notebooks/12_Clustering_TopicModelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering and Topic Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "import nltk.corpus\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "\n",
    "from gensim import corpora, models\n",
    "from nltk.corpus import wordnet as wn\n",
    "from operator import itemgetter\n",
    "\n",
    "import sklearn\n",
    "from sklearn import metrics, manifold\n",
    "import scipy\n",
    "from scipy import cluster\n",
    "import matplotlib as mpl \n",
    "import matplotlib.cm as cm\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# plt.rcdefaults()\n",
    "plt.rcParams['figure.figsize'] = (15, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering\n",
    "\n",
    "\n",
    "The dataset in `./data/blogdata.txt` contains the frequencies of meaningful words (columns), over several blogs (rows).\n",
    "\n",
    "\n",
    "**Task:** Use clustering to assess if there are **groups among these blogs** that employ similar words, talk about the same topics or have a similar writing style."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "blog2words = defaultdict(dict)\n",
    "\n",
    "with open(\"data/blogdata.txt\", \"r\") as infile:\n",
    "    words = infile.readline().strip().split(\"\\t\")[1:]  # word indices (first row contains words)\n",
    "    for line in infile:\n",
    "        splLine = line.strip().split(\"\\t\")\n",
    "        blog = splLine[0] # the first column contains the blog name\n",
    "        raw_counts = splLine[1:] # the other columns contain word counts\n",
    "        for i, c in enumerate(raw_counts):\n",
    "            if c != \"0\":\n",
    "                blog2words[blog][words[i]] = int(c) # only keep >0 and assume the rest is zero: efficient dictionary representation of sparse matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  0. ...  0.  1.  0.]\n",
      " [ 0.  0.  0. ...  2.  2. 10.]\n",
      " [ 0.  0.  0. ...  3.  0.  0.]\n",
      " ...\n",
      " [ 0.  0.  2. ...  3.  1.  2.]\n",
      " [ 0.  1.  2. ...  8.  3.  1.]\n",
      " [ 0.  1.  1. ... 23.  2.  4.]]\n"
     ]
    }
   ],
   "source": [
    "# populate matrix \n",
    "\n",
    "blogs = sorted(blog2words.keys())\n",
    "bwMat = np.zeros((len(blogs), len(words)))\n",
    "\n",
    "for ib,blog in enumerate(blogs):\n",
    "    for w, v in blog2words[blog].items():\n",
    "        bwMat[ib, words.index(w)] = v\n",
    "        \n",
    "print(bwMat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise\n",
    "\n",
    "Load and explore our dataset:\n",
    "\n",
    "\n",
    "- how many nonzero values are there in the two datasets? \n",
    "\n",
    "- what are the most frequent words, and in which blogs are they used?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hierarchical Agglomerative Clustering\n",
    "\n",
    "The algorithm:\n",
    "\n",
    "```\n",
    "Initialize each cluster to be a singleton\n",
    "while more than one clusters exist do\n",
    "\tFind the two most similar clusters\n",
    "\tMerge the two clusters\n",
    "end while\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](data/hr.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DISTANCE METRICS\n",
    "\n",
    "Clustering requires the use of a similarity/distance metric to estimate the distance between clusters. See the [SciPy documenation](https://docs.scipy.org/doc/scipy/reference/spatial.distance.html) for a list of measures.\n",
    "\n",
    "In what follows, we'll experiment with the **correlation** measure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the agglomerative methods available in the SciPy package requires the similarity matrix to be condensed\n",
    "# (condense distance matrix = a flat array containing the upper triangular of the distance matrix)\n",
    "distMat = scipy.spatial.distance.pdist(bwMat, \"correlation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.87852458 0.97632947 ... 0.88660713 0.92448712 0.88936377]\n",
      " [0.87852458 0.         0.85202354 ... 0.84220149 0.74722779 0.8871183 ]\n",
      " [0.97632947 0.85202354 0.         ... 0.86976633 0.93052498 0.91052894]\n",
      " ...\n",
      " [0.88660713 0.84220149 0.86976633 ... 0.         0.7849326  0.79955692]\n",
      " [0.92448712 0.74722779 0.93052498 ... 0.7849326  0.         0.7599039 ]\n",
      " [0.88936377 0.8871183  0.91052894 ... 0.79955692 0.7599039  0.        ]]\n"
     ]
    }
   ],
   "source": [
    "# if we want to have a look at the square matrix with the distances, we can use\n",
    "print(scipy.spatial.distance.squareform(distMat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original table: (99, 706) \n",
      "\n",
      "condensed dist: (4851,)\n",
      "[0.87852458 0.97632947 1.05504204 ... 0.7849326  0.79955692 0.7599039 ] \n",
      "\n",
      "square dist: (99, 99)\n",
      "[[0.         0.87852458 0.97632947 ... 0.88660713 0.92448712 0.88936377]\n",
      " [0.87852458 0.         0.85202354 ... 0.84220149 0.74722779 0.8871183 ]\n",
      " [0.97632947 0.85202354 0.         ... 0.86976633 0.93052498 0.91052894]\n",
      " ...\n",
      " [0.88660713 0.84220149 0.86976633 ... 0.         0.7849326  0.79955692]\n",
      " [0.92448712 0.74722779 0.93052498 ... 0.7849326  0.         0.7599039 ]\n",
      " [0.88936377 0.8871183  0.91052894 ... 0.79955692 0.7599039  0.        ]]\n"
     ]
    }
   ],
   "source": [
    "distMat_sq = scipy.spatial.distance.squareform(distMat, \"correlation\")\n",
    "\n",
    "print(\"original table:\", bwMat.shape, \"\\n\")\n",
    "print(\"condensed dist:\", distMat.shape)\n",
    "print(distMat, \"\\n\")\n",
    "print(\"square dist:\", distMat_sq.shape)\n",
    "print(distMat_sq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LINKAGE CRITERIA\n",
    "\n",
    "Scipy allows us to use several **linkage criteria** to perform the clustering. See the [documentation](https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html#hierarchical-clustering-scipy-cluster-hierarchy) for the full list. \n",
    "\n",
    "In class we mentioned that the strategy that works well for the majority of applications is the **average** linkage, so let's go with that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The hierarchical clustering encoded as a linkage matrix.\n",
    "linkage_matrix = scipy.cluster.hierarchy.average(distMat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descrition of a linkage matrix from the official documentation:\n",
    "\n",
    "*\"A (n-1) by 4 matrix Z is returned. At the i-th iteration, clusters with indices Z[i, 0] and Z[i, 1] are combined to form cluster n + i. A cluster with an index less than n corresponds to one of the original observations. The distance between clusters Z[i, 0] and Z[i, 1] is given by Z[i, 2]. The fourth value Z[i, 3] represents the number of original observations in the newly formed cluster.\"*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linkage matrix: (98, 4)\n",
      "# some original observations\n",
      "[27.         28.          0.08390102  2.        ]\n",
      "[37.         83.          0.09320044  2.        ]\n",
      "# final cluster\n",
      "[ 93.         195.           0.97908519  99.        ]\n"
     ]
    }
   ],
   "source": [
    "print(\"linkage matrix:\", linkage_matrix.shape)\n",
    "print(\"# some original observations\")\n",
    "print(linkage_matrix[0])\n",
    "print(linkage_matrix[1])\n",
    "print(\"# final cluster\")\n",
    "print(linkage_matrix[97])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DENDROGRAM\n",
    "\n",
    "A visualization of the structure produced by a hieararchical clustering algorithm:\n",
    "\n",
    "- datapoints = leaves\n",
    "\n",
    "\n",
    "- horizontal lines = cluster merges\n",
    "\n",
    "\n",
    "- y-axis values represent the similarity or distance of two clusters at the moment of merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's create the dendrogram\n",
    "scipy.cluster.hierarchy.dendrogram(linkage_matrix, labels = blogs, color_threshold  = 0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 20))\n",
    "\n",
    "# now with leafs on the left, and the root node on the right\n",
    "scipy.cluster.hierarchy.dendrogram(linkage_matrix, labels = blogs, color_threshold  = 0, orientation = 'right',\n",
    "                                  leaf_font_size = 10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### HOW MANY CLUSTERS?\n",
    "\n",
    "HAC does not require a pre-specified number of clusters, but we may want to partition our data as in flat clustering:\n",
    "\n",
    "- cut at a pre-specified **level of similarity**: by default, the `dendrogram()` method colors all the descendent links below a cluster node k the same color if k is the first node below a cut threshold t. Its default values is `0.7*max(linkage_matrix[:,2])`, but other values can be used instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_d = 0.75 * max(linkage_matrix[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0IAiCBiEEehAEQYMQAj0IgqBBCIEeBMGyEekv+hwh0IMgWDZWwZOe+joh0IMgWHYi/UWfIgR6EARBgxACPQiCoKB6KHtvHci+LDTsxqJq6tTmi5uZuMPESJ/ay6R2SZTtUxJtFfQW5W7QlPe8L+wCrYeG1dDL1KlNw5r8ZPNYtOltykkWaG+fkmgrmD27hRkzmmlra6WtrZUZM5qZPTuiSFYWaTfokg6iqGryM5pn9Lo237AaOuTUqdBRAwx6j7JdOiPaCubMmUJbWysXXng8AG1tNgkOHx5WS1+hmtelL+R0aWiBHgT9mcGDmxg3bhoAM2Y092pZgs4p87r0hZwuDetyCYIgWNUIgR4EQdAghEAPgiBoEEKgB0EQNAgh0IMgCBqEEOhVIoNcEAT9lBDoVSKDXBAE/ZQQ6J0RGeSCIOiHhEAPgiBYicxumb3C0gWEQA+CIFiJzJkyB6Am+VdPEQI9CIJgJVNP8q9lIXK5BL1OPSl1I51uEHRPaOhBr9NdSt1IpxsE9REaetAnWFJK3UinGwT1ERp6EARBgxACPQiCoEGoy+UiIgcAPwAGAD9X1XO6uO4DwO+Bt6nqbT1Wyn5Gy+zZTJljoUitbVsB0DxjJgAThw5l0vD+cT5hEAT9i24FuogMAM4H9gVmAbeKyFRVvbdy3brAF4CbV0RB+xNT5syhta2NpsGDabpwZvv7rW12RFUI9O4pI1+qUS8R8RIEnVOPhv52YKaqPgwgIpcChwH3Vq77FjAZ+EqPlrCf0jR4MNPGjat5r3lG7x9R1ZdIQrsU2ElYVw/5TqRrQ6AHQUfqEeibAU8Ur2cBE8oLRGQcMFJVrxCRLgW6iEwCJgGMGjWKwT0bUx/0M0qhDR2FdWeRLxHxEgRdU49Al07e0/YPRVYDvgcc2d2NVLUFaAEYP368dnN5sApQCu0Q1kGwfNQT5TILGFm8HgGU2WTWBbYHponIo8AuwFQRGd9ThQyCIAi6px6BfiswVkTGiMiawOHA1PShqs5X1Y1UdbSqjgamA4euylEuQRAEvUG3Al1VFwHHAtcA9wG/U9V7ROQMETl0RRcwCIIgqI+64tBV9Urgysp7p3VxbfPyFysIgiBYWmKnaBAEQYMQAj0IgqBBCIEeBEHQIIRAD4IgaBBCoAdBEDQIIdCDIAgahBDoQRAEDUII9CAIggYhBHoQBEGDEAI9CIKgQQiBHgRB0CCEQA+CIGgQQqAHQRA0CCHQgyAIGoQQ6EEQBA1CCPQgCIIGIQR6EARBgxACPQiCoEGo6wi6IOhNWm5vYcpdUwBofboVgOaLmwGYuMNEJu08qbeKFgR9itDQgz7PlLumtAvypmFNNA1rAky4J0EfBEFo6EE/oWlYE9OOnFbzXtLSgyAwQkMPgiBoEEKgB0EQNAgh0IMgCBqEEOhBEAQNQgj0IAiCBiEEehAEQYMQAj0IgqBBCIEeBEHQIIRAD4IgaBBCoAdBEDQIIdCDIAgahBDoQRAEDUJdAl1EDhCRB0Rkpoic3MnnXxKRe0XkThG5VkQ27/miBkEQBEui22yLIjIAOB/YF5gF3CoiU1X13uKyGcB4VX1FRI4BJgMfXhEFDoKUHz1yowdBLfWkz307MFNVHwYQkUuBw4B2ga6q/yyunw58rCcLGQQlSZinvOiQD74IgR6sytQj0DcDnihezwImLOH6TwJXdfaBiEwCJgGMGjWKwYPrLGUQVKjmR4/c6B1paWlhypQptLa6JdPczMSJE5k0KSa9RqUegS6dvKedXijyMWA8sGdnn6tqC9ACMH78+E7vEQRBz5CEeVOTn/Dkgj0EeuNSj0CfBYwsXo8AZlcvEpF9gK8Be6rqgp4pXhAEy0NTUxPTpk0DTEMPGpt6olxuBcaKyBgRWRM4HJhaXiAi44CfAYeq6tyeL2YQBEHQHd0KdFVdBBwLXAPcB/xOVe8RkTNE5FC/7DxgMPB7EWkVkald3C4IgiBYQdR1SLSqXglcWXnvtOLvfXq4XEEQBMFSEjtFgyAIGoQQ6EEQBA1CCPQgCIIGIQR6EARBgxACPQiCoEEIgR4EQdAghEAPgiBoEEKgB0EQNAgh0IMgCBqEunaKBstOy+zZTJkzB4DWtjYAmmfMAGDi0KFMGj6818oWBEFjERr6CmbKnDntgrxp8GCaPAl8a1tbu6APgiDoCUJDXwk0DR7MtHHjat5LWnoQBEFPERp6EARBgxACPQiCoEEIl8tKJi2SxgJpEAQ9TQj0lUwS5k3FCdlJuIdAD4JgeQiXSy+QFkknDh3a/l5rWxstszsc1RoEQVA3IdB7kaq2HmGMQRAsDyHQe5mkrZcumKB+Wm5vofniZlqfbqX16VaaL26m5faW3i5WEPQK4UMP+jVT7ppC69OtNA1rAqD16VYAJu08qTeLFQS9Qgj0oN/TNKyJaUdOA6D54uZeLUsQ9CbhcgmCIGgQQqAHQRA0CCHQgyAIGoQQ6EEQBA1CCPQgCIIGIQR6EARBgxACPQiCoEEIgR4EQdAghEAPgiBoEEKgB0EQNAgh0IMgCBqEEOhBEAQNQgj0IAiCBqEugS4iB4jIAyIyU0RO7uTztUTkt/75zSIyuqcLGgRBECyZbgW6iAwAzgcOBLYFPiIi21Yu+yQwT1W3Ar4HnNvTBQ2CIAiWTD0a+tuBmar6sKq+DlwKHFa55jDg//nffwD2FhHpuWIGQRAE3SGquuQLRD4AHKCqR/vr/wImqOqxxTV3+zWz/PVDfs2zlXtNAtJRMm8GHuipBwmCIFhF2FxVN+7sg3pOLOpM067OAvVcg6q2AHHgYxAEwQqgHpfLLGBk8XoEMLura0RkdWB94PmeKGAQBEFQH/UI9FuBsSIyRkTWBA4HplaumQoc4X9/APiHdufLCYIgCHqUbl0uqrpIRI4FrgEGABep6j0icgZwm6pOBX4B/EpEZmKa+eErstBBEARBR7pdFA2CIAj6B7FTNAiCoEEIgR4EQdAg1BO2uFIRkYnAn1X1VREZpqpPd3Hd9sBAYEtV/W3lsw2wUMqhwOuq+lAn35dlXbgVkTcBo4Anylh7ERmlqo+LyLaqeu8Svv/28rWq3iIig1W1rc7fHwO0x6Gq6i1L/RCd37est6eLv2ep6ksisp6qvrgM912euk51tQi4X1Vfqef3gI2w8j8DvNU/ugNYA1BVXejXdtrHRGRzbM1ojKpeW/lsk1QmVX3ed1MLsL6qPre0z9jNs2wDbA48qKoPi8i6nbVFtUzF+x36WuX+GwBbYe7XpepHy9OundyrvR2WYhxtqqpPichmqvpkT5RjRSEiOwAvqupjXXxe087L+jsrXaCLyIlYFMxtxXvbADv6y+uAz3o+mNdF5LvAQlWdKyK7AusAY7DBNhiY7/eYDCThOh/YwP8eIiKX+z1uEZEjsbDKocApRRneB6yHhWUuxOLoxf9fB3gNWKyqk4GvYKGbi0RkTvG9NhF5GBguIocAQ/z77wCuLKphM+BaYG9/xl2ABSIyEHhFVS/sou6+CKwFbOj3U7poQxEZp6ozRORtwNeAe70cDwLp/tsAw4vn+lBRb/P875HAsyLyV+A9wCki8h5Vvaz4rQ2BLb2+nksTaFHXW4jI/Vh7vQRcBvzUy7QtMAj4X0xoX6eqt4nIWsDWwH/5Z/sDm4vIZcD9wG7AaC/nhsDvgZ2L+v0/r59tgL972f7q9xIReUVVfwpMFJHfYm3e7N9fBIz1v18Grq30j22AW4B3iMiTXqfrA+uIyHRVvapaR14flxXP3Kqqp3dsuQ7XDQF+BWwpIrcDB6W2EJH5/oyLgH2Aq1OZVPXLfru1gfdh/W13ETmlLAOwLhaltkTBLCJfwRSoB/2e7WOoHJeq+nMRGaSqL3dyj11Udbr/fSzwJqy//JjadnhfGkde1vIe78f65XBgqIj8C+sLx4nIh/yyRZh8GADcp6p3FN/fpLjdsUAbeZwLtX3q88AmwDOq+npFUVitC0XgJHKbXFfIucOAHbwN8bK1YcrrLOAjwONYO8/xZ7xfVa+vPNetqvpE9XcTvaGhd7YJ6WjgR1hFfBh4A/gBcBom9AB+CeyCuYlexDSvf2KdAn99CbAYE0y3YYPhGXLjgu1Q/Y9fh4jsjQmCAzBB8zzwx0r5PqKq3y9eD8AafhGmqT/l97gZ+BxwraqeKyLHqOoFInJ25bkXAgv8+9cCNyZtS0Qucw08Dbibk4DANMtz/LqveJ2dIiIvFXUwEDgU2FlEbsMG3q3Ad4CbyEL8eWCTdD9nTb92iF9zm7/3qN/3Ir/uYBHZDpskJwPfAn6Htd+n0gRK3g28FnChR0ytgQnnVKavANsBPwOOBN7tv3smcD3W5pcDr2Lt/