{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove_input"
    ]
   },
   "outputs": [],
   "source": [
    "path_data = '../../data/'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import scipy.stats as stats\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing the Classifier\n",
    "We are now ready to implement a $k$-nearest neighbor classifier based on multiple attributes. We have used only two attributes so far, for ease of visualization. But usually predictions will be based on many attributes. Here is an example that shows how multiple attributes can be better than pairs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Banknote authentication\n",
    "\n",
    "This time we'll look at predicting whether a banknote (e.g., a \\$20 bill) is counterfeit or legitimate.  Researchers have put together a data set for us, based on photographs of many individual banknotes: some counterfeit, some legitimate.  They computed a few numbers from each image, using techniques that we won't worry about for this course.  So, for each banknote, we know a few numbers that were computed from a photograph of it as well as its class (whether it is counterfeit or not).  Let's load it into a table and take a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WaveletVar</th>\n",
       "      <th>WaveletSkew</th>\n",
       "      <th>WaveletCurt</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.62160</td>\n",
       "      <td>8.66610</td>\n",
       "      <td>-2.8073</td>\n",
       "      <td>-0.44699</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.54590</td>\n",
       "      <td>8.16740</td>\n",
       "      <td>-2.4586</td>\n",
       "      <td>-1.46210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.86600</td>\n",
       "      <td>-2.63830</td>\n",
       "      <td>1.9242</td>\n",
       "      <td>0.10645</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.45660</td>\n",
       "      <td>9.52280</td>\n",
       "      <td>-4.0112</td>\n",
       "      <td>-3.59440</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.32924</td>\n",
       "      <td>-4.45520</td>\n",
       "      <td>4.5718</td>\n",
       "      <td>-0.98880</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>0.40614</td>\n",
       "      <td>1.34920</td>\n",
       "      <td>-1.4501</td>\n",
       "      <td>-0.55949</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>-1.38870</td>\n",
       "      <td>-4.87730</td>\n",
       "      <td>6.4774</td>\n",
       "      <td>0.34179</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1369</th>\n",
       "      <td>-3.75030</td>\n",
       "      <td>-13.45860</td>\n",
       "      <td>17.5932</td>\n",
       "      <td>-2.77710</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1370</th>\n",
       "      <td>-3.56370</td>\n",
       "      <td>-8.38270</td>\n",
       "      <td>12.3930</td>\n",
       "      <td>-1.28230</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>-2.54190</td>\n",
       "      <td>-0.65804</td>\n",
       "      <td>2.6842</td>\n",
       "      <td>1.19520</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1372 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      WaveletVar  WaveletSkew  WaveletCurt  Entropy  Class\n",
       "0        3.62160      8.66610      -2.8073 -0.44699      0\n",
       "1        4.54590      8.16740      -2.4586 -1.46210      0\n",
       "2        3.86600     -2.63830       1.9242  0.10645      0\n",
       "3        3.45660      9.52280      -4.0112 -3.59440      0\n",
       "4        0.32924     -4.45520       4.5718 -0.98880      0\n",
       "...          ...          ...          ...      ...    ...\n",
       "1367     0.40614      1.34920      -1.4501 -0.55949      1\n",
       "1368    -1.38870     -4.87730       6.4774  0.34179      1\n",
       "1369    -3.75030    -13.45860      17.5932 -2.77710      1\n",
       "1370    -3.56370     -8.38270      12.3930 -1.28230      1\n",
       "1371    -2.54190     -0.65804       2.6842  1.19520      1\n",
       "\n",
       "[1372 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banknotes = pd.read_csv(path_data + 'banknote.csv')\n",
    "banknotes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at whether the first two numbers tell us anything about whether the banknote is counterfeit or not.  Here's a scatterplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_table = pd.DataFrame(\n",
    "    {'Class':np.array([1, 0]),\n",
    "    'Color':np.array(['darkblue', 'gold'])}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WaveletVar</th>\n",
       "      <th>WaveletSkew</th>\n",
       "      <th>WaveletCurt</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>Class</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.62160</td>\n",
       "      <td>8.66610</td>\n",
       "      <td>-2.8073</td>\n",
       "      <td>-0.44699</td>\n",
       "      <td>0</td>\n",
       "      <td>gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.54590</td>\n",
       "      <td>8.16740</td>\n",
       "      <td>-2.4586</td>\n",
       "      <td>-1.46210</td>\n",
       "      <td>0</td>\n",
       "      <td>gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.86600</td>\n",
       "      <td>-2.63830</td>\n",
       "      <td>1.9242</td>\n",
       "      <td>0.10645</td>\n",
       "      <td>0</td>\n",
       "      <td>gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.45660</td>\n",
       "      <td>9.52280</td>\n",
       "      <td>-4.0112</td>\n",
       "      <td>-3.59440</td>\n",
       "      <td>0</td>\n",
       "      <td>gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.32924</td>\n",
       "      <td>-4.45520</td>\n",
       "      <td>4.5718</td>\n",
       "      <td>-0.98880</td>\n",
       "      <td>0</td>\n",
       "      <td>gold</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>0.40614</td>\n",
       "      <td>1.34920</td>\n",
       "      <td>-1.4501</td>\n",
       "      <td>-0.55949</td>\n",
       "      <td>1</td>\n",
       "      <td>darkblue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>-1.38870</td>\n",
       "      <td>-4.87730</td>\n",
       "      <td>6.4774</td>\n",
       "      <td>0.34179</td>\n",
       "      <td>1</td>\n",
       "      <td>darkblue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1369</th>\n",
       "      <td>-3.75030</td>\n",
       "      <td>-13.45860</td>\n",
       "      <td>17.5932</td>\n",
       "      <td>-2.77710</td>\n",
       "      <td>1</td>\n",
       "      <td>darkblue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1370</th>\n",
       "      <td>-3.56370</td>\n",
       "      <td>-8.38270</td>\n",
       "      <td>12.3930</td>\n",
       "      <td>-1.28230</td>\n",
       "      <td>1</td>\n",
       "      <td>darkblue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>-2.54190</td>\n",
       "      <td>-0.65804</td>\n",
       "      <td>2.6842</td>\n",
       "      <td>1.19520</td>\n",
       "      <td>1</td>\n",
       "      <td>darkblue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1372 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      WaveletVar  WaveletSkew  WaveletCurt  Entropy  Class     Color\n",
       "0        3.62160      8.66610      -2.8073 -0.44699      0      gold\n",
       "1        4.54590      8.16740      -2.4586 -1.46210      0      gold\n",
       "2        3.86600     -2.63830       1.9242  0.10645      0      gold\n",
       "3        3.45660      9.52280      -4.0112 -3.59440      0      gold\n",
       "4        0.32924     -4.45520       4.5718 -0.98880      0      gold\n",
       "...          ...          ...          ...      ...    ...       ...\n",
       "1367     0.40614      1.34920      -1.4501 -0.55949      1  darkblue\n",
       "1368    -1.38870     -4.87730       6.4774  0.34179      1  darkblue\n",
       "1369    -3.75030    -13.45860      17.5932 -2.77710      1  darkblue\n",
