LSTM_TimeSeries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/Users/rohitsuvarna/anaconda/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pyspark as spark\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "sc = spark.SparkContext()\n",
    "sql = spark.SQLContext(sc)\n",
    "from pyspark.sql.types import *\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from elephas.utils.rdd_utils import to_simple_rdd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single Feature Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Sentiment_score</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-31T05:00:00.000-04:00</td>\n",
       "      <td>0.092263</td>\n",
       "      <td>6158.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-31T06:00:00.000-04:00</td>\n",
       "      <td>0.103770</td>\n",
       "      <td>6105.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-10-31T07:00:00.000-04:00</td>\n",
       "      <td>0.111691</td>\n",
       "      <td>6094.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-10-31T08:00:00.000-04:00</td>\n",
       "      <td>0.082134</td>\n",
       "      <td>6125.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-31T09:00:00.000-04:00</td>\n",
       "      <td>0.089370</td>\n",
       "      <td>6165.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        DateTime  Sentiment_score    Price\n",
       "0  2017-10-31T05:00:00.000-04:00         0.092263  6158.76\n",
       "1  2017-10-31T06:00:00.000-04:00         0.103770  6105.90\n",
       "2  2017-10-31T07:00:00.000-04:00         0.111691  6094.36\n",
       "3  2017-10-31T08:00:00.000-04:00         0.082134  6125.13\n",
       "4  2017-10-31T09:00:00.000-04:00         0.089370  6165.00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/Users/rohitsuvarna/Downloads/finalOutTwo.csv',header=None)\n",
    "df.columns = ['DateTime','Sentiment_score','Price']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df = df.drop(df.columns[1],axis = 1) Uncomment this for pure time series analysis, without sentiment score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the input variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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3MjUuiLAAX2UJDCKeoDAcTtUdzmwtqOOV7woI8deTUWahqa3vRIIjqW2xYndKmtoH/plT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\nEBBCiD5MgoAQQvRh/w+AXTZkTWmD1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ef607b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = df.values\n",
    "# specify columns to plot\n",
    "groups = [ 1,2]\n",
    "i = 1\n",
    "# plot each column\n",
    "plt.figure()\n",
    "for group in groups:\n",
    "\tplt.subplot(len(groups), 1, i)\n",
    "\tplt.plot(values[:, group])\n",
    "\tplt.title(df.columns[group], y=0.5, loc='right')\n",
    "\ti += 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set index to datetime and change the order of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Sentiment_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-31T05:00:00.000-04:00</th>\n",
       "      <td>6158.76</td>\n",
       "      <td>0.092263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T06:00:00.000-04:00</th>\n",
       "      <td>6105.90</td>\n",
       "      <td>0.103770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T07:00:00.000-04:00</th>\n",
       "      <td>6094.36</td>\n",
       "      <td>0.111691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T08:00:00.000-04:00</th>\n",
       "      <td>6125.13</td>\n",
       "      <td>0.082134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T09:00:00.000-04:00</th>\n",
       "      <td>6165.00</td>\n",
       "      <td>0.089370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T10:00:00.000-04:00</th>\n",
       "      <td>6170.77</td>\n",
       "      <td>0.102672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T11:00:00.000-04:00</th>\n",
       "      <td>6233.74</td>\n",
       "      <td>0.128146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T12:00:00.000-04:00</th>\n",
       "      <td>6201.03</td>\n",
       "      <td>0.105498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T13:00:00.000-04:00</th>\n",
       "      <td>6332.34</td>\n",
       "      <td>0.138458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T14:00:00.000-04:00</th>\n",
       "      <td>6363.13</td>\n",
       "      <td>0.117794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T15:00:00.000-04:00</th>\n",
       "      <td>6365.16</td>\n",
       "      <td>0.091320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T16:00:00.000-04:00</th>\n",
       "      <td>6364.78</td>\n",
       "      <td>0.093019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T17:00:00.000-04:00</th>\n",
       "      <td>6361.79</td>\n",
       "      <td>0.092068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T18:00:00.000-04:00</th>\n",
       "      <td>6335.64</td>\n",
       "      <td>0.106913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T19:00:00.000-04:00</th>\n",
       "      <td>6341.15</td>\n",
       "      <td>0.122171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T20:00:00.000-04:00</th>\n",
       "      <td>6370.08</td>\n",
       "      <td>0.128171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T21:00:00.000-04:00</th>\n",
       "      <td>6393.26</td>\n",
       "      <td>0.092386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T22:00:00.000-04:00</th>\n",
       "      <td>6383.94</td>\n",
       "      <td>0.106827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T23:00:00.000-04:00</th>\n",
       "      <td>6448.36</td>\n",
       "      <td>0.102079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01T00:00:00.000-04:00</th>\n",
       "      <td>6376.49</td>\n",
       "      <td>0.100317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01T01:00:00.000-04:00</th>\n",
       "      <td>6405.67</td>\n",
       "      <td>0.099254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01T02:00:00.000-04:00</th>\n",
       "      <td>6419.60</td>\n",
       "      <td>0.090378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01T03:00:00.000-04:00</th>\n",
       "      <td>6426.88</td>\n",
       "      <td>0.080843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01T04:00:00.000-04:00</th>\n",
       "      <td>6365.63</td>\n",
       "      <td>0.095285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01T05:00:00.000-04:00</th>\n",
       "      <td>6399.43</td>\n",
       "      <td>0.113686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01T06:00:00.000-04:00</th>\n",
       "      <td>6400.97</td>\n",
       "      <td>0.090430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01T07:00:00.000-04:00</th>\n",
       "      <td>6392.60</td>\n",
       "      <td>0.105440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01T08:00:00.000-04:00</th>\n",
       "      <td>6446.61</td>\n",
       "      <td>0.100443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01T09:00:00.000-04:00</th>\n",
       "      <td>6541.77</td>\n",
       "      <td>0.087040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01T10:00:00.000-04:00</th>\n",
       "      <td>6584.45</td>\n",
       "      <td>0.094840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-07T18:00:00.000-05:00</th>\n",
       "      <td>7100.78</td>\n",
       "      <td>0.090893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-07T19:00:00.000-05:00</th>\n",
       "      <td>7091.37</td>\n",
       "      <td>0.077261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-07T20:00:00.000-05:00</th>\n",
       "      <td>7003.36</td>\n",
       "      <td>0.076910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-07T21:00:00.000-05:00</th>\n",
       "      <td>7074.47</td>\n",
       "      <td>0.106283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-07T22:00:00.000-05:00</th>\n",
       "      <td>7078.19</td>\n",
       "      <td>0.111842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-07T23:00:00.000-05:00</th>\n",
       "      <td>7112.74</td>\n",
       "      <td>0.102838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T00:00:00.000-05:00</th>\n",
       "      <td>7086.68</td>\n",
       "      <td>0.115612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T01:00:00.000-05:00</th>\n",
       "      <td>7296.87</td>\n",
       "      <td>0.104877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T02:00:00.000-05:00</th>\n",
       "      <td>7312.77</td>\n",
       "      <td>0.095470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T03:00:00.000-05:00</th>\n",
       "      <td>7357.34</td>\n",
       "      <td>0.114608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T04:00:00.000-05:00</th>\n",
       "      <td>7358.13</td>\n",
       "      <td>0.099119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T05:00:00.000-05:00</th>\n",
       "      <td>7402.09</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T06:00:00.000-05:00</th>\n",
       "      <td>7412.62</td>\n",
       "      <td>0.096952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T07:00:00.000-05:00</th>\n",
       "      <td>7364.62</td>\n",
       "      <td>0.097009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T08:00:00.000-05:00</th>\n",
       "      <td>7386.44</td>\n",
       "      <td>0.078925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T09:00:00.000-05:00</th>\n",
       "      <td>7363.68</td>\n",
       "      <td>0.086040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T10:00:00.000-05:00</th>\n",
       "      <td>7363.09</td>\n",
       "      <td>0.072551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T11:00:00.000-05:00</th>\n",
       "      <td>7462.54</td>\n",
       "      <td>0.048587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T12:00:00.000-05:00</th>\n",
       "      <td>7467.79</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T13:00:00.000-05:00</th>\n",
       "      <td>7486.97</td>\n",
       "      <td>0.086756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T14:00:00.000-05:00</th>\n",
       "      <td>7505.69</td>\n",
       "      <td>0.116385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T15:00:00.000-05:00</th>\n",
       "      <td>7498.89</td>\n",
       "      <td>0.111292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T16:00:00.000-05:00</th>\n",
       "      <td>7476.00</td>\n",
       "      <td>0.098035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T17:00:00.000-05:00</th>\n",
       "      <td>7721.98</td>\n",
       "      <td>0.074300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T18:00:00.000-05:00</th>\n",
       "      <td>7316.73</td>\n",
       "      <td>0.070439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T19:00:00.000-05:00</th>\n",
       "      <td>7248.03</td>\n",
       "      <td>0.074165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T20:00:00.000-05:00</th>\n",
       "      <td>7259.95</td>\n",
       "      <td>0.074930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T21:00:00.000-05:00</th>\n",
       "      <td>7258.54</td>\n",
       "      <td>0.088518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T22:00:00.000-05:00</th>\n",
       "      <td>7397.59</td>\n",
       "      <td>0.066999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08T23:00:00.000-05:00</th>\n",
       "      <td>7467.97</td>\n",
       "      <td>0.069801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Price  Sentiment_score\n",
       "DateTime                                               \n",
       "2017-10-31T05:00:00.000-04:00  6158.76         0.092263\n",
       "2017-10-31T06:00:00.000-04:00  6105.90         0.103770\n",
       "2017-10-31T07:00:00.000-04:00  6094.36         0.111691\n",
       "2017-10-31T08:00:00.000-04:00  6125.13         0.082134\n",
       "2017-10-31T09:00:00.000-04:00  6165.00         0.089370\n",
       "2017-10-31T10:00:00.000-04:00  6170.77         0.102672\n",
       "2017-10-31T11:00:00.000-04:00  6233.74         0.128146\n",
       "2017-10-31T12:00:00.000-04:00  6201.03         0.105498\n",
       "2017-10-31T13:00:00.000-04:00  6332.34         0.138458\n",
       "2017-10-31T14:00:00.000-04:00  6363.13         0.117794\n",
       "2017-10-31T15:00:00.000-04:00  6365.16         0.091320\n",
       "2017-10-31T16:00:00.000-04:00  6364.78         0.093019\n",
       "2017-10-31T17:00:00.000-04:00  6361.79         0.092068\n",
       "2017-10-31T18:00:00.000-04:00  6335.64         0.106913\n",
       "2017-10-31T19:00:00.000-04:00  6341.15         0.122171\n",
       "2017-10-31T20:00:00.000-04:00  6370.08         0.128171\n",
       "2017-10-31T21:00:00.000-04:00  6393.26         0.092386\n",
       "2017-10-31T22:00:00.000-04:00  6383.94         0.106827\n",
       "2017-10-31T23:00:00.000-04:00  6448.36         0.102079\n",
       "2017-11-01T00:00:00.000-04:00  6376.49         0.100317\n",
       "2017-11-01T01:00:00.000-04:00  6405.67         0.099254\n",
       "2017-11-01T02:00:00.000-04:00  6419.60         0.090378\n",
       "2017-11-01T03:00:00.000-04:00  6426.88         0.080843\n",
       "2017-11-01T04:00:00.000-04:00  6365.63         0.095285\n",
       "2017-11-01T05:00:00.000-04:00  6399.43         0.113686\n",
       "2017-11-01T06:00:00.000-04:00  6400.97         0.090430\n",
       "2017-11-01T07:00:00.000-04:00  6392.60         0.105440\n",
       "2017-11-01T08:00:00.000-04:00  6446.61         0.100443\n",
       "2017-11-01T09:00:00.000-04:00  6541.77         0.087040\n",
       "2017-11-01T10:00:00.000-04:00  6584.45         0.094840\n",
       "...                                ...              ...\n",
       "2017-11-07T18:00:00.000-05:00  7100.78         0.090893\n",
       "2017-11-07T19:00:00.000-05:00  7091.37         0.077261\n",
       "2017-11-07T20:00:00.000-05:00  7003.36         0.076910\n",
       "2017-11-07T21:00:00.000-05:00  7074.47         0.106283\n",
       "2017-11-07T22:00:00.000-05:00  7078.19         0.111842\n",
       "2017-11-07T23:00:00.000-05:00  7112.74         0.102838\n",
       "2017-11-08T00:00:00.000-05:00  7086.68         0.115612\n",
       "2017-11-08T01:00:00.000-05:00  7296.87         0.104877\n",
       "2017-11-08T02:00:00.000-05:00  7312.77         0.095470\n",
       "2017-11-08T03:00:00.000-05:00  7357.34         0.114608\n",
       "2017-11-08T04:00:00.000-05:00  7358.13         0.099119\n",
       "2017-11-08T05:00:00.000-05:00  7402.09         0.074726\n",
       "2017-11-08T06:00:00.000-05:00  7412.62         0.096952\n",
       "2017-11-08T07:00:00.000-05:00  7364.62         0.097009\n",
       "2017-11-08T08:00:00.000-05:00  7386.44         0.078925\n",
       "2017-11-08T09:00:00.000-05:00  7363.68         0.086040\n",
       "2017-11-08T10:00:00.000-05:00  7363.09         0.072551\n",
       "2017-11-08T11:00:00.000-05:00  7462.54         0.048587\n",
       "2017-11-08T12:00:00.000-05:00  7467.79         0.069712\n",
       "2017-11-08T13:00:00.000-05:00  7486.97         0.086756\n",
       "2017-11-08T14:00:00.000-05:00  7505.69         0.116385\n",
       "2017-11-08T15:00:00.000-05:00  7498.89         0.111292\n",
       "2017-11-08T16:00:00.000-05:00  7476.00         0.098035\n",
       "2017-11-08T17:00:00.000-05:00  7721.98         0.074300\n",
       "2017-11-08T18:00:00.000-05:00  7316.73         0.070439\n",
       "2017-11-08T19:00:00.000-05:00  7248.03         0.074165\n",
       "2017-11-08T20:00:00.000-05:00  7259.95         0.074930\n",
       "2017-11-08T21:00:00.000-05:00  7258.54         0.088518\n",
       "2017-11-08T22:00:00.000-05:00  7397.59         0.066999\n",
       "2017-11-08T23:00:00.000-05:00  7467.97         0.069801\n",
       "\n",
       "[211 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('DateTime',inplace=True)  \n",
    "cols = df.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "df = df[cols]\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(df.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    '''\n",
    "    Converts the original dataframe to a format which contains\n",
    "    lag shifted values of inputs which can be used as input\n",
    "    to the LSTM\n",
    "    '''\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = pd.DataFrame(data)\n",
    "    cols, names = list(), list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):\n",
    "        cols.append(df.shift(i))\n",
    "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
    "        else:\n",
    "            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = pd.concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_hours = 3  #no of time lags\n",
    "n_features = 1 #no of features\n",
    "n_obs = n_hours*n_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.165131</td>\n",
       "      <td>0.166378</td>\n",
       "      <td>0.166144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.165131</td>\n",
       "      <td>0.166378</td>\n",
       "      <td>0.166144</td>\n",
       "      <td>0.164307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.166378</td>\n",
       "      <td>0.166144</td>\n",
       "      <td>0.164307</td>\n",
       "      <td>0.148241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.166144</td>\n",
       "      <td>0.164307</td>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.151626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.164307</td>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.151626</td>\n",
       "      <td>0.169401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.151626</td>\n",
       "      <td>0.169401</td>\n",
       "      <td>0.183642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.151626</td>\n",
       "      <td>0.169401</td>\n",
       "      <td>0.183642</td>\n",
       "      <td>0.177916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.169401</td>\n",
       "      <td>0.183642</td>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.217495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.183642</td>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.217495</td>\n",
       "      <td>0.173339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.217495</td>\n",
       "      <td>0.173339</td>\n",
       "      <td>0.191267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.217495</td>\n",
       "      <td>0.173339</td>\n",
       "      <td>0.191267</td>\n",
       "      <td>0.199826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.173339</td>\n",
       "      <td>0.191267</td>\n",
       "      <td>0.199826</td>\n",
       "      <td>0.204298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.191267</td>\n",
       "      <td>0.199826</td>\n",
       "      <td>0.204298</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.199826</td>\n",
       "      <td>0.204298</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.187433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.204298</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.187433</td>\n",
       "      <td>0.188379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.187433</td>\n",
       "      <td>0.188379</td>\n",
       "      <td>0.183237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.187433</td>\n",
       "      <td>0.188379</td>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.216420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.188379</td>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.216420</td>\n",
       "      <td>0.274886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.216420</td>\n",
       "      <td>0.274886</td>\n",
       "      <td>0.301108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.216420</td>\n",
       "      <td>0.274886</td>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.291002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.274886</td>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.291002</td>\n",
       "      <td>0.254801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.291002</td>\n",
       "      <td>0.254801</td>\n",
       "      <td>0.239798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.589640</td>\n",
       "      <td>0.555510</td>\n",
       "      <td>0.616317</td>\n",
       "      <td>0.618338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>0.555510</td>\n",
       "      <td>0.616317</td>\n",
       "      <td>0.618338</td>\n",
       "      <td>0.612557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.616317</td>\n",
       "      <td>0.618338</td>\n",
       "      <td>0.612557</td>\n",
       "      <td>0.558484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0.618338</td>\n",
       "      <td>0.612557</td>\n",
       "      <td>0.558484</td>\n",
       "      <td>0.602174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>0.612557</td>\n",
       "      <td>0.558484</td>\n",
       "      <td>0.602174</td>\n",
       "      <td>0.604459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.558484</td>\n",
       "      <td>0.602174</td>\n",
       "      <td>0.604459</td>\n",
       "      <td>0.625687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0.602174</td>\n",
       "      <td>0.604459</td>\n",
       "      <td>0.625687</td>\n",
       "      <td>0.609675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.604459</td>\n",
       "      <td>0.625687</td>\n",
       "      <td>0.609675</td>\n",
       "      <td>0.738815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.625687</td>\n",
       "      <td>0.609675</td>\n",
       "      <td>0.738815</td>\n",
       "      <td>0.748584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.609675</td>\n",
       "      <td>0.738815</td>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.775967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0.738815</td>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.776453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.776453</td>\n",
       "      <td>0.803461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.776453</td>\n",
       "      <td>0.803461</td>\n",
       "      <td>0.809931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>0.776453</td>\n",
       "      <td>0.803461</td>\n",
       "      <td>0.809931</td>\n",
       "      <td>0.780440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>0.803461</td>\n",
       "      <td>0.809931</td>\n",
       "      <td>0.780440</td>\n",
       "      <td>0.793846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.809931</td>\n",
       "      <td>0.780440</td>\n",
       "      <td>0.793846</td>\n",
       "      <td>0.779863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.780440</td>\n",
       "      <td>0.793846</td>\n",
       "      <td>0.779863</td>\n",
       "      <td>0.779500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0.793846</td>\n",
       "      <td>0.779863</td>\n",
       "      <td>0.779500</td>\n",
       "      <td>0.840602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0.779863</td>\n",
       "      <td>0.779500</td>\n",
       "      <td>0.840602</td>\n",
       "      <td>0.843827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.779500</td>\n",
       "      <td>0.840602</td>\n",
       "      <td>0.843827</td>\n",
       "      <td>0.855611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.840602</td>\n",
       "      <td>0.843827</td>\n",
       "      <td>0.855611</td>\n",
       "      <td>0.867113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.843827</td>\n",
       "      <td>0.855611</td>\n",
       "      <td>0.867113</td>\n",
       "      <td>0.862935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.855611</td>\n",
       "      <td>0.867113</td>\n",
       "      <td>0.862935</td>\n",
       "      <td>0.848871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.867113</td>\n",
       "      <td>0.862935</td>\n",
       "      <td>0.848871</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.862935</td>\n",
       "      <td>0.848871</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.751017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.848871</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.751017</td>\n",
       "      <td>0.708808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.751017</td>\n",
       "      <td>0.708808</td>\n",
       "      <td>0.716132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>0.751017</td>\n",
       "      <td>0.708808</td>\n",
       "      <td>0.716132</td>\n",
       "      <td>0.715265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>0.708808</td>\n",
       "      <td>0.716132</td>\n",
       "      <td>0.715265</td>\n",
       "      <td>0.800697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>0.716132</td>\n",
       "      <td>0.715265</td>\n",
       "      <td>0.800697</td>\n",
       "      <td>0.843938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     var1(t-3)  var1(t-2)  var1(t-1)   var1(t)\n",
       "3     0.039567   0.007090   0.000000  0.018905\n",
       "4     0.007090   0.000000   0.018905  0.043401\n",
       "5     0.000000   0.018905   0.043401  0.046946\n",
       "6     0.018905   0.043401   0.046946  0.085634\n",
       "7     0.043401   0.046946   0.085634  0.065537\n",
       "8     0.046946   0.085634   0.065537  0.146213\n",
       "9     0.085634   0.065537   0.146213  0.165131\n",
       "10    0.065537   0.146213   0.165131  0.166378\n",
       "11    0.146213   0.165131   0.166378  0.166144\n",
       "12    0.165131   0.166378   0.166144  0.164307\n",
       "13    0.166378   0.166144   0.164307  0.148241\n",
       "14    0.166144   0.164307   0.148241  0.151626\n",
       "15    0.164307   0.148241   0.151626  0.169401\n",
       "16    0.148241   0.151626   0.169401  0.183642\n",
       "17    0.151626   0.169401   0.183642  0.177916\n",
       "18    0.169401   0.183642   0.177916  0.217495\n",
       "19    0.183642   0.177916   0.217495  0.173339\n",
       "20    0.177916   0.217495   0.173339  0.191267\n",
       "21    0.217495   0.173339   0.191267  0.199826\n",
       "22    0.173339   0.191267   0.199826  0.204298\n",
       "23    0.191267   0.199826   0.204298  0.166667\n",
       "24    0.199826   0.204298   0.166667  0.187433\n",
       "25    0.204298   0.166667   0.187433  0.188379\n",
       "26    0.166667   0.187433   0.188379  0.183237\n",
       "27    0.187433   0.188379   0.183237  0.216420\n",
       "28    0.188379   0.183237   0.216420  0.274886\n",
       "29    0.183237   0.216420   0.274886  0.301108\n",
       "30    0.216420   0.274886   0.301108  0.291002\n",
       "31    0.274886   0.301108   0.291002  0.254801\n",
       "32    0.301108   0.291002   0.254801  0.239798\n",
       "..         ...        ...        ...       ...\n",
       "181   0.589640   0.555510   0.616317  0.618338\n",
       "182   0.555510   0.616317   0.618338  0.612557\n",
       "183   0.616317   0.618338   0.612557  0.558484\n",
       "184   0.618338   0.612557   0.558484  0.602174\n",
       "185   0.612557   0.558484   0.602174  0.604459\n",
       "186   0.558484   0.602174   0.604459  0.625687\n",
       "187   0.602174   0.604459   0.625687  0.609675\n",
       "188   0.604459   0.625687   0.609675  0.738815\n",
       "189   0.625687   0.609675   0.738815  0.748584\n",
       "190   0.609675   0.738815   0.748584  0.775967\n",
       "191   0.738815   0.748584   0.775967  0.776453\n",
       "192   0.748584   0.775967   0.776453  0.803461\n",
       "193   0.775967   0.776453   0.803461  0.809931\n",
       "194   0.776453   0.803461   0.809931  0.780440\n",
       "195   0.803461   0.809931   0.780440  0.793846\n",
       "196   0.809931   0.780440   0.793846  0.779863\n",
       "197   0.780440   0.793846   0.779863  0.779500\n",
       "198   0.793846   0.779863   0.779500  0.840602\n",
       "199   0.779863   0.779500   0.840602  0.843827\n",
       "200   0.779500   0.840602   0.843827  0.855611\n",
       "201   0.840602   0.843827   0.855611  0.867113\n",
       "202   0.843827   0.855611   0.867113  0.862935\n",
       "203   0.855611   0.867113   0.862935  0.848871\n",
       "204   0.867113   0.862935   0.848871  1.000000\n",
       "205   0.862935   0.848871   1.000000  0.751017\n",
       "206   0.848871   1.000000   0.751017  0.708808\n",
       "207   1.000000   0.751017   0.708808  0.716132\n",
       "208   0.751017   0.708808   0.716132  0.715265\n",
       "209   0.708808   0.716132   0.715265  0.800697\n",
       "210   0.716132   0.715265   0.800697  0.843938\n",
       "\n",
       "[208 rows x 4 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reframed = series_to_supervised(scaled, n_hours, 1)\n",
