Clustering Homework.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "df = pd.DataFrame(X)\n",
    "df.columns = iris.feature_names\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset we're using today contains information about flowers called an iris.  We're going to use K-Means clustering to atttempt to identify unique types of iris.\n",
    "\n",
    "Step 1:  Use my method below to identify how many groups are in the dataset.\n",
    "\n",
    "Step 2:  add the kmeans cluster labels into the dataframe df.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def find_k (X, k_range, sample_percent=1):\n",
    "    \"\"\"\n",
    "    k_range: a list of possible k values\n",
    "    X, the data we're clustering on\n",
    "    \"\"\"\n",
    "    from sklearn.cluster import KMeans\n",
    "    import matplotlib.pyplot as plt\n",
    "    import numpy as np\n",
    "    from scipy.spatial.distance import cdist\n",
    "    from sklearn.metrics import pairwise_distances\n",
    "\n",
    "    N = X.shape[0]\n",
    "    sampleSize = X.shape[0] * sample_percent\n",
    "\n",
    "    if sampleSize > 0:\n",
    "        index = np.arange(np.shape(X)[0])\n",
    "        np.random.shuffle(index)\n",
    "        X =  X[index, :]\n",
    "\n",
    "\n",
    "    mean_distortions=[]\n",
    "    for k in k_range:\n",
    "        #cluster using k, then calculate the mean distortion (average distance to closest centroid)\n",
    "        kmeans_model = KMeans(n_clusters=k, init='k-means++', n_jobs=-1).fit(X)\n",
    "        mean_distortions.append(sum(np.min(pairwise_distances(X, kmeans_model.cluster_centers_,\n",
    "                                                              metric='euclidean'),axis=1)) / X.shape[0])\n",
    "\n",
    "\n",
    "    #visualize results\n",
    "    plt.plot(k_range, mean_distortions)\n",
    "    plt.xlabel(\"K Value\")\n",
    "    plt.ylabel(\"Mean Distortion\")\n",
    "    plt.title(\"Elbow Graph for Mean Distortion per K\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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BawN3S7o7Ip4tV3hSyaz3hUKBQqFQcccTJsDnPgcLFsCQIbUM2cyscbW1tdHW1tbl59V9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\nP/UkSQwPm/JvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19b61c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_k(np.matrix(X), range(1,10),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=3, n_init=10,\n",
       "    n_jobs=1, precompute_distances='auto', random_state=42, tol=0.0001,\n",
       "    verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_model = KMeans(n_clusters=3, random_state=42)\n",
    "kmeans_model.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['labels'] = kmeans_model.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                 5.1               3.5                1.4               0.2   \n",
       "1                 4.9               3.0                1.4               0.2   \n",
       "2                 4.7               3.2                1.3               0.2   \n",
       "3                 4.6               3.1                1.5               0.2   \n",
       "4                 5.0               3.6                1.4               0.2   \n",
       "5                 5.4               3.9                1.7               0.4   \n",
       "6                 4.6               3.4                1.4               0.3   \n",
       "7                 5.0               3.4                1.5               0.2   \n",
       "8                 4.4               2.9                1.4               0.2   \n",
       "9                 4.9               3.1                1.5               0.1   \n",
       "10                5.4               3.7                1.5               0.2   \n",
       "11                4.8               3.4                1.6               0.2   \n",
       "12                4.8               3.0                1.4               0.1   \n",
       "13                4.3               3.0                1.1               0.1   \n",
       "14                5.8               4.0                1.2               0.2   \n",
       "15                5.7               4.4                1.5               0.4   \n",
       "16                5.4               3.9                1.3               0.4   \n",
       "17                5.1               3.5                1.4               0.3   \n",
       "18                5.7               3.8                1.7               0.3   \n",
       "19                5.1               3.8                1.5               0.3   \n",
       "20                5.4               3.4                1.7               0.2   \n",
       "21                5.1               3.7                1.5               0.4   \n",
       "22                4.6               3.6                1.0               0.2   \n",
       "23                5.1               3.3                1.7               0.5   \n",
       "24                4.8               3.4                1.9               0.2   \n",
       "25                5.0               3.0                1.6               0.2   \n",
       "26                5.0               3.4                1.6               0.4   \n",
       "27                5.2               3.5                1.5               0.2   \n",
       "28                5.2               3.4                1.4               0.2   \n",
       "29                4.7               3.2                1.6               0.2   \n",
       "..                ...               ...                ...               ...   \n",
       "70                5.9               3.2                4.8               1.8   \n",
       "71                6.1               2.8                4.0               1.3   \n",
       "72                6.3               2.5                4.9               1.5   \n",
       "73                6.1               2.8                4.7               1.2   \n",
       "74                6.4               2.9                4.3               1.3   \n",
       "75                6.6               3.0                4.4               1.4   \n",
       "76                6.8               2.8                4.8               1.4   \n",
       "77                6.7               3.0                5.0               1.7   \n",
       "78                6.0               2.9                4.5               1.5   \n",
       "79                5.7               2.6                3.5               1.0   \n",
       "80                5.5               2.4                3.8               1.1   \n",
       "81                5.5               2.4                3.7               1.0   \n",
       "82                5.8               2.7                3.9               1.2   \n",
       "83                6.0               2.7                5.1               1.6   \n",
       "84                5.4               3.0                4.5               1.5   \n",
       "85                6.0               3.4                4.5               1.6   \n",
       "86                6.7               3.1                4.7               1.5   \n",
       "87                6.3               2.3                4.4               1.3   \n",
       "88                5.6               3.0                4.1               1.3   \n",
       "89                5.5               2.5                4.0               1.3   \n",
       "90                5.5               2.6                4.4               1.2   \n",
       "91                6.1               3.0                4.6               1.4   \n",
       "92                5.8               2.6                4.0               1.2   \n",
       "93                5.0               2.3                3.3               1.0   \n",
       "94                5.6               2.7                4.2               1.3   \n",
       "95                5.7               3.0                4.2               1.2   \n",
       "96                5.7               2.9                4.2               1.3   \n",
       "97                6.2               2.9                4.3               1.3   \n",
       "98                5.1               2.5                3.0               1.1   \n",
       "99                5.7               2.8                4.1               1.3   \n",
       "\n",
       "    labels  \n",
       "0        1  \n",
       "1        1  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  \n",
       "5        1  \n",
       "6        1  \n",
       "7        1  \n",
       "8        1  \n",
       "9        1  \n",
       "10       1  \n",
       "11       1  \n",
       "12       1  \n",
       "13       1  \n",
       "14       1  \n",
       "15       1  \n",
       "16       1  \n",
       "17       1  \n",
       "18       1  \n",
       "19       1  \n",
       "20       1  \n",
       "21       1  \n",
       "22       1  \n",
       "23       1  \n",
       "24       1  \n",
       "25       1  \n",
       "26       1  \n",
       "27       1  \n",
       "28       1  \n",
       "29       1  \n",
       "..     ...  \n",
       "70       0  \n",
       "71       0  \n",
       "72       0  \n",
       "73       0  \n",
       "74       0  \n",
       "75       0  \n",
       "76       0  \n",
       "77       2  \n",
       "78       0  \n",
       "79       0  \n",
       "80       0  \n",
       "81       0  \n",
       "82       0  \n",
       "83       0  \n",
       "84       0  \n",
       "85       0  \n",
       "86       0  \n",
       "87       0  \n",
       "88       0  \n",
       "89       0  \n",
       "90       0  \n",
       "91       0  \n",
       "92       0  \n",
       "93       0  \n",
       "94       0  \n",
       "95       0  \n",
       "96       0  \n",
       "97       0  \n",
       "98       0  \n",
       "99       0  \n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   },
   "outputs": [
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       "<div>\n",
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       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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       "  </thead>\n",
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       "      <th>52</th>\n",
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       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
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       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>3.0</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "52                 6.9               3.1                4.9               1.5   \n",
       "77                 6.7               3.0                5.0               1.7   \n",
       "100                6.3               3.3                6.0               2.5   \n",
       "102                7.1               3.0                5.9               2.1   \n",
       "103                6.3               2.9                5.6               1.8   \n",
       "104                6.5               3.0                5.8               2.2   \n",
       "105                7.6               3.0                6.6               2.1   \n",
       "107                7.3               2.9                6.3               1.8   \n",
       "108                6.7               2.5                5.8               1.8   \n",
       "109                7.2               3.6                6.1               2.5   \n",
       "110                6.5               3.2                5.1               2.0   \n",
       "111                6.4               2.7                5.3               1.9   \n",
       "112                6.8               3.0                5.5               2.1   \n",
       "115                6.4               3.2                5.3               2.3   \n",
       "116                6.5               3.0                5.5               1.8   \n",
       "117                7.7               3.8                6.7               2.2   \n",
       "118                7.7               2.6                6.9               2.3   \n",
       "120                6.9               3.2                5.7               2.3   \n",
       "122                7.7               2.8                6.7               2.0   \n",
       "124                6.7               3.3                5.7               2.1   \n",
       "125                7.2               3.2                6.0               1.8   \n",
       "128                6.4               2.8                5.6               2.1   \n",
       "129                7.2               3.0                5.8               1.6   \n",
       "130                7.4               2.8                6.1               1.9   \n",
       "131                7.9               3.8                6.4               2.0   \n",
