vingkan/README.md

## README.md

      
    Raw
  

              README.md
            
          
    Spectral Clustering Demo

Run the Jupyter notebook in your browser with Binder:


## much_excite.csv

          
            Timestamp
            Secret Name
            On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Rap]
            On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Rock]
            On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Jazz]
            On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Pop]
            On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Country]
            On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Electronic Dance]
            On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Classical]
            On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Rhythm & Blues]
            On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Heavy Metal]
            On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Reggae]
            On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Cherries]
            On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Bananas]
            On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Apples]
            On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Pears]
            On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Coconuts]
            On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Blueberries]
            On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Strawberries]
            On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Cantelope]
            On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Watermelon]
            On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Pineapple]
            On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [National Air and Space Museum]
            On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [Smithsonian Institution Building]
            On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [American Art Museum]
            On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [National Portrait Gallery]
            On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [Museum of Natural History]
            On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [Museum of American History]
            On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [National Postal Museum]
            On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [Museum of African American History and Culture]
            On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [Museum of the American Indian]
            On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [National Zoological Park]

            
              2019/06/07 9:52:12 PM AST
              Giraffe
              5
              4
              2
              5
              1
              2
              2
              1
              2
              1
              2
              5
              1
              4
              3
              2
              4
              1
              1
              5
              5
              3
              3
              3
              3
              3
              1
              3
              4
              4

            
              2019/06/07 9:53:23 PM AST
              Dumb idiot
              5
              4
              3
              5
              3
              4
              4
              5
              1
              3
              5
              5
              4
              3
              3
              4
              5
              3
              4
              5
              4
              5
              5
              5
              4
              4
              1
              5
              3
              2

            
              2019/06/07 9:53:34 PM AST
              Rihanna 
              5
              3
              4
              2
              1
              1
              3
              5
              1
              4
              5
              4
              5
              5
              3
              5
              5
              4
              4
              5
              2
              4
              5
              5
              3
              4
              5
              4
              4
              1

            
              2019/06/07 9:53:45 PM AST
              Panda
              5
              5
              3
              5
              4
              4
              5
              3
              3
              3
              3
              3
              1
              1
              3
              2
              3
              1
              4
              3
              4
              3
              5
              3
              3
              4
              5
              5
              2
              3

            
              2019/06/07 9:53:48 PM AST
              foggybottom123
              3
              3
              2
              3
              1
              3
              2
              1
              1
              2
              4
              4
              3
              4
              2
              4
              4
              4
              4
              4
              4
              4
              3
              3
              3
              2
              2
              2
              2
              3

            
              2019/06/08 3:49:55 PM AST
              BBB
              4
              3
              4
              4
              3
              3
              4
              4
              2
              3
              5
              5
              5
              5
              5
              5
              5
              5
              5
              5
              5
              5
              5
              5
              5
              5
              5
              5
              5
              5

            
              2019/06/08 7:38:59 PM AST
              Walter
              4
              4
              1
              3
              5
              2
              2
              3
              3
              1
              4
              3
              3
              1
              1
              1
              3
              1
              5
              4
              4
              5
              3
              4
              4
              3
              1
              2
              1
              2

            
              2019/06/10 9:39:45 AM AST
              SG888
              2
              4
              2
              4
              2
              3
              4
              2
              2
              3
              4
              3
              3
              4
              4
              5
              5
              5
              4
              2
              5
              2
              3
              3
              5
              3
              3
              3
              3
              5

            
              2019/06/10 12:27:07 PM AST
              aaaassss
              4
              2
              1
              3
              2
              5
              2
              1
              1
              1
              4
              2
              3
              2
              3
              2
              4
              2
              5
              5
              4
              3
              2
              1
              4
              4
              3
              5
              2
              2

            
              2019/06/11 2:06:23 PM AST
              Bloop
              3
              4
              3
              2
              1
              1
              4
              4
              2
              4
              5
              3
              3
              3
              5
              5
              5
              2
              5
              5
              4
              4
              5
              5
              5
              5
              2
              4
              2
              1

            
              2019/06/11 3:47:39 PM AST
              old
              5
              4
              4
              3
              2
              3
              2
              3
              3
              2
              4
              4
              3
              4
              1
              2
              2
              1
              5
              4
              3
              2
              4
              3
              5
              4
              3
              5
              4
              4

