dlnd-your-first-neural-network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Your first neural network\n",
    "\n",
    "In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and prepare the data\n",
    "\n",
    "A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_path = 'Bike-Sharing-Dataset/hour.csv'\n",
    "\n",
    "rides = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking out the data\n",
    "\n",
    "This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\n",
    "\n",
    "Below is a plot showing the number of bike riders over the first 10 days in the data set. You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f65e9140630>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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znnQ08RbFhQo+FXxCiB3bCvQspBh0RQghQbBbdPJJ0fn6XadGt+9evARfs55dRAW/z0FX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IBSn8HaWr+QMbbb6j8Amz28DldMcAq+twKfFsqiFVrRyFTw62PRqZ1ZTBV86r/eurRLaEFQiXZu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\ndm5i60+ePo1b7rwPT9d4rZRMvmgMfErEI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/nu3u/efPfc55xjJeIC38zMzMysRFzgm5mZ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65eba9bb38>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 380
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8iCykMZdIYSp7zOJz83s96An4MJ8dHDyXSFl2XkRERESttHXrVqhcd5MOZFmbTKXUOIDL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65e91ad518>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rides[:24*10].plot(x='dteday', y='cnt')\n",
    "rides[:24*10].plot(x='dteday', y='temp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy variables\n",
    "Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hr_0</th>\n",
       "      <th>hr_1</th>\n",
       "      <th>hr_2</th>\n",
       "      <th>hr_3</th>\n",
       "      <th>hr_4</th>\n",
       "      <th>hr_5</th>\n",
       "      <th>hr_6</th>\n",
       "      <th>hr_7</th>\n",
       "      <th>hr_8</th>\n",
       "      <th>hr_9</th>\n",
       "      <th>...</th>\n",
       "      <th>hr_14</th>\n",
       "      <th>hr_15</th>\n",
       "      <th>hr_16</th>\n",
       "      <th>hr_17</th>\n",
       "      <th>hr_18</th>\n",
       "      <th>hr_19</th>\n",
       "      <th>hr_20</th>\n",
       "      <th>hr_21</th>\n",
       "      <th>hr_22</th>\n",
       "      <th>hr_23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "      <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>17349</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17350</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17351</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17352</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17353</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17354</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17355</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17356</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17357</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17358</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17359</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17360</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17361</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17362</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17363</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17364</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17365</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17366</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17367</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17368</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       hr_0  hr_1  hr_2  hr_3  hr_4  hr_5  hr_6  hr_7  hr_8  hr_9  ...    \\\n",
       "0         1     0     0     0     0     0     0     0     0     0  ...     \n",
       "1         0     1     0     0     0     0     0     0     0     0  ...     \n",
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       "...     ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...     \n",
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       "\n",
       "       hr_14  hr_15  hr_16  hr_17  hr_18  hr_19  hr_20  hr_21  hr_22  hr_23  \n",
       "0          0      0      0      0      0      0      0      0      0      0  \n",
       "1          0      0      0      0      0      0      0      0      0      0  \n",
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       "3          0      0      0      0      0      0      0      0      0      0  \n",
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       "7          0      0      0      0      0      0      0      0      0      0  \n",
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       "11         0      0      0      0      0      0      0      0      0      0  \n",
       "12         0      0      0      0      0      0      0      0      0      0  \n",
       "13         0      0      0      0      0      0      0      0      0      0  \n",
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       "16         0      0      1      0      0      0      0      0      0      0  \n",
       "17         0      0      0      1      0      0      0      0      0      0  \n",
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       "...      ...    ...    ...    ...    ...    ...    ...    ...    ...    ...  \n",
       "17349      0      0      0      0      1      0      0      0      0      0  \n",
       "17350      0      0      0      0      0      1      0      0      0      0  \n",
       "17351      0      0      0      0      0      0      1      0      0      0  \n",
       "17352      0      0      0      0      0      0      0      1      0      0  \n",
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       "17354      0      0      0      0      0      0      0      0      0      1  \n",
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       "17360      0      0      0      0      0      0      0      0      0      0  \n",
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       "17364      0      0      0      0      0      0      0      0      0      0  \n",
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       "17368      0      0      0      0      0      0      0      0      0      0  \n",
       "17369      1      0      0      0      0      0      0      0      0      0  \n",
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       "17378      0      0      0      0      0      0      0      0      0      1  \n",
       "\n",
       "[17379 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(rides['hr'], prefix='hr', drop_first=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>...</th>\n",
       "      <th>hr_21</th>\n",
       "      <th>hr_22</th>\n",
       "      <th>hr_23</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.81</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yr  holiday  temp   hum  windspeed  casual  registered  cnt  season_1  \\\n",
       "0   0        0  0.24  0.81        0.0       3          13   16         1   \n",
