Day_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "pleasant-norwegian",
   "metadata": {},
   "source": [
    "# Machine Learning Basics with Scikit-learn: Day 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "happy-gossip",
   "metadata": {},
   "source": [
    "Today we will learn about **supervised learning.** We will cover some popular methods implemented in *scikit-learn*. If you are interested in learning more, explore the [Supervised Learning documentation](https://scikit-learn.org/stable/supervised_learning.html#supervised-learning). \n",
    "\n",
    "Supervised learning consists in learning the relationship between two datasets: the observed data `X` and an external variable `y` that we are trying to predict, usually called “target” or “labels”. Most often, `y` is a one-dimension array of length `n_samples`.\n",
    "\n",
    "To run these models easily, all supervised estimators in *scikit-learn* implement a `fit(X, y)` function to fit the model and a `predict(X)` function to predicte labels `y` given observations `X`.\n",
    "\n",
    "### Distinction between classification and regression\n",
    "Supervised learning aims to predict an outcome `y` based on information available `X`. There are two kinds of strategies: \n",
    "* If the prediction task is to classify the observations in a set of *categorical* values, in other words to “name” the objects observed, the task is said to be a **classification** task. Given the input `X` and the real values `y`, the model predicts the outcomes `y',` which can be correct or wrong. The `y` is a vector of integers (i.e., 0 or 1) or strings (e.g., `class1`,`class2`).\n",
    "* On the other hand, if the goal is to predict a *continuous* target variable, it is said to be a **regression** task. Based on the input `X`, we will obtain a numerical value `y'`, which is an approximation of the real value `y.`\n",
    "\n",
    "We will focus on classification models:\n",
    "* Nearest neighbors\n",
    "* Decision trees\n",
    "* Logistic regressions\n",
    "* Neural networks\n",
    "* Support vector machines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "structural-parks",
   "metadata": {},
   "source": [
    "We will start loading all the packages required to run these models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "reserved-declaration",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn import datasets\n",
    "from sklearn import neighbors, datasets\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import export_text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blocked-courtesy",
   "metadata": {},
   "source": [
    "### Importing the dataset\n",
    "We will use the Wine dataset provided by `scikit-learn.` The data are the results of a chemical analysis of wines grown in the same region in Italy by *three* different cultivators. There are thirteen different measurements taken for different constituents found in the three types of wine:\n",
    "* Alcohol\n",
    "* Malic acid\n",
    "* Ash\n",
    "* Alcalinity of ash\n",
    "* Magnesium\n",
    "* Total phenols\n",
    "* Flavanoids\n",
    "* Nonflavanoid phenols\n",
    "* Proanthocyanins\n",
    "* Color intensity\n",
    "* Hue\n",
    "* OD280/OD315 of diluted wines\n",
    "* Proline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "complimentary-macintosh",
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_dataset = datasets.load_wine()\n",
    "wine = pd.DataFrame(wine_dataset.data, columns=wine_dataset.feature_names)\n",
    "wine['target'] = pd.Series(wine_dataset.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "historical-spotlight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "..       ...         ...   ...                ...        ...            ...   \n",
       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0          3.06                  0.28             2.29             5.64  1.04   \n",
       "1          2.76                  0.26             1.28             4.38  1.05   \n",
       "2          3.24                  0.30             2.81             5.68  1.03   \n",
       "3          3.49                  0.24             2.18             7.80  0.86   \n",
       "4          2.69                  0.39             1.82             4.32  1.04   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "173        0.61                  0.52             1.06             7.70  0.64   \n",
       "174        0.75                  0.43             1.41             7.30  0.70   \n",
       "175        0.69                  0.43             1.35            10.20  0.59   \n",
       "176        0.68                  0.53             1.46             9.30  0.60   \n",
       "177        0.76                  0.56             1.35             9.20  0.61   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  target  \n",
       "0                            3.92   1065.0       0  \n",
       "1                            3.40   1050.0       0  \n",
       "2                            3.17   1185.0       0  \n",
       "3                            3.45   1480.0       0  \n",
       "4                            2.93    735.0       0  \n",
       "..                            ...      ...     ...  \n",
       "173                          1.74    740.0       2  \n",
       "174                          1.56    750.0       2  \n",
       "175                          1.56    835.0       2  \n",
       "176                          1.62    840.0       2  \n",
       "177                          1.60    560.0       2  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "seasonal-puppy",
   "metadata": {},
   "source": [
    "For demo purposes, we will use two dimensions of the dataset. In your exercises, you will create the models using **all** the features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "vulnerable-angola",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = wine.iloc[:,:2]\n",
    "y = wine.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "optimum-greek",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid\n",
       "0      14.23        1.71\n",
       "1      13.20        1.78\n",
       "2      13.16        2.36\n",
       "3      14.37        1.95\n",
       "4      13.24        2.59\n",
       "..       ...         ...\n",
       "173    13.71        5.65\n",
       "174    13.40        3.91\n",
       "175    13.27        4.28\n",
       "176    13.17        2.59\n",
       "177    14.13        4.10\n",
       "\n",
       "[178 rows x 2 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "enormous-casting",
   "metadata": {},
   "source": [
    "### Datasets for your exercises\n",
    "As yesterday, we partition the dataset into training and testing datasets. We will use the 80/20 proportion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "opposite-immune",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "independent-transaction",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comic-expense",
   "metadata": {},
   "source": [
    "We will do the same using all the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "acquired-effects",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_all = wine.iloc[:,:-1]\n",
    "y_all = wine.target\n",
    "X_all_train, X_all_test, y_all_train, y_all_test = train_test_split(X_all, y_all, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "positive-relations",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>13.84</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.38</td>\n",
       "      <td>19.5</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.56</td>\n",
       "      <td>9.01</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.64</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>12.81</td>\n",
       "      <td>2.31</td>\n",
       "      <td>2.40</td>\n",
       "      <td>24.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.83</td>\n",
       "      <td>5.70</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.36</td>\n",
       "      <td>560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>13.83</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.60</td>\n",
       "      <td>17.2</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.60</td>\n",
       "      <td>1.24</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>12.85</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2.58</td>\n",
       "      <td>22.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.96</td>\n",
       "      <td>5.58</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.11</td>\n",
       "      <td>570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>12.37</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.30</td>\n",
       "      <td>24.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.22</td>\n",
       "      <td>2.45</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.12</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.78</td>\n",
       "      <td>342.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>13.08</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.36</td>\n",
       "      <td>21.5</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.14</td>\n",
       "      <td>9.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.33</td>\n",
       "      <td>550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>12.70</td>\n",
       "      <td>3.87</td>\n",
       "      <td>2.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1.19</td>\n",
       "      <td>3.13</td>\n",
       "      <td>463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>13.48</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.41</td>\n",
       "      <td>20.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.70</td>\n",
       "      <td>2.98</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.86</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.47</td>\n",
       "      <td>920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>12.52</td>\n",
       "      <td>2.43</td>\n",
       "      <td>2.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2.27</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.22</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2.78</td>\n",
       "      <td>325.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "156    13.84        4.12  2.38               19.5       89.0           1.80   \n",
       "132    12.81        2.31  2.40               24.0       98.0           1.15   \n",
       "51     13.83        1.65  2.60               17.2       94.0           2.45   \n",
       "162    12.85        3.27  2.58               22.0      106.0           1.65   \n",
       "128    12.37        1.63  2.30               24.5       88.0           2.22   \n",
       "..       ...         ...   ...                ...        ...            ...   \n",
       "149    13.08        3.90  2.36               21.5      113.0           1.41   \n",
       "79     12.70        3.87  2.40               23.0      101.0           2.83   \n",
       "35     13.48        1.81  2.41               20.5      100.0           2.70   \n",
       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "111    12.52        2.43  2.17               21.0       88.0           2.55   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "156        0.83                  0.48             1.56             9.01  0.57   \n",
       "132        1.09                  0.27             0.83             5.70  0.66   \n",
       "51         2.99                  0.22             2.29             5.60  1.24   \n",
       "162        0.60                  0.60             0.96             5.58  0.87   \n",
       "128        2.45                  0.40             1.90             2.12  0.89   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "149        1.39                  0.34             1.14             9.40  0.57   \n",
       "79         2.55                  0.43             1.95             2.57  1.19   \n",
       "35         2.98                  0.26             1.86             5.10  1.04   \n",
       "2          3.24                  0.30             2.81             5.68  1.03   \n",
       "111        2.27                  0.26             1.22             2.00  0.90   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  \n",
       "156                          1.64    480.0  \n",
       "132                          1.36    560.0  \n",
       "51                           3.37   1265.0  \n",
       "162                          2.11    570.0  \n",
       "128                          2.78    342.0  \n",
       "..                            ...      ...  \n",
       "149                          1.33    550.0  \n",
       "79                           3.13    463.0  \n",
       "35                           3.47    920.0  \n",
       "2                            3.17   1185.0  \n",
       "111                          2.78    325.0  \n",
       "\n",
       "[142 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_all_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electric-election",
   "metadata": {},
   "source": [
    "## 1. KNN (k nearest neighbors) classifier\n",
    "The simplest possible classifier is the *nearest neighbor*: given a new observation `X_test`, find in the training set the observation with the closest feature vector. The model considers *k* nearest neighbors to make a prediction.\n",
    "\n",
    "![Example of kkn](https://www.kdnuggets.com/wp-content/uploads/popular-knn-metrics-0.png)\n",
    "\n",
    "This happens for each and every test observation and that is how it finds similarities in the data. For calculating distances KNN uses a distance metric from the list of available metrics.\n",