## requirements.txt
numpy
pandas
matplotlib
seaborn
sklearn

## Spectral Clustering Demo.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spectral Clustering Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load and Explore Data\n",
    "\n",
    "The survey consisted of 30 ordinal scales, separated into three categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import SpectralClustering\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_palette(\"hls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>secret_name</th>\n",
       "      <th>rap</th>\n",
       "      <th>rock</th>\n",
       "      <th>jazz</th>\n",
       "      <th>pop</th>\n",
       "      <th>country</th>\n",
       "      <th>electronic_dance</th>\n",
       "      <th>classical</th>\n",
       "      <th>rhythm_&amp;_blues</th>\n",
       "      <th>...</th>\n",
       "      <th>national_air_and_space_museum</th>\n",
       "      <th>smithsonian_institution_building</th>\n",
       "      <th>american_art_museum</th>\n",
       "      <th>national_portrait_gallery</th>\n",
       "      <th>museum_of_natural_history</th>\n",
       "      <th>museum_of_american_history</th>\n",
       "      <th>national_postal_museum</th>\n",
       "      <th>museum_of_african_american_history_and_culture</th>\n",
       "      <th>museum_of_the_american_indian</th>\n",
       "      <th>national_zoological_park</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019/06/07 9:52:12 PM AST</td>\n",
       "      <td>Giraffe</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019/06/07 9:53:23 PM AST</td>\n",
       "      <td>Dumb idiot</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019/06/07 9:53:34 PM AST</td>\n",
       "      <td>Rihanna</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019/06/07 9:53:45 PM AST</td>\n",
       "      <td>Panda</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019/06/07 9:53:48 PM AST</td>\n",
       "      <td>foggybottom123</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   timestamp     secret_name  rap  rock  jazz  pop  country  \\\n",
       "0  2019/06/07 9:52:12 PM AST         Giraffe    5     4     2    5        1   \n",
       "1  2019/06/07 9:53:23 PM AST      Dumb idiot    5     4     3    5        3   \n",
       "2  2019/06/07 9:53:34 PM AST        Rihanna     5     3     4    2        1   \n",
       "3  2019/06/07 9:53:45 PM AST           Panda    5     5     3    5        4   \n",
       "4  2019/06/07 9:53:48 PM AST  foggybottom123    3     3     2    3        1   \n",
       "\n",
       "   electronic_dance  classical  rhythm_&_blues  ...  \\\n",
       "0                 2          2               1  ...   \n",
       "1                 4          4               5  ...   \n",
       "2                 1          3               5  ...   \n",
       "3                 4          5               3  ...   \n",
       "4                 3          2               1  ...   \n",
       "\n",
       "   national_air_and_space_museum  smithsonian_institution_building  \\\n",
       "0                              5                                 3   \n",
       "1                              4                                 5   \n",
       "2                              2                                 4   \n",
       "3                              4                                 3   \n",
       "4                              4                                 4   \n",
       "\n",
       "   american_art_museum  national_portrait_gallery  museum_of_natural_history  \\\n",
       "0                    3                          3                          3   \n",
       "1                    5                          5                          4   \n",
       "2                    5                          5                          3   \n",
       "3                    5                          3                          3   \n",
       "4                    3                          3                          3   \n",
       "\n",
       "   museum_of_american_history  national_postal_museum  \\\n",
       "0                           3                       1   \n",
       "1                           4                       1   \n",
       "2                           4                       5   \n",
       "3                           4                       5   \n",
       "4                           2                       2   \n",
       "\n",
       "   museum_of_african_american_history_and_culture  \\\n",
       "0                                               3   \n",
       "1                                               5   \n",
       "2                                               4   \n",
       "3                                               5   \n",
       "4                                               2   \n",
       "\n",
       "   museum_of_the_american_indian  national_zoological_park  \n",
       "0                              4                         4  \n",
       "1                              3                         2  \n",
       "2                              4                         1  \n",
       "3                              2                         3  \n",
       "4                              2                         3  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"much_excite.csv\")\n",
    "cols = [col[col.find(\"[\") + 1:-1] if \"[\" in col else col for col in data.columns]\n",
    "cols = [col.lower().replace(\" \", \"_\") for col in cols]\n",
    "data.columns = cols\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = cols[2:]\n",
    "music_slice = slice(0, 10)\n",
    "fruits_slice = slice(10, 20)\n",
    "museums_slice = slice(20, 30)\n",
    "music = features[music_slice] \n",
    "fruits = features[fruits_slice]\n",
    "museums = features[museums_slice]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e677b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "music_ratings = data[music].mean(axis=0)\n",
    "sns.barplot(music_ratings.values, music_ratings.index)\n",
    "plt.xlim(0, 5)\n",
    "plt.yticks(range(len(music_ratings)), music_ratings.index)\n",
    "plt.xlabel(\"Average Rating\")\n",
    "plt.ylabel(\"Options\")\n",
    "plt.title(\"Music Genres\")\n",
    "plt.gcf().set_size_inches(10, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113172828>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fruits_ratings = data[fruits].mean(axis=0)\n",
    "sns.barplot(fruits_ratings.values, fruits_ratings.index)\n",
    "plt.xlim(0, 5)\n",
    "plt.yticks(range(len(fruits_ratings)), fruits_ratings.index)\n",
    "plt.xlabel(\"Average Rating\")\n",
    "plt.ylabel(\"Options\")\n",
    "plt.title(\"Kinds of Fruit\")\n",
    "plt.gcf().set_size_inches(10, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11338a518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "museums_ratings = data[museums].mean(axis=0)\n",
    "sns.barplot(museums_ratings.values, museums_ratings.index)\n",
    "plt.xlim(0, 5)\n",
    "plt.yticks(range(len(museums_ratings)), museums_ratings.index)\n",
    "plt.xlabel(\"Average Rating\")\n",
    "plt.ylabel(\"Options\")\n",
    "plt.title(\"DC Museums\")\n",
    "plt.gcf().set_size_inches(10, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Apply Spectral Clustering\n",
    "\n",
    "To make ratings data comparable, we standardize each user's ratings by subtracting their mean rating for the category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 30)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R = data[features].values\n",
    "R.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 30)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "standards = []\n",
    "for rating_cols in [music_slice, fruits_slice, museums_slice]:\n",
    "    ratings = R[:,rating_cols]\n",
    "    X_section = (ratings.T - ratings.mean(axis=1)).T\n",
    "    standards.append(X_section)\n",
    "X = np.hstack(standards)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SpectralClustering(n_clusters=3, random_state=0)\n",
