stackedLSTM_carriage_service.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:07:56.917361Z",
     "start_time": "2021-04-28T05:07:52.318198Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import datetime as dt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "\n",
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "from torch.nn import functional as F\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from torch.utils.data import DataLoader, random_split, TensorDataset\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:07:57.164740Z",
     "start_time": "2021-04-28T05:07:56.965581Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda:2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "device = 'cuda:2' if torch.cuda.is_available() else 'cpu'\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:09:36.439264Z",
     "start_time": "2021-04-28T05:09:36.337455Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></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>1996-08-09</th>\n",
       "      <td>14.250000</td>\n",
       "      <td>16.750000</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>14.892752</td>\n",
       "      <td>1601500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-12</th>\n",
       "      <td>16.500000</td>\n",
       "      <td>16.750000</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>14.892752</td>\n",
       "      <td>260900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-13</th>\n",
       "      <td>16.500000</td>\n",
       "      <td>16.750000</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>14.779927</td>\n",
       "      <td>162800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-14</th>\n",
       "      <td>16.375000</td>\n",
       "      <td>16.625000</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>14.779927</td>\n",
       "      <td>59500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-15</th>\n",
       "      <td>16.625000</td>\n",
       "      <td>16.625000</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>16.625000</td>\n",
       "      <td>15.005575</td>\n",
       "      <td>62300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>35.270000</td>\n",
       "      <td>36.340000</td>\n",
       "      <td>35.099998</td>\n",
       "      <td>36.139999</td>\n",
       "      <td>36.139999</td>\n",
       "      <td>111400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>36.580002</td>\n",
       "      <td>36.849998</td>\n",
       "      <td>36.200001</td>\n",
       "      <td>36.529999</td>\n",
       "      <td>36.529999</td>\n",
       "      <td>114000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-06</th>\n",
       "      <td>36.650002</td>\n",
       "      <td>36.930000</td>\n",
       "      <td>36.349998</td>\n",
       "      <td>36.660000</td>\n",
       "      <td>36.660000</td>\n",
       "      <td>66100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>36.549999</td>\n",
       "      <td>36.680000</td>\n",
       "      <td>35.570000</td>\n",
       "      <td>35.759998</td>\n",
       "      <td>35.759998</td>\n",
       "      <td>83300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-08</th>\n",
       "      <td>35.750000</td>\n",
       "      <td>35.790001</td>\n",
       "      <td>34.910000</td>\n",
       "      <td>35.320000</td>\n",
       "      <td>35.320000</td>\n",
       "      <td>116200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6207 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close   Volume\n",
       "Date                                                                      \n",
       "1996-08-09  14.250000  16.750000  14.250000  16.500000  14.892752  1601500\n",
       "1996-08-12  16.500000  16.750000  16.375000  16.500000  14.892752   260900\n",
       "1996-08-13  16.500000  16.750000  16.375000  16.375000  14.779927   162800\n",
       "1996-08-14  16.375000  16.625000  16.375000  16.375000  14.779927    59500\n",
       "1996-08-15  16.625000  16.625000  16.375000  16.625000  15.005575    62300\n",
       "...               ...        ...        ...        ...        ...      ...\n",
       "2021-04-01  35.270000  36.340000  35.099998  36.139999  36.139999   111400\n",
       "2021-04-05  36.580002  36.849998  36.200001  36.529999  36.529999   114000\n",
       "2021-04-06  36.650002  36.930000  36.349998  36.660000  36.660000    66100\n",
       "2021-04-07  36.549999  36.680000  35.570000  35.759998  35.759998    83300\n",
       "2021-04-08  35.750000  35.790001  34.910000  35.320000  35.320000   116200\n",
       "\n",
       "[6207 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data.csv',parse_dates = True, index_col=0)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:08:04.116746Z",
     "start_time": "2021-04-28T05:08:02.289370Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f93ed8a7d90>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(data['Open'])\n",
    "plt.plot(data['High'])\n",
    "plt.plot(data['Low'])\n",
    "plt.plot(data['Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:08:06.324743Z",
     "start_time": "2021-04-28T05:08:04.568208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f93713a8490>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y1OfDabrksRFxNwMRIwiEkqnWCvifzVqlz2evirsZLUK1bkNAMf3p9QlavHZL3M0AgIpAZjuqyeOfztKYeWvibgYi1iruBrQUpknGixcre6NaT5qXPfm5JGlun14xt6SMVOvGAJSppgT7JAAAQDUgE6hADE/xlyoMzTUGCsT+BhQHx2cAAIDqQBAIRWddtlf6UJ5Kbz8AeOH4BiA2HH5QAo1NCY2ZtzruZgBlgSBQRDh/+eNOMwAAAIBSe3DADF3y2Gf6cn751bfhGgmlRhCoQNQB8mfwJcEp5DbBuREAgOKbtGiduvXuq0mL1sXdlLJVaJ+EbnE8pi3dIElavqEu5pYA8SMIBKD0uOUBAEDZ+XjKMknSgKnLYm5Jy0UXKB7lXKOUwCBKjSBQRMxyPKKUiVRNIL4i5IlzI1BcHJ+B4qq0fazS2gv4oS8JpBEEKhAHlACsyDuDepAnthwAAIovPZkHioWsDzgRdEWpEQRC0ZEJBAAAUP4IUBQf/eG4lc8PwP6GuAQKAhmGcb5hGNMNw5hpGEZvl+evNAxjhWEY45L/XR19U8tb+RxOyo9VGJrvCCmc9QAAiB3lDMLjO6tM5VgTqJzagurSyu8FhmHUSnpE0jmSFkr6wjCMd03TnOJ46Sumaf6qCG0sa1zLAnnI96zH/gYUBf1QoDqZpkdflqvTouHaIR5GGXci2SZQakEygY6TNNM0zdmmadZL+p+kbxa3WWiJKv3OSYU3v6Lx3QMAED3n6dW6UOa0C5QO/VyUWpAg0B6SFtj+vTD5mNMlhmFMMAzjdcMw9nRbkGEY1xiGMdowjNErVqzIo7nli53XG0UGAQAAyh8ZCWjpyul6hP0NcYmqMPR7krqZpnmEpP6S/uP2ItM0nzBNs6dpmj27dOkS0arjZVTg3jt96Qadeu9ArdlUX5L1leMYXABAWqVnagLID/s+qgXXI0BakCDQIkn2zJ6uycdSTNNcZZpmXfKfT0nqEU3zUAyPDp6pBau3aPBXy0uyPqMF5gLRaSotswVtOwAAlAuvsyvdHG98NZWpHO/bs58hLkGCQF9IOsAwjH0Mw2gj6fuS3rW/wDCM3Wz//IakqdE1sTJU0kVqqY+BRN5hKccTMAAAaMZpGi1dJV2zAcXiOzuYaZqNhmH8StJHkmolPWOa5mTDMO6QNNo0zXcl/dowjG9IapS0WtKVRWwzIkJQBqWW9zbHtgoAQOS8zstcKAPFx81RxMU3CCRJpml+IOkDx2O32v6+UdKN0TYNxWLVMSp1EKgldSc8p1RFUfGVA8XRko7PAIJzBnvo26ClSs18xwkPiKwwdNWrpANKXBV6Kuk7Qnlh0wEAoHRK2Wer1qwj+sUlRoATSCEIVKCKvGNS4janM4842wEAAJQLZ9esEme9BcLgagQgCIQSaClzg1V6+ysZ8UOguNjHANhxSPAW1fGSeFtp8XUDaQSBqlB6TGxpTvEtcXawFvRRAABAlWpJfbNKw3cfXiJhavLidXE3A6h4BIEKZFRgXDkVlIm3GahC3PUCyhVnBABpBChQjp4cOlu9HhqmMfNW570MylMABIFQQtVa+A9p+Z532XYAAIges4PFpxy+66+WbYi7CaFMXrxekrRwzZbQ76XeFZBGECgilRRUTh0CS9Rmo6UUBbLhLgIAACh3DU0JXfnsKI1bsNb1ea/uDDdfii/uruQ74xbp3AeG6OPJS+NtSAiFlJhIXY6U4aZdhk1CC0cQqECVGFRODwcrUU0gqwZRSdZWGuu3Nmrlxrq4m1E1yvGEDQBAuZu3arMGT1+h3706LtDrK7HMQam1lADZ9KXNWUAzlm+MuSUASo0gUBUr1YV1SywMfdxfB6jnXQPibkbVqcSgK1AJWtLxGUCaXzY2u3584u7TWOtPJCpnK0hvztG1eVNdYyogFpcwm0IiYaqpgn4zlCeCQBGppLsCpb7LU4wDdtwaOfjmJd8OD982AAD58zqPeg5v58RbdHEH3ysx68uq65PXcDCPkRA/f2GMzntwiBqbEoU2ryQufGio9rvpg7ibgQpHEKhAlXf4TOP8jlKLu8MDAEA1SddBcT8BOx9lBtniK7drh2r5rb1qAn02e1Xz46VtToYw654Wc9YSWgaCQFUoruFZBAAAoDxxeAZaphojXF3GcgtQtETlcrytqeByDfllAuXeuuPY9tnfEBeCQBGppANoyccgh+yAlCtmBIsP3z0AAOGl6r54ZQJxeg2txXxnqf555XygYkw4HGcfs3K+ebQ0BIEKFHdRt0KUbnaw5PpazFkTAACg/KVmaPUsCuTxMH22oim3S4eK+qkj+PKcn7ccPn65bRPFkEiYFVN3qRoQBKpK+RdVy2ttjC9HEoWhAQAonbAlAFrijK7lply+2poK7p/nE6T0yyKqlJpAlerK577Q/n/+MO5mIIkgUEQqaectdfZSanWV9CWhLFXiTBZAJeCCD2iZ/Pp8zqzwOM6zHH/ikc4Sq5wfINXm/N7sKs6PX0292iFfrYi7CbAhCFSgSr4oLdUxz68QG+CrcvonAACUDasPRk2g8lEuveJSds/XbWnQnJWbCl6OEUFRoHIPet35/hQ9OOCruJuBFo4gUETK/YBiF9fJp5IKz6E4Kmg3AQAgMnGd/7ymxbaU49AYlIbfthGlix8ZrjPvG1zwcgq5hvG7cV8ufdSnh83RgwNmxN0MtHAEgapQXMe4cjm4AgAAVIOwXS9qAlUPv5njohRFFpBdS7mx3DI+BSoRQaACVeJIJ+tYX6qml/JOA8pb/oWh2XiAYmIfA1o2r328kjLZEa1KLNcQRZDSO/uNfaGcjJ2/Rte9OFaJRPF/l399MkP73/RB0ddTTggCRYTDhjdmBwMAACg9K8jjdR1ViX2z+as26+g7PtaC1ZvjbkqLUEnbQCG1WKOoJxS1ygvDlc7PXxijvhOXaOXGuqKv6x/9v1JjCYJN5YQgUEQ+nU7Fcy+VOPuAmwpvfuz6T1mmgVOX5/VevnsAAMKzzp9hz6PlnBXx2pgFWrO5QT9/YUzcTalopRwOFrV8WpyOAVXe5610jU2JuJsAB4JAEbn+5S/jbkIIzQe/kmWBkgkEST97frQWr9sadzMAuKjAawAAoXjPDvbF3NV6Y8xCSekhQuV8TLC6r1OWrI+1HZWuEmc4LmbNqnLe5ivd3/pNS/1d6UkBLUWruBuA+FTiwR/VifMFAADh+WUCmTJ16eOfSZIu6dG1RK0qUMy1bFpKn6Qch0f5qcAyRpA0fOaq1N8tZf+pdGQCFagSi6rFhZ0ehWJ3A4qD4zPQsnnu4hW479MViIb1PVbWcLBkploeGy4z38XHvo0F/fqtt7w/YUn0DfJcZ/VsHASBqlCpt+/0ybp6dixEiy0HAIDwrIvloBf6lRBgqeGOUCjrtzaorrEp6/FK/BoLCeSkapRG2B6EV98Yrj7QHe9PKVJLslVRDIggUDUr1cGfyDsAAEDp+Q8HyxR3YGBrQzpYMWnROs1cviHrNXG3sdIccfvHuuyJzz2fp3+OqM1esTFj9j57EPqZ4XMCLSOO/byadgWCQAWqxPNQ6TOBiLwDAACUmtX38hrmUG4BgI8mL1W33n01b9Umff1fw3T2/UPiblJFGzZjpSRp7Py1Wc9ZGVVltgnkVMwyRnHuC27756oSTI1eLGf941Odeu+g1L/ts69vqmuMoUXBVNbQyMIQBKpiJZscjEwgFKiaxugCcWDKXKBlC7uHx3XefT05S9kHE5d6vibuG7CVdLz895BZvq+ppAvfdDHrll8TqMddA+JuQmTs21iijL//StoXCkUQCAAAAGiBUsGcHLOD2ZXLhFG5hoIwHCy4XPWTqm1ym1QQyGPrjjO41+J/C9tXGzTAHEc8popiQASBClWJ+2ypD3J+B13AD1sOAADhmY7/Zz3veCKOi1HTzP471x35Fn/BHKGcwbTk/yvpwrewEhNsN3Fpsm1kTWWcClRJ+0KhCAJVsVKfQ6tpxwKASsLxGSiuuG6EpQtDV8ZObn1PjU2V0d5yl6urnx4eVTnfdaUN6Qqqkn6DsP43ar7mrbIXiQ72vjhivQwHAyJEYWgUqoqOyQAARM4zE8jr8ZjPu7nWXwmJQJ9+tULrNjfE3YycWVNRf40fT16qP742PuKluiskaOI5U14Jtvmnhs7WnJWb0g9UwsZcgN++Mk6935yY8VjQQEscxyCCQAisEnfdks8OVoF3GlCeyuVcuWpjna5/+UttLOMZDgAAsMI8Xhc3zr5Z3EP4reZ8MXe152ty1bkpB2s31+vHz4zSNS+MjrspPplA0d6kveaFMXotWdi7WAqpWRX3ZrOlvkl39Z2qSx8fEW9DSuitLxdlPVYu14MDpizTiFkrMx4r45FqkSMIVGSrNtapW++++mzWqribkmJt30ZFhrCA+D30yQy9N36xXh+9IO6mAADgKT0cLPfz5cIKVg2budLzNXH3Xv2+s4bkULZZKzbmXk4JAm1BCmxXUvZDFPWgwmbFRW3DVtsNRMd3Xy4BkmIql0DL1c+P1uVPjpQk1ZRLRfwSIghUZF/OXyupOf2vWlVaAb8wB+CJC9dV9W9bOlV0VAYAICJ+haERvfKqW1O64WCllM93m/q8Mf0w6Sw7t+ean3xgwIzSNSgm5Rh0tL7/cmxbsRAEKpA9wOGW7VNbU30blZdK+QrCtPOih4fprr5Ti9cYAACAQgUtxmq9PKY+W5AsgXIfDhY0qaAUGfk5vyprOFiF9M/tKnE4WIpL460b0E8Oafk3lsuxMLS1qmq6XicIFKHLnvw86zFrAy6nSQ5S23epZwerkPtQldHK6lJFx2QAACLjN+V61sMR14kJLcCK3S4Oz7pvsM66b3DkzclH0KyCUvSLa3INB0v+v1yG5wThFRj4+r+G6toXxhS07DHz1hT0/qAyfnfHB9rS0FSSNsQpEXCDK2XfP1eWVktFEKjIUplAlXSEjVjcd5XCqobxuMXy5JDZ+mjy0ribUXRsIQCASmBdcLb089bslZs02z7rUoxqymg4WK5so7LJjMmDs68+adF69QvY//T6Wfzqt0Z1LVcO20WcyjHbhuFgCM3vAGqlrDZVcxCojE6GQZTrdKmllEiY+smzozQ8R2FGN3/9YKp+HuBOTNivsoq+eiAW1XR8A6qJ375dblnalXARFnSYl99NxTiGgz3/2Vy98Nlcx/rL/zu3FPKdpX8X9+f99oWDbvkw73Vnrsf+j8r57qNSjEviusYmrdxYl/f7yylwWyoEgYqspgwji9ZBrlQ3ACrtFFNGP1XR+HVMNtU3atD0FYECOqVULjPalUcrgOiU24VguRozb43mlkm2ARCG13nf+XDc2dtBVlspE474fZY4Zge79Z3JuuWdyRnPJRLRrrOYGfXPDJ+T93vTN6Xza19DRLU97OtPZ6BEsuiKUIzt4xf/Hauedw3I+/3W9UU5Xa8XG0GgIivnwtClOolWysnawsVQ8YOXYbeIqJpR19ikaUvXR7MwoEJtbWhSfWPEvf4KsrGu0f9FHi55bITO8Kg78vePpqlb7755LxsohnRNII/nHf9Od9ni6QsFuUDMVeemnHh9lNIWvPWfHSzqfm8pAhpF6Z4Wud1Wm+2raduq+VK8ms7JbtcWhQaGBk5bXtD7K23UShQIAhXI7zhem/yGqynC64VaO5UjdXeohf1mt7w9Sec/OFTL1m+NuylAbLrf0k9n/WNw3M2IxUeTl+qw2z7Sl/OjLwD6yKBZkS8TKNSYImzrcSuXGNC6zQ1ascF7CErQ7KtiyhVwKlYwqlL7jl6tXrx2S7Trsa2obetaSdLWxuIXhH5iyCx169037xIlUV3HleM1MbODoWCmaerRwTNTF5lGOdYEKnFTGA5WefzGTRdLt959dX//r4q2/NHJmR82bG0oaDlsIqh0C9dE26mtFFadswkL18XcEqA0bnl7Us7ny+0GXXm1Jrcj7/hYx/41ewhKORXjtmfjvzd+cervrQ1NRevrlWKTyid7KZ/rkXfHL9ZJfQZqxKxwNTLduLW5XTITqK4Es4Ld93Fz/7qhKd6soybXTKAYGmJjjYCIux2lRBAoYtOXbdC9/abrVy+NlSTVpjaq8tuqSn4npfy+Akg69d6BuvCfQ12fi2OzfeiTGdntiHzjKZf7iPlZt6VBd74/parSh1FcZXiKKopqvNsH5JI1HCymm0CWIOutiXk8WNA+fXkMB0u7/uUvU3/PXbUp9WQh96lfHjVfHztm5SrF8dVvFfWNCd31/hSt25y+6edXnsLtdx2bvHk4dcmG8I0MwMoEqouoP7d6U7269e6rAVOWZT1XaL2vqH7WcrwmVgsdAZELQaBCOQ4ojcmiYRvrmiO6qdnBqmijckqNsyyDKNA1z4/WHe9NyfmaavupFqzeoilL3Ovk5PubTV9anJNlwSL6beMOIf39o2l6etgcvT1uUcwtASqLUYV3+4Aw4qiNYV9VkIuwuM/BTqf8baBeH7Mw9e90HabChoOZpqlNBdQwk7wDTolENN/jjW9O1DWOSUTK4fj6/oTFemrYHPXpNzXrOc/ZwVweL7SYtJ+2qUygaIJAU5P9+Xs+zP7cqWyXmK/H3AqRx73JxF0QPw4EgYqsxqoJVEY37Eu/fZdPp/vjKct8ZxaI++BYTvK9O/To4Jk5n5+5fKOeGjo78PIiu/uQ/H+lFJX0stXqLLCpAqGU44ydqA7luslF2a5EwtTWPIa12Ov0VcK+edI9AzP+vXDNFv3x9fFZryv0k/z383k69LaPtGD15ryX4dXdSZhmOiju0tKFazZr3IK1ea2zJJlAPs83JjuwYWb0cntllLPSun0t7Vong0B51ARyC0xZrZ21InsWy5oCM7+i+lXLMTGipowncioWgkBFVs5TzkWVjjp7xcZUnYVirqdUyvCnKjmrQ5Dvduv3k//ns3m6q+/Ukg9nsk6YlTZjnVMi9TlibghQYWqqMOUbyC26feHPb09S91v6hc6auNlWtyhQuZKYT34bXLJz7B/ZzPojU9DmfzipeZjVvFUFBIE8VmaaubMfTvnbIF38yPC81lkOw8FybdZeT+XKBCqWtq2ah4ONmedfwD3hiN64fgc52lsuN0Hcjg9xDxFLDxWPtRklRRCoQM59zTPtsow6nFHvaGf941P94KmR/uuNdK3F43lyqJhPEJ18N5WogyxRffPltpy8159sQA1RIEQk7m26VKy7fWV0SgZi5dwXCpk2/OVR812XGYbzQtdNpZz5ouo3Ok/1Vz33hbr17hvsvR6PN2cCFdYuLyWZIj7gd2v/iMX4vEO+WqGNAYfsubXYygR6e9xil2czOTNoQl9bWkPb8rz/GtX1Y1lNlpRklHEN32IhCFRk1kGqPDf4Eq0n+f9K2a+q6QDgpVRfQdgTWFTbbKV0IL1Y31ttpY9rQ9n4a9/ctdJaCre7fU0JUze9NVHzC7jbDrQUUZxnC+lCNAaon1DuN0CsfmShXX+ri+T8tJ9MWx58ITluTher3k0p+tGFrMKrfbkCS25vWbpuq370zCj9xlZwO6zamuCX4s4+c9ivoFxqArmN0CtGi0zTDLwtWt3paroCJAhUImUYAyq5uA86QeXTSgJHmYJ2z8J+bYV+zVH9TMXufi5fvzVnXQXreFLm/WBUkC/m+qeitwRuNTDGLVijl0bO1w2v5N+RBypVlL2XKIIKbrVMvNYTlX8OmKFDb+0X7ULl/z08/mnu2ogJryhQCF41bRKmvWRF/sv3Wnbc3K45/Or7uA4Hy7G8Lcl+2qwVG4O1qcBOqDM+6t7e9Gdcv7Uh87kyqQkUJNsvX/bv+KKHh2m/mz4I+M7yGCpXSgSBCuR1Ipq2dL3WbW7wnSEgDsVsyYiZK9V3wpKMx+KYaaIQ+bSzUj5byQTssAQtDhdVkM06iRfagSz2z33c3Z/o547ZNuys40m53w1Fy/DwwBn65Yve22MlsZ+Plqzbom69++rNsc2z7EW1P3FTAJUkzExJgZeZ/1tdLV231f9FBXhgwFfaVB++MK8X0/F/L6s31QdaTiHHJq+3mkUcDlZOx8BCP2Oua5h0gCh/67c0+L8oydlndg102T5voyPlptCaQFH9rG6jY4qxyUxatD5wwCsVICujiZyKjSBQkZim9L0nPkv9uyyHgxUhn+Hyp0bqupfGuq6n/L6B6FTaZ/M72Bb6eYJuW34nojfHLtQnU5ellxvZcLDyD558+tUKz+esuyjFCgIlEqY2bA3eMUHLdt/HX+mDiUtjWfemusZABTODqrFlKsxY1nz39sWRzXVMotqdvE73S9Zt0czlG6JZCVAkhfTZ0sMto+0VnXDPJ3pn3KLUvytlJHTBX0PhiUCe35X9uiTqPmxJagLl8eXmc4zPVeMy7E1ut5c9N2Ju4LY4ryUnL16vw2//SMs3uAdJax1tj3L/NE1TL42cr3Wbw/cVi5kYke+iq3HSCIJARTRtabqzV0XbVJaKS1ao4t8qKkF/c7/idL97dbyu+s/owhtkra+F/LbpTKDiLP/BAV/p8Ns/1hqfu5RAsV3/8pe65LEReXU03diHPziPU1EVtPe6ODnxnoE6+/4hkawDiEpWNkEhQ49SxVXdn99U15j3DYbPZ69Or6fMb+SE7WtsqmvUwjXZNcnS2csFZALlGg5WpNmiyiETKFcTvLPfctQEKrA9UXAOo3p62Bxt2NqowdPTNw0zfm2Pc1y+2S72Y8WkRet101sT9YfXx4dfjmtNoKgy/vNT7seUYiAIVKCgG02xMoHGzl+jkbNXhXpPbMfmMjgpBJHPgagcTnhRKvTzBD2UFjPiPmbeas/9LsrApGmaGj5zZUm3gXRNoOKctN5LDulcvZkgEOI1YeE6SVJdUzRDNWpsd26dmXT1jdHkgbesswFaulJ2Xw6//SMdfvvHeb23IdDc8ZXpe098plP+Nijr8ULr/y3fsFWvjF7g+pxpmum+Wp7bwIoNdRn/LrTmTBj5bLf5zHxnvWd0BHXzCt3XnMPBrEwfz2tMx8NRZrvUNTafk/2GNLoJWgqilMgEQsGcQaFi1wT69qMj9L0nPg/1HqslJZsdrMIqrudVEyj6ZlSF4DWBwi13zLzVuuSxz/SvgTMKWo4X+67z5thF+sFTI/XamIWRLDtIMMl6TbFnB6u++yIoP9EeXe13vp3bt7OIZr6qqA+JCuMWSClKTSCP97pdqwa9gZLR9iLWsuk3aYm+byvnIElb6ptCBaHC3kyctGh91mMDpizT2PnNgQe/j7tg9WYNcRlCPmVx9nIt9mzIfLMwLnsy8/qjWMMB3dT5BO3drnH8rnvsra5vTOirZRtSH2qArTRB9vtKc9B3ZgJZXcBG2+P2m4PO3yHKmkCFfOLGItYEyveGbLpvEE07KgFBoBKppsiiU2p8eYV8Bfk0s1I+W6kEDTAWa79Yuq757tT0pcWpv2Fv9YJkCvfCNVsiWXaQE5D1mkJiQEvWbfHsIJZjZtumukbd/u5kbYmweCfKX3pynGiu+jJmL3Iscv2WxkjWUSkzYaJlGz5zpUbPXZ3x2C/+O9bj1Wn5ZEs4hXlv0NONPQhTrBsUpild+9+xGUPPJOngW/vpe//+zONd4bVv08r3NVc/Pzp9/DOkmcs36M2x7jebzrxvsH70zKisx1vXel/mNQfCC+ufO2fFKjTIEMbDg2bmfL7QSV5ue3eSzn1gSFa2k10prm/stbCcN05T33dGECj9vLNZUU7UU8jEdcXsY7otOVQAtwz7v8VCEKhA/lHl5o0pzsji6k31OvDmD/WFozNQapWyY3m1M+f44hbW6ff6NAOmLNPVAWr0BL1gK9Ym4bdfRpkF9+CAGf4vCiHIftJUYGHotZvrdeI9A3XhQ0Pzen8cnhw6W8+NmKtnhs+JuynwYJrFKyge1T5r7TOmso9TdQ3RBBgr5FSHFu4HT43Udx7PDFy4ZTM4+y+F1Z9JLtNnH8iVoeLFPuSlWJMi5MpOHjt/bfAF+Xz+f34Stt9g6Oz7h+h3r7rXX3HLrJByZwsnbLOD5V9M11F4uAxnA3brj3pmv9l+uFFzmq+Zct0cCL0Z5vG92K/dnD9zTfL39fr9nf3JYgTp8tkVrWHebuoam7RobTQ3VS0n9Rno+5qaZESETCBELuqaQDOXb1DvNyYEeu3ouatV35jQvz+dLSmGYEylDQeLuwFl7OrnR+dMiQ0r6Iko3yCbc/HW+opVSycKwTKBrM8RfvnTlq7X0Xf2D//GmFnH0HKcaRHNHvt0lg6//WPPmUryEfWvbR+u4Nx/fnrKPpGso5wugAA/UW6v6ZowuRdqvwGRz+qLdQq3t3tijgvVUgua9Xtvv2kZ/25dmzsIVCjn0tOF98Mv+7Z3Jumq574ouE0WtxYYtpsAru9xeSLIthbVPuQc7uVcttdwsIxMIPvyQgzLdPPWlwu1YHW6YLnVlmEzVuq7EWbG2f32lXE6uc9ANTqyd4Jeu7q9LFc2lyWd1VU9J3CCQEWWqgkU8YXLL/47Vv/7wr3YW7kp38ttd277/7kPfKoz7hsc6j1RWLelQd1699V74xcXZwUe8vk89inNawIeWYp1Qe+Xzl7O22SQzpP1knzuhp7/4FDf39d6upyDZSg/fZMFxZet8+9wxcW6c+pWGLpD29pI1tHSMkNRpQrYjIuxB5Ti2sw+a9JFDw/LezmRB68DnosfHTwr49+1OTpj9s+a7zErqw/iUhh6/dYGvTxqvu+y/vPZPH0ybXnoNnw8eWlWDaeMJtmaGHePxu979srosTj7zNb375kJ5FhfOtslSD/T1G9fGa9vPToi67nHP53l8o7CWE0aMLV5G3B+pqDXC/luy9Z28vKoBdoaUVZwuSMIVCDnAcVr44u6EnolXptVSnDV7Tf8atlGl1cW39yVmyRJTwyZHcv6w7jr/Sm2f4UfDpZzas6Q247X/lGMwtBRC9JG6wROYeiWa8PWBv3701mR30Aohahn35Oi3xbdpoiP6quulHMdSqtSNotC9rV8aqQEvsNv+zvf4WB1jU05j6lRZMfUNyZ0qstMX3FolaOPYP+keR/73GNAGb/pTW9O1I1vTsy5GGfWRxjXvDAmq4aTlB5GFWbWR9fsoXwblod1W7KHU9vblFUTqMaaHcxWL8veYMcHCpPtYr1k5cb0TR3r+mjYzJVZy4yK14xnDU3FPYJan+KNsQv194+mF3Vd5YIgUMSc+5X1z3IqDF3qlvilXxbTC5/N1dAZ2TMm5JRHQ+OqbdNSjZy9Ssf+dUDByynW71LMbTnIsaKQ4WCoDHe+P0X3fDhNg6aHvzPakpiO/xcqXRMoe3awqM7T5XO2B/x510cpZKGZ//xy/hqNW7C2oPUs31CX6s/le+476OZ++t2r4zyfD3PD9rNZq1wfX7GxTvVlMp29X2HoQg95zhhTuuZM+rFVG/2nEN9ShMyLN8c2F1Seu2pT1nNhZsSL9qZG7ucveSw768bOazhY5uZmnx0s8/3ur/dYV4mvWx8dPFMLVm9ObQvOfTHoPhVFfat8pr2vRASBIuY5LWZ5nA9i4XZnoFRueWeyrng6e8aEStXYlNAF/xyqQXmkzIZS4E+Vz0nTvnn84+OvMsbwht903Bvglya6bkuDlq+Prp5JPoJ81PTsYMWJApVRzLpqbdjaXIzSbxrcclKM7cZaZmRZfLbCpc4hFl8t3aARs1a6vCucaqopgPIwb9WmyLa7KE4rznPttx4doYsfGV7QMscvWOvan1u+Yate+cJ/uJHl7XHew+vNEIfbhzyKO9cW8AV6T0yS73CtYOvKd9NxZoFkzL4YQskSXlM1St1X+PKo+erWu2/g7KGou2DzbfV3LPav0mt2MK/gZdZwsBCFod1moS3mqe1fA2fq1HvTGXRNjsyfMDN85SNjVrUqOYcTBCqQ8wDg3OGsDamUNQLqGxO67InPNd7jrguavTNukR5xmWIyn1+q2L+vtfzVm+o1dcl6/fH1YEXBvZdXYHt8DpBBz4uei3EsYNrS8DOJhFpf0ql/G6jj7v5Ejw2eFVsB4kAn5wJnBwuKTKPqUs4dn6iOsVaHdt3mBjmPhG+PW6zLnxxZ8DoqcAQfKtiYeWt0+t8H66UAdVfc2PetzMBA/htyuOFgea9GkvSz/4zW/72Re7hRUGEygbyOSaMKmIk3SFZWmN8l1yvt19RRBZncMoGC1Igs1ZThQYcurbfNcpnrPamRDnkMaQwu/S6vmkBNGZkGOYJ7AQu3218TV21IZ02g4geBqq/DSxAoYs7O33Mj5kqKfsxkruV9tWyDPpu9ynsMbok7qOU4ZaQk/eZ/41zHfebTzqINB/MacF1kfhdcUaXMZnQ+7ctyvO7JoXPCL1zem7rX4+uT2Rd/6zdN74xb5Lv8Ypw0gtyJtE7OUZUEGjFzpfa9sa/WJFNg3e5GobTiOG4+PSy//cxSjKamOtcRLdzqNL8yeoHu+WCaz6vzVGbnOrRss1Y01yz8MswU5jaZtfkKDP7nMRtsPgFe+7l3ZYDhRpICFXsNMwTG66W/fvnLwMsIuv4HB6SzjqIKsCVMUwMLzCp39oHssy+mH/PfoIoZOHf7Dhb7TEGeMdtWGcUGnKNK/ApDO7en1HDoAN+39ZocJYayXxCh5oBXeo0NjQEDbXlntVUfgkARc0aD38mRdhoX64RbqouLStux8umQFPurLLcAml9zgp40vT6X1/uDBl08C0MHenezIGPU3e7+XPnsKH04cUmINWUKk6Yb1Wbx2KezlDClCYvWpS4opPLb7qpJqoBjCSMKd/WdqpnLN+T9/lQR5yhrKDj+H6XR89YUYaml/c2AQje3sDdLcnELAhRDPjdA3g3QHw9TiH/knOyMn6Pu+DhUm7LW77H6IbbZV8N8t4vXeQc7Vm+q1xtjF0rKfxPKmiLeJdMk2BTr0W0vP3t+tOdzVlv++3nurLmwk/kEfXU+nzPXcDCr5JN96JS9n2yazZmCb33Z/DuH2T/jrj15wj2fZAR4A9cEKnB2sGpCEKhAzgh3XGng74xbpC317het5dIdrZSOcX6ZQMX5bOWaReUnaOab92gwj5o+YU/MXqnVRfxCB09foV+8ODbv94c5OUfFntK8uS59HKmwzQ4ROPv+Ia4zlIQRaearFfCMOBOomBgOhkplKj1Fc0HLKeJwMNM0M44xwYfieL+uJhW8CNcWp7WbCzt2hrkJFMRPnv3C8zl7ZlS+fQpnUMIIkWliF+Uxs/+UZQUvoylhBrrpaN/27HUsJWnOyk2a51KUuhBemT3236G1Y/zdlc+O0m9fGa+VG+tch+v5rSszqBTfyS3MLG/5sPcNquUUThAocu6bTrEDIL/53zjdkTFFd/nI96QQl/9+Pk/vTyi/DK5S8vutTNPUO+MWZRRRtnfK8isMnV5pkDHkuXgdyithG7SamOs7tE7O0V0YZy7XuR7EJ46f4M4CzyXFiLNEdQ4txd2+TXWN+uWLY7Qkx114IDIFbtP2c+8Xc1er74QlycfzaIpP4d0oFOOY6Fdgt5j6fJgelhpk9eV0Xt5su/n88eSlqRsIYdtYquBC0F0lkQj2WqvZS9Zt1bF/HaARtqnTz7xvsE7/++DM1wdcv9s6mtvlHnSzP27f9xKmmaojtHjtFtdMLS/WIv2Gg9mfn750Q6hsujCC1gQqo92j7BEEilip7gC6dWSXBuxwlnoHSc0OVtrV5u3RwbP0q5e+DPWeog8HK/kac9tY16jf/G+cfvi0exHV4IWh3T+HVyZB8OFghWcSRV3HK6jU3Zdcr4nwhsikRetSReSdy+VkGqM8amtEJd/peou5vVRSJlDfiUv0wcSlxas5BNhFOBxsTVRTIxfxWJAwzYJnIHWqcbmYLpXHP52V+nv6subhuL98cYzn64McCzdsbdAyn5lO7YsJe3xdum6rfvxM5kxt17yQbrN9eYEyanI8N2/VJvW8q78WrildrcJXRs/PqzbiuIVrI2/Lio3pDKPswtDN/7fXBMqq8WX/O0Qh66DDu63nx8xbo/MeHKJnhs/xXXY+AgeB8lw+haERnnN2sBxb39J1W1XXmF/nOoiw566SDc+q0CFNYRStMHRWXWj/g9TfP5qmbr37FjS7ld87rWUvWeveyQh6MDU9/o7qWOw9HCya5c9dFX2nJFWML8eXkM4EKvyDfP1fw7QmmcI+f/Vmx3ffgnfaMhdnd6TQdRejJlBUd78LaVvQ/S2umQUrwfsTFmtTXWPczUBIG+sa1a13Xz0X8AIvzB4QejhYuJcHUi5D7y9+ZLgk6YOJSz1fE+RYeO4DQ3T83Z/kfE1GhknID/6vgTP0qa1OkVNmYWjnerPXlWv9r3yxQCs31uddY9Wrb5kr4PfIoFmqSw4/8jpnLFi9WQvXZN58L8b2Yy/e7TVFvNf353zUChrZ4ylPDZ2tgdOyh9ClM4Eyawx5sYa+TV4czWy+Ts6aQDOWbfAsg5KPqCZaqSQEgSLm1UlsSpg64Z5P9LtXxxdt3V4HgVzb9fkPDtELn80tSnucKqUmkNNsW6FcT0X+aGEu9p8YMluS1BhluogHe6usO1ih3u/xsQqNyHu9O+qfyZr9L0pBMoGs7+17T3we6brveH9KRhYI17Lxi2MM/vsTluhbjw4P/b5iHOPNVMAzmuUFDlCbpu75cKq+sh3XggZ3GpvYcdxMWrROv3rpS/35rWim80Y4R3Td3vXxbz86wvVx+1Zs1Tt51ueclypoX+RMoKjFORwsrCCff8m63FlAUmHH61Y+V8wJU5q5fKN+9+q4QO3NdWxtlax+HHVNGOu3nrx4Xc7Xed18PfXeQbrsycw+mN85otDNK2t2sOTvYD/n2Fdh/+5NuQeN7uo7VT99LruYdthZaK1spGJl2zbYPuPWhiad88AQXf9ydv3NfPtMVZgIRBAoams8isJZx4X+kwsvWOYl6HZvf920pRt0yzuTi9OgpLiG1UTllyGK/F757Cgd99cBkaVUexdIjmTxBbMOtgPyLsTnnsIa1Rbj/JpWR5Xq7iJomzfXN2pBjjTj1N2XADWBisFeLLJctjO7cmxTMcSdmpzvdNNStMf8qH/uoB3aVZvq9e9PZ+vyJ9NDXoNeIDY5eur9Ji3V8g3+F2UtnZUBtNgjgxTF9ftzD9KOHdrkfI39sGO/mAq6R4epOZJaT8i9vDg1gZr/Xw71dr6cvybn8/Y4g2maemro7Izng9a0dA4bCuM/n83zWbap374yTm+OXaSpS/wzQ3Ktv7UV6MjzxqbXtmsFbHo9NCyv5bop9vaTnQmUfDxjOJjX0DAz8P65YkNdKuiW0Rdxedvns5tnybMyq/wChPlqtGUCWbPYDpmxMut1eQ8Hq/Br1XwQBCqQc5P51Uv5zwoUar0uFwhhDz4zlwfIcIlQnOfWrXnWuAjK6sQMnr5CyzfU6eg7+xdlPWGuCwv5vv0i6db5xvr/EsfY80KvX+N+fz7LaEqYge5A/OjpUTr13kGez6fGYec4IWXc3SnijlUOHWJEZ/HaLerWu2/By3l62BzXQGYxNher8GipawKlMgNsFx5B22DdETXVfO659r9j9MOn3OunVaNKzQqudImEqda1ftt/7ucD32wM9rJQy7S/PupjTTFrAr0zbpFvtondtzwysyz2c/6IWat0V9+pGc8HrWnpVUw4CgkznaXi/ErdfrtcfQ0rEyjfDMuM4WC27bsx6G8doi/ot0j797wxGRQ/95Bdgi/fYwX24FDmK9JrNBVsop76xoSO/esA/d8bEyQF//jW91nre4zJj73NVuAuyuywmowAeGSLLWsEgSIW+KBSBGEv2B4ZNMv/RREohxS7BwZ8lfd7g00TGWxZy9Zv1aRFwTsD5So1RKPAjoPncLCClpr/egvRkEjo0cH++9Toebnv8qXaluNLKOQOnp9iLjsK5XA8KQV7MceoFJLdY1m1sU53vj9FP3IUBbWL6jeyF4KM6iIl6E1KtzusQYeD2Tvk1nk5nyKjLU2lzRTa0iRMU21a+XX73X8cK0ji3A9XbqxzLdhazJsTRQkipjIkol/0b/43Tr0eGhbZzciXRy1I/V3IMu3HqXmrNuvTr1aoW+++GjNvdUHtk5q3tVoj/Xcu33h4mJ4ZNsfzeStw2ZDvMFt7Rpvt+N8UcHlhTmdhgohWACPMp2pKmLrCNiGLtbpchaHtfxup93mv1TrPDbWybDISgfzfV6xMoKDXuHkfeqqlc2lDEKgFCXrsKfVduPTFTHw9v0KGZwU5nrl9ssamRGocveXUewfp6/8KnnrqdUwK8k3m83UvWrtF93ww1XfYgzMTyNlMQ4YamxKa4DNTgpnxd/pfUY0pLmSbC9uCSYvW6e8fTc97fZYgNYHsv0/UNQy8fhOEs6W+STe+OTE1ZW5YxeiPRLFMq6O3YWvxi/s2eXRsCxE05dstYBG0E7rUVo/DbX1zV25St959NchW8LMapKcPr05x9oGk5v2pdW3wbn9GBoVL4WTTNNXzrgEZtS7zCV6H/VZMM/PctDhA/Ru/9aQz/zJftXZzfcYwlEJ0v6VfJMv5W79oZh20BywWrtmSmu3rwxxFqQMv2zRVa2UCOb5T5+8wYeG6nMPLrG026jqXQZcXZmi27zmiwENAwjTTwRnZMlU9lpv51Zu2YY/e63B+XPsxI9fHS2UCFSkIFPir83ihaWZm6788an7G8/ZWV8s5iiBQgUox3WxQXh2MuDfmcpl1IV9BfmK37/6O96fo2L8OyHgs39RFa/HOpnzzkeFZ03Racp2MvLaVbz0yXP8eMlsTF+bOVkot2yuTx5Du7/+VvvHw8Jxp0N6FoT0ez9kq//cXU11DNB2UsDWBoh6ylTnULNJFV5WXR83Xy6Pm66FPZhS0nHILxFmtWbmxLiursZgtjWrZgY8NyRVmZPUE3MWtWWy8jrNfLliTfN2igI1pGcrhhlC1cPuOmxKm2oQIArkvN/33xOT+/974xZq5PPzEEOllhtseilMY2n3ZR93Rv+h1MwvxvE9tnlyKWQTbNO1FiAtbVqtUJlAi8Lby7nj/ukjFmMUxn+80zFuc2+dzw+e6LdH2V2Z/zkj9JrmuDzL/HTSzxwpIFSsT6OcvjCno/Zc9+bn2ufGD1L9vfDNzggJmB0NoccWA3FYbOBOoSMd9v4NzpXb78i0W1m9S4XdT0ndOHXdSkt/1+AVrPafpDPt9j5m3RsuTmUt+abepKcpl/T+TofQ0kcvX18mL8wSVuYT8pWYoCbDeqES1xCAdXPvFaOT7s0cqcblwa9PWhqaym5bbak2+7SrGcLAoTlf27TPXFMFRiypwEHh2sOQvaP+8Xh38/4yYqy/mug+hsJaz1RYkTs9EFKgpsRg6o3loyLSl0U33W0b3zFok+9frdtxpMs3UBXUgPtvnNx5OzyB49v1DtGz9Vm2KuIZXHs3yfl+ON+aaaruYwdpC94lCjsFeCU7DZmYX2w3LNNPBm3zPgS+NnK8ZyzaodU3zpWpDkxn4OufXL3/p+rj96w5aviPMTxQmEchwfTQ352+W2t88+tIZf8sW7Ex+9v4uk7o49wF7UCdXS1Ozg8UcTfHq31sFrN2MmLky9sk44kAQqEBht5n6poTr+OkoBO0kF+tayWu5lV5xPehwMGdHIZLixI7vLsxPF/Zu2ZyVm1J/+6XJWk97/uZGOiU0VwcgbCZQYDFkn4Xt6Hhm7qWyvoIVhi5m8eZyy0Jx+mzWKs1cvlHdb+nn2emLS8GbcJl2SOybmzPtO+rp3J0d2CgE7Z9a67YfCr32tdvenaxLH/8s53Iy2+B/NzZu1k2ML+auiXCpuYPzKIz9e3ULWCZMqTbP40qQt30+e1Xq7xdHztOHE5cEWnbY7cFM5HeMyXU+M3JkrRTzBkOchwCv48+0pRtyzmAadNlexznrPFHfmNCWeu+aRje9NVEX/HNoKpjU2JQIdczssm1bSdJRe3ZKP2jbkIP+rmF2Gb+aQLluigbhdSPC61zpzOx2buc/ez57avjljjIWQQs9WzWWipUJFFQ++9TlT40kEwjh5RPgyH867dyC3lUsViq2byZQpfb8AhaGfm7E3KI1ITX6KsB3GOa1dhl3SIJmAnnUrzEMo6ALnUosDL00YF0CvzZY32muE1JmECjUan3ZO8plllyT5bInP9fZ938qSeob8IKj0pTTcXPWio0ZHUznNmqm/h9No70zBfMXtENvrS5jXwu5Q7w/YYnOfWBI1uOpIrvl9OOWQKUPDa8kbvdxEgkzQHA5/fyQZDaY/dwWdJv995DZ+sWLuWfLPebO/lq3pSH87GBFCCO6FYK3lHOwthC5giBXPpsuM/DntybqkUEzQy3bXhPIay3feXyEDr7VvU6S1bZGWx2rhoQZ6rc466CdJUndd9su9Zhftpwba3hvEF6LXLx2i1ZvqtdLo9LD97Y0NIXe/p3noMP22C7rNc5MoPQ1gek57NHuzPsGZ/y7VY29JpD3+6wAVW2Ne2hhbp9eOnqvTp7vz9ejg2dq5cbCgmtS5Scs5IMgUIHyuakyck7hlfcLuUlcrHHAXkv1GtJUKYJ81abMrNdF8TUXUhg67NdtX5dftpp1ArHOR6+PWZj1miAnG6+nIisMXcB7wzZh6fpwQSCv7yVdE8i7AfafJ+q7lJlDzTKXPWnROp3cZ6DWbc6v2HE1qUld8Ob3+6SGg0XTnOZlFrhbXfDg0IzPk7Wf5hmA9pLZmY1moUE6evYCkpkzfYVf36K1W7IeS6fkh19eyRWhv1CZPYHyl3GB6/K7NSZM37vd1/43XXfDKv4+bsHadKH0gluZtnpTvcYtWJv6d+taQx3btvJ9n99+6NV/ybUpGzmO18W6GRJ35kGuvsOsFenM8BdHzg896YVpyzrzCp5PyFF70l4/MzU7WGMiZMHxdGbq8vVb9cLn8/RPW42+Yszm7NWvO6nPQJ1w9ycZx/yv/eNTHfmXj0PtUy85ihlPWtQ8XDfQDWLZSiWE+CKD1wRKBoEc/YLrz9pfvzvnQEnSW788OfB6g7q333T94bXxqX/7fbKtDU1a7HJersIYEEGgOESRMVJQEKhIZzT7MWVTXXrmmEq/+xd8SuHMFzpTKqMQJpAW9u6Vvfl+73VuQvaOnNR8LE0PB/NejtfnKfRitRKO5V67ofXdb6zznn3JfgKPOpsgI33Y8dw/P5mhRWu36DNb2n+p5eqsl5OCL5qKshGHW+gyR2CzvimR0Yl1HvNSmUAR/TRe2+K9/abp/AezM2yCCHIMTZjunyWqGyhBinPGrSiz01l/lPHnbins/bzOHZuHxSQSZl43WFrVFO8euSGldrbeFxwc6Lg+fekGrc0x6+IBf/7QdUbYXEv2mh3M67EotPLImCiVYh5/EqaZqg3jXM+Pnx2VcY3gNGzGyowMISuzpDFh5j0M8Li7P9Etb0/KeLwpYfrOYBtWru+03jGcbUuD91A4L6M8kggys2btNy4ys2mtTS7MJl0bsiaQs+7Y94/bS7/+2gHBV5iHzXXp73LZ+q2676PpnsHHX730pU7qMzDr8biDsnEgCFSgcpodzHl0nLJkva51qaZerOO+/SA0eLq9WF1l1wEI9Bubxd0WzKw/QrwnIHs3z69slW/nwZBnB8DOq4Cd13eZ6ys2TVPPDp+jFbbgW5wz5nnN2mbxzgQKcpFaWHZC7mWn/3Z+f6l6RTEe9qw2bI1oNrZi8/s5t/p0BOMMdp3lSAuXMgMhWcPBrOwZl42yoSmhDycuCfV57K+0v+3RwbM0bWnu2YhWbKjLu6hxwnS/2Ag7HMxLkGl6W6JiZJPAnX1b3Xun9pKkbdu10jeP2j30sh4aOEObi1Tw2X4uCXpauezJz7Mu6J3Wb3UJEuVofFQzWYWx3TatS7cyF2GDQDOXb1C33n01aPpy39fOW7U5VXTY+Z0On7lKA6Z6l8V4cWR6yFSNkT6vNLjUBJqy2PsY7/fxFq3dnFHcPAp+AUO3Z6M4x380eVlqOWPnr7Ut275uM9XPD/Pbt3aZUXDPHbfJeiyVCZQ8wW3TulY/O3Uf7dEp87XP/eTYwOt24/Z92eOpF/xzqB4eNFPjPAJ8XtveZNu2VO43GaNCEKhAZRUDcnms3+TsGapKkglUb4/ym9kviNh74xerW+++rnd/pMLGegaMARXlzr3XIoN8lYVkAvnd8Q6y7IKKn3oNg8uxqJnLN+ov703Rr17KXYegVPxm7fCuCeS/bPsuHP3dvMw7R27PlcNhb0Nddie/nGYICzIM9v0Ji9X9ln6uwQpr//nj6xOK0r4gNrkU7Vy0Jp1GbXX2Nmxt0EOfzEjdCbz6+S+y3vfIoJn6xYtjXWcj8eLswIZx9v2f6vwHh+ZcppeEabquL+p9LY6O5ooNdRozb03J1ysVZ8Y7uO8b9qEufzj3IN136ZE6/7Bd9cMT9ta0O8/X2QfvEnj5Exau04MDvvJcV6odeZ3qjaKUCnC7cLWvZeTsVRnnaCMVmC3dxrnLdm1Lti43YeeoGTitOfjzk2ezj+9Od7w/JefzuWag/dA2s27ClK5J3sxubDL1smM41KujF3gux+oPeP2kvd+Y6P5EAfy2H6+nj+i6fcHrtrLH/9ZvWnp9GSu3b+fZ7x86w73Par/ZY5rSW18u1PYuAcxGR2FoU+41yM5I1mrKl9t36JykIh/W8Fepem5UEAQqUCUUknK2sFg1gexypXqGMWHhWj08cIbv6/6THGI3Y/lG1+dnrdioYTPcp73064gHqyFRmgvjIL+c9ZpCfma/O95+tSxqDENWRmjQC3N7RzCf79LqVKzb0uBb/DLIxVex922vjm+gi1Tbd7pwzRbNtc3sVqiMTCDHc+lMoOi/m411jaEuijfVZQcoDr/9oyibVJAgF7xWxuR4x3BKqTgzbETxs/3w6ZGpv61sv7/1m6b7+3+lhckA0TKXGVCsMfirPQL1rjwyBYNYl2O4iO9qTbkebKMKMlqLKcW52OmbDw/TJY+NKPl6pfK6adZSTFy4Tk8NnZP1uP1itGPbVvpOj64yDEOGYahd61ptt41/7R27IJlA+QRzDCN4hunb150curB7xmO2B7/3xOcZ2bpxzNi3Tevakq3LTVPIomRRZrs35jFLcmMioXsdtYlyldewhq17/aLFKNng9pXaP6vbPhLVFuf2eZxDqlPZmC7buVcBbPsrR8xaqd++Mj5Vi8jO2p7sARm/LebWrx/i84psbudNt22zrEbqlCmCQAVybmMH75Zdqb0o63XZtTwzCxz/jiqlPdf63YJA+az1Gw8P130ff+X7OvsUkm5Gz1uTcfFi5/d1BMsEMiPv4M5esVEPDmgOgAWZetk0TfX5cFr67kcBpxa/i51AmUCp4WDer7EvZuWG9MWhV5Ch0o7pXoFHyb8mUC72k+DFjwzXGfcNDts0T/Y7yM5jhfWvqH+GWSs26rDbPtIrX3jf1XNyO8ZszjHdbMkFGPrStlXzKdheBNNSUwED1K0CkBu35h/0HzZjpWdgyL4v+A2bi5Jpuv9u0Z06zYiXF9zikLMYRilVlLRq7rNmWrDGpRipi8Nu+0i3vpN7uJPlooeHud78sp/D27bO7uqH7QemZgTN9RqP6/pcM/cYjr9zLb/GCB44CZtll2t2sGLZpk3cQaBwr48i28KSzwW6aYb7XUPdcIiIW/8tIzPHpfm5biLfeEH3wP1et+nn129pyCiQnXMWvAAFvHPdXGmyXacs37A10I2bPXbIHlaWS2NTwrXttTWGdmifmZ1UyObad8ISrYpgxrFyRxCoQM5tbLft28XSjjCKNztYerluwwgKXe3Pnh+tbz7iPn63la1wXBBehdPcBDkAn3nf4MgzR370zKjUtNfZGRnZbV64Zose/3SW7TXh1mcPvPgXhvZPebVOwLk6m/Zt5r0J6bsQ+XyTbplEQ2esdP2uStXNm7ncu25JVDWB/Iyeu1rdevfVxBwzcWQs2/Z7OYMq1nqjrmc5Y1nz92Slm+dixNBZz0eQTKC2rZovAupcgkDOGTaCGrdgrbr17qtJi4L93n5ydbrD1NFwOz42NiX0w6dH6odPuQfo7Yv91qMjNCDEUDIvgYeDubwuqiwBK2vR7bvdVNeobr376q0vs2dcrHSVPklEof5om8Eml411jXr+s3n+L8zBfnxs4zI0KuzMSM6hNW4zRHrtHxd79NskSUbmfp7ruG7ICJwh6WzKj58ZldE/ylp2KkMi0OIj0T7mIFDokgERrjufmxwJ0wwVOE99vBL+qG7tGzV3je15j/6ox/n+56fvF3ibdwvQXGkbumfKzHnODnJMyDWMz9p373h/io776yfNy/Np+pZkH/OoPTtp0B/O8F1/fVNCY+evyXq81jDUwTGz4CdT/fuTucxdtbmg91cCgkAFcu63uWb0KTavu2ub6zPblG8m0KWPj1DPu/p7r983Eyj/A3EiYar/lGWuwyYkWyZQwPTWMDVVggR3tjYkIs9SabQfbE3rf8G/w9BBINvf/plAPuuWmRrqkivoaH/Kvu8UesPJ/ls4Zy4LKopgac4sKI9N1f4e63f4YOISLd+QvoMfJou7f7II3rCZ3llJdvaOwE+ea+5AzF25KSNbJeqAp7XKIHcazdS+4K6hKaHHBs/S6LmrIwuESM3ZOn94bbyWrAt2Nz/I8aBNMhPINQiU505gBUoGOQJqM5dv0Gezws/qlutYEKT4u5P9lda2NmWJe3FPZ5Bk0PTlGhlgZrqgv5GXYtcEuvejaZ7LW5LM1PnXwJmRrKtQxbh8qtYgUCmHJ9i3LddMoJA/QjoboPl9R97xcdZrvBa50CcDytrPDcPI2S7DcK/14+Y/I+Zqnxv7piaK+PSrFal9y2vZUmlvLnRok3tInlXQu1jCBgL9fscwAv6MGUyF+31SmSnhV5W3hGlm9NWkzOHeQ12yw4d8tUJ1OTJdgx431m9p0Bl/H+T5vD0TyG0/C3JMaMiRPtboCBA1JUzfvqJ1o7H7rttqn84dfNdf15DQ5U9m3zT6ZNryrO3zn5/4lxLJpXVt6Y7XcSEIVCDnsBWv6ftKwWv//e0rmXef8j3HfTF3jVZu9E6vtC/WrV5HIR0/vwtyKxMoV5Q6sy22TCCfC+qo+m0fuxTpzsU5zWJYOQs4ujxm/5x+nQPfk0XAIFvmNpMOAuVTc8brZLMlzyFCUVzw5SrA2uC54aXX29CU0IatDfrli2N15TPpOzph2mYFfYN2upx1ArY2NOmM+wbrd6+OK9rFWyrDKMTv7vUdXPDPofpbv2n6zuOf6ev/GhZJ+6TmLKXXxyzUre9MTj02bel6vTRyvuvr09uj95fWJsdwsHyDQOmC1JnOvn9IzvoJXnIdC6zj6Oi53tu5s13Tlthn4Mj9HueqXxw5X9974nPfdZ14T/b0r6l1+r47OUW8ywujukBcsLq5s+p2CLDu+jo71EFsqW/SyX0GasSsYAHfXCqh3mGcEglTVzw90rPQ+ca6xqwbcFEOp/HjlwkUdlsOUmMrn3OmvW2G4d+uoP2ip4bNkWl6zwTkFEdNoPZtc2cCdWwbrm5TWGFrAkVZQyefgGjYG9hxZAsPmr5cx/31k6ybMBavIWqLcgTYgmYCfTZ7Vc7slWSotflvjyCQ33C7Ph9O83zutTHZ2at+P7M1HOzIPTvlfmGS2w2zYmkVdcp7GWr5n7DI4uomFRKYKN7sYOnl2js/fnfug7C3uVvvvlnPWwfJpoSplRvr1HfCktxttf3td9IPerLy+14fHhTuzq5bhzFM/yTsz2zv9OeK9je3I71wt05wxhTmAS4ipcxMoMKHg9mHtrmt1395dQ0JjZlXWFDXGs7n5mGPO/329m6qa0xdDC62ZTeEyVKyfsqg2/H/OWbMsDoz709Ykv6GIz7wWftOmOOa11cw06M4fKGs3dG+3vMfHKqb3nKfYSQ1C0eOXaltETKBggxDCyPXcS1hmpqzcpOWrg9eZ+Y/tmEufttxFLVj8jnfmab7mkNeM/lyO/fU2s5lYc1asVGL1m7RXe9PLbhtxeAVoKxE4xau1dAZK/Wz50e7Pn/YbR/pmDszM6fD7tP5FM+12Let1q3cgkD5LTfXLptP13LgtOWO/lju14e9MAt6PrB+mpkrinP+cONX32jbdsUOAoX7waLcb5eFOGdY8r10WbQ2ugwmP2uTwyQnBBx+b9mQYxSJW3kNN84MJKeNWxtzzg72xdw1OuIv2Rl+xXT6gV3U99en6PvH7hno9W43zIqFTCD4KsYsOcVWrDsdzwybm/o76EErKL8216aGg5m66rkvdN1LY7V2c46spQCZKukU5WjaGPZ7t3cYTTWnM//yRe/pz50n9EKmH/a7C23vQLp1gu2/f66OxqDp6SkpM4YQRrhb5Xshecf7U3TJY5/lrOtTiNkeM3rZg2Zn3/+pa5ZMuGBg+Cwbu1/YtrnUPpHXkrxZnyfMRVKpp9fONauG6+t9nj/gzx/o78mZTuoas4+XeWcNpApSF/791Bi576Sv29JQUNDN9yIkgp/YOUQ4yO+XMOVafyjqc6fb8pxDm1dsqPOshTR1yfqM81yq0HgBwYNiMnLcha40GxzF0H/63Be658PM4NvWhszfwb5PH3H7R54BZMsal7o7Qdk3AbdMgt+fe2BeyzXlPdzS7/O4eXTwrPTsYJL22rF5CNRhe2RPstI8HCzccfGzWasCbW/W+dFtprVi8Tsnd2ybPQ13lOI8TNz9gXdGiZd8z2n//nR2Xu8rRKGZ/PkYPtN9qLR1HLrupbE5h4Ot2FCXdVwLyq0PIwXrKx66+/ap/tV5h+6S13qKoVU+YxYrTMv/hEUWWyaQy2NB+1XFCgI9MCA9i5fbHaxCVus3PKl1KoU+kRoXmmtomP1k4rXofW78QK+PWRg40Od3QRP28zs7bj9+ZlSqvo3bopzfUfjC0Om//TKB/Lah1bZhg7m+lodsY3YzM4Hy37OmLd2g295Nz6zivv7gX47VEd/a0KS3v1wU+gJm6bqtru9p8LijYW/vms0NqWyJhqaE/vHx9NDD26ztMsqhCFEHv602BglUxZVN4D+4y51Xx9V+fHIdDpbndxxlJlDClH7zv3Gez9/9wbSMIHCn5Owcbqndbh/HHvC0ioPbRfEbvxpixrnUek1Tt707OevxqCdVyHVstPaJK54eqaufH+06O9oF/xyaMVlCruGFcZi/arO69e6rwdMLK9BZbmYs25AxvbjUnNHid7H5jSN3T/29fmuj61BSe1Bv1aY6TVq0Tr/47xjNXrHR97xsZ++PuB1XD95tO/3lG4cGXp5dmEyzULP6GYZeu/ZEPfWjnuqx1w4uy0qEvjCbsmS93h3vPvV15qpLPxzM7zxa7EygsJ817uCt27Tk5apcs0isvnXClPpNyj1iIowedw5wX1/Ir+Ghy452fbzLtm0lSUNyzLgbtaDD8CoZQaAClVMiUNDDcynGybqtw3kxtGD1ZvWblLtOTnpIhd848eTsYE2mVlmzUgUsSJzrxPbSyHmBixT7lXAI+7XX2tKe53hkjdhl3/EOtz77x/SrreS37I+mpH9Xv47GhIVrtXTd1owLl0KPvbNWpL+vqDou9300XTe8Mk6ffrXC/8U2J9zziZ4eln130fl7bapr1O9fHa81jgw262UbtjbqXwNn6l8DwxW7sy5co5hy3CpqGHVnMNXGAAfU1PDSgE3oN2mJ60w2YaVnNgqYCeQyfGzt5no9M2xO1jKKUhMohg67lQrflEhoa0OTfvb8aM1yGV7x6hcLNGDKMt3x/pTUY+c8MCRrRqwgFylTFq/XmfcN9qxZcss7mcGcIN+K81ht7b9Rf6cJ09TM5Rv0iG2osLUKK6jvd+yfZ6sBYe0/QYIFpdg+xsxvHk771peLmtdpm6q4kg2ennkOCFpwvYNPDRhJOuqO9BCy1Rvr9Zv/fakPJy3VWf/4VLe7BCa92Pcdr2PJ1w7eOfDyLKZphprafGyOunipZdr2yl22a6ezD9lFN/U6OOt1m+sb8wqOBylobC3Vq69ZjHpObVyG6dkVuyZQ2MLQFb7bllQ+9WTu/+6RRWhJJus3TyRMXftf75EFYXlNihT2hm7bVrU69YDOWY9b07/faeszFFsc2VylRhCoQKWc7SFDAestRa0010CT46ELHxqqa/87JudyrH6Mc3nOf1tR9/Vb0xcCQe9y5Po+AsxwaGtT7o733ACBHLupHjPmeGlodGQChTxl2zs5frOs5fput23XKqOT7xd0/MbDw3XqvQMz3uNWYE4KdnHjFNUFh1X3JEiBTKcRLhcJ9Y5A28uj5uuNsQszsqOk7M9s38btzvj7INeptlOFoSM8Vt36TvCLkSCsC9IgN3k3NzRqybotgffva/87Vr97dVwBrWtWkxpm1cxvW0wNfbE99qfXJ+iO96foS8eMdW6fJP+aQPF3XBqbTI2Zt0b9pyzTLW9Pynr+L+9N1tXPj04FByzOSQyC/MQPDPhKc1ZuymvmMy/OAInV8bT/5Pkci5wSpnTp45/p7x9NT9XRs7brpjwKQ1vN9soEGmGbITCGmqkpUQxVjJMzoP7+BP9MEyn88JsFazZn3NBwzvB4xdPZx/v0umxBII9jf9cdws8+ZUrqtE3wYUphAkb2VrZtlf2++sZEXsfFIFkZ1jW71zGnGEfVtj5BIOeU11ELXRg6jzo+1SqfTKCzD8k9FCoKVt++VBlvXt3On5zcTTt1aOP63AtXHa+5fXplPNZpG/fXFsrKXnZDYWj4i7+/neJ3Zy9dECzYzm+apv7Wb5rmrQoXvJDc0+adj4QZe+pcnrMDbnUM7Hc2cgUfNjtq1vy1r3t02VTwoS9+dXS2hEiL9st8cvsJnWNlw3by29umKy1kOJgzMBqkHQ1NZqDtMp+ZvtyWm8/5r5AhUAOnLc/6TZ3DwazvzXl3zrkd1zW4/zZzV212nQY+PRwsXJtzmb/aewaKfIQpXv3vT2frxHsGhvoNc00NHJijoGKvh4YGer29nVaWl/O3d/ssUc8OVkpNCTNje35v/GK9PCo9LCtozbiwn2FrQ1MkwRm389emusaMoS3WFO6FZNSYppk6F9305sTkY83POWcPDLIaK7jiFQT678h0Ue6g7Y7yWiFsFl+5cg4TCHozMFf9wYcHztACx3HVWaTfyTndtL0Z9m04iixQi2lKO3YMfkHml+0iNQ+ZlvyPefkGgWoDXMxZX1cph4P5DTc5ZPfmukgn7bdTUdYfdlTAFwFmgYxbuQzhWZhHMepSJBVYN0vivAkgSbdddKjG3HJO4NdvnyNYU4j9unT0fK5ch/RFiSBQgSpxEwl64J+/erMeGzxLV/8ns/BvkPe7BTE+nx3uTq19Pc4bFs7Cl1bE1l6LKNe53N4W0zT1pEcxQNM0gw8Hi+Co+uLIeRowZZleHOU+7bTFmanz0eSlGYEtKfzFif1zug0Hs18A5fqozvNY0E7V2gBDdpyfMYhCfxar+fbZofKpueHc/p0Xqw8lh3k562k5g0J+wURnDYZEqv3FOVo1JczQWW5Zy3AMWdtS36Qb35yYcxhXmO07yIWIn3Stneb1frUsd0HkdA0h23HM+i0cBxUrgLtuS0MqIyTf3yvKmkD5akqYqYu1RMLUvR8FLwJqfb/3fTRd597/aaj1dr+lny59/DOfFfgv57kRc7MeO/S2j/QjWx2YxclOfiHfc8I2C9nb4zKzSbIK/QdouPWWugCBsCEzVqhb776attQ94zTKw0Ul9pO8jJy9SpscU78H7SN43Siat2qz7vv4K9/MaD8ZE17kceI7ZLfsYsxODU0JbR8iEyhIssk3Hm6ua9Wude7jdNcd2ucVBAoSGLBuMHnV/XI+evnxe4Vuh5PfZzl09+30+Y1f04tXH1/wutxE0Wft/9vTtG2RM5bCKJcSHfkUo44yW9uLNRNpVMOBex2xW87nC/lEc/v00pUndZOUHg4WtVzfOYWh0SKF7Rw4Ay5B7rTaT6TWnxMWrtNXLsU/vdpjX09WJpDjItyK2NqDF7lOcPasmVxfR/NqAxaGzuOg2vOu/hkzavz5rUm6+vnRrkMo7JydyZ+/MCZrGFHY8/vwWem7im6/8Y1vptuZK7Dj/LaCdjS8Zsuyy+cuv2smUOilpD/X1CXr9b88is1KmTMOzXDMqmQFwZxBn7scWWpeY6+dy7FYn980HTOwReT+/tN1xn2DNX9VftlBpmnqL8laF1af+LUxC/TyqPkZxeaz3hdiHVEEgexBGXsNFy9umWPpmdoyH2+fHDJx5F8+1hl/Hywp/zua6Uyg+KJAs1du0qK1zdtDY8LUgtXB74o2JUwtWL1ZDw+aGShjKF1Dp/nYMM4x1C79OlPXvjBGf3pjgu8yRwe44211HgvJHJi0aH1WQNlZE8j5eC5Wx94rSG0fKvjBxOa6bWMC1GzJl7PNqUyg5L831zdq0LTiFo2esnh9JDXBpOYA+/ee+Fz39pue8XiQc5fkHcC3sqLrfG4uLF9f57uOBas3a/aKjaEv8If3Pks/OnFv39dtrm8K1bdY6hhClCvQ4zYETJLm3HOhhv7pTB3edfs8M4G832P1BTelhmMGW+YVJ/h/V/t27uD6uHU+OmHf3Bk+rWoM7bp9u6LNQhy2JpCb9m1bldXgzrMPLv6QKut646/fOkxP/ahnZMu1/8zfPnoP3XvJETlf/8o1J+S9rigy3nbdrp2O7Lp97hcVuO1aN8U6tS/OcLDGREKn7J9dg0gqn6yyYiIIVKFyzQ7mTCl2Ph80WJGezjXzcb+OiiRtqmvSH18br9Wb6h2PZ1+EerUnIwjkCHo4M1WsdF97hkyuz2nvJOcsIC0z8DEsn7sqKzfWu84Q4sft5O0MKkimLnvicz062P+CVZIeGTQrvXyXO5ZTFqfvGOeqV+HMYIiyEHk+y4rqjof1ufrnmDI7l2eGz9XVz4/2fZ3zMzqLkG70GUbpDBKNTGYg/emNCTr0to+CNDWUUXOai78u2+A/5Grdloas32Phmi2p7dn6jq3AsH3fdGaBhcnG8qu9EIR9OK01tbvFbRtL7QWm/XVZz0rKDFIt31CXsT6v5Xu3s/mN9v05DlZ9n7CdzUmL1wc+Ztm94VFHzPLBxKXqNzn3RASWIMHSMfObgydRXEjZ2YO2drnW8v6Exfpq2YZQF+dWxqH9Tuh/P5+nv/ULP3VzWNb2/MfXJugnz33h2Wexu/7lL/V2sobU4OnLA8/6duFDQ3XZk5/n31gbr03ZOSzrGZeJACRpS332dtV/yjJd9PAwSf4XHVsamlKZnm5TJRuGdOq9g3TWPz7Vb18Zl3NZTnt02ibwsLH1Ieri/cxxzvvTed09gzJeASLDMLRncur4fIKWr+TYVqzj5Oa65u/T67ziPAYH6Rc+f9Vxro+fdkAXzbr7Qh22R+4L6GIPD8onW8zJUPkMm/nbJYcHCs45nXZgF0nSKft31rkB6vJ07tg8U9URe3TKu47P2S5F2e37xf3fO0rfPXbPnMs4ft+d9NVdF+gFj+3smSt76iiXmTql5pk9C7VNm1rfbbTQLcPq94XJPnTaxyMYKzUfW/7x3SN1aY+uWc+1JhMIfuKKgOda7/39ve+eS8GLE3rVEAqSjTFn5Sa9NmahHnC05Y2xC7OKKHpd2Gdk9fjMpmN1nuzvyXWCCxoESiSCH8TmhcyEGDoj3CxTfjY7OpimKX02e1XqruWbYxdqjSMo58XtN7ZnFmx16YBanOeEKINAKzbUacy8NZq9YqOGu9S/cVNoDMiqf2NdXM9ekd/QpwEBg0d+F5V+mUDO7XlxFPVwXFi/q/W9LFu/Vd1699WHE5unHZ29YqNGz12dev28VZt05F8+1vOfzctYjv3zWh0Ka5n2j+KcYW1QiGmn20RwMreC4m61zNy2Mbfjp/WX8xrIbx/JdxfKJwDa+4Lu+a3Mg1+tNKeLHxmeUT/IX/Py/bKGwhR0DzLsdObyjZq/anOo7Dq/4S6ffrVCM7OC+c1ynad+9dKXOveBIQrTI2mwAq+2jfHmtyfpscHewcOhM1aox5399b1/f+Z7HHpq6Gz97tXmQGD/ZAZkanaw5GusrC17QOPjyUt18SPDdeCfP8xY3nvjF+uGZGDjyme/CJTRZe1XU3wmWWhKmLr8yc99i4sHDWje4ZjBZt2WBr0zbpFrJpA9SDJtaXamtFP3W/rpiNs/0kE398v5uqDH/W8fvYcuO655aFPQoIMzCB5G61pD3XZyL0jtzAR67ifH6sHvHZX3uiwTF63zfO6hT2aoW+++WUP8nOy//LbtWrkW4N+vS+bF5q7btXNd1nbbtAqU0VTs2YnGL/T+XoIyDGnbdukL9GIN2wlij07tU7MDB3X+obvqW0fvLknaqWMbPREgsyeKX+WiI3fP+PdLVx/vOxxs0B/OyHqsTasa7b+ze12bs7rvomeuPFYPfO9IdQhRoD2otq1qfAPXhd6Evf6sA7Rflw4679Bd815G9123zfn8Ltu1098vPTI1Db2lGDMClhuCQAX6Ys5q/xcFMGXxeq3c6J/qK0kfTlyi8R4p75J3N3Dp+i0aNWd14I6M2xTHUrg78M596L+fz9evXvoyowPp1Z6MTCDHVVBWTaDkydLetlwX0/ZsJme2kl3CNDXVo2ZCoa54epT/i0JwZkettV30LFi9Wb97dbyue6l5Ski/TeDjKbkDFls9ihM3y/zRo7xTvqm+SZc8NkJn/eNT/cA2E1bfCUtSxSWdVm2qz5rVIsx56Q+vjdf/Rs0vylhzt0ClX0DA7+LL7/1RfQ4rY8Dax63Z7F74vDnIc9Y/PtV3bPVZrCETAx3DP+zDGJ0XIS98Ps/zWOcMJuWSzx2dMfNWZ2QoWE1z284+mLQk6zHn97xmU73nZ/ELlEzKcRFjN23p+ozZ40bncdf82tP3C/2eXIpdaPXz2c3nYL8M1TCp3ZvrG9V1h218Xzdr5UZtqgtep8xv5rYfPzMqK1vQ+lyvjFqgy30yWnyH3dkLBye3uTDfy59en6BVm+o1cs7qrP3Ysm5Lg7r17qu7+k5NPbapvkn39puWPm8kN4kNyW3V+g4nLVqna14Yo3EL1mad4+3LDypoX2XlxjqNmLVKv/nflzlfl++2/H+vT9Bv/jdOEyK46Jak9R7ZoG41LP7yjUNzLuv+7x2le759uKTgtY0KYhieQW1nxuYZB+2si4/eowSN8g+023/6P194sOt5dKcOmReRXjVFjtlrh0BtKkWNmEIZMrRtu3RNIHt2k/3xYvv+sXvq+H13TA2tDqrf5KXpDOQc28A2rdPLtW44dGibf2BlR8fMWCft39mzb3Zctx0l5c5ocbJeu2OHNvrW0V31jxzTzzsDUpJ04eHNQZdO7Vt7niPatqrxzR60MpvzddCu2+qT35+hnbfL3Lf+81P37Cc3QYM5I2/8Wupv5+xkLRVBoAL5FcUK6sKHhurM+wYHmub2Fy+OdX3cusvm1VGZtGi9vvvvz0IEgdx3HHvHyi/K63WAOO+BIam/vYIE9vWc7SgQ2ufDaRnrti7y6nMEjuzsFwx+adNh6lmUkt8daHuBVCugtjiPGQss9kK4zuLDds5gprPQcdQamxK67qWxuuo/7kOtbn57ko67+5OM7WXojBWpC5Ager85sSjFTa0MtxG2ekx1PoWfF67J/Rv67d9RXZN/mhym5jzBei3f+v6dhxX7NOG1Nc2vu+3d9BT0+dZfssunJtAlj32mU+8dlPq31W63YP2vXvK+eOybzIz64+vjU49969ERGa9xnU3R9tA3HxnuuuzVm+q1dnM6iH3+g0MzClLm2k8tpx7QWc9cGV1dA6emhKljuwW76MmHFRSd7zOLpV9H8MLDd9Vlx+2ZWmaQ/eSa50f7ZhDYhb3Ittey+esHUzXCp39w9wdTcz5vZw2NqzEM9Z2wJGtYshV4vO3dyaprbFJTwsyYZa/WMDR7xcaMY5ckzyLxjw6epe/+u/mcZM2cYwUzrPPT+gDH5O/azmt+BemDbP+SbRiqz4+e7/2M5cmhskvXb00NJSkGt7v9R3oMB3FTyJ3vHnsH28cNSd88KvuiU5Latc7vovq7PbOHchRTbY3h2n945AfHBHr/932G+Vja2X5Pt4DKsz85NtByiq2jrTC0/Ts4dPftdN+l3sGHQvz05H0y/t3nkiPUurZGZ3XfWdectm/O9/7YUfsqyP4/vPdZqb//+f2jdM4hu2iv5BDFj397Wqi2S9IOLjVuvK65Xr7mBM346wWey3I7Lj1xRY+Mfx+dI/DYlEhknZuWJeuPnX/orp6ZRm1b1fqeJ3PdZA/DGSAOMwTRfly58YLu+u9V6ULr9mNelLMoVgqCQAXarl1+qY9uFxIbtjYWNHY96PSrzsKxXh10a8z9IkfgwJ6hM3xm7k6pV3qxfZlu2RDrNjekOoxuBkxdphW2CLO1GnvB6LHzve+C24NAs3IM7ynVDDtb6psCFZu1s1+g+rFmT8uneLWboJ1rqTkbwRl8+s3XDoikHVLzkLcg7JvZXX2n6ob/jQu1nmKMz7cClZc/mc5qCjp9tpcgs7FEwQq4Ou+ieU+DrIzXu6kxjKwMgFe+mK/pAYZJ5BLN7GD+dwsXrtmcCkpYn7OhydTitVtydobcAtZBgvXH3NlfR93R37fNuXTdYRud1b14xTSbEqbrbINRsNcHWeNT/DfIBa71lW9tSAT6/huazFDDwb5+hPvFr5cfPj0y6zHTNLVqY53+5zKDpFeLtzY0eQ6PfmjgDF330tiMCQokaez8tam/Jyxcp38NzJx4oLamOdvv8idHZpyLg/zSzgwdr+1jzaZ6jXAM+51um1zihlfGKZEw9ee3JuqFz+Zm3ZTKlR02c/mGVB0p65DUmDC1pb4pYzmvfrEg9V3nM7RhxKyVqWPGui0NnkOhouA2y2lTiBNCIQWIg16UGYZ0w9kHavyt52Y91yHALFPbuQRD7JkMN0Y8nNWL25BR+3CSfjec6vneoLMOtbMNj3Objaxrp20yMhb+fOHBnsv6dpEyqgyjuTaMxT5TWOvaGh22h/+Mc/mu1/1xw7euz1++eVgqcHj7RYfo6L06SZIuOSbzOxphC/zYM3d67L2DnvxRz9TveOAuuYcb2T37k2N1/qG7ZgSDT9h3x5zvqa0xUje63bYrt2P7AY425Tr/tW/TKnWNYFmWzJ5v19o70NN1x218M89XBBzh4sc5pL/GMFLZSmce1CXne3fbPj0s8+i9dtApB3TWyfs3F2Xfe8fM4/G3j9lDvQ6PJrmjEhAEKlC+Y3aLMUvPio11Wr2pPnTKsr0D9vaXi9Std1+t29w8ht1yx3vpMe72jpVfn+HpYXPU4NMJcTuIvPnlwoy7jm7qMjKSmv9vD1Dd+s5k51tSFvlkU1iWByh2mw/nZ77prYmhx9mHibCn6pPkGSBwBgLDBIHGLVirk/oMzHjst+ccmF9DXAQdVuf8vr5aHi6wUIzM7GlLN2j2itxTjYcVVaDPj3UhZxVFTRW0lfvxLT1Vvfcya2qMrFoXCVM678EhHu8Ipk2rGq3b0qAx8zKH79Y3JvTOuEUyzeZp7t3u7o6eu1pbG5p8f/93xy/WKX8b5DpF+fINdZ61XqTm44H9AvOLuatTtajsGpsSOv/BIfokYH2pGcs35HWuudjlTr1VNySsxoQZaV0wu1k5vlOnMEEgKfjQn1w3G5x27BhuhhO3WiZ1jQn93xsT1PvNzKBNjZGddWmaplZsqFP3W/rpbx9Ncw0JutU4c/5em+oas4Yy2oO5x/51QMY6g9hS32QLvrifmI6+s78uf2qk3hu/2PX5GqM5M/LFkfN1yzuT9fins9Wtd19NXrxOzw2fk7E/f/pVZg2+s+8fot+/llm8fO3mBh18az+98sUCDZiyTN1699WfbN91kM34249mZu1d/uTIVO0j08y8YC6FupxDtzPt0Sm7hs2TPjVSrKyLnbd1r3/jZAWmt3epHeMcIuPmcJfZiOw3Y/2yQKJgKjP7xc76DN13LTz4YQ+s/d953TX5L+el/n1zr4OzMjTcao7tnQw67rdzR9cAmuXOb+YeNujFkFLFh7dt1yojkNimtibQUOzdt8/cduyZXcN7n6VDd8/+LnMdzbd1uTl/wWGZNWWsyWRa1dao6w7tNbdPr6ybIbt3ch8SnOtG1iG75f7dzzxoZz1+RY9UoeMaQ3rp6uCzfLltV0EOubmGFrZrXZN1frz+rP0lSUfv1cnzXHjnNw9LPedVgHpFgcPBLM4AdW2NoUcuP0az777Q9+bKzrbaXFZG3Y0XNAdMv+/o19z/3aMCZ/S1BASBCpRv+my+feJc6c9rNzfomDv7hy5QbA+mWIUXr3tprBbYAiXPDE/fYapvSujAmz/U9S9/6Tkdr92749w7cBa/oRBe7HdirCDF1CXBLuzHL1wb6HV+d5fzdcd7mQEq+5CYYrAO1FbnfqpPoUynIY4OdO6aQMFY45wLEWamkHscQyUWrN4Sasr5Qsc2u7n4keE66x+f+r8whKAXr507ttEbvzgx7/XUNSbca9yYmUOfrIvCdLtyZQLl3Zyc2raq0ZXPjtIlj32Wyiq67qWxOvDmD/Wb/43TJ1OX64z7BrtmYn7n8c904T+H6vFPc8+29euXm4eETV2yXv/9PLNe0cWPDPes4yE175f249mlj3+mF11mDVy7pUHTlm7QH1/3L4orSX95b4p++eJY/eU974C4mwe/f3TGXVCpOfMjH82ZQMVJTwtSHFiSTrznE9+hVIaMjOL3QWOps5bnVyg+CLcLzS31Ta7bUrvWtZprO/dvbWjSCfd8kgrQvD469+xpds7MzTku/Y5CMyNvemti6sLErybW9S+7D7c0DCPjJtP/vmjeZ654epRuf2+K/mDbT25yBM0sI2auzLo58sjgmXrEZYa6IMdWewaVmyA1S9xqdOSrIURns8feO+q/Vx2vn52aHmpzjk9WhXWXfYf2rTX+1nM1yRaocHPqAe7TMUvuWT5Oj/6gh/5wbuZNpO22aZ0KgBRrOnUpvT8e221HHb3XDvpez+xhXe9df0rOwNmnfzzD9fFT9u+cCr4f2XV79bvh1IzPUlNjZGRKXX3qvlmf9bvH7qmfnryPjtyzk974xYma+dcLdKEtq+Gs7ukZqU4/MDN74ooTu+WXtWM0F+999ecnavTNZ2c81bq2Rq1r/E8cvzgjsw6dPUtmj07buGZO5fqZD7IVAj5ol201vPdZ+r1jm0nXPM3eP9657uRUzZknruihd391sqR0/yTXse+1a4P1qdq1rtEhu22nB79/dOhhSFee1C0j28l05F+6BVNzZdltv01r7bljZsDre8fupQG/O03fPGqP1HHPObS0Q9tWqSQCr4LgPQMOEw1ibp9eOiaZtWWoeV+vqTF0SY+uqX3n9AO76MHvHZVRw2lnW4aelbV12B7ba849FwYextpSEQQqUL7DweydCb9ihHZn3Dc4r/Xl4laDZNjMld6zdjUmVN+Y0HvjFwfKXvErDOy2niDdFisb5a0vF6bqGdhTxXNxu8teSm+OLW7Qx8kaomIF3LxqjLgZNmOl+nyYOZ1kmEwgyy6Owm5L1hdea+mSx0b4vyjpTZdA2wGOGWhy8SqEWm7u7jtVptk8BCnXdM+d2rdRj73zD8St3FiXsR1ZQysSppkxy019U0IDpizT2GTAzt7fcXbAilUI87NZq/Rl8uLsvAeH6PS/D1LfCeliztb+MWnRev3xtfFZQYvZKzdp8PTgs/nd/PYk/SbEcMPGhKlDb/vI93W1qaF3wS/sRs1ZrWeHzw38eotzCF2+v03CNH3PAYf7TJVcqCXrtupll+FTUvpz1jU2OTKBgi171abgweGwSXpu0+J+NHmp69Ab54XJvwbOSNV1kMIV6L/1nUkZ//7Le1OyXuN2A6yxKaHnRswNtI63vlyUanN9U0Ibtjbo9nfDBStb1xoaMzd9I6BL8uLRyvq0B6m9MpSWbdiadSNqweotqeOF5b+fz4sko22vHf2Hg0U5pClsTb5TDuisP/c6RF/ddYGm3Xl+ztfuvVP7jG16+/at1d6jrs/NvQ7W7LsvTE31LmVvQ0ECONtv01q/OitzOPl27Vrp4xtO1+M/LO4d/DO776y5fXqliu7+7TtHZL1mj07bZATO7vn24Xr0B8fo1AM666YLu2vvndyL+/7z+0fpe8c2ZyW0bV2bVyZR21a1uvWiQ/TOdSerx947qlVtTeqWi2mauuXrh6Reu43L77TnDunf5sqTugVapyFDtTWGjttnx6zZ3dq0qkkFW7Zt10r3XpL9fUnNASj7+rru0F5nH7xLKrBn30ysoVsXHr6bRt70NX3y+9Nz1uQxjObfxNq2rG1u/y7NWVT7udS7OXLPTqkg2bmH7qojunbKeG+uzdS+TT9nq9l036VHZmTTGoahD35zqr6RR8D39m8cmjGLmTO70K3cQq4h8du1a62/fOOwrMf337k5YLLb9s0BIvtMfVYwe02yJuEeHhMp9PH4zfNlHYKdxwqr7s95h+6qi4/eQ//8frqtVsbcPp07ZPw+xQwYVwqCQAVq17pWk/9ynvbwSBv08vd+0/XEkOY7y+94ZMokEqYSCTOvC+4wwi7fa9aOfG10nXLZv7NlTbc6dt7a0OsMM8NZMWwownDAXP45oLmeg1ddiFx++PTIrBlZck0R78V+QSLJ9S5aXIqVqRCH0fPW6O4PpuqkPgNzTvdsXdTbT5aW4/YJHxxaubG5M5AwzYwhJlsbErr6+dH695DmgsWmpBnJYG2pgrHOqZed2ZL23/+1MQs1dEbwgE8U7MWdc7H2XrfhOvaglp3fDCbOjrvFOSOIX4fJLWAhNQdg7EPh3DILbr3okKzHSsVK31+/pTHj5kPQYU0DpgYPDv8i5MxrzmG4UnORerdafM4hVc5jdpibzYNcAp7Ox5zHzC31Tfrv5/M8+zNurA55Y5OpRwbNyph8IIiT9uuckQ3mnOLXzuvXTCSyM13d3Pz2pIyZDPO1i21ogrNAraXQqcGtWhmSUhewYbVpVeNbqLn/b09P/W0dH7yyGvbotE3Wc+9ff0rqb+dwHT/2YvbbtmutvXZqr/MPa856eenq413f8/DlR6f+Dlsj58cn7q3bchynTtx3J9fHLztuL114+G564arjdc1p3vv/Th3bpoK7YQN3P3CpF2S5+tR9dfbBu+iHJ+ytnTq21YWH76pnrzw2VRh+n84dNOqm5lmRLrBlDeX6rHa5zi+ta9PDjDq0aaXv2gpiz+3TS3887yC9c11zls3t3zhUc+65UP/56XG67Lg99dSPe2ranc3FkO1bzd47Ng/dOnqvHbTLdu20X5eOOWvyfPOo5t/ZukazMtCvPKmb3v3VyTppP+/MNKf9urgXSJakvr8+RW9fd3IqsH3gLh11xkHpzKvv9OiamoUvajtv205v/vKk1H7vFfB/+7qTM7K/v5XcB7bfprVO3G8nz33iX5cdrX9+/6iM2cnOT+6v1u97/D476fQDu6Qygu68+DD99VuH5V3s3Yv1yZyHGWs91vZoHY++1n3n1LkqTDHpalG6+ftasA5tW6nvr09JFeg8Yd8dU9PWeuk3ean6TV6a86Sw700faNft2mnp+q2aePu5uvP97LtxUbAu3oKKOoDyx9cn6O3kiUBq7ny/4BhO4WZLkYNjUTj/0F1TJ9svbzlHR9/pXcS1mKzp4qOarn38guxaFWH96qwDdN/HX0XQmsL0nbBE1700Vnd/K32C7tS+dVYB9UriViDU6cxkarjVUenQpjZVlPoflx4ZqvC4nXMTm+iYFnnQtOXqP2WZnriiR9aY+/oiFRD24wxs//Q595nmiuULWzaDl3Pu/zQVLHEGc3NlEZ24X2fPmipS9qwbFmcKfq6hzxcctquWrt+alT3h5q6LD1P/KZk1jdp5BKJKoXOyTs+6LQ264oS99fqYhdplu7aqb0xoj07buAZi8nHD2Qe41kGJinNIlbPu3ZrNDa7FbPPlHFK9alOdbx0/J+sc3phIaEDAOld29/fPPH941WmRmoOR67Y0ZAUrV22q090feGdM2g13FKnOxwbbTa+OHsOfggyhyeX+7x6lDyY211bLFRgL66MbTsuoz9amVU0qs2e/LukLxGtO21dHdu2k615Kz2R77qHZQZ7uyWE7B+7SUY/9sEfW87nY67c4sxxO2r+zptxxnhas3qKXR81PZad9/YjdtecO7bVD+zbaa6f2rtnBXv7yzexMCbvnrwo+ZbUX65gbtp/2128drr9+yz3AsGOHNnrqx+mA2aM/aP6ea2sMNSVM7dO5Q6pmyjeO3F0fTV6qHdu3kWEY+lr3nfWJRwb0vd85Qo8PnqX2bbz3uV22a5tz5q3rztw/49+GYWQNU5MysxxzFXt3c+3pzTWi2rWu1Se/P12dOzTvDzU1RugA6QtXHa9xC9a6BjYO3T2dzfrkj3p61sgJ4uyDw0/UcMxeO+icQ3bRBxOXat8u7tlmzjZdfPQeeuvLRalA2L3fOUIXHL6bujqyenbo0CYVTHv3VyfLkJEacnf9Wftr9+3bqdfhu+miI3fX5vpGDZ+5yncYab6O3rOTxi9Yq506ZB7Xfn/ugdqxQ5tUUedUBpzS58Ztcmyr1YpMoIh0at8mlYIX5vg90mdmo6XJCu1PDp2jV0OM6Q+jvikRqrZK1FkTzrpCkxatD1TX6OcvjNFt70wqSsHeqPz1W80dh123a6cdOrQJNOa9GEbNaQ5KJhJmJFO2uxUsrVRWR/XpYemptcMGgKwUZTd/+cahoabpLYUnruihP553kKTmC6cT9t1Rj/6wR+oiarftgxX5dOPcn50zHFkdjP5TlmVltPjV3QnilWuCF1m0PDFktv+LYjZj+cZUEfQw5xivIE/qeY87dc5MIGcM6PA9ttfr156o4b3P0mM/7KHnfnKcTjuwi176mftdeMsu27XTJ78/PeOx9i53k53rL5ZO7dNBoCP37KSvdd9ZrWpqtGZzQ2QBoAN27qgbzo6uGL4b54WjM/tNinZI6yODMvfVRWu2eA6581PfmMhZOD2o18bk7iONmrM6K8MraABIklaFvGHm5gcnpLM2vC6gC80EalNboy7bttXfLok288Bez2jA75qH4JxzyC56/doT9cMT0llNN114sHodkTnDjlsQ2TAMjbv1HL3xi5Pyas8D3zsyI+sps62tdNCu2+omx4xZR+7ZSXsliyXnKnZvr4uUq2D1DWcfoHu+fXigAshuXrv2xNRU71amxU9O7pbXssJ4IDm0xzn1/COXH6M7L27utz71454ac/PZeu3aEzNmIfv56fvquz331MA/nOG67OOTmcS/OfsA7dSxjbps21a3XZQuPO2VNerlH989MvW3s71+7Bms+3XpWFAgvsu2bQMFN845ZJdU8LVzx7YZNY78TL/rfP37inABUcvFR+2h/r89TWfaMpByOf3ALprbp1dqf2hVW6NzDtlFB+cobn1E1046vOv2qcBr+zatdMWJ3VJZfu3btCpaAEhqPrb0/fUpqTZbtm3XWr/+2gGpQOpRyT75T0/eR4fstp2uOmUfPXzZ0c7FVT3CYhE6ab+d9M9PZoSaRjTolPDFHr705fw1gQtkuXXW7FkE+Vi+fmvqbkRdwKFG9Y0J/ecz/4yhOHVo20rPXNlTh+zWfJfgF2fsn7NOi5/9unTIOaW9LyOdFVROeuy9g7bfpnWsdXcK+V7/fUUPLV9fp2eGz9FtFx2qI//yceq5H5/UTZf06KrDAtR8sXyv5566pEdXPTFkdl53yP3Y78rW1hj63zXNKcJv/OIkTVmyLvAUtvmw6m+8NmZh1oVCFPKppRC2mH5YnTu2CZ1xmUtjIhE4GP+67cL4tAO7ZA19seouPHL5MdrNNjuQ86LGuuP/5wsP1iU9umZdGG2/TWs9/9PjtHCN93dpTWFsBRnP6r6znrnyWNf39P31qamsg7atakLfAQ7K+hxWGnl9UyKS4M+tXz9EH0xcotHz1uiNX+Z3kVuIsFk5hbrxzYk5i5/n8tDAwodZBfGz5wvL8HMbxn3o7ttp7eYGPf7DHrro4WG+y7DPonX5cXvp0+kr9H8XHKRLHkvPKugVUBh989m654NpemNs7mBXTY2hL/58ds7XhPHGL07SsvVbUxd6e3TaJlUvRJJ6ekzycNUp++jpYXN0hMuMXhYrCJuPbx3dVd86umvO17SuNfT9Y/fUN1xmPLy0Z9dU4PKCw3bVh5Oas7Zn/vUCtaqt0a+/doBmr9iU8yZOocHdY23f3fbbtM4Itrh565cnhQoqeDnv0F30wxP2ytl+wzC0U8e22im5vhsv6K57Ppym3/p85ld+nlkc2b4tDvjd6Z5FhL3svVMHzbnnQr00an7gGjpf3nJOWdwgHnXT1wLVOLV4Dc8OwjCMrKnhW5o2rWoysq68dO7YNmNfstfDQhqZQBGyTpBhij8GvaNb7Jold38wVcffPcD/hZLrEJ5Pfn9GQes/7u5P9NTQ5uldv+MyxXKptIn44rdd61qd1X0X7Zq86Dk/4Lj3XbdrpzE3n501NWYhHSapuQBcmGmVS6W2xtD/nR9dMcxC5Rpj72bH9m102B7b6/7vHpVxl+vN5MWfc5iCV7qupba2udDiUz/umTELhJ/j86jlY3fQrtv6dqoLZa8BduWzX0S2XCvjpV2b9D5cqmwSP1EHMBqazFBFzS1utU86JLMReh2xm47ZK30joLbG0Ec3nKbRN5+tm3sdrB8ev7e+uusC/ey0fXPeGfd6bo9O2+hnyemb27dppfevPyVVo8N+t7Zj21aa26dXxgwvA353up65sqcm/eW8VCDJzUG2DnCPvXfQ5QH2445tW+m+S49MzQYzdIb3kJ/+OQqQOv30lH309JXH6o1fnJgxgcT/PDLVCt134zY7x8ylfhasjibjKg59f32qhvc+K2vq8toaw/OO+N+/c4R6HbGbdujQRq9ee6J67L1jxuw8rWoMze3TK+v837mje3bPcd121Hd6FO+43WPvHTJmmQrKGu4bNDOhGAzDUJ9LjnCt/WLdsD1l/866/7tHpR63boJs26512WXxHr3XDhnFtfPVtlWt7rr48FABpZ+fvp/m9ulVUJ2X/XfumAoqhWEYhn5w/N6u07+72aFDm4L7y1GoqTHynkUaKDaCQBGy9vMwM7cE9fQw/xofhUiY2YV7w/A7yNk7OPd/90h9rXt2p+CuvlOzHiu1tq1z7xIn7bdTahiNlyF/PNPzOXthtVye++mx2qljW/34xG4Zj++b4/1BCvktWrtF33siO/usTasa/fCEvfIai1yIZ67sqVbJAJBb7YIbzj6g6DN+uAnbyXFmzrz0s+N197cOz7iottLWD9tjO731i5M1t08vHdvNPfvOXoDwiR/11NWn7OP6OqcoZq+xFKuI3poCai312HsHTbvzfD3wvSP1wPfSF+5S8ywsH91wmtq2qk3d9bn9G4fmnBWjVDbkmSERtbMP3kW7JjMureLfzplF7A7adVt17thWV5+6r2pqjEDfZfs2rfT5jV/LetxZw+2wPbZPDYexT06w0ZZt8e8remjA707Tnju211ndd1HHtq109an76O5vHa6T9ssuxPqibSjaA989SpfnGO5hqTGai3Z23SH7wupSx4X1/raZZIb9n/tx/t9X9ND0u5pnVdp+m9ZZM/CdsO9OuvObh2rX7drpaVutjpP3D16gNGpuMwW5KVWgKmymQFwO8rjr/p0eXdXvN6d6zh57ac899cjlmee13uc3Bzdvu+iQ1PnEnlFhzRjmPNe8fu2JevXaE/XnCw/WmQd1yXtoVRDtkvu/300Myw+O30u/PGM//TxZl6XcHLPXDrrxgu565PJjtE2bWt120SHqd8OpcTcLAEoiUO/YMIzzDcOYbhjGTMMwers839YwjFeSz480DKNb5C2tAEaqAFrMDYnBTh3a6Jdn7Jc1DbhlweotevHq4zXs/87Ut4/pqvsuPTKvqRHDyJWC7OVfPmNGH7+iR8Zyd3K56+0cqxpEvxtOTY23777rtqkhLc7ZFHLd2f7Jyfvo1jxTHn9w/F666+LD9dSPe+pVRypvvgb94YysFMyDdtk2o5N6VvddNPPuC9Vj7x20Y4c2GuioFXL9WQfo/MN209hbzgmVEVOodj7BQLtvucyocNJ+nbN+q0d/0ENz+/TS+9efmhqX/tq16e/iyK7ba8zNZ+ulq4/XTxzTs/7u3Oz062tOy+5YewWv5vbppbG3nCOpuY5CEBcdkd4/g0xrXAqH7r6d2rWuTQ0DOP3ALrrkmOYL9cYmM5U9ctUp++i1a0/UD47fS+/96pRci8zLti4FaAspBFkK5xyyi578UQ+9e/3JevyHPVKZU0EDAGHsun07ze3TS2celC7yuSnHjIjdduqgHyZrpfz+nPS2ft6hu2YMO5Gaz7OXH7+XXvpZdkZN545tNevuC1N1Dg7bY3uNvvls/fKM/TT1jvNTU9FfckzX1EX1oY7p6e0Fbr9/3F66yhaANQxDH/z6VN3c62B13aF9xs0NS5tWNb4p/Vec2E2f3/Q1fc0WdHfbn/Mx2KNOh13/356mgb8/Xa/+/ETd8+3DNe62cwIt+7A9ttfsuy8ssIX+vrz13KKvQ3I/dofhDBh8dMNpGvqnM3XfpUfqgF22VUefmfnseh2xm+b26aWfnJze3jq2bZWazejntpnlvn/snrq518Ea8LvTUkOxdujQRs/+5LjAw/rzsVPHtnr2J8fq4cuD3Zhp17pWfzq/e87iwXEyDEM/P32/1Pn4Jyfvk9dwYqDSXHZc+czOi/j4XukYhlEr6RFJF0g6RNJlhmE4rzSvkrTGNM39JT0g6W9RN7QSHLzrdtqncwfdeEF3fXTDaRkdBHuRuVLZO49gRL5qagz96fzuGvC70z1fc/L+nVN3W3fo0EYPXXZ0xhShUZp4+7mp9N8rTthbFyUDToNsHWT7jGSWMw7a2bMo7nH77Kjt2rXWCbapQOsaE+p3w6n6xRn7aeDvT9fMvzZPablHp208g1D3fPtwvferUzLGq3bfdTuNTE7TeVGO4NjRe+2gWTk64Vee1C2vzBnDNgnncfvsmCoMGNaJ++6km3sdrEd/cIz26dwhI5hx/D47qu+vT8nZSd23S0fN7dNL/76ih845ZJdUdt2OHdoETst2u0C39AqY0n7lSfuo1+G76eseNWseufyY1EWbPTsgH9aQpZsuPFg7dWyrk/bvnDWVrn12jAG/O11z+/TSnxwZad12aq9fnbV/6qLBaccObTS3T6/Aw73uvPgw3XbRIfr8xq+lhgVN+st5mnbn+amAkiR9t6f/8u67NFjgSVJqdhB7xt2pB3TWyfvvpD/3yh4K9PPT91Xnjm10+kGZs4oc221HGYaRMawo17bRvk1tznoMpx3YRbtv305P/ainWicDKFfatu+3rztZI3qflfOzdd91W42/7VzddtEhqfd+dIP3EKO3rzs5ss7avy47WoZhaOdt2+n8w3ZVt52agx1rAk5Pn9c6Lz9G1yYvXnMF5WtrDN118eGa26eXrk9OsBBE61ojq8ioMyu1c8e2+tP53bVNm1q9d/0pev/6U3T3tw/Tz0/fT8P+78ysoSrP/SSdXXbYHttlBbIP2X07XX1q877/wk+zi2Dvul24ourXnbmfnr3yWLVrXatpd56vO795qOvrDt19O/X99Sk695Bd9COXqcW/edTu+vOFB6tbgGzTA3bZVvt26ajj9tlRlx23l9q2qnVt95x7LtRpttl6jtyzk2pqDE2943y97BKEs9t7p/Yac/PZWUOipt5xfs73WUE4ezBQkv7gEgi3nNV9Z9dsycl/Oc9zn760R9dUcdxc+v/2tKxi538490Bd2qNrxjBGqTlzzj5U5/qvHaDLj98r70KvkvSqoyivJPW55Ahdfeq+WQHSUjjzoJ1DF/YFUD7m9umle759RNzNQBkw/IoYG4ZxoqTbTdM8L/nvGyXJNM17bK/5KPmazwzDaCVpqaQuZo6F9+zZ0xw9urTT8MbhoU9m6Lh9dtT+O3dUz7vca+688YuTNHflJv3+tfGpx7rvuq3r7B4W5zT0u27XToP/eIbu7/+VnhgyW/+49MhkYdlZWbNfnHFQF3Xp2FavjVmor3XfWXNXbcqrKO7Hvz1N5z4wRD8/bV/daKvTsLGuUQ2NCf2t3zT974sFqce9OmNzV27SGfcNznisda2hhuS0fgN+d5qmL92onbdrq0cHzdSg6dl1LSyXHbeXEglTf/vOEdpY16il67Zq/507qilhamtDkzq0baUx81Zr3IJ1uuqUfTRh4Vrt2KGNOrZtJdNsDk6t2VSvsfPX6Kr/pLfP3bZvp/euPyU1fvrNsQv1+KezdNfFh6eGVeTj1ncmafLi9ansmPrGhFrXGhmdy6eGztZ+XTqmpvSWpHs+mKrP56zW8vVbdcr+nfWH8w7SLrZO/Jh5a3TJYyNyrvulq4/XHe9P0bSlG1Lbi2XGsg0654EhGa+/+1uH65i9O+nO96do7srNevQHx+ibjwyXJN1+0SE6vOv2WUMfpOZC36PnrtFJ++2U1WkOI5Ewte9NH+i4fXbU7RcdqkN2306/f3W83hi7UE/+qKceHjhDJ+7XWTecfYAue/JzfTl/rbZt20r7dumg8QvX6dM/nqGRs1frT29MyFr2hNvP1RG3Nxdztm+ny9dv1VX/GZ01G9rcPr00YeFa/eW9Kbrjm4cGKlTnZcayDbrnw2l69AfHeGbymKapPv2m6aIjdtdhtsyFwdOX68pnv9DLPztBPbvtkCoo2q1336z2Ru2TZMHqrx28ixav3aJ/DZypsw/eWQOmLtNVp+yrf34yQ18/Yje1rjV05kE769nhc3XH+1Ncl/XLM/bTy6Pm6zs9uuqmCw+WaTYXMD/mzv5Zx5d8DJ2xQnvv2EF77dReg6cvV+vaGm2/TWt9MnW5dujQWre+M1kDf3+69u3SUZvqGrWprlHbtKnVgKnL1K5VrRat3ZK68Jeka54frY+nLNP4287V08Pm6HvH7qk9bNPdPzNsTtZnveCwXfXQZUdnFH3dXN+o9m1apX4v+3HvjV801wpZvalef35rYqpoafs2terZbUddedLeWVPZ//z0ffXvT2frgJ076pEfHKPN9U26OLmPOreBNZvqdeu7k3XHNw7VDjlq/ERh/IK16ta5Q+QXj9bwx1krNmrSonX69jHR1EVZsHqzVmysSw3nnLdqkzbWNbru52+MWahOyUyCDyct1d8uOaLgGhCj567WR5OX6rfnHKhtWtdqzeaGrFpLKzfWqeddA/STk7vpT+d1zxjWt3Jjnd4Ys1AXHr6bVm6s04+eGZUakthj7x1chwyNW7BWFz8yXL/52gG64ezmQJxhGFq1sU5NCVPLN9RlHHsk6cCbP9Tx++yop398rFZvqtcJ93yiPTpto79/5widlBze9snUZRnn0rl9eunDiUv0ixfHapvWtXrqxz318qj5uvy4vXTo7turQ9tataqtSfULXrjqOJ16QBc1JUz9/aPpqRkEb+51sO7qO1XfOnoPPfC9o7R2c71ue3eyBk1brvVbG/XP7x+Vmtb4Z8+PVv8pzcerG84+QN133U7nHbqLDMPQjW9OTBUH/tP5B+neftMlNRcx/8ZRu6fOqy98Nld3fzBNQ//vzLwK8975/hR126m9rnAM8QYAoFgMwxhjmmZP1+cCBIG+I+l80zSvTv77CknHm6b5K9trJiVfszD571nJ16x0LOsaSddI0l577dVj3rzyntkpaomEqTfGLtTitVv1s9P20bPD5+qbR+2urju0l2mauuo/ozV85kq99cuTddCu2+q5EXN1wr47qr4xoW89OkIn77+TOrVvo307d9D1Zx2geas2ad6qzdq2XSvttVN77bZ980WIaZoZF9uNTQktXrtVp/19kM48qIue+FFPGZIWr92qvXZqr3WbG/ThpCX6To+uWr+1Udtv01q1NYYamxJaun6rbnl7kv7vgu7afpvWGjl7tbrusI2O3LNToCkxt9Q36Yu5q3X4HtvnvNBoSpgav3CtZi3fqPMO21Ubtjbq+RFzteeO7TOmHpWk1Zua71zf22+a/njeQdpum9Z6oP9X2qdzB13aM9oUx0Vrt2jd5gYdsnvlpQh/MnWZDt9jexmGoWlL12tTXaN2234b9X5zol6/9kR1SGZFfLVsgw7YuWNWgGbZ+q1q17pWL46cp2tP2y8rO0WSRsxcqc/nrNbvzilshoygnNu21HzBlqtQ4tyVm/ThpKW6NlmXYMbyjRo+c6U+nLhUN5xzgA7cZVvt1KGN/vz2JH3jyN0zMr0sExeu08Bpy3XlSd20ZP2WskoZtwIJdkvWbVH7Nq20YPVmHbr7dgUF36I0YuZKbU7OIrhDhzbavVM7vTd+sa46ZV/XC+flG7aqc4e2rttenDbXN2rm8o06omsnz9dsqmvUa6MXpIb85NpGB0xZpl22a6fDu26vRMJ0/byL127RhIVrdf5h6ey0lRvrtHTdVn361Qr98oz9ZBiG5q7cpJ06tkkV0KxvTGhLfVNBU+OiPK3YUKcd2rcONJvfiFkr9djgWbrr4sO0907BarpEpbEpoQ1bG9WQSGTMjpWPBas3a/bKTTr9wC7a2tCk1rU1GceOdZsbNGHRWp16QGZm4AcTl+j4fXb0LUo7YtZKrd/SkLGfAQBQicomCGRXLZlAAAAAAAAApZIrCBSk+ukiSfb0iq7Jx1xfkxwOtr2kVeGbCgAAAAAAgGIIEgT6QtIBhmHsYxhGG0nfl/Su4zXvSvpx8u/vSBqYqx4QAAAAAAAASst33kbTNBsNw/iVpI8k1Up6xjTNyYZh3CFptGma70p6WtILhmHMlLRazYEiAAAAAAAAlAnfIJAkmab5gaQPHI/davt7q6RLo20aAAAAAAAAohJkOBgAAAAAAAAqHEEgAAAAAACAKkAQCAAAAAAAoAoQBAIAAAAAAKgCBIEAAAAAAACqAEEgAAAAAACAKkAQCAAAAAAAoAoQBAIAAAAAAKgCBIEAAAAAAACqAEEgAAAAAACAKkAQCAAAAAAAoAoQBAIAAAAAAKgCBIEAAAAAAACqAEEgAAAAAACAKkAQCAAAAAAAoAoQBAIAAAAAAKgCBIEAAAAAAACqAEEgAAAAAACAKkAQCAAAAAAAoAoYpmnGs2LDWCFpXglW1VnSyhKsBwiKbRLlhO0R5YZtEuWE7RHlhm0S5YTtsXztbZpmF7cnYgsClYphGKNN0+wZdzsAC9skygnbI8oN2yTKCdsjyg3bJMoJ22NlYjgYAAAAAABAFSAIBAAAAAAAUAWqIQj0RNwNABzYJlFO2B5RbtgmUU7YHlFu2CZRTtgeK1CLrwkEAAAAAACA6sgEAgAAAAAAqHoEgQAAAAAAAKpAxQWBDMN4xjCM5YZhTLI9dqRhGJ8ZhjHRMIz3DMPYLvl4G8Mwnk0+Pt4wjDNs72ljGMYThmF8ZRjGNMMwLin9p0FLYBjGnoZhDDIMY4phGJMNw/hN8vEdDcPobxjGjOT/d0g+bhiG8ZBhGDMNw5hgGMYxtmX9OPn6GYZh/Diuz4TKFeX2mHx+O8MwFhqG8XAcnweVL+Jj5L3JZUxNvsaI63OhMuWxPXZP9jHrDMP4g99ygLCi2iaTz3UyDOP15LXNVMMwTozjM6Fy5bE9/iB5rp5oGMYIwzCOtC3rfMMwpifP573j+kzIVnFBIEnPSTrf8dhTknqbpnm4pLck/TH5+M8kKfn4OZL+YRiG9Zn/LGm5aZoHSjpE0qdFbjdarkZJvzdN8xBJJ0i6zjCMQyT1lvSJaZoHSPok+W9JukDSAcn/rpH0mNR8cJV0m6TjJR0n6TbrAAuEEMn2aHOnpCGlaDharKiOkSdJOlnSEZIOk3SspNNL+DnQMoTdHldL+rWk+wIuBwgrqm1Skv4pqZ9pmt0lHSlparEbjxYn7PY4R9LpyevtO5UsFG0YRq2kR9R8Tj9E0mUcI8tHxQWBTNMcouaDn92BSl+k9JdkZfUcImlg8n3LJa2V1DP53E8l3ZN8LmGa5sritRotmWmaS0zTHJv8e4OaT7h7SPqmpP8kX/YfSRcn//6mpOfNZp9L6mQYxm6SzpPU3zTN1aZprlHztuwMeAI5Rbg9yjCMHpJ2kfRx6T4BWpoIt0lTUjtJbSS1ldRa0rJSfQ60DGG3R9M0l5um+YWkhoDLAUKJaps0DGN7SadJejr5unrTNNeW4COgBcljexyRvG6RpM8ldU3+fZykmaZpzjZNs17S/5LLQBmouCCQh8lKb1SXStoz+fd4Sd8w/r+9+wmto4qjOP49mnahVSqIVcTYgCJuSv8tFBT/YRZuihsRxKrtrnZh3YgiuJCCG8WFqIvSgiIFqwUVKlm5EFrFqguhSkExbUqrpYoldiHR4+Le0CfUkHmZPJJ55wMhyZ15lxk4zGN+c+8daUTSGLAJuEnS6rr9ZUnfSDogac1Ajzg6SdJaYAPwJbDG9um66QzlZhrKhfRkz8ematv/tUf0ZSF5rKMmXwX+M9Q8YiEWkknbR4DPgNP1Z8J2nnJH3+aZx6b9RPRtgZkcA84C+yR9K2mPpCsX7WCj8/rI43bg0/p37muWsK4UgbYBOyR9DVwF/FXb91ICdxR4HTgM/A2MUKqUh21vBI5w6SGVEfMmaRXwIfCM7fO922yb8hQ7YiBayOMO4JDtqUU6xBgyC82kpFuA2ynf3zcC90u6e5EONzqure/sufqJaKKFTI4AG4G3bG8A/uTilJ2IRprmUdJ9lCLQcwM7yOhbJ4pAtn+wPW57E7Af+LG2z9jeZXu97S3AauA4cA64ABysXRygXDQj+iJpBeVC+Z7t2Vz90jOt5gbg19p+iouj1aDc0Jyaoz2ikZbyeCewU9LPlCL5VkmvDODwo4NayuTDwBe2p21PU542ZtHTaKxhHpv2E9FYS5mcAqZsz45I+4Dc30QfmuZR0jrKGr1bbJ+rzbmvWcI6UQSSdF39fRnwIvB2/f+K2WGQkh4EZmwfq9XLT4B7axcPAMcGfdzRDZJEmX/9ve3XejZ9DMy+4esJ4KOe9q0q7gD+qMMrJ4BxSdfUBaHHa1vEvLWVR9uP2R61vZYyJewd23miGI21eI08AdxTp3ivoCwKnelg0UgfeWzaT0QjbWXS9hngpKTbalPub6KxpnmUNEoZWPG47eM9+38F3CppTNJK4NHaRywBKvWQ5UPSfkrx5lrKgpAvAauAp+suB4HnbbvOY5wA/qFUHrfbnqz93Ay8SxkddBZ4yvaJgZ1IdIaku4DPge8oWQN4gTJ/9n1gFJgEHrH9W724vkFZ9PkCJXtHa1/b6mcBdtveN7ATiU5oM489fT4JbLa9cyAnEZ3SVibrm0bepCx8asobcJ4d6MnEstdHHq+nLCtwdd1/mvLikXWX6sf2oQGdSnREW5m0fV7SesqIjJXAT5Tr5+9EzFMfedxDeSnTZN13xvbm2tdDlCVZLgf22t49qPOIuS27IlBERERERERERDTXielgERERERERERExtxSBIiIiIiIiIiKGQIpAERERERERERFDIEWgiIiIiIiIiIghkCJQRERERERERMQQSBEoIiIiIiIiImIIpAgUERERERERETEE/gXuAspHbgN5XAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(data['Volume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:08:07.050311Z",
     "start_time": "2021-04-28T05:08:07.045701Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_error(train_loss,val_loss):\n",
    "    plt.plot(train_loss,c = 'r')\n",
    "    plt.plot(val_loss,c = 'b')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend(['train','val'],loc = 'upper right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:08:08.288333Z",
     "start_time": "2021-04-28T05:08:08.283461Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_data(Y_test,Y_hat):\n",
    "    plt.plot(Y_test,c = 'r')\n",
    "    plt.plot(Y_hat,c = 'y')\n",
    "    plt.xlabel('Day')\n",
    "    plt.ylabel('Price')\n",
    "    plt.title('Stock Prediction Graph using Multivariate-LSTM model')\n",
    "    plt.legend(['Actual','Predicted'],loc = 'lower right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:09:43.704743Z",
     "start_time": "2021-04-28T05:09:43.664623Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Close       Open       High        Low   Volume\n",
      "Date                                                           \n",
      "1996-08-09  16.500000  14.250000  16.750000  14.250000  1601500\n",
      "1996-08-12  16.500000  16.500000  16.750000  16.375000   260900\n",
      "1996-08-13  16.375000  16.500000  16.750000  16.375000   162800\n",
      "1996-08-14  16.375000  16.375000  16.625000  16.375000    59500\n",
      "1996-08-15  16.625000  16.625000  16.625000  16.375000    62300\n",
      "...               ...        ...        ...        ...      ...\n",
      "2021-04-01  36.139999  35.270000  36.340000  35.099998   111400\n",
      "2021-04-05  36.529999  36.580002  36.849998  36.200001   114000\n",
      "2021-04-06  36.660000  36.650002  36.930000  36.349998    66100\n",
      "2021-04-07  35.759998  36.549999  36.680000  35.570000    83300\n",
      "2021-04-08  35.320000  35.750000  35.790001  34.910000   116200\n",
      "\n",
      "[6207 rows x 5 columns]\n",
      "(3876, 5) (1258, 5) (1073, 5)\n"
     ]
    }
   ],
   "source": [
    "data = data[['Close', 'Open', 'High', 'Low', 'Volume']]\n",
    "print(data)\n",
    "train_start = dt.date(1996,8,9)\n",
