introductory_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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "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\n",
    "import math"
   ]
  },
  {
   "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": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load Dataset via Pandas\n",
    "data_path = './data/bikesharing_hour.csv'\n",
    "rides = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a look\n",
    "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, and some summary statistics below (there's 17,379 entries in there!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>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>count</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>2.501640</td>\n",
       "      <td>0.502561</td>\n",
       "      <td>6.537775</td>\n",
       "      <td>11.546752</td>\n",
       "      <td>0.028770</td>\n",
       "      <td>3.003683</td>\n",
       "      <td>0.682721</td>\n",
       "      <td>1.425283</td>\n",
       "      <td>0.496987</td>\n",
       "      <td>0.475775</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.190098</td>\n",
       "      <td>35.676218</td>\n",
       "      <td>153.786869</td>\n",
       "      <td>189.463088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5017.0295</td>\n",
       "      <td>1.106918</td>\n",
       "      <td>0.500008</td>\n",
       "      <td>3.438776</td>\n",
       "      <td>6.914405</td>\n",
       "      <td>0.167165</td>\n",
       "      <td>2.005771</td>\n",
       "      <td>0.465431</td>\n",
       "      <td>0.639357</td>\n",
       "      <td>0.192556</td>\n",
       "      <td>0.171850</td>\n",
       "      <td>0.192930</td>\n",
       "      <td>0.122340</td>\n",
       "      <td>49.305030</td>\n",
       "      <td>151.357286</td>\n",
       "      <td>181.387599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4345.5000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.104500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.484800</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13034.5000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.621200</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.253700</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>281.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850700</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant        season            yr          mnth            hr  \\\n",
       "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean    8690.0000      2.501640      0.502561      6.537775     11.546752   \n",
       "std     5017.0295      1.106918      0.500008      3.438776      6.914405   \n",
       "min        1.0000      1.000000      0.000000      1.000000      0.000000   \n",
       "25%     4345.5000      2.000000      0.000000      4.000000      6.000000   \n",
       "50%     8690.0000      3.000000      1.000000      7.000000     12.000000   \n",
       "75%    13034.5000      3.000000      1.000000     10.000000     18.000000   \n",
       "max    17379.0000      4.000000      1.000000     12.000000     23.000000   \n",
       "\n",
       "            holiday       weekday    workingday    weathersit          temp  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.028770      3.003683      0.682721      1.425283      0.496987   \n",
       "std        0.167165      2.005771      0.465431      0.639357      0.192556   \n",
       "min        0.000000      0.000000      0.000000      1.000000      0.020000   \n",
       "25%        0.000000      1.000000      0.000000      1.000000      0.340000   \n",
       "50%        0.000000      3.000000      1.000000      1.000000      0.500000   \n",
       "75%        0.000000      5.000000      1.000000      2.000000      0.660000   \n",
       "max        1.000000      6.000000      1.000000      4.000000      1.000000   \n",
       "\n",
       "              atemp           hum     windspeed        casual    registered  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.475775      0.627229      0.190098     35.676218    153.786869   \n",
       "std        0.171850      0.192930      0.122340     49.305030    151.357286   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.333300      0.480000      0.104500      4.000000     34.000000   \n",
       "50%        0.484800      0.630000      0.194000     17.000000    115.000000   \n",
       "75%        0.621200      0.780000      0.253700     48.000000    220.000000   \n",
       "max        1.000000      1.000000      0.850700    367.000000    886.000000   \n",
       "\n",
       "                cnt  \n",
       "count  17379.000000  \n",
       "mean     189.463088  \n",
       "std      181.387599  \n",
       "min        1.000000  \n",
       "25%       40.000000  \n",
       "50%      142.000000  \n",
       "75%      281.000000  \n",
       "max      977.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d3c2588>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KJX3NGFPke0jHGfreGjc7rq8KVs0AgFPnHI85eXC2ctKT3C0IYWXsgEwVZqdK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\nfAAAACBE/j+9epA+eF3Z/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d3b70b8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Count against Day (For 24 Hours)\n",
    "rides[:24].plot(x='dteday', y='cnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d3b7940>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ffapq86tvD37PeA3MPoSUuDz4PvcDevBJ3Ykx6Co7Dz6bbAkhdcUsPOLk4I8q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IBSn8HaWr+QMbbb6j8Amz28DldMcAq+twKfFsqiFVrRyFTw62PRqZ1ZTBV86r/eurRLaEFQiXZu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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104936278>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Count against Day (For 24 Hours over 10 Days = 2400 Hours)\n",
    "rides[:24*10].plot(x='dteday', y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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. \n",
    "\n",
    "You'll be trying to capture all this with your model."
   ]
  },
  {
   "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 them **binary dummy variables.** \n",
    "\n",
    "This is simple to do with `Pandas` thanks to `get_dummies()`. To get a handle of how it works, let's see how many unique variables are in the `season` column as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many variables? \n",
    "rides['season'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore, `season` can take 1 of 4 values (Spring, Summer, Fall, Winter), so to convert these to dummies, we need to create four columns of the form `season_1` which is either a 1 or a 0 depending on whether it is Spring. This is exactly what `get_dummies()` does:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <th>10</th>\n",
       "      <td>1</td>\n",
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       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17363</th>\n",
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       "      <th>17364</th>\n",
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       "      <th>17365</th>\n",
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       "      <th>17368</th>\n",
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       "      <th>17369</th>\n",
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       "      <th>17370</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17371</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17372</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17373</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season_1  season_2  season_3  season_4\n",
       "0             1         0         0         0\n",
       "1             1         0         0         0\n",
       "2             1         0         0         0\n",
       "3             1         0         0         0\n",
       "4             1         0         0         0\n",
       "5             1         0         0         0\n",
       "6             1         0         0         0\n",
       "7             1         0         0         0\n",
       "8             1         0         0         0\n",
       "9             1         0         0         0\n",
       "10            1         0         0         0\n",
       "11            1         0         0         0\n",
       "12            1         0         0         0\n",
       "13            1         0         0         0\n",
       "14            1         0         0         0\n",
       "15            1         0         0         0\n",
       "16            1         0         0         0\n",
       "17            1         0         0         0\n",
       "18            1         0         0         0\n",
       "19            1         0         0         0\n",
       "20            1         0         0         0\n",
       "21            1         0         0         0\n",
       "22            1         0         0         0\n",
       "23            1         0         0         0\n",
       "24            1         0         0         0\n",
       "25            1         0         0         0\n",
       "26            1         0         0         0\n",
       "27            1         0         0         0\n",
       "28            1         0         0         0\n",
       "29            1         0         0         0\n",
       "...         ...       ...       ...       ...\n",
       "17349         1         0         0         0\n",
       "17350         1         0         0         0\n",
       "17351         1         0         0         0\n",
       "17352         1         0         0         0\n",
       "17353         1         0         0         0\n",
       "17354         1         0         0         0\n",
       "17355         1         0         0         0\n",
       "17356         1         0         0         0\n",
       "17357         1         0         0         0\n",
       "17358         1         0         0         0\n",
       "17359         1         0         0         0\n",
       "17360         1         0         0         0\n",
       "17361         1         0         0         0\n",
       "17362         1         0         0         0\n",
       "17363         1         0         0         0\n",
       "17364         1         0         0         0\n",
       "17365         1         0         0         0\n",
       "17366         1         0         0         0\n",
       "17367         1         0         0         0\n",
       "17368         1         0         0         0\n",
       "17369         1         0         0         0\n",
       "17370         1         0         0         0\n",
       "17371         1         0         0         0\n",
       "17372         1         0         0         0\n",
       "17373         1         0         0         0\n",
       "17374         1         0         0         0\n",
       "17375         1         0         0         0\n",
       "17376         1         0         0         0\n",
       "17377         1         0         0         0\n",
       "17378         1         0         0         0\n",
       "\n",
       "[17379 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Dummies in action, takes each unique variable \n",
    "pd.get_dummies(rides['season'], prefix='season', drop_first=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Naturally, every row will therefore have only binary value in these 4 dummy columns, but at least using zeroes makes this a fairly sparse array.\n",
    "\n",
    "Let's build a loop to iterate through all the categorial variables we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Which fields should we transform?\n",
    "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
    "\n",
    "# For each entry in the fields\n",
    "for each in dummy_fields:\n",
    "    # Get the dummy version\n",
    "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
    "    # Merge onto rides \n",
    "    rides = pd.concat([rides, dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<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>...</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",
       "    <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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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 × 68 columns</p>\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    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  \\\n",
       "0           1    ...          0      0      0          0          0   \n",
       "1           1    ...          0      0      0          0          0   \n",
       "2           1    ...          0      0      0          0          0   \n",
       "3           1    ...          0      0      0          0          0   \n",
       "4           1    ...          0      0      0          0          0   \n",
       "\n",
       "   weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          0          1  \n",
       "1          0          0          0          0          1  \n",
       "2          0          0          0          0          1  \n",
       "3          0          0          0          0          1  \n",
       "4          0          0          0          0          1  \n",
       "\n",
       "[5 rows x 68 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, that's all done, but we still have the old categorical values in there. Let's get rid of those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
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       "      <th>0</th>\n",
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       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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       "    <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",
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       "      <td>1</td>\n",
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       "      <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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the old ones\n",
    "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
    "                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
    "\n",
    "data = rides.drop(fields_to_drop, axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling target variables\n",
    "To generally make training the network much, *much* easier, we'll standardize each of the continuous variables **quantiative** variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1, which should sound fairly familiar:\n",
