notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COMP 527: Implementing the k-means clustering algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In the assignment, you are required to cluster words belonging to four categories: animals, countries, fruits and veggies. The words are arranged into four different files. The first entry in each line is a word followed by 300 features (word embedding) describing the meaning of that word."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation\n",
    " \n",
    "> (1) Implement the k-means clustering algorithm with Euclidean distance to cluster the instances into k clusters. (30 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we define Word and Dataset classes to handle the data. For the specifics, see the source-code in `kmeans.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                 # for handling vectors\n",
    "import matplotlib.pyplot as plt    # for plotting results\n",
    "import pandas as pd                # for neatly displaying results in table\n",
    "\n",
    "from kmeans import Word, Dataset   # defined to handle data from files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading in data from animals.\n",
      "Reading in data from countries.\n",
      "Reading in data from fruits.\n",
      "Reading in data from veggies.\n",
      "'cucumber' is already named, but we add it again anyway.\n",
      "You can remove with `.remove('cucumber')`\n"
     ]
    }
   ],
   "source": [
    "words = Dataset(['animals','countries','fruits','veggies'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we define a `euclidean_distance()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def euclidean_distance(u,v):\n",
    "    \"\"\"Return Euclidean distance between two np.array vectors.\"\"\"\n",
    "    \n",
    "    return np.sqrt( (u - v).dot( u - v ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4142135623730951\n"
     ]
    }
   ],
   "source": [
    "a = np.array([0,1])\n",
    "b = np.array([1,0])\n",
    "print(euclidean_distance(a,b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Euclidean distance between $[0, 1]$ and $[1,0]$ is, as we would expect, $\\sqrt2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "(cucumber_fruit, cucumber_veggie) = words.repeats()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euclidean_distance(cucumber_fruit.vector, cucumber_veggie.vector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that strictly speaking, `euclidean_distance()` is not a well-defined metric on the `words` set of data, since we have two different data-points with zero distance between them: cucumber labelled as fruit, and cucumber labelled as vegetable. (One way of dealing with this would be to somehow treat both cucumbers as a single data-point, but with two labels; another would be to choose one label over the other; yet another would be to remove one of the cucumbers. Here we simply note it, and continue.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now define a class to implement the K-Means algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "class KMeans():\n",
    "    \n",
    "    def __init__(\n",
    "                self, \n",
    "                k, \n",
    "                D, \n",
    "                metric = euclidean_distance, \n",
    "                normed = False, \n",
    "                max_iterations = 10**3, \n",
    "                seed = None,\n",
    "                ):\n",
    "        \"\"\"\n",
    "        Initialize KMeans Model.\n",
    "        \n",
    "        Args:\n",
    "            k (int): number of clusters to divide data into.\n",
    "            D (Dataset): as defined by Dataset() class.\n",
    "            metric (function): to measure distance between points.\n",
    "            norm (Boolean): whether or not to normalize vectors.\n",
    "            iterations (int): when to stop if no convergence.\n",
    "            seed (int): for reproducible (pseudo-)randomness.\n",
    "        \"\"\"\n",
    "        \n",
    "        self.k = k\n",
    "        self.D = D\n",
    "        self.normed = normed\n",
    "        if self.normed == True:\n",
    "            self.data = normalize(D.data)\n",
    "        else:\n",
    "            self.data = D.data\n",
    "        \n",
    "        self.metric = metric\n",
    "                \n",
    "        if seed:\n",
    "            self.seed = seed\n",
    "        else:\n",
    "            self.seed = np.random.randint(0, 2**32)\n",
    "        np.random.seed(seed)\n",
    "        \n",
    "        # we track centroid positions and cluster labels in nested dicts,\n",
    "        # of the form dict_name[iteration_number][centroid_number]\n",
    "        self._centroid = {}\n",
    "        \n",
    "        self._cluster = {}\n",
    "\n",
    "        # we record cluster labels explicitly as well\n",
    "        self._label = {}\n",
    "        \n",
    "        self.max_iterations = max_iterations\n",
    "        for i in range(self.max_iterations):\n",
    "            self._iteration = i\n",
    "            self._iterate()\n",
    "            if i > 0 and self._cluster[i] == self._cluster[i-1]:\n",
    "                break\n",
    "        \n",
    "        self.cluster = self._cluster[self._iteration]\n",
    "        self.label = self._label[self._iteration]\n",
    "        \n",
    "        self.convergence = self._iteration + 1\n",
    "        if self.convergence == max_iterations:\n",
    "            self.convergence = np.nan\n",
    "        \n",
    "        self._evaluate()\n",
    "        \n",
    "    def __repr__(self):\n",
    "        \"\"\"Representation of model.\"\"\"\n",
    "        \n",
    "        desc = f\"k = {self.k}\"\n",
    "    \n",
    "    \n",
    "    def _start(self):\n",
    "        \"\"\"Shuffle dataset and position centroids on first k datapoints.\"\"\"\n",
    "        \n",
    "        # to avoid the situation where some clusters have no nearest points,\n",
    "        # we start with cluster centroids positioned on randomly selected datapoints.\n",
    "        selected = np.random.permutation(self.data)[0:self.k]\n",
    "        \n",
    "        self._centroid[0] = {}\n",
    "        \n",
    "        for centroid_number in range(self.k):\n",
    "            self._centroid[0][centroid_number] = selected[centroid_number].vector\n",
    "           \n",
    "    \n",
    "    def _classify(self):\n",
    "        \"\"\"Assign each data point to cluster of nearest centroid.\"\"\"\n",
    "        \n",
    "        self._cluster[self._iteration] = {}\n",
    "        self._label[self._iteration] = {}\n",
    "        \n",
    "        for centroid_number in range(self.k):\n",
    "            self._cluster[self._iteration][centroid_number] = []\n",
    "        \n",
    "        for d in self.data:\n",
    "            distances = [] \n",
    "            \n",
    "            for centroid_number in range(self.k):\n",
    "                \n",
    "                distances.append(self.metric(d.vector, self._centroid[self._iteration][centroid_number]))\n",
    "            \n",
    "            closest_centroid = np.argmin(distances)\n",
    "            \n",
    "            self._cluster[self._iteration][closest_centroid].append(d)\n",
    "            self._label[self._iteration][d.name] = closest_centroid\n",
    "        \n",
    "            \n",
    "    def _reposition(self):\n",
    "        \"\"\"Move centroids to mean of each cluster.\"\"\"\n",
    "        \n",
    "        for centroid_number in range(self.k):\n",
    "            self._centroid[self._iteration] = {}\n",
    "        \n",
    "        for centroid_number in range(self.k):\n",
    "            \n",
    "            clustered = self._cluster[self._iteration - 1][centroid_number]\n",
    "            \n",
    "            if len(clustered) > 0:\n",
    "                vector_sum = np.zeros(len(clustered[0].vector))\n",
    "                \n",
    "                for datum in clustered:\n",
    "                    vector_sum += datum.vector\n",
    "\n",
    "                cluster_mean = vector_sum / len(clustered)\n",
    "\n",
    "                self._centroid[self._iteration][centroid_number] = cluster_mean\n",
    "\n",
    "            else:\n",
    "                # nothing assigned to this cluster so position is unchanged\n",
    "                self._centroid[self._iteration][centroid_number] = \\\n",
    "                        self._centroid[self._iteration - 1][centroid_number]\n",
    "           \n",
    "   \n",
    "    def _iterate(self):\n",
    "        \"\"\"Position centroids and classify data by nearest centroid.\"\"\"\n",
    "        \n",
    "        if self._iteration == 0:\n",
    "            self._start()\n",
    "        else:\n",
    "            self._reposition()\n",
    "        \n",
    "        self._classify()\n",
    "            \n",
    "            \n",
    "    def _evaluate(self):\n",
    "        \"\"\"Evaluate success of clustering.\"\"\"\n",
    "        \n",
    "        self.true_positives = 0\n",
    "        self.true_negatives = 0\n",
    "        self.false_positives = 0\n",
    "        self.false_negatives = 0\n",
    "        \n",
    "        data = self.data\n",
    "        \n",
    "        for i in range(len(data)):\n",
    "            for j in range(i+1, len(data)):\n",
    "                if data[i].category == data[j].category \\\n",
    "                and self.label[data[i].name] == self.label[data[j].name]:\n",
    "                    self.true_positives += 1\n",
    "                if data[i].category != data[j].category \\\n",
    "                and self.label[data[i].name] == self.label[data[j].name]:\n",
    "                    self.false_positives += 1\n",
    "                if data[i].category != data[j].category \\\n",
    "                and self.label[data[i].name] != self.label[data[j].name]:\n",
    "                    self.true_negatives += 1\n",
    "                if data[i].category == data[j].category \\\n",
    "                and self.label[data[i].name] != self.label[data[j].name]:\n",
    "                    self.false_negatives += 1\n",
    "                    \n",
    "        \n",
    "        self.precision = self.true_positives / (self.true_positives + self.false_positives)\n",
    "        self.recall = self.true_positives / (self.true_positives + self.false_negatives)\n",
    "        self.f_score = 2 * self.precision * self.recall / (self.precision + self.recall)\n",
    "        \n",
    "        self.score = f'Precision: {self.precision}.\\nRecall: {self.recall}.\\nF-Score: {self.f_score}.\\n'\n",
    "        \n",
    "    def describe(self):\n",
    "        \"\"\"Describe model clusters.\"\"\"\n",
    "        \n",
    "        desc = \"\"\n",
    "        for i in range(self.k):\n",
    "            desc += f\"Cluster {i} includes {len(self.cluster[i])} elements.\\n\"\n",
    "            \n",
    "            count = {}\n",
    "            for category in self.D.categories:\n",
    "                count[category] = 0\n",
    "                for word in self.cluster[i]:\n",
    "                    if word.category == category:\n",
    "                        count[category] += 1\n",
    "                if count[category] > 0:\n",
    "                       desc += f\"{count[category]} are {category}.\\n\"\n",
    "            desc += '\\n'\n",
    "            \n",
    "        print(f'K-Means Model\\n\\nParameters:\\nk={self.k}\\nD={self.D}\\nmetric={self.metric}\\nnormed={self.normed}\\nseed={self.seed}\\n')\n",
    "        print(f'Converged after {self.convergence} iterations.\\n')\n",
    "        print(self.score)\n",
    "        print(desc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Means Model\n",
      "\n",
      "Parameters:\n",
      "k=4\n",
      "D=Collection of 329 words from 4 categories, with 300-dimensional feature vectors.\n",
      "metric=<function euclidean_distance at 0x7f26b6d04b90>\n",
      "normed=False\n",
      "seed=29\n",
      "\n",
      "Converged after 8 iterations.\n",
      "\n",
      "Precision: 0.9079803630551433.\n",
      "Recall: 0.9074623459607485.\n",
      "F-Score: 0.9077212806026366.\n",
      "\n",
      "Cluster 0 includes 74 elements.\n",
      "22 are fruits.\n",
      "52 are veggies.\n",
      "\n",
      "Cluster 1 includes 48 elements.\n",
      "2 are animals.\n",
      "2 are countries.\n",
      "36 are fruits.\n",
      "8 are veggies.\n",
      "\n",
      "Cluster 2 includes 159 elements.\n",
      "159 are countries.\n",
      "\n",
      "Cluster 3 includes 48 elements.\n",
      "48 are animals.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clustering = KMeans(k=4, D=words, metric=euclidean_distance, seed=29)\n",
    "clustering.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our implementation appears to be working."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Precision, Recall, and F-Score\n",
    "\n",
    "> (2) Vary the value of k from 1 to 10 and compute the precision, recall, and F-score for each set of clusters. Plot k in the horizontal axis and precision, recall and F-score in the vertical axis in the same plot. (10 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are asked multiple times for different metrics to vary the value of k, to compute precision/recall/f-scores, and to plot the values, we define a function to do this for us without unnecessary repetition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define functions to avoid repetition while doing requested tasks.\n",
    "\n",
    "fig_num = 0\n",
    "\n",
    "def get_scores(max_k, D, metric, normed=False, seed=1):\n",
    "    \"Get model scores for range of values of k.\"\n",
    "    \n",
    "    scores = {}\n",
    "    scores['metric'] = f'{metric.__name__}, normed={normed}'\n",
    "    measures = 'precision', 'recall', 'f_score'\n",
    "    for measure in measures:\n",
    "        scores[measure] = []\n",
    "    model = {}\n",
    "    for k in range(1, max_k+1):\n",