jqwU6V+Pwzsrqrf9Lo5l9zGb/FyKbC6qn7PrznOn+U5ch/bHPgLFno7HvgGNrAB3ubt/DzwGCY8h3ndbIRNMh/BBuQO2GBFRPbDJrJRlWfeUkROxzSyX7sgGOC/W163G7AHNtjXBh7y/y8C9gN+7fU2uCyTR6GN8XZ7DFNIXqnc+1kv13DgDd8FPhzrZ5Mrk9IawBNeRxt4uy72z8pxCfBhEfkfVVUROdrL9h/gMyJyM9Y35qvqN0Xk05V2OBiYgY8jr8NL/PlHYZPqH/z3fuvPlpSlNxfttycWffdO4A4ROQfrAzt4ef7k9/ssroyo6p2VPnWmt/d8TAH5PllR2KiYgPZU1d+7AvNS0SbvdsXuFWwcfQkb82Ay7mPAN0TkEUwWp3YeBMwBdsLGQPlcuwAn0gW9EuVSdZeIyI+AuZiQu0lV/y0i78IqehDwH1W9UkR2wzr0RlhnHguspapHicgRZC1DVfVXInIA2dxW4F9lOVT1ZhE5QVXPE5HNsMFzA6btpHsdjJnqi3FNVkTWAbbHKn8jbLC8FQvjfExENvf/j8c0oOdV9XwRGYx1xDcwIXa3/z8BGyQbYB07DbidMUF2B9ZRdsQG1abYZHYscDYm5BTrBGt5xxyFNf712CDdC/gHJly/gQ2aIf4Mi6idcNTbYj42CRzq33vQ22sk0IT1n6k+YbVhnfvmou7m+e8mwTIP2FBVPyMi44syvc/rULy8L2OT8t8wAf87bHL7KJZb6CZVvdAH0EhMqO6CCdzXK2W4wdtGsARzrf5Zk6qeJSKfwvsYpqE/iPWrdYCZ5L63u2tL62OT5J7ANOBJ4NAk+ETkvar651SRInKa183LwO3FM7/L+90XVPWHIjLUy3g0JpjTdc9imvBqqjpdRL6M5VNaCHyGrFmmCewmL9Pn/LPFmKKzg7f5tsW9Z3ofWNPb+0BV/VlR9gu9bhdimvJumLUz1+/xT1W9S0Te6fW2pareICJfw/r4YnK/3BD4CabxfhxTxBYAa6rqVyrtcAsmbB9X1WeLOvqCt/MC4EZMPpTW92R/pq28ztYBbvF6/rSq/kxEPoMJxs8C78WsuYXYZH9cpU9th42B9VX1ZFcULvPn2saf7WDgBUwuzfLfTm1yPSYn/gC83++VWA34o7vP3oaN6zW8LO+htp+Xz3WDqv6FLugtH/puFO4S4GFV/Z5rKce6CSdYxc3BTO8rgUPwBsSE0B3Ayy64h/m9FBjmAm2ha3K7ALtjQqlKk4h8FRPinwOOUdVTReQorPPPwQbiYPKO2bOxmXobTFANwWbhAzBNM/1/PbArMEBEvo8J7M39WQQTIElzmo1pJK8DX8AG9Y+xWXoYNqDGYpPSDthA/yY2eV0nlnNna0xTBMuZcwnWcZOGcCkw3d9fANyrqneLyPaqendZKSLybr/fZzF/etLOAI7HJp7VMHP9VVU9y793gtfzg172i4AvAnMLzQdsQl7T//+D1//fVPUOEdmT7AbYEBvovwJ+g+2HSMK6zcv2CLCxqn7Xy3CK33uBql7nbZX8mI9h/epmv8cQch87HZtEbwX2xbSz1PdO9HItxLTEJ4FPe19pt1iwTXjbkq2X3/i1O1eeeZaIfN3LDjbZvAFcBRyE9YO9McFUukSeJVsUl6d6wya754DRrnRMwfrmcMzl9jzWbzZMZVDV232CUG/fnXwsvOFl/663eZrsTnGt+/jUrv6se2D9MilEj2PC9kFsnO4G/Buz5J73fzeR+3W1Hd6JjYcJwAXAI15XMyiOXxQAACAASURBVICXVfUX3p57Umt9/wmTJXO9rQcCW/hna/s9XsIUhCZgCjZWB2KKGdT2qWu97Df6Z1diQhevw0FYUsI3yPKhvU28L78f60vzsXEx2NvhAeAY8+IwB7MkE+v758n92f5cdENvCfS51LpLhvsgXBdL1bvA3x+NCeLr/PVV5Aa8ApuBt1TVq0XkPteKt8NM0l2B3Vwwj1XVr/qEkVC/74lYow3DtK6kqa6PdbZXsY6VtCwwIVya9P/EBsqWIvINrIHAZtrH/Z4PYQLwPuARN9E+gXWeNFFsj3WscsBNwDr3OzGza4CXazxuofhzjKHWTBNsoK3mf5+DdcCfe7kXYBPk3cAeInJQpY3asE54PzYw34WbwJhLZBDmTwbY1Nsv+S4fxwTBO7HF2yZsfWEjTPOhKNOXMQH235hVcoeq/quYGAZ5W83GhMD6ZEWgfOaBYrn6F5f3FlvAHuJ+/ZHAeoW290MvaxI45+DaGjaI0/tgWt0mmFvr65iG+1/+WRJ8r1BYgd7fdsImlM28rOdgWt4MsgsArN1/BHwVmK2W9wgRuQh3ifh162Hj5plKvbW7HCX7cROK9YPRmEBL9Z4+m4W15Zm45eWfHe71swUmXE4SkcXUtutAzPXxKNn1MURVT/c6nodNaGMq5RhC7td3VNphONmliX/3dUzBGisiw8hK3dbYeLkOWxO7WUQWYn1kENkN9GhhRZ2DCd5NMWVibPHMqU99EmvP67DJ/CrM0mv3ApAnoPXI8qGmL2MybSHWfkP9O3di1iT+2TpY/wBT0M4VkdW8DJTP5RNtl/TGouhJWCd+G2ZW3YQNjgmYWTGcnCtmsap+3s0gMDNkLawC0oJQ4r2SF1JGYB1kPtaBNhaRIV5R7e4C/94xmAb+EvBnzKQDE9LrYg38K6wh0gRUztRrYB0xWRPPFeW6ye8xEhPGs7EBu7eYn3yW18Gmfv1T1Ao6/F6HYGbobmTz+GXcQvHrXsK0qJvEIhsu92vvwDrNsdigONDr5BXgeZ+AHgAeJrun8O8m//kYTGCs5Rr2G/57SbtqIWuKG2Ma5N/LzucWxAZkjfQqL9OV2CA4gTyAwfrmE/78z2MD5FSv402Bk8tn9rrdAJswXynufabXyYGY++Zef4ad/HfGeR3u7HX/KNYXR5MFEWTtcj5mfe2CaY+QBd8IrN8A4P1tlKo+7nWwk5frq1g20p+KyOHeXg97nZwNvL0Qyt+ktp+v7sLyi/461dtcTBNMv5uUF8V8v+/EhNNmRd2ACbT9/f+q5VX6de9I9VsVKoWAHIb10xcK62OQqp7r1w0na7wfI/frajs8hykPSSNtU9Uf+j3K8Tsac6Gl6LDDMaXoLqyfl0pjaUWViuGp1FpAL2HupPuxfvUocL2qPim2AL6X33dbsrL5F7J8+Di1ffk0bEykZ0nf2YbsFx8IfFdVnxaR0UXbJ49C+VxLZKULdO9sx6nqD8QWYQC2KTrpEGyW/jSwSSH4Unkfx2bFn2Ba+P1eAUJeSEmCdKbaqvufgA9i0R3VTitYhb2CdeyF/lvNWEVvRXbnICJ3UuuXTzP1CCyKYhG5c2xfXNeEaexPYA2aTLQpbiIO989LQQfWoR71cu2CdZAvky2UsS6g5pHdESmy4e9+bXLxbK+qh/lzHOf1+RowSlUvFZFpXrcPYBExw7BBOZasjfwZy9vzZ68vqNVK5mIaxS9EJEWdpDqYg5n1u5H9jEdhmmWVWzFXwYPYIDoEG7hXY/5IMCG4CeZGeavf/6NkK2BfzA/7S7FIqo9jbpUBWB8Bs5aSb3VHzIJpAd6sqndWyr8atmi/npd9DDCZLPhGeF09DowSW2vZ0c3qZrL2vi8W3fR1bJJ4iuxCextZGLzZfy9FYUwG/lO4Dv7u1+1CrcuRipb3WXLAwa2pDGILtjt4eU/G+m9peV2OKSxpbMwCPioi72ivFBPupYDE62khplxt6W6c5I++DlOQvgBM1ryIV7bD0eQF8F8A2yVXEKblpvE7BRsDW/k9TsPWNS7GrKL0/k2YvEhKx95kzR5qLaAHvJwjvb6uw8bCcf695EK825XNH5In/ZF07MtHY4rBZphcm+bXrYspDgMxN9UHxSJwxOu9JD3XN7B26pLecrk8VPjEwML/vo4JpZewipsJ3FW4JiAvKm2PrTqfh4UWfcc//x9sYG+AdeDHAdTiclOoXnIXJDfHHzD3RTJvrvL/VVXPce0p+Sz/rqq3YkIlhVCtTp51P0X28T9eGVQpPGogtS6cd4nIIC/DE1inE2zQz8A6XprJh5KF/R7k0LGf+fOuj0UPPC62ur4A6whXeb095r5xsAnltaQ9Od/COt3HsUHzJKbZPZ6e0d1agzAhMqj4bnKRPISt8j9Z0dA/42W40V0eO/jr26msbYgtJC7CNLnryZEJM73u0gQ/QlXPEJEvkTWrG7F1guQ//R2Aqt4vtsj3Gjb5vIgJuuRbfdGf/VzM//u0iPwD84X/DtNyd8f6z4OYH/z/vBwXYlrYetikmZSVNbB+sQfmS78MV0LIi+EPuYvpS2QX2rFYH/8hcAbm4tnNf2skeTJJvz8DeElzmGoKD8bvl9wqgykUIe8npXLV6nV9n3/3cMx6/jDmWnwMWLsTsz9Zzg/66w197IzBJlwwa24eNtlOxxScI0RkS1V9X6UdLsX6+GQRuYOsxYONkUHASM3x2uX6j2JKyN/IAhxqlY4X/VlfxoT9wuL7u2D9fi3MlfQUNtGByYHkahrqikkLZlVsirV3Z+t0yT2Xxv1wv+drwCVqe27O8OfYt4t71BW90lsCfRPgzGJ2vtVX8HfB/NgDMI3sjqShi0UBvBkbWH/AHn4tv/YA8gMPImu3a4vIO9WjZlT1H/69vYA/FD7Oh8jhXMkttLH7hXdX1YOK95OGAzbrzlPVz/vncyk0lcqgmo6Zrc+Q/akbkSeL11T124WgS6Z7OZPPU9XviEX0pPDLN1zIfptad8QZ2OTWgmkJ38esk0cwgXAdcF6h+fwL03oWkBekk0m8ZnpG55vkxUOAy9QStp3iz3YNFhdchlfdimk+yU3zXi/TH7FJcB2yz/IsbIL7HjBRVcf4vVfHTN01sTDW/URkS0wTa1PVT4jIezGNajA2uZUDYRSm3Z+KWTCpbV73NjvW6+EozMrZBJjj7f4O4B5sYr0B09K+jAn4/cgL46WyMh7YSFWPFQvXO4CshMwurNKrKRb5qdXIngROwtr/Wmxzyk9F5M+YYFkHE9CvicU4pzDVtIFlISbAxmGad6kInVAp7yfJ4cN4mV7zfzdj0RxHlO3qwj25x9Ki+TYicio2wV3h7x2IWVS7kCOXpmN9oNoOc7C+d4N/5xlvi7mYfPinqj4kxV4LV7S+hU2UZ3j7lL5syK6Q1N+28me+wuspLZinfr+vv34Cs9yOwMbGKV7GAdhEsVBzOPEpmGBfhPXlFrJ7bmNszO6D9ZU1gNNF5MfABWqbpO713y/vUT7XEuktgb4pvsDiHWJXrHF3KfxtKYwxbVg5ENO898c2kdyBdfK/uIaTolLWVNUL/B5/wTbspKiZf5AFyfGu3Q3HfF+ieRfXuW5WrgPs6Vq6YC6g5N8SfIFHPKKG7PdKmkq5K+xMTHuc7/9G+edTVHWBiOwoFlu+TiqfiKyNrXAr5j87TCyiZxtM02rC/OBD6eiOOA/TPo7FhNtXMZfCm8ihainGWL0uU1rkx7x+p/tn75EcNTSZ2sXD4zB3wj3YgG9T1T+JyB4VDf2ruMbvzzDByzQc0/rOEYu4wMt1LyZIZnid74JNcmmhGkygJvfNaBc0L2GDrd1/KjkGeXNggKo+KiJpMnoJM5M/gU3si73+H/LybuaRLKd6nb4LG2j7k62817G+tSHWBxf6/zth1udJ2OT9T7ISUlqlYAJ8MdbfU5sAXKOq90leRxov5gsegp0SdpDX2//gOz79umTZnY1N+oeKbdJbUJQBCuVKRPbHrLMFmHvn+94+3wU+49EzC1T1B9RyK7WL5rMw18QbmBX0qtdRGg+XYWNgGOYmPaXSDp/CrDzUduJ+iey2+Dm2SLkv1jfa91qobWa6RfJGnHZftuYorBGYFfY41n4veF0MEJEm4NJCBn2TvGAMNplviE0wZVjvl7x/vO7v/YJs8R/orqRPY5ZMsvpu9t/YG5NJYBbZXsU9jvayl8/1O5ZAbwn0u7DOnjrVPWKbLG4oBOZcitVyN9OPAn5V0boSKSplHx/YgnWSH2Kz+jV+7yRIUnTBydgA3E4shjgJiwmYwL4T09YApqpqTSy75MWnRZiJfytZU3kz1nnfg3XI1fyzm8hC9BoRST7uXTBBezKmXf+L7At/J+b/3R7TkM8i+xKHYB1vAmbNTMA0gP9g5vMMcoTGJP/O2l6O1YEPuVshPdNrWNscjlk8f1Db3JQ63l3Uhno97+13P+YjvxeL3ilp13zcFXWef38YMNLrcRSAqh7iQmsDYGc3ydOi4iCyb/QYTAjsiE1w62ALZaPJputN1PpnB/lvpOiVjbBJSbAdzBf7b51JbSTLVZhW+y2yiT+9aP+h+CKu5s1ak8XOENgEcy0t9Hq7DGvnnbDdpRMwTe9KrA98yz/7FvBpsWiRtTDfcFp83Ap4pqi3r1G7uJcsu1GYlfEhTFBM8zKkWOZ25QoPHy7a7BxMUz0KWxB/jbyZCK+n9PzzMavtWkxY7uPvz1PV74tIckkIpmSshwnAFMGW2iHVdVJ6EqktzsaUjQ9i6w9pr0W5S3MUNm4foKOrYiS2WS9FOu2FudK2wdpIMZkB5gk4gLzRcC3yxrsF5LDeNIEd4894InkdZKT3u80wDVuxcMXRXpeXY+MaV+COwPr0Cbhfv/AuJD9/l/TWxqKTMIG9SFV/Vbz/YWwTzv/6DDwMG5yPqOolUrth4UeYNrOa2kaaJjwqRVV/Wdzza7jppapnishYVX1QRN7ims+nsA4+BhO6o7HGWQffxKQdY7S31Ly9/bNkzWJnrHGvVdVrxTYiPIwNvr9gg+c6THMchAuGNImILVReiK9ou2/tO5gf8B3qW8V9cA4g+xK/UVoo5A6oZJdUitC4nLzI9jG/biNVPUFyjpp3YJ32Ukwr2RHf3FRYP6t5ma4v6mV/TNjvhkUGPCW2M/Q93q7XFtf+DBO2i8v28s++4XV/Y/KTuhWSNJSjVPVsETmLHCL5DHm34tqYsHhZbYNZ2uC1GHORzPB6nysi48i+1m3JltNtmJsiRWQkq0w78SGncl+OCZHnsb492fvA896m13g//gwwVPPO1j39GR7FtNMXsclxtJelfJaTsYie9dS2zKffvghLa/CGqv5OLB3D9lhfS752tJJLREQOJStXafNXKvtZWJz1kf4M/8Em5M62vA8H3qOqP5G86zXtG1mMWeFXqG2UWU1VqxND2p+g2OR4LzBdVV8Wi2zZxdtiP3+mjbEJ8hLgY25FpbF4OqbgvYxF2XxSanesDsCs2xlYCoZZLtyHexnmkTdlpVDSV8mpPAZ4uyRr52XyYnRNX/Zn2wRr0+P8u+OTktIZYusZW5J3ER+G9cVpqjqtq+9B72noKQplFEASspjQeL+IXI/5vv6BdeZU0aVmP9k/W10s7nRJQfcnkE3fncR8jSOwlfYUh7oVNqv+ABOAI/AFTvFdgsX9yu3tpYa3ABNEd4pFOJyMCbcrMdPycmzgvo/sI50nImlWrfpPT/b/x/mzJubhu0U1Jx5rj5t3F9SRWAfchNr433PxPCSYMJ1Y3DflqNkX819+ANuw81E3e2e52T4TMx//jmtHzk1+jy2AD4nIff7bmwCDJEe9/ItsQXTQOtS2hH8KOFQsp8tjZHfC/l42MPP7ZWzyHYUJ0439vUFk98Vd2GS7Mya034z1oyOpXSy708u7B6ZlnQucqLaDsF3wS5FICdMUU/vdiA26m8TcfBR1vzmWi2YTrM9sksx0F56neL0NwXYI/sQn+NLfm+53CGaCi7tKhI7hjUljf64U4tIx6Vha7NyZHHOf+Blm3V6AaY6PYq7Abcj5YBZhGvV4bI2Gitv0MVfQbgIOEUsGBjZ+S9qtRWy8/Bc2Zt6HCdX7sAim59V2hr8dy4+S9lpAHoubY66UHxTjYwNypNbLmItyMZZDBkyBSpp5Smo2wp/tbH+Gz/j6xeGYspnCepOrbU0R+TXUJmpzxeFQzMISYDVXdNrzxFTqot0zUVhAa2JjbhpLoLcE+lqq+lcR2bcQsmnzxS+whjzBNe+Bqvqafy91vkWYNpHM/lswYdWGmThnk32mPyNrVteJ+YKHkWM6z8IG8v2YJrOhqv5YanehvSHmxnkDC5Uqw7TK3Xp3Yf6wP2Ia+VVkjeJqvz7VedoWPVNV23PHSPb3Js7COuOuPhj+2+thKuZLHKuq+1IbNw950TSFd+2ODdi0C3UYFpv7NbJvPOWoua8YlF8UkT0wS2M8NlD+iE0of/Nryjwk/8Qm18V4pkav+9OLZ9wTczusgU3Ix3tbpjUVyDsHP4RFGoz3/9fGJqPrsM1XyT+5Ftn8PtjLk2J9d0r3FVs8vprsRoPsa30EM7vX9np4mBzhUAr+NfDdkFobJZQSqN2EuTtuIkfHpI1aB/h3X9G8s3VN8v6Dy7GEcif6M5zp148UkS0wf/ZizHV2rFg+kaPImuN4TAimBFZvF9uIkxhCXsAtQ3yfIodyJibhO2Kx9t7F/38Vm0C+Qk5i9pqqLvDnSYumc/33rwf+6hbyunZJTnamqmdieXDAhONHMaUhTdwpYOEvWBjoiZgC89/YRJfCS/8H07xT3/+Az3mTKSK1sLZNwv1VbHymHaWJtSl2Y/t7AyWHmqZd4KPIm/1exSys9bFJ+BtS5Mcha/hTqeSJkZxTCuAp/53kmdhFLWgkKQld0lsCPTXyBG/kcvPFCdjgPk/sSLv7RWS0Wva41PnGYmFgA7DOvR5WsRtgq/gfVNtmfio2i/+WvFj0WVwDFpEZmBmW4lDLcKGdybGq0/29O/z7L5IXP99JHuj7AP9S1T3EVq4/hfnNpmKa+wJyxsFdsA5wrlikRuLX/vwpnjXtMN0A07pOxLTLXb08LX5ds38vmbJp0XR3bHFlsddX2hRzBXCqqi52QQHZR/94UZ4R2KD5IjZQnsa05JmYcPolheaBTR5rYZPza+RNK+24BXGEqv4/8Rw8WpskDPIAfIEc8dCmtekDBkjemfsvzLLYG1tkPcXNaMjpHd7AtM1dgSlivuu/k/kYNsi2w4R8Gd4I2a9Zbripco+X6UbpuGMzuTUWYxNZ2tn6JmyCOwFzIX47fUFq85h80MtzMKaxLsDa4EbMOkltACYA2zAFaB+//36YkPonsKGIvFtVrxCRT2LWzBH+3bQoCrW+a8GUnmEU6SlUdb7kRf12JQjTQK/AFLSpktNfJM02JTsDczUmK2wCJlCTZX6N/97lwAOq+nevm/+mdt0g7TG5vzrRYm6kz/vkXwr3zbTYqOX1LOTwwpQn6TqKVB7U7gK/maxMbYgph0P8d9cplKOUkmIEtZYb1Cod/4NFNiUBnoJGkpLQJb0l0K/AFkYuF/O7vtXNHsj+qfHAPYXpCbkytyF3BLCOPh3Tso6idtPGS9oxz3PqANMxjewd2CDeldx4i7BY1QHkBSRV1b+7cCjDtNIiyDzgcBH5ODazr6mq88RSx96Aae9/KssjIhQNeRIWUpeslS9gUS6CLeS8DpwplgxpK0wQfMTLWRWKqXO/gblRBmADdnNM4B6hqgf7te/FhNf3sA7zZclpPud6nYzvZJAk9idrHqdiQj9F/LyOLQ5Oa698e95txKJdjgRu986+qNDQF2MbUA4hh769xdv1UX/dSl4IHQZ8Xy2j4xmuxd0llhDtS0VZv+z1MpraJF6QB9mWFOGNYuszlyTXhZh/eiu/tkzper/X74vAGmqLv/thE+h04C2aw9suxvrNrthkdBomSPer1G2Zx+R+bN1lR6xPJo05rTWlNgAT4gMxITQIEwb7A19VT+nqytMV5ARgR2N97TS/R7kj9hCy5rmAIj0FgNomrDv9vl/QvLPzS15X2/ozrIZZ1ZPVElMlwXcmtj6yGLMk9yGH7h6I9aM9sTQVaZdvskKS2666x6Rkrpi78CJgnOZQ44O9flOa2w0xq/JFLCKqpbhHEuJbYW32Jq+3Zq/r7YEzVPU/xXfa8+NQuwv8CvKaGtQqHdOxaKMUqdSuJHTyXDX0ikBXy098B4AP6g3IGuT/YiFQz1Jrepa+ud9jAyFl7zukvH9hvhwMXJ80JRcW38Y0zbO8Q6UsbCdjZlOaWdJOzKPJOyLBNNKaMC33RSZz+17M/Hym+M4lWINfRa2pDyawU72c62UvF2GTz/vrIvJQcf0F2CAWqY2bT0Ixde59/O8FWObAyW4Gvq34jUFSiYxRD6vqjNKickZgA3wbTFj+CuuYh6lqi4g82slt0hrEgdgAa/c7+/OMwQTUM2TL6Q1sYKdUCSkSaQdsveFiEXkRa5f1sD7wAtktALVplo8i++BfIbtHvov1lxTeuCe10Q+l8AEz11NOmbWoTSiXNj9dgVmbKefNTeRBu42XazE2mb6D7KMW8kam1I/+5P+3ufaeorce97q5k1qLZS0srPQjmPvwbeRNbmCTwLHqC5Ui8nrFuhiDyYr7yKmV29NTSOGXxzTb0VJ7MEnaxDMd08q/ivWZ+1T1Bbe4kqW8iFqr9w5gjubcNod4u07HLO+S9j0m6Q2xAIKyLx1ETt63m9fT61iu8vW9zQ7zNr27IjuSh2B7VT3Rx9GLwEC1sNvNqVU0wTwG+2CT1OuYInsV5kq8HFtXUWqVjjVV9QnJUWUj/HcPweRjl/SKQJfahZlzJS82nIXNQodhplU1Z0TyzV2PLSaV5ndJMl86i0Y4BXO5fA0TAims6DBMOKzmDXUb1gkGkwVI6uDbYYN2deBIsQ1RCzGf8rrAGC9Xu/anRVhghSGuxazu99gQ0/KasQH/GGbajsY0pLUx8/QsatN+prj5NJjSYtALmHDbGhNWqEVL7OAaGpigGImZ12uQwxGB9kG0k//W17GkZx/xsv2ZrHlcggmHFLK2sQ+I9bGOXfJJ/70xmDVS+p3vKK77sJdpJNbRP1R8ditZY3ofecNQ8rc/7xNF2pilmPWwr3//VXIM/BV+TU0ki9qmtIO9HlIeosVk4QN5l2oKSSyTeqXNTy9jA//bPnHN8kG7l6omTTTV99fIKZTfSrHYXbnusEKre0CLRT1sQ1zaKPcDTAi+1cufJqNLij5wvt9LsR3RN1esi3FYH1qIWUhleor9yX75O70O0mSRFKMTsA1Ft2NjZB8fI8/4NdPJeYrGUoTtYbsyX8XCOF9NLhJVvZpa2veYuJAciEXynSsiH9S86/x6L9McVf15WZ9k98Z3vLxbkveVvOJl/w+0j6OrMA06TdSviR1Uc72q3khee/kLtnkotcm62JhfHVNq2qOosH0fv/HfgtpEgUukt1wu+2Na+cuYNpUWG7bFNJFfYloRIrKGqi70SQD/zu+xTTbt5rfUhiW1my/VScEpzeyfY76+EZh7YSTmS06r5ydhFZk2OIEN4NX8OW708nyFWnP5t5iL4zJsUFejC6wgFj1Thv5N8N853N9KZm/axTYLGwQpFjwlH0ra6ovYhLAI0zQXYrm3Z4nIn90SUWxQ7+73+ACmqbwLG6T7i8Ucp4MZ1iLnTYccpjUQG2SXYgM87fAc7P/S6v18TDssF+1STC6Y+6XdpFTLnjlbbVH81dKHXGnXA8hb2p8uNGrVnLvcb9nu6ijDO1cnZydcg2IjmOQTk/6jqicX729OTiCXNpw85grJLV73ZVKvz5H7TsqBcirwL7HF0EUUCb2c5DO+2tslbfVP0Rf49/4XG0cTsFOhBvrDTsYUld3Ih6mMxia++V7+RWqphc8rfrfmhCVgO61NrXCJ/1sbW3tI6SleJ2cc3ZWcH+aj2FqOYhbCpVgffgb4bCGYHxPb+q5Yf34W+K2qphQEL6jq2t4H1Mf9PKk9rrLcY7IjpnANBuYni09yXqhyrLSjqn/x/rGnP9uDmrNGQk4L8Hoxjr6ORYKlfRoP+3f3Fkuz8TLmWrsNOFBE3uz3P03zaUbViXoxtkB/JuYiqgY8dElvCfS0sy6FMKWwMgG2VdXLRGRsamQReQITLKeTtcFXMT/2I36PMizpU9QKYADE0reeiQ3ob/nb+2E+yC38Xt/FwoMGYIt6C+i4M/JFTGt/h9/vU1ilj8ZMzj+oxc8m3+UgOk5iHRCRrTHt/6NYtA+YoHyOfCjBWOA8VT1eLBY85Wa5BVuo3AizNh7FXE4Hqh1xhXeg1IkQkd0KQbcnptWvhXWiif4sr2A+/fYwU2zRaap/71CyxrsvtCfhUi1OeZJ8gENatPsk1kHXxwTTT7FY/mRSHieWS2UzyT7kKZhJW07I/8TcGn/yZ/y3iEz151koIkLhjiIf1vCqqv6mKN+llfue4s99JDnzJdig3gPP/y0iu2Pm+g5YitaqAnEG+TSn5At9qnB9rUNHks/4u+Qom8RErB/sT07tMFNVj69cV0ZinY75dxdjE1c5GXWK5Oizs7F2uhfr+w9gFu5CcmK8awvhdI3k/DAvYmNlNUyYn0iO7EgpB9L3FBO271TVd4ktsLYLdL92gdfbl7DJdwb5uMqLgBZfs9qAnD5jI1W9saKh34L19TdJcaKU99ekDR+Ibdz6OraOMQQb/98FzlXV84p6Su3/ErZfIGnhm2BJ3v4tlsTrWLWzbM8VkQ+LByN0Yr3fjo21z/nrZmoDHrqktwT6S5jgGoG5KXaqDgRVnSK1W28XFD6+eWTNI+X/fpzO81ofV9x2ot87Ldh8CNM2RmKNfxnmy0oLI4OwCIfqzsgR2ITyHOZj3xbTLB7BhPbamOD+JjnnSRMmyDesVoZY7OrjmOZ6HXC1SOvzQAAAIABJREFUqk7xj5PP913kCWoLMT/oNuTEQvtjJ9gMxNwg66vqvSKyhXTMZ5M6YtXvXpbpUrKJNwEzqe8Sd3uJL2L7AJvn90+m9+7ABpJdZBtgWtdc8qLdM/76beSUDjf6vQeR81ZsiQmlzTGfY4pVhmLRTopDkilyl6ulVWh3R2ECZF0qYWqFljjG63Bmen6xzV1z/fWmFEefqWqb2OlLu9L5otWLXh8LsTWfJOCSyd5Z9rw5vvZwN4WSUDzfa16WWdSmoC0po7Tej01OSbu/Djt7dHdq3YKTi7/PTf3G6yDF4e+AjaPzgM+rbUjbvfLbSVgr1l6vYmM1LTq/BZtYWrBJ/CpyGt+53m9GiciJqjpZffOhdAyh3ZravSNv83t+EBtno4F/VjT0DbG9CinZ2UBqzzpI2vAibNH8S5jbZKz/zgXYjuGyng7ExsidWBbLJv9YsElxVyxZXZkErIy7R0Q+oKp/cJm0HfmQkJ/QeRRYp/SWQG/3cfrrrlwkZa7mQVL4+DBBvhc5PAyyqVsVwIm3Q40vPnWyuzBf6nMi8pyqfiN9QexE93f5yzRgn8Mqe4qq/tqvuxzTPFKkTZnzZIL/xtZYh0oZ4BJpK/JTXu7nvYyPkiMfFmpeFP4w5t54kpwdMgnIN4rJAKxz7Cy1+WxSR9xVaxcjyw1UnyRrU+uSF/2qVk/S5Hb1cl6HTUCKLT4Px4TdXEzzSIt2KfXpzZiQ2BHrF9dh8c+D8NwmqnqE2ILzR/y+aVIsN5xsgbnP3uF//5F8FmS7ia2qrf7dJKgmufD8ASbEUwz9PeSokQ9hMfI/EpGvu6uipAxhq1I9zWkO5i5IJvvfO/lOWnvoTElISde+i2mTO2DCuX1twTW+cjFye8wV+QI2qTyFxYW3Se1O0SqLJe+/2AhTUK7F2qjMB6NSu09jU0xI7kX2V9/t323DLOJnKSZx8slBf1bV6mJnohpCexWmyS5W2ztysltAr2Lt/j5sP8tEybsvNyfnz1HyWQcbiUWPDfc6PwNTjM5R1UfEIneeBxCRZyvlKtN/zyUvuo/GBHeKzy/dZQdhVtiGYqG7iEXHQREW253iVaW3BPr5mAb1hL9OiznJP5ZiPwdhjXOQP0hKaPMhTNhcjc1eF1LB3RFrlu+p6scq15zif+7hv7022SWQ+Do5b/oJmPlansiS7n0IHUkTSxvm4+1qEbcMTWtNLhKxcE1Vi9cuNfvp5Nh4ROQGbPA+SWVguuDeEeuoZTgVmHZbLkaWA3MdTABsjmmje+Gpb6uFF4sYeCvWJgPIUSnr+O9/B9hKVR/w61fDLCfF+sC61C763IIN2AGYZpVM8zsxzSrF6JcHHq9J3jC1PrUxvqU7qiz3Jpg2OQXrWxtgg+3fWKRFWqS7QHP6h5tcA9sXP2aM2oNMqqTTnCZgwm434N+q+vtOrk1RX2mt4t3YvoRSSfgWpm2PwSbBH3kdPoVnh/Tvtp/oRT6QZR4dMwiWm/WqbIe10R1YEMEJ2ER3NbX5YLbz/8t9GnMw5WE4FgZ5P7Y4PwZbZEzRSsOwCWJ1L0dqsw6o7UVJnOsW/OOYxXoVeS1CsUn4MlW9yN9LY/bz/vy/xNya/1DVRzDh+TksquzHmEa+NXCCu1Oukpwjql1GiK1TpERrqT8nF+Rj5PBdsAkmucvWxNyLl2DjbQjm3nsFCx/egnyO7F7UHtfXJb0l0D+H+fEWYSbMWl6Wg7HV+hSidHzFDzsAq9CdyfHi90hlA4ebaYsx07RLNG/g+Hb1M+8sr2KHCZ/u76VA//YTWUTk/ar6x6TpVW5zJTaA/4SZYmUMdclo3K2gqlcU7w/y320P3fRyP1Z+WWzx5d3YYPk5HXk/+SixnxTvV+Nby4G5GTbw/g3sqqqf8N86ABMkqSyTsQW85zEhfC95v8DmblXthFlSh2JtPp/sMx2BubpKN8he5EPDn/V7bUEW5p/FFhbLxcPLMLfWxf7+xljIaipvMrHvEVvwPA6baNvIB4P8GnMnfBGzJlJo3rZiZ2emMzMnYBryl72uXvO6q1ovo708immg1QXTDqgtCF+H50PXnCwuUR5/+DZydsR0zulmXjdHYP13K6wd0/hIC+tp8kmTUWeCdCpm1a5BcbCCdgxpvVNVk4Lxd1da0sYdsMCCncm7qa+ouB8g5wC6trCaTtKu9z5A7QavtJV+Pia8q2NxfiF0BWvj27C9DXv7+5uRN+H9FbOe/ohp+S/TOe9Ui5L6L/Kh0/MwK3IutYfhlO6yzTAF5bNY+62D9ZUXMEsorbuA9bfZ5OP6llghvUHaYp60gk3JM18yN/fABuTJ5C3haVvta9jCzNHA11wLTEec3ScdFzrakWIrbjfmS8oSd04xM6e4+MFYkqIrROSd4hsWROQCrEGGYFrhlpgAOoacnmDrTn6reqwYUDPhdECKFX5MmK+PDYpnOrm8q52N7Ztg/PUVWEdcDWubLTFBOlb8+C73mQ8mH/cGJvwGYWGFyS10Atm3e5SqvlQp/w6YprIRZn7ehx8cgkUN7YbV1btUdYLXjWARAklD2o986PIGmCvseExrH+M+yfWpjV5pxrSda9QO8f2Ul/3t/v9vsUE8lBwzvJ/X7QuYZTQWswpS/+2wBuR8ELMMvgu1PupuqObzKUnaNhTZEaVjdsjn1dPcSscc+ykFNZgS0Z4gr8InsXWLk7D1Cvx+uxQCHOAUsXzvKYVEuSvzWlW9Syx/+yCsTb9Ix5DiTbCF6I0xS3EKFu5Xg9RupU9b/9NO6vFeP3uLSHv51A5XfgQLvfwFsIGv+eyNWY5LmjS6Q/03fiUim2E5+JMl/io2YaaJ/iFyOuLPkfvy436fCX5due5ynX/W1VpJDb0l0E+ndov5HVhDnob5I7fEBl1a1U0VkrbVnu/X3ImZVpOh5sDgIeQIjSrrdNeAPqEM8Mnk+XIgisiXVPU0EflcYRmkDQt3qIWvHYMJ/VIALcm0TceK3dbJZ11xHXmFXzBTr6vBUnb8kvKk9SuxkK/XsX4xi+wLTLHxyWVT1vX15JNuxkpenzgAaxvBohu+nn5URD6ImZspnv9aTPh9Akg+3f9V1YtE5Maibq7G+knaKFTuvEuLx+Mwgbe1u+ZSTvd0uPRHyScx3VHUwS2aM0mmnY4pS+H/uX/1BbVTfv7kz5v6ZVdrQGtgvu+D/d/SoFg456GqOtUn8Oep1fjK6J0WTCBcIHbi06lisc6vY+6R72P7Fz7t9f4mrL3HSW2u+5It/J7bYG3zADam9kkCHGv/r6rqPcX3yl2Z54plnLw8uT9E5ICKVZ1CZE/C2u+vmNW0HaYslJRb6cut/0KO0noS63/4dWkfyVxsbLwkeU1piy7arl6uldr1kO0LS/w1bAyk1CN7Y8rsuaq6l4hsopa46yRM2Uua/exi3eWDwG31Tjq9JdDPxgbLNpgv6yP++nQs9Ot0Edm+fAif8f6gtq32Tx1vWXNg8L+xzvemTq7bqbsGdI2/q8WigS60VscG0lPYrCvYYRpfxyaqmdhq/BrYoNqa2tC/9FxpQfd1TEhcRX18mGKFX4tUqp1QzZWNmP+/jAwCmyDTYvJcsi9wS2pdNuVxb5BPupmuOScPqvqs//2S1G6PH4vVy3mYpijARaqafIQ3Ykfz7eVlG4AJ7THkLI83UHv+6o5e3psp1hi042lO86jVdqp1ACawajYZqUV7pGuO9udNCseJfo/rqeVRimgj6udbWN+7ARvoYLHOU0Tk5UK7HICtg9xGTqR1DKaErCkix5K3/X8VmwwOwur9QLHonC53BHs5NsYE7BqaDyFpjyLCIqveITlVRNock5ScP2HC+j2YgK+6le7CBF5KxPV/mFAeRMc1H6jdSt++9V8tZfAnyArds+QDb75QfP9ssTWlbbysU/BDp6WyGdC5u+iXHdCOocBvwXNMqW3O2kU91YdYZJgAj7pFdaePk3NF5JhCs28uJuqHgY+LZyrtq4uipR8QzGxOvsINXShOrXxnOmZSd8WDmB9xA2x2XozFklbDfb5PrbbZFe0atVhM7EBso0GNG0QsdUDSGP4LP40G04jLpFuv4KF/5fe9MYdhgqpbk6rgKaxT79SNMIeOOS/AQtCup9ane7mX41nyxJNCz9Kq+xBsgP8Gc4GBDcDky00d7m7JUUl3YIM3afWPkgXdSZg5fLyI7KyqO5MTl62G9ZVWzAfbnuWxmAjBzNe0m0+1Nl3sUPJpTsdhkQvTiu++gbXJU8V7U1T10o7V2D4BvxnztS/y500HbSTffirIlOLvK6gDETnf/7wfs1au8t9cTWojigQLy0wbfyC7XKpKx6GYcvMYtRPMYnKyp84idI7G+sdB5MM8RmGCPkVATaVYoMf62lv93oKNs9fpuE5VupWu1eIUJMnRQMlyLElb6f+G+Zk3JqfO3pqcknlJuys/gLlelNpD4ztzPb3QyXtLopr6NrXbapiiolh/foPayfTVpNmr6jHpTbE1oJSSpDrZdKC3BHrpBwSPVsFWzUUrW5yhXdOaLBYytCM58kTVwrT2dFfISdhzXYotMlQ5nM4XCKuUOdvXoDYZV0mpMaRtyWWGvJS7ZTG1boqS4zABeTzdnOpdcD21MdlL4pv+f9khagaRk3ymR2NhkWD+/SfFQq7uwVbg/4JpZsnnXz3pBlW9kmIwiu2wTJEyZVjlzthEd67m3NXTsUW9l7A6/5G/LrM8/so/3xWruzepny8ptYdWNJF3B86j4wG8VfcRXQlz/+xcEfkRWbuEjr795eE/mPvjJ2JpAb4tljv7TFU9SWojlr4tllbgLdgaxFuBn6nqE+UNReSnWH9+Rf0ABl//SYvpbeQEaOk7KarqH8AOqnps8dk55Pz6q5PTKiTtexomXB8gHyZ+MLWCH3IK6fMr75fZEKuUaYwHUrsQnDKMPo9NXl3trmzCJuQ3yDmPRmgl2GAZaQ+YUEt9ezFmwZ0FPKt2wMdsVZ2ZvuAusmexNqguer6VpWClC3SfcUo/4HXUboI4q4uvQvaTDqdY/HMhvlVhpvw35oO7q5N7dLVAWKU9Z7uXsz0ZV/G7Y3Az0tk6Naa/btO8iekzZJdQeY8Pk2O1N6V+yvzRS0RzlsAPkZ+7Oogg+0x3wnzetwBHicgsbC/AR7A2GoxpNTthAna4LCFOViw6qdXXF3Yp3hMs/WqN5eWTdzK598UE8VGqOq+45wWYRXYeJtyflnxI8it4FIpaeuaTMMtpA8yH/Di2NX5nOrqP0v2HqCWOGqKqVS2tXbt00/nHZNfa8lJGNs1y/+wwLJwOaiOW2s9UVdVTi+9V2yCt3dSs37hQn44Jt3PIB2eDTeo/w/rlk5X7pSRm/8bG0t+x+n4aG89fxVwnR2Ba6ABqQ/1OojaF9NlYSGSieih5lTLPS7kQXGrbm2PKVAetVu181XWx/rcZ5uq8z8tWPXh6adkQU/7Sd1MStn2xnDQdDvhwK+vN2CamHcWOZyxzxdfNShfoPoM/ic2uya9YboJoPw1IO6a9Lf26W+G7+lT1gyKyuQuCzVlylECK8XyEJTNBPGc7psG8SD4zMbEGWeOb6PcsT1jazrX3JsxN8S86drDk701x1N0iIhu7pvhmfNNCN9dvrxZH3a6BqGpni7Npd+bfsJQGb8HCqdbAOub25FPaS23rt9jAr4btfYV8hNd8/05K9tR+EIALxerpLWmX7b/UNwNJccoLJghewOr/diyy5U1ehkOoPSA4ucSOxhbVkvU0Um2n43PkHbeJnTBhUW3zRNIud6A2m+MvO7m2brQ2F3paLHsBE1qTqVVIkqvnM8UtOrMSNsB8xTV9T2xn7FNY3pTq/oLVXNN/QmzDTpkraRImAD+GacFpPeT/t3fm4XJU1d5+F0NIZAgiRMaQxA9EEEQGE7moTMooH2pkUFSUQQYhokJAFFFQQa56AVGQDxUfLuoVRxBQBBkuCCKCoCgIIYAoQWVGEpKwvj/W3l27q6uqq6urTp1z2O/z5ElyTnVVdXfVrr3X8Pvt6l47AXuo7oDdP78gaFtPJ/jEjJtDchO1PlnoXuet6b7mftSZbRNMptJIty77w9g1qkFIy08Mqgzo95F4jc7BVqkLgFs1EGGT7pzSCe5v38T0ebobL0vTVshlG5xwjvv/NSSO56EGRQc3s/fOKz7p4rv6wJbh55Jkt/PiTR/Fajq3JLkQsrgEJ8wPbK8Zrbeqeo+IvBsbXOaplfSFDkvzsfjXd7EB/zxSg552CxP5Zo9+HIk9tN6FPUCKVjVgTvFzSbxGM0nFnm9wM+oLsEFiF3d++9Kt3QEZnZIuRDMPZ+FlP+qqeQ+NAE4hcG9xvz8Vp0YnZlQC9t357XxTi29w8Wbfy2Fm3p1BWLoddMImrjWDfaRXOsuLuepMJRjQM2aXb8ceUKtgs73aCB5Es3BKmdg19CxWVrkfFup5QBKzjKszdrUuVt3jY/5+/ydmbOu5Ktjnr7CHiI9rhw1dM+iWmXgntrLzdeC/cjPQOT1HSM7jr6kfXUK5RpqPYquC6S7M40N2dwD7SH7xgzeNmYR9Z+/FpEVOE9Naf5KCXoE+THWvv9v9/2CSezsUYetILqvqrmJlwce4479Zu7XiS9PWgP4P7M29THodzx8jcQPqEGTGvejW8tiX6W+i0L2mo3mecex0DXwPqbDQqcBdBSEFX69+tlhCK3RYuhabWW1MEmrJetCETTZl8CGAU7FZWj+mYWGB3ehV1OuQij37ip2rsZI+vyRdH/ML3Q74H7WqlqxOybSF1+ZYgvO1Lj7rK5bSMVnPndggshLdD+llgGmq2iXHKyZs5it0DpJE0hi6+waOJNGGeRI6NcTplZxfmXWVvubMLo/AZlRFSfuBEZFDsOt0A1U93v14L+xB+UqSUM9PNMO4OeAYsqtw/HGW11Sjjws33BJsM42kGuh+koauv6jqU+41l7r7ZD4WM/9P4J+SaowrgVc17GqkkV4HqPnuHJdg19tW2Ax9KUmidImIbJWagHiHp8nA3qn8z1YkUsBZsgz9mEDiyATBvS2J5stNBKE+F0NfiE24biPQih/04G0N6BtgZUMbYdnmUOVvNvkJyFB06zkSE+etsJvpX9gHmNY8D/mR28cfyOcx7IEjwEpqGuA9+AvVDQbrYs0qocOS90ZcARuEve5xelkeataUwcdWvcxmLi7Of7k7br+Qzm4kHZAP4Cp2VPVPIvIy7HP5ADZIC7ZK+BRWovVdSQT5s2zmwnzC+sC/goEkjMl6foSFUrbHkuATsAd1Zzt3TlOx6+VOLF47AVgzCL+9wyXLvXJd2MSVriEOuRAbpNN6QF2oyRL/N3ZDDpTAKsFj2OCyhputfRD73I/ArqcFwbZFoZ7MKhzohF1URB7SQBs8gxWwle10khWZ76gNB8T5JDP2r2rJ6p4UL2DJ2q6Qh5tBr07ynfuV1rLuepuBPeQOdvu4EKuf34PuHo/DsYnKYnrzP14Jtm9uKof/JfEaBbuuZmH39puDa+9mbGyY5rZ7lmTcOUBMxncF7S657EsrA3qwlDzKfRG+fnQK9mH2JCAdoejW1SShmTtJnsi4feWFXA5yr52BlT1lcTj2Zf8M2D5YsqfrQLclqZ9dgZTDEjbLmISFFhYC31bVdBzzROziewqXEOuH5ptlZBHG+ctwDPbeffnVd8U0biZiN/Nj2utss5kk8qEh3mZuI8wRvbMElm5p5A2xh/EMkpCAD43Nwh5Iz2FJo3C7j5FISOyK3SSLsZm3D791Svjc3390K4/faqqGOMUmWIx4OjYoFLENyayuNlT1YgCxRqbZbkCbpYnk8cHY6uPugt1AcRVO2dXhlOC4YRVXemY/cKlm14nag+sxbJWYpXfjr4sl2HUQdlSujV2/iq3ufE4lXS3jQ5yb0vv9r4w9AP5Br4heGdLhx62x/MViEs2Xx+kWjOtKimLj0p8oYWiRppUBPRggfRWKrx/dAIttP0l2Muq3WPJ0W+xG96GZz2NPNi9Yfwn2pP0MvfgvMxdVPUCsPOx0LF6cV8wflv5ldXKF3oh7AHNF5IXU/r7h3stUbEZf1OgxMC7OvyeW3FxKdimY5zp1MrskOuTLYWGtG7HBzXuqKtbt+YwbaD6CDQwhJ2MD46exWUsYFw0HktAWzuPDIqtg3/H0jO3C8FnY7HKgD79pb9+AD7n0a/Q5BFvuf6LPdjD8rC4TMdG18LqGoM0eOtrzWfXafh+vxd7DGmTH+PuuDiWRn/VVZH8K8hC3572uIkdjzXKCDXCfSv2+852r6h7hLzTprpxOsOLSXu2ZM7Dr8DXYuBOuMDJlOAYgHX78F9YhuoGqftLlGBZj1+m9mMrj5XQnRTenpKFFmrZCLv/Gnp6+8ebb2NPyOmwQn0qqrNBdVFtgS7zJWHzTh2bS4lQnk9ib/U+wj0+4MrYvunPIC6X8ArtRLqT4M8oq/QsJQxh/plv/xHMSifnv+ZTE3Vz3Y3ouhSJk2EV1FkkjUNb+wqaV7Ui8Fn+HPXhXwgxwnyR46IhpVK9OolF9ift5KOK1PTYbCQWGwoFkS5LqpW+7VctDWHjnEWwG7AevznbYze7rwX2zy2S3fx9+S7Osdru8f9kdQ1MP2mOw5PM36PaUzWIZrHN3BarN6vLoEV1LldztSn69tmc7Vb0Na8d/NzYIh8zGKZqSE4bUQBtdrEN1Y7LF5OrgX5rI1GblJE6iuwcgi+UJauMzCJO6r8dybRMx+YkqMhy48z1CVc92//bXqS/z9Ssgn4D3zkVbpZKim2LXdl6nbCFtDeiLVfXTYuI9YNl637nll1QzCapQ3EW1NjZo/Ab7Un1oJhSnWkwyM0/PmJ5xf59D8U2aO/CF5JT+pfkYNjPuaWBxzMHe69uxBoQdyhybpEPxOyJylRZ3i3aWouJMA9IbuM93gisb/AKAWPv7LOwi9HHBdDNSWqPa7+8KEXkNduOvQtByHyS3BKsd7nR5ur+fUdMeXxULUz0gphXd8XZNJci2J0mYTiJV65/iL/6GFesiDU2tQ6YDJ2m32Xcek6m4RO5Dj+iadJfc3YHNzIseIpPFxNTAwkJpHteUgF0a6e1QfX7IWWwRK0mGTG1A2APg5ZL/pCZj7HkNvbXxIaFK5/PY9/aCmETEEsx2sKwER8jGYjXkX8RKfq/Evr9HcTIkQQL+m/49SqLh/gPsPj2cPiuvPNpoLJoLTHEfntcrDzu3iqpQ3o/NAD+MLdcXYR/cZLf9hsBBqupNffehe0ayvDjDX7Ktv4Ce8r25YoazkLTfegPYove5KvZFKhYL3I+MxiKshvhm4GMlZtohH8JkSM+SxCEljwdxZrfqDDmycIN5uv38XZhp8MfFzIvT+IfDqqQ6c4NcybHYjP8akoHcc03w78Ui8hKSuPdS7KZ8BzZQ+20Xu3M7kWQg97PLt2EJJh+mCIXV3uNuqA2xa8xr8vvmsTCxuA0wq0SyEBKXm0H0WsowleS69qJrnZI7NW2V/4uFw2bm7OMMrCxPST2MpTupnw4Fdkg97BGR3avOYvuRDpHl4HsAfNNTWlY21AratefVFl/3HbbHYuPQOlhu7lzgfeKEswY8/XuwSeix2ANwIpazWwY4T7oT8Du7f/9UVe8IdyIiWfpCpWijsShd9jWXJLEo9CoxhszAZoxrAaup6iFiWiphs0E4WHQ94VX1dDFda1HVtFdjF2LlaJtjT/+0rkwZwljg+dig7nW3w3PqkQgtyRLg3WIOS/20XHoMObIQq7teBRvsfuYqdjYjGUivyXjZLnS/p86gGORKvLHB3/yvUq/xwv9vwWKjHw72cbgk5aj7hdsBd6npZh+K5R8ux66luyFTG34N9/ftmMvMBSLydlX9oXvvIWnd8EzcxGQx9rC8oGjbQQmv64CvYpORVdznOw+bKOTt43HyJS7CpH6Wlku4Hz+YVxWTGxrp7QHYBZs0pRsQ51MsiuYbGXdU1X8GIdh7sYF2gdv3oE1il7jP6RQxdc6tVXVDEflQ+h4Vs9LbEFtdfiC1n7CiaCDamKFPA55W1+6dMcCfQbcSY4hPaE7HatjDWb4nbIjoabLQAuW0FEeTtBFXGdDDWOCdWO3pFLqrcXC/75TfqVMoLEE6bJWJG8Qfk25Jgjy2xkr1lpBU7KyIJW7OILn5O7hB0bfWr4v7zsRKBsFmy9/CVlfXu/N+VEyAySe1vfC/F+Ly554uR01vt7p7X09hM/bLsSalBWRrw6/swg+/xeKXYDOpu7BQWKji6WP8mc5CAQuxWd0BFWd1pRHzGL2JJLY6D1sZbUlOjbl7XZ6MQZaeT94+vNb/au4cbqBXEqBRMsaKm1X152KG4OF2/SptvJvTVu7/5wA7D/tAVtV5wb+vF/NK6FRYpVZAYXd8mk5F0aBIqoquccS0kafhdI7Tyzz3+x/j5CdTv/sMNpivhXXM4faR50GYdfztyI67pbfbj6SN+HrspiltUiBJRYCPBa6IExJSp7sRbPt5XCmWOk3uPvueiz0cFmG6Fx8r2Pan2M3ekYLN2W4Ktkxd6rZb4H5+HDY4vg44X508bs4+DvLhCfddneSOeyo2IP8B2/m3xSplHsa+i3OwuOd9qvq3YH9vIBH/BxtQOttJd6PGy912e7r38Lv0+xXT0fC+pN9Vc4hfG9d0o6p/Dz5frzK5TbqaIvWe30cSRlJ14ldNIGbdtxOWgD0ZS+ItwFZgmW3u7nU7qupVIrKTqv4y+PlyJfNAiMihap3DN5BoLr1JVZuIo5dCRA7ABuZH1ZV4lnjNidj1sRh7qPvVyyZq5bSChV6kysNZuo1nirab4c79Far6k+Dnc7Ex7kFKeIimaSPkMteFPVYiO1OdVmIM8e3+E0m0RCblbJtHXtwtzQvY8u4u8h1pcknHAqXXUSakb/dqat8+Nr1HiSqDsrOwXVL/96p8p0pg7pD1wmAADG+ARZrUqz+OfXcvI6mZ9k3PIRAOAAAgAElEQVRDV9PtkxkOiG/ClvW/c/tfJ7WdN+c9h0Sn5lxVvTLrPHPCD4e6fU8mCdH8J5Y7WB8rncul7jBLH+Zig8APsRBB6AGQizoZhHAwd/8vdb05fOfwI9jKwGv7tEIQ+tmAwapB7sIe+n/EkqX30x2C/S+S663Kw/k6EuOZb6hVGGXxBre63TL18wvV1E3fQaC9VJa2qlzeS7541g64rDNBdtrNrr6DLdPX1kRB8ChsuV2WFyhn5/RrEnnaTWU4VxMIHGUyfheW3w1CmSRqv9JKT6j0mI7zh+YOWfwJC1mE+u9hvfovscF5AaZIeStWEfNfWCgmrY/vuQXTArkBOqu3cDvfqLGmqh7qtjkWqy4oRERWUtVnyG6s+SOWxJ2HNYoUfududQM2oxpYUGkA/OpldazpaVnssy9sUZdE2XKyppQtB2AJFjdfgl2nO9NfsbQxXKJ2PZw5xQCvu5jijuy8a7EsXcYz9JaJesKmu5C9ReR6bMX5egZMPLc1oEN+jWheGdXBfjAVkfPExHgEm8GVQkww6hqSaosi/NJ8NayM0WvIVCV0lOmEXKS3/G6QB8b/oc/NPMAszCfbqrARKXs9Fy4L69Wnk2Tud8Fivzu7bV7AlvHp72QdYGcxZ3Ww1Vu43b3YQ/LfktRmr9LvZMVUG/d3N87F7nw6wmWqmr7J+vElbIKwjYg8rN3u9LURrgbcg+wU7D1/guwmOk9H2ZLeRp2yhNZvR2KrrTIlnU0SyuUOnOeSlEORWgd2UYSgDJdjk7N+xjOfx8KHn079fANssvNRrHxxINoa0H0H4cnhD31yTbLLqMKKlesqzpQnYTf8k2TX5Iachw2YPgbsNWRyVeNK0BNycTON/bAvcl7mq/K5ZohzSfNqdw6+AqXUctPNiHfBBrWpBZsug4l1rYDV/+6NVf2cDfxYzQzgULprhq+i20ZwvdR2PuRyAva5qjuPovP1BuITsSqHMxl8ZZTmAfee1iQRZWoESSSEBavaEXpr6NOshs3kB1bvCwiNXJqsQx+EUC63Ct5vNqy6+g9s0rCI4oatPD6Ak+Pts90JWCPcJiQKo2DjyzKqukhEzs18ZQFtabk8S0bIww1ub1bVK92NF1JYvVKShVhI5zJsOVP0VD8F6zJ9gW4NmYFx4SJvFpD1JS2jpudQ+okcPPy2UtV0yd3AqOqvRWQzVT1XepUHi1iiqjtI0CWXQ/gewxn648D6LmyR9oANhcCuxK6BcLuONoaqFrlPhczCGsyeBXaXlHcodK2aChPJAeeS6HI3XfnhpYZnkNRYpyUX0izFSuQWS6/yYF+k18hlDfe5pw2cRxpvTp7uvi7Lq0i6mcFWx34CV3WVlWXHmEVHObTrh4HqpToVy0FoS8tlB1W9WlwNcOrXrxMzlngdQQec9sp5+soJAW5Q1UtKHHoiifphkXbFPtgsbm0sjHAf1jwyiMhViA8XPSTZjjLbuUTxlIzypjy+RX433cC4gfJeEbkI63QrS5f4VcG5d94jVqr4MHYzrY9V4exK78D0fJBYfcz9fldMbfI/1Jqq3okNVGVLP7/jjr0l8KRm2M25icURWAfpO0p8Bh/Clv7ThsixlMUPBI8MkJB9mnzlwTKkBd6mYJZ4I1si14s3J690HmrdzIvo1hi6nsR4vAo9dow5ZCmMAuUr8bJoK+SysoicRHbI4FISY4ki7nezyUOx2UcZFmJhlHuwGzVvhnGT+52PyS6LVTtMwmZIg7JMzr89Z2Lx8++qatnl/95YGV+VGvks/Gzv59gysNTsK13NU8CZqnqniMxUc0M/GruJblXV7+S8JkysfkEDByu3iriBRBsjVF7MLP2Ubj2gdbVYmnQ1bPnb1xGKbHGxpsgdCApYHXsvWcqDfVETePsUzroPkx6YIr1CcyNN2dlwETNxGkNutbYjNpi/k/yEZhGFdozQKVNegq0SvXhdSNlKvB7aav0Hi1P5JKX/3TuxpfUm2LL6jvTrAyZJ0lRS9uIOZ+i5Qlhq2iGHkiSSHsJmKfNLHidNv3DRjtiS9mhKxHPFTEHWwMJW+1L8OZXl79hM+TTs/dbNlm4m75OPvl1+el7tb0ZitbMddEyJvTZG39JPN/Oein0PoSftazJmQhdjq4cyJXGhNWJjdegOLyH8AVcYAPTtj7gEZ1wy6Iwv4GY1yVxfM/2HEiWzTVN2NlyEklS9lRof+nAZ/d2WFmChvDNy8hBlK/F6aKX1X0zT+SYxsZ+QfhoMIXdiy+aJ2us9msckLIyyNm7WV7CtTyStiemie/2IgUmHizLYERugNy8ZcpmIhS2GufDSbI49vL5N0kVZJ9Mw84HdXXjnX9j3/TCWFPo19hkXDYjhdmlzij9SrvTzCHrt7ragdya0L4kZc258XnqtEZvGrwa+RWLo3c8NaDcsjPYxBpzxBXjrvn9glTJ7VdxPnfSdDRch1mHqPX1VVa8TMyt5EDMRv7XCA6Mz4yf/s16TRGAuK1H9CKZJ9WTG7wppK+TyeuxDnEl3U8R8ijUYQqo0+1yBc+1xhftF3ObO7wmS2Rc0MANT1beKyArA7hk5hSz8g8nXjtex7P03sLGq/lhENui79QCISYNeqKr3isjz2ANsfVU9QUTOcse+h4JqDbcq6WyngTmFuzG8SmW/0s+eZJSqfjNju9CMOX9nFoddoM4pvmjbmghXA2uRKhctoGOaXZHdsHthU0w//PYB8j2NoKrHDLmLhdjDcF+sWuY6kpVIj3lH2dOiz+xaVfsVHfwc2FdEtlTVQwY5eFsD+h+l2zAYKKXBELK5DNfs08/Q15fEXUKik1H7DExEdsKW9tuQKuPMww0iD2A31zC18eE+vxL8+6KibSuwE/bZnYnNXDYHlrqB2Hu/Lk/x9TgRk4To2c6t+nySfBkReWtBkrwrBi0ia2q2H+elmOBWobaO1OMUX4qM1YA3U8gtF3UPwiremGl+r9b6fxhmAnFWDftsm42wksczVNUbTIcm4lXMO36rKc2ZPCTDy9Xxbuwav3DQg7c1oK+LXYhvpURXXxoxgaXCluyc14XlaP2qQ3xJ3I2kZGFrZgssfv2Aqs4f4HWhhnxtFFxkw7C12/dcYFNV3T/43WnS7T2ambTTXo/S9HbzNFFeLEqSn4OF0vYXkduBp0TkezjRsGC744GbRWRx0YRBE51+b3fWJLfRnah7C7ZSK6LI6GEQfOv/g1SI7TaBBAJvOoCeU8B62Cx8toj48tTpWEfsxhVzBB2D8hITzcOwSU6a72NNRwP3DbQ1oE/GkgZVDQE2xeKvnlJhkAGenPtgS9tHsRnl+7C4VuljlUVNEGh5rCNykCVsqCFf57I37yKrTGoAzyKvDXqQ7V5SMkkeutX8E1t27+h+1/luVXXPPucSchz1NJ71Iy1VPKnENb0NVk3xEixkVaVZBixc0zENr7iPulmOfEP5vqhqVsVax8y8IkeSmFj34wc5P59DxQ7Ytgb0X2F13pUMAVwTzNpYSWG/0EkVbgr+fTMm2nQ1gTxsnbgZ8aXuT1n2Iqn4qJO8i6xWROQ9JMnpS7ABMV2+laZou7uxnMfdfZLkoVvNvVj5Z5fdl4jsjM2QgFIKm0M1npXFrU6OIhGkm1Ei7PgciRnGMJIEpTT1R5hbyDeUr8omQ4ZyQxPrQuVUVc1rQvMdsIVa/Fm0NaBviy1RqwoFgWkZ/w2rWKgVDRyLHDuBycPWfawh+DLDqcJlUnCRDUW6EYxubZDzyfCAzeDAgu18zuNoXOI74xzmYqu7B4GjVXV/MWeptN3XTOzavJFyVSE/wmZlI6Ft0plBihlA70SxBd2GmL3asVScyTqekHKa+iPJJth3V8s4Jqa/P2w4yZubFyX4vYaM70QOfY9XxSa7L9BHxiKL0eIpWoWvYLoLPaYLdSCBrrFky8PWdZxQnKu03jrDq8Jlncsc4Ieq2kQderoRbENJtEG8cUk/irbryADkvdjFu+e4+l+vqfMgKaNlNYmCV2J5ms2wqqwiSs/KamCT4HNbnj7liKp6uIi8ChtkSmmGp5Ful6J+MfuR5GksLLExtvIalkkk+i4D4z6nx7FxVUTk9JxKnKJjhE5nH2FAMbXR4ilahcOwLHCXemGNeF3jmVhd/F4UDBZVcYPMnpSrJw4ZVhUui3OBD4qpG35LVau2P2eRbgS7jKRK50Bs1u2d1/Mo2s4rL97b5zzuC6oYwOLScwkGbRG5HJNkPlVVy8xIB9Kzr4privK5nO9hgmRlyhFL1dTnEaykJupgvrdN8xRWurlmvw1LktbfH7QsOu2olCka5hoXvVlP+loOnc4GjmC07ik6BILVCdeiZZKB1zW+FzgAyzzPauhYr6J8PbEvX/MxXqV6oivNHGzAOBCroKiTrkYwMR/Hs7AVhrfm67fa+EfBdn1DLo4pWFL+LVjO4iasfXxD4CcAqrqrq50/RkQ2VdU39DmvH2Gx/T/22W5YDiQRKzsRm3FPpH8uqlRNfR4SmIaLyDRtSB64AutiK9UePZ6KdOnvD4uqFomm5V3LK7nwWCWdpjb10IflbCx+2FQ97JWYG8wq2NLuOvpXYVSlbz1xiKtDf1bNt3DH/q8ozW8wYf0Zqlp3crTTCBbUn78Le4idj1Vh9JtpXVSwXd+QiyNdYXWZqj4kItv7DVyPxHNYgrhM88pBWGx/BsU5gGEJxcqexypYVsRmqkWch1W6rFjxuPdgYdKvurDcqECzfROGoUt/v+GmqYuwksuuog4tr42USVtqi8PWj4Ipx12KzSoHkXsty15YE8qHVdWbB5xTsH0lMmbbZV7jm1lejz3J68ryl2lbrkrYCHaa6xD1rfpegOshihtz5mRtJynlxT7nEWrI7AJsJiLh71DVftU2HcSkkf31sVrZ11UkFCu7EhOau4Ze2eE0O7v8xdvJlm/ux4rQ0b4fJCw4plDVb4jIWnTro9eOv+6C49TWjNbWDH2o+lHHDGwAWktE9g4zxcPiBsyZ2ECzWZ/Nh8LNtm9WMyx+acnXnCYiE1T1+bpPh4qiQEXkNIKdSGJDuIheG7i888va7g10Ky/mncd71MS8jgYWarZ86qCETlr7UE2hrxTaK1b2Bkzbvd+qZFm36lhQ8bhDzRrHEqr6WTEz7gMbPMYVIvKkK7/ul3AfiKbiz/24BVtyDyOQfwqW2T6Z4CKvA1U9TVV3V9XjVXV3EZkjIke5+uQmeGfq7zLnWPdgDtbE8H3q94rcFDOz8H88XvDsYiwU0q8KI2+7jvIiJu2axxru77CleyaWXO4XJ8+jnzRyk/wTa4pKG3ynuRsLIfW153ux4yZzH6O+RGsePh83s86dtjWgvxqL+6UdrwfhAKxcaf+MuvG6eRonXtXQ/ldzM6jSrb4icpCIfFhEdqvxPPbDHo6FLvKDoqq/xsICa2MrqrnYZzoLW2kdjNUUvztvHy6c8lDOdleJyHFuv0W6JSu71cJvsVwBJKuSSbmvKsYf+1iqO2lVZQL2uRbqzeDyF8PEhEVktvt776r7GO2IyBuxa/9m6is0yMPrWdWaSG8r5KKqeuqQ+3gUm5nUOjv3iMg2qnqjiGxL/eVR4XGm42K3A/JSehtihqUpOQHI+c7F5E+fwgbDIsnkXGllDZQX+3AmVtGiwBnSbRpeCe0vjdwkWU1RWQwrZAcWttkbSz6v11CvQtsswjqHv0eDujxB3mwh8Foq6Fnl0VYd+hoynI6xYMbCD4vZmjWBN2y+F+tKfQ6zL6ub5bCY8KBdhg9ijVWDONf0Y1/KaYpX4ZU53/l8ykkml90uF1V9nKAOW0QmMTIa5k3R0xSVwxzK64vk4cs6Z2Mrq2OH2NeoRM1Ja1Yf6YhaDgVc43IitdKWwcWuqnq5iLyt4m6OxMrdvoqVLlZNaBXxfjGN8kWYmYRvya2bg7AGg1sZrKZ8AmYOMb3GcwkTlXVXDj0HXEuq1l5LSiaX3a4fIrKqqj7h/r7WPWSWd+fV9DK7NlyIZyXg5yU+jzo6WVfG4r0Taag7u20kMV6fC6XMwauyDuY4dSvUWx7Z1gx9MxHZFIujVmEa5vjxOlKu2TWyDIn793RsabsFiTZ6XfwUeEJVB42ldbRQaqapGetPsJK3tsvetsDCO1ti5Z5PYnXwo0mnpwxLVPVEMTPrftTRyXoWZh5yZp+GmTFLQ/dT1nG+ISJPq+r36963aOvG3YMjZh3VQVWvbeAYW5Aouc10f25U1fNqPs4crEFkoKe1iJyLhSGGiYum97kilvh9WFX/Vsc+g33PxQaWjVT1vXXue8Dz2Bmrq56K9UL4h8zSsVSe58NX2KSsMHTpQkuvBu6s2rovIqdgUhv7q2oTUhujBmnGEyB9jPOw+7eMbnr5/Y7FAX0kEJFPY2GCF4B7VPXHDR6ro5qnJUx8XSJ1iv9/XbE4EfGenaqqjZgdi8hROpze9LDHPxGr3X7WNdu8B3gF1uT2322dV1Okm1iqDh4i8lksZr++qn68xlMcVTiNFQUeUtX/1+BxvGDaPao6r9/2ZWmrU/STmHLbUHEqyXZrr4t/YfXOS4FTnLZHrU/TgEFNfJfHZpdQbwjD68nULjIliapkEwnXQfgOlnvwJbOjUee7FCKyLDZQT1bVTCEn18SyVFWvFJFK+jxipt5fwa67JvofRhN5zWt1M5RgWh5tlS0uUNWv17CfLLf2unglZsb7OpLa5iaXM6VNfFX1Hqc3MRmbqVcpe8zipZjGRBPv816ssWVL4PIG9t8XL5mAqemtg1UIjUad77IcjF0DL6FYZvV1IvK/2LVcpJ2ex650XxONrN5GCRdjzW+VpIYHYCjBtDzaaixaQ0Tmukx9ZTTbrb0uVsYG2Luwwf0uSopnVeCHWDffIEuvDbGE3t9rPI91saadfWvcp2cr7DMdxtRkKFzS62z3t6/2eERVT6ZiW3zLrIZVYPVrrLsUOIIB7cwCJmLiXt5hajwzGxts62zYy+IsLD9X2+wc2puhP4iLW7Z0/DJ8EisNXITdDLOorlbXj4ENn1X1+AbO4yjsoVV36z/YALomzRsp5xKIms3CHl5HkcxeZ1Jjg0fTON2Yi1X1nj7bvRN7X5tgXaV3VDjcysBUVT1h2EnYGODxBlQcszgQs1SsVVywrQHdxy2PrLoDEbkQa5RYD7hVVb9c29kZB5Io+52GVQn8oeZjeJrs0BwEX688k/qdd+ZgM8TWZniuB2Iq9pD25zHs7LUtbqLcaie3w3YANgeWSmLhNy7xapIichzwQsPyuTOwSWKt4oJtDeh1xC1/o6pnumRWEyVGYXLkBOyGr9shyLvQHIklXEuXCjrFwBWo98Jr0nnnpdiSPddrcYTYHnuf24nIJQw/e20Fda43ruJpoqrmCbvNZ/gO2/2rnucYY1uSpqmmV5K+muaKOo/VyoDuVPGGZb57KNxOM5KlYXLkIzQ3GL0e+2IniMirVPWEkq+rQw8nzUk01/r/GUztsHZf1gH5M3YDbUg9s9c2KXJwAurrsH2RcJWqnjFCx9oXm1hspapvr2unY9mxaHXgFG2gkF5Evk8i9rQbZvul9DcSGBgNDD4GjE/maaMMQ5Ot/wdh5YLr0IwHbF/E9OYn4sS5sNn6ULPXlsl0vWkCV/K4EhZLH4kYcxuc3X+T2vgWFW3mimirDv0LOMnPIQajtYC5ItJErOskEr/SWzDN6a0xFbZaCeqzYbASzExtlBpoMsZ9NWZs3BahGcWx4XUzRmevc0mkc5tWfJyFNWT9u+HjtIaqNmrynWIXbGVVa+6mrRn6P7A24n6mwEU0WSmzNrYcWojNLH8KzFPVa+o+0BD6EU1oo3wOK9GstZPTzYzPxcJLVSzQ6qJNM4omqOM+KstPMW/R2lepLzZc5dFPgS9hUhu15W7auqgfxZp2hnEAWlVVT6IZH8c9sRLFl2ENMbcAjzVZsiUigyZcZ2FJnDpFpU7HEoQ71bhPsJnxQy6Tv1/N+x6E0IyirmasNqnjPirLkcDbsIdyZDg2wHI2p9PfnGQgWitbxJb2dw+xj8Y6/FQ1q5zy/LqP4/H6ESJSWj/Cz+zralkXkZnYcvoe6k/+joqZcctmFLXiNFq84cpIlIJ+hqQvIzIc82kod9PWgP40dmNvjPmCDoyvlJHmfD5x+5cmEq8pBtaPCFYLdVWkTAR+jF0TdV8XV/naXlqeGYvIWqr6dxFZR1UfbvNchuQ2kuqukRjQw76MxkSrXgw0WXnU1oA+tKVbIExUxnqsyv4PwHQyXg40rS5XRT9iIeaDuSn1aKNMwTRrvgQ8Qo1mD6NlZuy6K98oItdhksVzWj6lYdiFkdVXGSnRqhcNTcj0tjWgr8vwlm5lhYmq8kos/DASCafZ2OexG+W7UZ+pWYZ2RGqyReS1qtpE30AZbsK0b+ZTv1HJiKKqF4j5hD7KyHS5jpRo1YuCKmHWMrQVz5yHzdDfMcQ+ygoTVeUyJ/7VhL1dmsdV9dQByy83EZHja0zUzsfF9Rgut9GDiGwoIh8Qka9hD+G2mAXsgyX2xoMmyWnAE8APRuBYx2FFAse5LuXIcDSy4mlrhv4qEu3tqtzVlOlEIOL0eiysc1UTx3HHqqof4Tsul4jIVmrO95VpuKNwDhYWaqT0cwDCGfpYrDtPczRwraq+cQSOdXPDUhsvNhpZ8bQ1Q/81NiCtPMQ+dheRRtTfXAXJZ7Ebf37d+0+xLRbWUQbTdHgaE9BaCuzRwHnVhqoewQiUfpY4jwewkszPYp16Y53HgX1FZCQcoO53VWXzGHtCZqORRmR625qhr1+DnsuXcBZONZxPFrMZUNK2IlX1I1bHZr7CGHCrV9UFNFj6OQB99U/GEJOxkOBzI3Cs3bDwzu6YJk+TXgQvBhqR6W1rQH+tUxkcxtKtEQungJGStB1YP8Ip7HXKPevyFH2RcBFWorl22ydSA7/CVrkjoUPze1U9R0QOwzRdIhVpUqZ3xAd0F5+uwx2mEQsnj6ru2cR+M45TJY+wPInIVKQkInIDNgg+5X7UeillVUTkc1gs+y+qesEIHHKRC7k8CPxmBI43nmlMpnfEB3RnMnAEVjc7TJXLxViWeFod5xUSOKUDQwmINcVraKhmfJyzh/sznbHfHLMQ08U5QESmqGrTssRLMN2gJar6p4aPNd5pTKa3raToasCHsWaWgXGz/OOA9wN71XhegDmlY9UQL8EkX0cbjWlBjHP2wOLOd2Kx9LHMA1iVxCNYk1HTeO2kKM41PI3J9LZlcHHykK8/TUS2wEySp9dzVj0MbZPXIPMZ2zrebTEVm2luiJX8jbaVV2lGKMwS0ph20ouNJmV6pXmZkmYQkUMwka9/q+pXGtj/e7Bwzv2qemHd+49EhsUpdE4B/qGqzzd4nLnBf3UUhiAjjrYMLt4OrAKsq6qnVNzN85hhQu3yuS4LvRLmWzoeGlAi45NTgMewleo5TR0kUPacqKoLmzpOZHjaKlvcGotRl3Etz+PlWOnZusAv6jipgCWqeqJL3kYioxXF8mDrN3kQEfEx3z+LyDRV/WiTx4tUp5WQi6tBX9Mdf6gaahE5qE5xG7fPj2Ox1uWo17MzEqkNEVkX06e53jVuNXWcOVjPyFdFZM4IGilHBqStGfocrH248tMk8OKsvVxLVT9X9z4jkQZ4GyahvBT4UYPHWRGShpgGjxMZkrYG9Jdi4ZJKzjgisgdmkPxGxn75WSRSlauw0sWtaXBAjxOcsUNbA/pnMD2IqrPrrYDVVfVDLjzys9rOzCEiP8Z0MjYGbnc1uJHIaGI68JW6TRIiY5e2GosOwgblgyu+fgtgbRd2mVnbWXVzC/Bp9/ffGzpGJDIMs4BPiEidRuGRMUxbM3SwksP3VHmhqu4pIqthOgiX1HpWCT8HjgKuAO5t6BiRyDC8QLSFiwS0NaB/HZtZf22IfcwmqUFvwlVoV6zWfUdVvbWB/Uciw/IDLIb+y7ZPJDI6aCvkcgim533YEPtYATOIbmr2vEBVT4sli5HRiIjMxMTtrqNmk4TI2KWtAR0s5DIMy2BLzTtrOJcs1hCRuW067EQiBUwE7sMmNqPBOCQyCmhrQP86Jk87TMjlPizkcngtZxTg5HO9gE6tesWRSE1MwfJH78eKDCKRVgwu9sHqx6/DnNfnVNzVVEzD4s81nVoHVb1CRG5S1SdEZK269x+J1MAGmNHJ6Vi+JxJpJSl6M4nz+jD14xPcn/nDn1Imh4nIZdjNcmpDx4hEqjKfKKEcSTHiIRdVnY8lcdbDmnaq8nvMyq52fWZX374c8F2izVtkFKKqF6nq19y/oyJoBGhPnOtNuIFSVSvZp4nI8Zib0CpN6KFHIpHIWKOtOvQtMdeYCVT3w3wBeCsNGNYGwl/AqPQUjUQikR7aGtCXALcDz1Z5sZvh3+T+1L7EcBZ3e2ISA1FdLhKJjAnaGtAvxQyYX1Hx9QuxgXZfTNqzCdf7V2FGvI35/0UikUidtDWgv0FVLxCRLSu+fiOsqegMVb2vxvMCQESOAm7EbPKm1r3/SCQSaYK2BvSNXPPORhVfvx6wGJgtIk2Y1u6EdeIJscolEomMEdqqcnkJ8GrgTlV9bsRPoA/RZisSiYxFRnxAF5EvAT9R1WtH9MADICLLqWqMnUcikTFFG1ouv6cBH9A6iYN5JBIZi7QScolEIpFI/bQpnxuJRCKRGmllQBeR2e7vvds4fiQSiYxH2pDPnQtour0+EolEIsPRhtriaVjL/9cY5cnRSCQSGUu01Vi0GmZssail40cikci4o42Qy3TgFyN93EgkEhnvtDFDXx4zto1EIpFIjYz4gK6q97gqlwlYyKUJpcRIJBJ50dFWHbpgPp2xDj4SiURqoi1xri2AHYCrVPW2ET+BSCQSGYe0NUN+C2YhF4lEIpGaaGtAPwd4AjimpeNHIpHIuKOtkMvBmIRubCyKRCKRmmirsWg7YIaIrAfcqqpfbuk8IpFIZNzQVsjlZlU9HmFVZqgAAAFJSURBVPgNZvgciUQikSFpa4Z+v4h8ErgNuL2lc4hEIpFxRVsz9OnA88DGqvrXls4hEolExhVtzdCfUdUzWzp2JBKJjEvaqnL5IvBPYKmqfmHETyASiUTGIW2oLW6FJUMjkUgkUiNthFzmYcYWk4EnWzh+JBKJjEvacCx6DDgA03I5fKSPH4lEIuOVtqpcngUuB54SkSktnUMkEomMK9oa0P8JzAL+CuzS0jlEIpHIuKKtssVlgY2BCap6VEvnEIlEIuOKtgb0ycBdwHMtHT8SiUTGHW2FXH4F3IP5i0YikUikBtqaoX8Q00NfVUSWVdVvtnQekUgkMm5oa4b+e6e2eAewUkvnEIlEIuOKtgb0RU5t8Tngly2dQyQSiYwrWtFyiUQikUj9tDVDj0QikUjNxAE9EolExglxQI9EIpFxQhzQI5FIZJzw/wEqVmUqwdrFmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scipy.cluster.hierarchy.dendrogram(linkage_matrix, labels = blogs, color_threshold = max_d)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `fcluster()` method, if the `\"distance\"` criterion is selected, allows us to retrieve the cluster id for each datapoint, when we cut our hierarchy at a given distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[22 14 16  4  8  7 31 15 27 33  5 28  7 31 20  3  4 44 41 43  7  8  4 41\n",
      " 12 24 41 16 16 41  3  1 41 39 16 21 36  7 24 16  4 27  9 17 34  3 16 41\n",
      "  3 14 16 32 29 18 41 28  4 31 16 16 16  2 28 17 16 16 30 19 31 38 11 26\n",
      " 10 35 28  8  4 12 25 20  6 41 41  7 30  4 13 37 17 46 23 41 42 47 38 45\n",
      " 40 38 38]\n"
     ]
    }
   ],
   "source": [
    "clusters = scipy.cluster.hierarchy.fcluster(linkage_matrix, max_d, criterion = \"distance\")\n",
    "print(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22 ['43 Folders']\n",
      "14 ['456 Berea Street', \"O'Reilly Radar\"]\n",
      "16 ['A Consuming Experience (full feed)', 'Google Blogoscoped', 'Google Operating System', \"John Battelle's Searchblog\", 'Lifehacker', 'Micro Persuasion', 'Official Google Blog', 'Publishing 2.0', 'Quick Online Tips', 'Read/WriteWeb', 'Search Engine Roundtable', 'Search Engine Watch Blog']\n",
      "4 ['Andrew Sullivan | The Daily Dish', 'Daily Kos', 'Eschaton', 'Little Green Footballs', 'Power Line', 'Talking Points Memo: by Joshua Micah Marshall', 'Think Progress']\n",
      "8 ['Autoblog', 'Engadget', 'TMZ.com']\n",
      "7 ['Blog Maverick', 'CoolerHeads Prevail', 'Download Squad', 'Joystiq', 'The Unofficial Apple Weblog (TUAW)']\n",
      "31 ['Bloggers Blog: Blogging the Blogsphere', 'Copyblogger', 'ProBlogger Blog Tips', \"Sifry's Alerts\"]\n",
      "15 ['Bloglines | News']\n",
      "27 ['Boing Boing', 'MAKE Magazine']\n",
      "33 ['BuzzMachine']\n",
      "5 [\"Captain's Quarters\"]\n",
      "28 ['Cool Hunting', 'Pharyngula', 'ScienceBlogs : Combined Feed', \"Steve Pavlina's Personal Development Blog\"]\n",
      "20 ['Creating Passionate Users', 'Techdirt']\n",
      "3 ['Crooks and Liars', 'Hot Air', 'Michelle Malkin', 'NewsBusters.org - Exposing Liberal Media Bias']\n",
      "44 [\"Dave Shea's mezzoblue\"]\n",
      "41 ['Deadspin', 'Gawker', 'Go Fug Yourself', 'Gothamist', \"Jeremy Zawodny's blog\", \"Neil Gaiman's Journal\", 'PerezHilton.com', 'The Huffington Post | Raw Feed', \"The Superficial - Because You're Ugly\", 'Wonkette']\n",
      "43 ['Derek Powazek']\n",
      "12 ['GigaOM', 'TechCrunch']\n",
      "24 ['Gizmodo', 'Kotaku']\n",
      "1 ['Instapundit.com']\n",
      "39 ['Joel on Software']\n",
      "21 ['Joho the Blog']\n",
      "36 [\"Joi Ito's Web\"]\n",
      "9 ['Mashable!']\n",
      "17 ['Matt Cutts: Gadgets, Google, and SEO', 'Scobleizer - Tech Geek Blogger', 'Valleywag']\n",
      "34 ['MetaFilter']\n",
      "32 ['Oilman']\n",
      "29 ['Online Marketing Report']\n",
      "18 ['PaulStamatiou.com']\n",
      "2 ['Schneier on Security']\n",
      "30 [\"Seth's Blog\", 'The Viral Garden']\n",
      "19 ['Shoemoney - Skills to pay the bills']\n",
      "38 ['Signal vs. Noise', 'kottke.org', 'plasticbag.org', 'we make money not art']\n",
      "11 ['Signum sine tinnitu--by Guy Kawasaki']\n",
      "26 ['SimpleBits']\n",
      "10 ['Slashdot']\n",
      "35 ['SpikedHumor']\n",
      "25 ['TechEBlog']\n",
      "6 ['The Blotter']\n",
      "13 ['Topix.net Weblog']\n",
      "37 ['Treehugger']\n",
      "46 ['WWdN: In Exile']\n",
      "23 ['Wired News: Top Stories']\n",
      "42 ['flagrantdisregard']\n",
      "47 ['gapingvoid: \"cartoons drawn on the back of business cards\"']\n",
      "45 ['lifehack.org']\n",
      "40 ['ongoing']\n"
     ]
    }
   ],
   "source": [
    "# printing the contents of each cluster\n",
    "cluster2blog = defaultdict(list)\n",
    "for bid, clusterid in enumerate(clusters):\n",
    "    cluster2blog[clusterid].append(blogs[bid])\n",
    "\n",
    "for cId, blog in cluster2blog.items():\n",
    "    print(cId, blog)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- cut where a **pre-specified number of *k* clusters** can be obtained: `dendrogram()` method allows us to visualize only the last *k* merged clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scipy.cluster.hierarchy.dendrogram(\n",
    "    linkage_matrix,\n",
    "    truncate_mode='lastp', p = 10, # show only the last 10 merged clusters\n",
    "    show_leaf_counts=True,  # numbers in brackets are counts, others are ids\n",
    "    show_contracted=True,  # show dots where hidden clusters are merged\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `\"maxclust\"` criterion of the `fcluster()` method, we can retrieve the ids of our desired *k* clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6  6  6  2  4  4  9  6  8  9  2  9  4  9  6  2  2  9  9  9  4  4  2  9\n",
      "  6  7  9  6  6  9  2  1  9  9  6  6  9  4  7  6  2  8  5  6  9  2  6  9\n",
      "  2  6  6  9  9  6  9  9  2  9  6  6  6  1  9  6  6  6  9  6  9  9  5  7\n",
      "  5  9  9  4  2  6  7  6  3  9  9  4  9  2  6  9  6  9  6  9  9 10  9  9\n",
      "  9  9  9]\n"
     ]
    }
   ],
   "source": [
    "clusters = scipy.cluster.hierarchy.fcluster(linkage_matrix, 10, criterion = \"maxclust\")\n",
