customer_segments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Engineer Nanodegree - Tarun Parmar\n",
    "## Unsupervised Learning\n",
    "## Project: Creating Customer Segments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to the third project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with **'Implementation'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `'TODO'` statement. Please be sure to read the instructions carefully!\n",
    "\n",
    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.  \n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "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",
    "The dataset for this project can be found on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers). 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",
    "\n",
    "Run the code block below to load the wholesale customers dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "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": "markdown",
   "metadata": {},
   "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",
    "Run the code block below to 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": 2,
   "metadata": {
    "collapsed": false
   },
   "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())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "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>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5963</td>\n",
       "      <td>3648</td>\n",
       "      <td>6192</td>\n",
       "      <td>425</td>\n",
       "      <td>1716</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14276</td>\n",
       "      <td>803</td>\n",
       "      <td>3045</td>\n",
       "      <td>485</td>\n",
       "      <td>100</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0  22615  5410     7198    3915              1777          5185\n",
       "1   5963  3648     6192     425              1716           750\n",
       "2  14276   803     3045     485               100           518"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Select three indices of yur choice you wish to sample from the dataset\n",
    "indices = [4, 8, 27]\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": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quartile Visualization\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>4</th>\n",
       "      <td>85.7</td>\n",
       "      <td>64.8</td>\n",
       "      <td>63.6</td>\n",
       "      <td>77.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>96.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>38.9</td>\n",
       "      <td>50.5</td>\n",
       "      <td>58.2</td>\n",
       "      <td>15.3</td>\n",
       "      <td>62.7</td>\n",
       "      <td>42.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>69.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>37.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>10.2</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "4    85.7  64.8     63.6    77.5              63.0          96.8\n",
       "8    38.9  50.5     58.2    15.3              62.7          42.6\n",
       "27   69.8   8.2     37.7    17.7              10.2          29.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbsAAAFRCAYAAADttgP6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8695fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Quartile Visualization\")\n",
    "import seaborn as sns\n",
    "percentiles = data.rank(pct=True)\n",
    "percentiles = 100*percentiles.round(decimals=3)\n",
    "percentiles = percentiles.iloc[indices]\n",
    "sns.heatmap(percentiles, vmin=1, vmax=99, annot=True)\n",
    "display(percentiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
    "*What kind of establishment (customer) could each of the three samples you've chosen represent?*  \n",
    "**Hint:** Examples of establishments include places like markets, cafes, and retailers, among many others. Avoid using names for establishments, such as saying *\"McDonalds\"* when describing a sample customer as a restaurant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** \n",
    "\n",
    "I tried to create a heatmap to visualize the amount of spending for each of the 6 features as percentiles. Rows correspond to data points and columns correspond to the amount of spending as percentiles relative to the whole data set. For instance, the first row and column can be interpreted as \"86% of the customers spend less than customer 4 on fresh food\".\n",
    "\n",
    "* Choice 1 (index = 4): Customer 4 generally spends a lot in all of the 6 categories, particularly on ```Deli``` food. I would guess that it is a **large retailer** selling just about everything related to food.\n",
    "\n",
    "* Choice 2 (index = 8): Customer 8 spends about an average amount on all categories except ```Frozen```. This could correspond to an average-sized **convenience or small retail store** selling various kinds of food but having no freezers. \n",
    "\n",
    "* Choice 3 (index = 27): With low spendings in all categories but ```Fresh``` and ```Grocery```, I guess customer 15 is a **restaurant** or a **farmer's market**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Purchasing behavior of each sample to the mean of the 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>10615.0</td>\n",
       "      <td>-386.0</td>\n",
       "      <td>-753.0</td>\n",
       "      <td>843.0</td>\n",
       "      <td>-1104.0</td>\n",
       "      <td>3660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-6037.0</td>\n",
       "      <td>-2148.0</td>\n",
       "      <td>-1759.0</td>\n",
       "      <td>-2647.0</td>\n",
       "      <td>-1165.0</td>\n",
       "      <td>-775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2276.0</td>\n",
       "      <td>-4993.0</td>\n",
       "      <td>-4906.0</td>\n",
       "      <td>-2587.0</td>\n",
       "      <td>-2781.0</td>\n",
       "      <td>-1007.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0  10615.0  -386.0   -753.0   843.0           -1104.0        3660.0\n",
       "1  -6037.0 -2148.0  -1759.0 -2647.0           -1165.0        -775.0\n",
       "2   2276.0 -4993.0  -4906.0 -2587.0           -2781.0       -1007.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Purchasing behavior of each sample to the median of the 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>14111.0</td>\n",
       "      <td>1783.0</td>\n",
       "      <td>2442.0</td>\n",
       "      <td>2389.0</td>\n",
       "      <td>961.0</td>\n",
       "      <td>4219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2541.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1436.0</td>\n",
       "      <td>-1101.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>-216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5772.0</td>\n",
       "      <td>-2824.0</td>\n",
       "      <td>-1711.0</td>\n",
       "      <td>-1041.0</td>\n",
       "      <td>-716.0</td>\n",
       "      <td>-448.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0  14111.0  1783.0   2442.0  2389.0             961.0        4219.0\n",
       "1  -2541.0    21.0   1436.0 -1101.0             900.0        -216.0\n",
       "2   5772.0 -2824.0  -1711.0 -1041.0            -716.0        -448.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Purchasing behavior of each sample to the mean of the dataset :\")\n",
    "display(samples - data.mean().round())\n",
    "\n",
    "print(\"Purchasing behavior of each sample to the median of the dataset :\")\n",
    "display(samples - data.median().round())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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 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",
    "In the code block below, you will need to 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": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Milk prediction score of testing set is  0.669537264668\n"
     ]
    }
   ],
   "source": [
    "# TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature\n",
    "\n",
    "y = data['Milk']\n",
    "new_data = data.drop(['Milk'], axis = 1)\n",
    "\n",
    "# TODO: Split the data into training and testing sets using the given feature as the target\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(new_data, y, test_size = 0.25, random_state = 234)\n",
    "\n",
    "# TODO: Create a decision tree regressor and fit it to the training \n",
    "from sklearn import tree\n",
    "clf = tree.DecisionTreeRegressor(random_state = 234)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# TODO: Report the score of the prediction using the testing set\n",
    "score = clf.score(X_test, y_test)\n",
    "\n",
    "# Fresh -> -1.8124\n",
    "# Milk -> 0.6695\n",
    "# Grocery -> 0.6115\n",
    "# Frozen -> -0.2011\n",
    "# Detergents_Paper -> 0.5896\n",
    "# Delicatessen -> 0.1340\n",
    "\n",
    "print(y.name, \"prediction score of testing set is \", score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "*Which feature did you attempt to predict? What was the reported prediction score? Is this feature is necessary for identifying customers' spending habits?*  \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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** I started in the order of descending column names and noted all the regression scores as shown below. Based on these results I choose ```Fresh``` as feature to predict as it had the lowest negative R^2 score. If we drop ```Fresh``` and we try to predict what the value of this feature might have been using the remaining 5 features we will not be very successful since the coefficient of determination is negative so in essence we would have lost the information this feature provided without any means of recovering it. Hence the selected feature is necessary for identifying cutomers' spending habits.\n",
    "\n",
    "    * Detergents_Paper -> 0.5896\n",
    "    * Delicatessen -> 0.1340\n",
    "    * Fresh -> -1.8124\n",
    "    * Frozen -> -0.2011\n",
    "    * Grocery -> 0.6115\n",
    "    * Milk -> 0.6695\n",
    "\n",
    "You may note, ```Fresh``` and ```Frozen``` are dependent variables as they have negative R2 scores\n",
    "* Their negative scores imply that they are necessary for identifying customers' spending habits because the remaining features cannot explain the variation in them.\n",
    "\n",
    "Similarly, ```Detergents_Paper``` has very low R2 scores\n",
    "* Its low score imply that it is necessary for identifying customers' spending habits.\n",
    "\n",
    "However, ```Milk``` and ```Grocery``` has a R2 score of 0.67 and 0.59\n",
    "* These scores are a bit far from 1 but relative to the others they appear much higher\n",
    "* They may be not as necessary when compared to the other features for identifying customers' spending habits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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. 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. 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. Run the code block below to produce a scatter matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0wAAAH9CAYAAAAplPxOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x881d030>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Produce a scatter matrix for each pair of features in the data\n",
    "\n",
    "axes = pd.scatter_matrix(data, alpha = 0.3, figsize = (14,8), diagonal = 'kde')\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": {},
   "source": [
    "### Question 3\n",
    "*Are there any pairs of features which exhibit some degree of correlation? Does this confirm or deny your suspicions about the relevance of the feature you attempted to predict? How is the data for those features distributed?*  \n",
    "**Hint:** Is the data normally distributed? Where do most of the data points lie? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** \n",
    "\n",
    "Above scatterplot matrix shows a good degree of correlation pair of ```Milk``` with  ```Grocery``` and ```Detergents_Paper```. But it does not seem to correlate very well with rest of the features. \n",
    "\n",
