GRU.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GRU Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-01-2012</td>\n",
       "      <td>58.485714</td>\n",
       "      <td>58.928570</td>\n",
       "      <td>58.428570</td>\n",
       "      <td>58.747143</td>\n",
       "      <td>50.765709</td>\n",
       "      <td>75555200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04-01-2012</td>\n",
       "      <td>58.571430</td>\n",
       "      <td>59.240002</td>\n",
       "      <td>58.468571</td>\n",
       "      <td>59.062859</td>\n",
       "      <td>51.038536</td>\n",
       "      <td>65005500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05-01-2012</td>\n",
       "      <td>59.278572</td>\n",
       "      <td>59.792858</td>\n",
       "      <td>58.952858</td>\n",
       "      <td>59.718571</td>\n",
       "      <td>51.605175</td>\n",
       "      <td>67817400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06-01-2012</td>\n",
       "      <td>59.967144</td>\n",
       "      <td>60.392857</td>\n",
       "      <td>59.888573</td>\n",
       "      <td>60.342857</td>\n",
       "      <td>52.144630</td>\n",
       "      <td>79573200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09-01-2012</td>\n",
       "      <td>60.785713</td>\n",
       "      <td>61.107143</td>\n",
       "      <td>60.192856</td>\n",
       "      <td>60.247143</td>\n",
       "      <td>52.061932</td>\n",
       "      <td>98506100</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>2006</th>\n",
       "      <td>23-12-2019</td>\n",
       "      <td>280.529999</td>\n",
       "      <td>284.250000</td>\n",
       "      <td>280.369995</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>282.054138</td>\n",
       "      <td>24643000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>24-12-2019</td>\n",
       "      <td>284.690002</td>\n",
       "      <td>284.890015</td>\n",
       "      <td>282.920013</td>\n",
       "      <td>284.269989</td>\n",
       "      <td>282.322266</td>\n",
       "      <td>12119700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>26-12-2019</td>\n",
       "      <td>284.820007</td>\n",
       "      <td>289.980011</td>\n",
       "      <td>284.700012</td>\n",
       "      <td>289.910004</td>\n",
       "      <td>287.923645</td>\n",
       "      <td>23280300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>27-12-2019</td>\n",
       "      <td>291.119995</td>\n",
       "      <td>293.970001</td>\n",
       "      <td>288.119995</td>\n",
       "      <td>289.799988</td>\n",
       "      <td>287.814392</td>\n",
       "      <td>36566500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>30-12-2019</td>\n",
       "      <td>289.459991</td>\n",
       "      <td>292.690002</td>\n",
       "      <td>285.220001</td>\n",
       "      <td>291.519989</td>\n",
       "      <td>289.522614</td>\n",
       "      <td>36028600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2011 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     03-01-2012   58.485714   58.928570   58.428570   58.747143   50.765709   \n",
       "1     04-01-2012   58.571430   59.240002   58.468571   59.062859   51.038536   \n",
       "2     05-01-2012   59.278572   59.792858   58.952858   59.718571   51.605175   \n",
       "3     06-01-2012   59.967144   60.392857   59.888573   60.342857   52.144630   \n",
       "4     09-01-2012   60.785713   61.107143   60.192856   60.247143   52.061932   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "2006  23-12-2019  280.529999  284.250000  280.369995  284.000000  282.054138   \n",
       "2007  24-12-2019  284.690002  284.890015  282.920013  284.269989  282.322266   \n",
       "2008  26-12-2019  284.820007  289.980011  284.700012  289.910004  287.923645   \n",
       "2009  27-12-2019  291.119995  293.970001  288.119995  289.799988  287.814392   \n",
       "2010  30-12-2019  289.459991  292.690002  285.220001  291.519989  289.522614   \n",
       "\n",
       "        Volume  \n",
       "0     75555200  \n",
       "1     65005500  \n",
       "2     67817400  \n",
       "3     79573200  \n",
       "4     98506100  \n",
       "...        ...  \n",
       "2006  24643000  \n",
       "2007  12119700  \n",
       "2008  23280300  \n",
       "2009  36566500  \n",
       "2010  36028600  \n",
       "\n",
       "[2011 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/AAPL.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Date</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>58.485714</td>\n",
       "      <td>58.928570</td>\n",
       "      <td>58.428570</td>\n",
       "      <td>58.747143</td>\n",
       "      <td>50.765709</td>\n",
       "      <td>75555200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>58.571430</td>\n",
       "      <td>59.240002</td>\n",
       "      <td>58.468571</td>\n",
       "      <td>59.062859</td>\n",
       "      <td>51.038536</td>\n",
       "      <td>65005500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>59.278572</td>\n",
       "      <td>59.792858</td>\n",
       "      <td>58.952858</td>\n",
       "      <td>59.718571</td>\n",
       "      <td>51.605175</td>\n",
       "      <td>67817400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-06</td>\n",
       "      <td>59.967144</td>\n",
       "      <td>60.392857</td>\n",
       "      <td>59.888573</td>\n",
       "      <td>60.342857</td>\n",
       "      <td>52.144630</td>\n",
       "      <td>79573200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-09</td>\n",
       "      <td>60.785713</td>\n",
       "      <td>61.107143</td>\n",
       "      <td>60.192856</td>\n",
       "      <td>60.247143</td>\n",
       "      <td>52.061932</td>\n",
       "      <td>98506100</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>2006</th>\n",
       "      <td>2019-12-23</td>\n",
       "      <td>280.529999</td>\n",
       "      <td>284.250000</td>\n",
       "      <td>280.369995</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>282.054138</td>\n",
       "      <td>24643000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2019-12-24</td>\n",
       "      <td>284.690002</td>\n",
       "      <td>284.890015</td>\n",
       "      <td>282.920013</td>\n",
       "      <td>284.269989</td>\n",
       "      <td>282.322266</td>\n",
       "      <td>12119700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>284.820007</td>\n",
       "      <td>289.980011</td>\n",
       "      <td>284.700012</td>\n",
       "      <td>289.910004</td>\n",
       "      <td>287.923645</td>\n",
       "      <td>23280300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>291.119995</td>\n",
       "      <td>293.970001</td>\n",
       "      <td>288.119995</td>\n",
       "      <td>289.799988</td>\n",
       "      <td>287.814392</td>\n",
       "      <td>36566500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>289.459991</td>\n",
       "      <td>292.690002</td>\n",
       "      <td>285.220001</td>\n",
       "      <td>291.519989</td>\n",
       "      <td>289.522614</td>\n",
       "      <td>36028600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2011 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close   Adj Close  \\\n",
       "0    2012-01-03   58.485714   58.928570   58.428570   58.747143   50.765709   \n",
       "1    2012-01-04   58.571430   59.240002   58.468571   59.062859   51.038536   \n",
       "2    2012-01-05   59.278572   59.792858   58.952858   59.718571   51.605175   \n",
       "3    2012-01-06   59.967144   60.392857   59.888573   60.342857   52.144630   \n",
       "4    2012-01-09   60.785713   61.107143   60.192856   60.247143   52.061932   \n",
       "...         ...         ...         ...         ...         ...         ...   \n",
       "2006 2019-12-23  280.529999  284.250000  280.369995  284.000000  282.054138   \n",
       "2007 2019-12-24  284.690002  284.890015  282.920013  284.269989  282.322266   \n",
       "2008 2019-12-26  284.820007  289.980011  284.700012  289.910004  287.923645   \n",
       "2009 2019-12-27  291.119995  293.970001  288.119995  289.799988  287.814392   \n",
       "2010 2019-12-30  289.459991  292.690002  285.220001  291.519989  289.522614   \n",
       "\n",
       "        Volume  \n",
       "0     75555200  \n",
       "1     65005500  \n",
       "2     67817400  \n",
       "3     79573200  \n",
       "4     98506100  \n",
       "...        ...  \n",
       "2006  24643000  \n",
       "2007  12119700  \n",
       "2008  23280300  \n",
       "2009  36566500  \n",
       "2010  36028600  \n",
       "\n",
       "[2011 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'], format='%d-%m-%Y')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
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       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>58.485714</td>\n",
       "      <td>58.928570</td>\n",
       "      <td>58.428570</td>\n",
       "      <td>58.747143</td>\n",
       "      <td>50.765709</td>\n",
       "      <td>75555200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>58.571430</td>\n",
       "      <td>59.240002</td>\n",
       "      <td>58.468571</td>\n",
       "      <td>59.062859</td>\n",
       "      <td>51.038536</td>\n",
       "      <td>65005500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>59.278572</td>\n",
       "      <td>59.792858</td>\n",
       "      <td>58.952858</td>\n",
       "      <td>59.718571</td>\n",
       "      <td>51.605175</td>\n",
       "      <td>67817400</td>\n",
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       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>59.967144</td>\n",
       "      <td>60.392857</td>\n",
       "      <td>59.888573</td>\n",
       "      <td>60.342857</td>\n",
       "      <td>52.144630</td>\n",
       "      <td>79573200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>60.785713</td>\n",
       "      <td>61.107143</td>\n",
       "      <td>60.192856</td>\n",
       "      <td>60.247143</td>\n",
       "      <td>52.061932</td>\n",
       "      <td>98506100</td>\n",
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       "    <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>2019-12-23</th>\n",
       "      <td>280.529999</td>\n",
       "      <td>284.250000</td>\n",
       "      <td>280.369995</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>282.054138</td>\n",
       "      <td>24643000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-24</th>\n",
       "      <td>284.690002</td>\n",
       "      <td>284.890015</td>\n",
       "      <td>282.920013</td>\n",
       "      <td>284.269989</td>\n",
       "      <td>282.322266</td>\n",
       "      <td>12119700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-26</th>\n",
       "      <td>284.820007</td>\n",
       "      <td>289.980011</td>\n",
       "      <td>284.700012</td>\n",
       "      <td>289.910004</td>\n",
       "      <td>287.923645</td>\n",
       "      <td>23280300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>291.119995</td>\n",
       "      <td>293.970001</td>\n",
       "      <td>288.119995</td>\n",
       "      <td>289.799988</td>\n",
       "      <td>287.814392</td>\n",
       "      <td>36566500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>289.459991</td>\n",
       "      <td>292.690002</td>\n",
       "      <td>285.220001</td>\n",
       "      <td>291.519989</td>\n",
       "      <td>289.522614</td>\n",
       "      <td>36028600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2011 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "2012-01-03   58.485714   58.928570   58.428570   58.747143   50.765709   \n",
       "2012-01-04   58.571430   59.240002   58.468571   59.062859   51.038536   \n",
       "2012-01-05   59.278572   59.792858   58.952858   59.718571   51.605175   \n",
       "2012-01-06   59.967144   60.392857   59.888573   60.342857   52.144630   \n",
