solutions-06-part-01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Connect Intensive - Machine Learning Nanodegree\n",
    "# Lesson 06: Clustering and Dimensionality Reduction\n",
    "# Part 01: Clustering Mini Project\n",
    "\n",
    "## Objectives\n",
    "  - Perform [k-means clustering](http://scikit-learn.org/stable/modules/clustering.html#k-means) on the Enron Data Set.\n",
    "  - Visualize different clusters that form before and after feature scaling.\n",
    "  - Plot decision boundaries that arise from k-means clustering using two features.\n",
    "  \n",
    "## Prerequisites\n",
    "  - You should have the following python packages installed:\n",
    "    - [matplotlib](http://matplotlib.org/index.html)\n",
    "    - [numpy](http://www.scipy.org/scipylib/download.html)\n",
    "    - [pandas](http://pandas.pydata.org/getpandas.html)\n",
    "    - [sklearn](http://scikit-learn.org/stable/install.html)\n",
    "  - Lesson 02 in [the ConnectIntensive repo](https://github.com/nickypie/ConnectIntensive) introduces the Enron Data Set that will be used in this notebook\n",
    "    - You should have cloned [**ud120-projects** repo](https://github.com/udacity/ud120-projects) to your local machine, and you'll need the full path for this repo on your machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering Mini-Project\n",
    "\n",
    "This Jupyter Notebook is intended to provide a friendly guide through the \"Clustering Mini-Project\" lesson... but if you're feeling pretty confident about your Python skills, consider going off-script! Try to work through the lesson on your own -- you may encounter some snags, and you can always refer back to this Notebook if you need a little push forward."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "As usual, we start by importing some useful libraries and modules. Don't worry if you get a warning message when importing `matplotlib` -- it just needs to build the font cache, and the warning is just to alert you that this may take a while the first time the cell is run.\n",
    "\n",
    "**Run** the cell below to import useful libraries for this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully imported numpy! (Version 1.11.2)\n",
      "Successfully imported matplotlib.pyplot! (Version 1.5.3)\n",
      "Successfully imported pandas! (Version 0.19.1)\n",
      "Successfully imported display from IPython.display!\n",
      "Successfully imported sklearn! (Version 0.18)\n",
      "Successfully imported pickle!\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "try:\n",
    "    import numpy as np\n",
    "    print(\"Successfully imported numpy! (Version {})\".format(np.version.version))\n",
    "except ImportError:\n",
    "    print(\"Could not import numpy!\")\n",
    "\n",
    "    \n",
    "try:\n",
    "    import matplotlib\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.style.use('ggplot')\n",
    "    print(\"Successfully imported matplotlib.pyplot! (Version {})\".format(matplotlib.__version__))\n",
    "except ImportError:\n",
    "    print(\"Could not import matplotlib.pyplot!\")\n",
    "\n",
    "    \n",
    "try:\n",
    "    import pandas as pd\n",
    "    print(\"Successfully imported pandas! (Version {})\".format(pd.__version__))\n",
    "    pd.options.display.max_rows = 10\n",
    "except ImportError:\n",
    "    print(\"Could not import pandas!\")\n",
    "\n",
    "    \n",
    "try:\n",
    "    from IPython.display import display\n",
    "    print(\"Successfully imported display from IPython.display!\")\n",
    "except ImportError:\n",
    "    print(\"Could not import display from IPython.display\")\n",
    "    \n",
    "try:\n",
    "    import sklearn\n",
    "    print(\"Successfully imported sklearn! (Version {})\".format(sklearn.__version__))\n",
    "    skversion = int(sklearn.__version__[2:4])\n",
    "except ImportError:\n",
    "    print(\"Could not import sklearn!\")\n",
    "\n",
    "try:\n",
    "    import pickle\n",
    "    print(\"Successfully imported pickle!\")\n",
    "except ImportError:\n",
    "    print(\"Could not import pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A little review -- Magic Functions and `pickle`\n",
    "\n",
    "We're going to use the Enron Data Set from the [**ud120-projects** repo](https://github.com/udacity/ud120-projects) in this notebook. We last used this data set in [lesson-02.ipynb](https://github.com/nickypie/ConnectIntensive/blob/master/lesson-02.ipynb) to hone our `pandas` skills. In this notebook, we're going to perform [k-means clustering](http://scikit-learn.org/stable/modules/clustering.html#k-means) on the data to see if certain patterns emerge.\n",
    "\n",
    "Recall that we had used the Jupyter Notebook [line magic](http://ipython.readthedocs.io/en/stable/interactive/tutorial.html#magics-explained):\n",
    "\n",
    "`%cd \"...\"`\n",
    "\n",
    "By replacing the ellipses with a path, we can use this line magic to change the working directory for this Jupyter Notebook session.\n",
    "\n",
    "**Update** the Magic Function `%cd \"...\"` in the cell below to reflect the correct path of the **ud120-projects** directory on your local machine.\n",
    "\n",
    "Then **run** the cell to use [the pickle module](https://docs.python.org/2/library/pickle.html) to retrieve the Enron data dictionary. A message will print if the data dictionary was successfully loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/nick/Documents/Udacity/ud120-projects\n",
      "Enron data loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "# Be sure to write the full path, up to and including \"ud120-projects\"\n",
    "%cd \"/Users/nick/Documents/Udacity/ud120-projects\"\n",
    "\n",
    "try:\n",
    "    pickle_file = open(\"final_project/final_project_dataset.pkl\", \"r\")\n",
    "    enron_data = pickle.load(pickle_file)\n",
    "    pickle_file.close()\n",
    "    print(\"Enron data loaded successfully!\")\n",
    "except IOError:\n",
    "    print(\"No such file or directory! (Is there a problem with the path?)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing\n",
    "\n",
    "We couldn't find *all* the details for each person involved with the Enron scandal, so the Enron data set has some missing values denoted by `\"NaN\"` entries in the data dictionary. Because we must pass numeric arrays into `sklearn` modules, we need to preprocess the data dictionary a bit before we can use [`sklearn.cluster.KMeans()`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html).\n",
    "\n",
    "**Run** the cell below to complete our preprocessing steps:\n",
    "  - Remove the outlier: there is an entry `\"TOTAL\"` in the data dictionary containing totals of all features.\n",
    "  - Create a pandas `DataFrame` object from the Enron data dictionary\n",
    "  - Take the **transpose** of the Enron `DataFrame` with [`DataFrame.T`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.T.html), so that rows correspond to individuals (instances). Recall that we used [`stack()` and `unstack()`](http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-by-stacking-and-unstacking) last time to accomplish this task... but taking the transpose is more straightforward.\n",
    "  - Impute missing values: Replace all \"NaN\" values in the DataFrame with **zeroes**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
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      "text/plain": [
       "                         bonus deferral_payments deferred_income  \\\n",
       "ALLEN PHILLIP K        4175000           2869717        -3081055   \n",
       "BADUM JAMES P                0            178980               0   \n",
       "BANNANTINE JAMES M           0                 0           -5104   \n",
       "BAXTER JOHN C          1200000           1295738        -1386055   \n",
       "BAY FRANKLIN R          400000            260455         -201641   \n",
       "...                        ...               ...             ...   \n",
       "WINOKUR JR. HERBERT S        0                 0          -25000   \n",
       "WODRASKA JOHN                0                 0               0   \n",
       "WROBEL BRUCE                 0                 0               0   \n",
       "YEAGER F SCOTT               0                 0               0   \n",
       "YEAP SOON                    0                 0               0   \n",
       "\n",
       "                      director_fees               email_address  \\\n",
       "ALLEN PHILLIP K                   0     phillip.allen@enron.com   \n",
       "BADUM JAMES P                     0                           0   \n",
       "BANNANTINE JAMES M                0  james.bannantine@enron.com   \n",