    "print(clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *K*-means\n",
    "\n",
    "The algorithm:\n",
    "\n",
    "```\n",
    "Initialize K randomly selected centroid\n",
    "while not converge do\n",
    "\tAssign each item to the cluster whose centroid is closest to it\n",
    "\tRecompute centroids of the new cluster found from previous step\n",
    "end while\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](data/k-means.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RESCALE YOUR DATA\n",
    "\n",
    "Before running *K*-means, it is wise to rescale our observations. The `whiten()` method can be used to rescale each feature dimension (in our case our word counts) by their standard deviation across all observations to give it unit variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "rescaledMat = scipy.cluster.vq.whiten(bwMat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### COMPUTING K-MEANS\n",
    "\n",
    "The `kmeans()` performs *K*-means on a set of observations:\n",
    "\n",
    "- the stopping criterion is that the change in distortion since the last iteration should be less than the parameter `\"thresh\"`(default = 1e-05);\n",
    "\n",
    "    - **distortion**: the sum of the squared differences between the observations and the corresponding centroid.\n",
    "\n",
    "\n",
    "- The number of times *K*-means should be run (default = 20), specified with parameter `\"iter\"`.\n",
    "\n",
    "\n",
    "- For the iteration with the minimal distortion, it returns :\n",
    "\n",
    "    - cluster centers: a $k$ by $N$ array of $k$ centroids, where the $i$th row is the centroid of code word $i$. The observation vectors and centroids have the same feature dimension;\n",
    "    - the distortion between the observations and the centroids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         1.17632296 0.38193063 0.         1.08050018 0.44758365\n",
      " 0.1479538  0.         3.11886607 1.39443535 2.61777609 0.25533109\n",
      " 0.71277124 2.46470994 1.72268178 3.5157488  1.05430003 1.74113459\n",
      " 0.33218297 3.12120241 0.         1.80507217 0.15834306 1.51119251\n",
      " 1.42671441 0.35566597 2.09129003 0.34484623 1.23596372 0.50891504\n",
      " 1.33438208 1.36672035 1.23759669 0.93638218 1.95187696 0.57147677\n",
      " 1.45060901 0.53016614 1.69264433 2.00668164 0.64045816 0.52054819\n",
      " 1.32634341 1.66345446 0.         0.07569126 0.27621108 0.2302911\n",
      " 1.36024948 1.33073171 1.13996608 0.         2.59968443 1.26636549\n",
      " 2.0733043  1.6132166  1.2625655  4.63858192 1.62264559 0.30150505\n",
      " 1.96411501 0.53642583 0.44853815 2.57605393 1.5554189  2.14979082\n",
      " 1.8049463  0.92309949 0.58459753 1.46027512 1.41987926 0.46145949\n",
      " 2.13530962 0.13631709 2.11085201 1.25904723 0.         1.86820762\n",
      " 0.79989717 1.91808604 2.02351508 1.02132436 1.54198873 0.33410121\n",
      " 0.         2.57888108 0.         0.         0.21605114 1.28723494\n",
      " 2.44312919 0.56227768 0.8768963  2.81122972 3.93726105 1.63379084\n",
      " 0.19788526 3.19060009 1.3089468  2.86319096 3.54090046 0.19432621\n",
      " 0.         2.29067267 1.74657541 0.78865062 0.52717404 2.6031908\n",
      " 3.60749441 1.61054192 0.56266496 0.26846131 0.11436706 2.77933234\n",
      " 0.60239041 3.41022408 2.2248146  0.         0.61058785 0.91389428\n",
      " 2.13931809 1.59464955 1.84614121 1.02309529 1.83474958 2.59774078\n",
      " 1.42294963 0.         1.61405085 1.20521283 0.66810928 3.30191075\n",
      " 0.66527661 1.29698806 0.26507948 0.92109962 0.32839885 0.84507419\n",
      " 2.95181933 1.06210949 3.51009165 1.10656857 1.22265836 1.64181136\n",
      " 0.76723442 0.18046803 1.55047525 1.85496067 3.39892251 1.3391874\n",
      " 1.39839436 1.80895013 0.7528676  0.34620041 0.43445505 1.40152978\n",
      " 3.36415695 2.27302318 1.39222322 1.5397263  0.52249474 0.96909903\n",
      " 2.02932738 1.05063445 0.         0.954131   0.         1.66583383\n",
      " 2.47345457 0.48809946 3.05995705 1.81643732 1.63751044 1.14843894\n",
      " 2.00739838 2.55636873 0.27783133 1.51263099 1.67343308 1.92459908\n",
      " 0.92253822 0.71055885 3.15297995 0.         0.10714512 1.9547157\n",
      " 0.43731738 2.65581144 0.         1.03349527 0.         0.86573926\n",
      " 0.98020102 0.96087074 1.16865606 2.21726229 0.27607572 0.47130547\n",
      " 0.76869007 3.13805987 0.5062639  1.23508045 2.74312961 2.29038665\n",
      " 0.99329126 2.00255565 0.53286488 0.67821743 0.57961178 1.10363572\n",
      " 0.05581787 2.2539194  1.26020102 0.22806126 1.51993239 2.82256148\n",
      " 2.4021627  1.18116897 0.2618388  2.34809206 1.17740395 1.92216157\n",
      " 2.14061872 0.14431272 0.94204042 0.2816914  3.73707571 1.6081094\n",
      " 0.04223334 2.1343135  0.48831911 0.28799276 0.72754056 0.89951692\n",
      " 1.49604488 0.54802458 1.18547998 2.29712906 0.48785642 0.76389253\n",
      " 1.13531849 0.38255527 0.8356774  1.40242636 0.70857229 0.94487419\n",
      " 0.41704448 1.67388375 0.95466319 2.48893702 0.93048387 2.40825307\n",
      " 2.92263085 1.46772526 1.77045789 0.32607008 2.3040553  1.47413149\n",
      " 1.70023598 0.         0.90769018 0.         1.96040672 2.94507238\n",
      " 3.15606831 1.63967073 0.31224614 3.33857508 0.         0.78492363\n",
      " 0.20202271 0.         1.93690688 0.51038726 1.08491603 2.5774251\n",
      " 0.97603879 2.06766665 0.73130973 0.61582814 0.         2.21034922\n",
      " 2.4458842  0.         0.20776095 1.07349008 3.39851143 5.1223524\n",
      " 1.61601695 0.3278058  2.3544749  1.22592982 2.70545203 0.60949338\n",
      " 0.6718101  1.7605071  1.75934902 0.04501473 1.08082209 2.10763441\n",
      " 1.02539759 0.71433843 0.11661287 0.15916975 1.76557215 1.93457668\n",
      " 2.67135509 0.94556262 0.20641132 1.79033461 2.04657312 2.16707514\n",
      " 2.75682137 1.87461003 1.17112618 2.75508678 1.58361289 0.53240524\n",
      " 1.26075016 0.79723752 1.41198232 1.18841879 3.90103898 1.24329286\n",
      " 0.         2.69336159 2.26162051 1.55945869 0.21834682 3.06201032\n",
      " 1.14527425 0.89660673 0.77194302 1.05000271 2.65873625 2.61661314\n",
      " 0.41678761 1.27490123 0.24287331 1.01539797 2.6313083  1.82799202\n",
      " 0.72106391 1.65414053 4.52342546 1.51634014 0.         1.85031538\n",
      " 2.29212622 1.12181321 1.05630426 3.24372304 0.18044375 0.54881978\n",
      " 0.13280511 0.         1.39834824 2.3097546  2.17304146 0.6423157\n",
      " 1.84567915 1.37953137 1.34771861 3.66916355 2.83710681 0.60748916\n",
      " 0.1396601  0.24474679 0.78456238 2.82101992 3.66943406 0.372664\n",
      " 4.41588906 0.63626466 2.05656381 0.         0.64339344 1.88475292\n",
      " 2.67165852 0.8103872  2.29054851 0.10861023 3.70943767 2.46470994\n",
      " 1.70178918 0.94595102 0.16249751 3.68376078 2.23540611 2.85365932\n",
      " 2.54454548 0.68205791 1.57849351 0.         0.46031328 1.51240359\n",
      " 1.19446107 0.53136797 0.43278921 0.34073168 0.96565561 1.06600358\n",
      " 0.98136912 0.3890168  0.46364048 1.66239878 0.29989691 3.41488649\n",
      " 0.68219104 4.10699118 0.9130728  1.53476752 2.3304603  0.21626678\n",
      " 3.86115594 2.14898392 0.72952057 2.19248835 1.57823205 2.42268508\n",
      " 1.54212342 1.12427433 2.66092287 1.89017966 2.18235516 1.54654369\n",
      " 1.96711544 0.53745643 2.92580096 0.59437755 0.98342809 0.88070485\n",
      " 1.36307206 2.20727145 1.80391695 2.2470647  0.27296042 0.44783087\n",
      " 0.33878689 2.07642515 1.40109541 4.16926637 2.07914139 2.80477456\n",
      " 2.14991453 2.87606527 2.74867476 2.00816426 0.30683406 0.33447855\n",
      " 2.62803681 0.26710361 2.02335982 1.62123507 0.72183975 1.97029849\n",
      " 2.15644824 1.68613318 2.92604708 0.13811038 1.54698418 1.34004206\n",
      " 1.79294546 3.02658815 1.22917559 2.44896627 0.77702196 2.0446081\n",
      " 1.02600315 0.36177556 0.72727303 2.58648791 1.82155358 1.76881766\n",
      " 0.35354325 2.19196263 1.12408879 1.79215082 1.53467452 2.22922093\n",
      " 0.15960104 0.         0.99049537 0.24669493 3.23812781 1.52542554\n",
      " 0.55577647 0.35840876 1.42389603 1.0194342  1.87548443 0.35882113\n",
      " 0.29278154 1.72410039 0.99812699 3.01083365 2.97020198 0.27771324\n",
      " 0.         1.68722768 2.55659162 0.34421719 0.70363344 2.23390373\n",
      " 1.16971277 1.98456977 2.8894316  2.0864357  1.2001984  0.89107864\n",
      " 0.2901218  1.42913732 0.         0.         1.95742407 0.37403524\n",
      " 1.91886411 0.37006308 0.11317975 1.22447504 2.60938877 2.0522642\n",
      " 0.71153085 2.79492293 0.05334905 1.10348763 0.41748745 0.40778395\n",
      " 0.08079939 1.79996281 3.02325608 0.81715217 1.58091706 2.89241324\n",
      " 2.12379003 2.7378765  2.55654778 1.0884156  0.32523761 1.05654485\n",
      " 2.67296309 0.18493273 0.         1.56311674 0.         2.24962061\n",
      " 3.64717584 3.93080041 2.32061572 1.15753852 1.54526117 1.81672615\n",
      " 1.16111559 1.76913609 4.79917852 1.13380976 1.84411587 0.22090511\n",
      " 1.86280681 3.20481917 1.09976242 2.82946982 1.84776114 0.59706629\n",
      " 0.21075186 1.38379681 1.74080078 0.52357872 0.21974744 1.26016631\n",
      " 0.53211965 1.79139932 0.         1.49720994 2.09035746 2.56215436\n",
      " 0.12949643 0.47371132 2.37910555 0.27050998 0.65859999 0.94151308\n",
      " 2.38428788 2.99112938 0.         2.7777336  2.10456886 0.58927126\n",
      " 0.4533659  0.53386535 1.80619171 0.45222927 1.26964821 0.41728714\n",
      " 0.05975967 1.75461876 0.87959179 0.57302549 0.         0.29233891\n",
      " 0.27378587 0.         3.05614461 0.38608471 1.36817614 2.67861221\n",
      " 2.65169027 2.87561769 0.         1.70474923 0.14544362 2.00665833\n",
      " 2.52522121 0.34175337 1.37904611 0.6203027  1.12167352 0.69602984\n",
      " 1.75586018 1.04027343 0.50673372 0.29494869 2.80397008 2.16562289\n",
      " 0.         1.11605924 1.15742205 1.76567673 1.54679232 2.14249731\n",
      " 0.22895208 3.14524523 3.10074098 2.6837252  2.45626566 0.\n",
      " 2.43750584 0.69993073 2.51533743 1.97891795 0.61043114 2.00033066\n",
      " 0.35882286 0.69297306 0.48555671 1.48872349 0.8614397  0.26043255\n",
      " 2.08675755 0.36117919 3.79490743 1.21753397 0.15817563 2.1923613\n",
      " 1.44107704 2.17097955 3.63636517 2.17173394 2.10056428 0.01363256\n",
      " 0.88031306 2.08778509 0.         0.66158973 0.18255184 1.10546406\n",
      " 1.05691086 1.70876686 0.41423827 1.84425809 0.70637743 1.03883449\n",
      " 1.47526303 0.26577492 1.38791108 1.25913036 3.15485232 0.66885785\n",
      " 1.06777506 0.5778258  3.14642654 1.80265977 1.79879392 1.77827176\n",
      " 0.         2.05027592 3.65955947 0.16260992 0.40179716 1.80589595\n",
      " 0.69249021 0.76733812 1.55997422 2.64294294 2.44694603 0.34808732\n",
      " 2.80864199 0.30711297 3.91341968 0.         1.56522962 1.14933054\n",
      " 1.72128365 2.33818124 2.42571296 3.41264736]\n"
     ]
    }
   ],
   "source": [
    "centroids, distortion = scipy.cluster.vq.kmeans(rescaledMat, 10)\n",
    "print(centroids[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ASSIGNING DATAPOINTS TO CENTROIDS\n",
    "\n",
    "The `vq()` method can be used to assign each observation to a given cluster:\n",
    "\n",
    "- each observation vector is compared with the centroids and assigned to the nearest cluster.\n",
    "\n",
    "\n",
    "- It returns:\n",
    "    - an array holding the code book index for each observation;\n",
    "    - the distortion between the observations and the centroids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters, distortion = scipy.cluster.vq.vq(rescaledMat, centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9 3 9 9 3 7 5 6 3 9 9 9 9 9 9 9 3 9 8 4 3 8 9 0 9 8 9 9 3 0 9 9 3 8 9 9 9\n",
      " 9 8 8 8 3 9 9 9 9 9 8 9 6 3 9 9 3 9 9 9 9 9 3 8 9 8 9 9 9 9 9 8 8 9 9 9 9\n",
      " 9 9 9 9 9 3 9 8 9 3 8 9 9 9 2 9 9 0 9 9 8 9 9 8 1]\n"
     ]
    }
   ],
   "source": [
    "print(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 ['43 Folders', 'A Consuming Experience (full feed)', 'Andrew Sullivan | The Daily Dish', 'BuzzMachine', \"Captain's Quarters\", 'Cool Hunting', 'CoolerHeads Prevail', 'Copyblogger', 'Creating Passionate Users', 'Crooks and Liars', \"Dave Shea's mezzoblue\", 'Eschaton', 'GigaOM', 'Go Fug Yourself', 'Google Blogoscoped', 'Hot Air', 'Instapundit.com', \"John Battelle's Searchblog\", 'Joho the Blog', \"Joi Ito's Web\", 'Joystiq', 'Mashable!', 'Matt Cutts: Gadgets, Google, and SEO', 'MetaFilter', 'Michelle Malkin', 'Micro Persuasion', 'NewsBusters.org - Exposing Liberal Media Bias', 'Oilman', 'Online Marketing Report', 'PerezHilton.com', 'Pharyngula', 'Power Line', 'ProBlogger Blog Tips', 'Publishing 2.0', 'Schneier on Security', 'Scobleizer - Tech Geek Blogger', 'Search Engine Roundtable', 'Search Engine Watch Blog', \"Seth's Blog\", 'Shoemoney - Skills to pay the bills', 'Signum sine tinnitu--by Guy Kawasaki', 'SimpleBits', 'Slashdot', 'SpikedHumor', \"Steve Pavlina's Personal Development Blog\", 'TMZ.com', 'Talking Points Memo: by Joshua Micah Marshall', 'TechCrunch', 'TechEBlog', 'The Blotter', \"The Superficial - Because You're Ugly\", 'Think Progress', 'Topix.net Weblog', 'Treehugger', 'WWdN: In Exile', 'Wired News: Top Stories', 'flagrantdisregard', 'gapingvoid: \"cartoons drawn on the back of business cards\"', 'lifehack.org', 'ongoing']\n",