       "2012-01-09   60.785713   61.107143   60.192856   60.247143   52.061932   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2019-12-23  280.529999  284.250000  280.369995  284.000000  282.054138   \n",
       "2019-12-24  284.690002  284.890015  282.920013  284.269989  282.322266   \n",
       "2019-12-26  284.820007  289.980011  284.700012  289.910004  287.923645   \n",
       "2019-12-27  291.119995  293.970001  288.119995  289.799988  287.814392   \n",
       "2019-12-30  289.459991  292.690002  285.220001  291.519989  289.522614   \n",
       "\n",
       "              Volume  \n",
       "2012-01-03  75555200  \n",
       "2012-01-04  65005500  \n",
       "2012-01-05  67817400  \n",
       "2012-01-06  79573200  \n",
       "2012-01-09  98506100  \n",
       "...              ...  \n",
       "2019-12-23  24643000  \n",
       "2019-12-24  12119700  \n",
       "2019-12-26  23280300  \n",
       "2019-12-27  36566500  \n",
       "2019-12-30  36028600  \n",
       "\n",
       "[2011 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = df.set_index('Date').rename_axis(None)\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = dt['Close'].values.reshape(-1, 1)\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Using MinMaxScaler to scale the data\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "scaled_data = scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to create dataset with look_back data\n",
    "import numpy as np\n",
    "\n",
    "def create_dataset(dataset, look_back=1):\n",
    "    X, y = [], []\n",
    "    for i in range(len(dataset) - look_back - 1):\n",
    "        X.append(dataset[i: (i + look_back)])\n",
    "        y.append(dataset[i + look_back])\n",
    "\n",
    "    return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the look_back period\n",
    "look_back = 60\n",
    "\n",
    "# Creating Dataset\n",
    "train_size = int(len(scaled_data) * 0.80)\n",
    "\n",
    "train, test = scaled_data[:train_size], scaled_data[train_size:]\n",
    "\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "testX, testY = create_dataset(test, look_back)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "49/49 [==============================] - 5s 27ms/step - loss: 0.0046 - val_loss: 9.6990e-04\n",
      "Epoch 2/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 1.3594e-04 - val_loss: 4.7346e-04\n",
      "Epoch 3/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 1.1623e-04 - val_loss: 4.5555e-04\n",
      "Epoch 4/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 1.1032e-04 - val_loss: 4.3615e-04\n",
      "Epoch 5/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 1.0613e-04 - val_loss: 4.9821e-04\n",
      "Epoch 6/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 1.0088e-04 - val_loss: 3.9198e-04\n",
      "Epoch 7/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 9.5815e-05 - val_loss: 3.6628e-04\n",
      "Epoch 8/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 9.2576e-05 - val_loss: 3.4351e-04\n",
      "Epoch 9/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 9.1159e-05 - val_loss: 3.4319e-04\n",
      "Epoch 10/100\n",
      "49/49 [==============================] - 1s 13ms/step - loss: 9.1608e-05 - val_loss: 4.2386e-04\n",
      "Epoch 11/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 8.7786e-05 - val_loss: 4.3333e-04\n",
      "Epoch 12/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 8.6525e-05 - val_loss: 3.2327e-04\n",
      "Epoch 13/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.6041e-05 - val_loss: 4.0408e-04\n",
      "Epoch 14/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.6119e-05 - val_loss: 2.8681e-04\n",
      "Epoch 15/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 7.5406e-05 - val_loss: 2.8080e-04\n",
      "Epoch 16/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 7.4853e-05 - val_loss: 8.0661e-04\n",
      "Epoch 17/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 9.3646e-05 - val_loss: 3.0730e-04\n",
      "Epoch 18/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.2381e-05 - val_loss: 3.0942e-04\n",
      "Epoch 19/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 7.5895e-05 - val_loss: 3.6623e-04\n",
      "Epoch 20/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.1083e-05 - val_loss: 2.6371e-04\n",
      "Epoch 21/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.8354e-05 - val_loss: 3.1173e-04\n",
      "Epoch 22/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 7.5946e-05 - val_loss: 3.8082e-04\n",
      "Epoch 23/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.1166e-05 - val_loss: 2.7453e-04\n",
      "Epoch 24/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.4375e-05 - val_loss: 2.8580e-04\n",
      "Epoch 25/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 7.6947e-05 - val_loss: 2.8028e-04\n",
      "Epoch 26/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.1738e-05 - val_loss: 2.4559e-04\n",
      "Epoch 27/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.2943e-05 - val_loss: 2.4558e-04\n",
      "Epoch 28/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.1164e-05 - val_loss: 3.3034e-04\n",
      "Epoch 29/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.9206e-05 - val_loss: 2.5898e-04\n",
      "Epoch 30/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.7190e-05 - val_loss: 2.4333e-04\n",
      "Epoch 31/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.9288e-05 - val_loss: 2.5170e-04\n",
      "Epoch 32/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.2815e-05 - val_loss: 5.3609e-04\n",
      "Epoch 33/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.2961e-05 - val_loss: 3.9350e-04\n",
      "Epoch 34/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.9325e-05 - val_loss: 2.5256e-04\n",
      "Epoch 35/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 7.1786e-05 - val_loss: 4.1451e-04\n",
      "Epoch 36/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.0329e-05 - val_loss: 2.6803e-04\n",
      "Epoch 37/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.6024e-05 - val_loss: 2.5447e-04\n",
      "Epoch 38/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.7815e-05 - val_loss: 2.3514e-04\n",
      "Epoch 39/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.8729e-05 - val_loss: 2.3920e-04\n",
      "Epoch 40/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.5592e-05 - val_loss: 3.2796e-04\n",
      "Epoch 41/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.1120e-05 - val_loss: 2.5667e-04\n",
      "Epoch 42/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.6184e-05 - val_loss: 2.6838e-04\n",
      "Epoch 43/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.8874e-05 - val_loss: 5.5487e-04\n",
      "Epoch 44/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.9736e-05 - val_loss: 2.3710e-04\n",
      "Epoch 45/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.2535e-05 - val_loss: 2.7431e-04\n",
      "Epoch 46/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.6453e-05 - val_loss: 2.3616e-04\n",
      "Epoch 47/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 8.2438e-05 - val_loss: 3.8931e-04\n",
      "Epoch 48/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.3069e-05 - val_loss: 2.4902e-04\n",
      "Epoch 49/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.3675e-05 - val_loss: 2.4947e-04\n",
      "Epoch 50/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.3614e-05 - val_loss: 3.1968e-04\n",
      "Epoch 51/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.9417e-05 - val_loss: 3.0860e-04\n",
      "Epoch 52/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.6981e-05 - val_loss: 2.7111e-04\n",
      "Epoch 53/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.1991e-05 - val_loss: 2.4132e-04\n",
      "Epoch 54/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.5353e-05 - val_loss: 2.4550e-04\n",
      "Epoch 55/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.6950e-05 - val_loss: 2.4242e-04\n",
      "Epoch 56/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.6866e-05 - val_loss: 2.4441e-04\n",
      "Epoch 57/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.2539e-05 - val_loss: 2.7186e-04\n",
      "Epoch 58/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.4055e-05 - val_loss: 2.7939e-04\n",
      "Epoch 59/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.8306e-05 - val_loss: 2.8140e-04\n",
      "Epoch 60/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.0746e-05 - val_loss: 5.0008e-04\n",
      "Epoch 61/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.1953e-05 - val_loss: 3.0710e-04\n",
      "Epoch 62/100\n",
      "49/49 [==============================] - 1s 13ms/step - loss: 5.7224e-05 - val_loss: 2.9675e-04\n",
      "Epoch 63/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.0569e-05 - val_loss: 4.2526e-04\n",
      "Epoch 64/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 8.7915e-05 - val_loss: 2.3843e-04\n",
      "Epoch 65/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.3135e-05 - val_loss: 2.7663e-04\n",
      "Epoch 66/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.4243e-05 - val_loss: 2.4283e-04\n",
      "Epoch 67/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.8262e-05 - val_loss: 3.6622e-04\n",
      "Epoch 68/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.3630e-05 - val_loss: 2.4543e-04\n",
      "Epoch 69/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.4366e-05 - val_loss: 3.8029e-04\n",
      "Epoch 70/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.7647e-05 - val_loss: 6.0064e-04\n",
      "Epoch 71/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 7.6958e-05 - val_loss: 2.5307e-04\n",
      "Epoch 72/100\n",
      "49/49 [==============================] - 1s 13ms/step - loss: 6.1985e-05 - val_loss: 3.0084e-04\n",
      "Epoch 73/100\n",
      "49/49 [==============================] - 1s 13ms/step - loss: 6.1451e-05 - val_loss: 2.4929e-04\n",
      "Epoch 74/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.4775e-05 - val_loss: 2.3960e-04\n",
      "Epoch 75/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.4159e-05 - val_loss: 2.4063e-04\n",
      "Epoch 76/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.8041e-05 - val_loss: 2.4287e-04\n",
      "Epoch 77/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 8.2480e-05 - val_loss: 2.4737e-04\n",
      "Epoch 78/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.7398e-05 - val_loss: 2.3995e-04\n",
      "Epoch 79/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.1204e-05 - val_loss: 2.7535e-04\n",
      "Epoch 80/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.7878e-05 - val_loss: 2.4876e-04\n",
      "Epoch 81/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.7711e-05 - val_loss: 4.3672e-04\n",
      "Epoch 82/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.6088e-05 - val_loss: 2.4731e-04\n",
      "Epoch 83/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.7677e-05 - val_loss: 2.4136e-04\n",
      "Epoch 84/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.7045e-05 - val_loss: 2.5701e-04\n",
      "Epoch 85/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.7808e-05 - val_loss: 2.4554e-04\n",
      "Epoch 86/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.7461e-05 - val_loss: 3.0868e-04\n",
      "Epoch 87/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.4352e-05 - val_loss: 2.4309e-04\n",
      "Epoch 88/100\n",
      "49/49 [==============================] - 1s 13ms/step - loss: 6.0463e-05 - val_loss: 2.9847e-04\n",
      "Epoch 89/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.2311e-05 - val_loss: 2.5265e-04\n",
      "Epoch 90/100\n",
      "49/49 [==============================] - 1s 13ms/step - loss: 9.2436e-05 - val_loss: 5.8760e-04\n",
      "Epoch 91/100\n",
      "49/49 [==============================] - 1s 13ms/step - loss: 7.1601e-05 - val_loss: 2.8608e-04\n",
