STorricoMini_Project_Clusteringv1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customer Segmentation using Clustering\n",
    "***\n",
    "This mini-project is based on [this blog post](http://blog.yhat.com/posts/customer-segmentation-using-python.html) by yhat. Please feel free to refer to the post for additional information, and solutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Setup Seaborn\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "The dataset contains information on marketing newsletters/e-mail campaigns (e-mail offers sent to customers) and transaction level data from customers. The transactional data shows which offer customers responded to, and what the customer ended up buying. The data is presented as an Excel workbook containing two worksheets. Each worksheet contains a different dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>offer_id</th>\n",
       "      <th>campaign</th>\n",
       "      <th>varietal</th>\n",
       "      <th>min_qty</th>\n",
       "      <th>discount</th>\n",
       "      <th>origin</th>\n",
       "      <th>past_peak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>February</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>144</td>\n",
       "      <td>32</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>44</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   offer_id  campaign            varietal  min_qty  discount       origin  \\\n",
       "0         1   January              Malbec       72        56       France   \n",
       "1         2   January          Pinot Noir       72        17       France   \n",
       "2         3  February           Espumante      144        32       Oregon   \n",
       "3         4  February           Champagne       72        48       France   \n",
       "4         5  February  Cabernet Sauvignon      144        44  New Zealand   \n",
       "\n",
       "   past_peak  \n",
       "0      False  \n",
       "1      False  \n",
       "2       True  \n",
       "3       True  \n",
       "4       True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_offers = pd.read_excel(\"./WineKMC.xlsx\", sheet_name=0)\n",
    "df_offers.columns = [\"offer_id\", \"campaign\", \"varietal\", \"min_qty\", \"discount\", \"origin\", \"past_peak\"]\n",
    "df_offers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the first dataset contains information about each offer such as the month it is in effect and several attributes about the wine that the offer refers to: the variety, minimum quantity, discount, country of origin and whether or not it is past peak. The second dataset in the second worksheet contains transactional data -- which offer each customer responded to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_name</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Smith</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smith</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_name  offer_id  n\n",
       "0         Smith         2  1\n",
       "1         Smith        24  1\n",
       "2       Johnson        17  1\n",
       "3       Johnson        24  1\n",
       "4       Johnson        26  1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transactions = pd.read_excel(\"./WineKMC.xlsx\", sheet_name=1)\n",
    "df_transactions.columns = [\"customer_name\", \"offer_id\"]\n",
    "df_transactions['n'] = 1\n",
    "df_transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're trying to learn more about how our customers behave, so we can use their behavior (whether or not they purchased something based on an offer) as a way to group similar minded customers together. We can then study those groups to look for patterns and trends which can help us formulate future offers.\n",
    "\n",
    "The first thing we need is a way to compare customers. To do this, we're going to create a matrix that contains each customer and a 0/1 indicator for whether or not they responded to a given offer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set I</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Create a data frame where each row has the following columns (Use the pandas [`merge`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html) and [`pivot_table`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html) functions for this purpose):\n",
    "<ul>\n",
    "<li> customer_name\n",
    "<li> One column for each offer, with a 1 if the customer responded to the offer\n",
    "</ul>\n",
    "<p>Make sure you also deal with any weird values such as `NaN`. Read the documentation to develop your solution.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "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>customer_name</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>n</th>\n",
       "      <th>campaign</th>\n",
       "      <th>varietal</th>\n",
       "      <th>min_qty</th>\n",
       "      <th>discount</th>\n",
       "      <th>origin</th>\n",
       "      <th>past_peak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Smith</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rodriguez</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Martin</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jackson</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Campbell</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_name  offer_id  n campaign    varietal  min_qty  discount  origin  \\\n",
       "0         Smith         2  1  January  Pinot Noir       72        17  France   \n",
       "1     Rodriguez         2  1  January  Pinot Noir       72        17  France   \n",
       "2        Martin         2  1  January  Pinot Noir       72        17  France   \n",
       "3       Jackson         2  1  January  Pinot Noir       72        17  France   \n",
       "4      Campbell         2  1  January  Pinot Noir       72        17  France   \n",
       "\n",
       "   past_peak  \n",
       "0      False  \n",
       "1      False  \n",
       "2      False  \n",
       "3      False  \n",
       "4      False  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your turn\n",
    "df = pd.merge(df_transactions, df_offers)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>offer_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adams</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allen</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anderson</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bailey</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baker</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "offer_id       1   2   3   4   5   6    7   8    9    10 ...  23   24  25  \\\n",
       "customer_name                                            ...                \n",
       "Adams         NaN NaN NaN NaN NaN NaN  NaN NaN  NaN  NaN ... NaN  NaN NaN   \n",
       "Allen         NaN NaN NaN NaN NaN NaN  NaN NaN  1.0  NaN ... NaN  NaN NaN   \n",
       "Anderson      NaN NaN NaN NaN NaN NaN  NaN NaN  NaN  NaN ... NaN  1.0 NaN   \n",
       "Bailey        NaN NaN NaN NaN NaN NaN  1.0 NaN  NaN  NaN ... NaN  NaN NaN   \n",
       "Baker         NaN NaN NaN NaN NaN NaN  1.0 NaN  NaN  1.0 ... NaN  NaN NaN   \n",
       "\n",
       "offer_id        26   27  28   29   30   31  32  \n",
       "customer_name                                   \n",
       "Adams          NaN  NaN NaN  1.0  1.0  NaN NaN  \n",
       "Allen          NaN  1.0 NaN  NaN  NaN  NaN NaN  \n",
       "Anderson       1.0  NaN NaN  NaN  NaN  NaN NaN  \n",
       "Bailey         NaN  NaN NaN  NaN  1.0  NaN NaN  \n",
       "Baker          NaN  NaN NaN  NaN  NaN  1.0 NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1= df.pivot(index='customer_name', columns='offer_id', values='n')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "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>offer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adams</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allen</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anderson</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bailey</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baker</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "offer_id customer_name    1    2    3    4    5    6    7    8    9 ...    23  \\\n",
       "0                Adams  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 ...   0.0   \n",
       "1                Allen  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0 ...   0.0   \n",
       "2             Anderson  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 ...   0.0   \n",
       "3               Bailey  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0 ...   0.0   \n",
       "4                Baker  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0 ...   0.0   \n",
       "\n",
       "offer_id   24   25   26   27   28   29   30   31   32  \n",
       "0         0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "1         0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2         1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3         0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "4         0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df1.fillna(0).reset_index()\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Means Clustering\n",
    "\n",
    "Recall that in K-Means Clustering we want to *maximize* the distance between centroids and *minimize* the distance between data points and the respective centroid for the cluster they are in. True evaluation for unsupervised learning would require labeled data; however, we can use a variety of intuitive metrics to try to pick the number of clusters K. We will introduce two methods: the Elbow method, the Silhouette method and the gap statistic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing K: The Elbow Sum-of-Squares Method\n",
    "\n",
    "The first method looks at the sum-of-squares error in each cluster against $K$. We compute the distance from each data point to the center of the cluster (centroid) to which the data point was assigned. \n",
    "\n",
    "$$SS = \\sum_k \\sum_{x_i \\in C_k} \\sum_{x_j \\in C_k} \\left( x_i - x_j \\right)^2 = \\sum_k \\sum_{x_i \\in C_k} \\left( x_i - \\mu_k \\right)^2$$\n",
    "\n",
    "where $x_i$ is a point, $C_k$ represents cluster $k$ and $\\mu_k$ is the centroid for cluster $k$. We can plot SS vs. $K$ and choose the *elbow point* in the plot as the best value for $K$. The elbow point is the point at which the plot starts descending much more slowly. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set II</h3>\n",
    "\n",
    "<p><b>Exercise:</b></p> \n",
    "<ul>\n",
    "<li> What values of $SS$ do you believe represent better clusterings? Why?\n",
    "<li> Create a numpy matrix `x_cols` with only the columns representing the offers (i.e. the 0/1 colums) \n",
    "<li> Write code that applies the [`KMeans`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) clustering method from scikit-learn to this matrix. \n",
    "<li> Construct a plot showing $SS$ for each $K$ and pick $K$ using this plot. For simplicity, test $2 \\le K \\le 10$.\n",
    "<li> Make a bar chart showing the number of points in each cluster for k-means under the best $K$.\n",
    "<li> What challenges did you experience using the Elbow method to pick $K$?\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lower values of SS will be better since they measure how spread out the clusters are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
      "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32],\n",
      "      dtype='object', name='offer_id')\n"
     ]
    }
   ],
   "source": [
    "x_cols = df1.columns[1:]\n",
    "print(x_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>offer_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "offer_id   1    2    3    4    5    6    7    8    9    10 ...    23   24  \\\n",
       "0         0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 ...   0.0  0.0   \n",
       "1         0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0 ...   0.0  0.0   \n",
       "2         0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 ...   0.0  1.0   \n",
       "3         0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0 ...   0.0  0.0   \n",
       "4         0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0 ...   0.0  0.0   \n",
       "\n",
       "offer_id   25   26   27   28   29   30   31   32  \n",
       "0         0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "1         0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2         0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3         0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "4         0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = df1[df1.columns[1:]]\n",
    "x1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "cluster = KMeans(n_clusters= 4)\n",
    "\n",
    "labels = cluster.fit_predict(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "215.28023255813952\n"
     ]
    }
   ],
   "source": [
    "print(cluster.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAEsCAYAAAARos4IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xd4VNXWwOHfpJBQQgdBIFKUBdI0iEhTxIINxa7oVexe62fv13Kx965XReyKqKiIooKFJgpIhwXSe+8hpM33xz6JQ0xPJmeSWe/z5JnMabPmZHLW7HL2DgSDQYwxxphwiPE7AGOMMVWXJRljjDFhY0nGGGNM2FiSMcYYEzaWZIwxxoSNJRljjDFhE+d3ACa8RGQw8HYRm21X1bre9suATFU9MEzx1AHmAq+p6pAyHCcILFfVlvmsqw58DRwDDAcuUNXM0r5WRQv5m/1LVd/3ORzfhZyPB1X1AZ9jOVBV//J+bwksBd5S1cv9jCuSWZKJHl8AnxewLj3k9/8DwnLzlHfxHwk0C8fxvddIBL7EJZgPgYtUNStcr2eig4jUBr4D5gE5CWUj8C/gL7/iqgwsyUSPWcX5VqyqI8Px4iLSDvgY6BKO43uvkYBLYscB7wKXqGp2uF7PRJX6QA9ckgFAVXcDUV/SLIq1yZiwE5FrgVlAS+DZML1GNVxJrT/wFpZgjIkIVpIx+8ivTcYrhQwBjgKqA78CtwCzgfdVdXARhz0EV3V1N9AWuKmA1+4L/AS8U4xjhu4XD4wATgJeB/6tqsWq8vPe7wzgJeAhL9Y9wLfAHaq62tuuJQXUv4vIz0BvVY3zng/GtSH0Bc4FzgJqAX8A1wHrgKeAAbiqyZ+B61V1TZ7waovI68DZQDVgEnCfqk7J8/otgAeBE4AGwCpcW9QQ79t2znZB733GAxcBqcA5qjpORG4ABuP+PtneOXlOVQuqYkVETsOVHG9X1SfzrOuPq166U1UfF5HGwOPeOdkf2ASMBR5Q1SUFvUYhr/0AcD8gwF3AKUASMBN4SFW/ybN9Wc7R7bgvLgCXichlwNHAMvL5TIjIecCVuM9SErAF9ze+J6c9J5pYkokeNUSkYT7Ls1V1S0E7eQlmMhALvAisx104J1D8kvB1qrrXO17bQrabj6vjXlzM4+YkmOG4C/YISpBgQnTFteMMxSWHPl4cbXBVJKX1HrAc+A/QGrgZ+ArYjavHvxs4FHdBqoUrhYV6HFgNPALUAG4EfhGRo1V1MoCItMElnxhcgl0FHI67MB4jIkep6p6QYw4GluASfVvgNxG5DXgC+Ah4zXutK4HPROT0QqpQvwE2ABcCT+ZZ9y8gC3hPROKA74Fk3GdoBS45XAf0ExEJvdCX0Bjcxf5BoDbuy89XItJRVedD+Zwj7/dnccniLdxntXreYELO5RjgPu8c9AHOB44QkTaVqRNKebAkEz1u837y2g7ULWS/p3Dfxrqp6p8AIvIyMBo4vjgvnJNgirHdekpWxx2Ha+cZiCsRHAk0xiXCkmgOhF5M3xCRRsAJInKQqi4q4fFybAD65nQ8EJEDgHOAz1X1zJyNRKQDcKyIVFPV0E4Ym3Dnfae33QjcN/WngZ7eNi/jSjmHqOpyb9lrIjIOl+RuwCWrHDWAk1V1VcjrDwbmqeqgkGUfAhNxbWj5JhlVzRSR94GbvYv6HG/fWsDpwBhVXSMi3bzj7FPi8UqRVwMH40p5pTFDVU8POeZi3GfiQuAeb3F5nKNUXJJZnNO26ZVuCdkmFrgDmAKcGPJl5zURCeASTWdgeinfa6VkSSZ6vIdrDM8ro6AdvO7G/YHROQkGQFWzROQhiplkwqgZcAbwGLDNe3xHRE4sYWlmF64kE2oqrmqlCVDaJPNZnp5t873HT/Nstwjo5b3WipDlz+UkGABVnScio4FTveqnDNzf4Ctgd56S6hhgJ+78hF5ANfTi6VkBHCciQ4B3VXWhqm4ADirGe3wbV0K7ELjTW3YG7kKd03V+Ne4b/TUisgr3edquqq8ArxTjNQrzYZ7nU73HJgAiUo/yOUdF8v4vmgE1Qj9/3v9Rqve0VkmPW9lZkokeS1T1xxLu0wb3GVmQz7p5+Szzw2OqepeIxODq5fvjqkyeKsExNuaTlHJKX7FliG1dnuc51SR5S1o5iShv9WN+53ih99ga13YSAE7zfvJzQJ7n+ZXybsJdhO8B7hGR5bgL8Eeq+nMBxwVAVeeIyFTgfBG5yzuPF+HaIb7ytlnjtfk8g0sKmSLyB6667Z3SXNALeT95/24HUT7nqFhUda+IHCUiZ+OqBFvhSso5oq6zVdS9YVMi1bzH/Kq7UvNZVtFWqepdAF5PsotxpZKHRaRrCY5T1l5oBX1ZK6iUWNxSVn5xBbzHTP7+//0E1207v59z8+z/j3uGVHUB0B53b9HTuARxBfCTiDxfjDiH4tpbjvS+yR8NfBha9eeVWprh2juG4y6+QwAVkV7FeI2CFPW3K5dzVFwi8gkuQXfHfTl7Ctcm89/SHrOys5KMKcwi3AWxXT7r8ltW0fa5GKjqEhG5CXgD+EhEUlR1Vzm9Vk4p5B+NvXhVM2HQBtcDK1Q73N/kLyDRW5aQt5TqtQGciWsUL5DXKN8JyFDVccA4b3kL4EfgOhH5j6puL+QwH+FKKefgGsxjCBllQkTqe68xU1XfwVVpBnC95j7BlaQmFhZnGSzzHkt9jopLRHrjzsG7wOA8VWYXlMdrVEZWkjEFUtXNuIvcyaG9wrx/zlt8C6wQqvomMApXTfJSOR56E25khEO9qjkARKQHLhmEw5Ve77mc10oBTgR+VNVtqroOd3EeICKH5dn3Ilzbz2VFvEYsLrF8EPpaqroSWIlLaIX2hlLVbbjOAQNxF9lZqhrauH0srlfWlSH7BHG9Finq+GVRTucICq7SDJXT3jMnT4JphTsvEIVf7KPuDZsSuwnX/XOKiLyEq68eyN+9m8ptCBoR2Q9XfbE4p4tuKV0OzAEuFpExqvpRWWNT1TQRGY5r4P5KRL7EVflcAygQjrHeDgB+FZF3cFVNNwA7vMcc1wDjve1ew3UuOARX3bWcIqppvDaEx3CdJn72qnv24hLDMcDLxexe/DZwHu4emLz3QX0F/AkMEZHWwDRcj8bLcYn7hWIcvyzKdI48m3