Mini_Project_Clustering_Result.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Customer Segmentation using Clustering\n",
    "***\n",
    "This mini-project is based on [this blog post](http://blog.yhat.com/posts/customer-segmentation-using-python.html) by yhat. Please feel free to refer to the post for additional information, and solutions."
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Setup Seaborn\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "The dataset contains information on marketing newsletters/e-mail campaigns (e-mail offers sent to customers) and transaction level data from customers. The transactional data shows which offer customers responded to, and what the customer ended up buying. The data is presented as an Excel workbook containing two worksheets. Each worksheet contains a different dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>offer_id</th>\n",
       "      <th>campaign</th>\n",
       "      <th>varietal</th>\n",
       "      <th>min_qty</th>\n",
       "      <th>discount</th>\n",
       "      <th>origin</th>\n",
       "      <th>past_peak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>February</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>144</td>\n",
       "      <td>32</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>44</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   offer_id  campaign            varietal  min_qty  discount       origin  \\\n",
       "0         1   January              Malbec       72        56       France   \n",
       "1         2   January          Pinot Noir       72        17       France   \n",
       "2         3  February           Espumante      144        32       Oregon   \n",
       "3         4  February           Champagne       72        48       France   \n",
       "4         5  February  Cabernet Sauvignon      144        44  New Zealand   \n",
       "\n",
       "  past_peak  \n",
       "0     False  \n",
       "1     False  \n",
       "2      True  \n",
       "3      True  \n",
       "4      True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_offers = pd.read_excel(\"./WineKMC.xlsx\", sheetname=0)\n",
    "df_offers.columns = [\"offer_id\", \"campaign\", \"varietal\", \"min_qty\", \"discount\", \"origin\", \"past_peak\"]\n",
    "df_offers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the first dataset contains information about each offer such as the month it is in effect and several attributes about the wine that the offer refers to: the variety, minimum quantity, discount, country of origin and whether or not it is past peak. The second dataset in the second worksheet contains transactional data -- which offer each customer responded to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_name</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Smith</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smith</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_name  offer_id  n\n",
       "0         Smith         2  1\n",
       "1         Smith        24  1\n",
       "2       Johnson        17  1\n",
       "3       Johnson        24  1\n",
       "4       Johnson        26  1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df_transactions = pd.read_excel(\"./WineKMC.xlsx\", sheetname=1)\n",
    "df_transactions.columns = [\"customer_name\", \"offer_id\"]\n",
    "df_transactions['n'] = 1\n",
    "df_transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're trying to learn more about how our customers behave, so we can use their behavior (whether or not they purchased something based on an offer) as a way to group similar minded customers together. We can then study those groups to look for patterns and trends which can help us formulate future offers.\n",
    "\n",
    "The first thing we need is a way to compare customers. To do this, we're going to create a matrix that contains each customer and a 0/1 indicator for whether or not they responded to a given offer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set I</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Create a data frame where each row has the following columns (Use the pandas [`merge`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html) and [`pivot_table`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html) functions for this purpose):\n",
    "<ul>\n",
    "<li> customer_name\n",
    "<li> One column for each offer, with a 1 if the customer responded to the offer\n",
    "</ul>\n",
    "<p>Make sure you also deal with any weird values such as `NaN`. Read the documentation to develop your solution.</p>\n",
    "</div>"
   ]
  },
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     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>offer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adams</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allen</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anderson</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bailey</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baker</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "offer_id customer_name    1    2    3    4    5    6    7    8    9 ...    23  \\\n",
       "0                Adams  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 ...   0.0   \n",
       "1                Allen  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0 ...   0.0   \n",
       "2             Anderson  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 ...   0.0   \n",
       "3               Bailey  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0 ...   0.0   \n",
       "4                Baker  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0 ...   0.0   \n",
       "\n",
       "offer_id   24   25   26   27   28   29   30   31   32  \n",
       "0         0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "1         0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2         1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3         0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "4         0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## join the offers and transactions table\n",
    "df = pd.merge(df_offers, df_transactions)\n",
    "\n",
    "## create a \"pivot table\" which will give us \n",
    "## the number of times each customer responded to a given offer\n",
    "matrix = df.pivot_table(index=['customer_name'], columns=['offer_id'], values='n')\n",
    "## fill NAN as 0 and treat columns as index\n",
    "matrix = matrix.fillna(0).reset_index()\n",
    "matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Means Clustering\n",
    "\n",
    "Recall that in K-Means Clustering we want to *maximize* the distance between centroids and *minimize* the distance between data points and the respective centroid for the cluster they are in. True evaluation for unsupervised learning would require labeled data; however, we can use a variety of intuitive metrics to try to pick the number of clusters K. We will introduce two methods: the Elbow method, the Silhouette method and the gap statistic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing K: The Elbow Sum-of-Squares Method\n",
    "\n",
    "The first method looks at the sum-of-squares error in each cluster against $K$. We compute the distance from each data point to the center of the cluster (centroid) to which the data point was assigned. \n",
    "\n",
    "$$SS = \\sum_k \\sum_{x_i \\in C_k} \\sum_{x_j \\in C_k} \\left( x_i - x_j \\right)^2 = \\sum_k \\sum_{x_i \\in C_k} \\left( x_i - \\mu_k \\right)^2$$\n",
    "\n",
    "where $x_i$ is a point, $C_k$ represents cluster $k$ and $\\mu_k$ is the centroid for cluster $k$. We can plot SS vs. $K$ and choose the *elbow point* in the plot as the best value for $K$. The elbow point is the point at which the plot starts descending much more slowly. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set II</h3>\n",
    "\n",
    "<p><b>Exercise:</b></p> \n",
    "<ul>\n",
    "<li> What values of $SS$ do you believe represent better clusterings? Why?\n",
    "<li> Create a numpy matrix `x_cols` with only the columns representing the offers (i.e. the 0/1 colums) \n",
    "<li> Write code that applies the [`KMeans`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) clustering method from scikit-learn to this matrix. \n",
    "<li> Construct a plot showing $SS$ for each $K$ and pick $K$ using this plot. For simplicity, test $2 \\le K \\le 10$.\n",
    "<li> Make a bar chart showing the number of points in each cluster for k-means under the best $K$.\n",
    "<li> What challenges did you experience using the Elbow method to pick $K$?\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
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vfrUl8gAAAABUrMUutXjjjTfyxhtvZP78+enS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\ndMkll1ySL33pS3nyySfz9ttvp1evXs3aa+nSpVmyZElzI1dVuVyudgT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119eb9ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "\n",
    "# slice matrix to contain 0 and 1 dataframe\n",
    "submatrix = matrix[matrix.columns[2:]]\n",
    "kms = KMeans(n_clusters=5, random_state=42)\n",
    "kms.fit(submatrix)\n",
    "\n",
    "# get cluster assignment labels\n",
    "labels = kms.labels_\n",
    "\n",
    "# format results as a DataFrame\n",
    "matrix['cluster'] = pd.DataFrame([labels]).T\n",
    "\n",
    "# count clusters\n",
    "results = matrix.cluster.value_counts()\n",
    "\n",
    "\n",
    "results.plot(kind='bar')\n",
    "plt.title(\"Number of points in each cluster for k-means\")\n",
    "plt.xlabel(\"Cluster\")\n",
    "plt.ylabel(\"Number of points\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Challenges of Elbow method for k-means\n",
    "#### The idea of the elbow method is to subjectively select a range of values (e.g. 2 to 10) to run k-mean cluster for a dataset. However, sometime dataset may not have clear cluster patterns. The elbow method may not be useful to help us to do cluster for a dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing K: The Silhouette Method\n",
    "\n",
    "There exists another method that measures how well each datapoint $x_i$ \"fits\" its assigned cluster *and also* how poorly it fits into other clusters. This is a different way of looking at the same objective. Denote $a_{x_i}$ as the *average* distance from $x_i$ to all other points within its own cluster $k$. The lower the value, the better. On the other hand $b_{x_i}$ is the minimum average distance from $x_i$ to points in a different cluster, minimized over clusters. That is, compute separately for each cluster the average distance from $x_i$ to the points within that cluster, and then take the minimum. The silhouette $s(x_i)$ is defined as\n",
    "\n",
    "$$s(x_i) = \\frac{b_{x_i} - a_{x_i}}{\\max{\\left( a_{x_i}, b_{x_i}\\right)}}$$\n",
    "\n",
    "The silhouette score is computed on *every datapoint in every cluster*. The silhouette score ranges from -1 (a poor clustering) to +1 (a very dense clustering) with 0 denoting the situation where clusters overlap. Some criteria for the silhouette coefficient is provided in the table below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<pre>\n",