    "y = model.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0WloHYCcwAFjopz2Ed2uRlPfT8a5WmwInUtZVpiyPAB7xl78M/Lau53jllVdqpixdujTjfc+BjF/Tp00LtH9cnK55LVbzNhRnFmg6/z9daBMTYDUwR1V/6aflAsvwKqDUNrFKVZ3ub/NvwB5VnSEidwOl6t+fFZFKVW3lL48AeqvqvSJyHvAqcKOqviEi5wAJVd12pnO0NjHDMIyPY21iPurV0rcB14vIWyLy38DjwPhadv134C4RWY13K/HDNFz/i1cx/lZEXserPC8JcPq1krztGDYj/cv+xu50zWuxmjduTifaxFR1L/D1GlYv9bd56LR9tgNXpCRNSFnXKmV5DjAn5f2LQJ9gZ5w+hw4dCkv1MZL3thu70zWvxWreuDkb/ZWYYRiG0Xhp9G1icSBIm1hFRQVt/CeR6kqQNrEuF1zA7r/+NeP94+J0zWuxmrehOK1NzBEOHjwYiTeve3cnnK55LVbzxs1plVjM2blzZyTeZP+Oxu50zWuxmjduTqvEDMMwjNhilVjM6datWyTe5xctcsLpmtdiNW/cnFaJxZzWrVtH4n1nzx4nnK55LVbzxs1plVjMiaqz890RdJyMwuma12I1b9ycVokZhmEYscWJETsaOq+9+mrGfbaGjxzJIH+07bryYYA+ghs2bOC+DPYP0jdty+bNGe8bBJe8Fqt54+a0zs4NgCYi2iLDfZNTUmRCkErs1KlTZGXV/UI+SCUWJNYguOS1WM3bUJzW2dkRoppgLzm/WZi4NJlgVF6L1bxxc1olZhiGYcQWq8RizrGjRyPxNm0afnNqVLG65LVYzRs3p7WJNQCCtIkFIUibWKbYRJyGYaSDtYk5QsHo0ZF4y8vLQ3dGFatLXovVvHFzWiUWc7rk5kbiraioCN0ZVawueS1W88bNaZWYYRiGEVusTawBEKRNrFN2Nu/u3ZvRvkHaxCorK2nVqlWd9wvSJhYk1iC45LVYzdtQnNYm5gg98/Mj8e7bty90Z1SxuuS1WM0bN6dVYjGnf4ZDTgVl9+7doTujitUlr8Vq3rg5G1wlJiInRWSdiGwSkfUiMkZEzsp5isgIEXkkje3+S0TOrSb9IRG5318uEpEv1HKcH4jIOZmfsWEYhnEmGuIAwEdUtReAiJwPPAV8BngwrBNQ1S+lsU1hGof6ATAX+Fvgk6qBhQsW1Nehz0heXl7ozqhidclrsZo3bs4GdyWWiqq+B9wD3CseH7uSEpGFIjLAX64Ukaki8qqI/FlEPi8iS0Vkh4h8JeWwXUTkTyKyVUSqrRhFZKeIdPCXf+Rv+2ege8o2c0RkqL98k4i8JiIbRKRURJqLyGigM7BERJac5ayp4v1Dh+rr0GekefPmoTujitUlr8Vq3rg5G+KV2MdQ1R3+7cTza9n008BSVR0vIguAYuBmoAfwOPCsv93ngcvwro7WiMgiVV1b3QFF5Erg68Df4eVVOfDqadu0AOYAN6nqNhF5Aviuqv5MRMYAN6jq/mqOfQ9eBU3LFi0oLi4GvOm839mzp2oyuS2bN/PUnDlVA2keO3qUyRMnUjB6NF1yc8lJJJhw3330zM+vuv+8cMEC3j90iOEjRwKwYd06npk3j0m+43BFBVOLili7di2VlZUAXHXVVezZs4e3334bgO7du9OkSRM2+1MpdOrUiQsvvJBVq1YB8OGHHzJo0CBeeeUVjhw5AsA111zDW2+9xbvvvgtAjx49OHnyJFu3bgUgJyeHdh06MOaBBwDYt3cvs2fMYHxhIa3btAFg8sSJ3DZ0KJf36gXA3NJSzm3blsFDhpCTSPCfTz7J+vJy7h07FoDdu3ZRMmsWk4qLad7Ce8azcNw47hgxgkt69ADgsZISOicS3DpoEAAvLl7M9q1bKRg1CoAd27dTWlJC0bRpVaNuF44bx8iCArrl5ZGTSFA4fjx53btz4y23ZFROAI/MmFGncuqUnc237ryTUWPH0jE7G4CZU6Zwbf/+XN23LwDzy8o4fvw4w4YPB6B8zRpeeO45xhd6NwoOHjjAzIcfZsyECbRr3x6AqUVF3DxwIPl9+gBQNncuzZo14/Zhw8hJJHi6rIyXly/PuJwAli9ZUqdyykkkKJ40KVA5AZTMnp12OeUkEux4443A5ZT8PqVbTjmJBH9csCBQOQGsXrmyTuWUk0gwtagoUDnV9fvUJTeX3bt2ZVROj5aWkhaq2qBeQGU1ae8DHYERwCMp6QuBAf7yMf6vy0AR8CN/OQt4318eATyRsn8R8INqfDuBDni3A4tS0mcC9/vLc4ChQE9gWco2NwHzU49TW8xZoOdk+Jo5fXrG+wZhyZIlGe2X6bkGjdW8DddpsTZebxBnFmht/zv9/58NGxHpBpwE3gNO8PFboKndq46rVnV8OoVXqaGqp/j4FefpnaNq6yxV2/pIBwPcsG5dJN7zz6/twvjsE1WsLnktVvPGzdngOjuLSKWqtvKXzwN+A6xS1QdFpB8wDegH5ACbgK+o6tLT9nsI74pueuoxRWQE8FO824lHgFeAkXra7UQR2Qn0BnLxrriu4v9uJ5ao6nQRmYN3JbgQ2AbcqKpv+OmvqerPRWSDf35vnSnmIJ2dm7dokfFI0UE6O584cSKjkeyDdHYOEmsQXPJarOZtKM44d3ZumXzEHvgzsBj4sb9uJfAWsAGYjlep1JUVwJPAOuDp0yuwVFS1HChLbgssr2abo8DdwO/9SusU8Ct/9a+B5+rzwY7kffmwWbFiRejOqGJ1yWuxmjduzgb3YIeqNjnDOgXurGFdq5Tlh6pbp6pz8K6sajuHrinLPwF+Us02I1KW/4L38Mfp28wGZtfmMwzDMDKjIV6JGXXgcASjyUM0j9hHFatLXovVvHFzNrg2MRexSTENwzA+TpzbxIw6MMrv3xE2a9fW2JRYb0QVq0tei9W8cXNaJRZzkp0rwybZSTpMoorVJa/Fat64Oa0SMwzDMGKLtYk1AIK0ibXr0IGD+z8xqlVaBGkTO3LkCC1btqzzfkHaxILEGgSXvBareRuK09rEHOHa/v0j8e7Zsyd0Z1SxuuS1WM0bN6dVYjEnOcBo2CQHCg6TqGJ1yWuxmjduzgbX2dkIjyC39oqnT2dQBrO2BrmFuXTp0oz3t0f7DaNxYldiMWd+WZkz3u7du9e+UT3gUh5brOaNm9MqsZhz/PhxZ7xNmtQ4Ilm94lIeW6zmjZvTKrGYk5wM0QVvcoLOsHEpjy1W88bNaZWYYRiGEVusEos55WvWOOPt1KlT6E5wK48tVvPGzWmdnRsAQTo7t27TJpLRqTP1Bnk68dixYxmPnh/k6cS45XHcnFF5XYo1Km8Qp3V2doTxhYXOeFetWhW6E9zKY4vVvHFzWiVmGIZhxBarxGLOwQMHnPFmMlbj2cClPLZYzRs3p7WJNQCimhQzCqKYiBNsxA7DiBvWJuYIYyZMcMb7yiuvhO4Et/LYYjVv3JyxrsRE5KSIrBORjSLyRxE510/vLCLz/OURIvJItGdaf7Rr394Z75EjR0J3glt5bLGaN27OWFdiwBFV7aWqlwEHge8DqOo7qjo02lMzDMMw6ptYt4mJSKWqtvKXvwNcoarfE5GuwEJVvUxERgBfAc4BLgIWqOo4f59fAn2AlsA8VX3QT98JPA58GWgG/L2qbhGRh4BcoJv/92eqOsvf5xmgC9AC+Lmq/jrdOKyfWHpYP7HG6YzK61KsUXmtn1iaiEgT4Cbg2Ro26QUMAy4HholIFz/9R6raG7gCuF5ErkjZZ7+q5gO/BO5PSb8EuBX4PPCgiDTz00eq6pVAb2C0iIRy7X7zwIFhaBqE96233grdCW7lscVq3rg54z6fWEsRWQd0BV4FXqhhu7+o6gcAIrIZuADYDfyDiNyDlw/ZQA/gdX+f+f7fV4HbU461SFWPAcdE5D2gI7AHr+Ia4m/TBcgDany+1PfeA9CyRQuKi4sBeH7RIt7Zs4e7CwoA2LJ5M0/NmUPRtGkAHDt6lMkTJ1IwejRdcnPJSSR4edkyeubn09+f32vhggW8f+gQw0eOBGDDunU8M28ek3zH4YoKphYVMWrsWDpmZwMwc8oUru3fv2oSu/llZRw/frxqAM/yNWt44bnnqjovduzUifllZYyZMKHqvvfUoiJuHjiQ/D59ACibO5dmzZpx+7BhAKxeuZIjR45UPaDRqlUrevfuzapVqzh27BgA/fr1Y9u2bbz33nsAXHbZZRw7dozt27dTWVlJs2bN6NixI2vXrgWgTZs25Ofns2LFCk6cOAHAddddx6ZNmzjgP97bs2dP+t9wA7cOGgTAi4sXs33rVgpGjQJgx/btlJaUUDRtGllZWZw6dYrCceMYWVBAt7w8chIJ1qxeTV737tx4yy0ZlRPAIzNm1KmcOmVnM7+sLFA5HTxwgJkPP5x2OeUkEnz00Ue8vHw5Yx54AIB9e/cye8YMxhcW0rpNGwAmT5zIbUOHcnmvXgDMLS3l3LZtGTzE+wosX7KE9eXl3Dt2LAC7d+2iZNYsJhUX07yFd9+hcNw47hgxgkt69CAnkWB9eTmdE4mMywmgZPbstMspJ5Hg0ssvD1xOdf0+5SQSAIHKCbzvU13KKSeRYPOGDYHKCeCxkpK0y6lLbi69rrwyo3J6tLSUtFDV2L6ASv/vZ4DlwGj/fVdgo788AngkZZ+FwADgQuANoK2fPgcY4S/vBDr4y72Bpf7yQ8D9Kcfa6LsGACuAc/z0pcCAdOPIAj0nw9fM6dMz3jfIK1NvEJYsWZLxvlHEGlUex81psTZebxBnFmia/z/jj3pXWaOB+1Nu79VGG+BD4AMR6QgEue79DHBIVf8mIpcAVwc4Vp0omzs3LFXk3h7+L8GwcSmPLVbzxs3ZKCoxAFV9DVgPfD3N7dcDrwGbgFJgZQD9n4CmIvI6MBlYHeBYdaJZs3Tr7Ph7T548GboT3Mpji9W8cXPGuhJT/8nElPdfVtUnVXWneo/do6pzVPXelG0Gq+pSf3mEqn5OVQep6u2qOsdP76qq+/3ltao6wF9+SFWnpxzrMt91TFUHquoVqvr3qjog6ahvkvfHwyYK79atW0N3glt5bLGaN27OWFdihmEYhttYJRZzVq8Mchc0Xt6cnJzQneBWHlus5o2bM9adnRsLQTo7t+vQgYP795/V86lPb5DOzkeOHMl4JPsgnZ3jlsdxc0bldSnWqLxBnE51dnaZZB8RF7yRDQDsUB5brOaNm9MqMcMwDCO2WCUWc/bt3euMt1WrVrVvVA+4lMcWq3nj5rQ2sQaATYpZ/9ikmIYRL6xNzBGSY6+54F21alXoTnArjy1W88bNaZVYzEkO9OmCNzlIcNi4lMcWq3nj5oz7KPZGzAhyW694+nQG+SOL15UgtzGXLl2a8f52G9Mw6hdrE2sABGkTa96iBceOHj2r59NQvUGcQSqxEydO0LRpZr/3glRiccvjuHldijUqbxCntYk5wm1DhzrjjSrWbdu2ReJ1KY8t1sbpDcNplVjMSU5K6II3qliTE3SGjUt5bLE2Tm8YTqvEDMMwjNhibWINgCBtYpf06MGWzZvP6vk0VG8QZ5A2sf3799OhQ4eM9g3SJha3PI6b16VYo/IGcVqbmCOc27atM96oYo3q0X6X8thibZzeMJxWicWcwUOGOOONKtbt27dH4nUpjy3WxukNw2mVmGEYhhFbrBKLOcuXLHHGG1WsXbp0icTrUh5brI3TG4bTKrGYs7683BlvVLF27NgxEq9LeWyxNk5vGE6rxGLOvWPHOuONKta1a9dG4nUpjy3WxukNw+lcJSYiJ0VknYhsFJHfi8g5Z+m4lWfjOIZhGEb6OFeJAUdUtZeqXgZ8BHwn6hMKwu5du5zxRhVrm4hGHXcpjy3WxukNw+lcZ2cRqVTVVv7yd4ArVPV7IvIM0AVoAfxcVX+d3B74OTAYOAJ8VVX3iciFwFN4MwH8CbhPVVuJSCvgD0BboBkwUVX/cKZzcmlSzKiwyTgNI15YZ+daEJGmwEBgg580UlWvBHoDo0WkvZ/+aWC1qvYElgHf9tN/DvxSVfsA76Yc+igwRFXzgRuAGSL1959sUnFxfR26wXmjinXFihWReF3KY4u1cXrDcLo4n1hLEVnnLy8H/p+/PFpEkj3zugB5wAGBGintAAAgAElEQVS8W44L/fRXgZv95b7A1/zlJ4Gp/rIAPxWR64BTQA7QkY9XdIjIPcA9AC1btKDYL+znFy3inT17uLugAIAtmzfz1Jw5FE2bBsCxo0eZPHEiBaNH0yU3l5xEgk7Z2fTMz6e/P9fWwgULeP/QIYaPHAnAhnXreGbevKoP1OGKCqYWFTFq7Fg6ZmcDMHPKFK7t35+r+/YFYH5ZGcePH2fY8OEAlK9ZwwvPPVc1U2vHTp0AGDNhAu3ae/X91KIibh44kPw+fQAomzuXZs2acfuwYQCsXrmSl5cvZ8wDDwCwb+9eZs+YwfjCwqrJ8yZPnMhtQ4dWDRw6t7SUc9u2ZfCQIeQkEtw6aBDry8urGox379pFyaxZTCoupnkL73q2cNw47hgxgkt69ADgsZISdu3axY4dOwDo2rUr7dq1o9x/cqpt27b07NmTl156CVVFRLj++utZv349hw4dorKykoqKCg4ePMjOnTsB6NatG61bt2b9+vUAtG/fnksvvZRly5YB0LRpU/r161dVTgCPzJhRp3Lq5JdNkHI6eOAAMx9+OO1yykkkGDxkSKByAu/R6rqUU04iwUV5eXT2yxjgxcWL2b51KwWjRgGwY/t2SktKKJo2jaysLE6dOkXhuHGMLCigW14eACWzZ5PXvTs33nILcObvU04iwaTi4o99nzIpp7p+n3ISCW4fNixQOUHdv085iQSX9OgRqJzA+z6lW05dcnMpmjYto3J6tLSUtFBVp15AZTVpA4AVwDn++6XAgNO3B4YCc/zlA0BTf7lNcjtgBFAGNPPf7wS6numcskDPyfA1c/r0jPcN8orCG8QZhCVLlmS8r0t5HDevS7HGMY+zQM/0fzP5crpNLCXtq8C3VPXLInIJsA74oqouPa0NbSgwWFVHiMizwO9Uda6IfBf4V/XaxP4Z+KyqjhKRG4AXgQtVdWdN5xSkTSz5izRsovAGcQZpEzt16hRZWZndeQ/SJha3PI6b16VYo/IGcVqbWN34E9BURF4HJgOr09jnn4Hvi8ga4DMp6b8BeovIWuBOYMvZPtlU7hgxoj4P36C8UcW6adOmSLwu5bHF2ji9YTidaxM7/SrMTzuG95DHGbdX1XnAPH/5LeCalE2n+On7T0uvV5L3qcMmCm9UsR44cCASr0t5bLE2Tm8YTrsSMwzDMGKLc21iDZEgbWIX5eXxZgRThUThDeIM0iZ26NAh2mY4L1KQNrG45XHcvC7FGpU3iNPaxByhcyLhjDeqWA8fPhyJ16U8tlgbpzcMp1ViMSfZV8MFb1SxJvuXhY1LeWyxNk5vGE6rxAzDMIzYYpVYzHlx8WJnvFHF2rVr10i8LuWxxdo4vWE4rRKLOdu3bnXGG1Ws7dq1i8TrUh5brI3TG4bTKrGYkxyvzAVvVLGWRzQTr0t5bLE2Tm8YTqvEDMMwjNji3IgdjY0dEfQ3icobxBmkv9bIggIG+SOa15Ug/dPWr1+f8f6ZxmufJ/PGzWmdnRsANilm48Um4zSMzLDOzo6QnGfMBa9LsQK89NJLoTtdymOXYo3KG4bTKrGYk+kUIXH0uhQrQBR3SVzKY5dijcobhtMqsZgTxbxEUXldihVAIrgl6FIeuxRrVN4wnNYm1gCwNrHGi7WJGUZmWJuYI4wsKHDG61Ks4D2dGDYu5bFLsUblDcNplVjM6ZaX54zXpVjBmwImbFzKY5dijcobhtMqMcMwDCO2WJtYAyBIm1iXCy5g91//elbPp6F64xhrkDaxiooK2rRpk9G+mbaJxTGP4+R0zRvEaW1ijpDXvbszXpdiBTh48GDoTpfy2KVYo/KG4bRKLObceMstznhdihVg586doTtdymOXYo3KG4az1kpMREaLyP+IyG/q+2REZKeIdKjD9reJSI+U9yNEpPNZPJ8/icj7IrLwtPTfiMhWEdkoIqUi0sxP/6qIvC4i60RkrYj0O1vnYhiGYXySdK7Evgd8SVXvrO+TyYDbgB4p70cAZ60SA/4V+EY16b8BLgEuB1oC3/LT/wL0VNVewEjg0bN4LtXy/KJF9a1oMF6XYgXo1q1b6E6X8tilWKPyhuE8YyUmIr8CugHPishYEXnGv9JYLSJX+NucJyIviEi5iJSIyF+TV1MiMklEtvjrfysi94vIRSJSnuLIE5FXU7T/IiL/7b8+629zgYj8xXf/RURyReRa4CvAv/pXPuOB3sBv/PctReQmEXlNRDb4V0zN/ePtFJGfisgq/4opX0SeF5E3ReQ7yRNR1b8Ah0/PF1X9L/UB/htI+OmV+n9PynwaqPenZt7Zs6e+FQ3G61KsAK1btw7d6VIeuxRrVN4wnGecikVVvyMiXwRuAB4EXlPV20TkRuAJoJef/qKqPuxvew+AiPQGvgb8ne8pB15V1TdF5AMR6aWq64C7gTkp2gpV/byI/BPwM2Aw8AjwhKo+LiIjgVn+eTwLLFTVeb5zIHC/qq4VkRb+cW9S1W0i8gTwXf+YALtV9RoR+Td/u75AC2AT8Kt0Ms+/jfgN4J9T0oYADwPnA4POsO89ybxq2aIFxcXFgPfL5Z09e7jb7yS4ZfNmnpozp2ogzWNHjzJ54kQKRo+mS24uOYkEE+67j575+fT3pwtZuGAB7x86xPCRIwHYsG4dz8ybxyTfcbiigqlFRYwaO5aO2dkAzJwyhWv79+fqvn0BmF9WxvHjxxk2fDgA5WvW8MJzzzG+sBCAjp068e3hwxkzYQLt2rcHYGpRETcPHEh+nz4AlM2dS7Nmzbh92DAAVq9cycvLlzPmgQcA2Ld3L7NnzGB8YSGt/afwJk+cyG1Dh3J5r14AzC0t5dy2bRk8ZAg5iQT/+eSTrC8v596xY71C3LWLklmzmFRcTPMW3jOehePGcceIEVzSw7tIf6ykhM6JBLcO8orjxcWL2b51a9WEfTu2b6e0pISiadPIysri1KlTFI4bx8iCArrl5ZGTSFA4fjx53btX3eNPt5zKy8upqKgAoHfv3uzbt4/du3cDkJeXR/Pmzdm4cSMA559/PhdffDErVqwA4MMPP2TQoEGsXbuWyspKAK666ir27NnD22+/DUD37t1p0qQJmzdvBqBTp05ceOGFFE+fDsDBAweY+fDDaZdTTiLB02VlgcoJYPmSJXUqp5xEguJJkwKVE0DJ7Nlpl1NOIsGON9742PcJ4JEZM+r1+5STSPDHBQs+9n2qazll8n3KSSSYWlQUqJzq+n3qkpvL7l27MiqnR0tLSQtVPeML2Al0AF4DuqWk7wY+A6wDLkxJP+hv/wPgxynpM/EqGIA7gZ8DTYA3gfYprm7+cjPggL+8H2iWkr7fX54DDE1xLAV6+8s9gWUp624C5qd4cvzlkcB/pGy3Czg35f0AvIqyurz5D+BnNay7DvhzbfmrqmSBnpPha+b06RnvG+QVhTeOsQZhyZIlGe/rUh7HyemaN4gzCzTN/59pU93z+lpDek3bJ3kaGIh3lfWqqh447ZjVLZ/urY3a+hcc8/+eSllOvq91slAReRA4DxhT7QmqLgMuqsuDKpmwxf8FHjZReF2KFaC9/4s8TFzKY5dijcobhrMuldgyvCsoRGQA3tVQBbAC+Ac//Ragrb/9CuDLItJCRFqRcmtNVY8CzwO/BB47zTMs5e8qf/ll4Ov+8p3+scFrr0ptOEh9vwXommxXw7vtd1YmaBKRbwG3Av+oqqdS0j8r/tDjIpIPfAo4UP1Rzg5PzZlTn4dvUF6XYgW49NJLQ3e6lMcuxRqVNwxnXSqxh4DeIvI6MAW4y0//MXCL/7DGQGAvcFhV1wDPAuuB+cBa4IOU4/0G74pq8Wme5iLyCl47031+2mjgbt+d2gb1n3gPgrwmIhfh3V78lYisw7sSuxv4vYhswLvCSqutK4mILAd+D9wkIntE5FZ/1a+AjsAq/yGSQj/9a8BG3/8LYJhq/Q6JYhPsNV7vsmXLQne6lMcuxRqVNwxnrbfNVLVrytuvVrPJB8CtqnpCRK4BblDV5O256ar6kIicg3clNyNlv35AqaqerMb149POYSdwYzXntpKPP2L/Jt6tyiR/wXuwpMaYVHUOKQ+WnLau/+n7+unV5puqTgWmVrfOMAzDOPvUWomlQS7wOxHJAj4Cvp2y7td+Z+QWwOOqWg4gIguAi6imYjLqxrGjR53xuhQrQNOmZ+PrWTdcymOXYo3KG4bTBgBuANikmI0XmxTTMDLDBgB2hILRo53xuhQrQHl5ee0bnWVcymOXYo3KG4bTKrGYk+yg6YLXpViBqk7SYeJSHrsUa1TeMJxWiRmGYRixxdrEGgBB2sQ6ZWfz7t69Z/V8Gqo3jrEGaROrrKykVatWGe2baZtYHPM4Tk7XvEGc1ibmCD3z853xuhQrwL59+0J3upTHLsUalTcMp1ViMSc5SKkLXpdiBaoGCg4Tl/LYpVij8obhtErMMAzDiC3h96Y0zioLFyxwxhvHWIP017q6b18GrVyZ0b6ZtsW9/fbb3OdPy1FXgsRqn6fG6Q3DaVdiMef9Q4ec8boUa1Te5s2bh+4E+zw1Vm8YTqvEYk5yoj4XvC7FGpU3OUFn2NjnqXF6w3BaJWYYhmHEFqvEYs6Gdeuc8boUa1Te888/P3Qn2OepsXrDcFpn5wZAkM7OzVu0iGR06ii8LsUa1Jvpgx0nTpzIePT8IA922OepcXqDOK2zsyNMKi52xutSrFF5V6xYUftG9YB9nhqnNwynVWKGYRhGbLFKLOYcjmCk86i8LsUalTeqR+zt89Q4vWE4rU2sAWCTYhpnmygm47SJOI2zibWJOcKoDEdXiKPXpVij8q5duzZ0J9jnqbF6w3BaJVYDInJSRNaJyHoRKReRa/30riJyJGXdyyLS3V83QEQ+8Ne9LiJ/FpF6fWa5Y3Z2fR6+QXldijUqb2VlZehOsM9TY/WG4bRKrGaOqGovVe0JTAAeTln3Zsq6x4Efpqxb7q+7AlgDfD+8UzYMw3ALaxOrARGpVNVW/vLfA3eq6m0i0hVYqKqX+ev+BchR1R+IyADgflUdLCICzAbeUNWfnckVpE2sXYcOHNy/P8O9MycKr0uxBvVm2iZ25MgRWrZsmdG+QdrE7PPUOL1BnNYmFpyW/m3BLcCjwOSUdRf5694ExgAzU9b1F5F1wC7gC0BpfZ7ktf371+fhG5TXpVij8u7Zsyd0J9jnqbF6w3DaVCw1c0RVewGIyDXAEyJymb/uzZR1w4BfA1/01y1X1cH+uvHANOA7px9cRO4B7gFo2aIFxX6nwOcXLeKdPXu4u6AAgC2bN/PUnDkUTZsGwLGjR5k8cSIFo0fTJTeXnESCtatX0zM/v2oCuoULFvD+oUNVg29uWLeOZ+bNq+p4eLiigqlFRYwaO7bqnvXMKVO4tn9/ru7bF4D5ZWUcP36cYcOHA1C+Zg0vPPcc4wsLAejYqRMLFyxgzIQJtGvfHoCpRUXcPHAg+X36AFA2dy7NmjXj9mHDAFi9ciUvL1/OmAceAGDf3r3MnjGD8YWFtG7TBoDJEydy29ChXN6rFwBzS0s5t21bBg8ZQk4iwfGPPmJ9eTn3+g3Gu3ftomTWLCYVF9O8hXc9WzhuHHeMGMElPXoA8FhJCZ0TCW4dNAiAFxcvZvvWrRSMGgXAju3bKS0poWjaNLKysjh16hSF48YxsqCAbnl55CQSrC8vJ697d2685ZaMygngkRkz6lROnbKzWbhgQUbltHTpUgBatmzJVVddxSuvvMKRI0cAuOaaa3jrrbd49913AejRowcnT55k69atVW1iiUSCV155BYBWrVrRu3dvVq1axbFjxwDo168f27Zt47333gPgsssu4+q+fRk8ZAgAy5csqVM55SQS/M/GjYHKCaBk9uy0yyknkeDvrrwycDnV9fuUk0jwqU996mPfp4MHDjDz4Yfr9fuUk0jwxtatVd+nTMoJ6vZ96pKby+evuSajcnq0NM3f/6pqr2peQOVp7/cB5wNdgY0p6S2Bv/nLA/BuNSbXfQ7YXJsrC/ScDF8zp0/PeN8gryi8LsUa1JspS5YsyXjfuOVxHMs1bt4gzizQ2v53+v8/jdoQkUuAJsCBalb3A96sYdczrTsrzC8rq8/DNyivS7FG5e3evXvoTrDPU2P1huG024k109Jv2wIQ4C5VPek9r+G1ifnpHwHfStmvf8q6D05bd9Y5fvx4fR6+QXldijUqb5MmTUJ3gn2eGqs3DKddidWAqjZR71H5XqraU1UX+ek7VbVlSnofVX3FX7dUVT+Tsu46Vd1Wn+eZbAsJmyi8LsUalXfz5s2hO8E+T43VG4bTKjHDMAwjtlglFnPK16xxxutSrFF5O3XqFLoT7PPUWL1hOK2zcwMgSGfn1m3aRDI6dRRel2IN6s20s/OxY8cyHsk+SGdn+zw1Tm8Qp3V2doRkPxMXvC7FGpV31apVoTvBPk+N1RuG0yoxwzAMI7ZYJRZzDh6oruta4/S6FGtU3kzHTQyKfZ4apzcMp7WJNQBsUkzjbGOTYhpxx9rEHGHMhAnOeF2KNSpvcrzEsLHPU+P0huG0SizmJAcLdcHrUqxReZODBIeNfZ4apzcMpw07ZRiNkExv7RVPn84gf/T2uhLkFubSpUsz3t9uY7qNtYk1AKyfWMN1uuaNom8aRNM/zaVyjcpr/cSMWrl54EBnvC7FGpU3qljfeuut0J0ulWtU3jCcVonFnOSEeS54XYo1Km9UsSYn6AwTl8o1Km8YTqvEDMMwjNhilVjMKZs71xmvS7FG5Y0q1h49eoTudKlco/KG4bRKLOY0a9bMGa9LsUbljSrWkydPhu50qVyj8obhtEos5tw+bJgzXpdijcobVaxbt24N3elSuUblDcNplZhhGIYRW6wSizmrV650xutSrFF5o4o1JycndKdL5RqVNwyndXZuAATp7NyuQwcO7t9/Vs+noXpdijUqbxBnkM7OR44cyXgE/Uw7O7tUrlF5gzits7MjjHngAWe8LsUalTeqWKMYeNilco3KG4bTiUpMRP5NRH6Q8v55EXk05f0MERlzhv0r/b9dReSO+j1bwzAMI12cqMSAl4FrAUQkC+gAXJqy/lognZu3XYE6VWIi0qQu29eVfXv31ufhG5TXpVij8kYVa6tWrUJ3ulSuUXnDcDrRJiYinYH/VtWEiFwO3A9kA8OAvwH7gFxgAdAWaAZMVNU/+PtXqmorEVkNfA54C3gcmAVMAQYAzYFfqGqJiAwAHgT2Ar1U9Yw9OW1STKMxEMVEnGCj2DdW0m0Tc2IqFlV9R0ROiEgu3lXXKiAHuAb4AHgdrzIboqoVItIBWC0iz+rHa/kHgPtVdTCAiNwDfKCqfUSkObBSRBb7234euExVqx3Z1N/3HoCWLVpQXFwMwPOLFvHOnj3cXVAAwJbNm3lqzhyKpk0D4NjRo0yeOJGC0aPpkptLp+xsJt5/Pz3z8+nvT6GxcMEC3j90iOEjRwKwYd06npk3j0m+43BFBVOLihg1diwds7MBmDllCtf278/VffsCML+sjOPHjzNs+HAAytes4YXnnmN8YSEA7Tt04LsjRjBmwoSqOYOmFhVx88CBVeOllc2dS7Nmzar6iqxeuZKXly+vuk++b+9eZs+YwfjCQlq3aQPA5IkTuW3oUC7v1QuAuaWlnNu2LYOHDKFTdja/f+op1peXc+/YsQDs3rWLklmzmFRcTPMW3k+BwnHjuGPECC7xR4F4rKSEzokEtw4aBMCLixezfetWCkaNAmDH9u2UlpRQNG0aWVlZnDp1isJx4xhZUEC3vDw6ZWfz4x/+kLzu3bnxllsyKieAR2bMqFM5dTjvPL5z112ByunggQPMfPjhtMupU3Y2z8ybl1E5vf3222zfvh2ALl260LFjR9auXQtAmzZtyM/PZ8WKFZw4cQKA6667jk2bNnHgwAE+/PBDrr32Wg4fPsyOHTsA6Nq1K+3ataO8vByAtm3b0rNnT1566SVUFRHh+uuvryongJLZs9Mup07Z2fz1rbcCl1Ndv0+dsrP5r2efDVROmXyfOmVnM/0nP6n6PgEsX7KkXr9PnXNyeOfttz/2fUq3nB4tLSUtVNWJF/Ab4Ot4V1A9gS8BxcC/4F1NNQMewavQ1gFHgE7+vpX+3wHAwpRjzgO2+duvw7tCu8Xfbkm655YFek6Gr5nTp2e8b5BXFF6XYo1jHgdhyZIlGe8bp/x1zRvEmeV9pmr9/+nElZhPsl3scmAjsBsYC1QApcCdwHnAlap6XER2ArXd5RNglKo+/7FE73bih2fz5A3DMIxP4kSbGICI9ALmAztU9Qt+2qt4txUvw6vEPquqo0TkBuBF4EJV3ZnSJnYlMFNVr/f3vwfviu7v/YrvYuBtoA8ptx1rI0ibWPMWLTh29GiGe2dOFF6XYo3KG8QZpE3sxIkTNG2a2W/qTNvEXCrXqLxBnNZP7JNswHsqcfVpaR+o6n682429RWQtXoW2pZpjvA6cEJH1InIf8CiwGSgXkY1ACSG3M942dGiYuki9LsUalTeqWLdt2xa606VyjcobhtOZSkxVT6pqG1WdmJI2QlW7+8v7VfUaVe2tqt9S1c+p6k5/XSv/73FVvUlVe6rqv6nqKVX9oaperqqXqeoNqvqBqi5N9yosKMmG9bCJwutSrFF5o4r1vffeC93pUrlG5Q3D6UwlZhiGYTQ+nGkTa8gEaRO7pEcPtmzefFbPp6F6XYo1Km8QZ5A2sf3799OhQ4eM9s20Tcylco3KG8RpbWKOcG7bts54XYo1Km9UsR47dix0p0vlGpU3DKdVYjEn2WnRBa9LsUbljSrWZCfpMHGpXKPyhuG0SswwDMOILVaJxZzlS5Y443Up1qi8UcXapUuX0J0ulWtU3jCcVonFnPX+uHIueF2KNSpvVLF27NgxdKdL5RqVNwynVWIxJzlwpwtel2KNyhtVrMmBgsPEpXKNyhuG0yoxwzAMI7a4NABwo2T3rl3OeF2KNSpvEGeQeb0KRo9mkD/1SV3JtH9aeXl5oL5tmcZrn6ezi3V2bgDYpJiGkTk2GWfjxDo7O0JyYj4XvC7FGpXXpVhXrFgRuhPcyuMwnFaJxZzk7KsueF2KNSqvS7EmZ5gOG5fyOAynVWKGYRhGbLE2sQZAkDaxrKwsTp06dVbPp6F6XYo1Km8cY820TezUqVNkZWX+Oz7TNrE45nEUTmsTc4Q7RoxwxutSrFF5XYp106ZNoTvBrTwOw2mVWMy5pEcPZ7wuxRqV16VYDxw4ELoT3MrjMJxWiRmGYRixxdrEGgBB2sQuysvjzQimsYjC61KsUXnjGGumbWKHDh2ibYD5rjJtE4tjHkfhtDYxR+icSDjjdSnWqLwuxXr48OHQneBWHofhtEos5tw6aJAzXpdijcrrUqw7duwI3Qlu5XEYTqcqMRH5kYhsEpHXRWSdiFwlIk1F5Kcist1PWyciP0rZ5z5/n40i8lsRaeGn3yQi5f72K0Tks356rogsEZHXfM+XoorXMAyjseNMJSYi1wCDgXxVvQL4ArAbKAY6A5erai+gP9DM3ycHGA30VtXLgCbA1/1D/hK409/nKWCinz4R+J2q/p2/7b/XZ1wvLl5cn4dvUF6XYo3K61KsXbt2Dd0JbuVxGE6XRrHPBvar6jEAVd0vIucA3wa6qupRP/0w8FDKfk2BliJyHDgHeMdPV6CNv/yZNNLrhe1bt9bn4RuU16VYo/K6FGu7du1Cd4JbeRyG05mnE0WkFbACryL6M1AGHAIe96+aatrvn4GfAEeAxap6p5/eH3jGT68ArlbVChHJBhYDbYFPA19Q1VerOe49wD0ALVu0uPIn/kCZzy9axDt79nB3QQEAWzZv5qk5cyiaNg2AY0ePMnniRApGj6ZLbi45iQQT7ruPnvn59Pensli4YAHvHzrE8JEjAdiwbh3PzJtXNRjn4YoKphYVMWrsWDpmZwMwc8oUru3fn6v79gVgflkZx48fZ9jw4QCUr1nDC889x/jCQgA6durEt4cPZ8yECbRr3x6AqUVF3DxwIPl9+gBQNncuzZo14/ZhwwBYvXIlLy9fzpgHHgBg3969zJ4xg/GFhbRu49X7kydO5LahQ7m8Vy8A5paWcm7btgweMoScRIL/fPJJ1peXV022t3vXLkpmzWJScXHVOG2F48Zxx4gRVX1UHispoXMiUXV//sXFi9m+dSsFo0YBsGP7dkpLSiiaNq1qhIHCceMYWVBAt7w8chIJCsePJ697d2685ZaMygngkRkz6lROnbKz+daddwYqp4MHDjDz4YfTLqecRIKny8oClRN409LXpZxyEgmKJ03KqJxeXreOQ4cOAZCfn8/BgwfZuXMnAN26daN169asX78egPbt23PppZeybNkyKisrOffcc+nXrx/l5eVUVFQA0Lt3b/bt28fu3bsByMvLo3nz5mzcuBGA888/n4svvpjZP/85UPfvU04iwR8XLAhUTpl8n3ISCaYWFQUqp7p+n7rk5rJ7166PfZ8ASmbPrvX79GhpaVpPJ6KqzrzwbgcOAH4MvIt3q/C1lPV3A+vwbjN2wauIXgTOw7vF+Aww3N92PnCVv/wvwKP+8hhgrL98DbAZyDrTeWWBnpPha+b06RnvG+QVhdelWC2P039lypIlSzLeV1WdyuMonFle2db6f92ZNjEAVT2pqktV9UHgXuDLQK6ItPbXP6ZeG9cHeBXeF4C3VPV/VfU4XsV1rYicB/RU1Vf8Q5cB1/rL3wR+5x9vFdAC6FBfMe2IoL9JVF6XYo3K61KsQfqIBcGlPA7D6dLtxO7AKVXd7r8vBs4F/gZ0BApU9aiINAH+B7jFTy8F+uDdNpwDrMV7qONd4FpV3SYi3wS+pKpfE5HngDJVnSMinwP+AuToGTLaJsU0jMyxSTEbJ9bZ+ZO0Ah4Xkc0i8jrQA+8Bjh8Be4GNIvIasBx4HHjHv9KaB5QDG/Dy69eqegLvgZCnRWfBdnEAACAASURBVGQ98A28W4oAY4Fv++m/BUacqQILSrINJmyi8LoUa1Rel2J96aWXQneCW3kchtOZpxPVe7ji2hpWP+C/qtvvQeDBatIXAAuqSd8M9M38TOtGkKkk4uZ1KdaovC7FGtVdKJfyOAynS1dijZIo5iWKyutSrFF5XYpVIrod6FIeh+F0pk2sIWNtYoaROdYm1jixNjFHGOn3q3DB61KsUXldijXZdyxsXMrjMJxWicWcZOdBF7wuxRqV16VYkx2kw8alPA7DaZWYYRiGEVusTawBEKRNrMsFF7D7r389q+fTUL0uxRqVN46xZtomVlFRQZs2bWrfsAYybROLYx5H4bQ2MUfI697dGa9LsUbldSnWgwcPhu4Et/I4DKdVYjEnOYCmC16XYo3K61KsyUGCw8alPA7DaZWYYRiGEVucGbGjsfL8okXOeF2KNSpvHGPNtG2q/w03MMifFicTMm2L27VrF/f5U59kQqbxNtbPk12JxZx39uxxxutSrFF5Ldb6p3Xr1pF4G2seWyUWc+6OqONkFF6XYo3Ka7HWP1F1sm6seWyVmGEYhhFbrBKLOVs2b3bG61KsUXkt1vqnffv2kXgbax5bZ+cGQJDOzllZWZGMTh2F16VYo/JarOmT6YMdp06dCjRFSaYPdsQtj62zsyPYBHvmjbszKm9UsS5btiwSb2PNY6vEDMMwjNhilVjMOXb0qDNel2KNymux1j9Nm0bTPbex5rG1iTUAbFJMw4gfNhln/WJtYo5QMHq0M16XYo3Ka7HWP+Xl5ZF4G2seWyUWc7rk5jrjdSnWqLwWa/1TUVERibex5rFVYoZhGEZsiW2bmIg8A3QBWgA/V9Vfi8gvgT5AS2Ceqj7ob1sIfNlPfxkoUFUVkW8D9wCfAt4AvqGqfxORvwceBE4CH6jqdSJyKfCYv20W8DXgHeB3QAJoAkxW1TIRmQJ8BTgBLFbV+88US5A2sU7Z2by7d2+Ge2dOFF6XYo3Ka7GmT6ZtYpWVlbRq1Spjb6ZtYnHLYxfaxEaq6pVAb2C0iLQHfqSqvYErgOtF5Ap/20dUtY+qXoZXkQ320+f76T2B/wG+6acXArf66V/x076DV1n28p17gC8C76hqT//YfxKRdsAQ4FJVvQIorr8sgJ75+fV5+AbldSnWqLwWa/2zb9++SLyNNY/jPBXLaBEZ4i93AfKAXiJyD15c2UAP4HXgBhEZB5wDtAM2AX8ELhORYuBcoBXwvH+8lcAcEfkdMN9PWwX8SEQSeJXfdhHZAEwXkanAQlVdLiL/v71zj5eyrPb4d4EIKKAgCsoGFQ8HIxVEDPGepoZaqVGkUiGpdBGvR8wTUCIdlYBT4tFIQ0uORalkyvFWggEiigiheEGBkMSIW4ICcfmdP553YNhtNpu99zPvzLzr+/nMh3nf2TO/Z83LzJpnPetZay/Cj4j7zGwy8ERVg0/GeSVA0yZNGDEi+LqnJ0/m/WXLthfOfHPBAh564IHtmwY3bdzIrUOGMPDqq2nfoQPtKiqYN2cOXbt355SkrcQTkyaxds0a+g0YAMD8uXP53cMPMzTRWPfhh9wxfDiDbriBNgcfDMCY22/nxFNO4YSTTgLg0YkT2bx5M3379QNgzssv8+yTT3LTsGEAtGnblqcnT+b6m2+mVVJG547hwzmrd2+6H388ABMnTKBRo0Zc1LcvAC/OmMEL06Zx/Xe/C8Dfli9n7OjR3DRsGM2TNvG3DhnCBX36cHS3bgBMGD+e/Vu25PwLL6RdRQUA8+bM4aqklcV7S5cy7s47GTpiBI2bhPnssMGDuaR/f47s0gWA+8eN45CKCs457zwAnnvmGRa+9RYDBw0CYNHChYwfN47hI0durzAwbPBgBgwcSMdOnWhXUcGC116jU+fO25v87el1Arhr9Og9uk5tDz6YpydPrtN1Wr1qFWNuu63G16ldRQWN9t67TtcJYNqUKXt0ndpVVPDO22/X6ToBjBs7tsbXqV1FBZ/q1avW12nq1KkANG7cmF69ejF79mzWr18PQM+ePVm2bBl//etfAejcuTMNGzZkwYIFrF+/ns2bN3P44Yczc+ZMAJo2bUrPnj2ZNWsWGzZsAKBXr14sXryYDz74AIAuXbqwdetWRowaBez556ldRQV/Wby4TtdpTz9P7Tt04KTTTqvVdbpv/HhqhKSSuwGnA9OBfZLjqcBlhJBgy+TcA0B/Qrjxb0D75PwPgB8k9xcDXZP7/YEH8jR6AsOB94ADknNHAFcDi4AzknOtgH7JeIYl5xoD5wK/BJ7bnT0NQPvU8jZm1KhaP7cutzR0s2Srv8fFr1lbpkyZUuvnSsrMe9wgvMe79QelGk7cD1ijsH51JHAC0AL4CPiHmbUBeid/m1tuWmlmzYA+ea/THFhuZo2AS3MnzewISbMkDQNWAu3NrCOwSNKdwO+BY8zsEOBjSROAUUD3RGM/Sf8HXAt0i/IOJDwxaVLMly8q3SzZmpau2xqfTslspNCU63tcquHEp4BvmtmfgbeAF4F5wKuEUOEiQkgQSWvN7F5gPrAEeDnvdYYCs4C/JI/nutX9yMw6AQb8MXnt7wL9zGwz8AFhlnZ88rfbgM3At5LXeMzMmiTPvy6C/dtZu2ZNzJcvKt0s2ZqWrtsan8aNG6eiW67vcUnOxCRtktRb0jGSviTpdElTJfWX9AlJ50m6SNIDyd8PkfRvkj4j6TJJP0jO3yPp8OT5gyT1T85fJOloSUdJuiaZxd8m6ZOSukn6rKTVkp5OxtBNIUFktqTlkj6VnD9a0i9ivhe5OH2hSUM3S7ampeu2xue1115LRbdc3+OSdGKO4ziOA+7ESp75c+dmRjdLtqal67bG56CDDkpFt1zf45Ld7FxO1GWzc+MmTVKpTp2GbpZsTUvXba05td3svGXLljpVsq/tZudSe4+zsNnZge17irKgmyVb09J1W+Mzffr0VHTL9T12J+Y4juOULO7ESpx1KVXETkM3S7ampeu2xietFPtyfY99TawI8KaYjlN6eFPMuPiaWEYYlNQ8y4JulmxNS9dtjc/s2bNT0S3X99idWImTKwybBd0s2ZqWrtsan1yR4EJTru9xqZadchzHSZXahvVGjBrFeUmF/NpQ2zDm1KlTa/3cYg5h+ppYEVCXNbFWrVuzeuXKeh1Psepmyda0dN3W4tetrSPasGEDTZs2rdVza+vE6mKrr4llhBNPOSUzulmyNS1dt7V8dZctW1ZwzULY6k6sxMk1R8yCbpZsTUvXbS1f3VyDzkJSCFvdiTmO4zglizuxEufRiRMzo5slW9PSdVvLV7dz584F1yyEre7ESpzNmzdnRjdLtqal67aWr27Dhg0LrlkIW92JlTh9+/XLjG6WbE1L120tX90FCxYUXLMQtroTcxzHcUoWd2IlzpyXX86MbpZsTUvXbS1f3bZt2xZcsxC2+mbnIqAum52bt2iRSnXqNHSzZGtaum5r8evWdrPzpk2bal1Bv7abnetiq292zgg3DRuWGd0s2ZqWrttavrozZ84suGYhbM20EzOzrWY218zmmdkcMzsxOX+Ymb2W9vgcx3Gc6sl6AeANkroBmNk5wG3AaekOac9YvWpVZnSzZGtaum5r+erWtm5iXSiErZleEzOz9ZKaJfe/BFwq6QIzOwx4QtJRZtYEuAfoAWwBrpc0pZrz/YELgcbA4cBDkm6pbhzeFNNxnJqSRjPONKrY13RNLOszsaZmNhdoAhwMnFHF33wHQNLRZnYk8IyZ/Xs15wE+BRwFfAy8bGaTJe3UCc/MrgSuBGjapAkjRowA4OnJk3l/2TIuGzgQgDcXLOChBx5g+MiRAGzauJFbhwxh4NVX075DB9q0bcvQG2+ka/funJK0d3hi0iTWrllDvwEDAJg/dy6/e/hhhiYa6z78kDuGD2fQDTds7/cz5vbbOfGUU7bXOnt04kQ2b968fZ/HnJdf5tknn9we427ZqhXfGTCA62++mVYHHADAHcOHc1bv3nQ//ngAJk6YQKNGjbiob18AXpwxgxemTeP6734XgL8tX87Y0aO5adgwmrdoAcCtQ4ZwQZ8+HN2tGwATxo9n/5YtOf/CC2nTti0P/+pXzJszh6uSZnvvLV3KuDvvZOiIETRuEn4KDBs8mEv69+fILl0AuH/cOA6pqOCc884D4LlnnmHhW28xcNAgABYtXMj4ceMYPnIkDRo0YNu2bQwbPJgBAwfSsVMn2rRty/DvfY9OnTtzxtln1+o6Adw1evQeXacDWrfmW/371+k6rV61ijG33Vbj69SmbVsee+SROl0ngGlTpuzRdWrTti2333JLna4TwLixY2t8ndq0bcvSJUvqfJ329PPUpm1bnnz88Vpfp6lTpwLQrl07KioqmDVrFgDNmjWjR48ezJw5k02bNgFw8skn8/bbb7NixQo++ugjevbsyaZNm1i4cCEA7du3p02bNtsbdbZo0YLu3bszffp0tmzZAsCpp55KvwEDavV5OviQQ1j+/vu1uk73jR9PjZCU2RuwPu9+L+B1wIDDgNeS85OAM/L+bhpwTDXn+wO/zDs/HLi2unE0AO1Ty9uYUaNq/dy63NLQzZKt/h6Xp2Z96NaWKVOm1Pq5adjaINi62+/xTCd25CNpJtAaOLDSQ7uazlY3za08389uzNZxHCcivia2Y03sSGA60AZoz441seuBT0r6RhIufBbIhROrOn8x8F+EcOIGYBYwQJXCifn4PrHi1cyartta/Lq+T2xnsj4Ta5qk2M8FJgJfl7S10t/cDTQ0s/nJ3/SXtKma8xCc4YPAXOCR6hxYXTmrd+9YL110ulmyNS1dt7V8dRcvXlxwzULYmmknJqmhpG7Jraukycn5JZKOSu5vlNRf0tGSjpU0pbrzCSsknSeps3aTmVhXcou+hSYN3SzZmpau21q+uh988EHBNQtha6admOM4jlPaZHpNrFioy5rY0d26MX/u3HodT7HqZsnWtHTd1uLXre2a2IoVKzjooINq9dzaronVxVZfE8sIjRo1yoxulmxNS9dtLV/drVsrL/fHpxC2uhMrcXIbibOgmyVb09J1W8tX96233iq4ZiFsdSfmOI7jlCzuxEqcF2fMyIxulmxNS9dtLV/ddu3aFVyzELZ6YkcRUJfEjlatW7N65cp6HU+x6mbJ1rR03dbi161tYseGDRtqXcm+tokddbHVEzsyQq5IaxZ0s2RrWrpua/nq5goFF5JC2OpOzHEcxylZst6KpeT52/LlmdHNkq1p6bqtxa9b29DeoBtu4LykvcyeUtsQ5uzZs2v93B49etTo73xNrAjwppiO4xQzaTTi7NGjB7Nnz/Y1sXIn11QvC7pZsjUtXbfVdeuTmTNnRtdwJ1bi5DrtZkE3S7ampeu2um59kuswHRN3Yo7jOE7J4mtiRUBd1sQaN2nCpo0b63U8xaqbJVvT0nVbXbcqarsmtmXLFvbaq3b5g74mlhEu6NMnM7pZsjUtXbfVdeuTt99+O7qGO7ES5+hu3TKjmyVb09J1W123PlmxYkV0DXdijuM4Tsnia2JFQF3WxI7s0oU3Fyyo1/EUq26WbE1L12113aqo7ZrYypUrad26da2e62tiGWH/li0zo5slW9PSdVtdtz7xFHtnt5x/4YWZ0c2SrWnpuq2uW58sXLgwuoY7sXrAzNbv4vwDZpZOKpLjOE4GcCdW4kybMiUzulmyNS1dt9V165P27dtH13AntoeY2fVm9lpyu7bSY2Zmd5nZAjObDBwUezzz5syJLVE0ulmyNS1dt9V165M2bdpE1/BWLHuAmR0HXAb0BAyYZWbP5/3JhUBn4GigDbAAGL+L17oSuBKgaZMmjBgxAoCnJ0/m/WXLuGzgQADeXLCAhx54gOEjRwKwaeNGbh0yhIFXX037Dh1oV1HBzdddR9fu3TklabPwxKRJrF2zhn4DBgAwf+5cfvfwwwxNNNZ9+CF3DB/OoBtuoM3BBwMw5vbbOfGUUzjhpJMAeHTiRDZv3kzffv0AmPPyyzz75JPbi4i2aduWK/r14/qbb6bVAQcAcMfw4ZzVuzfdjz8egIkTJtCoUSMu6tsXCK3KX5g2bXujvL8tX87Y0aO5adiw7XXdbh0yhAv69Nm+p2XC+PHs37Il5194Ie0qKvj1gw8yb84crrrhBgDeW7qUcXfeydARI2jcJOR4Dhs8mEv69+fILl0AuH/cOA6pqOCc884D4LlnnmHhW28xcNAgABYtXMj4ceMYPnIkDRo0YNu2bQwbPJgBAwfSsVMn2lVUMOymm+jUuTNnnH12ra4TwF2jR+/RdWp78MFcfumldbpOq1etYsxtt9X4OrWrqOCRiRPrdJ0g/Orfk+vUrqKCEUOH1uk6AYwbO7bG16ldRQWL3nmnztdpTz9P7SoqeHzSpDpdp9p8ntpVVHDH8OG1uk7z589n1apVAHTt2pV169axaNEiAA477DBatWrFnMRJtmzZkq5du/L888+zbt06mjdvzmmnnca8efNYs2YNAN27d2f16tUsWbIEgI4dO9K8eXPmzZsHwAHJe1AjJPmthjfgGmB43vGtwNXA+uT4x8CAvMcfBfrs7nUbgPap5W3MqFG1fm5dbmnoZslWf4/LU7NUdWvLlClTav3c4447Trv77ky+P509oCbd6Aq68e69pUsLKZeqbpZsTUvXbXXd+qRFASrn+2bnPcDMugMPACeQhBOBrwLTJDUzs4uAgcC5hPWwBcAVkh6u7nW9KabjOMWMN8UsEyTNITixlwgO7D5Jr+b9ySRgITAfuAd4vvJr1De5uHyhSUM3S7ampeu2um59Mn369Ogantixh0gaA4ypdK5Z8q+Aqwo5ntzia6FJQzdLtqal67a6bn2yZcuW6Bo+E3Mcx3FKFl8TKwLqsiaWSzUuNGnoZsnWtHTdVtetitquiW3bto0GDWo3V/I1sYxwSf/+mdHNkq1p6bqtrlufvP7669E13ImVOLkNvVnQzZKtaem6ra5bn+Q2SMfEnZjjOI5TsviaWBFQlzWxIzp14t0CtDsoBt0s2ZqWrtvqulVR2zWxNWvW0LKWfcx8TSwjHFJRkRndLNmalq7b6rr1ybp166JruBMrcXLFUrOgmyVb09J1W123PskVCY6JOzHHcRynZPE1sSLAzP4O/KWWT28NrKzH4RSzbpZsTUvXbXXdYtE8VNKBu/sjd2IljpnNltQjC7pZsjUtXbfVdUtN08OJjuM4TsniTsxxHMcpWdyJlT4/y5BulmxNS9dtdd2S0vQ1McdxHKdk8ZmY4ziOU7K4E3Mcx3FKFndiTo0wsz/W5JzjOE4h2SvtATjFjZk1AfYBWptZSyBXkLMFcEhqAysQZnYQsL0+s6SlBdJtJWl1IbQKjZk1AP4s6aiUx9FaUhqbjguKmXUHTgYEzJA0J+Uh1Ss+EysxzGwfMxtqZvcmx53M7PyIkgOBV4AjgTnJ/VeAx4D/iaibKmb2eTNbCCwGngeWAE9G0jrJzN4ws9fNrKeZPQvMNrP3zKxXDM1Et7WZfd/MrjazZmZ2j5m9ZmaPmdm/xdKVtA2YZ2YdYmlUxsx6m9liM5tuZsea2evALDNbZmZnFkB/pJm1MLNGZvZHM1tpZv0KoDsM+