       "BAXTER JOHN C                     0                           0   \n",
       "BAY FRANKLIN R                    0         frank.bay@enron.com   \n",
       "...                             ...                         ...   \n",
       "WINOKUR JR. HERBERT S        108579                           0   \n",
       "WODRASKA JOHN                     0     john.wodraska@enron.com   \n",
       "WROBEL BRUCE                      0                           0   \n",
       "YEAGER F SCOTT                    0      scott.yeager@enron.com   \n",
       "YEAP SOON                         0                           0   \n",
       "\n",
       "                      exercised_stock_options expenses from_messages  \\\n",
       "ALLEN PHILLIP K                       1729541    13868          2195   \n",
       "BADUM JAMES P                          257817     3486             0   \n",
       "BANNANTINE JAMES M                    4046157    56301            29   \n",
       "BAXTER JOHN C                         6680544    11200             0   \n",
       "BAY FRANKLIN R                              0   129142             0   \n",
       "...                                       ...      ...           ...   \n",
       "WINOKUR JR. HERBERT S                       0     1413             0   \n",
       "WODRASKA JOHN                               0        0             0   \n",
       "WROBEL BRUCE                           139130        0             0   \n",
       "YEAGER F SCOTT                        8308552    53947             0   \n",
       "YEAP SOON                              192758    55097             0   \n",
       "\n",
       "                      from_poi_to_this_person from_this_person_to_poi  \\\n",
       "ALLEN PHILLIP K                            47                      65   \n",
       "BADUM JAMES P                               0                       0   \n",
       "BANNANTINE JAMES M                         39                       0   \n",
       "BAXTER JOHN C                               0                       0   \n",
       "BAY FRANKLIN R                              0                       0   \n",
       "...                                       ...                     ...   \n",
       "WINOKUR JR. HERBERT S                       0                       0   \n",
       "WODRASKA JOHN                               0                       0   \n",
       "WROBEL BRUCE                                0                       0   \n",
       "YEAGER F SCOTT                              0                       0   \n",
       "YEAP SOON                                   0                       0   \n",
       "\n",
       "                             ...        long_term_incentive    other    poi  \\\n",
       "ALLEN PHILLIP K              ...                     304805      152  False   \n",
       "BADUM JAMES P                ...                          0        0  False   \n",
       "BANNANTINE JAMES M           ...                          0   864523  False   \n",
       "BAXTER JOHN C                ...                    1586055  2660303  False   \n",
       "BAY FRANKLIN R               ...                          0       69  False   \n",
       "...                          ...                        ...      ...    ...   \n",
       "WINOKUR JR. HERBERT S        ...                          0        0  False   \n",
       "WODRASKA JOHN                ...                          0   189583  False   \n",
       "WROBEL BRUCE                 ...                          0        0  False   \n",
       "YEAGER F SCOTT               ...                          0   147950   True   \n",
       "YEAP SOON                    ...                          0        0  False   \n",
       "\n",
       "                      restricted_stock restricted_stock_deferred  salary  \\\n",
       "ALLEN PHILLIP K                 126027                   -126027  201955   \n",
       "BADUM JAMES P                        0                         0       0   \n",
       "BANNANTINE JAMES M             1757552                   -560222     477   \n",
       "BAXTER JOHN C                  3942714                         0  267102   \n",
       "BAY FRANKLIN R                  145796                    -82782  239671   \n",
       "...                                ...                       ...     ...   \n",
       "WINOKUR JR. HERBERT S                0                         0       0   \n",
       "WODRASKA JOHN                        0                         0       0   \n",
       "WROBEL BRUCE                         0                         0       0   \n",
       "YEAGER F SCOTT                 3576206                         0  158403   \n",
       "YEAP SOON                            0                         0       0   \n",
       "\n",
       "                      shared_receipt_with_poi to_messages total_payments  \\\n",
       "ALLEN PHILLIP K                          1407        2902        4484442   \n",
       "BADUM JAMES P                               0           0         182466   \n",
       "BANNANTINE JAMES M                        465         566         916197   \n",
       "BAXTER JOHN C                               0           0        5634343   \n",
       "BAY FRANKLIN R                              0           0         827696   \n",
       "...                                       ...         ...            ...   \n",
       "WINOKUR JR. HERBERT S                       0           0          84992   \n",
       "WODRASKA JOHN                               0           0         189583   \n",
       "WROBEL BRUCE                                0           0              0   \n",
       "YEAGER F SCOTT                              0           0         360300   \n",
       "YEAP SOON                                   0           0          55097   \n",
       "\n",
       "                      total_stock_value  \n",
       "ALLEN PHILLIP K                 1729541  \n",
       "BADUM JAMES P                    257817  \n",
       "BANNANTINE JAMES M              5243487  \n",
       "BAXTER JOHN C                  10623258  \n",
       "BAY FRANKLIN R                    63014  \n",
       "...                                 ...  \n",
       "WINOKUR JR. HERBERT S                 0  \n",
       "WODRASKA JOHN                         0  \n",
       "WROBEL BRUCE                     139130  \n",
       "YEAGER F SCOTT                 11884758  \n",
       "YEAP SOON                        192758  \n",
       "\n",
       "[145 rows x 21 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing the Enron dataset:\n",
    "# there's an outlier--remove it! \n",
    "enron_data.pop(\"TOTAL\", 0)\n",
    "\n",
    "# Create a DataFrame object from the Enron data dictionary\n",
    "enron_df = pd.DataFrame.from_dict(enron_data)\n",
    "\n",
    "# Take the transpose (.T) of the Enron DataFrame,\n",
    "# so that rows of the DataFrame correspond to individuals\n",
    "enron_df = enron_df.T\n",
    "\n",
    "# Change all entries in the DataFrame with \"NaN\" to zeroes.\n",
    "enron_df[enron_df == \"NaN\"] = 0\n",
    "\n",
    "# Display the DataFrame after preprocessing is complete\n",
    "display(enron_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Means Clustering - Cluster Visualization\n",
    "In this mini-project, you will perform [k-means clustering](https://en.wikipedia.org/wiki/K-means_clustering) on the Enron Data Set. K-means clustering is a variant of [Lloyd's Algorithm](https://en.wikipedia.org/wiki/Lloyd's_algorithm) or Voronoi Iteration. We're going to define a function `DrawClusters()` that will allow us to visualize the resulting clusters. \n",
    "\n",
    "**Read** the cell below to get an idea of what happens in the `DrawClusters()` function. The `DrawFunction()` function is a modified version of [the `Draw()` function from `\"ud120-projects/kmeans/k_means_cluster.py\"`](https://github.com/udacity/ud120-projects/blob/master/k_means/k_means_cluster.py). Here's a quick summary of changes:\n",
    "  - `DrawClusters()` now works with the `pandas` `DataFrame` object `enron_df`.\n",
    "  - Some of the functionality from `\"ud120-projects/tools/feature_format.py\"` is incorporated into `DrawClusters()`:\n",
    "    - The ability to remove instances with **all** zeroes from k-means clustering\n",
    "    - The ability to remove instances with **any** zeroes from k-means clustering\n",
    "    - The ability to rescale features, remapping them to the interval [0,1]\n",
    "  - `DrawClusters()` also draws the resulting decision boundary when `feature_list` contains only two features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DrawClusters() is ready to use!\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "def DrawClusters(n_clusters = 2,\\\n",