CJpp+IXIHrkp3XRFxV433eMDTLcNWQl/F3qbNO6d9G5WQlGVMor1tqH1yiuRF3MdrD39/MitU9uZja43rBXVWWg3hdoXO+Nb/mfZMsD9fiekMdhrvf43jc/SDfldPx87oKVy32OO7cjwO6e20oAKjqLNx9Pp/husi+hLsp9U2gVz43eP6Dqj4OXIq7AfEBXFfdg3Dj2N1YzFh/xJV8MoAP8hw/Ddch41Vc8noBdwPlIuAoVZ1UzNcolXI6R3tw3ZNr4v72/fLZZiPuff6OuwfoWVzJ8zX+/lLmd4/MChcIBsMyFqKpIkSkCbA+b+8rEemOu0nN95FxjTGRy0oypijjgfnejWahLvIey1KtZYyp4qxNxhTlTVwV2VgR+RRXL300rrpsFPnXTRtjDGDVZaYYRORCXONpOyABN7bYe8Cz0TYOkzGmZCzJGGOMCRtrkzHGGBM21iYTYtq0aVasM8aYUujatWsgv+WWZPLo2rUkQ1458+e7wXXbt29f3uGERWWKtzLFCpUr3soUK1SueCtTrFD2eKdNm1bgOqsuM8YYEzaWZIwxxoSNJZlykJ4VZNueLPZmlnqEcGOMqZKsTaYMpi3fytCJSxkzZy2Z2RD/2Qr6d2jCpb1bkZJcz+/wjDHGd5ZkSunDKSu4d+RsskP6o2VkBRk1ay2jZ69lyMBODOqe7F+AxhgTAay6rBSmLd/6jwQTKjsI946czfQVWys2MGOMiTCWZEph6MSlBSaYHNlBGDphacUEZIwxEcqSTAntzczi+7nrirXtmLnrrDOAMSaqWZIpoZ1pmWRkFW9ggIysILvSbPxIY0z0siRTQkmJccTH5jt6wj/ExwaolWh9K4wx0cuSTAklxMVyfIcmxdq2f4cmJMTlnevLGGOihyWZUrisdytiiijMBAJwae/ymlreGGMqJ0sypZCSXI8hAzsVmmgS42NpXrd6xQVljDERyJJMKQ3qnsyIf/fklM5NifPOYnxsgP1qJwCwJz2L2z+bhU0KZ4yJZtYqXQYpyfVIGVSPmXPmkZqRTUqn9uzYk8kJz/3K5t3p/Kwb+WDKCi484gC/QzXGGF9YSaYcVIsNUDcxloS4WBolJfDIGZ1y1z38zXyWbtrtY3TGGOMfSzJh0L9DE87q2hyAPRlZ3Dx8BplZ2T5HZYwxFc+STJjcP+BgmnkN/3+u2Mbrvy7xOSJjjKl4lmTCJCkxnqfP6ULA64H27A8LmbN6u79BGWNMBbMkE0ZHtG7AZb3cvTKZ2UFu+mQGaRk2lpkxJnpYkgmzW/sLbferBcCiDbt4aoz6HJExxlQcSzJhlhgfyzPnHJI73tlbE5cyefFmn6MyxpiKYUmmAnRsVof/O7YtAMEg3PrpTHakZfgclTHGhJ8lmQpy1ZGtSUmuC8DqbXt48Kt5PkdkjDHhZ0mmgsTFxvDMOYdQo5oblfmz6av4bk7xJj8zxpjKypJMBWrZsCb3nNw+9/ndX8xm4869PkZkjDHhZUmmgg06PJm+0giALbvTuetzG0TTGFN1WZKpYIFAgCfO7EzdGvEA/Dh/A8OnrvQ5KmOMCQ9LMj5oXDuRhwf+PYjmQ1/PY8XmVB8jMsaY8LAk45OTOzdl4CH7A7A7PYtbPp1BVrZVmxljqhZLMj568LSONK2TCMAfy7byxngbRNMYU7VYkvFRnerxPHV2l9znz3y/kPlrd/gYkTHGlC9LMj7rdWBDBvdsCUB6VjY3fTKDvZk2iKYxpmrwdfplEYkFbgSuAJKB5cArwMuqGsyzbUNgHvCKqj4QsjwBeAw4H6gJjAFuUNU1FfEeysMdJ7Tj10UbWbJxNwvW7eTZHxZx54nt/A7LGGPKzO+SzH3AI8D7wKnAcOA54LZ8tn0BaJTP8teAi4A7gUuALsBoL4FVCtWrxfLsOYcQG+MG0Xz918X8sWyLz1EZY0zZ+ZZkvCRwM/Ckqj6sqmO9EsrrwK15th0AHA+k5VneBpdgrlHVYao6AjgJ6AycFv53UX66tKjL9f0OBNwgmjcPn8GuvZk+R2WMMWXjZ0mmNvAu8Hme5Qo0EpGaACJSB3gVuAXIOwZLP+9xVO7OqouAucAJYYg5rK49+kC6NK8DwMotexgyygbRNMZUbr4lGVXdqqrXqeqfeVYNAFap6m7v+VPAPFV9J5/DtAXWhWybY4m3rlKJj43hmXMPITHe/Vk+/mMlP85b73NUxhhTer42/OclIpcDxwI3eM/74Rr0OxWwS21gZz7LdwItShPD/PnzS7zPnj17Sr1vfi45tB6v/u4mNrt1+J+8elpz6iaWXxNTeccbTpUpVqhc8VamWKFyxVuZYoXwxut3w38uEbkA14g/AnhJRGoAbwD3q+rSAnYLAAXdJp9d/lFWjFPa1ebQptUB2JaWxYuTN9ogmsaYSikiSjIicjOuWuwr4AJVDYrIw8B2XMIJjTNGROJUNdNbn5TPIZO8dSXWvn37ojfKIyf7l2bfgrzSrBX9n/2VHWmZTFqRyvw9tTmza/NyOXY44g2XyhQrVK54K1OsULnirUyxQtnjnTZtWoHrfC/JiMgjwNPAe8BZqprurTodOBTXoyzD+6mD6/acM3fxIqCJiFTPc9jWuA4ElVbTOtX578COuc8f+Gouq7ft8TEiY4wpOV+TjIjcCNwFPA8M9konOQYA3fL87MJVoXXzthkLxHrb5hzzIKCDt65SO7XL/pzcuSkAO/dmcuvwmWTbIJrGmErEt+oyEWkKPA7MBj4GuotI6CZT8yQdRCQLWKOqUwFUdbGIfAq84XV13go8CswCRob/XYRXIBDg4YEd+WPpFjbs3MvkJZsZOnEpl/dp7XdoxhhTLH6WZPoDCbieY5Pz+albzONcAnyCS1hvAjOBk1S1SgwAVrdGNZ44q3Pu8yfGKAvX59ehzhhjIo9vJRlVHQYMK+E+/0g83j0yV3o/VVJfacyFRyTz/m8rSM90g2h+cU0vqsX53qRmjDGFsqtUJXH3Se1p2aAGAHPX7ODFcYt8jsgYY4pmSaaSqFEtjmfOPQRvDE1e/ukvpq/Y6m9QxhhTBEsylUhKcj2u6esG0cwOws2fzCA13QbRNMZELksylcwNxxxEh/1rA7BscyqPjK4cw1YYY6KTJZlKplpcDM+de0huo//7v63gZ93gc1TGGJM/SzKV0EH7JXF7/7/vKbp9xCy27k4vZA9jjPGHJZlK6tJerejRugEAG3bu5d4v59ggmsaYiGNJppKKiQnw1DldSEpwtzp9M2stX81c43NUxhizL0sylVizutW5/9QOuc/vGzmHtdttEE1jTOSwJFPJnZnSjP4d9gNgR1omt4+YZYNoGmMihiWZSi4QCPDI6Z1oWCsBgPGLNvHeb8t9jsoYYxxLMlVAg1oJPH7m3zNUP/rtfBZv3OVjRMYY41iSqSKOab8f53VrAUBaRjY3fzKDjKxKOwO1MaaKsCRThdx7ysG0qO8mCZ25ajsv//SXzxEZY6KdJZkqpFZCHM+ccwgBbxDNF8f9xcyV2/wNyhgT1SzJVDHdWtbnyiPdzJlZ2UFuGj6DtIwqMX+bMaYSsiRTBd18XFvaNUkCYMnG3Tz27QKfIzLGRCtLMlVQQlwsz557CNVi3Z932KRlTFi0yeeojDHRyJJMFdW+aW1uPr5t7vPbRsxk+54MHyMyxkQjSzJV2BV9WnN4y/oArN2exv1fzgEgPSvItj1Z7M20thpjTHjF+R2ACZ/YmABPn9OFE577ld3pWYycsYYVW1KZtWobmdkQ/9kK+ndowqW9W5GSXM/vcI0xVZCVZKq4FvVr8J8BB+c+n77CJRiAjKwgo2at5axXJ/HhlBU+RWiMqcosyUSBAxvVKnR9dhDuHTmb6Su2VlBExphoYUkmCgydtKzIbbKDMHTC0vAHY4yJKpZkqri9mVl8P3ddsbYdM3eddQYwxpQrSzJV3M60TDKyije/TEZWkF1pmWGOyBgTTSzJVHFJiXHExwaKtW18bIBaidbh0BhTfizJVHEJcbEc36FJsbY9WhqTEBcb5oiMMdHEkkwUuKx3K2KKUZiZtWqbTXZmjClXlmSiQEpyPYYM7FRkolm3Yy9nvDKJyYs3V0xgxpgqz5JMlBjUPZkR/+7JKZ2bEuf91eNjAwzosj+vXpDCgY3dvTTb92Rw0dApfDp1pY/RGmOqCmvljSIpyfVIGVSPmXPmkZqRTUqn9rltMD0PbMg1H0xj4l+bycgKctuIWSzbvJtbjhNiilPXZowx+bCSTBSqFhugbmLsPo38darHM+ySwzmvW4vcZS//tJjrP/rTJj0zxpSaJRmTKz42hkfP6MRdJ7bLncL5m9lrOe9/v7Fx515/gzPGVEqWZMw+AoEAVx3VhlcvSCEx3n08ZqzcxsCXJ7Jw/U6fozPGVDaWZEy+TujYlE+u7EGjpAQAVm/bw5mvTOLXhRt9jswYU5lYkjEF6tKiLiOv7UW7JkkA7NybySXD/uCDKct9jswYU1lYkjGFala3Op9e3YO+0giArOwg93wxhyGj5pGVXbwx0Ywx0cuSjClSUmI8b150GBf1OCB32ZsTlnL1+9NITbcBNY0xBfP1PhkRiQVuBK4AkoHlwCvAy6oaFJHqwL3AuUATYBHwmKp+EnKMBOAx4HygJjAGuEFV11Tke6nq4mJjeOi0jrRuWJOHRs0jOwg/zFvPOa9P5q2Lu7Ff7US/QzTGRCC/SzL3AY8A7wOnAsOB54DbvPWvAtd6ywYC44GPReSckGO8BlwE3AlcAnQBRnsJzJSzwb1a8ebFh1Gzmju9c1bv4LSXJjJ3zXafIzPGRKISl2REpB9wPFCLfZNUHJAE9FHV5sU4TixwM/Ckqj7sLR4rIo2AW0VkGHAxcLmqvuWt/1FE2gC3AsO93y8CBuWUbkRkJqDAacDnJX1/pmj92u3Hp1f35LJ3/mDt9jTW7Ujj7Ncm89KgQ+nXbj+/wzPGRJASlWREZDDwA3A7cA1wFXC193M5rrQxuZiHqw28yz8TgQKNvJ/XgO/zWd/K+72f9zgqd6XqImAucEIx4zClcPD+tRl5bS86NqsNQGp6Fpe/M5W3J9oUzsaYv5W0JHMT8BdwClAD+BPXlpIB3ADcgUsMRVLVrcB1+awaAKxS1bnAv0NXeKWfE4EF3qK2wDpV3Z3nGEu8dSaM9qudyPCrenDjxzP4Yd56soPw4NfzWLZpN/edcjBxsX7Xxhpj/FbSJHMQ8B+vtICI7AKOVNUPgXtEpCNwNzC2NMGIyOXAsbiElZ8HgXa49htwpaH8bkPfCbTIZ3mR5s+fX+J99uzZU+p9/VDe8d7YtQZJ1OHzea5d5p3Jy5m7YgN3HrkfNauVLdFE+7kNp8oUK1SueCtTrBDeeEt6BcgGtoQ8XwgcEvJ8NNChNIGIyAW4UtAI4KV81t8B3AM8rapfe4sDQEE3a2SXJg5TcrExAa7o1oDrj2iYO2fN1NV7uPW7NWzYZV2cjYlmJS3JLAAOA4Z6z+cDXUPW18RVo5WIiNwMPAV8BVygqsGQdQHgaVxV3Sv83fMMYDuus0FeSd66Emvfvn2J98nJ/qXZ1w/hird9e+h28Eau/WA6O/dmsmxrOreMWc9bFx9GlxZ1S3VMO7fhU5lihcoVb2WKFcoe77Rp0wpcV9KSzDvAVSLyuojUAL4G+orI7SJyKvB/wOySHFBEHsElkfeAs1Q1PWRdDK5zwE3AI6p6bWgCwt0308S7nyZUa1wHAVPBjmzbiM+u6Umzuu5PsmnXXs7932S+m7PW58iMMX4oUZJR1ReBJ4DzgCzgU9zNj48BI4E6uMb/YhGRG4G7gOeBwaqat27laeBC4BZVvSefQ4wFYnGdBXKOeRCuyq5U7UKm7Nrul8TIa3txiFd6ScvI5ur3p/PaL4sJBm0oGmOiSYnvk1HVu0TkvpCEcJKIHAXUByaq6obiHEdEmgKP40o+HwPdRSR0k2zcaAA/AJNE5IiQdVmq+oeqLhaRT4E3RKQOsBV4FJiFS3rGJ42SEvj4yiO4ZfhMvpntSjGPfbuAZZt289+BHYm3nmfGRIVSDSuTt8Shqr+U4jD9gQSgE/nfW/MyrmH/OO8n1G7czaDg7vJ/FpewYoAfccPK2HSOPkuMj+XF8w+lZcMavPzTYgA+/mMlK7em8soFXalTPd7nCI0x4VZokhGRecBtqvpNyPOiBFW1yB5mqjoMGFbEZvndR5P3OLuBK70fE2FiYgLc1r8dLRvU5O4vZpORFWTiX5s545WJvD34cJIblLifiDGmEimqzmI9kBbyfIO3rLCfYlWXmehy9mEtePfS7rmll8Ubd3P6KxOZtnxLEXsaYyqzQksyqnp0nud9wxqNqdJ6tGnAF9f05NJhf7Bscyqbd6dz/htTeOrsLpzaZX+/wzPGhEFJxy4bJyLHFLJ+gIjMKntYpqpq3agWn1/Ti8Nb1gcgPTObGz76kxfHLrKeZ8ZUQUW1ydTDDSWToy8wTkTyG8olBjfvS5tyi85USfVrVuO9yw/nzs9m88WfqwF4+oeFLN20m0fP7ERCnM3SYExVUVTvsixcV+Cc8duDuPHDHixg+wDwWfmEZqqyhLhYnjmnC60a1uSZHxYC8Pmfq1m1bQ+vX9iVejWrAZCeFSQ1PZu9mVmWfIyphIpqk9khIqfguhkHcMPJ/I/8uxxnARuBceUdpKmaAoEANxxzEAc0qMFtn84iPSub35du4fRXJnLL8W35bu56xsxZS2Y2xH+2gv4dmnBp71akJNfzO3RjTDEVeZ+Mqk4HpgOIyPXA+6o6IdyBmehx2iHNaFa3Ole+N40tu9NZtjmV6z+asc82GVlBRs1ay+jZaxkysBODuif7FK0xpiRKett1a2wyMBMGh7WszxfX9KRZ3cRCt8sOwr0jZzN9xdYKiswYUxYlTTKZwOZwBGLMAQ1q0mH/OkVulx2EoRNsBk5jKoOSDitzA/CMiKQBE3BtMP+Yt6W445cZE2pvZhY/FfOjM2buOusMYEwlUNIk8wpuzph/TCqWh/3nmxLbmZZJRlbx7pXJyAqyKy2ThFr2UTMmkpU0yTxPwTNRGlMmSYlxxMcGipVoYgIQF2MjORsT6UqUZFT1gTDFYQwJcbEc36EJ38wqeoKz7CCc9MJ47jyxHad0bkogEKiACI0xJVWqof69+WNOBloAD+OG3u8JDFfVjPILz0Sby3q34tvZa8kuRnl59bY9XP/Rn7w7eRn3D+hAx2ZFdxowxlSsko5dFisiH+JuuLwFOAdoDHTDTZ88zps8zJhSSUmux5CBnYgpoGASE4AbjjmQHq0b5C77Y9lWBrw0gTtGzGLjzr0VFKkxpjhKWql9N258sutwY5TlXAq+xCWd7sB/yi06E5UGdU9mxL97ckrnpsR5n9D42AADuuzPiH/35ObjhA+v6M5rF6bQon51AIJB+GTqSo5+6mde/2UxezNtzjpjIkFJq8sGA2+p6qsikvtVUlX3As+KSGvgdFzCMabUUpLrkTKoHjPnzCM1I5uUTu336a4cCAQ4oWNT+kpj3pqwlJd/+ovU9Cx27c3k0W8X8NHvK7j35IM5pn1ja68xxkclLck0A6YWsn420LT04Rizr2qxAeomxhZ4P0xifCzXHn0gP93alzNTmucuX7Y5lcvfncpFQ39n4fr8Bg03xlSEkiaZlbjBMgtyJLCq9OEYUzr71U7k6XO6MPLaXhyaXDd3+fhFmzjx+fHc/+UctqWm+xihMdGppElmGHCliAzi76q2oIgkish9wPnAB+UYnzElckiLunz+7548d+4hNKntxkHLyg7yzuTl9H3qZ96dvIzMrH8MUmGMCZOSJpnHcPPFvA+s8JZ9DOzAzTHzHa5LszG+CQQCDDy0GeNuPYob+h1Igtd7YFtqBv/5ci4nvTCeCYs2+RylMdGhRElGVbNUdRBwHG5emW+BP3ElnIGqerLdJ2MiRY1qcdx8vDD2lqM4ufPfTYUL1++nM5boAAAgAElEQVTiwremcPk7U1m2abePERpT9ZXqZkxVHQuMLedYjAmL5vVq8PKgFC7usYUHv57L3DU7APhx/np+WbiBS3u34rqjDyQpMd7nSI2pekqcZLybLQfgpmTOr8tPUFWfLGtgxpS3w1vV56vrejNi2kqeHKNs2pVORlaQ139ZwmfTVnN7f+Gsrs2JKehOUGNMiZUoyYhIX2AUUJ2/b8TMKwhYkjERKTYmwLndkjmxU1NeGvcXb09cSkZWkE279nL7Z7N49zc3RE23lvX9DtWYKqGkDf+PA7twvcjaAa3y+WldngEaEw61E+O5+6T2fH/TURzbvnHu8jmrd3D2a5O57sPprN62x8cIjakaSlpd1hm4R1WHhyMYYypaq4Y1efPibvy6cCP/HTWPRRt2ATBq1lp+mLeeq45qw9VHtaZGtVI1XxoT9UpaklkfliiM8dmRbRsx+sY+PDDgYOpUdx0A9mZm88LYRRzz9C98OWM1waBNpWRMSZU0ybwOXCMidYvc0phKJj42hsG9WvHzrX25qMcBxHodANZuT+PGj2dw1muTmbVqm89RGlO5lLQOIA2IBxaLyM/ARiDv7dNBVb22HGIzxhf1albjodM6ckH3A3ho1Fwm/rUZgGnLt3LqSxM5q2tzbu8vNPZGFAiVnhUkNT2bvZlZBY63Zkw0KWmSeTrk99ML2CYIWJIxlZ40SeL9y7rzw7z1PDx6Pss3pwIwYtoqvp29luv6HcSlvVuSEBfLtOVbGTpxKWPmrCUzG+I/W0H/Dk24tHcrUpLr+fxOjPFPSadftknVTVQJBAIc36EJR0kj3p64jBfHLmJ3eha707N4/Ds3pUCfgxry0e8r9pnNMyMryKhZaxk9ey1DBnZiUPdk/96EMT4qNMmIyGjgCVX9OeR5UYKqenI5xGZMxEiIi+Xqo9pwRkoznhqjfDptFcEgrNiSygdTVhS4X3YQ7h05m3ZNk6xEY6JSUSWZ9kDtkOcH46rDjIlKjZMSeeKsLvzriJY8+PVcpi7fWuQ+2UEYOmEpKYMsyZjoU2iSUdVWeZ63DGs0xlQSnZrX4f3LD6fD/d+TlV30964xc9dZZwATlayNxZhS2rU3q1gJBlwbza60zDBHZEzksSRjTCklJcYRH1u8wTQDwM+6gQybMM1EGUsyxpRSQlwsx3doUqxtg8Atn87iqCd+4n+/LmZHmk27ZKKDJRljyuCy3q0oycwAa7an8cjoBfR4ZCwPfT2PlVtSwxecMRHA11H/RCQWuBG4AkgGlgOvAC+ralBEAsDdwFVAQ2AicL2qLgg5RgJuWujzgZrAGOAGVV1Tke/FRKeU5HoMGdiJe0fOJr/mmZgADBnYkeb1avDmhKX8unAjALvTsxg6cSnDJi3lxI5NubxPKw61Ls6mCvJ7aNn7gDuB/wK/AX2A54AawBPAf7z1dwDLgHuBsSJysKpu947xGnAqcAtuGoJHgdEi0lVVsyrurZhoNah7Mu2aJjF0wlK+y7njPzbACR2bckmvlrn3xxzZthG6bidvTVjCyD/XkJ6VTXYQvpm9lm9mr+WwA+pxeZ9WHHdwk9xx04yp7HxLMl4p5mbgSVV92Fs8VkQaAbeKyKvArcADqvqCt894XGnnMuAZEWkDXAQMUtVPvG1mAgqcBnxeke/JRK+U5HqkDKrHzDnzSM3IJqVT+3y7K0uTJJ44qwu39hfen7yc935bztZU1z4zdflWpi7fSnL9GlzaqyVnH9aCmgl+fw80pmz8bJOpDbzLPxOBAo2AfkAt4KvcFapbgV+AE7xF/bzHUSHbLALmhmxjTIWpFhugbmJskffDNE5K5ObjhUl3HsOQgR1p3bBm7roVW1J54Ot59Hh0LI99u4B129PCHbYxYePb1yQvYVyXz6oBwCqgufd8cZ71S3ClFIC2wDpV3Z3PNm3LKVRjwqZ6tVguPOIABh2ezLgFG3hzwhJ+W7IFgB1pmbz2y2LeHL+EAV325/I+reiwfx2fIzamZCKqLC4ilwPHAjfgSjp7VTU9z2Y7+Xuom9re87x2Ai1KE8P8+fNLvM+ePXtKva8fKlO8lSlWKFu8zQJwf5+6LDq4Ol/M286vS3eRFYTM7CBf/LmaL/5cTZcmiZzRoS6HNatOTKBs7TbRdG4rWmWKFcIbb8R0YRaRC3CN+COAl3D3rxV0O3XOHW3F2caYSuWgBgnc3qcxb5+ZzFkd6lAz/u9/05nr0rh/7Dqu/nIVoxfuYG+mfcxNZIuIkoyI3Aw8hWt/ucDrvrwdSBCReFUNvXMtCcjpWbbde55X6DYl0r59+xLvk5P9S7OvHypTvJUpVijfeNsDRx4GD+zNZPgfKxk6cSmrtrpvnCu3Z/Di5E18MGsHFx5xABf1OICGtRJ8i7UiVKZ4K1OsUPZ4p02bVuA630syIvIIbjK094CzQqrHFuFKKq3y7NIa1zkgZ5smIlK9kG2MqdRqJcRxaW83LfQrF6RwaPLfs59v2Z3OC2MX0fOxcdwxYhaL1udXe2yMf3xNMiJyI3AX8DwwWFVDRxCchJvueWDI9vWAo4Cx3qKxQCyus0DONgcBHUK2MaZKiIuN4aROTfniml589u+enNixSe5oA+mZ2XwydSXHPfsrFw/9nQmLNhEM2qwcxn9+3ifTFHgcmA18DHQXkdBNpgIvAv8VkWxgIXAPsAN4E0BVF4vIp8AbIlIH2Iq7GXMWMLKC3ooxFa7rAfXoekBXVmxOZejEpQyfupLUdHfv8S8LN/LLwo20a5LE5X1aM6BL03y7VKdnBUlNz7YpCExY+dkm0x9IADoBk/NZ3wg3pEw27qbMWrjSzcUhd/sDXAI8i0tYMcCPuGFl7G5/U+UlN6jBA6d24Kbj2vLR7ysYNnEZ63a4+2oWrNvJrZ/O5InvFnBxz5Zc0D2ZujWqMW35VoZOXMqYnNEJPltB/w5NuLR3K5u905Q7P++TGQYMK8amd3o/BR1nN3Cl92NMVKpTPZ6rj2rDpb1aMXr2Wt4Yv4S5a3YAsGHnXp4co7w07i86N6/DH8u27DPOWkZWkFGz1jJ69lqGDOzEoO7JPr0LUxX53vBvjCk/1eJiGHhoM0Zd35uPrjiCY9o1zl23JyOLKUu35DuQJ7hpou8dOZvpK4qeUtqY4rIkY0wVFAgE6NGmAW8N7sbYW45iUPfkYk1JkB2EoROWhj9AEzUsyRhTxbVpVIv7Bxxc7BECxsxdx95Ma9I05cOSjDFRYGdaJpkF1ZPlkZEVZFdaZtEbGlMMlmSMiQJJiXHExxavJBMIwJ50K8mY8mFJxpgokBAXy/EdmhRr22AQTnxhPB9OWUF2MUs/xhTEkowxUeKy3q2K1fgPrnrt7i9mc97/fuOvDbvCG5ip0izJGBMlUpLrMWRgpwITTUwA7j6pPQO67J+77PdlWzjp+fG8MHYR6TbisymFiBiF2RhTMQZ1T6Zd0ySGTljKdzl3/McGOKFjUy7p1TL3jv8zDm3GvSPnsHrbHtKzsnnmh4WMmrWGR8/oTNcDbFQAU3yWZIyJMinJ9UgZVI+Zc+aRmpFNSqf2/xi77Oh2jfn+piN56ntl2KRlBIOwcP0uznptEv864gBu6y8kJcb79A5MZWLVZcZEqWqxAeomxhY4OGbNhDjuH9CBL67pRbsmbtqmYBDenbyc4575lR/mra/IcE0lZUnGGFOoQ1rU5evre3Nbf6FanLtkrNuRxhXvTuWaD6axwRuQ05j8WJIxxhQpPjaGa48+kDH/dyQ9WjfIXT569jqOeeYXPvrdujub/FmSMcYUW6uGNfnwiu48cWZn6lR3bTI70zK56/PZnPfGbyzeaN2dzb4syRhjSiQQCHBOtxb8ePNRnNK5ae7y35du4cTnx/OidXc2ISzJGGNKpVFSAi8NSmHo4MPYv04i4KaBfvqHhZzy4nimLbcpA4wlGWNMGfVrtx/f33wUg3u2JGeg55zuzvd/OYdde22wzWhmScYYU2a1EuJ44NQOfP7vnvt0d35n8nKOe+YXfrTuzlHLkowxptwcmlzvH92d125P4/J3p3LtB9PZsNO6O0cbSzLGmHKV0935uxv7cETr+rnLv5m9lmOf/oWPf19BMGjdnaOFJRljTFi0blSLj644gsfP7ETtRDeC1Y60TO783I3uvMS6O0cFSzLGmLAJBAKc2y2ZH285ipNDujtPWbqFE54fz0vjrLtzVWdJxhgTdo2TEnl5UApvXbxvd+envl/IgBcnMH2FdXeuqizJGGMqzDHt/9ndWdfv5MxXrbtzVWVJxhhToUrb3Tk9K8i2PVnszcyqyHBNGdl8MsYYX+R0d/7fr0t43huKJqe788mdmnL/qQfTOCmRacu3MnTiUsbkTLL22Qr6d2jCpb1b5U6yZiKXJRljjG9yujuf2LEJd38xm9+WbAFcd+fxizZyTPv9+HLGakIHeM7ICjJq1lpGz17LkIGdGNQ92afoTXFYdZkxxncFdXf+4s99E0yo7CDcO3K2dRqIcJZkjDERoaDuzoXJDsLQCUvDHJkpC0syxpiI0jgpkWfO6UJsTKBY24+Zu846A0QwSzLGmIizMy2TrGLOtJmRFWT68q02VE2EsoZ/Y0zESUqMIz42QEZW8RLH+W9MYf86ifRt15h+0pieBzagRjW7vEUC+ysYYyJOQlwsx3dowjez1hZ7nzXb0/hwygo+nLKCanEx9GjdgH7tGtOvXWNa1K8RxmhNYSzJGGMi0mW9W/Ht7LUF9i4DiAnART1asnD9Tn5fuoVMb+P0zGx+WbiRXxZu5P6v5tKmUU36tWvM0e0a061lfeJjraWgoliSMcZEpJTkegwZ2Il7R87ON9HEBODh0ztx/uHuPpmdaRlMWLSJcQs28JNuZNOuvbnbLt64m8Ubl/LG+KUkJcTRp21DjpbG9JXGNEpKqKi3FJUsyRhjItag7sm0a5rE0AlL+S7njv/YACd0bMolvVruc8d/UmI8J3ZqyomdmpKdHWTOmu0u4SzYwMxV23O327k3k9Gz1zF69joAujSvw9FetVrH/esQU8xebaZ4LMkYYyJaSnI9UgbVY+aceaRmZJPSqT0JcbGF7hMTE6Bz87p0bl6X/zu2LRt37uVn3cBPuoHxCzexM2QgzpmrtjNz1Xae+3ERjZIS6Nu2Ef3aNab3QQ1JSowP99ur8izJGGMqhWqxAarFxhaZYPLTKCmBsw9rwdmHtSAjK5s/lm3hpwUbGLdgA4s37s7dbuPOvXw6bRWfTltFfGyAbi3r06+dq1Zr06gmgUDxSjnpWUFS07PZm5lVqnirEksyxpioEh8bQ882DenZpiH3nHwwKzan8pO6hDN5yebcSdQysoJMWryZSYs3M+Sb+STXr5HbeaB7q/okxv8zedhgnv8UMUlGRE4FPlDVpJBl1YEHgPOAusA04BZV/TNkmwTgMeB8oCYwBrhBVddUXPTGmMoquUENLu7Zkot7tiQ1PZNJf21mnLq2nLXb03K3W7EllWGTljFs0jKqx8fS68CGXtJpRNM61flwyop/dFKwwTwjJMmISE/gfSBvWfRZ4ELgDuAv4FZgnIh0UtVV3javAacCtwC7gEeB0SLSVVVtrAljTLHVqBbHsQfvx7EH70cwGGTBup25nQemr9iam0D2ZGTx4/z1/DjfzX1zQIMarNicSkG9rXMG82zXNCnqSjS+JhmvFHIj8F9gN1AtZF0MLsE8o6ove8smARtxJZunRKQNcBEwSFU/8baZCShwGvB5xb0bY0xVEggEaN+0Nu2b1ubaow9k6+50fl20kXELNvDLwo1sS83I3Xb55tQij5czmGfKIEsyFelE4C7gNqABrjSSIwaXdHaELNsN7AXqe8/7eY+jcjZQ1UUiMhc4AUsyxphyUq9mNU47pBmnHdKMrOwgf67YyrgFG/hx/noWrt9VrGPkDOYZTZ0B/L7t9Q+glaq+APuWNFU1E3gduF5EuolIPeBxoDrwmbdZW2Cdqu5mX0u8dcYYU+5iYwIc1rI+t5/Qjg+vOKLY+2VkBbnr89n8OG89qemZRe9QBfhaklHV1UVs8iBwBPC79zwbGKyq07zntYGd+ey3E2hRmpjmz59f4n327NlT6n39UJnirUyxQuWKtzLFCpEbb3pWkLgY8DqlFenz6av5fPpq4mMCdG6SSLfmNTi8eQ2aJvl3T044z63f1WUFEpEawCQgAdfusho4E3hLRHao6pe4jgIFtrVVSKDGmKhWLTZAz+Sa/Losb4XKP4VesDKyg0xbs4dpa/bw2u+baVEnnm7NXMI5uHEi8bFVY+SBiE0ywBnAQcDhqvqHt2yciDQAXgS+BLYDSfnsm+StK7H27duXeJ+c7F+aff1QmeKtTLFC5Yq3MsUKkR3v/9VswoRXJxU5mOc7lx7OrrTMfMdXW7k9g5Xbt/P5vO3USoijz0ENObpdY/pKIxonJYY1/rKe22nTphW4LpKTTAsgC5iaZ/kE4FwRqQUsApqISHVV3ROyTWtgfMWEaYyJdsUdzLPPQY0AihxfbdfeTL6ds45v57jx1To3r0NfceOrdW5WucZXi+QksxCIBboDv4Us747rxrwbGOttMwAYDiAiBwEdcDdxGmNMhSjJYJ6Q//hqvyzcyE8LNvDrwo37jK82a9V2Zq3azgtjF9GwVjWOausSTp+2Dakd4eOrRXKS+QqYAQwXkXuBNbhkciFwvaoGgcUi8inwhojUAbbibsacBYz0J2xjTLQqzWCeORolJXBW1+ac1bU5GVnZTF22NXe4m782/N1FetOudD6bvorPpq8iLibAYS3rcbRXyjmwca1ij69WUSI2yahqhogcCzwBPI3rujwfOFtVR4RsegluZIDHcV2yf8QNK2N3+xtjfFGWwTzBja/Wo00DerRpwN0ntWfllpDx1RZvZq/XlS0zO8hvS7bw25ItPPrtAprXq547vlqP1g3yHV8tP+Ec0DNikoyqPkCeKi5V3QxcVsR+u4ErvR9jjKlyWtSvwUU9WnJRj5bsSc9i0uJN/KQb+GnBRlZv+7s5etXWPbw7eTnvTl5OYnwMvdo0pK83V06zutX/cdyKGNAzYpKMMcaYolWvFssx7ffjmPZufLWF63fldh6YtmIrWV7Pg7SMbMYu2MDYBRu4D5D9knInZ0tJrsvwqasqZEBPSzLGGFNJBQIBpEkS0iSJf/dtw/bUDH5ZtJGfF2zg54Ub2bI7PXdbXb8TXb+T135ZTI1qsaSmF9yiUJ4DelqSMcaYKqJOjXhO7bI/p3bZn6zsIDNXbcudnG3umr+HgSwsweQorwE9LckYY0wVFBsTcL3dkutxy/HC+h1p/Kwb+HHeen6Yv6FYxyiPAT39HiDTGGNMBdivdiLndkvm0TM7F3ufjKwgu9LKNpCnJRljjIkiSYlxxR4XLT42QK3EslV4WZIxxpgokhAXy/EdmhRr2/4dmpT5vhlLMsYYE2Uu692KooY/iwnApb1blfm1LMkYY0yUyRnQs6BEkzOgZ3nckGm9y4wxJgqVdEDP0rIkY4wxUaosA3oWlyUZY4yJcmUd0LMw1iZjjDEmbCzJGGOMCZtAMFjIpNRRZtq0aXYyjDGmFLp27ZpvXzVLMsYYY8LGqsuMMcaEjSUZY4wxYWNJxhhjTNhYkjHGGBM2lmSMMcaEjSUZY4wxYWNJxhhjTNhYkjHGGBM2lmSMMcaEjY3CXAYiEgvcCFwBJAPLgVeAl1U14oZSEJFqwH+AfwENgSnArao63dfAiiAiCcAMYIqqDvY5nHyJSANgUz6rPlPVsyo6nqKIyDHAI0BnYAMwDHhIVbP8jCsvEekL/FTIJi1VdXkFhVMk75pwC3Al0ASYC9ylquN8DawAIlILeAw4G6gBTAJuV9WZ5fUaVpIpm/tw/6jvA6cCw4HngNv8DKoQzwI34D5UA4FU4CcROcDXqIp2P9DO7yCK0MV7PB7oEfJzl28RFUBEegHfAvOBk4GXgDuAe/2MqwDT2fd89gCOBjYD3wMr/QstX7fhrglDcf9ji4HvRORQX6Mq2GfAYOBJ4ExgHTBeRKS8XsBKMqXkfWO5GXhSVR/2Fo8VkUbArcATvgWXDxGpgytx3amqr3rLJuD+Wf8FDPExvAJ5/5w3kH8pIZJ0Btar6g9+B1IMjwHfh5QKx3klsaOBB32LKh+qugP4LXSZiDwHBIELVTXbl8AKdjHwoao+AiAiPwG9gcuA6/wMLC8R6Yr7UnS1qr7uLf5eRA4C/gucUx6vYyWZ0qsNvAt8nme5Ao1EpGbFh1So3UB34O2QZRm4f9YEXyIqgojE4b4RPgms9jmconQGZvkdRFG8L0G9gP+FLlfVO1W1ry9BlYCIHIy7WN+rqhv9jicfCcCOnCde9eN2oL5vERWsrfc4Js/yiUD/8noRK8mUkqpuJf9vJgOAVaq6u4JDKpSqZgJ/AohIDNASeACXZN73LbDC3QFUAx4FTvc5lqJ0BtJEZBKQgit5PQ88FWHtc52AALBbRL4GjsNdFF/BtclEWskgr4eBhcAbfgdSgJeB/4jIF8BUXFVUB+AeP4MqQE5VYzKwLGR5K6C2iNRX1S1lfRFLMuVIRC4HjsVV70Sy+3AJBuA/qqo+xpIvEWmP+8c8RlXTy7GKuNx5VacH40qLt+I6gJyMq5aqDjzkX3T/0Mh7fBf4EHgGOArXHrMHeNynuIokIq1xbZ9XRnAyfBXoB/wYsuxeVf3Kp3gK8wcuYb8iIoOBv4BzgZO89TWBMicZqy4rJyJyAfAaMALXkBrJvgD64urf/yMi//U3nH15Ja03gbdUdbLf8RTTKUAPVX1HVX9W1dtwVVJ3iEiiz7GFivcex6jqbar6k6o+gPvs3uslzEh1ObCVCC15i0gAV/V0BHANro3rYeB+EbnWz9jyo6p7gTOALFzC2YprU8ppT04tj9exkkw5EJGbgaeAr4ALIqx65B9UNaft4BcRSQJuE5GHVDXDz7hCXI8rwp/stcvkCIhInFf1FzG8evf8uqh+B1wNHAjMqdCgCrbLe/wuz/IfgGtx1aiLKzKgEhgIjPQujpGoF66R/xxV/dRb9rP3GX5CRN5R1V0F717xVHUu0EVEWgBxqrpURO4HsnFtSWVmSaaMROQRXDfVd4HLIu0CmENEmgAnAiNUdWfIqj9xjZUNcN0XI8HpQHPcN6tQXYCLRKSVqi6r8KgKICL740oyX+RpjK7uPUZSz7i/vMdqeZbnlHAi8guSiCQD7XHVkZGqhff4W57lE3Dtiy2JnC8biEgNXLflsaoa2hW8MzCnvK5lVl1WBiJyIy7BPA8MjtQE46mL66mV98bA43E3422o8IgKdhXQLc/PQmCU9/sa/0LLVwLwOnBhnuVnAgtVNVKSN8A8XE+9s/MsPxl3XpdVdEDFdLj3mPcCHkkWeo+98izvDmQCqyo2nCJl4KpJz8tZICKtcG0yo8rrRQLBYER+cYl4ItIUWIr7YF2ZzyZTIy3piMgIXKPkXcASXH3s1cClqvp2Yfv6TURmADMi+I7/D3GN0vfgbnI8G3dvxMBIa/QVkYuAd/i7DfFY3Dftf4fcLxFRROQB4FpVbVTUtn4SkVFAT1xHivm4ts+7gBdUNeJKYSLyPO7L0Y24XoaP4xr8D1XVzeXxGlZdVnr9cd9gOwH5NU43IrKqSQAuwt09fxfQFPet9mxVHeFrVFXDZbhee/+HO7fzgTMjLcEAqOq7IpIB3A1cguvKerWq/q/wPX3VGNjmdxDFcDbuxuZ7cPfGLML1No3I5A3ciasifRJIxLUt3lZeCQasJGOMMSaMrE3GGGNM2FiSMcYYEzaWZIwxxoSNJRljjDFhY0nGGGNM2FiSMcYYEzaWZIwxxoSNJRkTVURkmIik+R1HYUQkRUSmi0iaiJR6sMpwv1dv6P2IIyLLRCTvAKDGJ3bHvzGR5w2gDe5u7Ega9yyXiNyHG/Oqg9+xmMhmScaYyNMJ+FRVn/M7kEIcA0Ty3DMmQlh1mTGRJx7YWeRWxlQCVpIxFUJElgEjcYNy3gIcgJvb5MGcCZ5EpCVuZOu7VPWxkH37Aj8B56vqxyHP++JmSzwVN5T6O8DtuNn97gL2A6bgputdkieevrgpGgQ3iOGjqvphnm26AI8AfXBfyCZ6sU0P2SaIm8q6F24a499VtU8B5yAON9rxJbhJ2dYCH3vnINWbAjdnNOyrROQq4BJVHVbA8eriBmM8HTeVwzzgvwUNyikiw4DzVDUxz/KfgSaq2s57Xt87N/1w8wwt8+J6UlWzvb/lASHvPzdGEbkauA44CDdA7HDgvpzJukLe45nea9TD/c1e9c7jINy8LJtxkwDerap55xUqERE5CPe32wQcqaqRNnBtlWYlGVORzsBdFN8DbsNN6vWJiLQv5fE+wF1cb8eNhH0T8A3uYvUqbrbSPvx94c4R7233uxdHKvCBiOTOByMih+IuTC284z2Em3RqvIh0zXO824A9uOHShxUS73Dc+5+CG635B2/fb70E9CvwL2/bcd7vv+Z3IBFJwE2GdYV33FuA3cAXInJiITEUx3DcKOOv4BLGbOAx3HnGi30BLknmxuhN4PcKMAN3LobjpiH+Ls8Mp+Cm1/6fd9yfcaMW34Obx+Ra4CPcyNYfleWNeJP1jcHNCHqsJZiKZyUZU5H2Bw5W1YUAIvI7bhKqs3EX8ZJaBJyqqkEReR/YiGsr6KSq873XaAFcKiIJIdP2xgCPq+pD3jb/w10YHxWRD1U1G3gBWA50y9lPRF7BXXCfwZVacuzEDetf4PxBInISrsTxsKreG7J8HvA0cLGqvgUsEZH3gEWqWthc9lfgGt0HquqX3rHexs28eAfwbaFnruA4G+PO4a2q+rS3+E0RGY2bRhpVHSki/wcEcmL0Sgt3Aver6n9DjvcjLnH8i32T/dA8240AvlXVm0KW7QJOEpFEVS1xLzkRqY07DwlAH1WNtMnuooKVZExFmp2TYDwzvP4wSP4AAAUWSURBVMf9Snm8r1U1CKCqu3EzOy7KSTCepUAgz2tkA8/mPPGSyP9wUz53FpGGuLnavwGSRKShtywRGA309i5gOX4rxgR1A/h73o5QL+EmizqtGO831EnAypwEE/I+TuKfM3SWxHbct/6rReQ0EUn0jn2Sql5eyH6n4s7zqJzz5Z2zKcAW3Mybocbneb4KOFpErvX2Q1UfUNXDS5NgcInlS6AjcFze6lJTcSzJmIq0MfRJSMmitL2U8k4ZnZnPsizvMfSzvlZV/7+9cw2xqori+G+KlBSRCqYPZTlY86+g7AGhUCGFQi9iHA2mICLsoZCJMBD0KZgQLKQPWU2fKwjDLDTISpggIqjECyKLiiIRHMIgCamJGfuw9sl9T/fNvd0R1g8uh3s4e+3H5e511uuccmC9qEdZDhT1H+P4mPPPliTriqxt1bzqsByYNrPf85NmNpP6vqoFGTlFTKsKM/vezDp+zW/6TTbjCncfcErSR5LGJDX6nVak43f8d80uxd2OOeU1G8dfSvYaMC3pS0nbU9ypE9YAd+HemtsbXxr0klAywf/JXIft6m1utayHVt7CV2scA+k4m/W3C1hb53O8ibx68mtxITDTgoxym25RJSu5wJbhr+b+HHefvYsH4pvJuJfa67W1dH3VmpnZEdwdN4rH7FbgbsSKpMvanhH8mcZSAXamZIagD0RMJphPFFbHwtL5wS73M1jDz39tOv6Iu4wAZszss7yhpFXAEuAv2uNnYJ2kpbk1I2kBMIRv5u1wPBtzPr7HgVW4xVVmFrhI0kDhZkwMkpSzpMXALcBRM5sEJiUtwuMpD0saLrk8C35Jx5/MzEpj2kiDolJJFwA3A6fNbC+wN53bhiuaUdyd2Q5TZnYwPfFgCk8weKpNGUEXCEsmmE+cwq2TlaXzG7rcz0KyuIWki4GncffTMTM7gceLNhXxgXTdUjxj6nVqW1GN2I9bM+Ol81twpXWgTXkfA1dLWpeNb0GSf1NKXihzEv/P35i1WUm1shrG4yVPFCfM7AxwNH0t5j1L9f6xPx2fzzuUNIKv2UiDuQzgmXZ5nGwO+KbUZ9uY2Rd4htomSas7lRN0Tlgywbwh1Yp8CIxK2o1v9A8C13W5qz+AXZKuAU7gdStDwAPZHf424CDwraQ3U5sn8VjM+pIl0AoH8I34BUlDePrxrXia7lc0Tn2uxSReI7RPUpEJN4bX/dxdp817eP3Q+5KKGpWtnItHYWaHJR0CXpJ0JZ6tNgw8C3ySBdB/Be6U9BzwqZkdkTSJ1/cMpvkuS+1+AHbXm4iZzabxvChpD65wLsFTmU8CHwBIuhx3vVXMrNL6UjGOJya8Iek2M5tt1iDoHmHJBPONZ4C3gUfxOpfT+AbRTabx5249hGd7zQH3mdm/D1U0syk8cHwM35gn0ljuzzO6WiUppVE8VXs18Coe69gB3NNCdlpZ3hk8jfod3Op4Gf8/r01377XaVHBF9Dceb3qEpDxKl27AldgIbrWN4XVHG7NrXsFdgDs5lxm3Ga9VGsKtkseAPcAaM/utyZQmgO3ADfjaFLVPd2TFmNfj8Zr1TWRVkSzTCdxCLseGgh4zcPZsuzdkQRAE/SFZTovMbEe/xxK0RlgyQRCcF0hagsfSvu73WILWCSUTBMH5wmLgLTM71O+BBK0T7rIgCIKgZ4QlEwRBEPSMUDJBEARBzwglEwRBEPSMUDJBEARBzwglEwRBEPSMUDJBEARBz/gHbi0/pN1BBdQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#code to plot an inertia graph\n",
    "ks = range(2, 10)\n",
    "inertias = []\n",
    "\n",
    "for k in ks:\n",
    "    # Create a KMeans instance with k clusters: model\n",
    "    cluster1 = KMeans(n_clusters = k)\n",
    "    \n",
    "    # Fit model to samples\n",
    "    cluster1.fit(x1)\n",
    "    \n",
    "    # Append the inertia to the list of inertias\n",
    "    inertias.append(cluster1.inertia_)\n",
    "    \n",
    "# Plot ks vs inertias\n",
    "plt.plot(ks, inertias, '-o')\n",
    "plt.xlabel('number of clusters, k')\n",
    "plt.ylabel('inertia')\n",
    "plt.title('Fig 1: K numbers vs Inertia')\n",
    "plt.xticks(ks)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The challenge was to ensure that selected point was actually where inertia begins to decrease more slowly. For my model, I selected k = 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fig 2: Cluster Numbers Barplot ')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1['cluster'] = cluster.fit_predict(x1)\n",
    "df1.cluster.value_counts().sort_index().plot(kind='bar', color='g')\n",
    "plt.xlabel('cluster number')\n",
    "plt.ylabel('size')\n",
    "plt.title('Fig 2: Cluster Numbers Barplot ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing K: The Silhouette Method\n",
    "\n",
    "There exists another method that measures how well each datapoint $x_i$ \"fits\" its assigned cluster *and also* how poorly it fits into other clusters. This is a different way of looking at the same objective. Denote $a_{x_i}$ as the *average* distance from $x_i$ to all other points within its own cluster $k$. The lower the value, the better. On the other hand $b_{x_i}$ is the minimum average distance from $x_i$ to points in a different cluster, minimized over clusters. That is, compute separately for each cluster the average distance from $x_i$ to the points within that cluster, and then take the minimum. The silhouette $s(x_i)$ is defined as\n",
    "\n",
    "$$s(x_i) = \\frac{b_{x_i} - a_{x_i}}{\\max{\\left( a_{x_i}, b_{x_i}\\right)}}$$\n",
    "\n",
    "The silhouette score is computed on *every datapoint in every cluster*. The silhouette score ranges from -1 (a poor clustering) to +1 (a very dense clustering) with 0 denoting the situation where clusters overlap. Some criteria for the silhouette coefficient is provided in the table below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<pre>\n",
    "\n",
    "| Range       | Interpretation                                |\n",
    "|-------------|-----------------------------------------------|\n",
    "| 0.71 - 1.0  | A strong structure has been found.            |\n",
    "| 0.51 - 0.7  | A reasonable structure has been found.        |\n",
    "| 0.26 - 0.5  | The structure is weak and could be artificial.|\n",
    "| < 0.25      | No substantial structure has been found.      |\n",
    "\n",
    "</pre>\n",
    "Source: http://www.stat.berkeley.edu/~spector/s133/Clus.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fortunately, scikit-learn provides a function to compute this for us (phew!) called [`sklearn.metrics.silhouette_score`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html). Take a look at [this article](http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html) on picking $K$ in scikit-learn, as it will help you in the next exercise set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set III</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Using the documentation for the `silhouette_score` function above, construct a series of silhouette plots like the ones in the article linked above.</p>\n",
    "\n",
    "<p><b>Exercise:</b> Compute the average silhouette score for each $K$ and plot it. What $K$ does the plot suggest we should choose? Does it differ from what we found using the Elbow method?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>offer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adams</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allen</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anderson</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bailey</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baker</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "offer_id customer_name    1    2    3    4    5    6    7    8    9 ...    23  \\\n",
       "0                Adams  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 ...   0.0   \n",
       "1                Allen  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0 ...   0.0   \n",
       "2             Anderson  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 ...   0.0   \n",
       "3               Bailey  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0 ...   0.0   \n",
       "4                Baker  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0 ...   0.0   \n",
       "\n",
       "offer_id   24   25   26   27   28   29   30   31   32  \n",
       "0         0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "1         0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2         1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3         0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "4         0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2= df.pivot(index='customer_name', columns='offer_id', values='n')\n",
    "df2 = df2.fillna(0).reset_index()\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>offer_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "offer_id   1    2    3    4    5    6    7    8    9    10 ...    23   24  \\\n",
       "0         0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 ...   0.0  0.0   \n",
       "1         0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0 ...   0.0  0.0   \n",
       "2         0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 ...   0.0  1.0   \n",
       "3         0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0 ...   0.0  0.0   \n",
       "4         0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0 ...   0.0  0.0   \n",
       "\n",
       "offer_id   25   26   27   28   29   30   31   32  \n",
       "0         0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "1         0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2         0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3         0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "4         0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df2[df2.columns[1:]]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2 The average silhouette_score is : 0.09365573283492713\n",
      "For n_clusters = 3 The average silhouette_score is : 0.11889942863638556\n",
      "For n_clusters = 4 The average silhouette_score is : 0.12347053919571699\n",
      "For n_clusters = 5 The average silhouette_score is : 0.14092516241984757\n",
      "For n_clusters = 6 The average silhouette_score is : 0.1371798939109807\n",
      "For n_clusters = 7 The average silhouette_score is : 0.1161092456616906\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABBAAAAHtCAYAAACgUY2QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3XeYXVXVx/HvTHqDJNRQQ10GpIaOdEINxUaTEkSRJiJ5RamCIIIC0qUZUKp0EJAWuoBAlB6WigQEIr2TkDLz/rH2YW7O3Db1Tvl9nifPzZx7yr77tL3X2XufusbGRkREREREREREyqmvdQJEREREREREpOtTAEFEREREREREKlIAQUREREREREQqUgBBRERERERERCpSAEFEREREREREKlIAQUREREREREQq6lvrBIhI12BmQ4BfAN8GFgbeBO4EjnP3d9M8DwCbAOu7++NmNg1YGhjl7v8zs0bgC3cf2Pm/oImZLQTs6O6/L5i2KrCEu99Ru5SV1p55Z2aXAfsAu7v7Na1Y/pvAGcAiwJ/d/dttTVOpdJnZosATwJLAbcDOwO/TfAAnuvtxad7lgH9n63P3uvZIV2cys02B+4G73H2bdlpnHfA94Hp3/6AN65lGwfncHmnrjTpoH78EGLCMu0+rYv6hwAR3P7c9tl9hW/Ncu8ysH3Cou5+e/p4AXApc6O4HdHR6qmFmxwM/B45091PaaZ2dludtYWZ7AEcCywL/AU5w9+trmyoR6U7UAkFEMr8HDicCiw8A/YADgVvMLLtWPALcArxfiwRWw8wMeIkIhGTTdgemAKvWKl2d7O/Efnq9lcufDiwFPAc8316JyjOzgUQ6lySCCLu6+9zcbBuV+L80uQ24CBjQxvXcTeyPmW1OkdSMmY0EXgQO6aRN3gLcWvD308AvO2nbXUIN8rxVzGxL4ApgNPAosDxwrZltXMt0iUj3ohYIIoKZzQfsArwLLOfuM8xsEPACsAGwMvCcux9Tw2RWaxQwMjfN6EXXO3c/Gzi7DatYOH2u7+5z2iFJpUwC1gFeBsa7++e57z8F1jWz/u4+i6YAwqfA0A5MV3ezcnusxN33b4/1SM3NRwTlvDM25u475yatBHzRGdvuQjo1z9tgH6AO2NvdbzKz/YELgb2Ah2qaMhHpNnpNgVpEypoDNAIjgAlm9kd3/8zMtgUWIj3JzndhKLUyM9sZ+G1a9k/AAe4+O323OnAqEZiYTTy5muju76XvpzFvt4j1gMeAB9190zTPBsCZwGopbae5++/MbDTRdBhg69S0dl+iqSrAr8xsG3ffNAVNzga+nn77rcCPSjUBN7OdgBOBFYgntJOBA939nYImuscST+53Bz4EjnL3y9PyiwHnAxsDg4B/Ake4+11FtvXPtJ2vuvsLadqfgfHAOODxlL/bE/vsFeBkd78izXsZBV0FzGxd4LSUX3PT8j9093+Tk/IsM9vM9nX3y8xsM+AkYA3gY+AaovnvjJTvrwD3AJ8Dm6fvziuWl2k7x6V8ehfY1t3fKTLbE2ldaxFPyzYmgg19KQggFOTtuLT9q4GfuPsX6fu27ruq868gPWcBW6f5H0jzN2sRUqx5t5ndmZbdzN0fKLf9dE4unVY3vWB/FT1H0vqPJ86JXxMtdQYT+/UxmndJeoHY778iAks3AN8vyNvtie4uWReUp4jze193v6zI7+1DNJ/eD1iMqHD9ImtCXc3+KLLO7VIaDZgB3AccnB1TFfZ/lhcHATsR17dn0nZ/SHQN+Yg4V69O65sGLApsR7T8GAVcn9aZD4Jladwx5eHyRDecY939xhLz1hHdyQ4gWoqeWmSeDYmWQqvQdEwc6O4vE+dims0agWVIxwCwKxFgnQ6c5+6/KbLu64BvAau6+3Nmdnja1h/dfZ/U3e0D4HV3X7awC0PKG4ABafpmBavuZ2bnAXun5f/P3a8tkQfTgAWBHYFz02+YTFR8q2oBZ2brp988Nm3vGmI/zi4y72U07141ExiQdZUys72Bn6W0fETTPWMGRfLc3aeZ2feAo4hj/TniXvdQmvEB4ng7CpgIvOTuXzOznxD7fnHi+ng5cLS7N5TIp6Xz05Ni3V0uAB4mrkkAb6XPBUusQ0SkGXVhEBFSofdGoA9REXvPzP5CVPKfbGG/6v7Ek+VXiIred4nCeNaH/WFgK6Jp/LtEoe1eM+tfzcrNbBHgLmD1tK4BwPlmthdRefxrmvVtomntazQ9FXqR6IYBTf3sXyO6POxFVAKKbXM0cB1R+H+UqEB/k6jkFPoJUWCeBiwBXGBm86fvLiMqKK8SXQxWBa5OFaq8q9Pnzmn7g4Et02+6HzieqNh8SBQElwb+aGZrF0l7PVHQXQ94EvgXsA2xv4u5BWgo+P9rqTJ6T8E6ZgM/IiqThbYE1iSCI38rsX6Iysnx6f9Xuvu/Ssz3aPrcKI2VsHzBtOz31QE3EXn7PFEx+iFxHLd537U0/1J6bk6/8b9EH+OdibxssSq2/whRaYYYs+S1CudI/jf/D3je3aeXSMJo4gnlNKJb015EBRAzWzqlY0Wi2fraQKVWSucSlfmhxLm6InCdme1aJG2lzqUvWYx3cgPwFSIv/kcERSal70dT3f4/A1iAqGiuC/wD+A4wNW3/9ynomOlH7OfXievO3kRAphkzW4XIp6WBB4mxRa4zs02KzU8EV44BhhHBjKOJ4Ee2vqHAn4mK8VPEeDXjiKAixLkK0VLnlpS+I4hzdnZKw6LAr81szSLb/0v63CJ9Zi1/NkyfG6fff3uRZe9Onw1p2+8WfLdnSufLRMDpkhSMKGUAsW/fSuvbnqhoV5SuF3cR97AniBYRhwGtGu/AYgydy4hg0f3AZ8D3adrnzfI8BdcuBuYnnu6PAe4ysxWY1/HEfn7MzMandQ4mAmH1RNDixyWSlnU7KvavWTDL3f/q7he5+wfpWvWD9NUT1eSDiAgogCAiTb5HVKq/IApu2xCF8IdTBbZadcC33H1z4qkgRIEcoiA0lHhavj7R1PUhoqKTr0CUcnBaxyHuviXxBO5z4Mfu/jZNFZh/uPvO7n4f8eQJ4HJ3PyYFMr5FVChWdff1iEr75qmgmDeXeGq6s7tvQbRagIJCffIB8cR3DSJQMJgoNAJcCfzM3ddw9w2Jp1EjKP7k56r0mTUN3goYSAySN5cY/Ariada2RMH6+0TlKW8Y8eT4JWAPd1+HyMOfm1mzVmipOfLs7P8p/04ggksHuPsmRGXtX8C2ZlY4LkEd8dR8LXd/qkhaMt8kKgSNwA/MbJkS8/2NyPuNaarEPJqbZzOiG8TV7r6uu6+a5tk79Utu675rUf4Rla61icrsKu4+lji2XjCz1jzlK7v91K3o7TTvvml/lTxHcut+xN03SPOUMiRtdzOaKktj0+eBRMDwXHffgDif3yy1IjNbkqiwfEC0rtmceMIMcHJu9nLnUqFRxLnxCNENaw2iknl++r7a/f83d1+bpuvQfERLq3WJFgODgOUK5q8Hfp5aRa0DzAK+bzGuR95PiPPnG+6+FVGprScq9MUcmD53TXm0BfOW1wYQrSb2TOfjBrnflHVFeSOdw28T59IviWNyK6LlRLF8gOYBhK8R5+pyqWKeTW8WQCjoBjM7bbtwDJXXgK/SFGQcRhwzpfQFjknHXpZXY8vMX+iQtP6T3X1j4rh4mHRta4XRxPXtFuL6NRb4PyBrQVEsz3+apq2f8vwbxLGa7d/Mqe6+hbv/hKZr+++JoOgGRODjfopw9/3T9or9e7vYMgVOAbYlAtGXVJhXRORLCiCICADu/pG7f494OrYHUYmdTVSG9m3BqhqJJ1zQ1KwzC0Cslz4vT9ucQ9PT9uy7vPxI+1l/7wtTU9H30/pXtRj9uxpZoXUDoCGtZ/c0ba38zO7+X9JTYDO7lyiIQhQGCz3s7p+n35U1b88GtrsOeMfMrjSz14hKXbF14O5OtFIYa2ZLEAVJiO4gEM1QZwN3mNl0opXHf1M68+v6CPgjUXB/08z+TjSNfcqrH98g2zdXpHV+RtMT8ML99nZqQl2NA4mniwNpenKa9ynwLPHkc9M07a+5ebLjYXcza0z7cgOi8rF6W/ddK/IvO7buz5ocu/se7r63p7eZVOHLY76V+6/ac+SxKtOTdbN5KX1mx/SK6fPWlNYZNFU+i1mH+G13u/tbaZm7iSfMy6bWBJly51KhF4jK1TjiafeDRGX/ibT+avf/A+nzjfT5urv/J/0/a52R3372u18hWr/0p3hz8mx/3JX2R9Yiqtm1JlmOuI7ekdb/FAXBQY/uXjcCS5vZbUQrl2K/qdB9RDeHX5nZs0QrnaLLpNYoTwObpNYTC9I0SOKGRADhc5ryrFqPuvusdF68lqZVCk7fmT7zx14l2Xk4GcDdP3D3jd39Z1Wndt57z/3EPt6HOJ9uILr+TSmzfLbfPe337DzK7/fC8/BGIgh3dNrOucR18LliGzCzi8zs5hL/Fi62TFruZ0SrlAbgu168C5mISFEKIIgIZraBmV1vZkemQMLV7v4dmpqZf7UFq5vlTSPpZ59ZQaxZH84Cjbm/s+tTvsCYVYCyN0Jk/26jfAG62Dpeo3mzz/fyM1uMw/A80Xz7NpqCDfngRmGT0axyV5e6KTxCNAV/i3halTXxL/UqwqvSd18nWhi8mdaRVbqMKGROJZps32Vmh5ZY1wSiFcMFRH4eBTyfghPVqHa/fVzl+k5z94uJ9M8BdjKzrUvM+yjRBPg7RL/jF3LfZ/vyJZrvyxlt3XfpcwItz78vWyeUeCqdl3/CXKil26/2HKlmf80qCFTk86Vv7u9Kyh1HMO+xVG5/fClda7Knu5cTAdCTgH+Y2ZAW7P/szRNZGgu3Xyrdhd2uyl3jsv2Rb26