lstm_nasdaq_v_spx.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA and intial testing of LSTM on QQQ v SPX strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pandas_datareader import data\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "import copy\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, Normalizer\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.preprocessing.sequence import TimeseriesGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM, Dropout\n",
    "from keras import Input\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = [\n",
    "    '^GSPC',  # S&P 500\n",
    "    '^IXIC',  # NASDAQ\n",
    "#     'GSPX.L', \n",
    "#     'EQGB.L',  \n",
    "]\n",
    "\n",
    "yday = datetime.now() - timedelta(1)\n",
    "start_date = '1990-01-01'\n",
    "end_date = yday.strftime('%Y-%m-%d')\n",
    "\n",
    "panel_data = data.DataReader(tickers , 'yahoo', start_date, end_date)\n",
    "close_data = panel_data.loc[:, ('Close', slice(None))]\n",
    "close_data.columns = close_data.columns.droplevel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# titles = close_data.columns\n",
    "chart_data = close_data.iloc[-64:, :].pct_change()\n",
    "chart_data.iloc[0, :] = 0\n",
    "chart_data = np.cumprod(1 + chart_data) - 1\n",
    "\n",
    "plt.figure(figsize = (17,6))\n",
    "plt.plot(chart_data[tickers])\n",
    "plt.grid(axis = 'y')\n",
    "plt.legend(tickers, loc = 'best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "period = 505\n",
    "nasdaq_v_spx = close_data.iloc[-period:, 1].pct_change() - close_data.iloc[-period:, 0].pct_change()\n",
    "nasdaq_v_spx = np.cumprod(1 + nasdaq_v_spx) - 1\n",
    "\n",
    "plt.figure(figsize = (17,6))\n",
    "plt.plot(nasdaq_v_spx, label = 'NASDAQ - S&P500 spread')\n",
    "plt.grid(axis = 'y')\n",
    "plt.legend(loc = 'best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Use LSTM model\n",
    "- Inputs: weekly spread between QQQ and SPX over different time windows\n",
    "- Target: weekly spread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "pct_nasdaq_v_spx = (close_data.iloc[:, 1].pct_change() - close_data.iloc[:, 0].pct_change()).fillna(0)\n",
    "cum_nasdaq_v_spx = np.cumprod(1 + pct_nasdaq_v_spx) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pct_nasdaq_v_spx.hist(figsize = (8, 5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cum_nasdaq_v_spx.plot(figsize = (16, 7))\n",
    "plt.grid(axis = 'y');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "wkly_nsdq_v_spx = (cum_nasdaq_v_spx + 1).resample('W-FRI').last()\n",
    "pct_wkly_nsdq_v_spx = wkly_nsdq_v_spx.pct_change().fillna(0) ## for now just doing friday close to friday close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max weekly return: 0.11306082914061255\n",
      "Min weekly return: -0.16150905753141065\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pct_wkly_nsdq_v_spx.hist(figsize = (8, 5));\n",
    "print('Max weekly return:', np.max(pct_wkly_nsdq_v_spx))\n",
    "print('Min weekly return:', np.min(pct_wkly_nsdq_v_spx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pct_wkly_nsdq_v_spx.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1336, 1), (260, 1))"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_len = pct_wkly_nsdq_v_spx.shape[0] - 60 \n",
    "train_len = wkly_nsdq_v_spx.shape[0] - (52 * 5)\n",
    "\n",
    "# train = pct_wkly_nsdq_v_spx.iloc[:train_len].values.reshape(-1, 1)\n",
    "# test = pct_wkly_nsdq_v_spx.iloc[train_len:].values.reshape(-1, 1)\n",
    "train = wkly_nsdq_v_spx.iloc[:train_len].values.reshape(-1, 1)\n",
    "test = wkly_nsdq_v_spx.iloc[train_len:].values.reshape(-1, 1)\n",
    "\n",
    "\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_chart_data = copy.deepcopy(wkly_nsdq_v_spx)\n",
    "test_chart_data = copy.deepcopy(wkly_nsdq_v_spx)\n",
    "\n",
    "train_chart_data.iloc[:train_len] = np.nan\n",
    "test_chart_data.iloc[train_len:] = np.nan\n",
    "\n",
    "plt.figure(figsize = (16, 7))\n",
    "plt.plot(train_chart_data, label = 'Training data')\n",
    "plt.plot(test_chart_data, label = 'Test data')\n",
    "plt.legend()\n",
    "plt.grid(axis = 'y');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is it important that the test data set has some visual similarity to the training data set? The first testing done used the most recent 60 weeks as the test set. This period saw the spread between QQQ and SPX increase much faster than the general trend. The second run had a test set that was about 3 times larger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize = (16, 14))\n",
    "# plt.subplot(211)\n",
    "# plt.plot(wkly_nsdq_v_spx.iloc[:train_len])\n",
    "# plt.grid(axis = 'y')\n",
    "# plt.subplot(212)\n",
    "# plt.plot(wkly_nsdq_v_spx.iloc[train_len:])\n",
    "# plt.grid(axis = 'y');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scale Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 1.046837434328555)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "\n",
    "scaler.fit(train) # ONLY FIT TO TRAINING DATA\n",
    "scaled_train = scaler.transform(train) # each column ranges from 0 to 1\n",
    "scaled_test = scaler.transform(test)\n",
    "\n",
    "scaled_train.max(), scaled_test.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Generator\n",
    "- `n_inputs` = the rolling window length (number of weeks)\n",
    "- `n_cols` = the number of columns being trained (for univariate time-series, this is 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_input = 52 # a smaller number of inputs than the test set size will lead to predictions based on predicted values\n",
    "n_cols = scaled_train.shape[1]\n",
    "\n",
    "generator = TimeseriesGenerator(scaled_train, scaled_train, length = n_input, batch_size = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Given the Array: \n",
      "[[[0.08725221]\n",
      "  [0.08425994]\n",
      "  [0.08673244]\n",
      "  [0.08391931]\n",
      "  [0.07720955]\n",
      "  [0.08082631]\n",
      "  [0.08234924]\n",
      "  [0.08416985]\n",
      "  [0.08036153]\n",
      "  [0.08340131]\n",
      "  [0.08366648]\n",
      "  [0.0849889 ]\n",
      "  [0.07885197]\n",
      "  [0.07337806]\n",
      "  [0.07336946]\n",
      "  [0.07470871]\n",
      "  [0.07449419]\n",
      "  [0.07312472]\n",
      "  [0.06445883]\n",
      "  [0.07201058]\n",
      "  [0.07798969]\n",
      "  [0.07517125]\n",
      "  [0.07991156]\n",
      "  [0.08123662]\n",
      "  [0.08259065]\n",
      "  [0.08219255]\n",
      "  [0.07961842]\n",
      "  [0.07580115]\n",
      "  [0.06953624]\n",
      "  [0.06694189]\n",
      "  [0.0508968 ]\n",
      "  [0.05318566]\n",
      "  [0.04711904]\n",
      "  [0.03905026]\n",
      "  [0.04003644]\n",
      "  [0.03785053]\n",
      "  [0.03992787]\n",
      "  [0.03297801]\n",
      "  [0.01829099]\n",
      "  [0.01427785]\n",
      "  [0.00521124]\n",
      "  [0.00035672]\n",
      "  [0.00715935]\n",
      "  [0.        ]\n",
      "  [0.00415199]\n",
      "  [0.01055344]\n",
      "  [0.01095327]\n",
      "  [0.01353741]\n",
      "  [0.02092639]\n",
      "  [0.01894753]\n",
      "  [0.01806089]\n",
      "  [0.01922742]]]\n",
      "Predict this y: \n",
      " [[0.02486927]]\n"
     ]
    }
   ],
   "source": [
    "# What does the first batch look like?\n",
    "X, y = generator[0]\n",
    "\n",
    "print(f'Given the Array: \\n{X}')\n",
    "print(f'Predict this y: \\n {y}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # define model\n",
    "# model = Sequential()\n",
    "\n",
    "# model.add(LSTM(300, activation = 'relu', input_shape = (n_input, n_cols)))\n",
    "# model.add(Dense(1))\n",
    "# model.compile(optimizer = 'adam', loss = 'mse')\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_29\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_34 (LSTM)               (None, 52, 300)           362400    \n",
      "_________________________________________________________________\n",
      "dropout_34 (Dropout)         (None, 52, 300)           0         \n",
      "_________________________________________________________________\n",
      "lstm_35 (LSTM)               (None, 52, 300)           721200    \n",
      "_________________________________________________________________\n",
      "dropout_35 (Dropout)         (None, 52, 300)           0         \n",
      "_________________________________________________________________\n",
      "lstm_36 (LSTM)               (None, 300)               721200    \n",
      "_________________________________________________________________\n",
      "dropout_36 (Dropout)         (None, 300)               0         \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 1)                 301       \n",
      "=================================================================\n",
      "Total params: 1,805,101\n",
      "Trainable params: 1,805,101\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# define model\n",
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(300, activation = 'relu', return_sequences = True, input_shape = (n_input, n_cols)))\n",
    "model.add(Dropout(rate = 0.2))\n",
    "\n",
    "model.add(LSTM(300, activation = 'relu', return_sequences = True))\n",
    "model.add(Dropout(rate = 0.2))\n",
    "\n",
    "# model.add(LSTM(100, activation = 'relu', return_sequences = True))\n",
    "# model.add(Dropout(rate = 0.2))\n",
    "\n",
    "model.add(LSTM(300, activation = 'relu', return_sequences = False))\n",