       "1370    -3.56370     -8.38270      12.3930 -1.28230      1  darkblue\n",
       "1371    -2.54190     -0.65804       2.6842  1.19520      1  darkblue\n",
       "\n",
       "[1372 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banknotes = pd.merge(banknotes, color_table, on='Class')\n",
    "banknotes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "banknotes_darkblue = banknotes[banknotes['Color'] == 'darkblue']\n",
    "banknotes_gold = banknotes[banknotes['Color'] == 'gold']\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "\n",
    "ax.scatter(banknotes_darkblue['WaveletVar'], \n",
    "           banknotes_darkblue['WaveletCurt'],  \n",
    "           label='Color=darkblue', \n",
    "           color='darkblue')\n",
    "\n",
    "ax.scatter(banknotes_gold['WaveletVar'], \n",
    "           banknotes_gold['WaveletCurt'],  \n",
    "           label='Color=gold', \n",
    "           color='gold')\n",
    "\n",
    "\n",
    "x_label = 'WaveletVar'\n",
    "\n",
    "y_label = 'WaveletCurt'\n",
    "\n",
    "y_vals = ax.get_yticks()\n",
    "\n",
    "plt.ylabel(y_label)\n",
    "\n",
    "plt.xlabel(x_label)\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "\n",
    "plt.xlim(-7.5, 7.5)\n",
    "plt.ylim(-6, 16)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty interesting!  Those two measurements do seem helpful for predicting whether the banknote is counterfeit or not.  However, in this example you can now see that there is some overlap between the blue cluster and the gold cluster.  This indicates that there will be some images where it's hard to tell whether the banknote is legitimate based on just these two numbers.  Still, you could use a $k$-nearest neighbor classifier to predict the legitimacy of a banknote.\n",
    "\n",
    "Take a minute and think it through: Suppose we used $k=11$ (say).  What parts of the plot would the classifier get right, and what parts would it make errors on?  What would the decision boundary look like?\n",
    "\n",
    "The patterns that show up in the data can get pretty wild.  For instance, here's what we'd get if used a different pair of measurements from the images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "banknotes_darkblue = banknotes[banknotes['Color'] == 'darkblue']\n",
    "banknotes_gold = banknotes[banknotes['Color'] == 'gold']\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "\n",
    "ax.scatter(banknotes_darkblue['WaveletSkew'], \n",
    "           banknotes_darkblue['Entropy'],  \n",
    "           label='Color=darkblue', \n",
    "           color='darkblue')\n",
    "\n",
    "ax.scatter(banknotes_gold['WaveletSkew'], \n",
    "           banknotes_gold['Entropy'],  \n",
    "           label='Color=gold', \n",
    "           color='gold')\n",
    "\n",
    "\n",
    "x_label = 'WaveletSkew'\n",
    "\n",
    "y_label = 'Entropy'\n",
    "\n",
    "y_vals = ax.get_yticks()\n",
    "\n",
    "plt.ylabel(y_label)\n",
    "\n",
    "plt.xlabel(x_label)\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "\n",
    "plt.xlim(-16, 16)\n",
    "plt.ylim(-10, 3)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There does seem to be a pattern, but it's a pretty complex one.  Nonetheless, the $k$-nearest neighbors classifier can still be used and will effectively \"discover\" patterns out of this.  This illustrates how powerful machine learning can be: it can effectively take advantage of even patterns that we would not have anticipated, or that we would have thought to \"program into\" the computer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple attributes\n",
    "So far I've been assuming that we have exactly 2 attributes that we can use to help us make our prediction.  What if we have more than 2?  For instance, what if we have 3 attributes?\n",
    "\n",
    "Here's the cool part: you can use the same ideas for this case, too.  All you have to do is make a 3-dimensional scatterplot, instead of a 2-dimensional plot.  You can still use the $k$-nearest neighbors classifier, but now computing distances in 3 dimensions instead of just 2.  It just works.  Very cool!\n",
    "\n",
    "In fact, there's nothing special about 2 or 3.  If you have 4 attributes, you can use the $k$-nearest neighbors classifier in 4 dimensions.  5 attributes?  Work in 5-dimensional space.  And no need to stop there!  This all works for arbitrarily many attributes; you just work in a very high dimensional space.  It gets wicked-impossible to visualize, but that's OK.  The computer algorithm generalizes very nicely: all you need is the ability to compute the distance, and that's not hard.  Mind-blowing stuff!\n",
    "\n",
    "For instance, let's see what happens if we try to predict whether a banknote is counterfeit or not using 3 of the measurements, instead of just 2.  Here's what you get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure(figsize=(8,8)).add_subplot(111, projection='3d')\n",
    "ax.scatter(banknotes['WaveletSkew'], \n",
    "           banknotes['WaveletVar'], \n",
    "           banknotes['WaveletCurt'], \n",
    "           c=banknotes['Color']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome!  With just 2 attributes, there was some overlap between the two clusters (which means that the classifier was bound to make some mistakes for pointers in the overlap).  But when we use these 3 attributes, the two clusters have almost no overlap.  In other words, a classifier that uses these 3 attributes will be more accurate than one that only uses the 2 attributes.\n",
    "\n",
    "This is a general phenomenom in classification.  Each attribute can potentially give you new information, so more attributes sometimes helps you build a better classifier.  Of course, the cost is that now we have to gather more information to measure the value of each attribute, but this cost may be well worth it if it significantly improves the accuracy of our classifier.\n",
    "\n",
    "To sum up: you now know how to use $k$-nearest neighbor classification to predict the answer to a yes/no question, based on the values of some attributes, assuming you have a training set with examples where the correct prediction is known.  The general roadmap is this:\n",
    "\n",
    "1. identify some attributes that you think might help you predict the answer to the question.\n",
    "2. Gather a training set of examples where you know the values of the attributes as well as the correct prediction.\n",
    "3. To make predictions in the future, measure the value of the attributes and then use $k$-nearest neighbor classification to predict the answer to the question."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Distance in Multiple Dimensions\n",
    "We know how to compute distance in 2-dimensional space. If we have a point at coordinates $(x_0,y_0)$ and another at $(x_1,y_1)$, the distance between them is\n",
    "\n",
    "$$D = \\sqrt{(x_0-x_1)^2 + (y_0-y_1)^2}.$$\n",
    "\n",
    "In 3-dimensional space, the points are $(x_0, y_0, z_0)$ and $(x_1, y_1, z_1)$, and the formula for the distance between them is\n",
    "\n",
    "$$\n",
    "D = \\sqrt{(x_0-x_1)^2 + (y_0-y_1)^2 + (z_0-z_1)^2}\n",
    "$$\n",
    "\n",
    "In $n$-dimensional space, things are a bit harder to visualize, but I think you can see how the formula generalized: we sum up the squares of the differences between each individual coordinate, and then take the square root of that.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the last section, we defined the function `distance` which returned the distance between two points. We used it in two-dimensions, but the great news is that the function doesn't care how many dimensions there are! It just subtracts the two arrays of coordinates (no matter how long the arrays are), squares the differences and adds up, and then takes the square root. To work in multiple dimensions, we don't have to change the code at all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(point1, point2):\n",