eD4ZlGouk78v1phY7LxAtSv5KPEWmyDKFziK6PSxCtPbIxicptcwdxBP8rMvAqUTQcmeiZchkd2/p2zpmFPw/f28oJdsPJY+BCgrPw0FVzF+f5q2jab/h7p8QAbC9iC5TXyHGGHm4yDoy2fL569I/cvN9eR56jJOyMtFSJ+s2dglNweO8rYgAc7F/RYMzFuOM/Cr9+QN3v6nMbxARaUYBBBEB+IRoljnRzBYvmD46fb7RbInWyQpOewGkJuBZgT57CpMVMhdLn+vk1vFi+rwlNbf/PlG5vikV8rKCduH1LT9tavr8gOhusTNRoX+C4n339yQqC2e5+5mUDlTkgyCZNdK/Z9z9cKJSXGrgq8w1Kd1HEoNRXpc90TazE4kBIC9KTZzHp2XG5VdiZisS/XC3cPeD3X1loiXBfDR1Lakk2297pnUOISpsMO/Ts/wrGEuZAuDu/yQGzAM4u8Q4GFmXhfmJZub5Clp2PPw77cevE094HyGe2rVp37Ui/7Jja4uC8S3uMbP3zKzY2xLmOd7NbBgFTfWr3H7++K50jmSq2V+ljmloeiq8Q0rrEJqOxWKeTp/j0jgNmNlWRKX237kWGuW2+6UUIJhEDBj3XXdfjtj3SxKVvGr3f2t8K6VhSeKJ90yiu0Vetj8uTvvjKGJsh1KVwny+rkPT9RDiWj0/MX7Ir2i+H4tdA79L5OkEj0ECS3UbymQtSfYkmrj/jWgVtTtRiS82/kGmkfYrX1Z1HBSRnYdbA5jZcOAtM3uhxLgz+fvO2hQEKywGFJ4ENLj7HsS4GK8Ba5rZAhTP82y/n5T2+2+Jrl8357b95f4zsz2JgMHL7v4N4jieRZFrO4C7j3b3uhL/puXnN7OViEAkxEC36rogIi2mtzCICB4jbd9IVApfNLO/EQWplYlK9mXttKnTiH7KR5nZ5sSgZSsQBdOsL+nTRMH/GjN7nBiLodBFRJ/QX5vZDsTgaIsRg1oBZE0xNzKzR4nmttm0g83MPEYSv4colE01sw+IAuN/Kd6cPqsUnGzxhomsP361rxJ8g3iCNtbM/kqMLr9ouXW4+xtm9hBNTfcLKxvDiYraM2b2DNGnGIq/hms6MVr8qJTnn6b0zyAGYKvGL9MyF6QC7rJEAfo2d/9rGqiutU4gKikrAofTfJCzR0v8P3M3UeEan/Kinmgx86S7n2Zmbd13Lc2/yUTAZR3gOTP7nOgv/QxRocgPnPdM+tzOYvT75Zn3yWE123+HqBDeamZnUPkcaS+XpO0cYmZjia4VJZtNu/srZnYFsb+fN7PniO4mjUSgrDX+RwSN9kz7tz6t8x3iuGjr/i/nODMbR7QGGgD81t1nmVl+vnOJ696VZnYgMebLSKIyWcyZRADxynQNHEvsu2zAwew3/dDMVqOpG1H2mz4gKqXLm9nDRPDgVSLI8YSZfUG0IihcJu+xtJ4RxFgZDel6lAWt7iixHETeL5yuv0eVma8jnU8cm4enAMwSRIuKye4+t8g+yoJbx1iMg5N108jOxfeJgNE3zey7RL4sBbzo7u+Z2Sya5/nZxNg395vZE8Q1YSjlu37MIAJE25jZI0SguT9wb6tyobljiGN1LrC+mWXBjL+7+y/aaRsi0sOpBYKIZPYmXh02nShkjyKavG7iRV4/1xppQK2NiErfV4m+tZcB47zp1Vo/JZqFLkFUKr+TW8d/iCa7TxKF2XoiMHFk+n4q8AeigLQkUWi8gWjquwBRyYF4knZ5SsNKKU3jUj/uvHNpGqthDFHAfwVYOT0xrvS7pxPdFl4nxj54iTRKPLB+mUWzbb5G9F/OZBXtOcRrDj8nKuJnFNn2J0Rh+DaiIrkWkXc7uHuxp6XF0n8PEch5jAi09CcCLd+uZvkK636DKGhDFN4Xz30/jaaB+ZoFEFKLhG2JpsHLEvv8BpoG52vrvmtR/qX07EQ8ZVwqpekmYEd3b/Y01d1fIvbne8S+vIs01kQLtn8Kcd4aMLjSOdJeUguSXYk++KsT/ewvTF/PKrHYd4kBAD8h+tP/k2gFVPQNKFWkYRrRjPtBolK8MhHE2SaN1dGm/V/BvsT1ZCjRkuboEml8hMinfxIDEn4K/NRLvObU3f9E7KtPievkycxb6bweOIc471cjgouPAguY2QrpmDmVaBq/IlEJ3pcYTyRr+XRi+ix6/UldQ7I3C2TN9LOxbZ4tNt5KgROI43kMMR5Fp0vXlW2JVmXrENesM4kBLYu5lLgX9SP20U9pGsMHd/8b0X3jaSJgsxRxju+Yvm+W5+5+FTGY41tpnW8SLUBKtt5w9xuILkuvEIPHDifuZy0Zh6io1C1j+/Rnn5T2rLvDBqWWExHJq2tsbG3rMBER6UhmdiYx+vgZ7l7V68tEOouZZW+ceMHd/5ymXUUE57Z098m1TF9HMbNpREV8lLsXe/OJiIhIj6UuDCIiXYyZ7UU8sdyWaElxcW1TJFJUP2IwtgYzu59oYr8e0fT98XILirRW6spTasBYgPvc/ewy34uISBsogCAi0vUMIJqt/xc4PjVzF+lS3P1OMzuCGDF+E2KU/keI5vntPd6CSGYpml5tW8yHnZUQEZHeSF0YRERERERERKQiDaIoIiIiIiIiIhUpgCAiIiIiIiIiFSmAICIiIiIiIiIVKYAgIiIiIiIiIhUpgCAiIiIiIiIiFSmAICIiIiIiIiIVKYAgIiIiIiIiIhUpgCAiIiIiIiIiFSmAICIiIiIiIiIVKYAgIiIiIiIiIhUpgCAiIiIiIiIiFSmAICIiIiIiIiIVKYAgIiIiIiIiIhUpgCAiIiIiIiIiFSmAICIiIiIiIiIVKYAgIiIiIiIiIhX1rXUC2ouZTQAurTDbR+4+PM0/DZjj7su3YxpGAscD44FFgX8DFwHnuXtjK9ZX7TKvuvtoMzse+Dmwkbs/0tLttTczGw28Avze3b+Xpk2jIN+7WporMbM+wFLu/kpu+gru/q8apGc0uTxuxToWAz50988rzLcEMAnYEJgD7O/uf2rNNluQtnnytSPO21ozs02B+4Fj3f2kGienTTpr/9TqfBPpbXpa2crMNgQeASa7+5YV5v1n2t4o4NtEPuzl7le09De0t1Q+/PI3mNllwD7Aku7+egdud57yQmdttz2Y2cHAEcQ+fd7dx7ZhXRPohOOhVJmzK2jruW5mDwBfc/dW1UW7ct70Bj0mgFDgJuDGEt/NKvj/YUCLK/WlmNlg4D5gDPA74CVgW+AcYCXgoFasdq/c398Avk7cOB8umP5pK9bdGd4hfsO/a52Q9mBmSwN3AFcDJxVMPx/YElixRklrNTPbBzgP+ApQNoAAnAmMI47pZ4FHOzht3TZfW2gqcZ48XeuEdAdmdiRwLDC41mkR6UV6RNnK3f+aAgObmdmi7v6/EttdD1gBuMTdPzOzh4jr9F9b/WM61oXAvcD7HbWBEuWFDt9uezCzVYBzgX8BhxLl0y6tVJlTlDddQU8MIDxbTTTQ3W9u5+0eAKwG7Ofuk9K0C8zsVuAAMzvd3V9uyQrzv8PMlicCCI91hQh4Je7+GdDl09kCyxAFlrztiCfy3dFmwJAq510VeMfdD+3A9BTqzvlaNXd/i551nnS0rYH+tU6ESC/TY8pWxJPjXwG7EYHxYvZOn5MA3P0/wH9auJ1O4+6PAY918GaalRc6abvtYZX0eZa7X1jTlFSvVJlTlDc1pzEQ2s9g4B80rwjcDdQRN0CR7qw/8HGtEyEiIr1GR5St/gjMBfYo9qWZ9Qd2BaamCrJ0f1nQ+aOapkKkh+iJLRCqUqzvjpl9hWgKswkwCHgImAg8B1zh7hNKrS/1XS7WjGbN9DmtYDsTiAj4Ce5+fKt/RGkLm9kkYAciWvws8HN3v6twJjNbiehXuDkwDHgZ+ANwuruXffJrZkOAk4mmhEsRFctHgF+4+9NpntFU3z+/2jR/DTgK2AAYSDRHmwSc7e5zK223WJ+r1I/qUOC7RJPFz4EH0/afTfMcT4zVAHCimZ1IREBfKVhPIwX7tI35+wCwILAf8YRkdeBd4FrguNS6o9zyX03p3RSYD3gVuAb4lbvPSPNMA5ZOi/zXzB50902LrGsCBX1g0+/8ct4W7pNj028ZD3wAbObuL+W2l81buL15zhUz2wL4BbAGsb/uAH6SnuYXrmtv4IfAykRrhr8BJ7r7Q2Xyrh/wBtHaYuXcd/XAa8An7j4mTfsK8DPi6cyiRHPeF4DfFo4RkfbpcKIZ7knENeaXxNObZmMgmNkOxPVnLNAHeJ7o8/uHgnk2LbZs+m4aza9xhwITiG4hDUS3iTPdvVTT5ML1zQccA3wTWAyYDtxM5OcHJZY5niJjnJQYH6Waa0pjwToagT9k12UzWxI4AdgGWAB4nThfTio8X9Jy5wL9iKeMnwO7uPt9bckfEekeZSt3f9PM7gK2M7Pl3T3fzXJ7YCTRSiG/7i/7vJvZWjTdh0YQ94YbiWviZ2me46nyGpimbwX8CFiHuF98TNwjfu7uU0r9pvxYBLn7ezGbufsD1W6zVHmh2BgIqdvJz4gWHqOJSvv9xH55IZfmnYl73K+JstIA4HHg6GqCN2a2AHAcsBNxX3oXuBM43t1fK5L2y83s8sLfX2K9WwH/B6xNBKqeJcpPfymzzDzjUuR+Zz6PtgKOJFpGDCbKh1cCp7n7nFJlTneflpavWLYpWMf2RDlyaeAedx+fvl+fKJNtQOT7S0QZ45Jc+gen9ewGLEyURY4qlQ9F8qUPMfbEvsS9/d9E2afYvIunfNkGWIII9P0buNjdz839rmZ5Y2Yj0rZ2JMrodcQ14mrg5Eplb6leT2yBMNjMFizyb2S5hdIN7jFgK2KMgaOA+YkCbIvyycwGm9kqZvYb4oS5wd3/XjBL1peuowqlfyQu2scCpxD91W4zsy8rQ6l/3xPAusSF5cfAi2n+G1JFqZxrgf2JCsTBRL+4TYCHLQbb64g0705U7FcBziAuEm+l/19fRZqbMbM6omJ9OlGY+TFwNpEvj6fKMcS+Ojn9/3pi/2VjPLyb0vHlPm2H/IWojN5DVNT+jxj34nDgnnRBLvWbNgGeJMYPuDht+zkib+83s0Fp1sNoGkvjUEpc0Gk6Xgt/5y/Ttlq6T35KBEYOBf6YDx4kJfM1WQK4Nf3Gw4gnUXsRx2JhPpxOBGzeTuk6hbh53Wdmu5T4rbj7bOJp10pmtkbu682BxUkBFTNbjrhxb05cNw4i9veywDWpkFBoeeDElJZTiP3bjJn9NP3GhYnj7ljiRniZmZ1VKu3lmNlPgLOIQsLhRKFrQeJ43LnCskOIQt3/EcfDj4E/A4cAd5rZgNakKaeaa8peKf0N6f8XpvQtBzxFBCAnEYXhB4n9XnjMZyYA66ff8QfiXG91/oj0Aj2tbJV1hyjWCmEvYDZRLimVluWJvv+jgdOIa+ETxD2uVV3S0n3pTiIAehJxP7mOGHvoPjNbsAWrO4z4HYX/jkvfvQw808JtVlVeSPeKB4l71tNpuUuIrmdPFJSpMgPTeuuJiuNviWvzPVUcW4sS5YBDiH3xI6J89h1gipll4ycdRhx7EAH8vYixh0qtdz8iT5YBfkMEzucDbjezb5RLUzUsBvL8MxFMO4G4D/2XCFj9Js1WqszZmrLN1cR9dWL6P2b2deJ8WSpt56fAe8DFZnZOQVr7EOWUI9L8hxMBhNuBRar8yVelbXhafnJK/zyDWJrZcKI8tXtK58FpufmBc8xs/3J5kx7+3EccB3cSAZbjiADLCUSwT9pJT2yB8JP0L+8jIrJaymnEU+K13f0fAGZ2HvFkM18JqOQY4kII8TR2nvR0Ql+6B4AdstGJzewV4kb4LeCFVGmeRFyMVnf3rEnX+WZ2DFHB+TZQdIR9M1uI6J9+vrsfUTD9H8SFbA3i6V97pnl+4sL/Vkrze2m+c9JvmUDcNC5v4XZ3Tdv4kbufXfBbziMizhcBK7n7s+lmdhTwTEFf0CvM7CTiiUv2VKJN+VtgAeBcd/9h+vs8M3uDqMTtSVyA55Eq7JOIQazWKugben5B1Pb/iEj1zalitBFwU6kRlLPjtcjvbM0+mQ1s52Xe+JCNnZHfXoEBxBPj69LfF5nZIsDmZra0u7+aAjiHE60ADi/InzOJivD5ZnZ7mZYclxI39e8QzWczexER8ew3HUJcNzbIPVl5GLiLaGlxd8HyQ4Dvu/vVBfNuWrjhVBk+iSjgrV/QYuQs4C/AoWZ2vbsXDqRajQnAi+7+ZYE7kVH2AAAgAElEQVTZzK4iBgVbjVwAJucIYhCzfd39soLlXyeeHO1A3MhbpdprirtfYWbfA1bIHRfnEU1UV3f3V9O0C8zsPmJfHQqcWjD/YGD7wmPe4glja/NHpKfraWWrW4kg9e4UVCzSfX574HZ3f7vM8l8nKjbj3P3JNO0SM2sAVjSzAe7+RZVpyRxJVCQ3dfeZadrFZvZ++m5jqnzwlB+LIlXOHiP21/iCVmNVbbPa8gJRvlgLOMrdC1tw/IEIKEwys6+4e0P6agBwrbsfVjDvx8R1/5vEQ5BSfkVU8vfI3VNvIiqSvwO2SGkfTgSoHy1SpijMp2HEQ4AXgXULWpL8kQgu/4K2P/zbg7hf7eDuWVDgIqKssAJAqTJnK8s21+Tuq0OIfP070SJ3dvrqbDO7GDjEzK5w978RrfQ2YN79+Tsze5K475ZlZpsBuwAXufsPCqbfRQQh5hbMvg/xgGZ7d7+jYN7riODD+LSeUnmzE9HC9WB3P79g+QuJcup4WtByQsrriS0QLicip/l/O5VaIFWEtgbuyG5wAKn5dWsiVrcTB+oRRDO4p81s7Vasp7Uu93lfbfR4+lwsfa5GVAZuA/oVPk0gngJCvPGhlI/Sv13MbH8zWxjA3W9195Xc/c8dkOZxxM36rKyimrbZSBQqIC5SLbUbUdm+OZcPjUT+jDGzMS1cZ1vzN9NIRE0LZRfwb5ZYZk3i6fcfvPnAUqcQb+xoTT4V05p98ni54EGVPqP5DTx/vOyWPq/P5f9goqK7AFEwKsrdnwOmALunoEzWjO8bwJ3uPj3NejgwKhc86EvTtXVokdVPrvD7vk4Ed7/sbpLSNIfoEgOt24evRfLspOzJjLu/7e4ruHv+OCuWprdpHrQ6n3iKULJZZ5VafU2xaLK4FfHU67Pc/r4L+ITm55sXKQC3JX9EeroeVbYqaGn2FTNbs+CrXYnK3e8rrOK19HmqmW1hMW4C7r63u6/XiuABRHP5NQsq8lllL6vgFbufVJSezN5IVE53zbX8a+9tfpu4lp9eODFt8/KUhnzLvitzfz+VPhcttZF0X/4GMU7F1YXfufv9RIuEzaxlrTYgjumhwO8KK+Hu/jGwBXH8tVV27JxvZuubWb27N7r7uKx7QRmtKdvkyxzj0nzXA/Pn1nNNmie7Z+5MVPLPya3jQqIbaiVZ6715BitNAYJnc9POIlo1fFmeSA/k+hPl4bLHorvfkn7XJbmvFgI+rLS8tExPbIHwH3e/t4XLLEfkRbHm1C+2NAHunr3m5/YUZZtCPKXbrKXraqX8a4mySkjWzDhr1nVI+ldMyb5z7j7LzPYlntJeSDzpe4Y46S9z9392QJqXTZ8v5ObD3d8ws4+ISHRLrUg0DX+1zDxLU6a5W4l1Qivzt8Bb7v5u4QR3f9/M3iGO2WLK5dNMM3uZaEbfHlqzT97Kz9sK76QCaKFSx3i5V25V2geXEn3lNyNuwN8gbkCXZTO4e6OZDbTog7cm8XuXLUhHPkjbSOXXR5XM14JprTnWf0w8dTsaONrMXiUq2Fd7mb6gBdt7Ohfky1qL/L34ItVr4zVlBeIc3onSlZn8vi52HLYlf0R6up5YtrqUaN6+B03Xsb2BN6kcFL2eCKjuk7b/mZk9CNxCjOvQ4kC5R9/3FczsBGKE+dHEtSu7j7T2od9FKY0/8ty4Uh2wzWWJllyzinxXeP8qHM8hfz3Ogi8lu2oS3cvmo0Q3wLStLYnf826JeYrJ7q3Njll3b0k5sJxzifGpvpX+fZBay91ItMYo10+/NWWbfP5m6/h1+lduHcsB0919ntfFu/tcM3uJ6GZZTlZWLXYPf4EYwyHvZ2a2LnEsLUfTK5urORZnAz8ws43TsssRxwmUL+dLC/XEAEJrZKOzFosYt+lpaWpq8xzRpKuz5CtXedlJeDbRD6uYT8qtwN1vMrN7iGbHWxN9wI8EjjCz3dy9pc2ZK6W5rsL39RTff3n5Y76eiEx+u8wyz1Sx3vw6oQ35m5T6PX0o/XrD9sqnarRmW5X2czWqWUe2D8YXSUPGK6zjKuIpyp5EAGEvoo/grdkMqUnntcT+nEx0S3mOGByrWMW6IV8JL6Jcvma/q9pj/cvjxN1fSq1pNiHO282B7wP7m9nZ7v6jMuvqRzu+250i9542XFOyPPkTzZ88ZGbn/m52DLUxf0SkuS5dtkrrmALsamZHEBWW9YBTigSp88vOBSak4PFOxNPpjYlrx0/NbN38A4CcZtdAM/s10S3jZWJcgL8QXeiWJY330lIW3SYnABd6QTfNDtxma+5fDfkZ27idctuqpF/67LD7XQq8b2tmqxLd/7KWDd8EDjOzr5UIwEDryjb5Yzlbx1HEGBLFFHbfyY8hlF9PNQYRLWBLLp+6Z2RvV5lMlJ+fI47L16jAovvnI0TrpPuIVihnEsGWe1qYXqlAAYTwL+Ji8ZUi3xWb1oyZPQTM7+7FXik0jKYnpF1BNsJ9Q/6JgsVgY+OJgfuKSn3EVgGmufu1pGb5ZrYRccL+jDb0hy4h69e4MtE1oDA9SxJ5nF1gskpTsYtevkncK0Q09il3/zC33vWIyGVLCzptyt8Ci5vZoMJm7BZ9xUdSuhl8YT7Nw8wGEoWCf1Wx7Wq0ZJ90tmwfvO7u8wSAzGwVYiDGsm+ycPcPzOxmYGczO5a4yZ+fu7GfRoxYvZIX9JdN0fPWKszX53PfZfu17LGemqwuQLxNIutWsQow293vI26u2X66l+jzeJw3jdeRNw1YwczqCgMgFm9mmET0kb2qyHKlzsV5zsM2XlOmpc8BRc63OqJQNo0y2iF/RKS57lC2mkT05d6ACBpm0yqlayliLJbJxKDBZ1gMJnsqMYjbbsST5mqvgUsR4wfcB2xT0C89G1S3xcxsN6KryP0UaQ3ZEdsk7l/Lm1n/IpXg/P2rLd4lAvfFnmBn22qg5eNxTUufK5LuAxkzO4Do8jGxxLJzqaLcaWYrAIt4vJXjWeCXZjaUOO6+TXTJu63ZWkKbyzYF6/i8yD1zYWBDmsoh/wJWNrOFPI3XkOarJ8qTlV7tnZU3x9A8WLFC7u+TiNYGKxW2OrQY46qayv+RRF5vke7j2fJ9iG4M75VaUFpO0Rgg9d+eDGxvTaO2ZoXPUheKvP8Cq1puhFaLEVGXB25qp+S2h6eIpjz7mVm+qdMRROG9XD+sMURE75jc9ClERLQjXpNyN3GzOMzitT2Fsv7JWb/4d4lX6a1hBW8BsHhlTb7p/w1EtHOePs7pInozMRJsFh3Porj582Zublpb8zfTl2heWSgbAKbUAIx/J24O+6RobKGfEoP4FY4fUOo3VaMl+6Q18vnaEjekz5/njoEhxD69ldJR9UKXEgOEnU20/Lg09/2CRFeTwuBBH6JQBq0L0t5EHHNHWsHbA1Il99j0Z5avb6bP/FO4bxGjW2f6EIWhK1NwAQB3/y9x7Wqk/Hl7C3FjzrfU2ZuooJd6GlQqfXvl/m7JNWWe48Ld/5eW3cHitWr59F1HvA61nLbmj4jkdJOy1dXATGKcl28DD7t7NUH2nwH3mtk62YQ07kHWfz+7XlR7DRxJXEdfylXkRxItoaAF9xOLkf4vIypw3yrRLL6l26ymvHADMTbS/xVOTPv/O0RXmJa26mwmtQC5mRjDYp43aVi8iWpz4AEv8YrhMu4hglL7p4cu2TqHEmWojfMPmwq8SYybNbhguWWJCnmhs4DJKUCd/Z5PaRoTINtXxfK7Pco2dxOtAQ63GFyy0KlE+SIbWyQrax6bm28CUSmvJBv36+h03mfp3ZymV7FmFiTKlNNy07NgVqVjMRvvIv/g5WCi+6kemrcjZWaTHwOPAn8zs3OJPkM7E1FpqNyc6SjiCeUVZnYB0YRoLeJVQ070qwW+vKBsADzr7s8WWVeHSn2X9ieaB/09pfdV4GtEc+1/EIOjlVr+CTP7C3CgxSBJDxGVlT2Jk/T0Usu2Ic0fmdkhRAXuaYuRYj8gmn+NIwZXujLNO9PMrk3pudXMbiH6tR1E7IvCMQAuIwZNOjTd4G4nKtkHEH279i5oAZD1I9vZ4m0IN7n7+2n62mb2Y+Axd3+8Lfmbc1yKVj9J9GH8NnCru99QbOaCfXsb8JSZnU88hd40LTuFeGqeyX7TT83sHs+N3FxOS/ZJKzXL1xak7V6Ldy9PAB6xGMW3Efgu8WTiaHd/o4pV3UM8wfg6MdrvP3Lf/xnY08xuIF4bND8xqvcqRBBg/mrTXJD2f5vZz4m3dUyxGP15FnGcrkMM7vRwmvc/ZvYIsFWa76G07X1pesqAu39hZtmrIx8wsz8RFfMtievWeV76jRQQg3fuDFyVbvz/SNvZn6h4lwpo3UgUlo5LBbBXgW2JkZK/HLSrhdeUt4A6iz67D6cnKAcRzRwfSufb1LSN76dtnljmt7Uof2p9/RbpZrp02aqgpdkEokI9oapfFU2j9yDGY7iAqPQsS1yLXqfpmljVNZAYE+Jl4Ptm9gXRP3w0cc/KXmdY1f3EzBYnKtd9iFYQ4woDo8nLRHmgJdusprzwa2BH4qn6asTgtksQ+dKQ1t1efkaUbS63GO1/CtGy5QDgfaLi2CLu/p7FK33PBZ60eHvEbCLdSxL7rpQ/EEHwe9L9eGHid79CBMkzJxP3loct3r7wFvBV4EDiTRVZq4BiZc42l23SMX8oMVDos6ns9g7x9pHxRDeWrIz5JyLw80OL1ynfm9K6H5HHZbn7YxZvXTmYCJrcSLw68hCim0Thw6c/E/l3ZyrD9yfKXpsQ9+Nix+KXeZOW3yktP4k43rZK02bmlsfM9gQ+bUm5V5qoBULi7s8Tr6d5lGh+dgoRhcxGOy/bj8rj1WFrE9G27xAjlm5J3DjWy/WF25gYjbbN75NtLXe/m7jRPgT8gHjCuh5x8d/C3Sv10d+FKJSvSTTf+wURORzvTa/Xa+80/5HI06nE04tTiQjoj4CdvOm1QBAXq/OJgsY5xEVkL6KSV7jOOcRF8yji5nAaETl/GdjaC173k5pUnUEUEs6maSThY4lCwamkqH075C9EhHVT4q0Ov03bO4rSb2DI0nkv8R7l+4kb6RlEZe844pU9hU0+f0e82ul7lB5Mp9y2WrJPWqpZvrbQd4kbcn/indU/J47R3dz95HILZlL6s3eBX1ZkloOI42vt9PnDlOZ1iTdDbFT4FKNa7n4S0YrgfeKGegJxPOzt7gflZt+FCNRsTxxnqxOFnPwIx6cSedKPeJvDb4kmhIcR+6tcej4kjufziT6+ZxHn1CnAjqUGfUrLbUkcY4cR+3Imca3NX1OrvaacSjxhOJL0ZCJVFsYShZ7dicLfdsSYCBu6+5tU0IL8qfn1W6S76CZlq0lEhfkTosVSRak8sDHxCuoJxLVxAnENWj978l3tNTA199+WCLzvRVzLdyFaf61EBJGrfe3lCsTT2L5pPdcQ+VL47wet2GbF8kJ6kr4RsZ/HEoGW7xKv7VzL3R+s8jdUlK7rawMXANuk9H+TqMiv5vO+baIl6z2PCIJ8SNx7jyMquxu7e6lBGyHuWScTAZOziW4sRxODWBau/xGayk0/JI6dndLnFtn9tEyZsz3KNpcSD3qystsZxIO2I4FvFqShkajEHwOsmubbiHigUdWgku5+CFFWWpgoY3+DOBfyefmL9G9p4rj5KXGObEGcVyul4FjRvHH33xPXmEHAb4j9NoIoS50BDE0tkTOXk3s7hFSvrrGxPccJ6b7MbFGiKXJjbnpWETjB3Y+vRdqk9zGzB4jKvloJiYhIt6SylYhIz6MWCE0eBqam/suF9k6fj3VyekRERES6M5WtRER6GD3dbHIJ0eRqcupTNJfoc74L0Z/87hqmTURERKS7UdlKRKSHUReGAmlAjYOIQVgGEP3gLwd+W6qPr0hHUBcGERHpCVS2EhHpWRRAEBEREREREZGKavJ0c8qUKYpaiIiIlDB27Ni6ynNJW6k8IiIiUlqx8kjNmkePHTu2VpuuytSp8XaSMWPGVJizBhZbDKZPh1Gj4M2KbyeruS6dl92M8rL9KC/bj/Ky/UydOpXPP/+81snoVbp6eaQ96VwtT/lTnvKnMuVRecqf8rpa/kyZMqXodL2FQUREREREREQqUgBBRERERERERCpSAEFEREREREREKlIAQUREREREREQqUgBBRERERERERCpSAEFEREREREREKlIAQUREREREREQqUgBBRERERERERCrqW+sESCtMnAiffALDhtU6JSIiIiIiItJLKIDQHU2cWOsUiIiIiIiISC+jLgwiIiIiIiIiUpFaIIiIiEjNmdmOwJXuXrZ/npn9GRhf5Kth7v5phyROREREAAUQuqfZs5v+369f7dIhIiLSDsxsA+AKoK6K2VcDzgKuyU3/vL3TJSIiIvNSAKE7WnppmD4dRo2CN9+sdWpERERaxcwGAD8CTgQ+A/pXmH84sCRwp7s/3vEpFBERkUIaA0FERERqZVvgSOAnwDlVzL9q+ny2w1Ik3d7Hcz5m4j8ncukbl9Y6KSIiPY5aIIiIiEitPAks4+4fmtnxVcy/KvAFcJKZ7QQMAm4Hfuju/+u4ZEp3cszLx/Cnt/5EXV0dG4/YmOUGL1frJImI9BgKIIiIiEhNuPsbLVxkVWAA8AnwdWBZ4CTgPjNbw92/aGkapk6d2tJFuq0ZM2YAPf83D/t0GHPnzmVw3WDefuVtZtXPqmq53pI/raX8qUx5VJ7yp7zukj8KIIiIiEh3cQZwtbvfn/5+yMymAo8DuwCX1yxl0mXsOXRP1uq/FgvVL8Tw+uG1To6ISI+iAIKIiIh0C+7+EvBSbtrfzOxD4u0MLQ4gjBkzpp1S1/VlT7V6w28eQ8t/Y2/Kn9ZQ/lSmPCpP+VNeV8ufKVOmFJ2uQRRFRESkWzCz3cxs49y0OqJbw7u1SZWIiEjvoQCCiIiIdBcHAmeZWWH5ZTtiMMWHapMkERGR3kNdGERERKRLMrPlgIXc/fE06WTgL8AVZnYpsCJwInCDuz9ao2SKiIj0GmqBICIiIl3VscBj2R/ufhewI7A8cDNwNDAJ2KsmqRMREell1AKhO/rd72DmTBg4sNYpERERaRfufjxwfG7aBGBCbtptwG2dlCwREREpoABCd7TTTrVOgYiIiIiIiPQy6sIgIiIiIiIiIhUpgCAiIiIiIiIiFakLQ3f04oswZw707QsrrVTr1IiIiIiIiEgvoABCd7TlljB9OowaBW++WevUiIiIiIiISC+gLgwiIiIiIiIiUpFaIJTQ0NjADZ/fwPDXhtc6Kc3sN/czhtQ6ESIiIiIiItKrKIBQwhzmcN/M+xjyfterqn+n4QsFEERERERERKRTKYBQRh111Nepl4eIiIiIiIiIasfdUmOtEyAiIiIiIiK9jAII3cjshtkM7TOUYX2H1TopIiIiIiIi0ssogNBNzGmYw9eGf41JYybRv65/rZMjIiIiIiIivYzGQOgGBtYPZOLSE1lv+Hq1ToqIiIiIiIj0UmqB0MU1Njay5rA1FTwQERERERGRmlILhC7u84bPWW++XPDg3nthzhzoq90nIiIiIiIinUMtELq4wfWDGTvf2HknrrQSrLpqfIqIiIhIh/jlK79ksYcXY5fndql1UkREugQFELq4JQcuyXx956t1MkRERER6nbvfv5u5jXN5/KPHa50UEZEuQQGELmxO4xxWHbpqrZMhIiIi0isdM/oYVhm6ChOXmljrpIiIdAnqRN+FzWmYw9Yjt27+xS23wMyZMHAg7LRT5ydMREREpBcYt8A4xi0wrtbJEBHpMhRA6MLq6+oZ1GdQ8y8OPBCmT4dRoxRAEBERERERkU6hLgwiIiIiIiIiUpECCF1YHXXUaxeJiIiIiIhIF6DaaRfWt64vQ/oMqXUyRERERERERBRA6MoUQBAREREREZGuQgGELmxInyHU1dXVOhkiIiIiIiIiCiB0VY2NjSw9cOlaJ0NERESkR5rVMItr/ncNk9+fXOukiIh0G3qNYxc1s2EmGw7fsNbJEBEREemR7nzvTq5860oAVhi8Qo1TIyLSPagFQhc0a+4slh+0PJuO2LTWSRERERHpkZYauBR96MOIviMY3nd4rZMjItItqAVCFzK7YTbz952f/Ubtx04L7aTxD0REREQ6yOrDVufSlS5lYP1ABvUZxBu8UeskiYh0eQogdAGNjY000sjmIzbnkCUPYXCfweUXePXVzkmYiIiISA82ot+IWidBRKRbUQChC/ii4Qt+vfyvWWO+NapboF+/jk2QiIiIiIiISI7GQOgC+tX1Y/Sg0bVOhoiIiIiIiEhJCiB0AX3r+jKkz5BaJ0NERERERESkJHVh6ALq6+rpV9eCbgmnnw6ffALDhsHEiR2XMBEREREREZFEAYQuoF9dv5a9ceH002H6dBg1SgEEERERERER6RTqwtAF9K1XHEdERERERES6NtVcu4B6xXFERKTGzGwoMBHYHlgC2BWYCewP/NLdp9UudSIiItIVqOYqIiLSy5nZQsBTwDFAP2CR9Lkw8F3gcTNbsXYpFBERka5AAQQRERE5BVgUWBMYB9QBuPvtwDpEeeGkmqVOREREugQFEERERGQH4Bx3fw5oLPzC3acA5wAb1SJhIiIi0nUogCAiIiJDgTfKfP8BMF8npUVERES6KAUQuoDGeR/2iIiIdLYXgG2KfWFm9cSAii92aopERESky1EAoQsYVD+o1kkQEZHe7RRgRzO7CNggTRtlZuOAO9O0M2uVOBEREeka9BrHLmB43+EtW2D0aBg8GBZeuEPSIyIivYu732BmBwK/AfZLk/+YPmcDR7n7lTVJnIiIiHQZCiDUWENjAyP6jWjZQo8+2jGJERGRXsnM+rn7hWZ2NbAlsBzQB3gNuMfd36lpAkVERKRLUAChhhoaGxjWZxgHLH5ArZMiIiK923NmdoG7nwncWOvEiIiISNekMRBqpLGxkcF9BnPmimeyYP8Fa50cERHp3ZYGPqt1IkRERKRrUwChRgbWD+SM5c9g0QGL1jopIiIiNwB7mdn8tU6IiIiIdF3qwlADMxtm8v0lvs+Sg5Zs3QoOOAA++ABGjIALLmjfxImISG/0IbAT8D8zexF4B2jIzdPo7tt3espERESky1AAoQYW77844xcc3/oV3HorTJ8Oo0YpgCAiIu1he+Dd9P+R6V9eY+clR6T7mTF3BoP66NXcItKzKYDQiRoaG5jdOJtdF9mV+jr1HhERka7B3ZepdRpEurODph7EpOmTGDVgFK9s+EqtkyMi0mEUQOgEcxvnUkcdqw5dlf1G7cfyQ5avdZJERES6FDPbEbjS3YdVmO+rwFnAusD7wHnAr91dLSSkZu7/8H4aaWT6F9OZNXcW/fv0r3WSREQ6hAIIHWh2w2wG1Q9i/fnX53uLfY+FByxc6ySJiIg0Y2Z3VDOfu2/XQdvfALgCqKsw38LAvcDzwC7AmsAvgbnAaR2RNpFqnL786Uz890Q2G76Zggci0qMpgNBB+tKXbRbchr1H7c2wvmUfpoiIiNTaSjQf46APsAAwCJhGVNrblZkNAH4EnEi8RrJSzetgouyyo7t/DtyR1nGkmZ3l7rPbO40i1dhuoe3YbqEOia+JiHQpCiB0gMbGRr467KscvOTBtU6KiIhIRe4+uth0M6sHxgOXAr/tgE1vCxwJ/IQIVkysMP+WwOQUPMjcDBwDrA082gFpFBERkUQBhA4ws2EmOy64Y62TISIi0ibu3gDcamYXAqcC67TzJp4ElnH3D83s+CrmXxF4IDftPwXftTiAMHXq1JYu0m3NmDED6F2/uSWUP+UpfypTHpWn/Cmvu+SPAggdYGTfkYydb2ytkyEiItJepgFfbe+VuvsbLVxkPuCT3LRPCr4TERGRDqQAQgcY0neIXtMoIiI9gpmNBL4HtLSy3xHqaD5WQ6ahNSscM2ZM61PTzWRPtXrTb24J5U95yp/KlEflKX/K62r5M2XKlKLTFUDoAP3rOnj03XHj4P33YeTIjt2OiIj0Cmb2YomvBgCLE4Mb/rDzUlTSR0B+ZOJhBd+JiIhIB1IAoQP0r+/gAMIf/tCx6xcRkd7mbYo/2Z8LPAZc7e63d26SivoXsGxuWva3d3JaREREeh0FEDrA/H3nr3USREREqubum9Y6DVWaDPzAzIa4+2dp2s7Ae8DTtUuWiIhI76CO+u2ssbGREX1H1DoZIiIiVTOz+8xsizLf72Bmz3VmmtJ2lzOz9QomnU90p7jDzMab2dHEayBPcfdZnZ0+ERGR3kYtENrZrMZZjB40utbJEBERKcnMRgArFEzaFLjPzPJvOIB42LArzbsOdIZjgX2IwRNx9+lmtiVwFnA98BZwtLufVoO0iYiI9DoKILSz2Y2zWWzAYh27kS22gHfegYUWgsmTO3ZbIiLSE80FbgYWSX83Aiekf8XUATd0ZILc/Xjg+Ny0CcCE3LSngA07Mi0iIiJSnAII7ayusY6F+i3UsRuZOhWmT4dRozp2OyIi0iO5+8dmNh5YhQgOTAIuIgZMzJsLvAPc13kpFBERka5IAYR21r++P0P6DKl1MkRERMpy978Dfwcws6WBG92908c5EBERke5DgyiKiIj0cu5+QrnggZkNMLOtOzNNIgJH//tohtw/hFUeX6XWSRERAdQCQUREpNczs2HAecBWwFDmfcDQF+iT/t8HEek0N7xzA3Ma5/DPz//JjLkzGNRnUK2TJCK9nFogiIiIyG+APYGXgb8CA4m3HDwIzAFmAt+qWepEeqnjljmORfovwo4L7qjggYh0CQogiIiIyHjgBnffkAgkAJzr7tsC6wKzgTG1SpxIb7XHonvw2tde47pVr6t1UkREAAUQREREBBYG7gFw93eA6cB66e/ngIuB3WuWOhEREekSFEBoZwPqBzBf3/lqnQwREZGW+AjoX/C3E694zEwFlu7UFImIiEiXowBCOxs9cIH9pEAAACAASURBVLRe4ygiIt3No8BeZjY4/f0ssKmZ9Ut/rwZ8VpOUiYiISJehtzC0oy8avmD9+dfv+A0ddBB8/DHMp5YOIiLSLk4CHgJeM7MVgYuAQ4AnzOwVYEfgjzVMn4iIiHQBCiC0o770ZasFtur4DR1zTMdvQ0REeg13f9LM1gN+4O7vA++b2d5EYGEL4o0Mh9cyjSIiIlJ7CiC0o4X7L8zIfiNrnQwREZEWc/dngIMK/r4KuKp2KRIREZGuRgGEdrTEwCVqnQQREZFWM7NNgO2BJYFfEuMebABc6+6za5k2kZ6msbGR29+7ncH1g9l85Oa1To6ISFUUQGgnXzR8wepDV691MkRERFrMzPoAlwO7Fky+GFgwTT/AzMa7+0e1SJ9IT/Tghw9y4RsXAjBqwCjGDBlT4xSJiFSmtzC0k3rq2XjExp2zsaWWgn794lNERKTtjiKCB4cAywF1afotwERgXeC42iRNpGdapP8i9KEPA+sHMrKvusCKSPegFgjtZMF+C7Jw/4U7Z2Nz5jT9ExERabsJwO/d/XdmtkA20d2/AH5rZssCXyeCCSLSDsYMGcMlYy6hX30/5u87f62TIyJSFbVAaAdfNHzBhsM3rHUyREREWmtx4Kky3z8HjOqktIj0Ggv2X1DBAxHpVhRAaAcL9VuI/Rbbr9bJEBERaa3/AquU+X5j4PVOSouIiIh0UQogtNGshlnsM2of+tf3r3VSREREWusyYH8z24Om7o2NZjbQzI4FdgeurFXiRLqqj+Z8xAEvHcA+L+zDm1+8WevkiIh0OI2B0EZLDliScSPH1ToZIiIibXEKsDJwBZC9rvEaYARRVvgL8VpHESnw8ucv88YXbwDwwqcvsNiAxWqcIhGRjqUAQhvMnDuTbRbdhrq6usozi4iIdBFmNn/hKxndfS6wh5n9HtgZWBboA7wG3Obut9YmpSJd22rDVmOHBXdgZsNMNhq+Ua2TIyLS4XpcAOGZZ57htNNO4/LLL+/wbY3oN4IdF9qxw7cjIiLSzv5pZke6+yQAMzsOuNHdJwOTa5s0ke6jT10f9l98/1onQ0Sk0/SoAMLFF1/MrbfeyqBBgzp8W7MaZrH1yK019oGIiHRHw4CFCv4+HvgX8HxNUiMiIiLdQo8KICy11FKcc845HHHEER2+rWF9hrHvYvt2+HZEREQ6wFPAL8xsPPBJmnaEme1VZplGd9++45MmIiIiXVWPCiBsvfXWvP56x79lanbDbLZacCuG9BnS4dsq6owzYMYM6ISWFiIi0iPtBZwBfBVYAmgEFgeGl1mmsRPSJSIiIl1YjwogdJYB9QPYZ9Q+tUvAbrvVbtsiItLtufurwDezv82sATjM3a+qXapERESkq1MAoYUaGxtZeejKzNd3vlonRUREpL1sBrxY60SIiIhI16YAQgvNapjFbouoBYCIiPQc7v5grdMgIiIiXV+PCyAsscQSXHvttR22/iF9hrDy0JU7bP1VeeUVmDsX+vSBZZapbVpERERERESkV+hxAYSONqLfCPrU9altIjbcEKZPh1Gj4M03a5sWERERERER6RXqa52A7mZkv5G1ToKIiIiIiIhIp1MAoYUG1evViSIiIiIiItL7qAtDCw2oH1DrJIiIiLSJmR3RisUa3f037Z4YERER6TYUQGiBhsYGtUAQEZGe4JQi0xrTZ12J6QAKIIiIiPRi6sLQAjMaZrDWfGvVOhkiIiJttUzu3ybAx8ClwBrAYGAosApwAfAe/D97dx4eZXW+cfw7kz0hBBISCPt+RBBRVJSCSl1wl7bu1F2rFEGlRaHoT3FBKC51ty7V2mKrtbXuVepGVWwxFqsCB4QiIIFAQliSkGVmfn+8A8WQbZKZeWcm9+e6ck3mXea9MwYz88w5z2GUK0lFREQkZmgEQggyvBmMyB7hdgwREZE2sdZ+s+99Y8xvgHestVfUO/QrYLIxJht4EDgqShFFREQkBmkEQghyknPISc5xO4aIiEi4HQW808T+T4CDo5RFREREYpQKCCFIIsntCCIiIpHwLXBcQzuMMR7gTGBNVBOJiIhIzNEUBhEREXkEuMcY8yzwJE6xIB0YBFwLHA9c6l48ERERiQUqIISifl9qt7z6KtTWQkqK20lERCQBWGvvM8bkAj8HJu6zywNUAtdba591JZyIiIjEDBUQQpDqSXU7gmPkSLcTiIhIgrHW3myM+RXwfaBPcPN/gYXW2h3uJRNJDLt9u1lRuYIhWUNI86a5HUdEpFVUQAhBskdPl4iIJC5rbSnwJ7dziCSiX37zS5bsXMIRHY/g5n43ux1HRKRV9I44BOnedLcjiIiIRIQx5kTgHKArNNg1OGCtPTW6qUQSR4W/AoBKX2WD+30BHwvLFpKfks/IjhptKiKxSQWEFgoEAnRP6+52DMfbb0N1NaSlwYknup1GRETinDHmp8CDwbubgeoGDgtEL5FI4rmxz438a8e/OKLjEQ3uf6v0LR799lE8eHjsgMdi53WniMg+VEBooUp/ZaP/w4+6Sy6B4mIoLISNG91OIyIi8e964N/AqdbazW6HEUlEuSm5nJR3UqP7OyV3AiDDm0GGNyNasUREQqICQgulelI5IOsAt2OIiIhEQk9gvooHIu4Z3Wk0D6U9RIfkDnRO6ex2HBGRBqmA0EJJniQ6JHVwO4aIiEgkWKCX2yFE2rs+GX2aP0hExEVetwPEiyRPkoaTiYhIoroFuMYYc4zbQURERCR2aQRCC6V4Ukj26ukSEZGEdAmwC3jXGFMObAH89Y4JWGuHRjuYiIiIxA69I24hLeEoIiIJrBPwdfBLREREpEEqILSQpi+IiEiistaOc+vaxpgrgRtwGjkuBaZZaxc3cfyrwGkN7Mq21u6KTEoREREBFRBarEOyGiiKiEhiMcYkAQcAdcBKa22gkeNygYHW2n+F+foXA48BtwFLgCnAW8aYg621/23ktIOB+4E/1tteGc5sIiIisj8VEFogEAjQJaWL2zFERETCxhgzCbgDZ/oCQKkx5pfAPQ0UEk4GngWSwnh9DzAbeNxaOzu4bSHOihDXA1MbOKcTzmoRf7PWfhKuLCIiItIyWoWhBWoDtQzMGOh2DBERkbAwxvwYeBhYhvNmfQ5QA8wD3jbGZEchxkCgD/DKng3W2lrgdeCkRs4ZHrz9T2SjiYiISEMaHYFgjKkCGhzK2ISAtTarbZFiT12gju5p3d2O8T8rVkAgAB6P20lERCQ+TQfet9Z+f88GY8wdwEPA5TirMRxvrd0ewQyDg7f1GzeuAQYYY5Kstb56+4YD1cAdxpgzgQycgsMUa+2mCGYVERERmp7C8DyhFxASUoAAyZ4Ymu3RsaPbCUREJL4NBqbtu8FaWw1caYzZBMzC6UVwgrV2Z4Qy7PljVv/xd+KMkMwCdtTbNxxICx7zA6A/zjSMd40xhwR/hpAsX7481FPiVlVVFdC+fuZQ6Plpmp6f5uk5apqen6bFy/PT6Ltia+0lUcwR0wIESPGkuB1DREQkXHYBXRvaYa292RiTijNK4U1jTGPTCdpqzzC6xj6s8Dew7V7gD9ba94L3FxljlgOfAOcAvwtvRBEREdlXyB+rG2P64TRT6gU8BVQA/a21H4U5W8wIBAIkecLWN0pERMRtC4Gpxpg3GlpZwVp7ozEmC/gp8B7ONIFw2zM9IhvYvM/2bMDX0JKM1toVwIp62/5pjCnHWZ0h5ALCkCFDQj0lbu35VCuaP/M7Ze+wpWYLPyz4Iane1KhdtzXceH7iiZ6f5uk5apqen6bF2vNTVFTU4PaQCgjGmDk4n0gk4XxisBDIAf5sjHkR+LG1tqZtUWNTTI1AePhhqKiArCyYPNntNCIiEn9uBEYBi40xK4EzrbUr9z3AWnuNMWZn8NhDI5BhVfC2P9/tg9AfWLn/4WCMOQ/YaK1dtM82D860hq0RyCht8E3VN/xq/a8AyErK4vT8011OJCIibdXiVRiMMT8FZgD3Acfwv6GHi4AHgbOAG8IdMBb4A36SvTHUA+HOO+HGG51bERGREFlr1wMH4fQ6WAuUN3LcTOBU4IsIxFgFrAcm7NlgjEkJXu+dRs6ZBNxvjNn39cspOM0UFzV8irilc0pnOiV3Iokkeqf3djuOiIiEQSjviq8B/mStvcEYk7dno7W2FLg2uDbzhTjNjBKGz+/jrK5nMThzcPMHi4iIxAlrbSUwN/jV1HFv4vRCyAnz9QPGmLnAQ8aYbcBHOK81uuB8WIExZgCQb639JHjaHOBN4PfGmKdxmkHeDvzZWvtxOPNJ23VM7sjjBzxOtb+aTimd3I4jIiJh0OIRCDhDCt9tYv+HOH0REsLuwG7K/GXc2v9Wpvaaqh4IIiLSrkViSUdr7SM4UyMvBF4EOgHjrbVrgofcDCze5/i3gDOAgcBfcUZQ/CZ4vsSgjKQMFQ9ERBJIKCMQSoC+Tew/BNjSpjQxYn3Vem4pv4XxGeM5IucIt+OIiIgkLGvtPcA9jey7BLik3rbXgNciHkxERET2E8oIhOeBycaY7+2zLQBgjLkIuAL4SxizueaVra9Q4a8gzZPmdhQRERERERGRmBDKCIRbcDo2fwBswCke3G+M6Qz0AJYGj4l763avwxtSbUVEREREREQksbX4XXKw2dI44CrgPzjrMKcCy4HrgKOstTsiETLaNtdsbv4gERERERERkXYkpLUJrbU+4KngV0Kq8FWwtVZLSYuISPsSXBrxQpxlFHsBU4FKnGUWH7bWNrjUo4iIiLQfIRUQAIwxx+OsudwX8AFfA69Yaxc3dV68WFWxiip/ldsxREREosYYk4WzPOIYoAzoDGQDfXCWSbzQGDPOWlvsXkoRERFxW4unMBhjOhhj3gLewpmycAxwHHAj8KEx5lljTNyvdbh4x2JSvalux2ha165QWOjcioiItN3twBE4ow8OADwA1toXgR/h9Dq63bV0IiIiEhNC6RR4J3A8MBvItdbmWWtzgXxgLvBj4P/CHzF6AoEAS3YsIdkT8sCM6Pr3v2HjRudWRESk7c7BmabwJsEVlvaw1r4EPASMdyOYiIiIxI5Q3imfC/zaWnvbvhuttaXALGNMN+By4nglhi8rvuTb6m9jfwSCiIhIeHUBbBP71wWPERERkXYslBEIWcCyJvZ/AnRqWxx3vVTyEimeFLdjiIiIRNsq4HtN7D8VWB2lLCIiIhKjQikgvIrTRKmxj+fPAt5ueyT31Phr8Hg8bscQERGJtoeBHxtjfgH0DG5LMsYMMsY8BZwMPO5aOhEREYkJjU5hMMacU2/TP4DTgSJjzEM4n1b4cTo0XwocCFwVoZyyr+uvh/Jy6NQJ7rvP7TQiIhLnrLWPGWN64zRK3NMs8W/BWw/OFMYHXAknIiIiMaOpHgh/xGmkVP8j+aHAo8Hv6+9/EYjblRh2+Xa5HaFlnn8eioudlRhUQBARkTCw1v7CGPM0cAbQH+fv+TrgNWvtf1wNJyIiIjGhqQLCuKiliAErK1ZiKy3J3hhfgUFERCRCrLWrgHvcziEiIiKxqdF3y9baD6IZxG2PbHiEJE/cDp4QERFpE2PMgTijD7oBDfU7ClhrJ0c3lYiIiMSSkD5uN8bkAEcBHfhuA8ZkIBs41lp7fvjiRceXu75kReUKUrxagUFERNofY8wPgOdp+nVBAFABQUREpB1rcQHBGHMkTkOl7H027+l/EAjebg1Trqh6euPTJHs0dUFERNqtW4FvgR8Dn1prq92NIyIiIrEolHfNdwZvrwZSgIeAHwAZOKsvHEHTa0jHpLVVa1lWsUy9D0REpD0bDNxgrf3I7SAiiabGX8NbpW/RL6MfwzoMczuOiEibeJs/ZK/DgIettU8ATwC1OPMh/wicgLOs4+1NnB+T/rj5j3g9oTwNIiIiCWcdkOV2CJFE9MLmF3h84+PcvOZmdtbtdDuOiEibhPLOOQ34GsBaWwusBg4J3q8DfovTHyGulNWWqYAgIiLt3S+BqcYY43YQkUTTJbULAB2TOpLqbag/qYhI/Ahl3P56oO8+9y1w8D73K4H8MGSKqu11292OICIiElXGmDca2NwR+MIY8yVQAvjr7Q9Ya0+NeDiRBHNS3kkMzhxMl5QupHnT3I4jItImoRQQXgamGGNW4HRq/gC43RhzBE4x4SKcIZBxo8JXwZbaLW7HCN2YMVBaCnl5bicREZH4dCD/a4C8x54/iJ2DXyISJv0z+rsdQUQkLEIpINwOjAZ+D7yB0wfhOmAxzosQL/CTcAeMpD9t/hMVvor4G072wgtuJxARkThmre3rdgYRERGJPy2e/G+t3W6tHQ0cFfy+AmflhduAh4FTrbVPRihn2AUCAd7b9l78FQ9ERETCzBjzG2PMqCb2jzPGvBrNTCLxaFXlKpbuXOp2DBGRiAl57UJr7b/2+b4EmB3WRFHy3Kbn2FyzmRRvittRREREosoYk47T8wDAA1wC/MsY898GDvcCZwLHRyedSHzasHsDP1/1c/z4mdlnJqM7jXY7kohI2DVaQDDG3NCKxwtYa+e3IU9U2ArLHzf/UcUDERFprzoBK4Ds4P0AzmjChxs53gO8F4VcIiIiEsOaGoEwtxWPFwBiuoCw27eb2/97OwECePC4Had1TjsNtm6FLl3gtdfcTiMiInHGWrvJGHM+zlRED/B/wEvAfxo43IfTYPH56CUUiT8903ty96C7qfBVMCJ7hNtxREQioqkCQr+opYiiB9Y/wNbarfE9+uCzz6C4GAoL3U4iIiJxylr7JvAmgDGmD/CYtfaf7qYSiW+DMge5HUFEJKIaLSBYa7+JZpBo+KbqGz4o/yC+iwciIiJhZq291O0MIiIiEvtCbqIYz+5Zd4/bEUREREREJKi4uJgFCxZQVVWF1+vF7/eTkZHBxIkTKdRoW5GY024KCGW1ZXxd9TVJniS3o4iIiIiItGuVlZXMnTuX/Px8LrvsMnJzc/fuKysrY8GCBWzdupUbb7yRzMxMF5NGXlVVFT6fjw4dOrgdRaRZXrcDRMtXu76i2l/tdgwRERERkaiq8FWwYfcGt2PsVVlZycyZM7nmmmuYMmXKd4oHALm5uUyZMoXJkyczc+ZMKisrXUoaeVu3buXyyy/n4osvZtWqVW7HEWlWiwsIxpi4bhywZMcS0r3pbscQERGJOcaY04wxuc0fKSLxpsZfwzX2GibZSSwsXeh2HADmzp3LrFmzKCgo2LutvLycRe+9S9E/P9m7raCggFmzZjFv3jw3YkbF5s2b2blzJzU1Naxbt87tOCLNCmUKwxfGmMestb+KWJoI2la3Da+n3Qy4EBERCcWzwCPATW4HEZHwqvZXs612GwCbaja5nMbpeZCfn/+d4sHbr73EYzddwYAcH/68gYz8y6d79xUUFJCXl0dxcXFC9kQYOnQoP/nJT6ioqOCYY45xO45Is0J5R90HqIhUkEir8de4HUFERCRW+YBSt0OISPhlJ2dza/9buaTwEs4uONvtOCxYsICJEyd+Z9uQYSO4+BcPEOjzPXI7ZOx3zsSJE3nuueda9Piff/45r776KjU18fPa//TTT+e8884jObndtKeTOBbKb+mfgQuNMS9Ya7dHKlCk7PbvdjuCiIhIrJoK3GuM2Q18CGwB/PUPstaWRDuYiLTdiOwRjMge4XYMwGkYWL/nQa++/ejVtx9fLnoZduzY75y8vLwW9UEoLy/nlltuwefzUVlZybnnnhu23CLiCKWAUA6cCWwyxiyj4RcXAWvtqeEKFy7b67azdvdat2OEz6WXwvbtkJPjdhIREUkMjwBZwEPNHKeljESkTbze1k0pbsl5aWlpZGdnU15eTpcuXVp1HRFpWigFhFOBrcHvc4Nf9QXanCgCnvz2SWoCNSR7EmRY0J13up1AREQSy/3E6N9wEUksfv9+g5vCdl5GRgYPPfQQ27Zto2/fvq26jog0rcXvqK21/SIZJFJ2+3azePvixCkeiIiIhJm19la3M4hIaAKBAL/85pcsq1jGdb2v4/Odn+PDx8XdLibZG7uvezMyMigrK9tvGoPf7ycQCAAB/H4/Ho8Hj8cDQGlpKZmZmS16/JycHHI0SlckYlr1fxdjzIFAL+BToApn6kJVOIOFywflH1BeV05G0v4NWUREROR/jDHjcKYr9gZqgA3A69ba91wNJiL72eXbxYfbPwTguU3PsaJyBQCDMwYztvNYN6M1aeLEiSxYsIApU6bs3Wat5dLTRjOiq4fCrACnHVzAxGtvZeIV1wANN14UEXeEVEAwxpwCPADsGY1wApAGPGeMmWWtfSTM+drsg20fkO5NdzuGiIhIzDLGeIHfAhcAHmAbTr+DjsD1xpgXgAustZrmEOMqfBX8YdMf6J3emxPzTnQ7jkRQdnI2E7tO5KuKr/hRwY+4d929+AI+BmYOdDtakwoLC9myZQslJSV7l3Ls168ft/zq6b3HHAGMPPxIAEpKSnj//ffJycnh4osvdiNy3PH5fCQlqWWNREaLCwjGmO8DLwP/Ap4E5gR3rQO+Bh40xpRZa/8Y9pStFAgEWFO1Zu/wp4RhDGzaBN26gbVupxERkfg3HZgIPAjcuWe1BWNMV2AmzioN/wLucy2htMjLW17m5a0vA3Bwh4PpmtbV5UQSSed1O2/v988c+AwAXk/rmhRG04wZM5g5cyazZs2ioKCA1NRUxp96xn7HlZSUcM0117B7925efPFFJkyYoOkJzXjjjTd47LHHGDNmDDfccIPbcSQBhfJ/mNuAIuBo4Ik9G621XwFHAR8DPwtrujZaX72estoyt2OE386dzhI3O3e6nURERBLDZcCL1tpr912q0Vq72Vp7HfAicIVr6aTFTKYh2ZNMj7Qe5CTrjVZ74vV446J4AJCZmcldd93Fww8/zAMPPEBZ2Xdfr5eVlfHAAw/w8MMPM2PGDLp3784xxxxDx44dXUocP4qKiggEAixZssTtKPvZsmUL99xzDw888ECrm2mK+0KZwnAIMNNa6zPGfGeHtbbOGLMAmB/OcG31cfnHiTf6QEREJPz60PTogneB06KURdpgZMeRLBi6gFRPakw30pOG1fprWVi2kJzkHP5e9nc6p3Rmcs/JJHkSbzh6ZmYms2fPpri4mKeffprKykq8Xi9+v5/MzEwmTpxIt27dAHjyySddThs/LrroItLT0xk9erTbUfbzySefsHr1ajZv3swPfvADevXq5XYkaYVQ/rLsBppqJtAVqG5bnPAq2llEqjfV7RgiIiKxrgQY1sT+g4DSKGWRNspMalm3eok9fy75Mws2L2BLzRY6JXcixZvCCbknMCRriNvRIqawsJCf/SymBjG7aunSpVx44YXk5OTwxhtv0LFjRyorK7npppvo2bMn06ZNa/L8Pn36MH369CilDc3IkSP5/PPPGTZsGIWFhW7HkVYKpYCwELjaGPNE/R3GmEHAFJxPKGLC26Vv82XFl1q+UUREpHkvAFONMUXAM3uaJRpjPMClwJXAwy7mE2kXclOcpQ17pPUgNyWXgtQC+qXH5Urq0kovvPACJSUllJSUsGjRIk477TTmzp3LggULqKurY9myZcycOZMBAwa4HTVk3bt356abbmLIkMQtiLUHoby7ngH8E1iGUygIAJcaYy4HfoAzQuGmsCdsheLqYh7d8KiKByIiIi3zf8D3cJok32WMWR3cPgAoAD4LHiMiEXRi3okMzBxIXkqeeli0UCAQ4KOPPiIlJYXp06eTmZnJ66+/HrerEFx++eV8/PHHdO7cmeOPPx6AYcOG4fV6qaio4OWXX+aLL77gn//8p8tJpb1qcacVa+1aYCTwd+BUnGWeJgITgL8BR1prV0YgY0h2+3Yza/UsagI1bkcRERGJC9baSuAYnNGEnwGdgVzg38Fto6216twrEgX9M/qreBCChQsXMm/ePM455xw++eQT3n33XS666CLWrl3rdrRWGTBgAO+//z4vvfQS6enO7PFzzjmHDz/8kMMPPxyv10tWVpbLKd1VVFQUt/99E0FIH9FbazcAFwaHNHbBWSN6i7XWB2CMSbbW1oU/ZsvU+GuYtmoaxdXFahwkIiLSCGPMKmCWtfaF4P2LgEXW2keAR1wNJyISgj1vsrt160ZJSQk+n49169YxZ84cHn/8cZfT7e+LL76gsLCQLl26hHTegAEDeP3113nzzTcZP358o8ctWbKE5cuXM2HChIRcteLtt9/mwQcfJCUlhSeeeIK8vDy3I7U7LX6XbYxZA1xnrX0lODdyS7395+OsHx3av4YwqfXXMn3VdNZUrSHFm+JGBBERkXjRE6f58R5PAz8G1rqSRkSklY4++mjy8/PJyckhPT2dZ555hvfee4/u3bu7HW0/r7zyCk888QTZ2dk89dRTZGRkhHR+x44dOffccxvdv3v3bubMmUNdXR0VFRVMmjSprZFjTiAQcDtCu9doAcEYUwiM3WdTX+B4Y0xDKzF4gYsAV5Y8CAQC3LzmZlZUrtCqCyIiIs1bDtxmjDkc2IUzLfEyY8zYJs4JWGsnRyWdSDtSXF3M498+zuDMwZzf7Xy34+ynuLiYBQsWsHXrVpKSkkhPTycjI4OJEyfGTCf9fZvyXX/99fzwhz+kR48eLiZqWFVVFQDV1dX4fL6wP35qaio9e/Zk7dq19OuXmM03x48fT25uLvn5+Rp94JKmRiCUArcDg4L3A8A1wa/GuDLs8Ylvn2DpzqXtp3hwxx1QWQmZWqZJRERa5XKchonnAik4f+OPC341JgCogCASZq9tfY1Pd37Kpzs/ZXze+L0rMexrTdUaXtj8AmM6jWFMpzFRyVVZWcncuXPJz8/nsssuIzf3f7nKysr2FhVuvPFGMkN8TVpSU8KjGx6ld3pvLu1+aVhzezwe+vbtG9bHDJezzjqLgoICevfuTYcOHcL++F6vl3vuuYcdO3aEPEUinhx++OFuR2jXGi0gWGtrjDEnAP1wPpl4jj0oqQAAIABJREFUF5iDs5xjfT6cXgg2Iimb8FH5R7y85eX2NW3hssvcTiAiInHMWvtvnMbIABhj/MCPrbXPRTuLMeZK4AacaRVLgWnW2sVNHD8MuB8YBZThLC/5yz1LT4rEm6NyjmJR+SIGZQyiU3KnBo95tvhZinYW8e+d/45KAaGyspKZM2cya9YsCgoK9tufm5vLlClTKCkpYebMmdx1110hFRHeKn1rb9HkxNwT6ZEee6MFIiEpKYlx48ZF9BqpqakJXTwQ9zXZA8Fauw5YB2CMuRSnwdJ/oxGspd4qfYvBWYPD/rhbq7bSLzkxh/6IiIjUcynwcbQvaoy5GHgMuA1YgrPiw1vGmIMber1hjCnAWQ3qS+Ac4FDgTpwPMu6OVm6RcBrWYRi/G/q7Jo8Z1XEU/975b47MOTIqmebOnbtf8cDn8/HhRx9T2K0rgwc7r70LCgqYNWsW8+bNY/bs2S1+/CNzjuTdbe/SK60XXVO7Nn+CiMSMFjdRtNb+1hiTZYw5zFr7KYAxZjTwU6AWeLypTwwi5bYBt0XkcZcvXx6RxxUREYk11trfRvuawRWdZuO8fpgd3LYQsMD1wNQGTpuM89rljODSk28YY9KAmcaY+621tdFJLxJdJ3c5mZPyTsLj8UT8WsXFxeTn53+neLBt2zZOuuAqvlm/gaTUdM4+9Th+dfsswCki5OXlUVxc3OKeCIMyB/H0gU83m2PlypWMHj2alJR2NNJYJMZ5W3qgMeZAYDXwm+D9ATjTGs4GfgS8b4w5NgIZpb5Nm2DjRudWREQkPg0E+gCv7NkQLAC8DpzUyDnHA+8Eiwd7/BXIBTQpVuJKeW05k1dM5uoVV7O1Zmuzx0ejeACwYMECJk6c+J1tcx/4NeV1SRS99ScmnX8GL73/KcXFxXv3T5w4keeeC98MqEAgwPTp07n77rt5+ummCw2SWAKBANZaKioq3I4ijWjxCASc/gcB4OfB+1fiNF4aDXyBM6TwFuD9MOaThhx6KBQXQ2GhU0gQERGJP3vmH35db/saYIAxJslaW79N+WD2f52xZp99IU/DaE8jDvd0gG9PP3Moov38fF7zOct2LgPgzd1vcljaYVG5bnO2bt36nYaJAHZ9CaZ3N3r06MFPLr6Ax/76Pu9/uJjzz/4hAHl5eWzZsiVsz10gEGDXrl3s2rWLkpKSuPmd1b+xprXk+fnLX/7C3//+dwoLC7nuuuvIzs6OVrwW8fv9eL0t/gw+JPHy+xNKAWEsMNda+3bw/pmAtdb+E8AY83tgbpjziYiISGLqGLzdWW/7TpwRklnAjgbOaej4fR9PJCp2+XexqHoRg5MHMzBlYMjnD0kZwpFpR+LHz0GpB0UgYeskJSXtt62q1kfHdGcaQefOnUlLTWFDcUmz57WWx+PhxhtvZP369Rx44IFhe9xE4ff7+eqrr+jRo8d+xZ54t2PHDgKBAIsXL2bTpk1ceOGFHHlk5Hp/VFVVsWHDBvr379/s7/Dq1at58MEHycvL44YbbiAtLS1iuWJZKAWENGAbgDFmIGCAe/fZ7wXqwhdNREREEtie8diNrZ7gb+ScUI5v1r7rxye6PZ9qtaefORShPj/3r7uf9+ve5xPPJzx/wPN4PaF/KnkwB4d8TqSlp6fvty0jJYnqOmdA0LZt26iuqaVnYcF+54Xjd2vTpk0sWbKEsWPHctRRR7X58aIpWv/Gfve73/HCCy/QoUMH7rjjDgYMGBDR64VLS56fm2++mTfeeIMnnniC9PR0ampqIvp8/uxnP2PlypWccsopTJo0qcljv/jiC1JTU1m5ciVz5szhsssu46STGptxF7pY+390UVFRg9tDKSBY4GScdaN/ivMH/CUAY0wGcDHwVZtSioiIiCuMMR2AnwGn4iypeC6wG/gJcKe1dm2YL7k9eJsNbN5nezbgs9buauSc+uNZs/fZJxI1PdKcpQcLUwtbVTyIVRkZGZSVlX3nk23Tq4C/fvg53377Lc/84UWSvB6OHfO/N/elpaUhLePYlDvvvJO1a9fy6aefhrSyQ3tSV+d8ZltUVMTUqVO56KKLOPfcc11OFR4dOnTgnHPOoWfPnixfvpyzzjqr0WM3btyIz+ejV69erb7e9u3On47y8vJmjz355JPZtGkTr7zyClVVVbz55pthLSDEi1AKCPOABcaYbUAO8JG19iNjzEjgVaALcEYEMoqIiEgEGWPygX8AA3CWSOyK0+eoI3AZcLox5mhr7cowXnZV8LY/3+2D0B9o7Dqrgvv3tee+DV80kead1fUsjsw5kvzUfLejhNXEiRNZsGABU6ZM2bttxtSreO+Tqzj0xLNITsvg7FOP+86KCw01Xmytjh2d2Ug5OTlhebxEdOGFFzJgwADmz58POKM2Es3o0aMZPXp0o/vXrVvHtddei8/n46677mLo0KGtus7s2bP57LPPOPbYY5s9Njs7m6lTp3LQQQfx+uuv86Mf/ahV14x3oSzj+IIxZjPOussbgIeCu8qBfwG/sta+H/aEIiIiEmlzgW7AoUAxUAJgrX3dGHME8CZwB85rgHBZBawHJgBvAxhjUnBGQLzeyDnvAFcZY7KstXtadE8ASoGlYcwm0iLd07pjKy2903uTlZTldpywKCwsZMuWLZSUlOxdyrFz584sfu0PfPjRx3Qv7MagQYP2Hl9SUkJpaSndunULy/Vvvvlmvv76aw444ICwPF4iSk5O5uijj6Zr16785z//aZefgldUVOwdibFzZ/3WOC3Xo0cPevToEdI548aNY9y4ca2+ZrwLZQQC1toPgA/qbVuN88dbRERE4tPpwIPW2i+MMXn77rDWFhljHsSZvhg21tqAMWYu8FBwdONHwDU4Ixrvg71LRudbaz8JnvYIMAV4wxgzHzgYmAnMsNbWhDOfSEs8tfEpXtn6CgMyBvCrwb9yO07YzJgxg5kzZzJr1qy9RYSkpCSOOXrsd44rKSnhzjvv5K677grbtdPT0xk2bFjYHi+RGWMwxrgdwxVDhgzhpptuorq6OqJNFmV/LS4gGGNa9KmDtfaF1scRERERF3QAvm1i/zYisMqBtfaRYB+la4HrcUYRjLfW7lma8WacHkue4PHFxpjjgfuBF3F6J8yy1t4d7mwiLVFeV/6d20SRmZnJXXfdxbx588jLy+PHP/7xd3oilJWV8fvf/57S0lLuuuuusPU/EAnFqFGj3I7QLoUyAuGPOI0TPfW21++GrAKCiIhIfPkKOAl4rP4OY4wXp6Hiskhc2Fp7D3BPI/suAS6pt+1T4HuRyCISqqt7XM0BmQcwInuE21HCLjMzk9mzZ1NcXMzTTz/Nli1bSEpKIj09nczMTCZOnBi2aQsiEj9CKSA0NNEjCSgAzgaGoiaKIiIi8Wgu8CdjzOM4jZEBCo0xJwDTgdHARW6FE4lV2cnZnJ5/utsxIqqwsJCf/exnMbfEnIi4I5Qmih80sfuPxpiXgVk4Qw0lkp5/HmpqIDXV7SQiIpIArLV/NsZMAuYDlwc3P4sz6rAG+IW1doFb+URERCQ2hNREsRmv4rzwkEgbO7b5Y0REREJgrf21MeYPwPE4yzkmAeuAhdbaLa6GExERkZjgDeNjjWD/fggiIiISB4wxWTirKr1lrZ1vrZ2LMwLhDGOMhryJiIhISKsw3NDIrjScZZR+APw+HKFEREQkeowxvYC/AwNxGioWBXcdh9PEcLIx5kRr7VZ3EoqIiEgsCGUKw9wm9tUBf8FZgkki7R//+F8PBE1nEBGRtpsHdAaOt9buKR5grb3MGPMU8BJwF3ClS/niwpIdS1hVuYoz888kKynL7TgiIiJhF0oBoV8j231AqbW2Kgx5pCXOPReKi6GwEDZudDuNiIjEv+OAu62179XfYa39yBhzPzAp+rHix866ndzx3zvw46fGX8Ml3S9xO5KIiLikpKSEb775hsMOOwyPx+N2nLAKZRWGbyIZRERERFyTjjOasDEVQKcoZYlL6d50uqZ2pbimmN7pvd2OIyIiLqmrq2PatGls376d888/nwsuuCBi11q/fj1FRUUce+yxdOoUnT/TjRYQmuh50JSAtVYrMYiIiMSXfwFXGmN+ba2t2HeHMSYduJT/9UWQBqR4U3hg8APs9O0kPzXf7Tjtli/gY/438/m2+ltu6HMDvdJ7uR0p7LbWbOUXq39BkieJOQPm0Dmls9uRomLDhg2sXLmSMWPGkKqlzBPChg0bWL58OWPHjiU9Pd3tOGETCATw+XyAU0yIpNtuu41Nmzbx5ZdfctNNN0X0Wns0NQKhqZ4HjQmgpRxFRETizWzgHeALY8yzwNfB7f2BiUBf4AR3osWP9KR00pMS50VwPPq2+ls+2v4RAP8o/wcXdIvcJ39uWVaxjOKaYgBspeXInCNdThR5Pp+P6dOns2vXLtasWcMVV1zhdiQJgxkzZrB9