    "                 feature_list = [\"salary\",\"exercised_stock_options\"],\\\n",
    "                 remove_any_zeroes = False,\\\n",
    "                 remove_all_zeroes = True,\\\n",
    "                 rescale_features = False,\\\n",
    "                 x_label = 'Salary',\\\n",
    "                 y_label = 'Exercised Stock Options'):\n",
    "    \n",
    "    '''\n",
    "    Plots k-means clusters trained on a list of features from enron_df\n",
    "    @param n_clusters:        an integer, the number of clusters to form.\n",
    "    @param feature_list:      a list of strings, the features to use in KMeans clustering\n",
    "                              (first two features in the list will be plotted on x,y axes)\n",
    "                              if len(feature_list) == 2, decision boundaries will be plotted \n",
    "    @param remove_any_zeroes: a boolean, whether or not to remove points when clustering\n",
    "                              that contain ANY zeroes\n",
    "    @param remove_all_zeroes: a boolean, whether or not to remove points when clustering\n",
    "                              that contain ONLY zeroes\n",
    "    @param rescale_features:  a boolean, whether or not to rescale features from 0 to 1\n",
    "    @param x_label:           a string, the x-axis label of the plot\n",
    "    @param y_label:           a string, the y-axis label of the plot\n",
    "    '''\n",
    "\n",
    "    # Initialize color and shape lists for scatterplot\n",
    "    # Note: need to lengthen the color and shape lists if\n",
    "    #       you want to use more than 7 clusters.\n",
    "    color_list = [\"r\",\"g\",\"b\",\"c\",\"m\",\"y\",\"k\"]\n",
    "    shape_list = [\"^\",\"o\",\"s\",\"v\",\"D\",\"<\",\"h\"]\n",
    "    \n",
    "    # Initialize figure and axes:\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    # We are going to cluster the data using KMeans,\n",
    "    # based on the features from the parameter feature_list.\n",
    "    X = enron_df[feature_list]\n",
    "    \n",
    "    # We can see if clustering correctly identified POIs\n",
    "    y = enron_df['poi']\n",
    "    \n",
    "    # If desired, remove instances where ANY feature is zero\n",
    "    if remove_any_zeroes:\n",
    "        X = X[~(X.T == 0).any()]\n",
    "        \n",
    "    # If desired, remove instances where ALL features are zeroes\n",
    "    if remove_all_zeroes:\n",
    "        X = X[~(X.T == 0).all()]\n",
    "\n",
    "    # If desired, rescale features\n",
    "    if rescale_features:\n",
    "        for feature in X.columns:\n",
    "            min_feature = X[~(X[feature] == 0)][feature].min()\n",
    "            max_feature = X[~(X[feature] == 0)][feature].max()\n",
    "            range_feature = max_feature - min_feature\n",
    "            X[feature] = (X[feature] - min_feature)*1.0 / range_feature\n",
    "            \n",
    "    # If we removed any instances above, we need to keep \n",
    "    # only the corresponding instances in y (the POI Series)\n",
    "    y = y.loc[X.index]\n",
    "    \n",
    "    # By default, the first two features in the list are chosen\n",
    "    # as the x and y features.\n",
    "    x_feature = feature_list[0]\n",
    "    y_feature = feature_list[1]\n",
    "\n",
    "    # Determine the min & max values of x_feature, compute the range,\n",
    "    # and pad the minimum and maximum x values by 5% of the range\n",
    "    x_min, x_max = X[x_feature].min(), X[x_feature].max()\n",
    "    x_range = x_max - x_min\n",
    "    x_min -= x_range * 0.05\n",
    "    x_max += x_range * 0.05\n",
    "    \n",
    "    # Determine the min & max values of y_feature, compute the range,\n",
    "    # and pad the minimum and maximum y values by 5% of the range\n",
    "    y_min, y_max = X[y_feature].min(), X[y_feature].max()\n",
    "    y_range = y_max - y_min\n",
    "    y_min -= y_range * 0.05\n",
    "    y_max += y_range * 0.05\n",
    "    \n",
    "    # Compute k-means clustering.\n",
    "    kmns = KMeans(n_clusters = n_clusters).fit(X)\n",
    "\n",
    "    \n",
    "    # We can visualize the decision boundaries if the \n",
    "    # k-means clusters are formed from just two features\n",
    "    if len(feature_list) == 2:\n",
    "        # Return coordinate matrices xx and yy from arrays\n",
    "        xx, yy = np.meshgrid(np.linspace(x_min, x_max, num = 500),\\\n",
    "                             np.linspace(y_min, y_max, num = 500))\n",
    "\n",
    "        # Use the clustering to make a prediction for each point\n",
    "        # in the coordinate matrices, then reshape for plotting.\n",
    "        Z = kmns.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "        Z = Z.reshape(xx.shape)\n",
    "\n",
    "        # Plot semitransparent filled contours for the\n",
    "        # cluster decision boundaries\n",
    "        ax.contourf(xx, yy, Z,\\\n",
    "                    levels=list(np.arange(n_clusters+1)-0.5),\\\n",
    "                    colors=tuple(color_list[:n_clusters]),alpha=0.2)\n",
    "    \n",
    "    # Scatterplot all of the points in each cluster,\n",
    "    # with each cluster a different color and shape\n",
    "    pred = kmns.predict(X)\n",
    "    for cluster_idx in range(n_clusters):\n",
    "        X_cluster = X[pred == cluster_idx]\n",
    "        ax.scatter(X_cluster[x_feature], X_cluster[y_feature],\\\n",
    "                   s=30, marker = shape_list[cluster_idx],\\\n",
    "                   edgecolor='k', facecolor=color_list[cluster_idx], alpha=0.8)\n",
    "        \n",
    "    # Denote the centroids of each cluster with a white X\n",
    "    centers = kmns.cluster_centers_\n",
    "    if len(feature_list) == 2:\n",
    "        for center in centers:\n",
    "            ax.scatter(center[0],center[1],marker='x',facecolor=\"w\",linewidth=3,s=50)\n",
    "            \n",
    "    # Set the title and axes labels, and adjust the aspect ratio for rescaled data\n",
    "    if rescale_features:\n",
    "        title   = ax.set_title(\"K Means - {} Clusters (Rescaled)\".format(n_clusters))\n",
    "        # Force the axes aspect ratio to be plotted equally\n",
    "        ax.set(adjustable='box-forced', aspect='equal')\n",
    "    else:\n",
    "        title   = ax.set_title(\"K Means - {} Clusters\".format(n_clusters))\n",
    "    x_label = ax.set_xlabel(x_label)\n",
    "    y_label = ax.set_ylabel(y_label)\n",
    "    \n",
    "    # Set the x- and y-axis limits using the padded minimum and maximum values\n",
    "    xlim = ax.set_xlim((x_min,x_max))\n",
    "    ylim = ax.set_ylim((y_min,y_max))\n",
    "    \n",
    "print(\"DrawClusters() is ready to use!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises\n",
    "The exercises below correspond to the quizzes in the Clustering Mini Project. As you complete this notebook, you may check your progress by entering the solutions into the corresponding quizzes.\n",
    "\n",
    "## Quiz: Clustering Features\n",
    "**Run** the cell below, which will create a scatterplot of the data, then look at the resulting figure. What clusters you would expect to arise from this data if 2 clusters are created? That is, how would you partition the data into two clusters?\n",
    "\n",
    "In the accompanying quiz, \"Quiz: Clustering Features\", enter the two feature names from the cell below that we'll use in k-means clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NSfrll18wYsQILF++vMpxycnJmDp1KiIiIhAdHV2tNVASExNRWFiIS5cuaebK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\nbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109bd9190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(enron_df[\"salary\"],enron_df[\"exercised_stock_options\"])\n",
    "title   = ax.set_title(\"Exercised Stock Options vs. Salary\")\n",
    "xlabel  = ax.set_xlabel(\"Salary\")\n",
    "ylabel  = ax.set_ylabel(\"Exercised Stock Options\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quiz: Deploying Clustering\n",
    "**Run** the cell below, which will perform k-means clustering on the data using only the `salary` and `exercised_stock_options` features. Look at the resulting clusters -- because we're only using two features, we can also plot the decision boundary in the plane.\n",
    "\n",