       "132                6.4               2.8                5.6               2.2   \n",
       "134                6.1               2.6                5.6               1.4   \n",
       "135                7.7               3.0                6.1               2.3   \n",
       "136                6.3               3.4                5.6               2.4   \n",
       "137                6.4               3.1                5.5               1.8   \n",
       "139                6.9               3.1                5.4               2.1   \n",
       "140                6.7               3.1                5.6               2.4   \n",
       "141                6.9               3.1                5.1               2.3   \n",
       "143                6.8               3.2                5.9               2.3   \n",
       "144                6.7               3.3                5.7               2.5   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "\n",
       "     labels  \n",
       "52        2  \n",
       "77        2  \n",
       "100       2  \n",
       "102       2  \n",
       "103       2  \n",
       "104       2  \n",
       "105       2  \n",
       "107       2  \n",
       "108       2  \n",
       "109       2  \n",
       "110       2  \n",
       "111       2  \n",
       "112       2  \n",
       "115       2  \n",
       "116       2  \n",
       "117       2  \n",
       "118       2  \n",
       "120       2  \n",
       "122       2  \n",
       "124       2  \n",
       "125       2  \n",
       "128       2  \n",
       "129       2  \n",
       "130       2  \n",
       "131       2  \n",
       "132       2  \n",
       "134       2  \n",
       "135       2  \n",
       "136       2  \n",
       "137       2  \n",
       "139       2  \n",
       "140       2  \n",
       "141       2  \n",
       "143       2  \n",
       "144       2  \n",
       "145       2  \n",
       "147       2  \n",
       "148       2  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['labels']==2]"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   },
   "outputs": [
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       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
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       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "50                 7.0               3.2                4.7               1.4   \n",
       "51                 6.4               3.2                4.5               1.5   \n",
       "53                 5.5               2.3                4.0               1.3   \n",
       "54                 6.5               2.8                4.6               1.5   \n",
       "55                 5.7               2.8                4.5               1.3   \n",
       "56                 6.3               3.3                4.7               1.6   \n",
       "57                 4.9               2.4                3.3               1.0   \n",
       "58                 6.6               2.9                4.6               1.3   \n",
       "59                 5.2               2.7                3.9               1.4   \n",
       "60                 5.0               2.0                3.5               1.0   \n",
       "61                 5.9               3.0                4.2               1.5   \n",
       "62                 6.0               2.2                4.0               1.0   \n",
       "63                 6.1               2.9                4.7               1.4   \n",
       "64                 5.6               2.9                3.6               1.3   \n",
       "65                 6.7               3.1                4.4               1.4   \n",
       "66                 5.6               3.0                4.5               1.5   \n",
       "67                 5.8               2.7                4.1               1.0   \n",
       "68                 6.2               2.2                4.5               1.5   \n",
       "69                 5.6               2.5                3.9               1.1   \n",
       "70                 5.9               3.2                4.8               1.8   \n",
       "71                 6.1               2.8                4.0               1.3   \n",
       "72                 6.3               2.5                4.9               1.5   \n",
       "73                 6.1               2.8                4.7               1.2   \n",
       "74                 6.4               2.9                4.3               1.3   \n",
       "75                 6.6               3.0                4.4               1.4   \n",
       "76                 6.8               2.8                4.8               1.4   \n",
       "78                 6.0               2.9                4.5               1.5   \n",
       "79                 5.7               2.6                3.5               1.0   \n",
       "80                 5.5               2.4                3.8               1.1   \n",
       "81                 5.5               2.4                3.7               1.0   \n",
       "..                 ...               ...                ...               ...   \n",
       "84                 5.4               3.0                4.5               1.5   \n",
       "85                 6.0               3.4                4.5               1.6   \n",
       "86                 6.7               3.1                4.7               1.5   \n",
       "87                 6.3               2.3                4.4               1.3   \n",
       "88                 5.6               3.0                4.1               1.3   \n",
       "89                 5.5               2.5                4.0               1.3   \n",
       "90                 5.5               2.6                4.4               1.2   \n",
       "91                 6.1               3.0                4.6               1.4   \n",
       "92                 5.8               2.6                4.0               1.2   \n",
       "93                 5.0               2.3                3.3               1.0   \n",
       "94                 5.6               2.7                4.2               1.3   \n",
       "95                 5.7               3.0                4.2               1.2   \n",
       "96                 5.7               2.9                4.2               1.3   \n",
       "97                 6.2               2.9                4.3               1.3   \n",
       "98                 5.1               2.5                3.0               1.1   \n",
       "99                 5.7               2.8                4.1               1.3   \n",
       "101                5.8               2.7                5.1               1.9   \n",
       "106                4.9               2.5                4.5               1.7   \n",
       "113                5.7               2.5                5.0               2.0   \n",
       "114                5.8               2.8                5.1               2.4   \n",
       "119                6.0               2.2                5.0               1.5   \n",
       "121                5.6               2.8                4.9               2.0   \n",
       "123                6.3               2.7                4.9               1.8   \n",
       "126                6.2               2.8                4.8               1.8   \n",
       "127                6.1               3.0                4.9               1.8   \n",
       "133                6.3               2.8                5.1               1.5   \n",
       "138                6.0               3.0                4.8               1.8   \n",
       "142                5.8               2.7                5.1               1.9   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     labels  \n",
       "50        0  \n",
       "51        0  \n",
       "53        0  \n",
       "54        0  \n",
       "55        0  \n",
       "56        0  \n",
       "57        0  \n",
       "58        0  \n",
       "59        0  \n",
       "60        0  \n",
       "61        0  \n",
       "62        0  \n",
       "63        0  \n",
       "64        0  \n",
       "65        0  \n",
       "66        0  \n",
       "67        0  \n",
       "68        0  \n",
       "69        0  \n",
       "70        0  \n",
       "71        0  \n",
       "72        0  \n",
       "73        0  \n",
       "74        0  \n",
       "75        0  \n",
       "76        0  \n",
       "78        0  \n",
       "79        0  \n",
       "80        0  \n",
       "81        0  \n",
       "..      ...  \n",
       "84        0  \n",
       "85        0  \n",
       "86        0  \n",
       "87        0  \n",
       "88        0  \n",
       "89        0  \n",
       "90        0  \n",
       "91        0  \n",
       "92        0  \n",
       "93        0  \n",
       "94        0  \n",
       "95        0  \n",
       "96        0  \n",
       "97        0  \n",
       "98        0  \n",
       "99        0  \n",
       "101       0  \n",
       "106       0  \n",
       "113       0  \n",
       "114       0  \n",
       "119       0  \n",
       "121       0  \n",
       "123       0  \n",
       "126       0  \n",
       "127       0  \n",
       "133       0  \n",
       "138       0  \n",
       "142       0  \n",
       "146       0  \n",
       "149       0  \n",
       "\n",
       "[62 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['labels']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x1fb06898>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ul9OLvlEAdOvhNXdhNQGkKAqA3tjCelxg1QSQsQ0GCSDnIMFDOEal2jrAjvYQ5WrNAP5q\n/1CNRnt/+XUTLA0oZdkubH1QBhjhNUoFECvWyXFcw2vWTAAVi0XdEkoCyDlI8BC2U1pbx8yF5YX2\nEG6KKaMLq97eX0GCFTcEdrxNy7Ksx3ix1F/a8AkvYJYFVpq1aLcAMvYbYwKIWsI0Bgkewlaq1dYx\ntofo6OioGmjpdwuPm/FJfr1XlVLgg5oBRviLUhdcpaxFtwQQiwEiAWQdEjyEbVSrrcPaQ8TjccTj\n8cA+pEx4sBT7el1YrUq5w4Sl/rJ6KMbGj0FdS1bxWkxMNfw232pYFUD1WC2NAoi90DABxNY+CSBr\nkOAhGqa0GZ9ZbZ1cLgdJkupqD+FkdWGnxlZVFd3d3ba7sNh8W2lDq5QC30hLAYJwCqfqVrHfMRNA\n7FlgwooEUH9I8BANwcQMEzqlDxYryV5vewi/PajMpSfLMlKplK0uLL/dC6co1wS1tKKu0QJEEI3Q\nqEWqWQJIkiREIhFEo9E+afCtCgkeom6YCyuTyaCtra1fYHKxWEQul0MikUAsFgv8wcNcWGyTcaue\nUCvD3l7NmqBSBhjhVdwSQIVCAZFIpJ8F6N5778UZZ5zh0NV5l9aVekTdMCsGC6Izy8ISBAGFQgFt\nbW0Nxes46Xayc2xZltHd3Q2O45BMJm0Zk6gdZt2JRqNIJBK6lY3jOD2GLJfLQRRFKIriyyDvIBC0\nGJ5GYQIoFoshmUwilUrpAl4URQiCgHw+j2KxWPO6NSYF8DwPRVGwePFiB6/Gu5CFh6gJs9o6RuHQ\naunXRksWy8Jib1JE8zFu9tFoNLAZYCTcnMVtgWYWt8Ysl4VCQQ/cr8cCJIoiEomE05fgSUjwEJaw\nUlsnn8/bXlvHy6nWzcjC8uq98Au1uBJUVfXV/fajUCOsweLWmBWotHddOQFkJtQEQWhZKzQJHqIq\npe0hzDbWXC4HAL5Kv25ETBkbnZpZspw4KOlAs59KGWDMnaAoCmWA2YifRKRXqdS7jr2UsrXKUuLZ\n/sHiKlsREjxERay0h1AUBdFoFKlUqiUOZVEU+7iwSmnWPajUw4uwhvEgyeVy+oFCKfD246e9wusx\nR2YCSJZl3QUGAI8++ii2b9+OcePGNWThEUURBx98MIrFImRZxsKFC3HppZfach1OQ4KHMMVKbZ18\nPq8304tGo45sCE67tGoZm7mwZFlGW1ubbhUgggvL8CrNAGMp8JQBRngRJn6KxSJSqRRUVUVbWxse\nf/xx/Pa3v0Umk0E+n8ecOXMwe/ZsjB8/3vLajcVieOaZZ5BMJqEoCmbNmoX58+djxowZDl9V49CO\nTfSDBeKWs+ooigJBEMBxHDo6OiAIQpNm2hi1HE6snlAoFEJHRwcdbC2IWQsMSoFvDbxu4THDOGee\n5zFv3jzMmzcP//rXv7BkyRLst99+eOaZZ3DJJZcgFArhkUcewdSpUy2NzSxEoijqoQ5+gAQP0Yd6\n2kP4JXW8XowuLCuWLC/MmXCeSj3A2AuDWxlgflpvfhQPQaJQKGDkyJH49re/jW9/+9vQNA0rV67E\nzjvvbHkMVVWx77774uOPP8b3vvc9TJ8+3cEZ2wcJHgJA/8DkSu0hWsWd47VrNgopOjC8h1PF5Gr5\nfIJglBOW+XweqVRK/zfHcZgwYUJNY/M8j//85z/o6enBUUcdhXfffReTJ09ueM5OE/xTi6hKNReW\nLMsQBKGsO8dLcTa1UGne5MIiGsVMALFAUpZJU+r+onXmTTRNC0yAup1p6e3t7ZgzZw6WLl3qC8ET\njG+QqBtZllEoFEzFjqZpKBQKyGQyiMfjTcnCasYBIIoienp6EIvFWibzjHAe1tWaPUuJRAKhUAiK\noiCfzyOXy6FQKOhZkQTRCFYtPLWyZcsWdHd362M9+eSTmDhxYt3juQlZeFqUarV1VFVFLpezVFTP\nzzErxnnb5cLy8/0g3MOsCSp7JlkTVNYANUgB0BTD01zy+XxDFp4NGzbgm9/8pl79+bjjjsOCBQts\nnKFzkOBpQazU1hEEwRPtIZwOiGaQC4toJuw5ZBXKKQPMW/hRpJWbcy6Xa8jCs+eee2L58uWNTK1p\nkOBpIUrbQ5gFJhcKhZrbQ/jdosGysLze1b3affb790DsoFwKvCzLerwdazUQCoV8eSATzYFlnLYi\nJHhaBNbhXFGUsi6sbDYLwFvtIZw8xI1BpF7IwiKIclRKgRdFEUBvurEfmqD6UZQHac6NxvD4Gdrh\nWwBjWfxKtXVisRgSiUTNG6UfWxqwQFGgV+DZmYFBlhbCaUozwFhfN1VV+3TTZhYgr2WAeWkuVgnK\nnBuN4fEzJHgCDLNgZLNZaJrWT9Wz9hDFYhHpdFrvw+JF7DTZM4EXjUYhy3Jg0k0Je/GbmygcDoPj\nuH7dtKkHGGFEEASy8BDBorS2TqnFgQXp8jzfsIXDrcDiRinNwmKCkCCChlkGmNEFxlxkRgsQUR6/\niV+gclo6WXiIwMBSW8u1h/BLkK6dmAk8p8SOUwKQXGVEPbA9wCiAmpkB5kfxECTy+TzS6XSzp9EU\nSPAEiHLtIdhB6VS3b6cPYjZ+vZskc2EFTeAF5TqIxqi13Qg1QW0NylWHFkXR0+ELTkKCJyBUq62j\nqiq6u7sRDodbps6MlUKCZDEhWh0vNUH1KkGzSrVqDBcJHp9jpbaOJEmQZRmpVAqxWMz2Objhaql1\nfCsxSkHawAjCLprdBJWwh3IirZW/KxI8PsZKbR1BEKAoCkKhkCNixw1qfUCZCysejyMejzftAXfy\nrVBRFMiy3CfjhmJ8CCcoFUDGDDCzJqjVrAd+s5YE6ZkK0rXUAwken6KqKorFYtnAZNYeIhqNIhaL\noVAoODYXrxy0tabZOxlc7ASs3hETdDzPQxRFPd7CK98DEWyMGWAAWiYF3k8iDSgvLP0mOO2EBI/P\nKBeYbPx5aXsIlrHlV6wc5Ham2XsV9t1LkoRUKqXfE3bgsO85l8vp6cbkbiCcpjQF3rgejYLcGCdE\nEM2ABI+PKK2tU6k9REdHh2uHfrMtC15xYTmJqqoQRRGapulB58VisU/AaSgUQqFQQDQaNY23YIXp\ngnh/WhkvvbFbyQBja5C5ZL0y93J46f7Wgtm8WQ+2VoUEj09gG0Y5F1alQ7/ZgqRRys2/0UrRThdM\ntGujZO5J9ibN83