    "reframed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var1(t-3)</th>\n",
       "      <th>var2(t-3)</th>\n",
       "      <th>var1(t-2)</th>\n",
       "      <th>var2(t-2)</th>\n",
       "      <th>var1(t-1)</th>\n",
       "      <th>var2(t-1)</th>\n",
       "      <th>var1(t)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.039567</td>\n",
       "      <td>0.223441</td>\n",
       "      <td>0.007090</td>\n",
       "      <td>0.282312</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.322835</td>\n",
       "      <td>0.018905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.007090</td>\n",
       "      <td>0.282312</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.322835</td>\n",
       "      <td>0.018905</td>\n",
       "      <td>0.171625</td>\n",
       "      <td>0.043401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.322835</td>\n",
       "      <td>0.018905</td>\n",
       "      <td>0.171625</td>\n",
       "      <td>0.043401</td>\n",
       "      <td>0.208645</td>\n",
       "      <td>0.046946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.018905</td>\n",
       "      <td>0.171625</td>\n",
       "      <td>0.043401</td>\n",
       "      <td>0.208645</td>\n",
       "      <td>0.046946</td>\n",
       "      <td>0.276694</td>\n",
       "      <td>0.085634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.043401</td>\n",
       "      <td>0.208645</td>\n",
       "      <td>0.046946</td>\n",
       "      <td>0.276694</td>\n",
       "      <td>0.085634</td>\n",
       "      <td>0.407021</td>\n",
       "      <td>0.065537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.046946</td>\n",
       "      <td>0.276694</td>\n",
       "      <td>0.085634</td>\n",
       "      <td>0.407021</td>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.291150</td>\n",
       "      <td>0.146213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.085634</td>\n",
       "      <td>0.407021</td>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.291150</td>\n",
       "      <td>0.146213</td>\n",
       "      <td>0.459774</td>\n",
       "      <td>0.165131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.291150</td>\n",
       "      <td>0.146213</td>\n",
       "      <td>0.459774</td>\n",
       "      <td>0.165131</td>\n",
       "      <td>0.354060</td>\n",
       "      <td>0.166378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.146213</td>\n",
       "      <td>0.459774</td>\n",
       "      <td>0.165131</td>\n",
       "      <td>0.354060</td>\n",
       "      <td>0.166378</td>\n",
       "      <td>0.218619</td>\n",
       "      <td>0.166144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.165131</td>\n",
       "      <td>0.354060</td>\n",
       "      <td>0.166378</td>\n",
       "      <td>0.218619</td>\n",
       "      <td>0.166144</td>\n",
       "      <td>0.227313</td>\n",
       "      <td>0.164307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.166378</td>\n",
       "      <td>0.218619</td>\n",
       "      <td>0.166144</td>\n",
       "      <td>0.227313</td>\n",
       "      <td>0.164307</td>\n",
       "      <td>0.222447</td>\n",
       "      <td>0.148241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.166144</td>\n",
       "      <td>0.227313</td>\n",
       "      <td>0.164307</td>\n",
       "      <td>0.222447</td>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.298393</td>\n",
       "      <td>0.151626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.164307</td>\n",
       "      <td>0.222447</td>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.298393</td>\n",
       "      <td>0.151626</td>\n",
       "      <td>0.376450</td>\n",
       "      <td>0.169401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.298393</td>\n",
       "      <td>0.151626</td>\n",
       "      <td>0.376450</td>\n",
       "      <td>0.169401</td>\n",
       "      <td>0.407149</td>\n",
       "      <td>0.183642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.151626</td>\n",
       "      <td>0.376450</td>\n",
       "      <td>0.169401</td>\n",
       "      <td>0.407149</td>\n",
       "      <td>0.183642</td>\n",
       "      <td>0.224071</td>\n",
       "      <td>0.177916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.169401</td>\n",
       "      <td>0.407149</td>\n",
       "      <td>0.183642</td>\n",
       "      <td>0.224071</td>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.297952</td>\n",
       "      <td>0.217495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.183642</td>\n",
       "      <td>0.224071</td>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.297952</td>\n",
       "      <td>0.217495</td>\n",
       "      <td>0.273662</td>\n",
       "      <td>0.173339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.297952</td>\n",
       "      <td>0.217495</td>\n",
       "      <td>0.273662</td>\n",
       "      <td>0.173339</td>\n",
       "      <td>0.264645</td>\n",
       "      <td>0.191267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.217495</td>\n",
       "      <td>0.273662</td>\n",
       "      <td>0.173339</td>\n",
       "      <td>0.264645</td>\n",
       "      <td>0.191267</td>\n",
       "      <td>0.259207</td>\n",
       "      <td>0.199826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.173339</td>\n",
       "      <td>0.264645</td>\n",
       "      <td>0.191267</td>\n",
       "      <td>0.259207</td>\n",
       "      <td>0.199826</td>\n",
       "      <td>0.213801</td>\n",
       "      <td>0.204298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.191267</td>\n",
       "      <td>0.259207</td>\n",
       "      <td>0.199826</td>\n",
       "      <td>0.213801</td>\n",
       "      <td>0.204298</td>\n",
       "      <td>0.165018</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.199826</td>\n",
       "      <td>0.213801</td>\n",
       "      <td>0.204298</td>\n",
       "      <td>0.165018</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.238903</td>\n",
       "      <td>0.187433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.204298</td>\n",
       "      <td>0.165018</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.238903</td>\n",
       "      <td>0.187433</td>\n",
       "      <td>0.333041</td>\n",
       "      <td>0.188379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.238903</td>\n",
       "      <td>0.187433</td>\n",
       "      <td>0.333041</td>\n",
       "      <td>0.188379</td>\n",
       "      <td>0.214064</td>\n",
       "      <td>0.183237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.187433</td>\n",
       "      <td>0.333041</td>\n",
       "      <td>0.188379</td>\n",
       "      <td>0.214064</td>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.290857</td>\n",
       "      <td>0.216420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.188379</td>\n",
       "      <td>0.214064</td>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.290857</td>\n",
       "      <td>0.216420</td>\n",
       "      <td>0.265291</td>\n",
       "      <td>0.274886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.290857</td>\n",
       "      <td>0.216420</td>\n",
       "      <td>0.265291</td>\n",
       "      <td>0.274886</td>\n",
       "      <td>0.196723</td>\n",
       "      <td>0.301108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.216420</td>\n",
       "      <td>0.265291</td>\n",
       "      <td>0.274886</td>\n",
       "      <td>0.196723</td>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.236626</td>\n",
       "      <td>0.291002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.274886</td>\n",
       "      <td>0.196723</td>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.236626</td>\n",
       "      <td>0.291002</td>\n",
       "      <td>0.317037</td>\n",
       "      <td>0.254801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.236626</td>\n",
       "      <td>0.291002</td>\n",
       "      <td>0.317037</td>\n",
       "      <td>0.254801</td>\n",
       "      <td>0.190908</td>\n",
       "      <td>0.239798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.589640</td>\n",
       "      <td>0.262051</td>\n",
       "      <td>0.555510</td>\n",
       "      <td>0.230110</td>\n",
       "      <td>0.616317</td>\n",
       "      <td>0.146773</td>\n",
       "      <td>0.618338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>0.555510</td>\n",
       "      <td>0.230110</td>\n",
       "      <td>0.616317</td>\n",
       "      <td>0.146773</td>\n",
       "      <td>0.618338</td>\n",
       "      <td>0.216434</td>\n",
       "      <td>0.612557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.616317</td>\n",
       "      <td>0.146773</td>\n",
       "      <td>0.618338</td>\n",
       "      <td>0.216434</td>\n",
       "      <td>0.612557</td>\n",
       "      <td>0.146692</td>\n",
       "      <td>0.558484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0.618338</td>\n",
       "      <td>0.216434</td>\n",
       "      <td>0.612557</td>\n",
       "      <td>0.146692</td>\n",
       "      <td>0.558484</td>\n",
       "      <td>0.144900</td>\n",
       "      <td>0.602174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>0.612557</td>\n",
       "      <td>0.146692</td>\n",
       "      <td>0.558484</td>\n",
       "      <td>0.144900</td>\n",
       "      <td>0.602174</td>\n",
       "      <td>0.295167</td>\n",
       "      <td>0.604459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.558484</td>\n",
       "      <td>0.144900</td>\n",
       "      <td>0.602174</td>\n",
       "      <td>0.295167</td>\n",
       "      <td>0.604459</td>\n",
       "      <td>0.323610</td>\n",
       "      <td>0.625687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0.602174</td>\n",
       "      <td>0.295167</td>\n",
       "      <td>0.604459</td>\n",
       "      <td>0.323610</td>\n",
       "      <td>0.625687</td>\n",
       "      <td>0.277543</td>\n",
       "      <td>0.609675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.604459</td>\n",
       "      <td>0.323610</td>\n",
       "      <td>0.625687</td>\n",
       "      <td>0.277543</td>\n",
       "      <td>0.609675</td>\n",
       "      <td>0.342895</td>\n",
       "      <td>0.738815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.625687</td>\n",
       "      <td>0.277543</td>\n",
       "      <td>0.609675</td>\n",
       "      <td>0.342895</td>\n",
       "      <td>0.738815</td>\n",
       "      <td>0.287974</td>\n",
       "      <td>0.748584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.609675</td>\n",
       "      <td>0.342895</td>\n",
       "      <td>0.738815</td>\n",
       "      <td>0.287974</td>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.239852</td>\n",
       "      <td>0.775967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0.738815</td>\n",
       "      <td>0.287974</td>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.239852</td>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.337761</td>\n",
       "      <td>0.776453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.239852</td>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.337761</td>\n",
       "      <td>0.776453</td>\n",
       "      <td>0.258518</td>\n",
       "      <td>0.803461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.337761</td>\n",
       "      <td>0.776453</td>\n",
       "      <td>0.258518</td>\n",
       "      <td>0.803461</td>\n",
       "      <td>0.133723</td>\n",
       "      <td>0.809931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>0.776453</td>\n",
       "      <td>0.258518</td>\n",
       "      <td>0.803461</td>\n",
       "      <td>0.133723</td>\n",
       "      <td>0.809931</td>\n",
       "      <td>0.247430</td>\n",
       "      <td>0.780440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>0.803461</td>\n",
       "      <td>0.133723</td>\n",
       "      <td>0.809931</td>\n",
       "      <td>0.247430</td>\n",
       "      <td>0.780440</td>\n",
       "      <td>0.247722</td>\n",
       "      <td>0.793846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.809931</td>\n",
       "      <td>0.247430</td>\n",
       "      <td>0.780440</td>\n",
       "      <td>0.247722</td>\n",
       "      <td>0.793846</td>\n",
       "      <td>0.155208</td>\n",
       "      <td>0.779863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.780440</td>\n",
       "      <td>0.247722</td>\n",
       "      <td>0.793846</td>\n",
       "      <td>0.155208</td>\n",
       "      <td>0.779863</td>\n",
       "      <td>0.191609</td>\n",
       "      <td>0.779500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0.793846</td>\n",
       "      <td>0.155208</td>\n",
       "      <td>0.779863</td>\n",
       "      <td>0.191609</td>\n",
       "      <td>0.779500</td>\n",
       "      <td>0.122599</td>\n",
       "      <td>0.840602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0.779863</td>\n",
       "      <td>0.191609</td>\n",
       "      <td>0.779500</td>\n",
       "      <td>0.122599</td>\n",
       "      <td>0.840602</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.843827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.779500</td>\n",
       "      <td>0.122599</td>\n",
       "      <td>0.840602</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.843827</td>\n",
       "      <td>0.108073</td>\n",
       "      <td>0.855611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.840602</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.843827</td>\n",
       "      <td>0.108073</td>\n",
       "      <td>0.855611</td>\n",
       "      <td>0.195271</td>\n",
       "      <td>0.867113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.843827</td>\n",
       "      <td>0.108073</td>\n",
       "      <td>0.855611</td>\n",
       "      <td>0.195271</td>\n",
       "      <td>0.867113</td>\n",
       "      <td>0.346849</td>\n",
       "      <td>0.862935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.855611</td>\n",
       "      <td>0.195271</td>\n",
       "      <td>0.867113</td>\n",
       "      <td>0.346849</td>\n",
       "      <td>0.862935</td>\n",
       "      <td>0.320795</td>\n",
       "      <td>0.848871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.867113</td>\n",
       "      <td>0.346849</td>\n",
       "      <td>0.862935</td>\n",
       "      <td>0.320795</td>\n",
       "      <td>0.848871</td>\n",
       "      <td>0.252974</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.862935</td>\n",
       "      <td>0.320795</td>\n",
       "      <td>0.848871</td>\n",
       "      <td>0.252974</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.131546</td>\n",
       "      <td>0.751017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.848871</td>\n",
       "      <td>0.252974</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.131546</td>\n",
       "      <td>0.751017</td>\n",
       "      <td>0.111793</td>\n",
       "      <td>0.708808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.131546</td>\n",
       "      <td>0.751017</td>\n",
       "      <td>0.111793</td>\n",
       "      <td>0.708808</td>\n",
       "      <td>0.130855</td>\n",
       "      <td>0.716132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>0.751017</td>\n",
       "      <td>0.111793</td>\n",
       "      <td>0.708808</td>\n",
       "      <td>0.130855</td>\n",
       "      <td>0.716132</td>\n",
       "      <td>0.134769</td>\n",
       "      <td>0.715265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>0.708808</td>\n",
       "      <td>0.130855</td>\n",
       "      <td>0.716132</td>\n",
       "      <td>0.134769</td>\n",
       "      <td>0.715265</td>\n",
       "      <td>0.204283</td>\n",
       "      <td>0.800697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>0.716132</td>\n",
       "      <td>0.134769</td>\n",
       "      <td>0.715265</td>\n",
       "      <td>0.204283</td>\n",
       "      <td>0.800697</td>\n",
       "      <td>0.094192</td>\n",
       "      <td>0.843938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     var1(t-3)  var2(t-3)  var1(t-2)  var2(t-2)  var1(t-1)  var2(t-1)  \\\n",
       "3     0.039567   0.223441   0.007090   0.282312   0.000000   0.322835   \n",
       "4     0.007090   0.282312   0.000000   0.322835   0.018905   0.171625   \n",
       "5     0.000000   0.322835   0.018905   0.171625   0.043401   0.208645   \n",
       "6     0.018905   0.171625   0.043401   0.208645   0.046946   0.276694   \n",
       "7     0.043401   0.208645   0.046946   0.276694   0.085634   0.407021   \n",
       "8     0.046946   0.276694   0.085634   0.407021   0.065537   0.291150   \n",
       "9     0.085634   0.407021   0.065537   0.291150   0.146213   0.459774   \n",
       "10    0.065537   0.291150   0.146213   0.459774   0.165131   0.354060   \n",
       "11    0.146213   0.459774   0.165131   0.354060   0.166378   0.218619   \n",
       "12    0.165131   0.354060   0.166378   0.218619   0.166144   0.227313   \n",
       "13    0.166378   0.218619   0.166144   0.227313   0.164307   0.222447   \n",
       "14    0.166144   0.227313   0.164307   0.222447   0.148241   0.298393   \n",
       "15    0.164307   0.222447   0.148241   0.298393   0.151626   0.376450   \n",
       "16    0.148241   0.298393   0.151626   0.376450   0.169401   0.407149   \n",
       "17    0.151626   0.376450   0.169401   0.407149   0.183642   0.224071   \n",
       "18    0.169401   0.407149   0.183642   0.224071   0.177916   0.297952   \n",
       "19    0.183642   0.224071   0.177916   0.297952   0.217495   0.273662   \n",
       "20    0.177916   0.297952   0.217495   0.273662   0.173339   0.264645   \n",
       "21    0.217495   0.273662   0.173339   0.264645   0.191267   0.259207   \n",
       "22    0.173339   0.264645   0.191267   0.259207   0.199826   0.213801   \n",
       "23    0.191267   0.259207   0.199826   0.213801   0.204298   0.165018   \n",
       "24    0.199826   0.213801   0.204298   0.165018   0.166667   0.238903   \n",
       "25    0.204298   0.165018   0.166667   0.238903   0.187433   0.333041   \n",
       "26    0.166667   0.238903   0.187433   0.333041   0.188379   0.214064   \n",
       "27    0.187433   0.333041   0.188379   0.214064   0.183237   0.290857   \n",
       "28    0.188379   0.214064   0.183237   0.290857   0.216420   0.265291   \n",
       "29    0.183237   0.290857   0.216420   0.265291   0.274886   0.196723   \n",
       "30    0.216420   0.265291   0.274886   0.196723   0.301108   0.236626   \n",
       "31    0.274886   0.196723   0.301108   0.236626   0.291002   0.317037   \n",
       "32    0.301108   0.236626   0.291002   0.317037   0.254801   0.190908   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "181   0.589640   0.262051   0.555510   0.230110   0.616317   0.146773   \n",
       "182   0.555510   0.230110   0.616317   0.146773   0.618338   0.216434   \n",
       "183   0.616317   0.146773   0.618338   0.216434   0.612557   0.146692   \n",
       "184   0.618338   0.216434   0.612557   0.146692   0.558484   0.144900   \n",
       "185   0.612557   0.146692   0.558484   0.144900   0.602174   0.295167   \n",
       "186   0.558484   0.144900   0.602174   0.295167   0.604459   0.323610   \n",
       "187   0.602174   0.295167   0.604459   0.323610   0.625687   0.277543   \n",
       "188   0.604459   0.323610   0.625687   0.277543   0.609675   0.342895   \n",
       "189   0.625687   0.277543   0.609675   0.342895   0.738815   0.287974   \n",
       "190   0.609675   0.342895   0.738815   0.287974   0.748584   0.239852   \n",
       "191   0.738815   0.287974   0.748584   0.239852   0.775967   0.337761   \n",
       "192   0.748584   0.239852   0.775967   0.337761   0.776453   0.258518   \n",
       "193   0.775967   0.337761   0.776453   0.258518   0.803461   0.133723   \n",
       "194   0.776453   0.258518   0.803461   0.133723   0.809931   0.247430   \n",
       "195   0.803461   0.133723   0.809931   0.247430   0.780440   0.247722   \n",
       "196   0.809931   0.247430   0.780440   0.247722   0.793846   0.155208   \n",
       "197   0.780440   0.247722   0.793846   0.155208   0.779863   0.191609   \n",
       "198   0.793846   0.155208   0.779863   0.191609   0.779500   0.122599   \n",
       "199   0.779863   0.191609   0.779500   0.122599   0.840602   0.000000   \n",
       "200   0.779500   0.122599   0.840602   0.000000   0.843827   0.108073   \n",
       "201   0.840602   0.000000   0.843827   0.108073   0.855611   0.195271   \n",
       "202   0.843827   0.108073   0.855611   0.195271   0.867113   0.346849   \n",
       "203   0.855611   0.195271   0.867113   0.346849   0.862935   0.320795   \n",
       "204   0.867113   0.346849   0.862935   0.320795   0.848871   0.252974   \n",
       "205   0.862935   0.320795   0.848871   0.252974   1.000000   0.131546   \n",
       "206   0.848871   0.252974   1.000000   0.131546   0.751017   0.111793   \n",
       "207   1.000000   0.131546   0.751017   0.111793   0.708808   0.130855   \n",
       "208   0.751017   0.111793   0.708808   0.130855   0.716132   0.134769   \n",
       "209   0.708808   0.130855   0.716132   0.134769   0.715265   0.204283   \n",
       "210   0.716132   0.134769   0.715265   0.204283   0.800697   0.094192   \n",
       "\n",
       "      var1(t)  \n",
       "3    0.018905  \n",
       "4    0.043401  \n",
       "5    0.046946  \n",
       "6    0.085634  \n",
       "7    0.065537  \n",
       "8    0.146213  \n",
       "9    0.165131  \n",
       "10   0.166378  \n",
       "11   0.166144  \n",
       "12   0.164307  \n",
       "13   0.148241  \n",
       "14   0.151626  \n",
       "15   0.169401  \n",
       "16   0.183642  \n",
       "17   0.177916  \n",
       "18   0.217495  \n",
       "19   0.173339  \n",
       "20   0.191267  \n",
       "21   0.199826  \n",
       "22   0.204298  \n",
       "23   0.166667  \n",
       "24   0.187433  \n",
       "25   0.188379  \n",
       "26   0.183237  \n",
       "27   0.216420  \n",
       "28   0.274886  \n",
       "29   0.301108  \n",
       "30   0.291002  \n",
       "31   0.254801  \n",
       "32   0.239798  \n",
       "..        ...  \n",
       "181  0.618338  \n",
       "182  0.612557  \n",
       "183  0.558484  \n",
       "184  0.602174  \n",
       "185  0.604459  \n",
       "186  0.625687  \n",
       "187  0.609675  \n",
       "188  0.738815  \n",
       "189  0.748584  \n",
       "190  0.775967  \n",
       "191  0.776453  \n",
       "192  0.803461  \n",
       "193  0.809931  \n",
       "194  0.780440  \n",
       "195  0.793846  \n",
       "196  0.779863  \n",
       "197  0.779500  \n",
       "198  0.840602  \n",
       "199  0.843827  \n",
       "200  0.855611  \n",
       "201  0.867113  \n",
       "202  0.862935  \n",
       "203  0.848871  \n",
       "204  1.000000  \n",
       "205  0.751017  \n",
       "206  0.708808  \n",
       "207  0.716132  \n",
       "208  0.715265  \n",
       "209  0.800697  \n",
       "210  0.843938  \n",
       "\n",
       "[208 rows x 7 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reframed = reframed.drop(reframed.columns[-1], axis=1)\n",
    "reframed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(170, 4)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = reframed.values\n",
    "n_train_hours = 170\n",
    "train = values[:n_train_hours, :]\n",
    "test = values[n_train_hours:, :]\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split into input and outputs\n",
    "train_X, train_y = train[:, :n_obs], train[:, -n_features]\n",
    "test_X, test_y = test[:, :n_obs], test[:, -n_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(170, 3, 1) (170,) (38, 3, 1) (38,)\n"
     ]
    }
   ],
   "source": [
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], n_hours, n_features))\n",
    "test_X = test_X.reshape((test_X.shape[0], n_hours, n_features))\n",
    "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 170 samples, validate on 38 samples\n",
      "Epoch 1/20\n",
      " - 1s - loss: 0.3689 - val_loss: 0.3847\n",
      "Epoch 2/20\n",
      " - 0s - loss: 0.2041 - val_loss: 0.1684\n",
      "Epoch 3/20\n",
      " - 0s - loss: 0.0876 - val_loss: 0.0603\n",
      "Epoch 4/20\n",
      " - 0s - loss: 0.0884 - val_loss: 0.0589\n",
      "Epoch 5/20\n",
      " - 0s - loss: 0.0830 - val_loss: 0.0573\n",
      "Epoch 6/20\n",
      " - 0s - loss: 0.0767 - val_loss: 0.0550\n",
      "Epoch 7/20\n",
      " - 0s - loss: 0.0688 - val_loss: 0.0541\n",
      "Epoch 8/20\n",
      " - 0s - loss: 0.0635 - val_loss: 0.0522\n",
      "Epoch 9/20\n",
      " - 0s - loss: 0.0568 - val_loss: 0.0504\n",
      "Epoch 10/20\n",
      " - 0s - loss: 0.0506 - val_loss: 0.0497\n",
      "Epoch 11/20\n",
      " - 0s - loss: 0.0449 - val_loss: 0.0486\n",
      "Epoch 12/20\n",
      " - 0s - loss: 0.0420 - val_loss: 0.0476\n",
      "Epoch 13/20\n",
      " - 0s - loss: 0.0375 - val_loss: 0.0472\n",
      "Epoch 14/20\n",
      " - 0s - loss: 0.0356 - val_loss: 0.0465\n",
      "Epoch 15/20\n",
      " - 0s - loss: 0.0344 - val_loss: 0.0463\n",
      "Epoch 16/20\n",
      " - 0s - loss: 0.0337 - val_loss: 0.0459\n",
      "Epoch 17/20\n",
      " - 0s - loss: 0.0334 - val_loss: 0.0464\n",
      "Epoch 18/20\n",
      " - 0s - loss: 0.0337 - val_loss: 0.0460\n",
      "Epoch 19/20\n",
      " - 0s - loss: 0.0336 - val_loss: 0.0459\n",
      "Epoch 20/20\n",
      " - 0s - loss: 0.0338 - val_loss: 0.0460\n"
     ]
    }
   ],
   "source": [
    "# design network\n",
    "model = Sequential()\n",
    "model.add(LSTM(5, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mae', optimizer='adam')\n",
    "# fit network\n",
    "history = model.fit(train_X, train_y, epochs=20, batch_size=4, validation_data=(test_X, test_y), verbose=2, shuffle=False)\n",
    "# plot history\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s2KRVJQU8dNsqHrptFe7O0e5BXj0UHA08s/skf/laO/lRo21dzWRDcPPKCg0f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\nY5T4RURyjBK/iEiOUeIXEckx/x86iwI/Im7JjwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d7b0550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 101.986\n"
     ]
    }
   ],
   "source": [
    "# make a prediction\n",
    "yhat = model.predict(test_X)\n",
    "test_X = test_X.reshape((test_X.shape[0], n_hours* n_features))\n",
    "# invert scaling for forecast\n",
    "inv_yhat = np.concatenate((yhat, test_X[:, -1:]), axis=1)\n",
    "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
    "inv_yhat = inv_yhat[:,0]\n",
    "# invert scaling for actual\n",
    "test_y = test_y.reshape((len(test_y), 1))\n",
    "inv_y = np.concatenate((test_y, test_X[:, -1:]), axis=1)\n",
    "inv_y = scaler.inverse_transform(inv_y)\n",
    "inv_y = inv_y[:,0]\n",
    "# calculate RMSE\n",
    "rmse = np.sqrt(mean_squared_error(inv_y, inv_yhat))\n",
    "print('Test RMSE: %.3f' % rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12d82c208>]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(inv_y)\n",
