customer_segments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Unsupervised Learning\n",
    "## Project: Creating Customer Segments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In this project, you will analyze a dataset containing data on various customers' **annual spending amounts** (reported in *monetary units*) of diverse product categories for internal structure. One goal of this project is to best describe the** variation in the different types of customers** that a wholesale distributor interacts with. Doing so would **equip** the distributor with **insight into how to best structure their delivery service** to meet the needs of each customer. \n",
    "\n",
    "Many companies today collect vast amounts of data on customers and clientele, and have a strong desire to understand the meaningful relationships hidden in their customer base. Being equipped with this information can assist a company engineer future products and services that best satisfy the demands or needs of their customers.\n",
    "\n",
    "A wholesale distributor recently tested a change to their delivery method for **some customers**, by moving from a **morning delivery service** \"five days\" a week to a **cheaper evening delivery service** \"three days\" a week. Initial testing did not discover any significant unsatisfactory results, so they implemented the **cheaper option** for all customers. Almost immediately, the distributor began getting complaints about the delivery service change and customers were canceling deliveries — losing the distributor more money than what was being saved. You’ve been hired by the wholesale distributor to find **what types of customers** they have to help them make better, more informed business decisions in the future. \n",
    "\n",
    "Our task is to use unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories.\n",
    "\n",
    "The dataset for this project can be found on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers). This is product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data. For the purposes of this project, the features `'Channel'` and `'Region'` will be excluded in the analysis — with focus instead on the six product categories recorded for customers.\n",
    "- 1)\tFRESH: annual spending (m.u.) on fresh products (Continuous); \n",
    "- 2)\tMILK: annual spending (m.u.) on milk products (Continuous); \n",
    "- 3)\tGROCERY: annual spending (m.u.)on grocery products (Continuous); \n",
    "- 4)\tFROZEN: annual spending (m.u.)on frozen products (Continuous) \n",
    "- 5)\tDETERGENTS_PAPER: annual spending (m.u.) on detergents and paper products (Continuous) \n",
    "- 6)\tDELICATESSEN: annual spending (m.u.)on and delicatessen products (Continuous); \n",
    "- 7)\tCHANNEL: customersâ€™ Channel - Horeca (Hotel/Restaurant/CafÃ©) or Retail channel (Nominal) \n",
    "- 8)\tREGION: customersâ€™ Region â€“ Lisnon, Oporto or Other (Nominal) \n",
    "\n",
    "\"cold cuts, cheeses, a variety of salads, a selection of unusual, foreign prepared foods, etc\"\n",
    "\n",
    "[To Do]\n",
    "- First explore the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. \n",
    "- Afterwards, preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers. \n",
    "- With the good, clean customer spending data, apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. \n",
    "- Finally, compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wholesale customers dataset has 440 samples with 6 features each.\n"
     ]
    }
   ],
   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "\n",
    "# Import supplementary visualizations code visuals.py\n",
    "import visuals as vs\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the wholesale customers dataset\n",
    "try:\n",
    "    data = pd.read_csv(\"customers.csv\")\n",
    "    data.drop(['Region', 'Channel'], axis = 1, inplace = True)\n",
    "    print(\"Wholesale customers dataset has {} samples with {} features each.\".format(*data.shape))\n",
    "except:\n",
    "    print(\"Dataset could not be loaded. Is the dataset missing?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0  12669  9656     7561     214              2674          1338\n",
       "1   7057  9810     9568    1762              3293          1776"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Data Exploration\n",
    "In this section, you will begin exploring the data through visualizations and code to understand how each feature is related to the others. You will observe a statistical description of the dataset, consider the relevance of each feature, and select a few sample data points from the dataset which you will track through the course of this project.\n",
    "\n",
    "Observe a statistical description of the dataset. Note that the dataset is composed of six important product categories: **'Fresh'**, **'Milk'**, **'Grocery'**, **'Frozen'**, **'Detergents_Paper'**, and **'Delicatessen'**. Consider what each category represents in terms of products you could purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Fresh          Milk       Grocery        Frozen  \\\n",
       "count     440.000000    440.000000    440.000000    440.000000   \n",
       "mean    12000.297727   5796.265909   7951.277273   3071.931818   \n",
       "std     12647.328865   7380.377175   9503.162829   4854.673333   \n",
       "min         3.000000     55.000000      3.000000     25.000000   \n",
       "25%      3127.750000   1533.000000   2153.000000    742.250000   \n",
       "50%      8504.000000   3627.000000   4755.500000   1526.000000   \n",
       "75%     16933.750000   7190.250000  10655.750000   3554.250000   \n",
       "max    112151.000000  73498.000000  92780.000000  60869.000000   \n",
       "\n",
       "       Detergents_Paper  Delicatessen  \n",
       "count        440.000000    440.000000  \n",
       "mean        2881.493182   1524.870455  \n",
       "std         4767.854448   2820.105937  \n",
       "min            3.000000      3.000000  \n",
       "25%          256.750000    408.250000  \n",
       "50%          816.500000    965.500000  \n",
       "75%         3922.000000   1820.250000  \n",
       "max        40827.000000  47943.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display a description of the dataset\n",
    "display(data.describe())\n",
    "# IQR...the length of the middle 50% of that interval of space: where the bulk of the values lie? a typical value? a median?\n",
    "# The IQR approximates the amount of spread in the middle half of the data. \n",
    "#IQR = (local median after \"main_median\" - local median before \"main_median\") = the distance from Q_3 to Q_1\n",
    "# = About 50% percent of the data is between  Q_3 to Q_1..\n",
    "\n",
    "#percentile here..\n",
    "# here, percentile 50% means the \"main_median\". \n",
    "# here, percentile 25% means Q_1.\n",
    "# here, percentile 75% means Q_3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>16117</td>\n",
       "      <td>46197</td>\n",
       "      <td>92780</td>\n",
       "      <td>1026</td>\n",
       "      <td>40827</td>\n",
       "      <td>2944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "85  16117  46197    92780    1026             40827          2944"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Grocery == data.Grocery.max()]\n",
    "data.loc[data.Detergents_Paper == data.Detergents_Paper.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23     16523\n",
       "183    47943\n",
       "Name: Delicatessen, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Delicatessen.sort_values()[-2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "- I think in order to choose the three records disparate most each other, we should consider the difference of mean, median, mode, variance, maximum, minimum of each feature. \n",
    "![](meanmedianmode.gif)\n",
    "  - \"Fresh\": \n",
    "    - signigicantly huge total purchases;  \n",
    "    - the most significant variance(12647.33), huge difference between mean(12000) and median(8500); \n",
    "    - a right skewed significantly.\n",
    "  - \"Milk\":\n",
    "    - not outstanding mean(5796) and variance(7380), median(3627); \n",
    "    - a slightly right-skewed distribution.\n",
    "  - \"Grocery\":\n",
    "    - not outstanding mean(7951) and variance(9503), median(4756); \n",
    "    - a slightly right-skewed distribution.\n",
    "  - \"Frozen\": \n",
    "    - not outstanding mean(3071) and variance(4855), median(1526); \n",
    "    - a slightly right-skewed distribution.\n",
    "  - \"Detergents_paper\":\n",
    "    - not outstanding mean(2881) and variance(4768), \n",
    "    - small median(800), \n",
    "    - a more right-skewed distribution.\n",
    "  - \"Delicatessen\":\n",
    "    - not outstanding mean(1524) and variance(2820), \n",
    "    - small median(966), \n",
    "    - a slightly right-skewed distribution.\n",
    "\n",
    "## ==> Seemingly hard to determine..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Implementation: Selecting Samples\n",
    "To get a better understanding of the customers and how their data will transform through the analysis, it would be best to select a few sample data points and explore them in more detail. In the code block below, add **three** indices of your choice to the `indices` list which will represent the customers to track. **It is suggested to try different sets of samples until you obtain customers that vary significantly from one another.**\n",
    "\n",
    "We can filter samples based on the quartiles!!! The establishments in the first and third quartiles would be considered different significantly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# (q1, q3)\n",
    "Fresh_q1 = 3127.75\n",
    "Fresh_q3 = 16933.75\n",
    "\n",
    "Milk_q1 = 1533.00\n",
    "Milk_q3 = 7190.25\n",
    "\n",
    "Grocery_q1 = 2153.00\n",
    "Grocery_q3 = 10655.75\n",
    "\n",
    "Frozen_q1 = 742.25\n",
    "Frozen_q3 = 3554.25\n",
    "\n",
    "Detergents_Paper_q1 = 256.75\n",
    "Detergents_Paper_q3 = 3922.00\n",
    "\n",
    "Delicatessen_q1 = 408.25\n",
    "Delicatessen_q3 = 1820.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(    Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       " 16   1020   8816    12121     134              4508          1080\n",
       " 31   2612   4339     3133    2088               820           985\n",
       " 34   1502   1979     2262     425               483           395\n",
       " 35    688   5491    11091     833              4239           436\n",
       " 43    630  11095    23998     787              9529            72,\n",
       "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       " 4   22615   5410     7198    3915              1777          5185\n",
       " 12  31714  12319    11757     287              3881          2931\n",
       " 13  21217   6208    14982    3095              6707           602\n",
       " 14  24653   9465    12091     294              5058          2168\n",
       " 18  18601   6327    10099    2205              2767          3181)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data.Fresh < Fresh_q1].head(), data.loc[data.Fresh > Fresh_q3].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(    Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       " 3   13265  1196     4221    6404               507          1788\n",
       " 11  13146  1124     4523    1420               549           497\n",
       " 15  10253  1114     3821     397               964           412\n",
       " 21   5567   871     2010    3383               375           569\n",
       " 26   9898   961     2861    3151               242           833,\n",
       "    Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       " 0  12669   9656     7561     214              2674          1338\n",
       " 1   7057   9810     9568    1762              3293          1776\n",
       " 2   6353   8808     7684    2405              3516          7844\n",
       " 5   9413   8259     5126     666              1795          1451\n",
       " 9   6006  11093    18881    1159              7425          2098)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data.Milk < Milk_q1].head(), data.loc[data.Milk > Milk_q3].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(    Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       " 21   5567   871     2010    3383               375           569\n",
       " 39  56159   555      902   10002               212          2916\n",
       " 50   6269  1095     1980    3860               609          2162\n",
       " 54  27329  1449     1947    2436               204          1333\n",
       " 58  13779  1970     1648     596               227           436,\n",
       "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       " 9    6006  11093    18881    1159              7425          2098\n",
       " 10   3366   5403    12974    4400              5977          1744\n",
       " 12  31714  12319    11757     287              3881          2931\n",
       " 13  21217   6208    14982    3095              6707           602\n",
       " 14  24653   9465    12091     294              5058          2168)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data.Grocery < Grocery_q1].head(), data.loc[data.Grocery > Grocery_q3].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(    Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       " 0   12669   9656     7561     214              2674          1338\n",
       " 5    9413   8259     5126     666              1795          1451\n",
       " 6   12126   3199     6975     480              3140           545\n",
       " 8    5963   3648     6192     425              1716           750\n",
       " 12  31714  12319    11757     287              3881          2931,\n",
       "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       " 3   13265   1196     4221    6404               507          1788\n",
       " 4   22615   5410     7198    3915              1777          5185\n",
       " 10   3366   5403    12974    4400              5977          1744\n",
       " 22  31276   1917     4469    9408              2381          4334\n",
       " 23  26373  36423    22019    5154              4337         16523)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data.Frozen < Frozen_q1].head(), data.loc[data.Frozen > Frozen_q3].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(    Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       " 26   9898   961     2861    3151               242           833\n",
       " 27  14276   803     3045     485               100           518\n",
       " 39  56159   555      902   10002               212          2916\n",
       " 54  27329  1449     1947    2436               204          1333\n",
       " 58  13779  1970     1648     596               227           436,\n",
       "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       " 9    6006  11093    18881    1159              7425          2098\n",
       " 10   3366   5403    12974    4400              5977          1744\n",
       " 13  21217   6208    14982    3095              6707           602\n",
       " 14  24653   9465    12091     294              5058          2168\n",
       " 16   1020   8816    12121     134              4508          1080)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data.Detergents_Paper < Detergents_Paper_q1].head(), data.loc[data.Detergents_Paper > Detergents_Paper_q3].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(    Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       " 25  16165   4230     7595     201              4003            57\n",
       " 32  21632   1318     2886     266               918           405\n",
       " 34   1502   1979     2262     425               483           395\n",
       " 42  10850   7555    14961     188              6899            46\n",
       " 43    630  11095    23998     787              9529            72,\n",
       "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       " 2    6353   8808     7684    2405              3516          7844\n",
       " 4   22615   5410     7198    3915              1777          5185\n",
       " 7    7579   4956     9426    1669              3321          2566\n",
       " 9    6006  11093    18881    1159              7425          2098\n",
       " 12  31714  12319    11757     287              3881          2931)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data.Delicatessen < Delicatessen_q1].head(), data.loc[data.Delicatessen > Delicatessen_q3].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## I chose the sample '0', '3', '12' show the biggest disparity.\n",
    "This is because these indices appear more often than others, registering different quartile range of each field, thus seem more convincing.\n",
    "\n",
    "'0': higher in 'Milk', but lower in 'Frozen'\n",
    "\n",
    "'3': lower in 'Milk', but higher in 'Frozen'\n",
    "\n",
    "'12': higher in 'Fresh','Grocery','Delicatessen', but lower in Frozen'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen samples of wholesale customers dataset:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31714</td>\n",
       "      <td>12319</td>\n",
       "      <td>11757</td>\n",
       "      <td>287</td>\n",
       "      <td>3881</td>\n",
       "      <td>2931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0  12669   9656     7561     214              2674          1338\n",
       "1  13265   1196     4221    6404               507          1788\n",
       "2  31714  12319    11757     287              3881          2931"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Select three indices of your choice you wish to sample from the dataset\n",
    "indices = [0,3,12]\n",
    "\n",
    "# Create a DataFrame of the chosen samples\n",
    "samples = pd.DataFrame(data.loc[indices], columns = data.keys()).reset_index(drop = True)\n",
    "print(\"Chosen samples of wholesale customers dataset:\")\n",
    "display(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22095fada20>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22095fa7240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples_bar = samples.append(data.describe().loc['mean', :])\n",
    "\n",
    "#x_axis\n",
    "samples_bar.index = indices + ['mean']\n",
    "\n",
    "samples_bar.plot(kind='bar', figsize=(7,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22099c5c780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# radar plot super cooool !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "df = np.round(samples, 1)\n",
    "index  = df.index[:]\n",
    "categories = list(df)\n",
    "df = scaler.fit_transform(df)*100\n",
    "N = len(categories)\n",
    "angles = [n / float(N) * 2 * np.pi for n in range(N)]\n",
    "angles += angles[:1]\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "def Radar(index, title, color):\n",
    "    ax = plt.subplot(1, 3, index+1, polar=True)\n",
    "    ax.set_theta_offset(np.pi/2)\n",
    "    ax.set_theta_direction(-1)\n",
    "    plt.xticks(angles[:-1], categories, color ='grey', size = 8)\n",
    "    plt.yticks((25, 50, 75, 100), (\"1/4\", \"1/2\", \"3/4\", \"Max\"), color = \"grey\", size = 7)\n",
    "    values = df[index]\n",
    "    values = np.append(values, values[:1])\n",
    "    ax.plot(angles, values, color = color)\n",
    "    ax.fill(angles, values, color=color, alpha=0.5)\n",
    "    plt.title('Sample {}'.format(title), y= 1.1)\n",
    "\n",
    "for i, n in enumerate(index):\n",
    "    Radar(index=i, title=n, color='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Question 1\n",
    "Consider the total purchase cost of each product category and the statistical description of the dataset above for your sample customers.  \n",
    "\n",
    "* What kind of establishment (customer) could each of the three samples you've chosen represent?\n",
    "\n",
    "**Hint:** Examples of establishments include places like markets, cafes, delis, wholesale retailers, among many others. Avoid using names for establishments, such as saying *\"McDonalds\"* when describing a sample customer as a restaurant. You can use the mean values for reference to compare your samples with. The mean values are as follows:\n",
    "\n",
    "* Fresh: 12000.2977\n",
    "* Milk: 5796.2\n",
    "* Grocery: 7951.3\n",
    "* Frozen: 3071.93\n",
    "* Detergents_paper: 2881.4\n",
    "* Delicatessen: 1524.8\n",
    "\n",
    "Knowing this, how do your samples compare? Does that help in driving your insight into what kind of establishments they might be? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Answer:** \n",
    "\n",
    "\n",
    "## Based on the comparison chart above,\n",
    "\n",
    "### index_0: \n",
    "It might be **[a small sized upscale restaurant]** because they only use fresh ingredients.  \n",
    " - Average spending on 'Fresh','Milk','Grocery', 'Detergents_Paper', 'Delicatessen'\n",
    " - Almost zero spending on 'Frozen'\n",
    "\n",
    "### index_1: \n",
    "It might be **[a cheap takeout food chain]** because they mainly supply cheap frozen food and do not provide basic items such as paper, detergent, milk which are unnecessary for takeout food chains.  \n",
    " - less spending on 'Milk','Grocery','Detergents_Paper'\n",
    " - higher spending on 'Frozen'\n",
    "\n",
    "### index_2: \n",
    "It might be **[a fresh food retailer with cafe]** because they focus on supplying fresh food, but care offering some eating places at the same time.   \n",
    " - Extremely high spending on 'Fresh', higher spending on 'Milk','Grocery', 'Detergents_Paper', 'Delicatessen' \n",
    " - Almost zero spending on 'frozen'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "###########################################################################################################################\n",
    "###########################################################################################################################\n",
    "### Implementation: Feature Relevance\n",
    "One interesting thought to consider is if one (or more) of the six product categories **is actually relevant** for understanding customer purchasing. That is to say, is it possible to determine whether customers purchasing some amount of one category of products will necessarily purchase some proportional amount of another category of products? (We want the anti-Multicollinearity!!!)\n",
    "\n",
    "**We can make this determination quite easily by training a supervised regression learner on a subset of the data with one feature removed, and then score how well that model can predict the removed feature.**\n",
    "\n",
    "\n",
    "Implement the following:\n",
    " - Assign `new_data` a copy of the data by removing a feature of your choice using the `DataFrame.drop` function.\n",
    " - Use `sklearn.cross_validation.train_test_split` to split the dataset into training and testing sets.\n",
    "   - Use the removed feature as your target label. Set a `test_size` of `0.25` and set a `random_state`.\n",
    " - Import a decision tree regressor, set a `random_state`, and fit the learner to the training data.\n",
    " - Report the prediction score of the testing set using the regressor's `score` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper',\n",
       "       'Delicatessen'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Minkun\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6818840085440834"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature\n",
    "#new_data = data.drop(['Milk'], axis=1) #0.15\n",
    "#target_data = data.Milk\n",
    "#new_data = data.drop(['Fresh'], axis=1) #-0.38\n",
    "#target_data = data.Fresh\n",
    "new_data = data.drop(['Grocery'], axis=1) #0.68\n",
    "target_data = data.Grocery\n",
    "#new_data = data.drop(['Frozen'], axis=1) #-0.21\n",
    "#target_data = data.Frozen\n",
    "#new_data = data.drop(['Detergents_Paper'], axis=1) #0.27\n",
    "#target_data = data.Detergents_Paper\n",
    "#new_data = data.drop(['Delicatessen'], axis=1) #-2.25\n",
    "#target_data = data.Delicatessen\n",
    "\n",
    "# TODO: Split the data into training and testing sets(0.25) using the given feature as the target. Set a random state.\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(new_data, target_data, test_size=0.25, random_state=42)\n",
    "\n",
    "# TODO: Create a decision tree regressor and fit it to the training set\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor = DecisionTreeRegressor(random_state=42).fit(X_train, y_train)\n",
    "test_pred = regressor.predict(X_test)\n",
    "\n",
    "# TODO: Report the score of the prediction using the testing set\n",
    "from sklearn.metrics import r2_score\n",
    "score = r2_score(y_test, test_pred); score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Question 2\n",
    "\n",
    "* Which feature did you attempt to predict? \n",
    "* What was the reported prediction score? \n",
    "* Is this feature necessary for identifying customers' spending habits?\n",
    "\n",
    "**Hint:** The coefficient of determination, `R^2`, is scored between 0 and 1, with 1 being a perfect fit. **A negative `R^2` implies the model fails to fit the data.** If you get a low score for a particular feature, that lends us to beleive that that feature point is hard to predict using the other features, thereby making it an important feature to consider when considering relevance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Answer:**\n",
    "- I tried to predict each feature as a response variable in order to detect the sources of multicollinearity. \n",
    "\n",
    "- The prediction scores are reported : 'Milk'(0.15), 'Fresh'(-0.38), 'Grocery'(0.68), 'Frozen'(-0.21), 'Detergents_Paper'(0.27), 'Delicatessen'(-2.25). \n",
    "\n",
    "- As seen here, \"Fresh\", \"Frozen\" and \"Delicatessen\" as dependent variables have negative R2 scores and \"Milk\",\"Detergents_Paper\" have very small R2 scores. Their negative or small scores imply that they are useful to identify customers' spending pattern because the remaining features cannot explain the variation in them, thus tells us they are uncorrelated. I believe we can better understand customer's spending habit by tackling with those features of low, negative r^2 scores.  \n",
    "\n",
    "Based on this model, Fresh is an independent feature, so necessary. Thus if we have a high r^2 score(high correlation with other features), this would not be good for identifying customers' spending habits(since the customer would purchase other products along with the one we are predicting, as we could actually derive this feature from the rest of the features). Therefore a negative / low r^2 value would represent the opposite as we could identify the customer's specific behavior just from the one feature.\n",
    "\n",
    "Is it explaining that customers have a penchant of spending money on 'Grocery' whenever buying other products, hence 'Grocery' is the irrelavant or redundant field for understanding customer purchasing ???? Correct. Grocery can be derived from the other features, so not necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2209be55208>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2209bd65940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check out with features that can derive Grocery\n",
    "import seaborn as sns\n",
    "sns.barplot(X_train.columns, regressor.feature_importances_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Visualize Feature Distributions\n",
    "To get a better understanding of the dataset, we can construct a scatter matrix of each of the six product features present in the data. \n",
    "- If you found that the feature you attempted to predict above is relevant for identifying a specific customer, then the scatter matrix below may not show any correlation between that feature and the others. \n",
    "- Conversely, if you believe that feature is not relevant for identifying a specific customer, the scatter matrix might show a correlation between that feature and another feature in the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2209bed99e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Produce a scatter matrix for each pair of features in the data\n",
    "axes = pd.scatter_matrix(data, alpha = 0.3, figsize = (14,8), diagonal = 'kde')\n",
    "\n",
    "corr = data.corr().as_matrix()\n",
    "for i, j in zip(*np.triu_indices_from(axes, k=1)):\n",
    "    axes[i, j].annotate(\"%.3f\" %corr[i,j], (0.8, 0.8), xycoords='axes fraction', ha='center', va='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Question 3\n",
    "* Using the scatter matrix as a reference, discuss the distribution of the dataset, specifically talk about the **normality, outliers, large number of data points near 0 among others**. If you need to sepearate out some of the plots individually to further accentuate your point, you may do so as well.\n",
    "* Are there any pairs of features which exhibit some degree of correlation? \n",
    "* Does this confirm or deny your suspicions about the relevance of the feature you attempted to predict? \n",
    "* How is the data for those features distributed?\n",
    "\n",