    "    \"\"\"Returns the distance between point1 and point2\n",
    "    where each argument is an array \n",
    "    consisting of the coordinates of the point\"\"\"\n",
    "    return np.sqrt(np.sum((point1 - point2)**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use this on a [new dataset](https://archive.ics.uci.edu/ml/datasets/Wine). The table `wine` contains the chemical composition of 178 different Italian wines. The classes are the grape species, called cultivars. There are three classes but let's just see whether we can tell Class 1 apart from the other two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine = pd.read_csv(path_data + 'wine.csv')\n",
    "\n",
    "# For converting Class to binary\n",
    "\n",
    "def is_one(x):\n",
    "    if x == 1:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "wine['Class1'] = wine['Class'].apply(is_one)\n",
    "\n",
    "# This creates a column 'Class1' - we drop the 'Class' column, rename 'Class1' as 'Class'\n",
    "# then move the column to the first position using 'pop' and 'insert'\n",
    "\n",
    "wine = wine.drop(columns=['Class'])\n",
    "\n",
    "wine = wine.rename(columns={'Class1': 'Class'})\n",
    "\n",
    "class_label = wine.pop('Class')\n",
    "\n",
    "wine.insert(0, 'Class', class_label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Class</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 diulted wines</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</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</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>0</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>0</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>0</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>0</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>0</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Class  Alcohol  Malic Acid   Ash  Alcalinity of Ash  Magnesium  \\\n",
       "0        1    14.23        1.71  2.43               15.6        127   \n",
       "1        1    13.20        1.78  2.14               11.2        100   \n",
       "2        1    13.16        2.36  2.67               18.6        101   \n",
       "3        1    14.37        1.95  2.50               16.8        113   \n",
       "4        1    13.24        2.59  2.87               21.0        118   \n",
       "..     ...      ...         ...   ...                ...        ...   \n",
       "173      0    13.71        5.65  2.45               20.5         95   \n",
       "174      0    13.40        3.91  2.48               23.0        102   \n",
       "175      0    13.27        4.28  2.26               20.0        120   \n",
       "176      0    13.17        2.59  2.37               20.0        120   \n",
       "177      0    14.13        4.10  2.74               24.5         96   \n",
       "\n",
       "     Total Phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0             2.80        3.06                  0.28             2.29   \n",
       "1             2.65        2.76                  0.26             1.28   \n",
       "2             2.80        3.24                  0.30             2.81   \n",
       "3             3.85        3.49                  0.24             2.18   \n",
       "4             2.80        2.69                  0.39             1.82   \n",
       "..             ...         ...                   ...              ...   \n",
       "173           1.68        0.61                  0.52             1.06   \n",
       "174           1.80        0.75                  0.43             1.41   \n",
       "175           1.59        0.69                  0.43             1.35   \n",
       "176           1.65        0.68                  0.53             1.46   \n",
       "177           2.05        0.76                  0.56             1.35   \n",
       "\n",
       "     Color Intensity   Hue  OD280/OD315 of diulted wines  Proline  \n",
       "0               5.64  1.04                          3.92     1065  \n",
       "1               4.38  1.05                          3.40     1050  \n",
       "2               5.68  1.03                          3.17     1185  \n",
       "3               7.80  0.86                          3.45     1480  \n",
       "4               4.32  1.04                          2.93      735  \n",
       "..               ...   ...                           ...      ...  \n",
       "173             7.70  0.64                          1.74      740  \n",
       "174             7.30  0.70                          1.56      750  \n",
       "175            10.20  0.59                          1.56      835  \n",
       "176             9.30  0.60                          1.62      840  \n",
       "177             9.20  0.61                          1.60      560  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first two wines are both in Class 1. To find the distance between them, we first need a table of just the attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_attributes = wine.copy()\n",
    "\n",
    "wine_attributes = wine_attributes.drop(columns=['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.265012394048398"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance(np.array(wine_attributes.iloc[0]), np.array(wine_attributes.iloc[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last wine in the table is of Class 0. Its distance from the first wine is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "506.05936766351834"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance(np.array(wine_attributes.iloc[0]), np.array(wine_attributes.iloc[177]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's quite a bit bigger! Let's do some visualization to see if Class 1 really looks different from Class 0. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_with_colors = pd.merge(wine, color_table, on='Class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_darkblue = wine_with_colors[wine_with_colors['Color'] == 'darkblue']\n",
    "wine_gold = wine_with_colors[wine_with_colors['Color'] == 'gold']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "\n",
    "ax.scatter(wine_darkblue['Flavanoids'], \n",
    "           wine_darkblue['Alcohol'],  \n",
    "           label='Color=darkblue', \n",
    "           color='darkblue')\n",
    "\n",
    "ax.scatter(wine_gold['Flavanoids'], \n",
    "           wine_gold['Alcohol'],  \n",
    "           label='Color=gold', \n",
    "           color='gold')\n",
    "\n",
    "\n",
    "x_label = 'Flavanoids'\n",
    "\n",
    "y_label = 'Alcohol'\n",
    "\n",
    "#y_vals = ax.get_yticks()\n",
    "\n",
    "plt.ylabel(y_label)\n",
    "\n",
    "plt.xlabel(x_label)\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "\n",
    "plt.xlim(0, 5)\n",
    "plt.ylim(10.8, 15)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The blue points (Class 1) are almost entirely separate from the gold ones. That is one indication of why the distance between two Class 1 wines would be smaller than the distance between wines of two different classes. We can see a similar phenomenon with a different pair of attributes