    "plt.plot(inv_yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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STmZstWH+O2n87pM9AJycHHf4+sAQiE60Ho0QEWLCAg/vKeCObifC2Otg7WtW1tFuJ3r+\nvnyEBgGllMdWZBTy/z7YRF5JFb+c2Jd7pw0hKiTQs5uU7YF1b1o7fZXmQFg364N/zGzoMeiIU+PC\nYd714/n3ih38Y/k2hsVHNXHTxsWEBbmeHXS0M/8A69+2Bokvf9Wza32IBgGllNuKymt4ZGk6H6Xt\nYUDPCN799SRS+se5vtChoQF2fGHNx9/2GRgbJJ8JUx+EIRdZq3ab4Ocn3DFlINdM7EdMqGcBJzYs\nkAMVHgaByF4wcS5893cr+Vz8aM+ub4XSqjpq6mz0bGm3mgc0CCiljmFrMOQcqCRjbxkZe8vJKCgj\nY+9BsvZX4idw9zkDufWsEwkOcL3C9xBjYPHtkPamNZPnlDtg7GyPu1qaXfDVhJiwIHL2V3p8Hafe\nCamvwJcPwbUfuD7fS95Ymc0zy7fxwx8mt3kg0CCglAKgus7GI0vT2ZBbyvbCg4fSO4hAv7gwBveO\n5MKT+jB9dDwDekZ6/gI//dsKAKf9Bs76EwR4/mHeUrFhgazf7WFLAKx1Aqf/Fj6//7ilk6i3NfDG\nymxOTu6mLQGl1PHz1dZC3lyVw4TkOK45uR+De0cypHckA3pGuJW3v1k7lsMXD8DQ6TD5z+B3fLPY\nx4YFUVJZhzEGj5MXj7/JyjC6/EGY85UVFdvQ5+kF5JdW8/CM4zORUoOAUgqA5ekFxIQF8tZNJxPg\n78UP6aIdsOgG6DkcLvnPcQ8AYHUH1doaqKy1Ed7EyuUmBYbA2ffBR7dB+kcwfGbbVNJu3g9ZJMWF\nMnlIzzZ9HQfdVEYpRb2tgRUZhUwe3NO7AaCqBN6eBf6BcNVbTef0b2OxYdZAskdrBZyNusqatvrV\nI2Cr92LNjrR5Tymrsw4we2L/Fi+085QGAaUUa3NKKK6sY8rQXt67aYMN3rsRijPhytchpv2SQcbY\nU0e4vWr4aH7+1p4D+3fAute9WLMjvfZjFqGB/lyRkuT6ZC/RIKCU4sstBQT6C2cM6u69my7/izUW\ncMHfod8p3rtvC7S6JQAw+AJIOhm+fhxqWzDTyIUDFbV8lLaHS8YmEB3m4ZqLVtAgoJTiiy0FTDyh\nG5GeLvhqyvoF8ONzMH4OpPzKO/dshdhwRxK5FrYEwBoQPudBKN8Lq/7jlXo5W/BzDjX1DVx/Sn+v\n37s5GgSU6uJ27Stn174KzvFWV1BuKiy+E/qfbm3i3gHEhDmSyLWiJQBWi2bgefD9M1B5wAs1s9Tb\nGnjjp2xOObEbg3q1YPptK2gQUKqL+9K+s9eUoV6YjVK2BxZcY2XevPw1a0C4A4gJtbcE3NlTwJXJ\n91uZS9M/bP297L5IL2BPafVxbwWABgGlurzlWwoY0juy+e0a3VFRBG9eDrXlcNUCCO/m+prjJCjA\nj4jggNaNCTj0HmnlOcpb0/p72c37MYvE2FDvDsy7SYOAUl1YSWUtqdnFre8KOlgA8y60Zs9c+Tr0\nGuadCnpRTFhg67uDwBobSBgHud4JAlvyy1iVeYDZk1q+/0JraBBQqgv7OmMftgbDOcNaEQTK9sC8\nC6BkN1zzLpw42XsV9KLYsKDWDQw7SxgH+7ZCzcFW3+q1H7MICfQ7rtNCnWkQUKoL+2JLAT0igxnp\n4RaQh5TkwKvnWy2Ba9+H5NO9W0Ev8lpLACAhBTCwZ12rblNcUcsH6/K4ZEziobUMx5sGAaW6qNr6\nBr7N2MeUIT3xa0k3xIFd8OoFUFUMsz+CvhO9X0kvigv3ZktgrPWzleMC76Tupqa+getO6eeFSrWM\n5g5Sqg1V1tbzRXoBi9P2sGlPKfNvOJnBvY/vFMCmrM48wMGa+pYNRhZth9emQ30VzF58XHPtt5TV\nHeSllkBYHMSdYE2HbaF6WwOv/5TNpBO6MaS3Z5vkeJPLloCIDBaRNKdHmYjc7XT8tyJiRKS7U9l9\nIrJDRDJE5Dyn8nEistF+7FnxOJ2fUh1fbX0Dy9MLuPPtdYx7ZDl3LUgjPb+Mqlob972/gYYG095V\nBKxZQcEBfpw2wMNVwoVbrRaArRau/9gnAgBY3UEHq+uptzV454YJ4yBvbYsvX76lkLySKq5rh2mh\nzly2BIwxGcBoABHxB/KAD+y/JwHnAjmO80VkGDALGA7EA8tFZJAxxga8AMwBVgGfANOAZV58P0q1\ni4YGw6rMAyxev4dPNuZTWlVHbFggl45NYPqoeMb3j+PDtDzuWbieN1fncO3E9mv+AxhjWL6lgNMG\ndCc0yIONYfZuhPkzwC8Qrl8KPQa3XSW9LNaRP6iqju4RTe9g5raEFNi4yBoYj4r3+PJ5P2aSEBPK\nOd5Yn9EKnnYHTQF2GmOy7b//A7gX+MjpnBnAAmNMDZApIjuACSKSBUQZY1YCiMh8YCYaBFQn8JfF\nm3l9ZTZhQf6cO6wXM0YncNrA7gQ6ZeS8ZEwC763N5YllWzlvWK/mNwypr4WKfVBecPhneaGVhTM2\nGWL7Q2w/CAxtUX23FZSTW1zF3LMGuH/RhkXw8T0QHAnXLfG5zdedVw17JwiMs37mrfE4CGzdW8bK\nXQf44/lDvJu1tQU8DQKzgLcBRGQGkGeMWX9Ur04CsNLp91x7WZ39+dHlxxCRm4GbAfr2bb/Mg0q5\nY8XWQl5fmc3sSf247/yhTX6zFhH+b+ZJnPfMtzy0JJ1/XzP28MGsH6xcOwd2QUWhNdjqjsh4iHME\nhWRITIETznK58cnyLQWAm6uEq0vh499a33oTJ8AvXm7XjKAt5WgJeG1wuPdJVosoNxWGXuzRpW+s\nzCYk0I9Z49tnWqgzt4OAiAQB04H7RCQM+BNWV5DXGWNeBF4ESElJ6RgdqEo1oqSylj+8t4HBvSK5\n/8KhLvfcTe4ezh1nD+CpL7Zx2dYCJkftgS8fgZ1fQkRvSJpgTbOM6GXtwxvRCyJ6Wo/wHlBbAQcy\noTjLStF8INP6ueNLK7EZWN0UZ90HA6Y0GQyWbylgZGI0vVxtX5j9I7x/C5TlWVtCnv5b8PfN+SSH\ngoCnG843JTAEeo9o0QyhH3fs57QBPdptWqgzT/42zwfWGmMKROQkIBlwtAISgbUiMgFrzMA5vCXa\ny/Lsz48uV8pnPfDRZg5U1PLK9ePd3nT9ljNPJG3tKlh4HTT8BKGxMPURmDDHdfdOYCiEd4ek8cce\nq62wvq1/+3d48zLrW/tZf7QWbzkFg30Ha0jbXcJvzhnU9OvY6uDrx+D7f1jf+m/4rPHX9CGHu4O8\n1BIAK+Cuf9vaO8HPvb//kspadhVV8IuURNcnHweedEZdhb0ryBiz0RjT0xjT3xjTH6trZ6wxZi+w\nGJglIsEikgwMBFYbY/KBMhGZaJ8VNJsjxxKU8ilL1u9hyfo93H3OQEa4u9iqJIegpbfzUuUdTLCt\n47v4G+Gu9XDqnc0GgH0Ha7j9rbU8tmwLtfVNzG4JCodx18Mda+Gif1gDlm9cCq9Mg50rwFiN6hVb\nCzGmma6goh3w8lT47ikYdTX8+nufDwDgnE7aSy0BsMYFasuhaJvbl6TtLgFgdFKM9+rRCm61BEQk\nHJgK3OLqXGPMZhFZCKQD9cBt9plBAHOBeUAo1oCwDgorn1RYVs0DH21idFIMvz7TjQHS6jL46v8g\n9RUQP2TiXJ4pPZdX0ypYfACGNzOu+HVGIb9btJ7SqjrqbIZVuw7wr6vHNJ3wLSAIUm6A0ddYu2B9\n9zS8PhP6ngKTbiN77V4uiixnWHU47LJfYwxgYN82+PIh8A+ysoC28X66x1N4kD+B/uK9MQGwxmDA\nGhfoOdStS9J2lyACIxN9KAgYYyqAJlMC2lsDzr8/CjzayHmpwAjPqqhUx2KM4Q/vbaC6zsZTV4xy\nPbvj4F548xdQkA5jfgln3gvRidxRWceH277mvvc38sHcU49JHlZTb+PJTzN46ftMBveK5K05E9lR\nWM4f3t3Ahc9+z9NXjGp+oVdAMIy/CcZcC2vnW9/s37mG3zuOz2/iuuQzYeYLEN3ovA2fJSLEhAV5\nL3UEQNyJEBxtjQuMvdatS9bllDC4VyQRnm5430Y6Ri2U8iHv/LybFRn7ePDiYZzYI6L5k4u2W10y\nFfvh6oUw8JxDh6LDAnngomHctSCN13/K4vpTkw8d27WvnDsXrGNTXhmzJ/XjTxcMJSTQn0G9IhnW\nJ4q5b67lxtdSueXME/jduYOPmIp6jIBga7xhzLWs/Wk5T36azh/OH8ropFjAHnhErOcBwdBnNPh1\nzowysWGB3u0O8vOzUkjkubdy2BhD2u4Szh/R23t1aCUNAkp5YPeBSh5Zms4pJ3Zj9qT+Lk7+Gd66\nAsQPrl9yeF65k+mj4nlvbR5PfpbBeSN60zsqhHfX5PKXxZsJCvDjxWvHce7wIz8w+ncP5/25p/Dw\n0nT++80u1mYX89xVY+kd7WKmT2AI7+7vx4aAAIZOmgpuDmR3JjHezCTqkDDOGkCvrYSg5vdkyCyq\noLSqjjF9O0ZXEGgCOaXc1tBg+O2i9fiJ8OTlo5pPupaxDF67GEKi4aYvGg0AYHVRPDpzBDZjuP+D\nTdy1II3fv7uBkYnRLLvr9GMCgENIoD9/veQk/jlrNJv3lHHBs9/x7bZ9zdbfGMOXWwo4Y1APt2cy\ndTax3swk6pCYAsYG+etdnnp4UDjWu3VoBW0JKOWmV37IZHXmAf5++SgSYpqZyrlmHiz9DfQZBVcv\ngogezd43KS6Mu6YM4m+fbsXfT/jduYO49awBbm0wMmN0AsPjo5n75hque3U1F42MJyY0kAB/IdDf\njwA/IcDfj0A/oby2noKymnbZvaqjiA0LYm1liXdv6rxyuN+kZk9dl1NCRHAAA3q66EY8jjQIKNWE\nhgZDra2BWlsDmfsqeOKzDKYO68VlY5sYMDUGvn4cvnkcBkyFy+dBsHv/2W86PZmqOhtnDurBuH6e\nfUsc0DOCj247jYeXprN8SwH1tgbqbYa6ButnvVPCusiQACYPad9cNe3JMTBsjMFr+SsjekJ0X7fG\nBdJ2lzAyMbpddhBrigYB1eVV1dq45Y01bM0vsz706xuoszVQZztysXpceBCPXXpS4x8etnr4+DfW\nLJzRv4SLn/Fok/VAfz/umdrM4i0XQoP8eezSk3iMk445ZowVCOptBn8/ISig6/YCx4YFUmczVNTa\nvDs7J3Gcy5XD1XU2tuSXcfMZJ3jvdb1Ag4Dq8v726Va+3baPy8YmEh7sT5C/H4EBfgT5+xHk9PPU\nAd0bTzy2J83KrZOXCmf8Hs6+32XunuNJRAj0FwK75jDAEZxTR3g1CCSMg80fQPm+Jrv/NuWVUt9g\nGNO344wHgAYB1cX9tHM/837M4vpT+vPg9OGeXVxVbC0A+/llK5XDZS/DSb9om4oqr3BOHZEU58Ub\nJ9gXjeWtgcHTGj2lo60UdtAgoLqs8pp6fv/uevp3C+PeaR7kxTfGyhfz+QNQdQAm3Axn/wlCO9Z/\nbnWsNkkdAdYkAPG3WoNNBIF1OSUkxobSI9ILaay9SIOA6rL++skW8kqqWHTLJMKC3PyvULDZ6vrJ\n+QkSx8OF71sfAMonxNpbAl4PAkFh0GtYs+MCabtLOtT6AAcNAqpL+nbbPt5alcPNZ5xASn83+gWq\ny6yZP6v+Y839n/6cNQDcSVfWdlaO1M1ezSTq4BgXaGg45t9FYVk1eSVV3HBachMXtx/9F6y6nNKq\nOv7w3gYG9Ixwf0bOwtmw8nkrP8wda2DsbA0APigmtI1aAmCNC1SXWhsDHWVdBx0PAA0Cqgt6ZGk6\nhQdreOryUYS4M2WmJAd2rbA2arn4nxDmzRFFdTwF+PsRGRLQdi0BaHS9wLqcEgL9heHxUd5/3VbS\nIKC6lC+3FPDumlxuPfNERrn7rWzTe9bPkVe0XcXUcRMbFtQ2LYEegyEootFxgbTdxQzrE+Xel47j\nTIOA6jKKK2r54/sbGdI7kjunDHT/wo3vWoPAcR2vP1d5zsok2gYtAT9/iB9j7S3gxNZg2JBb6nlX\nkDk+O+tqEFBdxl8Wb6a4opanrhjl/qrZwi1QsAlOurxtK6eOG6/vKeAsYRzs3Qj1NYeKthUcpLLW\n5tkisYxP4dULoLywDSp5JA0CqktYtjGfxev3cOeUgQyPd3MrSLBaAeIHwy9pu8qp4yo2LJD95W0Y\nBBrqrEApatIhAAAgAElEQVRg5/EiscoDsOROa5A5xIN/qy2kQUB1ej/uLOL+DzdxUkI0t57lxlaQ\nDsZYG7efcJaVJEx1CqOSYsgrqWJDrpezicLh7SadxgXW5RQTGxZIv27N7zVwyLJ7oXI/XPIfa5Of\nNuYyCIjIYBFJc3qUicjdIvKIiGywl30uIvFO19wnIjtEJENEznMqHyciG+3HnhWvpfFT6lgbcku4\n9uVVXP2/VQQH+PGPK0c1vwPX0XJToSQbRmgqiM7kF+MSiQgO4JXvM71/86h4iOxzxLhA2u4SRifF\nuJe1NH2x9cXjjHuhz0jv168RLv9HGGMyjDGjjTGjgXFAJfAB8KQxZqS9fCnwZwARGQbMAoYD04Dn\nRcQxJP4CMAcYaH80vr5aqVbYUVjO3DfXMP1fP7Apr5T/d+FQVvzuLAb0jPTsRpveBf9gGHpR21RU\ntYvIkEAuT0lk6YZ8Csqqvf8CCYczih6srmN7Ybl7m8hUFB3eh+L0e7xfryZ42h00BdhpjMk2xpQ5\nlYcDjqHsGcACY0yNMSYT2AFMEJE+QJQxZqUxxmBtcz2zlfVX6pC8kirufXc95/7jG77J2MedUwby\n7b1nc9PpJ3g+Nc9WD5veh0HnHZd+WXV8XX9Kf2zG8PpP2d6/ecI4OLATKg+wIbcUY3CdLsIY+Pge\nqCmDmf/xKA15a3maNmIW8LbjFxF5FJgNlAJn24sTgJVO1+Tay+rsz48uV6pVqmpt/P3zjEP/oa8/\nJZnbzj6Rbo2lfXZX1rdQUaizgjqpft3CmTq0F2+uyub2yQO8O3/fMS6wZy3rcvoBuF6Tsuk9SP8I\npvzFykF0HLndEhCRIGA6sMhRZoy53xiTBLwJ3O6tSonIzSKSKiKp+/Y1v2+qUm+uyubl7zOZOSae\nFb8/iz9fPKx1AQCsWUHBUTDwXO9UUnU4N5yWTHFlHR+uy/PujePHAAIbFrExZx8n9ggnOrSZb/YH\nC+CT31lpJ06507t1cYMn3UHnA2uNMQWNHHsTuMz+PA9IcjqWaC/Lsz8/uvwYxpgXjTEpxpiUHj2a\n359Vqe0F5XSPCOaJX7jY+9ddddWwZQkMvRgCQ1p/P9UhnZwcx7A+UbzyQybGmwuzgiNh/I2wYQH3\nZs1hZrfcps81BpbcBXVV1mwg/+Of09OTIHAVR3YFOS+5nAFstT9fDMwSkWARScYaAF5tjMkHykRk\non1W0Gzgo1bVXikgc38F/d2dfueO7Z9bfbO6QUynJiLccFoy2wrK+X5HkXdvfuFT7Lv4dYJNNbdn\n3Q5L77Hm/R9t/QLYtgwmPwDdPVjF7kVuBQERCQemAu87FT8uIptEZANwLnAXgDFmM7AQSAc+BW4z\nxtjs18wFXsIaLN4JLPPGm1BdW/b+Cvp3D/feDTcugvCe0P8M791TdUgXj+pD94jgNpku+lNACufW\nPMH+ETfCmlfhXxOsKaCOVkdpHiz7A/SdBBNv9frru8uttocxpgLodlTZZU2cjjHmUeDRRspTgREe\n1lGpJlXW1lNQVkOyt4JAdSls+wxSftUuTXN1fAUH+HPtxH78Y/k2du4r58QeEV6797qcYhoCw4i5\n5EmYdI21CnjhtTD4QrjgCVh8p7W6eObzVt6hdqIrhpVPyyqqBHB/NaYrWz8GW40uEOtCrpnYlyB/\nP+b9kOXV+6btLmFkQgwB/n6QMBbmfA1TH4GdX8GzY2DnlzD1YYg7wauv6ykNAsqnZe+vAKB/Ny+1\nBDYugph+h6f5qU6ve0QwM0bH8+6aXEq9lF20pt7G5rwyRjuvD/APgFPvhNtWwoBzrHxUKTd65fVa\nQ4OA8mmZjiDgje6g8kLY9bW1NkAzmnQpvzo1mao6G2//nOOV+23JP0itrYExja0PiO0PV70Nl8/r\nELvTtX8NlGqFrKIKukcEExHshf77zR+AadAFYl3QsPgoJp3Qjdd+zKLO1tDq+63LKQY4siXQQWkQ\nUD4ta38lyd29NB6w8V3odRL0HOKd+ymfcuNpyeSXVvPZ5r3NnmdrMKzNKWZN9gG25JeRs7+SovIa\nqmpth9YbpO0uoXdUCH2ivbBupY3p9Afl07KKKjhzkBcWFB7IhNzVcM6Drb+X8kmTh/SkX7cwXvk+\nk4tGxh9zPL+0ioU/57IwdTd5JVWN3kMEwoMCqK6zcc7QXm1dZa/QIKB8VkVNPYUHa7wzHuDYR3hE\nkzOfVSfn5yf86pT+PLgknXU5xYzpG0u9rYEVGftYsDqHFRmFNBg4bUB37p02mJiwICpr6qmotVFZ\nW09FzeGfVXX1XJGS5PpFOwANAspnZe+3poe2emaQMVZXUN9JENPXCzVTvuoXKUk89fk2nvtqB8Pj\no1iYupuCshp6RAZz61kncmVKX/p6c3V6B6BBQPmsrEMzg1r5n3LHl7BvC0x/zgu1Ur4sIjiAK8cn\n8dL3mazIKOSsQT14eEZfJg/p6dmGRD5Eg4DyWZlFVhDo15qWgDHwzeMQnQQjZ3mpZsqX3TFlIP26\nhTF5aC/vJCTs4DQIKJ+Vvb+CHpGtnB66awXk/gwXPg0BQd6rnPJZ0aGBXDupf3tX47jpnO0b1SVk\nFVWS3NpWwNd/g6gEGPNL71VMKR+iQUD5rMz9Fa0bD8j8BnavhNN+AwGt3IRGKR+lQUD5pIqaevYd\nrGndeMA3T0BkHxhzrfcqppSP0SCgfJJjZlCLU0hnfgfZP1itAN09THVhGgSUT3KkkG7xGoFv/gYR\nvWHsdV6slVK+R4OA8kmOlkCL9hHI+gGyvoNT79JWgOryNAgon5RVVEHPyGDCWzI99Ju/WdtHpvzK\n+xVTyse4DAIiMlhE0pweZSJyt4g8KSJbRWSDiHwgIjFO19wnIjtEJENEznMqHyciG+3HnrVvOK+U\nx7Jauq9wzkprVtCpd0Fg518IpJQrLoOAMSbDGDPaGDMaGAdUAh8AXwAjjDEjgW3AfQAiMgyYBQwH\npgHPi4hjA80XgDnAQPtjmnffznFSshs+uRce7ws//bu9a9MlZe2vpH9LuoK+fhzCe0DKDd6vlFI+\nyNO29BRgpzEmG8h2Kl8JODZlnQEsMMbUAJkisgOYICJZQJQxZiWAiMwHZgLLWlH/46toO3z/DGxY\nYP3eYyh89ieoKoaz79fdqI6Tcvv0UI9bArtXWyuEpz4MQZ0rCZhSLeVpEJgFvN1I+Q3AO/bnCVhB\nwSHXXlZnf350edv4+LdQXQqRva254Id+2p970hWQvwG+ewrSP4KAEBh/E0y6HaLiYend8O2TViA4\n/8kOsV1cZ5dlzxnk8Wrhb/4GYd2svz+lFOBBEBCRIGA69m4fp/L7gXrgTW9VSkRuBm4G6Nu3hal9\nK/ZB/nooywdbzbHHQ2KsYBDREyJ6QUQvTEQvygPjyKuLYld1OP7VxZxb8g6y/XMIjoLT74GTb4UI\np01MLn4WQmPhh39aQWfmC+Af2LI6K7c4Ukh7tFAsdw3sWG5tGhPkpU3pleoEPGkJnA+sNcYUOApE\n5HrgImCKceyrBnmA824KifayPPvzo8uPYYx5EXgRICUlxTR2jktXzHfcDKpL4OBeOJhvBYWD+dSW\n7KG4IIeG/YUE5u4gsm4/wdQQCQyxPwDqQ+IImPyA9e0xtJH9QkWs7oXQWFj+IFSXWRtIa3dDm2lR\nCulvHofQOBg/p41qpZRv8iQIXIVTV5CITAPuBc40xlQ6nbcYeEtEngbisQaAVxtjbPaZRROBVcBs\noO0TuItYH9ChsdBzKGB1J1z/6mqy7N8oe0UFc0KfcIZ292NYZBUDwiqJaSjm6U/TGXPW1fzqjOGu\nX+e031iti6W/gTcug6sXQEh0W76zLiuzqIJeUcGEBbn5z3dPGmz/HCY/AMERbVs5pXyMW/+LRCQc\nmArc4lT8LyAY+MI+03OlMebXxpjNIrIQSMfqJrrNGGOzXzMXmAeEYg0IH/dB4TXZxcyZn4oxhlev\nH09K/1giQxrvvtmwagVVOVW4PZs85VfWB//7N8O8C+GXHxzZdaS8Int/hWddQSufh6AImKCtAKWO\n5lYQMMZUAN2OKhvQzPmPAo82Up4KjPCwjl7z6aZ87lqQRp/oEF791QSXeWcmJMfxeXoBDQ0GPz83\nZ/6MuNQaP3jnl/DqNLj2Q4jxjb1GfUVmUSVThvR07+SyfGv/4PE3actMqUZ0maksL3+fya1vrmVY\nfBTv3XqKW4nHJiR3o6Syju2F5Z692MBzYPaHUF4IH821xiWUVxysrqOo3IPpoT//DxpscPItrs9V\nqgvq9EHA1mB4aMlmHlmaznnDevP2nIl0i3Avd/zJyXEArM464PkL951orR3I/Nbaw1Z5xeHN5d0Y\nFK6thNRXYMiFEHdCG9dMKd/UqYNAdZ2N295cy6s/ZHHDqcn8+5qxhAT6u77QLjE2lD7RIazObEEQ\nAGtVamx/WP4X69uoarXDM4PcaAlsWGCt35g4t41rpZTv6rRBYH95DVf9byWfpe/lzxcN488XD8Pf\n3X59OxFhfP84Vmfux7SkSycgCKb8GQo2wYaFnl+vjpFV5Gb20IYGWPkC9BkF/U45DjVTyjd1yiBQ\nW9/AFf/9ifQ9ZbxwzThuOC25xfeakBxHQVkNOQcqXZ/cmGGXQPwY+Or/oK66xfVQlqz9le5ND935\nFRRtg4m3aToPpZrRKYNAUIAfd0weyNs3T2TaiN6tupdjXGBVS7uE/PysxWRlubD6v62qi7JaAm5t\nJLPy39amMcMvaftKKeXDOmUQAJg5JoGxfWNbfZ8BPSOICw9q+bgAQPIZMPBcK/9QZSvuo8jaX+F6\nZlfhFqslMGGO1SWnlGpSpw0C3mKNC8S2LgiAlbOmuswKBKpFrOmhta4Xiq183kr0N043jVHKFQ0C\nbpiQ3I2cA5XsLW1Fn36v4TD6Glj9IpTkeK9yXYhjemhyczmDKopg/TswahaEd2v6PKUUoEHALRP6\nt2K9gLOz/wTiB18ds5hauSGzyI3poamvWFljdVqoUm7RIOCGoX0iiQgOYHXm/tbdKDoBTv41bHjH\n2qNAeSTbsbl8XBNBoL4GVv8PBpwDPQYfx5op5bs0CLghwN+Pcf28MC4AVrbR0BhrAZnySGZRJb2j\nQggNamLB36b3oKJQWwFKeUCDgJsmJMexraCcAxW1rbtRaAyc8Xtr9srOr7xTuS7C2ly+ifEAY6wB\n4R5D4cTJx7diSvkwDQJucqwX+Lm14wJgZbSM6Qtf/MVa2arckr2/mTUCWd/D3o0w8VZdHKaUBzQI\nuOmkxGiCA/y80yUUEAyT/wx7N8Cmd1t/v7aUvx7WvdHumVAd00ObHBRe+by1f/DIK45vxZTycRoE\n3BQc4M/opBjvtAQARlxm5bVZ/iDUHPTOPb1tzzqYdxF8dBt8dn+7BoKsIkf2UKcgUF8DRdth8weQ\nsQxSboTA0HaqoVK+yZPtJbu8k5Pj+NeKHZTX1BMR3Mo/Oj8/uOApeHmqNWX0/Me9U0lvKdwKr19q\nbZk5bIaVhsFWC+c/YdX9eKo8QM3695nrv4qJmxZDah4cyISyPMAemEJjrW42pZRHNAh4YEJyNxq+\n2sGa7GLOHOSFbSOTxsP4G62cQiOvgISxrb+nNxzYBfNngH8QXPcRxCZbA9o/PmcFgoue8WogMMYg\nTfXjVxXDy1NJ2b+DlEAwu3ta6bn7n2rVKy7Z+r3HEKuOSimPaBDwwNh+MQT4Casz93snCICVanrL\nUlhyF8xZAf7t/FdSmmcFAFsN/GrZ4c1Ypj4C/sHw3d/BVgcz/gV+7u/N0JR9B2u48sWfSO4Wzj+v\nGnNkC8tWD4uuh+JsXkr6K28V9OOr31/U6tdUSh3m8uuciAwWkTSnR5mI3C0il4vIZhFpEJGUo665\nT0R2iEiGiJznVD5ORDbajz0rTX7965jCggIYkRDtncFhh5BoOP9v1iDxqv94774tUb4PXp8JlcXw\ny/eh59DDx0RgygNw1p9g/VvwwS3Wh3QrVNbWc+NrP5NXXMXX2/Zx+X9+Ir+06vAJn90Hu76Gi5/h\nk5rR9OyuaSCU8jaXQcAYk2GMGW2MGQ2MAyqBD4BNwKXAt87ni8gwYBYwHJgGPC8ijq+MLwBzgIH2\nxzQvvY/j5uTkONbvLqW6zos7hQ2bAYOmwYpH2y+vUFUJvHEJlOyGaxY23TV11h9gyl9g4yJ47war\nVdACtgbDnW+vY1NeKf++eiyvXD+e3QcqueTfP5K+pwx+ftnKszTpdhjzS7L3V7q1L7RSyjOe9j1M\nAXYaY7IdBY18mZ8BLDDG1ACZIrIDmCAiWUCUMWal/br5wExgWQvr3i7G94/jv9/uIm13CRNP8NI3\nUxG44O/w75Ph49/B1e94NNfd1mAoKq8hv7SavaVV9p/V5JdWs+9gDYN7R3Lu8F5M6B9HgH8jcb+m\nHN683BoMvnqB6524Tr/Hmub62Z+s1sDlr1q/u8kYw8NLNrN8SyEPzxjOOcN6AbDo15O4Yd7PPPmf\nF3nZ/zH8Bp4LUx+mrLqO/RW17u0joJTyiKdBYBbwtotzEoCVTr/n2svq7M+PLj+GiNwM3AzQt29f\nD6vYtsb3j0MEfs484L0gABCTBJPvtz5Y0z90azOUnP2VzH5lFbuLq7A1HDl9M8jfj97RIcSFB/H2\n6hzm/ZhFTFggk4f05NxhvTljUHdrd666alhwNeSlwuWvWXl33DHpNmvg+JPfwduz4Ix7IWmCW+ME\nL3+fyWs/ZTPn9GRmT+p/qHxonygWXx1PyLzZ7LD1Iq3fQ1zh509WkTWF1mUKaaWUx9wOAiISBEwH\n7mu76liMMS8CLwKkpKS07yqlo0SHBTK4V2TrM4o2ZsItVnK5ZX+wUh+ERDd7+pINe8jaX8mvzzyR\nhNhQ+kSF0Ds6hD72D39HK62ytp7vthfx+eYCvtxawPtr8wgOEG5JzOHamgX0KF4HM/8Dw6Z7WN85\n4B9otV52fgWhcTBwqtW1NWBKo/VftjGfRz/ZwgUn9ea+84ceebC6lB5LrsMEB/JS3F9ZuDSTnQeF\nYX2iALQ7SKk24ElL4HxgrTGmwMV5eUCS0++J9rI8+/Ojy33OyclxLFqTS52tgcDGuldayj8ALv4n\n/G8yLH8ILnq62dO/SC9gVGI0fzx/SLPnhQUFcN7w3pw3vDf1tTVkfvMGkWtfoPfe7RSaGF7qcS83\njb6qZXUedz0Mm2kFgW2fwvbPrUDmF2B1Kw06HwadB3EnsCanmLvfSWNMUgxPXzEaPz+nLq8GG7x7\nAxzYiVz7IX/teypBSzbz3292ERdu7Q7mcnN5pZTHPPkEuwrXXUEAi4FZIhIsIslYA8CrjTH5QJmI\nTLTPCpoNfORxjTuACcndqKy1sXlPmfdvHj/GSjed+grsXt3kaYUHq0nbXcI5Q3u5d9/qUvjhWQL+\nNYaBP9xD7wh/zPR/8er4xfw1bzRF5TUtr3NoDIy4FC59EX6/E371qTWgW15ozfB5biz1Tw6k/NVf\n8IfQj3jtjDJCakuOvMfnD8CO5dbYSPLpBPj78ciMEdx/wVCKK2vpEx1CSGDrp6QqpY7kVktARMKB\nqcAtTmWXAM8BPYCPRSTNGHOeMWaziCwE0oF64DZjjGMqzVxgHhCKNSDsU4PCDuOTrb2LV2fuZ3RS\nGyxQOvt+SF9srR245Vury+UoX20pBDg0qNqk0jxY9QKkzoPag9Z+xxf/E06cgvj5MTP+IC98n8uy\njflc69Q/32J+/tBvkvWY+hAcyKRi86d8/82nDGQ7Z9StRd61f5eI6WfNQgqNg9SXreCXcnhLSBFh\nzhknMLRPFHWaaE+pNuFWEDDGVADdjir7AGuqaGPnPwocs32WMSYVGOF5NTuWnpEhJHcPZ3XmAW4+\n40Tvv0BwBFz4d2vA9cdn4fTfHnPK8i0FJMSEMqR3ZNP32f4FLJxt5dgZfgmccrvV0nAyuHckA3tG\nsGSDd4JAQ4NhX3kNucVV5BZXklts45ONI9hR3Z+35kzkhF7+kJ8GeWut3ER5a6xpsQPPhXMb33Ht\ntIHdW10vpVTjdMVwC03oH8enm/fS0GCO7Nv2lsHnw9DpsOIxCOsO4647dKiq1sZ324u4akLfptMt\nrH8HPppr7W18xXwrtUITLh4Vzz+WbyO/tIo+0Z4lYCuuqOWZ5dvYVVRBbnEVecVV1NqO/NbeIzKY\nf84aw7h+VguK5DOsh0N1KQRHaQpopdqBBoEWmpAcxzupu9lWeJAhvaPa5kWmP2sNli650/r2PO1v\nEBDEDzuKqKlvaHo84Kfnrb74/qfDrLcgpPn6XTSyD09/sY2PN+Rz0+kneFTFf63YwRurcjgpIZph\n8VGcO7wXibFhJMaEkhgbSkJsqDUVtTkuZkEppdqOBoEWmnRiN/wEFqXm8sBFw9rmRUJj4Zp34cuH\n4Id/QsFmuGI+y7cUEhkcwAT7RjeHGANfPgzfP221Ii79HwSGuHyZE3pEMDw+iqUeBoHK2noWpu7m\ngpP68NxVY1xfoJTqcHQ/gRaKjwnl0rGJvL4y+8h8N97m5w9TH4ZfvAp7N2L+eyaF6d9yxuAeBAU4\n/fXZ6q0Ww/dPW9M2L5/nVgBwuGhkPGm7S9h9oNLtaz5Yl8fB6nquP6Wf++9HKdWhaBBohbumDMQY\nw7Nf7mj7FxtxKdy0nFoJ4j/1f+am0G8OH6urhkXXwdr51v7FFz3jcYbPi0b2AWDphny3zjfGMP/H\nbIbHRzG2b6xHr6WU6jg0CLRCUlwYV03oy6LU3WQVVbToHsaT3bp6DefFwS+x0gxnzPoHYfGdUFEE\nb/4Cti61xgwm/78WDbAmxYUxpm8MS9bvcev8VZkHyCg4yHWT+jc9OK2U6vA0CLTS7WcPIMBfeGb5\nNo+uM8Zw14J1zJmf6lEgWLq9hhcSHoPT7oG1r8HTwyDnJ7j0JZj4a0+rf4SLRsaTnl/Gzn3lLs99\nzZ6LaPro+Fa9plKqfWkQaKWeUSFcd0p/Plq/h4y97u8V/OoPWXyUtoflWwr5PN1VJg5Lzv5KMgoO\nMmVYPJzzFyvhW7cBcNU7MPLylr6FQy48qQ8isHR9811Ce0qq+Dy9gCvHJ+kqXqV8nAYBL/j1GScS\nERTA019kuHX+prxSHl+2lSlDejKgZwRPfLqVepvrFbHLt1jBYqpjlfDwmTD3RxjoZuZPF3pHhzC+\nfxxLNuxptnXy1qocjDH88mQdEFbK12kQ8ILY8CBuOv0EPttcwPrdJc2eW1FTz51vryM2PJAnLx/F\nvecNZue+ChatyW32OoAvtxYwsGdEm6ZUvnhUPDsKy8koaLxVU11n4+3VOUwZ2oukOE3oppSv0yDg\nJTec1p/YsED+/nnzrYGHlmwmc38F/7hyNHHhQUwd1otx/WL5xxfbqKxtervG0qo6Vu06wBR3E8a1\n0PkjeuMnNDlA/MnGfPZX1HKdN/IMKaXanQYBL4kMCWTuWQP4bnsRK3ftb/Scxev3sDA1l9vOGsAp\nJ1r5cESEP10whMKDNbzyfWaT9/9m2z7qGwxTh/Vsk/o7dI8I5tQB3Vm6Ib/RLqHXfszixB7hnDpA\n9/tVqjPQIOBF107qR6+oYP7+WcYxH6C7D1Ry//sbGds3hrvOGXjEsXH94jh3WC/+880u9jeR0nl5\negHdwoMYndT2c/IvGtmH7P2VbMwrPaI8bXcJ63NLue4UnRaqVGehQcCLQgL9uX3yQFKzi/l6275D\n5XW2Bu5csA6Af84a0+hGNPdOG0xlbT3PfXXswrM6WwMrMgqZPKQn/m2RrO4o5w3vTaC/HLNwbP6P\nWUQEB3Dp2MQmrlRK+RoNAl52ZUoSSXGh/P2zDBrs+/4+s3wb63JK+OulJzU5mDqgZyRXjk/izVXZ\n5Ow/MnXDz5kHOFhd73rvAC+JCQvijIE9WLp+z6H3UFRew9IN+Vw2NoGIYE05pVRnoUHAy4IC/Lh7\nyiA27ynj0817+XFHEc9/vZMrUhK5eFTzC6vuPmcQ/n5yzODyF1sKCArw4/TjmFf/olF92FNazbrd\nxQAsWJ1Dra3BOxvPKKU6DA0CbWDmmAQG9Izgyc8yuPudNJK7h/Pg9OEur+sVFcKNpyWzeP0eNuZa\n/fHGGJZvKeC0Ad1dp2T2onOG9iI4wI8l6/OptzXwxsocTh/YnQE9I45bHZRSbU+DQBvw9xN+O3UQ\nmUUVlFTW8eysMW5/gN9y5onEhgXy+KdbMMawvbCc3Qeq3N9L2EsiQwI5e3BPPt6Yz7JNe9lbVs1s\nbQUo1em4DAIiMlhE0pweZSJyt4jEicgXIrLd/jPW6Zr7RGSHiGSIyHlO5eNEZKP92LPSiaeYTBvR\nm6sm9OWJX4xkRIL7m6ZEhQRyx+SB/LBjP99tL+ILe0qJKUPbdmpoYy4eFc++gzU8tCSdxNhQJg85\n/nVQSrUtl0HAGJNhjBltjBkNjAMqsfYW/iPwpTFmIPCl/XdEZBgwCxgOTAOeFxFHgpkXgDnAQPtj\nmnffTschIjx26UnMHJPg8bXXTOxLUlwojy3byufpBYxMjKZXlPt7A3jL5CE9CQvyp6i8hmsn9jsu\nM5OUUseXp91BU4CdxphsYAbwmr38NWCm/fkMYIExpsYYkwnsACaISB8gyhiz0liT6Oc7XaOcBAf4\n87tzB7Mlv4z1u0uOe1eQQ2iQP+cO60VIoB9XpCS1Sx2UUm3L0yAwC3jb/ryXMcYxkXwv4PikSgB2\nO12Tay9LsD8/ulw14uKR8YxIsPYGbq8gAPCXi4fz4W2nEhse1G51UEq1Hbenm4hIEDAduO/oY8YY\nIyIe7I7i8rVuBm4G6Nu3r7du61P8/IS/XTaSZRv3MrRPZLvVIzY8SAOAUp2YJ3MOzwfWGmMcye8L\nRKSPMSbf3tVTaC/PA5z7DhLtZXn250eXH8MY8yLwIkBKSorXgouvGR4fzfB49weVlVLKU550B13F\n4a4ggMXAdfbn1wEfOZXPEpFgEUnGGgBebe86KhORifZZQbOdrlFKKdUO3GoJiEg4MBW4xan4cWCh\niF64nasAAAQ+SURBVNwIZANXABhjNovIQiAdqAduM8bY7NfMBeYBocAy+0MppVQ7EY82Om8HKSkp\nJjU1tb2roZRSPkVE1hhjUlydpyuGlVKqC9MgoJRSXZgGAaWU6sI0CCilVBemQUAppbqwDj87SET2\nYU1BbYnuQJEXq9MWtI7eoXX0Hl+op9bRtX7GmB6uTurwQaA1RCTVnSlS7Unr6B1aR+/xhXpqHb1H\nu4OUUqoL0yCglFJdWGcPAi+2dwXcoHX0Dq2j9/hCPbWOXtKpxwSUUko1r7O3BJRSSjWjUwYBEZlm\n3+R+h4j8sb3r0xQRyRKRjSKSJiIdIkueiLwiIoUissmpLE5EvhCR7fafsR2wjg+KSJ79zzJNRC5o\n5zomicgKEUkXkc0icpe9vMP8WTZTxw7zZykiISKyWkTW2+v4kL28I/05NlXHDvPn2JxO1x1k39R+\nG1bq61zgZ+AqY0x6u1asESKSBaQYYzrMfGcROQMoB+YbY/5/e3cPGkUURXH8f4kRJAqKiAgiGrAL\nEptUKVKJXdAiaBU7CxHtBBtFEERU7CxEIYgfBOJHWgVBbUQSRIVYiNhITIogmkowx+K9hWWzM6Ry\nbmbuD0ImswQOB3bv7pth30A+dxVYknQlD9Vtks45y3gRWJZ0rapc7fJGS7skzZrZFmCGtKf2CZx0\nWZJxDCdd5r1H+iQtm1kv8AY4AxzFT49FGQ/jpMcydfwkMAR8kfRV0h/gETBacaZ1Q9IrYKnj9Cgw\nkY8nSC8UlSnI6IqkeUmz+fg3MEfaU9tNlyUZ3VCynP/szT/CV49FGdeFOg6Boo3uPRLwwsxm8r7K\nXu3MO8MB/AB2VhmmxGkz+5CXiypdsmpnZnuBg8BbnHbZkREcdWlmPWb2nrSF7XNJ7nosyAiOeixS\nxyGwngxLGiTt33wqL3O4prR+6PFdzi2gHxgE5oHr1cZJzGwzMAWclfSr/TEvXXbJ6KpLSX/z82Q3\nMGRmAx2PV95jQUZXPRap4xAo2ujeHUnf8+9F4AlpKcujhbx+3FpHXqw4zyqSFvITcQW4jYMu8/rw\nFHBf0uN82lWX3TJ67BJA0k/gJWmt3VWPLe0ZvfbYqY5D4B2w38z2mdlG4BgwXXGmVcysL1+Ma+3h\nfAj4VP5flZkGxvPxOPCswixdtV4QsiNU3GW+WHgHmJN0o+0hN10WZfTUpZntMLOt+XgT6YaPz/jq\nsWtGTz2Wqd3dQQD5VqybQA9wV9LliiOtYmb9pHf/ABuABx5ymtlDYIT0DYgLwAXgKTAJ7CF9o+uY\npMouzBZkHCF97BbwDTjZtmb835nZMPAa+Ais5NPnSWvuLrosyXgcJ12a2QHShd8e0pvWSUmXzGw7\nfnosyngPJz2WqeUQCCGEsDZ1XA4KIYSwRjEEQgihwWIIhBBCg8UQCCGEBoshEEIIDRZDIIQQGiyG\nQAghNFgMgRBCaLB/YnrBKEGJEM0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d82cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inv_y = np.insert(inv_y,0,7000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6tzGs0oZb7imgNex/B/41H7Y9DQtuZfPy97ktaxHJiy5gfvLE7h9r8TGyUr6/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127740d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(inv_y)\n",
    "plt.plot(inv_yhat)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elephas Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spark_df = sql.createDataFrame(df)\n",
    "CleanDF = spark_df.withColumn(\"DateTime_casted\",spark_df['DateTime'].cast(TimestampType()))\n",
    "FinalDf = CleanDF.selectExpr(\"DateTime_casted as Date_Time\", \"Sentiment_score\",'Price AS Price1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.engine.training import _slice_arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "FinalDf.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import lag, col\n",
    "from pyspark.sql.window import Window\n",
    "for i in range(2,3):\n",
    "    col1 = \"Price%d\"%(i-1)\n",
    "    col2 = \"Price%d\"%i\n",
    "    w = Window().partitionBy().orderBy(col(\"Date_Time\"))\n",
    "    FinalDf=FinalDf.select(\"*\", lag(col1).over(w).alias(col2)).na.drop()\n",
    "    FinalDf.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "FinalDf = FinalDf.selectExpr('Date_Time','Sentiment_score','Price2 as Input_price','Price1 as Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], n_hours, n_features))\n",
    "test_X = test_X.reshape((test_X.shape[0], n_hours, n_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rdd = to_simple_rdd(sc, train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdd.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.optimizers import SGD\n",
    "#from keras.models import model_from_yaml,slice_X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.utils import np_utils\n",
    "from elephas.spark_model import SparkModel\n",
    "from elephas.utils.rdd_utils import to_simple_rdd\n",
    "from elephas import optimizers as elephas_optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(5, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mae', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adam = elephas_optimizers.Adam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark_model = SparkModel(sc,model, optimizer=adam, frequency='epoch', num_workers=2)\n",
    "spark_model.train(rdd, nb_epoch=50, batch_size=4, verbose=2, validation_split=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Multifeature Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Compound</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-31T05:00:00.000-04:00</td>\n",
       "      <td>0.032105</td>\n",
       "      <td>0.888699</td>\n",
       "      <td>0.076390</td>\n",
       "      <td>0.095283</td>\n",
       "      <td>6158.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-31T06:00:00.000-04:00</td>\n",
       "      <td>0.035658</td>\n",
       "      <td>0.862046</td>\n",
       "      <td>0.101670</td>\n",
       "      <td>0.130807</td>\n",
       "      <td>6105.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-10-31T07:00:00.000-04:00</td>\n",
       "      <td>0.034356</td>\n",
       "      <td>0.864839</td>\n",
       "      <td>0.099413</td>\n",
       "      <td>0.130343</td>\n",
       "      <td>6094.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-10-31T08:00:00.000-04:00</td>\n",
       "      <td>0.036202</td>\n",
       "      <td>0.879061</td>\n",
       "      <td>0.084736</td>\n",
       "      <td>0.110576</td>\n",
       "      <td>6125.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-31T09:00:00.000-04:00</td>\n",
       "      <td>0.043838</td>\n",
       "      <td>0.862753</td>\n",
       "      <td>0.092850</td>\n",
       "      <td>0.108861</td>\n",
       "      <td>6165.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        DateTime  Negative   Neutral  Positive  Compound  \\\n",
       "0  2017-10-31T05:00:00.000-04:00  0.032105  0.888699  0.076390  0.095283   \n",
       "1  2017-10-31T06:00:00.000-04:00  0.035658  0.862046  0.101670  0.130807   \n",
       "2  2017-10-31T07:00:00.000-04:00  0.034356  0.864839  0.099413  0.130343   \n",
       "3  2017-10-31T08:00:00.000-04:00  0.036202  0.879061  0.084736  0.110576   \n",
       "4  2017-10-31T09:00:00.000-04:00  0.043838  0.862753  0.092850  0.108861   \n",
       "\n",
       "     Price  \n",
       "0  6158.76  \n",
       "1  6105.90  \n",
       "2  6094.36  \n",
       "3  6125.13  \n",
       "4  6165.00  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('/Users/rohitsuvarna/Downloads/features.csv',header=None)\n",
    "df1.columns = ['DateTime','Negative','Neutral','Positive','Compound','Price']\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OpFLc7S3RG0SqG7XM6O/L1vhilt7Tn78NPicUvjiUxds7jesKLbqtB0+ND71k\nX9VrdNQ2afF2tDa3HYyxg7lro4nOqeLI4vGdEjCXS0ZZHW/uSOFgWjmBbrYIgJOtJb/+YyQrDmVx\nRz9f80B4+ydH2szKtZTLeOvuvowJc2fY2/t4+tYwnp1k3IBeFEV+PVNEZKAr3dxsqVVruf+Lk1Q2\naBgd6sHu5FLOvDoJC1PQXVmrprRWTZ1ax4Nrovjw3v5mYZtVXs+MT48yOsydLx7+Y/NcRVEktbQO\naws5QW62XX6/z6esVs3EDw/h62RDWZ2aGtMEwfPf6atBZ3dMuykEQomqiTs/O4YI5qySF2+P4J7B\n/giCgN4g8tjaaHOwNdjdjrsH+bF0TzpLZvRm2b4MBge6sOqRtv357YlcfJ1szAPGpfj6WA5LfjuL\nQiZw6D/jaWrWU1TTRINGx49R+eYZvq0HEL1B5LP9mbjYWZhN7Qtx69KDxuycZj239/Xmw3sHkF5W\nR18/pyv+cl8JWvpj2QMD2RRTyCvTexHqac+Da05Sp9axdeFoc9m00jpi8qqZPezCsZ78ykZsLOW4\n21vy9Po4/JxtWDA+hMPp5fT2dbqoS/B8Np8paqPdtrTh9mVHmNzLi7dm9sXBWsHEDw+RW9mIXCaw\neEoPnrglGI3OwOJNCeZ8/73PjSHU85z7JqOsjkkfHQbgvVn9uHdI17bT1OoNVNY34+10dXPZD6WX\n88a2s2Qo65kzIpAld/ZpVyauoIaT2ZU421jQw9uBpXvSOJZZyYhgN05kV7bri/M5llnBg2uiALit\nt1eHg7soikz48BD2Vgq2PDWKHYmlLPo5HkuFjC1PjSLQrfN/52vNulP5vPhLIkODXLljgC92lnJm\nDvS76t+nJBBa8cz6M+xLUfLzP0YQ7G6PIGDWOlpoyZzIqWhgal9vLOUyJnx4iPJaDXUaHV/PHdIu\nuHa5iKLIf39NxNfJhn9OCGtzTqc38OaOFM4W1/L948Pata8zfH4gk/XR+Tw4LJC5pokzNzLldRqG\nv73PnCI6ONCFn+aNYNjb+xgT5sEHFzH9rxeqRi2ONgrzB6xq0lJep8HZ1qKNdl6v0TFt2RGq6puJ\n/9/kNlq9KIqMfvcARTVNfPVoJLf27JxCcSOg0xvYm6JkcKALHg6Xtka0egOPf3OaQ+nlRPg4svM8\na/B8RFFk5vLjxBXU8NbMvhdUADZGF/CfTQk8OzGc5Qcz6eXryPIHB+HjdOO4Xy5Ednk93d3trqmS\nJgmEVtQAKfuvAAAgAElEQVSptWQo681B2M7y4e/pLNuXga+TNUcW34pcduNp2X92nvj2NKdzq3hw\nWCCfHcjk6VtDWbY/kxen9mTe2JBLX+AGpqCqkRKVusON01/ZnMR3J/PYunAU/fz/2MbpfxYaNDpe\n+CWRaX29mdKJjJrjmRUs+jmBXxaMvOA+AQaDyJ2fHyOxSIWjtYLfnxt7w+wpcCPSWYFwUwSVHawt\nLlsYAMzo78OyfRncOyRAEgZXiU/uH4DOIOJgpSCpWGWeVRp+iQyZPwMBrrYEuHYcZLw3MoCEIhXB\nlxmY/zNiZ6Xg0wcGdrr8yFB3jr1w4Q1lwJii+9qMXjzy5Sn+764+kjC4QtwUFkJXOJNfTS9fxxve\n/fJXQKc38PWxXHYnl/LlnCE42VpcupLETU2zzoClQtrW5VJILiMJCQkJCeAvKhAEQSgH8v5gdXdA\nWqj/4kh9dHGk/rk0Uh9dnOvVP4GiKF5ymYQ/lUDoCoIgnJY24rk4Uh9dHKl/Lo3URxfnRu8fyfkm\nISEhIQFIAkFCQkJCwsTNJBBWXe8G/AmQ+ujiSP1zaaQ+ujg3dP/cNDEECQkJCYmLczNZCBISEhIS\nF+FPNVPZ3d1dDAoKut7NkJCQkPhTERMTU9GZtNOrJhAEQZgCfIJxN7U1oii+c955F+ArIARQA4+J\nonjRbTSDgoKQJqb9talVazmcXs60vh3vYftXoaxWzcpDWfz39og/tJChhMTlIAhCp+ZvXZU3URAE\nOfA5MBXoBTwgCEKv84r9F4gTRbEf8AhG4SFxE1Nc08Q9K06w8MczpLXaw/mvyK6kUr4+lktW+ZXd\nU1hCoitcLdVkKJApimK2KIrNwHrgzvPK9AL2A4iimAoECYJwQ60DrNMbePGXBNJK/9qD043Csxvi\nSFca+7qyvvk6t+bqkl9l3DO6ukF7nVsi8Vdk3LhxrFmz5rLrXS2B4AcUtPq90HSsNfHA3QCCIAwF\nAgH/88ogCMKTgiCcFgThdHl5+VVqbsfkVzWy7lQBn+xLv6b3vRlpatYTk1fNhJ7GPSeqGm4OgaBq\n+ms/51+VoKAgPD09aWhoMB9bs2YN48aN6/K1BUEgMzOzy9f5I1xP5+U7gLMgCHHAP4EzgP78QqIo\nrhJFMVIUxUgPj0vGRK4oZbUaAPYkl6GsVV/Te99sxBfWoDOITDLtPlfd+OcfKH+JLeS/vyZ2eK6g\nxUJolCyEPyt6vZ5PPrn2nm6dTnfVrn21BEIR0HpfQH/TMTOiKNaKojhXFMUBGGMIHkD2VWqPeVeu\ny6HMJAR0BpGNpwsuUVqiK8TkVQMwIcIoEP4KFsL2hBJ+jink/Lk+oiiaLYSam0Qg1DQ206wzdKqs\nKIqote10wxuORYsWsXTpUmpqatqdS01NZdKkSbi6utKjRw82btxoPne+O2ft2rWMHm3cLnbMmDEA\n9O/fH3t7ezZs2MDBgwfx9/fn3Xffxdvbm7lz51JdXc306dPx8PDAxcWF6dOnU1hY2OVnuloCIRoI\nEwShuyAIlsD9wNbWBQRBcDadA3gcOCyKYi1XgTe2neXWDw5edr0WgTAgwJnvT+b/KV7SPyunc6sI\n9bTH3d4KJxsLqv8CAiG3soFmnYHK856lsqGZxmbju1TzF7CELoVOb2DyR4f5aG971+uBVGWbPjiY\npuT2ZUfpt2SP2Yq6EKIoEpNX9YeUvStBZGQk48aNY+nSpW2ONzQ0MGnSJGbPno1SqWT9+vUsWLCA\ns2fPXvKahw8b99qOj4+nvr6e++67D4DS0lKqqqrIy8tj1apVGAwG5s6dS15eHvn5+djY2LBw4cIu\nP9NVEQiiKOqAhcBuIAXYKIpisiAI8wVBmG8qFgEkCYKQhjEb6Zmr0RYAFztL8iobqddcnqlVWqvG\nzlLOott6UFqrZv2p/KvUwpsbg0EkJq+ayEDjrnaudpZU/ck1Z71BpKCqCTBmT7Umv9VA91dwjV2K\nMwU1KOs0nMyubHM8uVjF3LXRzFpxnMLqRkRR5IVNiVTWa2jWGTiRVXmBKxpZcSiLWStOsC2h+Go2\n/6K8/vrrfPrpp7SOb27bto2goCDmzp2LQqFg4MCBzJo1i59++ukP30cmk7FkyRKsrKywsbHBzc2N\nWbNmYWtri4ODAy+99BKHDh3q8vNctRiCKIo7RFEMF0UxRBTFN03HVoqiuNL08wnT+R6iKN4timL1\n1WpLmKdxm8KMy0xlLKtV4+VkzcgQN4YHu/LZgSyamq++laDVd860/qtwtqSWWrWOyCDj3sMutlfO\nQqjX6Bj3/gG2xBVduvAVpETVRLPp71hc0zb+1KL5WilkN4XL6GCaEoDk4to2bqMz+UZXS4lKzbzv\nYsgqb6C0Vs0zE8NwtrXgdF7VBa+592wZ7+9OA+D3s2VXsfUXp0+fPkyfPp133jk3zSovL4+oqCic\nnZ3N/3744QdKS0v/8H08PDywtj63TWhjYyPz5s0jMDAQR0dHxowZQ01NDXp918anm2JGTMv+vBll\nxpzvslo1D62Joug8ze18ymo1eDlYIwgCz0wIp6Jew+8pV+/lU2v1LN2dRu//7eaz/RlX7T43GquP\nZGNrKedWU4aRq51luxhCWmkd35+8/L2RdiSUkFvZyA9R19a6y6s8ZwW0sxBM5yJ8HG8SgVCOhVyg\nWWcgvZVSFl9Qg6udJS9M7UlycS1fHjWGEMeEeTC4mwun89rriKIosiE6n/nfx9Db15Hp/Xw4lF5+\nxZSo41kVl3RVnc+SJUtYvXo1RUVGpSMgIICxY8dSU1Nj/ldfX8+KFSsAsLOzo7GxkbzKBr48mkNJ\nSckl73H+JM0PPviAtLQ0oqKiqK2tNbuauro23U0hEAJcbbFSyMwv469nijiaWcG2+LamprJOzcbo\nAnOnlqrUeDsZpXJkkAuWChmJhe0DSFeK5Qcy+exAJv4uNizdk87W+OtnCl8MtVZPbkXDpQt2gE5v\naPPSZirr2RpfzCMjgnC1M4aUXGwt27lSVh/J5uXNSdSpL28AbUkGiM6tMseErgW5lef6p0TV3mXk\n5WiFt6M1NX+xtNPqhuY2GXnKWjXJxbX8bbAxxySuoIat8cUo69TEF9bQ39+J6f18UcgE1p0qoJur\nLQGutkQGuZJd3sCG6HymLTvCi78ksvJQFnPXRrN4UyIjQtxY98RwpvfzoU6tMycldIWUkloe/vIU\nr2656IIJ7QgNDeW+++5j2bJlAEyfPp309HS+++47tFotWq2W6OhoUlJSABgwYADfrdvIXR/v59Xv\n9rJiVdv5Al5eXmRnXzy/pq6uDhsbG5ydnamqqmLJkiWX1eYLcVMIBLlMIMzL3jz7dXey0XQ7nNF2\nXsOaIzn8Z1MCiUUqRFFEWafGy9EoECzkMiK8HUgsUl21diYUqYjwcWTnM7fQP8CZd3akXLV7dYXF\nmxK4fdmRyw6y12t0DHtrH7NWHOdMvvED/nhvOjYWcp64pbu5XIuF0FpwJBYa+721himKIoXVjeag\nZHpZXZvBN7u8ntN51dwz2B9RhJ2Jl9bEusLBNKW5fbkVDVgqZAS52bZzGeVXNdLN1RZnW4urknZa\n09jM/atOXJdZ0E+vP8Ood/fz9o4UdHoD+1ON7qKHhnfDxdaCFQezeHrdGZ7bEE+Gsp7+Ac642lky\nJtyYUj4q1B0wKmAAizclUlGvYVt8Me/sTOV0bjUvT4vg60eH4GBtwegwDyzkAh/sSeOVzUk0Nl88\nTqg3iGyNL24XzDcYRF78JRG9QeRIRgU1jc2d1rbjCmr4+z8XmeckODg4sGfPHlZ8+S0+vr54e3uz\nePFiNBpjGvs/Fj5NeoWahPfuo3L7RwydOKPN9V577TXmzJmDs7Nzm+yk1vzrX/+iqakJd3d3hg8f\nzpQpUzrV1kvxp1rcriuEezpwPKuSslo1Z/JrcLBSEJ1TTVOzHhtLOQBHMoxbnf4WX4yfsw1avYiX\no5X5Gn38nNgaV4zBICKTXfl1drLK6xkQ4IKVQs6M/r7837azKGvVeDpaX7pyJ6is1/D0+jNM6ePD\nw8MDzccLqhr5YE8a88aGEOHj2K7e9oQS/F1s6B/gTExeFVvijJZLQqGKod1dO33/A6lKKhua0eoN\n3LfqJK9Mi2BbQglP3xqKm/25fnaxs0SjM9Ck1WNrqaCxWUeGaQbz2ZI6Bge6Ular5oHVJ8kub6CX\njyO/LBjJ/atOEuZpz4Z5IwD4+lgucpnAott6kFikYltCCY+O6t5h27rKrqRS5n8fg1wm8NioIHIq\nGgl0tcXT0Yri8yyEinoNPb0dcba1RNWoRRTFy1q36ZfYQvxdbBna3ZWYvGoCXG3wdDj3juxNUXIy\nu4qo7CpCPOyv2DNeCp3ewOncatzsrPjicDbhXg78llBMkJstvXwc6R/gzMG0ciwVMo5mGr+1/gHO\nANw10I/9qUpGmwRCXz8nLOUyLBUyfpo3kgBXG5q0emSCgLWF3HxPeysFt/b0ZM/ZMqJzqxke7Ma0\nfj7m8waDSJ1ah5OtBQaDyKKf4/kltohAN1u+nDOEUFN8cXNcEXEFNTw2qjtfHcvhy6M5bIkrZu6o\nIOZ28M7k5uYCRqXk72ujiQxyQa0+J/jtvbpRPPxfvPZqOAtvDeNMfjU9Td/WzowGXGYtYdcTw/n7\nN9H0iQzg59UfmevOnz+f+fPnt7nf+Smlvr6+HDx4sM2xefPmmX8+/1xnuSksBIAwLwdKa9VsijV2\n7L8mhdOsN3Ayx5jJUF6nIaWkFrlMYFtCCSUq4x/Xu9Vg3NfPiTqNjjyTjzGvsoGKes1F75uprMNg\nSovT6Y0+VFUHWqFaq6ewuokQDzsA+vs7ARBfePkWSUf+1JrGZmavjuJYZiWf7E1HozNq94fSy7nj\ns6Nsjitm0c/x7VL40svq+Oe6WJ749jTVDc0s+e0s7vZG187Fgn7QPp98V1Ip7vaW7Ht+HO52lryy\nJRlPByvmjQ1pU8/V1nj9ljhCcnEtLc1KLTFmJn9/Mo+cigbuHuTH2ZJaXt6cRFVDM1E5VRRUNZJV\nXs+6U/k8MDQAT0dr7hrox+m86nbLkLy9I4X/23a2nTZYUa/ptF86t6KBf/8UT39/J2YN8mP1kRwO\npikJdLPD18mmXQyhVq3D0cYCZ1sLmvUGcwrqxfrx473prDmSTX5lI4t+TuC1rckoa9Xc+8UJ7ll5\ngvK6c+/h4XSj5aus65yLLKu8nuOmAfr1384y56tTnarXQqlKTVFNE+ll9TRp9Sye2oMQDzuWH8zk\neFYlMwb4IQgCQ4JcsZALfPfYUBysjLroAH+jQJje14eVDw1iSh9vAKwt5Pz39p58/uAgurnZIggC\ntpaKNsKghRUPDiZ5yW3YWco5kd12//r3dqcx5K297Eku5bmNcfwSW8TsYd1o0Oh4/Jto9AYRURRZ\ndTibHl4OvDwtgiA3Wz7dn0l+VSPv7067aD9mVzRQ2dBMSknb96rFhXUmv4akIhUzlx9n2rIjfHsi\nl+UHMxkT7sGIEDfCvRxILb0q2fZ/iJtGIIR7GTWBj35PJ8zTngeHdcNKIeNQmvHjOWb6IOaODKJE\npWaHyb3QWjvv42ccpBOLVFQ3NHPn58d48ZeOZ6ICnMmvZuKHh/nxVD4ZZXUMeXMvkz86zNilB8yZ\nEaomLadzq8ipaEAUMWt0vX2dkMsEEgprKFWpOZ5VQXYnXABppXUMev13Vh9u64PcEF1AWlkd/xgX\nQkV9M9viS3hnZypzvjqFl4M1i6f0JKmolrlro5nwwUE+/D2dqoZm3t6RgrWFHGWdhts+PkxCoYrX\n7+xDiIcdp3Mv7rfdlVRKvyV7iMmrQq3VcyBNyeTe3ng4WLH8ocG42lny8vRe2Fm1NVRdTLGElnV+\nEkxCMdjDjpQSY6bKulMFTOjpyVsz++JqZ8nPMYX4mOI9P8cU8sa2s1hbyPnXxHAA7h8SgLWFjK+P\n5bS5188xhXx5NIdP959bKqBeo2P8+wf54lBWm7J6g8jBNCWfH8jkSCt34xeHs9AbRFY8NJh3Z/Vj\nXA8PdAaRIDdbfJxtUNa1FS61TVocbRS42FoYn/MSqacf7Enn470ZvLE9hec2xqE3iJwtqeXdXWno\nDSKlKjWPf3saURRNLg9j21pm2l+IynoN2xKKmfHpUR756hRltWp+iingUHr5Rd1NC36I4dczhTRo\ndDy2NpoR7+zjrs+PmRWEgQEuPDw8kKxy4zs9o78vAI/f0p2Di8YzLNiN5yaHc1tvL/PfWiYTmNLH\nB3kry/vRUd0ZG37p1QlkMqOwGNLdlZPZ55SUinoNa4/noDeIPPldjFHpua0Hb83syxt39SG3spGd\nSSUcyaggtbSOx2/pjkwmMHOgcQWdV6b3olln4MM9F166pmXgz69qpKFVWnus6Xh8ocpsDdWpdby6\nJRm9QeQ/t/UAIMLHgdTSui4Hg68UN43LqK+fE9YWMoYEufLmXX2xtpAzvocnm2IKeWp8KEcyKnCx\nteCZiWGsjy5gzVHjwNESVAZjtpKlXEZSkYpTOZXUNGo5nlmBVm/ocAnjr4/lAkZtNja/Go3OwHuz\n+vHNiVye+PY0L0+LYGt8MQmFKhaZXpAWgWBjKSfM057Y/Gq2J5SQbQriTu7lxWszeuPrbNPhc352\nIJM6jY43d6Tg7mBpfrmPZFQQ7mXPosk92JFYwqKf4zGI8MDQbvzvjl5YKWQcy6zgRFYFffycWLYv\ng2X7jJlOL07tSXpZPZtiC1k8pSe39/XhcHo5OxJLLuo+255YQrPOwNPr4pgxwJfGZj1TTRrggABn\nTr80scO6rnbGgbLKNFAmFtbg7WjNmDAPNp4uYGdSCRX1Gh4aHoi1hZzZQ7vx2YFMnhwTzJ7kMj4x\ntfvV6b1wN7minG2NffFLbCH/vq0H7vZWVNRrqGxoxsPBig9/T+e23t708HbgQKqSOo2OQ+nlLLw1\njJrGZvacLWP14WwylMaB0t3ekuMvTECt07MlrpgZ/X3Nf5Ol9/Tn72ujGR3mTlmtGlE0atEBrrao\ntXo0OgOO1hY42RgHw5pGLf4uxvWctieWoGrS8tioIARB4HhmBZ8dyGTWIH9O5VZyOq+aKb29+T2l\njE2xhQwOdGHmQD9e3pxEUlEtelE0xyUuttzKp/sy+OB340DX3d2OnIoG/vtLInVq46D2a2wR/za9\nkwAbowsI8bSjp7cjOxJL2Z+qZGtcMYfSy5nR35ctccWsPJiFs60FgW623D3Yn/d2pxHsYWd2y1gp\n5PiZ+mjuqO4dumK6wvBgN97ZmYqyTo2ngzWrj2TTrDPw0/yRfHM8lyl9vLm9r9GdNKmXN8Hudny8\n1/iueDpYMWOAUXAtvDWUOwf4EuRuR0lNE2uO5jAq1J0MZT3NOgMvTO1pvmdsq2B2hrKeBo2OEA/j\ndwtGobT5TBHBHnbsePoWKhua8XKwQmEaL3p6O7LuVAFltZo2Y8314qYRCJ6O1px5ZTLWFjKzv3bR\nlB7s+7iM+d/HcLa4lom9vHCwtuCz2QN54lvjvgueDud825YKGRG+jnx5NAeDKBLmaU+Gsp64ghqG\nBLX1pZfVGq0MP2cbUkvrSCur45Hhgdw7JIA7B/ry1A+xvLE9hZbx8MujOQiC8eNsob+/MxtMWTIv\n3R6BWqtnxaEsHlsbzW//HN1OCOVUNLA9wej3TC6u5dkN8ZzIquSl23txKreKh4cHIpMJ/GNsCO/s\nSuX/7uzDHSbtDWDNnEg0OgNONhYkFNZwKqeKBo2eOSODMIgiswb5MSLEDYDIIFfWRxeQWV5PkJsd\nm2ILmd7PBwdr42Cu0xs4nF7OwG7OJBaqWHEwi8hAF4YHu5nvdyFB4mLbYiEYA3sJhSr6+jvR09uB\nxmY9/9uaTLC7HWPCjNrj47d0RxDgviEB+DhZE1dQw+t39uaeyIA2131sVBA/nS5g6idHeOfuvtiY\n3A8vT4vg2Q1xbEsopod3D3aZkg7iC1QUVDVyx2dHqWnUEuJhx6cPDEQhE/jHD7HsSi6lptE46/jB\n4d3M93G3t2LLQuNSBC3umxKTQGgZcB1tLMwWQk2jlqKaJu5efsys1RdVN/HK9AiWH8zCw8GKN2f2\nIbFIxVs7Unh5egQanZ4DaeX8bbA/U3p787+tyexOLkUuExAE6OfvTNkFXB2H08v5cG86U3p7M2uw\nP6ND3Zm5/Bj7UpXYWcrp4+fEr2eKcLRR0M3VFn8XW/6zKYGpfbzNiotaa+BAWjlPTwjj6VtDOZld\nSbFKzdhwDwRBwNHaglUPR+JsesZrwQjTu3Uyu4rh3V359nged/T3ZXCgC4NNkx5bkMsEnhwTzAu/\nJOLpYMW7s/phpZCbzwWZvsNFU3oQk1/NP9edMded0d+XHt4OCBgthBAPO7LKGzieVcEHe9Lp6+fE\n2eJaRoW6cSyzktTSOh4Y2g1ri3MCsYWe3saU+NTSWkkgXGtagscthHjYM3dUd1YdzmZIkAsvT4sA\nYFwPT1Y+NJjTedXtBt23Z/ZlR2IJlQ3NLBgXwtj3D3A0o6KNQEgorOG9XWnoRZFVjwzm3pUnaGjW\nmwOaVgo5nz84iLe2pzCgmzNfH8sloVCFv4tNmzb2C3Biw+kCevk4mgY9gXBvB+Z9F8OaIzn8Y9w5\n37soiry1IwULuYwF40JxtFHwyd4Mlh/MIqfCuIRCSybH/UO7cd+QgHaBTGsLudlH28/fmX4m/24L\nI00BP4AhpiyQLXFFWMrlfLQ3nfyqRmYN8uf/tp1lcm8vatU6nrglmAAXWywVMsK97DsVPG1JP61s\naOb93WlkVzTw6Kggc8BbozXw6eyBZoHibGvJ85ONA9WUPj5M6uXdxvXQQpiXA5v+MZJ//xTP4k0J\nzDfFLkaGuDM82I3tCSU8NT6UA6lKurnakl/VyCtbkqhp1PLD48MYGeKGIAgYDCKBbrZ8vj+TmqZm\n+vo5teurFvxdjANAbkUDQ7u7UmtKm3W0VpjdJSWqJt7emUKjRs8Pjw9jb0oZXx3LIau8nqOZFSye\n0hNrCzlDglz5dcEowKhhl9VqmNbPB0drC4YGubIlvojaJh2jQ93xdbLhgGlC2Pm8tDmRME97Prpv\ngPl9u3uQH2/tSGVcT08mRnjy7IZ43tqRikyAbq62ABSr1JSarI6nxoeg0Rp4+tZQFHIZswb5s/xg\nFgMCzvXD6DD39je/ivT2dcTBSsHPMYUcSitHZzDw3KTwC5a/NzIALydrhnV3xday46HQSiHni4cG\n8++fE5gY4cm7O1NZuieNUpUatVZPdkUDz08KZ/nBLNYcMbqn4gqMqekPDgvkVE4VWr3I8OCOky96\nehvf6dTSOsb18OxiD3Sdm0ogdMTzk8MZEezGLWHuZjMOjIustSy01ppevo708j2XidPX35ljmRU8\na3rxssrruXv5cWwt5bx0ewS9fZ14ZmIY5XWaNtq/lULOkjv7AEbtMaFQ1S4jZHiwGxZygX/fFm4e\nSG/r7c3kXl58vDedWYP8zDGO1Uey+f1sGS9Pi8DDZNX8Z0pPymo1bIotxFIhY2grodXV3cgC3ey4\na4Avyw9moZAJKGQCP5zMIzavmqicKg6llyOXCYwOc8fR+vK0REdrC2QCrDmSTYlKzQNDu/HQsEC0\nBqNQmzMikN6+Thes35EwaKF/gDMLxofw7AZjtomrnSXu9pZM6+fDS78m8daOFBqb9Sye0pOF62I5\nmFbOkCAXczokGC2b2UO78fbOVLq52vL23X0veL8gNztcbC2Iyqni3iEB1DaZBIKNBc42xn55f3ca\n5fUavpwTyahQd0aGuOFqa8nH+zKwt1K0sT5aGBPuYRbwAFP6GK0EhUzg1em9+C2+mIp6DTq9oc17\nXafWUlDVxOIpPdsoH3cN9OPrY7ncFxnAiBA31FoDff2ceHVLErH5NThaKyipaUJpsmBmDvQ3u4LA\n6HrcFFtonlx4PVDIZfxrUjj/t824ZtBjo7oT6GZ3wfIymcD4TgzCno7WfPvYUMA4qXDN0RxsLORm\nC29Id1fCU8qIL1QR7GGHnaWCxCIVw7q70svHkfhCVRvLuDVOthb4OlmbkyWuNze9QLBSyBnfhZd4\ndKgbKw9lG5e5cLTm470ZWCpk7Ht+nHlgfnJMyEWvMbWPD+/tSmsnEEI87El87bZ2mRUvTYtgb0oZ\nXx7L4cWpEVTWa3hvVxpTenvz99Ft/bIvT4vgYJqS3n5O7SykrvL23f3IUNZTVNPEu7P6Me+7GKJy\nqrgvMoBf44oYGOB82cIAjB+qi60lJSq10ayf0hOZTMBKJjd/mF1hTJgHgmBcMmNEsFHrv623N69s\nTuLbE3kM6ubM5N5eRHg7craktp3rCWDOyCBc7Cy5va8P9lYX/oxkMoFh3d2IMmWz1ba4jKwtcDIN\nKMo6DQ8MDeDWnkYFRBAE/jkhjEm9vdBoDZ3qw8m9vXhj+1keHRlEmJcDno7WGESjleXVKjEit8KY\nIdfd3bZNfU8Ha068OMH8+wNDjULo60eHciBNSaayns8PZlJYbax/vnsjwNWWqP9OvGQ7rzZ/H90d\nZxsLNsUW8s9bQ6/49Z8cE0xeVSNPjgmmp7cD0blVDOvuSg9vB+ILVdzRz5e7BvoRk1eNm70Vk3t7\nY2Mpb/M3OJ8e3sbA8o3ATS8QusrfBgfw5dEcFv2cwIJxIWxLKGbBuBCzMOgM3d3teG9Wvw61iI7S\n7ALd7JjWz5cfTuazYFwox7Iq0RlE5o8Laaf5u9hZsmXhKLN/9EpiYynn5/kjqdNo8XSwZkiQC9WN\nWt6Y2YeHhgfiaPPHX68ld/Y2LWdx5TfRc7O3or+/M3EFNfQw+XDd7a14466+WCpkzBzoh1wmMLaH\nB0U1TUzr69PuGtYWcu7tQFB0xPBgV3Yll1JQ1Wi2EJxsFFgp5NhaypELgtnl1ZoWd0Jn8HGyYe9z\nY/F3MQ70LQPQwTQlb25P4ZcFowj1tDfPoA5yv7Dm3BonWwvuGujH+lP5/8/eeYdHUa0N/De72U3b\n9Nt4Kj0AACAASURBVN47BEINkV4EkWZBpAiIvV5RsXzXi+1e7Oi9VlBRxIIogkgRpAnSIUBCQggh\nvffes0k2O98fu1kSUgkJAZnf8/CQnTnnzJmzO/Oe85bzIoo6rxlzpbxdIdjbzB7mzuxhLXJtdQuO\nliasvj/E8Lnx9znQzYqNYZncMdgFH3tzgzZg8UR/Fk9sXzAFulhyJKGQOo0WpVHvOn5eu9/qdYKP\nvTmv3taf17dGczi+AHuVksfG+V52O/Nu6tzLpZEnxvuy/Ww260LTSCuqwspUwUC31tUojS+JnsBU\nKTesPL57aDgNWhGFXMZA97ZVOp3h9kGuHRe6Aib2dSQyo9SwzxXAwhHNVTPPTQ7g4TE+LdxiL5eR\nekP8yZRiQ/xH46x/4XBPBrpbGbyhroSm6pFGZ4i1J9IoV2vYfyFPJxD03mpetp0TCI246I2hkRml\nOF0Dxs9rjXk3eTDU0wZ/R4uOC19CoLMFGq1IUkFli8DQbZFZpBRW8cykgHZVod2FJBC6gUUjPCmu\nrMPcWM6cYe5Ymyk7rnSFDHCzYnwfB747loKRTMZoP7ur8oNpj2t51ngptw1y4edTaYxow9gHOnWi\ng8WVr6z6OFpgY6YgNLnIoBa01NsPXru9/xW33xqNK4Tz2TrddGhyEU9M8COlqApnS5PLVh+66oVA\nQUUt/lcx+vl6wdhIbohTulwahUBcbkUzgRCaXMQLG3XBojHZ5Xy2YGirGoPu5IYJTOtJBEFgyeQA\nHh3ne1WEQSP/mKALMsstV191j47rHX9HFSdfmXxVtnaQyQSCPW04l1lGWU09SrkM4x5WDdirlDRq\nD+UygdOpJWgatKQWVuFtf/krRpcm7pJNt3ORuHJ87M1RymVcaBKxXKGu5+mfI/CyM2Pp9ED+vJDH\nwbiezykvCYTrmJG+toa9YMb5X9180xKXh6edGZkl1ZTpo5Sv1MurI4zkMoMaak6wO5W1GqKzy0kr\nqsa7Hc+btlAZG2FholsBSiqj7kUhl+HnqCK2yfYXu87lUlhZywezB/HkBD/2PjfesK1HTyIJhOsY\nQRB4a2YQz0/ug6ddz9kJJK4cN2tTquoayCyp7pLnVVdwtDBGKZfxtN7b5s+YXIqq6jptUL4UVyvd\nKsHJQhII3U0/5+Z7Gm2JyMLH3twQUBfgdPm2ia4gCYTrnEHu1iyZHNDb3ZDogMYAtQs55ViYXh2B\ncJO3LVOCnPCwNaOfiy7CHujSCgHAxVonCK6FiNq/GwPdrcgrryUxv5Ls0hpCU4q4S78p4NVEEggS\nEleBRk+vwso6LE2ujvF92Z1BrFwYDMDKhUMNKiRfhy4KhMYVgmRD6HYakwRtOJ3Or2GZiCLcNbRn\nPe1a4/pxC5GQuI5puoeN5VVaITTFz0HF1sVjOJ1S3MzV9nJo9DRqL8hKoms4WBgzJciJDaczUGu0\nTOnv1G6UdU8hCQQJiauAtZkCM6Wc6rqGq2ZDuBR7lTHTWwmy6yy3D3alqq7BYEuQ6F4WDPdk5zld\nzpB329kOpSeRBIKExFVAEATcrE1JyK+8ogju3sTH3rzZ1s8S3csYP3seGevDlP5O3RKo2BWuz1+m\nhMR1iLuNXiD00gpB4tpGJhN4vYcCFTvdh55qWBCEaYIgxAmCkCgIwtJWzlsJgrBdEISzgiCcFwTh\noZ7qi4TEtYCb3tOoN2wIEhKdoUcEgiAIcuBzYDrQH1ggCMKlom8xECOK4mDgZuBDQRCuXpivhMRV\nxs1a52lkJQkEiWuUnlohDAcSRVFMFkWxDvgFmHlJGRGwEHSOtiqgGNAgIfE3pTEW4Wq5nUpIXC49\nJRDcgIwmnzP1x5qyEugHZAPngCWiKGovKYMgCI8LghAmCEJYQUHP7+UhIdFT3ORty03eNs0SLElI\nXEv0ZmDaVCAScAWGACsFQWjxpIii+LUoiiGiKIY4OEj79UhcvzhbmfDrk6NxlLZ+kLhG6am1axbQ\ndIN/d/2xpjwELBdFUQQSBUFIAQKBU201Gh4eXigIQloX+2QPFHax7o2CNEbtI41Px0hj1D69NT5e\nnSnUUwLhNBAgCIIPOkEwH1h4SZl04BbgiCAITkBfILm9RkVR7PISQRCEMFEUQzoueeMijVH7SOPT\nMdIYtc+1Pj49IhBEUdQIgvA0sAeQA9+KonheEIQn9edXAW8B3wuCcA4QgH+JoijNLCQkJCR6iR5z\ndxBFcSew85Jjq5r8nQ1M6anrS0hISEhcHjfSbqdf93YHrgOkMWofaXw6Rhqj9rmmx0fQ2XQlJCQk\nJG50bqQVgoSEhIREO1xXIZP29vait7d3b3dDQkJC4roiPDy8sDNemteVQPD29iYsLKy3uyEhcUOS\nXFDJL6czWDotEJns6qZ2lLgyOhu/JamMJCQkOsWe83l8fTiZ3HJ1b3dFooeQBIKEhESnKK6qBaCo\nsq6Xe/L3ID09HZVKRUNDQ5tlVCoVycntxut2K5JAkJCQ6BTFVfUAFOkFw42It7c3pqamqFQqnJyc\nePDBB6msrOxSW56enlRWViKXywG4+eab+eabb5qVqaysxNfX94r73VkkgSAhIdEppBWCju3bt1NZ\nWcmZM2cICwvj7bff7u0udRudEgidyH4WKAjCCUEQagVB+L8mxz0EQTggCEKMPivakibnlgmCkCUI\nQqT+34zuuSUJCYmeoLhat0IorrqxBUIjbm5uTJ8+nejoaLKzs7nzzjuxtbXF39+f1atXG8qdOnWK\nkJAQLC0tcXJy4oUXXgAgNTUVQRDQaDS8+uqrHDlyhKeffhqVSsXTTz8N6HJxJyYmcvLkSZydnZup\nl7Zs2cKgQYMA0Gq1LF++HD8/P+zs7Jg3bx7FxcWXfU8dCoROZj8rBp4F/nfJcQ3woiiK/YGRwOJL\n6n4siuIQ/b+dSEhIXLM0rhAKb2CVUVMyMjLYuXMnQ4cOZf78+bi7u5Odnc2mTZt45ZVX+OuvvwBY\nsmQJS5Ysoby8nKSkJObNm9eirXfeeYdx48axcuVKKisrWblyZbPzI0aMwNzc3NAmwM8//8zChbo9\nQ1esWMHWrVs5dOgQ2dnZ2NjYsHjx4su+p86sEDrMfiaKYr4oiqeB+kuO54iieEb/dwVwgZaJciQk\nJK4DSvQ2hOIbXGV01113YW1tzdixY5kwYQKPP/44x44d4/3338fExIQhQ4bw6KOPsnbtWgAUCgWJ\niYkUFhaiUqkYOXJkl667YMEC1q9fD0BFRQU7d+5kwYIFAKxatYp33nkHd3d3jI2NWbZsGZs2bUKj\nubwklJ0RCJ3JftYhgiB4A0OBk00OPyMIQpQgCN8KgmDTRj0pY5rE3wJRFIlIL+F63C6mVtNAZa3u\n5VJ0g6uMtm7dSmlpKWlpaXzxxRdkZ2dja2uLhYWFoYyXlxdZWboUMGvWrCE+Pp7AwEBuuukmduzY\n0aXrLly4kM2bN1NbW8vmzZsJDg7Gy0uX5iAtLY1Zs2ZhbW2NtbU1/fr1Qy6Xk5eXd1nXuCpGZUEQ\nVMBvwHOiKJbrD38J+KLLlpYDfNhaXSljmsTfhWOJRcz64jhn0kt65fqRGaXM/vK44cV+OTSuDkAS\nCJfi6upKcXExFRUVhmPp6em4uenmzQEBAaxfv578/Hz+9a9/MWfOHKqqqlq0o0sv3zb9+/fHy8uL\nXbt2NVMXAXh4eLBr1y5KS0sN/9RqtaEPnaUzAqEz2c/aRBAEBTph8JMoipsbj4uimCeKYoM+j/Jq\ndKqp65aP/oznj6ic3u6GxDVMoyBIL67uleuvOZpCeFoJF3LKOy58CY2GZDOlnKJKyYbQFA8PD0aP\nHs3LL7+MWq0mKiqKNWvWsGjRIgDWrVtHQUEBMpkMa2trAGSylq9eJyenDmMOFi5cyKeffsrhw4eZ\nO3eu4fiTTz7Jq6++SlqaLiC5oKCAbdu2Xfa9dEYgGLKfCYKgRJf97PfONC7oRN4a4IIoih9dcs6l\nycdZQHTnunztUVhZy2f7E3h6/Rm2RXZaVkpc45zLLOPboynd1l5UZhkAeeVX/4Vaoa5n7/lcQLcF\nxeXSKBD8HVU3vNtpa6xfv57U1FRcXV2ZNWsWb7zxBpMnTwZg9+7dBAUFoVKpWLJkCb/88gumpqYt\n2liyZAmbNm3CxsaGZ599ttXrLFiwgEOHDjFp0iTs7e2b1b3zzjuZMmUKFhYWjBw5kpMnT7baRnt0\nuJdRZ7KfCYLgDIQBloBWEITn0HkkDQLuA84JghCpb/IVvUfRB4IgDAFEIBV44rJ7f40Qnqab+XnY\nmPHPTVHc0s8JlfHV2SZq57kcNpzOYPX9uqx8ak0DliaKq3LtvzvfHUthc0QWdwe7YW2mvOL2zmWV\nApB3lbZ+WBeaRmWthicn+LE7OpdajRaA5IKW6oqOKK7WCYEARwuiMsuortNgpryutkLrFlJTU1s9\n7u7u3qZtYN26da0e9/b2bmZPGjVqFPHx8c3KXGpv8vT0RKvVtmhLJpPxwgsvGFxau0qnvtFOZD/L\nRadKupSj6NJjttbmfZ3v5rVNeFoJSiMZb9wZxEPfn+Z0SjETAx27pe06jRalUesLuQatyPu7Y0kr\nqmZDWAYHYvNJyK/g0P9NlDYf64CcshoW/3SGFQuDcbNuOVsDiNGrViIySpnY98q+z7xytWFlkH+V\nVggbTmeQUVLN4+N82RaZjZedGUq5jKSuCAS9mijASQXogtPMbG88gfB3R4pU7gZOpxYzyM2KUX52\nKOUyjid1LjV0R94m6UXVDPjPHv6Kbe4pkJBXwT9/Pcs3R5JJK6rGxkzBWzti+Cs2n4ziGk6n6gJS\n6hu07D2fK+l8W+FwfAFn0ks5FNe651qtpoHEfJ1qJSKtbSPwnvO5xGR3rJM/p1cXqYyNrtrmcBkl\n1ZRW13Muq4yTKUVMC3LGz0FFcmEXVEbV9QgC+Nqb6z5LhuW/JT0aqdxeXUEQbAVB+FMQhAT9/626\nnV4NEvIq+OpQEtFZZYZjxVV1RHTCG0Rd30B0Vhkh3raYKOQEe1lzPKmow3qaBi13fX6MN7fHAJBW\nVEWdpvlSMDq7jLoGLct3xdKgvSg8PtmfwK/hmby3KxZvOzNWLAimTqNluI8tJgoZO/TG7Te2n+fx\nH8MZ8e5+vj6c1KmxuFZ49Icwhr65l0XfnKSqC14xHXFO/12fa/KdNyUxvxKNfszPpJe2OK/Viry9\nI4Ynfgzn5S3nOrxeVFYZMgHG+Nv1iMqooKKWMcv/Mjg2VKjrKdVHFq88kEh9g8i4AAd8HcxJL6qm\nvqGl2qE9SqrqsDZV4GhpAly7+xnllaspV9d3XFCiVXo0UrmDukuB/aIoBgD79Z+vOkcSCrj148O8\ntyuWD/fGAVCurmfB16Hc+83JDmfxZzNKqW8QCfHSybPRfvbE5JRTWt36DKqqVoO6voE/zuVwNrOM\nb4+lsOz389z8v4N8fiCxWdnUIt3SPj6vkh1R2YDOgL33fC4zh7hy91A3/nNHEGMD7PnuoZv4+r5h\n3BLoxK7oHD7dl8C60HQWjvDkJm9bVvyViLq+7V0Vu5vQ5KIuebM0ciKpEBtzJUcTC1l/Kt1wPLu0\nBs1lvsxa41yWrm/RlwiE/Ao1+2LyuJCjcyEc5WtHZEZpM4EM8PmBRL45moK/o4qzGaVkdOA5dDyx\nkD5OFnjbmZNfXtvtsQif7U8gq7SGn0/pvEwyimsM5/6MycPYSEaItw2+Dio0WrHD/gL8Z1s00z45\nzLLfz5NVWoONuRI7c50t5Vo1LM//OpT5X4VSq7l6v3XQTfA6M6bXOj0aqdxB3ZnAD/q/fwDu6uI9\ndIrqOg0Pf3+a74+loG3ycG+LzMbSxIgZA50JTytBqxV5dn0EcXkVVNc1UFLd9mxDXd/AOzsvYK6U\nc5O3LQCj/ewQRdgSkdXioU/Mr2DShweZ8vFhPtufgK+DOT725nx/PBVRhBPJzVcW6UXV2Jkr6edi\nyfu7YqlQ17MpPJP6BpFnJvnz0T1DDLaKiX0dsTZTcvsgFwor6/h4XzzTBzjz1swBPD3Jnwq1hj16\nL5POoq5vMBjML4eqWg2P/RDGst/PX3ZdgJq6BqrqGpgzzJ1RvnZ8fTgZdX0D+RVqbv7vQR747lSX\nfOkbqW/QciGnHLlMIC63wrAyyyiuZvaXx3l0bRjfHUvBRCFjVrAblbUaEvIryC6tYdYXx3h58zk+\n2Z/AzCGufPvATQDsir7ocqzVinyyL56UQp1Aj8woJSythLkhHjhZmlDXoDXM3ttjW2QWn+1PaPVc\nnUZrEGYphVWsP5WOhYkRJ5KKKKioJbNE93JytdLN6Ef42mGikOProFP5XGpYLqqs5daPDnFCv7rN\nLKlmbWgadRotP5xI5a/YfOzMldip9AKhEyqjFfsT+C08s8Ny3UVGcTUphVXE5JTz/q64q3ZdgA1h\nGYz/7wEOxOZzPKmQ1Yev3pbV3UlPRyq3V9dJFMXGpygXcOpkm13iQk45f8Xms2x7DC/+ehbQPbgH\n4/KZ0NeRSYFOlKs1bI/K5mBcAcP0M/62lveiKPLPTVGcyyrjk/lDsTLTefYM9rCmv4slb2yP4bG1\n4Yal+ZGEAu75KpQGre6FmVRQxZPj/VixYCiPjPVhwXAPzmaUNlMbpRVV42Vnxtt3DSCnXM1TP53h\ny4NJDPe2xd/RotV+3dLPiecmB7D+sZF8cW8wcpnAKF873KxN2XQZD2dRZS33fnOS2V8eJzytmFpN\nQ4vZdFtsi8ymolZDVGZZl2bzhXqbh725MU9P8ie/opYtEVlEpJdS16DlWGIRi38602b9Bq3I5wcS\nOZvRUtUTn1dBbI5OCEwKdKSuQUt8XgWiKPLgd6cor9HgYmXC+exy+jpbMsJHJ+j3ROex6lASUZll\nbAzLwNXahLfuGoCnnRmD3a2axaAciMvnk30JbInQuSB/fTgJCxMj7rlJJxAA8iraVxuJoshHf8bz\n8b54g5vo0YRCRr+3n6zSGn44nsodK4+SkFfB6iPJGMkFvrg3GK0Iu6NzyCjRrRBmDtU9buMDdC6K\nfvY6o/CldoStkdkk5Ffys341tv5UOgLw46MjeHSsDwA2ZkrMlEY4WhhzLLEQrVZk/al08lu5l8LK\nWj7Zn8D/9sY1m4D1JMcSdba7m/s68O2xFINAvhocTShEFOHZ9RHcv+YU7+y80Oq4ZBRX8989sVc0\noelJrgmjsqibSrf6q+murSvSinQzplG+dvxxLodaTQNRWWUUVtZxS6AjN3nrBMD7u2KRCfD4eN0e\n5JcKhNe3RjPri2O8sT2G7Wez+b8pfbm1/0VZppDL2Pb0GP41LZB9F/L497bz/GtTFPetOYWVmYKN\nT4xky1NjeHVGP+4a6sYANytev70/4wIcqNVoDZ4toAtg8rIzZ5iXDY+M8eFIQiHuNqa8P2dQm/ep\nNJLx3OQ+jPKzM0Q+ymQCs4e5czSxkOxS3YuiQl3P7C+P82tYRos2kgoqmfXFcaL1eu/D8YWsOZrC\n7SuOtrCrhCYXsfpwMpoGLVmlNUSkl/BjaBpGMoGa+gZicyvIK1dzOrWYxPyKZnWjs8r4cG9ci1mk\nQSBYKBntZ4e3nRn7YvI4m1GKkUzgiQm+HE4oaNVYLooiy34/z3/3xPHBnthm5w7G5TPl48M8uS4c\ngIXDPQGdHSEqs4ykgipeva0fS6cHAtDfxQIvO3OmBTnzxcFEfjmdwZxgd04sncS2xWMN7r23DXLh\nbGaZQUW2Rh+7kFVSQ165ml3RuSwa6YXK2AgnS2MAcsvaFwgJ+ZWkFVUjivDdsVRq6hpYujmK7DI1\nu87lsD82D1HUuZZuP5vNjIEujPW3x99RxfaoHDKKqzFXypkX4kGgswVTg5wBsDJT4GRpzMnk5jth\nNn4H+y/kUa6uZ8PpDCYFOuFmbcqLU/pyk7eNYZL0wGhvjiQU8vLmc7y8+RyvbI4mLreCGZ8eMQTf\n7TyXQ4NWJKdMTVgXVpmXQ3pRNamFVRxPKsLBwph3Zg0Emq/auos6jZavDyc1s2uJosiplGJG+toi\nlwt42poBFx0JGolIL2Hm58f4/EASm1p57q4FOuM3diWRyu3VzRMEwUUUxRx9kFp+aw2Iovg18DVA\nSEhIl6caaUXVCAIsHOHJieQizmWWcTihEJkAE/o4YG2mwMHCmOwyNaP97AhytQRaugjujcklr7yW\niPRSZg5x5amb/VpcSyGX8Y+b/cgrV/P98VSMZAKPj/flhVv7YKLQJcN4bHzzpBeND1t4WglDPKyp\n1TSQXVaDl53ux/XStEBG+9sx1t+hTTfU9pgT7M5n+xPYfCaTpycFsPJAoiFqdZSfHe42ZnywO5af\nTqajrm/AwsSIXx4fybLtMRxLLKSqTqeT/XBvPOseHQHo1HDPro8gv6KW385kklRQSX2D7it66mY/\nvjiYxLHEQlYdSqKkuh6lXMaJlydhpzImIa+CmZ8fo0ErYmFsxJ1DXFHIdfdVqNdP26uMEQSBsQH2\nbDmTRYVaQz8XS6YFOfPVoWSOJRVx52DXZvf52f5EfgxNw93GlNDkYooqa7FTGaNp0PLuzguYKGRk\nldagMjZifB8HLEyMiMosJbWwCoVcYGp/ZyxMjLiQU8H0AbqX6LI7gzj6USHVdRqevNnPYFht5J4Q\nT1bsT+TTfQk8e0uAwakgs6Sa2NwKRBFu7qPbdqVxhdCW62lMdjnh6SWU6lUyE/o4sCk8k9SiKjJL\narAxU7AtMpsLOeUIAqwNTUMUYc4wdwRB4PZBLny6PwF1fQMetmb42Juz+7nxza4xd5gHnx9MJL2o\nGk87M2Kyy4nJKWdKfyf2xuRx3zcnKays44HRun1yTBRyfn1ytKH+opFefHkwiQ1hGdiYKdh3IY/z\n2WXklKn5cG8cPz06kq0RWfjam5NdVsP2s9kM16+0eoIn14WTUVyNTCZwc18H3KxNGexuxZ7oXJ66\n2f+y22uc1Lw7a6DheW1k34U83t0Zi4lCzv2jvAHdBKqoqo6Xhrpx+yDd73Hgsj1EZZZxS7+Lk8V/\nbzuPqUKOj705WyOzeXCMT9dvuofo0UjlDur+Djyg//sB4PLjrC+D9OJqXK1MGe1nB8Cp1GL2ROcy\n1NMGG3MlgiAYDMO3DXLBwUI3k2u6QiipqiOvvJbHxvnw1swg3p89qN39R169rR9vzgxi7/PjeWVG\nvxY/rqY4WZrgbmPKGf1sKqO4BlHEIBCURjImBTp1SRgAeNqZMcLHlk3huhf3t0dTmKS3P7yyJZqy\nmnq+PZaCt50Z80I82PLUGIZ62jDW344z6TrB0cdJxdHEQoNb7ZojKeRX6MYjo7iamUPc+HxhMK/d\n1o8lkwOwVxmz8q9ESqrreX5yH+oatPwZo3Oh3axXpyy7oz8VtRrDfcPFFYKdSvcdjPW3p6qugVOp\nxQxyt2KQuzWWJkYcTSigslZDhd6rZF1oGh/vi2d2sDurFg2jQSsarvfbmUzi8yr5aN4QHhztzZxh\n7shlAhP6OPBbeBabwjMZ42+PlZkCmUxg6fRABnvothlwtjLhy0XBvDtrID56t8umWJkpeGisD7vP\n53LvN6FYGBsxKdCRzJIaUvVqi8Z6rf2uGlHXN/DkunBe3xrNigOJDPGw5rXb+iGXCVzIqeDZSf4s\nGO7JuawyNFqRh0b7IIrgZm3KSB/d7/r2Qa6Ioi4q2t2m9fiKRSO9kAsC7+yM4b41J5m76jgKucA7\nswZia67kbGYZD472ZlxA63uHWZkqeGK8L06WxmxbPBYXKxNyytTc2t+JY4lFrDqUxJn0UuaEuHNL\nPyd2nsvpFkeA1sgqrSEmp5yKWg1lNfWG53vqAGfOZpaRVVrTQQst+f54KpvPZPHJvgRCk4v4+nCS\nwWB8IFY3b913IZ+cshpe3XKOrRE6h48RPnaYGxthbmyEv6OKqMyLKsu8cjXnssq4d6QnC4Z7EJlR\nelVVWp2lw7eLKIoaoDFS+QKwsTFSuTFaWRAEZ0EQMoEXgNcEQcgUBMGyrbr6ppcDtwqCkABM1n/u\nMdKKqvC0NcNOZYyfgzlrj6cRl1fBvJCL8XQ393XAXClnapAzxkZybM2VzXzGY3N1Ko9xAQ7cN8q7\n3Rc86FYK94/yxtdB1ak+DvOy4VRqMVqtSHqx7sfiadvyBdRV5oZ4kFpUzazPj2GikLP87oG8NLUv\nh+MLePLHcNT1Wt6ZNZC37hqAh37ZO8bfnkYV8KpFw3C2NOF/e+LIKath1aEkpgU58+pt/Tm3bCr/\nmzuY2wa58Og4X4yN5AR7WlNRq2GopzXP3uKPl50ZO6Nz0WpFtkVkMT7AntnD3DGSCRyKv6gObFQF\nNXq0jPK1p1HuDvawRi4TGONvz8G4Am777AiTPjzEFwcTeX1bNJMCHVk+eyBBrpaG6wH8diaLPk4q\npg9wZtmdQSy7MwiAN2cOwF6lpKiqjhkDm+6m0pxxAQ7M16uYWuORsT7YmStxtjJl0z9GE+RqSW65\nmsT8SsyVcoMgMFHIsTFTtGpDWPFXAunF1Uzu50idRsuUICcCnCw4t2wKYa9N5oUpfZmsV0+qjI34\n59S++DuqeGiMtyEQ0d9RRaCzzr7kbmPWal+drUyYPtCFPefziMutYM4wd354eDgOFsYsnujPnGHu\nvHpbvzbvFeCZWwI4+q9JeNqZsWrRMD6dP4RP7hmClamC5bti8bA1Zc4wd2YMcKGoqo6zmaWczy7j\nf3viutXDqvEF/b+5gxnuY8ukQN34TNOryD7cE2fQ5R9NKOTjP+NbeNvti8njz5g8RFGkvkHLgdh8\njI1kfH04iflfh/LuzljG//cA2yKzOKj/nYYmFfH2Hxf46WQ6Kw8k4mhhbJi8AQxytyYqs8xwr/sv\n6Po5uZ8Tdw52QxBoVV3b2/R0pHKrdfXHi4BbLqezV4LuQdP9WG7ytuWX0xlYmymYOeSifXxeiAcz\nBrpgodcNO1oYN9t3Ji5XpyNufOC6m0mBjmyLzCY0uchg8/C2a/2h7gozBjrzxu/nMTc24tsHsOnJ\nMAAAIABJREFUb8LR0oT7Rnmz7Ww2J5KLGOJhzQA3q2Z1gj1tMFHICHS2xNdBxTO3+PPqlmjuXX2S\nBlHk5Rk6fXtrkdFDPW3YG5PHI2N9EASBaQOcWXMkhb0xeWSXqfnX9EAsTBQM87LhYFwBL03TtVVY\nWYeFiZFB4FqZKRjoZkVUZhlD9LP2sQH27IrOxUQhw8pUwQe74xjmZcPnC4MNqqfpA1z45kgyyQWV\nhKeV8Ph43xYrOltzJavuG8Zn+xOYplcRdQUrUwWHXpqIqUKOXCYQkW5Kg1YkNLkILzvzZtd1sjQx\n2BDmfXWCIR7WzB3mzteHk7k72I0P5w7maGIhI/Sz/qZ1h7hb42RpTIiXLaZKOftemNCiL3cMdiU2\nN84g1Fvj1Rn9GOlry6yhbs22oHhkbOfVGI3jPNjD2rCa+mzBULJKapg9zA1jIzmj/ewQBN1Or/F5\nFeyIymH6QGeCXK3aa7pVcsvU1NQ3NFul/RWbj6etGbOD3Zgz7OIryNdBxYLhnvxyOp0/Y/L4dMEQ\nnvslknK1hm2RWdRptFiaKpjQ14GvDuk8gkb52rFopBflag0fzh3Mj6FpDPGw5r5RXrywIZKlv52j\npr6BucPc+TU8kz+icpjY14HwtBIm9nVs9j0NdrdiU3gm3x9PpVaj5URSEe42pgQ4qnTPQpAzqw4l\nYW2moKq2gfF9HAxq497khog9r6zVUFhZh6f+5dooEObf5Nlsli8IgkEYgO7BbeopEJtbga250jDb\n626mBun017+GZ6IyNkJlbISt+ZXvodOImdKI7c+MxdpMYdibRy4TeH/2IGZ9fozHxrVM5m2ikPPB\nnMGG7R3mhXjw1aFkkgureHVGP7zs2l7BzAtxRyEXDLO16QNc+OpQMk+uC8fC2MhgjJ/Q14EPdseR\nX67G0dKEwspa7FXNx3hqkDMFFbX46Vdbt/ZzYuPpDJ6ZFEB/V0t+OZXOw2N9MFVe/D7nhriz6lAS\nL2w8S4NWbHP7iUHu1nyjdx+9EpruX9U4O0/Ir+S2S1YebtamZJWqqdNoOZ1azKmUYvacz8Xc2IhX\nZ/RDEIQ21TUymcCmJ0djYdL2oztziCvfHUsl2NO6zTLOVibcO8Lrcm6vU0zo07zfNuZK+rtYcjAu\nn/g8nWfTnzF5LQSCKIqcSS8l2NPa8GLdEZWNTBDwsDHjtW3RnM0oRSEX+P6h4ZzPLuNMWinHEgtZ\nMNyzVdXte3cP5JGxPty/5iQPfx+GiULGu7MGsv5UOu42pkRnl/HVoWQm93NkQh8H3tpxgbC0YoyN\nZEwf6MzsJgLmrbsGMPPzYwC8OKUvu8/n6tzOZw3E0lSB0SUTooHuurF/Qx94CvDgaG9DPz+aN4SH\nvz/Nuzt1jg8/nUxjxYJgXt4chbOVCbf2d8bT1oxJgY5U1Wl4ZfM5npkUQN8emow20imBIAjCNOBT\ndJvbfSOK4vJLzgv68zOAauBBURTPCILQF9jQpKgv8G9RFD8RBGEZ8BjQqCto3PSu20nTB3h5619e\nk/s7cU+IR4ezISdLY2JzL3r9XMitINDZosN9y7uKiULOHYNd2RSeiVYrcnNfh26/lncrOvA+ThZE\nLZuKvI39j5oabhVyGe/PHsSe87k8NMa73WvZqYx5tImQGexuxRMTfDGWy7h9sKthZjo+QCcQjicV\ncddQN71AaC4I/zHBj0fH+Rj66GhpwranxxrOvzClb4vr+zmoGOlrS2hyMRYmRu2+ILubpvp7b3uz\nFudOpRaTXaqzE8llAmlF1bw/e6DBbtIe7c38de2bEfba5K51vAcY7WfH6iM6zyszpZw/Y/J4bnKf\nZmV2nstl8c9nWLVoGNMGOJNXrua5XyIN0eKOFsYsnR7I5jOZLFpzElHUxViIItwxuG1Vn7+jirWP\njOCxtWE8Md6X+cM9WThCp/qrqWvgSEIBN/d1RGkkw9pMyTPrI5jcz6HFxn2D3K15fJwvmSU1OFuZ\nsOSWAF0f2tgHq7+LJeMC7AnxsiXAScXKvxKbrWBMlXK+e+gmItJLURoJLFx9kgWrQ7FXGZNbpuat\nHTpB8tzkAMyVRuyIyuHJCS0dWLqbDgVCk2jjW9HFEZwWBOF3URRjmhSbDgTo/41Al/xmhCiKcegS\n4DS2kwVsaVLvY1EUL83D3O2k69Uvje5gVqaKdl03G3GyNKGgopYGrYgAxOdWsKAdPXJ3MC/Eg59P\npjPY3YqP7hnSo9dqSlvCoDVG+dkxSm+8uxwEQeDl6S110/1cLLEwNuJUarFeINQR4Njc7iKTCRjL\n2rfZtMbCEV6EJhczPsABI/nV87J2sTZBEEAUL05EGnG3MaNCreG8fg+kd2cNoKq2gbnDPFpr6rpn\ntL89q4+kYKaU88R4Pz7eF09WaU2zTQW/O6YTGDuispk2wJmfT6aj0Yq8OTOIkqp67h/lhY25kplD\nXHlx41lmDHThXv2LvaNJk7+jigP/d3OL46ZKOVOCLqoJ7xjsisMltoCmvDzj4m/30VZW001RGsn4\n8ZERhs+t2adMFHLDc/TOrIF8fzyFz+YPxcfenMLKOl7eHMWaoymojI0Y6WvbQp3bE3RmhWCINgYQ\nBKEx2ripQJgJrNXHE4QKgmDd6FLapMwtQJIoimnd1PdOk6b3EGjri24LR0sTtKLOyJmQX0lNfUOP\n2Q8aGeJhzYbHR9Lf1bKZ+urvjFwmEOxlw+kUnW98UWUtI327x01xapATkwIdDbPCq4WxkRwnCxNy\ny9UtPJMaVw8nknXeWmP87ds0AP8dGO5ti0IuMD7AgTsGu/DxvnjuXHGUsQH2vHf3QJLyqwhLK8HG\nTMFfsflUqOtZfyqdCX0cDK6djbhYmfLzY13LSdwZRvpe/kSnO5gzzL3ZCsLBwpjnJvfh9hVHqVBr\nePuuAVelH50RCK1FG4/oRBk3dKkxG5kPrL+k3jOCINyPLpfCi6Io9kgES1pRNbbmyst+wTrpbQWb\nzmSyYn8ivvbmzYLQeooRvfSj7E2G+9jyX71HSEl1fQsbQlcxNpLz7YNXbh/oCu42puSWq1uo6Rpf\n/ieSijCSCbhYta52+LtgbmzE1/eH4O+gwsPWjPfuHsjplGK2RmaRWlhFZa0GM6Wcd2cN5B8/neHh\n70+TX1HLe3d3v43jemKAmxXTBziTUlh1xduvd5arlVNZCdwJ/NrkcKdyKndHpPLztwbww0OXn6Gz\nMYjog91xeNmZseGJUdh0o5FX4iKNe0HtPa+LG+iMLv1ax8PWDAsTI4P77MXjOgGQVFCFq7XpZanr\nrlcm9nU02D4WDPfko3uGsGJBMDE55SjkMj6aN5jJ/Z2wMVNwOrWEh8Z4G+JkbmQ+WzCUrYvHXLX8\nJt0VqdxRmenAGVEUDRv7N/1bEITVQKvphrojUtnRwgRHC5OOC15Co8FoiIc1Pzw03LBfkUT3M8jd\nCqVcxvazuiAfB9X1L3ifmeTP3cFuLXTcVqYKVMZGVNZq2gweuxG4bZALkwIdMVHImnnf1Ddom+n2\nb2QUchkdhDt1K50RCIZoY3Qv+fnAwkvK/A48rbcvjADKLrEfLOASddElNoZrMqeyg4Uxv/1jNP1c\nLG7IdIFXExOFnPF97NmnD+DpLpVRb+LroGo1KFEQBNxtTInNrcDjb2w76AxN3YSBbss0KNE1uiWn\nMrrAsxlAIjq304ca6wuCYI7OQ+nSnMnXRU7layFY5EZhxYJgvjqcxMG4AgKcetZ439s0CoQbeYUg\nce3RXZHKIrC4jbpVQAsr6d8pp7JE92CqlPPc5D4tfNT/jjQaljuKKZCQuJpcE9tfS0jcaDSuDBoN\nzBIS1wKSQJCQ6AXG93FgtJ8dgc6Wvd0VCQkDkqVUQqIX6ONk0aMBVhISXUHo7mTfPYkgCAVAVyOd\n7YHCbuzO3xFpjNpHGp+OkcaofXprfLxEUWx9x8QmXFcC4UoQBCFMFMWQ3u7HtYw0Ru0jjU/HSGPU\nPtf6+Eg2BAkJCQkJQBIIEhISEhJ6biSB8HVvd+A6QBqj9pHGp2OkMWqfa3p8rsiG0InEOfcC/wIE\noAL4hyiKZ/XnUvXHGgDNtaxXk5CQkLgR6LJA0Ce8iadJ4hxgQdPEOYIgjAYuiKJYIgjCdGCZKIoj\n9OdSgRBRFCWPBAkJCYlrgCuJQ+gwcY4oiseblA9Ftwtql7G3txe9vb2vpAkJCQmJG47w8PDCzrid\nXolA6EzinKY8Auxq8lkE9gmC0AB8pd/mugWCIDwOPA7g6elJWFjYFXRZQkKiqyQVVPJTaDqv3dbv\nqu3PL9E9CILQqfitq5UgZyI6gfCvJofHiqI4BF2uhMWCIIxvra4oil+LohgiimKIg0OHAk5CQqKH\n2B2dy7fHUsgqrentrkj0EFciEDqTOAdBEAYB3wAzRVEsajwuimKW/v98YAs6FZSEhMQ1SlFlHQD5\nFepe7olER6SmpiIIAhqN5rLqXYlAMCTO0afInI8uUY4BQRA8gc3AfaIoxjc5bi4IgkXj38AUrsEE\nORISEhcpqqoFIK+8tpd70jV+/vlnQkJCUKlUuLi4MH36dI4ePdrb3bqm6LINoZOJc/6NLhfCF/oU\neY3upU7AFv0xI+BnURR3X9GdSEhI9CiNK4S88utvhfDRRx+xfPlyVq1axdSpU1EqlezZs4fff/+d\nsWPH9nb3rh1EUbxu/g0bNkyUkJDoHaZ+fEj0+tcO8b2dF3q7K5dFaWmpaG5uLm7cuLHV82q1Wlyy\nZIno4uIiuri4iEuWLBHVarUoiqJ44MAB0c3NTXz//fdFBwcH0dnZWdyyZYv4xx9/iAEBAaKNjY34\nzjvvGNr6z3/+I86ePVucN2+eqFKpxKFDh4qRkZGG8zExMeKECRNEKysrsX///uK2bdsM5yZMmCCu\nXr3a8Pm7774Tx4wZY/gMiF9++aXo7+8vWllZiU899ZSo1WpFURRFjUYjvvjii6KdnZ3o4+Mjrly5\nUgTE+vr6xrphYifesTdSpLKEhMQVUNhoQ7jOVggnTpxArVYza9asVs+/8847hIaGEhkZydmzZzl1\n6hRvv/224Xxubi5qtZqsrCzefPNNHnvsMdatW0d4eDhHjhzhrbfeIiUlxVB+27ZtzJ07l+LiYhYu\nXMhdd91FfX099fX13HHHHUyZMoX8/HxWrFjBvffeS1xcXKfvZceOHZw+fZqoqCg2btzInj17AFi9\nejU7duwgIiKCsLAwNm3a1KWxkgSChIREh2i1IsV6G0LudSYQioqKsLe3x8iodQ35Tz/9xL///W8c\nHR1xcHDgP//5Dz/++KPhvEKh4NVXX0WhUDB//nwKCwtZsmQJFhYWBAUF0b9/f86ePWsoP2zYMObM\nmYNCoeCFF15ArVYTGhpKaGgolZWVLF26FKVSyaRJk7j99ttZv359p+9l6dKlWFtb4+npycSJE4mM\njARg48aNPPfcc3h4eGBra8vLL7/cpbGSEuRISEh0SEl1HVr9pgbXmw3Bzs6OwsJCNBpNq0IhOzsb\nLy8vw2cvLy+ys7Ob1ZfL5QCYmupSnjo5ORnOm5qaUllZafjs4XHR+VImk+Hu7m5oz8PDA5ns4jzc\ny8uLrKwWzplt4uzsbPjbzMzMcN3s7Oxm1216P5eDtEKQkJDokKIqnbrIXmVM/nXmZTRq1CiMjY3Z\nunVrq+ddXV1JS7sYt5Weno6rq2uXr5eRcTFeV6vVkpmZiaurK66urmRkZKDVaptdy83NDQBzc3Oq\nq6sN53Jzczt9TRcXl2bXTU9P71LfJYEgISHRIYWVOiEQ5GpJRa2GqtrL82/vTaysrHjzzTdZvHgx\nW7dupbq6mvr6enbt2sVLL73EggULePvttykoKKCwsJA333yTRYsWdfl64eHhbN68GY1GwyeffIKx\nsTEjR45kxIgRmJmZ8cEHH1BfX8/BgwfZvn078+fPB2DIkCFs3ryZ6upqEhMTWbNmTaevOW/ePD77\n7DMyMzMpKSlh+fLlHVdqBUkgSEhIdEijy2l/V0sA8iuur1XCiy++yEcffcTbb7+Ng4MDHh4erFy5\nkrvuuovXXnuNkJAQBg0axMCBAwkODua1117r8rVmzpzJhg0bsLGx4ccff2Tz5s0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hAAAg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\nAlAKQaFQKBRmlEJQKBQKBQD/D+evy7oB7moDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c420a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = df1.values\n",
    "# specify columns to plot\n",
    "groups = [ 1,2,3,4,5]\n",
    "i = 1\n",
    "# plot each column\n",
    "plt.figure()\n",
    "for group in groups:\n",
    "\tplt.subplot(len(groups), 1, i)\n",
    "\tplt.plot(values[:, group])\n",
    "\tplt.title(df1.columns[group], y=0.5, loc='right')\n",
    "\ti += 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1.set_index('DateTime',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Compound</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-31T05:00:00.000-04:00</th>\n",
       "      <td>6158.76</td>\n",
       "      <td>0.032105</td>\n",
       "      <td>0.888699</td>\n",
       "      <td>0.076390</td>\n",
       "      <td>0.095283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T06:00:00.000-04:00</th>\n",
       "      <td>6105.90</td>\n",
       "      <td>0.035658</td>\n",
       "      <td>0.862046</td>\n",
       "      <td>0.101670</td>\n",
       "      <td>0.130807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T07:00:00.000-04:00</th>\n",
       "      <td>6094.36</td>\n",
       "      <td>0.034356</td>\n",
       "      <td>0.864839</td>\n",
       "      <td>0.099413</td>\n",
       "      <td>0.130343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T08:00:00.000-04:00</th>\n",
       "      <td>6125.13</td>\n",
       "      <td>0.036202</td>\n",
       "      <td>0.879061</td>\n",
       "      <td>0.084736</td>\n",
       "      <td>0.110576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31T09:00:00.000-04:00</th>\n",
       "      <td>6165.00</td>\n",
       "      <td>0.043838</td>\n",
       "      <td>0.862753</td>\n",
       "      <td>0.092850</td>\n",
       "      <td>0.108861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Price  Negative   Neutral  Positive  Compound\n",
       "DateTime                                                                      \n",
       "2017-10-31T05:00:00.000-04:00  6158.76  0.032105  0.888699  0.076390  0.095283\n",
       "2017-10-31T06:00:00.000-04:00  6105.90  0.035658  0.862046  0.101670  0.130807\n",
       "2017-10-31T07:00:00.000-04:00  6094.36  0.034356  0.864839  0.099413  0.130343\n",
       "2017-10-31T08:00:00.000-04:00  6125.13  0.036202  0.879061  0.084736  0.110576\n",
       "2017-10-31T09:00:00.000-04:00  6165.00  0.043838  0.862753  0.092850  0.108861"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = df1.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "df1 = df1[cols]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(df1.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_hours = 3\n",
    "n_features = 5\n",
    "n_obs = n_hours*n_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var1(t-3)</th>\n",
       "      <th>var2(t-3)</th>\n",
       "      <th>var3(t-3)</th>\n",
       "      <th>var4(t-3)</th>\n",
       "      <th>var5(t-3)</th>\n",
       "      <th>var1(t-2)</th>\n",
       "      <th>var2(t-2)</th>\n",
       "      <th>var3(t-2)</th>\n",
       "      <th>var4(t-2)</th>\n",
       "      <th>var5(t-2)</th>\n",
       "      <th>var1(t-1)</th>\n",
       "      <th>var2(t-1)</th>\n",
       "      <th>var3(t-1)</th>\n",
       "      <th>var4(t-1)</th>\n",
       "      <th>var5(t-1)</th>\n",
       "      <th>var1(t)</th>\n",
       "      <th>var2(t)</th>\n",
       "      <th>var3(t)</th>\n",
       "      <th>var4(t)</th>\n",
       "      <th>var5(t)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.039567</td>\n",
       "      <td>0.078779</td>\n",
       "      <td>0.831199</td>\n",
       "      <td>0.195812</td>\n",
       "      <td>0.401475</td>\n",
       "      <td>0.007090</td>\n",
       "      <td>0.128849</td>\n",
       "      <td>0.579299</td>\n",
       "      <td>0.505289</td>\n",
       "      <td>0.542609</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110492</td>\n",
       "      <td>0.605697</td>\n",
       "      <td>0.477654</td>\n",
       "      <td>0.540763</td>\n",
       "      <td>0.018905</td>\n",
       "      <td>0.136519</td>\n",
       "      <td>0.740108</td>\n",
       "      <td>0.297985</td>\n",
       "      <td>0.462231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.007090</td>\n",
       "      <td>0.128849</td>\n",
       "      <td>0.579299</td>\n",
       "      <td>0.505289</td>\n",
       "      <td>0.542609</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110492</td>\n",
       "      <td>0.605697</td>\n",
       "      <td>0.477654</td>\n",
       "      <td>0.540763</td>\n",
       "      <td>0.018905</td>\n",
       "      <td>0.136519</td>\n",
       "      <td>0.740108</td>\n",
       "      <td>0.297985</td>\n",
       "      <td>0.462231</td>\n",
       "      <td>0.043401</td>\n",
       "      <td>0.244122</td>\n",
       "      <td>0.585976</td>\n",
       "      <td>0.397319</td>\n",