    "        model[k] = KMeans(k=k, D=D, metric=metric, normed=normed, seed=seed)\n",
    "        for measure in measures:\n",
    "            scores[measure].append( getattr(model[k], measure) )\n",
    "            \n",
    "    return scores\n",
    "\n",
    "\n",
    "\n",
    "def show_results(fig_num, scores):\n",
    "    \"\"\"Plot results for given scores and return scores as table.\"\"\"\n",
    "\n",
    "    measures = 'precision', 'recall', 'f_score'\n",
    "    max_k = len(scores[measures[0]])\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(15,8))\n",
    "\n",
    "    for measure in measures:\n",
    "        ax.plot(range(1,max_k + 1), scores[measure], label = measure)\n",
    "\n",
    "    ax.set_xticks(range(1,max_k + 1))\n",
    "    ax.set_xlabel('Number of Clusters (k)')\n",
    "    ax.set_ylabel('Evaluation Score')\n",
    "    ax.set_yticks(np.arange(0,max_k + 1)/max_k)\n",
    "    ax.set_xlim(1,max_k)\n",
    "    ax.set_ylim(0,1.01)\n",
    "    plt.title(f'Figure {fig_num}: Comparison of Precision, Recall, and F-Scores for {scores[\"metric\"]}',y=-0.15)\n",
    "    ax.legend()\n",
    "    plt.show()\n",
    "\n",
    "    scores['k'] = list(range(1,10+1))\n",
    "    scores.pop('metric')\n",
    "    table = pd.DataFrame(scores).set_index('k')\n",
    "    return table.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# explicitly set seed for reproducible pseudo-randomness\n",
    "seed = 29"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>k</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.324857</td>\n",
       "      <td>0.651405</td>\n",
       "      <td>0.824335</td>\n",
       "      <td>0.907980</td>\n",
       "      <td>0.907656</td>\n",
       "      <td>0.901078</td>\n",
       "      <td>0.905081</td>\n",
       "      <td>0.933899</td>\n",
       "      <td>0.927248</td>\n",
       "      <td>0.911342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930340</td>\n",
       "      <td>0.907462</td>\n",
       "      <td>0.574224</td>\n",
       "      <td>0.462517</td>\n",
       "      <td>0.400388</td>\n",
       "      <td>0.395767</td>\n",
       "      <td>0.357029</td>\n",
       "      <td>0.325479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_score</th>\n",
       "      <td>0.490403</td>\n",
       "      <td>0.788910</td>\n",
       "      <td>0.874136</td>\n",
       "      <td>0.907721</td>\n",
       "      <td>0.703428</td>\n",
       "      <td>0.611272</td>\n",
       "      <td>0.555178</td>\n",
       "      <td>0.555938</td>\n",
       "      <td>0.515550</td>\n",
       "      <td>0.479654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "k                1         2         3         4         5         6   \\\n",
       "precision  0.324857  0.651405  0.824335  0.907980  0.907656  0.901078   \n",
       "recall     1.000000  1.000000  0.930340  0.907462  0.574224  0.462517   \n",
       "f_score    0.490403  0.788910  0.874136  0.907721  0.703428  0.611272   \n",
       "\n",
       "k                7         8         9         10  \n",
       "precision  0.905081  0.933899  0.927248  0.911342  \n",
       "recall     0.400388  0.395767  0.357029  0.325479  \n",
       "f_score    0.555178  0.555938  0.515550  0.479654  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_num += 1\n",
    "show_results(fig_num, get_scores(max_k=10, D=words, metric=euclidean_distance, seed=seed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize\n",
    "\n",
    "> (3) Now re-run the k-means clustering algorithm you implemented in part (1) but normalise each feature vector to unit $ L_2 $ length before computing Euclidean distances. Vary the value of k from 1 to 10 and compute the precision, recall, and F-score for each set of clusters. Plot k in the horizontal axis and precision, recall and F-score in the vertical axis in the same plot. (10 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a function to normalize each feature vector to  unit $ L_2 $ length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(data):\n",
    "    \"\"\"Return normalized vectors (ie. parallel vector with unit magnitude).\"\"\"\n",
    "    \n",
    "    normalized_data = []\n",
    "    \n",
    "    for d in data:\n",
    "        normalized_vector = d.vector / np.sqrt( d.vector.dot(d.vector) )\n",
    "        normalized_data.append(Word(d.name, normalized_vector, d.category))\n",
    "        \n",
    "    return normalized_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Vector Magnitudes\n",
      "elephant's vector has magnitude 6.4487564189517705\n",
      "leopard's vector has magnitude 7.139715219834713\n",
      "dog's vector has magnitude 7.689131355830199\n",
      "...\n",
      "Normalized Vector Magnitudes\n",
      "elephant's vector has magnitude 0.9999999999999999\n",
      "leopard's vector has magnitude 0.9999999999999999\n",
      "dog's vector has magnitude 0.9999999999999999\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "print('Original Vector Magnitudes')\n",
    "for word in words.data[:3]:\n",
    "    print(f\"{word.name}'s vector has magnitude {np.sqrt(word.dot(word))}\" )\n",
    "print('...')\n",
    "\n",
    "print('Normalized Vector Magnitudes')\n",
    "for word in normalize(words.data)[:3]:\n",
    "    print(f\"{word.name}'s vector has magnitude {np.sqrt(word.dot(word))}\" )\n",
    "print('...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our `normalize()` function seems to be working."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>k</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.324857</td>\n",
       "      <td>0.651405</td>\n",
       "      <td>0.746907</td>\n",
       "      <td>0.638675</td>\n",
       "      <td>0.864610</td>\n",
       "      <td>0.856636</td>\n",
       "      <td>0.867821</td>\n",
       "      <td>0.968761</td>\n",
       "      <td>0.960047</td>\n",
       "      <td>0.959279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909174</td>\n",
       "      <td>0.544557</td>\n",
       "      <td>0.559619</td>\n",
       "      <td>0.509984</td>\n",
       "      <td>0.486194</td>\n",
       "      <td>0.482999</td>\n",
       "      <td>0.374258</td>\n",
       "      <td>0.340027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_score</th>\n",
       "      <td>0.490403</td>\n",
       "      <td>0.788910</td>\n",
       "      <td>0.820091</td>\n",
       "      <td>0.587873</td>\n",
       "      <td>0.679458</td>\n",
       "      <td>0.639345</td>\n",
       "      <td>0.623227</td>\n",
       "      <td>0.644611</td>\n",
       "      <td>0.538566</td>\n",
       "      <td>0.502085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "k                1         2         3         4         5         6   \\\n",
       "precision  0.324857  0.651405  0.746907  0.638675  0.864610  0.856636   \n",
       "recall     1.000000  1.000000  0.909174  0.544557  0.559619  0.509984   \n",
       "f_score    0.490403  0.788910  0.820091  0.587873  0.679458  0.639345   \n",
       "\n",
       "k                7         8         9         10  \n",
       "precision  0.867821  0.968761  0.960047  0.959279  \n",
       "recall     0.486194  0.482999  0.374258  0.340027  \n",
       "f_score    0.623227  0.644611  0.538566  0.502085  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_num += 1\n",
    "show_results(fig_num, get_scores(max_k=10, D=words, metric=euclidean_distance, normed=True, seed=seed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manhattan Distance\n",
    "\n",
    "> (4) Now re-run the k-means clustering algorithm you implemented in part (1) but this time use Manhattan distance over the unnormalised feature vectors. Vary the value of k from 1 to 10\n",
    "and compute the precision, recall, and F-score for each set of clusters. Plot k in the horizontal axis and precision, recall and F-score in the vertical axis in the same plot. (10 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a `manhattan_distance()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def manhattan_distance(u,v):\n",
    "    \"\"\"Return Manhattan distance between two np.array vectors.\"\"\"\n",
    "    \n",
    "    w = u - v\n",
    "    distance = 0\n",
    "    for x in w:\n",
    "        distance += abs(x)\n",
    "    \n",
    "    return distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n",
      "[1 0]\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "print(a)\n",
    "print(b)\n",
    "print(manhattan_distance(a,b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our implementation appears to work: the distance between $[0,1]$ and $[1,0]$ is, as we would expect, $ 2 $."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>k</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.324857</td>\n",
       "      <td>0.651405</td>\n",
       "      <td>0.824692</td>\n",
       "      <td>0.741136</td>\n",
       "      <td>0.920780</td>\n",
       "      <td>0.910362</td>\n",
       "      <td>0.937678</td>\n",
       "      <td>0.949247</td>\n",
       "      <td>0.937069</td>\n",
       "      <td>0.932424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.931025</td>\n",
       "      <td>0.566465</td>\n",
       "      <td>0.582211</td>\n",
       "      <td>0.508729</td>\n",
       "      <td>0.470390</td>\n",
       "      <td>0.438555</td>\n",
       "      <td>0.349156</td>\n",
       "      <td>0.323539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_score</th>\n",
       "      <td>0.490403</td>\n",
       "      <td>0.788910</td>\n",
       "      <td>0.874638</td>\n",
       "      <td>0.642134</td>\n",
       "      <td>0.713362</td>\n",
       "      <td>0.652710</td>\n",
       "      <td>0.626496</td>\n",
       "      <td>0.599938</td>\n",
       "      <td>0.508749</td>\n",
       "      <td>0.480390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "k                1         2         3         4         5         6   \\\n",
       "precision  0.324857  0.651405  0.824692  0.741136  0.920780  0.910362   \n",
       "recall     1.000000  1.000000  0.931025  0.566465  0.582211  0.508729   \n",
       "f_score    0.490403  0.788910  0.874638  0.642134  0.713362  0.652710   \n",
       "\n",
       "k                7         8         9         10  \n",
       "precision  0.937678  0.949247  0.937069  0.932424  \n",
       "recall     0.470390  0.438555  0.349156  0.323539  \n",
       "f_score    0.626496  0.599938  0.508749  0.480390  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_num += 1\n",
    "show_results(fig_num, get_scores(max_k=10, D=words, metric=manhattan_distance, normed=False, seed=seed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalized Manhattan Distance\n",
    "\n",
    "> (5) Now re-run the k-means clustering algorithm you implemented in part (1) but this time use Manhattan distance with $ L_2 $ normalised feature vectors. Vary the value of k from 1 to 10 and\n",
    "compute the precision, recall, and F-score for each set of clusters. Plot k in the horizontal axis and precision, recall and F-score in the vertical axis in the same plot. (10 marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fa4Z769KvvxGbFiiT772WlREiW72SNt3V5uGRSHubu1Na7xudTBuBzHCU64EQ9fgQljbale44j5pOn1V2+seHqDtTfVPX6R/eVq/D3vb+gJb6eG/bB49tn8G5svNa0mFPsU/eC34aKlu2LP7+/vz9998cP36cgIAAAFJSUmjYsCEnT56kZMmS+Pn5AeDq6gpAfHw8b775JiEhIej1ek6dOqXZexAWSlGgzVcwqxFs+RI6fK91RUI8Uui1GHrO3wsoLB/ckKol5D6Uz8reRk9xVz3Fs9jkJioxrRNqWliMTTJiNJkxmMykmNS7ywaTmvZ4/7LRpKZ9+Lp/OSVtxCMz+z3DrKQn0uuUuyEwM0HyXgB9eDnrITbdcloYzWh7FTXD0JbR9WwxiVkPbfq7oc0GF3tbXB2fHNoenDbpaCu3OdFa+kDp7myXbcdV1dSfxTvTVxPTRh/TT2m989pDo5QpaVNjk43cikshITLhvvVPCmWKQlr40d8XiuzSQpS9PjVsuTulC1MPhDG7DPbPKFA96rld2i+a8voviTIr7wW/TIzM5RRn59TrQlRV5fnnn2fZsmX3rT98+HCG/7B+//33FC9enEOHDmE2m3FwyPx/7CIfKVYN6g+GfXPAt3/qSLQQFuZIWDS9Fu7FwUbPz4MaULFoAa1Lyrds9DqKFLCnSBaa3OQEkzk1EBrNKgZjWkhMv/yI8Hhn2Wg2YzCmfng1pr2ekhYun7RfivH+YyQaTHeXU+tIt5y2r9GcfaMMT/JgaHNxsMHT3SktlNng6vjg9WwS2kTWKIqSNvqkxy3zM9IzJSXdCKXBZH4odNnoZAq5pcl7wc8C+Pv7M3z4cM6cOUOlSpVISEggLCyMatWqceXKFfbv34+fnx+xsbE4OjoSHR2Np6cnOp2ORYsWYTI9+loOkc81Hw1HfoW1o6HvP6m/ThPCQhy8dJs+C/fh6mDLskH+lCmczZ8yhFXS6xT0urTrrLTNoJl2p5HDfaHzUct3wqbZnBZm1bTQeS9Apg9xEtpEXnFnZK6gkzSdsxYS/HJA0aJF+fHHH+nWrRvJyckAjB8/nipVqvDLL7/w1ltvkZiYiKOjIxs3buSNN96gS5curFixghYtWtwdORTiIY7u8Nwn8PfbcPwPqPmy1hUJAcDecxH0/3E/RV3s+XmQPx5ZuH5NCEujKAp2Ngp26CD7Zt0JIYSmFDUrNzmxAL6+vuqDjU9OnDhB9erVNaoo75A/RythNsGcZpAUBcP3gZ2Mqght7Tx9i4GL9+Ph5sjSQf5Zug5NCCGEEFmnKMoBVVV9s7KPZfVsFUI8mU4P7SZB9GXYPU3rakQ+tzn0Ov0X7adcYWd+GdJQQp8QQghhoST4CWGNygWkTvPcOQWiLmtdjcin1h29xpAlB6ha3IVlg/w1byIihBBCiEeT4CeEtXr+f4AKG8ZqXYnIh/46dIXhSw9Sy6MgPw1skK3tx4UQQgiR/ST4CWGt3EpDwCg4thIu7NK6GpGP/HYgjFHLg6lX1p0lAxpQ0FE6ugkhhBCWToKfENYsYCS4esK60alNX4TIYT/vvch7Kw7RqGIRFvWrTwF7aQ4thBBCWAMJfkJYMzsnaD0Orh2Bg4u1rkbkcQt3nufjVUd5rlox5vfxxdFOr3VJQgghhMgkCX5CWLuanaFsAGz+HyRGaV2NyKNmbT3LuL+P06ZmcWb3rIeDrYQ+IYQQwppI8Msm06ZNo3r16vTo0UPrUkR+oyjQdiIkRMK2SVpXI/IYVVWZsvEUk9aF8mKdUkzv7oOdjfzXIYQQQlgbuTgjm8ycOZO1a9dSvnz5HD+X0WjExkb+6kQ6Jb2gXh/YNxfq9YWiVbWuSOQBqqoyad1JZm87yyv1PJnUxQu9TtG6LCGEEEI8hTyXHibtm0RoZGi2HrNaoWqMrj/6keuHDh3KuXPn6NixI/379+ftt9++b/22bdsYOXIkAIqisH37dlxcXPj6669ZsmQJOp2Odu3aMXHiREJCQhg6dCgJCQlUrFiRhQsX4u7uTvPmzWnUqBG7du2iY8eO9O7dm6FDh3Lp0iUApkyZQkBAQLa+b2FlnvsUjq6CdWOg58rUkUAhnpKqqoz7+ziBuy7Qo0EZ/vdSLXQS+oQQQgirleeCnxZmz57NunXr2LJlC0WKFHlo/eTJk5kxYwYBAQHExcXh4ODA2rVr+eOPP9i7dy9OTk5ERkYC0Lt3b3744QeaNWvG2LFj+eKLL5gyZQoAUVFRbNu2DYDu3bvz9ttv07hxYy5dukSbNm04ceJE7r1pYXmci0CLD1OD36l1ULWd1hUJK2U2q3zy51GW7r1Ev4ByjO1QA0V+kSCEEEJYtRwNfoqitAWmAnpgvqqqEx9Y7w4sBCoCSUB/VVWPPss5Hzcyp5WAgADeeecdevToQefOnfH09GTjxo3069cPJycnAAoVKkR0dDRRUVE0a9YMgD59+vDqq6/ePU7Xrl3vLm/cuJHjx4/ffR4TE0NsbCwuLi659K6ERfIbCEGBsO5DqPgc2NhrXZGwMiazyujfD/PbgTCGNa/IB22qSugTQggh8oAcu0JfURQ9MANoB9QAuimKUuOBzT4CQlRV9QJ6kxoS85wxY8Ywf/58EhMT8ff3JzQ0FFVVs/xhytnZ+e6y2Wxmz549hISEEBISQnh4uIQ+AXpbaDsBbp+H/2ZpXY2wMgaTmVG/hPDbgTDeblVFQp8QQgiRh+Rka7b6wBlVVc+pqpoCLAdeemCbGsAmAFVVQ4FyiqIUz8GaNHH27Flq167N6NGj8fX1JTQ0lNatW7Nw4UISEhIAiIyMpGDBgri7u7Njxw4AlixZcnf070GtW7dm+vTpd5+HhITk/BsR1qFSK6jSDrZ/A7HXtK5GWIkUo5k3lx5k9aErjG5bjZGtKkvoE0IIIfKQnAx+HsDldM/D0l5L7xDQGUBRlPpAWcDzwQMpijJYUZQgRVGCbt68mUPl5pwpU6ZQq1Yt6tSpg6OjI+3ataNt27Z07NgRX19f6taty+TJkwFYtGgR77//Pl5eXoSEhDB27NgMjzlt2jSCgoLw8vKiRo0azJ49OzffkrB0bb4EUwpsGqd1JcIKJBlMDP3pAOuPXWdshxoMa15R65KEEEIIkc0UVVVz5sCK8irQRlXVgWnPewH1VVV9K902rqRO7/QGjgDVgIGqqh561HF9fX3VoKCg+147ceIE1atXz/43kc/In6P2jGYj6y+s52bCTXrX7I1OeYbfzWz4DHZNgYGbwbNe9hUp8pSEFCODFx9g55lbfPlyLXo0KKt1SUIIIYR4AkVRDqiq6puVfXKyuUsYUDrdc0/gSvoNVFWNAfoBKKlzis6nfQmRrxjNRv459w/zjszjYsxFAK4lXGO03+inn27X9D04tAzWfgADNoBObrot7heXbKT/j/sJuhDJ5Ffr8Eq9hyZcCCGEECKPyMngtx+orChKeSAceB3onn4DRVHcgIS0awAHAtvTwqBVCgwMZOrU+/vTBAQEMGPGDI0qEpbOYDKw+txq5h2eR1hcGFXdq/J98+8JvhHM4uOLKWBbgDe933y6g9u7QKvP4Y9hcORXqPN6dpYurFx0ooG+gfs4HBbNlNe96VinlNYlCSGEECIH5VjwU1XVqCjKm8B6Um/nsFBV1WOKogxNWz8bqA4sVhTFBBwHBuRUPbmhX79+9OvXT+syhBVIMaXwx5k/WHBkAVfir1CzcE1G1x9NM89mKIpCyzItiTfEM+fwHArYFqBvrb5PdyKv12H//NRpn9VeSA2DIt+7HZ9Cr4V7OXktlhndfWhbq4TWJQkhhBAih+XoffxUVV0DrHngtdnplvcAlbPpXNKB7hnk1LWe4n7JpmRWnl7JgiMLuJ5wHa8iXnzi/wmNPRrf9/2rKAqf+n9KvCGebw98i7OdM69WefUxR34EnQ7afQ3zW8KOb1NHAEW+djM2mV4L9nLuVjxze/nSoloxrUsSQgghRC7I0eCXWxwcHIiIiKBw4cIS/p6CqqpERETg4OCgdSl5VqIxkd9P/c7Cowu5mXgT72LejAsYR8OSDR/5PavX6ZnQeALxhnj+t+d/ONs4075C+6yf3NMX6nSDPTPApzcUqvCM70ZYq+sxSXSf9x/hUYks7ONH48pFtC5JCCGEELkkx7p65pSMunoaDAbCwsJISkrSqCrr5+DggKenJ7a2tlqXkqckGBL49eSv/HjsRyKSIvAr4cdQr6H4lfDL9C8pkoxJDNs4jJAbIXzf4nual26e9UJirsIP9aBCc+i2NOv7C6sXHpVI93n/cSs2mYV9/WhQobDWJQkhhBDiKT1NV888EfyEsDTxhniWhS5j8bHF3E6+jX9Jf4Z4DcG3RJZ+Pu873sD1Azl1+xQzW82kQckGWT/Iju9g0xfQaxVUfO6p6hDW6WJEPN3n7SUmycCi/vXxKeOudUlCCCGEeAYS/ITQWGxKLEtPLGXJiSVEJ0cT4BHAUK+h1C1W95mPHZUURb/1/QiPC2d+6/l4FfXK2gGMyTCjAdjYw9CdoJfR3fzg7M04us/7j2SjmZ8GNKCWR0GtSxJCCCHEM3qa4Cc39hIiG0QnRzMzZCZtfmvD9JDpeBf1Zmn7pcxuNTtbQh+Am4Mbc5+fSxHHIgzbOIyTkSezdgAbe2jzJdwMhf0LsqUmYdlOXoul65z/MJlVlg/2l9AnhBBC5GMy4ifEM4hKimLx8cUsDV1KvCGelmVaMthrMDUK18ixc4bHhdN7bW9MZhOL2i2irGvZzO+sqrDkZbhyEN4KBme5ziuvOhoeTa8Fe7HV61g6yJ9KxQpoXZIQQgghsomM+AmRSyISI/j+wPe0/r0184/MJ6BUAL+9+BtTWkzJ0dAH4FHAg3mt52FWzQz6dxBX465mfmdFgbYTITkOtozPuSKFpoIv3ab7vP9wsrPh1yENJfQJIYQQQoKfEFlxK/EW3+z/hnYr2xF4NJDmpZuzsuNKvm3+LVULVc21OioUrMCc5+cQlxLH4A2DuZV4K/M7F6sG9QfBgR/h2pEcq1FoY/+FSHot2Iebkx2/DPGnXBFnrUsSQgghhAWQqZ5CZML1+OsEHgvkt1O/YTAbeKH8Cwz0GkiFgtreEy/4RjBDNgyhjEsZFrRZQEH7TF7DlXgbpvlAsRrQ9+/UkUBh9XafucWARUGULOjA0kH+lCgo9+YUQggh8iKZ6ilENrsad5Xx/42n3cp2LA9dTrvy7VjdaTUTmkzQPPQBeBfzZkrzKZyLPsfwTcNJMCRkbkdHd3juE7i4E47/kbNFilyx9eQN+v24n9KFHFk+REKfEEIIIe4nI35CZCA8Lpz5R+bzx5nUUNSpUicG1BqAp4unxpVlbNPFTby77V18S/gyo+UM7PX2T97JbII5zSApCobvAzunnC9U5Ih/j11j+NKDVCnuwpIBDSjkbKd1SUIIIYTIQTLiJ8QzuhRzibG7xtJhZQf+PPMnXSp3Yc3La/is4WcWG/oAWpZtybiAcey9upf3t72PwWx48k46PbSbCNGXYfcPOV+kyBF/H77CGz8fpGapgiwd6C+hTwghhBAZstG6ACEswfno88w/Mp9/zv2Djc6GrtW60q9mP4o7F9e6tEzrWLEj8YZ4JuydwNhdY/my8ZfolCf8bqdcY6jRCXZ+D3W7g1vp3ClWZIuVB8N4b8Uh6pV1Z2FfP1wcbLUuSQghhBAWSoKfyNfORp1lzuE5rL+wHjudHT2q96Bvzb4UdSqqdWlPpVu1bsSlxDEteBrOts583OBjlCc1bmn9Pzi1DjZ+Bq8szJ1CxTNbtu8SH606QsMKhZnfxxcnO/nnXAghhBCPJp8URL50MvIkcw/PZcPFDTjYONCnZh/61OhDYUfrv6H5wNoDiTPEsfDoQpxtnXm73tuP38GtDASMgm0TwW8glG2UO4WKp7Zo9wU+++sYzasWZXbPejjY6rUuKcuuxV/jfPR5GpZqqHUpQgghRL4gwU/kKyciTjDn8Bw2XdqEs60zA2sPpFeNXrg7uGtdWrZRFIVRPqOIN8Sz8OhCXOxcGFh74ON3ChgJwT/B2g9g8LbU6/+ERZq7/SwT1oTyfI3iTO/ujb2N9f1dhdwIYcTmEdxOvk23at143+99bHUyTVUIId41zGYAACAASURBVITISRL8RL5w9NZR5hyaw9awrbjYujCszjB6VO+R+fveWRlFUfiowUfEGeKYenAqzrbOdKvW7dE72DlB63HwW38IXgL1+uZarSLzfth0mm83nOIFr5JM6VoXW7319edac24Nn+76lBLOJWhdrjXLQpdxJuoMk5tNppBDIa3LE0IIIfIsCX4iTwu5EcKcw3PYGb4TVztX3qz7Jt2rd8fFzkXr0nKcTtHxv4D/kWBIYMLeCTjbOtOxYsdH71CzM+ybD5vGpTZ8cXTLvWLFY6mqyuR/TzJjy1k6e3vw9Ste2FhZ6FNVlbmH5zI9ZDo+xXyY2mIqbg5u1Clah893f063v7sx9bmpVCtUTetShRBCiDzJuj45CJFJB64fYNC/g+i1thfHbh1jpM9I1ndZz5A6Q/JF6LvDVmfLN82+oUHJBny661M2Xdz06I0VJfX2DgmRsG1S7hUpHktVVcb/c4IZW87SrX5pJr9ax+pCX4ophU92fcL0kOl0qNCBea3n4eaQ+ouFFyu+yOJ2izGpJnqt6cW68+s0rlYIIYTIm+QG7iLPUFWV/df2M/vwbPZf208hh0L0q9mP16q+hpNt/r45eYIhgcEbBnM84jjTW06nUanHNHBZnXa937DdULRq7hUpHmI2q4z96yg//XeJvo3K8dmLNZ7cpdXCRCVFMWrrKA5cP8DwusMZ4jUkw/dwK/EW72x9h+AbwQyoNYC3vN9CL9eaCiGEEBl6mhu4S/ATVk9VVfZc3cOcQ3M4eOMgRR2L0q9WP16p8gqONo5al2cxopOjGbB+AJdiLzHn+Tl4F/POeMP4WzDNBzx9oefvqSOBIteZzCofrjzMr0FhDGlagTHtqlld6LsYc5Hhm4ZzJe4K4wPG075C+8dubzAZ+GrfV6w4tYLGHo2Z1HQSrnauuVStEEIIYT0k+Il8RVVVdobvZPbh2Ry+eZjiTsUZUHsAnSt3xl5vr3V5FulW4i36retHRGIEC9osoHrh6hlvuGcmrP8Quv0CVdvmbpECo8nMuysO8WfIFUa0rMzbrSpbXegLuhbEqK2j0KFj6nNTH/2Lhgz8evJXvtr7FZ4unkxtMZUKbhVysFIhhBDC+kjwE/mCqqpsC9vG7EOzORZxjJLOJRlYeyCdKnXCTm+ndXnPLMlg4kZMMqULOebIh/2rcVfps64PScYkfmz3IxUKZvCh2mSAWY3AbIQ3/gMbCdK5JcVoZuTyYNYevcb7baoyvEUlrUvKstVnVzN291g8C3gys+VMSruWzvIxDl4/yNtb3ybZlMzEJhNpXrp59hcqhBBCWCkJfiJPM6tmNl/azJzDcwiNDMWjgAeDvQbzYoUXsdXnjXuAXYyIZ8CiIM7ciKNqcRc6+3jwUl0PShR0yN7zxFykz9o+6HV6FrdbjEcBj4c3OrMRfuoCrb6AxqOy9fwiY0kGE8N/Psim0Bt88kJ1BjaxrpEuVVWZeWgmsw/Npn6J+nzX/LtnumXKtfhrjNg8gtDIUN70fpNBtQdZ3cinEEIIkRMk+Ik8yWQ2seHSBuYcmsOZqDOUdS3LoNqDaF+hfZ666fOesxEM+/kAAAMCyrP55A2CL0WhKNC4UhE6+3jQpmYJnOyy5y4sJyNP0m99P9zs3VjUdhFFnYo+vNHS1+HCDnjrILgUz5bziowlppgYvCSIHadv8b9OtejlX1brkrIk2ZTMp7s+Ze35tXSq1Imx/mOz5RcyScYkPt/zOf+c+4fnyz7P+IDx+b5ZkxBCCCHBT+QpJrOJdRfWMffwXM5Fn6N8wfIM9hpM23JtsdHlrVtQLtt3iU//OErZwk4s6ONHuSLOAJy/Fc+qg2GsDA4n7HYiTnZ62tYqQWdvTxpWLIxe92yjH4duHmLQv4PwKOBBYJvAuy3274o4CzMagNdr0GnmM51LPFp8spEBi/az93wkkzp78Zpf1qdGaikyKZJRW0YRfCOYkT4jGVBrQLaOzKmqyuLji/nuwHdUdKvItBbT8HTxzLbjCyGEENZGgp/IE4xmI2vOr2He4XlciLlAJbdKDPEawvNln89z7d2NJjMT1oSycNd5mlYpyvTu3rg6PDxKYjarBF28zcqDYfxz5CqxSUZKuDrwkncpuvh4UqX409+bcO/Vvbyx8Q2quFdhfpv5ONs637/BhrGwayoM3Aye9Z76PCJjMUkG+i7cx6GwaL57rQ4v1c1g2q0FOxd9juEbh3Mj4QZfNvmStuVyrhnQ7vDdvLf9PXSKjsnNJuNf0j/HziWEEEJYMgl+wqoZzAb+Pvs3cw/PJSwujKruVRlaZyjPlXkOnWJdN6zOjJgkAyOWBbP15E36BZTj4/bVM3Vj7iSDiY0nrrPqYDhbT93EZFap5eHKy96edKxTiqIuWW/EsvXyVkZtGYV3MW9mtZqFg026awqTY+GHelCwNAzYALq893ehlaiEFHov3MfxKzH80M2bdrVLal1Sluy7uo9RW0dhq7Nl2nPTqFO0To6f81LMJUZuGcn56PO86/suPav3lOv+hBBC5DsS/IRVSjGl8OfZP1lwZAHhceHUKFyDoV5DaV66eZ79QHenicuFW/GMe6kW3RuUearj3IpL5q+QK6wKDudIeDR6nULTykXo7OPJ8zWK42Cb+RHSNefWMGbHGJp4NmFK8yn3X58V/DP8+Qa8PAfqvP5UtYr7RcQl03PBPs7eiGNmDx9a1bCuayhXnV7FuD3jKOtaluktp+fq1Mt4Qzwf7fiIzZc307FiR8Y2HCu3cBFCCJGvSPATViXZlMyq06tYcHQB1+Kv4VXEiyF1htDEo0meDXwA/52LYNhPB1CBmT18aFSxSLYc9/T1WFYGh/NHcDhXo5Nwsbehfe2SdPbxwK9cIXSZuB5wxakVjNszjrbl2jKxycR7U2vNZljQCqLD4a0gsH/6qaUCbsQk0X3+XsJuJzC3ly9Nq2TQWMdCmVUzPwT/wPwj8/Ev6c+3zb/V5CbrZtXMnENzmHloJrWL1Ob75t9T3Nm6wrMQQgjxtCT4CauQZEzit1O/EXg0kBuJN/Au5s1Qr6E0LNUwTwc+gF/2X+LjVQ83cclOJrPKf+ciWHkwnLVHr5KQYsLDzZHOPh687O1BhaIFHrt/4NFAvjvwHV0qd+Gzhp/d+zu5vD81/DV+B1p9lu115xdXohLpPu8/bsQms6CPHw0rFta6pExLMibxya5PWH9hPV0qd+Fj/48176y76dImPtrxEU62Tnzf/HvqFquraT1CCCFEbpDgJyxagiGBFadWEHg0kIikCHyL+zKszjD8Svjl+cBnMqtMWHOCBTvP06RyEaZ396GgY85/YE5IMfLvsev8fjCMXWduYVahbmk3uvh40MGrFO7OGd/w/ofgH5h7eC69a/TmPd/37v39rBwCx1bC8L1QyLruMWcJLkcm0G3ef0QnGPixvx/1yhbSuqRMi0iMYMSWERy5eYR36r1Dn5p9LObn9sztM4zYMoKr8Vf5pMEndKnSReuShBBCiBwlwU9YpHhDPMtDl7Po2CJuJ9/Gv6Q/Q7yG4FsiS9+rVis2rYnLlpM36duoHJ+8kLkmLtntekwSf4aEs/JgOKHXYrHVK7SoWozOPh60qFYMe5t71wOqqsrEfRNZGrqUN+q+wbA6w1JXxFxNbfRSsQW8/nOuvwdrdu5mHD3m7yUhxcSSAfXx8nR78k4W4mzUWYZvGk5EYgRfNfmKVmVbaV3SQ6KTo/lg+wfsvrKbrlW7Mrr+aM1HI4UQQoicIsFPWJTYlFiWhS5j8fHFRCdHE+ARwFCvoflqKtaliAQGLNrPuVvxfNGxJj0t5Kbcx6/EsPJgGH8eusLN2GQKOtrSwasknX088SnjhqIomFUzY3eN5c+zfzLabzQ9a/RM3XnHt7BpHPT6IzUAiic6fT2W7vP3YjarLBnQgBqlcv+auKe1+8pu3t36Lg42Dvzw3A/UKlJL65IeyWQ2MfXgVAKPBVKveD2+bfYthR2tZyqtEEIIkVkWF/wURWkLTAX0wHxVVSc+sL4g8BNQBrABJquqGvi4Y0rws3zRydEsPbGUJSeWEJsSSzPPZgzxGkLtorW1Li1X7T0XwdCfDmBWYVYPHxpVyp4mLtnJaDKz88wtVgWHs/7YNZIMZsoVduJlb09e9vaglLsdH2z/gA0XNzCu0ThervwyGJJgZgOwcYChO0EvoyqPc/xKDD0X7EWvU1g6sAGVn+Gei7ntt1O/Mf6/8VRwq8CM52ZQsoB13G7i73N/8/nuzynkUIipLaZSvXB1rUsSQgghspVFBT9FUfTAKeB5IAzYD3RTVfV4um0+AgqqqjpaUZSiwEmghKqqKY86rgQ/yxWVFMWSE0tYemIpcYY4niv9HEPqDKFG4Rpal5brft1/mY//OELpQqlNXMrnQBOX7BabZGDt0WusOhjOnnMRAPiVc6dj3eJsjZ5E0PW9fN30a9qUawOh/8Dy7tDua2gwROPKLdehy1H0XrgPJzs9Swf5W8X3AaR2zJxyYAqBxwIJ8AhgctPJFLB7fFMgS3Ms4hijtowiKimKLxp9QfsK7bUuSQghhMg2TxP8bHKqGKA+cEZV1XMAiqIsB14CjqfbRgVclNQOAQWASMCYgzWJHBCZFMmiY4tYHrqcRGMircq2YojXEKoWqqp1abnOZFaZuPYE83bkbhOX7ODiYMtrvqV5zbc04VGJ/BEczsqDYXz6Ryh2ti9QtFIEo7ePwV7nSPOq7aFCc9jyJdR6BZxlOt2Dgi5E0i9wP27Otiwd6E/pQk5al5QpicZEPtzxIZsubaJr1a6MqT8GG11O/leRM2oWrsnyF5bzztZ3GL1jNKG3QxnpPfLeLUqEEEKIfCYnR/xeAdqqqjow7XkvoIGqqm+m28YF+AuoBrgAXVVV/SeDYw0GBgOUKVOm3sWLF3OkZpE1txJv8ePRH/n11K8kGZNoW74tg2sPppJ7Ja1L00RskoGRy0PYHHqDPg3L8mmHGpo0cclOqqpyJDyalQfD+fPwWZKLzERvf52mrh8zokoZqqxsi1KvD3T4XutSLcqesxEMWLSf4q4OLB3UgJIFHbUuKVNuJtzkrc1vcTziOO/7vU/P6j0tpnPn0zKYDEzaP4lfTv5CgEcAk5pMoqB9Qa3LEkIIIZ6JpU31fBVo80Dwq6+q6lvptnkFCADeASoCG4A6qqrGPOq4MtVTezcSbhB4NJAVp1ZgMBt4ofwLDPQaSIWC+be9/+XI1CYuZ2/G83nHmvSykCYu2clgMvPP0dNMCHmLBFMkCZcG8a3ddl5KWcOtHhsoVjl/dGl9km2nbjJ4cRBlCjnx88AGFHN10LqkTDl1+xTDNw0nOjmaSU0m0aJM3mrcs+LUCibsnUAp51JMe24aFd0qal2SEEII8dSeJvjl5HBEGFA63XNP4MoD2/QDVqqpzgDnSR39ExboWvw1vvzvS9r93o5loctoV74dqzutZkKTCfk69O07H8lLM3ZxLTqJRf3q58nQB2Cr19GpTlVWv7KEUq5FKFxxEcvdWxOlOnFuyZt0n7uHFUGXiUvOv7O1Nx6/zqBFQVQoWoDlg/2tJvTtDN9J77W9MZvN/Nj2xzwX+gBerfIqC9ssJN4QT481PdhyaYvWJQkhhBC5KidH/GxIbe7SEggntblLd1VVj6XbZhZwXVXVzxVFKQ4cJHXE79ajjisjfrkvPC6cBUcWsOrMKgBeqvgSA2oPoLRL6SfsmfetCLrMR6uOUNrdifl9fKlQ1LoaYDyty7GX6bO2DwBT3VtTe9dXjLX/gMXRdXGw1dGmZgk6+3jSuFIR9DrrniqYWWuOXGXEsmBqlnJlUf/6uDnZaV1SpvwS+gsT9k2gsltlprecTgnnElqXlKOuxV9j1JZRHIs4xvC6wxnsNRidYt1TsoUQQuQ/FjXVE0BRlPbAFFJv57BQVdUvFUUZCqCq6mxFUUoBPwIlAQWYqKrqT487pgS/3PXryV/5au9XKIpC58qd6V+rP6UKlNK6LM2ZzCpfrwtlzvZzNK5UhBndfSjoZB1NXLLLmdtn6Le+H842Tiy6FUOxxFiCO/7L74cjWH3oCjFJRoq52PNS3VJ09vGkeknruXddVv0RHM47v4bgXcadwH5+uDpY/veCyWzi2wPfsuT4Epp6NuWbpt/gZGsdDWieVZIxiXF7xrH63GpalWnFl42/zDfvXQghRN5gccEvJ0jwyz3HIo7Rc01PGpRowOeNPs/zIwGZFZdsZOSyYDaF3qB3WhMXWytv4vK0jt06xoB/B1Dc1oXAE0EUajoGmo8m2Whi84kbrAwOZ0voDYxmlWolXOji48lLdUtZzRTIzPh1/2VGrzxMg/KFWNDHD2d7y++AmWBIYPSO0Wy9vJUe1Xvwvu/7+a7bpaqqLDm+hG8PfEuFghWY9tw0mcUghBDCakjwE9kmwZDAa3+/RpIxid87/i5d8NJcjkxg4KIgztyM4/MXa9CrYTmtS9Jc0LUghm4cSgWzjgWXL+Py5n4o6Hl3fWR8Cn8fvsLvB8M5dDkKnQKNKxeli48HrWuUwNHOegPHkj0X+PTPYzSpXIS5vXyt4r1cj7/OW5vf4uTtk4z2G0336t21LklTe67s4b1t7wEwudlkGpZqqHFFQgghxJNJ8BPZ5pOdn7D63Grmt56PXwk/rcuxCPsvRDJ0yQEMJjMze9SjceUiWpdkMXaE7WDE5hF4JSUw290fx1cXZbjd2ZtxrDoYzqrgcMKjEnG209Oudkk6+3jgX74wOiu6HnD+jnOM/+cEraoXY0YPH+xtLD/0hUaGMnzTcOJS4vim2Tc09WyqdUkW4XLMZUZsGcG56HO8W+9detXoZfW3sRBCCJG3SfAT2WLt+bV8sP0DhngN4U3vN5+8Qz7w24EwPlp5BA93R+b38aViPmnikhXrL6zng23v0zAhgWltF2BXvtkjtzWbVfZdiGTlwTDWHLlGXLKRUgUd6OTtQWcfDyoVc8nFyrNu+ubTTP73FO1rl2BKV2/sbCx/qu/2sO28t+09XO1cmdFyBlULVdW6JIuSYEjg450fs/HSRl6s8CJjG47FwSbvTEkWQgiRt0jwE88sLDaMV1e/SiW3SgS2DcRGZ/nXK+Wk9E1cAioVZkZ3H6vp1qiFVSeWM3bflzxvsuXr3nuwsbF/4j6JKSY2nLjOyoNh7Dh9C5NZxcuzIC97e9CxTikKF3jyMXKLqqp8t+EUP2w+Q6e6pZj8ah1srOD6zp9P/MzX+7+mqntVprecTjGnYlqXZJHMqpm5h+cyI2QGNQvXZEqLKXJtsxBCCIskwU88E4PZQN91fTkfdZ4VHVfgUcBD65I0FZdsZNTyEDaeuE5P/zJ89mLNfNvEJSuWbHqPr8PW09GtJv/ruDRLrfJvxCbxV8gVVgWHc+xKDDY6heZVi/KytyctqxfDwVa76ZSqqvLV2lDmbj9HV9/STOhc2+JvVWE0G/l6/9csC11Gi9ItmNhkonSvzIQtl7bw4c4PcdA78H2L7/Eu5q11SUIIIcR9JPiJZzLt4DTmHZnHN02/oW35tlqXo6mw26lNXE7fiOOzF2vQW5q4ZJ6qMuvHAGbqYuleqQtjGn32VNdLhV6LYdXBcP4ICed6TDIuDjZ08CpJZx9PfMu65+o1WGazyherj7Foz0V6+Zfli441Lf56xHhDPO9ve58d4TvoU6MPb9d7O9917nwWZ6POMmLzCK7EX+GjBh/xapVXtS5JCCGEuEuCn3hq+6/tZ8D6AXSq1IlxAeO0LkdTBy5GMmTJAZKNZmb28KFJ5aJal2R11CshTP6tE4sLujDYazBveb/11McymVV2n73FqoPhrD16jUSDiTKFnFKvB/T2oFwR52ysPOPzf7zqCMv3X2Zg4/J8/EJ1i2/8cS3+GsM3Deds1Fk+avARr1V9TeuSrFJ0cjSjd4xmV/guXqvyGmPqj8FWb/n3aBRCCJH3SfATTyUqKYouq7vgZOPELx1+yddTwX4/EMaHK49Qys2B+X38qFRMmrg8LfXPt/ji8j/87uLMO/XeoV+tfs98zPhkI+uPXWPlwXB2nb2FqoJPGTc6+3jSwatktl9/aTSZ+eC3w6wMDufNFpV4t3UViw99xyKO8damt0gwJvBts28J8AjQuiSrZjKbmBo8lcCjgfgU8+G75t9R2LGw1mUJIYTI5yT4iSxTVZVRW0axPXw7P7f/mRqFa2hdkibMZpWv159k9razNKxQmFk9pYnLM4u/hWmaD2NKebCOOD71/zRbR56uRifyZ8gVVh4M49T1OOz0Op6rVozOPh40r1rsmTttGkxmRi0P4Z8jV3n3+Sq81bJyNlWeczZf2syYHWNws3djRssZVHa3/JqtxZpza/hs92e4ObgxpcUUahauqXVJQggh8jEJfiLLfgn9hfF7x/O+7/v0rtlb63I0EZ9sZNQvIWw4fp3uDcrwRUdp4pJt9szAsP4jRnm3YUdUKBObTKR9hfbZegpVVTl2JYaVB8P561A4t+JScHey5cU6pejs40kdz4JZHqVLNpp4c2kwG45f5+P21RnUtEK21pzdVFVl8fHFfBv0LTUL1+SHlj9QxNHC7zN59RDcPAle1jMN9UTECUZuGUlkUiRfNPqCFyq8oHVJQggh8ikJfiJLTt8+Tbd/uuFbwpeZLWdmqftiXhEelciAH/dz6nosYzvUoE+jchY/lc+qmAwwqxFJZgNvVKnLwRshTGkxhealm+fI6YwmMztO3+L3g2FsOH6dZKOZCkWc6ezjQSdvDzzdnzyNOclgYsiSA2w7dZNxL9W0+MY+RrORCXsnsOLUCp4v+zxfNv4SRxtHrct6NFWF/fNh3YdgNsDz4yBgpNZVZVpEYgTvbnuXA9cP0LdmX0b5jJKmOUIIIXKdBD+RaUnGJLr9043bSbf5reNvlj86kAMOXLzNkCVBJBvNTO/uQ7Mq0sQlR5zeCD93Ib7lJwyMPsip26eY2WomDUo2yNHTxiQZWHvkKisPhrP3fCQADcoXorOPB+1ql8TV4eEmHQkpRgYuCmLPuQi+erk2r9cvk6M1PqvYlFje2/Yeu6/spn+t/oz0GWnZv8BJjoPVI+Do71C5Ndg6wfE/4KUZ4N1T6+oyzWA28PW+r1l+cjmNSjXi66ZfU9C+oNZlCSGEyEck+IlM+/K/L1l+cjmzW83Ol80fVgWHMfq3I5R0c2CBNHHJeUu7woVdRA3eTL9dowmPC2de63nUKVonV05/OTKBP4LDWRUczrlb8djb6GhdswSdvT1oUrkINnodsUkG+gXu5+Cl23z7Wh1e9vbMldqe1pW4KwzfNJwL0Rf4xP8TulTponVJj3cjFH7tBRFn4LlPIOBtMBth6Wtwfju8/jNUbad1lVny+6nfGb93PCWdSzKtxTQquVfSuiQhhBD5hAQ/kSmbL21m5JaR9K7Rm/f93te6nFxlNqtM/vckM7eexb9CIWb1qIe7szRxyXERZ2FGA/B6jZutv6DPuj5EJUcR2CaQqoWq5loZqqoScjmKVcHh/HXoClEJBooUsKdjnVIcuHSbY+HRTH3dmxe8SuZaTU/jyM0jvLX5LVJMKXzX4jv8S/prXdLjHV6ROtJn5wxdFkCFZvfWJcfCoo5w4zj0+gPKNtSuzqcQciOEUVtGkWhMZEKTCbQs01LrkoQQQuQDEvzEE12Pv06X1V0o5VyKn9r/hJ0+/4Se+GQjb/8Swr/Hr9OtfhnGvSRNXHLVv5/C7mkwaDPhBUvQZ20fjGYji9otoqxr2VwvJ8VoZuvJG6w8GM6m0OsoKEzv7k3rmiVyvZas2HBxAx/u+JAijkWY2XImFdwsuPGMMTn1Wr6gBVCmIbwSCK4ZhOr4W7CwLcTdgH5roESt3K/1GVyPv86oLaM4GnGUN+q8wZA6Qyx7yq0QQgirJ8FPPJbJbGLwhsEcuXWEXzv8SrmC5bQuKdeERyUycFEQJ6/F8GmHGvSVJi65LykGfqgH7mWh/7+ci71Av3X9sNfbs6jtIkoW0G6ULSohhZhEI2UKW+49LFVVJfBYIN8f+J46ReswtcVUy76f3O2LsKIPXAmGRiOg5Vh43M3Poy7BgjagmmHAenAvl2ulZodkUzLj9ozjr7N/8Vzp55jQZALOts5alyWEECKPeprgJ7+SzEcCjwWy79o+Pqz/Yb4KfQcu3ual6bsIi0xgYV8/+gWUl9CnBQdXaPU5hO2HIyuoULACs1vNJi4ljkEbBnEr8ZZmpbk52Vl06DOYDXy+53O+P/A9bcu1ZX7r+ZYd+k6ugzlNIeIcdP0ZWv/v8aEPwK0M9FoJxiRY0hnibuZOrdnEXm/P+IDxjPYbzbawbfRc05NLMZe0LksIIYS4S4JfPnHo5iGmB0+nbbm2dKrUSetycs0fweF0m/cfTnZ6Vg1vRPOqxbQuKX+r0w1K+cDGzyA5juqFqzOj1QxuJNxgyIYhRCdHa12hxYlJiWHYxmGsPL2SQbUHManpJBxsHLQuK2MmI2z8ApZ1BbfSMGQrVO+Q+f2LVYfuv0LMFfi5S+oosRVRFIWeNXoy+/nZ3Ey8yev/vM7u8N1alyWEEEIAEvzyhdiUWEZvH00J5xJ82vDTfDHaZTarfLM+lFG/hOBd2o0/hwdQqZiL1mUJnQ7aTYLYq7DjWwC8i3kzpcUUzkef541Nb5BgSNC4SMsRFhtGrzW9OHD9AOMDxjPCZ4TlXjsWex2WdIKd34FPHxiwAQo9xfWHZRrAa4vg2lH4pUfqdYJWxr+kP8teWEYJ5xIM2zSMRccWYW2XVQghhMh7LPQThMguqqoy/r/xXIu/xsQmE3G1c9W6pByXkGJk2M8HmLHlLN3ql2bJgAbSudOSlK4PXl1hz3SIPA9Ao1KN+KbpNxy7dYwRW0aQbLK+D/vZLeRGCD3W9OBW4i3mPj+Xlyq9pHVJj3ZhF8xpAmFB0GkWdJwGts9wE/kqbVLv7Xd+O6wcBGZT9tWa7isknwAAIABJREFUS0q7lOandj/RskxLJgdN5qOdH5FkTNK6LCGEEPmYBL88bvW51aw5v4ZhdYZRt1hdrcvJcVeiEnll1h42HL/Opx1qMOHl2tjZyLe5xWn1Behs4d9P7r7UsmxLxgWMY+/Vvby37T0MZoOGBWpr3fl1DFg/AGdbZ35q/xN+Jfy0Liljqgo7p8CiF8GuAAzaBHW7Z8+x63aD1uPh+J+w5r3Uc/2fvfsOj6ra+jj+Pemkd0IIvZfQewfpNVIEBAVpKvYG2N5rxy4iKkVBFBARkNCkinSQDqGHEiC0BEjvmf3+cUIIPYRMzkyyPs8zT5LJmZkf96LOmr33WlbG2d6Zr1p/xQt1X2DZyWUMWTGEi4kXjY4lhBCiiJJ3xIVYRFwEH2/7mAbFGzAieITRccxuz5lr9Jy0mbNXk/h5aEOGt5AmLhbLvQS0eg2OLIUT67Lv7lmhJ281fot/z/7Lu5vfxaRMBoYseEoppu6fyhsb3qCmb01md51NOY9yRse6s+RrMPdx/bxmtR4w6l8oXiN/X6PZC9D8Jdg5Hf79NH+fu4BomsaoWqOY2G4iEXER9F/an92XdhsdSwghRBEkhV8hlZ6ZzpgNY7C3tWd8y/HY2tgaHcmsQvdG0n+q3sRl4ehmtJUmLpavyXN6y/4V4/SmIFkGVh3IS/VeYtnJZXy87eMiczYqPTOddza/w3d7vqNb+W5M6zgNLycvo2Pd2fm9MKU1HF8FnT+Dfr/oXVvNof37UGcwrP8U/ptmntcoAG1KtWFO1zm4ObgxfNVw5h2dZ3QkIYQQRYwUfoXUd3u+49CVQ7zf7H0CXCx7IPXDMJkUX606yktz91KnlCeLnmtOpeLSxMUq2DtBx48h6og+4DuHEcEjGFZzGPOOzeOb3d8U+uIvNjWWUatHsfjEYkbXHs34FuNxsLXAc6lKwc4Z8HNHMGXAU39Dk2fAnCvrmgY9voXKXWD5GxC20HyvZWblPcszp9scmpRowofbPuSDrR+Qnll0tzQLIYQoWHZGBxD5b8v5Lcw4OIPHKj/GI6UfMTqO2SSlZfDavH38HXaR/g1K8WFITTnPZ22qdoPybWDdx1CzL7jcmE33cr2XSUxPZEbYDNzs3RhZa6RhMc3pTNwZnlv7HJEJkYxvOZ7u5R9g/EFBSkuEpa/C/rlQoR30/umm/7/MytYO+k6HWb1h4Sgo5gUV2hbMa+czdwd3JrWbxHd7vuPnsJ85EXOCr9p8hW8xX6OjCSGEKOTkXXIhcyX5Cm9vepsKHhV4veHrRscxmwuxyfSbvJWVBy/yTrdqfNpHmrhYJU2Dzp9CaoJe/N30K423Gr9Ft/LdmLhnInMOzzEopPnsvrSbQcsHEZMaw7SO0yy36Is+DtMegf1/QJu3YND8giv6rnNwhoG/g29l+GMwRFrvOTlbG1terv8yX7T6gkNXDjFg6QAORh80OpYQQohCTt4pFyJKKd7d/C5xqXF83vpzitk9RDt1C7b3bAw9J20m4koSPw9pyIiW5aWJizXzrwYNR8CuGXDxwE2/stFs+LD5h7Qt1Zbx/41n8YnFBoXMf0tPLmXEqhF4Onoyu+ts6hevb3SkOwtbCFPbQOJleGIhtBkLRp0ZLuYFgxeAszfM7gvR4cbkyCedy3Xmt66/YavZMmTFEJacWGJ0JCGEEIWYFH6FyOzDs9kYuZHXG75OZa/KRscxi8X7ztN/ylac7G30Ji5VpYlLodBmHDh5woo3b2vbb29jzxetv6Bxica8u/ld1kSsMShk/lBK8cPeH3hz45vU8a/DrK6zKO1e2uhYt8tIg7/HwvynwL86PL1B3+JpNPcSMPgvQIPfHoW4C0YneihVvavye/ffqeVXi7c2vcUXO74gw5Rx/wcKIYQQD0gKv0LiyNUjfL3ra9qUasOAKgOMjpPvTCbF16uO8uLve6gd5Enocy2oLE1cCg9nb2j3NpzeqM9tu4WjrSMT204k2DeYNza8wZbILQaEfHhpmWm8uelNftz3Iz0r9GRK+yl4OHoYHet2MWdhRhfYPhmajIahy8AjyOhUN/hWhMHzIfmqfu4v+ZrRiR6Kt5M3UzpM4fGqj/ProV8ZvWY0samxRscSQghRyGjW1i2vQYMGaufOnUbHsChJ6UkMWDaAxLRE5vecb7kt4PMoOS2T1/7cy/IDF+lXP4iPZSh74WTKhCmtICUOnv8P7G/fqhybGsvwlcOJiItgasep1PWva0DQvLmWco2X173M7su7eaHuC4wMHmmZW5SPr4GFI/QRGyHfQ/VeRie6u5P/wux+EFgPnvhLPwdo5f46/hcfbvuQ4s7FmdhuIpW8KhkdSQghhAXSNG2XUqrBgzxG3j0XAp/v+JzTsaf5pOUnha7ouxibwmNTtvJ32EXe7lqNz/vWkqKvsLKx1Ru9xJ6BLd/d8RIPRw8md5hMgEsAo9eM5vCVwwUcMm9OxZ5i0PJBhEWH8UWrLxhVa5TlFX2mTFj3iX52zi1QH8huyUUf6B1he0+Fs9v1LamFYDTCo5UeZXqn6aRmpjJo+SDWRqw1OpIQQohCQt5BW7lVp1ex4PgChgcPp3GJxkbHyVf7zsbQc9ImTkYl8NOTDRjZSpq4FHrlWurFxsavIfbcHS/xLebLtI7TcHNw4+nVT3My5mQBh3wwOy7uYPDywSSmJ/Jzp5/pXK6z0ZFulxClb5lc/xnUeRxGrNG3U1qDGo9Cty/h2ApY/OJtZ0StUR3/OsztPpdKnpV4+d+X+X7v95iUyehYQgghrJwUflbsQsIF3tv6HrV8azG6zmij4+SrJfvO89iUrTjY2bBwdHMeqVbc6EiioHT4EFCw+v/uekmASwDTOk7DRrNh5OqRRCZEFly+BxAaHsqo1aPwKebDrK6zqONfx+hItzuzTd9ie2Yb9JwEIT9Y35bJhiOgzZuwb849/95YE39nf6Z3nk6vCr2YvG8yL6/T51oKIYQQeSWFn5XKMGUwbuM4TMrEp60+xd7G3uhI+cJkUnyz+hgv/L6HWkEehD7XnCoB0sSlSPEqA81ehLAFEHH3Ji5l3MswpcMUkjOSGblqJFFJUQUY8t5MysTE3RN5Z/M71C9en1ldZ1HKrZTRsW6mFGz9Hn7pBnaOMHw11HvC6FR513qsXgBumQibJxqdJl842jryYfMPGddoHBvObWDQskGciTtjdCwhhBBWyqyFn6ZpnTVNO6ppWrimaePu8Ps3NE3bm3UL0zQtU9M0b3NmKiym7p/K7su7eafJO5b3hjKPktMyeeH3PXy79jj96gcxa0RjfFwdjY4ljNDiZXAvqY8TMGXe9bIq3lWY3H4y0cnRjFo9ipiUmAIMeWepmamM3TCWaQem0btSb35s/yPuDu5Gx7pZSizMewJWvgWVO+vn+UrUMjrVw9E06PK5vvVz9buwd47RifKFpmkMqjaIKR2mcCXlCgOWDWBz5GajYwkhhLBCZiv8NE2zBb4HugDVgYGaplXPeY1S6gulVB2lVB3gTWC9UuqquTIVFrsu7WLK/in0rNCT7uW7Gx0nX1xv4rI87AJvda3K531r4Whn0JBoYTwHF+jwAVzcD3tm3fPSWn61mNRuEmfizvDsmmdJSEsooJC3u5J8heErh7Pi9Apeqf8K7zV9z/JW4y8e0AeyH1kOHT+C/rOgmKfRqfKHjS08OgXKtYbQ5+HoCqMT5ZvGJRrze7ffKeFSgtFrRzMjbAbW1pVbCCGEscy54tcICFdKnVRKpQFzgXu1iBsI/G7GPIVCbGos4zaOI8g1iLcav2V0nHyx/9yNJi7TnmjAqFYVpImLgJp9oHRTWPsBJN97Ja9RiUZ81eYrjlw9wvP/PE9KRkoBhbzhZMxJBi0fpM/UbPM1w2oOs7y/x3tmwU/tIS1Jn83X7AV9pawwsXOEAbMhIBj+HKqfXSwkgtyC+K3Lb7Qv3Z6vd33N2I1jSc5INjqWEEIIK2HOwq8kcDbHz+ey7ruNpmnOQGdgwV1+P0rTtJ2apu2MirKcczwFTSnF+1vfJzopms9afYaLvYvRkR7a0v16Exd7WxsWjG5G++rSxEVk0TR9vEPSFVj/+X0vb1OqDR+3+Jjdl3bz6r+vkl6Arf23XdjG4OWDSclIYUanGXQo06HAXjtX0pMh9Dn9VqoRPLMRyjQ1OpX5OLrB4AXgURLmPAaXDhmdKN842zvzZesveaneS6w4tYIhfw/hQsIFo2MJIYSwAuYs/O70MfLd9qX0ADbfbZunUmqqUqqBUqqBn59fvgW0NguOL2B1xGpeqPcCNX1rGh3noSilmLDmGM/P2UPNQA9Cn29O1QALOwcljBdYR2848t8UiDp238u7lu/Ku03fZWPkRt7c9CaZ9zgfmF8WHFvAs6ufpbhLceZ0m0OwX7DZX/OBXDmhr/LtmQWt3oAnFoGrv9GpzM/FFwYvBHtnfVRFTOFpiqJpGiOCRzDpkUmcjT/LgGUD2Hlxp9GxhBBCWDhzFn7ngJxdR4KA83e5dgCyzfOeTsac5LP/PqNJiSYMrTHU6DgPJSVdb+IyYc1x+tQLYvbIxvhKExdxN+3+T3/zvvLNXM1o61e5H6/Vf42Vp1fywbYPzHYOyqRMfLPrG97b+h6NSjTi1y6/EugaaJbXyrNDi/XzfHGRMGg+tHtHPwdXVHiV0Vf+0pPgt0chMdroRPmqVVAr5nSbg7uDOyNXjeSPI3/IuT8hhBB3Zc7CbwdQSdO0cpqmOaAXd4tvvUjTNA+gNRBqxixWLTUzlTEbxlDMrhiftPgEG816p3BcitObuCw7cIE3u1Tly37SxEXch6uf3qo/fA0cW5mrhwytOZRRtUax8PhCvtj5Rb6/GU7OSOb19a8zPWw6/Sr34/tHvsfNwYLGjmSmw8q39c6dPhXh6Q1QycK2nxaU4jVg4B8Qew5m94XUeKMT5atyHuWY020OzUo246PtH/H+1vcLdJuzEEII62G2CkIplQE8D6wEDgPzlFIHNU17RtO0Z3Jc+iiwSiklk2nvYsKuCRy9dpSPWnyEn7P1bnU9cC6WnpM2ceJyAlOfaMDTraWJi8ilRqPAp5K+6peRlquHPF/neR6v+ji/HfqNyfsm51uU6ORohq8czpqINbze4HXebfIudjZ2+fb8Dy3uPPzSHbZO0v93G7YCPEsbncpYZZpCv1/gwn74YzBkpBqdKF+5Obgxse1ERgaPZMHxBQxbOYzo5MK1uimEEOLhada2LaRBgwZq586ic5Zhw7kNPLf2OQZVG8S4RreNQrQayw9c4NV5e/FxceSnIQ2oVkLO84kHdHwNzO6jj3lo/lKuHmJSJv5v8/8ReiKUMQ3H8ET1hxtQfvzacZ5b+xwxqTGMbzmeR0o/8lDPl+9OrIMFI/RmLj0nQnBfoxNZlr1zYNGzUKM39PmpUG57XXl6Je9ufhc3Bze+bfut1Z8HF0IIcWeapu1SSjV4kMdY757BIiAqKYp3Nr1DFa8qvFL/FaPj5IlSiolrjzN69m5qBHqw6LnmUvSJvKnUHip1gvVfQPylXD3ERrPhvWbv0aFMBz7f8TkLjy/M88tvjtzMk38/SYYpgxmdZ1hW0Wcy6Z1Pf3tUb2oyap0UfXdS53H9g4ODC+Hvsbk6M2ptOpXtxG9dfsNOs2PI30NYfOK2ExZCCCGKKCn8LJRJmXhr01skZyTzeavPcbS1vuYnKemZvDh3L1+vPkbvuiWZPaIxfm7W9+cQFqTzeMhI0Wf75ZKdjR2ftvyU5oHNeW/Le6w4/eBDvecdncdza58j0DWQOd3mUMOnxgM/h9kkXoE5/WDdxxDcD0b+A35VjE5luZq/pM8v3DEtV2NCrFEV7yrM7T6XOv51eHvT23z232dkmDKMjiWEEMJgUvhZqJkHZ7LtwjbGNhpLec/yRsd5YJfjUug/ZStL959nbOeqfPVYbZzsC9+2KlHAfCpAk2dh7yyI3JXrhznYOvBN22+o61+XNze8yYZzG3L1uExTJl/s+IIPt31I08Cm/NrlVwJcAvKaPv+d2wlTWsGpDdD9G+g9FRysf76n2bX/AGo/Dv9+Ajt+NjqNWXg5eTG5w2QGVRvErMOzeGbNM8SkxBgdSwghhIGk8LNAYdFhTNw9kQ5lOtCnUh+j4zywsMhYek7azPHLCUwZXJ9n20gTF5GPWr0BLv4PvFWvmF0xJj0yiUpelXj131fZcXHHPa9PSk/ilX9f4ddDvzKw6kC+a/cdLvYWUlQpBdunwPTOYGMDw1dBg2H60HtxfzY2+hnISp1g2WtwcJHRiczC3saecY3G8UGzD9h9aTcDlg3g0JVDMvJBCCGKKGnuYmES0xPpt6Qf6aZ05veYj4ejh9GRHsjfBy7wSlYTl2lPNqB6oJznE2awZxaEPgePToXa/R/ooVdTrvLUiqe4lHSJnzr+dMfmF5eTLvPCPy9w5OoRxjQcw6Bqg/Ir+cNLjYfFL8DBv6ByZ3h0MhTzMjqVdUpLgt9C4Pwefc5h+dZGJzKb/VH7eXndy0QlR+FXzI9g32CC/YIJ9g2mhk8NXB1cjY4ohBDiAeSluYsUfhbm7U1vs/TkUqZ3mk794vWNjpNrSikm/RPOV6uPUa+0J1OeaCDn+YT5mEzwUzuIvwjP7wTHB3vTeinxEkNWDCEhPYEZnWZQyatS9u+OXj3Kc2ufIy4tji9afUHrUhZUDFw6BPOehKsn4JH/g2Yv6atXIu+Sr8GMrhBzBoYuhcC6RicymyvJV1hxegVh0WEciD5ARFwEABoa5T3KZxeCwb7BVPSqiL2NvcGJhRBC3I0UflZu2clljNs4jmdrP8voOqONjpNrKemZjJm/n8X7zvNo3ZKM7x0s5/mE+Z39D37uAC1f04ugB314/FmG/D0EheLXzr9Syr0UG85t4I31b+Dq4Mr3j3xPVe+qZgieR/vmwpKXwdEN+k6Hci2NTlR4xJ2HnztBepK+bdangtGJCkRsaixh0WHsj96vF4NRB7iWeg0AJ1snqvlUyy4Eg/2CCXQJlG37QghhIaTws2Jn48/Sb0k/KntVZnqn6ZY1EPoeLsenMOrXXew9G8MbnaowWs7ziYK0cJR+Puu57eBd7oEfHn4tnKdWPoWLvQu9K/Xm+73fU8WrCt+1+47iLsXNEDgP0lNgxVjY9QuUaQF9fwY3C2owU1hEH4fpnfTmOMNWgXsJoxMVOKUUkQmRHIg+wP4ovRg8fPUwqZn6wHtvJ+8bhaBvMDV8a1jdcQQhhCgspPCzUummdIb8PYTTsaeZ33M+ga6BRkfKlbDIWEb+upOYpHS+6V+HzjXlzagoYHHn4bsGUKEtDJidp6c4GH2Q4auGk5ieSJugNnzW6jOc7Z3zOWgeXT2lb+28uB9avAJt3wFb6/hQyCpF7oaZPcCzDDy1HIp5Gp3IcOmmdI5fO86BqAPZK4MnY09m/76se1mCfYOp6VuTWn61qOJVBXtb2SIqhBDmJoWflfp297f8dOAnvmz9JZ3KdjI6Tq6sCLvAK3/sw8vZnmlDGlAjUD71FQbZ8CX88yE8GQrl2+TpKQ5GH2Rv1F4GVBmArY2FbFM+shwWPaN//+gUqNLF2DxFxYl1MLsfBDWAJ/4C+2JGJ7I48WnxHLxykANRBzgQrd+ik6MBvZNoNe9qBPtlFYO+tSjlVkp2ggghRD6Tws8Kbb+wnZGrRtK7Um/ea/ae0XHuSynF9+vC+XLVMeqU8mTqk/Xxd3MyOpYoytJT4PtGYO8Mz2yy/hWxzAy9kN08AUrUhn4z87SNVTyEsIUwf5jeNbX/LOv/O2VmSikuJl7MLgIPRB/g0JVDJGckA+Dh6JFdBNb0rUmwbzBeTtKJVgghHoYUflbmWso1+i7ui4uDC3O7zbWc7WV3kZKeybgF+1m09zwhdQL5tE8taeIiLMPhJfDHYOjyBTQeZXSavIu/qBccEZuh/lPQ+VOwlw9WDPHfNFj+OtQZDL0myYzEB5RhyuBEzImbisETMScwKRMAQa5BBPsFZxeD1Xyq4WgrnaCFECK38lL4yceYBlFK8X+b/49rqdf4vv33Fl/0SRMXYdGqdodyrWHdxxDcF5y9jU704E5t1Iu+tAR9a2ftAUYnKtoajYTEKFj/Gbj4Qof3jU5kVexs7KjiXYUq3lXoW7kvAEnpSfoW0egDHIg6wO5Lu/n71N/69Zodlb0r39RFtKx7WWw0GVcihBD5RQo/g8w9Opd/z/3L2IZjLatl/B0cPB/LyJk7uZaUzuTB9ehcs+h1uxMWTtP01bHJLeCfj6D710Ynyj2TSd/W+c+H4F1BP6tYvLrRqQRAmzf14m/zBHDxg2bPG53IqjnbO9MwoCENAxpm33c56XJ2IRgWHcbSk0v54+gfALjZu1HDt8ZNxaBvMV+j4gshhNWTrZ4GOHbtGAOXDqRxicZ8/8j3Fr1ytul4NCN/3Ymnsz3TnmxAzZLSxEVYsOVvwI6f4OmNEFDT6DT3l3QVFj0Lx1ZAjd7Qc6I+p09YDlMmzH8KDoXKSmwByDRlcir2VPb20LDoMI5dO0amygQg0CUwu4NoTd+aVPepTjE7acAjhCh65IyfFUjOSObxZY9zLeUaC3ouwKeYj9GR7iomKY32X2/A28WeWcMb4+8uZ42EhUu6Ct/Vg+I1YcgSyz6XFbkb/hwCcReg0yf61kJLzluUZaTC7L5wejMM/B0qW0f35cIiOSOZw1cO31QMRiZEAmCr2VLRs+JN5wXLe5S3nO68QghhJnLGzwp8ueNLwmPCmdJ+ikUXfQDvLzlETFIaM4c1lKJPWAdnb2j7tt6U41Ao1AgxOtHtlIKd02HFOHDxh2Er9NEBwnLZOUL/2TCzO8wbom/HLd3Y6FRFRjG7YtQrXo96xetl3xedHE1YdFj2NtGVp1Yy/9h8AJztnLO3iF4vBou7FDcqvhBCWAxZ8StAayPW8vK/LzO0xlBea/Ca0XHuae3hSwyfuZMXH6nEqx0qGx1HiNzLzIAprSA1Hp7/z7LmsKUmwNJX4MA8qNgeek+zzkY0RVVCFEzvqK8sD1sB/tWMTiSymJSJiLgIwqLD2B+lD5o/cu0IGaYMAPyd/W+cFfQNpoZvDVzsXQxOLYQQeSdbPS3YxcSL9F3Sl5KuJZnVZRb2tvZGR7qr2KR0Ok5Yj5ezA4ufb4GDnXRVE1bm1AaY2UNf/Ws9xug0uqijMO9J/Wvbt6Hla2Aj/2xZnWun4eeOoNnC8FXgWcroROIuUjNTOXL1yE3F4Jn4MwBoaFTwrJDdNCbYN5iKnhWxs5GNUEII6yCFn4XKNGUyYtUIDl45yJ89/qSMexmjI93T63/u4689kSwa3ZzgIGnmIqzUH0/A8dXwwk7wCDI2y4H5sPhFffWxz09Qoa2xecTDuRgGM7qCqz8MWwkulr1tX9wQkxKTfU5wf7ReDMakxgDgZOtEdZ/qNxWDJVxKWHQDNiFE0SWFn4Wasm8Kk/ZO4qPmH9GrYi+j49zTuqOXeWrGDp5rW4E3Oln2mAkh7ulaBExqCNV6QN+fjcmQkQor34Yd06BUE+g3A9wDjcki8lfEFvjtUfCvrjcScnQ1OpHIA6UU5+LPZReB+6P3c+TKEdJMaQD4OPlkF4HXt4i6O7gbnFoIIaTws0h7L+9l6IqhdCzbkc9afmbRnxzGpaTT6ZsNuDrasfTFFjjaSVc0YeX++Qg2fAFPrYAyTQv2tWPO6I1Azu+Gps9D+/fAgrd4izw4shz+GAzlWsHj88DOwehEIh+kZ6Zz7Nqx7C6iB6IPcCr2VPbvy3mUu+m8YGWvyhZ9fEMIUThJ4Wdh4tPi6bekHwB/9vgTNwfLns81bsF+5u08y8LRzalTytPoOEI8vLREfdXP2QdG/QsF1eL92CpYOBKUCXp9D9V7FszrioK3ZxaEPgc1+0Dvn+TcZiEVlxZHWHSY3kk06gD7o/dzNeUqAA42DlTzqXZTMRjkFmTRH/QKIayfjHOwIEopPtz6IRcTLzKzy0yLL/o2Ho9i7o6zPN26vBR9ovBwcIEOH8CC4fob9PpDzPt6pkxY9wls/BKKB8NjM8GngnlfUxir7mBIjII17+kfMHT5XOYxFkLuDu40C2xGs8BmgP7f+AuJF/QtolH6WIn5x+Yz6/AsALwcvajpWzN7RdDLyQtPJ088HT3xcPCQOYNCCENI4WcmoSdC+fv037xY90Vq+9U2Os49JaRmMG7BAcr7ufBKexndIAqZmn3gv2mw9gOo3guKmemDjYTLeoF5agPUfQK6fmFZoySE+TR/GRKjYeskfTZj6zeMTiTMTNM0Al0DCXQNpHPZzgBkmDIIjwnP7iB6IPoAmyI3obh5Z5WGhpuDG56OntnF4E23O93n6CnbSYUQD00KPzM4HXuaT7Z/QsOAhgyrOczoOPc1fvlhzscmM/+ZZjjZy6eQopDRNOjyGUxto5/36/Rx/r9GxBb48ylIidG3dtYdnP+vISyXpkGHD/Xib91H4OILDZ4yOpUoYHY2dlT1rkpV76o8VuUxABLTEzkdd5rYlFhiUmO4lnqN2NRYrqXoX2NSY4hKiuL4tePEpMaQnJF81+d3sXe5qRD0cPTAy8lL/+roddN9169xsnMqqD++EMIKSOGXz9Iy0xizYQwOtg6MbzHe4rdzbAmPZvb2M4xoUY76ZbyMjiOEeQTWgXpPwPbJUG8I+OXTyrZSsOU7fZufVxkYPB8CgvPnuYV1sbGBXpMg+SosexWcvfUVZlGkudi7UMOnRq6vT81MJSYlhpjUmBuFYlbReNMtJYaIuAhiUmNISE+46/M52TrdVCDmLBxz3ufl6JV9nbOds5xPFKKQksIvn03cPZHDVw/zbdtvKe5S3Og495SYmsHYhfsp6+PMax2rGB1HCPNq939wcBGsfEsv0B5Wcoze1OPIUqjWU3/T7yRzL4s0W3vo9wv8GgILRkAxL73jpxC55GjrSHGX4g/0/iHdlK6vHqbE3LFAzPnz0cSJKefCAAAgAElEQVSjXEu9Rlxq3G1bUK+zs7HL3fbTHPe5Obhho0ljIyEsnRR++Whz5GZmHppJ/yr9aVe6ndFx7uvzFUc4dy2ZP0Y1pZiDZa9MCvHQXP2g9VhY9TYcWwmVO+X9uS7sg3lPQuw56DQemjwrDT2EzsEFHv8DZnSB3x+Hp5ZBCcs+5y2sm72NPb7FfPEt5pvrx2SaMolPi79p62lMaoz+/S33nYo9xbXL+n2ZKvOOz2ej2eDh4JH7M4tOnrg7uGNnI29DhShIMs4hn0QnR9NncR+8nbz5vdvvFr+vfvvJK/Sfuo2hzcryXs/cb0MRwqplpMGPzfQxC6O3PfjcNaVgz2+w7HW9g2O/X6B0Y7NEFVYuNhJ+7giZqTBspXR3FVZPKUV8evwdzyveWjTmXG1MM6Xd9TndHNyyzydmdz3NOrOY87xiznOM0uRGCJ3M8TOISZkYvXY0Oy/u5Pduv1PJq5LRke4pOS2Tzt9uQClY8XJLnB3kEzdRhBxfDbP76s04mr+Y+8elJcGy12DfHCjfFvr8pDfxEOJuoo7B9E7g6AbDV4FbgNGJhChQSimSM5Jv23p6pwIx5+1eTW6c7ZxvamqTfXYxq3D0cvSilFspqvtUl7OKolCTOX4GmXVoFpsjN/NO43csvugD+GLlUSKuJPH7yCZS9Imip1IHqNQJ1n8OtfqDWy7O0kSH61s7Lx+C1uOg9ZiCGwYvrJdfZRg0H2b2gFl9YehS840TEcICaZqGs70zzvbOBLoG5vpxtza5uVuBeL3JTWxqLPHp8Tc9R1n3svSq2Ise5XtYfM8FIQqKrPg9pENXDjFo+SBalWzFhLYTLP7TpZ2nr9JvylYGNy7DhyE1jY4jhDGiw+GHJnrhF/L9va89uAhCn9cbd/SeBpXaF0xGUXiEr4U5/aFUIxi8QOY7CmEG15vcxKbGsj9qP4vCF7H78m5sNBuaBjYlpGIIbUu1xdHW0eioQuQL2epZwJLSk+i/tD9JGUks6LEATyfL/iQ3JT2Trt9uJDXDxKpXWuHiKKt9oghb9Y4+imHkP1Cy/u2/z0iD1f8H23+EoIb6eT6PoAKPKQqJA/P1Tp9VusJjv4Kt/PtXCHM7E3eG0BOhLD6xmIuJF3F3cKdLuS6EVAyhhk8Ni/+wXoh7yUvhZ9beu5qmddY07aimaeGapo27yzVtNE3bq2naQU3T1pszT3779L9PiYiLYHyL8RZf9AF8vfoYJ6MT+bxvLSn6hGg1Blz84O9xetOWnGLPwS9d9aKv8bMwdLkUfeLhBPeFLp/B0WWw9OXb/84JIfJdaffSvFD3BVb0XsGUDlNoUbIFi8IXMXDZQHov7s0vYb8QnRxtdEwhCozZ3v1rmmYLfA90AM4BOzRNW6yUOpTjGk/gB6CzUuqMpmn+5sqT31acXsFf4X8xMngkjUo0MjrOfe05c42fNp5kYKPSNK8oDSmEwMkdHvkfLH4e9s+D2v31+8PXwsKRkJEKfWdAzd7G5hSFR+OnITEKNnyhf+jQ/n9GJxKiSLC1saVZYDOaBTYjLi2OladXsih8EV/t+ooJuyfQomQLQiqG0DqotXQNFYWa2bZ6aprWFHhPKdUp6+c3AZRS43NcMxoIVEq9k9vntYStnpEJkfRb3I9ynuX4pfMv2NtY9r8kUtIz6f7dJpJSM1j5SivcnCw7rxAFxmSCaW0h4RI89x9s+wH+/RT8q+nb8Xwtv1mTsDJK6St+u37RZ0A2HW10IiGKrJOxJwkND2XJiSVEJUfh6ehJt/LdCKkYQlXvqkbHE+KeLOqMn6ZpfdFX8kZk/fwE0Fgp9XyOayYA9kANwA34Vin16x2eaxQwCqB06dL1IyIizJI5NzJMGQxdMZQTMSf4s8efBLlZ/vavz1Yc4cd/TzBzWCNaV/YzOo4QluXMdpjeEVwDIOEi1B4I3b4GB2ejk4nCypQJfw6Bw0v0hkG1HjM6kRBFWoYpg63ntxJ6IpR/zvxDuimdKl5VCKkYQtfyXfF28jY6ohC3MdsZP03Tymia1j7r+2Kaprnl5mF3uO/WKtMOqA90AzoB72qaVvm2Byk1VSnVQCnVwM/P2MJl8r7J7Ivax7tN3rWKom//uRimbjjJYw2CpOgT4k5KN9aLveRr0ONbCPlRij5hXja20PsnKNsSFj2rz5YUQhjGzsaOlkEt+bL1l6x7bB1vN34bOxs7PtvxGY/8+Qgvr3uZdWfWkW5KNzqqEA/lvmf8NE0bib7a5g1UAIKAycAj93noOaBUjp+DgPN3uCZaKZUIJGqatgGoDRzLVfoCtvPiTqYdmEavCr3oWr6r0XHuKzUjkzf+3I+vqwNvd6tudBwhLFfPSdDxIxnILgqOvRMMmAO/dNNnRD65GEo1NDqVEEWeh6MHA6oOYEDVARy/dlzfCnpyCWvPrMXbyZvu5bsTUjHEKuY2C3Gr+2711DRtL9AI2K6Uqpt13wGlVPB9HmeHXsA9AkQCO4DHlVIHc1xTDZiEvtrnAPwHDFBKhd3teY064xebGkufxX1wsnNiXvd5ONtb/orAV6uO8t0/4Uwf2oB2VWV4qRBCWJyEy/BzR0iJgadWgL+cKxLC0qSb0tkcuZlF4YtYf3Y9GSqD6j7V9a2g5bri4ehhdERRBJlrq2eqUiotx4vYcfuWzdsopTKA54GVwGFgnlLqoKZpz2ia9kzWNYeBFcB+9KLvp3sVfUZRSvG/Lf/jSsoVPmv1mVUUfWGRsfzw7wl61yspRZ8QQlgqV3944i+wdYBZvSHmrNGJhBC3sLexp02pNkxoO4G1j61lbMOxZJoy+WT7J7Sd15bX/n2Njec2kmHKMDqqEPeUmxW/z4EY4EngBWA0cEgp9bb5493OiBW/eUfn8eG2D3mt/msMrTm0QF87L9IyTPT6fjPRCamsfqUVns4ORkcSQghxLxcPwIyu4Bagr/y5+BidSAhxH0euHmFR+CKWnVxGTGoM/sX86V6hO70q9qK8R3mj44lCzixdPTVNswGGAx3RG7asRF+ZM2T6bEEXfidiTjBg6QDqFa/Hj+1/xEYz68z7fDFhzTEmrDnOtCcb0KG6rPYJIYRVOL0ZfnsUAoLhyVBwdDU6kRAiF9Iz01l/bj2h4aFsjNxIpsqkll8telXoRZdyXXBzyE1PRCEeTL4XfllD2GcqpQY/bLj8UpCFX2pmKgOXDeRK8hUW9FyAbzHLb/xw+EIcPb7bRLdaJfh2QF2j4wghhHgQh5fCvCegfFsYOBfsZMeGENYkOjmaZSeXsSh8EeEx4TjaOtKudDtCKobQOKAxtja2RkcUhYS5VvxWAj1ynvMzUkEWfuO3j2fOkTn88MgPtAxqWSCv+TDSM008+sNmLsamsPqV1ni5yBsGIYSwOrt/hcUvQM2++pw/G8vfaSKEuJlSioNXDrIofBHLTy0nPi