       "1   0        0  0.22  0.80        0.0       8          32   40         1   \n",
       "2   0        0  0.22  0.80        0.0       5          27   32         1   \n",
       "3   0        0  0.24  0.75        0.0       3          10   13         1   \n",
       "4   0        0  0.24  0.75        0.0       0           1    1         1   \n",
       "\n",
       "   season_2    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  weekday_2  \\\n",
       "0         0    ...          0      0      0          0          0          0   \n",
       "1         0    ...          0      0      0          0          0          0   \n",
       "2         0    ...          0      0      0          0          0          0   \n",
       "3         0    ...          0      0      0          0          0          0   \n",
       "4         0    ...          0      0      0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          1  \n",
       "1          0          0          0          1  \n",
       "2          0          0          0          1  \n",
       "3          0          0          0          1  \n",
       "4          0          0          0          1  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
    "for each in dummy_fields:\n",
    "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
    "    rides = pd.concat([rides, dummies], axis=1)\n",
    "\n",
    "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
    "                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
    "data = rides.drop(fields_to_drop, axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling target variables\n",
    "To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\n",
    "\n",
    "The scaling factors are saved so we can go backwards when we use the network for predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
    "# Store scalings in a dictionary so we can convert back later\n",
    "scaled_features = {}\n",
    "for each in quant_features:\n",
    "    mean, std = data[each].mean(), data[each].std()\n",
    "    scaled_features[each] = [mean, std]\n",
    "    data.loc[:, each] = (data[each] - mean)/std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data into training, testing, and validation sets\n",
    "\n",
    "We'll save the last 21 days of the data to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Save the last 21 days \n",
    "test_data = data[-21*24:]\n",
    "data = data[:-21*24]\n",
    "\n",
    "# Separate the data into features and targets\n",
    "target_fields = ['cnt', 'casual', 'registered']\n",
    "features, targets = data.drop(target_fields, axis=1), data[target_fields]\n",
    "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Hold out the last 60 days of the remaining data as a validation set\n",
    "train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
    "val_features, val_targets = features[-60*24:], targets[-60*24:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time to build the network\n",
    "\n",
    "Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\n",
    "\n",
    "The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\n",
    "\n",
    "We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\n",
    "\n",
    "> **Hint:** You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.\n",
    "\n",
    "Below, you have these tasks:\n",
    "1. Implement the sigmoid function to use as the activation function. Set `self.activation_function` in `__init__` to your sigmoid function.\n",
    "2. Implement the forward pass in the `train` method.\n",
    "3. Implement the backpropagation algorithm in the `train` method, including calculating the output error.\n",
    "4. Implement the forward pass in the `run` method.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class NeuralNetwork(object):\n",
    "    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
    "        # Set number of nodes in input, hidden and output layers.\n",
    "        self.input_nodes = input_nodes\n",
    "        self.hidden_nodes = hidden_nodes\n",
    "        self.output_nodes = output_nodes\n",
    "\n",
    "        # Initialize weights\n",
    "        self.weights_input_to_hidden = np.random.normal(0.0, self.hidden_nodes**-0.5, \n",
    "                                       (self.hidden_nodes, self.input_nodes))\n",
    "\n",
    "        self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes**-0.5, \n",
    "                                       (self.output_nodes, self.hidden_nodes))\n",
    "        self.lr = learning_rate\n",
    "        \n",
    "        #### Set this to your implemented sigmoid function ####\n",
    "        # Activation function is the sigmoid function\n",
    "        self.activation_function = lambda x: 1/ (1 + np.exp(-x))\n",
    "    \n",
    "    def train(self, inputs_list, targets_list):\n",
    "        # Convert inputs list to 2d array\n",
    "        inputs = np.array(inputs_list, ndmin=2).T\n",
    "        targets = np.array(targets_list, ndmin=2).T\n",
    "        \n",
    "        #### Implement the forward pass here ####\n",
    "        ### Forward pass ###\n",
    "        # TODO: Hidden layer\n",
    "        hidden_inputs = np.dot(self.weights_input_to_hidden, inputs)\n",
    "        hidden_outputs = self.activation_function(hidden_inputs)\n",
    "        \n",
    "        # TODO: Output layer\n",
    "        final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs)\n",
    "        final_outputs = final_inputs\n",
    "        \n",
    "        #### Implement the backward pass here ####\n",
    "        ### Backward pass ###\n",
    "        \n",
    "        # TODO: Output error\n",
    "        output_errors = (targets - final_outputs)\n",
    "        \n",
    "        # TODO: Backpropagated error\n",
    "        hidden_errors = np.dot(self.weights_hidden_to_output.T, output_errors) \n",
    "        hidden_grad = hidden_outputs * (1-hidden_outputs)\n",
    "\n",
    "\n",
    "        # TODO: Update the weights\n",
    "        self.weights_hidden_to_output += self.lr * np.dot(output_errors, hidden_outputs.T) \n",
    "        self.weights_input_to_hidden += self.lr * np.dot((hidden_errors * hidden_grad), inputs.T) \n",
    "        \n",
    "    def run(self, inputs_list):\n",
    "        # Run a forward pass through the network\n",
    "        inputs = np.array(inputs_list, ndmin=2).T\n",
    "        \n",
    "        #### Implement the forward pass here ####\n",
    "        # TODO: Hidden layer\n",
    "        hidden_inputs = np.dot(self.weights_input_to_hidden, inputs)\n",
    "        hidden_outputs = self.activation_function(hidden_inputs)\n",
    "        \n",
    "        # TODO: Output layer\n",
    "        final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs)\n",
    "        final_outputs = final_inputs\n",
    "        \n",
    "        return final_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def MSE(y, Y):\n",
    "    return np.mean((y-Y)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the network\n",
    "\n",