    "**Hint:** Is the data normally distributed? Where do most of the data points lie? You can use [corr()](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html) to get the feature correlations and then visualize them using a [heatmap](http://seaborn.pydata.org/generated/seaborn.heatmap.html)(the data that would be fed into the heatmap would be the correlation values, for eg: `data.corr()`) to gain further insight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fresh</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100510</td>\n",
       "      <td>-0.011854</td>\n",
       "      <td>0.345881</td>\n",
       "      <td>-0.101953</td>\n",
       "      <td>0.244690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milk</th>\n",
       "      <td>0.100510</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.728335</td>\n",
       "      <td>0.123994</td>\n",
       "      <td>0.661816</td>\n",
       "      <td>0.406368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grocery</th>\n",
       "      <td>-0.011854</td>\n",
       "      <td>0.728335</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.040193</td>\n",
       "      <td>0.924641</td>\n",
       "      <td>0.205497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frozen</th>\n",
       "      <td>0.345881</td>\n",
       "      <td>0.123994</td>\n",
       "      <td>-0.040193</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.131525</td>\n",
       "      <td>0.390947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <td>-0.101953</td>\n",
       "      <td>0.661816</td>\n",
       "      <td>0.924641</td>\n",
       "      <td>-0.131525</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.069291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delicatessen</th>\n",
       "      <td>0.244690</td>\n",
       "      <td>0.406368</td>\n",
       "      <td>0.205497</td>\n",
       "      <td>0.390947</td>\n",
       "      <td>0.069291</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Fresh      Milk   Grocery    Frozen  Detergents_Paper  \\\n",
       "Fresh             1.000000  0.100510 -0.011854  0.345881         -0.101953   \n",
       "Milk              0.100510  1.000000  0.728335  0.123994          0.661816   \n",
       "Grocery          -0.011854  0.728335  1.000000 -0.040193          0.924641   \n",
       "Frozen            0.345881  0.123994 -0.040193  1.000000         -0.131525   \n",
       "Detergents_Paper -0.101953  0.661816  0.924641 -0.131525          1.000000   \n",
       "Delicatessen      0.244690  0.406368  0.205497  0.390947          0.069291   \n",
       "\n",
       "                  Delicatessen  \n",
       "Fresh                 0.244690  \n",
       "Milk                  0.406368  \n",
       "Grocery               0.205497  \n",
       "Frozen                0.390947  \n",
       "Detergents_Paper      0.069291  \n",
       "Delicatessen          1.000000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2209bd78a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "corr = data.corr()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "cax = ax.matshow(data, interpolation='nearest')\n",
    "\n",
    "ax.matshow(corr)\n",
    "fig.colorbar(cax)\n",
    "\n",
    "plt.xticks(range(len(corr.columns)), corr.columns);\n",
    "plt.yticks(range(len(corr.columns)), corr.columns);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2209be675f8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2209be55a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "sb.heatmap(data.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Answer:**\n",
    "- It is obvious that every feature here does not hold decent normality as indicated by their too many outliers, uni-modal plots skewing to the right with most points flocking around '0'. There are so many outliers at the tails of the distribution.\n",
    "\n",
    "- 'Grocery' particularly shows certain correlations with all other features except 'Delicatessen'. If ranking them in order from strongest to weakest:\n",
    "  - positive: 'Detergents_Paper', 'Milk'\n",
    "  - negative: 'Fresh','Frozen'.\n",
    "\n",
    "- Interestingly, 'Delicatessen' shows absolutely no association with any others. \n",
    "\n",
    "- These features that are strongly correlated does lend credence to our initial claim that 'Grocery' may not be necessary for identifying customers' spending habits. 'Grocery' has a high correlation with 'Detergents_Paper' and 'Milk' that corresponds to a relatively high R^2 score when we regress 'Grocery' on all other features.\n",
    "\n",
    "- This denies my suspicions about the relevance of 'Grocery'. I thought 'Grocery' is evenly associated with other features, thus explained well by them. But we cannot predict the behavior of 'Delicatessen' because of its lack of relationship with others.\n",
    "\n",
    "- It seems that data of these features are all not normally distributed...I think clustering algorithms require them to be normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Data Preprocessing\n",
    "In this section, you will preprocess the data to create a better representation of customers by performing a scaling on the data and detecting (and optionally removing) outliers. Preprocessing data is often times a critical step in assuring that results you obtain from your analysis are significant and meaningful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Implementation: Feature Scaling\n",
    "**If data is not normally distributed**, especially if the mean and median vary significantly (indicating a large skew), it is most [often appropriate](http://econbrowser.com/archives/2014/02/use-of-logarithms-in-economics) to apply a non-linear scaling — particularly for financial data. One way to achieve this scaling is by using a [Box-Cox test](http://scipy.github.io/devdocs/generated/scipy.stats.boxcox.html), which calculates the best power transformation of the data that reduces skewness. A simpler approach which can work in most cases would be applying the natural logarithm.\n",
    "\n",
    "Implement the following:\n",
    " - Assign a copy of the data to `log_data` after applying logarithmic scaling. Use the `np.log` function for this.\n",
    " - Assign a copy of the sample data to `log_samples` after applying logarithmic scaling. Again, use `np.log`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2209be8c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Scale the data using the natural logarithm\n",
    "log_data = np.log(data)\n",
    "\n",
    "# TODO: Scale the sample data using the natural logarithm\n",
    "log_samples = np.log(samples)\n",
    "\n",
    "# Produce a scatter matrix for each pair of newly-transformed features\n",
    "log_axes = pd.scatter_matrix(log_data, alpha = 0.3, figsize = (14,8), diagonal = 'kde')\n",
    "\n",
    "log_corr = log_data.corr().as_matrix()\n",
    "for i, j in zip(*np.triu_indices_from(log_axes, k=1)):\n",
    "    log_axes[i, j].annotate(\"%.3f\" %log_corr[i,j], (0.8, 0.8), xycoords='axes fraction', ha='center', va='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Observation\n",
    "After applying a natural logarithm scaling to the data, the distribution of each feature should appear much more normal. For any pairs of features you may have identified earlier as being correlated, observe here whether that correlation is still present (and whether it is now stronger or weaker than before).\n",
    "\n",
    "Run the code below to see how the sample data has changed after having the natural logarithm applied to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.446913</td>\n",
       "      <td>9.175335</td>\n",
       "      <td>8.930759</td>\n",
       "      <td>5.365976</td>\n",
       "      <td>7.891331</td>\n",
       "      <td>7.198931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.492884</td>\n",
       "      <td>7.086738</td>\n",
       "      <td>8.347827</td>\n",
       "      <td>8.764678</td>\n",
       "      <td>6.228511</td>\n",
       "      <td>7.488853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.364514</td>\n",
       "      <td>9.418898</td>\n",
       "      <td>9.372204</td>\n",
       "      <td>5.659482</td>\n",
       "      <td>8.263848</td>\n",
       "      <td>7.983099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "0   9.446913  9.175335  8.930759  5.365976          7.891331      7.198931\n",
       "1   9.492884  7.086738  8.347827  8.764678          6.228511      7.488853\n",
       "2  10.364514  9.418898  9.372204  5.659482          8.263848      7.983099"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the log-transformed sample data\n",
    "display(log_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Implementation: Outlier Detection\n",
    "Detecting outliers in the data is extremely important in the data preprocessing step of any analysis. The presence of outliers can often skew results which take into consideration these data points. There are many \"rules of thumb\" for what constitutes an outlier in a dataset. Here, we will use [Tukey's Method for identfying outliers](http://datapigtechnologies.com/blog/index.php/highlighting-outliers-in-your-data-with-the-tukey-method/): An *outlier step* is calculated as 1.5 times the interquartile range (IQR). A data point with a feature that is beyond an outlier step outside of the IQR for that feature is considered abnormal.\n",
    "![](P37.jpg) \n",
    "Implement the following:\n",
    " - Assign the value of the 25th percentile for the given feature to `Q1`. Use `np.percentile` for this.\n",
    " - Assign the value of the 75th percentile for the given feature to `Q3`. Again, use `np.percentile`.\n",
    " - Assign the calculation of an outlier step for the given feature to `step`.\n",
    " - Optionally remove data points from the dataset by adding indices to the `outliers` list.\n",
    " \n",
    "\n",
    "\n",
    "**NOTE:** If you choose to remove any outliers, ensure that the sample data does not contain any of these points!  \n",
    "Once you have performed this implementation, the dataset will be stored in the variable `good_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper',\n",
       "       'Delicatessen'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Fresh':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.950323</td>\n",
       "      <td>10.732651</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>10.095388</td>\n",
       "      <td>7.260523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>7.335634</td>\n",
       "      <td>8.911530</td>\n",
       "      <td>5.164786</td>\n",
       "      <td>8.151333</td>\n",
       "      <td>3.295837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5.389072</td>\n",
       "      <td>9.163249</td>\n",
       "      <td>9.575192</td>\n",
       "      <td>5.645447</td>\n",
       "      <td>8.964184</td>\n",
       "      <td>5.049856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.979339</td>\n",
       "      <td>8.740657</td>\n",
       "      <td>6.086775</td>\n",
       "      <td>5.407172</td>\n",
       "      <td>6.563856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>3.135494</td>\n",
       "      <td>7.869402</td>\n",
       "      <td>9.001839</td>\n",
       "      <td>4.976734</td>\n",
       "      <td>8.262043</td>\n",
       "      <td>5.379897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>4.941642</td>\n",
       "      <td>9.087834</td>\n",
       "      <td>8.248791</td>\n",
       "      <td>4.955827</td>\n",
       "      <td>6.967909</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>5.298317</td>\n",
       "      <td>10.160530</td>\n",
       "      <td>9.894245</td>\n",
       "      <td>6.478510</td>\n",
       "      <td>9.079434</td>\n",
       "      <td>8.740337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>5.192957</td>\n",
       "      <td>8.156223</td>\n",
       "      <td>9.917982</td>\n",
       "      <td>6.865891</td>\n",
       "      <td>8.633731</td>\n",
       "      <td>6.501290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>2.890372</td>\n",
       "      <td>8.923191</td>\n",
       "      <td>9.629380</td>\n",
       "      <td>7.158514</td>\n",
       "      <td>8.475746</td>\n",
       "      <td>8.759669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>5.081404</td>\n",
       "      <td>8.917311</td>\n",
       "      <td>10.117510</td>\n",
       "      <td>6.424869</td>\n",
       "      <td>9.374413</td>\n",
       "      <td>7.787382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>5.493061</td>\n",
       "      <td>9.468001</td>\n",
       "      <td>9.088399</td>\n",
       "      <td>6.683361</td>\n",
       "      <td>8.271037</td>\n",
       "      <td>5.351858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>1.098612</td>\n",
       "      <td>5.808142</td>\n",
       "      <td>8.856661</td>\n",
       "      <td>9.655090</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>6.309918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>4.762174</td>\n",
       "      <td>8.742574</td>\n",
       "      <td>9.961898</td>\n",
       "      <td>5.429346</td>\n",
       "      <td>9.069007</td>\n",
       "      <td>7.013016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>5.247024</td>\n",
       "      <td>6.588926</td>\n",
       "      <td>7.606885</td>\n",
       "      <td>5.501258</td>\n",
       "      <td>5.214936</td>\n",
       "      <td>4.844187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>3.610918</td>\n",
       "      <td>7.150701</td>\n",
       "      <td>10.011086</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>8.816853</td>\n",
       "      <td>4.700480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>4.574711</td>\n",
       "      <td>8.190077</td>\n",
       "      <td>9.425452</td>\n",
       "      <td>4.584967</td>\n",
       "      <td>7.996317</td>\n",
       "      <td>4.127134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fresh       Milk    Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "65   4.442651   9.950323  10.732651  3.583519         10.095388      7.260523\n",
       "66   2.197225   7.335634   8.911530  5.164786          8.151333      3.295837\n",
       "81   5.389072   9.163249   9.575192  5.645447          8.964184      5.049856\n",
       "95   1.098612   7.979339   8.740657  6.086775          5.407172      6.563856\n",
       "96   3.135494   7.869402   9.001839  4.976734          8.262043      5.379897\n",
       "128  4.941642   9.087834   8.248791  4.955827          6.967909      1.098612\n",
       "171  5.298317  10.160530   9.894245  6.478510          9.079434      8.740337\n",
       "193  5.192957   8.156223   9.917982  6.865891          8.633731      6.501290\n",
       "218  2.890372   8.923191   9.629380  7.158514          8.475746      8.759669\n",
       "304  5.081404   8.917311  10.117510  6.424869          9.374413      7.787382\n",
       "305  5.493061   9.468001   9.088399  6.683361          8.271037      5.351858\n",
       "338  1.098612   5.808142   8.856661  9.655090          2.708050      6.309918\n",
       "353  4.762174   8.742574   9.961898  5.429346          9.069007      7.013016\n",
       "355  5.247024   6.588926   7.606885  5.501258          5.214936      4.844187\n",
       "357  3.610918   7.150701  10.011086  4.919981          8.816853      4.700480\n",
       "412  4.574711   8.190077   9.425452  4.584967          7.996317      4.127134"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Milk':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>10.039983</td>\n",
       "      <td>11.205013</td>\n",
       "      <td>10.377047</td>\n",
       "      <td>6.894670</td>\n",
       "      <td>9.906981</td>\n",
       "      <td>6.805723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>6.220590</td>\n",
       "      <td>4.718499</td>\n",
       "      <td>6.656727</td>\n",
       "      <td>6.796824</td>\n",
       "      <td>4.025352</td>\n",
       "      <td>4.882802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>10.029503</td>\n",
       "      <td>4.897840</td>\n",
       "      <td>5.384495</td>\n",
       "      <td>8.057377</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>6.306275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fresh       Milk    Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "86   10.039983  11.205013  10.377047  6.894670          9.906981      6.805723\n",
       "98    6.220590   4.718499   6.656727  6.796824          4.025352      4.882802\n",
       "154   6.432940   4.007333   4.919981  4.317488          1.945910      2.079442\n",
       "356  10.029503   4.897840   5.384495  8.057377          2.197225      6.306275"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Grocery':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>9.923192</td>\n",
       "      <td>7.036148</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>8.390949</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>6.882437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "75   9.923192  7.036148  1.098612  8.390949          1.098612      6.882437\n",
       "154  6.432940  4.007333  4.919981  4.317488          1.945910      2.079442"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Frozen':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8.431853</td>\n",
       "      <td>9.663261</td>\n",
       "      <td>9.723703</td>\n",
       "      <td>3.496508</td>\n",
       "      <td>8.847360</td>\n",
       "      <td>6.070738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>8.597297</td>\n",
       "      <td>9.203618</td>\n",
       "      <td>9.257892</td>\n",
       "      <td>3.637586</td>\n",
       "      <td>8.932213</td>\n",
       "      <td>7.156177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.950323</td>\n",
       "      <td>10.732651</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>10.095388</td>\n",
       "      <td>7.260523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>10.000569</td>\n",
       "      <td>9.034080</td>\n",
       "      <td>10.457143</td>\n",
       "      <td>3.737670</td>\n",
       "      <td>9.440738</td>\n",
       "      <td>8.396155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>7.759187</td>\n",
       "      <td>8.967632</td>\n",
       "      <td>9.382106</td>\n",
       "      <td>3.951244</td>\n",
       "      <td>8.341887</td>\n",
       "      <td>7.436617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>6.978214</td>\n",
       "      <td>9.177714</td>\n",
       "      <td>9.645041</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>8.696176</td>\n",
       "      <td>7.142827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>10.395650</td>\n",
       "      <td>9.728181</td>\n",
       "      <td>9.519735</td>\n",
       "      <td>11.016479</td>\n",
       "      <td>7.148346</td>\n",
       "      <td>8.632128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>8.402007</td>\n",
       "      <td>8.569026</td>\n",
       "      <td>9.490015</td>\n",
       "      <td>3.218876</td>\n",
       "      <td>8.827321</td>\n",
       "      <td>7.239215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>9.060331</td>\n",
       "      <td>7.467371</td>\n",
       "      <td>8.183118</td>\n",
       "      <td>3.850148</td>\n",
       "      <td>4.430817</td>\n",
       "      <td>7.824446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>7.932721</td>\n",
       "      <td>7.437206</td>\n",
       "      <td>7.828038</td>\n",
       "      <td>4.174387</td>\n",
       "      <td>6.167516</td>\n",
       "      <td>3.951244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fresh      Milk    Grocery     Frozen  Detergents_Paper  Delicatessen\n",
       "38    8.431853  9.663261   9.723703   3.496508          8.847360      6.070738\n",
       "57    8.597297  9.203618   9.257892   3.637586          8.932213      7.156177\n",
       "65    4.442651  9.950323  10.732651   3.583519         10.095388      7.260523\n",
       "145  10.000569  9.034080  10.457143   3.737670          9.440738      8.396155\n",
       "175   7.759187  8.967632   9.382106   3.951244          8.341887      7.436617\n",
       "264   6.978214  9.177714   9.645041   4.110874          8.696176      7.142827\n",
       "325  10.395650  9.728181   9.519735  11.016479          7.148346      8.632128\n",
       "420   8.402007  8.569026   9.490015   3.218876          8.827321      7.239215\n",
       "429   9.060331  7.467371   8.183118   3.850148          4.430817      7.824446\n",
       "439   7.932721  7.437206   7.828038   4.174387          6.167516      3.951244"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Detergents_Paper':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>9.923192</td>\n",
       "      <td>7.036148</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>8.390949</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>6.882437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>9.428190</td>\n",
       "      <td>6.291569</td>\n",
       "      <td>5.645447</td>\n",
       "      <td>6.995766</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.711101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "75   9.923192  7.036148  1.098612  8.390949          1.098612      6.882437\n",
       "161  9.428190  6.291569  5.645447  6.995766          1.098612      7.711101"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Delicatessen':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>7.335634</td>\n",
       "      <td>8.911530</td>\n",
       "      <td>5.164786</td>\n",
       "      <td>8.151333</td>\n",
       "      <td>3.295837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.248504</td>\n",
       "      <td>9.724899</td>\n",
       "      <td>10.274568</td>\n",
       "      <td>6.511745</td>\n",
       "      <td>6.728629</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>4.941642</td>\n",
       "      <td>9.087834</td>\n",
       "      <td>8.248791</td>\n",
       "      <td>4.955827</td>\n",
       "      <td>6.967909</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>8.034955</td>\n",
       "      <td>8.997147</td>\n",
       "      <td>9.021840</td>\n",
       "      <td>6.493754</td>\n",
       "      <td>6.580639</td>\n",
       "      <td>3.583519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>10.519646</td>\n",
       "      <td>8.875147</td>\n",
       "      <td>9.018332</td>\n",
       "      <td>8.004700</td>\n",
       "      <td>2.995732</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>10.514529</td>\n",
       "      <td>10.690808</td>\n",
       "      <td>9.911952</td>\n",
       "      <td>10.505999</td>\n",
       "      <td>5.476464</td>\n",
       "      <td>10.777768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>5.789960</td>\n",
       "      <td>6.822197</td>\n",
       "      <td>8.457443</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>5.811141</td>\n",
       "      <td>2.397895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>7.798933</td>\n",
       "      <td>8.987447</td>\n",
       "      <td>9.192075</td>\n",
       "      <td>8.743372</td>\n",
       "      <td>8.148735</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>6.368187</td>\n",
       "      <td>6.529419</td>\n",
       "      <td>7.703459</td>\n",
       "      <td>6.150603</td>\n",
       "      <td>6.860664</td>\n",
       "      <td>2.890372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>6.871091</td>\n",
       "      <td>8.513988</td>\n",
       "      <td>8.106515</td>\n",
       "      <td>6.842683</td>\n",
       "      <td>6.013715</td>\n",
       "      <td>1.945910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>10.602965</td>\n",
       "      <td>6.461468</td>\n",
       "      <td>8.188689</td>\n",
       "      <td>6.948897</td>\n",
       "      <td>6.077642</td>\n",
       "      <td>2.890372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>10.663966</td>\n",
       "      <td>5.655992</td>\n",
       "      <td>6.154858</td>\n",
       "      <td>7.235619</td>\n",
       "      <td>3.465736</td>\n",
       "      <td>3.091042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>7.431892</td>\n",
       "      <td>8.848509</td>\n",
       "      <td>10.177932</td>\n",
       "      <td>7.283448</td>\n",
       "      <td>9.646593</td>\n",
       "      <td>3.610918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fresh       Milk    Grocery     Frozen  Detergents_Paper  \\\n",
       "66    2.197225   7.335634   8.911530   5.164786          8.151333   \n",
       "109   7.248504   9.724899  10.274568   6.511745          6.728629   \n",
       "128   4.941642   9.087834   8.248791   4.955827          6.967909   \n",
       "137   8.034955   8.997147   9.021840   6.493754          6.580639   \n",
       "142  10.519646   8.875147   9.018332   8.004700          2.995732   \n",
       "154   6.432940   4.007333   4.919981   4.317488          1.945910   \n",
       "183  10.514529  10.690808   9.911952  10.505999          5.476464   \n",
       "184   5.789960   6.822197   8.457443   4.304065          5.811141   \n",
       "187   7.798933   8.987447   9.192075   8.743372          8.148735   \n",
       "203   6.368187   6.529419   7.703459   6.150603          6.860664   \n",
       "233   6.871091   8.513988   8.106515   6.842683          6.013715   \n",
       "285  10.602965   6.461468   8.188689   6.948897          6.077642   \n",
       "289  10.663966   5.655992   6.154858   7.235619          3.465736   \n",
       "343   7.431892   8.848509  10.177932   7.283448          9.646593   \n",
       "\n",
       "     Delicatessen  \n",
       "66       3.295837  \n",
       "109      1.098612  \n",
       "128      1.098612  \n",
       "137      3.583519  \n",
       "142      1.098612  \n",
       "154      2.079442  \n",
       "183     10.777768  \n",
       "184      2.397895  \n",
       "187      1.098612  \n",
       "203      2.890372  \n",
       "233      1.945910  \n",
       "285      2.890372  \n",
       "289      3.091042  \n",
       "343      3.610918  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For each feature find the data points with extreme high or low values\n",
    "for i in log_data.keys():\n",
    "    \n",
    "    # TODO: Calculate Q1 (25th percentile of the data) for the given feature\n",
    "    Q1 = np.percentile(log_data[i], 25)\n",
    "    \n",
    "    # TODO: Calculate Q3 (75th percentile of the data) for the given feature\n",
    "    Q3 = np.percentile(log_data[i], 75)\n",
    "    \n",
    "    # TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)\n",
    "    step = 1.5*(Q3 - Q1)\n",
    "    \n",
    "    # Display the outliers\n",
    "    print(\"Data points considered outliers for the feature '{}':\".format(i))\n",
    "    display(log_data[~((log_data[i] >= Q1 - step) & (log_data[i] <= Q3 + step))])\n",
    "    \n",
    "# OPTIONAL: Select the indices for data points you wish to remove\n",
    "outliers  = [65, 66, 75, 128, 154]\n",
    "\n",
    "# Remove the outliers, if any were specified\n",
    "good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Question 4\n",
    "* Are there any data points considered outliers for more than one feature based on the definition above? \n",
    "* Should these data points be removed from the dataset? \n",
    "* If any data points were added to the `outliers` list to be removed, explain why.\n",
    "\n",
    "** Hint: ** If you have datapoints that are outliers in multiple categories think about why that may be and if they warrant removal. Also note how k-means is affected by outliers and whether or not this plays a factor in your analysis of whether or not to remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Q1 = np.percentile(log_data['Fresh'], 25)\n",
    "Q3 = np.percentile(log_data['Fresh'], 75)\n",
    "step = 1.5*(Q3 - Q1)\n",
    "\n",
    "a = log_data[~((log_data['Fresh'] >= Q1 - step) & (log_data['Fresh'] <= Q3 + step))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Q1 = np.percentile(log_data['Delicatessen'], 25)\n",
    "Q3 = np.percentile(log_data['Delicatessen'], 75)\n",
    "step = 1.5*(Q3 - Q1)\n",
    "\n",
    "b = log_data[~((log_data['Delicatessen'] >= Q1 - step) & (log_data['Delicatessen'] <= Q3 + step))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Q1 = np.percentile(log_data['Frozen'], 25)\n",
    "Q3 = np.percentile(log_data['Frozen'], 75)\n",
    "step = 1.5*(Q3 - Q1)\n",
    "\n",
    "c = log_data[~((log_data['Frozen'] >= Q1 - step) & (log_data['Frozen'] <= Q3 + step))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>7.335634</td>\n",
       "      <td>8.911530</td>\n",
       "      <td>5.164786</td>\n",
       "      <td>8.151333</td>\n",
       "      <td>3.295837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>4.941642</td>\n",
       "      <td>9.087834</td>\n",
       "      <td>8.248791</td>\n",
       "      <td>4.955827</td>\n",
       "      <td>6.967909</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "66   2.197225  7.335634  8.911530  5.164786          8.151333      3.295837\n",
       "128  4.941642  9.087834  8.248791  4.955827          6.967909      1.098612"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fresh / Delicatessen\n",
    "pd.concat([a,b])[pd.concat([a,b]).duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.950323</td>\n",