       "      <td>0.455419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110492</td>\n",
       "      <td>0.605697</td>\n",
       "      <td>0.477654</td>\n",
       "      <td>0.540763</td>\n",
       "      <td>0.018905</td>\n",
       "      <td>0.136519</td>\n",
       "      <td>0.740108</td>\n",
       "      <td>0.297985</td>\n",
       "      <td>0.462231</td>\n",
       "      <td>0.043401</td>\n",
       "      <td>0.244122</td>\n",
       "      <td>0.585976</td>\n",
       "      <td>0.397319</td>\n",
       "      <td>0.455419</td>\n",
       "      <td>0.046946</td>\n",
       "      <td>0.186121</td>\n",
       "      <td>0.513127</td>\n",
       "      <td>0.542100</td>\n",
       "      <td>0.528573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.018905</td>\n",
       "      <td>0.136519</td>\n",
       "      <td>0.740108</td>\n",
       "      <td>0.297985</td>\n",
       "      <td>0.462231</td>\n",
       "      <td>0.043401</td>\n",
       "      <td>0.244122</td>\n",
       "      <td>0.585976</td>\n",
       "      <td>0.397319</td>\n",
       "      <td>0.455419</td>\n",
       "      <td>0.046946</td>\n",
       "      <td>0.186121</td>\n",
       "      <td>0.513127</td>\n",
       "      <td>0.542100</td>\n",
       "      <td>0.528573</td>\n",
       "      <td>0.085634</td>\n",
       "      <td>0.117236</td>\n",
       "      <td>0.544642</td>\n",
       "      <td>0.555482</td>\n",
       "      <td>0.628997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.043401</td>\n",
       "      <td>0.244122</td>\n",
       "      <td>0.585976</td>\n",
       "      <td>0.397319</td>\n",
       "      <td>0.455419</td>\n",
       "      <td>0.046946</td>\n",
       "      <td>0.186121</td>\n",
       "      <td>0.513127</td>\n",
       "      <td>0.542100</td>\n",
       "      <td>0.528573</td>\n",
       "      <td>0.085634</td>\n",
       "      <td>0.117236</td>\n",
       "      <td>0.544642</td>\n",
       "      <td>0.555482</td>\n",
       "      <td>0.628997</td>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.100541</td>\n",
       "      <td>0.701571</td>\n",
       "      <td>0.373207</td>\n",
       "      <td>0.492066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.046946</td>\n",
       "      <td>0.186121</td>\n",
       "      <td>0.513127</td>\n",
       "      <td>0.542100</td>\n",
       "      <td>0.528573</td>\n",
       "      <td>0.085634</td>\n",
       "      <td>0.117236</td>\n",
       "      <td>0.544642</td>\n",
       "      <td>0.555482</td>\n",
       "      <td>0.628997</td>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.100541</td>\n",
       "      <td>0.701571</td>\n",
       "      <td>0.373207</td>\n",
       "      <td>0.492066</td>\n",
       "      <td>0.146213</td>\n",
       "      <td>0.126593</td>\n",
       "      <td>0.578442</td>\n",
       "      <td>0.500330</td>\n",
       "      <td>0.552681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.085634</td>\n",
       "      <td>0.117236</td>\n",
       "      <td>0.544642</td>\n",
       "      <td>0.555482</td>\n",
       "      <td>0.628997</td>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.100541</td>\n",
       "      <td>0.701571</td>\n",
       "      <td>0.373207</td>\n",
       "      <td>0.492066</td>\n",
       "      <td>0.146213</td>\n",
       "      <td>0.126593</td>\n",
       "      <td>0.578442</td>\n",
       "      <td>0.500330</td>\n",
       "      <td>0.552681</td>\n",
       "      <td>0.165131</td>\n",
       "      <td>0.141180</td>\n",
       "      <td>0.532970</td>\n",
       "      <td>0.549913</td>\n",
       "      <td>0.543096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.100541</td>\n",
       "      <td>0.701571</td>\n",
       "      <td>0.373207</td>\n",
       "      <td>0.492066</td>\n",
       "      <td>0.146213</td>\n",
       "      <td>0.126593</td>\n",
       "      <td>0.578442</td>\n",
       "      <td>0.500330</td>\n",
       "      <td>0.552681</td>\n",
       "      <td>0.165131</td>\n",
       "      <td>0.141180</td>\n",
       "      <td>0.532970</td>\n",
       "      <td>0.549913</td>\n",
       "      <td>0.543096</td>\n",
       "      <td>0.166378</td>\n",
       "      <td>0.016336</td>\n",
       "      <td>0.942586</td>\n",
       "      <td>0.132895</td>\n",
       "      <td>0.395744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.146213</td>\n",
       "      <td>0.126593</td>\n",
       "      <td>0.578442</td>\n",
       "      <td>0.500330</td>\n",
       "      <td>0.552681</td>\n",
       "      <td>0.165131</td>\n",
       "      <td>0.141180</td>\n",
       "      <td>0.532970</td>\n",
       "      <td>0.549913</td>\n",
       "      <td>0.543096</td>\n",
       "      <td>0.166378</td>\n",
       "      <td>0.016336</td>\n",
       "      <td>0.942586</td>\n",
       "      <td>0.132895</td>\n",
       "      <td>0.395744</td>\n",
       "      <td>0.166144</td>\n",
       "      <td>0.124761</td>\n",
       "      <td>0.837867</td>\n",
       "      <td>0.171567</td>\n",
       "      <td>0.329644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.165131</td>\n",
       "      <td>0.141180</td>\n",
       "      <td>0.532970</td>\n",
       "      <td>0.549913</td>\n",
       "      <td>0.543096</td>\n",
       "      <td>0.166378</td>\n",
       "      <td>0.016336</td>\n",
       "      <td>0.942586</td>\n",
       "      <td>0.132895</td>\n",
       "      <td>0.395744</td>\n",
       "      <td>0.166144</td>\n",
       "      <td>0.124761</td>\n",
       "      <td>0.837867</td>\n",
       "      <td>0.171567</td>\n",
       "      <td>0.329644</td>\n",
       "      <td>0.164307</td>\n",
       "      <td>0.198284</td>\n",
       "      <td>0.755304</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.314862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.166378</td>\n",
       "      <td>0.016336</td>\n",
       "      <td>0.942586</td>\n",
       "      <td>0.132895</td>\n",
       "      <td>0.395744</td>\n",
       "      <td>0.166144</td>\n",
       "      <td>0.124761</td>\n",
       "      <td>0.837867</td>\n",
       "      <td>0.171567</td>\n",
       "      <td>0.329644</td>\n",
       "      <td>0.164307</td>\n",
       "      <td>0.198284</td>\n",
       "      <td>0.755304</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.314862</td>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.237637</td>\n",
       "      <td>0.541722</td>\n",
       "      <td>0.451360</td>\n",
       "      <td>0.465997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.166144</td>\n",
       "      <td>0.124761</td>\n",
       "      <td>0.837867</td>\n",
       "      <td>0.171567</td>\n",
       "      <td>0.329644</td>\n",
       "      <td>0.164307</td>\n",
       "      <td>0.198284</td>\n",
       "      <td>0.755304</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.314862</td>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.237637</td>\n",
       "      <td>0.541722</td>\n",
       "      <td>0.451360</td>\n",
       "      <td>0.465997</td>\n",
       "      <td>0.151626</td>\n",
       "      <td>0.280928</td>\n",
       "      <td>0.534492</td>\n",
       "      <td>0.421342</td>\n",
       "      <td>0.444023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.164307</td>\n",
       "      <td>0.198284</td>\n",
       "      <td>0.755304</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.314862</td>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.237637</td>\n",
       "      <td>0.541722</td>\n",
       "      <td>0.451360</td>\n",
       "      <td>0.465997</td>\n",
       "      <td>0.151626</td>\n",
       "      <td>0.280928</td>\n",
       "      <td>0.534492</td>\n",
       "      <td>0.421342</td>\n",
       "      <td>0.444023</td>\n",
       "      <td>0.169401</td>\n",
       "      <td>0.123146</td>\n",
       "      <td>0.607435</td>\n",
       "      <td>0.474041</td>\n",
       "      <td>0.560936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.237637</td>\n",
       "      <td>0.541722</td>\n",
       "      <td>0.451360</td>\n",
       "      <td>0.465997</td>\n",
       "      <td>0.151626</td>\n",
       "      <td>0.280928</td>\n",
       "      <td>0.534492</td>\n",
       "      <td>0.421342</td>\n",
       "      <td>0.444023</td>\n",
       "      <td>0.169401</td>\n",
       "      <td>0.123146</td>\n",
       "      <td>0.607435</td>\n",
       "      <td>0.474041</td>\n",
       "      <td>0.560936</td>\n",
       "      <td>0.183642</td>\n",
       "      <td>0.311738</td>\n",
       "      <td>0.447805</td>\n",
       "      <td>0.524477</td>\n",
       "      <td>0.472892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.151626</td>\n",
       "      <td>0.280928</td>\n",
       "      <td>0.534492</td>\n",
       "      <td>0.421342</td>\n",
       "      <td>0.444023</td>\n",
       "      <td>0.169401</td>\n",
       "      <td>0.123146</td>\n",
       "      <td>0.607435</td>\n",
       "      <td>0.474041</td>\n",
       "      <td>0.560936</td>\n",
       "      <td>0.183642</td>\n",
       "      <td>0.311738</td>\n",
       "      <td>0.447805</td>\n",
       "      <td>0.524477</td>\n",
       "      <td>0.472892</td>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.180111</td>\n",
       "      <td>0.387843</td>\n",
       "      <td>0.707781</td>\n",
       "      <td>0.580837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.169401</td>\n",
       "      <td>0.123146</td>\n",
       "      <td>0.607435</td>\n",
       "      <td>0.474041</td>\n",
       "      <td>0.560936</td>\n",
       "      <td>0.183642</td>\n",
       "      <td>0.311738</td>\n",
       "      <td>0.447805</td>\n",
       "      <td>0.524477</td>\n",
       "      <td>0.472892</td>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.180111</td>\n",
       "      <td>0.387843</td>\n",
       "      <td>0.707781</td>\n",
       "      <td>0.580837</td>\n",
       "      <td>0.217495</td>\n",
       "      <td>0.209864</td>\n",
       "      <td>0.405118</td>\n",
       "      <td>0.663783</td>\n",
       "      <td>0.550206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.183642</td>\n",
       "      <td>0.311738</td>\n",
       "      <td>0.447805</td>\n",
       "      <td>0.524477</td>\n",
       "      <td>0.472892</td>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.180111</td>\n",
       "      <td>0.387843</td>\n",
       "      <td>0.707781</td>\n",
       "      <td>0.580837</td>\n",
       "      <td>0.217495</td>\n",
       "      <td>0.209864</td>\n",
       "      <td>0.405118</td>\n",
       "      <td>0.663783</td>\n",
       "      <td>0.550206</td>\n",
       "      <td>0.173339</td>\n",
       "      <td>0.237665</td>\n",
       "      <td>0.487808</td>\n",
       "      <td>0.498664</td>\n",
       "      <td>0.468680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.180111</td>\n",
       "      <td>0.387843</td>\n",
       "      <td>0.707781</td>\n",
       "      <td>0.580837</td>\n",
       "      <td>0.217495</td>\n",
       "      <td>0.209864</td>\n",
       "      <td>0.405118</td>\n",
       "      <td>0.663783</td>\n",
       "      <td>0.550206</td>\n",
       "      <td>0.173339</td>\n",
       "      <td>0.237665</td>\n",
       "      <td>0.487808</td>\n",
       "      <td>0.498664</td>\n",
       "      <td>0.468680</td>\n",
       "      <td>0.191267</td>\n",
       "      <td>0.227426</td>\n",
       "      <td>0.506527</td>\n",
       "      <td>0.475111</td>\n",
       "      <td>0.465703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.217495</td>\n",
       "      <td>0.209864</td>\n",
       "      <td>0.405118</td>\n",
       "      <td>0.663783</td>\n",
       "      <td>0.550206</td>\n",
       "      <td>0.173339</td>\n",
       "      <td>0.237665</td>\n",
       "      <td>0.487808</td>\n",
       "      <td>0.498664</td>\n",
       "      <td>0.468680</td>\n",
       "      <td>0.191267</td>\n",
       "      <td>0.227426</td>\n",
       "      <td>0.506527</td>\n",
       "      <td>0.475111</td>\n",
       "      <td>0.465703</td>\n",
       "      <td>0.199826</td>\n",
       "      <td>0.181549</td>\n",
       "      <td>0.605828</td>\n",
       "      <td>0.421061</td>\n",
       "      <td>0.428070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.173339</td>\n",
       "      <td>0.237665</td>\n",
       "      <td>0.487808</td>\n",
       "      <td>0.498664</td>\n",
       "      <td>0.468680</td>\n",
       "      <td>0.191267</td>\n",
       "      <td>0.227426</td>\n",
       "      <td>0.506527</td>\n",
       "      <td>0.475111</td>\n",
       "      <td>0.465703</td>\n",
       "      <td>0.199826</td>\n",
       "      <td>0.181549</td>\n",
       "      <td>0.605828</td>\n",
       "      <td>0.421061</td>\n",
       "      <td>0.428070</td>\n",
       "      <td>0.204298</td>\n",
       "      <td>0.299932</td>\n",
       "      <td>0.499747</td>\n",
       "      <td>0.445098</td>\n",
       "      <td>0.394034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.191267</td>\n",
       "      <td>0.227426</td>\n",
       "      <td>0.506527</td>\n",
       "      <td>0.475111</td>\n",
       "      <td>0.465703</td>\n",
       "      <td>0.199826</td>\n",
       "      <td>0.181549</td>\n",
       "      <td>0.605828</td>\n",
       "      <td>0.421061</td>\n",
       "      <td>0.428070</td>\n",
       "      <td>0.204298</td>\n",
       "      <td>0.299932</td>\n",
       "      <td>0.499747</td>\n",
       "      <td>0.445098</td>\n",
       "      <td>0.394034</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.147860</td>\n",
       "      <td>0.596683</td>\n",
       "      <td>0.474037</td>\n",
       "      <td>0.477040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.199826</td>\n",
       "      <td>0.181549</td>\n",
       "      <td>0.605828</td>\n",
       "      <td>0.421061</td>\n",
       "      <td>0.428070</td>\n",
       "      <td>0.204298</td>\n",
       "      <td>0.299932</td>\n",
       "      <td>0.499747</td>\n",
       "      <td>0.445098</td>\n",
       "      <td>0.394034</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.147860</td>\n",
       "      <td>0.596683</td>\n",
       "      <td>0.474037</td>\n",
       "      <td>0.477040</td>\n",
       "      <td>0.187433</td>\n",
       "      <td>0.088094</td>\n",
       "      <td>0.605360</td>\n",
       "      <td>0.514699</td>\n",
       "      <td>0.588314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.204298</td>\n",
       "      <td>0.299932</td>\n",
       "      <td>0.499747</td>\n",
       "      <td>0.445098</td>\n",
       "      <td>0.394034</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.147860</td>\n",
       "      <td>0.596683</td>\n",
       "      <td>0.474037</td>\n",
       "      <td>0.477040</td>\n",
       "      <td>0.187433</td>\n",
       "      <td>0.088094</td>\n",
       "      <td>0.605360</td>\n",
       "      <td>0.514699</td>\n",
       "      <td>0.588314</td>\n",
       "      <td>0.188379</td>\n",
       "      <td>0.176758</td>\n",
       "      <td>0.574356</td>\n",
       "      <td>0.477885</td>\n",
       "      <td>0.519576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.147860</td>\n",
       "      <td>0.596683</td>\n",
       "      <td>0.474037</td>\n",
       "      <td>0.477040</td>\n",
       "      <td>0.187433</td>\n",
       "      <td>0.088094</td>\n",
       "      <td>0.605360</td>\n",
       "      <td>0.514699</td>\n",
       "      <td>0.588314</td>\n",
       "      <td>0.188379</td>\n",
       "      <td>0.176758</td>\n",
       "      <td>0.574356</td>\n",
       "      <td>0.477885</td>\n",
       "      <td>0.519576</td>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.134368</td>\n",
       "      <td>0.531768</td>\n",
       "      <td>0.569653</td>\n",
       "      <td>0.583945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.187433</td>\n",
       "      <td>0.088094</td>\n",
       "      <td>0.605360</td>\n",
       "      <td>0.514699</td>\n",
       "      <td>0.588314</td>\n",
       "      <td>0.188379</td>\n",
       "      <td>0.176758</td>\n",
       "      <td>0.574356</td>\n",
       "      <td>0.477885</td>\n",
       "      <td>0.519576</td>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.134368</td>\n",
       "      <td>0.531768</td>\n",
       "      <td>0.569653</td>\n",
       "      <td>0.583945</td>\n",
       "      <td>0.216420</td>\n",
       "      <td>0.137144</td>\n",
       "      <td>0.580677</td>\n",
       "      <td>0.504029</td>\n",
       "      <td>0.553042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.188379</td>\n",
       "      <td>0.176758</td>\n",
       "      <td>0.574356</td>\n",
       "      <td>0.477885</td>\n",
       "      <td>0.519576</td>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.134368</td>\n",
       "      <td>0.531768</td>\n",
       "      <td>0.569653</td>\n",
       "      <td>0.583945</td>\n",
       "      <td>0.216420</td>\n",
       "      <td>0.137144</td>\n",
       "      <td>0.580677</td>\n",
       "      <td>0.504029</td>\n",
       "      <td>0.553042</td>\n",
       "      <td>0.274886</td>\n",
       "      <td>0.123885</td>\n",
       "      <td>0.449502</td>\n",
       "      <td>0.547613</td>\n",
       "      <td>0.538201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.134368</td>\n",
       "      <td>0.531768</td>\n",
       "      <td>0.569653</td>\n",
       "      <td>0.583945</td>\n",
       "      <td>0.216420</td>\n",
       "      <td>0.137144</td>\n",
       "      <td>0.580677</td>\n",
       "      <td>0.504029</td>\n",
       "      <td>0.553042</td>\n",
       "      <td>0.274886</td>\n",
       "      <td>0.123885</td>\n",
       "      <td>0.449502</td>\n",
       "      <td>0.547613</td>\n",
       "      <td>0.538201</td>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.086307</td>\n",
       "      <td>0.721459</td>\n",
       "      <td>0.361015</td>\n",
       "      <td>0.479860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.216420</td>\n",
       "      <td>0.137144</td>\n",
       "      <td>0.580677</td>\n",
       "      <td>0.504029</td>\n",
       "      <td>0.553042</td>\n",
       "      <td>0.274886</td>\n",
       "      <td>0.123885</td>\n",
       "      <td>0.449502</td>\n",
       "      <td>0.547613</td>\n",
       "      <td>0.538201</td>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.086307</td>\n",
       "      <td>0.721459</td>\n",
       "      <td>0.361015</td>\n",
       "      <td>0.479860</td>\n",
       "      <td>0.291002</td>\n",
       "      <td>0.174027</td>\n",
       "      <td>0.747167</td>\n",
       "      <td>0.248548</td>\n",
       "      <td>0.367103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.274886</td>\n",
       "      <td>0.123885</td>\n",
       "      <td>0.449502</td>\n",
       "      <td>0.547613</td>\n",
       "      <td>0.538201</td>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.086307</td>\n",
       "      <td>0.721459</td>\n",
       "      <td>0.361015</td>\n",
       "      <td>0.479860</td>\n",
       "      <td>0.291002</td>\n",
       "      <td>0.174027</td>\n",
       "      <td>0.747167</td>\n",
       "      <td>0.248548</td>\n",
       "      <td>0.367103</td>\n",
       "      <td>0.254801</td>\n",
       "      <td>0.199921</td>\n",
       "      <td>0.697007</td>\n",
       "      <td>0.291954</td>\n",
       "      <td>0.368465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.086307</td>\n",
       "      <td>0.721459</td>\n",
       "      <td>0.361015</td>\n",
       "      <td>0.479860</td>\n",
       "      <td>0.291002</td>\n",
       "      <td>0.174027</td>\n",
       "      <td>0.747167</td>\n",
       "      <td>0.248548</td>\n",
       "      <td>0.367103</td>\n",
       "      <td>0.254801</td>\n",
       "      <td>0.199921</td>\n",
       "      <td>0.697007</td>\n",
       "      <td>0.291954</td>\n",
       "      <td>0.368465</td>\n",
       "      <td>0.239798</td>\n",
       "      <td>0.157873</td>\n",
       "      <td>0.492145</td>\n",
       "      <td>0.600605</td>\n",
       "      <td>0.578592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.589640</td>\n",
       "      <td>0.239830</td>\n",
       "      <td>0.626147</td>\n",
       "      <td>0.352967</td>\n",
       "      <td>0.375881</td>\n",
       "      <td>0.555510</td>\n",
       "      <td>0.213553</td>\n",
       "      <td>0.669811</td>\n",
       "      <td>0.322099</td>\n",
       "      <td>0.384069</td>\n",
       "      <td>0.616317</td>\n",
       "      <td>0.233207</td>\n",
       "      <td>0.634436</td>\n",
       "      <td>0.334049</td>\n",
       "      <td>0.385724</td>\n",
       "      <td>0.618338</td>\n",
       "      <td>0.402528</td>\n",
       "      <td>0.542865</td>\n",
       "      <td>0.307348</td>\n",
       "      <td>0.296855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>0.555510</td>\n",
       "      <td>0.213553</td>\n",
       "      <td>0.669811</td>\n",
       "      <td>0.322099</td>\n",
       "      <td>0.384069</td>\n",
       "      <td>0.616317</td>\n",
       "      <td>0.233207</td>\n",
       "      <td>0.634436</td>\n",
       "      <td>0.334049</td>\n",
       "      <td>0.385724</td>\n",
       "      <td>0.618338</td>\n",
       "      <td>0.402528</td>\n",
       "      <td>0.542865</td>\n",
       "      <td>0.307348</td>\n",
       "      <td>0.296855</td>\n",
       "      <td>0.612557</td>\n",
       "      <td>0.276576</td>\n",
       "      <td>0.566237</td>\n",
       "      <td>0.348924</td>\n",
       "      <td>0.372635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.616317</td>\n",
       "      <td>0.233207</td>\n",
       "      <td>0.634436</td>\n",
       "      <td>0.334049</td>\n",
       "      <td>0.385724</td>\n",
       "      <td>0.618338</td>\n",
       "      <td>0.402528</td>\n",
       "      <td>0.542865</td>\n",
       "      <td>0.307348</td>\n",
       "      <td>0.296855</td>\n",
       "      <td>0.612557</td>\n",
       "      <td>0.276576</td>\n",
       "      <td>0.566237</td>\n",
       "      <td>0.348924</td>\n",
       "      <td>0.372635</td>\n",
       "      <td>0.558484</td>\n",
       "      <td>0.179431</td>\n",
       "      <td>0.660310</td>\n",
       "      <td>0.324625</td>\n",
       "      <td>0.401482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0.618338</td>\n",
       "      <td>0.402528</td>\n",
       "      <td>0.542865</td>\n",
       "      <td>0.307348</td>\n",
       "      <td>0.296855</td>\n",
       "      <td>0.612557</td>\n",
       "      <td>0.276576</td>\n",
       "      <td>0.566237</td>\n",
       "      <td>0.348924</td>\n",
       "      <td>0.372635</td>\n",
       "      <td>0.558484</td>\n",
       "      <td>0.179431</td>\n",
       "      <td>0.660310</td>\n",
       "      <td>0.324625</td>\n",
       "      <td>0.401482</td>\n",
       "      <td>0.602174</td>\n",
       "      <td>0.164790</td>\n",
       "      <td>0.728385</td>\n",
       "      <td>0.276268</td>\n",
       "      <td>0.387183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>0.612557</td>\n",
       "      <td>0.276576</td>\n",
       "      <td>0.566237</td>\n",
       "      <td>0.348924</td>\n",
       "      <td>0.372635</td>\n",
       "      <td>0.558484</td>\n",
       "      <td>0.179431</td>\n",
       "      <td>0.660310</td>\n",
       "      <td>0.324625</td>\n",
       "      <td>0.401482</td>\n",
       "      <td>0.602174</td>\n",
       "      <td>0.164790</td>\n",
       "      <td>0.728385</td>\n",
       "      <td>0.276268</td>\n",
       "      <td>0.387183</td>\n",
       "      <td>0.604459</td>\n",
       "      <td>0.176119</td>\n",
       "      <td>0.604138</td>\n",
       "      <td>0.414651</td>\n",
       "      <td>0.420161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.558484</td>\n",
       "      <td>0.179431</td>\n",
       "      <td>0.660310</td>\n",
       "      <td>0.324625</td>\n",
       "      <td>0.401482</td>\n",
       "      <td>0.602174</td>\n",
       "      <td>0.164790</td>\n",
       "      <td>0.728385</td>\n",
       "      <td>0.276268</td>\n",
       "      <td>0.387183</td>\n",
       "      <td>0.604459</td>\n",
       "      <td>0.176119</td>\n",
       "      <td>0.604138</td>\n",
       "      <td>0.414651</td>\n",
       "      <td>0.420161</td>\n",
       "      <td>0.625687</td>\n",
       "      <td>0.185016</td>\n",
       "      <td>0.657552</td>\n",
       "      <td>0.350062</td>\n",
       "      <td>0.403081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0.602174</td>\n",
       "      <td>0.164790</td>\n",
       "      <td>0.728385</td>\n",
       "      <td>0.276268</td>\n",
       "      <td>0.387183</td>\n",
       "      <td>0.604459</td>\n",
       "      <td>0.176119</td>\n",
       "      <td>0.604138</td>\n",
       "      <td>0.414651</td>\n",
       "      <td>0.420161</td>\n",
       "      <td>0.625687</td>\n",
       "      <td>0.185016</td>\n",
       "      <td>0.657552</td>\n",
       "      <td>0.350062</td>\n",
       "      <td>0.403081</td>\n",
       "      <td>0.609675</td>\n",
       "      <td>0.233360</td>\n",
       "      <td>0.581522</td>\n",
       "      <td>0.379804</td>\n",
       "      <td>0.391969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.604459</td>\n",
       "      <td>0.176119</td>\n",
       "      <td>0.604138</td>\n",
       "      <td>0.414651</td>\n",
       "      <td>0.420161</td>\n",
       "      <td>0.625687</td>\n",
       "      <td>0.185016</td>\n",
       "      <td>0.657552</td>\n",
       "      <td>0.350062</td>\n",
       "      <td>0.403081</td>\n",
       "      <td>0.609675</td>\n",
       "      <td>0.233360</td>\n",
       "      <td>0.581522</td>\n",
       "      <td>0.379804</td>\n",
       "      <td>0.391969</td>\n",
       "      <td>0.738815</td>\n",
       "      <td>0.364671</td>\n",
       "      <td>0.529522</td>\n",
       "      <td>0.362977</td>\n",
       "      <td>0.335555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.625687</td>\n",
       "      <td>0.185016</td>\n",
       "      <td>0.657552</td>\n",
       "      <td>0.350062</td>\n",
       "      <td>0.403081</td>\n",
       "      <td>0.609675</td>\n",
       "      <td>0.233360</td>\n",
       "      <td>0.581522</td>\n",
       "      <td>0.379804</td>\n",
       "      <td>0.391969</td>\n",
       "      <td>0.738815</td>\n",
       "      <td>0.364671</td>\n",
       "      <td>0.529522</td>\n",
       "      <td>0.362977</td>\n",
       "      <td>0.335555</td>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.394019</td>\n",
       "      <td>0.563028</td>\n",
       "      <td>0.290954</td>\n",
       "      <td>0.276657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.609675</td>\n",
       "      <td>0.233360</td>\n",
       "      <td>0.581522</td>\n",
       "      <td>0.379804</td>\n",
       "      <td>0.391969</td>\n",
       "      <td>0.738815</td>\n",
       "      <td>0.364671</td>\n",
       "      <td>0.529522</td>\n",
       "      <td>0.362977</td>\n",
       "      <td>0.335555</td>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.394019</td>\n",
       "      <td>0.563028</td>\n",
       "      <td>0.290954</td>\n",
       "      <td>0.276657</td>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.357693</td>\n",
       "      <td>0.471372</td>\n",
       "      <td>0.438212</td>\n",
       "      <td>0.432692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0.738815</td>\n",
       "      <td>0.364671</td>\n",
       "      <td>0.529522</td>\n",
       "      <td>0.362977</td>\n",
       "      <td>0.335555</td>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.394019</td>\n",
       "      <td>0.563028</td>\n",
       "      <td>0.290954</td>\n",
       "      <td>0.276657</td>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.357693</td>\n",
       "      <td>0.471372</td>\n",
       "      <td>0.438212</td>\n",
       "      <td>0.432692</td>\n",
       "      <td>0.776453</td>\n",
       "      <td>0.468409</td>\n",
       "      <td>0.512129</td>\n",
       "      <td>0.295702</td>\n",
       "      <td>0.276640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.394019</td>\n",
       "      <td>0.563028</td>\n",
       "      <td>0.290954</td>\n",
       "      <td>0.276657</td>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.357693</td>\n",
