ShortTerm_StockPrice_Forecast_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Short-term Stock Price Forecasting with LSTM\n",
    "\n",
    "> This model is designed for short-term stock price predictions, utilizing a dataset that spans 1 year and employs a single-step approach. Each prediction is made based on the most recent data point available, making it particularly suited for short-term forecasting. The architecture of the model, which includes LSTM layers with dropout and regularization, is optimized to capture short-term trends and patterns within the stock market.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-26 02:27:19.915858: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import datetime\n",
    "import pytz\n",
    "\n",
    "import yfinance as yf\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense, Dropout\n",
    "from keras.regularizers import l1_l2\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch stock data and earnings dates\n",
    "def fetch_stock_data_with_earnings(ticker, period):\n",
    "    try:\n",
    "        stock = yf.Ticker(ticker)\n",
    "\n",
    "        data = stock.history(period=period)\n",
    "        data.index = data.index.date\n",
    "\n",
    "        earnings = stock.earnings_dates\n",
    "        earnings.index = earnings.index.date\n",
    "        \n",
    "        return data, earnings\n",
    "    except Exception as e:\n",
    "        print(f\"Error fetching data for {ticker}: {e}\")\n",
    "        return None, None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch stock data\n",
    "ticker = \"TSLA\"\n",
    "period = \"1y\"\n",
    "\n",
    "data, earnings = fetch_stock_data_with_earnings(ticker, period)\n",
    "if data is None:\n",
    "    raise ValueError(\"Failed to fetch stock data.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge S&P 500 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch S&P 500 historical data\n",
    "sp500 = yf.Ticker(\"^GSPC\")\n",
    "sp500_data = sp500.history(period=period)\n",
    "sp500_data.index = sp500_data.index.date\n",
    "\n",
    "data_sp500 = sp500_data[['Close']] \n",
    "data_sp500.columns = ['SP500_Close']    # Rename the columns for clarity\n",
    "data = data.merge(data_sp500, how='left', left_index=True, right_index=True)\n",
    "\n",
    "data = data.drop(columns=['Dividends', 'Stock Splits']) # Drop unused columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SP500_Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-02-16</th>\n",
       "      <td>202.059998</td>\n",
       "      <td>203.169998</td>\n",
       "      <td>197.399994</td>\n",
       "      <td>199.949997</td>\n",
       "      <td>111173600</td>\n",
       "      <td>5005.569824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-20</th>\n",
       "      <td>196.130005</td>\n",
       "      <td>198.600006</td>\n",
       "      <td>189.130005</td>\n",
       "      <td>193.759995</td>\n",
       "      <td>104545800</td>\n",
       "      <td>4975.509766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-21</th>\n",
       "      <td>193.360001</td>\n",
       "      <td>199.440002</td>\n",
       "      <td>191.949997</td>\n",
       "      <td>194.770004</td>\n",
       "      <td>103844000</td>\n",
       "      <td>4981.799805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-22</th>\n",
       "      <td>194.000000</td>\n",
       "      <td>198.320007</td>\n",
       "      <td>191.360001</td>\n",
       "      <td>197.410004</td>\n",
       "      <td>92739500</td>\n",
       "      <td>5087.029785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-23</th>\n",
       "      <td>195.309998</td>\n",
       "      <td>197.570007</td>\n",
       "      <td>191.500000</td>\n",
       "      <td>191.970001</td>\n",
       "      <td>78670300</td>\n",
       "      <td>5088.799805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close     Volume  \\\n",
       "2024-02-16  202.059998  203.169998  197.399994  199.949997  111173600   \n",
       "2024-02-20  196.130005  198.600006  189.130005  193.759995  104545800   \n",
       "2024-02-21  193.360001  199.440002  191.949997  194.770004  103844000   \n",
       "2024-02-22  194.000000  198.320007  191.360001  197.410004   92739500   \n",
       "2024-02-23  195.309998  197.570007  191.500000  191.970001   78670300   \n",
       "\n",
       "            SP500_Close  \n",
       "2024-02-16  5005.569824  \n",
       "2024-02-20  4975.509766  \n",
       "2024-02-21  4981.799805  \n",
       "2024-02-22  5087.029785  \n",
       "2024-02-23  5088.799805  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 7))  # Set figure size\n",
    "\n",
    "# Plot ticker 'Close' price with the first y-axis\n",
    "ax1 = plt.gca()\n",
    "line1 = ax1.plot(data.index, data['Close'], label='Ticker Close Price', color='blue')\n",
    "ax1.set_ylabel('Ticker Close Price', color='blue')\n",
    "ax1.tick_params(axis='y', labelcolor='blue')\n",
    "\n",
    "# Plot 'SP500_Close' with the second y-axis\n",
    "ax2 = ax1.twinx()\n",
    "line2 = ax2.plot(data.index, data['SP500_Close'], label='S&P 500 Close Price', color='red', linestyle='--')\n",
    "ax2.set_ylabel('S&P 500 Close Price', color='red')\n",
    "ax2.tick_params(axis='y', labelcolor='red')\n",
    "\n",
    "plt.title('Ticker Close Price vs. S&P 500 Close Price')\n",
    "plt.xlabel('Date')\n",
    "\n",
    "# Combine legends\n",
    "lines = line1 + line2\n",
    "labels = [l.get_label() for l in lines]\n",
    "plt.legend(lines, labels)\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After Hours.\n"
     ]
    }
   ],
   "source": [
    "# Function to check if the current time is within trading hours\n",
    "def is_trading_hours():\n",
    "    eastern = pytz.timezone('US/Eastern')\n",
    "    now_eastern = datetime.datetime.now(eastern)\n",
    "    \n",
    "    # Define market open and close times (9:30 AM to 4:00 PM)\n",
    "    market_open = now_eastern.replace(hour=9, minute=30, second=0, microsecond=0)\n",
    "    market_close = now_eastern.replace(hour=16, minute=0, second=0, microsecond=0)\n",
    "    \n",
    "    return market_open <= now_eastern <= market_close\n",
    "\n",
    "# Drop the last row if it's within trading hours (incomplete data)\n",
    "if is_trading_hours():\n",
    "    print('Trading Hours.')\n",
    "    data = data.drop(data.tail(1).index)\n",
    "else:\n",
    "    print('After Hours.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EMA\n",
    "short_span, long_span = 10, 50\n",
    "\n",
    "data['EMA_Short'] = data['Close'].ewm(span=short_span, adjust=False).mean()\n",
    "data['EMA_Long'] = data['Close'].ewm(span=long_span, adjust=False).mean()\n",
    "\n",
    "# RSI\n",
    "delta = data['Close'].diff()\n",
    "gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()\n",
    "loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()\n",
    "rs = gain / loss\n",
    "data['RSI'] = 100 - (100 / (1 + rs))\n",
    "\n",
    "# MACD\n",
    "exp1 = data['Close'].ewm(span=12, adjust=False).mean()\n",
    "exp2 = data['Close'].ewm(span=26, adjust=False).mean()\n",
    "data['MACD'] = exp1 - exp2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EMA (Exponential Moving Average)\n",
    "\n",
    "- EMA is a type of moving average that places a greater weight and significance on the most recent data points. It is more responsive to new information compared to the SMA.\n",
    "- Usage in analysis: EMA is often used to identify the direction of the trend, with prices above the EMA suggesting an uptrend and prices below indicating a downtrend. Traders might use short-term EMAs to make quick, momentum-based decisions and longer-term EMAs for overall trend direction.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(data.index, data['Close'], label='Close Price')\n",
    "plt.plot(data.index, data['EMA_Short'], label='EMA Short')\n",
    "plt.plot(data.index, data['EMA_Long'], label='EMA Long')\n",
    "plt.title('EMA Short vs EMA Long')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RSI (Relative Strength Index)\n",
    "\n",
    "- RSI is a momentum oscillator that measures the speed and change of price movements on a scale of 0 to 100. It is used to identify overbought or oversold conditions in a security's price.\n",
    "- Usage in analysis: Values above 70 are considered overbought (suggesting a potential sell signal), while values below 30 are considered oversold (indicating a potential buy signal). RSI can also show divergence signals, where the price trend and RSI trend move in opposite directions, suggesting a potential reversal.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LxSIFLjCMRmNAwdfV1RUyYS6R1LmKioohtxmNxqT+ZsGPaTQaAUB6zK6uLgBAbW1twHE6nW7I97a1teGtt94a8txNmzYNwNDX5IUXXgiDwQCn04nf/e53Ma2XrSv4eWeMGzcOa9euxZo1a/DKK69g1qxZWLFiBV588cWQx48aNQrXXHMN/v3vf2PXrl146aWX4HA4Yl6PyWSK+vyPHDkSgN8OHArWmydPQEzH+ylcv2M6KC8vD/jaYDBEvJ29b9ra2sDzPGpqaob8TqtXr47pHEEQhHKhNEKCILLOlClTpFCM4447Dl6vF48//jheffVVnHvuuaisrAQA3HzzzQGN8nLChQf09fXh7bffxu233x4Qk+50OtHd3Z3Uuk8++WTU19fjySefxMknn4wnn3wShx9+OKZOnSodw4IYwqWuyZWbWCkoKMDNN9+Ml156CZs3b475+4KTB9nz+uqrrw6JFE+UioqKkGESra2tKXn8dMMKqtbWVgwbNky63ePxSIUYo7KyEjNnzsRdd90V8rHq6+ulf3u9Xlx44YUoKyuD0WjElVdeia+++kq68A4H+xuFe62aTCbpvTNv3jwcd9xxmDZtGpYuXYrFixejsLAw4uOfd955WLFiRcyvo+7u7qjz5k466STccssteP3114conAw2D+6kk06SbkvH+0kNaZuVlZXgOE6apRZMqNsIglAPVGwRBKE47r33Xrz22mu47bbbcPbZZ2PSpEmYMGECNm3aJIVYxArHceB5fsgFy+OPPz4kbCJY5YiGVqvFxRdfjAcffBBffPEF1q1bh3/84x8BxyxevBgvvvgivF4vDj/88LjWDgAtLS0hd+eZ/VF+QR+v6nPyySdDp9Nhz549KQsOWLBgAV5++WW89957kp0OQEilRf58B8dxZwuW4Pjcc8/h0EMPlW5/+eWXpYRBxuLFi/Huu+9i3LhxUefB3X777fjiiy/w4YcfoqCgAMceeyx+97vf4a9//WvE72NWtGgJgIyKigrcfffduPzyy/Hwww/j5ptvBhD+dWSz2dDY2BjwOgqHx+NBY2MjTjvttIjHzZ07F4sWLcK///1vXHzxxZg/f37A/V9++SWeeOIJnHLKKQHPcSbeT0pk8eLFuPvuu9HU1ITzzjsv28shCCLFULFFEITiKCsrw80334wbb7wRzz//PC666CL84x//wKmnnoqTTz4Zl112GYYNG4bu7m5s27YNGzZswCuvvBLysYqLi3HsscfivvvuQ2VlJUaPHo3PPvsM//73v1FaWhpw7PTp0wEA//znP1FUVASTyYQxY8aEtLMxrrjiCtxzzz1YsmQJzGbzkNS9Cy64AM899xxOO+00/OpXv8Jhhx0GvV6PgwcPYtWqVTjrrLPw4x//OOzjT5s2DSeccAJOPfVUjBs3Dg6HA99++y3+/Oc/o6amJqDvZ8aMGfj000/x1ltvoa6uDkVFRRHjwkePHo0//vGP+P3vf4+9e/dK/XJtbW1Ys2YNCgoK4o7KvvTSS/GXv/wFF110Ee68806MHz8e7733Hj744AMAgRHjM2bMAADcc889OPXUU6HVajFz5syoak86mTJlCi666CI8+OCD0Ov1OPHEE7F582bcf//9QwbZ/vGPf8TKlStx1FFH4frrr8ekSZPgcDiwb98+vPvuu3jssccwfPhwrFy5EitWrMCtt96KE044AYAwP+23v/0tFi5cGPHvP3z4cIwdOxarV6/G9ddfH9PvcMkll+CBBx7A/fffj+uuuw7FxcW466678NVXX+H888+X4sUbGhrwyCOPoKurC/fdd1/Ux/3+++8xODgYUzLiM888gxNPPBGLFi3C9ddfL/3en3zyCf76179i8uTJIVNE0/1+UiLz58/HL37xC1x++eVYt24djj32WBQUFKClpQVffvklZsyYgWuuuSbbyyQIIlGyHNBBEEQewxLi1q5dO+Q+u93Ojxw5kp8wYQLv8Xh4nuf5TZs28eeddx5fXV3N6/V6vra2lj/++OMDktdCpREePHiQP+ecc/iysjK+qKiIP+WUU/jNmzcPSRXjeZ5/8MEH+TFjxvBarTYgPS84jVDOUUcdxQPgL7zwwpD3u91u/v777+dnzZrFm0wmvrCwkJ88eTJ/1VVX8bt27Yr4HP3jH//gzz77bH7s2LG8xWLhDQYDP27cOP7qq6/mGxsbA47duHEjP3/+fN5isfAA+AULFvA8H/l55nmef/311/njjjuOLy4u5o1GIz9q1Cj+3HPP5T/66CPpmEsvvZQvKCgY8r0suU7OgQMH+LPPPpsvLCzki4qK+HPOOYd/9913eQD8G2+8IR3ndDr5n/3sZ3xVVRXPcRwPQErGA8Bfd911Q35eqL9ZMJHSCIOfg1CvF6fTyd9www18dXU1bzKZ+COOOIL/5ptvQv7sjo4O/vrrr+fHjBnD6/V6vry8nD/00EP53//+97zNZuObm5v56upq/vjjj5cSM3leSAc844wz+NLS0iFpgMHceuutfFlZGe9wOAJuZ2mEoXjnnXd4APwdd9zB8zzPr169mr/uuuv4WbNm8eXl5bxWq+Wrqqr4U045hX/33XcDvjdcGuGtt97KV1ZWDllHOGw2G798+XJ+9uzZvMVi4S0WCz9z5kz+zjvv5G02W9jvS9X7KVKiYDQSSSN85ZVXAo4L95oL9/w+8cQT/OGHH84XFBTwZrOZHzduHH/JJZfw69atS+h3IAhCGXA8L0YCEQRBEESaWL58Of7whz/gwIEDGD58eLaXoyqam5sxZswYPPPMM0OUnkzh9Xoxfvx4LFmyJGyPGkEQBDEUKrYIgiCIlPLII48AACZPngy3241PPvkEDz30EM4//3w888wzWV6dOrnpppvw3nvvYePGjQFWzEzx9NNP47e//S127do1xH5LEARBhId6tgiCIIiUYrFY8Je//AX79u2D0+nEyJEjcdNNN+EPf/hDtpemWv7whz/AYrGgqakpIC49U/h8Pjz33HNUaBEEQcQJKVsEQRAEQRAEQRBpgIYaEwRBEARBEARBpAEqtgiCIAiCIAiCINIAFVsEQRAEQRAEQRBpgAIyIDT+Njc3o6ioCBzHZXs5BEEQBEEQBEFkCZ7nYbVaUV9fn3QCLBVbEGaYZCPdiSAIgiAIgiAIZdLY2Jj0bEgqtgAUFRUBEJ7Q4uLiLK+GIAiCIAiCIIhs0d/fjxEjRkg1QjJQsQVI1sHi4mIqtgiCIAiCIAiCSEl7EQVkEARBEARBEARBpAEqtgiCIAiCIAiCINJAVoutzz//HGeccQbq6+vBcRxef/31gPt5nseyZctQX18Ps9mMhQsXYsuWLQHHOJ1O/PKXv0RlZSUKCgpw5pln4uDBgxn8LQiCIAiCIAiCIIaS1Z6tgYEBzJo1C5dffjnOOeecIfffe++9eOCBB/DUU09h4sSJuPPOO3HSSSdhx44dUsPa0qVL8dZbb+HFF19ERUUFbrjhBixevBjr16+HVqvN9K9EEARBEARBEIqA53l4PB54vd5sL0VRaLVa6HS6jIx84nie59P+U2KA4zj873//w49+9CMAwoujvr4eS5cuxU033QRAULFqampwzz334KqrrkJfXx+qqqrw7LPP4vzzzwfgj3F/9913cfLJJ8f0s/v7+1FSUoK+vj4KyCAIgiAIgiBUj8vlQktLCwYHB7O9FEVisVhQV1cHg8Ew5L5U1gaKTSNsaGhAa2srFi1aJN1mNBqxYMECfP3117jqqquwfv16uN3ugGPq6+sxffp0fP3112GLLafTCafTKX3d39+fvl+EIAiCIAiCIDKIz+dDQ0MDtFot6uvrYTAYMqLiqAGe5+FyudDR0YGGhgZMmDAh6cHFkVBssdXa2goAqKmpCbi9pqYG+/fvl44xGAwoKysbcgz7/lCsWLECd9xxR4pXTBAEQRAEQRDZx+VywefzYcSIEbBYLNlejuIwm83Q6/XYv38/XC4XTCZT2n6W4tMIg6twnuejVubRjrn55pvR19cn/dfY2JiStRIEQRAEQRCEUkinYqN2MvXcKPYvUFtbCwBDFKr29nZJ7aqtrYXL5UJPT0/YY0JhNBqlAcY0yJggCIIgCIIgiHSg2GJrzJgxqK2txcqVK6XbXC4XPvvsMxx11FEAgEMPPRR6vT7gmJaWFmzevFk6hiAIgiAIgiAIIhtktWfLZrNh9+7d0tcNDQ3YuHEjysvLMXLkSCxduhTLly/HhAkTMGHCBCxfvhwWiwVLliwBAJSUlODKK6/EDTfcgIqKCpSXl+O3v/0tZsyYgRNPPDFbvxZBEARBEARBEER2la1169Zhzpw5mDNnDgDgN7/5DebMmYPbbrsNAHDjjTdi6dKluPbaazF37lw0NTXhww8/lGZsAcBf/vIX/OhHP8J5552H+fPnw2Kx4K233qIZWwRBEARBEAShMi677DJwHAeO46DT6TBy5Ehcc801AW1D3333HRYvXozq6mqYTCaMHj0a559/Pjo7OwEA+/btA8dx2LhxY5Z+Cz9ZVbYWLlyISGO+OI7DsmXLsGzZsrDHmEwmPPzww3j44YfTsEKCIAiCIAiCIDLJKaecgieffBIejwdbt27FFVdcgd7eXrzwwgtob2/HiSeeiDPOOAMffPABSktL0dDQgDfffFORM8UUG/1OEARBEARBEERq4Hkedrc3Kz/brNfGNefLaDRKYXnDhw/H+eefj6eeegoA8PXXX6O/vx+PP/44dDqhlBkzZgyOP/74lK87FVCxRRAEQRAEQRA5jt3txdTbPsjKz976x5NhMSRWduzduxfvv/8+9Ho9ACGN3OPx4H//+x/OPfdcxQ9rVmwaIUEQBEEQBEEQ+cfbb7+NwsJCmM1mjBs3Dlu3bsVNN90EADjiiCNwyy23YMmSJaisrMSpp56K++67D21tbVledWhI2SIIIuMc6BqEVsthWKk520shCIIgiLzArNdi6x9PztrPjofjjjsOf//73zE4OIjHH38cO3fuxC9/+Uvp/rvuugu/+c1v8Mknn2D16tV47LHHsHz5cnz++eeYMWNGqpefFKRsEQSRUTptTpz20Bc48+EvMeD0ZHs5BEEQBJEXcBwHi0GXlf/itfoVFBRg/PjxmDlzJh566CE4nU7ccccdAcdUVFTgJz/5Cf785z9j27ZtqK+vx/3335/KpywlULFFEERGeWNjM2xOD7oGXHh/c2u2l0MQBEEQhMK5/fbbcf/996O5uTnk/QaDAePGjcPAwECGVxYdshESBJFRXl1/MODf5xw6PIurIQiCIAhC6SxcuBDTpk3D8uXLccopp+DFF1/EBRdcgIkTJ4Lnebz11lt499138eSTT2Z7qUOgYosgiIyxpbkP21r6oddy8Ph4fLO3C43dgxhRbsn20giCIAiCUDC/+c1vcPnll+MnP/kJLBYLbrjhBjQ2NsJoNGLChAl4/PHHcfHFF2d7mUOgYosgiIzBVK1FU2vRZ3fjy92d+O+GJvzqxAlhv8ft9WF/1wCMOi0VZQRBEASR47B5WsEsWbIES5YsAQAsWLAg4mOMHj0aPM+nemkJQcUWQRAZwe314c2Ngtf6nEOHScXWc9/ux8wRJThybAX2dQ1gV5sNu9pt2N1uxc42G/Z1DsDj46HhgFW/XYhRFQVZ/k0IgiAIgiBig4otgiAywv6uQXQNuGAxaHHshCq4vTzuLdmBlj4HLn9ybdTv9/FAQ+cAFVsEQRAEQagGKrYIgsgI7VYHAKC2xASdVgOdFvjftfPx+Bd78cKaAxhweVFs0mFCTREmVBdifHWh9O9rn9uAjY29cHl8Wf4tCIIgCIIgYoeKLYIgMkKH1QkAqC4ySrfVlpjwh8VTccOiSbA63agqNIacxWHUCVMqXF4qtgiCIAiCUA9UbBEEkRHa+1mxZRpyn9mghdkQfrq8gRVbpGwRBEEQRMwoJSRCiWTquaGhxgRBZARmI5QrW7FipGKLIAiCIGJGr9cDAAYHB7O8EuXCnhv2XKULUrYIgsgIzEZYlUCxxZQtJxVbBEEQBBEVrVaL0tJStLe3AwAsFktIm34+wvM8BgcH0d7ejtLSUmi14Z01qYCKLYIgMkI769kqTqDY0pKyRRAEQRDxUFtbCwBSwUUEUlpaKj1H6YSKLYIgMoJUbIXo2YqGgQIyCIIgCCIuOI5DXV0dqqur4Xa7s70cRaHX69OuaDGo2CIIIiO09yfes0U2QoIgCIJIDK1Wm7HCghgKBWQQBJF2HG4v+h0eAAkqW+KHBNkICYIgCIJQE1RsEQSRdlg4hkGnQbE5fkGdot8JgiAIglAjVGwRBJF22mUDjRNJQ/L3bHlTui6CIAiCIIh0QsUWQRBpp0OcsZVI7DtAc7YIgiAIglAnVGwRBJF25MpWIlD0O0EQBEEQaoSKLYIg0k57f+Kx7wBFvxMEQRAEoU6o2CIIIu20WxOPfQcoIIMgCIIgCHVCxRZBEGmHpRFWFydnI6Q5WwRBEARBqAkqtgiCSDv+nq0kbYRUbBEEQRAEoSLiH3hDEAQRAp7n8c4PLbAYtDh8TAUKjP7TCyu2kk4jpJ4tgiAIgiBUBBVbBEGkhGe+2Y/b39wCANBpOBwysgxHT6jE/PEV6LIlaSMkZYsgCIIgCBVCxRZBEEnT2ufAfR/sAABUFhrQaXNhzb5urNnXjQdWCsdoOKCigIotgiAIgiDyByq2CIJImj++vQU2pwezR5Tiv9cchYM9dnyxuwNf7urE13u60Gd3Y/qwEmg1XEKPTzZCgiAIgiDUCBVbBEEkxde7O/HuD63Qajgs//EMaDQcRlZYcGHFKFx4+Ch4fTx2tVtRV2xO+GcYtFoApGwRBEEQBKEuKI2QIIi44Hke21r64RFVpn98vhcAsOSwkZhaXzzkeK2Gw+TaYpRY9An/TLIREgRBEAShRkjZIggiLpa/uw3/+qIB88dX4A+nT8VnOzvAccDPjxmbtp/Jii2as0UQBEEQhJogZYsgiJh54ssG/OuLBgDAV7u7cMkTawAAJ0+txcgKS9p+LilbBEEQBEGoESq2CIKIia92d+JP72wFAJw4pRoA0CHOz/rZMWPS+rMNWn9ABs/zaf1ZBEEQBEEQqYKKLYIgYuKhj3eB54GfHDoc/7pkLpYcPhIAMGtEKQ4dVZbWn82ULYASCQmCIAiCUA/Us0UQRFQ2N/Xh24Zu6DQcblg0CRzHYdkZ0zB7RCmOGlcBjkss0j1WjPJiy+ODUadN688jCIIgCIJIBVRsEQQRlX9/KfRpLZ5Zh9oSEwBBbTpv7oiM/HxmIwSob4sgCIIgCPVANkKCICLS2ufAW5uaAQBXHp2+xMFIaDQcdOJAZLIREgRBEAShFqjYIggiIk9/sw8eH4/Dx5RjxvCSrK2DEgkJgiAIglAbVGwRBBGWAacHz63eDwD4WRrnaMUCFVsEQRAEQagNKrYIggjLaxsOot/hwegKC06YXJ3VtbC+LRpsTBAEQRCEWqBiiyCIkPh8PJ4QgzGuOHoMNJr0Jg5GQ1K2qGeLIAiCIAiVQMUWQRAh+Xh7O/Z1DaLErMe5hw7P9nLIRkgQBEEQhOqgYosgiJA8/sVeAMCSw0fCYsj+lAg2W4uKLYIgCIIg1AIVWwRBDOGHg/4hxpceOTrbywFAyhZBEARBEOqDii2CIIbw7y8FVUs+xDjbGLXUs0UQBEEQhLqgYosgiABa+xx4+/sWANkbYhwKUrYIgiAIglAbVGwRBBHAS2sbFTHEOBgqtgiCIAiCUBtUbBEEEcCB7kEAwIJJVVleSSDSnC2yERIEQRAEoRKo2CIIIgCrww0AKDbps7ySQJiy5XR7s7wSgiAIgiCI2KBiiyCIAKwODwCgyJT9uHc5NNSYIAiCIAi1QcUWQRABWJ2ismVWprJFPVsEQRAEQagFKrYIggiAKVvFSlO2tFRsEQRBEAShLqjYIggiAL+NUFnKlpGULYIgCIIgVAYVWwRBSPA8j367YCOkni2CIAiCIIjkoGKLIAgJh9sHj48HoDxli2yEBEEQBEGoDSq2CIKQYLHvGg4oMGizvJpAKCCDIAiCIAi1QcUWQRAS/WK/VqFRB47jsryaQKQ5W2QjJAiCIAhCJVCxRRCEBFO2lGYhBEjZIgiCIAhCfVCxRRCEhFIHGgPUs0UQBEEQhPqgYosgCAlpxpbCBhoDpGwRBEEQBKE+qNgiCEKC2QiVNtAYAIw6IbCDot8JgiAIglALVGwRBCHRr+CeLRpqTBAEQRCE2qBiiyAICUX3bFGxRRAEQRCEyqBiiyAICVUUW2QjJAiCIAhCJVCxRRCEhJJthJRGSBAEQRCE2qBiiyAICTUoW04qtgiCIAiCUAlUbBEEIaGOocbeLK+EIAiCIAgiNqjYIghCQtHKljY3lS27y4sdrdZsL4MgCIIgiDRAxRZBEBLSUGMFKltGWUAGz/NZXk3quP/DHTj5wc/x4ZbWbC+FIAiCIIgUQ8UWQRAS/QoeasxshDwPeHy5U2xtae4DALy5qTnLKyEIgiAIItVQsUUQBACA53mZjVB5yhYrtoDcSiTsHRQK3M93dsBDsfYEQRAEkVNQsUUQBADA7vbCKypGSu7ZAnKr2OoZdAEA+h0ebGzsze5iCIIgCIJIKVRsEQQBwN+vpdVwsBi0WV7NUHRaDTSc8O9cGWzM8zx6RGULAD7d0ZHF1RAEQRAEkWqo2CIIAoA/9r3QqAPHcVleTWj88e+5UWwNurwBv8unO9uzuBqCIAiCIFINFVsEQQAQbGyAMi2EjFyLf2cWQq0o2W1u6ke71ZHNJREEQRAEkUKo2CIIAgAUHY7BMOgEe2OuKFssHKOiwIAZw0oAAF/u6szmkgiCIAiCSCFUbBEEAcBvI1SysiWftZULMGWrvMCAWSOEYmtf50A2l0QQBEEQRAqhYosgVEpTrx3NvfaUPV6/XbkDjRms2HppbSN+9eJ36JOFS6gRFo5RatGjpsgEAGjrd2ZzSQRBEARBpBBFF1sejwd/+MMfMGbMGJjNZowdOxZ//OMf4fP5d7V5nseyZctQX18Ps9mMhQsXYsuWLVlcNUGkH4fbi8UPfYHFD38Jd4pUHquCBxozWEDGC2sO4I2NzaoPlOgVla0yiwE1xWKxRT1bBEEQBJEzKLrYuueee/DYY4/hkUcewbZt23Dvvffivvvuw8MPPywdc++99+KBBx7AI488grVr16K2thYnnXQSrFZrFldOEOlld7sNPYNudA+40GVzpeQxrSoIyDDqAk9ZLNRDrfQMMGXLgOpiIwBStgiCIAgil1B0sfXNN9/grLPOwumnn47Ro0fj3HPPxaJFi7Bu3ToAgqr14IMP4ve//z3OPvtsTJ8+HU8//TQGBwfx/PPPZ3n1BJE+trX0S//utCV3cd7YPYgHPtyBVTsElUjJARkXHjEKR46twJyRpQAAm9qLLUnZ0kvKVns/KVsEQRAEkSsoutg6+uij8fHHH2Pnzp0AgE2bNuHLL7/EaaedBgBoaGhAa2srFi1aJH2P0WjEggUL8PXXX4d9XKfTif7+/oD/CEJNbG/1K7fdA8kpW3e+sxUPfbIbW5qF90FNiSmpx0sn580dgRd+cQRmDS8FANicau/ZGmoj7Bpw5UzaIkEQBEHkO8r1CwG46aab0NfXh8mTJ0Or1cLr9eKuu+7CT3/6UwBAa2srAKCmpibg+2pqarB///6wj7tixQrccccd6Vs4QaSZHbJiq2sgcWXL5+PxbUM3AODCw0diQnUhzjlkWNLrSzeFRuHUNeD0ZnklySEPyCiz6KHXcnB7eXTYnBhWas7y6giCIAiCSBZFK1svvfQS/vOf/+D555/Hhg0b8PTTT+P+++/H008/HXAcx3EBX/M8P+Q2OTfffDP6+vqk/xobG9OyfoJIF9tb/WpsMj1buzts6B10w6zXYtmZ03DZ/DGwGBS9BwMAKBT7yqwqtxH2yqLfOY5DtZRISFZCgiAIgsgFFH1V9bvf/Q7/93//hwsuuAAAMGPGDOzfvx8rVqzApZdeitraWgCCwlVXVyd9X3t7+xC1S47RaITRaEzv4gkiTXRYneiUFVidSRRba0RVa87IUui1it57CYApW7liIyy1GAAANcVGNPXaqW+LIAiCIHIERV9dDQ4OQqMJXKJWq5Wi38eMGYPa2lqsXLlSut/lcuGzzz7DUUcdldG1EkSmkKtaANCdhI1w7T6h2Jo3ujypNWUaf7GlcmVLTCMsswihJFL8OyUSEgRBEEROoGhl64wzzsBdd92FkSNHYtq0afjuu+/wwAMP4IorrgAg2AeXLl2K5cuXY8KECZgwYQKWL18Oi8WCJUuWZHn1BJEeWL8WxwE8n5yNcK2obB02RqXFlopthC6PD1axWCyTlC2yERIEQRBELqHoYuvhhx/GrbfeimuvvRbt7e2or6/HVVddhdtuu0065sYbb4Tdbse1116Lnp4eHH744fjwww9RVFSUxZUTRPrY1iIUW9Pqi7G5qR+dCaYRHuwZRHOfAzoNJ0WpqwXWs6VmZavXLvzdOA4oNgvKFs3aIgiCIIjcQtHFVlFRER588EE8+OCDYY/hOA7Lli3DsmXLMrYugsgmzEY4f3wlNjf1oyvBOVvMQjhtWIkqQjHk5IKNsFdMIiwx66HVCIE+NWJARruVlC2CIAiCyAUU3bNFEEQgHq8Pu9ptAID54yoBJD5na01DDwDgsNFlqVlcBikyqd9G2CP+3cpFCyFANkKCIAiCyDWo2CIIFbGvawAujw8WgxazRpQCAAZdXgy64i861BqOAQAFbM6Wywufj8/yahJDPmOLUUM2QoIgCILIKajYIggVwfq1JtUWodikg0EnvIXjDcnoHnBht6iQqbHYYjZCABhIoNBUAmzGVplM2aoWla0+uxsOt7oHNhMEQRAEQcUWQagKlkQ4ubYYHMehskC4UO+K00rIVK0J1YUoKzBEOVp5GHUa6LVCn5Na+7b8ypb/+S826WDSC6fldlK3CIIgCEL1ULFFECqChWNMrhXSNssLhQv1eGdtscj3eSqLfGdwHKf6+PceSdny2wg5jvP3bVFIBkEQBEGoHiq2CEJFMBshK7YqCoQen844bYRM2TpMhRZCBot/t6pV2RLVyGBlkSUSUkgGQRAEQagfKrYIQiX0O9xo6rUDEGyEAFAhKlvx9GwNOD3Y3CwoZGpVtgCgwKB2ZWtoQAYAVIkhGa19VGwRBEEQhNqhYosgVMJOsV+rrsSEEvECvYL1bMUxa+u7A73w+njUl5gwrNSc+oVmCBb/PqBSZYsFZMij3wFh7hYADDgpIIMgCIIg1A4VWwShEra1BloIAaCiUFBB4pm1tWafuvu1GKxnS7U2QrHYKg0qtvTigGOPz5fxNREEQRAEkVqo2CIIlbC9RQzHqCuWbmPKVmccxZYUjqHifi0AKDQJCpBabYS9oo2wrCDQRqjTCqdlt1ed88MIgiAIgvBDxRZBqIQdIZStSlHZitVG6PL48F1jDwDgsBxRttQY/e7z8ei1i8VWkLKlE5UtLylbBEEQBKF6qNgiCBXA8zy2y2ZsMcoL4gvI2NzcB4fbh1KLHuOrClO/0AxSaNQCUGfPltXhgdcnKFfBARk6cX4YKVsEQRAEoX6o2CIIFXCwxw6b0wO9lsPYqgLp9gppzpYLPB/94pxZCOeOKodGVFDUSqFRKFLU2LPF+rUsBi2MOm3AfTqNcFqmni2CIAiCUD9UbBGECmAWwnFVhdBr/W9bZiN0eX3oj6F3SZqvNaYsDavMLGzOlhp7tvwDjQ1D7mM2Qg8pWwRBEASheqjYIggVsL1VCMeYIgvHAACTXiv1LnVYI/dt+Xw81u0X+rXUHo4BAEUq7tkKF44B+AMyPD4qtgiCIAhC7VCxRRAqIFTsO6O6SFC32q2Rh+Du7rChd9ANs16L6cNKUr/IDJOrypZey5QtshESBEEQhNqhYosgVACLfZ8UqtgqFoqtaMrWGrFfa87I0gArolopULGy1SMqW8EztgBAK9oI3aRsEQRBEITqUf8VF0HkOA63Fw2dAwCG2ggBoLrIBABo749cbLF+rVywEALqjn7vlZSt8DZCL/VsEQRBEITqoWKLIBTO7nYbfLxwYc4sg3JitRGyJEK1z9diFJnUW2x1i0OoQylbehaQQWmEBEEQBKF6qNgiAhh0eXDBP7/BPz7bk+2lECLbZBZCjhsa185shG0RlK2DPYNo7nNAp+EwZ2RpWtaZaSRlS4U9W1JARghlS7IRkrJFEARBEKqHii0igHX7erB6bzee+WZ/tpdCiOwIMcxYjmQjjKBsMQvhtGElsBh0KV5hdmABGS6vD06PN8uriY/IARmijZB6tgiCIAhC9VCxRQTQaRPUka6ByP0/RObYLhZbU+qGhmMAfmWrPUJAxpoGIfL9sNHqn6/FKJAVjQNOtRVbLPo9xJwtLVO2yEZIEARBEGqHii0igC6bsOPucPsw6FKfPSsXYTO2JkVRtjoi2AhzLRwDEOx2FoMWgPqshBEDMqSeLVK2CIIgCELtULFFBMCULcBfeBHZo9PmRKfNBY4DJtYUhjyGKVtWpwd211CFp3vAhd3tNgC5VWwB/r4tq9Od5ZXERyQboU5DQ40JgiAIIlegYosIoFNWYLHENCJ7tPQKfVg1RaawvVZFRh1MeuGtHKpvi6laE6oLQ9rW1IwaBxvbXV443IJFsDRk9DsNNSYIgiCIXIGKLSIAea8WFVvZp88uKDYl5qEX5QyO42QhGUOthCzyfV6ORL7LKRKVrQEVWV6ZqqXTcJIyJ0dStiiNMCfw+Xjc/d52vPN9S7aXQmQZh9uLd75viTqAniCI3CI3YsmIlCG3EVKxlX36HdGLLUCYtXWgexBt/eGVrcNyzEIIAAXMRqgiZYsVW6UWQ8gof0nZojlbOcGW5n489tkeVBUZcfrMumwvh8giz317AH96eysKjTr8v+PH4/L5o2HUabO9LIIg0gwpW0QAXWQjVBT9orJVbI68L1JTLCpbQSEZA04PNjcLARu5qGwxG16Pil6rkWZsAYBeSwEZuUSn6BbosDrhcKsrNZNIjtV7u3D2377C5qY+AEBrnx2AMIj97ve248QHPsN7P7SA5/3v9dY+B1mICSLHoGKLkOB5PqDY6lLRBWyuwpStYlNkZauqKHT8+3cHeuH18RhWasawUnN6FplFaouF36mlL/yMMaUhhWOE6Z8jG2FuwZInAXW9Tonkef27Jmw40Iv3NgsWUptTUOCPHFuBmmIjGrvtuOa5DTj/n6vx1e5O/OaljThixcf4/f82Z3PZBEGkGCq2CIl+hwcu2Y5aN83ayjr9duHDuTiajVCatRV4MbdGinzPnflacupLBUWvWUUXsT1RlC2thmyEuUTPgD8ps7nXnsWVEJmGbZYxmzP7/6JpNVj124W4/oQJMOo0WNPQjQsf/xb//a4JALCz3ZqdBRMEkRao2CIkumyBxRXZCLMPC8goNkW2EUqztoKULRaOMTcH+7UAoF5U69R0Eds7ED72HQD0WlK2coleu7/YalLR65RIHlZcsbRUpmwVGnWwGHT4zUkT8clvF+Ks2fUAgApR7Q41woMgCPVCxRYh0Rk0V4tshNlHshHGEJABBPZsuTw+fNfYAwA4LAf7tQCgrkQoMltUdBHbLQvICAULyHBT34bq+GJXB9qDQmrkNkI1bQoQydPPFC2xyBqQFVuMYaVm/PWCOdhw60n424WHAAD19hFEjkHFFiHBlC2daGMiZSv7+AMy4rcRbm7ug8PtQ6lFj/FVoQciqx2mbLVZnfCqJFAiWkCGTkMBGWrk9e+acPG/1+D3rwf227C/N0DFVr5hFTfLbEE2wsIQToXyAoOUrsrm8BEEkRtQsUVIdIrF1ZjKAgBAt42KrWzDdkajBWTUiDbCnkE3nB5hV1SyEI4qh0YzNGI8F6gqNEKv5eD18SEHOisRKSAjrLIl2gip2FINPM/jH5/vBQDsabcF3NcToGyp4zVKpAapV8spFl0hlC05Jr0QA28nZYsgcgoqtgiJTrHfZ2JNEQDB+sAu3InsEGv0e6lFD4N4kc76tqT5WmNyMxwDADQaToq9V4tqwAIySsNFvzNli2yEquGbPV3Y1iKMWOgI6n3ts5Oyla8EK1us2CoK04Nr0gvncCq2CCK3oGKLkOgS0wfHVBZIiWjyJC0i8/gDMiIrWxzHobJQUEpY790Wcb7WoaNyt9gCgPoSFpKhDtWA9fCUh4l+Z+89Hw/4SN1SBY9/2SD92+rwBPTcyJWtpl57wEwlIndxe32SHdDm9IDnealnqyCMsmUWlS2Xx0fvfYLIIajYIiQ6rcJFQVWRUeon6aL496zCAjJKovRsAf5ZWx1WJ3w+XlK4hpVa0rdABVAnxr+39KlDNWADmMMHZPhPy2QlVD672234ZHs7OM5fKHfK1C15z5bT46PgoTyBWQgBwQ7u9PjgFhNGw9kIzQat9G8HuUoIImeI7E3KNwYGAK126O1aLWAyBR4XDo0GMJsTO3ZwEAi368lxgMWS2LF2OxBpZk+B0KPVNeCE0e1EjcaLep0XAy4Hejt6gBLdkGMBAA4H4I3wgRDPsRaLsG4AcDoBjyc1x5rNwvMMAC4X4I6g1MVzrMnkf63Ec6zbLRwfDqMR0AnPt9PuADcwCDOAYp8LGODDHguPB/V6H3a6HOhu70F3hR56hx0GDqjg3IBbC+j10rFwRiiiDQb/sV6v8LcLh14vHB/vsT6f8LpMwbHDLcJz29zrEN4Tg4PhH1enE543IPqx8bzvYzzW4/XBaR0A9P4NjeBj9S4PzC7hefQMDMBQUuS/M8vniLiPzYNzxLMfb4HZ5cDxU6qxrbkPLX1OdLV1Y3ixER5wsDo80HvdKOB4OD0+tDR3onJYaeDjJniOiOvYeN73OXaOiOt9n6JzhK1rQHofwwV0d/VL9xUYdCHPESYfD7PLAR/Hwe7ywmLQDXncISjsOiLuY/PgHBGWDFxH0DkiiXNEpPddvPAE39fXxwPg+4SneOh/p50W+A0WS+jjAJ5fsCDw2MrK8MfOnRt47KhR4Y+dOjXw2KlTwx87alTgsXPnhj+2slI67Lj7VvHfjJge/liLJfBxTzst/LHBL61zz418rM3mP/bSSyMf297uP/baayMf29DgP/a3v4187ObN/mNvvz3ysWvW+I+9997Ix65a5T/2kUciH/v229Kh/X//Z+RjX37Z/7gvvxz52Cef9B/79tuRj33kEf+xq1ZFPvbee/3HrlkT+djbb/cfu3lz5GN/+1v/sQ0NEY/dfvZF/Kib3uZ//vRa4bUR6XEvvdT/uDZb5GPPPZcPINKxcZwjvhkxnR9109u82+MVjo1wjnAfcmjg42b5HMHzvHCOC3csnSP8/61Zw3daHfyom97m71p4eeRjEzxH8E8+GflYOkcI/117rf/YLJ0jeo87iR9109v81FvfE46Nco5o7B7wP66KriN4nqdzBEMB1xF0jhBJ4BzRB/AA+L6+Pj5ZyEZISAQ3dhPZhZqkY4Pt/rb0qaNnCxCGVMvtgkRuwsJQTDr6W+c7To+g9ISKfQ8Fxb8TRO7A8TzPZ3sR2aa/vx8lJSXoa25GcXHx0APywEbo9Hgx6Q/vw+h24tv/Ow4PfbwLL6xpxNULx+JXJ0wMOFaC5P/4j41D0t+4twM/feRz1Jea8PENCyMeC48Hz322E3e+sw0nTa3BcZOrcct/f8D88RV4/NJ5OS3/b20fwGn/WIvKQgPW/f5ERdsI1+/vxpJ/r0VtbRk++91xYY+ddtv78PHAZzcdj+oaWcAJWYSEfyvgHOEctOOEuz9Cl82F+34yE4tn1uPW13/Aq+ub8P+OG4/rTpuO9Qf7cM7fv8HYYj1OmVSOJ7/aj0uPGoX/O3VK4OOSRWjosSq3EX60tQ2/fOE76eubTp+GZSv3YFxVgXA+D3OOWHjfKrRYXXjtNydg+rCSIY87hBDXEbzPh5nLPoTHx+Plq4/ADGZbpXNEYseq+DqCzhGJnyP6+/tRUl+Pvr6+0LVBHFDPlpyCgsA3dqTj4nnMWJGf2FJ5rPxEHIYuMcHOazShuKIURRWlsBs60ObVhf8d5B8y0YjnWKPR/6JP5bEGg/+Nl61j9Xr/CSgKfR7AbjDBUFIc/XWk06Gsugx2gwnNbg1aPBrYDSaUVJYN/V6dzn9yjYZWG/trOJ5jNZqUHVtXLTyfnTYXHB4fTLE+Lsel770c5tgu3gqn3hg4YyvEsW6TBS6vD25j0Psmi+eIhI7N4XPEm1s7cdClQW1lKU4+bByg1aC0qgx2QxdavVpAq5XSXIuKLaiqrYDd0Ib9jiivuzjOEXEdG8/7PsfOEQHE875P4hzRqzHAbvC/ppscTNnSDzk2+DGcDi7Q2RDndcSA0wOrTnjNN9g5zAj3/XSOiP9YlV1H0DkigWPZ+z5SYR8n5G0gAPiLrYpCAzQaDhVijDgNNs4ebMZWSZQZWwwpjdDmRHu/M+C2XKbUopcik1sVbiVkyXRlYWZsMXRaYcfV681744Ei4Xke/xbj3i+bPxp60RIqTwQF/LHvJRYD6kvZiAJ1pGYSycFmbDGYzbnQGCKESwYbbOxIwkYun+22vzOFTf4EQSQEFVsEfD4eT3wlXDjUiTOL2Aygboopzhos9j3ajC1GZaH/Qo/131XnQbHFcZwU/96s8Ph3dvFdFib2naETI8Tdkaw4RNb4ancXtrdaYTFo8dN5I6Xbpfeg+P5jF71lFj2GicVWYw/N2soH5NHvgH8jKFzsO4PFv9tdiRdb/fJiqzuCDZIgiIxAxVaew/M87np3G/73XRO0Gg5LT5wAwF9s0Zyt7NFvFz6si2OYsQX4d9UHXV7s7xJ2M6uLc7/YAvyDjVsUPtiYBSaEm7HFYEqJh5QtRfL4l3sBAOfNHYESmUrJ3oNszhYrrkvNeoyrKoRWw6F7wKWqMBciMcIrW5HP5yadWGylStnqImWLILINFVt5zneNvZId5t5zZmLhpGoAQEWBcNFAylb2iFfZKjBoJTvdzlYbAKCqMD+KrboSdQw2ZgONo9kI2XBcDylbimNXmxWf7ugAxwGXzx8dcF9VYaCNsFdWXJsNWkyqEWambWzszdh6iewQrGy19QvFVlGUNEKmbDmTSCOUF1v7ukjZIohsQ8VWntMoWgwOG1OOcw4dLt3OlK1euxteH+2uZwP2gVkcY88Wx3GoLBL+bi6v8EFdXRxHQ7GKqWP9MApXDCSlo4CULTXicHtxwyubAAAnTanBqIrAhutKmbo84PTIii2huJ49shQAFVv5ACu2dNLGifBeLojSs8U2zFKlbHVYnRhwRkjaIwgi7VCxleewJtxgHznbeed5/wUikVn8ARkxJglhqJKVDwEZADCM9WwpPHwg3oAMUraUxR1vbcH3B/tQatHj1sVTh9wvV5c7bU702gN79GaPKAVAxVY+wJwJNUEbXtFshEa9cFmWTLEl79kCgP2kbhFEVqFiK89hgxNN+sCXgk6rkXZjyUqYHfrFndFYbYRAYHFlMWijNmPnCnWq6dkS3kvlUXq2JBshKVuKYeXWNrywphEcBzx0wRyMKB8am81xXEAiIYt+Z31drNj64WAfPF4qpHMZpmyxYBRGtKHG5hSkEQYXWwe6qW+LILIJFVt5Djuhs6ZcOVJIBsW/Z4V+yUaYWLGVD0mEjHrVpBHGGJAhDsX0kIVXMXy7twsAcMG8ETh2YlXY4yrFsRmdNqcsjVC4bVxVIQqNOtjdXuxss6V5xUQ2YQEZLCmVES36PdU2QiD3+ra+2t2Je9/fDjdtWBAqgYqtPMfpEU5WRv3QD4AKin/PKv6AjNjVqUqZjTBfLISAX9myOjxDUsCUAs/z6GXR7wWx2QjpYkI5tIoBB+OqCiMeF6BsDQYGomg1HGYOLwEAbDrYm6aVEkqAKVvs3MSIZiNkARmOJKLfWbHFnA25lkh46+ub8bdP92Dtvu5sL4UgYoKKrTyHKVtG3dCXAtuN7ab494zR3GvHqX/9Ave8vz0FylZ+hGMAQIFRJ/W2KTVW2+r0SEpVrHO2yEaoHFiaXG1J5PcVew829TowKF4wl5r9f2+pb+tAb+oXSSgGv40wWNmKvHnmH2qcfBrh9GHFAIB9nbmjbNmcHuwVBzUH2yUJQqlQsZXn+Hu2QihbhWzWFilbmeKud7dhW0s/Hvtsj6QoxlVs5amyBfjj35UaktEr9u+Y9JqQ7zc5Oi3ZCJUGU7ZqoyR8MnV5d7sVAKDhAuO+Z1FIRs7j9vokG2CwshUt+t2UAhsh6/edObwUAHAghwYbb2vpl/494Ez8OSKITELFVp7j8Ig9W/qhLwXWs9VDxVZGWNPQjXe+bwEgpECy6+x40ggri/K32KoXG9GVqmz5LWWRVS1AHhdNNkIlwPM82voFhT84XS4Y9r7bdLAPgPD+1Yh/TwCYIxZbO9utsFEkd05ik83YCu7ZKoiibKWyZ4tZVpv77EkFbiiJLU190r8Hc+R3InIfKrbyHCkgI8ROe7k42JiUrfTj9fG4460tAIB5o8uk2zWcECcdK3JlK58CMgDZYGOFKlvSjK1Yii0t2QiVRO+gGy4Pm10X+X3F7LtssHFwcVZdbEJ9iQk8L6QSErkHK6JNes2QzZXoNkLhsiyZ4ogVW2MqC1Bk1IHnc0fd2tLsV7YGabOCUAlUbOU5bEq9KUTPFgVkZI5vG7qwpbkfRSYdHrvoUBw+phyAYCHkOC7Kd/upImULTQqNf2cztsqjhGMAgI7SCBUFsxCWFxhgDJHcKueYCZU4c1Y9TphcjSWHj8Tys2cMOYaGG+c2LNyoyKQfEvUezUaYiuj3PtmMxrHVQqDLrgylXzrcXtz7/nasS1N4xVaZjXAwiRARgsgkVGzlOZGVLSq2MgWL159aV4yKQiN+efwEAMDIELN8ImHSayXbYbRG/lyDxb+3KDT+PR5lSy8pW2QjVAKs2IpmIQSE9+BDP52Df182D8t/PAOHjCwbcswssZdmExVbOQkLxygy6VBo8BdXWg0XMoxKjsmQnI3Q4fZKKmyJWY9JNUKxtbPNmtDjxctz3x7A3z7dg7ve3Zbyx3Z5fAG/x6CLlC1CHeTHxFMiLP6erQhztqjYSjt2cYfOIn7QHj2hEi/+4gjUBzVXx8LtZ0zFrnYbJtUUpXSNSkcabKzYni02cym6ssWGGrtJ2VIEbX0sHCM1avFsCsnIafzFltCvV2jUweb0oNCoi+pUYDMvE00jZAl9ggVdh4ni50Cmiq3X1h8EAOzrTH3c/K52K9wyazUpW4RaoGIrz/GnEYawERb6AzJ4no/LzkbEB9uhs8h2QY8YW5HQY519yPCUrEltsMK0udeuyNcrC5qJKSBDTCP0krKlCFg4RqrU4hnDS6DVcGjtd6C1z5F3KnSuYw2akSgvtqLB5mzZEywk+mQjQzQaLqPF1raWfsnm1zPojvl3jpWtsn4tIPHniCAyDdkI8xynqGyFGmrMlC2Pj0e/neT6dMJSlcxxhGEQgdSUCKqD0+NTpPU1LhuhlEZIypYSiMdGGAsWmeJA6lbuIbcRhvp/JJLt2ZL3awGQXmf7ugalz/t08d8NBwO+bkxxKAcLx2ChUQNkIyRUAhVbeY6kbIVo+jbqtNKuVBcNNk4rwTZCIn6MOq0UCqJEK2FvHDZCpmy5KY1QEbTFOGMrHmaPEGK5qdjKPZiyVWQU3ussJCNa7DuQfBphcLFVU2xEsUkHr4/H3o7UW/sYHq8P//uuGYB/dEWqiq2DPYP495cNeH9zKwDgkFFCHyTZCAm1QDbCPIed0I0hbISAoG7ZnB50D7gwtiqTK8sv2IcGKVvJUV9iQofVieZeO6YPK8n2cgJIZM6Wl+ZsKYLWvtQqW4DQt/XCmkZsbOxJ2WMSyiBY2WKbljHZCGVztuKxQ9/59la4vT5paHaxSSi2OE6wEq7b34OdbVZMqSuO63eJldV7u9Fpc6K8wIBDRpbho21taOxJLKyI53nsbrfhgy2teH9LKzY3+e2DBp0GCydV44tdnVRsEaqBiq08J5KyBQjF1oHuQQrJSDPsQ8Oip7dkMtSVmLHpYJ8ilS0WB11sjv43ZnO2SNlSBm0pthECwOwRwu78Dwf74PXxUigKoX76ZQEZwv/FYisGGyFLI/TxgMvrizpqAAC6bE48/mUDAOAysThjyhYATJAVW+misUdQseaMKMXYqgJgW/zK1u52G17bcBAfbGkNUOE0HDBvdDlOnlaLU2fUYne7EGNPxRahFujKLs9xStHvoZUtmrWVGeyi97zASMpWMtSJ8e/NChxszHa72Y5zJPxztkjZyjYuj0/abEplkMX46kIUGLQYcHmxu92GSbX5lR6ayww4A8/nzE5YFIeyBQibobEUW02y890n29sBCAEZDH/8e/pmbbHfudCkw4gyIazoYE/sxdagy4MfP/oVrOLjGLQazB9fgVOm1+LEKTWoKPQngbLzO0W/E2qBiq08J1L0O0CztjIF2QhTg5RIqDBli+f5gDjoaOgoIEMxtFuF15JBp4mp3y5WtBoOM4aXYPXebmxs7KFiK4cYlDbPhEusUvF1I1ebwqHXaqDVcPD6eDjc3pi+p0lm1zsgqkny78tEIuGAU/gMKzDqMFycD9nYHfum1+c7O2F1elBdZMQfFk/FcZOqwp4rWWovKVuEWqCAjDzG6+Mlm1LYYkuMf2dDd4n0wAZYUkBGctSXirO2FKZs2d1eeMXCKZZEMhaQ4SEbYdbxWwiNKR8nwKyEFJKRWwwGBR6dP28Ezps7HBccNjKm75f6tmIsJppCnO/kduWJYiF/oHswbXHpLBmwwKDFiDKx2OoZBM/Hdg77cKsQfnHGrHqcOas+4qYUe14HnaRsKYk3NjbhzEe+jEvRzBeo2Mpj5GlH4WyE5WIzP2vuJ9KDpGxRz1ZSMBthqJ6t3kFXzB/8qYapWloNF1NBrRd7tjw0ZyvrtPYJSaw1RamfheUfbtyX8sfONE6PN+EEvVzDfz4X3utjqwpx77mzMKayIKbvZ5ufjhij2g+GCKKQK1uVhUaUFxjA85D6nVKNzelX84aLNsJBl3eIK8bt9WHJv1bjT29vlW7zeH2S/fGkqTVRf5akbIkhIoQyeHX9QXx/sA+rdnRkeymKg4qtPMbp8V/IRQrIAEABGWkmeCeUSAxmI2ztd0hKEiB8CMz+40o89fW+rKyLRUEXGnUxqSMsLMFNNsKsI83YSsPgYVZs7WjtV3X/Cc/zOP2hL3HiA5/RBgHkozwS2zxjm5+xqlChelSD7YcTpb6t9FgJmcpUYNDBpNeiWhzDEZxIuKfDhq/3dOG5b/dLt63d14PeQTfKLHrMFWPdI8E+J3neH/JFZB+2qdhL14tDoGIrj2G7kAatBpowSVgVhaxni+ZspRMWkEHFVnJUFRmhE/sdWK+N0+PF/R/sAAB8uaszK+vqD4qCjoZetBF6yUaYdQ50CalodSlMImTUlphQW2yCjxdSCdWK1enB7nYbDvbY0R3FBeFwe3GgK7dtRoNu8XyeYOCRPP49FpiNkAVaAaGKrfT2bdlkPVsAMELq2wr8W9vEc6HD7YNbLMxXbm0DABw/uUayUEdCHiKi5k2KXIOpmz3iTEnCDxVbeUy0GVsAUF4g7E51U89WWqGAjNSg1XBSPHdzr1BsvbLuoKRO7OuKPNTT7fXhnve34/OdqbVBxBOOAfgDMtyURph11u4T5mDNHlmalsdn6tamg71pefxMwF7fQHQ15pcvfIdj71uF3e3pC2vINoPO5JwK7HMgVlsmK7ZOm1En3RacepruYis4gZElEjYG9e9YZX1WVocHPM9L/VqxWAgBQKPhpIKLQjKUAyukqe1kKFRs5THSjK0w4RiAf6esayB7/S75QLK2E8JPvdS3ZYfL48PfP90j3dfYbQ+wFwbzzvct+Pune/CrF79Laf8JsxHGqmwxGyEFZGSXvkE3trUKA1UPG1Oelp/Bijg1h2Sw1zcQ/eJ3a7PwfO7PYXUr2bmJzNYfi0VuwOlBr6gknDm7Xro9vLKVnp4tKYHREKxsBdoIbbLCvN/uRveAS+o5O3ZiZcw/TwrJoGJLMTBli9Krh0LFVh7Dmm+NukjKllBsOT0+OqmlCZ7nMUhphCmjroQlEjrwxsYmNPXaUVVkhEGrgcvriziDa9UOoUm7Z9CN9za3pGxN/hlbcdoIqWcrq6zd1w2eB8ZWFqA6DQEZADBreCkAYOOB3rQ8fiaQK1uRPid4nkeHVbCkuzy5qdr6fLxk/0vUqcAGG8fSs8XOZ0UmHQ4ZWYZhpWYUm3RDBnCznq2mXntAcZwq5AEZgL/YCk6mk79WrA4Peu3iRpRRF9dmI7NoDpCNUBH4fLz0GuglZWsIVGzlMQ5poHH4DwSLQSsVY7RbkR5cXp90UU02wuRhiYRNvXapeLrw8JEYUS4UYeF21L0+PsA++J/VB1K2Jr+yFaONUEwjdFPYQFZZs68bAHD42PSoWgAwY3gJAGE2XH8aLoIzQaCyFf7it9/ugUt8Tbty9LUtTxBMdEi9mQVkxKCuHxSLrWGlZmg1HN65/mh8dMOCIZ8lpRaDFFqxKw2JhKzI9tsIw/RsOf2vlX6HG31isVUcwzwxOUw1TFeUPREf8qI3Wt9mPkLFVh7jlGyE4V8GHMcFWAmJ1CP/sLBEKHyJ2GCJhC19dqwT+22OGFuB0RVC7HJDmL6t7w/2omfQjQKDFjoNh/X7eyTLU7L02+MLyKChxsrg271dAIDDx1Sk7WcUGnUwiBta/Xa1FluxKVsdNv9IBmeOKltsuC8QPuU3GlL0ewzFFhtozOLWhaIqtArLBmfvSkPf1lBlS1hPU2+gddsWoGz5i61YhjfLkZQtmrWlCGyyv0PvgDrPY+mEiq08RlK2onwglFMiYVphFycGrSamJCYiMmyw8fr9PWi3OqHXcpg1vBSjxRk3+ztDF1ufirNBjp1YhZOn1QIA/iOLJ04GtvMf3LQeDp1GHGpMxVbWsDk92Nyc3n4tBntdsKJcbciLxEhKQ7vV/xmSqzZCu2zGVriU32iY4ym2ZMpWNCZUC8XWjtbUKls8z0tFT6FYbNWVmKHTcHB7eWkwOBAYkNHv8EivnbiLLUN8iY1Eegkoop2enH1/Jwpd2eUxzO4QyUYI+BMJuyiRMC1QEmFqqRPnIXWKr9dp9SUwG7QYXSHYWvaFsRF+KloIF06qwkVHjAIAvP5dU0r6G6xxRr/raKhx1lm3rxteH48R5WapgE8XrJcvHb00maA/VmUrD4otKfY9ifO5KY7od9azFctrdFKt0Le1K8VJkE6PD2xfiP3eWg0nrUluJQwOyEhY2RL7u+RKIpE9rEEKI/VtBULFVh4Ti40Q8CcSUs9WerDTQOOUEnzRwYZkMmUrVPx7l82J78Xo7YWTqnHE2HKMry7EoMuL179rSnpN/XFGv7OADEojzB5rWb9WGi2EjCKp2FKnshVoIwz/OwQUWzm6kZCKzTN/9Hv054jZCIeVxaBs1TBlK7XFltxCViALuWBWQvlg4+CAjL7B5JQtmrOlDGxB5y6atRUIFVt5jH/OVjRli4qtdMI+LEjZSg1lFn1Awubc0WKxJfZsHegaHJLy98WuTvA8MKWuGDXFJnAchwsPHwkAeHb1/qTHHiQc/U5ztrLG9+KQ4Tlpmq8lhxXhVqc6L1DkilwkG2GHzV9suXNU2WK/f0ESYzyYtT8WZSs+G6GgbLVbnSlVHuRzxeTWyVAhGbYAG6FM2bIkpmxRSrIysAUpW3S9GAgVW3mMQ/ywi9qzRQEZaYVi31MLx3EBFx6HjhL6bepLzdBrObi8PrT0Bca/fyqmFi6cVCXddvYhw2HWa7GzzSYNtk2UeG2Eei0FZGQTnuexRezXml5fkvafl1PKVoQCIR+ULda7lJyyJVyaOaIUEg2dA1I/VCzKVpFJL50bUzlvi11oB0e3S7O2ZPHvwUONE7cR0pwtJRGsbJGNMBAqtvIYf/R7bDbCHiq20oI9yQGYxFBY/PvoCguqxLhjrYaTPvzl8e9eH4/Pd3UCABZO9BdbJWY9zhKHhP5ndXJBGUyxiDn6XQzIcJONMCu09jvQPeCCVsNJCW7phBVb6k0jjFHZyoOeLXsKNs+kgAxP6OeS53k8+80+nPbXL+AT58BVir3V0ZggztvamcJEQhb7XRgUdc8SEg/KBhvbZK+VZNIIC8hGqCiCe7Yo/j0QKrbyGOYHN5KylVUoICP1sMHGTNViMCuhvG/r+4O96B5wociowyFifxfjgsMEK+HH29rgS0JlineoMYt+95KNMCtsbhJUrQnVhVEDhFIBSyPMCWUrxp6tXI1+H0xBDy6z9ocqXFv67LjkiTW49Y0tsLu9OGpcBZ792eExJx9OEvu2UlpsxaNsBQRkJK5smclGqCiG9GzR9WIAtJWex8SqbFHPVnqxu5JPryICuWDeCOxqt+Hy+aMDbpeKLVn8O4t8P3pCpRRMwZheXwyTXoMBlxcNXQMYV1UY91p4npfZCGMdakwBGdlkS7PQrzUtAxZCwP+66M+JYiv8xW+nLfdthP5iK/HLK6ZsWR0eeLw+6LQa8DyP/33XhNvf3AKrwwOjToObT52MS44cHVfE/IS0FFvC78xi3xmsZ6u13wGnxwujThvQ22N1uqVzXNzKlpGULSVhC+o3pYCMQBSvbDU1NeGiiy5CRUUFLBYLZs+ejfXr10v38zyPZcuWob6+HmazGQsXLsSWLVuyuGL14Iw5+p2KrXRCylbqmTu6HG9cNx/ThwVeLI+tEoqt7bI0LnnkezA6rQZT6ooBAJub+hJai93tlQI54o1+d5OylRWYsjWtvjgjP69I5dHv8nWHK7Y8Xl+AOyJnbYQp2Dxjxda6/T2YfOv7OObeT3DaQ1/iNy9vgtXhwewRpXj3V8fgsvlj4p7l5Ve2Ivdsub0+bG7qi0nRH5AGGgf+zpWFBpj1WvA80NzrgNfHB7w+rEnM2WLPESlbyoAV0UViwU3KViCKLrZ6enowf/586PV6vPfee9i6dSv+/Oc/o7S0VDrm3nvvxQMPPIBHHnkEa9euRW1tLU466SRYramfkJ5rOGKOfhe84DanRyrQiNSRCtsJERuzhpcCEJLmeJ4PiHxfMLE65PfMEAu2RIstNqhWq+Fi/htLNkJStrLCVlHZCi7W00VOBWSEURq6B1yQh3rmarE1kILNs7mjyzB7RCn0Wg4eH4/Gbju2tfRDr+Xwu5Mn4dWrj0xIZQeA8dWF4Djh7yFXGuXYnB5c+Pi3WPzwl3h1w8Goj8l6tixByhbHcVLfVmP34JDEumTmbBWIP2uQ5mwpAnYOGC5aR3uoZysARdsI77nnHowYMQJPPvmkdNvo0aOlf/M8jwcffBC///3vcfbZZwMAnn76adTU1OD555/HVVddleklq4pYla1isw46jXDS7x5wSf0wRGoYdIX2uxOpZ3JdEQw6DfrsbjR0DohFlxD5XisOQw6GpdH9kGCxxXb9C406cFxsu9BSQAalEWac7gEXmvuEhLcpdekPxwDkNkL1KVs+Hw+brMAKF5DRbg28sM/VYisVcxNLLQa8ft18eH082q0ONHbb0dJnx4xhJRibYJHFMBu0GFluwf6uQexstaJyfGCwRr/DjcueWIMNB3oBAJ/v7MB5c0dEfEymbBWG+AwbUW7BrnYbGnsGMa46cO39Do+k+sffsyUqW251blDkGqyQHlluxraWfnSTjTAARStbb775JubOnYuf/OQnqK6uxpw5c/Cvf/1Lur+hoQGtra1YtGiRdJvRaMSCBQvw9ddfh31cp9OJ/v7+gP/yEUnZihKQwXEcylhIho12K1KNZCPMQCN+vqPXajBdtIZtOtgbMvI9GKZubGnqTygkoz/O2HdhnWL0e472tSgZ1q81prIg5h67ZClWsbJlc3kCFKtwtq6OIBUld3u2Urd5ptVwqCsx47Ax5Thr9rCkCy3GhOrwfVsPf7wLGw70Sur6xsbeqI/H1LwCY4hiS1K27NLGE/usk887LI47jZCULSXBAjJGMmWLbIQBKLrY2rt3L/7+979jwoQJ+OCDD3D11Vfj+uuvxzPPPAMAaG1tBQDU1NQEfF9NTY10XyhWrFiBkpIS6b8RIyLv2uQq/qHG0V8GFdS3lTZSsRNKxM7sEULi4Ib9vSEj34OZUFMIg04Dq9ODA7LhnLHCLjCK47hwl4Yak40w47B+rakZ6tcC/BeaauzZCi4QwxZbeaJsqcEWPqlWKNp2hOjbYue4pSdOAMcBB3vsYe2GjHA9W0BgIiG7IK8uNkrnOEDo89HG2XtGc7aUBVO2RpCNMCSKLrZ8Ph8OOeQQLF++HHPmzMFVV12Fn//85/j73/8ecFywNYfn+Yh2nZtvvhl9fX3Sf42NjWlZv9LxpxFG/1CgkIz0oYYP51xi1ghBqXp9Y1PYyHc5eq0GU8RZS4lYCeMdaMx+JkBDjbMBU7YyMcyYoeaereDZYPYwQ41ZscXmI1GxlT0miiEZu0IoW8zxUltilvrCNoqWwnDYpGJr6DluuJhIeLB7UJrFVGTSBZwP41W1AP/zO0BphIqAnbtYsWV1eODOUfU6ERRdbNXV1WHq1KkBt02ZMgUHDhwAANTW1gLAEBWrvb19iNolx2g0ori4OOC/fMQfkBF7sUWztlLPoJs1VFPPViaYIypb7MMhVOR7MNOTCMmIN/Yd8KcReiiNMOVsONCDhz7eFfZCYEtzZpMIAf9rY9DlVZ11lL2+mfU1XEAGK7aGibYyp8p+z1hhv7+Sz+es2NrRZgXPB27o+Hu5NZg9ohRAdCshs/IVhCgwR5SLNsIeu6RsFRoDi614+7UAv03T7vIO+R2IzMMK7mGlZjCto5f6tiQUXWzNnz8fO3bsCLht586dGDVqFABgzJgxqK2txcqVK6X7XS4XPvvsMxx11FEZXasaYdPpTbp4bISR7QRE/NCcrcwyotwsbR4Akfu1GFIiYXMixRazEcZ+8cUsNW4vTxcSKebW1zfjgZU78fWeriH3WR1CcAqQ6WLL/9oITmxTOuz1XV0kBMw43L6QvY2sZ2tYqXDx7c5RZYvZwkMVHkphbFUBtBoOVocHbf2Bn+lsE9ao08ZcbDF1KWTPlqh0dA+40NYvBM8UGvUBtuqEii3Rsujx8Tnb/6cWeJ6XzlslZj1Kxb8nWQn9KLrY+vWvf43Vq1dj+fLl2L17N55//nn885//xHXXXQdAsA8uXboUy5cvx//+9z9s3rwZl112GSwWC5YsWZLl1SsfJzupxqRsCYlFZCNMPTRnK7NwHIdZw/0WsXCR73KYsrV+fw8OdMXXt5WQjVDjPzWTkzB1uDw+KRQguIcIALa1CPfVlZhQUWgccn+60Gs10ggONipALbDXd02x//kKZSUMVrZy9QJZDedzo06LURVCEbSrPdBK6PT4R8KwYmvTwd6I4UCRbITFJr1UTLH3V7EpBcqW7LolXAImkRkcbp8UdlJo1KHMQm0nwSi62Jo3bx7+97//4YUXXsD06dPxpz/9CQ8++CAuvPBC6Zgbb7wRS5cuxbXXXou5c+eiqakJH374IYqKMhPZq2b8PVvRXwblhf40wvZ+B7qiNMwSsSMFZFAaYcaYJV5ETK4tChv5LmdafTEOG10Oh9uH376yKSBFKxps5z8RGyEA8r0nSbvVgR8OCorkng4b3GLoSJ99qMWF9WtlUtViqDX+nb2+q4qMkn0oVGjBQTF4gfXw5H7PlnJthABQXRR6A9XJgrN0WkyqLYJRp4HV4cFeUfENhd9GGPp3ZlbCrS2CRbfQpEta2dJpNTCIrpwBKrayitUpnAM4TnDosPTqXlK2JBRdbAHA4sWL8cMPP8DhcGDbtm34+c9/HnA/x3FYtmwZWlpa4HA48Nlnn2H69OlZWq26kIqtKNHvgN9G+ENTHxbc9ykWP/xlQjHYxFDU8uGcS1wwbyTmj6/AbxdNiul4juNw/09mocCgxZp93Xjiy4aYf1YyARkAhWQkyy+eWY+zHv0Su9qs2NbiH/PRF+JCgCURTstgOAZDrfHvbLRBsUkvRXoHKw0HugbR3OeATsNhjrjRkbvFljps4ay4D7atMmXLqNNAr9VIFupIVkJbhDRCABghFti7RRVN6NmSFVuWxEYssOfYTiEZWUXei8dxnKRsddCoIAnFF1tE+vDbBWIPyGjpc8Du9qKlzxEwyJJIHH9DtbI/nHOJ2hITnvvZEThxavggnWBGVljwh8VCYM9fPtoZs3WlPwFlSx6D7KX496Ro7rXDxwOf7ugIKLZ6FapsqS3+XR4AEy4h7qs9woiF2SNKUSpeiOW8jVDhToVwCZjBKcX+vq2esI/FPsMKQ9gIAX/fFlOVC1NgIwT8StoAzdrKKqzYLhL//my0wHf7w79m8g0qtvIUj9cn7ZjHYiOskAUKMNS2A5sNGrsH8asXv5N29ELB+huUvhNKABfMG4FhpWYMurz4cndnTN+TyFBjnazYclMiYVKwi/pv9nZhe6v/fRhsI3S4vdjVLswdYj16mUSt8e9+m6xO2jAKthF+Jb5XjhpfKVm/clHZ8vp4aRMzVP+SkmAXxjZHeGULAGaPLAUQWdlixY4lXLEl9unJf7Y87j2R6HcAYV9vRGaRlC3xHDZ/fCUA4MvdnRTwJELFVp7ikH3QxaJsjSi3oKbYiDGVBdJJWm07sNngrx/vwhsbm/HwJ7tD3u/2+qTdPiq2lA/HcVg0TVDDPtwSfnA6g+d5qScinmKL4zip4KLBxsnBLurXNHRja7NM2QqKJd7ZZoXXx6PMokddDH18qaZYtT1b/s0Ei94fx83geR7fiMmP88dV5HSxJQ8GUfr5PJyS6o9+D1S2trdYJdVLjsvjkzY0CsNY4YeLyhZD6NlKhbLFii11bVDkGmx+GlM2Dx1VBpNeg3arU9rAyneo2MpT5CdNYwzR7ya9Fp/97jh8sPRYVIhhGWrbgc00Xh+PT7a3AxBm+4RCviNHNkJ1cJJoPfxoW1vEmUgOtxe/enEjdrfbwHHAmMqCuH4OsxLSrK3kYDv1NqcnYE5gsI2Q9WtNH1YCjuOQadSubBWb9DKlwf877GizomvABbNeizkjy2AQ+xFdXl/O7Xqz35vjYvtczSZMhbDKera8Pl7a/GPrH1ZqRmWhER4fH3LOoPxvbYnSs8UoSkH0OwCUUOqdIvArW8Lf0ajT4rAxFQCAL3dFd4BsONCD+Xd/gpfWHkjfIrOMss8GRNpwSIlDmpgvLEx6LQw6jWp7CzLN+v090odAY7c9ZNQ02wHWajjpIoRQNoeNLkeJWY+eQTfWR/Ck3/Ta93hzUzN0Gg53nz0DoyriK7ZYSAYpW4nj9fFhkyP7g4ot1q81NQv9WoDfSqW282qAssUCC2SbeV/tFlSteWPKYdD5E+SA3OvbYql8Fr02KwV7PIQq7pmqBfiVLY7jIs7bYv06BjFQIxTDg2yEqerZYomK7SE+W4nMEdyzBQBHjxeKra+i2O19Ph63v7EFTb12vP19S/oWmWXo6i5PYYMLY7EQBqPWHdhMs3JroM3suxDqlpRcpYIPZ0JAp9XghCnCbK4Pt7aFPe7znR0AgId/OgfnzxuZwM8hZStZQlnVxlcLzdvBscRbRIvh9CwkEQKQ2bPVdV4NFZAhV+y/Fi+25o8TLr7kik+uWQmlZFmF92sBoW2E7LoAQEBRPEfs2/ouRLHFfudw4RiAcJ3BCiN2rLxPK9Fiq0p8zFAbmUTmsAXZCAF/39bqvV0Rx5e880MLfhAV09Y+RxpXmV2o2MpT4pmxFQwVW9HheR4rxQtx9iGz4UDvkOPUMACTGMqiqbUAgJVb28JaodjfdsbwxC7epZ4tin5PmFAX80eOFS76++xuaXyFx+uTkgqzkUQIqPe8Kg/IYOMr2Gvf4/Xh24ZuAP6LL7mCn2vFlt2tjth3QBaQ4RyqbOm1XEAiqqRshfgMY98f7XceIevbKkq5spW7F+lqwBoUkAEAU2qLUV5gwIDLGzZcxe314f4Pd0hft/Xn7t+Riq08hZ1UjTHM2ArGvyOmrouCTLK73YZ9XYMwaDW4esE4AKGVLUoiVCfHTqyEUafBge5B7GgbmjTp8fqkXqFE56fpNGQjTBZ2nuM4oFLsNT1CLLZ8PKTxFXs7B+D0+FBg0GJ0nHbPVKHWocb9oWyE4vO66WAfbE4PSi16TK0TiliNxh/+4s6x17ZaYt+B0MW95HgJui6YObwEHAc09Q61ww+EUDVCIU8kLDQGDjUujiM8SE51kRBk095PylY2sYlDjeWvAY2Gw1HjIvdtvfN9C/Z3DaLUws59nphHqqgNKrbyFKdkI0xG2VLXRUEm+VgMxjhqfAWOmSDs6H5/sG9IoIJf2VK+7YTwYzHopL/rh1uGWgkHU5BKxmyEkSwYRGTkMdYrzp6JK48eg5On1UjnvT4xkZA1/k+tL4ZGkx07Lzuv9qtoE8vr4yVlo9isHxLFzSyER46tCHheczWR0D+gXvnFFlMhbCF6toxB1wVFJj3GVwn222CVQop9j0PZKjTpMKzMjMm1RThuUhV0CfYrVxeLNkJb5GLr/c0tOGrFx/h6T2zjOoj4sIUZb8I+I8P1be0SR+KcMbNeSpZszVF1i4qtPMURFO8aD6RsRYd5j6fVF2NcVSGKTTrY3V5sbxXic9c0dOPRVbvx0Me7AKjjw5kIhFkJP9w6NAJeHnySaCoZ2QiThwUwGLQanDS1BrcungqdViPZltisLdavNS1L/VqAOocayy1ocmWLFR1smPFRooWQIRVb3tzaxWY9uEqfsQWE/hxnylYox0u44cZM2Yr2O7NEQoNOA6NOC71Wg/d+dQyeuGxeYr8AZDbCfmdYO7fN6cEfXt+M5j5HyI0xInlC9WwBfuvwd429Ic9rnVahb7a6yIiaYkGlzNW+LeWfEYi0EM4uEAvFpGxFxb+jroVGw2H2yDJ8vrMDVz27Hu1WxxD7zMwE+3qI7HHClGpoOCEyvKnXjmGlfpsMuwBJJvhER2mEScMUfEPQea7UbEBbv1OatcWUrWz1awFAsVl9PVs9YtpqgUELo04r69kS7EAb9vcC8IdjMFjfljNHlS012QhdXh+cHi+MOi2c7tDKFiAMN35l/cEhypZUYEZxZwwvF86P8gvyZEOhmI3Q7vbC5vRIBaScf36+F5024XXaM0gR8elA6tsLKraGl1kwusKCfV2D+HZvN04Ux6YwOkVFslIstvZ2DuRs/x0pW3kKe3OYElBU1NrInUmYPYbt4B42ugyA4Hl3e3lUFRlx6vRa3Lp4Kt64bj5uWzw1a2slEqOi0Ii5o8oBACuDBhz7U8kSv+jS0ZytpGHKVrC6WCL2CPTaXfD5eGnY8fRh2dv0KFahstUtXryWFQj9cKzIGHR5sW5/N1xeH+pKTENmzOWqjdCuIhuhvDhin+UOT/hNWKZsfd/YJwXLALH3Hc8ZUYZDR5XhJ3OHJ7VuOWaDVgr6CBX/3tbvwL8+3yt9TfO40gPbPA41voapW1+GsBJKxVahETWiJZSULSKnaOgcAACMCprsHgtkI4yO3L4EAJfPHwONhkNNkQnzRpdjRLmZot5zgEXTarBmXzdWbmvDZfPHSLf7ezcSP8VK0e+kbCWMyxOm2JLZCBt7BmF1emDQaaRY+GzANrEcbh9cHl9A9LZS6RYVg3Kx2PIHZHil+VpHjasccq7L1WJLTT24Wg2HQqMONqcHNocHlYXGiMrWpJoimPVaWJ0e7OmwYUJNEQD/7xxt49Zs0OK1a45K8W8BVBUbYe3woL3fiXFVge/fv6zcCbvbC4tBi0GXl4qtNMF60fXaodc0R4+vxHPfHgjZt8UUx8pCA2pKRBsh9WwRucSuNhsAJHRxwWwAakvNyiQusSeOXVQUGHW4duF4nHPocIyssFChlSOcJNoiVu/tlsIWAGCAzU9LYodbSiOknq2ECVaYGaVisdU76Jb6tSbXFoUdypoJ5PaqPrs6zq1M2WLFljwg4xvWrxVkIQT8m1C5NtQ4Fe/7TFIYNNvNGWZzAhBszTNE5Vc+b0tStrJknawqDB3/vrPNipfXNQIAblg0CQApW+mCKVvaEOFCwmYLsKvdFhDtzvO8FGxSWWhEbXFuJ0tSsZWn7BZTYCYkUGyRjTA64S7yiNxiVEUBJtUUwevj8ckOf/M1sxNF62OIhF5StnLrgjSTOIM2PRgsarjP7lZEvxYgXMyyvr89HbasriVWWM9WuYUpW8Lrva3fIQ0qnR8UjgHkrrLlf9+ro9gKThb2z98Mvf7Z4nBjed+WPcuzIqvFi/TgSPq739sOHw+cPK0Gi8RNse4BV9ggDSJxmNU91GZViUWPmWKRLle3rE6P9P6vkgdkkLJF5AoOtxcHugcBAONrEim21NdbkGnC9YoQuceiacIHuTzpigVkJHMBwpQtNylbCRPVRihTtrKZRMiYPkwo+FgBqHSYUlAWZCPc2zkAHw+MrSpArWgPkiMpWzlWbKnJRgjIii1ndGULCD3cOOvFljTY2F9sfb2nE59sb4dOw+GmUyZLyqvT45P+RkTqYO4LXZixGVLflmzeVqf49yo06mDSa3M+jZCuBPOQBvGDsNikkyT4eGBphDanh3aJwiApW1m0JRGZgUXAf7azQ9oZZtaagmQCMkRly0sBGQkj9U4OCcgQLr56Bl3Y0qwMZQsAposFHysAlQ4rtoJ7thjzxw1VtQB59Htuvbabe+0AgMIk3veZpDCo/zqqsiUWWzvarFKRxWYKZiuBkRVbTNny+XiseHc7AGDJ4SMxtqoQFoNWes2RlTD1sL7icPPSjpaFZLBrRnm/FgBpU6bd6sjJ60q6EsxDdrULFpUJNUUJ9Q4xZcvHAwO0SxQSshHmD9OHFaOuxIRBl1eySfgHfSYRkKFhQ41z74MnUzjDbHowZWtXuw2dNhe0Gg5T6hRQbIl2mx9Uomz1DAYXW8Fzdob2awH+82IuRb/vbrfi6z1d4Djg2IlV2V5OTBRJg40Fl0o0ZauuxITqIiO8Ph6bxU0KR5YTGNlgY9az9db3zfihqQ+FRh2uP2ECACFivkJ8jVKxlXoiBWQAwCGjymDUadBudWK3eP0pTyIE/L13bi+fk38juhLMQ9iLfXxVYslbJr1GuhAkK2FonFRs5Q0cx0lBGcxKaE9FQAbN2Uoa+bw7OSwgg6WyjqsqSGjAe6qZJtoI93TYpPlFSqaL2QgtQ5UtjgOOGBum2MpBG+ETX+0DAJw4pQajKgoiH6wQioIDMqIoWxzHDbESSmmEWVO2/MEKDrcX976/AwBw9YKx0oU84H+NdtOsrZTjlmyEoa93THotDhsjjElhEfBMiWR/I4NOI6lcudi3RVeCeYgUjpFAvxYgnHApJCMywdHvRG7DrIQfbWuD18dLim8qlC2yESZO2DRCS+Dw0+kK6NcChAvH6iIjeB7Y1qJ8K2HPQOg0QkB4TkvFC9xg2N/DnSM2wp4BF/674SAA4GdHj4lytHIokrUEANGVLQCYKtpt2aatf85WdvrU5D1bz3yzD029dtQWm3Dl0WMDjqsQL+TZuAIidURTtgC/lZC5P/wDjf3nCNa31UbFFpELJBP7zqCQjMiQjTC/OHxsOQxaDboGXGjps0uqRCqULbIRJk649yGzETKmKqBfi8GshJublF9sRerZOiqMhRDIvTTC59ccgMPtw/RhxdIOvhpgn+P9MfZsAf73js0V+D3Z69kSLtD77G48/MluAMBvFk0cEthRJuvTJFKLJ0L0O4OFZKze2w231zfERgjIi63ci3+nK8E8w+31YV+XYJ1Jrthis7ZI2QoFFVv5hV6rkdSS3kG3bKhx4hcgevGDy0PKVsKEjX43ByouSkgiZEyvV0ciodvrk87/rNgyyeya4cIxAL9ykgvFlsvjw9Nf7wMAXHn0GFXNUGRztuJRtgrE72GJq4NZTiMsNuuk97fV4cHk2iKcc8jwIcex12hXDvYDZRt3hOh3xtS6YpRZ9LA5PdjU2IsOKwvIGFps5WIiYVy67/fffx/TcTNnzkxoMUT62d81CLeXh8WgRX2JOeHHkdsIr3p2HVr6HHj2isNREmTPyVco+j3/KDHr0W51os/ulgIyCoyJW2u0UrFFylaihIt+LzLpwHEAC71SkrI1jSlbCk8kZAoBx/nVDo2Gw4KJVWjps0dUeHJpqPE7PzSj3epEdZERp8+oz/Zy4iLcnK3gHkc5bHbgoDMweTVbyhbHcagqNKJJTIL8v1Mnh1RYWLHVQ8VWyvGynq0INkKNhsNR4yvxzvct+HJ3p6RsVRXJiy3h37loI4zrSmD27NngOC5kLCO7neM4eL2UUKdUWL/W+OpCaCJIvtFg9oP9nQP4QAwFuO3NzfjrBXOSX2QO4HSznq3sN90TmUE+KNfupoAMJRBOYdZoOBSb9OizuzGy3DLEVphNmI1wV5sVDrdXEcEdoegZEC7QyyyGgIvbpy6fBwARFZ5csRHyPI9/f9kAALj0qNGqczIE914zZcukj6RsCa9HpobZs5xGCAiJhE29dhw9vhILwiRBkrKVHniel6zu4QIyGEeLxdZXsmJLrmzV5nDPVlzFVkNDQ7rWQWQIFik8qaYoqcdhJ+l1+3uk297Y2IwTp9TgjFnq2t1LB+Hm+xC5S4loTesddKck+p01G3tyYPc/W0gKcwh7S6lFKLbYIGGlUF9iQpFJB6vDg/1dg5hUm9y5Ol1IA42D3Ayx2OhyJfr924ZubG7qh0mvwZLDRmZ7OXHDNk1tjmAbYfjCiVkPB1zCnE1J2cpisXXuocMx6PTi9jOmhn39kbKVHrwy50WkgAzAH5Lx3YFeaMS/k3zW6/jqQhw/uRpzxMTLXCKuK4FRo0alax1Ehli7TyiODh1VltTjFIsn6Q1isWXQaeDy+PCH1zdj3uhyaUBdPuL18dIJiIqt/IGpI312txSQUZDEBQizvrGLGSJ+JIU5xPuQ/b2U1K8FCMVKXYkJVocN7VaH4ostdhEbD/ocsREyVevsQ4ajLIHnIduEtRFGVLb8PVsur0/6rMtmsXXh4aNw4eGRr0/Lac5WWpDb3MMNNWaMKLdgVIUF+7sGAQjfJ08jnDu6HE9cpp6AmXiI60qwu7sbBw8eDLhty5YtuPzyy3Heeefh+eefT+niiNTi8viwqbEXgPCiTgbpJC1aCa48egxmDi9Bn92N3726Cb487jORW2Oo2MofpIAMuyslTeMsZas1B5OZMoW/d3Lo3+GQkWXQij1GSkM+O0ipdA8mXmzlgo1wX+cAPtomWOivmK+euHc5TKWyDgnIiN6zNeD0wuHy//2y1bMVK1KxRWmEKUU+vkEXQ2sKSyUEBOtptkYGZJq4rgSvu+46PPDAA9LX7e3tOOaYY7B27Vo4nU5cdtllePbZZ1O+SCI1bG7ug9PjQ5lFj3FVyQ1dZMUWY1p9Mf5y/myY9Bp8sasTz67en9Tjq5mAYovmbOUNTCnpt/vTCJMJyKgrFS64W8TGbyJ+IqWC3n7GVHx320lSj5SSkM8OUirBM7biIReGGj/19T7wPLBwUlVSyb7ZRLIROj3w+XhZ9Hv0ni272yvFv+u1XMQkOiXAXqe9g26yZqcQeU9xLMXW0bJiS96vlevE9e5YvXo1zjzzTOnrZ555BuXl5di4cSPeeOMNLF++HI8++mjKF0mkhvWShbA86XhadpJmTKopwriqQtxy2hQAwPJ3t0lDD/MNpywgJpqHmcgdAqPfhYuQZHZ768S00JYcjMHNFM4IxRbHcZIdWmlUq6BR3N+zFX+xpfbo9z67Gy+vawQguDrUCts05Xlg0O2NTdmSbSB1ipsBSg1xkVMqC8HptdN80FThlo0miTRni3Hk2Aqwy8/KQvVZbxMlrmKrtbUVY8b4TyyffPIJfvzjH0OnE958Z555Jnbt2pXaFRIpY93+bgDA3NHJ9WsBgcqWQavB6EpBKbv4iFE4dmIVnB4ffv3SxgCJOV+Q76araeYKkRxM2eoacMEh9golo2zVlzIboSOgCZmIHWnOlsJ33YNhylaHgpWtZHq2JBuhSj8fXlp7AIMuLybVFAXs1KsNo04jbQhaHe6YeraMOo2kYLBEuWwmEcaKTjYLkfq2Ugf7bNJruZiud8oKDJghuglI2QpDcXExent7pa/XrFmDI444Qvqa4zg4ncr9cMhneJ7HOlHZmptkOAYQqGyNqy6ULAQcx+G+c2eixKzHD019ePjj/Cu+pdk+KrvAI5KDFVvygYzJXIRUF5mg1XDw+nhFX3QrGem9GOHiUYnUqEDZ6klBz5ZaNuO+3tOJJ75skMbevLWpBQBw+fzRqt5Q4zhO+iy3Ojz+6PcIyhbHcdJ5jRVbSu/XYlBIRurxxBj7Lof1yY6qsKRlTUokrk+gww47DA899BB8Ph9effVVWK1WHH/88dL9O3fuxIgRI1K+SCJ59nUNomvABYNOgxnDk+9RkCtbk2oC/eo1xSYs//EMAMAjq3Zjw4Ee5BMU+56fBBdbGi65odZaDYcaUeFo6aO+rUSQ3osq2/ioLlZ+z1aXTbQRJtSzJVycqyX6/Q+vb8Yf394qjU7Z1zUAADgkBRuX2UYKyXB4YlK25N/TKb4G1GAjBIByCxVbqYZtmEQaaBzMdceNx5/OmoarF4xL17IUR1yfQH/605/wxhtvwGw24/zzz8eNN96IsjL/yebFF1/EggULUr5IInnW7RMshDOHlUT0Y8dKsbzYqh06p+b0mXX40ex6+HjgNy9txICYdpQPRGrKJ3KXUvGDnF3gFxh0Se9615VS31YyqPW9WMPSCK0OSU1RGpKylUDPltrSCPvFHp89HTb0DbqlIcAjytS/My+Pf/cPNY58jcDs0UxxV4ONECBlKx14JBth7OdYk16Li48cjYo8shHG1VAwe/ZsbNu2DV999RXq6upw+OGHB9x/wQUXoLhYWQMiCQHJQphk5DtDbiOcHGYOzB1nTce3Dd3Y1zWIu97dJqlduY5aL/CI5JA3YAOAxZj8BUidOK+umRIJEyJSQIaSYcqWw+1Dv8MjqaZKgef51PRsqaTYYvPa9nUOorF6EIDQb5LN2VKpghVbfXa333Yb5f0iFVvMRqiS54GKrdQjKVsxhGPkM3F/AlVVVeFHP/rRkEKrtbUV77//PhYtWpSyxRGpQwrHSJHtQZ7iNTFMsVVi1uPPP5kFAHj+2wPY2WZNyc9WOlKxpTLrEpEcxcHFVgrmh9STspUUrhjS1ZSISa+V3AMdVuX97Qdd/uS6pKLfVdKz5RCDVg50D6KxWyi2RpSbs7mklMHUheZe/+ssmrIl2QitrGdLHbOSWCADnU9Th79ni4qtSMR1Ndjb24sLL7wQVVVVqK+vl/q3brvtNowdOxarV6/GE088ka61EgnSPeDCng7BY35oioots0GLXx4/HlcvGIdhpeE/dI4aX4mZYo8Y+5DKdZzUs5WXaDVcQC9jKqw1TNminq3EiHWnXon449+V17fFLIQGrSah17malC2vj4dbvKDc1zWAA+Ln2Mhy9VsIAX/y5QHZ53O098uQgAyVKFvT6gXn1Xd51keeTjw+1rOlvnNsJolrO+KWW27B559/jksvvRTvv/8+fv3rX+P999+Hw+HAe++9R/1aCmX9fuHEMr66MKFm5nDcsGhSTMexEzMb9JrrkI0wfykx66V+jtQUW8JGhnzXmYgdKfpdhe/F6iIjdrfb0K5AZat3UOhhKrXoE+pLVNOcLfkaD3QNorFHVLZyoF8LAKrEYuug+HtpuOgqRXBAhkUlARmHimNvdrRZ0e9wK3bOnpqQlC2aKRqRuD6B3nnnHTz55JO4//778eabb4LneUycOBGffPIJFVpZZsOBHlzzn/Uh1aNUWwjjhdmp7PlWbNFOT97B5rgAqbIRkrKVDGp+L9YoWNlixVYiA40BfzO9GmyErGAHhBl621oEO3yu2AirxTAWdu1g0mujFtCsZ6tPDA5Ri7JVXWTCyHILeB747kBvtpeTE0gBGXFEv+cjcT07zc3NmDp1KgBg7NixMJlM+NnPfpaWhRHx8cSXDXhvcyv+u6FpyH2pDseIFzaDY9CVH4mEpGzlL/Igg4KUBGQIF3TtVqdqZhIpCXYxr7Y5W4Df3tXe78TKrW144MMd8ClkuHWvXVA0SiyJKQNqshEGx9NvbOwFAIzIMRthkxjCE4vlNnhYu1qKLcC/6bxeTGgmkiOR6Pd8JK5PIJ/PB73ef3LVarUoKChI+aKI+GF+69b+wB1wh9uLHw4Ks0GypWyxE7HdrfwP1lQgXeBRsZV3lJr9O/2paBqvKDDAoNWA55U94FaJ+GS9NmpUtljP1oHuAfz6pY146JPd+HxXR5ZXJSDZCBNMSVRVsRX0ueUVC95csRGy5Ev2XollZlZBUHGllqHGgN9KuHYf9W2lAr+NUH3n2EwS19UAz/O47LLLYDSKsbQOB66++uohBdd///vf1K2QiAlmAQhO2dnc1AeX14fKQmPWpnWz3hU7KVtEjiPf6U+FsqXRcKgtMeFA9yBa+hwYniMXeJlAblFT43uRKQ6rdnRIF/hr93Vj4aTqbC4LgN8+VpqosiWzEfI8n/Q8unQitxEytBpOCq9RO8xGyEhE2VLLnC0AmDtKcPhsbOyF2+uLaz4UMRQWkKGnNMKIxFVsXXrppQFfX3TRRSldDJEY/Q43esSdxtagYovt3swdVZa1DzRzvgZk0Ek875DbCFPRswUIiYQHugdp1lacyO1faot+B/w9W16ZdVApu/G9YhphaYI9W/Li1+X1KfrvE2wjBIBhpeac2ckvNeuh03BS700sylZhULEVy/cohQnVhSg26dDv8GBbSz9mDi/N9pJUDVNEtVRsRSSuq4Enn3wyXesgkkAeitEaZDVaz8IxRmfHQggAFtFONejOk2KLot/zltKAYis1FyD++HeyEcaD3KKmV2E/AVO25Gxq7IXT4816cdIzmJyyJVdP3F4eRgWPaXKE+NzKlXAMQFDPq4qM0vkl15UtjYbDoaPKsGpHB9bt66FiK0kkZStHNh/SBT07OYC82OoddEsfDj4fj3X7sxuOAchthPlRbDnJRpi3lKSh2BpWJlzY7e8aSMnj5QvyTQ8l29TCwXppAOCwMeWoKDDA6fFhc1N/1O/td7jx/ubWkIVCKvD3bCWobMkuzJTetxVK2cqVfi2GvLCPpZC3GNXbswUAs0aUAgC2t0Z/LxGRcVP0e0zQ1WAOcCAo7p1ZCfd22tA76IZJr5GG+WUDkyE/0wizvftMZJ5UR78DkHZe2by8ROF5Hpsae/Gnt7fimHs/wS9f+C4Fq1MuTrHQMKp0x9Vi0ElDss89dLjkTlgXQ4rao5/sxtX/WY/XNhxMy9r67MxGmJiypdFw0iwn5RdbwutoeJlfzcqVJEJGlaxvK5bkzmAboZrSCAH/pthAnmwApxNmc9ZR9HtEFCzeE7HS2B3Yy9Ha78DoygIp8n32iNKsSrwWfZ72bJGylXcUpzj6HfCniO5ss6F30BV3n8zudive3NiMNzc1Y1+Xf2OmsduOe8+ZqboLpVhRc+w745qF4/B9Yx/OmFmPfrsbH2xpw9p9PbgqyljLhk5BBW1P04yuZNMIAeH86HF5lV9siWmEdSUm9A26YXV6crDYik/ZKgjaSFKbspVvbpt04vEyGyEpW5GgYisHCKds+cMxsmchBPwntnRZWpSGyyv8nhSQkX/IbVWpUrYqCo0YW1WAvR0DWL+/BydMqYnp+wZdHlzy7zWSlRgQLopOnFqDD7e0wunxod3qwKiK3BzfkQtBNdcuHC/9m1nB1+3vhs/HQxOhIb1rQFCe0jU0uFdKI0zMRggIxdagyyudL5UKsxGa9FocOroMX+zqxKzhJVleVWqR2whNCShbqTrXZQqzuN58cdukEzdFv8eEut4hREhYz1ZtsQmt/Q4pJEMJ4RhAHqcRkrKVdwTaCFO32ztvVDn2dgxg7b7Yi60tzf1Yt78HWg2HhROrcObsepw4pQYFRh0W3LcK+7sG0dbvzP1iK0feh9Pqi2HWa9E76MaeDhsm1BSFPbabFVtpUI14npelESahbIkXZ6F6opQEsxEadRo8suQQ9A66UZsjse8MeX9gQj1bBnW9x5jbhpSt5KHo99hQ1zuEGILXx+Ngj2AjnDdG2Pls7XOgw+rEvq5BcBxwSJaGGTPYrle+nNj8PVv09so30hGQASCufh2GzSns2k6pK8K/L5uHs2YPk1LEasQejXZr7iYc5lpQjV6rwZyRpQCiR8B32gT7YDqKrUGXV9rNTqrYUslgY6esB9ek1+ZcoQUEztpKRNkyq0zZYsVivmwApxOKfo+N3PgUymPa+h1weX3QaTjMERN2Wvsckqo1qaYIxabEPxBTgSXflC2Kfs9bLAat5F0PjkdOhsPEjZTvD/bFbMcddHrFNQ1dR5W4k52unh4lkItBNZKVMELR7fL4YHV4pH+nGmYhNGg1SfXqqKbYcuf+5lm8aYRGnSbg4lp9PVvMRpgf1yTpxEM2wpigZ0flMAvh8DIz6kuFtKTWfgdW71WGhRDwDzzMF390LvSKEInBcRyOmVCFYaVmjExhE/3IcguqioxweX34/mBfTN8zIL7fCkIobEzZaiNlS1XME8/na/eHL7Z6RIsfkJ6eLWYhLLHok4rUZ+fHdPWVpQq2uaHmoJVoBARkxPB7chwXcF5RX7GVX9ck6cQ/Z4uUrUjk7tkjT2DhGCPKLdLw09Y+Bz7a1gYAWDCxOmtrY0jJP3kSkJGLF3lE7Pz70rn47HcLpU2GVMBxnP9CO0Yr4aBoI7SEUNhYj0ZHDitbrNcmlzY95owsg4YTkiRbwwy5ZhZCID2qUZ+YRFiWhIUQUJGylYMKaTCVhbKAjBh/T7mVMBbroZIw51lCcjrxUPR7TNCzo3IaZcUW85K39jtwsMcOo06Do8dXZnN5APzFltvLw63wXcxUkGuN+UR8cByXFksFSxWNtW+LzZApDGEjZLahXFa2cvF9WGjUYao4MzFc0c3CMYD0qEY9SQ40ZkjKluKLrdxXtgw6DcoLhL9nrL8n28Qx67WqGxrOrkmcHp80J4pIDIp+j43cPXvkCSwcY0SZBZWFxgAf9fzxlYqYoSNfQz6oW1LPVg7tqBPZZ54U/d0DXwwXCMwiE5wcBgA1xWJARg4rW9KcrRwqtoDoRXeXTVZspaVny28jTAZJ2VL4Blw+KFuAfwPGFOP7hfWkpjIIKFPI+1jz4Zoknfij36nYikRufQrlIVbRKlRi1kOr4QIaXU+Ykn0LISAUHawIzIdEwlzcUSeyz5S6IlgMWlgdHuxst0Y9fkAMyAgeQAr4L6zarTlcbOXo+5CFpYRLJOwaSHOxlYKBxoCKbIR5EJABANXiBkysM7MKxU2cVNqlM4VJrwET46hvKzlYzxbZCCNDz47KYc27zDMtj6U9YXJs83jSDcdxeeWRztWLPCK76LQaHDJS7NtqiG4lHHCGV7bYhVWf3Z2zw8ZztXdyrjjKY3trP/od7iH3d8l7thJUjZweL37z8kb8d8PBIff1iWmEZQUpshEqXtnyz9nKZa4+dizOml0f8yYt28RRo7LFcRzN2koRUhohRb9HJLfPHnkA23Vju0u14kXUjGElipoHYo4z/aff4cbjX+zFhY+vxg0vb8LTX++T5gYpnVy1LxHZh1kJo81ZAvwbG6GUrWKTTnp9duSoupWr8+6qi00YVWGBjwe+O9A75P7uFChb3+zpwn83NOGGVzZh1Y72gPukNMJ8UbbY60iFCk48HDW+En+9YA4qZGEZkWA2QiW0KiSCmeLfU4Kbot9jgp4dlePwBCpb04eVAADOnFWftTWFQkokjHJia+gcwLI3t+DI5R/jzne24avdXXhtw0Hc/uYWPPzxrkwsNWn80e/q/BAilMu8OIYbs+j3UDvPHMdJiYRt/bkZkpGLc7YYrG8rlMKZChshi4/neeBXL3yHfZ0DsvtEG2GepBFK0e85VrQnS4GomKst9p2Rb/M/0wVFv8eGusZ+E0OQbITiBcWVR4/B4WPKJbuRUohkI+R5Hl/t7sKTXzXgkx3t4MXe//HVhbhg3gh829CNlVvb0NRrz+SSE4ZshES6mD2yFFoNh+Y+B5p67RgmztYLBRtqHG64ck2RCY3d9pzt28rl4eLzRpfhtQ0HQyYSpsJG2DPgtyf2Ozz4xbPr8L9r56PAqJOi35NNIzSqpNhy5qhCmixqV7Zo1lZq8Ee/U7EVCSq2VI7f4iB8EJj0WswVrUZKItKsrRXvbcc/P98rfX385GpcPn80jh5fCY7jUGYxYOXWNvQ71HFSpGKLSBcWgw7T64ux6WAf1u3rxrDZw8IeG0nZAvyzttpzVNlyunNvzhZjnhiSsbGxFy6PL+BckwobYa/Yl7Voag02NvZiZ5sNv3t1Ex5dcoiURpj0nC3V9GzlrkKaDIUq7tkC5K0NpGwlA4t+JxthZOjZUTl+i4OyT3gs4SjYRrh2Xzf+9YVQaF18xCh8csMCPHHZPBwzoUqa3VEs9gb024c2gysRZw7vqBPZZ67UtxXZSij1bIVRtqqLhJ7ONlK2VMfYygKUFxjg9PiwubkPr3/XhLMe/QqN3YMB0e/OBIutPtFGOLGmCH+/6BDotRze/aEVj322V0ojTFn0u+KVrdyfs5UIoysLAAgzPtVIrK0NRGRYQAbZCCNDZw+V4wgKyFAqphA2Qofbi5te+x48D5w/dwT+9KPpGFtVOOR7i03CxWKo5C2lwfO8rGeL3l5E6vH3bUUOyZDSCKMqW7lZbOWy/YvjOCmV8NMdHbj9zS3Y1NiLZ77ZJ40DAQCXJ7ELSaZslVr0OHRUOZadOQ0AcO8H29Ep2hRLLUmmEYp/l0QLwkwhhVApfEMz05w+ow5vXDcfvzlpYraXkhBmPQVkpAK3ZCPMvfNsKqFnR+UER78rlVD+6H99vhd7OwZQXWTELadPCfu9fmVL+TZClswD5OaOOpF9DhXDEXa0WaX+mVBESiME/MpWuzU3bYS5budlyZSPfbZHimN/5/uWgGPk56N48IdgCAXVksNG4oJ5I8DzAJunneycLb3abIQK/4zNNBoNh1kjShXvqgkHC/ignq3k8NsISdmKBJ09VAzP89IHgdKVrVCS/Vd7OgEA158wIWKMsFRsqUDZkl845OKOOpF9qoqMGFtZAJ4H1h8IbSXked7fsxVizhYA1OSJspWrxdZcUeGU2/Ca+4TCmfWqJ1rIMBshK6g4jsMdZ03D7BGlAATLULK9OqqzEebo6yhfIRthavDP2aL3RyTo2VExcvuF0ostqRlVFpDRJl7kjQthHZTDbIQuj0/xA1jlFw5kIyTSBbvQDjdvy+H2Same+a5sqXXnPRrTh5VIjob6EhOGl/mTKWvEeYteHw+vL351S24jZBh1Wjx20aGYWFOI02fUST21iSIFZCi+2Mrt11G+ItkIFX5NoXTcPlK2YoGuBlWMvPBQ+q5b8C4Sz/NoFXdhow1fLjDopJ1aq8ITCdmFg07DQUNRqESaYCEZ4eZtDcisMeHm4FQXCcpWz6Bb8Re8iZDrNkK9VoPDx1QAAK5aMA7HTKiU7pOfUxP52/aIiYbBfVm1JSZ8sPRYPHjBnESWHAD7zHIr3EZIc7ZyE1K2UgPbzKGAjMjQ2UPFsB03rYaT/O9KJTiNsN/hkWLga4sjF1saDYdCozpCMnL9Ao9QBqxfZ1NjX0i1l83Yshi0YYv+ErMeTJzoU0nSZzww+1cuK8x3nzMDjy45BBcfMQpHjfMXW/UlfpUr3mLL6+OlMRuhBhcnq2gx1GAjlFv1qWcrt2BumwGnsjdwlY6bbIQxQc+OivEPNFb+n1FKIxTXzGb7FJt0MQ1FVEv8u8srXuCp4G9CqJfRFRZUFhrg8vqwualvyP3+GVvhRylqNByKxE2MXCy2WL9SLisSdSVmnD6zDhoNhyPHVUi3VxUZpULa6Y1v515+jo3US5ssUrGlYGXL7eUlOy7ZCHMLS4jWBiJ+KCAjNnL3UygPUEvsOyCX7IWLwNb+2CyEjGITC8lQ9i6Uk2LfiQwgRH8L6taaEFZCtltbECYcg8FmJeVksZXD0e+hqCw0YnJtkfhvQ8I9Uaxfq8ioS6tjwqAVXptKjn53etRj1Sfig2yEqcEj2Qjp/REJenZUjD/2XT3FFoujZv1aNVEshIxis2gjVPhFIdkIiUzBQjI2Hugdct+Ai9kIwytbgF+5UPr7KhHy8b143twRMOu1OHxsRcI2vR4xiTDZocXRUIONUF4IUrGVW5gNbM6WsjdwlQ7rudRSj3pEIn8SE4pGatxVgZfcHDTUuI0pW7EWWyZ1xL/n4wUekR3GVhUAAJp67UPuG2TKVhSLLiu2clHZyvXo91BccfQYXHrUaGg1nF/ZitOm1zc4NIkwHaip2DLqNCnrVSOUQQEpWymBRb/rqWcrIvTsqBiHiiJpgwMy4rYRqmSwMbuwIRshkW7qS4UQhOYQxRZTtgqMsSlbuVZsPfvNPmlDh23U5AtshznRYqbXzmZsGaIcmRyJFoOZxElJhDmLOchtQySGh6LfY4KULRXjlGyEyv8gYCc2lkDY2ifM2IrZRqgyZYs+nIl0UycmzvUMumF3eQOCZpg1JmrPVo4VWx6vD3e+sw1Pfb0PAHDBvBEYXVmQ3UVlCUOC0eo9A6RsMfxJhMrf0CTiwyLZCKnYSgYPRb/HBBVbKoYpWyYVKFtJ2wipZ4sgAig26VBo1MHm9KC5zx4wHHzAGVvPFlOMeweV/b6KBavDjV++8B0+3dEBAPjdyZNw7cJxWV5V9mDKUbwBFKEGGqcDowqKLZqxlbtYgjaAicTwUPR7TNCzo2IcKlK28iWNULIR0oczkWY4jkN9qfD+ae61w+nx4q53tuKbPV1+ZStPerYO9gzi3L9/g093dMCk1+BvFx6C644bn9d9NokqR32DmbER6tVgIySnQs7i3wBW9jWF0nFT9HtMkLKlYpxqTCN0e+H2+tBpi9NGKF4UWhVuI6TodyKT1JWYsbPNhuZeO1ZubcO/vmjAtw3dUiy8JQ96tjYc6MEvnlmHTpsLVUVGPH7JXMwaUZrtZWWdxHu2yEbIcKqoL5qID3ZN4nD74PXxlKaXIBT9HhtUbCkUj9eHQbc3YnO3muZssX4SngcO9tjB84LHt6Igtt3TIhPZCAkiGH9IhgNe8UOvoWMAU2qLAcSubCn9fRUOt9eHXzyzHp02F6bUFePfl86VnpN8J9EAih4pjTDNARlqKLZUlPhLxIfcYm13e1EYZWOKGArP89LnDhWrkaEziEL52TPrcPhdH6PD6gx7jJr85GZZQdjQaQMAVBeZoInxDaoaG6FUbCm/ACbUzzCZjXBHmxUAYBV7uIDY52ypVdnqs7sllfyVq4+kQktG8jbCNCtbsmKQ5/m0/qxEIRth7mLSa8BcxmQlTAy31/++pej3yNCzo0C8Ph5f7+mC3e1FQ+dA2OMcHvXYCHVajfThurdD+J1qio0xf79qAjIo+p3IICyRsLnPjp1isQUA21qEf+d6GuGAOE/MYtDSznQQiQZQZNpGCARetCkJshHmLhzHwaKnWVvJwGLfAerZigZdESqQ5l679AEZKSnH6WaxtOr4MzIrISsgYw3HANQX/U42QiITMCVnT/sADnQPSrcztSfXlS2rqHRToTUUSdmKO/pdVLYylEYIKDckw+lRTwgVET9min9PCtavBVCxFQ06gygQuZoVacdFUrZUsuvGGlL3dTFlK45iS7wodLh90gegEqE5W0QmGSYWW639DoRyYsWqbNndXkX3zoTDJipbhSYqtoKRbHpx/F29Pl6yaqe9Z0um/iv1tSdtaKrkM5aIDwsNNk4KD9kIY0ZVz86KFSvAcRyWLl0q3cbzPJYtW4b6+nqYzWYsXLgQW7Zsyd4iU4C82HJEULbUFJAB+JWtHa2CxSnWGVsAUGTUSf5qq4L7tlghSMoWkQlqSiJbcaMpW0WyAB41qls28VxQRMrWEPQJzNmS27RL0tyzpdFw0Ik9u0otttiGJm2e5Sb+kTRUbCWCR1SkNRxi7r/PV1RzBlm7di3++c9/YubMmQG333vvvXjggQfwyCOPYO3ataitrcVJJ50Eq9Ua5pGUT+zFlrosDuzE1mkTbCqjKiwxf69Gw0lWISX3bbko+p3IIEadFlVF/oIrON2zIEqxpdVwUtKnGoutATZPjIqtIbANH3ccFj3Wr1Vo1GUkylnpiYRqs+oT8WE20KytZHCLNkIdXe9ERRXPkM1mw4UXXoh//etfKCsrk27neR4PPvggfv/73+Pss8/G9OnT8fTTT2NwcBDPP/98FlecHHvlNsIYlC21WBwWTKyCXsvhsNHl+MPpU3DClJq4vl/piYReH491+3sA+AM9CCLdyBP4TphSHXCfJYqNEFB33xb1bIUnkUKmR0wiTLeqxfD3lSlTWaCAjNxGUrYiXGcR4WHKlo5Uraiooti67rrrcPrpp+PEE08MuL2hoQGtra1YtGiRdJvRaMSCBQvw9ddfh308p9OJ/v7+gP+UBItGB6IEZKisefd3J0/Gjj+dipevPhI/O2Zs3DunxQqfCfTsN/uwpbkfxSYdfjxneLaXQ+QJ9bKgmRODNjCiKVtAamZt2ZwefLOnS5q5kimoZys88RZbDrcXr60/CCD94RgMQwJWx0ziJBthTsNs1gNOKrYSgaWIUrEVHcWfQV588UVs2LABK1asGHJfa2srAKCmJvACo6amRrovFCtWrEBJSYn034gRI1K76CRwerw42GOXvnZECshwqyf6nZGMr7dYvKBSYs9We78Df/5wJwDgd6dMDrB2EUQ6kStbR46rkHZrgcwpW8vf3Yaf/ms1Vm4Nf95NB9SzFR5jHEONB5wenPXIV3ju2wMAgB/PGZbWtTEUbyMkZSunsZCNMClY9HsmLMdqR9HPUGNjI371q1/hP//5D0ym8GEKHBd4Ac/z/JDb5Nx8883o6+uT/mtsbEzZmpPlQNdgQKpYZGWLBWQo+s+YMooUHP/+4Me7YHV6MGt4CZYcNjLbyyHyCFZsDSs1o8ikx8hyfy+kJYaNmFQUWyz0prHbHuXI1MKULerZGko8hczKrW3Y0WZFqUWPf1x8KH52zNh0Lw+ACoot6tnKaSggIzlYGiHFvkdH0Z9Q69evR3t7Ow499FDpNq/Xi88//xyPPPIIduzYAUBQuOrq6qRj2tvbh6hdcoxGI4xGZSoPe4OGGEfu2VJX9HuyKHWw8aDLgze+awIA/N+pU6AlSZ3IIDOHlwAADh9bDgAYWW7B9lYrjDpNTI3LqSi2WvscADIfoUw2wvDEU8hYxQ2sI8dW4ORptWldlxxDHOpbNiAbYW5j1otztsRrKZ+Px1d7OvHKuoNYtaMdlx45Gr89eVI2l6ho2JwtHcW+R0XRn1AnnHACfvjhh4DbLr/8ckyePBk33XQTxo4di9raWqxcuRJz5swBALhcLnz22We45557srHkpGkILrZc4T+EpIAMFdkIk0Gpg43f+b4FAy4vRldYcIR4wUsQmWLe6HJ8csMCSeFiylasak+yxRbP82i3smIrs3YcshGGR+qHisVGKBbJ0UYFpJpEEhMzid89kh+fsfkGU7b2dw3ggZU78dr6g2jq9avz/91wkIqtCLCADD0pW1FR9CdUUVERpk+fHnBbQUEBKioqpNuXLl2K5cuXY8KECZgwYQKWL18Oi8WCJUuWZGPJSdPQIRRbpRY9egfd0pyPUDBlK1923fwBGcryV7+yTmgq/8ncERHtqwSRLsZWFUr/ZiMVog00ZhQnWWx1D7ikRmlStpSDQXQ8xKJsDUp2zMwWFYkMXs4k+fYZm2+w6Pd3f2gFIPSbFpt0OH1mPV5YcwDNfQ70DrrSPuBbrUgBGdSzFRXVf0LdeOONsNvtuPbaa9HT04PDDz8cH374IYqKirK9tIRgytbUumJ8vacr5wIykoEFZChJ2drbYcOafd3QcMA5h1ACIZF9xlULhVd5jBcIcmXrb5/uRrfNhd+fPiXmjYPWfof074FMK1usSMiwIqMG4rERZlvZUm4aIQVk5DJ1YpIrxwFHj6/ET+aOwKKpNTDptfh8Zweaeu3Y3mrFEWMrsrxSZcICMiiNMDqq+4T69NNPA77mOA7Lli3DsmXLsrKeVLM3qNiK2LOVZwEZ7EIg07vnkXhFjEpeMLEKtSXhQ1wIIlMcMaYCty6eikNHlUU/GP5ia3NTH1ZubQMAXDZ/NIaXxTZ0vE1WbGW60ZyUrfDEU2wx+2eBIcPKltIDMqRiKz8+Y/ONs2YPQ6lFj4k1RUPOd1PqioViq6Wfiq0wUEBG7NAnlILw+ngsmlaDho4BTKkrBhA+IIPneekDKl+ULaUlB3m8PmkuzXlzlTM+gMhvNBoOVx49JubjWbHV0ucvmvrtHiC2Wg2tfU7p3wOZLraknq3MzIVSEwbxAiiWfig2Z8iS4d43xQdkMBthnmxo5htaDYfjJ4cOU5tSV4SPtrVhW4s1w6tSD25pqDG9P6JBxZaC0Go4LP/xDADAF7s6AIQvLOS2i3wptpi/OtNWpXB8vqsD7VYnygsMOGFK+PRLglAyrNiSwxSjWGgNULayYyMkZWsokmoUQyFDylZoXGQjzFsm1wob3ttb+7O8EuXC0ggpICM6VI4qFLNYQDnCKFvy2015YnFgfRlKUbZeXiuoWj+eM0y6aCAItRG62Iq9L7JNpogxhSQT+Hy8tPGS6WAHNWDQxh6QkTVlS+HFFtkI85fJdULf/442K7w+PsrRuQvPh//dKfo9dugZUigmqdgK/SHEbtdquLxJgjFL096zX2x12pz4aJvQ30IWQkLNhCq2rI4Ela0IPaapZtDtlQbAk41wKHH1bIl/t0wrW0aFF1sOshHmLaMrCmDSa+Bw+7C/ayD6N+QgP3t6HU584LOwoWQs+p16tqJDZxCFwoqtcBcv/oHG+fMntCio2Hr9uyZ4fDxmDS/BpFp1Jl8SBCBEv1cUGGDUaaQByfHYCOUBGQNxfF+ysH4trYbLm5CgeIgn6Y9Fv2c8jVDhPVtWZlOlOW55h1bDYVKN8Nmej31bXh+Pj7e3YU/HAN7Y2BzyGBaQoc+TDf9koGdIoTAVJ2yx5cmv2HdAHpCR3Z4tnufx0tpGAMB580jVItSNVsPhpauOxBv/bz7Gi7HxtgSVrUxuhDCrY6FRR/PtQhCpkHG4vXjqqwa09AkDXNnfLeNzthSsbDncXmldxSHUXyL3yee+LavDLTkHXlp7IOQxbp/fYUVEhoothcJ6tlweX0i/MLMR5lOxJdkI3d6IPuJ0s+lgH3a122DUaXDGrPqsrYMgUsX46kJMri1GkbiDH6uy5XB70Tvot5gMujwZe2/axD4jUh1CE6mQeerrfVj21lb89aNdAPyhQzRnyw+zTnEcUEhz3PIS1reVj8qW/Ly+uakfm5v6hhzjV7ao2IoGFVsKxSwrokKFZORjJC27EOD57H44M1XrtBl1KDbRjieRO7BUv1h7tpiFkG1s+jL43mTqGxVboYnUD/XJ9nYAflVy0JkdZUuvYBthv1242Cw26aGhnfu8ZEK1UGw1dNqyvJLM0zPoCvj6xRDqFkW/xw49QwpFnn4UykooDTTOo0haeQGayd4QOXaXF29tEvzLP5k7PCtrIIh0USgGTcSqbLX1CzO26kvN0m2ZshJKNkKKfQ9JuOj3focbG/b3AAD67G64PD7pmGwpW24FKlt9duE9UGym11e+UlFoABCo8uQLveJmA7Mjv/Fd85AkaCmNkJStqFCxpVA0Gk4quEIpW1JARh4pW1rZc5KtkIz3NrfA5vRgZLkFR4yhqfJEbsEKl1h7tpgyUl9qlt6bmdoIsZKyFRF2keT18QFW9K93d0kXSf12d8AFlCXTc7aUrGw5/MoWkZ+UWcRiy+7OautCNugTC8xDR5VhZLkFVqcH7/7QEnAMO6/oSdmKCj1DCob1KEUqtvJt2GKBeGGVyYhpOUzVOvfQ4WQtIXKOeHu22Iyt2mJTxt+bA5QUFxH57D+3rJj5fFeH9O8+u0fq1zJoNRlPFVNy9DuzEYYajUDkB6UW4W/v9fHojyM0KBdgNsLyAgPOF4PAgq2Ebop+jxkqthQMs83ZXUM/iJxSQEZ+/QnZc5ItZYvZplhENkHkEqxwscZYbDFlq7bEJL03M6Vs2ajYioi8cGJ9dDzP47Md/mKr3+HGIAvHyMJgaCWnEcp7toj8xKTXSmpvb1APU67DrJOlFr2wucwBa/f1YHe7v3+Not9jh54hBWOOMGsrH6PfAfmsrezsMlnFPpEi6hMhchC/jTC2HgVWbNUUm6RwhWBff6p4dvV+fLqjXfpamoFE78WQyBPCWDGzp2MATb12KarZ5fGhyyZcRBZkIXEvXF+ZEmBKBvVs5TfMStiTZ31bfXZ/sVVTbMLxk6sBAC+va5SOoej32KFiS8FEGmzs79nKz2IrXRd00WC9LEW020nkIIVx2gh7BoQL9cpCA8zixfpAGt6bu9ttuPX1zbjmPxuk9z7ZCCPDcdyQYuaznYKqdcTYcilBskW0gma6XwsADFrhZyox+r2PbIQE/FZCdq7LF5iNsNQsFJsXzBsJAHht/UFp84YpW2QjjA4VWwrGHKGwyFsboSF7NkKe58m6ROQ0TLEdcMb2/rJKmw86FKRRdW7uFYbv2t1eqeeIot+jY9QG2vRYsbVwYrU0qFcqtrLwPJKNkFA6fmUrv4otuY0QABZOqkJ1kRFdAy58tK0NAOARN3EoICM69AwpGFZIhQzI8ORnQAaLJs6GsuX0+OAWd3LIukTkInJlyxdimHow/s0HvfTeTMdGSKfNKf37gy2tgT+b3othkRczDrcX3+7tAgAsmFQlKTYtfUIhW5ANZUvJxZZopS2xULGVz5QV5KeNsFeyEQq/v06rkcbdvCjOGnVT9HvMULGlYFjPVug0QuHDKZ+GGgN+ZWsgCz1b8kGvhVnobyCIdCMvXGJ5j8nj1y1pVJ07rP5i66OtbXB7fRT9HgPyYmb13i44PT7UlZgwobpQVmwxG2EWlC0FR7/3kbJFACgTi+18C8joE3/fMtlmw3lzhVTCL3Z1oLF7EF4KyIgZeoYUTEw9W/mmbGUxjVBuIaTYdyIXMeq00gVwLH1bNllgDAvIGExDGqG82Op3ePDt3m6pGCRlKzz+ni2vZCFcMLEKHMdJRYSkbFEaYQD9NNSYgF/Z6c67nq1AGyEAjKoowPzxFeB54JX1B6WADB1dD0WFii0FEzGNUOrZyrNiK0UBGcvf3YYV726La1Ah9YgQ+UCsg43dXp90Hioy6WDWpy8go0O0EbJC8P0tLfR+jAH2fDk9voBiC/AHP7T0Zk/ZUvScLQcFZBByZSt/bITCXDH2+jcE3He+GJTxyrpGKdhGR8pWVOgZUjDSUOMQFy/+6Pf8+hOyJu5klK1OmxP//Hwv/vH5Xmxu6o/5+1jsO+2kE7lMrLO25PO0Cow6WfR76pUt1rN12oxaAMB7P7SiS9xppmIrPEw52tsxgL0dA9BqOBw1vhKAX7Fhz2M2eraY/chNNkJCoZQX5F9AhtXhBtuHLg3qWVw0tQalFj1a+hz4Zo/QA0rKVnTy60pdZUSyEbILnbxTtqTnJPELuvZ+vyXptQ0HY/4+efIaQeQqUkhGFGWLvR9Meg30Wo2snzJ9PVtnzRmGMZUF6BpwUc9WDLBii6WHHTKyVFJqioMUG0oj9MPzvD+NkJStvKY0D+dssd+10Kgb0o9l0mtx6vQ6AH5rJQVkRIeKLQUTrthyuL1Y29ANAJhUW5TxdWWTVES/d8iSzd7a1BzzrirZloh8INZZW/5iR7gYLUhjUigrtoaVmvHn82ZBvpFKmx/hYTbCr8UdaGYhBIYqNtlMI3QqTNkacHnBwjjJRpjflOXhnC0WBhLutX/K9NqAryn6PTr0DCkYfxph4AfRpzvaMeDyYlipGXNGlGZhZdkjFfHS8mb7rgEXPhd7GaLBLj7p4o7IZWLt2Qp+P6QrKdTt9Uk7rZWFRhwysgzXLhwv3V9Amx9hCVaOFkyslu4LvpDKirIlmwMWT/9sumEWQoNWI/WVEflJPs7ZYrHvZQWhi60jx1YEXAeRshUdOosoGLPYjxWsbL39fQsA4PSZdeC4/HqRW1IwOFU+swcA/ruhacgx3QMu7OmwBdxGA42JfCDWni15EiHgV7ZSnRTaZROtKhoOpWKBcP0JE3DuocNx7cJxFDscAYPsuakoMGBafbH0dXCxlU1lC4A0w1AJ+C2Eurz7jCUCYXO2nB5fVuZ7ZgOmbJUGhWMwDDoNTpjs37ihgIzo0DOkYEIFZAy6PPh4WzsA4PQZdVlZVzZJiY1QVLaOmSA0iq/c1ibtZDIufWINTnrgMykWGfCnUxVRwzSRw8SqbAX3TKViIyQU7P1aWWiURi4YdBrc/5NZuPGUySn9WbmGvJg5dmJVwMiKIT1bWUwjBJQ1a6uP+rUIkQKDFnpRuckXdYslL0Ya6C23EuopICMqVGwpmFA9W6u2d8Du9mJEuRkzh5dka2lZIxXR7+zibcHEKkyqKYLL48O7P7RI91sdbvzQ1AcfD+zrHJRup54tIh8oknq2IjeEByu96Rpq3GEToskri0LvshLhCSy2KgPuG6JsZWPOlmxHPNMhGZFsi/2UREiIcBwnhWS09Nlx4eOrce/727O8qvTCiq2yCMXWsROrpDRsUraiQ8+QgglVbL39fTMA4PQZ9Xlpb0jFBR0rtqqKjPjxIcMAAP+VpRLubLNK/+6z+3eyqGeLyAdiDciQNh+YjZCNZXCmttjqtArvwapCY0ofNx+QFzPHTKgKuK846DyWDWVLo+Gk2OhMFVsujw+nP/QFrnx6XdiCq9/BBhpTsUX4i463NrXgq91deGltY5ZXlF6i2QgB4Xxx8RGjUFloxFSZPZkIDRVbCkYaaiwWFgNODz7ZLlgIF8/MPwshkKKADJu/2PrR7GHgOGDtvh4c6BJUrK0t/mJLHvdqo+h3Ig9gxZM1RhshU8LM6bIRyt6vxP9v787DoyrP/oF/zyyZJckkJEA2AkEUkKUsgggu4AYqWGj7Wtfa2Na3uFurdvH3vvB2EWvd6lZrLeButaDQitaFxbqhIIEAAqLs2Qgkmcky+/P7Y+acmUlmwkwyZ9bv57q4LjLzzORkzsyZc5/7fu4nNnJma3xFAQZ2C1Z7NMhIwpwtIPHt3w+1dGJHnRVrdzXhg6+aw46RywjZiZCAQJOMf+9oAODrCJ3J5AYZ3dfY6u6euWPw+T3no6LQlIjNSmsMtlKYfPIir9L93peNcLi9qCo2h0x0ziaBMsK+n9Apma08A0oLjDjLv8jn61t8jTK+rA8sdBy8arzNEdrqmigTRZ3ZcnTLbKnUICM4E02xKbEYAQAXjinpcV/3rE1uEjJbQFCw5UnMCWzwifJfP/gm7JhAGSEvrFEg2Kpv85U021NsXbh4k8975PLJ3mRjhVVfMNhKYd0zW2/6uxDO+1Z2lhACQVfPXZ4+tQp2uD3KVUv55O27cinhlsMQQmBXSLAVKCO0dSubIspE+TE2yJAbxsifTbdXxDVLEdwgg2Jz/dkn4Zlrp+C/zzmpx316rSYkm2VOwpwtIFDq6EjQCWzwUiof7m3Gjrq2HmPkZkjMbBHQswW6xyvgTqGGLvEWKCPk+z9eGGylsOA5Wza7C+v960HNzdISQiBQRihEz/XHotHsbyOt10rKF+mcsaUw52hx4FgnNh1owa6GQBlhcGZLbhjABhmUyeTM7YkzW6Gfh+AT93iWErKMsO9MOVpcMKZE+S7pLjiYSHpmK0HBlqNbCdhTG77pceGO3Qgp2IAwGZ5Mzm5FW0ZI0WOwlcLkK8VdLg/e+7IRTrcXIwblYnRpfpK3LHlM+v6d0AWXEMrZQXOOTmlj+qf3vgopgwpu9Spf6WdpCWWyaOdsdW8Yo9dqlCxFRxxLCZuDPrMUX3K3PUmC0lks0eRgK1HrbNndvvem/L7959Y63PDCFzjeETjWW7vkYz1PNilCsJXB87ZiKSOk6DDYSmHGoCt+q2v8XQizuIQQALQaSVmbpS9zQyLN//je5CEAfGUlweQrPEKIHnNUiDJRzN0IgzK9cilaf+ZUdqeUETKzFXdyZis3J3mL98oBeqIyW3JFxOjSfPy/uadCp5Hw9o4GzH74A6zzN6BiGSEFC5fhydRgq9PpVt7/RbkMtuKFwVYKMwWV5WzwlxBmaxfCYOacni3xoxUp2DrjpGKUFRiVn+XuOnLtssPtVa68soyQMpkyZ8vh7nVepM0RJtjyZ5474tT+3e7yKL+HZYTxZzH1LAFNtGQ1yDDqtfjJ2SfhjZvOxCmD89Dc7sB1yz/Hr1+vVbKp8utD2S04syWv35uoOYaJVnOwFUIA5QVGBltxxGArhRl1gS9ArwBGluRhZEn2lhDK+tP+PVKwpdVImD+xQvl5+ohiAIF0ulxSJUnJm9tAlAhy8OTxil7nRYZrGGM2xLcjofx5Neg0Sot5ih95TlJuEl/bZGW2DP7v13EVBfjnLWfhR2cOBwC8tPEgvmnuAMAyQvKpGOC7+Dq0yKw06snUzNamAy0AgNOqipK8JZmFwVYK0wSVzAG+LoQUvLBxH+Zstftat4ab/yF3JQR8mS7AV0YYUkKYo4NGk71lnJT5zDlayBVlNocr4rjAHMbgJgvxXWuruT3QiTCby6fVIpfJpUJmK3HdCOXMVuC71ajX4n8vHYMXfzItpMKBZYQE+EpO/3TFRDx1zWlKs5m+NOhKB3KwNWXYgCRvSWbhpcIUZ8rRKl9C2dyFMFhgra34ZbYAYGRJPr43eQj2Nbfj3FGDAPiutna5PIH5KZyvRRlOkiTkG3Sw2t2ob7VjcL6xxxi3x6uU8QaXEQYWNvbdd6zdgfd3NeHdnY3Y1WDFb+aPw7mjBke9LfKcye6tlyk+5EA5mdl6OaAJ7vyqJrlBRrgOjWeePBBv33YO/vjOLthdXgwrNidkmyi1SVKg8kUO0rt3tcwEHq/AFjmzxWArrnjmmOJMei1a4cKpZRaMGJSX7M1JCd1P6GJxogVSH/z+BAC+hhg5Wg2cHi9aO12w2dn2nbLHjBED8faOBrz82UFMqCzscX/wnKzgEjT5pH1VzRE898l+bD7QAm/QtK/XNh2KKdhqk7timTh3QA1KZitJa2wBgYWX5QVj1SZnJCJ1Xyww6/G7BeMTsi2UfuTy00ycs7Wn0Qabw43cHG1Wd71WA88cg3Q4O6B19vzS0Wq0MOqMIeMi0UgamPSmPo3tdHX2mJCu1znhhR2zxw4NuT3cWJkkSTDrA1fkulxd8IrIB4bcnNw+jbW77fB4Iwc8sYw1681KmZDD7YDbG7kMSW7/3ul0RzHWBI0kT8B2osHWCi/syDO6e+yb4LEurwt5Jjea25040taCox12eGGHyWBAh7MDRp0RWo1WeV6XJ/JV2eCxLo8LTo8z4liDzgCdRhfzWLfX91pEkqPNgV6rj3msx+uB3R35JEiv1SNHmxPzWK/wosvVFZexOo0OBp0veBZCoNPVGZexsXzuk3mMkHX/3PfnGHH1GYOxZsd+/GPL17jhvCEYNqAoZGxjezu8sMOg08Dl7YLL/zbV6Xz/ee9LX1c3ASdGl+VixKA8/GtbPXbUN4X8vSc6RjT6P69mo69ZR7THiNiOJ6HHiN4+y7GMTYdjxPhKA/JNHpwxIhcujyspx4iiPC+8sONQy3E4PU7VjxF2pxte2KGRHGE/ezxGhB+baecRfT1G5Og88MKO1i4bOpy5vY5Nt2PExn1H4YUd44cUw+HpQvc+R9l2HtHb5y5Wkuit3VSWsFqtKCgoAH4JoGfFDC455RK8edWbys+59+ZGPADPHDYT66vXKz8P+uMgNHc2hx07pXwKPr/+c+XnqkeqcKDtQNixI4tOxe5bdio/j31yLHYe3Rl27LCCYdh/+37l56l/nYpNdZvCjh1oHoijdx1Vfp61fBY2HNgQdqxZb0bHrwNvvrkvzcWar9aEHQsAYlHgrXXZa5fhHzv/EXFs+6/alYNq9RvVeHbrsxHHXjf8PazdaceiS8dgU+sDeHLTkxHH7rttH6oKqwAAd75zJx785MGIY7ffsB1jB48FACxevxj/t+H/Io797CefYWrFVADAHz/6I+5+7+6IY9f9cB1mVc0CADzx2RO4+a2bI47915X/wtyRcwEAy2uW47pV10Uc++p/vYrLxl4GAHhtx2v4/j++H3HssvnLUD2xGgDw5p43Me/leRHHPn7x47jp9JsAAOv3r8e5z54bcez9F9yPu868CwDw+ZHPcfozp0ccu2jmIiyetRgAsKNpB8b9eVzEsXdOvxN/nP1HAMD+1v0Y/qfhEcfeOOVGPDH3CQDA0Y6jGPxA5MzJDyf8EMsXLAfgO4HJWxI5W/xfY/4Lr132mvKz9H+R5wylwjFizKAx2HHjDuXneB0jcnUD0H7PceXn3o4RBq0Jp3rfwNjyAlw4pgTPfbUQa/f/O+xYQL1jRNOdTRiU6ysFvunNm6I+Rtz1zl144JMHIo7lMcInnY8Rd674AA9unxlxLI8RPpl+HtHXY8SYB6/Bl+0vRhybzseIuX+9H2vqfhFxbLYcI5TzCDuA+4C2tjZYLJaIj40GM1tpQqfl5HCZSd+3dbYStWgmUSZxuL2wuzxh57h0p9VI2PL/Zis/v7o/eeVpROE4MrSxASWGLoMbZH19NH6ZHArFzBYCma26o3Vho1em/8OPTVb6/w9rvsFznx7EzeeejFvOr4o6/f/10Vac++D7MOg0qPnf2b2OdXqc+OnzG/H+riYsunQMWjud+NP7e/G9yRX43YLxKZf+ZxkhywjjfYxwebyY8/AG1FsdeOB7p+P7UyuVset2N2DhC19gbLkF/1g4I+RxkT73P1y6EZ/tb8G93xmP70yq6HWs7BcrtmH11jrcPWcUbpo1lmWEyLxjxOGWTlz48Acw6DTYvvgS5fOp1jHi1pe/wBtb9+EXF41C9YyeV7p5jAg/NtPOI/p6jFj44kasqT2Mey4ZjWvOqOp1bDodI6x2F8YvfgsCLnz26/ORH2bZg2w7j7BarSgfVM7MVrzl5uSGfLB7GxfLc0Yr+MAWz7HBB+J4jg3+4ojnWIPOAAMiL2BqNvg+wJ1OzwnHBnO6NdDAiAJjzgn3S442B4PyLNDACrtTD6cb0MCIYrOlx2NztDnKB/pE9Fq9cgCK51idRgddlB3FYhmr1Wijfg/HMlYjaVQZK0mSKmMB9T73qXyM+PFZp+LeNbvwzIff4LIpQyBJEkx6E9weAzQwotCY3+v2B3/ux5WXYNP+Lhxodod9TLhjRIddCw2MGJxfENL6PZbPfSxjY/ksqzU2244RVcVGaGCEyw10OiTI/VbUOkY43F5oYITlBO9dgMeIYJl2HtHXsbl63/tVCGNU5xHpcow4anNAghYWowGllsITjs+G8whPTvw6TnKdLUo7Sut3V2xr+XTIa2VF2VGw0L9qfGunk63fKStdPnUozDla7Glsx4d7A3NG+vJ5GOXvbrWrwRb1Y+TW74Vc7yhjGXRaFOf6jrWJ6EiodCPU8fSHYid3scy0RY2brL13aqb+4dGG0o65j63fbf5gKzfqYCuw/kt7jIEaUSYoMOnx/Sm+8sFn/rNPub3dv9hxfgyfBznY2h1DsKW0fjez9XsmK/UvJNxgjVzmEy+BRY05n5Bil6mt34/6F5AflMdgSw0MtijtmP2p647ufUlPoCPWYMu/tk9Lpws2/5X8fGa2KMv86MzhkCRgw56j+KrRFyj1JbM1ssQXbDXZHNhR14Z7Xq/FtsOtvT6mpdM310C+8EGZqUwOttp6zgGx2V1Y+cVhZa3D/rK75XW2GGxR7DI1syWvQTrYEn2pJkWPwRalHVNO3w52crAV7dX4Af4TvLauQBlhuEmjRJlsaLEZc8aUAgCWfuTLbtn6kOnNM+hQWeSby3Hl05/ixY0H8degbFl3Xq9AG8sIs4KS2WoLzWx5vAL//dxm3PHqVjz/afh25rFyKJktnv5Q7OQg3Z5hXS3lYIuZLXXwaENpx6T3neB1xRhsydmpaDNbBf5g61i7E/uO+bpBDWA5E2Whn5zt69q24osjONbu6PPFh1Elvo5OVv/jOx2R513aHG54/Y3SLAy2Mlqp/2p69zlbj77/FT755hiAwJyS/mIZIfWHwT/Xz5Fhma0mm++zxzlb6mCwRWnHJDfIiHHOllx2GG2wJQdW3zR34KjNgUKzHqcNGxDT7yTKBKcNG4AJQwrgdHvxwqcHlQsXsTaMGe2ftyXrbd6DPF/LpNfyxDjDlRb4Mp4N1kCw9fHeZjy69ivl505nbA2RIpEzEia+p6gP5GNRxs3ZsrFBhpoYbFHakb8kY81sdThjm3fVfZ7IvG+VIYcdrCgLSZKEH599EgDgmQ+/wcdf+zoTxtIgAwCunDYU875VhuoZVQAAhzvyZ7i1yzdfawDna2U8ec6WnNk6anPgtr/XQIjA/o+1IVIkdjfLCKnvMn7OFoMtVfBoQ2nH3MfMllJGGOXaEHKDDJm8ECtRNrp4XCnKC4yw2d2w2t0w6bUYWx7bQo8VhSY8ftVknDNyIADA2cvV4VZ/ZquApbsZLzBnyw6PV+Bnf6/BUZsDo0rycdv5pwCI/XgfiXySLHeVI4qF/L6x93KhKB01tzOzpSa2VqO0Y+xrZkvpRhjdl6wpRwuDTgOH24vKIhMmD2UJIWUvvVaDp6+dgg17jmJMmQWThhb2uSV7NO2TucZW9pDnbLU73Lj/37vw4d5mmPRaPH7VJOystwKIT2ZLCBFYZ4tlhNQHcmbLkUENMtweL451+CoJGGypg8EWpZ2+ztmS18qKpX17oVmPRqsD35lYAUmSYvp9RJlmXEUBxlUU9Pt55HLc3udsse17tsg16GAx6mC1u/GXDd8AAH4zfyxOKcnH/mOdAIDOOJRtBb/fWEZIfWHQZ15m61iHE0IAWo2EIlYSqIJHG0o7Zv/Bzunxwu2J/upSe4zrbAHAmScPxACzHpf5F3Ylov6LpqNXq7KgMYOtbCCXEgLAdydXKMdcuWzcHofMVvA8G2a2qC+Musxr/S7P1xqYlwONhheV1cDMFqUdObMF+BaozNNGd82gow9rAz30/Ylwur1sjEEUR7GUERaYeKU1G5QWmLCnsR0jBuXit/PHKbfLx/tOV/+7EconyFqNBH2U3xtEwQwZ2CCDnQjVx2CL0o5Bp4EkAUL42gFHGzy19yHYAsBAiyjODFGUETKzlV2u83eo/N95p4ZUH/S1IVI4yhpbPKZTHxmjuFCUbrigsfoYbFHakSQJJr0WnU4P7M7oD3gdfSgjJKL4k68O99aNsM3f+p0NMrLDuaMH49zRg3vcLi/1EY8GGYG27ywhpL7JxNbvXNBYfby8Q2mpL2tt9TWzRUTxJZcROj1eeL0i7BhmtggIaojk8kCI8O+VaLETIfWX3CAjk7oRBtbYMp5gJPUVgy1KS0odvzO6On63x6t80TLYIkouQ1AZlzNCkxvO2SIAMPvXRRSi/00JlDW22ImQ+kguQe3tQlG6Oco1tlTHIw6lpVgzWx2OwDiWERIlV/A8yEhXiJnZIiBwrAeiv7gWSWDOFjNb1DfBWdFMmbfFBhnqY7BFaUnObEVbN93u/5LO0WnY8IIoyXQaCXKHYUeY9WqEEIE5Wwy2sppWIymZ0FgXsu8uUEbI7wDqm+CsfKbM22pisKU6HnEoLcU6abrdzvlaRKlCkqRe2793Oj1weXwlOoUsI8x68epI6GCDDOonnVYDnf9KUaYsbByYs8VgSy0MtigtmWL88g0saMwvWaJUIM+bCRdsyfO1cnQaZiFImbfV346EShkhgy3qB2MGNcnocLiVz9VAtn5XDb/FKC3Jma2oywiVToQsSSJKBYG1tnp+hls7A23fJUlK6HZR6pED7v4HWywjpP5T2r9nQGbreIfvWGvQaTifXUU84lBaCnQjjLZBhhxs8YomUSrorYywjc0xKIic2epysUEGJZ987Opvd8xU0KZ0feWxVk0MtigtxdqNkGtsEaUWuVFNuFKcFjnY4nwtQuwX1yKRT44NLCOkfjBk0MLGVjuDrURgsEVpKeZgyy7P2WKwRZQKei0j9HciLGBmixC/Bhl2pUEGT32o74y9ZOXTjdWf2bIw2FIVjziUlmL98u1gZosopQSCrTANMpTMFk8AKOh43+/W72yQQf1nzKDMFssIE4PBFqUlY6zdCJ0MtohSiTzvwRluzlYX52xRgEkfr26E/gYZnLNF/RCYs8Vgi6LDYIvSEssIidJbr63f5W6EZs7ZIsCUE59uhA4Xywip/4y9HLvSjbXLd27EYEtdPOJQWmIZIVF66731O6+2UoDSjdDZz26EXNSY4iCwzlbmZLYsRp4bqYnBFqUlY8zdCH3j8nhAIUoJObrIC4O2soyQgsiVDFxni1KBfKEok1q/s0GGunjEobQUe+t33wGFZYREqaG3BhnyOlsDWEZIiGM3QjbIoDiQ3z+cs0XRYrBFaSlQVhJtGaE/s8VFjYlSQlSt33kCQIh/N0IDG2RQPyhlhJkwZ4vrbCUEgy1KS/KE6dgXNeYBhSgV9NaNUGn9zjJCAmDKiXM3QpYRUj8EyggzJ7PFMkJ18YhDaUmZsxVt63eH3I2QVzSJUkGkboR2l0e5jd0ICQgqG4/bosb8HqC+M8hlhGGy8rLP9x/H2l2NidqkXm073IqjNkfY+6wsI0wIBluUlmIvI/QFW/nMbBGlhEhlhHJWS6eRkJvDk2IKlBF2uvrXjdChZLb4vqK+CyxqHL6McMOeo7ji6U9x/XObcaw9fJCTKDvrrJj/xEf40fLPIYQIuU8IwTlbCcJgi9JSLA0yPF6hlJ8ws0WUGnLkYKvbCYs8X6vQrIckSQnfLko9ppx4dSPkOlvUf/Ki2OHmbO1usOHmF7+Axyvg8QocTXKwtflgC4QAao+0YVeDLeQ+u8sLl8cXgDHYUhePOJSW5GDL7RVh53wE6wham4XdCIlSgyHCCQvX2KLu4t6NkA0yqB8M+vBzto7aHPjR8s9hcwTOOeRFg5Plq8ZAgLWqpi7kPjmrpdVIymeM1MEzT0pLpqADQ5fLo1wll1ntLnzy9TF88vUx7PZfzdFrJaV0iYiS60RlhJyvRbK4dSN0s4yQ+k8O1oODLbvLg+uf24QjrV2oKjZDI0n4prlDmROVLLuDsln/3FqHu+eMgkbjqxgILiFkFYG6GGxRWtJrJWg1EjxeAbvLE3IV/EhrF77zxEdo6jYhdFRpPg8oRCki0jpbbXIZITNb5BePboQujxcer69kimWE1B/dW797vQJ3vrYVNYdaUWDSY2n1VCxavcMXbNmTF2wJIbAnKLN1pLULmw+2YGpVEQCusZVIDLYoLUmSBLNeC5vD3eML+PlPDqDJ5sDgfAMuGleK8RUFqBhgwsTKwuRsLBH1IHf06l4GrJQRsu07+ZmD3iser4BWE/tFs+AsBDNb1B/KhSL/e+rh9/bgX9vqoddK+MsPTsNJg/JgMfqOX8nMbDW3O9HS6YIkAZeMK8ObtfVYVXOkR7DFtu/qS+nLO0uWLMHUqVORn5+PwYMHY8GCBdi9e3fIGCEEFi9ejPLycphMJsyaNQs7duxI0hZTIhnD1PE73B68tukQAOB3C8bhN/PH4bIplZgxYqDSwZCIki9SZqvVfwJQaGIZIfkEl413Ovs2Bya4cxzLyak/5GDd7vJixebDeGztXgDAvd8ZjzNOKgYAWEy+8w2rPXlztuSs1rAiMy6fWgkAeHNbPVwe32dBDgQtRp4bqS2ljzgbNmzATTfdhE8//RTvvvsu3G43Zs+ejY6ODmXM/fffj4ceegiPP/44Pv/8c5SWluLCCy+EzWbr5ZkpE4TrSPjvHY041uFEqcWI80YPTtamEdEJ5JxwzhavtpKPQaeBXAHe1yYZcmbL91wsJ6e+k8tQD7d04pcrtwEAbjp3BC6bUqmMSYXMlhxsnVKSjxkjijEwLwctnS58uLcZAMsIEymlg623334b1dXVGDt2LCZMmIBly5bh4MGD2Lx5MwBfVuuRRx7BPffcg+9+97sYN24cnn32WXR2duKll15K8taT2sJ1qHrx0wMAgCtOr4ROm9Jvb6KsZojU+r0z0PqdCAiUjQN9n7fl4ILGFCfye6jD6YHLIzB3fBl+fuGokDFyaV4y52zJwdaoknzotBrM+1Y5AGC1vyshg63ESauz0ba2NgBAUZGv3nTfvn1oaGjA7NmzlTEGgwEzZ87Exx9/HPF5HA4HrFZryD9KP8Zuma29TTZs3HccWo2EK6YOTeamEdEJsPU7xUJuktHXjoTyiWU+S6aon4LLUCdWFuLB709QOvzJ5NK8ZLZ+39PYDgA4pSQPAHDpBF+w9e8dDehyehhsJVDaBFtCCNxxxx0466yzMG7cOABAQ0MDAKCkpCRkbElJiXJfOEuWLEFBQYHyr7KyMuJYSl3dywhf3HgQAHD+6MEoLTAmbbuI6MQitn7vYut36sncz4WNj7X7MqbFeYa4bRNlp/JCEwrNelQVm/HXa6eEzZYmO7MlhMAef9v3UaX5AIDJQwsxZIAJnU4P3vuyUdk2NshQX9oEWzfffDO2bduGl19+ucd93euvhRC91mT/6le/Qltbm/Lv0KFDcd9eUl+gjNCNLqcHKzYfBgBcfcawZG4WEUVBnvfQvRthWydbv1NP/V3Y+HiHP9jKZRBP/ZNr0OHDX5yHf//sHAzKDx+8K3O2khRsNVjtsDnc0GokDB+YC8B3rjx/oi+7taqmTplPxsyW+tIi2LrllluwevVqrFu3DkOGDFFuLy0tBYAeWaympqYe2a5gBoMBFosl5B+ln+BuhP/aVger3Y3KIhPOPnlgkreMiE4kYhlhFxtkUE9GZc5W38qyjjHYojjKM+iUY1g4SjfCJJURfuUvIawqNods5/yJFQCADXuacPB4JwAGW4mQ0sGWEAI333wzVq5cibVr12L48OEh9w8fPhylpaV49913lducTic2bNiAGTNmJHpzKcECZYRepYTwytOH9qidJqLUE671u8PtUcrE2PqdgimZrT7O2ZLLCIvy+L4i9SU7s3W4pQsAMKw4N+T2kSX5GF2aD5dHKHO65G0l9aR0sHXTTTfhhRdewEsvvYT8/Hw0NDSgoaEBXV2+N5EkSbj99ttx77334vXXX8f27dtRXV0Ns9mMq666KslbT2qTv3w3HziOmkOt0GslXHYa598RpQO59bvHK+D2r/siT9iWJDYyoFD9nrPV4QDAzBYlhjJnq8sFIUTCf39dq+88ubyw5/x1ObslY2ZLfSn9bfbnP/8ZADBr1qyQ25ctW4bq6moAwN13342uri7ceOONaGlpwbRp0/DOO+8gPz8/wVtLiSZntt77sgkAMGdsacT6aSJKLcGlLQ63FzqtBm1BnQiZoaZgcjfCvgZbgTlb/I4g9cnZIq/wtYjPMyT2dDsQbJl63HfphDL84e1dys8MttSX0sFWNFcDJEnC4sWLsXjxYvU3iFJK9w5AV09jYwyidJET1D7Z4fYi1xA0X4tf/tSNvM6WnWWElAaMeg30Wgkuj4C1y5X4YKvNF2xVhAm2hgwwY2rVAHy+vwUAg61ESOkyQqLeyGUlAHDSoFyccVJREreGiGKh1UjQa33ZK7kjobLGFtu+UzemnP42yPCVEQ5kZosSQJKkpM7bqmu1AwDKCnoGWwDw7aBSwjyWbKuOwRalLVNQsHX1tGG9tvsnotQT6Ejoy1a0+tu+D2AnQupGvrgmn0TGQgihlBEys0WJEpi3ldiOhF6vQH1b5DlbADBvfBlKLAZMGloILUu2Vcdgi9KWXEZo0GnwvckVJxhNRKmme0fCNpYRUgTnjBwEAFhVcwRf1ltjeqzV7obL45uWwAYZlCgWo9z+PbGZreZ2B1weAY0ElFjCB1sDcnOw9uez8NpPpyd027IVgy1KW1OripBn0OG/zzkJhSw7Iko78rwthyu0jJCfZ+rujJOKMXd8GbwCWLRqR0wd3uSsVm6OtsdcXyK1KJmtBJcR1rX5sr8lFiP02sin+bkGHXS93E/xw1eZ0tbwgbnYtmg27rhwZLI3hYj6IJDZ8pcRdvlOijlhm8L59dxTYdJr8dn+4/jntvqoH3fcP1+LJYSUSMqcrQRntuROhGUF4bNalHgMtiitaTQS52oRpanAnK3umS0GW9RTRaEJ159zEgDgjS1HAAC7GqyY/Nt38cx/von4uOZ2tn2nxLOY/GWE9sTO2eqt7TslB4MtIiJKCoPe9xXk7D5ni8EWRXDe6MEAgC0HWyCEwOtfHMHxDide/uxgxMcE1thiZosSJ3mZLV8ZYbi275QcDLaIiCgpepQRypktE0+KKbwxZRYYdBq0dLqwr7kDn+47DgD4+mgHWvxBVXfH2v1lhAy2KIGSNmeLma2Uw2CLiIiSokcZoTxni5ktiiBHp8G3hhQAAP7zVTO2H2lT7vviYEvYxxyTM1t5LCOkxAl0I1S/jNDjFXht0yE0Wu3KgsYMtlIHgy0iIkqKiN0I2SCDejF52AAAwN8+3AePN9CVcPOB8MEWywgpGRKZ2VpVcwR3/WMbfrj0MxxpYYOMVMNlo4mIKCmCywjdHi9s/onkbP1OvZk81BdsHTzeCcDX0r3D6cGmCMHWMblBBrsRUgIpc7YSEGx9vt9XTrurwabcxjlbqYOZLSIiSorgRY3bgiaRy+U3ROHIwZbsmjOGAQC2HmqFy+PtMV4uI+ScLUokpRthAsoIaw61hfxs0mvZaCiFMNgiIqKkCJ6z1eoPtvKNXGiTejco34ChRWbl5ytPH4pCsx4Otxc766w9xssNMtj6nRIpUZmtTqcbexp9Ga2B/nmJ5YVGLouTQviNRkRESSG3fne4vVxji2Jymn/eVqnFiGHFZiXb1X3elhACLZ0sI6TEU+ZsdbkghDjB6L7bUWeFxytQYjHgN/PHAgAmVBaq9vsodgy2iIgoKYLnbLX5OxGy7TtF45yRAwEA544eDEmSlOCre7Bltbvh8vhOdFlGSIkkZ7a8AuhwelT7PTUHWwEAE4YU4pLxZXj/5zPx+wXjVft9FDsWxhMRUVIEdyNkZotisWBiBUotJqUNvBxsbTpwHEIIpYRKLiHMM+hg1GuTs7GUlYx6DfRaCS6PgLXLhTyDOqfcNYdbAQAThxYCAEYMylPl91DfMbNFRERJETJnyx9sFbDtO0VBkiRMH1GMXP8J7IQhhdBqJDRaHahrsyvjjtr887VYQkgJJklSQuZtbT3UCgCYOKRQtd9B/cNgi4iIkiK4jFBukMHMFvWFKUeLseUWAMAmfxtsADjS6ltziG2wKRny/J1VOxzqdCRsbnfgcEsXJAkY58/yUuphsEVEREkhB1tOtxdtnZyzRf0jlxJ+ETRvS17glcEWJYNcOiivIRhv2/wlhCMG5SlZNEo9DLaIiCgpDP45NHaXl5kt6jelScbBQLB12B9sDRlgDvsYIjXJwVa7SpkteX2tCSwhTGkMtoiIKCkG+deEOXi8g3O2qN/kYOvLeptStnW4tRMAMGQAM1uUePn+MsJ2lTJbynytSpYQpjIGW0RElBRy96yvmtpxqMV3UlxoZhkh9U1ZgQkVhSZ4vEI5CZUzWxUMtigJclXMbAkhsNVfRsh1tVIbgy0iIkqKgXkGDC0yQwjgm6MdAFhGSP0zOWi9La9XoK5VLiNksEWJp+acrQPHOtHa6UKOVoPRpZa4Pz/FD4MtIiJKmkn+7JaskGWE1A+n+d9Pmw+2oMnmgMsjoNVIKLUYk7thlJXkboRqZLbkrNaYcouyZiGlJu4dIiJKmkndyl9YRkj9MaWqCICvI+HB477S1FKLETotT3co8fIN6s3ZqlHmaxXG/bkpvnj0ISKipJk0dEDIz2yQQf0xujQfJr0WVrsbG/Y0AWAJISWPmt0I5XmJE9gcI+Ux2CIioqQ5tcyirLeVm6NlOQz1i06rUa70r6qpA8C275Q8ef61r2xxDrZcHi+211kBABMrB5xgNCUbv9WIiChpcnQajK/wXZllCSHFg9wCPrDGFjNblBxKZsvuiuvz7m6wwen2wmLUoaqYFxNSHYMtIiJKKrlJBksIKR5Oqwq90s+275Qs+So1yKhRSggLIUlSXJ+b4o/BFhERJdWMEQMBAJVFPCmm/pvcrayKmS1KljyVGmRsZXOMtKJL9gYQEVF2mzVqEJZWT8H4isJkbwplgAKzHqcMzsNXTe0AgErO2aIkkVu/92XOlsfrW7YgHGUx4yGFfd00SiBmtoiIKKkkScJ5o0swKN+Q7E2hDDHFX0qokYDSAq6xRckht37vcLghhIj6cc9/sh/jFv0bn3x9rMd9NrtLuZDwLXYiTAsMtoiIiCijTPYvKVBqMULPNbYoSeTMllcAXS5P1I97o6YOXS4PPvm6ucd9tUfaIARQUWjC4HxeSEgHPAIRERFRRpk9thTThheh+syqZG8KZTGTXgu5EjDaeVsujxfbj7QBAJpsjh73bz3ku4/ra6UPztkiIiKijFJg0uPvP52e7M2gLCdJEvIMOljtbtgcbgyO4jG7G2xwuL0AIgVbrQA4XyudMLNFRERERKSCfP/CxtFmtuS27gDQaLX3uF9pjsFOhGmDwRYRERERkQqU9u9RdiQMDra6Z7YarXbUt9mhkaAsBk+pj8EWEREREZEKlPbvUWa2tgYFW83tDrg93h73jSzJR66BM4HSBYMtIiIiIiIV5MaQ2bLZXdh71NfWXZIAIYBjHU7lfq6vlZ4YbBERERERqUBea6vd7jrh2NrDwW3dfesOBs/bkksMOV8rvTDYIiIiIiJSQSxztrb4g6mJQwtRYvGtodVk9c3b8noFtrHte1piwScRERERkQqUOVsnCLYcbg9W19QBACZVFsLhXwS50ebLbH3T3AGbww2jXoORJfkqbjHFGzNbREREREQqUDJbJ2iQ8ej7X2F3ow1FuTlYMKkCg7tltuTmGOPKC6DX8vQ9nTCzRURERESkgnxj5DJCj1fgwLEObDrQgj+v/xoA8LsF4zAwz6DM2WryZ7a4vlb6YrBFRERERKSC4MxWfVsX1tQ2YHeDFbsabNjTaIPdFWjtPn9iOS4ZXwYAPeZsbWVzjLTFYIuIiIiISAXBc7auf24Tth+xhtxv0PnmYE2pGoA7Lhyp3K50I7TZ4XB7sLPe97iJbPuedhhsERERERGpQM5sHTjWgUZ/lurW807G6DILRpfmY1hxLrQaqcfjgjNbX9bb4PIIFOXmoLLIlLiNp7hgsEVEREREpAJ5zpYcaJ1aZsEds0ed8HFyZqu53YHNB1oAABOGFECSegZmlNrYzoSIiIiISAV5Bn3Iz9NPKo7qccV5BmgkwCuA5R/vAwBMHV4U9+0j9THYIiIiIiJSgTxnSzZ9RHTBllYjYWCeL7t16HgXjHoNrpg6NO7bR+pjsEVEREREpAJ5zhYAaCTg9BiyU4MtBuX/V0wdiqLcnLhuGyUGgy0iIiIiIhUEB1tjywtQYNL3MjpUSb6vSYZOI+H6c06K+7ZRYjDYIiIiIiJSgVYjwZyjBRB9CaGsssgMAPj2xHJUFLILYbpiN0IiIiIiIpVYjHp0Oj1RN8eQ/XTmSSjOzcG106vU2TBKCAZbREREREQquf2CU1BzqBVnnTIwpseVFZhwy/mnqLRVlCgMtoiIiIiIVHLF6UNxxensJJitOGeLiIiIiIhIBQy2iIiIiIiIVMBgi4iIiIiISAUMtoiIiIiIiFTAYIuIiIiIiEgFDLaIiIiIiIhUwGCLiIiIiIhIBQy2iIiIiIiIVMBgi4iIiIiISAUMtoiIiIiIiFTAYIuIiIiIiEgFDLaIiIiIiIhUwGCLiIiIiIhIBQy2iIiIiIiIVMBgi4iIiIiISAUMtoiIiIiIiFTAYIuIiIiIiEgFDLaIiIiIiIhUoEv2BqQCIQQAwGq1JnlLiIiIiIgomeSYQI4R+oPBFgCbzQYAqKysTPKWEBERERFRKrDZbCgoKOjXc0giHiFbmvN6vairq0N+fj4kSQq5z2q1orKyEocOHYLFYknSFlKicH9nH+7z7MT9nn24z7ML93f2UGNfCyFgs9lQXl4OjaZ/s66Y2QKg0WgwZMiQXsdYLBZ+WLMI93f24T7PTtzv2Yf7PLtwf2ePeO/r/ma0ZGyQQUREREREpAIGW0RERERERCpgsHUCBoMBixYtgsFgSPamUAJwf2cf7vPsxP2efbjPswv3d/ZI9X3NBhlEREREREQqYGaLiIiIiIhIBQy2iIiIiIiIVMBgi4iIiIiISAUMtoiIiIiIiFSQdsHWkiVLMHXqVOTn52Pw4MFYsGABdu/eHTJGCIHFixejvLwcJpMJs2bNwo4dO5T7jx8/jltuuQWjRo2C2WzG0KFDceutt6KtrS3keb797W9j6NChMBqNKCsrww9+8APU1dWdcBtra2sxc+ZMmEwmVFRU4De/+Q2C+5B8+OGHOPPMM1FcXAyTyYTRo0fj4Ycf7ucrk7kyYZ+vX78ekiT1+Ldr165+vjqZKRP2eXV1ddh9Pnbs2H6+OpkrE/Y7ADzxxBM49dRTYTKZMGrUKDz33HP9eFUyW6rvc7vdjurqaowfPx46nQ4LFizoMaa+vh5XXXUVRo0aBY1Gg9tvv73Pr0emS+T+ljkcDkycOBGSJKGmpuaE23iizzj3d3QyYV/H7XxdpJk5c+aIZcuWie3bt4uamhoxd+5cMXToUNHe3q6Mue+++0R+fr5YsWKFqK2tFZdffrkoKysTVqtVCCFEbW2t+O53vytWr14t9u7dK95//31xyimniO9973shv+uhhx4Sn3zyidi/f7/46KOPxPTp08X06dN73b62tjZRUlIirrjiClFbWytWrFgh8vPzxQMPPKCM+eKLL8RLL70ktm/fLvbt2yeef/55YTabxV/+8pc4vlKZIxP2+bp16wQAsXv3blFfX6/8c7vdcXylMkcm7PPW1taQfX3o0CFRVFQkFi1aFL8XKsNkwn5/8sknRX5+vnjllVfE119/LV5++WWRl5cnVq9eHcdXKnOk+j5vb28XCxcuFE8//bSYM2eOmD9/fo8x+/btE7feeqt49tlnxcSJE8Vtt93W79clUyVyf8tuvfVWcfHFFwsAYsuWLb1uXzSfce7v6GTCvo7X+XraBVvdNTU1CQBiw4YNQgghvF6vKC0tFffdd58yxm63i4KCAvHUU09FfJ5XX31V5OTkCJfLFXHMqlWrhCRJwul0Rhzz5JNPioKCAmG325XblixZIsrLy4XX6434uO985zvimmuuiXg/BaTjPpeDrZaWlmj/TAqSjvu8u9dff11IkiT2798f8XkpVDru9+nTp4s777wz5HG33XabOPPMM3v/Y0kIkXr7PNgPf/jDsMFWsJkzZ/LkOwZq7+81a9aI0aNHix07dkR1Ah7rsZ37O3rpvq9lfTlfT7sywu7kVGJRUREAYN++fWhoaMDs2bOVMQaDATNnzsTHH3/c6/NYLBbodLqw9x8/fhwvvvgiZsyYAb1eH/F5PvnkE8ycOTNkYbU5c+agrq4O+/fvD/uYLVu24OOPP8bMmTMjPi8FpPM+nzRpEsrKynD++edj3bp1J/xbySed97nsb3/7Gy644AIMGzYs4vNSqHTc7w6HA0ajMeRxJpMJn332GVwuV+9/MKXcPid1qbm/Gxsbcf311+P555+H2WyOanv6cmyn6GTCvu7r+XpaB1tCCNxxxx0466yzMG7cOABAQ0MDAKCkpCRkbElJiXJfd8eOHcNvf/tb/PSnP+1x3y9+8Qvk5uaiuLgYBw8exKpVq3rdpoaGhrC/O3jbZEOGDIHBYMCUKVNw00034Sc/+Umvz03pu8/Lysrw9NNPY8WKFVi5ciVGjRqF888/Hx988EEUf3V2S9d9Hqy+vh5vvfUWP+MxSNf9PmfOHDzzzDPYvHkzhBDYtGkTli5dCpfLhebm5ij+8uyVivuc1KPm/hZCoLq6GgsXLsSUKVOi3qZYj+0UnXTf1/09X0/rYOvmm2/Gtm3b8PLLL/e4T5KkkJ+FED1uAwCr1Yq5c+dizJgxWLRoUY/777rrLmzZsgXvvPMOtFotrr32WmXy3NixY5GXl4e8vDxcfPHFvf7ucLf/5z//waZNm/DUU0/hkUceCft3UKh03eejRo3C9ddfj8mTJ2P69Ol48sknMXfuXDzwwAMxvgLZJ133ebDly5ejsLAw7OR6Ci9d9/v//M//4OKLL8YZZ5wBvV6P+fPno7q6GgCg1WpjeAWyT6ruc1KHmvv7scceg9Vqxa9+9auIvz8ex3aKTrrv6/6er4fPr6eBW265BatXr8YHH3yAIUOGKLeXlpYC8EWlZWVlyu1NTU09IlibzYaLLroIeXl5eP3118OWEgwcOBADBw7EyJEjceqpp6KyshKffvoppk+fjjVr1ihlISaTSfn93SPipqYmAD2j9+HDhwMAxo8fj8bGRixevBhXXnlln16PbJAJ+zzYGWecgRdeeCGWlyDrZMI+F0Jg6dKl+MEPfoCcnJy+vhRZJZ33u8lkwtKlS/GXv/wFjY2NSlY7Pz8fAwcO7O9Lk7FSdZ+TOtTe32vXrsWnn34aUiIGAFOmTMHVV1+NZ599Nq7f5xRZJuzr/p6vp11mSwiBm2++GStXrsTatWuVF0A2fPhwlJaW4t1331Vuczqd2LBhA2bMmKHcZrVaMXv2bOTk5GD16tU9auwj/W7AV5MPAMOGDcPJJ5+Mk08+GRUVFQCA6dOn44MPPoDT6VQe984776C8vBxVVVW9Prf8vBQqU/f5li1bQg4wFJBJ+3zDhg3Yu3cvfvzjH8f2ImShTNrver0eQ4YMgVarxSuvvIJ58+ZBo0m7r1zVpfo+p/hK1P5+9NFHsXXrVtTU1KCmpgZr1qwBAPz973/H73//ewDx/T6nnjJ1X/fpfD2mdhop4IYbbhAFBQVi/fr1IW2VOzs7lTH33XefKCgoECtXrhS1tbXiyiuvDGklabVaxbRp08T48ePF3r17w7bi3rhxo3jsscfEli1bxP79+8XatWvFWWedJUaMGBHSuaS71tZWUVJSIq688kpRW1srVq5cKSwWS0gryccff1ysXr1a7NmzR+zZs0csXbpUWCwWcc8996j0qqW3TNjnDz/8sHj99dfFnj17xPbt28Uvf/lLAUCsWLFCpVctvWXCPpddc801Ytq0aXF+hTJTJuz33bt3i+eff17s2bNHbNy4UVx++eWiqKhI7Nu3T50XLc2l+j4XQogdO3aILVu2iEsvvVTMmjVLbNmypUenM/m20047TVx11VViy5YtYseOHfF9sTJAovZ3d/v27YuqQ120x3bu7xPLhH0dr/P1tAu2AIT9t2zZMmWM1+sVixYtEqWlpcJgMIhzzjlH1NbWKvfLbbjD/ZO/ELdt2ybOPfdcUVRUJAwGg6iqqhILFy4Uhw8fPuE2btu2TZx99tnCYDCI0tJSsXjx4pA2ko8++qgYO3asMJvNwmKxiEmTJoknn3xSeDyeuL1OmSQT9vkf/vAHMWLECGE0GsWAAQPEWWedJd588824vUaZJhP2uRC+g7nJZBJPP/10XF6XTJcJ+33nzp1i4sSJwmQyCYvFIubPny927doVt9co06TDPh82bFjY5z7R3zFs2LB4vEQZJVH7u7toT8CFiO7Yzv19Ypmwr+N1vi75XxAiIiIiIiKKIxaQExERERERqYDBFhERERERkQoYbBEREREREamAwRYREREREZEKGGwRERERERGpgMEWERERERGRChhsERERERERqYDBFhERERERkQoYbBEREREREamAwRYREWWU6upqSJIESZKg1+tRUlKCCy+8EEuXLoXX6436eZYvX47CwkL1NpSIiDIegy0iIso4F110Eerr67F//3689dZbOPfcc3Hbbbdh3rx5cLvdyd48IiLKEgy2iIgo4xgMBpSWlqKiogKTJ0/Gr3/9a6xatQpvvfUWli9fDgB46KGHMH78eOTm5qKyshI33ngj2tvbAQDr16/Hddddh7a2NiVLtnjxYgCA0+nE3XffjYqKCuTm5mLatGlYv359cv5QIiJKaQy2iIgoK5x33nmYMGECVq5cCQDQaDR49NFHsX37djz77LNYu3Yt7r77bgDAjBkz8Mgjj8BisaC+vh719fW48847AQDXXXcdPvroI7zyyivYtm0bLrvsMlx00UX46quvkva3ERFRapKEECLZG0FERBQv1dXVaG1txRtvvNHjviuuuALbtm3Dzp07e9z32muv4YYbbkBzczMA35yt22+/Ha2trcqYr7/+GqeccgoOHz6M8vJy5fYLLrgAp59+Ou699964/z1ERJS+dMneACIiokQRQkCSJADAunXrcO+992Lnzp2wWq1wu92w2+3o6OhAbm5u2Md/8cUXEEJg5MiRIbc7HA4UFxervv1ERJReGGwREVHW+PLLLzF8+HAcOHAAl1xyCRYuXIjf/va3KCoqwocffogf//jHcLlcER/v9Xqh1WqxefNmaLXakPvy8vLU3nwiIkozDLaIiCgrrF27FrW1tfjZz36GTZs2we1248EHH4RG45u+/Oqrr4aMz8nJgcfjCblt0qRJ8Hg8aGpqwtlnn52wbSciovTEYIuIiDKOw+FAQ0MDPB4PGhsb8fbbb2PJkiWYN28err32WtTW1sLtduOxxx7DpZdeio8++ghPPfVUyHNUVVWhvb0d77//PiZMmACz2YyRI0fi6quvxrXXXosHH3wQkyZNQnNzM9auXYvx48fjkksuSdJfTEREqYjdCImIKOO8/fbbKCsrQ1VVFS666CKsW7cOjz76KFatWgWtVouJEyfioYcewh/+8AeMGzcOL774IpYsWRLyHDNmzMDChQtx+eWXY9CgQbj//vsBAMuWLcO1116Ln//85xg1ahS+/e1vY+PGjaisrEzGn0pERCmM3QiJiIiIiIhUwMwWERERERGRChhsERERERERqYDBFhERERERkQoYbBEREREREamAwRYREREREZEKGGwRERERERGpgMEWERERERGRChhsERERERERqYDBFhERERERkQoYbBEREREREamAwRYREREREZEK/j9u31slIGHazgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(data.index, data['RSI'], label='RSI')\n",
    "plt.title('Relative Strength Index (RSI) Over Time')\n",
    "plt.xlabel('Date')\n",
    "plt.axhline(70, linestyle='--', color='red')  # Overbought line\n",
    "plt.axhline(30, linestyle='--', color='green')  # Oversold line\n",
    "plt.ylabel('RSI')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MACD (Moving Average Convergence Divergence)\n",
    "\n",
    "- MACD is a trend-following momentum indicator that shows the relationship between two moving averages of a security's price. It consists of the MACD line (difference between two EMAs), a signal line (EMA of the MACD line), and a histogram (difference between MACD and its signal line).\n",
    "- Usage in analysis: MACD is used to identify changes in the strength, direction, momentum, and duration of a trend. A bullish crossover occurs when the MACD crosses above its signal line, suggesting a buy signal, while a bearish crossover (MACD falls below the signal line) suggests a sell signal. The divergence between the MACD and price action can also indicate potential market reversals.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(data.index, data['MACD'], label='MACD')\n",
    "plt.title('Moving Average Convergence Divergence (MACD)')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('MACD Value')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 251 entries, 2023-02-24 to 2024-02-23\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Open         251 non-null    float64\n",
      " 1   High         251 non-null    float64\n",
      " 2   Low          251 non-null    float64\n",
      " 3   Close        251 non-null    float64\n",
      " 4   Volume       251 non-null    int64  \n",
      " 5   SP500_Close  251 non-null    float64\n",
      " 6   EMA_Short    251 non-null    float64\n",
      " 7   EMA_Long     251 non-null    float64\n",
      " 8   RSI          238 non-null    float64\n",
      " 9   MACD         251 non-null    float64\n",
      "dtypes: float64(9), int64(1)\n",
      "memory usage: 29.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 12 entries, 2025-01-22 to 2022-04-20\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   EPS Estimate  9 non-null      float64\n",
      " 1   Reported EPS  8 non-null      float64\n",
      " 2   Surprise(%)   8 non-null      float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 384.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "earnings.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into features and labels\n",
    "features = data.drop(columns=['Close'])\n",
    "target = data[['Close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling\n",
    "feature_scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "features= features.bfill()\n",
    "scaled_features = feature_scaler.fit_transform(features)\n",
    "\n",
    "target_scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_target = target_scaler.fit_transform(target)\n",
    "\n",
    "\n",
    "# Convert scaled data back to DataFrame for further processing\n",
    "scaled_features_df = pd.DataFrame(scaled_features, index=data.index, columns=features.columns)\n",
    "scaled_target_df = pd.DataFrame(scaled_target, index=data.index, columns=target.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge earnings data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hd/pv9sg75d6b38kvxtq9c67bw40000gn/T/ipykernel_79512/2796264541.py:3: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  scaled_features_df = scaled_features_df.fillna(method='ffill').fillna(method='bfill')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SP500_Close</th>\n",
       "      <th>EMA_Short</th>\n",
       "      <th>EMA_Long</th>\n",
       "      <th>RSI</th>\n",
       "      <th>MACD</th>\n",
       "      <th>EPS Estimate</th>\n",
       "      <th>Reported EPS</th>\n",
       "      <th>Surprise(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-02-24</th>\n",
       "      <td>0.304672</td>\n",
       "      <td>0.267920</td>\n",
       "      <td>0.294787</td>\n",
       "      <td>0.525683</td>\n",
       "      <td>0.092682</td>\n",
       "      <td>0.282480</td>\n",
       "      <td>0.264176</td>\n",
       "      <td>0.350206</td>\n",
       "      <td>0.386067</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-27</th>\n",
       "      <td>0.344421</td>\n",
       "      <td>0.352568</td>\n",
       "      <td>0.356471</td>\n",
       "      <td>0.653861</td>\n",
       "      <td>0.102576</td>\n",
       "      <td>0.299116</td>\n",
       "      <td>0.269681</td>\n",
       "      <td>0.350206</td>\n",
       "      <td>0.409591</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-28</th>\n",
       "      <td>0.404114</td>\n",
       "      <td>0.365608</td>\n",
       "      <td>0.374626</td>\n",
       "      <td>0.600113</td>\n",
       "      <td>0.092771</td>\n",
       "      <td>0.309757</td>\n",
       "      <td>0.273986</td>\n",
       "      <td>0.350206</td>\n",
       "      <td>0.423552</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-01</th>\n",
       "      <td>0.373570</td>\n",
       "      <td>0.336575</td>\n",
       "      <td>0.336493</td>\n",
       "      <td>0.625393</td>\n",
       "      <td>0.077556</td>\n",
       "      <td>0.313912</td>\n",
       "      <td>0.276618</td>\n",
       "      <td>0.350206</td>\n",
       "      <td>0.427630</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-02</th>\n",
       "      <td>0.237796</td>\n",
       "      <td>0.239680</td>\n",
       "      <td>0.245279</td>\n",
       "      <td>0.793441</td>\n",
       "      <td>0.101854</td>\n",
       "      <td>0.298943</td>\n",
       "      <td>0.273068</td>\n",
       "      <td>0.350206</td>\n",
       "      <td>0.404375</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-16</th>\n",
       "      <td>0.344630</td>\n",
       "      <td>0.307543</td>\n",
       "      <td>0.328327</td>\n",
       "      <td>0.313955</td>\n",
       "      <td>0.932500</td>\n",
       "      <td>0.247752</td>\n",
       "      <td>0.456542</td>\n",
       "      <td>0.568797</td>\n",
       "      <td>0.186607</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.71</td>\n",
       "      <td>-0.0439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-20</th>\n",
       "      <td>0.303278</td>\n",
       "      <td>0.274620</td>\n",
       "      <td>0.268028</td>\n",
       "      <td>0.268767</td>\n",
       "      <td>0.908121</td>\n",
       "      <td>0.249238</td>\n",
       "      <td>0.447401</td>\n",
       "      <td>0.495796</td>\n",
       "      <td>0.202909</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.71</td>\n",
       "      <td>-0.0439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-21</th>\n",
       "      <td>0.283961</td>\n",
       "      <td>0.280671</td>\n",
       "      <td>0.288589</td>\n",
       "      <td>0.263982</td>\n",
       "      <td>0.913223</td>\n",
       "      <td>0.252017</td>\n",
       "      <td>0.439135</td>\n",
       "      <td>0.549739</td>\n",
       "      <td>0.219979</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.71</td>\n",
       "      <td>-0.0439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-22</th>\n",
       "      <td>0.288424</td>\n",
       "      <td>0.272603</td>\n",
       "      <td>0.284287</td>\n",
       "      <td>0.188273</td>\n",
       "      <td>0.998565</td>\n",
       "      <td>0.258376</td>\n",
       "      <td>0.432545</td>\n",
       "      <td>0.558761</td>\n",
       "      <td>0.241023</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.71</td>\n",
       "      <td>-0.0439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-23</th>\n",
       "      <td>0.297559</td>\n",
       "      <td>0.267200</td>\n",
       "      <td>0.285308</td>\n",
       "      <td>0.092350</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.255160</td>\n",
       "      <td>0.423428</td>\n",
       "      <td>0.512103</td>\n",
       "      <td>0.247258</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.71</td>\n",
       "      <td>-0.0439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open      High       Low    Volume  SP500_Close  EMA_Short  \\\n",
       "2023-02-24  0.304672  0.267920  0.294787  0.525683     0.092682   0.282480   \n",
       "2023-02-27  0.344421  0.352568  0.356471  0.653861     0.102576   0.299116   \n",
       "2023-02-28  0.404114  0.365608  0.374626  0.600113     0.092771   0.309757   \n",
       "2023-03-01  0.373570  0.336575  0.336493  0.625393     0.077556   0.313912   \n",
       "2023-03-02  0.237796  0.239680  0.245279  0.793441     0.101854   0.298943   \n",
       "...              ...       ...       ...       ...          ...        ...   \n",
       "2024-02-16  0.344630  0.307543  0.328327  0.313955     0.932500   0.247752   \n",
       "2024-02-20  0.303278  0.274620  0.268028  0.268767     0.908121   0.249238   \n",
       "2024-02-21  0.283961  0.280671  0.288589  0.263982     0.913223   0.252017   \n",
       "2024-02-22  0.288424  0.272603  0.284287  0.188273     0.998565   0.258376   \n",
       "2024-02-23  0.297559  0.267200  0.285308  0.092350     1.000000   0.255160   \n",
       "\n",
       "            EMA_Long       RSI      MACD  EPS Estimate  Reported EPS  \\\n",
       "2023-02-24  0.264176  0.350206  0.386067          0.85          0.85   \n",
       "2023-02-27  0.269681  0.350206  0.409591          0.85          0.85   \n",
       "2023-02-28  0.273986  0.350206  0.423552          0.85          0.85   \n",
       "2023-03-01  0.276618  0.350206  0.427630          0.85          0.85   \n",
       "2023-03-02  0.273068  0.350206  0.404375          0.85          0.85   \n",
       "...              ...       ...       ...           ...           ...   \n",
       "2024-02-16  0.456542  0.568797  0.186607          0.74          0.71   \n",
       "2024-02-20  0.447401  0.495796  0.202909          0.74          0.71   \n",
       "2024-02-21  0.439135  0.549739  0.219979          0.74          0.71   \n",
       "2024-02-22  0.432545  0.558761  0.241023          0.74          0.71   \n",
       "2024-02-23  0.423428  0.512103  0.247258          0.74          0.71   \n",
       "\n",
       "            Surprise(%)  \n",
       "2023-02-24      -0.0004  \n",
       "2023-02-27      -0.0004  \n",
       "2023-02-28      -0.0004  \n",
       "2023-03-01      -0.0004  \n",
       "2023-03-02      -0.0004  \n",
       "...                 ...  \n",
       "2024-02-16      -0.0439  \n",
       "2024-02-20      -0.0439  \n",
       "2024-02-21      -0.0439  \n",
       "2024-02-22      -0.0439  \n",
       "2024-02-23      -0.0439  \n",
       "\n",