AVwAKF6xv1mNiSS1kXV3WJogmcnlhxmNpHgRL4m6SgzawrMlNQtsu4gSWNjahQTZjYPOAP4g6RjzezTwMWSroyg9RLwDaAZ8DhwgaTpyS/osZJOqm/NRPcZYDbQHDgTuD/RPwW4VNLpMXQT7eeA44GXgI9y5yV9PpLeXOBiYH/gCeA8SS+a2SeA/5XUPYZuvr6kbmZ2IXABcB0wRVLXyLpvAMdK2pgcNwXmSPpEBK37k7sHAScCzyXHnwamSoriyDycWHocIamvmV0MIGmDme225049MM7MrgZOTY6nAuMkbY4pmoQwO7FzSO9PMTUTNktaZWYNzKyBpClmdkckrUaS5kOooylpOoCkOcmXTizaSPrP5P/PXyT9KDn/ppl9J6IuwC2RX78y2yS9AWBmH0t6EUDSG0l4MzaNkn/PBX4laXVhPrYsIXx2NibHjYF3YwhJugzAzJ4AukhanhwfTMSojTux0uOfyRebAMzsCGBTAXTvJnwQ706OvwrcA1weS9DMLgeuASqAucAJwEzCDCk2a82sGfAn4H/NbAWwJZJW/pfozZUe2zuSJsBWAEkys8prQ9si6iLpeTM7FOgk6Q9mtg/QMKLkWjMbSFjLXWNm1wG/AT4DrI+om+NxM3sT2AB828wOZIdjickm4PUkRC3gLGC6md0JIOnqCJqH5RxYwt+Af4+gA3g4seQws7OAIUAX4BngJKC/pKmRdedVDn1Uda6eNecTQk4vJqGYI4FbJPWNpZmnvS/hS8aAS4H9CGGnVRG0Pk8IW35c6fwRwBcljaxvzeT11xKctBFCiLkZrgEnS6pdS96aaV8BXAm0knSEmXUCfiopyvqUmbUnfG4E/IAQWvwGoXvEf+RmaTFJogofStqaOO0Wkj6IrPn16h6X9IsImncRoie/IrzfXwHekTSovrXAnVjJYGYnSZphZo0JaycnEL5sXixEhpWZzQG+JOnd5Lgj8HDMtQQze1nS8cl6Rk9Jm3JrC7E0s4SZnVbd45Kej6g9F/gUMEvSscm5+ZKOjqWZJmb2JeApSeuSxIruwIhCZAqa2d7smAm9FXsJING8iPDDCOBPkibF0vJwYulwJ3AcIYmjOzC5wPo3AlPMbBHBeR4KXBZZc5mZ7Q/8DnjWzNYA78cUNLN1JKHaqpDUIoJmQ0JYtoLwRTcj77EhkkbUtybEdVI1YJOkf+bWhcxsL6p53+tK8qNrCOH/z+3AfwO9gDeAGyUtiaWdMFTSb83sZOAcYBQhHN8zpqiZnU7ITlxC+Ny2N7Ovx15XlvQo8GhMjRw+EysRzOxFwgfuPODXlR+PFNuuPIbGQGfCh+FNSYVYi8tpn0YI6T0l6Z8F0BsOfAA8yI6QYvMYoT0zu4+wF+8lwlrj85KuTx6bE2u2m4Tw/hNYA4wB7iX8en4X+Iak2TF0E+2RwFrga8Ag4NvAAknfi6T3J0J4az+gHyET8zfA2YRMzKjrrGb2apLlehswX9JDuXORdV8BLpH0VnL874TEkuMiaOV+ABo7/yAxwtJrvf8ABHdiJYOZtSYsQt8BDKv8eIzYdqJ7hqTndrXPI/nFFY1kltKGvKhBITYcm9ksST13d66etP4s6Zjk/l6E5JnWhHWbF2N90ZnZdOCXhGSH64Br2ZFiPyKGrXnaDQhrUmcTvuSelnRvRL3tDsPMlkrqUNVjEfWfAP5K+AwfR0jweKkAKfbb/29Vdy6Cbjd2DifOi6Xl4cTS4UZJN5lZh1gOaxecRtjv8bkqHhMRQwZmNgj4PiG7KZctJyDqBzBhq5ldSpj1iuBQtkbS2p6BKGkLcKWFTarPEdY/Y9FM0s8AzOybkn6bnH/WzH5UzfPqg0GSfkKY/ZGM4ZrkXAy2JbOQ/YB9zKyHpNkWNnXHzIrM8WXgs8AoSWuTtPMbC6A728x+TogoQIgovBJTMNmKcwXhu8GAB83s3lj7TH0mViIkmXrdCQvhUTdmFgtm9g4hoaPeMwJroH0Y8BNC9qeAGcC1MdZOzGwCMEHSU5XOXw7cI6lR1c+ss+72UGXlsGXMMOauXj/mjMhCVY67CT+GriDMPLsSZqFXSHoshm6e/hHAsiQ56XTCD7FfSlobWbcx8B1C2SkjZKDeHXMpwMz+DPSS9FFyvC9hLT/Kj093YiVC8sv4SmBfID8VO2682ez66h6XNCaGbqI9BTgrmZ049YyZfQy8Q/g/dERyn+S4o6R9I2heDFxC+FKdlvdQc2CrpM/Ut2Y1Y2kNrJEUa4adrzUX6AEcBjwN/B7oLOncAmjvTVjLFgXITsxtjdGOKiFNgJdjZZ56OLFEkHQjcKOZPSbpCwWUbp7825mwZ+v3yfHn2LGvKBaLgKlmNpm8Dd0xHWeOZDPqFYQvnfz1uAGxtSuN4yxJz0Z6+XovPVQDXgCWE9b8RuedXwf8OZZo8kX+FeCvkv5oZpcQSiMtSEJdsdPOt0nakqwt/1jSWDN7NbJmWtmJ9xPqUubS6i8Afh5LzGdiTo2wUGfvi5LWJcfNgd9K+mxEze9XdV5S9JJFZvYCYabwCnlrYZIeia1daRw7JSEUQK81sEpl9sVgZv9L+DGyDyErshlhzeZMwvdgtZuC60F/FvBj4HvA5yQtNrPXFLmSfyGzEyvp5irnGyGxI5rD9plYiWFmJwBjCb+i9yYsSn8UK5yYRwcgP7X9n4RZSjQK4ayqYR9JNxVCyMx+v6uHCNXHY+meQNgztRq4lbD43xpoYGZfq7xGV0+au9qHFzUsDhwt6Zgk+/OvwCEKlTMmANEy5/K4DPgm8MPEgR0OTCiAbqOcAwOQ9LaZRVljzSfZxF2Qli/uxEqPuwhhkd8SYuxfA6K1zcjjQeClJEQg4EJCenY0kl+N/8G/hvQKUTvxCTM7V9L/FUDrFMLepco1/IxQ1SIWdxH2ie1HyITsrVDZ/UjCnqp6d2KSmu/+r6LQIAkp7kuYje1HcN6N2VGcNxqSFgBXw/byU80l3R5blxSyEwuNhxNLDDObLalHpb1FL0g6sQDa3dl570fUmL6Fdig/5V9DetE/hMmMYV/CWtxmIs4UzOxJYKSkKVU89idJp1bxtPrQ3V7Cy8zeUF57jkLsnUp0CtJ01ELB30GEyMVo4AuENdcTCOXTos76zWwq8HnCj7G5wN/J29QeUbfg2YmFxp1YiZFUHvgMcB+hosRyQgHgKJsmzayFpA/NrFVVjyti92EzeyV27D7LpJxi/3mCMzkEWEEoY/aGpE9G1DwEQNL7FsqZfQZYKumlWJp52rmKHZcD7SV9vxCbjrOAhxNLj68SWndcRdjr0h74YkS9h4DzCbOhfyklA3SMqP24mX0bmMTOl++TOAAADC1JREFU2YnRHGc+llIvMzNrwc7h01j2djWzDwnXsmlyn+S4ya6fVi/cSpgF7dR0NKagpPy6m/sSynxhZnsVYBvHXskG5y8TkjuikqS5V1cDtGycp8/ESggLJZh+ISl6W/NiwMwWV3FakmI6zpx2lb3MYq7HWeh3NZxQkij3wSyIvYUmLyw+j9B5eJuZvSQpyhqgmd1MSHIYnhwvJWQp7k34TN0WQzdP/0vAUGC6pG9bKEj8I0lRfoBa6NW2SyT9JYZuGrgTKzHM7GlCim70IrhZxlLoZWZmCwmVDqK31kkbM/sDYf/Q7YQMzBWEDbJR1nYttBI6Ja+KRC6815CwNnVyDN1ioly3T3g4sfRYAsxI0rI/yp0sxAbgNDCzowgNQPNDelGzIhM2StpoZphZY0lvmlnnyJrvsnM1lrLDzK4llPC6gGDrtexoOjo8pnbOgSX8JDm31UKn9KgkVSu+AXySnf8vR9k8n8b2ibRwJ1Z6vJ/cGrCjmkZZkmx2Pp3gxP4P6A3kKq/HpuC9zICbgReSjbH5a4DR2+wUkAqCAzmSUKHjBYJTezzyWmczM2uUq8wh6QHYnr0Xe48lBCfyJqGX2HCC447ZTbrg2yfSwsOJTo3J24UvYIYid6VNQnpdgVcldTWzNsB9kqqqqB9zHAXpZWZmLxGc9Hx2VO2P1mYnTZI9Wz0IpZ96Jbe1krpE0vsvoC1wlaSPk3P7Av8DLJd0cwzdPP1c+PLPyabrRoT2M1HWWIth+0Sh8JlYiWBmP5Z0rZk9ThVZR5I+H1l/GPAldrReud/MfqtIXYcTNiQL/luSjL0VxM2GzIV9vknYQD4f+LkK1wF5S+x9Q0VEU8IMaL/k9j7h/Y7FUOCHwFIzyyU1dCDU9BsaUTdHrjbj2iRE/gFxK95sy7u/odJjZTVzcSdWOuR23I/KO5f7z2gF0L+YkEWWq0x9O6GsTEwnNjsJ6d1LSPFfT5IWHZFfEL5wphHCl10IWYqFYIqZXUloTFnwLQWFwMx+RlgXWgfMIoQTx0haE1m6OyGMeQvhB8rphCLW+xCcaez3+GfJlo2hhCLazaiiuW09kub2iYLi4cQSwcy+AFRI+p/k+CXgQIIju0k7GhrG0n8SuFhJ/6PEuUyQdH5M3Tz9w4AWkqJVOk905itpGZHU2Xsp5qbfStqpbSkoFGb2FCHB4DWCA5sJvBY7Yy7JTvyMpNVmdiqh2ekgoBvwCUl9Yuo78fCZWOkwmFAzMUduTWFfQuuDKE7MzMYSHOUm4HUzezY5PouwfhMVM2tHqOawV3J8auQNx9tbcii0zogotTOSDi+YWEpI+qyFN/WThPWwG4CjzGw1YR9elZ0L6oGGeTPavsDPFDoSPGKh11d0zOw8/jU7MWpGZhZwJ1Y67C3pvbzj6Qodj1clC9SxmJ38+wqhckaOqRE1ATCzOwhfOAvYUTtRxO1j1rVS6KVpXlgmSu3EHMli/7eAXK3EqcA4xe91VVCSWddrZrYW+EdyO59Q7DiaE8urzHEmocFsjujfg2b2U0Lo8tOEknF9iB8azwQeTiwRzOwdSVVWqzezdyUdUegxxcbM3gKOKadipdVhZvcRKqrnshG/Suh2fHl6o6pfzOxqwgzsJMKsdwYhpDgDmC9pWzVPr4vu94BzgZWEhI7ukmRm/0ao2HFSDN08/VxWYu7fZsCjks6OqZsFfCZWOswysysk3Zt/MilVVIgCpucTNk3mQnvRZyaEKuONyEtyKHOOr1TI+bmkLFM5cRjwMHCdpOWFEpX0QzP7I3Aw8EzeGlwDwtpYbHIZgh8nhYhXAWUfPi4E7sRKh+uA31loq57bn3UcoR/SBQXQ/zFwEeHXcuxF+Nw63MfA3OTLp1w3/+az1cyOkPQuQFJfb+tunlNSpLmFQNKLVZx7u0DyTyTJUD8ifH5FCCs6dcTDiSWGmZ1BWBwGeF3ScwXSnQKcGSvcU0mr2lbx5bj5F8DMziQk6SwizHQPBQYU6ho7hSGpEtJE0j/SHks54E7MqRFmdjwhnPg8O8+KyrJmYxokX24AnQlO7E2ArKwJljsp1gEtazyc6NSUHxI2GzchpPdHw8x+I+nLtoueSCqjXkiVmJnsSdu+Fy7Z31SQfWpOPFKuA1rWuBNzakqrAmZS5SpkFGQjddqYWVugHSGd/1h2VGBpQUjLdkqfPuyoA3pZrg5oymMqC9yJOTXlD2Z2tqRnYgvlstYqN+5Lej99BSibhn4J5wD9CRXeR7PDiX1IqETulD4FrwOaFXxNzKkRZraOUB1kE2F/T7QU++RD/h3C7OT3wLPAVcB/AHMlfaG+NYsBMxssaWSlc4dLqqoclVNCmNndhB8kXyFUKVlPmJVF6SeWJdyJOUWHmT0GrCFsgj0TaElYh7tGUkFKBKWBmc2pXKfRzF6RdFxaY3Lqj6QAcCfCulhzYF7kEmqZwMOJTo1Iiqb+C5E+hB3zivDeR1JlQdK6CFqpkzQq/CSwn5ldlPdQC8qs4nhWMbPLCWu9FcBc4ATCj7Qo/cSyhDsxp6bcmHe/CaHO3SvE+RDmF+HdamaLy9WBJXQmJLHsT2gPkmMdcEUqI3Lqm2uA44EXJX06+eFyS8pjKgvciTk1onI3ZTNrD4zcxZ/XldSK8KaBpMeAx8ysl6SZaY/HicJGSRvNDDNrLOlNM+uc9qDKAXdiTm1ZBhwV44UlNYzxuiXAe2Y2iVAcV4R9RNdIWpbusJx6YFlSdup3wLNmtobQzdqpI57Y4dSIvHqGEIqmdgOWSOqX3qjKi6RX20Ps6OLdD7hU0lnpjcqpb8zsNGA/4ClJ/0x7PKWOOzGnRlSqZ7iF4MBmpDWecsTM5lWqYo+ZzZXULa0xOU6x4+FEp0ZI+oWZHZjc/3va4ylT/m5m/YBfJccXE1p2OI6zCxqkPQCnuLHAD8xsJaEg7dtm9nczG5b22MqQAcCXgQ+SW5/knOM4u8DDiU61mNl1hI64V+YqRyR9ru4hxPT/O83xOY6TbdyJOdViZq8CZ0laWen8gYQOucemM7LyI/lx8BPCRlgRNsNeJ2lRqgNznCLGw4nO7mhU2YHB9nWxRimMp5x5CPgNcDBwCPBbdqyPOY5TBe7EnN1RXQqwpwfXLybpQUlbktsEquin5jjODjyc6FSLmW0FPqrqIUKLdZ+N1REza5XcHQysBX5NcF59gcaSbk1rbI5T7LgTc5yUMbPFBKdlVTwsSd53ynF2gTsxx3Ecp2TxNTHHKRLMbLaZfTupsec4Tg1wJ+Y4xcNXCN2sZ5vZr83sHDOrKsToOE6ChxMdp8gwswaE/mL3ANuA8cBPJK1OdWCOU4T4TMxxiggzOwYYDfwIeIRQeupD4Lk0x+U4xYoXAHacIsHMXiGk2P8c+K6kTclDs8zspPRG5jjFi4cTHadIMLOOXmLKcfYMn4k5TvFwQRV5HP8AXpE0N4XxOE7R42tijlM89AC+SchQbAdcCZwO3Gtmg1Mcl+MULR5OdJwiwcyeBr4oaX1y3Ax4GLiQMBvrkub4HKcY8ZmY4xQPHdi5qPJm4FBJG4BNVT/FcbKNr4k5TvHwEPCimT2WHH8O+JWZ7QssSG9YjlO8eDjRcYoIMzsOOJlQDHi6pNkpD8lxihp3Yo5TJOS1ZMlnnaTNBR+M45QI7sQcp0gwsyVAe2ANYSa2P7AcWAFcIemV9EbnOMWJJ3Y4TvHwFHCupNaSDgB6A78Bvg3cnerIHKdI8ZmY4xQJZjZbUo+qzpnZXEnd0hqb4xQrnp3oOMXDajO7Cfh1ctwXWGNmDQnV7B3HqYTPxBynSDCz1sD3yctOBG4hlJ7qIOmdFIfnOEWJOzHHKTLMrAWwLVe5w3GcXeOJHY5TJJjZ0Wb2KjAfeN3MXjGzo9Iel+MUM+7EHKd4GAdcL+lQSYcCNwA/S3lMjlPUuBNznOJhX0lTcgeSpgL7pjccxyl+PDvRcYqHRWY2FHgwOe4HLE5xPI5T9PhMzHFSxsxyTmsacCDwKDAJaA1clta4HKcU8JmY46TPcWZ2KPB14NOE9Ppc2vC/tHp2HGcH7sQcJ31+Sig51RHIr1qfc2Yd0xiU45QCvk/McYoEM7tH0rfSHofjlBLuxBzHcZySxRM7HMdxnJLFnZjjOI5TsrgTcxzHcUoWd2KO4zhOyeJOzHEcxylZ/h82KarYYG40swAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1136c4828>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aff_mat = model.affinity_matrix_\n",
    "plt.imshow(aff_mat, cmap=\"hot\", interpolation=\"none\")\n",
    "plt.xticks(range(len(data)), data[\"secret_name\"], rotation=\"vertical\")\n",
    "plt.yticks(range(len(data)), data[\"secret_name\"])\n",
    "plt.title(\"Affinity Matrix\\nMin = {0:.3f}, Max = {1:.3f}\".format(aff_mat.min(), aff_mat.max()))\n",
    "plt.grid(linestyle=\"--\")\n",
    "plt.gcf().set_size_inches(6, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Evaluate Clusters\n",
    "\n",
    "To evaluate the clusters, we will compute **silhouette scores**.\n",
    "\n",