too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u374NuVyuVd63b1+EhoZi+PDh+OabbwRsKRERUeNJqhRwKYyH642hcCmMh6RKYdLnFRYW4tChQ4iPj4ebm5vW53K5HCqVCrGxsbh+/TrS0tKwZ88e5OfnIzY2FiqVSud99+zZgzfeeANTpkzBqVOnkJCQgBkzZmD//v0a9RYtWoSoqCicPHkSL774IgCgTZs2df4ZM2aM+vrt27dj0aJFmDt3Lo4fP47Q0FCsWLFC4xkrV67EsmXLMH/+fJw8eRJDhgzB888/jx9//FFn28vKyjB27FgEBgbi6NGjmD9/PubOnWtUv1oa0aZfAeDnn39GVFQUysvL4erqio0bN6JTp04GXXvgwAEcP35cYx2ej48PlixZgkceeQQVFRVISUnBiBEjsHfvXvTs2bPO++Xk5DTqXcg47G/zY5+bn732ubPkT7RxXgUnyXVUqrzwZ0UCKlTmWVxf0+chISFmeV5DSKoUcL01EtLq3HsFlYC0MgtlzXdB5RhgkmdeunQJKpUKHTp00Fvn2LFj+Omnn3D27FkEBNxrx5o1axAeHo7jx4+jb9++WtcsX74cMTExmDx5MgCgffv2+P777/HJJ59g8ODB6nqjRo3CCy+8oHFtfckIMplM/fdVq1bhH//4h/oer7/+OtLT0/Hbb79ptGXatGnqJVlvv/02Tp48ieXLl+Ozzz7Tuv/WrVtRUVGBTz/9FG5ubujYsSNmzJiBl156qc52WTJRg7qQkBCkp6ejuLgYaWlpmDJlCvbu3YuOHTvWeV1mZibi4+OxaNEiREREaNzvwf8hR0ZG4sqVK1i2bFm9QZ0l/wNga3JyctjfZsY+Nz977fN7Acu/7gcsAOSyCyYNWGpYS5/Lbi/Q6B8AkFbnQnZ7Ae56JpvkmfpG2h504cIF+Pr6qgM6AAgMDISvry9+/fVXnUHdhQsXEBsbq1H2xBNPaI3UhYeHa10bFBRkYOuBixcvagWFERER6qCupKQEeXl5ePzxx7Xaoi8J5MKFC+jUqZPGyKUxs4WWSNSgztnZWf1LDQ8Px3fffYcVK1Zg+fLleq85deoUxo4di9mzZ2PSpEn1PiMiIgI7duwQrM1ERHVRKEqwYEEWLl26gaCgP5CY2B0BAR5iN8tsxAhYrI1DdZ6e8nyTPTM4OBgSiaTO3SBUKhUkEonOz/SV6/usdpmrq6tWnfq2RnniiSewbds2g9pQF33XGRLoWhtRg7ralEolKioq9H5+4sQJxMTEYNasWZg6dapB9zx37hy8vb2FaiIRkV4KRQlGjtyP3NwSAEB2djGysq5j167BdhPYiRGwWBul1Beo1FXuY7Jnenp6YsCAAUhOTsZLL72kta6uqKgIDz30EP766y8oFAr1aN3ly5eRl5eHhx56SOd9Q0NDkZmZieeff15ddurUKb31H2TM9GuHDh3w3XffYfz48eqy7777Tv13Dw8P+Pr6IjMzE3369NFoS2hoqM77P/TQQ9iyZQvKysrUQae+ZE1rIVpQN3/+fERFRaFNmzYoLS3Ftm3bkJGRgdTUVABAUlISsrOz1dmt6enpiImJwaRJkzB27Fhcu3YNACCVStVblqxYsQL+/v4ICwtDRUUFUlNTsW/fPqxfv16clyQiu7JgQZY6oKuRm3tv5C45ub9IrTIvMQIWa1PunghpZZbGiGa1tB3K3RNN+tzFixdj4MCB6NevH95++2106tQJKpUK6enpWLp0Kc6dO4fOnTtj8uTJWLRoEVQqFd58801069YNvXv31nnP6dOnY8KECXj44YfRv39/HDp0CFu3btXaR1YXY6ZfExIS8PLLLyM8PBw9evTA3r17kZWVpZEsOX36dCxcuBDBwcF4+OGHkZKSglOnTuHYsWM67zlmzBi8++67mDZtGt58803k5+fjo48+MrhNlki0oO7atWuYPHkyCgoK4OHhgU6dOmHbtm0YMGAAACA/Px+5ufe/8Js3b8adO3ewbNkyLFu2TF3u5+eHc+fOAQAqKysxd+5c5OXlQSaTISwsDKmpqYiKijLvyxGRXcrLu6OzPD9fd7ktEitgsSYqxwCUNd8F2e0FcKjOh1Lqg3L3RJOvOQwMDMTx48fx0UcfYd68ecjLy0Pz5s3RuXNnLF26FBKJBJs2bcKsWbMwdOhQAECfPn3wwQcf6J3CHDp0KD744AMsW7YMs2fPhp+fHz766CONJAkhjB49GpcvX0ZSUhLu3r2LoUOHYuLEifjqq6/UdRISElBaWop58+ahoKAAISEhWL9+Pbp27arznm5ubkhJScHrr7+OPn36ICQkBPPnz8e4ceMEbbs5SYqKimxvUpksmrUsZrYl7HPziI8/gq1bf9cqj44OtpuROuBesoS5AxbAfN/z4uJiNGvWzOTPobrFxsaiqqoKKSkpYjfFrOr6/lnUmjoiImuWmNgdWVnXNaZg27XzQGJidxFbZX4qxwAmRZCg7ty5g88//xxPPfUUHB0dkZaWhq+++orLq2phUEdEJJCAAA/s2jX4f9mvNxEU1MLusl+JTEEikeDQoUNYsmQJysvLERQUhNWrV2PYsGFiN82iMKgjIhJQQIAHkpP7c8qbSEAuLi7YvXu32M2weBZxTBgRERERNQ6DOiIiIiIbwKCOiIiIyAYwqCMiIjKSLR4xRZavvu8dgzoiIiIjyGQy3LljPxtKk2VQqVQoKirSeY5uDWa/EhERGaFJkyaoqqpCcXGx2E0hO+Pu7g5HR/2hG4M6IiIiI9U1WkIkFk6/EhEREdkABnVERERENoBBHREREZENYFBHREREZAMY1BERERHZAAZ1RERERDaAQR0RERGRDWBQR0RERGQDGNQRERER2QAGdUREREQ2gEEdERERkQ1gUEdERERkAxjUEREREdkABnVERERENoBBHREREZENYFBHREREZAMY1BERERHZAAZ1RERERDZAtKAuOTkZPXr0gJ+fH/z8/PD000/j4MGDdV7z888/45lnnoGPjw/CwsKwaNEiqFQqjToZGRno06cPvL290a1bN6xdu9aUr0FERERkERzFenDr1q2RlJSE4OBgKJVKbNmyBbGxsTh27Bg6d+6sVb+kpASjRo1Cjx49cOTIEeTk5ODll19G06ZNMX36dADA5cuXMXbsWMTGxuKzzz5DZmYmZsyYgRYtWmDEiBHmfkUiIiIisxEtqBsyZIjGz3PnzsXnn3+Ob7/9VmdQt3XrVty9excrV66Ei4sLOnbsiIsXL2LFihWYNm0aJBIJ1q1bBx8fH3z44YcAgNDQUGRlZWH58uUM6oiIiMimWcSauurqamzfvh1lZWWIjIzUWefMmTN44okn4OLioi4bMGAA8vLyoFAo1HX69++vcd2AAQNw9uxZVFZWmu4FiIiIiEQm2kgdcG+NXFRUFMrLy+Hq6oqNGzeiU6dOOusWFBSgdevWGmVeXl7qzwIDA1FQUIC+fftq1amqqsLNmzfh4+Ojty05OTmNexkyCvvb/Njn5sc+N7+aPg8JCRG5JUTmJ2pQFxISgvT0dBQXFyMtLQ1TpkzB3r170bFjR531JRKJxs81SRIPlhtSR19byDxycnLY32bGPjc/9rn5sc/J3oka1Dk7OyMoKAgAEB4eju+++w4rVqzA8uXLteq2atUKBQUFGmU3btwAcH/ETl8dR0dHNG/e3BSvQERERGQRLGJNXQ2lUomKigqdn0VGRuLUqVMoLy9Xlx09ehS+vr4ICAhQ1zl27JjGdUePHkV4eDicnJxM1m4iIiIisYkW1M2fPx8nT56EQqHAzz//jKSkJGRkZCA6OhoAkJSUhOHDh6vrjxkzBi4uLpg6dSrOnz+PtLQ0fPzxx5g6dap6ajUuLg5//fUX3nrrLVy4cAHr16/H5s2bMW3aNFHekYiIiMhcRJt+vXbtGiZPnoyCggJ4eHigU6dO2LZtGwYMGAAAyM/PR25urrp+s2bNsHPnTsycORP9+vWDXC7Hyy+/rBGwBQYGIjU1FXPmzMHatWvh4+ODRYsWcTsTIiIisnmSoqIiVf3ViITDxczmxz43P/a5+bHPyd5Z1Jo6IiIiImoYBnVERERENoBBHREREZENYFBHZMMkVQq4FMajgywBLoXxkFQpxG4SERGZiKibDxOR6UiqFHC9NRLS6lw4OwIoz4a0MgtlzXdB5RggdvOIiEhgHKkjslGy2wsgrc7VKJNW50J2e4FILSIiIlNiUEdkoxyq8/SU55u5JUREZA4M6ohslFLqq6fcx8wtISIic2BQR2Sjyt0TUS1tp1FWLW2HcvdEkVpERESmxKCOyEapHANQ1nwXKmTRKKmKQIUsmkkSREQ2jNmvRDZM5RiAu57JyLmRgxC/hh+fpFCUYMGCLOTl3YGvb1MkJnZHQICHgC0lIqLGYlBHRHVSKEowcuR+5OaWqMuysq5j167BDOyIiCwIp1+JqE4LFmRpBHQAkJt7b+SOtHHDZyISC0fqiKhOeXl3dJbn5+sut2fc8JmIxMSROiKqk69vU53lPj66y+0ZN3wmIjExqCOLolCUID7+CIYO3Yv4+CNQKErqv4hMKjGxO9q101w7166dBxITu4vUIsvFDZ+JSEycfiWLwQX5likgwAO7dg3GggVZyM+/Ax8fZr/qo5T6ApW6yrnhMxGZHoM6shh1LchPTu4vUqsIuBfY8XdQv3L3REgrszSmYLnhMxGZC6dfyWJwQT5ZO274TERi4kgdWQwuyCdbINSGz0RExuJIHVkMLsgnIiJqOI7UkcXggnzD8MguIiLShUEdWRQuyK8bM4SJiEgfTr8SWREe2UVERPpwpI7IijBDmKyBpEoB2e0FcKjOg1Lqi3L3RGYAE5kBgzoiK8IMYbJ0D55/CwCoBM+/JTITTr8SWRFmCJOl4/m3ROLhSB2RFWGGMFk6nn9LJB7RRuqWLFmCfv36wc/PD8HBwYiJicH58+frvGbhwoWQy+U6/1y/fh0AkJ6ervPzixcvmuO1iEyuJkN4z56hSE7uz4COLIpS6qunnOffEpmaaCN1GRkZmDRpEh555BGoVCq89957GDlyJE6fPg1PT0+d10yfPh0TJ07UKJs4cSIkEgm8vLw0yjMzMzXu07JlS+FfgoioAWw5kYDn3xKJR7SgbseOHRo/r169Gv7+/sjMzMTgwYN1XuPm5gY3Nzf1z1evXsWpU6ewevVqrbpeXl5o0aKFsI0mImokW08kqDn/9l7Qmg+l1MemglYiS2Yxa+pKS0uhVCohl8sNvmbDhg1o1qwZhg8frvVZ3759UVFRgdDQUMycORO9e/cWsLVEpIstj0ABwrxfXYkEdz2ThWyuaGrOvyUi85IUFRWpxG4EAEyYMAG///47jh07BqlUWm99pVKJrl27YtiwYVi4cKG6PCcnB+np6XjkkUdQUVGBlJQUrF27Fnv37kXPnj313i8nJ0eQ9yCyV86SP9HBZRpkDlfVZeXKtrh4dzkqVG1EbJkwhHq/DrIEeDhma5WXVEXgYvmqBrWrjfMqOEmuo1LlhT8rEmyivxsrJCRE7CYQmZ1FBHVz5szBjh07cODAAQQGBhp0zcGDBxETE4NTp04hLCyszrrR0dGQSqX4v//7PwFaS42Vk5PDf3DNzBx97lIYD+fyrVrlFbJomxi1Mfb99PW5kP2kNZWLe+vXbGUq11j8t4Xsnej71M2ePRvbt29HWlqawQEdAHzxxRd47LHH6g3oACAiIgKXLl1qRCuJrJNCUYL4+CNISDiL+PgjUChK6r+ogSx1KwtJlQIuhfFwvTEULoXxkFQpGnQfod6v3D0R1dJ2GmUNTSTgnnBE9CBR19TNmjULO3bswN69e9GhQweDr8vLy8PXX3+N//znPwbVP3fuHLy9vRvaTCKrpFCUYOTI/eqzYrOzi5GVdR27dg02yTYoSqkvUKmrXLytLIRMShDq/YRMJLDUQJqIxCFaUDdz5kykpKRg48aNkMvluHbtGgDA1dVVneGalJSE7OxspKWlaVy7ceNGuLq6YtSoUVr3XbFiBfz9/REWFoaKigqkpqZi3759WL9+velfisiCLFiQpQ7oauTmlmDBgiwkJ/cX/HmWuJWFkEkJQr6fUIkElhhIE5F4RAvq1qxZAwAYMWKERvmsWbMwe/ZsAEB+fj5yczX/QVapVNiwYQOio6PRtKn2eZeVlZWYO3cu8vLyIJPJEBYWhtTUVERFRZnoTciWKBT3gp68vDvw9bXs0xrqa2te3h0EtLmJBa8eROtWJfirwAOJnwxEfr7uzWHrYkjWp5AjUEL9HoQcyTL0/Wr6qoPsEmSFQSbNALbEQJqIxCNaUFdUVFRvnZUrV2qVSSQS/Pjjj3qvefXVV/Hqq682pmlkp2pPVwIw6XRlYxjS1q5ht7Eu8TO0D7ilrvN4VwWWbv9/Rj3LmClMIUaghPw9mG4kS3d+2YN95ewIoDzbpHvQcU84InqQ6IkSRJairulKS2NIWxe8elAjoAOA9gG3sODVg0Y9y9yL8YX8PQiZlFATsDmXb4VjZQacy7fC9dZIjcQLMRIXagLpspZ7cNczmQEdkR1jUEd2pSYbdOjQvRrZoApFCY4d+1PnNfn5d8zZRIPk5elu04NtdZfd0FnHXXbTqGeZezG+Ie9mqJqRrApZNKqceqFCFt3gUTNDAjYmLhCRmCzmRAkiU9M3rbd8eS9Mm5aO69fLdV7n46O9dlOItjRmzZivr+42PdhWoaYezb0Y35B3M4ZQSQmGBGxMXCAiMXGkjuyGvmm9KVOOa5XXaNfOA4mJ3QVtR01wuXXr78jIyMPWrb9j5Mj9Ru0hl5jYHe3aaQaBtdsq1NSjkFOYhjDk3cSglOpOMHkwYPvbZQJUtf5bWQVH/O0ywZRNIyICwKCO7Ii+ab3i4r91lnt5yUySJCHEmrGAAA/s2jUY0dHB6NXLF9HRwVptfXDqsaQqosFTj0JOYRrCkHcTgyHBbZO7X0CCKo06ElShyd0vzNFEIrJznH4lu6FvWq9ZsyYoLtaeM+vbt41JAgmh1owFBHjUu99czdRjzo0chPg1/Pgkcx/Qbsi7mZshmaZcU0dEYuJIHdkNfdN6K1f2Met0X31rxvQlc5D46ss0NWSKlojIVDhSR3ajZlpvwYIs5OffgY/P/QQFfeWmMGHCQ9i58xKqqu7vdeboKMGECQ9Z1V55pmTIZseWqNw9EdK/T0Gquqouq5a0FX0zYGvtTyIyDoM6siv6pvXMOd33xRe/agR0AFBVpcIXX/wKAGY92ssSCXleqygk0NybWCJWQ/73eGvvTyIyGKdficysrjV1Qu7RZq3E2MBXKLLbCyBVXtUokyqvitp2a+5PIjIOR+rIalnTOa0Pasg+bA3do62mjy5duoGgoD+soo/ESDYQanpS6LYL0S4mbxDZDwZ1ZJWsee1ZYmJ3ZGVd12j7g4kZdX1mjNp9lJ1dbBV9ZO4NfIWcnhSy7UK1ixsiE9kPTr+SVRLjnFahslLr2odNyD3arOks2weZe7NjIacnhWy7UO0yd38SkXg4UkdWyVRrz/RN6Qo9MlhXYoZQSRuXLukOOvWdnmEpDNkProYg05NVuXrKLzeq7eWllyBzCxJ9KteY/rRWNd+DDrJLkBU2vM+JrB2DOrJKQp8PCtQ9pVvXqJelZqVev35XZ3lBge5yS2LIZsdCTU9KlAV6yq8ZfI8HCbXhs5DTpubePNqcHvweODsCKM+GtDILpa3Oit00IrPj9CuZTc30ZULC2UZvqmuK80HrCtysMSu1VSsXneXe3g0PfC2JUNOTKomXnvJWDW6bEDhtahh93wMie8SROjILoRftm2LD4LoCN0NGBi1tg9d27TyQlXVdqzww0F2E1txnaZmmSqcgoDpbR3k7HbXNxx6mTYWg73tAZI8Y1JFZmGL6UugNg+sK3OrLWLXEDV7ra7MYLDHTtNw9EdLKLI3RHUsZEbPlaVOh6PseENkjTr+SWVjD9GVdU7r1ZaVa4gavD7Y5IkLeqExaoVhipmnNiFiFLBpVTr1QIYvmaQtWRN/3gMgecaSOzMIUiQ1Cq29Kt66RQUvd4LWmzTk5OQgJafiifaEI2U9CTk9yRMx66cs4JrJHDOrILHRNBbZt64rS0goMHbrXYk6EaOiUrr4pIEm1ApIqBUd9/kfojXAZjBEgXMYxkbXj9CuZRe2pwGee8QcA7N//BzIy8rB16+8YOXJ/ozJixVTunoi/VdqBm1R5Ba63RkJSpRChVZaHGZ1ERKbDoI7MpmYUbNWqh+Hq6oSrV8s0PreG0w70ufynJ56Ki8elq55an4m9ts6ScP0aEZHpcPqVRGENiRPGWLAgCxmnm+Dyn80R1LZQ63Ox1tYJtdO+kNu1cMqUiMg0GNSRKKwhccIYNUHqXwW61wQas2ZMqABK3077Rp+4YIHbtRARkTZOv5IoTHEihJhqgtTETwbiN0Vzjc+MWTNWE0A5l2+FY2UGnMu3NnhNnlDbh1jidi1ERKSNI3UkClOcCGFuCsX9I8Tc3R3Rtq0rFFeBpyZNxoJXDyLI7w7CuoRB6pVk8IhWXQGUsVOWQm0fYqnbtRARkSYGdXbgweDDUrYOAYQ/EcKcah97BtzbouWZZ/xx+7Yv0r6NROLA7nBo6wGVEfcVMoASavsQobchsUSWdsQbEVFDiDb9umTJEvTr1w9+fn4IDg5GTEwMzp8/X+c1CoUCcrlc68+hQ4c06mVkZKBPnz7w9vZGt27dsHbtWlO+ikWrCT62bv3dJrYOsRS6jj27erUMrq5O2LNnKJKT+zcocFZKffWUGx9ACbV9iK1vQyLklDcRkZhEC+oyMjIwadIkHDx4EGlpaXB0dMTIkSNRWKidOVjb9u3bceHCBfWf3r17qz+7fPkyxo4di8jISHzzzTd4/fXX8eabb2