      "3 ['456 Berea Street', 'Autoblog', 'Boing Boing', 'Daily Kos', 'Download Squad', 'Google Operating System', \"Jeremy Zawodny's blog\", 'MAKE Magazine', 'Official Google Blog', 'PaulStamatiou.com', 'Quick Online Tips', 'Techdirt', 'The Unofficial Apple Weblog (TUAW)']\n",
      "7 ['Blog Maverick']\n",
      "5 ['Bloggers Blog: Blogging the Blogsphere']\n",
      "6 ['Bloglines | News', \"O'Reilly Radar\"]\n",
      "8 ['Deadspin', 'Engadget', 'Gizmodo', 'Joel on Software', 'Kotaku', 'Lifehacker', 'Little Green Footballs', \"Neil Gaiman's Journal\", 'Read/WriteWeb', 'ScienceBlogs : Combined Feed', \"Sifry's Alerts\", 'Signal vs. Noise', 'The Huffington Post | Raw Feed', 'The Viral Garden', 'kottke.org', 'plasticbag.org']\n",
      "4 ['Derek Powazek']\n",
      "0 ['Gawker', 'Gothamist', 'Wonkette']\n",
      "2 ['Valleywag']\n",
      "1 ['we make money not art']\n"
     ]
    }
   ],
   "source": [
    "# human readable\n",
    "cluster2blog = defaultdict(list)\n",
    "for bid, clusterid in enumerate(clusters):\n",
    "    cluster2blog[clusterid].append(blogs[bid])\n",
    "    \n",
    "for cId, blog in cluster2blog.items():\n",
    "    print(cId, blog)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PLOTTING OUR DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plotting the data directly\n",
    "\n",
    "Adjusted example and code from [scipy.cluster.vq.kmeans](https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.vq.kmeans.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster.vq import vq, kmeans, whiten\n",
    "\n",
    "# Create 50 datapoints in two clusters a and b\n",
    "pts = 30\n",
    "a = np.random.multivariate_normal([0, 0], [[4, 1], [1, 4]], size=pts)\n",
    "b = np.random.multivariate_normal([30, 10], [[10, 2], [2, 1]], size=pts)\n",
    "c = np.random.multivariate_normal([0, 25], [[10, 2], [2, 1]], size=pts)\n",
    "features = np.concatenate((a, b, c))\n",
    "\n",
    "# Whiten data\n",
    "whitened = whiten(features)\n",
    "\n",
    "# Find 2 clusters in the data\n",
    "codebook, distortion = kmeans(whitened, 3)\n",
    "\n",
    "# Plot whitened data and cluster centers in red\n",
    "plt.scatter(whitened[:, 0], whitened[:, 1])\n",
    "plt.scatter(codebook[:, 0], codebook[:, 1], c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise\n",
    "\n",
    "`Zebo` was a social network encoraging people to create lists of things that they own and things that they would like to own. The dataset `./data/zebo.txt` reports an item-to-user matrix of binary values\n",
    "\n",
    "- **Task**: Use a clustering method to group together the preferences expressed by these users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic Modelling\n",
    "\n",
    "> What follows is a short tutorial on using the LDA implementation available in [Gensim](https://radimrehurek.com/gensim/). \n",
    ">\n",
    ">\n",
    "> To install Gensim, either use the Anaconda Navigator or:\n",
    ">\n",
    "> ```conda install -c anaconda gensim```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-process the documents\n",
    "\n",
    "In this tutorial we will work with the `C-Span Inaugural Address Corpus` available in NLTK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C-Span Inaugural Address Corpus\n",
      "\n",
      "US presidential inaugural addresses 1789-2017\n",
      "\n",
      "(Thanks to Kathleen Ahrens for compiling this corpus from\n",
      "the C-Span sources.)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(nltk.corpus.inaugural.readme())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a first step, we lemmatize our corpus:\n",
    "\n",
    "- as usual, we need pos-tagging to properly use the WordNet based lemmatizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "un2wn_mapping = {\"VERB\" : wn.VERB, \"NOUN\" : wn.NOUN, \"ADJ\" : wn.ADJ, \"ADV\" : wn.ADV}\n",
    "\n",
    "inaug_docs = []\n",
    "\n",
    "for speech_id in nltk.corpus.inaugural.fileids(): # NB: fileids()\n",
    "    lemmatized_doc = []\n",
    "    for w, p in nltk.pos_tag(nltk.corpus.inaugural.words(speech_id), tagset=\"universal\"):\n",
    "        if p in un2wn_mapping.keys():\n",
    "            lemma = nltk.WordNetLemmatizer().lemmatize(w, pos = un2wn_mapping[p])\n",
    "        else:\n",
    "            lemma = nltk.WordNetLemmatizer().lemmatize(w)\n",
    "            \n",
    "        lemmatized_doc.append(lemma.lower())  # case insensitive\n",
    "        \n",
    "    inaug_docs.append(lemmatized_doc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Construct the document-term matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `gensim.corpora.Dictionary()` class encapsulates the mapping between normalized words and their integer ids:\n",
    "\n",
    "- we use it to create a dictionary representation of our documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique tokens: 7091\n"
     ]
    }
   ],
   "source": [
    "inaug_dictionary = corpora.Dictionary(inaug_docs)\n",
    "print('Number of unique tokens:', len(inaug_dictionary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'\"': 0, '(': 1, ')': 2, ',': 3, '-': 4, '--': 5, '.': 6, '.\"': 7, '14th': 8, ':': 9, ';': 10, 'a': 11}\n",
      "word with id 8: 14th\n",
      "frequency of token 8: 1\n"
     ]
    }
   ],
   "source": [
    "# let's check each token's unique id\n",
    "print(dict(itertools.islice(inaug_dictionary.token2id.items(), 12)))\n",
    "print(\"word with id 8:\", inaug_dictionary[8])\n",
    "print(\"frequency of token 8:\", inaug_dictionary.dfs[8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using built-in function `filter_extremes()`, we can remove rare and common words based on their document frequency. \n",
    "\n",
    "- the `filter_extremes(self, no_below=5, no_above=0.5, keep_n=100000, keep_tokens=None)` allows us to remove words that appear in:\n",
    "    - less than `no_below` documents (absolute number);\n",
    "    - more than `no_above` documents (fraction of total corpus size);\n",
    "    - if tokens are given in `keep_tokens` (list of strings), they are kept regardless of all the other settings;\n",
    "    - after the other parameters have been applied, keep only the first `keep_n` most frequent tokens (all if `None`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique tokens: 848\n"
     ]
    }
   ],
   "source": [
    "# Filter out words that occur in less than 10 documents, or more than 50% of the documents.\n",
    "inaug_dictionary.filter_extremes(no_below=10, no_above=0.5)\n",
    "print('Number of unique tokens:', len(inaug_dictionary))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The `doc2bow()` function is the most important `Dictionary()` method, whose function is to convert a collection of words to a **bag-of-words representation**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bag-of-words representation of the documents\n",
    "inaug_bow_corpus = [inaug_dictionary.doc2bow(d) for d in inaug_docs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Such a representation returns, **for each document**, a list of `(word_id, word_frequency) 2-tuples`:\n",
    "\n",
    "- we can use the dictionary mapping to retrieve the lemma associated with a given id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fellow-Citizens of the Senate and of the House of Representatives:\n",
      "\n",
      "Among the vicissitudes incident to life no event could have filled me with greater anxieties than that of which the notification was transmitted by your order, and received on the 14th day of the present month. On the one hand, I was summoned by my Country, whose voice I can never hear but with veneration and love, from a retreat which I had chosen with the fondest predilection, and, in my flattering hopes, with an immutable decision, as the asylum of my declining years -- a retreat which was rendered every day more necessary as well as more dear to me by the addition of habit to inclination, and of frequent interruptions in my health to the gradual waste committed on it by time. On the other hand, the magnitude and difficulty of the trust to which the voice of my country called me, being sufficient to awaken in the wisest and most experienced of her citizens a distrustful scrutiny into his qualifications, could not but overwhelm with despondence one who (inheriting inferior endowments from nature and unpracticed in the duties of civil administration) ought to be peculiarly conscious of his own deficiencies. In this conflict of emotions all I dare aver is that it has been my faithful study to collect my duty from a just appreciation of every circumstance by which it might be affected. All I dare hope is that if, in executing this task, I have been too much swayed by a grateful remembrance of former instances, or by an affectionate sensibility to this transcendent proof of the confidence of my fellow citizens, and have thence too little consulted my incapacity as well as disinclination for the weighty and untried cares before me, my error will be palliated by the motives which mislead me, and its consequences be judged by my country with some share of the partiality in which they originated.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Our first document, i.e. at index 0 of `bow_corpus`\n",
    "print(nltk.corpus.inaugural.raw('1789-Washington.txt')[:1890])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 2), (19, 1), (20, 1), (21, 1), (22, 1), (23, 1), (24, 1), (25, 1), (26, 1), (27, 2), (28, 2), (29, 1), (30, 1), (31, 3), (32, 1), (33, 1), (34, 2), (35, 1), (36, 1), (37, 1), (38, 1), (39, 1), (40, 1), (41, 1), (42, 1), (43, 1), (44, 1), (45, 2), (46, 1), (47, 1), (48, 1), (49, 1)]\n"
     ]
    }
   ],
   "source": [
    "# the BOW representation of the first document\n",
    "print(inaug_bow_corpus[0][:50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "myself --> 4\n",
      "ought --> 4\n",
      "circumstance --> 3\n",
      "establish --> 3\n",
      "happiness --> 3\n",
      "nature --> 3\n",
      "assure --> 2\n",
      "blessing --> 2\n",
      "care --> 2\n",
      "community --> 2\n",
      "decide --> 2\n",
      "department --> 2\n",
      "event --> 2\n",
      "execute --> 2\n",
      "executive --> 2\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "# which words (and how often) appear in the first document?\n",
    "for i, freq in sorted(inaug_bow_corpus[0], key=itemgetter(1), reverse=True)[:15]:\n",
    "    print(inaug_dictionary[i], \"-->\", freq)\n",
    "print(\"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Applying the LDA model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can train the LDA model by using the `gensim.models.ldamodel.LDAModel()` constructor.\n",
    "\n",
    "Parameters used in our example:\n",
    "\n",
    "- `num_topics`: how many topics do we want? In what follows, we set the number of topics to 5, because we want to have a few topics that we can interpret, but the number of topics is **data** and **application**-dependent;\n",
    "\n",
    "\n",
    "- `id2word`: our previous dictionary needed to map ids to strings;\n",
    "\n",
    "\n",
    "\n",
    "- `passes`: how often we iterateover the entire corpus (default = 1). In general, the more the passes, the higher the accuracy. This number is also called `epochs` in ML literature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "inaug_ldamodel = models.ldamodel.LdaModel(inaug_bow_corpus, num_topics=5, id2word = inaug_dictionary, passes= 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even if we are not covering these issues, it is importatnt to know that:\n",
    "\n",
    "- you can use this model to infer the topic distribution in **a new unseen document**:\n",
    "\n",
    "```python\n",
    "doc_lda = inaug_ldamodel[doc_bow]\n",
    "```\n",
    "\n",
    "\n",
    "- you can **update** you model with novel data (instead of retraining from scratch):\n",
    "\n",
    "```python\n",
    "inaug_ldamodel.update(new_corpus)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining Topics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An immediate way to inspect our topics is by using the `show_topics()` method, that prints the most representative words for each topic (each topic is marked by an integer id), along with their probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.014*\"congress\" + 0.009*\"business\" + 0.008*\"condition\" + 0.006*\"race\" + 0.006*\"executive\"'),\n",
       " (1,\n",
       "  '0.011*\"thing\" + 0.010*\"democracy\" + 0.009*\"economic\" + 0.008*\"civilization\" + 0.008*\"task\"'),\n",
       " (2,\n",
       "  '0.007*\"opinion\" + 0.006*\"object\" + 0.006*\"executive\" + 0.006*\"institution\" + 0.006*\"general\"'),\n",
       " (3,\n",
       "  '0.014*\"today\" + 0.014*\"century\" + 0.013*\"americans\" + 0.011*\"generation\" + 0.010*\"build\"'),\n",
       " (4,\n",
       "  '0.013*\"today\" + 0.011*\"help\" + 0.011*\"earth\" + 0.010*\"again\" + 0.010*\"americans\"')]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see just 5 words per topic (default = 10)\n",
    "inaug_ldamodel.show_topics(num_words=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  [('congress', 0.014140481),\n",
       "   ('business', 0.0093024345),\n",
       "   ('condition', 0.0075770696),\n",
       "   ('race', 0.0064101433),\n",