       "      <td>0.471372</td>\n",
       "      <td>0.438212</td>\n",
       "      <td>0.432692</td>\n",
       "      <td>0.776453</td>\n",
       "      <td>0.468409</td>\n",
       "      <td>0.512129</td>\n",
       "      <td>0.295702</td>\n",
       "      <td>0.276640</td>\n",
       "      <td>0.803461</td>\n",
       "      <td>0.420869</td>\n",
       "      <td>0.483130</td>\n",
       "      <td>0.383870</td>\n",
       "      <td>0.329324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.357693</td>\n",
       "      <td>0.471372</td>\n",
       "      <td>0.438212</td>\n",
       "      <td>0.432692</td>\n",
       "      <td>0.776453</td>\n",
       "      <td>0.468409</td>\n",
       "      <td>0.512129</td>\n",
       "      <td>0.295702</td>\n",
       "      <td>0.276640</td>\n",
       "      <td>0.803461</td>\n",
       "      <td>0.420869</td>\n",
       "      <td>0.483130</td>\n",
       "      <td>0.383870</td>\n",
       "      <td>0.329324</td>\n",
       "      <td>0.809931</td>\n",
       "      <td>0.190782</td>\n",
       "      <td>0.669622</td>\n",
       "      <td>0.342130</td>\n",
       "      <td>0.436503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>0.776453</td>\n",
       "      <td>0.468409</td>\n",
       "      <td>0.512129</td>\n",
       "      <td>0.295702</td>\n",
       "      <td>0.276640</td>\n",
       "      <td>0.803461</td>\n",
       "      <td>0.420869</td>\n",
       "      <td>0.483130</td>\n",
       "      <td>0.383870</td>\n",
       "      <td>0.329324</td>\n",
       "      <td>0.809931</td>\n",
       "      <td>0.190782</td>\n",
       "      <td>0.669622</td>\n",
       "      <td>0.342130</td>\n",
       "      <td>0.436503</td>\n",
       "      <td>0.780440</td>\n",
       "      <td>0.351867</td>\n",
       "      <td>0.426980</td>\n",
       "      <td>0.516512</td>\n",
       "      <td>0.450945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>0.803461</td>\n",
       "      <td>0.420869</td>\n",
       "      <td>0.483130</td>\n",
       "      <td>0.383870</td>\n",
       "      <td>0.329324</td>\n",
       "      <td>0.809931</td>\n",
       "      <td>0.190782</td>\n",
       "      <td>0.669622</td>\n",
       "      <td>0.342130</td>\n",
       "      <td>0.436503</td>\n",
       "      <td>0.780440</td>\n",
       "      <td>0.351867</td>\n",
       "      <td>0.426980</td>\n",
       "      <td>0.516512</td>\n",
       "      <td>0.450945</td>\n",
       "      <td>0.793846</td>\n",
       "      <td>0.223917</td>\n",
       "      <td>0.552018</td>\n",
       "      <td>0.461507</td>\n",
       "      <td>0.496876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.809931</td>\n",
       "      <td>0.190782</td>\n",
       "      <td>0.669622</td>\n",
       "      <td>0.342130</td>\n",
       "      <td>0.436503</td>\n",
       "      <td>0.780440</td>\n",
       "      <td>0.351867</td>\n",
       "      <td>0.426980</td>\n",
       "      <td>0.516512</td>\n",
       "      <td>0.450945</td>\n",
       "      <td>0.793846</td>\n",
       "      <td>0.223917</td>\n",
       "      <td>0.552018</td>\n",
       "      <td>0.461507</td>\n",
       "      <td>0.496876</td>\n",
       "      <td>0.779863</td>\n",
       "      <td>0.281935</td>\n",
       "      <td>0.479384</td>\n",
       "      <td>0.499772</td>\n",
       "      <td>0.441162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.780440</td>\n",
       "      <td>0.351867</td>\n",
       "      <td>0.426980</td>\n",
       "      <td>0.516512</td>\n",
       "      <td>0.450945</td>\n",
       "      <td>0.793846</td>\n",
       "      <td>0.223917</td>\n",
       "      <td>0.552018</td>\n",
       "      <td>0.461507</td>\n",
       "      <td>0.496876</td>\n",
       "      <td>0.779863</td>\n",
       "      <td>0.281935</td>\n",
       "      <td>0.479384</td>\n",
       "      <td>0.499772</td>\n",
       "      <td>0.441162</td>\n",
       "      <td>0.779500</td>\n",
       "      <td>0.244140</td>\n",
       "      <td>0.668685</td>\n",
       "      <td>0.292694</td>\n",
       "      <td>0.347635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0.793846</td>\n",
       "      <td>0.223917</td>\n",
       "      <td>0.552018</td>\n",
       "      <td>0.461507</td>\n",
       "      <td>0.496876</td>\n",
       "      <td>0.779863</td>\n",
       "      <td>0.281935</td>\n",
       "      <td>0.479384</td>\n",
       "      <td>0.499772</td>\n",
       "      <td>0.441162</td>\n",
       "      <td>0.779500</td>\n",
       "      <td>0.244140</td>\n",
       "      <td>0.668685</td>\n",
       "      <td>0.292694</td>\n",
       "      <td>0.347635</td>\n",
       "      <td>0.840602</td>\n",
       "      <td>0.517969</td>\n",
       "      <td>0.465373</td>\n",
       "      <td>0.322440</td>\n",
       "      <td>0.259376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0.779863</td>\n",
       "      <td>0.281935</td>\n",
       "      <td>0.479384</td>\n",
       "      <td>0.499772</td>\n",
       "      <td>0.441162</td>\n",
       "      <td>0.779500</td>\n",
       "      <td>0.244140</td>\n",
       "      <td>0.668685</td>\n",
       "      <td>0.292694</td>\n",
       "      <td>0.347635</td>\n",
       "      <td>0.840602</td>\n",
       "      <td>0.517969</td>\n",
       "      <td>0.465373</td>\n",
       "      <td>0.322440</td>\n",
       "      <td>0.259376</td>\n",
       "      <td>0.843827</td>\n",
       "      <td>0.292321</td>\n",
       "      <td>0.637478</td>\n",
       "      <td>0.292001</td>\n",
       "      <td>0.357619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.779500</td>\n",
       "      <td>0.244140</td>\n",
       "      <td>0.668685</td>\n",
       "      <td>0.292694</td>\n",
       "      <td>0.347635</td>\n",
       "      <td>0.840602</td>\n",
       "      <td>0.517969</td>\n",
       "      <td>0.465373</td>\n",
       "      <td>0.322440</td>\n",
       "      <td>0.259376</td>\n",
       "      <td>0.843827</td>\n",
       "      <td>0.292321</td>\n",
       "      <td>0.637478</td>\n",
       "      <td>0.292001</td>\n",
       "      <td>0.357619</td>\n",
       "      <td>0.855611</td>\n",
       "      <td>0.173882</td>\n",
       "      <td>0.607811</td>\n",
       "      <td>0.434054</td>\n",
       "      <td>0.489789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.840602</td>\n",
       "      <td>0.517969</td>\n",
       "      <td>0.465373</td>\n",
       "      <td>0.322440</td>\n",
       "      <td>0.259376</td>\n",
       "      <td>0.843827</td>\n",
       "      <td>0.292321</td>\n",
       "      <td>0.637478</td>\n",
       "      <td>0.292001</td>\n",
       "      <td>0.357619</td>\n",
       "      <td>0.855611</td>\n",
       "      <td>0.173882</td>\n",
       "      <td>0.607811</td>\n",
       "      <td>0.434054</td>\n",
       "      <td>0.489789</td>\n",
       "      <td>0.867113</td>\n",
       "      <td>0.107190</td>\n",
       "      <td>0.612963</td>\n",
       "      <td>0.469801</td>\n",
       "      <td>0.546337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.843827</td>\n",
       "      <td>0.292321</td>\n",
       "      <td>0.637478</td>\n",
       "      <td>0.292001</td>\n",
       "      <td>0.357619</td>\n",
       "      <td>0.855611</td>\n",
       "      <td>0.173882</td>\n",
       "      <td>0.607811</td>\n",
       "      <td>0.434054</td>\n",
       "      <td>0.489789</td>\n",
       "      <td>0.867113</td>\n",
       "      <td>0.107190</td>\n",
       "      <td>0.612963</td>\n",
       "      <td>0.469801</td>\n",
       "      <td>0.546337</td>\n",
       "      <td>0.862935</td>\n",
       "      <td>0.132756</td>\n",
       "      <td>0.694352</td>\n",
       "      <td>0.351126</td>\n",
       "      <td>0.469579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.855611</td>\n",
       "      <td>0.173882</td>\n",
       "      <td>0.607811</td>\n",
       "      <td>0.434054</td>\n",
       "      <td>0.489789</td>\n",
       "      <td>0.867113</td>\n",
       "      <td>0.107190</td>\n",
       "      <td>0.612963</td>\n",
       "      <td>0.469801</td>\n",
       "      <td>0.546337</td>\n",
       "      <td>0.862935</td>\n",
       "      <td>0.132756</td>\n",
       "      <td>0.694352</td>\n",
       "      <td>0.351126</td>\n",
       "      <td>0.469579</td>\n",
       "      <td>0.848871</td>\n",
       "      <td>0.130457</td>\n",
       "      <td>0.732844</td>\n",
       "      <td>0.308659</td>\n",
       "      <td>0.422066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.867113</td>\n",
       "      <td>0.107190</td>\n",
       "      <td>0.612963</td>\n",
       "      <td>0.469801</td>\n",
       "      <td>0.546337</td>\n",
       "      <td>0.862935</td>\n",
       "      <td>0.132756</td>\n",
       "      <td>0.694352</td>\n",
       "      <td>0.351126</td>\n",
       "      <td>0.469579</td>\n",
       "      <td>0.848871</td>\n",
       "      <td>0.130457</td>\n",
       "      <td>0.732844</td>\n",
       "      <td>0.308659</td>\n",
       "      <td>0.422066</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479350</td>\n",
       "      <td>0.489477</td>\n",
       "      <td>0.321666</td>\n",
       "      <td>0.311875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.862935</td>\n",
       "      <td>0.132756</td>\n",
       "      <td>0.694352</td>\n",
       "      <td>0.351126</td>\n",
       "      <td>0.469579</td>\n",
       "      <td>0.848871</td>\n",
       "      <td>0.130457</td>\n",
       "      <td>0.732844</td>\n",
       "      <td>0.308659</td>\n",
       "      <td>0.422066</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479350</td>\n",
       "      <td>0.489477</td>\n",
       "      <td>0.321666</td>\n",
       "      <td>0.311875</td>\n",
       "      <td>0.751017</td>\n",
       "      <td>0.891814</td>\n",
       "      <td>0.257590</td>\n",
       "      <td>0.262134</td>\n",
       "      <td>0.076990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.848871</td>\n",
       "      <td>0.130457</td>\n",
       "      <td>0.732844</td>\n",
       "      <td>0.308659</td>\n",
       "      <td>0.422066</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479350</td>\n",
       "      <td>0.489477</td>\n",
       "      <td>0.321666</td>\n",
       "      <td>0.311875</td>\n",
       "      <td>0.751017</td>\n",
       "      <td>0.891814</td>\n",
       "      <td>0.257590</td>\n",
       "      <td>0.262134</td>\n",
       "      <td>0.076990</td>\n",
       "      <td>0.708808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150689</td>\n",
       "      <td>0.303543</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479350</td>\n",
       "      <td>0.489477</td>\n",
       "      <td>0.321666</td>\n",
       "      <td>0.311875</td>\n",
       "      <td>0.751017</td>\n",
       "      <td>0.891814</td>\n",
       "      <td>0.257590</td>\n",
       "      <td>0.262134</td>\n",
       "      <td>0.076990</td>\n",
       "      <td>0.708808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150689</td>\n",
       "      <td>0.303543</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.716132</td>\n",
       "      <td>0.914538</td>\n",
       "      <td>0.211408</td>\n",
       "      <td>0.287951</td>\n",
       "      <td>0.047777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>0.751017</td>\n",
       "      <td>0.891814</td>\n",
       "      <td>0.257590</td>\n",
       "      <td>0.262134</td>\n",
       "      <td>0.076990</td>\n",
       "      <td>0.708808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150689</td>\n",
       "      <td>0.303543</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.716132</td>\n",
       "      <td>0.914538</td>\n",
       "      <td>0.211408</td>\n",
       "      <td>0.287951</td>\n",
       "      <td>0.047777</td>\n",
       "      <td>0.715265</td>\n",
       "      <td>0.730399</td>\n",
       "      <td>0.329642</td>\n",
       "      <td>0.284121</td>\n",
       "      <td>0.123753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>0.708808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150689</td>\n",
       "      <td>0.303543</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.716132</td>\n",
       "      <td>0.914538</td>\n",
       "      <td>0.211408</td>\n",
       "      <td>0.287951</td>\n",
       "      <td>0.047777</td>\n",
       "      <td>0.715265</td>\n",
       "      <td>0.730399</td>\n",
       "      <td>0.329642</td>\n",
       "      <td>0.284121</td>\n",
       "      <td>0.123753</td>\n",
       "      <td>0.800697</td>\n",
       "      <td>0.756721</td>\n",
       "      <td>0.384104</td>\n",
       "      <td>0.197833</td>\n",
       "      <td>0.094007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>0.716132</td>\n",
       "      <td>0.914538</td>\n",
       "      <td>0.211408</td>\n",
       "      <td>0.287951</td>\n",
       "      <td>0.047777</td>\n",
       "      <td>0.715265</td>\n",
       "      <td>0.730399</td>\n",
       "      <td>0.329642</td>\n",
       "      <td>0.284121</td>\n",
       "      <td>0.123753</td>\n",
       "      <td>0.800697</td>\n",
       "      <td>0.756721</td>\n",
       "      <td>0.384104</td>\n",
       "      <td>0.197833</td>\n",
       "      <td>0.094007</td>\n",
       "      <td>0.843938</td>\n",
       "      <td>0.680004</td>\n",
       "      <td>0.345968</td>\n",
       "      <td>0.292568</td>\n",
       "      <td>0.153739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     var1(t-3)  var2(t-3)  var3(t-3)  var4(t-3)  var5(t-3)  var1(t-2)  \\\n",
       "3     0.039567   0.078779   0.831199   0.195812   0.401475   0.007090   \n",
       "4     0.007090   0.128849   0.579299   0.505289   0.542609   0.000000   \n",
       "5     0.000000   0.110492   0.605697   0.477654   0.540763   0.018905   \n",
       "6     0.018905   0.136519   0.740108   0.297985   0.462231   0.043401   \n",
       "7     0.043401   0.244122   0.585976   0.397319   0.455419   0.046946   \n",
       "8     0.046946   0.186121   0.513127   0.542100   0.528573   0.085634   \n",
       "9     0.085634   0.117236   0.544642   0.555482   0.628997   0.065537   \n",
       "10    0.065537   0.100541   0.701571   0.373207   0.492066   0.146213   \n",
       "11    0.146213   0.126593   0.578442   0.500330   0.552681   0.165131   \n",
       "12    0.165131   0.141180   0.532970   0.549913   0.543096   0.166378   \n",
       "13    0.166378   0.016336   0.942586   0.132895   0.395744   0.166144   \n",
       "14    0.166144   0.124761   0.837867   0.171567   0.329644   0.164307   \n",
       "15    0.164307   0.198284   0.755304   0.213454   0.314862   0.148241   \n",
       "16    0.148241   0.237637   0.541722   0.451360   0.465997   0.151626   \n",
       "17    0.151626   0.280928   0.534492   0.421342   0.444023   0.169401   \n",
       "18    0.169401   0.123146   0.607435   0.474041   0.560936   0.183642   \n",
       "19    0.183642   0.311738   0.447805   0.524477   0.472892   0.177916   \n",
       "20    0.177916   0.180111   0.387843   0.707781   0.580837   0.217495   \n",
       "21    0.217495   0.209864   0.405118   0.663783   0.550206   0.173339   \n",
       "22    0.173339   0.237665   0.487808   0.498664   0.468680   0.191267   \n",
       "23    0.191267   0.227426   0.506527   0.475111   0.465703   0.199826   \n",
       "24    0.199826   0.181549   0.605828   0.421061   0.428070   0.204298   \n",
       "25    0.204298   0.299932   0.499747   0.445098   0.394034   0.166667   \n",
       "26    0.166667   0.147860   0.596683   0.474037   0.477040   0.187433   \n",
       "27    0.187433   0.088094   0.605360   0.514699   0.588314   0.188379   \n",
       "28    0.188379   0.176758   0.574356   0.477885   0.519576   0.183237   \n",
       "29    0.183237   0.134368   0.531768   0.569653   0.583945   0.216420   \n",
       "30    0.216420   0.137144   0.580677   0.504029   0.553042   0.274886   \n",
       "31    0.274886   0.123885   0.449502   0.547613   0.538201   0.301108   \n",
       "32    0.301108   0.086307   0.721459   0.361015   0.479860   0.291002   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "181   0.589640   0.239830   0.626147   0.352967   0.375881   0.555510   \n",
       "182   0.555510   0.213553   0.669811   0.322099   0.384069   0.616317   \n",
       "183   0.616317   0.233207   0.634436   0.334049   0.385724   0.618338   \n",
       "184   0.618338   0.402528   0.542865   0.307348   0.296855   0.612557   \n",
       "185   0.612557   0.276576   0.566237   0.348924   0.372635   0.558484   \n",
       "186   0.558484   0.179431   0.660310   0.324625   0.401482   0.602174   \n",
       "187   0.602174   0.164790   0.728385   0.276268   0.387183   0.604459   \n",
       "188   0.604459   0.176119   0.604138   0.414651   0.420161   0.625687   \n",
       "189   0.625687   0.185016   0.657552   0.350062   0.403081   0.609675   \n",
       "190   0.609675   0.233360   0.581522   0.379804   0.391969   0.738815   \n",
       "191   0.738815   0.364671   0.529522   0.362977   0.335555   0.748584   \n",
       "192   0.748584   0.394019   0.563028   0.290954   0.276657   0.775967   \n",
       "193   0.775967   0.357693   0.471372   0.438212   0.432692   0.776453   \n",
       "194   0.776453   0.468409   0.512129   0.295702   0.276640   0.803461   \n",
       "195   0.803461   0.420869   0.483130   0.383870   0.329324   0.809931   \n",
       "196   0.809931   0.190782   0.669622   0.342130   0.436503   0.780440   \n",
       "197   0.780440   0.351867   0.426980   0.516512   0.450945   0.793846   \n",
       "198   0.793846   0.223917   0.552018   0.461507   0.496876   0.779863   \n",
       "199   0.779863   0.281935   0.479384   0.499772   0.441162   0.779500   \n",
       "200   0.779500   0.244140   0.668685   0.292694   0.347635   0.840602   \n",
       "201   0.840602   0.517969   0.465373   0.322440   0.259376   0.843827   \n",
       "202   0.843827   0.292321   0.637478   0.292001   0.357619   0.855611   \n",
       "203   0.855611   0.173882   0.607811   0.434054   0.489789   0.867113   \n",
       "204   0.867113   0.107190   0.612963   0.469801   0.546337   0.862935   \n",
       "205   0.862935   0.132756   0.694352   0.351126   0.469579   0.848871   \n",
       "206   0.848871   0.130457   0.732844   0.308659   0.422066   1.000000   \n",
       "207   1.000000   0.479350   0.489477   0.321666   0.311875   0.751017   \n",
       "208   0.751017   0.891814   0.257590   0.262134   0.076990   0.708808   \n",
       "209   0.708808   1.000000   0.150689   0.303543   0.000000   0.716132   \n",
       "210   0.716132   0.914538   0.211408   0.287951   0.047777   0.715265   \n",
       "\n",
       "     var2(t-2)  var3(t-2)  var4(t-2)  var5(t-2)  var1(t-1)  var2(t-1)  \\\n",
       "3     0.128849   0.579299   0.505289   0.542609   0.000000   0.110492   \n",
       "4     0.110492   0.605697   0.477654   0.540763   0.018905   0.136519   \n",
       "5     0.136519   0.740108   0.297985   0.462231   0.043401   0.244122   \n",
       "6     0.244122   0.585976   0.397319   0.455419   0.046946   0.186121   \n",
       "7     0.186121   0.513127   0.542100   0.528573   0.085634   0.117236   \n",
       "8     0.117236   0.544642   0.555482   0.628997   0.065537   0.100541   \n",
       "9     0.100541   0.701571   0.373207   0.492066   0.146213   0.126593   \n",
       "10    0.126593   0.578442   0.500330   0.552681   0.165131   0.141180   \n",
       "11    0.141180   0.532970   0.549913   0.543096   0.166378   0.016336   \n",
       "12    0.016336   0.942586   0.132895   0.395744   0.166144   0.124761   \n",
       "13    0.124761   0.837867   0.171567   0.329644   0.164307   0.198284   \n",
       "14    0.198284   0.755304   0.213454   0.314862   0.148241   0.237637   \n",
       "15    0.237637   0.541722   0.451360   0.465997   0.151626   0.280928   \n",
       "16    0.280928   0.534492   0.421342   0.444023   0.169401   0.123146   \n",
       "17    0.123146   0.607435   0.474041   0.560936   0.183642   0.311738   \n",
       "18    0.311738   0.447805   0.524477   0.472892   0.177916   0.180111   \n",
       "19    0.180111   0.387843   0.707781   0.580837   0.217495   0.209864   \n",
       "20    0.209864   0.405118   0.663783   0.550206   0.173339   0.237665   \n",
       "21    0.237665   0.487808   0.498664   0.468680   0.191267   0.227426   \n",
       "22    0.227426   0.506527   0.475111   0.465703   0.199826   0.181549   \n",
       "23    0.181549   0.605828   0.421061   0.428070   0.204298   0.299932   \n",
       "24    0.299932   0.499747   0.445098   0.394034   0.166667   0.147860   \n",
       "25    0.147860   0.596683   0.474037   0.477040   0.187433   0.088094   \n",
       "26    0.088094   0.605360   0.514699   0.588314   0.188379   0.176758   \n",
       "27    0.176758   0.574356   0.477885   0.519576   0.183237   0.134368   \n",
       "28    0.134368   0.531768   0.569653   0.583945   0.216420   0.137144   \n",
       "29    0.137144   0.580677   0.504029   0.553042   0.274886   0.123885   \n",
       "30    0.123885   0.449502   0.547613   0.538201   0.301108   0.086307   \n",
       "31    0.086307   0.721459   0.361015   0.479860   0.291002   0.174027   \n",
       "32    0.174027   0.747167   0.248548   0.367103   0.254801   0.199921   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "181   0.213553   0.669811   0.322099   0.384069   0.616317   0.233207   \n",
       "182   0.233207   0.634436   0.334049   0.385724   0.618338   0.402528   \n",
       "183   0.402528   0.542865   0.307348   0.296855   0.612557   0.276576   \n",
       "184   0.276576   0.566237   0.348924   0.372635   0.558484   0.179431   \n",
       "185   0.179431   0.660310   0.324625   0.401482   0.602174   0.164790   \n",
       "186   0.164790   0.728385   0.276268   0.387183   0.604459   0.176119   \n",
       "187   0.176119   0.604138   0.414651   0.420161   0.625687   0.185016   \n",
       "188   0.185016   0.657552   0.350062   0.403081   0.609675   0.233360   \n",
       "189   0.233360   0.581522   0.379804   0.391969   0.738815   0.364671   \n",
       "190   0.364671   0.529522   0.362977   0.335555   0.748584   0.394019   \n",
       "191   0.394019   0.563028   0.290954   0.276657   0.775967   0.357693   \n",
       "192   0.357693   0.471372   0.438212   0.432692   0.776453   0.468409   \n",
       "193   0.468409   0.512129   0.295702   0.276640   0.803461   0.420869   \n",
       "194   0.420869   0.483130   0.383870   0.329324   0.809931   0.190782   \n",
       "195   0.190782   0.669622   0.342130   0.436503   0.780440   0.351867   \n",
       "196   0.351867   0.426980   0.516512   0.450945   0.793846   0.223917   \n",
       "197   0.223917   0.552018   0.461507   0.496876   0.779863   0.281935   \n",
       "198   0.281935   0.479384   0.499772   0.441162   0.779500   0.244140   \n",
       "199   0.244140   0.668685   0.292694   0.347635   0.840602   0.517969   \n",
       "200   0.517969   0.465373   0.322440   0.259376   0.843827   0.292321   \n",
       "201   0.292321   0.637478   0.292001   0.357619   0.855611   0.173882   \n",
       "202   0.173882   0.607811   0.434054   0.489789   0.867113   0.107190   \n",
       "203   0.107190   0.612963   0.469801   0.546337   0.862935   0.132756   \n",
       "204   0.132756   0.694352   0.351126   0.469579   0.848871   0.130457   \n",
       "205   0.130457   0.732844   0.308659   0.422066   1.000000   0.479350   \n",
       "206   0.479350   0.489477   0.321666   0.311875   0.751017   0.891814   \n",
       "207   0.891814   0.257590   0.262134   0.076990   0.708808   1.000000   \n",
       "208   1.000000   0.150689   0.303543   0.000000   0.716132   0.914538   \n",
       "209   0.914538   0.211408   0.287951   0.047777   0.715265   0.730399   \n",
       "210   0.730399   0.329642   0.284121   0.123753   0.800697   0.756721   \n",
       "\n",
       "     var3(t-1)  var4(t-1)  var5(t-1)   var1(t)   var2(t)   var3(t)   var4(t)  \\\n",
       "3     0.605697   0.477654   0.540763  0.018905  0.136519  0.740108  0.297985   \n",
       "4     0.740108   0.297985   0.462231  0.043401  0.244122  0.585976  0.397319   \n",
       "5     0.585976   0.397319   0.455419  0.046946  0.186121  0.513127  0.542100   \n",
       "6     0.513127   0.542100   0.528573  0.085634  0.117236  0.544642  0.555482   \n",
       "7     0.544642   0.555482   0.628997  0.065537  0.100541  0.701571  0.373207   \n",
       "8     0.701571   0.373207   0.492066  0.146213  0.126593  0.578442  0.500330   \n",
       "9     0.578442   0.500330   0.552681  0.165131  0.141180  0.532970  0.549913   \n",
       "10    0.532970   0.549913   0.543096  0.166378  0.016336  0.942586  0.132895   \n",
       "11    0.942586   0.132895   0.395744  0.166144  0.124761  0.837867  0.171567   \n",
       "12    0.837867   0.171567   0.329644  0.164307  0.198284  0.755304  0.213454   \n",
       "13    0.755304   0.213454   0.314862  0.148241  0.237637  0.541722  0.451360   \n",
       "14    0.541722   0.451360   0.465997  0.151626  0.280928  0.534492  0.421342   \n",
       "15    0.534492   0.421342   0.444023  0.169401  0.123146  0.607435  0.474041   \n",
       "16    0.607435   0.474041   0.560936  0.183642  0.311738  0.447805  0.524477   \n",
       "17    0.447805   0.524477   0.472892  0.177916  0.180111  0.387843  0.707781   \n",
       "18    0.387843   0.707781   0.580837  0.217495  0.209864  0.405118  0.663783   \n",
       "19    0.405118   0.663783   0.550206  0.173339  0.237665  0.487808  0.498664   \n",
       "20    0.487808   0.498664   0.468680  0.191267  0.227426  0.506527  0.475111   \n",
       "21    0.506527   0.475111   0.465703  0.199826  0.181549  0.605828  0.421061   \n",
       "22    0.605828   0.421061   0.428070  0.204298  0.299932  0.499747  0.445098   \n",
       "23    0.499747   0.445098   0.394034  0.166667  0.147860  0.596683  0.474037   \n",
       "24    0.596683   0.474037   0.477040  0.187433  0.088094  0.605360  0.514699   \n",
       "25    0.605360   0.514699   0.588314  0.188379  0.176758  0.574356  0.477885   \n",
       "26    0.574356   0.477885   0.519576  0.183237  0.134368  0.531768  0.569653   \n",
       "27    0.531768   0.569653   0.583945  0.216420  0.137144  0.580677  0.504029   \n",
       "28    0.580677   0.504029   0.553042  0.274886  0.123885  0.449502  0.547613   \n",
       "29    0.449502   0.547613   0.538201  0.301108  0.086307  0.721459  0.361015   \n",
       "30    0.721459   0.361015   0.479860  0.291002  0.174027  0.747167  0.248548   \n",
       "31    0.747167   0.248548   0.367103  0.254801  0.199921  0.697007  0.291954   \n",
       "32    0.697007   0.291954   0.368465  0.239798  0.157873  0.492145  0.600605   \n",
       "..         ...        ...        ...       ...       ...       ...       ...   \n",
       "181   0.634436   0.334049   0.385724  0.618338  0.402528  0.542865  0.307348   \n",
       "182   0.542865   0.307348   0.296855  0.612557  0.276576  0.566237  0.348924   \n",
       "183   0.566237   0.348924   0.372635  0.558484  0.179431  0.660310  0.324625   \n",
       "184   0.660310   0.324625   0.401482  0.602174  0.164790  0.728385  0.276268   \n",
       "185   0.728385   0.276268   0.387183  0.604459  0.176119  0.604138  0.414651   \n",
       "186   0.604138   0.414651   0.420161  0.625687  0.185016  0.657552  0.350062   \n",
       "187   0.657552   0.350062   0.403081  0.609675  0.233360  0.581522  0.379804   \n",
       "188   0.581522   0.379804   0.391969  0.738815  0.364671  0.529522  0.362977   \n",
       "189   0.529522   0.362977   0.335555  0.748584  0.394019  0.563028  0.290954   \n",
       "190   0.563028   0.290954   0.276657  0.775967  0.357693  0.471372  0.438212   \n",
       "191   0.471372   0.438212   0.432692  0.776453  0.468409  0.512129  0.295702   \n",
       "192   0.512129   0.295702   0.276640  0.803461  0.420869  0.483130  0.383870   \n",
       "193   0.483130   0.383870   0.329324  0.809931  0.190782  0.669622  0.342130   \n",
       "194   0.669622   0.342130   0.436503  0.780440  0.351867  0.426980  0.516512   \n",
       "195   0.426980   0.516512   0.450945  0.793846  0.223917  0.552018  0.461507   \n",
       "196   0.552018   0.461507   0.496876  0.779863  0.281935  0.479384  0.499772   \n",
       "197   0.479384   0.499772   0.441162  0.779500  0.244140  0.668685  0.292694   \n",
       "198   0.668685   0.292694   0.347635  0.840602  0.517969  0.465373  0.322440   \n",
       "199   0.465373   0.322440   0.259376  0.843827  0.292321  0.637478  0.292001   \n",