      "Epoch 92/100\n",
      "49/49 [==============================] - 1s 13ms/step - loss: 6.3469e-05 - val_loss: 3.7722e-04\n",
      "Epoch 93/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 7.5869e-05 - val_loss: 2.9716e-04\n",
      "Epoch 94/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.8815e-05 - val_loss: 3.7076e-04\n",
      "Epoch 95/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.4847e-05 - val_loss: 2.4036e-04\n",
      "Epoch 96/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.5474e-05 - val_loss: 2.5674e-04\n",
      "Epoch 97/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.9374e-05 - val_loss: 2.7752e-04\n",
      "Epoch 98/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.6006e-05 - val_loss: 2.4424e-04\n",
      "Epoch 99/100\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.5432e-05 - val_loss: 2.6262e-04\n",
      "Epoch 100/100\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.0726e-05 - val_loss: 2.8338e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2a49ee6a460>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building GRU Model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, GRU\n",
    "\n",
    "model = Sequential()\n",
    "model.add(GRU(units=50, return_sequences=True, input_shape=(look_back, 1)))\n",
    "model.add(GRU(units=50, return_sequences=False))\n",
    "model.add(Dense(units=25))\n",
    "model.add(Dense(units=1))\n",
    "\n",
    "# Compiling model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Training Model\n",
    "model.fit(\n",
    "    trainX,\n",
    "    trainY,\n",
    "    batch_size=32,\n",
    "    epochs=100,\n",
    "    validation_data=(testX, testY),\n",
    "    verbose=1\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49/49 [==============================] - 1s 5ms/step\n",
      "11/11 [==============================] - 0s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "# Making Predictions\n",
    "train_Predict = model.predict(trainX)\n",
    "test_Predict = model.predict(testX)\n",
    "\n",
    "# Inverting predictions to original scale\n",
    "train_Predict = scaler.inverse_transform(train_Predict)\n",
    "test_Predict = scaler.inverse_transform(test_Predict)\n",
    "trainY = scaler.inverse_transform(trainY)\n",
    "testY = scaler.inverse_transform(testY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluating Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score (RMSE): 1.6979980264629058\n",
      "Test Score (RMSE): 3.9682277073377623\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "train_Score = np.sqrt(mean_squared_error(trainY, train_Predict))\n",
    "test_Score = np.sqrt(mean_squared_error(testY, test_Predict))\n",
    "\n",
    "print(f'Train Score (RMSE): {train_Score}')\n",
    "print(f'Test Score (RMSE): {test_Score}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shifting train predictions for plotting\n",
    "train_predict_plot = np.empty_like(scaled_data)\n",
    "train_predict_plot[:, :] = np.nan\n",
    "train_predict_plot[look_back : len(train_Predict) + look_back, :] = train_Predict\n",
    "\n",
    "# Shifting test predictions for plotting\n",
    "test_predict_plot = np.empty_like(scaled_data)\n",
    "test_predict_plot[:, :] = np.nan\n",
    "test_predict_plot[len(train_Predict) + (look_back*2) + 1 : len(scaled_data) - 1, :] = test_Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting baseline and predictions\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(scaler.inverse_transform(scaled_data), label='Original Data')\n",
    "plt.plot(train_predict_plot, label='Train Predict')\n",
    "plt.plot(test_predict_plot, label='Test Predict')\n",
    "plt.title('GRU Model - Original vs Predicted Stock Prices')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Forecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the function to forecast next n days\n",
    "def forecast_next_days(model, data, n_days, look_back):\n",
    "    forecast = []\n",
    "    last_sequence = data[-look_back:]\n",
    "    current_sequence = np.reshape(last_sequence, (1, look_back, 1))\n",
    "\n",
    "    for _ in range(n_days):\n",
    "        next_value = model.predict(current_sequence)\n",
    "        forecast.append(next_value[0, 0])\n",
    "        current_sequence = np.append(\n",
    "            current_sequence[:, 1:, :],\n",
    "            next_value.reshape(1,1,1),\n",
    "            axis=1\n",
    "            )\n",
    "    \n",
    "    return np.array(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n"
     ]
    }
   ],
   "source": [
    "# Number of days to forecast\n",
    "n_days = 30\n",
    "\n",
    "# Getting the last look_back days from the dataset\n",
    "last_sequence = scaled_data[-look_back:]\n",
    "\n",
    "# Forecasting next 30 days\n",
    "forecasted_values = forecast_next_days(model, scaled_data, n_days, look_back)\n",
    "\n",
    "# Inverting the scaled predictions back to the original scale\n",
    "forecasted_values = scaler.inverse_transform(forecasted_values.reshape(-1,1))\n",
    "\n",
    "# Creating a Dataframe for plotting\n",
    "forecast_dates = pd.date_range(\n",
    "    start = dt.index[-1] + pd.Timedelta(days=1),\n",
    "    periods = n_days,\n",
    "    freq = 'B'\n",
    ")\n",
    "\n",
    "forecast_df = pd.DataFrame(\n",
    "    forecasted_values,\n",
    "    index = forecast_dates,\n",
    "    columns = ['Forecast']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lhx9+mN///vfMnDmTDz/8kN27d7No0SKmT5/OokWL2LNnDwBf+9rX+P73v8/ChQv50Y9+VCf7dPDgQc4//3xmzJjBjBkzAsHVeeedx+zZs5kyZQqPPvpom8adlJTEww8/zEsvvURBQQGlpaUsWrSII444gmnTpvHyy87Ur1tuuYXt27czc+ZMbr755ib3ExERERHpamFtrd3VfvbfDWzcXxzSc04enMJPz57S7D4VFRXMnDkz8LygoIBzzjmnwX733HMPO3fuJDY2lsLCQtLS0rj22mtJSkripptuAuDss8/miiuu4Morr+Sxxx7jhhtu4KWXXgLgiy++4J133iEyMpInnngicN4bbriBBQsW8OKLL+L1eiktdda+feyxx8jIyKCiooIjjzySCy64gH79+rX6vaekpDBq1Ci2bt3K7NmzefHFF0lJSSEvL4958+ZxzjnncM8997B+/XrWrFkDOFmrxvZT0wQRERER6Wq9OhjqKvHx8YFgAJw5QytXrmyw3/Tp07n00ks577zzOO+88xo917Jly3jhhRcAuPzyy/nhD38YeO3CCy8kMjKywTHvvvtuYF5SZGQkqampgJOJevHFFwHYu3cvW7dubVMwBM66QP6vt912Gx988AERERHs27ePgwcPNrp/Y/sNHDiwTdcVEREREQm1Xh0MtZTB6WqvvvoqH3zwAa+88gq/+MUv2LBhQ4vHBGdUEhMTW32t9957j3feeYdly5aRkJDACSec0Oa1fUpKSti1axfjx4/nn//8J7m5uaxatYro6GhGjhzZ6Plau5+IiIiISGfTnKEu4vP52Lt3LwsXLuQ3v/kNhYWFlJaWkpycTElJSWC/Y445hmeeeQZwAovjjjuuxXMvWrSIP//5zwB4vV6Ki4spKioiPT2dhIQENm/ezCeffNKm8ZaWlnLddddx3nnnkZ6eTlFREVlZWURHR7NkyRJ2794N0GD8Te0nIiIiIj3L5gPFFFXUdPUwQkrBUBfxer1cdtllTJs2jVmzZvG9732PtLQ0zj77bF588cVAA4U//OEPPP7440yfPp2nnnqKBx54oMVzP/DAAyxZsoRp06Yxe/ZsNmzYwGmnnYbH42H69OncfvvtzJs3r1XjXLhwIVOnTmXu3LkMHz6cRx55BIBLL72UlStXMmfOHP75z38yceJEAPr168exxx7L1KlTufnmm5vcT0RERER6lu8+vYbr/rmqq4cRUsY/B6QnmjNnjq0/F2fTpk1MmjSpi0Yk7aWfm4iIiEj3tb+wgmPueZdbT5/ItxaM6erhtJkxZpW1dk797coMiYiIiIhIs370n7UAnDAhq4tHEloKhkREREREpFmbcpzlasYPSOrikYSWgiEREREREWlWfEwkd49ag3n52109lJBSMCQiIiIiIs0qqfQwwrMLNr7S1UMJKQVDIiIiIiLSJGstJZUeTHQ8eCq6ejghpWBIRERERESaVFnjw+uzRMQkgs8D3t6z1pCCoTCIjIxk5syZgT+7du3q6iEBcP/991NeXt7VwxARERGRHqSk0gl+ImPjnQ01vSc7FNXVA+iN4uPjWbNmTZuP83g8REWF70dy//33c9lll5GQkBC2a4iIiIhI71Jc6QEgOi7R2VBTAXEpXTii0FFmqJOsWbOGefPmMX36dM4//3wOHz4MwAknnMBtt93GggULeOCBB1i1ahULFixg9uzZnHrqqeTk5ACwbds2TjrpJGbMmMERRxzB9u3bKS0tZdGiRRxxxBFMmzaNl19+GYCysjLOPPNMZsyYwdSpU/n3v//NH/7wB/bv38/ChQtZuHBhl30fRERERKRnKXYzQ9Gx7gfqvWjeUO/ODL1+CxxYF9pzDpwGp9/T7C4VFRXMnDkTgFGjRvHiiy9yxRVX8Mc//pEFCxZwxx138LOf/Yz7778fgMLCQt5//31qampYsGABL7/8Mv379+ff//43P/7xj3nssce49NJLueWWWzj//POprKzE5/MRExPDiy++SEpKCnl5ecybN49zzjmHN954g8GDB/Pqq68CUFRURGpqKr/73e9YsmQJmZmZof2eiIiIiEivtWqX8yF+Rlqqs0FlctKc+mVyRUVFFBYWsmDBAgCuvPJKLrzwwsDrF110EQBbtmxh/fr1nHzyyQB4vV4GDRpESUkJ+/bt4/zzzwcgLi4OgJqaGm677TY++OADIiIi2LdvHwcPHmTatGncdNNN/OhHP+Kss85i/vz5nfG2RURERKQX2pVfRkZiDAP7ZTgbanrPHPTeHQy1kMHpLhITnfpLay1Tpkxh2bJldV4vLi5u9Lh//vOf5ObmsmrVKqKjoxk5ciSVlZWMHz+eVatW8dprr3HrrbdyyimncMcdd4T9fYiIiIhI73OgqJKBKXEQ7XM29KLMkOYMdYLU1FTS09P58MMPAXjqqacCWaJgEyZMIDc3NxAM1dTUsGHDBlJSUhg6dCgvvfQSAFVVVZSXl1NUVERWVhbR0dEsWbKE3bt3A7B//34SEhK47LLLuOmmm/jss88ASE5OpqSkpBPesYiIiIj0FjlFlQxMjYNod85QLwqGendmqBt58sknufbaaykvL2f06NE8/vjjDfaJiYnh+eef54YbbqCoqAiPx8ONN97IlClTeOqpp/jWt77FHXfcQXR0NM899xyXXnopZ599NnPmzGHmzJlMnDgRgHXr1nHzzTcTERFBdHQ0f/7znwG45pprOP300xk0aBBLlizp1PcvIiIiIj3TweJKZg5Pgxg3j9KLyuSMtbarx9Buc+bMsStXrqyzbdOmTUyaNKmLRiTtpZ+biIiISPfzzSc/5Z1Nh/j+yeO5YVYk/GEWnP8IzLi4q4fWJsaYVdbaOfW3q0xOREREREQa2FtQzjubDgEQGWFqy+Sqy7pwVKGlMjkREREREWlgT0FtOdyxYzMhuveVyfXKYMhaizGmq4chrdSTSzVFREREequDxZUALLnpBEZlJoLP67xQ3XuCoV5XJhcXF0d+fr5usHsIay35+fmBtZNEREREpHs44AZDA1JinQ0RkRAZCzUqk+u2hg4dSnZ2Nrm5uV09FGmluLg4hg4d2tXDEBEREZEgB4sqSY6LIiEmKGSIju9VmaFeFwxFR0czatSorh6GiIiIiEiPdrC4yllsNVhMYq+aM9TryuRERERERKTjDhRXMqB+MBSfDuX5XTOgMFAwJCIiIiIiDeSWVJHlny/klzYCDu/qkvGEg4IhERERERFpoKSyhpS46LobM0Y5wVAvaVamYEhERERERBqoqPESHxNZd2P6SPBUQsmBLhlTqCkYEhERERGROqo9Pmq8loTo+sGQ26isl5TKKRgSEREREZE6KqqdBVYTYus1n04f4Xwt3NPJIwoPBUMiIiIiIlJHeY0HgIT6ZXJxqc7XquJOHlF49Lp1hkREREREpP3W7yviz+9vB2BwWnzdF2MSna/VZZ08qvBQMCQiIiIiIgFn/fGjwOOJA5PrvhidABioLu3cQYWJyuRERERERKRRaQn1WmsbAzFJvSYzpGBIREREREQCUuNrA6DYqMiGO8QkQlVJJ44ofFQmJyIiIiIibDtUQrXH0j85lqKKmqZ3jEnsNZkhBUMiIiIiIn3Y9txS0uKjOf+hpZRUepg8KKX5A2KTes2cIQVDIiIiIiJ9VGWNl0X3vc/kQSmUVDrttDfmtNA2OyYZqnpHMKQ5QyIiIiIifdTbGw8CDQOggSlxfPjDhY0f1IsyQwqGRERERET6qI+35TW6ffbIdIZlJDR+UExSr2mgoGBIRERERKSPenbl3ka3xzXWRc5PmSEREREREenJ9uSX47ONvxYf00yYEKs5QyIiIiIi0oOV13gabLt6/iigifWF/GJTwFMB3obH9zQKhkRERERE+qDDZXXXEspMiiUtIQaAyAjT9IExSc7X6p4/b0jBkIiIiIhIH3TPG5sBOH58/8C2qOaCIL/YZOdrL2iioGBIRERERKQP2pHrzPv54yWzmDgwme+dPA6vdSYRmeZiIgVDIiIiIiLSU5VU1lBW5eGGReNIjY/mjRuP59KjRlBZ4wNa6ianYEhERERERHqo1XsK8Vk4cmR6ne2VNV4A4qJbaKAACoZERERERKTnWb2nkAgDRwyvGwwNSYsHYFRmEwuuAiT2c76WHAjX8DpNVFcPQEREREREOtfBkkoyEmNIjK0bDlw+bwRj+idx7Nh+TR+cNgKi4iF3c5hHGX4KhkRERERE+pjPdh8OtNEOFhFhOG5cZvMHR0RC/wlwaGOYRtd5FAyJiIiIiPQhSzYfYvOBEmKiOjBjJmsybH83dIPqIpozJCIiIiLShyzefBCA31wwvf0nyRwHpQd6fBMFBUMiIiIiIn3Ih1vzWDQxi/NmDWn/SRLcOUWVRaEZVBdRMCQiIiIi0odkH65gwsDkjp0kzm2vXVnc8QF1IQVDIiIiIiJ9hNdn8fossc0tqtoa/rWGlBkSEREREZGeoMbrAyA6ynTsRHFpzlcFQyIiIiIi0hNUeZxgKCayg2FAXKp7QpXJiYiIiIhID+DPDMV2pK021AZDygw1zhgzzBizxBizyRizwRjzXXf7ncaYfcaYNe6fM4KOudUYs80Ys8UYc2q4xiYiIiIi0hdVu5mh6FBlhioLO3aeLhbORVc9wA+stZ8ZY5KBVcaYt93Xfm+tvTd4Z2PMZOBiYAowGHjHGDPeWusN4xhFRERERPoMfzDUoQVXAaJiIDoBKgo7PqguFLbMkLU2x1r7mfu4BNgENNfM/FzgGWttlbV2J7ANmBuu8YmIiIiI9DX+MrkOB0PgZIdUJtcyY8xIYBaw3N30HWPMWmPMY8aYdHfbEGBv0GHZNB88iYiIiIhIG1SFqkwOnI5yPbxMLuzBkDEmCfgPcKO1thj4MzAGmAnkAPf5d23kcNvI+a4xxqw0xqzMzc0Nz6BFRERERHqh659eDYQwM6QyuaYZY6JxAqF/WmtfALDWHrTWeq21PuAv1JbCZQPDgg4fCuyvf05r7aPW2jnW2jn9+/cP5/BFRERERHqNovIaduaVASForQ0Qn6YyuaYYYwzwN2CTtfZ3QdsHBe12PrDeffwKcLExJtYYMwoYB6wI1/hERERERPqS9ftrA5fQZIbSenxmKJzd5I4FLgfWGWPWuNtuAy4xxszEKYHbBXwLwFq7wRjzLLARpxPdt9VJTkREREQkNNbtCwqGQpYZKuz4ebpQ2IIha+1HND4P6LVmjrkLuCtcYxIRERER6au2HCgJPA5JA4WEflBVDNVlEJPY8fN1gU7pJiciIiIiIl2rqKIm8DgkZXIDpzlfc9Z2/FxdRMGQiIiIiEgfUFrpCTyODUUwNPgI5+v+zzp+ri6iYEhEREREpA8oqaoNhkJSJpc8AFKGwr5VHT9XF1EwJCIiIiLSBxSVVwceh6RMDqDfGCjKDs25uoCCIRERERGRXu5AUSX7iyoBMAYSYiJDc+L4dKg4HJpzdYFwttYWEREREZFu4LCbFfrjJbNYMKE/cdEhCoYSMqC8IDTn6gLKDImIiIiI9HJl7nyh1PhoUuKiQ3dif2bI2tCdsxMpGBIRERER6eX2FVYAkBgb4sKw+AywXqgsannfbkjBkIiIiIhIL/fdZ9YAEBcd4tv/hH7O14qeWSqnYEhEREREpI8YlpEQ2hMmZDhfe+i8ITVQEBERERHp5YZnJDBjWFpo5wtBbWaohwZDygyJiIiIiPRiPp8lp6iCIWnxoT95IDOUH/pzdwIFQyIiIiIivVhuaRU1XsuQtLjQnzzeHwzlhf7cnUDBkIiIiIhIL5Z92OkkNyQ9DJmhuFSIiOqxZXKaMyQiIiIi0gtZaznvoaV8vrcQgCFpIW6eAGCMM29ImSEREREREekuqr2+QCAEMDIzDMEQQFIWFO8Pz7nDTMGQiIiIiEgvVO3x1XkeGxUZngsNPgL2fgo+b3jOH0YKhkREREREeqHgYGh0ZmL4LjTyOKgqggPrwneNMFEwJCIiIiLSC1V7nWDoJ2dO4tUb5ofvQsPnOV/3rQrfNcJEwZCIiIiISC/kzwylJ8QQHxOmEjmA5EHO17Lc8F0jTBQMiYiIiEine27lXo7/zRKstV09lF7LHwzFRIX5lj8yGuLTe2QwpNbaIiIiItLpbn5+LQBVHh9x0WHMWvRhVZ0VDAEk9ofSQ+G/TogpMyQiIiIiXaaypud1IOsp/HOGYiI7IxjKgvL88F8nxBQMiYiIiEiXKa9WMBQunVYmB5CYqcyQiIiIiEhbKBgKn7IqDwCJsZ0wMyYpC8oUDImIiIiIAJBTVMErn+9vdp8KBUNhU+oGQ8lxnRAMJWZBZRF4qsJ/rRBSAwURERERCbnDZdUcffe7AMwfm0l6Ykyj+1VozlDYFFd2YjCU1N/5WnoI0oaF/3ohosyQiIiIiITcf9fWZoRyiirrvObz1bbTXr+viCqPAqJQW5tdyO0vrQcgOTY6/BdMGuB87WGlcgqGRERERCTkDpfVBB7vyi/jd