    "Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\n",
    "\n",
    "You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\n",
    "\n",
    "### Choose the number of epochs\n",
    "This is the number of times the dataset will pass through the network, each time updating the weights. As the number of epochs increases, the network becomes better and better at predicting the targets in the training set. You'll need to choose enough epochs to train the network well but not too many or you'll be overfitting.\n",
    "\n",
    "### Choose the learning rate\n",
    "This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\n",
    "\n",
    "### Choose the number of hidden nodes\n",
    "The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 99.9% ... Training loss: 0.059 ... Validation loss: 0.197"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "### Set the hyperparameters here ###\n",
    "epochs = 2000\n",
    "learning_rate = 0.05\n",
    "hidden_nodes = 20\n",
    "output_nodes = 1\n",
    "\n",
    "N_i = train_features.shape[1]\n",
    "network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n",
    "\n",
    "losses = {'train':[], 'validation':[]}\n",
    "for e in range(epochs):\n",
    "    # Go through a random batch of 128 records from the training data set\n",
    "    batch = np.random.choice(train_features.index, size=128)\n",
    "    for record, target in zip(train_features.ix[batch].values, \n",
    "                              train_targets.ix[batch]['cnt']):\n",
    "        network.train(record, target)\n",
    "    \n",
    "    # Printing out the training progress\n",
    "    train_loss = MSE(network.run(train_features), train_targets['cnt'].values)\n",
    "    val_loss = MSE(network.run(val_features), val_targets['cnt'].values)\n",
    "    sys.stdout.write(\"\\rProgress: \" + str(100 * e/float(epochs))[:4] \\\n",
    "                     + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
    "                     + \" ... Validation loss: \" + str(val_loss)[:5])\n",
    "    \n",
    "    losses['train'].append(train_loss)\n",
    "    losses['validation'].append(val_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.010227633461907797, 0.5)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S0LFjR69ZcaU6dOiAZ599Fl999RWuvfZa9O3bF5GRkYiLi0OVKlVQtWpVj21e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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65e807d9b0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 252,
       "width": 373
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses['train'], label='Training loss')\n",
    "plt.plot(losses['validation'], label='Validation loss')\n",
    "plt.legend()\n",
    "plt.ylim(ymax=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check out your predictions\n",
    "\n",
    "Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ag4ODeOihh7BmzRp85jOfwUMPPdSQ94I0HgbtGSQw8bRgRJdEEJeLkx6vmbgu\nefujBIJ2b3p8fBUOSOa9vOqvr+Ht1Rvw84ffwL8ePB2Tx3ZF3lc3Hqp56aYV+nY7rn/By/Sc0r2u\nJMo2vGUvNvq0A6XMpHwu2AKoXnyfN69NQghJhIsuuggA0NnZie7ubmy77bY48cQTccwxx+Czn/0s\ncjl/AnM+n8e9996LCy64APfffz+uvvpqbLnlljjllFNwwQUXYNdddw08x8SJE3HHHXfgoosuwpw5\nc9DX1wcA+NrXvobx48fj4osvxmabbYZf/OIXuP766zF+/HgcccQR+OEPf4gf/OAHyb8JpGkwaM8i\n6kTSOCAOPpa0wmWqtOtTupOou/em+NoxoktCKVu3sdSzU0qgt79oFLRTaW89WkNp9/tBmJ73Wvf4\nBK6dvIjjW9GYDhHechUq7YQQYpc4JU2TJk3Cddddp/3b0qVLtY/PmjULs2bN0v6tUCjg7LPPxtln\nnx3425w5czBnzpx6h0pSDmvas0gS7vGJKFz117RrVbiEzfJspccn8V4Oxah5pdLeerRCjbN0a2U6\nObjmQbu2TCdZb40czBY4G9XWsRU+b0IIIYTUD4P2DCLi9mlvgZr2RtW7CsRQ4cIm9AkoZXHcpbXu\n8QwMUo3v806p8ioVpd0xjLf113jcUQWJkx4fdpnYV9oZtBNCCCHtDIP2LBIzaNenS6fLPb5RKd3e\nBRAb7aCGezwORU80Y0dpp3t8mvEZk6U0iJM+I7p6lPbgY7bPS9eV/pZvFlL4Afv3olboFkAIIYSQ\n+mHQnkXiBu1N6I8casMcgi4ASKblW/3p8aFKe8J9nI1dujXp+uzTnm5aQWn3lunk4Bpfn41Q2ouu\ntJ7xM9zj9UKlnRBCCGlvaESXQYLp8fHVI+sqthKkuxaM6JJW2k3d48MCjCQm3fvJZ/Bvnb/HXGc/\nFN2DjPZlTXvr4V2YSSJl3AZSaflmurigXzy0+2IdRWk3N+1sVE27J5MmrYs0hBBCCKkbKu1ZpBWU\n9phmeY0yqRLSH3iYEGpEl0BA/D1xK/bOvYZzC7cAG9YY7at7L1nTnm6KLZYeXxDmLd90br62z8sh\n143VxaJRQXsrlEMQQgghSRDH3b+VYNCeQUTcgFizufUgThmTaZ923XgS6SXvrXe1ZERne9ItpcQk\nsQ5AKTjKDfQY7U+lvfXwnkOpVV4D13j8RS/b7c6KjvQvxllKj7de0+60wOdNCGkphBAAAJceNiTl\nVIL2yjlXNwnGAAAgAElEQVTbrjBozyLqpM6KM7vlm7oapFswoku6T7u1lm+Wx+lK/+KC6xi2rWLQ\n3nK0gtLuKte4Y3BeSim1hmu2vRaKruu7rk0XD8OGk0QafwUps6M6EEKSo6urCwDQ19fX5JEQMjyV\nc7RyzrYrDNozSFylXdsD3bYxmarCpbXlW5ya9jCl3bZaqAQerlMcZmvN/prPlkF7uoljPNgwlADY\nldHPy9BWaoko7Z7r2vA+FBY8J2GY5z9+Sj9zQkjL0N3dDQBYsWIFent74bouFwRJapBSwnVd9Pb2\nYsWKFQBq52y7QiO6LBK3T3sjAuK4CwtN6NNunB4fprRbXgBxXH+Kr6pwjry/rk870+XSjN89vokD\nGYbAQpzBOdVIV/Y4QXt4Nk1ySjtQWrww+ee+YbCIu55ajh0378a+202yOjZCSGsyadIk9PX1YcOG\nDXj33XebPRxChmXMmDGYNKm9/38xaM8ggeDShtJuuw7bdeGtTLFhAJW0e7yt9HjraqESeJgG7axp\nbz2851Cc9PiP+gaxYl0/dpk2zsaw/KgdIgzS4xtl8DbkuInUtCfpHl/P8X/251dx4/y30JnP4U/f\nOwTTJ29ic3iEkBYkl8th6623xpo1a9Db24uBgQEq7SRVCCHQ1dWF7u5uTJo0CblceyeQM2jPIkrQ\nJl0HJtYNjVCxHafoPzlNW741KNDM+dLj7RjRWa93Vcy0ZB017aPRj4lYj/cwGQDd49OOjb7dq9cP\nYMal89A7UMQl//hJnLD/traGV0KtaTc47xvpyl4Q3pp2O+7xtq+fgNJuePwb578FABh0XFx834v4\n5Un7WhsbIaR1yeVymDx5MiZPntzsoRCSedp7SYLoUQJgU+VVNw+174asLiykMIUfgECclm9mj9dL\nUek17bpmNe0dQ+uwoOsMzO86E0fmHgdApT3t+NPj6/usLvvzq+gdKJ0r59/1vJVx+VHT46Ofl40y\ncRwq+scY8AMZgbDh2DYHVF93nHW/J98yawlJCCGEkORh0J5FYgbtjXCPD5ilpVRpF56ASFgyokvC\nWdpXEmGotH+sdxEmivXICYnP558AQKU97djo2/3S++tsDUePakRncB8Ku0RsB8NO4D5Uf1u6jnwt\nnylxpd1wnJPH1hx31w8UuShHCCGEpAwG7VlEqiq24US0AfXiRVVutlBLmoR5WpyWb6FGdAnUu/qU\ndkO1ULiD1Z+7UApiOKlPNzaU9vfXbrQ1HD2BoN3AiK5BaedFZ8j/QAxvjY587d+t9Zp2xW3Q9Phb\nTRzt+/2F99bGHhMhhBBC7MGgPYOoqeamSrs29dyyRbWqtKe15ZvX4C0v6lfhvFhXC1UjOkOl3Zu2\nXCgH7XSPTzc2ato/WDdgazh61LaOBunxYbXittvbOYHFw/qzkjoLtX+3SSvtpu+Duv1jb6yOPSZC\nCCGE2INBexaJmx7fEKU9nsLViDECmt7sBpPlhqmFStAuDT9vbzp9oVzDb3uRhtjF5x5vIZD1pk9b\nI0bGT6PaJRaLcZX22s9epd32wtxQTPd4dfvH32TQTgghhKQJBu0ZRAQUrugTUSmlNi61rWK7qsJl\nqOzqgspEUrrVN8OoLrdBaqHSp904aJc1BbRDlPZlTXu68Z7/9Zz3/UP+c2RKt/2gPXgfMjCiC/OD\nsHztqAua6phH3t+jtOcbp7THDdoXLf0o9pgIIYQQYg8G7VkkhtLeKNdmpxjPiE43eU+m5Vv9Zlre\nMRZyHpMq22qho7StMk6P1yjtDNpTjb9Pu/n+7360wfe7MOkJGRF1sdBk8bBR/c+dopINEOM+5E2P\nt202qd4z4qbH9w4U2Y+ZEEIISRFWg3YhxGFCiLuEECuEEANCiPeEEH8SQnxBs+2BQoj7hRBrhBAb\nhRDPCiHOEkLkhzn+N4QQTwoh1gsh1goh5gkhvmjzNWSCQKAZ3wDKunu8GvzGULgqJKO0q72m61Pa\nfSZVCdflmhvReWvaqbS3AnGN6Jat8QfttheSAGjuQ/Hd462Xlijp8cI0GG4RpV03HsbshBBCSHqw\nFrQLIX4K4EEA+wC4F8DPAPwRwGYAZijbHg3gEQCHALgLwDUAOgFcAeDWkONfBmAOgGkAbgBwE4Dd\nAPxBCPFvtl5HJoiRHt+oybKjqsEWWr4lUtOujCsw7mEIM6my7yyt1OXGSI+n0t4axDWiW7baH7Sr\nNdNWiHMfCjOiS7hMx7xPu2dhrlBLV7B+javmojEWFyrYXjwkhBBCSP0UbBxECHEqgP8D4NcAviWl\nHFT+3uH5eRxKQbcDYIaUclH58QsB/A3AV4QQx0kpb/XscyCAcwC8AWBfKeVH5ccvBbAYwGVCiPuk\nlEttvJ62R5mMGaWlhirt6QraG5Uer44rkNY/DF7xMsl2UKpbvGl6vGB6fMvhPf/rSXNetsbf7i2J\nzztY015faYkX+yaO6vUcIz0+wWs8qLQb7q95P217axBCCCGkfmIr7UKILgCXAFgGTcAOAFJKr9T3\nFZTU91srAXt5m34AF5R//Y5yiG+Xv19SCdjL+ywFcC2ALgAnx3sl2UFVi9JY0662fDMO2htkRCeU\nSbxJmYBX3epKVGmv//MG/HX7HezTnnpc128Wmdb0ePU+ZBK0h5o4JrzgZV6mU/u5I8H0ePV4pten\n7rbFmJ0QQghJDzbS449AKQi/E4ArhDhSCPF9IcSZQogDNNvPLH9/QPO3RwBsAHBgeTEgyj5zlW3I\nSMRQuFxXYhpW45qOq3Bh4TfVoNV6n/aYNe2NavkWJ/DwjrEjn1zqbNz3UmjS49mnPb2o576p6goA\n76hBewKft2rqZmLy5n2NHg9H6+MMZvzU78qeZAlM3D7tuveNC3OEEEJIerCRHr9v+Xs/gKcAfNL7\nRyHEIwC+IqVcVX5op/L3V9UDSSmLQoi3AHwCwPYAXhJCbAJgSwDrpZTva57/tfL3HaMMVgixOORP\nO0fZvx1Q01JNHc8v6piDz+ZLb+Nrckvc6sxMXU27bsJp2ywPCL6XRunxjZrQq+nxhkq7Lz2+3PLN\nlaUFnFwuAVtxEotAAGd4PkkpG6K056QLeE+fOjN+Ogs59A+VrkPbcaaqtKvdIkbcv0Hp8UVX4vj8\nX/FJ8RauLR5dR8u34GNMjyeEEELSgw2lfUr5+/8BIAF8GkA3gH8A8GeUzOZ+79l+fPn72pDjVR6f\nUOf2ZCRiKFyuK6sBOwB8NT8PQCPc4+P1HQaSUtqVoN1gUu+GBO1JlxqY9MMG/NkEHaj9TKOqdBKo\nbzb8nAaKLjYqfdoT6RaglukYpASEp53bVtrjlen4gvYEF+a2dt7FjzpuxPGFv+H0wl3Gn7kuQKfQ\nTgghhKQHG0p7ZSZSBHCUxwzuOSHEPwJ4BcChQogDpJSPWXi+WEgp99Y9Xlbg92rwcJpC0ADKxLXZ\n//toDABIok+7f0KvjnkkdJPQRtS0SwNF0p8eX5vQ21a41Jp2k88b8KfH571BuyvREdqgkTQL9Vo0\nVdp151+xnhz7EQhk/JgsHoYp2LbLdJRrR6D+xcMka9o3dz+o/ry1WFWH0q5r+caonRBCCEkLNpT2\nnvL3p1T3dinlBgB/Kv+6X/l7RRkfDz2VxyvHNd2ejIiaHl+/e/wm6C89bnkSGkjhNm351iAjukDL\nNwMVO7SHs+XAI+AWb5jimwtR2tmrPZ2oQbq56hp8rBFKu3dxaCTCFrxsZ3/ELdPxvm1+pd3uIojw\n3HfywrUStLOmnRBCCEkPNoL2V8rfw4Lmitv7aGX7QA26EKIAYDpKqv2bACCl7AOwHMBYIcQ0zfF3\nKH8P1MgTPXFaLakBwWiRkNKuBr8pbfkmoKT4Fs38ASr4JvQJK+1aq+hh8KbHF7xKewJ1ziQ+xbJZ\n5M0dl+D6jsvR6WwYeScPjSgtkVIiF2PxMKy0JGkTR3WRbuT9QxYXbCcueMaVg2ucXaF73xizE0II\nIenBRtD+V5Rq2XcVQuiOVzGme6v8/W/l77M02x4CYAyABVLKAc/jw+3zeWUbMgKBVHMT12ZlJjem\nnB5vXWkP9BKPb0RXdKX1lE+hHM8oa8GzaZImVep7adzyzfW6x9d+poN8OnGlxHGFh3BQ/gV8Lr8I\nny6aVSXprhHH8rXjuBI5NdW8TiO6RDsvxL0Ped6zrgSVdu97l4drvPCn257p8YQQQkh6iB20Synf\nBvAHANsAONP7NyHEZwF8DiUVvtKu7XYAHwI4Tgixj2fbUQB+WP7158rT/Hf5+/lCiImefbYDcBqA\nAQC/ivtasoJalxkrPT4xpd0/JjU4Hnn/kD7OluehOUVpd0yyFsJSfK2/l2oasqF7vGf7TuEA5fOH\n6bPppOhKTERv9fdu2TvM1kHCPlab17ijUdqNFg99WSo1Y4WkTRxNvTXCFhdsjlNK6Ss1yMM8PV6n\nzNNokhBCCEkPNozogFLgvCeAy4UQR6LU+m06gC8DcACcIqVcCwBSynVCiFNRCt7nCSFuBbAGwFEo\ntYO7HcBt3oNLKRcIIS4HcDaAZ4UQtwPoBHAsgEkATlfr6Uk4gd7iho7n/bIDo8SQ73HbypGM6doc\nNml1XIm8xTZlatsqE2f2RrV8C7rHGyrtysJEHi4c5FnTnlJcVyLvCYhtmDgCdo0HdUq7yeKhd4id\nnmDYNC18JIILXqbt82o/J3WNuxIoeD9vyDr6tDM9ntjltQ968eoH63H4rlPQVaBjKSGExMVK0C6l\nfFcIsTeA/4dS8H0IgHUoKfA/llI+qWx/txDiUADnAzgGwCgAr6MUlF8tNXl5UspzhBDPobRA8C2U\n8hSXALhUSnmfjdeRFVSlXRrMzhwp0YdRGAV/0G7bPE2dwKsu7SMxXNBuk0BdrkGxapizdNJKu2lN\nu9qbugAHDvJU2lNK0fWr2LaC9iHHxShLUbujLCyUnrje9PjkXNnVBS7Vw2IkQg3zLI6z6Lq+z7uk\ntEffP2yhw/YCCMkOa/oGceR/zcdg0cW/febj+PfP7dTsIRFCSMtjS2mHlHIVgNPLX1G2/zuALxg+\nxxwAc0zHRvwElHbDyfJGdAHoDTxuk4BibSnwKNVh21v1V4P2etPjk1Xa68+sAMpBuyeboAAHA6B7\nfFpRA2LTBa8wkdbmeem4NtPjG3ftxGn5ltQ41c/bND0+LA2e2fGkXn45/y0MFkvn5DUPvc6gnRBC\nLGDDiI60GAGl3aQ/sgv0yVGBx63XtCtSkWlaath4bE6WpZSxnPh9E3qvmZb1tlVqcFR/yzeg5iBv\n3UyLWMGVEnnhVdrNPu9wpd2mOhxMjzct06mQZMu3QNBudK/0j6Ujl0xGQNGVyHk+7xxco/T40Kwk\nRu2kTob4v4EQQqzDoD2DBANNM4VrIzr9x6vD+GgkgsGvnfR4q2ZarkRO+I8X6Os8DI1S2qXrL2UQ\nhjXteSUluKMatMcbF0mGoqOkxxtOoMON6Ox94GrdPWB4HwoxeJPSblq32mnBRGn3Xt/5nPB5aVhV\n2h3pa8VorLSHmnYyaCd1wlOHEEKsw6A9gwTSUo3VYb+R2xgM2HdtDhhA2QnabU7oi9rAo06lveBV\n4exGw6oibhIcAcH3vtL2jS3f0okr46XHh10jNn0r1Lr70hObLHjVfhZCoJBLJlMl2Kfd4Pr2jCMn\ngEJCrenU+5Cx0h6aHs/Ii9QHzxxCCLEPg/YMElDaTVI+pQwor93YYN89PqGg3bbSrgbtZkZ0tZ+9\nKb62Y+FAz3vDNGT18y6IitLOqVkaiWtEFxarWc9SUaf2hvehCnkhkEtIxQ4a0Rko7Z6XkxMCOZFM\nyzfVH8BUaQ81ouPlTeqEJoaEEGIfBu0ZJOgeb5aWmlf27xYbE3dtNl27b4R7fNGRARXTldFbvoWl\nx9tWsNW6XFPDr0JIejyN6NJJcDHJcMErtOWbvfPScf1194DZ4mHtOi61cCwkFLTHyfhxlPR4/xjt\nvZdF1/Ub0Qk76fFclCP1wjOHEELsw6A9g6hpqWZGdMG01pLSnnCrJVOlPdQ93m6rpUB6vEEKsd+I\nzlvTHn9sXgILIIYp/OprrBnRcWqWRtTPLGet5ZtFdVjGTY+X2Eksw7zOs/Hv73wX3bn+6t9sXuOx\nlHZfery/pj3JjB9bRnSsaSf1wlOHEELsw6A9gwQCYJN0aYmA8totNlrv0x5nsgwMpx5ZVgsDSnv8\nmnbbpQbq8UzcxIuurKbDV+io1LRb/syJHRxX+q4XAdcoXTWsljlVLd9cics7fo7tch9gev9LOAu3\n+P5mC7W0RMCNXOvtfc9zAti092XsLV4J/C0uwZp2abTwx5ZvxDZc8CGEEPswaM8gsdLjNQpZEkq7\nGwg07dS021Sxh3Rtq5zo6fF+B+wk3ePr73nvOMGFicrvVNrTibZvt8EkOuxjHbJ48egWvEwXDz+R\ne7v6+8F42ndsW6hZSHnIyLXe3nEcKJ7DZx7+Ku7oughfyD2eqNJu+nkzPZ4QUi/PL1+LI69+FGfc\n8hTvGYQkDIP2DBJH4dK1auoWGxKvabeltNusF3ec4AKGutgw7P7emvYEg/bAmAzSkIuuq6lpp3t8\nmnGkJogzMSZrkNIeuKYNzidVqd4EG3zHtkWgT7vBe+m9vv9bXlz1v/hy/u/WvTVyanq8kRFdyONU\nS0mdsPNAdjjhF0/ghffW4d5n3sMdi99t9nAIaWsYtGeQODXtOgOpZNzj7SntSfVA19W0mwTt3ol1\nh3eMtic8itJu0gLM0bnHs6Y91Tiu63ePhzQKwMJOYZs17dp2iXWavAHAJugP/VssFPW/lHoe7fiV\nYewpXvM/DmFdafeWsOThGh0/bPGNlzepF5462WHtxqHqzwuXrmniSAhpfxi0Z5CgAZTZZFmdbI9t\niHt8vUZ0El15rzN7enpNt4bSLlFQjegE3ePTjOOiJZT2QHq8YRcLV9aM3bowWPubxcUFVWnPQUZe\nFKi8X6cU/uh7/EM53nJNuxtPaWefdmIZZmlkE29bS0KIfRi0Z5BATXvc9HhstK+6StU93jA93nFx\ndcd/YXHXt/G5/JPVx61OljX13mqLqGHH6MsGSKZlFaDrHR9Pae+g0p5qHNf1ZcPkIE3i4VADsiHL\nJo7BkhezPu3rMUp/7ESVdoP0eFdiItZhVm6h7/E8nJTVtIcfl5B6YMyeTXKMKAhJFF5iGSSWa7MM\nBnHjGlDTHhjzCOzovIqj8o9hU9GLy9zLqo9bnyyLGAsgnpmN14jOlXYXFwL+AAbBly6NuVCtaefM\nLI04LoLKq5ERXYjSbrPlm1ZpNzCicyX6MDrk2PYWF9QsFRMV25USU0RP4B5hmvkwEkOORN6zAGJ6\n/PCWb7GHRjIKT51sIqi0E5IoDNqzhvRP8ABD93it0p5En3alpt1wGjDG7VP2t+94XtQ4xQfT+sPx\njiWv9HG2qRbKQNaCyRiDRnS1mnYa0aUR1WvB3D2+ASaOWvd4k8VDYL3UB+1WF5MCi4cy8vFdieBr\nBJAXbgLvZW2cpunxYfdEpseTeuGpk00YshOSLAzas4buv6lRq6VgHffYcnq8zUleINA0VNrXu12+\n37cUqwFYVto1QbtaAzsc3nl7LqcE7Ukq7QbBUVFjPFipcWef9nSiXqM5IQ1rnPWPW712ZDA93mQx\nqaS0h6THJ9jyLWdg6qddmEApkLe53qUu0pjU3QPhC4RU2kn98OTJIqxpJ2nhnqeX48irH8VNj789\n8sYtBIP2rKEL2Izc4xEwJusWG8p/s6lwqe7xZscWijq8vXgPgF11uFgMBhmmPe8r5HMCeZFU0F6/\noll0pEZpLy1W0GwonahGdDlbSrvV9Phg5wXTMp2N0r8wVzkv7V47/oW5nIieeu7K4LUDVGra7Srt\n3kWagrCjtFvvYkEyA0+dbOIVHghpJmfe+jReeG8dLrj7eWwcjC4IpB0G7RlDnYSWHotvRAfYVeKC\nfdoNJ7nK/tPFCgDhpkv1oFPVTdvnlfdCTggUkkqPD7yXZin8ASM6usenmlJAXPtsTMzTgHA/hSGL\nF4/jIpDBYeoen1P2H4fkFw9NWr7pspIA+zXtpQ4P/mvUMfEHCFXaeX2T+uCpk00otJM0smEwGPe0\nKgzaM4ajCTRN0qUdzUQ0GaVdDTQNlXbZAKXdGQo8plsUCcOVEjNzS7Co6zv4+F+/Be8itU3Trzg9\n77Ut3+gen2pUIzrTdOywj9Vuyzc3mB5v4h6vabc4XvSVj53ctWPqHq9eO0ApU8m2Iab6XkiDBRbW\ntBPbcMEnmwhWtZMU0k4GiQzaM0bRgjpcUVorjE1CadfUkpogXDVofx+A3TG6cdPjXYlfdl6GyWId\nxi/7M47I1VrTJWtEF6/lWyVoZ017OlFTz03T48OCtSHL6rCasWPWeSFYpjMe9oN2dfHPpF7cdRHI\nBigdw77SHjAXldEXD0PT4+kzSeqE/xmyCbPjSRqw2X0pbTBozxg68zSTWlKdO3qXKKILg4lOlk0C\nTQABc73puUp6fLJGdPWlx5f4GJaH/i0OQSO66KmzRdcNBEcdCdQOE3uoyqtJSjcwjNJuNT0+qA6r\nC23D7q9pPTlBrK8e2xbqgpeJM7sTUtNegGM9a0EtNZAGhpjhLd94fZP64KmTTXIpj9qLXInMBKo4\n105zVQbtGUObHm6icOmCfpTq2m2aKwXTUs0csHNKALCV+BCjMGDZTCue0q5Oir0ZDMmWGlhS2tvo\nRthOqCp23lKf9qRTuk30OVeTej6urLRbPS81GT/RW77p3eOtK+1O8Bo1aj0Z8nkzPZ7UC8+dbJLW\nLGTHlTj+hsex58V/wYMvftDs4ZCEUf+/ttP9iEF7xtCldMdV2gFYV9pVpVwIszZG0Chc08UKq2nn\nugWMevu0A/503yTrck3T44Pu8ZWe981ftZZSWjVIawfUYNHYiK5BQXtc93g1UK3WtFs1cay/pt3V\nLHgBZSM6i2PUvZdG3hqhSnusYZEMw1Mnm6S1pv3up5ZjwRur0dtfxCn/u6jZwyEJowqI7dQJhUF7\nxijGTY8PSbvsFEWrKnawpt1wsqx5TdPF+3bTUnU17YZ1uV46crV97aqFMWraHRd5oS4ulM6hZivt\nfQNFfP6qR7HvJQ/iybfWNHUsaaKoOKubpseH/X+zmVpYUtrj9WlXF5OqNe0W70OBMh0bfdqFa/Ve\nWdRkLbgGC2ph13ESKYUvvrcOZ9zyFO5c8q71Y5P00E7KFolOWrPj3/xwfbOHQBqI+r+L6fGkZdG1\nKTMpQAtTcDoxZLem3VVrSQ0myyFpqdPFCrtGdDHb56nvV0eDlPacQXCkq9sviHS4x//PI2/i5RW9\n6NkwhK/f+ERTx5Im1LaMqUyP17VDM1rwCl7j1Zr2BJV2k3ZtYfch2y3f9FkLZgsg2scTCLx+dP9L\nuPeZ9/D9O57Fmr5B68cn6aB9psjEhFxK8+O5hpQt1LlKCpJCrcGgPWPEVdp1gSoAdKKYuHu8Sasl\nXVpqt9hg10xL917GSY9PqqY9MIE3COA0r7EjJTXtL7y3rvrzQLGN7soxUWvaS+pw9P3DtrXrBxEM\nNM3KNvzXC2DfPV5KGRiTyX1ISoQa0dm8dnRtGWWI94iO8Jr2WMPSsryn1GlkyJFY1Ttg/wlIKlCv\nkXZ2cyY10qq08+zLFgGlvY1WbRi0ZwxXF1SaOLOHpcfbVtqVMQmDXtOOKwNuyoD9hQVtCqqJwqXc\nSBKraVcDDyOlPdiLPi192jvyKZ0hNBm1h7mpshu2rX0jOjU9Pp7SbrtPu84sz3TxMGi2V3agt1rT\n7mrS4+O7xydxfXuP2ez7B0mOdp40kxqtshjDThjZQvU5aqf/NQzaM4ZWaTYI4sLS47vEkFX3eL1r\nc7Tjh6WldqBod7JsoU+7l0a5x+sCiTCkJmivtHxrdp/2Qp63Lx0B93hhFiSG1aPar2mPZ5CYdNCu\nqxUXBiZypZZvwddUgGv1vdT1vI+T8VM9RAITXQbt2aCdWy6RGoHPOa3BcUqHRZIhkOmT1vOyDjjr\nzRj6Pu0mNe2NUdrVhYQcorvHO0540G41hd8NBrRmRnSq0u4J2m3eZNSsBZPgqKipaa8q7c1NSe9I\nay5ek1HrxYWlPu2Ju8ebtCLUucdbbvlW1GTsmGQtuIohYAXbLd8cJ7i4EGfxsHqMBOY53oXX1E7w\nSWxUBZYfdXsScOlOaZUaT79s0c6LhgzaM4ajSW83CeJClXbLAXGwltQgPV4zoQeADlG06iytM/WL\n0/LN69JuNSBWW74ZKe3Bzzufkpp2tbvMIOvaAQQXrfJwjdIYw43o7KrDcdLjpdS4x5eVdlspm0XH\nhUDwGo0ctDe0pl15HoP7UNjnTaWd1EvLKLAkFkPKfCqtimarpPETO9A9nrQNrjY9Pn6fdvtKuxpo\nGijtIa2WbNe0awPrGO9lJ2rKvdUVa80CSFRcbXp8adzN/gfdN+BfUOjZSDdqIFgekjPsCx72udp2\nPFdVaJOWb9r0+CSUds21ErWdWth9yH5NezAjwGzxMOTxBK5v72fT7PsHSY52njSTGmqZT1qD43SO\niiSFWrrZTv9rGLRnDJ2xmJERXWjQbrdPuzqBz0FG/ocQ5h7fAcfqxattn2egRuakP+jskjU3ZZuq\nZtCIziRoD2/51uya9vVq0L5Bc25nELVe3DQdO+wSUVWVOOjblJmY5QVV7DFioLR4aOkaLzrBbAAg\nxBdEg84sD0hGaQ+kxxstgOhfTxLzb2+mEwO59kX9/8W+7e1JqxgOtlPQRkamnRcNGbRnDF3NtVG6\ndEjQ3iWSVdrNW741oqZd06fdYLIslP1HoaYU28yODy6AxAvaO1LiHr++n0G7DvX8t9an3boRnf95\njM7LkIB4PPqstXUsalzZAcDVeFnoCFXahYSU9lQpx3UTMaJLItDyTurTqsqR+LTzpJnUGGqR1n6M\n2bOFumjYTos2DNozhs7x3OiOFtL/txN23eOFqwbtBq7Nmr7FQClot9pKLWbP+7z0T/47k1LaNe9l\n1MTSJGEAACAASURBVH+uOvf4Qkpq2lWl/aMNTI8HSunbXn+E0oKXwf6ej9XbVs9qaYnUtEMzMUh0\nXF+3hQqjxQBsJQQUQwwt3YhP4IZ4a9i+frRp/CZKe8gwkpiAe19zWlU5Eh/WtGcDdSGXnzNJA0Ej\nuiYNJAEYtGeM2H3aQyaD1mvaA62WDNLjdQEBgE5hN4Vfm1ZqEGznFcVuFGpBe7JO/NEXQHQLE4Vy\ny7dmqyfB9HgG7UDQbNI0Pd67Kt3paatn99rRt1OLSlhGS2lhzpbSrk+Pj9oD3Q1p+VYJsG2t/usU\nfZOa9rD7ahLVLzSiywaBlkttNGkmNdSSqbQGRyzPyBbqXKWd/tcwaM8Ysd3jQ4P2pN3jZeTjO66s\n1l17sTmhB6BNQTVp+abWtHfKWtBp8yajvpd5g1IDXdDeUVUKm/sfmunxetRrNAdZd5/2joInaE+4\n5ZuJ10LY4qHNevHw9PhoAbHj6lP+bXdfiKu0h43D9kRXSv99p51SFomfdjaCIjWCizPp/JxTOiyS\nEOzTTtoGvfOxhfR4yzXtupZvJjXtuslyB4pW07f0Pe+jBR6lllVq0N5fO3bCSnvUm9hwLd+auXrp\nuhJ9g/7X9RGDdgCAVBbmTHqLA361xKe0W1yk0dW0G3VeCLkPFQzb2w1HUdP/HNAvfOpwda3YUFPa\nbbWf1PVpT2PLt2Cds9XDkxTBmvZsMNQi6fGS/vGZQp2rtNP9h0F7xtAZi5ko7cO6x9sM2pXJbl5E\nVwsbVdOufS8M2kF1BIJ2j9Ju6Z+f60oI5Vh5uJE/K53ZXqWWuNE17ctWb8B7PRsBAH2DwXExPb6E\nqgSbthjzbtvhCdqtt3xTlXaD9Piw+1DB4n0orOVb1NRzVwbb2gG1oN3WIoi+T7uBP0BYerzloFq9\np7XTRIr4aWcjKFJDvdemVWnn6Zct2llpLzR7AKSxJFXT3oUhu6nnUgLC/1BUV+gwpd12Wzp91kLE\nMUpZTTOv0OnaV9odTeBgZESnCdoLTVDaH39zNf7lhscBAPedfjAmbdIZ2Ibp8SVUFTpOenyXJz1e\nVVXioAuITWra1c4LFQpwrLqy62vao1/jwyrt1mraNWn8hj3vdSSttLfTRIr4odKeDVrFiI6nX7YI\nLCal9LysByrtGUMXtJtMlsMUri4MWQ2IdfWtkWtJQybL1t3jtUp71NRZBJT2DjmASqmCtaA9pLVW\n5OMPE7Q3sk/7Ob97BlKW1nJO/+1T6BsIjovu8SXU87KUHh99f++p0VlISmnXBZoGx5fh6fG2lPah\nsPT4yNe4RF4T9NsuL9EtgKgdI4ajUS3fgo6+7TORIn6Ck+YmDYQkSutc02kdF0mCdi7FYtCeMdyY\nRnSh7vHCbkCspscDBgpXWJ92Yc+kCtCXGkTNWtAtLOQg0VkO5JNM8c3HdI9vRp/299ZurP785od9\n6O0PjmvtRirtQDBozwmzOm+fe7xPabcZtEO7mBQ5UAxLjxf2fCvCWr6pngFhuBLalm/V9HhbNe2u\nRE74jyWMWr7V9s15sptsX95qDX96J/gkLsyqyAatYjiY0mGRhGidxSRzrATtQoilQggZ8rUiZJ8D\nhRD3CyHWCCE2CiGeFUKcJYTID/M83xBCPCmEWC+EWCuEmCeE+KKN15AVXM2Sk5F7fGhN+5C1QFPK\nYB02EN0AKjRot2xEp13AiBq0uxKdIhh4Vtq+WUvxdTRKu4juHq9X2isLC41bvhzb5a/kUdu9AVTa\nq2jc403Oe++pcejQfJyZvwPjsd660l7QlW1EfIqw9PgOONYM3oquGwiGARP3+JD0eCEBgw4OI6Gv\naa+v5VshIQ8DoL0nUsQPP+tsMNQihl8M2rOFWraR1sWkerBZ074WwJWax9erDwghjgZwB4B+ALcB\nWAPgSwCuAHAQgH/W7HMZgHMAvAvgBgCdAI4D8AchxOlSymvsvIz2Rt+yLfoJHTZZ7rSYeh4WdEdN\n+QzbvxNFaxN6IET5j6hwuRojOgAYjUGsg02lPZiGbJIen5aa9u6ugk9d16fHD5UWfIQI/C1LxHWP\nr6jd+4iXcc7aHwMdQLfYgNud71obo+7ayZcN8/KqmYX2AOE17daunZD0+Kj3IVdKdISUHplku4xE\n0XE1Ld/qS4/vzOcwWCztazs9PpCy2EYTKeJHXXRup0kzqRHsh92kgYwAz79s0c6LhjaD9h4p5eyR\nNhJCjEMp6HYAzJBSLio/fiGAvwH4ihDiOCnlrZ59DkQpYH8DwL5Syo/Kj18KYDGAy4QQ90kpl1p8\nPW2Jtqa9zvR4V+SRK//ehUGrKd26yXLRQOHSpaV2WHa41wYOMdLjAWCUGASkvX8yOlO+knt8xANo\n3vOC5T7TUdhEUdp16fGDRRf9Qy5Gd4Ym62QCNRtG1Okef3HHnOpjpxTm4jb3OzaGV3oOrbdGSX3u\niPDxiZB7QR6OtWunqGtLBzOlfXRI0F6AY1Vpj9PyzXsdd+RrCybW0+OVzyWtTtMkPkFX8SYNhCRK\nqyia6q2Gi/vtTTuX5zSjpv0rADYDcGslYAcAKWU/gAvKv6qzw2+Xv19SCdjL+ywFcC2ALgAnJzXg\ndkKncAkTkw7P/m5+VPXnktJu5z9zmPt71FrSYdPjLc4edIpb1AUQ163Vr3sZjVKKt616V11New5u\n5NR2rdIuGq+0jx01cno8wBR5IBhUmirtlU13yS3zPW5zkUZ3XuUNFhf0GUOl9HibWSq6+4jOF0SH\nK8Pb2OXgWq1pDywAGtS0e99zX3q8baVdVeXaaCJF/DCrIhu0iqKpzvvSOk5ih1Y5L+vBZtDeJYT4\nmhDiP4QQZwohPhNSnz6z/P0Bzd8eAbABwIFCiK6I+8xVtiHDoDMWM1Hahce12SmMrv7cKezVtIcp\nXCYt3/Ka/sgF4UZWySKhbZ8X7T0otXzTBe2lmnabpQbqooxJcKR7jc0wolNr2nVKO8CgHUDgMzMP\n2iXGoc/32AvutlY9DHTeGqXWdNH2F6Hu8TZbvsVPj9dl0wAll3u7SnsMIzqv0p7zKu22a9oVVa6N\nJlLEj/pZt9OkmdQIXNMpXZwZ4iJSplBjhXa6/9hMj58K4DfKY28JIU6WUj7seWyn8vdX1QNIKYtC\niLcAfALA9gBeEkJsAmBLAOullO9rnve18vcdowxSCLE45E87R9m/1dEr7fW1fHPznqDdYr14WNDt\nRpyIOlKvtAOAcO05jOtM+aJOlsNMqirp8dbqXcPc4yOnx6ejpl19O97r2ajdbi17tWvS4836tLuu\nxKdyL/oe24Auqy3+dNdOzsCcLSw9vmCxQ8SQI9EVMz0+p7mPAWVfCYslMHFq2r3nRoenW4DteS17\nd2cH9bO17Y9A0oHaUSStQbs6N2W5RnvDPu0j8ysAh6EUuG8CYDcA1wPYDsBcIcTunm3Hl7+vDTlW\n5fEJdW5PhkEfaBqc0J6g1Ke0W3SPL7quNmXfLD0+ZNuiPSVW66Qf8b10XaBD6x5fGp89pd0NqnAm\nztXD9Wlv4KR7UCnCf3v1Bu12HzFoD6RG19On/aDc877HbKadA/rAN4forekao7Tr0+OlQYeIwrA1\n7XZmjrqFOZOg3bsY0+FJj7ethAdSFttnHkUU2jk9ldQIGtGl83NWz8dGdr4hjaed+7RbUdqllBcp\nDz0P4NtCiPUoGcjNBvCPNp4rLlLKvXWPlxX4vRo8nIajN4CKfkbnvEZ0nqC9C0OJu8e7FibLNpV2\nXd1o5Am9DKtpt5serzOpMkqX1vZpLz3WyH/Qg8pdd9maWtA+tqtQrXF/e40/rTuTqH3aTZV2KXGw\nErTn4QRMh+KgV9oN1OcwpR0ONlhU2nX3Rl2JkQ45TMaP3Zp2N7BIGZaJoN3fW9PuSY+3XtMeMCdL\n5wSfxMN1ZWDtmunI7Ym68JjWWDhYmtOkgZCGEFwgbp/7T9JGdP9d/n6I57GKMj4eeiqP99S5PRkG\nvXmaiRGdJ2jvGFP9uVPYc2YvanqLl57aJPU85K7s2EyP19Tlxk2PrxjRJfhemrSb0qX715T2xv3n\nU5X25Z70+P2mT6r+/MLydQ0bU1pRF45MWvwBgHAG8bGcvxKpACfx9HgTr4VcSFDa0YDWk5EXD2V4\nxo/VmnZHBnrew2Ah1hs8dzYyPb6NJlKkhu5/Fz/q9kRNj0/rNc17T7ZQ5yrttECcdNC+qvx9E89j\nr5S/B2rQhRAFANMBFAG8CQBSyj4AywGMFUJM0zzHDuXvgRp5EiSu0u5Nu5QBpT1Z93gTpT3UtTkk\nrbYudAF6VPf4MCM6UQrakzTTMgritO7xLmCSYm8BVWn3csD2m1Z/fm55WBVNhtAY0Zko7cINlpDY\nTo/XlboIyMgKSFh6vKnp3nCEGWLKyIaYCF08zAt7Ne26xUwzpb32cyFRI7rWSKUl8dB9rvys25NW\nMRwcamNjMhKknbsFJB20f6r8/U3PY38rf5+l2f4QAGMALJBSDkTc5/PKNmQYtDXtBi3fhC893qO0\nW61p1ytcTjGeig2UVERbxHkvHVdWXdi9jCqnx1t14hdq0G7B8MtyEDcSqtLuZa9tJ1T7Sy9bs4Fm\ndLr0eJPPKsTHwGZmha6MJAcZKwMEsLu44IYuHkZt+TbM4qHF9Hj9fSj6Z+Wd4Phq2pkeT+pAd5+g\nstmetIwRXRsbk5EgNKIbBiHELmWHd/Xx7QBcU/71Js+fbgfwIYDjhBD7eLYfBeCH5V9/rhyukmZ/\nvhBiovIcpwEYQMkMj4yAPj0+uirj3VZ2KO7xVtVhXYpd9LTU0Mmy1Zr2+t/LUtCuM6Irjc/WTUb3\nXhaEgRo5jOGXrW4BURguaJ84phM7Te2u/v7CexlX25VzMCekURaM0GZXWFbadenxwsCILnQxqWjv\n2gmrSY+oYrvDLB4W4ATUn3rR9byvu+Wbt0+75eoXNZhr5KIfaRy6/y1coGlPWqUjRCCNP6XjJHZo\nlfOyHmwo7ccCWCGE+KMQ4johxP8nhLgdwEsAPg7gfgCXVTaWUq4DcCqAPIB5QohfCCF+CuBpAAeg\nFNTf5n0CKeUCAJcD+BiAZ4UQVwghrgWwCMAkAP8upVxq4bW0PyGtlqLiC9qV9HhbRlVF19UrXBED\nj2KjjOi07vEG6fEa9/jRoqy0WwqIiyEO2FHbVukCOMB+uvRIDJceP3ZUAZ/comZ58XzGg3ZdQBz1\n8y5trDcflNLeP7+wLJWoAbfQLHgBdhcXSi3bElo8hBuYSNaN7r00SY/3DLGSsQLYb9NFtSsb6K6/\nNpozEw/qnC+twVE7B3EkSDsb0dlwj38Ipd7rewI4CKX69R4A81Hq2/4bqfz3l1LeLYQ4FMD5AI4B\nMArA6wDOBnC1un15n3OEEM+hpKx/CyWnnSUALpVS3mfhdWQCXV24SSqlbzKY74Ir8shJBzkhI7sq\nj0RJhda4NhsoXKH9kS0G7UKrtMdLjx9dNqJLUmkHACfiZxWm2BVQxEBK0uPHdhXwyS3HAwvfAQA8\nl3EzOt1npsuwCd1fc515zQfzuXz9g6uOR29EF71sI7yVmi1VLyw93uQ+FN7yzbWmtLs6pd3EiM7r\nHt/A9HhOnNsT1rRnh6EWWYhjTXu2UD/flJ6WdRE7aJdSPgzg4Tr2+zuALxjuMwfAHNPnIjXi1rR7\nU29FLgc314mcU3bydgZCdjIjrKY9auCh1rRLiOprzEmL7vHagDaeEV3NPd5eD2dt4BHRiT88aLdb\n4zwSYUF7TgCjO/KloL3M8xk3o9Nd47oU6lBCatqBUgZIl42lXs15lTNxjx+mbMPWeVlSynWpvgZK\ne8jiYR7usNkjRuju6SZ92r3u8Qmmx9PBORvolXZ+1u1IyyrtPB/bmlYxSKyHpI3oSMqI2/LNF8Tl\nC3BzHbW/WQraHVdqFxKipviqrZq8tffJK+1Rxwh90C7s9ml3QtrnOVGD9mHS413ZmFpFKWVogDO2\nqwAhBHae2o182fn6rQ/7qn3bs4juvHRNIjDNNVJT2m2lx+uM6NzIabTDGdHZunbckMXDqBb3rkRo\nTXveak17/WU6gP8aLiSYHh8wB2qjiRSpofM6aadJM6kRNPxq0kBGgCaY2UItL22n+w+D9qyhrWmP\nPsHL+ZT2AtxcZ+13S87sRSdMaY8YEKv9kQvJBO16hSvazaHouijoWr6VlXabbav0Ne3RgtqwvvMF\nUXq8ESvWw9X+7je91O5tVEceU8eNqj7+UZ+9LgEthzaIMzCbHC493pb8qlXao3c1CDsv8xaDdkdK\nbZp51MXD0KAfJaV9aJiSDyO09yEnctAdZkSXfHq81cOTlKD7n0ClvT1pleBoSFVeeT62NXSPJ22D\nvqbdRGmv7S9yebj5rurvOYtKe6y0VFci79nfa5iXt5gerws8otaSui7QKXQt3yrp8fZq2rWBh4X0\n+Mrxk2a4NOJvH7p99efRnbVa641DBsZrbYbuM3NN/Ca0RnR2P2/dApxRK8KQ9HirSruEfvEwqhGd\nqyweeu6VJaU9ufcyDxlZ9apMYDfFWnTlPC09LV/a7TyRIjV0nSr4WbcnrZKGrGZ/pHWcxA7t7J/C\noD1rxOzp6w/aC3DzNaUdtpR219XWgpqlx3u27agpsDmT2t4R0KfHR6931aXHV43orCntIe7xkY3o\nwmuHgQYF7SGK5LTxo7DPdpOqv4/xBO0bBrMbtGu9HwzSpXWfeU6UvBFU46G60YwxDzeyYUxoBggc\nayqKqyz+ef4QaX9HKkZ0BW/QHl7yYUxI9lTUa9NxJf4x9yie6DoNZ7/6dXSW207aVqPUYK6dJlKk\nhm7BmVkV7Umr9GkPGObVcT7aLhciydHO7vEM2jOGvpbURGmvTehFLg/pSY/PWQraQ/u0G7R88wWq\nnpr2glWlvX4n/pKz9DAt31LS8z4sOOqwXOM8HGFB+//74q6+30d1eJT2DAftQlNHbdbyLewzL2rr\nVetCm6UiI/9z9Z6X0qNgF4RjrV1iWJZK1D7tUiqlR54Fzrxwhu2IYIRmITJvYOrnuBJXdP4cBeFi\n04F38fX8XwAk0fKtpOafkv8jdhNvttVEitTQXX9pDeZIPFpF0YxrRLfgjQ9xwI//hmOvfwyvftBr\nc2gkAdQF4nbyMLDhA0xaiZgt33yT0FzeN2HOubaU9ng17W5AaffUtFsK2sPaQZm0fOvUBFddllu+\nhbnHO8WISnvIe15ZcGiG0v6p7SfhgO0nY9Ynp/oeH+NLj8+uER1cB1C6spm0fBvOmV2tDawbzX3I\nqOWbdIGKZ1rHqGrnioLlmna9H0R9XSz8Srudlm8ypO4+L6K/l+q9ZguxuvS4dfd4F7M7fo0v5R/H\nOjkalw3ta/cJSCrQnXftNGkmNdR7WFo/5rgt3+b8fSlWrOvHinX9+OwVj+DKY/fAl/fc0uYQiUWC\nXgtNGkgCMGjPGNpa0jrd43P5AqRHPbIVtDuh/ZGjp6V6J9uiw1vTbieYGwpJO4+6AOJIic5h0uNt\n1buGZy1EWwAZLg0ZsNeabjgGPfX322+2CW791gHa7ZgeX0JbtmGgtIsQvwObJm+6oL3ulm+F0QBK\nbf5sp8drs5AMSmBClXZL7vFh98oc3Mjvg/qZVhY8baujRVdiV/E2AGCc2IhxA+9bPT5JB9r0eCrt\nbYkaHKU1oyJuRkDPRr/Yc8Hdz+PoPbaAECJkD9JMAt0CUnpe1gPT47NGTCO6nGJE5w3a8xb7tOsm\nojoTPR2OK5EXIUG7Jfd419WXFUStaXfdkJr2Snq8paXBovJeVIgatOtSrQGgQzSupn3Ao7R7+0ir\nMD0+3LFcmrjHD2PyZqtNmc4sT0BGVuRyIQp2Byymx6tBd5mopSVy2Jp218rCXFg2QB5u5PcyGLSX\njpeEe7xvrJYWUEm6oNKeHVrFiC6uy726/fqBYkNKA0l9qB4GaT0v64FBe9bQTJbD2hLp8E6WRa4A\nFLwmb7aUdr2KHVlpD9S0j6n+WEDRSq1m2ITeRGnvGMY93pZJleO4sXrej6i026pxHgZvenxXIfyW\nNYbu8eELXiYZEcN85tY+77D0+Dpq2r0tHfNwrC14OSELc1Fr2h1X+g01PQucBbhWrnHX1feCN3Hi\nD1faYw/Ph9rKU5h0NCAtgy77qo3mzMRDoCNESj9o9Zw0XZDULVa3UyDYbgRMT6m0k5ZF8w+1bvf4\nQHq8HRW76EhtoFlvqyWv0t6JopWbbWBhoPJcEbMWnBClfVTZuXlgyKLSHqPX9EhBuy3ldTi8QXvn\nsEF7rdonq+nxbojyqjMrCyOsw0KHcOypCyEt36KuLXgzfrzdIQpwMGhpYSE0ayHy4iFCa9pzlvq0\nhy0eGqXHK9sVElLaXelfxIia7UNaC93/VwY47UlAwU5hcOS6wfaXpuejzjS0EXMfUh+Bso02uv8w\naM8YusDXxD3eG8SJXN5fp2mxpj2OEV1Qaa8F7R2wk9YUZkRXals18vHdkJZvXWIIsNgOKtQfIGKf\n9pGD9uRvht7nGC5oZ3p8WWnXlkPEa/kGlLJUbKnYujKSnIhW0+4qBm/Cd33bS+FXg8wKUUsNAgso\nXpd7izXt4enx0Y6hTmjyCZW+qAuIYecZaW10/1/bqaaU1Ihr8NYIbHgsUGlvLVqlq0E9MGjPGpoJ\nZw4ycsq4Nz0+ly/41SOL7vFxW775a0lrSlyHsKS0h6bHR0tLddxa2zSVDthrBxWWLi0jKq/5kDE2\nVGn3LDAMV9PO9PjhFrzspMcna0QXreWbI2U1sCwNzNvyrWjtnCxd4/Ub0QWC9oInK8lSTXtYNoCJ\n0q5Oaiv3TttxVuDcNMj+IK2Dri0kg/b2JJAen8LP2YbHgu5ezZr29MI+7aRt0CrtIroBlFchU4N2\nm0p7nLrcgAruqWm3qbSHTZajHD/MiA4AOjFkL2h39IGHW4fSLr2LH+WxN7pP+3BK++gOr3t8NgOC\nsKA9LBDXIcLS4+EEDF7qRae0RzVPc4ZZlCtYaqUGDJceb5Lx49nWp7Rbqmm3YEQXUNqTco93lPsy\n0+PbEq2yyUzitiQQHKUwkNW1KTUdJ5X21iLgHt9GnxWD9qwREviqxg1h+GrFc3mIBIL2UKU9YuDh\nSKU/sqfmtRNFKxewKxFSSyojTXadkPT40hiHfI7pcXBcN+S9HPn4ar97UfDXDgONUdq978VuA08B\nv/4S8OjPAtuN9irtg9mcJcZtlwiE92kvtXyz9b7q3OOj9RYvBapepd17XhYx5ETPHBp2hFLv9xHV\nA2RYpV3YWVwoZR2EmPpFNaJrUE27ajAatjhEWhutstlGShepoZZLuRJW7r020WV+2AjaWdOeXoJK\ne5MGkgDs054xwlqSlYzJRj4dfEp7Lo9cR+Pc46MWaQYClwSU9lKrpeBxok6WHVfvHg+UFhZs1bSH\nGdFFSY8P7NsxGujvAQAUROON6L6UW4B/e/ea0oNvPQLs+mVg049Vt/Onx2czILCjtIeXbdhI6ZZS\nQmgmdqUFr5H3d6XS8UKzmFR0JTry8XroxjeiUzICEqhpL7WejGlEp+xeVdotX9qBUp2IZQaktdC5\nx1OVbE903URcCcS89VpFq7QbLizoRBSe0+lFFReotJPWZdigfYRdlTruXKEDOa/SLm25x7vIifpr\n2odLn+2wVJfrhvQ/FxEdsF0pUfAq7Z6FhU5hLz3ecUOc+CMEDI5i+KW+j0BjjOgGHRefFG/iqo5r\n/X9Y/brv19E0ogvt222SihymtNuqaR/WPC1KlkpgMcmfHg/YWUwKXwCJqrQrAbVHac/DtXKNB7KK\nPMeP3qfdP45CQunx6rlJI7r2RHfeUGlvT2yknieNjW4Guv8nrGlPLwH3+Da6/zBozxhhKeZOhBpn\nV/pbGIlcHrkOj3pkSWkPqxl1o06WHccf9CtGdDolwJRSMFy/wuW4Ep3eoL1zk9qPKFo1oqtfaXf9\nCxNKv3sgmB6XBINFF1/MPxFcyFn3nu9Xb3p8Vlu+OWrdcBmT9loizIhOWFKHVaW8TNTe4qp7vLdP\ne+W8HCrayabRtnA08NbwZdN4lPY8XCut6cK6WORENFM/QNenPaH0eIdGdFlAq74ywGlL9Ep7uj5r\nG2PUiRNpW5wgNVrBa6FeGLRnjND0+IjKq2+CKPw17baC9tCxRAw8vFkDrsj7FC5rfdpDDaCiuscr\ngUfnWN8YE2/5FmEBZKDoKt4A/tZ5QIOU9qKLcegL/qH3fd+v3j7tmXWPD1PaDdLjw9r8dVi6dtyQ\nYDgXVWlXa9o7vItypcetmLyFLXhFrWn33IckBJDvqP6et9WnfTj3+KhGdMpmlWve9qUdaPlGI7q2\nRKtspiyQI3awoWL3Dzl468O+xGrh4xojOq5+Tsea9vQSMKJro/sPg/asERKsOREmUP8/e28eJUt2\n1gf+bkRkZtVbu193a0WgBYRklmPEYkvWCCywDzAwjAd5RjP2gLENYxvGWCwezCaOjbBBLAIj0BEa\nIwRjSQyMhNhBIKmRaBm11KhbLfWi3tXL69dvrS0zI+6980dkZHx3i7gRcSMrKyu+c96pV5VL3cqM\niLzf99tySjd5fJQgJhvmUPR44WgafC/q1BldMjVLPljj4dgsM8/Gw8hpn9CmPXV+UDStTIjWyOs0\n5VpzVDbt41VGvmUCp9iBecOVh4F3/CPgTS8Hzn9yoMejMB5sN6Qpyp3Tzq2oRdNyN5p+Jo6i0j0+\n3HHpinxjnki7YrTGYiAqh0pxqJz2Cvd4byM67e8p2DWhN9H6+z7Q4zez7Dnth7CQoXovq6t6g+uG\nEBL/4xs/hL/70+/HD77rjl4adxsbsAnzw3Wd3iT0dtNKZ9Nu0ns1NO3HrTog7ULPJo8ixGOyYQ7U\ntMNB1fel+CqbwZ6adldOewTppXXiXGJMqbMjlR4PIIzm1eXEz+s3zNNUqM2RZugH2E2HQtecC5zE\n1Lzh428H7vo94Ik7gN//XsWI7tjS44Wdeu7baAJVSDsPaOLoQoc9H6/ktJPBYcCm3ZUQ4TsAHLDB\nVQAAIABJREFUoTIDGcUAKz9u40DRdK7hYZOmXfcKKa4/4SPfVK+SAWnfzAqRiz3U0SjrgKbBe/3g\nxT3c9cQOAODtf/UI3vmRR4KtrSgr0t7g2ua6Tg+a9vUt/Rq0Se7xQ9N+zKraPb66DISMqZr20QIh\n7lpupL0FLTWKFVpquJx2WJthbwMoUQ44OEtUCj/LbwvVtNsM83xM/aqQ9hELpx2uqzl3IO20Hv5L\nNfLtmNLjDR+CohrR492RbyGGNKLCD8KPpYJKo0kgUNPuGHi5NP960SGjNJB2EURaUjUA8W26mTZs\n3UIucwo9j5NC/T2+r+NQR6vsdOQN2jUPtayu+eW6K/tr33MnPv3kbud10bKxw5rsAV3X6eGYXt/S\n399NGhoOTfsxK3fTXo+8ClFGfQEAokTRtI+RBtksSyfS7olwERRZp8ePWThdrp0e70drl7zcwAqW\nKCZVBdI18zAHrCsuYDfT8hiATFNei7Tb3GND1zwTOIWaph2DezzgjgBrQo93Ie1JoMi3KiM6nw/X\nHF2mSLsapQYA8xBGdB3d4xUkOUqAqDw+cyO6UEwaF9Lu9xyRdu2fFE17YKRdZ3MN9PjNLF1uAQya\n9k0tK6uiwVutP36WCfz2xz7TdVlK2QbNIejxg6Z9fYtr+5RNGrAMTfsxKxe6ITwaRIMSHsVKszlB\nFgjhcqzFc7Os0Oj7ose7XJs93eNlNiufi42UxmOMvKGfpSEovi73eB9Nu6jQtBfu8asxomvctKd8\no6arvhUi8q06pz1Qo2mJdEyYf/KCGvlG3eMDatqFPXoSng2I1Bk/FGkP5cTvkEP4GtEZAxAAkwXT\nJ3TTridWDPT4zSwbijn07JtZXZ3ZbdfAK/uBZJaL6mqW52I8blIjuGmlg0mbNDQcmvZjV/aD16fB\nsdHjabM5wTwIEudqKL3RQrI57Ise76al+qGFPCNIe5Qog4VJoWkPhsRZNlEeG+aDlHu4x68GaT/J\niKadDIpoRWKOSVJe0nTq3XEoLgTilsyKopw57YwHifgzvDHobT4pFrohZk9GdK5Bhys2Uy/DW4Op\nSHsI93ghpcp+Is/vs3nOhFxKCopa0uMD73P0YayL0THU0S4d5QKGBmdTq2tOu20vFirutijbnrTr\nYAEYNO3rXIZ7/Aa9V0PTfsyqi6bdQG2jREWIWRZkU++kwfsi7XQzyBINaefB3OOtmnZPtFBkZTye\njFSkfRTSiM7VIPm6xzty2lca+cYFTlOk/aYX2u+4+6RmRnf86LfcQY8PFfkWIlvcSTuH3zDJpMeT\npp0JADLM8NDxOriuoZX30+jxSUBNu+065J15L7XoSdCmPTDSrrG5Bnr8ZlZX46+hjkZxIa0MikZN\nu+UaGCrutqiuSLtT075J7mYbVkZO+wZdf4am/ZiVa8PJfejx+mY7ijQtdhrkgutEsjz101JB2iOD\nHh9isOCix3tr2gk9XkQjTXcfzohOOHPa/Zp2J9LOVoe083RWUnZZDJx7vv2Ou08qWe3H0UHeFfHn\n22gCQISqyLdQSLuL8eOTYgH1uIwTBcUOReN3DQ+9X0sl8i1SmvZooWnvGnHkco/3lelwYTbtk4U8\nJzQ6ofumDEj7ZtbgHn88ymVK2mTYZ3uOeRb2umBlAwRB2o8fk+8olLAMkzbp+jM07cesbK7NgB/1\n3E6PJ80msjBGVY6LpHfUktCRdpUeP+sxH9kX4aL0eB1pHwemx9s17b7u8XakvdS09//BFc1LN1me\nnAROP8t+x93z2BqVl7TpMXSQd2mcm1iB02ZKsnIIkgRqhl1meYAf0m4OD9VzPFQGumuw5S/T0Y3o\