       "200   0.637478   0.292001   0.357619  0.855611  0.173882  0.607811  0.434054   \n",
       "201   0.607811   0.434054   0.489789  0.867113  0.107190  0.612963  0.469801   \n",
       "202   0.612963   0.469801   0.546337  0.862935  0.132756  0.694352  0.351126   \n",
       "203   0.694352   0.351126   0.469579  0.848871  0.130457  0.732844  0.308659   \n",
       "204   0.732844   0.308659   0.422066  1.000000  0.479350  0.489477  0.321666   \n",
       "205   0.489477   0.321666   0.311875  0.751017  0.891814  0.257590  0.262134   \n",
       "206   0.257590   0.262134   0.076990  0.708808  1.000000  0.150689  0.303543   \n",
       "207   0.150689   0.303543   0.000000  0.716132  0.914538  0.211408  0.287951   \n",
       "208   0.211408   0.287951   0.047777  0.715265  0.730399  0.329642  0.284121   \n",
       "209   0.329642   0.284121   0.123753  0.800697  0.756721  0.384104  0.197833   \n",
       "210   0.384104   0.197833   0.094007  0.843938  0.680004  0.345968  0.292568   \n",
       "\n",
       "      var5(t)  \n",
       "3    0.462231  \n",
       "4    0.455419  \n",
       "5    0.528573  \n",
       "6    0.628997  \n",
       "7    0.492066  \n",
       "8    0.552681  \n",
       "9    0.543096  \n",
       "10   0.395744  \n",
       "11   0.329644  \n",
       "12   0.314862  \n",
       "13   0.465997  \n",
       "14   0.444023  \n",
       "15   0.560936  \n",
       "16   0.472892  \n",
       "17   0.580837  \n",
       "18   0.550206  \n",
       "19   0.468680  \n",
       "20   0.465703  \n",
       "21   0.428070  \n",
       "22   0.394034  \n",
       "23   0.477040  \n",
       "24   0.588314  \n",
       "25   0.519576  \n",
       "26   0.583945  \n",
       "27   0.553042  \n",
       "28   0.538201  \n",
       "29   0.479860  \n",
       "30   0.367103  \n",
       "31   0.368465  \n",
       "32   0.578592  \n",
       "..        ...  \n",
       "181  0.296855  \n",
       "182  0.372635  \n",
       "183  0.401482  \n",
       "184  0.387183  \n",
       "185  0.420161  \n",
       "186  0.403081  \n",
       "187  0.391969  \n",
       "188  0.335555  \n",
       "189  0.276657  \n",
       "190  0.432692  \n",
       "191  0.276640  \n",
       "192  0.329324  \n",
       "193  0.436503  \n",
       "194  0.450945  \n",
       "195  0.496876  \n",
       "196  0.441162  \n",
       "197  0.347635  \n",
       "198  0.259376  \n",
       "199  0.357619  \n",
       "200  0.489789  \n",
       "201  0.546337  \n",
       "202  0.469579  \n",
       "203  0.422066  \n",
       "204  0.311875  \n",
       "205  0.076990  \n",
       "206  0.000000  \n",
       "207  0.047777  \n",
       "208  0.123753  \n",
       "209  0.094007  \n",
       "210  0.153739  \n",
       "\n",
       "[208 rows x 20 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reframed = series_to_supervised(scaled, n_hours, 1)\n",
    "reframed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var1(t-3)</th>\n",
       "      <th>var2(t-3)</th>\n",
       "      <th>var3(t-3)</th>\n",
       "      <th>var4(t-3)</th>\n",
       "      <th>var5(t-3)</th>\n",
       "      <th>var1(t-2)</th>\n",
       "      <th>var2(t-2)</th>\n",
       "      <th>var3(t-2)</th>\n",
       "      <th>var4(t-2)</th>\n",
       "      <th>var5(t-2)</th>\n",
       "      <th>var1(t-1)</th>\n",
       "      <th>var2(t-1)</th>\n",
       "      <th>var3(t-1)</th>\n",
       "      <th>var4(t-1)</th>\n",
       "      <th>var5(t-1)</th>\n",
       "      <th>var1(t)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.039567</td>\n",
       "      <td>0.078779</td>\n",
       "      <td>0.831199</td>\n",
       "      <td>0.195812</td>\n",
       "      <td>0.401475</td>\n",
       "      <td>0.007090</td>\n",
       "      <td>0.128849</td>\n",
       "      <td>0.579299</td>\n",
       "      <td>0.505289</td>\n",
       "      <td>0.542609</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110492</td>\n",
       "      <td>0.605697</td>\n",
       "      <td>0.477654</td>\n",
       "      <td>0.540763</td>\n",
       "      <td>0.018905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.007090</td>\n",
       "      <td>0.128849</td>\n",
       "      <td>0.579299</td>\n",
       "      <td>0.505289</td>\n",
       "      <td>0.542609</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110492</td>\n",
       "      <td>0.605697</td>\n",
       "      <td>0.477654</td>\n",
       "      <td>0.540763</td>\n",
       "      <td>0.018905</td>\n",
       "      <td>0.136519</td>\n",
       "      <td>0.740108</td>\n",
       "      <td>0.297985</td>\n",
       "      <td>0.462231</td>\n",
       "      <td>0.043401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110492</td>\n",
       "      <td>0.605697</td>\n",
       "      <td>0.477654</td>\n",
       "      <td>0.540763</td>\n",
       "      <td>0.018905</td>\n",
       "      <td>0.136519</td>\n",
       "      <td>0.740108</td>\n",
       "      <td>0.297985</td>\n",
       "      <td>0.462231</td>\n",
       "      <td>0.043401</td>\n",
       "      <td>0.244122</td>\n",
       "      <td>0.585976</td>\n",
       "      <td>0.397319</td>\n",
       "      <td>0.455419</td>\n",
       "      <td>0.046946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.018905</td>\n",
       "      <td>0.136519</td>\n",
       "      <td>0.740108</td>\n",
       "      <td>0.297985</td>\n",
       "      <td>0.462231</td>\n",
       "      <td>0.043401</td>\n",
       "      <td>0.244122</td>\n",
       "      <td>0.585976</td>\n",
       "      <td>0.397319</td>\n",
       "      <td>0.455419</td>\n",
       "      <td>0.046946</td>\n",
       "      <td>0.186121</td>\n",
       "      <td>0.513127</td>\n",
       "      <td>0.542100</td>\n",
       "      <td>0.528573</td>\n",
       "      <td>0.085634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.043401</td>\n",
       "      <td>0.244122</td>\n",
       "      <td>0.585976</td>\n",
       "      <td>0.397319</td>\n",
       "      <td>0.455419</td>\n",
       "      <td>0.046946</td>\n",
       "      <td>0.186121</td>\n",
       "      <td>0.513127</td>\n",
       "      <td>0.542100</td>\n",
       "      <td>0.528573</td>\n",
       "      <td>0.085634</td>\n",
       "      <td>0.117236</td>\n",
       "      <td>0.544642</td>\n",
       "      <td>0.555482</td>\n",
       "      <td>0.628997</td>\n",
       "      <td>0.065537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.046946</td>\n",
       "      <td>0.186121</td>\n",
       "      <td>0.513127</td>\n",
       "      <td>0.542100</td>\n",
       "      <td>0.528573</td>\n",
       "      <td>0.085634</td>\n",
       "      <td>0.117236</td>\n",
       "      <td>0.544642</td>\n",
       "      <td>0.555482</td>\n",
       "      <td>0.628997</td>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.100541</td>\n",
       "      <td>0.701571</td>\n",
       "      <td>0.373207</td>\n",
       "      <td>0.492066</td>\n",
       "      <td>0.146213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.085634</td>\n",
       "      <td>0.117236</td>\n",
       "      <td>0.544642</td>\n",
       "      <td>0.555482</td>\n",
       "      <td>0.628997</td>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.100541</td>\n",
       "      <td>0.701571</td>\n",
       "      <td>0.373207</td>\n",
       "      <td>0.492066</td>\n",
       "      <td>0.146213</td>\n",
       "      <td>0.126593</td>\n",
       "      <td>0.578442</td>\n",
       "      <td>0.500330</td>\n",
       "      <td>0.552681</td>\n",
       "      <td>0.165131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.100541</td>\n",
       "      <td>0.701571</td>\n",
       "      <td>0.373207</td>\n",
       "      <td>0.492066</td>\n",
       "      <td>0.146213</td>\n",
       "      <td>0.126593</td>\n",
       "      <td>0.578442</td>\n",
       "      <td>0.500330</td>\n",
       "      <td>0.552681</td>\n",
       "      <td>0.165131</td>\n",
       "      <td>0.141180</td>\n",
       "      <td>0.532970</td>\n",
       "      <td>0.549913</td>\n",
       "      <td>0.543096</td>\n",
       "      <td>0.166378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.146213</td>\n",
       "      <td>0.126593</td>\n",
       "      <td>0.578442</td>\n",
       "      <td>0.500330</td>\n",
       "      <td>0.552681</td>\n",
       "      <td>0.165131</td>\n",
       "      <td>0.141180</td>\n",
       "      <td>0.532970</td>\n",
       "      <td>0.549913</td>\n",
       "      <td>0.543096</td>\n",
       "      <td>0.166378</td>\n",
       "      <td>0.016336</td>\n",
       "      <td>0.942586</td>\n",
       "      <td>0.132895</td>\n",
       "      <td>0.395744</td>\n",
       "      <td>0.166144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.165131</td>\n",
       "      <td>0.141180</td>\n",
       "      <td>0.532970</td>\n",
       "      <td>0.549913</td>\n",
       "      <td>0.543096</td>\n",
       "      <td>0.166378</td>\n",
       "      <td>0.016336</td>\n",
       "      <td>0.942586</td>\n",
       "      <td>0.132895</td>\n",
       "      <td>0.395744</td>\n",
       "      <td>0.166144</td>\n",
       "      <td>0.124761</td>\n",
       "      <td>0.837867</td>\n",
       "      <td>0.171567</td>\n",
       "      <td>0.329644</td>\n",
       "      <td>0.164307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.166378</td>\n",
       "      <td>0.016336</td>\n",
       "      <td>0.942586</td>\n",
       "      <td>0.132895</td>\n",
       "      <td>0.395744</td>\n",
       "      <td>0.166144</td>\n",
       "      <td>0.124761</td>\n",
       "      <td>0.837867</td>\n",
       "      <td>0.171567</td>\n",
       "      <td>0.329644</td>\n",
       "      <td>0.164307</td>\n",
       "      <td>0.198284</td>\n",
       "      <td>0.755304</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.314862</td>\n",
       "      <td>0.148241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.166144</td>\n",
       "      <td>0.124761</td>\n",
       "      <td>0.837867</td>\n",
       "      <td>0.171567</td>\n",
       "      <td>0.329644</td>\n",
       "      <td>0.164307</td>\n",
       "      <td>0.198284</td>\n",
       "      <td>0.755304</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.314862</td>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.237637</td>\n",
       "      <td>0.541722</td>\n",
       "      <td>0.451360</td>\n",
       "      <td>0.465997</td>\n",
       "      <td>0.151626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.164307</td>\n",
       "      <td>0.198284</td>\n",
       "      <td>0.755304</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.314862</td>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.237637</td>\n",
       "      <td>0.541722</td>\n",
       "      <td>0.451360</td>\n",
       "      <td>0.465997</td>\n",
       "      <td>0.151626</td>\n",
       "      <td>0.280928</td>\n",
       "      <td>0.534492</td>\n",
       "      <td>0.421342</td>\n",
       "      <td>0.444023</td>\n",
       "      <td>0.169401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.237637</td>\n",
       "      <td>0.541722</td>\n",
       "      <td>0.451360</td>\n",
       "      <td>0.465997</td>\n",
       "      <td>0.151626</td>\n",
       "      <td>0.280928</td>\n",
       "      <td>0.534492</td>\n",
       "      <td>0.421342</td>\n",
       "      <td>0.444023</td>\n",
       "      <td>0.169401</td>\n",
       "      <td>0.123146</td>\n",
       "      <td>0.607435</td>\n",
       "      <td>0.474041</td>\n",
       "      <td>0.560936</td>\n",
       "      <td>0.183642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.151626</td>\n",
       "      <td>0.280928</td>\n",
       "      <td>0.534492</td>\n",
       "      <td>0.421342</td>\n",
       "      <td>0.444023</td>\n",
       "      <td>0.169401</td>\n",
       "      <td>0.123146</td>\n",
       "      <td>0.607435</td>\n",
       "      <td>0.474041</td>\n",
       "      <td>0.560936</td>\n",
       "      <td>0.183642</td>\n",
       "      <td>0.311738</td>\n",
       "      <td>0.447805</td>\n",
       "      <td>0.524477</td>\n",
       "      <td>0.472892</td>\n",
       "      <td>0.177916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.169401</td>\n",
       "      <td>0.123146</td>\n",
       "      <td>0.607435</td>\n",
       "      <td>0.474041</td>\n",
       "      <td>0.560936</td>\n",
       "      <td>0.183642</td>\n",
       "      <td>0.311738</td>\n",
       "      <td>0.447805</td>\n",
       "      <td>0.524477</td>\n",
       "      <td>0.472892</td>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.180111</td>\n",
       "      <td>0.387843</td>\n",
       "      <td>0.707781</td>\n",
       "      <td>0.580837</td>\n",
       "      <td>0.217495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.183642</td>\n",
       "      <td>0.311738</td>\n",
       "      <td>0.447805</td>\n",
       "      <td>0.524477</td>\n",
       "      <td>0.472892</td>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.180111</td>\n",
       "      <td>0.387843</td>\n",
       "      <td>0.707781</td>\n",
       "      <td>0.580837</td>\n",
       "      <td>0.217495</td>\n",
       "      <td>0.209864</td>\n",
       "      <td>0.405118</td>\n",
       "      <td>0.663783</td>\n",
       "      <td>0.550206</td>\n",
       "      <td>0.173339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.180111</td>\n",
       "      <td>0.387843</td>\n",
       "      <td>0.707781</td>\n",
       "      <td>0.580837</td>\n",
       "      <td>0.217495</td>\n",
       "      <td>0.209864</td>\n",
       "      <td>0.405118</td>\n",
       "      <td>0.663783</td>\n",
       "      <td>0.550206</td>\n",
       "      <td>0.173339</td>\n",
       "      <td>0.237665</td>\n",
       "      <td>0.487808</td>\n",
       "      <td>0.498664</td>\n",
       "      <td>0.468680</td>\n",
       "      <td>0.191267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.217495</td>\n",
       "      <td>0.209864</td>\n",
       "      <td>0.405118</td>\n",
       "      <td>0.663783</td>\n",
       "      <td>0.550206</td>\n",
       "      <td>0.173339</td>\n",
       "      <td>0.237665</td>\n",
       "      <td>0.487808</td>\n",
       "      <td>0.498664</td>\n",
       "      <td>0.468680</td>\n",
       "      <td>0.191267</td>\n",
       "      <td>0.227426</td>\n",
       "      <td>0.506527</td>\n",
       "      <td>0.475111</td>\n",
       "      <td>0.465703</td>\n",
       "      <td>0.199826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.173339</td>\n",
       "      <td>0.237665</td>\n",
       "      <td>0.487808</td>\n",
       "      <td>0.498664</td>\n",
       "      <td>0.468680</td>\n",
       "      <td>0.191267</td>\n",
       "      <td>0.227426</td>\n",
       "      <td>0.506527</td>\n",
       "      <td>0.475111</td>\n",
       "      <td>0.465703</td>\n",
       "      <td>0.199826</td>\n",
       "      <td>0.181549</td>\n",
       "      <td>0.605828</td>\n",
       "      <td>0.421061</td>\n",
       "      <td>0.428070</td>\n",
       "      <td>0.204298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.191267</td>\n",
       "      <td>0.227426</td>\n",
       "      <td>0.506527</td>\n",
       "      <td>0.475111</td>\n",
       "      <td>0.465703</td>\n",
       "      <td>0.199826</td>\n",
       "      <td>0.181549</td>\n",
       "      <td>0.605828</td>\n",
       "      <td>0.421061</td>\n",
       "      <td>0.428070</td>\n",
       "      <td>0.204298</td>\n",
       "      <td>0.299932</td>\n",
       "      <td>0.499747</td>\n",
       "      <td>0.445098</td>\n",
       "      <td>0.394034</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.199826</td>\n",
       "      <td>0.181549</td>\n",
       "      <td>0.605828</td>\n",
       "      <td>0.421061</td>\n",
       "      <td>0.428070</td>\n",
       "      <td>0.204298</td>\n",
       "      <td>0.299932</td>\n",
       "      <td>0.499747</td>\n",
       "      <td>0.445098</td>\n",
       "      <td>0.394034</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.147860</td>\n",
       "      <td>0.596683</td>\n",
       "      <td>0.474037</td>\n",
       "      <td>0.477040</td>\n",
       "      <td>0.187433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.204298</td>\n",
       "      <td>0.299932</td>\n",
       "      <td>0.499747</td>\n",
       "      <td>0.445098</td>\n",
       "      <td>0.394034</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.147860</td>\n",
       "      <td>0.596683</td>\n",
       "      <td>0.474037</td>\n",
       "      <td>0.477040</td>\n",
       "      <td>0.187433</td>\n",
       "      <td>0.088094</td>\n",
       "      <td>0.605360</td>\n",
       "      <td>0.514699</td>\n",
       "      <td>0.588314</td>\n",
       "      <td>0.188379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.147860</td>\n",
       "      <td>0.596683</td>\n",
       "      <td>0.474037</td>\n",
       "      <td>0.477040</td>\n",
       "      <td>0.187433</td>\n",
       "      <td>0.088094</td>\n",
       "      <td>0.605360</td>\n",
       "      <td>0.514699</td>\n",
       "      <td>0.588314</td>\n",
       "      <td>0.188379</td>\n",
       "      <td>0.176758</td>\n",
       "      <td>0.574356</td>\n",
       "      <td>0.477885</td>\n",
       "      <td>0.519576</td>\n",
       "      <td>0.183237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.187433</td>\n",
       "      <td>0.088094</td>\n",
       "      <td>0.605360</td>\n",
       "      <td>0.514699</td>\n",
       "      <td>0.588314</td>\n",
       "      <td>0.188379</td>\n",
       "      <td>0.176758</td>\n",
       "      <td>0.574356</td>\n",
       "      <td>0.477885</td>\n",
       "      <td>0.519576</td>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.134368</td>\n",
       "      <td>0.531768</td>\n",
       "      <td>0.569653</td>\n",
       "      <td>0.583945</td>\n",
       "      <td>0.216420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.188379</td>\n",
       "      <td>0.176758</td>\n",
       "      <td>0.574356</td>\n",
       "      <td>0.477885</td>\n",
       "      <td>0.519576</td>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.134368</td>\n",
       "      <td>0.531768</td>\n",
       "      <td>0.569653</td>\n",
       "      <td>0.583945</td>\n",
       "      <td>0.216420</td>\n",
       "      <td>0.137144</td>\n",
       "      <td>0.580677</td>\n",
       "      <td>0.504029</td>\n",
       "      <td>0.553042</td>\n",
       "      <td>0.274886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.134368</td>\n",
       "      <td>0.531768</td>\n",
       "      <td>0.569653</td>\n",
       "      <td>0.583945</td>\n",
       "      <td>0.216420</td>\n",
       "      <td>0.137144</td>\n",
       "      <td>0.580677</td>\n",
       "      <td>0.504029</td>\n",
       "      <td>0.553042</td>\n",
       "      <td>0.274886</td>\n",
       "      <td>0.123885</td>\n",
       "      <td>0.449502</td>\n",
       "      <td>0.547613</td>\n",
       "      <td>0.538201</td>\n",
       "      <td>0.301108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.216420</td>\n",
       "      <td>0.137144</td>\n",
       "      <td>0.580677</td>\n",
       "      <td>0.504029</td>\n",
       "      <td>0.553042</td>\n",
       "      <td>0.274886</td>\n",
       "      <td>0.123885</td>\n",
       "      <td>0.449502</td>\n",
       "      <td>0.547613</td>\n",
       "      <td>0.538201</td>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.086307</td>\n",
       "      <td>0.721459</td>\n",
       "      <td>0.361015</td>\n",
       "      <td>0.479860</td>\n",
       "      <td>0.291002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.274886</td>\n",
       "      <td>0.123885</td>\n",
       "      <td>0.449502</td>\n",
       "      <td>0.547613</td>\n",
       "      <td>0.538201</td>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.086307</td>\n",
       "      <td>0.721459</td>\n",
       "      <td>0.361015</td>\n",
       "      <td>0.479860</td>\n",
       "      <td>0.291002</td>\n",
       "      <td>0.174027</td>\n",
       "      <td>0.747167</td>\n",
       "      <td>0.248548</td>\n",
       "      <td>0.367103</td>\n",
       "      <td>0.254801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.086307</td>\n",
       "      <td>0.721459</td>\n",
       "      <td>0.361015</td>\n",
       "      <td>0.479860</td>\n",
       "      <td>0.291002</td>\n",
       "      <td>0.174027</td>\n",
       "      <td>0.747167</td>\n",
       "      <td>0.248548</td>\n",
       "      <td>0.367103</td>\n",
       "      <td>0.254801</td>\n",
       "      <td>0.199921</td>\n",
       "      <td>0.697007</td>\n",
       "      <td>0.291954</td>\n",
       "      <td>0.368465</td>\n",
       "      <td>0.239798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.589640</td>\n",
       "      <td>0.239830</td>\n",
       "      <td>0.626147</td>\n",
       "      <td>0.352967</td>\n",
       "      <td>0.375881</td>\n",
       "      <td>0.555510</td>\n",
       "      <td>0.213553</td>\n",
       "      <td>0.669811</td>\n",
       "      <td>0.322099</td>\n",
       "      <td>0.384069</td>\n",
       "      <td>0.616317</td>\n",
       "      <td>0.233207</td>\n",
       "      <td>0.634436</td>\n",
       "      <td>0.334049</td>\n",
       "      <td>0.385724</td>\n",
       "      <td>0.618338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>0.555510</td>\n",
       "      <td>0.213553</td>\n",
       "      <td>0.669811</td>\n",
       "      <td>0.322099</td>\n",
       "      <td>0.384069</td>\n",
       "      <td>0.616317</td>\n",
       "      <td>0.233207</td>\n",
       "      <td>0.634436</td>\n",
       "      <td>0.334049</td>\n",
       "      <td>0.385724</td>\n",
       "      <td>0.618338</td>\n",
       "      <td>0.402528</td>\n",
       "      <td>0.542865</td>\n",
       "      <td>0.307348</td>\n",
       "      <td>0.296855</td>\n",
       "      <td>0.612557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.616317</td>\n",
       "      <td>0.233207</td>\n",
       "      <td>0.634436</td>\n",
       "      <td>0.334049</td>\n",
       "      <td>0.385724</td>\n",
       "      <td>0.618338</td>\n",
       "      <td>0.402528</td>\n",
       "      <td>0.542865</td>\n",
       "      <td>0.307348</td>\n",
       "      <td>0.296855</td>\n",
       "      <td>0.612557</td>\n",
       "      <td>0.276576</td>\n",
       "      <td>0.566237</td>\n",
       "      <td>0.348924</td>\n",
       "      <td>0.372635</td>\n",
       "      <td>0.558484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0.618338</td>\n",
       "      <td>0.402528</td>\n",
       "      <td>0.542865</td>\n",
       "      <td>0.307348</td>\n",
       "      <td>0.296855</td>\n",
       "      <td>0.612557</td>\n",
       "      <td>0.276576</td>\n",
       "      <td>0.566237</td>\n",
       "      <td>0.348924</td>\n",
       "      <td>0.372635</td>\n",
       "      <td>0.558484</td>\n",
       "      <td>0.179431</td>\n",
       "      <td>0.660310</td>\n",
       "      <td>0.324625</td>\n",
       "      <td>0.401482</td>\n",
       "      <td>0.602174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>0.612557</td>\n",
       "      <td>0.276576</td>\n",
       "      <td>0.566237</td>\n",
       "      <td>0.348924</td>\n",
       "      <td>0.372635</td>\n",
       "      <td>0.558484</td>\n",
       "      <td>0.179431</td>\n",
       "      <td>0.660310</td>\n",
       "      <td>0.324625</td>\n",
       "      <td>0.401482</td>\n",
       "      <td>0.602174</td>\n",
       "      <td>0.164790</td>\n",
       "      <td>0.728385</td>\n",
       "      <td>0.276268</td>\n",
       "      <td>0.387183</td>\n",
       "      <td>0.604459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.558484</td>\n",
       "      <td>0.179431</td>\n",
       "      <td>0.660310</td>\n",
       "      <td>0.324625</td>\n",
       "      <td>0.401482</td>\n",
       "      <td>0.602174</td>\n",
       "      <td>0.164790</td>\n",
       "      <td>0.728385</td>\n",
       "      <td>0.276268</td>\n",
       "      <td>0.387183</td>\n",
       "      <td>0.604459</td>\n",
       "      <td>0.176119</td>\n",
       "      <td>0.604138</td>\n",
       "      <td>0.414651</td>\n",
       "      <td>0.420161</td>\n",
       "      <td>0.625687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0.602174</td>\n",
       "      <td>0.164790</td>\n",
       "      <td>0.728385</td>\n",
       "      <td>0.276268</td>\n",
       "      <td>0.387183</td>\n",
       "      <td>0.604459</td>\n",
       "      <td>0.176119</td>\n",
       "      <td>0.604138</td>\n",
       "      <td>0.414651</td>\n",
       "      <td>0.420161</td>\n",
       "      <td>0.625687</td>\n",
       "      <td>0.185016</td>\n",
       "      <td>0.657552</td>\n",
       "      <td>0.350062</td>\n",
       "      <td>0.403081</td>\n",
       "      <td>0.609675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.604459</td>\n",
       "      <td>0.176119</td>\n",
       "      <td>0.604138</td>\n",
       "      <td>0.414651</td>\n",
       "      <td>0.420161</td>\n",
       "      <td>0.625687</td>\n",
       "      <td>0.185016</td>\n",
       "      <td>0.657552</td>\n",
       "      <td>0.350062</td>\n",
       "      <td>0.403081</td>\n",
       "      <td>0.609675</td>\n",
       "      <td>0.233360</td>\n",
       "      <td>0.581522</td>\n",
       "      <td>0.379804</td>\n",
       "      <td>0.391969</td>\n",
       "      <td>0.738815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.625687</td>\n",
       "      <td>0.185016</td>\n",
       "      <td>0.657552</td>\n",
       "      <td>0.350062</td>\n",
       "      <td>0.403081</td>\n",
       "      <td>0.609675</td>\n",
       "      <td>0.233360</td>\n",
       "      <td>0.581522</td>\n",
       "      <td>0.379804</td>\n",
       "      <td>0.391969</td>\n",
       "      <td>0.738815</td>\n",
       "      <td>0.364671</td>\n",
       "      <td>0.529522</td>\n",
       "      <td>0.362977</td>\n",
       "      <td>0.335555</td>\n",
       "      <td>0.748584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.609675</td>\n",
       "      <td>0.233360</td>\n",
       "      <td>0.581522</td>\n",
       "      <td>0.379804</td>\n",
       "      <td>0.391969</td>\n",
       "      <td>0.738815</td>\n",
       "      <td>0.364671</td>\n",
       "      <td>0.529522</td>\n",
       "      <td>0.362977</td>\n",
       "      <td>0.335555</td>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.394019</td>\n",
       "      <td>0.563028</td>\n",
       "      <td>0.290954</td>\n",
       "      <td>0.276657</td>\n",
       "      <td>0.775967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0.738815</td>\n",
       "      <td>0.364671</td>\n",
       "      <td>0.529522</td>\n",
       "      <td>0.362977</td>\n",
       "      <td>0.335555</td>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.394019</td>\n",
       "      <td>0.563028</td>\n",
       "      <td>0.290954</td>\n",
       "      <td>0.276657</td>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.357693</td>\n",
       "      <td>0.471372</td>\n",
       "      <td>0.438212</td>\n",
       "      <td>0.432692</td>\n",
       "      <td>0.776453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.394019</td>\n",
       "      <td>0.563028</td>\n",
       "      <td>0.290954</td>\n",
       "      <td>0.276657</td>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.357693</td>\n",
       "      <td>0.471372</td>\n",
       "      <td>0.438212</td>\n",
       "      <td>0.432692</td>\n",
       "      <td>0.776453</td>\n",
       "      <td>0.468409</td>\n",
       "      <td>0.512129</td>\n",
       "      <td>0.295702</td>\n",
       "      <td>0.276640</td>\n",
       "      <td>0.803461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.357693</td>\n",
       "      <td>0.471372</td>\n",
       "      <td>0.438212</td>\n",
       "      <td>0.432692</td>\n",
       "      <td>0.776453</td>\n",
       "      <td>0.468409</td>\n",
       "      <td>0.512129</td>\n",
       "      <td>0.295702</td>\n",
       "      <td>0.276640</td>\n",
       "      <td>0.803461</td>\n",
       "      <td>0.420869</td>\n",
       "      <td>0.483130</td>\n",
       "      <td>0.383870</td>\n",
       "      <td>0.329324</td>\n",