       "      <td>10.732651</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>10.095388</td>\n",
       "      <td>7.260523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fresh      Milk    Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "65  4.442651  9.950323  10.732651  3.583519         10.095388      7.260523"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fresh / Frozen\n",
    "pd.concat([a,c])[pd.concat([a,c]).duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Fresh, Milk, Grocery, Frozen, Detergents_Paper, Delicatessen]\n",
       "Index: []"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delicatessen / Frozen\n",
    "pd.concat([c,b])[pd.concat([c,b]).duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Answer:**\n",
    "- the data point with index**'75'** is the shared outlier of 'Grocery','Detergents_Paper'. The data point with index**'154'** is the shared outlier between 'Delicatessen','Grocery','Milk'. The data point with index**'66, 128'** is the shared outlier between 'Fresh','Delicatessen'. The data points with index**'65'** are the shared outlier of 'Fresh','Frozen'.\n",
    "\n",
    "- I guess they should be removed as they are not only outliers in one categories but more than once. \n",
    "- What will happen if we remove those outliers? I think outlier should be discarded for the better result because they are not of any importance. They are not representative of our general customers.\n",
    "- Outliers can greatly affect distributions, influence a **distance based algorithm** like clustering and/or PCA! The loss function of the K-means algorithm is defined the terms of sum-of-squared distances, making it sensitive to outliers. In an attempt to reduce the loss function, the algorithm would move a centroid away from the true center of a cluster towards the outlier. This is clearly not the behavior we want."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "# Feature Transformation\n",
    "In this section you will use principal component analysis (PCA) to draw conclusions about the **underlying structure** of the wholesale customer data. Since using PCA on a dataset calculates the **dimensions** which best maximize variance, we will find which compound combinations of features best describe customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Implementation: PCA\n",
    "\n",
    ">Fight against high dimensionality\n",
    "\n",
    ">Fight against multicollinearity\n",
    "\n",
    "Now that the data has been scaled to a more normal distribution and has had any necessary outliers removed, we can now apply PCA to the `good_data` to discover which dimensions about the data best maximize the variance of features involved. In addition to finding these dimensions, PCA will also report the *explained variance ratio* of each dimension — how much variance within the data is explained by that dimension alone. Note that a **component (dimension)** from PCA can be considered a new \"feature\" of the space, however it is a composition of the original features present in the data.\n",
    "\n",
    "Implement the following:\n",
    " - Import `sklearn.decomposition.PCA` and assign the results of fitting PCA in six dimensions with `good_data` to `pca`.\n",
    " - Apply a PCA transformation of `log_samples` using `pca.transform`, and assign the results to `pca_samples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2209a807550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Apply PCA by fitting the good data with the same number of dimensions as features\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=6).fit(good_data)\n",
    "\n",
    "# TODO: Transform log_samples using the PCA fit above\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "# Generate PCA results plot\n",
    "pca_results = vs.pca_results(good_data, pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(pandas.core.frame.DataFrame,\n",
       "              Explained Variance   Fresh    Milk  Grocery  Frozen  \\\n",
       " Dimension 1              0.4430  0.1675 -0.4014  -0.4381  0.1782   \n",
       " Dimension 2              0.2638 -0.6859 -0.1672  -0.0707 -0.5005   \n",
       " Dimension 3              0.1231 -0.6774  0.0402  -0.0195  0.3150   \n",
       " Dimension 4              0.1012 -0.2043  0.0128   0.0557  0.7854   \n",
       " Dimension 5              0.0485 -0.0026  0.7192   0.3554 -0.0331   \n",
       " Dimension 6              0.0204  0.0292 -0.5402   0.8205  0.0205   \n",
       " \n",
       "              Detergents_Paper  Delicatessen  \n",
       " Dimension 1           -0.7514       -0.1499  \n",
       " Dimension 2           -0.0424       -0.4941  \n",
       " Dimension 3           -0.2117        0.6286  \n",
       " Dimension 4            0.2096       -0.5423  \n",
       " Dimension 5           -0.5582       -0.2092  \n",
       " Dimension 6           -0.1824        0.0197  )"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pca_results), pca_results # shows the eigenvalues(explained var) of the cov-matrix and the corresponding eigenvectors ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dimension 1    0.4430\n",
       "Dimension 2    0.7068\n",
       "Dimension 3    0.8299\n",
       "Dimension 4    0.9311\n",
       "Dimension 5    0.9796\n",
       "Dimension 6    1.0000\n",
       "Name: Explained Variance, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cumulative explained variance should add to 1\n",
    "display(pca_results['Explained Variance'].cumsum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# I don't know..Feature Weights...they can be positive , nagative,.. a bunch of coordinates of the eigenvectors of the cov-matrix?????\n",
    "\n",
    "Remember that the sign of the features in the component really wouldn't matter too much, since if we multiply the entire PCA dimension by -1 it would still be the same PCA component (so the first PCA component refers to the spending of Milk, Deter, and Grocery regardless of the direction of the vectors). As for each component the prevalent features refer to the highest absolute magnitude.\n",
    "\n",
    "And a principal component with feature weights that have opposite directions can reveal how customers buy more in one category while they buy less in the other category.\n",
    "\n",
    "For example, in terms of customers spending, since PCA deals with the variance of the data and the correlation between features, the first component would represent that we have some customers who purchase a lot of Milk, Grocery and Detergents_Paper products while other customers purchase very few amounts of Milk, Grocery and Detergents_Paper, hence spread in the data. So maybe this component could represent retail spending.\n",
    "\n",
    "![](pca_example2d.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Question 5\n",
    "\n",
    "* How much variance in the data is explained* **in total** *by the first and second principal component? \n",
    "* How much variance in the data is explained by the first four principal components? \n",
    "* Using the visualization provided above, talk about each dimension and the cumulative variance explained by each, stressing upon **which features are well represented by each new dimension**(both in terms of positive and negative variance explained). Discuss what the first four dimensions best represent in terms of customer spending.\n",
    "\n",
    "**Hint:** A positive increase in a specific dimension corresponds with **an *increase* of the *positive-weighted* features** and a *decrease* of the *negative-weighted* features. The rate of increase or decrease is based on the individual feature weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Answer:**\n",
    "- As seen from the chart(0.44 + 0.26 = 0.70), roughly 70% of variance in the data is explained by the first and second components.\n",
    "\n",
    "- 0.44 + 0.26 + 0.12 + 0.1 = 0.92, roughly 92% of the variance in the data is explained by the first four principal components.\n",
    "\n",
    "\n",
    "- Components breakdown:\n",
    "  - The first principal component (PC1):\n",
    "    - A decrease in PC1 is associated with large increases in \"Milk\", \"Grocery\" and \"Detergents_Paper\" spending. This is in line with our initial findings where the 3 features are highly correlated.\n",
    "    - Perhaps, it represents a group of **cafe**. \n",
    "\n",
    "  - The second principal component (PC2):\n",
    "    - A decrease in PC2 is associated with large increases in \"Fresh\", \"Frozen\" and \"Delicatessen\" spending. Interestingly, the features in PC1 have very small positive weights.\n",
    "    - Perhaps, it represents a group of **fresh food retail**.\n",
    "\n",
    "  - The third principal component (PC3):\n",
    "    - An increase in PC3 is associated with a large increase in \"Delicatessen\" and a large decrease in \"Fresh\" spending.\n",
    "    - Perhaps, it represents a group of **high-level restaurant**.\n",
    "\n",
    "  - The fourth principal component (PC4):\n",
    "    - An increase in PC4 is associated with a large increasing in \"Frozen\" and a large decrease in \"Delicatessen\" spending.\n",
    "    - Perhaps, it represents a group of **low-level restaurant**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Observation\n",
    "How the log-transformed sample data has changed after having a PCA transformation applied to it in six dimensions? Observe the numerical value for the first four dimensions of the sample points. Consider if this is consistent with your initial interpretation of the sample points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dimension 1', 'Dimension 2', 'Dimension 3', 'Dimension 4',\n",
       "       'Dimension 5', 'Dimension 6'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_results.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pca_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dimension 1</th>\n",
       "      <th>Dimension 2</th>\n",
       "      <th>Dimension 3</th>\n",
       "      <th>Dimension 4</th>\n",
       "      <th>Dimension 5</th>\n",
       "      <th>Dimension 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.7580</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>-0.9590</td>\n",
       "      <td>-1.6824</td>\n",
       "      <td>0.2680</td>\n",
       "      <td>-0.3891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.1553</td>\n",
       "      <td>-1.4052</td>\n",
       "      <td>0.5422</td>\n",
       "      <td>0.4127</td>\n",
       "      <td>-0.6865</td>\n",
       "      <td>0.6409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.2406</td>\n",
       "      <td>-1.2419</td>\n",
       "      <td>-1.0729</td>\n",
       "      <td>-1.9589</td>\n",
       "      <td>0.2160</td>\n",
       "      <td>-0.1782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2  Dimension 3  Dimension 4  Dimension 5  \\\n",
       "0      -1.7580       0.0097      -0.9590      -1.6824       0.2680   \n",
       "1       1.1553      -1.4052       0.5422       0.4127      -0.6865   \n",
       "2      -2.2406      -1.2419      -1.0729      -1.9589       0.2160   \n",
       "\n",
       "   Dimension 6  \n",
       "0      -0.3891  \n",
       "1       0.6409  \n",
       "2      -0.1782  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display sample log-data after having a PCA transformation applied\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = pca_results.index.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Implementation: Dimensionality Reduction\n",
    "When using principal component analysis, one of the main goals is to reduce the dimensionality of the data — in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being explained. Because of this, the **cumulative explained variance ratio** is extremely important for knowing how many dimensions are necessary for the problem. \n",
    "\n",
    "**Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards.**\n",
    "\n",
    "Implement the following:\n",
    " - Assign the results of fitting PCA in two dimensions with `good_data` to `pca`.\n",
    " - Apply a PCA transformation of `good_data` using `pca.transform`, and assign the results to `reduced_data`.\n",
    " - Apply a PCA transformation of `log_samples` using `pca.transform`, and assign the results to `pca_samples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# TODO: Apply PCA by fitting the good data with only two dimensions\n",
    "pca = PCA(n_components=2).fit(good_data)\n",
    "\n",
    "# TODO: Transform the good data using the PCA fit above\n",
    "reduced_data = pca.transform(good_data)\n",
    "# TODO: Transform log_samples using the PCA fit above\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "# Create a DataFrame for the reduced data\n",
    "reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Observation\n",
    "How the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions? Observe how the values for the first two dimensions remains **unchanged** when compared to a PCA transformation in six dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dimension 1</th>\n",
       "      <th>Dimension 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.7580</td>\n",
       "      <td>0.0097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.1553</td>\n",
       "      <td>-1.4052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.2406</td>\n",
       "      <td>-1.2419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2\n",
       "0      -1.7580       0.0097\n",
       "1       1.1553      -1.4052\n",
       "2      -2.2406      -1.2419"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display sample log-data after applying PCA transformation in two dimensions\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = ['Dimension 1', 'Dimension 2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Visualizing a Biplot\n",
    "A biplot is a scatterplot where each data point is represented by its scores along the principal components. The axes are the principal components (in this case `Dimension 1` and `Dimension 2`). In addition, the biplot shows the **projection of the original features along the components**. A biplot can help us interpret the reduced dimensions of the data, and **discover relationships between the principal components and original features.**\n",
    "\n",
    "produce a biplot of the reduced-dimension data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2209e8fe5f8>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2kAAAH1CAYAAABss7a3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XucXWdd7/HPWrNncpmkSZpMJg20oQ3hSaAzKVTxoD0tNNzLoak3VEARBY/KET3eFRCPeAUFVFTECwerVuW0KdiWiwotCKW2pZkppE+nSZtUk84lmUySmUwye/Y6fzx7zey9Z1/Wvq+99vf9euWVZPZlnnXZaz+/9Xue3+MFQYCIiIiIiIjEg9/uBoiIiIiIiMgyBWkiIiIiIiIxoiBNREREREQkRhSkiYiIiIiIxIiCNBERERERkRhRkCYiIm1hjPHa3YZupv1fHe0vEWmlVLsbICISZ8aYLwI3FPz4PDAGfMxa+8cFz+8Ffhx4M2CAC8Ao8AFr7d11tuWlwBeAb7XWPljPe7WaMeYp4J+tte8wxqwCfg+3LQcKH29BW15Klfsxex6cs9a+roHteAvw18CAtXaqxHNeAfwp8Gzc+fa/GvS7rwN+BviuRrxfnBhjPg58i7X26ga+53uAk8BHsv//Ig0+H0REcilIExGp7N+Bn8v5/zrgLcAfGWMIAzVjzCXAZ4HnAx8G3gX0At8H3GWM+Rlr7Yda2fAYuQWYzv77MuCngC+1qS0PAy8BDlXxmp8AFpvTnLJ+B3dT4DXA0w183x/F3URIot8A+hv8nr8O/HzO/9t1PohIl1CQJiJS2Wlr7f25PzDG/BvwLcA7gDCb9iFgGPgOa+0jOU//Z2PMWeADxpg7rbVPtqLRcWKt/Xq72xCy1p4B7q/4xPzXfLNJzankUuBua+0X2vT7O4619nALfke7zgcR6RIK0kREamCtzRhjDgKvBzDGbAV+EPhIQYAW+g3gIrC22PsZY54DPAm8AfhfuADwSeC91tp/LNUOY8wbgZ8GXpD90SPAL1lr78s+/nFc5u9LwP8GtuIClJ+w1h7KeZ9XAO/DBZkngb8Cft1auyJbYIy5Bvg68DJr7RezP/tp4IPAPmvtv2V/9rPALwDbstvyz8AHsv8G+CdjzL3W2pdm/7/GGPMnuMxjD24o5DustWfLbP8w8LvAt2V/dBfwc9ba8Zzt3wjMATcD/5JtZ95wR2PMT2T3z7NwmdO/AT4OXGmtfSp3eFvOcMnrcZmua4HjwG9Za/8ip20vBt4LfDvuuD8J/IG19qOltifntc/J2U8/kW1f2Jayxyo75PZdwPcDO7Lb/gXgndbap7P75Ieyzw2AlwHPoWDopTFmIy77+cPW2o8bY94LvA53Lv0IcNha+0JjTAp4Dy67vBV4FPhFa+2/ltm+LwLfBOaBtwILwD/gjt189jlPAbcBLwX2Au+x1r4/4jFfGu4YpX3GmEtxQ3D/B7AG9xn5WWvtaHYfAbzfGPMOa+1zCoc7GmO2AL+Ny3hemn39L+ScX2/BnftvAH4f2AMcxn1WP5V9Tk/2Pb4PGMw+/ofW2j8rtR9FJLlUOEREpHa7WO5I78MFFkXnnVlrj1tr32mt/UaF9/xz3HC8W4CHgNuMMa8s9kRjzHfjgom7gNcCP4wLSP7BGNOX89SX4zrl7wTelG33x3PeZx9wT3ZbbgHeD/ws8Icl2ngQeAa4MednL83+fV3Oz14FfMZaG+T87ATwndl//wpu2FjoLbgO7vfigps3Zv8uKhss3g/05Wzf9cC9xpjc4W434b7vXo/Ldha+z9tx2dA7gP24znHFQAoXQPw/3L7/OvAxY8zzs+95BS4wOgd8Dy5AfBz4M2PMUIT3PoEbkvkM8Mnsv09EPFYfxAX6vwO8EvhV3PkZbvtv4M7TI9n3fThCe0J7s39uwQWCAB/LtuHDuP33GHCPMebbK7zXG3HH64dwwwnfAvxlwXN+FrgTtw8/VcUxz1W2fdkg7l9wx/GXs79rDfA5Y8wm3D4C+KPsducxxqwDvoL7nP0SLhDzgPsKjvV6XED9EVywO4X7rF6affyXccHvu8h+doA/Nca8qsR2iUiCKZMmIlKZl+3Iget8XYYrDvJCXPEFcIUdAI7W+bs+Y619Z/hvY4zBdd4+V+S5z8Vl7t4b/sAYcxG4HXgeLmMArnN4k7X2RPY5zwI+bIzZbK09icvK3G+t/b6c33sK+Lgx5v3W2qdyf6m1NjDGfAaXgcEY4+M6yl8H/nv2Z6uz/35LwWsvGGPCoY9jBcPGLPD92aDuX4wxLwt/RwnvBiaB11hrL2Z/70O4Qi1vxXWqwX3X/bi1djr7nJcWvM97gE9Ya8M5R581xmzHdaTL+bC19g+y7xkG1q/BZYheAHwVeKO1diH7nPuBU7hCNKPl3thaewG43xhzARgPh9saY6IcqwFcZumvss+5N3sevTH73oeNMZPAjpz3rbCpS1K4DNPXs6/bjTvGb8vJIn7GGHMZ7ry6sei7OD3Aq3MydwHwx8aYX805575prf3t8AXGmP9HtGMePj9K+27CfZavt9Z+Kfu6h4EHcBm5z2f3z7ESw3Z/GNgJDIXnszHms7jiQu9luThLH/DzYWbcGDOOu+HxMlywfx3woLX2E9nnf9EYM4fLhIpIl1GQJiJS2Wtxw7FyncdlLML5aOGwwHpHKNxW8P9PAe/JBkJ5rLW/A0vD0nbjCkG8PvvwqpynHg0DtKz/zP7db4w5D7wY+NWcQBTcXXwf14H86yLt/AzwN8aYtbiAcB0uU/HH2WFb1+OKpny2/Obmub8g6/YUUK5C3/XA34eddXBzhYwxI7hAKOywT4YBWiFjzC7cEMcDBQ/9E5WDtKV5bdba08aYc2QLVlhr78Fla1Zns2u7cPsZ8o9NZNl9XfFYWWvfkH3+s3DnxB5cAFDT7y3i8Zx/vzT7990Fbbob+G1jTF/u8SnwLwVVLe/EfZ6uwx17cIF7rqjHPHL7cMNRZ8IALfuek8CVJdpd6HrgG7k3HKy1F40xt+OqvObKnQu59DnM/v0l4H3GmLDq6aette9CRLqSgjQRkcq+zHLGLMANYTsSZkiywgzaFbhMygrGmGdba/+z2GM5ThT8fxIX7Kwr8n7bcMPDXoOb7/YNlju3uWs6Fd6Jz2T/9oFN2b9/O/un0GUl2vm57Ouuw1WzfDD7s3W4rMQrga9aa0+XeH0xxdpZLujdBIwX+fk4cEnO/yfKvMeW7N+TRd6jkpLtzQaqvw/8GC6Dchi4L/u8WtfbinSsssP4/hQ3Z20Gl+E8X8fvzTVrrZ3N+f/m7N//VeL5W3Dz9Yopdq6DG/IaKjx2UY95Ne27tMjvqUY1bco9Z3I/h+CGp87hhjx+CPiQMebLwFtaUQxFROJFQZqISGUzEdbT+gKQBl6Ny2zkyQZUTxljft1a+xtl3mdzwf+34oorFCue8Xe4YZYvAR6y1qaNMa9lec5XFGeyf78Pl8koVLSDba2dNsY8gMveGOA+a+0JY8wYbpjjK3CFIJrpFK7AQqFtRC+vH3beBwp+Xvj/av0q8HZcMZm7rbWz2UzYj9TxnhWPlTFmA65Ay5eB77LWPgFgjPk94Joy7x1mMHOD4hU3BoqYyb7223Hnf6Gi679lFTvXoXzAVO0xj9K+GYoc7+xw2ycLh/uWaNPuEm06WeG1S7KFXz4IfDA7p3E/bq7eH+NuxIhIF1HhEBGRBrDWnsIV8Xi7MabYEL334TIZhcMZCxUOsbsZ+GLBMMDQS4DbrLVfs9aGHdBXZ/+OlDXJVk48COy01j4Y/sFl5n4buLzMy+/BBWnfwXKW6D5c4Y8hShRRoXHrS30ZuDm3SIoxZk/2d/97xPf4T1z28fUFP7+5zra9BDe/6J9yMk9VHZtCEY/Vblxm50M5AZqPC5pzf2/hMQgDwO05P/vvEZr15ez7XlLQppfjss/FAqPQy7KBa2g/Lrt0X4nnh7+vmmMepX1fATbmFjrJFgy5B5cRhuWsV6k2vSDbjvD1fbg5ilHPQ4wxnzPG/AGAtfaYtfYPccMer4j6HiKSHMqkiYg0zi/iyoJ/yRjzIVwHbQOuCt3/wJWTH6vwHm/LFnX4Ci4Lsxc356WY/wDekp2PM43rFP549rGipf5LeA9wwBgzg6twuAUXVGYoX+DiM8D/yT7vy9mf3YfLFh23xZciAJe5AHi5MWbMWnuwirbm+k3cfrrHGPNB3L5+Hy7o+r9R3sC6pRR+A1eZcQL4V9wcxLCKX7nOeTn/AfySMeYduH34rbj9HFDdsSlU6Vj14rKu784OuVwD/CTuPAqMMV424D8NPNu4cv4P4jLB87iCMu/DBQbvBi6Ua4y19pFsMY9bjSvRfwg3D+xdwO9Za8vtv83Ap40xv48rgvNbwJ9Ya0sNj4Qqj3mU9hljPo0bEvoPxphfwWXXfgmXRQ6zwaeB64wxX7LWfq3g1/w1bhmMu40x78Kd3z+Dy/j9ZpltKfQl4F3GmBO482cPrtLkB8MnGGP+G26OpYY/iiScMmkiIg2SLTZwHW540vfiOtF/huuUv9Ja+5EIb/OruPLbB3DFJl5lrf1qief+MK7T+de4DN0eXGZrluWy4VHa/Slc5uhbcIVKPoSrTPgya225ynIP4uYRHbRugWiAe7N/31Pm953BrXP1Zlz2sSbW2odw1fl6cYU+Pozr6H6HLbO2WpH3+Svg53Drin0aV5nxfdmHz9XYvN/BBQ2/hht++AO4hc8/TxXHpkhbyx4ra+0MrprgpuzjH8EFHd+D+84P1xb7KG7O1D/jzs3TuHN2IPuzn8Qdnyjb/0bcOfjLuMD9+3FBzq9UeN1ncYHlP+BucLwfF+yU2/5ajnnZ9mXnlr4SF6B/GPh7XKD18uz+BFel8WW44DDvBnf2914PfA23v2/DBc3Xl6gGWcpv4TKiP47bN7+EC9B+Pec5X8UFzyKScF4QFBtBIyIirWSWFy/+HmvtJ9vcnK5ijPkBXJGTJ3N+9lvAj1lrC+dNSQOYgsWgG/zetwFXWWtfXPHJIiIxpeGOIiLS7d4K/HJ2ONwUrsz9T+MyO9IhjDGbcQU2bgC+2N7WiIjUR0GaiIh0uzcBv4cbqrYRN7/p3cAftLFNUr1vwQ3jPMzycFURkY6k4Y4iIiIiIiIxosIhIiIiIiIiMaIgTUREREREJEY0J60JJifPagxpgmzatJbp6XJVyKWT6fgmm45vsun4JpuOb7Lp+DoDA+u9Yj9XJk2kglSqp91NkCbS8U02Hd9k0/FNNh3fZNPxLU9BmoiIiIiISIwoSBMREREREYkRBWkiIiIiIiIxoiBNREREREQkRhSkiYiIiIiIxIiCNBERERERkRhRkCYiIiIiIhIjCtJERERERERiREGaiIiIiIhIjChIExERERERiREFaSIiIiIiIjGiIE1ERERERCRGFKSJiIiIiIjEiII0ERERERGRGEm1uwEiIpJMQQAjIx6joz3Mznr09wcMDS0yPBzgee1unYiISHwpSIvIGPPLwOuBPuBPrLV/2eYmiYjEVhDAHXekeOwxn1T2m2ZmxuPYMZ8jRzLs359WoCYiIlKCgrQIjDEvBb4d+A5gLfBzbW2QiEjMjYx4eQFaKJWCQ4d8rrrKY+/eoD2N6zDKSIqIdB8FadG8ChgF7gAuAX6+vc0REYm30dGeFQFaKJVyj+/dm25tozqQMpIiIt1JQVo0W4AdwOuAK4FPGWN2W2uL3gbetGktqVRPK9snTTYwsL7dTZAm0vFtPM+D/v7Sj/s+DAy0pi2dfHwfegiOHYMNG1Y+dvQoPP00XHtt69sVJ518fKUyHd9k0/EtTUFaNCeBx6y1FwFrjJkHBoCJYk+enp5rZdukyQYG1jM5ebbdzZAm0fFtjiBIMTtbuoDwhg0ZJiebn0nr9ON7330pLl70uXix1OMZrriiezOSnX58pTwd32TT8XVKBaoqwR/Nl4FXG2M8Y8x2oB8XuImISBFDQ4ukS8QO6bR7XCqbnS0/lnFuTmMdRUSSSEFaBNbafwa+DjwAfBr4SWutehgiIiUMDwfs2ZNZEail07BnT4bhYRUNiaK/v/x+WrtW+1FEJIk03DEia+0vtLsNIiKdwvNg//40O3d6jIz0MDfnsXatqhJWa2hokWPHVlbJBGUkRUSSTEGaiIg0hee5jNrwcPfOmarX8HDAkSMZDh3KD9SUkRQRSTYFaSIiIjGljKSISHdSkCYiVdPiuiKto4ykiEj3UZAmIlXR4roiIiIizaUgTUSqMjLi5QVooVQKDh3yueoqj71765sno0ydiIiIdDMFaSJSldHRnqKV5sAFaqOjPezdW/uwLGXqREREpNtpnTQRqUqzF9etlKkbGVGEJiIiIsmmIE1EqtLsxXWjZOpEREREkkxBmohUZWhokXSJ0YyNWFy32Zk6ERERkbhTkCYiVRkeDtizJ7MiUGvU4rrNztSJiIiIxJ0Kh4hIVZq9uO7Q0CLHjq2ckwaNydSJiIiIxJ2CNBGpWjMX1x0eDjhyJMOhQ/mBWqMydSIiIiJxpyBNRGKl2Zk6ERERkbhTkCYisVNPpk4LYYuIiEinU5AmIomhhbBFREQkCVTdUUQSQwthi4iISBIoSBORxNBC2CIiIpIECtJEJDG0ELaIiIgkgYI0EUkMLYQtIiIiSaAgTUQSY2hokXSJgpBaCFtEREQ6hYI0EUmM4eGAPXsyKwI1LYQtIiIinUQl+EUkMbQQtoiIiCSBgjQRSZR6FsIWERERiQMNdxQREREREYkRBWkiIiIiIiIxoiBNREREREQkRhSkiYiIiIiIxIiCNBERERERkRhRkCYiIiIiIhIjCtJERERERERiREGaiIiIiIhIjChIExERERERiREFaSIiIiIiIjGSancDRERERKS0IICREY/R0R5mZz36+wOGhhYZHg7wvHa3TkSaQUGaiIiISEwFAdxxR4rHHvNJZXttMzMex475HDmSYf/+tAI1kQTScEcRERGRmBoZ8fICtFAqBYcO+YyMKEITSSIFaSIiIiIxNTrasyJAC6VS7nERSR4FaSIiIiIxNTtbPlM2N6dMmkgSKUgTERERian+/qDs42vXln9cRDqTCoeIiIhIXVR9sHmGhhY5dmzlnDSAdNo9LiLJoyBNREREaqbqg801PBxw5EiGQ4fyA7V0GvbsyTA8rEyaSBIpSBMREZGaVao+eNVVHnv3KpColefB/v1pdu70GBnpYW7OY+1aZSpFkk5BmoiIiNQsSvXBvXvTrW1Uwniey6gND2s/inQLFQ4RERGRmqn6oIhI4ylIExERkZqp+qCISOMpSBMREZGaDQ0tki4xCk/VB0VEaqM5aSIiIjEX5xL3qj4oItJ4CtJERERiLO4l7lV9UESk8RSkiYiIxFgnlLhX9UERkcbSnDQREZEYi1LiXkREkkVBmoiISIypxL2ISPdRkCYiIhJjKnEvItJ9FKSJiIjEmErci4h0HwVpIiIiMTY8HLBnT2ZFoKYS9yIiyaXqjiIiIjGmEveSFHFe708kbhSkiYiIxJxK3Euni/t6fyJxo+GOIiIiItJUldb7GxlRhCaSS0GaiIiIiDSV1vsTqY6CNBERERFpKq33J1IdBWkiIiIi0lRa70+kOgrSRERERKSptN6fSHUUpImIiIhIU2m9P5HqqAR/FYwxW4GHgFdYax9rd3tEREREOoHW+xOpjoK0iIwxvcBHgfPtbouIiIhIp9F6fyLRabhjdB8A/gw43u6GiIiIiIhIcnlBoDHAlRhj3gI821r7PmPMF4H/WW64Yzq9GKRSWu9DREQaJwjg4YfhkUfg7FlYvx6uuQZe9CI0VExEpHMVvYIrSIvAGHMfEGT/XAM8DrzeWvtMsedPTp7VTk2QgYH1TE6ebXczpEl0fJMtKcc3COCOO1I89piftyBwWHRh//50VwZqSTm+UpyOb7Lp+DoDA+uLXr01Jy0Ca+314b9zMmlFAzQRkTgLAhgZ8Rgd7WF21qO/XxP3O8HIiLciQANIpeDQIZ+rrvLYu1f3B0VEkkJBmohIlyiWjZmZ8Th2zOfIke7NxnSC0dGeFQFaKJVyj+/dq2IMIiJJoSCtStbal7a7DSIitVA2pnPNzpaPnufmFF2LiCSJqjuKiHSJKNkYiaf+/vLB89q1Cq5FRJJEmTQRkS6hbEznGhpa5NixlVlQcMVDhoYWW98okQYrnDN72WWwY4enObPSlRSkiYh0if7+gJmZ0j0dZWPaI0oxl+HhgCNHMhw6VLy64/Cwjp10tmJzZtNpePTRXs2Zla6kIE1EpEsoGxM/UYu5eB7s359m506PkZEe5uY81q5VZU5JDs2ZFcmnIE0k4VRyXULKxsRPNR1Tz3PHcHhYVRwleVTBVCSfgjSRBFPJdcmlbEz8tLJjqhs2EmeaMyuST0GaSIJp+IgUUjYmXlrVMdUNG4k7zZkVyacS/CIJppLrIvHWqtL6lW7YjIwoQpP2GhpaJF3i3pHmzEo3UpAmkmAaPiISb63qmOqGjcTd8HDAnj2ZFZ8HzZmVbqXhjiIJpuEjIvHWqmIuumEjcVdszuy2bXDddQuaNyldSUGaSIKp5LpIvLWqmItu2EgnKJwzOzCwmslJnZvSnRSkiSSYSq6LxF8rirnoho2ISGdRkCaSYCq5LiKgGzYiIp1GQZpIwqnkuojoho2ISGdRkCYiItIFdMNGRKRzqAS/iIiIiIhIjChIExERERERiREFaSIiIiIiIjGiOWkiLRAEMDLiMTraw+ysR3+/JuyLiIiISHEK0kSaLAjgjjtSPPbYcunrmRmPY8d8jhzJsH9/WoGaiIiIiCzRcEeRJhsZ8fICtFAqBYcO+YyMKEITERERkWUK0kSabHS0Z0WAFkql3OMiIiIiIiEFaSJNNjtbPlM2N6dMmoiIiIgsU5Am0mT9/UHZx9euLf+4iIiIiHQXBWkiTTY0tEg6XfyxdNo9LiIiIiISUpAm0mTDwwF79mRWBGrpNOzZk2F4WJk0EREREVmmEvwiTeZ5sH9/mp07PUZGepib81i7VuukiYiIiEhxCtJEWsDzXEZteLjEuEcREWm7IHDLpoyO9jA769HfrxtqItIeCtJEREQkUWoJtoIA7rgjlbeu5cyMx7FjPkeOZNi/P61ATURaRkGaiIiIJEatwdbIiJf3mlAqBYcO+Vx1lcfevZpDLCKtoSBNRGJJw45EpBa1Blujoz0rXpP72tHRHvbu1ZB1EWkNBWkiEjsadiQitao12JqdLX9RmZvTRUdEWkdBmkiXinOmSsOORKRWtQZb/f0BMzOlX7t2ra45ItI6CtJEYq4ZwVTcM1UadiQitao12BoaWuTYsZU3h8Ctazk0tNioJoqIVKTFrEViLAym7rqrl+PHfWZmPI4f97nrrl4OHEgR1Hhjt1KmamSkvak0DTsSkVoNDS2SLnEPp1ywNTwcsGdPZsVr02nYsyfD8LAyaSLSOgrSRGKsWcFUlExVO/X3l+8MadiRiJRSa7DlebB/f5rXvW6B7dszbNwYsH17hptuWmj76AIR6T4a7igSY80a9hf3TJWGHYlIrcJga+dOj5GRHubmPNaujTZM3PNckDc8rOHUItJeCtJEYqzRwdTy/DafyUmf3t6AwUH3J1e7M1XDwwFHjmQ4dCg/UNOwIxGJQsGWiHQ6BWkiMdbIamO5xUL6+gLm5sD3PWZmPKanA3bvzgDNyVRVW/yknjvhIiIiIp1OQZpIjDVy2F/u/LbBQZieDpia8vB9mJz02LTJY/PmoOGZqlorSepOuIh0qzgvkSIiraEgTSTGGjnsL3d+m+fB7t0ZJiZgfNxjYcHjwgW46aaFujsBhZ2L6Wk4ccJj+/b852nNMxGRleK+RIqItIaCNJEYa+Swv8L5bZ7nMmpuPlrAxo1B3cFSsc7Fo4+6pQPOnHFDKnPbrDXPRETyVarq26obW8rmibSXgjSRmGvUsL9Gzm8rpVjnYmHBDamcmvIYH4dt2/Jf0+5KkiIicdKsqr7VUDZPpP0UpIl0iVaUtS/WuejtDZifd4HaxITHtm3lK0nq7q2IdLM4LJESl2yeSDdTkCbSJRoxv61SAFWsczE46DJ4vu+yarD8ewqDQ929FZFu14pRD5XEIZsn0u0UpIl0sGqyTvXOb4sSQBXrXAwOBkxPB0xOevT25gdohcGh7t7GjzKbIq3VilEPlcQhmyfS7RSkiXSoWrJO9cxvixJAlepc7N6d4ZJL3O8fG3ONuuqqDFdemd/Z0N3beFFmU6T1GlnVt1ZxyOaJdDu/3Q0QkdpUCppGRhrbe44SQA0Pu3XW0gVxVDoNa9bAli2wa1fArl0BPT0ed9/dy4EDKYLs973u3sZLq88xEVke9fC61y2wfXuGjRsDtm/PcNNNCy27MTI0tLjiOh5qVTZPpNspkybSoVqddYoSQJUaUrlqVYbHH0/R27uynbnDGHX3Nl6U2RRpj0ZV9a1VHLJ5It1OQZpIh2p11ilqAFWsc3HrrSsDtFBuZz8OczFkmTKbIt2pkWt0ikhtFKSJdKhWZ53qCaCidvZ19zZelNnsDCruIs3Q7myeSLdTkCbSoVqddaongKomC1fp7q06pK3T7symjnVlKu4iIpJMCtJEOtTwcMDhwxnuu89naspnYcGVuN+yJcP11zc+61TP8JdqOvvl7t6qQ9pa7cxs6lhHo2UrRESSSUGaSAcLArKVEcNOWLBUKbEZah3+0qjOfqd0SJOSAWrnvJROOdbFtPL4q7iLiEgyKUgT6VAjIx7W+jzrWfCsZwUsB2rw2GOuPHorOrFROqSN6ux3Qoc0aRmgds1L6YRjXUyrj7+Ku4iIJJOCNJEOFYdObDUd0kZ09juhQ9rJGaBS2pEZ7IRjXUyrj7+Ku4iIJJOCNJEOFYdOrDqkK8UheG6kdmUG43CsawlOKx3/e+5JMToaNCzYbXdxFxERaQ4FaSIdKg6d2FYHJJ3QIY1D8NxI7coMtvtY1xqcljv+hw75nD0Lvp+J/H6VaNkKEZFkUpAmElHcikE0qxNbzXa2OiDphA5pHILnRmpXZrDdx7rW4LTU8R8f95ia8tiwIVPV+1WiRYdFRJJJQZpIBHEsBtGMTmy129k5oNpRAAAgAElEQVTqgKQTOqTtzgA1Wrsyg+0+1rUGp+HxP3nSY3zcW1oa48wZjyCArVtXfibqDXa16LCISPIoSBOJIE7FIHIzXefOefh+QCYDmzZRd3Yv6naGbXj6aY+HHvJZvRoGBwMGB5f3QbMCkrh3SNudAWq0dmYG23msaw1Oh4YCbruNvM/R/LzHf/2Xx+bNGbZure79RESkOylIE4kgLsUgimW6fN8FAFu31p/Ri7Kdw8PppTb09MD69TA15TEz4zE9HbB7d6ZjA5JGaHcGqNGSlhmMqtbgdHTUY9062L17MS+Ttn27RxB4TEzAtm3R369TxW14uIhIp1GQFoExphf4K+A5wCrgfdbaT7W1UdJScSkG0eyMXpTtLGzD7t0ZJibcnJszZzwymYCbbkonsjMWteMZ92xfNZKWGYyq1uB0dLSH3l4YHCSbWXb7Z3wcHn/cY2LCY9u2/H2WtGA3jsPDRUQ6jYK0aN4EnLTWvtkYcynwCKAgrYvEpRhEszN6UbazsA2el98h3b496Lh1wKLo1o5n0jKDUdUanJa60TE4GDA97eam5S48n8RgN07Dw0VEOpWCtGj+Cfhk9t8e0Pm3x6UqcRny1eyMXpTtvP/+8peNuMytafRwq27ueMY9M1juWNeq1uC03I2O3bszZDLuRkaSg924DA8XEelkCtIisNaeAzDGrMcFa+8q9/xNm9aSSvW0omnSIvv2rWNqCkZHobd3+ecLC3DttbBv36qWdLIuu8wFS6Vs2wYDA6trfv99+6i4nUePNrcNjRAEcNtt8Oijy9sxMwNf+ILbvje8gbzjNTCwvuJ7Hj0KGzaUe3wVL395nQ2XqkU51lGObyn79rk/UV1/Pdx+e/7nJ7SwAN/5ne6zlGSeB/39pR/3fRgYaNzvq+f4Svzp+Cabjm9pCtIiMsZcDtwB/Im19u/KPXd6eq41jZKWGBhYz9TUWW68EQYGit9Vn5pqTVt27PB49NHekpmu665bYHKyvmxOpe1sRRtqkZtNGRvzefxxj8suCxgczA/IvvY12LJlYSnrNTCwnsnJsxXf/8SJ3rKZzGeeCZicXKh7O6Q6Bw96PPCAOx8vXsx/7Gtfg127VnHFFZWPb6Ncfjns2JEqOUzy8svTTE62rDltEQQpZmf9ko9v2JBhcrIxmbSon1/pTDq+yabj65QKVBWkRWCMGQQ+B7zDWvuv7W6PtEcchny1oohDpe2MYyGJwvlihw/7zM15jI2xVHEyDNRqHW4Vl3mJkq/S0LpHHoErrmhde+I+h68VVRfjMjxcRKSTKUiL5leATcC7jTHvzv7sNdba821sk3ShRncAa+mwxa0TGgTwyU/63HFHisVFV+785EmPvj43rGpqymN8PL/seS3z5tTxjKdK8zTPnWtRQ3LE4YZOMa0qfhPHGzkiIp1GQVoE1tp3Au9sdztEoHEdwHo6bHHphIbbcPvtPUuB1/y8x6lTHqkUbNkS4PusKHteS9YrSR3PJK1hVSnDuW5dCxsTc60qfhO3GzkiIp1IQZpIl6q3wxaHjn64DYuL+b8wXGB79eqAdetgYWG57HmtWa+kdDyTtpRApQznNde0vk1x1cqqi3G5kSMi0qkUpIl0qXo6bHHp6Ifb0NcXcOHC8i/s7w84fx7OnfNYty6gt3c5QKsn61VtxzMOgWyhpC0lUCnD+aIXUXNhnzgev3o0ewkPERFpHAVpIglRbYeyng5bXDr64TZs3RowM+PmoIW2bAlYWHAB286dAdu3Z1rawY5LIFsoaWtYNSvDGdfjVw8VvxER6RwK0kQSoJYOZT0dtrh09MNtGBx0VRynpry8QG3jxoDv+Z50WzrUcQlkCyUxm9KMoXVxPX71UPEbEZHOUXohExHpGJU6lCMjKzveQ0OLJRelrtRhi0tHP9wGz4PduzM873mLrF+fYfXqgLVrM9xyy8VYZ6zaob+/fGChbIoT1+NXj+HhgD17Mis+951Y/EZEJOmUSRNJgFoyW/VUK4zLsKnCbRgchMHBgHTadUb378+0bUhaXALZQsqmRBPX41ePpBS/ERHpBgrSRBKglg5luQ7b0FBQdn5bXDr6ce50xiWQLZSkpQSaKa7Hr171Dg1NWjEVEZG4UpAmkgC1diiLddiizG+LU0c/rqW+mxHINqKDHOfANk7iciMiTpJYTEVWUiAuEg8K0kQSoJEdyqgFE26+OU067XPvvb2cPevWJrvhhgVuvrl9QwzjpNGBbCM7yHENbOMkTjci4iKJxVQknwJxkfhQkCaSAI3sUEaZ3zY8nObAgRRjYz5XXLH83mNjKe68U1/k0PiMlTrIrdXqjGMnZC/iUtVVmqeTrjOd8JkRqYeCNJEEaGSHstL8ttlZj09+0uf223tYXPTo6wvYujVgcDCeX+RRNOvLvpEZK3WQW69VGcdOyV4ksZiK5OuU60ynfGZE6qEgTSQhGtWhLDe/LQjAWo9nnulb6pBduOAxM+PWKdu9OxOrL/IoOuXLXh3k5OqU7EVSi6nIsk65znTKZ0akHlonTaQLBAEcPOhx660pPvrRXm69NcXBgx5Bke+wcuunHT8OmQwsLuZ/Ufs+TE15jI+7/8flizyKhx+m6jXm2kHrmyVXp6zJVs/aitIZOuU60ymfGZF6KJMmknDVZorKzW9LpTwGBwOmpgLm51cGahMTHtu2BbH5Io/ikUfoiOE9Sa02qHkl8ctelDomQ0MqppJ0nXKdidtnRqQZFKSJJFy1w0LKzW/76ldTnDnjArWZGQ+/IBe/sOCRTgd1f5G3suN+9mz5x+PyZZ/EaoOdMtS02cJhhOPj7s/Cgkdvb8DgoPvTypselY7JzTe3ppiKgvf26JTrTH9/wOnTbvTGxITHxYv586M76UahSCmJCNKMMTcBbwQ2Ap8H/tRaO5/z+CbgTmvt9W1qokjb1DIRvNT8ttHRYClIm54OmJzMD9R8P6j7i7zVHff168s/Hpcv+ySub6Z5Jc7VVy/y+c+nOHVq+fM0P+8xM+MxNRXwylcutKwtUY9JM4upKHhvn065zlx99SKf+1yK6enlz0w4P/rkyYBXvap1nxmRZun4IM0Y81bgI8AngDPArwE/Yox5nbX2qezT+oDvaE8LRdqrkcNCcofC7N6dYdOm5Tv/PT0Bt9yywP799a2T1uqO+zXXwKOPFh/yGKfhPRC/9c3qzXZ0SiW5Zgn33913pzhyBC5e9Fi/vvK8oGaKwzGJW/DebVm9Rl1nmr3fkrjvRXJ1fJAG/DzwI9bavwMwxvwacDvw78aYG6y1T7S1dRILlb4syj3e6RpZka1wKEw4HCscClNvgAat7yS+6EXw0EPxH94TN43IdnTzvJLc/ffNb3ps2uQxOxtw9qxHOg3PfnZmaejWo4/2cM01rQlW43BM4hAohlqV1UtaINjs/fbooz284AWZkkOEW/mZEWmWJARpzwbuD/9jrR03xrwC+CzwBWPMfwfOt6tx0n5R5lgcOFD68be9rX1tb4RGTgRvxVCYKOu0HTzYuM5Mo7cpaZ2tUhqR7ejmku65++/iRQ/Pg3XrYN26gEwGBgfdH2htsBqHYxKHQDHUiqxepwzvrOba1uz9Fp4jYVBWKMk3eKLolu+hpEtCkHYI+B7gd8MfWGvnsvPUvgh8AXhre5omcVDpyyKd9hkbK/34ww/DFVe0rr2N1uiJ4M0echdlnbaTJ3sb2plp5PCeTuhsNUIjsh2dUkmuGXL3X19fwIULyyeG78P4uLfU+WxlsFrqmARBuASHx0c/2ltXp69SB7LRgWI9HdZWZPXiNryzmGqvbc3eb3G4mRBX3fQ9lHRJWCftl4FfM8b8uzHmheEPrbVngFcA47ismnSpSl8W997bW/bxRx5pXttaIcwUve51C2zfnmHjxoDt2zPcdNNCLC/WUdZpi+uaZpU6W+