    " It denies my suspicisions about relevance of the feature ```Fresh``` that I attempted to predict. Instead it seems like it has no correlation with correlating features like ```Grocery```, ```Milk``` and ```Detergent_Paper```.\n",
    " \n",
    " The data on all the features is skewed right and not normally distributed noticeably with some outliers present in the picture.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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",
    "In the code block below, you will need to 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": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAHrCAYAAAAHTOgXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nj7tHxOkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb256f70>"
      ]
     },
     "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",
    "\n",
    "# Produce a scatter matrix for each pair of features in the data\n",
    "\n",
    "axes = pd.scatter_matrix(log_data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');\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": {},
   "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": 9,
   "metadata": {
    "collapsed": false
   },
   "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>10.026369</td>\n",
       "      <td>8.596004</td>\n",
       "      <td>8.881558</td>\n",
       "      <td>8.272571</td>\n",
       "      <td>7.482682</td>\n",
       "      <td>8.553525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.693329</td>\n",
       "      <td>8.201934</td>\n",
       "      <td>8.731013</td>\n",
       "      <td>6.052089</td>\n",
       "      <td>7.447751</td>\n",
       "      <td>6.620073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.566335</td>\n",
       "      <td>6.688355</td>\n",
       "      <td>8.021256</td>\n",
       "      <td>6.184149</td>\n",
       "      <td>4.605170</td>\n",
       "      <td>6.249975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "0  10.026369  8.596004  8.881558  8.272571          7.482682      8.553525\n",
       "1   8.693329  8.201934  8.731013  6.052089          7.447751      6.620073\n",
       "2   9.566335  6.688355  8.021256  6.184149          4.605170      6.249975"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the log-transformed sample data\n",
    "display(log_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
    "\n",
    "In the code block below, you will need to 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",
    "**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": 10,
   "metadata": {
    "collapsed": false
   },
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 65  66  75 128 154]\n"
     ]
    }
   ],
   "source": [
    "all_outliers = np.array([],dtype='int64')\n",
    "# For each feature find the data points with extreme high or low values\n",
    "for feature in log_data.keys():\n",
    "    \n",
    "    # TODO: Calculate Q1 (25th percentile of the data) for the given feature\n",
    "    Q1 = np.percentile(log_data[feature], 25)\n",
    "    \n",
    "    # TODO: Calculate Q3 (75th percentile of the data) for the given feature\n",
    "    Q3 = np.percentile(log_data[feature], 75)\n",
    "    \n",
    "    # TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)\n",
    "    step = (Q3-Q1) * 1.5\n",
    "    \n",
    "    # OPTIONAL: Remove data points from the dataset by adding indices to the outliers list\n",
    "    outlier_pts = log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))]\n",
    "    all_outliers = np.append(all_outliers, outlier_pts.index.values.astype('int64'))\n",
    "    \n",
    "    # Display the outliers\n",
    "    print(\"Data points considered outliers for the feature '{}':\".format(feature))\n",
    "    display(outlier_pts)\n",
    "    \n",
    "# OPTIONAL: Select the indices for data points you wish to remove\n",
    "all_outliers, indices = np.unique(all_outliers, return_inverse=True)\n",
    "counts = np.bincount(indices)\n",
    "outliers = all_outliers[counts>1]\n",
    "\n",
    "print(outliers)\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": {
    "collapsed": false
   },
   "source": [
    "### Question 4\n",
    "*Are there any data points considered outliers for more than one feature based on the definition above? Should these data points be removed from the dataset? If any data points were added to the `outliers` list to be removed, explain why.* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** There are several data points which are outliers for more than one feature [65, 66, 75, 128, 154]. I believe they should be removed because they represent truly unusual points with respect to the rest of the data, and therefore will be of little use in forming clusters of similar customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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": {
    "collapsed": false
   },
   "source": [
    "### Implementation: PCA\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",
    "In the code block below, you will need to 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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0UAAAH8CAYAAAAe3QQhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nT98pmcIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb032fb0>"
      ]
     },
     "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",
    "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "print(pca_results['Explained Variance'].cumsum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 5\n",
    "*How much variance in the data is explained* ***in total*** *by the first and second principal component? What about the first four principal components? Using the visualization provided above, discuss what the first four dimensions best represent in terms of customer spending.*  \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 indivdual feature weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** The first principal component explains 44.3% of the variance, and the second principal component represents for 26.4%. Together the first 2 account for 70.7% of all variance in the data. When you include the 3rd and 4th principal component (12.3% and 10.1% respectively) we've now accounted for 93.1%. \n",
    "\n",
    "* First principal component has large positive weights on ```Milk```, ```Grocery``` & ```Detergents_Paper```. It also correlates with a decrease in ```Fresh``` and ```Frozen```. This pattern might represent spending in household staples products. \n",
    "\n",
    "* Second principal component has large positive weights on ```Fresh```, ```Frozen``` and ```Delicatessen```. This pattern might represent spending in frozen fresh fruit and deli products. \n",
    "\n",
    "* Third principal component has large positive weights on ```Frozen``` and ```Delicatessen```. It also correlates with a decrease in ```Fresh``` and ```Detergents_Paper```. This pattern might represent spending in frozen vegetables and cleaning supplies. \n",
    "\n",
    "* Fourth principal component has large positive weights on ```Frozen``` and ```Detergents_Paper```. It also correlates with a decrease in ```Delicatessen``` and ```Fresh```. This pattern might represent spending in frozen products and cleaning supplies.\n",
    "\n",
    "**Q**: So what information did PCA provide concerning cutomer spending ?<br>\n",
    "**A**: The features fell in PCA components that define various customer segments, in this case viz. Small Retailer or Grocery Store, Large Retailer or Wholesale Store and Cafe/Restaurant or a Farmers Market. As per the outcome of Component1 it appears ```Milk```, ```Grocery``` and ```Detergent_Paper``` represent highest variance in data followed by others.\n",
    "\n",
    "**Q**: What does a high value in the component represent?<br>\n",
    "**A**: A high value in above plot depicts its Feature Weight of individual features as per the component they fall into describing variance contributing to the data\n",
    "\n",
    "**Q**: What type of customers could the dimension relate to?<br> \n",
    "**A**: Each dimension could relate to a particular customer segment e.g. Dimension 1 relates to Small Retailer or Grocery Store.\n",
    "\n",
    "**Q**: How do the features vary?<br>\n",
    "**A**: The features vary in weight based on which dimension you look at. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "Run the code below to see 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": 13,
   "metadata": {
    "collapsed": false
   },
   "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>0.7848</td>\n",
       "      <td>2.3943</td>\n",
       "      <td>0.4798</td>\n",
       "      <td>-0.3483</td>\n",
       "      <td>0.3191</td>\n",
       "      <td>-0.0613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.8636</td>\n",
       "      <td>-0.6650</td>\n",
       "      <td>-0.5376</td>\n",
       "      <td>-0.7922</td>\n",
       "      <td>0.1551</td>\n",
       "      <td>-0.0344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.4162</td>\n",
       "      <td>-0.6069</td>\n",
       "      <td>-0.7652</td>\n",
       "      <td>-1.3209</td>\n",
       "      <td>-0.1614</td>\n",
       "      <td>-0.8089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2  Dimension 3  Dimension 4  Dimension 5  \\\n",
       "0       0.7848       2.3943       0.4798      -0.3483       0.3191   \n",
       "1       0.8636      -0.6650      -0.5376      -0.7922       0.1551   \n",
       "2      -2.4162      -0.6069      -0.7652      -1.3209      -0.1614   \n",
       "\n",
       "   Dimension 6  \n",
       "0      -0.0613  \n",
       "1      -0.0344  \n",
       "2      -0.8089  "
      ]
     },
     "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": {},
   "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. Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards.\n",
    "\n",
    "In the code block below, you will need to 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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# TODO: Apply PCA by fitting the good data with only two dimensions\n",
    "from sklearn.decomposition import PCA\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",
    "\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": {},
   "source": [
    "### Observation\n",
    "Run the code below to see 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": 15,
   "metadata": {
    "collapsed": false
   },
   "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>0.7848</td>\n",
       "      <td>2.3943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.8636</td>\n",
       "      <td>-0.6650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.4162</td>\n",
       "      <td>-0.6069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2\n",
       "0       0.7848       2.3943\n",
       "1       0.8636      -0.6650\n",
       "2      -2.4162      -0.6069"
      ]
     },
     "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": {},
   "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",
    "Run the code cell below to produce a biplot of the reduced-dimension data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb09dbd0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4sAAAIACAYAAAArE/nMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNVLp0HLxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0064b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a biplot\n",
    "vs.biplot(good_data, reduced_data, pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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?\n",
    "\n",
    "**Answer:** ```Milk```,```Grocery``` and ```Detergents_Paper``` are the strongly correlated features with the first component while ```Fresh```, ```Frozen``` and ```Delicatessen``` are associated with second component. The observation clearly agree with ```pca_results``` that we plotted above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "source": [
    "### Question 6\n",
    "*What are the advantages to using a K-Means clustering algorithm? What are the advantages to using a Gaussian Mixture Model clustering algorithm? Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** \n",