    "In the accompanying quiz, \"Quiz: Deploying Clusters\", state whether the two clusters partition the data as you expected... there's no wrong answer here :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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55zB58mTY29vD0dERc+bMAQDExsZixIgReO655/Dhhx9i4MCBcHR0RHZ2NlJTU3Hu3Dkc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\n4Ngl1G63s2nTJsLCwrBarcTFxWG1WpkyZQo6nQ4/Pz9++OEHEhISiIi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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109bd9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DrawClusters(n_clusters = 2,\\\n",
    "             feature_list = [\"salary\",\"exercised_stock_options\"],\\\n",
    "             remove_any_zeroes = False,\\\n",
    "             remove_all_zeroes = True,\\\n",
    "             rescale_features = False,\\\n",
    "             x_label = 'Salary',\\\n",
    "             y_label = 'Exercised Stock Options')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may notice in the cluster scatterplot that the decision boundary between the two centroids (denoted by white X symbols) does not appear to be perpendicular to the line-of-centers between the centroids... do you understand why?\n",
    "\n",
    "## Quiz: Clustering with 3 Features\n",
    "\n",
    "**Run** the cell below to add a third feature to features_list, `total_payments`. The clustering is now performed using 3 input features instead of 2 (obviously we can still only visualize the original 2 features, `salary` and `exercised_stock_options`). We also can't visualize the decision boundary anymore, because the points all have different values for the `total_payments` feature. Compare the clusterings to those you obtained with 2 input features. Do any points switch clusters? How many? This new clustering, using 3 features, couldn’t have been guessed by eye -- it was the k-means algorithm that identified it.\n",
    "\n",
    "In the accompanying quiz, \"Quiz: Deploying Clusters\", state how many points, if any, change clusters after adding the `total_payments` feature to `feature_list`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sDBkjERERWRHJxQcA2NjYwN/fH/7+/oaKB7/88guOHz+O1NRU2NnZwd/fH2PH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\n/weAcZIKLNW7WQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ab752d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DrawClusters(n_clusters = 2,\\\n",
    "             feature_list = [\"salary\",\"exercised_stock_options\",\"total_payments\"],\\\n",
    "             remove_any_zeroes = False,\\\n",
    "             remove_all_zeroes = True,\\\n",
    "             rescale_features = False,\\\n",
    "             x_label = 'Salary',\\\n",
    "             y_label = 'Exercised Stock Options')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quiz: Stock Option Range\n",
    "\n",
    "We're going to cover [feature scaling](http://sebastianraschka.com/Articles/2014_about_feature_scaling.html), a type of preprocessing that you should perform before some classification and regression tasks. Here’s a sneak preview that should call your attention to the general outline of what feature scaling does.\n",
    "\n",
    "What are the **maximum** and **minimum** values taken by the `exercised_stock_options` feature used in this example? You will enter these values in the corresponding quiz, \"Quiz: Stock Option Range.\"\n",
    "\n",
    "**Note:** In one of the preprocessing steps, all `NaN` entries in `enron_df` were changed to zeroes. We do this because all features passed into `sklearn` classifiers must be numeric. So, to answer this question, you may want to look at the original data *before* this preprocessing step was performed. To help with this task, **run** the cell below to create the data frame `df` from the `enron_data` dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame df has been created!\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame object from the Enron data dictionary\n",
    "df = pd.DataFrame.from_dict(enron_data)\n",
    "\n",
    "# Take the transpose (.T) of the Enron DataFrame,\n",
    "# so that rows of the DataFrame correspond to individuals\n",
    "df = df.T\n",
    "\n",
    "print(\"DataFrame df has been created!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum and minimum values of exercised_stock_options are 34348384 and 3285.\n"
     ]
    }
   ],
   "source": [
    "print(\"The maximum and minimum values of exercised_stock_options are {} and {}.\".\\\n",
    "      format(df[~(df[\"exercised_stock_options\"]==\"NaN\")][\"exercised_stock_options\"].max(),\\\n",
    "             df[~(df[\"exercised_stock_options\"]==\"NaN\")][\"exercised_stock_options\"].min()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quiz: Salary Range\n",
    "\n",
    "What are the **maximum** and **minimum** values taken by the `salary` feature used in this example? You will enter these values in the corresponding quiz, \"Quiz: Salary Range.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum and minimum values of salary are 1111258 and 477.\n"
     ]
    }
   ],
   "source": [
    "print(\"The maximum and minimum values of salary are {} and {}.\".\\\n",
    "      format(df[~(df[\"salary\"]==\"NaN\")][\"salary\"].max(),\\\n",
    "             df[~(df[\"salary\"]==\"NaN\")][\"salary\"].min()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quiz: Clustering Changes\n",
    "\n",
    "**Run** the cell below to plot the original two clusters, with the features `salary` and `exercised_stock_options`. Note that the features are not yet scaled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I9EOr4uP8+fMAgF69eqkdN6a2vy5+//13jBs3Dt7e3lAqlYiMjMSqVavwwQcf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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ceb05d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DrawClusters(n_clusters = 2,\\\n",
    "             feature_list = [\"salary\",\"exercised_stock_options\"],\\\n",
    "             remove_any_zeroes = False,\\\n",
    "             remove_all_zeroes = True,\\\n",
    "             rescale_features = False,\\\n",
    "             x_label = 'Salary',\\\n",
    "             y_label = 'Exercised Stock Options')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now **run** the cell below to do simple feature rescaling, mapping the original ranges of `exercised_stock_options` and `salary` onto the interval [0,1]. You should find that some of the points change clusters! You should also find that these clusters are not as stable as in the previous examples. **Run** the cell multiple times. You should find that the reported clusters are sometimes different, depending on where the centroids were initialized for k-means.\n",
    "\n",
    "In the accompanying quiz, \"Quiz: Clustering Changes\", one possible clustering result is depicted after feature rescaling. **Check** the boxes next to points that change clusters after feature rescaling in this example scatterplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JghBCiEKUIAghhCik1gSRn5+P7OxsdV6SEEJIB+E1Ue748ePIyMjA4sWLpdu2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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d512a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DrawClusters(n_clusters = 2,\\\n",
    "             feature_list = [\"salary\",\"exercised_stock_options\"],\\\n",
    "             remove_any_zeroes = False,\\\n",
    "             remove_all_zeroes = True,\\\n",
    "             rescale_features = True,\\\n",
    "             x_label = 'Salary',\\\n",
    "             y_label = 'Exercised Stock Options')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Exploration\n",
    "\n",
    "At this point, you've completed the k-means mini project -- congratulations!  Below are a few more examples of k-means clustering on the Enron Data Set. Feel free to **run** these cells to see additional k-means clustering examples.\n",
    "\n",
    "You can also read [the `sklearn` documentation on `sklearn.cluster.KMeans`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) and [the KMeans user guide](http://scikit-learn.org/stable/modules/clustering.html#k-means) to learn more about the algorithm and about other parameters that you may set when calling KMeans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gRIuoFUH0CSUIQgghKlGCIEQHqJuJ6ANKEIRoGXUzEX1BCYIQQohKlCAI0RHq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\nfBEHBwe0Wi3e3t7mb/9VysvL+eKLL9i7dy8FBQU3PK6HhwdhYWEkJiY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d5254d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of k-means clustering with 3 clusters, different features, rescaled\n",
    "\n",
    "DrawClusters(n_clusters = 3,\\\n",
    "             feature_list = [\"total_payments\",\"exercised_stock_options\"],\\\n",
    "             remove_any_zeroes = False,\\\n",
    "             remove_all_zeroes = True,\\\n",
    "             rescale_features = True,\\\n",
    "             x_label = 'Total Payments',\\\n",