zZeZsFnLJ+YayVRhDdDYQ3MRNArPUFZYC5jyAILVt0ECDB43msuLBY6nWz2kM0\nQ1AxaxbHcYF2YYmiqDf7Y2/KtcBxHCKRSL+Ku+XcDXTYEE7C9jCO4xCNRikDzEHMXrhaucoyQILH\n01SrrWM1bsXvFp5SWsGFxYpEKoqid69nb8b1UqniLnvb5nme4n8I1yiXAs9i1QD0E+Rur0m/urTM\nyOVyJHgIb1FaW6f0IWfmYKvtIdyqhOz0+E41O/XahqYoCjKZDCKRCNrb2/W52T1Ho7uh2tt2K8b/\neG1dBIVyFYCByinwQc4As5ty+zEJHsJTVKutY3zzt7M9hNdRVRWZTAYAbHNhOXmY1SsCWZ+zZDJZ\ntW6S3QdypcOG4n+IZlCuCSpzybI1Sy5Zc0rvBwkewjNUq60jyzKy2Wy/N3+rOP3G6tT4zLITdBdW\ntTYYpZTeB7stbWaHDYsno/gf79AqlqhmNUEN0v2lGB6i6RhdWFZr69SC0w+rU+MzoSPLMuLxOBKJ\nhO2fYWc2Vb34IQCbHTZs7ZnF/1CwKeEmlVLgWz0mrdyelsvlkEqlmjAjb0CCp8kwFxUAPU7CCGsP\noaqqHrxaD04f7HaPb6wpFIlEPCkC7ECSJGSzWcvWK68En1uN/2GHTavF/xDlcWofqiUmrdUEEINc\nWkTTYFYdURQRCoX6BeGywzAWiyGdTrfMw1kqAnK5XLOnVDPV3EtGq53VAGwvf/+1BJsShBuUywBT\nFEXPeLQSlN9sC3A9lJszubQI17FSWyefz0MURduykfyQqeXEdVejWTWEmNXOzYrYblIp/gdAn/YX\nFP9DuIFRAMVisZYMys/lchg6dGizp9E0SPC4jFltHeOhqygKBEEAx3G2HoZer8VTqS2G1+deC8bA\n81ax2pXG/2SzWUSjUT1In1wNjRGUZ8NtymWAlVolg2ThYaVMWhUSPC5RrbYO4O+Ceo2IEqPrLpFI\n+Oq6y2F2P2pJOQ8yLNaCWfDMXA1G608Q37Ttxk/PjBcFRLUMMJZF6fesRApaJhyn1IVl9qAUi0UU\ni0XHaut40UpiNY7Fybm7cV/qSTmvNFbQqORqML5pGwOgCcJJSjPAVFVFJBJxPAXeLiiGxxwSPA5j\npbYO6yDMumD7lVoO42Z1dncbY/uPRr9fP6+NWih1NbhRa4UgysEqQ5emwBtFuV/csqwvX6tCgsch\nrNTWYY0h2cbuxcq/tYxvlSC6sMxgYtZK+w878KIVr1HK1VqRZdlXBw0RLCplgHkhBb5SDA9ZeAhb\nYb2uyjX9VFUVuVxObwzJHhK/U+2wrbeAIsdxUFXVjimajm23SGCHci0p54Q1zASQMf4HCH6mjd/x\nYgxPo1Qqy2DWBLVZ65JieAhbqebCkiQJgiD0aQ+hqqrjb+bNtvCwVGxN0wLtwmKuOk3TkEgkSOw4\njJX4n3rS39mz4oeDOWhWPa9RqdlpOYxuWcD9JqiVYnhI8BANY6W2TiPtIRrFreBcM5jIi0ajdbmw\n/OKqYSnn7LsNqqjzMlbjf8LhcKDcX0G5jqDilb50ZOEhGsaKC4sF6Jq1h/DLgV6JchVKmynyrGBX\nwUQWj8Wuk3V2t5MgrBM3qeT+8kKcRaviN5eW3fOt1JfOrsD8clYpSZJa2upMgqcBaqmtUylA142D\nzG0Lj9GF1UgPMMDbBz3rhcbisdh1ennOrUq5QFNZlvvFWZB1jnALtwPz/SQ27YYET51Uq63D2iQU\ni8WWCFw1Xn+jLiy/wFLOQ6GQHo9F+IdKgaYsiYD1uaP0d8It7LBMlrNKtfoaJsFTB2btIYwYa6+0\nt7dXfVsMioVHVVXk83lPu7BKqfe+MMudWynnhPMY4yxYJiXHcYGP/yEq02wXXDXLJGuCWq0wJ4sd\namVI8NRAqQvLTMiw9gG1HIRuuT+c/oxisQiO4xp2YZXiJfeQ0XLnVFVsovmw59YYZ+HV+J9mH8i1\n4KfsN69SyTLJMsCA3hdvJuJL/75Vod3aIpqmQZIkKIpS1oUlCAJkWfbkQejkImeZBkF37bDgcybq\nKlnunBBppWNWepML6nfQLMzesmVZ9lydFaL1MMsAy+fz+tp84YUX8Oijj+KQQw5pyOq+du1afOMb\n38Dnn38Onudxxhln4Pvf/76NV+I83jqVPYqV9hDZbBbhcLiu9gHs9508qJwqsMeysKLRqGNvuV7o\npcWqQ3u5sasX5xRUOI5DJBLp12nb7TRjwnn89ALBzieO4xCLxcDzPHbffXe89957WLx4MV588UXs\ns88++K//+i/MnTsXBx10ENLptKWxw+Ewfvvb32LvvfdGNpvFvvvui8MPPxwTJ050+Krsg15DKsAO\ndJZiXHqgG3+eSCSQTqcbejC84raxArN2FItF3YXlp/lbhb0tZbNZpFKpQAdhE/XB6m5FIhEkEgmk\nUindnc0C+HO5HIrFIhRFCeRzQngL48v5uHHjcP755+NPf/oTZs+ejRtuuAHt7e24+uqrMXz4cNx/\n//2Wxhw2bBj23ntvAEA6ncakSZOwbt06Jy/DdsjCUwZWW0eSJIiiiHg83ufnLO1aVVVbYlb81GeJ\nWbQikYgu8oIoAjRNQzabrfs7poOtNaklyyYcDgf2+THDT9YSRlCe43w+j3Q6jVmzZmHWrFm4+OKL\nkcvl6mrb8+mnn2LFihXYf//9HZipc5DgMcEY/FWueBNLu27UqsPwUmBuOcwK7JX+3Ama4dIyE3W1\njus0fjw8WpFKQab5fB4Axf94Hb89Z2Z7g1nj0HoaiWazWSxcuBDXX3+9ZXeYVyDBY4LRHGhM5XO6\ncrDTva4aGb+aRctvG0IlWKZdMplELBZr9nR0/CCKieqYBZk2Ev9Dwpewgh2d0mVZxsKFC3HKKafg\nyCOPtGlm7kGCxwSjiZkdMsb2EE40v3TLIlAPVq0dfrTwGNE0DblcDpIkeTLTjnAPt0QE22vM+n+x\nKrs8z+u08RwmAAAgAElEQVQ1Vvxe/8dv4syPLxjlUv/tEDzf+ta3MHnyZJx77rkNjdMsyHZqgnGh\nsMO2u7sbkUgEbW1tjpicnT7U69lkjEHZyWQSqVSq7Dh+2sSMsPuuKAp6enp0C1ajYoesMUQ9sPif\naDSqP3PRaFR3JwuCgHw+rxc+pTXmDn7d34w02in9xRdfxF133YWnn34a06ZNwz777IOlS5faOEPn\noVfYCrA3fgCutIfwkkuLVZot7RFVCb9aeJjY8XLKeTn89sZM1IbVInM8z9NaIACU3xMEQWjIwjNr\n1iwoitLI1JoOCZ4yGNtDAHDcveGljcrowrJaSNBL87cKKybJikX6qd+ZMcas9H+ngy+4mMX/FAoF\nPcaO6v/YS5CepXw+37BLy++Q4DFBkiT09PTo7SG6urocX/huuLSqjW/MwqonYNeNbux2fQfGIOxo\nNOoLsUNuMsIIE71M2LAeYMb4H6+0v2AESUB4lXL32I4YHr9DgscEZtlgbpxWOGhYa4xaXFhGnBaD\ndmK0YIXD4brqUFSD4zhHxiWIUjRN08UMEzcs7ses/o8xAJpoHXK5HEaMGNHsaTQVEjwmsI3D7c9s\nloWnHheWXylNOS8UCs2eEkE4gpX4n9ICiERf/GiRKjfnfD6PRCLRhBl5BxI8FnDDwuNm6rWxd1cj\nLiwjbgm2ejcfY3PXVrPeEQTQP/6HCSDq/9Ua5HK5hrK0ggAJHgsE4VAs3bwadWH5CRaAHgqF6mru\nShBBw6z9haqqkGXZ0fgfv1lM/DbfSjSalh4ESPCYULrAg2LhMdacsduF5VVRWCwWIQiCHoBudq1O\nzNvJ+8E2YUVRWqoPE+Eclfp/FQoFaJqmx/5Q/I+3oaDl8pDg8QhuBLkyF1ahUPBc24Rq1CogWJfz\nYrFYsYaSn8SC0RXJum+zgFV2EHlRdBL+wxj/E4vFysb/MBHkp+eoVaG0dBI8ZTEesF61XtQCm78o\nio64sLx0j4xtQNrb2wP3NprJZMDzPJLJpF6LhbkiAKBQKOiHFVmAgo8bbpdy8T+SJKFQKFiO//HK\nHmEVP7q0Kll4yKVFVMXvLi2WhQX0Vox2Ml7HqQ3C6v1h1xqNRpFIJCzNxS8uLUmSAPSWTUgkEvq/\n2WED9FZTZan2pZ24KROHsING439oDTYHEjwkeCzjV8FjTMN2MgW72ZuYMeOslk72zZ63FYzXBqCi\nkDP2YjJaf0ozccLhsCcK0XkVP77ZN4ty8T+yLOuinFyu7lEpLZ1cWoQppS4tv2FMw2adv1nwoV8p\nN/cgZ5yVXlt3d7flv2XuLCb+qBAd4QaV6v/Isqz/nh/S34MkfGVZ9kVFeSchwWMBv7m0yqVhO/3g\nOtnHqdyYxmutN+PMqyLQ7NoaucelBxGz/lAgKuEkxvgfZmXmOK5f/A9ZHe2BJTKUQvF8JHgs4UYG\nlV2Cx+jCikaj/XznXj3c68FKynk1nNoAGr3XkiQhm81W7ODe6Gew/kuVAlGN1p9W3yy9ip+eaatW\nR6/0//LTva1GkK6lXkjwWMAPQoGlKkuSpLuw3MbJ+2Qc2wvX6hSs+3WhUKiYTm83leqwkPvL+/hV\njJpZHcvF/zRr3fnt3ppZgL1+frlFcE4Km3F7kTciFqxWEvaDcKsGSznnOM62lHOv3BMvxSJZ6cNk\nLETnt0OB8CaV1p0oivrPad3VR6vfLxI8FvByDA9zYTXi1rELpy08sixDEISKbp56xvUCdsQiOYmV\nOizk/iKqUS6+pBxW6/84Ff9T63y9QJACre2GBI8FvGgZqcet48XrsAIzc0uS5KqbpxFqude1xCJ5\n4TukNgREM7DqdjXWnKKDvxdJkgLl+q8XugNlcNsHWstB1kgzTDc6mtuJqqoQBAGqqiIej9sudpop\nIJyK13H7eiq1ITDrwk0QdlAp67C06GYrCW8zCw/10eqFBI8F3HxLqGaObMSF5be3HVY1mWUS+W3+\nldA0DdlsFqqqoqOjw7bN2Av3qJwbglXhBXa8cZL7q3GabfHzCqVZh8YAaDPhbbUKexDWJxUd7IUE\njwXciuGphB2ZSU5fh53jG9PrY7EYBEGwZVwvoCgKMpkMIpEI0ul0IDbUchjdEKz6syAIunULaM23\ncLvxyxpyS0Awd1Yrxp2Rhac8JHgs4Jbbo1xROebC4nk+kM0wjZQTdk59B26Pa4zXicfjtn+u1zHW\nYeF5Xj+EGnkLJ4hqtHr8DwmeXkjwlKEZdQzMDkk7iusZx3eygGKj4sEo7GqNTfI6mqYhn8+jWCw2\nVDvIC0HLdmLm/jJrQhnEQ4hoHpXS343xP04XnLWbcnsDCZ5eSPBYgG2ybvpzg1xczwwm7CqlnPv1\noGeB15qmBd5C1wjl3sJlWXa987tf15rX8ep9LRXexqa7xvR3v1gezVxard4pHSDBYwm3Fjd7e3fK\nheXFGB6rmUpOz91uMcvmawy8TiaTnt8ovYTxLbwZnd/pu3IGr99Xo9tVkiTd9WwMvPdS+wsrkIWn\nFxI8FikXX2M3xWIRoig6UkjQa+4Qo+XDzkylWnDaSpDJZPTAa7vx+iZrJ2Y9mJj7qxViMCoRlEwi\nr8KCn42B95XarjRz7ZVbC5Sl1QsJnjK4vWDZBi6Kom9dWLUIKmb5iEajSCQSVe+30/FHdsLidQD4\n9rv0OmbuL7MaLNT5nbAbK21X3HK9WoVcWr3QTmwRJ60jzIUFAMlk0rED0gsWHk3TIIoi8vk8UqmU\n/sbeTOy03rFeXwwnvstmf4dexKwGC2tA6UYLAsIafrNGWZlvpfgftzMPy803l8thp512cuxz/QIJ\nHos4JRaMwbqyLNs+vptUu0eNNMf0glirhtFqFY/H0dXVZftnWLGEef0+OY2Z+4s6vxNuUM712uz4\nn3w+j0Qi4fjneB0SPGUoXYR2HyRmacpGy4ATNPMw9HpzzEYpLZTY6qLDS1hxQVAHbqIUO55hs8Kb\nTorvShYecmmR4KkJuw6xcllYfn87LxdnY2ctISdo5L43u3yA31wEXsBKB26e53X3BN3f1sbuxBEr\n8T92x56R4OmFBE8FjAehXQuvWr0Zr6WNN4JdxfYAb4pBFq/DcRzV1/Ep5Wr/SJKkZxF6ufO73fuT\n05CA7IsV8V1L/A9laVWGBI9FGj1wjYd/uXozTm8EbtbhMQbvBlEMVMsyc7JYpdeEX614Ubwy2Bs4\nIxaL9XFBsJ+T+4uwGzPxXa7yeK3xPyR4eiHBY5FGNmlFUSAIQlVLgJcPglqQJAnZbBaxWMxSyrkV\nnLw3tY7N4nWakWVGB6y7VOv87rcCdIR1mm2NqtT/q1AoQNO0ftbHSjE86XTa7UvwHCR4HMZKywQj\nfndpsVRgr6Sc24kxXqfWLDPC/1QKQKXO79UJwstcMzHG/8RiMdP4H5YlVip8qNJyLyR4KlAaw1PL\nA2vFhWX2eW7glJuFZR10dHQ4IgaauWEa43WC1tiUqI9yBxB1fq+MX+5Dsy081TCzPrI9WBAECIKA\nm266CXPnzoUoig0JntNPPx1LlizB0KFD8eabb9p4Fe5CryAWqUXwqKqKTCaj15uxInZq/Yx6cOrh\nVRQF3d3d/UywduLkxlPtvkuShO7ubkQiEaTTactzaYaLMihuUT/CDp9EIoFUKqVnJDIrL3sBUhSF\nviPCVox7byQS0TOyOI7DpZdeildeeQVHHHEErr32Wrzxxhs1V60/7bTT8PjjjzsxdVchwWMRqwdJ\nsVjsczjWatb220YoiiJ6enqQSCQsuez8BGtsms1mkUqlbItHsgsvzSVoNPp2zw6gaDSKZDKJVCqF\nSCQCVVVRKBSQy+VQKBQgSVLDyRC0DggGWw8cx2Ho0KG4/PLL8a9//Qt77bUXzjzzTHz88cdYtGgR\nhg8fjhtvvNHyuF/60pcwYMAAB2fuDuTSsgnmwhJF0bILqxQ3Ni672iiY1Z+RZdkzgcWN0khVaCch\nC44/Mbq/WH0fu9xfqqriL3f/Bf988Z9IxBI485QzMW3aNAevpj78Js78Nt9ysODmr3/96zj66KMB\nAKtXr9bjzloJsvBUwLjYKx00zIUlyzI6OjrqEjvVPsMu7PgMdr2qqqK9vd33zTFL74miKOjp6QEA\nT4kdIhhwHGfq/gLQz/2lqmrV5/Wue+7CvS/9FRPO3Q0Dju7ABT+/AB9++KEbl0L4CON5NmbMGOy+\n++5NnE1z8PdJ5SLlhAJLwbaahVUNr7+9V7peL6WO14ud3ydZYwgrmKUf19L5/bFnH8Xsa2ZjyK6D\nAQBbP92GZ/71DCZMmODuhRBNJyhWKacgwWOR0sPLDheW2Wc4Tb2HMItnKRQKtl2vl2DXl8/nA3l9\nhH/gOOud3wEgGolCzIr63xezRUTjwSoJ0QxIPPSFrUU/Qy6tCpRb7Ha5sMw+z4sLiqVkS5JU8Xq9\nOn8rFAoFiKJYU1YdQTgNc3+xit7G+laiKCKfz+Oko0/GUxc/jeX3L8fzt7yAjU9uxIL5C5o88/6Q\ngHAes3ssSVLDe9qJJ56IAw88EB9++CFGjx6NO+64o6HxmgVZeCzCDnMnqgi7Sa2ihLVQiEQiSCaT\nTbtep8QUCxxljVztbhRo95z9LCqJxiltPinLMg479DAM6ByAf730LEYmRuEn1/4UAwcOJIHRIEG5\nf4IgNFx08O6777ZpNs2FBE8NMEuHUy4Ptw4zq5/BWigkk0k9qLISfjuMmXjled6TXdwJohosBfmQ\nQw7BwQcfbNp8ksX+UOuL4GMm0qjK8g5I8FhAVVXkcjlomobOzk7HSsa7laVVDbOUc69gV0q9MR6J\nlWUPCn4SnYR9VOq9xCrwernzO+EM1Dh0B945yTwIx3G6FSAajequD6dx0pRaTVQpioJsNotQKFR3\nCwUn5m/XeJqmIZvN6i0weJ4PlODxm5WNcI5S95ex95Lbnd8lScK2bdt8dfD60aVFFp7KkOCpgCiK\nepXdSCQCURSr/1EDNPvhYjVAEolEXS6eZs+/GkzMhcPhmlpE1AuJD8JLlOv8ztxfTnV+f+2113Dl\nDb+CElYQVaO4/Ic/x+TJk20Zm6iOIAh6q4lWhwRPBaLRqG4FYAeX06rfrkrI1cY3YmeKvZPzb2Rs\no5iLx+Om4/oBNlf2ncmyrL/Fe11wEvZT77qt5P4qFAp6dd5G3V/bt2/Hr276FeZccQiG7T4Mq15b\nhcuuuQyLb1lsKS6QsE65tUAurR2QE7cCLCWU/f9+Ohit4lSKvVdgwkAQBKTT6X5ix49omqZXug6H\nw1BVFfl8HrlcDrIsW6rOSyAwTTztELrMvRWLxZBKpZBMJhEKhaAoCnK5HHK5HERRrLl9zNq1a5Ee\nlcbwicMBDRi771hoKQ2bNm1qeM5O40eXFtB/PZBLawdk4alAMxa7Gx3T2fgs5ZzV+LBr4/RKtWVV\nVSEIAjRN0y115fDLwaeqKiRJQiwWQywWgyRJugVSVVW9HYEgCI65KPxOoVDAs088gXUrVyIcj2PW\n4Ydj19126/M7fj3s7KKc+0sURSx5dAnefP8NDOwchJOOPQlDhgwpe68GDx6MnrU9yG4TkOpMYvv6\n7RC7xEA0ovQa5dYsWXh2QBaeGvBLr6tqsI7NmUwGyWSyqfV1nIL1w+J5Hm1tbRXFTrMCxGulWCyi\nWCwiHA73+86MLopwOKzHnbGMNLu6cweBl559FuLKlZg5ciSmpNN44aGHsHnz5mZPy7MYO7/ffd/d\neODff0N8XgyrB3+Kc350NjZu3KivLVVV+/ztsGHDcMoRp+Chsx/CY5cuxaMXLsU53zwH6XS6SVdj\nnaA8JxTDswOy8NRAUFxakiRBlmVHGmN6wcLD4nWs1g/yOswtVywWEY1GLX1nxgydWCwGVVUhy3Kf\n7tytWp9l7ccfY6+hQ8FxHJLxOAZwHLZs2YLBgwc3e2qeRtM0PPjYAzh28SIkO5PY/WDg8XVP4K23\n3sKcOXPKdn5f+PWFmLHvDKxduxbDTxiOcePGNftSLOOn56KShYfWdi8keCpgtnj8bOExpqPaXVXY\nCxiFQa31g7wqZFkavaZpaG9vhyiKdc2VtSdgY7ZyfZZURwe6BQGDOzuhaRoERQlEbJcbaAA43mBZ\nDPWuFWPvLyaumXs1FAph2LBhGDp0qGefsyCTz+eRSCSaPQ1PQIKnBrzc3LMazOrBgpL94saxCquC\nDQDt7e01HdpeFX7GNHo73Y7l6rOUvqGHw+FAWn++dNhh+L877kDXm2+iqCjY4+CDMWbMmGZPqy7c\nfNY4jsMRh34NSy9/HHsdOxVbVm1Fz5s9mP7N6X1+xyz7i1kXAegp8F7PLPRbHFe5tZDL5XzhQnQD\nEjxVMB7gfnRplVo9WN0NP1Lu/jsRfG0HjawXVvCy1ppIHMf1i6OohlmAKhM/7A3d6P7yO6FQCBGe\nxy6DByMSiSC7cSM+//xzDB8+vNlTqws31/t3vn0WdnpgIP79wL8xuH0ILvzVD8oGIGuahsV3L8YD\nSx8AACyYvQCnnHgKeJ5vGXHtNmb3j7K0dkCCpwb8FrTMrB7MhcXzPBRFsWXscrgtCmvt91UOrwhZ\nTdP0LthO9WyrROkbejOr8zrFB2+/jTGxGEZ/YdXZsHUr3v3Pf3wreNyE53kct/A4HLfwuKq/+8hj\nj+CRNx/B127+CjiewxO/eBIDHh2ARccsAmDuWjUG3rNSIERjUJbWDkjw1IhfBA+zEMTjccTjcX3j\n8KOVimGcu539vryyqWqaBkEQoCiKIwHl9VAuPdkYn+G3A0pVVXAGSxUHQKnRKkZUZ9kby7DXsXui\nfUg7AGDqcVPx+t9f1wVPqWtV0zTIsgxFUZDP5wGgj3XR7bXlR5cWWXgqQ4KnBvyw+I2NMVOplB6o\nynCzzo9TmFmuvEgt94IVgOR5vmpAebMEq9H6E41G+8RneOGAssqEPfbAYytWILRlC8RCAf9etQp7\nDBqE9evXY8SIEc2eXmAY0DYA69as1f+9/bPtGJDeqezvcxzXL/iZOr83DrMWEyR4quJ2DE8jn2Fs\njOkVC4HdsMqvdsfrNNPqxWKQYrFYH2ucGaU/a+amb3xDd7M3U6MMHToU8048EcteeAGvPfssJgwe\njLZNm/DkPfdgzqJFGD16dLOnaBuapmHp0qV4+Kl/IBQK44QjT8CBBx7oymefsPAEnPuTc5FZnwHH\nc9jy6lZ877Kz9XlVW+fVOr8HLbasUcjCUx0SPDXgZcHDDs1IJFKxMaafLTyshUI6ne5nuWqEZh7E\nLAbJzBrnJ6r1ZgKg/9wLLtVhw4Zh53HjwH/2GcaPHAkASHR3481ly3wleKoJhyeeeAI3//1mzDp3\nJqSCjF9cdwV+Hr0C++23n+NzGzZsGG655ha88sorAIC9j9wb27dvx2lnn4b1G9dj3Kix+PF5P7GU\nIVcus5C5VwHYHlvmN5dWOSiGZwckeGrEC5t1KXYF7noVFq+jKAoSiYSvhQGjkZpBfqBS4UMWmN1s\n94Smqv2qVWsOB/W7zSPPPIIDvjsDY/btFRXCtiyeePYJVwQPAOy0005YsGABAGDLli247NrLsO85\n0zB//y/j3affw8+u/Blu//3tNT/T1Tq/t6L7q5xAY9YwglpLVKV0Q3Tj86yKKhbkms/n0dbWZkns\n+M3Cw2JbVFVFNBp17Dtw4p6UuxcsBolVuw6a2DGDFT5kbkjmBhNFEYIglG1N4CRjd9kF23ge6zdv\nxuauLny8fTsmTpvm2ue7QTQcgZgr6v8uCkVEI815Yfjss88QGx7DrgfuCj7EY8phe0CMiti4cWND\n4xpbXyQSCd1ayuIZ61lfXnyxbYRWEHxWCP5OayNecmmxonQsyDWIPuzSTLNcLufI57i5GSiKgkwm\ng0gkUncxQb9vxuVS392uzTJw4EDMO+EEvPXvf6MgSThw7lzsuuuuAKzd42KxiPvvvhvvvfYaUp2d\nOOrkkzFp0iRH5lovJ3z9RPzs2p8ht02ALMpYed9H+N4vz6l5nJUrV+Lam6/F51s2Ys+JU3HB2Reg\no6OjpjHa29uR2ZiBKIiIpWIQtgsodPW+rNmJmXXRGP9T2vqilpg5L6NpWr9zwO97hd1wVW5Iy98t\nliYJ9G5woija/oAaEUURkiRVjKovl3JuBVVV0d3d7Vi34nw+D03TLPmMN27ciBdffBHLlq1AMpnA\ncccdjYkTJ/apRWOMbREEATzP214mXZIk5PN5tLe32zpuLpcDx3H6fO3o8WVcH6w7unGTk2UZkiR5\nupR8LpdDLBYzNbMbCx8qitK04FRWc6iSq+WPt96KNY8/jinDhmF7Po/3FQXnX3kldt55Z1fmCPSu\nKU3TKq6nt99+G48//TjCfBhfW/A17LLLLjV9xnPPPYezf3w2OnfvxJ7z9kB+awGpT9K44Tc31DRO\nPp/Hn+/6M/751pMYsucQbFy+EcfOtVbTxy6M7i9ZlvuUVjC6v5j13E/ZTSxRwFi7S9M0LFiwAC+8\n8EITZ+Y6ZQ9EsvDUSDMtPMaU83qL0nmhDo+qqrjnnr/isst+g88/74Km7Y1BgwbgwQfPxF/+cjPG\njRtn2tzUybctJ++JHd9bOfz0BmoFtwsfZrNZrFy5EookYcy4cZabLMqyjHdfeQVzRo1CMh7HwLY2\nbFq1Cu++846rgscKU6ZMwZQpU+r62w8++ACX/O4S7HPhNAyZNAiv3fxvTJs/DcsfW45sNluzIPjW\nN7+F/d/ZHxs2bMCYA8dg8uTJdc2rXsqVVigNrvejxTwoQdZOQoKnCm7H8ADmh6+qqhAEAZqmoaOj\nw7MPZKXWBuvWrcP55/8Yy5e/iQ0b1kNVw9C0AxEO746uLhXxeBL/+79/wi9+cYmrzU2d7CvGvjdF\nUWz/3lphc3Oy8GE2m8U/7rkHqe5uhEMhvPPCCzjs+OMt1eLheR7haBT5YhHJLxqPiqqKqMtJA04f\nck89+xQmHT8RO00fgCG7D0bo/DBe++3rgIq6rJQcx2HvvffG3nvv7cBsa6ec+4v1/crlcpbdX16k\n2S+3XoMETw24FcNTijHl3K4mkm6/DWiahrPOOh+rVg2FouwNRWGuggFfuAxlqCoPWVbLptV7wTpV\nC8zlFIlEAtmd3m2sFj6UJAnvvfMOcpkMRowejd0mTDC99x9+8AFS3d2YMGoUACC1bRtWvPIKRhx9\ndNW58DyPwxctwmO33YaR27YhI8vgx43DjBkz7L3oJhMJRxBGGHEuji0fb0XP+m5sfOtzXHj6ha63\nPXEDJrBDoZDuei3t/O7F2lKA+Z4uSVIgslrtggRPDTQjaNnuOi1OP6Dl7lF3dzc+/XQ9Bg78KrZt\nuwfAVAA5AK9C0/aHqmYRCq3FiSde1ZwUZZu/V0mS9NTrVCpl6zX5SfQ5iVnhw0KhgIfvuw+hzz9H\nRzKJl5YtQ9ehh2L6jBn9vgNFkhAxZMjFIhF0iaLlz5972GEYOGQI3n/nHUzo6MAhs2cHrt7JvMPn\nYcmPlyAUCUHmJfznzjdx1sKzcOopp9Y8lp9cLiwAuFznd9aAudTC6DUEQUAqlWr2NDwDCR4Pomma\nHjRnFsvSKEyUuPmAplIphMOAKG5HPN6OcLgLijIaPC9DVZ/DzjsnceON12LWrFkV5+1E2rLd96FQ\nKCCfz+tp9HaO78VN1Qsw68/HH3+Mj15/HaM7O9GeSGDKiBF4/bnnMHmPPfTYH3Y4jdllF7z70ktI\nd3UhEgrho61bse/MmTV97l577YW99trLoatqPqNGjcLvf/l7/P3hB1GQRHzzp6dh+vTpzZ5WUygV\n2Kz3l1c6v5vt6blcztMJDG5DgqcK/QqTuWTh6enpQSgUQkdHh68OuXL3KBKJ4NJLf4BLLrkO8fgg\nhMOvI5XajI6ODgwbNg733Xcnhg0b1oQZ24exoWl7ezskSXK8O72f3pqdJpvN4pl//AORjRsRkyS8\ns2YNdp06FZFIBIlEol/qe2dnJ2YfcwzeWrYMsiRhnwMOwCSXg2j9wJgxY3Du2ec1exp90DQNDz/y\nMF5Z8Qrakm048ZgTLVVsrmX8SrAXGWZ1N2YXeqnzO1VZ7gsJnhpwQ/CwSrSxWAyxWMyRh6RZsTBH\nHXUkJk7cHe+++y54/iS9GN3MmTNrrulhJ3bcD7OGpszs7SQkdnawZs0aDA+FkB02DFo+j2GRCJ5f\nvhwLzzmnbNuLwYMHY/b8+br1hwSkP7jn3nvw2LuPYtoJ09C9oQsX/fIiXHfFdRg6dKhtn1HLOjBr\nreKFzu+sDAbRCwmeOnBiU2StBsQvYgjiX2R+OIHTgqdSWv2YMWMwdOjQutKzvRq0zILK7W5oWi9e\nvEduEQ6Hsff++2P1J59g+/btGLnLLti/pFlmub5MRusP0BvA6nUBZFZszk3WrFmDTCaDMWPGVE1R\nt/NePvLsI/ivq+eic1gHgDHoWtuNV199FUcccYQt4zcKx/Xt/M4EkLH1hd3ur3IuLYrh2QEJniq4\nkZbOrANAbzXS7u5uRz7HDcrdIxaTpChKoDq5Vyom6JRAqzSmlw9npxk1ahSWp9PY2NWFzmHD0JNM\n4stf/WpVQWCW+i6KIhRFgSAI1JXbBE3TcMMtN+Cfr/8T6SEpSJ/LuPKnV+qVqp2G53ko0g53sSqr\ntn43doozM/eX3Z3fy+0J5NLqCwmeGrE74FeSJAiC0M864OSbpdsWHtYGIxQKNZSe7dS86xm3Wc0/\nW1nQVKOtrQ1fPeEEvLV8OQq5HGZNnFjzAWx0TTBLkJOFD/3ImjVr8MOLf4gVH67AtCP3wvwfzcMn\nr67C1Tdcjf+9/n9dmcMx847Bvb++F3sumoKudV3YvqwLM39ZW8B5syhnYbSj87uZhYcEzw5I8NSI\nXYduufYJfqf0gWMWkEQi4VhMkttomoZsNgtN0wLbx8yvdHZ24qC5c20bz8nCh36kq6sLp373VIw6\ndiTm/b/D8eFDK/HXC+/FCdcej1eufaXi39r5snLk145ER3sHlr24DGOTY3HB5Rdi4MCBto3vJk52\nfmxygskAACAASURBVCeXVl9I8NSIHYKnmnvH6bRxNyw8Zu0Utm3bhp6eHgwdOrTuVMlmx6cwa1U4\nHLatCCThPcyeP6uFD90OTHWT5cuXIz0hhRmnzcCW7Vsw54rZuH3mHVix5A3sMmZ81b+30000Z/Yc\nzJk9x5bxSmlW7JZZ8HOp+8to/WEvW+Xmm8vlfNUPzGlI8FTB7kVvxb3j1eBcK7C5MwsIa6fwyCOP\n4Y47/g4ggbY2FRdffF7NTQyd3oCqbXKsaatVa5Wfv0ei+nozK3xofDP3clXeeolGoyhmiohFY2hL\nprH5ky3Ib8tj3YPr8JvLr2n29AJHJfeX0cXKAuxLKRQKtmau+R2yxddII4dYsVhET08P4vF42fYJ\nbuDkQawoip450tbWBp7n8emnn+L22/+BwYPnY+TIBZDlqfj1r2/yjBio9j0waxVrllhrh3oi+LA3\ncxaLl0qldBdFoVBALpdDoVCAJEk1r/tMJoPnnnsOr7zySr9SB25bIqZPn452sQNLf/w4Pn5sFV79\n2TKcdPRJ+POtiz3XNDWIMNdXPB5HKpXS9yK2Llhc4aeffqoH3TcSw7N06VJMnDgREyZMwNVXX23X\nZTQNsvBYwCgQ6g1wZQXprAS4Om0ZcGp8Fq8DoI/feNOmTeD5gYhGex+8nXYajTVrXoYoijWl3zfD\nYuK17LJWthqtWbMGrz33HCRRxIS99sK0ffc1PezXrl2LTZs2IZ1OY/z48U35zoxv5sz9VVqV10pc\nxrp16/CN73wDsdFRiBkRg7jBuOPWO5oWiBqLxXDHLXdg8V2Lsf6N9Tjn69/HUUceZemlAQA+/PBD\nfPDBBxg0aBAOOOAAz8a/eb0cAdDX/RUKhVAoFBCJRKAoCk4//XSsWrUKU6dOhSiKmD59es2FXVVV\nxdlnn42nnnoKI0aMwPTp03HkkUdi4sSJDl2R85DgqZFaDxyzgnR2f0azMWYspVIpPcWeMXToUKjq\nVoiigFgsha1bV2HYsAF1dVt2CrO4KVVVkclkwPM8Nf9sMps2bcKTf/0rdk2nEYtE8Nbjj4PjOEzb\nd98+v/f2m2/i9UcfxcBwGBlZxkcTJ2LeEUc09WC1kpZsFpcBAFf97iqMPX4MZn5nf2iahkcvXIo/\n/flP+O+z/rtZl4NUKoWzzjyr5r978p9P4nd/+h3SU1LY/N5mjAztjHv/fK+n9gG/wqzqTGQ/9dRT\n+OSTT/DLX/4Sy5Ytw6RJkzB69GgcfvjhWLBgAebMqR77tGzZMuy22256Bevjjz8eDz30kK8Fjzfl\ndUCQJAnd3d2IRCJIp9OeeZuxU1AxQcd6fpkVExwzZgzOPPPr2Lp1KdatewSx2Hs477wz8Lvf3Yj5\n84/DokXfwvPPP+/qvKshyzK6u7sRjUbrdj/6Tbh6mTWffopBAAZ1dKAtmcRugwfjo7fe6vM7qqri\n1aefxtThwzF+5EjsNXo0tn7wATZs2NCcSZeBWX9isRiSySSSySRCoRBkWUYul0Mul9PrAK3dsBaj\nDxil/92IGcOx9vO1Tb6C2lEUBdfcfA32vmhPHHz5QVh439FYI63GJZdf0uypmeIHC081dtllF7S1\nteG6667D5s2bceuttyKdTmPJkiWW/n7dunUYNWqU/u+dd94Z69atc2q6rkAWHgvU6tIyy1Cq9/O8\nDKswHIlE9IwlNu/SDWPevMMxc+b+6OnpweDBg3HbbX/E3/72JoYM+QpyuQx+9rPrceutgzBp0qRm\nXY6O3R3q3cQva6dWItEoioa+ZGKxiEhbW5/fUVUVqiQh9sXzxnEcol8ICS9jlpbMXF+Td5uMN+56\nE8P2GAY5L+GDv6/EGYef0ewp10w+n0euKGDQpEFItPe6sUfsMwIvPfdSk2cWDCplafU2bg5j5syZ\nmFljc9yg4Q2Tg4+odqAwi0exWCxr8Wj0MxrFjvFFUUQmk9EDNNnDVumtqKOjA6NGjUI8Hsczz7yM\nQYMORDSaRjo9HIoyDitWvNHQnBqB43o7sedyOeTzebS1tflO7ASZ3SZMgDRoEN5ZvRor167FJ/k8\nph98cJ/fCYfDGDNpEt777DMIhQLWbd4MKZnEkCFDmjTryuTzeVx08UXY90v74uB5B+Ohfzykx2Uw\n689FF1yEzo0DcOv+/4P/+dIfcMCoA3DE147Qe+75RdymUikMah+Mt+99B4qsYMOKDVj/2nrsNHCn\nZk8t0LA6b/UwcuRIrFmzRv/32rVrMXLkSLum1hTIwlMjlcSC0eLRzCwsK9S7UVqtMFzNJNzR0YbP\nP9+OeLzji9/PoK2tcr0IJ4UgCywH4OligkG14FQjmUziqJNOwkcffQRZkvClMWMwaNCgfr83+/DD\n8WoyiY8/+QTtO++M+YccUnfNJ6f51W9+hXfld/CNp09Gz/oe/OqsX2HE8N7gUEZbWxtuv/l2dHV1\nIRQKIZVK9Sl8CPS6i1jgc6N7jqZp2L59u95N3k6u/cW1OPmsk/CfP76BZGcCaS6Nc759jq2fYRd+\nc2lVsvDUG+A+ffp0fPTRR1i9ejWGDx+Ov/71r7jnnnsanWpTIcFjE8wNYtZTqVbcsPDUQ2nPr3Ki\nwMr45513Bv7f//sF1q//DJomYNddQ5hrY4XcWjCm0hutVY3SquLEKZLJJKZOnVrxd6LRaG+l5Sat\npVp4/pXnsGDxfCQHJJEckMTE43bHy6++3EfwAL3raMCAAfq/jYUPc7kcVFXVCx820vaiUCjg51dd\njhUrV0DTNBy8zyH4wXk/sK1tyvjx43HFRb/Ar6/7NaSchMP+63DMneP978nPNNJLKxQK4cYbb8Th\nhx8OVVVx+umneyLkoBFI8FigtIEoe7MCak85t/p5XnNp1doRvNr406ZNwx//eA1WrFiBZDKJgw46\nqOqD6cR9Yan0HMdRfR0XISEIdLR3Yusn2zBgdK+Y6f64G50TrFtVjNlfPM/XVPhQkiQ88cQT2Lp1\nK/bee29MnToVd/7lTnze+TlOuf9kKLKCpZc9jgf+/gCOXXisLdf7n//8Bzfc9Xt85eb5iLfH8a+r\nn8Of7/ozTj3lVFvGb2XKPU/sRa5e5s2bhw8++KDuv/caJHhqxHjosqrJPM+jo6MjsIdlrUG8Vu/D\nuHHjMG7cuEanVxfGwPK2tja9fhBBuMWPz/sxzvnhOVjztdXIbhCgrQKOvujousaq1JKgUCgA2NH2\nQtM0nHXuWdjAr8fAiTvhtp/chh+d9SO898l7mPiN3cGHePAhHrsevivef+59W65V0zQ8/8rzmHLy\nHhi9T2/mz6xzZ+KfV//Tk4LHby4toPy+67frcBISPDXCBA+zDMTjcdstA16x8DRivXK6V1cj99tY\nTJC1viAItznggANw9//cjZdffhnJ0UnMv2x+2QBTRVGwZcsWtLW1WXJRlCt8KEkSnn76aayVPsPC\nPx+DUIjHlKO34KpTrsKRXz0Sny1fi9HTRkPTNKxfvh4HDLUvqycRTUDYtOPFIrM5i2Tcm/FVQYCs\nqP0hwVMj7M1JEIS6Us6tUOo2awb1FExkONn0tFHK9TJzQmQ6PSazvBk7dvsBP7xxunVY7Lbbbtht\nt90q/s6qVatw5jlnYHu+C5JQxA/P/RFOOuEkANbmWVr4sFgsonNsJ3ieg6pp6BzdCSEn4BvHfwM/\n/vmP8dBb/4BcVDBIG4TjrjiuruvKZDK45vfX4D9vL8fggYNxwXcvxFFHHIUnf/AknpaeRawjhg//\nbyWuuuiqusYn+lLJdeWH580t/LFDNhm2YFRVRaFQgKqq6Ozs9K1loNpBXGu8jhlm42uahp6eHiQS\niaakfLPmn05Y5dyEWagkSUIikdDf3lngKvt3UDt2u0Wle9fV1YXP1qwBz/MYM3asox2pz/3RuRh/\n+i7Y5+Rp6FrbheuPuw5Tp0zFnnvuqc+zu7sbr776KjRNw/77718xw2ratGn4za2/wepX12DY5GF4\n8eaXMH2f/dDZ2YlrrrgGH330EcLhMKZMmVJ3AsbPrvgZto/YhkNvnIv1b2/AuT89F7dffzvuuOkO\nPPLoIxAzIs795XmerdrrR5cWUR0SPBZhIoD5wJ0UO810aRUKBb12Q72ixGyj2Lp1K8477yK8887H\n4HkN559/Bk466cS6xq5nM7LjuryAMX6sra0NyhfF+Ni6ZDEbLG2ZWX5KWxYEGU3TsGL5crz3738j\nFA5j2qxZmLD77raNv3XrVjxx//3okCSomoZ30mnMX7QIbSWFEBtFlmU888wzeGP5G5h54/4AgM6d\nOzH6oNF47733dMGzefNmfPfC/0ZiUu/Lya2Lb8Ut19xStnfS2LFjce1l1+Lnl12Ordu2Yfo+03HF\nFVcgkUggHo9jwIABkGVZt2RXWkM9PT1Yt24dxo8frz9XoijitRXL8J3rzwAf4jFg1ACsfm4N3nrr\nLXzlK1/BaaeeZut9IswFGom2/pDgsQArspdKpcDzfL9eUXbTrCaZLF7HjiaZpfO/9NIr8fbbYQwa\ndDpkWcA11yzG7rtPwH777dfQ51iZh5Xr8oNLS1EUZDIZANAPV8VQfZgFrgK9KdzM0sN6NvE83ydz\nJ6ib4TtvvYV3/vlPTBo2DIqq4uV//APx447D6NGjbRn/rddfxzCOw4gvirCtWr8eH7z3HvabMcOW\n8YFea+RpZ52GDfJ6jP7yKPzxqDuw6NaFGLHXcGxcsREj5ozQf/fOu+9E/MA4Rh42HBo0rGlfi/+5\n4zZc8uNLy44/c+ZMPPbA0n7/e2nwM8v8Mq4hTdNw/wP3457778H7q95DojMBLQ/ceOWNOOywwxAO\nh8GBh7BNQNvgtl6L5CahpkbBzSYIYkEURepTVgIJHgtEIhH9sDQeMH6l9CA2Nsm0I9vM7O9ffXU5\nQqGvolgsIhZLQ1XH4P33369Z8NQiIoxxSH7PojO64wqFgqX7wHEcIpFIv5YFQbf+fPrhhxi3005I\nfnHADo/HsXbVKtsET7FYRNpgJYxGIpBE0ZaxGQ8//DA2Rzdh0d0LIRWLWPHICtx72n0YPGwIDp1x\nKGbNmqX/7pvvvInQ/BAGTx4MPsxj60fb8dwt1XvTWcGs7cUVV1+Bf3/+Oj4TP8NRdx+J1OAUtryz\nFWf/8Ht4Z867CIfD+M43v4O7vvsXjP/qeGx5ZwsGyYMdf7lpZcwEWiNFB4MKCR4LhEIhPYjYDeuL\nmxYeu+Na2KZoZMmSR7B69WeQpDfA88MwatQw8PxmDB06tKHPqoQdcUhegQUnp9NphMNhPVYHsP4m\nauXNnQmg0potfiOeTCK3YQN2am8HAORFEQNt3Ph3mTgRry1Zgkg4DEVRsFEUMXv8eNvGB4AtW7Zg\n4JSB4HkesXgckw/eA6+GXscfrv4D9thjj77fjwR88uQnmHTU7gDH4ZMnPkG+p2DrfIDeNZTL5fDS\nipew5xlTUGjLY9TMnbH9k+0YP3ccno09iw8//BATJkzAKSeegl3G7IIVb67AkMlDsODCBb4Jqg8K\nuVzOs1XGmwWtwBoJguBhWWB2x7X8/e8P4YYb/ohcLo+5c2fh4ot/BFEU8Ytf3IDx40/Cxx8/Bln+\nHJ9++ihOOGGuY5WV66l67cWqyMY2HszCaNccKzWsVFW1T8Vev1l/pu2/Px799FMIa9dCUVXIgwZh\n0h57NDzu6tWrsW7dOowdOxbT5s3Dh2+8AT4ex4GzZ2PEiBHVB6iBffbZB7f/7A+Yeuye6BzViVdv\nXoaDZn4JU6ZM6fN7mqbh4FkH449PrsTd8/+K7JYsYuk4RneMdtAto2HgyJ2wdeU2FLoLAMdh07ub\noORVjBgxQi98uO+++2LGjBm6ZbzZmadWMTaK9gtk4bEGCZ468bOP15jWbEe8DgC8/vrr+PWv/4zO\nzq8ikYjjySefRUfHrTjmmK8BSGLAgInYa6+dIQjrIAgv4eyzz+z3ud3d3bj33vuxZct2HHjgdMye\nPbvf51QSJlb7fLlFIyJK0zRks1lomuZ4b69y1h8mgPxm/Rk4cCCOPPlkrFu3DjzPY/To0chkMli8\neDGKUhHzvjyvaip4KXcuvhPX/c91GDxhEDa/vwW//OkvccTxxzt0BcB+++2HC06/EFceeSVEUcSM\n/WbgV1ddafq7J55wIu7921+x+qM1GLbPUIw9ZAzWP7EBt/3vbTjrzLNsnVdbWxtmTfsS3n3wPQwb\nNxR3L7gXqcFJdH3YjfO/fb6eHVZa+JDtl5Q96B6sWCyxAxI8FihtLeHG5zllbWB1aAD0qUPTKCtW\nvAlN2wXxeCcURcGAAfvh5Zd7hU08LmHz5uXYvPk95HKbkUzm+qXxZrNZnHzyGfj00yRCoZ1w771X\n4aKLNuKEE6ofKqtXr8aDDz6MbDaLQw+djRkzZvjOKmGEfUfhcBjJZNL1w8Gq9SccDnv24Gpvb0f7\nFy6t9evXY8HXF2DEocMRaYvg5mNvxl2334V99tnH0ljLly/Hb274DU5+7CQMGN2JTR9sxk8W/gSH\nHHyIo+noxy46FosWLoIsyxXrfX3yySfYuP1zzLxwf0TTUbz1p7dx+GWH4faf/MF2wQMAP7nwJ7jr\n3rvw7kfvYvzk3TBhlwk45IeH9OlzVlr4sFgs9ml7wfN8n/gxr64jv0AWHmuQ4KmDelOjax3fbozx\nOsY4EDvYaadOqOp2fd75/GaMGbMTkskkrrjih1i48AxI0jREImMxYICIq666Djff/Fv9Hj7//PP4\n7LMQhgw5FAAgiuNwww1/7Cd4Su/N6tWrccop30MmMw48H8HDD/8cN930c0eDoZ2EfUeJRAKxWKzi\nGmMF5Zxei9WsP14/uG77w20Yt2gs5l42GwAweI9BuOp3V+K+xfdX/DtVVfHTy36KBx99ECIn4v++\n8zccf+exGLL7YMQHxLBp0yZHBQ+wI/C8Erf/5XYc8IMZGPu1MUgOSiHWHsfbf38HqurMeo5EIjj1\n5FMt/z5bp6FQCLFYzNT64yUXqp+t90YaaRwaVEjw1IFXDkerGPtGserQdgue+fPnY8mSp/D++0ug\naXEkEptwwQVXAwBisRgmTvwSOjrmIhKJgOM4vP32YmQyGf0tvFgsAtgRRxQKxVEoFKt+7n33PYCe\nnnEYPnwmeJ7H9u0d+MMf7vJURojVDbSWnmXN2pRLrT9m/Zq8Zv3pynShc3KH/u8BYzvxWWZd1b97\n+OGH8fzK53Hmy6dju7Adby5+C0svfhzTT50OJaNi+PDh+u+y7E073MOlaJqGTCaDdDptKgYKxQI6\nhnRAyskQewoIJ8P47KXPcPrCb9s+FzswWn9isZjvXajNptxZRC6t/pDgqRMv9LqygrFvlDFex27L\nQCKRwC23/BbLli1DV1cX9txzT4wdOxZAb00YRckiGo2A43gUiwI4Tu0TUDxjxgwkkzdj27Y3EY8P\nhiAsw6JF88pek6ZpEEURPT1ZRCJx/SAIhWIQxepCqdy4dmL13poFJ9c7Jvu5G4LI7OCSZdlz1p8v\nz/0yLrryRxg+bThibVG8cMVLOHZu9ZYJ7698H7vMG4tEewKIAGNmj8HfbnwQ3a/14KZrb0IikYAo\nivjpZT/FY08+Bp7n8a1TvoXzzj7Ptmt9++23cdZ5Z2F793YkonFc/+vf90lJB4AjDj8CV/7uShx0\nySxsy2zFS1e8jAUHfgUXnHeBLXNwmmouVLfLJ/jVwkMureqQ4LFA6UJy62Fo9MEr1zfKKWKxGA46\n6CDk8/k+4mHy5Mk46KCJeO65JQAGgeM+w/e+d3IfwTN8+HDceecNuOaaG7F58xuYPfsQfPe73+n3\nGUyoMRF31FEL8MQTF6Orqx2hUBT5/Ms45pgzap57szY4N4OTnYbned0yVa1bt5ssmL8Am7dsxg3f\nvAGSJGHRUYvw/e99v+rfjR01Fg8/8g+MnD8SoUgIG1/+HPvvsz/+cvtf9CJ61990Pd4T38V3l5+F\nYq6IB099EGNHjcXXj/o6isUiCoWCbsWsFVEUccY5Z2DGZdMxacFErHl1Dc456xw8+dCTGDhwoH4f\nv7LgKygWi7jrd38Bx3H4/+ydd3gc5dX275lt2qLemyVLluUm27jJveGGjXHBgG3iOKGTxBBCeUmA\n9yN5cSAhJCSUFBLAQKgGjE21DTbGVe4N25Jl9S5Zbevs7Mz3h/wMs6vd1XbtyvO7rlwx0mp2ZnZ2\nnjPn3Oc+f3j4D1h2wzKf3jNYePqZe2qfMNDNM73B1TphNBp9vvYGKlLA4wOhaBv3FzLN3ZUWJNjH\nIN42TdN46qknsHv3btTW1kKjmYzi4uJefzN06FD8619/63O7JpMJcrkcMTExGD9+PJ5//gn8+99v\nw2plcdNNd2DJkiUBP55g0N/i5GDiKvtjtVrBcZyd+WEosj/r163H+nXrPX49z/PQG/VoONWAj9Zs\ngTpRjabjTdhw2wY7x+CDxw5g/BPjoFAroFArMOpHI3Dg4AFUVFXgxb+/CFpOY3TRaPz7pX8jISHB\nq32uq6sDF8Vh+OKeeVODigchYUg8Ll68iMTEROF1FEVh5YqVWLlipVfbDwUWiwVbt25FbX0tikYW\n4dprr/Xqs3aW/bHZbHbXj7j8JfEDJpPJruwqIQU8HiMOEELpxePtQuBMrxNqnO2zTCZDbm4unn32\n79DrlbDZ3sQttyzA/ff/3ONjJOUS8h7k76ZMmYIpU6b4vd+hzDx4I04eCIizP2TCO8dxsFqtAGBX\ntgiXc/HNd99g4V8WIKkwCVYDg47qThzeVGL3mpTEVDSdaULWuCwAQMuZFtjaeHx9eCfuOnYHNIlq\nfPP4Ljz6v4/iXy/+y6v3T0xMhLHNgI6aDsRlx8HYbkTrpTYkJycH7BiDCcuy+OX//BKX49uQPCoZ\nO97bjktVl3DX7Xf5tD1x9kepVNqNTiGaRLH42dfrKNJKWu4yPFJJyx4p4AlTfAmqSHmE4zjExsa6\nfeLpD+H144//Ht3dI0DTcgAmvPHGF5g2rRgTJ07s828tFgsMBgN27PgGmzd/BZ6nMW7cUNx//z0B\nEeYF63w4C1y9EScPRMjCRcpbZOEiLcuOwuf+WnwS4hLQerEZI1YMBwDU7K9FfGy83Wsevu9hrLtr\nHZqONMPSbQFfC4wYOwpDxxdAl9xzXY6/axy2rvzM6/ePjY3Fow/8Gn9a8SdkTcpEw4lGrL9pPfLy\n8vw/uBBw7Ngx1NvqcePTKwAARUtG4s0Vb+In634SkOve2egUceu7uPR1NYqfiamsxA9IAY8PhGOX\nFhksqVAooNPpPBK3BusYXG27oqIKra3tMBqVADSwWptRUlLiNuARD/+srKzE22/vRnr6YqjVGhw7\ndghvvPEu7r339qAcR6DxRpzsjmC3oocSEtA4an9Ylg3oUzuhqakJJ0+eRFJSEq655hq32/vVhl/h\n+pXXo6uyC6ApVGyrxGcf2wcu+fn52PreVhw4cAByuRwzZ87E5s2b8e3eb8FxHGiaRs3BWpeTy/vi\n1jW3YsK4CSgtLYVynhKjRo2KmM/ebDZDk6AGTdPgOA5RsWpQsp5ye6ADfUftj/g6CucsYjCR2tJ7\nIwU8HtJfJS1PEOt1wnkicWysBhcv2hATMx0833Mz2r//BH7+c+evJ8M/gR4jucrKatB0BpRKNSiK\nRnLyCJw+fSRg+xdsTdNAEScHE7H2J9CajX379mHdbT9C+rh0tJa1Yf6M+Xjhzy+4XPzy8vKw66td\n2Lp1K3iex9IvlyIrK6vX65KSkrB06VLhv9esWYOtX23F2wvfQ3SaDk1Hm/DO6+96dyJEZGZm4pHH\nHsHZ0rPgOA5TJk7Bq/94Neyzg0VFReh8oQuntp1CxqgMnHj/JMYOvybo3kVA7+vIMYvoroMwUgJK\ngqv9NRgMUobHAemu6wOhyvD09R4k+2E0GhEdHe1VsNMfGZ6VK6+HUimHxVIBi6UCQ4cWgqad37RZ\nlkVXVxdkMhmio6NB0zQSE+Nhs/1gbtjV1YD09ESnf+/LPgcDiqJgs9nQ1dUFmqaFYxko8DyP8vJy\nlBw4gPPnzgl+NIGAPLUrlUpoNBpotVooFApwHAeTyQSDwQCLxQKWZT26lu/ecDeue30Rbty2Aj89\nth7fHtuNnTt3uv2bjIwM3HPPPbj33nudBjvOUCqVeHfTu5hZOBPnvjiPttbL2PjsRnR2dnr0945s\n/MNG6DO78bOyu/Gz0ntQxVfhby/9LewX5vj4eLzwzAvQf2bE7gf3ILtjEDb+78aQ7wdFUYKGTK1W\nC6Vkonc0GAwwm82CoH6gIGV4eiNleMKUvm5kHMfBYDBEVMZg1qyZGD16H6KihkCrjUVr62FMnXpN\nr9eRoaaOwz+nTp2Kb789hBMnvoRCoUNMjAm33RbeXiMkszNQxclHDx/Gpf37kaRWo85sRm1FBeYu\nWhSU69GT7I8rvxaO49BU14TcObkAAKVGgYzJGaiuru71PhzH4fLly0hNTfW57Lhv3z58sf8L3Hns\ndkSn67Dz4W/wP48/gn+88E+vt3Xy7EmMfHQ4bLwNoIChNw/BsfePYf/+/bBarSguLg7bhS0/Px8v\n//llQVPTH00UjvRlfEi+ozabLSK0P64CX0nD0xsp4PGB/i5psSwLvV4PhULhcztzf2R4hg0bht/8\n5g784x//hcFgxOLFk3HbbeuE31dUVOC3v30WlZV1yMvLxhNPPIjBgwcLv1coFHjggXtx6dIlUBSF\nwYMHIzo6OmD7HejzYbFYwPM8NBpNSEuNodL4MAyD8yUlGJeVBblMhkyex8nycly+fBlJSUlBfW93\nHTvO/FpomsawomE4/spxTLh3PDprOnHpq0sYvWq03XYPHjyIW9ffCoPRAJVShTdefQOzZs3yev8O\nHDyA4WsLEZ/TM0xz8oOT8OF1W3w61uyMbJzbeh7p0zNAAbiwrRSXT7Tjld2vgLWy2PTBJjz/++eF\nwZ3hSDhnoxxb34nhYX8ZHwYKg8EQtoFwfyEFPB4i/rJSFBWS1KezBZh0+DhmPyKFmTNnYObMCmW3\nJgAAIABJREFUGb1+bjKZ8PDDv4NeX4icnAVoabmERx/diE2bXrQLFuRyOQoKCoQvckdHBz766BO0\ntLRjwoTRmDt3js8BYKAQi5M9mYXkLeEimuc4DhTPQ3ZlEaAoCvSVEl6ocdaxw7KsXfbnlRdfwdqf\nrMWhPx6GuduMJ37zhJ1gXq/XY/WPVmPBa/NQsHgIKr6pxLrV63Di8AmvPXRSU1LRcqhVWOgbjjci\nJTnFp2ObMGYCvvzbl2g40ggbYwPPAbIEGRY/tQgcx2HfK/vxzvtv4967fubT9iV+gJS/KIqyy/6E\ns/Ghq2DSbDZLAY8DUsDjA6HK8IgRL6LR0dGQy/376Pojw+MKm82GsrIydHbyyMwcAYBCYmIBmprO\nob6+3mUbrsFgwC9/+Rhqa6OhUiVi+/b/oqmpGWvX9j1hPVg4ipO7u7sjZgyJt0RFRSF96FBcuHAB\n6fHxaNfroUhK8jo4CDSOHTvHjh3DR1s+glKhxJv/eRMqlQrx8fGIiYmB2WwWSo2XLl2CJlmDgsVD\nAACD5+YiPjceZWVlTo0y3bF27Vp88MkHeG/JB4jJikblN9V485U3fToeuUKO2T+bhdwpOaBoCkaZ\nEV89uF34feqIFDR93eTTtiV6Iw4gIt34MBwCsnBCCnjCFPFC5titFIgvVrhkCYgBX1JSEmiagdVq\nhkKhhtVqAscZnFqjk/0+fvw46utpZGX1zBZimGz897+fYM2aW3z6ovt7PsSjPDyxBhgIzJg7Fydi\nY9FcV4fYrCxMmzQpLHQahD179mDdHesw5p4iMJ0MNt24CTs+34H29nZse+cdcGYzYlNTMXvxYqSk\npKC9rh2dNV2IzY6BvlGP1kutPrnVRkVFYcv7W/D111+ju7sbUx+YiuzsbJ+OYfq06Xj9V68he0IW\n4jJjsfepfdCotbBarLBZbTi39TxuHLfKp21LeE6ojA+9xV258Gq4B3mDFPD4QCi7tIheh3QYRMIF\n7On5IeJknU6H+Ph4/PSnK/Dqq9tAUSng+RbcdtuKXloQ8fH3lE5+EJXStFxIP3ubAfP3vJLALSoq\nClFRURHxOQUChUKBiZMn9/duuGTjnzbi2r/OwYibeswD5Wo5XvrHSxifm4+iuDho1WrUNjVh1+ef\nY9GKFXj4wYfx10nPY9C0Qag5WINfbvglBg0a5NN7q1QqLF682O9jKCgowB8ffxYvvPICuvVduK54\nMSyDLXjjxjfB88DSa5dixbIVfr9PMAlnDY+vhLPxYTg8zIYjUsDjIY4anlCUtFiWFeqwgdbr9GeG\nR2wmKDbgW736JowdW4S6ujpkZmZi2LBhbrczduxYxMe/iYaGY9BoknDx4jeIiurArbf+HEuWzMKP\nfrQ6JGnmq905OZwxGA3QZfzg+6JN16KtvA0GlQanvv8eHMchKy8PRpaFUqnE/b+4H7Omz8K5c+eQ\nd2cexo0bB6vVKrg+9xfFxcW9ymq/NP4SDMOEtVg5EuF53uv7Rn8aH7rb34EWZPqLFPD4QCgGbxJP\niEDodUKNu/NDynMURSE2NrbXF3LYsGFOAx2e53Hs2DFUVVUhOTkZ06dPR2xsLJ5//v+wadM7OHr0\nMDQaDpMm3QeZTIEtW3YjJSUJixYt8Hi/fRnl0ZeuKhjXSriUIyOBFdevwKYHN2HBP+fB3GHG4T8d\nxaMbHsW5L7/E7IwMKBQKXDh6FOywYVAoFKAoCuPHj8fw4cOF0hxpV3ZnVtcfRGLTwtWCP8aHEsEj\nslbSqwASEPA8D4VCEbRgJ1SdZmK8Lc+ZTCb85S8v4rvvDqO9vRUcFw+NJg8834Bbby3DXXfdhvT0\ndDz66K/w3HMvQ6ORQ6ns6UqIiRmKkyfPexzw+DKkVXJODn8euO8BMAyD9259Dwq5An/43z8gMyUF\n3MWLKG9thZqi0KVQYFB6Otrb2/H555+DZVlMnToVQ4YMsROsknKp2WwGgF4zvyRcMxBLWp7ianyK\ns2vJl9Z3Z+f2aj7f7pACHg8JRUlLHBCEIiAJZZeWJ2WfU6dOoaTkKGJjdVi4cCH+/OcX8eWXlYiJ\nmYZLl96HXJ6N4uKxUCjG4f33P8ENNyyGVquFTqdDcnIcTKYqAD2+PUZjK1JTfROJ9sXVKE6OVGia\nxq8f+TV+/civhZ8dKSlBRmoqUgoLYWYYdBkMsEZHY+ENC5FYnACFVoFnX3gW77/xPkaOHAmgb7O6\nUD+xX7x4EX98/o9oudyCeTPm4Z677vHZJFHCnmAHC55cS+LOL1/2xWw2h/WYof5CCnh8IBgBj2NA\nYLFYgupnEopFmpwjT9rpd+/ejccf/xt4fgg4rhsffvg5amsbkJJyKximC3J5HHg+Bp2dHUhNTYPF\nAtx9969gNgNarQLLls1FVFQlqqu7AFDIyACWL1/i0/66g2VZdHd3eyVODnX5SSp5uWf4yJEoP3MG\ndW1toCkKeo0GZ0+fQPayTMx9cg4A4GjRMTz956fx1n/ecroNx3ZlZ0/swRpU2dDQgFU/WoXRvyhC\nbsEgvPu3d9Da1oL/9/iTAX0fidDgrPWdBD+eGB86C9CIV5uEPVLA4wOBXFBcCXiDTbB9eICeY/N0\n/MULL7wGnW4udLqeqdLV1dvBsiYwTBdUqngolRz0+osAktDUdAp1daWIj1+H2Ng0HDy4CyUlL6Kg\nYBhGjtRi/fo1GDlypFdfeE8WJV/EyVL2J/zQarW4Yc0aVFdXg+d5ZGRk4Iv77kG7oQOnPziD4TcM\nQ1JhIs6+d96j7Tl7YhfrNRzLFZcvX8afX3wOF6suIn/QEDy44UEkJno+E27nzp3ImpeJ4rsnggeP\n1JGp2DT9TSngCRD9WQ5yFD/7anxIhklL2CMFPH7g7xfDnYB3IDyld3V1eTz+wmg0Qan8Ye4LRWmw\nZMlcbN++A52dg5GQEIvo6EoolSySk+MAFCIpaQgOHToKuXwIbLY2JCRMxYULJ8AwTECfbgJt+hio\nfZLwHbVajcLCQgDAtk+34cylM8hYkI7vvziHE++chIyVY+U031q9yaBKoHe3jtVqxb0P3APdDB3G\n/eQalO4sw89+9TO89e+3PPYvomkaNssP2V8bw4KWhbeGTNKU+IYnxofk5+KHZWmOlnP6/84dITgG\nI/7Sl29LsAOeYG6fYRgAPV0knj5lXHfdbLzzzrdISJgMs7kTSmU11q//JVatWoVTp04hJiYG1157\nLWQyGZqamnDrrb/A2bMn0dbWDrU6CoABKlUMgERcvnzZ6312dT7CUZwsLRyew7IsKioqYDIakZqW\nhtTU1F6veeb5Z3DLm6ugylChvaMDn93zBYYqc3Hfz+/3+/0du3XKysrQznXgul8sBCgKKUOT8c7u\n93Dp0iUMHTrUo8920aJFeP7vz+PbZ75DwtB4HH/5BG7/8e1+76tEeOPK+NBiscBisYBhGBw6dAhm\nsxlxcXEBe+jbvHkznnzySZw7dw6HDx/GuHHjArLd/kAKeHzE1wGNZDgdicAHkm8Lz/Mwm82CjsGb\nttl7770TSqUSW7duh1zOYs2aG5CdnQ2ZTCYIR4GeBay0tBRWK4e6up2wWCgYDHswbtyiK3NwXI+i\n8BaO49Dd3e2XOHkgZOoiFZvNhu3btsFQXg6NTIaTPI8pS5diSEGB8Bqe59Hd3Y34wQlQqhWIi4tD\n4aShWJy2xGUmj5QXvF1QyHwm1sz2zMNS0LAxLBhjjwWF0Wj0yKk3MTERr//zddz58ztwovMk8rLy\nsPaWtV7ti4RrIiUbRYwPGYYRBMrt7e145ZVXcPLkSaSlpeG5557DokWLMGLECJ+PqaioCB9//DHu\nvvvuQO5+v9D/j6sRii8LGdG0WCwWxMTEuA12Ii3DQ46NYRghE+LN9hUKBYYPL4DBwMJoHIT//Ocb\n/OY3v3Uq3P7kk50YMmQZFiy4A3PmLEJycjS6ug6jo+NTbNhwk12A5Cssy6KzsxNKpRJarTZsb4Dh\nul/Bpru7G+fPn0dZWRksFovT19TV1aG7ogJFOTnIz8rCiKQkHPrmG7vXUBSF6VOnY9fG3TBcNqLy\nYBUqvqp0+RT7+huvY/DQwRgyfAgWLl2IpibvZlgNGjQIE4aOx9YHtuH4hyew7cHPMKFwAgoLC4VM\nL8MwMBgMQhlV3K3Z2NiIOzfcietXX492qh1zn5mNmPnRWLN+tcvz0N9IAX9wIcaDMpkMN954I778\n8kv861//wsSJE1FeXo7rr78egwYNwp133gmDweD19gsLC1FQUDAgPkcpwxMixK3MMTExfS5UkRTw\niDMhMTExYBjG6w4znufxzDMvIjp6ATSaZPA8h/37P8GRI0d6OczabBxoWgadLhk6XTI4zoaVK7Ox\ndu0an/U14vMhOSeHN21tbdj23/9CYzCA5XkcSUvDirVrERUVBavVioaGBgA9nSpKUQkySqWCtaOj\n1xP8008+jSeeegJvL3wHcXHx+PPv/uw0S3jo0CH8/i+/x0/2r0Pc4Hh8++Qe3H3f3djy3haP952i\nKDz9u2ewefNmXDx9EZOvmYJVq1YJpVJxuYJof8icJpqm8YuHf4H4RXFY8fNlMLYZcehPJVj73mp8\nuONjXLhwAaNHj/bpnIaCqzU4DybuDF7Hjx+Phx56SCilfvPNN1d955YU8HiIPxqbgTxnSdymzXEc\nHnroMXz77UFQFHDvvT/GT3+63qPj7RFwG5GW1tOtQlE0aDoO3d3ddq+jKAoLF07HK698AZYdA6vV\nDJWqBrNn/8hvMTHpmAsncbIrBsLTlq+UfPcd0mw2ZF+ZcfV9dTW+P3MGhcOH4+O334atqQkcz4NK\nTgZoGk2XLyNGq0VFczMGjxnT63qMjY3F3579m93PnD0JHz16FENXFiAhv2ca/NSHJ+PF/L97vf9y\nuRyrV692+xpH7Q/Hcairq0OLvhnz112LiqpLSB+fjwsflaLpTDMseosUnAeISClpiXHXlk5RFIYO\nHYqhQ4e6/Pv58+fbZSvJOdi4cSOWLl0anJ3uB8L3jh7meBLwiDUtOp3OqynSkZDhccyEbNz4LHbv\nbkRKyu2wWLrx0ksfoaAgHzNmzOhzWzKZDBMnjsHhw/uRkjIJBkMzZLIGp2Mmpk+fhvj4OHz99QGo\n1SrceONDPg94JJBzwbJsL3EywzAoKyuDwWBCZmY6MjMzPd5usEZLXK2YzWY0NzQgW9SRolEqYTIa\nceTgQaiamzHsylTy76urET12LLrMZjR1dyNrwgRMnDLF7fbb29vRcSULlJuba/e71NRUNO9ogY3l\nIJPTqC2pQ0pqcsCP0REiVo2Pj4fVyMJmYKHV6HC5tA1tZW3Y98f9GJE7Enl5eRG5WEsEHoPBgPj4\neI9fv2PHjiDuTfggBTxe4Lh4uVvIiKbFZrP55K8TzmJXV95BJSUnEBs7BTQtg0KhA0UV4PjxUx4F\nPADwv//7CH7/+z+jpOQdJCbGY+PG3yArK8vuNeRmPn36dEyfPj0gx0PsAQAgOjrabsGwWq3YvPkz\n1NfboFBoYLWexdKlU1BY6Pppqb8J52vHH+rq6rD388/RVlWFcxcuYPbkyYhPTESj2YxROTk4VVKC\neFErbpxGA57jsPimmzzafkVFBQ589hl0PI92kwlNU6ZgiujaXbZsGd796F38d9bbSChIQMU3lXjt\nH68F/DhdER0djdtW34Z373gHg2YPQtm3ZYg1xWHNdWuw7kfrwLIsGIbp06hOwjWR9r1xFeCazeag\ntKVH2vlxRAp4fMTdU5S3eh13BOuJzddFURwcOGZC0tOT0dzcCLU66Uoavhnp6ZM93nZcXBz++Mff\neb1P/iAuyXV0dKCxsRFKpVIwgquurkZ9PYNBg4oAACZTEnbvPuw24LFYLGhqaoJSqUR0dHTQj+Fq\neKpnWRb7vvwSQ7RajCkuxmGdDp8fP45RkyZh2rJlyMnJQWtzM06fPYvEmBgAQF13N8bn5AjbaGpq\nwolDh8AyDPJHjsSw4cOF33EchwPbt2NYfDy0ajWMJhPOHTmCguHDkZSUBKCnFPXuG+9i165daG9v\nx8SHJvbKAgWbO356B4pGFKG0tBSrf7wG06dPt9NluDKq82dMgb9E4vUZafvrSCCdlrds2YINGzag\ntbUV119/PcaOHYsvvvgiINsONVLA4yOuAgbSYaFWq6FSqXz+4oTqC+fNzaiv4Z8PP7wBd931INra\namG16lFUlBjw+m8gsxfiklxXVxc2bfoQNpsWHGfB1KnDcO21s8FxHGj6h1KkQqFCdzfrcpttbW14\n+eVNaG3lwHEWjB+fi5tv9s3Ari+IISLHcVAoFEEZYxAumM1mgGEQfSX4KC4qgi4xEdNXrUJyck9Z\naey4cehsb8eBw4fB8zxGzZyJoisi3ra2Nnz17rvIViqhUyhwZNs2cByHEVc6+liWBccw0F7xjaIp\nCmq5XLBYIMhkMsybNy9Uh+2U4uJiFBcXw2q19moO6GtMgbjtXcr+RD6u7t+BNB5cvnw5li9fHpBt\n9TdSwOMF4sXWWXnLV71OX+8XrAyPN5BATqPRuPTXyc/PxwcfvIpTp07BZrOhuLg4LAfYOXNO/uyz\nb2C1pmPQoCHgOA579x5Bfn4u0tLSEBV1CK2t9VCro9HSUoEpUwpcbnvz5m3o6EhGdnYBOM6Gffu+\nxYgRZzBp0qSAHgPHcejq6hIWOPEYA47jgj54NtSo1WrINBq0d3UhPiYGepMJjExmd1OXyWSYu2AB\nZsyZI4h+CVWVlUjgOKRdydzJaBqlJ04IAY9SqUR8ZiaqGhsxKDUVHXo9TAqFVzqIUNPXvcHVmIJA\nDqkcaERiNsoZ0iwt50gBjx+QgIe48XIch9jY2Ih5cvIkoCKBnMVi8ahzKSEhAbNnz4Zerw9I0Odu\nv3y5MRFtFcuyiI6OFhaD5uZ2xMb2lDhoWgaZLAbd3d3Izc3FzTcvwt69R9DdXYdZs4ZgwoRrXG6/\nvr4V8fFjhe0oFPFob++we41er8c772zGuXMVyMpKwY9/fItQNvH0GEwmE6KioiCXy2Gz2QTfI1LK\nYBgGVqvVIyO7SEAmk2Hm4sXY8/nnqKqrA69UYup11zm9qTu77uRyOWyiIJC12SBzeN3shQvx3Y4d\nKKmpgUylwtxlywaUPb+n2R+5XB7Qa2WgBBHhiKtzSzLXEvZIAY+PkIuM6HXkcrnPbrzu3qM/RWLi\nQG4gjFXgOA6dnZ348MMt+Oyz3aAoCjffvAS33LIKgwal4fvv6xAbGwur1QKO60BCQk/7cVJSEpYv\nX+TRe+TlZeHYsUvIzh4NlmXAsq1ISflBx8TzPP7613/i9GkGSUlFOH68DtXVz+OZZx73KBvGMAxY\nlhXGdrDsD+U1ktUggQ5N03ZzdyK9nJGcnIxlt94Ko9EItVrtVUCdl5+PsyUluFhbC6VcjiaWxeyF\nC+1eo9VqsWj5csH1eCAvGJ5kf8TCZylgiSxMJpOU4XGCFPD4CEVRYFkWZrMZarU6KKWb/mxN91d4\n3d/BmiNEf7Rr17fYvPkw0tOXAuDx+utfIzExAYsXz0Vz83uoqTkAmuaweHGxV+3nhJUrl+Dy5f+i\nsnIneN6GxYvH2bXWd3Z24syZagwatBQURUGrjUdt7deoqalBQYHrUhnQo2MxmUyCP4s7HOfuDJQF\nTS6XI+aKKNkbdDodrl+9GqXnz4O1WjEmLw9paWlOXxuJwaC/OGZ/iPCZaJjItRTo7E+4EWnZKCnD\n4x1SwOMF5MLieR5Wq1UoiwSzdNMfEKNEf4XXwcJbbRPRH2m1Wpw4cR7R0aOgVPY8/Wi1I3D48CnM\nn38t1q5dAZlMJhhE+kJ0dDTuv/8uYVK844iNnmvFBpvNCrlcCZ7nYLMxbq8hseYoJiZGcN4VlyXI\nguVqsfZkQSMBUCA/b6PRiKMHD6KtsRGJaWkYP3kyGhsbUVNeDpVajcKRI0Omk4mOjsb4iRND8l6R\njNj0UKVSDZhg+WpCmpbuHCng8RKO42AwGITOmGAGO6HO8IgHm/orvA6HDI8z/VFychyOHWsFkAsA\nMJsvIympp3WZpumAlO4sFgu+++4AKisbER+vxbXXzhDSy1qtFsuWzcZHH30DhSITDNOCqVPzXBon\nuprWToIdnuchl8vt2pHJ73o6zHofi7MFjWVZO+GzWMzqKzabDV9/9hkULS0YFBeH5vPn8X5ZGSi9\nHoOio2HgeWw/dw4Lb7oJcXFxPr+PRHDxN1iOtKxJJOHOhyccG0b6Gyng8QKWZYUnd4VCYaefCAah\nDBqImSBxGvbWKDHUeOJyLTZ+JAv3LbesxOHDT6K2tgsAh5QUC1atuk/4u/LychgMBiQkJPjk3szz\nPD74YBvKyhgkJeWjtLQejY1bcN99twvW/7fcciOGDMlFZWUNUlPHY+rUqU4DC3fT2m02Wy8xMsdx\ngl6H53mwLHtlgjwt/L8zaJoW9s1xhhMpjfnSydPV1QVjUxOuuVIa1KnVOPDVV5g6YgTSEhMhk8lg\nq69H5aVLGOtiWGeoKC8vx+mDB8GxLPJGjUJeH+XFcKA/AglPg+VIzf4MpODsaizN9oUU8HiB1WoV\nyjwMw/R7BsNfSEBFzAQpivLbKNFx28Ggr/1zHGYqfn1KSgpeeOFpnDx5EhRFYezYsYI54O7de3H2\nbCvk8ljYbMcwf/5oTJo0wat9MxgMKCtrwKBBMwEASmUeqqtL0NzcLLhGUxSFCRMmYMIE19smmiOV\nSmU3f41MRrZYLLBarVAoFEIwwjAMGIYRgiPy2RKvFpvNJgQ+rm6GzmY4+drJI5fLYeN52DgOMpqG\njeNg43n7YJqiwPdzC31DQwNOfv01hqakQC6TofzoUbAsi/EBthIYiDgLlh2zP+GQ7R2oSOfVO6SA\nxws0Go2Q1QnFlzgUJS2WZWEwGFyaCUYargIFMbGxsZg5c6bdz9ra2nDiRDXy8qZBLpeDZa3Ytesw\nxowpEnyHjEYjjhw5joqKaqSnJ2HKlMm9nJR7AgUbTp48hZqaJlAUkJzc7VV50JXnkc1mA8dxUCqV\nUCqVdpkYjuNAURRUKpXdk7VMJoNCoRACH3EGh/zeVfbH306e6Oho5I4dizNHjyJOpUKHxYJx8+ej\nvqoKivZ2cDyPdpkME/PzPT43waC5oQHJKhXUV851ZkICamtqACng8QpxsEwmvrMs22PqyHEwmUx2\nwXI43msiMcPjuL9SEOQaKeDxg0gPeMhNSKfTBXzSMkVRQTO/68vlmgwz9Qar1QqZTAWa7rl5yOUK\n8HyPoZ9KpYLFYsGbb36I/fvL0NpqBcNYMGLEXvz61/fadftERUUhNpbG9u3fIi5uGEymVlitjbBY\nLB7tB+nEEnseiTU54oWCZHZYlhUCG9I5SP5bHJCQoIaUvEgABUDYRl+lr760HAzD4PD+/WhraEBS\nejomTZ+O9KwsdLa3Y0h8PHJzc1FWVoa6igoolUrMGz263839ojQaNIo+H73JhCgvfJEkekOuU6VS\nKTxYkeuTiO4HikdUuCKd095IAY+PRPLFRLp+bDYboqKiAh7shBpvzRGdkZCQgNhYHs3NNYiPT0Nb\nWy0GDUoQOh1qa2tRWtoCg0GDrKwicJwNVVUn8dlnu3D77WvstsUwNGbMmAWrlYFSmQGzeTAqK6vc\naoJcDWR1FewAPRkfo9EIuVwuZLNUKpXdk7XBYBCevMn4CRLQOGZuyPuQEpgzv57SCxdw7uhRgKIw\ncuJEDBkyRHiaZxgGX2zZAnl9PbISEtB69iy+am3F8jVrIBs8WNhGdnY28vLyfPqcvMVms+Hs2bPo\naGtDSno6CgsLe313B+floaq0FOdrayGnKJjVakwcMybo+3Y14axUKvaICpRQ/mqDlLglPEMKeLxA\nfKOM1JIW6TLjeV5YAINBsDU8YpdrZ+JkADhz5gz27j0IrVaN665b6NbNWKlUYsWKhThw4Bja2r7H\nsGGpuPbaGXafudXKgKY1QuChUmnR3t7da1s6nRoWSywSEtLBsjaUlx+CWu26Y6KvTixnwQ7LsjAa\njVCpVL1GfVAUJQjro6KiwHGcICq12WxC8EMWF8fsj7jjiwQ/NE2j4tIlHP38cwxJ6hkOW/Lpp1Cs\nWIGcnBxQFNXTOt/SgqLBg8HZbNBpNDhSW4uGhgYkJyeHfDHjeR6fb9mCxqNHkaBS4XuLBfWzZ2Pu\nggV2r1MqlZhz3XVobGwEx3FITk4ecKM5wglHjyhHoTzQf9mfSCxpOeKqO1NCCnh8JhIDHpvNhu7u\nHj2JRqOB0WiM6HqvO3Hyvn378MQTfwNQCJvNiE8+2YF//vM5YQq6M+Li4rBy5RKnepvMzExkZ+tQ\nWloOmSwGDGNAUhKPgoLsXq9dtmw+XnrpHVRVNYNhjMjJoVFUVCT83mKxoLm5GSqVComJiYJTt0aj\nsRMnE82NY7DDMIxgeNmXNsiZDod01Yg1FcQziPyPtLqLn8YrLlxAhlYLbVQUaJpGpsWC6vJy5FyZ\nSC6Xy2HleVQ1NICxWhGj1YK+clzkuEnwJi6hBQKe51FdXQ2j0Yjk5GQkJSWhtbUVVcePY2pODmia\nRo7Nhn1796J42rReHiUKhQLZ2dnCtgwGQ0D2K5hEytO9JzO/+sr+iEuzEj/g7NySsTMSvZECnqsE\n8RT3UHwZgh0Q2mw2GAwGl+Lk//znPWi10xET07OI1dTswa5du7Bq1Sqf3i8qKgr33LMeiYkf49tv\njyMpKRozZozHggWze702NzcXDz98OyorKwH0DFUl57y5uRmvvPIOOjsp2GwmTJiQh2XLltgJxklJ\nCrBvLSU+SQzDQKvV+pSdI1014idrq9UqBL8k8yPOxhA9kEarhelKpxfHcTCYzYiVyYTuL61WCz3H\nYc/XXyNRoUADy2Ls8uXCiA6gJ+Aym81CpjEQJnY8z2PnF1+goqQEOppGB0Vh3i239JiCirJXcpkM\nNOCxnUSkP+lHIs6yPyzLCgGQ+PeS9sc50lgJ10gBj4+QBT2YKdBACH/dTXGP1HZRoncNdDuuAAAg\nAElEQVRxJ7Y2my2QyX4I7GhaCbPZM+GwK6Kjo3HbbT/GT3+6TigNuSIlJQUpKSlC+zhh8+bPYDZn\nIj09CyaTEQcPHsOECXXCWAmipXHM6oh1VzqdLiBPuu5a0Ik2SJz9KRo/Hl+Wl4NpaOjJgmi1mDJ6\ntHCNNjQ0IB5A0fz5MBmNGK1Sof7KLDayvySDRJ7aHYXPvpQy6uvrcamkBMXZ2aBpGt1GI3Z+/DHu\neOABqNLSUFpXh9S4ONRcvoyUIUN8Gk0h0T+Iy7PiazRY2Z9IK2k521/S4SnRGyng8QJHDU8o3s+f\ngMRR3+KYEQiVziZQkODNZrNBrVa7FVsvXToXf//75+C4ybBaDVAoKjBlyp0e73NZWRlee+19dHR0\no7i4CKtXrxK0MiRQ8ATH81Bf34ro6MFgGAu0Wi0Uinh0dna61euQz5GiqIAPqBXvJwk0iPCZjE+x\nWCygKApqtRoLb7oJ9fX1AHoyWTqdTgh4OI6DSiZDcnKysN3a+nphmruz9xS3MftayjCbzVCLMjnR\nGg1sra2gKAo3rluHb3fsQGVDA9KLizFj7tyIWtAkfsCVTYLNZhNsEnw1yRxISBke10gBjx+QxSwc\nv1ju9C2RiDh4IxkHd9xyy02QyWT46qvvoNWqcccdjyHfQ7+XpqYm/N//vQSFYgy02nh8+ukxWK3v\n4o471vt9DFlZSbhwoRy5uaNgs1nBcZeRlJTkMtghpR9xJ1YoIC3FjqJShUKBQYMG2WWGyGeRkZEB\nLiYGzR0diNVqUdXcjPQrmSuGYdxmbdyVMvpazJKTk2FUKNDa2YmE6GhcbGhAWn6+kBm4fuXK4J8w\nCZcE6x7paJPgyiRzIGt/XGl4pIDHOVLAE8b4miUhwz/JEEx3i0yovXJ8wTF480RQStM0br55FW6+\n2XvNTmlpKSyWJKSl9bSRZ2dPxnfffeFXwEPcrJcuXQCz+TPU1e0BYMOKFTOQnZ3tdSdWKBFnYsRd\nX2Lhs0KhgFKpxKKbbsKBXbtQ3t6OjAkTMGnqVCgUCjvPH5tIB+TO8dlZKcNisYC/4tZMAqCYmBhc\nv24ddmzZgjP19cgaMgQLli7t87hMJhN4nh8QhptXO56YZHqS/YkUIbg79Hq9FPC4QAp4vMDxSxIK\nDYy32yf6C1/M98IRdyMWAg35PHtKOibh52ZzN7Ra328gPM+ju7sbcrkcGRkZuO++O9DV1SVkUJwF\nOySY8KQTK9TQNC0EYY6eP0qlEtcuWSJYHogdn0lJkmxD3PZOFipfHJ9lMhlSUlKw/mc/c7kNMRzH\noeTAAVReGS+Skp+P6XPmhN157otwzS6HA55mfzwZkRLOuMrwSJPSnSMFPH4Q7IDHmy+iK+O6vrYf\nzhoeVyMWgn2DGjNmDAoLd+Lcud2Qy6MB1OGBB271aVvkRkuOgQQ2sbGxYFm21xMlMfCzWCw+d2L5\nSlNTE77bvh1dbW3IyMvDrPnzoVar3f6Np54/MplMWGyI6JqU8RznfXky7NTR8dkbD5fyixfRcPw4\nxmZlgaYolJWV4XR8PMZNnOjnGZQIRzzJ/pBrhgRDkYxU0nJNZH+y/YB4IQ9FwOPJ9h2Hf3qTkg3H\nLi1xZ5mvzsn+vHdUVBQef/xBHDp0CHq9AYWFN3qs/xHDMAxMJhNomrazAghlJ5anGAwGfPHee8iR\nyTAkIQEV589jJ8NgqRdt/H15/ly8eBHNtbXQxsZi3IQJdsfo6PkD+D7stC/h8+WWFiRqNJBd+e/k\nuDi0NTb6fO4k3BNuZSJ3I1LIAwfP82Gf/XF175a6tFwjBTwRDin5+DL8M5hfZl+DQbE4OTY21mWJ\nI1glLYJKpeo1YNRTxKMu1Go1GIYRfu6uE8toNAJA0Dqx3NHa2oooiwUpmZkAgKFZWdh36ZIwkd0X\nyJOzxWLB2dOnceLLL5GhVqPVZMK5Y8dw809/Cp1O59Tzx3HYKfm3p8NO3QmftbGxaDQakXalHNDe\n3Y34vDzfT55ExCIOmFUqFYxGozCfzpMBueGAs5KWO4PVqxkp4PGD/s7wuCr5BGr7ocYxUxWONxdH\nOjs7odfrkZSUJGhajEYjWJZFTEyMsFC7C3a86cTieR779u3DV1/tg0wmw/Ll8zB27Fi/j0OpVMIs\ncnY2ms2g/Rw9QswhlUolzhw8