L37t2mfB2LVdeZq/ZEoShBfPwRPPXULnTtugUDBuxCfPyRBge3psreFTKAenD7kJKqiAZvH6JyDMAdj+WodvCHEs1Q7eCPOx7LTTqSJalSwKUwHq43hsKlMN6kARbXDBKRrRBt+nXHjh0aP69evRr+/v7IzMzE4MGD67y2efPm8Pb21vnZunXr4OPjgw8//BAAEBoaiqysLCxfvhwjRowQpvFWxNa2DmkIXVOlV66UIjv7OrKyrjfoPFRTZe8KefRVzf0au9O+pEqBpiXTIFVeuVegLEbTkmkoczRN9qu5s225ZpCIbIXFZL+WlpZCqVRCLpfXW/f5559H+/btMXDgQK0RuDNnzqB/f811WgMGDMDZs2dRWaljYZCNs7WtQxpC12hljYaOWpoye7cmECtruQd3PZNFX9tl7pEscz9PyClvIiIxWUyixFtvvYUuXbogMjJSbx03Nze8++67ePzxx+Ho6IivvvoKcXFxWLlyJWJiYgAABQUF6Nu3r8Z1Xl5eqKqqws2bN+Hjo/sf6pycHMHexZLExrbEqVN/4urVcnVZ27YyxMa2FPWdzfHsP/+8i1WrcpGRcbPOepcu3WxQe5YuDcOqVbm4fr0CXl7OSEhoh4qKa8jJudbQJgvOWfIn2jivQgfZdVRe8cKfFQmoULUx6h4dZJfu7XNXS3npJeTc0Oy3muc5Sa6jUqX7efXVEfp59XGWxKKDyynIHK7ef5ayLS7ejEXFjcZ9T2313xVLVtPnISE8A5bsj0UEdXPmzEFmZiYOHDgAqVSqt16LFi0wffp09c/h4eG4desWPvnkE3VQBwASiUTjOpVKpbP8Qbb6D0BICLBvX6BFbR2Sk5Nj8v5WKErwr3/t1ztC96CgoBYNak9ICNC3b9eGNM8s7k1j/ktj1Esuu2D0NKasMAgoz9YudwvSmNI15HmG1BHyeYYJQUXVPjg8MOVd4Z6IgEaOkJrje06a2Odk70QP6mbPno0dO3Zgz549CAwMNPr6iIgIbNq0Sf1zq1atUFBQoFHnxo0bcHR0RPPmzWtfbheseeuQhqpryvVB1rzhcX2E2vOu3D0R0sosjXvpSt4w5HmG1BHyeYbi0WVEZAtEDepmzZqFHTt2YO/evejQoUOD7nHu3DmNpInIyEjs27dPo87Ro0cRHh4OJyenRrWXrIe+BBF3dyd4ejZBq1Yu6oDOEvbsMwVDEwDq28ewJvu1afEUSJTFUDk005n9qu95jn8fg6RKAZVjgEFtMjRZhAkORESaRAvqZs6ciZSUFGzcuBFyuRzXrt1bh+Tq6go3NzcAQFJSErKzs5GWlgYA2Lx5M5ycnNC1a1c4ODjgwIEDWLNmDebPn6++b1xcHJKTk/HWW28hLi4Op0+fxubNm7FmzRqzvyOJR1+CyKBB/nYzamnIpsG6MoNrZwQbmv2q73kOqutwvTUSZc13GbyRsSEjZ/awKTIRkTFEy35ds2YNbt++jREjRiA0NFT9Z9myZeo6+fn5yM3VnF5ZvHgx+vXrh/79+2P79u1Yvnw5Xn75ZfXngYGBSE1NxcmTJ9GrVy8sXrwYixYtssvtTMytZi+4oUP3GrQHnLH1jWFrZ8s2hCF73i1YkAVlRS42fLAZh79YhQ0fbIayIlcjI9jQbFRdz6tdX8h9+Gx9U2QiImNJioqKjDnFiEgnXSM+7dp56NwDLicnB87O3gbXb0ybLClBpD6mOM6t5vir8tJLkLkFaU1jvvjPtVg4ZQHaB9xSl/2maI45qxKR/MVEAIDrjaFwrMzQuneVUy+Utdyj9Ty3G0/BQXVdb/37R3I1fh8+Ie8lNC7aNz/2Odk7o6Zfq6urceTIEVy+fBmFhYXqrNIaEokEb775pqANJOtQ18kVuqY7ja3fENaUIGLINGhD1Lf58CvjdmoEdADQPuAWpj+3E8C9oM6YaU6VYwCqmvSFc/lWvfWFTEpgggMR0X0GB3U//vgjxo8fj6tXr2oFczUY1NkvY0+uqK++KUatLJk5glxdwrtU6S2v/t/fDc1GRQPrExGRMAwO6mbOnInS0lJs2LABPXv2NOjkB7J+hgZXxp5cUVd9U41aWTJTHedWMz3ZQXYJskLt6Vfnpm2Bcu3rnJu2xd3//d3Yo8vqq39/yjQPSqmvRU2ZEhFZM6NG6mbPno0hQ4aYsj1kQYwJrhITuyMr67rWGjl9iQl11Rdr1EpMpjjO7cEzVJ0dAZRna52hauiomrHTnPrqm/tcVyIie2Jw9murVq3g6Cj6XsVkRnUFV7UFBNxLcoiODkavXr6Ijg6uc2StrvqGTM2aKmtWLKbI1jUka1XlGICLdzcg/YdeyP6lI9J/6IWLdzeYLMAy97muRET2xOAobfLkyfi///s/TJ48mZv42gljpwSNTUzQV99ep2bDwuQoLa0AAHTv7oX33+/RqHcyZHPee/35M3Jzh6vL2rX7Gbt2+ZukP4XeMFioqVxOCRORLdAb1O3cuVPj59atW8PR0RFPPPEExo8fj7Zt2+o8p3XUqFHCt5JEYYopQUPY29SsrkD111+LG31fpcRdT7mb+u/m7k8hNwwWaiqXU8JEZCv0BnUTJ06ERCLRmemalJSk8xqJRMKgrhZrzuI0dp2cUGqmZnXtMWeqhAIxmSywkkjqLTe0P4UayRIyM1aos1+FPEOWiEhMeoO6PXv26PuIDGTtU4V1BVfmeLaxU7PWylSBqoNS91pDB+Vt9d8N6U8hR7KMzaSti1BTuTxDlohshd6g7sknnzRnO2ySsSMwljiqZ2kb+Io1emhKDQ1U6xs9M2Sq05D+NGYky7gRvcYdZiPUVC7PkCUiW9GodNa///4be/bsQVFREQYNGoS2bdsK1S6bYMwIjLWP6pmLmKOHptKQQNWQ0bNy90RI/z4Fqeqq+rpqSVuNqU5D+tOh8pLONjhUagZ6hrRJyFE/oaZyuVkyEdkKozYfzszMREbGvTMgq6qqMHDgQPz4449QqVRISkrCgQMH0KlTJ5M11toYMwJjiwkApmJpo4eN1ZBA1eDRMwk0B8R0LLOrrz8lOs5xvVdeYHSbhFy/JtRUrpBTwkREYjI4qDt+/DiGD7+/7cHOnTvxww8/4KOPPkLXrl3x4osv4sMPP8QXX3xhinZaJWNGYGwxAcBS1DWtXXu68PeSVzFvQYHZp8B1BVZ1tduQdWCy2wsgVV7V+FyqvGp0AKVyaAUor+go96717PrbJPT6NaHOfuUZskRkCwwO6vLy8hAQcP+/XL/66it07twZEyfeO/R74sSJWLVqlfAttGLGjMDYYgKAJahrWjuwTaHWVKDLjWM4c3ISFH+20KgrVGBn6Jqz+qbjDVkHJlQApXRsB1RpbzitdAzU/FnPPydK3N/6iOvXiIhMx+ATJRwdHXH37r3TIFUqFb755hsMGDBA/blcLsetW7eEb6GVqxmB2bNnKJKT++sNDkxxogDVPa2tayowsPV1LHj1oFZdIdSsJ3Mu3wrHygw4l2+F662RkFQpjGo3cG8dWLW0ncbntdeBKSV6giyJ9v6SdTHkWQAgrb6g8/oHyw29FxERGc/goK5jx45ITU1FUVERNm7ciMLCQjz11FPqz69cuYKWLVuapJH2wNhjtsgwdU1r6xvJau1VolVXCMYckVXfdHzNOrAKWTSqnHqhQhatlWwgrdATZOkp18eQZwGARFmm83qJ8v67GHovIiIynsHTr7NmzUJMTAyCgoIAAI899pjGticHDx7EI488InwL7YitJQBYgrqmtfVNBf513UOrrhCMmQ41ZDq+vnVgEugJsmB8kGrImjOVgxzQsTeeyqGZ0fciIiLjGRzU9enTB8ePH8fRo0fh7u6O0aNHqz8rLCzEk08+icDAQFO0kajB6kpWKXcP1trK4vJfXkj8ZKBWXSEYs55MiP34DA2yhHKn2Uq4FY6ABFX3nwVH3Gm20iTPIyIiTZKioqJG7QCan5+PrVu3IiUlBefPn+e6OqpXTk4OQkJCzPa8mizS/Pw7cHd3gkqlwu3bVfD1bYqkxFYI9vhEvZVFTfZrQ/bAqy8JQlKlgOvNIRoZqdUObVHWYp9WPdntBai4cxVnzzli2f+NApwCjc7EdSg/oTPIKvXcDaWsp8H3MYZD+Qk0LZ4CibIYKodmuNNspdazDEkWEepYMjGZ+3tO7HOiBm0+XFpairS0NKSmpiIjIwPV1dUICwvDa6+9JnDziOpX30kcNdPa+jNKP1LXb+0JJDdgZtDgTXVr/ydUrZ8fvI+zA9CrG9Djkav/u49x6yuVsp4o9dyNpsVToKq6BYljc51BlpCUsp4olf2o9/PGblAMwOqDPSIiUzE4qKuursahQ4eQmpqK/fv34+7du5BIJHjxxRfx8ssva2x3QmQuxpzEYcoNng3eeFdVa9841VWTbc4L3A+yLGUEo1EbFBe/BWn1r4KcRkFEZIvqzX7NysrCG2+8gdDQUDz33HP45ZdfMGPGDOzatQsqlQp9+/ZlQEei0ReoDRu2DwqFZrkpN3gWauNdY5IpJFUKuBTGw/XGULgUxuvcGkUoQj2rMX3gWGv9I6A/e1gXId7BnH1ORGSsOkfqIiIikJubi7Zt2+L555/HmDFj1MeAXbmivcM8kbnpC9SuXCnFyJH7NUbsTLnB8+3ylvDUsf3b7fIW6v9yMiRRwtBkCiHPUK2PkM9SStz1lLvd/7uePtDHkM2UhXgHc/Y5EVFD1DlSd+nSJfj7++Pf//433njjDZ7rShZHX6AGaG8c3NANnhWKEsTHH8HQoXsRH39EawQQABI/GYjfFM01yn5TNNfIpDVk411DN+c1Zs+7xjLmWfWOZEl0HD5bq1xfH1Q5P6rzUkNOoxCiv8zZ50REDVHnSN2nn36KrVu34qWXXoKLiwsGDhyI0aNH4+mnnzZX+4i0ZJ8+g+Kct9GiWSFiesuRe3EIsn5w01n3walVY45tq2Homr0ff3HHhDljsX5RCuTud1F02wUT5oyFg+v9kSmVYwDueCzXzA71WK4xymPo4fJCn6FaF0OfZchIloOOLVbuld9W/11/P7WF9NYvGoGVoadRCNFf5uxzIqKGqDOo+8c//oF//OMfuHbtGlJTU5GamorY2Fh4eHjgySefhEQigUTff3kTmUD26TPwxzi0H3BTXdatQy6ee2Masn/UDuxqT60au8GzockVXcNu41+jUxHUthAA0LxZOb54LxVLt3dT15FUKdC0ZBqkyv8tXVAWo2nJNJQ57tIK7OpLijBkGlMohk4JG5IEYci99PZT810GBbyNeQdT34OIyJQMOibM29sb06dPR3p6Ok6ePIm4uDj88MMPUKlUmDZtGqZMmYK0tDSUlenewZ5IKMU5b6O9/02Nsvb+NzE7fp9Jzs69dEn3yFLt5IoFrx5E+wDNPRrbB9zSOEdW0Ok7A6YxhWLolLAhI1nl7omodmireS+Hthr3qqufagLespZ7cNcz2eC1bEKcOctza4nI0hl89muNsLAwzJ8/Hz/99BPS0tIwePBg7Nu3D//85z/Rvn17U7SRSK15s0Kd5S3lRYKfnatQlODXX3U/r/YIoLvshs567rL7AaiQ03eGTGMC99e4dZAlGJytWXtdHACDzmtVSn113k9rJKuevfpMMc0pxJmzPLeWiCxdgzYfrtGrVy/06tULH330Eb766iukpqYafO2SJUuwZ88e/Pbbb3B2dkb37t0xb948dOzYUe816enpWLFiBb777juUlJSgXbt2mDJlCp5//nmNOsOGDdO69syZM+jQoYNxL0gWJ69Ad5CWV+CBhwU+O3fBgiyUlVVplbu6OmqNAAqZ2WoIQ6cx1RsZOwIoz643W7OudXH1TQmXuydqHbtWeyTLkL36TDW1LMSZszy3logsmdEjdbo0adIEo0aNwpYtWwy+JiMjA5MmTcLBgweRlpYGR0dHjBw5EoWFukdGgHuBWadOnfDll1/i1KlTmDRpEl577TVs3bpVq25mZiYuXLig/hMcHNygdyPLsnZPtM4s03V7o9U/G5Ktaoia7VIC2tzEhg824/AXq7Dhg80Y0LtKawRQyMxWQxhyr4ZM9zZmitiQkSyDRuHMOLVMRGRLGjVS1xg7duzQ+Hn16tXw9/dHZmYmBg8erPOaGTNmaPw8adIkpKenIy0tDdHR0RqfeXl5oUWLFsI2mkQn9wrFU5MmY8GrB9HaqwR/XfdA4icDEdkjFIBxJ0zUx9e3KQLa3MShzz/TWC/3VI+/IKmK0kpuECqz1RCG3Ksh05iNnfqsbyTLkBFGQ6eWhWQLZ80SEYkW1NVWWloKpVIJuVxu1HW3b99G69attcr79u2LiooKhIaGYubMmejdu7dALSUxJSZ2x8iR1/H8m/9Qlz2YECHkUWCJid1xKeN9rQQInxbXUFHr2C4hM1sNJUQAJcQ1xjBkitbcWabcVJiIbIWkqKio9rJlUUyYMAG///47jh07BqlUx9b8Ohw4cADjx4/HwYMHERERAQDIyclBeno6HnnkEVRUVCAlJQVr167F3r170bOn/oPMc3JyBHkPMr0//7yLVatycf16Bby8nJGQ0A5t2rgAABISziI7u1jrmogIOVatetjoZwVIJsPL9axWeUlVBC6Wr1L/3K7JXLRwOqBV72blIOT+/a7RzxWCs+RPhLokoInD/VG2v5U+uHB3FSpUbfRe08FlGmQO99e9lSvb4uLd5XqvaUi72jivgpPkOipVXvizIkHj3uZow4Ms8XdHjWcJZx0TmZtFjNTNmTMHmZmZOHDggMEBXWZmJuLj47Fo0SJ1QAfc+x/yg/9jjoyMxJUrV7Bs2bI6gzr+A2A+jT1cPiQE6Nu3q87PgoL+0BnUBQW1aNAzXQrbA+XaQZ3MLQghfvfv53r9BqCdU4FmLjcR4i/Od0tS5QzHG44a2aWOUkcEBrarYwQqBBVV++DwwLRuhXsiAgQdsQoB0Bcq3PsHSPvO5mjDfa43SnWODDZrWtao311jv+dkPPY52TvRg7rZs2djx44d2LNnDwIDAw265tSpUxg7dixmz56NSZMm1Vs/IiJCaw0f2abExO7IyrquMQXbmP3qDJkuBABJ1Z86r5dUXdVZbg6GZJrqYgkZnuZsAzcVJiJbIWpQN2vWLOzYsQN79+41eLuREydOICYmBrNmzcLUqVMNuubcuXPw9vZuTFPJgigU99bI5eXdga+v5lFfDTkKrC6GJjdIJH9r779WUy6S+pIemBxwj6GBOxGRpRMtqJs5cyZSUlKwceNGyOVyXLt2DQDg6uoKN7d7+1ElJSUhOzsbaWlpAO7tQRcTE4NJkyZh7Nix6mukUilatmwJAFixYgX8/f0RFhaGiooKpKamYt++fVi/fr0Ib0lCMyS71dijwOpj0KiRSs/SVH3lZlDXCBSTA+4TMiuZiEhMogV1a9asAQCMGDFCo3zWrFmYPXs2ACA/Px+5uff/63nz5s24c+cOli1bhmXLlqnL/fz8cO7cOQBAZWUl5s6di7y8PMhkMoSFhSE1NRVRUVGmfiUyAyGzW4WkcnABlNp7LKocXDR+dig/8b9tT4qgcpDjTrOVUMp6ao6aSdwBiQQOypJGjaDVNQJlyDmtDWGto3+WMOVMRNRYFpP9SvajMYuZhw7di4wM7WnFXr18sWfP0MY2rcFcrz8Fx6osrfIqx0dR5vVfAPcCOrfCEZA8kFGhgiPKPFbDpUw7yKpRLW2ncwTNkACqpk556SXI3ILUdVxvDIVjZYZ2e516oazlHqPfv+ZZGqN/dbTdkHtZY3D4IC7aNz/2Odk70RMliIzh69tUZ3nts1jNTenYDtAR1CkdA9V/b1o8RSOgAwAJqtC0ZDoccEfvvXWNoBk6fVozApVzI0cjW9cUyQFCjf5xapiIqGEEOSaMyFwSE7ujXTvNpIfGZLcKxZBjuyTKIp3XSlB/MkXtEx0ac5yXoe01VmNPo6jR2HcjIrJXHKmjOtWVaSoGobNbhWLIYnuVgxzQcQSWCk0gqWOkDtAeQRPiOC9jkgP0rQXUbKMwo39CBYdERPaGQR3pJeQ5qkISOrtVKPUttr/TbKXONXV