29/QY3b3WxNdmHgtZ//byN3vLShs4fX6z347rbA47joTrjlT8pyvpb2rLWGFAyJiIiISMgVV9YGQ79+YzN/WLyV/6zKBuCN9Qfq7Pvp7oJOHVtfUOl2kgMwxoT/gqnDna+HNob/WiGkYEhEREREQq64ojYYKnIf+wOkpdvzGJeVFHg9Xouuhlx1Z5ceJvWHgdNg2zude90OUjAkIiIiIiFXXFlDP7dpgtedI1RV42QrduWVM2t4WmDfOAVDIfXG+hw+2dEF2baxJ8OeT6CqtPOv3U4KhkREREQk5IorPAxMjQOgxO1qVunxYq2losZLv6TYwL7KDLWfz2d5flU2lUFd+a7752eBxz88bULnDWbAFLBeKN7XedfsIAVDIiIiIhJyJVU1ZCXH1tlWVuWlxmvx+iyJMZG8ceN8AMa6JXNlVZ5ASZ20zqe7Crjpuc/54fNr2VtQDoC/Wd/Egclcd8LYzhuMv6NcSU7nXbODFAyJiIiISMgVV3hIjY8mJWiNm7IqDwVl1YBTGjdxYArRkYZNOcV8uquAuXe9w4yfvcWBoko+2prXVUPvUSIinOYIr3y+n/m/WYK1toUjwih5oPO15EDz+3UjCoZEREREJOSKK2tIiY8mMyg7VFbt4dv/ckq4CsudDFCN17J8ZwEXPryMsmqn1OuMP3zIZX9b3vmD7oFqgrrGAXUya50eFykYEhEREZG+zlpLSaWHlLho+gfNDTpcVsOq3YcBKK3yNHm8P3skLav21g2G7nl9c+CxpZOjodhkiElSMCQiIiIifVd5tRevz5IcF8WozMTA9j3unBYAX1eWc/UiNd6638dnPt0beBzRGesL1Zc8EEoVDImIiIhIH+VfTyglPppxA5ID2/cVVgQef//k8Z0+rt6opl5mKFhy0HytTpM8CIrVQEFERERE+qj8UqfMLT0hus7iqn7fP3k8aQkxLZ7H51P2qCXNB0PRnTgS/0UHQcn+zr9uO3VBuCgiIiIivVleaRUA/ZPjAmsNBRuSFt+q83itJYIuKPXqQao9jQdDc0akc+fZUzp5NEDKIGfOkM8HEd0/79L9RygiIiIiPcL/1u7njfUHyD7slMNlJccyODWOby0YzVePGh7YL7Pe+kNN8Soz1CL/nKHXvzs/sG3+uEye/79jGN4vofMHlDkevNVwcH3nX7sdlBkSERERkZD4zr9WA3DW9EFkJccyND0eYwy3nj6Jao+PGUNT2ZRTwjFj+rXqfOqx0DJ/mdzAlDhuOX0i67KL+NFpE7tuQONPAxMBm16BQdO7bhytpGBIRERERELqf2tz+NKsIZigbmYxURFcdOTwZo5qyKtoqEWVNc7aTDFREVy7YEwXjwZIzIQRx8KWN+DEn3T1aFqkYEhEREREQu64cZkdPofK5FpWXFlDZIQhISayq4dS6+wHILF/V4+iVTRnSERERERCIriVc1uDoW13nc7sEel1tqmbXMuKKmpIjY+uk4Xrcv3GQFxKV4+iVRQMiYiIiEhIVNZ4GZuVxL0XziAruWEXucYkuhmNqMgI/nLFHKIiam/qVSbXsqIKDyldsZ5QL6FgSEREREQ6rMbro8ZrOXfGYL48e2irj3v3phN48bpjAMhIjOHyo0cEXvMpGGrW1oMl/G/tfiYN6hlZmO5IwZCIiIiIdFh5tTORP76Nc1cGpMQxa3hteVxqfO1Cob6m1xPt9dbsLWTkLa+yPbc0sM3rs7yxPocqj/O9fvj9HVgLN540vquG2eMpGBIRERGRDiuv9gCQGNuxkq3oyNrb055SJufzWTze0EZuL63eB8D7W3ID21buKuDaf3zG3a9tBqCoopqBKXFMGJgc0mv3JQqGRERERKTD/JmhjnY1u/KYkaQlONmhntJA4ZqnVjL2x6+H/Tr5ZdUAgUVty6u9DE2PD/t1ezMFQyIiIiLSYeVVbplcdMeCoaTYKO44azLQc1prv7PpEADVnvDW9RW4wZC/a195tbfNZYlSl4IhEREREemw0iqnTC4hpuOdzSLdjnKvrsuhzD1vd5V9uDzweG/Q446ybolgcMfs51buBWqDoYpqb/daX6gHUjAkIiIiIu1mraWgrJpNOcUAjO6f2OFz+tfM+e2bW7jx32s6fL5wCs4G7cgtC9l5n1y2G4Dg1YP8pYiREYYar4/yGk9Igs++TN89EREREWm3n/13I08s3QXAkLR4Bqd1fA5LZFA6ZPWewg6fr7PszCsFBnToHIfLqvnm31cGnvss7C+s4Jh73g1sW7GzgHHuHKX545QZ6ghlhkRERESkXWq8vkAgBHDixKyQnDeooRxFFdUhOWe4eILmNe0tqOjw+dbvL2LV7sOB51UeH2uzC+vss2F/ceBxQgfnaPV1ygyJiIiISLt8urOgzvPzZg0JyXkjgjJDNd7u2UTB57Nc+49VzByeFthWWePt8HkPFFXWeV5Z4w2UDTZGc4Y6RsGQiIiIiLRLcMOAxJhIjggKDDoiqYNrFXWG4soa3tp4kLc2Hgxsqw7BWkMHi2uDoZioCCcYamb/WGWGOkRlciIiIiLSLlVu84DXbpjP+p+d2mwGoy36JcWG5DzhVFLZsMtdVU3HgiFrLVsPlQKw9JYTiY+OpLLGWydTVv9bvK+w46V5fVn3D7tFREREpFvyd1IbmhEfskAIICMxJmTnCpfSRlp+dyQzZK1l4b3vsSu/nIzEGAanxRMXHUFljY+IiOD96h43OrPj3fv6MgVDIiIiItIu/sxQTGRoi43SE6JDer5waCwz1J5FVzflFNM/OZadeWXsynfKDv3rLMVFR1Lp8WKaKJR7/+YTGJqe0OZrSi0FQyIiIiLSLtVhCoaiQny+UKus8fKr1zbV2RYTGUGVp20NFKo8Xk5/4EMAbj51QmB7PzczFhcVSUW1F1/9dJBrRD9lhTqqe/+miYiIiEi3Ve31ER1piIgIXYlcT/DellzW7C3k/otmBrbFRUdQWF7TpvPsL6xtlvDbN7cEHvsbSMTHRLJsRz7feHJlg2P/d/1xbRy1NEbBkIiIiIi0S7XHF/KsUGO8vu7VXnvboRIATps6kGlDUgGwwNZDpVRUtz47lB3UjS9YohsMJcZGNlqOBzDVva50jMrkRERERKRdqjzeTmntXF7tITmu+8wjKqv2Eh1piIuOJCbKCQanDk5l2Y58ckuqGN6vdfN4sg/X7QQXHx3JUaMzuOOsyQAkxDS8Vf/1BdNC2qyirwtbKG+MGWaMWWKM2WSM2WCM+a67PcMY87YxZqv7NT3omFuNMduMMVuMMaeGa2wiIiIi0nHVHqdMLhwmDkwOPG5LtqUzVFR7iXeDQP/7nzI4BYDD5dWtPs/egrqZodvPmswTV81ldP8kwFm7qb6LjhzOV+YMa9e4paFw5jU9wA+stZOAecC3jTGTgVuAxdbaccBi9znuaxcDU4DTgIeMMVpFSkRERKSb8vgsURHhuZ187tqjuemU8YCTiekOKqq93PD0ar44WEK8G6ikJzjNDtLcDngFbQiGsg9XMCwjPvD8q0cNr/N6fL3M0LJbT2zXuKVpYQuGrLU51trP3MclwCZgCHAu8KS725PAee7jc4FnrLVV1tqdwDZgbrjGJyIiIiId4/NZosKUGUqOi2bcACc7VNbImj5d4aTfvc8rn+9n6fb8QAnbXedP44ZF4zhp8gCgdWO11rJq92Fe+Xw/w5ppjR2cGZo6JCUQeEnodMqcIWPMSGAWsBwYYK3NASdgMsZkubsNAT4JOizb3Vb/XNcA1wAMHz68/ssiIiIi0kk8PhtYEyccEt2Ao6Kme2SG9hXWzvHxl8llJMbw/ZPHB0reylvIYhWUVXPEL94OPE+IieRvV84JNE0IluBuG5oez/+un9/h8UtDYW//YYxJAv4D3GitLW5u10a2NWgdYq191Fo7x1o7p3///qEapoiIiIi0kddniQzjZP6EWCfg6C6ZoaSggOXLs4fWeS3BzeK0NL/ptXU5dZ7HRkWyaNIA5o3u12Bff2YonAFnXxfWzJAxJhonEPqntfYFd/NBY8wgNys0CDjkbs8GgmeDDQX2h3N8IiIiItJ+4c4M+QOMlrItnSG/tIrSKg/fOn40Xz1qeIMFT/2ZnZbG+pOX1td5np7YdJe8hEayRRJa4ewmZ4C/AZustb8LeukV4Er38ZXAy0HbLzbGxBpjRgHjgBXhGp+IiIiIdEw45wxBbZlcdwiGtueWAXD0mH4NAiGA2KgIjHHagDdnxrC0Os9vOmVCk/smuKV4yguFTzjDzWOBy4F1xpg17rbbgHuAZ40x3wD2ABcCWGs3GGOeBTbidKL7trW263/zRURERKRRTmYofLMuajNDXV8mtzOvFIAxbtvr+owxJERHthi4ZSXHMrp/Ijvc4CqtmaYIiW6ZoNYVCp+wBUPW2o9oOpBd1MQxdwF3hWtMIiIiIhI6zpyh8J3f37GtrKrrPx/fW1BBZIRhUGpck/vEx0S1GAxV1nhbvYCs//0rFAqfsDdQEBEREZHexVrLtkOleMO4zhBAXHTrSs/C5cmlu1i/rwiAZTvyGZeVRFRk0+83ISaSiibGaq1lV14ZVTU+4qKcc5w2ZWCz1/dnhiR8NCtLRERERNrktXUH+Pa/PgPg6Ea6oIWKMYbEVmRbwuWnr2wAYNc9Z/LFwRLOn9Vg1Zc6EmKaLpN7d/MhvvHkSgBOmNCfrXed3mInPn9mSKmh8FFmSERERKSPK6/2MOEnr/P0ij2t2n9XflngcbjbPifGRvLprgIOl1WH9TrNKamsoaTSw5C0+Gb3i28mGNpfVBl4HBcVSXRkBBEtfO+i3RpExULho2BIREREpI/7fG8RVR5fIBPSksqgRVDDHQylJ8SwNruIC/68NKzXqc/rq13usrC8xhlLYtPNDsCfGWq8TM7j9QUezx6R3qox+DNDUwantmp/aTuVyYmIiIj0cXsLygGo9vhYvOkgiyYNaHb/3JKqwOOoMAdD/oVOd+SVtbBnaNUEBS8llU6AkxLX/K1zQkwU+aWNZ7CCM1tfmTOs0X3qG5wWzzPXzGP6UAVD4aLMkIiIiEgfl11YEXjsn9fSmNIqD5c8+gnPfLo3sC3cmSHb8i5hUTcYcjJDLXWBS4iJpKKm8TK5zQdKyEqO5cMfLiQ1oXXd5ADmje5XO3dIQk7BkIiIiEgft2x7Xp3nZVUNS73W7C1k6k/fZNmO/Drbwx0MVXRR84Qab20Yludme1JaEQztzi/n010FDV5bv6+IeaP7MSwjIbQDlQ5pVTBkjDnOGHOV+7i/MWZUeIclIiIiIqFWWePlQNBEfoD80ipW7T7MDYvGcfJkpzzulc/3N9jnvD99XGfbSW4pnccX3tyNz3ZNbig4M/Ta+hzSEqIZP7DxBVf94qOdDM6FDy+rsz23pIr9RZUqd+uGWgyGjDE/BX4E3Opuigb+Ec5BiYiIiEjofedfq5l392J8QQHMppwSfBbmjc7g/otmAnC4vO68lzV7C2vPsXAsL1x3DF8/diQAyS3Mo+moLoqFqPbUBkOvrs3hnBmDiY1qft2fhJja14MbMOx05zuNH5Ac4lFKR7Xmt/d8YBbwGYC1dr8xRj9JERERkR7mnU0HAcgvq6Z/cizVHh+lbklcanw0ibFRxEdH8r/Pc/jmcaOJcRcH3bi/OHCOm06dADiLiD5y+WyOGRO+dYYAbBfNGgrODAHMGp7W4jHxQcHQ7vwyRvd3Mkn+eURaRLX7aU2ZXLW11uLOXzPGJIZ3SCIiIiISDv7ObJf+9RPe/yKX8T95nWv/sQqARHeS/vCMBDbmFLNky6HAcRtzihucyxjDqVMGtthUoKOCq/DqByjhdCioYx7A2P4t5wKCM0P7C2vLEf2tyFvKLEnna00w9Kwx5hEgzRhzNfAO8JfwDktEREREQm3GMGfOyhcHS7nysRV1XktwsxZ/+9ocAArdUjlrLV8cLOnEUdZlg+rk/C2uO8PFj35S5/nUISktHhPcZjy/rDaYqnJL7uKi1busu2mxTM5ae68x5mSgGJgA3GGtfTvsIxMRERGRkCptJpjwZ438mR5/4HHMPe+SU1TJwgn9ufnUieEfZD3BRXLFFTVktLDwaSg0loEypuWuecEd6LYcqA0glRnqvlrTQGEU8KG19mZr7U3AR8aYkWEfmYiIiIiE1L6g9YQA7r9oJg9+dRbzx2USH+3cqPuDol++uomyKg85bve5SYNSmDy45exIqN1w4rjA46KKmk655trswsDjiQOTmT8us1XHeXy1QdRD721nXXYRUJsZilVmqNtpTQOF54Bjgp573W1HhmVEIiIiIhJyJZU15JVWc/2JY9l6sJRpQ1M5b9YQAM6aPjiwX/C6Qb9/+4vA45T48M4Nasp5s4YwJD2eCx9eRnFl5wRDG3OcrM4DF8/k3JlDWn2cP6D023KwhGlDU6lyM0Nx0coMdTetCYairLWB/orW2mpjTPjzkyIiIiISMmvdLMXMYWn84JQJze47a3gaq/cU8tePdga2ZSbFhnV8zYmJdDIq/vK1N9bnMDgtnulD08JyvcNlzq3v6VMHtem4i+cOp7TKy6/f2Fxne22ZnDJD3U1rfiK5xphz/E+MMecCec3sLyIiIiLdzMtr9pEYE8nRrWiF/fuvzGywbeawrlswNCrSyVb55+Rc+4/POOfBj5s7pEMOl1eTFBsVaC3eWtGREfzfCWMabC+p8hATFaE5Q91QazJD1wL/NMY8CBhgL3BFWEclIiIiIiH11saDnDplIAkxLd/+Dc9IaLBtdGZSOIbVKlERTlDi8do63eXCpbC8hrSEjpcF+tyxllR6SAnz4rTSPq3pJrcdmGeMSQKMtbbreiuKiIiISLsUV9QwJD2+VftGBM0bWvyDBaTFR9fZ1tn8mSGPz0dZtTfs1ztcXk16QsdnhfjL40oqPWFfj0nap8lgyBhzmbX2H8aY79fbDoC19ndhHpuIiIiIhIDXZ/FZp4yrrQakxAU6zHWV6KDMkH8+Tzgd7mBm6IObF3L8b5dQVuUPhmpIVmaoW2rup5Lofm15uV0RERER6TRXPb6CwooaXrzu2Fbt72880JZg6NlvHc2Lq515Rl0tODNU0AnBUEFZFSMaKRVsrWEZ8UQYKK921mpyMkMKhrqjJn8q1tpHjDGRQLG19vedOCYRERERaYLH62PJltw2HVMdCIZaX+o2d1QGc0dltOk64RLlluhV1vg4909O44S4MK3ZU1rlYW9BBRfOHtbucxhjSIyJqpMZ6p/UdXOupGnN/hZZa73AOc3tIyIiIiKd53O3RTY45W+tUeMu+tnW7mjdRZSb0cotqQpsC9ccnBx3YdqRmYkt7Nm8hNhIZYZ6gNb8jVhqjHnQGDPfGHOE/0/YRyYiIiLSBxSWV/PR1tavWrJhf20wlF9a1cyetarbUSbXnfjL5Ko8tc0TwhVc5LtleP0SO9ZAITE2KrBIbGmlhyQFQ91Sa/5GHANMAX4O3Of+uTecgxIRERHpK/7vH59x2d+WsyO3tMl9qjxept35Jv9avoeSSk9g+7Id+a26Ro3HySD12GDILZMrD+okFxemNXv8c5IyOhgMjc9KZsXOAvJKq6j0eImP7vq5V9JQa/5GXGitXVjvz4lhH5mIiIhIH7DPLctavrOgyX32FpRTUunhthfXUVpVGww9sHhro/t7vD7OffAjXlq9D2jfnKHuxL/OUEVQMNTaEsG2ClVm6P9OGENeaTVzfvkONV7bYwPR3q7Jn4ox5mxjTC6w1hiTbYw5phPHJSIiItLrHSqpDLSt/scnu5tcUHRXXnngcWmlh/SEaMb0T2wyICgoq+bz7CJu/PcaoLabXGxPnTNULzMUFWGo8fnCcq2CUicYSu9gMDRjWBqp8bXzmnrqfK3errmfyl3AfGvtYOAC4O7OGZKIiIhI73KopJKiipoG28944EM25hQDsGF/Mat2H270+F35ZYHHT32ym+JKD+fMGMKegnLKgjJFfvWv5X/eU7MTERGGmKgItrulhAkxkXi84ckMFZRVkRIXFZLv1Z8vq51m31MD0d6uuZ+Kx1q7GcBauxytNyQiIiJSx+JNB5l255stNjKYe9diznnwowbb80rrrpnjL5mrb3d+eZ3nXp9l4qBkrIUvDpY02L9+MLTO7UA3dUhqs+PszmYPT2frIScY6p8cS15pVZOZtPZauj2P3QXl9EuKDcn5jhxZ25q8pwaivV1zP5UsY8z3/X8aeS4iIiLSpz2xdBcllR4+2uZ0gysqrwl0EPPzBya788uprHHKvDxeHweLKwEY0z+Rv105B6BOc4RgwZkhv0kDUwB4Z9PBBq+t3+cEP/4SPP+cobSE8LSj7gyDUuMCjycPTqW82ttk8Ngey3fk89W/LOe9Lbkdbp7gFxwAqUyue2rup/IXnGyQ/0/95yIiIiJ9WpzbIWxnXhllVR6Ovmcxx9z9bp0mBxv21bbC/vGL6wF45IMdHPWrxQBcu2AMR4/pBzQdDB0ur5tBGpuVxND0eJLjohqU1llreXzpLgBG9EsAaucMRUf03BvytITaAGXKYCcQ3Hqw6Q58zamfUdq4v5iLHv0k8Dw9ITTBUDBlhrqnJhueW2t/1pkDEREREelJrLWB8rMnlu7i+PH9AxP8H/9oJ9cvGgfA2qBgyJ/FWb2nNoCZNCiF+OhIIgyUVjWcVwRO04SzZwzmW8ePpn9yLAkxkUREGI4cmVFnIVKALQdLAmV11e5iqzVeH1ERhoiIntlNDupmtYamxwO1nd/a4qllu/jpKxt476aFDHeDxeuf/qzOPh3tJBds4sBkNh8o6bGd/Ho7hagiIiIi7bDlYAkHiiv50qwhFJbX8P6W3MBrwW2yd+eXkZ4QzewR6US6wUiEMaTGR/P3r89l6pBUjDEkxUZRWi8zVFrl4XBZNaVVHpLjopg6JJUBKXEkxzmBQUJMJGXVdY/55yd7ACd75C+Pq/HawMKlPVV6UDA0Z4QzF8ef8WqLF1bvw2drSw8PFVeyPbduGWJcdOhukeeNdrJ+B4oqQ3ZOCR0FQyIiIiLt4A9+LpwzDIC9h51szKDUOPYUOI9X7Czg6RV76Z8cy6lTBlBQVk1ReQ2bD5QwbUgqx4/vHzhfclw0JfU6w335z0uZ9Yu3Ka70kBzbsKAnMSaKHbllHHnXO+SXVnHy797nqU92A072JDgz1OPLtIwTzJ0/a0hg/o3//bWFvxu5vwvfWxtr51wlxkS6lwpd4HjDonGcPHkAZ88YHLJzSui0+LfCGNOgnYYxJqOxfUVERET6io05xQxJi2fCQGcq9V43ABqcFs+egnJKKmv4yiPLACcTNCozCYBX1u5nT0E5Y7OS6pwvOS6qwZyhzQecTnHVHh8p8Q2bHyTEOjfvuSVVLNmSG+i2Bk5Q1puCoYEpTgOFhROzAiVn7QmG/POF/PO6fvKSM49rx6/O4D/XHUNyXBSXzRseiiEDkJEYw1+umMPgtPiQnVNCpzV/K14wxgT+9hljBgFvh29IIiIiIt1fRbWX5LgoUuKcjM2nu5x5QP5yqFteWBfY9+vHjWJUpjM/5Xb35vvyo0fUOd/mAyW8vfFgndKveaNrP3+ObGS+T2JMbbYoeO7QWdMHERcdWRsMeWyPn7Ny0qQs3rhxPufMGBzIDJVXe9tcfuZzg6HD5dWUB5UYRkQYJg5MYd2dpzI2S73C+orWBEMvAc8ZYyKNMSOBN4FbwzkoERERke6uosZLXHQkUfUyLplJzuT7xZsOkuWWx31lzjCGZSQE9kmOi2JM/7qZIf8Nfvbh2nbRNV5LvNux7mh37kmwxKDSuQq3bTc4LbVjoiKo8s8Z8vX8zJAxTrACEOO+l9+/8wXz7l4caFneGv4A8VevbWbyHW+GfqDSo7T4t8Ja+xecTNBLwH+Ba621b4V5XCIiIiLdWlWNLxCoBPNncCprfBwqqWLGsDQAYqMiA00AhjRSMvWvbx4FwK682sn81R4fR4/px867zwicJ1hibO31C4PabyfGRhEbGUG1x4e1lhqv7fHBUDBjTJ1MV1UbyuUOl9ft2PfVo4az9a7TQzY26Vma/FtRb4HVOGAYsAaYp0VXRUREpC+rrPGyYldBg65jt54+scFcoFH9EgOP7zp/GlC7/k+wkZnOfjuDgqEqj5eYyIgmJ/QnBJXJ5ZfWBkOTBqUEMk01XkuNx9fjy+Tqq/HWrhVU5WldZqjG6yOvtIqpQ5wM05wR6fzq/Gm9KlCUtmlynSEaLqz6YhPbRURERPqU37yxBYBNOU6Dg+PH9yfCwLcWjKGi2ss5M4bw1Ce7WLGzgOPGZQaOO358f86cNoifnDWpwTn7JcaQHBsVaPkMTmYotpk2z/7uZwC5pbVzhk6Y0J8XPst2zuH1Ue31BYKj3qiqpnWZobzSKqyFS+YOJzkumuPGZrZ8kPRqWnRVREREpI125jld2/zzdP7+9bmB1+JjIjluXCZHj+mHz9YtT0uKjeJPlx7R6DmNMYzMTOTvy3Zz9fzRDMtIoMrjC8yPaUxC0JyhFe7aRmkJ0WQmxQauW+3xUV7tISG6uc/Ae55L5g7n6RXOmkqtLZM7WOwEjANT4lg0aUDYxiY9R2taa79tjEkLep5ujNFsMxEREemz/GvV9HObJTQmMsK0ufxqf6HTPOHet5zMU5Wn+YxOcGbIb/XtJwPUWYunotpLXCP79mR3f2kaD182G2h9i21/57kBbptukdb8De1vrS30P7HWHgaywjYiERERkW7OP//m/JlDQnreK48ZCcCg1HistRRX1DS6vpBfYiMLsfrnF8UEZYYqarwkNNLsoafzlxC2ds7Qun2FgIIhqdWaYMhrjAmsPGWMGQHYZvYXERER6dW8PktmUizfOXFsSM97vXu+6EhDaZUHj8+SkdB09il4naGrjh3J41cdGXjuzwxVeryUV3tJ6GWZIYBYN+A7/6GlrN9X1OL+y3cUMCwjPtD+XKQ1xaM/Bj4yxrzvPj8euCZ8QxIRERHp3vYUlHPkyPQmu7y1lzGGmKgIqr0+Ct0W0GkJTWeG4mKcYCA1Ppqfnj2lzmvjspKJjDD8+vXNVFR7ie+FwVDwnKnlOwuYOiS12f33FJRzwoT+If+5Sc/VYjBkrX3DGHMEMM/d9D1rbV54hyUiIiISfjXuoqRtmdvj81n2Hq7gpDBNwI+JjKDGYznsrhuU3kxmKCXOCZS+tWB0g9cmD07h4iOH8c/lTpOBwY2sbdTTTR6UEnjcmsxXSaWHtGa+n9L3tLatyDE4GSG//4VhLCIiIiKd6vyHPiY+OpLnrj2m1cccKqmi2uNjWEbDtYJCITrS4PH5AouDpic2kxmKjmTXPWc2+Xq/pNjA43H11j/qDYKbSzS2AG59Xp8NLIorAq3rJncP8F1go/vnu8aYu8M9MBEREZFwWr+viPX7ivl012FyS6paPsC1p6AcgOFhCoaiIiN45fP9vLPxIECHMhn+7nQA4wb0zqUi//GNowCwrZjS7vH5iFIwJEFakxM+AzjZWvuYtfYx4DSg6Y8gRERERHqAdzYdDDx+Y8OBVh+X5y5u2j85toU92ycmMoLC8hqe+mQ30HyZXEvOn1Xb7S5cwVtXG5OVCLS88KrPZ/FZlBmSOlpbIJsW9Lj5mWkiIiIi3dTS7Xls2O90HdtfWEH/5FjGZSXx6AfbWbY9v1Xn8K9pE9vM+j8d4W/bDWCM0xyhvY4dm8mPTpvIKZMH9NogIDbKKY9raeFVr3UyR8oMSbDWzBm6G1htjFkCGJy5Q7eFdVQiIiIiIVRYXs3dr23m3yv3ArDrnjPZdqiUYenxnD9rCLe/vIFL/vIJz1wzj3mj+zV7Lv+aNs0thtoRwc0cUuKiOxzE/N8JYzo6pG7NH5S2tNaQ110pNzIiPD836Zla003uaWPMe8CROMHQj6y1rc8li4iIiHSx3765JRAIAWQfLuezPYXcsGgcozJrGwtsyiluMhh65P3tlFR6GJDilMf5MxKhFhwMpTfTVlsc/mCosoUyOY9PmSFpqDUNFBZba3Osta9Ya1+21h4wxizujMGJiIiIhMLewxV1nh/36yUALJqYRUp87WfDP/vvRg4WVzY43lrL3a9v5sEl2wI33eHKDPULWhBUbaBbFhUZQWSEaTkz5PVnhhQMSa0m/xYbY+KMMRlApjEm3RiT4f4ZCQzutBGKiIiIdFBOYUWj26cNSQ2s1eN35ysbGuyXW1rbbc6//k+45gwNCVoPKCNRwVBrxEVFtNhAweNzXo+KVDAktZork/sWcCNO4LMKp0QOoBj4U3iHJSIiIhI6u9122MF+evZkIiIMafVK0V5ff4Ciipo6jQu2HiwNPPav/xPThoVa2yJ4cdT6Y5PGxUZHBhoo+HxOk+36GaDaOUMKhqRWk3+LrbUPWGtHATdZa0dba0e5f2ZYax/sxDGKiIiItFuVxxvoABds7qgMwClF+9/1x/GL86Zi3Pvk/NK66w59cbAk8LioopqYyAgiwnRT7Z+TBB1rq92XxEZFUOXxUlRRw8Q73uBrj6+o83pZlYfz/vQxoDlDUleTmSFjzJHAXmvtH93nVwAXALuBO621BZ0zRBEREZH2m/CTNxrd3i+xNuiYOiSVqUNSGZoWz1VPfEphRU2dfdfvKw48fm1dePtIZSXHBR53pK12XxIbFcGSLbk8u/ItAD7cmhd4bfmOfPYVVrC/yJkLpm5yEqy5MrlHgJMAjDHHA/cA1wMzgUeBL4d7cCIiItL37MorI/twBceNywzL+Vf8eBFbDpQwMDWuwWv+srRCd16Qfzz/+Sw7LGNpzLCgxVHDNS+pt4mNimRXfm0p5Mh+td/Dix79pCuGJD1Ec3/DIoOyPxcBj1pr/2OtvR0YG/6hiYiISF/0s/9u4LK/LefpFXsC23bnl/HmhgOtXhi1KRHGybzMH9e/0df93dsK3XlBOUUVXPjIsgb73XvhjA6Nozljs5JIjnU+r1ZmqHVio+ve0rrTgyiurGmwb25JVYNt0nc1lxmKNMZEWWs9wCLgmlYeJyIiItJu/tbVf/1wB5fMHQ7AVY9/yo68MgD+dfVRHDOmdVkj/6R5Y8BauOjI4c3unx7IDDk30X9YvJXckipuOmU8Vx8/mtySKoamJzR3ipC45KjhPPrBDkZlJob9Wr1BXNCaTydNymJTjjPH64sDJQ32rT8fTPq25oKap4H3jTF5QAXwIYAxZixQ1AljExERkT7G4/WxbIeT/amo9vLprgJGZSZSVu0J7LPvcONtshtT43UCqx+eOpFL5g4jOa75TEtyXDTGwAdbc3lg8VaK3LlD3zlxHECnBEIA3z95PHNGpHNUEwvASl3BmaH4mCh81uLz2cA8oWD6nkqwJoMha+1d7uKqg4C3rLVuwpEInLlDIiIiIiH11492Bh7vL6rkwocblqhVe5tfT8ZvXXYRW9wucNGRplULmEZGGFLionlvS24rRxwecdGRnDJlYJeOoScJnlsVHx2Bz1rO+MOHbK6XGXrlO8cyfWhaJ49OurNmy92stQ1mnFlrvwjfcERERKQv25lb1uI+jbXJbszZD34UeBzdhjWB0hOiAxkhgB+fManVx0rXiHXL5GIiI4iMiMDrsw0CIYCEmMgG26RvU4sSERER6Ta8biHKoolZANzzpWls/PmpTB6Uwn1u04LWBENVHm+d520JhlLdDNLMYWnsuudMrj5+dKuPla7hzwzFRkUQGQHFFbVllTFREfzq/GkA9E9q2EFQ+rawBUPGmMeMMYeMMeuDtt1pjNlnjFnj/jkj6LVbjTHbjDFbjDGnhmtcIiIi0n3tKSjnyJHplFQ5N7PDMxJIiInite/O55yZg4HWBUOr9xTWeR4d2fqFNvsnOesPqXlBz+GfMxQbHUGkMXVKKas9Pr561HB23XMmqQnqzid1hTMz9ARwWiPbf2+tnen+eQ3AGDMZuBiY4h7zkDFGeUwREZE+JrugnGEZCVTWOJmd+KCypqgIJ6Bpac7Q2uxCLq63tkxMG9brOXmyk5XamddyyZ50D/4yudioSIxpfeArErZgyFr7AVDQ4o6Oc4FnrLVV1tqdwDZgbrjGJiIiIt1PtcdHTnElw9ITAmVPSbG105uNMcRERbSYGXrm070NtrWlTO70aYMAOGpURquPka6VHOf8nkRGGCIjFAxJ63XFnKHvGGPWumV06e62IUDwv1zZ7rYGjDHXGGNWGmNW5uZ2bacXERERCZ19hRVYC8MyEnjg4ln88LQJjM1KqrNPbGQEVc0EQx6vj61uB7kfnDw+sD0xtvVLJKbERbP69pO5+dQJbXwH0lWGpscDcLi8uk4wFBsVwQVHDO2qYUkP0NnB0J+BMcBMIAe4z93eWAhvG9mGtfZRa+0ca+2c/v0bXz1aREREep7sw+UADEuPZ3BaPNedMLZByVNsdPPB0D8+2c2nuw7zq/On8c35tY0P/DfLrZWeGENUG7JJ0rX86z+VVHoI/pV548bjue8rM7poVNITdOrfcmvtQWut11rrA/5CbSlcNjAsaNehwP7OHJuIiIh0rfzSagAyk2Ob3CcrOY6coqYXXf3iUCn9EmP46lHDiY+J5ImrjuS4sZkM66TFUqVrBP98SyprO8n5y+dEmtKpwZAxZlDQ0/MBf6e5V4CLjTGxxphRwDhgRWeOTURERLqOz2e58d9rAEiNb7rj14h+CezJL2/y9UPFVfQPCqZOmJDFP755VJsaKEjPMzC1tmV29uHaYLm53yURaGHR1Y4wxjwNnABkGmOygZ8CJxhjZuKUwO0CvgVgrd1gjHkW2Ah4gG9ba72NnFZERER6odbewA7vl8Dr6w+wO7+MgalxxERG1CmlO1RSyYAUrSXT1wQHu16fU0b51DfmtqlxhvRNYQuGrLWXNLL5b83sfxdwV7jGIyIiIt3XF27TA2i+85u/HGrBb98DnO5hd549mcuPHgk4maEJA5LDNk7pvh64eCaZSbE8sHgrAFERCoSkZfotERERkS63xQ2GPv/pKc3ulxhbdxlCr89y+8sbWL3nMDvzyjhQXBmYTC99y7kzh3Ds2Ey8PqcHV1sW2pW+S8GQiIiIdJrHPtrJI+9vr7Nty4ESnl+VzeDUuBbneJw2ZVCj23NLqnj8451ERhgunjus0X2kb/C4wZDWG5LWUIsNERERCbuDxZXc9NznfLg1D4Cr548mwr1ZPfX+DwA4enS/Fs8THxPJ5fNG8NQnuxmekcCeAqeZwjVPrQLgsnnDNWeoj/N4nTlDKpOT1tBviYiIiITdmxsOBAIhgJ++sgGP10eVp7Zf0szhaa06l79z2ILx/RvMDzpnRqNrtksf4lVmSNpAwZCIiIiEXVF5DQB/vWIOEwcm89Qnu/k8u5Dfvf0F4GR0rj9xbKvO5Z8LEmHgleuP5emr5wVey0yKCfHIpafxl8lFac6QtIKCIREREQm70ioPsVERnDR5APdfPBOAv320k0fe3wHAnWdPISGmddX7I/slAjBxUAqxUZEcPaYfH9y8kBtOHMuozMSwjF96jhEZTgONxFjNBpGW6bdEREREwmbptjy++tflAGQmOYuhDk6LB+C1dQcAOHP6IKLasB7MyZMH8J//O5ojhqcHtg3vl8D3T5kQqmFLD/b7i2eyclcBQ9zfM5HmKDMkIiIiYbFxf3EgEALISHQ6xaXERXPSpKzA9n6JbSttM8Ywe0RGncVWRfxS4qI5ceKArh6G9BDKDImIiEjIvLo2h005xXx74Vh+/cbmOq/99sszAo8fuXwOpZUeHlyylW8cN7qzhykiAoCx1nb1GNptzpw5duXKlV09DBEREXGNvOXVRrc/ffU8jh7TcutsEZFwMMasstbOqb9dmSEREREJicY+YD1l8gAevaLB/YeISLegYEhERERCosrjLHb5rQWjGZAcx8zhaUwZnNLFoxIRaZqCIREREemw9fuKeP+LXACGpsVz+dEju3ZAIiKtoGBIREREOsTrs5z1x48Cz8cNSO7C0YiItJ5aa4uIiEiH3PXqpsDjZ66Zx7zRapQgIj2DMkMiIiLSbh9tzeOxj3cyc1gaL153jNb+EZEeRZkhERERabel2/MAePCrsxQIiUiPo2BIRERE2i23pIqBKXEMTU/o6qGIiLSZgiERkW5k1e4Clm7L6+phiLTavsIKslJiu3oYIiLtomBIRKQbueDPy/jqX5e369hbX1jLsfe822D786uy+eHzn1NW5WnT+Xw+y+Gy6naNRfqGRz/YztLt+Rw3NrOrhyIi0i4KhkREuqFqd/HKtnh6xV72FVY02H7Tc5/z7Mpspvz0TTbuL271+e5+fROzfvF2m4Mo6Rs8Xh+/em0zAJfOG9HFoxERaR8FQyIi3URRRU3gcXl12wKQ4OCpuWNX7Mxv9Tn/8uFOgEYDLJFNOSUA3HzqBIakxXfxaERE2kfBkIhIN7E7vyzw+MOtbZs3tOVASeBxfmltadvB4so6+9353418uDW3Tefer2BIGrGnoByAhROyungkIiLtp2BIRKSb2JlXGwxd//TqNpWnfbqrIPC4IGiez83Pr22w7+V/W9Hi+YKzVJU1bS/Zk97vw625xEdHMiozsauHIiLSbgqGRES6iY/rdZG77p+ftfrYjTm1c4F+++YWyqs9rNpdwAdf5DJreBo3LBrHRXOGMX+cM9H96RV7OFQvaxRse25p4HGVx9vqcUjfkH24nGc+3cu5MwcTHxPZ1cMREWm3qK4egIiIgLWWZ1dm19n2/hdOOVteaRUXPbKMhy+bzbgByY0ev7+wgsykGPJKq/loWx5/fm87u/KdMqafnzOVaUNTAXhvyyE+3JrHrS+sY3BqHEtvXcS+wgoqa7yM6Z8UOF/24drSuPY0c5DezR+of+mIoV08EhGRjlFmSESkG/hsz2EAjhqVwUOXHsH4AUkYA16fZcnmQ2zPLeOBxVsD+5dU1nCopDLweOn2fIZn1C56+cd3t7Ejt5Tk2KhAIATUaYG8v6iSkbe8yrH3vMui+96vM559QcFQlYIhqSctIQaAI0emd/FIREQ6RsGQiEgXK63ycO0/PmNIWjx/vmw2Z0wbxMVHDsdaOOpX71Be7ZSp/W9tDo9/7HR4O/G+95l712LOefAjTnQDmTV7C+ucd8P+YvrXWwwzKjKCX18wrdFxbD1Ygs9nAacMyhhnuzJDUl9yXBRj+idi/L8kIiI9lIIhEZEu9sn2fHJLqvj1BdPJSHQ+cU+NjwYgr7Sa1W7WCOBn/93IJY9+Qm5JFQBrs4sCj5+4am6DcxdXNGzCcNGRw3nthvmB+UN+J//+A065/wNyiirYV1jBaHdivDJDUl+Nx0d0pG4hRKTn079kIiJdbH+RU5I2YWDtfKCFE7OYPcIpQdoW1MwAYNmOxtcKOn58/wbbBtTLDPlNHpzCU984ine+fzwrblsU2L7tUClff2Il723JDXQJq6hRAwWpq8arYEhEegf9SyYi0sUKy5021mkJ0YFtGYkx/PzcKQBsPViKMTB5UEqd406cmMXTV89r8rzfXjiGx686stlrj81KJislLvD8qFEZbHI7000YmExcdAQffNG2dYmk9/P4LNGRKpETkZ5P3eRERLrY4fJqkmKjGnzS7p+kXuXxcfLkAVhbt4X2Y187Eq87x+cHJ48H4Llrj6aksoblOwq48aTxrf70/qhRGSzfWcARI9JZvtNZs+ibx43mYHEVr6zZT3m1h4QY/ZchjmqPjyhlhkSkF9D/bCIiXayovKZOVsjPP28I4Kzpg/juM2sCz++7cAYAkRGGXfecGdh+5MgMAE6cOKBNY/jLlXPYfqiUz90mDMmxUaQnxnDuzME8vyqb+9/Zym1nTGrTOaX38vgs8dFaX0hEej59rCMi0sUOl1c3GgwlBi1meeqUgXzjuFEAXDJ3OBfMDu36Lilx0cwank5ynDOOoW6bbn9w9egHO9hfWNHk8R2xv7CCGq+aNPQkzpwhlcmJSM+nYEhEpIsdLq8h3S2JCxbctjguOpLbz5rMhz9cyM/OmRK2sSTHOQUD/hvduOhI5riNHMIxd6iyxssx97zLjf9eE/JzS+hZa/l0VwG78spUJicivYL+JRMR6WJFFTWB+UH13XbGxDpNEIZlJBATFb5/upPcYMhnbWDbc9ceTVJsVKCxQijlFDkLx766Nifk55bQW7o9nwsfXkZxpYeVuwq6ejgiIh2mYEhEpIsdLq8mLb5hmRzANcePYeGErE4bS6LbJMEXVLVmjGFsVhJfHCxt4qj2e2/LocDjNzccCPn5JbT2Ha4tlbz0qBFdOBIRkdBQAwURkS60fEc+heU1pDcyZ6grREY45XHBmSGAcVlJLNkS2jK5pdvz+Nl/Nwae//TlDZw6ZWBIryEd5/NZqjw+/vrhDu57+wsANv78VHUXFJFeQZkhEZEusu1QCRc9+gngrCvUHYzun8iAlFhurdc5bvyAZPJKq3jw3a2MvOVVSiprOnQdn8+yYV9t2d23jh/N4fJqwAmScooq6uwrXef6p1cz6Y43AoHQuKwkBUIi0mvoXzMRkXbYmVfGwnvf48SJWTz2teYXNm3MQ+9t4zdvbAk8P23qoFAOr90SYqJYfttJDbaPG5AEwL1vOTfERRU1gc5z7XHZ35azdHs+AI99bQ5rs4uo8vgoLK/mq39ZDsCTX5/L5pxi7nvrC1bdflKHriftk1NUwavrnPlcc0dmcO+FMxjeL6GLRyUiEjrKDImItMPCe98D4N3Nh/C0oy10cCD0+FVHMjA1LlRDC4sx/ZPqPPd1sBO2PxACWDghiwS3jfhqd50jgCsfW8Hdr2+m2uvje+o21yaf7irgrD9+SHm1p0PnefzjXQDcevpEnvz6XAVCItLrKDMkItJBB4orGZrevpvE31wwvVMbJLRXar05TdmHy4mJighJEGeMId4tu9qRW9boPqv3FHb4Or3djtxSTrzvfcZmJbHtkNPsYvuhMqYNTW31Oao8XsqrvCzfWcDJkwfw6tocTpk8gG8tGBOuYYuIdCkFQyIibZRfWlXnefbhinYHQ6dMGRCKIYVdQnRknedf/etyoiIM2351RoN9qz0+iipq6J8c2+bz78l3gqHHvjaHnXnl/OJ/ToOFoooarLV11l6SuvxrNfkDIYBKj7fVx1d7fBz36yXklji/3986fjQ5RRVccMSQkI5TRKQ7UZmciEgbvbxmPwAPXDwTgD++uxVr2zbJf2S/BI4d26/J9YW6m6jICKIi6gYiHp+lqpGb7XP/9DELfruk2fP5y+L8RmYmAvDJDmftmuPG9ufUoEDR47OszS5q19j7mse+NifwuLii9Y0u7np1YyAQAnjkgx34LIyuVyIpItKbKBgSEWkDay0/d7MVp7tNDz7els8HW/PadJ4ar2VgSnzIxxdOnka6uh0qrpsls9ayKaeY8movL67OpqK68cxEclzdwoSZw9JIT4hmy8ESoiMNMVERDE6t+/15cumujr2BXuLU33/AQ+9tq7PtUHEla7OL+MHJ4zlx4gCW3HQCAIXlrQuG3ttyiCeX7ebM6YN48Kuz+OFpEwCYNCiFEyb0D+n4RUS6E5XJiYi0QXFF7YT0mKjaz5OWbD7EgvGtv2ms8niJje75n0cdKK5kWEZtieCKnQWBx9/79+d8j8/Z/IvTiAsqs7PWNrhJj4wwDEiJ43B5DfHuvhERhvSEaIakx7N+XzGTBqWE+d10f9Zathws4TdvbCHCGL5x3CiiIyNY7n7fj3d/BwenxREZYdiZ1/gcrPr++7nTMe6OsyYzICUOj9fH5fNGqIOfiPR6Pf9/YhGRTnTWgx8CMMOdlH7r6RMBeGLpLt7acCCw31Of7OaX/9tIobt2Tn2VNT7ioiIbfa27umze8EBA4g8ED5fVfX//WL6nwXH1y9vyy6qp8jjt6GYNTwtsT4x1Pp8rrqwNOFffcQovf/s4AEqrOtYZrTeorKlt43fP65u5/l+r+WzPYZZsPkRSbBRTBjs/n9ioSEZnJrJ67+EWz7lhfxH/+SybiQOTGZDiNMSIioxQICQifYKCIRGRVqqo9rK3wFkM9JHLnXkZ31owhkvmDgfgmqdWAU7W5/aX1vPXj3by1oaDDc6zM6+M0ipPj8sM/fK8adziBn9zRqQDUFBWzZ+WbGPboRIAduaVkhpf9yZ69Z66N+T+78n9F83kxeuODWxvan5LZIQhMSZSwRA0aJX9xoYDfOmhpbyweh8nTx5AVGTt79QRw9P5eFs+f/toZ7Pn/NVrmwDIK208cBcR6c161v/EIiJd6EBxJQD3XTijTkvprKCuaS98ls2En7wReL6vsKLOOXbklgbWKPI2Mgenu5s/NpPbzpjI774yE4BX1+Xw2ze3cNLvPiC/tIoduWUsmpjFdxeNY6E712RN0NpBAIUVzk33qVMG1tm+1e2C9sRVDRexHd4vkbc2HujzAVG5OwdrxrC0Bq+dNb3uwr23nz0ZY2DDvuYbT8S4AdT3Th4XmkGKiPQgCoZERFopp8gJbAbVW1vn+KC5Qt9/9vM6rx0oquTtjQe55/XNAIG5HeC0Mu5pIiIM1xw/JhAA7ikoD7w2+5fvUF7tJTE2iu+dPJ7Hr5rL6VMHsuVACT6f5eJHl3HsPe/ytw93Eh8dSXy9jnKz3WxTY3Ovbjl9InsLKvjgi9wwvru6yqo8XPzoMl5ava/TrtmSihonGLp6/iiuOnZkYPu80RksmlS3TXtSbBTThqSSU1RZZ/uG/UX8+b3tbD5QDEBBeQ3zx2Vy6VEjwjt4EZFuSA0URERa6YB7U1l/odHZI9I5c9og3t50MBDgnDV9EOv2FXG4vJqr/74SgG/OH1WncUBb23F3JxERhqgIQ0ll85ma1PhoSqs8PLF0V6BtNjitxet78utzKavyNLqW0KRByQDc99YWzpg2qMHroVTl8fLS6n0MS0/gkx0FHCqu4rxZ3WOtHX9mKDEmip+ePYXL541gX2EF88c13ryjvNrL2uwiVu85zNisJOKjIznzDx8B8Os3NvPAxTPZW1Bep425iEhfomBIRKSVcpoIhgCOHZvJq+ucjlyXzxvBL86bykWPLKsT/KzLLqKksvZ5D6ySqyM6MqLReT7BsUxibBSHSqoC7cj9bjl9UoPjkmKjSIpt/L+ldHc9pu25reuO1hE/fnE9z6/KDjwvq+4+pXn+OUP+rNro/knNrgN00qQBbDtUyvkPLWXGsDRuc+d8AcRFR/DcymwKyqoZnam1hESkb1KZnIhIKx0oqiQ1PpqEmIY37P2SahdPTUtwGggMSIljV34Zye4N/lVPfEpBUPc1Xw/ODAFER5pG1x4aFLQ+0O78xoOX06YObHR709fqnP+uPF5fnUAICLT67kxNZQ396zbVX7S2KSdNygo8XptdyO0vrycqwrDuzlOYNiSVj7Y562ONG6BgSET6JgVDIiIteOqT3Zzz4Ee8tHpfg/lCfkPSagMAf3bj+PH9OVRSRUnQpP/gdXh6emYoeJ0lv0vmDuOb80cFnh8qqbso63cWjuWjHy1s1/W+edyoVgcB7fXdf69psK2ooqbTShqttby54QCjbn2Nhfe+R35p3e9feRuDocFBv5dxUZF8cbCUI4ankxwXTUpQ6+wJA5NDMHoRkZ5HZXIiIs2w1nL7S+sDz6cMTm10v8mDUjAGrIWkOOef1nmjMxrstyOvjGlDUlm3r4hL5g4Lz6A7SWPZmp+fO7XO9kvmDmdt9jrev/kENuWUcMrkAURENJwT1Bop8dGUV3u59YV1HC6rZl9hBf+9/rh2j78x/mA1MymWPDcQOVxew678ckZlJob0WvX5fJYT73uPXflOU4qdeWXM/uU7bPr5acTHRLJxfzG73EVU4xvJTjbGv24Q1DZf8Dde+PWXp/ONJ1dSVeNlYErjQb6ISG+nYEhEpBnBbaEvnzeCby8c2+h+ERGGo0f3Y+n2fBLdG9XBqfHER0dSUePluLGZgZKkc2YMDvlNfFeoHwzNH5fZYNslc4dz8ZHDMMYwol/HgolkN8h8ekXtwq4+n213cNWYo0Zl8OHWPH553hT++3kOX549lKue+JTVew6HPRh6a+OBQCB086kTeHnNPr44WMpnew5zoKiSHzxX26kwoZWle5ERhv/83zFc8OelgW3+YD0zKZaXv31sU4eKiPQJKpMTEWnGVx5ZBsDy2xbxi/OmNto8wc//mr+jXESEYUh6fJ3XAGaPTA/XcDtVdKQThES5wchX3cVn62usO1x7BJd1+RU2sVBrex0ur2ZM/0ROmzqIP116RKBtev2W6eHw+Me7ACdYvmzeCF647lgiIwwPLN5aJxACpzFFa80ekc5Pz54ceN5UkwoRkb5IwZCISBNqvD5qvM5ckeCFVZsyNsuZhB68fo7/pnXCgNo5GVObKLXrafxZoGEZCey650xOD3PL65T4hsFQbr05Se31zsaDzL3rHfYWVAQ614GTWfGrP38n1Hbnl3Ph7KH84ZJZpMZHkxQbxTFj+tWZZwZOQ4fG5ms156pja+dxBb8/EZG+TsGQiEgT/Dfad549uVXZjWvmj+aBi2dyZlBQsHCCk1kIzga19Ua2u/K/j9hOej/+TNRX5gzlrvOnAqEJhm54ejXf/PtKDpVUsaegnLR6wcIDF88EYPOBkg5fqylf/vNSDhRX1pnjA/DQpUcEHs8flwk4azd1RHPZTRGRvka5chGRRvh8lq89vgKA4Y0sENqYqMgIzp1Zd3HO7ywcy/mzhjCiXyIPXzab/sm951N5/015XCe1nj5ubCa/OHcKF84Zxv7CCgAu+9tyXrjuGI4Y3v7Sw1c+31/neVJs3fezcGIW/RJjuP3l9fzv+uMaba3eEZU1XlbuPgw4JW3BkuOieeKqI/FZy6trDwDtD4ZuPGkcL67e12k/LxGRnqB3fDwpIhJia/cV8cXBUqIjDVOHtL+sLSoyItA44LSpA5k9omGHuZ5q0qAUoDZjE25RkRFcfvRI4qIjGdkvkbNnDAZgSwcyNpVuh7WbThnP8tsWccncYZw7q25AmxIXzW++PJ0duWV88EVu+99AE/61vLYhRGPzyU6YkMWJEwcEGkiMb2cb7BtPGs/7N7evrbmISG+lYEhEpBE5bubhpW8fS1ayyooaM2mQc1O+v7Cy068dEWG4+0vTACit9LSwd9P8ZXZZyXEMSInj7i9NbzTLdPSYfgBsz218EdmOeOi9bQCcP2tIo00i/Ko8TuA2vQPBuYiI1KVgSET6hD8t2cbpD3yIt5UrneaVVQPQP6nlxgl9lT8zVF7d/mCkIxKiIzEGSirb3lFuxc4Clm3PJ9dtitC/hQYZcVFOaZm/U2AoxURGcMKE/vz+opnN7negyAk6x2SFt8W3iEhfojlDItLrvbflEL99cwsAZdWewKfvr67N4fqnP2Ptnac2aDe8OaeYhJhIMhJ7zxyfUBvbP4nzZg7ma0GdyjpTRIQhKSaKkqq2B2P+lun+hW9bCoYiIgxREYYab+iDoYLyasYPaLn07dYzJpGeGMNxY/uHfAwiIn2VgiER6dUOFVfytcc/DTyvrPby9PI9vLRmP5tyigEn8Jkzsu5cnu25pUwZnEJUpBLoTYmKjOD+i2d16RiS4qLqlMkVV9YQFWFa3eTg6RV7mTE0lQmtmIcTExUR8syQtZYqj4+4VnTkGz8gmd99ZWZIry8i0teF7X95Y8xjxphDxpj1QdsyjDFvG2O2ul/Tg1671RizzRizxRhzarjGJSJ9x+Gyaub+anGdbf9dm8Pdr28OBEIAn+05THFlDat2F/D0Cmcye0mlp9n5G9I9JMdFURqUGZp+51sc/5slzR5TXu0huFP6j06bGFgzqTnRkREhzwx5fBZradX1RUQk9ML5r+8TwGn1tt0CLLbWjgMWu88xxkwGLgamuMc8ZIxR788e4JMd+Tzx8c6uHoZIo27895rA4wuOGArA4k0HG+z3q9c2M/3Ot/jyw8u49YV1geCosUU+pXtJiq0bDAHklVbja2Zu2OJNh7AWpg1J5azpgxrt4NaY6MgIqr2Nn/eJj3fyu7e2BJoctIa1NpBp6i1rT4mI9DRhK5Oz1n5gjBlZb/O5wAnu4yeB94AfudufsdZWATuNMduAucCycI1POq6i2svFj34COJ9ufnP+6C4ekUhd6/cVMSg1jo9+dCLvf3GI/3yWTW5JFUPS4ln8gwWUVHo48q53Avtb9z73929/QUmlJ9DKWLqvpLhoPtmez8hbXuU3X54e2H7GHz7kjRuPb/SYbYdKAXjxumPaVAYZ20yZ3K9e30y1x8emAyX8+IxJvLv5EFcdO7LJxXoLy6v50kNLAxkqBUMiIl2js//1HWCtzQFwv2a524cAe4P2y3a3NWCMucYYs9IYszI3N/TrPUjrvbxmX+Dx4x/vwtrWdekSCbVqj49f/G8ju/Nr2x6XVNaQX1bNV+cOJzLCBLqBbT1USlJsFHHRkcTHNJ6A/nBrHoXlNQzPaN1iq9J1kmOjqHZL1374/NrA9s3NrD1UUeMlLjqizfPBoiMbb6BQWeMNBElLt+Vxwr3v8fP/bSSnqOmW4798dRM78soCrbpVJici0jW6y7++jX101uidtbX2UWvtHGvtnP791VGnK/137X7GZSXxs3OmsK+wgj0F5V09JOmj3tl0kL99tJM7Xt4Q2Pbfz3MAOGassz5MRlJtV7gtB50b5fjo2mDo5lMnANAvqHtc/aYK0v3U7wKY4Aa4p0weUGd7fmlVYHHW8mpPnZ99a8VENT5naL+7JtXEgcmUVdeWyS3fmd/oeQ6XVfP8quzAWP3nFhGRztfZ//oeNMYMAnC/HnK3ZwPDgvYbCuzv5LFJG2UfrmDioBRG9HM+Pd+eW9rFI5K+6pU1zj8XW4KyAU+v2MOkQSmBBTTH9E8KvDYkLR6AyAjnc5jk2Ci+vXAsn/74JH57YW2p1ZTBKWEfu3RMUr1SxsoaJxjZU1BeJ1t91h8/4tT7PwCgvNrb6m5zwaIjGy+T2+cGQydOzApsS4yJ5Hv//pzVew432P8LNxi/6/ypjMtyfi9jFQyJiHSJzv7X9xXgSvfxlcDLQdsvNsbEGmNGAeOAFZ08NmmlVz7fz8hbXmV3fjmDUuNIdSeZf/2JlV08MumL1u8r4o0NB4DaxTfXZheybl8RF84eGpizER0ZweZfnMaHP1zIi9cdEzj+8auO5PUb5wPOWjOjMmuDJpUudX/1F9H1P918oITbX14fCF78JWs/fnEdL3y2L1Ba1xaJMVGUVzdskJB92AmGvnTEUL4yZyjvfP94LpjtNOw4/6GldfatqPZykTvXMj0hhhMnOQGUftdERLpGOFtrP43TAGGCMSbbGPMN4B7gZGPMVuBk9znW2g3As8BG4A3g29ba1rfkkbArq/Jw4n3vcftL67nh6dWB7cHBEHTdSvTStxwoquSN9Qe46JFlbHRbZE8dkkJZtZfDZdUs2+6UJ500qW6pVFx0JMMyEshKiQtsWzghi6HptXOD/Fkj6RmKK5wA+Psnj2/w2j8+2RNYXNXvn8ud1ukTWrHIaX1JcQ071wHszi8nMsIwsl8Cv/nyDMZmJXPbGZMAJ+u4ZPMhKmu8eLw+Hli8NXDc1CGpnDplIIAW9xUR6SLh7CZ3SRMvLWpi/7uAu8I1Hmm/yhovl/1tOTtyy9iRW1bntUGp8WQGrdw++Y43uf+imZw3q9H+FyIdtnF/MWf84cPA88luKVtWchxQzPG/WUJsdATGwKC0uCbO0rSYqAgunD2UY8dmhmrIEkY/PG0i04amcuXRI7ls3giueGw56/fVriG1Zm8huSVVdY4ZkhbPn756RJuvlRQbxY565cA+n+XVdfs5cmR6nYYMcdGRfGnWEF5YvY+rnviU48f3Z/Xuw5RUeZg/LpOnvnEUAJlJsSy56QRG9lOzDhGRrqC8vLTojD98yOo9hY2+NjgtrsHClE99srsTRiV91Rvrc+o8/2RHAVCbGSip8pBXWs03jh3V7tKj3144QwF9DzEwNY6rjh1FRIQhIzGG/10/v8E+x/36XQC+edwoAIZlxJOa0PY1pJLiotiVX87IW14NzBNatiOfvQUVXDJ3eIP9hwcFOB98kUuJm1VaML5u859RmYlNtuAWEZHwUjAkLQrOBo3KTOS1G+YHJp4PTHU+ef+xWxICEBWh/9Sl497/Ipe/frijwfY3NhwgJiqCq44dCcCmQJlcKl/88vTAftefOK5TxindX5U7b+iSo5yA5ZwZ7Qt0MxJqS9k+3ekE4Zf+dTkAR4/u12D/axeM4RfnTgk8j4owfGnWEAXaIiLdiFYUlGYVllcHHn9+xymBT1PfvPF4Xl+XQ/8kp0Tu6uNH47WWe17frBaxEhJXPub0UDlv1hAy3d+z19bl8MXBUu48ezJfO3YUj3+8C4D545yStpioCP53/XGUV3vb9cm/9C4j+yWwK99p+Z8UG8WY/kl8/tNT6sxzbNP5MhMDjz/alhfI9IDTfKO+uOhILj96JPe+9QVFFTUcP74/v7toZruuLSIi4aFgSJq1bl8RAGfPGFzn5nJsVhLXL6r7yfu1C8bw6c6CQGelxz/eyalTBjJYE9KljXbl1WYjd+aVBYKhp1c4k9/PmD6ozv4PXlI7/2PqkNROGKF0Z984bhT/+SybJTedQGmVh6c+2c3JbjON9gZCACdPGsAVR4/g78t28/yqbJ5flQ3AD0+b0GyZ24CUWIoqagJttEVEpPvQR/jSrM05znoYwaUezZk6JJUvDpWwM6+Mn/13I1/9yyfhHJ70QtZaTrj3vcDzCx9exjMr9rBhfxEfbs3jhkXj3GYJ8OJ1x/DCdccoCyR13H7WZNbccQrGGJLjornuhLGMa0f3uPpSE6L5+blT+YY798jv6vmjmz1uXiMldCIi0j0oGJJmHSiuJCEmkrSE1rV9nTsqA2th0X3vAQRKVERaa7+7HkywW15Yx1PLdhMXHVHnRnTW8PTAoqoineUnZ07ini9NA5yFe1tq1HHF0SMAWDChf7P7iYhI51Mw1M2VVXm4760tPPL+9i65/qGSKrIaqYVvyqzhaUDtwodQuzq7SEt25ZXxq1c3AfCvbx5FXHTtP1HPfLqXcVnJHSpzEgkFY0xgjlBaK34fx2Yls/PuMzhmjNq1i4h0NwqGurlr/7GKP767jbtf34y1tuUDQqigrJr/fr4/UJLUGgkxUYzun1hn27H3vBvqoUkv9bP/buDVdU7r7BGZiXz645NY+ZOTAmuwBE9gF+lK6e4iqZMGpbRqf7XOFhHpnhQMdWN5pVV8uDUv8HxHXlkze4feE0t3AXDurMFtOu6OsyaHYTTSF7z3RW7g8cCUOJLjoslMiuWV64/jmuNHc+vpE7twdCK1pg9J5cdnTOKBi2d29VBERKQDFAx1Y/uDyssiDPz38/2dct2iihrO/dPH/GHxVk6aNIBLjxrRpuOPG5vJHy6ZxXcWjg1s8/k6N6slPc+2Q6X4k5/Ba1kBpMRFc9sZk9SZULqNqMgIrj5+NP2SWl9GLCIi3Y+CoW7qrQ0H+PLDywB4+up5DEyJC7SsDhWfz2Kt86eksiaw/bGPdvL53kIAbjyp7QtXRkVGcM6MwUQE3cxuzy3t8Hild3vGbZv9v+uPY/Lg1pUeiYiIiHSEgqFu6pqnVlHtrpo+bWgqKfHRHCyupLTKQ15pFUUVNS2coXmF5dXM/dU7/ODZz/nz+9uZdudb5JdWkVdaxQOLt3LMmH58/tNTOrRmS15pVeDxfW990aHxSu9mreWlNfs4fepArRMkIiIinUaLrnYzJZU1rM0uqrMtKTaKlPhoPtyax9SfvklyXBQllR523XNmu68z8+dvA/DC6n2BbfsKK/jVa04nr3FZSR3u2jVxYO26HvExkR06l/Ru23NLySut1nosIiIi0qmUGepmfvrKBi796/LAc38DovSgRSVLKj0A5JZU0R6VNd5Gt1/y6CcUljsZpwvnDGvXuYNddtQI3vn+AhaM789new5T4/V1+JzSO/h8lqXb87DWUlnj5RtPriQ2KoJjx6r1sIiIiHQeBUPdzMb9xXWeHzPG+aR8aHpCg33X7y9qsK1V18hxrvGt451V048f35+vHTOSsmov23NLmTQoJSSlShERhrFZSXz1qOHszi/n7D9+FCj9k77t0Q938NW/LGfJlkP857NsdueXc88F0xibldTVQxMREZE+RGVy3YjPZ9nnNkkYkhbPPRdMY/rQNABG9GsYDG3OKWHhhKw2X2fLgRIALj1qBINS4zhj+iAKy2t4YukuaryWjMTQLmp56pSBTB+aytrsIj7enteuMUvvcdXjK1iyxWmh/dCS7azcfRiA82YO6cphiYiISB+kzFA3Mu/uxZRUebjr/Km8/f3jmT+uf2DezrCMhsHQks2HWr0Q6xcHS3h740EA3t18iMykGIamx/O1Y0eRlRxHQtCcnnCskn7Pl6YDUFyv8cPegnJG3vIqI295ld35nbuOknS+4sqaQCAEBAIh0KKUIiIi0vkUDHUjh9w5QMeNzSQhpm7SbkQjwdCKXQX87aOdLZ7XWsvpD3zI1X9fyVsbDrB400G+PHtYndbXwdc7Z0bbFlltjcwkZ7X27z6zpk4p4BvrDwQen3jf+60O7qRn2pHrBLyXzRvOT892FuedOSyNv399blcOS0RERPoolcl1E/4gYMawNEb0S2zwev05Q4kxkZRVe3n/i1y+OX90ndfW7yviT0u2cfXxo/nSQ0v58RmT8LqLnl7z1CoAjh9fN/sTnBlqLAvVUclxtaV3r3y+P7COzP6i2rWTvD5LYXkN6YkxIb++dA873PWmvnbMKEZlJjI2K4njxmYqKyQiIiJdQpmhbqKs2unwdua0gY2+HhMVwf0XzQw8v+zoEYzLSiI2qmHL6ieX7uL19Qe47YV1ANzltssOllEv4IiNCu+vQlx07fk9QV3lNuwv5ojhaTx06REA5BRVhnUc0rW255YSFWEY0S+ByAjD/HH9FQiJiIhIl1Ew1E3459KkxDXdvOCcGYNJc1tsbztYSlpCNO9sOsjIW15lU04xFdVept/5Js+tygZgs9soAWD8gCSe+kZtKVJ6Qt1gyBjDnWdP5i9XzAnZe6p//lU/OYmMxBjKqp3W4F6fZdP+YiYPTiEx1klSVtR4wnL9vu5QcSUfbs0NtFUvKq/h/Ic+Zl12+zoSttemnBJG9EsgOlL/9IiIiEjXU5lcN+FfOyi5mWAoIsLwlyvmcOHDyzhQXElVUJvqZ1bs4fKjR1BcWTeYyEyK5fazJnHMmEwyk2K45vjRbDlQQmZSbIPzf+3YUSF6N43rlxRLSlwUpVXODfmMn71FaZWH2SPSA5mpKrXeDou5v1ocePzxLSeyaX8xq/cUculfP2Htnad22jjW7yti/rj+nXY9ERERkeYoGOomiivdzFB88z+SQalxgJNV+eV5U9meW8onOwp4cfU+1u5r+Cn/148byblBLYtvO2NSCEfddomxUZRVebDWUlrlBG5zRmSQV+o0j1AwFHoV1XUX2b33zS2BVu3FlR5G3vIqr393PpMGpVBe7aGqxhe2eVuVNV6S4/TPjoiIiHQPuivpJlpTJgfO+kM/OHk8Z80YzKjMROaN7seIjET++/l+Vu8pBOBvV86hyuNjQEocRwxPC/PI2yYxNorSKk8ggzV7RDrDMhICmbGqGgVDoXbmHz8MPM5MiuHF1fsAiIwwgcYaf/1wJ/d9ZQZff+JTPtlRwDkzBhMdGcF9X5kR0rFUe33EhHl+moiIiEhrKRjqJrLdxVb96wo1xRjD9YvG1dk2MyjgiYowLJo0IOTjC5Wk2ChyiioDmaDL5g0HIDbaXybnbXBMZY2XNzcc4JwZgzXZvo1yiioC7awf/OosrIXrn14NwMSByVx3wli+/a/PyHG7+n2yowBwOv4BHDEijUuPGhGy8dR4LTGaLyQiIiLdhO5KutgTH+/klc/389jHO5k6JCVQvtQWSbFRjHSPO2dm6NcICqUduaVsyinmz+9tBwjMXfLPGfruM2saHHPvm1v47jNrWLo9v9PG2Vv4s4VnThvEwglZnD1jMH+70mmS4fVZzpw+iNOnDmTp9vw6az75/eSl9YHH6/cVcfNzn3PD06ubXA/K57P8cfFW9hdWUF2v5NHrs3h9Vs0TREREpNvQXUkX2rC/iDv/u5Ebnl7N7vxy/m/B2HZnPganxTtfU+NDOcSQ25VfDsDzbsc7fzAUXDr1/X+vqXOzvbvAOWZ/Ye2aRNI6O/OcrNC9F84IdOw7Yng66QnRgflj/gV3r/3HqgbHzxyWFnj88/9t5LlV2bzy+X4OFlc1er31+4u47+0vOOaedxn/k9f5cGtu4DV/cKQyOREREekudFfShTbnlNR5PntEervPVeOu3TPQbbDQXU0dklLnef9kJxhKjKmt2Hxh9T4+2pYXaLDgbwddUFbdSaPsPQ4WV5ISF0V80KK66YkxrL7jFI4f73R1u2TusAbHLf7BAhZO6B+YU1Re7WHFzgLS3dbu293FUwHWZRdx/kMfs7egnNfW1c0uBWebFAyJiIhId6O7ki7kz3j4ZSa1v4NXjde5aR3UzYOhv3/9KJ791tGB5/71jhJjo7jgiKGB7Zf/bQUXPrwMgENuFuJweU0njrRneHnNPs558CM+3pYX2GatDQTHB4srGZDS/O/EnJEZbLvr9MDzN26cz5j+SURHRgQCmM92FwIEOhNuzy2lvNrDuQ9+xNkPfsTqPYWc/eBHPPz+dqYPTeVrx4wEYPGmQ7zwmZMFfG7VXsAppRMRERHpDtRAoQttzy1lSFo880b3o7C8mqgOzKW49KjhZB8uZ/LglJZ37kIZiTHMHZUReB4ZUVsWeNOp4/mPe+MMsCmnGJ/PsivfKfV6+P3tnDV9EFOHpHbegLs5/xyrS/+6nAtnD+XoMf3YW1DB79/5gs2/OI2DxVWtyhZGRUbwxo3zSYiOYrg7/ywmKoJqN6has/cwAN9eOJYnlu7ijpc3cMfLG+qco9ANVm87YxLzRvcjJT6aPyzeyvef/ZzMpFh++eomALYeqpsRFREREekqygx1kfe2HOLVtTkcM6Yf931lBn/72pEdOt+Fc4ax8icnM6ibzxny+9KsIXx74Zg62walxvPpj0+qs+2ZT/dS5fEF5had9cePOFhc2Wnj7M7yS+vO23luVTbff/Zzfv/OFwBMvP0N1uwtZEha634nJg5MCQRC4AZDHh/WWl5YvY8jR6bTPzmWr8ypzeB947hR7Lz7DM6cPoiYqAh+fcE05o3uB9Qtv7visRWBx8dp0VURERHpJhQMdZH739lKfHQkvzx/alcPpUv87qKZ3HzqxAbb67cWv+3FdQA8cPHMwLaPtjolYf65RH2Vf72glvzfCWNa3qkRMZER1Hh97MwrY0duWaBE7jdfrl176MdnTMIYw/0XzWTjz07loiOHB14blBrP40FB/qjMRLbedTrnzOjeHQ9FRESk71Aw1MnKqjws3Z7Hmr2FREYYYqMiWz6oDwmeXP/L82oDxZGZifz50iMAp3zutXU5TLz9DbYe7LslV0u355OZFMu7P1jAqVMG8GO3O9yRI9P573eOA+CmU8Yzol9iu87vzwz9fdluAKYFlSf+5/+O5s6zJxPhljlGR0Y0Wua5cGIWq28/md9cMJ0nrjpSbbVFRESkW9GcoU425advBh4/flXHSuN6syFp8Vx61PDAOjeDUuIYMm0QM4am8tePdjLJXXNod3454wYkd+VQO2TlrgIefn8Hf7p0ViAwLq3y8Ob6A3zpiCFNtlqv9vj4ZEc+XzpiCKP7J/HI5XOorPGyr7CCG08aR1pCDGvvPIXk2Pb/Ffc3UFi+s4CYqIg689Fmj8hg9oiMZo6ulZ4Yw1eObNixTkRERKSrKRjqRMFlXefOHMyRI1t3M9nXrLhtEUlxURhj2PCzU8krrQpkICYMTObz7CI25RQDkJoQ3dypurUqj5cvux3zJvzkDX58xiSuPn401/x9JUu35zNtaCrjmwj0PtmRT3m1l/lB82/ioiO585wpgecpcR373qQnRFNW7WVTTjHXLhijrI6IiIj0OgqGOtF7W2oXoLz0qBFdOJLuLSuoFXRibFRgsVCAgfXaRHt7cJvm+9/ZWuf5w+9vJzkuiqVu1utQcVWTwdAzn+4hLSGaBePD14xgTlCwftRoBe4iIiLS++ij3k7i81l+/cZmAK6eP6pOe2lpPX92wr8mk8fbc4OhzW52a9PPT+PLs4eSX1bNLS+sC7x+qMTpmrevsCKwbhDAvz/dw2vrDnDRkcOIiw7fnLN5o/vx7LeOZtVPTmLhhKywXUdERESkqygz1EkKK2rYmVfGxUcO48dnTu7q4fRY/nK50f2TyCstwOPztXBE93WguIoTJ2YRHxNJUUXDBWXLqjxUebwce8+7JMZEMrp/EsbA2uwijhiexvdPHh/2MSpoFxERkd5MmaFO4r/ZVblRx/g7ms0angb07MxQUXk16QlOhut7JzmBzbCMeH51/jQAyqu97C0oB6Cs2su6fUWszS4C4Muzh6kToYiIiEgHKTPUSQrLqwFIi4/p4pH0bMeP78/7N59AWZWXR97fgcedM+TzWa58fAUj+yXyi/N6xtpNpVUekuOcv4KTB6ew654zAee93PbiOsqrvezILWv02HkKqkVEREQ6TJmhTpJf6gZDPbj7WXcxol8i0ZFOuZy/TO6/a/fz4dY8nvpkd1cOrdWstZRVe0mMbZjd8ZcCfrg1l5c/3w/AJXOdxUzf/cEC3vre8Yzun9R5gxURERHppZQZ6iS78p1P+Ee2cwFMqSvSDRj8ZXKr9xQGtnu8vkYXAO0u/vzediwWr8+SFNt0cPzZnkJw39fdX5rGzadOICNRmUURERGRUOm+d4y9zJq9hfRPjiVdN7Mh4e8q5y+Tyz7szK3x+myz2SFrLS+v2cebGw60eA2vz/Lgu1s5VFwZghE7dueX8es3NvObN7YAzlo+raVASERERCS0FAx1gg++yOXVdTmcPnVgVw+l14hyy+SWbc9ne24p72w6xEmTshiQEsvP/ruRp1fsafS419Yd4LvPrOFbT62ivNrT7DW+/c/PuPetL7jtxfUhG/ezK/fWeX5kE93aHv/akYHHT359bsiuLyIiIiK1FAyF2bZDJVzx2AqshVtPn9TVw+k1/GVy//ksm0X3vQ84C7T+8NSJAPxv7f4Gx2zYX8S3//VZ4Pket1NbY6y1vOFmj97ZdJBPduTXec3atnexs9bypyXbmTiwdiHV0ZmNl00eMTw98HhMf5VWioiIiISDgqEwe/j9HQDcevpE4mPUCjlUoiMa/up+efZQLpg9lAXj+/Pxtnx+9Pxayqpqsz/1s0X7Dlc0ef7NB0qc67gZqH8trz32isdWMOrW13jovW1tGvOnuw4DMHlQCtefOJafnDkJY0yj+6YGlc8NTo1v03VEREREpHXUQCGMfvT8Wp5flc21C8bwrQVjuno4vUpSXMNf3UGpcQDERDmB0r9X7qXG6+N3F80E4KOteQzLiOePlxzBeX/6mOxmgqFX1+YAsPj7J/D1Jz+lsKKGlbsKeG5lNh9uzQPgD4u38o3jRrV6vZ/Fmw8CcPNpExjUigDn9xfNIK+kOtBdTkRERERCS5mhMHpzo1Nm9X8KhEIuOqhbnH/NnYzEWIDAoqUAy3cWALAzr4xd+eVcefRIZgxNJT46kq2HSho9d5XHy4NLnKzP8H4JDM9I4IMvcvnyw8v4tzvn58xpg6is8XH+n5ZS7fG1ON7nV2XzyPs7OHvG4FYFQgDnzxrK1cePbtW+IiIiItJ2CobCaPrQNGYMS6tT8iShc+XRI7jxpHE8/rW5/OvqowLd1vonx7L9V2cwc1gaVR4fewvKWXjvewAMTY/HGMP8cZm8vfEgPp/lsY928p9V2YHz5pZU1blOcBe3axeM4cdnTOK7J40DYGNOMeN/8jo+X/NziO58ZQMA1584tsPvW0RERERCQ2VyYeDx+vhsTyHlVR4SojVPKFx+du7UwONjxmTWeS0ywrBgfH8eWLyV+b9ZEtg+IMUppTtz+iDe2niQMT9+DX8vhLNnDCYmKiIQDP3tyjkAbM8tBWDuqAxuOX1i4FzLb1vEUb9aDMCYH7/G0ltObJD12XaolB88u4Zqr4/BqXGMH5CMiIiIiHQPygyFWJXHy0tr9vOVR5axcvdh1u8r6uoh9Vljs5IabJs0KAWARZMGABDcFO6k372Px+tj6yEn+BmWkQDA/LFOoHXfhTPqnGtAShwPfnVW4Dyf7S6s8/ov/7eRk373Pp9nF1Ht8TXZLEFEREREuoYyQyFysLiScx78iIPFdUusSqqaX8tGwmfhxKzA439dfRSp8dHEuZm6pNgo3rhxPqfd/2Fgnz0F5ezKL+PjbXlkJsUwzg2mvnvSeC6bN4IsN6sU7KzpgzlxYhaT73iTHW4G6dNdBazNLuKvH+0EIDEmkrJqL/sKm27YICIiIiKdT8FQiLy7+VAgEEqOiyIpNoqcokquO0HNE7pKUmwUc0dmYLENyugAJg5M4flrj2ZkZiJzfvkOANUey8fb8jl2bGYgkxMZYRoNhPwSYqIYkhYfKKe78OFlgde+d9J4vnvSOEbe8irjBzTMVImIiIhI11EwFCKLNx0kKzmW5bctosrjC2QgpGv9+1vzmi1PmzPS6UT3xFVH8rXHPyX7cDl5pVVMG5LapuvMGJbK0u35lFfXZgIzk2K59gSnG9yyW08kMVZ/3URERES6E92dhUBFtZe9BRVcfOQwjDEKhLqR1s7TSY5zOv75F2Yd7s4Xaq1TpwzktXUHeGn1fgDuv2gmZ00fRJTbAry17bRFREREpPMoGAqB+JhIXvvufGq8La83I91TYqwTwC7Zkgs4nePaYuHELCIjDLe9uA6AOSPTA4GQiIiIiHRPulsLkcgIZYR6soyE2rWEzp81hLSg562REhfNkSPTAUhLiGZoetsySyIiIiLS+RQMiQD9kmIDj0+dMrBd5/jBKRMAmDI4JSRjEhEREZHwUpmcCE5mb0haPGXVHk6alNXyAY2YPTydn5w5iXNnDgnx6EREREQkHBQMibje+f4CKmq87Z7rExFh+Ob80SEelYiIiIiEi4IhEVd8TCTxMZr3JSIiItJXaM6QiIiIiIj0SQqGRERERESkT1IwJCIiIiIifVKXzBkyxuwCSgAv4LHWzjHGZAD/BkYCu4CvWGsPd8X4RERERESk9+vKzNBCa+1Ma+0c9/ktwGJr7ThgsftcREREREQkLLpTmdy5wJPu4yeB87puKCIiIiIi0tt1VTBkgbeMMauMMde42wZYa3MA3K+NrnxpjLnGGLPSGLMyNze3k4YrIiIiIiK9TVetM3SstXa/MSYLeNsYs7m1B1prHwUeBZgzZ44N1wBFRERERKR365LMkLV2v/v1EPAiMBc4aIwZBOB+PdQVYxMRERERkb6h04MhY8z/t3f/sVfVdRzHn6/xRSpAUn5Nw2QuGEIJKivtx0YyrZgullGSU7ZqlmXLmX/kyvWHbFmZE6I0ljNyydTIiXOTFcNKsmbgFwRREaSGMUFz6wtGBbz743y+7UZ85dfn3HMO5/XYzu6955774f1578M53/c9n3vOUEnD+58DlwAbgOXAvLTZPODhbsdmZmZmZmbtUcU0ubHAQ5L6//37IuIxSU8BD0j6HPAXYE4FsZmZmZmZWUt0vRiKiK3A1EOsfw2Y2e14zMzMzMysnep0aW0zMzMzM7OucTFkZmZmZmat5GLIzMzMzMxaSRHNvVWPpF3An6uOIxkFvFp1ECcw57dczm95nNtyOb/lcn7L49yWy/ktT1Nze2ZEjD54ZaOLoTqR9KeImF51HCcq57dczm95nNtyOb/lcn7L49yWy/ktz4mWW0+TMzMzMzOzVnIxZGZmZmZmreRiKJ/FVQdwgnN+y+X8lse5LZfzWy7ntzzObbmc3/KcULn1b4bMzMzMzKyVfGbIzMzMzMxaycWQmZmZmZm1kouhAUg6Q9IqSZskbZT01bT+VEm/krQ5PZ6S1o9M2++WtKijnbdJelTSc6mdW6vqU53kym967zFJ61I7d0kaVEWf6iRnfjvaXC5pQzf7UUeZx+7jkp6X1JuWMVX0qU4y5/ckSYslvZD2wZdX0ac6yXhsG94xbnslvSrpjoq6VQuZx+5cSc9IWp+OcaOq6FOdZM7vp1NuN0r6bhX9qZNjyO3FktakMbpG0kUdbZ2f1r8oaaEkVdWvI+ViaGD7gK9FxNnABcCXJU0Gvg6sjIgJwMr0GmAvcDNw4yHaui0iJgHnAh+Q9LHSo6+/nPn9VERMBd4NjAbmlB18A+TML5I+AewuPepmyJpb4MqImJaWnSXH3gQ58/sNYGdETAQmA78pO/gGyJLfiOjrGLfTKG6A/ssu9aGusuRWUg+wAPhwRJwDrAeu604Xai1XfkcC3wNmRsQUYKykmV3qQ10dbW5fBS6LiPcA84B7O9q6E7gGmJCWj3anC8fOxdAAImJHRKxNz/uATcA7gI8DS9JmS4DZaZs9EfEExX++znbeiIhV6fm/gLXAuG70oc5y5Te99/f0tAc4CWj9VUFy5lfSMOAGYH75kddfztza/8uc388C307bHYiIJt4xPasyxq+kCcAY4HflRV5/GXOrtAxN36qfDPy19A7UXMb8ngW8EBG70utfA60+a3wMuX06IvrH5EbgLZKGSDoNODkinoziCm0/6/9MnbkYOgKSxlOc1fkjMDYidkAxeCgOAEfaztuByyiqa0ty5FfSCmAn0Af8opxImylDfm8Bvg+8UVaMTZVp33BPmmZ0cxOmE3TT8eQ37W8BbpG0VtKDksaWGG7j5Dq2AXOB+8OXp/2v48ltRPwbuBZ4hqIImgzcXWa8TXOcY/dFYJKk8eks3GzgjPKibZZjyO3lwNMR8U+KAmp7x3vb07paczF0GOlb8WXA9R1nII6lnR5gKbAwIrbmiq/pcuU3Ij4CnAYMAS46zOatcbz5lTQNeFdEPJQ7tqbLNHavTNMMPpSWq3LF13QZ8ttDcRZ+dUScBzwJ3JYxxEbLte9NrqA4vhlZ9ruDKYqhc4HTKabJ3ZQ1yAY73vxGxOsU+b2f4mzmNoppYq13tLmVNAX4DvCF/lWH2Kz2X5K4GHoTaYe0DPh5RPTPhX4lnQYkPR7pHP/FwOaIuCN7oA2VOb9ExF5gOcVp3dbLlN8LgfMlbQOeACZKeryciJsj19iNiJfTYx9wH/DeciJulkz5fY3ibGZ/If8gcF4J4TZOzn2vpKlAT0SsKSXYhsmU22kAEbElnW17AHh/ORE3S8Z97yMR8b6IuBB4HthcVsxNcbS5lTSOYv96dURsSau3878/BRlHA6Z4uhgaQJqucjewKSJu73hrOcWPxUiPDx9BW/OBEcD1mcNsrFz5lTSs4z9qDzALeC5/xM2SK78RcWdEnB4R44EPUsyznpE/4ubIOHZ7lK4QlQ5ClwK+Wl++sRvAI8CMtGom8GzWYBso57EtmYvPCgFZc/syMFnS6PT6YorfcLRa5r/LxqTHU4AvAT/JG22zHG1u0zTkR4GbImJ1/8ZpKl2fpAtSm1dz5PuS6kSEl0MsFH/4BcXp6d60zAJGUvzmZ3N6PLXjM9uAv1FcdWs7xTzfcamdTR3tfL7q/lW9ZMzvWOCp1M5G4AcU31JW3scTIb8HtTke2FB136peMo7docCajrG7ABhUdf+qXnKOXeBM4LeprZXAO6vuX9VL7n0DsBWYVHW/6rBkHrtfpPi7YT1FUT+y6v5VvWTO71KKL0eeBa6oum9VL0ebW+CbwJ6ObXuBMem96RRf7G0BFgGqun+HW5QCNzMzMzMzaxVPkzMzMzMzs1ZyMWRmZmZmZq3kYsjMzMzMzFrJxZCZmZmZmbWSiyEzMzMzM2slF0NmZlZ7kvZL6pW0UdI6STdIetNjmKTxkj7TrRjNzKx5XAyZmVkT/CMipkXEFIqbUM4CvnWYz4wHXAyZmdmAfJ8hMzOrPUm7I2JYx+uzKG64PIri5qr3UtzIFuC6iPi9pD8AZwMvAUuAhcCtwAxgCPDDiPhx1zphZma142LIzMxq7+BiKK17HZgE9AEHImKvpAnA0oiYLmkGcGNEXJq2v4biLunzJQ0BVgNzIuKlbvbFzMzqo6fqAMzMzI6R0uNgYJGkacB+YOIA218CnCPpk+n1CGACxZkjMzNrIRdDZmbWOGma3H5gJ8Vvh14BplL8FnbvQB8DvhIRK7oSpJmZ1Z4voGBmZo0iaTRwF7AoirneI4AdEXEAuAoYlDbtA4Z3fHQFcK2kwamdiZKGYmZmreUzQ2Zm1gRvldRLMSVuH8UFE25P7/0IWCZpDrAK2JPWrwf2SVoH/BRYQHGFubWSBOwCZncnfDMzqyNfQMHMzMzMzFrJ0+TMzMzMzKyVXAyZmZmZmVkruRgyMzMzM7NWcjFkZmZmZmat5GLIzMzMzMxaycWQmZmZmZm1koshMzMzMzNrpf8AszFANdhMHSYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 7))\n",
    "plt.plot(dt['Close'], label='Historical Data')\n",
    "plt.plot(forecast_df, label='Forecast')\n",
    "plt.title('GRU Model - 30-Day Stock Price Forecast')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function to create a GRU model\n",
    "def create_model(units=50, activation='relu', optimizer='adam'):\n",
    "    \n",
    "    model = Sequential()\n",
    "    model.add(GRU(units=units, return_sequences=True, input_shape=(look_back, 1)))\n",
    "    model.add(GRU(units=units, return_sequences=False))\n",
    "    model.add(Dense(units=25, activation=activation))\n",
    "    model.add(Dense(units=1))\n",
    "    model.compile(optimizer=optimizer, loss='mean_squared_error')\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wraping the model using the function\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "\n",
    "model = KerasRegressor(build_fn=create_model, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the hyperparameter grid to search\n",
    "\n",
    "param_grid = {\n",
    "    'batch_size' : [16, 32, 64],\n",
    "    'epochs' : [50, 100, 200],\n",
    "    'units' : [50, 100, 150],\n",
    "    'optimizer' : ['adam', 'rmsprop'],\n",
    "    'activation' : ['relu', 'tanh']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Splitting\n",
    "\n",
    "# Setting the look_back period\n",
    "look_back = 60\n",
    "\n",
    "# Creating Dataset\n",
    "\n",
    "train_size = int(len(scaled_data) * 0.80)\n",
    "train, test = scaled_data[:train_size], scaled_data[train_size:]\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "trainX, trainY = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 108 candidates, totalling 324 fits\n",
      "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000002A6B5EB5160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000002A6B5EB5160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000002A6A49F8670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000002A6A49F8670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    }
   ],
   "source": [
    "# Grid Search\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "grid = GridSearchCV(\n",
    "    estimator = model,\n",
    "    param_grid = param_grid,\n",
    "    scoring = 'neg_mean_squared_error',\n",
    "    cv = 3,\n",
    "    verbose = 1,\n",
    "    n_jobs = 1\n",
    ")\n",
    "\n",
    "grid_search = grid.fit(trainX, trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: -0.0002559135546745299 \n",
      "using {'activation': 'tanh', 'batch_size': 32, 'epochs': 200, 'optimizer': 'adam', 'units': 100}\n"
     ]
    }
   ],
   "source": [
    "print(f'Best Score: {grid_search.best_score_} \\nusing {grid_search.best_params_}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Forecasting using Optimized Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "49/49 [==============================] - 3s 10ms/step - loss: 0.0047\n",
      "Epoch 2/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 1.4078e-04\n",
      "Epoch 3/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 1.1005e-04\n",
      "Epoch 4/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 1.0101e-04\n",
      "Epoch 5/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 9.8319e-05\n",
      "Epoch 6/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 9.4069e-05\n",
      "Epoch 7/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 9.0612e-05\n",
      "Epoch 8/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 8.7308e-05\n",
      "Epoch 9/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 8.3389e-05\n",
      "Epoch 10/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 8.6499e-05\n",
      "Epoch 11/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 9.1428e-05\n",
      "Epoch 12/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.5208e-05\n",
      "Epoch 13/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.6502e-05\n",
      "Epoch 14/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 7.2106e-05\n",
      "Epoch 15/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.0687e-05\n",
      "Epoch 16/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 8.0262e-05\n",
      "Epoch 17/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.7564e-05\n",
      "Epoch 18/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.7020e-05\n",
      "Epoch 19/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.6094e-05\n",
      "Epoch 20/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.8480e-05\n",
      "Epoch 21/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.3881e-05\n",
      "Epoch 22/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.2900e-05\n",
      "Epoch 23/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.4385e-05\n",
      "Epoch 24/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.8415e-05\n",
      "Epoch 25/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.6187e-05\n",
      "Epoch 26/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.2890e-05\n",
      "Epoch 27/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.6438e-05\n",
      "Epoch 28/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.0445e-05\n",
      "Epoch 29/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.3333e-05\n",
      "Epoch 30/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.8720e-05\n",
      "Epoch 31/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.6118e-05\n",
      "Epoch 32/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.0966e-05\n",
      "Epoch 33/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.2501e-05\n",
      "Epoch 34/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 8.1771e-05\n",
      "Epoch 35/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.0638e-05\n",
      "Epoch 36/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.7823e-05\n",
      "Epoch 37/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.5438e-05\n",
      "Epoch 38/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.6276e-05\n",
      "Epoch 39/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.9167e-05\n",
      "Epoch 40/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.5722e-05\n",
      "Epoch 41/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.4294e-05\n",
      "Epoch 42/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.3250e-05\n",
      "Epoch 43/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.5109e-05\n",
      "Epoch 44/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.6898e-05\n",
      "Epoch 45/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.0796e-05\n",
      "Epoch 46/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.4204e-05\n",
      "Epoch 47/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.2617e-05\n",
      "Epoch 48/200\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 6.6709e-05\n",
      "Epoch 49/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.6495e-05\n",
      "Epoch 50/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.5732e-05\n",
      "Epoch 51/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.3918e-05\n",
      "Epoch 52/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.9000e-05\n",
      "Epoch 53/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.8603e-05\n",
      "Epoch 54/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.7246e-05\n",
      "Epoch 55/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.9240e-05\n",
      "Epoch 56/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.7794e-05\n",
      "Epoch 57/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.6841e-05\n",
      "Epoch 58/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.3675e-05\n",
      "Epoch 59/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.6608e-05\n",
      "Epoch 60/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.9133e-05\n",
      "Epoch 61/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.1017e-05\n",
      "Epoch 62/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.7875e-05\n",
      "Epoch 63/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.7380e-05\n",
      "Epoch 64/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.9906e-05\n",
      "Epoch 65/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.0771e-05\n",
      "Epoch 66/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.8284e-05\n",
      "Epoch 67/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.9872e-05\n",
      "Epoch 68/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.7763e-05\n",
      "Epoch 69/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 7.1596e-05\n",
      "Epoch 70/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.5790e-05\n",
      "Epoch 71/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 9.4671e-05\n",
      "Epoch 72/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.8717e-05\n",
      "Epoch 73/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.4184e-05\n",
      "Epoch 74/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.4830e-05\n",
      "Epoch 75/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.8992e-05\n",
      "Epoch 76/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.9056e-05\n",
      "Epoch 77/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.4155e-05\n",
      "Epoch 78/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.4742e-05\n",
      "Epoch 79/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.0906e-05\n",
      "Epoch 80/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 7.7856e-05\n",
      "Epoch 81/200\n",
      "49/49 [==============================] - 1s 12ms/step - loss: 5.8225e-05\n",