       "      <td>0.809931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>0.776453</td>\n",
       "      <td>0.468409</td>\n",
       "      <td>0.512129</td>\n",
       "      <td>0.295702</td>\n",
       "      <td>0.276640</td>\n",
       "      <td>0.803461</td>\n",
       "      <td>0.420869</td>\n",
       "      <td>0.483130</td>\n",
       "      <td>0.383870</td>\n",
       "      <td>0.329324</td>\n",
       "      <td>0.809931</td>\n",
       "      <td>0.190782</td>\n",
       "      <td>0.669622</td>\n",
       "      <td>0.342130</td>\n",
       "      <td>0.436503</td>\n",
       "      <td>0.780440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>0.803461</td>\n",
       "      <td>0.420869</td>\n",
       "      <td>0.483130</td>\n",
       "      <td>0.383870</td>\n",
       "      <td>0.329324</td>\n",
       "      <td>0.809931</td>\n",
       "      <td>0.190782</td>\n",
       "      <td>0.669622</td>\n",
       "      <td>0.342130</td>\n",
       "      <td>0.436503</td>\n",
       "      <td>0.780440</td>\n",
       "      <td>0.351867</td>\n",
       "      <td>0.426980</td>\n",
       "      <td>0.516512</td>\n",
       "      <td>0.450945</td>\n",
       "      <td>0.793846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.809931</td>\n",
       "      <td>0.190782</td>\n",
       "      <td>0.669622</td>\n",
       "      <td>0.342130</td>\n",
       "      <td>0.436503</td>\n",
       "      <td>0.780440</td>\n",
       "      <td>0.351867</td>\n",
       "      <td>0.426980</td>\n",
       "      <td>0.516512</td>\n",
       "      <td>0.450945</td>\n",
       "      <td>0.793846</td>\n",
       "      <td>0.223917</td>\n",
       "      <td>0.552018</td>\n",
       "      <td>0.461507</td>\n",
       "      <td>0.496876</td>\n",
       "      <td>0.779863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.780440</td>\n",
       "      <td>0.351867</td>\n",
       "      <td>0.426980</td>\n",
       "      <td>0.516512</td>\n",
       "      <td>0.450945</td>\n",
       "      <td>0.793846</td>\n",
       "      <td>0.223917</td>\n",
       "      <td>0.552018</td>\n",
       "      <td>0.461507</td>\n",
       "      <td>0.496876</td>\n",
       "      <td>0.779863</td>\n",
       "      <td>0.281935</td>\n",
       "      <td>0.479384</td>\n",
       "      <td>0.499772</td>\n",
       "      <td>0.441162</td>\n",
       "      <td>0.779500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0.793846</td>\n",
       "      <td>0.223917</td>\n",
       "      <td>0.552018</td>\n",
       "      <td>0.461507</td>\n",
       "      <td>0.496876</td>\n",
       "      <td>0.779863</td>\n",
       "      <td>0.281935</td>\n",
       "      <td>0.479384</td>\n",
       "      <td>0.499772</td>\n",
       "      <td>0.441162</td>\n",
       "      <td>0.779500</td>\n",
       "      <td>0.244140</td>\n",
       "      <td>0.668685</td>\n",
       "      <td>0.292694</td>\n",
       "      <td>0.347635</td>\n",
       "      <td>0.840602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0.779863</td>\n",
       "      <td>0.281935</td>\n",
       "      <td>0.479384</td>\n",
       "      <td>0.499772</td>\n",
       "      <td>0.441162</td>\n",
       "      <td>0.779500</td>\n",
       "      <td>0.244140</td>\n",
       "      <td>0.668685</td>\n",
       "      <td>0.292694</td>\n",
       "      <td>0.347635</td>\n",
       "      <td>0.840602</td>\n",
       "      <td>0.517969</td>\n",
       "      <td>0.465373</td>\n",
       "      <td>0.322440</td>\n",
       "      <td>0.259376</td>\n",
       "      <td>0.843827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.779500</td>\n",
       "      <td>0.244140</td>\n",
       "      <td>0.668685</td>\n",
       "      <td>0.292694</td>\n",
       "      <td>0.347635</td>\n",
       "      <td>0.840602</td>\n",
       "      <td>0.517969</td>\n",
       "      <td>0.465373</td>\n",
       "      <td>0.322440</td>\n",
       "      <td>0.259376</td>\n",
       "      <td>0.843827</td>\n",
       "      <td>0.292321</td>\n",
       "      <td>0.637478</td>\n",
       "      <td>0.292001</td>\n",
       "      <td>0.357619</td>\n",
       "      <td>0.855611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.840602</td>\n",
       "      <td>0.517969</td>\n",
       "      <td>0.465373</td>\n",
       "      <td>0.322440</td>\n",
       "      <td>0.259376</td>\n",
       "      <td>0.843827</td>\n",
       "      <td>0.292321</td>\n",
       "      <td>0.637478</td>\n",
       "      <td>0.292001</td>\n",
       "      <td>0.357619</td>\n",
       "      <td>0.855611</td>\n",
       "      <td>0.173882</td>\n",
       "      <td>0.607811</td>\n",
       "      <td>0.434054</td>\n",
       "      <td>0.489789</td>\n",
       "      <td>0.867113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.843827</td>\n",
       "      <td>0.292321</td>\n",
       "      <td>0.637478</td>\n",
       "      <td>0.292001</td>\n",
       "      <td>0.357619</td>\n",
       "      <td>0.855611</td>\n",
       "      <td>0.173882</td>\n",
       "      <td>0.607811</td>\n",
       "      <td>0.434054</td>\n",
       "      <td>0.489789</td>\n",
       "      <td>0.867113</td>\n",
       "      <td>0.107190</td>\n",
       "      <td>0.612963</td>\n",
       "      <td>0.469801</td>\n",
       "      <td>0.546337</td>\n",
       "      <td>0.862935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.855611</td>\n",
       "      <td>0.173882</td>\n",
       "      <td>0.607811</td>\n",
       "      <td>0.434054</td>\n",
       "      <td>0.489789</td>\n",
       "      <td>0.867113</td>\n",
       "      <td>0.107190</td>\n",
       "      <td>0.612963</td>\n",
       "      <td>0.469801</td>\n",
       "      <td>0.546337</td>\n",
       "      <td>0.862935</td>\n",
       "      <td>0.132756</td>\n",
       "      <td>0.694352</td>\n",
       "      <td>0.351126</td>\n",
       "      <td>0.469579</td>\n",
       "      <td>0.848871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.867113</td>\n",
       "      <td>0.107190</td>\n",
       "      <td>0.612963</td>\n",
       "      <td>0.469801</td>\n",
       "      <td>0.546337</td>\n",
       "      <td>0.862935</td>\n",
       "      <td>0.132756</td>\n",
       "      <td>0.694352</td>\n",
       "      <td>0.351126</td>\n",
       "      <td>0.469579</td>\n",
       "      <td>0.848871</td>\n",
       "      <td>0.130457</td>\n",
       "      <td>0.732844</td>\n",
       "      <td>0.308659</td>\n",
       "      <td>0.422066</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.862935</td>\n",
       "      <td>0.132756</td>\n",
       "      <td>0.694352</td>\n",
       "      <td>0.351126</td>\n",
       "      <td>0.469579</td>\n",
       "      <td>0.848871</td>\n",
       "      <td>0.130457</td>\n",
       "      <td>0.732844</td>\n",
       "      <td>0.308659</td>\n",
       "      <td>0.422066</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479350</td>\n",
       "      <td>0.489477</td>\n",
       "      <td>0.321666</td>\n",
       "      <td>0.311875</td>\n",
       "      <td>0.751017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.848871</td>\n",
       "      <td>0.130457</td>\n",
       "      <td>0.732844</td>\n",
       "      <td>0.308659</td>\n",
       "      <td>0.422066</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479350</td>\n",
       "      <td>0.489477</td>\n",
       "      <td>0.321666</td>\n",
       "      <td>0.311875</td>\n",
       "      <td>0.751017</td>\n",
       "      <td>0.891814</td>\n",
       "      <td>0.257590</td>\n",
       "      <td>0.262134</td>\n",
       "      <td>0.076990</td>\n",
       "      <td>0.708808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479350</td>\n",
       "      <td>0.489477</td>\n",
       "      <td>0.321666</td>\n",
       "      <td>0.311875</td>\n",
       "      <td>0.751017</td>\n",
       "      <td>0.891814</td>\n",
       "      <td>0.257590</td>\n",
       "      <td>0.262134</td>\n",
       "      <td>0.076990</td>\n",
       "      <td>0.708808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150689</td>\n",
       "      <td>0.303543</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.716132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>0.751017</td>\n",
       "      <td>0.891814</td>\n",
       "      <td>0.257590</td>\n",
       "      <td>0.262134</td>\n",
       "      <td>0.076990</td>\n",
       "      <td>0.708808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150689</td>\n",
       "      <td>0.303543</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.716132</td>\n",
       "      <td>0.914538</td>\n",
       "      <td>0.211408</td>\n",
       "      <td>0.287951</td>\n",
       "      <td>0.047777</td>\n",
       "      <td>0.715265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>0.708808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150689</td>\n",
       "      <td>0.303543</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.716132</td>\n",
       "      <td>0.914538</td>\n",
       "      <td>0.211408</td>\n",
       "      <td>0.287951</td>\n",
       "      <td>0.047777</td>\n",
       "      <td>0.715265</td>\n",
       "      <td>0.730399</td>\n",
       "      <td>0.329642</td>\n",
       "      <td>0.284121</td>\n",
       "      <td>0.123753</td>\n",
       "      <td>0.800697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>0.716132</td>\n",
       "      <td>0.914538</td>\n",
       "      <td>0.211408</td>\n",
       "      <td>0.287951</td>\n",
       "      <td>0.047777</td>\n",
       "      <td>0.715265</td>\n",
       "      <td>0.730399</td>\n",
       "      <td>0.329642</td>\n",
       "      <td>0.284121</td>\n",
       "      <td>0.123753</td>\n",
       "      <td>0.800697</td>\n",
       "      <td>0.756721</td>\n",
       "      <td>0.384104</td>\n",
       "      <td>0.197833</td>\n",
       "      <td>0.094007</td>\n",
       "      <td>0.843938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     var1(t-3)  var2(t-3)  var3(t-3)  var4(t-3)  var5(t-3)  var1(t-2)  \\\n",
       "3     0.039567   0.078779   0.831199   0.195812   0.401475   0.007090   \n",
       "4     0.007090   0.128849   0.579299   0.505289   0.542609   0.000000   \n",
       "5     0.000000   0.110492   0.605697   0.477654   0.540763   0.018905   \n",
       "6     0.018905   0.136519   0.740108   0.297985   0.462231   0.043401   \n",
       "7     0.043401   0.244122   0.585976   0.397319   0.455419   0.046946   \n",
       "8     0.046946   0.186121   0.513127   0.542100   0.528573   0.085634   \n",
       "9     0.085634   0.117236   0.544642   0.555482   0.628997   0.065537   \n",
       "10    0.065537   0.100541   0.701571   0.373207   0.492066   0.146213   \n",
       "11    0.146213   0.126593   0.578442   0.500330   0.552681   0.165131   \n",
       "12    0.165131   0.141180   0.532970   0.549913   0.543096   0.166378   \n",
       "13    0.166378   0.016336   0.942586   0.132895   0.395744   0.166144   \n",
       "14    0.166144   0.124761   0.837867   0.171567   0.329644   0.164307   \n",
       "15    0.164307   0.198284   0.755304   0.213454   0.314862   0.148241   \n",
       "16    0.148241   0.237637   0.541722   0.451360   0.465997   0.151626   \n",
       "17    0.151626   0.280928   0.534492   0.421342   0.444023   0.169401   \n",
       "18    0.169401   0.123146   0.607435   0.474041   0.560936   0.183642   \n",
       "19    0.183642   0.311738   0.447805   0.524477   0.472892   0.177916   \n",
       "20    0.177916   0.180111   0.387843   0.707781   0.580837   0.217495   \n",
       "21    0.217495   0.209864   0.405118   0.663783   0.550206   0.173339   \n",
       "22    0.173339   0.237665   0.487808   0.498664   0.468680   0.191267   \n",
       "23    0.191267   0.227426   0.506527   0.475111   0.465703   0.199826   \n",
       "24    0.199826   0.181549   0.605828   0.421061   0.428070   0.204298   \n",
       "25    0.204298   0.299932   0.499747   0.445098   0.394034   0.166667   \n",
       "26    0.166667   0.147860   0.596683   0.474037   0.477040   0.187433   \n",
       "27    0.187433   0.088094   0.605360   0.514699   0.588314   0.188379   \n",
       "28    0.188379   0.176758   0.574356   0.477885   0.519576   0.183237   \n",
       "29    0.183237   0.134368   0.531768   0.569653   0.583945   0.216420   \n",
       "30    0.216420   0.137144   0.580677   0.504029   0.553042   0.274886   \n",
       "31    0.274886   0.123885   0.449502   0.547613   0.538201   0.301108   \n",
       "32    0.301108   0.086307   0.721459   0.361015   0.479860   0.291002   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "181   0.589640   0.239830   0.626147   0.352967   0.375881   0.555510   \n",
       "182   0.555510   0.213553   0.669811   0.322099   0.384069   0.616317   \n",
       "183   0.616317   0.233207   0.634436   0.334049   0.385724   0.618338   \n",
       "184   0.618338   0.402528   0.542865   0.307348   0.296855   0.612557   \n",
       "185   0.612557   0.276576   0.566237   0.348924   0.372635   0.558484   \n",
       "186   0.558484   0.179431   0.660310   0.324625   0.401482   0.602174   \n",
       "187   0.602174   0.164790   0.728385   0.276268   0.387183   0.604459   \n",
       "188   0.604459   0.176119   0.604138   0.414651   0.420161   0.625687   \n",
       "189   0.625687   0.185016   0.657552   0.350062   0.403081   0.609675   \n",
       "190   0.609675   0.233360   0.581522   0.379804   0.391969   0.738815   \n",
       "191   0.738815   0.364671   0.529522   0.362977   0.335555   0.748584   \n",
       "192   0.748584   0.394019   0.563028   0.290954   0.276657   0.775967   \n",
       "193   0.775967   0.357693   0.471372   0.438212   0.432692   0.776453   \n",
       "194   0.776453   0.468409   0.512129   0.295702   0.276640   0.803461   \n",
       "195   0.803461   0.420869   0.483130   0.383870   0.329324   0.809931   \n",
       "196   0.809931   0.190782   0.669622   0.342130   0.436503   0.780440   \n",
       "197   0.780440   0.351867   0.426980   0.516512   0.450945   0.793846   \n",
       "198   0.793846   0.223917   0.552018   0.461507   0.496876   0.779863   \n",
       "199   0.779863   0.281935   0.479384   0.499772   0.441162   0.779500   \n",
       "200   0.779500   0.244140   0.668685   0.292694   0.347635   0.840602   \n",
       "201   0.840602   0.517969   0.465373   0.322440   0.259376   0.843827   \n",
       "202   0.843827   0.292321   0.637478   0.292001   0.357619   0.855611   \n",
       "203   0.855611   0.173882   0.607811   0.434054   0.489789   0.867113   \n",
       "204   0.867113   0.107190   0.612963   0.469801   0.546337   0.862935   \n",
       "205   0.862935   0.132756   0.694352   0.351126   0.469579   0.848871   \n",
       "206   0.848871   0.130457   0.732844   0.308659   0.422066   1.000000   \n",
       "207   1.000000   0.479350   0.489477   0.321666   0.311875   0.751017   \n",
       "208   0.751017   0.891814   0.257590   0.262134   0.076990   0.708808   \n",
       "209   0.708808   1.000000   0.150689   0.303543   0.000000   0.716132   \n",
       "210   0.716132   0.914538   0.211408   0.287951   0.047777   0.715265   \n",
       "\n",
       "     var2(t-2)  var3(t-2)  var4(t-2)  var5(t-2)  var1(t-1)  var2(t-1)  \\\n",
       "3     0.128849   0.579299   0.505289   0.542609   0.000000   0.110492   \n",
       "4     0.110492   0.605697   0.477654   0.540763   0.018905   0.136519   \n",
       "5     0.136519   0.740108   0.297985   0.462231   0.043401   0.244122   \n",
       "6     0.244122   0.585976   0.397319   0.455419   0.046946   0.186121   \n",
       "7     0.186121   0.513127   0.542100   0.528573   0.085634   0.117236   \n",
       "8     0.117236   0.544642   0.555482   0.628997   0.065537   0.100541   \n",
       "9     0.100541   0.701571   0.373207   0.492066   0.146213   0.126593   \n",
       "10    0.126593   0.578442   0.500330   0.552681   0.165131   0.141180   \n",
       "11    0.141180   0.532970   0.549913   0.543096   0.166378   0.016336   \n",
       "12    0.016336   0.942586   0.132895   0.395744   0.166144   0.124761   \n",
       "13    0.124761   0.837867   0.171567   0.329644   0.164307   0.198284   \n",
       "14    0.198284   0.755304   0.213454   0.314862   0.148241   0.237637   \n",
       "15    0.237637   0.541722   0.451360   0.465997   0.151626   0.280928   \n",
       "16    0.280928   0.534492   0.421342   0.444023   0.169401   0.123146   \n",
       "17    0.123146   0.607435   0.474041   0.560936   0.183642   0.311738   \n",
       "18    0.311738   0.447805   0.524477   0.472892   0.177916   0.180111   \n",
       "19    0.180111   0.387843   0.707781   0.580837   0.217495   0.209864   \n",
       "20    0.209864   0.405118   0.663783   0.550206   0.173339   0.237665   \n",
       "21    0.237665   0.487808   0.498664   0.468680   0.191267   0.227426   \n",
       "22    0.227426   0.506527   0.475111   0.465703   0.199826   0.181549   \n",
       "23    0.181549   0.605828   0.421061   0.428070   0.204298   0.299932   \n",
       "24    0.299932   0.499747   0.445098   0.394034   0.166667   0.147860   \n",
       "25    0.147860   0.596683   0.474037   0.477040   0.187433   0.088094   \n",
       "26    0.088094   0.605360   0.514699   0.588314   0.188379   0.176758   \n",
       "27    0.176758   0.574356   0.477885   0.519576   0.183237   0.134368   \n",
       "28    0.134368   0.531768   0.569653   0.583945   0.216420   0.137144   \n",
       "29    0.137144   0.580677   0.504029   0.553042   0.274886   0.123885   \n",
       "30    0.123885   0.449502   0.547613   0.538201   0.301108   0.086307   \n",
       "31    0.086307   0.721459   0.361015   0.479860   0.291002   0.174027   \n",
       "32    0.174027   0.747167   0.248548   0.367103   0.254801   0.199921   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "181   0.213553   0.669811   0.322099   0.384069   0.616317   0.233207   \n",
       "182   0.233207   0.634436   0.334049   0.385724   0.618338   0.402528   \n",
       "183   0.402528   0.542865   0.307348   0.296855   0.612557   0.276576   \n",
       "184   0.276576   0.566237   0.348924   0.372635   0.558484   0.179431   \n",
       "185   0.179431   0.660310   0.324625   0.401482   0.602174   0.164790   \n",
       "186   0.164790   0.728385   0.276268   0.387183   0.604459   0.176119   \n",
       "187   0.176119   0.604138   0.414651   0.420161   0.625687   0.185016   \n",
       "188   0.185016   0.657552   0.350062   0.403081   0.609675   0.233360   \n",
       "189   0.233360   0.581522   0.379804   0.391969   0.738815   0.364671   \n",
       "190   0.364671   0.529522   0.362977   0.335555   0.748584   0.394019   \n",
       "191   0.394019   0.563028   0.290954   0.276657   0.775967   0.357693   \n",
       "192   0.357693   0.471372   0.438212   0.432692   0.776453   0.468409   \n",
       "193   0.468409   0.512129   0.295702   0.276640   0.803461   0.420869   \n",
       "194   0.420869   0.483130   0.383870   0.329324   0.809931   0.190782   \n",
       "195   0.190782   0.669622   0.342130   0.436503   0.780440   0.351867   \n",
       "196   0.351867   0.426980   0.516512   0.450945   0.793846   0.223917   \n",
       "197   0.223917   0.552018   0.461507   0.496876   0.779863   0.281935   \n",
       "198   0.281935   0.479384   0.499772   0.441162   0.779500   0.244140   \n",
       "199   0.244140   0.668685   0.292694   0.347635   0.840602   0.517969   \n",
       "200   0.517969   0.465373   0.322440   0.259376   0.843827   0.292321   \n",
       "201   0.292321   0.637478   0.292001   0.357619   0.855611   0.173882   \n",
       "202   0.173882   0.607811   0.434054   0.489789   0.867113   0.107190   \n",
       "203   0.107190   0.612963   0.469801   0.546337   0.862935   0.132756   \n",
       "204   0.132756   0.694352   0.351126   0.469579   0.848871   0.130457   \n",
       "205   0.130457   0.732844   0.308659   0.422066   1.000000   0.479350   \n",
       "206   0.479350   0.489477   0.321666   0.311875   0.751017   0.891814   \n",
       "207   0.891814   0.257590   0.262134   0.076990   0.708808   1.000000   \n",
       "208   1.000000   0.150689   0.303543   0.000000   0.716132   0.914538   \n",
       "209   0.914538   0.211408   0.287951   0.047777   0.715265   0.730399   \n",
       "210   0.730399   0.329642   0.284121   0.123753   0.800697   0.756721   \n",
       "\n",
       "     var3(t-1)  var4(t-1)  var5(t-1)   var1(t)  \n",
       "3     0.605697   0.477654   0.540763  0.018905  \n",
       "4     0.740108   0.297985   0.462231  0.043401  \n",
       "5     0.585976   0.397319   0.455419  0.046946  \n",
       "6     0.513127   0.542100   0.528573  0.085634  \n",
       "7     0.544642   0.555482   0.628997  0.065537  \n",
       "8     0.701571   0.373207   0.492066  0.146213  \n",
       "9     0.578442   0.500330   0.552681  0.165131  \n",
       "10    0.532970   0.549913   0.543096  0.166378  \n",
       "11    0.942586   0.132895   0.395744  0.166144  \n",
       "12    0.837867   0.171567   0.329644  0.164307  \n",
       "13    0.755304   0.213454   0.314862  0.148241  \n",
       "14    0.541722   0.451360   0.465997  0.151626  \n",
       "15    0.534492   0.421342   0.444023  0.169401  \n",
       "16    0.607435   0.474041   0.560936  0.183642  \n",
       "17    0.447805   0.524477   0.472892  0.177916  \n",
       "18    0.387843   0.707781   0.580837  0.217495  \n",
       "19    0.405118   0.663783   0.550206  0.173339  \n",
       "20    0.487808   0.498664   0.468680  0.191267  \n",
       "21    0.506527   0.475111   0.465703  0.199826  \n",
       "22    0.605828   0.421061   0.428070  0.204298  \n",
       "23    0.499747   0.445098   0.394034  0.166667  \n",
       "24    0.596683   0.474037   0.477040  0.187433  \n",
       "25    0.605360   0.514699   0.588314  0.188379  \n",
       "26    0.574356   0.477885   0.519576  0.183237  \n",
       "27    0.531768   0.569653   0.583945  0.216420  \n",
       "28    0.580677   0.504029   0.553042  0.274886  \n",
       "29    0.449502   0.547613   0.538201  0.301108  \n",
       "30    0.721459   0.361015   0.479860  0.291002  \n",
       "31    0.747167   0.248548   0.367103  0.254801  \n",
       "32    0.697007   0.291954   0.368465  0.239798  \n",
       "..         ...        ...        ...       ...  \n",
       "181   0.634436   0.334049   0.385724  0.618338  \n",
       "182   0.542865   0.307348   0.296855  0.612557  \n",
       "183   0.566237   0.348924   0.372635  0.558484  \n",
       "184   0.660310   0.324625   0.401482  0.602174  \n",
       "185   0.728385   0.276268   0.387183  0.604459  \n",
       "186   0.604138   0.414651   0.420161  0.625687  \n",
       "187   0.657552   0.350062   0.403081  0.609675  \n",
       "188   0.581522   0.379804   0.391969  0.738815  \n",
       "189   0.529522   0.362977   0.335555  0.748584  \n",
       "190   0.563028   0.290954   0.276657  0.775967  \n",
       "191   0.471372   0.438212   0.432692  0.776453  \n",
       "192   0.512129   0.295702   0.276640  0.803461  \n",
       "193   0.483130   0.383870   0.329324  0.809931  \n",
       "194   0.669622   0.342130   0.436503  0.780440  \n",
       "195   0.426980   0.516512   0.450945  0.793846  \n",
       "196   0.552018   0.461507   0.496876  0.779863  \n",
       "197   0.479384   0.499772   0.441162  0.779500  \n",
       "198   0.668685   0.292694   0.347635  0.840602  \n",
       "199   0.465373   0.322440   0.259376  0.843827  \n",
       "200   0.637478   0.292001   0.357619  0.855611  \n",
       "201   0.607811   0.434054   0.489789  0.867113  \n",
       "202   0.612963   0.469801   0.546337  0.862935  \n",
       "203   0.694352   0.351126   0.469579  0.848871  \n",
       "204   0.732844   0.308659   0.422066  1.000000  \n",
       "205   0.489477   0.321666   0.311875  0.751017  \n",
       "206   0.257590   0.262134   0.076990  0.708808  \n",
       "207   0.150689   0.303543   0.000000  0.716132  \n",
       "208   0.211408   0.287951   0.047777  0.715265  \n",
       "209   0.329642   0.284121   0.123753  0.800697  \n",
       "210   0.384104   0.197833   0.094007  0.843938  \n",
       "\n",
       "[208 rows x 16 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reframed = reframed.drop(reframed.columns[-4:], axis=1)\n",
    "reframed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(170, 16)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = reframed.values\n",
    "n_train_hours = 170\n",
    "train = values[:n_train_hours, :]\n",
    "test = values[n_train_hours:, :]\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split into input and outputs\n",
    "train_X, train_y = train[:, :n_obs], train[:, -n_features]\n",
    "test_X, test_y = test[:, :n_obs], test[:, -n_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(170, 3, 5) (170,) (38, 3, 5) (38,)\n"
     ]
    }
   ],
   "source": [
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], n_hours, n_features))\n",
    "test_X = test_X.reshape((test_X.shape[0], n_hours, n_features))\n",
    "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 170 samples, validate on 38 samples\n",
      "Epoch 1/20\n",
      " - 1s - loss: 0.0688 - val_loss: 0.1399\n",
      "Epoch 2/20\n",
      " - 0s - loss: 0.0660 - val_loss: 0.1376\n",
      "Epoch 3/20\n",
      " - 0s - loss: 0.0631 - val_loss: 0.1317\n",
      "Epoch 4/20\n",
      " - 0s - loss: 0.0609 - val_loss: 0.1252\n",
      "Epoch 5/20\n",
      " - 0s - loss: 0.0585 - val_loss: 0.1208\n",
      "Epoch 6/20\n",
      " - 0s - loss: 0.0566 - val_loss: 0.1157\n",
      "Epoch 7/20\n",
      " - 0s - loss: 0.0545 - val_loss: 0.1100\n",
      "Epoch 8/20\n",
      " - 0s - loss: 0.0517 - val_loss: 0.1066\n",
      "Epoch 9/20\n",
      " - 0s - loss: 0.0496 - val_loss: 0.1011\n",
      "Epoch 10/20\n",
      " - 0s - loss: 0.0477 - val_loss: 0.0964\n",
      "Epoch 11/20\n",
      " - 0s - loss: 0.0454 - val_loss: 0.0897\n",
      "Epoch 12/20\n",
      " - 0s - loss: 0.0437 - val_loss: 0.0840\n",
      "Epoch 13/20\n",
      " - 0s - loss: 0.0410 - val_loss: 0.0795\n",
      "Epoch 14/20\n",
      " - 0s - loss: 0.0385 - val_loss: 0.0735\n",
      "Epoch 15/20\n",
      " - 0s - loss: 0.0363 - val_loss: 0.0701\n",
      "Epoch 16/20\n",
      " - 0s - loss: 0.0339 - val_loss: 0.0642\n",
      "Epoch 17/20\n",
      " - 0s - loss: 0.0320 - val_loss: 0.0605\n",
      "Epoch 18/20\n",
      " - 0s - loss: 0.0303 - val_loss: 0.0559\n",
      "Epoch 19/20\n",
      " - 0s - loss: 0.0286 - val_loss: 0.0511\n",
      "Epoch 20/20\n",
      " - 0s - loss: 0.0269 - val_loss: 0.0456\n"
     ]
    }
   ],
   "source": [
    "# design network\n",
    "model = Sequential()\n",
    "model.add(LSTM(5, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mae', optimizer='adam')\n",
    "# fit network\n",
    "history = model.fit(train_X, train_y, epochs=20, batch_size=4, validation_data=(test_X, test_y), verbose=2, shuffle=False)\n",
    "# plot history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2jOPiHrHEhumYZ+XjctfZCmDnJzbpj7jbLg/ZKdrpyFqNp9v4A7Cdu5cDudjO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ca7b128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 110.902\n"
     ]
    }
   ],
   "source": [
    "# make a prediction\n",
    "yhat = model.predict(test_X)\n",
    "test_X = test_X.reshape((test_X.shape[0], n_hours* n_features))\n",
    "# invert scaling for forecast\n",
    "inv_yhat = np.concatenate((yhat, test_X[:, -4:]), axis=1)\n",
    "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
    "inv_yhat = inv_yhat[:,0]\n",
    "# invert scaling for actual\n",
    "test_y = test_y.reshape((len(test_y), 1))\n",
    "inv_y = np.concatenate((test_y, test_X[:, -4:]), axis=1)\n",
    "inv_y = scaler.inverse_transform(inv_y)\n",
    "inv_y = inv_y[:,0]\n",
    "# calculate RMSE\n",
    "rmse = np.sqrt(mean_squared_error(inv_y, inv_yhat))\n",
    "print('Test RMSE: %.3f' % rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12ddcf9b0>]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(inv_y)\n",
    "plt.plot(inv_yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jHC8q9/TL6Rd2u3IEAceicP52409LjfHaMXIFYHgcNXnKsZK1jlJnXr49FHQQ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\nudtev3KYWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c61c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38,)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv_yhat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_sol = pd.DataFrame({'forecast':inv_yhat,'actual':inv_y})\n",
    "df_sol.to_csv('Predictions_nosent.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}