    "The score `s(i)` for item `i` is defined as:\n",
    "\n",
    "$$s(i) = \\frac{b(i) - a(i)}{max(a(i), b(i))}$$\n",
    "\n",
    "Where:\n",
    "\n",
    "- `s(i)` = 0, if an item is the only item in its cluster\n",
    "- `a(i)` = mean distance from item `i` to members of its cluster\n",
    "- `b(i)` = mean distance from item `i` to members of the next nearest cluster\n",
    "- Any pairwise distance metric may be used\n",
    "\n",
    "The silhouette score for each item suggests how well it was grouped:\n",
    "\n",
    "- When `s(i)` is close to `1`, the item is much closer to its cluster than any others\n",
    "- When `s(i)` is close to `-1`, the item is much closer to another cluster than its own\n",
    "\n",
    "The mean of all silhouette scores provides a single metric of how well the dataset was grouped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group 0: Giraffe, Dumb idiot, Rihanna , Panda, foggybottom123, BBB, SG888, old\n",
      "Group 1: Walter, Bloop\n",
      "Group 2: aaaassss\n"
     ]
    }
   ],
   "source": [
    "data[\"cluster\"] = y\n",
    "for i, names in data.groupby(\"cluster\")[\"secret_name\"]:\n",
    "    print(\"Group {}: {}\".format(i, \", \".join(names)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cluster</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rap</th>\n",
       "      <td>4.250</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rock</th>\n",
       "      <td>3.750</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jazz</th>\n",
       "      <td>3.000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pop</th>\n",
       "      <td>3.875</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>2.125</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>electronic_dance</th>\n",
       "      <td>2.875</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classical</th>\n",
       "      <td>3.250</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rhythm_&amp;_blues</th>\n",
       "      <td>3.000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heavy_metal</th>\n",
       "      <td>1.875</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reggae</th>\n",
       "      <td>2.625</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cherries</th>\n",
       "      <td>4.000</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bananas</th>\n",
       "      <td>4.125</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apples</th>\n",
       "      <td>3.125</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pears</th>\n",
       "      <td>3.750</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coconuts</th>\n",
       "      <td>3.000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blueberries</th>\n",
       "      <td>3.625</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strawberries</th>\n",
       "      <td>4.125</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cantelope</th>\n",
       "      <td>3.000</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watermelon</th>\n",
       "      <td>3.875</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pineapple</th>\n",
       "      <td>4.125</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>national_air_and_space_museum</th>\n",
       "      <td>4.000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smithsonian_institution_building</th>\n",
       "      <td>3.500</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>american_art_museum</th>\n",
       "      <td>4.125</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>national_portrait_gallery</th>\n",
       "      <td>3.750</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>museum_of_natural_history</th>\n",
       "      <td>3.875</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>museum_of_american_history</th>\n",
       "      <td>3.625</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>national_postal_museum</th>\n",
       "      <td>3.125</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>museum_of_african_american_history_and_culture</th>\n",
       "      <td>4.000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>museum_of_the_american_indian</th>\n",
       "      <td>3.375</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>national_zoological_park</th>\n",
       "      <td>3.375</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cluster                                             0    1    2\n",
       "rap                                             4.250  3.5  4.0\n",
       "rock                                            3.750  4.0  2.0\n",
       "jazz                                            3.000  2.0  1.0\n",
       "pop                                             3.875  2.5  3.0\n",
       "country                                         2.125  3.0  2.0\n",
       "electronic_dance                                2.875  1.5  5.0\n",
       "classical                                       3.250  3.0  2.0\n",
       "rhythm_&_blues                                  3.000  3.5  1.0\n",
       "heavy_metal                                     1.875  2.5  1.0\n",
       "reggae                                          2.625  2.5  1.0\n",
       "cherries                                        4.000  4.5  4.0\n",
       "bananas                                         4.125  3.0  2.0\n",
       "apples                                          3.125  3.0  3.0\n",
       "pears                                           3.750  2.0  2.0\n",
       "coconuts                                        3.000  3.0  3.0\n",
       "blueberries                                     3.625  3.0  2.0\n",
       "strawberries                                    4.125  4.0  4.0\n",
       "cantelope                                       3.000  1.5  2.0\n",
       "watermelon                                      3.875  5.0  5.0\n",
       "pineapple                                       4.125  4.5  5.0\n",
       "national_air_and_space_museum                   4.000  4.0  4.0\n",
       "smithsonian_institution_building                3.500  4.5  3.0\n",
       "american_art_museum                             4.125  4.0  2.0\n",
       "national_portrait_gallery                       3.750  4.5  1.0\n",
       "museum_of_natural_history                       3.875  4.5  4.0\n",
       "museum_of_american_history                      3.625  4.0  4.0\n",
       "national_postal_museum                          3.125  1.5  3.0\n",
       "museum_of_african_american_history_and_culture  4.000  3.0  5.0\n",
       "museum_of_the_american_indian                   3.375  1.5  2.0\n",
       "national_zoological_park                        3.375  1.5  2.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"cluster\").mean().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0094560093170212075"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(X, y, metric=\"euclidean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>secret_name</th>\n",
       "      <th>cluster</th>\n",
       "      <th>silhouette</th>\n",
       "      <th>interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BBB</td>\n",
       "      <td>0</td>\n",
       "      <td>0.170708</td>\n",
       "      <td>belongs in cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foggybottom123</td>\n",
       "      <td>0</td>\n",
       "      <td>0.114770</td>\n",
       "      <td>belongs in cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Giraffe</td>\n",
       "      <td>0</td>\n",
       "      <td>0.093085</td>\n",
       "      <td>belongs in cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SG888</td>\n",
       "      <td>0</td>\n",
       "      <td>0.090263</td>\n",
       "      <td>belongs in cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rihanna</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036958</td>\n",
       "      <td>belongs in cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>old</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.030891</td>\n",
       "      <td>does not belong in cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dumb idiot</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.061021</td>\n",