    "\n",
    "Please see the [Nearest Neighbors section](https://scikit-learn.org/stable/modules/neighbors.html#neighbors) of the online Scikit-learn documentation for more information about this type of classifier.\n",
    "\n",
    "Let's start creating the knn model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "renewable-symposium",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blond-words",
   "metadata": {},
   "source": [
    "Now, we use this model to predict the values of the wine' class (`y_predict`) using the testing set (`X_test`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "satisfactory-riverside",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 0, 2, 0, 1, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 1, 1, 1, 0,\n",
       "       1, 2, 2, 0, 1, 0, 2, 2, 0, 1, 0, 1, 1, 2])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = knn.predict(X_test)\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unsigned-think",
   "metadata": {},
   "source": [
    "Let's compare how good are the predicted values with respect to the real values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "intellectual-functionality",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 2, 2, 0, 0, 2, 0, 2, 1, 0,\n",
       "       1, 2, 2, 0, 1, 2, 1, 0, 0, 1, 0, 1, 1, 2])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "possible-belarus",
   "metadata": {},
   "source": [
    "We will consolidate all the results in one single table and check if the predictions are correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "central-raleigh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_real</th>\n",
       "      <th>y_predicted</th>\n",
       "      <th>is_correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     y_real  y_predicted  is_correct\n",
       "31        0            0        True\n",
       "101       1            1        True\n",
       "150       2            2        True\n",
       "53        0            0        True\n",
       "122       1            2       False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({'y_real':y_test, 'y_predicted':y_predicted})\n",
    "results[\"is_correct\"] = [i['y_real'] == i['y_predicted'] for k,i in results.iterrows()]\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nominated-psychiatry",
   "metadata": {},
   "source": [
    "Let's count how many are correct and how many are false"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "regional-meaning",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct predictions: 27\n",
      "Wrong predictions: 9\n",
      "Percentage of correct predictions: 75.0%\n"
     ]
    }
   ],
   "source": [
    "print(\"Correct predictions: \" + str(len(results[results.is_correct == True])))\n",
    "print(\"Wrong predictions: \" + str(len(results[results.is_correct == False])))\n",
    "print(\"Percentage of correct predictions: \" + str(round(100*len(results[results.is_correct == True])/len(results),2)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "clean-editing",
   "metadata": {},
   "source": [
    "Since we are using two dimensions, we can see visually how the model predicts.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "subsequent-conditioning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create color maps\n",
    "h = .02  # step size in the mesh\n",
    "cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])\n",
    "cmap_bold = ['darkorange', 'c', 'darkblue']\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "x_min, x_max = X_train.iloc[:, 0].min() - 1, X_train.iloc[:, 0].max() + 1\n",
    "y_min, y_max = X_train.iloc[:, 1].min() - 1, X_train.iloc[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                     np.arange(y_min, y_max, h))\n",
    "\n",
    "# Predict the areas according to the model\n",
    "Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.contourf(xx, yy, Z, cmap=cmap_light)\n",
    "\n",
    "# Plot also the training points\n",
    "sns.scatterplot(x=X_train.iloc[:, 0], y=X_train.iloc[:, 1], hue=y_train,\n",
    "                palette=cmap_bold, alpha=1.0, edgecolor=\"black\")\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.title(\"3-Class classification\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "contemporary-cooling",
   "metadata": {},
   "source": [
    "### Exercise 1:\n",
    "Now, it's your turn to create the model with all the features: `X_all_train`, `y_all_train` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cosmetic-nature",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run a new model. Remember to use X_all_train and y_all_train\n",
    "# knn = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "developing-villa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predicted value. Remember to use `X_all_test`\n",
    "# y_all_predicted = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "covered-accent",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the new result dataframe\n",
    "# results = pd.DataFrame({'y_real': y_all_test, 'y_predicted': y_all_predicted})\n",
    "# results[\"is_correct\"] = [i['y_real'] == i['y_predicted'] for k,i in results.iterrows()]\n",
    "# results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "criminal-agreement",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the new results\n",
    "# print(\"Correct predictions: \" + str(len(results[results.is_correct == True])))\n",
    "# print(\"Wrong predictions: \" + str(len(results[results.is_correct == False])))\n",
    "# print(\"Percentage of correct predictions: \" + str(round(100*len(results[results.is_correct == True])/len(results),2)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fatal-soldier",
   "metadata": {},
   "source": [
    "Which model is doing better? The one using two variables or all the variables?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "systematic-vertex",
   "metadata": {},
   "source": [
    "## 2. Decision Trees\n",
    "Decision Trees are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. A tree can be seen as a piecewise constant approximation.\n",
    "\n",
    "Decision trees learn from data and set *if-then-else* decision rules. The deeper the tree, the more complex the decision rules and the fitter the model.\n",
    "\n",
    "Here is a simple example:\n",
    "\n",
    "<img src=\"https://miro.medium.com/max/2000/1*jojTznh4HOX_8cGw_04ODA.png\" alt=\"drawing\" width=\"700\"/>\n",
    "\n",
    "Some advantages of decision trees are:\n",
    "* Simple to understand and to interpret. Trees can be visualised.\n",
    "* Requires little data preparation. \n",
    "* Other techniques often require data normalisation, dummy variables need to be created and blank values to be removed. Note however that this module does not support missing values.\n",
    "* Able to handle multi-output problems.\n",
    "* Uses a white box model. If a given situation is observable in a model, the explanation for the condition is easily explained by boolean logic. By contrast, in a black box model (e.g., in an artificial neural network), results may be more difficult to interpret.\n",
    "* Possible to validate a model using statistical tests. That makes it possible to account for the reliability of the model.\n",
    "* Performs well even if its assumptions are somewhat violated by the true model from which the data were generated.\n",
    "\n",
    "The disadvantages of decision trees include:\n",
    "* Decision-tree learners can create over-complex trees that do not generalise the data well. This is called *overfitting*.\n",
    "* Decision trees can be unstable because small variations in the data might result in a completely different tree being generated. \n",
    "* There are concepts that are hard to learn because decision trees do not express them easily, such as XOR, parity or multiplexer problems.\n",
    "* Decision tree learners create biased trees if some classes dominate. It is therefore recommended to balance the dataset prior to fitting with the decision tree.\n",
    "\n",
    "For more information, check the [documentation](https://scikit-learn.org/stable/modules/tree.html).\n",
    "\n",
    "\n",
    "### Creating a decision-tree\n",
    "Let's create a decision tree classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "sacred-acquisition",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "prescribed-amazon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct predictions: 27\n",
      "Wrong predictions: 9\n",
      "Percentage of correct predictions: 75.0%\n"
     ]
    }
   ],
   "source": [
    "y_predicted = clf.predict(X_test)\n",
    "results = pd.DataFrame({'y_real': y_test, 'y_predicted': y_predicted})\n",
    "results[\"is_correct\"] = [i['y_real'] == i['y_predicted'] for k,i in results.iterrows()]\n",
    "\n",
    "print(\"Correct predictions: \" + str(len(results[results.is_correct == True])))\n",
    "print(\"Wrong predictions: \" + str(len(results[results.is_correct == False])))\n",
    "print(\"Percentage of correct predictions: \" + str(round(100*len(results[results.is_correct == True])/len(results),2)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "further-venue",
   "metadata": {},
   "source": [
    "We got a high accurate model using a decision tree. We can visualize the rules created by the decision tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "controlling-extreme",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- alcohol <= 12.78\n",
      "|   |--- malic_acid <= 2.96\n",
      "|   |   |--- alcohol <= 12.49\n",
      "|   |   |   |--- class: 1\n",
      "|   |   |--- alcohol >  12.49\n",
      "|   |   |   |--- malic_acid <= 1.32\n",
      "|   |   |   |   |--- class: 2\n",
      "|   |   |   |--- malic_acid >  1.32\n",
      "|   |   |   |   |--- malic_acid <= 2.10\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- malic_acid >  2.10\n",
      "|   |   |   |   |   |--- alcohol <= 12.56\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- alcohol >  12.56\n",
      "|   |   |   |   |   |   |--- class: 2\n",
      "|   |--- malic_acid >  2.96\n",
      "|   |   |--- alcohol <= 12.10\n",
      "|   |   |   |--- class: 1\n",
      "|   |   |--- alcohol >  12.10\n",
      "|   |   |   |--- alcohol <= 12.61\n",
      "|   |   |   |   |--- malic_acid <= 3.50\n",
      "|   |   |   |   |   |--- malic_acid <= 3.10\n",
      "|   |   |   |   |   |   |--- class: 2\n",
      "|   |   |   |   |   |--- malic_acid >  3.10\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- malic_acid >  3.50\n",
      "|   |   |   |   |   |--- class: 2\n",
      "|   |   |   |--- alcohol >  12.61\n",
      "|   |   |   |   |--- alcohol <= 12.74\n",
      "|   |   |   |   |   |--- malic_acid <= 3.71\n",
      "|   |   |   |   |   |   |--- class: 2\n",
      "|   |   |   |   |   |--- malic_acid >  3.71\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- alcohol >  12.74\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|--- alcohol >  12.78\n",
      "|   |--- malic_acid <= 2.23\n",
      "|   |   |--- malic_acid <= 1.30\n",
      "|   |   |   |--- class: 1\n",
      "|   |   |--- malic_acid >  1.30\n",
      "|   |   |   |--- alcohol <= 13.02\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |   |--- alcohol >  13.02\n",
      "|   |   |   |   |--- alcohol <= 13.50\n",
      "|   |   |   |   |   |--- alcohol <= 13.48\n",
      "|   |   |   |   |   |   |--- alcohol <= 13.43\n",
      "|   |   |   |   |   |   |   |--- malic_acid <= 1.84\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- malic_acid >  1.84\n",
      "|   |   |   |   |   |   |   |   |--- malic_acid <= 1.94\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 2\n",
      "|   |   |   |   |   |   |   |   |--- malic_acid >  1.94\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- alcohol >  13.43\n",
      "|   |   |   |   |   |   |   |--- malic_acid <= 1.74\n",
      "|   |   |   |   |   |   |   |   |--- class: 2\n",
      "|   |   |   |   |   |   |   |--- malic_acid >  1.74\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- alcohol >  13.48\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- alcohol >  13.50\n",
      "|   |   |   |   |   |--- alcohol <= 14.32\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- alcohol >  14.32\n",
      "|   |   |   |   |   |   |--- alcohol <= 14.36\n",
      "|   |   |   |   |   |   |   |--- class: 2\n",
      "|   |   |   |   |   |   |--- alcohol >  14.36\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |--- malic_acid >  2.23\n",
      "|   |   |--- alcohol <= 14.18\n",
      "|   |   |   |--- malic_acid <= 5.73\n",
      "|   |   |   |   |--- malic_acid <= 2.43\n",
      "|   |   |   |   |   |--- malic_acid <= 2.33\n",
      "|   |   |   |   |   |   |--- class: 2\n",
      "|   |   |   |   |   |--- malic_acid >  2.33\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- malic_acid >  2.43\n",
      "|   |   |   |   |   |--- malic_acid <= 4.05\n",
      "|   |   |   |   |   |   |--- malic_acid <= 3.77\n",
      "|   |   |   |   |   |   |   |--- alcohol <= 13.58\n",
      "|   |   |   |   |   |   |   |   |--- class: 2\n",
      "|   |   |   |   |   |   |   |--- alcohol >  13.58\n",
      "|   |   |   |   |   |   |   |   |--- malic_acid <= 2.93\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 2\n",
      "|   |   |   |   |   |   |   |   |--- malic_acid >  2.93\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- malic_acid >  3.77\n",
      "|   |   |   |   |   |   |   |--- alcohol <= 13.07\n",
      "|   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |--- alcohol >  13.07\n",