3PJbVu6au9giaEAGUockBWmsBlSVwKxyBUs/dGoGdUFuDcN84IqKG4fQr6VVXpikZTynriVLP3ah28IcSzVDt4H/vZ9fRKGu+CxWyaFQ59UKFU19UO/iq69zxWK41gmaK6VN99K4FLJ6iUVYz+qd+D1l0g9bBmfPdiIhsCUfqSK+8PN1Tgvn5dU8Vkn5KWU+Uyn7UKq8ZNVMnCSj/NwWpLEbTkmkoc9ROgDDX3mp61wIqtY9jE2JrEO4bR0TUMAzqSC9LzTS1ZcZkkJprbzUVXAHoWgtouu8B940jIjIep18FplCUID7+CIYO3Yv4+CNQKLT/z9BaWGqmqZAs7fdliUkC1c6hRpUTEZE4OFInIEtNLGgoS800FYol/r4sMUnAQVWlp7xaq8wWNg0mIrJWDOoEZKlHWDWGpWaaCsESf1+WeLi8oYEmNw0mIhIXp18FxMQC8xBqytSSfl817zRk5DnMXxWHv5Vt68x+NSdDs1G5aTARkbg4UicgJhaYnpBTppby+3rwnQLa3MQ/Ez9DE4db9z7Uk/1qToZmo1riekAiInvCkToB2UNigdiE3DvPUn5fD77TglcPon3ALY3PLWG0qyYbtazlHtz1TNYZYCqlvjqv5abBRETmwaBOQDWJBdHRwejVyxfR0cFWmyQhlvqmVoWcMg0I8MDy5b3g7++GZs2c4O/vhuXLe5n09yWpUsClMB6uN4bCpTAekiqFxju1bqV7Krmho101z+sgS1A/z1S4aTARkbg4/SowW04sMDVDplaFnDJVKEowbVo6rlwpBQAUF1di2rR0kwXi+hIJuoa9iYyMe0V/Feh+bkNGux58nrMjgPJskyYucNNgIiJxcaSOLIYhU6tCTpma+xg0fYkEC149qH6nxE8G4jdFc406DR3tEiNxwZBpWiIiMg0GdWQxDJlaFXKK29zZr/oSCdxlN9Xv5B/UGUu3/z8UVo9s1PmpdT2PiQtERLaJ069kMQydWhVqitvc2a917fem/U7jUGbC5xERke3hSB1ZDHNno5r7eeZOJGDiAhGRfeFIHVkMcx9LZu7nmTuR4MHnlZdegswtiIkLREQ2TFJUVKQSuxFkX3JychASEiJ2M+wK+9z82Ofmxz4ne8fpVyIiIiIbwOlXK6FQ3NtqIy/vDnx9TTtNSERERNaHQZ0VEPK8UyIiIrJNnH61AubeJJeIiIisD4M6K2DuTXKJiIjI+nD61QqYe5NcWyapUvxvS5E8KKW+3OKDiIhshmgjdUuWLEG/fv3g5+eH4OBgxMTE4Pz58/Ve9/PPP+OZZ56Bj48PwsLCsGjRIqhUmruyZGRkoE+fPvD29ka3bt2wdu1aU72GWZh7k1xbVXPAvXP5VjhWZsC5fCtcb42EpEohdtOIiIgaTbSgLiMjA5MmTcLBgweRlpYGR0dHjBw5EoWFhXqvKSkpwahRo9CqVSscOXIE77//PpYtW4bly5er61y+fBljx45FZGQkvvnmG7z++ut48803sXv3bnO8lkkIed6pPRPjgHsiIiJzEW36dceOHRo/r169Gv7+/sjMzMTgwYN1XrN161bcvXsXK1euhIuLCzp27IiLFy9ixYoVmDZtGiQSCdatWwcfHx98+OGHAIDQ0FBkZWVh+fLlGDFihMnfy1SEOu/UnlnLAfcO5SfQtHgKJMoiqBzkuNNsJZSynmI3i4iILJzFJEqUlpZCqVRCLpfrrXPmzBk88cQTcHFxUZcNGDAAeXl5UCgU6jr9+2sGPwMGDMDZs2dRWanjdHOyG0qpr55yyzng3qH8BNwKR0CqvAIHlECqvAK3whFwKD9h9rZIqhRwKYyH642hcCmM5zQ1EZGFs5ig7q233kKXLl0QGRmpt05BQQG8vLw0ymp+LigoqLNOVVUVbt68KXCryZpYwwH3TYunQIIqjTIJqtC0eIpZ28H1h0RE1scisl/nzJmDzMxMHDhwAFKptM66EolE4+eaJIkHyw2pU1tOTo5RbabGaUx/uzpko12TJDhKbqNK5Y7cv+ehTBlh0LXOkqVo47wKTpLrqFR54c+yBFQUVwCwjN//w01v6fxPLVXVrUZ/R/Vd7yz5U7NPKhLQxnkVpE7a6w8r/5qF3L/fbVQ77An/XTG/mj7nGbBkj0QP6mbPno0dO3Zgz549CAwMrLNuq1at1CNyNW7cuAHg/oidvjqOjo5o3ry53nvzHwDzacyh2/emJ6epR7McUYqHmk5DqeduA9edhQDoCxXuffktbTMTybXmgLJUu9yxeaO+o/r6/N6I3L80EkjksgtQSVqg1oAhAKBZ0zKE+PN/K4bg4fLmxz4neyfq9OusWbOwbds2pKWloUOHDvXWj4yMxKlTp1BeXq4uO3r0KHx9fREQEKCuc+zYMY3rjh49ivDwcDg5OQnafjI/S5meNJU7zVZCVeu/tVRwxJ1mK03yPH0ZwRJlgc76lrT+kIiINIkW1M2cORObN2/GmjVrIJfLce3aNVy7dg2lpfdHKZKSkjB8+HD1z2PGjIGLiwumTp2K8+fPIy0tDR9//DGmTp2qnlqNi4vDX3/9hbfeegsXLlzA+vXrsXnzZkybNs3s70jCkyiL9JQXm7chJqKU9USp525UO/hDiWaodvA3YhTSePoyglWSVha//pCIiDSJNv26Zs0aANDaZmTWrFmYPXs2ACA/Px+5ufdHEZo1a4adO3di5syZ6NevH+RyOV5++WWNgC0wMBCpqamYM2cO1q5dCx8fHyxatMiqtzOxVqY4vUHlIAeUJTrKmzXqvpZEKeuJUtmP5nmW1BfQkRSudGqHcvfP//f7y4dS6sPTN4iILJykqKhIVX81IuPUZE8+OLVXLW2Hsua7cDG3opFr6kZoTMGq4GjS0SxbUPeaOt2/JwZwjcP1XebHPid7ZzFbmpBtMdXpDeaenhSDOfeHUzkGoKz5LlTIolHl1AsVsmgGdEREVkr07FeyTaY8vcGc05PmpjVyVglIK7NMGmipHANw1zPZJPcmIiLz4UgdmYQ1nN5giXg+LRERNRSDOjIJU57eYKnHVwnRLmNHOC21L4iIyPw4/UomUbNWS3f2ZMN32RdjetKc7dKbjapjhNNS+4KIiMTBkToymZq1WmUt9+CuZ7IggYalTk8K1S5jRjgttS+IiEgcHKkjq2LKBIzGMLRd9e3dV/cIZ8OeSURE9oFBHVkVY6YnzcmQdhk6XWpoNqql9gUREYmD069kVUyZgNEYhrRL6OlSS+0LIiISB0fqyKoYMz1pae0yxXRptWMYJMp75yVXOXVHebP3Re8LIiISB4M6sjqWullufe0ScrpU1/Fe0upfjb4PERHZDk6/EpmJkNOlzHwlIqLaOFJHZCZCTh0z85WIiGpjUEdkRkJNHTPzlYiIauP0K5EVYuYrERHVxpE6IitkqVnAREQkHgZ1RFbKUrOAiYhIHJx+JSIiIrIBDOqIiIiIbACnX4mslKRK8b81dXlQSn25po6IyM4xqCOyQlonSlQC0soslDXfxcCOiMhOcfqVyArxRAkiIqqNQR2RFeKJEkREVBuDOiIrpJT66inniRJERPaKQR2RFeKJEkREVBsTJYisEE+UICKi2hjUEVkpnihBREQP4vQrERERkQ0QNag7ceIEnnvuOYSFhUEul2PTpk111l+4cCHkcrnOP9evXwcApKen6/z84sWL5nglIiIiIlGIOv1aVlaGjh07Yty4cUhISKi3/vTp0zFx4kSNsokTJ0IikcDLy0ujPDMzE56enuqfW7ZsKUyjiYiIiCyQqEFdVFQUoqKiAABTp06tt76bmxvc3NzUP1+9ehWnTp3C6tWrtep6eXmhRYsWwjWWbAKP1iIiIltl1YkSGzZsQLNmzTB8+HCtz/r27YuKigqEhoZi5syZ6N27twgtJEvCo7WIiMiWWW2ihFKpxKZNm/Dcc8+hSZMm6nIfHx8sWbIEGzZswIYNGxASEoIRI0bgxIkTIraWLAGP1iIiIltmtSN1//3vf3H16lW88MILGuUhISEICQlR/xwZGYkrV65g2bJl6Nmzp9775eTkmKytpE2M/u4guwRnHd/48tJLyLlh+79/fsfNj31ufjV9/uD/DxDZC6sN6r744gs89thjCAsLq7duREQEduzYUWcd/gNgPjk5OaL0t6wwCCjP1i53C0KIn23//sXqc3vGPjc/9jnZO6ucfs3Ly8PXX3+tNUqnz7lz5+Dt7W3iVpGl49FaRERky0QdqSstLcWlS5cA3Fsjd/XqVfz444/w9PSEn58fkpKSkJ2djbS0NI3rNm7cCFdXV4waNUrrnitWrIC/vz/CwsJQUVGB1NRU7Nu3D+vXrzfLO5HpNTSDlUdrERGRLRM1qDt79iyGDRum/nnhwoVYuHAhxo0bh5UrVyI/Px+5uZoL21UqFTZs2IDo6Gg0bdpU656VlZWYO3cu8vLyIJPJEBYWhtTUVPXWKSSemmCsg+wSZIVBDQqoGpvB2pCjtbgNChERWQNJUVGRSuxGkO3TCsZwb+rT2O1EXArj4Vy+Vau8QhZtknNQhWq32LjWyPzY5+bHPid7Z5Vr6sj6CLWdiEN1np7y/Aa3rS7cBoWIiKwFgzoyC6GCMaXUV0+5j9FtMoS5g0giIqKGYlBHZiFUMGbuDFZzB5FEREQNxaCOzEKoYKwmg7VCFo0qp16okEWbdH0bt0EhIiJrYbWbD5N1eXA7kfLSS5C5NSz7teZepkiK0PcsboNCRETWgEEdmU1NMJZzI8eqTnAwZxBJRETUUJx+JSIiIrIBDOqIiIiIbACDOiIiIiIbwKCOiIiIyAYwqCMiIiKyAQzqiIiIiGwAgzoiIiIiG8CgjoiIiMgGMKgjIiIisgEM6oiIiIhsAIM6IiIiIhvAoI6IiIjIBjCoIyIiIrIBDOqIiIiIbACDOiIiIiIbwKCOiIiIyAYwqCMiIiKyAQzqiIiIiGwAgzoiIiIiG8CgjoiIiMgGMKgjIiIisgEM6oiIiIhsgKhB3YkTJ/Dcc88hLCwMcrkcmzZtqrO+QqGAXC7X+nPo0CGNehkZGejTpw+8vb3RrVs3rF271pSvQURERCQ6RzEfXlZWho4dO2LcuHFISEgw+Lrt27ejc+fO6p89PT3Vf798+TLGjh2L2NhYfPbZZ8jMzMSMGTPQokULjBgxQtD2ExEREVkKUYO6qKgoREVFAQCmTp1q8HXNmzeHt7e3zs/WrVsHHx8ffPjhhwCA0NBQZGVlYfny5QzqiIiIyGZZ5Zq6559/Hu3bt8fAgQOxe/dujc/OnDmD/v37a5QNGDAAZ8+eRWVlpTmbSURERGQ2oo7UGcvNzQ3vvvsuHn/8cTg6OuKrr75CXFwcVq5ciZiYGABAQUEB+vbtq3Gdl5cXqqqqcPPmTfj4+Oi8d05OjqmbTw9gf5sf+9z82OfmV9PnISEhIreEyPysKqhr0aIFpk+frv45PDwct27dwieffKIO6gBAIpFoXKdSqXSWP4j/AJhPTk4O+9vM2Ofmxz43P/Y52TurnH59UEREBC5duqT+uVWrVigoKNCoc+PGDTg6OqJ58+bmbh4RERGRWVh9UHfu3DmNpInIyEgcO3ZMo87Ro0cRHh4OJycnM7eOiIiIyDxEnX4tLS1Vj7IplUpcvXoVP/74Izw9PeHn54ekpCRkZ2cjLS0NALB582Y4OTmha9eucHBwwIEDB7BmzRrMnz9ffc+4uDgkJyfjrbfeQlxcHE6fPo3NmzdjzZo1YrwiERERkVmIGtSdPXsWw4YNU/+8cOFCLFy4EOPGjcPKlSuRn5+P3NxcjWsWL16MP/74A1KpFMHBwVi+fLnGerrAwECkpqZizpw5WLt2LXx8fLBo0SJuZ0JEREQ2TVJUVKQSuxFkX7iY2fzY5+bHPjc/9jnZO6tfU0dEREREDOqIiIiIbAKDOiIiIiIbwKCOiIiIyAYwqCMiIiKyAQzqiIiIiGwAgzoiIiIiG8CgjoiIiMgGMKgjIiIisgEM6oiIiIhsAIM6IiIiIhvAoI6IiIjIBjiK3QBrIKlSQHZ7ARyq86CU+qLcPREqxwCxm0VERESkxqCuHpIqBVxvjYS0OvdeQSUgrcxCWfNdDOyIiIjIYnD6tR6y2wvuB3T/I63Ohez2ApFaRERERKSNQV09HKrz9JTnm7klRERERPoxqKuHUuqrp9zHzC0hIiIi0o9BXT3K3RNRLW2nUVYtbYdy90SRWkRERESkjYkS9VA5BqCs+a7/Zb/mQyn1YfYrERERWRwGdQZQOQbgrmey2M0gIiIi0ovTr0REREQ2gEEdERERkQ1gUEdERERkAxjUEREREdkABnVERERENoBBHREREZENYFBHREREZAMY1BERERHZAFGDuhMnTuC5555DWFgY5HI5Nm3aVGf99PR0jBs3DqGhofD19UWPHj2wYcMGrTpyuVzrz8WLF035KkRERESiEvVEibKyMnTs2BHjxo1DQkJCvfXPnDmDTp064dVXX4WPjw8OHz6M1157DTKZDNHR0Rp1MzMz4enpqf65ZcuWgrefiIiIyFKIGtRFRUUhKioKADB16tR668+YMUPj50mTJiE9PR1paWlaQZ2XlxdatGghXGOJiIiILJjVr6m7ffs25HK5Vnnfvn0RGhqK4cOH45tvvjF/w0ivkJAQsZtgd9jn5sc+Nz/2Odk7UUfqGuvAgQM4fvw4Dh48qC7z8fHBkiVL8Mgjj6CiogIpKSkYMWIE9u7di549e4rYWiIiIiLTsdqgLjMzE/Hx8Vi0aBEiIiLU5SEhIRr/tRYZGYkrV65g2bJlDOqIiIjIZlnl9OupU6cQHR2N2bNnY9KkSfXWj4iIwKVLl8zQMiIiIiJxWF1Qd+LECURHR+PNN980KLkCAM6dOwdvb28Tt4yIiIhIPKJOv5aWlqpH0JRKJa5evYoff/wRnp6e8PPzQ1JSErKzs5GWlgbg3h50MTExmDRpEsaOHYtr164BAKRSqXrLkhUrVsDf3x9hYWGoqKhAamoq9u3bh/Xr14vzkkRERERmIOpI3dmzZ9G7d2/07t0bd+/excKFC9G7d2+89957AID8/Hzk5uaq62/evBl37tzBsmXLEBoaqv7Tr18/dZ3KykrMnTsXPXv2xODBg5GZmYn58+dj8+bNejc5VqlUWLhwIR566CH4+PhgyJAh+OWXX8zTCTaqvo2lp0yZorVB9FNPPSVSa63fkiVL0K9fP/j5+SE4OBgxMTE4f/68Rh1+z4VlSJ/zey6s5ORk9OjRA35+fvDz88PTTz+tkSjH7zjZO0lRUZFK7EaY2tdff43MzEx069YNCQkJWLx4MWJjY9Wff/zxx1i8eDE+/fRThISE4IMPPkBmZia+/fZbuLu7i9hy61Vfn0+ZMgV5eXlYvXq1uszZ2Vljw2gy3LPPPotnn30WjzzyCFQqFd577z18++23OH36tLpP+T0XliF9zu+5sPbt2wdnZ2cEBwdDqVRiy5Yt+OSTT3Ds2DF07tyZ33Gye3YR1D2oTZs2+OCDD9QBhkqlwkMPPYT4+HjMnDkTAHD37l2EhITg3XffRVxcnJjNtQm1+xy49392t27dQkpKiogts12lpaXw9/fHpk2bMHjwYH7PzaB2nwP8nptDYGAg5s2bhwkTJvA7TnbP6hIlhKZQKHDt2jX0799fXebi4oIePXrg9OnTIrbM9p06dQrt27dHREQEXnnlFVy/fl3sJtmM0tJSKJVK9cbc/J6bXu0+r8HvuWlUV1dj+/btKCsrQ2RkJL/jRLDifeqEUpNs4eXlpVHu5eWFvLw8MZpkF5566ikMGzYMAQEBuHLlChYsWIDhw4fj2LFjaNKkidjNs3pvvfUWunTpgsjISAD8nptD7T4H+D03hZ9//hlRUVEoLy+Hq6srNm7ciE6dOqkDN37HyZ7ZfVBXQyKRaPysUqm0ykg4o0ePVv+9U6dOePjhh9GlSxccPHgQw4cPF7Fl1m/OnDnIzMzEgQMHIJVKNT7j99w09PU5v+fCCwkJQXp6OoqLi5GWloYpU6Zg79696s/5HSd7ZvfTrzX71xUUFGiU37hxQ+u/+Mh0fH190