    "\n",
    "![](http://d2r5da613aq50s.cloudfront.net/wp-content/uploads/360188.image0.jpg)\n",
    "\n",
    "Yep, all we are doing is **normalizing** our variables, and scaling them such that they will follow a traditional normal distribution. The standard function for normalizing (also called the standard score or z-score) is:\n",
    "\n",
    "![](./assets/normal.png)\n",
    "\n",
    "So lets pipe all of our **quantiative** variables through this normalization process. As an example, let's start with `cnt`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         16\n",
       "1         40\n",
       "2         32\n",
       "3         13\n",
       "4          1\n",
       "5          1\n",
       "6          2\n",
       "7          3\n",
       "8          8\n",
       "9         14\n",
       "10        36\n",
       "11        56\n",
       "12        84\n",
       "13        94\n",
       "14       106\n",
       "15       110\n",
       "16        93\n",
       "17        67\n",
       "18        35\n",
       "19        37\n",
       "20        36\n",
       "21        34\n",
       "22        28\n",
       "23        39\n",
       "24        17\n",
       "25        17\n",
       "26         9\n",
       "27         6\n",
       "28         3\n",
       "29         2\n",
       "        ... \n",
       "17349    125\n",
       "17350    102\n",
       "17351     72\n",
       "17352     47\n",
       "17353     36\n",
       "17354     49\n",
       "17355     34\n",
       "17356     19\n",
       "17357     11\n",
       "17358      1\n",
       "17359      3\n",
       "17360      9\n",
       "17361     40\n",
       "17362     85\n",
       "17363    196\n",
       "17364    157\n",
       "17365    120\n",
       "17366    157\n",
       "17367    224\n",
       "17368    203\n",
       "17369    247\n",
       "17370    315\n",
       "17371    214\n",
       "17372    164\n",
       "17373    122\n",
       "17374    119\n",
       "17375     89\n",
       "17376     90\n",
       "17377     61\n",
       "17378     49\n",
       "Name: cnt, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = data.loc[:, 'cnt'] # From dataset, get all rows with label `cnt`\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean = test.mean() # Mu\n",
    "standard_deviation = test.std() # Sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       -0.956312\n",
       "1       -0.823998\n",
       "2       -0.868103\n",
       "3       -0.972851\n",
       "4       -1.039008\n",
       "5       -1.039008\n",
       "6       -1.033495\n",
       "7       -1.027981\n",
       "8       -1.000416\n",
       "9       -0.967338\n",
       "10      -0.846051\n",
       "11      -0.735789\n",
       "12      -0.581424\n",
       "13      -0.526293\n",
       "14      -0.460137\n",
       "15      -0.438084\n",
       "16      -0.531806\n",
       "17      -0.675146\n",
       "18      -0.851564\n",
       "19      -0.840538\n",
       "20      -0.846051\n",
       "21      -0.857077\n",
       "22      -0.890155\n",
       "23      -0.829511\n",
       "24      -0.950799\n",
       "25      -0.950799\n",
       "26      -0.994903\n",
       "27      -1.011442\n",
       "28      -1.027981\n",
       "29      -1.033495\n",
       "           ...   \n",
       "17349   -0.355389\n",
       "17350   -0.482189\n",
       "17351   -0.647581\n",
       "17352   -0.785407\n",
       "17353   -0.846051\n",
       "17354   -0.774381\n",
       "17355   -0.857077\n",
       "17356   -0.939773\n",
       "17357   -0.983877\n",
       "17358   -1.039008\n",
       "17359   -1.027981\n",
       "17360   -0.994903\n",
       "17361   -0.823998\n",
       "17362   -0.575911\n",
       "17363    0.036038\n",
       "17364   -0.178971\n",
       "17365   -0.382954\n",
       "17366   -0.178971\n",
       "17367    0.190404\n",
       "17368    0.074630\n",
       "17369    0.317204\n",
       "17370    0.692092\n",
       "17371    0.135273\n",
       "17372   -0.140379\n",
       "17373   -0.371928\n",
       "17374   -0.388467\n",
       "17375   -0.553859\n",
       "17376   -0.548346\n",
       "17377   -0.708224\n",
       "17378   -0.774381\n",
       "Name: cnt, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.apply(lambda x: (x - mean)/standard_deviation) # Let's apply the normalization function to the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, it's been transformed for individual integers to a float representing its position on a standard normal distribution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's do this for all the quantitative features in our dataset, stored their former mean and standard deviation in a dictionary called `scaled_features`. \n",
    "\n",
    "Since the normalization function works by just subtracting the mean and dividing by the standard deviation for each value, we can quickly invert normalization by just doing:\n",
    "\n",
    "![](./assets/unnormal.png)\n",
    "\n",
    "So let's keep track of them for later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# What are all the continous features in this dataset (as opposed to discrete above)\n",
    "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
    "\n",
    "# Store scalings in a dictionary so we can convert back later\n",
    "scaled_features = {}\n",
    "\n",
    "# For each feature\n",
    "for each in quant_features:\n",
    "    mean, std = data[each].mean(), data[each].std() # Grab it's mean and standard deviation (just for the col `each`)\n",
    "    scaled_features[each] = [mean, std] # Store the original mean and we are scaling by just in case\n",
    "    data.loc[:, each] = (data[each] - mean)/std # Select all the rows from col `each`, and normalize every one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scaling factors are saved so we can go backwards when we use the network for predictions after training (!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data into training, testing, and validation sets\n",
    "\n",
    "We'll save the data for the last approximately 21 days 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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Save data for approximately the last 21 days (-21 for LAST 21) \n",
    "test_data = data[-21*24:]\n",
    "\n",
    "# Now remove the test data from the data set \n",
    "data = data[:-21*24]\n",
    "\n",
    "# Separate the data into features and targets\n",
    "target_fields = ['cnt', 'casual', 'registered'] # which variables do we wanna know about\n",
    "\n",
    "# Extract training features and targets, and use this as the primary dataset \n",
    "features, targets = data.drop(target_fields, axis=1), data[target_fields]\n",
    "\n",
    "# Extract the test features and targets the same way; just only for the last 21 days\n",
    "# Note: Unlike the validation set below, the training is NOT removing the last 21 days, so we're training with it\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).\n",
    "\n",
    "Therefore, let's make another quick dataset called validation that makes sure we are forecasting correctly. In general, validation is used to find the best approach, and testing is used to further minimize the error of each approach (as explained [here](https://stats.stackexchange.com/questions/19048/what-is-the-difference-between-test-set-and-validation-set).)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use everything but the last 60 days as the final training set \n",
    "train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
    "\n",
    "# Hold out the last 60 days or so of the remaining data as a validation set\n",
    "# Note: Validation because these are days the network has never seen, therefore they can be used\n",
    "# to check if our network it'd work outside the experiment on data it's never seen before\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",
    "<img src=\"assets/neural_network.png\" width=300px>\n",
    "\n",
    "### Layers & Activation Functions\n",
    "The network has two layers, a hidden layer and an output layer. A **layer** is a series of \"neurons\" at the same level with a given activation function; they can be hidden, input, output, etc. so long as they have the same activation function. An **activation function** is a function that takes the input signal and generates an output signal, but takes into account the threshold (i.e. only fires within certain conditions); here we will be using the venerable [Sigmoid function](https://en.wikipedia.org/wiki/Sigmoid_function). The output layer has only one node and is used for the regression, and the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. \n",
    "\n",
    "\n",
    "### Forward Propogation & Backward Propogation\n",
    "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",
    "![](https://lh3.googleusercontent.com/proxy/KEylW2oPMQGONIVnFvROm9Qn_XReGdYT33KX8Zl9rFY37v-Y1ansWLeZbYR2G8KsYvME30d8c215fdhzn0ClqZF4QxQWSLf8ekVQX8sO2prQ6QoIeguz=w465-h317-nc)\n",
    "\n",
    "We use the weights to propagate signals forward from the input to the output layers in a neural network. We use these weights to also propagate error backwards from the output back into the network to update our weights, and decide how much each neurons signal should be weighted.  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 $f(x)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODO:\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "class NeuralNetwork(object):\n",
    "    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
    "        \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 for input > hidden and hidden > output layers, and grab the learning rate\n",
    "        self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5, \n",
    "                                       (self.input_nodes, self.hidden_nodes)) # Root of 1/input_nodes as sd? Why? \n",
    "\n",
    "        self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5, \n",
    "                                       (self.hidden_nodes, self.output_nodes))\n",
    "        self.lr = learning_rate\n",
    "        \n",
    "        self.bias_input_to_hidden = np.ones(self.input_nodes)\n",
    "        self.bias_hidden_to_output = np.ones(self.hidden_nodes)\n",
    "        \n",
    "        ## Activation Function\n",
    "        # First we set self.activation_function to our implemented sigmoid function \n",
    "        # We also set the derivative of this function, which we will use later as part of gradient descent\n",
    "        self.activation_function = lambda x : 1/(1 + math.e**-x)  \n",
    "        self.sigmoid_derivative = lambda x:x*(1 - x)\n",
    "\n",
    "                    \n",
    "    \n",
    "    def train(self, features, targets):\n",
    "        ''' Train the network on batch of features and targets. \n",
    "        \n",
    "            Arguments\n",
    "            ---------\n",
    "            \n",
    "            features: 2D array, each row is one data record, each column is a feature\n",
    "            targets: 1D array of target values\n",
    "        \n",
    "        '''\n",
    "        \n",
    "        ## Defaults\n",
    "        # Start with a count of the dataset and empty weights for the delta weight holder\n",
    "        n_records = features.shape[0]\n",
    "        delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)\n",
    "        delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)\n",
    "        \n",
    "        for x, y in zip(features, targets):\n",
    "            #### Implement the forward pass here ####\n",
    "            \n",
    "            ### Forward pass ###\n",
    "                        \n",
    "            ## Hiden Layer\n",
    "            # We take the weight vector and input vector, and get the dot product (matrix multiplication)\n",
    "            # Since the weights are initially random, each node is now randomly-weighted array for every input\n",
    "            # To get the outputs, we just run the activation function on these new nodes and see if they pass\n",
    "            hidden_inputs = np.dot(x, self.weights_input_to_hidden) \n",
    "            hidden_outputs = self.activation_function(hidden_inputs) \n",
    "            \n",
    "            ## Output layer\n",
    "            # Again, we are taking the random initial weight and multiplying it by a vector, so do products\n",
    "            # However, each node is now like a randomly weighted average of a randomly weighted average of inputs \n",
    "            # Since the output layer has no activation function, we will just pass \n",
    "            final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n",
    "            final_outputs = final_inputs # signals from final output layer\n",
    "            \n",
    "            #### Implement the backward pass here ####\n",
    "            ### Backward pass ###\n",
    "            \n",
    "            ## Output Errors\n",
    "            # This output errors variable measures the difference between desired targets and our actual output\n",
    "            output_error = y - final_outputs\n",
    "                        \n",
    "            # The hidden error represents errors between the hidden layers, which can get a little messy\n",
    "            # First, lets get the dot product of these two vectors again\n",
    "            hidden_error = np.dot(self.weights_hidden_to_output, output_error) \n",
    "            \n",
    "            # Weight Updating\n",
    "            # Now that we have the vectors, how do we translate our error such that we can modify \n",
    "            # the weights to reduce it. First though, we need to reverse our activation function to get weights\n",
    "            # so we do the derivative of the activation function (here the Sigmoid)\n",
    "            output_error_term = output_error\n",
    "            hidden_error_term = hidden_error * self.sigmoid_derivative(hidden_outputs)\n",
    "            \n",
    "            # Weight step (input to hidden)\n",
    "            # To get this, we take the learning rate and multiply it by the dot product of the \n",
    "\n",
    "            \n",
    "            # Apparently transposing a 1D array in Numpy doesn't work, but we can reshape with a new (empty) axis\n",
    "            # Also it's element-wise multiplication now, not dot products \n",
    "            delta_weights_i_h += self.lr * hidden_error_term * x[:,None] \n",
    "            # Weight step (hidden to output)\n",
    "            # Same idea, but since the final layer is just input to output, don't need a derivative\n",