      "|   |   |   |   |   |   |   |   |--- alcohol <= 13.16\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 2\n",
      "|   |   |   |   |   |   |   |   |--- alcohol >  13.16\n",
      "|   |   |   |   |   |   |   |   |   |--- malic_acid <= 3.88\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |--- malic_acid >  3.88\n",
      "|   |   |   |   |   |   |   |   |   |   |--- alcohol <= 13.32\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |   |--- alcohol >  13.32\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 2\n",
      "|   |   |   |   |   |--- malic_acid >  4.05\n",
      "|   |   |   |   |   |   |--- class: 2\n",
      "|   |   |   |--- malic_acid >  5.73\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |--- alcohol >  14.18\n",
      "|   |   |   |--- class: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "r = export_text(clf, feature_names=wine.columns[:2].tolist())\n",
    "print(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "endless-kansas",
   "metadata": {},
   "source": [
    "We can visualize the model as a decision tree. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "wrapped-vision",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAAIuCAYAAAC7EdIKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAACk3UlEQVR4nOzde3xU9Z34/9cnBJIQlQRIQCIglwAKtQIqXlDZGpRaBUVbbyutu+3udm337l6+3f3tdh/Vdtt1+92l+630YkXkIgqiWIhIaECjQAy1BAJoJDeiECj3kDEmef/+OGeGJOQylzPnzJl5Px+PecBkzpzP5XzOmc/5nM/FiAhKKaWUUskszesIKKWUUkrFm1Z4lFJKKZX0tMKjlFJKqaSnFR6llFJKJT2t8CillFIq6WmFRymllFJJTys8SimllEp6WuFRSimlVNLTCo9SSimlkp5WeJRSSimV9LTCo5RSSqmkpxUepZRSSiU9rfAopZRSKulphUcppZRSSU8rPEoppZRKelrhUUoppVTS0wqPUkoppZKeVniUUkoplfS0wqOUUkqppJfudQSUUpCVlXU4EAiM8DIOmZmZR1paWkZ6GQellIoXIyJex0GplGeMEa/PRWMMImI8jYRSSsWJtvAolaDWrl1LR0cHzc3NFBQUcOzYMdLS0pg/fz6ZmZkAiAjGnK+jHDhwgMrKSsaOHcuhQ4e49957KSsro7KykqysLAoKCigqKvIqSUop5Rmt8CiVoPLy8jhx4gQXXXQRHR0d5ObmkpGRAUBtbS27du1i2rRpNDQ0ICIUFRUxefJkampqmD59OvX19QBMmTIFEWH37t20trZ6mSSllPKMdlpWKkEdP36czz77DIC0tDSOHz8e+iw/P5+MjAza29tpa2ujra0NgMbGRnbv3s2SJUsYPnw4NTU17Nixg1mzZjF48GDS0/UeRymVmrQPj1IJoK8+PJs3bw49htq2bRs33HADAwcOjEcctA+PUippaYVHqQQQTqflhoYGRo8ezZkzZ2hvbycnJ6fL5+vXr6ejo4MFCxbwyiuvMGbMGPbs2cOsWbPYt28fY8aMYebMmX3FQSs8Sqmkpe3bSiW4ZcuW0dLSwpgxY3j22WcpKipi2LBhDBgwgLKyMvLz85kxYwYZGRkEK00NDQ3k5eWRn59PIBAIvVdKqVSlfXiUSnB2ywsAs2bNorKyErBGaLW1tdHe3g5AIBAgEAhQU1PDpEmTaGhoICcnh/r6+tB7pZRKVfpIS6kE0NcjrYqKCmpqarj//vvjHQd9pKWUSlpa4VEqAUQz8WBxcTHz5s3rc5vgvDxFRUUsW7aMq666ipaWlh6/pxUepVQy0wqPUgmgpwrP4sWLWbhwIYsXL6awsJCzZ8+SnZ1Neno6gUCA7Oxshg0bxqFDhxg4cCCzZ8+msLCQkpKS0Lw8YFWMMjMzCQQC3HjjjZSVlfHFL36xpzhohUcplbS007JSCWr8+PGUl5czcuRIJkyYEBqVVVxczJAhQwBob2+nsLCQ9PR0qqqqKCwspK2tLdTnJzgvz5VXXkl1dTUVFRXcddddXiVJKaU8oy08SiWASB5plZaWcv3114eWl3AwDtrCo5RKWlrhUcpjxloMq8Prc1ErPEqpZKbD0pXygLFcbYz5AVDT/fPa2lr279/f736WLl3Kzp07KS4uZvXq1V2+8+STT9Lc3MySJUvYvXs3AM3Nzfzyl7/k3Xff5ZlnnqG+vp5169Z1jtdfGmNGxZ5CpZRKLFrhUcpFxpgpxph/A/YBrwACzAerb05xcTFPPfUUR48epbS0lPfff5933nmHH/3oRwDU1dVRXFxMbW0tACNGjKC5uRkg9G/QsGHDCAQCDB06lOrqaoBQp+dRo0aRkZHBoUOHuj8amw7sNcb8xhjzp8aY4fHKC6WUcpNWeJSKM2PM5caYfzDG/BbYAgwBvgqMF5F/EpHdAOfOnWPo0KEMGTKE0tJS5syZw5YtW9i/fz9TpkwBoKOjg7a2Njo6OkL7b2ho4MiRI5w6dYqmpib27NlDIBAgLy+PTz75hE8//ZSJEyeyZ88eTp48SVZWFsYYPvvsM6644ooucRWRrwGXAv8N/AHwkTFmgzFmkTHmkvjnllJKxYf24VEqDowxlwJfAR4EJgJrgFXAWyLS3sP2Ec/DA10XFo3G6dOnOXDgANdee22PfXiMMRcBd2OlYw5QgpWO10XkXNQBK6WUy7TCo5RDjDHDgPuwKgfTgVexKgclIvJZP9+NqsLjpP46LRtjcoF7gIeA64BfY6Vvk4h86koklVIqSlrhUSoG9mOeBViVnNlAMVYlYKOIBMLdT1ZW1uFAIDAiPrEMT2Zm5pGWlpaR4WxrjMkH7sdK91RgHbASKBWRtrhFUimloqQVHqUiZIwZDHwJ68e+CCjFquSsF5GzHkbNE8aY0cCXsVp+xgAvYeXHOyLS0dd3lVLKLVrhUSoMxphBwO1YP+pfAnZi/ai/IiInvIxbIjHGTAQewKoM5gAvYuVThefP7JRSKU0rPEr1whiTjtVR90HgXqAK67HNGhE54mHUfMEYMw0r7x7EGn6/ClglIns9jZhSKiVphUepTowxacANWD/SXwYasH6oV4tIg5dx8yt7JumZWHn6AHCS85WfjzyMmlIqhWiFR6U8+wd5Bud/kE9jteS8KCLVXsYt2dgVyhs5X6Gs43yF8pCXcVNKJTet8KiUZYy5kvOPXNI43+qwx9OIpQj7keEfcP6R4R6sY/CyiDR5GTelVPLRCo9KKcaYCZzvVDsUq1PtSrRTraeMMRlYncIfxOoUvoPzncJPehg1pVSS0AqPSnrGmALOz3o8jvPDpst02HTiMcZkc37Y/22k+LB/pZQztMKjkpIxJo/zE+N9DmuhzlXAb3RiPP8wxgzBmtjxIay+PxuxjmNxJBM7KqWUVnhU0jDG5GAtffAgcD2wAevH8Q1d+sD/7JXbg0t3XE0ES3copZRWeJSv2Y8/gotb/gHWauQrgV+LSLOXcVPxY4wZhTXK60FgAl0XZ9XHlEqpC2iFR/mO3cF1HtaP3ReBd7F+7NaJyCkv46bcZ4wZx/mO6HnAaqxKb7l2RFdKBWmFR/mCPYT5NqwftQXAbs7PenzMy7ipxGGMuQKr8vMQMBB7qgGgUis/SqU2rfCohGVPUjcbq5JzP3AQ68frJRFp9DJuKrHZk0lezfl5lpo5P5nkBx5GTSnlEa3wqIRi/1Bdi/Uj9RXg91iVnBdF5KCXcVP+ZFecZ2G1+nwFaOR8mar3Mm5KKfdohUd5zq7kdF5osp3zd+NVXsZNJRdjzADgVqxythA4gFXWXhaRw17GTSkVX1rhUZ4xxhRyvpJzEef7W7yv/S1UvBljBgFFWC0/dwPvYZW/tSJy3Mu4KaWcpxUe5SpjzBisxwoPAQVYI2pWAdt1OLHyijEmC7gTq/J9O7ANq1y+JiJnvIybUsoZWuFRcWeMGcH5OVOuANZiPUbYKiLtXsZNqe6MMRdjjQR8ELgZ2IRV+dkgIi1exk0pFT2t8Ki4MMbkYvWReBC4Bngd60fjTRFp9TJuSoXLGDOU8+V4JrCe8+VYZ3dWyke0wqMcY98Zz8f6cbgFeBOrJUfvjJXvGWNGcr6lcjJWS+UqtKVSKV/QCo+Kid334YtYPwJ3AG9xvu/DaS/jplS8GGPGYvVFexAYRde+aHpRVSoBaYVHRcwYMxCYi3WxvxuowLrYvyIiv/cybkq5zRgzmfOzO2cBL2K1bP5OKz9KJQ6t8Kiw2POX3ML5+Us+4Pysxzp/iUp59nxSn+P8VAut2FMtiMh+L+OmlNIKj6eysrIOBwKBEV6EnZmZeaSlpWVkX9vYF/DrOT/r8SdYd66rRaQu/rFUyp/sc+c6rHPnAaCJ85Np1oazD6+uD+FcG5TyI63weMgY41mLtzEGETE9/N0An+f8XWoL5y/UB9yNpVL+Z7eOdl4TrprzraMf9/E9T64PvV0blPI7rfB4qLcL2tq1a+no6KC5uZlp06Zx9OhRmpqaWLRoUWgbEcGqm1jKy8s5dOgQ9957L8XFxZw6dYpBgwYxZswYZs6c2VPYoYuaMeYSYAWwB7gHGISuMq2U4+z+b7dhVX4WAL8FtgJDgb/uPPlmT9eHWK8NW7dupaWlBWMMGRkZzJkzp6c4aoVHJaU0ryOgLpSXl8egQYMYMWJEqLKSn58PQGVlJevWraOxsZHi4mLKysoAmD59Oh0d1rVy+PDh1NfX09DQQEtLWKPBS4EvYU0K+FVggoj8HxHZrZUdpZwjIp+JSLGIfA24FPgfrIrPXwC/6u/7sV4bpk+fjoiQn5/PZ5/pNEIqtWiFJwEdP348dDGqrq6muro69FlBQQFpaWm0t7fT1tZGe7s1/ceSJUsYPnw4NTU1pKWlUVhYyKRJk2hoaAgnyHlYHZL/TER2aCVHqfgTkYCIrAMWYU3t8Df9fSfWa8PixYsZNWoUTU1NZGZmxiFVSiUufaTlof6e0W/evJmioiIASkpKuO2225wMW5utlUpgfV0f9NqgVOS0wuOhcDolNjQ0MHr0aM6cOUN7ezs5OTldPl+/fj0dHR0sWLAg9Hz+yJEjFBQUUF1dzec+9zluuummnsLWi5pSCayv60Mk14WlS5cya9YsduzYwaxZs9i3b1+vffvscPXaoJJSutcRUD1btmwZLS0tjBkzhmeffZaioiKGDRvGgAEDKCsrIz8/nxkzZpCRkUHwojh9+nTKyspobm6mtbWVEydOeJwKpZSTorku5OfnEwgEQv82NDSQl5fncUqUcp/24UlQ9l0WALNmzaKyshKwRmB0fj4fCAQIBAJdns8PHjyY9PR0hg4dGuqsqJTyv2iuCzk5OdTX14f+jaBvn1JJRR9peaivJuuKigpqamq4//774xW2NlsrlcB6uj7E+7pgh6vXBpWUtMLjoWgmFisuLmbevHl9bvPOO+9w8uRJxo0bx3vvvcejjz7aU9h6UVMqgUV6fQjn2rB//37WrFnD7Nmzqamp4Wtf+1pP4eq1QSUl7cOTYBYvXszChQtZvHgxhYWFnD17luzsbNLT0wkEAmRnZ7NhwwYOHTrEwIEDmT17NoWFhZSUlCAiFBUVcfDgQS655BIuu+wySktLvU6SUsoBTlwbpkyZwtSpUxk+fDhbt271OklKuUorPAlm/PjxlJeXM3LkSCZMmBAafVFcXMyQIUMAaG9vp7CwkPT0dKqqqigsLKStrS30bH/SpEmUl5dTV1dHbm6uV0lRSjnIiWvDJ598wqhRoxgwYACFhYVeJUUpT+gjLQ9F0mRdWlrK9ddf79hkYdpsrVRiC/f6oNcGpcKjFR4PJeLioUqpxKCLhyrlLB2WrpRSSqmkpxWeBFNbW8v+/fv73W7p0qWsXr2aTZs2hf72zDPPUF9fz8aNG9m2bVvo708++STt7e384Ac/4PTp06xbty4eUVdKOcQYM7773yK5NuzcuZPi4mJWr14d+k5zczM//vGP+eCDDwA4fPgwzzzzDFu3buWnP/0pO3fuDGv/SvmVdlpOAMXFxQDs2rWLuXPnUlFRQSAQ4Ny5c5SVlfHEE09QV1fHvn37mDJlCpdffjkjRowgLS2Nzk3eeXl5tLS0MHXqVBobG0N/nzlzJgMGDODqq6/mkksu0UUDlUpQxpirgH8Eboforw3Nzc0AoX8BsrOzGTduHJMmTQJg5MiR5OXlkZ6ezvHjx0MzMdvx+A/gxyJy2L3UKxVf2sKTAM6dO8fQoUMZMmQIpaWlzJkzhy1btrB//36mTJkCQEdHB21tbV1mTj5x4gTGGLZv3w4Qmkl1yZIl5OXlhf6+d+9eGhoaKC8v59ixY+4nUCnVJ2PMjcaY9UAx8D4wHqK/NjQ0NHDkyBFOnTpFU1MTe/bsCX324Ycf8vvf/56PPvqIvXv3IiLk5eXR1tbWOUpZQJUx5qc9tTYp5Ufaadkjxpgrgb3R5n/n1ZIjcfr0aQ4cOMB1110HcJGINPf3HaWU84wxBqsl5/8Ao4EfAb8SkYD9eVSdlqO9NlRVVZGbm8uoUaMQEWOMyQf+EvhT4A3gByJSGfGOlUoQWuFxmTEmE+sC9+eZmZmDAoHAxV7EIzMzMxAIBI4Afy4iG7yIg1KpyBgzAFgI/BMwEPgB8KKIdG1iyco6HAgERrgdv8zMzCMtLS0jg++NMUOAPwP+CigHvi8i77odL6VipRUeFxlj5gBLgL3AX4jIIY/jczvwDLAD+CsROeJlfJRKZsaYQcAfAv8AHAe+D7wuIr5Y4dcYkwU8BjwB1AFPAW96NreGUhHSCo8LjDFDsZqr7wC+LSKveBylEGPMYOD/A/4Iq+XpWb9cgJXyA2NMNvAN4G+BKqyKzla/VhSMMQOBB7A6V3+KlZ5XRKTd04gp1Q+t8MSR/Yz+IeBp4GXgOyJy2ttY9cwY83ngZ1gXsD8RER2fqlQMjDG5wLeAbwPbsB4FVXgbK+cYY9KAu7EezeUA/wEsF5FWL+OlVG+0whMnxphxwE+BAuAbIrLd4yj1y+5b8OfAvwI/wbpAf+ptrJTyF2PMpcDfYLWavgr8MJlvIOwbuzlYFZ8pWDd4v9ABESrR6LB0hxlj0o0xf4fVua8UmOGHyg6AiLSLyGJguv163xhzi8fRUsoXjDETjDHBPnoDgatF5I+SubIDIJbfiMjtWJ2xbwEOGmP+xW7lUiohaIXHQcaYa7EqOncAs0TkByLymcfRipiINAD3AN8BVhhjfq4XLqV6Zoy5yhizAtgONAGTReSv7PMopYjIeyJyH1aLz3ig2hjzQ7vVSylPaYXHAcaYi4wxPwbWA/8F3C4iH3kcrZjYd21rgalAK7DXGPOA3XytVMqzJwt8HWuywN8CE0TkX0TkqMdR85yI7BORx7BaijOxrh/P6CSGykta4YmRMeZurCbsocA0EVnm19EXPRGRUyLyOHAf8C/A68aYsR5HSylPGMs8Y8xW4AXgdWC8iPwoUQckeElE6kXkL4DJwDFgpzFmuTHmcx5HTaUg7bQcJbuJ9n+w7mD+VERKPI5S3NnziPwdVofMp4D/6T5ZmlLJKNzJAlXfjDGXAN/EmsTwPayBEe94GimVMrTCEyF7KOY3gO9hDeP+noi0eBsrdxljCrEmUByCNYQ9aYbaKtWZXcl/FPh74PdYc878Wueqio09ieHXsPK1DitfNyVT67hKPFrhiYAxZirWD/0ArB/6lF1Xxu7Lswj4IVbT/r+KyFlvY6WUM+zJAv8EqzWzCqtFc5v+IDvLGJMOPMj5SQx/AKzVSQxVPGgfnjAYYzKNMf+ONcx8BTA7lSs7EOrUvBSYBuQDe4wxd3ocLaViYowZaoz5/4Aa4EbgHhG5Q0R8OzNyIhORNhF5AbgK+C52BdMY80d265pSjtEWnn50Wv9qD9b6V42eRihBGWPmYq3LVY61Ltdhj6OkVNiMMaM4P1ngOuA/ROSAp5FKQXbL8a1Yy9xcgTWJ4c91EkPlBG3h6YV9p/dLYBnwDyJyn1Z2eicibwKfw7oz3m2M+Ybd30mphNVpssA9QDrweXuyQK3seMBuOS61JzG8F5iNTmKoHKI/SN3Yw04fxhpqfg6YKiLrvI2VP4jIORH5J2Au8HWg1BhzhcfRUuoC9mSBK4EdpPhkgYnKnsTwfqwWn+Akhj/SSQxVtLTC04m9/tVGrA5094jIt3VujciJyO+w+j+sBt4yxvybMSbD42gphTHmpk6TBe7CmkNHJwtMYCKyv9MkhoPQSQxVlLTCQ2j9qyew5oUoBWaKyA5vY+Vv9rpcP+H8uly/03W5lBfsVtsvGmO2YT2i1skCfciexPAvsSYxPIo1ieEKY8xVHkdN+UTKd1q217/6GdYsoH/m9yUhEpHdEfFerIkaNwJ/LyInvI2VSnb2ZIH3Y7XYpmPN9bJaJwtMDvYkhn+GNYlhBTqJoepHyrbwGGMuNsb8X5Jo/atE1cu6XA/qulwqHowxGcaYrwP7gL8E/j+szsgrtLKTPETktIj8EKt/zwbgBWPMVmPMHXptUT1JyRYee/2rnwC/Af5ORI55HKWUYoy5AatVrQH4cxGp9TZGKhkYYy7i/GSBe7BadHSywBRhT2L4AFaL3mdYx18nMVQhKVXh6bT+1dVYj6+Sfv2rRNVtXa7vA/+td98qGsaYocC3gceBrcAPdLmT1GVPh3EX1rpnQ4H/AF4QkVZPI6Y8l/SPtOwOi1caY/4M2A18AFyllR1viUiriDwF3ADcidUBcabH0VI+YowpMMY8DVQDY4CbReTLWtlJbSLSISKvYY0U/VOsVp+PjDF/ZS8ZolJU0ld4sJo3K7DmhfmCiHwn1Rb7TGQi8iFQBPw3sMEY87wx5v94HC2VwIwxE40xPwMqsda1+7yI/LFOFqg66zSJ4R3APcBNQI1OYpi6kv6RljHmLay1nr5lzwasEpQxJg9rbpTLsCqnv/E4SiqBGGOuxrqBKQL+H/A/2v9ORcIYMwVrhfYFwLPAf4nIJ97GSrnFlQpPVlbW4UAgMCLuAXWSmZl5pKWlZaSbYarY2T9qdwDPBy9EbpYfLTddeX3uGmPuALKAb2DN5/RjYInOn6NiYYwZDfwtsAh4CXgLeE9E9ge38brsK+e5UuExxrg+UMIYg4jo0MQk4Gb50XLTlZfnrjHmHmAtcBz4DrBURAKuRkYlNbtV+a+wBlC0ApeJyCn7M/3dSjLpbge4du1aOjo6aG5upqCggGPHjpGWlsb8+fPJzMwEQEToPI3CgQMHqKysZOzYsRw6dIh7772X6upqysvLycjIYODAgdx9991uJ0V5xKkytGbNGqZOncqUKVO8Soovdc7/adOmcfToUZqamli0aFFom+75X1FRwdGjR5k3bx5vvfUWZ86c4c477+wvqBbgVWC9iDwbj7So1CYiR40x3wXagClAj0PYY7nmFBUVsWzZMr797W+Hrjk7duygoKCA3bt38+ijj5KXl+dCapXrFZ68vDxOnDjBRRddREdHB7m5uWRkWMss1dbWsmvXLqZNm0ZDQwMiQlFREZMnT6ampobp06dTX18PQENDA7m5uQwePJjKykq3k6E85FQZmjlzJoGANhhEqnP+z5w5k+LiYvLz8wGorKzko48+4pprrmHPnj1cfPHF3HTTTaHtAAoKCti1a1e/4YjIG8Ab8UyLUvZw9X/ta5tYrjnvv/8+hYWFwPlrTnNzM62treTn59PSomNo3OL6KK3jx4/z2WefWYGnpXH8+PHQZ/n5+WRkZNDe3k5bWxttbda0LI2NjezevZslS5YwfPhwampqOHbsGHV1daSlpTFp0iS3k6E85FQZqq6uprq62pM0+Fnn/O+ehwUFBaSlpYXyv729vct2NTU1HDp0iEsuucSTuCsVjViuOWfPnmX//v1drjmDBw8mPT2dnJwcGhoaPElTKvKsD8/mzZspKioCYNu2bdxwww0MHDjQyTD1WWiS6O1ZejzKkJabrvrqx9A5/0tKSrjtttucClOPgfKc/m4lH887LTc0NDB69GjOnDlDe3s7OTk5XT5fv349HR0dLFiwIPT88/Tp0xw/fpyqqqpen39qwUkevZWfSMpOsM9Xa2srBQUF1NTUcPvttzN27NjuYWm56cSpczfYb+fs2bPk5OTQ3t5OdnY2t9xyS09h6jFQnnO67B89epSCggI+/vhjLrvsMr7whS/0FKaW/ThyvQ9P0LJly2hpaWHMmDE8++yzFBUVMWzYMAYMGEBZWRn5+fnMmDGDjIwMgoWu8/NPQJ9/pqhoyk6wz9fBgwdpbW2loaFBy06Uosn/YL+dnJwcRISpU6fS2NjocUqUikwsZT/YbycvLy/Uj1C5y7OZlu2aLACzZs0KdTwWkS7P/gOBAIFAoMvzz4aGBpqamvT5Z4qKpuwE+3wFn51PnjxZLzpRiib/g/12Tpw4gTGGJUuW6MgU5TuxlP3gtQfQkaEe8eyRVkVFBTU1Ndx///3xClObBpNE9/ITz7Kj5aYrPXdVqtKyn3w878MTxzC14CQJnXjQO3ruqlSlZT/5JOTiocH5Ovry+uuvU1ZWxoEDB3j55ZddiJXyg3DKTrDMlJeX88orr7gQq9QRTv63t7fz1FNP8cYbb/DWW2+5ECul4i+csr9v3z6WLVtGRUUFa9ascSFWqjNPOi0vXryYhQsXsnjxYgoLCzl79izZ2dmkp6cTCATIzs5mw4YNHDp0iIEDBzJ79mwKCwspKSkJTeqUl5fH2bNnQ5M7qdTgRNnpaRJCFR4n8n/nzp1MnTqV+vp6Lr/8cq+TpFRYnCj7l112GaWlpYwePZo9e/Z4naSU40mFZ/z48ZSXlzNy5EgmTJgQGtJXXFzMkCFDAOsusLCwkPT0dKqqqigsLKStrS3UYSw3N5eqqqrQ5E7z5s3zIinKZU6UnWCZ+eijj5g2bZpXSfElJ/L/3LlzVFVVceONN/LRRx95lRSlIuJE2a+rqyM3N5e6uroLhrKr+EuoPjylpaVcf/31obVJYgxTn4UmiXDKj1NlR8tNV3ruqlSlZT/5JFSFx+EwteAkCe207B09d1Wq0rKffDzptFxbW8v+/fv73W7p0qWsXr2aTZs2hf62c+dONmzYwIoVK7r8/Yc//CGnT59m3bp18YiySiCxlJ9nnnmmS7+dTZs2sWzZMp5//vm4xDXZRJL3b7zxBqWlpTzzzDOUlZWFPnvyySdDk4c2Nzfzy1/+krq6Oj13VUKLpOwvX76cffv2hf62ceNGtm3bBlhz9qxZs4Zdu3bx5JNPcurUKVatWhW3eKvzXOvDE+zBvmvXLubOnUtFRQWBQIBz585RVlbGE088QV1dHfv27WPKlClcfvnljBgxgrS0NDrXspubm0lLS2Po0KGhaenb2tpobW1l0KBBjjQrqsTjVPnJy8vrMsNyXl4ekydP7nJxUl1Fm/f5+fkcO3aMEydOdNnfsGHDaG1tJTs7O9TpMzMzU89dlXCiLfvvv/8+V1xxRWg/nWcWN8Ywfvx4srOzGTZsGB0dHdqfxyWutfCcO3eOoUOHMmTIEEpLS5kzZw5btmxh//79oVknOzo6aGtro6OjI/S94Mys27dvBwjNshwIBGhra2PPnj2hO/ampia3kqNc5lT5ycnJob6+PvS+rq7ugvW0VFfR5n1TUxODBg1i6NChdHR0sGfPHgKBAHl5eTQ2NrJnzx5OnjxJVlYWn376qVfJU6pX0Zb9yZMn09DQELrOBGcW3759Ox0dHSxfvpzs7OzQuaDckdB9eDqvTBuO06dPc+DAAa699lp9FppE3Cg/wdW+tdx0peeuSlVulf3W1lZ27NjBzTffrGU/zhK6whNjmFpwkoR2WvaOnrsqVWnZTz6u9OHJzMw8YowZ4UZYncN0MzwVP26WHy03Xem5q1KVlv3k40oLTziMMeuAdSLyXC+fXwusACa5Xu1WCc0YMxN4ESjsrWwYY4qBX4nIi65GLgUYY34EfCoi/9zL53nAh0CBiDS7Gjml4sgYcxPwM2BaH9eeUuD/isg6F6OmepAQa2kZY3KBPwDW9rHZe4AA17gSKeUnjwDL+6kIr7C3Uw4yxgwAHsLK3x6JyFHgHWCBW/FSyiUP0/+1Z7m9nfJYQlR4gPuATSJyurcN7AK1Ai04qhP7B/dBYGU/m64DbjXGDIt7pFLLzcAxEanqZzs9d1VSMcYMBL5M/9eeNcAdxphL4h8r1ZdEqfA8Qh93iJ2sBB60f+SUArgV+ERE+pwRzK5Mv4FVuVbOeRjrDrY/rwI3G2OGxzk+SrmlCKgWkT5XrxaR40ApcK8bkVK987zCY4y5DPg8sLG/bUXkANAIzIlztJR/PEx4lWXQVgZHGWMysCqQ/U4TKyJnsM7x++MdL6Vcotcen/G8wgM8ALwiIoEwt9e+GAoI/eAuxOqwHI6NwOeMMaPjF6uUcgewR0Qawtxez12VFIwxg4G7gZfC/Mp6YJbbo75UV4lQ4XmY/p+BdvYicI8xRuehV18EdovIoXA2FpFPgVewKtkqdpGeu8XAlcaYMXGKj1JuuRvYLiJhDSMXkXNYlR699njI0wqPMWYKMAr4TbjfEZFG4H2sHzuV2sLtP9KZNi07wBhzMdY5GO4dLiLSitWB88F4xUspl0TyOCtoBdaIRuURr1t4HgJWiUh7hN/TH60UZ494uAPrBzQSW4GRxpgr+t1S9WUBsE1Efh/h9/TcVb5mjBmK1Y90XYRf3QxMMMaMdzpOKjyeVXiMMYboaslg/cjdbowZ4myslI/cA5TaIyDCZleuV6E/urGK9tzdBgw3xkx1OD5KueV+4I2+plHpiYh8htUiqq08HvGyhecarIkE34v0iyJyAtiCDvNLZeFOZdCT5cDDdqVbRcgYkw/cCLwW6XdFpAOr349WOJVfRfMoPWg58Ihee7zhZYXnYWBFDMtEaNN4irJHOszC6gQYjV1AG3CdY5FKLV8Gfh3DMhEr0Aqn8iF7hOfnsDrgR+NdIAu4yrFIqbB5UuGJYHbcvrwOXGuMGelMrJSPfAVYb498iJjO2h2zaB9nBb0PBIDrHYmNUu55AFhrj/iMmH3t0RZOj3jVwjMHaLQnEoyKiLRgNal/xalIKd+I9QcXrIvOA8aYdAfikzKMMeOAScCmaPehFU7lY05ce1YADxljvB40lHK8ynAnCg3oRTPl2CMcJmCNeIiaiHwANGAtWqvC9yDwkt0BMxYrga9ohVP5hT2ycwRWx/uoicge4CRwkwPRUhFwvcJjTxh4L+