       "      <td>does not belong in cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Panda</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.171522</td>\n",
       "      <td>does not belong in cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Walter</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.061826</td>\n",
       "      <td>does not belong in cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bloop</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.076507</td>\n",
       "      <td>does not belong in cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>aaaassss</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>inconclusive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       secret_name  cluster  silhouette              interpretation\n",
       "5              BBB        0    0.170708          belongs in cluster\n",
       "4   foggybottom123        0    0.114770          belongs in cluster\n",
       "0          Giraffe        0    0.093085          belongs in cluster\n",
       "7            SG888        0    0.090263          belongs in cluster\n",
       "2         Rihanna         0    0.036958          belongs in cluster\n",
       "10             old        0   -0.030891  does not belong in cluster\n",
       "1       Dumb idiot        0   -0.061021  does not belong in cluster\n",
       "3            Panda        0   -0.171522  does not belong in cluster\n",
       "6           Walter        1   -0.061826  does not belong in cluster\n",
       "9            Bloop        1   -0.076507  does not belong in cluster\n",
       "8         aaaassss        2    0.000000                inconclusive"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"silhouette\"] = silhouette_samples(X, y, metric=\"euclidean\")\n",
    "messages = []\n",
    "for score in data[\"silhouette\"]:\n",
    "    if score == 0:\n",
    "        messages.append(\"inconclusive\")\n",
    "    elif score > 0:\n",
    "        messages.append(\"belongs in cluster\")\n",
    "    else:\n",
    "        messages.append(\"does not belong in cluster\")\n",
    "\n",
    "\n",
    "data[\"interpretation\"] = messages\n",
    "data[[\"secret_name\", \"cluster\", \"silhouette\", \"interpretation\"]].sort_values(\n",
    "    by=[\"cluster\", \"silhouette\"], ascending=[True, False]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [\"overall\", \"music\", \"fruits\", \"museums\"]\n",
    "slices = [slice(0, 30), music_slice, fruits_slice, museums_slice]\n",
    "n_vals = range(2, len(data))\n",
    "scores = []\n",
    "for n in n_vals:\n",
    "    model = SpectralClustering(n_clusters=n, random_state=0)\n",
    "    y = model.fit_predict(X)\n",
    "    result = {}\n",
    "    #for metric in metrics:\n",
    "    for label, sl in zip(labels, slices):\n",
    "        score = silhouette_score(X[:, sl], y, metric=\"euclidean\")\n",
    "        result[label] = score\n",
    "    scores.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113995ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for label in labels:\n",
    "    plt.plot(n_vals, [s[label] for s in scores], label=label)\n",
    "plt.xlabel(\"Number of Clusters\")\n",
    "plt.ylabel(\"Silhouette Score\")\n",
    "plt.title(\"Spectral Clustering\\nSilhouette Scores by Category and Number of Clusters\")\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.gcf().set_size_inches(10, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Compare to PCA\n",
    "\n",
    "Spectral clustering is often compared to principal component analysis (PCA). What makes them different?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1131c3b38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "comps = pca.fit_transform(X)\n",
    "var_explained = 100 * pca.explained_variance_ratio_.sum()\n",
    "pc0 = comps[:, 0]\n",
    "pc1 = comps[:, 1]\n",
    "\n",
    "plt.scatter(pc0, pc1)\n",
    "for xi, yi, name in zip(pc0, pc1, data[\"secret_name\"]):\n",
    "    plt.text(xi, yi, name)\n",
    "plt.xlabel(\"Principal Component 0\")\n",
    "plt.ylabel(\"Principal Component 1\")\n",
    "plt.title(\"PCA(2)\\nVariance Explained: {0:.1f}%\".format(var_explained))\n",
    "plt.grid()\n",
    "plt.gcf().set_size_inches(8, 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a90c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(pca.components_.T, cmap=\"RdBu\", annot=True, fmt=\".1f\")\n",
    "plt.yticks(np.arange(len(features)) + 0.5, features, rotation=\"horizontal\")\n",
    "plt.gcf().set_size_inches(3, 10)\n",
    "plt.title(\"PCA(2), Feature Weights\\n\")\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.1"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
  }
 },
 "nbformat": 4,
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}
Timestamp	Secret Name	On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Rap]	On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Rock]	On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Jazz]	On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Pop]	On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Country]	On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Electronic Dance]	On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Classical]	On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Rhythm & Blues]	On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Heavy Metal]	On a scale of 1-5, how much do you like each genre of music? (1 is extreme dislike, 5 is extreme like) [Reggae]	On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Cherries]	On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Bananas]	On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Apples]	On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Pears]	On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Coconuts]	On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Blueberries]	On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Strawberries]	On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Cantelope]	On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Watermelon]	On a scale of 1-5, how much do you like each fruit? (1 is extreme dislike, 5 is extreme like) [Pineapple]	On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [National Air and Space Museum]	On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [Smithsonian Institution Building]	On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [American Art Museum]	On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [National Portrait Gallery]	On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [Museum of Natural History]	On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [Museum of American History]	On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [National Postal Museum]	On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [Museum of African American History and Culture]	On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [Museum of the American Indian]	On a scale of 1-5, how likely are you to visit each Smithsonian museum? (1 is extremely unlikely, 5 is extremely likely) [National Zoological Park]
2019/06/07 9:52:12 PM AST	Giraffe	5	4	2	5	1	2	2	1	2	1	2	5	1	4	3	2	4	1	1	5	5	3	3	3	3	3	1	3	4	4
2019/06/07 9:53:23 PM AST	Dumb idiot	5	4	3	5	3	4	4	5	1	3	5	5	4	3	3	4	5	3	4	5	4	5	5	5	4	4	1	5	3	2
2019/06/07 9:53:34 PM AST	Rihanna	5	3	4	2	1	1	3	5	1	4	5	4	5	5	3	5	5	4	4	5	2	4	5	5	3	4	5	4	4	1
2019/06/07 9:53:45 PM AST	Panda	5	5	3	5	4	4	5	3	3	3	3	3	1	1	3	2	3	1	4	3	4	3	5	3	3	4	5	5	2	3
2019/06/07 9:53:48 PM AST	foggybottom123	3	3	2	3	1	3	2	1	1	2	4	4	3	4	2	4	4	4	4	4	4	4	3	3	3	2	2	2	2	3
2019/06/08 3:49:55 PM AST	BBB	4	3	4	4	3	3	4	4	2	3	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5
2019/06/08 7:38:59 PM AST	Walter	4	4	1	3	5	2	2	3	3	1	4	3	3	1	1	1	3	1	5	4	4	5	3	4	4	3	1	2	1	2
2019/06/10 9:39:45 AM AST	SG888	2	4	2	4	2	3	4	2	2	3	4	3	3	4	4	5	5	5	4	2	5	2	3	3	5	3	3	3	3	5
2019/06/10 12:27:07 PM AST	aaaassss	4	2	1	3	2	5	2	1	1	1	4	2	3	2	3	2	4	2	5	5	4	3	2	1	4	4	3	5	2	2
2019/06/11 2:06:23 PM AST	Bloop	3	4	3	2	1	1	4	4	2	4	5	3	3	3	5	5	5	2	5	5	4	4	5	5	5	5	2	4	2	1
2019/06/11 3:47:39 PM AST	old	5	4	4	3	2	3	2	3	3	2	4	4	3	4	1	2	2	1	5	4	3	2	4	3	5	4	3	5	4	4