1uXyM1ItsxPOwKzRQe724Yapq7/7ZuddmzXAsL7vFWB6vFjkkQwDe+4XP6tI/vu4Imx4/73HVXLwcOpIqut1hK2IG8665ejh/3i75XI9dji/L7ymlFVq8T1vyq9trW7P2mNftK66bvoaTr+EyatfZfjTF7ge8FzhY8djI73PF/A9/djvZJ+1X6sqhUgv3cuQY2pk3iVnCinCjrtBUTh0ITcZpL02yV7mSvWRNUHJbaKZXkmiF3/w0OwvR0wNTUcqW63t6g5mC1nsxRsWNy8iRcemmG7dvzn1tLpidK1qje7H/u9o+N+Tz+uMdll7n5feH2R217ozI25Y5JnIZ3llLtta3Zma5OWSqgHbrpeyjpOj5IA7DWjgG/WeKxBdxQyN8t9rgkX6Uvi0ol2Neta3CDpKwo67SV0u7OTCd0thql3FDFhQWYmoK77qo8LLWTbiA0Uu7+8zxXLXViwg1znJ/3uPrqRV7zmnTVwWojhjoVHpNbb03R01P8RdV2+qJ2IGsN3gu3//Bhn7k5j7ExFwjv3p3JC9Qqtb0RQ3IrHZO1a+M/dK/aa1uzhzJ38w2eSrrpeyjpEhGkiZRT6cvihhsWGBtLlXz8mmua38ZaJHlicKV12kppd2emFXfd43Jsy93J7u8PmJuL9xybdivcf57nMmqbNwfs2bNY81DkZsxvamSnL+p71Rq8F25/OGzU92Fqyi1+vG1b9LY3ImNT6Zjs2pUmnY733Mxqr22tyHR16w2eSjRfLzkUpEniVfqyuPnmDHfeWfrxF73IZQXipFsnBse90EQr7rrH5diWu5M9OtrD8eONybwkVS2ZgCjBezOGOjWy01fre5Xa9n378p9XuP29vQHz88uB2sSEx7Zty7+jUtsbkbGpdEwuXPDZsyfeQ/eqvbY1I9PVCTev4iDu35MSnYI0SbwoXxadNmyiE6qBNUPc5yG04q57nI5tqTvZ99+//KEJAhgfd53jixc9+voCZmd9gkCL0VaTCYgavDdjqFMjO321vFe5bZ+aghtvXD6XCrd/cNAFheFcP5dZC6pqe70ZmyjH5I1vXIj1d1At17ZGZro65eZVHMT9e1KiU5AmXaHSl0WnDZvo1onBcQ+oW3HXvROObZgtCQJ47DE/ryDGhQseY2MBBw6kuq5jVU8mIGrw3oyhTo3s9NXyXrnbXhj0P/44nDrl893fnSlavn9wMGB6OmBy0p2Dvb1Bxd/XaFGOSdy/g9p97e2km1ft1u5jJY2TqCDNGHMl8FvAtwK9QN6paK3t4NWuRJZ188TgTujMNPuue9yF2ZKpKfICNHBLKFx2WdB1Hat6MwFRg/comapqg8Ww03fVVR733JPi8GF3QK+6KsOVV1Y3dKqWDmS47cWCfrdf+0ilXLGRYtu/e3eGTZs8Tpzw2LnTLUHSyg5rUoaftfPam4SbV60U9+9JiSZRQRrwN8ClwIeBM21ui0jTaGJwciXh2IbZkn/8x54VAdrAQLC0jEIjO1ZR5yy1S7WZgMLtefBBn/Xr88vI5wqD90qZqqGhoOZg8fDhHjIZj127wnZ63H13L08+Wd1ws2o7kOGNi/HxlUE/wOKiV7F8/+bNAdddV3tBlnpo+Fn9knDzSqRaSQvSrgWutdZ+s90NEWmmpNyZlZWScGzDbMkjj/gcPhywsODR2+uCs9x17hrVsapmzlK7VJMJKLY95897PPOMv6KMfCgM3itlqmodNtbO4WbhjYuJiZUBGrghjI0o398scRh+1ulFN5Jw80qkWkkL0h4HtgIK0iTRGnFnttO/tJMqCXfdw3Pr5EmvZIAGjetYlQsgRkdhy5bGBxDVfn6qyQQU256wAEaxMvKFwXu5TFWtw8baOdwsvHFx8eLKfbi4yNJ5VW/5/mZqZ5uSUHQjCTevRKqVtCDtA8DHjDEfBI4AF3MftNb+W1taJV2tGcFQvXdmk/ClnVRxuOtej9xzq68vYG4OfN9jZsZbygJBYztW5QKI3t7GBxC1fH6qyQQU257cAhhhGfkggOPHIZXy+OpXU4yOVj5Pah021srhZsWumWvXBvT0BORONc9kYPv25SBN2ZTiklB0Iwk3r0SqlbQg7f9m//7jIo8FQE8L2yLS1GConjuzSfjSTrI4ZgKiyj23BgdhejpYmkc0OemxaZOXXbC5cR2rVs9XqeXzU00moNT2hAUwzpyBDRsCrPWyi2C7Rd7PnFl5bSkMeEZHfVatYkVWM1Qq0GnVcLNS18x0Gq64IsOxYx6ZzHJ29sorYW5O2ZRyklB0o9NvXonUIlFBmrW2yGh1kfaJazCUhC/tJOvkoai555bnucBiYgLGx93QxwsX4KabFhq6La2er1LL56eaTEC57RkcDHjhC111wpMne8teW4aHVxYJ6esLeOyxnrysZm5bSgU61Q43q/UcLnfNvOQSj2/7tjRzc37eOZb0bEq914OkFN3o5JtXIrVIVJAGYIzpB34Q2IPLnD0G/L21dqqtDZOuFNdgKClf2o0Wh+Co1UNRG73NheeWy/SEmZuAjRuDht+YKBdALCw0PsNSy+enmkxAlIAoyrUFFovMbXPZzfFxl9UMM2qVAp1qgsx6zuFKQ1c3b4Ybb1xY2ofbtsF11zU26I+TRlwPVHRDpDMlKkgzxgwDnwEWgAdxQdrrgfcYY15qrf1GO9sn3SeuwZC+tFeKyzy9VmZfm7HN7Ti3ygUQ115LwzMstW5j1ExAlIDo/vsrX1uKBTxhdvPSS93C4hs3BpGGjVUTZNZzDle6Zp4/7+Xtw4GB1UxOxvt6VelGSLnHG3E9UNENkc6UqCAN+EPgs8DbrLVpAGNMCvgY8CHgFW1sm3ShuAZD+tJeKS5DU1uZfW3GNrfj3CoXQOzbt4qpIuMo6skgNnsbowREUa4tpQKeMLu5cWOGt799oap2RQky6zmH43rNrFWlGyE335zmwIHSj8/OUvf1QEU3RDpT0oK0FwM/FgZoANbatDHmd4CH2tcs6VZxDYY67Uu7FcMQ4zI0tZXZ12Zsc6vOrVLnxBvfmJ/9K3Z+FHacgwCs9bjrrh4uvRRe8pLFsudXK7axUkAUdUhkrQFPLZ+58DX33ut+b19fwNatKxfgLncOx/WaWatKN0LSaZ+xsdKPX7gA/f2l3z/K9UBFN0Q6U9KCtOPAcwFb8PPnATOtb450u7gGQ530pd2qYYhxGZraykxC7jaPj3tLxT3CynkbNlS/zYXn1uysx6lT4Pvud/zt36bqPs/qPSdyO85BAI895i9VoDx3Dlavpux7xeHzE+3aUlvAk7t/e3pgfBwmJnwOHEixY0fAm998kb1787cz9zXnz3tcuOD+zMywYgHucudwXK+Ztap0I+Tee3u54ori25RKwfHjLkAuJer1QEU3RDpP0oK0jwJ/YYz5NeCB7M++DXgv8GftapR0rzh05sq1rRO+tFs1DDEuw6xamUkIt/nQoeUgBWB+3lvaF0FQPBtVTnhuDQ2lueOOFCdPus7+mTMULRNfTDPn6YyO9nDypAtKT51yi26vX+/2RxhMDg4GZd+r3YsTj4x4zM7ChQuuI795c8Bzn5theHj52lJrwBPu356e/AAW4PHHPT7+8V5uuCH/+OUvveDOK993wXnuAtwLC7BqVYZbb00VzdBFvWaG++DoUThxoje2FVAr3fw5e7b86zdvDkiniw957MTMoohEl7Qg7QNAP/BbwKXZn50A3g98sF2Nku7WKcFQXLVqGGJchlm1MpMwNLTIgw/md8JzpdMBBw+6tbhqGWpaazBVKVNWzzydIICvfKWHp5/28X04dcplD0+ehPPnYcuWgIUFL9J7tUPhvunvd8FlOg3r1pF3XGq9SRR+5p55hhXnhgu6fA4dgiuvXD437r23h/PnXXC7devywtthoDYx4QLJc+dgbCxVNgNa6ZqZuw82bHCBUKOz62EQODLSw9iYz6lTrv27dmWqCgYr3fxZv77865/73Azr1pGYzKKIRJeoIM1aG+CyZu81xmwFzltrK9ynEukucSgzX41qhyHWun1xGWbVyuzr8HBAT8/Kn2cyLli57DL4xCd62bKFmoYV1hpgVwru6pmnMzLihl/OzZEdiume29sLQeCxZg2sX7+8flip94pynjXjs1Zt4FvLTaLwMzcxUTx4X1jwSKXgE5/oY8uWgFTKnRdueKPH9HSAMW7h7XAI7Zo18LznpfMCtEptb9Q+qFYYBB465PPEE8s3MY4ehaef9qsKBivd/LnhhoWi+yR8PMyMxnE0hog0V8cHacaYtwJ/a629kP134eNL/7bW/lUr2yYSN7XM5Wl3UFfNMMR65ypdeeUi1nqMjfl4Hlx5ZYbXvja9Yv5Ns7Uq++p5sGuXm7uUOx8tLPYwPu46pdu25S96HLUzXOs8v0rBXT3zdEZGerhwwWNqyqOnx/3+xUWPxUWXjTpzhrxtKvZeUc4zaM5cylZklsPP3MWLxRvY2+vWWTt2zGPbNrd/+voCLlxwwczk5PIabOE6bNu3Z7hwoXiwErXtQQAHD3q8//19HD3q43keW7fCjh3L7WjEPgiDwJMn8zOJudsWNRisdPPn5psz3Hln+ZtDtV4P2n3tFpH6dHyQBrwbuBO4kP13KQGgIE26WrV3oOOwdlg1wxAbNbzOBS6QTns8+WS8hrs12rp1AYOD3tJi07kmJjxWrSr+uiid4Vrn+VUK7uqZpzM25rOw4LFuncum9fXB3FyA53ksLLj25C7wXOy9opxnQFOyPa0ocBN+5sLAK1cm4/bP+LjH6tXL7d+6NWBmhqXhjeG8PoDjx10gfOiQTyaTH7xFbXsQwO23p/jUp1JY694H4Kmn4Phxn927XXGS8XF44on6gpIwEC6WSczdtijBYJTMeDMy53G4dotIfTo+SLPWXlns3yKyUrV34eOwdlg1wxCbNbyuVWuktUO5IHh+3mPHjtLbXSkgqHWeX6Xgrp55OqdOefT0uOGcs7MB5855ZDLutX19y4FjufeKcp4FAUxNuY7+xYv55ejryfa0osBN+JkbH/eXAi9wAdrAgAuwjh718qoSDg7CqVPu5+fPe0xOeoDbv5dcApddliGTgbNnl4dE7t6dn6EtnwH1+NKX3NywVAouXnQ/9323wPWTT8LMjAvAN2xw+6jWoCQMhEtlEsM5i1ED4kqZsGZkzrv5miaSFB0fpBUyxrwCOGitnTDG/BDwvcCDwPustdFX7RRJoGrvwsdh7bBq7jQ3a3hd3IpHVKvcsKdyQfCOHcUzHqFKAUGt8/wqBXe583QOHuzhiSd8Tp50hR1mZ922lspCbN4ccPSo69yHRTcGB2F2NuDsWY9NmzJs316+OESl82x21uPgQX+pOAmwohx9rRmvVhS4CT9zV13l8YlPuOUIVq1iKQPmzo0MW7eufF24v1w1T58zZ2DTJnecc7Ntk5MeGze6wiPj4x7z83Dtta5QTbH9Pjraw9SUnz1uLsMXPsf3PU6fdsGaC4SXz6tagpIwEC6WSQQ33BPivbB20q9pIt0gUUGaMeaXcEMe9xljdgF/AXwceAOwEXhn+1on0n7V3oWPy9phUe80N2t4Xau2sxmiDHsqFQQHAdx9d2/NAUGtQ7miBHeeB0NDAU88AatWsZTVOX7cXzE3LNeuXRmeftpfqjwYWrMGrrgiw403LvKmN9V3nk1Pw+nTxYfKheXon/Ws2jr4rSpw43lubt4HPnCR0dHK58b4uNu2cCmD5z0vw/i4h+d5S9s8OOiC1HCe18GDPUufyS1bAnzf4667eotmvmZnvaUM1rp1MD8f5H0uL1xwxV/CTF+uaoOSMBDODSpD4XDPuJe/T/I1TaRbJCpIA/4n8L3W2vuNMX8GfMVa+zZjzIuBT6MgLdY0ybn5qr0LH5e1w6Jq1vC6uG1nNaIOeyoWBAcBPPlkfQFBLUO5ogZ3Ubbt5S/Pfyw8R3IrD4aLd2/eHETqeFc6zzwPLrss4PHHiwdqJ054NXfwW732YqnjV3huhPO3wsqgQQBHj/rZYiIBnucK0OzenWFiws0NnJ11wyDDYaCeVzrz1d8f0NsbMD/vNjAcrjo/73PxYkBPj6tGWjiEMlRNUBIGwt/8pr8UVHqeW9OspweOHvWW9kEt6wjm7sNmfecl+Zom0i2SFqRtBUay/34d8KHsv08Ca9rSIolEk5xbo9q78LUGPe0KuJs1vC7Od8wrqWfYUzsXY48S3EXZtsIgLfccyc24VBN4VjrPxsc9enry1woLZTKweTN1ZbxaUf2z0me48Nz4xjd6WL8+XCPNyxZocfsEPI4f93nsMRdEhZVDL7mk+P4udl6Ga/rNzPQs7c/+fti0yWXVMpkMu3aV3p5qgpLcbQuH0z78sE9fHwwMLA/7vPvuXp58Mvr3U+4+PXfO4/HH3XzI7dvd4438zkvyNU2kWyQtSPsm8BZjzASwHbjTGNMH/DzwSFtbJmVpknNrVNvpriXoaXTAXU3A18zhdZ2q3mFPcV6MvdK2jY15/OVfwokTvfT3B1x9teuYnjvnhscdP+6xeXOGXbuiB57h+VjuPf72b1OcOeOxe3emaMbuhS9cjMVNp1KfraGhgAMHKn+GC8+N48d9xse9peGMuXPHUqn80vzz826IahC4gK2wwMqGDfk7aHg44PrrM0xPu+IhYaC2uAiXXhpwzTUZzp8vvlNrCUpyt+3gQY9Vq1YO+41SNTY/KPNZXAzYvt1t89iYW6TwzJnlIirhe+YuFF7Lja4kX9NEukXSgrSfAz4JXAr8kbV2zBjzEeD1uMyaxJQmObdONZ3uaoKesENyzz0pHnywh9Wrg0jDmMqpJeBr5vC6TpTkYU/ltu3QIZ/5+YCtW10wd/q0x+c+l8Lz4AUvyCwVDUmnXQc4aoCWez6Weo/cLEZhufm4ZDHKfba+8IUMs7M+vb35ryn3GQ63eXx8OYAK546dO+f2U1i+fvPmgB07MgwMuGUKctciCwuseN5i3lBCz4Nbbkmzc+ci99yT4vBht5bh1VfD9ddfYHg44M47U00JSmr5fircv+PjLvhq1yAAACAASURBVLsILii7eDFY2ubc4DV8z9yFwqH6G11JvqaJdItEBWnW2i8aY7YCG6y109kf/x/gp1XZMd40yTm+ogQ9uR2Sb37TlcFeWMivZhcGaoUdmnKZsnrWPat2uGWcM0b1SPKwp1LbNj7uMTHhYUwm52cwPe0tPZ7bIY5686Ca+X1xz2KU25YHHkixbVuGbdvyHwuXFfjYx/oYGsoUrRL68MP5k/A2bXJB6po1LphduzbgppsWCAL467/uzQvQci0uujbmHpOwmMnevctf5wMDfUxOuuc0Kyip5fupcP/mBq9TU97Skg+wcl25woXCQ9Xe6ErqNU2kWyQqSMu6BHiBMaYXWLpyGmOw1v5b+5ol5ST5bn83yO2QhBXYIL+aXdjhy+3QVMqUzc4WX7AYot/BLnzPOM5vrHcOX60l9uMSMNSq1LadOOEtZXFDuQsT53aIIXq2PmpGpROyGOW2ZXHRBbm5QUIQLGe91q6FmZmgaJVQaz0efdRfGt6Zm02HgO3bM+zd64Y5plIrd0S4Ftv27dWPoGhWUFLL91Ph/i28Lp4/vxykFT5euFB4Lo0sEekeiQrSsuui/QnFi4QEQE9rWyRRJflufyerFDyEj3/sY31MTvr09gacP59f7cz3yevw5XZoKmUmLlxwQ8pKiXIHu/A94za/sd6gsp4S+3EJGGpVKhhyw+vyz8PchYlzO8ShKNn6ajIqcc9ilNuW3t4gu4+WPydhiX3fX14nDFZ+rl7zmjSZTC+p1MrPWO613PPccgjuvfPn7IUBdFxGUNTy/VS4f3MrUwKsXesC0vDGQe4+DefrlRKX/SLxoMrYyZWoIA14H/DnwHustWfb3RiJLsl3+ztVpc7/zTenl4oLTEx4XLgA8/OuoML8vMfAwPIxCzt8hR2aSpmJ48fdF04pUe5gF77nPfekGB0Nlr7Mrr8eLr+89jLa9ao3qKynxH4SFAuGbr01xfHj+WPochcmzu0Qh6Jk65OU8S+3LYODAceP5/8st8R+uXXIol7LgwBOny4doEHj92cQwMGDbt7skSPu/Ni5M8OrX51m797GFhYq3L+Dg+7/YVC2aVNAX58LfMPHw/cstlB4rk46z6S5OnHkiESXtCBtE/BhBWidpxOGB3WbSp3/dNpnbMw9ntsBXr/e3Qk+e9b9G1ynuFiHplJmYvNm97p67mDnOnTI5+xZ8H13B39mxuP222HHjlTbvszqLZqjojsrFct8hAsTw8ogI2q2PkkZ/3LbsnlzwOWXu8Wiw8cvXvSWhiIW7j9Yzu5EuZaHHcsTJ7ylwGV+3v07nMPa6P0ZBHD77SnuvDPF9PRysPTMMz6HDvVw881pbrml+DWglu+nwv07OLi8JEP4/zAYTqU8nve85Tl+9S4iL92j00aOSHWSFqR9Cvgu4Pfb3RCpXtyHB3WbSp3/e+/tXRqSE3aAw47PwICbc7J+fcCFC66a3mtfm17RoamUmXjuczOsW0ddd7BDYWnwDRvyF7vt7W3vl1m9RXNUdGel3MxHaHAQTp4Msv9uzvponZTxr7Qtr399mttv97n33l7OnnXLFmzdmsGY4tuYm92pdC0PO5bbt7tKh+EwSt93lQ4vuQRuuKGx+3NkxOO++/y8AA3c7zx1yj22c2fpa0C130/F9u/u3RkuucQtiP285wX09we8+tXFK+XWu4i8dAfdpEu2pAVpE8BvGmO+DzgMXMx90Fr7g21plUgHqtT5P5uTrx4cdFUccyu1rVkDz39+hj178odc5I6fHxvzefxxj8suyy0u4KTTMDzsOjD13MEOjY+7J2/dGm3x3FIaPf6/3iF0jRyCl5S5DbmZj6eeWsUzzwSsXRvwqlctLK09VUu2PkkZ/3LbEq6TNjbmL92IGR93hUM8b7laa6ja7E5ux3L37gwTEyuHPTY6sz062sPJk8UrSboCR37Fa0Aj1mx81asqnytJOs+kuXSTLtmSFqRtAP6+3Y0QSYJKnf9wKCO4TkVhZ2tgwJXazu1UFFtnas0aD2v9vFL9uXeMG3EHG9wQzC1b8iv+5YryZdaM8f/1DqEr9vpwgeATJzxmZ31uvTVVsYOXu20nTy4vwHz33Sle/OI0P/VT6aId3LgKz5t9+2ByMn8FlmIVQQ8ejN75TkrGv9S2HDzorTgPensDVq0KmJjw2LRpuVprLdmd3I6l57mbPC676d5j48bGzxGdnfXyiscUWljwyl4DWrVmYyNeW4+k3KgpJWnbl6R5srJSooI0a+0Pt7sNIklRKXi44YYFxsZSS4/ndrbSaRegFQ4dKjZ+fvfuDJs2eZw44TE35yq+1fOlWeou9LXXBvi+V/I9o3yZNWP8f71D6ApfH5ZKn5hwZeb7+wOOH/crBpLhto2N+QVrV3l8/vNuRZN3vnOhJR2ZVnakSnW+H3zQp6cHdu0KWLeu8u9PUucvzHLnngfz825O2qpVbn7axo2ZvMxbNdvejo5lf3+QN3e2UG9vUPb3dsPcn6QXoUji9iVpnqyslKggDcAYcxPw08Au4AbgR4GnrbV/3taGiXSYSsHDzTdnuPPO6oKLUuPnw0n027dneNOb6r9zXOwu9MGDHnfdVd9k/GaM/693aFPh68fG3NIFu3dnVqwFltuZLAwqRkd9zp2j6JCwVAq+9jV/xeLCzdDqjlSxzvehQ/5S1b0gWGTbtvK/P2mdv5WBuuP7sLAAmzdnePvbXXaylm2vtWNZLBCOWp11aGiR//gPP2/ubCiTgS1bMmWvAd0w96dRgWhcb1gkMdBO0jxZWSlRQZox5k3AHwIfAr4Dty7aM8AfGGP6rbUfbGf7RArF9csMogUP1QYX7Rw/X+rLbGEh+pdZ2P7xca9o6fBa21/v0CbPg6GhgCBY5P77e1hcdAuIA0UXbR4eTq/oWE9MeBw96v6/ZcvKfZHJeC3piLaiI5X7uTtwoIepKY81a+DSSwN6e11Z9J7sqprhGn/lfn+xNgcBTE3BP/5jD4884pfNEDfiOlDLe5R6TbEALeT7cPLk8oO1HK9KHcuhoWDF8NOrr17k8OEerM0PBqNWZx0eDrjhhgynT/ucOrW8fZmMO+7XX59/DSjcNw8+6HPJJSsrg4aSMPenEYFobtDe0+OGXU9M+Bw4kGLHjoA3v/li2eUOmimJgbbmLyZbooI04BeBH7PW/pMx5ucBrLUfMcaMA+8HagrSjDE+bpHsvcAF4EettU80qM3SpZp9970RHb9KwUO1wUU7x8+X+jJzd+Kj7ev+/oD77+9ZMQwsLB1+443t+YLPPZdy16zLLWkempvzinas+/oCgsANOT13LmDduvzf0dtbexBajWZ3pHL31diYz1NP+WQyHqdPB8zMBKTTHqlUwJYt7pzJXdS51O8vbHM45DQ8Tw4fDujvp+hnuxHXgVreo9xrTp2CxUU4f97dmFhcdBUJ+/sD1qxxJfpLbXuuUvsrStGSYsNPp6d9nv/82qqzeh7cckuanTsXl9ZJC4Li66QV2zfnz7ty/YWfp1AS5v404iZaeG3p6cn/DAA8/rjHxz/eyw03tCe7nNQiG0maJyv5khak7QQeLPLzR4BtdbzvfmC1tfYlxpj/hivxf3Md7yfS1IxBXIdftXv8fLEvs4EBmJyM9vpVqzJMTHgr2u/7Lru2atXKzlsr5J5LufNuwpLmmzZ5SxmAtWuDoh3rrVsDxsYCPM9jdtZj3brlcy+TcY+3oiPa0I7UzGkYPwqDO5Z+FO6rkyfdkgypFFy8CL7vAtQg8Lh4EWZnXaAaLnwdFmN54omVNz0K2zw+Tl7n1AV6xT/bjbgO1PIe5V4TBC5oPXvWp6fHtT0M2gYHM+zcuXye13q8KhUtKWzX1JTPzIzLEG8r+DaPGrx7HuzdG7B370LZ5xXbN+EyI4WfJ0jO3J9G3EQLry3PPMOKjGxYRfPQIdoytFBFNjpHnEcZtVLSgrRR4Cbgj7P/Dz9xbwVG6njf64DPAFhr7zfGfEsd7yUCNDdjENex9508fj4IYGysh/l5F/C66pQuQxIErhM3P+8DrQ/Ucs+lwjXrwgDSFXRxncn771954g0OwrOeleHwYZ9Mzia4+ToBmzfX0RFdXMQ7P4c3M4N3+jT+zGm806fxZk7jnz6Nd3qaU0fOcO7pGV79X6dZO3+aDcE069OnWXthmtTixfz3e1d1v773tjtYuHEfsLyvxsddB3LdOjh50nXgfd9jfj5g9WqP2VnXadu6NcjLjG3Y4Dp6uTc9Cjt/ExP5ndMw0IOVn+1GXAdqeY9yr1m92p3LW7YEzM66c8D33fmeSnmsWbN8glTb8a3U+SrVroUFt0/D4aeFGpkFKdaG3GVGws8TdMa1K6pG3EQLg/bCz0BoYcFr29DCdt8klGiadZO5EwO/pAVpPwvcZYzZB/QB7zHG7AJeCLyujve9BJjJ+f+iMSZlrS16hdm0aS2pVE8dv07iZmBgfeUnVcnzXAn6UnzfZXlqcfQobNhQ7vFVvPzltb13vd72Nvj6192fc+dcJ/maa+BFL2p82e2oKh3fIIDbboNHH4VNm9wQq3Pn3J9UCq69FrZvh56e3pqPWT1yz6WrrnIZj/FxluZVeR709QZ8694F9l1zgVMPPMPp09Osnj/NmvlpVp+fZs38NDcvTjPjTdN3apotZ0+zPj3Nxsw0/QvTrL1rGj7cvG0o81Go28YfffPSwn7hvvI86Otzx3JhwR1L33fVCz3PHfNnPxuuuirFiRMwM+Oeu2NH/uf26FHYvdvNP+vtZel39PW5fy8urnxN7me7EdeBWt6j3Gt835XBv+QSuPTS5Z8vLrpgpa8vtfR+118Pt9++vO25Fhbc4+Fzcz9H4fNnZuALX3D77w1vKN2udetcsFjs8f7+VWzbBgMDq0vvhCqUasO118KJE+5Uevaz43HtaqR9+9xxGB3NP54LC27b9+1bVXE7L7vMBTy5n4Fc69ZBf3+qqu+3Rn3/NmL7pPEKj+9DD8GxY8X7MEePwtNPu+NVjajXnrhJVJBmrf2yMcYAPwGkgY3Al4EfsNYeq+OtzwC5Z5FfKkADmJ6eq+NXSdwMDKxncvJs5SdWKQhSzM6WXnhqw4YMk5O13Wk8caK37DCkZ54JVqwd1UqXX+7+5Jqaak9bohzfgwc9HnigF9/3WVjwWLXKdebBdRzn5xeZm4ONGyMcsyCA8+fxz55x2aTTp/Fnpt2/z8y4zFKYYZo5vZx5msk+Nje74i1/OsqGfnb5n98b5flJcu4cp774VRZfcPXS5y4I/KV1szZuhN5ej3PnYPVqssd3kec8J2BuDo4e9VhY8BkYCNiwIcNswSGYnMywYwc5yyB4XLzoMpLFXpP72W7EdaCW9yj3mnPnfLZtc0UyihXIGR9fvn5cfrkr3FEqO3755eml4cTh5ygcYgrLRXi+/GV44IHFpYxmoY0bPSYmfFatyjA7u5y16u9fxcz/Z++8w6Oq0j/+OXdm0kMS0iAgEJAmhK5gRVlEigUsa8FV96fuurs2LFjWdVddK6y69i6ryFoRdEEEC4hiA4UEhNBrII30PnPP74+TO5lekgkEvN/nyZOZO+eee+6de+683/O+7/etaOCUU5ooLo6MN8vftTHGGhsr0XUHPXs6OOYYedieXe2BceMgPd23CEUo59mzp2D9eptzDrhC16FHD/X9hfr7Funf37aen4nIwtf3+9VXVhobNeczwhNffaXTo0d4tpGvZ4+B77+HtDTvkkGHEv4WIo4qkgaQn59/ALg3wt1+A5wDvNuck5YX4f5N/ArRnqEXR3rs/SEJS7DbFeGpPYhl215nCJ5bKF6F+t/nlwpurqggqqaM6NpyOjnKsEqXH4nvXfq9JULj64DQExKRycnITp2QtiiQOqK+HlFZiSgtRWvy86vaBjRq0UTpDcHbjRtPzYyZ2E8YTdox6QiPX+LOZ5xEcVGlc95lZqo5YoRkGaIYffs6SEuDvn3tNDRo1NYKYmMFmZm6X2W/ujrB5Zc3OYUwamo0tmyRdO2qwiVd1UAtFkl2th0pDVXO0J8D/ubF4MHhP0sCHddikc6ah77O2fX5EY66nGcYoVHqwPgONmzQSE2VlJdrDBrkHjacmSkpKZEkJ7uPJxx11lDh69q4lmXIyNApKNDYtUvjyy9lc1jokRE+BcGfr20RoTBC2gsL3csdGAsWrmHXhwOmyEbHR3sIvBypyp4hkTQhxBzgSo/NjUARsBx4REq5oTUDEEJkADVSSu/l4TDR7EX7JzAAiPb8PD8/v18ru/4QOLN///6rAAH4LJothIgC0ouKKlt5mMDIyOjktc1qtZKcnMJJJ53CzJl3069f/3Y5tonIoz3zs46Y2HspobERUVXV4k2qqGD1sirKtlcwoKmM2PpyYhvKiKkrp8lSTkZUWQuhqqwIfowQ0DnI59kROUpkIC0WZHIyeqckRZiSktGTktle1pl9dSk0xKZQF5NEXXQK1bZkMgcmMfa8REhOQiYkYtwUUkJenothHePg+MydDLavw7Y+F+uGPKzr87Ds3eM8tlZdBdWRWdW2D8rBPjiHFVXD2Z44lP3pOcTWl3HGD7MZk/uas50/glY/9Xxqb7oNx6DBbtu17ducBK3m1juI/9ejzs/iZj1Mzq138eWXOnl5VqqqBHV1kJgIsbHSmXs3cKDO1Kk6QiiiEBdnpaDAv6cqLs7duJUSFixQ3iVXhTuV3webN1tZsEDlVoT6HAiUpzFggM6AAbpXHmqgZ0mg444erfs1hHw9P0I1fF2Nr8JC4SUs0dQkyMqSlJdDQYEKITYIbn298sKceGILeQ5XnTVUuF6b0lLBli0aBQWKYGdlSTIy1PexdatSUzVqEnYEcaZgaG9RKYO09+4teOMNpWgaHd1C+I+mHD4T7YP2WGQ+UpU9w/WkzQAMh3A8Sk3xauBCIcQkKeXycDoTQkwC5qFyxtpM0oD/Ag7gVaAuAv0BkJ+frwPXBWojhOgJLAUejtRxfaFv337cfPNtzvcOh4Pdu3fxyisvsHz5F3z22Vf06tWRTEoT/tCe9U3CJoC6DrW1zhA8pzepvAytssJd5ME1FK+iQrWti9h0A+AwpcuFhToRS41NESE9OZlux3VS5KmZOMlkRZ5kcgoyKan5dTJ6YieIi/OuqNtGpEnYnyfY4nEvDe1ZhvWX9ViXLlaka30etvVKR+k3zX+RgqNbd+yDc7APHtL8l4N+TA+/52rZsJ5eM/7FWWtvD9r33slXsXTYbRTE9mlZ+T9O4jlNUscMd76unXk38VbgUUXU4mc9zNvdZlBT04msLB1Nk5SVqTDHjAzJuHEOhgzxnn+eix6BPGPQMrcdDo28PBtxcUo8JCNDeaiEcBfwCeU5EEgMaNMmjcmTmzj2WEfIz5JgMvgLF/oPYWytge1qfBnCLa6w2dRYjztOR0rJgQMau3cLoqMl8fHKI/nOO1Fu9bYyMkJXZw0VQsB559nZudNGQYHg4EGahYIUkczP10hO1p3qoK5CIodbnCkYDoWolKGiOXt2o/tCkFm/y0QIaI9F5iM1uihckrZASrnTdYMQ4mmU7P27QojeUsrqMPobjcobixT6A8fn5+f/EsE+Q0U20FpPXchIT8/goosu8do+cuQoLr30Qp555t/Mnv2k/w4cDmw/fId9cA4y0dszZ+LQwmsFuqkJUVON2NWSh9SigOc7FM9tu4ss3x8CHfiP7XpahxSGR8kgQTKpmSh5bHd+npyM3ikZmZgIUVGkZ3Tym/Owbp1g0SIbJSVK2dHVqNR16NdPJzVVMmVKE4mH0iBrasKyZTPW9blY1+c1e71y+U1ZWURJF4CMi1fEK6eFeNn7DYDY2DA7klh/+J74xx8l6svPnZv9eTGXj5rBl8Nv5JSL0ti+3aIMy0ag0f/Kv2V9SyR61UOPqQn24INOkgZwyR392XTDfmc4nyECbLfDkCEOnwaq66LHli2BPWOuRK2+XmP48JZjgDvBKygQXHttY/MzILAnyl+4jlE0+5VXosjJUUqTo0fbQzKEA3nA2mMBydX4MsoSGDDKPBjjqq1VqqKZmbLZG6n5rLd17bUt1yGSIdJ5eYK6OsHw4bI577TlOywpEZSWepdYMNCRw6cOZdiXGVpoojVojyijIya6yANtzkmTUu4RQtwKvImSun+qzaNqPZYAJwKHg6QdVvzmNxNITOzEjz9+7/1hMzGLXjifqP99hKWokLLPV2LPGXroB3okQEpoaEBUVqJVlMPWRqJ27oOycvZvrKR0WwWWinIS7GVk2MpIls0kqtnjpEUoHOxIgrTZkEnJ1MUkUamlUBOdgj0hmfjuSXTuk+TtTXISqiRkXDyuT84XX7QFXPFKTpb84Q+HRvTEMGhc5bddpe337xeccoojMqE7UqIVHsCat04Rr/V5WNbnYt2xve19+4C9X383j5d9UA7SqOAcCUhJ1OdLiXt8FrbVPwQeizWapcffyXcjr6MuJkVta/5BBkfIK/+dx53s/Lz+mubgB4sFe3Zv53WMbygje89KdhxzqjFMCguVZPiOHVGMHevwMu7D9YwZ8Ayx8czBKi5WiwChhJn5CtdxLQ0QFwcVFZELuWsPA9vV+LLZJPX1anBGmYfMzJa2paUaPXrIoPW2fvpJiZfMn29l5UpF5gyhk9WrNU47TWfatPCvgyuZcR2rcfzSUo3U1JbPPdFRw6eO1LAvE78etEeU0ZFa/idSwiHvA68AE2kmaUKIE4H7gTHNbb4F7pFS/tD8+Rxa8tx2CCFWSClPb/7sOOBB4AyUlP7PwP1SSqc+mRBiOVCP8uLdDNQmJiZO79q169vdu3f/U3FxcbempqY0IURjQkLCZkBWVlYOl1IKlz66Aw8Bk1DqjRuB2VLKt1zazGk+h98Bs4HjgSrgHeAOKWWdEOIq4PXmXV7PyOiEkZc2Z86rvP76K+zcuZ2YmBjGjDmZu+76GwMGDGzVhQ4ETdNwOJp/UB0ODn66mMcffZBl+fns0x3EACOBB4DBhQcgZyjffLOSadOmMG/eeyxc+CGLFn1MdHQUEyZM4t57HyDV+BUCdF3nhReeZe7cOezevYvOnVM555zzuPPOe0hs9soZ/T311PM8//zTbN++jfPPv4h///u58E8oWG2lijK0igCheP6kgVqBJI//HRWNUfE0xCVDSjLRXZpJUFJLCJ6enMyuihS+3ZRGY1wStdEp1MUkUx+dhN0aw779GlJCt27efdvtMGWKbwUkX3kOxj4qvyd0I6kjhSUYBo0QMGCATlERbmp3ffrI4OdWXY114wYn8bJuUN6vSN6fBvT0DJpyhlCQMYRfbMPYlTKUxmOyGTxMtH+IkcNB9EcfEvf4Y1jzNwUeZ1oatTNup276lRAXh5QQnSdIybUQXSvdfpDfessa0sq/7btVzu2Vz73s1q7ig49JHTHI+f7P75zJ7bfWIRFuOWNNTVBQIH2SHH+eMV9jMeAZ3udJNmw26SSb2dnCWSfMlyfI17xwLZrtWYvtUIbcherFcjW+NM3K6tUWYmLcyS6o50bnzmrcRr2tmhoVlqrrAk2TNDWp81y7Fg4eFHz0kZWDB1uub329oKLCQlmZRu/eDoYNC+86uJIZT5EZ41xAEcxg4iodCR3p+WrChD9EepGoPdNL2hMRIWlSynohxDZgKIAQ4kxgEbAW+BtKxOP3wFdCiDOllCuBF1H1x6ahct02NO+bg5LNP4AiUE3ApcBiIcRlUsp3XA59Ciov7nYgu0uXLrfU1dVpBQUFwzRNq0tKSsrVdd1aVVV1nOeYhRBZKE02gSKWZcB5wFwhRJaUcpZL8wxUvtm7wFwUqbsBRRJnAl81j/Vu4KVnn33pDwDvv/8OM2fO4Le/vZRrrvkjpaUlvPTSc0ybNpnvv19Lp06RM/nXrfuZiopyJgwbTsJdt+H4eCFnFxVSAVwPdAM2A88DZwG5W7eSOP4s5/4zZ95CfHw8M2fexb59+3j11RdZu/Ynli1dQZQQiOoqZtx6E+9+8j8uHXsGfxlzEpt37uDV115m9YL5fH7mBOKqqonftROAu276M1fZbFzX2EiP/84l/b9zI3auHR1SiGYFvCT05BQ3kuT0IiX5CcUzhB1CfGK0hhwtm2ulwOE7R6ikRAMk3bqFZoQaiGSeQ0cKS3A1aITAGSIndDtp5dsYVL2WhAfXYl2fi2V9HpaiwoiPQdpsKtRwULPHa3AOjoHHqXvFs62v+6EQdi0i8mIGDQ3EvP0WcU/MwlKwL2DT2qw+rBx7B2v6X0JsJ6tPT5W/H+RQV/6Tz53YMrQLL3Zro3f3qPcAXLDsep4d8qwbcTKIjr/7NlwvhOu97JmD5RreZ7HAG2/YSEtrcSp7esR8zQuDwPgiCocq5C6YEMV559nJy/MmcDNnNrFwofS7sl1dDfv3qzIJJSWC2lrhcu8KSkuVF7FbN1iyxJ2gGdA0ReCWLLEybFh43nfXuZ+ZKSkrkxQXtxwjOVnHbtd8KmB25PCpjvR8NWHiUOJIDL+NpAR/GdBHCKEBLwA/AGOllA4AIcQzKNL2FDBcSvmtECIXRdJcc92eBoqBEYbiY3Pe2xfAv4UQH0opjSXoeOByKeX3AP3796/dt2/feinlsQ6Ho39ZWVlx8/7DUR43VzwExACDpZT7m9s9C7wFPCCE+I+Usqi5bQpwo5Ty6eb3LwshfgGmAzOllNuFEMtQJO3biy665A8AH3zwLgMGDOSZZ150HnTw4Bzuu+9vbNy4kdGjxxAumpqaKC0tdb6vqqrkl48XcN+T/8IK3LHiS2JXfMk7wFZU/OdZLvv3RimgrL33Ls6/9y5nQqDYu4cfgE733g3AKODajb/wv2PS+SNKwvO/qC/2jy75JFOBs4qLeGPeXG4CjPqQp0rJM+3gKQgHMjraPU+p2avkRY58heDFxjkrAaenJ/LEE3UB1d2ysnQuv/zQT/zWkKNAhmZpqaCqChobBVFRg/tNKQAAIABJREFU3ivc/kJhIpnncNjCEqRElJa25Hmtz+XaNXkk7ArsFWot7Nm9cQxuIV72QTnoXbPaFG7o736wWGD5co38fBspKbQqX0dUVxHz+qvEPTEraEhv09Dh1M64nYazJvPhwqiWMVVDWTVhheOFsvIftWyJ833FW+/6bFf5wqt0uu5q5/sxua/yr873o2kZgDfRCeYZ8zcWV7jey655S57hfYWFsGePRpcu7rLzrvPY17xobBRu0uaeCBa65uoBq64WlJerbSkpkJAQ2j0S7Bm0c6eNujrhl8D5W9nOzRXs2aNRUqLCQqUUCCGdBcgTEhRpKi2Fbds0vzo8mqY+DxeeZGbAAJ2UFJVP2NAAw4frxMU5qK1177ujh08dqWFfJkz8GhFJkmZDxYAMR3GB54EU4f50/xiYIYToJqX0WnoVQqQCY1FELVYI4ZqZ/iHwOCrc8JvmbXXAj0YDu92+Udf1kcCzUkqn3pOU8mchxFJUOCbNRHIq8CXQJIRIcznOByjP3ZkowmbA85d/HUFqwmZldWP58i+YNethLr74Mnr06Mn48WcxfvxZgXYLiB9//J6BA73VG3sC76HCGQEuBsYBrifmSpk81V3+gnJrGrgK5SL8CKUx8QHK5TiZFnlPgBFAF+B/wE0u209zee2sreQRducp7OCqhqd3SlLCDjExHaIMfEeN428NOfJnaG7cqHHwoEAIQUODpKFBUFGhcrEGDNARwn8oTCSvT8TDEurrseZvdBIvQ+FQq2mZBelhdukPekqKyvFqlpe3Dx6C49i+EBUVoSMEhq/7wTVvqbRUMmSIHlLekigtJfbFZ4l/cnbQ4zaechq1N99G06lj3eZr7rq2e1hDWflPOrPlUdx45kTvhkDD+ReBC0kDeG95N046sckv0QnkGfM3Fle43ssFBYLiYuGVywbKIxbtVTRGwXUee86LjAxJdLRvggaBQ9c8PWCudcDS0tScD4VMB3oGlZaq8x42TOWWFRUJ5wJQYaEK8Rw2zPfKdk6O5O23VTinw3lZBbW1koQESUKCIruhIFI5LJmZktRU6YxSAI449cIjNezLhIlfIyJJ0lJRHrA+ze9nNf/5Qg/AV3yMse8NzX/+9jVIWqmU0vmYrqmpWQyMSEpKGtC/f/8/AM4nf0xMjKW+vt54m4ZKL5ra/OfvOK7wFPltAAIuz9166x2sXv0Ds2Y9zKxZD9O//wDOOmsy06dfQXZ270C7+sVxxw3mvvsedL6Pjo6m17w3Gfzuf92U/WTz4B4BVgHbUJ41I+BDR9UZaqirh08Xk33xdKpOGO0WlnfM9X9km91O8Vffs/F3FyM/X+Z1UQzE9zmW4m9/ovyblTBtCrFPPEPx9CtadY4dEbGxDr7//kPWr3+D0tJfqK0tJiYmlW7dTmLUqBvIyjrhsIyrNeTIl6Fp5MvEx0uki12nEvQFhYVKxc5fKEyk8xyChiXoOtqe3S3Ey0dNr0iiqs8Qdncewr70oZT3GkLG+IEcd3JyhzNofN0PrnlLrt4cT6Kk7d1D3LP/JvbVl4Iep2HiFGpn3IZ9+MiA7SLhYQ228n/85red28o++tRHDy7jPmcq0R8vcNs2oW4B23PODSmvqDVeCONevvbaRhYtsvm8HvX1gp49/c8RYx57zgtDfdQXgoWuuXrAPPPljDnfpUtwMh3oGVRYqAjWpk3u/RsLQG++CUOHNvqcR3l5goQEyMqS7N0raWhQ5x8VBZomqKqS9Oyp6tr17q1z4IBvb5quQ3Z2iGzOBaGSmSMtfAqOzLAvEyZ+jYgISRNCdEJ5zxYBlubNfwO+87OLv/ghY99ngQV+2rgWzXb7BbLZbFcDREVFjQD6un4WExOT7ELSjOO8j8qN8wU3STVXMhgqsrK68eWXq/j6669YsmQRX3zxGU899TgvvPAM7767gJNOOiXcLklOTmbs2DPcN445idIHHsb2/bfYvl6JbdXXbMtdyyko79kElGdtOIq8TQUcvbKpemkOdd+shE8XI6ddQP049+pUDosVTWhgteJwOEhISOT1133nlsXEuMtxWywWn+2ORFRUVPDOOxfx44/L6N79VIYP/wuxsZ2prNzNhg1vMW/e6aSkzCaI6H27oDXkyDA0t+bWM6BgORv7TKawUPVhGIq+VAxPPdW/imGk8hxERTnWDevdPF5GTa9IoyzxGAoyhlLcbQS7UgaxL30IJfE9mXy2bxn2jOa/jgxf94ORtwTu4hIZpZsY9/1jjHxyXtB+1w2/nJ8n3Er38ceGtdruT5HQUFPcsEE9J1pbz2vIEElS5rXOtvYxJwYcT8W/nyfDg6T9Y+1F3D6uFs81t2CesXC9EIEIXs+e/r1h4H+Roy2ha64E2jNfTtPU99OliwxKpgM9g5qaBHV1OnV13gRK01TYa26ubwKYl2fBZlNevagoSU2NpKZGhXdqmjrugAE6iYkwcaKd776zcOCAQNcFFov6PDYWUlMlkyb5HnswwROTzJgwYeJwIlKetAtR0XALgZ3N26qllJ+5NhJCHI8qi+Ov8q2xr93HvsehapHV+hvE3r17hwGFxcXFc4qKim5z/UwI8T5wQfPb4uZ+bD6O0wMVxdfm4tq//KL45Gmnnc5pp50OwPfff8f550/h5ZdfaBVJ8weZlEzjhEk0TpgEwP1/uoay+e/x88WXMbDZ6BVSYphjorLSbf+dO3e4vW9qamLPnt2ceupYAI45pifLl3/BsGHDSUpyL2338ccL6NzZX7WjIx/XXXcdq1d/xlVXvURy8hVuxtDIkTP5/PPzeemlu7j88jPo06ev/47aAa0hR5Yd27j851exzX0TS201j9+1l9jYzmRk6M4QLNdaTkrFMHDIUyBjcVDfeoZbN2J7172ml1ZWFqGr0AI9PgHH4JZQw6ZBOczfNJgN2+Od48rNFVRUaKSnKyMvPj6ampoGdW2AvDw6ZH2jUODrfmhsVF9a/8ofuWHPQ4xY+r+g/dRe/Ufm97mVNUU9WvpywLowBUg8DXjX0EtNg8RESUGBFjz00o+xHPPKC87XB7/4xnM3N0gJHy5N4loEmoc640VvXsi7V8x3UxcM5hkL13APRPCkhMWLfXvZAi1yhEsaXUnJ0qUWdF0VYTbuEVcor6s6/0DhyoGeQRZLC9nxheho/wTQIPgZGZKKCkhIUHloBmJilJdu6FBYvdpCUhJUVkJdnfKeVVcLEhN1zj3XHrIibaTKF5gwYcJEJNBmkiaE6IqS2t+HyuGyA/uBG4UQrxjFrZu9be+ilB6NqDnjl0cDkFLuF0KsBq4SQjwmpSxo3tcGvIZSj+zuevz+/fuPA77Kz8+39+vXb/DWrVs3SCmv7NGjx1exsbHVAJWVlV2AKcY+Ukq7EGIxMFUIMVRKuc6ly8dRZG4wEI5Um9u5AFxzzRVER8fw2WdfOT1LOTlDsNmi2t3TVFZTTVx8Aqmz/015VBSi7CDy65U899eZcGA/jqpKXOPa3njjdX73u6uw2VTozNy5/6GysoKzzz4XgIkTJ/Hmm6/zxBOz+cc//unc79NPP+Hqq6/gz3++kZyjsO7a999/x9tvv81vf3spjz56CXl5TR7GkIXLL/8XY8YM57//fYt77vnHIR1fyCvpDgdRXywj5rWXif58mVsf1/b+DEvqBW7CKJ6KZVlZHgaflGgH9ruIbOTxf+1Y06s4fSAlxwzBOnIwaWfm4Aixpte6dYIN221epEXTlBhBSoqgt0fk8ZFcJ8h5P/wiGFDwJeO/fZg+e1cG3a9mxm3U/eEvyOaSG+vWCdb4CM0LV63T04B3Db10FepolWS8lCTePdP51jE4J2BzI7zvqcu/4ea5JwFQkJ5DVnEeJxQv5tPynWjZPYmLk0RH61RXa7z0kq3NRZFd4Y/gSQk7doTnEfPlBQpUxNqTlOg6VFUJKioEdXUQH+/e3tXrGihcOdAz6IQT7KxcacXug88a37+/+WYQfF81CkERQFVHD/LzNQYP1klPd19cSkvT6dPH4fN6RFKR1hORLqxtwoSJXyfCJWlThRCGbkQsMAC4ovn1RCllHYAQ4kZUHbGfhBCvoKTqr0XpW0yXUhqPbCPP63YhxCdSyo+AG1FKjmuEEM8BpSghj9HAXVLKFmlDhc9Q2hVFwGfdunVj79694sCBAwuTkpKklJLy8nIhhEBKtwfunShtja+aVR13AWc3/70opdxAeDDO5fK33nqDiy++jL/85SZmzLieCy44h3POmQpI3nvvbRoa6vn9768Js/vwMG7cmSxZspjLLruIc8+dSmVlJe++O4+d5cp7UXjrHW4kbfv2rZx77kQuvPC3bN26hTlzXuXkk09l2rQLARg//iwmTpzCc889xe7duzjttNPZs2c3r776Et27H8Of/3xju57P4cIHH6iKDzfddGuAFfQ+fPDBx4wYMcq5ZeTIwYwdewa6rjN//nukpHTmiy++ITU1le++W8WsWY+wZo3SvBkxYiS3334XJ554sluva9b8yOzZj/Djjz9gsWiMHHk899xzH8cd11LvafXq73nrrYf48ccf0XXo3n00Z599L2efPYIhQyRaWSkjRw/nTLsdWVPNPFTy6Dkopc4NQPbyz8m5Yiq7d2vEOip57pXjOC6xO493GUlW0TqyitZh1ZvglsheW0dGZrPXa0iLyEavbLBaA5cWKNOZmhbaKrevnKioKCWKomnKY+hJ0jwN0o5ucEkJuWsldf9dxKhPH+MP+9cGbF8X1YnPx9zJd8OupUZL9Fn7LlJqnZ4GvKtkvKdQR7gqoHGzHna+Lv3u56DtjXPa12WEc1tWcZ7z9d9eHUBRYWXzfWc9pN4VIeC88+zY7RorVtioqoLERBg7tonzztN9esTC9QJ5khLDQ2V8H8YxARwORYJycwX19YJRoyTr1vmutRfIm5eTI9mzx8Lmzd7lB4zv3x8BdCX4njUKNU0ybVoTU6fqfPxxS9kCX3L469dbGDbM+56KpCKtK0wPnQkTJiKFcEnaEy6vG1Hes4+AR6WUm40PpJTvCyEmAH9F5abpwHrgXCmla6zN2yiv1e+B04GPmqX5TwbuA25FqUbmA1dJKf/jOaD8/HzN87UQ4oympqaHSkpKhqNI3nPAQFRYpjHGbUKI0Sgv4LVAAioP7RaaC3KHAynlpuZSAVfdc8+dnHTSKUyffgVWq5VXXnmRhx66H4fDwbBhw/nvfz/g5JNPDfcQYeHKK/+Piopy5s79D3/960zS0zMYNep45sx5iylTzmTlT6v5o8uv5t/+dh+rV//AAw/8nU6dkrjuuuuZOfNutOY2QgheffUNnnnmSd59978sXfoJqalpnH32udx55z1kZHT0bJ3WYdWqr+natSt9+/YL2M4IC3XF/Pnv069fPx544BGKigpJTU1lyZLFXHXVZfTqlc0ttygvwNy5c7jggnN47bW5TJw4GYDvvlvFhReeS2ZmF66//iZiY2N56aXnmTZtMsuWfUWPHir8dPr0ixg8OIe77/4rDQ2NvP32XF54YQITes0m8bXviZn/PqKhnrdRE+BJVAHC81Ek7V3gH2/OYfybcxiPkjt9GLi9roQxRYGNfV+QUVHNcvLBa3oFQrir3P6IVHW1tzXkapy6CmmAd2hZhzW4mpqI/uBd4v/1KJZdOxkfoKmjW3e+Of0OPkq5AhHTIiEYyEsTKbVOTwN+wwYLiYnSpzEdTr9ISfzsR9TLqCj03n2C7OB+Tp+c8g8mff0PAN6b8DwXLf0TACWPzGGT/EO7eFcCQUpYsMDKli0aPXq09L9li5WFC73vs9bMj08+sfLLL5rTy5SRIUlLUx6qxESa872UcFBjo6S01IIQikxpmhIo8XfPey5gGfNx3jxrsxiRREpBbKzy0Bnff6BQTk+Cb9QotNsNdUVFXqsCV4Pwe0+1l2Jve3roTJgw8euC8PAuHdHo379/79ra2pPi4uIcQAWwPj8/fzeAEOJjYKiU0p9AYcRQXFx1xFzUb75ZybRpU3jqqee55JLph3s4HQ69e3dj4MABLFr0udv22tpa6urcUystFo3k5BRAedL27dvL2rUb6dKlKwB2u51Ro3IQQvDVV9+RmKiKHlRUlHPaaapm3urVedhsNiZOPIN9+/axYsW3dO6sQtC2bdvCyScfz5/+dAN/+9t9jB49jMzMLixc+IkzfLZ27x7GnziChIYGDN9CL2BP81+Wy3iHoGJ0XV3Gf0S5wAtRcckG2qOmVyDMnWsNuS5dIK9baSlkZLgP0zUvKilJcuKJNmpqGnwWADfU8/zlCvnyQrULamuJnTuHuMcfQzt4MGDTA6nH8dmJd/JT7wuYfI7O0KHK8A5HKjyc6x8OItVvwh23EPv6KwCU5uajN88xX0hPT6S4uMrt2ELqPPavOAA29JnCoG2LnO3vmFGFbvGtmNhe9RDDvc9aMz/mzbP6rNXWubNOYaES2xg4UOfAAcHevRoxMd6eqVDuec/5aMy3oiKV/zZggO7sy3O++eor2H370UeJbNrUENK1cEVHv8dNKBjz18TRCfP7VUhPT/T5FIykBP9hQ3Ne2hPA4AMHDmhRUVF0795dArJ///5rqqurHwDOQNV2NmEiZOi67hkmC8Cjjz7I888/7bbtmGN6sGbNeuf77OzeToIGkJu7loKCfVxzzf0sXNjZxeOTwv/93x948MF/sHbtT/Tq1ZufflrDn/50g5OgAfTp05dly1bQrVt38vLWsWvXTq666hrKy8udbYTDwTkNDTyBcnMbpOxY3AkawGXAXSgXd7/Tx1F+z328f9F5TJ44mcp/P9eayxUQ4YQNhrPKHWjl2uGAggLo1q1luxAqfKqgALp2laSk2EhK0n2Opb1CooJBlJcR+8qLxD0xC9HUFLDtzqwxfHbi3eT3OtONjSoRFMHQofawxS4ipdbZLv06HE6C5uiaFZCg+Tu2FBp7M4bRvWgtg7Yt4u9Xb+O+V5U37qa5J/PElT/47KO98hXDvc9aMz9iYqQbSTPKa3TurMIYDfIwd66VLM+HRYCxeMJzPhrzrXNnKCgQ1NRI+vb1Pff9PSOmT/dP5IYNg/XrCfueaq97vKPW1DRhwsSRhyOepPXv338CSvr/v8BfdF0/p7a2duaWLVs+iI2N3RkVFXVGTU3NQlR45n2HdbAeWL78C4qLi4K2S0/P4PTTxx2CEZnwRJcuXThw4IDX9iuv/D3jXEoW/P3vf6Wqyl0xMy3NvUTyrl27ACgsHEhyslppNULnGhoGArB37x6szVZDbx8hXIY4y8qVKwC47757uO++e3yOfUeXrnQ7sB/wLR1/GXA3qgj63VYrXxQXcrDsIOeff5HP/tqCcMMGwyktEMjAzcpSeSx2u3Rr43DA6aer42ZkxFBc7NvoPFQGl1Z4gNjnnibOg/j7wo4BE1hxyh2UDRzDlq0WL8GHSIyvLdLu7d1voktB6rLlq1p97FcvWMjfn+8JwBlNy6g/73xiFs4nqziXzOINFKYP8uoj3Hp/oSLc+6w188NqhQMHBFKqnK6EBNVPYaEgNVU6SUlb73lf81GIlnDFrCzJ9Ol2cnMFb71ldZKxwYMdbNtmIT8/vNDiESNgzZrw76n2uscjXTPShAkTv14c8SQNuBd4PD8//w4AIcQqYJeU8o+1tbWTa2tr66Kjo7d27979wJ49e/ICd3Vo8eSTs1m16uug7U466RSTpB0mHH/8aN55Zx7bt29zI029ex9L797HOt8nJyd7kTRPBc9mjoansKfVClu3qtc2WxQOhzKWRIBQQqPNnXfew8iRx/tskzl0GCVWK/qYEZCQQN2ppyv5+182IGpr6AGcTHNe2jdfsyA5hYyMTE455TSvvtoqnhFunkY4q9zBjMq+fXXS0hwsX+5fkMHf+cXFtY/Bpe3YTtzTTxA71yvN1gv1U8+n5sbbeH/zMPdruB82bxbExgpnCFmkxteWemDt2m9DAzEL5wPQNHgIMiX00h/ex25Zujj9jesoLqxw9n3bf0Zy+231bvu3xbsSbP6Ea9iHMz+qqwUbN6rwXotFUlurikGXliq5eptN5Xjl5ChxkLw8FZoYFaXy1oyyHK5j8Xc+OTmSzZs1tm0THDyoVCNjY6FzZyMPTc1XXws2q1drlJVpHHec+70cLJertfdUe93j7eWhM2HCxK8PRwNJG4pLFeHmotPPNf8B0L9//8HAMu9dDy8WLFh8uIfAySefSlFRZfCGv1JceOHFvPPOPF588VkeffTxNvVVXZ0NQGlpPscee47bZxUVSnenW7dudO2qYo08a9cB3H//vSQnJzuVIOPj472Km//88xrKysqIiYlFxsRAdDR6l65Uz2rW/dF1tF07sW5YzwVvvcGMz5eyMTmFZZ8u5oJLpnuRy0iIZ4QbzhXOKncwA3fzZo2DB4VfQYZA5xcXp9PUBDYfKUrhGFyW9XnEPTmbmI8+DNq27ne/p/aGm9F7ZTu3rVvnm+R27SrJz9dISRFeQhxtNQjbq5BvW/pNuvQC5+vyjz9t87ErerxN0hWXAKDt2kn52/NJvuR8AMZ/+xCfnXg34N+7EsriRSjzJ1zDPpz5UV5OM0GDtDSai0KrnDS7HY45Rue88+wsWKDGGB0NdXWChgZBRYWSvx8wQC1o2O0weLDD5/ns2qXx9tuwc6fGrl2C2lqBpqk+KislFRWSsjJJ374OSku9z7WkRKOiQlBYCF26uH8WKMyyLQtI7XGPt5eHzoQJE78+HA0kLQ4InEkPJUB6kDYmTHhh7NgzuPjii5kz51UGDDjOZ+mE999/h7VrfyY1NdVHDy3o3HkE8fFdWbv2RYYN+wPR0Uo4pKGhkrVrXyQxsQtDhw5H0zQGDcrhww/fZ8aM25wCIzt37uDll5/nqquuZtiwEWRmduHll19k6NCr2Lo1iZoagcVSwVNPXYWUDfz88y++B6Jp6Nm9aczuzaSTTmZmTj/+Ono0BxfMd5ZccEUk1MrCDaEKZ5U7kIFbUABSSqeAQWGhkoFvbBTk5Qnsdp0TTsDv+dXWasTHq1pOIRtcUmL7/lviHn+MqOVfBDxvgNq/3ETtddcjMzP9tvFHco0aUvv3Cy+Bh6POIKyuJurrrwBoPO0M78JerUBjs5oqQPLUyRxcuxEpBEJKzvrmftb95kZsyXF+86dCWbwIZf6Ea9iHMz9cU2qFcC8KretK7TQvr2WMmZmKTBUXC2feWmGhInhGXTJf51NaCvn5Fmw2nZoaDYtFOI9ZWyuIiZEUFQmSkvCZ86ak9dX87NIlNOVPKeHtt+GHH2ytXkAKhNYQwPby0JkwYeLXh6OBpAmUxH8gyOZ2JkyEjZdffpmamnruuOMW5s17k0mTppCZ2YWCgn0sWvQxv/yynvT0DO6776GA/XTqZGXcuH/xv/9dzty5J5GT83sA8vJep7p6P1dd9Zaz5MEDDzzMxRdPY8KE05k+/Uo0TePVV1+kU6ckrr9+BjabjQcffIxrr72KK644lSFDfo/VGkNu7muUlOzm6qtfx2IJPr07d07l9NPHsWDBfHr27MWoUSd4tYmEeEZr8jRCXeUOZOBaLErd0VXRUdOguhpqajSefFKjSxfo2dO3YWi1KgW8IUPs/g0uKYn67FPiHn8M25rVAccqo6OpnXE7dVf/AZmUHLCtK/yRXEOUoa5OqcYdzQZhypSWHNCKee9FrN+6y35H7Lw3sRTsA7ud0s27SOurRIBve7Y/pRu9PdoQ+uJFqPMnXMM+1PmRkoJTat+zVplSd/Se4wMG6KSktBSGLiwUpKcrFcjPP48iOhovz21RkVrIKC/XSEiA2tqWMEkhVNhlz56KwKmfZHfYbJL6etEsbuL9ua9nRG6u8CkaEqmC1K2NIGgvL7QJEyZ+XTgaSBrApf379w+k4dnpkI3ExFGHxMREXnvtTZYu/YR58+Yyd+5/KCoqJDExkUGDhvDII//ikkumExcXF7Af5fE5nwsv/B/ffvswq1Y9iMVio2vX4xk//gUuu2w0hnFyyimnMX/+Ih577EFmz36E2NgYxow5mXvvvZ/MZo9Lz57TmDYtjR9/fJRvv30YITTS0o5j2rQPSEycTG5uaPLwF1zwW5Yt+9SnFw0iI57RnnkagVauv/3WSmWl4MABnEZqSYkKxRICrFbB3r1w8KBGdrb0mdtVW6s8HTk5Suxg/VpJ1YsfYFn5KKmFmwKOrTYhna/G3smWU69k4MjoVhOnQCRXCDj22KNP1tvVi6EXH+TWjcozvGn4b3n/9fiIFRWvfvRxYue9CUD8ffdQ88Aj1F53PXEvPINWWor1u2+xjznRa79QyVeo86e9DPuEBOlVDNqok5aZqe4tX2M05Pc3btSoqhJYLILKSkXG6uqEMwzSQGOjcP5PT1chldXVoOtKqKRzZ9V+61bf1yMzU93jNpv3M8sIs1Q5cxa2bNEoLVUevsRESE7GK3eureqrR2K9s7bmDpswYaJj4WggabuBm0NsZ8JEqyAlZGZOZvz4czjxxOA/fq5S/AYMjw+M45JLWoRgWkKa3I2J0aPH8MEHH/sdU16ehd69x9G7t29RGcNA8TUWVxjeuwsvvNjn5/HxkvJy4RYq6CoqEEicwjAacnMt7NsnKCsTdO3aUnspUmF5/gzcvDxJZaWgqKjFg2YQNHXuKhSyrk5QXIzP3K5EWx3Rc/6D9tAsxpcXBCwcbc/uTe0tM3lHu5RftkS3GHglsH0RrQ7B+rWJEXh6Mf72/CjnZ3dk/Yd+FSJyYW3R0eiJndCqKol78TlqHniEmvsfIu6FZwBIOfcsigsrvGoChkq+Drfan3HvGOqKrl4q497Jy7P4HGNhoWiuJ9hCxqKiJA0NguJi4TZfjO1RUep9fLxsjkhV7xMT1f/sbB27XXjdy5mZkpISSXKyd27lgAG6U/lxy5YWj/i+fYK4OOjUyeKWO2egLeqrh6v8RmsRidxhEyZMdCwc8SQtPz+/1+Eeg4m5vkmNAAAgAElEQVSjG6358fO3onneeZHLVYiEh0tKyRtvvM7IkaPo16+/zzaDBztYutRKWVlLuJQhKlBaKjnrLN91vDyvW48ekpgYSUGBEiUYM8bR7qu8hoFqrPLX1LQQNClVfo7VCiUlygbfvFlQta+SiTtf4opdDxHrqAnY/57MESw9/k563nAWQ4erjtetE/ziozBxW1bgf21iBK5ejKSqvXSqUWUw3u1yPYUlVpIKdTIzZcS8GuWLltH5tNEA2L5aTtNpp1O25AtSJqoFkIQ7bqH6sSfc9gmVfB1ugh3aveN7jIWF6vwyMlqubUaGpKJC1VkrLGwhaRkZkrIy6NVLp7RU8wqtzMyU2O0webKdHTssPsejno+KNLo+H6WExYttlJYKt7BNqxVqapSwj5E75yo6Ei4Bdn1uL11qcebseXrpQD1LDM9eR/BaBfP8ZWerZ19HGa8JEyaC44gnaSZMtDd++sm/sIQvAzEUUheJkKa2rNDb7Xb++Mf/Y9++Pfz00xpee21uwGO15kfcV1FbYzXfME7bO1zIMFDz8gQgaK5cgJTq+sTHS9JkMdfV/otrv3s0aH9bepzO52PuYNsxp7tdlMoNOkOHq++0PVbghVAGrN2usWKF/1ICRwtycy2UlCjP7eKl/Zzbn+z1OJpwJweR8Go4Bgx0vk6+8FyKiyqxjxiFo1t3LPv2EjvnVWruvAfpUlw+VPJ1uAl2KEIW/sZYX6/y1lw1bQyxmpIS4VYcOzUVhg51EB8PQrTkwOk6pKdLUlOV1P/QoZKhQwOPx/O7nDvXitWqvndX8peQAGVlijAlJEg30ZFwCbDnc1vXBVVV3gqXBvLzNUpL20ewpDUI9tx5440o0tJkhxmvCRMmgsMkaSZMBMHatd6J6QZ8GYiHKpchnBV6b8+elQ0btlFcvIvbbruTs88+1+9x1q+3MGiQEg1wzWkxclbWr7cwbJi3gRxJstLaXAvDQLXbdT78MIqYGEFa3S6ur5nNJfuegy2Bj5vX9zyWjZrJlqSRJCT4b+fqtfT0cHpet5oaVWcq0Lg9zzcuTlJSAnV1mt9SAkeLkSUlfPuthT17NHo2bMbSrAs1K/E+Sko10tKkGzmA8MPafN1Pk//8GP2fmwmAKDuITOnMwVVrSO+pGEragGyKXcqVhEq+OoLaX7B8N39jHDlSomnCbYyGWE1RkfKoJyVJDh5UIj3HHAMVFdC3r4MePQSlpYLUVJ2+fb3PN5z8O2NOeX7v8fGSpiblTWv5XLaKAHs+t40cOVeFS8NL56oa64rDma8WKLKisFCwe7e3OFJHzq8zYcKESdJMmAiKqkCSNHgbiIcqlyFUI9GfZ+/cc39k4EBl4AeC8eNvkDJP+DOQIxGOCW3PtbBuyef/vpzNnz97J+ixVvb+Hd+OvY3iVPfQz4O7hVO23BdcvZauHk6jiLCx+l9fL9i8WWPBAqvfcfs63/x8QX6+hYwMSUqK7pYbWFioQpmGDTs6jKzcXEF5ubpm76wd5Nz+VKe/ImsF1dXSmd9kIJywNn/30wu2G3gCRdI6/d/vqPhwEcTGUv33f5Jw3z0ARC1Z7JTtD4d8HQlqf77GuG6dYJGP0F0hlOds8uRGtm+3UFqqYbGoZ6XhPRs2LHKLB8acMhQgXZGeDklJOomJ6j7IytJbRYA9n9uepQgML52raqwvHK58tUCRFYWFqgSCL3TE/DoTJkwomCTNhIkgSEwM/LmngRgpchIMoRqJbfXstTasMlKCCWGNX0qsP60m7snZRH/6SdC+l/b/M3kT7+Sjn7vQqRP0768IUGGuu8cwNdW32AF4ey0ND6dn/gwo47VrVxnwunuer5SwZYvqa/t2QXS0RmqqEmUwcgPffBOGDm08KrxpeXkWunaV8PNa57Z/Zj2NLmkWfxFkZrYIWYQb1ub3frIJtnY7hWP3fU3UNysx3J11f7nRSdKSrriE4gPlGF/qoSBfgbzIxvm0V55RsIUgCC8UvLVoET9p8W4Z0HWlbpqWBlOmhKZo6wu+ntuupQisVpwE0FCN9YdIPePDQaDIivp63Dzwnjgc4zVhwkRwmCTNhIkgGDYMn7V4wLeBGA45aatkcihGYls9e60VPoiUYELA8VskFR+sIOnvjxC16uugfdXccju11/yZ3II0J7Ht0iWGUZZ6hBButdRAeb4qKgR9+6p8G0+D1FdYlWHYvvee1WddKkOEwN91dz1fo77b/v0a9fWChgaceXV1dZK0NMUXdu/WyM09OkKWampUvtms9S01+xb1uI64EiXr3qkTbVIHDXQ/vTH1Xe5/VlVajnn1ReqvuQ6Ag6vW0PmkkQB0uuoyKt94uzWnFjYCeZG3bdORUuVGtVeeUbCFoLfesh7yqIH09Bbvlq6rwtipqW3P7/P33DYiCLKyWspcGKqx/tDeip2+EIhQ9+yp+/X8weEZrwkTJoLDJGkmTATBiBGwZk3oif+hkpNQwvighcQZtYE6d5b07aszZEhoZK6tnr3WCh9ESjDBdfxC6gzespDx3z5MVnFuwP3qopP4fMydfD3oGnoPjXczWoektRDb9PQYPvvMzuuv27w8XwYcDknv3g6OPdYRUmjb1Kl21q7V2LbNuy6V0dafOlx1dUtnhYUqH8ZqhcZGEEIgmz1KtbVQUyNJSIDo6KMnZCk+XpK6fqXz/cM5bxAbC9nZEptNJyEBkpNlq/O6As2HutjOzteJd890kjTHsX1pGnk8tjU/Er1kMdrePejdjwn/5MJEIC/yihUaQiiS4vlZJL1YgRaCXK+lr5zVpKT2iRrYskVQWqo8yiNGWOnVq6nN3sNwFpUOt2KnLwQi1IY6ZkcarwkTJoLDJGkmTARBuIn/oZKTUCSTt2+3sGmTe22gXbtgzx4t5BXztoYdtlb4ICKCCY2NnLD+LUYueZTUip0Bm9and2fhkLv4OedyHNZot882biSg0TpkiMRi8d5ueL+yspSAyuWXh6bMKQT07asTH6/hWpfKgPKACJ/qcKWlKt9FCJz13eLjlVqe6lv1p2mCmhr1/WVmylaFLHXE4rc5OQ7G3zLB+f7gWb9lJMqItNvbFtIGwefD+9ct4sIXpgBgyd+Eo/8AAMo//pT0LEXiUkcMchMRaS8E8vqVlirF0qws7wLskfRiBbpHjGvpK/fSuMbBRHKMNuvWCZYssbJtm+qkd2+dSZPsDB2q7kV/ZDE9PZri4raT0XAWlQ63Yqc/+LtGUsKOHR1vvCZMmAgMk6SZMBECwsk9CZWcBAtD/OQTK1IKr9wmQ20sJSW0FfNIrPq2Nvcm7P1qaoidO4e4J2ahHTwIwAQ/TfenDWLpCXfR7cazGTpCY+5cKwUFPtxgBDdaFalS9aJcvQGu3q9wSVCg615QoPr0RdAdDvV5t24467slJEBMjPKeRUW1tLfblbx5ZqZsVU0oX57c1auVCETfvpKEhENP2kYVLHK+fuWCj5yvI2VQBpsPyRecBi+o98lnT6B0y271xmql8ukX6HSD8q7FvPUG9dOvCHistpLgQF6/xkaBCNBJJPKMgnn7Bw92sHq15tcDbbfLoGG4UsL8+VY++sjKwYMt/Rw4oLFpk4XzzrMzbVr7q5eGu6iUne0gP1+wZYvyaGZn60ye3EIqOxI6gsKoCRMmwodJ0kyYaAeEQk6ChSHu2KFx7LHSqzYQ4KY2FmzF/HCu+gYzUkV5GbEvv0Dc448hHIHJ4vasE/nixLvI73UmCNEy/ub6ZG0N60xIkGRmGvW3vK9JuCQo0HW3WoVPpUxQ4WuFhQK7XRIVJWloUONOT9epqBBERakaTpomycjQGTBAb1XIki9PruERAZDSQZcuh76WUvKVlzhfV508nuTa1oc2+kIo86H22uuIe/kFtIpyaGhQ8aRAw8WXQTNJS5xxPfW/vVRVUvaBtqqSQmCvX1RU4PsxLk62udhycG+/I6gHOtjzKTdXsHKl5kbQQD3jDh4UrFih0aePInq+niennaak/13PqS0lO4I9tz2/V7W4A3a7YMeOjhtyfCQojJowYcIdJkkzYeIwIVjYlWy2wTxrAxkwagIFIx+RXkXVdu/C9s1KGi69PGA7X0aq3Lcf65tPkvHzU0GP0/CbM6mdMRP78ScgEezIE1TlWkiuhbg4b5nttoZ1RjrPJNB1D6YO16+fzokn2hHCyk8/CaKjoU8fSVmZ4VWVTlW71pJtT09uYaG7x9ZYBDiUtZSi35nnfF32v2VcfkLkDcpQ5kPNfQ8R97JypyXccQvVTz7r3L903SZSh6oQyOTJ4ylftsLncSJRLzHQPZmaKp2hr56w26G42F0+vzWiIsG8/evXW9rsgc7Ls1BSovn0xGmaCuvMy7MwZIjdJ+mdPx969mwpaREJchwIh6oOpgkTJkyYJM2EicOEYKSgTx8d8F0bCMBmU4ZAKB6eiKyiSknMm3OI//vdiNpa9B49aTr5VL/Nc3MFJd/t4JI1sxidNydo9/XTLqD2pttwHDfI6zNB8PG3lWS1h8fR33UPpg4XHy8ZMkSSk9PEggXSOaYuXZRndf9+JSAzfHjoAjKe8FV029VQNhYB4NDVUjJCCQHsJ4xut+MEnQ9WK46sblgK9hE77003kqZ3zaLhnKlEf7wA27qfsWzaiGPAQK8uIlEvMdA9OXasbwl8u10tYtTUaF5OvnCJRCje6bZ6oGtqhN+FKFD3YW2t8EuObDb3c2pvEnWo6mCaMGHChEnSTJg4TAhGCrKzHSxebCMzU/LTT1dTUvImoDFq1F4slnQyMqQX+TjjjJPZsCGPiy++jKeffoGpUyezZ89u1qxZD+D1/oYbruOdd+ZRFEQEQdu3l8QZ1xO1/AvntsQ/X0vZ8lXIlBZFPGveOuKemE30/xYyHhgfoM9VQ65h0zkzKIjp3RKONFDS2kXutpKsSHscA4VchUoofY0pK0vniivaHvrn6Xn0NJSNRQAD7V1LKfaFZ5yvDy7/tl2PFQrKP1xE6uhhAER9+gmNZ00C1Pe68oY3GP9xJwA6nzaaz5ZVeX0fkaiXGOyeBMjL8/4sN9dCY6PvPsMhEqF4p13vZSmVIqlRbN1ikWRn6wHFQ+Ljpd+FKFD3YVycDIkcgYOXX46iuFhzKky6hhVHgkQdqjqYJkyYMGGSNBMmDhNCMcAMRa6YGGMvnYMHFzFw4JVetYF27drJhg15bse4+ebbqK2tbf0gpST6nXkk/HUmWlUVEpwkyrK/gKRLzkd2SiJqxZdBu1p+/C18NepGquK7sHGjRlUVnNCgQ0Prw5E8iVBcnKRfPzv19VqrSFak8jaChVydd549ZELZXrkknkTR1VDWdcjIcCdp7VpLSUoS7r3b+daXN/VQQ8/u7Xyd9LuLKS6qdPted52/gKvnTwWg/u9PsODK29zu3UgVcw/2/fv67LvvIkMkQllMMBZHfvlFY+vWFhERXYf0dNiyxcqCBf7ndU6OEh+pqLB4hTzqOnTuLImO1lm61Nack+ldzgLg228t7N6tUVSk6gkaCpNlZZIBA1oUMNtKoiL1vf5a0BEVZE2YOFJgkjQTJg4jghlgBon75hvJ3r0QH98LXf+IceOme4W5LV78P9LS0igpKXHuf/rp41o9Nq3wAAm33kj00iUt4/VoY/v5J5/7ypgYVp1xB0v6/In6mGS3z4zcp6Qkd+nwcMOR/BEhu11j4ECd6dObDpsREErI1eFWW/P0PGZmthifRtFtA+1dSynukQecr0t/WNduxwkXVU8+S+LNfwFAFBay7kAX5/e6qfdEZ7uzV93LzOHXk9vb5rx3D2ctrUgRiVC808Zik92usWFDFHFxeHmxAs3rIUMkp52mU1bmLh6i65CSIomLU0Svrk7Q0KD+KipwI1+FhYKDB6FnT9zEdjRN5ealpLQI9bSVRHXEGmkdFe2dH2jCxNEOk6SZMNGBYZC43r11fvwRLr98Mm++OYcLL6wiNjbWre3ixR9z1lmTeeutN9p2UCmJnv8eCXfdhlZe7uY9C4Sqh2dRf+nvIC4OgLp1gupFNq+HTGGh6s3TUwPhhSO1V+5JJFZ+Q81bOZxqa56eXKPwsN2uVPmMc213FVBdJ/6J2QDIuDj0Xtntc5xWoP7Sy50kLWn6ReRdtcrte733+v3c/0xXAP72+kBe6bPDee8eTlXVSBGJUEOAhYCGBo1hw7xrtkHgeS0ETJtmp08fB598ouqkGZL2xx7rYMsWq9sigqa1lCEpLFTlKfbvF3Ttqo6dkSGpqMCtZElhoSJpkSBRHbVGWkeEKbJiwkTbYJI0EyaOIEyadDYvvvgcX321nLOac2QAiouL+fHH77npplvcSJpnDlow2CsquPbkUSwpKuRt4EJCI2hNw4ZT/7vfuxXx8mfM1Nd7e2pcEWo4Unsk8Edq5fdIyVvx9ORK6TvHqT29ewm3z3C+Lv2+43jRABCChgkTiV66BFvuWmqq3A3KupgUVo74C6f+9CyJtYWkbPoOGGXseti8pZEkEsG8/caixooVFiorfeeCQeB7XggYOlQydGiT2/a5c63O8WdmSsrKJMXFLUStqEjQo4fyuBnPk8xM5WVzVSptahIRI1FmzbHQYYqsmDDRNpgkzYSJIwijR59IamoqS5YscpI0KeGVVxZhs8WTn38mAAcPtkj4hwopJTffdRufFBXyHxRBCxW2tT+r4r6/v8a5zZ8xM3KkRNOEX2Mm1HCk9iBCkVr5PRx5K5HwAB7yWkp2O7Fvvg6Ao/sxSH/M/TCi6oVXie7dDYDTv5/FwuPudPv8ozNmc+pPSv3xqpfGUfxAhdMNebhqUx0qIuG6qFFXpxZg/OWCtabY+ubNGtu3KxESlYumk5wsKCpSipCxsXD++bBihYP9+zXnuQ8YoFNUhLMsQHq6ZMqUpoidu1lzLDQcKYtVJkx0VJgkzYSJIwgWi4Uzz5zIp59+gq7rCKHx4YdWPvxwEdnZE6mpUQoje/dqLFgQ3vS+9967ee+Dd/n3lHO50Galoa4OUVuHqK1B1NVBXS2irg5RW4uoq0U0tax6y+hop/qdK3wZM+vWuddvckU44UjtQYQitfIbLNwsOlpn7lxrxBLpj9Tcj07XXuV8XfblN4dvIAEgExKdr09Z/A8+6Hen+/cqBE9ftoIb5o0FIOHu26l+ePYhHqU3DgWRcF3UcA0z9MwFCzfM0Lift27VnIa+kYuWlibJydERArKydEaOhLIyB3v2tNz7QiiPmjq2ImhmWN2hhymyYsJE22CSNBMmjjBMmnQ2b7/9FqtX/0h09BjWratmz54vmTTpNWcbTVOen+rq0Pp84olZvPjis9x++11ccvtdVIWyU1OTImt1daDr6F2zQjpWpEKx2iOBP1Irv4HOsaoKNm+2OmtYRULZcssWjc2bBV27toR9GVLoP/1kJT9fMGmS3UkEO4TiWn090Ys+AlS4rExKDrLD4UPZomWkTFFe6lPjVrOydpTb97o9YzQ1SV2IrzhA7KsvUTPzbrfSFO2NUL7P9vjOXRc1PMMMjVyw1FQZdpihQf66dpVs3izc8suMXLS0tJY5buaJdUyYIismTLQNJkkzYeIIw9ixZxAbG8unny4mO/tk9uxZghAavV3U5kB5foKRDgMPP/wAmqbxww/fhT4Qmw1pS0J2Sgpn+BELxWoPwyySsum+zjE6WncjaAbaqmy5bZsqObBlCxw8qK6ha07O+vUWdF045f8XLDj8Xrek3051vi5f8En7H7ANsB/fUlh72iOnkbS0wuverbl5HfG9FENO69+L4iC1ByOFULyoEFqbUEmcax6aqyx+//46nTu3hBnGxYUeZuhKIlessFBXJ8jIkKSlueeXaZoSCjn11JZSJWaeWMeESZ5NmGgbTJJmwsQRhri4OMaOHceSJYu44oqH2LLlI3r2/A1RUQlebR0hLlTefPNtaJrG448/xgcfvMsFF/w2wqN2RyRCsdrDMIvkyq+vc5w715ugGWiLsqVRiFrTYNcule+X2BKlR1OTcBJBu11jy5bDq7gmqiqJ+m4VAI3jxjsVQTsyam65nfjHZyF0nSHH1jBkiOeYY6m+5z4S/vl3AKKWfkLjBO8Q4EgjlDxKIGCb7GzB9u2WkIi7ex6ab1l8JRoiycrSw150sFhg715BWZnGpk2QlATHHOPAblfEz2aT9OnjvZhg5ol1PJjk2YSJtsEkaSaOOnSIUK52xuTJZ3PjjX+isnI9O3Z8yhln+M6BsVhCI2p3330vdXV1vP/+O9x7792MHz+BpA4cfmYg0oZZe6/8Riqc0jN3zrUQdV2d+p+YKN0+B2WUr1hho0cP3+dxqBTXkie21O+reOPtdj1WpFA786/EPz4LgMSb/kzVy3O82tTdOMNJ0pIuv5jiwhYRkfZCKHmUxmt/bT75xIqUIiTi7koK/cnid+kS3qKG0afFosjkwYOac+FBiSBp9OolGTJE9ZeVdfQ8y492mOTZhInWQzvcAzBhIpIwVmQXLbJRUKBRUSEoKNBYtMjGggXWsBUPOyomTJiIxWLhyy/voqmplmOPPdurjd2uwvdCRWxsLP/856MUFxdx//1/j+RwjxgYK79nn91EVpZOcrLyBkyZ0hSRMMBg30drlS0zMyV6s5Cew4HzNajXrnLoVUESDttbcU2UlGDdshmA+osucSvb0KGhadj79gMgZuF8v80Ofv2j83Wn31/e7sMKhfgHa7Njh2/vMbgTPfDMQ5Okp7fce4YsfriLGkafhYWK6CUkSHS9JZSxtlZQXCwoLBRmLpMJEyZ+NTA9aSaOKvxaimd27pzK6NEnsmrVZ/TvPxabLdXtc11XRtIPP0BZWej9Tpw4mTPPPIu5c+dwySWXcbxLLs6vBe258hupcErP3DnXGlKWFnsaXYf0dPeaVa5hkL4QFyfb1Rvd+dTjna+rnn7BZ5uO6g2veG8hqcMGAhA9/z0azr/Iq42jX3+aho/A9vNPRC/+GK1gH3pWt3YbU6h5lIHaBFu8ciXunoRvwACdlBThzEOLjSVsuXujz6Ii5ZWLj4e6OkltLWiaQNcFmibZv19wyikOM5fJhAkTvwqYnjQTRxVCDf05GjBp0hQArrzybDfPD0D37i1iAOHiwQcfIzo6mttuu4mmpqbgO5gIGUOGKKU7u8dX0xplS88+BgzQ6ddPJzVVJylJkpgo6ddPd6tVZbfD2LFNXvu6fj54sKPdvNHavr1opaUA1F53PU41CBd0ZG+4K9nqdN3VftuVL/p/9u47PKoq/+P4+04mhYQklIRApAgIF5QkFOu6giIgigguthUUlbLu+rO7inXtbbGuDRUbUVEUsCBWVFzXhkiIitddaSotlARISJ37++MmQ8okmSSTzGTyeT0Pj2dm7px75t6ZeL/3nPM9H3rLFUFdc/H1XahQEfjXt03fvh7fL5ar3MPrqzc4JcUmPd3DsGFljBhRRkZGw4LpijqLi/evL5eUBJ0720RF2URFeYiP99CvX+guJyEiEmiGHS7jv0JITs4eHdQgmTMnss47xh062Myc2bDAIzk5npwcv5LSS4iqq2emS5eq57e5e3FsG7KzmzaR3rZh8WK3z7lzFUFZ9R7likBwwoRS3njD93sHDvTQu3cZ77xT+zp2TVlzKrlLgrdc23yt+tbRa+j+A/37jXpzEYnTpwKw47sf8RzQ3ed20S9nknDp3wDY/fDjFJ01OWBtqKy+70KfPmV8/30EX3wRwc6deJdpMIzGnfNAn5/Kdf74o8GePVUDd48H+vd3kpGkpnqYMqVqtKm/z+FN5ze86fw6kpPjff7fX8MdJaxo8Uyprr4U5TNm+L9tIO7it0RmS6g7EKzrvS++6A7Igt7VRZTPQwPIv+6mWhNqBGpBcX81NCgvPuVUwAnSEieNZ9eX3/mst+jPU6A8SEu45K/knHZm7dk7mqC28zloUBm//BLhDb569bKJiTHYvNlFcbHNEUeUkZ6+//uybp1/CXOaI7lORZ1bt7q8C2JD1eG6mosmIm2NgjQJK1o8U6qrb57iypXQs6d/21ae0xjseVP1BXt1vVbXewOVgbK6Tkcfur+OS6+sdbvm2r8vjQ3KC0+dRMyi13Gv/cXJ1BLhexj1jlVrvMMdO5w8mtx3Pw5Y2yvzdT6zsgwsq+p3OSVlf8CTnl5WpcfL31TpgUyrXvk3tHevQdeuNrm5HvLzDWJiqrZX62qJSFujIE3CihbPlOrq65lZtWp/kOZvL05tF/cbNrj4+GNnAd5QSnjREM3RG+1etdJb3jP7oTrT0lffv207Wf+2bTMoLnYWOM7KMrzHtPqFfm6u81zHjtC+vc3w4dCjh+9dNjbR0J4HHyNm0etOe++6jfwbbvb5WTypB1B00nii33mLyJXfEvGzRVl/s44jFTgN7ZFsSA9vIHqDff2GIiKgf3+buDgPSUl2i62rFewbLiIivihIk7CixTOluvp6Zvbu9X/bil4cXxf3tg3/+5+LbdsM74K+gR4q2RKaoze645hjveXCc8/3e/+27cyt277dyfrn8UB0tM2SJZGsXevMr1u8eP+F/po1zrYASUnOwsoLF0KvXm6fx7/RQyvbtcOOisIoLib24fv5z/hbar3A3/1sJskpiQB0+uNh5Gzb7fdxa4qW7JFsjNoC5MhIp21paaUtkom3JYY4i4g0hrI7StipuMs7ZUopM2eWMGVKaYOzjUnosG1n6FZmpps5cyLJzHSTlWX4neWvvrXJ2rf3f9uKXiRfF/cVazw56z3t/7JVBA+rV7eOL2CgMlBWiPzsU2857+nnG7T/imNaEaBVzE+qOKavv+7yXlxv3Wp4t628sHJkZO3HvymBzK6ly7zlH5/8uvZMlIZBXuYr3m3bPfxAvccgEAK1Jl9zCZVMvPX1praW362IhB8FaSISsgKRjr2+9OODB/u/bUUvkq+L+4o1ngBKSqq+3pqWfwj0gt4dJo33lp2kG/7vv7jYIDYWn8sJuN3w6b6p9+sAACAASURBVKf7swxu3WpUyehfsbByxba+jn9TApmytHRv+ZIFx1d5rfoFfvGYE72vtb/9H7BvX537DQR/v8vBEio9fZWDRduGLVucwG3FChc//mjwzjvBXfZBRNouDXcUkZAViMXJ65unOHQobN/u37YVvUi+5m1VrPEEEBlZs031XXSG0ryYQC3oHbXkLW8595VFDd5/WpqHvLzaz++eSpmbqwfG1Z/zdfybOrTzs3G3ccySGwGIKcqjMDrR+1r14ZLbrfUkmQcC0OmIwexcbdVZtz/q+s6E+vzcUMnEWxEsVh9aC1BUZLBypcHixbaGPYpIi1OQJiIhKxDp2BsyT9HfbX1d3EdF2RQVGXg8Tla66uq66AzXeTGJ5+9fG6zkuOPr2NK3+i7k4+OdHrStWw02bTIoKXHeUzGEtXKw7Ov4NzWQ+fSIK71B2uS3zmHuaW9Web1yYGh37MS+aTNpN/dJIrZsxr3ia0oPPbzO+uti27BwoZvly13s2OEkVYmKsvnmGxcjRng49dTSkJ6fGyqZeCu+Y5WH1lYWHe3/DSERkUBSkCYiIStQQ6ICnbnO18V9ly42u3btTxteWX0XnYHoMQw1MS/N85Z3Lf2oUXXUdyEfG2vzww9OIB8dDXv3GhQVGRQW2nTsaNOli+3d1tfxb2qiobj2sLHrYfTc8g0D1r/vRE6V3lQ9MNx75z9pN/dJADqeNKrWBb39kZVl8MYbbnbtqtrzk5cHubku+vQpY/BgOyA9os0hVHr6Kr5jlYcrV6i44dIc6/OJiNRHQVqYCqWhUyKNFSpDoqrzdXHfrZtNz54eCgqqXun5c9HZnAs4B+tvQfxlF3nLpcMOa1QddV3It2vnYfNmF4WFNnl5zusul01ZmcHevU7QnJICJSUwYIAH24bMTLfPY9DYQCYtrYynTnmd25501nA4PPsZvk6f5m1jjcDQMNj19gd0PHk0AHE3ziL/9nsadWyWLq0aoFVwuWDnToOlS90MHlzSqLpbQqhk4q34jq1cWfUHWDlRDQQ/G6aItD0K0sJQuA6dkrYnVIZE+eLr4t62ITu74RedzZVEIVh/C9o98pC3vHP5V42up7YL+UGDynj++UjWr48gLg7AWZfOMCA21kOnTs5adQcc4OGYY2DFCnjnnciAHwPnAj/J+/j09y/i6/RpdQbmpYcfgScpGdf2HGKffJyCq2Zhd+hY776qB9vLlkVg24bP5CcuF6xb17i8YC0Z1Adq7mNT2zBxYimWZfD99xGUlBhERto1esSDnQ1TRNoeBWlhKByHTknbFCpDovzV2IvO5uoxDMrfAtum/a03eh+WDRjYpOp8HdOsLINff3V5e5Hat3cWrganB6RfPw8DBjjLcGzcCJbVPMeg4gJ/1fbXGHz9aQAcHPkzPU7oXWdQs2NFNskHdgUgqX8vn2unVV+k++efnfmOqanO6/v2GeTnG+zb56wJ5+v9DdVWb/AZBpx4YikejxGSN4QaQqNoRMKHUvCHoVBZf0akqQKdDj5UNVe69GD8LYi7/WZveceK7IDXD067Y2J8v1aRer8isF21imY9BoYBB8wY43187tzR9a/LGBtL/nU3eR9GffhelZerLz3x888G//1vBL/8EsFPPzn/2+7UyVmOoKDAqNET6/FA374eGqotrxkW6PUBgyEQS5aISOhQT1oYCpX1Z0QCIRSGRDW35uoxbPG/BR4Psf9yFmv2xCfg6dkrsPWXy883SElxeh+rz8kCKCw0vIFt5TT9vgTqGOw753zazXuWiG1bnYlwkZF17/eyq4i781YAEs8+vUoSkerBUuWkFjk5Bh07GvTta5OTY1NY6MzBc4Z9OgFap042Y8c2/PfSnHMjA62ix2jDBti8ObLJPUahMkeuKTSKRiS8KEirh2maiUAmkABEAVdYlvVFcFtVt1BNtiAivgXyArHycKcVK1zs27c/iUb1egL9t6D9lZd4yzu/WBnQuiuLi3MyN65bZ/P77y5s28DlclLvt2tn06uX7Q1s4+Prris21g7IELG9d/2TdvOeBaD9jbPYe/d9QN3Dz3Yu/4pOw48AIGH6VHbPfQGoGSxVXoPP5XKWHUhLsxkwwMOGDQYlJQYxMc6SA0lJHoYP9zTqYry13OCrPCwzMdFpdyCGZbb2G0KtKcgWkfopSKvfFcBHlmU9aJqmCbwMDA1ym+oUyskWRMS3QFwgVp9TlJAAW7a4yMuDXbuci/qKi9eA/y0oKaHdi06QUdbrQOwuXXy2LxDzZQYNKuODD9yUlBjEx9vs3ev0IO3Z48xNmzKl2Fvf4MHw/fe+hzyWljp1BWQeVlQUno4dce3aRbtnnmLv3ff5McdrICXpg4lcvYrotxbj2rwJT7fUGsFSxRp8FUpKnCQpAwZ46NTJSb2fnu5pcs9Pa7nBpx4j31pLkC0i/lGQVr8HgKLyshsoDGJb/NLaki1I26PJ7c2j+sVrSorNrl02OTkG27c7C/Z27do8fwsSpp3jLe/66LMarzdXUoq4OLwZDj0eSEys2mM4dCh8+23tfw+BgF3w5779AZ2OPhSAyI8/YkWnUfXXvfQjkg/oDEDnjAHkbNtdI1jq0sUmLw/vkMeKRboNAzp3hnHjigMSlAT6Bl9z/c7VY+RbawmyRcQ/CtIqMU1zGnB5tafPtyzrG9M0u+IMe7ysvno6dozF7Q5uco4ZM+C775x/e/c62c8GD3YuWHQR3HDJyfWMmRK/2TbMn+/0blRM28nLg48/hu3b4cwzW/47Gi7nd8MGJ0ipbOhQ2LIFNm+G0lI3AwY0w9+Cffvg3Xec8hFHkNS3e41Nvv0WNm6s2b6Kdv/6Kwwb5t/uNm6Eww5zPtOWLVBcDFFRTgDarRts3BjJ6NH7t58xo12tfw+fecZ3m/a3LZpRo/xrF8n7P0CHM09lw9O2f3U//TRMn+5UsXQRw4efy8KF+38fffo4h3jrVudxr15OcFpS4hyz44+Prvdc2jasXOkkUtmzxxkGWv17cPzxzm8wO7vqlLqG7Kfy/vz9nfvTtsoMY/8cPIC4uOgqr7tckJzsXzvDyfDhVPneVFZS4rzeGo9LuPx9Ft90fmunIK0Sy7LmAnOrP2+aZhowH7jKsqxP66tn166CZmhdw/Xo4fyrbPv24LSlNajtru/xx7dn+/Z6sg+I37KyDL7+2lmzqri46mtffQVJSSUtOlQpOTmenJzwOL+bN0f6HPKUkOD869DB5pRTnAWOA/m3oMO40VRcF+a8+ib4OJ7Ll7spLnbVOOf7X/fQs6d/vR+bN0dSUGCQmFgzwCoogC1bbHJynM+ZnBzP9u17av17WNsxq1BRl7+9QjF330f8rCsByP3ld/JLkmqpuVI7TzmDZJwgjalT6fH7eHr1iqnS+3fggU4g6nYb9OzpqbL/+s6lr15McAKob7+t2os5ciQkJ/ueG9mQ74y/v/OGtG3/53GTn+90K8bFRZOfX1Tl9cREDzk5ba8nrUcP6NXLXWuvcY8epeTkBK99jRFOf5+lJp1fR22BqoK0epimeTCwADjTsqysYLdHmkddQ7G2b3cuXNQDGRgaqtR8gjHcydidR+Q3zoLVRWPGQrt2PrcL5HyZQH5Of+pqyFDNwvOne4O0s+afzoOTPvarnTtW/kDnoYcA0HHCWCYu+bBGIpmxYxs3VLAhc7gClTzD3995Y+aXad61b+GQoVJE9lOQVr+7gBjgISdvCHmWZU0IbpMk0Oq6UMjOhs6dnYn6mkPVdJrc3nyCcfHaYfQIb3n3sy/Wul0gA6tAfk5/6mpQIGEYFB8zgqjPPuWAdV9QWmLjjqz5uau309O9B0UnnEj0e0uJXPE17v/9THp6/4BkGgzGjRF/f+eNaVvledeVad51689QKSL7KUirhwKytqG+C4UXXogkKYl676JL/TS5vfk0NmlQYxM8GDk5uNetBaDwrMl1rg0WyMAqkMmR/KnrxRfdDQokdj8zj6R+PQE44/eHeLX7ZX61c/fzL5PctQMAnY4+lJxtu/3+HHUJxo0Rf3/njWlb5R6j9euj2bLFVo+RiIQdBWki1H6h4Fy8wo8/RnDAAU5WtZQU519bT/fcWBqq1HwaM9ypKVkXO/1hf6KMPQ8+WmfbAhlYBXJYlz91NTSQsBM7eMtHvTaLgg8v8q+dLhd5L8wn8dyzAGj36MPsu+gSmioYN0b8/Z03tm0VPUbHH493/qGISDhRkCaC7wsF23ZSc1sWeDwGhYU2hYXOoqkVa05pDlXDaYmI5tXQ4U6NXXPK9etGXHm5ABRcdOn+/PB1tCuQ82UCOayrvroaE0jkLlpCh1PHATDE/T3pUw7xqy3FY0/yltvfcgP7LphR6zw/fwXjxoi/v3PdtBER8U1Bmgi+LxS2boXt2w3KypyU0BVcLsjJMejY0SAlxdYcqgbS5PbQ0tj5Sp2HDfKW82+61a99tdb5Mo0JJEqOPsZb7njSKLav3+z3/rb/tI6kAb0BZ9jjzpU/NLzRlQTjxoi/v/Nwummj9R9FJJAUpIng+0Jh2zbn/6odO0J0dNULBZcLtm51gjTNoWq41nqxHo4aMycowvrJW957461hn/q0sYFEwV8vJvbxf2EU5ENhIcTE+LU/u1Nn9k2dRrvn5xLx26+4V66gdOihjW5/sG6M+PM7D5ebNs21WLuItF2GbesCM9BycvbooLZCtg3Z2fsvFL75xkVCAkRHR5GdXVpjNFdMDGRklDFuXMuu6yWB1dbXacnMdLNpU+1DFVNTPUyZUvUiO7lLgrccqOQWzSVQ57eil+Sdd9ysW+fCtqFvXw9jx5aSkVFLMFFWRnK3jgAUnvFn9jwyp0E7TE7ZvwhcqB/nYAmV329WlsGSJZG19rZW/H9CvW0NEyrnV5qHzq8jOTne56+/7kkEIm1IxV3fKVNKmTmzhBEjykhJsUlNhaQkG4+n6vYREXarG44jUl1aWhmltXR0bNoEv/5qMGdOJJmZbrKyDCK+XeF9fc8Dj7RQK0PDL79EYNsGBx1k06+fjctl8M47kSxe7Mbn/c6ICMp69gIg5tWXG7Yzw2DXm+95H8bddF0TWi7NzZ9hwxW9bUuWRLJpk4u8PINNm1wsWVLHd0hE2iwFaSK1qLh4NQwYMMBD//5lxMd7iImxiY31cOqpxRrCIq1eerpzs6F6oPbDDy527XLhchlVLiY7nTjSu03h5HNbuLXBU1+CldWrff8hyF34trccteQtwOmVy8oyyMx0VwmAq1+klx55FJ5OnQCIfeIRjLxcv98rLcufYcON/Q6JSNukOWkitaiYh7JhgxOopaRASopNaalzUTtxokcBmrR6vuYE7dwJHTp4SE2tOt1s4O8fect5z2QGobXB09gEK57ynjSAxPMns23r7gbNXdrx7Q8k9+4GQGfzQJ58bK/mPYUgfzKABmNRcRFpvdSTJlKLiovXSZOceTkdOtikpnoYN65EF0OtjHof6lZ9qG/37jYHHFAzH8jMBeO85eKTT2nhVgZXUxaE3l1pLtpPy7Y0rDclLo78a64HwPB4sN/9SD0xIaiuYcMVGUCDsai4iLReCtJE6mAYMHQo3ovXKVPqSBIgIUnzQBrO18VkmrXQW37x/HdasjkhIS6u7i9KXVlei874s7c88IpJ9famVFdw5TXe8oWLx+PrS1vbe6Vl1DZsuHIG0KZ8h0Sk7VGQJiJhrb55ICtXBqddoczXxeS5b53tLW9LO7YFWxMa/OkpqUvRiScDkLw5G8P21Lpdbb0pOz/90lue/LbvuYDqiQmeipEXJ59cUuvIi6Z+h0SkbVGQJiJhrb55IKtWtWx7WoPqF5OHr37GW77/rH+3yYtJf3pK6rL70Se95eO/uKvW7WrrTSkbeDA53dIAGGwtIGHvJr/fKy2j+rDh6iMvmvod0rBtkbZFQZqIhLX65oHs3dtCDWlFql9Mnv7+37yvtT9uaJtcdsKfnpI6tW/vLZ7wn9t8blJfb8rqZz71lm98ok+D3ivB15TvkIZti7Q9yu4oImGtvqxrla6dpVzljI/RDzzgff4/T3/DxPFtN2lORU9JenrjMvDtencZHcc6Sxik/vYNm7of5n3Nn96UtKFulk/+F8NfvBiAIT++zHcH/9nvnhgJvsZ+h+obtt2nj0FGhs6/SDgxbN1+CbicnD06qGEkOTmenJw9wW6GNFJWlsGSJZE+hzyWlsKUKdH07Knz65Ntk5yS6H2Ys213EBvTOM39+7Vt5wI6OzuC/HyDuDibtLQy0tN9JxhK7pLgLT9wfwEFBQaxsXW/p/r+uqTsr+PB2XsYlIFf7w1HbeXvc2amm02bah/8lJrqYcqU8Evf31bOb1ul8+tITo73+ddbPWkiEtYq1rtbs6bqXeiK3oehQ2H79uC1L5TF3XyDt7xj5Q9BbEloqhiC1pB1y/L/fi1x/3TmpE2ZmNvgrlzDgB3ffk/nYYMAuGjBGHLPfa/Jn0VCm9L3i7Q9mpMmImGtyXOJ2iqPh9jH/+UUO3TA071HkBsUeuobguZr3bLK6fQT/u8vjdqvp0dPikaNASDyqy+I+OW/japHWg+l7xdpe9STJq1eQ4cbSdvT1LlEbVH8pfuThez8/NsgtiR01Zc5NDs7goyMat85l4vSgYfgXvMD0e+81eh97858leSuHQDodNSwVjkUVfyXllbGxo01bwiAksaIhCsFadKqNWa4kUgoCOmbCyUlxLzyEgClfQ/CTk4OcoP2C6Xj1tghaHmvLqJzWn8Aol99ucpi135zuch79kUSz58MQLsnHmHfhf/X8HqkVahv2LaSxoiEHwVp0qop45W0RqF+cyFh6v6gIff9T4LXkGqa47g1JeirL3NobUPQPCldveWE//sLOY0J0oDiceO95fY3Xce+86ZDTEyj6pLQVjnj6urVEQ1OOCMirY+CNGnVGjXcSCTIQvrmQkEB0R++D0DJ4UdixyfU84aWE+jj1tSgrylD0PKeySTxgikAuDZuwNOzl9/trmz7mnUkDewNQKc/Hs7OFasbVY+EPg3bFmlblDhEWjVlvJLWyJ+bC8HS4U/jvOXc194MWjt8qe+4rV4dQVaWQWammzlzIsnMdPPtt9S60G9jEn9UVn3R7wp1DUGzbWdZiGdy/+R9Lvbkkxu9GLHduTP7zjkPgIiN63GvWtm4ikREJKSoJ01atcYONxIJplC9uWDk5RK50kkSUjR2XMgNnavruNk2fPllBL/+WrVXbOFC6NXL7bNXrKk98Q0dgla95+7bg89m2I8v0W7LBjJfhwmTnDobOgRz7+yHaDfvOQA6jjlWSURERMKAetKkVUtLK6txF7uCMl5JqArVdNodjz/GW979zLygtKEudR23rVth1y6jRtAVGVl7r1ggguWKIWhTppQyc2YJU6aUkpHhO5iq3nP32pjHvK/1nfsPVq82vIHckiWRbNrkIi/PYNMmF0uWRLJ4sdt3j5thkPvGUu/DuFturLfdIiIS2hSkSavWmOFGIsEWijcXjK1bidi4AYB9U6ZSaxdTENV13DZvNujWzffvvbYhpC0dLFfvuSt1x1DsjgVg5LcPkJ0d0eghmCVHHY0nIdFp96MPYezOC2jbRUSkZSlIk1ZNCxVLaxSKNxc6HTnEW947+6EW378/6jpunTpBSkrtx81Xr1hLB8u+eu7+NXm5t7z99c955JEofvzRxdatNbetb77ijlVrvOXOB/dtYmtFRCSYQu9WqUgDKeOVtDahlk7btWE9rvy9ABRccgW4QvP+XV3HLTs7gk2bam+3r16xll57ytcc2i3Jg7zlOz4fzXHHFlNUZJCXZ7Brl82AAZ4q29c5BLN9e/KvmkXc7LsxiouJ/GQZJceODOhnEBGRlqEgTUQkCELp5kLnw9K95fzr/xHEltSv9uPW8HT4LR0s+0rZv3WrwUM97+XSjVcD0NHYxRY64XJBTo5Bx45GlR7C+oZgFlx9HXGz7wagwxkTydmah4YUiIi0PqF5u1RERFpExJofveW9N9/Rai/oaxsKWVJSd69YQxJ/NEcbt241eLnrZd7Ht675M57yzjOXiyrDHv0dgrnz4/94y/F/m9H0houISItTT5qISBvWacSR3vK+v10cxJY0TW29YsOHQ48ejZ+f2tB0+A1tY7t2Nl262Kz77Uh6b/qSQVuWkTTUw/YdLlwuKClxdtKQIZhlhwyidMBA3D+tIeb1V9l78x3YKSmN+fgiIhIkCtJERNoo9zdfecu7H348iC0JDF9DIZOTISencfVVX9cMnLXXNm50sXatp1HJiaq3MTbWzaZNLp6b+Bq3PNYdgPOL5vBG/5ls3WrQrh2kpnoaHBju+vAzkrsnAZCU1k9rp4mItDIa7igi0kZ1HDfaWy46a3IQW9KybBuysgwyM93MmRNJZqabrCyjxhpkjU2H3xAVGSYLYpO8z0366BJSUuDgg23+7/+KGzcEMyqKPffc730YvXBBk9sqIiItR0GaiEgbFLnsQ28577mXgtiSltWQxaKrr2tWWX3p8P1VeZ7aU6e95X2+47afm5xhsvD86d5ywoXToKzl198TEZHGUZAmItIGdTjrT95y8UknB7ElLashvWO+1jWrrM50+H6qvNbj3j8c733+qsXHBmStxx0rsr3lxNNOaVplIiLSYhSkiYi0MdGLX/eWcxctCWJLWl5Desfi4uruxaovHb6/KmeY3Ffe+xW1eydGSXGT6/b07EXxcU7wF/X5Z7jW/tLkOkVEpPkpSBMRaWMSZp7vLZccfUwQW9LyGtI7VjFfzBd/0+E31N7b7/GW21/794DUmffy/qC885FDAlKniIg0LwVpIiJtSMxzc73lXR98GsSWBEdDesdqW3utIenwGywyEk9yFwDazXs2MHW6XOTNned92O7JxwJTr4iINBsFaSIibYVtE3/15d6HpRltr1elIb1jleeLpaZ66NDBJjXVw7hxJQGZL1ab3Lfe9ZajPnwvIHUWj5/gLbe/YRYUFQWkXhERaR5aJ01EpI2IfXC2t7zzP98GsSXNw9fC085i1ngDqvR0m7VrPaxZUzV5SG29Y77WXmtuZX0O8pYTzz49YGucbf/hF5IO6QtAx+FHsOurVQGpV0REAk89aSIibYFtE3fXbd6HZQf1C2JjAq+21PoLF1IltX6wescaas99D3vLxvbtAanTTk5m39nnAOBetxb3agVpIiKhSkGaiEgbEHfjLG95x6o1QWxJ86gttX5kZM3U+pWzKc6cWdK4xaKbWeGUqd5y4pTTA1bv3gce8ZY7jhoesHpFRCSwFKSJiIS7sjJin3wcAE9SEp7UA4LcoMBriYWnW5RhUDxyFACRK7+lyirbTaw3d+Hb3odxt98cmHpFRCSgFKSJiIS5+P/7i7e887NvgtiS5tMSC0+3tN1P7s/u2O6RhwJWb8kfh+OJaw9A7MP3Y+wJzJw3EREJHAVpIiKtXXExkZ8sw9i6tWaPS3ExMa+/CkBpfxO7c+cgNLD5tdTC0y3JTkj0ltvfdlNA696ZbXnLnQeF1/xEEZFwoCBNRKS1i4qi/TVXkJTWj86H9CVx0njibriGmJfm0XH0/nlHue8uC2Ijm1cwFp5uCbve3J+CPyJ7dcDqtdvHk3+Fs1i2sW8fkcs/CVjdIiLSdIYdqHHu4pWTs0cHNYwkJ8eTk7Mn2M2QZhIu5zfuhmu8885qU3TSeEoHHkzpwYMoO+SQKqneWzvbdrI4Vk+tHxUVTa9e+0Iqc2NDJXdJAMCOimL7b4HJ9Fi9boCcrXkAuDasJ3LVStzfrcS9aiWlhx9J/vX/COh+AyVcfr/im85veNP5dSQnx/v8v5PWSRMRCQPFo06oN0iLfuctot95y/t4+383Yid2aNB+fK1FlpZWRnp6cLMjVqTW79vXYPXqCAoKDGJjK9ZJa70BGkDBJVc4c8eKi2HfPmjXLmB1585fSIez/gRAckoink6dcO3cWWWbwsnnBmx/IiLiHwVpIiJhoOSoo7Fj4zAK8v1+T8S6tZQOHur39hVrkVVOdZ+XZ7Bxo4u1az1B763ytfB0cjLk5ASvTYGQf+2NxD58PwDxl1/EnieeaXRdxt49xDzzFJHlvWQRv/9W5fXqARpA6dBDG70/ERFpHM1JE5EGsW3IyjLIzHQzZ04kmZlusrKMgGUIl0aKjqZ4xHF+bVrapy+5C95oUIAGta9F5nbXXItMAigigtLefQCIWfhak6pyZ62i/e03E73kzRoBmi+ehETK+vRt0j5FRKThFKSJiN8qelKWLIlk0yYXeXkGmza5WLIkksWL3QrUgqx49Ak+n684LXZUNPlXX8euT76gxM+ArrKwW4usFcl7ff8w1ag3FzW6HjvCjSc+3u/tSzOGgEuXCiIiLU1/eUXEb+pJCW3Fo8YA+4OyCgZQPOI4di7/koKrZkFMTKPqD8e1yFoLT/ce3nLi9KmNrqf0yKPY+VUW+86fjh3hBNV13VspHTqs0fsSEZHGU5AmIn5TT0po83TtRklaBpVDpbIuKex+8lnyXl2Mp4nD1sJxLbLWZPcTc71l16bfG12PnZTE3nvuZ9enX1I0Zix1hdYlQxSkiYgEg4I0EfGbelJCX/Ho8t40l4uC6X9h139WUDRxEoHI6BGua5G1FkV/Ot1bTjxjYpPrK+tvsjvzVXJfe5PSQ9J8blM6pGHzFkVEJDAUpImI39STEvqKR51AScYQct9dRv6d/8ROSAxY3enpNgMHemoEaqWlMHCgh/R0nf/mVjTeCc7cP1vg8QSkzpLhx7Lrw+XsfugxylK6ep8v69oNT7fUgOxDREQaRkGaiPhNPSmhr3TooeS+u6zBmRv9UbEW2cknl5Ca6qFDB5vUVA/jxpUEPf1+W7H7X094y7H33hG4iiMiKPrzFHZ++R35V83CbhdLqYY6iogEjWErHVvA5eTs0UENI8nJ8eTk7Al2M0KCbcPixW7WrKmaPKSiJ6U1XahfumOROwAAIABJREFUfPGFvPLKS1Wei4qKIikpmT/84Y9ccskVDBgwsMH1Dhs2iB49erJ48TsATJx4Er/+upFvv/0+IO2ubu/ePRQWFpGUlNQs9bd24fj7TeraAaO8Fy1n2+5m2Ydr8yYi1v5CydHHNEv9gRKO51f20/kNbzq/juTkeJ9XTlrMWkT8VtGT0revwerVERQUGMTG2qSllZGebreaAK2yBx54gKioOAAKCgpYv34dL730Am+//QYvv/w6RzfxIvWyy66ioKAgEE2tISvrO8455ywef/xpkpJC+2JaAif3vY/pOHoEAO5vvqL0sCMAuPfeO5k9++463/vRR/8mLS293n14uqVqqKOISBApSBORBjEMZ25Senot4x5bmYkTJxIX17nKc9On/4XRo0cwY8ZUvv56Ne3bt290/cceO7KpTazVmjU/smXL5marX0JTacYQb7njuNE1etMuu+wq+vXr7/O9PXr08Pm8iIiEFgVpIiLVHHBAd2655Q4uumgmL788jxkz/hrsJolUkX/dTcTdeSsAxt492O33L1A9YsRxTe4BFhGR4FLiEGnVbBuysgwyM93MmRNJZqabrCwDTbWUpho/fiLR0dEsW/ah97lvvvmK006bQO/eqfTuncrpp09g5coVddYzceJJDBs2qMpz//3vz0yfPpUBAw6kb9/uTJx4El9++Z8q27z55iImTDiRvn27c8ABnTn00DRuueVGioqKAGdo2yWXOMHjqaeOq7KPTZt+56KLZjJwYG969Ehm5Mg/8tprr1Sp37ZtZs++m6OOGkqPHskcfHBf/va3Gfz++29VtnvuubmMGHEUvXqlYJq9mDr1bH76aU2VbQoLC7nrrls59NC08ramc/fdt1NcXOzdZv78F+nSJYHvv8/mwgsvoF+/nhx4YDfOPffPbNy4oc5jKDUVXHKFtxx/4bQgtkRERJqDgjRptWwbFi1ys2RJJJs2ucjLM9i0ycWSJZEsXuxWoCZNEhMTw4EH9uaHH5yEH598soyJE09iz548Zs26nssv/zu///4bEyacWCPAqsvatf9j7NiR/Pvfn3LBBTO57rqb2LVrF6efPoHvvvsWgMzM55k+fSqJiYnceOMt3Hzz7XTv3oNHH32Iu+++HYBx407hnHPOB5zhbbfd5sxF2rJlM2PHjmT58k+YPv1Cbr75djp16szf/jaDRx55yNuOBx+czezZdzNy5Cjuvvs+zjlnKkuXLuGMMyZSVuZk6XzttVe4+urLSUtL5/bb7+Gvf72Yb775klNPPYndu/MAKCsrY/LkM3j88Uc44YQTueOOeznmmOE8+OBsLrhgCtWTU5177lnk5uZy3XU3MXXqBXzwwbvMmDG1MaeobXO5KEkfDED0++/SkD94w4YN4oorLuayyy6iZ88uZGQMYMeOHQB8+eV/mDTpFA48sBsHHtiNP/3pZL744nPvez///DO6dEmo9d/8+S96t/XnpsawYYO46qrLWLBgPsccczg9eiRzxBGDmTv3yaYcHRGRVk/DHaXVWr3a4KefqmYZBHC7Yc0aF336GGRkKFKTxktM7MD69evweDz8/e+XMWTIMN54YykREREATJs2k5Ejj+a6665m2bJ/+1XnXXfdTmlpCR988Cl9+vQF4NRTJ3H44YN59NGHefrp53n88X9x6KGH8/zzL2OUZ2M5//wZHHpoGsuWfcg//nEbhxwyiMMOO5x5856tMrztjjtuoaiokOXLvyKlfM2rCy6YyV//Oo177rmdM888m+TkZF5//VWOP340d9xxr7dtqandee65uWzcuIHevfvw+uuvMmDAQB55ZI53m0GDnB69NWvWcMQRR7JgwXw+++wT5s9fyMiRo7zbDRkyjKuuupR3332HE08c530+I2MIzz6b6X1cUFDA88/PZe3a/9Gnz0ENOj9tXd5Lr5E0yDlm0ZWCo927d3uDrsoSEhKIjIwEYOHC1+jfvz+33XY327ZtpXPnzrz77jucd97ZHHhgb6644moAMjOfY9Kk8TzzTCZjx55Ev34mjz5aNYAqLS3l5puvp6zMwxFHHAU4NzUmTz6dQYPSmDXreoqKipk/P5MJE05kwYI3OPLIP3jfv2zZB7z55kKmTfsLXbqk8MILz3LttVfRq1cvRo06IbAHTUSklVCQ5ifTNAcAXwEplmUVBrs9AtnZETUCtAput/N6RkZ4JLeQ4CgtLcEwDLKzs9iwYT3nnTed3NzcKtuMGXMic+Y8yubNm+hWTzY8j8fDhx++z/HHj/EGaACdOnXmrbfeo1MnJ4HJJ598QUFBvjdAA9i+PYfExA7k5+fXWf/SpUs4+uhjcLsjq1yojxs3gYULX+PTT5dx2mln0q1bKp9//hlPPvkYEyeeRpcuXZg69QKmTr3A+57U1AP45JNl/POfd3HmmWfTs6dz0Vz5wvntt98gKSmJjIwhVfY3atQYIiIi+OCDd6sEaRMmnFqlzYMGpQGwbds2BWkNZHfp4i0nXPo3uGoWAFOn/tnn9osWLfEG84WF+3jhhfl07doNcAKtWbOupFu3VD744FPi4xPK6zqf4cOP5JprruD440fTpUsXTj/9rCr1XnPNFeTm5vLCC/Pp3btPg29q/P77byxb9jmHHOIM2T3ppPGkp/fntddeVZAmIm2WgjQ/mKaZANwHFAW7LbJffn7d+d4LClphPngJKTt37qRz5yTWr18HwC233MAtt9zgc9vffvu13iBt586d5OfvrRKgVRg48GBvOTIyklWrvmPRotf4739/Zt26tWzfngNAjx49a61/x44d7N6dx9Klb7N06ds+t6mYc3bzzXdwzjlncsMNs7jxxmvJyBjMCSecxJQp55GSkgLAlVdew4oVX/PPf97FP/95F6Y5gBNOOInJk8+ld+8+AKxfv47t27czcGDvWo9LZZ07V13PLTo6GsA7xFIaJm/eKySecyYARu4uwDm3FQFPZZWf6927jzdAA1i9ehWbNv3OjTfe6g3QwOlNnjZtJrfffjOrVq3ksPJ0/xUyM5/n2Wef5oorruaEE04EaPBNjYMO6lelbSkpKSQndyEnZ1sjjoiISHhQkFYP0zQN4EngOuCNIDdHKomLs8nLqz0Qi43VUEdpvD17drNhw3pGjz7BG0DMmnUDw4Yd5nP72lKeV1ZRj1HPgnLXXnsVc+c+SVpaBoceehinn34Whx12BNdee1WNxB6VeTxO/ePHT+Tcc8/3uU2vXgcCzgX7l19+x7JlH/L++0tZtuxD7rnnDh5//BGWLv2Ifv36k5p6AB9//B/+/e/lvPvuEpYt+5CHH76fJ554hFdfXcwf/vBHysrK6NOnL/fcc7/P/XXo0KHKY5dLU6EDqbg8MAKIme8szp6RMbje7I5JSclVHlckbznooH41tu3XzwScgLtykPb1118xa9aVjBw5iquvvs77fENvalQP3MEJ3hW4i0hbpiCtEtM0pwGXV3t6AzDfsqws0zT9qqdjx1jc7ohAN0+qGT4cFi6E8ikWVZSUOK8nJ9d8rTGSk+Pr30halZiY/V8cX+f3rbcWYNs2p58+iUMOGQhASkpnTjvtlCrbffPNN+zcuZMePboQExODy2UQGRnhrTMyMgKXyyA5OZ6OHdvRrl07Nm/+tcY+Z8+ezebNm7nkkkuYO/dJzjnnHF544YUq2+zYkeOtCyA+PgaADh1ivfXHxsbictk12rlx40ZWrlxJz54pdOoUS1ZWFgkJCZxzzpmcU94T8+qrr3LmmWfy+usvcd9995GdnQ3ApEnjmTRpPACff/45xx13HC+88HR59sk+rFixgj/96eQqAVhJSQkLFy6kRw+T5OT4Gm2tUNvzgRT2v98LLoBnnsG1dw9Q/7F0uQxiYqJ8nofExHY13hsf7/R2JiUlel/7/fffmT79HFJTU1mw4BU6dUr0bh8XFwXAbbfdxpFHHumzDcOGDaNjx3hcLoPo6Mga+6z+O6pL2J/fNk7nN7zp/NZOQVollmXNBeZWfs40zf8B08oDuK7A+8DwuurZtaug2doo+/XoAb16uVmzpmrykNJSGDjQQ48epeTkNH0/ycnx5OTsaXpFElIKC0u85ernd+vWLdxww41065bKmDGn4Ha7SUnpygMPPMSECWd6F7fes2c3p512OkVFRXz33Y+43SV4PDYlJWXeOktKyvB4bO/jESNGsmTJO6xatYYDDugOQG7uLu65514OO+wIfvnFGR7Yq1ffKu368MP3+O9//0u3bqne5/PznRT3O3fu9T53/PFjWLLkbT7++D/e+V4AF110CW+//QbLl39FaambY489jiOPPIoXX1zg3eaggw4BoKjIaf+f/jSJ6OgYPvxwuXdeUffuBxEZGUVpqfOZjjtuDO+//z733vsA06bN9Nb19NNPcN11V3PXXbOZNi2VPXsKyz9rQZXPVdvzgdImfr+33EPyM894H9Z3LKt/RwESE507WitWZPGHP1RdgH3lytUAtG/fiZycPRQWFnLKKePJzc1lyZIPKCuL9FkXuMnIqDo88rvvvmXXrl3s3VtKaeken22prY2+tInz24bp/IY3nV9HbYGqgrR6WJblnclumuZ6YEzQGiNVGAZMnFhK374Gq1dHUFBgEBtrk5ZWRnq6TT0jykQAWLx4MVFRcYCz3td///szCxa8zL59hcyfv5B27doBcOed9zJjxnmMGnUMkydPJSYmmszM5/n11408/vjTuGvLYlPNDTfczNixIznhhOOYNm0m8fHxzJv3PAUF+Vx77Y307t2H7t178OCD91FYWEhq6gF89923zJ//IjExMezdu9dbV1KSM0zsuefmsm3bViZNOoMbbriZf//7UyZMOJELLphB9+49+OCDd3n//Xc599wLGDDA6RWcMeMv3H//P5k69WxGjhzFvn0FzJv3HLGxsZx99jkAXHTRpVx++f8xadJ4xo+fCNgsWDCfoqJCzj9/OgBTpkzl1Vdf4rrr/k52dhZDhgxjzZofeOGFZ0lPH8yf/zwlIOdJ6hAdjSchEcqXRWiMjIwhpKR05bnnnub886d556Xt2bObZ599mpSUrmRkDAHgyisvYdWq7/jXv54gLS2jRl2DBw8lJaUrTz01h8mTp1a5qTFjxnnemxoiIlI7BWnSqhkGpKfbpKcri6M0zuWX7x/hHBUVRdeuqZxwwklcfPFl9O27f37O+PETefXVxTz44Gzuv/9eXC4XAwYMZN68+YwZc6Kvqn3q39/knXc+5M47b+GRRx7C5XIxZMhQHnnkCW8A9dJLr3HTTdfy1FNPYNs2Bx7Ym9tvv4fS0hKuv/4asrK+IyNjCMcccywTJvyJ999fymeffcK4cafQu3cfli5dxr333kFm5nPk5+fTq9eB3HrrncyY8VdvO66++no6dOjIyy9n8o9/XI/b7ebww4/gscee8s6vmzz5XNxuN08/PYc777yVsrIyBg8ewssvv+6d8xQdHc3rr7/F7Nn38NZbi3nttVdISenKeedN46qrZhEbG9uk8yP+yV3yARxzOADurFVQz5y06iIjI703IkaPHsHkyc7adS+++Dxbtmxm7tx5uFwunnnmKRYsmM9RRx1NbGwcr732SpW18A48sDeHHXZEwG5qiIi0VUb1hUal6XJy9uighhF1x4c3nd/w1pbO76NdErgF+Bg4ZNvuWrcbNmwQPXr0ZPHid2q8tnz5J9x//72sWrUStzuSoUOHccUVV3vXNbv44gt55ZWXaq37zDPP5l//esJb14MPzua771Z6b2pceukVVW5q1NaWutpYWVs6v22Rzm940/l1JCfH+xz7pSCtGShICy/6IxLedH7DW1s6v+2eepz2118DwHZrPXbHTkFuUfNrS+e3LdL5DW86v47agjTlQhYRkTrZNmRlGWRmupkzJ5LMTDdZWQa6xxda9k2/0FtOOF9zAUVEWjMNChcRkVrZNixa5Oann/ZnUc3LM9i40cXatR4mTixVkp5QYRgU/+GPRP3n30T959/OydPJERFpldSTJiIitVq92qgSoFVwu2HNGherVysICCW7n830lts9/UQQWyIiIk2hIE1ERGqVnR1RI0Cr4HY7r0voqDwPrWJ+moiItD4K0kREpFb5+XX3lBUUqCct1OS+9qa3HPHTmiC2REREGktBmoiI1Couru7sILGxyh4SakqGH+stdzh5TPAaIiIijaYgTUREapWWVkZpLWvFl5Y6r0voKZjpLFzu2p0HRUVBbo2IiDSUgjQREalVerrNwIGeGoFaaSkMHOghPV09aaEo/+Y7vOX211wRxJaIiEhjKEgTEZFaGQZMnFjKySeXkJrqoUMHm9RUD+PGlSj9fihzuylLPQCAdi/NC3JjRESkobROmoiI1MkwnB619PRaxj1KSMpd/A6dD88AIOrddygee1KQWyQiIv5ST5qIiEgY8hzY21tOPPesILZEREQaSkGaiIhImNr90GPesrF1axBbIiIiDaEgTUREJEwVnTXZW06cfHoQWyIiIg2hIE1ERCRcGQZFY8YCELl6FXg8QW6QiIj4Q0GaiIhIGNvzxFxvOfah+4LYEhER8ZeCNBERkTBmt4/3luPuui2ILREREX8pSBMREQlzu5Z84C27s74LYktERMQfCtJERETCXOlhR3jLHcYcG7yGiIiIXxSkiYiItAH5V/wdAMO2IT8/yK0REZG6KEgTERFpAwquvt5bjr/0bzU3sG2MHTtwbd7Ugq0SERFf3MFugIhIINg2rF5tkJ0dQX6+QVycTVpaGenpNoYR7NaJhACXi9L+Ju6fLWLeXETxKycQse4XItatJWLtWiLWrcW1O4+8Z1+keFxqsFsrItKmKUgTkVbPtmHRIjc//eTCXf5XLS/PYONGF2vXepg4sVSBmrRJUe8txf3dCiLW/kLEunVErP3F+1rCxRfW2N6TlExx+bpqIiISPArSRKTVW73aqBKgVXC7Yc0aF336GGRk2MFpnEiweDzE/206rj17/H5L4VmTITKyGRslIiL+0Jw0EWn1srMjagRoFdxu53WRtsbYnYedkOjXthW3MAonn9N8DRIREb8pSBORVi8/v+6xjAUFGusobY/doSO7Pv2CwjP+XO+2BlB81NGU9e3X/A0TEZF6KUgTkVYvLq7uoYyxsRrqKG2TnZDInkfmkPfsi3g6dXaeq2XbwsnntlzDRESkTgrSRKTVS0sro7TU92ulpc7rIm1Z8bjx7Fz+FUVjT8JXv7InIYGikye0eLtERMQ3BWki0uqlp9sMHOipEaiVlsLAgR7S09WTJmJ36cLu519m98OP42nfvsprRaedCbGxQWqZiIhUp+yOItLqGQZMnFhK374Gq1dHUFBgEBurddJEajAMis6aTMnRxxB/yV+J+vwzAPZNnhrkhomISGUK0kQkLBiG06OWnl7LuEcR8fL06Ene62/R7qnHiX7rDcrS0oPdJBERqUTDHUVERNoil4t9f7mI3IVvB7slIiJSjYI0ERGRtiwqKtgtEBGRahSkiYiIiIiIhBAFaSIiIiIiIiFEQZqIiIiIiEgIUXZHERFpMbYNq1cbZGdHkJ9vEBenpRJERESqU5AmIiItwrZh0SI3P/3kwl3+f5+8PIONG12sXeth4sRSBWoiIiJouKOIiLSQ1auNKgFaBbcb1qxxsXq1IrSmuPfeO+nSJaHOf9nZq1ukLRMnnsSwYYNaZF8iIuFIPWkiItIisrMjagRoFdxu5/WMDC1G3lSXXXYV/fr19/lajx49Wrg1IiLSGArSRESkReTn191TVlCgnrRAGDHiOI4++phgN0NERJpAwx1FRKRFxMXZdb4eG1v36yIiIm2FgjQREWkRaWlllNYymrG01Hldmt+wYYO44oqLueyyi+jZswsZGQPYsWMHAN988xWnnTaB3r1T6d07ldNPn8DKlSuqvD83dxcXX3whQ4YcTPfuSRx2WDq3334zhYWFNfb18ccfMXr0CHr0SGbIkIO5//578Xg8LfApRURaNw13FBGRFpGebrN2rYc1a6omDykthYEDPaSnqyctEHbv3u0NuipLSEggMjISgIULX6N///7cdtvdbNu2lc6dO/PJJ8uYPPl0Bg1KY9as6ykqKmb+/EwmTDiRBQve4Mgj/wDA9Onn8f33WcyY8VdSUrqyYsXXPPzw/ezatZP77nvYu79t27ZywQXncN5505g8+Vxef/1V7r77duLi4vjLXy5qmYMhItJKKUgTEZEWYRgwcWIpffsarF4dQUGBQWys1kkLtKlT/+zz+UWLlnjnqhUW7uOFF+bTtWs3ADweD3//+2UMGTKMN95YSkREBADTps1k5Mijue66q1m27N/k5OSwfPnH/OMft3PRRZcAMGXKVGzbZv369VX2V1RUxBNPPMO4ceMBOO20M8jIGMiSJW8pSBMRqYeCNBERaTGG4fSopacri2NzufnmOzjkkJrp7ys/17t3H2+ABpCdncWGDes577zp5ObmVnnfmDEnMmfOo2zevIlOnToTF9eeZ599mp49ezFy5Cji4uJ46KHHauwvNjaWsWNP8j5u3z6efv36sW3b1kB8TBGRsKYgTUREJIxkZAyuN7tjUlJylcfr168D4JZbbuCWW27w+Z7ffvuVbt1SmT37Qa688hKmTTuH6OhojjrqaE4+eQJnnPFnYmJivNt37NjJ2yNXISamHSUlOY35WCIibYqCNBERkTamevBUVuYkbZk16waGDTvM53sq1l6bNOkMRo4cxdKlS/jgg/dYvvwTPvlkGc8++zTvvruM6OhoAFwu5SYTEWks/QUVERFp43r06AlAXFwcI0YcV+VfQkICZWVlxMS0Y+/evXz55RcYhsHZZ5/Ds89msmbNWmbO/Cs//JDNJ58sC/InEREJDwrSRERE2rjBg4eSktKVp56aw969e73P79mzmxkzzuPSS/+G2+3mp59+5JRTTuDFF+d5t4mKimLQoHQAIiJ0WSEiEgga7igiItLGRUZGcued9zJjxnmMGnUMkydPJSYmmszM5/n11408/vjTuN1uhg07jCOP/AN33XUrv//+KwcfPIjff/+NuXPn0K9ff4YPPy7YH0VEJCwoSBMRERHGj5/Iq68u5sEHZ3P//fficrkYMGAg8+bNZ8yYEwEwDIPnn3+J2bPv5r333mXevOdITOzAySdP4JprbiAqKirIn0JEJDwYtq3FQwMtJ2ePDmoYSU6OJydnT7CbIc1E5ze86fyGN53f8KbzG950fh3JyfE+VwnV4HEREREREZEQoiBNREREREQkhChIExERERERCSEK0kREREREREKIgjQREREREZEQoiBNREREREQkhChIExERERERCSFaJ01ERERERCSEqCdNREREREQkhChIExERERERCSEK0kREREREREKIgjQREREREZEQoiBNREREREQkhChIExERERERCSHuYDdApLUwTXMA8BWQYllWYbDbI4FhmmYikAkkAFHAFZZlfRHcVklTmabpAh4DMoAiYLplWf8LbqskEEzTjASeAQ4EooHbLct6M6iNkoAzTbML8C0w2rKsn4LdHgkc0zSvBU7B+X/uY5ZlzQ1yk0KSetJE/GCaZgJwH87FnoSXK4CPLMsaAZwHPBrc5kiATARiLMs6CpiF8/uV8DAF2GFZ1jHAWOCRILdHAqw8EJ8D7At2WySwTNM8FvgDcDQwAugR1AaFMAVpIvUwTdMAngSuAwqC3BwJvAdwLgbAGV2gXtLw8EfgXQDLsr4EDg1ucySAFgA3lpcNoDSIbZHmMRt4AtgU7IZIwJ0AZAOLgLeAt4PbnNCl4Y4ilZimOQ24vNrTG4D5lmVlmaYZhFZJoNRyfs+3LOsb0zS74gx7vKzlWybNIAHIq/S4zDRNt2VZuqBv5SzL2gtgmmY88BpwQ3BbJIFkmuZ5QI5lWe+VD4uT8JIE9AJOBnoDb5qmOcCyLDu4zQo9CtJEKikfF11lbLRpmv8DppVf4HcF3geGB6F50kS+zi+AaZppwHzgKsuyPm3xhklz2A3EV3rsUoAWPkzT7IFzJ/4xy7JeCnZ7JKAuAGzTNEcBg4EXTNM8xbKsLUFulwTGDuAny7KKAcs0zUIgGdgW3GaFHgVpIvWwLOugirJpmuuBMUFrjAScaZoH4wyfOtOyrKxgt0cC5nNgPPCqaZpH4gyvkTBgmmYKzs2y/7Ms66Ngt0cCy7Is701Q0zQ/AS5UgBZW/g1caprm/UA3IA4ncJNqFKSJSFt3FxADPFQ+nDXPsqwJwW2SBMAiYLRpmv/Bmbd0fpDbI4FzHdARuNE0zYq5aSdalqUkEyIhzrKst03THA58jZMb4yLLssqC3KyQZNi2hoCKiIiIiIiECmV3FBERERERCSEK0kREREREREKIgjQREREREZEQoiBNREREREQkhChIExERERERCSFKwS8iImGlfD3DXuUPbaAAyAJutSzrvUrb2cBoy7I+bOk21qa87bdblvV0M+7jIJx14+K1wLeISGhST5qIiISjK3EWSu0OHImzuPUS0zRHVdqmG7A8CG2ry2HAi81VuWmaPYC3cdYGFBGREKWeNBERCUe7LcvaUl7eBFxtmmY34AEgDaDS6yHDsqyc5qrbNM2JwJPA5ubah4iIBIaCNBERaSueBJabpnmQZVn/qzzcsXyY4Z3ADGAQTg/bTJygbixgAWdblrUGwDTNPwL34wR8a4G7LcuaV/7ac0Ae0AU4BdgF3GBZ1nPlrx8L3AccDOQAj1uWdVf5a+spH+5omqYLp0fwQiAV+Bq4xLKsrPJtbWAqcBXQH/gWONeyrF9q+fzjgBvLP8vHjTuEIiLSEjTcUURE2oofy/97cC2v3wpcBxwDHAp8B7wLHA6UAbcDmKbZFXgHZ1hiWvn7/mWa5vhKdf21/P1pwGvA46ZpdjJNMwJ4HXgLGAhcBNxkmuYJPtpzE04AdjkwFFgHvGuaZnylbf5R/vqhQCecQNNgxzQzAAACfklEQVQny7JmWJY1p7bXRUQkdKgnTURE2oq88v/G1/L6C5ZlfQBgmuYnQJJlWU+WP34RmF6+3UXAx5ZlPVT++H+maQ4ALsMJvgCyLcu6t/y9NwGX4vTQfY8TTG21LGs9sN40zeNxeuO8TNM0gItxeuDeLH9uBvALcC7waPmmD1qW9VH564+Xt0FERFo59aSJiEhbkVD+3921vF45UNoHbKj2OLq8PBA40TTNvRX/gGtxhhxW8A45tCyrYn+RlmXtBB4BHjNN83fTNOcALh/z47rgBHNfVaqnBFhRvv8a+yn/XJG1fDYREWlFFKSJiEhbkV7+3+9reb16OnpPLdu5gZeBwZX+DQKGV9qm2Mf7DADLsi4GBgAP4Qy9/NQ0zfOrbbuvln1HlP+rbT9GLe8TEZFWREGaiIi0FRcA31qWta6J9VhAP8uy/lfxDziB/cMha2WaZlfTNB8DNliWda9lWccAzwJnVNmB0/u2GTii0nsjgWHl+xcRkTCmOWkiIhKOEsoTfBhAEnA2cBYwOgB1PwZcYprmXcAzQAZwL06Sj/rsBE4FIkzT/CfOkMZjgAU+tr0PuNk0zd+Bn4FrgHY4vXgiIhLG1JP2/+3aoU2FMRSA0Y9FWIAlCEHCTAj0CxIYBAWGBQj2SgSCFTA/4iERJE/QkHN027Tyyy0A/9Gu/STqvXpq/yXxbGaeDz14Zt6qi+q8/dfJXXU1M7e/2PtZXVYn1Wv1UD1W1z8sv6nuqvvqpTquTmfm49A3ALC2o23b/voOAAAAfDNJAwAAWIhIAwAAWIhIAwAAWIhIAwAAWIhIAwAAWIhIAwAAWIhIAwAAWIhIAwAAWIhIAwAAWMgXv7sGe+ecKxoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2209c388438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a biplot\n",