HPjtuXEmCcMWaCA/tS/vAQsNqBH1zQCnM0HLlZEZFqoBa4LdZ9KeWSaKdR6YleezzgRQvPncD79gSCMRGRNmA1OswvJdidXGMZndVdcNbuLIf2l9SMMZ8DLgHecWiXetFXvhDjNCo9WQV82RgzyKH9qTB4UeFxstBg70uH+aWGq7BGOLzrxM5E5GOsEVt3OrG/FPAwsNIeWu6E1cB8rXAqH7gWaMe6XsRMRGqB/cDtTuxPhcfVCo89UeBcIp8dty/bsX4E/83BfarEFPzBjXYqg56sBf7SXuZE9cLuYPkQDt6siMgnWCO2fqwdOFWiMsaMAv4Za3SWk9cebeF0mdsXmXuBLfbEgU65BBgJ/KWD+1QJJh4/uLYJwM3AbIf3m2xuBM4ClU7t0G6VvRL4E0A7L6tENRlrsdArHd7vauBOY8xFDu9X9cK1Co89O+ujOPyDJSKnsPp1vOXkflXC+TJw0h7h4KR/xCo7jv2QJxt73bI/IraJQi9g7+sOYKe9sKhSiegA1qOsP3RypyJyDCgD/khbON1hnG2h6yMgY/4b+BbwRyKy1JVAVVIwxgwGTgEfAVc43Kys+mGM+TqwBPhPEfkHr+OjVLIwxryN1T/oqljmpVPhcbNW2QYcAV51MUyVBOwZlT8DfqKVHU80Ay3A//M6Ikolme9j/Q4f8joiqcC1Fh6wntnrD5aKhpYdb2n+KxUfem65x9UKT7SysrIOBwKBEW6Fl5mZeaSlpUXX6EoSbpYfLTtduX3ugh4DZdGyp7rzRYWncwV47dq1dHR00NzczLRp0zh69ChNTU0sWrQotL2I0HlanoqKCo4ePcq8efN46623OHPmDMYYsrOzee+993j00UfJy8vrHB4iovP6JIlg+elcdgoKCjh27BhpaWnMnz+fzMxM4MKyc+DAASorKxk7diyHDh3i3nvv5ZVXXmHMmDHU1tYydepUSktLufPOOxkzZoyWnW56O3cjzf9x48ZRW1vLfffdx/r16+no6CAnJ4eWlhbmzZvXPUw9BoruDSfR/Ha89NJLTJw4kenTp/Pb3/6W0tJSZsyYQUtLC7W1taHzvlOYWvYSWFh9eLKysg4bY8TNV1ZW1uGe4pKXl8egQYMYMWIEM2fOBCA/Px+AyspK1q1bR2NjI8XFxZSVlQGEtgMoKCjg3LlzTJ06lYEDB5Kfn09LS0tsuaj65Gb56a3cQNey09HRQW5ubqjs1NbWsnbtWj788ENKSkrYvNlaqmvy5MlcdNFFTJ8+nY4Oa769hoYGWlpaQuUqLy8voctQMuT/6NGjOXv2LAAZGRlkZmYyffp0/HDDphJDNL8dnc/tq666KlT5EZGwzvtEOfeUJawKTyAQGCEi9PWqr69HRDh9+jQnTpy44PPXXnuNdevWXfD/3l69NUUeP36czz6zllGqrq6muro69FlBQQFpaWm0t7fT1tZGe3t7l+1qamo4dOgQl1xyCUuWLCEvL4+cnBwaGhpizkjVu/7Kj5Nlp68m7M5lJy0tjePHj4c+y8/PJyMjI1R22traAGhsbGT37t0sWbKE4cOHU1NTw6RJk2hoaAiVq5ycHOrr6+Oci9FLhvyvq6sjJyeHmpoaAoEAgUCAxYsXM2rUqDjnnkoW0fx25ObmUl9fT01NDe+99x7XXnttqNyFc94nyrmnLGE90uqrT9WyZctoaWlhzJgx7Nixg6KiIoYNG0ZBQQFlZWXk5+czY8YMNm3ahIhwxx13dPl/H2GGmgZ7Cn/z5s0UFRUBUFJSwm23ObcGoTZLOqu38hOPstPTsesefueys23bNm644QYGDhzoVFoTrux4mf/9nbtO539PcVCpqbdy7+Zvh9fXPtVVzLOb2pkMwKxZs6isrGTOnDmISJeaciAQQERCd2ixNkUHCyzApEmTADhz5gzt7e3k5OR02Tb4zH/BggVUV1dTXl5Oa2srBQUFXfaj3JUIZWfcuHEMHDgworKTk5NDdnY2t9xyS0zx8JrX+d/Q0MAtt9zCmTNnaG5u7jPvt27dSktLC3v37uXP//zP2bBhA+PGjWPGjBkxxUWlnqKiIhoaGhg9ejTXXXcdJ0+e7LPsde/3WVtby2WXXcYXvvCFqOPg1bmX6mKu8FxxxRXU1NRc0HEQ4K677gr9f/78+aH/jxs3LtZgga615GeffTZUSx4wYECXWnJGRkaooDQ0NJCbm8vBgwdpbdXJXb3k17Jz5ZVX0tjY6Eg8vORV/keT99OnT6esrIyZM2dijGH8+PFkZ2fHHBeVWqIpewUFBezatYvrrruOxsZG8vLyYn6E7eW1L5XFPPHgzJkzuf/++52IS8R6qiUDPdaSA4EANTU1HDt2jLq6OgYPHkx6ui7f4yW/lp1g/y+/8yr/o8n7YL+J6upqPvjgA5YvX64VHhWxaMpe936fAFOmxLbWsJfXvpTWV4eq4MvaLHwbN27sd5u2tjZ58skn5b333utxezvMPsN/77335KWXXooobuHoHLa+Yn/Fo/xUVlbKCy+8cMHfezp2PYWfSmUnHvlfVVUlzz//fL/pdzPve4uDvlLzlQhlz+trn766vqLqtLx48WIWLlzI4sWLKSws5OzZs2RnZ5Oenk4gECA7O5thw4Zx6NAhBg4cyOzZsyksLKSkpAQRoaioiHfffZempiYWLFhAcXFxn3Np9NVpOh6085ez4lF+AF588UUeeOCB7mFdcOzcLD+JWHbikf9nzpzhhRde4Jvf/Gb3sLqk3+1zt6c4qNSUCGXP62uf6iqqR1rjx4+nvLyckSNHMmHCBG699VauueYaDh8+HKzV0t7eTmFhIRMnTqSqqgqgy3DTc+fOUVVVdcHwwFgVFxf3u82BAwd4+eWX2bx5M6tWrXIsbBUeJ8rP6tWrGTBggONxS4Xy40T+19XVkZub63jcwsn/9vZ2nnrqKSoqKlizZo3jcVCpKZyyt2/fPpYtW0ZFRUVY23eXyNe+VBDzsPTOSktLuf7660OzpsYUsV5aeJyqIRcXF5OWlobIhcP8tKbsLDfLT38tPPEuP4lYdrzM/3i2Dt9www1s3LiRr371q33GQaWmeLds9vdkoqc49CZe1z7VlaO9dufMmePk7nrUvYYcHE5YXFzMkCFDgPM15PT0dKqqqigsLKStrS1Ugw5OZjZjxgyOHj0a9zir8Gj58ZZf8j/YOhyc/E2pcDhR9oItm04/mXDj3FNRtvDU1tYSCAT67am+dOlSsrKyyMnJ4fbbbwdg586dHD9+nIqKCr7zne8A8Nlnn/GLX/yCm266iaqqKh588MGI+/DEq3VJxc7J8rNx48Yuc+A899xzTJw4kYMHD7Jo0aKo+/A4VX4Ssew4mf9vvPEGGRkZoQt0f/nvdeuwSl2JUPZiOffy8/N7vNbNnj27y/u+rn2qq7BbeILPK3ft2sXcuXOpqKggEAhw7tw5ysrKeOKJJ6irq2Pfvn1MmTKFyy+/nBEjRoSa/YOam5uBrutbNTU1ccstt3Do0KGo79i0hpzYnCo/U6dO7TIHzqhRo2hubg6tiROtZC8/TuV/fn4+x44dC73X/Fd+F++yF+25132+r+C51v19rOdeKgm70/K5c+cYOnQoQ4YMobS0lDlz5rBlyxb2798fqq12dHTQ1tYWWmQR4MSJExhj2L59O2BN3tbU1MTevXtpaGhgz5495Ofns23bNi699NKwI15bW8v+/fv73W7p0qWsXr2aTZs2hf62fPly9u3bF3q/c+dOiouLef7558MOX0XGqfITnAsj+L6xsTHi+VhiKTtvvPEGpaWlofiuWLGCTZs2JXznZafyv6mpiczMzKjzP5K8D+b1M888E1rM8cyZMyxdupSPP/4YgKNHj/KTn/yEpqamqDqRqtQRSdkL/iaANXXLmjVr2LVrF3C+DL7zzjv8/Oc/5/Tp06xbt67X/UV77vV2rYvl2pfqwm7hWbhwIQDXXXdd6G/dm+XGjRvXZTbI9PT0C4bOLVq0qMv70aNHA/DNb36T1tZWTp061WscnLpLbWho4Iorrgi9D9aataYcP06VnyeffBKAiRMnAvDYY48B1po4fYlHC0daWhpDhw7llltuCVWCEpVT+d+9g384+R9t3gfz+sSJE6F9XXzxxVx66aVkZWUB1mrWra2tDB06NOy8UKkj2rLXuSWl+8zewTI4YcIEqqurGTx4cJ+Pw6I993q71gWFe+1T58U803JfIl2natCgQdx88829fu7UXerkyZNpaGi4oNVJJZZIyk9/CwDGq4UjEAg48uw/ETmV/9HmfVNTE4MGDWLo0KF0dHSwZ88eTp48yZYtW6iurub3v/89n3zyCTk5OaFVsJXqLNqyF/xN2L59Ox0dHSxfvpyMjIwuZTA9PZ329vY+b9Kj5eS1T3USzuyEmZmZhwFx85WZmXk4GD4RzlbZ2Ztvvhn2tps3bxa7h5mEky/6Cu/lZvnpXG6Cr2jLT7hl59NPP5Vt27aJSGKWHS/zP955H3TkyBHZvXu3iCTmMdCX+y+3yt6pU6dk586dIlag0jkOXl/79NX1FdYoLa/pTMsqFqk+07KXEmG2W5WatOyp7nyxemZmZuYRY8wIN8NzKywVf26WHy07Xbl97gbDdDM8lZi07KnufNHCEw5jzDJgu4j8by+fLwLuE5EF7sZMJRpjzFeBvwOuFZFAFN8fBGwHfioiP3c6fqnGGPOvQK6I/FUvn98I/BK40vVbdpXUjDH5wAdAgYg097LNe8A/iIj2Dva5uHZadosxZjBwF/BSH5utA+YYY3Q4RwozxowH/hN4JJrKDoCItAKPAE8ZYwqdjF+qMcYY4GFgRR+bvQtkAp93JVIqlXwZeL23yo5tBVYZVT6XFBUeYD5W606vQ61E5DTwBnC/a7FSCcUYkw48D3xfRHbHsi8R2Qf8G/CCMWagA9FLVTOAAUB5bxvYrTr6o6Pi4RH6rmwDvAjca4xJzuGYKSRZKjz93SEG6UUztf0j8Cnwfx3a3/8DjgP/4tD+UtEjwIowHlWtAB4yxiTLNUt5zG7tLQTe7Gs7EWkEfgvc6Ua8VPz4/uJhjBkG3Ir1yKo/G4GrjDGj4xoplXCMMdcBfwF8VUQ6+ts+HPaP9GPAn9j9TFQEjDEDgAeBlf1tKyJ7sSqXs+MdL5UyHgReEpFwJnHSm+Uk4PsKD3AfUCwiZ/rbUEQ+BdZiFXSVIowx2cALwLdE5JCT+xaRw8CfAsuMMZc4ue8UcCtw2H48GI4VWC1CSsUkzL5jna0F5hpjhsQvVirekqHCE0mhxd72oTjFRSWmp7H6eK2Ox85F5FWgBOcelaWKSM/dVcB99ig5pWLxOeBi4J1wNhaRE8AWYGE8I6Xiy9cVHvvR1OeASFYN3AqMNMZc0e+WyveMMXcDdwDfinNQfwPcbIy5L87hJAVjTAbWj8eL4X5HROqAfcDt8YqXShkPAysjfLytN8s+5+sKD/AAsNZ+VBUWEWnHulPUgpvk7EnHfgYsskfpxY2InAX+EPh/xpiCeIaVJL4I7BaRhgi/p30pVEzsju8PEVnrIsDrwLXGmJHOx0q5we8VnkibxINWAA/bz3FVErKP7bPAsyLylhthisgO4H+BX+loon5Fe+6+BNxpjLnI4fio1HEjcAaojORLItICvAZ8JR6RUvHn24uy/UhqBLAtiq9XAB3AdY5GSiWSP8MqH991OdynsPoGfNvlcH3D7tx9B/BypN8VkWNAGaAzpqtohTsVQk+0hdHHfFvhwSp0q+xHVBGxC/pytOAmJWPMFODfsWZTbnUzbBFpw3q09c/GmGluhu0j9wClInI8yu/ruauiYnd4v58wpkLoRQkwzhgz0blYKbf4ssITxZDCnqwEHrBn31VJwr6gLQf+RUQOeBEHEfkI+Adgud05V3UV67n7GnCTMSbPofio1DEX+EBEaqL5sn1DsxrtA+pLvqzwYD2KagN2RbsDEfkAaAD+wKlIqYTwb8AnwBKP4/EroBp40uN4JBS7I/n1wPpo92F3EN+ALhOjIhdrZRv7+49oH1D/8WuF52GifwbbmT6PTSLGmJuxZj7+Y69X1bbD/1Os5RBu8zIuCebLwHoRORfjfvTcVRGxJyD9En0vMh2O7UAGcHWscVLu8l2Fx34E9QCx19LBmgPkHmNMlgP7Uh6yZ0BdBnxDRI54HR8IdbD9I6xRW7lexydBOHGHDbAJuMIYM9aBfanUMB94t69FpsOhi9n6l+8qPFiPoOpF5MNYdyQiH2M9FtNF4fxvMbBRRF73OiKdicgbwCvAM6neBG4v1jgR2BzrvuzO6C+jy8So8DlV2Qarn6AuZuszfjxYThZa0Jq67xljHgBmAX/ndVx68Y/ANHQdqIcIf7HGcOi5q8JiLzJ9C+EtMt0vEakCjgE3O7E/5Q5fVXjsR0/3EMF09GFYCxQZY65ycJ/KJfbyIouxhqA3ex2fntgTlj0C/NgYc7nH0fGE3br1CM7erLwNDDXG3OHgPlWSsbtBfIMwF5mOgFa4fcZXFR6sR08VIvKJg/vMxBrxtcHBfSoX2OtWlQD/T0Te8zo+fRGR94EfA5uNMak4pPUqYDDwroP7TAPagddS/XGh6tPnsSYgzXF4v7qYrc/4rcLj9B0iwGGsVXADDu9Xxd+3gEKgyuuIhGkPMJ74L2SaiIIjKyNZrLFP9pwozwMtXo/KUwntENZv3VNO7lRE6rGuPfOc3K+KH+OX64Qx5j+AvwLmikg0y0n0t3+jF01/McYsBI7FozzEizHmRmC0iDj5WDahGWMew3rs+Nci8vM47F/PXdWneJURY8xSrMkM54rIXqf3r5zlp1mGC+1/4zLkWC+Y/iMia72OQ6RE5B2v4+CBMViPs2IeWdkTPXdVf+JYRj4EFmF1i1AJzk8tPJOAi0Qk6tmVlVLus0fIXC0iJV7HRSmn2X0J12rFO/H5psITi6ysrMOBQGCEW+FlZmYeaWlpGelWeInOzfxPxLxP9fTHwu1zF5IvD/1Kj71yWkpUeNx+xG+MQUR01IjNzfxPxLxP9fTHwovuOcmWh36lx145zZU+PInYwrJ27Vo6Ojpobm5m2rRpHD16lKamJhYtWhTaRkToPNq1vLycQ4cOce+997JmzRqmTp3KlClT4peQJBZN/ldUVFBbW8t9993H0qVLmTVrlq/yv3OaCwoKOHbsGGlpacyfP5/MzEzgwjQfOHCAyspKxo4dGyp7ZWVlVFZWMm7cODIyMti1axePPvooeXnxWTw8kVqoYsnDcePG+br8pLpYr9nV1dWUl5czdOjQsM+bRCr7KnauDEsPBAIjRIS+XvX19YgIp0+f5sSJExd8/tprr7Fu3boL/t/TK5wCmpeXx6BBgxgxYgQzZ84EID8/H4DKykrWrVtHY2MjxcXFlJWVATB9+nQ6OqxRtcHvqOhEk/+jR4/m7NmzoW0DAX/NJNA5zR0dHeTm5obSXFtby9q1a/nwww8pKSlh82Zr9YXJkydz0UUXdSl7U6ZMYdq0aeTn5/PZZ5+Rn59PS0tL3OLd3/nr5rkbSx76vfykuliv2Q0NDaHyEu55k0hlX8XO83l4li1bxs9+9jP27t3Ld7/7XXbv3s3hw4c5c+YMxcXF7Npl9VHOyMgI3cF1/n+0jh8/zmefWTPcV1dXU11dHfqsoKCAtLQ02tvbaWtro729HYAlS5YwfPhwampqLviOikw0+V9XV0dOTg41NTXk5ORQX1/vSdyj1TnNaWlpHD9+PPRZfn4+GRkZoTS3tVmDPhobG9m9e3eXsrdjxw5mzZpFU1MTmZmZ5OTk0NDQ4Hp6vDh3Y8lDv5efVBfrNfvYsWPU1dU5ct549bulYuNKH56+nsW+8MILNDc3M3astehxbW0tc+bMYdSoUWzbto0RI0Zw7bXX8tprryEiXHXVVVRWViIiLFiwoLfwujyH7Sv8zZs3U1RUBEBJSQm33XZbTGntKfxU52b+J2Led09/5zRv27aNG264gYEDBzoVluPp7+34eXnu+i0PVeTCOfZOXbM7hRlW+XOj7CvneV7hqaiooKamhvvvv9/J8MKu8DQ0NDB69GjOnDlDe3s7OTk5XT5fv349HR0dLFiwgLfeeoszZ86Qnp7OkSNHOHr0aI/PgLXgdtVb/keS91u3bqWlpYWhQ4dy/PhxqqqqfJP3TqQ/2P9gwoQJHD9+nNOnT3PVVVdd0AfFzQqPn87dYJ+7+vp6jhw5QiAQ4Pbbbw/9YPUVB+UNp4598Npx5MgRLr30Uo4dO8bw4cO5/fbbewozrPLnRtlXzvN84sGZM2d60h9m2bJltLS0MGbMGJ599lmKiooYNmwYAwYMoKysjPz8fGbMmEFGRgbBAl9QUMCuXbuYMGECkydP5q233opr34lkFU3eT58+nbKyMpqbrfVB491vJZ6iSX+w/0Ew/cF/veSnc3fmzJkEAgHy8vKYPHkyv/zlL31bflJZrNeOtrY2hg4dyi233BJTPLwq+yo2nvfh6U1xcXG/2+zZs4fly5dHtX+7Ng3ArFmzqKysBKxe/p2fAQcCAQKBADU1NRw6dIhLLrmEuro6xo4d61nfCb+LJu8XL17MqFGjaGhooKmpydd5H036g/0PGhoaOHLkCKdOnaKpqcmzNPQlnHP3wIEDvPzyy1HtP5r8C/b5CJ67kydP1j48PhTLtWPw4MGkp6cTCATi1pcm3mVfxcazR1qLFy9m4cKFLF68mMLCQs6ePUt2dnaoQGZnZzNs2DAOHTrEwIEDmT17NoWFhZSUlCAioWe4L774Ig888ED38PptloxHk2Rv4ae67vmfannv9/R3j79T525xcTHz5s3rHpan525PcVDeSIRj72XZV87zrIVn/PjxlJeXM3LkSCZMmMCtt97KNddcw+HDh0M1+Pb2dgoLC5k4cSJVVdaC2J1HX6xevZoBAwZEFf7MmTN7PWnCqaWXlJTw3HPPRRV2qusr7yG8/N+/fz9PPvmkk9FyjRPp37dvH8uWLXMyWmFz4twNjpyKRqznbnt7O0899RTl5eW88sorUcVBeSPWYx88bzZv3syqVasiDt/rsq9i43mn5e5KS0u5/vrrY2pydKOWvnHjRg4ePMjjjz/eb/ipLl53SevWreOee+7pHlbC5X080n/mzBleeOEFvvnNb3YPy7VOy9355dx99913aWpq4ktf+hKvvvoq9913X59xUN6I53kzYcIERIQ77rije5hhd5rvLB5lXznP807L3c2ZMyfuYXSvpQd7+BcXFzNkyBDgfC09PT2dqqoqCgsLaWtrC9Xi09PTycrKintck5ET+f/JJ58watQor5IQEyfSX1dXR25urldJ6JFfzt1z585RVVXFoUOHmDZtWtzjrJzh5HmTlpbG0aNHHYubG2Vfxc6zFp7a2loCgUC/U7svXbqUrKwscnJyQsMIV69e3eX9xo0byc7Opra2lkWLFnlaS7fD05p6J6l+l+T39HePfyzn7tNPP83jjz8eSuNzzz3HxIkTOXjwYEKcu3aYCVeGUlEiHHsvy75ynqstPMFnrLt27WLu3LlUVFQQCAQ4d+4cZWVlPPHEE9TV1bFv3z6mTJnC5ZdfzogRI0hLS6NzocvJyenyfurUqTQ2NoamGY+W1tK9ler5n8jpd+rc7T6Ud9SoUTQ3N+u5q6IW72Of6GVfhc/VTsvnzp1j6NChDBkyhNLSUubMmcOWLVvYv39/qMbc0dFBW1tbaP0TgBMnTmCMYfv27T2+X7JkSdwWTlRKOXfuBoeHB983NjaSnZ3tfoKUCpOW/SQifSyM5tTLCiY6b775Ztjbbt68Wez2R+kv/JqaGtm3b1+/+3zuuefkxRdflDfeeCP0txdeeEGqqqpC7zds2CBbt26VpUuX9hh+qr+6538sef+f//mf0tLSEnr/s5/9TGpraxM6751Mf7CsBX3ve9+TTz/9VFauXBm39Ed7/sbj3I0k74qLi+U3v/mN/PSnP5W3335bRETa2trk+9//fmi7pqYmWbx4sRw5ckQ2btzYYxz05b/zZseOHaHjKWKdJ0GnT5+W5557TsrKyuRnP/uZnDp1Sl555RUR6b/8hcuJsq8v518J12m5u+CInHD0t6aKU02TDQ0NXHHFFaH3Tj1SS2bxahZuaGigpaUl4fPeqfQHy1rQzJkzGTRo0AVT6ycCp87daPMuPz+fY8eOceLEidC+BgwYwNVXXx16n5eXR2trK0OHDo08gSruoj323Wci73zduPjii7n00kuZMGEC1dXVDB482PGJCJ383VLOceWRVmZm5hFjDG69MjMzj/QUD6eaJidPnkxDQ4M+UotAvJqF/TJjrlPpD5a14Pu9e/fGfcZpN8/fns7daPOuqamJQYMGMXToUDo6OtizZw8A5eXllJeX8/vf/55PPvmEnJyc0CrcKrFEe+yDM7J3P0/27NnDyZMn2bJlC+np6bS3t3Pq1Klew/e67CuHed3E5MYLjx+ppfor2vxPlryPd/o//fRT2bZtW8KmP5aXG2VHROTIkSOye/duEUm+PPTry61jf+rUKdm5c6eI6LFP9lfCP9Jygl1LH+FmeG6F5Qdu5n8i5n2qpz8Wbp+7wTDdDE/1TI+9cpor8/D4gTHmp0C9iHy/l89/ACAi/+hqxFKAMWYg0AhcLyIHe/jcAAeBe0Tkd27HL96MMVcCm4CxItLew+ejgfeBUSLyqcvRSzjGmB8DBcADEsUFzBjz18D9wK0i0uZ0/FT8GGMuBX4L3Csi70bx/Yvs7/+TiOgKnikmYVdLd5MxZhDWBXBlH5utAB4yxmieOa8I+Kinyg7YbcxW/j/saqzc8xCwqqfKDoCINACVwLyePk8lxpi5WOfqn0VT2bH9N3AO0JsXH7FvfH4FLImmsgMgImeBPwT+1xhT4GT8VOLTH2/L7cABEantY5tK4AxwoysxSi0PY1Vo+pKUFU77Ih5u+h+Jf4wSlzFmGNYP3tdE5Hi0+xGRDuBrwLeNMdc5FD0Vf48DOcD3YtmJiOwAfgL8KtmuJ6pverAt/f7g2HeTy0neVgZPGGMGA3cDq/vaTkT2AieA2W7Ey0WzgM+wmtn7sga4wxhzSfyjlHjsiuES4EURKYl1fyLSiPUD+oL9mEMlMPux778Cj4qIE0Pqvg9cBPyFA/tSPpHyFR77Yvcl4KUwNl8FfNnuc6KccTewXUTC6SyYjI+1HgZW9Pd4RkR+D2wF7nEjUgnoq8Ak4DtO7dDuw/EO8LRT+1TOM8ZkYN1s/pOIfOjEPu2+W48C3zHGfM6JfarEl/IVHmA+UCYi/S6dKyI1wAdYj8CUM8J5nBO0Crjf7nPle8aYdOAr9N13rLNkrPD1yxgzHvgR8IiIBBze/V8Ac40x8x3er3LOvwN1wC+d3KmIfAT8PbDcGOPszIMqIWmFJ7IfXOxtH4pTXFKKMWYoMAdYF872IlIH7CN5KpxfABoiuGtdD1zv9lBdL9mVwmXAUyJS6fT+ReQ01p3+EmPMSKf3r2JjjJmD1cn4GzF0Uu/Lc8CHxNgvSPlDSld4jDHDgZuBVyP42kvAXcYYXfUtdvcBb9g/OuFKps67D2M11YdFRJqB17FahVLFP2KNqPrveAUgImXAL4Bf2n2FVAIwxuQAS4E/DqcFPhp2JepPgAeNMbrGQ5JL6QoP1vDWjSJyJtwviEgT8C5W3xMVm0hb18CqcH7R7x1NjTFZwALgxQi/mjItjPYIqm9jjcrq6G/7GP07kA98M87hqPD9L/CaiBTHMxC7f9wfYY3a0kXVkliqV3ii+cGFFO1L4SRjzGXAVcDGSL4nIseAMqy+V372JaBCRD6J8HtvAoV2v5akZVdoXwAet0dUxZU98ucR4LvGmCv6217FlzHmYWA68A9uhCcim4C1wDPaype8UrbCY4wZA0wForl7WAfcas8LoqLzAPBKlDMHJ0OFM6rKtv3D/BLwoOMxSixPA++4ORuuiHwA/DPWUPWk6BjvR8aYscD/Bf5QRM65GPQ/AVdi9RlSSShlKzxYPxhrRKQ10i/aj8CKsR6Jqeg8QnSta2D1uZpt98HyHWNMLnAb1h1lNFYAjyTrnag9Ymou3syR8jPgY+DfPAg75RljBmD123laRHa5GbaItGBdl/7LGHO5m2Erd6RyhecRIugw2gOdhDBK9iODEVjzykTMnh5+A/BlJ+PlooXAZhE5GeX33wGysR4JJhV7pNQSYFGEndkdYXdi/WPgMWPMLW6Hr/hbwAD/6UXg9lp9/wEssytfKomkZIXHGDMNGAa8FcNuioFp9sKOKjJ9rh0VJj8/1oq27xgQWhphJf5Nf4/sFqtfAr8Ukbe9ioc9MOEbwPPGmCFexSPVGGOmA3+HVdmN5doQq//Cmv3clf5Dyj0pWeHB+sFdGcvID/tR2BqSvy+FoyJYO6o/m4Ar7Of9vmGMGQXMwGqhikUyri32TayRUt/1OiIi8jrWMfqJ13FJBfYSM8uBv7Ln2/KM/bvwVeCvjDHXeBkX5axkuliGxcEfXPB3K4NXrgXagZiez9sVzpfxX4XzAWCd3V8gavYkfKeAmxyJlcfsx5zfxZpN2Ym1kpzwd8C1xhi/lTE/+g/gfRFx4rocMxFpwJoSYbnOuZY8Uq7CA1wPBID3HdjXW0CevbCdCk9Ya0eFyY8VTqcq2+DP9F/AHhH1AvDP9kiphGCPEPpD4H/00XX8GGO+iDXNxONex6UzEXkR2IlH/YmU81KxwuPYD679nHkVKTIRXKzsToAPEP7aUf15Gxhq98lKeMaYScBo4DcO7TJZ1hb7N6yRUT/zOB4XEJH3sIZIP59kjw8TgjEmD2uW66+JyAmv49ODb2FNdHqX1xFRsUupEziKxRrDsQJ4OFmHCDvsD4BDTt3Fd+q865cK50PAi/ZKzTHrtJjtXCf25wVjzM3AY1jLB8RjrSQn/AcwEPgbryOSTOxr5s+A5SLi1E2Ao0TkFLAI+HkqrWGXrFKqwoM190mtiFQ7uM9G4BKgxMF9Jh1jzASshfo+cnjXvqhwGmMewPrBrHF41759rGWM+SbWo6xv2COjEpLdkvso8A/GmO/aN04qBnbn/cXAOOBfPI5On0RkG9a1a6Wut+VvqVbhcbL/RFAAqxPuGIf3m2xagAKg1uH9/s7e9/UO79dpo7Aqxvsc3u9LwJf81rHSrqD+X6z5hN70Njb9s1vTPsT6cZ7qcXSSwVeBP8eajyqa2dbd9grWAIEfex0RFb2UqfAYY/4YuBdrtWnH2E2eM7AWn1O9EJGPga+IyD86vF/BGj78VIIPUX8W+KKIvOHkTu2WkfeBpT6cKO0A8JBPfvDAauWpwHq8pWLTijVj+ve8jkiY3gO+AzR4HREVPZO4j82dZYwpwepDcouXk5op5xljXsJa5uMGEdnudXzcZLeUNGLNXD3QhVXFlVLKl1LpWfQrQLFWdpLSH2I9GqnyOiJuExGxl0D4D63sKKVU71KmhScWWVlZhwOBgCs99DMzM4+0tLSMdCOscGn6Nf2pmH43091ZquZBIqUbvDn+iZYHyUYrPGEwxrg2YtYYg4gk1IgjTb+mPxXT72a6u4WbknmQSOkGb45/ouVBskmlR1qOWLt2LR0dHTQ3N1NQUMCxY8dIS0tj/vz5ZGZmAiAidB4lfeDAASorKxk7diyHDh3i3nvvZenSpcyaNYt9+/YxZswYZs6c6VWSotI5H6ZNm8bRo0dpampi0aJFoW2650NFRQW1tbXcd999rFmzhqlTpzJlyhQvoh+TWNNeVlZGZWUlzc3NPP7446Fyk8hiKffjxo0Lpb26upry8nJaW1spKChg9+7dPProo+Tl5XmVtLDFkgdFRUUsW7aMb