    "train_end = dt.date(2011,12,31)\n",
    "train_data = data.loc[train_start:train_end]\n",
    "\n",
    "val_start = dt.date(2012,1,1)\n",
    "val_end = dt.date(2016,12,31)\n",
    "val_data = data.loc[val_start:val_end]\n",
    "\n",
    "test_start = dt.date(2016,1,1)\n",
    "test_end = dt.date(2020,4,7)\n",
    "test_data = data.loc[test_start:test_end]\n",
    "\n",
    "print(train_data.shape,val_data.shape,test_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:09:46.276747Z",
     "start_time": "2021-04-28T05:09:46.220796Z"
    }
   },
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "train = sc.fit_transform(train_data)\n",
    "val = sc.fit_transform(val_data)\n",
    "test = sc.fit_transform(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## torch dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:09:50.662470Z",
     "start_time": "2021-04-28T05:09:50.657702Z"
    }
   },
   "outputs": [],
   "source": [
    "def convert2torch(data, time_step):\n",
    "    X_data, y_data = list(), list()\n",
    "    for i in range(time_step, len(data)):\n",
    "        X_data.append(data[i-time_step:i])\n",
    "        y_data.append(data[i][0])\n",
    "    X_data, y_data = torch.Tensor(X_data), torch.Tensor(y_data)\n",
    "    return X_data, y_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:09:56.364757Z",
     "start_time": "2021-04-28T05:09:51.839355Z"
    }
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "\n",
    "X_train, y_train = convert2torch(train, 50)\n",
    "train_data = TensorDataset(X_train, y_train)\n",
    "train_loader = DataLoader(train_data, shuffle=False, batch_size=batch_size)\n",
    "\n",
    "X_val, y_val = convert2torch(val, 50)\n",
    "val_data = TensorDataset(X_val, y_val)\n",
    "val_loader = DataLoader(val_data, shuffle=False, batch_size=batch_size)\n",
    "\n",
    "X_test, y_test = convert2torch(test, 50)\n",
    "test_data = TensorDataset(X_test, y_test)\n",
    "test_loader = DataLoader(test_data, shuffle=False, batch_size=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:09:56.668744Z",
     "start_time": "2021-04-28T05:09:56.625658Z"
    }
   },
   "outputs": [],
   "source": [
    "class MultivariateLSTM(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim):\n",
    "        super(MultivariateLSTM, self).__init__()\n",
    "        self.input_dim = input_dim\n",
    "        self.hidden_dim1 = hidden_dim[0]\n",
    "        self.hidden_dim2 = hidden_dim[1]\n",
    "        \n",
    "        self.lstm1 = nn.LSTM(input_dim, input_dim, batch_first=True)\n",
    "        self.lstm2 = nn.LSTM(input_dim, self.hidden_dim1, batch_first=True)\n",
    "        self.lstm3 = nn.LSTM(self.hidden_dim1, self.hidden_dim2, batch_first=True)\n",
    "        \n",
    "        self.fc1 = nn.Linear(input_dim, 40)\n",
    "        self.fc2 = nn.Linear(40, 35)\n",
    "        self.fc3 = nn.Linear(35, 1)\n",
    "        \n",
    "    def forward(self, inp):\n",
    "        h0, c0 = self.init_hidden(inp, self.input_dim)\n",
    "        output, (h_t, c_t) = self.lstm1(inp, (h0, c0))\n",
    "        h_t, c_t = self.fc1(h_t), self.fc1(c_t)\n",
    "        output, (h_t, c_t) = self.lstm2(output, (h_t, c_t))\n",
    "        h_t, c_t = self.fc2(h_t), self.fc2(c_t)\n",
    "        output, (h_t, c_t) = self.lstm3(output, (h_t, c_t))\n",
    "        output = self.fc3(h_t)\n",
    "        return output\n",
    "        \n",
    "    def init_hidden(self, inp, c_size):\n",
    "        b, seq_len, c = inp.size()\n",
    "        h0 = torch.zeros(1, b, c_size)\n",
    "        c0 = torch.zeros(1, b, c_size)\n",
    "        return [t.cuda() for t in (h0, c0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:10:14.392750Z",
     "start_time": "2021-04-28T05:09:56.906763Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([128, 50, 5])\n",
      "torch.Size([128])\n",
      "torch.Size([1, 128, 1])\n",
      "tensor([[[-0.0333],\n",
      "         [-0.0333],\n",
      "         [-0.0332],\n",
      "         [-0.0332],\n",
      "         [-0.0332],\n",
      "         [-0.0330],\n",
      "         [-0.0326],\n",
      "         [-0.0324],\n",
      "         [-0.0322],\n",
      "         [-0.0321],\n",
      "         [-0.0322],\n",
      "         [-0.0323],\n",
      "         [-0.0324],\n",
      "         [-0.0326],\n",
      "         [-0.0328],\n",
      "         [-0.0330],\n",
      "         [-0.0331],\n",
      "         [-0.0332],\n",
      "         [-0.0332],\n",
      "         [-0.0332],\n",
      "         [-0.0332],\n",
      "         [-0.0333],\n",
      "         [-0.0334],\n",
      "         [-0.0335],\n",
      "         [-0.0335],\n",
      "         [-0.0336],\n",
      "         [-0.0336],\n",
      "         [-0.0336],\n",
      "         [-0.0337],\n",
      "         [-0.0338],\n",
      "         [-0.0338],\n",
      "         [-0.0339],\n",
      "         [-0.0340],\n",
      "         [-0.0340],\n",
      "         [-0.0341],\n",
      "         [-0.0342],\n",
      "         [-0.0342],\n",
      "         [-0.0343],\n",
      "         [-0.0343],\n",
      "         [-0.0344],\n",
      "         [-0.0344],\n",
      "         [-0.0344],\n",
      "         [-0.0344],\n",
      "         [-0.0344],\n",
      "         [-0.0344],\n",
      "         [-0.0344],\n",
      "         [-0.0344],\n",
      "         [-0.0344],\n",
      "         [-0.0344],\n",
      "         [-0.0343],\n",
      "         [-0.0343],\n",
      "         [-0.0342],\n",
      "         [-0.0342],\n",
      "         [-0.0341],\n",
      "         [-0.0341],\n",
      "         [-0.0340],\n",
      "         [-0.0340],\n",
      "         [-0.0340],\n",
      "         [-0.0340],\n",
      "         [-0.0340],\n",
      "         [-0.0339],\n",
      "         [-0.0338],\n",
      "         [-0.0337],\n",
      "         [-0.0336],\n",
      "         [-0.0336],\n",
      "         [-0.0336],\n",
      "         [-0.0335],\n",
      "         [-0.0335],\n",
      "         [-0.0335],\n",
      "         [-0.0335],\n",
      "         [-0.0336],\n",
      "         [-0.0336],\n",
      "         [-0.0336],\n",
      "         [-0.0337],\n",
      "         [-0.0337],\n",
      "         [-0.0338],\n",
      "         [-0.0338],\n",
      "         [-0.0339],\n",
      "         [-0.0339],\n",
      "         [-0.0336],\n",
      "         [-0.0333],\n",
      "         [-0.0329],\n",
      "         [-0.0326],\n",
      "         [-0.0324],\n",
      "         [-0.0324],\n",
      "         [-0.0324],\n",
      "         [-0.0325],\n",
      "         [-0.0326],\n",
      "         [-0.0328],\n",
      "         [-0.0330],\n",
      "         [-0.0331],\n",
      "         [-0.0333],\n",
      "         [-0.0334],\n",
      "         [-0.0336],\n",
      "         [-0.0337],\n",
      "         [-0.0338],\n",
      "         [-0.0339],\n",
      "         [-0.0340],\n",
      "         [-0.0341],\n",
      "         [-0.0342],\n",
      "         [-0.0343],\n",
      "         [-0.0343],\n",
      "         [-0.0343],\n",
      "         [-0.0343],\n",
      "         [-0.0344],\n",
      "         [-0.0344],\n",
      "         [-0.0344],\n",
      "         [-0.0344],\n",
      "         [-0.0344],\n",
      "         [-0.0345],\n",
      "         [-0.0345],\n",
      "         [-0.0345],\n",
      "         [-0.0345],\n",
      "         [-0.0345],\n",
      "         [-0.0345],\n",
      "         [-0.0345],\n",
      "         [-0.0345],\n",
      "         [-0.0346],\n",
      "         [-0.0345],\n",
      "         [-0.0345],\n",
      "         [-0.0346],\n",
      "         [-0.0346],\n",
      "         [-0.0346],\n",
      "         [-0.0345],\n",
      "         [-0.0345],\n",
      "         [-0.0345],\n",
      "         [-0.0345],\n",
      "         [-0.0345]]], device='cuda:0')\n",
      "tensor(4.1062, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    dataiter = iter(train_loader)\n",
    "    X, y = dataiter.next()\n",
    "    print(X.shape)\n",
    "    print(y.shape)\n",
    "    model = MultivariateLSTM(5, [40, 35]).cuda()\n",
    "    output = model(X.cuda())\n",
    "    print(output.shape)\n",
    "    print(output)\n",
    "    criterion = nn.MSELoss()\n",
    "    loss = criterion(output.squeeze(), y.cuda())\n",
    "    print(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:10:20.441667Z",
     "start_time": "2021-04-28T05:10:20.429326Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopping:\n",
    "    \"\"\"Early stops the training if validation loss doesn't improve after a given patience.\"\"\"\n",
    "    def __init__(self, patience=7, verbose=False, delta=0, path='carr_ser_stackedLSTM.pth', trace_func=print):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            patience (int): How long to wait after last time validation loss improved.\n",
    "                            Default: 7\n",
    "            verbose (bool): If True, prints a message for each validation loss improvement. \n",
    "                            Default: False\n",
    "            delta (float): Minimum change in the monitored quantity to qualify as an improvement.\n",
    "                            Default: 0\n",
    "            path (str): Path for the checkpoint to be saved to.\n",
    "                            Default: 'checkpoint.pt'\n",
    "            trace_func (function): trace print function.\n",