       "[251 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge earnings data\n",
    "scaled_features_df = scaled_features_df.merge(earnings, how='left', left_index=True, right_index=True)\n",
    "scaled_features_df = scaled_features_df.fillna(method='ffill').fillna(method='bfill')\n",
    "\n",
    "# Display the merged dataframe\n",
    "scaled_features_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 251 entries, 2023-02-24 to 2024-02-23\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Open          251 non-null    float64\n",
      " 1   High          251 non-null    float64\n",
      " 2   Low           251 non-null    float64\n",
      " 3   Volume        251 non-null    float64\n",
      " 4   SP500_Close   251 non-null    float64\n",
      " 5   EMA_Short     251 non-null    float64\n",
      " 6   EMA_Long      251 non-null    float64\n",
      " 7   RSI           251 non-null    float64\n",
      " 8   MACD          251 non-null    float64\n",
      " 9   EPS Estimate  251 non-null    float64\n",
      " 10  Reported EPS  251 non-null    float64\n",
      " 11  Surprise(%)   251 non-null    float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 33.6+ KB\n"
     ]
    }
   ],
   "source": [
    "scaled_features_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training 201 201\n",
      "testing 50 50\n"
     ]
    }
   ],
   "source": [
    "test_size = 50 # Number of samples to be used for testing\n",
    "\n",
    "training_features = scaled_features_df[:-test_size]\n",
    "training_target = scaled_target_df[:-test_size] \n",
    "\n",
    "test_features = scaled_features_df[-test_size:]\n",
    "test_target = scaled_target_df[-test_size:]\n",
    "\n",
    "print('training', len(training_features), len(training_target))\n",
    "print('testing', len(test_features), len(test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model parameters\n",
    "\n",
    "num_layers = 1\n",
    "size_layer = 32\n",
    "epochs = 50\n",
    "dropout_rate = 0.2\n",
    "dense_nums = 32\n",
    "reg_rate = 0.001\n",
    "batch_size = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TimeSeriesSplit for cross-validation\n",
    "tscv = TimeSeriesSplit(n_splits=5)\n",
    "\n",
    "# LSTM model with l1_l2 Regularization and Dropout\n",
    "def build_model(input_shape, num_layers=2, size_layer=128, dropout_rate=0.2, dense_nums=64, reg_rate=0.01):\n",
    "    model = Sequential()\n",
    "    reg = l1_l2(l1=reg_rate, l2=reg_rate)   # Add Bidirectional LSTM layer with L1/L2 regularization\n",
    "\n",
    "    for i in range(num_layers):\n",
    "\n",
    "        if i == 0:  # If it's the first layer, specify the input shape\n",
    "            model.add(LSTM(size_layer, return_sequences=(num_layers > 1), kernel_regularizer=reg, input_shape=input_shape))\n",
    "        else:  # Subsequent layers will automatically infer the input shape\n",
    "            model.add(LSTM(size_layer, return_sequences=(i != num_layers - 1), kernel_regularizer=reg))\n",
    "    \n",
    "        # Add dropout after each LSTM layer\n",
    "        model.add(Dropout(dropout_rate))\n",
    "\n",
    "        # Optionally, add a Dense layer with ReLU activation after each LSTM layer (except the last one)\n",
    "        if i < num_layers - 1:\n",
    "            model.add(Dense(dense_nums, activation='relu'))\n",
    "    \n",
    "    model.add(Dense(1, activation='linear'))\n",
    "    model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n",
    "    \n",
    "    return model\n",
    "\n",
    "# Setup early stopping to avoid overfitting\n",
    "early_stopping = EarlyStopping(\n",
    "    monitor='val_loss', patience=10, restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "8/8 [==============================] - 3s 102ms/step - loss: 0.2262 - mae: 0.2046 - val_loss: 0.1874 - val_mae: 0.0849\n",
      "Epoch 2/50\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 0.1913 - mae: 0.1285 - val_loss: 0.1727 - val_mae: 0.0783\n",
      "Epoch 3/50\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 0.1676 - mae: 0.0686 - val_loss: 0.1669 - val_mae: 0.1013\n",
      "Epoch 4/50\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 0.1545 - mae: 0.0590 - val_loss: 0.1606 - val_mae: 0.1173\n",
      "Epoch 5/50\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.1434 - mae: 0.0551 - val_loss: 0.1502 - val_mae: 0.1170\n",
      "Epoch 6/50\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.1334 - mae: 0.0519 - val_loss: 0.1389 - val_mae: 0.1114\n",
      "Epoch 7/50\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.1244 - mae: 0.0591 - val_loss: 0.1284 - val_mae: 0.1072\n",
      "Epoch 8/50\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.1142 - mae: 0.0550 - val_loss: 0.1177 - val_mae: 0.1011\n",
      "Epoch 9/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.1066 - mae: 0.0544 - val_loss: 0.1091 - val_mae: 0.1008\n",
      "Epoch 10/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0983 - mae: 0.0569 - val_loss: 0.1014 - val_mae: 0.1020\n",
      "Epoch 11/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0909 - mae: 0.0580 - val_loss: 0.0934 - val_mae: 0.1006\n",
      "Epoch 12/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0821 - mae: 0.0480 - val_loss: 0.0860 - val_mae: 0.0995\n",
      "Epoch 13/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0753 - mae: 0.0540 - val_loss: 0.0792 - val_mae: 0.0986\n",
      "Epoch 14/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0681 - mae: 0.0509 - val_loss: 0.0735 - val_mae: 0.1004\n",
      "Epoch 15/50\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 0.0630 - mae: 0.0510 - val_loss: 0.0677 - val_mae: 0.0997\n",
      "Epoch 16/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0574 - mae: 0.0535 - val_loss: 0.0617 - val_mae: 0.0965\n",
      "Epoch 17/50\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 0.0518 - mae: 0.0492 - val_loss: 0.0565 - val_mae: 0.0948\n",
      "Epoch 18/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0478 - mae: 0.0548 - val_loss: 0.0530 - val_mae: 0.0995\n",
      "Epoch 19/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0433 - mae: 0.0519 - val_loss: 0.0500 - val_mae: 0.1037\n",
      "Epoch 20/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0389 - mae: 0.0522 - val_loss: 0.0467 - val_mae: 0.1053\n",
      "Epoch 21/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0346 - mae: 0.0471 - val_loss: 0.0416 - val_mae: 0.0982\n",
      "Epoch 22/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0315 - mae: 0.0484 - val_loss: 0.0388 - val_mae: 0.0988\n",
      "Epoch 23/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0300 - mae: 0.0536 - val_loss: 0.0370 - val_mae: 0.1020\n",
      "Epoch 24/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0269 - mae: 0.0490 - val_loss: 0.0347 - val_mae: 0.1013\n",
      "Epoch 25/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0245 - mae: 0.0447 - val_loss: 0.0322 - val_mae: 0.0989\n",
      "Epoch 26/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0221 - mae: 0.0421 - val_loss: 0.0296 - val_mae: 0.0943\n",
      "Epoch 27/50\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.0210 - mae: 0.0427 - val_loss: 0.0292 - val_mae: 0.0980\n",
      "Epoch 28/50\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 0.0208 - mae: 0.0503 - val_loss: 0.0282 - val_mae: 0.0975\n",
      "Epoch 29/50\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 0.0183 - mae: 0.0411 - val_loss: 0.0296 - val_mae: 0.1066\n",
      "Epoch 30/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.0198 - mae: 0.0543 - val_loss: 0.0288 - val_mae: 0.1066\n",
      "Epoch 31/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0167 - mae: 0.0407 - val_loss: 0.0263 - val_mae: 0.0993\n",
      "Epoch 32/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0183 - mae: 0.0517 - val_loss: 0.0240 - val_mae: 0.0924\n",
      "Epoch 33/50\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.0175 - mae: 0.0528 - val_loss: 0.0227 - val_mae: 0.0891\n",
      "Epoch 34/50\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 0.0158 - mae: 0.0441 - val_loss: 0.0234 - val_mae: 0.0945\n",
      "Epoch 35/50\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 0.0164 - mae: 0.0471 - val_loss: 0.0243 - val_mae: 0.1005\n",
      "Epoch 36/50\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 0.0152 - mae: 0.0483 - val_loss: 0.0254 - val_mae: 0.1066\n",
      "Epoch 37/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.0147 - mae: 0.0483 - val_loss: 0.0247 - val_mae: 0.1058\n",
      "Epoch 38/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.0130 - mae: 0.0363 - val_loss: 0.0234 - val_mae: 0.1022\n",
      "Epoch 39/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0137 - mae: 0.0464 - val_loss: 0.0221 - val_mae: 0.0985\n",
      "Epoch 40/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0130 - mae: 0.0420 - val_loss: 0.0215 - val_mae: 0.0975\n",
      "Epoch 41/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.0125 - mae: 0.0427 - val_loss: 0.0205 - val_mae: 0.0949\n",
      "Epoch 42/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.0114 - mae: 0.0367 - val_loss: 0.0202 - val_mae: 0.0947\n",
      "Epoch 43/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.0119 - mae: 0.0421 - val_loss: 0.0203 - val_mae: 0.0966\n",
      "Epoch 44/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.0125 - mae: 0.0472 - val_loss: 0.0201 - val_mae: 0.0969\n",
      "Epoch 45/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.0118 - mae: 0.0476 - val_loss: 0.0202 - val_mae: 0.0986\n",
      "Epoch 46/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.0108 - mae: 0.0439 - val_loss: 0.0203 - val_mae: 0.1001\n",
      "Epoch 47/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.0106 - mae: 0.0415 - val_loss: 0.0196 - val_mae: 0.0982\n",
      "Epoch 48/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.0113 - mae: 0.0491 - val_loss: 0.0194 - val_mae: 0.0984\n",
      "Epoch 49/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.0098 - mae: 0.0408 - val_loss: 0.0191 - val_mae: 0.0982\n",
      "Epoch 50/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.0101 - mae: 0.0455 - val_loss: 0.0196 - val_mae: 0.1010\n",
      "Epoch 1/50\n",
      "14/14 [==============================] - 4s 58ms/step - loss: 0.2505 - mae: 0.2556 - val_loss: 0.6307 - val_mae: 0.6745\n",
      "Epoch 2/50\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.1803 - mae: 0.1304 - val_loss: 0.4128 - val_mae: 0.5053\n",
      "Epoch 3/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.1476 - mae: 0.0762 - val_loss: 0.3006 - val_mae: 0.4005\n",
      "Epoch 4/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.1292 - mae: 0.0633 - val_loss: 0.2601 - val_mae: 0.3689\n",
      "Epoch 5/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.1156 - mae: 0.0703 - val_loss: 0.2539 - val_mae: 0.3804\n",
      "Epoch 6/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.1005 - mae: 0.0666 - val_loss: 0.2330 - val_mae: 0.3705\n",
      "Epoch 7/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0878 - mae: 0.0637 - val_loss: 0.2273 - val_mae: 0.3790\n",
      "Epoch 8/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0757 - mae: 0.0599 - val_loss: 0.2103 - val_mae: 0.3710\n",
      "Epoch 9/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0659 - mae: 0.0633 - val_loss: 0.1944 - val_mae: 0.3622\n",
      "Epoch 10/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0568 - mae: 0.0612 - val_loss: 0.1790 - val_mae: 0.3525\n",
      "Epoch 11/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0489 - mae: 0.0567 - val_loss: 0.1679 - val_mae: 0.3466\n",
      "Epoch 12/50\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0422 - mae: 0.0564 - val_loss: 0.1604 - val_mae: 0.3443\n",
      "Epoch 13/50\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0375 - mae: 0.0580 - val_loss: 0.1591 - val_mae: 0.3492\n",
      "Epoch 14/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0325 - mae: 0.0579 - val_loss: 0.1450 - val_mae: 0.3342\n",
      "Epoch 15/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0291 - mae: 0.0541 - val_loss: 0.1353 - val_mae: 0.3237\n",
      "Epoch 16/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0273 - mae: 0.0578 - val_loss: 0.1349 - val_mae: 0.3265\n",
      "Epoch 17/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0237 - mae: 0.0480 - val_loss: 0.1348 - val_mae: 0.3289\n",
      "Epoch 18/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0225 - mae: 0.0505 - val_loss: 0.1347 - val_mae: 0.3306\n",
      "Epoch 19/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0217 - mae: 0.0538 - val_loss: 0.1314 - val_mae: 0.3274\n",
      "Epoch 20/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0203 - mae: 0.0507 - val_loss: 0.1270 - val_mae: 0.3219\n",
      "Epoch 21/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0193 - mae: 0.0519 - val_loss: 0.1214 - val_mae: 0.3145\n",
      "Epoch 22/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0178 - mae: 0.0488 - val_loss: 0.1210 - val_mae: 0.3150\n",
      "Epoch 23/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0174 - mae: 0.0526 - val_loss: 0.1248 - val_mae: 0.3218\n",
      "Epoch 24/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0164 - mae: 0.0473 - val_loss: 0.1267 - val_mae: 0.3256\n",
      "Epoch 25/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0152 - mae: 0.0455 - val_loss: 0.1200 - val_mae: 0.3155\n",
      "Epoch 26/50\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0154 - mae: 0.0486 - val_loss: 0.1280 - val_mae: 0.3286\n",
      "Epoch 27/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0152 - mae: 0.0542 - val_loss: 0.1267 - val_mae: 0.3270\n",
      "Epoch 28/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0141 - mae: 0.0476 - val_loss: 0.1325 - val_mae: 0.3361\n",
      "Epoch 29/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0137 - mae: 0.0483 - val_loss: 0.1329 - val_mae: 0.3371\n",
      "Epoch 30/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0137 - mae: 0.0531 - val_loss: 0.1273 - val_mae: 0.3290\n",
      "Epoch 31/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0131 - mae: 0.0495 - val_loss: 0.1352 - val_mae: 0.3412\n",
      "Epoch 32/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0132 - mae: 0.0551 - val_loss: 0.1277 - val_mae: 0.3302\n",
      "Epoch 33/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0122 - mae: 0.0492 - val_loss: 0.1324 - val_mae: 0.3377\n",
      "Epoch 34/50\n",
      "14/14 [==============================] - 0s 15ms/step - loss: 0.0123 - mae: 0.0497 - val_loss: 0.1315 - val_mae: 0.3366\n",
      "Epoch 35/50\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0123 - mae: 0.0523 - val_loss: 0.1327 - val_mae: 0.3387\n",
      "Epoch 1/50\n",
      "21/21 [==============================] - 3s 26ms/step - loss: 0.3270 - mae: 0.3142 - val_loss: 0.2507 - val_mae: 0.2813\n",
      "Epoch 2/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.2070 - mae: 0.1639 - val_loss: 0.1540 - val_mae: 0.0677\n",
      "Epoch 3/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.1638 - mae: 0.1346 - val_loss: 0.1327 - val_mae: 0.0445\n",
      "Epoch 4/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.1385 - mae: 0.1087 - val_loss: 0.1154 - val_mae: 0.0417\n",
      "Epoch 5/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.1212 - mae: 0.1050 - val_loss: 0.1002 - val_mae: 0.0390\n",
      "Epoch 6/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.1022 - mae: 0.0801 - val_loss: 0.0872 - val_mae: 0.0379\n",
      "Epoch 7/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0881 - mae: 0.0754 - val_loss: 0.0757 - val_mae: 0.0369\n",
      "Epoch 8/50\n",
      "21/21 [==============================] - 0s 7ms/step - loss: 0.0786 - mae: 0.0733 - val_loss: 0.0667 - val_mae: 0.0398\n",
      "Epoch 9/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0679 - mae: 0.0668 - val_loss: 0.0580 - val_mae: 0.0348\n",
      "Epoch 10/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0610 - mae: 0.0667 - val_loss: 0.0519 - val_mae: 0.0360\n",
      "Epoch 11/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0547 - mae: 0.0621 - val_loss: 0.0482 - val_mae: 0.0413\n",
      "Epoch 12/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0500 - mae: 0.0616 - val_loss: 0.0443 - val_mae: 0.0391\n",
      "Epoch 13/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0465 - mae: 0.0594 - val_loss: 0.0413 - val_mae: 0.0382\n",
      "Epoch 14/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0428 - mae: 0.0543 - val_loss: 0.0381 - val_mae: 0.0338\n",
      "Epoch 15/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0403 - mae: 0.0538 - val_loss: 0.0356 - val_mae: 0.0315\n",
      "Epoch 16/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0381 - mae: 0.0529 - val_loss: 0.0339 - val_mae: 0.0333\n",
      "Epoch 17/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0358 - mae: 0.0520 - val_loss: 0.0319 - val_mae: 0.0305\n",
      "Epoch 18/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0344 - mae: 0.0469 - val_loss: 0.0308 - val_mae: 0.0347\n",
      "Epoch 19/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0330 - mae: 0.0491 - val_loss: 0.0289 - val_mae: 0.0304\n",
      "Epoch 20/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0319 - mae: 0.0484 - val_loss: 0.0286 - val_mae: 0.0399\n",
      "Epoch 21/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0289 - mae: 0.0411 - val_loss: 0.0267 - val_mae: 0.0366\n",
      "Epoch 22/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0289 - mae: 0.0469 - val_loss: 0.0252 - val_mae: 0.0316\n",
      "Epoch 23/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0260 - mae: 0.0417 - val_loss: 0.0240 - val_mae: 0.0297\n",
      "Epoch 24/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0257 - mae: 0.0421 - val_loss: 0.0233 - val_mae: 0.0317\n",
      "Epoch 25/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0246 - mae: 0.0438 - val_loss: 0.0222 - val_mae: 0.0289\n",
      "Epoch 26/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0240 - mae: 0.0444 - val_loss: 0.0213 - val_mae: 0.0302\n",
      "Epoch 27/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0225 - mae: 0.0422 - val_loss: 0.0204 - val_mae: 0.0286\n",
      "Epoch 28/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0238 - mae: 0.0465 - val_loss: 0.0200 - val_mae: 0.0292\n",