3/727z11lucOXMGYwzZ2dnccos/FmWPptyXl5eHrnnBdA8YMICMjAx27drlm2MfFE0enDx5MnTs169fT0dHBydPnmTWrFm+vP75mS9GaWVlZR02xohbr6ysrMO9xSUvL49BgwYxYsQIOjo6yM3NJT8/H4Da2lrWrl3Lhx9+SElJCZs3bwZg8uTJXHTRRUyfPp2ODqubRX5+PoFAgIaGBlpaYlpWyROd8yFYWQvmQ2VlJevWraOxsZHi4mLKysoAGD16NGfPngWIqILn5vHv69g7lfYpU6Ywbdq0sPMgEdIfS7nvnPaGhgZyc3Npbm6mtbWV/Px835T/WPLg/fffp7CwEICCggLOnTvH1KlTGTjQPwO+oin3na95wXTn5+fz2Wef+erYB0WTB52PfUZGBpmZmaHrv3KXLyo8gUBghIjQ16u+vh4R4fTp05w4ceKCz1977TXWrVt3wf97evX13Pb48eN89pm1tmFaWhrHjx8PfZafn09GRgbt7e20tbXR1mZNqNvY2Mju3btZsmQJw4cPp6amhpycHOrr65k0aRINDf4b6dg5H6qrq6muPj+XY0FBAWlpaaF8aG9vB6Curo6cnBxqamou+E5f+jv+bh17p9K+Y8cOZs2aFXYeJEL6Yyn3ndN+7Ngx6urqGDx4MOnp6eTk5Pim/MeSB2fPnmX//v3U1NRw6NAhLrnkEpYsWeKr1o1oyn3na14w3U1NTWRmZvrq2AdFkwedj30gECAQCISu/8pdvujD09ez1GXLltHS0sKYMWPYsWMHRUVFDBs2jIKCAsrKysjPz2fGjBls2rQJEeGOO+7o8v9ewuvyHLWn8Ddv3kxRUREA27Zt44YbbnDkbi0Rn+H2lf+d86GkpITbbottItKe0t9b+G4c+77CdzrtkYbvRfrjVe77Ct8r4Rz3VMyDeJR7O6yESTe4e93rFGZC5UGy8X0fHruAADBr1iwqKyuZM2cOItKllh0IBBCRUC071opesLA3NDRwyy23cObMGZqbm8nJyemyXfCZ7YIFC0L9FyZMmMDx48c5ffo0V111lW+f4zY0NFBUVMSZM2dob2+/4KSPd9q9OvZBncvAbbfdFsqHvspAsO+WE8fci/QH0wwwbtw4Bg4cGFa6g8d/wIAB5OTkICJkZGQwZ86cqOPilWjO/eD/Ozo6GDNmDEOGDKG8vJyHHvLLMnDndS4DkyZNAnC97Htt8uTJgJXunh5Nd0731q1baWlpoba2ljvvvJN9+/b5tuz7ne8rPFdccQU1NTXMmzfvgs/uuuv8Arfz588P/X/cuHExh9v57vrZZ58N3V0PGDCgy911RkZG6Aemc/8FIPSv3yRK2r069kHR5IOTz+79WPbT0tIQEfLz8zl27FjMcfFCNOkP/v/AgQPk5eVx+vRpcnNzPU5J9Lwu+16JJt3Tp0+nrKyMvLw8WlpafF32/c4XfXj6MnPmTO6///4ufysuLu73e6+//nqoU1k0erq7Bnq8uw4EAl36LzQ0NHDkyBFOnTpFU1PCrpnYq0RJe0/HHsI7/u3t7Tz11FMxhR9NPjj57D6W9B84cICXX345qnBjOf4nTpzAGBPqx+FH0aQ/+P9gn71gfviV12XfK9Gke/HixYwaNSqUfj+Xfb/zZR+exYsXs3DhQhYvXkxhYSFnz54lOzub9PR0AoEA2dnZDBs2jEOHDjFw4EBmz55NYWEhJSUliAhFRUXs2LGDs2fP9vjsNZw+PBUVFdTU1PT4gxNjWhPuGW739Mcr7XZY/fZhceL4v/vuuzQ1NbFgwYKIww+KRz64lX6wKkbdW4fCCd/t4+8VN497t3BTMg8SKd3g7nW/U5gJlQfJxpePtMaPH095eTkjR45kwoQJoefGxcXFDBkyBLDu4AsLC0lPT6eqqorCwkLa2tpCtfPc3FyqqqJfiWDmzJm+mzvHKV6n3Ynjf+7cOaqqqi6o8ETCq3xwIv3B0UM9PQ7rj9fH32upnn5I3TxI1XQnjd6GpybSy4pm/37zm99IS0tLWNv2xQ4v4vCDNm7c2O82VVVV8vzzz/cbdiK8vE6/m8c/lvCDwkl/ZWWlvPDCC46GnwjpDyft+/fvl5deekl27twpa9eu7Td8r17xLPdvvvmmrFy5ssdtkj0PIin7yZr21157TV588cWEz4Nke/myhac3bvV6D+eRwoYNG/p8pHDZZZdRWlrqSnydlqjp99PxnzZtGnv37nU0Xm6k34m0T548mZqaGqZPn+6rPh1OlvvgxIV+k6hl3w1Opr2lpcV3ky4mg6Sq8LjFiUcKdXV1vrzggabfifSvXr2aAQMGeJWEqDn5OO2jjz5i2rRpXiUlYk6W+7S0NI4ePepVUqKmZd+ZtA8aNCg0A7Vyjy87LYM1lXsgEOh3ToelS5eSlZVFTk4Ot99+OwDPPPMMd955J2PGjAGsQpiTk8Phw4dZtGhRWJ2We1JaWsr1118fUw/8ROy05nX6u4cfy7EPHuvg+507d3L8+PHQejiRdFruzg/pf/rpp7us3/Xcc88xceJEDh48GFP6nUi7HVbClH83j3u3cFMyDxIp3eDN8U+0PEg2vmrhCQ633bVrF3PnzqWiooJAIMC5c+coKyvjiSeeoK6ujn379jFlyhQuv/xyRowYEZr7Iyg4H0JQcCK04Joo0Ur1iaTimX6njn3wWAcF5wOK9diDP9LfvcPlqFGjaG5u1rIfg1ROe1Aq50Eqp91vfDUPz7lz5xg6dChDhgyhtLSUOXPmsGXLFvbv3x+62+3o6KCtra1Lc2Fw7o/t27cDhOZDCL4Pfh6J2tpa9u/f3+92S5cuZfXq1WzatCn0t6effjo0AZeIsGbNGnbu3MmqVasiioNXvEi7U8e++/uGhoaI5wNyKv0ATz75JK2tra6lP7j+T/B9Y2Mj2dnZ4SeeyNK/du1a1q9fH/pbKpX9jRs3sm3bNp5//nm2bNkS+uy5557j7bffDr0PtwwkgkjS372s+zndEFna33jjjS59FJ988snQ/8+cOcPSpUv5+OOP+fd//3eamprCmjtLxc5XLTwLFy4E4Lrrrgv9rXuz/rhx47rMJpuens4DDzzQZZvuc+8EPy8pKekz/HjcZRtjGD9+PNnZ2RdMy55IvE67U8e++/tFixYB3hz74PtBgwa5lv6vf/3rXd4/9thjQPzSP3jw4NDkbMH0BiV72b/yyitpbGwkLy+vS+fsYKtaULhlwCvRpn/s2LFd9uO3dEP0ae8+m3Lncn/xxRdz6aWXkpGRQVZWliOtyyo8vmjhyczMPGKMIZrX3Llzw962qKgIYwyZmZlHeopHPO6yOzo6WL58ecR32W7zMu3RHv9EP/YAe/fu7XfFaD+nPy0tjUmTJqVk2e+8GvqUKVMuaFWLpAx4Kdr099aa6Jd0Q/RpD86m3D2te/bs4eTJk2zZsoX6+npaW1s5deqUJ2lLSV6Pi/fDiwjnY+jszTffDGu7Tz/9VLZt25aQ8zBEm/5I0y5WYJr+BEizpt+d8z4o2fLA78fejbQHHTlyRHbv3i0iiZcHyfby1SMtr9h32CPcCsuNcCKh6df0p2L63Ux393DdDrM3qXrswZvjn2h5kGx8MSzdD4wxPwJaReQ7vXz+GvCyiDzvbszcYYwpA54UkQ09fJYOHAJmi0i165GLM2PMRUAjMFFELphcxRgzBfgNcJmItLsdv3gzxtwI/ByYJj1cUIwxjwF3i8hC1yPnAmPME8AkEflGL5//AtgvIv/pbszcYYx5BXhVRJ7r4TMD7AX+WETedTtu8WaMGYB1bZsjIgd6+Dwf+AAoEJHm7p8rd/miD0+iM8akAQ8BK/rYbAXwsDsxcpcxZhwwCXizp89FpA1YjZVHyWg+UNZTZQdARPYDHwNz3IyUix4GVvRU2bGtBW4zxuS4FyVXPUzqnvu5wBeAV3r63C4TSZt+4A+Axp4qOwAi0gS8i3WNUB7TCo8zbgZ+LyJ9zZe+HrjeiyZyFzwIvCQin/WxzQrgERPp+H9/6O8HD5L0om+MGQh8BVjZ2zYicgrYDCRdC48x5kogH9jWx2ZbgRHGmCvciZWrFgJv2se4NyuBr9gtvckmZc99P9IKjzMeBpb3tYHdnLke+LIrMXKJXYF5hP5P+h3AIGB63CPlImPMcGA2sK6fTVcB9xpjnJmON3EUAR+JyMF+tluBVU6SzUPAyr4eVdqfrSI5Wzj7PfdF5COgBqusJA37XL4H69j2ZR1wszFmWLzjpPqmFZ4YGWMGAffRf6GH5Kzpfw64GHinr42SuGn7fmCjiJztayMRaQTeB77oRqRcFM4dLsCvgenGmFFxjo9r7Mp+uOlfATycTC2cxpgC4Grggn57PUjGc/9O4Lci8nFfG4nIGaAY61qhPKQVntjNA6pEJJxlnzcDE40x4+McJzcF+2+EsxLecuAhu6NfsgindSsoqVo5jDGDgbux+mf1SUQCWHe6D/SzqZ/MAj4DfhvGtruAduDauMbIXQ8Ar9jHtj8vAnfbZSZZhFvZhSQ79/1KKzyxC7vQ231cXiJJmrbD7KwdIiL7gCasPk++Z4wZC1wBvBHmV9YAc40xQ+IXK1fdDewUkXCH0ibbXX5/nbVDOrVwJtOPXiTXviPATuCuuMbIJfY5PBerQ344ioGpxpgx8YuV6o9WeGJgjLkY6xHFyxF8LZk6794InBGRyn63PC+ZLvoPAmtFpDWcjUXkBNbw9GTpvNtv37VufgNcZoyZFKf4uMbugNtnZ+0eBDvv+r6F0z6GBUBpBF9LpnP/XmCLfU73y75GrCFJbnb9Sis8sVkAvCUix/rd8rx3gcHAVfGJkqsi/cEDq6/TQmNMRhzi47Zo0r+CJLjoGWOGYg2z73E4ck/szrsvkgTpxxqKXS8iH4b7BRH5AGvOlj+IW6zc8zCwKsJ5pV4B5thD2f0uksdZQUlx7vuZVnhiE3Ght/u6rMTnTfv2cOQvE15n7RARaQD2AHfEI15uMcZMA4YBb0X41deB64wxI52PlavuAzaJyOkIv5csnXej+cGDJHisF2Fn7RC7rGzCKju+ZZ+712Kdy5F4CxhujJnqfKxUOLTCEyVjTB5wE/BaFF9fgdV518/5Pxf4QERqoviu7y/6nB+OHE5n7RAROYdVZr4Sl1i5J9of/HJgADDD2ei4xxiThdW6+2IUX38RuMfn0xPMBAzwXhTfTYZz/yvAayLSEsmXknx6Al/w8w+u174M/Lq/4cg9sfu8nMKqMPlVtD94YPV5+qLdB8p3or3D7cTXF31jzGXA54GNkX43SaYn+BJQISKfRPpFewjzb7GGNPtV2J21e7ARuNoe0u5XMZ/7SdDC6Uta4YleLIUefHzRN8ZkY422eCma74vI77Fmpl3gZLxcdD0QwJpXJxqbgXHGmAmOxchdDxL+cOSerAQe9HHnXSfOfV923rWP2YNE1lk7xC4zr+DT6QmMMROBcUBJlLv4LdCKNaWBcplWeKJgjLkcmEz4w5F7shK435640G/mA+/a68REy7cXfezOylHe4SbD2mLRdNYOsacnOALc6liMXGKvB3Yb1oibaK0Biny6ttgc4BN7fbho+fncfwh40T6HI2ZfM5bj05tdv9MKT3QexFr5vK+1o/okIrXAAay+MH4T6x0uWP1YbrBXE/aNKIcj98SX0xPY60GNxFofKhZ+beFcCGzuZ+2oPonISawWgnudipSLnDj3S4FRxpgpsUfHPREso9OflcADSbq2WELTCk90nDjpwYcXfXs9mFvof+2oPtlri/0a/60tdhtQY68PFIvtQCZWXxg/eYjIhyP3xK/TE6TyuZ+JVUmLprN2iI87716NtR7gjlh2IiLVQC3W1AbKRVrhiZAx5nNADlDmwO5eAr5k94nxi/uBYnt9mFj57qKPQz94fuy860Bn7RAROQTsxkdrixljLsUaoRTO2lH9+TVwjb1Pv/gi8Dv72MXKj513Y+ms3Z2vzv1koRWeyEU1HLknInIUa9HN+THHyj1O3eGCNSfHJGPMOIf2F1f2cOT5hLF2VJj8Nj3BtUAHUOHQ/vx20X8AWBfpcOSe2Pt4FX9NT+DkuR8c0n6NQ/uLq0iX0QnDamCBfU1RLvHLhTYhOHmH24lvLvrGmNHANKx1YWJm94F6GatPlB/cBZSLyGEndiYie4HjwGwn9ucCJ+9wweq8e4cx5hKH9hdvqXzuXwLcTmydtUN82MJ5M3BMRKqc2Jk9pcF7WFMcKJdohScyNwDNWE3xTnkVuNnuG5PogmtHfergPv100XP6Bw98kn5jzP3Ao1iPYhxhT0+wFbjHqX3GizGmEBiDtR6YU7YCE4wxTzm4z3i5FygVkeMO7nMFPpiewBgzHvguUcw71Q9fnPvJRCs8kXH6Dhe7L0wxVt+YRBfTcORelAGX2H2jEpa9/s8XiGDtqDCtwh/TE3wFGAo4ParOL0N0YxqO3It84GLgDx3cZ7w4fu6LyAGgEWuoeyIbjTWFgtMrna8FbvPp9AS+pBWeMBljZuLMcOSerAC+lsh9WYwxDwB5RL52VJ86rS32LWPMECf37RR73bC/Bt6MZThyT0SkDtiHlf5E7sC5HnhSRBxr4em03+uNMQm7tpo9fPpRHG7ds9eVewjrsW7CMsbMA64j8rWjwrEC+JMEn56iEquj+mNO7tS+lmwG/tq+xqg40wpP+P4XGILzd7gAI7AuKH8bh3075VdAFtawTKeNA76O9Zw8EY0H/g9weZz2fynwQ6zRfwlJRJaJyD/HYdeXYK2t9Vwc9u2U72OVUcfPfRFZKyJ/4/R+HfYckI51rJw2BmtqioSddV1EjovIl0SkNQ67vxz4DtY1RsWZVnjCdxh4kxjnYOjFC8B+4Ggc9u2UT4D/cGKESg/+DmttscY47NsJ9cA54PE47f9vsNJ/Mk77T1h2B/BfYp1fieoI1qKnTvfh8ItPgF841Vm/m+9h5e+ROOzbD/4c69pS73VEUoFxsDuKUkoppVRC0qmtlUpyWVlZhwOBwAg3wsrMzDzS0tIy0o2w/EDz3jtu5n2QHoPEpi08SiU5Y4ysWbOGjo4OmpubKSgo4NixY6SlpTF//nwyMzMBEBE695s+cOAAlZWVjBs3jtraWu677z6qq6spLy9n6NChZGRksGvXLh599FHy8vKCYSEiidz52lXGGBER1q5dG3X+FxUVsWzZMr797W/z1ltvcebMGerr67nzzjvZsGEDd955J2PGjNG87yaY9511Pg7Tpk3j6NGjNDU1sWjRotA23Y9DRUVFqPyXlZVRWVnJ+PHjOXLkCI8++mj3MPUYJLCUaOFxu6bfvZbv9V2ehp/a4QPk5eVx4sQJLrroIjo6OsjNzSUjw1rGqra2ll27djFt2jQaGhoQEYqKipg8eTI1NTWMHj2aPXv2ANDQ0EBubi75+fkcO3aM/Px8Wlr67tblVfq9uMPvHoegWPL//fffp7CwEICCggJ27dpFXl4eLS0toX9743XZ8zr87jofh5kzZ1JcXEx+vtUXvbKyko8++ohrrrmGPXv2cPHFF3PTTTd1Kf9TpkxBRMjOzmby5Mn9xinR0p/qUqLTciAQGCEi9Paqr69HRDh9+jQnTpy44PPXXnuNdevWXfD/3l7dC7ib4fd0cmn4qR0+wPHjx/nss88ASEtL4/jx8/PH5efnk5GRQXt7O21tbbS1WVPNNDY2snv3burq6sjJyaGmpoZjx45RV1dHU1MTmZmZ5OTk0NDQkDDnX+f09xduPMLv7RjEkv9nz55l//791NTUcOjQIS655BJycnKor68P/ZtoeZ8o4fd1HKqrq6murg59VlBQQFpaWug4tLdb6+N2Lv87duxg1qxZ1NXVMXbs2P6CS7j0p7qUaOHpzbJly2hpaWHMmDE8++yzFBUVMWzYMAYMGEBZWRn5+fnMmDGDjIwMRKym0c7/1/A1fL+Ev2CBNep38+bNFBUVAbBt2zYGDBhAZmYmX/qSNcP9FVdcEfpOQUEBf//3f99lP+PGOTdVlOa/d/mfSnnfWefjMHHiRL71rW9RUlICwNChQ5k/31rWsHNl5tprrw39P5j/99xzT0zx8Dr/U1VKtPD0xn7eCsCsWbOorKwErGe4nWv4gUCAQCBATU1N6P8avobvx/CDP7ZgXbwHDhzImTNnOHny5AXbrl+/nldffRWArVu3UlxczNq1a1m/fj1Lly5l8+bNUccDNP9vueUWDh+2RnqHcwzeeustNmzYwMaNG9m2bVvUcYDUzPvOgo+jzpw5w8yZMy/4vKd8f+ONNygtLeW//uu/OHo0thlEvE5/qkqJTss9dV4DqzNaTU0N99/v7KoO3TuuuRl+T53mNHwNv3P4ne8ud+zYEbq7LCgo6HJ3uWnTJkSEO+64g9OnT1NWVkZ2djaVlZUYY7j88su58847Ezb9vYUbz/DDjUM0x+DgwYPs2rWL6667jsbGRm644YZew+30t4Qqe26G31PYseT7hAkTOHbsGEeOHOGWW25hzJgLV5pItGu/6iqlKzxxDC+sQu9G2Bq+ht89/BdeeIHm5uZQs31tbS1z5sxh1KhRbNu2jREjRnDttdfy2muvISJcddVVrFixgrvuuoszZ87Q0tJCY2Mjo0aN4vbbb484/HiKpMLjZRyiOQYNDQ0EAgG2bt3KY489xsSJE3sNt9PfEqrsuRl+T2HHku8iwuDBgzl16hS5ubncdNNNUcUhXrTCEwbpo0NVsrysZIZv48aN/W6zf/9+eemll3r8zA4vruFXVlbKCy+80G/YyRp+b/mv4fcf/nvvvddr2Y1VIqU/nuV+9erVsmvXrh63CScO8TgGiZT3iRB+T2HHs+yHG4e+hJP+zZs3y69+9at+w9bXha+U7LS8ePFiFi5cyOLFiyksLOTs2bNkZ2eTnp5OIBAgOzubDRs2cOjQIQYOHMjs2bMpLCykpKQEka5DRr0Kf9q0aezduzdlw/c6//0c/syZM3vst+AWr9LvZLnvbzh4f7w6Bl6XPa/DT4ay39raSnNzs2dp8LOU7LQ8fvx4ysvLGTlyJBMmTODWW2/lmmuu4fDhw8FaOe3t7RQWFjJx4kSqqqoAehwy6lX4q1evZsCAASkbvtf57+fw+1JcXNzvNgcOHODll1+moqIirO278yr9Tpb73NzcPoeDRyuc/Hz99dcpKyuLav9elz2vw+9LOHm/Z88eli9fzubNm1m1alXEYTiR/vT0dLKysiIOW2kfni5KS0u5/vrrQzOfxhAeEsVzXCfCj+U5uoaf/OGHc4c5bNiwPu8wwfpxmDdvXujfREy/2+d9OHFwIv937NjB2bNnue2223oNt9PfEqbsuR1+PPIe4MUXXyQ3NxcRq1NzJHFwM/3qQin5SKs3c+bM0fA1/KQOv/sdZk5ODmBVYIYMGQKcv8NMT0+nqqqKwsJC2traQnegwTvsiRMndpm4LVZe5b+b4TqR/7m5uaE7f6do2Q8v74MtfGlpaTEPTe/M6/xPFSnbwlNbW0sgEGDKlCl9fnfp0qVkZWWRk5MTGpHy9NNP8/jjj4dq48899xwTJ07k4MGDLFq0qN9afixhr169usv7jRs3kp2dTW1tbY9hJ1v4keZ9vNMfjM/q1at9k/6exKt1s6fw3Up/rOd9fn4+2dnZvPfee13WCwuGOXv27Kji0BO3Wlj8VPaPHTvW47EOJ997Crs3TrbwuXntX7p0KQUFBXz88ce95r/qKqVaeILPaHft2sXcuXOpqKggEAhw7tw5ysrKeOKJJ6irq2Pfvn1MmTKFyy+/nBEjRpCWlkbnQtu909uoUaNobm4OrckSz7BzcnK6vJ86dSqNjY19hp1M4UeT9/FMfzA+iZ7+/sT7DtOP596VV14ZKtudOygHw4wkDv2JZ/77tex3X7ohHvkO/in73fO/ubmZ1tbWmNOfSlKq0/K5c+cYOnQoQ4YMobS0lDlz5rBlyxb2798fqnF3dHTQ1tZGR0dH6HsnTpzAGMP27duB82uwBN83NjaSnZ3tStjd3y9ZsiR055kK4UeT9/FMf/f1eBI1/Z3V1tayf//+frdbunRpaGbloNWrV7Np06bQ++Csv88//3yf+/LjuRcs28H1wrqHGe0xiCT/d+7c2aUz7XPPPcfbb78dev/kk0/S2traZwdav5b93o61G/kenFU56Mknnwz9v729nR/84AccP36cJUuWcPr0adatW+d4+vvL/8GDB5OenlJtFrGTBBgbH+8XEc6F0Nmbb74Z9rabN28WsQIUJ8J3ImwNX8MHZOPGjbJx40Z58sknZefOnfLTn/5Ufvvb30pZWZn88Ic/FBGR2tpa2bhxo9TU1IiINSfI1q1b5Sc/+UkonDfeeEOKi4tD7+vq6uSdd94JzR+SSOl367wPJw7R5v+WLVu6zM3SPf+Dn23cuDGh8j4Rwo+l3O/atUs2bdoUCqP7/DgbN26UsrIy+cUvfiGnT5/utfx7nf/66vpKiephZmbmEWOMayvJZmZmHvEq/O5ha/gaPlh3mZdddlnoLvPuu+9mw4YN5OTk9HmXmZaWxqRJk9i+fTvXX389J06cIDc3N/R+yZIlPPbYY5w6darPOHmRfrfP+57iEBRt/gdXog/md2NjI4WFhaH3e/fuZerUqX3GJZXLfrT53tTUxODBgy/I51OnTjFt2jTKy8v5kz/5E373u99x7NixPuPk9bmvzkuJTstKpbJop7fvvLJ3f0pKSrjtttu042Q3sSwtEG7+t7a2smPHDm655RbN+07cKPcAp0+f5sCBA1x77bVa/hOcVniUSnK6no93NO+948VaanoMEltKPNJSKpVps7p3NO+948UjTT0GiU1beJRKIcYYA7wE1IvI30S5jzzgd8BDIrLVyfilImPMNuBHIrK+l8+/BVwvIn/obsySjzHmEeA7wDUici6K7xvgZaBWRP7W6fip+NIKj1IpxBjzVeDvgGtFJBDDfu4Efgp8XkROOhS9lGOMGQPsAkaJSGsv24wADgAFIqKrRkbJGDMWeA+4XUR+G8N+hmFV+L8qIiVOxU/FX0rNw6NUKjPGjAf+E3gklsoOgIhsAF4H/teJuKWwB4E1vVV2AETkCLAduNu1WCUZY8wAYBlWS1rUlR0AEfk98EfAc8aYoU7ET7lDKzxKpQBjTDrWBf8pEXFqqekngBnGmIcd2l8qegRYHsZ2y+1tVXSeADqAp53YmYhsAtYAS+zHXMoH9JGWUinAGPPPwBys5vyOfjaPZL8zgGKsPhH1Tu03FRhjpgEbgbH9HRNjzMXAIWC83cKgwhSvMmqMyQLKgR+KSN9TjauEoC08SiU5Y8y1wLeBrzlZ2QEQkV3AfwHP248NVPgeAlaGc0xE5AzWj/b9cY9VEjHGDMZqHfsrpyvkItKC1er2tDFmnJP7VvGhFR6lkpgxJhvrgv8tETkUp2B+hHUt0VErYbIfgzwErIjgayvQx1qR+iHwvohEks9hE5HfAf8BLNMKf+LTR1pKJTFjzBIgU0S+GudwxmI1798Ra6fQVGCMuQF4Frgy3NnxjDEZwMfA1SLSEM/4JQO3RhIaY9KAzUCJiDzZ3/bKO9rCo1SSMsbMB+ZiPc6KKxGpA/4aWG73bVB9exhYHslUwCLyKbAWa2SX6oMxJh/4BdbQ8ZPxDMt+JPlV4C/tx8cqQWkLj1JJyBgzEvgt8GURedulMA3WY5ejIvIXboTpR/aIuUbgRhH5KMLv/gHwXyIyPS6RSwJ2OVwH7BeRf3Ax3AeAfwdm6HxJiUlbeJRKMvYF/5fAL92q7ADYrRV/DtxjjJnnVrg+dBtQE2llx7YNyDfGXOlwnJLJ14ExwP/nZqAi8iKwE2uuK5WAtMKjVPL5MyAf+K7bAYvICazm/V/aS1CoCz1MZJ2VQ0SkHViF1eFZdWOMKQSewppc81MPovAt4IvGmLs8CFv1Qx9pKZVEjDFTgLeA2SJywMN4/AiYCCx0fcnqBGYPk24ErhCRw1HuYwbWemgTNW/PM8YMBN4GlonITzyMx83AaqzO5bqYaALRFh6lkoQxZhDWEPR/9rKyY/tnYBzWFPzqvLuA8mgrO7bfAp8Bs5yJUtL4Z+A4Hi93IiJvAb8CfqGzMCcWrfAolTz+DWvY8s88jkdwRNEjwA+MMRO9jk8CifpxVpDdqrPC3pcCjDE3An8K/FGCtHr9GzAKK04qQegjLaWSQKdm9M+LSJPX8QkyxvwF1g/zbBFp8zo+XjLG5AK1wBgRORXjvgqxHt8UaL6ai4H3gb8VkXXexua8RHm8rM7TFh6lfM4YMwRrYdBvJFJlx/YT4BTW44ZUdx+wKdbKDoCIfAjUAV+IOVb+99/AlkSq7ACIyH6skWLL7f5FymNa4VHK/xYDG0Xkda8j0p09KdtjwDft2YVTWcyPs7pJ+cdaxpj7gJuxJr1MRM8AR4B/9ToiSh9pKeVrfpnszBizEGtdo+n2QpgpxRhTAFQCo0Qk4NA+LwWq7H22OLFPPzHGjMLqwD1fRHZ4HZ/eGGNGYD1yc20SUNUzbeFRyqeMMb/C6qD8tUSu7ACIyFqgAthtjPm81/HxwIPAK05VdgBE5BOsPE25OV/seW52AM8ncmUHwB6a/pfAq8YY1+fGUudphUcp/7rH/vcTLyMRgUrgclJs0jx7cUmnH2cFLQceTsHhz48BwVYzPzgIZAN/6HVEUpk+0lLKp4wxC7BWaD7rdVzCZY8maxCRWq/j4hZjzHqs5SRuFJH3Hd73XVgLiv6viCRqPxbHGWOuxhqh/zuv4xIu+9HW1SLyhtdxSVXpXkdAKRUdEXnV6zhEyp6ULdXkAM1AQxz2vRdoA4bHYd8Jy+mKoxvsR1ta2fGQtvAopVQcGWNGAy0icixO+x8LnBGR4/HYv1LJQis8SsVZVlbW4UAgMMKNsDIzM4+0tLSMdCOscKV6+lOVm8c9KNGOv5b9xKIVHqXizBjj2mz3xhhEJKE6sKZ6+lOVm8e9U5gJdfy17CcW7cOjlMvWrl1LR0cHzc3NTJs2jaNHj9LU1MSiRYtC24gInQfeVFRUcPToUebNm8fq1avJyspixIgRHD9+nHnz5nmRjKjEmvaysjIqKyuZMWOGK2l3u5Wi+1261+E7qfOxLygo4NixY6SlpTF//nwyMzOBC4/9gQMHqKysZNy4cdTW1nLfffexdOlSZs2axe7du8nKyuLuu++OR3Qd41S6169fT0dHBx0dHYwZM4Y9e/Ywa9YspkyZ4lXSfEeHpSvlsry8PAYNGsSIESOYOXMmAPn5+QBUVlaybt06GhsbKS4upqysDCC0HUBLSwstLS00Nyf01Ds9ijXtU6ZMYdq0aa6lPRAIjBARenvV19cjIpw+fZoTJ05c8Plrr73GunXrLvh/b6/ulRuvw3dS52Pf0dFBbm5u6NjX1taydu1aPvzwQ0pKSti8eTMAkydP5qKLLmL06NGcPWsNRszPzycQCITOg0TnVLozMjLIzMykoaGBlpaWUD6o8GkLj1IuO378OG1tbQwaNIjq6mqqq6uZONFaULygoICamhra29tpa2ujvb0dILRdTU0NmZmZZGRk0NAQj0E/8RVr2vft28fcuXNZuXKll8lg2bJltLS0MGbMGJ599lmKiooYNmwYAwYMoKysjPz8fGbMmEFGRgbBRxqd/+/38KPR+dinpaVx9OhRLr30UsCqxGRkZISOfTCejY2N7N69m2HDhpGTk0NNTQ05OTnU19eHzoNE51S6A4EAIsKkSZNoaGjg8ssvp76+nquvvtrD1PmL9uFRKs56e46/efNmioqKACgpKeG2225zIqyEe47fU/rjkXY7LEfT39uxe+GFF2hubmbs2LGAdac+Z84cRo0axbZt2xgxYgTXXnstr732GiLCVVddRWVlJSLCggULwo6/1+HHIpxyv23bNm644QYGDnRmbc1EK/+d8yCe6bbDSqi0JyKt8CgVZ71d+BsaGhg9ejRnzpyhvb2dnJycLp8Hn9kvWLCANWvWMHXqVKqqqhg4cCDHjx+noKAgdAHtFFbCXfT667gZST4E+2/01m/BrQpPRUUFNTU13H///U4FFQwvrAqPW+HHuK9ej3s0Zb+mpobs7GwOHz7MVVdd1WMZSLTy71QevPLKK4wZM4YhQ4ZQXl7OQw9dOFl5oqU9EekjLaVcFs3jiJkzZxIIBBg+fDiVlZUYY2htbfU4JbGJJh8Spd/CzJkzu/QtSrXwoxVL2Z86dSqNjY2+7LvWWTR50NDQQF5eHqdPnyY3N9fjFPiXdlpWymX2nRgAs2bNorLSWg5IRLr0XQkEAgQCAWpqakL9WNLS0pg0aRKDBw8mPd3f9yvR5EOw/0aiKi4u7nebAwcO8PLLL3sWfnt7O0899VRcwu9PLGV/yZIlXHTRRZw6dYqmpiZP4u+EaPIg2G/n2LFj1NXVeRZ3v9NHWkrFWfdm7Xg9jrDDSrhmbTcfy8T7kdbixYtZuHAhixcvprCwkLNnz5KdnU16ejqBQIDs7GyGDRvGoUOHGDhwILNnz6awsJCSkhJEJPQIsri4uMch9f090nIi/HfffZempqYe+/LE+5FWPMu+HWZClX838yDR0p6ItIVHKZfNnDmzz4tdOHfpO3fu5Kc//amT0XJdb/kQTvr37NnD8uXL4xGtPo0fP57y8nJGjhzJhAkTuPXWW7nmmms4fPhw6K69vb2dwsJCJk6cSFVVFQBtbW20tbUB50fgeBX+uXPnQn93W19lP5LWsZdeeonf/va3TkfPFbHmweuvvx6askFFRlt4lIqzeNylHzx4kJ///Od8//vf7x5Wwt3lxauV5MUXX+SBBx7oHpYrnZa7Ky0t5frrrw9NJBdDeGF1WnYr/Bj3FbfWsczMTAYNGsSNN94Yt/g7IR55sGPHDs6ePXvByMZES3si8ncnAKV8qPtdenB0RnFxMUOGDAHO36Wnp6dTVVVFYWFhl3k60tPTQ/PX+I0T6V+9ejUDBgzwKgkXmDNnTkqHHw4njnuwdeyOO+5g3759F1R4Ep0TeZCbm+tZC53faQuPUnHm5l16It7l+Tn9PcW9traWQCDQ75T+S5cuJSsri5ycHG6//XbAqqh1fv/000/z+OOPs3r1ahYtWtRvC08kYV9xxRVdlt947rnnmDhxIrNnz+7y/uDBg6GlPdwalt6ZU61TdpgJVf79XPaTkbbwKJUg/HCXHk+JnP5g34pdu3Yxd+5cKioqCAQCnDt3jrKyMp544gnq6urYt28fU6ZM4fLLL2fEiBGkpaV1md04Jyeny/vuy2s4GXb34dujRo3q8rfg+77CdkMiH3e3aB64QzstK+Wy2tpa9u/f3+92S5cuZenSpaH1dcC6K3/77bdD75988klaW1tZtWpVXOIaD5Gk/+mnn+4y745X6T937hxDhw5lyJAhlJaWMmfOHLZs2cL+/ftDrS0dHR20tbXR0dER+t6JEycwxrB9+/Ye3weHXMcj7IaGBpqamkJhNTY2kp2dfcF7tzh93AF++MMfcvr0adatW+d4fOMhkjzYuHEj27ZtC/0tmGaAM2fOsHTpUurr632T9kSgLTxKuSDau/SDBw92mWCw+136zJkzGTRo0AWztCaaaNMfXDohyKv0L1y4EIDrrrsu9Lfuj5XGjRvHuHHjQu/T09Mv6FTd/f3Xv/51AI4cOeJ42J1XoAd47LHHenxfUlLSa9ixiudxb2tro7W1lUGDBjnyOCxeos2DK6+8ksbGxtB+Ok80efHFF3PppZdy8cUXJ3TaE4228Cjlgmjv0oMTDPZ2l753715fLCIabfqDLSB+TH/3ZT/64tRaYokQdmfxPO7BodmJPglhtHmwZMkS8vLyLijre/bs4eTJk2zZssWT9PiZdlpWKs7C7bjYXefFBvvS2trKjh07uPnmmxOy46Kf05+VlXU4EAiMcGp//cnMzDzS0tIyMlHCj0W8j3vQ6dOnOXDgANdee23Clf9450Eipz0RaYVHqTiL9qIXZVgJd9FL9fSnKjePe6cwE+r4a9lPLNqHR6k4y8zMPGKMceUuPTMzs/fOIB5J9fSnKjePe+cw3QyvP1r2E4u28CjlMWPMDOAlYGJPt4PGmHzgA6BARPy9VHQPjDHfBq4TkUd7+fwu4B9E5GZ3Y6bizRiTBtQAd4tIj+ttGGO2Av8lIq+6GjkXGGPGALuAUSLS2sPnWcDHwBUictjt+CUb7bSslPceAVb01vYtIk3Au8B8V2PlnoeBvhbGegOYYowZ28c2yp9uAk73VtmxrcAqI8noIeDlnio7ACLSArwGPNDT5yoyWuFRykPGmAHAg1gX9b4k5UXfGDMemAD0OjZaRD4DXsb6cVDJ5WH6L/svA/OMMRe7EB+3hZP+pDz3vaAVHqW8dQvQJCL7+tluHXCLMWZY/KPkqoeB1Xalpi960U8yxphBwP1An7NGisjvga3APS5EyzXGmGnAUODtfjYtAS43xvhz8bwEohUepbzV3+McAETkDFCM9QORFIwxBvtxXhiblwE5xpjPxTdWykVzgQ9EpCaMbZOxwvsQsFJEOvraSETagNVoC2fMtMKjlEeMMRnAQvq5w+1kBVYFIVl8HsjE6p/UJ/tHYSV60U8m4TzOCVoP3GB34Pc9u7IfSfpXAI/Y31NR0gqPUt6ZB1SKyKEwty8GphpjRscxTm56GOsON9yhoiuAh/Wi73/GmGzgS1ijE/tlj058HfhyPOPlouuBFuB3YW6/HRgEXB2vCKUCrfAo5Z2HsVotwiIinwJrsTo5+5o9HPkhIkg/sBtoBm6MS6SUm+YD2+0RiOFKpsdaEVX27e1Wkjzp94RWeJTygD3iZB7WCJRIJMtFfzZwQkQqw/2CfdFfgT7WSgZh9V3r5k1gkjFmXL9bJjBjTDrwFcJ/nBW0AnjIvllQUdCMU8ob9wBb7REokdgG5BtjrnQ+Sq6KpP9CZyuBrxhjBjocH+USe6ThLVgjD8Nmj+R7Cf+3cN4G1IjIR5F8SUT2Ar8HdALOKGmFRylvRPWDLyLt+Lxpu9Nw5EgeZwEgIgeBj7B+NJQ/3Q8U2yMPI7Uc/3fejbayD3b6HYxLStEKj1Ius0ea3IA18iQafu+8ezuwT0Tqovy+XvT9LZrHWUHvAhcBvpyewBgzGKv/0uood7EKuM8e4akipBUepdz3FeD1GNbF+i3QCsxyLkquiuUOF6zHGnfbPx7KR+wRhtOwRhxGrNP0BH5t4bwLKI92XSwRqQf2Anc4GqsUoRUepdwX0w9+p867vrvoG2MuAu4kzOHIPRGRI1jDdO92Kl7KNQ8Ca3pbOypMfu68G2tlH3x67icCPxYYpXzLXjtqItaIk1isBB6wR3z4yXygTESOxbgfvej7U8w/+PbIvtP4bHoCY0wu8AfAKzHu6mXgi0m6tlhcaYVHKXc9CLwUxtpRfRKRD4E64AuOxMo9TtzhgjXCZ44xZqgD+1IusEcW5gFvObA7P1Z47wM2icipWHZi3yy8BSxwJFYpRCs8SrkkwrWjwuGri74xZjjW/DuvxrovETkNvIH1I6L84WFglT3SMFargC/7bHoCpyr74LNzP1FohUcp93wOyCaMtaPC9CKwwBiT5dD+4u3LwEYROevQ/vSi7xNRrB3VJ3vB0Q+wFiBNeMaYAqxlITY6tMvXgJuMMXkO7S8laIVHKfcEp5Pvc3XkcInIJ0AF1sgPP3DyDhesH4+rjDGXObhPFR+zsEYW/tbBffppeoYHgFdEJODEzuybhl9jjfhUYdIKj1Iu6LR2lJM/+ADHgZ8bY65yeL+OMsaMBa7AegzlCHttsVewfkxUYnsYWBHBQrHhaML6wf93B/cZL05X9gFOAN83xhQ5vN+kpRUepdxxE3A6krWjwrQFGALE1BHSBQ8CL8c4HLknOglhgrNHEj6A8z/4u4EOYLjD+3WUMWYyMAoodXjXm4GLgTaH95u0tMKjVJzZix3+GGdGp3QhIs8AV8cwa3HcGWPuBL4NvB2H3ZcDY4wxS+Kwb+WMtVgtkbVO7lREDmC1Gv6jk/t1kj2K8FfAWw511g4RkVeAzwNbndxvMvPbHB5K+dHFwExgfzx2LiK/i8d+HXQtUEB8brAuAQbjk86rKepW4DNgEA63RtidlxPZAKxlZBzpu9OdiOyOx36TlVZ4lIq/fcDPgW96HRGPrAWMiDzv9I5F5GNjzBexKpQqMf0PVv+dc15HxAPHsM79v/A6Isq6CHkdB6VUH7Kysg4HAoERboSVmZl5pKWlZaQbYSnVn1Qv+6mefqdphUepBGeMcXhwS59hISJ+GOarUkCql/1UT7/T9JGWUv1IlLustWvX0tHRQXNzM9OmTePo0aM0NTWxaNGi0DYiQudpSSoqKjh69Cjz5s2juLiYU6dOMWrUKM6cOUN9fT133nknY8aM6TNOXqbfzbB7Cj+VuZ330H/+R3MOlJeXc+jQIe69994LzoE777yzz/gk4rlfUFDAsWPHSEtLY/78+WRmZgIXpvvAgQNUVlYybtw4amtrue+++1izZg1Tp06loqKCGTNmcMUVV7iRtISho7SU6kcgEBghIrjx6uvimpeXx6BBgxgxYgQzZ1pdVvLz8wGorKxk3bp1NDY2UlxcTFlZGUBoO4Dhw4dTX19PQUEB586dIy8vj5aWloROv5th95f/qcbtvA8n/6M5B6ZPn05HhzXXZ/dzIJHyINxzv6Ojg9zc3FC6a2trWbt2LR9++CElJSVs3rwZgMmTJ3PRRRcxevRozp61JjcP5llDQ0NY536y0QqPUjFqaGgA4MyZM5w8efKCz9evX8+rr756wf8jdfz4cT77zFpztLq6murq6tBnBQUFpKWl0d7eTltbG+3t7V22q6mpIS0tjcLCQg4dOsQll1xCTk4O9fX1UcWlM7fSn6jhpzK38z6ac2DJkiUMHz68x3MgVl6c+2lpaRw/fjz0WX5+PhkZGaF0t7VZA+EaGxvZvXs3dXV15OTkUFNTE8qzyZMnh+KeSvSRllJRWrZsGS0tLYwZM4Znn32WoqIihg0bxoABAygrKyM/P58ZM2aQkZGBiPUcvvP/I7VggbU48ubNm5k4cSLf+ta3KCkpAWDo0KHMnz8fgLFjx4a+E9wOYNy4ccyYMSPq9HbndvoTLfxU5lXeR3MOPP7446H/O3UOeHnuFxVZEytv27aNAQMGkJmZyZe+9CWALo+oCgoK+Pu///su+xk3blxU4ScLbeFRKkp2Jz8AZs2aRWWlNYmyiHS5wwwEAgQCAWpqakL/j0XwgtfQ0MBtt90W1t1ldXU1K1euZOfOnRQXF7N69Wr2749tWiCv0p8o4acyr/N+8uTJgNWy0vmxbVCylv3guQ9wyy23cPjwYSC8FqalS5fGnG6/01FaSvWjt5ESFRUV1NTUcP/99zsZFtJtpET38DvfXe7YsSN0d1lQUNDl7nLTpk2ICHfccQe/+c1v+PTTT8nIyODTTz/lk08+4YYbbmDKlCkRh+9m+vsapeJW/qcqt/PeDrPP459KZb+v8KPJh40bN3LppZdy9dVXhx1+stEWHqWiNHPmzB4veMXFxf1+95133mHDhg1RhRvN3eWxY8eoq6ujoaGBI0eOcOrUKZqamqIKPyiW9B84cICXX37Z1+Gnsp7yPpJ8Ly8v55VXXok4XC37lmjywak+e36mLTxK9aP7XdbixYtZuHAhixcvprCwkLNnz5KdnU16ejqBQIDs7GyGDRvGoUOHGDhwILNnz6awsJCSkhJEhKKiIl544QUuueSSUJ+DTmH1e5cXr7vrcMN3Iv1g/TjMmzevz/B7usN1M/xUFs/jXlRUxKuvvsp9993XPcw+j38qlf2ewg/S1s3oaKdlpSI0fvx4ysvLGTlyJBMmTCAnJwewLmJDhgwBoL29ncLCQtLT06mqqqKwsJC2trbQXdmkSZMoLy+PKvyZM2f22G/BLU6kPziCpPtF3w/hpyon8/2jjz5i2rRpEcch1ct+kNf54FtuzTGgL3359WWdJv37zW9+Iy0tLWFt2xs7rKjCD9q4cWO/21RVVcnzzz/vaPjxSH8kaY9X/qfqy83jHhTL8RdJrrIfSfhB4aS/ra1NnnzyybDCT7aXPtJSqh9eT+8ej2b1M2fO8MILL/DNb34z4vDjKZJOy26En8rczns7zD6PfyqV/Z7CdyL97777Lk1NTaGh7n2Fn2y007JSEaqtrQ1reOfSpUtZvnw5+/btC/1t9erVbNq06YL3zz8f/kLi3ZvVb731Vq655hoOHz4cvCsMNatPnDiRqqoqgC6TktXV1ZGbmxt2mJ1Fkv7e0hv09NNPEwgEIkp/JOEH9x/03HPP8fbbb1/wPpLwU5XTx72lpYU1a9awc+dOVq1aFVYc/Fz2eyuLbp/7586dC/091WgfHqXCEBx9sWvXLubOnUtFRQWBQIBz585RVlbGE088QV1dHfv27WPKlClcfvnljBgxgvfff7/LZGA5OTl0vmMLvg9OEx+O4CRj3Z08eZLrr78+tLZOd3fccUfo/9OmTYuoD0W06U9LS+sxvUHdlwdwOvzOE9ABjBo1iubm5gveR5L/qSSex90Yw/jx48nOzg71hemPn8t+9z430ZQ9J9J/2223cdttt4UdZjLRFh6lwnDu3DmGDh3KkCFDKC0tZc6cOWzZsoX9+/eH5vPo6Oigra0ttG4PEJrCffv27QCcOHECY8wF750wZ86cXi94sYo2/b2lN/i++/IATocf3H8wvMbGRrKzsy94r3oWz+P+wQcfsHz5ckfy3w9lv7ey6IR4pj+peN2JSF/6SvQXEXYcDHrzzTfD3nbz5s0iVmDSX/g1NTWyb9++fvf53HPPyYYNG2Tr1q2hv/3qV7+St956K/T+e9/7nnz66aeycuXKsMMPlxPpjzZsp8JP5Zcbx11E5NNPP5Vt27aJSP/HP5Ky/5//+Z9dOhJ3LvsdHR3y8ssvy44dOxK27PcUvlPnfrjpT7aXPtJSKk46TwPfn/6amKNtVr/yyitpbGwM7af7I52ZM2cyaNCgsB8pRMLJ9Psx/FQVSb4DDBo0iJtvvrnXz+PxODOax2mRSPRzP97pT1T6SEupfmRmZh4xxuDGKzMz80hPcYi2WX3JkiXk5eX1+khn7969/a6a7GX63Qy7r/xPRW7nfW/5H4/HmR0dHWE9TkvWcz/c9CcbHZauVIIzUQ6N7byycl9aW1vZsWMHN998M8Yk/9BU5R+pXvZTPf1O00daSiU4+y5zhFthuRGOUuFI9bKf6ul3mrbwKOVzxpjJQClwmYi09/D5TcDPgGlR3S4qlaCMMQbYC3xdRN7p4fN0oAG4VUQ+cDt+8WaMyQM+xDr3z/bw+SRgm/15m9vxSzTah0cp/3sIWNVTZcf2DjAY+Jx7UVLKFVcBWcC7PX1o/8i/iHWOJKMvA7/uqbIDYFfyGoA/cDVWCUorPEr5mH2H+wiwordt7FadlfZ2SiWTR4AV/bRcrgAesc+VZNPnuW9bATzsQlwSnlZ4lPK3a+x/3+tnuxXAQ8YYPedVUrDL8kP0/4NfjvVbl1TLixtjxgGTgE39bLoKuMcYkxX/WCU2vfgp5W8P0/8dLiKyBzgJ3ORGpJRywWzguIjs7Wsj+9xIxlaOB4GXReSzvjYSkU+ACuBOV2KVwLTCo5RPGWMGYF30Vob5lWS86KvU9TD9t+4ErQQetM+ZZBFJ+vXcRys8SvnZHOBjEel/+WbLKuB+Y8yg+EVJqfizy/D9WGW6XyKyDzgM3BrPeLnFGPM5YAhQFuZX1gJFxpicuEXKB7TCo5R/PUz4rTuISC1wALg9XhFSyiW3A/tFpC6C76wgeTruP4w1MrOj3y0BETkJlAD3xjNSiU4rPEr5kDEmE+viFdYdbicrSN4huip1PAwsj/A7q4B7jTEZcYiPazp11o40/Sn/WEsrPEr50xeB34nIoQi/9xLwJWNMai2io5KGMeYirA64L0XyPftc2Y117vjZDUAzVloi8WvgGmPMpc5HyR+0wqOUP0XSYTFERI5iTUQ43/EYKeWO+UCZiByL4rvJ0MoR1sjM7kSkBXgV+EpcYuUDWuFRymeMMZdg9WFYE+UukuGir1JXVJV928vAHfY55DvGmIFYsyuH3Xevm+UkTz+miGmFRyn/uRf4jYgcj/L764CbjTHDnYuSUvFnl9nZWC0VEbPPmVLgHudi5aq5QLWIHIzy+78BxhhjCh2Mk29ohUcp/4nlDhd73Z2NWMN6lfKTLwMbels7Kkx+buGM9dxP9rXF+qQVHqV8xBgzArgOeD3GXfn5oq9SV0w/+Lb1wPX2ueQb9kCDu4iws3YPVgAPJ+naYn3SCo9S/vIVYL2InItxP28AVxpjxjgQJ6XizhgzFriC/teO6pN97qzHai3yk7uB7SJyJMb97AQGAtNjj5K/aIVHKX9x4g4XEWnF6vT8YMwxUsodwbWjWh3Ylx9bOJ0695N1bbF+mQhHtimlPGKMGQ9sBwr6WzAwzP3dCvy3iFwd676UijdjzO+Ab4vINgf2NRBoBK6PoQOwa4wxw4CDwGgROe3A/q4ANgNjRKQ91v35hbbwKOUfDwOrnajs2N4Chhtjpjq0P6XiwhgzDRgKvO3E/uxz6CX803n3PuANJyo7EFpbrAm42Yn9+YVWeJTyAbuD4SM40KQdZK/DsxL/XPRV6noIWBnu2lFhWgE84pPOu448zupmG/C0MWaiw/tNWFrhUcofPg9kAu86vN+UHbGh/MEum/H4wX8XGAxc5fB+HWWMGQ18DmsqCSddAszAWnU9JWiFR6kEZ0+29gTWHa7Tne7eBwLA3zm8X6Wc8gRwDvidkzvt1ML5hN1HJuHYQ9H/GXhFRD51ePdfB34O7HF4vwlLOy0rleCMMUVYQ3E3isiXHN73IOAYkCEivl5FWiUnY0wr0ALkOTRCq/O+f421mOhcESlxct9OMMZ8Dquit0tErvE6Pn6nLTxKJb6PgHrga07v2P4B+RNgn9P7Vsoh+4A/cbqyY/sa1rmVqCO1DgIf4785gxKStvAopZRSSSgrK+twIBBwZUbpzMzMIy0tLSPdCCtaWuFRSimlkpAxJg7d/noNCxFJ6MEP6V5HQCmllFLxs3btWjo6OmhubmbatGkcPXqUpqYmFi1aFNpGROg8WLOiooLa2lruu+8+Vq9eTVZWFhkZGRw5coSjR4/y6KOPkpeX50VyoqZ9eJSKs6ysrMPGGHHjlZWVdTjRwlepy+uyl+rhB/3/7d1dbFTnncfx72Mbe7wT1RNcxhSHd49wQ192MQR3ZcBbDMkqLaiENm2qRorUm4ru3dK7vWQvKqG98EWVG2QHAsSkiBcJT1KbeAnOmhCTFS7GKG5mjPEGFBZn8cscyHievTCe4NcZ2/Niz/w+0lHmeM48/wd0hvz9f/7nnGXLlpGfn09JSQkVFRUAeL1eADo6Ojhz5gx9fX34/X5aW1sBWLlyJYODow+lD4VChEIhli1bxvbt2/F6vYRCoUScIimlhEckyRzHKbHWMt12+/ZtrLU8fPiQ/v7+Se+fO3eOM2fOTHo91TbVen2640v2Sve5l+3xxzx48ICvvx69QXt3dzfd3d3R90pLS8nJyWFkZIRwOMzIyOiTJnp6evB4PAQCAVwuFwUFBfT09LB69Wo8Hg+9vb0JOENSS0taImly9OhRQqEQq1at4siRI9TU1FBcXExubi6tra14vV42bdpEQUEBY+vwT79e7PEle6X73Mu2+Hv37gWgqamJsrIyfv/739PcPHoV/tKlS9mzZw8Aq1evjn5my5Yt0ddr164dN95PfvKTOc0j3VThEUkTM9rkB8DWrVvp6OgARtfSn/5Ny3EcHMchEAhEX2dCfMle6T73sjV+TU0NAL29vezcuZOBgQG++uqrScedP3+es2fPAqMVoRMnTvDxxx/j9/tpaGigq6trXvNIF12lJZJkxkx9pUR7ezuBQID9+/cnMtakKyXSHV+yV7rPPcUfH//pytKVK1eilaXS0tJxlaX3338fay0vvvgiH3zwAY8ePaKgoIBHjx7xxRdf8KMf/Yjy8vKY8RcaJTwiSTbdP3pJihX3P7qpii/ZK93nnuKPj3/s2DGGhoaiS1fBYJDq6mpWrFjBpUuXKCkpYcuWLZw7dw5rLT/4wQ/45JNPePDgAYWFhVhr6e/vZ9OmTWzfvj1m/AVnpoYqbdq0zX8b/ZrFr7GxMeYxXV1d9tSpU5N+/iRW0uN3dHTYY8eOxRVfW/Zu6T73MvG7N5/4n3zyyZSfTYTF8N1X07JIitXW1rJv3z5qa2vx+XwMDg7idrvJy8vDcRzcbjcXLlzgzp07LFmyhKqqKnw+H83NzVhrqampYcOGDQQCgbTF/973vseNGzcS/DcjmS7d514mfPfmE7+ioiJ6WXo2UtOySIqtW7eOq1evsnz5ctavX8+OHTvYvHkzd+/eHfutjJGREXw+H2VlZXR2dgIQDocJh8MA9PX1cf369bTFb2hoIDc3d15/D5J90n3uZcJ3bz7xY/H7/TGPuXnzJkePHk1K/GRTD49IksW7jt/S0kJlZSUul2s+sbBz7CNIVnzJXuk+9xR/fPx4KkzFxcUzVpgGBgY4duwYv/vd72LGX2i0pCWyQFRXV2d1fMle6T73siX+xAqTx+MBRis7RUVFwDcVpry8PDo7O/H5fITD4WgFqqenh2effTYl8000VXhEkmzib1nBYBDHcSZd1jlRfX09hYWFeDwedu/eDYyW85/eP3z4MAcOHKChoYHXX389rt/yZhPf6/XidrujV2TU1dVRVlZGVVXVuP3PP/982viSvZJ57jc2NuJ2uwkGg0k59+/fv8+BAweiVZe5nPsL7bs/nWyp7qrCI5ICY2vj165dY9euXbS3t+M4DsPDw7S2tnLw4EF6enq4efMm5eXlrFmzhpKSEnJycnj6HyyPxzNuf+JzcRId//nnn6evry86zooVKxgaGpq0Hyu+ZK9knfsbN26kr68vaef+03cdhrmf++n+7scj3RWuVFHTskgKDA8Ps3TpUoqKimhpaaG6upqLFy/S1dUV/W0vEokQDoeJRCLRz/X392OMoa2tbcr9ic/FSXT8N998k2XLlkXj9fX14Xa7J+2LTCdZ5/7YuZms+GPfrfme++n+7j8tGAzGdZfk+vp6GhsbuXTpUvRndXV1XL58Obp/6NAhHj9+zMmTJ2c1h7RK93Xx2rRl+sYs78Ux5i9/+UvcxzY1NVk7GswutPjasndL97mn+NjGxkbb2NhoDx06ZD/++GP7pz/9yX766ae2tbXV/vGPf7TWWhsMBm1jY6MNBALW2tH7AfX09NiPPvooGue9996zfr8/uj92z6Cx/y6G776WtESSzOVy3TPGpOQp4i6X695Ciy/ZK93nXrbHh9EK03PPPRetMP30pz/lwoULeDyemNXdN954g7a2NiorK+nr68Pn80X3b9y4wcaNG1PxR0sYNS2LiIhkoLk+2qKpqSn6oNGZPH78mCtXrrBt27ZF0bSshEdERCQDpftZXguNlrREREQy0EJYUltIVOERERHJQsaY3wA/t9bumeb9AuB/gL+31vamdHJJoMvSRUREstOvgbene9Na+wg4DfwyZTNKIlV4REREssyTpa5bwApr7fAMx1UD/2Gt/YcUTS1pVOERERHJPj8Hzs+U7DzxIbDMGPN8CuaUVEp4REREss9rwPFYB1lrR4CTwK+SPqMk05KWiIhIFjHGrAPagFJr7ddxHL8JOAWUpew69yRQhUdERCS7/BJ4N55k54lPga+BF5I3peRTwiMiIpIljDGGGFdnTfSkqnOc0WWwRUsJj4iISPb4PuAG/muWnzsBvGqMWbQ3LFbCIyIikj1eA05YayMxj3yKtfYz4DbwT0mZVQoo4REREckCxpgcRq+2inl11jQW9bKWEh4REZHs8I/AQ2ttxxw//w6w1xhTmMA5pYwSHhERkewwq2bliay1XwDtwMsJm1EKKeERERHJcMaYfGA/ozcRnI9Fu6ylhEdERCTz7QJuWWuD8xznNLDTGOOZ94xSTAmPiIhI5ovrURKxWGv/D2gC9s17RimmhEdERCSDGWPcjPbdnErQkItyWUsJj4iISGbbA3xkrf0yQeNdACqMMd9J0HgpoYRHREQksyVkOWuMtTYEnAX+zRjzd4kaN9n0tHQREZEMZYwpBv4GrLTWDiRw3GvAD4HvW2s7EzVuMqnCIyIikoGMMbnAbwB/IpOdJ34OfAEEEjxu0ijhERERyUyVwL8DCV/Ksdb+zVr73JPlrUVBCY+IiEhmug+4gLfSPZGFQD08IiIiGcoYU/Tk3jlZTwmPiIiITFJYWHjXcZySVMRyuVz3QqHQ8mTGUMIjIiIikxhjbKpyBGMM1lqTzBh5yRxcRERE5mahVFhOnz5NJBJhaGiI0tJS7t+/T05ODnv27MHlcgFgrcWYb/KVW7du0dHRwdq1awkGg7zyyit8+OGHDAwMYIzB7Xazffv2VPzRotS0LCIisgA5jlNirWW67fbt21hrefjwIf39/ZPeP3fuHGfOnJn0eqptpsRq2bJl5OfnU1JSQiQS4dlnn8Xr9QIQDAY5ffo0n332Gc3NzTQ1NQGwYcMGnnnmGVauXMng4CAApaWlDA8Ps3HjRpYsWZKCv8HxVOERERFZRI4ePUooFGLVqlUcOXKEmpoaiouLyc3NpbW1Fa/Xy6ZNmygoKGBsSerp17P14MEDwuEw+fn55OTk8OWXX/Kd74w+VcLr9VJQUMDIyAjhcDgao6+vj+vXr1NcXIzH4yEQCHDnzh2+9a1v8eabb/LGG28k5i9jFpTwiIiILCJP+l0A2Lp1Kx0dHVRXV2OtJRwOMzIyAoDjOFhrCQQC0ddzsXfvXgCampqoqakB4NKlS+Tm5uJyuXj55ZcB+O53vxv9TGlpKX/4wx/GjbN27VoAdu/ePad5zJealkVERBag6ZqG29vbCQQC7N+/P5GxJjUNx2pa7u3tZeXKlQwMDDAyMoLH4xn3/vnz54lEIuzdu5f6+nq2bt1KeXl53PETTRUeERGRRaSiooKKioq0xZ/LkprX68VxnLTNGdS0LCIikhH8fn/MY27dusW77747rzhTLakBUy6pOY5DIBDA4/Fw+/btecWdLy1piYiILEATl5Rqa2vZt28ftbW1+Hw+BgcHcbvd5OXl4TgObreb4uJi7ty5w5IlS6iqqsLn89Hc3Iy1Ntp/4/f7eemllybGintJK1VLaommCo+IiMgisG7dOq5evcry5ctZv349O3bsYPPmzdy9ezdacRkZGcHn81FWVkZnZycA4XCYcDgMfHP11HxUVFRMmezEU2H661//yttvvz2v+HOlCo+IiMgCFO+djltaWqisrIzeBHCOsWJWeBJVYXrnnXd49dVXY8ZPNDUti4iILGLV1dUpiTOxwjR2VZbf76eoqAj4psKUl5dHZ2cnPp9v3P15GhoayM3NTcl8J1KFR0REZAGaWGEJBoM4jjPtpd1j6uvruX//PgcOHIhWferq6igrK6Oqqmrc/ueff87rr78+p8vSxySrwpRoqvCIiIgsUGN9MdeuXWPXrl20t7fjOA7Dw8O0trZy8OBBenp6uHnzJuXl5axZs4aSkhJWr149bpwVK1YwNDQ0aX/sERHzkaoK03ypaVlERGSBGh4eZunSpRQVFdHS0kJ1dTUXL16kq6srWumJRCKEw2EikUj0c93d3XR3d9PW1gaMNiu73e5J+7MRDAbp6uqKeVx9fT2HDx8ed9+duro6Ll++HN0/dOgQjx8/5uTJk7Oaw3yowiMiIrJA7du3D4AXXngh+rOJS1pr166NPrYBIC8vj9/+9rfjjpn47Kqx/ebm5hnjJ6vCVFFRQX5+/qS7MyeTKjwiIiIZZOxqqHjs3LlzxveTVWG6ceMGvb29s/2jzYualkVERBagwsLCu47jlKQilsvluhcKhZY//bN4m5YnevohozN5/PgxV65cYdu2bSlpWlbCIyIiIpPMNeGZYyxdpSUiIiKp53K57hljUlZhSnYMVXhERERk1owxPwP+xVr742neXwNcBUqttY9TObepqGlZRERE5uI14Ph0b1prg8AtYFeqJjQTJTwiIiIyK8aYImA38OcYhx4Hfp38GcWmhEdERERm62fAB9ba/hjHnQJeNsY8k4I5zUgJj4iIiMzWjMtZY6y1XwKtwJ6kzygGJTwiIiISN2PMcmALcD7Oj7zNaIKUVkp4REREZDZ+AZyz1obiPP4sUGWM+XYS5xSTEh4RERGZjdeAE/EebK0dBBqB/UmbURyU8IiIiEhcjDHrgbVA0yw/epw0L2sp4REREZF4/QposNaGZ/m594DnjTGrkjCnuCjhERERkZiMMYbRe+rEvDproid3Wv4z8MtEzyteSnhEREQkHj8EXEDbHD+f1mUtJTwiIiISj18Dx+fxCPUPgW8bYzYmcE5xU8IjIiIiMzLG5DDavzPr5awx1toIo1d3paXKo4RHREREYtkG/K+19sY8xzkOvPakHyillPCIiIhILHE9SiIO/w04QGUCxpoVJTwiIiIyLWNMPvAKcHK+Yz3p/0lL87ISHhEREZnJi8BNa21PgsY7AfzCGJOXoPHiooRHREREZpKo5SwArLXdQBDYmagx46GER0RERKZkjHkG+GfgVIKHTvkT1JXwiIiIyHT2ApettfcTPG4DsMcYU5jgcaelhEdEREQmMcZsBQ4B15Mw/AOgF2hJwthTSmnDkIiIiCwaS4HVQH8SxrbAM8C6JIw9JSU8IiIiMpX/BP7VWns40QNba782xlQyerl7Spi5PxJDREREZHFQD4+IiIhkPC1piYiIZKDCwsK7juOUpCKWy+W6FwqFli+k+BNpSUtERCQDGWNsqv4fb4zBWmsm/Cyt8SfSkpaIiEgW6u3tBWBgYICvvvpq0vvnz5/n7Nmzk14v1vha0hIREckiR48eJRQKsWrVKo4cOUJNTQ3FxcXk5ubS2tqK1+tl06ZNFBQUMFahefr1Yo2vCo+IiEgWebL8A8DWrVvp6OgAwFpLOBxmZGQEAMdxcByHQCAQfb2Y46uHR0REJANN10PT3t5OIBBg//79iYwVdw9PquJPOkYJj4iISOaZbdOw3+/npZdemvGYW7du0dHRMSlZSUTTcqLjT6QeHhERkSxQW1vLvn37qK2txefzMTg4iNvtJi8vD8dxcLvdXLhwgTt37rBkyRKqqqrw+Xw0NzdjraWmpoYNGzYQCAQWZXz18IiIiGSBdevWcfXqVZYvX8769evZsWMHmzdv5u7du9GempGREXw+H2VlZXR2dgIQDocJh8MA9PX1cf363J4lmu74WtISERHJQPEuKbW0tFBZWYnL5ZpPrDkvaSUr/qRjlPCIiIhknnTf+C/d8SfSkpaIiEgWCAaDdHV1xTyuvr6ew4cPj7sMvK6ujsuXL0/af+utt5ISv6Ghgffffz/6s4n7Y/ObTXw1LYuIiGQov98PwLVr19i1axft7e04jsPw8DCtra0cPHiQnp4ebt68SXl5OWvWrKGkpITVq1ePG2fFihUMDQ1N2vd6vUmJn5OTM+5Ggx6PZ9x+RUUFQMz4T1OFR0REJEMNDw+zdOlSioqKaGlpobq6mosXL9LV1UV5eTkAkUiEcDhMJBKJfq67u5vu7m7a2tqA0WZht9s9aT9Z8fv7+zHGRONN3B+b32yoh0dERCQDzbWHpqmpiZqamriObW5uZufOnQnt4UlU/ImU8IiIiGSgdDcNpzv+ROrhERERyUAul+ueMaYkVbEWWvyJVOERERGRjKemZREREcl4SnhEREQk4ynhERERkYynhEdEREQynhIeERERyXhKeERERCTjKeERERGRjKeER0RERDKeEh4RERHJeEp4REREJOMp4REREZGMp4RHREREMp4SHhEREcl4SnhEREQk4ynhERERkYynhEdEREQynhIeERERyXhKeERERCTjKeERERGRjKeER0RERDLe/wPoI7ajDBzbjwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(10, 10))\n",
    "tree.plot_tree(clf)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "heavy-given",
   "metadata": {},
   "source": [
    "And also, we can see these regions in a 2D plane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "helpful-somerset",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1, figsize=(8, 6))\n",
    "plt.pcolormesh(xx, yy, Z, shading='auto', cmap=plt.cm.Paired)\n",
    "\n",
    "# Plot also the training points\n",
    "plt.scatter(X_train.iloc[:, 0], X_train.iloc[:, 1], c=y_train, edgecolors='k', cmap=plt.cm.Paired)\n",
    "plt.xlabel(wine.columns[0])\n",
    "plt.ylabel(wine.columns[1])\n",
    "\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "medium-proxy",
   "metadata": {},
   "source": [
    "### Exercise 2:\n",
    "Now, it's your turn to create a decision tree using all the features available. How well does this model perfom? Is it better or worse?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "treated-gathering",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create your model here with X_all_train and y_all_train. Don't forget to run the fit function:\n",
    "# clf = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "valuable-organizer",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predicted value. Don't forget to use `X_all_test`\n",
    "# y_all_predicted = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "lonely-nancy",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the new result dataframe\n",
    "#results = pd.DataFrame({'y_real': y_all_test, 'y_predicted': y_all_predicted})\n",
    "#results[\"is_correct\"] = [i['y_real'] == i['y_predicted'] for k,i in results.iterrows()]\n",
    "#results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bigger-store",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the new results\n",
    "#print(\"Correct predictions: \" + str(len(results[results.is_correct == True])))\n",
    "#print(\"Wrong predictions: \" + str(len(results[results.is_correct == False])))\n",
    "#print(\"Percentage of correct predictions: \" + str(round(100*len(results[results.is_correct == True])/len(results),2)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frank-bachelor",
   "metadata": {},
   "source": [
    "## 3. Logistic regressions\n",
    "\n",
    "Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. The logistic regression (or logit regression) estimates the parameters of a *logistic model* (a form of binary regression). \n",
    "\n",
    "<img src=\"https://scikit-learn.org/stable/_images/sphx_glr_plot_logistic_001.png\" alt=\"drawing\" width=\"400\"/>\n",
    "\n",
    "Mathematically, a binary logistic model has a dependent variable with two possible values, such as pass/fail which is represented by an indicator variable, where the two values are labeled \"0\" and \"1\".\n",
    "\n",
    "We will create a logistic regression with the current data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "experienced-sterling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=100000.0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log = LogisticRegression(C=1e5)\n",
    "log.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "earlier-representation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_real</th>\n",
       "      <th>y_predicted</th>\n",
       "      <th>is_correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     y_real  y_predicted  is_correct\n",
       "31        0            0        True\n",
       "101       1            1        True\n",
       "150       2            2        True\n",
       "53        0            0        True\n",
       "122       1            2       False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = log.predict(X_test)\n",
    "results = pd.DataFrame({'y_real':y_test, 'y_predicted':y_predicted})\n",
    "results[\"is_correct\"] = [i['y_real'] == i['y_predicted'] for k,i in results.iterrows()]\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "delayed-ethics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct predictions: 25\n",
      "Wrong predictions: 11\n",
      "Percentage of correct predictions: 69.44%\n"
     ]
    }
   ],
   "source": [
    "print(\"Correct predictions: \" + str(len(results[results.is_correct == True])))\n",
    "print(\"Wrong predictions: \" + str(len(results[results.is_correct == False])))\n",
    "print(\"Percentage of correct predictions: \" + str(round(100*len(results[results.is_correct == True])/len(results),2)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afraid-acquisition",
   "metadata": {},
   "source": [
    "We will plot the classification results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fallen-electricity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = log.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1, figsize=(8, 6))\n",
    "plt.pcolormesh(xx, yy, Z, shading='auto', cmap=plt.cm.Paired)\n",
    "\n",
    "# Plot also the training points\n",
    "plt.scatter(X_train.iloc[:, 0], X_train.iloc[:, 1], c=y_train, edgecolors='k', cmap=plt.cm.Paired)\n",
    "plt.xlabel(wine.columns[0])\n",
    "plt.ylabel(wine.columns[1])\n",
    "\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "important-score",
   "metadata": {},
   "source": [
    "### Exercise 3:\n",
    "Now, it's your turn to create a linear regression mdoel using all the features available. How well does this model perfom? Is it better or worse?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "associate-check",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create your model here. Don't forget to run the fit function:\n",
    "#log = ...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "optical-breakdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predicted value. Remember to use `X_all_test`\n",
    "#y_all_predicted = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "static-preserve",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the new result dataframe\n",
    "#results = pd.DataFrame({'y_real': y_all_test, 'y_predicted': y_all_predicted})\n",
    "#results[\"is_correct\"] = [i['y_real'] == i['y_predicted'] for k,i in results.iterrows()]\n",
    "#results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "seeing-maintenance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the new results\n",
    "#print(\"Correct predictions: \" + str(len(results[results.is_correct == True])))\n",
    "#print(\"Wrong predictions: \" + str(len(results[results.is_correct == False])))\n",
    "#print(\"Percentage of correct predictions: \" + str(round(100*len(results[results.is_correct == True])/len(results),2)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blessed-salem",
   "metadata": {},
   "source": [
    "# 4. Support vector machines (SVMs)\n",
    "\n",
    "Support Vector Machines belong to the discriminant model family. These models try to find a combination of samples to build a *threshold* maximizing the margin between two classes. After using the training examples to create this threshold, SVM maps new examples into that same space and predicts their category to belong based on which side of the gap they fall.\n",
    "\n",
    "The main features of SVMs are:\n",
    "* allowing to make a certain number of mistakes during the training process.\n",
    "* keeping margin as wide as possible so that other points can still be classified correctly.\n",
    "\n",
    "This process is known as *Soft Margin Formulation.*\n",
    "\n",
    "<img src=\"https://miro.medium.com/max/1400/1*M_3iYollNTlz0PVn5udCBQ.png\" alt=\"drawing\" width=\"500\"/>\n",
    "\n",
    "This method is highly effective to solve classification problems in non-lineable separable spaces. SVMs performs a non-linear classification using what is called the **kernel trick**, implicitly mapping their inputs into high-dimensional feature spaces.\n",
    "\n",
    "<img src=\"https://miro.medium.com/max/1400/1*mCwnu5kXot6buL7jeIafqQ.png\" alt=\"drawing\" width=\"600\"/>\n",
    "\n",
    "If you are interested in learning more, check out this [link](https://towardsdatascience.com/support-vector-machines-soft-margin-formulation-and-kernel-trick-4c9729dc8efe).\n",
    "\n",
    "\n",
    "## Creating the threshold.\n",
    "There are three main kernels used to identify these thresholds, which will separate the classes using SVM:\n",
    "* Linear: A lineal formulation will be used to separate the classes\n",
    "* Polinomial: A polinomial function (i.e., x^2, x^3, x^4, ...) will be used to separate the classes.\n",
    "* Radial Basis Function (RBF): It uses normal curves around the data points, and sums these so that the decision boundary can be defined by a type of topology condition such as curves where the sum is above a value of 0.5. \n",
    "\n",
    "We will start running the **SVC lineal kernel** in the next line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "clinical-context",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = svm.SVC(kernel='linear')\n",
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lovely-relaxation",
   "metadata": {},
   "source": [
    "Let's check the predicted values from the *testing* dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "musical-balance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 0, 2, 0, 1, 0, 2, 0, 0, 0, 2, 2, 2, 0, 2, 0, 1, 1, 1, 0,\n",
       "       0, 2, 2, 0, 1, 2, 2, 2, 0, 1, 0, 1, 1, 2])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = svc.predict(X_test)\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "radical-atlantic",
   "metadata": {},
   "source": [
    "We are going to consolidate the predicted results with the real results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "proof-header",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_real</th>\n",
       "      <th>y_predicted</th>\n",
       "      <th>is_correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     y_real  y_predicted  is_correct\n",
       "31        0            0        True\n",
       "101       1            1        True\n",
       "150       2            2        True\n",
       "53        0            0        True\n",
       "122       1            2       False"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({'y_real':y_test, 'y_predicted':y_predicted})\n",
    "results[\"is_correct\"] = [i['y_real'] == i['y_predicted'] for k,i in results.iterrows()]\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "medical-coverage",
   "metadata": {},
   "source": [
    "Checking the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "domestic-holmes",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct predictions: 25\n",
      "Wrong predictions: 11\n",
      "Percentage of correct predictions: 69.44%\n"
     ]
    }
   ],
   "source": [
    "print(\"Correct predictions: \" + str(len(results[results.is_correct == True])))\n",
    "print(\"Wrong predictions: \" + str(len(results[results.is_correct == False])))\n",
    "print(\"Percentage of correct predictions: \" + str(round(100*len(results[results.is_correct == True])/len(results),2)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "negative-alabama",
   "metadata": {},
   "source": [
    "Let's plot the classification model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "celtic-tyler",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the line, the points, and the nearest vectors to the plane\n",