nVPd4oDtTpcqIzkumY6HXJ0wgQRY+W1voSPvxOz+PQ9lz2jdn0zxUXi4/m85Ie2B6vA0Rb9LEufZh\nAz1+PWvTmT5D037MijkOXh+kPafHaxtRihAHco93aklbIO25WR5B2hkP8qEgJazRWr75yBRplzrS\njrCRb3b3eD8jOremfaEdXsEHV5ztLf/PR6eAM8+033FvQNpdSHtberzQ9OJ9xpQBfgwQQ8cdJUBE\nB3N9I+1+r4E6/NA17WG099xCbwcWMh1PIzrb4ydIw7vHD5r2Y1FW9/gBlNy4cjXtXfXioenxNkS8\nya9weY8MPg3rWXY5xCEspKcamvZjVsyyQQPaIlyxslnOs4d7pMe3jVrS6PEhDMpczTDzzZqmsXRG\n0x5W097WTXyacrUBtGnaV2D2Fqcl0i5GpyqQ9ieVrPbj2LS70gKaGNHFtGmP9aa9P3NEwNOIzsb4\niSkjIIz23uVu7osQMyIzYLEZ+QZ0b9qr3OPbGtEBua49OKWQ6037oGnfxBqQ9uNRrrjXJpc0W4MV\nGmm3Iq8NmGdOpH3QtK9ldY34W/camvZjVLJCS+qDYhuuz9pmeRSIHu9sKH2RGcWILlJoqWNkvTbD\nOT3e4wmIER1ijR6/0G+HGi5Y33MfpD3Tc9pN9/hVUMRGBGkX4wqkffe8Evt2HOnxGXcYDzbStBOE\nmCDtI8aDbFSq3ON9kHYDXdao50kgZ3bnIFP6adGVGE0Wa037Ik8+ANLeJafdFvkGLJr24O7xg6b9\nOJRtgzw07ZtXLqS5kcnbKpp2W5rBoGnf2LIi7Rt0/Rma9mNUVQiXz+SRC6hNnJE9HAhp75jTbmhJ\nDaS9+2bRbQDlaUTHNff4niLfXA2Sz5DmYF6R087CrbGuaNMuJ6eBU0+333H3vOYef/yaghxptzBA\nGtHjy+ZKpcdnYTTtFfR44SHTkYZMR2X8hGIEuLTrEaRXQyu5jrRTTXv+BF2HnK7rUOxrRCftSPuE\nhafHQ89pH5r2jSw7srk5m+ah8nJdYxsZ0Vnp8WGPFfvx6P/4eWZfz6BpX8+yRvxt0Hs1NO3HqJzx\nX/CLfONCImJ6065pSYM4nrtQOL8Tj1Vp2pFhlvbnHu+bjyx5aUSHeKwZ0YXVtNsGNS7qL60caadN\nu0mPX4UR3Yjvl9+MTwHXPxc4+9n590QnbNLjjx/9lgu714KvDhvQtNi6pj3Ah1/V8NDPEBPqYCJK\nVHo8CzNccJ0jEfOMU6NNe6Q63Bea/K7XS5fRZORrRCckEktaQB9Iuz6MtSH8tc8h5bFk0BylsrrH\nbxDSNVReQYzorEh72PPbBkiFyGkfBlHrWZt+/Rma9mNUUqIzPd7UkqquzUGQ146adoViu8hHlmAA\ncmfklDi3ty1XbBWD9JvAKvR4kw0AhIp8c7iJe6Bcs3mKmOV/iwSzZsmvQtc14Xvkm9P5Mfet7wG+\n4Q3At/5uedtAj3cP5hqgmrHStKvmg2GGcm73ePD6QQvXKd0aPX4EHgStEU6k3XMwJ2jTrrKSokCa\n9s4pFkIusu3Vypv20Eh7NyO6WcbxdT//F3jJf/hT/MrN94dc2VABa0Daj0e56OFNqMiryGm3Ia/N\nctoHTftRKqvx4AY17Un9XYbalHKhMoCKCrnK6h4fqfnIIS5kXenxqhFdDDAGzkZIZN4op/O545H+\nxasMoHw+EES5BmYg7UXT3r3p5EIgYu007bOUxNKxBEyLzgNWQ4+fEKSdTU7n/zn3vPzf9Gp5x90n\ncYJEvh1LerzjHG9Ej6ea9rg8LhPwIDq+SsaPxzkurJr28IaYrjz2CKJ2MMeFVB6vG9ElgTTtQkh3\nikXLnHagH3q8fl1v2rTfct9F3PXEDgDgdX/wKVzan+P/+toXBVvfUGHKjnQdwkKG6rVc18AmVGR7\nTnvYPYXdTXzQtG9qWa8/G/RWDUj7MSoupTun3UvTXu0eH0xL6lpLC007W6BbgqyTp9PWa1v+ikoD\nKI8PBDokiUdAbBrRBdG0O6UG9c+dzmmWvCozGK8SaRcl0s62Tms3ngEKCne6jzNxOQw5OAyk/dGP\nAu/7CeDSA6v/3agYzDUxoqPNlEaPD4Ng2xvN/MaWKRbaQKlPI7oI0pr9SyvlQvX/0IzoSqS9o6Zd\n2lkLked1KB+AmAPbSQ/u8fo1p2nTvjtT1/nL778PH33oUud1DRW2rO7xQ9e+cXVUctpte9ImA0nX\nZ96gaV/P2nSmz9C0H6MSVVFLHptlkx6vOrOHo8e7NvSeSLtuUgW1ac8CIO0uMy0G6YVwMWJEx5Ix\nkJg57SG0905E3eP9TlPyOjGzMQK603t9aluUSHukN+2MAaeetvz2nLyy/P/BqpH2bAa89RuBD/wk\n8JvfstrfvSgXiu1CjW1FkVdJzAcT8CAeBsLhBwH4y3TMnHbqHh9Ge+86d3IJTPU651wgZjrjhyLt\n4ejx1px2z6bdMBdd1AQpQjMKDaS9oabdtpm/7eErlnsOdZhlzWnfIHrqUHk5td5N6PGHhLQ3abid\nOe0DPX4ta3CPH2pjqooe728ARRGuRGvkeK+Rbz5rBHQtqYm0Cz7rsLi8XDntEaQXqsBEiWKzeGzP\nae8Rafdp4jLCSJDaGkeFkdYKJphbsmzak+2z5h2Im/w5eXn5/5XT45+6F0gXrIAnbl/t716Ukx7v\n0pBbKnJo2hPWf067UxpDyhiYaXrxUIwf15DQpyFOM1G5xiXS3nGD6hqA+Oe0C2vTv4V5+I2ObkTX\nWNNu/p0hYjGHCls29HSDgK6hFuVqfJtcNmz7xdCSu67MDzc9fjio17Gs158Neq+Gpv0YVU6Pb29E\nZ6fHxxALk7eISaRZd8duV3PuixbSqKUoztEtSenxQTTt3aKWQJr2KFHp8ZOA7vF6zNKyPDbMIi2H\nG0bTviL3eCklTpCmPdaRdkBp2s/yki67cno8GWABaLZ7CVROvbhHM1yU4ltBc9qRBRnKuczTAED4\n5LQLmJr2WJPphDh3HGuJPMwmU17t/xFK0155HfKhx4vyXKa11YumXctpD4C0D037+pVdU7o5m+ah\n8gpBj7fdN+V+wIdvWXPaB037xtaAtA+1MVXl2uyTj2zouBd0T0FonyLr3hA7GwxvpN3UtCtNe9Yd\naXfR4yMmwT024hFF2pOJSo9fZKCH2JA6ByA+TRzV/idbSmM0XhE9PhMSJ1GuI9o6Y95p+7rlf0/K\nUv++cnq8/sGQ7tvv12O5kPYIwjsyMXI07aEQ7Er3eE/H8yr3+FCMANf1xkcvnnIdaVeb9uK2roM5\n1wAkZp70eMfjJz1EvulD1xjc+5gEcvd4n58Ndbi16ZrSofIK4x5vf440YEPc1U2catonSdkyDUj7\nepbt2Nmk608vTTtj7B8zxuTi3z933OdljLE/YIxdYowdMMZuZ4z9G8Zo8LLxmG9ljP0VY2yXMXaV\nMfZ+xtg39PE3bGLlGzSHpt2LHm9xbQYgWLlhztLucWpuGrwHpZsLhd4bLfKbJUGJQwwWuICaWa/c\n5vGBQ1CnKBmrRnQIZ0TnRlg9tMMEaUdiR9qDyCEqap4JnGIH5Q8mp8w7kcZyi5Wv68pz2nXZxWx3\ntb8f+UbCFQHm+7mVUHr8qIem3RGXCIShx4eKfHM17T6xjrPMYkTHzKa9sxFdVU67p3v8iDly2kMb\n0WlIe+wZS1eUzeMjiO/HUEFr05GuofJyIe2NqOeO+4bUtYekx58Yl9fwQdO+nrXpTJ/gTTtj7DkA\nfhGAc8fKGPsmADcDeAWAdy3uPwbwcwDe4XjMTwN4K4BnAvgVAL8B4IsA/C5j7LvC/QWbW0JIZ6Pp\ns1nmQixzuwGUJm9kM8oDZKC7kfb6Ey+npWobeqhIu0gDNO1VFF8P1kKk0+OTvnLa25v6yaxEuFmy\n5Wja+900zzOBUwRpx8RCjydNeyEtAA6BHq8Pg+arb9p5hcbZt0FS6fFqTnuItIDKc8fHINGGYsc0\n8i2Upt2+Fi9NOxfq+6ANFgr3/L5y2mOPWDrAHfm2hfD0eP2aE4M3RLwGevxRKBuy2YRRMdTRKKcR\nXUd6PBC4abess8nHGJVabY/Kve6AtK9nWeUQG3T9Cdq0M8YYgF8FcBHAmxz3OYO86eYAvkpK+c+k\nlN8P4G8CuAXAqxhjr9Ye8zIA3wvgPgBfLKV8jZTyOwF8KYBLAH6aMfbckH/LJpbLPA2oaO7ofUgz\nKsBy526oSDsP0BA79dYebIC5DeEClE295N3XKDuihbRpj2MdaQ/XtDsdsH1MoDLqcD/RKMh5U9J3\n5NucC5wCoZmPLUh7bDrvA4dAj9ePq9nOan8/KmQbkN5NmBLFpbjHZ4HMEd3u8V6pBrw6p30Uqml3\n0uPrEeI88k1zj1fo8eFy2u1DmvpYOqAqpz08PV6/FiXgjfJz7UZ0Az1+3aprLvZQR6Nc72mTYZ/r\nOh3SjK4r0k7XsjWmTfswMFzHGnLam9W/BvBKAN8GYM9xn1cBuAnAO6SUtxY/lFJOAfzw4tt/qT3m\nXyy+vk7K0h5aSvkggDcCmCx+51AVpaMygrz9XlFLxOBNoLx4SdK0C5fxWYPqYkQ341xDuBZrI42d\nDEKPdzcedQMQKSViSZD20QRILPT4IFnTjueoeS2llAAnWvLRVj6k0dD2kNozW80zgZOMIu0WTTtB\n2mnTvnL3eIMev/qmnQtYM9B9jcny+xJPCKppZ2GGNFXu8bw10t6De7wz8k3UbthMpD1x0OO7G9HZ\nmu7IE2kXjsi4rcV5FBQh1d3jfU07F2W7HoaOhxqqe1k17UPPvnHlorY3uaS5Pk9Cntddh0hOevww\niFrL6uphsO4VrGlnjL0YwH8C8PNSypsr7vrKxdc/stx2M4B9AC9jjE3Iz6se84fafYZylI4Oc2of\n4NEQU02iYOWhIwgCywM0xC6TNJ/It5TrGc7538ho0x4Aaa+Kz6uj+GZCKo7NkaYXL4zoetW017yW\ns0wsEX9ggbQDZtPeMz1+lgmcQp2mvbxUjGTZOK+cOltHjz+4DDx0S69jX8MsclG+GmdAbdr1nPZQ\nmnb3uePBpkn5MnIQQN4MK5FvGeZZ9w9p15DQB2mfZ9Jk/FB6fKDINy7t3hqxx2ABqKLHz5e3hyr9\n9UzAm2narUZ0Q9O+bmV7Twd6/OaVy0SuWU57/027zTekCbWdfpacGBH/pmEStZa16UyfpP4u9cUY\nSwD8OoCHAfxgzd0/f/H1Hv0GKWXGGHsAwBcAeD6ATzHGTgJ4NoBdKeXjlue7d/H1hZ5r/ajjphf5\nPP4oFxdqXrNAAiwQFR8tqUqPJyg9QdplAHq8G2mvP/HmmUVLCiia8RBIe2XjwavZBilXG2IkEyvF\nOwjS7nwtq9/vWSowAXmdCsQ1pjTkrPcPrnR2gMliiJEhRkKQX2NtABLCYJiuWtNu0ONJ057NgF96\nKbDzOPB3vhv4e/++nyVUGdF5Hk60iYs0enyYpt2dG+8jLckUxk+EKIoUenwC0XmdUkpn0+6jF0+5\nQMx0Cn95zVyFEZ2vpn1kpccvPhdCnt5SM6JjohFNdchpPxq16ZvmofIKYUTnavxDntc2Y+C2FP6t\nAWlf+3J97gkhEUVsxasJX6GQ9h8F8CUA/omU8qDmvmcXX686bi9+XuQ4Nb3/UI7SN/TUQM5ns6wi\n7eSxEXXU7G5E56bBt9e0M9K0G81Vi8oHIO38AdJMRdoRj5WhwiSgpt3FWqiTGkwzrg0WFutTkPZA\nNOSKyqYlxXyfnVj6KChFkPZElO/tyjf0RtN+rfz/ne/KG3YA+NDP97cEh2wjZv5UZMWIbqQa0QXJ\naRfuFAvpYeKYErNLWTB+YjXyravesEr+EjE/TbtK4Y80pD2Qpr3KiM5T0x5XIO0hzeiY9p4kDY3o\nrE37qgdzQ9XW0LQfj3I1R03eayfSHlLTHjCnfXtUtkwuecBQh1uuz71Noch3RtoZY38LObr+M1LK\nW7ovqd+SUn6p7ecLBP4lK17OSkuPWuJK0+5Bjycbakk17dSZvQZl9innAEHmWdPM1rgtyqolBRDF\ntGnvPljQN8ucJYgXSFIda2HOhaK9hmFEl982C/DB5ZQU1KzxYM4VJ/YSaVdp/Ds9I+38oGzap2wb\nFkW70rTHpGlfOdKeaZp2So8/uLKSJbiN6Fpq2jV6fAjzHSGlVXcP+DF+eKoOD2NAi3zLkHakxwsJ\np+6eQdY2xFbdfR+a9oqm3TenXZETLao490M17VJKwyMgbkqPt8S7hdzcDxWmrMZfG7JhHqqsEDnt\nrutfV9kQra7Ho9q0U6R9uPasYzmPSyFB3r4jW52Q9gUt/m3Iqe4/4vmwAhk/67i9+Hmxy216/6Ec\npaNH1PVderiJZ6TZVZF28jwBIt+qtKR1e7w8H9nUtEej0Ei72bQv/1+DFs65ULOR47FmRJcBkIHo\n8e007dOMY8y0wQKg0OMTZE56W6gS05JgM41P2u+kNO2qpn2lWspKevwUqygXit1I006ODWbktMvO\nr2meYtGhaVeQ9sV5F9HINxEGwXYMFnwGIHMjejKxa9oDuMe7hjQ+zy1c9Pgl0t5pecuyyTaSBoMk\nwBH5NuS0r11Z3ZuHnn3jyukeHyLyLah7vMWYrNF1p7zv9njQtK97hUg1WOfqSo8/hVxL/mIAU8aY\nLP4BeO3iPr+y+NkbFt/fvfhqaNAXQ4DnAcgA3A8AUso9AI8COMUYe6ZlDZ+3+Gpo5IdSi2tIu2iI\ntGc2WioAUPf4ACi2iwbvsxG1IlwAItLYMRFG0+4agNTltKeZWFLgAeTU8yheInERyze4QeKMHO9r\nnaZ9mopapD0UXbqq0oOSYj6PTtjvRDTtLJthHJfH5kop8lWRbzoK39cSHEZ0sYd5WnlfirSXr20h\n6eiaTyskrI1mfqNH024bHmrDpBCu7C56vI/reaoPD5ke+RZG084FHO+3X0OcuejxLCw9PrO8ns2R\ndpsR3UCPX7eyNUlNGrmhjka5zt1mSHv/RnSd6fGOnPZB8rGe5dS0b8jb1ZUePwPwfztuewlynfsH\nkTfqBXX+zwH8IwBfC+Dt2mNeAeAEgJullHSX++cA/vfFY35Ve8zXkfsMVVFC6E07jWrzcBomtFRJ\nneeJnlSG0LQ71uLn2izUTehiuBARzTgLQY+vYC3Uatq5MDXtQI4Yp3km+RhprznttUh7ylUKf2Ii\n7eNF5FudZKFLZdMyoz2LLSZ0gIK0g88wGUXLSf0sFdhaFSfKoMfTpn01SLsLeWUNUM3EQY8vkOeU\nC4zi9vNe1xrz25oODxfvrUKP7+61oA84aTHIWmqkKdNRm/aCkt71HHfS45n0GggI6ULaF5FvgfbO\nNtlG0oD9ATiQ9sGIbu2K25qkDUG5hirLde42MqIj19FxEi2vh0Gb9pD0+PGgaV/3cg6TNuT96tS0\nL0zn/rntNsbYjyFv2n9NSvkWctNvAfhJAK9mjP3nIqudMbYF4McX9/ll7enehLxp/yHG2LuLrHbG\n2HMBfCfy4YHezA+lVSU93mezzO1Ne3BNu8vxHLIW5cubdnKfxWY+JvT4SKadG00uBGJGBiCRP9I+\ndzXt8Zg07VmYD64c1jSqDmk/SOs17SNkkHIRGxX307SnM+JrSX0JaFFH+WyGrVGMnWn++uZI3Mj+\nuNBV5x6/iiVUIK++GxWlaR+X7IaiueuMDkvpNHH0McTkGbkOFeedEvnWnQFSNViIPDXtid60k2tm\n8R51pvE7ItsAv2txbeRbQKRdlxsE0bQPTfvalTWnfUM2zEOV5bp0NXmv6X1PjOOyaQ9Kj+8W+TZo\n2o9WVbnHb0IFy2n3LSnlNQDfjryVeD9j7C2MsZ8C8NcAXoq8qX+n9pi/BPCzAF4A4HbG2M8xxt4I\n4FYA5wB8n5TywdX9FUezdOosbdp9IBUrwgWAkQ1zCL24q6H0Qdpzenx1TnsiswCb+vL1kmDKhrzW\niC6zGNEBmq49DfTB5XaPr9Imz1K+zIvP12jPaQe606WrKkvLZlcSsz6l6M+zKSZJeVmbrlDzKrTG\nPCPUfvBVNe2ikzFZfl+XEV3+foegnrsaYh9vDWG7DlF6POPdEWzhHix4MX64NA0xlcHCgrUQAGl3\n0fgznwFITdMeih7P9dcD+XClyfMPOe1Ho+w57YewkKF6LSfS3uC9pp8lJ4levPfIt0HTvrHFHfuT\nTWH7rLxpBwAp5bsBfCWAmwF8M4D/E3lg+PcAeLW0dBNSyu8F8G0AngDwHQC+BcCdAL5RSvmLK1r6\nkS4hVEdk2VTTTunxJHNYocd7bBTryt20i1rjs7lD0643m10bYvp3ChbnjXvxfc1rkHKJEXVsXiLt\nZfM5Yd2RdimlW9Ne08SZmvaiaSc57SxME1dV2Zw0u06knTbtM4UOP12h5nVvf1/5/oHHniiP11UZ\n0Tno0sxX064xSCLNiA7ovllxOdwDftchBUGOTCO6JBA93rVG35z2Kk170cCGMaJzsBY8jn1X017m\ntAdE2nVNOxNOtM5Wdnr8oGlftxqQ9uNRIQy/MqUhLq+PIRk0NoCmrcP9oGlf/9p0pL1z5JurpJQ/\nBuDHKm7/EICvb/icbwXw1g7LOtalOyILson0c48nm2WK0kdhNe0u1D/ypcczbbMMGFFls5Tj1KT9\n4U8z6wG21M4DAK9t2gVO1jTEITTtVbFV8eK1TBxy7wND024i7eMl8ton0l42uyzxocerSPsq3aV5\nqiPtO3jbLQ/hn778eaulx1tcz33M0wAoHgipjBHTIc2SHt+90exEj+c1Oe0BmnahywxYtLwu+fgD\npPp1yEDaA0kNhLS+3wAgRP21mEuJsSXyrUDaQ4ETdvf47vT4IiGiL0+NoZqXNad9Q1CuocoKoR2m\n+7kTPTXt1uOxwdMPmvajVa4eYVOuQYeCtA91OKVrSZtq2mnUEkWNGNnYw2OjWFeuyDcftNBE2k36\n7AhZZ/qVklnP4kasBVPTvlibFvuWCdlpOljpgM2q0cKpoWmvoMf3iLQLpWl30OPXBGnP5iqafpod\n4OZ7L+TfpAfqnXv6AHFHgNXrsAFAEnkLR6QYOCaBmnZe4R7vhbQr16F+NO2GER15fp/hoZliESmD\nvVCadi4rNO0eA5A88s3Uvhfnfig0KRPC4h7f3YhOyn6HhkM1L5t7/EqjN4daSbne0ybDH61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64i7UC3WEIhpDocoOeIR3zeLBVLxky+KNVfIJx7fDekfW407QPSvo6lGtEBlDkcirVxXEq/Dq5b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8Fi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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65e8175860>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 272,
       "width": 502
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "mean, std = scaled_features['cnt']\n",
    "predictions = network.run(test_features)*std + mean\n",
    "ax.plot(predictions[0], label='Prediction')\n",
    "ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n",
    "ax.set_xlim(right=len(predictions))\n",
    "ax.legend()\n",
    "\n",
    "dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n",
    "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
    "ax.set_xticks(np.arange(len(dates))[12::24])\n",
    "_ = ax.set_xticklabels(dates[12::24], rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thinking about your results\n",
    " \n",
    "Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?\n",
    "\n",
    "> **Note:** You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter\n",
    "\n",
    "#### Your answer below\n",
    "The model, first of all and obviously, performs much better on the training data set then the validation data set - consistently. Implying that some overfitting happens here. Also the model is much less extreme, in general often averaging out. It also fails to ultimately detect the \"trend\" of decreasing bycicles usages during the holiday period. Would be nice to actually compare with some other \"oldschool\" forecasting methods.\n",
    "No idea why I am failing part of one of the unit tests :(\n",
    "#### Update\n",
    "Ok, now I know what is wrong, for some reason I do not have to divide by the number of observations I have, but I really do not understand why... But now my predictions got much better, yet we are still not able to detect the decrease during Christmas, yet I feel this works better with fewer iterations, so maybe another sign that we might be overfitting here?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unit tests\n",
    "\n",
    "Run these unit tests to check the correctness of your network implementation. These tests must all be successful to pass the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".....\n",
      "----------------------------------------------------------------------\n",
      "Ran 5 tests in 0.008s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=5 errors=0 failures=0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unittest\n",
    "\n",
    "inputs = [0.5, -0.2, 0.1]\n",
    "targets = [0.4]\n",
    "test_w_i_h = np.array([[0.1, 0.4, -0.3], \n",
    "                       [-0.2, 0.5, 0.2]])\n",
    "test_w_h_o = np.array([[0.3, -0.1]])\n",
    "\n",
    "class TestMethods(unittest.TestCase):\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for data loading\n",
    "    ##########\n",
    "    \n",
    "    def test_data_path(self):\n",
    "        # Test that file path to dataset has been unaltered\n",
    "        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n",
    "        \n",
    "    def test_data_loaded(self):\n",
    "        # Test that data frame loaded\n",
    "        self.assertTrue(isinstance(rides, pd.DataFrame))\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for network functionality\n",
    "    ##########\n",
    "\n",
    "    def test_activation(self):\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        # Test that the activation function is a sigmoid\n",
    "        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n",
    "\n",
    "    def test_train(self):\n",
    "        # Test that weights are updated correctly on training\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "        \n",
    "        network.train(inputs, targets)\n",
    "        #print(network.weights_hidden_to_output)\n",
    "        #print(network.weights_input_to_hidden)\n",
    "        \n",
    "        self.assertTrue(np.allclose(network.weights_hidden_to_output, \n",
    "                                    np.array([[ 0.37275328, -0.03172939]])))\n",
    "        self.assertTrue(np.allclose(network.weights_input_to_hidden,\n",
    "                                    np.array([[ 0.10562014,  0.39775194, -0.29887597],\n",
    "                                              [-0.20185996,  0.50074398,  0.19962801]])))\n",
    "\n",
    "    def test_run(self):\n",
    "        # Test correctness of run method\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "\n",
    "        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n",
    "\n",
    "suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n",
    "unittest.TextTestRunner().run(suite)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}