iGuysqCNigLP8zhRVYWamhrk5eU59fzpa9ipY+mroaEBB3ftgtlg\nQE5hISZNnSpsz1HHkZ2djersbBytqoJCJoMmIwOjr7nG73MoMTAgJVhXA3JJANTf9ydXGi6ppOUa\nKeDxg/4KeMQZhFCXfDzF23PjTaYqmOfdm+3u2PE1Nm36BIAasbEUHn74HiQkJNgFbGKPmr46schk\naXccOHAQf//7p0hMHA+Os+G55/6Lxx6LwrBhw3w6XkJaWhqyx4zBsRMnoJPJ0MHzmLFsmc+lCHJc\nZDitzWYTSkgURUF2Rfuj0+mcev44TnoHXA87bWtrwxfvvIM8tRo6tRrlBw+C53lMmzVL2B9Hn6GF\n11+PlpYWsCyL6OhooZQR7mUMMeH0sDIQccz+iEu0ZrPZzvE5nLI/pGlFojfht1KGOaHU8DjD1aBJ\nXwiXDI+/mapA4c0Nq7q6Gq+++inS0xdAqdSgpaUCzz77Mv74x//XayYWx3FOF1NfOrH27j2CuLgi\nxMSkAADM5gKUlJzwO+ChKApzFy1C1fDhMBqNSExMRGpqqk/bcnZcY6dNw6nt2zE4IQHdJhMssbHC\n9HhXnj+kbVyc/XHU9HAch9bWVsRarUi8sr/5KSk4e+oUpsyY4fL7IZPJkJaWBgC93pMQ6HlfwSCc\n940wULrJyFgWAGGd/TEajVKGxwVSwBPGOAYkNpsNer0eMpmsX3QevuDuZtef4mR/aWpqAs/HQCZT\nXREZp6Gh4bBdloaUspRKpSBgFgtsGYaBRqPx6rijolSwWn8YkcGyZqhUganXUxSF3Nxcv7ZhsVic\ndpgVT52KKLUalaWl0Oh0WDl9OrRaLbq6uvDV1q2oKy9HQmoqFixfjrS0tD49f8ST3qOiomC7stjw\nPA/GYIBcpRIWJG+Fz2QRk4ZXXn14Gpw5uoQTvZhj9oc85ATjXi2VtLwnclaYMCRUGRLyZdLr9VCr\n1UJ7s78Euy3dHZ6Ik91tO1iGiZ6cj66uLnzwwTYcObIXZ8+aUVg4BLGxNDIzk4TghWQMAAgdWiQl\n3tXVhf37D6GlpQN5eZmYNm2qkL3oi6VL5+PUqb+jurobPG9DTEwL5sxZ7ccRBwYSvLIs67TDjKZp\nXDN+PK4ZP97ubz557z2oGxowPS0NLR0d+HjTJqzfsEG4Ybvz/CGLTmZmJk5lZ+N0dTXUMhku8zzm\n3HSTUEoTjyAg23QnfCa/12g0bluYw6mMIdF/kIDGMfvjjVVCIJF8eFwjBTx+ECofHlfDPwOx/VBo\nkBy/4OEqTvb0fLz++rtobk7A+PELcfp0CU6cOIni4sF44on/AdAT7Bw9egzbtx8Ay3IYPTof1103\nDwqFAmazGe++uwVVVYBWm4jTp0+jpqYBy5cvsZtU7uqcDB48GL/97f04duwE5HIZJk6cgKSkpICe\nB28hQm2e573KPBqNRrTX1KDoSmkrPTER9bW1aG1tFcpdYlx5/rAsi5kLFqCmpgYcx2FKVpZQjqNp\nGkajEY2NjaAoSvg5uS598fwJxzJGOBNJJa1A7KsnVgli1+dA76vBYJACHhdIAY+XiC+wYGYagB+y\nDRaLxWMzwXDHnzZ6Qn9rj77/vhyJidMhkymRnT0SFRUlWL9+OnJycsBxHCoqKrB16yFkZk6EXK7E\n8eOnERW1F1OnTkRrayuqqw3IzZ0OAEhMzMTZs99i2bIerxLxpHJx6UZMZmYmMq+0j/c3HMcJrbxi\n7ZInKJVK8HI5TBYL1CoVbBwHs83mkY7L0fNHo9EgJiZGCID0ej3kcjkMBgO+3rIFKpMJLMchKjMT\n1y5ZIjhci40P3QU+5D09EbH6u5BJDBycWSUEO/sjlbRcIwU8YQrJggA9vizBCHZC3WUWLuJkd7g6\nHwzDYPv2HaisrENnZwsYph6ZmYXgOAU0Gh4pKSnC4tnY2ASlMhVKZU8pKykpF2fPnsPs2dOulCPF\nW+75D3Hphiyk5KZIFlHSsk1RFC5fvowje/fC2N2N9NxcjJs0KeSjEfwdbKpQKDD7hhvw3YcfIpai\n0G2zoXDGDJ/E0kRQKl5UrFYrDu7Zg2iDAVmpqZDJZCitqcGlS5cwYsSIXl1f4tZ38u++sj/9WcYI\nh4YDCe/wxSjTFe40PDqdLliHENFIAY8fBCtgEGdByE03kgm0ODkUxoNiOI7DM8/8BQcPtkAuT0ZX\nlwzANnBcEzjOhJkzCzFixAih7Tw6WgeG6QLQU966fLkZeXkJUKlUSEtLQ15eHC5dOgGdLgWdnXWY\nODEf0dHRwvu5WrxJ2chqtWLnxx8jnaKQqtOh+vhxHGQYzJgzJ+DnxBXEY8ffwaZjxo5FSmoqWltb\nER0djZycHL/3TbyogGWRGBcHiqJgtVoh43m0t7bCYrE49fwhAYtcLheytyzL9nqdu/cUe/6QhSxY\ns5si/d4QToQ6gHRnlMkwjN3vvQmaLRbLgJijGAykgMdLHEtagf6SOA7/tFqtQRcWB6vGTkp+ZFxC\nJJXleJ5HaWkpWltbwbIsDh+uQFraQigUCmRkjEB19Qe4996FSElJQWZmpl0mYNiwYRg2rBTnzh0A\nx9GIj+ewaNGNAHo0H+vX34LvvtuPxsY25OQUYerUYpf74WwhraioAK3XIyY1FRSAwampOHHuHKbN\nmhWSUkqgB5ump6cjPT09AHvWm4y8PFTs3YvCzEzwHIduAKOyswX3ZbHnD4Be0+nFnj+Ow07dBT+e\nzm66GoTPkaThAfoviBRr1DzJ/rg7r1JJ1TlSwOMHgQx4yBwlhmHssiD9rVfxByJmpWk6rMTJ7iDn\n+sMPP8HHHx8ATSego+MCmpu7kZmpFG40crkSOTk5SExM7NV2KpPJsHTpAoweXQWlUomMjAw7EWFU\nVBTmz5/r9b6RhVSj0YCWy6GOioKN42AwGmG9knEhN8xgLaQMw8BsNnvdTt9fjB47FhazGcdPnoRM\nocA1ixZh8ODBAHqXociiIhbbO3r+iDU/nnR9kd9LwmcJb3DM/oivORI0k9dFWkDZn4T/HesqgGgh\nAmEm6C3B+sKwLAuO46BUKqHVagOexg9mSaulpQVbtnyHzMwFAGjExOSjvPx51NQcQnx8Ljo6LuKa\na3IFV2XxsYlnYo0YMSIoN6K0tDTE5uXh+4sXoZHL0c5xmLZgAdRqtV3py5OuL0/heR4WiwUMw4R8\nirs/yGQyTJ42DcVTp/Y6ByR7RjyRiH2AO8+fQA477cu/RXpKDy3hHDg4Bs0cx8FisQhrR0dHB77+\n+mvMnz8/YO/5yCOPYNu2bVCpVMjPz8drr72GmJiYgG2/P5A9+eST7n7v9pdXI+RiI/8W3yh9wWaz\nobu7G3K53Kl/CcMwdunxQEPqvYG8uTIMA71eD5qmoVarA77vRB8RaOEzKV10dHRgz57T0GpzwfMc\ntFodlMpuTJyYDJpuwtSpebjrrp/08kMiNx9fOpaAnoX2zJkzKC+/BJ7nEBcX5/R1FEUhJy8PqpQU\naLKyMGLCBOTk5go3RKVSKdwYSUaGLMq+mKCR7KPNZouoYEeMq2MWZ6yUSqUQiKhUKtA0LWRirFar\n3WgQsgA5C07EmSB370+2RQIrUh4kixl5aCCvc9wGGewaros0IVL2kwSf4a5/IdcfyUiqVCo0Njbi\ntddew+OPPw69Xo/Ozk6o1WpkZGT4fG+nKAp/+tOfcO+99+L48ePYu3cv5s2bF+CjCQq/dfULKcPj\nJYHU8JCuJbVa7TJoiqSSllicrNPphNEAgSaY54TnezquoqIsaG2tQGpqPpqaypCdHYeHH75fuNE4\nLkDiQZm+GEOyLIu33tqMCxcMUCh0sNmO4OabZ2Hs2DFOXy+TyZCfn+/0d+J0uKddX+7OB8lYBTpT\n19+4coUGXHv+kODR0TbA22GnznCm4SC6H2fC53DOSIiJlP2MNHieF667wsJCvPXWW7BYLFiwYAGs\nVivuvvtu1NfXY8GCBXjssccwcuRIr7YvDm4mT56MDz/8MNCHEHKkgMcPfF14HQODULcTiwlU8EAW\nRjIlXCaTCRqFSIGcC6vVioceugubNn2A6uqzyM/Pxt133yOIsB2DHVICIYMyfaGyshJlZV3Iy5sE\nADCbs/Dpp9+6DHi8oa+uL1elL5KxkslkPnsmhSN9uUI74kyETMpQzgLIvoadetr11Zfwmfz8ahA+\nh4KBEJiRSsEzzzyDZ555BrW1tfjiiy+gVqv92u6rr76K1av739HdX6SAJ8SIRyp40rUUCRked87J\n4b7vYkgWRKvVoqCgAE899Rth/1mWFZ6oxJAsgb8i3pqaGpw6dQoXL9YjPT0DBQVFYBhWWNCAnsCq\nuroaAJCTk+PT+7lqn3Y0PKQoCiaTyeeMVbhCynMcx0Gr1fqU7u8rgBRnf1wJn4kAlfj/EMGyN8Jn\n8tAEhHZ0gUR44CxAMxqNdsFNVlYW7rzzTpfbmD9/Ppqamnptc+PGjVi6dCkAYOPGjVAoFFi7dm2A\njyD0SAGPH3gbjHAch+7ubshkMo+7lkJtDugtRIPkzDk5WDfeYNkBkCdnsTaILFDOxMneZAnc0d7e\njj17ToOm02CzZaK8vBkNDR9j7drZwnYNBgOeeeavuHixAwCPoUMT8T//c59fjqquSl8Mw9j5Pw2U\nLEIwynN2nj+AXRaGBJCO2R/Slu6q9OWp8BkA1Gq1sI1wHHYaSQ88AyHD4+2k9B07drj9/euvv47P\nP/8c33zzjb+7FhZIAY+XOPtCePJFsVqt0Ov1iIqK8smVNhzxxDk53G94YjsAjUYDi8Ui/NxdsOPL\n7ChXNDY2QiZLxvz543D69AXo9Rqo1a1YsmQBWlpa8MknX2Hnzr2oqTFh/PhVUCo1OH/+AD777Evc\ndNNKv95bDAlqOI6DWq0GTdNB6/oKNeIRGMEsz4kDSHEHlqPnjzgYcVb68nbYqTPzOhLAi4XV/ekx\nIxFYnN1bvQ143PHll1/i2WefxZ49e8LWGd9bpIDHDzw17nM0E/T2PcItw0NalImFuSsNUrhneHie\nh16vF+wAyALjLtjxZ3aUK3qyKybExMRi2rRJ0Os7wXHR4DgOr7/+IRgmAwyTD4bpRGnpQYwaNQca\nTTpqa5v63rgXOBPx9lX6ioT2aaJFUigUIS3POYqQxZ4/pPQlLiG68vwhmSDymkAKnyUiG8fPMJBz\ntDZs2ACGYYRW98mTJ+Pll18OyLb7Cyng8RNPOlysVqvPLsPkiTtYeBs8OBMn9/X6cERcXiRZGqKn\ncBXs+NuJ5YqcnBwMG5aA8+ePgKY1oKh2rF49H62trejqopCdnY2MjA7U1prQ3d0Bi8UAg6ECQ4dO\nC8j791WeC2TXV6gJ1AgMfxGXvsRdX756/rAsK/zO1XfQE+GzOGMXTp9bfzAQSlok4x4IysrKArKd\ncEIKeLzEmXmZs0XdUcgb7k/BnuBOnOyMcM3wkFllKpWqV3mR4zgwDNPrCTgQnViukMlkuPHGpbh0\n6RIsFgtSU1ORnJyMlpYW8LwFHGfDyJEj0NzcggsXDqCpyYxZs67BggX+e2L4Up5zJdo1GAwAEDal\nL5ZlYTQag/KZ+Yt4WCwpQ7EsC4PBYBcciTNoJCiy2WwwmUyCRYJ43IW3wudgDzsdCEFEuOLs3EqT\n0t0jBTx+4mzxFQ//9FcvEC4lLXfiZHeEW4aHaKkcdUc8z4PneSiVSjAMIwyQJItDIDqx3CGTyVBQ\nUGD3s+TkZEydOgzffXcIMlkshgxR4K67HsbEieMDMqojEOU5Z5mLcCh9kQA1EkZguPL8cXYOKYqC\n2WwGRVHQarXCdSsWPrMs22fw46xbL1yFzxKeQ6QTEs4J7ztBmOIYJIj/7YmQ15/36g98PaZg77u3\nT49ms9mp7ogsFjRNCwaQ4kWH/I5lWTsdRSi47rr5KCzMR1dXF5KSkpCdnR2Q7RJdCwlUAtWx5Kz0\n5cqvJljnkLgnR6IrtCvPH3Emhgivyb8B154/3g47DWfhc7CJtGyUlOHxHing8ROxcNlsNsNisdgN\n/wx33AUlnoqT3RHMmVfe7APpxBLrjtzpdSiKEgIcnU4nLDzikoNCofAo/a/X67F797fo7jZgzJhR\nGDFihFfH6spR2VdCpWsJdemLXK9WqzUigx1nkHMol8sFt2aapu2Ez954/pBt+ip8Jp5B0rDT/sXV\nfTWQXVoDkchYlcMYIioWd/sEMgXcXxkeb8XJzgiHm6FjJxb5bLztxCL6CccxA6TzRdxtI8ZgMOCx\nxzaislIBhSIa77+/Cw89tA7Tpk0N6XkgkGyLWq322eG7ubkZn27ejNb6egwqKMCSlSsRHR3t9m9c\niXYDVfoSC699NRQMV1x1mfni+ePLsFNXLtOeDDuNpKxJJO0rwXF/jUYjUlJS+mlvwh8p4AkARqMR\nSqUyYG3KYvpDw+OtONkdwdp3st/u9s1ZJxbZJ9KR5W0nlrMFQNxt47joHDlyBFVVNHJzpwMA9PpM\nvPHGR/0S8IgHZfqagTSbzXjjpZeQYTBgYnw8Lh47hnc6O3Hnhg0eXyfuFlFfSl9i9+RA+CKFE+6y\ncb56/igUCiHwId8FT7M/JOME/PA9CrbwWcJzyMOMhHOkgMcHyGJLFjqFQhGUYCcUOLa9+ypOdrXt\n/sJVJ5a7YMeXrh5n3TbiRafH2VcBngcoClAo1DCZLME4ZJeQUg/DMH6XehobG0F3dCD/ipZodHY2\ntl+8iO7ubsTExPi0TcfSl7hjCXBf+iKjWoiINxK/g64gwY4n16O/nj/OHJ89KX31JXymabrfNYgD\nEVcPewaDQRItu0EKeHyApM9NJpNwkwnXFmxvcNXB5A/9cbNzJbImiymAXjfxQGQ/yKJDRM5yuRzD\nhg2DQvExGhsvQKOJR1vbCdx00xTfD85LHLMf/pZ6lEolzFcWN5qmYbFawYue+v3Fm44lAAEXXocL\nJPj2pfToi+cP0JP9EQc8Yg2QN6UvpVIp/J3VahXK4+JsUzh+Vs5m5UUakmjZPVLA4wMGg0EwEyRC\nvmARipIWx3EuO5j83XawcHVe+urEcszqkOxHW1sbvvuuBE1Nl5Gbm4H582d5fePgOA6ff74d+/ad\nAscBxcXDsWzZYjz99CN4/fX30NFRhzlzxmLJkoWCJiOY7drBmB2Vnp6OgsmTsXffPsTTNFo4DjNv\nvFHobgskfZW+gB9KNAOJQOisxPji+SPO/jiWvohw2RPPH4qiwDAMlEqlMPAUgCR89hNXGR6TySRl\neNwgBTw+oFKphHJPOLSN+4M4le2rOLmv7YcCV67Wfc3EMplMMJvN+Oijr9DVFYe4uOE4caIa7e1b\nsX79LV7djI8ePYbdu8uRkzMLFEVh//5jSE4+hBkzpuKppx4TXhcKp2IidJXJZAGdHUVRFG5cvRpn\ni4rQ0dGBWWlpvfyDggUpfclkMuj1eiGzSoK6cDE89IdAZBrd4U0GzZXjs6Pnj+PrnL0n+Vwc7Qo8\nET5LeI7UpeUeKeDxAfK0QojUDA/HcUKGKjY2NiiC62AhPi/+dmJZLBZcvswhO7un/Ts7eziqqvZ5\nrUupqKhDdHQmZLKer1V8fDYuXarFjBn2r3OnWfG25d0ZwRqBId7/oqKigG7TU0ipR6PRCNmPYHR9\n9Qeh9g/qy/PHMRD3dNgp+X2kCZ8jqUvL1b5KxoPukQIePwnVFyTQX0YiTiY3p2AdR7AzPKQTSy6X\n2wnH++rEMhqNQpuv0WgExzEig0EreN7mdTkhOTkOR4+WA+gR9Or1bUhOTnP7N66euD1peXdGOI9T\n8BdX2Y9Ad331B84Gt4YaV75JZPSIJ54/jm3vfc0B7Ev4LA5+IiVw7U+kkpZ7pIDHT0KhsQk0YnEy\nMTELBsFeWMhTYVRUlF+dWImJiZgwIQ+HDh2GUhkPq/Uy5s27xuv2zilTinHhQgUqKg6AoihkZqow\na5bnAz7FC3d1dTW++moXzGYGU6Zcg4KCgl4t744EWvsRTngTEPjT9RVqxGaJgRCVBwpxIAJ47vkD\n2Je+rFYrKIoS/t8X4bPjsNNgCZ8jSZogaXh8g+rjQ46cKyCEkC8h0PPUSdyVg0V7eztiY2MDcjN0\nFPWShSA2NjYAe2pPMLfd0dEBnueh1WrtMhm+dmJxHIfS0lK0t3cgOTkJQ4YM8Wm/WJZFfX09eJ5H\nenq6T1mW2tpa/O53L4Ln8yGTyWEynceDD96K4cOHg2EYnDhxAufPX0RycjzmzJkDnU4nXIcDxWGY\n4Oie7M93gGQQrFarMDqhP0tfpNvTZrMJDx+RgNh+gbiRiz1/yEMg0VaRzCU5/2QbfQmfHd+TlM3I\n9zvQwmej0QiVShUR3x+r1QqbzdarWWDRokXYs2dPRBxDEHF5MUgZngggEFmkQDgne0swsl9kAeQ4\nDhqNxi6g6KsTy50PDU3TGDZsmN/7J5fLMWjQIL+2sX9/CVh2ELKyCgEAbW1KbN++F6NHj8auXbvx\nz39+CoUiFxbLRezZcxiPPLJBENJ7umhyHIfLly9DpVIFNVj3B3FLfSDck8Op9OV4bP2dafKGvjx/\nSHu72BtJfM799fwRZ+4k4XNvrvbjd4cU8PhJJHRpiZ2To6Oj7b4Qwd7/QG5bHLSJb2yedGIFyocm\nFDg7ZzzfMxX7zTc/QUbGfKhUOvA8j0uXtuPChQuYOHFirynvrm7+nZ2deP75f+LSpTYALG64YQZW\nrlwWVotuMFrqHemv0lcoji1UOHr+EH0c+U462i8EatgpRVEuhc8kwPJ22OlAEC1Hyv73F1LA4yeh\nCHj8eQ8iTu4PN+hALxJ6vR4AEBMTI/xbHOzYbDbU1taC53lkZ2dDJpPBaDRGnFkOMIQAACAASURB\nVAvv1KmTsHPnS2hsVICmFTCZzmHhwluvuHuzkMtVMJnMaGpqRHu7XtAxAUBJSQl27twHhUKGBQtm\nIT8/v1fW4q23PkBFhQpZWdfBZrPi4493YciQwRgzZkw/H3kPxD2ZTAUPxefmbbu2r/THsYUK8nBB\nrBAA9BlEBmrYqaPwmeh+xMLncNBsBRPyUCThGing8QHxlyZUGR5f3oOIk9VqtUtjuEjI8NhsNuj1\nertOLLLfRJxssVjwf//3LA4erADHAaNHp+I3v/kl4uLigtKaHUyys7Px+OP3YseOPbBaWcyYsQ6j\nRo0CAMydW4ytW79GZSUHk8kMubwM7777KcaMGYOKiko899zb0OmKYLVacOLEv/G73/0SGRkZdi3v\nFy5UIimp+Mp/K0HTqaivbwiLgIdkBfrTPTlYpa9wOLZgQWweZDKZ3bH54vkjHnbqWPryRvhM9iuU\nwudQ4S4bFanHFAqkgCcC8OUC9tQ5OZgBTyC+eCzLoru7G2q1ulfgIq79b978MT79tBw63STQNI2d\nOw8hO/sdPPzwA37vQ3+Qk5ODO+5Y1+vn69atRknJQ6iqqkVOTi6GD78TXV0N+PTT7aiqqkds7HjE\nxWUCAGprzSgpOYY1a/LtFpz09AScO1eJjIzhVxaVFqSkTHe7P2azGXt270ZbYyNyhg7F5MmTA14e\nFA/KVCqVYXPjDkTpy9XE84GAJ4FcsDx/PBl2StO0nd7IleNzJJW0XBHp+x9spIDHT8KtpOXKcbg/\n8fVGQmZiOevEomkaZrMZDMNAoVDg4MHjkMmyoNPFXxGCDsGhQ2cCeRj9DpmHNHLkKADjkZQ0GADA\nMAZ0dRmuXCc/+J70nKee8y5ecO64Yx3+8IeXUF+/GzabCVOmDMXQoUNhsVictryzLIt//vWvsJw/\njxS1Gjt270ZjbS1W3nxzwI4tUvyDfCl9uZt4Hun4Gsj56/kjzv6Q+4uvwmfi+QP0fMciQfjsbO4X\neQCUcI0U8PhAf5S0PEEsThY7DntCMJ5ufN0eadcl7f7iFnJykyM3S3KjTEiIhsVSDpttKChKBqu1\nDikpgW+H7y/ELfVTpozDoUObodHEAaDQ3X0OxcXLERUVhaeffh0MYwLLWqBS1WDGjLW9tpWcnIyn\nnnoUjY2NUCqVSEtLE84jmfIubjOuqqpCe2kp5ubkgKIoDLLZ8MVXX2HxDTcEZIZWpPoHeVL6kslk\nYBgGUVFRAzbYIY7evuKL5w/ww7BTsY7Pm2GnRPhMPH+MRqNgekjTtE/C5/6EDEyWcI0U8ASIYKZD\nPQmqfBUnh9sX2bF9vq9OLLIor1u3GocOPYbGxq0AaMTF2fCTnzwqPLGF23F6irOW+uLiSbjjDgO2\nbv0aPM/j9tsXYMqUKaAoCv/v/ymxZ88hKJVaLFq0ChkZGU63q1KpkJiYCKVS6TTtL24zNhgMoHle\nMLegKQoUAqPPCvbsqFDimLUgZokURdkJaCP5eiQEM2tFAkXxsFNxMC4eveKq9OXtsFPyeajV6ogQ\nPjtbbyTTwb6J7DtMGBCqDhJ3i4sn4mRPth+MY/Fm244ZKvI3nszEyszMxKuv/gXbt+8Gw1gxc2Yx\n8vPzwTCM06fESMBVSz1FUZg3by7mzZvb629GjRolCJxd0d3djZdffg0XLtRCJgNWrZqPhQvnC9sW\nT86+cOFCT4k0Lg6HKyqQFhODys5ODJ8+3e/sTjiMUwgWpFRCjm0gzPoikGAnFOXHvjx/HCe9B2rY\naSQKn41Go9fu8FcbUsATAIIZMPSFp+LkcMdVhsqTmVhELKnVanHbbT+y2y55SiSusMSkLNxnKwXT\nq+Xttz9EaSmP7OzrYbVa8Pbbu5GTk/3/2zv36KjKq/9/ZyaZJEMCCaBBgVIFRWgVBARFQRDBGJPM\nwMJyUeAVaLVar7WIii20RQq2UtEqRSoU8VIykws3QS5SSliW14oVfmgRiwgIyC0kk0zmds7vD95n\nPJmcM9dzn/1Zy7VAkpznnJw5z/fs/d17t2q82NTUhIULl+Dw4RZYLDYUFuagx5AhONPQgGuGDcOw\nkSMjRtVkryNLWYZCIcP0RkoGsahVvNSXUPzo8X5kqCl2oonu+RPvOiY77FTquktFQIPBIABthp2K\n7Tc0KT0+JHhSIPpGU9rHI/bz5TQnK12pFe9nS0WoEpmJlUhIXZirFxok5ZpOLjfMG8H6mci9ps8/\nP4xLL73p/65LLmy2YnzzzTetBM+WLdtx+LAN3/veSADAsWP/hi2vAx558EEA340XSHbKu9zdk/VG\nIlGrRKq+9HQ/MthnTi9eq2Sr52INOxVGg5Lt+SMcdiqs+lL73ibBEx8SPDKgtuBhGyLP80mbkxP5\n+WrCQvypzMRK5cEr9pbI3hDFqkPUhr1BMyOoEmu4/PLOOHz4BC69tBc4Loxw+DwKCwtbfc3p0+eR\nm9sp8vf8/Etw6tSZyN+lqpViTXk3U4fhaJhnJ9khoLGuYzgcbrVpaykO9SZ2okm2ek4ofliKLDs7\nO+WeP0LRFQ6HI6kvpYzP5OFJDRI8KaKVSNCyc3IqSF2nRCqxoqM6iczESnZt7IGlZHfdRFGrNPve\ne8fjhRf+jGPHvgHH+XDLLVehf//+rb6mT58rsX37e+jYsTssFivOnz+IO+8cIvrzxDwPrISejbqw\n2WwIBoOKRa20RJiiSydqlUjVlxapL3Z8oxjLE+35w15qmNhh0WUx4zOQWMdnod9IaHxmxmkljc8U\n4YmP/u9eA6BGhIdtIumYk2P9fDXFG6v8CYfDCVVisX/z+XwIh8OK+T6sVmskRSb0/agxWFLN0uzL\nLrsMv/3tLBw7dgw5OTn43ve+1+Z8hg4dihMnvsW6dRsAAKNHD0ZJyeiEfn70dWQROYbf75d1yrWW\nKJmii5WyUSsVazSxI4ZUzx/2e2ORGGE/n1jG50SHnUqJLr/f30b8JPv7Iw9PahjzDtYZaggeVvVh\nNHOyWDou2UosJpAsFgvy8/NVq4xTa7PRolrJ4XDg6quvlvx3q9WKu+8eh7FjK8DzfMr3HMdx8Pv9\nkaiVWJWNlinEdFAzRadF6osJVTNV0bHPrtVqRSgUirRlkKrmjDfsNJGqL+A70QW0HXYKyGN8ZtYA\nQhoSPDKgpOBhaZxwOIwOHToo8uBRK8LD0nF2u71VWiOWOVkP84ekNpt0N20jVCul81YvlqKLnqwd\n3WDOKKXaQrGjxVBepVNfZhQ7DLGGiYn0/JEyPguHnSba8ycd47PUs9rn86FLly4yXSVzQoInRdQQ\nCUJzMvsAGA12nZSuxFILuXw/bMPkeV61qJWaJJKiEzaYizdbSU/Xh/V+0svEc7lTX2buj8QizGLP\nlFR7/sg17DRZ47NYSov68MSGBI8MKCF+hObknJycVh4IuVFavAWDQdF0XKxKLKOMGxD6VWK9aQs3\nDuGGaQTjebKk0j1ZD36VRNBDxDEW6aa+WFGAXiOO6RBL7ESjVM+fZI3PYsNOpUQopbTiQ4JHBuQW\nDNHRENbgSimUEjzCt6RkKrECgYAh3zDjbdpswxZWhehtw0wHuaroUil5VwOjTTxPJvVlsVgQCAQQ\nDAZN2R8pGbEjhlw9f1Iddhr9QgUg8ju8cOECLrnkEtnK0n/5y1+itrYWVqsVxcXFWLlypWlSZZY4\nG50+pmLqEBZ2BNAql58uLDUijIawD1aHDsoMw2RN7uSs/GJG42Aw2CqNFc+cLEd5r95gwo9tKMB3\nzeWMaNYVQ63fnfChHwqFIpu20iNDzDbxXLhpB4PByAsPi6ia4Z5kKPm7Y88zdj+Gw+GYaW3hsFO2\nfwCIm/qK/hnMGxcKhTBixAj4/X5cffXVqKiowPTp09Nqa+H1epGfnw