+3astVxzzTVuxwmblJQU7r77blavXs3o0aMjeq3c3Fw2bdpEbm5uRK+zr6YKCI31PBAREZEEYq390BhzEs6HAP9Xb/d/gFOstYuin0wkND3TenJC7gls2L2BcZ3HuR0nIkbljOK4zseR5EliZPZIt+NEhcfj2TvqIC0tzeU0Ei6ZmZls376dzMxMt6OEXY8ePejRo0ezx9XV1fHpp58yYMCAVl1n9uzZfPPNNwwaNKhV57dGowUE9TwQERFpP4INFA8zxhQAvYEkYL21Vt16JW54PV6m9prqdoyISvOmcV3v69yOEVVer5f77ruPtWvXcsghh7gdR8Jk/vz5/Pe//2X48OFuR3HNs88+y0svvUTnzp2ZMWNGyA0X09PTMcZEKF3Dmm2iaIwZA/zEWntRA/seAQxwp7X23QjkExERkSiy1pYAJfW3G2OyrbU7XYgkIkJubm5Uh2lL5OXk5DBixAi3Y7jK7/d/5zYeNFlACDZSnAv4jDF3WGtX1jukA3A0cKwxZra19rYI5RQREZEIMsZcBpyI87fdu8+uZCAbGAFkuBBNREQkIV1yySUccMABDBo0iLKyMrfjtIi3sR3GmDNxigdvAAMbKB4QHJXQD/gAuMUYMz5SQUVERCQyjDHTgSeBM4HDgfHAgcBY4HhgOPCwawFF4pytsLxU8hIVvormDxaRdiM5OZkxY8bQtWtXt6O0WKMFBOA64HPg9Kb6IVhrNwCnAP8FpoU3noiIiETB5cBnQD4wBvAAxwE5wFVAOs5SjyISIl/Ax01rbuI3xb/hmY3PuB1HRKRNmiogHAossNYGmnsQa+1u4HfAqHAFkyZ89hl8+61zKyIi0nZ9gWettbustauAcuBoa63fWvsE8BzOBwsiEiIvXvJS8gDIT813OY2ISNs01QPBA4TSLKkYp2OzRFq3bm4nEBGRxFINVO5z3wIH73P/A+DkqCYSiWHlteXct/4+OiR14Lpe15HiTWn0WI/Hw72D7mVTzSb6Z/SPYkoRkfBragTCGpyGSS01AljXtjgiIiLigi9wpizssYzvjirsij4kENnr4+0f89nOz1hUvojlFcubPT4zKVPFAxFJCE2NQHgeuMkYc29wOGOjjDGDgIuAx8MZTkRERKLiEeD3xphc4CzgBeDN4HLNK4DrgSUu5hOJKYd1PIz+pf3pkNyBwZmD3Y4jIhI1TRUQHgGuBBYZY64F/lS/H4IxxgucDdwN7AbujVTQaPP5fDz11FN07tzZ7Sj7GfHZZ6TU1lKbksLSQw91O06ztmzZAkB+fmLM+6uqqiIvL49JkyaRlZXldhwRkTaz1j5njMkGrgWqrLVvGWN+DVwdPGQ9ThFBYsiSHUt4eMPDjOo4ikk9J7kdp10pSC3gfnO/2zFERKKu0QKCtXa7MeZ04K/AH4BfG2OKgM04wxgLgJE460VvAMYHV2RICD6fj6VLl9KhQwe3o+xn8sKF5FVXU5qWxhM+n9txmrVr1y6AmHwuWyIQCFBVVUVubi79+vVj1KhRnHDCCSoeiEhCsdb+Gvj1PvcnGWPmArnAV9baGtfCSYPeKXuH0tpS3ix9k6t7XI3H43E7ksSw8tpyllcuZ2T2SFK9qW7HEZE41dQIBKy1XxljhgPXAOcAR+9zTg2wGPgL8Li1tjqSQUWiqa6uDp/PR7du3Rg4cCDjx49n5MiRJCc3+U9GRCSuGWM6AMfirMpQB3wNLFLxIDb9IP8HbKvbxqiOo1Q8kGbdvOZm1u5ey7jO45jWWyuvi0jrNPtuyFpbBcwH5htjPEAe4LPWbot0OJFo8/v95ObmcuSRR3LaaafRu3dvvSgTkXbBGDMduBnIwlmJCSAAbDPGTLPWPutaOGmQyTLMGzjP7RjiAlthAed3oKV8Ad93bkVEWiOkj1ODPRC2RiiLiOuqqqq46qqrGDNmjNtRRESixhhzFTAPeA94EGfkQRIwCJgKPG2M2WGt/at7KUXi0466HaR6UklPSg/L4y2vWM6NX98IwLyB8xiSNaRF590+4Ha+2vUVR3Q8Iiw5RKR90nhskX2kp6czdOhQt2OIiETbNGChtXZ8ve2fG2P+AryDMzpBBQSREHy560tuXnMz2UnZPGgeJCc5p82P6Qv4CBDY+31L5aXkcXTno9t8fRFp31RAEAny+Xx06dIlJlfeEBGJsF5Agy3lrbV+Y8yfcKYzikgI1u5eS12gjm112yitLQ1LAWFYh2Hc2f/Ovd+LiESTCggiQE1NDQMGDGDOnDluRxERccPnwHE4Szg35DBgRfTiiCSGE3NPZEfdDnJTcumf0T9sjzs8e3jYHqu92bhxI8uWLWPMmDGkp4dnWolIe6ICgrR7tbW1jBs3jp///OdaZUFE2qufAG8bY34L/BKwgA/oDUwGzgUmGGMK9j3JWlsS7aAi8STVm8oF3S5wO4bsY+bMmZSVlbF8+XKmTJnidhyRuKN3S9KuVVdXc8kll3DBBfrjLiLt2sdACnAh8GOc1Rf8OI0U96zI8LcGzkuKSjoRkTBJS0sDICMjw+UkIvFJBYQ4VJWcTIXPR5U+LW+zLl26cN5557kdQ0TEbfdAsCubiEgCmz9/PmvWrGH4cE0DEWkNvQONQ5PGjXM7QkLw+/0MGzYMr9frdhQREVdZa291O4OISDTk5ORwyCGHuB1DJG6pgCDtVlVVFaeeeqrbMUREYoIxJgsYYq39NHh/NPBToBZ43Fq72M18EtvW715PXaCOfhn93I4iIiIRpI9epd1KT0+nV69ebscQEXGdMeZAYDXwm+D9AcC7wNnAj4D3jTHHuhZQYtraqrVMsVO4duW1fLXrK7fjhF15bTlba7a6HUNEJCaogCDtVlpaGp07d3Y7hohILJiD0wPh58H7V+I0VTwa6AYUAbe4E01iXZW/Ch8+AgSo9Fe6HSesNlVv4soVV3LliitZXrEcgC93famCgoi0W5rCEIcuXLGCzNpaKlNS+N0BB7gdJy4FAgHy8/PxeDzNHywikvjGAnOttW8H758JWGvtPwGMMb8H5roVTmLbkKwh3NLvFmoDtRze8XC344TVtrpt7PbvBqCkpoSVlSt5cuOTZCdl85shvyE9KX2/c5buXMpTG5/ieznf47xuatQsIolFBYQ4dNz69eRVV1OalqYCQivV1tZy+eWXux1DRCRWpAHbAIwxAwED3LvPfi9Q50IuiROHdTzM7QgRMSRrCNf3up5KfyVHdzqa5zc/D0C1vxofvgbPeW3ra6zdvZYN1RtUQBCRhKMCgrQ7fr+foUOHMmrUKLejiIjECgucDDyJ0zgxALwEYIzJAC4GEm9yu0gLfD/3+3u/P6vgLPJT8+mb3pespKwGjz8l7xQ2Vm/ke52+F62IIiJRowKCtDvJyclMmzbN7RgiIrFkHrDAGLMNyAE+stZ+ZIwZCbwKdAHOcDOgSCxI9iZzXO5xTR5zaMdDeaTjI1FKJCISXWqiKO1KXV0dkydPpmfPnm5HERGJGdbaF4DjgeeAWcApwV3lwL+AE621f3MpnkjUVPmr+Me2f7C9brvbUUREYpJGIEi7UVNTw0knncSJJ57odhQRkZhjrf0A+KDettXABHcSiUTfsxXPsnrdagZlDOLewfc2f4KISDujAoIktLq6Ovx+P7169WLkyJFcddVVbkcSERERF6yqXMVTG5/isOzDOKvrWQ0e48FZncnr0SBdEZGGqIAgCaeuro5AIEDv3r0ZMWIEEyZMoHv37m7HEhERERf9dctf+ariK76q+IoJ+RNI9u7/MviiDhdR2b2SYR2GuZBQRCT2qYAgCaGuzlldrHfv3hxyyCFMmDCBbt26uZxKREREYsX3O3+fZRXLOCz7sAaLBwDpnnQO6XRIlJOJiMQPFRAk7gUCAQoKCpg/fz4FBQVuxxEREZEYsLF6Iy+WvMiojqMYlTOKkR1H8vSBT7sdS0QkrqmAEIfW5OSwpaaGnampbkeJCX6/n+nTp6t4ICISJsaYNKDWWut3O4tIaz1T/AyLty9m0bZFvDj8RbfjiIgkBBUQ4tBtRxzhdoSYUVtby7HHHsuwYZqrKCLSFsaYnsDtwKlALnCiMcYH3ATMsNYWuZlPJFQHZR3E4u2LGd5huNtRREQShgoIEtdqa2s555xz3I4hIhLXjDH9gY+BdGARThEBwAscBSwyxhxjrf3UpYgiITs9/3ROyD2BNG+a21FERBKG1qgRERGReUAdcABwGThr2Vlr3wOGACXAba6lE2ml9KR0PB6P2zHixlulb/G30r+5HUNEYpgKCCIiInIc8Ki1dhMQ2HeHtXY98DCg+XMiCaxoRxEPbXiIhzc8zJIdS9yOIyIxSlMY4tCNRUVkB5sozhs50u04rsrOzqawsNDtGCIi8S4VKG9ivx8I+zhwY8ww4H5gFFCGU6j4pbU20MQ5PwIa6og3xVr7ULgzirQXXVK6kOpJJUCALildwvKYlZWVBAIBsrKywvJ4IuI+FRDi0JCyMvKqqylN05y+gQMHkpGR4XYMEZF49ylwDs4b+O8wxqQDlwD/DucFjTEFwN+BL4PXPhS4E/ABdzdx6sHA18CF9bb/N5z5RNqbPhl9eGLIEwDkpuS2+fE2b97MtddeS11dHXfffTd9+/Zt82OKiPtUQJC4VVVVxbHHHut2DBGRRHAr8LYx5m/AKzjTGA4xxgwArsXpjXBq46e3ymSc1yFnWGsrgTeCy0fONMbcb62tbeS84UCRtfaTMOcRaffCUTjYo7i4mIqKCgDWrVunAoJIglAPBIlbgUCAIUOGuB1DRCTuWWvfByYAg4GHcJoozgd+DXQBLrTWvhXmyx4PvBMsHuzxV5wlJA9v4rzhwH/CnEXauZe3vMz/lf8fi6sXux0lYRx88MFcccUVXHTRRYwZM8btOCISJhqBIHHL4/GQlJTkdgwRkbhnjOlmrX3DGDMQGAEMAJKAdcCSJkYDtMVg4P1629bss+/jBnJmA31xRkesBPoBy4EZ1to3IpBR2omXt7zMVt9WFu1exGVc5nachODxeDjzzDPdjiEiYaYCgsQ1FRBERMLiU2PM49ba24DPgl+tZoxJwSlCNGYz0BHYWW/7nvsdGznvIJzREf2AaThLT/4UeNUYc3xw2cmQLF++PNRT4lZVVRXQvn7mljqq+iiqqOJo79F6fhqh35/m6Tlqmp6fpsXL86MCgsSl2tpaTj75ZHr27Ol2FBGRRNAZ2BTGx+uBMzKgMdfjFAIaW23B38j2ZTi9GD601u4AMMYsBD4HbgJCLiBIdFQHqin3l9M1qavbURo0Jn0MIwPte2UrEZGWUAFB4k5tbS2nnnoq11xzDR6Px+04IiKJ4DFgijHmH9baNn/0Ya1di1MgaJQxZhaQXW/znvvbG3nccuCNett8wSJC/VUZWqQ99dLZ86lWtH/mQCDAZDuZ9bXruaLrFZyZH5vD2t16ftwWCAR4/vnnqaur47zzziM5ueG3B+31+QmFnqOm6flpWqw9P0VFRQ1uVwFB4kptbS1nnnkmkyZNcjuKiEgi6Y/TW+BLY0w5sIX9RwEErLVDw3jNVcHr1s8BYBs6wRhzCDDSWvtkvV0ZwNYwZpMwqgvUsblmMwAbdm9wOY3UV1RUxIIFCwDo06cPY8eOdTmRiMQyFRAkbgQCAQYOHMjVV1/tdhQRkUTTCfg0ytd8B7jKGJNlra0IbpsAlAJLGzlnBPCEMabIWvtvAGNMBnAK8GakA0vrpHhTuKXfLSyrWMapXcK9Gqi0Ve/evcnOzsbv99OvXz+344hIjFMBIQ592L07WbW1VKSkuB0lqgKBANOmTdO0BRGRMLPWjnPhso8AU4A3jDHzgYOBmTgrKtQAGGM6AgcCq621W4A/BY/5U3AKRBUwHegA3BH9H0Faanj2cIZnD3c7hjSgoKCAZ555BoDU1FR3w4hIzPO6HUBC9+TQodw/YgRPDg3nSNLYFggEOOSQQ+jfv/5oVxERiUfW2mLgeJwPM14EfgLMstbevc9hhwKLcRonYq3dBRwHLAEeAP4AVAJHW2vXRy+9SGJJTU1V8UBEWkQjECQuBAIBCgsL3Y4hIpKQjDHLWnKctfbAcF7XWvsp8L0m9r9PvWaMwULB+eHMISIiIi2jAoKIiIiUsP+SiklAATAI+Bp4O9qhREREJLaogCAiItLOWWuPbWyfMeZgnOLBoqgFEpGweu655ygtLeWyyy4jKyvL7TgiEsfUAyEO/WrRIp5ZuJBfLWo/r+X8fj8ZGRluxxARaXestZ8DDwK3uJ1FRJpXWlvKom2L2O3bDcCqVav4wx/+wNtvv83ChQtdTici8U4jEOJQp+pq8qqr3Y4RVX6/n8zMTLdjiIi0V2XAALdDiEjzZq2exbfV3zKu8zim9Z5G9+7d6dmzJ9u2bWNoO2rALSKRoQKCxDy/34/H46Fv375uRxERaXeMMcOAa3H6IIhIjPMGBxjvuc3KyuLRRx8lEAhoKWwRaTMVECRmBQIB6urqOOigg7j22mvp2bOn25FERBKSMaaK/ZsoAqSeL+25AAAgAElEQVTwv+mO50UvkYi01p0D7mRF5QpGZo/8znYVD0QkHBKmgOD3+7n11lux1pKamsodd9xBnz593I4lrVRTU0Pfvn2ZPHkyw4cPdzuOiEiie56GCwg+YBPwvLX2i+hGEpHVlat5eevLHNPpGEZ2HNn8CUDnlM4clXNUhJOJSHuVMAWEv//979TU1PD888+zdOlS5s6dy6OPPup2LGmFlJQUJk+ezPjx41UtFxGJAmvtJc0dY4xJttbWRSGOiAQ9tfEpvqj4gs93fs5vh/7W7TgiIomzCkNRURFjx44FYMSIEXz55ZcuJ5LWqKurY+rUqZx00kkqHoiIRIkxZo0x5vQm9p+PMxJBpN1aXrGcl0peospXFbVrHpJ9CACHZh8atWuKiDQlYUYg7Nq1iw4dOuy9n5SURF1dHcnJCfMjJryamhpOOukkjj32WLejiIgkNGNMITB2n019gROMMQ2tl+sFLgJSoxBNJCbV+Gu4afVN1ARq2FK7hZ/0+ElUrnt217OZkD+BFG9KVK4nItKchHl33aFDByoqKvbe9/v9Kh7EkZqaGg466CCmTp3qdhQRkfagFLgdGBS8HwCuCX415pFIhxIJ1crKlXjxMjBzYESvk+RJIi8lj+KaYvJT8iN6rfpUPBCRWJIw77APPfRQ3nvvPU455RSWLl3K4MGD3Y4kLeTz+TjjjDP46U9/qmkLIiJRYK2tMcacAPQDPMC7wBxgYQOH+4At1lobxYgizVq2axkzVs8A4O5BdzM4M3Kv/ZI8Sdw3+D5Kakrol9EvYtcREYl1CVNAOOGEE/joo48477zzCAQCzJkzx+1IEfPCoEGk19WxO85HWPj9ftLS0vj5z3/OMccc43YcEZF2xVq7jv9v787D5KiqPo5/J5NkQkISSAgMkJCEkBwCyCqgYScBNSiKIC4sioqyhUVFQEDCJrLv+64iEngVFFkkIILvC4pRQSQcEQggm+xbtgnT7x+nOnSanullpru6p3+f58kzmeqq6lN3qvveOnXvLXgWwMz2Ae5z96fTjUqkdEsyS8gkDw9Z0ln9+T2HtA5R8kBEml5jX4Hm6NevHyeccELaYdTEbePGpR1Cj3V2djJ8+HDOOuss2tvb0w5HRKSpufu1AMkcCENYdpLl/sBQYFt3vzSF8EQKWn/o+pyw5gn0ox/rLL9O2uGIiDSFPpNAkMaRyWQYPHgw55xzDiuvvHLa4YiIND0zWw34BbBFkVWVQJC6kn1KgYiI1IYSCFJTmUyGgQMHcsYZZyh5ICJSP04HphBJhA7iqQunACOBzxO9EqamFp1IFb286GWufPFKBi8YzCeW+0Ta4YiI1LV+xVeRerNcRweDOzpYrqMj7VDK9t577zFjxgzGjh2bdigiIvKBacA17r4HcAjxVIY73X0/YEPgbWDXFOMTqZpbXr2FB956gFvm38LbnW+nHY6ISF1TAqEBXXzvvdxw551cfO+9aYdStv79+zNhwoS0wxARkWWtADwI4O5vAc8BmyW/vwBcDnwmtehEqmizYZsxpN8QJg+YzPIty6cdjohIXdMQBqmptrY2Ro4cmXYYIiKyrNeIiRKzHFgv5/dngNE1jUikRjYcuiG/+MgvmDt3btqhiIjUPfVAkJpqbW1l8ODBaYchIiLLuhf4lpmNSX7/KzDNzIYlv28DvJ5GYCIiIlI/lECQmmtpaUk7BBERWdZMYBXgSTMbCVwEjAAeN7M5wB7AjemFJ1IbmUyGs549ixk+g6cXPJ12OCIidUcJBBERkSbn7v8C1gGOcffX3P05YDoxlKE/8ZSGH6QYokhNvN75Or9/4/fMWziPe9+4N+1wRETqjuZAEBEREdz9JeC0nN/vJYY2iDSNEf1G8Mnhn+TphU8zbcS0tMMREak7SiCIiIgIZtYP2AvYCRgDHAy8B+wCXOjub6YYnkhNtLS0cOCYA9MOoyx/f+fvXPfSdWy34nZMX2l62uGISB+nIQwiIiJNzsyGEL0Nrga2Jx7hOJQY1nAi8KCZrZpagCLSpZv+exOPz3+cn7z4k7RDEZEmoASC1FRra2vaIYiIyIedSCQNdgLWBloA3P0mYFdg9WQdEakzO47YkZEDRrLTSjulHYqINAENYZCaamtrSzsEERH5sN2JYQq3J09hWMrdf2VmFwB7phOaiHRn6xW3ZusVt047DBFpEkogNKBzN9yQ/p2dLOnXeB1IlEAQEalLKxFPXOjKs8k6IiIi0sSUQGhAfxs1Ku0QKqYhDCIidekJYAvgsi5e3wl4snbhiIjURkdHB7Nnz2bcuHFMnjw57XBE6p4SCCIiInIhcKGZOfDbZFmrmU0EjgQ+BRyWVnAiItVy4403cv311zNgwACuvfZahg4dmnZIInVNCQQREZEm5+6XmNkaxESJ2ckS70h+tgCXuvt5qQQnIlJFK6ywAgBDhgxhwIABKUcjUv+UQGhAE958k/6ZDEtaWngy+dJrBIsWLWLjjTdOOwwRESnA3X9gZlcBnwXWBFqJuQ9udfdHUg1ORKRKpk+fzsSJExk1ahSDBg1KOxyRuqcEQgM69qGHGLloEa+1tfG1HXZIO5ySjRo1in333TftMEREpAvu/m/gzLTjEBGppYkTJ6YdgkjDaLxp/KWhZDIZFi5cyOLFi9lnn30YOHBg2iGJiDQ9M3vfzL5SYPkwM1PbQERERApSDwTpVZ2dnSxYsIDll1+e9vZ2Ro8ezahRo9hggw2YMmVK2uGJiEhoyV9gZiOB/wI7APfUPCIRERGpe0ogSI8sWbKEjo4Ohg8fzuqrr87YsWPZYostWH/99Rk8eDAAc+fOTTlKEREp0YcSCyIiIiJZSiBIWTo6OshkMowcOZLRo0czceJEtt56ayZMmED//jqdRERERERE+ipd8Um33n//fTo6OlhxxRVZY401WG+99Zg6dSqjR4+mpUU3qkRERERERJqFEgiyjM7OThYuXMjw4cMZM2YMkyZNYurUqUyaNIl+/TSvloiIiIiISLNSAkGAGJowbtw4Jk6cyPbbb896663HgAED0g5LRESqZ6SZrZHz+4jk58p5y5dy92erH5aIiIjUKyUQmtzixYsZOXIk++23H1tvvXXa4YiISO2ck/zLd10327RWKRYRnlrwFPe+cS/TRkxjjUEFc1giIpIyJRCaVCaTobOzk2nTpjFjxgwGDRqUdkgiIlI7x6cdgEi+s589m3kL5/HYe49xxsQz0g5HREQKUAKhAR2xxRb0y2TorHASw87OTlZccUWOOeYY1l577V6OTkRE6p27K4EgdWf8cuOZt3Ae4weNTzsUERHpghIIDejlwYN7tH1bWxtnn302o0aN6qWIRERERHrmsDGHsWf7nowaoPaJiEi90rT6TWbJkiXMmDFDyQMRERGpKy0tLaw8cGU9JlpEpI4pgdBEOjs72Xzzzdluu+3SDkVEREREREQajIYwNKCtnn+egZ2dLO7Xj/tXX73k7RYuXMhnP/vZKkYmIiIiIiIifZUSCA3oG489xshFi3itra2sBMKwYcNYd911qxiZiIiIiIiI9FUawtBE2tvbGThwYNphiIiIiIiISANSAqGJDB8+PO0QREREREREpEEpgdBEll9++bRDEBERERERkQalORCaQCaTAeDjH/94ypGIiIh8mJkNBR4FvuvuNxVZtw34MfBlYAhwJ3Cwu79Q9UBFRESanHog9HGdnZ0MHjyYE044galTp6YdjoiIyDKS5MEtwBolbnIJsDdwJLAPsAFwm5m1VidCERERyVIPhD5s0aJFTJgwgZNPPpkRI0akHY6IiMgyzGwbIiGwSonrTyCSB19x9xuSZQ8DDnwW+GWVQhURERHUA6HPWrx4MVtuuSXnn3++kgciIlKvbgb+AXyyxPW3T37eml3g7k8A/yxjHyIiIlIh9UDogxYvXsymm27KscceS0tLS9rhiIiIdGUrd3/UzMaVuP4k4CV3fy9v+VPJa2WbO3duJZs1pAULFgDNdczlUPl0T+VTnMqoeyqf7jVK+SiB0IA6W1pY0tJCZ4HkwOLFi1lvvfWYOXOmkgciIpIKMxsATOhmlZfd/Q13f7TMXQ8D3imw/B1gTJn7EhERkTIpgdCAvj5t2oeWdXZ2ArDllltyxBFH0L+//rQiIpKa1YHubqEcBpxTwX5bgEwXr3VWsD8mT55cyWYNKXtXq5mOuRy9XT7PLXyOc587l/GDxnPA6AMa/saOzp/iVEbdU/l0r97KZ86cOQWX6yqzwWUyGZYsWcIGG2zAjBkzGD16dNohiYhIk3P3ecTFfm97CxhaYPnQ5DWRunH363fj8x2f7+y68q60t7WnHZKISI8pgdCgMpkMHR0dTJo0iQMPPJC111477ZBERESq7Qmg3cyWc/cFOcvXBO5PKSaRgrZZcRvmvDOHcYPGsfLAldMOR0SkVyiB0ICWLFnCsGHDOOSQQ5gyZUra4YiIiNTK3UAr8BlgFoCZTQTWBWamF1ZzWPD+Aq564SqG9x/OHu17NHyX/Gobv9x4zrfz0w5DRKRXKYHQYDo6Ojj03XfZbtIk2u65B5RAEBGRPsrMhgHrAE+6+yvu/qSZ3QhcbmbDgTeAU4BHiEdCShXd88Y93PH6HQBsMmwTJg+pj3G6IiJSO/3SDkBKk8lkaGlpYZ999uETTz1F23nnwUUXpR2WiIhINW0MPADslLNsH+AG4FTgCuBhYLq7v1/78JrLOkPWYUi/Iaw6cFVGt2nOJRGRZqQeCA0gk8kwYMAATj31VCZNmgSHHZZ2SCIiIr2mq0kX3f3e/OXu/h7wreSf1ND45cZz/XrXa+iCiEgTUw+EBtDa2spJJ50UyQMRERGRlCh5ICLS3JRAaABHH3006667btphiIiIiIiISBPTEIY6lclkaG1t5cgjj2SzzTZLOxwRERERERFpckog1KFMJkNbWxsnnXQSkydrhmMRERERERFJnxIIdaazs5Nhw4Zx5plnstpqq6UdjoiIiIiIiAigBELd6d+/P2effTbt7e1phyIiIiIiIiKylCZRrCOLFi1it912U/JARERERERE6o56IHRjyZIlZDKZmr3fhAkT2HPPPYuvOHkyrLQSjBpV/aBEREREREREUAKhSwMHDuScc85h0qRJNXvPQYMGlfZ85bvvrn4wIiIiIiIiIjmUQOjG4MGDGTp0aNphiIiIiIiIiKROcyCIiIiIiIiISFHqgSAiIiLSpDozncx6eRaddPLFVb5Ia0tr2iGJiEgdUwKhEX31q/D66zBiBFx7bdrRiIiISIN66O2HuO7l6wAYN2gcU1aYknJEIiJSz5RAaER33QUvvgirrpp2JCIiItLAxg4ay9DWoXRmOhk7aGza4YiISJ1TAkFERESkSbW3tXPNOteQIUNbv7a0wxERkTqnBIKIiIhIExvYb2DaIYiISIPQUxhEREREREREpCglEEREREREpCreeecdZs2axeOPP552KCLSCzSEQUREREREquKqq65i9uzZDBo0iFmzZtHS0pJ2SCLSA+qBICIiIiIiVbFq8tSw9vZ2JQ9E+gD1QBARERERkarYfffd2XzzzVlllVXSDkVEeoESCCIiIiIiUjVjx45NOwQR6SVKIDSinXeGN96AFVdMOxIRERERERFpEkogNKJLLkk7AhEREREREWkymkRRRERERERERIpSAkFEREREREREilICQURERERERESKUgKhEU2ZAmutFT9FREREREREakCTKDaiefPgxRdh/vy0IxEREREREZEmoR4IIiIiIiIiIlKUEggiIiIiIiIiUpQSCCIiIiIiIiJSlBIIIiIiIiIiIlKUEggiIiIiIiIiUpQSCCIiIiIiIiJSlBIIIiIiIiIiIlKUEggiIiIiIiIiUlRLJpOp+ZvOmTOn9m8qIiLSIDbZZJOWtGNoBmqPiIiIdK1QeySVBIKIiIiIiIiINBYNYRARERERERGRopRAEBEREREREZGilEAQERERERERkaKUQBARERERERGRopRAEBEREREREZGilEAQERERERERkaKUQBARERERERGRopRAEBEREREREZGilEAQERERERERkaL6V/sNzGw94Fxgc+B14ELgNHfPlLj92cBEd/90L8e1L/B9YDTwd+A77v5AN+v36Dj6sgrKcgpwMrARMB+YDRzu7i/XINy6Vm5Z5m17HDDT3VuqGGLDqOC8HAWcCXyaSK7eBxzm7k/WINy6VuFn/DRgA+AV4FrgR+7eUYNwG4KZ7Qxc5+5Di6ynukd6zMyGAo8C33X3m4qs2wb8GPgyMAS4EzjY3V+oeqA1Vsnny8x2BQqV4Qx3v6AqgdaQ2sfdq6B8fkO0K/INdfd3qxNl+lTHFVdGGdXdOVTVHghmtjJxcZgBdgcuIy4cv1vi9gcBh1Yhrq8ClwA/A3YF3gTuNLPxXazfo+Poyyooy8nA3cA7ROPke8AWyTYDahJ0nSq3LPO2XQ/4QXUjbBwVnJcDgLuAzYB9ga8BE4DbzGxgLWKuVxWU5QTgd8C7yfpnA0cAp9Qk4AaQJFh+BnSb7FPdI70hSR7cAqxR4iaXAHsDRwL7EInA28ystToRpqMHn68NgH8DH8/7d2PVgq0RtY+7V2E7bQPiAjn/fJlf3WjTozquuFLLKFF351C1eyAcmLzHzu4+n6iA2oCjzOzcru5GJSfUacBewFu9GZCZtQDHA5e5+/HJsrsABw4DDu6t4+jrKizLg4AXgV2z5WZmTwB/BnYAbqtB6HWnwrLMbtsKXEXc6V29+tHWtwrLcm9gErC2uz+bbDOPOB8/AsypeuB1qMKy3A1oJT7j7wG/M7NVgYPM7PBmuKvQlaTeOAQ4EXgPKJacUt0jPWJm2xAXPKuUuP4E4vvwK+5+Q7LsYeIz/1ngl1UKNQ2Vfr7WB+a4+4O1CrQW1D7uXiXlY2YrAGOAO/ra+VKI6rjiyi2jej2Hqj0HwjTg7uSkyLoZGAFs2s12PyDuSn+C6B7Um9YCxgK/zi5ITtDfAp/sYptKj6Ovq6Qs/wmcmfel4MnPonfa+7BKyjLrMGAocH7VomsslZTlLsSX87M52/zd3Vdz96ZMHiQqKcs2oANYkLPsNWD55LVm9ingKOBwSvu8qu6RnroZ+AfF65Gs7ZOft2YXuPsTRN1d6j4aRaWfr/WBR6oZWErUPu5eJeWzfvKzL54vhaiOK67cMqrLc6jaCYRJRDevXE/lvNaVi4HJ7j67SjFB4bgmdNFFr9Lj6OvKLkt3v8jdL8xb/Jnk5+O9HF8jqeS8xMzWIjLi+wKLqhdeQ6mkLNcHHjez48zsJTNbZGa/NbNSu/z2VZWU5XXA+8ApZjbCzDYlhqL9yt0XVi/UhvAQMN7dzyO6bBajukd6ait33x34b4nrTwJeSnoP5XqKvnfOlf35SoaDjAM2MrN/mVmHmT1iZtOrF2bNqH3cvUrbFouAk8zsNTObb2Y3mll7NQNNkeq44soto7o8hyoewpCMGZ7QzSovA8OIse65sr8P62pDd/euXusF2fctFFc/YsKgtwtsU/ZxNIFKynIZZjYGOAP4C3BPbwfYQMouy6Q73RXAT9z9j2b20apH2RgqOS9HEWN95wHfSNY5FfitmW3k7kuqFm19K7ss3f1JM/seMZbx+8nivxLl29Tc/fkyN1HdIwWV0gZz9zfc/dEyd13onCNZNqbMfaWmim3UjxBjlscD3wGWAAcAvzGzae7++4qDTp/ax92rpHzWJ3revUP0dFwTOAm4J2lb9KkbP6rjiqugjOryHOrJHAirA3O7ef0w4ku2q+xKZw/euyeyk1WUE1c9Hkc9qKQsl0qSB3cTX7xfauax0VRWlt8mutTtXJWIGlclZTmAGIf2KXd/E8DMniIyxZ8HZvV2kA2i7LI0s28ClxMJhBuA1YATiGTMtL7WYKoy1T3SlVLaYOdUsN++cs5Vq436GLAT8Ed3fxuWjoN/GDgGaOQEgtrH3aukfM4Crs9JLN1nZnOBB4lJA3/auyE2nGY6fypVl+dQxQkEd59H8dk1jybGZufK/t6rkyOWIfu+Q4kMNDm/v9/F4zDeov6Oox5UUpbA0qcG3E5cuO2gR+WVV5ZJ8uU04q7ufDPrTzIkKfl/p7s365dvJeflu8CfsskDAHf/i5m9SdxxatYEQiVleSRwm7t/O7vAzP5CNOb3ICb8lNKo7pGCSmmDVajQOUeyrGHOuWq1UZM64ra8Ze8nSYS9Kgq2fqh93L2yy8fdHydveK67/ylpW2yAEgjNdP5UpF7PoWrPgfAE0dUiV/b3ag5T6M4TeXGQ8/u/utmm3o6jHlRSlpjZ5sD9xDjprdy9riYGSUm5ZTmV+JK9iZiwrgM4M3mtA/hhFWJsFJWcl/+m8Ey4/SltjFpfVUlZjiEy40slFeBrwDq9Gl3fp7pHau0JoN3MlstbviZ975wr+/NlZhslvazyLQe82ouxpUHt4+6VXT5m9iUz2zpvWQvRJb3Rz5fe0EznT0Xq9RyqdgLhbmCamQ3JWfY5oiHZ209XKNUTwHNJHMDSsXI7EfEWUo/HUQ/KLsvkWbm3Ay8BU5LZnaX8svwNMUNt7r+zktc2JbqPN6tKPuO/A7Yws9VyttmGeHLA/1Uv1LpXSVn+C5iSuyCZ7HMk8HR1wuyzVPdIrd1NPIY1O7kxZjYRWJeuP/ONqpLP14bA5Wa2UXZBkmyZDvyhWoHWiNrH3aukfPYHzjWz3Out6UTC6b4qxdlImun8qVRdnkM9mQOhFBcBM4jnep5OdLU4CjjS3RcDmNkw4q7Uk+7+SpXjwd0zZvZj4AIzewP4X+AgYCXg7CSmCcConOdtFj2OZlRhWZ5DTIxyILBG3iz3z7j7izU7gDpSblm6+2vEF+xSZrZlsq+/1DT4OlPheXk28HXgdjM7DhgMnE4kD35X40OoGxWW5QnALDO7ArgeaAdmEhNU/qSmB9BgVPdIreW3wZJJUG8kLpKHA28ApxCPELs5xVCroZI26o3JOjcmQyAWEI9jW56Y2KxhqX3cvQrL50fETbOfmdnVxJMFTgT+x92b7uZEM58/pWqUc6iqPRCSi8FpRKLiJuBbwNHufkbOahsDDxAZvJpw94uIL/y9krhWAD7h7tlHhxybxJRdv5TjaErllGWSqZ1O3N34ebI8998eNQ2+zpR7XkrXKviMvwJsQdwh/ylwAXAXsFMTzyUBVFSWNwK7Et/ttxEXH/cBm7t7odnd5QOqe6TWCrXB9iEmQD2VeNLPw8B0d3+/9uFVTyVt1GSc+1Rigt3ziCTpfGBrd3+udtFXh9rH3augfO4kJrpei0jAHU3MA9To82VUqqnPnxI1xDnUksk08/BeERERERERESlFtedAEBEREREREZE+QAkEERERERERESlKCQQRERERERERKUoJBBEREREREREpSgkEERERERERESlKCQQRERERERERKap/2gFIfTGza4CvlrDqPsC9wNPAUe7+4yqG1S0zywCXuvt+ZjYuN6b839OKsTtmtmbOM4S7XNYozKwNuBT4PJABvgzcXWDZb0n+biXud1vg98CX3f0XvR95bcvdzL4GXA183N0frMV7iojUOzObCRzX3Tru3lKl924DRrr7C9XYfzMzs42BK4B1gOfdfUKV3mdV4E13X5D8Pg943N0/WY33K/D+ewJXuPugCrcv2g4xsxHAtcD2wCJgG3f/RyXv18176LMgXVICQfJdCszO+f3zwC7AYcCrOcv/r5ZBFbEX8K+0g6iEmX0dOAtYIWfZscCXgHXTiquHvkUkoa4CHgD+2sWycv9uc5NtHujNYLMK/S1ERCQ1+e2OqjKzscBdwA+BqiSpm9zlwATgSOClaryBmX2K+NsZsKAa71Hk/dcHLuzB9qW2Q44GPg2cDvw7+ddr9FmQYpRAkGW4+wPkXKCZ2VpEAuFmd5+Xu25ydz917v6ztGPoga2B/Cz1VKA1hVh6y0eIXgb7u/tiADP70DKgrL+bu79c7jZlKvS3EBGRdHyo3VFl44GJNXy/ZvMR4EZ3P6eK77E5MKyK++9Skrz4efL+iyrcTantkI8AL7j79yt8n2L0WZBuaQ4EEeltA4HFOYmCrpaJiIhIcxgAvJN2ENVgZqcDtwHzgN/V4C0H0kfLUhqDeiBIb2gzszOAPYGhwIPAIe7+aHYFMxsCzCS65q8MPAVc4O7ddvVKxnmdS4zzGkl8OV8NnO7unck6S+dA6IUYjwe+mMT4DNHl/nR3fz9ZZyYxNnNVd38pZ9trgC/ljnkzs/HAKcCOREb5b8AP3f3u5PV7gW1yjuF44GvA2Jxl+7j7Ncnv+wEHEVnhV4FZwLHu/m6RMlwBOInoSbIC8Bhworv/OmedNZNYdwCWA/4B/Mjdb87b1wbAj4CtiATk/xLzS/w1J2Zy/v+H7DHmLnP3bQv93ZJ5AQ4huh/+l+g6d7y7Lyg0B0Ip51Xyt9kwKbvTk/+/ClwJnODunYX+Fu4+s0BZPgG84u5T8pZ/G7gE2NDdHzaz1Yjz5FNAO/Au8EfgCHefm7/fnGP/0JwIhc45MxsFnAx8FhhODO84xd1n5WxX9LMjItLoSqkbzcyI79HtiO/DN4nhmoe7+/M5378A15vZj919XKG6PdnfvUC7u6+d8/ubRB20X/L/j7r7C0nddTzwUWAxMSfQEe7+ZJHj2p3o7m/EHe37gR+4+z+T12cC3wOmEMNPNwSeA85y90vy9vUV4EDiznUbUR9cSdQHufX2lkk5bQ4szIn12Zx1dgWOIoZZvgv8BjjS3f/bxXF8jQ/K9ttJfbmPu19TRrvrUOAA4Dzi2mXv3DZMst41fDCH14tmdq27fy3n9W8C3wfWIIZPznT3X+bto6J2FjGvw4lEvXxpF+XQBpxJDD1YFXgBuCGJY2Ep7ZCceb3IWW/pcfbVz4LUH/VAkN7wfaLb/YnEl+PHgdvNbCCAmfUHbgf2B64nKoJHgRQv/G8AAA+6SURBVAvMrNjEhrOATwAXEV+K/wB+nLxnb8bYRnyBHgrcSoy9fIS4qP5Jme+FmY0hkhQfA04DfkBk3+80s08nq51MNAg6iLH9v0ze/3HgxWTZfcn+fkSUwd+JC+xZRGV6R1K+XcXRRly47pts813gPeBXSXc7zGwC8CdgGnA+0TBoSdb5Vs6+NiISBmOIi/YTgHHA/Wa2SbLaXnnHdHIXywrFejRRab0KHA78Kon3qi7WL+e8Wp34uz5ElN8TRAWaPb5Cf4tCrgc+Zmar5y3/AjA3SR4sl+zr00RD4gBi6MWOxN+rR8NTzGxosv/PAxcTDchXgRvMbP+cVXvrsyMikoYVzWylAv+WTqBYSt2YTKr3ALAJcAbxfXgrcdF6Q7Kr+4jkOMQY9kMriHca8Z37HeCy5IJpOjGWvB+RDDiLuOB/wMzW6GpHZrYN0R3+WaI9cipxUf/7pA7IGkDc8X6JqDefAS42syNy9vVt4DrgeaK+OIJoB5wKfCNnve2Ae4gL7BOTWKcBs7PvmezrJqKN8l3gMmBX4I9m1tXQgfuIepVk/3sB95XZ7hoCnJ28dhGF5+G6lGg3QPyNcy/ktyJupFyTHP8wYJaZrZdz/BW1sxK7uPsP3b27oQsXEjeJfpbs964kluyQjlLaIa8kr+W2Ey8tNf5G/CxIfVIPBOkNLwNTcma8XUxUPh8lvuS/CmwJbOvu9yXbXGzR5etwM7u8UPbRzFYmLvq/5+5nJouvMLPbgLV6OcZvEBf7+7r7Fck2F5nZecAMM7sq23OgRNkv34+6+6vJe15I3JE/z8xuc/e7zGwPYLOceRweMbNDgZbsMjObSHzZHufuJ2bfwMxmE1/8e/FBtjjfvsRdgs+5+y3JdlcTF9pHEBfgpxA9E9bP3h03s4uIZMGZZjbL3d8kMv/PAJtmK8lkvX8QFcE27v4zM5uWd0wUWpbLzEYCxxIV5m7ZOyJm9g5wrJkdVWCzcs6rlYBvuPtVyX5/QmT/dwcu6eJvUcj1SZy7JuWR7Q2wLZFVB9gZWBPYyt3/mHOM7xLJGSN6gVQqewdlQ3f/V7LvC4nK/1Qzu47o8dJbnx0RkTT8tYvlKwJvllE37k1cMG6cM6fCZWY2CPiymQ1x96fM7C4i2f/H/N53JRoCfDHbszFJFl9IPLFqx5x67Uqi19iJdP3Uqy8SF/m75Gz3MHHRNxn4c7LeQGC2u++ZrHMRcVF+rJld7O5vExeAd7v77jlldCVxMboj8WQEiB56LxB1/NvJen8m7hLvama/St7/KnfPTTzMAuYQCYUPPT0jeaLAU2b2U+CJnLbNAZTe7upP9Jy8oIvywt0fMLNHiN6W/5PbS5TodbGpuz+WvPcc4mJ9Z+DRHrazKHF45leAK939mOT3K82sH0mv01LaIe7+HvAzi94UlbQTG/GzIHVICQTpDb/OXpgn/pL8bE9+fg74D/CYma2Uux2RDf8UUKhSeIvoHrefmf0buNPdF7r79CrE+BmiMs2vIE4GZhBdxUtKICQVws7EeDgKHPMpRHe3Rz+8dUE7Ez0Cbs3b15+A14GdCsSdNR14Lps8AHD3RUkmeEHypT6dKJ+5Oessthjy8QtgezO7j7hYPx0YmncH5DbibzQs2+iowFSigr8otzslkZi4kbhzMi5vm3LPq//JOb6FZubAKuUE6e5zk0bcbiQJBKInQCuRXMDdbzCze9z9lex2ZjaYmEQSYPly3rOAzxHDYV7PO+6biZ4QWxNZ/t767IiIpGFPIvmfL9sdu6S60d1PTS5Gc7+ThxNdqCEudt7rhXjf9JxhkcSQgnFEr8eRZpZdvpi4y7tTN/v6D3Ghd5aZXeTuT7j7HcAdBdY9LfufZEje+UQyfluiPtyAGJqYqx14m6Q+MrNViLvSJ+XW4+5+j5ltRtzx3jFZ/9d55f080Z7ZiSKP38xTbrvr/jL2ne/RbPIgkd8G7Ek7q1T/AXY3sz8Rba633f2bPdxnVl/+LEgdUgJBekP+uLeFyc+Byc8JRLf3VyhsTKGFyYXu/sSjf24G5pvZ3cSF2qzs+LheinEc8GT+Pt39ZTN7g7jjW6qViIr/S8m/QsZQegIh+6zkru7GFCy/xFgKPN7H3Z+ApY2GIRR+nGI2obAGcUcdoovk4V281+pEg6QSY5OfT+TF+SYxfo6cCiernPOqw93fynt9EZU97eJ64Edm1p7c4fgCMMfdc8s5Y2bHEENljDi/su/V06FjE4jGYJfH3cufHRGRNPyvd/8UhnLqxuXM7BTiItmS17JDIXprOG/+d3I2vvOTfx9iZsvl3dzIuoC4qDoUODRJeN8CXJ5f1xAX97myr4+DpTcEtjOzLxI3L4wPHhOYPfZsG2eZOjjZ/qEk1uzxdHVHuuAcCN0YR3ntrq7qvFIsE1tyEwGWbadCZe2sUh1A3BD5KbA4uTFzEzGHwcJutyyuL38WpA4pgSC9odiEbK3AP+l6HNWzXSwn6RJ/B9FlfCfiTvVniDsT5WQsi8XY0s1rrXyQne1unfz//5yuM9YPF9lfoX1/ClhS4PXuZuItdoFc7Lghjj37/7OIYQ+FPFfkvbpTyYV8OedVb04aeD3Ri2TXpOvmtsRwEADMbDIx70QL0RPgaqJ755pU9nzo/LJpJe7KdDV/iEOvfnZEROpRSXWjxcRttwNvEN37ZxNDAHYkhpX15L1z5dcz2XWOoOsLu45CC939bYsJDbckuuRPJ4avHWpmU3OGx3UC+Qnh7PsuATCzi4nJ7P5M3JG+ghhOObvI8eTLrvNVYqhDScfSjXLbXT2px0tpp0Jl7aySuPvsZKz/54g5knYk5grYz8w2c/dyyy9Xn/0sSH1SAkFq4Vki6323Lzvb70pEd+uC3aUsZufdCPinu18KXJp0Bb+a6AY2KTsGvBfMAzY2s9bcbLiZtRO9CZ5PFmVfa8vbfuWc/78CLAD6uXtuBY3FhD1rJK+XKnsh/LS7e97+vkBMntSV5yjwLF+LWXY/RszKPJ/IQn9oteTn8zkxLC5wTB8jnmxR6XOPs3FCXGTnzvY8hhg2cVaBbSo6r3rK3Z81s/8jugwuJjL2N+Ss8n2iPCa6+zM5cR1ZZNelnFsQxz2kwN9hPDHD9vwaf3ZERNJQat14HDEkcl13fyNnna56COZ6HxhgZi15w+tW5oNhacXie6vA9/X2QKe7F7rYw8zWAlZw9/uJrvvfMbOPExf+BxBJaogLszXImZmfD+a5edJi1v79iInsvp2z/1ZiBv6s3Do4P5arickPs8fzcoHj+TTl90CcR2ntrlroSTurKIsJuzcC/uPuPwV+amYDiIksDyOeiNCTxz/22c+C1Cc9hUFq4VZgNWLyllzHEuPSP1RhJSYRFefXswvcfT5x1xkKZ1l7EuMoYJ+85dmLvt8mP7NfwhtkV7B4ZN8WOTEuAe4EdjGzSTnrDSAu4K7ngy/b9/nw5zB/2a15sWT3twsxy+4u3RzXbcBYM9sxZ7uBxDCE9ZNK+3bgM8md89x1vkMkOu5x9+eJmX2/mTu+Lhk7N4uY+bcnf4/ZRPZ537zlexOTSb1ZYJtKz6uuFPpbdOV64nFLXwbud/f/5Lw2kqiglzZ+kjkjshMEdZW4LXRuDSHuKOS6lXgSxLZ5y88mZqBentp+dkRE0lBq3TgSeDHvgmk1Yv4a+OA7OXsRm1sPvJT8/pGcbTegQGK+gIeIrvOHWDydJ7v9GGJugmO72fZM4DdJHZD1MJGoz//+PjBn363AwcQd5j8AI5KX8h8f/HVi+GJ/gKSOfwTYI0k2Z/e3BfHkgOWIHnWLiUmKW3PW2SQ5nqUJihKV2u4qR6G/Yamx5L43UHI7qxTDiImpl+4/6XHw9+TX7N+0nHZIrr78WZA6pB4IUguXEhXQlcnd6r8RF9x7A79MMuwf4u5/M7N7gJPNbDQxZ8AkYnKdO5OZfXvL5UQldomZbUw8WWA7Ynz7jTkZ05uJ8VuXJncI+hGV9wssO17vyGT7B5IJjV4mZuD9KHBwMpMuRG+FAcl4+d+5+5+TZVuZ2SHAXR6PBryUeH7yykSlOiYph3/Tfbf4S4FvAjdbzGz8DHHRa8D2yTpHEd3b/2hmFxAT7uwBbAoc5O7ZrnuHEhnyOWZ2CTGR1b7E3Aefz8tIlyUZ83gyMNPiUVC3ETNN70+M+fxXUsnlH9vXKPO86kahv0VXbgTOJf7G++e9djsxVOAWM7uZaCB9g0h2QPROKOReooI+KUk4vE2Ub/7YyB8RwxJus3j6wr+JIQmfIZ7//QzwTA0/OyIiNVdG3Xg78H2LpwD8gairv8UH38XZn9lx23ubGe7+c6J32VHATWZ2LvEEiIOBos+tT+YeOIx4bN+fzewaotv+QUTPge66jJ9DXLD/wcyuJbqE70E8YeeyvHUPtHiS0V+ICX63Ar6ejPP/J9G74IdmtjzRFtk62ddClq2PvpuU1YNJr4PBRL3/CPCTZH/HEUP4/mBmNxBzKRwMvEYXj2juRqntrnJk/4ZHmNkt7n5PKRv1sJ1Vyv5ftXj604HJBfSDRNvpYCKxn32SVDntkErib8TPgtQh9UCQqksmh9mO+AL7NDF7/RRgJlGJdWc34kJxF+Iu95eBi4kKpjdjXERcUF9IzPx7NpFlPTx5z+x6rxAXa/8hLuQOILrXX5a3PyeGCPye+II9g6iM93T33AlkLiUufH/IB1n4M4iufaclsUBcpB4GjE9i25u4iN3W3V/v5rjmE3fKryPuOJxOfO538OTRh8mEih8jLmAPJhoBHcSjHy/M2dcfiIbHXOLL/iTiIncnz3nKQ6Xc/fjkOCcQjafpxDlyUBfr9+S8KqTQ36KrWP9LzEOwhJgEKdclRDZ9nSSmbxJluyHRCNy2i312EMc8BziGKONfJfHkrvcKMTnjDcR5cA5RZoew7ASXNfnsiIikqJS68Tjie3I74jv5S8QcRVOT17cFcPfHie/ILYELzGyguz9CfHd2EHX9V0gSsaUEl1x47UT0SjuBeDSeJ/F1eXHo7r8nhsktIh5xd2oSwydz5j/I+hzRc+0M4oJ+N3e/OtnPouT9/0rUD6cSd4y/QtQLGyY9CUku2HdIYj2ZuDnyG6K9sDBZ58dEGS9HtFEOIi5+tyo3MV1qu6tMNxD17X7E05jKUVE7qwwHEO2mbYhJMvcn6vipOd33S26HFNAnPwtSn1oymYpvGoqIiIiISI2Z2UzignBVjycCiYjUhHogiIiIiIiIiEhRSiCIiIiIiIiISFFKIIiIiIiIiIhIUZoDQURERERERESKUg8EERERERERESlKCQQRERERERERKUoJBBEREREREREpSgkEERERERERESlKCQQRERERERERKUoJBBEREREREREp6v8BXE/fPeYyQbMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your turn.\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA  \n",
    "\n",
    "average_scores = []\n",
    "range_n_clusters = range(2, 8)\n",
    "\n",
    "for n_clusters in range_n_clusters:\n",
    "    # Create a subplot with 1 row and 2 columns\n",
    "    fig, (ax1,ax2) = plt.subplots(1, 2)\n",
    "    fig.set_size_inches(18, 7)\n",
    "\n",
    "    # The 1st subplot is the silhouette plot\n",
    "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
    "    # lie within [-0.1, 1]\n",
    "    ax1.set_xlim([-0.1, 1])\n",
    "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "    # plots of individual clusters, to demarcate them clearly.\n",
    "    ax1.set_ylim([0, len(x) + (n_clusters + 1) * 10])\n",
    "\n",
    "    # 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)\n",
    "\n",
    "    # The silhouette_score gives the average value for all the samples.\n",
    "    # This gives a perspective into the density and separation of the formed\n",
    "    # clusters\n",
    "    silhouette_avg = silhouette_score(x, cluster_labels)\n",
    "    print(\"For n_clusters =\", n_clusters,\n",
    "          \"The average silhouette_score is :\", silhouette_avg)\n",
    "\n",
    "    average_scores.append(silhouette_avg)\n",
    "    \n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(x, cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(n_clusters):\n",
    "        # Aggregate the silhouette scores for samples belonging to\n",
    "        # cluster i, and sort them\n",
    "        ith_cluster_silhouette_values = \\\n",
    "            sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "        color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                          0, ith_cluster_silhouette_values,\n",
    "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "        # Label the silhouette plots with their cluster numbers at the middle\n",
    "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "        # Compute the new y_lower for next plot\n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "    ax1.set_title(\"Fig 3: 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([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "    \n",
    "    \n",
    "\n",
    "    # 2nd Plot showing the actual clusters formed\n",
    "    pca = PCA(n_components=2).fit(x)\n",
    "    x_2D = pca.transform(x)\n",
    "    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n",
    "    ax2.scatter(x_2D[:, 0], x_2D[:, 1], marker='.', s=30, lw=0, alpha=0.7,c=colors, edgecolor='k')\n",
    "\n",
    "\n",
    "    # Labeling the clusters\n",
    "    centers = clusterer.cluster_centers_\n",
    "    # Draw white circles at cluster centers\n",
    "    ax2.scatter(centers[:, 0], centers[:, 1], marker='o',c=\"white\", alpha=1, s=200, edgecolor='k')\n",
    "\n",
    "    for i, c in enumerate(centers):\n",
    "        ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,s=50, edgecolor='k')\n",
    "\n",
    "    ax2.set_title(\"Fig 3: Visualization of clustered data.\")\n",
    "    ax2.set_xlabel(\"Feature space for the 1st feature\")\n",
    "    ax2.set_ylabel(\"Feature space for the 2nd feature\")\n",
    "\n",
    "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \" \"with n_clusters = %d\" % n_clusters),fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.show()\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.09365573283492713,\n",
       " 0.11889942863638556,\n",
       " 0.12347053919571699,\n",
       " 0.14092516241984757,\n",
       " 0.1371798939109807,\n",
       " 0.1161092456616906]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Silhouette Scores for clusters\n",
    "average_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing $K$: The Gap Statistic\n",
    "\n",
    "There is one last method worth covering for picking $K$, the so-called Gap statistic. The computation for the gap statistic builds on the sum-of-squares established in the Elbow method discussion, and compares it to the sum-of-squares of a \"null distribution,\" that is, a random set of points with no clustering. The estimate for the optimal number of clusters $K$ is the value for which $\\log{SS}$ falls the farthest below that of the reference distribution:\n",
    "\n",
    "$$G_k = E_n^*\\{\\log SS_k\\} - \\log SS_k$$\n",
    "\n",
    "In other words a good clustering yields a much larger difference between the reference distribution and the clustered data. The reference distribution is a Monte Carlo (randomization) procedure that constructs $B$ random distributions of points within the bounding box (limits) of the original data and then applies K-means to this synthetic distribution of data points.. $E_n^*\\{\\log SS_k\\}$ is just the average $SS_k$ over all $B$ replicates. We then compute the standard deviation $\\sigma_{SS}$ of the values of $SS_k$ computed from the $B$ replicates of the reference distribution and compute\n",
    "\n",
    "$$s_k = \\sqrt{1+1/B}\\sigma_{SS}$$\n",
    "\n",
    "Finally, we choose $K=k$ such that $G_k \\geq G_{k+1} - s_{k+1}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aside: Choosing $K$ when we Have Labels\n",
    "\n",
    "Unsupervised learning expects that we do not have the labels. In some situations, we may wish to cluster data that is labeled. Computing the optimal number of clusters is much easier if we have access to labels. There are several methods available. We will not go into the math or details since it is rare to have access to the labels, but we provide the names and references of these measures.\n",
    "\n",
    "* Adjusted Rand Index\n",
    "* Mutual Information\n",
    "* V-Measure\n",
    "* Fowlkes–Mallows index\n",
    "\n",
    "See [this article](http://scikit-learn.org/stable/modules/clustering.html) for more information about these metrics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Clusters using PCA\n",
    "\n",
    "How do we visualize clusters? If we only had two features, we could likely plot the data as is. But we have 100 data points each containing 32 features (dimensions). Principal Component Analysis (PCA) will help us reduce the dimensionality of our data from 32 to something lower. For a visualization on the coordinate plane, we will use 2 dimensions. In this exercise, we're going to use it to transform our multi-dimensional dataset into a 2 dimensional dataset.\n",
    "\n",
    "This is only one use of PCA for dimension reduction. We can also use PCA when we want to perform regression but we have a set of highly correlated variables. PCA untangles these correlations into a smaller number of features/predictors all of which are orthogonal (not correlated). PCA is also used to reduce a large set of variables into a much smaller one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set IV</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Use PCA to plot your clusters:</p>\n",
    "\n",
    "<ul>\n",
    "<li> Use scikit-learn's [`PCA`](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) function to reduce the dimensionality of your clustering data to 2 components\n",
    "<li> Create a data frame with the following fields:\n",
    "  <ul>\n",
    "  <li> customer name\n",
    "  <li> cluster id the customer belongs to\n",
    "  <li> the two PCA components (label them `x` and `y`)\n",
    "  </ul>\n",
    "<li> Plot a scatterplot of the `x` vs `y` columns\n",
    "<li> Color-code points differently based on cluster ID\n",
    "<li> How do the clusters look? \n",
    "<li> Based on what you see, what seems to be the best value for $K$? Moreover, which method of choosing $K$ seems to have produced the optimal result visually?\n",
    "</ul>\n",
    "\n",
    "<p><b>Exercise:</b> Now look at both the original raw data about the offers and transactions and look at the fitted clusters. Tell a story about the clusters in context of the original data. For example, do the clusters correspond to wine variants or something else interesting?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>offer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>cluster</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adams</td>\n",
       "      <td>0</td>\n",
       "      <td>1.007580</td>\n",
       "      <td>0.108215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allen</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.287539</td>\n",
       "      <td>0.044715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anderson</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.392032</td>\n",
       "      <td>1.038391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bailey</td>\n",
       "      <td>0</td>\n",
       "      <td>0.699477</td>\n",
       "      <td>-0.022542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baker</td>\n",
       "      <td>3</td>\n",
       "      <td>0.088183</td>\n",
       "      <td>-0.471695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "offer_id customer_name  cluster         x         y\n",
       "0                Adams        0  1.007580  0.108215\n",
       "1                Allen        3 -0.287539  0.044715\n",
       "2             Anderson        1 -0.392032  1.038391\n",
       "3               Bailey        0  0.699477 -0.022542\n",
       "4                Baker        3  0.088183 -0.471695"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "df1['x'] = pca.fit_transform(df1[x_cols])[:,0]\n",
    "df1['y'] = pca.fit_transform(df1[x_cols])[:,1]\n",
    "df1 = df1.reset_index()\n",
    "\n",
    "customer_clusters = df1[['customer_name', 'cluster', 'x', 'y']]\n",