    "                            Default: print            \n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.verbose = verbose\n",
    "        self.counter = 0\n",
    "        self.best_score = None\n",
    "        self.early_stop = False\n",
    "        self.val_loss_min = np.Inf\n",
    "        self.delta = delta\n",
    "        self.path = path\n",
    "        self.trace_func = trace_func\n",
    "    def __call__(self, val_loss, model):\n",
    "\n",
    "        score = -val_loss\n",
    "\n",
    "        if self.best_score is None:\n",
    "            self.best_score = score\n",
    "            self.save_checkpoint(val_loss, model)\n",
    "        elif score < self.best_score + self.delta:\n",
    "            self.counter += 1\n",
    "            self.trace_func(f'EarlyStopping counter: {self.counter} out of {self.patience}')\n",
    "            if self.counter >= self.patience:\n",
    "                self.early_stop = True\n",
    "        else:\n",
    "            self.best_score = score\n",
    "            self.save_checkpoint(val_loss, model)\n",
    "            self.counter = 0\n",
    "\n",
    "    def save_checkpoint(self, val_loss, model):\n",
    "        '''Saves model when validation loss decrease.'''\n",
    "        if self.verbose:\n",
    "            self.trace_func(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ...')\n",
    "        torch.save(model.state_dict(), self.path)\n",
    "        self.val_loss_min = val_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:10:23.584737Z",
     "start_time": "2021-04-28T05:10:23.561559Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultivariateLSTM(\n",
      "  (lstm1): LSTM(5, 5, batch_first=True)\n",
      "  (lstm2): LSTM(5, 40, batch_first=True)\n",
      "  (lstm3): LSTM(40, 35, batch_first=True)\n",
      "  (fc1): Linear(in_features=5, out_features=40, bias=True)\n",
      "  (fc2): Linear(in_features=40, out_features=35, bias=True)\n",
      "  (fc3): Linear(in_features=35, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = MultivariateLSTM(5, [40, 35]).cuda()\n",
    "\n",
    "lr = 0.0001\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,\n",
    "                                                       mode='min',\n",
    "                                                       factor=0.2\n",
    "                                                    )\n",
    "criterion = nn.MSELoss().cuda()\n",
    "patience = 20\n",
    "n_epochs = 300\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:10:24.601671Z",
     "start_time": "2021-04-28T05:10:24.586811Z"
    }
   },
   "outputs": [],
   "source": [
    "def fit(model, batch_size, patience, n_epochs):\n",
    "    \n",
    "    train_losses = []\n",
    "    valid_losses = []\n",
    "    avg_train_losses = []\n",
    "    avg_valid_losses = [] \n",
    "    \n",
    "    early_stopping = EarlyStopping(patience=patience, verbose=True)\n",
    "    \n",
    "    for epoch in range(1, n_epochs + 1):\n",
    "\n",
    "        ###################\n",
    "        # train the model #\n",
    "        ###################\n",
    "        model.train()\n",
    "        for data, target in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "            output = model(data)\n",
    "            loss = criterion(output.squeeze(), target)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_losses.append(loss.item())\n",
    "\n",
    "        ######################    \n",
    "        # validate the model #\n",
    "        ######################\n",
    "        model.eval()\n",
    "        for data, target in val_loader:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "            output = model(data)\n",
    "            loss = criterion(output.squeeze(), target)\n",
    "            valid_losses.append(loss.item())\n",
    "\n",
    "        train_loss = np.average(train_losses)\n",
    "        valid_loss = np.average(valid_losses)\n",
    "        avg_train_losses.append(train_loss)\n",
    "        avg_valid_losses.append(valid_loss)\n",
    "            \n",
    "        epoch_len = len(str(n_epochs))\n",
    "        \n",
    "        print_msg = (f'[{epoch:>{epoch_len}}/{n_epochs:>{epoch_len}}] ' +\n",
    "                     f'train_loss: {train_loss:.5f} ' +\n",
    "                     f'valid_loss: {valid_loss:.5f}')\n",
    "        \n",
    "        print(print_msg)\n",
    "        \n",
    "        train_losses = []\n",
    "        valid_losses = []\n",
    "        early_stopping(valid_loss, model)\n",
    "        \n",
    "        if early_stopping.early_stop:\n",
    "            print(\"Early stopping\")\n",
    "            break\n",
    "\n",
    "    model.load_state_dict(torch.load('carr_ser_stackedLSTM.pth'))\n",
    "\n",
    "    return  model, avg_train_losses, avg_valid_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:10:25.604349Z",
     "start_time": "2021-04-28T05:10:25.598881Z"
    }
   },
   "outputs": [],
   "source": [
    "def evaluate_model(model, test_loader):\n",
    "    test_loss = 0.0\n",
    "    y_test = []\n",
    "    y_hat = []\n",
    "    model.eval()\n",
    "    for data, target in test_loader:\n",
    "        data, target = data.cuda(), target.cuda()\n",
    "        y_test.append(target)\n",
    "        output = model(data)\n",
    "        y_hat.append(output.view(1))\n",
    "        loss = criterion(output.view(1), target)\n",
    "        test_loss += loss.item()\n",
    "    return test_loss/len(test_loader), y_test, y_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:19:46.964748Z",
     "start_time": "2021-04-28T05:10:26.866280Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1/300] train_loss: 0.98419 valid_loss: 0.95097\n",
      "Validation loss decreased (inf --> 0.950969).  Saving model ...\n",
      "[  2/300] train_loss: 0.97730 valid_loss: 0.94784\n",
      "Validation loss decreased (0.950969 --> 0.947840).  Saving model ...\n",
      "[  3/300] train_loss: 0.97388 valid_loss: 0.94447\n",
      "Validation loss decreased (0.947840 --> 0.944474).  Saving model ...\n",
      "[  4/300] train_loss: 0.97008 valid_loss: 0.94057\n",
      "Validation loss decreased (0.944474 --> 0.940572).  Saving model ...\n",
      "[  5/300] train_loss: 0.96548 valid_loss: 0.93583\n",
      "Validation loss decreased (0.940572 --> 0.935828).  Saving model ...\n",
      "[  6/300] train_loss: 0.95967 valid_loss: 0.92978\n",
      "Validation loss decreased (0.935828 --> 0.929775).  Saving model ...\n",
      "[  7/300] train_loss: 0.95202 valid_loss: 0.92162\n",
      "Validation loss decreased (0.929775 --> 0.921621).  Saving model ...\n",
      "[  8/300] train_loss: 0.94148 valid_loss: 0.90995\n",
      "Validation loss decreased (0.921621 --> 0.909952).  Saving model ...\n",
      "[  9/300] train_loss: 0.92623 valid_loss: 0.89214\n",
      "Validation loss decreased (0.909952 --> 0.892140).  Saving model ...\n",
      "[ 10/300] train_loss: 0.90293 valid_loss: 0.86311\n",
      "Validation loss decreased (0.892140 --> 0.863112).  Saving model ...\n",
      "[ 11/300] train_loss: 0.86509 valid_loss: 0.81242\n",
      "Validation loss decreased (0.863112 --> 0.812423).  Saving model ...\n",
      "[ 12/300] train_loss: 0.79972 valid_loss: 0.71804\n",
      "Validation loss decreased (0.812423 --> 0.718037).  Saving model ...\n",
      "[ 13/300] train_loss: 0.68464 valid_loss: 0.54582\n",
      "Validation loss decreased (0.718037 --> 0.545817).  Saving model ...\n",
      "[ 14/300] train_loss: 0.50859 valid_loss: 0.33455\n",
      "Validation loss decreased (0.545817 --> 0.334550).  Saving model ...\n",
      "[ 15/300] train_loss: 0.31908 valid_loss: 0.27122\n",
      "Validation loss decreased (0.334550 --> 0.271221).  Saving model ...\n",
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     ]
    }
   ],
   "source": [
    "model, train_loss, valid_loss = fit(model, batch_size, patience, n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-14T14:05:12.841612Z",
     "start_time": "2021-04-14T14:05:12.833174Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load('carr_ser_stackedLSTM.pth'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:20:13.352756Z",
     "start_time": "2021-04-28T05:19:47.473649Z"
    }
   },
   "outputs": [],
   "source": [
    "test_loss, y_test, y_hat = evaluate_model(model, test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:22:19.479332Z",
     "start_time": "2021-04-28T05:22:19.474535Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14020268761376617"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:20:32.184758Z",
     "start_time": "2021-04-28T05:20:31.918241Z"
    }
   },
   "outputs": [],
   "source": [
    "target, predicted = list(), list()\n",
    "for i in range(len(y_test)):\n",
    "    target.append(y_test[i].item())\n",
    "    predicted.append(y_hat[i][0].item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:20:52.228737Z",
     "start_time": "2021-04-28T05:20:51.021368Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 3))\n",
    "plt.plot(target[:200], label='target')\n",
    "plt.plot(predicted[:200], label='predicted')\n",
    "plt.legend(loc='upper left', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:32:23.684743Z",
     "start_time": "2021-04-28T05:32:22.446061Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(target, label='target')\n",
    "plt.plot(predicted, label='predicted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}