8AePnll3HgwAG89tprKf88\nDZC8ec2xo2iMHA8H5unw+Xxo3759qzSO2o0N04XjODQ2NgJAmxRWLLHT3NzcZm6UGWDXNxQKweFw\nID8/HzabDX6/Hw0NDWhqaooYFo2IGi0DGOxNu127dmjfvj1ycnIQDofh9XrR2NgYEV1y3s/shcNM\nE89ZFC03NzeyMdvtdgQCATQ0NKC5udnQ9yRDOApDid8di6Ll5uYiPz8fBQUFyM7ORigUQmNjI7xe\nb6t7knly2LW32+2RNC17KQoEAq28QNGwn8PSbXV1dVi+fDmCwSBeffVVXHrppbj77ruxcuVKfPvt\nt0mfExM7wMWXYTM9iymlJQOsT06q8DwPr9crW+dkLYmuxGLRL71XYimJ1GylZHw/ekXYMkDt7smJ\nmEzTjaLpvcNwOgh7CAmN83ppeJguWsz9itXzR+yeTGTYabyqL5vNhoEDB6J9+/ZYsWIFsrKy8N57\n72H9+vX4/PPP8bvf/S7p85gzZw5WrVqFwsJCfPDBB6lfEJ1BKa0UEaa0hC3lk4W9nWZlZUkaWDmO\nw4ULF1BUVJT2usVgH8Z0U3LMe+RwOCJvU83NzZGNKV4llt1uN8zDNBGS9bQIy2JDoVDkuum1SZ+e\nhapwwGO8NIMUZmi6JwUTO+xzL/W7i0596clAHgsth5xKwe5J9l90z59ooud8CQmFQm0qsiZMmIDV\nq1ejY8eOcdcyevRonDp1KvJ39myeP38+ysvLI/9/4cKF8Pl8mDt3bpJnqymSNyUJnhRhNy+AiMm2\noKAgqZ+RaOdknudx/vz5hG7kVJBD8Ih5j4CLgicUCiEnJ6fNpmGUSqxUEL49OxyOpKN2wogF81hE\nGyO1hE2qN4KBV1jyzsZbxCt5N3MfmlR7CEX7VYQGcj31TtKj2IlG7OUmlpAU9vwRPg+Exufy8nJs\n3rxZ1tTd0aNHUVpain379sn2M1VA8oY212uLgZASCLEQaycuB+mk5NjGHggE2kxtD4fDsNlskc2R\n5Z2zsrIifSbM+vacbgfe6LJYPTXpM5qBN9mSdzP3oWH3psViSbqHkBoND9PFCGIHSL3nD4v4sOgO\nyzII25iky6FDh9CrVy8AQE1NDfr06ZP2z9QL5tppNCIZ028sgRDr5ytJqqZl5t/gOE5yJpbNZoPD\n4WhVqcRKOYWt1vUcIUgGluaRe0imWJO+6I1G6UolwPielnh9aqxWK3ieN63YYSZUOe5NvfVOMorY\niSbRnj8cxyEQCKCgoCDyOWf/f+PGjfB6vZHZYOkwe/ZsHDx4EFarFT169MDSpUvlOE1dQCmtFGEP\nSiDxsnGhOTnZapbz58+jQ4cOimxoqXiQWCWWzWZrFcVIpBKL5/lISaUe0zWpwh64rGW9Guehpu8n\nEzwtbDYSi06qJSSVhokdVlGk5L0pVqqtxKwvIeyzZ1QhLgX7fLPxQuzz/eWXX6Jnz57Iy8vD+++/\njyVLlqCmpgbt27fXesl6gFJaShMvQpKIOVkrko3whEIheL3etGdiSaVr2MPRSP1AxOZGqYFYaFxY\nFSKXkDR7mof5rQoKClq1ERDOVdKzgTwWapvLk0l9yXEvmVXsAN89mzmOi7xYBoNBLFiwAFu3bsXg\nwYNx7Ngx1NbWkthJAIrwpIgwwsPyp4WFhaJfKzQnp/rmX19fj4KCAkU2m2RM12KVWEBssSP0fMSr\nxIpuLBevkkEP6NV8HV2plIqQZJVmZu3Am4iBV8xAbpQp73rrDi131ZfRU6zxiGWe37hxI1577bVI\n6fiVV16JsrIyuFwu9O/fX6MV6wKK8ChJrAgJi1q0a9curTd/JZsPJvqzW1pa4PP5FJ2JFe0LYMKn\npaVFlz1q9Bz5SNf3I4x8mFXsJOJp0bOBPBZCsSNnF/V0SHRsSCLXMpPFTl1dHRYvXoy1a9eiU6dO\nCAaD2L17N9avXw+3253pgkcSivCkCJv5xP4cXTYuNCfLEZm5cOEC2rVrp4h3IhgMorm5WdKDFOtc\npGZiAd+JATk8H2JvhlqmGIw8BkPK9yMsc021dNkosPb86XpaUil5VwOjVdIB0mNDxF5wzC522IuJ\nmNj58MMPMWfOHNTW1uKSSy7RaIW6hiI8asDSOUp0TlY6wiOF1LkkOhNLrlED0W+G0WWcapqeheZr\nYYdaoyDl+2HnxNoGmHWau5xpnkSGSqrdpM+o1UqJVn2xF7BMFDsfffQR5syZg+rqahI7KUCCRwai\n/SpKmJO1SGkJK7GEG7tw+F30W6waYiBeikFJ0zOLDJhFDAivJdtkfD4fAET+bDQDeSyUjHwkmq5R\n8loaVexEI3UtWYrVZrOB53lTtbQAYoudvXv3YtasWaiqqkJxcbFGKzQ2JHhkgrnnWbWAHgyC6cAq\nsXJyclqF/IXm5GixI+xBo6YYiPaqBIPByINDTn+FnkcpyAHHcfD5fJGyeuahEl5LvRvIY6GmGEi0\nUknOa2nWNA+7lsxGwJruGXX+nBTCtg/R57Fv3z488cQT8Hg8uPzyyzVaofEhwZMiYptdU1NT2ubk\nWMdTK8LDmgOmUomlZg8aMaxWa+TtXWh6TvfBaERPRDKIldVbLJZW11JYps26ZmvtVUkUrdoGMMTS\nNXKWvJtV7DDY+Qn9gHppeCgHsXpcHThwAA8//DDWrFmDbt26abRCc0Cm5TTw+/2RfHJLSwvy8/MV\ne5gKm4bJjXA4aTqVWHoOo6djetZr2blcJHt+Qt8Pa82ghVclUfT8+5Oj5N3MDSEBcbEjRqxZX3pO\nycY6v88//xz3338//va3v+HKK6/UaIWGg4aHKoHf70djY2Pkg5afn6/YA0dJwcOqzHJychAMBjWr\nxFILqcGcYhs2Kw010vklQ7rnJ7XJ6KVHjdHEQLJT3o12fsmSzvklU/WlFbHEzhdffIGZM2fi7bff\nxlVXXaXRCg0JCR654Xkep0+fjpiTGxsbFX2DFA78kxuO41BfX4+srKxWVVWJVmKJ5ZyNBItWRL8V\nsv+vxx47cqBEDyGh+NG6caTRJ57HK3lnhmgSO/GRu+GhHMQSO//9739x33334c0338Q111yj+toM\nDgkeJWDVOhaLBY2NjZFOwkodC7jYE0VOWCVWOBxGYWFhG7ETrxLL4XAY0sAqBduwWbrSZrPBbrfr\nrqlcOqjVQ0jooVK7R42eG0Kmglgakc2ki9e93IgoGbkSRiWFaUQ1U1+xxM7XX3+NqVOnYsWKFfjB\nD36g+FpMCPXhUQI2qVYNlDAtCyuxWCkyEL8Sy0xl2dFYLJZI+DsvLy/yVmiWahA1uydbLJZWRl22\nYStpLhWOwpCrB5QeELYPsFgs8Pv9sNvtEXFuBK9KoiidphNW0MWaTq7U51xoMI8+v2PHjmHq1KlY\nvnw5iR0FIMEjE0pWUbGfL6e4YpVYrKqspaUFgDEqsZRCWFbPRg3EaoQW3Z1Y7wi7Jwsn3KtBdO8k\nYV8VuWZTschVOBw2XPfrRPH7/ZGO5+z81Ch5VwstPEnxKujkFOaxqulOnDiBKVOm4M9//jOuu+66\ntI5DiEOCRyaUFjxA/Insif4Mv98Pn8+HgoKCyEOFCSp2DCNWYqVDPDEn1ulZ2J1Yz1VKQOvIXKy5\nUWog9oYdDAYRCARSnk0VHbnS4+8gHWJFrpQueVeLWE331EKsC7mwo3s6kbRwOCwpdk6ePIl77rkH\nL7/8Mq6//no5T4kQQB6eNAgGg5Goi5KmYgCtHnapwt7w2cgH4UOlvr4eOTk5opuM2SuV0unRovcq\nJcBYDRNT8f2Yfe5XqhPrjTTl3QgG82hDfjKpL/ZCJSZ2vv32W0yaNAkvvvgibrrpJkXXP2jQIHTr\n1g1r165V7Dg6gDw8SqB2SiCdCA+biQUA7du3bzMmIjs7Gy0tLa1CuFarFX6/P2Ju1euDKB3S7dEi\n1lE33WiFnBitYWK8NGJ0eiHRiedGJR2DebwRLFrfmwwjiB2gbVPTRFNfscTO2bNncc8992DRokWK\nih0AeOmll9C3b180NDQoehw9Q4JHJvSc0pKa7yWsxGIf5OiZNRaLBbm5uYbzAiSCEpU8SnV6TgWt\nuwunS6x5SqyCjvWqMavYYZ9DOebSCUewiN2bWnTONorYiSbR1JfVao18BqPFzvnz5zFp0iTMnz8f\nw4YNU3S9x44dw8aNG/Hss8/ixRdfVPRYeoYEj0zIbSoW+/mpEAqFIj2ChN4UqUos9nbCeqjYbLbI\nQ0mv4fBkUWKauxhi0Ypob4VSG4yeuwunQrTvh4k5AK2aR2odrZALpT1JsSroAHU6ZxtV7EQjZcgP\nBAKRth4s7craitTX12PSpEmYO3cuRowYofgaH3/8cbzwwgu4cOGC4sfSMyR4ZEKNKq1kf350JRYj\nlUosvaVqUkWNae5iiL0RKmV6Nrvnipk/hVFJNcuKlUbtajqpDVvJKe9mETtisDlzfr8/Eh0PBoP4\nxS9+gbq6OowaNQp79+7Fc889h9tvv13x9WzYsAHFxcXo378/duzYoXgmQs+QaTkNWBgTkMdUHAvm\nY+jQoUPcr2URDL/f32bcRayZWImmQIRTtIXmPb2XweqpUokhp+mZmVsDgYApNxIg/sTzdGam6QG9\nGbClxjOk81k3s9gBLl4zr9fb5h4Nh8Ooq6vDSy+9hC+++AL19fUoKSlBeXk5SkpKEnq2p8IzzzyD\n1atXIysrCz6fD42NjRg3bhxWrVqlyPF0AHVaVgKh4AkEAvD7/SgoKFDsWI2NjSgsLIz5dcJKLGGv\nDvYzpGZipRoVEKZqQqEQrFarLt+ujVKpJDaaIZFImlrdk7Uk2YngRqpSAqB7A3b0Zz0VMZkpYkes\nSKCpqQmTJk3CQw89hLFjx+LYsWNYv3491q1bhwMHDuDLL79U/HP797//HX/4wx+oSotIDz2ktNiH\nzWKxiFZiSc3EEpa8JvsQik7V6LE5n5EqlcRMz/EaysltbtUjqTSkM0qVEqB/sQMkZtSNJSbNNu4j\nmlhix+fz4d5778X999+PsWPHAgC6deuGBx54AA888EDkZZFQForwpAF7GweSSzmleqwLFy6gqKhI\n9N9ZBCg7O1uyEktsJhbbKOWeiSX0qYRCIc2a85nFvBvr7Zq1E9BDCkQJlPAk6WmSNku1MkO2EX+H\n8aa8M7FjpnEfQlgEmXkfhbS0tODee+/FlClTMGnSJI1WmFFQhMfoxIrwBINBeL1e5OXlITc3N/L/\nE52JpVQVCHu7FvpU5BwlEA8zmXfF3q6ZKR0A7HY7wuGwIXwqyaBUVEAv3YnZRpmdnW3ocS2xSt7Z\ns8usgjyW2PH7/Zg2bRomTpyIiRMnarRCgmHsXUBHqNGHB0CksorBwvPJVmI1Nzer9pCVGiXA1s42\nF7mqQMxu3rVYLLBarQiHw7Db7cjOzkYoFGpVVaNXn0qiqDkENF6qhl1Lua+nWcRONMKS95aWFgQC\nAWRnZ0cGFOt9DEsyRP8OhQQCAUyfPh0ulwtTpkwx/LmaARI8MqGGh0eIsBJLOBOL/Vu6lVhKIvSp\nRJcUp+uriPazmDF8LuZJYr//aDGpJ59KomhpwFbL9xPL72EWogWrMNLLhrzK/bKjJkKxI4ysAxej\n7jNnzsQdd9yB6dOnG+7czAp5eNKA53kEAoHIn8+fP4+OHTsqdrzz58+jQ4cOsFgsaGpqQjgcVqUS\nSy3E5iglUwKrt5JeJUimUkmJkmKlUdJXli5yXU8jmehTpaWlJe7sLyPen4xYYicUCuEnP/kJbrnl\nFjz00EOmfA7pHCpLVwIxwVNUVKTYDV5fX4927dpF8uLCipxEKrGMlOKRMulKVXyxB5DNZtNtlUu6\npFKpxEj2emqBsCmk3ieep1qiHa+PkBlIROxEI0fJu1rESkWGw2E8+OCDGDBgAB577DFdrTuDIMGj\nBELBAwDnzp1TVPCcP38+kh8XbupaVWKphbDiS6yZXLRp0IwPGTnNu9HXE9DeV2Hk6JxURWK070cP\n6WSlSUXsRKPn/kmsfYBYRV04HMYjjzyCPn364Be/+IWh7mGTQYJHCaIFD0s5KSEqgsEgGhsbkZub\nG5nHwtYgZU5mlVhmKlkWexjyPA+73W7Ykt5YCM27DodD9uic0Feh1eZihB40yRBdos2iaH6/Hw6H\nw9DtEaSI7ucl5zMwXsm7WsQSOxzH4fHHH8f3v/99PPPMM4a/hw0OCR6l8Pv9kT/X19ejoKBA9k2J\nGSYtFgvatWsXeWAmUoml987C6cBmUWVnZ0fSeXp5E5QDLcy7QvGjxuZilA7YqcJxXCSdDMBQPpVE\nUVLsiB0r2uenxpT3eGJn1qxZuOSSSzB37lzT3cMGhASPUigpeFg6KhAIID8/v5VZleW8xczJmRA6\nF0vxSG3WRqwA0YOfRWlTqVnLsoUIfVc2m033PqpkUVPsiB1bjdRsPLHz7LPPol27dpg/f74hf4cm\nhASPUgQCgUg5+oULF9CuXTtZKqDYh0xYidXY2IicnJyIbyVWJZbROwtLkWjUI3qzFk581vubtR5T\nPHKbnjOhUimWyVwvncjTQU/z26KH8MpV8h7rs8hxHObNmwee57Fo0SLdP1cyCBI8SiEUPA0NDbII\nDdajI7oLcmNjYysjpNErsZIl1aiHcLMWlrvrbcApYIwUT7pv1pkQgUxmSGb0Zs1Ss3ruT6MnsSOG\nHNHJWGKH53nMnz8fTU1NWLx4se7OP8MhwaMU0YIn3Yc4m4klVonV1NSEUCgU6a7LPmRmqMSKh3AU\nRjpRD+GAU2HFlx7SCizqYaRqs2RNz8lOPDci6U4EF4ofPUYnmdgJh8O6bx8ApFbyzl6uLBaLqNhZ\ntGgRTp8+jVdeeUWR34nf78fw4cMRCAQQCoUwfvx4/OpXv5L9OCaFBI9SBINBcBwHAJGUU6qCh83E\ncjgcrcL87I2alZ5HG/aCwSCsVqtpKrGiUSrqoae0glmiHrFMz+FwOOU+QkZB7tlf0Zu11WpVxaQb\naz1GEjvRJFLyHk/sLF68GEeOHMGf//xnRQVoc3MzHA4HwuEwbr75ZixZsgSDBw9W7HgmgoaHqkE6\n4yVYJVZ+fn6rN9/oAaA2my0y8JC9SbJjBwIBXaZp0kFJr0e8AadKzVCKOTg1ZQAAIABJREFUxiwT\n3YHYY0OAi0NO9RClUALWg0bOkSZic76CwWBkaKyaAt3oYgdIbHQI80dGv0DyPI+XX34Zhw4dwl/+\n8hfF72PWfsTv90ciU0R6kOCRkVQEj7ASq3379q3EirASK/rDFQ6H4ff7IxEBFv5uamrSVZomHdQU\nAtEDTqMfhOx6yi1+9D7uIx3YRHJ2H7Pr6vV6TXOPAupVKkVv1sK5VEq3ZBCmzY0qdsQQTnlnkWT2\n8tPU1IT33nsPvXv3xg9+8AO8/vrr+H//7/9h5cqVqrxUchyHgQMH4ssvv8RDDz2EG264QfFjmh1z\nPWF1QDKCh/lyOI5D+/btk56JJRQCbPNgmwp7C9Rzi/ZYaC0Eoh+EwWAQgUBA1gGScqc/9IZQCAhn\nvkVHKthGbqQKJYZW5l2hQAcgeo/K5fsxq9gRws6RWQOAi8/gTz/9FM8991zEH/nKK6+A4zhVPq9W\nqxV79+5FQ0MDXC4XDhw4gL59+yp+XDNDHp40YSWQAFrlfePBcRwaGxths9laPUTknIkllq/We+mr\n3qvNWOOzdKs/tOpdohaJCgE9dHpOFb2meITN+dL1/WSS2OF5XjSN9de//hXbtm1Dv379sGHDBhw8\neBB33HEHfvSjH2HcuHGqrPE3v/kN2rVrhyeeeEKV4xkcMi0rhVDwCD808b7H6/VKzsSSEjs+ny/y\ncE12kxTbWNTyqCSzRiNVm8UylEoJNaOdYyqks0kapXmkUYRAdAuBZKJpRjnHdIgndt5++21s3LgR\nf/vb3yLFBCdOnMCGDRtw7tw5zJo1S5F1nTlzBtnZ2ejQoQN8Ph/uuOMOzJ49G6WlpYocz2SQ4FEK\noeARvu1JEa8SS2xMBEt9yTkTSxj50cNIBlYZARhveCQQf8CpsPoDMOY5JoKc56jX5pGxNkk9k0w0\nzajnmAzxBN3f/vY3VFVVwe12q94cc9++fZg2bVrkBXjChAl49tlnVV2DgSHBoxTJCJ6Wlhb4fL6Y\nlVhiA0CVbkQX/VYtR4fSZI/PWrfrpbNwOkiVvrJp9mbeQJTqEK2X8mwziVaxfj8s8uP3+zNa7FRV\nVeHtt99GVVUVcnNzNVolkSIkeJSCbWwAIr6M/Pz8Vl8jrMSKnrUlrMSSatKmZvt9sbdqJYdHGrHZ\nXrIIy7IBaB5NUwK5GkMmQjppmnSPK9WfxehEe9MAICcnxxRVdNHE816tXbsWK1asQHV1dVx7AqFL\nSPAohVDwBAIB+P1+FBQURP6d53l4vV7wPN+qP0csvw6gj94schh0Y2GWZnuxiO4jZBSPSjJoOQ5D\nLdOzktErvSAc3ZKTk9OqG7lRq+iiiSd2Nm7ciKVLl6KmpqbNiythGEjwKIVQ8DCR0r59ewCpV2Ix\n4aSnKiVhSkE4jypV8aMHQac08QSd2tE0JRBG6PQQ+hemEuVKz7Loldi0bLMglaozchVdNPHEzpYt\nW/DSSy+hpqYm8gwnDAkJHqVgDwOgteBhlVg5OTmtHpLxxI6eB/IxpGbTxKpOEmL2/jNA8oJOWEos\nh6BUA71PPJfD9GyEYa7pkowvKdboEL3ep0B8sfPBBx9g0aJFWLt2LTp06KDRKgmZIMGjFELBwzod\n5+XloampKelKLCOaIaWqk8Ry/0YRdOmSbtPEWIJSL34Ko6UjUzE9M7GTnZ1tWn9ZOs8dqYnkehtv\nE6/v1c6dOzF//nysXbsWRUVFGq2SkBESPEohFDzhcBgNDQ0AoLtKLDUQip/oYZxWqzVS5mrmnh5y\npyO1MujGwujpyESuKftMMjO9GZHThM0EpdD3o5cO72zGmZjYqaurw9y5c7F27Vp06tRJoxUSMkOC\nRymY4GGmxkAggA4dOohWYgFo84ETVmLZ7XbTCIHo3D/HcbBarcjNzTVc7j8R1Ihe6aF5JBM7Zpn9\nJXZNbTYbQqFQJB1tRpSsOJNqy6CF7yeW2PnnP/+JZ599FrW1tbjkkktUWxOhOCR4lIKFS1klVigU\nQseOHSP/pvdKLKUR9tixWq0IhUK6aHQoJ1p1pFW7eSRL1Znde8XK6zmOU70nlRqoXXEWbSRXy/cT\nS+x89NFHeOqpp1BdXY0uXbootgZCE0jwKEUoFMK5c+eQlZWFvLw81NfXo2PHjglXYpnlTVkMKVOr\nWKNDo4ofvXivpJrIybWpZILRnN2vzJek107P6aB1eb1avh/hPL7o39Unn3yCJ554AlVVVbj88stl\nOyahG0jwKEU4HI5UY1ksFpw7dw6FhYURwWPUSqx0STR6JVWabYQ3ajWb7SWDnP2T4hk+zQJLLUvd\nr6nMTdMbWosdsfWImfPT9f3EEjv79u3DI488Ao/Hg27duslxGoT+IMGjFCxawzh//nykYZVZKrGS\nJdUqpeiNWs8lr0ap4Elno84UcR5P7ESTyNw0vaE3sRONmO8nFX8ai0QKm7wyDhw4gAcffBBr1qzB\n97//fdnP4dixY5g6dSpOnToFq9WKH//4x3jkkUdkPw4RFxI8SuL3+wFc/NBeuHABFosFdru9VZTC\nbPOixGDRAPZ2lc6br9yNDuVE7/1npEi2hYDZJ2UD6Zuw5dqolYSJHSM1TkzF9xNL7Hz++ed44IEH\n8M4776Bnz56KrPnkyZM4efIk+vfvD6/Xi4EDB6K2thbXXHONIscjJCHBoySBQCCSmokuzWZvfX6/\nP+INMMIDJ1mEG6TD4ZBVlOgpnWC0/jNSJNJCADB3JFKJijMlOj2ngxHFTjRSvh/hyw+LKouJnS++\n+AIzZ87E22+/jauuukq1dbtcLjz88MMYNWqUasckAJDgUQ5hB08pvw5Leek5RZMOaqbqkolSyI3Z\nSrIZYi0ELBYLcnNzDeGlSgU1Ks60Nj2bQexEI+b7YdWf7dq1a/O5/O9//4vp06dj1apVqkZavvrq\nK4wYMQL79++nmVzqI3mjm2fX1YiDBw9i9OjRWLx4Mb788ksIBeQbb7yBb775Bu3atUP79u2RnZ2N\nUCiExsZGeL1e+P1+cByn4erTh+M4eL1eWCwWVaIBrElcXl4eCgoKkJeXF3mwe71e+Hy+SMRCTvx+\nP3w+n+hD1ehYLBbYbLZIxCo7Oxt2ux2BQAANDQ1obm5GIBCQ/ZpqhVrl9VarFXa7PfL5t9vtCIfD\nrT7/4XBYkWObUewAiLzgOBwOFBQUICsrKyJ8mpubsXfvXmzatAktLS34+uuvMX36dKxYsUJVseP1\nejF+/Hi89NJLJHZ0BkV4ZKChoQEbNmyAx+PBN998g9tuuw3Hjh3D7t27UV1djSuuuKLV1+vZn5IM\nwsGRWht3pZrypduRWFil5HA4DFORkyxSJmwzDDgVoofyeqW7Epulc3s8ooVrOBzGli1bsGDBAhw8\neBAdO3bE/fffjwceeACFhYWqrCkUCqGsrAx33nknHn30UVWOSbSBUlpqcerUKdx11104d+4cunfv\njqFDh2Ls2LHo27ev6AYh5k8xgvjRu5dFmPZKtdNrplQpJWrCNuKAUyGxypW1Qm7Tc6aIHZZeFhOu\nJ06cwPTp03Hrrbdi7969+Pvf/47BgwfD6XTi3nvvVXRe1tSpU9G5c2e8+OKLih2DiAsJHjX45ptv\nUFZWhv79+2Pp0qUIh8N4//334Xa7cfDgQQwfPhxjx47Fddddl5D4SXYKuVoYrUN0tD8lEfHDfElm\nnv0FtG22lyhGGHDKMFIvoXRMz0ZplZAuscTOyZMnMXnyZCxZsgSDBw8GADQ1NWHLli2ora3FvHnz\n8L3vfU+RddXV1WH48OG49tprI37O559/HiUlJYocj5CEBI/ScByH66+/HhMmTMDTTz/d5mHj9/ux\nbds2uN1u7N+/HzfffDNcLhcGDhwoKX60MufGQg8pgXSI7vIs1uiQ+R/U8iVpRbL9Z6TQ44BT4dqM\nGqVLpns2iR3g9OnTmDhxIl588UXcdNNNGq2Q0AEkeNTgzJkz6Ny5c9yvCwaD2LFjByorK7F3717c\neOONcDqdGDJkiKiI0IP4MfLGIYWYP8VmsyEQCCA7OzsjUgJyR+mU8lKluhZWQSl3qwS1kUonsihl\nJokdsSrJs2fPYsKECVi4cCGGDRum0QoJnUCCR6+EQiHs2rULlZWV2LNnDwYNGgSn04mhQ4eKVgNp\n8TadCekdlvZgTSSN5k9JhlQ7YaeCsNePmkNjzdw4Mdr0zCa85+bmah5RU4pYYuf8+fOYMGECfvOb\n32DkyJEarZDQESR4jEA4HMbu3bvh8XhQV1eHfv36weVyYdiwYZLzfZR+m9brvCi5EXpZWPuAaCO5\n3AMOtUDLlGQi6UQ5MLPYEcJaQrAUl147PacLS72KiZ0LFy5gwoQJeO655zB69GiNVkjoDBI8RoPj\nOOzZswdutxs7d+5E37594XQ6MWLECNFKGiXET6ZVfIild6RKiLX2UqUCa4KpB/+VUk35MmVeHfts\nspYQDK0iakoRS+w0NDRg0qRJeOqpp8gYTAghwWNkOI7D3r174Xa7sX37dvTq1QsulwujRo1Cbm6u\n6PdEl2Un+9Zn1HlRyZJMeseIQyMB/fuvpPpSJRtRy3SxI/Z1iZqe9UgsseP1ejFx4kQ88cQTKCsr\n02iFhE4hwWMWOI7D/v37UVlZia1bt6J79+5wuVwYM2YMHA6H6PcIS10TeeszWtl5qqST3pHqn6KH\nyiQhSs44U4JUI2p6nwYuF4mKnWhimZ61jvaJEUvsNDU1YfLkyfjpT3+KcePGabRCQseQ4DEjPM/j\ns88+g9vtxubNm1FcXAyn04mSkhIUFBSIfk+8njRqGlq1Qu6+LFLpRK19FEb3ssQacCoUlZkkdrxe\nb9pRV6U7PadLrHYJPp8P99xzD2bMmIG7775boxUSOocEj9nheR6HDh2C2+3Gxo0bUVRUBKfTidLS\nUnTo0EH0e6JNpFarNbI5mlnsKB3xSDaipgRmS++IiUomKFtaWkzfRkAusRNNoqJSLWKJnZaWFkyZ\nMgX33HMPJk+erOq6CENBgieT4Hkehw8fhsfjwYYNG9CuXTuUl5ejrKwMRUVFbR5ibHNkokfJChot\n0UIERIvKZDrnpkomRDw4jkMgEIi0EcjKyoLdbjesOTcWavnphKJSC9MzO08xseP3+zFt2jSMHz8e\nU6ZMMd3vmJAVEjyZCs/zOHr0KKqqqrB27VpkZ2ejvLwc5eXl6Ny5MxobGzFz5kzMnj0b119/PSwW\ni+TASCOLHz2U16sxiDNTKuuEnYXtdruhzbmxSHX0hxyoaXqOJXYCgQCmT5+Ou+66C9OnTzftPU3I\nBgke4qL4OXHiBKqrq1FbWwufz4fTp09j4MCB+NOf/iT6QGVmRyNPy9ajCBC7rumWZWfKeIFYEY/o\n62rkBpJaip1olDQ9xxI7wWAQM2fOxG233YYHHnjAtPc0ISskeIjWHDhwACUlJRg8eDC8Xi8CgQBK\nS0tRUVGBrl27ij5YjDgt2wjl9VJl2clcVyOcpxwkc57RA06tVmurruR6Rk9iJxo5B8fGOs9QKIT7\n778fQ4cOxc9+9jNFxM6MGTOwfv16FBcX49NPP5X95xOaQIKH+I5//OMfGD9+PBYtWoRp06aB53mc\nO3cOtbW1qK6uRkNDA0pKSuB0OtGjRw9J8ZPuJq00zACpx01DilibidQmbcTzTIV0RICReijpWexE\nk47pOdZ5hsNhPPjgg7j++uvx+OOPK/b72bVrF/Lz8zF16lQSPOaBBA9xEY7jMGrUKDzzzDOSrdjr\n6+uxbt06eDwenDlzBnfccQecTid69uwZV/wIRzFoKX7M0EsokaGxck081ztynqeeeygZSexEk4zp\nOZ7YefTRR9G7d2/MmjVL8d/HkSNHUF5eToLHPJDgIb6D5/mEHyINDQ3YsGEDqqqqcPz4cdx+++1w\nuVzo3bt3wuJH7TSCGXsJib1J22w2hEIhOBwOEjtpoJdxDEYWO2JImZ6tVqtkRJLjODz++OPo0aMH\nnn32WVWuPwke00GCRyl++ctfora2FlarFcXFxVi5ciW6dOmi9bIUoampCRs3boTH48GRI0cwYsQI\njB07Fn379hWN5MSKUCghfniej5Qq62FelFIIJ7uzDUEvEQq5iTUlWwnUGnAaTSzjrhmINpNbLBbk\n5OTAZrNFfq8cx2HWrFno3Lkz5s2bp9p9TILHdJDgUQqv14v8/HwAwMsvv4wDBw7gtdde03hVyuPz\n+fD+++/D7Xbj4MGDGD58OMaOHYvrrrsuafEjxxBOvc+LkpPokRh6iVDIjdpiJxqlBpxGY3axw2DN\nE+12O2w2G4LBIJYsWQKPx4PS0lLU19ejU6dOWLBggar3LQke0yF585gj3q8hTOwAiDR7ywTy8vLg\ndDrhdDrh9/uxbds2LF++HPv378fNN98Ml8uFgQMHRq6HxWKJbBi5ubmRTbqpqSltA2n0CAWz/g6E\nIzHy8/Mj52mz2SLRLBah8Pv9aG5ujohKo4kfPaQlrVYr7HY77HZ7qwrFlpYW2Uz6meLBYi0ThNV1\n2dnZePLJJzF06FAsW7YMf//735Gfnw+fzweXy4Vhw4ap8rvneR5xXvwJk0ARHhmYM2cOVq1ahcLC\nQnzwwQfo1KmT1kvSjGAwiB07dqCyshJ79+7FkCFD4HK5MGTIENEUk9QcqkTTM2YboSBFKhEsKQ+F\n3htIMrGj17SkXGXZmSZ2xAae8jyP+fPnw+v1YvHixfj8889RU1OD6upqfPXVV9ixYwd++MMfKra2\nyZMnY8eOHTh79iyKi4sxb9483HfffYodj1AFSmmlw+jRo3Hq1KnI35npd/78+SgvL4/8/4ULF8Ln\n82Hu3LkarFJ/hEIh7Nq1C5WVldizZw8GDRoEp9OJoUOHir65JSt+9NA9WQ3kGAJqlAaS6Uyw1wKx\nVK3QpC/1u8o0scNmnQnheR6LFi3Ct99+iz/96U9t7sOjR4+iuLjYFAZuQlVI8KjB0aNHUVpain37\n9mm9FN0RDoexe/dueDwe1NXVoV+/fpGwtdQDX7iRRIsfNi/K7F2FlYhg6bWHktHETjRSgp2lctnv\njsTOxWv1xz/+EYcPH8ayZct0JboJw0OCRykOHTqEXr16AbhoWv7HP/6BNWvWaLwqfcNxHPbs2QO3\n242dO3eib9++cDqdGDFihGT3XGHflHA4DOCiB8DskR3mcVIqXaeHNgJCb5KZPFjCe5aZya1WK/x+\nv+lbCcQTO6+88go+++wz/OUvfzGkuCV0DQkepRg/fjwOHjwIq9WKHj16YOnSpbjsssu0XpZh4DgO\ne/fuhdvtxvbt29GrVy84nU6MGjUKeXl5bb4+GAxGzLgcx5mqKkmIFuk6pSvppI5pRrETDcdx8Pv9\nCAQCAKDblKIcxJrpxvM8li1bhn/9619YuXKlafpkEbqCBA+hfziOw/79+1FZWYmtW7eie/fucLlc\nGDNmDBwOB6qrq/Hmm29izZo1rXp3CPumGLUqSYgehp2m6k1J9hiZ0kqApbEcDkekJNsMA06jYVFJ\nm83W5t7leR5vvPEG6urqsHr1ahI7hFKQ4CGMBc/z+Oyzz+B2u7F582ZkZWXh888/x6pVqzBs2DDR\n7zFqVZIQPU48T7eSTupnpmvENgpCsRO9yeshpSgX8cTOm2++ia1bt+Kdd94xdTqP0BwSPIQx4Xke\nv/nNb7B8+XJMnjwZdXV1KCoqgtPpRGlpKTp06CD6fWJN4/Qufowy8TzaTJ5sSjGTxE4yzRO1SCnK\nRTyx8/bbb2PDhg1Ys2YNVV0RSkOChzAebIjgrl27sGnTJnTp0gU8z+Pw4cPweDzYsGEDHA4HKioq\nUFZWhqKiIsn5XnovyTbqxPNkU4qZ0jcJSK9TdDpTyNUmltgBgDVr1sDj8aCysrKNgZkgFIAED2E8\nPv74Yzz99NNYs2aNaCSH53kcPXoUVVVVWLduHbKyslBeXo7y8nJ07tw5pvhh0R89+CfMUqYcbw4V\niZ3UEEsp6sWoz8SOlLm+qqoKb731FqqqqkSLEAhCAUjwZDqzZs3CunXrkJOTg549e2LFihVo3769\n1suKS6KT3Xmex4kTJ1BdXY3a2lpwHIeysjJUVFSguLg47mR3rcSP1vOilEIspRgOh2Gz2UjspIlY\nVI0JIDWvazyxs27dOvzlL39BTU0NHA6HausiMh4SPJnO1q1bcdttt8FqtWL27NmwWCxYsGCB1stS\nBJ7ncfr06UiLer/fj9LSUlRUVKBr165xxQ8zjyotfvQ+QkEu2NBI4OJ11kNUTSm0mO4u5lVTOl3L\nonUWi0VU7Lz33nt47bXXUFNT02reIEGoAAke4jtqamrg8Xjw5ptvar0UxeF5HufOnUNtbS2qq6vR\n0NCAkpISOJ1O9OjRI2HxI3fljNG7CicK6yfE/B0AVBeWasHEjla/U7XStfHEzpYtW/DHP/4RtbW1\nhogiE6aDBA/xHRUVFZg4cSImT56s9VJUp76+HuvWrYPH48GZM2dwxx13wOl0omfPnpLiR6pyJpVN\nLVMa7QHx+wkxYck2aCNVJUWjtdiJRq4Bp2I/N5YP64MPPsCiRYtQW1uLwsLCtM+DIFKABE8mkMiQ\n0/nz5+Pjjz+Gx+PRapm6oaGhARs2bEBVVRWOHz+O22+/HS6XC717905a/CSyiWRSo71k+wlJXdus\nrCzdVSVFo/fUpFxNJOOJnZ07d2L+/Pmora1Fx44dZT8PxqZNm/DYY4+B4zjMmDEDTz31lGLHIgwJ\nCR4CWLlyJV5//XVs375d131etKCpqQkbN26Ex+PBkSNHMGLECIwdOxZ9+/YVFSbJbtCZ1HuGeXZS\n7SfEri2LUOi5JFvvYieaVJtIxhM7u3fvxq9+9SvU1taic+fOiq2f4zhcffXV2LZtGy6//HLccMMN\nePfdd3HNNdcodkzCcJDgyXQ2bdqEn//859i5cyc6deqk9XJ0jc/nw/vvvw+3242DBw9i+PDhGDt2\nLK677jpJ8RNrEwGQMeXYcjdPTHQCuRYYTeyIIez1IzWXjol1nudF7989e/bgmWeeQU1NDS699FJF\n1/vhhx9i3rx5eO+99wAAv/vd72CxWCjKQwiRfCiYpw6WiMnDDz+MQCCA0aNHAwBuvPFGvPrqqxqv\nSp/k5eXB6XTC6XTC7/dj27ZtWL58Ofbv34+bb74ZLpcLAwcOjIgfi8UCm80Gm82GnJycyAbNNgkA\nsFqtGSN25GyeKLy2ubm5kciP3++Hz+fTrB+NGcQOgMi1Bb4rd/f7/Whubo6IylAoBEBcrP/rX//C\n008/jerqasXFDgAcP34c3bt3j/y9W7du2LNnj+LHJcwBCZ4M4YsvvtB6CYYkJycHpaWlKC0tRTAY\nxI4dO/DWW2/hySefxJAhQ+ByuTBkyJDIpiHcoO12O5qamiJN97xer25TM+miVqdoMWHJNmi1+tGY\nRexEY7VaI5E54bVlrQQCgQCam5tRVFQEAPj3v/+NJ598EtXV1ejSpYvGqyeI+JDgIYgEyc7OxujR\nozF69GiEQiHs2rULlZWVePrppzFo0CA4nU4MHToUWVlZOHr0KJ588kksXboUhYWFsFgskehES0uL\nZPrAiGjVKTp6g2aen+jIj5zmcLOKnWjY/coik+FwGC0tLRg8eDAuu+wy3Hrrrdi6dSvWr1+Pyy+/\nXLV1de3aFV9//XXk78eOHUPXrl1VOz5hbMjDQxBpEg6HsXv3bng8HtTV1aFXr17YtWsXZsyYgVmz\nZol+j9CXYmTxo8exGGKz09j1TUf8ZErvJFZNGA6H2xjs/X4/1qxZg9dffx1ff/01Lr30UowbNw5j\nx45F//79Fb93w+EwevfujW3btuGyyy7D4MGD8c4776BPnz6KHpcwFGRaJgg12Lt3L8aMGYNbbrkF\nx48fR58+feByuTBixAhJE2+yAzj1ghHGYsg1PoSJnfz8fFO3E4gldgDgP//5D37yk5/g3XffxRVX\nXIEPP/wQ1dXVqK6uRq9evbBp0ybF17hp0yY8+uijkbL02bNnK35MwlCQ4CEIpamrq8O4cePwpz/9\nCePHjwfHcfjkk09QWVmJ7du3o1evXnA6nRg1apTkIEUmflhDvugBnHrBCGInmljN+GJFbEjsXOTQ\noUOYMWMG3nrrLVx99dVtvvfs2bOKlqQTRIKQ4CEIJWlqasK1116LpUuXYsyYMW3+ned57N+/H5WV\nldiyZQu6d+8Ol8uFMWPGSA5WlJqTpLX4MYOPJdEmkn6/H4FAwPSNIoVNMfPz89vcX4cPH8b//M//\nYNWqVZQ+IvQOCR7COLjdbsydOxefffYZ/vd//xcDBgzQekkJ0dTUhHbt2sX9Op7n8dlnn8HtdmPz\n5s0oLi6G0+lESUkJCgoKJL8n2peixpDIaMwgdqIRip9QKBTp9QNcjGRlQmQn1riTr7/+GlOnTsWK\nFSvwgx/8QKNVEkTCkOAhjMN//vMfWK1W3H///fj9739vGMGTCjzP49ChQ3C73di4cSOKiorgdDpR\nWlqKDh06SH6PUPyoNX08E0y7rNGhz+dDOBxuk/bSU1pRLlpaWiTFzvHjx3HPPffg9ddfR79+/TRa\nIUEkBQkewniMHDkSf/jDH0wteITwPI/Dhw/D4/Fgw4YNcDgcqKioQFlZGYqKiuJOdldyQnasTdFs\nCM9VOIHcyNV0UsT6vZ44cQKTJ0/Ga6+9ljGfQcIUkOAhjEemCR4hPM/j6NGjqKqqwrp165CVlYXy\n8nKUl5ejc+fOccVPKBSC1WpNW/xk0nR3ILYAMGo1nRSxzvXUqVOYNGkSlixZgsGDB2u0QoJICRI8\nhL5IZLJ7JgseITzP48SJE6iurkZtbS04jkNZWRkqKipQXFycsPgRTshO9LiZMt09WWFnlGo6KWKZ\nsU+fPo1Jkybh97//PYYOHarRCgkiZUjwEMaDBE9beJ7H6dOnUVNTg+rqavj9fpSWlqKiogJdu3aV\nFD9SFUlS4ideibKZSDeKJVVNp7ahPFFiiZ2zZ89i4sSJWLBgAYZcia9KAAARMUlEQVQPH67RCgki\nLUjwEMZj5MiR+P3vf4+BAwdqvRRdwvM8zp07h9raWlRXV6OhoQElJSVwOp3o0aNH0uKHlWOz6dgc\nx5HYSeHnqeGpSpVYPYXOnz+PiRMn4te//jVGjhyp0QoJIm1I8BDGoaamBg8//DDOnDmDwsJC9O/f\nH++9957Wy9I99fX1WLduHTweD86ePYsxY8bA6XSiZ8+eCYufrKwshMNhAMgIsaNkyk7KU5WVlaVJ\nlVsssXPhwgVMnDgRc+bMwejRo1VfG0HICAkegsgkGhsbsWHDBng8Hhw/fhy33347XC4XevfuHVP8\nNDc3R/xUZi7HVtuflGijQ6WI1T+psbEREydOxKxZs3DnnXcqug6CUAESPASRqTQ1NWHjxo3weDw4\ncuQIRowYgbFjx6Jv376Rjd7r9WLz5s248847kZub26rXD2vEZxbxo7U/SSqyptT1jSV2vF4vJk2a\nhMceeyxSLEAQBocED0EQgM/nw/vvvw+3242DBw9i+PDhGDNmDJ577jlceeWVWLZsWZtoh3BzNrr4\n0VrsiK1HWO4u9/WNJXaam5sxadIk/PSnP8W4cePSOg5B6AjJD432LjqCIFQjLy8PTqcTb775Jnbu\n3IlBgwbh3nvvBQBceuml+Ne//gWO41p9j81mQ25uLgoKCiLpn5aWFjQ2NqK5uTmyUesdvYkdALBY\nLG2ur8Vigc/nS/v6xhI7Pp8P9957L3784x+rJnbcbjd++MMfwmaz4eOPP1blmAQhhAQPQWQoFy5c\nwIIFCzBlyhTs3LkTd911F9566y2MHDkSs2bNwu7duyMGZobNZkNOTg7y8/ORn58Pm80Gv9+ve/HD\nKs/0JHbEEIqfdK5vMBiUFDstLS2YOnUqpkyZgh/96EdKnUobrr32WlRXV+PWW29V7ZgEIYRSWgSR\ngZw+fRq33nor7r77bsydO7eVAAiFQti1axcqKyuxZ88eDBo0CE6nE0OHDkVWVpboz9NzF2IzlNlH\nX99YjQ6DwSB8Pp+o2AkEApg2bRrGjRuHqVOnanItqL8WoTDk4SEI4juCwSBqa2sxfvz4mF8XDoex\ne/dueDwe1NXVoV+/fnC5XBg2bFhkong0eupCbAaxE02sRofhcFhS7ASDQdx333246667MH36dM2u\nBQkeQmFI8BCEnGzatAmPPfYYOI7DjBkz8NRTT2m9JMXhOA579uyB2+3Gzp070adPH7hcLowYMQI5\nOTmS3yO2OashfswodqIRVtOFQiEAgN1uR05OTivzeTAYxMyZM3HbbbfhgQceUOxa0MgYQgeQ4CEI\nueA4DldffTW2bduGyy+/HDfccAPeffddXHPNNVovTTU4jsMnn3yCyspKbN++Hb169YLT6cSoUaOQ\nl5cn+j3Rm7OSIxgyQewICQaDaG5uRk5OTkRkPv744+jTpw8qKiqwcOFCDB06FD/72c80vxYkeAiF\nkbzBxRPyBEFIsmfPHlx11VXo0aMHAGDixImora3NKMFjtVoxYMAADBgwADzPY//+/aisrMTixYvR\nvXt3uFwujBkzBg6HI/I9FosFdrsddru9lfjx+XzIysqKCCA5xjv4fD7wPJ8RYicUCkXSWMxjxfM8\n7r77btTU1GDUqFHIzc1Fnz59cODAAfTt21fza6JHYzthfqhKiyCS5Pjx4+jevXvk7926dcPx48c1\nXJG2WCwWXHvttfj1r3+N3bt3Y+7cuTh06BCcTifuvfdeVFZWorGxsc332O12tGvXDu3bt4fdbkc4\nHEZjYyO8Xi/8fn+b8vhEEIodh8Oh+cauNKFQCM3NzXA4HK0M5RaLBWPGjEFWVhaefPJJvP322zh3\n7hzuvPNO9OnTB3PmzFFddNTU1KB79+748MMPUVZWRl2dCdWhlBZBJInH48HmzZuxbNkyAMDq1aux\nZ88eLFmyROOV6Que53Ho0CG43W5s3LgRRUVFqKioQGlpKQoLCyW/R2z+VCKRH57n0dzcDAAZLXaA\niynHJ554At27d8ecOXMi14LneXz00UfYs2cPHnroIS2WTRBKQx4egpCLDz/8EHPnzsWmTZsAAL/7\n3e9gsVgywricKjzP46uvvoLH48H69evhcDhQUVGBsrIyFBUVSc73ihY/whEM0V9LYuciHMfhqaee\nQqdOnTBv3jzTXwuCiIIED0HIRTgcRu/evbFt2zZcdtllGDx4MN555x306dNH66UZAp7ncfToUVRV\nVWHdunXIyspCeXk5ysvL0blz54QnuwsjP5kodvLy8tq0BuA4Ds899xxycnLw/PPPKz4UlSB0CAke\ngpCTTZs24dFHH42Upc+ePVvrJRkSnudx8uRJVFVVoba2FhzHoaysDBUVFSguLk5I/LDSZ4fDYcj5\nXskQS+zwPI958+YhHA7jhRdeILFDZCokeAiC0Dc8z+P06dOoqalBdXU1/H4/SktLUVFRga5du7YR\nMhzHRSI7NputVeQnKyvLdOInHA6jqalJUuw8//zzaGhowEsvvURih8hkSPAQBGEceJ7HuXPnUFtb\ni+rqajQ0NKCkpAROpxM9evSAz+fDxIkT8dhjj2HkyJGwWCyKTx7Xknhi54UXXsDJkyfx6quvktgh\nMh0SPARBGJf6+nqsW7cOHo8H3377Lfx+P7p164a//vWvsNvtbb7eTOKHiZ3c3Nw258rzPP74xz/i\nv//9L5YtW9bGzE0QGQgJHoIgjE9TUxNKS0sBAJdeeimOHz+O22+/HS6XC71795YUMtGeH6OIn3hi\n55VXXsGBAwfwxhtvkNghiIuQ4CEIwth4vV6UlZXhiiuuwPLly2Gz2dDU1ISNGzfC4/HgyJEjGDFi\nBMaOHYu+fftKpnaY+AmFQuA4rtWICz2Jn3hiZ9myZfjoo4/w17/+VXKKPUFkICR4CIIwNrNnz8aZ\nM2ewbNkyUTHj8/nw/vvvw+124+DBgxg+fDhcLhf69esnKX6EaS89iZ94YueNN95AXV0dVq9eTWKH\nIFpDgocgMpUZM2Zg/fr1KC4uxqeffqr1clKmpaUFdrs9IVOu3+/Htm3b4Ha7sW/fPtxyyy1wuVwY\nOHBgQuInHA5H0l5qix+O4+D1eiXFzptvvoktW7bg3XffbWNgJgiCBA9BZCy7du1Cfn4+pk6damjB\nkyrBYBA7duxAZWUl9u7diyFDhsDlcmHIkCGSvhcmfkKhUKvJ7tnZ2YqKHyZ2cnJykJOT0+rfeJ7H\nO++8g/Xr12PNmjWiZm2CIEjwEERGc+TIEZSXl2ek4BESCoWwa9cuVFZWYs+ePRg0aBCcTieGDh0q\nmRriOK7ViAulxE8ssQMAlZWVqKyshNvtRm5urmzHJQiTQYKHIDIZEjxtCYfD2L17NzweD+rq6tCv\nXz+4XC4MGzZMMlXE83wk7SUUP1lZWWn1v+E4Dk1NTbDb7aJip7q6GqtXr0ZVVRXy8vJSPg5BZAAk\neAgikyHBExuO47Bnzx643W7s3LkTffr0gcvlwogRI0QFCCAufpgASkb8xBM769evx/Lly1FTUwOH\nw5HyOSbDrFmzsG7dOuTk5KBnz55YsWIF2rdvr8qxCSJNSPAQRCZDgidxOI7DJ598gsrKSmzfvh29\nevWC0+nEqFGjJKMrwsnuwWAQNput1XDTWMdqampCdna2aJpq06ZNePXVV1FdXY2CggLZzjEeW7du\nxW233Qar1YrZs2fDYrFgwYIFqh2fINJAUvBQPSNBZAA8zyPOyw3xf1itVgwYMAADBgwAz/PYv38/\nKisrsXjxYnTv3h0ulwtjxoxpFW0RTm8Xih+/3w+r1SoqfuKJna1bt+Lll19GbW2tqmIHAG6//fbI\nn2+88UZ4PB5Vj08QSkARHoIwOZMnT8aOHTtw9uxZFBcXY968ebjvvvu0Xpbh4Hken332GdxuNzZv\n3ozi4mI4nU6UlJRIChKh+AmFQhHxY7PZ4PP5kJ2djZycnDbm5x07dmDhwoWora1FYWGhGqcnSUVF\nBSZOnIjJkydrug6CSBBKaREEQcgFz/M4dOgQ3G433nvvPRQVFaG8vBylpaWSAoXneYTDYQQCgchk\nd7vdHvH+MHbu3Inf/va3WLt2LTp27KjYOYwePRqnTp1qtT6LxYL58+ejvLwcADB//nx8/PHHFOEh\njAQJHoIgCCXgeR5fffUVPB4P1q9fD4fDgYqKCpSVlaGoqKhV9Ka+vh4cxyE3NxfZ2dkIhULYuXMn\nZs+ejYqKCvTt2xevv/461q5di86dO2t4VsDKlSvx+uuvY/v27ZLGbYLQISR4CIIglIbneRw9ehRV\nVVVYt24dsrKyUF5ejvLy8sifp02bhpkzZ0aEUDgcxj//+U+sXr0aNTU16NSpE370ox9h/PjxGDBg\ngCYjLjZt2oSf//zn2LlzJzp16qT68QkiDUjwEARBqAnP8zh58iSqqqrg8Xjw5ZdfYuDAgXjhhRfQ\npUuXVkLm448/xi9+8QtUVVXh+PHjcLvdcLvdCIfDeO655zB9+nRV137VVVchEAhExM6NN96IV199\nVdU1EESKkOAhCILQgqamJtx555244oorcNNNN6G2thZ+vx+lpaWoqKjA2bNn8dhjj6Gqqgpdu3aN\nfB/P8/j0008RDAYxaNAgDc+AIAwFCR6CIAi1aW5uxl133YWePXtGprzzPI9z586htrYWbrcb//73\nv/Hhhx+ie/fuWi+XIMwACR6CIAi1Wb9+PaqqqrB8+XLJBoR+v59MwQQhHyR4CIIwBseOHcPUqVNx\n6tQpWK1W/PjHP8Yjjzyi9bJShpV7EwShCiR4CIIwBidPnsTJkyfRv39/eL1eDBw4ELW1tbjmmmu0\nXhpBEPpHUvCkPt6XIAhCAbp06YL+/fsDAPLz89GnTx8cP35c41URBGF0SPAQBKFbvvrqK3zyyScY\nMmSI1kshCMLgkOAhCEKXeL1ejB8/Hi+99BLy8/O1Xg5BEAaHBA9BELojFAph/PjxmDJlCpxOp9bL\nIQjCBJBpmSAI3TF16lR07twZL774otZLIQjCWFCVFkEQxqCurg7Dhw/HtddeC4vFAovFgueffx4l\nJSVaL40gCP1DgocgCIIgCNNDZekEQRAEQWQuJHgIgiAIgjA9JHgIgiAMzC9/+Uv069cP119/PUpK\nSnDy5Emtl0QQuoQ8PARBEAbG6/VG+hS9/PLLOHDgAF577TWNV0UQmkEeHoIgCDMibMrY1NQkOZWd\nIDKdLK0XQBAEQaTHnDlzsGrVKhQWFuKDDz7QejkEoUsopUUQBCEDfr8fw4cPRyAQiHSK/tWvfiXL\nzx49ejROnToV+TvP87BYLJg/fz7Ky8sj/3/hwoXw+XyYO3euLMclCANCfXgIgiCUprm5GQ6HA+Fw\nGDfffDOWLFmCwYMHq3b8o0ePorS0FPv27VPtmAShM8jDQxAEoTQOhwPAxWhPKBSCxSL57JWNQ4cO\nRf5cU1ODPn36KH5MgjAi5OEhCIKQCY7jMHDgQHz55Zd46KGHcMMNNyh+zNmzZ+PgwYOwWq3o0aMH\nli5dqvgxCcKIUEqLIAhCZhoaGuByufDKK6+gb9++Wi+HIDIJSmkRBEGoRfv27TFy5Ehs2rRJ66UQ\nBPF/kOAhCIKQgTNnzuDChQsAAJ/Phy1btuCaa67ReFUEQTDIw0MQBCEDJ06cwLRp08BxHDiOw4QJ\nE1BaWqr1sgiC+D/Iw0MQBEEQhFkgDw9BEARBEJkLCR6CIAiCIEwPCR6CIAiCIEwPCR6CIAiCIEwP\nCR6CIAiCIEwPCR6CIAiCIEwPCR6CIAiCIEwPCR6CIAiCIEwPCR6CIAiCIExPvNESkh0LCYIgCIIg\njAJFeAiCIAiCMD0keAiCIAiCMD0keAiCIAiCMD0keAiCIAiCMD0keAiCIAiCMD0keAiCIAiCMD3/\nH7tpQN0Qph9/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2022ccc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model = TSNE (n_components =3, learning_rate =12, random_state=10)\n",
    "X_tsne= model.fit_transform(X)\n",
    "plt.figure(figsize = (10,10))\n",
    "ax = plt.subplot(111, projection ='3d')\n",
    "ax.scatter(X_tsne[:,0],X_tsne[:,1],X_tsne[:,2], c=kmeans_model.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=4, n_init=10,\n",
       "    n_jobs=1, precompute_distances='auto', random_state=42, tol=0.0001,\n",
       "    verbose=0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_model = KMeans(n_clusters=4, random_state=42)\n",
    "kmeans_model.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['labels'] = kmeans_model.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x1ffa0cf8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ahxUSDIfDZV1YhcYKikuLZeFVepyEeziN/wmFQk3l/mqW42hW/NKXjiw8RM04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\nx8NRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fef1048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model = TSNE (n_components =4, learning_rate =12, random_state=10)\n",
    "X_tsne= model.fit_transform(X)\n",
    "plt.figure(figsize = (10,10))\n",
    "ax = plt.subplot(111, projection ='3d')\n",
    "ax.scatter(X_tsne[:,0],X_tsne[:,1],X_tsne[:,2], c=kmeans_model.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}