2eAJcAepTvQUjFEEq7lzY6orByeSn87jvOATgNbNY0bTGQeP1OpdTXDxbPuvx79l/mHJnD4GqDraLoA5i64SRhkXFMHlxPij4hhLBW9Z6ExChY+4E+YqTzp6DdaTSuEMJSaZpGTd+a1PStyRsN32Dd2XUsCl/Ez2E/M+3ANOr51yOkYggdy3bExd7F6LiiiMjNit//7nS/Uup9syS6j4JY8bucdJk+i/sQ4BLA7K6zcbC1/CLq2KV4uk/cRIcaxfn+8XpGxxFCCPEwlIKVb8G2H6Ddu9DqdaMTCSHywaXESyw5uYTQ8FBOx52mmF0xOpTpQEjFEOoXr28VvSSEZTDLVs8cT+4GKKVUQl7C5RdzF34mZWLU6lHsj9rP3O5zraIrU0amiT4/buHstWRWv9IKH1dHoyMJIYR4WCYT/PU0HJgHPb6F+kONTiSEyCdKKfZF7SP0RCgrTq0gIT2Bkq4l6VWhFz0q9CDILcjoiMLCmWWrp6ZpNYHfAO+sn6OBJ3MOYi9MZoTNYPuF7bzf7H2rKPoAftp0in3nYpn0eF0p+oQQorCwsYGQHyD5Kix9BZx9oFoPo1MJIfKBpmnU8a9DHf86jGk4hn/O/MOi8EX8uO9Hftj3A40CGtGrYi/al25vFfOjhXXIzVbPLcDbSql1WT+3AT5RSjUzf7zbmXPF70DUAZ78+0nalW7Hl62/RLOCMxXhlxPoOnEj7ar48+PgelaRWQghxANIS4SZPfVZf08shLItjE4khDCTCwkXWHxiMaEnQjkbfxZnO2c6le1ESMUQ6vrXlfd5Ipu5unruU0rVvt99BcVchV9CWgL9lvQjU2Uyv+d83B3c8/018lumSdF38hZORSey+pXW+LnJap8QQhRKSVdhemeIvwBDl0GJWkYnEkKYkVKK3Zd3syh8EStPryQ5I5nSbqXpVbEXPSv0JMAlwOiIwmDmKvz+Anajb/cEGAw0UEqF5CnlQzJX4ffmxjdZfmo5v3T+hbr+1jH/7qeNJ/lo2WG+HVCHXnVKGh1HCCGEOcWeg587QmY6DF8J3tZxHEEI8XCS0pNYHbGa0BOh7Li4Aw2NJiWaEFIxhHal2+Fk52R0RGEAcxV+XsD7wPW9JRuA95VS1/KU8iGZo/BbcmIJb216i9F1RvNs7Wfz9bnN5WRUAl2+3UjLSn5Me7K+LP0LIURREHUUpncCJw8YtgrcihudSAhRgM7Gn2XJCb0r6PnE87jZu9G5XGd6VexFLd9a8n6wCDFrV09Lkd+F35m4M/Rb0o+q3lWZ3mk6tja2+fbc5mIyKfpP3crRi/GsebU1/u7ySY8QQhQZ53bCzB7gU0Hf9unkYXQiIUQBMykTOy7uIDQ8lNURq0nJTKGcR7nsrqD+zv5GRxRmZq4Vv9VAP6VUTNbPXsBcpVSnPCd9CPlZ+KVnpvPk308SER/Bgh4LKOFaIl+e19xmbD7F+0sO8VW/2vSpL+1+hRCiyAlfA3P6Q6kmMHgB2MsHgEIUVQlpCayKWMWi8EXsubwHG82GZoHNCKkYQttSba1iHrV4cOYq/PYopere776Ckp+F3ze7vmF62HS+bvM1Hcp0yJfnNLeIK4l0mrCBpuV9mD60oSzpCyFEUbX/T1g4Aqp2h8d+BSvYsSKEMK+IuAhCw0NZfGIxl5Iu4e7gTtdyXQmpFEJ17+ryvrEQyUvhZ5OLa0yappXO8SJlAOvaH3oHW89vZUbYDPpW7ms1RZ/JpBgzfz/2NjZ80jtY/uEVQoiirFY/6PwpHFmqz/mzsqMbQoj8V8a9DC/We5GVfVYypf0UmpdszsLjCxmwdAC9F/dm5sGZRCdHGx1TGOS+A9yBt4FNmqatz/q5FTDKfJHM72rKVd7a9BblPMoxpuEYo+Pk2uztEWw/dZXP+9SihEcxo+MIIYQwWpNnITEKNn4FLn7wyLtGJxJCWABbG1ualWxGs5LNiEuLY8WpFYSGh/Llzi+ZsGsCLYJaEFIhhFZBrbC3tTc6rigguWruommaL9Ak68dtSinDPip42K2eSime/+d5tp7fyu/dfqeKd5V8TGc+Z68m0WnCBhqU9WbmU7LFUwghRBalYMmLsPtX6PwZNHnG6ERCCAt1IuYEoSdCWXJiCdHJ0Xg5etGtfDdCKoZYzXtiocvLVs+7rvhlbemMUUrFKqWiNU1LBEKAypqmTVJKpT1kXkPMOTKHDec2MK7ROKv5C66UYtzC/dhoGuNli6cQQoicNA26faMPeV8xFpx99G2gQghxiwqeFXi1/qu8WPdFtpzfwqLwRfxx9A9mHZ5FVe+qhFQMoWu5rng5eRkdVZjBXVf8NE3bDjyqlDqvaVodYA0wHqgFpCulRhRczBseZsXv6NWjPL7scZoGNuW7dt9ZTQE1Z/sZ3vrrAJ88GszjjUvf/wFCCCGKnvQUmNUHzm6DgX9ApfZGJxJCWIGYlBiWn1pO6IlQDl05hJ2NHW2C2hBSMYTmJZtjZ5Obk2GioOVrV09N0/YrpWplff8lYFJKjdE0zQbYe/13BS2vhV9yRjL9l/YnIS2B+T3n4+3kbYZ0+S8yJplO32ygdikPZg1vbDXFqhBCCAOkxMKMbnD1BAxZAkEP9J5ACFHEHb16lNAToSw7uYyrKVfxcfKhR4Ue9KrQi4peFY2OJ3LI766eOSuMdsBaAKWUKQ/ZDPf5js85HXuaj1t8bDVFn1KKcQv2Y1KKT3vXkqJPCCHEvTl56HP9XP1hdj+IOmp0IiGEFaniXYUxDcewpt8avm37LbX9ajPr0CweXfwoA5cOZO6RucSmxhodU+TRvQq/fzRNm6dp2reAF/APgKZpJQCrOt+3JmIN84/NZ2jNoTQNbGp0nFz7c+c5Nh6P5s0uVSnl7Wx0HCGEENbArTg88RfY2MFvvSH2nNGJhBBWxt7Gnnal2/Ftu29Z028NYxqOIc2UxsfbP6bdvHa8vv51NkVuItOUaXRU8QDutdVTA/oDJYB5SqnIrPvrAv5KqZUFljKHB93qeTHxIn0W96G0W2l+7fKr1bSsvRCbTMevN1CjpDtzRjTBxkZW+4QQQjyAC/vgl+7gVgKGrQBn69jtIoSwTEopDl89TGh4KMtOLSM2NRb/Yv70qNCDjmU7Usa9DC72LkbHLDLy9YyfpXqQwi/TlMmwlcM4cvUI83vMp5R7KTOnyx9KKYb9soNtJ6+y4uWWlPGRf4iEEELkwamNesOXErXgyVBwkP+eCCEeXlpmGuvPrWdR+CI2R24mU+krf56OnpR0LUmQWxAlXUvq37sGUdKtJIEugVazAGMN8nWcQ2Ew9cBUdl/ezSctPrGaog9g4e5I1h2N4n89qkvRJ4QQIu/KtYQ+P8GfQ2DekzBwLsgbLyHEQ3KwdaBDmQ50KNOBqKQodl3eRWR8JJEJ+u3wlcOsPbOWDFNG9mM0NPyd/bMLw+sF4fUC0d/ZHxvtXqfQxMMqtIXfnst7mLxvMt3Kd6NHhR5Gx8m1S3EpvL/kIA3LejGkaVmj4wghhLB21XtC929gyUsw93Go8ziUaa43gBFCiIfk5+xH57Kdb7s/05RJVHIU5+LPZReEkQmRnIs/x7YL24hKikJxY+ehvY09ga6BN60SZn/vWhIPRw9pdPiQCmXhF5cWx9gNYwl0CeSdxu8YHSfXlFK8/VcYqRkmPu9bW871CSGEyB/1h0JqAqz7GI6v0u/zqQRlmkHZFvpXjyBDIwohChdbG1sCXAIIcAmgAbfvSEzLTON8wvkbBWHCuexVw4NXDt7WPdTF3uXG9tGsraTXi8JA10Cc7aUR4v3ct/DTNK058B5QJut6DVBKqfLmjZY3Sine3/I+UUlR/NrlV1wdXI2OlGuL951nzeFLvNOtGuV8ZYunEEKIfNTseWj8tN70JWIzRGyBg4tg90z9956l9ZXAMs31QtC7PMin60IIM3GwdaCsR1nKepS94+8T0hJuKwgjEyI5G3+Wree3kpKZctP13k7e2YVg9hnDrFXDAJcA7G1km/t9m7tomnYEeAXYBWT3bFVKXTFvtDu7X3OXhccX8r8t/+Olei8xInhEASZ7OJfjU+j4zQbK+7rw5zPNsJXVPiGEEOZmyoRLB/Ui8HoxmBSt/86thF4AlmmmF4O+VcBGzt8IIYynlOJKyhW9GMxRFF4vEi8kXshuOANgq9lS3Ln4zdtH3W6sGPoW87W6baRm6eqpadp2pVTjh0qWj+5V+J2MPcmApQOo5VuLqR2nWs0BUaUUz87azT9HL7P8xZZU9LeeVUohhBCFiFIQfUwvAk9v1r/GX9B/V8z7RhFYphkEBIONrbF5hRDiDjJMGVxKupRdFJ5LOHdTkRiVHHXT9Y62jtnnC0u6lqSUW6ns70u6lcTdwd2gP8ndmaur5zpN074AFgKp1+9USu1+wHxmlZaZxtgNY3G0deSTlp9YTdEHsOzABVYcvMi4LlWl6BNCCGEcTQO/KvqtwTC9ELx2OseK4GY4slS/1tEdSje5UQwG1pWOoUIIi2BnY5dduN1JSkYK5xPO31YQRiZEsi9qH/Fp8Tdd7+bglr06eKetpI62jgXxx3pouSn8rq/25awoFdAu/+Pk3YTdEzhy9QjftfsOf2fr6VR2JSGV/ws9SO0gD0a0KGd0HCGEEOIGTQPvcvqt7iD9vthIOLMVTm/SC8LrzWLsnSGooV4Elm0OJeuDfTHjsgshxF042TlR3rM85T3v3LIkNjX2RifS+BsrhidiT7AxciOpmak3Xe9XzO+mQjDINSi7OCzuXBxbC9kdUSgGuG88t5HRa0czsOpA3mr8lkHJ8ua5ObtZffASS19sQeXibkbHEUIIIR5MQhSc2XJjVfBiGKDA1kEv/q6vCJZqBI7y3zkhhHUzKRNXkq9wLuHcbaMqIuMjuZh0EZMyZV9vp9kR4BJw05nCnAPuvZ2883S+0Fxn/DyA/wGtsu5aD3yglIq9+6PM59bCLzo5mj6L++BTzIffu/1uNUutAH8fuMCzs3fzRqcqPNe2otFxhBBCiIeXHANntt1oFnN+D6hM0GyhRO0bIyRKN4FiXkanFUKIfJVuSudi4sWbi8IcZw2vply96fpidsVunCe8dVyFWxAu9nfu9G+uwm8BEAZk9XvmCaC2Uqr3fZ9c0zoD3wK2wE9KqU9v+X0bIBQ4lXXXQqXUB/d6zpyFn0mZeHbNs+y6tIs/uv9BBc8K94tkMa4mptHxm/UEeDjx1+jm2Ntaz5lEIYQQItdSE+Dcf1krglvg3E7ITAU0KF7jRrOYMs1kqLwQotBLSk+66XzhrauGiemJN13v6eh5U6OZINcgglyDaB7U3CzNXSoopfrk+Pl9TdP23u9BmqbZAt8DHYBzwA5N0xYrpQ7dculGpVT3XCfO4bdDv7Hl/BbebfKuVRV9AO8vOUhscjq/DW8sRZ8QQojCy9EVKrTTbwDpKRC5K6sQ3AR7foP/pui/86mknw+8XgzKUHkhRCHjbO9MRa+KVPS6fbefUir7fOHZhLM3NZ05eu0o686uI92UnufXzk3hl6xpWgul1CbIHuienIvHNQLClVInsx43F+gF3Fr45cnBKweZsHsC7Uu3p1/lfvnxlAVm1cGLhO49zyvtK1OthOW1hxVCCCHMxt5JL+7KNgfegMz0G0PlT2+GsL9g1y/6tZ5lbl4RlKHyQohCTNM0PJ088XTypIZvjdt+b1ImLiddJjIhkgY80GIfkLvC71lgZtZZPw24CgzNxeNKAmdz/HyOGx1Cc2qqado+4DzwulLq4K0XaJo2ChgFULp0aZLSkxi7YSw+Tj681+w9qxq4GJOUxtuLwqhWwp3Rba1rlVIIIYTId7b2ENRAvzV/6Zah8pvg+ErYN0e/9tah8n5VpRAUQhQZNpoNAS4BBLgE5Onx9y38lFJ7gdqaprln/RyXy+e+07+Jbz1QuBsoo5RK0DStK7AIqHSHDFOBqaCf8ftk+yeciTvDz51+xsPRI5dxLMMHSw9xLTGNGUMbyhZPIYQQ4lY2tlCiln5r8syNofLXx0dEbIawBfq1zj5QuumNERLFa8pQeSGEuIu7Fn6apg1WSs3SNO3VW+4HQCn19X2e+xxQKsfPQeiretlyFpFKqeWapv2gaZqvUir6bk8amxpL6IlQnq71NA0DGt4ngmX558glFu6O5MV2FalZ0roKViGEEMIQOYfKNxyeY6j85huF4G1D5bPOCQbWkaHyQgiR5V4rftd7h95p6E5uhv/tACppmlYOiAQGAI/nvEDTtADgklJKaZrWCLABrtzrSS8kXuBRv0d5pvYzuYhgOWKT03lz4QGqFHfj+Xa3LWoKIYQQIjduGio/WL8v9hxEbL1RDN46VL5sC317qAyVF0IUYbkZ59BcKbX5fvfd5bFdgQno4xymK6U+1jTtGQCl1GRN055HP0OYgd4w5lWl1JZ7PadreVd1dP9RSrqWvN/LW5Qx8/exYHckf41uRq0gT6PjCCGEEIXXfYfKZzWMKdVY7zoqhBBWxlxz/HYrperd776CUqNODXVw7239Xyza+mNRDJn+H6PbVGBM56pGxxFCCCGKluRrcGZ71orgZji/98ZQ+cA6N5rFyFB5IYSVyEvhd68zfk2BZoDfLef83NFX8AxRzM66tmjEp6QzbsF+Kvq78uIjssVTCCGEKHDFvKBKZ/0GN4bKn87aGrp9Cmz5Dn2ofM2bO4e6+hkaXQgh8su9zvg5AK5Z1+Q85xcH9DVnqMLkk+VHuBSXwoJnm+FkL53GhBBCCMPddah81opgzqHyvpWzisCsc4Ie1nXURAghrrtr4aeUWg+s1zTtF6VURAFmKjQ2HY/m9//O8HSr8tQtLVtHhBBCCIt001B5bgyVvz5C4k5D5ctmnRP0KiezBIUQViE3Z/z8gDFADcDp+v1KqXbmjXZnDRo0UDt37jTipR9IQmoGnb7ZgKOdDctfaimrfUIIIYS1MmXCpbAbzWIitkBSVhPy7KHyWSMk/KpIISiEMLt8PeOXw2zgD6A78AwwBIh68HhFy2d/H+F8bDLzn2kqRZ8QQghhzWxsoURt/dbkWX2WYNTRm2cJ5hwqn10INpOh8kIIi5Gbws9HKfWzpmkv5dj+ud7cwazZ1hNX+G1bBMNblKN+GW+j4wghhBAiP2ka+FfVb9lD5U9lFYFb9C2ih5fo19o6gGsAuBUH1+LgFpDj5xxfXXylQBRCmFVuCr/0rK8XNE3rBpwHgswXybolpWUwdsF+yvo483rHKkbHEUIIIYS5aRp4l9dvtw6Vv3QA4i9B/AW4Eq4XhSkxd3gOW3Dx0wtDt4AcReKtxWJxsLUv2D+fEKJQyE3h95GmaR7Aa8B36OMcXjFrKiv2+YqjnLmaxB+jmlDMQT65E0IIIYokjyCo1Q/od/vv0lMg4ZJ+i7+Y4+tFvUiMi9S7jCZGA3foxeDsk1UI3q1IzPpqb10jsIQQ5nXfwk8ptTTr21igrXnjWLcdp68yc+tphjYrS+PyPkbHEUIIIYQlsncCrzL67V4yMyDx8i3F4S1fo47o35sybsLkgwsAACAASURBVH+8o0eOLaYlcmwvvaVIdHSThjRCFAH3Lfw0TZvBHT5uUkoNM0siK5WclsmY+fsJ8irGmM6yxVMIIYQQD8nWDtwD9du9mEx6l9HrK4YJF28vEs9u179mpNz+eHvnW1YLS9x+BtEtAIp5SYEohBXLzVbPpTm+dwIeRT/nJ3L4atVRTkUnMmdkY5wdcvM/qxBCCCFEPrCxAVc//RYQfPfrlIKU2BsFYc7tpde/XgqD8LWQFn/7420d9MLw1i2ltzaskUY1Qlik3Gz1XJDzZ03TfgfWmC2RFdoVcY2fN59icJPSNKvga3QcIYQQQojbaRoU89RvfvfZnZSWeIetpRduFIlXTuhjLJKv3eF1rjequXXVMGs18fr3Lv5g52CeP6sQ4jZ5WZqqBJTO7yDWKiU9kzfm7yPQoxjjulQzOo4QQgghxMNzcAGfCvrtXjJSs4rCO2wxjb8I8efh/B5IjOLejWruViRKoxoh8ktuzvjFo/+TqmV9vQiMNXMuq/HNmmOcjErkt+GNcHWULZ5CCCGEKELsHMGztH67l8wMvfi7dWtpzq9Rx7Ia1aTf/vibGtXcYczF9SJRGtUIcVe52erpVhBBrNHeszFM23CSgY1K0bKSn9FxhBBCCCEsk60duJfQb/diMkHy1TufP7z+9dwO/WtG8u2Pt3cGn4pQq79+c5X3Z0Jcd9fCT9O0evd6oFJqd/7HsR6pGZm88ec+irs78WZX2eIphBBCCPHQbGz05jAuvkDNu1+nFKTG3bzF9Po20zPbYNXbsOZ/UKkT1B0ElTrK4HtR5N1rxe+re/xOAe3yOYtVmbj2OMcvJ/DLUw1xd5J/kQghhBBCFBhNAycP/eZX+fbfXz4Ce2fz/+3deZgcVbn48e+bhAAhkLCEJRAW2RchYEAURBBEEBRUEFFR1Ivivbhd/CkqKorb9XoVvaKIgMgFQVZFRMSFRVSEIEvYgmFNSIAAWdgCJHl/f5wa6DQ9k5kwPZ3UfD/PU89MLV391unT1f32OXWKW34Jk39bBpvZ9hAY/25Ya+uBj1daCkRmiwttl2ITJkzIiRMndjSGSdPmcOCP/srbt1+X/z54u47GIkmSpG4smA9T/gg3nQmTLyvXD64zHrZ/L2zzDhixWqcjlJZIRNyQmRP69JjeJH4RsQ2wFeU+fgBk5hl9jrAfdDrxe27+Qt76w2uY9fRzXP6p1zNqRVv7JEmSlnpPPQaTzoUbz4KHJ5X7Em6xH4x/L2y8h/ce1DJlSRK/3ozq+WVgd0ridymwL3AN0JHEr9N+eMUU7nzoCU59/wSTPkmSpGXFSqvDzh8t04ybSwI46Vy47SJYeSxs9y4Y/x5YY5NORyq1xZBebHMQsCfwUGZ+ANgOWL6tUS2lbps+hx9dMYW3b78ue265VqfDkSRJ0pJYZzt487fh6Mlw8M9h7W3gryfAD18Fp74J/nkGPPtEp6OU+lVvbjz3TGYujIj5EbEK8AjwijbHtdR5fsFC/t95t7DqSsP50lu26nQ4kiRJermGLQ9bH1imuTPglnNKS+DFH4PffRa2OqC0Am6wSxlxVFqG9SbxmxgRo4GfAjcATwLXtTWqpdCPr7yb22fM5SeHvYrRI4Z3OhxJkiT1p1XWgV0/Bbt8EqZNLAPC3Hoh3Hw2jN6gJIDjD138zeqlpVSfRvWMiA2BVTLzlnYFtDidGNzlzofm8pb/vYZ9t1mHHxy6/YA+tyRJkjrkuafhzkvgxjPh3quAgI12K6OCbrE/DB/R6Qg1SLVrcJdfA78Efp2Z9y1hbMus+VUXz1ErLsdxb/W+L5IkSYPG8BGw7TvLNPsBuOnscn/AC4+A5VeBbd5eRgVdb0K5t6C0FOtNZ+XvArsCt0fEeRFxUESssLgH1cVPrr6HSQ/O4asHbMNqK9nFU5IkaVAavT7s/ln4+E3w/kvKrSBuORdO3QtO3AmuOQGeeKjTUUrd6nVXz4gYCrwBOALYJzNXaWdg3RnIrp7/evgJ9vvBNbxxq7U48T07DMhzSpIkaRkxby7c/qsyIMzUayGGwiZ7wfh3w+b7lsFjpDZoS1fPascrAm8BDgF2AH7e9/CWLfMXLOTT59/CyBWG8ZUD7OIpSZKkJiusAju8r0yPTindQG8+B857P6y4GrzyYNj+PeX2EVKH9eYav18CrwYuA04ErszMhe0OrNNOveZebp46mx8cuj1rjPTXGkmSJPVgjU1gry/DG46Fu68oo4Le8DO47iew1itLAvjKd5YbyUsdsNiunhGxD/CHzFwwMCH1bCC6et4980n2/f5f2GPzMZz03lcRXqwrSZKkvnr6cbj1gjIq6IybYMhysPk+ZUCYTfaCob3qfCe9xJJ09ex2cJeI+AxAZl4GvL1p3TeWKMJlwIKFyWfOv4URw4dy/IHbmPRJkiRpyYxYDXY6Aj5yFXz0b7DTh+H+v8PZh8D3toLLvwgzJ3c6Sg0SPY3q+a6G/z/XtG6fNsSyVPjZX+/lhvtn8eW3bMWaKw+awUslSZLUTmttDft8A/7zDjjkLFj3VfD3E8uIoD/dEyaeBvPmdDpK1VhP7cvRzf+t5mvhvkef4juXT2avLdfkwPHrdjocSZIk1c2w4bDl/mV68hG45ZdlVNBLPgWXfQ62fAuMfw9s9HoY0ps7r0m901Pil93832p+mbew6uI5fOgQvv62V9rFU5IkSe01ck147cfgNUfB9H+WBPDW82HSeTBqHGx3aLk1xGobdTpS1UBPPyNsFxFzI+IJYNvq/675V/Zm5xGxT0RMjogpEXFMD9vtGBELIuKgPsbfb874+31cd9/jfHH/rVhrFbt4SpIkaYBElK6f+38Xjr4LDjoN1tgMrv5v+MF4+Nl+cNMv4LmnOh2plmG9voF7n3dcbvh+F/BGYBpwPXBoZt7eYrs/APOA0zLz/J72245RPR947GnedMLVvPoVq/Gzw3e0tU+SJEmdN+dBuPnscn/Ax++B4SNh6wPLqKDr71wSRg1KbbuB+xLaCZiSmfcARMQ5wAHA7U3bfQy4ANixjbF0a+HC5DMX3MywIcE3324XT0mSJC0lRq0Lu30aXnc0PHBtuTfgbb8qt4dYbePSDXS7Q8t20mK084rRdYGpDfPTqmUviIh1gbcBJ/W0o4j4cERMjIiJM2fO7Ncgz7ruAa6953G+sN+WrDNqxX7dtyRJkvSyRcAGr4EDToSjJ8OBP4aV14E/Hw8nbAP/9/Zyv8Dn53U6Ui3F2tni16rprLlf6QnAZzNzQU8tbZl5MnAylK6e/RXgtFlP861L7+B1m67BITuO66/dSpIkSe2x/MjS0jf+3aX7501nl+6g538QVhgFrzy4jAo6dnu7gmoR7Uz8pgGN2dR6wPSmbSYA51RJ3xrAmyNifmb+qo1xAZCZHHPBJAC+9Y5t7eIpSZKkZctqr4A3fAF2/xzce1W5FvDGM+H6U2DNrUpyuO0hZfRQDXrtTPyuBzaNiI2AByk3hH934waZ+cLYtBFxOnDJQCR9AOdcP5VrpjzK1w7chnVH28VTkiRJy6ghQ2DjPcr0zGy47cJya4jLj4U/Hgeb7l1aATd7EwxdrtPRqkPalvhl5vyIOAr4PTCUMmLnbRFxZLW+x+v62mn67Gf4+m/v4LUbr867d1q/U2FIkiRJ/WvF0TDhg2V65M7SCnjLL2HypTBijdICuP17YK2tOx2pBljbbufQLi/3dg6ZyeE/u57r73uc339yN8atNqIfo5MkSZKWMgvmw5Q/llFBJ18GC5+HdcbD9u+Fbd4BI1brdITqoyW5nUM7R/VcKp13wzSuumsmn91nC5M+SZIk1d/QYbD5PnDImWVU0H2+BQsXwKWfhv/ZHM47HP71x7JMtdXOa/yWOg/Nmcfxl9zOThutxmE7b9DpcCRJkqSBtdLqsPNHyzTj5nIt4KRz4baLYOWxsN27yvWAa2zS6UjVzwZN4peZfP6iSTy/YCHffse2DBniKJ6SJEkaxNbZrkx7Hw+Tf1euB/zrCXDNd2HczuVawK3fBsuv3OlI1Q8GTVfPi258kD/f+Qj/701bsOEaK3U6HEmSJGnpMGx52PpAeM958KnbYa/j4OnH4OKPwXc2g4uOhHv/AgsXdjpSvQyDYnCXR+bO443fu5pN1hzJuR95DUNt7ZMkSZK6lwnTJpYBYW69EJ6dC6M3KN1Axx8Kox0Zv5Mc3KWFzOTYX93KvOcX8O2DtjXpkyRJkhYnAsbtCG/5fhkQ5u0/hVU3hCu/ASdsCz9/K9xyLjz3dKcjVS/V/hq/39wyg8tvf5jPv3kLNh4zstPhSJIkScuW4SNg23eWafYDcNPZ5XrAC4+A5VcpN4hffZPSCjh6fVh1gzJQzNDapxrLlFp39Zz5xLPs/b2r2GD1lbjgo6+1tU+SJEnqDwsXwv1/LQngvVfD3OlAQ14xZBissu6LieDorqmaH7k2DKl958O2WZKunrVOw7988a089dwCvnOwXTwlSZKkfjNkCGz0ujIBzH8O5k6DWfeXVsHZ1d9Z95d7BD750KKPHzocRo1btJWwMTkcuWbpbqp+U9vE77e3zODSSQ/xmX02Z5M1HYJWkiRJapthw2G1V5SpleefgTldieH9LyaGsx+AO38LTz/atL8VqqSwoZWwK0kcvSGMWM3EsI9qmfg99uSzfOnXt7LteqP48Ou6qXySJEmSBsZyK8Iam5apleeeejERbE4Op10P82Yvuv3wkQ2JYHNyuAGsOLr9x7SMqWXid9xvbmfuvOf5xUE7M2yofYclSZKkpdrwlWDNLcvUyrw5MHvqol1Iu7qU3vdXeO6JRbdfftRLk8HG+UF4U/raJX6X3foQv7l5Oke/cTM2X3vwvaCSJElS7awwCtYeBWtv89J1mfDMrEWvLexKDh+7G+7+MzzfdNuJFVdrkRg2dCcdPmJgjmsA1Srxm/XUcxz7q1vZeuwqHLn7xp0OR5IkSVK7RZRr/kasBmPHv3R9Jjz92Eu7kM66Hx6+HSZfBgueXfQxK41p3YV09AYwaj1YboWBObZ+VKvE76uX3M7sp5/jjA/uxHJ28ZQkSZIUASutUab1XvXS9QsXwlOPtL6+cPqNcMdvYOHziz5m5XW6v75w1HowdLmBObY+qE3i98fbH+aiGx/kE3tuylZjV+l0OJIkSZKWBUOGwMprl2ncTi9dv3ABPDGj6drCqlvp1Gvh1gsgF7y4fQx58R6Gjd1Hu5LDVdaFIUMH7vgqtUj85jz9PJ+/aBJbrL0y/7HHJp0OR5IkSVJdDBlaWvFGrQcbvPal6xfMh7kPthh45gG496rub27/Qivhhosmhm26uX0tEr/jf3s7jz31HKcdviPDh9nFU5IkSdIAGTqsJG2rbtB6/fznYM7Ul97YfvYD8K8/wJMPN+2v4eb2zdcXrrpBuf5wCSzzid8Vkx/h/BumcdQem7DNuqM6HY4kSZIkvWjYcFh94zK18vwz1a0qHnjp4DN3XNLi5vYrLlkYS/SopcTcec/zuQsmsdlaI/nYnnbxlCRJkrSMWW5FGLNZmVp59snSYth470K+0eenWaYTv2/89g4eeWIePzlsF5YfNvAXSEqSJElSWy0/ssXN7fue+C2zF8RdfddMzrl+Kh/ebWO2Gze60+FIkiRJ0lJrmUz8nnx2Pp+7cBIbj1mJT+61aafDkSRJkqSl2jLZ1fObl97BjDnPcP5HX8sKy9nFU5IkSZJ6ssy1+D357HzO+scDfGjXjdhh/VU7HY4kSZIkLfWWucTvwVnPsNEaK3H03pt3OhRJkiRJWiYsc4nfcwsW8u2DtrWLpyRJkiT10jKX+I0dtSI7brhap8OQJEmSpGXGMpf4rT5yeKdDkCRJkqRlyjKX+EmSJEmS+sbET5IkSZJqzsRPkiRJkmrOxE+SJEmSas7ET5IkSZJqrq2JX0TsExGTI2JKRBzTYv0BEXFLRNwUERMjYtd2xiNJkiRJg9Gwdu04IoYCJwJvBKYB10fExZl5e8NmfwIuzsyMiG2Bc4Et2hWTJEmSJA1G7Wzx2wmYkpn3ZOZzwDnAAY0bZOaTmZnV7EpAIkmSJEnqV+1M/NYFpjbMT6uWLSIi3hYRdwK/BT7YakcR8eGqK+jEmTNntiVYSZIkSaqrdiZ+0WLZS1r0MvOizNwCOBA4vtWOMvPkzJyQmRPGjBnTz2FKkiRJUr21M/GbBoxrmF8PmN7dxpl5NbBxRKzRxpgkSZIkadBpZ+J3PbBpRGwUEcOBdwEXN24QEZtERFT/7wAMBx5rY0ySJEmSNOi0bVTPzJwfEUcBvweGAqdl5m0RcWS1/iTgHcD7IuJ54BngkIbBXiRJkiRJ/SCWtTxrwoQJOXHixE6HIUmSJEkdERE3ZOaEvjymrTdwlyRJkiR1nomfJEmSJNWciZ8kSZIk1ZyJnyRJkiTVnImfJEmSJNWciZ8kSZIk1ZyJnyRJkiTVnImfJEmSJNWciZ8kSZIk1ZyJnyRJkiTVnImfJEmSJNWciZ8kSZIk1ZyJnyRJkiTVnImfJEmSJNWciZ8kSZIk1ZyJnyRJkiTVnImfJEmSJNWciZ8kSZIk1ZyJnyRJkiTVnImfJEmSJNWciZ8kSZIk1ZyJnyRJkiTVnImfJEmSJNWciZ8kSZIk1ZyJnyRJkiTVnImfJEmSJNWciZ8kSZIk1ZyJnyRJkiTVnImfJEmSJNWciZ8kSZIk1ZyJnyRJkiTVnImfJEmSJNWciZ8kSZIk1ZyJnyRJkiTVXFsTv4jYJyImR8SUiDimxfr3RMQt1fS3iNiunfFIkiRJ0mDUtsQvIoYCJwL7AlsBh0bEVk2b3Qu8PjO3BY4HTm5XPJIkSZI0WLWzxW8nYEpm3pOZzwHnAAc0bpCZf8vMWdXstcB6bYxHkiRJkgaldiZ+6wJTG+anVcu68yHgd61WRMSHI2JiREycOXNmP4YoSZIkSfXXzsQvWizLlhtG7EFJ/D7ban1mnpyZEzJzwpgxY/oxREmSJEmqv2Ft3Pc0YFzD/HrA9OaNImJb4BRg38x8rI3xSJIkSdKg1M4Wv+uBTSNio4gYDrwLuLhxg4hYH7gQOCwz72pjLJIkSZI0aLWtxS8z50fEUcDvgaHAaZl5W0QcWa0/CfgSsDrwo4gAmJ+ZE9oVkyRJkiQNRpHZ8rK7pdaECRNy4sSJnQ5DkiRJkjoiIm7oa4NZW2/gLkmSJEnqPBM/SZIkSao5Ez9JkiRJqjkTP0mSJEmqORM/SZIkSao5Ez9JkiRJqjkTP0mSJEmqORM/SZIkSao5Ez9JkiRJqjkTP0mSJEmqORM/SZIkSao5Ez9JkiRJqjkTP0mSJEmqORM/SZIkSao5Ez9JkiRJqjkTP0mSJEmqORM/SZIkSao5Ez9JkiRJqjkTP0mSJEmqORM/SZIkSao5Ez9JkiRJqjkTP0mSJEmqORM/SZIkSao5Ez9JkiRJqjkTP0mSJEmqORM/SZIkSao5Ez9JkiRJqjkTP0mSJEmqORM/SZIkSao5Ez9JkiRJqjkTP0mSJEmqORM/SZIkSao5Ez9JkiRJqjkTP0mSJEmqubYmfhGxT0RMjogpEXFMi/VbRMTfI+LZiPh0O2ORJEmSpMFqWLt2HBFDgROBNwLTgOsj4uLMvL1hs8eBjwMHtisOSZIkSRrs2tnitxMwJTPvyczngHOAAxo3yMxHMvN64Pk2xiFJkiRJg1o7E791gakN89OqZZIkSZKkAdTOxC9aLMsl2lHEhyNiYkRMnDlz5ssMS5IkSZIGl3YmftOAcQ3z6wHTl2RHmXlyZk7IzAljxozpl+AkSZIkabBoZ+J3PbBpRGwUEcOBdwEXt/H5JEmSJEkttG1Uz8ycHxFHAb8HhgKnZeZtEXFktf6kiFgbmAisAiyMiE8CW2Xm3HbFJUmSJEmDTdsSP4DMvBS4tGnZSQ3/P0TpAipJkiRJapO23sBdkiRJktR5Jn6SJEmSVHMmfpIkSZJUcyZ+kiRJklRzJn6SJEmSVHMmfpIkSZJUcyZ+kiRJklRzJn6SJEmSVHMmfpIkSZJUcyZ+kiRJklRzJn6SJEmSVHMmfpIkSZJUcyZ+kiRJklRzJn6SJEmSVHMmfpIkSZJUcyZ+kiRJklRzJn6SJEmSVHMmfpIkSZJUcyZ+kiRJklRzJn6SJEmSVHMmfpIkSZJUcyZ+kiRJklRzJn6SJEmSVHMmfpIkSZJUcyZ+kiRJklRzJn6SJEmSVHMmfpIkSZJUcyZ+kiRJklRzJn6SJEmSVHMmfpIkSZJUcyZ+kiRJklRzJn6SJEmSVHMmfpIkSZJUcyZ+kiRJklRzbU38ImKfiJgcEVMi4pgW6yMiflCtvyUidmhnPJIkSZI0GLUt8YuIocCJwL7AVsChEbFV02b7AptW04eBH7crHkmSJEkarNrZ4rcTMCUz78nM54BzgAOatjkAOCOLa4HREbFOG2OSJEmSpEGnnYnfusDUhvlp1bK+biNJkiRJehmGtXHf0WJZLsE2RMSHKV1BAZ6NiFtfZmzqmzWARzsdxCBjmQ88y3zgWeYDzzIfeJb5wLPMB55lPvA27+sD2pn4TQPGNcyvB0xfgm3IzJOBkwEiYmJmTujfUNUTy3zgWeYDzzIfeJb5wLPMB55lPvAs84FnmQ+8iJjY18e0s6vn9cCmEbFRRAwH3gVc3LTNxcD7qtE9dwbmZOaMNsYkSZIkSYNO21r8MnN+RBwF/B4YCpyWmbdFxJHV+pOAS4E3A1OAp4EPtCseSZIkSRqs2tnVk8y8lJLcNS47qeH/BP6jj7s9uR9CU99Y5gPPMh94lvnAs8wHnmU+8CzzgWeZDzzLfOD1ucyj5F6SJEmSpLpq5zV+kiRJkqSlwDKT+EXEaRHxiLdyGDgRMS4iroiIOyLitoj4RKdjqruIWCEirouIm6sy/0qnYxoMImJoRNwYEZd0OpbBIiLui4hJEXHTkoxMpr6LiNERcX5E3Fmd11/T6ZjqLCI2r+p31zQ3Ij7Z6bjqLiI+VX1+3hoRZ0fECp2Oqe4i4hNVed9mHW+PVnlQRKwWEX+IiH9Vf1dd3H6WmcQPOB3Yp9NBDDLzgaMzc0tgZ+A/ImKrDsdUd88Cb8jM7YDxwD7ViLdqr08Ad3Q6iEFoj8wc7xDgA+b7wGWZuQWwHdb5tsrMyVX9Hg+8ijKI3UUdDqvWImJd4OPAhMzchjK44Ls6G1W9RcQ2wBHATpTzyv4RsWlno6ql03lpHnQM8KfM3BT4UzXfo2Um8cvMq4HHOx3HYJKZMzLzn9X/T1C+JKzb2ajqLYsnq9nlqskLcdsoItYD9gNO6XQsUrtExCrAbsCpAJn5XGbO7mxUg8qewN2ZeX+nAxkEhgErRsQwYAQt7g+tfrUlcG1mPp2Z84GrgLd1OKba6SYPOgD4efX/z4EDF7efZSbxU2dFxIbA9sA/OhtJ/VXdDm8CHgH+kJmWeXudAHwGWNjpQAaZBC6PiBsi4sOdDmYQeAUwE/hZ1a35lIhYqdNBDSLvAs7udBB1l5kPAt8BHgBmUO4PfXlno6q9W4HdImL1iBhBuU3buA7HNFis1XX/8+rvmot7gImfFisiRgIXAJ/MzLmdjqfuMnNB1TVoPWCnqhuF2iAi9gceycwbOh3LILRLZu4A7EvpRr5bpwOquWHADsCPM3N74Cl60S1IL19EDAfeCpzX6VjqrrrG6QBgI2AssFJEvLezUdVbZt4B/BfwB+Ay4GbKpUJaCpn4qUcRsRwl6TsrMy/sdDyDSdUN60q8trWddgHeGhH3AecAb4iIMzsb0uCQmdOrv49QrnvaqbMR1d40YFpDD4LzKYmg2m9f4J+Z+XCnAxkE9gLuzcyZmfk8cCHw2g7HVHuZeWpm7pCZu1G6I/6r0zENEg9HxDoA1d9HFvcAEz91KyKCcj3IHZn53U7HMxhExJiIGF39vyLlQ+zOzkZVX5n5ucxcLzM3pHTF+nNm+utwm0XEShGxctf/wN6U7kJqk8x8CJgaEZtXi/YEbu9gSIPJodjNc6A8AOwcESOq7zB74iBGbRcRa1Z/1wfejvV9oFwMvL/6//3Arxf3gGFtDacfRcTZwO7AGhExDfhyZp7a2ahqbxfgMGBSdc0ZwOcz89IOxlR36wA/j4ihlB9mzs1MbzGgulkLuKh8L2MY8IvMvKyzIQ0KHwPOqroe3gN8oMPx1F51zdMbgY90OpbBIDP/ERHnA/+kdDe8ETi5s1ENChdExOrA88B/ZOasTgdUN63yIOBbwLkR8SHKjx4HL3Y/mQ4YKEmSJEl1ZldPSZIkSao5Ez9JkiRJqjkTP0mSJEmqORM/SZIkSao5Ez9JkiRJqjkTP0mSJEmqORM/SVK/i4iMiP9pmP90RBzXT/s+PSIO6o99LeZ5Do6IOyLiihbrNouISyNiSrXNuRGxVkTsHhFLdO/NiPhkdd+3fhcR20fEKdX/x0XEp1ts852IeEM7nl+S1HkmfpKkdngWeHtErNHpQBpFxNA+bP4h4N8zc4+mfawA/Bb4cWZukplbAj8GxrzM8D4J9Cnx68PxfB7438Vs87/AMX15fknSssPET5LUDvOBk4FPNa9obrGLiCerv7tHxFVV69ldEfGtiHhPRFwXEZMiYuOG3ewVEX+pttu/evzQiPjviLg+Im6JiI807PeKiPgFMKlFPIdW+781Iv6rWvYlYFfgpIj476aHvBv4e2b+pmtBZl6Rmbc27XeRlrVq/xtGxEoR8duIuLladkhEfBwYC1zR1cIYEXtHxN8j4p8RcV5EjKyW3xcRX4qIa4CDI+LjEXF7dczntDi+lYFtM/PmFuuOiIjfRcSKmXk/sHpErN28nSRp2Tes0wFIkmrrROCWiPh2Hx6zHbAl8DhwD3BKZu4UEZ8APkZpFQPYEHg9sDElWdoEeB8wJzN3jIjlgb9GxOXV9jsB22TmvY1PFhFjgf8CXgXMAi6PiAMz86tVt8dPZ+bEphi3AW7owzE1JDSnnQAAFbRJREFU2weYnpn7VTGMysw5EfGfwB6Z+WjVUnossFdmPhURnwX+E/hqtY95mblr9fjpwEaZ+WxEjG7xfBOAW5sXRsRRwN7AgZn5bLX4n8AuwAUv4/gkSUshW/wkSW2RmXOBM4CP9+Fh12fmjCoRuRvoStwmUZK9Ludm5sLM/BclQdyCksS8LyJuAv4BrA5sWm1/XXPSV9kRuDIzZ2bmfOAsYLc+xLskJlFaLP8rIl6XmXNabLMzsBUleb0JeD+wQcP6Xzb8fwtwVkS8l9LS2mwdYGbTssOAfYF3NCR9AI9QWh4lSTVj4idJaqcTKNfKrdSwbD7V509EBDC8YV1jErKwYX4hi/ZSyabnSSCAj2Xm+GraKDO7EsenuokvensgDW6jtBAuzgvHWVkBIDPvqh4/Cfhm1a20VVx/aDiWrTLzQw3rG49nP0rr6quAGyKiuTfPM13P3eBWSiK9XtPyFartJUk1Y+InSWqbzHwcOJeS/HW5jxcTpwOA5ZZg1wdHxJDqur9XAJOB3wMfjYjl4IWRN1fqaSeUlsHXR8Qa1UAphwJXLeYxvwBeGxH7dS2IiH0i4pVN290H7FCt3wHYqPp/LPB0Zp4JfKdrG+AJYOXq/2uBXaourETEiIjYrDmQiBgCjMvMK4DPAKOBkU2b3QFs0rTsRuAjwMVVPF02o0W3UEnSss/ET5LUbv8DNI7u+VNKsnUd8Gq6b43ryWRKgvY74MjMnAecAtwO/DMibgV+wmKuZc/MGcDngCuAm4F/ZuavF/OYZ4D9gY9FxL8i4nbgcEo3yUYXAKtVXTU/CtxVLX8lcF21/AvA16rlJwO/i4grMnNmtc+zI+IWSiK4RYtwhgJnRsQkSjL3vcyc3RTvncCoapCXxuXXAJ8GflslvstREsTmaxolSTUQmc29ZSRJUp1ExKeAJzLzlB62eRuwQ2Z+ceAikyQNFFv8JEmqvx+z6PWTrQyjtM5KkmrIFj9JkiRJqjlb/CRJkiSp5kz8JEmSJKnmTPwkSZIkqeZM/CRJkiSp5kz8JEmSJKnmTPwkSZIkqeZM/CRJkiSp5kz8JEmSJKnmTPwkSZIkqeZM/CRJkiSp5kz8JEmSJKnmTPwkSZIkqeZM/CRJkiSp5kz8JEmSJKnmTPwkSZIkqeZM/CRJkiSp5kz8VGsRsX5EPBkRQzsdy2AWEa+LiMmdjqNLFD+LiFkRcV0H43hPRFzei+1OiogvDkRMAy0iNoyIjIhh1fyVEfFvHYjj8Ii4ZqCfd2kQEW+LiKnVuXL7TsfTFxFxX0Ts1ek4Xq7G44iIz0fEKZ2OabBpPhdJdWTip1qoPjSfqb64dE1jM/OBzByZmQs6HWOjiPhy9QHTpy8sEfGmiLg6Ip6IiJkRcVVEvLVdcfaXzPxLZm7e6Tga7Aq8EVgvM3dqXlklAQuqejQ3Im6KiP37O4jMPCsz9+7Fdkdm5vH9/fzNqqRrXnXcj0bEhRGxTrufd2nX8IWw8fxycw/bH1DVmblVOf4pIjYcuIj77DvAUdW58sZOBzNQWv3IUL3Om3QqJoDM/EZmLvbHj079SKIXRcRtDeeEBQ3nzycj4vOdjk9qZuKnOnlL9cWla5rezidb0l8FI2Jj4CBgRh8fdxBwHnAGsB6wFvAl4C1LEsdAWUp/Pd0AuC8zn+phm79n5khgNHAqcG5ErNa80VJ6fC/HUdVxbwKMpCQFKkY3nF+2a7VBlTScARwNjAI2An4ELOyvIKoW6/78/N4AuG0JY7E3hQbE0niuzcytu84JwF948QeUkZn5jebtl8Zj0OBi4qdaa9GNbKOGFrM/RsSJEXFmtW73iJjW9PjG7jfHRcT5EXFmRMwFDo+IIRFxTETcHRGPRUTL5KDJD4HPAs/14TgC+C5wfGaekplzMnNhZl6VmUdU2wyJiGMj4v6IeCQizoiIUU3l8IGqS9esiDgyInaMiFsiYnZE/LDh+Q6PiL9GxP9GxJyIuDMi9mxY/4GIuKMqx3si4iMN63aPiGkR8dmIeAj4WXPZVuserB4/uWvfEbF8RJwQEdOr6YSIWL5pv0dXxzcjIj7QQ5mNjYiLI+LxiJgSEV3l9CHgFOA11a+yX+mp7DNzIXAasCLwilbHV+13/6qVZ3ZE/C0itm2IZVyU1rOZVT35YUM5X9P1GkfE96pjm1O9LttU606PiK817O+I6pger45xbMO6rF7bf1Wv84lV/emTzJwN/AoY37DvHut7ROxaHfvsqp4dXi3fLyJujNICNjUijutrPK30sh62rC8RsXpVdnOjdPfduD9iopTXvZn5pyyeyMwLMvOB6nmHRunKd3cV9w0RMa5a99qIuL56/a+PiNc2xHtlRHw9Iv4KPE2pi6Mi4tTq2B6MiK9FlYhFxCZRegTMidLq+MsW5bd8RDwJDAVujoi7q+VbVs83O0qLxlsbHnN6RPw4Ii6NiKeAPVrs98oqlr9V77HfVOV9VlXe10dDC2hEfL+qF3Or8nhdw7rjqnp2RlVet0XEhOYyr94vcyLilxGxQvXYVSPikup9N6v6f71q3deB1wE/rGL8YURcXe3v5mrZIT3to+FYj49yvnwiIi6PiDUWV0ki4rAo5+rHIuILTeuOixc/l1aI8pnzWPV6XB8Ra7WK/+WWZXRznqrWfTDKe21WRPw+IjZY3DFWjzs8Iq6JiO9Uj703IvZtWN/yPN0Qb/Nn7nERcV617ImImBQRm0XE56K8z6dGxN4N++jpPTK0iuvRiLgH2K83x9QXEfFvUb5z/CAiHgeOrWI4vWGbTSIiG+ZHR7kUYUaUc9hXo39/6NFglplOTsv8BNwH7NVi+YZAAsOq+b9TWjCGU7r7zQXOrNbtDkzrbr/AccDzwIGUH01WBD4JXEtpgVse+Alwdg9xHgz8ulXMwLuBW7p53BbVcWzUw74/CEwBXkFpqbkQ+L+mcjgJWAHYG5hH+WK/JrAu8Ajw+mr7w4H5wKeA5YBDgDnAatX6/ShflAN4PeWL6A4N5Tgf+K+qTFZsLFtgc2AqMLYhto2r/79aleeawBjgb5Rkt3G/X61ienP1vKt2Ux5XUVpaVqB8GZ8J7NlwfNf0UJYvrAeGAZ8AnqC04LQ6vh2q8ns15Uv0+6vXd/lq/mbge8BKVTy7tnieNwE3UFoYA9gSWKdadzrwter/NwCPVs+5PPC/wNUNsSdwSbWf9avj3qeX76MrgX+r/l8d+CNVfa2WdVvfq+d6Aji0en1WB8Y3vHavpLxvtgUeBg7s5j36Qgy9iLc39bBlfQHOAc6tXpNtgAe7qxPNMS4mpldQ3lvfoyRFI5vW/z9gEuV9EMB2VVmtBswCDqPUuUOr+dUbyuUBYOtq/XKU9+9PqmNYE7gO+Ei1/dnAF6oyf6HOdRNzAptU/y9HOY98nnKefEP1um7eUBfnALt07bubejSlem1GAbcDdwF7VbGfAfysYfv3VmUwjNJS+lDXfinn3XnV6zcU+CZwbdM5+jpgbFWGdwBHNtThdwAjgJUpPSZ+1aq+tyqLPuzjbmAzyrngSuBbi6kjWwFPArtR3kffpdTVxs+ars+ljwC/qZ5/KPAqYJUe4l+isqTn89SB1eu5ZbXfY4G/9fI9ejjlc/OI6jk+CkwHohfn6eN46Wdu1zG8iRfr0r2Uur5c9Tz3Njx/T++RI4E7gXGUunMFi56LfgTM7mZ6yWd1N6/Hv1Wv7Uer418R+BpwesM2mwDZMH9J9dwjgLUpnwsf6k15Ozktbup4AE5O/TFRPvyfbDgp/6pavmHXiZzyxXQ+MKLhcWfSt8Tv6qb1d3R9SFXz61QfVC/5gkhJxv5FlbzRTbLazfHtUh3HS75kNWzzJ+DfG+Y374qloRzWbVj/GHBIw/wFwCer/w+n4cO5WnYdcFg3z/0r4BMN5fhcY6wsmvhtQkmS9gKWa9rP3cCbG+bfROmS2bWPZxrLttrPzi3iGQcsAFZuWPbNrg9bepf4za/q0qOUZGevHo7vx1QJasOyyZRk5DWULzOt6sQLcVC+YN8F7AwMadrudF5M/E4Fvt1Ur54HNqzmk4Yv+ZTk5phe1rMrKcnRnGo/NwHr96a+A58DLurl85wAfK/5PdoQQ68Sv17Uw5b1hfIF7Hlgi4Z13+iuTjTE2PjF79M9xLFzVe4zKV9ST6dKAKt6cUCLxxwGXNe07O/A4Q3l8tWGdWsBzwIrNiw7FLii+v8M4GTKdayLK7fGxO91lGRhSMP6s4HjGuriGb2oR19omP8f4HcN828Bburh8bOA7ar/jwP+2LBuK+CZhvn7gPc2zH8bOKmb/Y4HZjXF2WPi18t9HNsw/+/AZYspny8B5zTMr0Q5p7RK/D5I+QFs227Kucf3Sm/Lkp7PU7+jIfGgJGFPAxv0om4dDkxpmB9RlfHaLP48fRwv/cw9DvhDU116Ehhaza9c7X80i3+P/JnqR4Jqfm96+QNPD/W+VeJ3T9OybhM/yo+wzwDLN6w/rPGYnZxezmTTserkwMwcXU0Htlg/Fng8M59uWDa1j8/RvP0GwEVVF5zZlC/GCygfOM2+QmmBu7ePzwklSYPyRbs7Y4H7G+bvp3whb4zl4Yb/n2kxP7Jh/sHMzKb9jQWIiH0j4tqqe85syi/Ijd2bZmbmvFZBZuYUSsvRccAjEXFOvNhVsdUxjG2Yfywz5zfMP90Uc5eu1/qJpn2t2yqmblxb1aU1MnPnzPxjw7rm49sAOLqrHlRlMq6KYxxwf1PcL5GZf6Z0Az4ReDgiTo6IVbo5tvsbHvckpX40HttDDf93V0bd+XhmjqK0zK1Kad3r0lN9H0dJ3F8iIl4dEVdUXcjmUH5pX2x3uMXpRT3srr6Mobw3Gt/PjfWuO2s0nGO+U8XQOLjD6wAy89rMfGdmjqEkUrtRWiSg+3JqrvtdMTW+ro3xbkBp4ZjR8Hr8hNKqAfAZSovidVWMH+zF8XXFMTVLF+fexNGdXp9ronTHvSNKV83ZlFbCxtexuT6vEIteK9WyvkfEiIj4SdWlci5wNTA6+nBdYi/30df321gayjDLtcaPdbPt/wG/B86J0v392xGxXA/xLmlZ9nSe2gD4fkM9e5xSt3p7Pn3hORs+f0fSu/N0q7rWXJcezRcHcHumYf+Le48s8jrQu3PAkujL94wNKK3ADzfEfCKtv1NIfWbip8FkBrBaRIxoWDau4f+nKL9GAi8MWjCmaR/ZND8V2Lfhy+DozFwhMx9s8fx7Ah+PiIeiXBs2jjJgyGd7Efvk6rne0cM20ykfGl26Wjgfbr35Yq0bsci1YesD06Ncc3cBpcvsWpk5GriU8kWgS3M5LSIzf5GZu1bxJqXbZHfHsCSD9EynvNYrN+2r1euyJFrVg6831YMRmXl2tW796MVF/Zn5g8x8FaU732aUboHNFimjiFiJ0rWrv46tK5ZJlF+mG68R7Km+T6X76+R+AVwMjKuSypNYtL70WS/rYXdmUt4bje//9ZckjmwY3CEz/9Ji/fWUbtfbVIu6K6fmut8VU+Pr2ljvplJaMxqT0VUyc+vqeR/KzCMycyylu+CPonejVU4HxjVdU9RTHC9LlSx/FngnpRvuaEqL88uqH5WjKT0fXp2Zq1AScBr23ZvjWNw+lsQMGupe9Zm0eqsNM/P5zPxKZm4FvBbYH3hf1+rGbV9mWfZ0nppK6R7Z+L5fMTP/1ov99qQ35+mXU9d6fI/Q9DrQdA6IchudJ7uZ+jIYUvMxLPJdg9L62Rjz05TLKhpj3hapH5j4adDIzPuBicBxETE8Il7DoiNi3kX59XO/6hfVYym/vPXkJODrUV3oHhFjIuKAbrbdk/Llb3w1Tad8ITuxF7En8J/AF6MMaLFKlIE2do2Ik6vNzgY+FWUAm5GUrmu/XFxLUw/WpCSqy0XEwZTrOy6lXPezPNWX5ygX6i/2lgRdImLziHhD9cV9HuUX2q5fa8+mXPw+JsoACV+idMftk8ycSuke9c0ogyNsC3wIOKuv++qlnwJHVi1bERErVfVoZUoX2RnAt6rlK0TELs07iDLQzquruvcUpWxa3YbkF8AHImJ8VYbfAP6RmfctLsh4cZCfDXt5XD+n1IOuwT16qu9nAXtFxDsjYliUwTy6BoZZmfLL/ryI2IlyPetiLSbeJa6HVevAhZRzwYiI2IpyXebLVr0nj4iINav5LSjld221ySnA8RGxaVVXto2I1Snvrc0i4t1V+R1C6Yp3STfHMAO4HPifhvPBxhHx+up5D44XByGZRfny2Zvb2vyDUv8+U733d6ecJ8/pc2H0zsqUJHwmMCwivgS0aule0n0/A8yOMgjRl5vWP0y5JrOnZYvbx5I4H9i/qivDKdehtvw+FhF7RMQrqx8i51K6KHe9jq1iXdKy7Ok8dRLwuYjYuoppVPWZ0BXjlbEEAza1+zy9uPcIpTv2xyNivYhYFTim6fFH5qIjhTdOW7PkbgJeH2UwndGNz1uVyVXAdxpi3iQidutuZ1JfmPhpsHkP5VqGxyitGb+k/CJIZs6hXJ9xCuUXx6eAaa1384LvU1oyLo+IJyhf7l7dasPMfKz6Ff6hzHyI8uE9q+qqR5SbeXf7K2Jmnk8ZZOWDlKTx4eoYfl1tchqlW9DVlIvd5wEfW0z8PfkHsCnlGrevAwdVx/AE8HHKh+Ysypf4i/uw3+WBb1X7fYiSWHTd7+hrlOT8FsoAGP+sli2JQynXZk0HLgK+nJl/WMJ99SgzJ1IGFfghpUymUK5t6Uoy3kK5juMBSp06pMVuVqEkkLMoXY4eo8WtFDLzT8AXKa1dMyitR+/qZajjqn33qnUwM58DflA9H/RQ37OMWvlmSgvJ45QvN123PPh34KvVY75EqTsvK95+qIdHUbqDPUS5bu1nfXhsT2ZTEr1JUUbMvIxS/75drf8uJebLKV/kT6Vcg/QYpTXnaMpr/xlg/8x8tIfneh8lAb6dUgbn82J38B2Bf1QxXEy59nGx3cyr1/ytwL6U9+iPgPdl5p29Ovq++z3lGrK7KK/1PPreBb87J1AG0+i6TveypvXfBw6KMtrkD6plxwE/j9LN7p292EefZeZtwH9QfsSZQXntuvusWZvyus6ldK2+ihd/DGuOf4nLsqfzVGZeROmVcU6U7q63UupHl3HAX3vzPC20+zzd03vkp5Qyu5nyWXNhPz5vT7rOCZMoCXfzeeu9lOs+u2I+j0VbBaUl1jWqkjQoRRni/M7M7I9fcWsjyjD8/1Z1x1RNRMSxlOsTf9LpWHpjWYtXGmyqVuXzMvM1nY5F0uKZ+GlQiYgdKa0R91K6hf0KeE1m3tjRwJYyJn6SJEn1YldPDTZrU4ZcfpLShe2jJn2SpP5Udd1/uYOCSFK/ssVPkiRJkmrOFj9JkiRJqjkTP0mSJEmqORM/SZIkSao5Ez9JkiRJqjkTP0mSJEmqORM/SZIkSaq5/w/KyQ/1gsPDJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>k</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.324857</td>\n",
       "      <td>0.651405</td>\n",
       "      <td>0.831604</td>\n",
       "      <td>0.752643</td>\n",
       "      <td>0.961514</td>\n",
       "      <td>0.945145</td>\n",
       "      <td>0.940383</td>\n",
       "      <td>0.953102</td>\n",
       "      <td>0.943330</td>\n",
       "      <td>0.949613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.969192</td>\n",
       "      <td>0.597159</td>\n",
       "      <td>0.762551</td>\n",
       "      <td>0.525901</td>\n",
       "      <td>0.473357</td>\n",
       "      <td>0.452191</td>\n",
       "      <td>0.370379</td>\n",
       "      <td>0.342994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_score</th>\n",
       "      <td>0.490403</td>\n",
       "      <td>0.788910</td>\n",
       "      <td>0.895142</td>\n",
       "      <td>0.665946</td>\n",
       "      <td>0.850552</td>\n",
       "      <td>0.675782</td>\n",
       "      <td>0.629729</td>\n",
       "      <td>0.613373</td>\n",
       "      <td>0.531913</td>\n",
       "      <td>0.503961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "k                1         2         3         4         5         6   \\\n",
       "precision  0.324857  0.651405  0.831604  0.752643  0.961514  0.945145   \n",
       "recall     1.000000  1.000000  0.969192  0.597159  0.762551  0.525901   \n",
       "f_score    0.490403  0.788910  0.895142  0.665946  0.850552  0.675782   \n",
       "\n",
       "k                7         8         9         10  \n",
       "precision  0.940383  0.953102  0.943330  0.949613  \n",
       "recall     0.473357  0.452191  0.370379  0.342994  \n",
       "f_score    0.629729  0.613373  0.531913  0.503961  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_num += 1\n",
    "show_results(fig_num, get_scores(max_k=10, D=words, metric=manhattan_distance, normed=True, seed=seed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Cosine Similarity\n",
    "\n",
    "> (6) Now re-run the k-means clustering algorithm you implemented in part (1) but this time use cosine similarity as the distance (similarity) measure.Vary the value of k from 1 to 10 andcompute the precision, recall, and F-score for each set of clusters. Plot k in the horizontal axis and precision, recall and F-score in the vertical axis in the same plot. (10 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using cosine similarity directly with our k-means clustering algorithm will not work properly, as it is a *similarity measure* rather than a *distance measure*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cosine_similarity(u, v):\n",