    "model.add(Dropout(rate = 0.2))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer = 'adam', loss = 'mse')\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1284/1284 [==============================] - 103s 80ms/step - loss: 0.0055\n",
      "Epoch 2/25\n",
      "1284/1284 [==============================] - 100s 78ms/step - loss: 0.0025\n",
      "Epoch 3/25\n",
      "1284/1284 [==============================] - 98s 76ms/step - loss: 0.0021\n",
      "Epoch 4/25\n",
      "1284/1284 [==============================] - 97s 76ms/step - loss: 0.0019\n",
      "Epoch 5/25\n",
      "1284/1284 [==============================] - 101s 79ms/step - loss: 0.0018\n",
      "Epoch 6/25\n",
      "1284/1284 [==============================] - 108s 84ms/step - loss: 0.0016\n",
      "Epoch 7/25\n",
      "1284/1284 [==============================] - 106s 82ms/step - loss: 0.0015\n",
      "Epoch 8/25\n",
      "1284/1284 [==============================] - 102s 79ms/step - loss: 0.0015\n",
      "Epoch 9/25\n",
      "1284/1284 [==============================] - 104s 81ms/step - loss: 0.0011\n",
      "Epoch 10/25\n",
      "1284/1284 [==============================] - 117s 91ms/step - loss: 0.0014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7f93cac39110>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "early_stopping = EarlyStopping(monitor = 'loss', patience = 1)\n",
    "model.fit_generator(generator, epochs = 25, callbacks = [early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_per_epoch = model.history.history['loss']\n",
    "plt.plot(range(len(loss_per_epoch)), loss_per_epoch)\n",
    "plt.grid(axis = 'y');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Evaluate on Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1336, 1)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = []\n",
    "\n",
    "first_eval_batch = scaled_train[-n_input:]\n",
    "current_batch = first_eval_batch.reshape((1, n_input, n_cols))\n",
    "\n",
    "for i in range(len(test)):\n",
    "    \n",
    "    # get prediction 1 time stamp ahead ([0] is for grabbing just the number instead of [array])\n",
    "    current_pred = model.predict(current_batch)[0]\n",
    "    \n",
    "    # store prediction\n",
    "    test_predictions.append(current_pred) \n",
    "    \n",
    "    # update batch to now include prediction and drop first value\n",
    "#     current_batch = np.append(current_batch[:, 1:, :],[[current_pred]], axis = 1)\n",
    "    current_batch = np.append(current_batch[:, 1:, :],[[scaled_test[i]]], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inverse Transformations and Compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((260, 1), (260, 1))"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_predictions = scaler.inverse_transform(test_predictions)\n",
    "true_predictions.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(260,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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efhhEfXp6ACIiIiIiIv1WXop5dbFi7s7MYry9LMZFBg3MGVHLsmIsy/rYsqwky7L2WZZ1byttwizLesuyrF2NbW7zzHBFRERERET6kNwkCAiH4OEu3bYzs5j44SHMjR3CvuzSflewyJmlufXAT2zbngLMA75nWdapC5y/ByTZtj0TWAL80bIsP7eOVEREREREpK/JTTH7Q12omOtw2OzKLGZWTDjTo8Ioq6kno7DSg4Psfh0GUdu2j9q2vaPxfRmQDESd2gwIsSzLAoKBQkyAFRERERER6V8qCmDVXfDuz9pvZ9uQl+xyxdzDBRWUVtczKyaMaVFhQP8rWOTSHlHLsmKB2cCWUz56ElgD5AAhwPW2bTtauf9O4E6A4cOHs2HDBpcHLCIiIiIi0lMi8jYTv/8p/OpKcFg+bPQ/F4d3QKtt/WoKOKu6hAPFPmS7kH0+z64DoO5YGsfKLHwseHfzXkKK9rvjS+gVnA6ilmUFA68DP7Rtu/SUjy8EdgLnAuOADy3L+uzUdrZtPwc8B5CYmGgvWbKkC0MXERERERHpRut+DfsehxHTYcb1eK39BYvH+ML4Ja23P/gxfAETzlrGhLFnO/2Y9W/uJcgvi69feg7eXhaT922kxMuHJUvmuefr6AWcOr7FsixfTAh91bbtN1ppchvwhm2kAYcB109sFRERERER6Y3qqmHLMzD5a/Ct9TDnNvDygfTP2r6n+Ih5HTLWpUftyixmenQY3l5mX+m0qDD2Zpdg2/2nYJEzVXMt4AUg2bbtx9tolgEsbWw/HJgIHHLXIEVERERERHpUxhdQVwmzbwYfP/APhqg5cLi9IJphwmrISKcfU13XQNLRUmbFDD5xbXpUGKXV9WQWVnXlK+hVnJkRXQDcDJxrWdbOxv8usSzrLsuy7mps87/AWZZl7QE+Av6fbdv5HhqziIiIiIhI90pbB95+ELuw+VrsIsj5CmrKWr+nOANCR4G386V5ko6WUtdgMysm7MS16f2wYFGH3xHbtjcC7dYatm07B7jAXYMSERERERHpVdLWwZgF4BfUfC1uEXz2GBz5AuJbiUPFmRA+xqXH7MosBmgxIxo/Ihhfb4s92SVcOsP52dXezKk9oiIiIiIiIgNWcSbkpcD481pejznTzJKmf9rGfRkQPtqlR+3MLGZ4qD8jwpor8fr7eBM/PIR9Of1nRlRBVEREREREpD1pH5rXCee3vO4bCNFzW98nWl8DZUddCqIOh82mgwXMjR1y2mfTo8LY048KFimIioiIiIiItCftIwiLgYj40z+LXQTHdkNVccvrJVmA7VIQTTpaSl5ZDUsmDjvtsymjQimurCO3rMbFwfdOCqIiIiIiIiJtqa+FQxvMslyrldI5cYvAdsCRTS2vF2eYVxeC6Cf78wA4Oz7ytM/GRQYDcDC33On+ejMFURERERERkbZkboHa8tP3hzaJngs+AaefJ9qJILohNZdpUaFEhvif9tmJIJqnICoiIiIiItK/pX1ozgKNW9z65z7+EHPG6ftEizPA8oaQUU49pqSqjh0ZxSyJP31ZLsDwUH+C/Lw5mFfhyuh7LQVRERERERGRtqR9BKPnQ0Bo223GLITje6H6pKq2xRkQFuX0GaIbD+TT4LBZMvH0ZbkAlmUxNjJYM6IiIiIiIiL9Wn0t5CaZY1raE50I2JC9o/lacQaEubYsNzTAh1kx4W22GRcZxCHNiIqIiIiIiPRjJZmmENGQse23i5pjXrO3NV9z4QxR27b5ZH8ei+Ij8fFuO6KNiwwmu7iKqtoGp/rtzRRERUREREREWlOUbl4Hx7bfLjDcHO2Std382cUzRJOOlpJbVsOSVqrlnmxsY8GiQ/l9f3mugqiIiIiIiEhrnA2iAFGJZkbUtl0+Q3RDauOxLW3sD20yblgQQL8oWKQgKiIiIiIi0pqidPD2g5CRHbeNngMVeWZJbkmmueZkEP0kNY+po0IZFhLQbrvYoUFYFhzqBwWLFERFRERERERaU5QO4WPAy4nYFJVoXrO+dOkMUdu22ZtTwtzYIR22DfD1JmbwoH4xI+pcLWEREREREZGBpijduWW5AMOngk8AZG8HvyBzhmhoVIe35ZXVUFnbwNjIIKceMzYyiIO5mhEVERERERHpf2zbtSDq7QujZkPWNjMjGurcGaKH8s3sZlyEc0F0XGQwh/LLcThs58bVSymIioiIiIiInKqqCGpKnQ+iYI5xOboLCtKc3h96uDGIxg51PohW1zk4Wlrt/Lh6IQVRERERERGRU7lSMbdJdCI01ED2DgiPceqW9PwK/Hy8GBUe6FT7cY1LePv68lwFURERERERkVN1Jog2FSxy4eiWQ/kVxA4dhLeX5VT7prNED/bxyrkKoiIiIiIiIqc6EUTHOH9PWDQEjzDvXVia6+yyXICIYD9CA3w41Mcr5yqIioiIiIiInKooHQZFgH+I8/dYllmeC04F0QaHTUZBJXFOVsw1j7AYNyxYM6IiIiIiIiL9TlE6DIlz/b7ouebViSW9OcVV1DY4GOtkxdwmYyMUREVERERERPofV45uOdncb8H1rzo1I3rIxYq5TcYNC+J4aQ1l1XWuj6+XUBAVERERERE5WUMdlGR1Loj6B8PkZU41TW86Q9SFpblgjnABOFJQ6drYepGOT1gVEREREREZSEqywG7oXBB1weH8CoL9fYgM9nfpvrPjI9n90AWEBvh6aGSepyAqIiIiIiJyss4c3dIJh/IriI0YhGU5d3RLkwBfbwJ8vT00qu6hpbkiIiIiIiIn66Ygeji/nLiIYI8+o7dSEBURERERETlZUTp4+0HISI89oqa+geyiKuJcrJjbXyiIioiIiIiInKwo3VS99XJu+esXBwv4y0cHXHpEZmElDhviIgZ1YoB9n/aIioiIiIiIVJeCfwhYlktHt6QeK+PbL2+jvKae6xJjGBEW4NR9h/IaK+Zqaa6IiIiIiMgAdDwJHh0Df5kFH/wPFB5yKojml9dw+/Ivaao19NmBPKcfmV7QGERdPEO0v1AQFRERERGRgS31HbAdMGQcbHkWakph6Ph2b6mua+A7r2ynoKKGf95xJhHB/mxMy3f6kYfzKxga5EfYoL57BEtXaGmuiIiIiIgMbGnrYeRMuPkNqC6BzC9hzPx2b/nj2lS2HyniqW8kMDMmnIXjh7IxLR+Hw8bLq+PjWA7lVRA7QAsVgWZERURERERkIKsuhaytMG6p+XNAGEw4D/zaD4nv7zvGeZOHc8l0U1l34YRI8strSTlW5tRj0wsqBmzFXFAQFRERERGRgezwp+Coh/HnOX1LVlElmYVVLBg/9MS1heMjAOf2iVbU1HO8tEZBVEREREREZEBKWwd+IRBzhtO3bDlUCMC8sc1BdERYAPHDg53aJ3o4v6liroKoiIiIiIjIwGLbcPAjiFsM3s4XDdpyuIDwQb5MHB7S4vrC8ZFsPVxIdV1Du/efqJirICoiIiIiIjLAFByE4gwYf65Lt20+VMgZsUNOK0q0aEIENfUOvkwvbPf+w41niMYO0KNbQEFUREREREQGqrR15rWpUJETsouryCisbLEst8mZY4fg622x8UD7y3MP51cwMiyAQD9vl4bbnyiIioiIiIjIwHTwIxgyFobEOX3LlkMFAK0G0UF+PiSMHsxnHQXRAV4xFxRERURERERkIKqvgfSNLlXLBdh8qICwQF8mjQhp9fNFEyJIOlpKUUVtm30czlcQVRAVEREREZGBJ2Mz1FW6tCwXzP7QM+NO3x/aZHp0OACpx1s/T7SoopbiyjoF0Z4egIiIiIiISLfL329eR81y+pacdvaHNpkwLBiAA7nlrX5+SEe3AAqiIiIiIiIyEJVkgZcvBA1z+pYth83+0DPHDmmzzciwAIL8vElrY0Y0XUEUUBAVEREREZGBqDQbQkeCl/ORaPPBQsICfZk8IrTNNpZlMX54SJszoofzK/D2sogZMsjlIfcnCqIiIiIiIjLwlGRDWIxLt3x5pJC5sYPb3B/aZMKw4HaDaMzgQHy9B3YUG9hfvYiIiIiI9A/v/xy+etX59qVZEBrldPPymnoO51cwo7EYUXsmDAsmr6yG4srTK+eqYq6hICoiIiIiIn2bbcP25bD1OefaOxqg9CiEOR9E92WXYNswPSqsw7bxw83RLmmnzIrats3h/ApiFUQVREVEREREpI+rKoK6Cji2G6pLOm5fnguOOpdmRPfmlAIwNart/aFNxrdROfd4aQ1VdQ2MVRBVEBURERERkT6uOMO82g7I2NJx+9Js8xoW7fQj9maXMDzUn2EhAR22jQoPJNDXmwPHWwbRwycq5gY7/dz+SkFURERERET6tpLM5vdHNjrRPsu8uhhEp43qeFkugJeXxfhhwRzIbXmES1MQjY0Y2BVzQUFURERERET6uuLGIDp0AqR/3nH7phlRJ5fmVtbWczCvnGlO7A9tMmFY8Gl7RA/nl+Pn48WosECn++mvFERFRERERKRvK8kE30Ew+WuQ8xXUtH50SnP7bNM+cLBT3ScfLcVh41IQHT88mKMl1ZRW1524dji/krihQR0e/zIQKIiKiIiIiEjfVpxhzgSNXQB2A2R2sE+0JNPMhlrOBcI9WaYAkjMVc5tMGHZ65dzD+eU6uqWRgqiIiIiIiPRtJZkQHgMxZ4LlDUc6WJ5bmu3S0S17skuJCPZjeKi/0/fEDzcFidIaCxbVNzjIKKzU0S2NFERFRERERKRvK840M6L+ITBqVst9osWZkLOzZfuSbJcKFe3LKWFaVBiWkzOoANGDB+Hv43WiYNEn+/Ooa7BPHO0y0CmIioiIiIhI31VbAVWFZkYUYMwCyN4OtZWQnwbPnwMvXQYN9ebz+looPw6hzgXR6roGDuSWu7QsF8Dby2JcZDAHcsvJKa7ip//dxaQRISybMdKlfvorBVEREREREem7mirmho02r7GLwFEH+96Aly+DygKoKYHje8znZUcB2+mluclHS2lw2Ex18uiWk00YHkzqsTLu+dcOausdPPWNBAJ8vV3upz9SEBURERERkb6r6QzRphnR0fPA8oI374Hacvj6SnP9yBfm1cWjW/ZmNxYqiu5EEB1mKufuyCjmkatnMDZSy3KbKIiKiIiIiEjfVZxhXsMag2hAKIyabY5n+cbrEH8hhI9pLmBUktXY3rmluXuzSxk8yJdRYQEuDy1+uKmce9O80Xxt5iiX7+/PfHp6ACIiIiIiIp1WkglePhAyovnaNf8wx7gMGWv+PGYBHPgAbLs5iDo5I7on2/VCRU2WTBzG49fN5FLtCz2NZkRFRERERKTvKm48E9TrpL2Xg8c0h1CAMfPNXtH8/WZpbkA4+He8TPZ4aTUpx0pJGD24U0Pz8/HiqoRo/H20L/RUCqIiIiIiItJ3lWRC+Oj224xZYF6PbHLp6JZVX2XjsOHyWVpW624KoiIiIiIi0nc1nSHaniFjIWiYCaKlWU4ty7Vtm9e3Z5EwOlxFhjygwyBqWVaMZVkfW5aVZFnWPsuy7m2j3RLLsnY2tvnE/UMVERERERE5SX2tOY4lvIMgalkw5izI+MLsEXXi6Ja92aUcyC3n6jnOzZ6Ka5wpVlQP/MS27R2WZYUA2y3L+tC27aSmBpZlhQNPARfZtp1hWdYwD41XRERERETEKMvBnAnaQRAFE0STVpv3TizNfX1HFn4+XiyboWW5ntDhjKht20dt297R+L4MSAZO/RXCjcAbtm1nNLbLdfdARUREREREWig+5QzR9ow5q/l9aPtBtLbewZs7szl/ynDCAn27MEBpi0vHt1iWFQvMBrac8lE84GtZ1gYgBPizbdsvt3L/ncCdAMOHD2fDhg0uD1hERERERARg+LH1TAa2pBylKmND+43tBhb4BOFbX8FXh/MoKWq7/fbj9RRV1hHvW6jM4iFOB1HLsoKB14Ef2rZd2ko/c4ClQCDwhWVZm23b3n9yI9u2nwOeA0hMTLSXLFnShaGLiIiIiMiAtmELpMCZF1wFPv4dt89ZCAc+YPbiS2FIXJvN/vXyNiJDivneVefi4636rp7g1HfVsixfTAh91bbtN1ppkgV8YNt2hW3b+cCnwEz3DVNEREREROQUJRkQPMK5EAow8WIYFNFu1dzS6h8FZOAAACAASURBVDo+Ts3l8pmjFEI9yJmquRbwApBs2/bjbTR7E1hoWZaPZVmDgDMxe0lFREREREQ8ozjTuf2hTebcCj9OBh+/NpvsySqhrsFmcXxk18cnbXJmae4C4GZgj2VZOxuv/RwYDWDb9jO2bSdblvU+sBtwAH+3bXuvJwYsIiIiIiICQEkmjHRhIaZltRtCAXZlFQMwIzqsKyOTDnQYRG3b3ghYTrT7A/AHdwxKRERERESkXbWV5kzQScvc2u3uzBLGDB1E+KD2A6t0jRY9i4iIiIhI73V8HzwxHXJ2try+9TloqHV/EM0qZkZ0uFv7lNMpiIqIiIiISO+1ayUUZ8BbP4CGenOtugQ2/gkmXAijz3Tbo3LLqskpqWamluV6nIKoiIiIiIj0TrYNKW9DUCQc3QVfPm+ub3oSqovh3F+49XG7M0sAmBmjGVFPUxAVEREREZHeKS8VCg/Bkvth/