    "vs.biplot(good_data, reduced_data, pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Observation\n",
    "\n",
    "Once we have the original feature projections (in red), it is easier to interpret the relative position of each data point in the scatterplot. For instance, a point the lower right corner of the figure will likely correspond to a customer that spends a lot on `'Milk'`, `'Grocery'` and `'Detergents_Paper'`, but not so much on the other product categories. \n",
    "\n",
    "From the biplot, which of the original features are most strongly correlated with the first component? What about those that are associated with the second component? Do these observations agree with the pca_results plot you obtained earlier? yes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Clustering\n",
    "\n",
    "In this section, you will choose to use either a K-Means clustering algorithm or a Gaussian Mixture Model clustering algorithm to identify the various customer segments hidden in the data. You will then recover specific data points from the clusters to understand their significance by transforming them back into their original dimension and scale. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Question 6\n",
    "\n",
    "* What are the advantages to using a K-Means clustering algorithm? \n",
    "* What are the advantages to using a Gaussian Mixture Model clustering algorithm? \n",
    "* Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?\n",
    "\n",
    "** Hint: ** Think about the differences between hard clustering and soft clustering and which would be appropriate for our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Answer:**\n",
    "- We use a K-Means as a hard-clustering. Each point is assigned to a particular cluster. It's easy to implement and interpret.\n",
    "- We use a GMM as a soft-clustering. Each point is assigned to all the clusters with different weights(or probabilities). It allows flexibility in clustering shape(a cluster can contain another cluster). \n",
    "\n",
    "- I'd say we have to use GMM because we need more flexibile clustering. We know that the underlying structure of our dataset is not defined by individual features, but defined by the mixture of those features. There is no clear demarcation in our dataset. \n",
    "\n",
    "As the main two differences in these two algorithms are the speed and structural information of each:\n",
    "\n",
    ">Speed:\n",
    "\n",
    "> - K-Mean much faster and much more scalable. GMM slower since it has to incorporate information about the distributions of the data, thus it has to deal with the co-variance, mean, variance, and prior probabilities of the data, and also has to assign probabilities to belonging to each clusters.\n",
    "\n",
    ">Structure:\n",
    "\n",
    "> - K-Means straight boundaries (hard clustering). GMM you get much more structural information, thus you can measure how wide each cluster is, since it works on probabilities (soft clustering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Implementation: Creating Clusters\n",
    "Depending on the problem, the number of clusters that you expect to be in the data may already be known. When the number of clusters is not known *a priori*, there is no guarantee that a given number of clusters best segments the data, since it is unclear what structure exists in the data — if any. However, we can quantify the \"goodness\" of a clustering by calculating each data point's *silhouette coefficient*. The [silhouette coefficient](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html) for a data point measures how similar it is to its assigned cluster from -1 (dissimilar) to 1 (similar). Calculating the *mean* silhouette coefficient provides for a simple scoring method of a given clustering.\n",
    "\n",
    "Implement the following:\n",
    " - Fit a clustering algorithm to the `reduced_data` and assign it to `clusterer`.\n",
    " - Predict the cluster for each data point in `reduced_data` using `clusterer.predict` and assign them to `preds`.\n",
    " - Find the cluster centers using the algorithm's respective attribute and assign them to `centers`.\n",
    " - Predict the cluster for each sample data point in `pca_samples` and assign them `sample_preds`.\n",
    " - Import `sklearn.metrics.silhouette_score` and calculate the silhouette score of `reduced_data` against `preds`.\n",
    "   - Assign the silhouette score to `score` and print the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4, 5, 6, 7, 8, 9, 10]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imports\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "# Create range of clusters \n",
    "cluster_ranges = list(range(2,11)); cluster_ranges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### what \"Distance Metric\" to use ?\n",
    "\n",
    "The Silhouette Coefficient is calculated using the mean intra-cluster distance and the mean nearest-cluster distance for each sample. Therefore, it makes sense to use the same distance metric here as the one used in the clustering algorithm. \n",
    "- **Euclidean** for KMeans \n",
    "- **Mahalanobis** for general GMM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.49093664 -0.10743169]\n",
      " [-2.17322969  0.15659534]]\n",
      "When the cluster_size is 2, the AVG silhouette_score is : 0.42628101546910846\n",
      "[[ 1.74488838  0.00598088]\n",
      " [-1.62798915 -1.21631317]\n",
      " [-1.8404065   2.11247443]]\n",
      "When the cluster_size is 3, the AVG silhouette_score is : 0.3974234200078325\n",
      "[[-2.11389956  2.09087287]\n",
      " [ 0.88977881 -1.06160784]\n",
      " [-2.22044761 -0.86020807]\n",
      " [ 2.20735485  0.97157732]]\n",
      "When the cluster_size is 4, the AVG silhouette_score is : 0.3327470971178821\n",
      "[[ 2.64218703  0.52461516]\n",
      " [-2.30665683 -0.85231133]\n",
      " [-2.55451657  2.41003117]\n",
      " [ 0.34084753  0.85722602]\n",
      " [ 0.95448386 -1.62649872]]\n",
      "When the cluster_size is 5, the AVG silhouette_score is : 0.3509907789312652\n",
      "[[ 2.28433265 -0.08359701]\n",
      " [-2.51427159 -0.80101731]\n",
      " [ 0.04321922  0.35397759]\n",
      " [-2.58215121  2.21704992]\n",
      " [ 0.24358515 -2.11640401]\n",
      " [ 1.57776098  3.28825217]]\n",
      "When the cluster_size is 6, the AVG silhouette_score is : 0.3634526356380664\n",
      "[[ 0.02817376  4.79374534]\n",
      " [ 1.76497491 -0.60543628]\n",
      " [-2.48922899 -0.85049075]\n",
      " [-0.12288249 -2.14773124]\n",
      " [-2.7621493   1.78038848]\n",
      " [ 0.12294135  0.71994255]\n",
      " [ 3.18435884  1.09641784]]\n",
      "When the cluster_size is 7, the AVG silhouette_score is : 0.36304376625697876\n",
      "[[-0.2607615   0.09496101]\n",
      " [-0.02928744 -2.37792555]\n",
      " [ 3.56943731  0.96946287]\n",
      " [ 1.30116591  1.13190263]\n",
      " [-2.6493212   1.80100895]\n",
      " [ 0.02817376  4.79374534]\n",
      " [-2.52400132 -0.90749928]\n",
      " [ 1.81263652 -0.80279809]]\n",
      "When the cluster_size is 8, the AVG silhouette_score is : 0.36708624288541813\n",
      "[[ 1.30116591  1.13190263]\n",
      " [ 1.81263652 -0.80279809]\n",
      " [-2.40816199 -0.52932901]\n",
      " [-2.642744    1.9902839 ]\n",
      " [-0.05991529 -2.26985599]\n",
      " [ 3.56943731  0.96946287]\n",
      " [ 0.02817376  4.79374534]\n",
      " [-3.62670496 -2.70880761]\n",
      " [-0.24565538  0.10662025]]\n",
      "When the cluster_size is 9, the AVG silhouette_score is : 0.37065893692255675\n",
      "[[-0.1744554   0.31823588]\n",
      " [-2.00710946 -0.55754644]\n",
      " [ 1.68430848 -0.9812806 ]\n",
      " [-0.17079869 -2.45013   ]\n",
      " [ 1.45493716  0.86914743]\n",
      " [-1.31948733  5.41268758]\n",
      " [-3.74582414 -1.40843232]\n",
      " [-2.7349983   1.9586826 ]\n",
      " [ 2.25703274  3.67562117]\n",
      " [ 3.55958055  0.43641876]]\n",
      "When the cluster_size is 10, the AVG silhouette_score is : 0.3645360979697626\n"
     ]
    }
   ],
   "source": [
    "# \"k-means\": Loop through clusters\n",
    "for i in cluster_ranges:\n",
    "    # TODO: Apply your clustering algorithm of choice to the reduced data \n",
    "    clusterer = KMeans(n_clusters=i).fit(reduced_data)\n",
    "\n",
    "    # TODO: Predict the cluster for each data point\n",
    "    preds = clusterer.predict(reduced_data)\n",
    "\n",
    "    # TODO: Find the cluster centers\n",
    "    centers = clusterer.cluster_centers_\n",
    "    print(centers)\n",
    "\n",
    "    # TODO: Predict the cluster for each transformed sample data point\n",
    "    sample_preds = clusterer.predict(pca_samples)\n",
    "\n",
    "    # TODO: Calculate the mean silhouette coefficient for the number of clusters chosen\n",
    "    score = silhouette_score(reduced_data, preds, metric='euclidean')\n",
    "    print(\"When the cluster_size is {}, the AVG silhouette_score is : {}\".format(i, score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.25648563 -0.17661209]\n",
      " [-2.2256852   0.31284314]]\n",
      "When the cluster_size is 2, the AVG silhouette_score is : 0.3761556577675909.\n",
      "[[ 1.17792711 -0.46220755]\n",
      " [-2.27761367  0.10719722]\n",
      " [ 1.36212676  0.71791426]]\n",
      "When the cluster_size is 3, the AVG silhouette_score is : 0.342254608880423.\n",
      "[[-1.97097258 -0.46137624]\n",
      " [ 1.62707733  0.336509  ]\n",
      " [-2.19201767  1.92346849]\n",
      " [ 1.09367956 -0.79500258]]\n",
      "When the cluster_size is 4, the AVG silhouette_score is : 0.3009035343910248.\n",
      "[[ 2.89055604  2.74631729]\n",
      " [ 1.76865762 -0.40780594]\n",
      " [-2.7795463   0.51412705]\n",
      " [-0.97372042 -1.33286692]\n",
      " [ 0.25511448  0.61821233]]\n",
      "When the cluster_size is 5, the AVG silhouette_score is : 0.2606932111101687.\n",
      "[[-2.10353425 -0.64271788]\n",
      " [ 2.68575132  2.384453  ]\n",
      " [-2.8387016   1.73166301]\n",
      " [ 1.8312541  -0.26856849]\n",
      " [ 0.20697997 -1.89774056]\n",
      " [ 0.07957196  0.53148599]]\n",
      "When the cluster_size is 6, the AVG silhouette_score is : 0.31846415002209216.\n",
      "[[ 1.33643768  3.1137108 ]\n",
      " [-2.00217465 -0.61737301]\n",
      " [ 2.96492956  0.67884626]\n",
      " [-0.06899514  0.23378575]\n",
      " [-2.84492999  1.71435436]\n",
      " [ 0.5354548  -1.90527744]\n",
      " [ 1.74431564 -0.30376639]]\n",
      "When the cluster_size is 7, the AVG silhouette_score is : 0.29994914110484183.\n",
      "[[ 1.72555269 -0.1274271 ]\n",
      " [-2.49531466 -0.51381836]\n",
      " [ 1.13481433 -1.79058245]\n",
      " [-2.9516649   1.64686654]\n",
      " [ 0.19642057  0.35098976]\n",
      " [ 3.0294223   0.73409856]\n",
      " [-1.45889685 -0.88112066]\n",
      " [ 1.19994903  4.05109098]]\n",
      "When the cluster_size is 8, the AVG silhouette_score is : 0.2847350674576839.\n",
      "[[-1.87263833 -0.72729745]\n",
      " [ 3.34567059  0.89478576]\n",
      " [ 0.50109459 -2.03602976]\n",
      " [-0.04343837  0.5569138 ]\n",
      " [ 1.91083721  0.28166871]\n",
      " [-2.68299881  2.08441502]\n",
      " [ 0.90506111  4.37385573]\n",
      " [-3.44029132 -0.69473948]\n",
      " [ 1.58608204 -0.92392948]]\n",
      "When the cluster_size is 9, the AVG silhouette_score is : 0.30096633847215276.\n",
      "[[ 1.81574308e+00  2.52013918e-01]\n",
      " [-2.13102947e+00 -6.38714743e-01]\n",
      " [-2.77051602e+00  1.81375496e+00]\n",
      " [ 1.09985843e+00  4.18780589e+00]\n",
      " [ 1.22270875e-02 -2.12944086e+00]\n",
      " [ 4.10374385e-03  2.06820620e-01]\n",
      " [ 1.67753367e+00 -1.05452739e+00]\n",
      " [-3.04137875e+00 -3.19584931e+00]\n",
      " [ 4.28520163e-01  1.61947582e+00]\n",
      " [ 3.40989842e+00  7.28465284e-01]]\n",
      "When the cluster_size is 10, the AVG silhouette_score is : 0.34632006690431155.\n"
     ]
    }
   ],
   "source": [
    "# \"GMM\": Loop through clusters\n",
    "for i in cluster_ranges:\n",
    "    # TODO: Apply your clustering algorithm of choice to the reduced data \n",
    "    clusterer = GaussianMixture(n_components=i).fit(reduced_data)\n",
    "\n",
    "    # TODO: Predict the cluster for each data point\n",
    "    preds = clusterer.predict(reduced_data)\n",
    "\n",
    "    # TODO: Find the cluster centers\n",
    "    centers = clusterer.means_\n",
    "    print(centers)\n",
    "\n",
    "    # TODO: Predict the cluster for each transformed sample data point\n",
    "    sample_preds = clusterer.predict(pca_samples)\n",
    "\n",
    "    # TODO: Calculate the mean silhouette coefficient for the number of clusters chosen\n",
    "    # what metric argument to use?? metric='mahalanobis'\n",
    "    score = silhouette_score(reduced_data, preds, metric='mahalanobis')\n",
    "    print(\"When the cluster_size is {}, the AVG silhouette_score is : {}.\".format(i, score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianMixture(covariance_type='spherical', init_params='kmeans',\n",
      "        max_iter=100, means_init=None, n_components=4, n_init=1,\n",
      "        precisions_init=None, random_state=None, reg_covar=1e-06,\n",
      "        tol=0.001, verbose=0, verbose_interval=10, warm_start=False,\n",
      "        weights_init=None)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2209f3ea5c0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2209f344b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fit GMM with EM....using BIC ???information-theoretic criteria (BIC) ???????????????????????????????????????\n",
    "# BIC could sometimes be a better criterion for deciding on the optimal number of clusters.\n",
    "\n",
    "lowest_bic = np.infty\n",
    "\n",
    "bic = []\n",
    "\n",
    "n_components_range = range(1, 7)\n",
    "cv_types = ['spherical', 'tied', 'diag', 'full']\n",
    "for i in cv_types:\n",
    "    for j in n_components_range:\n",
    "        # Fit a mixture of Gaussians with EM\n",
    "        gmm = GaussianMixture(n_components=j, covariance_type=i)\n",
    "        gmm.fit(reduced_data)\n",
    "        bic.append(gmm.bic(reduced_data))\n",
    "        if bic[-1] < lowest_bic:\n",
    "            lowest_bic = bic[-1]\n",
    "            best_gmm = gmm\n",
    "print(best_gmm)\n",
    "\n",
    "bic = np.array(bic)\n",
    "\n",
    "import itertools\n",
    "color_iter = itertools.cycle(['navy', 'turquoise', 'cornflowerblue',\n",
    "                              'darkorange'])\n",
    "clf = best_gmm\n",
    "bars = []\n",
    "\n",
    "# Plot the BIC scores\n",
    "spl = plt.subplot(2, 1, 1)\n",
    "for i, (cv_type, color) in enumerate(zip(cv_types, color_iter)):\n",
    "    xpos = np.array(n_components_range) + .2 * (i - 2)\n",
    "    bars.append(plt.bar(xpos, bic[i * len(n_components_range):\n",
    "                                  (i + 1) * len(n_components_range)],\n",
    "                        width=.2, color=color))\n",
    "plt.xticks(n_components_range)\n",
    "plt.ylim([bic.min() * 1.01 - .01 * bic.max(), bic.max()])\n",
    "plt.title('BIC score per model')\n",
    "xpos = np.mod(bic.argmin(), len(n_components_range)) + 0.65 + \\\n",
    "    0.2 * np.floor(bic.argmin() / len(n_components_range))\n",
    "plt.text(xpos, bic.min() * 0.97 + 0.03 * bic.max(), '*', fontsize=14)\n",
    "spl.set_xlabel('Number of components')\n",
    "spl.legend([b[0] for b in bars], cv_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since in unsupervised learning processes, we do not have a **ground truth** to generally compare to, this makes evaluating a clustering model quite interesting and there's a lot of literature on this, some other methods to consider are the information criterion methods such as AIC or BIC. (if it is possible to make a likelihood function for the clustering model. For example: The k-means model is \"almost\" a Gaussian mixture model and one can construct a likelihood for the Gaussian mixture model and thus also determine information criterion values)\n",
    "\n",
    "**AIC**(Akaike information criterion) is an estimator of the relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Thus, AIC provides a means for model selection. AIC is founded on information theory: it offers an estimate of the relative information lost when a given model is used to represent the process that generated the data. (In doing so, it deals with the trade-off between the goodness of fit of the model and the simplicity of the model.) AIC does not provide a test of a model in the sense of testing a null hypothesis. It tells nothing about the absolute quality of a model, only the quality relative to other models. Thus, if all the candidate models fit poorly, AIC will not give any warning of that.\n",
    "\n",
    "**BIC**(Bayesian information criterion) is a criterion for model selection among a finite set of models; the model with the lowest BIC is preferred. It is based, in part, on the likelihood function and it is closely related to the AIC. When fitting models, it is possible to increase the likelihood by adding parameters, but doing so may result in overfitting. Both BIC and AIC attempt to resolve this problem by introducing a penalty term for the number of parameters in the model; the penalty term is larger in BIC than in AIC."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Question 7\n",
    "\n",
    "* Report the silhouette score for several cluster numbers you tried. \n",
    "* Of these, which number of clusters has the best silhouette score?\n",
    "\n",
    "http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Answer:**\n",
    "- In GMM, '2-clusters': 0.38, '3-clusters': 0.34, '4-clusters': 0.30, '5-clusters': 0.26,....\n",
    "- In k-means, '2-clusters': 0.43, '3-clusters': 0.40, '4-clusters': 0.33, '5-clusters': 0.35,....\n",
    "\n",
    "It seems '2-clusters' has the best score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Cluster Visualization\n",
    "Once you've chosen the optimal number of clusters for your clustering algorithm using the scoring metric above, you can now visualize the results by executing the code block below. Note that, for experimentation purposes, you are welcome to adjust the number of clusters for your clustering algorithm to see various visualizations. The final visualization provided should, however, correspond with the optimal number of clusters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "clusterer = GaussianMixture(n_components=2).fit(reduced_data)\n",
    "preds = clusterer.predict(reduced_data)\n",
    "centers = clusterer.means_\n",
    "sample_preds = clusterer.predict(pca_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzwAAAH4CAYAAACPEOhlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XucVVX9//HXmTkjM8wgKFJ4hTRZmpqoUyB20aTMUktES/CWmaRgZkVNmWF+1TCyvip5+6qFiv5MEUsrLU37ago5KopfdUFqmJqJKCMzzIxzZs7vj7XPcObMuV/32ef9fDx4AOey99qXgfXZn7U+KxSNRhEREREREQmiuko3QEREREREpFQU8IiIiIiISGAp4BERERERkcBSwCMiIiIiIoGlgEdERERERAJLAY+IiIiIiARWuNINEJHCGGPqgbOBWbif6a2Au4EfWWt7jTG/Bp611v4sz+3/CZhlrX2rgDYW1IYC9rsDcIe1dlo595uKMWYi8CKwOu7lEHCZtfYG7zONwLnAEd579cDNwE+ttdG4bZ0FXA4caK1dkWaf5wNzgdfi9rc1sBz4dvw2s2j/TGCetfbgbL+TD2NMK+66TUzyXhR4FujHHQvATdncW8aY04CtrLVXFrG5GGNOBr4ONOF+/h4Bvmut3VjANv8HuNpa+0QO30l5vxtjFgNvWWvPz3JbpwC/Av7LWvujuNdDuHt4s7V272zb5n33n8BMa217Lt+L+37S+8L7uXrWWtuSz3a9bUSBi6y1P4x7raj3uzHmIWCxtfaOYmxPRLKnDI9I9bsKOBA41Fo7GfgIYIDrirT9TxdpO2VnrX3dL8FOnG5r7eTYL+BzwKXGmA97ncm7gNG4QGYycDBwNHBBwna+DiwFvpnFPm+L2+e+wP7AscBninNIZXdI3LEcCsw0xnw7i+99DBhZzIYYY34AnAZ80bte+wJ9uIcOhfg0WwK6rJTgfn8FmJ3w2scp8jn0kW8ZYz5R6UaISPEpwyNSxYwxH8B1SLa31r4LYK3tMsZ8HUj2lDcKjItla2J/B3pwT3N3BwaAJ4A5wPXeVx80xnzOe28xsAvQAPw/a+3F3hPWh4HngYnAJ621/87yGKYBlwDN3vbPt9beY4xpxgVzk4BtgU24TJP1npS+DezhfeYY4DHgIK9tDwMne39+1lrb4mU6JgLbAxOA9cCXrLWvG2M+ClyJezr/ovf+t6y1DyW0dS/v+McCUeBSa+2NxpiDgYuAl4C9gRHAXGvtg5mO31r7mjFmrXec2wB7Ap+31vZ7728wxpzotT3WjoO9c/Jd4EVjzM7W2n9l2lec9+M6re9429sTuMw7rnrg8riM0wW4e2wDsDauDb8mLmsX/3djzCTgGuB9uGt6obX2NmPMjiS5f7zvnwGcA3QwNAOWlrX2LWPMt4Blxpife/u8xjvG8cA64DjcvXEU8GljTDdwR7LPWWvfzHbf3j36A2A/a+1/vPb0GWPmA0cbY7ay1r5njDkXd4/WAf8EzvTuu4dIft/+F7ADsNQYcxLu5yP+fl/u/T4RFxQtsdYuis90GGO2xj302Bf4NxDBZZ5i5/rrwHu4n/051trnkhziamBnY8w0a+2j3msn4zKOn/W29f5U59HL6KwEPuydp9h5awH+ADxmrf1eke6LOmPMdcABuIDzG96+XwDOstb+ydve/3jn6LIk2zgXuNkYs6+19p34N7x/P7az1s5L/Lt3HZ8APoW7/y7zzscncf+uHWetjbX9aGNMG+7nb6m19iJve6n+HTwF+Kr3eoe19pA050BEUlCGR6S67Q/8XyzYibHWvmGtvTOH7RwNjIrLEAHsaq39ivfnQ7wO9U3ADdbaA4CPAtONMcd5n9kJN/xlUg7Bzja4QOtEa+3+uA7pVcaYXYDDgY3W2qnW2knA48C8uK+/Y639kLX2Cu/vu+GyIfvgOh6fTLLLjwPHWmv3wHX25xhjwsAy4Dxr7Ydxw8QmJ2lrGPgdcIX3ucOBi40xB3ofmYILgPbDBYrnZ3kODgQ+iOuctQIrY8FOjLV2rbX2z3EvnYHrLL0O/IWh5yWZLxljVhlj1hhjNgBX4Dq5f/eO6w6gzbuunwS+Y4yZaoz5Aq6jPhkXQI/O5piA/wfcbq3dC5fButjrgCe9f4wxk3Hn6xPW2o/gOuK5eBrX2d4O+DKuI30gsCuwGXd/Lcddv19Ya3+Z6nM57ncP3NCutfEvWms3W2uXesHOSbh78qPez9cfGJp9HXbfWmvPBV4HZltrV3qfi7/flwIPWmv3wQVLJxhjvpzQth8D3V4bj8VlfWNDYP8b+Kx3rq/FZb5SuRHvvBhjRuJ+hu6Nez/TeXzWWrund/7B3UN/An5vrf2e91ox7osm4M/ez995wG9wwdNVuAwc3j34BWBJim3chAs6r02zn1QmevuegQtcHrLWtuLO1Vlxn9samOr9OsEYc3iGfwcB9gIOVrAjkj8FPCLVbYDi/Bw/AuzlPalsA/7bWvuP+A94T7M/CfyXMWYVsAL3RDYWHERwT6tzcSAu43KXt80/4DInH/bGuf/aGHOWMeYyXKcwfoz+wwnbuttaO2Ct3QT8A5cBSfRQXHD4lPeZfQCstX/0fn8QN0ck0SSgMRZIesHGMrwn3cA6a+0q789Pptg/QJMXfKwyxjwL/ATXsf0XWVxPY8x4XIAa67QtAb7mXZ9UbvM623t7bW4G/hh3XLsBN3jX4K+4zuN+wHTgTmvtJmttBLghXdu89m2LyypcB2Ct/Ze1djfcnJtU98+hwJ+stW94m8m1wxmbh9TtPbl/1Mv6XOkd87C5Hdl+LoNsfv6OwHVu273jPgsv+PBkc9+Cd7971/kg4JfecXQAv8YF4PGmAzdaa6PW2vW4rBBeMH077tgX4zIn15PaUmCGMaYBd9/9Dvezjre9TOcx8ef0JtzDkcvjjqcY98VGa+1tXpvuw2W+9sCdm08bY8bhMpX32PRzq84ADvDme+Ui9oDpRe/3e+P+Hn9Nr7PWRrx/h+7ADV1M+e+g951nEh9qiUhuNKRNpLr9HdjTGDPK6zAB4A0RuRaYmeQ7Ie8zW8VesNa+bIz5IC6o+BRwvzHmLDt0cm29991p1trN3ja2ww2J2Q7o9TrFuagHnrfWTolr+w7Aem8oy+m4oS634Ib0fCDuu50J2+qO+3OU5PMfkn0mkuSz/QyXrGNbh3uKnO3+wZvDk+K9FcA3jTH18VkeY8xHgG9Ya0/EPa2OAncbY2Jt2Bo42RhzF66zFPO5+I17GYd5uOE3P8UVM6jHdRYH2+QNU+rwPhN/HPHXN/EYt0r4THyBBQO8Qer75/Q0+8nGR4CXrbWdxphLcFmCG4AHcddn2LXI4XN/wA0vA1cI5Hdxbz8HNBhjPhj/gMC4whN34q5VPXCJtfYq770RuKGLMdneN7H7vS7JZ+Lvw1Tbig9STjDG7I0Lir6HGzL1hWQ7tda+YYx5EncvnQx8C/fzHjvWTOcx8ef0QuAQ3L11Fun/Xcnlvkj8mQ0BfdbajcaY24ETcIVd5qbZBtbad40xs3EPBH4a91aq+z2mN2E7fVm0M4Qbfpfy30FckJZ4DkUkR8rwiFQxa+1ruCewN3jDNWLDNq4ENlhruxO+sh43bArc0Au875yBG1LxJ2+YyX24J7Xg/oNu8J4wrsB1eDDGjAH+RoqOUpZWALsbb6KwN4RlLa6DeRjwa2vt9YAFjsR1DIrteaDXGBObk/BRXNYnsXqZBd4zxszwPrcDbrjXnykSa+1juDkHP/c6zbHg4wrgZW840unA1621E71fuwAX4yr1/dvGFUTwslCJ+3gP9xR7jjFmf++4eowxJ3j72xmX4ToA95T6WGPMGGNMHUOHKg3eS14H9ePe9t/FBVQnx23vb7isUar758/AZ4wxO3nbPiXbc+Zdh0uAWJW2w3AZypuAN3FP0GP3TYQtgUG6z8Wfr8/Fnc/fJbzX6+37Bu86xQKaXwDN3vm/Dzgt9vOJKz5xUxaHFt/W+H1uwp3Hud7+RgMnMfw+vBf4qjGmzhsy9QXv89sZY/6F+/fhv4Ef4jJy6dwIfBsYba1NzH5mdR7j/B04E3dffSbDvyu53BdjjTFHeNs4EhcwxYYa/hI3p6fOWvv3DMca+zm8FPhR3MvrcZmfkJeVyrfgx0neNrYBvoQLrNL9OygiRaCAR6T6nYl70vyoNxxipff3ZEMyvgH80ntiux9uMjO4Dk098Jwxph2XMYhN6r0TeMR7IjwLmGqMWe3t51Zr7dIs23mRMaYz7tet3lCbY4BFxpincR3BE62163Ad2DneMT2AGyb2wWxPSra8rNQxwPnGmKdwHbs3cHMR4j/XB3wRONsY8wxwP3CBzaIwQY6OwT35fcI7Jw/ghqEtwA2PqsMFufF+gZvD8jmyYK19xNvGYtwT5i/gOuXP4OZXnGet/Zu19g+4J/ftuOvdEbeZK4DtjTHW29ZDce/NAo7z2n83cJo3LCnp/WPdhO7vAg94919jhkN40BsS+ARuiNWNdkup6QuAn3nv3Ykbrhm7b/4IfMMY8/0Mn8uadZPrlwH3effq07jrF3sQcB1wD7DCGPN/uGFKp2Sx6buA24wxyTrWs4FDvfP4d2//v074zPm4a/sC7hqs9tr7Fi7L8oB37AtJ/m9FYlv2JXmglvN59H7uz8QFittQnPviTeAY7xp8HzgmlnG21j6Nm7N3dYbjjHeR15aYpbigZy1ewYUcthWvA/dA4FHcfMCHMvw7KCJFEIpGs16CQUQkkIwxi4CfWWv/42UknsYVbch7HRUR8QdjzG64gNzEhs2JSG3RHB4REVdK9wFjTB/u6fxpCnZEqp9xZdVPB85WsCNSu5ThERERERGRwNIcHhERERERCSwNaRMRXzPGXA58wvvrh4CX2VLK98AkleiKue/JuAnhHcAMa+0/S7WvDO3YDlhvrU1WNnl73EKSH8JVlusGLrbW/rYM7XoIWJxQvjzTd/6JK+HbjRs+GFvQ9bxMZc2NMZ8Hplhrf5TucwnfORhXLvkma+1JCe89CHzEWpvT+jv5HHfC99NdzygwzisukM+2/8mW81uHK0ZymbX22kK3n+1xexUGz8UV2Qh5bbgZ+Km1VsNKRKTsFPCIiK9Za78R+7PXmZttrW0v0+6Pwq1on+sihOV0HXC/tfZLAMaYDwF/M8ZMs9Y+X9mmpTR4Db0Sv0txlebOSvstt95OqoU50/k3cIQxZmTcWi8TGLoAaJDEn9+dgTXGmD9at7htSRljQriqbmtwDyR6jDFjgd/jFiQ9r9RtEBFJpIBHRKqaMaYX+C2ubO5sXNnfObiFAbcFFlprrzLGnIJbKX4A2B14DzjJWvust7bOD733+oH5wM640rn1xpgma+1sY8x5wPG4NVLWAPO8hRkfwi2MugdwFa7E7BO4RVzfhyvx/X7civLNwHHW2tXeGiqX4db9acCVoJ5vrY14bboIVx778TSnYHugyRhTZ60dsNY+Z4w5CleGF2PMqWnOxzG49XEmAq/g1iuZB0wCfm6tvdT73PG4bMGOwGvAyYlr/BhjpuHWpGn2zuP51tp70rQbAGttl3GLob5ojDnXO/9XeW3YFtiEK1s8Bvi6dz06cGsPDfuctdYm2c3buBXvv4hbxBbc2jW3eNuMBV5Jt5fk+saOOextow+37lAzhV9PcCXcP4I75z+01t5jjPkzcHtcpuZcYDtr7TkZtrUN0EXC4pUZjnc8roTzHrhrebW19vJUx52QmfsEsCfw+djiudbaDcaYE3H3GUnO53Lv94m4jNASa+0ibz9XAB/D/by+BHwFt8bOsNettVqgU0SS0hweEal2WwF3W2sNbs2RrwGfs9buh1vYL3619E8CZ1lr98Ytbjjfe30RcKa1thX3BPpgb32hq4HbvGDnK8DhuCFQH8YtzvnruG2/Y639kLX2Cu/vE702zMAFAg9527+XLZmMXwBPWGsPwK2LtB3wLW8Ryxtwa4kcgKsil8p3cEHKm8aY3xpj5gMveYFYS4bz8XFcB3ISLiD7MnAobj2fC73FRgEOAuZaaz+EC+Quj9sG3loqv8KtHbI/LjN2lTFmlzTtHmStfRV4F5dxORzYaK2daq2dhAsO5llrV7Llepyb6nNpdnMjQxdO/RJbgh+y2F7i9d0KuB23/ssJXqe/GNcT3PXbHzgBWGKMGYcLRk8D8K7LaaReV2apt07RC8BTuIDlnYTPpDveK4E11to9gAOB040xsbV1kh13vFZgZSzYibHWrrXWxi+OGn8+l+Iyqfvg7rUTjDFf9vZ9MPBh77y9hHugkep1EZGklOERkSB4GMBa2+mttv55Y8zuwGTcMJqYJ7zONbiFTGd4f/5/wHJjzO9xq7vHBwUxhwO/stZ2eX+/DDjXGLNVfBvi3On9/qL3+71xfz/Y+/MRwEeNMV/1/t7k/f4xYLW19jnv79fgMhrDWGv/4gUWU3FP148EfmSM+ZS19vEM5+Px2DAnY8zLwJ+stQPGmBdxizyO9D73J2vtGu/P/wOsSmjGgbhM013GDI4Si+I6oa8ka3cSUWCztfYOY8xLxpizcAtYHkySRR6z/Vycu3FB2PtwGb4XcFmGbLeXeH0vBUYBu8XNSyn4enqu9tr0rDHmOdz5vRu43BizL7AD8HKKbBYMHdK2A/AXY8z/WWtvzfJ4p+MW/MRa2wHs7W0r1XHHGyC7h6kPe9tsxgU5n4ntzxjza9zP29m4jN9KY8x9wDJr7d+NMWOSvZ7FPkWkRinDIyJB0AlgjNkJ1xmfgFvx/YcJn4svcBDFDZ/ByxgcBLQDpwCPxWU3YpL9PRzbBglDhnATxwdZa/uStLseONZaO9laOxmYgnvKPtg2T9LJ/MaY9xljrgSi1tpHrLUXW2s/AdwGnJzF+ehN+HuyNibuvw7X2Uw8judjx+Edy1TgvhTbSzyOCbhA7EVjzBnA9bihX7cAtzL0XMS+k9XnYqy17+EKUByPG3726xy3l3h9b8INw/qfuNcKup5x4s9vCOjzMiZXA6d6v1Jld4bwhh7+ji2FP4CMxxvx2hz77K7GmK29vyY77ngrgI8YY+oT9vcRY8xNcS/Fzmcdw69bHdBg3VpY++KymP3AbcaYc1K9nvIkiEjNU8AjIkHSCqwHLrTW3od74k5i5yueMSbsFUNottZejZu3syduDka8+4CveE+kAb4B/K+1NjFoyMV9wDnGmJAxZgSuYzoP9/R7L+9pPrggLJm3gU8DZ3uTxTHGjAR2wWWwcj4fKRxqjNnR+/PXcdmGeCuA3Y0xn/C2PxlYi8tEpOU9rb8CV/2rBzgM+LW19nrA4jJWsfZG2HJd0n0ulRtx5/ITbMm4xeS6vb/jhj9+0BjzNe+1Qq8n8e8bY/bHZaNWeq9fh5uHdgBu3ktG3v36aa+98dId7/24oY5488we8NqR6rgHWWsfw2XPfm5ctTa8IX1X4CosJn5+E+7+mRu3v5OAP3vZyQeAR6215+Ou376pXs/mfIhIbVLAIyJB8ifgVcAaY57CdfzX44bsJOXNQfgmcIsx5knc/IRTkwQy1+M6gn83xjwP7I8rklCIb+Amuq8GnvF+/6m1dj1uov5Sr00fSNP2z+CGPL1sjHkW1zm+z1p7A3mcjxReBW7yjnsi7nzFt2M9rgDCImPM07gswInW2lRzVWJzTJ4AHsLNH2nz3vsZMMcYswrXqX0yrr0PAEcZY67I8LmkvM54M3BPkrkn+WyvBxecLDLG7EaB1zPOrt71ug74srX2bW9/b+KykLemyBjGxM7vU7g5PPdYa3+Vw/HOA/Y0xjyDm+v2E2vtE2mOO9ExuKzNE9798AAuu7YgRXtn44Lq1biAahkuA/dH4P+AZ40x7cA04Pw0r4uIJBWKRlUSX0REkjOuSttMa+0RlW5LrTNu/Z7HgU/YMpSYFhEJCmV4REREfM4bPvY8cLmCHRGR3CjDIyIiIiIigaUMj4iIiIiIBJYCHhERERERCSzfLzy6fv0mjbkLiG22Gck772yudDOkRHR9g03XN7h0bYNN1zfYdH23GDduVMq12JThkbIJh3Nd+kOqia5vsOn6BpeubbDp+gabrm92FPCIiIiIiEhgKeAREREREZHAUsAjIiIiIiKBpYBHREREREQCSwGPiIiIiIgElgIeEREREREJLAU8IiIiIiISWAp4REREREQksMKVboCIiIiIiOQm3L4ann2e8N57Emndp+DtvfTSi1x11eX09PTQ3d3NgQcexKmnns5TTz3Bb3+7jB//+Cc5bW/Zsts45pgv5dyOV1/9FxdddD6hUIhdd92Nb33re9TVFZajUYZHRERERKSKNLctYsyMufDdSxkzYy7NbYsK2t6mTZs4//wf8I1vfJsrrriGa675FS+++A9++9tleW9zyZIb8vreFVf8nK997QyuvPI6otEoDz/817zbEKOAR0RERESkSoTbn6HplrsJ9fQCEOrppfHWe1zGJ0+PPPJX9t//I+y88y4A1NfX88Mf/pjPf/4LQz531FGHDf55wYLv8+ST7bzyyjrOOONU5s07nTPPPI3//OcNliy5nnff7eBnP1tIJBLhJz+5gLlzv8YZZ3yVJ59sB+DEE4/jBz+Yz4IF3x+yD2tfYL/9DgBg6tRptLf/Pe/jitGQNhERERGRKtGw4unBYCemrruHhpWr8h7a9tZb69lhhx2HvDZy5Misvvv44yvZc8+9OPPMs3n66afo6urk5JO/yrJlv+E732lj+fI7GD16DN///o/o6NjI3Lmnc/PNv6G7u5tTTvkqkybtMWR70WiUUCjktaGZrq7OvI4pngIeEREREZEq0Td1MtHGEUOCnoGmRvqmTM57m+9///asWfPCkNdef/013nzzPym/E42634844gssXbqEb3/7LJqbW5gzZ+6Qz7344j945pmneO65ZwHo74+wceNGAHbZZeKw7cbP19m8uYuWlpZ8DmnoNgvegoiIiIiIlEWkdR+6Zx1JtHEE4IKdnuOPKKhwwUEHfYyVKx/ltddedfuIRLjiil/w0ksvDt13JMLmzZvp6+vj5Zfde4888lf23Xc/LrvsKg455FCWLl0CuEwNwIQJE5k+/TAWL76WSy+9nEMOmc7WW28NMJjJibf77mZw2NuKFY+y77775X1cMcrwiIiIiIhUka6F8+md+Vm2efYFOvbeo+Aqbc3NLZx77o+55JILGRgYYPPmzRx00Mc5+uiZPPXUE4OfO+6445kz5xR22GFHxo/fHoA99vgQF164gCVLrmdgYICzzvoWABMnfoALLjiPtrbzuOSSC5k373S6ujo5+uhj01Zdmzfvm/z0pxdxzTW/ZMKEiRx88KEFHRtAKBZ9+dX69Zv83UDJ2rhxo1i/flOlmyEloutbeuH21TSsWEXf1MlFKUGaC13f4NK1DTZd32DT9d1i3LhRw9NFnrJneIwx3weOArYCrrTWXl/uNoiIVJvmtkWDVXmijSPonnUkXQvnV7pZIiIivlfWOTzGmIOBacBBwCeBncu5fxGRalSKEqS1LNy+mqbFN+n8iYjUiHJneA4DVgPLga2BjI8nt9lmJOFwfanbJWUybtyoSjdBSkjXt0SefQGSlCDd5tkX4PBpZWtGIK7vvP+CG+6E7l5oGgGnzoDF51W6VRUXiGsrKen6Bpuub2blDni2AyYARwAfAH5njNnDWptyns4772wuV9ukxDTONNh0fUsnvPeejElSgrRj7z2IlOmcB+H6htufYcz1d245j929DNywnI7PH1r2OVF+EoRrK6np+gabru8W6QK/cpel3gDcZ619z1prgR5gXJnbICJSVUpRgrQWpVusT0REgqvcGZ5HgLONMT8HtgeacUGQiIikEStB2rByFX1Tyl+lLQhKsVifiIj4X1kzPNbae4CngL8DdwNzrbX95WyDiEi1irTuQ/fcExXs5EmZMhEJknB7Jyx6xf1eBC+99CLz55/NWWfN4bTTTuL6668hGo3y5JPtLFjw/Zy3t2zZbQW15/LLL+Wuu+4oaBsxZS9Lba39brn3KSIiAsqUiUgwNLeto+mWDdATZUxjiO5ZY+laOCHv7W3atInzz/8BF120iJ133oX+/n7OO6+N3/52GbvsMjGvbS5ZcgPHHPOlnL/3zjvvcOGFC/jXv9Yxa9aJee07UdkDHhERkUqKtO6jQEdEqla4vZOmWzYQ6nE1v0I9URpv3UDvzLFEWlvy2uYjj/yV/ff/CDvvvAsA9fX1/PCHP6ahoYHVq58e/NxRRx3G7353HwALFnyfL3zhGLbbbhw/+cmPqa8PMzAwwIIFF3Lvvb/n3Xc7+NnPFvLNb36HRYsu5tVX/8XAwABf+9oZ7L9/KyeeeBw77zyBhoYwP/7xTwb30d29mVNPPZ0VK/6W7ykaRgGPiIiIiEiVaFjRORjsxNR1R2lY2Zl3wPPWW+vZYYcdh7w2cuTIrL77+OMr2XPPvTjzzLN5+umn6Orq5OSTv8qyZb/hO99pY/nyOxg9egzf//6P6OjYyNy5p3Pzzb+hu7ubU075KpMm7TFkezvssCM77LCjAh6RoAm3r6ZhxSr6pmqIjYiIiKTWN7WFaGNoSNAz0BSib0p+wQ7A+9+/PWvWvDDktddff4033/xPyu9Evd0fccQXWLp0Cd/+9lk0N7cwZ87cIZ978cV/8MwzT/Hcc88C0N8fYePGjQB5D5fLVbnLUotIgua2RYyZMZeWCxYzZsZcmtsWVbpJIiIi4lOR1ha6Z40l2hgCXLDTc3z+w9kADjroY6xc+Sivvfaq20ckwhVX/IKXXnpx6L4jETZv3kxfXx8vv+zee+SRv7Lvvvtx2WVXccghh7J06RIAol5ENGHCRKZPP4zFi6/l0ksv55BDprP11lsDEAqF8m5zLpThEamgcPszNN1y92CZ3FBPL4233kPvzM8WNdOjDJKIiEhwdC2cQO/MsWzzbB8dezcUFOwANDe3cO65P+aSSy5kYGCAzZs3c9BBH+foo2fy1FNPDH7uuOOOZ86cU9hhhx0ZP357APbY40NceOECliy5noGBAc4661sATJz4AS644Dza2s7jkksuZN680+nq6uToo4+lrq68OZdQLPryq/XrN/m7gZIUralqAAAgAElEQVQ1rQY8XNPim2i5YPGw1zsXzKN7bnEqkzS3LRoMqqKNI+iedSRdC+cXZdvxdH2DTdc3uHRtg03XN9h0fbcYN25UynSRhrSJVFBsIcR4xVwIMVUGKdy+uijbFxEREfE7BTwiFVTqhRAbVjw9ZFV5gLruHhpWrirK9kVERET8TnN4RCqslAshxjJI8UFPMTNIIiIiIn6ngEfEB0q1EGIsgxQb1lbsDJKIiIiI3yngEQm4UmaQRERERPxOAY9IDShVBklERETE71S0QEREREREAksBj4ikFW5fTdPim1TKWkRERKqShrSJSErlWrRUREREpFSU4RGRpLRoqYiIiASBAh4RSUqLloqIiEgQKOARkaRii5bG06KlIiIiUm0U8IhIUrFFS2NBjxYtFRERkWqkogUikpIWLRUREZFqp4BHRNLSoqUiIiJSzTSkTUREREREAksBj4iIiIiIBJYCHhERERERCSwFPCIiIiIiElgKeEREREREJLAU8IiIiIiISGAp4BERERERkcBSwCMiIiIiIoGlgEdERERERAJLAY+IiIiIiASWAh4REREREQksBTwiIiJSM8LtnTQtfoNwe2elmyIiZRKudANEREREyqG5bR1Nt2wg1BMl2hiie9ZYuhZOqHSzRKTElOERERGRwAu3dw4GOwChniiNt25QpkekBijgERERkcBrWNE5GOzE1HVHaVipgEck6BTwiIiISOD1TW0h2hga8tpAU4i+KS0VapGIlIsCHhEREQm8SGsL3bPGDgY9A00heo4fS6RVAY9I0KlogYiIiNSEroUT6J05loaVnfRNaVGwI1IjFPCIiIj4SLi9k4YVnfRNVYe8FCKtOq8itUYBj4iIiE+obLKISPFpDo+IiIgPqGyyiEhpKOARERHxAZVNFhEpDQU8IiIiPqCyySIipaGAR0RExAdUNllEpDRUtEBERMQnVDZZRKT4FPCIiEjN8XPpZ5VNFhEpLgU8IiJSU1T6WUSktmgOj4iI1AyVfhYRqT0KeEREpGao9LOISO1RwCMiIjVDpZ9FRGqPAh4REakZKv0sIlJ7VLRARERqiko/i4jUFgU8IiJSc1T6WUSkdmhIm4iIiEiVCbd30rT4DVUYFMmCMjwiIiIiVURrSYnkRhkeERERkSqhtaREcqeAR0RERKRKaC0pkdwp4BERERGpElpLSiR3CnhEREREqoTWkhLJnYoWiIiIiFQRrSUlkpuKBDzGmPcBTwCftta+UIk2iIiIiFQrrSUlkr2yD2kzxjQA1wDd5d63iIiIiIjUlkrM4fkZcDXwegX2LSIiIiIiNSQUjUYzf6pIjDGnADtZay80xjwEfD3TkLZIpD8aDteXo3kiIiIiIlKdQinfKHPA879A1Ps1GVgDHGWtfSPVd9av31S+BkpJjRs3ivXrN1W6GVIiur7BFrTrG27vpGFFJ31TNQ8iaNdWhtL1DTZd3y3GjRuVMuApa9ECa+0nYn+Oy/CkDHZERESKrblt3eBK9dHGEN2zxtK1cEKlmyUiIiWidXhERAoQbl9N0+KbCLevrnRTJAvh9s7BYAcg1BOl8dYNhNu1Sr2ISFBVbB0ea+3Bldq3iEgxNLctoumWuwn19BJtHEH3rCPpWji/0s2SNBpWdA4GOzF13VEaVnbW/NA2EZGgUoZHRCQP4fZnBoMdgFBPL4233qNMj8/1TW0ZXKE+ZqApRN8UBTsiIkGlgEdEJA8NK54eDHZi6rp7aFi5qkItkmxEWlvonjV2MOgZaArRc/xYZXckkMLtnbDoFQ3ZlJpXsSFtIiLVrG/qZKKNI4YEPQNNjfRNmVzBVglkrsDWtXACvTPH0rCyk74pqtImwRQrzkFPlDEqziE1TgGPiEgeIq370D3ryMFhbQNNjfQcfwSR1n0q3bSalm0FtkirAh0JrlTFOXpnKpsptUkBj4hInroWzqd35mdpWLmKvimTFexUmDp5Io6Kc4gMpYBHJEfh9tU0rFhF31R1cMVlenQf+IM6eSJOrDhH/M+DinNILVPAI5IDlSEW8a9ydvIyzRMSqaRYcY5YxlPFOaTWKeARyVKqMsS9Mz+rJ/wiPlCuTl6284REKilWnGObZ/vo2LtBwY7UNAU8IllKV4ZYAY+IP5S6ApvmCUk1ibS2wOGjiKzfVOmmiFSUAh6RLKkMsUh1KGUFNs0TEhGpPlp4VCRLsTLE0cYRACpDLFKDYvOE4mkyuIiIvynDI5IDlSEWqW2aDC4iUn0U8IjkSGWIRWpbqecJiYhIcSngERERyVEp5wmJiEhxaQ6PiIiIiIgElgIeEREREREJLAU8IiIiIiISWAp4REREREQksBTwSFUKt6+mafFNhNtXV7opIiIiIuJjqtImVae5bRFNt9xNqKeXaOMIumcdSdfC+ZVuloiIiIj4kDI8UlXC7c8MBjsAoZ5eGm+9R5keEREREUlKAY9UlYYVTw8GOzF13T00rFxVoRaJiIiIiJ8p4JGq0jd1MtHGEUNeG2hqpG/K5Aq1SERERET8TAGPVJVI6z50zzpyMOgZaGqk5/gjiLTuU+GWiYiIiIgfqWiBVJ2uhfPpnflZGlauom/KZAU7IiIiIpKSAh6pSpHWfRToiIiIiEhGGtImIiIiIiKBpYBHREREii7c3knT4jcIt3dWuikiUuM0pE1ERETSCrd30rCik76pLURaWzJ+vrltHU23bCDUEyXaGKJ71li6Fk4oQ0tFRIZTwCMiIiIp5Rq8hNs7Bz8PEOqJ0njrBnpnjs0qWBIRKTYNaROpEeH21TQtvolw++pKN0VEqkSq4CXdMLWGFZ2Dn4+p647SsFJD20SkMpThEakBzW2LaLrlbkI9vUQbR9A960i6Fs6vdLNExOfSBS+psjV9U1uINoaGfG+gKUTfFGV3RKQylOERKRK/ZlDC7c8MBjsAoZ5eGm+9x3ftFBH/iQUv8TIFL5HWFrpnjR383kBTiJ7jNZxNRCpHGR6pOeH21TSsWEXf1OItWurnDErDiqcHg52Yuu4eGlau0lpGIpJWLHiJDWvLNnjpWjiB3pljaVjZSd+U7AodiIiUigIeqSmlCExSZVB6Z37WFwFF39TJRBtHDAl6Bpoa6ZsyuYKtEpFqkW/wEmlVoCMi/qAhbVIzSjW0K10GxQ8irfvQPetIoo0jABfs9Bx/hC+CMRGpDpHWFrrnjlcAIyJVSRkeqRmlGtpVDRmUroXz6Z35WRpWrqJvSvGG8omIiIj4nQIeqRmlCExi84F6p09jxP2PEurp9W0GJdK6j+/aJCIiIlJqCnikZsSGdsWGtRUamCTOB+qZPo3+A/YqSwalFIUXRERERIJIAY/UlGIN7Uo2H2jEA4/Rcebskgcgfq4IJyLiN+H2ThpWdNI3VUUURGqVAh6pOcUY2lWpUs9+rwgnIuInzW3rBktqRxtDdM8aS9fCCZVuloiUmaq0ieQhNh8oXqkKFcQvaOr3inAiIn4Rbu8cDHYAQj1RGm/dQLi9szJtWfxGRfYtIsrwiOSl2POBUkkcvtY7fZrvK8KJiPhBw4rOwWAnpq47SsPKzrIObVOWSaTyFPCI5KnUpZ6TDV/b6oHH6Jk+jcYcKsKpwIGI1KK+qS1EG0NDgp6BphB9U8oX7KTKMvXOHKv5RCJlpIBHpACFzgdKF4ykGr7Wf8BebDxzdlaBlgociEitirS20D1r7GDAMdAUouf48gYafskyidQ6BTwiccqZDckUjKRbNyibQEsFDvxJGTeR8ulaOIHemWNpWNlJ35TyV2nzQ5ZJRBTwiAwqZzYkm2Ak0zyhTB3nSlWSk9SUcRMpv0hr5cpR+yHLJCIKeESA8mdDsg1GUs0TyqbjnC5DJOWnjJtIbap0lklEVJZaBEgfgJRCLmWtI6370D33xLjMTvKOc7h99fDvzTpycD+lqiQn2VFJcZHaFWltoXvueAU7IhWiDI8I5c+GFFLWOpehaqWuJCfZU8ZNRESkMhTwiFC+dXXi5RuM5NpxLrSSnBRHJe4xyV24vZOGFZ30TdXQIxGRoFDAI+KpRDYkn2Ck2B1nVQ0rH2Xc/E0LRIqIBJMCHpFE0cwfqbRidZxVNaz8KplxU3CbmhaIFBEJLgU8Ip5q6/wXvuhpdVQNUye9OKrt/o5XjmFmWiBSRCS4FPCIUD2d/2KqhnV6qrmT7ifVfH+Xa5iZFogUEQkulaUWwV8lg8Ptq2lafNOwMtPFlktp7ErItvx2NSrXNY7x0/2di1TDzMLtnUXfV2yByGhjCEALRIqIBIgyPCL4p2RwOTMafq8aVg0ZqHxUImvlh/s7n2FpmYaZFXuomxaIFBEJJgU8Ivij81+JYUd+rhrmh056sVVqaFml7+98h6WlG2ZWqqFukVYFOiIiQaOARyrGb5PRS9X5z/Y4K5XR8Os6PZXupJdCJbNWlQpuC6l+FhtmFvt+bJgZoIpqIiKSNQU8UhF+nYxe7M5/LscZxIxGofycgcpHpa9xJYLbQquf9c50AQ4h6D3GBTRNi99QRTUREcmaihZI2fltMnqpJpDncpyxLFDv9GmDhQSCkNEohkjrPnTPPTEQ5yGWtaqlaxwblhYv2+pnzW3rGDNjDSNveIumpRsYcceGgrcpIiK1RxkeKTs/TUYvZaYp2+NMbEPP9Gn0H7BXIDIaMlzQslaZpBqWlikTk2koXD7brFblWIdIRCTIyhrwGGMagBuAicAI4EJr7e/K2QapvEoP64kp9QTybI4zWRtGPPAYHWfODnxHGPw3j6tc/DpvqlTyqX6WaShcrVRUK9c6RCIiQVbuIW0nABustR8HPgssLvP+xQf8Mqyn1GuTZHOc1bo+SjE0ty1izIy5tFywmDEz5tLctqjSTZISirS20D13fNaBSTbD1nLdZrUp5zpEIiJBFopGo5k/VSTGmBYgZK3dZIwZCzxurd013Xcikf5oOFxfngZKea14Gh5+Aj5+AEzdtzL7/9Qp0B0XcIxshAd+Vdz2pDvOcrWhGFY8DQ+3w8dbC2/bY6vg0K9Ux3HXohUd8HAHfHw0TB1duXbMWwM3/Bu6ozCyDr4yHhZPqlx7ym3RK/Ddl4a//tNdYf4u5W+PiIi/hVK9UdYhbdbaTgBjzCjgDuCHmb7zzjubS90sKZNx40axfv2mLS/stqv7BRD/ernstivNxyeUPf7yEXTttmtx25PuOMvVhgJlM9dp2PVNo+neR2npHprZYnMPnff+je7d0j4DkRJLNYQql+tbNAu2J/z5UUOHrfno56LUwns3MCbJOkQdezcQKeJ5qMi1lbLR9Q02Xd8txo0blfK9shctMMbsDCwHrrTW3lLu/YvE88MEcj+0IZnY/Jr+MS1Fn+vkl3lcMlS6QgEcnvo/klKq5YVAa604g4hIqZS7aMH7gT8B86y1D5Rz3yKp+GECuR/aEG9IRiccJhSJDHm/0Kp6QVxUNAjSFQrg8Ao1ysfKUT2tVooziIiUUrkzPD8AtgHOM8ac5712uLW2u8ztECmJIFQd2+qmu2i68a7BICcUiRBl6MDYYmRj/JrZqmWxQgGJQ6i0vs1w5ayeVstZLhGRYij3HJ6zgbPLuU+Rcinlmj7l0ty2iKYblxOK9A95PQREw/WEIv1Fzcb4LbOVjyAEuTEaQpWdTGsEiYiIv2jhUZEiKMaaPpXuOA8eQ0KwAy6js+mic6jv2KRsTJwgBLmJNIQqs0xrBImIiL8o4BEpgnTr6WQTHPih45zsGACiDWF6jj+C9074Ylnbk6jSAeHw9pR24dpKKtUQqnLMeSkHDf0TEakuCnhEUsilg11I1TG/dJyTHkM4zKZL5lc82PFDQJio0CC31pRzzkupaeifiEh1qat0A0T8qLltEWNmzKXlgsWMmTGX5rZFaT8fqzoWbRwBkNM8l3Qd53JKegwnfbHiwU6qgDDcvrqi7YoFiPFUWju5VHNewu2dFW5Z/roWTmDjnZPoXLAjHcsmVW3wJiJSC5ThEUmQb8Yl36pjflqTxo+V0/yaSVFp7ewFdc6LqqeJiFQHBTwiCQrpYOdTdcxvHWe/VU7zU0CYyI8Boh9pzouIiFSSAh6RBKXoYGeaD6SOc2p+CwgT+S1A9CPNeUktKIUcRET8TAGPSIJid7CznXCvjnNqpQoI/Vb5LchU7nq4IBVykOQU0Ir4QygajWb+VAWtX7/J3w2UrI0bN4r16zdVuhlZC7evLriDHW5/hjEz5g3LFnUsWzxsm9Xe+a626+vHym9+Vm3X1+/C7Z2MmbFm2DC/jmWTyt4x1rUtDb8EtLq+wabru8W4caNCqd5ThkckhWJkXLKdD6TOd3n5pRS4lI7fn6wHtZCDOKkqE/bO9OdQTr//vIgUSgGPSAllMx9oq5uX03TjckKRfiAYnW+/Z6v8WvlNisMvT9bTUSGHYKumgLYafl5ECqV1eERKKNP6PM1ti9j6uz8bDHZiKrEOT7HkuoZRJWgNneCqljV/YoUcoo1uBIYKOQRLLKCN58eAtlp+XkQKpQyPSB5yyWCkmnA/OKwqEhn2nWrtfFfLUDG/V36T/FXTk3UVcgiuaqlMWE0/LyKFUMAjkqN85tskmw+UbFgVwEA4XLWd72oaKhbEUuAah++/oWKZrokWLw2uagho/fbzIlIqCnhEclDMDEay+T3RhjCbLpnPeyd8scjtLs+cGj8vEppMkEqBaxy+M/hk/ea3CL0H0RFU7Mm6X66JAuHK8XtAOywTFYbeQ0f5us0i+dAcHpEcpMtg5CrZ/J7uE79Y9GCnnHNqMs1ZktLQOPwkYg+sByqze79ck+a2dYyZsYaWC15jzIw1NLetK+v+xf+6Fk6gd/poomGoi0Dj/Zt0n0jgKMMjkoNiZzBKPayqEnNqgjhUrFzyzcRpHP6WLEb/6DoXaPS510N9VKQcsB+uiR9LIyvblJ9Snrdweycj7u8g5E0n9cN9IlJsCngkL5k6Zn4vS5yvUkx2L+WwqkrNqQnSULFyKWQtplofhz9k6FiYwY5bTCWCPz9cEz8EXfHKNcQvaEFVqc+b3+4TkVJQwCM5y9QxC/oimtWUwcgmI+X34NTv7SuGQjNx1VIRqhSGZTEibjRbfEHgSgR/frgmfgi6YsqVbfLLvKlMsg3KynHe/HSf+FHQAuhapYBHcpKpY5bufQ6fVsmmF1W1ZDAyZaT8Hpz6vX3FUoxMXDVUhCqFZE+nQzCY6alk8JfumhSzE5VqW6UKuvJpezmyCH4cwpdMLkFZOc6bH4Jzv6qWAFoyU8AjOcnUMUs7qT9AAU81ybgOkE/XzPF7+4qpWHPD/F4RqhRSPZ3edNFO1HcMVDz4S3ZNitmJyrStYgfC+ba9HFmEahialWtQVq7sS60+MEmnWgJoyY6qtElOMq1QrxXs/SnSug/dc08cEigUs+JcKfi9fcWUTXW7cHsnTYvfqO3Ka0nEnk7HVrWPPZ1+74T30T13fFGyGcU878Ws3pbttiKtLQWdi9g52OrmN/Nue6rrlE+bUl2TWHAQz29Ds9IFZckU87xlUuh9EjS5XivxN2V4JCeZhkhpBfvq4fc1c/zevmJLNzdMwyrSK9XT6VKc92JmIcqR0ShmQYhiXKd016Qahmblk7FR9qUyNLcpWBTwSM4yTdqvpkn98Wphcnw8vwenxWxftVzbZHPDNKwiO8Uezleq817MTlSpO2SlKAhRyHXK5pr4PTjINyirxeGqlVYNAbRkL2XAY4wJA3OBXYC7rLUPx713vrX2/NI3T/wq06T9apnUH1Mrk+MT+T04LUb7qv3aVsO8hGqVbvJ9qc57MTtRhWwr/tg5fFTSz/itIES218TvwYEfgjJVHsuOH66VFEe6DM81QD2wGrjRGPM/1tqLvfeOAs4vcdtEyqKWJscn4/fgtJD2BeHaJn2KH4b+0ZqCWYhMw9VKmT0pZicqn20lHjtf3QQLth/2Ob8VhAjSEKNKBmUaIpsbvwfQkp10/2O2WmtPsdZeCnwU+JIx5pvee6E03xOpKrU0Ob7WBOHaJk5ajgJ1Edj6B6/S3Lauso2roEKKCWQz2b/Uk8WLOUE8l20lO3auez3peSx1QYhclXMCf1AVs2iGSDVJl+GpM8Y0W2u7rLXrjTGfAx4xxryJ+z9XJBBqbXJ8LQnKte1aOIG+vZrY+nv/Gpw0XstzeQp9Qp3t0Khssif5Dg2q1JCiZMdOLzQveJWO3+8x7PN+G9Ljt/ZUGw2RlVqVLsNzBfCkMeZTANba14DDgZ8Ae5ahbSJlkU1JYKlOQbq29R0DKStkFZufS2Dn+4Q6/phyKV+cLnvS3LaOMTPW0HLBa4yZsSbrjFu+3yuGvqktRBuGv97wTFfKc+i3csV+a081qYbS3SKlkDLDY6291hjzINAb99oLxpi9gNPK0TiRcvH75H3JX1CubbnmL/h9fH8+T6iTHVOhhQPyreJW6ap7kdYW+iaPZKvHNw95PdSLnvJnqZon/KvymNSqtGWprbVrk7zWCfx3yVokUiGFTt6vltLHtcjvhRkyiXWweqePZsT9HSXrqKTrjKeq5FXMfWfTicw18Et1TB3LJhU0NCrfoUF+GFLU9eOdafiiJfTeltf0lD87fn8gkA0NC5RapHV4pOr4MbCo9tLH4l+JHaye6aPoP2BUSToqaVcWP7youxoil05krk+o0x1T4rCoXJ7c55txq0SlscTjirS20H3CdlsCwZF19Hx5W3V8M6h0dq6YVHlMao0CHqkqfgwsglD6WPwpWQdrxAOb6Dhz+5J0VirVGc+1E5nLE+psjynboCs+eMh3AclyDilKdVzx57Dls++jazeVOc/ED9k5EclPxoDHW4D0MGBb4spRW2tvLGG7RIbxa2CRrvSxAh5/8GNWMBvl7mBVYnx/vseY7RPqbI4p26ArWfCw8c5JOQ8NynVIUSGV4NIdV+xXy7hRsH5T1tutdvmezyCtAyRSa7LJ8NwCTACeZ0s56iiggEfKyq+BRVBKH5eCHwKNcmYFi328lehglXt8fzmOMdMxZRN0pQseuueOz7lN2QZshcwZCWpGopCiAYWcT034F6le2QQ8H7bWDi/OL1Jmfg0sYqWPY53qai59XEx+GH5YzqxgKY63Uh2sco7vL9cxpjumbIKuSgQPhc4ZCWJGIpuAJVVAVIw5OJrwL1Kdsgl4njfGbG+t/XfJWyOShp8Di2orfVzqzItfhh+WKytYyuOthg5WoWV6K32M2QRdlQgeCg2ygpaRyCZgSRcQFSto1YR/keqTTcAzErDGmGeBntiL1tpPlaxVIin4ObColtLH5ci8+GX4YbmygqU+3vgOVinWACn2EKHemWNz3l6lO5GZgq5Cgod8z2//mDqiYYYsOJtrkFXpYLKYMgUsmQKiIGa8RCQ72QQ8F5e8FSI5qJbAwo/KlXnxy/DDcmUFkx1vtCFM/+jirl1TijVACtlm0g7mkrdouvktQu9RdeuUZAq68gkekpYV339UxuBn8HsRN2k2BCkLLmQKpiodTBZLpoAlU0AUtIyXiGQvYx1Ka+1fcVmeI4GjgTHeayJSZdJlIoopFmhEG0cAVHT4YdfC+Wy885d0LphHx7LFJZlHlHi8USDUF2HrH/yc5rZFRdlHqqfX4fbO7L67+I1hny1km5Cig9nP4IKWuW6vUlKdn2QirS3D1u9Ju93E83vPu7Rc8BpjZqyhuW1ddt8Dog2w6aKdhgSPzW3rGDNjTcbtZbSiI+vjr6RYwBJtdAVjEwOWWEAULzGD07VwAhvvnETngh3pWDapaoJxESlMNmWpvwscAyzF/bt7rjFmL2utMj8iVaacmRc/DT8sR1awa+F8+vb+IFt/dxGhSD+QPIOW7/CmfOcflHJOQ/+YusHsQyp+rwpWiqxZTLLzGztXicOt4u+LpN/rg/qOgcG/F2sRzOa2dXDrBlq6i3/88W2NHRtQsvle2WZwgpLxEpHsZTOk7QRgirW2G8AY8z/AE2iomwjgj9LL2cpniFchx1drww/rN3YOBjsx8XN5Culc5zP/oNRzGuo3DqQNdrLdXrZBYLHnLxUraEgl2fmNFwsGR9yxYdiwt3JUjYsdPyU6fkgIKOvda6H+woY7pgtYgjRnSUSKJ5uApy4W7Hh6gEiqD4vUknwKAFQ6QMol81JIgYNKH2clpMugFdq5zmf+QannNISf7BqW4Rmoh1C9G9aWzfayDQJLkYkpdanpxPM77Fw1hegfXUfzJUPvixEPbKJ3+mhG3N9Rsqpx4fZORl76ekmPf9g9H/csoBTBVUwpMjilKBYiIuWTTcDzgDFmGfBr7+8nA38pWYtEqkQ+BQD8sDYNZJd5KaTAgV+Os9zSZdCaFr9RcOcy16fX2XSK830iHm7vdB3yuNeiQO/hW9Nz5vZZbS/bILBUmZhyL3oafqJrWBBTv3Eg6X0ROaCZ7jPfP+w8xne88w1WhwSPDA/C+qa0FKWDnyygTDxOPw93jCnlsEcRKY9sAp5vAl8HTsIVOfgLcE0pGyVSDXItReyXtWmylW+p5Wo7znyky16lyqAVq3Ody9PrUs5pSDU/pf+AUVlvL9sMy4g7NhQcLCZTiUVPu9s7hwQx4fbO9PdF3GEn63hvvHMSDSs76R9dR/3GAcLt6c9JsoIIMVHgvUNHDxtil28HP9OQvmooCV3qYY8iUh4pAx5jzHhr7RvAzsDvvV8xOwCvlLhtIr6WawEAv6xNk0okEuHPf76Pp59+kq6uLlo6uvhYQw9H9I0gTKwqUuYCB34/zkJlk71KlkGrVEncXDM42T7ZL0YAl802mtvW0XTzW8O+W6zOcrnnfCQGg6nui8SgI36IGwzteNf9q3dwWFymACVd1iUEDLwvXLQO/rBjq3f7CPVnN9zRD0o97FFEyiNdhuc64Ajgr2x5xhR7GBQFdi1hu6QIanEORTnlWgDAL2vTJOrq6uKaa37JjTf+itdff23Ie5cCO9HAHMZwduP21GdRWjpJrdAAACAASURBVNqvx1kMhWavKjWhOtuMSy5Dd4oRwGXaxuDT9feGfm9gK4raWa501a7E+wJgzIw1Q+f13LeRUN/Q79V1RxmxbENOAUq6rMtAU2hwG4n7ybeDn+zYqqmggBYrFQmGlAGPtfYI7/cPlK85Uiy1Ooei3HIpAFCuRTBzsX79embPnsmqVU8BsPvuu3Pcccex7bbb8vbbb/Ob3/yGtWvXch7ruXPnsdz87VMYl2GbfjzOYilG9qrSnetU8hm6U4wALt02UmUjek7cjq6fBGsORfx9kWy+V6gPomEIxZUMGmgKQTS3ACVVIYVYsNk7c+yQ+yC2n0I6+Mnu+YYVnYPvFUspCgtosVKRYMhmHZ6PAh8DFgP3APsBX7fWLitx2yRPtTCHwk9yKb2c79o0pcjWdXV1DQY7H/jAB7jmmms49NBDefz5//DY6n/zvh1D3PmVs/n3S6uYM2cOT619gdmzZ7J8+R9obm5Ov20frcFTTEHOXuU7dKcYAVyqbaR6ut57zNiC9lcJuXTGkx13FIjsGCb8n/4hHe98ApT4ILN/dB2jIw107N2wZZ5RCTv4xSwAEH9OizXvKBmVuhapftkULbgc+B4wE+gGDgCWeb/Eh4I+h6La5bo2TbGzdbHg6b9fXjUY7Dz66KOMHz+eJb//P+7635fYfecxvNfXz8PXvcYpR3yIRx99lGnTprFq1VNce+2VnHNO5v0HcQ2eIGevMg7dWdFB0x/fHOywF+tperrtVNvT9VTHkmsnP9La4ubs3LNxy0KlQP2b/bx78U7UdwwM6XgPOUdbQWTvpoxtHRJkjhtFZP2mwfdK1cEvtABAygCnARjYUvY62XYLvV/9mpkVkexkuw7PX40xS4E7rLWvGGOy+Z5USJCfQteScPtqRtzxR5pu+i2hPjeOpdBsXSx46u/p4UZeBOCaa65h/PjxvNvVy5///gpT9no/3z3hIwxEoyy49jF+/7eXOfzAT3H11Vdz2GGHceONv+Kss84hHK7NfwaCmr1KF1w0t62DWzfQ0u067JEPbEX45fcKfpqeTSBQLU/XUx1Lvp38yP7NNN6zcchrdd1R6jsG6J47fsjrsXPUvOBVGp7uYqvHN9MwY01BWY5SdPALKQAw5PxuBfTHBTh9wz8fv12VlRaRuiw+s9kY823gUOAeY8zZwKYM35EKij2FjjaOAAjUU+ha0dy2iDEz5jLyhmWDwU5MLFuXSrh9NU2LbyLcvjrh9S1DHX9PJ6/Sx6RJkzj00EMBWPHsG3R0vsdHPzSeuroQ4fo69vngWF57s5N/vLqR6dOns/vuu/Paa69y//1/Kv5BV5FI6z50zz0xcD9TXQsnsPHOSXQu2JGOZZOGdNjp3tJhDz/fO6wDH27vzGlfqQKBZNuJtLbQPXe8b4OddMeSrpMfbu+kafEbSY85lnGLl2moWsPqzYMFHvK9LqWUzzFBkvP73tBFTJOJX08o2/tMRIIrm4BnNtAMzLDWvoMrSX18SVslBetaOJ+Nd/6SzgXz6Fi2WAULqkjiHKxE6bJ1sUCp5YLFjJkxl+a2RYPvxQ91bKcbgGOPPZa6OvfPwKv/6SQUAjNhm8HvjN+2hYEoPPvSBurq6jj22GMBWLXqycIPtIRSBX3l+n41SwwuUq23Ey/Wgc9FukCg2qQ7llSd/PATXYyZsYaWC15jzIw1LosWJ5Zxi30303C+ajifuR5TTKYFTAEG6nGZn4TtVsN5EZHSyzgmxVr7mjFmGbCtMeYTuPV4dgNeS/9NqbQgzqGodpmKD4TbVzPy0hvSBjupsnWZilXED3Xs9CrNb7vttoPf7+vvZ8RW9TQ3bjX42rajXZawr69/yOc3v/TPYcfD4dNyPR0lUeicJ1U4HCrVBPr4Lnw+VbyCVO433bEkGyrY+6lRNKZYUye+85/LcL5yn89weycj7tgAkNMaPfkMUUx6D9YDdW44W3wBh8TtBuk+k9IrRaU/8YdsqrT9EjgSeIkt6/FEgU+VsF0igZOpIz3kfRJWQB/RQPcJX6D3mNRzdzIVq4ifcN/S47b+9ttvb/l+fT297/XT1fMeY0a5QOftDre9hob6IZ8f/famYcfDV2fAgm8WdI4KVWiFQlU4HC7WYR95qxvWNtAUon/iljk8+RYSqLaCBOlkOpbETn7Dik5Cv393yDZSzWXJZi5NrJMWvzhpKc9nc9s6mpa8NTisrGnJW3SfvF3W82JynR+U6vwmC3CqvfCFVI7megVbNrOOPwMYa213qRsjElSZOtLD3oe49TFcVqfrJ+mzDNkUq4hNuN9ryRK47Vp+85vfcMEFF1BXV8dO728hGoU16zay47hRALzxdid1Idh717EMDAxw++23A7DvBycNOx5+tZzw5w9NGxhEo1FCocQBUfl/LlGhFQpV4TC5roUTGPm1nem8983BzmW4vbPgQgLVUpAgG+mOJdlT42JlHRI7aT3TR9F/wKiSnc9weydNN701ZA5NqB+alr6VU6YnV6nObzb7C9J9JqVRaAVB8b9s5vC8xPAh2yKSg3Qd6VTvh4DeT0/Leg5WtsUqIq378MlfLGSHHXZk7dq1PPDAAwBM3Xs8o1u2YuVz/2ZgIEpfZIDV/9jAju9r4YM7jeH+++9n7dq17NS8NYfvsNvwYXeb0xdT6OnpYfbsY1m+/I60x7F8+R3Mnn0sPT09GY85USzoi5dLhcJCvx9oU0cPmdtTrEICfi9IkItkx9Lctm7YXJ1857IkStZJG/HAppJ26htWdCatihbqpeTzYgq5V4J0n0nxaa5X8GWT4XkbeM4Y8ygw2AOx1p5aslaJBEym7Euq9zefc2rGzEL8PJpsSyaHw2FOOukrLFx4IXPmzBlch+fTH92Fu/73JdqufIT3+vp57c1OTjniQ7z55n+YM2cOACd+42yi0w4Y1l5Gpg4Menp6OPnk43nwwQf4y1/uB+Doo2cOa//t/7KcccZpDAwMcPLJx7Nkya00NjZmOLtbFLpOTqrvAzQtvimnhV9jT/X7x9RRv3FAY8JrULqnxsXIOhRS5jlffVNbiDYMLwUdHUFOGSrNlah+QbqGmusVfNkEPPd6v0QkT5k64vl21FPNC8qmUz5nzlzuvff3rFr1FNOmTePqq6/mxMOns8eEbXls9b+pqwtx2lF78fqLTzFt2tf55z//yX777c/pp59JpLl5WHvrvnJ00v1Go1FOPfUEHnzQZZIGBgY444zTADhh5cuD27i1YTNnRF5hIOr+w3nwwQc49dQTWLr09pyGtxW6Tk7i90fccS9jZszNqYjBkGFGeEMUa2hMeGJHKEgdo1xkCkhSzWXJ9nxVopMWaW2h+8TthszhidZD9+ztsr62tTJXIsj3fdCuoeZ6BV8oGk1f6hHAGDMR2Au4D9jZWvtyids1aP36TZkbKFVh3LhRrF9f20s4hdtXp+2IZ3p/6GefYcyMecOyQh3LFmfdyV+/fj2zZ89k1aqnANh999059thj2XbbbXn77be5/fbbWbt2LQD77bc/S5fewXbbbZe0vdscPi3l9V2+/I7BzE1MXV0dN9fvzPF9I/l/dDCb1xmI+05dXR1XXXXdYCaoEvI5x+H2TsbMWJO0jO5AU4iOZZPK/p9oMTpe2f78JnaE8l2kNAidxXB7J2OOtEPmu0TrYePdJuUx5dqRjP98rJOWa8czn3+bw+2djFjmVWk7JvuOYbKfj0r9XJSSnwKCYv/fG+RrWIy5ieWmvtUW48aNSvl0NJsqbV8Cfgg0AdOAx4wx37HW3ly8JorUhkylwnMpJV6MCfbjxo1j+fI/cO21V7JkyQ2sXbuWiy++eMhndtxxJ0466SucfvqZNDc359XeWNASH/QMDAxwwsA6bqOFu+n0XbADuZ3jWAe97tXepMGO+25phxslU86OV7IhXOHnewcngWY7EdhPncViS/cEL5+J0/kOjYsPKDl8VK6HkXOltZhKDMMrt2JOgPfj0NggX8N872vxv2yGtH0PF+j8r7X2TWPMfsD9QM4BjzGmDrgS2BfoBU6z1v4j1+2IpJJpnZsgyaYqWzaam5s555z5nHXWOdx//59YtepJuro6aW5uYfLk/Zk+/TOEw9n8U5Fe0qAH+C3/v717j3Ojvu/9/5ZW65W8u/YaMGCuIQQPTezihm1wSElDgIChNGDsBBwTgiGliUmbcArHoW1M6Eni1L9c2m6aklOcBszlhw3kAgeXY0N6koBpFmJjn4bBSYMbLg62iZe9aC9a6fyhlazLjDSSZjSj2dfz8eCBrdXOfEYj734/+ny/n2/xotAgJDu599FkT7ej17hogN6e/RTfaif4Zs8Jb0bnocKBc8fmg2UDIbtNSu3ObxfzxIKE4wFfo9Wher+/9Pvatw+VvQ+ik7K9/noHkpUGaVbXUppQ6rpBae08x9dZj8KBe9jXSriVEAR1aizrXdCKnIxiJk3THDQMQ5JkmuZrhmGkq3yPncskxU3TfLdhGIslfUXSB+s8FlDE6w0jg5ZMNbpAv1QsFtNFF12siy662OVID7NKegpFFfE92Sl9H6VOOUGxX71s+xqXDdAnsru+a4YUGS9sL978OeFefxJbNCBrk2Txm8Fuk1K7pMIu5lm3/FqRVPUBX6PVoXq/v+OWX+qJex7VsxOmBtuSii84Qgv/eLE+1HGS2sfaDl//DPvF/W4PJK2uZWzZkWUJpb69T7FLuj17b1aa5hjGtRJu3MeynytTjwehXTLrXVpLGKYHu8FJwvN/DcO4UVK7YRiLJH1Skn3v2cr+QFMNEEzT3G4YRm+dxwGKeL1hpNfJVL0aXaDvh8svX6bvf/XretR8vuxrS4yFvq/ZKX0ftb30it784k1qGxi0fI0tB+iT0sjHjlL6hA5Nzo6qbSDty5xwLz+JLRuQWVW0ZkiTp3aUDW47Nh+0TSqsYs5IiqSyf6404Gu0olXXlLLhYf3Pz31Nd939bb2s/dkHJyXtlLTzW/rvncfohvFLdFNmmTqVUGRS6th80PJ4tQ4kKw1k7K4l9+ciI2nPpiNZxdH20rje/OIJvv278JobCYHVz5WcIEwfY2+j1hDm6cG1cpLwrFZ2DU9S0gZJT0j6b3Web5akgYK/TxqGETNNM2X3DXPmzFQs1mb3ZbSYuXNrnyvuyO4XJIu1FnN2vyAtObuxYz+9Q7rvB/njR0bHNPP+RzTz41dIi89o7NhuWHJ249foEif39/7779dje3Zbfu2xPbu1bdujuvLKK90OzRmb99Hs1IR02yetv2dJWlr/qpQsGJzMjGrm9SdKi2d7GGwF2wekHw1I58yWrpsnbXgtG9/MqKLXHqs5S+qbvlR0f3f/VrIZkOVE/+Q4Rf9hfj6e6DmzNTMj6bwd+e+NjGY08/43NPPjU6/Xku7sFKtczLHDyU7+uMmM5uyeKF97YhGT7XOt1Pj9r7/+upYvv1T9/f2Ssk0/PvShD+WbfjzwwAPas2ePPqcN+r5+okf1JR09Oaf4ekvduUD6eMHrtXi2ZlrFeuOLh1+jRERaNU/qm1/1WmbOnJF9fsn7teuio9Xlxc9nmzhmp9ql205y/3xB4fQ+2rH6uZJTx/3y5Hfvkm5pifuHRe0s7+/TA9J9B+1/1jai8HeMX7/nalQ14TFNc1jSZ6f+a9SbkgrvSrRSsiNJv/3tiAunRRB42UkktuB31GOx1mJgwelKNXjOxJan1JUs32RzaMtPlDz1rQ0dO0yc3F+rbm2F0um0PvKRj+jNN5O+VHrqeh+dGlXnVSWf5l55hIZPjUo+dM6xnMb04PziT2LriKv0/sYWtKunpBJTKJ2IaODi7uzrdmpUOnWOJCnRt09dpYO4kbSGtryu5KlTe2GvnafYJd1qf2ZIk7OjmnXry+UdoRa0l90Tq5jsnmullu8fHh7W5ZdfrB07fqZTTjlFd9xxh45deIJeOPBLvT58UHNSXfrJmqe14+nndMMNN6j/V6Yu0Wf1Q31NnSOJ4ustVfB6Wd2rWP+Qeu587XCcyYzSG17T4Klt+XVOsruWi7vVMTJe9H6NXnus9nv0fm30nrS0Kvex2vcW/lwpmhpb488XuniFm939TWx5vfrP2joEuWpUKbF30qXt05I+JymXwkUkZUzTrKfs8hNJl0p6YGoNz646jgGUcXs9SyG3mgP4KQjrjyxbU0u6tKRLW+E+Pc1Oeup9HwVlekelKVnJ1ce6eq7SaTuZqd8IkcnK65WcTrMrXIif3J10ND2o0alEtXz/HXd8I5/s5Dbu/Ystf6PXBl9XZ/tMHRp7U0vfvkQXXHCBnnrqKZ199tnq/5Wpr+tBfTZxdUPTCp2uc7K7llRvV9H7dc6SeZ4l51av6fh5s9W+fSj/dVgr/Lni59RYtCYvpjQ3oxGOV6ruw2MYxkuS3mua5n81erKCLm2/q2zidK1pmi9U+h724QmPZnzKVMs+NrUoXMOTGwQHYQ2PE81af1Tp/tolO/foOF2p2dl9eCKv5Tcdlfzt1ubV+8hrib596rr9lbLHh9Ye33DCY3d/C/etkOQo6atn75j8vi8ZVf3l6nQvDbs1MNW+P5VKqbd3oV599RU9/vjjuuCCCyRJT//Xs3rrnJO09T9/rO+98Li+/IFbdcqcEyVJjz/+uC688EKdGDlau6/5V4397akVr7cSq31QrJpDDDyYneJW7bVozs/m7P1rf2pQsV+MKTJR3ICiVRdWt0LcVHjCrdL9dWOfrkJe/o5xQ0P78Ej6D0m/cSMQ0zTTkv7UjWMBVmrZx6YWrdgcQPK+mYMTmUxGmzbdb5vsSNKHEsdo6M9u0A3rP1+0T8+mTffrssuuUCRi+zPME169j7zW7HaxVoM9J4O+eipihY0OEvcerDiNwsleGpWmZVT7/v/9v/9Vr776iubPn6/zzjsv//i7TzpTkizfr+eff75OO+007dmzR997v6mLVH/CU1Zda892ByyUW9ieXH1s/lr8HJx3bD6oxMYDiowffiz36XD09ZQ6tg4EcopMJUGe2gNI7s8+aOWW5E4m8f29pF2GYdxlGMaG3H9eBwYETap3oZKrr26pgXCljTObJRKJaMOGjTr33OzAMJfsfHgq2clVzD743z6jb37znxWNZn8snXvuedqwYWPTk51WlhsIZ+LZ18xuSlasf0iJvn2K9Q9ZHcaRzjV71bP0RXXd/op6lr6ozjV7a4514qzsXjXV4rCbRlFv/JWO5+S12bnzOUnS8uXL8+/XaqLRqJYvXy5J2rHjubriLjS87mQdemi+htYerze/fGL+nueUDkKK7tcHTc2+5OcVr9GN90jRse49WJTs5ESTGXVsOeTavW0Wt9+TgFdSvV1FH3w0fCwHv2OCyEmF5++V3WS0tt9mwDRSaY1Mvetn3Fh3E5T1R/F4XPf8+a26/t9+qqvTXfnKTjoW0+AXPqPxlZdJOrxmZ9Om+7Vhw0bF4/GmxhkG1T7Rc+NTaTfmcdcSh92alY4HD9ZVsbA7Xufal9W+a6RqTMPDw5KkI444wvE5C58/POzOoNjpOiervaJm/HRE7ZeZSq48KttRrIDblYtKLZbTMSlq0YXP77bL1Xi9xxUQVEFZs1orJwnPqGmat3seCaadICykd0OlNTL1rp9xa92Nl80catX93At6JH2cIgUrDaKplNoGiuceX375Ml+msYWJ3ZQstxacVhrs5b5eKQmpNQ7LvXnapMRdB8rWgjhhOS1jhtS+czhfhagUU2dnpyTpjTfecHS+nNzzOzsrv9b1TD2rNAixSzgi48ruzfPxgWxHMXmzKNnq9Zayr/nYB2YpvnWw5abItPLUHqBRTqYNB42TWvxWwzC+YhjG+YZhvDf3n+eRIdQ616xXz9LV6rq9Tz1LV6tzzXq/Q8qL9e9Sou9uxfqrNxG0WyMT699V8Wv1HrMew+tu1qGHvqGhtTdq4ME+35otTCxeJJVUbOyqTUFKdmp5PwRdtUTFqdxgr1A6EVHs2WFH09xqjaN0GkWmI/t4bt1K6XSiatOxrKZlpM6YWTblyi6mM854pyTpgQcesG2xXiq7Jm2TJGnRonfaPq+RqYJ2U1es7ldONJnJ7qcxxa33SFlcJfdv/Pc7NfBdQ0MbTmvJKTKtPLUHmI6cVHh+b+r/hT+hM5Le7344mA6CsJDeTq2VlUprZKK/3mf7tUrXWemY9b4+QViEH6Rqk1PN6nBXi0YWnrv1qbRVq+Gx93crPrXwXKpcGagnjsIKRvTXY5q54UDR13OD8sLmBhWnpZVURCSpvaTzmV1MF1xwoY477njt2bNH27Zty3dpq2Tr1q3as2ePjj/+BJ1//gcsn+OkulLP/c/fr7sPlDU3yLRJkXMObxw42RMt6/iWkTQ5u/59O6TKFahWnSLTqnED05GTjUfPbUYgmD68GNC7oZ5EzG6NTNuz/1fxx39c9nwn62eCsu7GC63U7c7u/TCx4DS1HRr0ZSpmo2srGt2nplDpYK99+5Aij75Z9By7NQ31xpGbRlGaGEjZ5GRyVlSdNUzHKp2W4TSmWCymj370Wq1b9z90ww035Pfh+fZzD2jvoZf15li2GrLh2fvVFm3TO494h/78hmyD0o9+9FrFYta/equtC2nk/g+vO1mTXVF1/f3rZclM4d/bDqVVWguKSGobcFbJqqTSNJhWnCIjtW7cwHRjm/AYhvEt0zT/xDCMJ5X9mVjENE0qPKhLUAf09SRiVlWL8fMWK771KUXGiz9KTc9od1TRaMVKSKx/l7T754ot+B1H1xfka8mxez/MumW9IqlU0ys+bq2tcPNT6dLBXi1Vm0bisEuY2gbSDS0kryWmG25YrS1bHtWOHT/T2WefrX/6p39S93Gd6knMVk9itk7qOV7KZLRv32+0dv3n9NJLL+n3fu+d+pM/+aTtMStVvizv/8YDju9/55q92TVPJY9HJ5Wd0nbqnKoxAECrqlThuWPq/7c1IQ5MI0Ed0NebiJVWLdq371D8kR+WPW/06g9q+EvOBsetVAnJTfvS6Jh6AjLtyw1W74eMpEgq21Kq2VMx3ewK5cWn0vVUbRqJwyo5ifUPNTxYdxpTZ2en7rlnsz7ykWXaseNnuvDCC3Xaaadp+fLlOuKII/TGG29o06ZN2rNnjyTpnfMXaeM9m/MND+zObfcaJvr2ld//calz7csaePT0irHmk6VU+dfSiYiiBVPaGqkCtsImnACmJ9uExzTNZ6f+eFDS6ZKSkv7DNM1fNSMwhFsQB/SNJGKlVQurxGnsiotqjicIr0upwu56Uiaw67EaVfZ+iMUUTRWPGGuZitnoYNDuk/fJ2VEl+vY1fZBpdT3NXtNQmpy4OWXPiXl7E9py0bf1zeiduvO5+7Vnzx598YtfLHrOiTpaN+hSXX/FpxQ96qiqx7R7DScWd1luMNr+/LBi/ZWTXrsubZl2afSqIzVz8WypYKf2sWVHZv8QkcauqKGCxCacAAIqkslY98Y3DONoSZslLZC0R9kPNw1JT0taYZrmoWYEuH//oHWAaDlz53Zr//7B6k/0Wax/V8OJWOFi91zi5HfVw4024KWL+CcWzteMn5Z3Lxtae6OSq69uNORAyL0fJmd3a9atXy1LZAce7Kv6ero1GCw8TjoR0eRbZij2q/GmDDIL//0GfXAb6x+qOemqNSEteg3apdTkpB5Lb1e/TA0qqW4l1CtDF2uxoomYBh6c33DiNfuSn2vGT0fKHh9ae7ySq4+teG09pQ0ZYtLg356o8ZVHN3xvLY+fiLhyzbnjUzmqX6v87kV9uL+HzZ3bbdvitdKUtn+Q9GNJ55mmOSFJhmHMkPR5SV+X9DEXYwQCw43KStAqWG50G7NaxB/b+YIyM9qL1isFYT2WmwrfD8nde2quALq5r0nhp/+Ts6Ka9Zcvu7pfihO1Xo8fg9Vap8rVOsi32sizra1Nl854j/54/D3KtGWfF5l0t13x8OdPVPtlZlH7bCdT9uwqX+Mrj658XQ7fU15uwhn05BpAa6iU8PyuaZofLnzANM1xwzBulbTD27CA1heUKWlutQG3XMQ/PqHx31+o9l0vOk4Cgr7hbKX46klk3R4M5gbzlms6mrDTey3X4+dg1WmiVc8g3/I1mJRGPnaU0id0HG5zXdDy2o1ph6neLiVXHlXXlD0n0w3rfa961ejAi01QAUxPlRKeUasHTdPMGIbReH9KAE1RT/c5q0G/XVOH4c//uSRpzu4XNLDg9IpJQBD3tSnkJL5aE1mvBoN+ddNyel4/B6u1JFr1DPIne6LKxFTUBCCdiJStd2m0lXROYfLWaHc7LxIXr9ZOeVk5AjC9VNpJrNLaGdbVAC0il6gUqjTtrHPNevUsXa2u2/vUs3S1Otesl3R4EX/uWIXVnFTvQunmVVUqO9aVplh/+RogP3gVn1c7svu107vT81YarHrJLtGK9VufNzfIL1RpkN+5Zq9m3fqyIqnDvwjtXoNaY7E7X8/SF9V1+yvqWfqiOtfszd6D1cdWvdex/iEl+vY5Pl8j76nhdSfr0EPzNbT2eA08ON+VSl6t9wYA7FSq8LzDMIz/tHg8ImmeR/EANQn69CgvOb32WrrPVZv+1sjapKBuOJvjZXxedS/za6d3J+f1qwJVa1WglupEWQKjqcX/XzihbD1MPbFUPV8NVbJ6K0teVpBq1eyuewDCq1LCM79pUQB1CPr0qEoaTdRqvXaniYqTQX+9a5OCtuFs6T3wOj6vdmT3a6f3auf1a7BaT6LldJBvmcCkpLYB61nejSZ9HZsPOkqYStcrNTqd0K/3lBW/knoA4VJpH569zQwEqIVbC/H90GiiVu+1O0lUnAz6nSRrVs8J0oazdvcgKPGFhVeD1UoNCepNtJwM8icWdynTlu2+lpNpk2UCk4tx9PxuxbcO2sZidy2da/YqsfFA2XHTJeezquSkT+iou7IUxBbQQUrAALSmShUeILC8mn7k9RQ5NxI1L6deVUtKnCRrlZ4TEkOM/AAAIABJREFUhHbdle5BEOILm1oGq04G206majWzKmC1oLU0xrHzZyt1ZmdZLHbXkq/QjJcfu3BFi10lZ/ALJ9RVWaIFNICwIuFBS/Ji+lEzpsi5kax4PfXKbtBvmSjc9V1NLDhN4ysvs39OSULnd7vuavfA7/imKyeD7VqmanlRFWjfPlRU3ZGy7agLKydWMc7YNqDkJ48pq+zYXYvV1LmcSMH57NYItQ2ka65yed1VL4iVIwDTR6UubUBgVeoYVo9mdRCrtWOaFbev3fYcq68uOqZlopBKadbNX9bsS66Xtu+smEwEhRv3oNVYdeuqtYOXl5x2M/Or81uOk65h1WLMve6V1udYncfqfJXiqdQ1zeree/naWnWaA4BmosKDluXm9KNmdRBzax2LH1OvrCpLkhSZTGvGT3dJf3i12j5wjuPqUy3TB92cahiktUTNYFU5kRSoqUtOu5n51fktx8n6oEoxFt2LdpWtB8o9r/Q8GU11hCs5X7V4rKpcVtPt9N4jsnsLsXloEapSQHiQ8MAXtQ5g7Z7v1vSjZnYQG153syYWnKb4//qhRi9+X346mBOlr0MzB+n5ROGuhxVJTZY/YTyljm1Pa+z8s9Wx9alsMhGLaey8d5fFWcv0QS+mGk6XtTqWg82NBxSR8utDgjAAdZrIBKFNcbX1QXYxSiq+FxPZBgSakb0XpddSeJ7J2VG1DaQtz1fLeiWr90PHI4ekRw5pVjyi1CkzFPvVuC+bhwYtuag2xTJo8QKojIQHTVfrALYZa2ua+al/4fXM+PFzSu7e4+h6gtCGO5eszbplvSKpVNnXo8lRpc58hySpY8uPFE2lFN/6lDJr1udjraVxg1fd+KbL/k2Wg02LhfB+715fSyIThDbF1dYHWcWY6NtXfi8mpZGPHaX0CR22yZObbaSt3g+5CXGR0YzaXhrXm188wTa5qkUuIXBSOQpas4RqVamgxQugOhIeNFWtA9hmtp+u5VP/egfM9V6P269DIwP+8ZWXKbl7jxIbv6fI+ETR19KJuCZndatz61P5hKg01lqmD9o9t+PBLXXf/yAkjs0Se244Px0qJz1DRRUeqbGpS2590l1LItMKbYpLY7SrYo1dcTix87pqYBVDoXzDg9XHNnSe0oSgUuUoiFPeqq1nClq8AKoj4UFT1bpWpllra3KcTBNrZMBc7/W4+Tq4MeDPJYeda/9O7Tt+rshESpoZ1+iVf6S2gcGKsdYyfdBu3VDiru9KGTmKuzC5kzJVO82FRax/SB1bB4qSnYyksQ/MUuboGa5MC3P7k+5WSGTqVa2K1Yyqgd3aoByv1uxUqhw5nfLWTJWmWAYxXgDVkfCgqWpdK9PMtTVONFppqfd63Hod3KwUpXoXauDRf84mFM/sUNdF79HwqW9VrH9XWayZjvZ8rLVMH8w/t6SaFJlIOYq7NLmbWDjfutPcLesdTy1sFXbTlybP7FZy9bENTwsL4ifz1VSqoDRjTYZdFauZr2VhDG3PDlbcFLUeFVtlW1SO/G5EYaVachq0eAFU13bbbbf5HUNFIyPjt/kdA9zR2dmhoZ4eRX5zQLGf/1KR1GR+sDv20cstvyd93DE1PT/Wv0sdDz0uRaNKH3eM69fQ8dDj6tj2dNFjkVRKk287Sal3nVH1+2u9nka/r9H4nbye6eOOUepdZ6jTOEkjI+OHY33+BUUyhwcFmRntmjj/PZKkifPfo/FzejX5tpM0ctOqitcxcf57FHn9oNp/9h+O487G/ry6/+rrh5O71KQir+3P/rnkuZF0Wm0v/Kcmzun15H3ji6gUf/ANRQqWWqUTEY3cNE/p42YofdwMpd7VpfRxMxwdrrOzQyMjh+fBdTz0hjq2vVn0nEhKmnxbXKl3BW/w17lmr7r/6mV1bHsz+7r8ZlwT5/dU/ZrbrF73Zr+WuRjGP3ikxs/pVuKM2Tr0qaM19tGjGz94lfedVSyR34wr9vOkIqnDzRtciaUBE+f3aPycbk2+La6Rm+bl4wlqvJWU/ttFuHB/D+vs7Pi83deo8KDpau2Q5fT5TqZq5aY3TfZ0q+3QYM1rWNyotNTbIcyNzmK1xN/I1LexZRcqcfd3pcm0pGzr6kY2IB1bdlFRZapS3DmW0wAz1msXJG+nSvrB645mQfxk3k6lCork/5oMP1/LVG+XtKRbqf2Drh2v1vddEBpRWLGbYhnUeAHYI+GBL2ptqVzt+U6mahUN4JX9lL/Wgbxb3dzqbSndaCtqp/E3OvWtffvO7LqeAo0kFNXitmrCYLf+x44bUyWD1qrWy4FZEFpEO1VxEXpGvq/JKFtb0y6Nnze77qmGHZsPSpJv0wvred+12vqtVosXmO5IeBAK1Rb1lw3gp55TzxqWVt/DxUn8jTZJ8GLtlV3cdpWosiRpRrsik5OKTFWdJBVs6Nh4G3KrDR1T7+z0PfnxcmDW7E+6600oq1VQmlVdqRT/8LqTFXl9XPEtbyoyIXVsHVB6zd6aGhd0rtmrxHcO5DczTXzngJLXHOVLy2QSAgBBQsKDUKg2wLYawOfUU3lo9qafbkv1LtTEmQsUiZSuZskqfD0zyiiiSE0Ji1f7GpW+7tUqUaVJUsfmLUUxjZ/3bqXOfEfDiavdho7xRw6Ffp+OZg1sG+liVq0a5aRS1Wj1zslGlvGtg/m1L7VOrYv1Dymx8XCyI0mRSSm+8UBDlR6vq5ZBq4oCCCcSHoRCtQF2pelNfnZ9q5VbG2aOjo5q1aqVWr78Sl1++bKyr+dez+/dfZc2ThzQpvjbpBoTlmZUwpxUogqTpFTvQk9iqrahY9C7lwWdG13MKlWjqlWqGm0Z7ST+Rtsdt28fKtpbKX+McdkeI5dsaElaOjVa9nWvW2WzgSeAZiHhQWhUGmCXJkRuTmVqFrc2zBwdHdU111ylJ5/cpiee2CpJlknPxrNO0Sf+5ddKK61L3xHXv9z2KcVrPJfXlbB6ps55EZOTDR3Zp6N+bu19UqkaZfc1N5ItJ/E32rhgYnGXMjNUlvSkZ8jyGIXJhta/qs6ryitOXjZzaMW25gBaV/lHOkALS/UuVHL11ZYD2uF1N+vQQ9/Q0Nob9eZXP6uhtTdq4MG+lth7xW7qVqx/19TXdynRd3f+73YymYxWrVqpJ5/cJklKp9P6xCeu18MPby563sMPb9YnPnG90unsepcnnn1Gq1atVKZClzOvWV1jLpHNxDsk+ZfA5qZMZeLZuk7pqxTU7mWtIpcMFGrWa1qx4YFDTuIvfQ/V2gQi1dul5MqjlGkrOEebNLryKMvpeYXJhpLZZCPWf/ia3LjuSrw+PgAUosKDaaVV195UmrpVuC6lWuUnEolo+fIr9cQTW/PJTC7pkbKVntJkR5Ki0aiWL7/Sds2P1ypVt4LSRMLrDR3DyOn6DT87wrnRMtpp/KVT6yQp0bfP8fqW3Pd3PDjVpe0K67VIM7/yatWK02RPVJmYyvbTcSvJbKW25kHBeiegfhE/P7F1Yv/+wWAHCMfmzu3Wfpf2ephuYv271LN0ddnUrcEvfFqzbv1a2eMDD/ZVHPjbJTVLTlugx/bsLnv8m9/8Z8tpb4W8ur+x/ufVs/TGmq/Rb7H+oVDt0+H2/a1n/YZfr2lhrLlkpZ61JrXE78X6lqJjqngD3nQiooEH5yvV22X5vEau20k8Xhw/TBp5P/C7N9y4v4fNndtt+6ksFR6gBdg1ZWg7NFRX++hc8lKY9KTTaT1qPl98LIfJjpcabZHtF9ry2qt3/YZfr+nwupM1esURmvHvw1WTlUwmY1sJdRq/F+tbyo6pw23ZNTOq0SuPUKq3y/J56Zg0+IUTNL7y6LrObYcNPJ1hvRPQONbwAC2icA1Sbu1RbtF+Iadd5y6/fJm++c1/VjRq/WPArWTH6foiO41cI4Kp1dZvjI6O6sNfu1b3HvfjigPMhx/erI98ZLlGR0