      "Epoch 82/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.7423e-05\n",
      "Epoch 83/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.5523e-05\n",
      "Epoch 84/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.0847e-05\n",
      "Epoch 85/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.5491e-05\n",
      "Epoch 86/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.0053e-05\n",
      "Epoch 87/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.8626e-05\n",
      "Epoch 88/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.2269e-05\n",
      "Epoch 89/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 8.7951e-05\n",
      "Epoch 90/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.4638e-05\n",
      "Epoch 91/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.4278e-05\n",
      "Epoch 92/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.7669e-05\n",
      "Epoch 93/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.4373e-05\n",
      "Epoch 94/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.8978e-05\n",
      "Epoch 95/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 7.1372e-05\n",
      "Epoch 96/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.3608e-05\n",
      "Epoch 97/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.0741e-05\n",
      "Epoch 98/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.4559e-05\n",
      "Epoch 99/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.5473e-05\n",
      "Epoch 100/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.9638e-05\n",
      "Epoch 101/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.6719e-05\n",
      "Epoch 102/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.7529e-05\n",
      "Epoch 103/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.9694e-05\n",
      "Epoch 104/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.4962e-05\n",
      "Epoch 105/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.6591e-05\n",
      "Epoch 106/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.1879e-05\n",
      "Epoch 107/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.4449e-05\n",
      "Epoch 108/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.7819e-05\n",
      "Epoch 109/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 8.8397e-05\n",
      "Epoch 110/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 7.2998e-05\n",
      "Epoch 111/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.5254e-05\n",
      "Epoch 112/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.1341e-05\n",
      "Epoch 113/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.6659e-05\n",
      "Epoch 114/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.8782e-05\n",
      "Epoch 115/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.9822e-05\n",
      "Epoch 116/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.0399e-05\n",
      "Epoch 117/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.5556e-05\n",
      "Epoch 118/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.4082e-05\n",
      "Epoch 119/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.4122e-05\n",
      "Epoch 120/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 8.6314e-05\n",
      "Epoch 121/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.1569e-05\n",
      "Epoch 122/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 7.1681e-05\n",
      "Epoch 123/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.5097e-05\n",
      "Epoch 124/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.2610e-05\n",
      "Epoch 125/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.5213e-05\n",
      "Epoch 126/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.7111e-05\n",
      "Epoch 127/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.9160e-05\n",
      "Epoch 128/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.3305e-05\n",
      "Epoch 129/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.6788e-05\n",
      "Epoch 130/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.4677e-05\n",
      "Epoch 131/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.2735e-05\n",
      "Epoch 132/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.2188e-05\n",
      "Epoch 133/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.4636e-05\n",
      "Epoch 134/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.5272e-05\n",
      "Epoch 135/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.9786e-05\n",
      "Epoch 136/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.2293e-05\n",
      "Epoch 137/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.0876e-05\n",
      "Epoch 138/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.4361e-05\n",
      "Epoch 139/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.7362e-05\n",
      "Epoch 140/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 8.2995e-05\n",
      "Epoch 141/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.2703e-05\n",
      "Epoch 142/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.8749e-05\n",
      "Epoch 143/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.3902e-05\n",
      "Epoch 144/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.1292e-05\n",
      "Epoch 145/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.8185e-05\n",
      "Epoch 146/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.2713e-05\n",
      "Epoch 147/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.3712e-05\n",
      "Epoch 148/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.5737e-05\n",
      "Epoch 149/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.1144e-05\n",
      "Epoch 150/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.8024e-05\n",
      "Epoch 151/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.9118e-05\n",
      "Epoch 152/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.3063e-05\n",
      "Epoch 153/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.1862e-05\n",
      "Epoch 154/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.0791e-05\n",
      "Epoch 155/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.8947e-05\n",
      "Epoch 156/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.0077e-05\n",
      "Epoch 157/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.5554e-05\n",
      "Epoch 158/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.1521e-05\n",
      "Epoch 159/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.6806e-05\n",
      "Epoch 160/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.6911e-05\n",
      "Epoch 161/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.0750e-05\n",
      "Epoch 162/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.5079e-05\n",
      "Epoch 163/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.5075e-05\n",
      "Epoch 164/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 8.5147e-05\n",
      "Epoch 165/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.9960e-05\n",
      "Epoch 166/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.2607e-05\n",
      "Epoch 167/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.0613e-05\n",
      "Epoch 168/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.8655e-05\n",
      "Epoch 169/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.8915e-05\n",
      "Epoch 170/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.3186e-05\n",
      "Epoch 171/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.4516e-05\n",
      "Epoch 172/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.6566e-05\n",
      "Epoch 173/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.1379e-05\n",
      "Epoch 174/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.7015e-05\n",
      "Epoch 175/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.1722e-05\n",
      "Epoch 176/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.5945e-05\n",
      "Epoch 177/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.5646e-05\n",
      "Epoch 178/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.1327e-05\n",
      "Epoch 179/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.7076e-05\n",
      "Epoch 180/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 7.1095e-05\n",
      "Epoch 181/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 8.1319e-05\n",
      "Epoch 182/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.0779e-05\n",
      "Epoch 183/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.0699e-05\n",
      "Epoch 184/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 7.1855e-05\n",
      "Epoch 185/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 8.0968e-05\n",
      "Epoch 186/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 5.7772e-05\n",
      "Epoch 187/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 7.3882e-05\n",
      "Epoch 188/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.0286e-05\n",
      "Epoch 189/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.4126e-05\n",
      "Epoch 190/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.3959e-05\n",
      "Epoch 191/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.9906e-05\n",
      "Epoch 192/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 5.9078e-05\n",
      "Epoch 193/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.2894e-05\n",
      "Epoch 194/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.1800e-05\n",
      "Epoch 195/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.3229e-05\n",
      "Epoch 196/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 5.6471e-05\n",
      "Epoch 197/200\n",
      "49/49 [==============================] - 1s 11ms/step - loss: 6.0808e-05\n",
      "Epoch 198/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.0730e-05\n",
      "Epoch 199/200\n",
      "49/49 [==============================] - 0s 10ms/step - loss: 6.0221e-05\n",
      "Epoch 200/200\n",
      "49/49 [==============================] - 1s 10ms/step - loss: 6.5174e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2a6b48b2fa0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building Model using optimized parameters\n",
    "best_model = Sequential()\n",
    "best_model.add(GRU(units=100, return_sequences=True, input_shape=(look_back, 1)))\n",
    "best_model.add(GRU(units=100, return_sequences=False))\n",
    "best_model.add(Dense(units=25, activation='tanh'))\n",
    "best_model.add(Dense(units=1))\n",
    "best_model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Training Model\n",
    "best_model.fit(trainX, trainY, batch_size=32, epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 547ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n"
     ]
    }
   ],
   "source": [
    "# Number of days to forecast\n",
    "n_days = 30\n",
    "\n",
    "# Getting the last look_back days from the dataset\n",
    "last_sequence = scaled_data[-look_back:]\n",
    "\n",
    "# Forecasting next 30 days\n",
    "forecasted_values = forecast_next_days(best_model, scaled_data, n_days, look_back)\n",
    "\n",
    "# Inverting the scaled predictions back to the original scale\n",
    "forecasted_values = scaler.inverse_transform(forecasted_values.reshape(-1,1))\n",
    "\n",
    "# Creating a Dataframe for plotting\n",
    "forecast_dates = pd.date_range(\n",
    "    start = dt.index[-1] + pd.Timedelta(days=1),\n",
    "    periods = n_days,\n",
    "    freq= 'B'\n",
    ")\n",
    "\n",
    "forecast_df = pd.DataFrame(\n",
    "    forecasted_values,\n",
    "    index=forecast_dates,\n",
    "    columns=['Forecast']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Forecast\n",
    "plt.figure(figsize=(14,7))\n",
    "plt.plot(dt['Close'], label='Historical Data')\n",
    "plt.plot(forecast_df, label='Forecast')\n",
    "plt.title('GRU Model - 30-day Stock Price Forecast')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TimeSeries_Analysis",
   "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}