Pmw/mGzRPeLv8HUq2DkDLc+bndWMV4WTB0V6tZ+5XQKoiIiIiIi0julvGVeJ14Klz4Gjgb4xyVQXw3n/I/bH7crq4T44SEM8nPmcBHpCgVRERERERHpnVLegahECB0Jg2Nhyf+DugqYdSNEjHfro2zbbixUpP2h3UFRX0REREREegfbNmd9gjmaJecrOO+h5s/n3wMB4TDlcrc/OquoiqLKOlXM7SaaERURERERkZ732ePwl9mQt9/8OfU983ry8SzevpB4Gwwa4vbH78oqBmCmgmi30IyoiIiIiIj0rNIc+OT3UF8Fyy+Bb74JyW9BRDxETOhS1//Zlsk7u4/i5+NFoK83YyODuHfpBKymmddGuzKL8fPxYuKIkC49T5yjGVEREREREelZGx4BRz184zXw8oHlyyB9I0y6tNNd2rbNYx+k8rPXdpNRWElmYSVbDhfwxLoDHMwrP639rqwSpowMxc9HEak76LssIiIiIiI9J28/fPUKzL0DJpwPt70LfkFgN7RcluuC+gYH97++hyc/TuOGuTF8+KPFvP/Dxbxx9wIANqTmtWjf4LDZm13CTBUq6jYKoiIiIiIi0nM++jX4BsHi+8yfh4yF2z+Aq56HqDmd6vI3byfx722Z/ODc8fzuqun4eJvYExUeyIRhwXyyv2UQTT1WRmVtgwoVdSMFURERERER6RmZWyHlbVjwAwiKaL4eFgUzrmuuoOuidUnHuXjaCH58wcTT9oIumRjJlkOFVNbWn7i2ZlcO3l4Wi+MjO/U8cZ2CqIiIiIiI9IxPHoWgYTDvbrd1WVRRS05JNbNiWp/dPDt+GLUNDr44WACYZbmrvspiSXwkkSH+bhuHtE9BVEREREREul/hYUj7yOwN9Q92W7dJR0sBmDqq9f2ec+MGM8jP+8Ty3I1p+RwvreGaOdFuG4N0TEFURERERES6346XzNLb2Te7tdt9OSUATBkV2urn/j7enDVuKBtS87Btm9e3ZxEW6Mu5k4e5dRzSPgVRERERERHpXvW18NU/If4isx/UjZJyShkZFsCQIL8225wdH0lGYSW7s0r4YN8xLp81Cn8fb7eOQ9qnICoiIiIiIt0r9R2oyIM5t7m96305pUwZ2fpsaJOz483s5wNv7KGm3sHVCVqW290UREVEREREpHtt+weExcD4pW7ttqq2gYN55UxtY1luk9FDBzE2Ioiko6WMHxbMDJ0f2u0UREVEREREpPsUHITDn8CcW8DLvcthU4+X4bBhShuFik529kRzVMs1c6JPO+JFPE9BVEREREREus/2f4CXj9uLFEFzoaKOZkQBrpodzeSRoVyV4N49quIcn54egIiIiIiIDBBpH8HmZ2DyZRAywu3d78spJTTAh+jBgR22nR4dxnv3LnL7GMQ5mhEVERERERHPy9oO/74ZIifCsj955BFJOaVMGRWqpbZ9gIKoiIiIiIh4Vt5+ePUaCI6Em16HwHC3P6LBYZNyrJQpI1V4qC9QEBUREREREc9J+wheWmb2hd68yiNLcgEO5ZVTXedwan+o9DwFURERERERcb/aSnj3PvjnVRA4GG5ZA0PGeuxxSUdLAZgapSDaF6hYkYiIiIiIuFdROrx6HeSnwry7YemvwLfjAkJdsS+nFD8fL8ZFBnv0OeIeCqIiIiIiIuI+x/fBK1dBfTXcvBrGnePW7osrawkL9G1RkKjBYbMzo5iJw0Pw9daiz75AQVRERERERNwjYzP86zrwHQS3vw/DJru1+8/T8vnmi1uJCg/kqoQols0YyeZDhfz9s0OkF1Ty3SXj3Po88RwFURERERER6br8NHj5CggdZYoSDR7j1u5zy6q5d+VORg8ZxKjwAP780QGeWHcAgJnRYTz1jQQunOqZQkjifgqiIiIiIiLSdZufArsBbn3bhFE3anDY/HDlTspr6nj1W2cycUQI2cVVrEs6zsQRIZwZN0Rnh/YxCqIiIiIiItI1VUWwawVMv9btIRTgyfVpbDpYwKNXT2fiiBAAosIDueWsWLc/S7qHgqiIiIiIiHTNjlegrhLOvMst3dU1OHh3z1F2ZhazJ6uE7RlFXDFrFNclxrilf+l5CqIiIiIiItJ5DfWw9XkYsxBGznBLl79/P4XnPztMoK83U0eFcufisfzg3AlaftuPKIiKiIiIiEjnpb4LJRlw0f+5pbvMwkpe2nSEqxOiefTq6fjoOJZ+SX+rIiIiIiLSeVuegfDRMPESt3T3+w9S8fKC+y6cqBDaj+lvVkREREREOid9Ixz5HM64E7y8u9zdzsxi3tqVw7cXjWVEWIAbBii9lYKoiIiIiIi47tAGePU6GBwLCd/scne2bfN/7yQTEezHd84e1+X+pHdTEBUREREREdckvwWvXguDx8DtH0BAWJe7XJt0nK3phdx7XjzB/ipl098piIqIiIiIiPO+ehX+800YORNufQdCRnS5y+LKWn65ei/xw4O5Ya6OaBkIFERFRESkZ9m2+/s8tAFS3nV/vyID3ean4c27Ie5suHk1DBrilm5/sXovhRW1PH7dLHxVoGhA0Jy3iIiIuJ/DAemfQXQi+AW13qaiAN74NmRtgzFnwdizIf5CGDK2a8+2bXj7x1BXCRMvBp07KNJ1tg0bfgefPAqTvwZXvwA+/m7pes2uHN7efZSfXhDPtKiuL/GVvkG/bhARERH3ytwKf18KL18Gn/y+9TbH9sDzS0zFzfgLIS8F3r8fnl4I9TVde35eChQehLKjUJLZtb5ExNjwiAmhs26Ca5a7LYQeK6nmF6v2MHt0OHepQNGAoiAqIiIi7tFQB6vughfOh9IciJwE+944feltyjvwwgXQUA+3vwdXPw/37oQrn4W6ChNSuyL5reb3mVu71pd4XkUBHN1lllMnvw01ZT09IjlVxhb49Pcw4wa4/Enwds+iyk1p+dz4/GbqGmwev26WzgwdYPS3LSIiIu5xcD3sWgHz7obvb4cF90JxBuTsaG5TVw1rvg8RE+DODRA1p/mz2EXmNXt718aR/Jbp1zcIMjZ3rS/xrJyv4E9T4NnF8PLl8O9vwPqHe3pUcrLaClh9F4RGwyV/cMtS99zSan6w4itu/PsW6h02f78lkbiINpbwS7+lICoiIiLucXA9+ATC0gfBPxgmXgJevrBvVXObfaugsgDO/w2EDG95f1gUhIw0e0Y7q+gIHNsNU66A6DmQuaXzfYln2Tas/aXZQ3z9P0311UnLYOe/TPiR7tNewbAPfwWFh+CKpyAgtMuP2nKogIv+/Bnv7zvGvUsnsPZHi1kwPqLL/UrfoyAqIiIi7nFwPcQuAN8A8+fAcBi/FPatbv6H7pfPQ0S8qbjZmqg5XZsRTXnHvE5eBjHz4PheLfXsrdLWmYJWZ/8/U/wmdiGc9X2oKYU9r/X06AaOqmJ4Yjr891bz/mQHPoQv/25WOcQt6vKjVm7N4KYXthAe6Mu7P1jEj86PJ8DXu8v9St+kICoiIiJdV5wJ+fth3NKW16deaQoGZW+HrO3mde63217eFzXHFBqqLOzcOFLehmFTTeXdmDPBdnR9qa+4n6MBPnwQBsfBnNuar8ecCcOmwLYXPHOsj5xuy7Pm/9Hkt+CZRWZfdW4y/Pc2ePVaiJgIS3/V5cf837vJ3P/GHuaPi2DV9xYwfliwGwYvfZmCqIiIiHTdwfXmddy5La9PvBi8/cyS3C+fB79gmHlD2/007Rk9eV+ps8rz4MgmM7sG5ugYLFNoRXqXXSshd58JOD5+zdctCxJvN8WLOvMz4AkOh9nr3B9Vl8Lmv8HES+H2D8z3/8WL4Kn5cGAtLPwh3P4++AZ26TGrv8rmuU8PcdO80bx4SyJhgb5u+gKkL1MQFRERka47uB5CRkHkxJbXA8Jg/Hmw+z+w93WY+fX295mNmg1YZva0NY4G2PRXKDt2+mep7wK2WZYLZmnwsMnaJ9rb1FbAx7+FUQlmxvxUM643haa+fLH7x9aajx+Gv8yGgoM9PRL32/osVJfA2feZX9zc9RnMuRUW/QR+uAfOewgGDenSI7KKKvnl6r0kjhnMry+bpsq4coJ+EkRERKRrHA3m6I1x57a+5HbqlVCRCw21MPdb7fcVEGrCbFvLabe9CGt/AZ8+dvpnyW9B+BgYPq35WsyZkPWlmdWSnnc8Cf5+HpRmwwUPt/7zEhAKM641v7ioKur+MZ4sPw0+/ws46s3PXn9SUwZf/A3iL2r8BRDmF0fLHoelv+xyAAVocNj8+N+7sIE/XT8Lb6+uV9yV/kNBVERERFxzdBes+m5zYZOcr6C6GMaf23r7+IvA298czzJsUsf9RyVC9rbT9whW5MP6/wUs2P3vlpVVCw/BwY9g+jUtw03Mmab4TV5y+88sOw5/m9f1M0yldQ31sPV5eG4JVOTBN143ha3akngH1FfBjle6bYinsW1472dmWerYc+CrV/pXNd8v/26C/uKfeewRz3xykK3phfzm8qnEDBnksedI36QgKiIiIq75/M+w61/wn29CfW3j/lAL4pa03j4gFG56DS5/0rn+oxLMES/FR1peX/eQCQJfe8KEy71vNH+2+WmwvE0hpJONPtO8Zm4xweLAh2Y29dQZ0v3vmbB6aINzY5SO5aXCm/fAs2fD76Lg3Z9C3GL47iaYcF77946cYWbYN/wO8vZ3z3hPlfqu+eXGkgdgyf1mCevu//TMWDqjIh/e+iE8PhXe+A4krTFFwI58ARufMP8fjz/PHHPkAWm55fzpw/1cOmMkV86O8sgzpG/z6ekBiIiISB9SVw37PzBHsBz+BN7+ERSkwahZEDS07fviFjv/jOhE85q1DQbHNr//6hVzvEfCLfDFU7B9OSTcbP5x/dU/YcZ1EDqyZV+D4yAo0hwhk/xWc1GluLMhZm5zu7R15jW/h0JPf+NoMMeBFGeYv8+534KYM2DyZW1XTD7V5U/BMwvgtdvgWx81HwvUHeqq4P37TQXfM+4EL28YMcPM6s651fmvoSc01JlxbngE6irMbO7+92H3ypbtIiaa83w95HfvJhPo681vLpuK1Zu/X9JjFERFRETEeYc2QG05XPg7yNoKnzxqri/6ifueMWwK+ARC9g6z1LahHt75CQSPMGdOWhYk3maCwrE9JhjXVcL8753el2WZ5bkpb0NAOCx90BTKSXmrOYg21MGhT8z7npp962++egVyk+Dal2DqFZ3rI3QkXPE0/Os6sy/40lb2BbvT/g8g9T0oOmz2hpZmwa3vgHfjP5fPuBPW3ANHPjdnnp7qeJL5xYlfDy5BLc4wvwDI3m6OUrrod2bPdUOdqSid9aUp4BVzJgRFeGwYGw/k81FKLg9cPImhwf4ee470bQqiIiIi4rzkt8A/zMxwjl8KRelmv+aEC9z3DG9fGDnT7BM9+DG8/4BZNnv1C+AfYtrMvMEs1d3yjFluO24pDJ/aen+Lfmz6m/stU4Dl8Kfm6zjv1yaoZm0zS32Dh2tG1B1qymD9byFmHky5vGt9xV8I8++BL56EsWc3H83jTmXH4b37IOlN88uKoeNg9Dzz831y4Jx+DXz4S9j6XMvrNeXwwc9hx0swej7c9EbPhNHU92HVd8zZudcuhylXNM/cevua79/Ysz0+jAaHzcPvJBEzJJBbzor1+POk71IQFREREec01EPqOzDxouazHy//G8z7bnPVTXeJTjTh45UrzCzT9f9sGUICB5tqvF/90/z5ymfa7itqTvP5pGD6eefHkJsMw6eYZbmWN8y5DT55xOyt8+BsUb/3+Z9NleSvr3DPEtalD5oZ6/UPuz+I7lppChLVVcO5v4QF95rQ1hrfQEj4Jmx6EtZ8H6LPgEFD4YMHoOiI+Xnct9rsnb7hXy3PR/W0zU+bFQIjZsB1L8GQsd337FP8d1smKcfK+NuNCQT4evfYOKT3U7EiERERcc6RjabK5slhwNvX/SEUYOIlEDLShJC7t7QeQObcZl6HTzP74Jw16VLAMst1wRSkiTkDohuX6mpW1HkNdSbMbXnWLP3MTTFBbdrVzXt9u8rHz+wFzkuB/APu6bO+Ft7+sZlBHDYFvvs5LP5p2yG0yfzvw8SLTeGfNffAyq+bGcjb3jWzkMv+BGkfmn4dDe4Za0dKj8K6X8OEC+GOD3s0hJZU1vHY2v0kjhnMJdNH9Ng4pG/QjKiIiIg4J/kt8B1klsF6WuwC+ElK+21izjDLNuMvdG3mLWSECZ3Jb0Hi7ZCzE875H4iYYD7PS4UxZ3V+7AOBwwFJq80sZeHBlp95+5tfILjTpEvNzGXyW2aptavK86Chxvz81lXB63dAxhdw1g/MWL2d/CdxcCTc8Kr5+gvSzC8t4habytBg9i7XlMKHv4KcHeaXJJGTICzK7Hv2DYTAcBMWQ6PByw1zQp/+Hhx1cPGj3VvQ6RRVtQ3c8dKXlFbV8auvTVGBIumQgqiIiIh0zOGA5LfNcQ89WYzlZJYFF/62c/dOXmbCwvblgG32A4bFmLDgrlm3/srhMEumD38CkZPh6yth5Cw4vtecMRsRD4PHuPeZYdFm5j3lbdeDaOFh+NsZ0FDbfM0n0Ow5nn5N58bj5QWR8ea/Uy241ywdP7DW/FIj9T2wW5kd9fY3x9QsfRDiFnVuHAUHYcfLppLvkLjO9eEGdQ0O7n51O9szivjbjQnMiA7vsbFI36EgKiIiIh3L+hLKj5njN1xU3+DgD2tTSRg9mAun9pLlepMag+hnfzT7/EbOMuEiYjzkp/b06Hq3wxtMCD33F7Dwx+ZoEzBVbiec77nnTv4afPQbKM2B0FHO37f5aXOG7KV/NEuJ6yrN0u9hkz031oRvmv/ALAOuKjQzsXVVUJEHhYfMf0mr4aVlMPUquOBhM3Pqio//D7z9YPF97v8anORw2Nz33118nJrH/105nUumj+z4JhEUREVERMQZe18HL1+Id606rm3bPLhmH69uycDPx4tVd5/F1FFhHhqkC4aOM3sDc5PMss+mJZIRE82xNNK2L1+AQRFmWatXNxajmdQYRFPegTO+7dw9VUWmoNX0a0zV5J7g42eWg5+sqXrtOT83xZ02/skUzbrny9PbNslPgze+bfaxTr/WLCXf+5r5ZUBb93iYbdv85u0kVu/M4b4LJ3LjmaN7ZBzSN6lYkYiIiLSvstD8Y37aVRDgWoh8asNBXt2Swc3zxjBkkB93v7qDkqo6Dw3URZOWmdeT97xGxENxJtRW9syYeruSLEh918z2+Xj2fEjbtrFtu/lCZLz5+0le43wn2/4BdRWtnzHbG/gGwpL74bb3zN7SpgJap8rYAi+cb84JrS6Fd38KL19ujptZcG/3jvkkT65PY/mmdO5YGMfdS8b12Dikb+owiFqWFWNZ1seWZSVZlrXPsqw2f9oty5prWVa9ZVmdXHAvIiIivc7W58w/5hf80KXbXtuexR8+SOWKWaP49WVTefLG2WQXVXHff3e1DBinOFpSxT8+P8xDa/ZRU+/ByqNzboFp18CkS5qvRcYDtilEI6fbvtwsc028zaOP+TgllwWPrOdH/96Jw3HSz8qkZZD+ufnlCJhjU9LWtd5Jfa352R27BEZM9+h4u2zUbFPAKPW90z9LfhtevswUOfrWh3D3F3DXRjMTesXT5noPeGXzEf744X6uSojify6ZrOJE4jJnlubWAz+xbXuHZVkhwHbLsj60bTvp5EaWZXkDjwJrPTBOERER6Qm1FbDlGYi/2Jy56aT9x8t44I3dLBg/lN9fMxMvL4vE2CHcf/EkHn4nmZ+v2svN88YweWQIlmWRXVzFe3uO8t7eY2w/UnSin2lRYVwzJ9oTX5kpgHPNCy2vRTQWn8nfbwrJDGRHd8Hqu82xJlOvNMFu+0umSnG4Z5ZgllbX8fDbSfxnWxbDQvxZvTOH6MGD+OmFE02Dyctg4+Nmb2V5nnlfX336ObNglpOXHYXLn/TIWN3Kssy+1a3PQU05+Aeb64WHzbmko2bDjf9uPt92xPQeDdcrt2bwqzf3ct7kYTx69Qy8vBRCxXUdBlHbto8CRxvfl1mWlQxEAUmnNP0+8Dow192DFBERkR6y42Wzz86FSqW2bfOLVXsJ8vfhLzfMxs+neQHWHQvjOJhXwb+/zGDF1gxihw4iLNCXXVklAEwZGcpPL4jnomkj+e4/t7N802GuTojq0mzL+pTjrNmZw3VzY5g/dmj7fQ0ZB5aXqXY6kNVWwGt3QMEB+O+tpjrrkDioyPXYXsvs4iquf/YLcoqruHvJOO49bwIPrdnHkx+nERsRZH4hMSoBQqPg7R+Zm6ZeCUXp8Ob3YOTM5oBs27Dpr2YfcHccN+QO8RfBF0/CwfUwpbEo2LbGX5Rc/0pzCO1BdQ0OfvtOMss3pbNoQgRP3piAr7d2+knnuFSsyLKsWGA2sOWU61HAlcA5KIiKiIi4l6MBdr4Kn/zBFF05z81nNJ4sN8XMMI2caSqMbvorjFlgzux00mvbs9iaXsijV09naHDLfYSWZfG7q6bzkwviWbvvOO/tPUp5TT3/76JJXDxtBLERQSfa3nJWLL9YvZcdGUXMGTOk01/SH9fuZ19OKat35jBpRAjfWjS27XDrGwDhY8yM6ED2/gNmefI3XofdK2H9/4JvkPneeCDYFVfWcsuLWymprOO/d80/8ff9m8unkVFYyQNv7CZ6cCDzxg6FM+40M6LnPWSW3RYehmcXm+B827vmFyfv3ge5++Dyp1w7Y7YnjZ5n9nymvmeCaF2V2Zs9eZlrVYI9wLZtjhRU8vNVe9h0sIA7FsbxwMWT8FEIlS6w2tuj0aKhZQUDnwC/tW37jVM++y/wR9u2N1uWtRx427bt11rp407gToDhw4fPWblyZReHLyIi0r8NLtzJuIMvElxxhDqfEHzqK9iW+AQVwe49p9Fy1BObvpLRGa9hYVPtH0FF0BiGFm5n9/QHKRya4FQ/5bU2D3xWyfAgL35+ZgBeXQgB1fU2P9pQyfQIb+6eFdCpPrLLHPzP51VcF+9LsJ/Fh0fqySxzsCjKh1um+uHTypLCaXseJqA6l21z/wK2zYhj6ygPjqM8ZHynv5a+JCLvC6bte4SMmKs4NO4WsG3GHPk3cekrSBt3O1kxl7v1eTUNNn/4spr0Egc/SQxg8tCWlXgr6mz+94sq6hzw6OLAVv/OInM/Y2rSY+QPPYOwkiS8G2pIj72ejNFXmxnuPmJy0uMMLvqKTWctZ8SxDUxK/Qs7Zz5M8eDuW4Zr2zb5VTbppQ7SSxyklzaQXuqgog58LLh1mh8Lo3y7bTzSt51zzjnbbdtObO0zp2ZELcvyxSy7ffXUENooEVjZ+JvFCOASy7LqbdtefXIj27afA54DSExMtJcsWeL0FyEiIjLgZO+A5x+CwWPgkpfwjV0Ef01gbsFrcOka9830FKXD698yZ4XOugnGzCcg5V0CDn4EoxKYcdWPnH7W/a/vprKhiidvXcCkEaFdHtq26iSWb0pncsI8hoe6HkZ//34K3l6HuO/as4kM8eeXts0T6w7w548O0BAQxtM3zSEs8JR/VNeugy3PsmTxIvj8CUh90iz5/N6XZsa0NQ11UFMGgzo/c9utju01e0AnXADBkeZaQx0c/Bi2PAsjZzH6lmcZ7ePXeMM5UPxzxofFMN6NM4zFlbX85D+7OFhSyd9uTGjzDMpBMbnctvxLcoPGtXFEyBJYk0vEjpdh9Hy47K+MjZjAWLeN1CipqmNDai4XTxvZYsm520QUwmufsGRcEBz4DCInMeuKe7ptVnd3VjE//s8u0nLLAfDxspg4IoSvzQpjenQYC8ZFtFi1INIVHQZRy6TLF4Bk27Yfb62NbdtxJ7VfjpkRXd1aWxEREXGCw2GWFwZFwnc+bT425dxfmKMbktfAFDfMTDXUwwsXmmWA17wI064212ffZK5hOfWP4JRjpfz1ozTe2XOUOxePdUsIBfjm/Fhe+Pwwr24+wo8vmOjSvQ6HzZs7c1g0IYLIELNE2LL+P3v3HVdl2QZw/PewNyogMhURBw5QUNy5V2pqmbm1bNrO5tt8m29lUy1TM7dljhy5cm9cuFA2giCIKIhsznneP55MTcY5eFDU6/v59CHPue/nuR/F4jrXfV+Xwks9G+Jby443lh3lwR92s+yZ9jjZXBOMujYCXRFs/kgrhuPTFpL3wt5ppZ+VPXsUlj8Jl1Lh5ZNgZXczj1z1LsTDnAGQf0HLFvq0hZr1IHqttq3V3g0enKX1v7yWCQsUpV8qYOaOeBbsSyKvSMeHg5qVGYQCdGnkRrBPDaZuieXBEC+sLUrpX9pvMrQYBr7tr/aFNaF1x9N494/jnMspZFnDFH4cFYKtlYn7qDboDmYWsOVjSD0M/b40WRAaey6H7zbFkl+sVaI2VxRa+tagexN3/Fzt+XFbHF9vjMbN0Zr/PtCUIO8aNKrjiI3lLewVK+4phmREOwCjgWOKokT8/dpbgC+Aqqo/VtHahBBCiHtXxAJIOQCDp1/fuzNkvNYbcf3bWjbLwc18WgAAIABJREFU0vbm7pN2BC6naYHHlSD0CgOunZ1fzJvLjvLnsTQcrC2Y2NWf57oF3NyaruHrYkf3xrVZGJ7ExG4NSg9AyrA/8QIpWfm81ufGAPbBEG9cHa0Z+3M4Sw+eYXwHv6tvXqmcu/Mr7Tzk8MVawZ4dkyF4JDi6a+/rSmDn17DtMzC3guI8SN4H/l1v4olN7HKGFhhb/Z3Fys+ChcMAFUb+DmcOwMlVWv/Khn207wH/bjcGoSZyLqeAaVviWLgvCZ2qMqCFB093aUCjOo7lzlMUhZd7NmTMz+H8duAMo9uWsjXdwgrqdTTZWot1ek5n5hGfcZllh1JYdyKNQA8nRoT58u2mGMb+HM6scaE42phwm6qNs/YM8VvBykELrE0gPOECj889gF5V8a6pfVBSUKxj3Yk0Pl17CicbCy4VlHB/cw8+GdwcZzvZeiuqniFVc3cCBn8Uo6rquJtZkBBCCHHPy78If72nZar+/YOouQX0/R/M6Q+7voMur9/cvU7v1r5W8gf4aVtiWXc8jee6NeCxjn7UsDN9ADOuvR+jZu1j/t4kHuvoV/GEvy0/nIK9lTm9AuuU+v59Dd0I8nZmUXgS49rXu1q8yK2hlin0bAkPz9UCnF4fwtQw2PIRDPxe29a68nk4G6EFbz3eh+9aQsL26hOIJu6EhY9o2cHQx6D1Y1p12QsJMGaF9mce0BO6vlnlS8kpKGbKlljm7E6kWKcyNMSbZ7o0wNfF8OxxpwBXQurWZNqWWIaGeFdZpi6/SMd/V59gyYEzlPzdw9TawozX+zRmQic/LM3N8Hdz4KVfIxgxYx9zH21DTXsTft836qcFoi2Ggc3N7yxYc/QsL/0WgXdNW+aMb4NPrau/56lZ+Ww+dY59CRfo0tCNITdZoVoIYxhVNVcIIYQQt8CWT7RgtN8XpW/L8+uk9fXcP0PbKmp+E9mL07u1liWOpQdr5cnKK2L+3tP0b+HJK0ZumzVGhwYudG7oxjcboxkY5PnPNtvyFBTrWHPsLL2b1Sl3++TwNr68sewYh5KyCKlbU3vRtiaMXwu1m1zt5+jiD2FPwp6p2rbpI4vAzgWG/qK1EAHwCtEC0Vvt0llYMFQLgDu+pJ1TjV6v9Z+sURfcGmmZ251/n7B6YJpJM4cVScnK59HZ+4k+l8OgYC9e6B5QqXOGV7KiI2fu49f9yYxtX8/ka409l8PEBYeJPpfD8Da+hNatSX03BxrUdsDB+uqPzQOCPLG3Nuep+Yd4bM5+Fkxoa7ptuk2HQMwGaP/cTV9qxeEUXvotghDfmswYE3pDwOxZw5ZRbesyqrQMsxBV7M4pIyaEEELcCzLjYP9MLYPl0aLscSFjITcDYjZW/l56vRaI1m1fqemzdyWSW6RjYteqrSarKArvDQikoETH5+tOGTRny6lz5BSUMLilV7njBgR5Ym9lzqLwpOvf8G17/ZZogM6vakFqxHxoNRqeDb8ahAL4ddbO9RVcMmiNJnNwNqQf01rtfBsMq16AxSPArbEWUA+bB88dhLCnoPen0HLkLVva8ZRsBk/dRWpWPvMeDePrYcE3Veymvb8LberVYvq2OPR6wzo/GGrd8bMM+H4X5y8XMmd8Gz4Z3JwhrbwJ9qlxXRB6RbfG7nw7LJjDyVk8t+gwJTq9aRbi4Aajlmp9W29CVFoObyw7Sut6tZg/Icy0WVshTEACUSGEEKI6OTwPUKDzpPLHNegB9rW1s6SVdS4SCrK0PqFGulxYwi+7E+kZ6F7h+T5T8Hdz4NGOfiw5eIZDSRfLHXupoJipW2Op7WhNe3/XcsfaW1swMNiL1UdTuVRQXP4ibGvA2JXw+GYY8K0WlF7LrzOoOkjaY8gjmYauGA7OgQY94eldUK8DHPwFfMJg7Cqwd9HGufhrW7rbPXNLllVYomNReBIPT9+DhZnC70+3p2NA+X8WhlAUheFhPqRmF3A4ufzvA2McT8nm+cURNPZw5M8XOtG5oZtB8/o29+D9AU3562Q67/xxAkPbIla1y4UlPL3gIA7WlkwZ3lIKDolqSQJRIYQQorrQ6+DIYi3IrGirrLkltHgYotdpBWkq48r50EpkROfvPU12fjHPVnE29FrPdQugtqM17/1xgriMy+yMOc/vB8/802oCtCB0zKxwTp3N4aNBzTAvpefkvw1v40NBsZ4/DqdUvIg6zbUtuKXxbgPm1rd2e27Un1qxqdaPgXtTGL4InjsEo1eY5HyhsXILS/hpexydP9/Cm8uO0biOI8sndjDphxU9mrhjZWHGmqNpJrleVl4RT80/iIu9FTPHhBrdJmhs+3o808WfReFJjJ4VzsbIdHQmztYaQ1VV3lx2jMTzuXw/vCW1K9H2SIhbQc6ICiGEENVF3BbIOatlrgzRchTsmQLHfoN2E42/3+ld4Oyj9Sk1QkGxjpk7EugU4EqQTw3j71tJDtYW/Of+JrywOILuk7dd917PQHfGtKvLl+ujiDx7iWkjW9GrqWHnXpt7OdPU04mF4cmMalsXRVFQVdX4oi2WNuAbBgnbKh5rKgd+1v4MA3pdfc3F/9bd/xqXC0sY+uMeTp69RHt/F754KIhOAa4mL37jaGPJfQ3d+PPYWd6+vwlmBnzYUBa9XuXFXyNIv1TAb0+2w8Wh4vPHpXm1dyOcbS2ZvSuRx+cewKuGLY929GNkmO8tz0YuDE9i1ZFUXu3diHb+Lrf03kIYQwJRIYQQorqIWAC2tbRCRIao3QQ8W8HhBdD2GeP6DaqqlhGtRIXX7zfHcP5yIRO7tjR67s0aGORJiU5FUbRCK7XsrVh99Cxz9ySyMTIdK3MzfhgZQo9Ad4OvqSgKj7Tx5Z0Vx+n4vy1cKigmr0jHp4Ob83BrH+MWWK+zVlk374JWNKgqZcZp1VW7vQ1mt3frZYlOz8QFh4hOz2HGmFB6GvH7Xxn3N/dgY2Q6h5MvElK38r/PU7bEsjUqgw8HNaOlb82KJ5RBURSevM+fRzv68VdkOr/sTuTD1ZFM3xbHM138eaTNrQlIE8/n8tHqk3QKcOXp+27PBxJCGEoCUSGEEKI6yL8Ip9ZAyLgKezjq9Sr/WXEcRYH/Bo3AYu0krZ2IZ3A518+Colxw/rt4T2Ys5J4zelvub/uTmboljodDvQnzq+JAqxSKovBgiPd1r73c05Gn7qvP8sMp+Ls50La+8VmgIS29OJx0EVUFZ1tLtkVnMGNHPENDvY3L6Pl1hi1A4g4IfMDodRjlwM9gZgEtx1TtfSqgqirvrjzBtugMPh3SvMqDUIDuTWpjZWHG6qNnKx2Ixp67zHebYhgY5MmoMF+TrMvS3Iy+zT3o29yDvfGZfLUxmvdXRfLF+ijaN3Clc0M3WvrUwNHGAjsrCyzNFXKLdOQWllCiU6nvZl/pgFWnV5m05AgW5gqfP9TipjLFQtwKEogKIYQQ1cGx30FXaFBF0282xfxT5fV8fT+mm1ujRCy4MRBNOw4rn4XMeCjM1l7r9Ap0e0fblgtGFSraFp3Bm8uP0SnAlY8HN69W/QbtrCwYGVb5FhT21hZ89fDV37/f9ifz2tKj7E+8SBtjAm6vVmBpr50TrcpAtDgfDs+Hxv3BseoDv/L8tD2ehfuSeKaLP8PbmCagq8iV7blrj6Xxzv2BRgddqqry/soT2FqZ8+6AwCr5Xm5b34Vfn2jL3vgLrDmWytaoDDZGppc7x8JMIcDdkZC6NXi1d2OcbQ1vzTRzRzwHTl/k62FBeDjb3uzyhahyEogKIYQQ1UHEQnBvBnXKadkCrD12lu82xTA0xJs2frV4Y9kxtjm0pXPEQsy6vHn9dtCN78LFRAgaBjV8Ie0Y7JgMRXmQlwn2buBiWLGhE6nZPDP/IA3dHZk2shWW5nd3vcP+QR58uCaShftOGxeImltqWeaEHVW3ONBatRRkQesJVXufCvwVmc5n607Rv4UHk6qwl2xp+rfQtuceSrpIaD3jsqLrT6SxM/Y87w8IxLWS50INoSgK7fxdaOfvgqqqxJ/PJSY9h9xCHXlFJRTpVByszbG3tkBV4eTZSxxPvcTCfUlYmJnx/sCmBt0nKi2HyRui6dO0DoOCy29ZJER1IYGoEEIIcbudOwmph7Qej+VkZk6evcQrS47Q0rcGHw1uhrWFOa4O1ny1oD+dzbej7p6C0uNdbXDKIYjbBD3eh44vaa+pqnYGdd8PgAKBAw06V5qalc+jv+zHydaS2eNa42hjeJbmTmVnZcGQll4sCk/m3QFF1DKmB6NfZ9j4DqSf0CrZGutyBuiKrm6j/rczB2DrZ9DsIajX0fjrm0h0eg4vLD5MM09nvhwadMu3gna/Uj332FmjAtH8Ih0frj5J4zqOjGpb+Sy6sRRFwd/NAX83hzLHDAjyBODNZcdYsO804zvUo65L+X1XD56+yDMLDuJoY8HHg5tVq50KQpTn7v44UwghhLgT7JkKFjZaO5Yy6PQqExcewtHGgumjQrC20M6RdW1cm4f69WKNLgz93h8gN1ObsPMrsHGG0MeuXkRRoM+n0GkSoEL9LhUuLTu/mHGzw8kr1DF7fGvqON87rSBGhNWlSKdn6cEzxk0MHqFlm5c9CSWFZY8ruAR6/fWv5aTDjK6wYGjpcwpzYOkEcPKC+ycbV6DKhC7mFjFhzgHsrC34aUzIbelT6WBtQZe/q+cWlugMnvfD1lhSsvL5YGBTLKppZv/FHgGYmylM3hBd5hhVVZm39zSP/LQHawtz5k8Iq3TVXyFuh+r5t08IIYS4V1xK1XqHthwF9q5lDtsWfY74jFze6R94Q1/AgUGeTNU/hFJSALu+gXOn4OQqaPPkjb0kFQW6vwNP7YJW48pdWlGJnqfnHyThfC7TR4fQuM6t70t5OzWq40ho3ZosDE9Cb0xfSHtXGDgF0o/Blo9LH5N/Eb4Lhtl9tOATtHOfi4dDdjKcOwHZpfQ1XfsGZJ2GIdPB9ta1zgE4eiaLReFJfPLnSYbP2EtadgHTR4fc1vOIo9vVJf1SId9vijVofPqlAn7cHs/AIE/CKlHU6lZxd7JhQsf6rDySyvGU7BveLyrR8/rSo1ql5waurHq2I0087q2/n+LOJ4GoEEIIcTvtnQaqHto/V+6wuXtOU9vRmt6l9MasYWdFvcatWKd0RA2fARveBks7CHuq7AvWaQZm5f8Y8MmfJ9kdl8n/HmxB+wZlB8l3s5FtfUk4n8ue+EzjJjbqo1VA3vUdJO688f3wmdo53bNH4acucOYgLH9K21Ld5U1tzL/7kUZvgIj5WsEpI6sdA2ReLiQmPQedMUE1kFtYwmu/H2HglF28uewYv+xORFXhm0eCaXUTLU9MoVOAGw+28uaHbXGlBmz/Nm1LLHq9esvPs1bGE/fVp6adJf9bd+q617Pyihg9ax+/HTjD890aMGtsa5zt7v7t8uLuI2dEhRBCiNsl/yIcmA1NB0PNemUOO52Zy7boDJ7vFlBmkaAhrbz4JPIB+tjsQondCG0ngn3lMz7ZecUsCk/i4VBvhrTyrnjCXapvMw8+XH2ST/48ye9PtcfWyogtqL0/0YoWLX8Kntp5NYNZlKud0w3orfUAXTwSZnYHVOj1kfZnFz4D4rZo23yvODQHHD3gvteNfo71J9KY9NsRcgpLsLcyp5mXM141bLlUUEJOQTEAgZ5OtPB2pnEdp3/aiyRm5vLyrxGcvpDHM138GRHmi6ezbbVqDfJu/0B2xGQwackRVj7bESuL0v+OpGblsyg8maGh3vi62N3iVRrPycaSZ7sF8OHqSF5cfJg2fi7UdbHj7RXHSbmYzzfDghnUUgoTiTuXBKJCCCHE7bJ/FhRdhg4vlDts4b4kzBSl3NYYXRrV5nVbX/Y69KZ97iZo/+xNLW354TMUlugZ067eTV3nTmdjac7nD7bg8XkHeG3pUb57JNjwYjBW9jBkBvzcC5Y/CY8s0rLQh+Zq2dBOL4NHC3hiC6x8Tqtg3O5Zbft0/S4Qv1UrMKUo2nnSmI0Q+qhWmddAJTo9X2yIYvq2eFp4OzMyzJcTqZc4ciabfQkXcLK1xMnGgmKdnsXhyczelXjDNTydbVj0eNtK9We9FZztLPl4cHMen3uAaVtjebFHw1LHTd0Si4rKxK6GVYquDka19eVESjZbos6xIiIVgJp2lix4PIzWRlYKFqK6kUBUCCGEuB2K82Hfj9CghxaMlKGgWMevB5LpFehebqEgKwszBgZ58uT+R9g98QMcnTwrvTRVVVkUnkwLb2eaeTlX+jp3ix6B7kzq1Ygv1kfRxMORZ7oYEch4h0Cfz+DPSbD9C62C8e7vtf6tvm21MfauMHzR9fPqd4Hjv8O5SK3ybvQ6rc9s08EG37pYp+fRX/azI+Y8o9r68k7/wH+KXJVGp1eJy7hMdHoOuYUl5BVpBYCGtPSu9ls/ewa680CwJ1M2x9LU05megdf3Vk2+kMdvB5IZ1toH75rVPxt6hbWFOV8NC0ZVVRIz8ziekk1I3Zp41pA+oeLOJ4GoEEKIu9+VrFJ1cvRXyM2ADi+WO2z10bNk5RUz2oA2E4NbeTNnz2n+PGPDMI/KL+1Q0kWi0nP4dEjzyl/kLvNMF39OpeXwxfooGtZ2pMe/Ap1ytZ7wd8uVTyEzFi6lwMDvyp/j31X7GrdFC0RPLNcq5Xq3Nvi2n609xY6Y83w8uBkjwyr+/jE3U2jo7khDd0eD71Gd/HdgMxLP5/LU/IN8ObQFg1te3VI+dUssCsodlQ29lqIo+Lna4+dafisXIe4kUqxICCHE3S0nDb5qov0gX50cmgu1AyvsAzlv72n83exp51/xtsggb2fqu9mz9FAp1VaNsHBfMvZW5gwMqnxW9W6jKAqfP9iCpp5OvPhrBDHpOcZMhv5fawWijv0GdVqAf/fy5zh7g0uAtj03Pwti/9KyoRUUmLpi9dFUZu1MYFz7egYFoXcDZztLFjzeljC/Wrz06xGmbonlh61xPDB1F4v3JzO8jc9trfArhLieBKJCCCHubpv+CzlnIX5bxWNvlXMnIeWg1rKlnEztqbRLHEnOYmRYXYPOJSqKwuBgL8ITLpCWXVCppWXnFbP6aCoPtPTC3lo2Tl3L1sqcn0aHYmNpzuNzD5CVV3TDGFVVmbI5hg6fbebYmWuquFrZwbD54NkKev7XsAx9/S5wehdErgBdkcHbcmPSc3jt96OE1K3JW/2aGPZwdwkHawt+HteanoHufLE+iv+tO4WqqrzauxFv9L23fi+EqO4kEBVCCHH3SjkEEQu0fz8XeXvXcq3D88HMAloMK3fY8kMpWJgpPBBseGayb3OtvcvGyLRKLe1KkaIR5RRGupd51rBl+uhWpGTl8+zCw5To9P+8l1+k49lFh/lyQzSZuYWM/2U/yRfyrk6uWU8rTHRl221F/LtCcR5s+RScfcErpMIpxTo9Ty84hJ2VOVNHtCqzguzdzMbSnB9GtmLuo23Y/UY3Vj7bkYldGxhX8VgIUeXuvf86CSGEuDeoKqx7E+zdoMUjkB6pvXa76Yq186GN+mpFasoapldZEZFCl0ZuuDhYG3z5BrUdqe9mz/oT6UYvTVVVFoYnSZGiCoTUrcXHg5qzM/Y8E+Ye4PN1p/hxWxxDp+/mz2NneaNvY1Y/15FinZ6xP4dzIffGzKlB6nUExRwup0HTQQZlUdefSCP23GU+GtS83OJWdzsLczM6N3SToj5CVGMSiAohhLg7nVgGyXuh2zvgGwZFOZCVdLtXBTEbtCJFwaPKHbY77jzplwqvK7hiqN5N67AnPrPUraPlOZR0kej0y5INNcDDrX14oXsAR89k89P2eD5be4rTmXnMGhvKU/f506C2I7PGhnImK5/H5uynoFhn/E1snK9mQQ3cljt392l8a9ndUDVWCCGqGzn8IYQQ4u5TnA8b34M6zbVzmGcOaK+fi4Sat7lwy+H54OCutW0px/JDKTjaWNC9SW2jb9GnaR1+2BrHppPneDDE8EB2wb4kHKwtGCBFigzyUs+GvNSzIaqqkl+sw0xRsLG8uv0ztF4tvh0WzNMLDjFjezzPdQ8w/iatxoBtDfBsWeHQyNRLhCde4O37m2BuVs2qRAshxL9IRlQIIcTdZ/uXkJ2s9W80M4fafxcpST9xe9eVkw7R6yHoETAv+7Pg3MIS1h5Po38Lj+sCG0O18HbGw9mG9ScMPyeanVfMmqNneSDYU4oUGUlRFOysLEr9s+rb3INege78uC2OjJxC4y/eajSMXGLQttw5uxOxtTRnaIiP8fcRQohbTAJRIYQQd5eMKNj1LQQNv9oaxcZJK/ZyuwoW5WfBiRWw8llQdRVuy11/Io38Yl2ltuWCFhj1CnRne0wG+UWGbQld9neRouGyLdfk3ujbmMISPd9uiq6ye1zMLWJFRAqDWnrhbGdZZfcRQghTkUBUCCHE3UNVYc0rYGUPPT+8/j33QK1g0a1UUggrnoHP68OSsZC0Dzq8CG4Ny522/HAK3jVtCa1bs9K37t20DgXFerZFZ1Q4VlVVFoUnESRFiqpEfTcHRoT5sig8mdhzl6vkHr8dSKawRM/Y9vdGz1AhxJ1PAlEhhBB3j6O/QuIO6PE+OLhd/17tQMiMgZJKVjA1Vv5FmDdEax/TegKMXwevxUPPD8qddjY7n12x5xnS0guzmzjn18avFjXsLA3annvwtFakSLKhVeeF7gHYWprz2dpTJr+2Tq8yb+9pwvxq0biOk8mvL4QQVUEOgQghhLhzlRTCqTVQkKUVKNrxFXi3hlZjbxzr3hT0JXA+Guo0q9p1ZSXDgqGQGQtDZkKLoQZPnbUjAUVRGBp6c+f8LMzN6NHEnQ0n0rhcWIJDOec+F0qRoirn4mDN0138+WJ9FHvjM2lb38Vk1568IYozF/N5t3+gya4phBBVTTKiQggh7lyrX4bfx8Pql2D9W9r5y/5fg1kp/3ur/fcP6VVxTlRXrJ0BXf0y/NgRvm0Bl1Jg1FKjgtCsvCIWhicxoIUHPrXsbnpZw1r7kFukY8SMvWRevr5QTl5RCb/tT2bQ1F0sO5zCkFZeUqSoij3awQ8PZxs++fMker1hPW2nb4tj3OxwsvOLS31/9dFUpm2NY0SYL72a1jHlcoUQokrJ/3GEEELcmc4e1ba9tnkSOr4IlnZg5VB2NVrXADCzNG3l3MLLcGgu7J2mVem1cgTvEOj8KjR/GFwbGHW5ObtPk1ek46ku/iZZXut6tZg+KoSJCw8xdPoe5j0WxsXcIhaFJ/FHRCqXC0toUNuBd/oHSu/QW8DWypxXejVi0pIjrDqaygPBXuWOn7M7kU//3so7fnY48x4Lu+7DgsjUS7y65CihdWvy/oCmVbp2IYQwNQlEhRBC3HlUFTa8rfVX7PqW9rUi5pbg2tA0GdGcdAifDvtnaduCfdtDvy8hoKfWLqYS8opK+GV3At0b1zbpOb8ege7MnxDGo7/sp9uXWyks0WNtYcb9LTwY0caXkLo1UQxoDSJMY3BLL2btTODzdVH0blqnzPY8f0Sk8N7KE/QMdOeBYE+eX3SYJ+cdZObYUBQFdsdl8s6K4zjZWjBtVCusLGSTmxDiziKBqBBCiDtPzEZI2AZ9/mdYEHqFeyD5sTs4k55DgLuj8fctytO2AEcsBF0RNOkP7V8An9bGX+tfft2fzMW8Yp7papps6LVa16vFb0+2Y/KGKDo2cGVwS29p8XGbmJspvNWvMaNnhTN3TyJPdL7+z7ugWMfKI6m8tewYYX61+H54S2wszSko1jNpyREGfL+Ts9kFXC4swdnWkjmPtqG2o83teRghhLgJEogKIYQwnYJsrYCQQ+2qu4euRMuG1vKH0EeNmppgVhe//CW8uWAHv73Y1/iqtHunwcHZEDIO2j8PLqYJGotK9MzYHk+berUIqVvLJNf8tyYeTswce/MBs7h5nQLc6NzQjSmbY2lX35W8ohLOXy5ie3QGfx47S05hCUHezswcG/pPxvShEG/yi3VM3xZH/xYe9G5ah3b+LmVmVIUQorqTQFQIIYTprHwOTu+GZ/aCvWvV3OPwPDgfBcMWgIWVwdNyC0uYdtKaLwAl4yQbT7aitzHFXQpzYM8UCOgNA741ft3lmLM7kdTsAj4e3Nyk1xXV11v9GtPv2x0MmLLzn9fsrczp08yDwS29aOfvgvm/PigZ3bYuo9tKn1AhxN1BAlEhhBCmUVIIMX9BcS6seRmGzgFTnz3U62HXt+AVCo3vN2rq5A3R7LrkDjbQ3jGN7zbF0CvQ/er5yLRjELFI6zV6PgbsasHwxVezu+E/ab1Bu7xu0kc6efYSX6yPolegO10auVU8QdwVGtdxYu6jYWTmFlLL3opa9lbUd3XA1koynEKIe4OcbBdCCGEap3drQajffRD5Bxxfavp7xKyHiwnQbqJRQe7hpIvM3p1At7CWYOfCGLt9xKRmsunkOW1AVjLMGQgHZsGls+DRAtIjYfFIKC7QquPungIBvcArxGSPU1Cs46VfI3CyteTTIc2laNA9pmOAKw8Ee9EpwI2mns4ShAoh7imSERVCCGEaMRvB3BqGzYd5g+DPSVCvEzi6G36NkkKtLUvyPjizXwv8Wo68+v7eaeDkDU0GGnzJgmIdbyw9Rh0nG17v2wQCvsDl90f5yn4u321yoXuAM8qSsVov0Kd2XW25cmIFLBmrbTeu3QTyL8B9bxj+LAaYvCGKU2k5zB7XGhcHa5NeWwghhKjOJBAVQog7RU4anFwFrSeYdsurXgeXUqDGTfaRjNkAfp3AxgkG/QjTO8HyJ2DQD+DkWfH848u04DUvU/u1tROcWq21XPFpDWnHIWE79Pig7F6hpfh4zUmi0rVgz9HGEpo9COdO0n/7FxxK8yTlt0V4pxyEh+dd3/ez6SDIfBs2fwSKOTToqfUINYG07AJ+3Z/MjB0JjG5bl66Nq7C4kxBCCFENSSAqhBB3Ar0elk6AxB3gEQQ+bUx37Q1va5nGgN5w3+ulvlSTAAAgAElEQVSVC7YuxGtnK9s8rv3arSH0+QzWvALfBkHLUdDhRahZSqGVvAvauBPLtG2v/b8BnzCwsIbpneH38fDkdtj3A1jaQasxBi/rz2Nnmbf3NBM6+l0f7HV5C336Sd6Omo9ZjArtn4PAUrKsnSZBRjQcWwJdbj4bujc+kx+3xbE9OgO9Cvc1dOOtfk1u+rpCCCHEnUYCUSGEuBMcnK0FoQBRa00XiKZHwr7p4N0azoTDzG7adtgHZ2mZTUPF/KV9bdDj6muh48G/K+z8Bg7Ph8MLYOQSqH/f1THnTsLcQVoWtNs7WrB6bbZz6GyY1Rt+f1Q7g9pypFZEyABJmXm8/vtRgnxq8Fqfxte/aWaG2ZDppH3fk+hL5gS0fh2P0i6iKDD4R+j6JtSqb9B9S3Mht4iP15xk6aEzuDtZM7FrAx4K8aaui32lrymEEELcyaRYkRBCVHcXT8PGd6F+V+3MZdRa01xXVWHta2DtCMN/hRePQfd3IfYvWPemcdeK2QAuDW7sq1mzHgz4Bp6P0N5bPAJSDmnvZURrBYIAHt8MnSfduOXWKwR6/hfit4CuEMKeMmg5hSU6nl10CEWBKcNbYmVRyv/urB3IH7uBMUVvsOrYubIvZmZe6SBUVVWWHjxD98lb+SMihYld/dn2alde6dVIglAhhBD3NAlEhRCiOlNVrVgOCgz8Hhr1hYyTcCHh5q8d+YeWZe32Nti7aAFpp1eg40sQMR9OrTHsOkV52nUCepU9xtkLRi3TspkLHoLo9TBngPbe2FValdqytH1a244bMg7cGlW4HFVVeXv5cY6eyebzh4LwqWVX5lg/N0eCvGuw4nBqhdc1VnzGZUbO3McrS45Q382BNc934tXejbGxlMqoQgghhASiQghRnR2aAwnboNeHUMNHC0QBoteVPefgHMiMK/+6RXna2VD3ZhAy/vr37nsD6jSHlc/D5YyK15i4E0oKrt+WWxonDxi9AhQzWPgw6IpgzB/aedLyKH8H4QO+rXgtwOxdiSw5eIbnuzWgT7M6FY5/INiLyLOXiEnPMej6hpi5I54+3+7gWEo2Hw9uxpIn29GojqPJri+EEELc6SQQFUKI6io3Eza+p23HDRmnvVarPrg2Knt7bk4arHoedkwu/9p7p0J2MvT9343bYS2sYPBPUHgJVr2gtTUpjV4H52MhYoFWRKhuh4qfycVfy4wG9NKCUPfAiucYYXt0Bh+tiaRXoDsv9qggwP1b/yAPzBRYEZFikjXM2pnAR2tO0qWhG5tevo+RYXUxM5P+oEIIIcS1pFiREEJUV5s+gKLL0O+L69u1NOoLe6ZAQTbYOF8/5/Ru7WvcZm1bb2ltXlQVIhZpAW69jqXf2z1QOy+64W34yB2cvf+ueKtAcT4U5cLFBCjO08Y3HQKWNoY9l0cLrWiRiSVfyOPZhYdo6O7I18OCDQ7+ajva0KGBK39EpDKpVyOUm2iNs+JwCh+ujqRP0zpMHdkKcwlAhRBCiFJJRlQIIaqDjCjtnyvOHIRDc7XiPLX/1d6jUV/Ql2hFhf4taa/2NecsnIss/V7pJ+BCHDQdXP6a2k6Eob9oZ0a9W2sBaHE+WNlpQWmrsfDAVHhiGwz5yeBHrQqqqvLW8mPoVZgxJhR7a+M+Zx0U7MWZi/kcPH2x0mvYGnWOSUuO0LZ+Lb55JFiCUCGEEKIckhEVQojbLT8Lfu4D+Re1fptd/wN/vgIO7qX3rvRuDXYu2vbcZg9e/17SbnBrDBmntEDVvemN8yP/0M5pNhlQ/rrMzLRgtaKAtRpYeSSVHTHn+WBg03KLE5Wld7M6/GfFMVZEpBBaz7D2MNeKSsvhmQVaNnbGmFApSCSEEEJUQDKiQghxu+34UgtCg0fCkcXwTTNIPQy9P9Yq2f6bmTkE9NZaplx7frMgG9KOQ+AgqB0IsZtunKuqELlCO8/pULvqnukWys4r5sPVkQR5OzOqbd1KXcPB2oI+Teuw9GAKpzNzjb7/E/MOYG9twezxrXG0sazUGoQQQoh7iQSiQghxO11IgH3TtSB00FSYuA8a3w/Nh96Y7bxWo75a4Jmw/epryeGACnXbgX83SNqjneW8VsYpOB8NgQ9UyePcDv9bf4qLecV8MqT5TW2Hfb1vYyzMFV5dchS9XjVojk6v8vziw6Rm5fPjqFa4Oxl4TlYIIYS4x8nWXCGEuJ3+eh/MLLRenqBVlX14bsXzAnqBbS04+As06K69lrQHFHNt666q1woaJe6Chtf09zyxAlCgyUATP0j5Es7nsmDvafKKdRSX6LGyMOOZrg3wqmF7U9fddDKdhfuSmNDRj6aezhVPKIeHsy3v9g/k1d+P8svuRB7t6FfhnMkbotgWncEng5sTUtf4Lb1CCCHEvUoCUSGEuF2S9mrbZLu8qfXYNEKOzpxk9/40iVqIkpMOju5weg94BIGVPfi2BwtbiNt0fSAa+QfUba+Nr4BOr7IxMg3vmnY08XCqdLZxd9x5np5/iPwiHU62lliZK1zIK2JjZDqzxramuXfFAWRMeg5Z+cUE+9TA0tyMgmIdn6+L4uddCTSu48hLPQ1r1VKRh0K8WXc8jc/Xn6JLIzfquzmUOfbnnQlM2xrH8DY+jAjzNcn9hRBCiHuFBKJCCHE7ZMbB6pfA0QPaP2fU1NzCEsbN3s/FpBZstp5L0cG5WHV8HlIOQpvHtUGWNlCvw/XnRDOiIOMk9P3CoPv8uC2OL9ZrlXwdrC0IqVuT57o1MKqYz+LwJN5ecRw/V3tmjW2Nr4tWSCg6PYfxs/fz8PQ9fD+8JT0CSw+MI1Mv8d2mGNadSAPA0dqCTg1diTuXS1R6DuPa1+ONvo1NVhxIURQ+GdKcXl9v56n5BxnfwY8ujdzwcL4+cztzRzwfrTlJ76bufDCwmUnuLYQQQtxLJBAVQohbSVcMu7+Hbf8Dcyt4cJaWwTRQfpGOx+bsJyI5i9Ht2rPnQCD1d8ykhndbrHWF4Nvu6mD/7rD+Tbh4WusDemgu2rbcCqrlAoeTLvLVxmj6NqtDn2Z12J94gU0nz/HIT3t5f2BTg4oCzdmdyHsrT9C5oRtTRrTE6ZoiPg3dHVk+sT0T5hzgiXkH6Na4Nn2bedCjiTvncwvZEZ3B5qgMtkdn4GhtwfPdA2hSx5Ft0RlsjcoAYPb41nRtZPqCS+5ONnz1cBDvrDjOm8uO/b1eB0Lq1iTYpwapWQV8uymG+5t78M0jwViaS7kFIYQQwliKqhpWkMHUQkND1QMHDtyWewshxG1RnK+1aTkboQWDfT8HJ0+DpxcU63h87gF2xp7nm2HBPBDsxYHVMwg9MIkTVkE0LToCr8aBvas2ISMKpraBep20AkWX06FBTxj1e7n3uVxYQr9vd6DTq/z5QiecbbUAMju/mBcWH2ZrVAbD2/jy/sBArC1Kz0SmZuXT46tthPnVYsaYUCzKCNbyikr49q8YVh1JJTW7AEXRCvsC1HOxY2CwF4918MPZ7moQe+X/W4pStX06VVUl5txltpw6x87Y8xxJzuJSQQkAA4I8+frhoDKfSwghhBCgKMpBVVVDS31PAlEhhLhF9k2Hta/B4OkQ9IhRU4+nZPPybxFEp1/mi4daMDTUR3ujpJDCzxthXXSRC3b1qPXakauTVBWmhEJWMgT0hOYPQcM+YFl+gaBXfjvC8sNn+PXJdrT+1zZcnV5l8oYopm2NI8jbme+Gt6Suy40Z3afnH2RL1Dk2vnSfQX09VVXlyJlsNp86h7uTNZ0auP2zjbe60OtVEjJzSc8uoI1fLQlChRBCiAqUF4jK1lwhhLgVSgph5zdaESEjgtBinZ5pW+L4fnMMLg5W/DK+NV2u3Y5qYY116CjY/T07CgPor1evFhVSFJjwFyhmYGNYRdktUedYeugMz3cPuCEIBTA3U3itT2NaeDvz2u9Huf+7nXw8uBkPBHtdd421x9N4tXcjg4JQbakKwT41CPapYdD428HMTMHfzQH/cgoYCSGEEMIwEogKIcStELEAclJh0DSDhquqyvoT6Xy+/hTxGbkMCvbkg4HNrtui+o+Q8ej2/sTq/ObYnkynV9M6V9+zrWnUMn/cGoensw3PdWtQ7rg+zTxo5uXMi4sjeGFxBMsOpTCopSedAtx4748T+LvZ83in+kbdWwghhBD3DglEhRCiqumKYcfXWn/P+l0qHH4iNZu3VxzncFIWDWo7MHNMaJlVZQFw8Ud9NY7j34STv+f09YGoEY6dyWZfwgX+06+JQQV4vGvasfiJtkzfHs/8vad56dcj/5zxXDghDCsL2boqhBBCiNJJICqEEFXtyGLIToL7J2vbZcuRW1jC43MOUKRT+d+DzXmwlbdBZxEtbB0ZGebLlxuiicu4XKntozN3xuNgbcGwNj4Gz7EwN2Ni1wY8fZ8/h5MvsvroWVwdrGnfwNXo+wshhBDi3iEfVwshRFXSlcCOyeARrBUMqsC3m2JIzS7gx1GtGNba16iCOI+08cXK3Ix5e04bvczUrHzWHD3LsNY+17VZMZSZmUJI3Vq8N6ApE7uWv61XCCGEEEICUSGEqEpRa+BiAnSeVGE29OTZS8zamcAjrX0ILaVQUEVcHazp17wOSw+eIbewxKi5c3YnoldVxneoZ/R9hRBCCCGMJVtzhRCiKu2fCc6+0KhfucP0epX/LD+Gs60lr/dpXOnbjW5XjxURqfT8ahtOtpbYWJrzQLAn4zv4lTnncmEJC8OT6NvcA++a1atlihBCCCHuTpIRFUKIqnI+BhK2Q8hYMDMvd+ivB5I5lJTFW/2aUNPeqtK3bOVbg5d6NCSkXi18atmh06t8sCqSz9aeoqy+0dO3xZFTUCJVboUQQghxy0hGVAghqsqBn8HMElqNKXdY+qUCPvnzJGF+tXiwlVe5YyuiKAov9Aj459d6vcrbfxznx21xXC4s5r8Dm2FmdnWL8MbIdL7fHMuDrbyrdQ9PIYQQQtxdKsyIKorioyjKFkVRIhVFOaEoyguljBmpKMpRRVGOKYqyW1GUoKpZrhBCVCNHFsOyJ+DS2RvfK8rTeoc2GQAOtcu8hKqqvLPiOEUlej4d0hylgnOkxjIzU/h4UDOevK8+8/cm8cS8AySczwUg9lwOL/0aQZC3Mx8PbmbS+wohhBBClMeQjGgJ8IqqqocURXEEDiqKslFV1chrxiQA96mqelFRlL7AT0BYFaxXCCGqhwsJsOpFKMmH6PVaa5bmD119/8QyKMiG1hPKvcyaY2fZEJnOG30bU78SLVcMoSgKb/RpjJuDNZM3RNPzq20Ma+3D7rhMbCzN+HF0CDaW5W8dFkIIIYQwpQozoqqqnlVV9dDf/54DnAS8/jVmt6qqF//+5V7A29QLFUKIakNVYfWLYGYBY1eBSwNY+hgsGg7Hl0LeBa1IkVsTqNu+zMtcyC3ivT9O0NzLmQkdyy4mZAqKojChU322vdaFEWG+/Lo/mTMX8/hhVAgezrZVem8hhBBCiH8z6oyooij1gJbAvnKGPQasLWP+E8ATAO7u7mzdutWY2wshRLXgnraZJvFbiQ54itTTehT/t/CxWopv7Aosov5ERUFBJabBE6Rs21bmdaYfLSArT8eLwebs3LH9lq2/mzM072hDbrFKbuJRtibeslsLIYQQQgCglFVF8YaBiuIAbAM+VlV1WRljugLTgI6qqmaWd73Q0FD1wIEDRi5XCCFus8sZMLU1uDaC8WvB7JqNJboSSD0EsZvgYiLc/yVYO5Z6mb3xmTzy016e69aAV3o1ujVrF0IIIYS4hRRFOaiqamhp7xmUEVUUxRJYCiwoJwhtAcwE+lYUhAohxB0pIwpWvQBFuTDg2+uDUABzC/Bpo/1TjhKdnvdXnsCrhi3PdGlQhQsWQgghhKieDKmaqwCzgJOqqn5VxhhfYBkwWlXVaNMuUQghTChqLSx/CgpzDJ+TdwHWvg7T2kF6JAz8Hmo3rvQSFoUncSoth//c3wRbKykSJIQQQoh7jyEZ0Q7AaOCYoigRf7/2FuALoKrqj8C7gAsw7e/WAyVlpWCFEOK20JXA5v/Crm+1X5tbagFlRbKS4ec+kJMKIeOg63/A3rXSy7iYW8SXG6JpV9+Fvs3qVPo6QgghhBB3sgoDUVVVdwLlNrZTVXUCUH6PAiGEuF1y0mHJOEjaDaGPgqUd7JkCDftC435lz8s9D/MGQ+EleGwjeN/852uTN0ZxubCE9wc2NXnPUCGEEEKIO4VRVXOFEOKOcyEe5g6C3AwY/BMEDYOSIojfBiufA+/W4OB247yCSzB/CGSfgdHLbzoIVVWV6dvjWbAvibHt6tGoTulFjIQQQggh7gUVnhEVQog7Vnqktq228BKMW60FoQAWVjDkJ+31Vc9rfUGvdTkDFg6D9BPw8Fyo2+6mllFUoue134/y2dpT9GvuwRt9K3++VAghhBDibiAZUSHE3SN6A5wJBwtrMLOAnd+Apa3WZqV2k+vHugdC9/dgw3+0YLXPp+DVCuI2a8WM8rO0YLVhL6OXcfLsJRaHJ1GsV1GAE6mXiEjO4vluDXixR0PMzGRLrhBCCCHubRKICiHuDvkX4ffxUHT56mu1/GH0MqhZr/Q57SZqfT43fwgzukK9TpC4A9waw6hlUKeZUUsoLNExZXMsP2yNw9LcDHtrC1RVxcrCjK+HBTG4pXfln08IIYQQ4i4igagQ4u6wf6YWhD61C1wbQkk+WNprvT3LoigQMhaaDoYdX8K+6RAyHnp/AlZ2Rt0+Jj2Hp+YfJC4jlyGtvHjn/kBq2lvd5EMJIYQQQtydJBCtCkl7IXIldHvb6B9mhbgtcjPBxrn8oK06K86HvT9Cg55Xs5gWRgSBNk7Q87/Q7d1K/R7kFpbwxLyD5BSUMOfRNtzXsJTiR0IIIYQQ4h9SrMiU8i7AH8/Cz71h71SIWX+7VyRE+VQVDvwMXzWBX0eCXn/9+1FrIWLh7VmbMQ7Ph7zz0PFFg6eU6PT8tj+Zx37Zz28HkinW6SsdiL/7xwlOZ+YyZURLCUKFEEIIIQxwh6Y/qqEzB2HBQ1CQDe2fgwOzIWGHtuXvdsmMgz1TwcFd+wHdwvr2rUXcHsX5WnbewkrLeNrWBEdPsHeD4lxY9SIc/x1cG0H0OtgxGe57VZsbtQ4WjwRVDzV8oV7H2/ss1zp3Cpy9tPOduhLY/b3WhqVuhwqn6vUqK4+k8u2mGBLO51LL3opNp87x3aYYnu7iz7BQHyzMDf+MbsXhFJYeOsPz3QNoW9/lZp5KCCGEEOKeIYGoqfz1HphbwVM7wL2p9oNy4o7bs5bsM7Dtcy1LZGYBukKIXAGDfgDPYMhK1oKOosvaeTjbGrdnnaLqHVkMq0vJEppbgYUtFOVoW8g7vqxVit3yMXi1BGsnWDIOPFpo1WNXPANP7wZrh1v+CDfIjIMf2mlrDHtSC6qzTmvnOpXyq9GePHuJN5cdIyI5iyYeTswcE0q3xrXZGn2O7zbF8p/lx9kWlcH3I1pibWFe4VISz+fyn+XHaFOvFs93a2CqJxRCCCGEuOtJIGoKZw5qQWevj7QgFMCvE2zcCDlp4FjHNPe5kAB7f4Du72iZoNIkh8P8B7VMWOsJ0OkVOHtE65U4szu4NICMU1fH7/wGOk+C1o+DpQ3odVoGzNzSNGsWt1fSXi1QG7NSy9bnX4BLqdqHFbnnIXgE1Ps7izjgG0g/DksnaL928oARSyAzFmb3hY3vQP+vb9+zXHF4vvbVtx1s+5/2764NoVG/MqcUFOv45q8YZu6Ix8nWkq8eDmJQsNc/bVS6NXana6Pa/LI7kQ9WRfL43INMHxWCrVXZweiBxAs8veAQFuZmfPNIsFFZVCGEEEKIe50Eoqaw62tt22PIuKuv1eukfU3cCc0fMs19Nv0XTiwDM3Ot5+G/Je3VglCH2jBqKdSqr73u2Aue2QMb34OLCRA8Ehr1heI8+OsD2PA2bPlEOy9Ykg/m1vD0LnANMM26xe2TvBd8wrSemRWxsodh8+GnLmBho7UvcXDT/mk3EfZMgcb9oUH3Kl92mfQ6OLJIK0o0YjGcOwn7Z0GTAWBWeiB4/nIhj/6yn6Nnshka4s1b/ZqUWs1WURTGd/DD3sqC15cdZdzscH4aE4qz7fUfyqiqypzdiXy05iTeNW2Z91gbPGvYVsnjCiGEEELcrSQQvVnnY+Dkai3zeG2W0iMIrJ0hYbtpAtGLidr2WtuasO9HCBqubZu8InEXLBiqZbHGrta+Xsu2Jgz87sbrjl4G8Vvh1BrtDKm5tdbG4uRK7ZnEnSsnXfu+aT3B8Dku/vDEVrC0BSfPq693extiNsDK5+H5Q2WfN9brtUDxYoKWldeXQOhj4NbwJh7kGnGbIecs9P07E1q7Cdz/ZZnDE87nMvbncM7lFPDT6BB6Na14d8LDrX2wsTLnpV8j6Pz5FsZ3qMf49n5YmCtsjEzn94Nn2Bl7nh5NajP54eAbAlUhhBBCCFExCURv1u7vtB/Kw566/nUzc6jb3nTnRPdMA8Ucxv0JcwbA6pfgsY1aFujoEm3rrbMPjF1p/Fbg+l20f66I3QjR6yUQvdMl79W++rQ1bp6L/42vWdpqwd+8wVoV3dDxpc89vhT+eAYUM+0ManEelBRq235N4fB8sHOBhn0rHHokOYvxv+wHYNHjbWnpW9Pg2wwM8qS+qz3fborhm79imLE9Hp2qUlCsx9PZhv/0a8JjHf3+2dorhBBCCCGMI4HozchJ04rBtBytbV/8N79OEL0WslO0Cp+VlXcBDs+D5kO1LZa9P4blT8L+GXA+GvbP1M7LPTxX25Z7sxr2ge1faL0l7aUK6B0raZ+2xdYjyOApJTo9W6MycLK1pI1frevfrN8VPFvBrm+07/l/tzrRlcC2z6B2U3hqp/YhyfwHIWmPCR4G7e9B1J9ahrWCHqH5RTqeWXAIOytz5j0Whp+rvdG3a+blzIwxoZw8e4mfdyZgbWnGwCAvQuvWlABUCCGEEOImSSBaWXq9drZSX6K1aynNP+dEd0DQI5W/1/5ZWmbpyn1aDNMyQ2tf037d7lno8b7pCgwF9NaKwMT+BUHDTHNNcesl79UCxwqCNtACt98PJjNjRwJJF/IA6BTgyut9GtPMy1kbpChalvzXkXBiObQYev1Fjv+uFTZ6eN7V85q+bWHzR1oQafevwNZYx5aArghajqxw6JQtMaRk5fPbk+0qFYReq4mHE18MNTyYF0IIIYQQFZMyj5WhqrDuDe0H4y5vQi2/0se5N0O1rUnE9pVEJGdV7l7FBRA+XSvOcqXgjKJo1Ut9wrQf+nt/bNoqt54ttUqrMetNd01xaxXna9WSfcMqHHrsTDbdJm/lnT9OUMveih9GtuLt+5twLCWb/t/vZMzP4UzbGkt4wgUK/HuDWxPY+ZX2YcwVuhLY+hnUaa4VNLrCt732NWnvzT/T4XladrdO83KHxZ67zE/b43mwlfeNWV0hhBBCCFEtSEa0MrZ8rAWH7Z6Fzq+WPc7MjESHlrikh9Plh9280D2AZ7r4G97moSAbNrwDuRnQ4fnr33MNgMc2GLzkEp2edSfSiM/Ipaa9FS72VtR2tMbP1Z5a9lYo1/ZfNDODgF5warUWYPx7C6ao/lIOadn6Cs6Hrjl6lleWROBib82ix9vStn6tf74XHm7tw0/b4vnz+Fk+X5cBgKuDFQvDHqfhrpe1beeN79cudHSxVqDokYXXV6/1CtF6libthsZlt1ep+HkOQtox6PtFucNUVeXdP45ja2nOm/0aV/5+QgghhBCiSkmEYazwGdr5yVZjtL6hStlnxQpLdCy94Mcks82MaQxfbYxme3QGU0e2wt3Jpux76HVwaA5s/hjyMqHNE1e3+Ropv0jHbweSmbEjnjMX80sd42xrSeM6jgwN9aF/Cw9sLM2hYW+IWADJ+672mRR3jn8KFbUpc8j3m2KYvDGakLo1+XFUCG6O11fCdbKxZFLvRkzq3YgLuUUcSLzAF+ujuH+zGwecvXHa/BFK7nmt7cu2z7Vs5b97eVraaNuDT9/kOdEdX2ktkirY4r7ySCq74zL5cFAzXB3KqOwrhBBCCCFuOwlEjRU+Q9sS2/+bcoNQgOWHUvgzrwmTrOG9Wn8R/MgrvLH0GB+ujmTKiFZlT1zzChycrW1r7PMpeAYbvczkC3nM33eaX/cnk5VXTCvfGrzbP5D7GrmRnVdMZm4RZ7PzSTifR3zGZfbEZzJpyRE+WhPJ0BBvhjQNobGZJUr0OglE70RJ+8C1UZnnMjecSGPyxmgGt/TiswebY21hXu7latlb0atpHTo0cOX1pUd5+/ggvimYhvmqazL1/b4s/e+Eb1utB2lRHljZGf8s6ZFadv6+18HGqcxhF3KL+HB1JC28nRnRxtf4+wghhBBCiFtGAlFjXD4H56O0wkBm5f/grtOrTN8ej4NnE9SGz6LsmcID9bsQ07ERU7fG8nx6Dg3dHW+ceClVK0TUaiwM+LbCYPdaqqqyOy6TX3YnsulkOoqi0CvQnfEd/Ghdr+Y/Wy5rO5lT28mGJh5O183dE5/J/L2n+XlXIjN2qCy1b0Ldw6uw6/IedlbyrXLH0Ou1THbgwFLfzisq4YNVkTRyd+Tzh1pg+f/27js8qyLt4/h30kiDBEihJIEAofdeRLFipwgqgmJDxbK6q7u6um5R1/XdVdeColgQFUFWEcXKWpAOoQkkAQKBBAKplISSkDLvH+dhAUnnSYPf57q4SM6ZM2cG53rMnZm5p6JLxYGABl68Oq4X0xYF0eObnvzxohaM79nUGaehHUp+qNVgJ9Nu6mqIPr/sF+QdhP3Jp56Ru+RF8Ak8/YikX3ly3iYOHi3gwzsH4KmstiIiIiJ1mpIVVUbyUufvVueVW/SbTXvZkXWYe4e1xVz8FycB0Bf3M6mbFwE+Xrz8Q2LJD658A2wRDP1dhYNQay2zV6Vw2b8XMf7tlaxJ3s/kYW1Z/NACUkoAACAASURBVIcLmTqhD/2jm5y6B7QExhgGtw3h9fF9WPX4xTw7qhvxgYMIObqDaZ//VKF2SB2RtRXyDpS6P/TlH5yMss+M6lqpIPQ4Ywx3nd+GQZ2jeXpxDimeUaUHoeCsIMCcujy3qABy053EX+AkV1r6MrzUHd4c6ix/txaytztnk/a9vcysu/N/2cNXG/fy0CXt6dis9FlTEREREakbNM1VmvxDTrIXv+AT13YuBe+AcpfKWmuZunA7bUIDGN6lGXgYGPMuvHE+Qd9M5rZB/2bKzzvZkpZLh2YnzYrm5cDq6dB5BDRuXaFmHiss5rG5G5i7NpVuLYN4fmyPE/s8q6hpYANuGhAF7e6GV9/k8C+fkXDegFNmUKWOKC6CjARntjErEQ5nQeZm517U6YHolrRc3lm8g7F9IujXuuoZZY0xPDWiC5e+uIgn5m3k/dv7l/7LDr9gCO9y4jzRowfg3eFOO/0aQ1hn2LcDcvc42aEbBDpHvhxIcSXL8nESg5UiIzePJz/fRI/IYO4+v02V+yQiIiIiNUczoiXJy6Hg+Y7kLnz51Os7lzjHYZRzVMrSbdnE7cnhngvanjj4vkkbuOYl2LWSez0/J8DHi1d+PSu6dgbk58Dg35xeaQkO5Rdyx4xY5q5N5eFL2/PF/UMY0yfijILQUzRtS2F4D0Z5Lecvn8dhj89egRMAnfy91Kz8XJh3L/wjEt4YAvMfhNi3IXmZs2y8z23OmDuJtZYn520i0NeLP17Z6Yyb0DzIjz9c3oHFiVnMW59aduGogbA7Fgrz4ZPbnfNGhz3u/NKluNDJAn3rVzDhExgzHYY+Amvfh18+chKDNQwvteon523i6LEiXhjbo+IZqUVERESkVmlGtAT7i3zZdKwtnWLfx//SJ/D08nLNNCVAtzHlPv/Bip00DfBhRM8Wp97oNga2fI3f8hd5uNcH/G3FXh5Iy3GWEhYVwIqpzrLflmUkMnLJzSvgxmkr2JyWyz/HdOf6vpFV7W6ZvHreQOfvHicreRNf/BLFiJ4tnbZOHexk1r3smWp5r5QhPQ7mTIR926HXzdBqCET0dQLPMpZgf75+D6t27uO50d1oEuDjlqaMH9CKz9al8tT8eIbGhJaeqTZqkBMozxoH239w9j/3ubXkssbAxU9C41ZOcrAhD5b6/vW7DvBdXDq/H96BdmGBZ94hEREREakRmj4oQeMAH7z7TSSkOIv5cz9wLh7fH1rOMSppB/P4PiGDsX0jS85Eevn/QYNAbs58gYYNPHhj4Xbn+qa5kJN6+nmhpfhwRQpxe3J4c0KfagtCAegyGothUvBa/v5VArl5BbBhjrMPcf0sZ+mk1IyDu2H5a/DWxc7M+S1fwLWvQI8boGnbMoPQI8cKee6bzXRrGeTW8eLpYfi/67pz5FgRD81eT1FxybPkBRGuZcLbf3CSDpUWhJ6s9y1wz2IIiii1yNSF2wjy82bi4NaVb7yIiIiI1BoFoqUYePkEcj2D8ds4k5VJ2c7+UC8/J+lQGWatSqHY2tKPjwgMheH/wCs1luciV/H1xr0cWjEdvvk9hHZy9siVo6ComBnLdjKkXVMu6Vz6kkW3aNQcEz2U0d7LyMjNY8aS7U4WU29/OJIFyUuq9/3nuuztsOBJmNIf/t0FvnscIvvB3YshuuJny775cxJpOXn8+ZrOJ5aLu0n78IY8PaIrS7ZllZiEa+/Bo9w4O4WE4kgWFfdgdYeH3fLebRm5fBeXzsRBrQhsoMUdIiIiIvWJAtHSePnQoO8ELvZcy1Ozf6Joh2t/qFfpSxoLi4qZHZvC+TGhRDUt47zEHjdC24u4Iv1N3vd8isBvH4LwrnDjTPAo/z/J1xv3kpaTxx3nRVelZ5XXbSwNcnZyc1Q26av+4+zvu+oFJ3FT3LyaacO5JvF7+GAUvNobVrwOjVo4y6AnL3dmQsvYM/lrew4c5c1F27m6e/MzSlBUluv7RTK2TwSv/pjIwi0ZAOQXFrEgLo2rXlnC5r05bLziM/7W8C/cNfMXkrMPn/E73/g5CV9vD82GioiIiNRDmkYog0+/W2HlFEYcnYfnsTjo+qcyy3+fkEF6Tj7PjGxVdsXGwNX/xuP1QXTzTOFf3vfy8C3P4OFZfpIhay3vLNlBm9AAhrUPq0RvzkCna+Grh5kYuIr8tMUcDY7Gr/sNkPhfSPgCrnwePDWU3Cbxe5h5HTRsARc+4ZwpW07gmZGTR9zeHPILiskvLMLLw4OY8ECiQwL4v283Yy08dkXHam32UyO6sjH1IA/OXk/rpv7E782hoMjSPjyQqRP60DY0kH4xLRj1+lJuey+WzyYPIci/7MRfpUk9cJR561KZMLAVTUvblyoiIiIidZaih7KExEDUYO5M+RqArJC+hJRRfObKZJoH+XJhh9Dy627cGu5ZwsLEQ7w2bzeDd+xnSLuyanfE7tzPht0H+fuorm5fYlkqv2CIuYy2iXMwHsf4OPAxbvDwhC6jIG6uszy3zbCaacvZrqgAvn0MmrSFe5eDV+lBVmFRMT9tyeTj2BR+2pJZ4v5Mb09DQZHlgYvaEdG4jFl6N/Dz8eT18b2Z9P5qfL09uf28aHpGBHNhx7D/ZXKODgngzQl9mPDOSiZOX8XbE/uWnuCoDG8tSgJgko5rEREREamXFIiWp/cteKQsI8968+7OJvyhS8nFdmYdZnFiFr+7tH3Fj5Bo2paLGxURvCCdmSuTKxSIvrMkiWB/b0b3Kj2BS7XoNhaz+Uv2e4fz993duDq/kICYS13Lcz9TIOouq96C7ES4aU6ZQWh6Th63TY8lfm8OoQ0bcNf5bbi4Yxj+Pl408PYgr6CIxPRDbE7L5eDRAu65oG2NNL9NaCA/PDyszDID2jRlyk29eXD2Oka+tpR3JvY79TzdcsTu3Mfs2BRG9GxJy2C/M2yxiIiIiNQG7REtT+cR0CCIZP8ufBCbxuH807PEWmt55qsEfDw9uKFf5TKS+np7MqZ3BAvi0snIzSuz7M6swyyIT2fCgFb4+bjprNCKaj8cwruyf+AfyDlm+GZTGnj7QYfLIWG+sue6w+EsWPgctLsEYi4rtVhiei6jXltKcvZhXhnXi+WPXcSjl3ekb+smdG7RiLahgXRpEcTIXi157IqO/GN0NwLqWDKf4V2aMefuQRwrLOa6qctYEJd26jm1pfguLo0Jb6+kRZAfjwxvXwMtFREREZHqoEC0PD7+MH4ORVc8T25eIXNW7zqtyAcrkvk+IZ1Hr+hIeCPfSr9i3IAoCostc2JPr/u4omLLHz7dgJ+3J7cMKmcPanXw9oPJS4m+6A5aN/XnkzWutnYeCUeyYefimm/T2ebHZ6DgMAx/ttSjWFbt2Md1U5dRUGz5+O5BXNujRcVn4OuY7hHBfH7/EFo19eeuD9Yw9o3lLNySgbWW/MIiEtNz+XlrJsu2Z7EuZT/Tl+5g8odr6Ni8EZ9MHkzzIM2GioiIiNRXdWuapK6KGkjnKOi9ZCnvLt3BLYNa4+nan5mwN4dnvkrgwg6h3D6kdZWqbxsayNCYEKYtSmJs38gSg9lXf0xk1Y59vDC2B2FVCHbdxRjDmD4RPL9gK7v2HSHy+PLc+HnQ9sJaa1e9l7EZ1s6A/ndDaIcSixw8WsCdM2IJadiAGbf1J7JJ9e75rAnNg/z4dPJg5qzexRsLt3Pr9FhCAhuw73A+JR1JemGHUF4b3xt/H310iYiIiNRn+mmuEiYNbcPkmWv5euNeLu0cTn5BMb+ZtY4gP2/+NbYHppRZrIp4akRXrnh5EX+cu5F3JvY9pa6VSdm88kMio3u15Lo+Nbw3tASjekfwwn+38v7ynTxxVWfnPMuUFbXbqPxDsO5D2PEzXPsqBJS/37ZO2fAxYOD8R0ot8taiJHLyCpl118CzIgg9ztfbk1sGtebGflF8unY3K5OyiWziT5vQAFoG+1NYXEx+QTEeHobBbZviXU9ngEVERETkBAWilXBZl2ZENvHjgVnr/nfNGPjg9gFVyvx5suiQAP4wvCNPfRnPJ2t2M7avs9c0+1A+D85eT1QTf54a2fWM3uEuLYP9GNM7grcW76BPq8ZcHt4Ftn0PhfllJthxm1VvweIXILQjNHP9m6x9H/IOOl//MgQG31/97XAXa50Z5eihpQbQ2YfyeXfpDq7q1pwuLYJquIE1w8fLg3H9oxjXP6q2myIiIiIi1UyBaCV4ehhev6kPixIzMQYMhk7NG3JejHtm324d3JpvN6Xx1Px4OjRryDeb0vhweTL5hcV8OnkwgXUo4czTI7uyLfMQv/34F7pc0prI4kLISjwRGFYXa2HZq+DhDUf3wco3obgQOl0Dgx6Ab34PG+fUr0A0PQ72JcHgB0otMnXhdvIKivjtpUrQIyIiIiL1X92JbOqJbhFBdIuonhkpDw/Dv8Z25/KXFnPtlKUYA1d2a859w9rRuUWjanlnVfl6e/LmzX0YOWUpjy0pZCZARkL1B6Ipy+FAMox6E3rc6Jy7eewQ+DV27ne7Hr77I2RuhdB6ErQlfAHGAzpeXeLttIN5fLAimVG9ImgXFljDjRMRERERcT9ttqpjWjUN4MXre3Dr4Nb8+PAwXrupd50LQo8La+jLWxP7sjEvjEI8KU7bVP0vXf+Rkxyp0zXO957eJ4JQgK7XOUHdxjnV3xZ3if8cogZDYFiJt6f8lEhRseWhS2JquGEiIiIiItVDgWgddEW35vz12i5EhwTUdlPK1aVFEE9f14ttxS1I3rym4g9mbYM178Fn98C0YbBpbvnPHDsCcfOcs119Svm3aRgO0RfAxv84y3jruozNkLnZ6VMJ9h0+xsexu7i+X+RZlaBIRERERM5tCkTljI3o2ZIjQTF4ZyewJnl/2YWLCuG7J2BKH5j/ICT+10ky9OmdEP9F2c9u/gqO5ULPcWWX6zYW9u+E3asr1Y8S7f0F5t0Hrw2Ag6lnXt+vJbj6fHyG91e+2rCHgiLLzQNr4exYEREREZFqokBU3KJzr0FEmCwen72U3LyCkgsdyoD3R8DyKdD3Drh/Dfx+G9y9CFr2gU9uh60LSn/JLx9BUBS0Oq/sxnS6Brx8z2x5btY2ePcKePN8iJsL2ducTL3uFv85RA6ERs1LvD1v/R46hDekU/O6uTxbRERERKQqFIiKW/i2cJIUBR5M5Okv408vkL3dCepS1ziJhq5+EULaOeffNGgI4/8D4Z3h4wmQ+P3pz+fsgaSF0OMG8Chn2Po2oihmOHnrPyE753DVOvTTM5C2ES57Bn6XAL1vcY6IOZBStfpKkr0d0jeVuiw3JfsIa5L3M6JXC/e9U0RERESkDlAgKu4R3hmAie2OMndtKgeP/GpWdMm/nSW4dyxwst3+ml8w3DwPQtrDR9fD6ukn7lnrnB1qi6F7Cc/+SmZuPv/c0x3fY/v4+5Q32JqeW7m+HN0Pm7+Gnjc5R6r4BcPQh52gedHzlaurLGveA0ypy3I/X+8sBb62hwJRERERETm7KBAV9wiKAu8ABgamU1hs+WFz+ol7R/Y5yYO6Xw/Nu5deh38TuO1raHsRfPkQLPgTJHzpzKQueRHaXerMopYhfk8OI6YsYda+GAo8fBlUuIrRry9j4ZaMivcl7jMoyj91L2pQBPS5FdbPhH07Kl5XaQ6mwqpp0P0GCI487ba1lnnrU+kf3YSIxkpSJCIiIiJnFwWi4h4eHhDWidAj2wlv1IDv4tJO3Fv3IRTmQf+7yq/HtxGMmw39JsGyV+Hj8c45oSNeh3Gzynx06bYsxryxjGILH90zDO+YixgVsImoxn7c/l4s38enl/n8/6yfBaGdoHnPU6+f9zswnu6ZFV34D2eG98LHS7wdtyeH7ZmHGdmz5Zm/S0RERESkjlEgKu4T1gmTEc/wzuH8vDWTo8eKoLgIYt9yEgyFd6lYPZ5ecNXzMGoajH4b7ouFXuOdM0NLsWxbFnfMiCWqiT9f3D+Eri2DIOYyvHJ388mYJrRqGsDrC7eV/+7s7bB7lbN82JhT7