    "customer_clusters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>offer_id</th>\n",
       "      <th>campaign</th>\n",
       "      <th>varietal</th>\n",
       "      <th>min_qty</th>\n",
       "      <th>discount</th>\n",
       "      <th>origin</th>\n",
       "      <th>past_peak</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>n</th>\n",
       "      <th>cluster</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.041879</td>\n",
       "      <td>0.122314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Jackson</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.839578</td>\n",
       "      <td>-0.328823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.342938</td>\n",
       "      <td>0.302186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Peterson</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.797379</td>\n",
       "      <td>0.475995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Wood</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.551215</td>\n",
       "      <td>-0.395670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   offer_id campaign varietal  min_qty  discount  origin  past_peak  \\\n",
       "0         1  January   Malbec       72        56  France      False   \n",
       "1         1  January   Malbec       72        56  France      False   \n",
       "2         1  January   Malbec       72        56  France      False   \n",
       "3         1  January   Malbec       72        56  France      False   \n",
       "4         1  January   Malbec       72        56  France      False   \n",
       "\n",
       "  customer_name  n  cluster         x         y  \n",
       "0        Thomas  1        2 -1.041879  0.122314  \n",
       "1       Jackson  1        2 -0.839578 -0.328823  \n",
       "2      Mitchell  1        2 -0.342938  0.302186  \n",
       "3      Peterson  1        2 -0.797379  0.475995  \n",
       "4          Wood  1        2 -0.551215 -0.395670  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## To create Scatter Plot, create a dataframe\n",
    "df01 = pd.merge(df_transactions, customer_clusters)\n",
    "df01 = pd.merge(df_offers, df01)\n",
    "df01.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (291600988)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from plotnine import *\n",
    "\n",
    "ggplot(df01, aes(x='x', y='y', color='cluster')) + \\\n",
    "    geom_point(size=5) + \\\n",
    "    ggtitle(\"Fig 4: Customers Grouped by Cluster\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From observing the resulting PCA plot, it seems that K=4 is the ideal number of clusters. This result is different to the one obtained using silhouette_score method. The result from the SS method showed that K=5 is the ideal one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the PCA explained variances\n",
    "features1 = range(pca.n_components_)\n",
    "plt.bar(features1, pca.explained_variance_)\n",
    "plt.xticks(features1)\n",
    "plt.ylabel('variance')\n",
    "plt.xlabel('PCA features')\n",
    "plt.title('Fig 5: PCA Explained Variances per features')\n",
    "plt.show       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_2   varietal          \n",
       "False  Pinot Noir            38\n",
       "       Espumante             35\n",
       "       Prosecco              32\n",
       "       Champagne             28\n",
       "       Malbec                20\n",
       "       Pinot Grigio          16\n",
       "       Merlot                13\n",
       "       Cabernet Sauvignon    11\n",
       "       Chardonnay             8\n",
       "True   Champagne             53\n",
       "       Cabernet Sauvignon    21\n",
       "       Malbec                12\n",
       "       Prosecco              11\n",
       "       Chardonnay             7\n",
       "       Merlot                 7\n",
       "       Pinot Noir             6\n",
       "       Espumante              5\n",
       "       Pinot Grigio           1\n",
       "Name: varietal, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df01['is_2'] = df01.cluster==2\n",
    "df01.groupby(\"is_2\").varietal.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing further analysis, it can be observed that customers belonging to cluster 2 did not buy that many Espumante bottles, however, they purchased a lot more Cabernet and Champagne ones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min_qty</th>\n",
       "      <th>discount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>46.330189</td>\n",
       "      <td>57.660377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>81.267857</td>\n",
       "      <td>62.928571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         min_qty   discount\n",
       "is_2                       \n",
       "False  46.330189  57.660377\n",
       "True   81.267857  62.928571"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df01.groupby(\"is_2\")[['min_qty', 'discount']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above analysis, shows that customers belonging to cluster 2 love to buy in bulk and with heavy discounts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we've done is we've taken those columns of 0/1 indicator variables, and we've transformed them into a 2-D dataset. We took one column and arbitrarily called it `x` and then called the other `y`. Now we can throw each point into a scatterplot. We color coded each point based on it's cluster so it's easier to see them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Exercise Set V</h3>\n",
    "\n",
    "<p>As we saw earlier, PCA has a lot of other uses. Since we wanted to visualize our data in 2 dimensions, restricted the number of dimensions to 2 in PCA. But what is the true optimal number of dimensions?</p>\n",
    "\n",
    "<p><b>Exercise:</b> Using a new PCA object shown in the next cell, plot the `explained_variance_` field and look for the elbow point, the point where the curve's rate of descent seems to slow sharply. This value is one possible value for the optimal number of dimensions. What is it?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your turn\n",
    "# Initialize a new PCA model with a default number of components.\n",
    "import sklearn.decomposition\n",
    "pca = sklearn.decomposition.PCA()\n",
    "pca.fit(x)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.4096489 , 0.30753551, 0.2022926 , 0.16703717, 0.15015248,\n",
       "       0.1434373 , 0.13818887, 0.12192294, 0.11636172, 0.10804271,\n",
       "       0.09937813, 0.09495961, 0.08690352, 0.07256738, 0.0660996 ,\n",
       "       0.06245473, 0.05634388, 0.05327395, 0.04728801, 0.04393911,\n",
       "       0.03900424, 0.03625783, 0.03455714, 0.03235091, 0.02940632,\n",
       "       0.02618221, 0.02308167, 0.02142632, 0.018814  , 0.0165252 ,\n",
       "       0.01426187, 0.0077789 ])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pca.explained_variance_)\n",
    "plt.title('Fig 6: Explained Variances')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Clustering Algorithms\n",
    "\n",
    "k-means is only one of a ton of clustering algorithms. Below is a brief description of several clustering algorithms, and the table provides references to the other clustering algorithms in scikit-learn. \n",
    "\n",
    "* **Affinity Propagation** does not require the number of clusters $K$ to be known in advance! AP uses a \"message passing\" paradigm to cluster points based on their similarity. \n",
    "\n",
    "* **Spectral Clustering** uses the eigenvalues of a similarity matrix to reduce the dimensionality of the data before clustering in a lower dimensional space. This is tangentially similar to what we did to visualize k-means clusters using PCA. The number of clusters must be known a priori.\n",
    "\n",
    "* **Ward's Method** applies to hierarchical clustering. Hierarchical clustering algorithms take a set of data and successively divide the observations into more and more clusters at each layer of the hierarchy. Ward's method is used to determine when two clusters in the hierarchy should be combined into one. It is basically an extension of hierarchical clustering. Hierarchical clustering is *divisive*, that is, all observations are part of the same cluster at first, and at each successive iteration, the clusters are made smaller and smaller. With hierarchical clustering, a hierarchy is constructed, and there is not really the concept of \"number of clusters.\" The number of clusters simply determines how low or how high in the hierarchy we reference and can be determined empirically or by looking at the [dendogram](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.cluster.hierarchy.dendrogram.html).\n",
    "\n",
    "* **Agglomerative Clustering** is similar to hierarchical clustering but but is not divisive, it is *agglomerative*. That is, every observation is placed into its own cluster and at each iteration or level or the hierarchy, observations are merged into fewer and fewer clusters until convergence. Similar to hierarchical clustering, the constructed hierarchy contains all possible numbers of clusters and it is up to the analyst to pick the number by reviewing statistics or the dendogram.\n",
    "\n",
    "* **DBSCAN** is based on point density rather than distance. It groups together points with many nearby neighbors. DBSCAN is one of the most cited algorithms in the literature. It does not require knowing the number of clusters a priori, but does require specifying the neighborhood size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering Algorithms in Scikit-learn\n",
    "<table border=\"1\">\n",
    "<colgroup>\n",
    "<col width=\"15%\" />\n",
    "<col width=\"16%\" />\n",
    "<col width=\"20%\" />\n",
    "<col width=\"27%\" />\n",
    "<col width=\"22%\" />\n",
    "</colgroup>\n",
    "<thead valign=\"bottom\">\n",
    "<tr><th>Method name</th>\n",
    "<th>Parameters</th>\n",
    "<th>Scalability</th>\n",
    "<th>Use Case</th>\n",
    "<th>Geometry (metric used)</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody valign=\"top\">\n",
    "<tr><td>K-Means</span></a></td>\n",
    "<td>number of clusters</td>\n",
    "<td>Very large<span class=\"pre\">n_samples</span>, medium <span class=\"pre\">n_clusters</span> with\n",
    "MiniBatch code</td>\n",
    "<td>General-purpose, even cluster size, flat geometry, not too many clusters</td>\n",
    "<td>Distances between points</td>\n",
    "</tr>\n",
    "<tr><td>Affinity propagation</td>\n",
    "<td>damping, sample preference</td>\n",
    "<td>Not scalable with n_samples</td>\n",
    "<td>Many clusters, uneven cluster size, non-flat geometry</td>\n",
    "<td>Graph distance (e.g. nearest-neighbor graph)</td>\n",
    "</tr>\n",
    "<tr><td>Mean-shift</td>\n",
    "<td>bandwidth</td>\n",
    "<td>Not scalable with <span class=\"pre\">n_samples</span></td>\n",
    "<td>Many clusters, uneven cluster size, non-flat geometry</td>\n",
    "<td>Distances between points</td>\n",
    "</tr>\n",
    "<tr><td>Spectral clustering</td>\n",
    "<td>number of clusters</td>\n",
    "<td>Medium <span class=\"pre\">n_samples</span>, small <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Few clusters, even cluster size, non-flat geometry</td>\n",
    "<td>Graph distance (e.g. nearest-neighbor graph)</td>\n",
    "</tr>\n",
    "<tr><td>Ward hierarchical clustering</td>\n",
    "<td>number of clusters</td>\n",
    "<td>Large <span class=\"pre\">n_samples</span> and <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Many clusters, possibly connectivity constraints</td>\n",
    "<td>Distances between points</td>\n",
    "</tr>\n",
    "<tr><td>Agglomerative clustering</td>\n",
    "<td>number of clusters, linkage type, distance</td>\n",
    "<td>Large <span class=\"pre\">n_samples</span> and <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Many clusters, possibly connectivity constraints, non Euclidean\n",
    "distances</td>\n",
    "<td>Any pairwise distance</td>\n",
    "</tr>\n",
    "<tr><td>DBSCAN</td>\n",
    "<td>neighborhood size</td>\n",
    "<td>Very large <span class=\"pre\">n_samples</span>, medium <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Non-flat geometry, uneven cluster sizes</td>\n",
    "<td>Distances between nearest points</td>\n",
    "</tr>\n",
    "<tr><td>Gaussian mixtures</td>\n",
    "<td>many</td>\n",
    "<td>Not scalable</td>\n",
    "<td>Flat geometry, good for density estimation</td>\n",
    "<td>Mahalanobis distances to  centers</td>\n",
    "</tr>\n",
    "<tr><td>Birch</td>\n",
    "<td>branching factor, threshold, optional global clusterer.</td>\n",
    "<td>Large <span class=\"pre\">n_clusters</span> and <span class=\"pre\">n_samples</span></td>\n",
    "<td>Large dataset, outlier removal, data reduction.</td>\n",
    "<td>Euclidean distance between points</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "Source: http://scikit-learn.org/stable/modules/clustering.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Exercise Set VI</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Try clustering using the following algorithms. </p>\n",
    "<ol>\n",
    "<li>Affinity propagation\n",
    "<li>Spectral clustering\n",
    "<li>Agglomerative clustering\n",
    "<li>DBSCAN\n",
    "</ol>\n",
    "<p>How do their results compare? Which performs the best? Tell a story why you think it performs the best.</p>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AffinityPropagation\n",
    "#Affinity Propagation \n",
    "Ap=AffinityPropagation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "labels = Ap.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12346523604478911\n"
     ]
    }
   ],
   "source": [
    "silhouette_avg = silhouette_score(x, labels)\n",
    "print(silhouette_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Estimated number of clusters\n",
    "len(Ap.cluster_centers_indices_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Spectral Clustering \n",
    "from sklearn.cluster import SpectralClustering\n",
    "\n",
    "Sc=SpectralClustering(n_clusters=4)\n",
    "labelsc= Sc.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.029065698932750638\n"
     ]
    }
   ],
   "source": [
    "silhouette_avg = silhouette_score(x, labelsc)\n",
    "print(silhouette_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Agglomerative clustering\n",
    "\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "Ac=AgglomerativeClustering(n_clusters=4)\n",
    "labelac = Ac.fit_predict(x)\n",
    "Ac.n_leaves_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.128937578158815\n"
     ]
    }
   ],
   "source": [
    "silhouette_avg = silhouette_score(x, labelac)\n",
    "print(silhouette_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dendogram for Agglomerative Clustering \n",
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Calculate the linkage: mergings\n",
    "mergings = linkage(x, method='complete')\n",
    "\n",
    "# Plot the dendrogram, using varieties as labels\n",
    "dendrogram(mergings,labels=labelac,leaf_rotation=90,leaf_font_size=6)\n",
    "plt.title('Fig 7: Dendogram')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n",
      " -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n",
      " -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n",
      " -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n",
      " -1 -1 -1 -1]\n"
     ]
    }
   ],
   "source": [
    "# DBSCAN\n",
    "\n",
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "Db = DBSCAN()\n",
    "labeldb = Db.fit_predict(x)\n",
    "print(labeldb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### After using the different methods, the Agglomerative Clustering (AC) had the best silhouette score , followed by the Affinity Propagation. One of the potential reasons why AC performed better due to its a \"bottom-up\" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy until all possible clusters (Ks) are achieved.  The plotted dendogram confirmed that best K is 5, which was also the intuition provided by the inertia method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}