    "            delta_weights_h_o += self.lr * output_error_term * hidden_outputs[:,None] \n",
    "\n",
    "        ## Final Weights\n",
    "        # These weights exist outside the loop above, which means they are adjusted for each tuple (x,y) in the\n",
    "        # training dataset provided. The longer the set, the more opportunities to adjust the weights on each\n",
    "        # pass through\n",
    "        \n",
    "        # update input-to-hidden weights with gradient descent step\n",
    "        self.weights_input_to_hidden += delta_weights_i_h / n_records\n",
    "        # update hidden-to-output weights with gradient descent step\n",
    "        self.weights_hidden_to_output += delta_weights_h_o / n_records\n",
    "\n",
    "    def run(self, features):\n",
    "        ''' Run a forward pass through the network with input features \n",
    "        \n",
    "            Arguments\n",
    "            ---------\n",
    "            features: 1D array of feature values\n",
    "        '''\n",
    "        \n",
    "        ## Initial Forward Propogation\n",
    "        # We are initializing the network, so all we are doing is a forward pass the way we defined above\n",
    "        # Hidden layer\n",
    "        hidden_inputs = np.dot(features,self.weights_input_to_hidden) # signals into hidden layer\n",
    "        hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n",
    "                \n",
    "        # Output layer\n",
    "        final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n",
    "        final_outputs = final_inputs # signals from final output layer \n",
    "        \n",
    "        return final_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def MSE(y, Y):\n",
    "    return np.mean((y-Y)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unit tests\n",
    "\n",
    "Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".....\n",
      "----------------------------------------------------------------------\n",
      "Ran 5 tests in 0.013s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=5 errors=0 failures=0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unittest\n",
    "\n",
    "inputs = np.array([[0.5, -0.2, 0.1]])\n",
    "targets = np.array([[0.4]])\n",
    "test_w_i_h = np.array([[0.1, -0.2],\n",
    "                       [0.4, 0.5],\n",
    "                       [-0.3, 0.2]])\n",
    "test_w_h_o = np.array([[0.3],\n",
    "                       [-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() == './data/bikesharing_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",
    "        self.assertTrue(np.allclose(network.weights_hidden_to_output, \n",
    "                                    np.array([[ 0.37275328], \n",
    "                                              [-0.03172939]])))\n",
    "        self.assertTrue(np.allclose(network.weights_input_to_hidden,\n",
    "                                    np.array([[ 0.10562014, -0.20185996], \n",
    "                                              [0.39775194, 0.50074398], \n",
    "                                              [-0.29887597, 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)"
   ]
  },
  {
   "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 iterations\n",
    "**This is the number of batches of samples from the training data we'll use to train the network.** The more iterations you use, the better the model will fit the data. However, **if you use too many iterations, then the model with not generalize well to other data, this is called overfitting**. You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. **As you start overfitting, you'll see the training loss continue to decrease while the validation loss starts to increase.**\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": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_features' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-97bc6258531f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0moutput_nodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mN_i\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_features\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mnetwork\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNeuralNetwork\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden_nodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_nodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_features' is not defined"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "### Set the hyperparameters here ###\n",
    "iterations = 10\n",
    "learning_rate = 0.15\n",
    "hidden_nodes = 30\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",
    "# Borrwing this dictionary for plotting!\n",
    "losses = {'train':[], \n",
    "          'validation':[], \n",
    "          'iterations':[iterations], \n",
    "          'learning_rate':[learning_rate], \n",
    "          'hidden_nodes':[hidden_nodes]\n",
    "         }\n",
    "\n",
    "for ii in range(iterations):\n",
    "    # Go through a random batch of 128 records from the training data set we did earlier \n",
    "    batch = np.random.choice(train_features.index, size=128)\n",
    "    # Using those random indices, use pandas.ix() to get the values, and get the targets for the same rows\n",
    "    # from train_targets, but since we can only use 1 of the 3 targets for the one output node, lets use `cnt`\n",
    "    X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n",
    "    \n",
    "    # Train the network by going through each x,y in the feature target list\n",
    "    # The magic happens because the weights are stored outside the main loop, so we can \n",
    "    # iteratively improve the weights using gradient descent through each pass through of this training step\n",
    "    network.train(X, y)\n",
    "    \n",
    "    # Now let's meausre the success of those weights using MSE, and feeding forward the training data and \n",
    "    # training targets, and measuring the loss using the current weighted nodes \n",
    "    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n",
    "    # Also, let's use that validation set (data it's never seen) to test it's performance in the \"wild\"\n",
    "    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n",
    "    \n",
    "    # Printing out the training progress\n",
    "    # Updates that dict up there with values, prints to stdout, then flushes and does it all again\n",
    "    # In that way, yields a cool table and a great little load animation which powers the plot below\n",
    "    sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n",
    "                     + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
    "                     + \" ... Validation loss: \" + str(val_loss)[:5])\n",
    "    \n",
    "    sys.stdout.flush()\n",
    "    \n",
    "    # At the end of the loop, append the dict with latest loss\n",
    "    # Now iterate again\n",
    "    losses['train'].append(train_loss)\n",
    "    losses['validation'].append(val_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_features' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-97bc6258531f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0moutput_nodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mN_i\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_features\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mnetwork\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNeuralNetwork\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden_nodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_nodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_features' is not defined"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "### Set the hyperparameters here ###\n",
    "iterations = 250\n",
    "learning_rate = 0.15\n",
    "hidden_nodes = 30\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",
    "# Borrwing this dictionary for plotting!\n",
    "losses = {'train':[], \n",
    "          'validation':[], \n",
    "          'iterations':[iterations], \n",
    "          'learning_rate':[learning_rate], \n",
    "          'hidden_nodes':[hidden_nodes]\n",
    "         }\n",
    "\n",
    "for ii in range(iterations):\n",
    "    # Go through a random batch of 128 records from the training data set we did earlier \n",
    "    batch = np.random.choice(train_features.index, size=128)\n",
    "    # Using those random indices, use pandas.ix() to get the values, and get the targets for the same rows\n",
    "    # from train_targets, but since we can only use 1 of the 3 targets for the one output node, lets use `cnt`\n",
    "    X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n",
    "    \n",
    "    # Train the network by going through each x,y in the feature target list\n",
    "    # The magic happens because the weights are stored outside the main loop, so we can \n",
    "    # iteratively improve the weights using gradient descent through each pass through of this training step\n",
    "    network.train(X, y)\n",
    "    \n",
    "    # Now let's meausre the success of those weights using MSE, and feeding forward the training data and \n",
    "    # training targets, and measuring the loss using the current weighted nodes \n",
    "    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n",
    "    # Also, let's use that validation set (data it's never seen) to test it's performance in the \"wild\"\n",
    "    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n",
    "    \n",
    "    # Printing out the training progress\n",
    "    # Updates that dict up there with values, prints to stdout, then flushes and does it all again\n",
    "    # In that way, yields a cool table and a great little load animation which powers the plot below\n",
    "    sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n",
    "                     + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
    "                     + \" ... Validation loss: \" + str(val_loss)[:5])\n",
    "    \n",
    "    sys.stdout.flush()\n",
    "    \n",
    "    # At the end of the loop, append the dict with latest loss\n",
    "    # Now iterate again\n",
    "    losses['train'].append(train_loss)\n",
    "    losses['validation'].append(val_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sXr3adj2nmvVJkyZBCIFVq1bh9ddfx6BBg5CdnY2CggJcfvnl2Lt3r+PYVAsWLIAQAo8/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\nvh7FyCBWqh1nry84NmBnfbvOrfuUjYErHoL612HCEVD/WuLnC8uSX98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FAAAg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\nzkjx2Bbk78OVM5M9tt2+cBS3P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RqhP931GKxrZF0GCW+qs972l6cVuNSyrGzX453GmCuA\nfcBCY8zco6XEWJY1q7vtrhH3mX3UXhEROQGRwf68+4MFlNc1U1DZQFltM9NSIt0lKt+6Yz6vbMpj\nxe4iNmWXuwPxzhpbHKw54BnYLpkQT1x4IBdPHcq7PUyGtSy45+VtvHb7XCYNPfoKra+l5dNidzZi\nTFyoxzcMGYdrqGls8SgbeeXMJGqb7O5gfcXuIn5+0QT3/IAnv8rkNx+kkzokhDe+P9+9qFWbDVld\nU26a7Q6yy+oZHXdsk0xXZZSQVVrHNbNSuv1AcCw+3FnIhqxybl4wkpTo4OM6x4noGJS3pTaBVjGV\nwaMvgvW2YYae/tK1bT/aEm9tz2/tEKgDYFlWvTFmBXAzzpKQyl8XERkAjDHEhAYQE9o1Hz00wJeb\nFozkpgUjqapv4VBVAzWNrZTVNpFbXs/Bklo2ZpWTXea5MqqvzbBssnO0/KFrpnH59KEcLGmvEjM2\nIYynVmWSU1ZPQ4udqx5fS4i/Lw7Lwt/XRqCfD8OigzlzRDRzRsUwLSWCLdkVPNih7OTiCXFEBPmR\nEh1EXnkDLXaLx7846K4HPjwmmJnDomixW4QF+FLT1Ep+RQPphTVMHBpOQWUDf/p4HwAHS+p4aWMu\nty1MdZ/fsiw2dgjWh0YEcsi12FVGcc0xBes78iu5/pmNWBbkltXz84snHlOfdJRZUssPXtqK3WGR\nXVbHv75zZq/PcaI6ButJUUHkuPpbOesyWPRFsL7PdT+2h+fHuO57ymnvfJ6egvoK133QMbZLREQG\niIhgPyKCu6/em19Rz66CKnLL6zlc3cT80bHu8ob+vjaWTIhnyQTPFUbnjormisfWUtPUSmOLg8YW\nz/znnLJ6VmWUAhDga8Nm2kd0JyaGc9fiMe6f2z4E/POrTPfxV85IxhiDv69h0fg4d6nLj/cUMXFo\nOA9/st9jQajn1uVwy4KRGGP4NL2YtNwKyuqcbYoM9mPZ5ESeWZMFOPP7lx/DoqxvpBW4K9e8ta2A\nn1w4odcrfn6Wfhi7632vO1hGc6sDf99Tm4LTsZLPsOhgd7CuNBgZLPoiWP/cdb/UGGPrWBHGGBMG\nzAfqgfVHOc9ngAVM7Hwel7YJp1l90GYRERkgkqOCSY7qXXrG6LgwHv/WLG59brN70aeeNHUIquPD\nA3j6xtmEBDj/+5w0NIIVu50VaOwd8nSumJHk/nnppPj2YH13MRdNSeT1DrntAAWVDby/s5CXNuZ2\nKdd45ohoxiW0j6RnHD76JFPLslixu71cZGltM1tzK5g9Ivqox3bUVtUHnL+H9MJq9yThU6VjUD6s\nQxpOX5XjFPF2JxysW5Z10BjzMc4JoHcAj3Z4+kEgBPinZVl14F7YKBVosSzrYIfz5Bhj3gUuxbny\n6V/bnjPGLAUuwDnq/tGJtllERGTBmFi2/Pf5VDW04GMzGJw54XVNrewsqGL9wXI2ZJW502xC/H14\n6vozSIxo/4L3mtnJPLsum9Ja5yi4zcBN80cyLKY9qFw4dgj+Pjaa7Q72FFZz7f+ud+ff+/va3CPs\n976y3T1639FVs5I9yla21VrvuBBTZ9vzqyh0pc20+XhPca+C9frmVo9UHIC03Ip+DdaHd/i99lR/\nXWSg6asJpt8H1gJ/M8YsAdKBOThrsO8HftZh3yTX8znAiE7nuQOYAfzFVWd9K85qMJcDduAWy7J6\nqjojIiLSK4F+PgT6dZ14OTouzF3Wsbi6kd2HqhiXEO5ezKlNYkQQ63+yhMKqRgL9fAgN8O0ykTMs\n0I/5o2P43LWqa3lde5D5xLdm8r1nt9DqsDwC9atnJTN7eBSzhkcxJj6Mmsb2gDWztJYr/7GGrXmV\nXHfGMH5z+eQuK6R2twjTit1F/GT5+B4XwOpsQ2Y5zXbPL7nTciv5zvxjOrzP9DiyrjQYGST6JFh3\nja7PBn4FLAMuBAqBR4AHLcuqONLxHc6Tb4yZBTyAc4T9HKAaeBf4nWVZG/uivSIiIscqPjyQ+COs\n3OrrYztqlZR7l44jp6yezNI697avn5nC4vHxLJ+S6FGx5oa5w/nlpZM8guqwQD/3JNMWu0VarnN6\n10sbc/H3MR77W5bFR7vaq9K0ySmrZ19xDQ3Ndhpa7MwdFXPEwL1jCkybtJxj+u+8T3mOrLcvhq5q\nMDJY9FnpRsuy8oDvHMN+2UCPfx1cix7d6bqJiIic9iYnRbDyR+dSUdfM/uIamu0O5rpqrn/v7FF8\ntKuQFrvFeRPieOCSSd0G0WPiw9wVYTr697oc8isaqG1qpbHVwbTkCI/UnbmpsXya7syrv/lfmymo\ndE6IvWXByCNWiPmqm2C9oLKB4urGI3546Wsdg/XEiED8fAwtdoumVgeNLfZuvxkRGUi8qc66iIjI\ngBYV4t9lYaQpyRG8cutc8isaWD45oceKLZOGhrtHuyOD/ZiSFOGuWPPZ3sPu/bbntRdVWzQ+jvMn\nxruD9bZAHZyruqZEB3PDvBFdXiuvvP1bgEA/G+MSwt3nTcupYPmUxN6+9eNid1jUNLa6H4cF+hER\n5E9pbRPgDOQVrMtAp2BdRESkn80YFsWMYVFH3OfG+SPYnl+Jv4+NX1wyiaGRQdz8703ugL07yycn\ncvbYWPdodGcPvrubT9OLKalpIsjfh0lDw0mKDGb1gfZR9TkjY5g4tEOwntserOeV17vz40fEhjAt\nJYK4sL4bda/uMKoeFuiLj80QGeznDtYr61tO6Si/SH9QsC4iInIaiAsL5IVbzvLY9uT1s3llcx7N\nrQ5Sh4RS32znza35fLm/hBnDojhvYhwBvj5cNTOZlzflERPiz4OXTeKpVVlsy6vEYeER7G/N7brU\nyTljh3hM7Nziylt/e1sBP31jp0fpS39fG7+9YgpXz0r2OEdFXTM7CqqYkhThXqH2SHYVVPHChlxG\ndKj+0rbCa8eVXrubZPr2tgI2ZJVzx6LRXSYEi5yOFKyLiIicpgL9fLh+7giPbRdNTcThsDAGd+77\nb6+YwrfOGs6oISEE+/ty1qgYrvzHWnLL67s5a7vhMcFcOSMJu9U+Kr8jv4rLHlvjkW7TprnVwY9f\n30FkkB/nTYzH4bB4aVMuv/9wLzWNrRgD01MiSQgPpKy2GQuLxePjuWpWkntE/r0dh7j3le0e9e2h\nPUiP7BCsV9Z7lm/cmFXO3S9vAyC9sJo3v3+KS9eInAQK1kVERAaYzqUcbTbD5KQI9+PY0ADevmM+\nX+4vISTAl4TwQMrrm9lVUEVhVQNj4sKYMSySCYnh+Pk4VywdHuNcPbTVYXkE6iNigpk/OpbVB0rJ\nKavH7rC448U0lk9OYG9RDXuL2hdxsqyuo/ebsiv488f7mJAYRlSwf49pPZHBXUfWKzuNrP/lk33u\nn7fmVpJeWM2ExPBj+p11x+GwWHOwlCFhAYxPOP7ziJwIBesiIiKDUFSIP5d3WGkVnAs49eS6M4bx\nh4/2emy7bPpQfnPFFEIDfDlc08hVj68lr7yBplYHb2075LFvdIg/lfXNdLPuE3aHxa6C6iO2t+1D\nQ0Rwe7C+t7D9g8Dag6VdVn99ZXMev7hk0hHPeyRPr87iNx+k42szvHvnghMK/EWOl4J1EREROarb\nFo5iyYQ4SmuawDgXhBoZ2173PC4skOdumsNVj6+lrMPCT742w20LU/nB4tE0NNvZkFVGi90iJtSf\nQ5WNvLIpj43ZnkH2vNQY7jlvLF/75zr3tsQIZ5rMmSOi+b812QA8tz6bb541jFGxITz8SUaXNr+1\ntYD7l48nwLf3FWPsDounV2cB0OqweHljLg9eNtn9vGVZ/OOLg2QU1/D/lo1nqPLj5SRRsC4iIiJH\nZYxhbHwYY+PDetxnRGwIb/9gPh/uLCI8yJfEiCAmDg0nNjQAcObYL5vsWfbx6lnJlNQ0kVteT1FV\nIz42w3kT4vD1sfG/357FHS+m0WK33KP+F0xKYOawSNJyK2mxW/zynd2cNyHeHfD7+Rh3eceK+hY+\n3XOYi6b2vtTk6gOlFFW317X/YFcRD1wyyV1ac8XuYv60wpl2U9tk56kbZvf6NUSOhYJ1ERER6TPJ\nUcF895xRvTpmSFgAQ8ICumxfOimBT3+4kIYWuztn3GYz/OqyyVz699XuajYd89yvmZ3CkNAAHvnM\nOdL+0sZcd1Wc7mzILOP9nYUsmRDvkQb06uY8j/1KaprYkFXGvNRYAF7YkON+7vN9hzlc09inZStF\n2tj6uwEiIiIiPRkeE9JlcufkpAi+OWd4l32To4K4Z8kYj9KRqw+UMvkXK7jiH2v4+8oM9hfXYFkW\nlmXx2OcHuO7J9Ty7LocbntnIf7+1i8YWO1X1LXy8p7jL+d/bUQhAblm9xwcEu8Pi7a2HuuzvzWoa\nW7CsbiYQiNfRyLqIiIicdn60dBwr9x6moLKB0ABfvnv2KG4+eyShAc7Q5uwxse6AusVusTW3kq25\nlfz54/2E+PsQHepPXnmDxzmfW5/DmoOlTEwMp9lVOjIy2I/KemfVmY92FfHgpZN4aVNul/a8tiWf\nW84e6S6X6a0amu38/K1dvLE1n3mpMTx705weV80V76BgXURERE47EcF+vHvnAtJyKpg5PKrLYku/\nv2oqD3+yn03Z5WSXedaTr2u2U9chUI8LC+BwjXNV1MySOjJL6tzP3bNkDE98mUlRdSPldc18tb+k\nS4oMwL7iGnYWVDE1ObIv32afyq+o59bntrD7kLPyzpoDZWzMKmduakw/t0yORMG6iIiInJaiQ/w5\nb2J8t88lRQbxp2umAc4VVL/KKOHjPcWs2l9CdWOre78b543gZxdN4LUt+fz6vT3Ud1yR1cfG5TOS\nyKtocFeGue35LbTYnekj8eEBnDUqhrddZSp//PpO6ppacVgWdy4ezTWzUrrUvAcor2vmxQ05RIX4\nc9XMZAL9el+tprdKapq44h9rKXF9KGnzWXqxgnUvp2BdREREBrSoEH8um57EZdOTsCyL8rpmcsrr\nCfH3ZVyCs7rN188cxoWTE3lrWwH/2ZRHXkU9954/lshgfy6dNtQdrLcF6gDXzk5hbmqsO1hPL2yv\nFf/j13fywoZcrpiRxLDoYBIjgogN9WddZhkPvruHcld5y4c/zeD756ZyxYwkIoM9vx3ord2Hqvjp\nGzux2QznTYhn2eQEUoeEAvDUqswugTrAp+nF/OyiCV6fvjOYmcE0ucAYs2XmzJkzt2zZ0t9NERER\nkdPIY58f4MUNuRRUOtNnokP8+eCus4kLC2DxQ190SbXpLZuBWcOjWDB6CGeMjGJETAjF1Y0crmki\nNjSA0XGhHqu3dlbV0MKFj6xyt6/NveeP5fp5I5j/+5XUNjm/UfjzNdN44O1d7m8RPv3hQkbHhbqP\nKa5uxBiOq7qNZVkK/F1mzZpFWlpammVZs07kPBpZFxERETmKOxaN5o5FoymsamB/cS1j40NJcC3U\n9Pi3ZvHPLw8SFeLP4vFxbMqu4IkvD7onqXZnaEQgdsuiuNo52u2wYFN2BZuyK3o8JiU6iKUTE7hw\nSgJj48MIDfDFGINlWfz0zZ1dAnWAhz7Zz+acCnegPjoulCtnJPHJniJW7HZWvPksvdgdrL+6OY+f\nuEbnH7l2OsunHHuN+v9bk8WfV+xjyYR4Hr52ercpQNJ7GlkXERER6WP5FfV8sLOQrNJ6csvrOFzd\n5E59uXT6UO5dOg5fm+Hljbm8vf0Q2/Iq6W1IFuBrY2hkEEPCAtiY1b4K7PfPTWXNwTK251V2OeZP\nV0/lmtkpvLI5j//32g7AuSrsK7fN5Y20fO59dbu7HX4+hv/99mwWjY87altWZZRw/TMb3cc+df3s\nHucTnEqZJbUMiw7G1+fUVyvvq5F1BesiIiIi/aystolVGaVsyi5nU3Y5ZbXNJEQEMiQsgKKqRjJL\n6mi29zxS3+brZw7jd1dOoaKumYv+topDVe2rsCZGBPLlfYvw97VRWtvEGb/5FMtypuDcNH8kz6zJ\nwtEpLAzwtfHryydzydShBPl3PxG2pKaJ5Y+sorS2PSd+XmoML373rOP7ZfQRu8Ni7u8+o9VhsWxy\nAj9eNv6IqUR9TWkwIiIiIgNETGgAl89I4vIZSd0+32J3sPZgGR/sKGRdZhmHaxppbPEM3sfGh/LA\nxRMB56Tav39zJl97Yh2trgj85gUj8fd1jjDHhgYwIyWStNxKHBY85ZpACzA+IYzaplbyKxpoanXw\n/17bwa/e3cOi8XHMHBbJ1OQIkqOCiQjyY1N2OY98muERqAOsPVhGemE1ExI9F7Q6lTZnl7tLcq7Y\nVcSvLp3Ub205EQrWRURERLycn4+NhWOHsHDsEMA5kbO2qZW88gaySuuobmxh2aQEj9HvmcOi+P1V\nU/n5WzuZmhTJN+YM8zjneRPjScv1TJWZkBjO8zefSW1TK1/75zp3Tn1tUyvvbj/Eu9t7XqnVGBgX\nH8beohrAmcP+x6undbtvRV0z+4prOFhSS3igH2eNimFIWEDvfzFH8P7OQvfPyyYn9EsqTF9QsC4i\nIiJymjHGEBbox8Shfkwc2vPo9dWzkrliRlK3q5TeOG8EG7PKyS2r54wR0Zw7bghLJsTj72sjJjSA\nD+8+h5c25vL6lnwyS+u6Obune5aMZcGYGK56fB0Ab207xKJxcQwJCyC7rJ59RdXsLaphX1GNe8S7\no4mJ4dy7dCxLJpx4rrvdYfHBziL344unDj3hc/YXBesiIiIiA1h3gTpAsL8v//rOmT0eFx3izx2L\nRvP9c1PZVVDNlpxytuVVsq+4lmLXiq5JkUGcPzGeS6YlMmt4NJZlMS05gu35VTS3Orj9hbRjbuee\nwmpu/vdmvnfOKO67YBx+nUbCHQ6LDVnlbM4uZ09hNfkVDVw2fSg3LxjZpVzkhqwyd2rOkLAAzhwZ\nfczt8DYK1kVERESkR8YYpiRHMCU5wmN7q93RJbXEGMOtC1P5/lGC9ABfG2PiQxkTF0ZBRQNb8yrc\nC07971eZPL8+h7BAXyKD/BkdH0p8WCCfpBeRV+5ZnnJnQRUHS2r59WWTPdry/o72FJgLJyf0+IHl\ndKBgXURERER6racc8OWTE3jkuumszyynsKqBslrnCPy4hDD3bURMiEcAXV7XzI9e3c7KvYcBqG+2\nU99sp7i6iX3FNUdsx0sb89hXVMP4xHAig/xYMDqWj3a1p8BcdBqnwIBKN4qIiIiIF3A4LJ5clclj\nnx+gurG1230igvy4cEoCM1KiWHuwlLe29TzhFSA+PIB19y/plwWaVLpRRERERAYMm82ZQnPzgpHU\nNdmpb2mlqKqR/cU15Fc0MDY+jPMnxhPo56x4c83sZBIjg3j8i4M9nvPCKYmn/UqqCtZFRERExGv4\n+tiICLYRgR+JEUHMGBbV7X7GGH68bDyXT09i96Eq6ppa2V9cy/s7Cymva8bXZrjujGHdHns6UbAu\nIiIiIqettjz4Ng9cMpGtuZUMCQtgZGxIP7asbyhYFxEREZEBw8/HdlqXauzs9FzKSURERERkEFCw\nLiIiIiLipRSsi4iIiIh4KQXrIiIiIiJeSsG6iIiIiIiXUrAuIiIiIuKlFKyLiIiIiHgpBesiIiIi\nIl5KwbqIiIiIiJdSsC4iIiIi4qUUrIuIiIiIeCkF6yIiIiIiXkrBuoiIiIiIl1KwLiIiIiLipRSs\ni4iIiIh4KQXrIiIiIiJeyliW1d9tOGWMMWVBQUHREyZM6O+miIiIiMgAlp6eTkNDQ7llWTEncp7B\nFqxnAeFAdj+8/HjX/d5+eG05ddTPg4P6eXBQPw8e6uvB4VT38wig2rKskSdykkEVrPcnY8wWAMuy\nZvV3W+TkUT8PDurnwUH9PHiorweH07WflbMuIiIiIuKlFKyLiIiIiHgpBesiIiIiIl5KwbqIiIiI\niJdSsC4iIiIi4qVUDUZERERExEtpZF1ERERExEspWBcRERER8VIK1kVEREREvJSCdRERERERL6Vg\nXURERETESylYFxERERHxUgrWRURERES8lIL1k8wYk2yMecYYc8gY02SMyTbGPGyMiervtknvuPrO\n6uFW1MMx84wxHxhjyo0xDcaYHcaYe4wxPqe6/dLOGHO1MeZRY8wqY0y1qw+fP8oxve5LY8wNxpiN\nxphaY0yVMeYLY8zFff+OpDu96WdjzIgjXN+WMeblI7yO+rkfGWNijDG3GGPeNMYccF2fVcaY1caY\nm40x3cY6uqZPL73t54F0Tfv21wsPBsaYVGAtEAe8DewFzgTuBpYZY+ZbllXWj02U3qsCHu5me23n\nDcaYy4DXgUbgP0A5cAnwV2A+cM3Ja6Ycxc+BaTj7LR8Yf6Sdj6cvjTF/Bu51nf9JwB+4DnjXGHOn\nZVl/76s3Iz3qVT+7bAfe6mb7ru52Vj97hWuAx4FC4HMgF4gHrgSeApYbY66xOqwCqWv6tNTrfnY5\n/a9py7J0O0k3YAVgAXd22v4X1/Yn+ruNuvWqP7OB7GPcNxw4DDQ+gO1/AAAFwElEQVQBsztsD8T5\nAc4Cruvv9zRYb8AiYAxggHNd/fF8X/UlMM+1/QAQ1WH7CKAMZ4Awor9/DwP91st+HuF6/l+9OL/6\n2QtuwGKcgbat0/YEnAGdBVzVYbuu6dPwdhz9PGCuaaXBnCSuUfWlOAO8xzo9/QugDvi2MSbkFDdN\nTo2rgSHAy5ZlbW7baFlWI87RPoDb+6NhApZlfW5ZVobl+it8FMfTl7e57n9jWVZFh2Oycf49CAC+\nc5zNl2PUy34+HupnL2BZ1krLst61LMvRaXsR8ITr4bkdntI1fRo6jn4+Hl7ZzwrWT55FrvuPu/mH\nVQOsAYKBs051w+SEBBhjvmWM+akx5m5jzKIe8hsXu+4/6ua5r4B6YJ4xJuCktVT6yvH05ZGO+bDT\nPuJdhhpjbnVd47caY6YeYV/1s/drcd23dtima3rg6a6f25z217Ry1k+eca77/T08n4Fz5H0s8Nkp\naZH0hQTguU7bsowx37Es68sO23rsf8uyWo0xWcAkYBSQflJaKn2lV33p+rYsCai1LKuwm/NluO7H\nnozGygk733VzM8Z8AdxgWVZuh23qZy9njPEFrnc97Bh86ZoeQI7Qz21O+2taI+snT4TrvqqH59u2\nR56Ctkjf+D9gCc6APQSYAvwTZy7bh8aYaR32Vf8PHL3tS/X96ake+DUwC4hy3RbinMh2LvBZp7RF\n9bP3+z0wGfjAsqwVHbbrmh5YeurnAXNNK1gXOUaWZT3oypkrtiyr3rKsXZZl3YZzwnAQ8Mv+baGI\nHC/Lsg5blvWAZVlplmVVum5f4fwGdAMwGrilf1spx8oYcxfOih57gW/3c3PkJDlSPw+ka1rB+snT\n9gksoofn27ZXnoK2yMnVNrHlnA7b1P8DR2/7Un0/gFiW1YqzLBzoGj8tGGN+ADwC7AEWWZZV3mkX\nXdMDwDH0c7dOx2tawfrJs89131Nu0xjXfU857XL6KHHdd/w6rcf+d+XXjcQ5ESbz5DZN+kCv+tKy\nrDqgAAg1xiR2cz5d+6efLte4+tk7GWPuAR7FWUN7katSSGe6pk9zx9jPR3JaXdMK1k+ez133S7tZ\nVSsM56IL9cD6U90w6XNtFX06Bt4rXffLutn/HJyVgNZaltV0MhsmfeJ4+vJIxyzvtI94v+6ucVA/\nexVjzI9xLmq0DWcAd7iHXXVNn8Z60c9Hcnpd06e6sPtguqFFkQbMDZgAhHSzfQTOGeIW8NMO28Nx\nfnLXokhefuPYFkXqVV/ipQtrDObbMfTzTDottuLavsTVXxYwT/3snTfgv119sRmIPsq+uqZP01sv\n+3nAXNPG1Qg5CVwLI60F4oC3cZbom4OzBvt+nP9IyvqvhXKsjDG/xDmJ5SsgB6gBUoGLcP6B/wC4\nwrKs5g7HXA68hvPifhnnctaX4iwb9hrwNUsXYL9w9c3lrocJwAU4R1hWubaVWpb1o07796ovjTEP\nAT/EuWT1aziXrL4WiMH5AV5Lk59kvelnVym3MTj/Zue7np9Ke03l/7Ys63+6eQ31cz8zxtwA/Auw\n40yN6K6aR7ZlWf/qcIyu6dNMb/t5QF3T/f0paaDfgBScJf8KgWacgd7DdPjEppv333CWe3oJ54zz\nSpwLMJQAn+Cs72p6OG4+zkC+AmgAdgL/Bfj093sazDeclXusI9yy+6IvgRuBTThXLK4BvgQu7u/3\nP1huveln4GbgPZyrTtfiHHXNBf4DnH2U11E/e3c/W8AX3Ryna/o0uvW2nwfSNa2RdRERERERL6UJ\npiIiIiIiXkrBuoiIiIiIl1KwLiIiIiLipRSsi4iIiIh4KQXrIiIiIiJeSsG6iIiIiIiXUrAuIiIi\nIuKlFKyLiIiIiHgpBesiIiIiIl5KwbqIiIiIiJdSsC4iIiIi4qUUrIuIiIiIeCkF6yIiIiIiXkrB\nuoiIiIiIl1KwLiIiIiLipRSsi4iIiIh4KQXrIiIiIiJe6v8DqqsDeaHjZl0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104639080>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "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()\n",
    "\n",
    "# Let's add a title, and save it somewhere\n",
    "title = 'Loss ' \\\n",
    "             + '' + str(losses['iterations'][0]) \\\n",
    "             + ',' + str(losses['learning_rate'][0]) \\\n",
    "             + ',' + str(losses['hidden_nodes'][0])\n",
    "            \n",
    "plt.suptitle(title, fontsize=11,)\n",
    "plt.savefig(\"./figures/\" + title + \".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79220210807200098"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "losses['validation'][-1]"
   ]
  },
  {
   "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": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1KGVDinTfT6LbZQui3cp3+1C55yMiQ00bX/YZdG/agtyH2H///RkYGODll19m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\nEEIIIYQQoiL/H1a8SnnF7FXVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a7fa4a8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 299,
       "width": 391
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "mean, std = scaled_features['cnt']\n",
    "predictions = network.run(test_features).T*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)\n",
    "\n",
    "# Let's add a title, and save it somewhere\n",
    "second_title = 'Results ' \\\n",
    "             + '' + str(losses['iterations'][0]) \\\n",
    "             + ',' + str(losses['learning_rate'][0]) \\\n",
    "             + ',' + str(losses['hidden_nodes'][0])\n",
    "            \n",
    "fig.suptitle(second_title, fontsize=11,)\n",
    "fig.savefig(\"./figures/\" + second_title + \".png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "### Hyperparameter Tuning\n",
    "\n",
    "#### Iterations\n",
    "\n",
    "100 Iterations|500 iterations|1000 Iterations\n",
    "-|-|-\n",
    "![](./figures/Loss 100,0.1,24.png)|![](./figures/Loss 500,0.1,24.png)|![](./figures/Loss 1000,0.1,24.png)\n",
    "![](./figures/Results 100,0.1,24.png)|![](./figures/Results 500,0.1,24.png)|![](./figures/Results 1000,0.1,24.png)\n",
    "\n",
    "As discussed earlier, increasing the number of iterations does yield an overall reduction in training & validation loss, but too many iterations and validation loss starts to curve upwards again (as you can see on the top right.) Therefore, given this tradeoff between minimal loss, runtime, and validation loss the final parameter chosen was **250**. \n",
    "\n",
    "\n",
    "#### Learning Rate\n",
    "\n",
    "0.01 Learning Rate|0.15 Learning Rate|0.5 Learning Rate\n",
    "-|-|-\n",
    "![](./figures/Loss 250,0.01,24.png)|![](./figures/Loss 250,0.15,24.png)|![](./figures/Loss 250,0.5,24.png)\n",
    "![](./figures/Results 250,0.01,24.png)|![](./figures/Results 250,0.15,24.png)|![](./figures/Results 250,0.5,24.png)\n",
    "\n",
    "A low learning rate means that the weights we are shifting in the neural net (`self.weights_input_to_hidden` & `self.weights_hidden_to_output`) do not move as much, since `self.lr` is one of the parameters used to calculate `delta_weights_i_h` & `delta_weights_h_o`. I think this a great name, because it really does represent \"learning\"; how much should we allow new information and data to change our neural nets existing beliefs? \n",
    "\n",
    "If it's too high, we become too gullibe, changing our weights significantly with every new piece of information and hugging the testing data so tight it increases validation loss. However, if we are too low we learn nothing, ignoring data and keeping the random weights we initialized. Consequently, the final learning rate used was **0.15**.  \n",
    "\n",
    "\n",
    "#### Hidden Nodes\n",
    "\n",
    "10 Nodes|100 Nodes|250 Nodes\n",
    "-|-|-\n",
    "![](./figures/Loss 250,0.15,10.png)|![](./figures/Loss 250,0.15,100.png)|![](./figures/Loss 250,0.15,500.png)\n",
    "![](./figures/Results 250,0.15,10.png)|![](./figures/Results 250,0.15,100.png)|![](./figures/Results 250,0.15,500.png)\n",
    "\n",
    "\n",
    "Increasing nodes may increase effeciency, but only up to a point; after a certain threshold, we are sacrificing significant computational resources to generate nonsense (far left). Initially, I was going to test 1000 nodes, but that took so much time it nearly crashed my machine. On the other hand, too few and we cannot appropriately reflect the complexity of the data; and our testing and validation loss is too high again. Amusingly enough, this threshold is **much** lower than I expected; with the best loss being reached around **30**.\n",
    "\n",
    "### Analysis\n",
    "\n",
    "### How well does the model predict the data? \n",
    "\n",
    "Frankly, I'm still not completely impressed with the model. At 250 iterations, a learning rate of 0.15, and 30 hidden nodes, the final training loss was still `0.71109919954875223` and the validation loss was still `1.2181699251630218` which for MSE (where lower is better) means the average squared error for this estimator is still waaaay off from being perfect.\n",
    "\n",
    "### Where does it fail? \n",
    "\n",
    "It fails hardest with regard to the validation set, which implies we are suffering from some variant of overfitting; we are far too dependent on our training data. However, even there the training loss is rather high, which means we aren't even learning from our model well. It is disappointing to submit a model with such subpar results, but at least by building it I can sort of see how the different elements can yield very different results depending on their implementation; there seems to be a lot more to Deep Learning than just building a big ANN and switching it on. \n",
    "\n",
    "\n",
    "### Why does it fail?\n",
    "\n",
    "To be perfectly honest, I'm not 100% sure (though I'm open to any feedback.) After testing all the hyperparameters up there I realized that there must exist some magic combination of these values that could yield lower training and validation error. I figure if we recursively search over the entire hyperparameter space, we might eventually stumble upon it. It'll cost a lot of computational power (and I'm pretty sure there are already some pretty good packages for it), but that's not something I explored for this assignment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feedback"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After submitting the project, I recieved some pretty great feedback regarding how to further optimize this model. That process will be detailed below, while the above model/analysis is preserved for posterity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter (Re-Tuning)\n",
    "\n",
    "#### Iterations\n",
    "\n",
    "\n",
    "![](./figures/feedback2.png)\n",
    "\n",
    "\n",
    "Evidently, I stopped my analysis much too early to see the real benefits of increased iterations; above is an example of what the loss might look at 3000 epochs (alongside a higher learning rate, which we will cover further below). My current machine cannot effeciently process 3000 epochs, but I will be testing at **2000** epochs and seeing if it yields similar benefits.\n",
    "\n",
    "\n",
    "#### Learning Rate\n",
    "\n",
    "A rough heuristic for testing the learning rate involves using the **quotient the quotient of the learning rate / the number of records to see if it generates a value between 0.01 and 0.001** for this network. A (admittedly poor) formalization of this is:\n",
    "\n",
    "![](./assets/learningrate.png)\n",
    "\n",
    "Consequently, since `0.15/128 = 0.001`, **0.15** is actually the very bottom limit of effeciency. We will test a suggested L of **0.5**, since `0.5/128=0.004`.\n",
    "\n",
    "\n",
    "#### Hidden Nodes\n",
    "\n",
    "Evidently the [number of nodes is fairly open](https://www.quora.com/How-do-I-decide-the-number-of-nodes-in-a-hidden-layer-of-a-neural-network); it was suggested that for this network, **more epochs would be more beneficial to performance than more nodes**, so we will leave the number of hidden nodes in the same range at **35 nodes**. \n",
    "\n",
    "#### Data Availability\n",
    "\n",
    "As an interesting aside, the neural network performs quite well during normal days but **fails during the holiday week around Chrismas because there isn't enough data for the neural network to learn about lower ridership during those days**. The dataset only has two years of data and the network only saw this last week **once**, which makes it a particularly difficult validation problem for it. \n",
    "\n",
    "![](./figures/feedback1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 100.0% ... Training loss: 0.214 ... Validation loss: 0.402"
     ]
    },
    {
     "data": {
      "image/png": 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eDhw4sMa5AQMG0KRJ7XFtwoQJnHDCCWy77ba0aNGiYjw333wzkFvXXx/l68cH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\ni4iIiIiU0P8Dd5xp34i9dCIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11489d6a0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 377
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QERGRfh0dHbzjHe8A4I033ui7fYsttqgJGgD+/d//nXXWWYebb7451XE22GCD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qxDUO5qJpNKk4et6MOCxXUJUooO2IOAGHlMCpfEBjaigQf58tY6K1qB5HKE+oxpGSzKUxXMuP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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e79f98>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 299,
       "width": 391
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "\n",
    "## Training\n",
    "### Set the hyperparameters here ###\n",
    "iterations = 2000\n",
    "learning_rate = 0.5\n",
    "hidden_nodes = 35\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",
    "# Borrwing this dictionary for plotting!\n",
    "losses = {'train':[], \n",
    "          'validation':[], \n",
    "          'iterations':[iterations], \n",
    "          'learning_rate':[learning_rate], \n",
    "          'hidden_nodes':[hidden_nodes]\n",
    "         }\n",
    "\n",
    "for ii in range(iterations):\n",
    "    # Go through a random batch of 128 records from the training data set we did earlier \n",
    "    batch = np.random.choice(train_features.index, size=128)\n",
    "    # Using those random indices, use pandas.ix() to get the values, and get the targets for the same rows\n",
    "    # from train_targets, but since we can only use 1 of the 3 targets for the one output node, lets use `cnt`\n",
    "    X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n",
    "    \n",
    "    # Train the network by going through each x,y in the feature target list\n",
    "    # The magic happens because the weights are stored outside the main loop, so we can \n",
    "    # iteratively improve the weights using gradient descent through each pass through of this training step\n",
    "    network.train(X, y)\n",
    "    \n",
    "    # Now let's meausre the success of those weights using MSE, and feeding forward the training data and \n",
    "    # training targets, and measuring the loss using the current weighted nodes \n",
    "    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n",
    "    # Also, let's use that validation set (data it's never seen) to test it's performance in the \"wild\"\n",
    "    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n",
    "    \n",
    "    # Printing out the training progress\n",
    "    # Updates that dict up there with values, prints to stdout, then flushes and does it all again\n",
    "    # In that way, yields a cool table and a great little load animation which powers the plot below\n",
    "    sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n",
    "                     + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
    "                     + \" ... Validation loss: \" + str(val_loss)[:5])\n",
    "    \n",
    "    sys.stdout.flush()\n",
    "    \n",
    "    # At the end of the loop, append the dict with latest loss\n",
    "    # Now iterate again\n",
    "    losses['train'].append(train_loss)\n",
    "    losses['validation'].append(val_loss)\n",
    "    \n",
    "\n",
    "    \n",
    "## Loss \n",
    "plt.plot(losses['train'], label='Training loss')\n",
    "plt.plot(losses['validation'], label='Validation loss')\n",
    "plt.legend()\n",
    "_ = plt.ylim()\n",
    "\n",
    "title = 'Loss ' \\\n",
    "             + '' + str(losses['iterations'][0]) \\\n",
    "             + ',' + str(losses['learning_rate'][0]) \\\n",
    "             + ',' + str(losses['hidden_nodes'][0])\n",
    "            \n",
    "plt.suptitle(title, fontsize=11,)\n",
    "plt.savefig(\"./figures/\" + title + \".png\")    \n",
    "\n",
    "## Validation\n",
    "fig, ax = plt.subplots()\n",
    "mean, std = scaled_features['cnt']\n",
    "predictions = network.run(test_features).T*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)\n",
    "\n",
    "# Let's add a title, and save it somewhere\n",
    "second_title = 'Results ' \\\n",
    "             + '' + str(losses['iterations'][0]) \\\n",
    "             + ',' + str(losses['learning_rate'][0]) \\\n",
    "             + ',' + str(losses['hidden_nodes'][0])\n",
    "            \n",
    "fig.suptitle(second_title, fontsize=11,)\n",
    "fig.savefig(\"./figures/\" + second_title + \".png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These paramaters are implemented above, and they yielded a **much more effecient network** with a training loss of only 0.21414819659326614 (decrease of **69%**) and a validation loss of 0.40253867612225269 (decrease of **40%**). I will strongly consider using **more epohcs and a higher learning rate** for networks in the future.\n",
    "\n",
    "Thanks Udacity!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feedback (Round 2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of now, the final parameters chosen were:\n",
    "\n",
    "    iterations = 2000\n",
    "    learning_rate = 0.5\n",
    "    hidden_nodes = 35\n",
    "    output_nodes = 1\n",
    "\n",
    "The training loss is around 0.2, but the more important metric that**tels us how our system will behave with real data (validation loss)**, is much too high at 0.4. As the reviewer pointed out, **a validation loss of 0.5 means that you are not predicting at all or any better that you could do by just chance.**\n",
    "\n",
    "Their recommendation is:\n",
    "\n",
    "1. First to choose a moderate number of hidden units (from 8-20). \n",
    "2. Try to achieve the red curve (\"good learning rate\") in the learning rate chart they've provided. \n",
    "3. Expand the number of epochs to see what is the validation loss achieved when the curve is stationary/flat. \n",
    "4. If it is below 0.2 for the validation loss, then we are done. \n",
    "5. If not, try to decrease the learning rate a little bit and see if you can improve the validation loss."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter (Re-Tuning)\n",
    "\n",
    "After a submitted again, I recieved yet more incredibly valuable feedback, which I will attempt to parse below:\n",
    "\n",
    "#### Iterations\n",
    "\n",
    "The reviewer suggested that the gird comparing the different the different parameters and performance is a viable approach, but a *sub-optimal one*, since we cannot compare different learning rate for the same number or epochs. **The number of epochs depends strongly on the learning rate and the important thing is what is the minimum value of validation loss achieved when the curves reach a constant value.**\n",
    "\n",
    "#### Learning Rate\n",
    "\n",
    "Another good way of choosing the learning rate is to keep the quotient of the learning rate / the number of records should end up around 0.01 (i.e. `self.lr / n_records` ~ 0.01). This nturally depends on the structure of the network itself since more complicated networks usually require higher learning rate, but simple networks can apparently yield just as valuable results. \n",
    "\n",
    "![](./assets/learningrates.jpg)\n",
    "\n",
    "\n",
    "#### Hidden Nodes\n",
    "\n",
    "The network has too many hidden units and may incur in overfitting: it is recommended to decrease the number of hidden units a little bit. A good value is the mean between inputs and output, but [the number of nodes is fairly open](https://www.quora.com/How-do-I-decide-the-number-of-nodes-in-a-hidden-layer-of-a-neural-network); it was suggested that for this network, good performance could be earned from networks anywhere from 5 to 25 hidden nodes, but 35 is far too much.\n",
    "\n",
    "![](./assets/neuron_model.jpg)\n",
    "\n",
    "Also, since I did not incorporate a *bias* element into my neural network, making the performance of the nodes marginally poorer than anticipated, but it shouldn't have a significant impact for this toy network.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 100.0% ... Training loss: 0.068 ... Validation loss: 0.169"
     ]
    },
    {
     "data": {
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X9Nvf/rbKNceNcy6mD0n9JDlJ02LQVx1J2yUdlNQ2zPH23muFe8yV1K4Sr7U4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\n5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b1b780>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 380
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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x6pKV7gerI4Nk+5EDi/7uyTaHp4VUH1NoUGLCIaZniekjmcnWz+eUEBKPiBT9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LPfcCW+7/f+uXUsrPT+LENjU1V2A71jk6N9GQcrjnUejIM8x6E454ahLVVacmTZYS1ralmnwEFr\nfb7WWgE4H8APImMNPqeUelqX67QprfXTuX8Avrjbj82pyEmrERhNrA0cJmk624EDiM9AmaZRwDFd\nKNWsVCmALjXBtzxoA4dcqmQ2NYB2S1VKITIOHHCIWI8DNwDO3xydENASody8YLAG/NDbzcs4Y3ZD\ng7TBOEik4XDf7HmvqjGGmHQnVSK77mNEM+Dwk8+6JHt89DG18jgIjIMEHOLidRmrgZVk1UKqRDwO\nK3F5/+ICRfTvoSBVmqb9uEx8paAlJfcXSB4HY3L0NALYksbFqXbzeFCZFDnOQ+Y1j5MUayDAYaUw\n5KuG5mjFMXvC+80yDjPg0J1UiQKHJ15wAL/23K+d/X7nEWbTYE5155EN/MifXz1jaD9wHUfAu5fv\nXIFlqZJUaYeAkvS6upqj26cquTexdqpSb45uVrxUaRceSMc1F4+D1vp+rfX7AXw3gLMBvJP8Of/m\nPVi6oXn5sYbXX+oyddzK8jiYTVqc6JnmF0DRyDRmHHitf6A0Tlsm41sePMXv+gf5RN18fe3ocTAZ\nB02amoTzONSkKuXZ95sNzNFpUDSEIazm5YJLgf/nVuASy3ITmrIVoDlwiEmDnAYCcFDKlCvhdHfy\nDYtx2DfKjsGnP/owLrvkbCS6I3O0r8chthkHOgAu8cprp1p7ChTXWODwcPELfT+iZvMUaPOsrV1/\n1uNAZXKzeSkNZzmkFCDT16/8Pk4SjX0UOIxMxqGJlncvTo4+TYDDvlGEiw6vzX6/8yjjbZpDpanG\nK99znXHZ5+5oFwnrO1dgWaoUx7rLHgcJYNPHGYUKK4M5MA4OqUqjKLTiWJf7+OiyfFieRaq5mqO1\n1l8BcCOAJymlzplefNP0/5InQSkVAXgMshkQt07v4zSAuwHsU0pdwCyTJzaVPBPLWHS3VSM0gYP1\nBRtbyUbFrr8ptQEctf4V6UIb2xZweMACDqSJsnf+m8Sx0p3YJKlLVaqIY3UefEd2YsMKxmH1sDm/\nYXbF8trOuf5WTbaLZi2VpEqAaZBWpzBOHKUydbVVNM+n9OqMcQCA5z75EYi7GgAnSpWEy8lgvFgN\nzAFwSAzNcF2ZUiUCHJITpes+ggIHiXGYnocufh5jlkJId/35wYGsJLDp8UY9VJRxYM3RKdYUDxyy\nONb65UrFvUfC+73FzHGYh8fh1Hax/voowoVnFef33Uc3d8Ts+M7P3F5KdjqxNWmVqS83so3vciHK\n9jrtmMfBN1XJ9jhE7RgHVjYjgADbHB0aA+B6qZJr9R6H5vWI6f/5kf7x6f/PYa77bQDWAHxaa03D\n+atu873WdZa6yoxD8RZqS6o0ts2LkZyq5CKhsKdW09oqMQ6nrZhIov02BnMlznGsxiwFazeZVoDU\nmDI9e76sVMk/VcmQcNiMw4pAjFnSEaA5cEhiao4eyFckkax5spLL+1xbBDicwLoBHJ5x8WHD42Af\nk14lmaOl4WwxjakdGFPKIySYeOxCG3Ih8n5zgwBN4CB5HDzmC9DNgdBkt+rnOHDsmscuvOYBMrdh\nEKcp1g3GoZAqBUgbpvP4mKN3Zo7DKYtxOLAywMHVwXSdFA+eLM+R6bLe+NGb8ZoP3Fi6fJJo3HW0\nuVTK16y7LGU3czuVIiUzC/x5GdsehxaMQ5pqViLjYo4eRgEGJFWpZxzca1lTlVoDB6XU1yilSt2S\nUipQSr0WwHnIgEDOq74XwEMAXqSUega5/gqA/z799Y+su3vL9P9fVUodJre5GMDLAWwD+PO2z2UR\nik7TTREYO852qlKcJOWpsgA7x8Hlg0hKVQLK8xBObcd8HGtp/cRtbcvjoCuaGrquDqeab+sx5OZo\n29TNrl2aIUEYBxWbzYsIHEjSzcwc3ayJp/GzWpIqAUKyUgcN1VahCjyu17FvWDyGx567D4o07Pcf\nt/wfHiVNMZZTlQpANcGgdJx57SwKwIGrCyTgYHgcGjIOhDkI0pjVF7PnOAUOyodxEECLLscHTxKN\nNZCmecUEDk121tj3tpJxMIHDPMzRtlQJAC4irMOdLZr3urrxnhN408e+NPv98V+1H099VHFe3/ZQ\n8/PLJQZ0Gcv+HNgpxkFquKX3gTIjg1C1Yhx8ZVIG4xAGiHrGoVH1UiW5vg/AfUqpjyql/kQp9ZtK\nqbchi0n9FQD3Afix/Mpa6xPT30MAVyil/kwp9ToAnwfwzciAxV/RBbTWnwbwuwAuAXC9UuqNSqk/\nAHANgLMAvGo6DG7py5AqqQCoYBziyWQ2ZTpGUOy8c5OjXQfAkZ38lEhStrbHpeuzEgrA2AkeIGmU\nqqSt4V60Ik6mBLC7/i4NRsnjQJ5LBEsuIQIHOkOincchJTvrlcCBneXQwYf+JgEOFuMQBAr714qm\n6pb7WliPJKmSdDlNVVKmx8E3jlUJJmGuDHO0EMfqt+tPpUrFfXDJRoCehgzk1x+2W1tIdIpUgiTV\neODEFh44kbEMcWKlKhmMA7/DWVsMSFCC3C2ebGFNmbv98/A4UMYhP9YvPFT4HO6ao8/h/hPmbNV3\n//g34QnnF6/zrS2Ag9QALv8cBztVaWcaYel1lQCFCRyCVoyD1KyKjINtjg57c3STYuVhS/DydTHH\n4Z8APBZZ/OpTkcWinkbmOXgXgN/TWhviTK313yilvh3ArwJ4PoAVAF8G8AvT65deWq31K5VSNyBj\nGH4cGaf9rwB+R2v9wQ6ex0KUIVWyGIcScCCDwiYYFG82M4TNZSc0tT5gUxXMJspyJmPTXyFLlVzW\n1hXm6LypV0ihEVjGaHKIM2DDJd/ejqE15SOJG+PAGGWbAgdqBqfpUqUijMMhNY1k7UL7bTMOI3MW\nxcH1FeQbwbc/cBzf0nAZbhgYgIo4Vtnj4G2OFoAiVxdA8jiszH7swuMQMWyfAc6DAWHXGhqzhUSn\nECmuv/s4XvjHn0GSavz1T16GSZLigGSOVs38NCzLJDR2wdbJ0mUzj0OXUqWtGsbhyPyAA2WJnvOk\n83HW+hCXnLs+u+y2h05xN3MqqV/eq8DhD6/4Mq6+7Qhe9T2Px5MeIWWl1Jf9ObBT0hu6bxGowkvi\nMr15GJU9Dlpr5zkevgla/eToboqTAy6DVKk1cNBa/xuAVzS43aeQsRU+t3k7gLf7rrVUZTEO1Bxt\np48khuZ7iNlXHfU4KA24JhsRYJIgzBiP6TmwPWGGzzlIlQbKzeOQJNr0LVgg4JXRe/Cy8EP4k+S5\neFf8XbO/KYFxGHgyDpJMqsw4CONEmKnVWw3nOCQG4+DoccilSl00VIbHYQ37VsyPkfXVomE+vWnu\nmHpVhx6HgfKb42DEHofVjENoHJcUqDZjuOg5TidRU0B8aG2AYxsTXqYE8Mebt1TJXPtX3nfDbLfx\nx955DX7pOU8wGQcCmlVTczT33grHQTgpJ1zNGIcOzdE0MS4/1i88TBmH+UmV6OZCvuP8mHMK4HDr\ng90zDntxMPC/3PowXvfhLFcl1cA7/vM3NL6v3fI40N3nURTO3lupEac7+/kshUGoMEkyNm+SaAwj\nN+AggRMX0DKKAiNVqZ8c7V79HIe+9kTR5l1T+REYj8N28YU2AWkwlTIkTiFSpx0605htzpAYj8sN\nojE5OuKlSqGzVIl6O5QBPoaI8dPR32BVjfGz0ftwUJFduLAGODj5K2zGwUyb8fY45I1cwyY+JeZo\n+EqVumioNk3GgUqVAECRxyRFaTqV6HFwSFUKbMYhxbgh41AHHIySGId8joMDcDAkSZHJbuV17r7s\nctYYbd2uqTlaWwEEdGf9oVNjxKk25ziM7AFwDb4gOamSAByiMcM4zNnjsD5l10yPw/wYBwocVofZ\n2hQ43N5CqrRXGYc01Xj7p27DGz968yx++D3X3DX7+5U3Pyjd1Kkmu+RxoLv7IyI7cpULAWjsc5AT\nnRwGwFlzHPrJ0e7Vexz62hNFaS6bcYA9OXpiab5pWXIlt2QjKpNShkxqPC5Hog6lpqZJqpIl0aKP\n34iEBPCNAZnlZwAHOnzOfWq11uZur46oxyHxT1WayimaTo5OyMC7yqaWMUd3IuEgUqUTWJ/JN/Ki\n5ug2cayiVEkADsqYHD20jrPYK7rSWLtGqmSU4XHgGAeXXX/KYJjHWl5rwxCv/T++DhcfitjrckEA\n7RiHtMQsZQPgiMegFMfakVRJOA72j+8vXbYyh1Slk4xUiTIO9xxrwarVFJ2vszIdAHbegQKQHt0o\nf+66luhx2OXG5p+//BBe84Eb8aaPfQm///HMGH7Lg80lWXYluzQ5mjbcQ+IZkDwWtscBgOFz8Am6\n8AYOVqpSSOc4LEHjm9cHrrsHb/74l+YmN+wHwPW1Nyo1GQdNGAfb45BOii+0KuAQIHXzGZAPuFSF\n0AZwqDNHkwY3NE2rLrvgdG2tAqM5XYVpkPyO4PPFLyPSyDeVKllRsDqwgINyMEczufpNPQ6azq3w\nZBxcBv3Vls04DGXgoJkZG84lbokK92l7HAw5W+qnZRaSjWqLgqbGHgfKOJBdf2U2kS/+xkfj/f/3\nM/nH2VAmpcQo2KQEEMcV5miFtBm75eFxOGd8d+mynNnZjpsNoLMrTtLZ6xYoYHXavJ+zr3htHj41\nvzjWLUOqlK29RiYIb06Sxo2vGA+6y53NOz99++znP7ziFmitS8ChzXtbSlXaIY8DBWQ5g1C1vt28\nAybj4HN++Q77s1OVBjRVaUmkSv9293H89Ls/h9d/5GY8+w1X4E8/cWvna/Dm6MVHDj1wWLSyGAea\nkKStnblkYnocjFJ0CFzq6HGwjdnFfXC7w7LHwUx1choAR4GDJVVas4DDs4N/LX4hzTMHHJoMvjMZ\nh+ZzHBoDB0Oq5Odx6JpxsFOVACAIdolxIB6HRA1Kgw59BsAFQrJRbYV88z4DDk5fusU5rgTGIZet\ngB4LwvC5mVTJewCcCbz2r5jHWsY4yFKlRscaAxyk4+C8+J7SZStTNi/XgLet09bwt9yMemh1gLyX\nOrEVz033TT+fctASBMoAcae2m51j0s7xbjMO5+43z7c7jmwYrA/Q/LMTKD/vnWrk6DoUOMhyoTLj\nQCVOPueXGL3LnCNxks78SYECojBAtIRzHG6+v5A6ThKN137oC/j76+/t7P6l2Rm9VKmvHS9jOrQy\nPQ52k5ZOCo9DmXEobhe4fsmXhs8V9zFQ5duLjIMVk+kbE5kiMHT0q1Yko2FWpY28xXQAbruw2eRo\nfgc6VP4eh7bmaCQNpEpdMg7UHK2rpUptPA6KjR+FCEZUansczIF/TVOVMFiRr2iXIVWiHgd3025g\nyKT4eSV5E0lZFkmqNJp5avzYDtvLE1g+zCOntisGwOlGxxoHEqR5HudzwIF85nQBkk9uF+faPit2\n+PBa8focPV1mXLsoKmekMpV1kmR2uiFwEJN2drmvsT9P3nvtXaXrHGsh0bKf9041cnQdyhxIAG5S\nwzj4nF/SsD9ubfqdeCCaALdeOfMOVT3eRSvuvPnF916He451E3YggbVlSFXqgcOCFWUVUhUYciE6\nHA4AtjaLE6DUYFq7sS4NDTVfpwiNKFB7ejNgmaMFqVKE2FEmZRqzaXNqMw5GCYxDmzkOCIuGMEKC\ndZAPGrLrahSTctN014xq+VEFHAjjcLbKmv1OGIfSHAczjjUgwErK4HcpJQ5645sG+rpM1LBkjvab\n40CTjXwYB2EAXL7r7/AYFP1iicqmeoACB3os8GsXQQAumwPUX0EHHabYsL5o7z96vJgTo4bmsDqk\nzcz/zJeq0vwxdEF6X+myHKAB3RikKeNgN7RnrRfP9+E5AQf6Gq4SiRJ9LF0Dh92WUpy2or1//+Nf\nLl2nDXCwPwd2wxztyzgMpx6Dla4ZB+Yzlq77J+HrgHc+Dy/80qtmly2LVOnUdvn12xgn+OMrb+nk\n/vfq+dVF9cBh0co40U3GQU+btCTVuOGu4zh2kuhC7eanlDjj0rzLxmx7CBtQZY4mxmzlKJMypEoW\n41AFHGg8amjuQAPabY6DDRwsc/Qq2Y3BYA1s0UZuZlZt9gEcxoWnIhmsy1fcdwliWNMAACAASURB\nVB4QZekvZ6lTOIwT3aTNlOY4WFKljszRkilWNkcX70NizXEYqAQTj+dOGQcJOJzQzHtdl2zkcLxR\npkUZqUrE48BJlaQ4VuWeqkR3/FUQZoMjp7U1Ns+zh48enf08CVcN+WPGOPgDB2fGId7G+XiodPEK\nORe7ONa54W95UeBwZG6MA4nFFIBDU6mSb7b/ThXX0Nl1bLP5612KY90pxoHGsRoD1erN0QXj0GwI\nnGTA5kBT3gvsxwa+Af8OAHj08atnm4N2KtWiFgXcz3h0scl2+dV34oGT7QMPZEN667ve9eqBw6KV\ntfNOv6zzv7367/4N3//mT+J9V5GdmkERHwigBBycPoTIzp9tzOaBg5AxTxs6xA2iYJXBOKyripOc\nAocgnL1egcrAgIt8wzZH053YdWwV8yXCkSkdo9Xh5OgwLqRRugo4BCFwzuNmvz5O3d2omTMqTUtz\nHEpxrOS9EX0KDkWb94R+VHFgJIlnDWaqVcbEBQFScrvEw6hNH3cgmKPv1WeVLzSYtTLj4ALQlcMc\nB16qxK899GEc6HEehEjIqJ+xNR3++DEKHNZKEc+NgLFrqtKxO2YDIU/o4rNtRD5z3J5vddGm3AbI\nZ+/bAcaB8TgAJojpGjjstgb71Fb9eXq8VZrU7sSxGlKlgQPjwKYqNYtjdZFDzdadfic+QpnAPGf2\nd2rS9ryLnjfP+brz8fWPzGTG23GKy6+6s/X971UPURfVA4cFKzuO1fQ4ZEbjv/jsHQBg7IQreyec\nAI5QuRmUbbmQVqZ50i7RHD0wv+j9o2ADKNKIO0uVrMcxQIwk1bXUa2rFsSqied+viEzJBmfGuswc\nh4aNzSAu1kyHFcABAM574uzHxwV3t9+FHZ+cNXen9AoSFWFtaIKlkO66t5IqFa8PNfcrLlWJSHbG\niKCmueMpnSmRuD8Wao4OowgTXQaED2hm2B81hjNJWi4AXQIO9R4H3hztkyBm7O6rAAloiovZHG9v\nnJj9HEcmcFCOQyXL65e/VAMGOOiHCznBzfqi2c/DjhmH0xXAwWAc5pSsxKUq2Y9l2TwOp50YhxbA\noRTHujONMH29aRyrPMehuHxmjm7MONQPmZvd7ww4PGxcnjP7y2KOts/tH7ns4tnvV99+pPX9SwBh\nt+ekdFE9cFiw0jSOVZnzDJBOcN2dxW7wCjENhyObcbA8Dr5yIRWW0pHskodTFY33CsZucawJXTuA\nImuvuEqVgEY+h9SaHE0f/36aqFTVxHOpSg3N0VHiyDgAwLlPmP34OHVX+wFwNtswLJJm8gq6Ag7k\nNTfM/dwOdEyBwwD5I6JyOjr/wmftMAgQowwcHgJjhBd2/XN5mgvjEAgyqSyAIPvSmaUqJZJUiR5v\n2fN2AaqGVEmFSAgYmEzM148ao5NorRy40JFUKWCOoYQAhy/jwtnP2XPNXqP5S5WK13tuUiWBcTCl\nSg3nwezRxuYkA4QOrET4wac9cvZ7G4/DrjEOvqlKjFSpKeMgA4fyORKfehiAxiNtxmHK7HeRVrYX\ninpp1kcRnnlxwSBfd+ex1syAdFwtg8ehIgS+rz1Z1o4gbaC1TvDZW4tdAqr3DUdWg2l7HDyTjbRS\n0NYQObuGhjmaNDWE/VhV246To02PA21O15QP42D7HLIGY73C/2qbo+ku8Kqi/oYKxoHcJjdwNpUq\nDVJHsAJYwOFufLKtOdqa4bA6KjfUQUTSZ1pIlQIijYtVjeGaNNDbiJAPOqXhAcbE7ZpSFuNgA4dt\nHeGY3le+oTCpfOjBONBzXAUREgSz4y+ARgpVNBCxYI6mqUqqGeOgwhAp6AaDeXt63mWMQwEgM6lS\nk/eeYxzKTWJy7O7Zl9d96quyz5cp6zTCBNsYtpflAThlDH8zj4Gzd8IcbTAONFWJAAcHaQ9XsmF2\nl83RBDi8/6cuwx1HNvDkCw/hQzcUUZmL6HEwJkdH9YzDxJqlYN+uG8bBuo8rX4cn/K/X4q2Dp+Jm\nfaHxp+WTKpnBBxedtYqz1oc4cnqME1sxbnv4NC45l/mMdywJgPdSpb52voxGzGQcVGoCBypVGq7Y\nwIGyBSliB8mOOb059DRH81KlFYy941gzj0PRJFWbo61d4QazHBJtAwcBZVRKlbobADdMCqmSqpUq\nFcDha4IuGAdzarQtUwKyRntWbTwOpIlNghrgYDEOwbSJpclfTaVKUVgGDmMMcAI15mgmSWucVL8e\n2j7WAlMulB87tXGsUTPGwd6YSJTMKtI0sWC0zzRHK+3kH7KLA5oBk6qUbhUyqc1wnzVsrxgC17YM\nOcPKLpijmcnR9mM53ZC5FDXYu52qRF7zCw+v4Qee8kg85px1HForzqc2Hgd7x3znPA7FzybjwB+n\nlHHIwb8Zx9oF42Bd/r9eCwD438LP4Vl0kCqK79lOAjb2QJ222ESlFC69sOgXPn/HMe5mzrXMjEMP\nHBas7AnKFADEcYxrv1IYFul8gzJwKLMFdTKKkseBxrGq8m1FqZIFHHzjWIHASO6p9DiUpEqEcXDc\nibVTlZSU618lG2LNqs0+gIcp8VUMa3ZEDj0acZA93nPVcQRbLbWbNuMwYBgH8hqrNG6cWy0BB94c\nXTRuY03kU8b54cE4kPc7iCIjXQgAtjHACc2830F1qlLdsZ7qzB8wexxBZACH/DhcHU4fj4M5uqnH\nQQXVjAOVKg1W9xseh6AB45AdJ+VjJWQ8LXqzAA7b4T4++rYLxmEsS5V2gnHYdvA4NDVH71UN9knB\nV3JotXi9281xsD0OOwUcytIjoN64/NrorXja/3wy8Kk3WXGszRgHc2q1fB9PDEyDcM4wbjSdP7TH\nivMvPeWiIl3purvaAQd6flE1rzRTY5GqBw6LVhZwoFKlE6c3sR2nOBvH8SPhh3GpKnTAK2tWg2kl\nGwH1jazWMmipZRzoLj0BDqsYO7IdxcmWKtMcHTGgpVjAxePgwDgo2kg2YBwYo2xTxmFEgIMa1QCH\nIMSp9UfNft23UR6m5FXbxbTNk1gttPbGknS+R9r4i5nq3RPicWCTmkqMQ/YzBbfJpBlwiIIQsaXq\nHCPyYhxcm/fSzBBr1z8/z1Y5qVLEm6N9ZpYYr20QGtPhQ2vII5UqjdYOWMDB3xydatMAPltXxzi9\nHeP/ettV+IE3fxJ3HtmA3ibAIbIYB4/42boypUoW47BvJxgHZlo4gHXy86mtZsBB3BHdRSnFOC5k\ns2GgjEaZMg5tpEp7wuMQEu9VxRyHc3EUL44+hiDZBj76680HwJE1VghwGNMmdlIdQZpv0C0LcOAS\n057yqKJfuO7O7hgHKjFbhjjbHjgsWtEvdhUaAGASZyfCbw7+DK8ZvBPfEV43+1sJOBCJS94A1Bo3\nqczCMmZT4PCj33IxALdUpVXHtbU216Y6+sqqMEfPpkfXfAAnljlaZhyEGQ7WurM41oYfwBQ4BLZ3\nhal4WNCvanK64poOZezsD1jGgQLKAeLGZjoqF0rIDnrgkqrEPJaJB+NAJTNRFJlxsAC29QDHOcbB\n2PUnx9q06a5nHPQsZhQAoAJj1z+XC812n2nEbCjMcfCRKtHj3EpVsjcHKOOwsn7AMkdrJKn2GroX\np6kZQjCtEDHec82d+MTND+K6u47jJ/7iWoAAh0m4bkz3njEOHQAH2iStDXdeqmR4HEjzsW+leH+b\npipJjMNu5swb8pFhaAQvHFwlwKGNOdr6PNqxOQ6CVElKKRonaSlqvOkAONrEUubKOD9J8AVXqzPg\n0GI2zx4qM/gge00ed17RJ91zvN0sB3FS+BIMcujN0QtW9q6/IjsX8TT15LvDa0u3C+zdcCJxyRuA\nesbBmiER0DjW4m8v/sZH4dz9I5z9GWCmIjJSlUypUr72WsUQZNNfYUqVKsvO4Gd2gesaKm3FsQYD\ngXEYOgKHaTrO5iSB1rqUSlRXI10Ah1CaVE0qJYAmmGxUXNOhiEwoRigAh/JwwVUmlai+CONApUos\n40AADfE40GN0MvZhHIoP/VkcK3mbMo8DJ1Uix2XIDIBzAg4M4zB9OLlcaKVucjST6OTSSAe2VEmV\nZVJ5rZGmJlwpS5WA7NwahG77U0mqjdc9r0jHuPLmB2e///s9JxCcXwy3jIf7DYZl9pnSwZT0TQM4\nmMfwYfKBdXRjnLFFgd+5XLu+wDhQo3ZTqdJe9DjQ57KfgCPAZByOt4hjtXf4d2NytGmOFjwOcVpq\n0FbIIejFOJD3lB5HcZJCa41/+sIDGB39Mr6t4j7yDcamnpq9VrbHATA3A46eHjf6fs7LYHkGAY5P\nv7aXIc62ZxwWraydd0UaoziJEUH4ErF3w0cUOGRHdN0XrQkc7DjW4kNsfRThp571WOyPyAdbyEuV\nVhyNjNTbAcsc7VWMSdlFPkKbuUBkHCqkSkoZTayPfMQorbGii4YtsL0rXBF2KYjbMg7FF3aMsJhg\nTMtioprusNAmVpP3LeAmR1PGQRfAgQ6ji+Nm5uggCI2ddyD3OHBSJco4lEGqi8fBYBwsn0FYkioJ\ncaw0xctjbkjJ46AoeyQzDhiuW5Ojc+DgJ6fgpUoTfPU5JmOqxgXjkETrljk69zi039mjkZd0txfI\nsvXzXXCtgWMb3bIOWmvj9Vshu5ZdDICTAEJdI31sY4y/u+4ePNzB7IrbHzptNFjcLnBeh9a68TjY\njfqOeRzo5GiHAXCTJDXlvgDWAzqnxMMcTZpVehxNEo0rbnoQP/bOa/A/PnhV5X2sTs/3jYbH216q\n7TiZMeFRoGZAbmVQbIbFqW58bgEVjEMvVeprp0ulpoyBehxUGuMsnGRuhXJTSxiHfdOdw7omVqXm\nTqgkVZqdJIZxk5qjzTjWbO060FKsnaoQoSvjYJex8+8IHKykm7CJVAlgh3J5y5Xirdlj2dYDDCT2\nQ3hcARke16iITGiMqJ5xUGljqRJtYtOASJWYlB3aQG9jMDOjUYDp43EwgGIYIla2x0GSKknmaEc/\nTWr6aUoeh6nkiZ3jIMikXNkOAFYUrJmcVk5VIsBhUI5jBfyTXwKOcUCCQWju+initUmGB+bmcTCl\nQuVj3dih7Bg40Mc/jAIEhM3oYgAc3fkcORpmAeAlb7sKP/Puz+Fl77ym0bp5/eY/fAHPev0VeMFb\nPj0LUKgauLc+DBFNX4PNSdI4btfe8d05c7TfALhJoo1J6ACwTpISmzIOFACPkxR/eMWXAQAHVDUb\nvUweBzpkME9UystkHbphtlwYpkWqHjgsWJnJRqGxoxogxTlK0CnazS6RuKzBDTgYA+AQGE2SCRzy\nxBeqv5biWB2/5G2PQ2PgwBlWqz8Is1QlkpAgNet1wIHTnfvKKcYFY3AaIwwjBxqVMA5R3FKqZDEO\ndcAhQuKlczfuhhxTmjBWigMOtsdhBhyIB8gDONizFFLG43AMvnMcdC3joEtSJXPXPz/PZk2sIVXi\nU5VcQQtgvuaZOVqe1XJQEfZq9bDBQKoGQ9hie9Bivq6ODcmOQopBUqyth3aqUndSJdqcjZhj/cBq\nN/IZruimgn2edZGqZDaT5i60VJMkxXV3Zd8xn7vjWCvW4Y+vvHV2Pzfcnd1n1cA9pRT2r7R/3naj\n7gIcvnjfCbz9U7e1er5SIyml7IzjdPY9kddqUIB1L8aBrj0wpUr5Rt8B1ACHmVRp8RmHKoB6eL04\np9tsBkgM0yTRjZMG90r1wGHRymiglcE4RJXAwWpqSTNZMA41H0RW4orRIJLElZnxy0h8kVKVpubo\nuobKyL9XCKLupEp1OzcZ41CsH4qpSnXAgTIO2f15Mw7jQtt9Wq866ccDIkuLkraMA/U4RGyqkulx\nSJymJXNlSpUocHBJVcqQQ0De78TDHB3aJmGLcdjGAJtqFce/5vnmDQ8/hjyBcKb7D1TGWvnK4srm\n6NzjwIBzQarkepwDyDQ3s6VDa46DefvDKI5FrB42PA5NGIdUYBwGiE2TMrZn1zutRxgOh7xUqWvG\nYVA+1w7OEThUyaS6kCrZGuy8qoC+/Tn9XW/8BH74Tz6LO4+025DId4BNj0N5c4ga1JuGS5Q9DtXH\nyeY4wQ+95TN4zQduxC+/74ZGawLy5GiJcRgn6Yw9y2u1KeNA1li1QOJFZ2XfxwdUtYx1Zo5uOKl8\nL1WVJI56l460AQ7kuBqEAaj9abeHLLatHjgsWBlIVQVQVpN2DiTgUGWOzj0ONR9E1hwHZZlgASBQ\nmWYQaWIBDfIlQBkH5bY7SJ+3VuEuMA5UuhJhrJmGucrjAFgyqYbTow3GYcUJOKhRAWgGbYEDkcZM\ndGjsVM7KiPptzjgoyjiQRpiVKtmTo3PgQABm4uNxsKRKtsdhjAHe9KKn4uAPvxX40Q8D3/oLwA+9\nCzj8aPOOLIO0i8fBaNADnnGYyQHFydFkXeU+18B43iowJm/bjMMhJQOHYMY4+CW/sIwD4lmTOECM\nZwQ3z/52CqvZ7i1nju7A40A/E7ljfZ7AoZJxoAPgGjZyJnCgGmx34HDk9BifufVhvO1TtzV6DHlx\nUqX1YfkzngKcplIl+/Mo1ajcAb7p/pM4MY28/ciN9zdu+lJB887dn9YZO2lLldbQnnGwpUr5MX4A\n1cBhnSQgNv1M3yvFGaPzMkIPWqSl0ZcoVAqRgzxtUaoHDgtWyjIo0wY6rGIcIqupHZVTlWoHsVlT\nZRHSBjE7EYeRgjpyq5H3j3BoTkDhzNGeiU6hnZaU14XfUPz87b9U/ntITaNuJspSHKs1lGtWdcAh\nsqUrDb78CHDYwMgJOITkvR6kbYGDKVXiJkfbqUpNUySox4E2wtx0YYNx0ANWqoR04mzUpqBFBRHL\nOHzPk87PjutHfzPwna8GvvZ55TuyGC6XVCVlx7EazXt2+xHHOBhxrMyx1nqOQ3b7rzqQrXOQAoe1\nsyxzdPYcujBHD1WCE5tjBEjxvuGv4x3D35797aRey5pewjgU3qVuU5U4Wd7B1eK9aTPNmKstYWo0\nYDbVp8dxo0hRSTpTJVWS2MM//9Tt3usbj2XauJ+kczNqGIemWnuuUa8CA5GVlHXbQ80CJsQhbAxQ\nyxtLW6o00sXnnA8wzu/vXBzFSx56I34q/BsA2fyk/Bg/WMM47CfG7EX3OXAzHPIyfUvdmPDDQGFA\njqNFB159HOuCla4wKIcqxTk4wdwKDONQeBzWHc3RJX8FnRw9/cL/mfD9wO+/B1g7p7hhaDX5UVmq\nVM92mKlKQcg0rABw6QuBJ3xflkn9za8o/50MhDs8bXzqmzlrtzUIeOAwrEk4Ypq5zbHnB4ghVVox\nTHbisisFcBim7bKpqVRpInkcLEDZhVSJ7iizwIHOl0BhdqPH6AAJtuIU+2peM601ArIDGQSmXChf\nwzbsshUNZ5HEQyROwMEeAGcyDjEO4PS0gViTPQ7MXINxnNbGC1KwFoSRYY7Oz4FHHlrF/Se2cQiW\nx0GIY3UtKY4VAE5ubOFSdQu+PrjduHzGOKTMhkAXUiVDLlTHOHSr/d4UpkYDWSOyOginkc7AxiQp\nNUB1JTIOHlKlvC698CB7uWvlD4WyJ9zzoZ83TQdocru9carBeN8BlMHSF+49gceeVzN4kymaYkU3\nfFI9lemRxjJ/nW3GYQXT8x5+/rgcWL568C5cdvyzuGwA3KIfiX9Mnzlrous8DvsM4BAbx/6ilWGO\nHs6HcaAtSxjkjEO27qJHsvaMw4KVqhiEFiLF2a4eB4ZxqJUyWIwDnSERIcEBnMZP4T3ZBRsPFde1\ngYM1ORpw8VcQvbsVQ2tUtAp8688D3/kaYOVA+e/r581+zNmZWqmS1ggUjcgsDwQD4CVVajw9ersA\nDhtYwcDBHB2RIXGjThkHh1QlpJg0bOAMlieqS1XiJ0fbfgsXXbTWMJKNMq2/+eUSq6FbvreVbjR2\nSFUqxbGStZ8R3IR/Gb0c6g2PB45+BYgJEDQmRxfAYVXRCMea5DTD26HYVKW1YYQnn78yM0vqIMqk\nj8YAuNRpPVqxwDgAwMbmJh6pHipdfmLGODSblF1Xu+pxqFnblCv5gxY59cWfcXj02Q6x0BWVN9Wn\ntovX0JaQADDin5sDh/JzqGIcbLD0hXuFzbnadclsmMBkMuzXPN+RLnscis85n/c8v//nhp+dXfbD\n4ccBACemx22dx2E9oGsvNuNQKVUi5ug2HocS40A2miYLnqzUA4dFK9q8BwGCwPxiP1f0OFipSoY5\n2t/jwPkrfjD8Z/52JeBQgJjC4+DOdtgTs42SjMt57Tt39uPZ09eq1hxtNzQqRMxKlerM0eWIzDZS\nJVePw2C1YJdGui3j4DLHgaRtqaT55GjK8pCmOMzB82SzMPNaqUqzAXBW8pfL653YyUZWLCkApIEg\nlbOLempUvVRJa5TM0XTtXxv8ZQYEtk8Af/9K4MQ9xXUJKKbAYYUCh5pjPaRpUmHEyqRWBgF+9/sv\nKq63ejiTbFFztNIAtH8cq+KPlY3NTTxG3Vu6/GTOOHDxsw0by7yq5ijktVPAgQPobZOVmqQqSYyD\nr+6/ZFBOcuBQzTisUcahoVyG2+2tBEsdAQf6nAOljGGB9ushMQ65zwDwY7gSxsOR33d+3B6s8zgQ\n0LLo06NNqZJsjm4zm4UyTGGgEAX108IXpXrgsGilzQbaNiifo4QPNdvjwEyOrvc4WAPgrKbsP4b/\nxN+uBBxIU+NqZLT9FRJwsNeyizRX505fq9psfVs+YqXNzMpjjkOuw/bdNUsp46BHJf0tV9TjsKq3\n2o28T1ykSqYBvXEcK034IY1woGPgix8CXvfVwB//h4xtoJOjNWUczNQxF21uNr2Z+gzK73fsDBzM\nnfDxdFKrVCXGwYpjNerOqzLWIS9qzGYijwEX3b/JtOigzDiMohCP3Ucah9WzpjdQoOO1FbS3x4Ez\nRwPA5tYWLgnuKV1+Sq/h/IOrc2EcjDkKoTlHIa+5mqMrpEqA2VhTb4BrSQOqfFKVXG7jcv38fqti\nMgFz6nFXqUoAKj0iZeAgzEqqKdpIRkFgbPrYLEjO7NjAgaYqndicOMd6Jsz7k28oHJ8xDtVSpVW1\nnIyD7aWhHocjLaRKFBxEgUJEGIceOPS1s2WnKoXmjiBrjg6Hhu4cgDHHwT1Vydx1D0iDeJ46hscy\nX+zZA5MZh5nHoeaLx5whoYyG0FyrhnFYLxiHQqpUt7YZxwpVTtkB4CBVKk+O9mUcEgIcttSqk1xG\nEeCwprbaNVSEcZhoSapkRnh2kaoUkNkZARLg8h8GJhvAfTcA//IWi3EYFK8LZT8QOwE1bScbWQZl\nwBxIV1mWr0Xr6h1dzuNgsx2z2j5eSALDIbD/guJvRjype4SjPTEbnDE7CoDNo8WNVg8bjzevEGkn\nA+CADIBeosqfLyexikvOXTcA2sgxqa2uto0ZDvxXJZ3jcKJzxqFYnzdmtwMttHmhUqhKj4PwN9+U\nGPv6+Xt1cqt4HhxwWJmjx0Eq+/PrvhNbjbTvBuMQoBHjMEg3Z5IXmohUuzbz9PL7zhOj6jwOq8SY\nvTlZcMZhLEuVDq2ROQ4tBsBRoBgoZWzy9VKlvna2rJ136nGIEOMszhxtsw2AyTg4T2+mBmFlgJbK\nRAabBQgHM8YgUikixA7+ClMm1Zhx2Ec8DlOpUn2qUjkikwcOPnMcmk2OTreK3a4tJUywrnhca9hu\nHGMIoDwAbsh8hFiG5OZSJdLEkkY4tM3Rd19rMg6Iin3vwGx8XV7v0s53wACHuuNstmjZ11JlFk9L\nMqkg8xDU1aFHmWA6slk9t3hUW6JlG7OBaRO9eaS4ngAcAmgvA2ecpiLjMFQxLmGkSqewiq8+dx+b\nWNaWcdiskQoBO8c4cMPnDpIGp4mkwhhQFbWTKnkzDtb95PdLU2xoA5cXfR8aT4729Tgwz+2hBoPg\nDLOs1UiWPQ7Z77bHQU02Gh1zCfOc8w2F/Lnz3+GKXL+Que4E49BFKppUVcxWV9Pg6XsahVYca884\n9LWTpSypUkiahbPVCUSK+QDndsJH/nMcjHhMa2r1/qrdipVD5csi0yDtxXZYw+fM+/VlHOqbG07z\nzkqVhv6To313zfR28eG+HdasxzyuNbXdrqGicxwQ8XMcQnOXv7lUichmBhQ4WLtdG0cMxmGbDICj\nTFuk3DwOnFRJWwwXHUhXWZ7a+5TxONigha1D1vyIwNT9j2aeGvdzPLCHPM4Yh9BkHNbOIuua06N9\nIiNLoInUReqBmRnbuM3wQNZIWfMygPZzHLZqpELAfIHDdg1wOWwAB/+1aTNpMA6OcxzMCFdP4GCt\nkX8mUQB0eL0Mzmn8c+M4VqZp4zwA9mOj5SPBy8s2y1LGYTtO8befvxsf+ff7ABSvsx3Hislmo2nl\nHKNighLNz3Eg5/aIJPLN0+OQphov+pPP4NL/9hH87efvnssaFPisVaUqbYwbT3m2PS0mUOwZh752\nsixztCJN2rliohIDHAaFOXpdbUMhrfc4ULmQJVWq1Ec+86WVj2kFDs0sfd5owTiM9s92Y1fVGGvY\nrm0w0lTPmiYAme68CeMQMYyDr8eBxLFuqxppVF6EXVpDW6kSnRwteBws303jOFbKONhSJVqbR0uM\nw8yHFpg+HJfXuzSEjWne3YGDaY4GqhmHzIhvgxYH4HD44vJlBJyPHNPLVJXHYTodPmMcHKVKPoxD\nIkuVnqDuYC9f3z/dlGAZh3Y7llWTm/PaqQFw3Pp2g+Nb9DA041jddt+pxMN3B9W+fv7dQzXl9Pnl\ntdpBqtKEm+PgybI0WZsuEQRmI/nX196Fn7388/jxd12LK256QPQ4YHwaB1b85XGch4NKGNexNQ00\nsGr/I2Y/DvX27PNhnozDx774AD576xFsTVL87OWfn8saFPjYs4hWBsX32iTRnUxmjwJlelriFDjG\nf6YtQvXAYdHKkiqFVC4kpSJwwCEITPCALYdIVDIUS5mgZT+EmM9vejnw9S+ofEwrauwlk9KqwuNQ\nBxyUKkWyusSxmlKlSDBH+0yOnnocPHfNKOMwCR2BwxylSvZuDQBrcnQLo2NxFgAAIABJREFUxoG8\n5iEBDqXIzs2jpsdBU4+DOfnYRaqUlqRK5VQlLQ0gtIuRp1UB1Sz619HjQIsFDkT37zDbQFvHeRAE\n0IoCL4FxILNRzCFwqdeuvzQADgDrbwCA/YfOnj44OgCumzkOVQPY8to3imY7x5uTpH7zxaPqpFIU\ntLRlHFzZA/r8aMPle47bwGE7ThAn6UxvrxTYOQErHaQqcbIkn2nZQDPgkFqNZEjMsr/70WIa+sve\ncU0Rx1piHJpJlVjGYXrfB3AKfzv8Nf6G//sbSuoAYL6Mw73HW0aGOxRlq1aZZEAqV2pybgG2p8U0\nR6/edaUp91yw6gfALVpZUiU6OZqj8gHIDe1oHzDJGlGXnXdj7cA0Zu+njMPjvgd42n/KXKZP/H7h\nMVGD9Ng/VSksf6kAqJcqAVkk6/EM7Z+D47UNBitV4hgHzktCi5vm60t5E8ZhHLgCh1WkUAigsaIm\n2B632Bml5miHVKVIJY3mOJSa2GHxXCNOqhRbcazcY3E0R3NSpdSeGxI6+ksMuZCDxyEt+wxEkEyL\nAw4DK5JVV+vCUw1jAJsKIjZVaWUQACdcPA6e5mjrdR+rIYY6a1QOCR6qgwemoIV5ndsDB7LjL0wH\nU0rhwEo00+Yf35zg3P2ObFTt+tXApW1spMQ4uHoc6OAsXx+TfQ6M4xTHSBN8cHVgSHnyWusgVYkz\nf1d5HDhQ1GTzpSxd4fdt41RXSJVM4HBiqznjkCca/mD4SSPY5IvpRfjD+Hn44W9/Mr75Ud+YSV3j\nrJlfwzY2sILTc5wc7TLUtG3RY2eNObf2k6Ql19fYrhLjQN7v4ZEvNbrPvVI947BgpSoYB7GkXXgi\nYdmnNv3kQipAGFHGgQCH4VoGGL72edOIRqZIU7PqK1Wq8ji4mFaJz+FcdbzWmJ0ycxxKZtloBRC+\nCGZlybOABjtX4waMg1LYJkbqyWazOEEARhxrrCOsOJijfRNXgLLWPyTHS4DEaFARb2YzHaY1ph4H\nK47ViXGomaUAAMqVceDM0RXHehm0BNAuHgcaxTpbuxzJWnWecYlONmMDAAeTY8BNHyqut0o9DpY5\n2uP4jq3J0TFhOyQ29UA0/VKPyh4HXzbPri3DnCyf200aOZeiyTXcrigdVHV0pzwOpIleI/n3vppt\n+/rbcWr6GxiZEjC/ydFVHgfufG0CHOxcfwqM9luRoLNUJWVLlTZwYLW47nHH9517zsOp9PCr1NH/\nn703D7flqupFf7OqVrf7s0+f5uQkpCWtJDEhGAiNIIKIwqW5PETg4pVPQBQ7ru3nA9/zygOfilfQ\nJ6h4QYUPLyLoVQFDI9L3BNKdJCc5yWn2Obtda9Wqqvn+qG7MWXPOmtWs5Jy4x/ed7+y9dtWea9Wu\nZozxa4bw+h8FP4QPRU+Ac9H3xy8QZsKAxTqHrZr0nTpRt0A0BUUcVKg5/Xts1LA6BsRzypUQh3FH\nofs8g2K7cDjTQh4A51kkFVxzU++KVKXyOQ4mcTSBF8u4/tI2A+aXrs2FB41B42CDOEgC6VLEIYyk\nydFOQePAy9AGQCzUEoeKyg8/QsmJbLvegFA4BGPzoB9TcEJVCpmr7g5JgwHraBxk2grVOHQQFM/p\n1fuyL+MBcNmO2ev2Gofi3I6CzsCrjjhkol0DNa4wM8RW4yCLowHlvBRTwlOgCkloh4sIy1jDcz/3\nYmDzWL6dQeNQpesfhuL6AcuPnWqq7Yh3cMUTnpUsRu1Y4wf9+jhoRMsrG8CWxrR0DjRhUlOVGiIO\nBlclnSC0LcShSFWKhOJnh8JRCZiiHaulriONpoiDK2kc5ELp8Mm4EVdOVbJLatO/9YgXjytt+r13\nx3/F30bfh11zPVx9bpLcdkWqK1BfmG4TsqbgxGZ1B6uyEGiAiqK86XBFQPH3Js/KUJeTnSGxTVU6\nw4IiDkwSKGsj0pz4ZJZDjDiUCCcFqpILl6wtJNZWhQPthpZrHJhUMEHH+7YRrRJL1p1Ys0A78vcW\nsZgGUxjKZfOZqZNV0rmp/AAiiTu3tQRFQmuK4s5S2ABxiEI/K5lcr6OeI+GKXepJUAdxkAsHwmGH\n4nxeuSv7UkQcJI1DncJBYf/LbApUQBJHx2uX6Qxo193KjvXcG0SdQRqKWQ7mtUWqkgpx+Anv7zE7\nPiruaLJjrUxVyt/fxOkh1cHTxsTWda/Gbcd8zF96Cy7atS95c/m1MOvmax5dG+PATkv3MSlsNA4A\narnc2AQ9V2UBJyC5KtVYlyY2HdeBw2K0Lf0Z7ZCm0ZbGQd5+HESlwmh5zbpFoYqWZLRjVWkcaiTO\nBaoSOb6yZuBr98dGJ2WFgy3ClYq/x+gIAyGB+NmfxrMffxWC8HLceMHO/JzvPLKFw8