    "    \"\"\"Return cosine similarity of two np.array vectors.\"\"\"\n",
    "    \n",
    "    if np.array_equal(u, v):\n",
    "        # we specify this to avoid rounding errors\n",
    "        cos_theta = 1\n",
    "    else:\n",
    "        cos_theta = u.dot(v)/( np.sqrt(u.dot(u)) * np.sqrt(v.dot(v)) )\n",
    "    \n",
    "    return cos_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(cosine_similarity(a,a))\n",
    "print(cosine_similarity(a,b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our clustering algorithm relies upon a distance metric such that $ d(x,y) \\to 0 $ as $ y \\to x $, whereas cosine similarity gives us $sim(x,y) \\to 1$ as $y \\to x$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than rewriting our k-means algorithm, we will instead define `angular_distance()` in terms of `cosine_similarity()` and use that as our metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def angular_distance(u, v):\n",
    "    \"\"\"Return angular distance between two np.array vectors.\"\"\"\n",
    "\n",
    "    cos_theta = cosine_similarity(u, v)\n",
    "\n",
    "    theta = np.arccos(cos_theta)\n",
    "    \n",
    "    if theta < 0:\n",
    "        theta += 2 * np.pi\n",
    "    \n",
    "    return theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n",
      "[1 0]\n",
      "0.0\n",
      "0.0\n",
      "1.5707963267948966\n"
     ]
    }
   ],
   "source": [
    "print(a)\n",
    "print(b)\n",
    "print(angular_distance(a,a))\n",
    "print(angular_distance(b,b))\n",
    "print(angular_distance(a,b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having seen that `angular_distance()` behaves suitably, we then plot its results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>k</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.324857</td>\n",
       "      <td>0.651405</td>\n",
       "      <td>0.805828</td>\n",
       "      <td>0.717341</td>\n",
       "      <td>0.960442</td>\n",
       "      <td>0.956548</td>\n",
       "      <td>0.947196</td>\n",
       "      <td>0.968664</td>\n",
       "      <td>0.960158</td>\n",
       "      <td>0.959606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.951335</td>\n",
       "      <td>0.581755</td>\n",
       "      <td>0.605317</td>\n",
       "      <td>0.548836</td>\n",
       "      <td>0.494295</td>\n",
       "      <td>0.479690</td>\n",
       "      <td>0.373973</td>\n",
       "      <td>0.338829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_score</th>\n",
       "      <td>0.490403</td>\n",
       "      <td>0.788910</td>\n",
       "      <td>0.872557</td>\n",
       "      <td>0.642472</td>\n",
       "      <td>0.742607</td>\n",
       "      <td>0.697481</td>\n",
       "      <td>0.649597</td>\n",
       "      <td>0.641636</td>\n",
       "      <td>0.538288</td>\n",
       "      <td>0.500822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "k                1         2         3         4         5         6   \\\n",
       "precision  0.324857  0.651405  0.805828  0.717341  0.960442  0.956548   \n",
       "recall     1.000000  1.000000  0.951335  0.581755  0.605317  0.548836   \n",
       "f_score    0.490403  0.788910  0.872557  0.642472  0.742607  0.697481   \n",
       "\n",
       "k                7         8         9         10  \n",
       "precision  0.947196  0.968664  0.960158  0.959606  \n",
       "recall     0.494295  0.479690  0.373973  0.338829  \n",
       "f_score    0.649597  0.641636  0.538288  0.500822  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_num += 1\n",
    "show_results(fig_num, get_scores(max_k = 10, D=words, metric=angular_distance, seed=seed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare and Discuss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> (7) Comparing the different clusterings you obtained in (2)-(6) discuss what is the best setting for k-means clustering for this dataset. (20 marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_metrics(seed):\n",
    "    \"\"\"Return k=4 results for all metrics (normed and not), given seed.\"\"\"\n",
    "    \n",
    "    metrics = euclidean_distance, manhattan_distance, angular_distance\n",
    "    \n",
    "    normed = False, True\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    for m in metrics:\n",
    "        for boolean in normed:\n",
    "            if (m == angular_distance) and (boolean == True):\n",
    "                # don't need to take norm for angular_distance\n",
    "                pass\n",
    "            else:\n",
    "                results[f'{m.__name__}, normed={boolean}'] =\\\n",
    "                KMeans(k=4, D=words, metric=m, normed=boolean, max_iterations=100, seed=seed).f_score\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = compare_metrics(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "With a randomising seed of 29, our best f-score was given by euclidean_distance, normed=False\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>euclidean_distance, normed=False</th>\n",
       "      <th>euclidean_distance, normed=True</th>\n",
       "      <th>manhattan_distance, normed=False</th>\n",
       "      <th>manhattan_distance, normed=True</th>\n",
       "      <th>angular_distance, normed=False</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f_score</th>\n",
       "      <td>0.907721</td>\n",
       "      <td>0.587873</td>\n",
       "      <td>0.642134</td>\n",
       "      <td>0.665946</td>\n",
       "      <td>0.642472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         euclidean_distance, normed=False  euclidean_distance, normed=True  \\\n",
       "f_score                          0.907721                         0.587873   \n",
       "\n",
       "         manhattan_distance, normed=False  manhattan_distance, normed=True  \\\n",
       "f_score                          0.642134                         0.665946   \n",
       "\n",
       "         angular_distance, normed=False  \n",
       "f_score                        0.642472  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'\\nWith a randomising seed of {seed}, our best f-score was given by {max(results, key=lambda key: results[key])}')\n",
    "pd.DataFrame(results,index=['f_score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know that our data should divide into four categories, so we are primarily interested in the case k=4. It seems like the best f-score is clearly given by `euclidean_distance()` when the data is not first regularized with the $ L_2 $ norm. Looking at the plots, we also see that this is the only case where the f-score peaks at k=4.\n",
    "\n",
    "So *based on the clusterings obtained in tasks (2)-(6), that would seem to be the best setting for k-means clustering for this dataset*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*However*, such a conclusion does not account for the possibility of our results being due to the idiosynchrasies of the initial random centroid positions.\n",
    "\n",
    "We can investigate whether this is the case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "seeded_results = {}\n",
    "for i in range(30):\n",
    "    seeded_results[i] = compare_metrics(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = pd.DataFrame(seeded_results).transpose()\n",
    "table['best'] = table.idxmax(axis=1)\n",
    "\n",
    "table = table.sort_values(table.columns[0], ascending=False)\n",
    "\n",
    "table.index.name = 'seed'\n",
    "table.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table: Cluster F-Scores for Different Metrics given Different Seeds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seed</th>\n",
       "      <th>euclidean_distance, normed=False</th>\n",
       "      <th>euclidean_distance, normed=True</th>\n",
       "      <th>manhattan_distance, normed=False</th>\n",
       "      <th>manhattan_distance, normed=True</th>\n",
       "      <th>angular_distance, normed=False</th>\n",
       "      <th>best</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>0.955827</td>\n",
       "      <td>0.926310</td>\n",
       "      <td>0.946709</td>\n",
       "      <td>0.972068</td>\n",
       "      <td>0.974981</td>\n",
       "      <td>angular_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0.951888</td>\n",
       "      <td>0.960005</td>\n",
       "      <td>0.958406</td>\n",
       "      <td>0.961017</td>\n",
       "      <td>0.969215</td>\n",
       "      <td>angular_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>0.951727</td>\n",
       "      <td>0.963565</td>\n",
       "      <td>0.961017</td>\n",
       "      <td>0.961017</td>\n",
       "      <td>0.966422</td>\n",
       "      <td>angular_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>0.946924</td>\n",
       "      <td>0.963565</td>\n",
       "      <td>0.944333</td>\n",
       "      <td>0.960767</td>\n",
       "      <td>0.966422</td>\n",
       "      <td>angular_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0.942758</td>\n",
       "      <td>0.961002</td>\n",
       "      <td>0.958384</td>\n",
       "      <td>0.969213</td>\n",
       "      <td>0.974981</td>\n",
       "      <td>angular_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>0.942758</td>\n",
       "      <td>0.958201</td>\n",
       "      <td>0.958201</td>\n",
       "      <td>0.960849</td>\n",
       "      <td>0.966422</td>\n",
       "      <td>angular_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24</td>\n",
       "      <td>0.939274</td>\n",
       "      <td>0.960053</td>\n",
       "      <td>0.944162</td>\n",
       "      <td>0.683858</td>\n",
       "      <td>0.974981</td>\n",
       "      <td>angular_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21</td>\n",
       "      <td>0.937913</td>\n",
       "      <td>0.672434</td>\n",
       "      <td>0.949567</td>\n",
       "      <td>0.797838</td>\n",
       "      <td>0.662653</td>\n",
       "      <td>manhattan_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>0.937709</td>\n",
       "      <td>0.649431</td>\n",
       "      <td>0.938577</td>\n",
       "      <td>0.972068</td>\n",
       "      <td>0.651866</td>\n",
       "      <td>manhattan_distance, normed=True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0.932914</td>\n",
       "      <td>0.958201</td>\n",
       "      <td>0.944333</td>\n",
       "      <td>0.960756</td>\n",
       "      <td>0.966422</td>\n",
       "      <td>angular_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>29</td>\n",
       "      <td>0.907721</td>\n",
       "      <td>0.587873</td>\n",
       "      <td>0.642134</td>\n",
       "      <td>0.665946</td>\n",
       "      <td>0.642472</td>\n",
       "      <td>euclidean_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18</td>\n",
       "      <td>0.883603</td>\n",
       "      <td>0.958384</td>\n",
       "      <td>0.944333</td>\n",
       "      <td>0.972068</td>\n",
       "      <td>0.966422</td>\n",
       "      <td>manhattan_distance, normed=True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.775315</td>\n",
       "      <td>0.689468</td>\n",
       "      <td>0.670690</td>\n",
       "      <td>0.690260</td>\n",
       "      <td>0.724263</td>\n",
       "      <td>euclidean_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19</td>\n",
       "      <td>0.753795</td>\n",
       "      <td>0.757331</td>\n",
       "      <td>0.748283</td>\n",
       "      <td>0.679056</td>\n",
       "      <td>0.753795</td>\n",
       "      <td>euclidean_distance, normed=True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.752118</td>\n",
       "      <td>0.961002</td>\n",
       "      <td>0.775827</td>\n",
       "      <td>0.969213</td>\n",
       "      <td>0.969215</td>\n",
       "      <td>angular_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5</td>\n",
       "      <td>0.719766</td>\n",
       "      <td>0.713318</td>\n",
       "      <td>0.709995</td>\n",
       "      <td>0.694792</td>\n",
       "      <td>0.713318</td>\n",
       "      <td>euclidean_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>15</td>\n",
       "      <td>0.713309</td>\n",
       "      <td>0.687420</td>\n",
       "      <td>0.686183</td>\n",
       "      <td>0.680755</td>\n",
       "      <td>0.683981</td>\n",
       "      <td>euclidean_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>0.705581</td>\n",
       "      <td>0.701061</td>\n",
       "      <td>0.684090</td>\n",
       "      <td>0.690868</td>\n",
       "      <td>0.695682</td>\n",
       "      <td>euclidean_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9</td>\n",
       "      <td>0.697747</td>\n",
       "      <td>0.694601</td>\n",
       "      <td>0.679511</td>\n",
       "      <td>0.683146</td>\n",
       "      <td>0.688588</td>\n",
       "      <td>euclidean_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10</td>\n",
       "      <td>0.696591</td>\n",
       "      <td>0.687666</td>\n",
       "      <td>0.763871</td>\n",
       "      <td>0.679056</td>\n",
       "      <td>0.683238</td>\n",
       "      <td>manhattan_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>28</td>\n",
       "      <td>0.691521</td>\n",
       "      <td>0.689468</td>\n",
       "      <td>0.683417</td>\n",
       "      <td>0.681133</td>\n",
       "      <td>0.688895</td>\n",
       "      <td>euclidean_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>0.689571</td>\n",
       "      <td>0.691673</td>\n",
       "      <td>0.682424</td>\n",
       "      <td>0.672323</td>\n",
       "      <td>0.688588</td>\n",
       "      <td>euclidean_distance, normed=True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0.689468</td>\n",
       "      <td>0.689468</td>\n",
       "      <td>0.508260</td>\n",
       "      <td>0.682495</td>\n",
       "      <td>0.684889</td>\n",
       "      <td>euclidean_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>26</td>\n",