    "\n",
    "<b>K-means Clustering:</b>\n",
    "\n",
    "  Advantages\n",
    "  \n",
    "* With a large number of features, K-means can be computationally faster if K is small.\n",
    "* K-Means could result in tighter clusters than hierarchical clustering.\n",
    "  \n",
    "Disadvantages\n",
    "  \n",
    "* Difficulty in comparing quality of the clusters produced.\n",
    "* Since preset K value is required, it could be difficult to predict which K is best.\n",
    "* Assumes data is globular with doesn't always occur in real-life data.\n",
    "\n",
    "<b>Gaussian Mixture:</b>\n",
    "\n",
    "Advantages\n",
    "\n",
    "* Capable of \"soft\" classification\n",
    "* Works with different distributions of the data\n",
    "\n",
    "Disadvantages\n",
    "\n",
    "* Could fail if the dimensionality of the problem is too high"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Chosen Algorithim:</b>\n",
    "\n",
    "In this submission, the chosen algorithim is Gaussian Mixture Model because of its ability to apply \"soft\" classification and since we've reduced the dimensionality of the problem with PCA, GMM should be able to do the job.\n",
    "\n",
    "If our dataset was considerably larger, we could reconsider and use K-means."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
    "In the code block below, you will need to 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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The silhouette_score is: 0.41181886438624477\n"
     ]
    }
   ],
   "source": [
    "# TODO: Apply your clustering algorithm of choice to the reduced data \n",
    "from sklearn import mixture\n",
    "clusterer = mixture.GMM(n_components=2, random_state = 234)\n",
    "gmm = clusterer.fit(reduced_data)\n",
    "\n",
    "# TODO: Predict the cluster for each data point\n",
    "preds = gmm.predict(reduced_data)\n",
    "\n",
    "# TODO: Find the cluster centers\n",
    "centers = gmm.means_\n",
    "\n",
    "# TODO: Predict the cluster for each transformed sample data point\n",
    "sample_preds = gmm.predict(pca_samples)\n",
    "\n",
    "# TODO: Calculate the mean silhouette coefficient for the number of clusters chosen\n",
    "from sklearn.metrics import silhouette_score\n",
    "score = silhouette_score(reduced_data, preds, metric='euclidean')\n",
    "\n",
    "# Print the score result\n",
    "print(\"The silhouette_score is: {}\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "*Report the silhouette score for several cluster numbers you tried. Of these, which number of clusters has the best silhouette score?* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "| Number of Clusters | Silhouette Score   |\n",
    "|:------------------:|:------------------:|\n",
    "|         2          |      0.4118        |\n",
    "|         3          |      0.3735        |\n",
    "|         4          |      0.3313        |\n",
    "|         5          |      0.2954        |\n",
    "|         6          |      0.2779        |\n",
    "\n",
    "As we can see in the table above, <b>the best number of clusters is 2.</b>\n",
    "\n",
    "As we increase the number of clusters, the Silhouette score gets closer to 0 which indicate overlapping clusters. Where as when using 2 clusters, we get score values closer to 1 indicating dense and well seperated clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAz8AAAIECAYAAAA3n5CZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb261a30>"
      ]
     },
     "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": {},
   "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",
    "In the code block below, you will need to 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`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "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>4316.0</td>\n",
       "      <td>6347.0</td>\n",
       "      <td>9555.0</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>3046.0</td>\n",
       "      <td>945.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment 1</th>\n",
       "      <td>8812.0</td>\n",
       "      <td>2052.0</td>\n",
       "      <td>2689.0</td>\n",
       "      <td>2058.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>712.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Segment 0  4316.0  6347.0   9555.0  1036.0            3046.0         945.0\n",
       "Segment 1  8812.0  2052.0   2689.0  2058.0             337.0         712.0"
      ]
     },
     "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 8\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. *What set of establishments could each of the customer segments represent?*  \n",
    "**Hint:** A customer who is assigned to `'Cluster X'` should best identify with the establishments represented by the feature set of `'Segment X'`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** \n",
    "\n",
    "The average customer in <b>Segment 0</b> has an above average spending on ```Milk```, ```Grocery``` and ```Detergents_Paper```. This could represent customers who purchase a lot of household items which possibly includes **Wholesale or Retail Grocery** Stores.\n",
    "\n",
    "<b>Segment 1</b> shows the opposite spending habits by being far below the average in those categories mentioned above. This cluster could represent **Cafes or Restaurants or Farmer's Market selling fresh produce**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 9\n",
    "*For each sample point, which customer segment from* ***Question 8*** *best represents it? Are the predictions for each sample point consistent with this?*\n",
    "\n",
    "Run the code block below to find which cluster each sample point is predicted to be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "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 0\n",
      "Sample point 2 predicted to be in Cluster 1\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": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**Answer:**\n",
    "\n",
    "For <b>sample point 0</b>, the values for ```Grocery``` and ```Milk``` are above average and mimic the <b>Segment 0</b> center in those categories — so, the predicted cluster seems to be consistent with the sample.\n",
    "\n",
    "The same behaviour is seem when inspecting ```Fresh``` and ```Milk``` purchases in <b>sample point 2</b> (3rd point from above) which is also consistent with the initial findings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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
   },
   "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. *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",
    "**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": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "I can think of two theories one could have:\n",
    "\n",
    "* No fresh food theory: You could reduce the delivery frequency for customers segment 1 since it is not ordering a lot of fresh food. Since fresh food should be fresh and therefore delivered often, these customers would likely react negative to reduced delivery frequency. Customers from segment 0 may not care that much (provided they can store a sufficient amount of food).\n",
    "\n",
    "* Retail does not like stocks theory: Retailers usually like it a lot to be able to quickly adapt to changing demands and also to minimize inventory to save cost. Thus, they could react negatively to reduced delivery frequency. Restaurants may not be as sensitive to inventory.\n",
    "\n",
    "Unfortunately, both theories predict negative effects for one or the other customer segment. Only an A/B test will tell if actually any of the segments will actually not react negative.\n",
    "\n",
    "In terms of the question wording, I actually cannot think of a reason why any customer would react positively, since we talk about a reduction of service level with no compensation. If cost saving are transferred (patially) to the customers, they may.\n",
    "I would do the A/B test by selecting a random sample from each cluster and reducing the delivery frequency for each of them (and maybe reduce prices a little). Talking to the selected customers after one or two weeks and asking for their happiness will provide a quantitataive score of customer satisfaction. An equally sized control group selected from the remaining customers should also be contacted to get a comparison score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
    "**Hint:** A supervised learner could be used to train on the original customers. What would be the target variable?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "I can see two different ways to assign new customers to a segment:\n",
    "* Use the clustering algorithm itself to create the assignment: Both k-means and GMMs perform during training some kind of E-step in which they assign data points to clusters based on the current parameters for the clustering. We could keep the cluster parametrization fixed and perform these E-steps for the new data points to include them into the clustering (without changing the clusters themselves).\n",
    "* Use a supervised learning algorithm to \"learn\" how to distingusish between clusters: You could interpret the cluster assignments as labels for a supervised learning algorithm. If the algorithm is sufficiently good at reproducing the cluster assignments, it will also do a good job at assigning cluster labels to new data points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAz8AAAIECAYAAAA3n5CZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtAzff/B/DnOZUuJFRuyz1KuY0il+ayzNcsl9jQ8GU2\nlwkzmzWjTHOZyw+T68yXMblMRpsZctsQHZeQjs2dJClSdKpzzuf3R+vo6Haqc+08H//g9Pm8P6/z\neX9OPq/zfn9eb5EgCAKIiIiIiIgqObGhAyAiIiIiItIHJj9ERERERGQWmPwQEREREZFZYPJDRERE\nRERmgckPERERERGZBSY/RERERERkFpj8EJFZS0xMhIeHBwYNGoRBgwZh4MCBGDhwIHbv3q3aJicn\nBytWrFBt079/f3z//feF2tqyZQvc3d1x6dKlEo8ZHh6Ozp07q9obMGAA/Pz88O2335Y5/j/++AMj\nR44s835ldeXKFfTq1avIn7m7u6N///6q9zJw4EBs3LhRo3Z37dqFiIiIMsWyZ88eTJgwoUz7nD17\nFv7+/mXaBwB69eqF+Pj4Mu0TFhaG8PDwQq+Hh4fjm2++KXMM+UaOHKnW7p49ezBs2DAMGjQI77zz\nDkJCQpCRkaGVY2lDwXPeq1cv/PLLLwaNh4gIACwNHQARkaHZ2Nhgz549qn8nJyfD398frVu3RosW\nLTBx4kQ0bdoUO3bsQJUqVZCeno5x48YhKysLU6ZMUe23Y8cO9O/fH5s2bcL//d//lXjMfv36Ydas\nWap/P3v2DP3790e3bt3QtWvXMsUvEonKtH15FXcckUiELVu2wMHBAQDw9OlTjB8/HmKxGKNHjy6x\nzfPnz6NFixbaDrVSsrW1hZ2dHQBg7dq1+Ouvv7B69WrUqlULCoUC8+bNw8SJE7F161YDR1qYra0t\nbG1tDR0GERGTHyKiV9WpUweNGjXC7du3kZ6ejlu3bmHDhg2qm38HBwcsXrwYiYmJqn3OnDmD9PR0\nfP755/Dz80NycjLq1Kmj8TFTUlIgk8lQvXp1AMCNGzcwf/58PH36FEqlEiNGjMDgwYMBACtWrMCv\nv/6KmjVromHDhqo2vvzyS7Ro0QJjxowp9O/bt28jJCQEaWlpEIvFmDBhAt5++20kJycjLCwMSUlJ\nkMvl6NevH8aNGwcA2LZtGzZv3ozq1aujefPmxcYuCAIKrpddo0YNBAcHY/LkyRg9ejRSU1MREhKC\n1NRUPH78GPXr18fy5ctx/vx5HDlyBKdOnYK1tTX69OlT5Ha1atXS+DwePXoU69atg1wuR1paGgYM\nGICpU6cCAJ4/f44pU6bg7t27qF69OubOnYvGjRsjNzcXS5YsQWxsLJRKJVq2bIlZs2ahatWqam0f\nOXIEa9euhVwuh42NDWbMmIF27dohMzMTs2bNwrVr1+Ds7AwLCwt06NChyPiuX7+OESNGID09HR4e\nHggNDYVUKsWnn36KY8eOAQBkMhl69eqFX3/9Ve29d+jQAa1bt0ZWVhbWrVuHffv2qX5uYWGBL774\nAocOHUJubi6AvGto1KhRSElJgZOTE5YtWwYnJ6diz9HZs2exbNkyNGjQAP/88w9yc3MREhKCjh07\n4ssvv0TVqlXx999/4+HDh2jatCmWLVsGW1vbQtfqyJEjERAQoPa+O3bsCA8PD437kYhIV5j8EBG9\n4sKFC7h79y7atm2L/fv3o23btoVGPRo2bKiWeGzfvh39+/eHs7MzOnfujK1bt2L69OnFHuO3337D\nuXPn8OLFCzx9+hSenp4ICwtD69atoVAoMHXqVCxevBgtW7ZEZmYmhg4diubNmyMlJQWHDx/Gvn37\nYG1tjY8//lij9/Tpp5/ivffew7Bhw/Dw4UOMGjUK3bt3x4wZMzBmzBj06NEDOTk5+Oijj9CwYUM0\nbtwYq1atQlRUFGrVqoXQ0NAynUN3d3ekpqbiyZMn+O233/D666/jww8/BACMGzcO+/btw+jRoxEd\nHY0WLVogMDAQP/74Y7HbaWrTpk1YtGgRGjZsiEePHqFnz57473//CyBvRG/ZsmVo27Ytdu7ciRkz\nZmDnzp1Yv349LC0tERkZCQBYtmwZli5dipCQEFW7d+7cwbJly7B161Y4ODjg+vXrGD16NA4fPozv\nvvsOtra2+P3335GWloaAgIBik5+7d+8iMjISNWrUwOeff441a9Zg+vTpqFmzJk6cOIE33ngDv/32\nGzp37lwo6Rs/fjwAID4+HnZ2dmjQoIHaz62trfHOO++o/n3//n3s2rULNWrUwKRJk7Br1y5MnDix\nxHN0+fJlzJkzB25ubvjf//6HlStXYsuWLQCAq1ev4scffwQAvPfeezhw4AD69+9f5LXq6uqqFltZ\nrx8iIl1h8kNEZk8mk2HQoEEQBAEKhQI1a9bE0qVLUadOHYhEIiiVyhL3f/z4MQ4dOqSaOte/f398\n/fXXmDRpEmxsbIrcJ3/am1wux9y5c/HPP//A19cXAHD79m3cvXsXM2fOVI2oZGdn4+rVq7h+/Tp6\n9+6tmkI0ePBg1c1pcdLT0yGVSjFkyBAAQN26dXHw4EFkZWUhNjYWz549w/LlywEAWVlZSEhIQFJS\nErp166a6AR86dCj++usvTU4ngJdT5GxsbDBq1ChIJBJs2rQJt2/fxvXr19G2bdtC+2i6XUnWrFmD\nY8eOYd++fbh586bqPQGAm5ubqr1Bgwbh66+/RmZmJo4dO4aMjAycPHkSACCXy+Ho6KjW7smTJ/H4\n8WOMHj1a1SeWlpa4ffs2Tp8+ja+++goAUKtWLfj5+RUb31tvvYUaNWoAAAICArB48WJMnz4dgYGB\n2LVrF9544w3s2LEDM2bMKLYNsVhc6jUJAF26dFEdKz8ZLe0c1a9fH25ubgAADw8Ptemgvr6+sLTM\nu21o0aIF0tPTS7xWmzZtWmqMRET6xuSHiMzeq8/8FNSuXTv8+OOPEARBbfTn0qVL2Lp1KxYtWoSd\nO3eqppIBedPAnj9/jj179uDNN99UTSMTiURYv369WvuWlpaYPXs2AgICsGjRIoSEhEChUKB69epq\nMaWmpsLe3h6LFi1Sm2JmYWGh1l7Bn+Xk5Ki2EYlEavHfunULzs7OAKB6lgkAnjx5Amtra+zcubPE\n45Tm0qVLcHFxga2tLRYvXowrV65g8ODB8PHxgVwuV2s7n6bbFScrKwuDBg1C79694eXlhSFDhuDw\n4cOqNsTilzV+8vvTysoKCoUCX331lSr5zMrKQnZ2tlrbSqUSnTt3VnuW6+HDh6hduzZEIpFanPkJ\nQlEKxgAAVlZWAAB/f38sW7YMZ86cQVZWFry8vIpto1mzZpDL5bh3757a6E9OTg6CgoIwb948tbaB\nl8loVlYWBg4ciLfeeqvIc2Rtba22T8H3VTCRz/9ZSdfqxYsXi30PRESGwmpvRGT2SrrBbteuHZo2\nbYoFCxaokonHjx9j3rx5aNiwIZRKJXbt2oW5c+ciOjoa0dHROHLkCMaNG4fNmzejdu3a+OWXX/DL\nL79gz549qoSjICsrK8yZMwc7duxAQkICmjRpAmtra+zbtw8AkJSUhHfeeQfx8fHw9fXFgQMHkJGR\nAaVSib1796raqVWrFq5cuQIASEtLw7lz5wAA1apVg6enp+oGNSkpCYGBgcjOzkbbtm3xww8/AMgr\nujB8+HAcOXIEXbp0wcmTJ5GcnAwAqilhmkhOTsbSpUsxduxYAHmjJv/973/Rv39/1KxZE6dOnVKN\nXFhYWKieUSlpO03cuXMHz58/xyeffIIePXrgzJkzyM3NhUKhAABIpVJIpVIAeQlf+/btYW1tDV9f\nX/z000/Izc2FUqnEV199VahghY+PD06ePKkaKTl+/DgGDBiAnJwc+Pr64ueff4YgCEhPT0d0dHSx\nMR45cgQZGRlQKBTYsWOHKuGysbGBv78/Zs6ciWHDhpX4PqtUqYKPPvoIM2fOVI3m5OTkYN68eZDJ\nZEVeYwXP0YsXL4o9R2VV0rVKRGSMOPJDRGavtGppK1euxNKlSxEQEABLS0soFAoEBARgzJgxiI6O\nhiAIas9aAMDo0aOxZcsWHD9+HN27dy81hg4dOqB///6YO3cuIiIisGrVKsybNw8bNmyAQqHAtGnT\n8PrrrwMA/vnnHwwePBgODg5wd3fHkydPAOSVQv7ss8/Qt29fvPbaa+jUqZOq/SVLluDrr7/Gli1b\nIBaLMW/ePDg6OmLJkiUICwuDv78/5HI5/P39Ve/l888/x3//+19Uq1YNbdq0KfH8jRo1SjXCJBaL\nMWTIENVN/KRJk/Dtt99i1apVsLS0RIcOHXDnzh0AwBtvvIGwsDAAQFBQULHbveqvv/5C+/btAeQl\nrw4ODjh27Bi6d++O//znP6hevToaNWoEV1dX3L17F1ZWVmjWrBlWrVqFu3fvwsnJSVVa/OOPP8ai\nRYswaNAgVcGDL774QvXeAMDV1RVz587Fp59+CiAvaVuzZg1sbGwwefJkhIaGom/fvnB0dFRNGyuK\nq6srxo0bh4yMDLRv3141KgjkTYPbuXMnBgwYUOz++caNGwdbW1uMHTsWIpEI2dnZ6NixI1avXl3i\nfu7u7iWeo7KysrLC6tWr8c033xS6Vs+ePVvm9oiIdE0klGVOAREREenE+vXrkZSUxOIAREQ6xJEf\nIiIiA3vzzTfh6OiINWvWGDoUIqJKjSM/RERERERkFljwgIiIiIiIzAKnvRERadE333wDiUQCALh+\n/ToaNGgAa2triEQitZLSuiCVSjF58mTY29sjPDwc9evX19mxSvLkyRN07txZVVmtoJSUFMyfPx/X\nr1+HSCSCra0txo0bhzfffFPncY0cORIjR47EW2+9pfE+vXr1grW1NWxsbKBUKqFUKtGzZ09MnTq1\n1PLfx48fR1xcHKZMmVKmOHNyclRr8QCAQqGAv78/PvroIwDArFmz0K9fP3Tu3Fmj9hITE/HOO+/g\nwoULZYrD398fISEh8Pb2LtN+RETGjMkPEZEWzZo1S/X3N998E0uXLoWHh4dejh0dHQ0fHx9V9TRD\neXVNpIJmzZqFLl26YNmyZQCAGzduIDAwEBEREUa7KGbBPpTJZJg+fToWLFig1tdFuXz5Mp49e1bm\n43388cdo0qSJKllOT0/HuHHjkJWVhSlTpuCbb74pc5ulVTQkIjIXTH6IiHREEIRCawi1bt0ab775\nJq5du4YlS5YgISEBO3fuhFwux9OnTzFu3DgMGzYMe/bswaFDhyAWi3Hnzh1YWVlh0aJFcHV1xcGD\nB7F27VqIxWJYWFjg888/R1JSEiIiIqBUKiGTybB48WKsWrUK+/fvh6WlJRo3boyQkBA4Ojpi5MiR\nqFGjBm7duoXhw4fjwIEDaNWqFWJiYpCWloaRI0ciNTUVZ8+ehUwmw/Lly9G8eXNkZmZi3rx5+Pvv\nvyGXy9G5c2fMmDEDYrEYBw8exPLly2Fra4tWrVoVe05SUlKQnZ2tSpCaNWuG1atXw8HBAQDw888/\nF3s+/vjjD2RnZyMxMRH16tXD+++/j61bt+LOnTsYM2YMRo8ejT179uDXX3+FIAhITk5G3bp1sXDh\nwkJr35w/fx5Lly5FVlYWxGIxgoKC0KNHj2L7MZ+NjQ1CQkLg5+eHadOmQSwWY86cObhz5w6ePn2K\nqlWrYunSpXj27Bm2b98OpVKJatWqYfz48UVu17hxY7VjxcbG4ubNm/j+++9VCYuDgwMWL16MxMRE\nAC9HsDw9PfH++++jWbNmSExMxNatW3H58mWsWLECgiDA1tYWc+bMgb29vdox1q5di4MHD0IQBLz2\n2msIDQ2Fs7Mzbty4gZkzZ0Imk6FJkybIysoq/uImIjJVAhER6UTPnj2FK1euqL3m5uYm7Nu3TxAE\nQXj+/LkwdOhQ4enTp4IgCMLFixeF119/XRAEQYiMjBS8vb2F5ORkQRAEISwsTAgODhYEQRD8/PyE\nuLg4QRAE4eTJk8KqVasEQRCElStXCmFhYYIgCMLPP/8sDBs2TJDJZKqfjR07VhAEQRgxYoTw1Vdf\nqWIaMWKEMHnyZEEQBCEuLk5wc3MTjh07JgiCIMyfP1+YPXu2IAiC8OWXXwpbt24VBEEQFAqF8Pnn\nnwsbNmwQHj9+LHh5eQk3btwQBEEQ1q1bJ7i7uxd5TmJiYoRu3boJPj4+wsSJE4UNGzYIDx8+1Ph8\n5G/br18/YerUqYIgCEJCQoLQpk0b1Xavv/66cOfOHUEQBGHJkiXClClTVO/zjz/+ENLT04U+ffoI\niYmJgiAIQnJystC9e3chKSlJoz4UBEHo3LmzcPnyZeHAgQPCN998o3o9JCRE1QcF+6Ok7QrauHGj\n8MknnxR57vLlv4/79+8Lbm5uwrlz5wRBEFT9IJVKBUEQhIMHDwofffSRcP/+fdV53LNnjzBt2jRB\noVAIgiAIO3bsED766CNBEARh4MCBwu7duwVBEIRz584JLVu2FM6ePVtiLEREpoYjP0REetahQwcA\ngJ2dHdauXYujR4/izp07SEhIUPu23dPTE7Vr1wYAeHh44NChQwCAfv364eOPP0aPHj3QpUsXfPjh\nh4WO8eeffyIgIADW1tYAgFGjRmHt2rWQy+UAAC8vL7Xt85+DadCgAUQiEbp16wYAaNiwoWqxymPH\njuHy5cvYtWsXACA7OxsikQjnzp2Dm5ubatra0KFDVdPaXtWpUyccP34cFy9ehEQiwdGjR7F69Wps\n3rwZrVq1KvF8tG7dGnXq1AEAuLi4oGvXrqoYc3JyVNt269YNDRs2BAC89957GDhwoFoMFy5cQEpK\nCiZNmqQa1RGLxbh27Rrq1q1bZNxFsbW1RZ8+fdCgQQPVCNTZs2dVi9EWpOl2IpEISqVS4xgsLS3R\nrl07AHmjWS1atFAtstq7d2/07t1bNWIEvOzDgIAAAIBSqUR2djaePn2Ka9euqRZYbd++PVxdXTWO\ng4jIVDD5ISLSMzs7OwBAcnIyhg4diqFDh8LLywt9+vTB8ePHVdvlJy5A3k1x/o36J598giFDhuDk\nyZPYs2cPvv/+e+zZs0ftGK/eQCsUCigUClUb+THke7UQQ1EP8ysUCqxYsUKV5GRmZgIATp8+rTY1\nrLhCAGlpaVi5ciVCQkLQvn17tG/fHuPGjcOsWbPwyy+/wNnZucTzYWVlpdbeq/8u6vgKhaJQPEql\nEq6urtixY4fqtUePHsHR0bHI9l6VmJiIrKwsNGjQANu2bcOuXbswYsQI+Pv7w8HBQS3ZyKfpdu3a\ntcOPP/5Y6LmpS5cuYevWrVi0aJHa9lWqVIFYLFa971ef7bl27RqqVaum9t4/+ugjDBs2DACQm5uL\nZ8+eQSQSqV1jQF5iRURU2bDUNRGRgVy+fBm1atXCxIkT0bVrVxw9ehQACj0nVJBCoUCvXr3w4sUL\nDB06FKGhobh58yZyc3PVtvP19UVkZKRqNGTLli3w9vYuNmEoqLjjd+vWDZs2bQKQV5FswoQJ+Omn\nn9ChQwdcv34d165dAwBERkYWub+DgwNOnTqFzZs3q17LyspCUlISPDw8ynU+ior59OnTePToEQBg\nx44d6NWrl9q2bdu2xe3bt1VV+RISEtCnTx/VPiV59uwZvvnmG4wYMQJVqlTByZMnERAQgMGDB6Nx\n48Y4evSoKvG0sLBQ9UtJ2xXUrl07NG3aFAsWLEBOTg4A4PHjx5g3b55qNKu49922bVvcuHEDN27c\nAAAcPnwYM2bMUNuuW7du2LVrlypxXb58OWbMmAEHBwd4enqqRvXi4+Px999/l3o+iIhMDb/WISLS\nkaIqbBV8rVu3boiMjESfPn1QtWpVtG7dGrVq1cKdO3eKbdPCwgJfffUVpk+fDisrK4jFYixYsKBQ\nUjNkyBA8fPgQ7777LgRBQMOGDbF48eIi4yrt3/lmzZqF+fPnw9/fH3K5HF27dsWHH34ICwsLLFmy\nBJ999hmsrKzQsWPHYmPfuHEjFi1ahC1btsDOzg4ikQgBAQEICAiATCYr8/koKua6detixowZePTo\nEVxdXVXV7/K3qVWrFlauXIlFixapii8sXrwY9erVK7Ldzz77DDY2NqqRkT59+mDChAkAgA8++AAh\nISGIjIyEWCyGp6enKmno3LkzJk+eDCsrK4wdOxazZ88ucrtXrVy5EkuXLkVAQAAsLS2hUCgQEBCA\nMWPGFHqvBf/u6OiIJUuWYMaMGapCC/nTD/O3e/fdd/Ho0SMMHToUYrEY9erVw4IFCwDkVbX78ssv\nERERgUaNGqFZs2alnnciIlMjEjT5Sk3L1q9fjyNHjiA3NxeBgYEYPHiwvkMgIqJKKL8q3Nq1aw0d\nChERGSG9j/ycPXsWFy5cwPbt2/HixQts3LhR3yEQEREREZEZ0vvIz//93/9BJBLhn3/+wfPnzzFj\nxgx4enrqMwQiIiIiIjJDeh/5efLkCR48eIB169bh3r17mDhxIg4cOKDvMIiIiIiIyMzoPfmpUaMG\nmjVrBktLSzRp0gTW1tZIS0tDrVq1it3n1ZKfREREREREZaX35KdDhw7YsmULRo8ejeTkZMhkMtSs\nWbPEfUQiEVJSMvQUIWmTs7M9+86Esf9MG/vPdLHvTBv7z3Sx70ybs7N9qdvoPfnp0aMHJBIJhgwZ\nAkEQEBoaylEdIiIiIiLSOYOs8/PZZ58Z4rBERERERGTGxIYOgIiIiIiISB+Y/BARERERkVlg8kNE\nRERERGaByQ8REREREZkFJj9ERERERGQWmPwQEREREZFZYPJDRERERJWCQqHAw4dJkMvlWmnvwoVz\nCA2dqfba2rXh+P33X4vcPjn5IU6e/FPj9kJDv8Tt27fQp093TJkyAZMnj8f48WMwbdokZGZmlinW\nZ8+e4dChA2Xap6Ju3ryOuLgLqn+vWLEUSUkPkJGRgYULwxAUNA4TJ47FnDlf4fnz4t+PQqHA5Mnj\nMXHi2DK/77Ji8kNEREREJk8S+Qv+9BuErE4DcLLXQMRs26GVdkUikcbbnj8vweXLcRq1l5OTA0EA\nbGxs0KRJM3z33VqsXLkO69b9D+7uHvj111/KFOf163/jr79OlGmfijp27Ahu3bqp+ndSUiLq1auP\nOXO+QteuvggPX481a36Ah4cnFi9eUGw7KSkpyMrKwpo1P6BatWo6jdkgi5wSEREREWlL2uPHwJzl\nePdhFgAROkpTcDQsHEndOqNew4YValsQhCJfDw9fjkuXLkIkEqF37z4ICHgPW7duQnZ2Nlq3bot6\n9eph+fIlAIDq1R0wc2aI2v7nzsWiffsOhY4hCAIePXoIF5cGAIDdu3fg0KE/IBKJ4Of3FgYPHorj\nx4/gp59+hJWVFZycnDBnznxs2fI/3LhxHVFRv8DTszXCw5dBqVQiPf0ppk//Eq1atcaAAX2wd+8f\nAIDQ0JkYNGgIkpIe4Lff9kEQBIwdOx63bt3EiRNHIZPJ4OBQA/PnL8ahQwdw+vRJyGQyPHiQiPff\nHwVv7074/fdfYWVlBXf3lrCxsUWjRk3w8OFDPHmSCl/fHqr39O67w5GV9eLf97OzUPtLly7A/ft3\nsWTJAkycOBkLFoQhI+MZAGDq1Olo2tS1Qn1YEJMfIiIiIjJp5/bsxbCHLwC8HKXpkZqNbXv2od7U\noAq1ff68BFOmTACQl5gkJT3A8OEj8fDhA6xfvwlyuRwff/wh2rf3xogRo3H37h107eqL8ePHYObM\nUDRq1Bi//roXW7duhrd3J1W7p0//hREjRkOpVOL27ZuYMmUC0tPTkZ2djT59+qJv33dw+/YtREcf\nwpo1P0AQBEybNgne3j6Ijj6E998fhe7de+GPP/bjxYvnGDXqA+zdGwl//4GIjj6EoKBpaNq0GQ4d\nOoD9+/ehVavWauenIHv76liwYAkEQcClSxexYsUaAMCnn06GVHoVAPD8+XMsXfod7t+/hy++mIa+\nfd9B377vwNHRCe7uHti27Ud06eKLx49TUK9efbX2RSIR7OyqQhAEZGQ8K9T+9OnBmDPnK3z22ZdY\ns2YlvLw6YuDAwbh//x7mz/8aq1dvqFAfFsTkh4iIiIhMWnWX15AiBuooX76WDiXs6tWpcNsdOnhj\nzpx5qn+vW7cKOTk5aNPmdQCApaUlPD1b4/btW2r73blzC0uXLgQAyOVy1UhOvuTkh6hduw4ePkxS\nTXvLzs5GcPCnqFmzFsRiMW7evIGHD5MwdepECIKAzMwMJCbeQ1DQJ9iyZRN+/nkHGjVqojbKAgDO\nzs7YtGkDbGxs8Px5JqpWzZ9KVnAU6+XfGzZsBCAvSbGwsERo6EzY2tri8eNHquenmjdvAQCoXbsO\ncnJyC52ny5cvYfjwkUhNfYxHj5LVfiaXy3HkyGG89dZ/im0/382b13H+vARHjhxSJUvaxOSHiIiI\niEyad5+3ENltKz468TcsIYISAnZ0aob+AYO0fixBEFClShVcuHAO7703HHK5HFeuxOHtt9/BjRvX\noVTmZWANGzbGrFlfo3btOrh8OQ5paamqNm7cuI5mzZqrtQkA1tbWCAkJw+jRgWjdui0aNmyEpk2b\nYcmS7wAAO3dGoFmz5ti3bw/Gjh2PGjVqYPHi+Thx4ijq1asPQcg79vLlSzBnzjdo2LAxfvhhHZKT\nHwLIKywgk8lgYWGh9qyOWCxWxfXnn8ewfv0mZGfLMHbsSFVs6s8+Car9BEGJjIwMVKtWDSKRCE5O\nzqhRoyb++us4unXrror72rWraNbMtdj28zVq1AR9+rSEn18fPHnyBL/+urdiHfYKJj9EREREZNLE\nYjH6bl6HiJVrYH3rPrIb1UPvoAmwtNT+rW7eFC471K9fHxMmfAC5XI5evXqjeXM3AMCWLf9Dixbu\n+OyzYISFhUChUEAsFiM4eDZSUh4BAE6d+gtduviqtZmvZs1amDTpEyxePB9r125E+/bemDhxLHJz\nc+Hh4Qln59po2dITn38+FXZ2VWFnZ4cuXXyRk5ONGzduYNeu7fjPf97GrFlfoHp1Bzg710Z6+lMA\nwJAhwzB+/GjUr/8a6tZVn5oGAC4uLrC1tcPHH38IQRDg6OiMx49TijoLAAA3N3esXv0dqlathk6d\nOqt+OntpHgUWAAAgAElEQVT2XCxduhDbt/+E3NxcvPaaC774YhYsLCyKbL/gNLlRo8ZgwYIw7N0b\niRcvXuCDD8aVv7OKilwo7ikuI5OSkmHoEKgcnJ3t2XcmjP1n2th/pot9Z9rYf6aLfWfanJ3tS92G\npa6JiIiIiMgsMPkhIiIiIiKzwGd+iIiIzIhUIsH9mFi4+HjD3cvL0OEQEekVkx8iIiIzERU8G523\nHYKvTECCzWZEBfaG/8IwQ4dFRKQ3nPZGRERkBhIksei87RBayvLqHLWUCfCJOAypRGLgyIiI9IfJ\nDxERkRlIjJGoEp98HllK3D8Ta6CIiIj0j8kPERGRGXDx8UaCjUjttau2Yrh08jZQRETap1Ao8PBh\nEuRyuVbau3DhHPz938KUKRMwZcoEjB07EiEhXxbb/s2b1xEXd6HY9v75529s2rQBADBgQB+txEhl\nw2d+iIiIzIC7lxeiAnsD/059u2orRsxwP/iz6AFVEpGSQ1iZeRI3G4nQ6KQSE6w7IdDn7Qq326GD\nN+bMmaf699dfz8LJkyfQvXuvQtseO3YEtWo5om3b14tsq3nzFmjevMW//xIVuQ3pFpMfIiIiM+G/\nMAzSIQMQcSYWLp28mfhQpfE4LRVz8CcevusCAJB2BMKOnkW3pDZoWM+lQm0Lwsvporm5uUhLS4W9\nfXWsW7cKcXEXIAhKDB36Plq1aoPff/8VVlZWcHdviYcPkxAZuQsKhQIikQjz5y/GjRvX8csvu/H1\n1/MB5LV748Z1rFixBABQvboDZs4MwbVrUqxZsxJVqlRB//6D8NZbfSv0HuglJj9ERERmxN3LiyWu\nqdLZc+4wHg6rr/Zaao962LPtGKbWG1Ghts+fl2DKlAlIS0uDWCzCgAEByMnJwYMHiVi9egNycnIw\nfvxohIevR9++78DR0Qnu7h6IjT2LxYtXwNraGosXz8eZM6fh5OQMkSh/xCfvz0WL5mHmzFA0atQY\nv/66F1u3boa3dyfk5uZg/fpNFYqdCmPyQ0REREQmzaV6bYhTEqGsU+3li+ky1LNrUOG286e9PXuW\njmnTglC3bn3cvHkd165JMWXKBAiCAIVCgaSkJLX9atasgXnz5sDGxgb37t1Bq1Ztimz/zp1bWLp0\nIQBALpfDxSUv5oYNG1U4diqMyQ8RERERmbQ+3t3RLfIYTnxkC1haAEolOu14jID+k7R2jOrVHTB7\n9lxMmTIBEydORocOXvj885kQBAGbN/+A115zgVgshiAo8fx5Jn74YT0iI3+DIAiYNq2oOPKmvTVs\n2BizZn2N2rXr4PLlOKSlpQIARCLWJdMFJj9EREREZNLEYjE2952BlRE7ccs6A42yqyKo92ewtNTu\nrW7jxk3w7rvDcPr0SdSuXQeTJn2ErKwsvPFGD9ja2sLNzR2rV3+HRo2aoE2bthg3bjQsLS1gb++A\nx49TULduvQKt5U17mz49GGFhIVAoFBCLxQgOno2UlEdajZteEgkFn+IyYikpGYYOgcrB2dmefWfC\n2H+mjf1nuth3po39Z7rYd6bN2dm+1G04nkZERERERGaByQ8REREREZkFPvNDREREpGdSiQT3Y2Lh\n4uPN0uNEesTkh4iIiEiPooJno/O2Q/CVCUiw2YyowN7wXxhm6LCIzAKnvRERERHpSYIkFp23HUJL\nWV69qZYyAT4RhyGVSAwcGZF5YPJDREREpCeJMRJV4pPPI0uJ+2diDRQRkXlh8kNERESkJy4+3kiw\nEam9dtVWDJdO3gaKiMi8MPkhIiIi0hN3Ly+cDuytSoCu2ooRM9yPRQ+I9IQFD4iIiIj0yH9hGKRD\nBiDiTCxcOnnDn4kPkd4w+SEiIiLSM3cvL472EBkAp70REREREZFZYPJDRERERERmgckPERERERGZ\nBSY/RERERERkFpj8EBERERGRWWDyQ0REREREZoHJDxERERERmQUmP0REREREZBaY/BARERERkVlg\n8kNERERERGbB0tABEBERkWmRSiS4HxMLFx9vuHt5GTocIiKNMfkhIiIijUUFz0bnbYfgKxOQYLMZ\nUYG94b8wzNBhERFphNPeiIiISCMJklh03nYILWUCAKClTIBPxGFIJRIDR0ZEpBkmP0RERKSRxBiJ\nKvHJ55GlxP0zsQaKiIiobJj8EBERkUZcfLyRYCNSe+2qrRgunbwNFBERUdkw+SEiIiKNuHt54XRg\nb1UCdNVWjJjhfix6QEQmgwUPiIiISGP+C8MgHTIAEWdi4dLJG/5MfIjIhDD5ISIiojJx9/LiaA8R\nmSSDTXtLTU1Fjx49cOvWLUOFQEREREREZsQgyY9cLkdoaChsbGwMcXgiIiIiIjJDBkl+vv32Wwwf\nPhy1a9c2xOGJiIiIiMgM6T35iYyMhKOjI7p27QpBEErfgYiIiIjIBEmkcQg/vA0SaZyhQ6F/iQQ9\nZyAjRoyASJRXIlMqlaJJkyZYs2YNHB0d9RkGEREREZHOBG1fio3t0pDlXgu20jR8cLEWwodNN3RY\nZk/vyU9BI0eOxNy5c9GkSZNSt01JydBDRKRtzs727DsTxv4zbew/08W+M23sP9Olrb6TJFxEgPPv\nkLV8+eW+7dU07E7tCy/3thVun4rm7Gxf6jYGXeQ0fwSIiIiIiKiyiEm8qpb4AECWRy2cuR9voIgo\nn0GTnx9//FGjUR8iIiIiIlPh4+IJm4RUtddsr6ahk4ungSKifAZNfoiIiIiIKhsv97YIPG2vSoBs\nr6ZheEw1TnkzApaGDoCIiIiIqLJZ6B+EIdI4nImIRyeXTvDyZ+JjDJj8EBERERHpgJd7W472GBlO\neyMiIiIiIrPAkR8iIiIiMyaRxiHmfjx8XDw5SkGVHkd+iIiIiMxUcFQ4Apz2Y+7wTAQ47UdwVLih\nQyLSKSY/RERERGZIknAR2zpnqNajkbV0RIRPJiTSOANHRqQ7TH6IiIioVFKJBIfD10AqkRg6FNIS\nLsRJ5ojJDxEREZUoKng2nAKCMHzuJjgFBCEqeLahQyIt4EKcZI6Y/BAREVGxEiSx6LztEFrKBABA\nS5kAn4jDHAGqBLgQJ5kjJj9ERERUrMQYiSrxyeeRpcT9M7EGioi0aaF/ECIfv43QiGrYndoXC/2D\nDB0SkU6x1DUREREVy8XHGwk2m9USoKu2Yrh08jZgVKRNXIiTzAlHfoiIiKhY7l5eOB3YGwk2IgB5\niU/McD+4e3kZODIiorLjyA8RERGVyH9hGKRDBiDiTCxcOnnDn4kPEZkoJj9ERERGSCqR4H5MLFx8\nvI1ilMXdy8so4iAiqggmP0REREZme9DnaPvDfvjKBCTYbEZUYG/4LwwzdFhERCaPz/wQEREZkQRJ\nLNpt3M/S0kREOsDkh4iIyIgkxkjgnsXS0kREusDkh4iIyIi4+HhDaitSe42lpYmItIPJDxERkRFx\n9/LCxQ/eZmlpIiIdYMEDIiIiPdKkituw8MX4s9/bLC1NRKRlTH6IiIj0JCp4NjpvO6RRFTeWliYi\n0j5OeyMiItKDBEksOm87xCpuREQGxOSHiIhIC6QSCQ6Hryk2mUmMkagSn3ys4kZEpF9MfoiIiCoo\nKng2nAKCMHzuJjgFBCEqeHahbVx8vFVFDPKxihsRkX4x+SEiIqoATaezuXt54XRgb1ZxIyIyIBY8\nICIis6FJpbWySoyR4I0iprNFnIktdAz/hWGQDhnAKm5ERAbC5IeIiMxCWSqtlUXedLbNas/zlDSd\njVXciIgMh9PeiIioUpNKJPgpeCact+zTSaU1TmcjIjIdHPkhIqJKK3+05xOZAClE2I50DIMDgOKn\nppUHp7MREZkGJj9ERFQpvVqIwB3WAIArkKEVbFRT07T1HJC2p7Pp4vkkIiJzx+SHiIgqpaIKEbjD\nGlHIgNjWDjHD/YCf9+rkOaBXlTWR2R70Odr+sF/ncRERmRs+80NERJVSUevqXLEW8PfYgUjdvRKu\nQ/prVKK6ojRZA6igBEks2m3cX2xcpS2maipSUlJx/HgsHj1K0XrbleUcEZH2MfkhIqJKqahCBGff\n74NRC+bD3csLiTEStQptQN5zQPfPxGotBk3XACooMUYC96yi4yprImWsQkN/RUDAWmzffhUBAd8j\nNDQKgiCUvqMGKss5ItMnkcYh/PA2SKRxhg6FCmDyQ0RElZb/wjA8jgxHROhopO5eqTZ1rKiRoZJK\nVJdHeRIsFx9vSG0LxyV3qKqXkSpdi4w8ja1ba+Hbb/2xZs1/sWSJP378sQ5+/vlUhdsuT7JJpAvB\nUeEIcNqPucMzEeC0H8FR4YYOif7F5IeIiCo1dy8v+E2aWOhZG32UqC5PguXu5YWLH7xdKC7Lp891\nPlKlD3v3PkXTppno0qUNAMDHpzVcXdMRFZVR4bb1MZpHVBpJwkVs65wBWUtHAICspSMifDI5AmQk\nmPwQEZHZKmlkqChlfZakvAnWsPDFheLSx0iVPty4YYU6ddRfq1NHwI0bFa/BVFnOEZm2mMSrqsQn\nX5ZHLZy5H2+giKggVnsjIiKzpmmJ6vw1g8paga28awC9Gpe7lxeiAnsD/07ryk+kTGlNoZycHDx5\nYo2WLWVqr9vaivD0qS2ys7NhbW1d7vYrwzki0+fj4gmbhP1qCZDt1TR0culkwKgoH5MfIjIbXDeF\nyquoZ0mEiMOQDhmg0bWkrTWAjGkxVUEQkJz8EGlpabCwsEDt2rVRs2atEveRybKQk2MLsVg9+RGJ\ngJwcG8hkWRVKfgDjOkdknrzc2yIw6k9sQypkLR1hezUNw2Oqwcu/raFDIzD5ISIzUd5v7YmAotcM\n8shSIuJMrN4TaW0vplpWmZkZ+Pnnnfjf/zYgIUF9Gs8bb/TEmDEfok+fvrC0LHyLUa2aPezsnkEu\nVz+XcrkAK6ss2NraaSVGQ58jooX+QRgijcOZiHh0cunExMeIMPkhokqvot/aE+U9S7JZ7WF6c3yW\n5ODB3xEUNB5Pnz4t8ucnThzFiRNH0ayZKzZvjkCLFm5qPxeLxahRQ4nnz9Wfy3n+HKhVS4YqVaro\nLHYiffNybwsvdyY9xoYFD4io0mMFKKoofVSGM3Z79vyMUaOGqyU+1tbWcHNzR9OmzSASvUxobty4\njn79eiM+/kqhdtzd5XjwAKp1fQRBQFKSCE2ayHX/JgyAa70QGRcmP0RU6bECFGlDWSvDVSYXL57H\n5MkToFQqAQB169bD/PmLEB9/HX/+eRYxMRdw7twVTJ06Hba2tgCA9PSnCAwcgqdPn6i11bOnHW7e\nbIyDB/O+fDh8WIJbt+rB399ev29KD7jWC5HxsZgzZ84cQwehiRcvcgwdApVD1arW7DsTVln6z6l+\nfUQn34FNwk04y19+a9911AhDh6ZTlaX/NCGVSHAxci9yxCI41a+vs/2c6tdH047eZTpGeRhb382Y\n8SmuXZMCANzc3PHbb4fh69sd1tY2qm2qV3fAG2/0QK9efvjll0jk5GQjMzMDDg410alTZ9V2np4N\nkZl5Chs3nsWFCzewfXscBgywwpQpvfX+vnSlalVrnDh3FrPc4lUVv+TOdpBap8P3nj3qO9U1cIRU\nHGP77FHZVK1aesEUkZA/7mzkUlIqvvgZ6Z+zsz37zoRVtv6TSiS4/28FKHOYrlTZ+q84+cUsWsoE\nJNiIcFrDYhbl3U8fjKnv7t+/By+v1qpRn+PHY9CypYfq5xJpHGLux8PHxVP1fMPWrZvx6aeTAQAN\nGzbCmTMXYWFhodZucvIjSKV34O7eBHXqOOnp3eiHs7M9QiPWYe7wzEI/C42ohkl+gQaIijRhTJ89\nKjtn59JHkDntjYjMhruXF/wmTSxX4lPWxS1JP4oqZuETcbjUfirvfq8yh+ti167tqsTnjTd6qiU+\nxU3rGjJkKGrWrAkAuHv3Dv7660ShduvUqY3u3b0rXeKTL2+tl1S11/LWevE0UEREBDD5ISIqVVTw\nbDgFBGH43E1wCghCVPBsQ4dE/ypvMQttFMEwl+vixo3rqr/36+ev+rsk4SK2dc5QTeuStXREhE8m\nJNI42NjYwM+vj2rbW7du6i9gI+Hl3haBp+1VCZBqrRdW/yIyKCY/REQl0HSEwBxGAIxReYtZVLQI\nhrZGjkxBVlaW6u81atRQ/T0m8araCvYAkOVRC2fu5639kz/y82ob5mShfxAiH7+N0Ihq2J3aFwv9\ngwwdEpHZY/JDRFQCTUYIzGUEwBiVtwR1RUtXm1P59OrVq6v+fu/eXdXfS5vWdffuy23t7StfJTdN\nebm3xSS/QI74GLn8kuQxV84bOhTSMSY/REQlKG2EwJxGAIxVeUtQV6R0tTmVT3/99Q6qv+/YsU21\nPk9J07pOHTqIQwcPFNkGkbEp+OxaL8vdLEleyTH5ISIqQWkjBOY0AmDMylvMoiL7mcuipwEBQ1C1\najUAwD///I3ffotS/ayoaV1RwbOxd9R4KJQKAECz2vXg6dnKILETlUQijUPwT0uwxfmmagpnlnst\n1bNrVDmx1DXpFEtGmjb230vFlcmWSiRwCghSS4Cu2oqRunulwW+E2X+6p6vy6cbWd1988Sn+978N\nAIBq1eyxdesOdOnSrdB2CZJYHO7/PsLkD1WvLbR6DV32bjH450GfjK3/qLDgqPCXBTukj4CLicCw\n11U/Z0ly06RJqWsucko6xcXCTBv776XiFrc05gVU2X+6p6tFT42t79q0aYe9eyORkZGBnJwcbN/+\nEy5fvoRq1aqhWjV7pKc/xf79UZg5Yzp+SX+g2q8H7LBGWRsxrrXRtGPlmxJYHGPrP1InSbiotgAt\nnKoClhbAw2dAbXvYXk3D2w+cceL2RYhzlFyU1oRwkVMyOH77ZdrYf5ozxgVUTbX/pBIJ7sfEwsXH\neM5lSXQRrzH2XULCVbz77gA8epSs0fbesMEfaIgkWyujGAnVJ2PsP3op/PC2IhegRVQ87JrXQ6Nt\nt3BreGPIWjrCJiEVgaftWanPRHDkhwyO336ZNvaf5nQ1AlARmvSfRBqHyItHSv12UyqR4GLkXuSI\nRTp9j1HBs+E2Kxw9oy/ixe7fEZ18B25+vXR2vIrSVbzG+NlzdnaGv/9A/PPPNdy+favY7SzFFggQ\nO+AX4TXct7UympFQfTJE/2n6WSZAnKPE7heXIHe2U71mfSUFo8/WwIdKD0T0yFGNCsmd7SC1Tofv\nPXueVxOgycgPkx/SKWP8D5w0x/4zbaX1X3BUOGa5xSO6pyV2v7iE5Og4+Ll1LLSdvhKSBEks3GaF\nq56fcpYD1tJbuOfbzqiSyny6jFeXn72KJLIODg54991hGDRoCCwtLfHsWTpEIsDevjqaNnXFuHET\nsWr1BrR5+z+Ida0Dq08/NLvEB9D/705NP8uUp75TXSRHxyHB5inkznawvZqGwDPVsfC9aYh5cAW/\nd1XfXu5sC9dT6ejYtLVhAiaNaZL8WOohDjVyuRwzZ85EYmIicnNzMWHCBPTqZbzf6hERmZr8aVit\n+/ZAnWYti9xGknDx5cO+AGQtHREhpGGINE5tPZKiSnkLEYchHTJA69OYEmMkeKOIynkRZ2L1OmVK\n02lsZYlXG1PjtNFGVPBsdN52CL4yAQk2mxEV2LtMJb7zubo2R1jYQoQVs6uzs3OZYjS1qY7GRNPP\nMqlb6B+EIdI4nImIRyeXTvDyzztXvk3awCbhZ7UFfPPWr+pkqFBJy/Re6nrfvn2oWbMmfvrpJ3z/\n/fcIK+43JxERlVnBBVcte40pdsHVmMSrav+5A0CWRy2cuR+v9po+S3kbw9o5ZVmwVtN4tbEIrjba\nMNY1qbhIcMVo+lmmwopagNanVfti16+iykHvyU/fvn0xdepUAIBSqYSlpd4Hn4iIKqVXb27ds4q/\nufVx8VT9554v79tNT7XX9JmQGHrtnLImB5rEq42EQ1tJizGuSWWsCZkp0fSzTJorav0qqjz0nvzY\n2trCzs4OmZmZmDp1KqZNm6bvEIiokpJKJDgcvsZsb5zKcnPr5d5Wo283tZmQaNI//gvD8DgyHBGh\no5G6e2W5pmSV9Zj5ypMclBavNhIObSUtxjCy9ipjTMhMjaafZSqbokaFqHIwSKnrpKQkBAUFYcSI\nERg0aJC+D09EldD2oM/RbuN+uGcJkNqKcPGDtzEsfLGhw9KrKzFnYdlrDNyzXv5al9qJIY/eiFY+\nRT/8HHPlPP68dQm+TdrAp1X7Etu+9WcMmvj6FNtWSfTVP3lxnkYT3864snVXmY5ZnvOnSTyltVkw\n5qKOo8241PrBToyLY/oa9HOii3NurjT9LBOZO70nP48fP8aoUaMQEhICHx8fjfdjvXzTxLUOTJup\n9F+CJBbOAZPVvkG+ais2u7VFgJcPtLeU5d3cnhrmV+HRk4rSV/8UfO/xVZQ4rsjAxwqHMh2zYBv5\no1wVPX8ltVnwZwk2Ipz+twDBq5+9qODZcN6yD8jNhVClCh6P8C93XIZek+rV4ga6OOeGZiq/O6kw\n9p1mJNI4xNyPh4+Lp1GNjmmyzo/eH7hZt24dnj17htWrV2PVqlUQiUTYsGEDqlSpou9QiKiSMJYq\nYcbAf2EYpEMGIOJMLFr/pwf8i6n2pk+66p+CN9ECBLVnRzxzxLCADa5Ahlaw0fiYBc+fSydv+Gvh\n+imuzZIq6Tn37VmonaaiKvCEDeKhxOMKxOPu5WWwz0Vx1ea0fc6JSHeCo8LzKgz6OsImYT8Co/40\nqeeiDDLtrTyYhZsmfoNi2kyl/6QSCZwCgjjy8wpj6T9d9E9U8Gx02noAnjlixFdRYnfb1xASm1R4\nO2TAH/ZaOaa2HQ5fg+FzNxV6PSJ0NIbPmaHqu/yRs3SxHH82sILvvVxUF6yM6r1oQtcjgMb0TbSx\nfPao7Nh3JZMkXESA8++FSoHvTu1r8M8doNnIj94LHhARaZuhq4RVVGUv1KDt/kmQxMJ783545uT9\nF+aZI8bAc3cRZSVT2+6yhRywstLKMXVB0wIEiTESrGpvjV4n+mGGdAx6neiH1a9XMbmiALosbhAc\nFY4Ap/2YOzwTAU77ERwVXuE2iaiwylBanXWmiahSMNWpM9padNLYabN/zv+8B58o1P/7aqO0wka3\nmnC99Vz17Ejs8LfR3IivCXcvL0QF9gZefd7llTifvlYTG77rgewOrwEAsjq8hh++64FW52oaIuxy\ny0v2Nhca+alotbnKvsinMY1oEeWVVt9v0ovAMvkhokrDkM8ylEdJz3yY0vvQlLb6RwFAimy4w1r1\nmhTZaNDlTTxeGlAo2dHmudT2jagmSeFDe6Uq8ckn6/AaHv6trPDx9UnTZK+sYhKvQvZGEd9ER8Sb\nfLJg6s9WUOXj5d4WgVF/YhtSIWvp+LK0ur/pfNaY/BARGUhxhQBW7N5TKZMfbek4JABHNkcCCsAd\n1pAiG9EW2eg4OECnCbCubkSLizm/oINIng6rS7nIbVNH9bOC37S+Wj3NmOlihLYi30Qb86hKZR/R\nItO10D8IQ6RxOBMRj04unUwq8QEAizlz5swxdBCaePEix9AhUDlUrWrNvjNh7D/dyhGL8GL373CW\nv3xNimwIV/7GhZQHcPPrVaH2y9J/UokEFyP3IkcsglP9+hU6rq451a+Pm4+TIbtyDX8rs5BaxQK5\nowah66gROjumJOEiZrnFq25E5c52kFqnw/eePeo71dX68aK+mIWmwSuQHH0cvf6UQnnpAS61c4Si\nfnXVN62juvZHVPBsuM0KR8/oi3ix+3dEJ9+p8HWja07166NpR2+tXWf1neoiOToOCTZPIXe2Uzs/\nJQmOCscst3hE97TE7heXkBwdBz+3jpBI4xB58QjEOcpy9602fndGxh1BdE/176jlzrZwPZWOjk1b\nV6htKh7/39NMfae66Ni0tU5+/1VE1arWpW7Dam+kU6yaYtrYf7pXsGqZFNm4CBmGwUErVbA07b/i\n1poxdvpcryb88DbMHZ5Z6PXQiGqY5BdY6v5lGWFIkMSi9uDJyM3KghVEqul9MXYK/NykClynTMHg\nQcO4vtUrJNI4nLkfj04anOPiKla9uTcThwdWhaylI2wSUhF42r5co3va+N0pkcYhwKnwiJaxVNWq\nrPj/nmljtTciIiPnvzAMB0b0RhQyIIeAYchblFNbVbBKU9RzRz4Rh42i8lxpVfDcvbzgN2miXm70\n86ZWpaq9lje1yrPUfctaiSwxRgL3LAG3kav2XJPPCwssiVfA4cET1Xa6qp5mirzc22KSX6BGiUFx\nFasOOCerTzPzyYREGldof4k0DuGHtxX5s1eVt5qjl3tbBJ62V113qmcrmPgQVQiTHyIiA+s4JACu\nNtVVi3EC2qmCpQljvYGOCp4Np4AgDJ+7CU4BQYgKnm3QeMp7I1rkcxvF3FDnc/HxhtRWhCawghTZ\naj8reF0UVSr7irWAB/fu6T15NbVy7UUls1aXH0Hu46L2WlElfMuSzFb0Ol7oH4TIx28jNKIadqf2\nZbEDIi1g8kNEZGCGXKdI07Vm9MlYR6PKcyNanjUx3L28cPGDt2FhY4uLkOHqvwnQq9fFq9fNZQs5\nTsifYfrGaL0mjMaWqGqiqGS2z0E5bCyqqG336uheWZJZbV3HZRnRIqLSseAB6RQfHDRt7D/9cfPr\nhbu+7XDKtTasPv1QKw/va9J/TvXrIzr5DmwSbsJZ/vIGW5fFA0oTF7kPPaMvqr3mLBdwyrU2mnY0\nXFIGlP0hX3GOErtfXILc2U71mu3VNHxq1aXENjq9+w6kHTzwzLUBHgzogXgfjyKvi/zrZo9NNsSX\nrmGEvBoAwFkOWEtv4Z5vO50WsEiQxMJtVrjqBl9fx9UGP7eO8L1bHa6n0vGpVRdMfHNYqYUTNC1C\nULWqNU5t3WW01zEVr6L/72mjYAaVnyYFD1jqmojISOhznaKC5ZGNbYFYXS2GaQgVWRND0+vB3csL\n92Ni4Z9ro/a6R5YSEWdidXpNFVeuXdfH1RYv97ZqIyqllfAtS1ntynQdk2a4LpNp4MgP6RRHDkwb\n+5CP0q0AACAASURBVM+0Fdd/BcsjP9uxD5Hx5/HmxPEVLj+sabns0rYzxtGoinh1hKG0EsxA2T97\nRZVNv2orhtWnH+p0BMZQx9Wlkkb3NC2rXbWqNexqOFWq69hclPf/PX2Xw6eiaTLyw+SHdIo3z6aN\n/Wfaiuq/V6cp1VGK4fD3HUQknEf7Af7lPpam681osp1UIkHWswwk9utW5HQvU1qTKF9Zp8uV9bNn\nqIRRk+OW1l/FTROq6PQhXU0/0iSZze8/XUxnJd0q7/97XJfJOHCdHzI41ss3bew/01ZU/x0OX4Ph\nczcV2naDZSbc9m0u11QlTdeb0WS70tYcMtU1icqqvJ89fa59pMlxS+sv1TShV9bVKe51TVV0/4ri\n707TVd6+47pMxoHr/BARkRpFjWpYL36KK5CpXpMiGz5yy3KXt9a0XHZp25VWHctYq8AZE32ufVTa\ncUvrr6Iqp231eoJFa5eUuTx4QeUpL076UZb1kUwN12UyHUx+iIjMRFTwbHSYuQrjlDUgBrAd6ZAi\nGxchg9jWrlwPYkslEjy4fw/xVZRqrxf1YHdpZbVLS46MdU0iKlpp/VVUGfCcNnVw9nBkmcuDF1Se\n8uLlUZlv5HWhrIv9miKuy2QamPwQEZmBV7+F94ANPGCNa8hGG9ua5VpXKH99l+kbo3FckYHLFnlP\nvRe3TlFp6xmVlhwZ45pEVLzS+quohUbtzt3HyPgsWJ9LVHv91fV2SlJUu2XZXxPmcCOvTeY0Gsd1\nmYwfkx8iIjNQ1LfwbWCDuN7eSN29sszPzbyaTH2scMAtCwVWjPUrsT3/hWF4HBmOiNDRhbYrLTky\n5GKwVHal9Vf+NCGrS8kA8hKfMVOO48PUKvhwyjHV62WdPqTr6Ue6uJGv7KNI+hqNI9IECx6QTvGh\nT9NWlv4ruG4Mb0b1p6TzXrD//ti6BW1nLEdr+ctqREUVJdD0mEeXfoeA6Di0gvraMhGho+E3aWI5\n383L9kt6aN9QD/UXRVfXfWX63Vlaf+3esx03vluBwbdy4fPCAkDetXli+VQ8tFeik4tnuRIXiTQO\nZ+7Hl3v/4oQf3oa5wzMLvR4aUQ2T/AIBlK3/tFGcQSKNQ8z9ePho+b1qiykVA6hMnz1zpEnBAyY/\npFP8JWLaNO0/c6nAZWxKO+/5/Ze/XZzsKdr8O90t/1v4svZTwWNeRTYuQYZhcABQ/mTKVOnyuje3\n351q11U5r01NVTRR0ORGXtP+kyRcRIDz7xVKCgxd2U5TBePMH40zxjjN7bNX2TD5IYPjLxHTpkn/\naVrmmLRLk/Pu7GyPE78fUdvuCmSIsZTDZlEw+owYWeFjxiMbAgSIbe10esNqbHR93Zvj7059jOhp\nK1Eo7UZe0/7TZBSpJNpInvRJV6Nx2mSOn73KhKWuiUjnWIHLMGJ/3oPrsmdqJatfPe9XYs7i2NKV\nav3TCjb4UF4NFumFb7hKU1Rfe8Iakb3bluu5IVPG6177dF2mW5vP6mirqldFizOU9iyNsT1LxGIA\nZAyY/BBRhbACV8mkEgkOh6/R6lo0UcGz8Z+th+APe1hChO1IB6B+3qOCZ8Oy1xgERMfhKrLV9i9v\n/xTX1z2nTdHZDasuzp82jsnrvmjGdrNdkLYfutfGjXxFizOUlDyxIh1R0SzmzJkzx9BBaOLFixxD\nh0DlULWqNfvOhGnSf0716yM6+Q5sEm7CWf6yolPXUSP0FKXxigqeDbdZ4egZfREvdv+O6OQ7cPPr\nVaE2EySxcJsVDo/svBtvJ1jCEiKcqqLA3+/3RddRI1TbtJQJqA1LROM5xBDBGZaF+kcqkWD/d+GI\nPxwNqxoOcKpfv9hjl6evpZL/Z+/c46Kq0z/+meE23AR1BhSoFTVBRTEbgTQsFctLqKG7CWlrt+0i\ntq3Vb9k203Rb3dp2S9kuW2upJbaJpRTqBlpZAjIp95nU1BS8cUm8cRtmfn/AHGeGc+Zc5szMOfB9\nv169Xjmcy/f7fc7lec5z06Fsx060KxUOj02HK9ZPrHO6+rqX47MzKy8bL8ZUo3CKN3KvV6By616c\naToPZbsJEepBnh4elO0m5F6vgFETQP3mX9OE5T4TRR8fH/mlxCQg+XQ/DD/YjOU+E/HgpDmczxOh\nHoQLheXQqy7BqAmgjKdRA27GizHVlLFn1ATA4NeM5DPBkpCFlJHjvUe4QWCgH+s2JOeH4FJI7Ky8\n4V3tTSIVuKSAq3JCCrLfRvrqD3v8/uYjKchY+wrjNlVoxY7p8TZemrysFWjd9CmmdqoQCz9UehlR\n+ttZrOFr1rIGwFjtzJmCAJ7IJRNyTldd93J7dvbIPdl2BBgbAYwK90gSPlNRA3cl3btbfva5NM7m\nEnkCi8xCO31xyavdY5Xr5HbvEWzhkvNDPD8El0K+oMgbPvJTR0RgaMIE3l/3eyvlO3ZhSmGZzW8a\noxkHh4dhaILw0Kh2pQLXc3dDY7zxW42/EqGrllNrT7dNg78vwv/5MqWg63WlUP7pNWiNPohF15ey\ncLMSPjXHUTv5VlYP0NCECSha/zajl8Ta+9Q1d8DPcBJnksdxukZctX5in9NV173cnp07yvehcEp3\nGfXKc0BkCDAyHID7vQ72HqgLheVIiUkA4JyXhQ9s8tMZyrGjbJ9oXrEI9SAkDB1DHcudXi4xoGR2\noRJ7x7Xhm6m+PWQHiL9udMjt3iPYwsXzQ3J+CAQCwQW4KieES6NPyzYGf+Zt6op1UHR0UIaPhbg2\nBaekffsmpyNbzUjKKaDyZJwtCMBl/Rzl5gjJFSJ5PMKxyT051QTEhtn8XcyGlo7yirgUNRAr6V5o\nfpM7cnFc3ehVTCiZGduBcZHAqC6j2V52JIeJIBbE80NwKeQLirwh8hOOK3NCYlKm4nTyOBwcHgaf\n5Y/SHjMmZSquzJmEwqhQ2m3alQq0/vcLmE2dUONG49MqPzN8n/0dqyeDzUvC5KHyWf4oJy8J2/o5\nys0Rmiskpfw1ud17NrknIb5Aw3VAE0T93Vmvg+WL/zvf5WJt/MkbeUXbC3Gm/izlCbDxQHVj1Phj\n+MFmJAwd49QcrXHkXQKY5afTl7ktF8ddXi5noWR26DQwKdrmbxbZKds63bZucrv3CLZw8fwQ44fg\nUshDRN4Q+TkHFyNFKFzCraJjhmLQ6LG026gjInCk/iyOVVQg2KyAGt5dOT+LZ3Iap7VxU4VWlKAF\nP/oC4c89CXVEhCiGBNP6OQqpqz9b51S4nStlZg1bIQg53nuUsn28E8FHGnC6f7tNEr5Q5ZsyNNT1\nODrCC8ZRGgBdCvDRkOv4JqwRuUEncKGwHHdHa1nDvZwNneJiwDDJj69x5uxY7cPhXI2Q8VIheiG+\nQFMLoA6k/maRXXFdjVuMWkCe9x7hBsT4IXgc8hCRN0R+wrEot/2iIpEwb65DxduZimiOYJNfTMpU\neN2VhK9VRpSOH4b+a57nrOxbjJuiisMYZvbBJASgHUClr4nysohhSNAZeY68TldqzzqdK+Tq/DUu\nnimh956rriWuWJTtufF3ieJ1sDE0Dp0G7rD1DCAsCCg5DeOkX8Hg14z7W26BV/n5HtXPLOdn89hw\ngYsBwyQ/Prk4YozVnQgdL+U1vLkTxvJaQKkENEE2suObw+SM0Ujee/KGGD8Ej0MeIvJGqvLztILH\nBp+wK1eWc+Zaqjw+ZRrGpEzjvZam0GCM/G8BRnX6AOgqmGDvZXGFIeEopK5fVKRT4XauhmshCCH3\nnidKgztCDK+DjaHR0dnDMwDDRSBEBYQFUwbICzMfoTW8xAo5s1HEq84BJafh+2MTngufzur5YSpN\nbW8cijVWIUaAoH2cHC/lNbzsj7lnByGp2stGdlzXDeBvhNnPV6rvPQI3iPFD8DjkISJvpCg/NgXP\n04YRnypnzlZEc4RBp0PF57tQXVWFU98edMl6lO/Yhan7bJO9XV2RDXCcmyOlvB06uFaU43vvufJa\n8iTKdhO2XylDZ1ggEBYMFBwFFIouj0/1eaDiLDD1FgC2ngA6w8vGkOo2Woyhvhh+vNNh6JS9cmxR\nxCuKDsE8bGBXnkp7J3wrL1JKtiP5ccnFESN3SYgnRqj3RozxWmQ2dvgoWqOZy7rxNcLo5jv31mTJ\nvfcI3CHGD8HjSFF5JnBHavJjU/Ck8OWbT7lkV5VztqzDmd2FGLe3FFO/qXLJenApauAqY9RRSJ27\n8naEwLUQBJd7z3pt64p1bi8N7g4i1IPww/qtOKcxwRjRDwFtnZjzxF489FoZLtV1oiltBOe8Ispj\nU/gjEBHSZbQ0XEfwkQbMjb+Ldh8mYyDU5Iv/jmxE56iuqnbm8CBKyT7bcAGfV+yD8WoHo9eDzSvm\nbKlqIZ4YZ7w37iqtzbZufIwwpvlOu9AfA4IGijZmgnshpa4JBEKvwlH5ZLbSy+6CT4nmztAg0Usr\nW9bB2NqCcVBhVHcpa1esB1vZ7bysFVCnZSJ99YdQp2UiL2uFaOe2nD9l6ZO0zUUd/c2TcClVzgX7\ntf1h08eoQZvNNr2lTPeKux/Fxulf4tWYD1A4OR/bi02YfQZ4fcFy7GiYhZU5QchtnMnarFQbG49p\nn13rar5qKcU9KhyFM1W8y2YX19WgfbTGZvuWUQOw8n/vIU2dj/9LbcTcS5swe30m71LYlrE6U6q6\nuK7mRsNZq/E5KjcuZB8h4xVaIpwLNiXXu/GvaUJi1Oge2zLN98DJCtHHRZAWxPNDcClS8xwQ+CE1\n+Tn6ai6VL998SjT7fFOKnOh+GHS13ekQLYsX4KfiEtxbegKH0IJJCLDZxhXrIaQim5zDsMTwZHHx\nTDm69+jWNrK5BXtxBf3hBTW8UY027JlxG6Y9+bigMUoJdUQEquvOYNp3P+HWFoXNfcI3r6j2l/P4\nZqqvzW9MngFHXoSkqNE9PB1+VfU4F9iO9kk3A9uOwDQ6HGd/MxSfXi1DVe4+m5LcXHCmVLUQT4yz\n3hsu43V1EQc+uUFM830pdArx/MgYEvZG8DhSU54J/JCa/BwZFs72lRETPgZB+LUOHP7rUlQnjRIc\nomVtUNVX6dGuAPqbFWiCbQ8fZ9fDovgfO2qwySOyLmpg2ebH4hLMKT1hs7/F+GpXKhwaEJ7K22I7\nr5hhlWyFIBzde3ThkmHwRgM6EQIlStCCUCjRkn6vrEPerBErlJGPgu9oW21svI2S7VN5EcO2nMCF\nzHFA5TkgMoTyLnWGBfYoyc1V4RdaNIKPEWC/T7VvEzrDAuFXVY+Mkn68jC5H43VXjyOuRiPTGi29\n5zeSeu8R+EGMH4LHkZryTOCHFOXHpARJLdGdT4nm6qRRSFn6pCBF396gijF5Y7vyKqJ9/FHReR1K\nKKCBN+f1YDIArBV/770HcPGb7zE0dx9jc9GQ6p+wHZcxwayijlHjr8QRlRnxaz9gNCA8lbfFdl53\ne7Ic3Xt0hn412hAKJeKgQgz80ODvK5kKd2KgM5Tj24sGDB93G8aOHS/4OHyMArZtU2ISULl1L078\neBTGm4JxeZwa2F4Bs9nUo1mnfUluVzTntGAp0HB3tBb3X7+Fl+eo4OghlLefhenoBSgvtSL+en/R\nPDOuaEDLVJmOq9FIZyhJ8b1H4A4X40dhNpvNrFtJgPr6K54eAkEAGk0wkZ2MkaP8DDodaktKEZU4\nQXL5HgadDuq0TJu8pRp/JRpzNwgea0H220hf/WGP319/ZBpGxNyCZm9veDVf7bEeBp0OtcWliEqa\nYJOjY8mb0qsUKMqYjtR1a6DXlUKTtsxm3Aa0wQgzlP4BaMzdADPMPbap9DLilNKI1A4VavyV+Hzq\nGNxXWME4f7rzOLs+XOByXqZ1zlm5BClLnxR9TGz3nrWsavyV2DYkGOknL1P/Lk5PQeq6NaKPyxNk\n5WVTuTcqfSMyioJZ83vY0BnKUVJbjcSo0ax5NEzb6vRlSNPstskb8aq8AHx3Ep1Tht7IKwK6SnIb\nO4G4wQCAlTlBWJqS4dQc6HBmrejm41/ThNzGmZxzjRwe31CONHW+aMd3xXUByPO9R7iBRhPMuo03\n6xYEAoEgI2K1WskZPRZitVrkZUwHrJTW4vQUpDox3q4CC5t6KO4J89OQPHMK7Uvcojgnt5qhV21C\nXsZ0DF8wp0fBCHNOAQwL5qKuWIfJdoUmYuGHPFxBaosJOSWlgBk9thnT6Y19S2YgJyoKUYkTEF1c\nipFf2iY5j+reP1artTlPFVpxEh2IbvFBbfffXQXd/KzHBTCvs6cKCqSuWwPDgrnI6Tb0l2m1MOh0\n1L+duaakBG3RAXMTFhjKnVLItbHxnPdn2ra4rgatk21zQzrHhOORfQGo+awWP8xRdBVFqL4AVJ4F\nFt4KwJKAnyh47Ew4u1Z082kZNQAlOdWiGD/a2Hhk5B3AVjSideTAG0URUvkf21XXBaFvQKq9EQgE\nghtJXbcGDTuykbNyCRpzNzj9dZ5v9TCmqnhHtn/GWEmProKdAW2Ihg9lADBVuRs//z7KM3K29gyq\nfU09trEYEJZjbEMzvKFAKoKhhAJnf7ANFRSKpcqefcU7LhX6xKrSJib2Fe2kWuGOL9ZycqYCmSvH\nBTBXFps/PgUH//Q+Pm+6FytzgnDvrjao4m+m/m5dBU3MymfOrhWfSmlCWZeayatCHxNSui4I8oPk\n/BBcComdlTdEfq6BLdmdL0x5UHTyY8o7+n78UPTXn6ItGBGr1WJ7ZSlCj/4MDbxRjVZUoA3h8KEq\nijnKubLk08wpPYHtuAyV0hvhZmWPPCR1RAQ+rjiEO49dwMjuEt0aeCP0zEWnc2sc5fRwzRfjm3Tv\nTOGGvnrv2cupDB0oHe3j8v4xfMdVeOFnTFmYzpgTFBjoh9CA/kgYOgZzx0+lTcAXu/KZs9XahBRJ\nEILQIg7WuLKvUF+993oLpOABweOQh4i8IfJjx1OVyeyhM6jo5MdUFW/AqmdR7tXBaADUn6lF2Dc6\nlKAFjeiEH5Q9KorRGQf2hQImmFUo8enAniUzELpqeQ8D4pfaWkz9psrmN0clurmsP5diBVwNG66G\nq7OFG/rivUcnp8EnLuDM4JtxamCHSxVyvuOyXD8Lp8yjNWzs5Wev8POtfMaU2G+NGMaLM+W13Ykr\nDbW+eO/1JrgYPyTnh0AgEARClzsjRpI5XTECsbZ3lHcUq9Xa5JFY541EJU2Al2oTUlttq7fZ57zY\n51zR5dPMafdDTlQU7Vg7Q4PwvvcVJBl9EAcV43kA7uvPJaeHbuxCoQsttORP2R+fr6ydRWcoR3Ft\nNZI4JPpzQczxM8np1y2D8VjDRJTkVCMxKlFQjogrxmW5ftjyh+jWnE9+DZXYnzwQKn0+MvIOMIaL\nrUvNxAJDuVNrxScfypOIMVdC34R4fgguhXxBkTdEfsy4qvQxX4+Bo+2Z5OfIy8Hk2RBaSpxP/6W8\nrBVIeGMb7mn3RQM6UYhrUPoH0J6Hz/q7uwcUU2ihvfdKiOycQewwK7HLkjuS09ix450OlXLFuNj6\nNDGtOdewLSG9ccQIK5MLrpgree/JGy6eH1LwgEAgEARQV6xjLBAgFKZiBPYJ+kK3t4Zrcrx1kreQ\nYg1shQIsx9/z0WabuYyCH0b7BOLwK08xenO4rr+7ixVwKaDgjOyEQFsdK+mq4ER7V4xfikUlhIxL\nZyjHa3kf4qM92xnXXBsbj4yiYKrAgH0hBAsksZ9AEB8S9kYgEAgCcEXpY67hWUK35xuiZB1WVun9\nPjbP0OLBje/yVkbtyzLfgq6+OWcPl2FWQXn38Y0oN17DSIRQ+43p8EZV81XaY/Jdf/sxuLIUNJeS\n5nxl5yxilzF2ZvyOrkN3yokPXMdlHaLmXXkRxvI6wMp4sV5zLmFbXRXYevbGcUWpbC6IHTZJIHgC\nEvZGcCnEfSxviPyYERoG5gi+4TVs21vkZ9DpsPWxpzDwjU2Yv7+KU4iSfVhZuEmJkKM/I0d/GOPn\npvKemyWcrmj924h5MRvqwmKMOFqHUUYv6vgKKHARRoR1f5dzNHch66+OiEC7UoHa4lKXF6hgK6DA\nVXZiwRRmNf94f5wtPMB7PYSGEjKFylkn9I8dO17UaohiwVbswj5EzRQeCCiUwMUrQFhX40X70Da2\nsC13VWDjghhhk1wKN3ga8t6TN6TaG8HjkIeIvCHycwzf0sds8FXo2bYPDPTDJ0//Ebe88CbuP3MN\nZlMnCnANU4x+rPlJdHkrYfDGjyd+Au5MhDoignelO2uD6hBacAcCbP4eBm/s8m7BeJMfJ2OG7/qL\nnaPChr2ybK/gs8lOzHuPTom+/Z/VeOrVfEHrIcT4ZMrTWh5yGq/cekq0XCRPsaN8Hwqn2AXUhAXB\ne5cBpvGRgg0XKVRgE5J7ZI/YOWfOwmSIkfeevOFi/CjMZrOZdSsJQNelnCB9NJpgIjsZQ+TnGQw6\nXVdz0URu4Wl7P9qCE/lfYeis6bhn0WLq9/PHa+A99WGb0DAD2mCEGXFQIWflEqoBKd0YQuc8jjFG\n7x77Vq58Ai1nzlI5H3qVAkUcKt0VZL+N9NUfAgCq0ApvKBCLGy+qGn8lDr/yFLyar3KeO1f0ulJo\n0pb1CJNrzN0AAC6vuEaFQ40cCJW+ERlFwViXmskoa1fdezpDOUpqqxF+RYG7fv8m7XrwWQM+16q1\n/C0UBRhxZ9E8dIwNp37zr2lCbuNM2YVV6QzlSFP3DFF75fBwNHu1I1ECoWJCq/NlbX0dG38f3OP3\nlTlBWJqSwbq/Tl+GNM3uHmvjKTkz3Y8Aee/JHY2m53VqD/H8EFwK+YIib4j8PAOfJqh5WSsw4PUP\nMPh4LVT7SnCk/iz19b5qVx4m7dahCq0oQQs6YMYoqFCCFnT6+zsMUVJHRGBH9WGqsakBbShDKwb5\n90PdzElIfHMb70p31qFSYfBGAa5BCQU08KY8B7OWP8Np7ny9TkwV2N4/egSJG/4r2BvEZRyOvpoz\nhXi56t6zhFmdLfyOU0U6Nvhcq3Shcm+O8MF3L99hs51R44/hB5uRMHQM53FIAaYQteWzlkii+ppQ\nz2dWXja2RJ2GyWwC1IHU7/41TZh1VoNvT5WxhrHRecU8JWc2LxZ578kbEvZG8DjkISJviPykjV5X\nirN//Au0Rh9MQgDMpk4craiE111JUEdEwCdYhe0fbsIwc9ff62HEBjTB5O2D84vuZQ0Ti5+bihz9\nYfx44jgiTEoM8u+H4vQU+Pn5CVKc7UOllP4B2DljPGrTZ/IKGxSixNEp3lV+ZgSea8Ckdu/uOfAr\nV851HEyKX7//1qDzuwpaw4nrvSe0ya67y38D9KFyNcm3onS0D2vJZ0/DNVfFEqIWX9GKZeZEyTQJ\nFVqa32IotE+6GSg4Cnh7AepA+FXVI/qTn5F7t4lTGBvX0t7ugM0QI+89eUOMH4LHIQ8ReSM1+QlV\n9DyBo7GKNY/d67Nxz+FTVOiYGt7oZwa+VhkRnzINV5vq8asPvsBosy+ALm9LBLzRoVTgenwsp6++\n4+emAncm4vjwCMpAcUZxts/TufvJx3kltzMpcd9p/HDq24OMa0qneOeOGYzfnWm12U5jNGO7qg31\nhqMO5cNHmaRT/HzKz+OZR3cgfW8FreHE5d5zJofJFQU7uGAv/3sffUwyCf1M8M1ViVAPwvRbExEa\n0N+No3QM195T9tgYCnGDgfOXgZLTmJnfjsOLIjjnAEmpcAObISa19x6BH8T4IXgc8hCRN1KSn7uT\n1Z2BaawGnQ6fPvYUbnszB1P3lTs9j+qCfbj3yGmb39TwRun4YRiTMg1Vu/KQ/L8jPf5+1NSKsYaz\nnD0c9qFNzirOfEKl7GFS4g4VFuK3+/UO19Re8Y6eMrmHEfcvr8tIqaxllQ8fZdJe8fOrqsf8ZQVY\n+aOpe7+ehhPbvSdGk12xC3ZwxV7+UkjoZ0Joor8rnp3OVEoT+sGih6EQFgz/Tm8kXQxB6b2hNtuy\nhbFJRc5shpiU3nsE/hDjh+BxyENE3khFfmIoeu6Caaw5lYcQ/Je3MPlMM0Z1+tj8Teg8fEJDcHnb\nLoSbb/SrrvQyov+a56mwt8aPd9ooPAa0IQRKjDZ62SjqfL1RnlKc6ZS4arQhyqREGLxZ19Ra8bY3\n4nb5tmGc2Q9x3eW3HR2LrzJprfhN3nUR6/9rsPm7veHEdu8J/ZLvaD08CVvJZ08hNFdF7Gens5XS\nhH6wYDIUFsRNERTGJhU5OzLEpPLeIwiDGD8EGzwRMkQeIvJGKvITS9FzB0xj/fHEcQQZTZhkV97Z\nmXmoIyKwv6EWvtXHENapQJWfGaWLZ1IKTXTMUOSdPEb93VK0YCqCbBR1e0/V1spDaDpTy/qs8ITi\nbK/EVfoYUWVqwVQEUdvwWVNrI65uSDjmHP7Z5u9MxxKiTFoUv+CAYFbDie3e42p8ySlUVIocPfYj\nCg99C5OvkrFXDx1iPjvFKDMNCP9gQWcoSCmMTShMhphU3nsEYRDjh0DhipAhLi9V8hCRN1KRnyeS\ns4VCN9ZKHyNuMirQH15oQifUuPEl2dl5xKRMxZnJ43FweBh8n/2djUITGOiHqImTcGbyeLx/9Aj8\n63/BfFOQjaJu76kqNDbjzqMXMPUbbs1QPYG1Enfu3sm49ZtKp64NixHnExrC6zqzjGO7qg2n4qIR\nt+A+zmGEbIYT273H5RhyChWVIll52Xgj4Rza7xkONFwHCo/BX+nHSckX89kpZqU0oR8s6AwFqYSx\niY1U3nsEYUjS+DGbzVi1ahXeffdd7Nq1C1qtFiEhIaz7kQtROK4IGeL6UiUPEXkjFfl5KjlbCHRj\n3Tv9VsScbsRoY1d5Z28ooIY3qvzMKMm4W5TmqI7KJasjIpD4wEJcvXNCj6++1p6qSrQiEj4Y2V1A\nQczwQqaPJUI8EzpDOb69aMDwcbdh8tS7Rbs2hFxnRevfxtSthbi39AQnA8MyX+XNg1EVHozvxw/F\ngFXP9jgHl3vP0Zd8OYWKWuNMXouo47DztkATBB+zF9aWjcDyWUtY9xfz2SmlSmn2OApjk4osivhO\nzQAAIABJREFU+SKV9x5BGJI0fgoKCnD8+HG88847GDp0KN544w3Mnj2bdT9yIQpH7JAhPi9V8hCR\nN3zl58oQG0/lmAjBfqzTnnycUqqnGP1w0LcT2SNC0DxrMmdvgRBOVpajeOt2Sh4WI6nh7FlKTv2i\nIilvxyG0iBqWZ4HpY4kQzwRd7sPSpVmcrw22a5SPN4evgWGZ74XCbzBubylSj5xGf/0plHt19Jg3\n13uPyfCVSqgoHwXY2bwWMaHztpjCA5FU7cXJ2yLmu0+OIWaukqU7DCqit8gbSRo/n376KRISEnDL\nLbdg0KBB+Nvf/oaHHnqIdT9yIQpH7JAhPi9V8hCRN3zkZ63IXv30C2wuzEdwzHBRFXupJGdzwX6s\n1gbRzwHeyDhcy9lbIIS8rBX41fP/YDU4yr06UDtmGFT6EwgxmtCATmhEDMtjMhAOaHyR+MYnvDwT\nQpqF2q8JF2OLzptjDu3Xw2ji8yy0rIOxtYWTd03os9Ni3HWGBsHnm1KPhoryUYDFymsRi3f2fYKj\nA1sBzY1cMj7eFrHffXIKMXOVLN1lHBO9Rd5wMX6UrFuIzNWrVxEcHEz929vbGyaTyd3D6FPEarUo\nypgOvUoB4EYoR6xWK+h4UUkTqGNZqPFXIipRWonnBPeh15Xi9q1fUYrs6HYlflN6CpfmPoK8rBW8\nj2fQ6VCQ/TYMOp3YQ/UosVotIhO1mFVQTq3VyFYzknIKRJ2rRR6xLbbn2LNli42cLL/fsmAuGnZk\no3LlE9h9byLnZwUXOdUV66jzWRjVYsLJ/ALa32tLShmPVVxXcyMMqZuWUQNQUlvNuI8F+2uUad3p\ntpuwKR+X5j6C9NUfQp2WSV3Tjp6F9mtzZPtnGNlqxil0UH2ZuM6bK3lZK6BOy0T66g9x2wv/Qk50\nP9Ge+3zR6cuw9fYrlLxaRw5ETtJV6AzltNs7I1ux0enLUDAvEKg4Cxgudv1YfQHTdrdCGxvv9vFY\n0MbGY2lKhkfHwAVXyJLv9UQgOMKbfRNxCQoKwrVr16h/m0wmKJXsNphGE8y6DYGZh//zBqoeO4S8\nA8WITk7Cw0nCv5ZoZk7BtkdmQbExH7EtZhgClKh4aCYWzpxCvz2RnUupKj6EkweKEJ18O+KckCsT\nXORXXFWJyXaKbCz8cKzjCiZuK8CFxzI4j21b5vMYtzEfyS1mGPw3ofDhWViY/ZqgsUsFaxk106zV\nqBYT8qoqoWG4h/hCJ49RLSbkFe6nNTjyqiqR+vzTQPf5q4rZnxVc5TRm5l2ofOV9jOm88bqp9DYi\n/v45MHz/d8pAAwBDgBJjZtzFeM3NHDMBrxly0RI7gPotwNCEGWOmsF6njGtit+50243p9MapzhYA\nKoxsNUPRfU0nMzwL8WW+zdq8PSwU44+egwEKRMMHBrTZGEBM8+bz7KwsKsHEnK8Qa2W0Zfx8BT9u\neA7Hf7ni9HOfC1XFh7Dn81ycjQpCu5cZrU/2VICr8o5jpuYOm9+Lqw6j8UoDfL84hfZ7Y6nfucpW\nbKqKf0Lr5IHAyIFA1TkgrxqIHoDJI8bwGou7x11cdRgHTlYgOXoskuLGu/Xc1jhznzJBycQKputJ\nDIje0rtxu/Ezfvx47N+/HzNmzEBZWRlGjBjBab/6+isuHlnvJ3zYSIQPGwnA+fWctvIlGGbPwpGS\nUkQlTsA0rZb2mBpNMJGdC8nLWoHbt36F1FYz9Kp/Y2PGdKSuWyPa8bnKLzRuLPQqhY1ibUAbouGD\n2Osm5Oz5mrr2HKHXlSL+P/mUAhfbYoZp424cmD3LbV+sxcZeRkdT4nusVY2/EqFxY0S7V+jkUeOv\nRGTKFOj31bCem+1ZwUdOTb9cRRWuwQcqxMIPBrThW3MbxkYOwcH06TB3e1lq/JUoXpiC1GEjGddh\nWPgtSM8LwlZzI1pHDoR/TRMWFgdhWOotrGvHtCb2c3d0LVuwvqbtn4URMEOTtoxam46WFiyoasUo\nqLANzRgHFcrQik6YMRoqxnnzfXZW7fkG6S12HyCum3Ckth4pS58E4Nr3aF7WChQc/xaf/+MutNwW\nAO/yc/B6qwKdT91ObeNf04S40ESbcWTlZXd90f/dQHhV9ofXW0XofOp2XrIVm0sfF8IvQoO22yKB\nuMFA3ODusQ/nPBZ3v/uodUwdCJV+OzI27sW61Ey3nd8aZ+5TJuJCh0OlP2rjUaK7nsSA6C3yhovh\n6vawt+nTp8PX1xcLFy7EunXr8Kc//cndQyCIRKxWi5SlT8pWKZU7XMN43IEltLLatyuE1dJPJq5b\nueMaEskUIiVGSJAnoJPRzMIKfJ4y1qXhSBZ5GPxtz3HPosWihMDykVNdsQ5PdYbgR7Thb6jHj2jD\n0s5+qC0pReq6NWjYkY2clUvQmLuBk+G+LjUTOxpmYWVOEHIbZ3JW8LiG/9pvV+Vnxj6vrmvZgv01\nbf0stF+bU+jAqG4vz0KEwAgzAqHER/dO4DVvNjwZjqzXlcL/s93dhk8kAMAYPxi4Ixo+eV3NXC1J\n+tYhW/ahTJ1jwuE9eTgeef0SL9mKPZdFOw/j0ae/hv8PdQAA1Q91mJF3XbLhZlIMCRN6nzKhjY1H\nRlEwVPpGAPTXE4HAFUbPj9FoxMcff4xz584hJSUFWqsXxIYNG7Bs2TJBJ1QoFHj55ZcF7UsgEG5Q\nV6yjDePJKSn1iEGaum4NDAvmYsXKvyCh7CQWdoRQCmYqx/F0KXCbenydl2s+GZOMym+7FQ1P/Q45\n3d4CR+tj0OlwePsOGAEkLEhDZEwMDh82IDJyIIYNi4ZCoaDdL3XdGlx4LAM5e762OYdFTkznNuh0\nqC0uRVTSBMbriI+copIm4C2vNzG1U4W56AcD2vAvr8sY271trFbL+3rVxsYLUnrY5s60nWr7Tuit\nPVQOrmn7tYmGD2rQRhlAcVBB6R+A0KeeEN3gzcuYDnAcp5jUFetQG+ZDGT4WOscOwpL9gYjKCUJi\nVCK0qbYyK66r6RHK1BanQVSl55Rayz2bfbAViyZ/iQNRPkiu7cDJ5x/hfSydoRzFtdVIihrt0vnQ\nrWPLqAEoyan2eI6SmOdfl5qJBYZylORU015PBAJXFGaz2Uz3hxdeeAEmkwkjRozARx99hN/85jd4\n4oknAAD33XcfPvvsM7cOlLgg5YnU3cdcFD2pYtDpoE7L7KGANuZuEG0uQuVn0OlQ26048h2LJUzM\nRoETMZTPnTgro7ysFZiwKR9jOr1hQBv+rNCgOGQwRoy7G8ePX0Nk5CCsXDkSWi19+DBf+VmvvV6l\nQJGDMEquctLrStE/9QnbnB8vIy7lvSure47PNW2/NtuGBCP95GVe17Qn7j2hGHQ61D3wBB7+araN\nAeRXVY/PfrmXUQHWGcqRps7vEcqU2zjTY0q7WM/Vlwv/jf/Ed4V9qfSNyCgKdpknS+g6shln7jLe\npIbU9RaCY7iEvTEaP3PmzMGuXbsAAE1NTViyZAnS0tKwZMkSzJs3D59//rm4o2WBXIjyRMoPET6K\nnlRxtaHgKfl5QoFzFUJlpNeVYuC8pzC6vSs6uRC+yMRNWOVdhYhdGxEYfhMWLDgKb++r2Lt3MoKC\nAnscg4/89LpSaNKW8VL6uMipIPttpK/+sMfvOSuXULkovRH7teF7TUv52UmHbc5PJHzKz2PxDwNY\nFX4qV6U7N2Ta7lZEdPTHnNsnI2HUrbzHIYbC7uxzVacvw/ywPTYJ/xZjBIBLDAr7dUwvDnK49tbb\n0xlnbH/vzcjt3iPY4pTxk5qaik8++QQBAV0N7y5cuID09HT84Q9/wMaNG4nnh8AJqT5EhCh6UsWV\nhoJU5Sc3hMjI3mh4GqPxAZ7A/XgR01beh5SlT2L58j346KO5+NvfvsBDD03vcQw+8hPTSLH2qAJw\nuYeyNyLHe8+g02H/njycjwpCatLdnJV7naEc+brv8O13BlS13QRTSRj8PjuOB44O4qVwi6mwO/Nc\nzS7YitXpV3v8PmF1FSp/PVhUg8La2AOAktpqJLIYVjp9GdI0uxk9RWx/7+3I8d4j3ICL8cOY87No\n0SLcd999WLVqFW6//XaEh4fjvffew6OPPorGxkZRB0oguBup5cs4g5CcCYJ7ESKjqKQJqPbdSHl+\nbkE9AmGAr1JB5dYEBgKAD65c6XR6jGLlW1m+mie3mqFXbUJRxnQc81AuiqsRI2xWzqG39gh9Fmlj\n4/HWP79BVesgmNqvA63xaBvRiJwBV7HAUM5J4aZN+jc3cd5frLkAQFLUaPgbdtt4fnx3GVA+Pxzt\nPMfnyJNFGXvJA6HS53M2praX/g+tI84Dne1d1exgmyMk1RwiAkEsvFatWrWK7g9xcXFITk7GwIED\nqaakAwYMwK9//WuEhIRg3Lhx7hwn6bYrU6TaKbldqcD13N0e7X4uB6QqPzEx6HQo27ET7UqFQ9nb\nb8d1P6GoIyKw7+IZeFf8iHCzEiFoQqRyP4YsuRuTHlwEAHj33WNobGzDSy9poFb373EMrvKzKODF\n/maoT1+ExnijGprlXFzQ60oR82I2ZUBpjICf4ST8X/4Drt8/CweHh8Fn+aM2x3T1OrqKvKwViHkx\nG1MKy3A9dzcKL/yMmJSpTh/DHNoPZTt2QuHvg6ABGheNXnqc8f8JX6/zAQ4DOBIN/P4kjENCMfxg\nMxKGjmHdf0f5PhROsf2ea9T4c95fTCLUg3C5qAaVXo0wagLgX9OEsXt/wZn7h/IaX1ZeNl6MqUbh\nFG/kXq/AhcJypMR09WrS6cvwYkw1ZewZNQEw+DUj+UwwItSDGMeWlZeNj+66js47hgBNLUDBUaqU\n93KfiYhQD4Ky3YTc6xUwagKo/az/3tvpC++93kxgoB/rNg77/AwZMoTmoIFYsmSJ0DERCJLAk5WR\nCNLB3kuRx5D3Zb/dhuh+SD95mXU/a4R84bdUHXsz9zMYYUbC/DRq3z17dCgqugUPPFCJ2Nj7+E+e\ndm4K5KfEo+y2cYzV0BzNw5FHNSpxAmAXZM11/aUGXQlzc04BDAvmcpYt3TGMm/JxassupHeoYHht\nE/LS5bEeYpAUNRoqfT5a4Uv95l/ThMSoRH7724Vqcd1fbLIXPovZB76jKpPhbiCNx/jYPFl8vTM6\nQzm2H9qLj2Z0oH10t1EdGwagyyuV3jCUqp6mjY1HRt4BbEWjTQ4Rqa5G6C0wen6kBrHC5YmUv6DE\npEzF6eRxtF+jCV1IWX7OwuSlOJM8zsazc/SoHolvfEJtd97YirEN1zHK6EW7Hx3WX/gbt3+J10sK\nUXW+FgcOfI2amir4+PgiPDycdl91RATGpExDfMo06vh1dRfx9NMVmDLlLF57LY2x3DWb/OjWIPTM\nRfgsf5RWiWfzdjB5VEtUJoxf+4HNfqbQYIfrL2XKd+zClMIym980RjMODg/D0ARuYYJ0xwg3K3HU\n1IIY+EEto/UQgwj1IFwoLEd5zTWYDbdANeMIMioG4MFJc3jtr1ddorwt6cVBnPcXm8BAP4QG9EfC\n0DGIUA/iPT42TxYf74zFg1SqONfl8bFGHYgle/ywbsEzNj+nxCQg+XQ/DD/YjOU+Ez22jp7A0+89\nnaEcO8r2Qdlu6hOeNrHh4vkhxg/BpXj6IcKGOiICQxMm9AnlQghSl58zOFJgq3fsopR8ZWERKtuv\nUE0uD6EFdyCAdj86