      "Epoch 29/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0211 - mae: 0.0379 - val_loss: 0.0201 - val_mae: 0.0403\n",
      "Epoch 30/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0210 - mae: 0.0436 - val_loss: 0.0183 - val_mae: 0.0279\n",
      "Epoch 31/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0194 - mae: 0.0354 - val_loss: 0.0186 - val_mae: 0.0384\n",
      "Epoch 32/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0188 - mae: 0.0373 - val_loss: 0.0179 - val_mae: 0.0381\n",
      "Epoch 33/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0199 - mae: 0.0478 - val_loss: 0.0169 - val_mae: 0.0312\n",
      "Epoch 34/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0190 - mae: 0.0432 - val_loss: 0.0166 - val_mae: 0.0324\n",
      "Epoch 35/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0184 - mae: 0.0440 - val_loss: 0.0161 - val_mae: 0.0298\n",
      "Epoch 36/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0195 - mae: 0.0454 - val_loss: 0.0158 - val_mae: 0.0301\n",
      "Epoch 37/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0179 - mae: 0.0425 - val_loss: 0.0152 - val_mae: 0.0292\n",
      "Epoch 38/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0176 - mae: 0.0459 - val_loss: 0.0151 - val_mae: 0.0314\n",
      "Epoch 39/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0159 - mae: 0.0387 - val_loss: 0.0160 - val_mae: 0.0467\n",
      "Epoch 40/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0159 - mae: 0.0412 - val_loss: 0.0155 - val_mae: 0.0460\n",
      "Epoch 41/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0145 - mae: 0.0350 - val_loss: 0.0149 - val_mae: 0.0438\n",
      "Epoch 42/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0141 - mae: 0.0332 - val_loss: 0.0135 - val_mae: 0.0302\n",
      "Epoch 43/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0158 - mae: 0.0422 - val_loss: 0.0136 - val_mae: 0.0334\n",
      "Epoch 44/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0146 - mae: 0.0401 - val_loss: 0.0137 - val_mae: 0.0393\n",
      "Epoch 45/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0152 - mae: 0.0447 - val_loss: 0.0143 - val_mae: 0.0478\n",
      "Epoch 46/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0129 - mae: 0.0336 - val_loss: 0.0146 - val_mae: 0.0537\n",
      "Epoch 47/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0139 - mae: 0.0392 - val_loss: 0.0151 - val_mae: 0.0610\n",
      "Epoch 48/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0135 - mae: 0.0403 - val_loss: 0.0126 - val_mae: 0.0391\n",
      "Epoch 49/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0131 - mae: 0.0374 - val_loss: 0.0128 - val_mae: 0.0428\n",
      "Epoch 50/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.0130 - mae: 0.0402 - val_loss: 0.0128 - val_mae: 0.0433\n",
      "Epoch 1/50\n",
      "27/27 [==============================] - 2s 19ms/step - loss: 0.3593 - mae: 0.3552 - val_loss: 0.2684 - val_mae: 0.3141\n",
      "Epoch 2/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.1852 - mae: 0.1522 - val_loss: 0.1456 - val_mae: 0.0807\n",
      "Epoch 3/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.1457 - mae: 0.1179 - val_loss: 0.1229 - val_mae: 0.0827\n",
      "Epoch 4/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.1244 - mae: 0.1140 - val_loss: 0.1018 - val_mae: 0.0723\n",
      "Epoch 5/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.1010 - mae: 0.0975 - val_loss: 0.0853 - val_mae: 0.0719\n",
      "Epoch 6/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0835 - mae: 0.0853 - val_loss: 0.0711 - val_mae: 0.0644\n",
      "Epoch 7/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0683 - mae: 0.0727 - val_loss: 0.0609 - val_mae: 0.0662\n",
      "Epoch 8/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0614 - mae: 0.0782 - val_loss: 0.0529 - val_mae: 0.0615\n",
      "Epoch 9/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0532 - mae: 0.0681 - val_loss: 0.0471 - val_mae: 0.0542\n",
      "Epoch 10/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0484 - mae: 0.0669 - val_loss: 0.0429 - val_mae: 0.0523\n",
      "Epoch 11/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0449 - mae: 0.0657 - val_loss: 0.0393 - val_mae: 0.0498\n",
      "Epoch 12/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0412 - mae: 0.0610 - val_loss: 0.0363 - val_mae: 0.0461\n",
      "Epoch 13/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0400 - mae: 0.0644 - val_loss: 0.0340 - val_mae: 0.0443\n",
      "Epoch 14/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0353 - mae: 0.0567 - val_loss: 0.0324 - val_mae: 0.0564\n",
      "Epoch 15/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0325 - mae: 0.0566 - val_loss: 0.0296 - val_mae: 0.0453\n",
      "Epoch 16/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0326 - mae: 0.0594 - val_loss: 0.0280 - val_mae: 0.0437\n",
      "Epoch 17/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0290 - mae: 0.0531 - val_loss: 0.0266 - val_mae: 0.0464\n",
      "Epoch 18/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0290 - mae: 0.0584 - val_loss: 0.0253 - val_mae: 0.0416\n",
      "Epoch 19/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0261 - mae: 0.0527 - val_loss: 0.0239 - val_mae: 0.0445\n",
      "Epoch 20/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0263 - mae: 0.0602 - val_loss: 0.0228 - val_mae: 0.0446\n",
      "Epoch 21/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0227 - mae: 0.0422 - val_loss: 0.0219 - val_mae: 0.0465\n",
      "Epoch 22/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0236 - mae: 0.0531 - val_loss: 0.0214 - val_mae: 0.0487\n",
      "Epoch 23/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0225 - mae: 0.0481 - val_loss: 0.0201 - val_mae: 0.0420\n",
      "Epoch 24/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0217 - mae: 0.0509 - val_loss: 0.0195 - val_mae: 0.0450\n",
      "Epoch 25/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0222 - mae: 0.0574 - val_loss: 0.0187 - val_mae: 0.0396\n",
      "Epoch 26/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0198 - mae: 0.0451 - val_loss: 0.0180 - val_mae: 0.0395\n",
      "Epoch 27/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0197 - mae: 0.0488 - val_loss: 0.0176 - val_mae: 0.0396\n",
      "Epoch 28/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0196 - mae: 0.0525 - val_loss: 0.0175 - val_mae: 0.0401\n",
      "Epoch 29/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0177 - mae: 0.0427 - val_loss: 0.0178 - val_mae: 0.0552\n",
      "Epoch 30/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0183 - mae: 0.0489 - val_loss: 0.0160 - val_mae: 0.0391\n",
      "Epoch 31/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0169 - mae: 0.0456 - val_loss: 0.0156 - val_mae: 0.0414\n",
      "Epoch 32/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0176 - mae: 0.0491 - val_loss: 0.0152 - val_mae: 0.0384\n",
      "Epoch 33/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0159 - mae: 0.0439 - val_loss: 0.0148 - val_mae: 0.0389\n",
      "Epoch 34/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0171 - mae: 0.0527 - val_loss: 0.0145 - val_mae: 0.0397\n",
      "Epoch 35/50\n",
      "27/27 [==============================] - 0s 4ms/step - loss: 0.0160 - mae: 0.0474 - val_loss: 0.0145 - val_mae: 0.0379\n",
      "Epoch 36/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0164 - mae: 0.0497 - val_loss: 0.0140 - val_mae: 0.0399\n",
      "Epoch 37/50\n",
      "27/27 [==============================] - 0s 4ms/step - loss: 0.0157 - mae: 0.0481 - val_loss: 0.0136 - val_mae: 0.0396\n",
      "Epoch 38/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0149 - mae: 0.0455 - val_loss: 0.0136 - val_mae: 0.0429\n",
      "Epoch 39/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0151 - mae: 0.0487 - val_loss: 0.0134 - val_mae: 0.0444\n",
      "Epoch 40/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0147 - mae: 0.0488 - val_loss: 0.0128 - val_mae: 0.0386\n",
      "Epoch 41/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0133 - mae: 0.0421 - val_loss: 0.0125 - val_mae: 0.0402\n",
      "Epoch 42/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0132 - mae: 0.0407 - val_loss: 0.0122 - val_mae: 0.0386\n",
      "Epoch 43/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0156 - mae: 0.0513 - val_loss: 0.0122 - val_mae: 0.0361\n",
      "Epoch 44/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0154 - mae: 0.0530 - val_loss: 0.0123 - val_mae: 0.0417\n",
      "Epoch 45/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0143 - mae: 0.0474 - val_loss: 0.0120 - val_mae: 0.0364\n",
      "Epoch 46/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0134 - mae: 0.0448 - val_loss: 0.0125 - val_mae: 0.0495\n",
      "Epoch 47/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0135 - mae: 0.0470 - val_loss: 0.0116 - val_mae: 0.0367\n",
      "Epoch 48/50\n",
      "27/27 [==============================] - 0s 6ms/step - loss: 0.0131 - mae: 0.0436 - val_loss: 0.0111 - val_mae: 0.0365\n",
      "Epoch 49/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0448 - val_loss: 0.0109 - val_mae: 0.0380\n",
      "Epoch 50/50\n",
      "27/27 [==============================] - 0s 3ms/step - loss: 0.0128 - mae: 0.0491 - val_loss: 0.0109 - val_mae: 0.0403\n",
      "Epoch 1/50\n",
      "34/34 [==============================] - 2s 17ms/step - loss: 0.3111 - mae: 0.2955 - val_loss: 0.1636 - val_mae: 0.0702\n",
      "Epoch 2/50\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 0.1653 - mae: 0.1231 - val_loss: 0.1296 - val_mae: 0.0319\n",
      "Epoch 3/50\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 0.1302 - mae: 0.1019 - val_loss: 0.1033 - val_mae: 0.0344\n",
      "Epoch 4/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.1014 - mae: 0.0846 - val_loss: 0.0816 - val_mae: 0.0292\n",
      "Epoch 5/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0824 - mae: 0.0801 - val_loss: 0.0645 - val_mae: 0.0229\n",
      "Epoch 6/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0676 - mae: 0.0765 - val_loss: 0.0531 - val_mae: 0.0252\n",
      "Epoch 7/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0558 - mae: 0.0691 - val_loss: 0.0453 - val_mae: 0.0238\n",
      "Epoch 8/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0496 - mae: 0.0693 - val_loss: 0.0397 - val_mae: 0.0224\n",
      "Epoch 9/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0426 - mae: 0.0580 - val_loss: 0.0351 - val_mae: 0.0189\n",
      "Epoch 10/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0406 - mae: 0.0660 - val_loss: 0.0330 - val_mae: 0.0329\n",
      "Epoch 11/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0367 - mae: 0.0640 - val_loss: 0.0301 - val_mae: 0.0306\n",
      "Epoch 12/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0316 - mae: 0.0493 - val_loss: 0.0266 - val_mae: 0.0184\n",
      "Epoch 13/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0295 - mae: 0.0485 - val_loss: 0.0247 - val_mae: 0.0196\n",
      "Epoch 14/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0285 - mae: 0.0548 - val_loss: 0.0235 - val_mae: 0.0270\n",
      "Epoch 15/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0271 - mae: 0.0564 - val_loss: 0.0219 - val_mae: 0.0265\n",
      "Epoch 16/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0251 - mae: 0.0529 - val_loss: 0.0203 - val_mae: 0.0221\n",
      "Epoch 17/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 0.0233 - mae: 0.0503 - val_loss: 0.0200 - val_mae: 0.0322\n",
      "Epoch 18/50\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 0.0233 - mae: 0.0560 - val_loss: 0.0189 - val_mae: 0.0309\n",
      "Epoch 19/50\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 0.0209 - mae: 0.0473 - val_loss: 0.0172 - val_mae: 0.0222\n",
      "Epoch 20/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0207 - mae: 0.0528 - val_loss: 0.0172 - val_mae: 0.0322\n",
      "Epoch 21/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0200 - mae: 0.0487 - val_loss: 0.0156 - val_mae: 0.0228\n",
      "Epoch 22/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0188 - mae: 0.0482 - val_loss: 0.0153 - val_mae: 0.0273\n",
      "Epoch 23/50\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 0.0181 - mae: 0.0508 - val_loss: 0.0144 - val_mae: 0.0240\n",
      "Epoch 24/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 0.0183 - mae: 0.0514 - val_loss: 0.0140 - val_mae: 0.0260\n",
      "Epoch 25/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 0.0160 - mae: 0.0453 - val_loss: 0.0130 - val_mae: 0.0191\n",
      "Epoch 26/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 0.0160 - mae: 0.0465 - val_loss: 0.0125 - val_mae: 0.0194\n",
      "Epoch 27/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 0.0166 - mae: 0.0525 - val_loss: 0.0126 - val_mae: 0.0245\n",
      "Epoch 28/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0163 - mae: 0.0509 - val_loss: 0.0120 - val_mae: 0.0206\n",
      "Epoch 29/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0159 - mae: 0.0522 - val_loss: 0.0115 - val_mae: 0.0195\n",
      "Epoch 30/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0145 - mae: 0.0477 - val_loss: 0.0110 - val_mae: 0.0199\n",
      "Epoch 31/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0161 - mae: 0.0552 - val_loss: 0.0124 - val_mae: 0.0347\n",
      "Epoch 32/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0148 - mae: 0.0513 - val_loss: 0.0109 - val_mae: 0.0214\n",
      "Epoch 33/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0148 - mae: 0.0514 - val_loss: 0.0106 - val_mae: 0.0215\n",
      "Epoch 34/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0134 - mae: 0.0461 - val_loss: 0.0104 - val_mae: 0.0228\n",
      "Epoch 35/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0137 - mae: 0.0490 - val_loss: 0.0100 - val_mae: 0.0208\n",
      "Epoch 36/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0139 - mae: 0.0529 - val_loss: 0.0105 - val_mae: 0.0282\n",
      "Epoch 37/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0141 - mae: 0.0480 - val_loss: 0.0100 - val_mae: 0.0242\n",
      "Epoch 38/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0135 - mae: 0.0493 - val_loss: 0.0096 - val_mae: 0.0198\n",
      "Epoch 39/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0443 - val_loss: 0.0101 - val_mae: 0.0298\n",
      "Epoch 40/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 0.0129 - mae: 0.0496 - val_loss: 0.0101 - val_mae: 0.0350\n",
      "Epoch 41/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0484 - val_loss: 0.0090 - val_mae: 0.0202\n",
      "Epoch 42/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0458 - val_loss: 0.0117 - val_mae: 0.0510\n",
      "Epoch 43/50\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 0.0122 - mae: 0.0483 - val_loss: 0.0089 - val_mae: 0.0209\n",
      "Epoch 44/50\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 0.0120 - mae: 0.0447 - val_loss: 0.0086 - val_mae: 0.0220\n",
      "Epoch 45/50\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 0.0110 - mae: 0.0425 - val_loss: 0.0086 - val_mae: 0.0287\n",
      "Epoch 46/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0115 - mae: 0.0469 - val_loss: 0.0082 - val_mae: 0.0204\n",
      "Epoch 47/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0112 - mae: 0.0466 - val_loss: 0.0092 - val_mae: 0.0335\n",
      "Epoch 48/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0130 - mae: 0.0564 - val_loss: 0.0083 - val_mae: 0.0215\n",
      "Epoch 49/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 0.0105 - mae: 0.0436 - val_loss: 0.0081 - val_mae: 0.0291\n",
      "Epoch 50/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 0.0115 - mae: 0.0478 - val_loss: 0.0082 - val_mae: 0.0256\n"
     ]
    }
   ],
   "source": [
    "best_val_loss = np.inf\n",
    "best_model = None\n",
    "best_history = None\n",
    "\n",
    "\n",
    "# Cross-validate using TimeSeriesSplit\n",
    "for train_index, val_index in tscv.split(training_features):\n",
    "    X_train, X_val = training_features.iloc[train_index], training_features.iloc[val_index]\n",
    "    y_train, y_val = training_target.iloc[train_index], training_target.iloc[val_index]\n",
    "\n",
    "    # Reshape input for LSTM [samples, time steps, features]\n",
    "    X_train = X_train.values.reshape((X_train.shape[0], 1, X_train.shape[1]))\n",
    "    X_val = X_val.values.reshape((X_val.shape[0], 1, X_val.shape[1]))\n",
    "\n",
    "    # Create and fit the LSTM model\n",
    "    model = build_model(input_shape=(X_train.shape[1], X_train.shape[2]),\n",
    "                        num_layers=num_layers,\n",
    "                        size_layer=size_layer,\n",
    "                        dropout_rate=dropout_rate,\n",
    "                        dense_nums=dense_nums,\n",
    "                        reg_rate=reg_rate,\n",
    "                        )\n",
    "\n",
    "    # Fit the model with Early Stopping\n",
    "    history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(\n",
    "        X_val, y_val), callbacks=[early_stopping], verbose=1)\n",
    "    \n",
    "    # Track the best model based on validation loss\n",
    "    val_loss = np.min(history.history['val_loss'])\n",
    "    if val_loss < best_val_loss:\n",
    "        best_val_loss = val_loss\n",
    "        best_model = model\n",
    "        best_history = history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Training and Validation Loss\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(best_history.history['loss'], label='Training Loss')\n",
    "plt.plot(best_history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Model Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.0104 - mae: 0.0446 - val_loss: 0.0075 - val_mae: 0.0190\n",
      "Epoch 2/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0107 - mae: 0.0446 - val_loss: 0.0073 - val_mae: 0.0174\n",
      "Epoch 3/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0098 - mae: 0.0430 - val_loss: 0.0071 - val_mae: 0.0171\n",
      "Epoch 4/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0101 - mae: 0.0448 - val_loss: 0.0071 - val_mae: 0.0192\n",
      "Epoch 5/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0107 - mae: 0.0467 - val_loss: 0.0072 - val_mae: 0.0195\n",
      "Epoch 6/50\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.0100 - mae: 0.0441 - val_loss: 0.0073 - val_mae: 0.0212\n",