    "\n",
    "| Range       | Interpretation                                |\n",
    "|-------------|-----------------------------------------------|\n",
    "| 0.71 - 1.0  | A strong structure has been found.            |\n",
    "| 0.51 - 0.7  | A reasonable structure has been found.        |\n",
    "| 0.26 - 0.5  | The structure is weak and could be artificial.|\n",
    "| < 0.25      | No substantial structure has been found.      |\n",
    "\n",
    "</pre>\n",
    "Source: http://www.stat.berkeley.edu/~spector/s133/Clus.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fortunately, scikit-learn provides a function to compute this for us (phew!) called [`sklearn.metrics.silhouette_score`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html). Take a look at [this article](http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html) on picking $K$ in scikit-learn, as it will help you in the next exercise set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set III</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Using the documentation for the `silhouette_score` function above, construct a series of silhouette plots like the ones in the article linked above.</p>\n",
    "\n",
    "<p><b>Exercise:</b> Compute the average silhouette score for each $K$ and plot it. What $K$ does the plot suggest we should choose? Does it differ from what we found using the Elbow method?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('For n_clusters =', 2, 'The average silhouette_score is :', 0.40108724865480644)\n"
     ]
    },
    {
     "data": {
      "image/png": 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2xtrammfPnrFv3z4GDhyYZh761Oqg/Zeec/To0SM1mK3RaChSpAh//fUXn332\nWZrrCiGE+O+QHtpCCPERyZUrF3v27CEmJoazZ8/So0cPvddMdRmbnpV0ez9pB4DUevjwITdu3FDz\nP2ppf6QmDzbpDvalpTsI2YYNG3B0dASSAgLnz5+nWLFiej3ytAGD5D94dQMVuvN0yz969Kj6Y+jV\nq1fcvHmTokWLGnwVWkvbe1pRFIYOHcqAAQOApIHU3ta+ffu4ePEiiqIwdepUNeWDNjdwerVv354d\nO3bw6tUr3NzceP78OYqiqMEISDoXV69eJTw8nKZNm9K3b180Gg0XLlxg8ODBPHr0iNWrV/Ptt98a\n3IZGo+H69euEh4djaWmJm5sbkJTbcs6cOezcuZOzZ89y7tw59Ryml5WVFZ999hmPHz8mLCyMmJgY\nNYCl0WgICgoCkq4r3d6lWpnpraU7qFetWrVo3Lgxvr6+as5lY4OoAXoDjT179gxFUYymG8mdO7fa\nM7N27dr8+eef6rybN29iYmKipgZ4/fo1ixYtUvOXenh4AElBEUP5dzPibc8/JLWlK1eucPPmTaKi\notRgyPnz55k2bRpFixalc+fORs+/bvAqPfeGf/75Rx0QcOrUqUBST9Q///yT5cuXc/PmTY4cOZJq\nD0bd9v/777/TsmVL9f/DwsIoXLiwwYEw0woaZ5b2XAOEhITo5YLVpnVIy9u2l8zIaPufP38+iYmJ\nFC1alM2bN6vlaNd7l0JDQ4mPj1fPYVhYmDpPd/De5HTnjRw5Ur3XA1y8eJHPP/88zVQJBQsWVNtZ\ndHS0+tn1zz//pOg1/TbtXfczzsPDQx3AdPPmzWq6D23e6/Ro1KgRU6ZMIS4ujmXLlqkpN7744gsK\nFizI7du3WbRoEZAUgHVwcEi1vLQeOnysvvzyS7Xu58+f15u3bt06fHx8KFq0KEOHDjV6LRi7z8XE\nxKifzVrx8fFcu3aNGzdu8OWXX/LHH38ASW8STJ48GX9/f7y9vZk4cSKfffZZln/PSX6fe/36NVeu\nXCE8PJwqVaqog5lev36dUaNGcenSJdatW8eoUaOMnuMtW7ak+TAgrfvrmzdvGDRokPrGWokSJVi9\nerXeGyVCCCEESA9tIYT4qJiZmal5j1+8eEG/fv04evQot27d4syZMwwbNox79+6pA7O9a4ULF6Zq\n1apoNBqOHDnCrFmzuHLlCkFBQQwZMkTtcaQbmNGmXoiKimL79u1qkMXQQEBNmzZVf2hNmTKFoKAg\nbt26xezDv4VEAAAgAElEQVTZs+nUqRNVq1bVG4hLu+zz58+5fPmyOpCf7o/IixcvcunSJWJiYmjd\nurU6fezYsYSGhnL9+nV++ukn2rZti6OjY6qvvuoGFo4dO8bVq1cJCgrSy8uamV7Jycv28fEhPDyc\nI0eOMHfuXHV6enqJVatWTf1Bqw0eFSxYUO9V+8uXL/Ptt98yceJERo8ezZkzZ7hz5w73798nNjYW\njUZj9DV6rSFDhvD9998zYsQIdu7cye3bt7l//75ertLMBgI7dOgAJL2hMHToUEJDQ7l48SLjxo1T\nBzzr0KFDit54WWX8+PHqtRUYGMjWrVuNLuvg4KAONKelzcVtSOvWrdFoNAQEBLB06VLCw8M5deoU\nPXr0oHHjxjg7O/PmzRvi4uLU3qWXLl1SUzWMHz9eDYxk9lrLivOvfeASExPD6NGjuXDhAufOnWPa\ntGmEhoayc+fOVAMOefLkwczMTD0Wly9fJj4+nnXr1hESEpJi+SlTpuDq6sro0aNZtWoVN2/e5OHD\nhzx8+FBdRnu96QZr/vnnH65fv86DBw+oVauWmrd57ty5+Pn5cfv2bVasWEGnTp2oXr36ewmyapUu\nXZoSJUqg0Wjw9vbmzz//5MaNG2zbtg13d/d0l/Mh2ktG2n90dDQajYZ79+7h5+fH9evXmTVrFteu\nXUNRlExfx+nx4MEDRo8ezaVLlwgMDNR74yW1VBklS5ZU84//+eefeHl5cfv2bXbt2kX79u2pU6eO\n3jgThmjLj42NZdiwYYSFhXHmzBl++OEHFEXRC0JmtL3rXuPnz59XA/W6nyPbt2/n5s2bbN68WW9Q\nxLSOt52dnfogSvu2iPbBXZ06ddBoNOrDu/SkGzHWHj92NjY21KpVC41GQ3BwMH/88QfXr1/nyJEj\nuLu7ExQUxJEjR1J9sKGbemrHjh28ePGCyMhIpk6dmuI8vHnzhh49evDDDz8wfPhwfH19uX37Ng8e\nPFDHwVAURX1o+66+52g9efKELl26MHbsWEaMGMGJEyfU+rx8+RJAHbDUmKzIob1s2TIuXLiAoihY\nWFgwY8YM4uLi1Pv/w4cPszSPuRBCiE+X9NAWQoiPzA8//MC5c+c4f/4858+f1+spBqg9oocOHfpe\n6uPm5kb37t158OABq1atYtWqVXp16dWrl94ryK1atVJ7CU6cOFFN6VCpUqUUgavPP/+coUOHMnfu\nXC5duqQ3eKKiKJQrV04d+BCSAkI3btzg1atXtGnTBmdnZ5YsWaKX73LNmjWsWbOGrVu3Ur58eb79\n9ls2btxIQEAAAQEBeuXXr1+fhg0bGt33qlWrkjdvXiIiIjh16pT6w1G3p3hmBp8CqF+/PnPmzOH1\n69d4e3urPbN1fywmL9vYq9jt27dn7ty56rq6vbO1+9GlSxc2bdpEWFiY2vNKK1u2bAwZMsRoXbW9\nyAcNGkRsbKyag1l3vrOzM+XLl09jrw0bPHgwISEhBAYG4u/vj7+/v17ZVatWZezYsZkqOz0KFChA\n7969WbJkCQC//fYbzs7ORgc+q169upoP2Vj+bC1XV1f8/Py4e/cu8+bNY968eeo8ExMTRo8ejZmZ\nGWZmZtSuXZuAgACePHnCd999B/zf69tvc6297fkH6NOnD35+foSGhuLn54efn586T1EUBg8enGoP\nWFNTU5o2bYqnp6fafrXXq6OjY4p7w4QJE+jVqxfPnj1j1qxZan5Z7fbKli2rtl1bW1s+++wznjx5\nwtmzZ2nZsiVjx46lT58+TJgwgbFjx3Lv3j0GDRqkV0aBAgXo06eP3nYzmwokvcaNG8egQYNISEjg\nt99+47fffgOS0kpdu3ZNrVtq3nd7yWj7b9q0KVu3biUmJibFMdcGRxMTE7MsPYQue3t7fHx89N50\nURSF9u3bp/rgCeDHH3+kf//+REZG6qV6UBQFW1vbNAPaffv2xcfHh2vXrumdFwsLC4oUKaKXJszK\nyipD7V33M2727NkoisKJEydo2rSpmiZk7ty56gNR3Wvo8ePHKR7CJdekSROCg4OBpPuSNo1SnTp1\n2LBhg5pbOj3pRlJrj+/D27ThyZMn07VrVyIiIliyZIn6mQBJD2y0b4sYU6lSJQoUKMDdu3e5dOmS\n+vaOlZUVJUqUUAcehaTBQ3/++WfGjRvHs2fPUnynUxSFrl27qg8K39X3HK0vvviC4cOHM2/ePG7d\nupXifGnfVHuX4uLi1EHGNRoNr169olOnTimWO3ToEPnz53+ndRFCCPHxkx7aQgjxkbGysmL9+vVM\nmDCBypUrkzt3brJnz06ePHmoUqUK48aNY+PGjSnyH2p/CCefpvvf1JY1tnyBAgXYs2cPgwcPpkSJ\nElhYWGBlZUXNmjVxd3dn3LhxemV07NiRiRMnUrBgQXLkyEGxYsWYOHEiU6ZMMbjd/v374+7uTo0a\nNbC2tsbc3JwiRYrg6urK6tWr9fKnfv/999SsWZOcOXNibW2tBhKLFi3KiBEjKFCgADly5NAbXGzy\n5MlMnz4dR0dHrKyssLCwoGTJkowdO5b//e9/qZ4La2trVq5cSd26dbG2tiZ37txUqlSJ2bNnU6dO\nHRRF4ejRoynytBo7trrTCxUqhIeHB1WrVsXKygobGxucnJxYvny52uNat1eVbg7K5FxcXNQcrYqi\n0KZNmxTL/PLLL/z2229Uq1YNOzs7smfPjq2tLc7OzqxcuTJFj//k26pRowZbtmzBxcVFPbcWFhaU\nLVuWMWPGpJlXPLXjY2pqysqVK5k6dSrVqlUjd+7cmJubU6ZMGcaNG8eqVatSvDJt7FhkdNtaAwcO\nJF++fCiKQmRkpF4vfEPHQjstebqR5MvmzZuXzZs306NHDwoWLIiZmRl58+alfv36rFq1ilatWqnL\nzp07lw4dOvD5559jYWFB8eLFGTx4MEOGDEFRFG7fvq0XFMtIO87o+U/OzMyMNWvWMGzYMIoXL06O\nHDmwsbGhatWqzJ07N8WgrobqNm3aNLp164adnR0WFhZUqVKFpUuX0rp16xTLlyxZkq1bt9KtWzcK\nFy6MhYUF5ubmlChRgkGDBrFmzRq1V7miKEybNo1SpUphbm6Ovb29mge7ZcuWrF69mgYNGmBjY4OZ\nmRlffvkl3bt3Z+PGjXo5WVNrY8mPZ1r7amz5unXrsnz5chwdHTE3N8fOzo6+ffvqBezTGpAyK9uL\nsc+I5DLS/idNmkSfPn3Inz8/5ubmFCpUiB49ejB58mQURSEuLo7jx4+nq24ZmQ5JKYQWLlxIyZIl\nMTMzo2DBgowePZrp06enud9OTk5s3ryZ5s2bY2dnh5mZGfnz56ddu3Zs2rSJ4sWLp3qMLC0tWbdu\nHZ07d8bW1hYLCwvq1q3L+vXrKV68eIp6Z6S916lTh+7du2Nvb4+5uTmlSpXi9evXtG7dmmnTplG8\neHHMzc1xcHCgadOmrFy5Un1gkJ7euU2aNFHrV6lSJSwtLYGkntrZs2dHUZIGSjQUGE++X4bao25q\nn4y0F2PSujYym/akUKFCbN++nU6dOpE/f35MTU1xcHCgSZMmbNiwgbp166ZaD23bdHZ2xsrKity5\nc/P111+zZcsWihQpkmL5Vq1asW7dOpo0aUL+/PkxMzPDysoKR0dHpk6dyqRJk9Rls+p7TmrHZsCA\nASxdupR69erh4OBA9uzZsba2pmbNmsyfP5/evXtn6rim14ULF9QUOsb+vYsHYUIIIT5NiuZdd0UR\nQgghxDv16NEjGjZsSEJCAk5OTqxZs+ZDV0kIkUxiYiIHDhwgX758ODg46A3+FxISQpcuXVAUhSlT\nphjslSgMu3v3Lo0aNUJRksYOmDlz5oeukhBCCCGEeMck5YgQQgjxCUpMTOTBgwfExcUxdepU4uPj\nURSFjh07fuiqCSEMyJYtGxMnTlTzHk+ZMoU6deoQERGh14uyQoUKH6qKQvyr6ObcT8tnn30mvX+F\nEEKIT4gEtIUQQohPUEJCgl5eTG1u9ZYtW37AWgkhUtOzZ08WL14MJKUJ0KXNRV2mTJkPUTUh/nUa\nNGiQ7mUlL7MQQgjxaZGAthBCCPEJMjU1pWzZsly/fp3cuXNTt25dxowZIz3MhPiIDRs2jHz58rFz\n506uXbtGdHQ0ZmZmFCpUiObNm7/zHLX/VunNBS7+W9J7Pch1I4QQQnx6JIe2EEIIIYQQQgghhBBC\niE+CdOMSQgghhBBCCCGEEEII8UmQgLYQQgghhBBCCCGEEEKIT4IEtIUQQgghhBBCCCGEEEJ8EiSg\nLYQQQgghhBBCCCGEEOKTIAFtIYQQQgghhBBCCCGEEJ8ECWgLIYQQQgghhBBCCCGE+CRIQFsIIYQQ\nQgghhBBCCCHEJ0EC2kIIIYQQQgghhBBCCCE+CRLQFkIIIYQQQgghhBBCCPFJkIC2EEIIIYQQQggh\nhBBCiE+CBLSFEEIIIYQQQgghhBBCfBIkoC2EEEJ8ZHbs2EHp0qUz9a9Hjx5qOdppmzZt+oB78/a6\nd+9O6dKlGTVq1FtN/zcLDAzkxIkTBuc9efKEv/766z3XKHPe1bmLiopi2bJlmVr34MGDfPvttzg5\nOeHo6EijRo04e/ZsltYvs/bv38/Fixf1pp08eVJt++Hh4R+oZp+Gf8s9Miu5u7tTunRp6tat+6Gr\nAsDFixfx9vb+0NUQQgghxEdGAtpCCCHERyZHjhzY2dml+Gdra4uiKCiKQq5cuQwukydPHr2yFEX5\nQHuRtYztR0an/xsNHTqUXr16cfv27RTz9uzZw9dff42np+cHqFnmZPW5CwgIoGnTpqxZsybD6x47\ndowhQ4YQEhJCVFQUOXLk4MmTJ+TPnz9L65hR0dHRdO/eneHDh/P06VODy/yX2sDbkOP08fr1119p\n3749Fy5c+NBVEUIIIcRHJvuHroAQQggh9LVo0YIWLVqkmH737l0aNWqEoij8+OOPuLi4fIDavX/5\n8+enSJEifP755x+6Kh8lX19fo0G5EydOEB0d/Z5r9HE5c+YMERER2NnZZXjdXbt2AWBra8vWrVvJ\nly9fVlcvU54/f86pU6ckGPuWihYtCkDu3Lk/cE2EIb6+vmg0mg9dDSGEEEJ8hCSgLYQQQoiPmpub\n24eugviPevr0KYqi4OTk9NEEs0XW8fLy+tBVEEIIIYQQmSApR4QQQgghhDAgPj4eAAsLiw9cE33S\na1UIIYQQQvyXSQ9tIYQQ4j/g1q1bLF68mICAAJ4+fYqNjQ1OTk7079+f0qVLG13v6NGjbNq0idDQ\nUJ4/f06uXLkoW7YsLi4utGrVKlMpD3x9fdm6dSvnzp3jxYsX5MyZkyJFivDVV1/RrVs3rKys9Jbv\n3r07p06domXLlsyZMydD24qKimL58uUcOHCAu3fvYmFhQZkyZejWrRuNGzc2ut7p06dZv349wcHB\nPHnyhJw5c1K8eHFatGhBx44dMTMz01temw4GwMPDw+CAaseOHaN///4oisLBgwcN5mH29PRk+/bt\nXLhwgZcvX5InTx4qVqxI586dadCggcHjoj0HkydPZvLkybRt25bq1aszYcIEddmQkBD1PF+6dEmv\nnAcPHrBixQqOHTvG/fv3yZYtG19++SXOzs707NkTW1vb1A5xCuPHj2fnzp307NmTESNG4O7uzv79\n+3n06BGfffYZVapUoW/fvqled8ZcvnyZNWvWcPLkSR4+fIiZmRmFCxemSZMmKa6dkydP6g2S+uTJ\nE3Wbhw4dSjUPtvbYau3YsYMdO3YAMGvWLL10Pxmpk1bp0qVRFIXdu3dz8OBB1q5dy8uXL8mXLx/j\nx4/H2dnZaN0aNmzIvXv31PPer18/ICmX+tChQ1MsHxAQwKpVqwgNDSUmJgYHBwe++uorBgwYYPTc\nxsfHs3XrVjw9Pbly5QqvXr0ib968VK1alW7dulGlShWj9Utu2bJlzJ07l5w5cxIQEJCi7Wj98ssv\nbNy4kTJlyqjHGpKC9/v372ffvn2cO3eOiIgINBoNNjY2VKxYkU6dOhlsb+k5xtrrYcqUKXTu3DlF\nGYcPH2bLli2EhYXp3f+++eYbWrduTbZs+n2DdK+5ffv2UaRIkRRlbty4kV9++QVFUVIM6BkVFcWa\nNWs4ePAg4eHhxMXFYWtrS8WKFXFxcVHvMRl1/fp1NmzYgL+/Pw8ePEBRFIoVK0bLli3p1q0bpqam\naZbh7u6Ou7s7dnZ2HD9+3OAyI0eOZN++fVSvXj1Fzvrbt2+zYsUKTpw4wf379zExMeHzzz/HycmJ\n7777Tu9+oL2HQFKO82XLlrFs2TKD5YaFhbF69WrOnDnD06dPsbS0pESJErRo0YIOHTqkuN6058jG\nxoYDBw4wffp0Dhw4AEDhwoVZuHAh+fPn582bN2zcuBFvb2+uXr1KTEwMefLkoUyZMrRs2ZJvvvkm\nxfkXQgghxPsjAW0hhBDiX+7YsWPMnDmT169fkyNHDhRF4fHjx3h6euLj48PSpUupXbu23jrx8fFM\nmjSJnTt3qoGzXLly8fLlS06cOIG/vz/btm3D3d3dYMDOmN9//x0PDw+1zNy5cxMdHU1oaCghISFs\n3bqVdevWpciXnZnA+d27d3FxceHu3buYmJhgZmbGixcvCAgIICAggO+//54hQ4akWG/69OmsXbtW\nr46vXr0iODiYM2fOsHHjRpYsWcIXX3yRYt3M5jSOjo5m2LBh+Pv762332bNnHD58mEOHDtGyZUtm\nz56NiYkJADY2NtjZ2fHkyRN1oNAcOXKQK1cuzM3NsbOzIyoqitjYWExNTbG2tk5RP19fX8aMGUNM\nTAyKomBubg7AlStXuHz5Mps2bWLBggVUr1493fuiHbg0Ojqabt26ceHCBbJnz46lpSX37t3j7t27\n7Nu3jxkzZtCmTZt0l7ts2TLmz59PQkICiqJgZWXFmzdv+Pvvvzl//jwbNmxg8eLFlClTBgBTU1Ps\n7Ox49eoVr169wsTEBBsbGxRFUY+hMdpj++LFC+Lj48mRIwdWVlYoikKOHDkyXafkFixYwIEDBzA1\nNcXExIRbt25RvHjxVOuWN29eYmNjiYiIQFEUrK2tMTU1JWfOnCmWdXd3x9PTE0VRsLS0JD4+nlu3\nbrFq1Sr27dvH1q1b+eyzz/TWefz4MQMHDuTChQt6g9A+evQILy8vPD096dOnD2PHjk21nlpt2rTh\njz/+4NWrVxw8eJDmzZunWCY+Ph5vb28URaFdu3bq9KioKFxdXdUHN9q6REVF8fDhQ3x8fPDx8WH4\n8OG4uroa3L6hY1yiRAl1vqE2GxcXx6hRo/Dx8dHbru79b+vWrSxcuDDFQLzGykxLZGQkXbp04caN\nG+o1amlpyaNHj/D19eXAgQN06tSJqVOnZqjc9evXM3PmTOLi4tRrNCYmhvPnz3Pu3Dn27NnDqlWr\nyJUrV4brnJz2WCUXFhZGnz59iIqKUu8ziqJw69Ytbt68yfbt25k1axbffPMNgDro8bNnz0hMTMTS\n0hJLS8sUx3ru3LksW7ZM3aZ2386cOcPp06fZsmULS5Yswd7ePkWdEhMTGTx4MKdOncLc3Jy4uDgi\nIyPJnz8/cXFx9OnTh9OnT6vnImfOnDx9+pTjx49z7NgxvLy8WLp0qeSxF0IIIT4QeawshBBC/Mv5\n+vri4ODAihUrCAkJITQ0lMWLF5M7d27i4+MNBkhmzZrFzp07sba25scff+TUqVOcPHmS4OBg5s6d\ni52dHYGBgYwePTrd9bh48SIeHh5ky5aNsWPHcvbsWYKCgggLC2PevHlYWFhw584d/vjjj7feZ41G\nQ0hICM+ePWPatGkEBwcTHBzMrl271J6AixYt4uHDh3rrLViwQA1md+zYkSNHjhAUFERwcDC//fYb\nefLk4erVqwwcOJCYmJi3rqfW6NGj8ff3x8HBgdmzZxMcHExQUBCnTp1iypQp5MyZEy8vL2bOnKlX\nV92ekqNHj+b48eNMnDiR5s2bc/z4cTV4WK5cOTUQoxUaGsqIESOIjY2lTZs2eHl5cfbsWYKDg9m5\ncyf16tXj+fPnDBkyhNu3b2dofzQaDTt27ODixYv079+fwMBATp48iaenJ1WqVCE+Pp4ff/yRK1eu\npKu8bdu2MXfuXBITE2nSpAn79+/n1KlThISEqA8XHjx4QP/+/Xn8+DEAlStX5vjx4/Tp0wdIClJr\nj4GhAJcu7bF1dHQEUI/nsWPH1GOamTol5+vrS5cuXTh9+jQhISGsXbuWAgUKpFq3LVu2sGXLFvX/\n586dy/Hjx+ndu3eKZb28vHB2dsbT05MzZ85w9uxZRo0aBcCjR49YuHCh3vJxcXEMGjSICxcuULx4\ncRYvXkxISAhBQUEEBgYycuRITE1NWblyJStXrky1nlr29vbqA7O9e/caXObo0aM8f/4cExMTWrZs\nqU6fPXu2GnCcOXOmWpewsDDWrFmjBqb/97//8fTpU4NlGzrGhh5G6Zo0aRI+Pj5ky5aNQYMGceLE\nCbU9Tpo0CXNzc06dOsXw4cPTdQzSY8GCBdy4cYP8+fOzfv16zp8/z8mTJzlx4gQdO3YEks796dOn\n013m8ePHmTp1KvHx8TRq1Ahvb29OnTpFaGgoM2fOxMzMjAsXLjBt2rQs2w9D6XB++uknoqOjqVu3\nLj4+Pup9Zu/evVSpUoXExESmTZumDmD7448/cvz4cbWdfvfddxw/fpwFCxaoZa5atYply5Zhbm7O\n0KFD8ff3V9vf8uXLKVy4MBcvXsTV1ZWEhIQUdXrx4gVnzpxh+vTpnD17ltOnTzN//nwA1q1bx+nT\np7G2tmbJkiWcO3eOoKAgzpw5w+DBg4GkB8XaQWOFEEII8f5JQFsIIYT4l8udOzfr1q2jVq1aQFIv\nOmdnZwYOHIhGo+HmzZvcv39fXf7GjRusW7eObNmysXz5cr777ju1F7aZmRnNmzdn2bJlmJiYcOTI\nEU6ePJmuegQEBABQqlQpevfurfYGzpYtG82aNWPQoEGYmZkRHh6eJfutKAp//PGH3mvnJUuWVIM3\niYmJap0AHj58qPYe79atG1OnTlV7ipuamtKqVSs8PDzInj07169f56+//sqSevr7+3P48GEsLCxY\nv349rVu3VnM2W1pa0qlTJ3777Tc0Gg0bN27McHDZmJkzZ5KQkEDXrl1xc3PTS49QqlQpli1bRvny\n5YmKimLRokUZLj8xMZG+ffsyatQo9fopWrQof/75J4UKFSIhIYG5c+emWU5sbCxz585Vr9uFCxdS\nsGBBIOkcN2jQgL/++ovcuXPz9OlT3N3dM1zXjMqqOjk4OPDzzz+rvb4zkspDK7V82o6OjixZsoSi\nRYsCSe23X79+NGjQAI1Gw4kTJ/SW3759O3///TcODg5s3LgRZ2dnte3kzp2bAQMGMHbsWDQaDYsW\nLSIqKipddWzXrh0ajYZjx47x8uXLFPO1gUFnZ2c1DcqrV6/UN0SGDx+Oi4uLWhcTExOcnJzUh3EJ\nCQmEhoYa3HZGj/H58+fZtWsXiqIwZswYhg8frvYMtrCwoFu3bup1e/LkySwbWDIoKAhFUejVqxeV\nK1dWp9vY2DB16lSKFClC7ty5OX/+fLrLnDlzJoqiUKtWLf73v/9RqFAhIOn4ubi4MGbMGDQaDV5e\nXkRERGTJfiT37NkzLl++DCQ9dPvyyy/VecWKFWPevHlkz56dnDlzpvsB14sXL5g/fz6KouDm5saQ\nIUPU6yZbtmzUrVuX1atXkzNnTi5evMju3btTlKEoCs2bN6d9+/YAmJubU7ZsWQACAwNRFAUXFxca\nNGig9sK2tLTk+++/p06dOuTMmVPdLyGEEEK8fxLQFkIIIf7lGjVqRN68eVNMr1atmvr3o0eP1L93\n7dqFRqOhdOnSVKxY0WCZZcuWpVy5cgD4+Pikqx6WlpYA/PPPPwQHB6eY379/f0JDQ9mwYUO6yktL\n/vz5qVevXorpFSpUUINbuvvt6+vLmzdvMDU1NZiLGKB8+fJ8/fXXaDQag0GSzNDmC65bt67RvM4N\nGzbEzs6OhIQEDh48+NbbvHnzJiEhIQB06tTJ4DKKotC+fXs0Gg0HDx7M8ECElpaWBlO6WFhY0Ldv\nXzWgqu2VaUxQUJDa+/aHH34wuEz+/Pn59ttv0Wg0eHp6kpiYmKG6ZlRW1almzZrvNA+vobzQAFWr\nVgVI0XNcG0Bu1qyZ0VRC7du3x8TEhKioKL0HQqlp3LgxuXPnJi4uDm9vb715UVFR+Pn5qQFErezZ\ns7N27VrmzJlDhw4dDJarmzrE2HWU0WOsDVDnzZtXLwe7roYNG6q997Oql66lpSUajQZfX1+DQf89\ne/YQFBREr1690lXe9evXuX79OgADBw40uEy7du0YOHAgU6dOTTMNT2ZZWFiox3/Xrl0pekvb29sT\nEhKCn5+fXiA/NT4+PsTExJA7d26+/vprg8vY29tTv359dXlDDOVeh/87F/7+/ine4gFYunQpp0+f\nZty4cemqrxBCCCGynuTQFkIIIf7lihUrZnC6bpBbt6elNtB59epVoz/4AV6+fIlGo1GDJmlp0qQJ\nv//+O1FRUXTt2pVy5cpRu3ZtateuTdWqVY0OFpcZ2kHPjMmbNy/379/XC4KdO3cOSBpMzlBeXK3a\ntWvj6enJ9evXefXqlRqoz6yQkBAUReHYsWOpHu/nz5+j0Wi4du3aW20P4OzZs+rfvXv3Nhrwi4uL\nA5LO9aNHj9JM1aGrSpUqak/z5JycnNTyL168qPdwJbmwsDAAbG1t9QKYydWqVYulS5cSHR3NjRs3\n0sxF/Tayqk6GBg3MStqe2cnZ2dkB8ObNG+Li4jA1NSUhIUHt/bt582aj6UHg/3qFX7t2jSZNmqRZ\nD+2bHZs2bWLPnj1qCg2A/fv38/r1a2xsbPQGwzQzM6NixYp6D9Xi4+O5f/8+//zzD5cvX9ZLv2Eo\nrQRk/BifO3cORVFwcnJKNchbu3ZtQkJC1GvhbbVt25bQ0FBOnjxJ/fr1qVmzpnp/LFasGNmzZ+xn\n2+KFrF4AACAASURBVN9//w0k9Vg2FijOmTMnI0eOfOu6p8bc3JxmzZrh5eXFypUr2bNnD3Xr1qVW\nrVrUqVMHOzu7DO+b9v6lTWNiTHR0dKqfUcauDRcXF7y8vLh27RqNGzfGyclJrW/ZsmUzXF8hhBBC\nZD35NBZCCCH+5Yz1tNQdzEq39612kMH4+HijeWl1y4iMjExXPfLmzYuHhwfjxo3j1q1bXLhwgb//\n/pvly5djYWFBvXr1aN++PQ0aNEhXeWlJz2CVuvutfeU+raCt7vxnz569dUD7yZMnALx+/ZrXr1+n\numxGjnd6tglJ+5DWNhVF4cWLF+kOaCuKkmqeYt2BCHXrYkhmzsu7Sp+Q1XXKioH4UpORtv/ixQt1\n4MCYmJh05YjPyLXYrl07Nm3axJkzZ3j06JGazkeb3qNVq1YGA4Xnzp1j/fr1BAcHc+fOHTVwrShK\nunpeZ/QYZ/TcvnjxIkPlG9OlSxciIiJYunQpsbGxHD58mMOHDwPwxRdf0LRpU7777rs0839rae/d\nVlZWWfqwMDOmTZuGRqPB29ubp0+fsnPnTvVtgHLlytGiRQu6dOmS7nup9p6RkJDwVp9RxtpH/fr1\nmTZtGm5ubkRHRxMQEMCJEyeYM2cOdnZ2NG7cmK5du1KyZMl01VcIIYQQWU8C2kIIIcS/nG7wKj20\nqRFatGjBnDlzsrQujo6OeHt7ExAQwMGDBzl+/Di3bt0iNjYWHx8ffHx8aNu2rd7gh5mV0f3WaDTp\nWkc3dURGtmEsDYZ2+oABA955b0lDdTl37tw76XGYWu9W3e2bmpqmWk56U51k9rxkRlbV6V2mGzG2\nTWN06zplyhSjqWgyq1KlShQtWpTw8HA8PT3p3bs3Dx8+VHtZ66Yb0Vq+fLlenvV8+fJRvHhxihUr\nRoUKFahcuTJfffVVqtvN6DHOzLnNCGM9yQEGDx5M165dOXDggDo+wcuXL7l37x4rV65k3bp1/O9/\n/zOYSik57dsV75OxfcuZMyfz5s1jxIgR7N+/n2PHjhEaGkpcXBx///0358+fZ+3ataxbt458+fKl\nuR3tsa9UqRIbN27MdH1TuzY6duxIy5YtOXToEIcPHyYwMJCIiAiePn3Kxo0b2bJlC1OmTDGaDkcI\nIYQQ75bk0BZCCCGEHhsbGzQaDQ8ePHgn5SuKQu3atfnpp5/Yv38/fn5+TJs2jVKlSgFJeXzTm5s3\nK9na2qZrv3Xnawci0w0cGgt0GRtAz8bGBkBvYM53TbvNd7VdjUaTas9J3dzl2p66xmiPsaFctroM\nnZd35WOs09uytrZWA3zv6lp0cXFBo9Go6Uz27NlDYmIixYsXV3Pya4WFhanB7DZt2nDkyBEOHTrE\nsmXLGDduHC1atFBz4Wcl7XlK731Aty2l5z6QVs74PHny0LFjR9zd3QkKCmLLli0MHDgQCwsL4uLi\nmDx5crr2Q1uvqKgo3rx5Y3S5f/75J0M97VML+Kc1SGihQoUYMGAAf/31F6dOncLDw4M2bdqgKAr3\n799P1yCx8H/79q4+o7QsLS1p1aoVc+bMwd/fn927dzNy5EhsbW1JSEjg119/TffAqEIIIYTIWhLQ\nFkIIIYSeChUqAHD+/Hmjr9MnJibStm1b2rVrx19//ZWucr29vZk7dy779u3Tm25vb0+HDh1Yt26d\nGqAKDQ19iz3IHG2u3kuXLvH8+XOjy504cQKAggULYm5uDqD3Sr+xAIex3NcVKlRAo9EQEBCQajC8\nUaNGdOzYMcXxywztOQY4evSo0eUWLVrE119/jaura4Z7pGpzsRsSGBgIJL3yX6ZMmVTL0Z6XiIgI\nLl++bHQ57XkxNzenUKFCGaprRn2MdXpbpqamlCpVCo1Gw7Fjx4wud+vW/2PvzsPkqgt08b9VXb1v\n2VgCSSCQsBqMgKCsgiiCsgQUR0VcUAZGVBS5CiKyjOCCosKMCFcFBkVUZAZcRs0YcGbA6+Xi9ojI\nooAoonEkobvT3emq8/sjv7TEJBA6nVQq/fnkydNdp06d81Z3nequt779PY/koIMOyt/93d/l//7f\n//us9nHMMcekXC7nnnvuye9///t861vfSqlUynHHHbfaut/5zndSFEU6Ojryj//4j2t84+O+++4b\n/fzZnrR0bVYej3fdddfTjqZe+b3dZZddRpc99XlgbcX1mp4H/vKXv+S6667LBRdcsMoUPKVSKc95\nznNyxhln5Oyzz05RFHnsscee8Y2UZMUJbJMVz9Vrez4dHh7Osccem3322Sc33njj025v5X17ukL+\nwQcfXO2vAu6///5cddVV+chHPrLK8tbW1uy///758Ic/nCOPPDJFUazzfOQrn78ef/zxpz2nwFvf\n+tYcffTRueyyy9Zpu8mKqZ++9KUv5UMf+tAqj69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AAAAANASFNgAAAAAADUGhDQAAAABA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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c558510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('For n_clusters =', 3, 'The average silhouette_score is :', 0.45886477013014015)\n"
     ]
    },
    {
     "data": {
      "image/png": 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RIwYMGKA3yNz9+/fZuXMno0ePpn79+mzatClLtpWQkMCyZcvYs2eP+gpnfHy8Xs+iCRMm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\nt9yi0aNHy1rb5PnItzx+rV+/fodrClxwwQUaM2a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BAAAg\nAElEQVT5xje+kRtvvDGXXHJJzjnnnEEHBQCAUWf9+qRa3fy1fn3RaQAAoHADLrT/4z/+I2vWrEln\nZ2f+8pe/JEn6+vpy+eWXJ0kOOOCAvP71r8/EiRNz66235otf/GJtEgMAAAAAMCoNuND+9Kc/nSQ5\n99xz85GPfCRJ8v3vfz+PPvpo2tvb88lPfjLnn39+rrzyyiTJl7/85RrEBQAAAABgtBpwof3Tn/40\nkyZNesJyIt/85jeTJC94wQvS0NCQJFm4cGH22GOP/Pa3vx1kVAAAAAAARrMBF9qPPvpodt1115RK\npa2Pffvb306pVMrBBx/8hGNbW1vT1dU18JQAAAAAAIx6Ay60Gxsb89hjj2393/fee2/+67/+K0me\nUGhXKpX88Y9/zPjx4wcREwAAAACA0W7AhfbChQtz77335kc/+lGS5POf/3ySZO+9984uu+yy9bhr\nrrkmjz32WPbee+9BRgUAAAAAYDSrH+gTV6xYkdtvvz2rV6/OrFmz8pvf/CalUiknn3xykuTuu+/O\ne97zntx6660plUp52cteVrPQAAAAAACMPgMutE888cT853/+Zz73uc/l17/+dZLkmGOO2Vpc9/T0\n5JZbbkldXV3OP//8HHXUUbVJDAAAo8VppyU9PZu3y+Vk3bpi8wAAQMEGXGgnyZo1a7Jq1ar87ne/\nyx577JF99tln674999wzZ555Zo499tjMmTNn0EEBAGDUue66pKNj83ZbW7FZAABgGBhUoZ0kc+bM\necrCurm5OW984xsHe3oAAAAAAEgyiJtCAgAAAADAjjTgGdqnnXbadh1fKpWydu3agV4OAAAAAIBR\nbsCF9m233fasx5RKpSRJtVrdug0AAAAAAAMx4EL7LW95y9Pu6+npyUMPPZRvf/vb+dOf/pSzzz47\nhx122EAvBQAAAAAAAy+0X/3qVz/rMZs2bcqFF16YT3ziE3nhC1840EsBAAAAAMDQ3hRy7NixWbNm\nTerq6vLhD394KC8FAAA7nxNOSFau3Px1wglFpwEAgMINeIb2tmpvb8/s2bNz++23D/WlAABg57Ju\nXdEJAABgWBnSGdpbPPLII+np6dkRlwIAAAAAYCc15IX2xz72sTzwwAPZc889h/pSAAAAAADsxAa8\n5MiZZ575jPt7e3tzzz335P7770+pVMqKFSsGeikAAAAAABh4oX3rrbdu87HHHHNMVq1aNdBLMQpt\n2LAhF154YSZMmJC6uh2yMg5QkEqlkvXr1ydJxo0bZ8zDKGDcDw+dnZ0pl8tZt25dJk+eXHQcAADY\nJgMutM8999xn3D9mzJiMGzcuBxxwQBYsWDDQyzBK9ff3p1qtFh0D2EFKpVLREYAdzLgvTrVaTVdX\nV+bPn593vOMdymwAAEaUISu0AQCA4aWnpydjx47Nm970phx77LH+YgEAgBFnwIU2AAAwMmyZlb3f\nfvvl7W9/eyZOnFh0JAAAGJBtKrRvvPHGmlzshS98YU3OAwAAo8G/fv3rae7rS5J019fnZX/3d9t9\njp6enjQ0NOTv//7vc+SRR5qVDQDAiLZNhfYb3vCGQX/wLZVK+dWvfjWocwAAANvm8bOy//Ef/zHt\n7e1FRwIAgEHbpkJ71113HeocAABAjWzYsCH19fW54IIL8j/+x/8wKxsAgJ3GNhXaN99881DnAAAA\nBqlSqaSrqysLFizIO9/5zkyePLnoSAAAUFNuCgkAACPUlmVFkqS1tTUzZ87Msccem2OOOcasbAAA\ndkoDKrR//etfZ+HChU+7/1Of+lT23XffLF68eMDBAACAJ6pWq9mwYUM2bdqUlpaWTJ06Nccdd1wO\nO+ywzJ07N2PGjCk6IgAADKntKrTvueeeXHrppbn99tvzla98JbNnz37SMRs2bMg///M/p7+/P4sX\nL8673vWu7L777jULDAAAo1G1Ws24ceOyfPnyHHXUUXnOc56TxsbGomMBAMAOtc2F9i9/+cucccYZ\nWb9+fZLkJz/5yVMW2n/6058ybdq0/PGPf8ztt9+elStX5uqrr868efNqlxoAAHZi3d3dT5pt3dzc\nnM985jMFJQIAgOFhmwrtDRs25Lzzzsv69eszZ86cXHjhhVm+fPlTHrvXXnvlpptuyne/+928853v\nzN13353zzjsvN9xwQxoaGmoaHgAAdkZjx47N2rVr0/y97yV9fUmSUr3b3wAAwDZ9Kv785z+fP/3p\nT9lvv/2ybt26NDU1Petzli1bln/913/Ny1/+8tx999350pe+lJe//OWDDgwAADu7urq6TJ06NVmx\nougoAAAwrNRty0H//u//nlKplLe97W3bVGZvMW7cuFx66aWpVqv5xje+MeCQAAAwmpRKpaIjAADA\nsLRNhfbvfve7TJw4Mc997nO3+wIHHXRQJk2alN/85jfb/VwAABiN6uq26WM6AACMOtv0SbmrqyvT\npk0b8EV23XXXdHR0DPj5AAAwWlSr1YwfP77oGAAAMCxtU6Hd0tKShx9+eMAX+etf/5rGxsYBPx8A\nAEaLrq6uLFu2rOgYAAAwLG1Tob3XXnvloYceyiOPPLLdF3j44Yfzpz/9Kbvtttt2PxcAAEaburq6\nHHXUUUXHAACAYWmbCu3ly5enUqnkqquu2u4LrFu3Lv39/Vm8ePF2PxcAAEaT/v7+NDc3Z86cOUVH\nAQCAYWmbCu2XvexlKZfLWbduXb72ta9t88lvuummXHnllSmVSjnppJMGHBIAAHZ2mzZtSrVazbve\n9a6USqXND157bfKFL2z+uvbaYgMCAMAwUL8tB02cODFvf/vbc9FFF+XCCy/Mt7/97axevfppZ47c\ncccd+eQnP5kvf/nLqVQqWbVqVRYuXFjT4AAAsLPo6elJe3t7PvCBD2TXXXf9/ztOPz3ZcnP1trZk\nxYpiAgIAwDCxTYV2kpxwwgm5++678/GPfzzXXXddrrvuukyaNCnz589Pe3t7+vv78+ijj+b3v//9\n1rW2q9VqTjzxxLztbW8bshcAAAAjWVdXV2bNmpX3v//9aWtrKzoOAAAMa9tcaCfJ+eefnyVLlmTN\nmjW599578/DDD+fhhx9OqVRKtVp9wrHz58/POeeckxe+8IU1DQwAADuLrq6uHHjggbnsssvS0NBQ\ndBwAABj2tqvQTpJly5blxhtvzG233ZYf/OAHueuuu9LR0ZGmpqbssssumTVrVg4//PDMnDlzKPIC\nAMCIV61W09XVleOOOy7nn3/+/18zGwAAeEbbXWgnSalUytKlS7N06dJa5wEAgJ3aljL7rLPOyskn\nn1x0HAAAGFEGVGgDAADbr7+/P729vXnHO96RQw89tOg4AAAw4ii0AQBgB+nr68sVV1yRhQsXFh0F\nAABGpLqiAwAAwGjR3t6uzAYAgEEwQxsAAHaQcrm8fU9Yv35oggAAwAhlhjYAAOwAlUolLS0tRccA\nAIARzQxtAAAYYt3d3RkzZowbQQIAwCAptAEAYAhUq9V0dnamXC7n6KOPzmte85pMnjy56FgAADCi\nKbQBAKCGthTZbW1tednLXpZVq1alra2t6FgAALBTUGgDAEANVCqVdHV1ZcKECTnzzDOzYsWKNDY2\nFh0LAAB2KgptAACoga6urpx11llZuXJlxowZU3QcAADYKdUVHQAAAHYGDQ0NOfroo5XZAAAwhMzQ\nBgCAGmhoaMiECRNqe9LTTkt6ejZvl8vJunW1PT8AAIwwCm0AAKiB1tbWlEql2p70uuuSjo7N224s\nCQAAlhwBAIDBqlQqaW9vLzoGAADs9BTaAAAwSBs3bszcuXOLjgEAADs9hTYAAAzShg0bss8++xQd\nAwAAdnoKbQAAGKSGhobstddeRccAAICdnkIbAAAGaezYsZk+fXrRMQAAYKen0AYAgEFqbGxMa2tr\n0TEAAGCnV190AAAAGOna2tqG5sQnnJD09GzeLpeH5hoAADCCKLQBAGAQ+vv7M27cuKE5+bp1Q3Ne\nAAAYoSw5AgAAA1StVrNx48aceeaZRUcBAIBRQaENAAAD1NXVlde//vVZtGhR0VEAAGBUUGgDAMAA\ndHV15bDDDstJJ51UdBQAABg1FNoAALCdNm3alN133z1///d/n1KpVHQcAAAYNRTaAACwnTo7O3PK\nKadk7NixRUcBAIBRRaENAADbqampKfPnzy86BgAAjDoKbQAA2E6NjY2ZMWNG0TEAAGDUUWgDAMB2\nqFQqmTBhQsaMGTP0Fxs3LimVNn+NGzf01wMAgGFOoQ0AANuhu7s7p59+etExAABgVFJoAwDANqpU\nKpk6dWqOOuqooqMAAMCopNAGAIBt1N3dnfPOOy+lUqnoKAAAMCrVFx0AAACGq2q1mg0bNqS3tzfN\nzc1ZtGhRDjnkkKJjAQDAqKXQBgCA/1atVtPd3Z1KpZJyuZxJkyZl3333zWGHHZZ99tknra2tRUcE\nAIBRTaENAMCoValU0tXVlbq6upTL5UyZMiX7779/li9fnvnz56epqanoiAAAwOMotAEAGDX6+/vT\n2dmZhoaGlMvlzJgxI0uXLs2yZcsye/bs1Nf7eAwAAMOZT+wAAIwK3d3dmTp1ak4//fQsXbo0M2fO\nHP43d1y7Nunr27ytbAcAAIU2AACjQ319fT7wgQ9k8uTJRUfZditWFJ0AAACGlbqiAwAAwFDr6enJ\nQQcdNLLKbAAA4EkU2gAA7PTq6upyzjnnFB0DAAAYJIU2AAA7ta6urjz/+c/PlClTio4CAAAMkkIb\nAICdVqVSSUtLSy644IKiowAAADWg0AYAYKfV3d2dCy64IC0tLUVHAQAAakChDQDATqmnpyeLFi3K\nC17wgqKjAAAANVJfdAAAABgK9fX1ueSSS4qOMTjXXpv09W3erq9PVqwoNg8AABRMoQ0AwE6ns7Mz\nxx13XCZNmlR0lME5/fSko2PzdlubQhsAgFHPkiMAAOxUqtVqyuVyXve61xUdBQAAqDGFNgAAO41K\npZJHH300L33pS9Pa2lp0HAAAoMYsOTKC3Hrrrbn66qvzi1/8Ij09PZkyZUoOO+ywvPa1r820adOK\njgcAsMNVq9V0d3enWq2mpaUlM2bMyKpVq/LSl7606GgAAMAQUGiPEB/60IdyxRVXpFQqZcyYMSmX\ny7nvvvvy6U9/OjfccEM+/vGP57nPfW7RMQEAhlxvb296enrS3NycyZMn54UvfGGOOOKILFiwIPX1\nPt4CAMDOzCf+EeAb3/jG1jL7nHPOyerVq1Mul3PnnXfm4osvzs9//vOcc845+frXv562trai4wIA\n1FR/f386OjpSLpczfvz4PPe5z82LXvSiPO95z0tLS0vR8QAAgB1IoT3MVavV/Mu//EtKpVJOOumk\nnHvuuVv3zZkzJ1dddVWOPfbYPPTQQ7nqqqvyxje+scC0AACDV6lU0tXVlTFjxqSlpSVz5szJ0Ucf\nnaVLl2bKlClFxwMAAAqk0B7mfvCDH+Suu+5KqVTK6tWrn7R/3LhxWbVqVS6//PJcf/31Cm0AYMSp\nVqvp6elJf39/WlpaMm3atCxbtiyHHXZY9tprr5RKpaIjAgAAw4RCe5j7wQ9+kCSZPn16Zs6c+ZTH\nHHLIIbn88stz//3354477sjcuXN3ZEQAgO3W29ub3t7eNDU1ZcqUKTnyyCNz5JFHZuHChRk7dmzR\n8YaP9euLTgAAAMOKQnuYu/POO5Mks2fPftpj9txzz63bv/vd7xTaAMCw09fXl66urpTL5bS1tWXR\nokVZsmRJTjzxxEyYMKHoeAAAwAih0B7mHnjggZRKpUyfPv1pj2ltbU1zc3N6enry4IMP7sB0AABP\n1N/fn40bN2bjxo2pr69PQ0NDWlpaMn/+/Bx99NE58MAD09ramp///OdJ4qaOAADAdlFoD3OdnZ1J\nknK5/IzHlcvl9PT0bD0eAGAoVCqV9Pb2ZuPGjUmSxsbGjBkzJs3NzWltbc3EiRMza9aszJs3L7vv\nvnumTZuWSZMmPWEd7N7e3qLiAwAAI5xCe5jr7+9PkmddS7KhoSHJ5n/OCwAwUNVqNZs2bcrGjRvT\n39+fxsbG1NfXp7GxMW1tbRk/fnz22GOPzJkzJ3vuuWemTZuWKVOmWPcaAADYIRTaw1xTU1OSZNOm\nTc943JaZTn6ZBACeSbVaTV9fXzZu3Jje3t40NjZm7NixGTt2bFpbW9PW1pYZM2Zk9uzZmTNnztbC\nurm5uejoAAAACu3hbsu6khs2bHjG43p6epJsXk8bAODx+vr6smnTpkyYMCGtra2ZOnVqZs+enblz\n52bXXXfN1KlTM27cuCcsCwIAADAcKbSHuWnTpuWnP/1pHnjggac9pqurK93d3SmVSpk6deoOTAcA\nDHednZ2ZOnVq/tf/+l+ZPXt20XEAAAAGRaE9zM2dOzdf//rXc/fddz/tMXfdddcTjgcA6O/vz4YN\nG/KSl7wk5557rmXJRqrTTkv++1/ipVxO1q0rNg8AABRMoT3MLV26NFdccUX+8Ic/5P7778/06dOf\ndMz3vve9JMmkSZMyZ86cHR0RABhmurq6MmHChLz3ve/Nc57znKLjMBjXXZd0dGzebmsrNgsAAAwD\ndUUH4JktXrw4u+66a5LkIx/5yJP2d3R05NOf/nRKpVJe8YpX7Oh4AMAw0tnZmd7e3hx99NH5zGc+\no8wGAAB2OgrtEeDNb35zqtVqvvCFL+Q973lPOjs7kyR33HFHzjjjjDz44IOZNGlSTj311IKTAgA7\nWqVSyfr165MkJ554Yj772c/m4osvTmNjY8HJAAAAas+SIyPAi1/84vz85z/P//k//yef/OQns3bt\n2jQ3N6ejoyOlUiltbW258sorM27cuKKjAgA7yKZNm9Lb25tp06blrLPOygtf+MI0NDQUHQsAAGBI\nKbRHiLe+9a05+OCDc8011+TnP/95Ojo6suuuu+b5z39+Xve612XGjBlFRwQAdoDOzs40NDRk4cKF\nee1rX5u99947pVKp6FgAAAA7hEJ7BFm+fHmWL19edAwAYAfr6elJf39/2tvb85KXvCSnnnpqJk+e\nXHQsAACAHU6hDQAwDPX09KRSqaS9vT2HHHJIVqxYkfnz55uNDQAAjGoKbQCAYaBaraanpydJ0t7e\nnkMPPTQnnHBC5s6dq8QezU44Ifnv/y5SLhebBQAAhgGFNgBAgbq6ulIqldLe3p7DDz88xx9/fGbP\nnq3EZrN164pOAAAAw4pCGwCgIJ2dnTn00EPzqle9KrNmzVJiAwAAPAuFNgBAAXp6ejJv3rz84z/+\noyIbAABgG9UVHQAAYLSpVCppbGzMu9/9bmU2AADAdjBDGwBgB6pWq+nu7s573vOetLe3Fx0HAABg\nRFFoAwDsIL29valUKjnzzDNz4IEHFh0HAABgxFFoAwAMsWq1mq6uruy555657LLLMmPGjKIjAQAA\njEgKbQCAIdTb25tqtZrXvva1ecUrXmHNbAAAgEFQaAMADIEts7Jnz56dyy67LNOnTy86EiPRuHFJ\nR8fm7ba2ZP36YvMAAEDBFNoAADXW39+fjRs35jWveU1e+cpXmpUNAABQIwptAIAa6u7uTltbW/75\nn/85CxcuLDoOAADATqWu6AAAADuDarWazs7O7LPPPrnmmmuU2QAAAEPADG0AgEHq7e1NX19fzjjj\njJx66qmWGAEAABgiCm0AgAHq6elJqVTKvvvum/POOy+zZs0qOhIAAMBOTaENALCdOjs709jYmGXL\nluWss87KtGnTio4EAAAwKii0AQC2wZY1sltaWnL88cfnVa96Vdrb24uOBQAAMKootAEAnkVPT0+a\nm5vzqle9KieffHKam5uLjsRosXZt0te3ebveR3cAAPCpGADgGWzcuDG77LJLrrzyyrS0tBQdh9Fm\nxYqiEwAAwLBSV3QAAIDhqq+vL01NTfngBz+ozAYAABgGFNoAAE+hUqmkUqnkiiuuyKRJk4qOAwAA\nQCw5AgDwBP39/ens7ExTU1Pe/e53Z+bMmUVHAgAA4L8ptAGAUa+vry9dXV1pbW3NrFmzcsIJJ2TZ\nsmVu/ggAADDMKLQBgFFp06ZN6enpSWtra+bPn58TTjghBx10UJqamoqOBgAAwNNQaAMAo0Zvb282\nbNiQcePGZZ999smKFSty4IEHpqGhoehoAAAAbAOFNgCwU+vt7c3GjRszbty4HHDAAVmxYkWe97zn\npb7exyBGgGuvTfr6Nm/X1ycrVhSbBwAACuY3OQBgp9PX17d1OZFFixZl5cqV2X///ZXYjDynn550\ndGzebmtTaAMAMOr5rQ4A2Cn09/enq6srLS0tmTt3bk466aQsXbrUciIAAAA7EYU2ADBiVSqVdHR0\npKWlJTNnzsxLX/rSHHrooW7sCAAAsJNSaAMAI9LGjRvT0tKSCy+8MIcffnhaW1uLjgQAAMAQU2gD\nACNOX19fmpqactVVV2XixIlFxwEAAGAHUWgDACNKpVJJX19fPvjBDyqzAQAARhmFNgAwYlSr1fT0\n9OSyyy7L7Nmzi44DAADADlZXdAAAgG3V1dWVM844I8uWLSs6CgAAAAUwQxsAGBF6e3uz1157ZdWq\nVUVHgR1n/fqiEwAAwLBihjYAMOxVq9VUq9W8853vTKlUKjoOAAAABVFoAwDD3palRqZPn150FAAA\nAAqk0AYAhrWurq485znPyctf/vKiowAAAFAwhTYAMGxt3Lgx06dPz3vf+15LjQAAAKDQBgCGp76+\nvjQ2NuYDH/hAyuVy0XEAAAAYBuqLDgAA8Leq1Wr6+vry4Q9/OJMmTSo6DgAAAMOEGdoAwLCzadOm\nHHrooZk1a1bRUQAAABhGzNAGAIadvr6+TJgwoegYULzTTkt6ejZvl8vJunXF5gEAgIIptAGAYae/\nvz/t7e1Fx4DiXXdd0tGxebutrdgsAAAwDFhyBAAYVqrVarq7u62dDQAAwJMotAGAYaG3tzddXV1p\nbm7OG97whhxxxBFFRwIAAGCYseQIAFCYSqWSzs7OtLS05HnPe15OP/30LFiwIKVSqehoAAAADEMK\nbQBgh9uwYUP6+/szbdq0rF69On/3d3+X5ubmomMBAAAwzCm0AYAdolqtbl1SZNmyZTn11FMze/bs\nomMBAAAwgii0AYAh19nZmebm5qxatSqnnHJKmpqaio4EAADACKTQBgCGTFdXVxobG3PiiSfm1a9+\ndVpaWoqOBCPLCSckPT2bt8vlYrMAAMAwoNAGAGquu7s79fX1OeaYY/L6178+48aNKzoSjEzr1hWd\nAAAAhhWFNgBQUz09PTnssMNy7rnnZuLEiUXHAQAAYCei0AYAaqa3tzf77rtvLr300qKjAAAAsBOq\nKzoAALDz6Ovry7nnnlt0DAAAAHZSCm0AoCb6+vqy1157Zfbs2UVHAQAAYCel0AYABqVarWb9+vXp\n7+83OxsAAIAhZQ1tAGBAent7s2nTpuyyyy45/fTT8+IXvzgtLS1FxwIAAGAnptAGALZZtVpNR0dH\nmpub89znPjdnnHFG5s+fn1KpVHQ0AAAARgGFNgDwrB4/G3vVqlU57rjjzMaGHWHcuKSjY/N2W1uy\nfn2xeQAAoGAKbQDgGXV1dWXhwoU555xzsnDhQrOxAQAAKIxCGwB4Wl1dXVm8eHHe/e53K7IBAAAo\nXF3RAQCA4amrqyvz5s3Lu971LmU2AAAAw4JCGwB4kp6enuy222553/vel/p6/6ALAACA4UGhDQA8\nyZgxY/LBD34w5XK56CgAAACwlUIbAHiS5ubmjB8/vugYAAAA8AQKbQDgSZqbm4uOAAAAAE9iUUwA\n4An6+voU2jBcrF2b9PVt3raePQAAKLQBYDTr6+tLV1dX6uvr09jYmPb29uy22245+eSTi44GJMmK\nFUUnAACAYUWhDQCjxKZNm9Ld3Z36+vo0NTWlvb09M2bMyJIlS7Lvvvtm1qxZaWhoKDomAAAAPC2F\nNgDshDZt2pSurq40NDRsnXk9c+bMLFmyJPvss09mzpypvAYAAGDEUWgDwAjX39+fjo6ONDY2pqGh\nIRMnTnxSeV1v7V0AAAB2An67BYARpq+vL52dnSmXy2lpacns2bNz2GGH5bnPfW5233135TUAAAA7\nLb/xAsAwt+XGjY2NjWlra8vcuXNz+OGHZ/HixZk2bVpKpVLREQEAAGCHUGgDwDCz5eaNTU1NaWtr\ny/z587cW2FOmTFFgAwAAMGoptAGgQNVqNb29venp6Um5XM64ceOycOHCHHHEEdl///0zZcqUoiMC\nRbr22qSvb/N2fX2yYkWxeQAAoGAKbQDYgSqVSrq7u5MkTU1NmThxYubNm5fnP//5WbRoUSZPnlxw\nQmBYOf30pKNj83Zbm0IbAIBRT6ENAEOot7c33d3dKZfLaW5uzm677ZYlS5ZkyZIlmTdvXhoaGoqO\nCAAAACOGQhsAamTL7OtqtZpyuZwJEyZk9uzZOfTQQ7Pffvtl6tSp1r8GAACAQVBoA8AAVavVdHd3\np66uLi0tLZk+fXoOOOCAHHTQQZk/f36ampqKjggAAAA7FYU2AGyHarWajo6ONDY2ZuLEiTnmmGPy\n4he/OHvuuafZ1wAAADDEFNoA8Cz6+/vT2dmZcrmcqVOn5uUvf3mOPvroTJ06tehoAAAAMKootAHg\nKfT19aW7uzstLS3Zfffdc+yxx2b58uUZP3580dEAAABg1FJoAzDq9fX1paenJ5VKJU1NTWlsbMyC\nBQvykpe8JAcddFDK5XLREQEAAIAotAEYRTZt2pSenp4kSWNjY5qamjJ+/PhMnz49++yzT/bee+/M\nnDkzkydPth42MDysX190AgAAGFYU2gDsVKrVajZt2pQNGzakVCptLa7b29uz2267Zd99982CBQsy\nc+bMTJgwQXENAAAAI4hCG4ARqVqtpre3Nz09Pamvr09DQ0Oam5vT3t6ePfbYY2txvfvuu1v3GgAA\nAHYSCm0ARowtM6/b2toyZcqU7LXXXtl3330zb968zJgxI21tbUVHBAAAAIaQQkhkShwAACAASURB\nVBuAYa2vry/d3d1pbW3Nc57znKxcuTIHHnhg6uv9CAMAAIDRRhsAwLDT19eXrq6utLa2Zt68eVm5\ncmWWLl2ahoaGoqMBAAAABVJoAzAsVCqVrF+/Pm1tbdlrr72ycuXKHHzwwWlqaio6GgAAADBMKLQB\n2G7VajX9/f2pVCpP+rNSqaSuri5jxozJmDFjUldXl1KplLq6uiTJmDFjUl9fv/VryzHlcjmnnXZa\njjjiiDQ3Nxf8CgEAAIDhSKENsBPbUjA/XfG8pUze8ueW0rlUKm0tnseOHfuEAnrs2LFpaGhIS0tL\nWltbt361tbWlra0t48aNS3Nzc5qamp7y62/Xvu7t7c3Pf/7zJMm+++5rWRGAxzvttKSnZ/N2uZys\nW1dsHgAAKJhCG2AY+Nvi+fGl85bi+fHlc6lUSqlUSrJ5xvOW0vlvy+fGxsa0trY+oXzeUjq3tbWl\nubk5jY2NKZfLTyqex4wZU/D/KwDkuuuSjo7N221txWYBAIBhQKHNsNXf359NmzZtnTEKw8nfznp+\nfPFcX1+/dYbz44vnLYXz3xbQW2Y8t7a2prm5OW1tbWltbd1aOre1tW0tnP+2eG5sbFQ8AwAAAKOG\nQpthqbGxMYsXL8748eOVdQxL5XJ5a+n8+D/L5XLK5fLWWc+P/9NfzgAAAAAMjkKbYam+vj6rV6+2\nni4AAAAAsJXpggAAAAAAjAgKbQAAAAAARgSFNgAAAAAAI4I1tAEAYLg64YSkp2fzdrlcbBYAABgG\nFNoAADBcrVtXdAIAABhWLDkCAAAAAMCIoNAGAAAAAGBEUGgDAAAAADAiKLQBAAAAABgRFNoAAAAA\nAIwICm0AAAAAAEYEhTYAAAAAACOCQhsAAIarceOSUmnz17hxRacBAIDCKbQBAAAAABgRFNoAAAAA\nAIwICm0AAAAAAEYEhTYAAAAAACOCQhsAAAAAgBFBoQ0AAAAAwIig0AYAAAAAYESoLzoAAADwNNau\nTfr6Nm/X++gOAAA+FQMAwHC1YkXRCQAAYFix5AgAAAAAACOCQhsAAAAAgBFBoQ0AAAAAwIig0AYA\nAAAAYERQaAMAAAAAMCIotAEAAAAAGBHqiw4AAAA8jWuvTfr6Nm/X1ycrVhSbBwAACqbQBgCA4er0\n05OOjs3bbW0KbQAARj1LjgAAAAAAMCIotAEAAAAAGBEU2gAAAAAAjAgKbQAAAAAARgSFNgAAAAAA\nI4JCGwAAAACAEaG+6ADPpFKpJEk2bNiQsWPHFpyGHWXTpk1bt3t6etLX11dgGmCoGfMw+hj322Hu\n3KSra/N2S0vS3V1sHhgg4x5GF2MeRp/Hj/stne5QKVWr1eqQXmEQHnnkkdxzzz1FxwAAAAAAYBvs\nueeemTRp0pCdf1gX2n19fXnsscfS2NiYujqrowAAAAAADEeVSiUbN27M+PHjU18/dAuDDOtCGwAA\nAAAAtjDtGQAAAACAEUGhDQAAAADAiKDQBgAAAABgRFBoAwAAAAAwIii0AQAAAAAYERTaAAAAAACM\nCAptAAAAAABGBIU2AAAAAAAjgkIbAAAAAIARQaENAAAAAMCIoNAGAAAAAGBEUGgDAAAAADAiDItC\n+7bbbsurX/3qHHLIIdl///3z8pe/PP/3//7fQZ/3Va96VV7wghcMPiA1UYv3ubOzM+973/tyzDHH\nZNGiRTnssMOyZs2a/OUvfxmi1MBg1GLc33PPPbn44otz+OGHZ5999snSpUuzevXqfOc73xmi1MBA\nDcVnuhtuuCELFizIW97ylhqlBGqpVuP++uuvzytf+cosXrw4ixYtyoknnpjPf/7zQ5AYGKxajPuH\nHnool1566dbP+AcddFDOPvvs/OxnPxui1EAt3HbbbVm4cGH+7d/+bbueV+s+b8yaNWvWDOiZNXL9\n9dfnnHPOyZ///Ofsv//+2X333fOTn/wkX/3qV5MkS5YsGdB5/+mf/ilf+cpX0tramle96lU1TMxA\n1OJ97urqymmnnZavf/3rGT9+fA488MB0dHTk29/+dr761a/mmGOOSWtr61C/FGAb1WLc/+hHP8op\np5ySX/ziF5k0aVIOOOCA1NfX50c/+lGuv/76tLS05HnPe95QvxRgGwzFZ7o///nPOeuss9Lb25v5\n8+fn6KOPrnVsYBBqNe4vvvjiXHHFFXn00UezZMmSTJkyJb/85S9z0003ZcOGDTnkkEOG8mUA26EW\n4/6+++7LypUr88Mf/jDt7e1bn3P77bfnS1/6UubMmZM5c+YM6esAtt9dd92VM888Mz09PTniiCOy\n9957b9PzhqTPqxbo4Ycfri5atKi6//77V3/9619vffyuu+6qLlu2rLpw4cLqr371q+06Z09PT/Wt\nb31rdf78+dX58+dXDzvssBqnZnvV6n1+17veVZ0/f371oosuqvb39299/J/+6Z+q8+fPr5599tlD\nkh/YfrUY9319fdUjjjiiumDBguqVV175hH3f+973qvvss0917733rt5xxx1D8hqAbTcUn+mq1Wr1\n1FNPrS5YsKC6YMGC6oUXXljLyMAg1WrcX3vttdX58+dXX/ziF1fvv//+rY/feeed1SVLllQXLFjw\nhPMDxanVuD/vvPOqCxYsqF522WXVSqWy9fEvfvGL1fnz51eXLFlS3bhx45C8BmBgvve971UPPvjg\n6vz586sLFiyofuELX9jm5w5Fn1fokiPXXHNNNm7cmFWrVmXBggVbH581a1be9KY3pVKpZO3atdt8\nvptuuinHH398vvzlL2ePPfYYisgMQC3e587Oznz+859PuVzO2972ttTV/f//dC+88MLsvvvuufnm\nm3PvvfcO2esAtl0txv1tt92W++67L/vuu29Wr179hH0HH3xwXvayl6VSqeRrX/vakLwGYNvV+jNd\nknzyk5/MbbfdlsWLF6dardY6MjBItRr3H/7wh1NfX5/3ve99mTZt2tbHZ8+endWrV2f69On5xS9+\nMSSvAdg+tRr33/3ud5MkZ599dkql0tbHTzzxxOy5555Zv359fve739X+BQDb7S9/+UvWrFmT1atX\nZ/369dl111236/lD1ecVWmh/85vfTJIceeSRT9p31FFHpVQq5ZZbbtmmc3V0dOTcc8/Nfffdl9NP\nPz0f/ehHa5qVgavF+3z77benp6cnBx54YNra2p6wr66uLocffvgTrgUUqxbjvqurK/vtt1+WL1/+\nlPv33HPPVKvVPPjgg4MPDAxKLT/TJclvf/vbvP/978+RRx6ZE088sWY5gdqpxbj/zW9+kz/84Q9Z\nunRp5s2b96T9r3vd63LzzTdn5cqVtQkNDEqtft5vKbQeeOCBJzze19eXzs7OJMn48eMHGxeogY9+\n9KP53Oc+lz333DNr167N0qVLt+v5Q9XnFVpo//73v0+Sp/zwMm7cuEyePDnr16/fprKiVCrluOOO\ny/XXX5+LLroojY2NNc/LwNTifb7jjjuSJHPnzn3K/XPmzEm1WvW3uDBM1GLcH3XUUfn85z+fc889\n9yn3/+xnP0upVHrCbC6gGLX8TNfb25s3v/nNaWtry2WXXVbzrEBt1GLc//KXv0yS7LfffkmSb33r\nW3n3u9+dSy65JFdffXXWr18/BMmBgarVz/vly5enWq3mLW95S374wx9mw4YNueeee/KmN70pjzzy\nSI4++ujsvvvuQ/IagO2zxx57ZM2aNbnhhhtywAEHbPfzh6rPq9/uJDXy2GOPZePGjWltbU1TU9NT\nHrPLLrvk4YcfziOPPJIpU6Y84/laW1vz3ve+dyiiMgi1ep8ffPDBlEql7LLLLk97jiR5+OGHaxMc\nGLBaf39/Kr/97W/zta99LaVSyU3ioGC1HvOXX3557rzzzlxxxRWZOHHiUEQGBqlW4/4Pf/hDSqVS\nWlpasnr16nz3u9/duvxAtVrNRz7ykXzoQx9yA2gYBmr58/6SSy7Jn//85/zoRz/KqlWrtj5eV1eX\nM888M+ecc07N8wMD8/gxOhBD1efVtNC+4IIL8qtf/epZj1u0aFHe+MY3JsnTfiN8/L6urq7aBGSH\n6+npSTL493nLecrl8jOeo7u7e0A5gdqp1bh/On/5y1/yhje8IZVKJS996Uszf/78gQUFaqKWY/77\n3/9+1q5dm+OPPz5HHXVU7UICNVWrcd/R0ZFqtZqPf/zjGTNmTC6//PI8//nPz2OPPZarrroqn/vc\n53L22WfnhhtuyOTJk2v7IoDtUsuf9+PHj88JJ5yQO++8M+PHj8+8efPyxz/+Mb/97W/zpS99KQcc\ncECe//zn1y48UJih6vNqWmjff//9ueeee571uClTpmxdM+nxNwB4Om4ENHLV6n0eM2bMNp2nUqls\nRzpgKAzl9/cHHnggr371q/Nf//Vf2W+//XLJJZcMKCNQO7Ua8x0dHbn44oszffr0/MM//EPN8gG1\nV6tx39vbm2Tz+L/66quzePHiJJuXLlizZk0eeOCB3Hrrrbn66qtz/vnn1yA5MFC1/Ix/wQUX5Gtf\n+1rOP//8vP71r9/6+E033ZQ3vvGNOffcc/PFL34xs2fPHlxooHBD1efVtND+zGc+s83Hblnof+PG\njU97zIYNG5Ikzc3NgwtGYba8d4N9n7fs23LsQM4B7Bi1Gvd/63e/+13OPPPM3H///Vm0aFGuvPJK\n90uAYaBWY37NmjV58MEHc9VVV6W1tbW2IYGaqtW43zIra86cOVvL7Md7xStekVtuuSU/+MEPBhMX\nqIFajfvvfve7+drXvpaDDz74CWV2svkeOqtXr87HPvaxfOpTn8o73/nOGiQHijRUfV5ha2i3tram\npaUlHR0d6e3tTUNDw5OOeeihh5LkaddZYfir1fs8derUVKvVp11TZ8s5BrIWL1BbQ/H9/bvf/W7+\n5//8n+nq6sqhhx6af/mXf3naf7IE7Fi1GPO/+MUv8tWvfjXt7e350pe+lC996Utb9917771Jkh//\n+Me58MILM3v27Jx55plD8EqAbVWrn/Vb1smfMWPGU+7fbbfdkiSPPvroYCMDg1Srcf+DH/wgpVIp\nhxxyyFPuX758eT72sY9t03K2wPA3VH1e3aCTDcKWO1xuuVPu4z322GN5+OGHM27cOCXlCFeL93nL\nOe68886n3H/HHXekVCo95d2WgR2vlt/fb7jhhrz+9a9PV1dXTjrppHz0ox9VZsMwM9gx393dnVKp\nlMceeyxf+cpXnvD1s5/9LKVSKffdd1++8pWv5Pvf//6QvhZg29TiZ/2Wz+4PPPDAU+7f8suvG8TC\n8FCLcb9+/fokSX39U8+v3PL4pk2bBhsXGAaGqs8rtNA+9NBDU61Wc9NNNz1p37//+7+nWq3mBS94\nwY4PRk3V4n1evHhxyuVybrvttq3L1WxRqVRyyy23pFQq5dBDD61ldGCAavX9/eabb85FF12U/v7+\nnHfeeXnHO96xdf0+YPgY7JhfsmRJfv3rXz/l17ve9a5Uq9Ucd9xx+fWvf521a9cO4SsBtlUtftYf\ndNBBaWxszG9+85vcfffdT9r/zW9+M8nm7xFA8Wox7mfPnp1qtbp1fP+t73znO0mSvffee9B5geIN\nVZ9XaCuwcuXKlMvlfOpTn8pPfvKTrY/fddddef/7359SqZRXv/rVT3jOQw89lLvuumvrlHSGv+19\nn5/qPW5qasrKlSvT2dmZt7/97U/429r3vOc9+eMf/5ijjjoqM2fO3DEvCnhGtRj3jzzySC6++OJU\nKpWcddZZOfvss3foawC2XS3GPDCy1GLct7a25uSTT06lUsmFF16Yv/zlL1v3fec738k111yTcrmc\nk046ace8KOAZ1WLcH3vssWlpacl//Md/5Morr3zC+b/zne/kE5/4ROrq6nLKKacM/QsCampH9nml\n6rPdfnaI/du//VsuvfTSlEqlLF26NA0NDfn+97+f3t7eXHDBBXnNa17zhOMvuuiiXHfddVmxYkX+\n9//+30973vvuuy9HHnlkpk2blltvvXWIXwXPZnve56d7jzs7O/PKV74yd9xxR6ZPn5599903d9xx\nR+66667svvvu+exnP5vJkycX8fKApzDYcf/e9743V111Verr6/OiF73oaWdm77///nnFK/5fe/ce\nVlO+/wH8vSoUGRJjQtKJ9kK2KGKcQnXKKKTpuOduyBkOHs5TTi4nl9zOmFEck4kMB8NQiHEflfut\nGteMpFKNg+hCW1Ot3x/OXr+29k7pYprzfj3PPM883+93re9lrTWXz/76fEfWypyISLfq+He9NpGR\nkQgICMDgwYOxatWqmp4GEVVCdXz3BQUF+Oyzz3DlyhUYGRmhZ8+eeP78OX766Sfo6elhyZIlGDp0\n6PuYHhFpUR3f/enTpzFr1iyoVCq0bdsWoigiIyMDN2/ehJ6eHgICAuDr6/s+pkdEbxEQEICoqCgs\nWbIEPj4+GnW1Gc97b4dCqvn4+MDMzAxhYWFITEyEvr4+bGxsMGHCBLi6upZpLwiC/NfbVLQd1bzK\nPGddz9jY2Bg7duzAhg0bcPToUZw+fRotW7bEmDFj4OfnB1NT09qcEhG9RVW/+7i4OAiCgOLiYhw+\nfFhnP4IgMKBN9BtQHf+u14X/TUf021Qd372RkREiIiLw73//G1FRUbh48SIaNGgAJycnTJkyBXZ2\ndrU5JSJ6i+r47vv164e9e/fim2++wfnz5/Hjjz/C2NgYzs7OmDhxIuzt7WtzSkRUTWoznvfed2gT\nEREREREREREREVUET9YiIiIiIiIiIiIiojqBAW0iIiIiIiIiIiIiqhMY0CYiIiIiIiIiIiKiOoEB\nbSIiIiIiIiIiIiKqExjQJiIiIiIiIiIiIqI6gQFtIiIiIiIiIiIiIqoTGNAmIiIiIiIiIiIiojqB\nAW0iIiIiIiIiIiIiqhMY0CYiIiIiIiIiIiKiOoEBbSIiIiIiIiIiIiKqExjQJiIiojovMjISoihW\n+q/Q0FCN6wcNGvSeZ1I5ly5dgiiK6N69u0a5s7MzRFHEsWPH5LK6OseKysnJwdOnT7XWJScn1/Jo\nfpsiIyMxaNAgdO3aFQ4ODli8eLFcFx4eDnd3dyiVSvTu3RsbN25EaGgoRFHEtGnTqtSvr68vRFHE\nli1bqjiDqlGpVMjMzHyvY9BG2/dKRERERLoZvO8BEBEREVWVqakp7OzsypSnpqbi6dOnMDU1hYWF\nRZn6Vq1a1cbwapQgCBAEQWv5/4rvvvsOa9euRUhICExNTeVylUqFL7/8Etu3b8eNGzfe4wjfvxMn\nTiAgIACCIMDMzAwmJiZo06YNAGDr1q1YvXo1BEFA27Zt0ahRI7Ru3Rqpqak636/Ket/v4/Hjx7F8\n+XL89a9/hZeX13sdizbve32IiIiI6hIGtImIiKjOc3JygpOTU5nygIAAREZGwtHREcHBwe9hZDWr\na9euOHz4MPT0/rf/0N2KFSugUqnKlN+7dw8REREMFgL44YcfIAgCHB0dERYWprVu+PDhGru2nz9/\nDg8PDzRs2LBKfa9evRoFBQUaPzbUtnXr1uGXX355b/0TERERUfVhQJuIiIiojmrQoAEsLS3f9zCo\nDsjOzgYArX+SQV1nb2+vUd60aVM0bdq0yn1/9NFHVb4HEREREZHa//Z2HiIiIiL63ZIk6X0P4Tej\nqKgIAFC/fv1K1RERERER/dYwoE1ERERUSm5uLlauXAlXV1d06dIFjo6O+Nvf/oa0tDSd18TGxmLq\n1Kno3bs3unTpgv79+2P+/PlISUmpdP9JSUmYO3cu+vXrBxsbG/Ts2RMjR47E1q1bUVhYqNFW16GQ\nNTHHqKgojB07Fj179kSXLl3g7OyMBQsW4MGDB2Xavu0AyqNHj0IURbi4uGitr+h6qg8tVKcbUR8+\nGBkZCV9fX/z5z3+W042oDwJ981DA6nx2wOsDKBctWgQ3NzcolUo4ODhgypQpOHPmjNb2Dx8+RFBQ\nENzc3NClSxf06NEDo0ePxp49e1BcXKyzn4qOOyAgAKIo4vLlywCAlStXQhRFODs7y+ulXpOZM2dC\nFEWMHTsWABASEqLzUMji4mLs3r0bo0aNQq9evdClSxcMGDAA//znP5GXl6fRtrxDIXNycvDFF1/A\n09MTtra2sLOzw4gRI/Ddd99pnb/6XteuXUNCQgL8/PzQq1cvKJVKeHh4YP369RrpZ9Tv4s8//wwA\n8Pf31zgQVhd1P+W1mzRpEkRRxKZNmzTK4+LiMGvWLDg7O6Nr166wtbWFq6sr5s+fX+EDSt92IOfm\nzZs1ntWboqOjMW7cODg4OECpVMLNzQ1Lly7Fo0ePtLbPyMjAwoUL4erqChsbG9jb28Pb2xuhoaHI\nz8+v0JiJiIiIagsD2kRERET/lZOTg08//RQRERHQ19dHu3bt8OzZMxw4cACffvop0tPTy1yzdOlS\nfPbZZ4iNjYWenh4UCgVevnyJffv2wcvLCydPnqxw/5cuXcKwYcNw6NAhqFQqKBQKmJqaIjExEcHB\nwZgwYQJKSkpqdY6FhYWYPHky/P39cfnyZXzwwQcQRRE5OTnYs2cPhgwZgiNHjlRpTKVVZj3NzMxg\nZ2cnB62tra1hb2+P5s2bQ6FQoGPHjvIubTs7O9jb26NBgwbv1FdFHDx4EN7e3ti9ezeys7NhbW2N\nBg0a4MyZM5g8eTJ2796t0f706dMYNGgQdu7cicePH0OhUKBZs2a4du0aFixYgAkTJuDly5dVWiNL\nS0vY2dmhcePGAIA2bdrAzs4OSqUSCoUCdnZ28s5sKysr2NnZQaFQANB9UGFeXh7GjRuHhQsXIj4+\nHk2aNIGVlRUyMzOxadMmjBw5skwQVNu9kpKS4OnpibCwMKSmpsLc3BwfffQREhMTsWjRIkyePBmv\nXr0qc50gCDhy5AhGjx6Ns2fPomXLlmjevDnu37+PkJAQTJ8+XW6rPjDWyMgIANCuXTvY2dm99UBY\n9cGRhw8f1lr/9OlTXLhwAfr6+hg8eLBcHhQUhClTpuDo0aOQJAnW1tYwNTVFZmYm9u3bBx8fH9y6\ndavcvquiqKgIM2fOxNy5c3Hp0iUYGRnB2toajx8/xvbt2zF48GAkJiZqXHP//n14e3tjz549eP78\nOaytrWFmZoa7d+8iNDQUw4YNY1CbiIiIflskIiIiot8pf39/SaFQSP7+/uW227dvn6RQKCSFQiE5\nOTlJV69elevu3bsn9enTRxJFUQoMDNS4btu2bZJCoZD69OkjnT59Wi4vKiqSNm7cKImiKNna2kqp\nqakVGq+3t7ckiqIUHBwsFRUVyeXXr1+XevToIYmiKB04cEAuv3jxoqRQKKRu3bpp3Kd///6SKIrS\n0aNHqzzHBQsWyHO8dOmSXK5SqaQlS5ZICoVCUiqV0u3bt8v05enpqXWeR44ckRQKheTs7KxR/q7r\naWtrK4miqDE+9bopFApJFMUyY6juZ5eSkiIplUpJFEVp1apV0qtXr+S6LVu2SAqFQurcubOUkpIi\nSZIkpaWlSV27dpVEUZQWLFgg5efny+0TEhKkvn37SqIoSrNnz66WcY8ZM0YSRVHavHlzmbFre18k\nSZJCQkIkhUIhTZ06VaNc/V25uLhIN2/elMszMjIkT09PSaFQSPPmzSu37/z8fLlff39/KTc3V667\nd++e5OnpKa/Nm/NQv8dz5syRnj9/Ltft2rVLft7nzp3TuE59v8jIyDLz1yY/P19+r0q/22oRERGS\nQqGQJk6cKJfFxMRICoVCsrW1lc6ePavR/u7du5KLi4vWZ6pt/XWtvVp4eLikUCgkX19fjfIVK1ZI\nCoVCGjBggJSYmCiXFxQUyN9rnz59NNZtxowZ8rgKCgrk8rS0NHls//rXv8pbLiIiIqJaxR3aRERE\nRP8lCAJWrFihkcLDysoK48aNgyRJuHr1qlxeWFiI9evXQxAEfPnll+jbt69cp6+vj6lTp8LT0xMq\nlapMSgJd1GkRvLy8oK+vL5fb2Njg888/h5ubW5XzHFdmjpmZmdi7dy8EQcDatWvRo0cPua5BgwYI\nDAxE//79UVhY+NYUDm9TE+tZm32Fh4fj1atX6NevH+bNm6fxnMaPH49+/fqhuLgYBw4cAAB8/fXX\nUKlU6NmzJ4KCgtCoUSO5fdeuXeX1/OGHH3Dnzp0aG3dlPXr0CFFRURAEAevWrUOnTp3kulatWiE4\nOBgAcPz4ca27y9V2796NzMxMODg4IDg4WN5BDrx+H9etWwd9fX3s27evTJoYADA3N8fq1avRpEkT\nuWz48OEQRREANN7jd9GoUSO4uroCeJ2+400HDx6EIAgYMmSIXHbmzBnUq1cPo0aNwscff6zRvkOH\nDhgzZgwkSZK/8+r25MkTbN++HQYGBggLC4NSqZTrDA0NERgYiO7du+Pp06fYuXOnXKcej4eHBwwN\nDeVyc3NzzJ07Fy4uLjAxMamRMRMRERG9Cwa0iYiIiP6rYcOG6N27d5nyDh06AACePXsml8XHx+PZ\ns2f48MMPYW9vr/V+np6ekCQJp0+frlD/FhYWkCQJixcvxuXLlzVyCI8dOxZfffUV3N3dKzGjsioz\nx7i4OBQXF8PKykojmF3a2LFjIUkSzp49Kx8u+C5qYj1rs6+YmBgIggAfHx+t9f/4xz9w/PhxzJw5\nU6P9yJEjtba3sbGRf3T48ccfa2zclRUbGwtJkiCKokYwu/S4o6KicObMGTRs2FDnfU6dOgVBEODh\n4aG13tLSEqIoori4GHFxcRp1giDAyckJenpl/1fG0tISAKolRYaXlxckSSqTdiQ1NRU3btyAkZER\n3Nzc5PL58+fj+vXrmD17ttb7qdOelM7xXZ1iY2Px66+/QqlUwtzcXGsbDw+PMu+H+p87a9asQUxM\njEau/oEDByI0NBTDhw+vkTETERERvQuD9z0AIiIiot+K5s2bay1XB+ZK5/O9d+8egNcHLI4aNUrr\nderA1ZMnT5Cfnw9jY+Ny+58zZw5mzJiBxMRE+Pr6onHjxujVqxecnJzg70qjaAAADIJJREFU7OwM\nU1PTSs/pTZWZo/rAR22BSzUbGxsAr+ealZWlM5D2NjWxnrXVV2FhIf7zn/9AEARYW1trbdOyZUv5\n71+8eIHHjx9DEAR07txZ5307d+6Mq1evys+hNtdIF/XBobrmCUDeJV0e9a7gLVu2IDIyUmubjIwM\nSJKk9YDO0utZmnqHcVV+XFH7+OOP0bJlS2RlZeHatWvyDwzqXfbu7u4aO5rVSkpKcP78edy7dw8P\nHz5Eamoq7ty5g19++UWurwnqNU1OTtb5fuTm5gKAxpp+/vnnuHjxIh48eICpU6fCyMgI9vb2cHR0\nhIuLC1q3bl0j4yUiIiJ6VwxoExEREf1XZdJ55OXlAXgdQIyPj9fZThAECIJQoeBi//79sWvXLoSF\nhSEuLg75+fk4ceIEjh8/jsWLF2PgwIFYtGhRlYKUlZmjepdr6XQYbyq9C/fFixfvPK6aWM/a6qv0\nrvby1kqt9O7hiqytel1rc410ef78ucbY3pV6DbQFq0sTBEGed2n16tWrUv8VIQgCBg8ejE2bNuHQ\noUNyQDs6OrpMuhG1TZs2YcuWLcjOzpYPwjQwMEDHjh3RsWNHebd9TVCvaW5u7lvfj9LvoHpX/caN\nG3Hy5Enk5eXhzJkziIuLw/Lly9G3b18EBQXp/BGBiIiIqLYxoE1ERET0DtTpA3r37o3NmzdX231t\nbGywbt06FBYW4urVq7hw4QJiYmKQlJSEgwcPQqVSISQkpNr6K4862Fpe+obSwcaKBHMBoKCgoExZ\nTa2nNtXdV+ng7suXL9GsWbNy25dep/z8fJ3t3/xBoTbXSBf1GLQ9w8reJz8/Hzt27EC3bt2qY2g1\nwsvLC5s2bcKRI0cQGBiI69evIzU1Fa1atUKvXr002q5btw4bNmyAoaEhxowZgx49eqB9+/awsLCA\ngYEBoqOjqy2gXd435OPjgyVLllTqfhYWFggODkZJSQkSEhJw4cIFxMbGIjExETExMfDz88O+ffuq\nZexEREREVcUc2kRERETvQJ2r9/79+zrbPHv2DFeuXEFWVtZb71dSUoL09HRcuXIFwOud1L1798bs\n2bMRFRWFhQsXAgBOnDhR5WBiRanneOvWLZ1tbt68CeD1jlkzMzMAkA+0LJ2Lt7THjx/r7Ku61rM8\n1d1X48aN5XQw6rQgb4qNjcXo0aOxdu1aGBsbo0WLFgD+f/20uXHjBgRBQNu2bWtk3O+iXbt2AFDu\nwYZ/+ctf4OfnJx9mqY16LrrWCwASExORlJRUa++7NlZWVrCxsUF2djauXLki59MeNGiQRruioiJs\n3boVgiAgKCgIgYGBcHd3h5WVFQwMXu8hUqccqYiqfEPJyck675uVlYX4+HiN67OysnD+/HkAgJ6e\nHrp3747p06dj165d+PrrrwEAt2/fLve+RERERLWJAW0iIiKid2Bvbw9jY2M8evRI567LtWvXYsyY\nMZg2bdpb7/fw4UP86U9/wvjx45GdnV2mvvRBjqUPi6xJjo6O0NfXx/3793Hx4kWtbb799lt5fOrA\nXZMmTQAAjx490hqMPHXqVJmyqqyn+nBASZK0lldnX7o4OjpCkiSd+aD379+Pq1evyik7+vbtC0mS\nsGPHDq3tf/rpJyQkJMj3rqlxV5ajoyMEQcCtW7e0BrVTUlJw8uRJxMbGwsTEROd91PPfvXt3mecG\nvM6fPXr0aAwZMgRnzpyp8rjV6T+09fU2Q4cOBQAcP34cx44d05pu5NmzZ3JqGG05xH/99Vfs378f\nQMW+3w8++ADA/+exf/NecXFx8pzUHB0doaenh4SEBJ0/JgQGBmLkyJHyD2QvX76Eu7s7Jk6cqPWH\nKwcHB/k7qq1/7hARERG9DQPaRERE9Lv2ZtCnujRs2BCTJk2CJEnw9/fXCDAWFxcjPDwcu3fvhiAI\nmDx58lvv17ZtWyiVShQXF2PevHl48uSJXJefn48vvvgCANC9e/dqz4usS6tWreDt7Q1JkjBnzhxc\nunRJrlOpVFi6dCliYmJQv359zJo1S65TKpUwMDDAq1evsGrVKvmAvoKCAixbtgzx8fFlnktV1lOd\n8iMzM1Nr+Zt11f3sAGDy5MmoX78+jh07htDQUHnOkiRh27ZtOHToEAwMDODr6wsAmDJlCoyMjHDl\nyhUEBgZq5B9PSEjAjBkzIAgC3NzcoFQqa2zclWVubg5PT09IkoQZM2Zo7LBOT0/HnDlzIAgCPvnk\nk3JzLo8ePRrNmzfHjRs3MHfuXDnQD7wOik+bNg1FRUWwtLSEq6trlcetfhcyMjIqfa2HhwcMDAzw\n/fffIzMzEzY2NvjDH/6g0aZZs2byDzlhYWEah6ump6dj2rRp8g8AFdlxbmdnB+D17mn1LmngdQ7z\nefPmlXnXAaBNmzbw9vZGSUkJpk+frpFHW6VSITg4GGfPnoWenh4mTZoE4PW6uLq6QpIkBAYGIj09\nXb6msLAQq1evRklJCVq3bo327du/ddxEREREtYE5tImIiOh37V12ZFbUtGnTkJqaiv3798PPzw8t\nW7ZEixYtkJ6ejpycHAiCgIkTJ5ZJT6DLypUrMWLECJw7dw79+/eXc++mpqaioKAAJiYmCAoKqrH5\naPP3v/8dmZmZOHfuHMaOHYs2bdrAxMQEycnJePnyJYyMjLBs2TJ07NhRvsbExATjx49HeHg4du7c\niaNHj8LMzAwPHjxAQUEBZsyYoTUP+LuuZ6dOnRATE4NFixZh27Zt8PX1hZeXF1q3bo0mTZogNzcX\nQ4cOhbm5OVauXAkrK6tqf3bt27dHcHAw/P39ERoaim3btsHc3ByZmZnIzs6Gvr4+Fi9eLAcFLSws\nsGbNGsydOxd79+7FoUOHYGVlhby8PKSmpkIQBDg4OGDZsmXVskbVadGiRcjIyEB8fDwGDRoEKysr\nCIKAlJQUFBcXo2PHjvIOYF2aNm2K0NBQ+Pn54fDhwzh27Bg6dOgAlUqFBw8eQJIkfPjhhwgLC6v0\nj1LavvlOnTohISEBGzduxKlTp+Du7o6pU6dW6H5NmzZFv379cPz4cQiCAC8vrzJt9PX1MXPmTCxd\nuhTR0dGIiYlB27ZtkZ+fj7S0NACvdztfvHgReXl5UKlUMDQ01NmnKIr45JNPcOTIEaxduxY7duyA\nqakpkpOTUVJSAj8/P2zYsKHMdYGBgcjKysK5c+cwcuRImJub44MPPkBqairy8/MhCAICAwNhb2+v\ncU1iYiJu376NAQMGwNzcHA0bNkR6ejry8vJgaGiI4OBgnX/igYiIiKi28b9KiIiI6HdNEIQKBcTe\n1k5bvSAIWLFiBdavX49+/fqhqKgId+7cgSAIcHR0REhICObNm1fhsVpaWuL777/HsGHD0KpVK6Sl\npckH0E2YMAHR0dGwsrKq0Lh1lVV2joaGhvjmm2+wfPly9OzZE3l5efj555/RokUL+Pr6IioqCgMH\nDixzr7lz5yI4OBi2trYoKChAeno6unXrhoiICAwfPlzrGN91PRcvXgwnJycYGBggJSUFKSkpAF7n\n9Q4JCUHHjh2hUqmQkZEhBxer+9kBr3fyRkZGwsvLC0ZGRkhKSoIkSXBzc8POnTvh4+Oj0d7FxQUH\nDhzA8OHD0bx5c9y9exd5eXlwcHBAcHAwIiIiyuzGr4lxq++rq/zNOmNjY3z77bcIDAyEUqlEVlYW\n0tLSYGlpidmzZ2PXrl1yyozy2NraIjo6GpMmTYKFhQVSUlLw8OFDWFhYYMKECYiKioK5uXm1zGXW\nrFnw8PBAw4YNkZKSUul80F5eXhAEAfXq1YOHh4fWNqNHj0Z4eDh69eqFBg0a4O7du3jx4gX++Mc/\n4quvvsLWrVvlPPNvpozRNuY1a9bA398foigiJycHWVlZ6Nu3L/bs2YO+ffuW+70GBwejV69eyM3N\nRVJSEgwNDeHm5oZt27Zh1KhRGtc0a9YMe/bswfjx42FpaYlHjx4hOTkZJiYmGDFiBA4ePIgePXpU\nar2IiIiIapIg1eS2JSIiIiIiIiIiIiKiasId2kRERERERERERERUJzCgTURERERERERERER1AgPa\nRERERERERERERFQnMKBNRERERERERERERHUCA9pEREREREREREREVCcwoE1EREREREREREREdQID\n2kRERERERERERERUJzCgTURERERERERERER1AgPaRERERERERERERFQnMKBNRERERERERERERHUC\nA9pEREREREREREREVCcwoE1EREREREREREREdQID2kRERERERERERERUJzCgTURERERERERERER1\nwv8BuysFyQuRyxEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e0d8ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('For n_clusters =', 4, 'The average silhouette_score is :', 0.5260948672798107)\n"
     ]
    },
    {
     "data": {