       "   ('executive', 0.0061434684),\n",
       "   ('increase', 0.005809959),\n",
       "   ('revenue', 0.0057905284),\n",
       "   ('necessary', 0.0057278504),\n",
       "   ('ought', 0.005417626),\n",
       "   ('labor', 0.0053896834)]),\n",
       " (1,\n",
       "  [('thing', 0.010629142),\n",
       "   ('democracy', 0.009719583),\n",
       "   ('economic', 0.009120638),\n",
       "   ('civilization', 0.008227594),\n",
       "   ('task', 0.00819473),\n",
       "   ('opportunity', 0.007753881),\n",
       "   ('republic', 0.007554688),\n",
       "   ('thought', 0.0067014294),\n",
       "   ('problem', 0.006527296),\n",
       "   ('order', 0.006225285)]),\n",
       " (2,\n",
       "  [('opinion', 0.0065768915),\n",
       "   ('object', 0.0064775813),\n",
       "   ('executive', 0.0062681353),\n",
       "   ('institution', 0.006263179),\n",
       "   ('general', 0.005626477),\n",
       "   ('necessary', 0.0048418315),\n",
       "   ('limit', 0.0047417297),\n",
       "   ('exist', 0.004713355),\n",
       "   ('authority', 0.004605751),\n",
       "   ('regard', 0.004553977)]),\n",
       " (3,\n",
       "  [('today', 0.0141304415),\n",
       "   ('century', 0.013810536),\n",
       "   ('americans', 0.013469474),\n",
       "   ('generation', 0.01125395),\n",
       "   ('build', 0.009897642),\n",
       "   ('cannot', 0.009764098),\n",
       "   ('promise', 0.0097165285),\n",
       "   ('child', 0.008655375),\n",
       "   ('challenge', 0.008239936),\n",
       "   ('help', 0.006768532)]),\n",
       " (4,\n",
       "  [('today', 0.012636174),\n",
       "   ('help', 0.011172456),\n",
       "   ('earth', 0.010979441),\n",
       "   ('again', 0.009931568),\n",
       "   ('americans', 0.009760998),\n",
       "   ('friend', 0.008434962),\n",
       "   ('child', 0.007865357),\n",
       "   ('moment', 0.0076750075),\n",
       "   ('word', 0.0065424535),\n",
       "   ('ask', 0.0063457866)])]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the setting formatted=False allows you to get rid of the word*probability format when retrieveing topics\n",
    "inaug_ldamodel.show_topics(formatted=False, num_words=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `get_term_topics()` method returns the odds of that particular word belonging to a particular topic:\n",
    "\n",
    "- topics below a given threshold are ignored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.014050201), (2, 0.0040861676), (3, 0.0022967777), (4, 0.0017318719)]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inaug_ldamodel.get_term_topics(\"congress\", minimum_probability = 1e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `get_document_topics()` returns several statistics describing the topic distribution in a document:\n",
    "\n",
    "- the topic distribution of the document;\n",
    "\n",
    "\n",
    "- (if `per_word_topics=True`) the the topic distribution for each word in the document;\n",
    "\n",
    "\n",
    "- (if `per_word_topics=True`) the probability of each word in each document to belong to a particular topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.00085566),\n",
       " (1, 0.0008522134),\n",
       " (2, 0.99658746),\n",
       " (3, 0.0008539308),\n",
       " (4, 0.00085071207)]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the topics of the first document of our corpus\n",
    "inaug_ldamodel.get_document_topics(inaug_bow_corpus[0], minimum_probability = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(2, 0.9965882)]\n",
      "[(2, 0.96297276)]\n",
      "[(2, 0.9976793)]\n",
      "[(2, 0.9331537), (3, 0.06482706)]\n",
      "[(2, 0.9978613)]\n",
      "[(2, 0.9960719)]\n",
      "[(2, 0.8845566), (4, 0.112198606)]\n",
      "[(2, 0.9985426)]\n",
      "[(0, 0.019416338), (2, 0.9797845)]\n",
      "[(2, 0.998448)]\n",
      "[(2, 0.9961048)]\n",
      "[(2, 0.9960783)]\n",
      "[(2, 0.99893945)]\n",
      "[(2, 0.9927283)]\n",
      "[(0, 0.06547872), (2, 0.9338433)]\n",
      "[(0, 0.1779427), (2, 0.81926435)]\n",
      "[(0, 0.056809053), (1, 0.102318816), (2, 0.84018743)]\n",
      "[(0, 0.21903944), (2, 0.7797757)]\n",
      "[(0, 0.23327568), (2, 0.7656988)]\n",
      "[(0, 0.15348287), (2, 0.4902972), (4, 0.3524243)]\n",
      "[(0, 0.34364608), (2, 0.5783606), (4, 0.07571455)]\n",
      "[(0, 0.19144925), (1, 0.13776785), (2, 0.6690763)]\n",
      "[(0, 0.6068058), (2, 0.39188114)]\n",
      "[(0, 0.6623133), (2, 0.18303588), (4, 0.14898701)]\n",
      "[(0, 0.38732672), (1, 0.09118435), (2, 0.5200746)]\n",
      "[(0, 0.9412506), (2, 0.057959408)]\n",
      "[(0, 0.55285263), (2, 0.4039236), (3, 0.0420234)]\n",
      "[(0, 0.8915584), (2, 0.10762441)]\n",
      "[(0, 0.65482104), (1, 0.016614672), (2, 0.23132607), (3, 0.096735254)]\n",
      "[(1, 0.84584856), (4, 0.15072589)]\n",
      "[(0, 0.9992161)]\n",
      "[(1, 0.9972869)]\n",
      "[(1, 0.9032427), (2, 0.09420975)]\n",
      "[(0, 0.06419885), (1, 0.93136346)]\n",
      "[(0, 0.60605216), (1, 0.29745507), (2, 0.09590383)]\n",
      "[(0, 0.6207049), (1, 0.3782951)]\n",
      "[(0, 0.37020472), (1, 0.42778748), (3, 0.13296522), (4, 0.06842444)]\n",
      "[(1, 0.85310525), (3, 0.14517203)]\n",
      "[(1, 0.7034551), (3, 0.2934458)]\n",
      "[(1, 0.29791728), (3, 0.16629267), (4, 0.5317153)]\n",
      "[(1, 0.998083)]\n",
      "[(1, 0.36756182), (2, 0.16151309), (3, 0.15916783), (4, 0.31125578)]\n",
      "[(4, 0.99712265)]\n",
      "[(3, 0.30329126), (4, 0.69432026)]\n",
      "[(4, 0.9960071)]\n",
      "[(3, 0.22497562), (4, 0.7733299)]\n",
      "[(3, 0.99670833)]\n",
      "[(3, 0.9953769)]\n",
      "[(4, 0.99777514)]\n",
      "[(4, 0.99807715)]\n",
      "[(4, 0.9974958)]\n",
      "[(3, 0.99705726)]\n",
      "[(3, 0.9979085)]\n",
      "[(3, 0.996969)]\n",
      "[(3, 0.99759656)]\n",
      "[(3, 0.9968414)]\n",
      "[(3, 0.9977003)]\n",
      "[(4, 0.9965788)]\n"
     ]
    }
   ],
   "source": [
    "# the topics of ALL the documents of our corpus\n",
    "for doc_topics in inaug_ldamodel.get_document_topics(inaug_bow_corpus):\n",
    "    print(doc_topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Document topics: [(2, 0.9965868)]\n",
      "\n",
      "- Word topics: [(0, [2]), (1, [2]), (2, [2]), (3, [2]), (4, [2]), (5, [2]), (6, [2]), (7, [2]), (8, [2]), (9, [2]), (10, [2]), (11, [2]), (12, [2]), (13, [2]), (14, [2]), (15, [2]), (16, [2]), (17, [2]), (18, [2]), (19, [2]), (20, [2]), (21, [2]), (22, [2]), (23, [2]), (24, [2]), (25, [2]), (26, [2]), (27, [2]), (28, [2]), (29, [2]), (30, [2]), (31, [2]), (32, [2]), (33, [2]), (34, [2]), (35, [2]), (36, [2]), (37, [2]), (38, [2]), (39, [2]), (40, [2]), (41, [2]), (42, [2]), (43, [2]), (44, [2]), (45, [2]), (46, [2]), (47, [2]), (48, [2]), (49, [2]), (50, [2]), (51, [2]), (52, [2]), (53, [2]), (54, [2]), (55, [2]), (56, [2]), (57, [2]), (58, [2]), (59, [2]), (60, [2]), (61, [2]), (62, [2]), (63, [2]), (64, [2]), (65, [2]), (66, [2]), (67, [2]), (68, [2]), (69, [2]), (70, [2]), (71, [2]), (72, [2]), (73, [2]), (74, [2]), (75, [2]), (76, [2]), (77, [2]), (78, [2]), (79, [2]), (80, [2]), (81, [2]), (82, [2]), (83, [2]), (84, [2]), (85, [2]), (86, [2]), (87, [2]), (88, [2]), (89, [2]), (90, [2]), (91, [2]), (92, [2]), (93, [2]), (94, [2]), (95, [2]), (96, [2]), (97, [2]), (98, [2]), (99, [2]), (100, [2]), (101, [2]), (102, [2]), (103, [2]), (104, [2]), (105, [2]), (106, [2]), (107, [2]), (108, [2]), (109, [2]), (110, [2]), (111, [2]), (112, [2]), (113, [2]), (114, [2]), (115, [2]), (116, [2]), (117, [2]), (118, [2]), (119, [2]), (120, [2]), (121, [2]), (122, [2]), (123, [2]), (124, [2]), (125, [2]), (126, [2]), (127, [2]), (128, [2]), (129, [2]), (130, [2]), (131, [2]), (132, [2]), (133, [2]), (134, [2]), (135, [2]), (136, [2]), (137, [2]), (138, [2]), (139, [2]), (140, [2]), (141, [2]), (142, [2]), (143, [2]), (144, [2]), (145, [2]), (146, [2]), (147, [2]), (148, [2]), (149, [2]), (150, [2]), (151, [2]), (152, [2]), (153, [2]), (154, [2]), (155, [2]), (156, [2]), (157, [2]), (158, [2]), (159, [2]), (160, [2]), (161, [2]), (162, [2]), (163, [2]), (164, [2]), (165, [2]), (166, [2]), (167, [2]), (168, [2]), (169, [2]), (170, [2]), (171, [2]), (172, [2]), (173, [2]), (174, [2]), (175, [2]), (176, [2]), (177, [2]), (178, [2]), (179, [2]), (180, [2]), (181, [2]), (182, [2]), (183, [2]), (184, [2]), (185, [2]), (186, [2]), (187, [2]), (188, [2]), (189, [2]), (190, [2])]\n",
      "\n",
      "- Scaled phi values: [(0, [(2, 0.9997233)]), (1, [(2, 0.9998885)]), (2, [(2, 0.9999256)]), (3, [(2, 0.9999008)]), (4, [(2, 0.99976414)]), (5, [(2, 0.9999195)]), (6, [(2, 0.9998337)]), (7, [(2, 0.9999243)]), (8, [(2, 0.9998542)]), (9, [(2, 0.99984765)]), (10, [(2, 0.9999104)]), (11, [(2, 0.9996001)]), (12, [(2, 0.99872994)]), (13, [(2, 0.99991095)]), (14, [(2, 0.9998139)]), (15, [(2, 0.9996725)]), (16, [(2, 0.99987274)]), (17, [(2, 0.99989456)]), (18, [(2, 1.9993075)]), (19, [(2, 0.99990016)]), (20, [(2, 0.9997945)]), (21, [(2, 0.9997712)]), (22, [(2, 0.99963295)]), (23, [(2, 0.9998732)]), (24, [(2, 0.999825)]), (25, [(2, 0.9998498)]), (26, [(2, 0.99977666)]), (27, [(2, 1.9998863)]), (28, [(2, 1.9996969)]), (29, [(2, 0.9998779)]), (30, [(2, 0.9997223)]), (31, [(2, 2.9998906)]), (32, [(2, 0.99990046)]), (33, [(2, 0.9999004)]), (34, [(2, 1.9995705)]), (35, [(2, 0.9999484)]), (36, [(2, 0.999903)]), (37, [(2, 0.9999005)]), (38, [(2, 0.9999174)]), (39, [(2, 0.99986255)]), (40, [(2, 0.9998153)]), (41, [(2, 0.99993145)]), (42, [(2, 0.99983454)]), (43, [(2, 0.9998885)]), (44, [(2, 0.99961454)]), (45, [(2, 1.9997336)]), (46, [(2, 0.999877)]), (47, [(2, 0.9998934)]), (48, [(2, 0.9989754)]), (49, [(2, 0.9999107)]), (50, [(2, 1.9998888)]), (51, [(2, 0.99986714)]), (52, [(2, 0.9998816)]), (53, [(2, 0.9997452)]), (54, [(2, 0.99993354)]), (55, [(2, 0.9998305)]), (56, [(2, 0.9998309)]), (57, [(2, 0.9998942)]), (58, [(2, 0.9998471)]), (59, [(2, 0.99986494)]), (60, [(2, 0.9998933)]), (61, [(2, 0.99948955)]), (62, [(2, 0.99992627)]), (63, [(2, 0.99978137)]), (64, [(2, 0.9999175)]), (65, [(2, 0.9999074)]), (66, [(2, 0.99983543)]), (67, [(2, 0.9999268)]), (68, [(2, 0.99993783)]), (69, [(2, 0.9998428)]), (70, [(2, 2.9996352)]), (71, [(2, 1.9998808)]), (72, [(2, 1.999835)]), (73, [(2, 1.9999073)]), (74, [(2, 0.99994725)]), (75, [(2, 0.9999604)]), (76, [(2, 0.9998357)]), (77, [(2, 0.9998082)]), (78, [(2, 0.9999441)]), (79, [(2, 0.9998406)]), (80, [(2, 0.9999306)]), (81, [(2, 0.99946856)]), (82, [(2, 0.9999174)]), (83, [(2, 0.9999006)]), (84, [(2, 0.9998788)]), (85, [(2, 0.9997968)]), (86, [(2, 0.99961436)]), (87, [(2, 0.998735)]), (88, [(2, 1.999832)]), (89, [(2, 0.9995237)]), (90, [(2, 0.9999007)]), (91, [(2, 0.9998143)]), (92, [(2, 0.9999011)]), (93, [(2, 0.99978375)]), (94, [(2, 0.99984366)]), (95, [(2, 2.9997768)]), (96, [(2, 0.99993974)]), (97, [(2, 0.9993068)]), (98, [(2, 0.9993592)]), (99, [(2, 1.9997202)]), (100, [(2, 0.9998786)]), (101, [(2, 0.999833)]), (102, [(2, 0.9999108)]), (103, [(2, 1.9990524)]), (104, [(2, 1.9997959)]), (105, [(2, 1.9997281)]), (106, [(2, 0.999673)]), (107, [(2, 0.9999226)]), (108, [(2, 1.9998415)]), (109, [(2, 0.9997989)]), (110, [(2, 1.9998693)]), (111, [(2, 0.9995657)]), (112, [(2, 0.999907)]), (113, [(2, 0.9986791)]), (114, [(2, 1.9995644)]), (115, [(2, 0.9999003)]), (116, [(2, 0.9997168)]), (117, [(2, 0.99985385)]), (118, [(2, 0.9997393)]), (119, [(2, 1.9994888)]), (120, [(2, 0.99995965)]), (121, [(2, 0.9998034)]), (122, [(2, 0.99982727)]), (123, [(2, 3.9997966)]), (124, [(2, 2.9997208)]), (125, [(2, 1.9998677)]), (126, [(2, 0.99991256)]), (127, [(2, 0.9996666)]), (128, [(2, 1.9999572)]), (129, [(2, 0.99990326)]), (130, [(2, 1.9997449)]), (131, [(2, 0.9997639)]), (132, [(2, 1.9984819)]), (133, [(2, 1.9995583)]), (134, [(2, 3.9995277)]), (135, [(2, 1.9997599)]), (136, [(2, 0.9999135)]), (137, [(2, 0.9999098)]), (138, [(2, 0.9998612)]), (139, [(2, 0.9997838)]), (140, [(2, 0.99987864)]), (141, [(2, 0.99982333)]), (142, [(2, 0.99985266)]), (143, [(2, 0.9998777)]), (144, [(2, 1.9994143)]), (145, [(2, 0.9999348)]), (146, [(2, 0.9999007)]), (147, [(2, 0.99988794)]), (148, [(2, 0.99991834)]), (149, [(2, 0.9997543)]), (150, [(2, 0.99693197)]), (151, [(2, 0.99991024)]), (152, [(2, 0.9999081)]), (153, [(2, 1.9996036)]), (154, [(2, 0.99995387)]), (155, [(2, 1.9997892)]), (156, [(2, 0.99993104)]), (157, [(2, 0.99986684)]), (158, [(2, 0.9999324)]), (159, [(2, 0.9997852)]), (160, [(2, 0.99988604)]), (161, [(2, 0.9995194)]), (162, [(2, 0.99988055)]), (163, [(2, 0.9998451)]), (164, [(2, 1.9996909)]), (165, [(2, 1.99988)]), (166, [(2, 0.9999069)]), (167, [(2, 1.9994385)]), (168, [(2, 0.9997424)]), (169, [(2, 1.9998605)]), (170, [(2, 0.9995081)]), (171, [(2, 0.9994803)]), (172, [(2, 0.9998135)]), (173, [(2, 0.99988216)]), (174, [(2, 0.9997835)]), (175, [(2, 0.99982125)]), (176, [(2, 0.999859)]), (177, [(2, 0.99941874)]), (178, [(2, 0.9999224)]), (179, [(2, 1.9996685)]), (180, [(2, 0.99990326)]), (181, [(2, 0.9997892)]), (182, [(2, 0.99979216)]), (183, [(2, 0.9996362)]), (184, [(2, 0.99992085)]), (185, [(2, 1.999393)]), (186, [(2, 0.9992053)]), (187, [(2, 0.99971855)]), (188, [(2, 1.9998513)]), (189, [(2, 0.99771374)]), (190, [(2, 1.9998316)])]\n"
     ]
    }
   ],
   "source": [
    "# the topics of the first document of our corpus, of its words and the scaled prob values of each word.\n",
    "doc_topics, word_topics, phi_values = inaug_ldamodel.get_document_topics(inaug_bow_corpus[0], per_word_topics=True)\n",