zVqDv3ugF9mOcmMqipjszOz2vcOaFxyNtzP1qXi7Wm4sluzqr9HRERERKSOUiAq7hPeBY5kc01bb/IKivl5ayYkLnAS/PSfVPn6etwA3cc6gWkZViRlc/uMWFo1CWDmnQMIa+Tr3Gg/HAD/nd8zfkAUa1MOsDktp+x3/jILjIezZLYk5/0WvP3g+79Utjcn/Pg0eAfA+Y+UeLuo2PLFL3sY1iGMYH+fqr9HRERERKSOUiAq7hPmJCzq7buHYH9vZ3nuqmnQsAV0vKpaXrlqxz5umx5LZGN/Zk4aQNPABiduNmoBzbpB4gKu6x2Bj5cHH60sI+ttcTH8MhvaXFjqcSoEhsF5D8HmL2HH4oo1MmkhzH8IFjwJ//2z8+yQ30BASInFFyVmkpmbz6heWpYrIiIiImcnBaLiPq5A1DMrgUs6hbM9YS1s/xH63V7mstrSFBYVk19YVOr91Tv3cev0VbQI9uWjSQMJOTkIPS5mOKSsoLHHYa7s2ozP1qY6S4ZLkrwUDu6CHuNKvn/coPuhUQR897iz9Lgs+5Nh1k2w4WMnKF/6svPswHtLfWT2qhSaBPhwSafwsusWEREREamnyl7zKFIZgaEQEAq7Y7nHZBJip1Hk5Ytn74mVrurHzek8+ulGDh4poFtEEH1bNaZ/dBMGtmlKQAMv1iTvY+K7q2jWyJdZkwYS2rCEIBSc5bmLn4dtP3DTgIuYt34P8zfs4fq+p2eqZc108GlY/uyttx9c8leYeyerPn+d1hdPOrEc+GTWwvwHnb2m966A4CgozHeW/pYSmGfk5vFDQga3DWmNj5d+TyQiIiIiZycFouJeYZ0g7jPaAT/Rm41tH+A3gWEVfvxQfiHPfBnP7NhddGzWkJE9W7AmeT/vLt3Bm4uS8PIw9I5qTPzeHMIa+TLrroElB4HHtewD/k0hcQH9Rl1Hu7BAPlqZcnogmh4Pm+Y6e0B9yj8uZXPoZVjPDrRa/zzjNrfl7UkXEB0ScGqh9TMh6Se48nknCAXwKiVgdvl0TSqFxZYb+kWV2wYRERERkfpKgai4V69bwK8xDP4Nn/zswcqkfdxbVIyXZ/mze4fzCxkxZQk7sg4zeVhbHrokhgZengDkFRSxJnk/ixOzWLItk5jwQKaO70N4WUEogIenc/5o4gKMLWZc/yie/jKe+D05dG7R6ES5hc9Cg4Yw+IEyq7PW8tbiJJ7/bitDfG9hunmCKfmP88Trk3n8jhudbL0AuWnO0t2owU523Aqw1vJxbAr9WzehXVhghZ4REREREamPFIiKe3Uf6/wBrumRxlcb9/Lj5gwu61L+MST/+m4LSVmHee+2/lzQPvSUe77engxpF8KQdiFAx8q1qf1lsGE2pK7hut49+ee3m5kdm8JTI7o69/esg4T5MOyP4N+kzKoWxKfz7NebuaxzOP8YfRHsak3M/N/y/pHHeH/aEgIuuIjogm2Q+F9nGe6IKc4ZqxWwImkfO7OP8MBFMZXrn4iIiIhIPaNNaFJtLukURrNGvnywIrncsrE79zFj+U4mDmp9WhB6xtpeDJ4NYMGfCPbMZ3iXZny+fs+JREg/PevM4g6cXG5V05fuoGWwH6+P7+1k6O10NV4PrOJY1xu53XxB9KKHsLFvO7OrI6dC07YVbubs2BQa+npxZbdSMvaKiIiIiJwlFIhKtfHy9OCmAVEsTsxiR9bhUsvlFRTx6CcbaBnsx++Hd3B/Q/yCYfQ02L0aPryOG7oHcfBoAT8kZEDKSues0yEPgm9QmdUk7M1hRdI+bhnU6tSlxn6N8R8zlfiR33Llsef4U6dvYdIP0HV0hZt44MgxvtmUxsieLfHz8axqT0VERERE6gUFolKtbuwXiZeHYWYZs6IvfZ9IUtZhnhvdnYAG1bRavMtIGDsdUtcwePnd3B6wnMbf3gsfXe9k+u1/V7lVTF+6A19vD27oV0LGXaBzz0EMHTqMmbF7WLQ1s8JNs9byt/nxFBYVM36gkhSJiIiIyNlPgahUq7BGvgzv2oz/rNld4vmdi7ZmMm3Rdm7oG8l5MSHV25jOI2Dse5g9a/lz0avEHFrN0ehLYdzH4BNQ5qPZh/KZt34Po3tHEOzvU2q5317SnnZhgTz66QZy8goq1KxP1uzms3Wp/ObiGDo2a1T+AyIiIiIi9ZwCUal2Nw9sxcGjBczfsOeU64npudw3cy3twxvy5DWda6Yxna6Be1eya+y39Mt/nQ+a/xEi+pT72KxVKRwrLOa2wa3LLOfr7ckLY3uQkZvP43M3Ulxsyyy/LSOXP38ex8A2TZSkSERERETOGQpEpdoNiG5C+/BA3l++k7wCZ1Y0+1A+t8+IpYG3J+/c2o/A6lqSW5KQdkR2GUSPyCZ8uiYVa8sOFguKivlgRTLntQshJrxhudX3iAzmkcs68OWGvfxrwZZSy+UVFHH/R+vw9/Hk5Rt74elhKt0VEREREZH6SMe3SLUzxnDr4Gge/2wj3f+6gN6tgsk5Wkh6Tj4f3zWQlsF+tdKuMX0i+NO8TWxKzaFbROmJir7asJf0nHyeHdWtwnXfc0Ebdu8/wtSF22kR5MvNg1r/715hUTGfrUvlpe8TST1wlPdu61f+eagiIiIiImcRBaJSI8b1j6RlYz+WJGaybHs2iRm5vHh9T3pFNa61Nl3TvQVPfRnPnNW7Sg1Ei4str/20jZiwQC7sEFbhuo0x/O3aLqTn5PHnL+LIPHQMbw/DgaMF/LQlg6TMw3SPCOKfY7q7zkYVERERETl3KBCVGmGM4YL2of87I7So2Nb6UtQgf2+u6d6Cj1fvYtLQNkQ19T+tzIL4NBIzDvHyjT3xqGR7vTw9eHVcb25+ZyWv/JAIQICPJ9GhAbwxoQ/Du4RjjJbjioiIiMi5R4Go1IraDkKPe2R4e77euJdnv07gjZtPTVpkreXVH7cRHRLA1d1bVKl+Px9P5tw9iH1HjtHI1xsfL23LFhEREREp96diY0ykMeYnY0y8MSbOGPNgCWWMMeYVY8w2Y8wGY0zv6mmuiHs1D/Ljvgvb8m1cGsu2ZZ1yb+GWTOL25DB5WNszCpw9PAwhgQ0UhIqIiIiIuFTkJ+NC4GFrbWdgIHCfMebXZ21cAcS4/twFTHVrK0Wq0Z1D2xDR2I+nvoynsKgYcGZDX/kxkZbBfozq1bKWWygiIiIicnYpd2mutXYvsNf1da4xJgFoCcSfVGwE8L51zsFYYYwJNsY0dz0rUqf5envyp6s6cc+Ha/nnd1uIbOzH1vRDrEs5wNMju+LtqZlMERERERF3qtQeUWNMa6AXsPJXt1oCu076frfr2imBqDHmLpwZU8LDw1m4cGGlGitSXRpYS6cmHkxblASAp4FOTTwIP5zEwoU7arl1IiIiIiJnlwoHosaYQOBT4CFrbU5VXmatnQZMA+jbt68dNmxYVaoRqRZ9BhWQlHmYFkG+hAQ2qHSWXBERERERqZgKBaLGGG+cIHSmtXZuCUVSgciTvo9wXROpNxr5etMzMri2myEiIiIictarSNZcA7wDJFhrXyyl2BfALa7suQOBg9ofKiIiIiIiIiWpyIzoEOBmYKMxZr3r2uNAFIC19g3ga+BKYBtwBLjN/U0VERERERGRs0FFsuYuAcrcLOfKlnufuxolIiIiIiIiZy+dSyEiIiIiIiI1SoGoiIiIiIiI1CgFoiIiIiIiIlKjFIiKiIiIiIhIjVIgKiIiIiIiIjVKgaiIiIiIiIjUKAWiIiIiIiIiUqMUiIqIiIiIiEiNUiAqIiIiIiIiNUqBqIiIiIiIiNQoBaIiIiIiIiJSoxSIioiIiIiISI0y1traebExmUByrbxcyhMCZNV2I0RcNB6lLtF4lLpCY1HqEo1HKU0ra21oSTdqLRCVussYs9pa27e22yECGo9St2g8Sl2hsSh1icajVIWW5oqIiIiIiEiNUiAqIiIiIiIiNUqBqJRkWm03QOQkGo9Sl2g8Sl2hsSh1icajVJr2iIqIiIiIiEiN0oyoiIiIiIiI1CgFoiIiIiIiIlKjFIieI4wxkcaYn4wx8caYOGPMg67rTYwx/zXGJLr+buy63tEYs9wYk2+MeeRXdQUbYz4xxmw2xiQYYwbVRp+kfnLXWDTGdDDGrD/pT44x5qHa6pfUT27+bPytq45NxphZxhjf2uiT1E9uHosPusZhnD4XpSqqMB7HG2M2GGM2GmOWGWN6nFTX5caYLcaYbcaYx2qrT1L3aI/oOcIY0xxobq1da4xpCKwBRgK3Avustc+5PhwaW2sfNcaEAa1cZfZba58/qa4ZwGJr7dvGGB/A31p7oKb7JPWTO8fiSXV6AqnAAGttck31Reo/d41HY0xLYAnQ2Vp71BgzB/jaWvtezfdK6iM3jsWuwGygP3AM+Ba4x1q7rcY7JfVWFcbjYCDBWrvfGHMF8Fdr7QDX/5+3ApcCu4FYYJy1Nr42+iV1i2ZEzxHW2r3W2rWur3OBBKAlMAKY4So2A+dDBmtthrU2Fig4uR5jTBBwPvCOq9wxBaFSGe4ai79yMbBdQahUlpvHoxfgZ4zxAvyBPdXcfDmLuHEsdgJWWmuPWGsLgZ+B0TXQBTmLVGE8LrPW7nddXwFEuL7uD2yz1iZZa4/h/JJkRM30Quo6BaLnIGNMa6AXsBIIt9budd1KA8LLeTwayASmG2PWGWPeNsYEVFdb5ex2hmPxZDcCs9zaODnnnMl4tNamAs8DKcBe4KC1dkG1NVbOamf42bgJGGqMaWqM8QeuBCKrqalyDqjCeLwD+Mb1dUtg10n3druuiSgQPdcYYwKBT4GHrLU5J9+zzjrt8tZqewG9ganW2l7AYUDr/aXS3DAWj9fjA1wL/MftjZRzxpmOR9c+qRE4v6xrAQQYYyZUU3PlLHamY9FamwD8H7AAZ1nueqCoelorZ7vKjkdjzIU4geijNdZIqbcUiJ5DjDHeOB8mM621apfIsAAAActJREFUc12X0137AI7vB8gop5rdwG5r7UrX95/gBKYiFeamsXjcFcBaa226+1sq5wI3jcdLgB3W2kxrbQEwFxhcXW2Ws5O7Phutte9Ya/tYa88H9uPs0ROplMqOR2NMd+BtYIS1Ntt1OZVTZ+QjXNdEFIieK4wxBmdfZ4K19sWTbn0BTHR9PRH4vKx6rLVpwC5jTAfXpYsBbTiXCnPXWDzJOLQsV6rIjeMxBRhojPF31Xkxzp4qkQpx52ejK5ERxpgonP2hH7m3tXK2q+x4dI21ucDN1tqTf/ERC8QYY6JdK5hudNUhoqy55wpjzHnAYmAjUOy6/DjOev85QBSQDFxvrd1njGkGrAYaucofwskGmWOM6YnzGy8fIAm47aQN6iJlcvNYDMAJANpYaw/WbE/kbODm8fg34AagEFgH3Gmtza/J/kj95eaxuBhoipPI6HfW2h9qtDNS71VhPL4NXOe6BlBore3rqutK4CXAE3jXWvv3GuuI1GkKREVERERERKRGaWmuiIiIiIiI1CgFoiIiIiIiIlKjFIiKiIiIiIhIjVIgKiIiIiIiIjVKgaiIiIiIiIjUKAWiIiIiIiIiUqMUiIqIiIiIiEiN+n/KyqbfhGaoMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dates = wkly_nsdq_v_spx.index[-test.shape[0]:]\n",
    "print(dates.shape)\n",
    "\n",
    "plt.figure(figsize = (16, 7))\n",
    "plt.plot(dates, true_predictions, label = 'Predicted')\n",
    "plt.plot(dates, test, label = 'Actual')\n",
    "plt.grid(axis = 'y')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Room to improve the model..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter search\n",
    "\n",
    "- From the Generator section onwards in one block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hyper_search(\n",
    "    n_input, \n",
    "    n_nodes_list, \n",
    "    rate_dropout, \n",
    "    act_funct = 'relu', \n",
    "    opt = 'adam', \n",
    "    batch_size = 1,\n",
    "    epochs = 25,\n",
    "    patience = 2\n",
    "):\n",
    "\n",
    "    ## Time-Series Generator\n",
    "    n_input = n_input \n",
    "    n_cols = scaled_train.shape[1]\n",
    "    generator = TimeseriesGenerator(scaled_train, scaled_train, length = n_input, batch_size = 1)\n",
    "\n",
    "\n",
    "    ## Build LSTM model\n",
    "    seq_bool = [True] * len(n_nodes_list)\n",
    "    seq_bool[-1] = False\n",
    "    \n",
    "    model = Sequential()\n",
    "    \n",
    "    for layer in range(len(n_nodes_list)):\n",
    "        if layer == 0:\n",
    "            model.add(LSTM(n_nodes_list[layer], activation = act_funct, return_sequences = seq_bool[layer], input_shape = (n_input, n_cols)))\n",
    "        else:\n",
    "            model.add(LSTM(n_nodes_list[layer], activation = act_funct, return_sequences = seq_bool[layer]))\n",
    "        if rate_dropout != 0:\n",
    "            model.add(Dropout(rate = rate_dropout))\n",
    "    \n",
    "    model.add(Dense(1))\n",
    "    model.compile(optimizer = opt, loss = 'mse')\n",
    "\n",
    "\n",
    "    ## Fit LSTM model\n",
    "    early_stopping = EarlyStopping(monitor = 'loss', patience = patience)\n",
    "    model.fit_generator(generator, epochs = epochs, callbacks = [early_stopping])\n",
    "\n",
    "\n",
    "    ## Generate Predictions\n",
    "    test_predictions = []\n",
    "    first_eval_batch = scaled_train[-n_input:]\n",
    "    current_batch = first_eval_batch.reshape((1, n_input, n_cols))\n",
    "    for i in range(len(test)):\n",
    "        current_pred = model.predict(current_batch)[0]\n",
    "        test_predictions.append(current_pred) \n",
    "        current_batch = np.append(current_batch[:, 1:, :],[[scaled_test[i]]], axis = 1)\n",
    "    true_predictions = scaler.inverse_transform(test_predictions)\n",
    "\n",
    "    return generator, model, true_predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list_n_inputs = [4, 8, 12, 18, 26, 38, 52, 78, 104, 156]\n",
    "# list_n_nodes_list = [[500], [300], [200, 200], [500, 200], [100, 200, 300], [400, 250, 100], [50, 50, 50, 50, 50]]\n",
    "# list_rate_dropout = [0.0, 0.01, 0.05, 0.1, 0.2, 0.3]\n",
    "\n",
    "list_n_inputs = [4, 8]\n",
    "list_n_nodes_list = [[10], [10, 5], [2]]\n",
    "list_rate_dropout = [0.0, 0.05]\n",
    "\n",
    "len(list_n_inputs) * len(list_n_nodes_list) * len(list_rate_dropout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0074\n",
      "Epoch 2/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 9.4920e-04\n",
      "Epoch 3/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 5.8753e-04\n",
      "Epoch 4/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 4.7466e-04\n",
      "Epoch 5/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 4.4126e-04\n",
      "Epoch 6/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 4.3941e-04\n",
      "Epoch 7/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.9748e-04\n",
      "Epoch 8/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.4122e-04\n",
      "Epoch 9/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.3801e-04\n",
      "Epoch 10/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.4683e-04\n",