bp1a24S3UizZ8/G9u3bkZaWhsDAQHU5v+emo6/PdeH3vPGcnZ0RFBSE8PBwzJs3D126dMGKFSv4HScCgzoEBATA29sbR48eVZeVl5fj1KlTeOyxx0RsmX25efMm8vLyuEl0I8yaNQvbtm1DWloaOnTooPEZv+emUVef68LvufCUSiUqKir4HSeCnUy/1rfJ8ZQpU/DRRx8hJCQE7du3x+LFi+Hq6ooxY8aI3HLrVVefe3p64v3338fw4cPh7e2NK1eu4J133oGXlxeGDh0qcsut08yZM5GSkoKNGzdCLper19C5urrCzc0NEomE33OB1dfnpaWl/J4LbP78+YiKikKbNm1QWlqKbdu2ISMjA6mpqfyOE8FOtjRJT0/HsGHDtMrHjRuHlStXQqVS4f3338cXX3yBoqIiREREYPHixejYsaMIrbUNdfX5kiVLEBsbix9//BHFxcXw9vZGr1698Pbbb6Nt27YitNb61c64rDFr1izMnj0bAPg9F1h9fX737l1+zwU2ZcoUpKeno6CgAB4eHujUqRNeeeUVDBgwAAC/40R2EdQRERER2Tq7X1NHREREZAsY1BERERHZAAZ1RERERDaAQR0RERGRDWBQR0RERGQDGNQRERER2QAGdUS1dOnSBVOmTDHZ/YcMGYIhQ4ZolMnlcixcuLBB95syZQq6dOkiRNMEtW3bNjz22GPw8vLSu6ebKSgUCsjlcixdutRszyQisgQM6siubN68GXK5HOHh4WI3xaQ+/PBDjUPOzS03NxcJCQnw8fHB0qVLsXr1aoOuy8jIgFwuR9u2bXHnzh0Tt5KIyLbYxTFhRDVSU1Ph7++P3NxcnDlzBpGRkWI3CQCQn58PR8eG/c/xP//5D5RKpUbZ4sWL8eyzz4p2HNXJkydRVVWFd999F127djX4utTUVPj5+eHq1av46quveLwTEZEROFJHdiM/Px/ffPMNEhMT4efnh9TUVLGbpCaTyRoc1Dk5OaFJkyYCt6hxbty4AQBo1qyZwdf8/fff2L17NyZMmIAnnnjCon4/RETWgEEd2Y2tW7dCJpPhmWeewejRo7Fjxw5UVlYadG1FRQU+/PBDPProo2jVqhVCQkIwbtw4/PLLL+o6y5Ytw8CBAxEUFARvb2/06NED69evN+j+tdfUbdq0CXK5HCdPnsQ777yD0NBQ+Pj4YNSoUbh8+bLGtbXX1Mnlcvz999/YsmUL5HI55HI5hgwZgt9//x1yuRyffvqp1vN//fVXyOVyfPbZZ3W2U6lU4uOPP0ZERARatWqFsLAwvPHGGyguLlbX6dKlC+bNmwcA6NatG+RyuUFrFA8cOIDi4mKMHj0aY8aMwZEjR9TB4YO+//57REdHIzg4GD4+PujWrRteeukllJWVadXdsmWL+nfWo0cPHDt2rN52EBFZK06/kt1ISUnBwIED4ebmhjFjxuDjjz/GoUOHMHjw4DqvUyqVGDduHA4fPozhw4cjPj4ed+/eRXp6Or7//nuEhYUBAFasWIGnnnoKI0eOhEQiwd69e/HKK69AqVRiwoQJDWrznDlz4OLign/961+4efMmli9fjsmTJ+Prr7/We83q1asxbdo0dO/eXf3cVq1aITg4GI899hhSUlLw8ssva/WNk5MTRo8eXWd7ZsyYgXXr1mHw4MFISEjAL7/8gs8//xzZ2dk4ePAgnJycsHDhQuzYsQM7duzAe++9hxYtWqBdu3b1vmtKSgoiIiIQGBiIkSNHYtasWdi+fTteeukldZ0bN25g1KhRaNGiBV599VXI5XJcvXoV+/fvR1lZGVxdXdV1d+/ejZs3byIuLg4ymQwrV67E+PHjce7cOXh6etbbHiIia8OgjuzCL7/8gp9++gmzZs0CAHTu3BlhYWFITU2tN6jbsmULDh8+jMTERMycOVNd/uqrr0KlUql/zs7ORtOmTdU/JyQkYOTIkfjPf/7T4KCuadOm2Lt3Lxwc7g2qe3p6Ys6cOfjll1/UwWRtMTExeOWVVxAYGIiYmBiNz8aNG4fXXnsNv/76Kx566CEAgEqlwtatWzFgwAC0aNFCb1vOnz+PdevWYezYsRojeiEhIZg9eza2bNmCF154AUOHDkVOTg527NiBIUOGICAgoN73LCwsxKFDh9QjfM2bN0e/fv2QmpqqEdSdPn0ahYWF2LFjh0ayy5w5c7TumZubi+zsbLRs2RIA8OSTT6J3797Ytm0b4uPj620TEZG14fQr2YWUlBR4eHggKipKXTZ69Gjs378fJSUldV6blpaGZs2aYfr06VqfSSQS9d9rArrKykoUFhbi5s2b6N27Ny5duqQxPWmMuLg4dUAHAD179gQArSlYQ40aNQoymQwpKSnqsoyMDFy9ehXPPfdcndcePHgQAPDKK69olE+cOBEeHh7qzxti586dqKqqwqhRo9RlY8aMQXZ2Nn7//Xd1mbu7O4B7U7X1TZ2PHDlSHdABQNeuXeHh4dHgviMisnQM6sjmqVQqbNu2DT179kR+fj4UCgUUCgUeffRRlJeXIy0trc7rc3Nz0b59+3qTEfbt24e+ffvCx8cH7dq1Q3BwMN555x0AqDdw1MfPz0/j55r93goLCxt0v2bNmuGZZ57B1q1b1aOMqamp8PDwwKBBg+q89sqVK5BIJAgJCdEob9KkCQICAnDlypUGtQm4F3R36dIFlZWV6t9P586d4eTkpBGA9urVC8OGDcOiRYsQFBSEmJgYfPHFFygtLdW6Z+2+A+69f0P7jojI0jGoI5uXnp6uXnfVrVs39Z8RI0YAQL1ZliqVSmNETpfMzEyMHz8eLi4uWLp0KVJTU7Fr1y5MnToVALS2HDGUVCrV26aGGjduHK5evYoTJ06oM05HjhwJmUzW4Hs2pj2XL1/G6dOn8cMPP2j8fnr06IHKykps3bpVXVcikWDDhg04fPgwEhIScOvWLbz22mt44oknUFBQoHFfU/QdEZEl45o6snmpqanw9PTEsmXLtD47fvw4Pv/8c/z1119o3bq1zuuDgoJw+vRpVFRUwNnZWWedXbt2QSaTYefOnRrBUXp6ujAvYaS6gtD+/fvDx8cHKSkpuHHjBkpKSrTW3uni7+8PlUqFnJwcdO7cWV1eUVGBK1euoFevXg1qa2pqKqRSKdasWQMnJyeNz86fP4/33ntPa0/BiIgIRERE4O2338Z///tfREdHY/369RprHomI7A1H6sim1UyvPv300xg6dKjWn5dffhlKpRLbtm3Te4/hw4ejqKhI51YgNaM+UqkUEolEY0SuqKgIGzduFP6lDNC0aVMUFRXp/EwqlSI6Ohq7d+/Ghg0b4O/vjx49etR7z5r1iLX7Yd26dSgpKcHAgQMb1NbU1FRERkZi1KhRWr+fadOmwcXFRT2aWlRUpDXS1q1bN/VnRET2jCN1ZNNqEiGeeeYZnZ8HBgYiLCwMKSkpWgkANZ577jmkpqYiKSkJP/zwA3r27Iny8nJkZGRg1KhReO655zBo0CB8+umnGDVqFGJiYlBYWIgvv/wSrVq1wrVr10z5ijqFh4fj+PHjWLZsGVq3bo2WLVuiT58+6s/HjRuHZcuW4fDhw5g5c2a908sA0KlTJ8TFxamDuH79+uGXX37BunXr8Mgjj2DcuHFGtzM7Oxu//fYbXnjhBZ2fN23aFL1798aOHTuwcOFCbN68GWvWrMHQoUPRrl073L17F5s2bYJUKlVPpxMR2SsGdWTTUlJS4OzsjAEDBuitM2jQICxduhQ//fSTxrRiDalUipSUFHz00UfYtm0b9u3bB09PT3Tv3h0PP/wwgHsL+FeuXImlS5di9uzZaN26NSZPngy5XI5p06aZ6vX0ev/99/H666/j/fffR1lZGXr27KkR1HXs2BHdunXDDz/8YNDUa42PPvoIAQEBWL9+Pb7++mu0aNECkyZNQmJiotbUqSFqkiDq2lZm0KBBOHjwIA4dOoSePXvi7Nmz2LlzJwoKCuDu7o6uXbvigw8+wKOPPmr084mIbImkqKiIq4aJ7NDTTz8NpVKJw4cPi90UIiISANfUEdmh8+fP49tvv23QlCkREVkmjtQR2ZHz58/j+++/x6pVq/Dnn3/ihx9+gJubm9jNIiIiAXCkjsiO7N69Gy+//DLu3LmDzz//nAEdEZEN4UgdERERkQ3gSB0RERGRDWBQR0RERGQDGNQRERER2QAGdUREREQ2gEEdERERkQ1gUEdERERkA/4/oBC0CE5Lu90AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_darkblue = wine_with_colors[wine_with_colors['Color'] == 'darkblue']\n",
    "wine_gold = wine_with_colors[wine_with_colors['Color'] == 'gold']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "\n",
    "ax.scatter(wine_darkblue['Alcalinity of Ash'], \n",
    "           wine_darkblue['Ash'],  \n",
    "           label='Color=darkblue', \n",
    "           color='darkblue')\n",
    "\n",
    "ax.scatter(wine_gold['Alcalinity of Ash'], \n",
    "           wine_gold['Ash'],  \n",
    "           label='Color=gold', \n",
    "           color='gold')\n",
    "\n",
    "\n",
    "x_label = 'Alcalinity of Ash'\n",
    "\n",
    "y_label = 'Ash'\n",
    "\n",
    "#y_vals = ax.get_yticks()\n",
    "\n",
    "plt.ylabel(y_label)\n",
    "\n",
    "plt.xlabel(x_label)\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "\n",
    "plt.xlim(10, 30.2)\n",
    "plt.ylim(1.25, 3.5)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But for some pairs the picture is more murky."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_darkblue = wine_with_colors[wine_with_colors['Color'] == 'darkblue']\n",
    "wine_gold = wine_with_colors[wine_with_colors['Color'] == 'gold']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "\n",
    "ax.scatter(wine_darkblue['Magnesium'], \n",
    "           wine_darkblue['Total Phenols'],  \n",
    "           label='Color=darkblue', \n",
    "           color='darkblue')\n",
    "\n",
    "ax.scatter(wine_gold['Magnesium'], \n",
    "           wine_gold['Total Phenols'],  \n",
    "           label='Color=gold', \n",
    "           color='gold')\n",
    "\n",
    "\n",
    "x_label = 'Magnesium'\n",
    "\n",
    "y_label = 'Total Phenols'\n",
    "\n",
    "#y_vals = ax.get_yticks()\n",
    "\n",
    "plt.ylabel(y_label)\n",
    "\n",
    "plt.xlabel(x_label)\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "\n",
    "plt.xlim(70, 165)\n",
    "plt.ylim(0.9, 4)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if we can implement a classifier based on all of the attributes. After that, we'll see how accurate it is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Plan for the Implementation\n",
    "It's time to write some code to implement the classifier.  The input is a `point` that we want to classify.  The classifier works by finding the $k$ nearest neighbors of `point` from the training set.  So, our approach will go like this:\n",
    "\n",
    "1. Find the closest $k$ neighbors of `point`, i.e., the $k$ wines from the training set that are most similar to `point`.\n",
    "\n",
    "2. Look at the classes of those $k$ neighbors, and take the majority vote to find the most-common class of wine.  Use that as our predicted class for `point`.\n",
    "\n",
    "So that will guide the structure of our Python code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def closest(training, p, k):\n",
    "    ...\n",
    "\n",
    "def majority(topkclasses):\n",
    "    ...\n",
    "\n",
    "def classify(training, p, k):\n",
    "    kclosest = closest(training, p, k)\n",
    "    kclosest.classes = kclosest.select('Class')\n",
    "    return majority(kclosest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation Step 1\n",
    "To implement the first step for the kidney disease data, we had to compute the distance from each patient in the training set to `point`, sort them by distance, and take the $k$ closest patients in the training set.  \n",
    "\n",
    "That's what we did in the previous section with the point corresponding to Alice. Let's generalize that code. We'll redefine `distance` here, just for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(point1, point2):\n",
    "    \"\"\"Returns the distance between point1 and point2\n",