xddiYAS0tmMNGrC8sw7fnTqKb7/dj++/P4DCwq+wefNG7Nv3FXx9fREbOwpK\nJXP/6ebmy3jggY9w550DsG5dGkwmE0wmE+0+bPLjo8SzGYtA1/1UeOFnqPQnqNC53dPGIrWgosd+\n33u14t7SE5zXUUqIETZLdwwD2hACJcK6gzKs10Ou4YF8SIlJgGHnGZzUh+GD1Aj8btpveO8vFYWd\n7t7jMz4244arMWUTHtfR2RXqpg60Oeaq0HtolewI9SDKeOtLePK95yjUkcANp8PegK5mp9999x0u\nXbpk8/u8efOEj4xAIBA8DFOCv7FfkE040hijN5RQoQqtiIOqR9NKy35MhQHqinVoa72C+ajDFZgY\nx/PDDzr88IMOubn/xX/+sxlBQT0r1rS2tmHRon9hxoxxWL68q2zua6/tRkiIGU89xV/Ro1uDSh8j\nOkOCaOdhCWmrQitOogPRLT6otSsSYt8gNKK4FCO/sDWwRrWYsFsB6FUKSTe0ZQrxEyNs1v4YVX5m\nfGtsxVOdIdQ2lvWQS3jgmTOnsX37Jzh9+me0trYiJCQEiYm3Y/bsOfD1vRHO5ih0Mry/Gj7ePogf\nPlrQGMRurCk2XMfHJfSMS9NPm/C4uMHAtiNd/x8bRsLZJIbYRTsIzLAaP88++yzOnj2LYcOG2YRW\nEOOHQCDIGSYF1r/5qo1CDgCj4Yf3va8gzqiC0j+Avmklg+JbH+CDB3EGVh/4cZPCBwmpqbh52FAc\nP34ce/fmo72960vj/v2FWLTofnzyyWfw87thYJlMJjz66EZ0dFyGyaTEq6/ugcmkwBc7T2FhciMM\nugje1ans16AabajuaMVtL/wLeVVHKQXboNPhYu0Z5Pm04lpHG8ZBhVQEowZtOPtDGe1xrcdCZ2Qm\nzE9DkdlLsnl3bAaHvZEnZNz2x1Bt3wl993oYApQoXpiC4TA7nV/kakpLS7B+/T/wv//tgX33jI0b\n34NarcHixb/F0qW/xzd//bssDDkpwMW4YTOmeuRCLbwVvrsMWLzXH/PHzySGj4QgVfbcB2OfHwsz\nZszAnj173DUeRkjNdXlC6uXLG7nIz5lywXTNKOn60hx+5Sl4NV/l1bSyufkSEhPHoampCQAQBW/8\nyTcCvg/Mw9y//YXarqGhAW+++Treffdf1G+Zmc/gpZdWU//+61934Y037gRg6xEKxS6cwSs4o/Ky\nadRr0OlQ9eUXaG9px/gFaQ7XZc9Hm9H2f+uQZPShwvssvXiObd9JKd+fKa5huNkLY7q3sd7O0fEd\nNY2UYkNbT/YBs6zHmBl3IXzYSMk3if3448147rnfo7OTvdx69K+G4P3zPpjaduO7q/26/vnPe7F1\naxQOHQqDRiP9andMzx4pPTv5NkAFxGkWK1c8JTudoRxp6p5FMfpKfyWx4NLnhzXnp6ioCBMmTEBg\nYM/u4e6kt+Yd9HZ6c85IX0AO8nO25LB93hdd3kpxegpmLX+mx3Zs+WJbtnyAL7/MAwD0D+2PPz/+\nNEIXzIKfn8omdyMgIABTp6bAx8cHBw58AwDQ62vw6KOPU+FCYWF+uOuuE7jvvlrcd18tJozZj/uK\nluFx0wGMgm0OTtH6t3H2j3/B1JLjuPfIaVzetgv7G2oZ1+XUtwfx2/16KtcE6Mo32e7XiqlbCykj\n4DjakAzbdwGXPB1HxUWkmHf31fp/seYjOZuDw7S/ZT2iY4bi+vV2SZflf23D3/HSC3+08fbcdddU\nPP74U5g7Nw2xsSNx+vTPuHKlS5G81HwJJZ1XkYEQ+KMrR81+XQsLj6OyMgSPPRbgcb2DDUfPHkfP\nTncntPPNheKbd9LbEvQ99d6TWtEOucIl54fV8/PII4/gyJEjGDFihE3M7ubNm50fIQ+k8gWFwA8p\nff0i8Efq8nPlF3pnPRJmsxmTJmlx/PgxAMCrr/4TAw2nKA9Ita8JufGRmPLyi9TxTSYTJk68DSdO\n/AQAeP319Vi8eAnt8Zk8Am88Mg1DN3+JER0KxFrlJVX5mfHLZ2/RzoXJ2/VV+hT8fmPhjWOgFd6w\nPa5lvQH0imadeVkroNmyC7d00M8zVqu18WTpVQobjxvXc7Dtb33vOfKceYrff7QWOS+8BrR2WWUD\nb47Azq07MWJEjM12RqMRH3+8GS+88Dw6OjoAAEsQgg/QZbhZ1nXPdw34/vsr0OsH4eLFcYiLy0dY\nWBv+9rdp+NWvIt07OQ6wPXuYnp3WXhiVvpFzY1J3odOXIU2zm7P3QerzEYKn33s6QzlKaquR2Ae9\nbmLAxfPDmvPz+OOPizIYAoFAEBtHfWWcVcCd6fAOAD/+aKAMn8DAIIwZdgt+9dJ7lLI0ul0Jr9JT\nODb3EeQtnoPUdWugVCrx4IMPY9WqPwMAvvhiJ6Pxw1SwodMMnO+4jjkYYLN9XJuCcV3sc392+bah\nNG4IYuJGQq/aR50jDir8y+syOr18MLpdSSni6A6N81QehzNhj9ZQvXc6VNiGZgBALPxQ5WfGoe58\nJGd7/AjZn2t+kVjrwIZOX4ZPT31PGT64RY1rmxfickdrj229vb3x298+jH79+uHxxx8GAHyMy3gV\nYaj396XyvDr9f0JMzIXuvSoB3AQA0GgG9DimFBDy7JFDQjufvBM5zEeOSL1oR2+AuZ5qNwkJCWhp\nacH+/fvx1Vdf4fLly0hIIGX3CASC5+kyAGx73EilYtjFixeo/x87Nh6/lFf3KKQQCz+gowNJOQUw\n6HQAgIkTJ1kd4yLj8WO1WhRlTKfmTxkiFxsxCD4woM1m+yo/s8N1SV23Bg07srF6wmAMgw/WlJ7D\nbS/8CznR/WzO4f/bBWj6/G3krFyCxtwNGL5gTg9l3no+Bp0OBdlvU/8Wm7ysFVCnZSJ99YdQp2Ui\nL2uF4GPVFeuoeSxECIwwIw9XsHfRDU+L9TYWRrWYUFtSyvscfPaP1WqRsvRJRsVa6DoIkc/B05Uw\nflZx44dVd6M1KQIltdWMx7zvvgW47bausXfAjKenDUNj7gZqXUePHoaZMyf2+C8gwJ/zuNyJkGdP\ncV2NjUcF6DYsutdNCnQVSGi0+a2rGWvP6ntymA+BQAer8fPee+8hOzsbgwcPRlRUFN555x288847\n7hgbgUAgOITJAJBC2JXJdKOstZeXF62yZEAbouFjo/x6eXnTHoMOi8FibYjMKijHHASjDK2UAVSh\n7MChB+5hXRczzPh15XmMbu96NYxsNWPhqSv44a9LqXNQHp1u/d2RMm+tkA+Y9yTenj1PVCOIzoti\nbXjxxV5GcVBhmH8IEuanMW4DcDO4LQZB7aUGVMHWQ1KNNtry4o6OYz1Hoesg1GAabAwAznS3vxgQ\nAMwfSynIjo65ePFD1P//fP2KJO5ToQh59vAxLICu8Kfsgq3QGcrFGzgL2th4ZBQFU+OkymHTeCL4\nzodAkAqsxs+uXbuwZcsWPPjgg/jtb3+LLVu2YOfOne4YG4FAILBibwCIEW4lhrdi4MAbX0SrqysR\nPWYMijKmo9q3y6AxoA1l3b2DrJXnI0d+oPZTq9Ws57H2CDB5Lr56aAandWEyZLyar1LnsFduzx4u\nozUG7PsljW5X4jelp3Bp7iNOeWe4jJerF8YeLgqtEKXXes3u2bADObhMGaaVaEUlWuHVfJV1fEyG\nhZB1cMZwDA+2+to/ZhD8f7qM9OIgBF5td3jMcePGU7s1NdkqzXKE77OHj2GRlZeNNHU+VqdfRZo6\nH1l52S6ZAx3rUjOxo2EWVuYEIbdxJmMOD5/5EAhSgjXnx2w2Q6W6UdbUz88P3t6suxEIBILbcDY/\nxxq2/i5c8ypGjhyNyMgo1NXV4pdffsEXX+zE/O7cjRUr/4KEspNY2BFi09/GbDZj06aN1DGmTbub\n19jt84Di0NWXKNTKc8Fnf8DWq0Gbr1JYgc9TxgIFFaz9kmLhh2MdV7oUYhH61LCNVwhc8mv49Pix\nX7OxZl/4IgQ/og3H0A4FgABfFfqzjNlRrpCQdXAmX06pvPHd9KZzCrzb2NUvpiD7bZtjVqEVJ1s6\ncDR3B2K1WhiNHbTHkCtCcqy49O6RQi4N17wTLvMhEKQG69MnKSkJy5Ytw759+7Bv3z4888wzSExM\ndMfYCAQCwSUweXbYvobzCRPy9va2KVbw6qt/xaVLvyBWq8XTX36O0J3/6fHF+L//zUFlZVeIi0ql\nQnr6A7zmZe+VMATwCwNk82oc2f4ZrYdhyG239vgCzjXMzxlcFfbIll/DdRuA3isTCz8ooUAqgnEv\nglEaP0TQcSzrKGQdnMmXCw+/Uc64sfYcRkQM6XHMbWiGd/ccZ2z5CnlZK3Dw4HfUfmFh4aznYcPV\n+WSOcCbXTBsbj6UpGYzGhdxyadjmQyBIDdY+P8nJybh06RIKCgqg1+uRlJSEJ554wu1fbaTea4RA\njxz6xBCY6Y3yc9Sbo3zHLkwpLLPZ3tKHpE0JxLyYTSmg1n11mPqtDB06HB9++D6MRiMuXfoF+/fv\nQ0JCEjQajU1/m/b2dnzwwXvIynqWyvNZuPABzJs3n/f8rHvqaNb8HuN/8xvB+1v35MnLWoGoLV/A\nbOqEGrZNKn2WP4pYrZa2X5Jv9TGEdSqoML+pCOLcp4ZLLx1HPYSkAF2fHgPaEAIlwuCNKj8zIt/5\nW4/52d97bP1++K4DUz8rLus3YMAAfPnlLjQ01KOjowODBg3G+PFa6phnqqox2uRDlQoP61TAR38C\ny6u/w+UrlwEATz+9HPHx41jPxYSz/b2cQa8rZX0WOPPsVLabkHu9AkZNAPWbf00TlvtM7BV9dKRO\nb3zv9SW49PlhNH7q6+sRGBiIc+fOYciQIbjjjjuQnJyM6OhoXLt2DcHB7HW0xYRciPKEPETkTW+T\nH5vS4kjBrCvWMRpGTA0+g4KCMGRINL74oitP8uLFC/jww/dRVPQ9fvmlCQaDHjt2bMfTTz+JvLzP\nKcMnJiYW//73BzYhx3ywb5RpD5tRYd941LJuk9q9UYBr8IYC6m7FvSTjbkaFOSZlKs5MHo/3jx6B\nf/0vmG8K4qxkc1FuLfPoFxWJhHlzPd70kw57I6PSy4hvlC2Ybe5aC6b1s7/3uBgrfBvGCjUcFYou\n705BwV4AXXlqs2fPQf/+/RGTMhUH6s/g3iOnbfZ5z9iAz6+cBwAEB/fD+vVv2/QO5AMX48OVOPpI\nYnkWOPPsJM0u+SNmo9Xe9t7razjV5PTxxx/Hu+++i6lTp1IPOsumCoUChYWFdLu5DCk3WiQw4+lm\nYQTn6G3yY2oMmrNyCVKWPgmAuaEkUyNQLg1Vc3I+wvLly9DZ2ck6xtGjxyAnZzsGDRo3KtaHAAAg\nAElEQVTMb3I00MlPSINO+3WrQitOogNHH5mHB9f+ldNY+DSN5dK81tlGo84gJNfDev4AWNeC6d5z\ntvku6xg5zuvq1StISIhHQ0MDgK4wtnXrXsfMmbNx7MgR6l45DyP+igZswC/UvpmZz+Cll1YLHieX\n+9iVcHkWiPHslHqzS52hHMW11Ujy8PjEbrTa2957fQ0uTU4ZjR+pQS5EeUIeIvJGyvITqoByMWCY\nFEwmw4gLhw/rsH79P7Fnz5e0JawHDRqMBx98CE88kYmgIG5lj9mwlx8Xo4IOZww/IbApt0LnIQbu\nMrrcfe8JmVdxcRF+/es5aGu70VMqMjIKKSn34MLhMrRXH8W3piuwcqRi0qRkfPLJZ4K9PoD7r0c6\n2J4FUn52ioHYBodQdPoypGl22+RI+dc0IbdxpmCDrLfLrrfDxfhhzfmpqKhAfn4+Ro8ejcceewxr\n167FTTfdhOHDh4s1Tk4QF6Q8Ie5jeSNV+QmN9+ea58AUPuRMfsngwRGYN28+0tMXQaMJw9ChwxAb\nOwqTJk3G0qW/x6uv/hPJyXc6pRTaYy8/LuE6dDiTHyIEtvwWrvPgkjPEB3eGW1lkJ/Yc6BA6r6io\nm5CYeDt27/6SMoCuXLmM8vIjOH7xHE6a22Ft5k+bNh0ffPAx/P2da1zq7uuRDrZngVSfnWKg05fh\nxZhqyuAwagJg8GtG8plgt+ck7Sjfh8IpthWIjRp/DD/YjIShYwQdszfLri/AJeyNtWb1X/7yFzz3\n3HPYu3cv/Pz8sGPHDixbtgz33HOPKIMkEAgEPjgq+cvlqy+fMsV0OFtWOzIyCk8//QfB+zuDM6Wh\n+ayb4LAwq33yMqYD9l/Wu4/FZR5sJcuF4Ex5aCG4Yg50ODOvSZOS8e23xdi48T18/PEmNDb27N+T\nkJCEhx56FPPmzYeXl5coY3b2PhYDMUvsy4niuhq0TqapRpdT7fbwt65Gq/k9PD+JUaQqMYEZVuPH\nZDIhISEBzz77LO655x5ERERwilsnEAgEVyCGAtpXlJaq4kOo3P01ZVCwGRVscFk3IQo70z5Myi3b\nPIQYyFwMNlf0FWKisqjEKSOfD87OKyIiEi++uArPP/8nFBZ+hTNnfkZraytCQkKRkJCEUaNGizpe\nC33lPpYaUjI4tLHxyMg7gK1oROvIgTcarZJ+QwQHsBo//v7+2LhxI4qLi/HSSy9h06ZNCAwMdMfY\nCASCGxDyld6TuFMBlTN5WSswMecrpLeYUen9PjbP0OLBje+69Iu5EKODbR+m/RzNg6+BzNVgc9Z4\n5MOp74qR6gYvk+X+/46mUS3fefn5+WHWrHtFGxtBmkjN4CCNVgl8YTV+/v73v+PTTz/Fhg0bEBIS\ngosXL+If//iHO8ZGIBBcjLvCasTEnQqoXLEYFLHdyvMYozeUX5Tg/Yd/h0c3/ttlX8yFeOWc8eTZ\nz8OiyHeGBkGvUnAykPkabO4Kt4pOvh161b9dauTb3v8K5KfEo+y2cR4LIyPIB2cNDrErxWlj4yVZ\nEY8gTVgLHgQFBaF///5obW1FXV0dhgwZgubmZkRGRrppiF2Q5DN5QhIHpYurG/W5Eqk3tvQ0dAUB\nwuCNH0/8BNyZ6LLEebZCBWLtQ4d1EQyfb0qRE90Pg662sybECykCwbefjhCiY4Yi7+QxlyX1093/\noWcuUg1r5YKY/V3ERKrPTjGJUA9CwtAxvNc9Ky8bL8ZUo3CKN3KvV+BCYTlSYhJcNEr+9AXZ9WZE\nKXjw8ssvY//+/bjpppuo3xQKBTZv3uzc6AgEgkdxd/K22Hgq3l8qYYKOxhGVNAGV3u9jjPHGI96A\nNiQZvVHpQvkK8cpx3cfRfOm8NwtPXcEPf12KsuarDj0ZUg6jdKWXSe73P2BVbjl5IFT6fGTkHfBI\nuWUCd3T6MqpENgC0jhyIHHMTFhjKieeG4DZYjZ/vv/8ee/bsEdxpnEAgSBMpK31SRSphgmzjiNVq\nsXmGFl5flGAU/GBAG8rQirH+/V0uXyEKO9s+bPNlUuTLm6+yNr2Uehilq4x8+/u/OKAT26N9MTyi\nv+jncgVEiZYnUqoUR+i7KNk2uOmmmyCTPqgEAoEHsVotijKmQ69SALgRViOXr77uhs67kJRTAINO\n55bzG3Q6FGS/jT1btnAax4Mb38We+bfjfe8rMMKMsf79OcnXch6n58XztRGr1SJl6ZOcvDr28+1S\n5BU2+/Ex5FPXrUHDjmzkrFyCxtwNks97EwPr+z9zogp3fTsbr1ctwR+STyIrL9vTw2OluK7GptoY\n0K1E11Z7aEQELnRVirMth95VKc41FQEJBDpYPT8hISGYPXs2br31Vpvme2vXrnXpwAgEVyCVkCWp\n4MleGXKThSfDhKw9H+97X8FIo20Ha6ZxLN/+EQ7s3o9KjvIVw7MltneMy7qL4b3pi2WTU9etwfbE\n0fh30jF0jA0HIB8PihjllsVOuhcLqY5LDKRWKY7QN2EteHD9+nUkJibi5ptvRmRkJPXfyJEj3TRE\nyzhI8pkckVLioHVC9PXc3Si88DNiUqZ6elgex1HytqvkJ2VZGHQ6lO3YiXalwmZNxErM54t9Yrqv\nyYwGdEJj9e2KaRyBgX4ICFVzSs7nUgCD71iFHMMeruve24pguOvZua+uAvtTfG1+M2r8MfxgMxKG\njnH5+YUSoR6EC4Xl0KsuwagJoJToByfN4bS/q5PuhcpP6sUAxCAlJgHJp/th+MFmLPeZyFlm7sJa\ndo4Kaki12EZfh0vBA1bjZ+TIkQgODkZLSwumT5+Ofv36ITHR/Y2spKJAE/ghFePHFUpZX4Cv/JgM\nB2ukLAtHRpk6IgKFF352uvoWlzWyxr4aWRi8UYhrMHt7I9ykdDgOPvITUvXMFcewh8+686nCxlcO\n7sZdz05luwm51ytg1ARQv/nXNGG5z0REqAdJep2EKtE6fRlejKmmvEZGTQAMfs1IPhMsmhIrRH7u\nGJdUEFopzh1YZOfIEO0LRqpcEcX4yc/Px6pVq7B//36kpqZi/vz5UKvViI2NFWucnJCCAk3gj1SM\nH1coZXKFjzLDR35cvTlSlQUXo8xZ74IQjxed50PpH4CydctQnTTK4Tjo5OdKz5arvGNie3Wk7Hm0\n4K5npyMPihzWSYgSvaN8Hwqn2Eb9i+3tEiI/tnERT4N7CAz0w7c/HGI0RM/Wn+8zRqoc4WL8sBY8\neO+995CTk4PAwEAMHDgQn332Gf7973+LMkACwV04mxDdW8jLWgF1WibSV38IdVom8rJWiHJcPsUA\npCqLumKdTeU7oCu3pLak1OY3psR8NoQWTGAqTHHPosW8x+FI/mIUwHBlEQ2h626NQafDx1kvQLNl\nl8cKV0iRdamZ2NEwCytzgpDbOBPrUjM9XuDDlUg16d7RuLLyspGmzsfq9KtIU+fLoiiFnHFUUIMU\n25A/rMaPUqlEUFAQ9e+wsDAolay7EQiSglQ2c221Mq6GAyBdWbjaKOOzRvaIUY2Mi/zFOI+jY4hW\nSU4AFsPvmY2FuKVDgW1opv7GVQ5yhcu6a2PjsTQlg0qwd+Z6lTra2HhkFAVThgaVdO/h4gJM44LZ\n3LOsd9JV6Azlnhxur8aRISpV45nAHdawtyNHjuD8+fOoqKjArbfeivXr1yM0NBR33323m4bYhRRC\npwj8kUrYG9D7EqL5IiTcjKv8+IY7SVEWYuX0MOFsSBiffBYL1vLjKn8h5+EyVk+GUNmHNKrhDW8o\ncB5GhMHbLYUr+CLWs1PounuqwIe7cHXSvVD50Y3LHWF6YtEbQvMCA/0QGtCfMRzU2WIbBNciSs7P\nxIkT8fXXX+PcuXMoKCjA0KFD8fzzz9uUvXYHUlGgCfyQkvEDiKPYyRUhygxX+QkxHKQoC1caZa42\nruiwlp8nlVlPF7mgM/zU8EYJWtDp7+9yOQhBjGenM+vuievV3YiVdE+n8DsjP/txsRWlEAtnDZfe\nUgTAIjtHBrLUK9b1ZbgYPwqzTDqY1tdf8fQQCALQaIKJ7CREXtYKJH60B6PblajyM+PQA/c4DG3i\nKz+DTofa7p4yng5jkyruXCN7+Vl68Nj0wnFDQ8+C7LeRvvrDHr/nrFyClKVPuvz8Bp0O6rRMdLa2\n4CQ6EA0fwM8PexelIGF+miSvVTGenWKsO7mnHZOVl02FpKn0jcgoCsa61EzR333W57F4GtalZrrk\n+Nbz4IpOX4Y0ze4efZdyG2d6PJyQL0RvkTcaTTDrNqxNTj/88EO89dZbuHKl60Iwm81QKBTQ6/XO\nj5BAILidE+Z2nEAHYPIR/dh9sVEkXzy5Rp5qatuVT7XJJofEnUUuYrVabIjuh4X6VqQiGDVow7ah\noVi29q9uOb+nEGPdyT3NjE5f1jMXp7tB7EzNHaKea11qJhYYylGSU43EqETRmoLqDOXYfmgvPprR\ngfaRGgDCGt0W19WgdTJNEYCcatkZP4TeD2vlgs2bN+Pzzz+HXq+HXq+HwWAghg+BIEMsCe+pHSqk\nIhipHSpJVm/yZFJ8X0CMqmmOoJOfp4tc6HWlSD95GaPQFQ4xCn5YeOpKr7/GPL3uYqIzlCO7YKuk\nkvzdXfXLviiFs1gqyG0cUYf20Rqbv/GdBykCQJATrMbPsGHDoFar3TEWAoHgQuRQvclVpbgJ7sGR\n/MSoJCcUOVz7rsKT6y4WUi3zLGeF38ZrFT0AMFy0+bv1PLgYnlKtoEcg0MEa9rZ48WKkpqYiPj4e\nXl5e1O9r16516cAIBIK4eDr0iA26UszmnAIYFsyV5ZfqvgYX+XkqhErq176rkXPomqPQMk8r1trY\neGTkHcBWNNrk4ogVkuZKbMLU4gYD2450/X9smM08qFyg5IFQ6fORkXeAMRfIVaF5BILYsHp+Xnnl\nFaSmpuL2229HQkIC9R+BQJAXUg+B6ctf53sDUpaf1K99AjNSbyhJ1yBWDvTwWi28Fb5Hm/DIm1eo\nedAaniz9hcQOzSMQXAGr58fX1xeZmfK4mQkEgmM8lfDOhb7+dV7uSF1+Ur72Ccx0Ken5PaqIJUYl\nenBUtmhj42Wn7NN6rRqGYm3GDX2PFDEg9FZY+/z8/PPP+P7776FUKnH+/HmcPXsWZ8+eRWRkpJuG\n2IWUesUQuCO1Pj8Efv113Cm/vtBXxN0Q+dkixd5STJBnZxdybSgpB/mx9aoRs7+QnJqfykF2BGZE\n6fOzePHinjspFNi8ebPwkQmA1FyXJ6RevrzxhPxIXxHxIPJzPwadDrXFpYhKcm7+5Nlpi85QjpLa\naiRGjXba66AzlKO4thpJIhyLCbnIj20txOgv5GwPIXcjF9kR6OHS54c0OSW4FPIQkTdEfvKGyM+9\nWDeR1asUKMqYLri6Wm+TnVhGobO4SxGXg/y4roUzhqccm5/KQXYEZpxqcrpixQqsWbMGixcvhkKh\n6PF3d3t+CAQCgUCQKqRaITMWozC51Qy9ahPynDAKnUGqlePc4YnqcU4ea0GX08TVmCV5QwQpwmj8\n3H///QCAZcuWuW0wBAKBQOi7SMU7IIS6Yh0m01S7yykpld1cxERKRqEUFXE+paTFxJm14GPMyqFg\nBaHvwVjqOi4uDgAQGhqKpqYmXL9+HYMHDyalrgkEAoEgOnJvcNtV7c42SkJK1e48hZRKoEutKen2\nz7Zhi/YXXqWkxULoWtAZs0k5BTDodLTbk+anBCnCaPw0NjbigQcewKJFi/Cf//wHb731FtLS0vDY\nY4/h8uXL7hwjgUAgEHoxfBUqKUJ6CdEjJaNQSop4XtYK/LT+TXSMDbf53V09jISuhRBjVq69kAi9\nF8aCB8888wxuvvlmLFu2DD4+PgCA9vZ2bNiwAfX19Vi3bp1bB0qSz+QJSRyUN0R+8kYu8ivIfhvp\nqz/s8XvOyiVIWfqk+wfkBGJVu7OWnZzDAQHbQhAWo9ATOT8WxKwcxwTTvaczlGNX8V7cufa/GNIG\nTP12Nlpuu9E6xN3FAPiuhUGngzots0c/r8bcDbK8NumQy3OTQI9T1d5mzpyJ3bt39/jdbDZj7ty5\n2LVrl6BBXb16Fc899xyuXbuGjo4OZGVlYdy4caz7kQtRnpCHiLwh8pM3cpFfX1Co+GKRnZgV5DxJ\nXyuBTnfv2ZSN/qEODz/9NQBg4/q70HJbJHwrLmCRrr/kPSNSM2bFRi7PTQI9TlV78/OjbxKkUCig\nVDJGy7HywQcfYOLEiXjwwQdx8uRJPPvss9ixY4fg4xEIBAJB3sRqtcjLmA7YK1R9QEl2hJSKBThL\nrFYruzED4nnd7KurtdwWiQ/W34nCyflYNPlLbI/2xfCnn8b8+xaKNXSXkbpuDQwL5iKn25jt6/cp\nQX4wGj905a25/I2Nhx56CL6+vgAAo9HIaGQRCAQCQVq4MvyKKFQ9IRXkPIuYJbrpqqtdvy0KB6J8\nMPsMMCpxGlJlYPhYEMuY9USZbwKB0fg5duwYpk2b1uN3s9mM+vp6Tgffvn07Nm3aZPPb2rVrERcX\nh/r6evzf//0f/vznP/McMoFAIBDcjTt6tUjRO+DJfJuuYgGbeoQD9vUKcu5AbK8bXclnv6p6tMy4\nG40zUj1q7HvKAPFUmW8CgTHnp66uzuGOkZGRDv/uiB9//BHPPfcc/vjHP+KOO+4QfBwCgUAguJ7K\nohL4THsYsS03XheGACWMhRsRl9R7Wx9sy3we4zbmI7bFDIO/AmUPz8LC7Nc8N4YAJcoemun2MfRF\n8l57E6n/927P3199AqnPPy3omJnbXsfGcU1oiR2AAEMTHiobgOyFzzo7VKewHpO/oQkPu2lMRZU/\nYJrPDrTEDqB+CzA0odA4H0lx411+fgvFVYdx4GQFkqPHuvW8BM/CaPy4iuPHj2PZsmV44403EBMT\nw3k/knwmT0jioLwh8pM3YsmvN1Vj44peVwpN2jKPFWHoUe2tDxULkALOFuFwVO3N1ZXmuKLTlyFN\ns7tHA1K6anNie4eyC7ZidfrVHr+vzAnC0pQMp4/PBesCFCp9IzKKgrEuNZO892QOl4IHwisXCOQf\n//gH2tvb8corr2Dx4sVYunSpu4dAIBAIBB5IqVeLKzDodCjIftumr5DQ5px0x3KWWK0WKUufJIaP\nG3FV3yZtbDyWpmR43PABuvOQRtrmIdH1GcrKy0aaOh+r068iTZ2PrLxsp8/t6Yaz9gUo3NlgluB5\nGHN+XMVbb73l7lMSCAQCwQl6czU2plwmIfk27siLkgp9IVG9txfhoMtD6jJAEql/0xoJ5iYsMJQ7\nJXdtbDwy8g5gKxq7Sn9bmqymuudaoitA0TJqAEpyqjETJB2jt+N2zw+BQCAQ5EfqujVo2JGNnJVL\n0Ji7oVco9XRJ7Uk5BTDodLy//Ds6Vm/DFZ4AqdLbvW5jttbBJ88AADcMECujhqt3SAjrUjOxo2EW\nVuYEIbdxpluLHXja80TwLG73/BAIBAJBnkixGpszsJWSvmXBXPwPnchXAAnz0xx++e8rZald5Qnw\nBJ6s5OdpqHyXNWPhW12PCaur8PKUR3p4Xrh4h5xBGxvvkevG054ngmchnh8CgUAg9Ekc5TLlZa2A\nOi0Tv99YiFkf78Ox7TsFH8sduCLXiA5XegLciUW+6as/hDotE3lZKzw9JLdhb8C2j9agakEE7bba\n2HhkFAVTXhI675CgMRjKkV2w1aM5Np70PBE8CzF+CAQCgdAnYQptM8PMO4TNVQnyXHCnIt8bwoWk\nHKLoDqOArwErtpEgpbBJKRWgILgPYvwQCAQCQXaI5emgy2USWunNE3lR7lbkXeUJcCdC5etq3GUU\nCDFgxTISSJU1ghQgxg+BQCAQZIXYng77pHZnQtjcnSDvCUVe7uFCng5RpMOdRoEnDdjeEjZJkDfE\n+CEQCASCbHCHp8OTIWx88ZQiL+dwISnK191GgacM2N4QNkmQP6TaG4FAIBBkg7uqqsmlx0tv7sHk\nSqQmXzGrqnHtweSJSmukyhpBCijMZrOZfTPPU19/xdNDIAhAowkmspMxRH7ypjfKz6DTQZ2W2aP5\naGPuBkl6ZoTCV3YGnQ613Yq8u9ahL5eKZkPIvUeVn7YyCvh6ZKyPodI3IqMoWHJhiTpDObYfLoDC\n2In5CfdIznvYG5+bfQmNJph1G+L5IRAIBIJsIJ4OetzdgykvawVu3/oVklvN0Ks2IS9jeq9ofOtJ\n1qVmYoGhHCU51UiMSuTtDXG2BxNXj5EzUMbZ77uMM3PRAckZP4TeDzF+CAQCgSArpBay1Negy7sy\n5xTAsGAu8QA5CVsomiNvW3FdDVon0+QN5VSzGhiUUZI8ECp9PjLyDojuMepNDXIJ8oYUPCAQCASC\n7HB3VTXCDaRaKrq3w1blUGgxAXdVmiOV3ghSgRg/BAJBErirQz2BQHAOKZaK7u1wqXLIVsKaqYGq\nu4wSUumNIBWI8UMgEDyOOzvUEwgE55BiqejeDldvG1MJa0cNVN1llPSGBrmE3oHXqlWrVnl6EFy4\nfr3d00MgCCAw0I/ITsa4Q356XSliXsymXuwaI+BnOIkzyeOgjohw6bmBLo9T2Y6daFcq3HI+d0Lu\nP/kiddnFpEzF6eRxODg8DD7LH8WkBxd5ekiSQmz5tSsVuJ67Gxrjjd9q/JXwWf5oj+dWhHoQ+jW1\n4eK3JWhXKnCq+SJejKmmvDtGTQAMfs1IPhOMCPUgRKgH4UJhOfSqSzBqAiij5MFJc0Qbv4WUmAQk\nn+6H4QebsdxnokvO4SxSv/cIjgkM9GPdhhQ8IBAIHsVdfVvoIBWrCAThuLvCXF+GT5VD++fa+gUx\naN0yyWYb+0IIzlaa44Mn+gsRCNYQ44dAIHiU/2/v7oOjqu89jn8WCSR3oGgCOGIYp+NtHoASK5vJ\nImKBa65EJvLQ8JAgDD4wtAo+UKSxkiBQhlQGp3eE0lSumgls+IMHZVuuDFjLCLIhy4XcQrK0InUI\nZRBCpQSIMWbvH5jU1AjZsLvnnPzer7/w7MnZb/bnLvvh9zvf37X7B8q+sW9LtO8foGMVACfpTJfD\njj7Xpuyske9oij4fNqDtvI42UL2ZUBKLNtlApHDPDwBLWXX/AB2rADjNjbocdvS5ln+hhx7471NR\nu9fmevcTAXbEzA8Ay1mxb4tVM04AEC3f9rlW9J9PquF8XMSXtbF3D5yI8APAFmJ9/0A4a+gBwAlu\n9LkW6UByMxurdoTlc4gFwg8AY1kx4wQA0RTLz7Vbv+ylnhuq1OxJlobdIanj+4k6o9C39tos0ugk\nxdfuVIHvg7ZW3UAkEX4AGI2OVQC6m1h8rrWGleb0TKnmrLT5sBKG33XtfqIwl9WxfA6xRMMDAAAA\nGwsEq7V2j1eBYLXVpUj6ZljRkNsVN/ROrfzff+/SbI3/dM0/r/WVq0MSVVl3LBLlAu0QfgAAAGzK\njt3UOgorX3x/oC7e0rXNQT3JQ9u60bW6tnxuaJdrBL4N4QcAHCYYCGjP2vUKBgJWlwIgijpcDuZp\nsHwGKNJhxZ2WoYIDfaPWjhv4OsIPAFgo3CDjKyxS/ynzlb/8LfWfMl++wqIoVwjAKnZdDhaNsFKS\nO1/bzj+spRV9tLU+h2YHiBoaHgCARXyFRRrp3a3RjSHVxpfJV5Ct3JIV33p+R7u3hyr2KJg3kaYN\nQDd0bYZlZ7sA1NVuapFWkjtfecHqiO4d5E7LYLYHUcfMDwBYoKMg46nYc90ZoI52bx9ytUV1lVVR\nrRWANey+HMydlqGnHyywTT1AZxB+AMACXQky13Zvd7U7VpPQQ8lZmVGpEYD1WA4GRBbhBwAs0JUg\nk+Z260BBdtvPte7ezpI3oHtjhgWIHO75AQALpLnd8hVkS18tfWsNMjfajT2Wu7cDANDdEH4AwCJd\nDTKx2L0dAIDuiPADABYiyAAAEDuEHwAAADhWIFgtf90xeZKHcl8UboiGBwAAAHCkQt9aTem/U8vz\nGzSl/04V+tZaXRJsjvADAAAAxwnUHpF35KW2TWAb05NU4WlQIFhtcWWwM8IPAAAAHMd/uqYt+LS6\nOiRRlXXHLKoITkD4AQAAgON4kocqvra+3bGEmgvKSh5qUUVwAsIPAAAAHMedlqGCA33bAlBCzQXl\n+/vQ9ADXRbc3AAAAOFJJ7nzlBatVWXFMWclZcucSfHB9hB8AAAA4ljstg9kedBrL3gAAAAAYgfAD\nAAAAwAiEHwAAAABGIPwAAAAAMAINDwAAthEMBFTnr1KyJ1NpbrfV5QAIUyBYLX/dMXmSh9KEALZE\n+AEA2IKvsEgjvbs1ujGk2vgy+QqylVuywuqyAHRSoW+tvCMvqXF0kuJrd6rA94FKcudbXRbQDsve\nAACWqw1UaaR3t9IbQ5Kk9MaQPBV7FAwEIvo8wUBAe9auj/h1AdMFao9cCz7pSZKkxvQkVXgaFAhW\nW1wZ0B7hB0CHgoGAfKv/iy+JiInT/kBb8Gk15GqL6iqrIvYcvsIi9Z8yX/nL31L/KfPlKyyK2LUB\n0/lP17QFn1ZXhySqsu6YRRUBHSP8APiG1i+JuYtL+ZKImEj2ZKo23tXuWE1CDyVnZUbk+rGaWQJM\n5Ukeqvja+nbHEmouKCt5qEUVAR0j/ABohy+JsEKa260DBdltAagmoYf8+Q9GrOlBLGaWAJO50zJU\ncKBvWwBKqLmgfH8fmh7Admh4AKCd0/6AHujgS2JFZRXdtxBVuSUrFMybqIrKKiVnZSo3gv+/XZtZ\nKmsXgCI5swRAKsmdr7xgtSorjikrOUvuXIIP7IfwA6AdviTCSmlud1RCdprbLV9BtvTVrGbrzFIk\nAxa+He2PzeFOy2CMYWuEHwDt8CURTnWjPYKiObOEb0f7YwB24gqFQqEbn2a9c+cuWV0CumDAgL6M\nnUMFAwF9dvRPunXY91nu5lAmvf9a9whKbwypNt6lAw7fI6i7jF2g9oimDPifdl3AEmouaGt9Tree\nHegu42cixs7ZBgzoe8NzaHgAoENpbrdyX3iG4APbo0mHfdH+GIDdWBZ+Tpw4IVCq/yYAABKuSURB\nVLfbraamJqtKAAB0A3Rysy/aHwOwG0vCT0NDg1555RX17t3biqcHAHQj0d4jCF1H+2MAdmNJ+Cku\nLtbChQsVHx9vxdMDALqRaO8RhJtTkjtf284/rKUVfbS1PodmBwAsFdVub1u2bFFZWVm7Y4MGDdKE\nCROUmpoqh/RaAADYHJ3c7I32xwDsIubd3h566CHdfvvtCoVCqq6uVkZGhsrLy2NZAgAAAAADWdrq\nety4cdq1a5fi4uJueC5tB52JlpHOxvg5G+PnXIydszF+zsXYOZvtW127XC6WvgEAAACIiaje83Mj\n7733npVPD+AmBAMB1fmrlOzJ5MZyAADgCJaGHwDO5Css0kjvbo1uDKk2vky+gmzllqywuiwAAIDr\nsnTZGwDnqQ1UaaR3d9umkumNIXkq9igYCFhcGQAAwPURfgCE5bQ/0BZ8Wg252qK6yiqLKgIAAOgc\nwg+AsCR7Mts2k2xVk9BDyVmZFlUEAADQOYQfAGFJc7t1oCC7LQDVJPSQP/9Bmh4AAADbo+EBgLDl\nlqxQMG+iKiqrlJyVqVyCDwAAcADCD4AuSXO7me0BAACOQvgBAHQb7D8FALgewg8AoFtg/ykAwI3Q\n8AAA4HjsPwUA6AzCDwDA8dh/CgDQGYQfAIDjsf8UAKAzCD8AAMdj/ykAQGfQ8AAA0CV266zG/lMA\ngBsh/AAAwmbXzmrsPwUAuB6WvQEAwkJnNQCAUxF+AABhobMaAMCpCD8AgLDQWQ0A4FSEHwBAWOis\nBgBwKhoeADCO3bqUORGd1QAATkT4AWAUu3YpcyI6qwEAnIZlbwCMQZcyAADMRvgBYAy6lAEAYDbC\nDwBj0KUMAACzEX4AGIMuZQAAmI2GBwCMQpcyAADMRfgBYBy6lAEAYCaWvQEAAAAwAuEHAAAAgBEI\nPwAAAACMQPgBAAAAYATCDwAAAAAjEH4AAAAAGIHwAwAAAMAIhB8AAAAARiD8AAAAADAC4QcAAACA\nEXpaXQAAAIApgoGA6vxVSvZkKs3ttrocwDiEHwAAgBjwFRZppHe3RjeGVBtfJl9BtnJLVlhdFmAU\nlr0BAABEWW2gSiO9u5XeGJIkpTeG5KnYo2AgYHFlgFkIPwAAAFF22h9oCz6thlxtUV1llUUVAWYi\n/AAAAERZsidTtfGudsdqEnooOSvToooAMxF+AAAAoizN7daBguy2AFST0EP+/AdpegDEGA0PAAAA\nYiC3ZIWCeRNVUVml5KxM5RJ8gJgj/AAAAMRImtvNbA9gIcIPAACAzQWC1fLXHZMneajcaRlWlwM4\nFvf8AAAA2Fihb62m9N+p5fkNmtJ/pwp9a60uCXAswg8AAIBNBWqPyDvykhrTkyRJjelJqvA0KBCs\njsz1g9Vau8cbsesBdkf4AQAAsCn/6Zq24NPq6pBEVdYdu+lrM6MEExF+AAAAbMqTPFTxtfXtjiXU\nXFBW8tCbum60Z5QAuyL8AAAA2JQ7LUMFB/q2BaCEmgvK9/e56aYH0ZxRAuyMbm8AAAA2VpI7X3nB\nalVWHFNWcpbcuTff7e3ajNLOdgHo2oxS1k1fG7Azwg8AAIDNudMyItri2p2WoQLfB/KqXo3pSf+c\nUYpAsALsjPADAABgoGjMKAF2R/gBAAAwVKRnlAC7I/wAALqNYCCgOn+Vkj2ZSnO7rS4HAGAzMQ8/\nLS0tWrVqlY4dO6ampiYtWLBAP/zhD2NdBgCgm/EVFmmkd7dGN4ZUG18mX0G2cktWWF0WAMBGYt7q\n+p133tGXX34pr9erdevW6ZNPPol1CQCAbqY2UKWR3t1KbwxJktIbQ/JU7FEwELC4MvsLBgLas3Y9\nrxUAI8Q8/Ozbt08DBw7UvHnzVFxcrLFjx8a6BABAN3PaH2gLPq2GXG1RXWWVRRU5g6+wSP2nzFf+\n8rfUf8p8+QqLrC4JAKIqqsvetmzZorKysnbHEhMT1bt3b5WWlqqqqkovvviiNm7cGM0yAADdXLIn\nU7XxZe0CUE1CDyVnZVpYlb11NFsWqtijYN5E7pcC0G1FNfzk5eUpLy+v3bGFCxe2zfZkZmbqr3/9\na6euNWBA30iXhxhh7JyN8XM2U8ZvQM5YbX7iYbne2Km0qyEF/62H/u+xHM3Ice7qgmiPnf/on/RA\nB7NlvqN/0gAHv252Ycp7rzti7Lq3mDc8GDFihPbu3avs7GwFg0ENGjSoUz937tylKFeGaBgwoC9j\n52CMn7OZNn7/sbRYwQkP63BllZKzMvUfbrdjf/9YjN2tw4arNt71jdmyW4d937Gvm12Y9t7rThg7\nZ+tMcI15+Jk6dapefvllTZ8+XZK0bNmyWJcAAOim0txulmx1UprbLV9BtvTV0reahB7y5z+oXF4/\nAN2YKxQKhW58mvVI4c7Ev6A4G+PnbIyfc8Vy7IKBgOq+mi0jOEYG7z3nYuyczZYzPwAAwD6YLQNg\nkpi3ugYAAAAAKxB+AAAAABiB8AMAAADACIQfAAAAAEYg/AAAAAAwAuEHAAAAgBEIPwAAAACMQPgB\nAAAAYATCDwAAAAAjEH4AAAAAGIHwAwAAAMAIhB8AAAAARiD8AAAAADAC4QcAAACAEQg/AAAAAIxA\n+AEAAABgBMIPAAAAACMQfgAAAAAYgfADAAAAwAiEHwAAAABGIPwAAAAAMALhBwAAAIARCD8AAAAA\njED4AQAAAGAEwg8AAAAAIxB+AAAAABiB8AMAAADACIQfAAAAAEYg/AAAAAAwAuEHAAAAgBEIPwAA\nAACMQPgBAAAAYATCDwAAAAAjEH4AAAAAGIHwAwAAAMAIhB8AAAAARiD8AAAAADAC4QcAAACAEQg/\nAAAAAIxA+AEAAABgBMIPAAAAACMQfgAAAAAYgfADAAAAwAiEHwAAAABGIPwAAAAAMALhBwAAAIAR\nCD8AAAAAjED4AQAAAGAEwg8AAAAAIxB+AAAAABiB8AMAAADACIQfAAAAAEYg/AAAAAAwAuEHAAAA\ngBEIPwAAAACM0DPWT9jQ0KDnn39eV65cUe/evbV69WolJSXFugwAAAAAhon5zM+2bduUmpqqTZs2\nKScnRxs2bIh1CQAAAAAMFPPwk5KSooaGBknXZoHi4uJiXQIAAAAAA0V12duWLVtUVlbW7lhxcbH2\n79+vCRMm6OLFi/J6vdEsAQAAAAAkSa5QKBSK5RMuWLBAo0eP1rRp03T8+HG98MIL2rFjRyxLAAAA\nAGCgmC9769evn/r06SNJSkxM1OXLl2NdAgAAAAADxXzm59NPP9WSJUt05coVNTc369lnn9XIkSNj\nWQIAAAAAA8U8/AAAAACAFdjkFAAAAIARCD8AAAAAjED4AQAAAGAE24efhoYGzZ07VzNnztTjjz+u\n+vp6q0tCGFpaWrRy5UoVFBQoLy9Pe/futbokdMGJEyfkdrvV1NRkdSnopIaGBv34xz/WrFmzNGPG\nDB05csTqktAJoVBIS5cu1YwZMzR79mydOnXK6pLQSc3NzVq8eLFmzpypadOm6Q9/+IPVJaEL6uvr\nNWbMGJ08edLqUhCG3/72t5oxY4Z+9KMfaevWrdc91/bhZ9u2bUpNTdWmTZuUk5OjDRs2WF0SwvDO\nO+/oyy+/lNfr1bp16/TJJ59YXRLC1NDQoFdeeUW9e/e2uhSE4c0339R9992n8vJyrVq1SsuXL7e6\nJHTCnj171NTUpM2bN+unP/2pVq1aZXVJ6KQdO3botttu06ZNm/T6669rxYoVVpeEMDU3N2vp0qWK\nj4+3uhSE4eDBgzp8+LA2b96s8vJynTlz5rrn94xRXV2WkpKijz/+WNK1L2FxcXEWV4Rw7Nu3T9/7\n3vc0b948SdKSJUssrgjhKi4u1sKFC/XUU09ZXQrC8Nhjj6lXr16Srv2FTnh1hkOHDmn06NGSpIyM\nDB09etTiitBZOTk5Gj9+vKRrqx569rT9Vyz8i1/+8pfKz89XaWmp1aUgDPv27VNKSoqeeuopXb58\nWYsXL77u+bZ6Z27ZskVlZWXtjhUXF2v//v2aMGGCLl68KK/Xa1F1uJGOxi8xMVG9e/dWaWmpqqqq\n9OKLL2rjxo0WVYjr6Wj8Bg0apAkTJig1NVV0xbevjsZu1apVGjZsmM6dO6fFixfrpZdesqg6hKOh\noUF9+/Zt+++ePXuqpaVFPXrYfqGG8RISEiRdG8Nnn31Wzz//vMUVIRzbtm1TUlKSRo0apd/85jdW\nl4Mw/P3vf9ff/vY3lZaW6tSpU/rJT36id99991vPt/0+PwsWLNDo0aM1bdo0HT9+XC+88IJ27Nhh\ndVnopIULFyonJ0fZ2dmSpPvvv1/79u2zuCp01kMPPaTbb79doVBI1dXVysjIUHl5udVloZOOHz+u\nRYsW6Wc/+5nuv/9+q8tBJ5SUlOiee+5pm0EYM2aM/vjHP1pbFDrtzJkzmj9/vh599FFNnjzZ6nIQ\nhkcffVQul0uSFAwG9d3vflfr169XUlKSxZXhRtasWaOkpCTNmTNHkjRx4kS9+eabSkxM7PB8W838\ndKRfv37q06ePpGuzCJcvX7a4IoRjxIgR2rt3r7KzsxUMBjVo0CCrS0IYdu3a1fbncePG6Y033rCw\nGoTjo48+0nPPPadf/epXSk1NtbocdNK9996r999/X+PHj9eRI0eUkpJidUnopPPnz+uJJ55QcXGx\nPB6P1eUgTF9flTJr1iwtX76c4OMQI0aMUHl5uebMmaOzZ8+qsbFRt91227eeb/vw88wzz2jJkiXy\ner1qbm7WL37xC6tLQhimTp2ql19+WdOnT5ckLVu2zOKK0FUul4ulbw7y6quvqqmpSStXrlQoFNJ3\nvvMdrVu3zuqycAPZ2dnav3+/ZsyYIUk0PHCQ0tJS/eMf/9Cvf/1rrVu3Ti6XSxs2bGi79w7O0ToD\nBGcYM2aMAoGA8vLy2jpmXm8Mbb/sDQAAAAAigTsoAQAAABiB8AMAAADACIQfAAAAAEYg/AAAAAAw\nAuEHAAAAgBEIPwAAAACMQPgBAHTZ6dOnNWzYME2ePFmTJk1STk6OnnvuOdXX10uSjh49qqKiopjX\nNW/ePJ07dy4i1/riiy80Z84cVVVVReR6AADrsM8PAKDLTp8+rdmzZ+u9995rO/bqq6/q0KFD2rRp\nk4WVRcbJkyf185//XLW1tXr99deVmZlpdUkAgJvQ0+oCAADdy4IFCzRq1Cj9+c9/1meffabXXntN\n5eXlmjVrloYMGaIPP/xQTU1Neumll1ReXq4TJ05o9uzZmjNnjq5cuaLly5frL3/5i1paWjR37lw9\n/PDD2r59uz744ANdvHhRp06d0qhRo7R06VKdPXtWixYt0tWrV9WjRw8tWbJEw4cP17hx47Rx40bd\ncccdWrlypfx+v1wulx555BHNnTtXBw8eVGlpqeLj43XixAmlpqZqzZo16tmz/V+LW7du1ZNPPqmy\nsjKLXk0AQCQRfgAAERUXF6e77rpLH3/8sRITE+Vyudoec7lc8vl8Wrt2rVauXCmfz6fz589r0qRJ\nmjNnjtavX69hw4appKREDQ0Nys/P1/DhwyVJR44c0e9//3u5XC6NHz9eBQUF2rVrl8aOHavHH39c\nBw8e1KFDhzR8+PC256yoqNDZs2f1u9/9To2NjZo1a5ZSUlKUkJCgw4cP691339XAgQM1depU7du3\nT2PGjGn3uyxatEiS9NZbb8XktQMARBfhBwAQcS6XS/Hx8d84/sADD0iS7rzzTmVkZKhXr14aNGiQ\nLl26JEn68MMP9fnnn2vLli2SpMbGRn300UeSpB/84AdKSEiQJA0ePFgXL17UfffdpwULFujYsWMa\nM2aMZs6c2e75/H6/Jk+eLEmKj49Xbm6u/H6/xo4dq5SUFA0cOFCSdPfdd+uzzz6LwisBALATwg8A\nIKKampp08uRJ3X333Tpz5ky7x+Li4tr+fMstt3zjZ1taWrR69Wqlp6dLkurr69WvXz/5fD716tWr\n3bmhUEj33nuvdu7cqffff187d+7U9u3b9cYbb6j1dtZ/va01FAqpublZktpd7+uzUwCA7otubwCA\nm/L1gBEKhfTaa6/pnnvu0eDBg8O+hsfjkdfrlSR9+umneuSRR74RoL5u9erVevvttzVp0iQVFRWp\npqam3eMej0dvv/22WlpadPXqVfl8PmVlZYXz6wEAuhFmfgAAN+XcuXOaPHmyQqGQWlpaNGTIEK1Z\ns+Yb511vdqX1saefflrLli1Tbm6uWlpatHjxYg0ePFiBQKDD82fPnq2FCxdq+/btuuWWW7Rs2bJ2\nj0+fPl0nT57UxIkT1dzcrIkTJ+rBBx/UwYMHw/odmRkCgO6BVtcAAAAAjMCyNwAAAABGIPwAAAAA\nMALhBwAAAIARCD8AAAAAjED4AQAAAGAEwg8AAAAAIxB+AAAAABiB8AMAAADACP8PEg/8hYxwwsMA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb592b50>"
      ]
     },
     "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": {},
   "source": [
    "### Question 12\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? Are there customer segments that would be classified as purely 'Retailers' or 'Hotels/Restaurants/Cafes' by this distribution? Would you consider these classifications as consistent with your previous definition of the customer segments?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "It seems to perform quite well since 2 clusters are what the shilouette score recommended to use and, coincidentally, 2 clusters are what the creators of the dataset grouped the customers into. Regarding data points that could be classified as purely one or the other segment, there are some data points of this kind. Most data points though are somewhat ambiguous and do not clearly belong to one cluster or the other. There is considerable overlap in the true distribution. \n",
    "\n",
    "I believe the algorithm did a reasonable job of clustering these customers according to broad categories of business type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \n",
    "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
   ]
  },
  {
   "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}