      "image/png": 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vp2FERARdunRBURQ8PT3p1KnTO6zph+XGjRs0atQIRUkdO2D69Onv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\n/7fC7Xp7e3P99dev9D6Xfb7nzZuXL33pSyvd54t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AAAAAQFNQaAMAAAAA0BQU2gAAAAAA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lMURfqGi+zaVcnbXtKT5y5orXckAACA7UKhDQDQIMaK7Gp6Wpty8ZruvHRpR0ols7IBAICdh0Ib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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10420a190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('For n_clusters =', 5, 'The average silhouette_score is :', 0.53680444175593633)\n"
     ]
    },
    {
     "data": {
      "image/png": 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KlWpuzFWrVuHt7a1XbkZfjb137x6zZs0CoHLlyqxfv549e/bQuXNnIPnHzLlz5yhYsCD+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+P2Atg+tZXKeXXXzPTPmpVqxcwi2J4Nj4zk8V/+Mkkye9YcYx4mAeN+6yzu7c/+U3fIjLZOBTYA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\nERERERG1Cf8H0DsUjusPDikAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a422850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('For n_clusters =', 6, 'The average silhouette_score is :', 0.46233458987503845)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Npk2bYm9vT0xMDIGBgYwePZrvv/8+w/W87nXO0ANBV1dX9u7dy6RJk3B1dcXG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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e0bfcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "\n",
    "# set number of clusters\n",
    "range_n_clusters = [2, 3, 4, 5, 6]\n",
    "\n",
    "# save data to X\n",
    "X = submatrix \n",
    "for n_clusters in range_n_clusters:\n",
    "    fig, (ax1) = plt.subplots(1)\n",
    "    fig.set_size_inches(18, 7)\n",
    "\n",
    "    # The 1st subplot is the silhouette plot\n",
    "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
    "    # lie within [-0.1, 1]\n",
    "    ax1.set_xlim([-0.1, 1])\n",
    "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "    # plots of individual clusters, to demarcate them clearly.\n",
    "    ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n",
    "\n",
    "    # Initialize the clusterer with n_clusters value and a random generator\n",
    "    # seed of 10 for reproducibility.\n",
    "    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
    "    cluster_labels = clusterer.fit_predict(X)\n",
    "\n",
    "    # The silhouette_score gives the average value for all the samples.\n",
    "    # This gives a perspective into the density and separation of the formed\n",
    "    # clusters\n",
    "    silhouette_avg = silhouette_score(X, cluster_labels)\n",
    "    print(\"For n_clusters =\", n_clusters,\n",
    "          \"The average silhouette_score is :\", silhouette_avg)\n",
    "\n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(X, cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(n_clusters):\n",
    "        # Aggregate the silhouette scores for samples belonging to\n",
    "        # cluster i, and sort them\n",
    "        ith_cluster_silhouette_values = \\\n",
    "            sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "        color = cm.spectral(float(i) / n_clusters)\n",
    "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                          0, ith_cluster_silhouette_values,\n",
    "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "        # Label the silhouette plots with their cluster numbers at the middle\n",
    "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "        # Compute the new y_lower for next plot\n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "    ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
    "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
    "    ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all the values\n",
    "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
    "    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "\n",
    "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
    "                  \"with n_clusters = %d\" % n_clusters),\n",
    "                 fontsize=14, fontweight='bold')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### K = 5 seems the best cluster for the dataset based on silhouette_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing $K$: The Gap Statistic\n",
    "\n",
    "There is one last method worth covering for picking $K$, the so-called Gap statistic. The computation for the gap statistic builds on the sum-of-squares established in the Elbow method discussion, and compares it to the sum-of-squares of a \"null distribution,\" that is, a random set of points with no clustering. The estimate for the optimal number of clusters $K$ is the value for which $\\log{SS}$ falls the farthest below that of the reference distribution:\n",
    "\n",
    "$$G_k = E_n^*\\{\\log SS_k\\} - \\log SS_k$$\n",
    "\n",
    "In other words a good clustering yields a much larger difference between the reference distribution and the clustered data. The reference distribution is a Monte Carlo (randomization) procedure that constructs $B$ random distributions of points within the bounding box (limits) of the original data and then applies K-means to this synthetic distribution of data points.. $E_n^*\\{\\log SS_k\\}$ is just the average $SS_k$ over all $B$ replicates. We then compute the standard deviation $\\sigma_{SS}$ of the values of $SS_k$ computed from the $B$ replicates of the reference distribution and compute\n",
    "\n",
    "$$s_k = \\sqrt{1+1/B}\\sigma_{SS}$$\n",
    "\n",
    "Finally, we choose $K=k$ such that $G_k \\geq G_{k+1} - s_{k+1}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aside: Choosing $K$ when we Have Labels\n",
    "\n",
    "Unsupervised learning expects that we do not have the labels. In some situations, we may wish to cluster data that is labeled. Computing the optimal number of clusters is much easier if we have access to labels. There are several methods available. We will not go into the math or details since it is rare to have access to the labels, but we provide the names and references of these measures.\n",
    "\n",
    "* Adjusted Rand Index\n",
    "* Mutual Information\n",
    "* V-Measure\n",
    "* Fowlkes–Mallows index\n",
    "\n",
    "See [this article](http://scikit-learn.org/stable/modules/clustering.html) for more information about these metrics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Clusters using PCA\n",
    "\n",
    "How do we visualize clusters? If we only had two features, we could likely plot the data as is. But we have 100 data points each containing 32 features (dimensions). Principal Component Analysis (PCA) will help us reduce the dimensionality of our data from 32 to something lower. For a visualization on the coordinate plane, we will use 2 dimensions. In this exercise, we're going to use it to transform our multi-dimensional dataset into a 2 dimensional dataset.\n",
    "\n",
    "This is only one use of PCA for dimension reduction. We can also use PCA when we want to perform regression but we have a set of highly correlated variables. PCA untangles these correlations into a smaller number of features/predictors all of which are orthogonal (not correlated). PCA is also used to reduce a large set of variables into a much smaller one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set IV</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Use PCA to plot your clusters:</p>\n",
    "\n",
    "<ul>\n",
    "<li> Use scikit-learn's [`PCA`](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) function to reduce the dimensionality of your clustering data to 2 components\n",
    "<li> Create a data frame with the following fields:\n",
    "  <ul>\n",
    "  <li> customer name\n",
    "  <li> cluster id the customer belongs to\n",
    "  <li> the two PCA components (label them `x` and `y`)\n",
    "  </ul>\n",
    "<li> Plot a scatterplot of the `x` vs `y` columns\n",
    "<li> Color-code points differently based on cluster ID\n",
    "<li> How do the clusters look? \n",
    "<li> Based on what you see, what seems to be the best value for $K$? Moreover, which method of choosing $K$ seems to have produced the optimal result visually?\n",
    "</ul>\n",
    "\n",
    "<p><b>Exercise:</b> Now look at both the original raw data about the offers and transactions and look at the fitted clusters. Tell a story about the clusters in context of the original data. For example, do the clusters correspond to wine variants or something else interesting?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>offer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adams</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.095967</td>\n",
       "      <td>-0.730059</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allen</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.104426</td>\n",
       "      <td>1.435039</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anderson</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.228685</td>\n",
       "      <td>3.915361</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bailey</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.786007</td>\n",
       "      <td>-0.538732</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baker</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.300455</td>\n",
       "      <td>-2.768331</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "offer_id customer_name    1    2    3    4    5    6    7    8    9   ...     \\\n",
       "0                Adams  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   ...      \n",
       "1                Allen  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0   ...      \n",
       "2             Anderson  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   ...      \n",
       "3               Bailey  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   ...      \n",
       "4                Baker  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   ...      \n",
       "\n",
       "offer_id   26   27   28   29   30   31   32         x         y  cluster  \n",
       "0         0.0  0.0  0.0  1.0  1.0  0.0  0.0  1.095967 -0.730059        2  \n",
       "1         0.0  1.0  0.0  0.0  0.0  0.0  0.0 -3.104426  1.435039        3  \n",
       "2         1.0  0.0  0.0  0.0  0.0  0.0  0.0  2.228685  3.915361        4  \n",
       "3         0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.786007 -0.538732        2  \n",
       "4         0.0  0.0  0.0  0.0  0.0  1.0  0.0 -1.300455 -2.768331        0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# save a list of the 0/1 columns\n",
    "x_cols = matrix.columns[1:]\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "x = pca.fit_transform(matrix[x_cols])[:,0] \n",
    "y = pca.fit_transform(matrix[x_cols])[:,1]\n",
    "matrix['x'] = x\n",
    "matrix['y'] = y\n",
    "matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>offer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>cluster</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adams</td>\n",
       "      <td>2</td>\n",
       "      <td>1.095967</td>\n",
       "      <td>-0.730059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allen</td>\n",