    "             y_label = 'Exercised Stock Options')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LIEaDEgQhRoRqEcSYUIIgxMhQLYIYC0oQhBgZqkUQY0EJghAjRI8lJcaAEgQh\nRoxqEcSQKEEQYqSoL4IYGiUIQowU9UUQQ6MEQYgRo1oEMSRKEIQYMVrEjxgSJQhCWgBaCpwYAiUI\nQoycj5+DoUMgrRQ9UY6nr776DPn5TxU/C4VCSKVSxc/Ozub44IOFhgiNmCCJvyUyJJXwt7QwdCik\nFaEEwVN+/lNUVHyt+FkgMINM9mwzwPv6D4qYLH9mhQcPq+HvZ+hISGtCTUyEtCA05JXoEyUIQloI\nWn6D6BslCEJaGBrRRPSFEgQhLQiNaCL6RAmCkBaI+iKIPlCCIKSFob4Ioi80zJUnZ2dzPDuUVd08\nCEKa0/E7tRjiR4mCNB9KEDw9PwlOLBajtLTUQNGQ1sbHzwEP7xQbOgxi4qiJiZAWjPoiSHOiBEFI\nC0VLgZPmRgmCkBaKHihEmhslCEJaMKpFkOZECYKQFoweKESaEyUIQlo4ib8lLb9BmgUlCEJaOOqL\nIM1FpwkiNzcXWVlZujwlIUQL1BdBmgOviXK//fYb0tPT8d577ym2rV+/HqdOnQIA+Pr6YuHChbC3\nt9dNlISQevkzKzxEtaHDICaGVw0iMTFR6cP/2rVrOHXqFMLDw/H6668jLy8Pu3fv1lmQhJCGUV8E\n0TVeNYgnT57Ay8tL8fOFCxfg6uqKyMhIAEBxcTFOnz6tmwgJIVqR1yIyqmvo2dVEJ3TSB5GSkoLg\n4GDFzy4uLigupnViCNE3ib+loUMgJoRXgvDw8EBSUhKAuualwsJChISEKPYXFhbCxsZGNxESQhqF\nOquJrvBqYho7diy+/fZbzJo1CxKJBN7e3njxxRcV+2/cuAFfX19dxUgI0RI1MxFd4pUg+vXrBzs7\nO1y5cgU2NjYYPnw4zMzqvrWUl5fD1tYWAwYM0GmghBDtSPwt8SCjGv5+ho6EtHS8nwcRFBSEoKAg\nle22trb48MMPmxQUAMTFxeHw4cMoLi6Gr68vZs2aBT8/zX/xsbGxiI+PR35+Puzs7NC7d2/MmDED\n5ub04B7SulAtguiKUc6kPn/+PH7++WdMnToVX3zxBV544QWsWLFC4wN5zp49ix07dmDq1Kn45ptv\nEBUVhfPnz+OXX37Rc+SEGAd6LCnRBV41CMYYEhISkJiYiLy8PJSXl6uU4TgOO3fu5BVUbGwswsPD\nMXDgQABAZGQkrly5ghMnTmD8+PEq5dPT0xEYGIi+ffsCAJydndGvXz/8+eefvK5PiKmgWgRpCl4J\nYtu2bfjRQQkDAAAgAElEQVT111/h6+uL/v3763TEklQqxd27dzFhwgTFNo7j0K1bN6Snp6s9JiAg\nAGfOnMGdO3fg5+eH3NxcXL16VZFgCGmNqC+CNBWvBHHq1Cn06tULf//733UdD8rKyiCTyVSW6bC3\nt0d2drbaY0JDQ1FWVoZPPvkEjDHIZDIMHToUr7zyis7jI6SlkPdFHL9TiyF+1NxEGo9XgqipqVHb\nQd3cOI5Tuz01NRX79+9HZGQk/Pz8kJOTg02bNmHv3r2YNGmSnqMkxHj4+Dng4R2atEr44ZUgunbt\nijt37iA8PFzX8cDOzg4CgQAlJSVK20tKSjQu/rdr1y4MGDAAgwYNAgD4+PhAIpHghx9+0Jggzp49\ni3Pnziltc3NzQ0RExH+bzFij4hYKhRCLxY06pqUx9Xs01fuzsqpGtjlDGxO9v2eZ6msox+f+zGzq\nao+bN29Gbm6u0r5+/fohNDRU8/UaHyLwxhtvYMWKFdi3bx+GDh0KOzs7PqdRH5BQiPbt2+P69evo\n2bMngLpO8Rs3bmDkyJFqj6muroZAoDwgS17bYIyprXmEhoZq/MVUVFSgtFTaqLjFYrHGUVamwtTv\n0VTvr8hbhqpb1QjsbmWS9/csU30N5fjcnwUs4AhHRERENPp6vBLEe++9B8YYYmJiEBMTAwsLC5UP\naADYsmULn9Nj9OjRWLduHdq3bw8/Pz/ExsaiuroaYWFhAIDvvvsObdq0wYwZMwAAPXr0QGxsLHx9\nfRVNTLt27ULPnj01NksR0lrQUuCEL14JolevXs36wdu3b1+UlZVh165diolyixYtUlStCgoKlBLS\npEmTwHEcYmJiUFhYCLFYjB49euDVV19tthgJaUkk/pZIq6qCp6EDIS0KxxhrXGN7K3D+fBE1Malh\n6vdo6veX/6gafb1M+7Gkpv4a8mpiElvAu683r+sZ5UxqQkjzoAcKkcbgvRZTZWUlYmNjceXKFeTn\n5wOom8Hco0cPjBo1CtbW1joLkhDSdO07uSD1ygOaXU20xqsGUVhYiI8++gh79uyBRCJBx44d0bFj\nR1RXV2P37t346KOPUFRUpOtYCSFNJPG3pDWaiNZ41SC2b9+O4uJifPTRR+jevbvSvqtXr2L16tXY\nvn073nnnHZ0ESQjRDRrRRBqDVw3i2rVrGDVqlEpyAICQkBCMHDkSV69ebXJwhJDmQX0RRBu8EkR1\ndbXGWc0A4ODggOpq+pZCiDHy8XMwdAikheCVILy9vXHu3DlIpapDQaVSKc6dOwdvb37Dqggh+kG1\nCNIQXn0Q48ePxzfffIMFCxZg+PDh8PDwAABkZ2cjPj4e9+/fx/vvv6/TQAkhukOL+BFt8EoQffr0\nQXV1NbZv346NGzcq7ROLxYiKikLv3r11EiAhpHlI/C2RIamkIa9EI97zIMLCwtC/f3/8+eefyM/P\nB2MMLi4u6NChA8zMaBgdIS3Bg4dm9EAhohHvBAEAZmZmCAgIQEBAgK7iIYToiXzIK02cI5polSBu\n3rwJAOjcubPSzw2RlyeEGCd6LCmpj1YJIjo6GkDdBDmhUKj4uSExMTH8IyOENDuqRZD6aJUgli5d\nWldYKFT6mRDS8lEtgmiiVYJ4vqmImo4IMR1UiyCa8JooFx0djevXr2vcf+PGDa2boQghhufj50CL\n+BEVvBLEzZs3UVJSonF/aWmp1h3ZhBBCjFOzPDAoJycHVlZWzXFqQkgzkfhb0vIbRInW8yBOnjyJ\nU6dOKX7et28fjh8/rlKusrIS9+/fR0hIiG4iJIToBfVF6E5GdfM82tWmqgoVz537ctqTeo9xd7NB\nJM9HjmqdIGpqapSehVpVVQWO45TKcBwHS0tLDB06FJMnT+YVECHEcExxRNP2Px7p/Zql7bmGC/Fg\nJalAVU2V8sb2HIYElGs8RmzFv29J6wQxbNgwDBs2DAAwd+5czJo1Cz179uR9YUKI8ZHXIpqrqSmn\nIqdZzisnEuVDIlF+1EBpAx+gLYnYXoDSEv3dC6+lNtatW6frOAghRkLiVH+TRVM4OJnBv3NpwwV5\nEtubobSkotnO39o0aS2m5ORkXL16FU+e1P1Bubi4ICQkBD169NBJcIQQw2jOD3HScvBKEBUVFVi1\nahVu3rwJgUAAR0dHMMaQkpKC+Ph4dOrUCfPnz4eNjY2u4yWEEKInvBLEpk2bcOvWLcycORPDhg2D\nSCQCAEgkEhw7dgw7duzApk2b8M477+g0WEIIIfrDK0EkJSVh+PDhGDdunNJ2kUiEcePGIT8/X2lI\nLCGkZcgoyqDmJaLAa6KcUChUPGZUHU9PT8XCfoQQQlomXgmiV69euHjxImQymcq+2tpaXLhwgR45\nSkgLk1GUYegQiJHh9TW/f//++M9//oPFixcjPDwc7u7uAIDHjx8jISEBUqkU/fv3x927d5WOa9++\nfdMjJoQ0G2peIs/ilSCWLVum+P8///xTbRl1z4ygBwgRQkjLwStBREVF6ToOQgghRoZXgggLC9Nx\nGIQQQowN76FGT58+RU5ODqqqqiASieDh4QFzc3NdxkYI0RMa3krUaXSCSE9Px969e3Hjxg1IpdL/\nnUgoRNeuXTFp0iQEBAToNEhCCCH616gEERcXhy1btgAAAgMD8cILL0AkEkEikeD+/ftISUlBSkoK\nIiIiMHz48GYJmBCiWzS8lWiidYK4ffs2Nm3ahMDAQMydOxeurq4qZfLy8vD9999j06ZNaNeuHdUk\nTIC7uyVEIgEyM6tU9vn6WkEikSEnp1rNkaQloeYloo7WE+UOHToEd3d3LF68WG1yAABXV1csXLgQ\nbm5uOHTokM6CJIbh7m6JoCA7BAbawtdX+RGyvr5WCAy0RVCQHdzdLQ0UISGkOWmdINLT0xEWFtZg\nR7S5uTkGDhyI27dvNzk4Yjjy5CAQ1D0ZKzDQFp6edU+mkicHABAIOEoShJgorZuYKisr4eDgoFVZ\nR0dHVFZW8g6KGJ5IJFAkBzlfXyHc3dtAJFJ+hKFAwEEk4rVqCyHEiGn9rnZwcMCjR9o92zUrK0vr\nZEKMU2ZmFdLSVB9t+HxyAIC0tHK1fRTE+FEHNamP1gnixRdfxPHjx5GXl1dvuby8PCQmJuLFF19s\ncnDEsDQliWdRcmj5qIOaaKJ1gpg4cSJkMhmWLFmCs2fPKs2BAACpVIqzZ89iyZIlYIxh4sSJOg+W\n6F9mZhUkEvUPsJdIaik5EGLCtO6DcHZ2xoIFC7B69WqsXbsWGzZsgKenp2IeRHZ2NmpqaiAWi/Hx\nxx/D2dm5OeMmeuLra6W2WQmoa27y9bWiJEGIiWrURLnAwEB8/fXXiI+PR3JyMrKysiCRSCASieDr\n64vu3btj6NChsLW1ba54iR49O1pJE/l+ShItD/U/kIY0eqkNGxsbvPLKK3jllVeaIx5iJDQlB4mk\nVqVGQUmi5aL+B1IfGptI1JJIZJDJmNK2zEwpTp4sVOm4lskYJBLVpwsSQlo2o31wdFxcHA4fPozi\n4mL4+vpi1qxZ8PPz01i+srISO3bsQFJSEsrLy+Hi4oKIiAgEBwfrMWrTIV8+Qz5ZLi2tHIWFdZMk\n5TWFwEBbyGQMKSlltNxGC0PNS0QbRpkgzp8/j59//hlvvvkm/Pz8EBsbixUrVmDNmjUQi8Uq5aVS\nKT799FPY29vjgw8+QJs2bfDkyRPY2NgYIHrTIf/Ql6/FJBb/bxa9PEnQWkwtFzUvkYYYZYKIjY1F\neHg4Bg4cCACIjIzElStXcOLECYwfP16lfGJiIioqKrBixQoIBHWtZjSKSjfq+/CnPgdCTJvRJQip\nVIq7d+9iwoQJim0cx6Fbt25IT09Xe0xycjICAgLw448/IikpCWKxGKGhoRg/frwiYRBCCGkcXp+e\njx8/brDM77//zufUKCsrg0wmg729vdJ2e3t7FBcXqz0mLy8PFy5cgEwmw8KFCzFp0iT8+uuv2L9/\nP68YCDFl1P9AtMUrQSxfvrzeJTfOnDmD1atX8w5KE47j1G6XyWRwcHDAW2+9hXbt2qFv376YMGEC\njh07pvMYCDEF1P9AtMGricnBwQHLly9HdHQ0nJyclPbFx8fjxx9/RL9+/XgFZGdnB4FAgJKSEqXt\nJSUlKrUKOUdHRwiFQqUE4u3tjeLiYtTW1sLMTHUm8NmzZ3Hu3DmlbW5uboiIiPhv5zZTOaY+QqFQ\nbQe6KTH1e2wt92fz1AZi9W+lFk8oFEJsb+KvYSPvz8aibrDO5s2bkZubq7SvX79+CA0N1Xy9xocI\nLF68GNHR0YokIV+59eDBg9ixYweGDBmCyMhIPqeGUChE+/btcf36dfTs2RMAwBjDjRs3MHLkSLXH\ndOzYUeXDPjs7G46OjmqTAwCEhoZq/MVUVFSgtFSqdp8mYrEYpaWm/a3M1O+xNdxf8v1kAEBpiWne\np9hebLL3BvC8v/8+6ysiIqLR1+OVIGxsbBRJIjo6GsuWLUNsbCwOHjyIsWPH4rXXXuNzWoXRo0dj\n3bp1aN++vWKYa3V1NcLCwgAA3333Hdq0aYMZM2YAAIYNG4a4uDhs2rQJI0aMwOPHj7F//36MHj26\nSXEQ0/DVV58hP/+pxv3Ozub44IOFeozIsKh5iWiL9ygmsViMJUuWYOnSpXj//fdRUVGBqVOnYtKk\nSU0Oqm/fvigrK8OuXbsUE+UWLVqkqP4XFBQojU5ycnLC4sWLsWXLFsyfPx9t2rTB6NGj1Q6JJa1P\nfv5TVFR8XU+J9/UWCyEtiVYJ4u7duxr3/eUvf8HatWsxcOBAhISEKJVt374978CGDx+O4cOHq923\ndOlSlW3+/v745z//yft6hBBClGmVIBYsWNBgmVOnTuHUqVNK22JiYvhFRQjRubSCNEOHQFoYrRJE\nVFRUc8dBCNED6n8gjaFVgpB3DhNCCNG/grwCJMQnIK8gD65OrggfGg4nV6eGD2wiXp3UtbW1qK6u\nhrW1tdr9lZWVsLS01DjElBBCiHbOnDyDmLgYPO32FJadLXEj9wbOrD6DaSOmoX9Y/2a9Nq+Z1Js2\nbcKSJUs07l+yZAm2bt3KOyhCCCF1NYeYuBhwwzhYellCYCmApZcluGEcYo7GoCCvoFmvz6sGce3a\nNQwYMEDj/t69e+PMmTOYNWsW78AI0RVnZ3PUN5S1br9pyyjKgI0tLX/f0iTEJ9TVHASWSts5AYea\nLjVIiE/AtJnTmu36vBJEUVER2rRpo3G/o6MjCgsLeQdFiC61pklw9QnsVoHSkobLEeOR9TgL5t3U\nf4ExdzbHoxuPmvX6vJqYbG1tkZ2drXH/o0ePYGVlxTsoQgghgLeHN55qWAXgaf5TeHl4Nev1eSWI\n4OBgJCQk4N69eyr77t69i4SEBISEhDQ5OEJI09Hy3i1X+NBwmF83B3vu+fBMxmCeao7woeHNen1e\nTUzTpk3DtWvXsHDhQvTo0QM+Pj4AgIcPHyI5ORlisRjTpjVfuxghpHHq5j+Y7iqnpsrJ1QnTRkxD\nzNEY1HSpgaWbJapzq2Geao5pw6c1+1BXXgmiTZs2WLlyJbZv347ff/8dSUlJAAArKyuEhoZi+vTp\n9fZREEII0U7/sP7o3Llz3TyIW/+dB/G+Ec+DAOo6ot955x0wxlBaWgrGGOzt7TU+1IcQQgg/Tq5O\nmDZzmt6XM2/yM6k5jlN0SFNyIMS4UP8DaQreCSI/Px+7du3C1atXFQ9ZEYvFCAkJwZQpU+Di4qKz\nIAkh/NH6S4QvXgni0aNH+OSTT1BRUYGgoCB4edUNtcrOzsbp06eRnJyMTz/9FJ6enjoNlhBCiP7w\nShDbt28Hx3H44osv0LZtW6V9Dx48wKeffort27dj/vz5OgmSENJ41LxEmorXPIhbt25h5MiRKskB\nANq2bYvhw4fj5s2bTQ6OENI01LxEmoJXgpBKpbCwsNC439LSElKplHdQhBBCDI9XgmjXrh0SExNR\nWVmpsq+yshKJiYlNetwoIYQQw+PVBzF16lSsWLEC7733HsLCwhSd0dnZ2Th16hTKysowe/ZsnQZK\nCNEe9T8QXeCVILp27YoFCxZg27ZtOHjwoNI+X19fvPPOO+jatatOAiSE8EP9D6SpeM+DCAoKwhdf\nfIHi4mI8efIEjDG4urrCwcFBl/ERQggxEF59EHv27MGDBw8AAA4ODvD390dAQIAiOTx8+BB79uzR\nXZSEEEL0jleC2L17tyJBqPPw4UPs3r2bd1CEEEIMj1eCaEh5eTmEwiYv80QI4SGjKIP6H4hOaP0p\nfvPmTaXJb5cuXUJOTo5KuYqKCpw/f17tJDpCCCEth9YJIjU1Valf4fLly7h8+bLast7e3nj99deb\nHh0hpFFoeCvRJa0TxPjx4zFixAgwxhAZGYnIyEj06tVLqQzHcbCwsKh3ljUhpHlR8xLRFa0TxLMf\n/N999x3EYjEsLS2bLTBCCCGGxasn+flnPZSWluLatWsoKiqCp6cnevToAYGgWfq/CSGE6InWCeLs\n2bNITEzEe++9B7H4fw8/T09Px+eff47y8nLFNj8/PyxZsgQikUi30RJCCNEbrb/mnzt3DrW1tUrJ\ngTGGtWvXorKyEpMnT8ZHH32EoUOH4s6dOzh06FCzBEwIUY+GtxJd07oGcf/+fQwYMEBp2+3bt5GX\nl4fhw4djypQpAIDu3bujoKAAly5dwtSpU3UbLSGEEL3RugZRUlICV1dXpW0pKSkAgL59+yptDwoK\nQl5eng7CI4Rog4a3kuagdQ3Czs5OqZ8BANLS0iAUClWe/UCjm1qXr776DPn5TzXud3Y2xwcfLNRj\nRK0TNS8RXdM6Qbzwwgs4f/48Ro8eDTMzMxQWFuL27dvo0qWLyryH3NxctGnTRufBEuOUn/8UFRVf\n11Pifb3FQgjRHa0TxIQJE7B06VJ89NFH6NChA27cuAGpVIoxY8aolE1OTkaHDh10GighhJgqdwd3\niIQiZOZnquzzdfaFRCpBTrHq0kbNTes+iMDAQLz33ntgjOHs2bMwNzfH22+/jaCgIKVyN27cQF5e\nHnr27KnzYAkhxNS4O7gjqG0QAr0C4eviq7TP18UXgV6BCGobBHcHd73H1qiJcn369EGfPn3qLdO1\na1ds3bq1SUERQrRHw1tbLnlyEHB139UDPQMBAJlPMuuSw39/FnACBLUNQsaTDJSW6O+1pjW5CSHE\nQERCkSI5yAV61tUkRObKE40