qmj3N21LuG\ndWESRwPAHBGgt1I4MIYOpd5NRoo9zpzYLhzOtJASaNcGcYg0F7nktjOs0vWXxNHi77W4yCWf+bLk\nndPPwBzAcRDCKYopbSgksji65AHECT2HJ4lRrcJBQBzim3XlBxCxQ9Uef0X4TjuIQxRO8sKhoznW\nhTkO1bsrsjVnaaIe0snRXm4/LuktVi3nODAmJtAyNc3p2iIOCnG0kS5UdHQq1Tg88RfUr9NrjE0A\nbraCLaIdYuGwh53Ci9yPF3ekdqzkumg6OZpO56aIw8z+y/C4Z79M3Jkc51kv/4xHVocNCodyVyVA\noiq1WDjQ5KyvLByIq1LDgWSuA3iukyXJQcShknXoXJWqFw5FcTRFTZZ0VKWGdqycc404upod67CG\nHStFeDyHwSW0Pjk5/drhuHAoaBz8LcFVqao42pdSPhehUJTPLyzjpZceFHfuUqrSGODA5hTF0ZsF\nxKH+LAVd0HNnVkFVajqVHZCvL0coFNnEPGzvdI9tqtKZFnU0DvLArDSEWQ7lNp2OhDgw3dpVqUpV\nNQ5JchKpTl8bxGFmZ/blDraOUWnRUl44MJtiiU7rZvWoSowkyFZ6jiSoHiIi06crR5A/qBxdMi9R\nleoVDmIC7XoeAm53uxobEAebLmVhcrTjFsT4Tg2qUs9ivkBhejJzzRqH/dcAFz5V/TMJ1QNgPNcj\nzuEy8XNTcfTz3FuLBgznXA8snkvebwM7Vumzh2Q6tzDVVnV/oYiDk1+vD67V7+zZzHEApkdVouur\nBJwLg04mIVsbBcaJz6oIpMSGdkR116wOcahux1oUR6+QKb3Ls+qmyKChq5KuQIgMGodpiKNlqpJc\nYN9xNL5HFzQONV2V0s/nSY22WYwExAH9heLOKsRhinasG9LvXtlov3AYlrgqCRqHVqhKEKhKCM5s\nxKFx4cAY28kY+y+MsQ8yxu5gjA0ZY6uMsU8xxl7JGFOuwRi7iTH2EcbYSrLP1xhjr2dM7z3IGHsZ\nY+xzjLGNZI1PMMae3fQznEnBBEtU0VVJGzrhmiCOLrdj5XLyrqNQVKUqMd9icjRdO77hKp2NbLrw\nJMnvY1L+EKBWsMmacgeakSJMv25x6F7VB5BQONjSZQAEZFgcb6RxyNf3dOgOHXRX01VJ7j67rotA\n9fdWxAhdonFog6pUtGOlmgtjKMTRpuusMARNNyXd7QJXvRB44V/oDQgK06NLrGAlcTKYC0bWXqTO\naU9/E/DaLwE//vfi+qTQiF2V7P/2USQWLhFBHITBlqoinTQM+owiDg0KB3LemgqHedIBrtudVEWZ\nZaTrsFrd5zQiKbHpCJasGo1DW4hDJCMOkRXi0G84OVpHSTJqHNoSRydr78ZJLH36TXji+BOl+5RS\nlSoiDp40A2cWI7EoJ82tLLyiTmuaiIOKqtRm+EGUHw+HoesVU1RB49CKOFoszLcRB+A/AfhjADcA\n+HcAvwvgAwCuAPAnAP6aSURoxtgPA7gVwBMBfBDAHwDoAngbgPepFmGMvQXAuwHsT9Z7D4ArAfwd\nY+w1LXyOMyNoZ8RxBJ2Bfp9yqtIshpWmyjJdQgPYUZU61SZHF9YGCrMUuNvVJ1GatfvJXAPTdEge\nEo1DkhjJ3W9Gu6666ImFGlDdjpURO1RWAXEIiHib+/ULB+qq5HU16zMmIDJhWL0LKztZOa5nXTis\n8HkyOVoSR9tSleTkXUYcupaOVgoLXiNVSWnHqrjOnvzLwI++E1g6oF/bk+xYASMCoKZJaa7xxXOB\nnY8pFq/k+nNYVGkIm+yqFDqa80tlL01nVpAu7YNNCgfBjlX/qGyjO6mKMo0D0EznICMO1B40CCOs\nbk0KSVt74mgV4lBN41CHqqQrcIwah7YQh+QZ86bOuzD/xbfjp0/9Ni7RDDZMQ0lVqoM4JJ+vI+nD\nZtkwo80CAHoKxMEr6sumqXGYNlWpDG0AWhJHh2JhTqlpLGhf8P1wRhuFw3cBPAfAOZzzl3DO38g5\nfwWASwHcB+B5AH403ZgxtoA48Q8B3MI5fyXn/OcBXAPg3wA8nzH2IroAY+wmAG8AcCeAqzjnP8M5\n/ykA1wJYAfAWxtjBFj7LaR/C1FzHheNaJFNajUPOXbQZDCaLoyF726dRWRxdTlWiaEdKn5ARB2Y7\nzZcOtGE+Im5+8HFpYjYABPKls3hO+boKxKEJVcnaEhRASBAHNCkcSOHi6TQOgNChD4MahYNEm/Fc\nD6HF7SrkDKcwl+evtRAHBVVJ0hl4togDKRzSydEmdE0pzFahilSQrIsOFUfb0aSKn1tXOGjOd2EA\nHK9EJwmjSOiIRjq7YSVVidqxEqpSg8KBnisqjUEabXQnlesLk6PV6y81mOUg2IMyhg7hYH/zyBqu\n/61/xo2/9S/42uFT2etC4dDAjrUojg4FVyUrqlKLiENVO9Ymaz/D/UL22vPcTxr3KSAO4RiLvfw+\nWBVxkAuHeQzFGUyqwsEtFuV1XLxsQ76GVlp2VdqamIXRADDXb0HjICMOVOMQ/AdHHDjnH+Oc/x3n\nor8U5/xBAH+UfHsL+dHzAewG8D7O+RfI9iMAv5J8+2ppmZ9M/n8z5/wk2ecQgLcD6AF4ebNPcoZE\nZNn1p6GCHwGRRsGCSsk7Mw2mqqhx6DMffunUaimZQrHrbyWMBpTcb9ODgFFXqmTtCZc++5IN4kA0\nDqkda8XOjUMRhwqFAyfHO2xAVaKFQ0eHOECkctUpHGSHHcd1MbHwcljBPDicRpOj5eFzceddTGTc\nnqXg1i1SlUwoUyi7KukogTaFg1c8z00IQOzoJBkg6O4vC2erX5c0DlW6dWEkUilCncWx6v5CkhuP\n50nNkQYaB1pk6QbAAWLhIHdLm4SIOKj/DksEcTi5WRFxEDqiosvPa/7yS/CDCH4Y4XXv/XL2uqhx\nEBEHE2orh9yo8S2pSrRDXCd512kcwsqTo+vRL+XQTUpPo6BxAJldAmSTtssiSlBUl4nvYZaNJMRB\nRVUiNMCkkDm5NamEJlaJaVOVymY4AMC8gDg0HwDnSi5aTriNOJgiPeL0THhK8v8/KLa/FcAWgJsY\nY7SlZ9rno9I2j+pgJZ3QLB73Y/nXP/S76m1csUtXjjhY0hgIkqENiapUmSYFFKkrtoiDJMwGzF1g\nURztJWs3QxzmiDi6ygNXoCpV0Dh0B/kDYbRV346VFlFdQ9edWohGNahKskCZOXaIwwqPO2ZNNA6F\n5F3Refe61RGHnsUch2LRwuoXDp2iHWtZ0WKDdnDHEyyNhSCFAwPHxjiwPr/DKEKnbuFAimg3olSl\nYXFby7AVR1Ou/3qbhUPJHAdAclZqgjhIk23pZOBDJ/LuKKXt9DxHGGJWZdCjagDcCnn/y7PqwqHn\nORma6AeRkWKkCh1V6eGe45CGU1Y4yIgDgHnHz47BxthOFB9EXLi20tiNU5l+KHB66uYbec4s9/PP\ncGx9OslvcY7DFKlKmutqrmVxtOeKk8KdoP596XSIqdmxMsY8AGn2ShP+S5L/vyvvwzkPGGN3A7gc\nwAUAvs0YmwVwNoANzvkRxVLp7O6LLd/XFzU/utRm/0c6rHUG17wEuPrFcQdh35XqbaRuqInGEK8t\nUZV0+goVB1kOT6RRlAuUFYiDnEjWQRxYOeLAqThahzhU1DjMki7PaBJpuZbSG4Eb5TdRpwLi0Jtb\nzH/N1or1fnLQwsGEONDCgQf1rCI7kkBZKYaXIi8cFBoHZklViopcfy6d6x1bxEEhjjYm7xEKzkbK\nrr9qbkNh7aLGwSzMBly5YFJd4/P79Q0LR6QqcR53+GhyrYsg4iJVSVs4KF4nTQNGrpGj62NMwggd\n1aDCkrC1Y21jyqwcYcQFBFi3PkUcqoqjaaLvSYiDLmgS3fEceA7LEqQg5DDUV0KoEYf8/S9pBsAx\nxjDouFnXeDgJBcSnLHSIY9U5DvUmRxdfK0McChoHAE6whfmel6ENa6NAW2ilEUrXVhr7Wf4s8L05\ndUJICocdXY50iPvR9XHr8xUAhcahZVclEXGw0Di0YMfqSFTA0P8PTlUyxP+NWCD9Ec75P5LX0wxm\nVbNf+nr6ZKy6/aM7SJeIOa5+EFp3DjjvJn3RAIjuNwjgh+YODhM6wA1dlaiYEZPyhE4uWqCgKjVA\nHIyIh0BVSvQVXHrI2iAOHTKpm42zbpO1Q0UUZp3wgDvo2MzwSKK3+4Ls66XRYev95GACVckWcah+\n41VNbw4s+hwnoC8cOggwsqAqqVyVHClRNn12IYTiPB0AZ9Y4mIawZWFFVSpqHEzneaQQhauKFmY6\n1yWqEmDfsYuTG4Lu6e4jKvMFcj9hwRi7kkSK8/qdUdsBcG0IKYtr5+fIYzsPgn37Q8qhUUuD+oiD\nkNg4TFtc0YSHJtFd1xEmx08q6Bzkzv9InuMw0CfCTSxZdc8Zo8ZhiohDKVVJUThgMhScvGwSW/na\nSmMvKRwCT+MMSJ6rO3r5+z3agAZoChm1a5+qlP9+XcOONgPqooj0771/45u4cP3z2f11tNWALnwa\nxFQKB8bY6xCLmW8D8NJprFE3OOfXqv4hfq+nfQhdf9NwKBu6kOD4Uk6jgDQArtHaNKlhfjllR0FV\nKnT9ayAOAwuNAxWXp6Lfeb4mbmPzmR1HEkjHN15rT2wijJ7Aq9RFXTg7B9T2Tg5XokdlwTlcTqhK\npuSZaALqUJVUXf9Q7ewsxAkucXSl5NnWjlWmKsnN2F7Hsmij1xizS97lokU5L8WKqlRN46AqmJRo\nh07fkOyThlO1cAgjdImVKq9CVRKaKBznLObnX91ZDtSByoQ4UFpDWxqHtCu6E6v4oPuLwF//GPDx\nNxe2Wxzka68Nq60dFhAH9WekFAtaOPQ8cahVFdtlmRp0anOSdeQHHVdpkZlGE0tW3faVJ0e3Vjjo\nf4+LULQhzt7QVuViVUdVoohD2NVpIck8lU7+fh5aa5+q5AdR4XhvjMtp1FViaIU4VCvMVJEielex\nO/HcL7wUL7ztdfgh57MAADf8Dz7HQY7EGvX/BfAtAE/mnMu8iBQhWIQ60tdTK4eq2z/Kw9YS1WKu\nALkh5I4v+pu/Y4042FCVRMSBc3VnJwsudWGBIufd1p6UJJMD5oMhMt+YqKtSUiztimrSfRTOStaI\nAxFUTeAJvutlMbMvZ/KdhwextlVD8EWQl4A7GHT1VsDUVYnXdFVypOQ9qkNVks4zGxtBlS2p/AA3\ndaCFUIijTcl7qPjc8nUWOd3KqF4/awxUc1VSFi2LpsKBCADTwsHywRuS8yuCA9YpDtnjcPTXOfm8\nu8gtqK4rio2rESBNmW0ZcXiK++W86/yZ3xPvg4Bgzbk2qnadFagUjpqq5OkQB88Rio0qGgcZcaDH\njRZiqmhiyTr0q2scVLqIOsPnVKiGCXFQ0ZQAAMFIcLSyeX5EXE1V2kcKh0hbOOTX4YJHEIf19pNf\nXeH97SNrytdrrWEhjm5D45BqiK53vpO9dqPzTQCAt1045MEYez2A3wfwDcRFw4OKzdKjWNAkJLqI\n8xGLqe8CAM75JoD7AcwxxvYrft9Fyf8FzcSjMRzB2chriDiINA7A7PMuODoZ7VgrIg4pjUJzUwcg\nUZXSwqGmONpxqnWiBZQnKRxQs3CgOodEIL1lXTjkD5JxRcSBze7EKuIHwwwb49iRu633Va0fwDXr\nMtxmiENRqFuVqpS8ICBbE4yDSBh8pYqIc7FQYK7I/UfcbbUKQRxdrnGYhFFhAJycvIfdBbt5JV5R\ny2O2gkWBJqUsHBYMVCVJ4wBUePCSAjNiHhyFviLqzOg/OznW817+OYc1h1XZDoCbhh1rmhBPuHT8\nV+6K/58MgXv/XbDmrKpxKIg3NRoHep+hzZ2uJ1GVKgyBMxUZ8yWahSbOSlqqUmWNQzuuSo4KUUhC\nSVMCgGAsaIZsUK4g4ugwM+LALdwX58h1NQ3EQXev+MydJ1pbY2hBVZrpuNltZssPK4vwAbUF7iKL\nKUoe33ZVAgAwxn4R8QC3ryAuGo5qNv1Y8v8PKH72RAAzAD7DuXBkTfs8U9rmUR30wc4cjeMKmF3X\nXzGcynRDZKRjwZyGA+CkTjBQIlAmRYvjaJyNKoiFZRqHMaGiHP0kMdqduwJXC+qslCIONalKXQsx\nI42jnbxTvPHAdwxbaoLoGybwzF13UlTyFgbAwXGFoXK6OJEiDmnl4CmchUpsBKNQ+rlTRBx6BntO\nISiql11j+vUngTw5uog4hH1LOZdqjoNp+JzCVUlJVTIiDkWNg23Hn54nEfPUrmGm+xrZfpFQKqyv\nLynoPcFkxzrTzZOM4SS0crkpi/ReOMOkBOPQJ2PU4U9/APjTp+PqL/237Ee2nv5p0A54LN7UUZXU\niEPHlahKFYbAmWhNZYhDv4HGQUtVqjjHoSp1RtesMCEO+sJhJAzfszm/4xkpxetwN8ulo6yvmOEA\nCPfQOTf/HQ9NQeOgKxz+rcXCQRBHa55hjsMw122GOqiG7i0myvI+tgsHMMZ+FbEY+osAnso5P27Y\n/P0AjgN4EWPsOvI7+gDelHz7P6R90nkQv8wY20H2OQjgpwCMAbyrwUc4Y0J0NvLU4ujunF1HUrJj\nBcw3RNGOVWcFy4QbjTakTjBQpXBoiDgAkkDaN64dBPmFz5LP/Bv8ldlrvzZ5mf26dJZDgjhY86LJ\ntMkJr4Y4AMCpmQP5/kdvN2ypCVJABXC1/FAAoo1nLXE0Cgm0iqr0EBeT6JSqlJ39qgK1JNmIyLkW\nJbdIVy4cDJx3IRSonil598OoYMfqSF1/3rfQNwDKOQ6m61seuhcPmFQkcfMq4Dd9v6IdK2D/0BUK\nB8dTuoYxU1OCXP+0M2qN6Elh66rEmJhkbLYwVTd9z/OQ3FcOfQo4cSdw5CsAgF13fjD7ka2nfxoF\njYOGqtTRoApd1xH2qVIwTUyIQwWqUtUEXnefNyEgbYijdYWJsXBQzHAAkFCVqiEOOlclGp2Bhg1O\nrquBm/+Ooy0jDvetbOGXPvC17Pv9i3mO8PlDK63NjbBxVQKa05UyxIHl+y4kw/bS+/GZGo3tWBlj\nLwPwm4gnQX8SwOtYMWk9xDl/NwBwztcYY69CXEB8gjH2PsTTn5+D2Kr1/QD+iu7MOf8MY+ytAH4W\nwNcYY+8H0AXwQgDLAF6bDIN71AejrkqMAY6DiDM4dLCLDU0JUFOVjJSd/Cbn6qwaTVQCGkJClwiU\nDQ9cTgTKaeFQmOMwt7t83ex90unRY7NFJuXoJx3YDwQ3Y4GtIoSLvwyfht+0XVeFONgmGiSx8tGp\nXDiM5s/PFEPOyTsr7QtAQBwCuFoP7HiB/NZy3/FV/PXn78MLrrewrE1CxbcPmQuJMYTDfDf2slze\ndCKhY2X3IAUlrozeQBGmCA4cAJ60cB2NQy6ONiAOYVQQZjPpOuM2wmhA1DhkdqxmjYMVVUk3wwFQ\ni6NtufdU48A8OF7x/uKY7m3k885TxKFmIm/rqgTESUbK098YB1gc6PU/dmvH71mY6gvEhcNNaivH\nJlQl1+CqZNI4iEVFhTkOhiKjzF6V3neqahy0iIPh/aiumdTMQ5HrqH+/pjAxzXHQaxzGmCMaB5uk\nNtSIo4X15jRIJrmuBqSYaVvj8Jsf/ha+ejhHQC7cM4d+x8XdxzcxDiJ8+d5TuPGCnY3Xoff/gUbj\nADSnIOaIQ75eijgM/qMXDog1CQDgAni9Zpt/BfDu9BvO+d8yxp4E4JcBPA9AH8AdiAuD3+MKyxfO\n+RsYY19HjDD8BGKV8JcA/A7n/MMtfI4zJIjOIHmoh3Dg0JuCdeFAk5rybih1F3I8T01VsqEpAZUT\nOjpLIbXG3L80B1DNlIl7XVhfdFYyFS0hoa6kiMNm1MEf4Efs10ujp3BVstY4SK5KFcTRAMCXLwAS\nJ9bB2qFK+8brU6qSa0ymaLLrIsQvfOBruPrcJVyyT8OjlSKKIrEYZk5cOEhxmO/GtcjRkxPyADha\noFok7vHiZJaADnGw1jgUqUqma2xSQBycQuHAbGY4ABpXJf3a8tC92FVJkQDP7NKvKVCV4r+fbeIu\nIw6uasCgJVVpzg2RAupbNcWNoqtSSeHQss4hFfEKU30BYP1IrnPIggNg1alKtoUDQRXGkquSDo0o\nC1OHnzraqILy0q2NJZLQznEw1DyqzxUlZh62lEVd4SBrp2hoqUphdY2DDeLQmy0vHLoI4CazO9Lp\n0da0zZL4p289JHw/1/PwuAM7cPfxONm+69hmK4UDfd7aIw7V6bZ6jQPHgJ3ZhUNjqhLn/Dc456zk\n3y2K/T7NOf9BzvkOzvmAc34l5/xtnGaIxX3ezTm/nnM+yzmf55w/6T9W0QA45PAwnbuQbeGgHE5l\n1/V3XY2rko22AlBSSMzTmwlVyY0v9n3LknOUiXsth5BUjY2895AkNOnaNFwNxK+MblEcXUfj4MOt\nrHHo7rko+3p+9EClfQGIiAMvEUeThDMV5X3sNp3sSbEU6T6HcADGlFSlI3xZ+P4U4uPrmBAHkwgf\nElUp+T3Hd9+QvXaY76pQOOTHoWejcQglVyWFJapjjTioJkcbzvOo6OgkU5XG7qygnSgEFUez+hoH\nzjy4Ks2SyU2KNEJmCKWialdatZ+RlofmSUZx7YSqJCMOAHDXx4WOq3MyAAAgAElEQVRvZ910PkiJ\nO5wUcuGgE0fT7WRxNN1HngZtCpVuII0yqtJ8xaSZhl4cbaAPkvcq0qTsP29OVRILBZMdq604esOi\neIoRB/N2eo0DnZEywu65/Pu26Uo05noelmfz++d6RdcwXdDr2vQME9zSajQD0r85Pe4L2MpmR53J\nMc0BcNsxlZCsGqEqHCysWIHK4miBqqQtHGyLliYaBzd5D1JnyuQvL4dAVfKNg8FCQeNQ/MyVCode\nkapUB3GoQ1Va2J0fn7mghribUHgm8IxUJUpxSTtdOg61KrhEFwKAkBWP/Yo0t4En2zIV4pA8iMuO\nd0TndiTFyuaOy/Drk5fhw+ENeLn/C/ZUJYU42pQ0+YHCVUka9OfMLsMqyDluK46W15Z1BqNuydoq\nO1bLRJoOF4wRB1XhYIc4zJLCoQ5VKYx49ndirBxhEn31m3OxM6qSjDgAwJ2iD8iuXv5MqGLJWigc\nHPVnpOeMPACu49SkKhkS9TKq0lyDSd1VNQ5BGGXzJRwGIWGvUqSpaCuAoTiAWeNAj5HNHKBQY8cq\nLqhBg6l2MPSxa77+0EFTyLM7Nv0AC2TQXVUqni5s5jgA0kT4GqhlGBYLB4dx7GK6WcZnTmwXDmdY\nOIpBaAWRcB2qktUAOIo4PIJUpbTrL69vM705DdK57MMXrBflCEkSmybEr7r5/Oy1Vz/pMfbrdlXi\n6BquSjXE0XM79mVfL/A14e9pFRU0DjJVCQCOb9h3WqizUVo4cAVV6d+jy3BnFIt1/yq4JV8//ULR\ndS/TOPCoWLQwMPxZ+Ay8ZvLTuJ2fY18EVdQRFahKThFxcG0LB4GbHH/2MOJaSolqABwkhM3vG2hK\n6T5JZHastREHFVWpDuJQ/aEv8KA7bimXvW2qUtoVVRYOp+4Vvt3VJ4VDheSKCnY9hwkTomn4msKh\n4znoeNRVqf4AOBpliAOlMlWdm0EbRLMkaQw170dGWAY1h8+lRZrsbCQXB/S20qoda8jhKexYxTej\nEUfT6zAYCcl81aGDppCfJ34QNZpTogtrcXTDazpHHMTjvhc1HRlPo2hD47AdD2Mw8mBPE+jaVCXF\ncCrdzZBzDi4gDprCwWYwFVDd7SaKsjLXTbtc8iThuogDxma3mTDIMtEU7XjtUy/CcBLCcxy8+pYK\nhQPp6lRGHIL6k6MBYHFuFqf4LJbYZpwgbq1UE5STwiWe42BwmhES5vjYHlu3LxyE5J3pEYcNDPAc\n/024nB3CF3k+GibrMgs6nhBO2bA/QJjbka0tdSRtRZEqHZGpSI3nOEjaDqkxYE9VonQ8MgMkiJTn\nTsHJynEKqF44KCscinas1t06AXHoaBAHw/2F3FNmHII41EAA6L3IaAKQhIg4NE9w0sJlTkVVkmKZ\nFA6rFRI5mrw7TE9VoohD0VWJDICrYsdq2LbMjpUWFlUpJLQgnO93svuEzuVJtp+l7lpVZkioaCtA\n0V1n93wvm49gsmOdhjgafZvCwcfCQvvJPFBsWv7nGw4IhUlbRYp4bevPtXlSIJ2qgXaEClclAIKZ\nx5ka24jDGRYUcUgHofV70gO2BlUp5aHrqAyTUOxGOro5DraFgyu7KnFrfUXGux5JF2DP8nMDIuLA\nzOJo6rKTFmsL/Q7e9Nwr8RvPudyetiK9x1Qcbe+qRKlKHrpeNY3DfN/DcZ4/HII11XxG0/oiVcn0\nuR0FVelYTcSBZ4hD8Xwb8h42McDn+GVCgp2hG0y0B+6WDfsDEJHkKF27zgAgAGpxtHGOQ1EUHnDp\nONsWDkSL0CdiPN36BY0Dc4pD2GbtC4eqdqyMzvtwPHhVxdHkfjYghUMtxMGSB53GXINk1rS+gDho\nbK6Xu/l7rZLIRRRxcPXi6DR5jiIuUHo6rohSVEEcTNuWUZWaUEiGhIpLna909EGKOPQkxKHKDInQ\nkqq0a67YUCtEMBYmHtsIxGOqUsl2OzUNMHreBSMsDPK1qwrydcE5F2jS73r59XjyJXuEtdqiKm1N\n7MTRexfyv8WDq9UdpEKFOBoQp3WfqbFdOJxhQREHltACBj3pAduAqqRL3gsTbZ2G4miCWLgs5l/q\nErqYH0q6Y2mXa71i4iu8z/xmOMBY2wUOIy4ULUp7yiohiKMTxMH24ScVDlURB8dhOOnkzhlbJyse\nP2EAnJmq5AjC++qIQ6SgC6kGwA2hHvonrCWhW2XiaBXaYXKBMQZJvDMdkRFxyNfmYABjRdvhGohD\nfH1z4/oqqpIj6SuYyYoVkCZHV0MchOPuePC6isLBRIUUHLTy31VHHC1TlcpiTqCONNc45IgDKRzO\n/V7ltju6+d+sSiJHz2nXMADOD+Np6zJth0n7tKVxKKcq0SKtWjJJk32rwkHSdPTIufCez96Lp731\nX/Hn/3aodF1dEikjDjtp4aBz3pHmONhoaspclba8JWBGQ4EkeQJCX+jEt4U4+BKS9eRL9oAxJtKi\nHmaq0llL+f3zgVMKymBJpH/zrnTc97Azn6q0XTicYUGHsKW0mcIQuBlLyzKlq5L+Biq7vcBxsqQu\nC9uiBSjoHHRrTyKxaEktUbHxkHJ7q+jY2bH6gcg5Z4qud6UgVKW0m2ibWN39UH7DmcDDuTss0R0S\n626edI5OHqm2M+kIB9wTul5yqDQORysUDhCcjdzk/+J6W1B3YcXCQTzPyjUOKsSh5jRgRXEeRlzr\nYx+Sbliq6ShQEW3tWN18QKSLKCvgdIhD7OgkD4ATEQdvvoTapqIqWXbgRcShA68qVYkOqnLy39VU\n41DmqARMgaqUIQ6EqrR0QLntEkUcKhQOUUEcrUcw/TASrViTgsEjhUMVVyUjVanEjrUJ95w2xih/\n3penxaevS3MraBH5gS8dxh1HN/Br/+ubRsMDIL+dyToDWeNAHYsE9x3SBIjnOFS3YzVRldZmz9f+\nrIA49KnTUTv0IdnmNw1a3LWFbhTQxM3jwNffD2yK06n3L+bH/EgDxEFGevZtFw7b8XCHgDiknXd5\ngvOOg3a/TOmqpEccBC/7JEEodOD7lkkNUOwE6xwv5IQm5ZdPyEO1ytRoQJwczcZaUfg4CCWxasNL\nhnSLY09n+47oP3w1F0Xu3bGAg7sqFGlJbHbyrpK/Vq3wigIRcTA6zRA0KqXBrWz61nQGcQhbfH5z\n6TyPOMMY6iTjMbsJbc0Tu3hlGgeKMHGNxsE6FK5KgL7rH1DEIVn7gr3SNWWLOADKWQ7m5oDkqiRd\n392Fveb1mlCVCOLAHQ8dFeJg6arUI8e6DgJAiw0bKmLTKbOF9SchupjkyInjaSd2L5Jhd1XoHIFc\nOBgQzHEQFYTRANBxKFXp4RkARzve1alKFREHgzhat50qdEJZGXGgjkWztHCgzcBghFmicWhjjsPW\n4gX6namzWuBPhao0JvekHtGR0OKuin7HFALi0HGBP3sO8IFXAu99UTzMJomzlvKC6chqfcRBRpn2\nbhcO2/GwB0Uc0od6oXA4z+53FQoHrk0qxnJSkaxZGBBVJamRO8GaBLooGFXcvOdLEho5OqJwVLf2\nOJAHcjUcdkOOzw6sA7DjqA79EPefyG3crjqvgqiZxJjYaUbr9nMVAGAyyR9ykePBMTkLkcJhieR/\nJzbs7Pto8p7ShWRXpTXM4MI9RQvB5dku3viDl+YvSOdZWQeaKwbA1aYqOW7e9Wc5z1irM6Cdz+Rz\n75iTkmWdiFEV5LP3k0REVyQXh8+5cKXpzf2lfTCGAnGw7koSKhx3OhpXJbvJ0bSTWwdxoMWllcah\noed7YX0/FNGG3ry2KbPgUY2D/dqFAXCG63kchAU6CYDaA+BMRUbpHIcm4mifIg7577GhKsniaBqT\nEsTBlqq0a5YI/BnpclMaUV3EgdD3IkdE88LlC/U7GxCHKuebKej1RgfKTYOqJNixBqeAo9+Mvzn8\nOWG44p75fma1fnyjvOEkR1YsSijTnkeBq9J24XCGBRVHu2nBIGsNbBEHktQ4LOY36wahaZN3eW1b\nGgVg3Qn2wyjrXsZrJ6ftM34rf+1Zb7VfF5DsWPUD4MYTuWBqSFUiD4AltgHAzod7OAkzVAgA5maq\n05QAYEztNDerFQ6+nz/IVEJlIQjFZbGX32ZsdQ4qS9RIOvan+BwukFCXr//G0/GZX3oKzttJXm+g\ncUi7/s+8Mu/2PvHiikUbGay0kCSDOsTBD4poh3DO9ReLjQLLtecTvrzOAMEPIrhUmO248CMxmews\nlGgcSOGQAlLjILJLKslxh+MJ51D+BuzE0fRaqaNxqDL8DZCpSu3YsQr6ht6C9t664FHnmQZzHEyI\nwyQq0HYAiAPgWhJHT1PjQO/zNCnVIQYTGXHQnAtl53ekQRx6RHsEiHMiUvMMAFLhIGocNv0QnJsb\nGzLiEM6JDQC262J5lzyoxiGaYKGfH4PWEIdAjTj0O05WpPoVBxyqIgij7G/NGNAbSqj7Hf+Sfek6\nDHvn6wukg0zjsI04bMcjHuRmn3qsjzfETeYqdN8lS9axjsZQ6Eam/qQ1hZuANfdcdnTKkpNrXw48\n/c3Ac/8IuPBp9utKa5s0DuMgFCHeKkmbKnqL2ftfYEN4CKwQBz+IxG6VqxYFl0U4kye9ztbxSvv6\nft4d42UFFEGilnp5cnF03e7mK+gMMsRBKhwwh93zYld6vt8pUkta0DicvTTA//ey6/BTT34Mfuf5\nV1l9hizINZEWi7oHIKVoZec5TZZt9UtpEHQiK1p0BfpEOg8Zw+5FqcM/W1I0ketjhvy5bLqijlA4\ndKoXDqRA9Lif3aLGQVQpqQXErmThfDp6GzAUHd1mKw7kKl1/IiMOC1rEYc7Nj1sVqlIBcTBMovfD\nqJBEAzLiUEUcrd92tsoAuHFQmjTTGFUUR48lcbSOtlZGVQoUw8CAuGFHE8ue5+CdL70WN16wjKdc\nQK49gao0Rsd1sr9BGHHjYEcgPt70ORZJhUN/36XyLnkwJlCBKTWuLRSAoqAUcWCMtUqN2qLapY4L\nJusk7/wX4dv9VCBdka4UaTQOs2x7cvR2tBSxHVlohNU553BUg9DWHxA3tPWYBwriTV3nvcB/1qEd\ndREHA10okNGOdO3uDHDTa4BrXlztMwMFjYOJoiVqOxoWDo4jJABL2MSWRcdoHIStFA6MJH7dUbXC\nYUIRB5miJgdJIBfIW7VFHERXpdQEoIg43HBB/kA9e0mTVEpTyutoHADgqZftxc8/41LsXVALsrVB\nCwfEhYOuQBc1Dsnn3nEQOP9JcSFx3SuqrU0Lh0RTo0cc8odyam1bYGaUNQbI8Rp08mvShlJCJ0dz\nt6M+x03mCyS5YaGPWSLe36rYqaTF5T6cAMbrMf/5I78A/OENwB8+Pp6DksRsRXvM0vX9MEOIAMTI\nkebeOkuE4JUmR5N7jslVCVAgDhlVqe4AOPW2/Y5T6hZHKUMRrzZPQadxMKFwaXQ9R/g70ygrmlLE\nQWWJSm1X+x0XT798H973E4/HwXnyOyWNA1AN5YokcTSX9DLz+0tmEZFn9aLg4jVdcTTQLl1JFEZ7\nRWfGuz8pzEuizkpHTtVDHErnZ5yBsV04nAbxj998EOe/8SO49Ff/Aa9/31e02wWSzzpTJbGzJVQC\nOQS7yNDguKKhKsldwQaIg46+UbCClQe/1QnJVUmXTI6DUCpaWlibwM6LbMOqYzQOInSpA0fNwsEh\nWpD++IRhy2IEE8pBt6cqzdcoHKBI3gtUJczi3B0D/N6LvwfPumo/3v6Sx6l/l2WBmq0n6CsaFoqA\ncE2kybuuQFciDowBP/a/gF+4C7jptdXWroA4BCqa1AyhtiVOasYgx2tA5ozY0HeYTFVSFad7L9f/\nAkHEORYoRlVRgPQc+X7nC/i5bz0feOtjgX/+DeBz74g3WH8A+MYHsu0phaWKv78utiaBQuOg1rbM\nuJSqZGl9y7mCqmTWOIyVVCXqqmTf+fc1iXaZo5Jquyo6h8riaKlY0qEhZa5K+TCw4rlBdQ6ChoKa\nfygKhyoC6RhxyLcJ587CP4Xx/fJdwTOwMFPSDCH30HlBU9MO4iBqHKTCQRBIN0QcZAqiXDhMNoEH\nvpR9e9ZiflyqWrJGGl3LoyG2J0efBtElN19TAllwF1I9xJfOrba45Ppi7LyrkvdGVCVRzDjSWqJy\ntcahSQjiaP3k6PFE+txNNQ4AMMgLh1QgveWHRvcWP4hEP2gVjcMiuot5YTkTnIy7qJZozWRCkv5S\nxEFdOBy3HAJHB8Bl4mjpXDvF5/DYnofnXH0WnnP1WfpfVpGqRA0IspHhTUJCmAA94iB0bWnRwli1\naytbmyIOcSKiu8cEBHFIKVro9IGXfAD45geB6y3QDnJtUqq6DTdZKBxcBVVp31VaS9J4H0JbC/04\nyUsK1aooQJpg/FbnT+J77ngN+PTviht95yPA974KgJTEtYA4bI5DcfibgapEO9a23Xea4zsspoR0\nDEWhH0TCPhni4LSLONjaHs/3vexesj4KsHehZAfEiRx9vtGEVPfeKZLQ9RxhYrO4na2rkgJxYJOM\ngSw8A/zN/GuJqgSIKFdZYR5yEXHo93p4LX4ei6MTiOb34+VlzwBybc0TqtJU7Fil5+CCYMnabD3K\n6JjpusCGYpbRyXuAAzcCAPbTwqGyxiH+TLLG4dEQ24XDaRBUDGR6wMbzDEoS6IWzqy1OEQcDjUPl\n8R7/38SOlSZ0PlZNVrCtIw6UquRjOInpQky6gY6CMJtDEK/dQgeaIA472AbA447R8qweRShQlVSO\nMxYxP7+ADd7HHBvB4wEwPKkf/CNFQFyVSgsXcl4MnPxvZzslmxNKHtdRlTBnJVyVEYeTlahK7SIO\nmcZBhzgEFHFoo2ihiENatGiuM2GGBLnGLnpa/M8mSPJJNPFWCa3DSzQOlz7b/AskxKHulF8gvw/v\nZmv6je6+NdY6DJaEmSZtaBw2x4Ekjp7XUpW6PC/GbT8nnbngOUWhsxzjIKJPHiXiUKlw0KATJ7fs\nOsp1pkfLdBja3ddpFOh8h65nQBzKxNEZ37349+npEAefaBclcTQg6WpK/u5hxNFx8uPkel2855U3\n4qPfeBAvuM6i2UjuobMsAGNxz2ljHCAII6Ow3iZoI6VfoCoRjUOrVCUF4gAAa4ezL6nGoaola3qK\ny7M7Hg2xTVU6DYKKgUxTZcOQl9uS7r6k2uKyONoA2arpQtLDppJVpOUcB5uCqWp0qE2lj4ireapF\nxGE6iWRZl3I8ibIBYgBqU5UWBh2c5MTCdGjv8EALB1ZWOBD//y4tHGwdZ1R2rApXJR3nWAhB46AX\nwqehsoJtFIq/tw5xEKhKbZxrVRCHkBZMNT832Y82Z+0Qh/z8Zq5XPMcvfZb5FxQQh2pe9zSsnJii\nALj9nwCIzkubfjXBrhxBMmxNoCr1F+Kp84p7vsfz42brOkMb+2mtZ0r+/EDtqkT1CEEFcTQtMg7u\nrO4QV2cInDANvOtmnwFoTlUqs2MNDLSVPrmv01xAQBwGoh0rAGl6tFkbWZjj4HZw3cFl/OqzH4tL\n9hUtrQtB7qFO5GO+ZRcxQRwtIQ6LDydVCQBW78++pM28quhKWpw/GqlK24XDaRC0y6DrBgIJ4qAY\nwoYn/0r8/2AH8PjXVFucPGy7CMwD4FTzDOjNDaiW7FjOcYipSlNEHBJ/e1XhUqBoTYmqVDakahzK\nrkr1qEqLgw5WQQSmo1P6jaUIaeHg2VOVunUQB8UgNFXhMKOhDghhWaCmEQizFNotFBfTrr8GcaBz\nHFgb5zlBADPEwYKqVPsa0xQOZRa4AMC4dH47LnDw5vj7AzeZ9Q2A0AzAZEtEASoiDuk5cpjvKv6Q\n6si+8pfx0sTlJuJmymlZpPeCAuLAmLIx04kI4mBZOKgQB/Mch0hMvJPkThRH1ysc3vTcK7Pf8+pb\nSkS6SdSxZJXfP6UIW81x8BztcLpScbRBKEsRB8HuVUtVSsXRdoVx1vkW3AErPsckNK9N+hAgDYAz\naBwauypRxKGjEEcDwFpeOAgNgYoFUnqJbVOVtmMqQXmN1TQOyX5P/Dng/CcCyxdUczUCJHF0gHVr\nV6Xk4vY3lNtbhaxxsBZmt6xxYPGNezwJgYGYEI8LVKU2xNFkCFw6y8ECcaADfCpPyk5icdDBPZwU\nDkP7woEmlqWIA3kwdQlUu2V58+VEZ5BSlZhUlK6xeeHhrw3LAjUNpUC5SZBrMp0WrrdjbdH6F1Ai\nDtrrTKAq1Vyb7Ecbh7WoSgDwkvcD930WOOtx5dStHkmqx+uNdAfp+xWaBmk887fjSbM8Au76OHD8\nDmDXhZjpulmiuTkOrCZOqyJ9r4KPfzfpCisoip5UOKgol3IIiEOyqdFVKQgFRCFNcL3aGof8d126\nfx7v+4kbcefRTTznGoNWiQS1ZF23vKcINJWOHeIg27FqEQdLjYOSqpRoHBwmOcMJVCWzxsGU1Oad\n7wYaOToELhwnTkdxYduGQFq0Y03+Lon+rs2Bc/Q5O9thgGzHCgiIA20+VHHvAvRD/x4NsY04nAZB\nK2wT1BxGmnkGjAEHbgDmakwTlqhKOnG0r0veGxUOkqvSI6RxmClFHBRWsE2CIA6LiT1nKeIQhOi1\ngDgszXRxiiIOFahKEbGpc8qoUoJ2Jv9strC2GnEQP3PYXypNkAAUEIcyOkdIi+c2XLRUdqy6rn9E\ni9SWCwc8zFSlqhoHIo7OCtNOH7jgFmGQnTboNqPVRrqDNMlMURohLvlB4OIfyL///J8AEBO5OkPn\n0kiTwBnq995N7lcKUwIn9LMCmluiHQLi4JZrHHwd4kCeXYGlsBkQE+2O4+Da85bxguvPtS62aDJp\nS1Wi131fLhw0iT89lv2Oi1mNpqrsmOeuSiqqUnxfPW/nrCSOpq5KksaBc8yTY2Ci0aR/FsEKtszc\nQg56vw9GgsakjSFwwnH2HOBvfhx4y8XAdz4qUpUsNTC6oNflLndDmFafBdE4zAqIQ7VrOjDoWs70\n2C4cToOgNwujOHoanXcCQXaYnqrk28wz6M5VXJsmdLlAWY5JyOGwlpP3Hpmoiy0wRMqiqTA5ehri\naJQjDm0NgJvtulhH/neabFUpHPKbrFNKVcofLB1y/KwTKiqOzqx/ZSG+pcuQNMehnKpEEYcpiaMt\nBsCxNooWxRwH3dph0ALSQq5NmmPpHNOEXbmicKgSPVo4rIl2rFURBz+Eh0A9rKnTB657Zf59MjRK\nXK9+spAW1ymFMl4zLRwU52MwlOiu5Qk8neHgJMW3GXGIlNO0qRNT3QFwpoJFFyJVqZ7GoeeWI/0j\nqViqizjoJkcDuSvWhXvI8zPw86TW8eJ7GL3nh771YDQ14lCVqkQQh8AX6UOtIA758XvsxmdiF7fN\no8B7XyR+zhbF0buxmv9g18V5MTU8mRVtM4IAvSJViccUa5UF75ke24XDaRDWVKWItz/PQBoAp1t/\nEkaiJarqAVa5cBARh4irOz+FtdsqmJKkymMRdmBDgziEU9A4FKlKZdz/sVw41HRVYoxh5OViuPG6\n/SyHKMgTGccrKVzIcaKuErY3X9UQNs7EZJLN2BYORY2DSbwaChqHdhGHxRI7VkpVkqlZtUKFOFgM\nn6tdMAkah/wYV6Uq1SocKP9/vCYgDrbamjS2JiHmoHBRueWN8f/7r85f2zwGQEwymliypgn6jKpw\nUB2XYCw8Q2yONZ3hkNKNPKPGIRR+b7qeV3MAHN22VuEguCpZahyEaeCOFVVJdOGpT1XSTY4GcsTh\nIlo4UCS/O1uY3hx3/e0oPDniQJHUqlQlEXFokz4EiNrOcze/KfyszQFwtPjdzckA1Pn9wAKhySU6\nB+rMtuWHmVbFJoIwelQOfwO2C4fTImSqki6pCQquSm0XDkHs1624OHzdHAcavQaIQzLcbKQQUU6F\nqgQIIsddbFXJE40nR7fMOydUpZS6Usb9Hweyq1I9qhIA+J28MzvZqIc4uGWIA3l/9GFpTVUitIcU\ncZDzms6spZ5Hsv0to3NQS9R2kncyx6HMjjVs+TxXuipp3MvaQBzIfp2qVCVaOJSdX6qQqEqzDYSN\nIz/MjlcWP/yHwBNeH39NZ2oMTwFRiBmaZDSwZM0QB0uqEiZDcQBdxcLBTQuHElelISmG0vXquCpx\nzgV0wjQ/Qhd17FhlqhUVdvthpHzuyvvoqEq2iINycnTy7LtoLy0cCEUubcjRZlEwtrYpTREHwRa0\nkcbBHu2wDeomOeDiddeqqxLRcZ2/8eX8BzsfAyyek3+/GtOVXIcJaJ7uvq2KiJv1DbyNGUGPUGwX\nDqdBdFwnu3nrbEEBhS1pG0mNoHGILwpVUqV1VaLRs7B1oyEhDoD6oTeVydEAMCcWDip3juLgu7ap\nSomrUklH1A8kyLMmVQkAJt08mQw3V6z34yEtHOwRBxdhpmsdTSIhadEG4buniEOfix3ghZkBrEJC\nHACz330oTDBuo1Ak4mhsAuDKrn9smzhNxCGlKmmGb7UhCif7dWnhYNHxd1UahyrRmcmvz2CEhW7+\nOataRhYmN++7Eviel+TOTa5HCkIODE+1NgQuReVEqtJsvq4cNWZWqAqHTskcB3pvzqhKNRAHeW3H\ngHTognbbT27aJZOyxsFznawZwbl6toSMsniuOP8hDd0k7DTSZ6qqA50jDuT5SQuHFG0S6EIS4mBw\nNlIOn6v6/JDQDprMn2qoOwBExGEuEJtZi17++5s6OOXXBseFJz6e/+DiZ4ozsFapzoGK0O0LhyCK\njPqG6AxOv8/cd/4oCzr0RNcRLAxhmwJVCVBzoP1Apiop1m5IVQJ0hcMUJkcDwGxutbgLa8puxngi\nU5XaoK6IiEMPvgXiEIq2bjVdlQAg6uWJLK8gjgYtHDol65PEj0WBePO1SKpUQ9h6kdiJWpqxTC4l\njQNg7spGbVuier0s8fNYhDkMlZ2rQDI/YG0UqZ2ZrIjrswl68A2IQwvImqZwsJkv4DalKkl2pTvd\n3JWoaqdy6Eci4qAabEkFq1snRAeWRhoHBVXJhDgEo0ZUpVYUOXsAACAASURBVLxwEM/1XXP5NS5r\nHDKqkqBxsCscAgVNqmpQ96F7V7YMW+Yh6xUAlNKVhMFkyT4qS1Yd1Un+PSaq0mN26xCHpGiUEYcB\n1Xnoz+9QJdKtbMcqrr1jJs8bTm75ih2qBW1WLgwPCz9bDPNnVFtUpQvZ/VgY3he/2J2LXSkXSeFw\n6p7sS2r5XUXnEEVmK9aojfv7IxTbhcNpEj1BIK3pCBY0Di0jDonjgyqp8UONoxONyohDsROsSjCm\nIgoHClQlVTdjKnMcujMxpxJAjwV4tfchK41DW1SliCRXbLRq2FJ6D36eyHS69ogDwkAUjtp0bRQa\nh34kOtzssC4cFIiDyb2sbUtUQBJIbyqv8VjL03KRKiXT8xhq7y9BG4gDOV4dp77GoVRDowtCV9pB\nCoeqdIqhH4iOSj2FqxO1yNw60RrisKmiKqVd531XFHcIRgLiYFOklSEOPfh4s/cn+G3vnVjAJvwg\nEn5vhjgIrkp2VCWqYzMJsk1Bh8bdc2LTauBeYWowUDrLQeUkpdI5lBVNabGumiKc3pOEGQ4TFVVJ\nRBxsdQYphUweAFcp5MKBDEZrBXHIjj3H/NZ9ws/mo9wyfG04qaQzkCM9B57ufCF/8cKnxUjiLjI8\n9yvv1djeVkMcjFSl7cJhO5pG38KSNZgG198TNQ6AWjxZ1DgkJ/33vDR/7YafrLa2SzUOcddCVzhM\nReMgUZWUiMM0qEoAcPMbsi9f7X4I3sYR4+ZtuSoBACNJrDO2KxyCMMLaZk4VOntniT0m7YxGE+Fh\na4U4KFyVxt1lYZvFGctjoEK2DIWa6GzUVuFA6UobajpgYFGc1wnJWUmHOLQyQ0LQOJDCwYaqRBKb\nWogDIHzWJSc/X6sIODnnGE4kjYNi8JpQOAxXGtm/0kjRRxFxSLrOT/11YG6fuEMwEpJOq8KBJNou\nS8XR+d/uZ72/wTNGH8ULvU/gee6tGAehNDwrdVWqTlWiWggTPcoUy7PdbHrxph/i+EZ513tInms5\n4pAfN5Uxh6rYUE2rL5scnV7vqg702XPAn/74deKLNoiDQFXSJ+9KR6fK4miRqkSbNiubzRGH9Jxd\nwga6wbq49PBEVqhGvB0a4LXO7fmLqbXyY38YmE0s7dcOA19+DwBI+iHbOUQ81jgo7HezbbYLh+1o\nGjbOSpOIlzsbVQ23WDioEAetq9JTfz2eVv3stwEXPKna2krEQdWJnYIoHMhvEgB2YVUJg46DUD10\nr2lc9wqsLV0GIEYd9m983bj5OAhbcVUCAHc2T8A7vl3h8K0ja2DE83p5ftawNUQudhSI3VgbvnlU\nTKC/c+BFeIAvI+QMr/Vfg6VBHcRBX6CmEbbtbAQULFmVdEAbHVGdEBCHLe39JWyjYKID4CqKoylV\nyalbOBBkgCb+VahK4yBCxHMXKgDqORIDmarUDuKgpCqlAytnloGf+Qbw6s/kP5uMBN69zbGmyXsu\njs6T+P/q/X329Wu9D8YaB0USTQXVtnasgeCoVO9ezhjDwV35PeieE4p5G1KoXKF6JVQl1T4qqlI5\n4pAIlBWc9xdesxtPuXSv+KKycJAQB0uqUj5PgDaeKiLnlBob+lOjKp3HFAPZNo+15uKUFr+XOATV\nOOua+P/uDPCEn85f/9wfA6iHOCjpYXJsFw7b0TS6FohDGMmJRQuqfIX7jSp517oqze0GnvFm4LpX\nVF9b0QlWdUNjfcUUkndSOOxkOo3DFKhKAOC4GO68PPvWHa8bNo7fR1eYHF2fqtSZzZPY7mTNap/P\n3b2S/Y3i9atSlSrefAVxdPz35t4ATxr/Lm4Y/yH+LrqpkcZBN/AJEPUVrRUOki2qzoBgKloeyVlJ\ne39pw4ZWgzhU1jjUcVUChM86R6hGVahK6XstRxxo4SAhDg0HwDmIsnMVAOARIwC3I9AsCxoHi7Uj\nbtI4iAXAbdEBjCWq0kBhxxpYIg4TQjXp1NQ4AMB5hK506ES5zkF4/ylVqWQInOoz0yZIvq+5aEp/\nj5K6MlG8d9mOFSh0/YVZFuNAS+GJsuFzLSIOs20XDvF70xUObQ2BG05CLGAT57DEitXpADsvzDe4\n9uX5s+3Yt4GNo7XmwaRFtEnj0Fou8QjEduFwOkQYYI+7gYPsCM7GsRJxdNt2rPkNIS8cbJyNWkio\nLBGHgpvUlKhKquRiHEjOCG2tDYCRZKRTksDHGod2qEr9uSVEPH5g96ItQfSsi88fWsE89bUv07PI\nVKWKN1/VHAfXdTCBh+OIj9vSoD5VydQdjaIpUJXI8ZplIztK3sNYtADiALjan5vQXTokJ7RCHMj5\nXWr3qwvyWWd4PcQhfa+Cq1IZVUnWOFR0caKx6QfF4W+y3kVK5KpqHAKFxiHtQ13ARNrkESxjPJEH\nwMVJD9UIbIwDvP3jd+Cdt95p7MBTWo8RceAcGG9of3xwZ0XEQUG1qqRxSKlKdcTRBlcldeFQhjiM\n4blOdl/lHNjQ3FeVE4wbaRx8LAuIw8RKY2KKlB59gB0t/nDzeGtD4Lb8EJcwgjbsvlQ8Fr054GxC\nGzv0KeHvbdsQGKoKRcnQpNup3/x7pGO7cDgd4lt/iz8/8WJ8ovcGvLHzPw0DmjicsunNVYNSlZhe\noDyeRlKj6ASriqaJjTC7TlCqEltVQqBToyoBcGZy3rvM65SjTY3DwqCHVRCqkYVA+kv3nsIcK6Fu\n0KA34ygQbr421pgiXSjeV3ZgsUccigWqqTsakp+1Vjh08+M9i5EWcRAmpE9J46ATR0dtIC0C4pCv\nY6VxILoWVvf8JlSl7mQtO2fkjrkp0uRAQByU4mg94tDEVWlzHKiHv9HoEARCKhxsirRI4Wy0Z76P\n7z1/Gdc63xW27WECP4yUQmGKONx5bBO/84/fwW995DZ85Ot6zVZAaIhajUMUAn/5n4D/62zg1rco\nN6mKOKjef5mrkqrYqEVVStZWUld828JBLBYBCBOcdRO01a5KFZNWer75G/Hk7eTY+ZLjVp1I6dG7\n2aniD2WqUoNZDkM/FGlKey8vbnTw+/Kv3/9y3Lz+9xnjwXaQZNocE57ZpFEJAMzdpiptR5OQOoK6\nISNT6byTBK+b3FiUOoMgygqLeL/6iWsWltzzghVsGxQtQEQcsIpVBeQ6NXE0AI8UDr2SwqFox9qk\ncPCwyknhUGLJyjnHiY2xhDiUiaPJcQonmO242c3X5iGjEii7UuGwWIOq1LVAHDhZ22nD2QgQrIpn\nMRJ8y9PwgynYLQMKxEFnvtCCDS3Zj+Rk2mKFRtuIAxuvC4mVbacyTRYrIQ7DFXHgXCNXpVA9/I2G\n4+XHOgow06FC9PJjTREHOkfhf/6XG/DLV4ro5wA+xpNQrXHQXB//54e/pV3bD6g4WnOeffW9wB3/\nFH/9md9XbtJI49CAqkT3ScNW46AUy04U790CcQDEQXi6hDotHAS0o6rGgep5kudFmzqHtFm6kyme\ngzJVqUHhsOUHuIzdm7+w97HFjWjhAOBHD/93PMWJh8WV2aankVHTKD2M2L8D2KYqbUfDoOJFZrJL\n5O13v4UBcImrktKOVaLKNBDn5r+jSCHR2VS2TpMCgO4soqSb12MBomGx8z6eRHApytPixd6ZybUG\ng0gPyQPxg6c1xKHfERGHoaLLQ2ISxg4Rc6xK4UASv42jeMOdL8One6/DReywFY0jUgiUZTh8XtH5\nU4ZiQrnpQS9oHKo+YHVBCwc2tLMdngZViW1qqUpCsVb3c5Nr0yPoSVkXnHMOjyAObdixYrQqJBy2\nw6OOrcdJWak4WqIqDQQ6XgPEwZcRB4URAWPC/XPOzdezmW4rD0NLw3MdLK3eJmw7wLgwAE5lZ0pD\nRedRvT+6dhZRCHzqbWSHU4BCAyYgDsfLC4dRDaoSfR71k8EkjqJxZdJMxb/HoHEoRRxUk6MTxMGi\nE68UR1dFHGjSuxnrAwSdg+UQPl2k96QdUBUOx6UGQDNxdCnicM71hefrNc6d8VuxpSr5irkdcvOh\nrQboIxDbhcPpECQJmzd1BKMpTFAmN6O0G6uiSk1CeY5AC4hDJ3/wDQyIQzAtVyVAoCt1RscLPx4H\n8gC49hCH7nxeOMxE5gffZBJIk6Pr8yMXBh0RcRiZC4f0QS90YMs0DvT9bTyIPaO7cRZbwa95f24l\njhYcfhJIV4bime2NVyhQ4/OM0iUKa5OfOa1pHPLCYSZJxOSYmu0wGV62gC34QaQUUvI23KQo4kAL\nh5IHbhhxweO+th0rLWhHa1joV+dGf/vBuOO+wMg1qUIcJFclwXK4icZhHGAG+QwKJeIACOf1jJOv\nZ0OTooUNRUoAACcPCd8OmI+hHyqpPpR7TmNGYVmaBk3gVVOYcdvfAyfukN7TPYXNds/1MqrT2igo\n/dyiQ1K8romqFIRRVhAwpi+SgHJHqUoahyNfBe4ncwYsEQcdVSkVwjfSOMyQwmErKRwI4tsYcUie\nMctMofXbPCZexzURhzDiGAcRzqcant2XFTfszgCXPkt4aT9bARDPd7EJG43DmRzbhcPpEH3RQlCL\nOERyAt0G4qBwVdI4G/XaRhzIQz7tZmsTKjaFhAoAI4XDXLhaKFyKVKX21qZUpblkyJIuwiC/MUdO\nt1G3ooA4bK0Ytx9NQnQxQS+F2R1P5LyqQoPM3Ox+w0ocTREHJ/ldNtoIZZBztW9BVaLD51orHIjG\nYQ5qxMGXB8BNwY415e2rOqShoHFoPgBORhxMAsog4uJDtmpHNA2a4I/XhE6lLcXhtiNx17OaOHpF\ncl+pjzhsjEP0GUnEVBoHQEQcSOFgo+WghY2Q5I9WC42EPsY4fHIL6Z+v6zkZbXBx0FEm1HMK56Fs\niTLE4avvK74mFTNA3DjYM58fg6Pro8I2NMo0DvKzZ0S+H3TcrFGhuvXaznHwlIhDUqCGAfCe5wPv\neCLwwJfzn6dWziUaB11hPFEVLVWRcwldA9Cqs9LISFU6joV+fp7UpSopTQ/o56LxnN8Hrn5x9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T/yrPcUgRB0pT2nFAonHm70emKjXSONCEnOobZpNiTSgcihTHxUEnS/43xoF2SKRO6NyTqEo/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\nmUtmfF5zjXcwzG00RORgmdtKWuyh5zblcxu5r2ZibiuoBKAKMlvemCq/TP8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113dcca58>"
      ]
     },
     "metadata": {
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       "height": 299,
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      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "\n",
    "## Training\n",
    "### Set the hyperparameters here ###\n",
    "iterations = 5000\n",
    "learning_rate = 0.45\n",
    "hidden_nodes = 10\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",
    "# Borrwing this dictionary for plotting!\n",
    "losses = {'train':[], \n",
    "          'validation':[], \n",
    "          'iterations':[iterations], \n",
    "          'learning_rate':[learning_rate], \n",
    "          'hidden_nodes':[hidden_nodes]\n",
    "         }\n",
    "\n",
    "for ii in range(iterations):\n",
    "    # Go through a random batch of 128 records from the training data set we did earlier \n",
    "    batch = np.random.choice(train_features.index, size=128)\n",
    "    # Using those random indices, use pandas.ix() to get the values, and get the targets for the same rows\n",
    "    # from train_targets, but since we can only use 1 of the 3 targets for the one output node, lets use `cnt`\n",
    "    X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n",
    "    \n",
    "    # Train the network by going through each x,y in the feature target list\n",
    "    # The magic happens because the weights are stored outside the main loop, so we can \n",
    "    # iteratively improve the weights using gradient descent through each pass through of this training step\n",
    "    network.train(X, y)\n",
    "    \n",
    "    # Now let's meausre the success of those weights using MSE, and feeding forward the training data and \n",
    "    # training targets, and measuring the loss using the current weighted nodes \n",
    "    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n",
    "    # Also, let's use that validation set (data it's never seen) to test it's performance in the \"wild\"\n",
    "    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n",
    "    \n",
    "    # Printing out the training progress\n",
    "    # Updates that dict up there with values, prints to stdout, then flushes and does it all again\n",
    "    # In that way, yields a cool table and a great little load animation which powers the plot below\n",
    "    sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n",
    "                     + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
    "                     + \" ... Validation loss: \" + str(val_loss)[:5])\n",
    "    \n",
    "    sys.stdout.flush()\n",
    "    \n",
    "    # At the end of the loop, append the dict with latest loss\n",
    "    # Now iterate again\n",
    "    losses['train'].append(train_loss)\n",
    "    losses['validation'].append(val_loss)\n",
    "    \n",
    "\n",
    "    \n",
    "## Loss \n",
    "plt.plot(losses['train'], label='Training loss')\n",
    "plt.plot(losses['validation'], label='Validation loss')\n",
    "plt.legend()\n",
    "_ = plt.ylim(0,2) # better axis for the smaller value\n",
    "\n",
    "title = 'Loss ' \\\n",
    "             + '' + str(losses['iterations'][0]) \\\n",
    "             + ',' + str(losses['learning_rate'][0]) \\\n",
    "             + ',' + str(losses['hidden_nodes'][0])\n",
    "            \n",
    "plt.suptitle(title, fontsize=11,)\n",
    "plt.savefig(\"./figures/\" + title + \".png\")    \n",
    "\n",
    "## Validation\n",
    "fig, ax = plt.subplots()\n",
    "mean, std = scaled_features['cnt']\n",
    "predictions = network.run(test_features).T*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)\n",
    "\n",
    "# Let's add a title, and save it somewhere\n",
    "second_title = 'Results ' \\\n",
    "             + '' + str(losses['iterations'][0]) \\\n",
    "             + ',' + str(losses['learning_rate'][0]) \\\n",
    "             + ',' + str(losses['hidden_nodes'][0])\n",
    "            \n",
    "fig.suptitle(second_title, fontsize=11,)\n",
    "fig.savefig(\"./figures/\" + second_title + \".png\")"
   ]
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