       "      <td>3</td>\n",
       "      <td>-3.104426</td>\n",
       "      <td>1.435039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anderson</td>\n",
       "      <td>4</td>\n",
       "      <td>2.228685</td>\n",
       "      <td>3.915361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bailey</td>\n",
       "      <td>2</td>\n",
       "      <td>0.786007</td>\n",
       "      <td>-0.538732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baker</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.300455</td>\n",
       "      <td>-2.768331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "offer_id customer_name  cluster         x         y\n",
       "0                Adams        2  1.095967 -0.730059\n",
       "1                Allen        3 -3.104426  1.435039\n",
       "2             Anderson        4  2.228685  3.915361\n",
       "3               Bailey        2  0.786007 -0.538732\n",
       "4                Baker        0 -1.300455 -2.768331"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_clusters = matrix[['customer_name', 'cluster', 'x', 'y']]\n",
    "customer_clusters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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27bP3AXtn7wP2/rf3jRwxMrtO23V+EJEkK2bF1I+vz5QpU1JVVZWWOPQS4KOcGQEAAIvo\nnRnvpGu6LjTedV7XzJw5M9/4xjcyoTQh0zJtges35sYMWn9QUWUCVBw7IwAAYBFt9O2NMvaxsflq\n01fnjzWlKZO6TMqKK66YJDn10lNz2LDDsm22Tb/0y925O8+1fy4v3P1CucoGKDs7IwAAYBENP2x4\n7l3t3txde3fmZE4mZmJ+vuzPc+SZR6aq6v2/au+11155esbTee3br+X6Va/Ppr/YNBPmTEhdXV2Z\nqwcoHzsjAABgEXXq1Ck3jb0pV/zmilx6w6Xp3a93fnncL7PKKqsscN9SSy2V6268rkxVAlQeYQQA\nACyGDh065IBDDsgBhxxQ7lIAPjM8pgEAAAAUShgBAAAAFEoYAQAAABRKGAEAAAAUShgBAAAAFEoY\nAQAAABRKGAEAAAAUShgBAAAAFKqm3AUAAEClaG5uzkNjH8oV512RvgP6Ztj3h2XZZZctd1kAbY6d\nEQAAkKSpqSk/PujHeea4Z/I/l/9Pqk+pzs5r75x77rqn3KUBtDl2RgAAQJJrLr8mQ54akq3qt0qS\n9G/pn6GvDc1JB56UMePHpKrK/+MBLCl+ogIAQJKbrrgpm9VvtsBYXeryhRlfyN/+9rcyVQXQNgkj\nAAAgSYeOHTIncxYan1s9Nx07dixDRQBtlzACAACSDDtiWK7tcu0CY1MyJe/0fSf9+/cvU1UAbZMz\nIwAAIMk3N/5mbtn2loy4fUTWnb5upnWelr/1/Vt+98fflbs0gDZHGAEAAP+0z6H7ZNoe0zJz5sz0\n7ds3X/7yl1MqlcpdFkCbI4wAAIAP6dGjR77+9a+nrq6u3KUAtFnOjAAAAAAKJYwAAAAACiWMAAAA\nAAoljAAAAAAKJYwAAAAACiWMAAAAAAoljAAAAAAKJYwAAAAACiWMAAAAAAoljAAAAAAKJYwAAAAA\nCiWMAAAAAAoljAAAAAAKJYwAAAAACiWMAAAAAAoljAAAAAAKJYwAAAAAClVT5GKjRo3KNddckxdf\nfDG1tbVZZZVVstNOO2WbbbYpsgwAAACgjAoLI04++eRcddVV6dChQ9Zdd91UVVXlkUceydFHH51x\n48ZlxIgRRZUCAAAAlFEhYcS9996bq666Kn379s3VV1+dZZddNkny5ptvZpdddskf//jHbL755tlw\nww2LKAcAAAAoo0LOjLj55ptTKpVy6KGHzg8ikmTZZZfN7rvvnpaWltx3331FlAIAAACUWSE7I848\n88wceOCBWW655Ra69t57771fSE2hx1cAAAAAZVJIAlBdXZ2BAwcuNP7444/nqquuSk1NTbbeeusi\nSgEAAADKrCzbEY488si89NJLef7559O9e/ecffbZWXXVVctRCgAAAFCwwsOIGTNm5NZbb02pVJr/\n629/+1s23XTTVFUt/hEWTU1NaWhoWAKVwqJrbGz82K+hnPQllUZPUmn0JJVIX1JpllQfllpaWlqW\nyEz/pYaGhrz77rtp165dHnvssZx66ql5+eWXs9NOO+Xkk09e5Hlnz56d559/fglWCgAAAHySwYMH\np2PHjov0vYWHER81adKkfPvb305DQ0PuuOOOLL/88os0jzACAAAAirM4YUTZX2HRr1+/rLXWWhk7\ndmzGjx+/yGHEBwYMGJAuXbosoepg0TQ2Nmb8+PFJkkGDBqW2trbMFYG+pPLoSSqNnqQS6UsqzYd7\ncnEUEkacc845eeWVV3LGGWekffv2C12vq6tLsmSePampqZk/H1SC2tpaPUnF0ZdUGj1JpdGTVCJ9\nSVuy+CdG/hfuu+++3HHHHRk9evRC12bNmpUnnngiSTJkyJAiygEAAADKqJAwYpdddklLS0vOPPPM\nvPzyy/PH33nnnRx11FGZMWNGNt100/Tr16+IcgAAAIAyKuQxjZ133jnjxo3L7bffnq233jprr712\nampq8tRTT+Wdd97JkCFDMmLEiCJKAQAAAMqskDCiVCrlnHPOyQYbbJBRo0bNfyxjwIABOeCAA7Ln\nnnt69gkAAAA+Jwp9m8b222+f7bffvsglAQAAgApTyJkRAAAAAB8QRgAAAACFEkYAAAAAhRJGAAAA\nAIUSRgAAAACFEkYAAAAAhRJGAAAAAIUSRgAAAACFEkYAAAAAhRJGAAAAAIUSRgAAAACFEkYAAAAA\nhRJGAAAAAIUSRgAAAACFEkYAAAAAhRJGAAAAAIUSRgAAAACFEkYAAAAAhRJGAAAAAIUSRgAAAACF\nEkYAAAAAhRJGAAAAAIUSRgAAAACFEkYAAAAAhRJGAAAAAIUSRgAAAACFEkYAAAAAhRJGAAAAAIUS\nRgAAAACFEkYAAAAAhRJGAAAAAIUSRgAAAACFEkYAAAAAhRJGAAAAAIUSRgAAAACFEkYAAAAAhRJG\nAAAAAIUSRgAAAACFEkYAAAAAhRJGAAAAAIUSRgAAAACFEkYAAAAAhRJGAAAAAIUSRgAAAACFEkYA\nAAAAhRJGwH/Q0tKSu+++Nzvt9IPsvvuRefjhh1t9zaampkyZMiVNTU2tvhYAAEDRaspdAFS6/fY7\nJjfcUJsZMw5P0pg77jg/w4ffmxEjjlria7W0tOTkk8/LpZf+JQ0N/VNX90r23Xfz/PjHhyzxtQAA\nAMrFzgj4N55++uncfPPczJhxapIBSVbK22+fl9/97om8/vrrS3y988+/NOecMzcTJ96S11+/MBMn\n3pKf/3xWLrjg8iW+FgAAQLkII+DfuPrq2zN16i4Ljb/xxva5/fbRS3y9kSP/mHfeOSpJ6Z8jpbzz\nzo/yq19dt8TXAgAAKBdhBPwbvXotlaqqtxcab9fu7XTv3nWJr1df3y5J9UdGa1JfX7fE1wIAACgX\nYQT8G8OG7Zg+fS5MUv+h0Vnp02dUttzyf5f4er16VSWZ8ZHRaVl2Wce7AAAAbYcwAv6NHj165De/\nOTIDBnw7Sy11Vrp3H5EvfnGHXHvt6WnXrt0SX+/cc3+U3r33STLxnyMT0qfPfjn33B8t8bUAAADK\nxX+3wn+w+ebfzN/+tmHGjRuXmpqarLvusamqap0cb/3118ldd43IMceckVdemZoBA3rl9NNPy+DB\ng1tlPQAAgHIQRsB/oba2NhtssEEha6266qq56aaRhawFAABQDh7TAAAAAAoljAAAAAAKJYwAAAAA\nCiWMAAAAAApV2AGWLS0tGTVqVP74xz/mH//4RxobG9O3b99ssskmGT58eLp06VJUKQAAAEAZFRJG\ntLS05JBDDsno0aPToUOHrL766unQoUOefvrp/OY3v8mdd96Zq6++Oj169CiiHAAAAKCMCgkjrrvu\nuowePToDBw7Mb37zm/Tp0ydJMnv27Bx11FEZM2ZMTjnllPziF78oohwAAACgjAo5M+KGG25IqVTK\n0UcfPT+ISJKOHTtmxIgRKZVKGT16dBoaGoooBwAAACijQsKIrl27ZuDAgVljjTUWuta9e/d07do1\nTU1NmT59ehHlAAAAAGVUyGMaI0eO/MRrkyZNysyZM1NXV5fu3bsXUQ4AAABQRmV/tefZZ5+dJNlo\no41SV1dX5moAAACA1lbWMOLSSy/Nn//853To0CGHHXZYOUsBAAAAClLIYxof59JLL80ZZ5yRqqqq\nnHbaaVlhhRWWyLxNTU0OwqTsGhsbP/ZrKCd9SaXRk1QaPUkl0pdUmiXVh6WWlpaWJTLTp3DWWWfl\nkksuSU1NTUaMGJFtttlmseecPXt2nn/++SVQHQAAAPCfDB48OB07dlyk7y10Z0R9fX2OOuqo3Hnn\nnenQoUPOOeecbLTRRkWWABXj/vsfyoUX/imzZrVL+/Zzstdem2brrTcpd1ltRlNTU6qrq1Mqlcpd\nCgAA8BGFhRHvvvtu9t9//zzxxBPp2bNnLrzwwgwdOnSJrzNgwIB06dJlic8Ln0ZjY2PGjx+fJBk0\naFBqa2sXuD5mzH0ZMWJcpk69MUmHJI0577zj07v3c9l3352LL7gNueyy6/Kzn/0+s2d3Sfv27+S7\n3/12Dj98v3KXVRH+U19C0fQklUZPUon0JZXmwz25OAoJI5qamnLAAQfkiSeeyBe+8IX89re/zfLL\nL98qa9XU1HgrBxWltrZ2oZ486aSLM3XqxXk/iEiS2kyffnp++ctt8r3v7Vl4jW3FddfdkmOOGZdp\n025OUp2kJaeffmo6drwqhxyyd5mrqywf15dQTnqSSqMnqUT6krakkLdpnH/++XnsscfSq1evXHHF\nFa0WRMBnxYwZzUm6fmS0Ou+9174c5bQZp512SaZNOz3vBxFJUsrMmcflV7+6rpxlAQAAH9HqOyNm\nzJiRyy+/PKVSKUsvvXR+9rOffeK9xx57bHr06NHaJUHZLb10TZLpSbp/aLQxnTvPLVNFbcO771bl\nX7tNPlCdOXOEPAAAUElaPYx4+OGHM3fu+//AeuGFF/LCCy987H2lUimHHnqoMILPhTPOODQ77HBQ\n3nprZN7fITEnPXocneOO27fcpX2mLbtsXf7+97eTLP2h0Tnp1s1rsAAAoJK0ehix6aabeuUmfMSG\nG3451147L0cfvV+mTWtJ587zcsIJ+2f77bcsd2mfaWeffWS22WZ4Jk/+VZJlk0xLr16H57TTDil3\naQAAwIcU+mpP4F++8Y2v5qGHvlruMtqU9dZbO7feekJ++MMfZfLk2enRoy4jRhyUr33tK+UuDQAA\n+BBhBNCm/M//rJm77rqs3GUAAAD/RiFv0wAAAAD4gDACAAAAKJQwAgAAACiUMAIAAAAolDACAAAA\nKJQwAgAAACiUMAIAAAAolDACAAAAKJQwAgAAACiUMAIAAAAolDAC+My5//4Hst12B2WrrQ7ITTfd\nlpaWlnKXBAAAfAo15S4A4NP40Y/OyG9/OzXTph2TpF3uu++SbL75bRk16v/KXRoAAPBfsjMCqChP\nPPFETjnll7nssqszZ86cBa69/vrr+f3vn820aT9P0i/JMpk165jcdVenPPLII2WpFwAA+PSEEUBF\naG5uzvbbH5hNNvl9TjxxrQwfPi+rrrpNnn76mfn33Hnn3XnjjW0W+t5p03bM1VffUWS5AADAYvCY\nBlARrrzyutxxx+C8996hSZL6+mTixC2z22575emnb06S9OzZPR06TMhHNkykqurN9O7do+iSAQCA\nRWRnBFARLr74T3nvvf0/Mto9U6eukIkTJyZJNtts0/Tpc2OS6R+6Z2769Lkg++67U0GVAgAAi8vO\nCKAilEqlJM0fc6UlVVXv56a1tbW58cZfZMcdd8/UqWulubl9und/ICNHHpull1660HoBAIBFJ4wA\nKsL3vrddHnvsorz77pEfGn07yyzzcvr37z9/ZMiQ1fLcc7fmmWeeSUNDQ9Zc87hUV1cXXzAAALDI\nhBFARdhll+1y882H5a67Ds1bb22dDh1eTp8+o3LttectdG+pVMrQoUPLUCUAALAkCCOAilAqlXLV\nVb/M888/n9tvvzf9+i2bbba5JXV1deUuDQAAWMKEEdCGtbS05PLLR+WCC65PfX1TNt98vZxwwsHp\n3LlzuUv7RIMHD87gwYPLXQYAANCKhBHQhh144I9z1VXdMmvWZUna5+mn78hf/rJrHnzwejsOAACA\nsvFqT2ijJk+enJtvfjmzZh2VpEOSUpqbN8/48dvkmmtuKHd5AADA55gwAtqoxx9/PG+//Y2FxufM\n+VZuu+3B4gsCAAD4J2EEtFH9+vVLly4vLDReVTU+q6zSrwwVAQAAvE8YAW3UkCFDMnDgS0me/tDo\nO1luuV/k4IP3LFdZAAAADrCEtuz22y/ObrsdmWeeeTfNzR3Tq9eM/Pa3I9KrV69ylzbf3/72tzz9\n9NNZZZVVMmTIkHKXAwAAFEAYAW1Y9+7dc/vtl2T27NlpaGhIt27dFmu+mTNn5o9/vCUNDY3Zdtst\nsswyyyzyXI2NjfnOd76XRx/tlLfeWj89e16WwYNfzW23/SadOnVarDoBAIDK5jEN+Bzo2LHjYgUR\n9fX1GT78h1l22c2z336lHHjgUllrrYNy7rmXLPKcJ5xwdkaP3jJvvnleWlp2y5QpP8v/+3/D873v\n/XiR5wQAAD4bhBHAvzV37tx8+cvb59e/Hpf6+vvT0rJbWlq2zeuvj8qZZ47Oyy+/vEjz3nTTX9PQ\nsO0CY81hFNl+AAAgAElEQVTN38i1196f1157bUmUDgAAVChhBFSIGTNm5MwzL8iwYUfn6quvT2Nj\nY7lLSpKMHHlFnnxy5ST7ZcEnu0qZPHl4Lrnk+kWat7m5OklpofHGxt7ZY4+jF2lOAADgs0EYARXg\nmWeezRpr7Jzjj++fyy8/IPvv/3bWX3/7vPvuu+UuLX/4w11pbl43SfXHXK3OvHnzPvWcd911X155\n5e9JHvnIlQlJls4//jE3kyZNyve//+Osu+4O2WGHg/Pcc899+uIBAICKJIyACrD33j/OK69clXnz\ntkoyMLNnH5AnnjgoJ598XrlLy9JLL5VkzSTXJGle4FrPnhdln322+1TzjRv3SHbd9VeZO/e2JHsn\nuTTJ35Ncm+R7SU7JvHmN2XDDfXPhhVvlkUf+kOuvPzwbb/yTjB5932J/HgAAoPyEEVBm7733XiZP\n7pBk6QXGm5s3y5///HB5ivqQ44/fLz17nptkWJLtk9yW5K7U1m6dQw9dLwMHDvxU8x177PmZMuW8\nJP2T7JXk6SSXJJmT5IYkDZk3b3Jefvm8JF/K+49yDMzkyZfn8MN//m/nfv755/PAAw9kzpw5n+5D\nAgAAhfJqTyizmpqaVFU1fMyVxtTWlj8v/NKX1stPfvJ8fv7zyzJjxpqZN+/cdO8+KzfeeEHWWmut\nBe598803c911t6S2tiY77vjtdO/efaH53nprdpJl//m7w5Lsk2SFJP2SXJQuXS5Lt27L5a23Bn/k\nOztk+vROqa+vT7t27Ra48vrrr2frrb+fSZNWSn1973TrdmZ+9KMdctBBey2ZPwQAAGCJKv+/dOBz\nrl27dllttS55f4fAv3TqdHH23//b5SnqIw4+eFjGj/9D/vKXrfPkkyPzyitjFwoizj//0qy11sE5\n5JBeOfDAzhk6dFiuuebGheZabrmuST54W0ZdkiuTbJ3kR1lqqRvywANXpGvXjklmL/S9NTWzUltb\nu9D4NtscnMce+0WmTPlZ3nnnyLzyyo356U/vy+OPP764Hx0AAGgFwgioAFde+fOst95J6dnz+FRX\nX5G+fffPdtu9lO99r3L+Z799+/ZZb731suKKKy50bdKkSTnttD/njTdGpaXl22lu3j6vvXZDjjrq\n4sycOXOBe88667D07n1Ikrf/OVKfUumK9OlTl+uvPzVDhw7NYYftnPbtd02yc5LvJvlr6uquyxZb\nrJWqqgV/bL344ouZNKl/3t9d8YFSpkw5Lqef/rsl9wcAAAAsMR7TgArQo0ePPPTQ9Xn88cfzyiuv\n5H/+5yfp169fq675+uuv50c/+nmefPLldOlSm+OO2ydbbfWtRZrr8stvyOTJ382Cr+qszuTJu+em\nm27LnnvuOn909dWH5sYbj8+RRx6SyZMb0qnTvBx++K4ZNuz/kiSNjY05//yr0ti4XZJdk0xLcnTm\nzXsyzzyzSt5999107tx5/nwzZ85MY2PPj6mqV95+e8YifR4AAKB1CSOggqy11loLPf7QGt588818\n9av7ZMKEnyVZPck72Wuvk3Liia/lsMP2/dTztbS0ZMEg4gNVaW5e+NWf6623du6//6qPneuqq67P\nU09tkXnzhv1zpHeSSzNv3rb5f//vgOy//3G55pp/vWVkyJAh6dr1hEybNi8ffv1o+/ajsscem3/q\nzwIAALQ+j2nA59DJJ/8qEyacnPeDiCTpmunTz8555/0xjY2Nn3q+vfbaNr17/yZJy4dGm9O795XZ\nZpstPtVcf/jD3ZkzZ5uPjJaSfCMtLbV58MGJ/ww/3ldXV5dTTtk/yy67Z94/d2NqOnS4KGuv/efs\nsceOn/qzAAAArU8YAZ9DDz74bJL1PjJayuzZg/Laa6993Lf8W/3798+RR34jffrsluSOVFXdkr59\nt89pp+2Vbt26faq5ll++V5JXP+bKq0mWybx5NQuEEUmyxx7b5Z57Tsw++1yZLbY4Lr/+dY/cc8/V\nH3vYJQAAUH4e04A2ZNasWbn22j9l6tSZ2X77b2WllVb62PuWX75XHnvslSRfWGC8ru6V9Oz5cecv\n/Mu99/41Rx55dqZNK6V9+/occshOOfDAPXPUUQdk1123zNVX35S6uprsuuuv06tXr0/9GX74w31y\n443HZPLkq5J8ECb8I8lLSbrlC19ot9AhlkkyaNCgXHLJGZ96PQAAoHjCCGgj7r77/2XYsFPzxhv7\npqlpcM4992fZcce+Of/8kxa695RTvp8HHzwmb711Wd5/vWZSV3dTvv71fgscDvlRjzzyWHbe+Zd5\n881Lk3RJ0pRjjx2R9977dY466oAst9xyOeqoAxfrcwwcODAXXLBvjjhiy0yatFLmzZuV5J1UV2+X\nfv2G5Xe/+7/Fmh8AACg/j2lAG9DU1JT99z81kybdkKamnZJsnDff/HWuvHJ2Hnjgrwvdv/rqq+e3\nv903gwZtm+WWG5YvfGGb7Lnn2P+4s+CYY87Lm2/+Mu8HEUlSk5kzT8yFF/5poUcnFse22/5v/vGP\n2/PII9/Nz362bnbbbWBGjHg3Tzwx6hN3ewAAAJ8ddkZAGzBu3LhMm7ZRkg4LjE+f/r383/+dnw02\n+MpC37PVVptmyy03ybvvvpsOHTqkpuY//ziYPPndvP92iw8rpb6+V2bPnp1OnTot+of4iOrq6qy5\n5ppZc801l9icAABAZbAzAtqAUqn0CTsT/v1uhVKplC5duvxXQUSS9O3bJcnrC63Rvv2UdOzY8b+a\nAwAAQBgBbcB6662Xnj3vSTJngfEePS7MIYfstMTWOeusw9K796FJZvxzpCHduv04hx66Y0ql0hJb\nBwAAaNs8pgFtQHV1dS655MTsscd3Mnny3mls7JHevf+QnXf+Qr7ylS8vsXXWXHONXH/9j3LkkcMz\nZcq8dOjQmCOO2C377LPzElsDAABo+4QR0EZ87WtfyXPPXZ/rrrsp06ZNyHe+c1xWXHHFJb7OV76y\nXsaOvXaJzvn4449n7NjHs9pqX8zXvrahXRYAANDGCSOgDencuXP23nu3cpfxX2toaMgWW+ybJ55Y\nPm+//bUstdQDWXHFn2f06EvTo0ePcpcHAAC0EmEEUDY/+ckvct99O6axcZskycyZW+Txx5/KPvsc\nmxtvvKjM1QEAAK3FAZZA2dx009g0Nn77I6Or54knJn/C20EAAIC2QBgBlM0n5Q0tLc6MAACAtkwY\nAZTNN7+5eqqq7vnI6EtZZZWlHGIJAABtmDMjgLI544wf5aGHds8LLzyWWbO+lg4dnkz//n/IZZf9\nrtylAQAArUgYAZRN586d89BDN+TPf74z99xzV9Zc84vZYYebU1dXV+7SAACAViSMAArx3nvv5aKL\nfp+77348Q4eumMMP3ye9evVKVVVVttjiW9lii2+Vu0QAAKAgZTkzYty4cRk8eHCuu+66ciwPFGzq\n1KlZZ53tc8wxPXPLLafmjDPWzTrr7JVnnnm23KUBAABlUHgY8dJLL+WII44oelmgjI488oyMHz8i\njY3bJ+mZlpaN8sorV2S//U4qd2kAAEAZFBpGjB07NnvssUemTp1a5LJAmT388ItJ1v7IaM+8/npL\n5s2bV46SAACAMirkzIhp06b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YtGmTqlatqiVLlhh9aQAAgHzl2LFj8vePzwoiLmvV6rw+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\nz2vVqlWaMmWKYmJitH79en3xxRc5Fqr09fXVihU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c9d0a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# label color for each cluster\n",