    "    where each argument is an array \n",
    "    consisting of the coordinates of the point\"\"\"\n",
    "    return np.sqrt(np.sum((point1 - point2)**2))\n",
    "\n",
    "def all_distances(training, point):\n",
    "    \"\"\"The distance between p (an array of numbers) and the numbers in row i of attribute_table.\"\"\"\n",
    "    attributes = training.drop(columns=['Class'])\n",
    "    def distance_from_point(row):\n",
    "        return distance(point, np.array(row))\n",
    "    return attributes.apply(distance_from_point, axis=1)\n",
    "\n",
    "def table_with_distances(training, point):\n",
    "    \"\"\"A copy of the training table with the distance from each row to array p.\"\"\"\n",
    "    training1 = training.copy()\n",
    "    training1['Distance'] = all_distances(training1, point)\n",
    "\n",
    "    return training1\n",
    "\n",
    "def closest(training, new_point, k):\n",
    "    \"\"\"Returns a table of the k rows of the augmented table\n",
    "    corresponding to the k smallest distances\"\"\"\n",
    "    with_dists = table_with_distances(training, new_point)\n",
    "    sorted_by_distance = with_dists.sort_values(by=['Distance'])\n",
    "    topk = sorted_by_distance.take(np.arange(k))\n",
    "    return topk\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how this works on our `wine` data. We'll just take the first wine and find its five nearest neighbors among all the wines. Remember that since this wine is part of the dataset, it is its own nearest neighbor. So we should expect to see it at the top of the list, followed by four others.\n",
    "\n",
    "First let's extract its attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alcohol                           14.23\n",
       "Malic Acid                         1.71\n",
       "Ash                                2.43\n",
       "Alcalinity of Ash                 15.60\n",
       "Magnesium                        127.00\n",
       "Total Phenols                      2.80\n",
       "Flavanoids                         3.06\n",
       "Nonflavanoid phenols               0.28\n",
       "Proanthocyanins                    2.29\n",
       "Color Intensity                    5.64\n",
       "Hue                                1.04\n",
       "OD280/OD315 of diulted wines       3.92\n",
       "Proline                         1065.00\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "special_wine = wine.drop(columns=['Class']).iloc[0]\n",
    "special_wine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now let's find its 5 nearest neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Class</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 diulted wines</th>\n",
       "      <th>Proline</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</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</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>13.74</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.25</td>\n",
       "      <td>16.4</td>\n",
       "      <td>118</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.62</td>\n",
       "      <td>5.85</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.20</td>\n",
       "      <td>1060</td>\n",
       "      <td>10.392805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>14.21</td>\n",
       "      <td>4.04</td>\n",
       "      <td>2.44</td>\n",
       "      <td>18.9</td>\n",
       "      <td>111</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.65</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.24</td>\n",
       "      <td>0.87</td>\n",
       "      <td>3.33</td>\n",
       "      <td>1080</td>\n",
       "      <td>22.340748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>14.10</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.40</td>\n",
       "      <td>18.8</td>\n",
       "      <td>103</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2.38</td>\n",
       "      <td>6.20</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1060</td>\n",
       "      <td>24.760232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>14.38</td>\n",
       "      <td>3.59</td>\n",
       "      <td>2.28</td>\n",
       "      <td>16.0</td>\n",
       "      <td>102</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.27</td>\n",
       "      <td>2.19</td>\n",
       "      <td>4.90</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.44</td>\n",
       "      <td>1065</td>\n",
       "      <td>25.094663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Class  Alcohol  Malic Acid   Ash  Alcalinity of Ash  Magnesium  \\\n",
       "0       1    14.23        1.71  2.43               15.6        127   \n",
       "54      1    13.74        1.67  2.25               16.4        118   \n",
       "45      1    14.21        4.04  2.44               18.9        111   \n",
       "48      1    14.10        2.02  2.40               18.8        103   \n",
       "46      1    14.38        3.59  2.28               16.0        102   \n",
       "\n",
       "    Total Phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0            2.80        3.06                  0.28             2.29   \n",
       "54           2.60        2.90                  0.21             1.62   \n",
       "45           2.85        2.65                  0.30             1.25   \n",
       "48           2.75        2.92                  0.32             2.38   \n",
       "46           3.25        3.17                  0.27             2.19   \n",
       "\n",
       "    Color Intensity   Hue  OD280/OD315 of diulted wines  Proline   Distance  \n",
       "0              5.64  1.04                          3.92     1065   0.000000  \n",
       "54             5.85  0.92                          3.20     1060  10.392805  \n",
       "45             5.24  0.87                          3.33     1080  22.340748  \n",
       "48             6.20  1.07                          2.75     1060  24.760232  \n",
       "46             4.90  1.04                          3.44     1065  25.094663  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closest(wine, special_wine, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bingo! The first row is the nearest neighbor, which is itself – there's a 0 in the `Distance` column as expected. All five nearest neighbors are of Class 1, which is consistent with our earlier observation that Class 1 wines appear to be clumped together in some dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation Steps 2 and 3\n",
    "Next we need to take a \"majority vote\" of the nearest neighbors and assign our point the same class as the majority."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def majority(topkclasses):\n",
    "    ones = len(topkclasses[topkclasses['Class'] == 1])\n",
    "    zeros = len(topkclasses[topkclasses['Class'] == 0])\n",
    "    if ones > zeros:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def classify(training, new_point, k):\n",
    "    closestk = closest(training, new_point, k)\n",
    "    topkclasses = closestk[['Class']]\n",
    "    return majority(topkclasses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify(wine, special_wine, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we change `special_wine` to be the last one in the dataset, is our classifier able to tell that it's in Class 0?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "special_wine = wine.drop(columns=['Class']).iloc[177]\n",
    "classify(wine, special_wine, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes! The classifier gets this one right too.\n",
    "\n",
    "But we don't yet know how it does with all the other wines, and in any case we know that testing on wines that are already part of the training set might be over-optimistic. In the final section of this chapter, we will separate the wines into a training and test set and then measure the accuracy of our classifier on the test set. "
   ]
  }
 ],
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