    "\n",
    "# \"Topic distribution for the whole document. Each element in the list is a pair of topic_id, \n",
    "# and the probability that was assigned to it.\"\n",
    "print(\"- Document topics:\", doc_topics)\n",
    "\n",
    "# \"Most probable topics for a word. Each element in the list is a pair of word_id, and a list of \n",
    "# topics sorted by their relevance to this word.\"\n",
    "print(\"\\n- Word topics:\", word_topics)\n",
    "\n",
    "# \"Relevance values multipled by the feature length, for each word-topic combination. Each element\n",
    "# in the list is a pair of word_id, and a list of the values between this word and each topic.\"\n",
    "print(\"\\n- Scaled phi values:\", phi_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise\n",
    "\n",
    "What follows is the raw text from the Trump inaugural speech. \n",
    "\n",
    "Use your model to **infer and have a look at the topics distribution** of this document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "trump_speech = \"We, the citizens of America, are now joined in a great national effort to rebuild our country and to restore its promise for all of our people.\\n\\nTogether, we will determine the course of America and the world for years to come.\\n\\nWe will face challenges. We will confront hardships. But we will get the job done.\\n\\nEvery four years, we gather on these steps to carry out the orderly and peaceful transfer of power, and we are grateful to President Obama and First Lady Michelle Obama for their gracious aid throughout this transition. They have been magnificent.\\n\\nToday\\u2019s ceremony, however, has very special meaning. Because today we are not merely transferring power from one Administration to another, or from one party to another \\u2013 but we are transferring power from Washington, D.C. and giving it back to you, the American People.\\n\\nFor too long, a small group in our nation\\u2019s Capital has reaped the rewards of government while the people have borne the cost.\\n\\nWashington flourished \\u2013 but the people did not share in its wealth.\\n\\nPoliticians prospered \\u2013 but the jobs left, and the factories closed.\\n\\nThe establishment protected itself, but not the citizens of our country.\\n\\nTheir victories have not been your victories; their triumphs have not been your triumphs; and while they celebrated in our nation\\u2019s Capital, there was little to celebrate for struggling families all across our land.\\n\\nThat all changes \\u2013 starting right here, and right now, because this moment is your moment: it belongs to you.\\n\\nIt belongs to everyone gathered here today and everyone watching all across America. \\n\\nThis is your day. This is your celebration.\\n\\nAnd this, the United States of America, is your country.\\n\\nWhat truly matters is not which party controls our government, but whether our government is controlled by the people.\\n\\nJanuary 20th 2017, will be remembered as the day the people became the rulers of this nation again. \\n\\nThe forgotten men and women of our country will be forgotten no longer.\\n\\nEveryone is listening to you now.\\n\\nYou came by the tens of millions to become part of a historic movement the likes of which the world has never seen before.\\n\\nAt the center of this movement is a crucial conviction: that a nation exists to serve its citizens.\\n\\nAmericans want great schools for their children, safe neighborhoods for their families, and good jobs for themselves.\\n\\nThese are the just and reasonable demands of a righteous public.\\n\\nBut for too many of our citizens, a different reality exists: Mothers and children trapped in poverty in our inner cities; rusted-out factories scattered like tombstones across the landscape of our nation; an education system, flush with cash, but which leaves our young and beautiful students deprived of knowledge; and the crime and gangs and drugs that have stolen too many lives and robbed our country of so much unrealized potential.\\n\\nThis American carnage stops right here and stops right now.\\n\\nWe are one nation \\u2013 and their pain is our pain.  Their dreams are our dreams; and their success will be our success.  We share one heart, one home, and one glorious destiny.\\n\\nThe oath of office I take today is an oath of allegiance to all Americans.\\n\\nFor many decades, we\\u2019ve enriched foreign industry at the expense of American industry;\\n\\nSubsidized the armies of other countries while allowing for the very sad depletion of our military;\\n\\nWe've defended other nation\\u2019s borders while refusing to defend our own;\\n\\nAnd spent trillions of dollars overseas while America's infrastructure has fallen into disrepair and decay.\\n\\nWe\\u2019ve made other countries rich while the wealth, strength, and confidence of our country has disappeared over the horizon.\\n\\nOne by one, the factories shuttered and left our shores, with not even a thought about the millions upon millions of American workers left behind.\\n\\nThe wealth of our middle class has been ripped from their homes and then redistributed across the entire world.\\n\\nBut that is the past. And now we are looking only to the future.\\n\\nWe assembled here today are issuing a new decree to be heard in every city, in every foreign capital, and in every hall of power.\\n\\nFrom this day forward, a new vision will govern our land.\\n\\nFrom this moment on, it\\u2019s going to be America First.\\n\\nEvery decision on trade, on taxes, on immigration, on foreign affairs, will be made to benefit American workers and American families.\\n\\nWe must protect our borders from the ravages of other countries making our products, stealing our companies, and destroying our jobs.  Protection will lead to great prosperity and strength.\\n\\nI will fight for you with every breath in my body \\u2013 and I will never, ever let you down.\\n\\nAmerica will start winning again, winning like never before.\\n\\nWe will bring back our jobs. We will bring back our borders.  We will bring back our wealth.  And we will bring back our dreams.\\n\\nWe will build new roads, and highways, and bridges, and airports, and tunnels, and railways all across our wonderful nation.\\n\\nWe will get our people off of welfare and back to work \\u2013 rebuilding our country with American hands and American labor.\\n\\nWe will follow two simple rules: Buy American and Hire American.\\n\\nWe will seek friendship and goodwill with the nations of the world \\u2013 but we do so with the understanding that it is the right of all nations to put their own interests first.\\n\\nWe do not seek to impose our way of life on anyone, but rather to let it shine as an example for everyone to follow.\\n\\nWe will reinforce old alliances and form new ones \\u2013 and unite the civilized world against Radical Islamic Terrorism, which we will eradicate completely from the face of the Earth.\\n\\nAt the bedrock of our politics will be a total allegiance to the United States of America, and through our loyalty to our country, we will rediscover our loyalty to each other.\\n\\nWhen you open your heart to patriotism, there is no room for prejudice.\\n\\nThe Bible tells us, \\u201chow good and pleasant it is when God\\u2019s people live together in unity.\\u201d\\n\\nWe must speak our minds openly, debate our disagreements honestly, but always pursue solidarity.\\n\\nWhen America is united, America is totally unstoppable.\\n\\nThere should be no fear \\u2013 we are protected, and we will always be protected.\\n\\nWe will be protected by the great men and women of our military and law enforcement and, most importantly, we are protected by God.\\n\\nFinally, we must think big and dream even bigger.\\n\\nIn America, we understand that a nation is only living as long as it is striving.\\n\\nWe will no longer accept politicians who are all talk and no action \\u2013 constantly complaining but never doing anything about it.\\n\\nThe time for empty talk is over.\\n\\nNow arrives the hour of action.\\n\\nDo not let anyone tell you it cannot be done.  No challenge can match the heart and fight and spirit of America.\\n\\nWe will not fail. Our country will thrive and prosper again.\\n\\nWe stand at the birth of a new millennium, ready to unlock the mysteries of space, to free the Earth from the miseries of disease, and to harness the energies, industries and technologies of tomorrow.\\n\\nA new national pride will stir our souls, lift our sights, and heal our divisions.\\n\\nIt is time to remember that old wisdom our soldiers will never forget: that whether we are black or brown or white, we all bleed the same red blood of patriots, we all enjoy the same glorious freedoms, and we all salute the same great American Flag.\\n\\nAnd whether a child is born in the urban sprawl of Detroit or the windswept plains of Nebraska, they look up at the same night sky, they fill their heart with the same dreams, and they are infused with the breath of life by the same almighty Creator.\\n\\nSo to all Americans, in every city near and far, small and large, from mountain to mountain, and from ocean to ocean, hear these words:\\n\\nYou will never be ignored again.\\n\\nYour voice, your hopes, and your dreams, will define our American destiny. And your courage and goodness and love will forever guide us along the way.\\n\\nTogether, We Will Make America Strong Again.\\n\\nWe Will Make America Wealthy Again.\\n\\nWe Will Make America Proud Again.\\n\\nWe Will Make America Safe Again.\\n\\nAnd, Yes, Together, We Will Make America Great Again. Thank you, God Bless You, And God Bless America.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing Topics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we have several document or topics, usually plotting data is the best way to make sense of your results.\n",
    "\n",
    "- First of all, let's encode our document to topic mapping in a numpy matrix to simplify our processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs_id = nltk.corpus.inaugural.fileids()\n",
    "\n",
    "doc2topics = np.zeros((len(docs_id), inaug_ldamodel.num_topics))\n",
    "\n",
    "for di, doc_topics in enumerate(inaug_ldamodel.get_document_topics(inaug_bow_corpus, minimum_probability = 0)):\n",
    "    for ti, v in doc_topics:\n",
    "        doc2topics[di, ti] = v\n",
    "\n",
    "# print(doc2topics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can check the **share of a given topic in the documents of our corpus** in a barplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "which_topic = 2 # try to change this and see what happens!\n",
    "\n",
    "ind = range(len(docs_id))\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "plt.bar(ind, doc2topics[:,which_topic])\n",
    "plt.xticks(ind, docs_id, rotation = 90)\n",
    "plt.title('Share of Topic #%d'%which_topic)\n",
    "plt.tight_layout()  # fixes margins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can check the **share of all the topics in all the documents** by using a heatmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 12))\n",
    "plt.pcolor(doc2topics, norm=None, cmap='Blues')\n",
    "plt.yticks(np.arange(doc2topics.shape[0]), docs_id)\n",
    "plt.xticks(np.arange(doc2topics.shape[1])+0.5, [\"Topic #\"+str(n) for n in range(inaug_ldamodel.num_topics)], rotation = 90)\n",
    "plt.colorbar(cmap='Blues')  # plot colorbar\n",
    "plt.tight_layout()  # fixes margins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A nice way to visualize the distribution over words that characterizes each topic is by printing, for each topic, the top-assocaited words resized **according to their strength of association** with the topic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Credits**:\n",
    ">\n",
    "> The following code has been adapted from the **Text Analysis with Topic Models for the Humanities and Social Sciences** tutorials by Allen Riddell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 10))\n",
    "\n",
    "num_top_words = 10\n",
    "\n",
    "topic2top_words = dict(inaug_ldamodel.show_topics(formatted=False, num_words = num_top_words))\n",
    "fontsize_base = 25 / max([w[0][1] for w in topic2top_words.values()]) # font size for word with largest share in corpus\n",
    "\n",
    "for topic, words_shares in topic2top_words.items():\n",
    "    plt.subplot(1, inaug_ldamodel.num_topics, topic + 1)\n",
    "    plt.ylim(0, num_top_words + 0.5)  # stretch the y-axis to accommodate the words\n",
    "    plt.xticks([])  # remove x-axis markings ('ticks')\n",
    "    plt.yticks([]) # remove y-axis markings ('ticks')\n",
    "    plt.title('Topic #{}'.format(topic))\n",
    "    for i, (word, share) in enumerate(words_shares):\n",
    "        plt.text(0.3, num_top_words-i-0.5, word, fontsize=fontsize_base*share)\n",
    "        \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise\n",
    "\n",
    "* Try to play with the topic mode, and especially try to change the number of topics. What happens? How do they distribute over time and presidents?\n",
    "\n",
    "* Use Gensim's `gensim.models.Phrases` class to calculate high-frequency bigrams and trigrams, add them to your bag-of-words document representation and train the topic model again. Does this improve your results?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}