       "      <td>0.687666</td>\n",
       "      <td>0.687420</td>\n",
       "      <td>0.676935</td>\n",
       "      <td>0.683026</td>\n",
       "      <td>0.687256</td>\n",
       "      <td>euclidean_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>20</td>\n",
       "      <td>0.674104</td>\n",
       "      <td>0.672012</td>\n",
       "      <td>0.658591</td>\n",
       "      <td>0.671208</td>\n",
       "      <td>0.678985</td>\n",
       "      <td>angular_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>0.663455</td>\n",
       "      <td>0.691673</td>\n",
       "      <td>0.676255</td>\n",
       "      <td>0.683274</td>\n",
       "      <td>0.692569</td>\n",
       "      <td>angular_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>17</td>\n",
       "      <td>0.655823</td>\n",
       "      <td>0.966417</td>\n",
       "      <td>0.655623</td>\n",
       "      <td>0.674704</td>\n",
       "      <td>0.974981</td>\n",
       "      <td>angular_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8</td>\n",
       "      <td>0.582002</td>\n",
       "      <td>0.667937</td>\n",
       "      <td>0.594844</td>\n",
       "      <td>0.673047</td>\n",
       "      <td>0.683155</td>\n",
       "      <td>angular_distance, normed=False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11</td>\n",
       "      <td>0.504114</td>\n",
       "      <td>0.724263</td>\n",
       "      <td>0.502615</td>\n",
       "      <td>0.706068</td>\n",
       "      <td>0.709443</td>\n",
       "      <td>euclidean_distance, normed=True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>27</td>\n",
       "      <td>0.500655</td>\n",
       "      <td>0.503199</td>\n",
       "      <td>0.505028</td>\n",
       "      <td>0.504945</td>\n",
       "      <td>0.502824</td>\n",
       "      <td>manhattan_distance, normed=False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    seed  euclidean_distance, normed=False  euclidean_distance, normed=True  \\\n",
       "0     25                          0.955827                         0.926310   \n",
       "1      4                          0.951888                         0.960005   \n",
       "2     13                          0.951727                         0.963565   \n",
       "3     23                          0.946924                         0.963565   \n",
       "4      2                          0.942758                         0.961002   \n",
       "5      7                          0.942758                         0.958201   \n",
       "6     24                          0.939274                         0.960053   \n",
       "7     21                          0.937913                         0.672434   \n",
       "8      6                          0.937709                         0.649431   \n",
       "9      1                          0.932914                         0.958201   \n",
       "10    29                          0.907721                         0.587873   \n",
       "11    18                          0.883603                         0.958384   \n",
       "12    12                          0.775315                         0.689468   \n",
       "13    19                          0.753795                         0.757331   \n",
       "14    14                          0.752118                         0.961002   \n",
       "15     5                          0.719766                         0.713318   \n",
       "16    15                          0.713309                         0.687420   \n",
       "17    16                          0.705581                         0.701061   \n",
       "18     9                          0.697747                         0.694601   \n",
       "19    10                          0.696591                         0.687666   \n",
       "20    28                          0.691521                         0.689468   \n",
       "21    22                          0.689571                         0.691673   \n",
       "22     0                          0.689468                         0.689468   \n",
       "23    26                          0.687666                         0.687420   \n",
       "24    20                          0.674104                         0.672012   \n",
       "25     3                          0.663455                         0.691673   \n",
       "26    17                          0.655823                         0.966417   \n",
       "27     8                          0.582002                         0.667937   \n",
       "28    11                          0.504114                         0.724263   \n",
       "29    27                          0.500655                         0.503199   \n",
       "\n",
       "    manhattan_distance, normed=False  manhattan_distance, normed=True  \\\n",
       "0                           0.946709                         0.972068   \n",
       "1                           0.958406                         0.961017   \n",
       "2                           0.961017                         0.961017   \n",
       "3                           0.944333                         0.960767   \n",
       "4                           0.958384                         0.969213   \n",
       "5                           0.958201                         0.960849   \n",
       "6                           0.944162                         0.683858   \n",
       "7                           0.949567                         0.797838   \n",
       "8                           0.938577                         0.972068   \n",
       "9                           0.944333                         0.960756   \n",
       "10                          0.642134                         0.665946   \n",
       "11                          0.944333                         0.972068   \n",
       "12                          0.670690                         0.690260   \n",
       "13                          0.748283                         0.679056   \n",
       "14                          0.775827                         0.969213   \n",
       "15                          0.709995                         0.694792   \n",
       "16                          0.686183                         0.680755   \n",
       "17                          0.684090                         0.690868   \n",
       "18                          0.679511                         0.683146   \n",
       "19                          0.763871                         0.679056   \n",
       "20                          0.683417                         0.681133   \n",
       "21                          0.682424                         0.672323   \n",
       "22                          0.508260                         0.682495   \n",
       "23                          0.676935                         0.683026   \n",
       "24                          0.658591                         0.671208   \n",
       "25                          0.676255                         0.683274   \n",
       "26                          0.655623                         0.674704   \n",
       "27                          0.594844                         0.673047   \n",
       "28                          0.502615                         0.706068   \n",
       "29                          0.505028                         0.504945   \n",
       "\n",
       "    angular_distance, normed=False                              best  \n",
       "0                         0.974981    angular_distance, normed=False  \n",
       "1                         0.969215    angular_distance, normed=False  \n",
       "2                         0.966422    angular_distance, normed=False  \n",
       "3                         0.966422    angular_distance, normed=False  \n",
       "4                         0.974981    angular_distance, normed=False  \n",
       "5                         0.966422    angular_distance, normed=False  \n",
       "6                         0.974981    angular_distance, normed=False  \n",
       "7                         0.662653  manhattan_distance, normed=False  \n",
       "8                         0.651866   manhattan_distance, normed=True  \n",
       "9                         0.966422    angular_distance, normed=False  \n",
       "10                        0.642472  euclidean_distance, normed=False  \n",
       "11                        0.966422   manhattan_distance, normed=True  \n",
       "12                        0.724263  euclidean_distance, normed=False  \n",
       "13                        0.753795   euclidean_distance, normed=True  \n",
       "14                        0.969215    angular_distance, normed=False  \n",
       "15                        0.713318  euclidean_distance, normed=False  \n",
       "16                        0.683981  euclidean_distance, normed=False  \n",
       "17                        0.695682  euclidean_distance, normed=False  \n",
       "18                        0.688588  euclidean_distance, normed=False  \n",
       "19                        0.683238  manhattan_distance, normed=False  \n",
       "20                        0.688895  euclidean_distance, normed=False  \n",
       "21                        0.688588   euclidean_distance, normed=True  \n",
       "22                        0.684889  euclidean_distance, normed=False  \n",
       "23                        0.687256  euclidean_distance, normed=False  \n",
       "24                        0.678985    angular_distance, normed=False  \n",
       "25                        0.692569    angular_distance, normed=False  \n",
       "26                        0.974981    angular_distance, normed=False  \n",
       "27                        0.683155    angular_distance, normed=False  \n",
       "28                        0.709443   euclidean_distance, normed=True  \n",
       "29                        0.502824  manhattan_distance, normed=False  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Table: Cluster F-Scores for Different Metrics given Different Seeds')\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table[table.columns[1:]].plot(figsize=(15,8))\n",
    "plt.ylim(0.4,1)\n",
    "plt.xlim(0,29)\n",
    "plt.xticks(range(0,30), labels=list(table.seed))\n",
    "plt.grid(True)\n",
    "plt.xlabel('Randomizing Seed')\n",
    "plt.ylabel('F-Score')\n",
    "\n",
    "\n",
    "our_case = table.loc[table.seed==seed].index.values\n",
    "plt.annotate('Our Best Result', \n",
    "             xy=(our_case, table[table.iloc[our_case].best.values[0]][our_case]), \n",
    "             xytext=(14, 0.55),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            )\n",
    "\n",
    "plt.annotate('Our Randomizing Seed', \n",
    "             xy=(our_case, 0.4), \n",
    "             xytext=(14, 0.45),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            )\n",
    "\n",
    "fig_num += 1\n",
    "plt.title(f'Figure {fig_num}: Effect of Randomizing Seed on Cluster F-Scores for Different Metrics',y=-0.15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table: Number of Times a Metric gives Best F-Score for Given Seed\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>best</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>angular_distance, normed=False</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euclidean_distance, normed=False</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manhattan_distance, normed=False</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euclidean_distance, normed=True</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manhattan_distance, normed=True</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  best\n",
       "angular_distance, normed=False      13\n",
       "euclidean_distance, normed=False     9\n",
       "manhattan_distance, normed=False     3\n",
       "euclidean_distance, normed=True      3\n",
       "manhattan_distance, normed=True      2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Table: Number of Times a Metric gives Best F-Score for Given Seed')\n",
    "pd.DataFrame(table.best.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table[table.columns[1:]].boxplot(figsize=(15,8), rot=5, fontsize='large')\n",
    "fig_num += 1\n",
    "plt.title(f'Figure {fig_num}: Box-Plots showing Range of Cluster F-Scores for Different Metrics',y=-0.15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further analysis demonstrates that the superior performance of the clustering algorithm with the parameters `metric=euclidean_distance, normed=False`, was a fluke of the initial centroid positions generated by the randomizing seed (see Figure 6).\n",
    "\n",
    "When we run the clustering algorithm with k=4, and the different combinations of metric and normed, using a range of different randomizing seeds (from 0 to 29), and compare f-scores, we find that it is actually `angular_distance` which returns the best f-score on the most occasions. (Aside: since the angular distance between two vectors should be unaffected by the vector's magnitudes, we only run the normed=False version for this case; if we did, we would actually find that slight rounding errors would give subtly different scores, but this is not what we are interested in.) We also see that every combination returns the best f-score on at least two occasions.\n",
    "\n",
    "When we plot the range of f-score results for each metric/normed combination (Figure 7), we see that `metric=euclidean_distance, normed=False` had the best *median* result, but the worst maximum result. And this analysis is based only on thirty different possibilities, out of a set of $ {329 \\choose 4 } = \\frac{329!}{4!(329-4)!} = 479,318,126$ ways that we could choose 4 out of 329 datapoints on which to place our centroid's starting positions. So we cannot really say that any one metric/normed combination works best for this dataset -- all we can say is that one would be well advised to run the clustering algorithm with several different randomizing seeds."
   ]
  }
 ],
 "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": "3.7.6"
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}