    "fignum = 1\n",
    "plt.figure(fignum, figsize=(8, 6))\n",
    "plt.clf()\n",
    "\n",
    "plt.scatter(svc.support_vectors_[:, 0], svc.support_vectors_[:, 1], s=80,\n",
    "            facecolors='none', zorder=10, edgecolors='k')\n",
    "plt.scatter(X_train.iloc[:, 0], X_train.iloc[:, 1], c=y_train, zorder=10, cmap=plt.cm.Paired,\n",
    "            edgecolors='k')\n",
    "plt.axis('tight')\n",
    "\n",
    "Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(fignum, figsize=(8, 6))\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired, shading='auto')\n",
    "plt.contour(xx, yy, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],\n",
    "            levels=[-.5, 0, .5])\n",
    "plt.xlabel(wine.columns[0])\n",
    "plt.ylabel(wine.columns[1])\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "fignum = fignum + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outer-garlic",
   "metadata": {},
   "source": [
    "### Exercise 4:\n",
    "Now, it's your turn to create a SVM model with a linear kernel using all the features available. How well does this model perfom? Is it better or worse?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "proper-double",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create your model here. Don't forget to run the fit function:\n",
    "#svc = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "baking-keeping",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predicted value. Don't forget to use X_all_test\n",
    "#y_all_predicted = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "urban-adapter",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the new result dataframe\n",
    "#results = pd.DataFrame({'y_real': y_all_test, 'y_predicted': y_all_predicted})\n",
    "#results[\"is_correct\"] = [i['y_real'] == i['y_predicted'] for k,i in results.iterrows()]\n",
    "#results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "descending-spice",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the new results\n",
    "#print(\"Correct predictions: \" + str(len(results[results.is_correct == True])))\n",
    "#print(\"Wrong predictions: \" + str(len(results[results.is_correct == False])))\n",
    "#print(\"Percentage of correct predictions: \" + str(round(100*len(results[results.is_correct == True])/len(results),2)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suspended-horse",
   "metadata": {},
   "source": [
    "### Polynominal kernel\n",
    "Now, we will use a polynomial kernel with degree 2. This will include a quadratic function in the formula. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "false-making",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(gamma=2, kernel='poly')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = svm.SVC(kernel='poly', gamma=2)\n",
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suited-prompt",
   "metadata": {},
   "source": [
    "Now, we calculate the predicted values from the testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "endless-server",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct predictions: 26\n",
      "Wrong predictions: 10\n",
      "Percentage of correct predictions: 72.22%\n"
     ]
    }
   ],
   "source": [
    "y_predicted = svc.predict(X_test)\n",
    "results = pd.DataFrame({'y_real':y_test, 'y_predicted':y_predicted})\n",
    "results[\"is_correct\"] = [i['y_real'] == i['y_predicted'] for k,i in results.iterrows()]\n",
    "\n",
    "print(\"Correct predictions: \" + str(len(results[results.is_correct == True])))\n",
    "print(\"Wrong predictions: \" + str(len(results[results.is_correct == False])))\n",
    "print(\"Percentage of correct predictions: \" + str(round(100*len(results[results.is_correct == True])/len(results),2)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "configured-geneva",
   "metadata": {},
   "source": [
    "We will plot the plane to see the new thresholds "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "familiar-fitness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the line, the points, and the nearest vectors to the plane\n",
    "fignum = 1\n",
    "plt.figure(fignum, figsize=(8, 6))\n",
    "plt.clf()\n",
    "\n",
    "plt.scatter(svc.support_vectors_[:, 0], svc.support_vectors_[:, 1], s=80,\n",
    "            facecolors='none', zorder=10, edgecolors='k')\n",
    "plt.scatter(X_train.iloc[:, 0], X_train.iloc[:, 1], c=y_train, zorder=10, cmap=plt.cm.Paired,\n",
    "            edgecolors='k')\n",
    "\n",
    "plt.axis('tight')\n",
    "x_min, x_max = X_train.iloc[:, 0].min() - 1, X_train.iloc[:, 0].max() + 1\n",
    "y_min, y_max = X_train.iloc[:, 1].min() - 1, X_train.iloc[:, 1].max() + 1\n",
    "\n",
    "Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(fignum, figsize=(4, 3))\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired, shading='auto')\n",
    "plt.contour(xx, yy, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],\n",
    "            levels=[-.5, 0, .5])\n",
    "plt.xlabel(wine.columns[0])\n",
    "plt.ylabel(wine.columns[1])\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "fignum = fignum + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intensive-brunswick",
   "metadata": {},
   "source": [
    "### RBF kernel\n",
    "Lastly, let's use the Radial Basis Function kernel to detect other thresholds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "uniform-fireplace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(gamma=2)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = svm.SVC(kernel='rbf', gamma=2)\n",
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "harmful-compression",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct predictions: 27\n",
      "Wrong predictions: 9\n",
      "Percentage of correct predictions: 75.0%\n"
     ]
    }
   ],
   "source": [
    "y_predicted = svc.predict(X_test)\n",
    "results = pd.DataFrame({'y_real':y_test, 'y_predicted':y_predicted})\n",
    "results[\"is_correct\"] = [i['y_real'] == i['y_predicted'] for k,i in results.iterrows()]\n",
    "\n",
    "print(\"Correct predictions: \" + str(len(results[results.is_correct == True])))\n",
    "print(\"Wrong predictions: \" + str(len(results[results.is_correct == False])))\n",
    "print(\"Percentage of correct predictions: \" + str(round(100*len(results[results.is_correct == True])/len(results),2)) + \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "three-sleeping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the line, the points, and the nearest vectors to the plane\n",
    "fignum = 1\n",
    "plt.figure(fignum, figsize=(8, 6))\n",
    "plt.clf()\n",
    "\n",
    "plt.scatter(svc.support_vectors_[:, 0], svc.support_vectors_[:, 1], s=80,\n",
    "            facecolors='none', zorder=10, edgecolors='k')\n",
    "plt.scatter(X_train.iloc[:, 0], X_train.iloc[:, 1], c=y_train, zorder=10, cmap=plt.cm.Paired,\n",
    "            edgecolors='k')\n",
    "\n",
    "plt.axis('tight')\n",
    "Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(fignum, figsize=(4, 3))\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired, shading='auto')\n",
    "plt.contour(xx, yy, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],\n",
    "            levels=[-.5, 0, .5])\n",
    "plt.xlabel(wine.columns[0])\n",
    "plt.ylabel(wine.columns[1])\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "fignum = fignum + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weighted-disposal",
   "metadata": {},
   "source": [
    "## 5. Neural Networks: Muli-layer perceptron\n",
    "\n",
    "Multi-layer perceptron (MLP) is an artificial neural network. The model learns from the dataset through three types of layers:\n",
    "* *The input layer*: The input layer receives the features to be processed.\n",
    "* *The output layer*: The prediction and classification is performed by the output layer.\n",
    "* *The hidden layers*: An arbitrary number of hidden layers that are placed in between the input and output layer perform the computation. Each layer has nodes (also known as neurons) that transforms the values from the previous layer into an activation funcition. The output layer receives the activated values from the last hidden layer and transforms them into output values.\n",
    "\n",
    "<img src=\"https://static.javatpoint.com/tutorial/tensorflow/images/multi-layer-perceptron-in-tensorflow.png\" alt=\"drawing\" width=\"400\"/>\n",
    "\n",
    "Multi-layer perceptrons are networks of nodes that pass a linear combination of their inputs from one layer to another. As they do this, the nodes decide how to modify their inputs, utilizing a given activation function. The activation function of a neuron is the key here. By selecting non-linear activation functions, such as the logistic function \\sigma(x) shown below, the neural network can embed non-linearity in its operation:\n",
    "\n",
    "<img src=\"https://www.baeldung.com/wp-content/uploads/sites/4/2020/06/Copy-of-Copy-of-Blank-Diagram1.svg\" alt=\"drawing\" width=\"400\"/>\n",
    "\n",
    "Neural networks are good for non-linear classes of problems. The first advantage of neural networks is, therefore, their flexibility in addressing problems with non-linear shapes. MLPs are implemented in diverse fields such as speech recognition, image recognition, and machine translation software. From artificial neural networks, **deep learning** has emerged as a sub-field of machine learning.\n",
    "\n",
    "Let's create a simple perceptron with 5 nodes in two layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "buried-corpus",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(alpha=1e-05, hidden_layer_sizes=(5, 2), max_iter=10000,\n",
       "              random_state=1, solver='lbfgs')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1, max_iter=10000)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assured-philip",
   "metadata": {},
   "source": [
    "We will use the trained perpcetron to predict the wines' classes and compare with the real values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "spanish-painting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_real</th>\n",
       "      <th>y_predicted</th>\n",
       "      <th>is_correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     y_real  y_predicted  is_correct\n",
       "31        0            0        True\n",
       "101       1            1        True\n",
       "150       2            0       False\n",
       "53        0            0        True\n",
       "122       1            1        True"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = clf.predict(X_test)\n",
    "results = pd.DataFrame({'y_real':y_test, 'y_predicted':y_predicted})\n",
    "results[\"is_correct\"] = [i['y_real'] == i['y_predicted'] for k,i in results.iterrows()]\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "loving-restoration",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct predictions: 25\n",
      "Wrong predictions: 11\n",
      "Percentage of correct predictions: 69.44%\n"
     ]
    }
   ],
   "source": [
    "print(\"Correct predictions: \" + str(len(results[results.is_correct == True])))\n",
    "print(\"Wrong predictions: \" + str(len(results[results.is_correct == False])))\n",
    "print(\"Percentage of correct predictions: \" + str(round(100*len(results[results.is_correct == True])/len(results),2)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "common-infrastructure",
   "metadata": {},
   "source": [
    "Let's see visually how the neural network performed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "religious-microwave",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1, figsize=(8, 6))\n",
    "plt.pcolormesh(xx, yy, Z, shading='auto', cmap=plt.cm.Paired)\n",
    "\n",
    "# Plot also the training points\n",
    "plt.scatter(X_train.iloc[:, 0], X_train.iloc[:, 1], c=y_train, edgecolors='k', cmap=plt.cm.Paired)\n",
    "plt.xlabel(wine.columns[0])\n",
    "plt.ylabel(wine.columns[1])\n",
    "\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gentle-government",
   "metadata": {},
   "source": [
    "### Exercise 5:\n",
    "Now, it's your turn to create a neural network using all the features available from the wine dataset. Try with 20 nodes and 15 layers => (20,15). How well does this model perfom? Is it better or worse?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "imperial-tobacco",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create your model here. Don't forget to run the fit function, and use the X_all_train and y_all_train dataframes.\n",
    "#clf = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "after-final",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predicted value. Don't forget to use `X_all_test`\n",
    "#y_all_predicted = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "welcome-oasis",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the new result dataframe\n",
    "#results = pd.DataFrame({'y_real': y_all_test, 'y_predicted': y_all_predicted})\n",
    "#results[\"is_correct\"] = [i['y_real'] == i['y_predicted'] for k,i in results.iterrows()]\n",
    "#results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "retired-plaintiff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the new results\n",
    "#print(\"Correct predictions: \" + str(len(results[results.is_correct == True])))\n",
    "#print(\"Wrong predictions: \" + str(len(results[results.is_correct == False])))\n",
    "#print(\"Percentage of correct predictions: \" + str(round(100*len(results[results.is_correct == True])/len(results),2)) + \"%\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}