FnAAWQv2uc0cJghBCDCQzPxPg/ldzkHs+OQBAWnYaCmsK9RRZHVow\niRBCDCjzSSbSstPqLZOWnYbMJ5n6CegZVIMgTebsbI76hrLW7SfNgfofTIO8z0FdzUHyVGKQ5ABQ\nglDrASR4wtVN/PJnVgaOxvjRJDj9yuCqYP8wGQAgrSpAUlxHvDSiwsBRkabQlByAuuYmXxdfqkEY\ni7Zta+FQI0VGhhAZXJVWx9jIBKjgqiihkGaRl3VW8f8d3WSAG+DXxQY1GS44kn4bgLfhgiNN8uxo\nJU3k+/XdB6FVgsjPz+d1cmdnZ17HAUBcXBwOHz6M4uJi+Pr6YtasWfDz82vwuHPnzuHbb7/FSy+9\nhA8//JD39QHA31+qdVmxmCE5GVonFIBqJ0Szh3eKYSm6ofjZ3U0Gvy42KuUs/C2BdOB4ui2GBJSr\n7CfGzddZfXKQPJWo1CgCPQNxr/Ce8Q1znTt3Lq+Tx8TE8Dru/Pnz+Pnnn/Hmm2/Cz88PsbGxWLFi\nBdasWaO03Pjznjx5gm3btqFTp068rttUjUkoDdVOKHm0HvK/A3mzEQC88IIMbS2EdQmgASMDXHAk\nPQ3HEUhJooWRSCWQMZnSUFd5h/TzNQsZk6FGWqPX+LRKEFFRUUo/M8bw22+/IT8/H6GhofD09ARj\nDNnZ2Th37hxcXFwwcuRI3kHFxsYiPDwcAwcOBABERkbiypUrOHHiBMaPH6/2GJlMhrVr12Lq1Km4\ndesWKisreV9fH+pLJo1p2gIombRUD+8UAwDsRTfg/kyzUWNZ+FuiS40jkJkGBFBTU0siXz5DPlnu\n2dFK8v8GegZCxmRIeZCCSqbfzzWtEkRYWJjSz/v27cPTp0/x7bffws7OTmnf1KlTsWTJEhQXF/MK\nSCqV4u7du5gwYYJiG8dx6NatG9LT0zUet2fPHojFYgwaNAi3bt3idW1j0ZiaCABkZKhPJpQ4jIem\nWkJdQmj66+TXxQapmUXIuCmmUU0tjDxJqFuLKfNJJsCgWItJbK+5BaU58Oqkjo+Px+jRo1WSAwCI\nxWIMGTIER44cUfqQ11ZZWRlkMhns7e2Vttvb2yM7O1vtMWlpaThx4gS+/PLLRl/PFGhKKJoSh8bz\nUELROXnnsj3QpFqCNrr4OiI18yYybnamJNHC1LcQn7oF/PSFV4IoKytDdXW1xv01NTUqz47QBY7j\nVLZJJBJ89913eOutt2Bra6vza7ZkuuwTAf43UktxfkooKuTNRvIOZk2dy83Br4sNcnIFuP+gFuis\nl0sSE8crQfj7++O3335DSEiIysOC/vzzT/z2229ajThSx87ODgKBACUlJUrbS0pKVGoVAJCTk4Mn\nT57g888/V2yTyWQAgOnTp2PNmjUqT8IDgLNnz+LcuXNK29zc3BAREQEbGxtApH5MsiZCobDeDnRj\n16NHw2WEQjNIpdYAgLQ0Durrc0CgoGUm6sa+hmmyui9BNvcuKbYFdmLwtRQBEMMqUP8JdNQrYsTs\ny4DY3l9ln1Ao1HsThb6Z+j3yuT8bi7ovKJs3b0Zubq7Svn79+iE0NFTjsRxjjDU2yKysLCxbtgxl\nZWUICAiAu7s7OI7D48ePkZ6eDltbWyxbtgw+Pj6NPTUAYNGiRfDz88OsWbMA1HWKz5kzByNHjsS4\nceOUykqlUuTkKFfPfvnlF0gkEsyaNQseHh4wMzNr1PXPZ2WhtKZxowXEYjFKS027Wq/NPWZkNP47\nh7HURLR9DZ+dk+DuVvdlRF+1BG0cjM0CAHQaoNxhLbYX63WIpCGY+j3yuT+xlRh9A/o2XFANXjUI\nb29vrFq1CgcOHMC1a9dw9+5dAICLiwtGjRqF8ePHw8HBgVdAADB69GisW7cO7du3Vwxzra6uVnSW\nf/fdd2jTpg1mzJgBoVAIb2/lN4KNjQ04jlPZTppf4zvYNTdtGUPieDY2eQezPpuN+Bg/2hsHY7No\nbgRpMt4zqR0cHBAREaHDUP6nb9++KCsrw65duxQT5RYtWqSo/hcUFEAgoHUGTYHmDnbDDvVV6VwG\n0NZHu3kJxkA+NyJDSh3WhD9eTUzPKioqQklJCdzd3SFqZLu9saImJvWM9R6b2qyVwVVBlFENKysr\nyFgSAOOvJWjjTmoFUjOLFE1Npt78Apj+PbaIJiYASEpKwvbt2/H48WMAwJIlS9C1a1eUlpbin//8\nJyZPnoyXX36Z7+kJ0VpTmrXsHybDHsDL/gLY2NiiokLQYmoJDZHPjSCEL17tNL///jtWrVoFOzs7\nTJkyRWmfWCxGmzZtcPLkSV3ER4jO+ftLFf/c3WQIHWwFC39LWAVamUxykHOycsKt01mGDoO0ULwS\nxN69e9G5c2d8+umnGD58uMr+gIAA3Lt3r8nBEdKcBKlX0dbCtBc0Dh1sBScrJxxPb5lDj4lh8UoQ\nDx48QJ8+fTTut7e3N8q2akKeZ2o1BnVCB1vBM6f+J5YRog6vBGFpaQmJRKJxf25uLs1qJkZNkHrV\n0CHo3dVjGYYOgbQwvBJEly5dcOrUKdTW1qrsKy4uxvHjx/Hiiy82OThCmlNLH6XUGONH141kyrhp\nurOMie7xShDTp09HYWEhFixYgPj4eADAtWvXsHPnTnzwwQcAgMmTJ+suSkJ0qDXWHgAgqIMzpPk3\nDR0GaUF4JQhPT08sX74cdnZ2iocCHT58GPv370fbtm0RHR2tdv0jQoxFa6o9yHV60REAkBTX+u6d\n8MN7CIePjw+WLFmC8vJy5OTkgDEGNze3Fr1gHTF93PUatBWZ9sil+tQtw0HPsCbaafI7xdbWlvfK\nrYToGydIhYV/6/4G7WTlhKQ4G7w0osLQoRAjx6uJ6fr16zh06JDStsTERERFRSEyMhKbN29WLLlN\niLForX0Pz3vZRwBb69s0N4I0iFeC2L17NzIzMxU/P3jwABs3boRYLEbnzp1x5MgRlQRCiCFx1+vW\n1mqNfQ/Ps/C3xMgAF5obQRrEK0E8evQIHTp0UPx8+vRpWFlZYfny5Xj//fcxZMgQnD59WmdBEtJU\nnCDV5GdNN4Z8giDVIkh9eCUIiUQCK6v/rYZ57do1BAcHw9Ky7o/Oz88PT5480U2EhOhIa5g13Rhd\nfB2pFkHqxStBODs7488//wRQ98jPhw8fIigoSLG/vLwc5ubmuomQkCaivgf1/LrY0GJ+pF686tyh\noaHYs2cPCgsLkZWVBRsbG7z00kuK/Xfv3oWHh4fOgiSkqajvQb3QwVY4GFs3w5oeLESexytBTJw4\nEVKpFFevXoWzszPmzJkDG5u6N2B5eTlSU1MxatQonQZKCB9Ue2iYk5UTCvJvguZGkOc1+Ylypoie\nKKdeS7xHQepVrWsPLfH+GqO++zsYm4Xyyo4tfm4EPVFOzTGGeKIcIcautc+aboy6Z1jTDGt9KMgr\nQEJ8ArIeZ8HbwxvhQ8Ph5Opk6LDU0urds379enAch7feegsCgQDr169v8BiO4xAVFdXkAAnhi2ZN\na8/C3xJOD+s6rOXPsCa6d+bkGcTExeBpt6cw72aO9Px0nFl9BtNGTEP/sP6GDk+FVgkiNTUVHMdB\nJpNBIBAgNTW1wWM4jmtycITwRX0PjSfvsD6eboshAeWGDsfkFOQVICYuBtwwDpaCuiHXll6WYB4M\nMUdj0LlzZ6OrSWiVINatW1fvz4QYE+56DSCgkUt8dPF1BDLTgACqRehaQnwCnnZ7qkgOcpyAQ02X\nGiTEJ2DazGkGik49XvMgCDFmNGuaP3lSpRnWupf1OAvmzurnh5k7m+PR40d6jqhhvBLE3bt3cfTo\nUQb0OCYAACAASURBVI37jx49qrRWEyH6RrOm+ZOv00RPn9Mtbw9vPM1/qnbf0/yn8PLw0nNEDeOV\nIHbu3Inr169r3H/jxg3s3LmTd1CE8CVflI/wZ+FvCScrJ3r6nI6FDw2H+XVzMJnyzAImYzBPNUf4\n0HADRaYZ7xpEYGCgxv2dOnVSLMVBiD5xglTqe9CB0MFWDRcijeLk6oRpI6aBxTNUZ1VDJpGhOqsa\nLJ5h2vBpRtdBDfCcB1FVVQUzMzON+zmOQ2VlJe+gCOGDRi7pVhdfR6TSsFed6h/WH507d0ZCfAIe\n3XgELw8vhL/fwudBPM/DwwN//PEHRo4cqXb/tWvX4Obm1qTACOGDag+649fFBqmZRfT0OR1zcnUy\nutFKmvBqYho8eDCuXr2KLVu2oKLif384FRUV2Lx5M65du4bBgwfrLEhCGiJIvUojl5rB+NHesLW+\nbegwiIHwekeNHDkSmZmZ+O2333DkyBE4OjoCAIqKisAYQ//+/TF69GidBkpIQ2jkUvOhGdatE68E\nwXEc5syZgwEDBuDSpUvIy8sDYwwvvfQSevXqhS5duug6TkI0or6H5jV+tDcOxmbRkuCtUKMTxNOn\nT/HHH3/AxcUFXbt2RdeuXZsjLkK0Ygyzphlj+P336ygtLcaQIQMMFkdz6uLriNRMWhK8tWl0H4RQ\nKMTq1atx+za1SxLDM+SsacYYkpJSsHjxJ/j99/VISrpokDj0QZ6A6elzrUuj31kcx8HDwwNlZWXN\nEQ8hjabvvgd5jeHAgd1o164As2cLYGYmxC+/6DUMvZM3NZHWg9dXrwkTJmDr1q3o06cPPD09dR0T\nIVrR9/Me1CUGa+u661dXy/QWhyE5WTnRsNdWhNe7Kz09Hba2tvjggw/QuXNnuLi4wMLCQqkMx3GY\nNWuWToIkRB19P+/hwIFjOHRoB+bPt0GbNq1zSO3LPgIUVN3G8fRAWhK8FeD1V/7sQn03btzQWI4S\nhOGcPn4cF5OSNO7v/dJLGDBkiB4j0i1DjFx65ZVh8PHxxPbtu9CuXT4GDzaDtXXrWhDZwt8SI+GC\nI+m0JHhrwCtBxMTE6DoOomMOYjHu2ttDOGqUyj5pbCxG2tvr7FqGSkb6HrnEcRx69uyGHj26Ijn5\nBn766X+Jop6VZ0yOhb8lkE5zI1qD1llPbgW69ewJlwMHUFRbC4G1tWK7rLISLo8eoWuPHjq7lj6T\nEWD4WdPqEoWX1xODxWMINDeidWjSuyw9PR2pqakoKSnB8OHD4eHhgerqajx69Aienp4QiUS6ilO/\n7jwF2ho6iKbhOA5TXnkF/05MhGDMGMX22sRETJ0wQaePhNVnMpIzhlnTzyeKkpIiQ4ekV118HXHp\nZi3Q2dCRkObCqwFVKpVi1apVWLJkCXbu3IkjR46goKAAQN2bZsWKFfjtt990Gqg+PXx02tAh6ES3\nnj3hdO8eZP9dWVdWWQnne/d0/oEtT0ayxESl7c2RjIxx1rQ8UZjqJDlN/LrYwNb6NpLiaIFEU8X7\ngUHJycmIjIzEN998o7TPwsICvXv3RlI9bdItwZ8H9xg6hCZ7/oO7OT6w5fSVjABasdWY0GJ+po1X\nE9O5c+cwbNgwhIeHq50w5+XlhYsXW+6sUp92/sjPy8WfB/egw/jJBotDF52/3Xr2hNOBAygoLKz7\nwJ49W9dhAlBt0mqu2gOt2Gqcjqfb0rBXE8SrBlFaWoq2bTU30gsEAlRXV/MOyhh06BQEwLA1CXnn\n74Pp01X+3RWL4ahF56/8g7viyy+brfYgJ69FSOXJyET7Hogy+TOsj6fbGjoUomO8EoSTkxMePXqk\ncf/t27fh7u7OOyhjYegk0a1nT7hkZQG1tRBYWir+oba2UZ2/3Xr2xIxx45rlA/tZzZmMuOs1VHsw\nUhb+lookQUwLr3dcaGgofv31V/Tu3RseHh5K+xISEnDhwgXMmDGjSYHFxcXh8OHDKC4uhq+vL2bN\nmgU/Pz+1ZY8fP47Tp0/jwYMHAID27dtj+vTpGss3RodOQfjzVopBmpt0NRKJ4zgMnzChucJU0lzJ\nSN+zpknjyOdG0LBX08KrBjFx4kR07NgRS5cuRXR0NABgy5YtiIqKwsaNGxEcHIwxz3ygNdb58+fx\n888/Y+rUqfjiiy/wwgsvYMWKFSgtVf+Hd/PmTfTr1w/Lli3DihUr4OTkhBUrVqCoSDfDDuU1CUOM\noNFn568uyJORqY9cIqq6+DpCmn/T0GEQHeKVIIRCIRYuXIioqCi4urrCy8sLUqkUL7zwAubMmYOP\nPvoIAgH/JQhiY2MRHh6OgQMHwsvLC5GRkbC0tMSJEyfUlp83bx6GDRuGF154AZ6ennj77bchk8lw\n/fp13jE8r0OnIGTc+VNn59OWPkciGSPueg0AGrnUEvh1sVEs5kdMA+9GXY7jMGDAAAwYoNux31Kp\nFHfv3sWEZ5pEOI5Dt27dkJ6ertU5qqurUVtbC1tb3XaauZubGaSpSV8jkYyRIZ/3QBovdLAVCmJv\nI+NmZ2pqMgG83nkPHjyodxQTAFy8eBG9e/du9LnLysogk8lg/9wIHXt7e2RnZ2t1ju3bt6NNmzYI\nCgpq9PXrY+PXBe53UvWeJOS1iK++/BJRc+caZe2hOddjopFLLYuTlRMK8unpc6aAVzvQxx9/jP37\n90MmU10Dv7y8HF9//TW+/vrrJgf3PG0+GA8cOIALFy5g/vz5EAp1/83Txq+LoiahT/oaicSXLobk\nPo/6Hlqm0MFWAEDDXk0Ar0/QAQMGYOfOnfj9998xd+5cxUODLl++jB9//BFVVVWIiIjgFZCdnR0E\nAgFKSkqUtpeUlKjUKp536NAhHDx4EJ988gl8fHzqLXv27FmcO3dOaZubmxsiIiJgY1N/G6q4ex+I\nbv2BrKOH0XnKTAB1/TL/396ZxzdVpf//fZN0TVda6IqUUgpYNhUR2TcBAUG/Qt0HZ/g5rjMqiDqi\nDiKbIjg6bjMOioIgxRFBCohgVZAqi0ALAi2UtaUrTbe0aZJ7f3+UZJo2bZN0r+f9evWl3HvuzfMk\n7f3knGc5fn5+9V7XWGbOmtWs92+I+nwcNnYsX27dyhVFqdWPKTQnh6Fjxjg98zF7e9JnQGCjbHaG\nlvgMW5OW9G/QtWGknDmDn3/PFnk9CxqNBj//Dv4ZOumf1r3qebZ69WpycnJszg0bNozhw4fXea2k\nKIrivJlw5MgRPvjgA0pLS5kxYwYXLlzgp59+olevXjz22GONqoOYP38+MTEx1v0kFEXhscce49Zb\nb2XatGl2r9myZQubNm2yXtsY9u3bV2fGVHXKTh8n22imx/QZ+Pn5OXRNe6YhH1MOHOCDo0fRVMtg\nM27dymMDB9Jv0CCnXssye2jJ4HRH/wxb2r/NiZco1fdq0d3n/Pz9KC7qwJ+hC/75efkxNHaoS6/n\ncqrRwIEDWblyJd26dWP9+vX89NNP3HHHHbzyyiuNLpKbMmUKu3bt4ocffiAzM5MPP/wQg8HA6NGj\nAXjnnXdYt26ddfzmzZvZsGEDjz76KMHBweh0OnQ6HRUVFY2yoyEsy01iKaSKpk7JFZlL7RtLn6b0\n3zruN/qOjsuL9BUVFaxdu5bTp0/TrVs3Ll++TFJSErGxsVx//fWNMmro0KGUlJSQkJBgLZSbP3++\ndXpcUFBgk0a7c+dOTCYTK1assLnPzJkzmTGjeYPJ2pg40k+k0N/bE7r3adbXaus0VT+mlt5rWtB8\niIB1+8alJaZjx47xwQcfUFhYyMyZM5k2bRo5OTm89957pKWlMWbMGGbNmoWXl1dz2NzsOLrEVJ1L\nZ09xTWQkctx1zWRV4wjVavHUaDhXI7YDEOXvT4XJRHZZ/UsBjixRKIrCyy++SMF99xH02WcsXLTI\naYFQHT/cKrMHscTUPGxOvERWaMvsYS2WmOxc09JLTK+++iparZZly5Zx++23o1KpCAsLY+HChdx3\n333s3buXZ555xiWD2ivXDriR9NNn2uRyU6hWS/8uXegdFERUjUB/lL8/vYOC6N+lC6ENBOcdobH9\nmNri+ydoHKJPU/vFJYG48847WbJkSa1MIUmSmDZtGsuWLWsw46gjYqm2bksPOYs4qK4+qKuLhEUc\nAFSS1GQi4WpKrqia7pi49/QgyCuIEz9eam1TBE7i0kJvfHx8vecjIyNZtGiRSwa1d3r06U/6iRR6\nQptYbvLUaKziYMEiEp416kRUklTrWE0cLYhzpTmgqJruuAwf68XmxNa2QuAsDv81nj59mtDQUIfa\nV+Tm5nLixAlGjRrVKOPaKxaRiJHjUPq5t6otlpiDZaZgwZ4QnCwosBujqI6lIE4zeXKtc6bERG5t\n5MxRVE13XCyziD4jRcC6veDwEtP8+fM5cuSI9d+lpaXcf//9/PZb7e6Np06d4r333msaC9spkdFe\nnM7Y0tpmAFUicfLqnuF14Yg4QNPtUVGTtrQsJ2geLBXWoplf+8Hl+byiKBiNRrvtNn5v5OTksGHD\nBkaPHk1ISAgAHh49Adf3kWjq3kbniorsLisBVJhMDokDNN0eFfYQsYeOz/QpkWxOPIVIe20fiAXf\nRqDX60lISODnn39Gp9ORlJTEzTffzMyZM/H29m7UZkNNvZRTlzhA1XJTlL+/wyJh6S5bqNej8vb+\nX0Gci11mxezh94dYamofuL5pw++csrIyXnjhBbZv3052djbl5eVkZ2ezbds2XnjhBcqu1hS4um1p\nUy7lVM9Wqgt7KbB10Rx7VIjZw++H6VOqhEFUWLd9hEC4SH5+PufPn7e287A8HCsqKjh//jz5+fnW\nsa6IRM2HsAVnH8Z1iUO+nd32nBEJS1sNk2WPikbEHkTm0u8Psftc+8ApgcjNzSUjI4OMjAzOnz8P\nwOXLl63HLD+5ubnNYmxbory8HEVRaj2oJUlCURTKy8ttjrsiEk3R26jCZEKuUSy/9PRphhw5wtLT\np22Oy4pChcnk0H0bWxBXHZG59PvDMmMUtRFtG6e+um3YsIENGzbYHPvPf/7TpAZ1ZJyNSTRFb6Ps\nsjLIzbUWyy1NS+Pj/HxUHh58nJsLsszfYmORFYWU3NwG221Up7F7VIjYw++bqoC1EIi2jMMC8eij\njzanHe0OLy8vJEnCbDbbNA6UZRm1Wl1nHyqLSEiplQ7VSDTFdqMWkbiSlcWHGzfatOP+9+rVDImP\nJzAszEYc7GVRubm5YTQagcYVxMHVqmmViD383gnyCmJ3mk+L9GkSOI/DAmFptS2oIjg4mGuuuYbs\n7GwMBgOyLKNSqfDy8iIsLIzg4OA6r7XUSMQwrUGRaKrtRrPLylD8/Ag6e7ZW9lGFr2+tmYO9LCqV\nJCErSpMUxDVF1bSiKBw8mEpxsY5x45p2b3RByzC4q4qCtJPspmWa+QmcQwSpXUSr1bJ06VImT55M\naGgoXl5ehIaGMnnyZJYuXVrvrnQeHj2tImHpP1QfTbXdqDPZR3azqDw9G10QVx1XYw+KonDgQAov\nvvgyBw++x4EDPzfaFkHr4N7TQzTza8OI9JFG4O3tzaxZs5g0aRLff/+9TaFcQ1SJRLp1JvFD7p4G\nC+MaEwi24OiSVXMWxLmy34NFFH79NZWLF0/RvXsBs2erUKs1rF/vsimCNoB7Tw9Iq0p77Xltx23V\n3R4RAtGKVBeJwIrwZu1xZMGZJaumLogDSE/X4J93lJ5jHdsrxCIMq1Z9jF5/nmuvVTN7dije3lW/\nugaDqOTvCMRFBXL8nNhYqK0hBKIRWCqpDx06hNFoZO/evQwaNMhaSe0IFpG4eCaTzpcuUWg2o6p2\nrazXN9mSjgVHl6xqziLkpCSXZg/p6Roy0/db/y3rzlGZ3rPeJSZLfOGrrzai159Fry9izhwV4Ia3\nt1gZ7WhYkhV+2aFt0T2sBfUj/tJcpKysjBdffJGff/4ZSZLw9PREkiSSk5N56aWXrJXUjuDh0ZMw\ndw1DgwMaXRjnCJIkMdHBe9oUxJ0757RQ7T6URWb6fq7Rm5gb48PcGB/KinvUOb56fOHo0Q+YPbuQ\nZ58N5P77Q1izRs327TJ6vZg1dERi4rT4eJ8SzfzaEEIgXESv12MymXB3t81Ccnd3x2g0or9a3OYo\n2pg4enaPwuuX5EYVxjU11QviHrjrLodEJT1dw/fbfuX7bb+izslmbowPM/sHWM8XX2PmmwPpdq/9\n6qudvPfeCu67r4CpUzV4e6uQJIkBA7z561/DyMvryapVgWzdahRC0QGZPiUSH+9TrW2G4CpiiakN\nEXPtAMafPccXX32F9t57m2X24AqWJamBN91ESUlJneN2H8pCnZMNwDV6k40oVGdujA/rDtq/x+23\nT6Br13A++yyB7t3zGTtWbV1SkiSJkJBAnntuHocOHWPVqgQiIvIa55ygTSJqI9oGQiDaGJMmT2PX\nG6+jO5tBeCMDwk1FfUtS6ekaLhRfQJ2TjRoYH3aZAdqeDt23Mt1QKw4hSRKDBvXjhhv6WkXAIhRq\ntf0xRUW1+0oJ2i9xUYFw7iTppmtFVlMrIwTCRby9vdFoNOj1eptlJoPBgFardThIXRNJkph16xSW\n/eMtht3cNKmtzUH1wPPEsMsQxlVhcEwcLgdEsf+izPA6htsTipqzBcuYtogo4nMdS8BaZDW1PkIg\nXESr1bJo0SI2btzIwYMHqaiowM3NjaFDhxIfH19nqw1H6BcXx6wxY+gRGuTyhkPNxffbfrX+//+W\nkf73lFcUhS927mTGhAn1its1ehOFunNA33pfr73NFqpnX0VG5lFUFC0EwgVi4rQcP1coaiNaGSEQ\njaAxhXL1IUkSE8eOBeDMiRRUxw8jx13X6Ps2ht2HsvDWHQeqYgh18UtqKtvWreOa8HBu6lf3t/uZ\n/QNYd9DxRm1tebYAtsIgiviahiCvIAryxSyiNREC0QSEhITw0EMPUVzc9N90IqO9SD99hh6tJBCW\nGUN3vYnr+uuJIbzOsYqi8PXGjXyg1bIwIYHBffu22SWypsKeMIgivqZh+Fgv9n4nmvm1JiLNtY3j\n4dGTUDe10zvSNYbdh7KsaaqANU31er/e9V73S2oq1xcUEKLRcF1+PvuPHat3fFlxD75eV/+Yto69\ntFxB0zG4q0r0aWpFxG9zE5CTk8OHH35ITk5Os9xfGxPXIiJhqV9Q52QzPuyytbDNESyzh7uutj6/\nW61mS0ICSo3NiqrzkIPtNtoyt98+gccff4bPPgsStRnNgCXLTWws1DoIgWgEer2e1atXs2jRIpKS\nkli0aBGffPKJ00VyjtCcImGZLVSveK4vVVVRFDZ+843Nw98ye/C5KhA+KpVDswiA08frrjq3VFbv\n3v2jEx61HJbYyKJFCxk48DFRxNcMiD2sWw8Rg3CRsrIyXnrpJWuaq6enJ7Isk5ycTEpKCgsXLqy3\n5bcraGPiCD19vEkym2r2R3J0pgC1A9GW2cNrKtvvG3er1TzXQCzickAU8rnCWhsHNSYbSKsNRaPx\npKjoXK1z/v5RmEwVlJVlO+asgziSlitwHdHMr3UQAuEijrTaaGqBAFuRyPQJbLBF+Mhx42yOVa92\ndqaozYK9QPQvqan0zctDrdFQLv/vm7MaiMvLY/+xY3VmNFVVVZ/D8ofvSjZQ9ZqDadPi6dKlP5JU\nJVbVRcLfP4qgoN4oikxuLk0uEtD+0nLbC5a01wOimV+LIgSiCTAajRQXF+Pn54ebm1uT3TcnJ4dv\nvvmGiRMn2qTPWkQiLeVXh1uEW4RBTfXZgnPiAPYD0SU6Heeio1lQxzWdCxt+SLqSDVRzltGz559s\nxCEoqCqoXlR0zioOAJKkokuX/s0mElWv0bbTctsjVXtYnyL9N1Fh3VIIgWgEsixz5coV9Ho9sixT\nVlaGt7c3nTp1atR9q7cRNxgMHDhwoFYbcW1MHD0rTXRO+pHCiRPttgj38LuJ9HSJzPT9TrfBgNpF\nbzWXkixLSIsWLuSWkY0rBvv7wg1cPLedefO0dOpU/6+lLMt89tlmfvhhF0OGKNZZRl5esFUcLAQF\n9cbfPwqNxtPmuCSpah0TtH1EbUTLIoLULqIoCllZWdbmdaqrD82SkhKysrLqzd6pD2faiHft4c3Q\nqGuQt2+zuUf59iR6hsWRdfoA6qP7rBlJzi4nWWINlkDzvsOHXQ5E10dcHw+uv2FYg9lAsiyzZs0m\n7rrrHi5e/JiQkFyb1NJdu76goKB2SqQ9ISgoOGk3RiFo2wy/mvm2O83xmJnAdYRAuIgkSYSFheHr\n64uiKBiNRhRFwdfXl7CwMJcLxJxpI+7h0ZMx4/rgdeQw8pUCissqKcrOx+PoEYaF9LDWLzgrDGAb\na9iSkIAsy3yxdq01jdWCI+msDTFA2xN1cRH9/WPtZgMpikJOTiF//vNjnDmzildeKSc+XkKjqf0e\nFxWdsysS1dm48X0bcbBkSu3Y8V3dFwnaDGIP65ZDCEQT0VoVw56esdw7fgBFW7YimYxo9+zkzVvH\nEj8g0KX7WVJYf0lJsYk1fLJlC9fm5qIGymXZ+qNSFAwZGfySmmo3/dVRLJsIVU8bHTDgUZYtU/PU\nUxcxGk/wt7+peeCBUMrL3dDpFEwm+6+j051Fr7ffltxgyObbbzdafbVsTnTw4HskJ+9x2m5ByyNq\nI1oOEYNwEb1eT1ZWFrIsI0kSGk3VW1lWVkZ5eXmjayGcCXxfP3AiPptfxXvCLYSeucDNE6a4/Lq/\npKaS+NlnaDt14gNJ4jOdjomyzJM7d9KnZ08WGI0247MKC8nKzOTTL78ERXGoD5M9AoINfHPgErf1\n/F/zvj17DpCdfZbRoxXy8yW0WjXgjbe3F4WFes6cqWDrViNjxqg4caKCzMxcAC5erKRHD1+7r+Ph\nEcr48TM4cCClVqbUFy1XrC5oJFUBayEQzY0QiEZi+bZsNptRq9WNWmqxcOXKFWtfJ0vg28fHBx8f\nH7ttxCVJYnBYJPtWrmTJnXe6PJuxLCvNVqtJuXCBo4GBfJKXh4ckMcbNjeumTaNvjx424198+WUW\nBgbywKFDrNHpHO7DVDMAbmneZ5vNlM+SJcGUlOhYtUqFXi9bd5jTar3o3z8WT89ePP/8egYOLEen\nM+PvH8Xo0fW3BImPf4xt2xYze3ah6JvUjgnyCuLEj5foM1IErJsLscTkIt7e3oSFhREfH8/jjz+O\n2WymsrISlUpFWFgY3t7eREVFERoa6vA9y8rKWLp0qVUQVCqVNTuqvLycF154oc7aisiQMOYOH87N\n115r97wjyz+/pKZyXX4+u4qL+YPRyHs5OSwBvlIUJuh0JKxZU7t6Oj8f9+JihisKsWfPEqJWOxS4\nrhkAl2WZHTu3MuepF6x7UU+d6kZwsA9hYWEEBsZa4xJlZWZSU8tJTU2homIny5YFc8cdnfnTn+ZY\nU1mrc+VKbq1jkyfPp0eP1t+MSeA6ImDd/AiBcBG9Xs/QoUNZvXo1K1euZM6cOUiShNFoJCsri5iY\nGHr37k3//v0dFglLgNrT05Pg4GBrEDw8PJzAwMB6H+6SJHHf2LF1fmuv+UCuiWX2EFVZSZzRSLos\nc63ZzA1AL+BkZSVR589br7eMn2ow4GU0UibLzKispKy8vMHAdc0AuKIovLbmYy5m7+HOOy/Xanon\nSRJBQQG8+uoreHpO5Pnn8zl+PI+//KWSqVM1aLVqtFovzOYKFMV2JlBQcJLCwkMcOZJkc9xsNlFa\nernO91PQPhAB6+ZFCISLREZGsnr1amvsYcWKFTz99NPIsswTTzzBzTffDFSlvzojEtVxc3MjKCio\n0cV39h7INbEUwFXKMhc8PPi7onDf1XP3A28oCjsrKym+WvRmmT2Yioo4IctcpyiESxL5+fn4SFK9\ns4iaxXa/pKZSkn6WZ68Es3JxSZ1prps3f8vu3ZtYsKATM2Z0ITj4f98cFUUhMXET3323wSoSllRW\nSZLw8zOQn38CAFk2s3//NpYsSRZ9k9o5ImDdvAiBcBEfHx+rOFhYsWIFly5d4o033rA5rlKp8PRs\nvaKshtpwV+/EOtnXl4l+foxRFKIBNyAaGKMoXO/uTq8hQ/Ds1s1m9vC5LHMn0Ompp9Defnu9swjr\na0kShUVF3K1S8Z+PPuKGggLu6eTPXcW+eLpPtNv0ztI5dd26YJKS3NBotCiKwpEjet5++zKBgels\n2PAhubkpdusciovPU1Bwkry8VEJC3G1SahMTTc3y3guaH9HMr/kQAuEi+/bt45lnnql1PCIiotax\nkydPcu7cuQbvadnnurKy0ua4wWBAo9G4tM+1I224Lb2UVIrCx4WFrMjNZTZVvZS4+t/ZQNDYsQyc\nPBnfPn14YPp0SnU6jsoyvRQFnzlz0KxcSejatRSPGoVaUYjLzeX1jz6y2/VVMhgounIFKipQzp9n\nqsEAwF0qNb99/QuvvvpKrYd3zRTY117T8Pzzl8jIyOPPfzYzYULV8lpZWXadRXBFRees7TWq32/w\n4Me5+eYRTr+/grZBXFQgugvm1jajw6FesGDBgtY2oq1x8eJFDFcfWHWRnZ3N4sWLKS4uZuLEiXWO\nc1QcoKoYbsSIERQXF5OVlYXBYECtVnPTTTfx17/+tV6ByDh1muG9Y2od/yU1FZ89exiirnrcu0sS\nl0tLKe3Wjcir/Z1OnDrFb2VlrFcUdly4wECTidsAGTBd/fGLj2fkmjWor86auo0cydbSUv69bx+9\nn36aYStWACCpVPhMn85HOTkcyMoi9cABzpeUMOy6qh3xPnj3XZ6qqKAoP5+uisJ2vZ4Ik4keJhN+\nvr7IZhWXi3RU9Ith0OB+jB07BpUqiqCgYKKju9lkOcXFlXDXXW6EhhoBGaNRzblzEQwfPsyh99uC\nJEmEh4cQF9e7wc+9PePh4dFh/evUxZ2z585y/mQYIVHlrW1Os+Hh6fxn6OHmQdegri69nkhzdRFF\nUVAUxRqgtjdzqKiocFgcLFTf59peoz5nbayvDfeNcXH899tvmTFhAuNHjODFl1/mQY2Gj00mVVkR\nswAAG/5JREFU/gIEAxKgACMjIpheY0ntwaVLmfzEE3Sp4btaoyG0Rw881Go+0mr5w+bNjLrxRgD6\n5uVRYTQiGY1UAtuMRl4DzFcD3IHe3sSXqFj0rwRuGNy3VtO7r77ayZYt6672bHID3FAULXp9OZJk\noqCgkN27f3S4Nbig4zB9SiTbd50AGr8vvKAKMYOwgyMziL1793L48GHmzJnDjBn292bQaDSYTCZ0\nOp3TNvj4+DBgwAB8fBpO4du5+yekyspaM4hfUlNx/+EHrlepMCmK9UcFZJaWcshsZtfGjYTFxHAp\nJwefH39kgk6HjyzzFWBQq8lXq8lRqfjm4EEwm7lh9Gib19D61V73Xfvuu6z629/onJ/PyIoKvGWZ\nL86do2f37vxWVsZ7mZlclCQ+B0JMJvoAxYDGywu1mxt6g5rs0hIq+nQnPNL2j7137x5ERsaQkHCW\nvLxSIiMl3NwkjEYThYU69u69gsGA07MI6NjfsKHj+wdwOqOMw/pQooMqGx7cDhEziHZCbm4uc+bM\nYcXVpZW66N27Ki/f2ZmEM6hKS3hu+qRaxxtqw53+7bd8oNXyyoYNSMDLBgPuKhWzNBq+MZt5NiSE\nYVfrLv5jNNIjKIgPFi3ikRdfrNMW3YIFvLlwIW+r1fzLZKJSkrjfzY0d58/jGxDAmEmTiD13jtka\nDW/m53PAYODfQIa7O37h4QQHBHCixISnsZTO+bXbhFffb+HgwVRWrvyI0NALTJhgIihIIiLCvbZR\ngt8N066N5IvDJ9lNb8bFlra2Oe2eNisQO3bs4Ouvv0an0xEVFcUf//hHYmJqr7FbSE5OJiEhgdzc\nXMLDw7n33nu57uq6d3MwadIkHn/88VrHMzMzay03tYRI2OOWkSPrbMP9c0oKsR98QIhGg/+lS0RI\nEqbiYkIBsywzXVH4Z34+Q729kSSJu9Vq7njjDVSZmfzx//0/POyk7eozM/l60SJmKAqdTCaGAQcU\nhWmKwmRZ5tWVKxkQEcHrKhWKonDOYGCVmxveksQpSeItlYrn581DkiTWHTzGmCl9a70G2O4DMWCA\ngZCQQDZu1NG1q9HueMHvB6/eXtxa1pntaSchVlRYN5Y2mcW0b98+1qxZQ3x8PK+//jrdunWzBoTt\nkZaWxttvv824ceNYvnw5N954I8uXL+fSpebLjb5w4QImk21q5Ny5c4mMjGTu3Lk2x2VZpqKiotls\ncZbqmU2KolBSXMyYK1fAYABFQaUo3EtVP6iksjLKZRlJlqm4dIkvn37arjgAeEdEUPDkk0wHtMAY\n4Hsg22hkmiwj5+SgPnMGNZCk1zPQZEIrSUiKQmBlJb0zMx1qHf7VVzt5770V3HdfAbfd5sbgwVrm\nzAmnV68u9O3b9Lv4CdoXltoIQeNpkwKRmJjI+PHjGTVqFBERETz00EN4eHiQlJRkd/y2bdsYOHAg\nU6dOJTw8nPj4eLp3786OHTuazcZvvvmG++67zyoSc+fOZeXKlQCsXLnSuvQkyzIpKSlkZ9e9c1lO\nTg6ffvopOTk5zWZvdX5OScF89ixaSWKfXk9cZSVKZSUlZjP5ZjN6qjKXRgF/KSjgpYgIRpaU8Pen\nnyZg+fJ67/3UihXEzpmDB2AGrgH2AYVXYw17Skp4OTKS1ysqGOnuTpa7OxfUaopkmdsMBpsU3Lqa\nsVnqISx7R5SVmTl6tJzSUjNDhjQcs7F0cd29+0enzrVnFEUhOfnXDudXXVj6NAkaR5tbYjKZTGRk\nZHDHHXdYj0mSRL9+/UhLS7N7TVpaGlOnTrU5NmDAAA4ePNhsdprNZhISEoCq2oc333zT5vyyZcu4\n5ZZbkGW5TnFwZOe4pkZRFFZ9/DFyURHJnp7klJeTbDazDhgMHAf6eXhgqKig1M0Nf6ORSl9f/vDA\nA9xaowAQwJSZiabGkppmxQpUioL3m28yCXgY8OBq0Z0sY/TzY7qvL+EaDSgKWZcvE67RcLmigrjc\nXPYfO0ZcH0+On9Bhb+cwSxzi+uvjWLduC88/v57rriunrMyDQYPqnkHU3KK0qCjamu1keYCuW/dJ\nrXPtmeo+d+tWwJUrUR3Cr4YYPtaLvd8FiT2sG0mbE4iSkhJkWca/2n7KAP7+/mRlZdm9RqfTERAQ\nYHMsICDApewhRzGbq4pyLCJRk6KiIu655x4WLlxot8FeWVkZL730Enq9Hnd3d2uldXJyMikpKXVe\n11h+TknBfP48/1apWHjlCk8qCieA3sA9wFp3d7a5uTFUpeKU0ci/vL25d8cORmm1KCYTUrVU19K5\nc7m4ciXd5szBu1qwXjaZ0GZmkg/4UDUTyQDeBl4Cjv38Mx59+7IAyC8sRJ+TQ4ynJ6dlGe+gIDoX\nFnKTdiTHOUZluqHWkkHN/auXLQvGYKhg4cIKuxXR9va7Vqs1rF9vey4mppDZs7Gea8/Y81mj0bBu\nXWtb1nIMH+tFQeIpxPakrtPmBKI+nGljrShKs27i4+trf78BCxqNxroDnL0HvSM7xzWHQBxKTWWw\nWo3ZzY0uRiOPmExogDcBJIm7NBqOmEzcERhIRF4eYZ6ehJSVcX1CAkbA7bPPkDQaPpo7l6MrV3IJ\nMKxcyUxg1ooVmE0mXrrvPuSEBNKBEqAA+D+qstNvBgJMJsbeeiuD+/blxZdf5q3QUHxUKkplmefU\nasaPqKpotmwiZMHeQ8/Srluj8WLAgF4MHnwrRUWFDY6vqDCTk1PIiy++bD3n46NBluV23fq7Pp8r\nO2bmZ4OIluCu0+YEwtfXF5VKRVFRkc3xoqKiWrMKC/ZmC/WNh6o6hp9++snmWEhICA8++KBDD+bu\n3buzf//+Os97eHigUqnw8fHBz06tQHl5uXUfa3vUdZ091BqNQ2MVRSHv9GmWhYfjo1Jx98WLpJrN\nTAbiJIlLgFJezmSVig8LCnhPkkgsKGA4VUVzxoQEDMDXEREMefNN7gP0wHRg8sqVGBWFC5mZXEpI\n4IWrr/k4Vd/fbqOq6O4e4KmiIrZ+8QVenp7cWFiI/9VZib9KxaDCQo5nZDD0uutwczez+8hp7rqh\nqsguIWErGzd+xLx5PnTqZCuskgTu7m6MHfu/+of6xu/eXcSFC1ksXhxhPSdJEiqVCkmqapTo6Pvf\nlqj/PVLarV+Ooqnxt/DAPdey4ct0si6E0btf+19q0mg0+Pk79/lp3aueZ6tXr64V5xw2bBjDhw+v\n+/WcN7F50Wg0REdHk5qayqBBg4CqB9uxY8e49dZb7V4TGxvLsWPHmDx5svVYamoqsbGxdb7O8OHD\n63xjysrK6syYstCtW7d6z7u7uyPLMqWlpXh5edU6X1paiizX/U21ruvsYTaZGrQXqpaX+ufm4qXR\nUFxaSqfKSgYoClFUZSuoFQVfs5luikIvkwmVRkMakAYsujrmUkIC3YGRVFVYHwGmUNWvSXnzTTyB\ngcBpYDJwAxABBAGlgC8wWJZJSUvjn+++y8dUpdVamAk8t3o1cdHRPDhczbqDRqtvEyeOIDg4kDVr\nEujePZ+xY9XWtuCKIlt34bNQ3/hx43w4diycNWs01nOWGYS9e7UX6n+PaLd+OYqfn18t/3qG+XD8\n3AEOlV5Lz2vbt+9+/n4UFznpw9XHyIMPPuj067XJLKYpU6awa9cufvjhBzIzM/nwww8xGAyMvlrF\n+84777Cu2mLq5MmTOXz4MFu3biUrK4uEhAQyMjKYNKl28VhTERcXV2t5qDoajabeBnvN0ZivPmya\n9ikKusJC3MxmZgFfAnpFoYyqYPJXsswfFIUco5F8YBXwOvAEEAj8GSgD8oC1wB+By1SJgpmq5aRt\nVPVyKgJuoUog8oBsYKwkcaCkhPD8/Fp7XKuBuLw8m3TXvd9V9dap3lzP0sivvnbd9Y2XJImQkMBa\nHV3be+tvZ9+j3wMxcVrRzM9F2twMAmDo0KGUlJSQkJBgLZSbP3++depYUFBgszwTGxvLk08+yeef\nf8769esJCwtj3rx5REY237pjly5d+Oijj1iyZAmnTp2yBq3d3d0JDw9nyJAhxMfH1zkL0Gq1LFq0\niI0bN3Lw4EEMBgMeHh4MHTq03utqUnb6uEPjLB1b1RoN+Xo9ssGAXlEwUfXwvgtwp2om0Asop2p2\n0JuqpaFy4MDVfwcCV4DDwACqRMWdKkHwuTq+F/APII6qLzAF1e4fplYzSZY5HBjIgq72WwB0vrrv\nhJt73VlMN9zQl0OHjrFqVQIREXl1+l7f+OrnTpzIYNWq1fXeq71gz+drrilobbNajZg4LcfPiYC1\ns0hKU2yi3MHYt2+fU9Pw48eP8+mnnzJ16lTOnj3rdIO9nJwclxvzlZ0+zqkL0Tw0tn5B+fbHHzn4\n888AXM7N5dKlS1RW6+lSLkl4KQrlJhNukoRao6HCaEQty9a6BIOioKFKAAAqqXroQ9VykwKoJQkU\nBYWq+IT31fGKJFWdU6nw0Wrx8vTk+ptvZu7DD9dr99GydI6fMHDbvfarqqFqdnTo0DGKigodSuGs\na7yfnx9FRUVO3au9oCgKJ05kcPlyZofyqyb2lpgs7P2unILygnYdsHZlicnPy4+hsUNdej0hEHZw\nViCg/l/M5sRRgWgKWsvHdQeP1SsQTUVr+ddSdHT/oGEf935XTqp/53bbp6mlBaJNxiAEgppY4hAC\nQWMY3FUl9rB2AiEQgjbPvYP6Uph9prXNEHQAxB7WziEEQtBuqEzv2HsZCFoGsYe14wiBELQLLgdE\ncaGydhsNgcAV4qICMeX/1tpmtHmEQAgEgt8dMXFV1cUHdoj28PUhBELQLhgfdpkTR8+2thmCDsT0\nKZH4eJ9qbTPaNEIgBO2CAdqerW2CoIMiAtZ10yYrqVsbV7qoNkfnVUc4UawQEuGPxs+z2V9L0mpb\n9RdG6jmQ5P0ljBruXDGho6jVWurpntLu6ej+gfM+zrzjWn7Ym4PuchjXRLf9Zn5ad621t5LD13i4\n/mwShXICgUAgsItYYmoiVq9e3domNDsd3UfhX/uno/vY0v4JgWgiWmo/6dako/so/Gv/dHQfW9o/\nIRACgUAgsIsQCIFAIBDYRQiEQCAQCOyiXrBgwYLWNqKjcM0117S2Cc1OR/dR+Nf+6eg+tqR/Is1V\nIBAIBHYRS0wCgUAgsIsQCIFAIBDYRQiEQCAQCOwiBEIgEAgEdhHN+hxkx44dfP311+h0OqKiovjj\nH/9ITExMneOTk5NJSEggNzeX8PBw7r33Xq677roWtNh5nPFx9+7d/Pjjj1y4cAGA6Oho7rnnnnrf\nk9bG2c/Qwk8//cTbb7/NjTfeyDPPPNMClrqGs/7p9XrWrVvHgQMHKC0tpXPnzjz44IMMHDiwBa12\nDmd9TExM5NtvvyU/Px9fX1+GDBnCvffei5ubWwta7RgnTpxgy5YtZGRkoNPpmDdvHoMGDar3muPH\nj/Ppp59y6dIlgoODueOOOxg9enST2SRmEA6wb98+1qxZQ3x8PK+//jrdunVj8eLFFBcX2x2flpbG\n22+/zbhx41i+fDk33ngjy5cv59KltttW2Fkff/vtN4YNG8aCBQtYvHgxQUFBLF68mMLCwha23DGc\n9c9CXl4ea9eupU+fPi1kqWs465/JZOLVV18lPz+fuXPn8tZbb/Hwww/TqVOnFrbccZz1ce/evaxb\nt474+Hj+8Y9/8Oijj7Jv3z7Wr1/fwpY7hsFgICoqitmzZzs0Pjc3l2XLltGvXz+WL1/Orbfeyr/+\n9S9SUlKazCYhEA6QmJjI+PHjGTVqFBERETz00EN4eHiQlJRkd/y2bdsYOHAgU6dOJTw8nPj4eLp3\n786OHTta2HLHcdbHv/zlL0yYMIFu3boRHh7OI488gizLpKamtrDljuGsfwCyLPPPf/6T+Ph4unTp\n0oLWOo+z/n333XeUlZXx7LPPEhsbS3BwMH369GnTNQTO+piWlkbv3r0ZOnQowcHB9O/fn2HDhnHm\nzJkWttwxBg4cyF133cXgwYMdGr9z505CQkK4//77CQ8PZ9KkSdx0000kJiY2mU1CIBrAZDKRkZFB\nv379rMckSaJfv36kpaXZvSYtLc1mPMCAAQPqHN/auOJjTQwGA2azGR8fn+Yy02Vc9e+LL77Az8+P\nMWPGtISZLuOKf4cOHSI2Npb//Oc/PPTQQ8ydO5dNmzYhy3JLme0UrvgYGxtLRkYGp0+fBqoa3R0+\nfLjNL/U6Snp6eq3nzMCBA5v0OSNiEA1QUlKCLMv4+/vbHPf39ycrK8vuNTqdjoCAAJtjAQEB6HS6\nZrOzMbjiY00+++wzOnXqRP/+/ZvDxEbhin8nT54kKSmJ5cuXt4SJjcIV/3Jzczl27BgjRozghRde\n4PLly6xatQpZlrnzzjtbwmyncMXH4cOHU1JSwssvv4yiKMiyzC233MLtt9/eEiY3Ozqdzu77odfr\nMRqNTRJnEQLRCCRJcnisoihOjW8rOGLzV199RXJyMgsWLECjaV+/Uvb8q6io4J133uHhhx9ukzMi\nZ6jr85NlmYCAAB5++GEkSaJ79+5cuXKFr7/+uk0KRH3U5ePx48fZtGkTDz30EDExMWRnZ/Pxxx/z\n3//+t9356CiWxhhN9axpX3/NrYCvry8qlYqioiKb40VFRbXU24K92UJ941sbV3y0sGXLFjZv3szL\nL79M165dm9NMl3HWv+zsbPLy8njttdesxyxLL/fccw9vvfVWm4pJuPL5BQYGotFobB4kkZGR6HQ6\nzGYzarW6WW12Fld8TEhIYOTIkdYlwq5du1JRUcG///3vDiEQAQEBtd6P4uJivL29m+yLmohBNIBG\noyE6Otom+KooCseOHaNXr152r4mNjeXYsWM2x1JTU4mNjW1WW13FFR+hShw2bdrE/Pnz6d69e0uY\n6hLO+hcZGcmKFStYvny59WfQoEH07duX5cuXExQU1JLmN4grn1+vXr3Izs62OZaVlUVgYGCbEwdw\nzUeDwYBKZfuIswhiR2hBZ+85c/To0SZ9zohurg7g5eXFhg0bCA4Oxs3Njc8//5zz58/zyCOP4OHh\nwTvvvMPp06etAaNOnTqxfv16PD098fHxYfv27fz88888+uij+Pn5tbI39nHWx82bN5OQkMATTzxh\n/WZWUVEB0CaXmZzxT6VS4efnZ/Nz9OhRACZNmlTrodMWcPbzCw8Pt9YThIaGkp6ezpo1a5g8eTK9\ne/duZW/s46yPhYWF7Nixgy5duuDh4cGZM2dYs2YNffv2ZciQIa3sTW0qKiq4dOkSOp2OXbt2ERMT\ng7u7OyaTCW9vb9atW8cPP/xgzXIKDQ3lyy+/RK/XExwczL59+9i6dSt/+tOfCAkJaRKb2t5fchtk\n6NChlJSUkJCQYC3QmT9/vvVhX1BQYPPQiI2N5cknn+Tzzz9n/fr1hIWFMW/ePCIjI1vLhQZx1sed\nO3diMplYsWKFzX1mzpzJjBkzWtR2R3DWv/aGs/4FBQXx4osv8sknnzBv3jw6derElClTmD59emu5\n0CDO+njnnXciSRIbNmzgypUr+Pn5ccMNN3D33Xe3lgv1kpGRwSuvvGL996effgrAqFGjeOyxx9Dp\ndBQUFFjPd+nShb/97W988sknbN++naCgIB599NEmTRQR7b4FAoFAYJf2+5VJIBAIBM2KEAiBQCAQ\n2EUIhEAgEAjsIgRCIBAIBHYRAiEQCAQCuwiBEAgEAoFdhEAIBAKBwC5CIAQCgUBgFyEQAoFAILCL\nEAiBoAVYsGCBTRsFgaA9IHoxCQR1cOHCBTZu3GjdRN7X15fIyEgGDRrEpEmTnLpXe9wLRCAQAiEQ\n2OHUqVMsXLiQ4OBgxo0bR0BAAAUFBaSnp7N9+3anBUIgaI8IgRAI7PDll1/i7e3NsmXL8PLysjlX\nXFzcSlZVIcsysiy3ybbqgo6F+A0TCOyQm5tL165da4kDYLOnR1JSEnv27OHixYvo9XpCQkKYNGkS\nEyZMqPf+JpOJ//73vxw+fJjs7GzMZjPR0dHEx8cTFxdnHZeXl8cTTzzBAw88gEqlYvv27eTn5/P3\nv/+dxYsXM27cOB588EGbe1+5coXHHnuMu+++u8PsvyxoHYRACAR2CA4OJj09nYsXL9a7leq3335L\n165dGTRoEGq1mkOHDrFq1SqAekWivLycpKQkhg0bxvjx4ykvL+e7775jyZIlLFmyhG7dutmMT0pK\nwmg0csstt6DRaOjcuTODBw8mOTmZWbNm2cQ49u7dC8DIkSMb8xYIBEIgBAJ73HbbbSxdupRnn32W\nmJgYevfuTb9+/YiLi7PZkvOVV17Bzc3N+u+JEyeyZMkStm7dWq9A+Pj48O6779rca9y4cTz11FNs\n376dRx55xGb8lStX+Oc//4mPj4/12KhRo9i7dy8pKSkMGDDAenzPnj306dOHTp06Neo9EAhEmqtA\nYIf+/fuzaNEiBg0axPnz59myZQuLFy/mkUce4eDBg9Zx1cVBr9dTUlJCnz59yMnJoby8vM77S5Jk\nFQdFUSgtLcVkMhEdHc3Zs2drjb/ppptsxAGgX79+BAYGsmfPHuuxixcvcuHCBTF7EDQJYgYhENRB\njx49mDt3LmazmfPnz7N//34SExN58803ef3114mIiODkyZNs3LiRtLQ0Kisrba7X6/V2YxgWvv/+\nexITE8nMzMRsNluPd+nSpdZYe8ckSWL48OF8++23VFZW4u7uzp49e3Bzc2uTey4L2h9CIASCBlCr\n1URHRxMdHU1oaCjvv/8+ycnJjBgxgldffZXIyEhmzZpFcHAwGo2GX3/9lcTEROrbzffHH3/k/fff\nZ/DgwUybNg1/f39UKhWbNm0iJyen1nh3d3e79xk1ahRff/01Bw4cYNiwYfz0008MGjSoXmESCBxF\nCIRA4AQ9evQAQKfTcejQIUwmE88995zNen9qamqD9/nll18ICQlh7ty5Nsc3bNjglD1du3YlKiqK\nPXv20KlTJ/Lz85k9e7ZT9xAI6kLEIAQCOxw/ftzu8V9//RWA8PBwVKqqPx9Zlq3n9Xo933//fYP3\nV6lUtaqr09PTSUtLc9rWkSNHcvToURITE/H19WXgwIFO30MgsIeYQQgEdvjoo4+orKzkxhtvJCIi\nApPJxKlTp0hOTqZLly6MHj0anU6HRqPhtddes0lVDQgIQKfT1Xv/G264gf379/P6669z/fXXk5ub\na02ZraiocMrWESNGsHbtWg4cOMDEiROtwiUQNBYhEAKBHf7whz+QnJzMkSNH2L17NyaTieDgYCZO\nnMj//d//4e3tjbe3N3PnzuXzzz9n7dq1BAQEMGHCBHx9fXn//fdr3bP6jMEiMLt27SIlJYXIyEj+\n+te/kpyczIkTJ5yy1c/PjwEDBnD48GFGjBjRaN8FAguSUl8kTSAQtAveeOMNLl68yFtvvdXapgg6\nEGIuKhC0cwoLC/n1119F7YOgyRFLTAJBOyU3N5eTJ0/y3XffodFoGD9+fGubJOhgCIEQCNopv/32\nG++//z6dO3fmiSeewN/fv7VNEnQwRAxCIBAIBHYRMQiBQCAQ2EUIhEAgEAjsIgRCIBAIBHYRAiEQ\nCAQCuwiBEAgEAoFdhEAIBAKBwC5CIAQCgUBgFyEQAoFAILCLEAiBQCAQ2OX/Axb95IfDkSuzAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d54b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of k-means clustering with 7 clusters, rescaled\n",
    "\n",
    "DrawClusters(n_clusters = 7,\\\n",
    "             feature_list = [\"salary\",\"exercised_stock_options\"],\\\n",
    "             remove_any_zeroes = False,\\\n",
    "             remove_all_zeroes = True,\\\n",
    "             rescale_features = True,\\\n",
    "             x_label = 'Salary',\\\n",
    "             y_label = 'Exercised Stock Options')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QMxMxFpQgCDEi1MxEjAklCEIIISpRgiDECFEzEzEGlCAIMTLUzESMBSUIQowQ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\nFkuDwsLC3xW/n58fbm5uVFRUtBirr68vRUVFTbY3xNDQ4vL396eiooI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d52f510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of k-means clustering with 3 clusters, different features, rescaled\n",
    "\n",
    "DrawClusters(n_clusters = 3,\\\n",
    "             feature_list = [\"restricted_stock\",\"total_stock_value\"],\\\n",
    "             remove_any_zeroes = False,\\\n",
    "             remove_all_zeroes = True,\\\n",
    "             rescale_features = True,\\\n",
    "             x_label = 'Restricted Stock',\\\n",
    "             y_label = 'Total Stock Value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "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
}