      "Epoch 7/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0098 - mae: 0.0433 - val_loss: 0.0069 - val_mae: 0.0206\n",
      "Epoch 8/50\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.0111 - mae: 0.0525 - val_loss: 0.0069 - val_mae: 0.0175\n",
      "Epoch 9/50\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.0102 - mae: 0.0445 - val_loss: 0.0066 - val_mae: 0.0164\n",
      "Epoch 10/50\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.0091 - mae: 0.0425 - val_loss: 0.0065 - val_mae: 0.0166\n",
      "Epoch 11/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0111 - mae: 0.0516 - val_loss: 0.0072 - val_mae: 0.0249\n",
      "Epoch 12/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0099 - mae: 0.0452 - val_loss: 0.0067 - val_mae: 0.0228\n",
      "Epoch 13/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0090 - mae: 0.0412 - val_loss: 0.0062 - val_mae: 0.0164\n",
      "Epoch 14/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0096 - mae: 0.0468 - val_loss: 0.0063 - val_mae: 0.0173\n",
      "Epoch 15/50\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.0090 - mae: 0.0414 - val_loss: 0.0065 - val_mae: 0.0221\n",
      "Epoch 16/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0099 - mae: 0.0474 - val_loss: 0.0062 - val_mae: 0.0171\n",
      "Epoch 17/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0101 - mae: 0.0500 - val_loss: 0.0064 - val_mae: 0.0191\n",
      "Epoch 18/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0093 - mae: 0.0466 - val_loss: 0.0059 - val_mae: 0.0170\n",
      "Epoch 19/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0097 - mae: 0.0452 - val_loss: 0.0059 - val_mae: 0.0164\n",
      "Epoch 20/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0082 - mae: 0.0402 - val_loss: 0.0057 - val_mae: 0.0169\n",
      "Epoch 21/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0097 - mae: 0.0505 - val_loss: 0.0061 - val_mae: 0.0183\n",
      "Epoch 22/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0081 - mae: 0.0386 - val_loss: 0.0059 - val_mae: 0.0194\n",
      "Epoch 23/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0082 - mae: 0.0410 - val_loss: 0.0057 - val_mae: 0.0192\n",
      "Epoch 24/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0084 - mae: 0.0427 - val_loss: 0.0055 - val_mae: 0.0165\n",
      "Epoch 25/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0084 - mae: 0.0425 - val_loss: 0.0055 - val_mae: 0.0171\n",
      "Epoch 26/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0077 - mae: 0.0410 - val_loss: 0.0071 - val_mae: 0.0437\n",
      "Epoch 27/50\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.0090 - mae: 0.0452 - val_loss: 0.0079 - val_mae: 0.0523\n",
      "Epoch 28/50\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0100 - mae: 0.0522 - val_loss: 0.0056 - val_mae: 0.0170\n",
      "Epoch 29/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0081 - mae: 0.0425 - val_loss: 0.0059 - val_mae: 0.0232\n",
      "Epoch 30/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0081 - mae: 0.0441 - val_loss: 0.0053 - val_mae: 0.0164\n",
      "Epoch 31/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0074 - mae: 0.0399 - val_loss: 0.0055 - val_mae: 0.0235\n",
      "Epoch 32/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0073 - mae: 0.0382 - val_loss: 0.0058 - val_mae: 0.0275\n",
      "Epoch 33/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0075 - mae: 0.0415 - val_loss: 0.0056 - val_mae: 0.0232\n",
      "Epoch 34/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0074 - mae: 0.0406 - val_loss: 0.0050 - val_mae: 0.0165\n",
      "Epoch 35/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0078 - mae: 0.0440 - val_loss: 0.0050 - val_mae: 0.0174\n",
      "Epoch 36/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0078 - mae: 0.0416 - val_loss: 0.0050 - val_mae: 0.0169\n",
      "Epoch 37/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0072 - mae: 0.0385 - val_loss: 0.0053 - val_mae: 0.0213\n",
      "Epoch 38/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0076 - mae: 0.0433 - val_loss: 0.0059 - val_mae: 0.0352\n",
      "Epoch 39/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0072 - mae: 0.0410 - val_loss: 0.0056 - val_mae: 0.0277\n",
      "Epoch 40/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0070 - mae: 0.0385 - val_loss: 0.0064 - val_mae: 0.0364\n",
      "Epoch 41/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0077 - mae: 0.0412 - val_loss: 0.0052 - val_mae: 0.0242\n",
      "Epoch 42/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0074 - mae: 0.0417 - val_loss: 0.0048 - val_mae: 0.0169\n",
      "Epoch 43/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0074 - mae: 0.0416 - val_loss: 0.0059 - val_mae: 0.0320\n",
      "Epoch 44/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0070 - mae: 0.0396 - val_loss: 0.0047 - val_mae: 0.0174\n",
      "Epoch 45/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0070 - mae: 0.0418 - val_loss: 0.0046 - val_mae: 0.0160\n",
      "Epoch 46/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0062 - mae: 0.0361 - val_loss: 0.0047 - val_mae: 0.0185\n",
      "Epoch 47/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0071 - mae: 0.0413 - val_loss: 0.0047 - val_mae: 0.0198\n",
      "Epoch 48/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0074 - mae: 0.0437 - val_loss: 0.0046 - val_mae: 0.0164\n",
      "Epoch 49/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0070 - mae: 0.0419 - val_loss: 0.0073 - val_mae: 0.0533\n",
      "Epoch 50/50\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.0072 - mae: 0.0425 - val_loss: 0.0048 - val_mae: 0.0202\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x13c8a8d50>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the best model from cross-validation\n",
    "X_final_train = training_features.values.reshape((training_features.shape[0], 1, training_features.shape[1]))\n",
    "y_final_train = training_target\n",
    "\n",
    "# Re-train the best model on the entire training dataset (excluding the final validation set)\n",
    "best_model.fit(X_final_train, y_final_train, epochs=epochs, batch_size=batch_size, validation_split=0.1, callbacks=[early_stopping], verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_4 (LSTM)               (None, 32)                5760      \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5793 (22.63 KB)\n",
      "Trainable params: 5793 (22.63 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Summary of the model\n",
    "best_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "# Prepare the test data\n",
    "X_test = test_features.values.reshape((test_features.shape[0], 1, test_features.shape[1]))\n",
    "\n",
    "# Make predictions on the test data\n",
    "test_predictions = best_model.predict(X_test)\n",
    "\n",
    "# Inverse transform the predictions and actual values to their original scale\n",
    "predictions = target_scaler.inverse_transform(test_predictions)\n",
    "actual = target_scaler.inverse_transform(test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "def plot_predictions(predictions, actual, dates, title=f'Actual vs Predicted Prices ({ticker})'):\n",
    "    plt.figure(figsize=(14, 7))\n",
    "    plt.plot(dates, actual, label='Actual Prices', marker='o')  # Use test_target's index for actual prices\n",
    "    plt.plot(dates, predictions, label='Predicted Prices', linestyle='--', marker='o')  # Use the same index for predictions\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Date')\n",
    "    plt.ylabel('Price')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.xticks(rotation=45)  # Rotate date labels for better readability\n",
    "    plt.gcf().autofmt_xdate() \n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "plot_predictions(predictions, actual, test_target.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 8.49\n",
      "Mean Absolute Error (MAE): 2.36\n",
      "Root Mean Squared Error (RMSE): 2.91\n",
      "R-squared (R2): 0.99\n",
      "Adjusted R-squared: 0.99\n",
      "Mean Absolute Percentage Error (MAPE): 0.01\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error\n",
    "\n",
    "# Calculate evaluation metrics\n",
    "mse = mean_squared_error(actual, predictions)\n",
    "mae = mean_absolute_error(actual, predictions)\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "r2 = r2_score(actual, predictions)\n",
    "\n",
    "# Adjusted R-squared\n",
    "n = test_size  # Number of observations\n",
    "p = len(features.columns)   # Number of predictors\n",
    "adj_r2 = 1 - (1 - r2) * (n - 1) / (n - p - 1)\n",
    "\n",
    "mape = mean_absolute_percentage_error(actual, predictions)\n",
    "\n",
    "print(\"Mean Squared Error (MSE):\", round(mse, 2))\n",
    "print(\"Mean Absolute Error (MAE):\", round(mae, 2))\n",
    "print(\"Root Mean Squared Error (RMSE):\", round(rmse, 2))\n",
    "print(\"R-squared (R2):\", round(r2, 2))\n",
    "print(\"Adjusted R-squared:\", round(adj_r2, 2))\n",
    "print(\"Mean Absolute Percentage Error (MAPE):\", round(mape, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Squared Error (MSE) and Mean Absolute Error (MAE):\n",
    "\n",
    "- These metrics measure the average error between the predicted and actual values.\n",
    "- Lower values indicate better performance.\n",
    "- However, the absolute scale of these metrics is dependent on the scale of your target variable. Therefore, it's difficult to define a universally \"good\" threshold. It's more informative to compare these metrics across different models or variations of your LSTM model.\n",
    "\n",
    "### Root Mean Squared Error (RMSE):\n",
    "\n",
    "- RMSE is in the same units as the target variable, which can make it more interpretable.\n",
    "- Similar to MSE and MAE, lower values are desirable.\n",
    "- Like MSE and MAE, it's more useful for comparing models rather than establishing absolute thresholds.\n",
    "\n",
    "### R-squared (R2) and Adjusted R-squared:\n",
    "\n",
    "- R2 measures the proportion of the variance in the dependent variable that is predictable from the independent variables.\n",
    "- R2 ranges from 0 to 1, with higher values indicating better fit.\n",
    "- A value of 1 indicates that the model explains all the variability of the response data around its mean. However, a high R2 value doesn't necessarily mean a good model, as it may overfit.\n",
    "- Adjusted R-squared adjusts the R2 value based on the number of predictors in the model, providing a more accurate measure of model performance, especially with multiple variables.\n",
    "- Generally, R2 values closer to 1 and higher Adjusted R-squared values are desirable, but the interpretation depends on the context and the complexity of the model.\n",
    "\n",
    "### Mean Absolute Percentage Error (MAPE):\n",
    "\n",
    "- MAPE measures the average absolute percentage difference between predicted and actual values.\n",
    "- Lower values indicate better performance.\n",
    "- A MAPE of 0% indicates a perfect fit, but in practice, it's rare to achieve such a value.\n",
    "- It's common to interpret MAPE in the context of the variability of the target variable. For example, a MAPE below 10% might be considered good in some domains.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n"
     ]
    }
   ],
   "source": [
    "# Function to predict future 'n' days\n",
    "def predict_future_days(model, last_input, feature_scaler, target_scaler, n_days):\n",
    "    future_predictions_scaled = []\n",
    "    input_sequence = last_input.copy()  # Copy the last known input sequence\n",
    "\n",
    "    # Iteratively predict the next 'n' days\n",
    "    for _ in range(n_days):\n",
    "        # Reshape input for model prediction [samples, time steps, features]\n",
    "        reshaped_input = input_sequence.reshape((1, 1, input_sequence.shape[1]))\n",
    "\n",
    "        # Predict the next day\n",
    "        next_day_prediction_scaled = model.predict(reshaped_input)\n",
    "\n",
    "        # Store the scaled prediction\n",
    "        future_predictions_scaled.append(next_day_prediction_scaled[0, 0])\n",
    "\n",
    "        # Prepare the next input sequence for prediction\n",
    "        next_input_sequence = np.append(input_sequence[:, 1:], next_day_prediction_scaled, axis=1)\n",
    "        input_sequence = next_input_sequence\n",
    "\n",
    "    # Reshape the predictions to fit the target_scaler inverse transform\n",
    "    future_predictions_scaled = np.array(future_predictions_scaled).reshape(-1, 1)\n",
    "\n",
    "    # Inverse transform to get predictions in original scale\n",
    "    future_predictions = target_scaler.inverse_transform(future_predictions_scaled)[:, 0]\n",
    "    \n",
    "    return future_predictions\n",
    "\n",
    "# Predict future 'n' days\n",
    "n_days = 5  # Number of days to predict\n",
    "last_known_input = scaled_features_df.iloc[-1:].values  # Use the last known input features\n",
    "\n",
    "future_predictions = predict_future_days(best_model, last_known_input, feature_scaler, target_scaler, n_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([195.03387, 187.52551, 224.81596, 230.78749, 214.24088],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "\n",
    "# Function to generate future dates excluding weekends\n",
    "def generate_future_weekdays(start_date, n_days):\n",
    "    future_dates = []\n",
    "    current_date = start_date\n",
    "    while len(future_dates) < n_days:\n",
    "        current_date += datetime.timedelta(days=1)\n",
    "        # Check if current_date is a weekday (Monday=0, Sunday=6)\n",
    "        if current_date.weekday() < 5:  # Weekdays are 0-4\n",
    "            future_dates.append(current_date)\n",
    "    return future_dates\n",
    "\n",
    "# Historical data\n",
    "historical_dates = data.index[-10:]  # Last 20 days from the historical data\n",
    "historical_prices = data['Close'][-10:]\n",
    "\n",
    "# Generate future weekday dates for predictions\n",
    "last_date = datetime.datetime.strptime(str(historical_dates[-1]), '%Y-%m-%d')\n",
    "future_weekday_dates = generate_future_weekdays(last_date, n_days)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(14, 7))\n",
    "plt.plot(historical_dates, historical_prices, label='Historical Close Prices', marker='o')\n",
    "plt.plot(future_weekday_dates, future_predictions, label='Predicted Future Close Prices', linestyle='--', marker='o', color='red')\n",
    "\n",
    "plt.title(f'Predicted Future Close Prices ({ticker})')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(mdates.DayLocator())\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "plt.gcf().autofmt_xdate() \n",
    "\n",
    "plt.show()"
   ]
  },
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   "metadata": {},
   "source": [
    "Here's the refactored explanation to match your updated code:\n",
    "\n",
    "## Future Price Prediction Explanation\n",
    "\n",
    "The future price prediction in this model leverages a function that iteratively forecasts prices for a specified number of days ahead, utilizing the most recent input data. This approach is particularly pertinent for predicting stock prices where future values (like the next day's 'Open', 'High', 'Low') are unknown.\n",
    "\n",
    "### Preparing the Last Known Input\n",
    "\n",
    "The prediction process commences with the preparation of the last known input data, which is the most recent data point available from the dataset. This last input is crucial as it serves as the starting point for future predictions. The code snippet for this preparation is:\n",
    "\n",
    "```python\n",
    "last_known_input = scaled_features_df.iloc[-1:].values  # Use the last known input features\n",
    "```\n",
    "\n",
    "Here, `scaled_features_df.iloc[-1:]` fetches the last row of the scaled feature dataset, representing the most recent day's data.\n",
    "\n",
    "### Iterative Future Prediction\n",
    "\n",
    "The essence of the future price prediction lies in an iterative mechanism where each prediction is subsequently used as input for the next. This iterative forecasting is encapsulated in the `predict_future_days` function:\n",
    "\n",
    "```python\n",
    "def predict_future_days(model, last_input, feature_scaler, target_scaler, n_days):\n",
    "    future_predictions_scaled = []\n",
    "    input_sequence = last_input.copy()  # Copy the last known input sequence\n",
    "\n",
    "    for _ in range(n_days):\n",
    "        reshaped_input = input_sequence.reshape((1, 1, input_sequence.shape[1]))\n",
    "        next_day_prediction_scaled = model.predict(reshaped_input)\n",
    "        future_predictions_scaled.append(next_day_prediction_scaled[0, 0])\n",
    "        next_input_sequence = np.append(input_sequence[:, 1:], next_day_prediction_scaled, axis=1)\n",
    "        input_sequence = next_input_sequence\n",
    "\n",
    "    future_predictions_scaled = np.array(future_predictions_scaled).reshape(-1, 1)\n",
    "    future_predictions = target_scaler.inverse_transform(future_predictions_scaled)[:, 0]\n",
    "\n",
    "    return future_predictions\n",
    "```\n",
    "\n",
    "In this function:\n",
    "\n",
    "- `input_sequence` starts as the last known input, and with each iteration, the model predicts the next day's price.\n",
    "- The prediction (`next_day_prediction_scaled`) is appended to `future_predictions_scaled`, accumulating the future price predictions.\n",
    "- `input_sequence` is updated with the new prediction, effectively simulating the input for the next day's prediction.\n",
    "- After completing the predictions for the desired number of days, the `future_predictions` are inversely transformed to the original price scale using the `target_scaler`.\n",
    "\n",
    "### Conclusion\n",
    "\n",
    "This iterative prediction method allows for forecasting future stock prices based on historical data patterns. However, it's crucial to recognize that the accuracy of these predictions may diminish over time due to the cumulative effect of prediction errors and the absence of actual future inputs. This strategy presupposes that historical trends will persist, which may not always be the case in the volatile stock market environment.\n"
   ]
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