    "LABEL_COLOR_MAP = {0 : 'r', 1 : 'k', 2 : 'y', 3 : 'b', 4 : 'm', 5 : 'g'}\n",
    "label_color = [LABEL_COLOR_MAP[l] for l in customer_clusters['cluster']]\n",
    "\n",
    "# plot scatter of cluster\n",
    "plt.scatter(customer_clusters['x'], customer_clusters['y'] , c=label_color)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we've done is we've taken those columns of 0/1 indicator variables, and we've transformed them into a 2-D dataset. We took one column and arbitrarily called it `x` and then called the other `y`. Now we can throw each point into a scatterplot. We color coded each point based on it's cluster so it's easier to see them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### K= 5 is the best for the dataset based on PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Exercise Set V</h3>\n",
    "\n",
    "<p>As we saw earlier, PCA has a lot of other uses. Since we wanted to visualize our data in 2 dimensions, restricted the number of dimensions to 2 in PCA. But what is the true optimal number of dimensions?</p>\n",
    "\n",
    "<p><b>Exercise:</b> Using a new PCA object shown in the next cell, plot the `explained_variance_` field and look for the elbow point, the point where the curve's rate of descent seems to slow sharply. This value is one possible value for the optimal number of dimensions. What is it?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  7.48991521e+00   4.91570888e+00   4.94699474e-01   1.78002472e-01\n",
      "   1.56728622e-01   1.43394269e-01   1.35975915e-01   1.30142486e-01\n",
      "   1.16717666e-01   1.03639916e-01   9.92647102e-02   9.49353382e-02\n",
      "   8.73189841e-02   8.43345179e-02   6.93092325e-02   6.58625369e-02\n",
      "   5.87592951e-02   5.44222160e-02   5.09194596e-02   4.33708199e-02\n",
      "   4.01202568e-02   3.86705418e-02   3.72533298e-02   3.32691316e-02\n",
      "   3.03698506e-02   2.64153743e-02   2.32689230e-02   2.16310616e-02\n",
      "   2.09615527e-02   1.85580928e-02   1.47701925e-02   1.37038534e-02\n",
      "   7.54414808e-03   1.91334029e-04]\n"
     ]
    }
   ],
   "source": [
    "# Initialize a new PCA model with a default number of components.\n",
    "import sklearn.decomposition\n",
    "pca = sklearn.decomposition.PCA()\n",
    "pca.fit(submatrix)\n",
    "print pca.explained_variance_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Clustering Algorithms\n",
    "\n",
    "k-means is only one of a ton of clustering algorithms. Below is a brief description of several clustering algorithms, and the table provides references to the other clustering algorithms in scikit-learn. \n",
    "\n",
    "* **Affinity Propagation** does not require the number of clusters $K$ to be known in advance! AP uses a \"message passing\" paradigm to cluster points based on their similarity. \n",
    "\n",
    "* **Spectral Clustering** uses the eigenvalues of a similarity matrix to reduce the dimensionality of the data before clustering in a lower dimensional space. This is tangentially similar to what we did to visualize k-means clusters using PCA. The number of clusters must be known a priori.\n",
    "\n",
    "* **Ward's Method** applies to hierarchical clustering. Hierarchical clustering algorithms take a set of data and successively divide the observations into more and more clusters at each layer of the hierarchy. Ward's method is used to determine when two clusters in the hierarchy should be combined into one. It is basically an extension of hierarchical clustering. Hierarchical clustering is *divisive*, that is, all observations are part of the same cluster at first, and at each successive iteration, the clusters are made smaller and smaller. With hierarchical clustering, a hierarchy is constructed, and there is not really the concept of \"number of clusters.\" The number of clusters simply determines how low or how high in the hierarchy we reference and can be determined empirically or by looking at the [dendogram](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.cluster.hierarchy.dendrogram.html).\n",
    "\n",
    "* **Agglomerative Clustering** is similar to hierarchical clustering but but is not divisive, it is *agglomerative*. That is, every observation is placed into its own cluster and at each iteration or level or the hierarchy, observations are merged into fewer and fewer clusters until convergence. Similar to hierarchical clustering, the constructed hierarchy contains all possible numbers of clusters and it is up to the analyst to pick the number by reviewing statistics or the dendogram.\n",
    "\n",
    "* **DBSCAN** is based on point density rather than distance. It groups together points with many nearby neighbors. DBSCAN is one of the most cited algorithms in the literature. It does not require knowing the number of clusters a priori, but does require specifying the neighborhood size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering Algorithms in Scikit-learn\n",
    "<table border=\"1\">\n",
    "<colgroup>\n",
    "<col width=\"15%\" />\n",
    "<col width=\"16%\" />\n",
    "<col width=\"20%\" />\n",
    "<col width=\"27%\" />\n",
    "<col width=\"22%\" />\n",
    "</colgroup>\n",
    "<thead valign=\"bottom\">\n",
    "<tr><th>Method name</th>\n",
    "<th>Parameters</th>\n",
    "<th>Scalability</th>\n",
    "<th>Use Case</th>\n",
    "<th>Geometry (metric used)</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody valign=\"top\">\n",
    "<tr><td>K-Means</span></a></td>\n",
    "<td>number of clusters</td>\n",
    "<td>Very large<span class=\"pre\">n_samples</span>, medium <span class=\"pre\">n_clusters</span> with\n",
    "MiniBatch code</td>\n",
    "<td>General-purpose, even cluster size, flat geometry, not too many clusters</td>\n",
    "<td>Distances between points</td>\n",
    "</tr>\n",
    "<tr><td>Affinity propagation</td>\n",
    "<td>damping, sample preference</td>\n",
    "<td>Not scalable with n_samples</td>\n",
    "<td>Many clusters, uneven cluster size, non-flat geometry</td>\n",
    "<td>Graph distance (e.g. nearest-neighbor graph)</td>\n",
    "</tr>\n",
    "<tr><td>Mean-shift</td>\n",
    "<td>bandwidth</td>\n",
    "<td>Not scalable with <span class=\"pre\">n_samples</span></td>\n",
    "<td>Many clusters, uneven cluster size, non-flat geometry</td>\n",
    "<td>Distances between points</td>\n",
    "</tr>\n",
    "<tr><td>Spectral clustering</td>\n",
    "<td>number of clusters</td>\n",
    "<td>Medium <span class=\"pre\">n_samples</span>, small <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Few clusters, even cluster size, non-flat geometry</td>\n",
    "<td>Graph distance (e.g. nearest-neighbor graph)</td>\n",
    "</tr>\n",
    "<tr><td>Ward hierarchical clustering</td>\n",
    "<td>number of clusters</td>\n",
    "<td>Large <span class=\"pre\">n_samples</span> and <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Many clusters, possibly connectivity constraints</td>\n",
    "<td>Distances between points</td>\n",
    "</tr>\n",
    "<tr><td>Agglomerative clustering</td>\n",
    "<td>number of clusters, linkage type, distance</td>\n",
    "<td>Large <span class=\"pre\">n_samples</span> and <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Many clusters, possibly connectivity constraints, non Euclidean\n",
    "distances</td>\n",
    "<td>Any pairwise distance</td>\n",
    "</tr>\n",
    "<tr><td>DBSCAN</td>\n",
    "<td>neighborhood size</td>\n",
    "<td>Very large <span class=\"pre\">n_samples</span>, medium <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Non-flat geometry, uneven cluster sizes</td>\n",
    "<td>Distances between nearest points</td>\n",
    "</tr>\n",
    "<tr><td>Gaussian mixtures</td>\n",
    "<td>many</td>\n",
    "<td>Not scalable</td>\n",
    "<td>Flat geometry, good for density estimation</td>\n",
    "<td>Mahalanobis distances to  centers</td>\n",
    "</tr>\n",
    "<tr><td>Birch</td>\n",
    "<td>branching factor, threshold, optional global clusterer.</td>\n",
    "<td>Large <span class=\"pre\">n_clusters</span> and <span class=\"pre\">n_samples</span></td>\n",
    "<td>Large dataset, outlier removal, data reduction.</td>\n",
    "<td>Euclidean distance between points</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "Source: http://scikit-learn.org/stable/modules/clustering.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Exercise Set VI</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Try clustering using the following algorithms. </p>\n",
    "<ol>\n",
    "<li>Affinity propagation\n",
    "<li>Spectral clustering\n",
    "<li>Agglomerative clustering\n",
    "<li>DBSCAN\n",
    "</ol>\n",
    "<p>How do their results compare? Which performs the best? Tell a story why you think it performs the best.</p>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated number of clusters: 5\n"
     ]
    }
   ],
   "source": [
    "## 1. AffinityPropagation\n",
    "from sklearn.cluster import AffinityPropagation\n",
    "from sklearn import metrics\n",
    "af = AffinityPropagation(preference=-50).fit(submatrix)\n",
    "cluster_centers_indices = af.cluster_centers_indices_\n",
    "labels = af.labels_\n",
    "\n",
    "n_clusters_ = len(cluster_centers_indices)\n",
    "\n",
    "print('Estimated number of clusters: %d' % n_clusters_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(algorithm='auto', eps=0.5, leaf_size=30, metric='euclidean',\n",
       "    min_samples=5, n_jobs=1, p=None)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 4. DBSCAN\n",
    "from sklearn.cluster import DBSCAN\n",
    "dbscan = DBSCAN()\n",
    "dbscan.fit(submatrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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YAQAAABRKGAEAAAAUShgBANDFVavVjBhxZqrV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\nAKBQ/z9B5TPow1eOwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a88d490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2).fit(submatrix)\n",
    "pca_2d = pca.transform(submatrix)\n",
    "for i in range(0, pca_2d.shape[0]):\n",
    "    if dbscan.labels_[i] == 0:\n",
    "        c1 = plt.scatter(pca_2d[i,0],pca_2d[i,1],c='r',marker='+')\n",
    "    elif dbscan.labels_[i] == 1:\n",
    "        c2 = plt.scatter(pca_2d[i,0],pca_2d[i,1],c='g',marker='o')\n",
    "    elif dbscan.labels_[i] == -1:\n",
    "        c3 = plt.scatter(pca_2d[i,0],pca_2d[i,1],c='b',marker='*')\n",
    "\n",
    "plt.title('DBSCAN finds 5 clusters')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DBSCAN's performance was consistent with other clustering algorithms that 5 clusters is the best to describe the dataset."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}