frPle1KWf1suskOHbBLGnbGflqgeW9SUltA2l5IdXbpeTqYxm4V9Bq/16AICLhAUo0OkD3UmlThkYX7V9++TItOW2B5deWnLag4WSnc8169Sxdra7b+9SzdLU616yv+RhBaUwA9/jZhKBWua6GW7c+btngIyc3TXTr1sd1zTVX1ZX0dK7Zq56lL6pj26DrjS/sXvORz8yTFs+u+rwg3pvpgnsCNI4pbUABt1o/N1Mji/YffnizHtuz2/Jrj+3ZrYcf3lx30lNtU9Ba+NGYgAXC3vGzCUEt7LoaSsWt3EvXxD355DatWrVS99yzyXGjj0pTztx6fSYWJtS+Y0SRCftjtsq9mU64J0DjSHiAKW4O0Jutnu5zVo0LCtkN7pxye+2N3TW6tRlr4XE6Ns9iQ0SPtcL6jWZ2Naw05WzkM/Maen2KFrzPkMZ/v1PDnz/B9pitcG+mG+4J0BgSHmBKqy6Or4fTLm2NJD31bApaK7cqckXHaT9NSn9Kkcnsj8fIaEbxuw5oYkHCdtE21aD6BLWxQ+H9tGvw8YlPXK/vf/UePbbnybq6Gpaea7Inatmmud5kJ3/c2dHiytG4FNs9UvX7g3pvpjPuCVA/Eh5gSjMG6EFgl+zkBmmlX6836bHrLOdW8uhWRa7sOBMnq/RHYzQlzbrl10ruTpZVerxoHwz/WN3Py9fZdTXcVvS9hf+OnCTBpedKnTJDsV+NNzxtqei4USlSUsQt3XMHAMKOhAeY4vUAPQgymYw2bbq/4ifSdp9ob9p0vy677Iqapul4ufbGrYpc+XF+IWlMUnFnuEhKZa1gaRcbLpXup9W/i0KF/46cJMFW52p7aVxvfvEEtQ2k6562VHZcixmrLHgHMN3QpQ1VBblrmdusWj+HSSQS0YYNG3XuuedJsp9+U9qy+txzz9OGDRtrSnZySjvLucWtdtXlx3lJ6bZ/V8bip2NpK1jaxYZLtfuZ7Wp4ruX3Ljnt3HxlxyppivUXvyfsztU2kG6oTbPVcQtlOsSCdwDTDhUeVNSKXcsaVU8DgFYSj8f1ne/cp1WrVmr58ittp6nlHt+06X5t2LBR8Xi8mWFW5VZFzvo4E5pYcKJm3fJrRVKHn1v6yXiuXWzpugs+PW9N1e5ntqvhk5bf+9ieJ/Xww5u14pU/sE2aCpMMr947VsctlFx5lIa/xJRLANNLJJOx/yQoCPbvHwx2gCEW639ePUtvLFvTMvBgX10Jwdy53dq/f9DNENGASjvC1/M8v+5vrH+XK1PmrI5TODUpt6ai0hoeq+f43dDArfNPl3+/dvezWldDKVsxvePmPl33d28vS2QGHpxf9vo7eX/VfQ13H1Bkovhxuzimy72drri/4cb9PWzu3G7bwQoJD2wl+u5W1+19ZY8Prb1RydVX13w8/lGGg10baLv761bbaL/E+oeqtoK1e47fDQ3cPP90+vdbej/tuxqea9mlbcN71+mj29/lKJFx8v6q9xo6176s9p3DioyrYhzT6d5OR9zfcOP+HlYp4WFKG2xNl65lcK7WKY5hmBLppBWs1XP8bmjg1/n9rmi5ofB+WiY7yiY1Fz/wp5ZdDVf9nzWauLlPV8bPr5rIeNVqONXbpYFHT/csoQKAVkLTAtjKrW3ILegOY9cyOGfXBtqumUXtzw9Xcwy/Gxr4cf7ONXvVs/RFdd3+inqWvqjONXs9O1czWHY1VFT36C919TPvUqx/qKzBh5RNev7/Z7+rkU8e07QkI9Y/pETfvrLmCKneroaaIABAGFDhQUVethVGa6m1DXQtz7erBLXydDi/Gxq4cf7Cao2WdFd9bthadOe6Gq469wpt/eWP8snOlXq/VNCIoLRldSNdDevh99RJAAg6Eh5UFfauZXCm1imOTp9vVwmKvn5QHVufatnpcKneLiVXHFm2KL1Zg3+780tSom+f7ZSzXJLT9tyg4lsH84NoXTcorZ1ne75KFaVarjloU+Li8bju+sp3dP3SFbo6fUE22VF58uhXV8MgJppBu4cAQMIDTCONVExqbQPt9Pl2laCOLT9SJJXtCZ1LgsaWXdRSyffwupM1tuxI39ZQlJ6/Y/NB9Sx90bYSUFQpkJSrT0RGM9K39yl2SbftNbhRUQpqpSJ29lF64JpvaeZ9b0gVktfLL19W8+a8jXIr0XRLUO8hgOmNhAeYJtxoIFDrFEcnz7esBMViiqZSRc+rNH0uyLxalF7r+atVAsq+XnqgkXTFQXSjFa0gVioKjXz5LRpfflTV5LWZyY7k/9TJQpb38K4DmliQ0PjKo5seDwDkkPAA04DdtLF6Kia1TnGs9nyrStDYee9WfGo6W04tHQJbee2PV6pVAqy+XmRmtOogupGKVtAqFVb8Tl6t+D11spDlPUxJs275tZK7k1R6APiGhAeYBmptONBsVpWgTEFFqpYOgWFohe2FapWAyZ6oMjEpUlBYy01rSyciil7rrNNXvUlBECoVrbr2xO+pkzlW91DKvqeCVK0DMP2Q8ADTQCvsqVRaCaqnQ6CblaywqVQJyK+7SBUnOePnzVbqzE5NnNWlOUvmSR5ubud3paJzzV4l7j6gyISUaZeSVx/VUhWJIFSf8vfwrgNFibMUvGodgOmFhMchpsigldXacCAoap0+14xKVqtWASTrSoDV2p10TBr8wglNX3fhV6Ui1j+kxHcOKDKZ/XtkQop/5wAViToMrztZEwsSmnXLr4uSHr/WFQGARMLjCFNkEAbTYU8lrytZYehAVVoJsFt30TaQLv3WPC+TvmZUKkrj79h8MJ/s5EQnpY4HD5Lw1GF85dFK7k4GYl0RAEgkPFUxRQZhEvY9lbysZAW9i1i9JmdHi1pQS9lpbZOzo5bPb8WkrzDB6dh8sCz+Zp27ld8ntQrKuqJGTNd7B4QRCU8VQV/sDaCYV5WsVugiVo+2gXRZC+qIrCs8rZj0FSVo7ZLSOjx1bSr+wS+coEybiqo8mTZp7IrGkqFWTA7dFIR1RfWa7vcOCBvrj/CQl5siUyhoi70BFEv1LlRy9dWufiiR60BVKAzrEmq5rkpJXxCVJWgTKp+6lsyobSCt5DVHKTMj+1imQ0pec1RDg3W75DDWH8zXCodx74DwaWqFxzCM2ZI2SpolaYakm0zTfLqZMdSqVRd7A3BH4bSWoOx34qZ8Z62NBxQZzw727a7Laetor6YC1XrcqnsL6XD8qdXHujoFq6UqgtsHlHjsdaZuTWmpewfAkWZPabtJ0jbTNL9uGIYh6T5J72xyDDWbDou9AZSzmtZy6KH5gVmX4GpikRvf2fcqcNQ62qupQPUc1zJBa5MibVJkXGXxuzkFKwj7CjnRuWavdN9BdSWZupXTKvcOgHORTKbyp19uMgyjR9KYaZpJwzDeIel/mqZ5dqXvSaUmM7FYW3MCBFrV9p3Sj/qlc3qlxWf4HU04PD0gnbdDShb8jJwZlbadIS2e7V9cOTe+KG14LRtfIiKtmif1za/9OPVc5/YB6UcD0jmzi5/j1WvWyHELX6eZUenaY6WVx1jH7zarc9dzjyT717wRQX+P+8nNewegWUqXpOZ5VuExDOM6SZ8pefha0zR/ahjGscpObft0teP89rcjXoQHH8yd2639Hm5cOF0FpW162O5vYsvr6kqWfCA0ktbQlteVPNXf5Y+x/iH13Pna4U+gkxmlN7ymgUu6a65QOL3Oovt7alQ6dU72zwX33KvXrKHjrp2n2CXd5VU5i/idclxZszp3HeertbrlNL4gv8d959K9C4Kw/WxGMe7vYXPndtt+zbOExzTNOyXdWfq4YRgLJd0v6S9M0/w3r84PTAe0TfdOkKe1uLnGwM3rrOVYtUzHazRGN6eq1Zp8NHruWjvj1RJfkN/jQdDKXeYAFGvqRziGYbxd0iZJK0zTfKyZ50bzxfp3KdF3t2L9u/wOJbQqtU1HY3LrVXIdzILUpMDNjnFuXqfTY3Wu2auepS+q6/ZX1LP0xew6kibF2Ag/unfV0hmv1vhyr6sSwXuPA4Cbmt204EuS4pL+LtuzQAOmaX6wyTGgCaymWenO2/0OK3RybdMLkx7aprsnqJsnOmkeUAs3r7PaserdyycI98KP7l21VGHqiW943cma+fETNbTl9UC9xwHATU1NeEhupge7aVb6+BXSqW8teN4utW/foYnFdL6rF23TvRfUaS1uJwBuXmelYzWSNPh9L/yYAlZLclt3fItns2YHQKg1u8KDacBumpV+9Gw+4QnKQvswoG16sHi1B40VvxOAerTyuhG3K2tOOU1u/YoPAIKOhAeus5tmFT3nTMX6d6lj8xYlNn5PkfEJSSy0d0OqdyGvXQB4tQdNmLT6oNyvqXVOk9sgTP0DgKAh4YHr7KZZzdz4ffXc+VBZ9Uc6vNCeQTtaVb1rU6ajVh+UB72yFvT4nGhmpRRA+JHwwBOl06ykjGZecaNlsiOx0B6tz48F7a0sDINyeINKKQC3sUoRnkn1LlRy9dVK9S5U+/adUtI+2WGhPVqdm62ip4tY/5ASffs8beuM1lJLa23ePwCcosKDpphYvEhKdBQlPekZ7Rq9+oMau4K1O2h91damMEWnGJ/iw4rTSinvHwC1IOFBU6R6F0qrlioztYYnV9UZ/hKd2RAedmtTGJwVc3u9UyPJJIlosDjp4sd6OQC1IuFB8/T9tQ5dch7tkxFqpWtTGJyVc3O9UyPJJIlo8Djp4sd6OQC1IuFBU9E+ORzYNNY5Bmfl3NqLp5FkkkQ0uKp18WvlvZwA+IOEB0BN2DS2NgzOyrm1F08jySSJaLBV6uLX6ns5AWg+Eh4AjsX6n88nO1LxprFacrbP0QUTgzNrbuzF00gySSLa2lp9LycAzUXCA8Cx9u07y/ZSym0aS8Jjj8GZtUb34mkkmSQRbX3s5QTAKRIeAI5NLF6kTLyjKOlh01hnGJx5o5FkkkR0eqNDHzB9kPAAcCzVu1DJFZfmp7WxaSyCoJFkkkR0eqJDHzC9kPAAqMnwups1tuwi2ovDE3zqDq/RoQ+Yfkh4ANSM9uLwAp+6oxno0AdMP1G/AwAAwO5T91j/kM+RIWxyHfoK0aEPCDcSHpSJ9e9Sou9uxfp3+R0KgGmi0qfugJtyHfpySQ8d+oDwY0obirCpJFpVrH+X2rfv0MRi1hXVIihrZtgXB81Ehz5geiHhQV6lTSUZQCLISNTr48WamXoTKPbFQbPRoQ+YPkh4kFdpU0kSHgQViXp9vOhU1WgCxafuAAAvsIYHeblNJQuxqSSCrlKiDntO1szE+oeU6NvnqHGAW00HUr1dSq4+1lGyU0t8AIDpiwoP8thUEq0ol6gXJj0k6tVVWzNTWq3RdYPS2nm2x2t2q1+n1aSgrFECAPiHhAdF2FQSrYZEvT6V1sxYVWv07X2KXdJtmzQ0s+mA0+l47OsDAJBIeGCBTSXRakjU62O3ZsaqWqORdMVqTTObDjipJnmxRgkA0JpIeACEAol6faw6VVlVazQzWrVa06ymA9WqSbH+Ic38yqtNnWIHAAgumhYAAIpYbcyoa501Eqil6YCb8eWqSZ1r9qpn6Yvq2DaokhoV+/oAwDRFhQcAUKa0WjNnyTxp/6DfYeVZVZPKprFJykz9n319AGD6IuEBAFgK+saMpfFZre2JSBq7YJZGPjMv0NcCAPAOU9oAAE3j5d45ubU9hdKJCMkOAExzVHgAAE3hdZvoZnaKAwC0DhIeAEBDnGzu2aw20c3qFAcAaB0kPACAujmt2jjZO8ctQV97BABoLtbwAADqYle1sVqfY7e+hjbRAACvkfAAAOpSqWpTqtLeOQAAeIkpbQCAuuSqNoVJT6WqDetrAAB+IOEBANSlnq5orK8BADQbCQ8AoG5UbQAAQUfCAwCwVNhuWku6bZ9H1QYAEGQkPACAMqXtpnXdoLR2nt9hAQBQM7q0AQCKWLWb1rf3WbabBgAg6Eh4AABFrNpNayRt2W4aAICgI+EBABSx2iRUM6NsEgoAaEkkPACAIlabhOraY2lMAABoSTQtAACUKW03PWfJPGn/oN9hAQBQMxIeAIAl2k0DAMKAKW0A4KNY/5ASfXRAAwDAK1R4AMAnpXvdJFccqeF1J/sdFgAAoUKFB/BZrH+XEn13K9a/y+9Q0ERWe93E7ztIpQcAAJdR4QF81LlmvRL3/kCR0TFl4h1KrrhUw+tu9jssNIHVXjfRZEbtzwyxbgYAABdR4QF8Eut/Pp/sSFJkdEzx+x6h0jNNWO11k05E2OsGAACXkfAAPmnfvjOf7OREk6Nqf2aHTxGhmaz2uhm96kiqOwAAuIwpbYBPJhYvUibeUZT0pBNxTZy1yMeo0Eyle92Q7AAA4D4qPIBPUr0LlVxxqTLxDknZZGf0qj9Sqnehz5GhmVK9XUquPpZkBwAAj1DhAXw0vO5mjS27SO3P7NDEWYtIdgAAAFxGwgP4LNW7kEQHAADAI0xpAwAAABBaJDwAAAAAQouEBwAAAEBokfCgSKx/lxJ9d7P5JQAAAEKBpgXI61yzXol7f6DI6Jgy8Q4lV1yq4XU3+x0WAAAAUDdfKjyGYZxuGMaAYRhxP86PcrH+5/PJjiRFRscUv+8RKj0AAABoaU1PeAzDmCXpK5LGqj0XzdO+fWc+2cmJJkfV/swOnyICAAAAGtfUhMcwjIikb0m6VdJIM8+NyiYWL1Im3lH0WDoR18RZi3yKCAAAAGhcJJPJeHJgwzCuk/SZkof3SrrfNM27DcN4SdLppmmOVjpOKjWZicXaPIkRJW78G2nDQ1JyTJoZl669XOr7a7+jAgAAAKqJ2H7Bq4THimEYv5D08tRfF0v6d9M031vpe/bvH2xegFCsf5fan9mhibMWKdW70NVjz53brf37B109JoKD+xtu3N/w4t6GG/c33Li/h82d222b8DS1S5tpmm/L/XmqwvOBZp4f1aV6F7qe6AAAAAB+YR8eAAAAAKHl2z48pmm+xa9zAwAAAJgeqPAAAAAACC0SHgAAAAChRcIDAE0W6x9Som+fYv2SdK6hAAAJeUlEQVRDfocCAEDo+baGBwCmo841e5W496Aioxll4hElVxyp4XUn+x0WAAChRYUHAJok1j+UT3YkKTKaUfy+g1R6AADwEAkPADRJ+/ahfLKTE01m1P4MCQ8AAF4h4QGAJplY3KVMvHgj6HQioomzunyKCACA8CPhAYAmSfV2KbniyHzSk05ENHrVkUr1kvAAAOAVmhYAQBMNrztZY8uOVPszQ5o4q4tkBwAAj5HwAECTpXpJdAAAaBamtAEAAAAILRIeAAAAAKFFwgMAAAAgtEh4AAAAAIQWCQ8AAACA0CLhAQAAABBaJDwAAAAAQouEBwAAAEBokfAAAAAACC0SHgAAAAChRcIDAAAAILRIeAAAAACEFgkPAEux/l1K9N2tWP8uv0MBAACoW8zvAAAET+ea9Urc+wNFRseUiXcoueJSDa+72e+wAAAAakaFB0CRWP/z+WRHkiKjY4rf9wiVHgAA0JJIeAAUad++M5/s5ESTo2p/ZodPEQEAANSPhAdAkYnFi5SJdxQ9lk7ENXHWIp8iAgAAqB8JD4Aiqd6FSq64NJ/0pBNxjV71R0r1LvQ5MgAAgNrRtABAmeF1N2ts2UVqf2aHJs5aRLIDAABaFgkPAEup3oUkOgAAoOUxpQ0AAABAaJHwAAAAAAgtEh4AAAAAoUXCAwAAACC0SHgAAAAAhBYJDwAAAIDQIuEBgBYV6x9Som+fYv1DfocCAEBgsQ8PALSgzjV7lbj3oCKjGWXiESVXHKnhdSf7HRYAAIFDhQcAWkysfyif7EhSZDSj+H0HqfQAAGCBhAcAWkz79qF8spMTTWbU/gwJDwAApUh4AKDFTCzuUiYeKXosnYho4qwunyICACC4SHgAoMWkeruUXHFkPulJJyIavepIpXpJeAAAKEXTAgBoQcPrTtbYsiPV/syQJs7qItkBAMAGCQ8AtKhUL4kOAADVMKUNAAAAQGiR8AAAAAAILRIeAAAAAKFFwgMAAAAgtEh4AAAAAIQWCQ8AAACA0CLhAQAAABBaJDwAAAAAQouEBwAAAEBokfAAAAAACC0SHgAAAAChRcIDAAAAILRIeAAAAACEFgkPAAAAgNAi4QEAAAAQWiQ8AAAAAEKLhAcAAABAaJHwAAAAAAitSCaT8TsGAAAAAPAEFR4AAAAAoUXCAwAAACC0SHgAAAAAhBYJDwAAAIDQIuEBAAAAEFokPAAAAABCi4QHAAAAQGjF/A4A049hGKdLekbSMaZpjvodD9xhGMZsSRslzZI0Q9JNpmk+7W9UaIRhGFFJ/yjpDEljkq43TfMX/kYFtxiG0S5pg6S3SOqQ9D9M0/y+r0HBVYZhHC3pWUkXmKb5gt/xwD2GYXxW0h8r+/v2H03TvNPnkAKNCg+ayjCMWZK+ouzgCeFyk6Rtpmn+oaSPSfqGv+HABZdJipum+W5Ja5T9t4vwWCnpoGma50i6SFKfz/HARVMJ7R2Skn7HAncZhvE+SWdLeo+kP5R0oq8BtQASHjSNYRgRSd+SdKukEZ/Dgfu+puwvVylbPaZ61/r+QNIWSTJNc7ukXn/Dgcs2SfrrqT9HJKV8jAXu+/8k/ZOkV/0OBK67UNIuSQ9L+oGkR/wNJ/iY0gZPGIZxnaTPlDy8V9L9pmnuNAzDh6jgFpv7e61pmj81DONYZae2fbr5kcFlsyQNFPx90jCMmGmaDIxDwDTNIUkyDKNb0mZJf+VvRHCLYRgfk7TfNM1/nZr6hHA5StLJkv5I0imSvm8YxummaWb8DSu4SHjgiam5pEXzSQ3D+IWk66YGy8dKelzSe30IDw2yur+SZBjGQkn3S/oL0zT/remBwW1vSuou+HuUZCdcDMM4UdlPif/RNM17/Y4HrlklKWMYxvmSFkm6yzCMPzZNc5/PccEdByW9YJrmuCTTMIxRSXMlve5vWMFFwoOmMU3zbbk/G4bxkqQP+BYMXGcYxtuVnSLzYdM0d/odD1zxE0mXSnrAMIzFyk6hQEgYhnGMsh883Wia5ja/44F7TNPMf5hoGMYPJf0pyU6o/FjSnxuG8VVJ8yR1KpsEwQYJDwC3fElSXNLfTU1ZHDBN84P+hoQGPSzpAsMwnlJ2jce1PscDd90qaY6kvzYMI7eWZ4lpmixyBwLMNM1HDMN4r6R/V3Y9/mrTNCd9DivQIpkM0/0AAAAAhBNd2gAAAACEFgkPAAAAgNAi4QEAAAAQWiQ8AAAAAEKLhAcAAABAaNGWGgBQF8Mw3iLpRUn/MfVQQtLzyu7r8hvDMHqV3f/j+ibH9b8kXW+a5qsuHKtd0hZJf2Oa5g8bPR4AoPlIeAAAjXjVNM1FkmQYRkTSFyVtlnSOaZr9kpqa7EiSaZoXu3EcI7uh1AZJ73TjeAAAf5DwAABcYZpmxjCMtZJ+YxjG70o6QtJtpmm+b2q3959JOl/ZStCnJP2ZpHdI+pppml8zDKNL0jckLZDUJunLpmneZxjGxyRdNHW8t0p63DTNTxqGcYKke5TdZTwt6c9M09xuGMZLkt4n6b8kfV3SeZIyku42TfPLhmG8T9lNN0ck/Y6kXZJWmKY5XnJJ10laL+nTbr5OAIDmYg0PAMA1U0nDHkmn23x9oaS7Jf2DpCsknSPpc1Nf/itJz5qmeaak90r6S8Mw3jr1tbOnnv+7ki41DGOhsgnJI6Zp9kq6RdIflJzuTyWdOPU975J0hWEYlxQc70ZlE56TJF1oEestpml+t6YXAAAQOFR4AABuy0hKWjz+2NT/90rabprmiKS9hmH0TD1+vqSZhmGsmvp7p7IVIEl6yjTNQUkyDOM/la32bJX0kGEYvyfpUUl9Jed7v6R/MU1zUtKIYRj3KFvt+b6k3aZpvjx1vJ9PHQ8AEEJUeAAArjEMY4YkQ4cbGRQqnDKWsvh6m6SVpmkumloXtFjZhgGSNFrwvIykiGmaP5H0dkn/KunDkn5QcrzS33ERHf6gr+x4lhcEAGh5JDwAAFcYhhGV9Hllqze/rOMQT0j6xNSx5inb8e2kCuf7W0lXm6b5HWWnp5U2F3hC0jWGYbQZhjFT0kckPVlHXACAFkbCAwBoxHGGYewwDGOHpJ2Sjpe0os5jfV5SwjCM3comK7dUSZz+Qdl1OTskPaypZKnAHZJenorrZ5K+b5rmw3XGBgBoUZFMJuN3DAAAAADgCSo8AAAAAEKLhAcAAABAaJHwAAAAAAgtEh4AAAAAoUXCAwAAACC0SHgAAAAAhBYJDwAAAIDQ+n/nTSR1WyD5swAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2209f3565f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the results of the clustering from implementation\n",
    "vs.cluster_results(reduced_data, preds, centers, pca_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Implementation: Data Recovery\n",
    "Each cluster present in the visualization above has a central point. These centers (or means) are not specifically data points from the data, but rather the *averages* of all the data points predicted in the respective clusters. For the problem of creating customer segments, a cluster's center point corresponds to *the average customer of that segment*. **Since the data is currently reduced in dimension and scaled by a logarithm, we can recover the representative customer spending from these data points by applying the inverse transformations.**\n",
    "\n",
    "Implement the following:\n",
    " - Apply the inverse transform to `centers` using `pca.inverse_transform` and assign the new centers to `log_centers`.\n",
    " - Apply the inverse function of `np.log` to `log_centers`, using `np.exp` and assign the true centers to `true_centers`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Segment 0</th>\n",
       "      <td>3552.0</td>\n",
       "      <td>7837.0</td>\n",
       "      <td>12219.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>4696.0</td>\n",
       "      <td>962.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment 1</th>\n",
       "      <td>8953.0</td>\n",
       "      <td>2114.0</td>\n",
       "      <td>2765.0</td>\n",
       "      <td>2075.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>732.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Segment 0  3552.0  7837.0  12219.0   870.0            4696.0         962.0\n",
       "Segment 1  8953.0  2114.0   2765.0  2075.0             353.0         732.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Inverse transform the centers\n",
    "log_centers = pca.inverse_transform(centers)\n",
    "\n",
    "# TODO: Exponentiate the centers\n",
    "true_centers = np.exp(log_centers)\n",
    "\n",
    "# Display the true centers\n",
    "segments = ['Segment {}'.format(i) for i in range(0,len(centers))]\n",
    "true_centers = pd.DataFrame(np.round(true_centers), columns = data.keys())\n",
    "true_centers.index = segments\n",
    "display(true_centers)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Question 8\n",
    "\n",
    "* Consider the total purchase cost of each product category for the representative data points above, and reference the statistical description of the dataset at the beginning of this project(specifically looking at the mean values for the various feature points). What **set of establishments** could each of the customer segments represent?\n",
    "\n",
    "**Hint:** A customer who is assigned to `'Cluster X'` should best identify with the establishments represented by the feature set of `'Segment X'`. Think about what each segment represents in terms their values for the feature points chosen. Reference these values with the mean values to get some perspective into what kind of establishment they represent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Segment 0</th>\n",
       "      <td>-8448.297727</td>\n",
       "      <td>2040.734091</td>\n",
       "      <td>4267.722727</td>\n",
       "      <td>-2201.931818</td>\n",
       "      <td>1814.506818</td>\n",
       "      <td>-562.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment 1</th>\n",
       "      <td>-3047.297727</td>\n",
       "      <td>-3682.265909</td>\n",
       "      <td>-5186.277273</td>\n",
       "      <td>-996.931818</td>\n",
       "      <td>-2528.493182</td>\n",
       "      <td>-792.870455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Fresh         Milk      Grocery       Frozen  \\\n",
       "Segment 0 -8448.297727  2040.734091  4267.722727 -2201.931818   \n",
       "Segment 1 -3047.297727 -3682.265909 -5186.277273  -996.931818   \n",
       "\n",
       "           Detergents_Paper  Delicatessen  \n",
       "Segment 0       1814.506818   -562.870455  \n",
       "Segment 1      -2528.493182   -792.870455  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(true_centers - data.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Segment 0</th>\n",
       "      <td>-4952.0</td>\n",
       "      <td>4210.0</td>\n",
       "      <td>7463.5</td>\n",
       "      <td>-656.0</td>\n",
       "      <td>3879.5</td>\n",
       "      <td>-3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment 1</th>\n",
       "      <td>449.0</td>\n",
       "      <td>-1513.0</td>\n",
       "      <td>-1990.5</td>\n",
       "      <td>549.0</td>\n",
       "      <td>-463.5</td>\n",
       "      <td>-233.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Segment 0 -4952.0  4210.0   7463.5  -656.0            3879.5          -3.5\n",
       "Segment 1   449.0 -1513.0  -1990.5   549.0            -463.5        -233.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(true_centers - data.median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Answer:**\n",
    "- I'd say we should use deviations from the **median** since mean is sensitive to outliers and would not yield meaningful comparisons. When we use mean, for example, there are too many negative values in the Segment_1. \n",
    "\n",
    "- If we substitute median for mean, we can say..Establishments in the Segment_0 have above median spending on \"Milk\", \"Grocery\" and \"Detergents_Paper\". It might represent a bunch of **'cafes'**. Establishments in the Segment_1, 'Fresh' and 'frozen' are dominant. I guess, Segment_1 represents a bunch of **'markets specializing in fresh and frozen food'**.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segment 0 centroid percentiles:\n",
      "  Fresh is at 27\n",
      "  Milk is at 78\n",
      "  Grocery is at 80\n",
      "  Frozen is at 30\n",
      "  Detergents_Paper is at 80\n",
      "  Delicatessen is at 49\n",
      "Segment 1 centroid percentiles:\n",
      "  Fresh is at 52\n",
      "  Milk is at 34\n",
      "  Grocery is at 34\n",
      "  Frozen is at 58\n",
      "  Detergents_Paper is at 32\n",
      "  Delicatessen is at 41\n"
     ]
    }
   ],
   "source": [
    "# we could also examine the reduce PCA plot. Anything interesting about dimension 1 and how the clusters are split?\n",
    "# we Could also check out the percentiles with...\n",
    "\n",
    "from scipy.stats import percentileofscore\n",
    "\n",
    "for idx in true_centers.index:\n",
    "    print(str(idx) + ' centroid percentiles:')\n",
    "    for col in data.columns.values:\n",
    "        print(\"  {} is at {}\".format(col,int(percentileofscore(data[col],true_centers.loc[idx,col]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Question 9\n",
    "\n",
    "* For each sample point, which customer segment from* **Question 8** *best represents it? \n",
    "* Are the predictions for each sample point consistent with this?*\n",
    "\n",
    "Find which cluster each sample point is predicted to be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample point 0 predicted to be in Cluster 0\n",
      "Sample point 1 predicted to be in Cluster 1\n",
      "Sample point 2 predicted to be in Cluster 0\n"
     ]
    }
   ],
   "source": [
    "# Display the predictions\n",
    "for i, pred in enumerate(sample_preds):\n",
    "    print(\"Sample point\", i, \"predicted to be in Cluster\", pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample point 0 predicted to be in Cluster 0\n",
      "The distance between sample point 0 and center of cluster 0:\n",
      "Fresh               9117.0\n",
      "Milk                1819.0\n",
      "Grocery            -4658.0\n",
      "Frozen              -656.0\n",
      "Detergents_Paper   -2022.0\n",
      "Delicatessen         376.0\n",
      "dtype: float64\n",
      "Sample point 1 predicted to be in Cluster 1\n",
      "The distance between sample point 1 and center of cluster 1:\n",
      "Fresh               4312.0\n",
      "Milk                -918.0\n",
      "Grocery             1456.0\n",
      "Frozen              4329.0\n",
      "Detergents_Paper     154.0\n",
      "Delicatessen        1056.0\n",
      "dtype: float64\n",
      "Sample point 2 predicted to be in Cluster 0\n",
      "The distance between sample point 2 and center of cluster 0:\n",
      "Fresh               28162.0\n",
      "Milk                 4482.0\n",
      "Grocery              -462.0\n",
      "Frozen               -583.0\n",
      "Detergents_Paper     -815.0\n",
      "Delicatessen         1969.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Could also check out the distance to each cluster centroid for justification\n",
    "for i, pred in enumerate(sample_preds):\n",
    "    print(\"Sample point\", i, \"predicted to be in Cluster\", pred)\n",
    "    print('The distance between sample point {} and center of cluster {}:'.format(i, pred))\n",
    "    print((samples.iloc[i] - true_centers.iloc[pred]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2209f7cbdd8>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2209f6f0828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples_bar = samples.append(data.describe().loc['mean', :])\n",
    "\n",
    "#x_axis\n",
    "samples_bar.index = indices + ['mean']\n",
    "\n",
    "samples_bar.plot(kind='bar', figsize=(7,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Answer:**\n",
    "- I selected sample points because they look best disparate. \n",
    "  - Sample_0 (index:0): It is evident that this belongs to **Segment_0** where spending on \"Milk\", \"Grocery\" And \"Detergents_Paper\" is dominant.\n",
    "  - Sample_1 (index:3): It is evident that this belongs to **Segment_1** because spending on \"Fresh\" and \"Frozen\" is high.\n",
    "  - Sample_2 (index:12): The spending on \"Milk\", \"Grocery\" and \"Detergents_Paper\" is high, but spending on \"Fresh\" is super high as well. Given that spending on \"Frozen\" is almost zero, it seems we can cluster it under **Segment_0**.  \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In this final section, you will investigate ways that you can make use of the clustered data. First, you will consider how the different groups of customers, the ***customer segments***, may be affected differently by a specific delivery scheme. Next, you will consider how giving a label to each customer (which *segment* that customer belongs to) can provide for additional features about the customer data. Finally, you will compare the ***customer segments*** to a hidden variable present in the data, to see whether the clustering identified certain relationships."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Question 10\n",
    "Companies will often run [A/B tests](https://en.wikipedia.org/wiki/A/B_testing) when making small changes to their products or services to determine whether making that change will affect its customers positively or negatively. The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively. \n",
    "\n",
    "* How can the wholesale distributor use the customer segments to determine which customers, if any, would react positively to the change in delivery service?*\n",
    "\n",
    "**Hint:** Can we assume the change affects all customers equally? How can we determine which group of customers it affects the most?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Answer:**\n",
    "- In Segment_0:\n",
    "  - Here, the impact of the change in the delivery method might not be considerable since the products(\"Milk\", \"Grocery\" And \"Detergents_Paper\") are not super perishable. Even \"Milk\" and \"Grocery\" still last more than a week these days.  \n",
    "\n",
    "- In Segment_1:\n",
    "  - Here, the impact of the change in the delivery method might be considerable since the products(\"Fresh\",\"Frozen\") are super perishable such as fish, pork, tofu, etc. \n",
    "\n",
    "- Hence, the distributer should be careful when they apply their new change in Segment_1. \n",
    "\n",
    "- Of course, we can formalize the impact by running AB-tests. \n",
    "  - Sample 2 groups from each Segment.\n",
    "  - Apply the new change to the customers in each one of the 2 groups.\n",
    "  - As a metric to measure, we cross-reference their satisfaction level with their spending.\n",
    "  \n",
    "The key takeaway here is that we should run separate A/B tests for each cluster independently. As if we were to use all of our customers we would essentially have multiple variables(different delivery methods and different purchasing behaviors).\n",
    "\n",
    "https://stats.stackexchange.com/questions/192752/clustering-and-a-b-testing\n",
    "\n",
    "The two clusters that we have in our model reveal two different consumer profiles that can be tested via A/B test. To better assess the impact of the changes on the delivery service, we would have to split the segment 0 and segment 1 into subgroups measuring its consequences within a delta time. Hypothetically we can raise a scenario where the segment 0 is A/B tested. For this we divide the segment 0 (can also be implemented in segment 1) into two sub-groups of establishments where only one of them would suffer the implementation of the new delivery period of three days a week, and the another would remain as a control with five days a week as usual. After a certain period of time, we could, through the consumption levels of the establishments, come to some conclusions, such as: whether the new frequency of deliveries is sufficient or not for a buyer. Where a sensible increase in overall consumption of all products may indicate the need for the establishment to maintain a storage because of the decreasing delivery frequency; or if it negatively affects the consumption profile of certain products, like groups of costumers who have greater buying fresh produce that can be negatively impacted, precisely because of the demand for fresh products with a higher delivery frequency. We can not say that the change in frequency will affect equally all customers because of the different consumption profiles that are part of the two segments. There will therefore consumers that will be affected, and possibly groups of buyers who will not undergo any change."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Question 11\n",
    "Additional structure is derived from originally unlabeled data when using clustering techniques. Since each customer has a ***customer segment*** it best identifies with (depending on the clustering algorithm applied), we can consider *'customer segment'* as an **engineered feature** for the data. Assume the wholesale distributor recently acquired ten new customers and each provided estimates for anticipated annual spending of each product category. Knowing these estimates, the wholesale distributor wants to classify each new customer to a ***customer segment*** to determine the most appropriate delivery service.  \n",
    "* How can the wholesale distributor label the new customers using only their estimated product spending and the **customer segment** data?\n",
    "\n",
    "**Hint:** A supervised learner could be used to train on the original customers. What would be the target variable?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Answer:**\n",
    "- we can use some typical classification methods - SVM, RandomForest, Logistic Regression, etc...using their estimated product spending and the customer segment data. \n",
    "- Target variable would be the Segment membership (0/1)\n",
    "\n",
    "Cluster assignment as new labels(Semi-Supervised)\n",
    "\n",
    "https://en.wikipedia.org/wiki/Semi-supervised_learning\n",
    "\n",
    "Another cool idea would be to use a subset of the newly engineered PCA components as new features(great for curing the curse of dimensionality). PCA is really cool and seem almost like magic at time. Just wait till you work with hundreds of features and you can reduce them down into just a handful. This technique becomes very handy especially with images. There is actually a handwritten digits dataset, using the \"famous MNIST data\" where you do just this and can get around a 98% classification accuracy after doing so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Visualizing Underlying Distributions\n",
    "\n",
    "At the beginning of this project, it was discussed that the `'Channel'` and `'Region'` features would be excluded from the dataset so that the customer product categories were emphasized in the analysis. By reintroducing the `'Channel'` feature to the dataset, an interesting structure emerges when considering the same PCA dimensionality reduction applied earlier to the original dataset.\n",
    "\n",
    "Run the code block below to see how each data point is labeled either `'HoReCa'` (Hotel/Restaurant/Cafe) or `'Retail'` the reduced space. In addition, you will find the sample points are circled in the plot, which will identify their labeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2209f7418d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the clustering results based on 'Channel' data\n",
    "vs.channel_results(reduced_data, outliers, pca_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Question 12\n",
    "\n",
    "* How well does the clustering algorithm and number of clusters you've chosen compare to this underlying distribution of Hotel/Restaurant/Cafe customers to Retailer customers? \n",
    "* Are there customer segments that would be classified as purely 'Retailers' or 'Hotels/Restaurants/Cafes' by this distribution? \n",
    "* Would you consider these classifications as consistent with your previous definition of the customer segments?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Answer:**\n",
    "- I'd say this classification is consistent with our clustering results.\n",
    "  - The size of clusters is consistent with the underlying distribution with 2 major clusters.\n",
    "  - There are customer segments that classified as purely 'Retailers' or 'Hotels/Restaurants/Cafes' on the extreme left and right accordingly. \n",
    "  - I saw the Segment_0 as 'cafe' and it turns out it's Hotels/cafes.\n",
    "  - I saw the Segment_1 as 'markets' and it turns out it's retailers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of correctly classified customers: 64.60%\n"
     ]
    }
   ],
   "source": [
    "#Maybe also fully examine how well the clustering algorithm did!\n",
    "\n",
    "#find percentage of correctly classified customers\n",
    "data = pd.read_csv(\"customers.csv\")\n",
    "data = data.drop(data.index[outliers]).reset_index(drop = True)\n",
    "\n",
    "# might need to switch around the 0 and 1, based on your cluster seed\n",
    "df = np.where(data['Channel'] == 0, 1, 1)\n",
    "print(\"Percentage of correctly classified customers: {:.2%}\".format(sum(df == preds)/float(len(preds))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    294\n",
       "2    141\n",
       "Name: Channel, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Channel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}