      "Epoch 11/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.3262e-04\n",
      "Epoch 12/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.1770e-04\n",
      "Epoch 13/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.1145e-04\n",
      "Epoch 14/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 2.6522e-04\n",
      "Epoch 15/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 2.8620e-04\n",
      "Epoch 16/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 2.8946e-04\n",
      "Epoch 1/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0119\n",
      "Epoch 2/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0021\n",
      "Epoch 3/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0017\n",
      "Epoch 4/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0015\n",
      "Epoch 5/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0015\n",
      "Epoch 6/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0012\n",
      "Epoch 7/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0012\n",
      "Epoch 8/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 9.7216e-04\n",
      "Epoch 9/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0011\n",
      "Epoch 10/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0011\n",
      "Epoch 1/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0381\n",
      "Epoch 2/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0243\n",
      "Epoch 3/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0243\n",
      "Epoch 4/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0242\n",
      "Epoch 5/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0242\n",
      "Epoch 6/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0242\n",
      "Epoch 7/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0242\n",
      "Epoch 8/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0242\n",
      "Epoch 9/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0242\n",
      "Epoch 10/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0242\n",
      "Epoch 1/25\n",
      "1332/1332 [==============================] - 5s 4ms/step - loss: 0.0371\n",
      "Epoch 2/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0242\n",
      "Epoch 3/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0242\n",
      "Epoch 4/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0242\n",
      "Epoch 5/25\n",
      "1332/1332 [==============================] - 4s 3ms/step - loss: 0.0243\n",
      "Epoch 1/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0106\n",
      "Epoch 2/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 9.6843e-04\n",
      "Epoch 3/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 8.7006e-04\n",
      "Epoch 4/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 7.8986e-04\n",
      "Epoch 5/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 7.7532e-04\n",
      "Epoch 6/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 6.9279e-04\n",
      "Epoch 7/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 6.0977e-04\n",
      "Epoch 8/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 5.9134e-04\n",
      "Epoch 9/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 5.3367e-04\n",
      "Epoch 10/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 5.0008e-04\n",
      "Epoch 11/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 4.6812e-04\n",
      "Epoch 12/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 4.4363e-04\n",
      "Epoch 13/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 4.1205e-04\n",
      "Epoch 14/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.8491e-04\n",
      "Epoch 15/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.7603e-04\n",
      "Epoch 16/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.5414e-04\n",
      "Epoch 17/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.5227e-04\n",
      "Epoch 18/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.4589e-04\n",
      "Epoch 19/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.3085e-04\n",
      "Epoch 20/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.1327e-04\n",
      "Epoch 21/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.1958e-04\n",
      "Epoch 22/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 3.2642e-04\n",
      "Epoch 1/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0375\n",
      "Epoch 2/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 0.0243\n",
      "Epoch 3/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 0.0243\n",
      "Epoch 4/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 0.0242\n",
      "Epoch 5/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 0.0242\n",
      "Epoch 6/25\n",
      "1332/1332 [==============================] - 2s 2ms/step - loss: 0.0242\n",
      "Epoch 7/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0243\n",
      "Epoch 8/25\n",
      "1332/1332 [==============================] - 3s 2ms/step - loss: 0.0242\n",
      "Epoch 1/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0098\n",
      "Epoch 2/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 9.1527e-04\n",
      "Epoch 3/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 7.4378e-04\n",
      "Epoch 4/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 6.4526e-04\n",
      "Epoch 5/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 5.3357e-04\n",
      "Epoch 6/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 4.9584e-04\n",
      "Epoch 7/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 4.6248e-04\n",
      "Epoch 8/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 4.7337e-04\n",
      "Epoch 9/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 4.2472e-04\n",
      "Epoch 10/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 4.1188e-04\n",
      "Epoch 11/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 4.1414e-04\n",
      "Epoch 12/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 4.0652e-04\n",
      "Epoch 13/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 3.7546e-04\n",
      "Epoch 14/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 3.6043e-04\n",
      "Epoch 15/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 3.6350e-04\n",
      "Epoch 16/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 3.4713e-04\n",
      "Epoch 17/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 3.4251e-04\n",
      "Epoch 18/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 3.4862e-04\n",
      "Epoch 19/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 3.1266e-04\n",
      "Epoch 20/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 3.2288e-04\n",
      "Epoch 21/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 3.0680e-04\n",
      "Epoch 22/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 3.0502e-04\n",
      "Epoch 23/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 2.8989e-04\n",
      "Epoch 24/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 2.8806e-04\n",
      "Epoch 25/25\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1328/1328 [==============================] - 3s 3ms/step - loss: 2.8077e-04\n",
      "Epoch 1/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0066\n",
      "Epoch 2/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0023\n",
      "Epoch 3/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0020\n",
      "Epoch 4/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0018\n",
      "Epoch 5/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0017\n",
      "Epoch 6/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0013\n",
      "Epoch 7/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0014\n",
      "Epoch 8/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 0.0011\n",
      "Epoch 9/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0013\n",
      "Epoch 10/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 0.0012\n",
      "Epoch 1/25\n",
      "1328/1328 [==============================] - 6s 5ms/step - loss: 0.0317\n",
      "Epoch 2/25\n",
      "1328/1328 [==============================] - 6s 4ms/step - loss: 0.0244\n",
      "Epoch 3/25\n",
      "1328/1328 [==============================] - 6s 4ms/step - loss: 0.0244\n",
      "Epoch 4/25\n",
      "1328/1328 [==============================] - 6s 4ms/step - loss: 0.0243\n",
      "Epoch 5/25\n",
      "1328/1328 [==============================] - 6s 4ms/step - loss: 0.0243\n",
      "Epoch 6/25\n",
      "1328/1328 [==============================] - 6s 4ms/step - loss: 0.0243\n",
      "Epoch 7/25\n",
      "1328/1328 [==============================] - 6s 5ms/step - loss: 0.0243\n",
      "Epoch 8/25\n",
      "1328/1328 [==============================] - 6s 5ms/step - loss: 0.0243\n",
      "Epoch 1/25\n",
      "1328/1328 [==============================] - 7s 5ms/step - loss: 0.0075\n",
      "Epoch 2/25\n",
      "1328/1328 [==============================] - 6s 5ms/step - loss: 0.0028\n",
      "Epoch 3/25\n",
      "1328/1328 [==============================] - 6s 5ms/step - loss: 0.0023\n",
      "Epoch 4/25\n",
      "1328/1328 [==============================] - 6s 5ms/step - loss: 0.0022\n",
      "Epoch 5/25\n",
      "1328/1328 [==============================] - 6s 5ms/step - loss: 0.0019\n",
      "Epoch 6/25\n",
      "1328/1328 [==============================] - 6s 5ms/step - loss: 0.0016\n",
      "Epoch 7/25\n",
      "1328/1328 [==============================] - 6s 5ms/step - loss: 0.0018\n",
      "Epoch 8/25\n",
      "1328/1328 [==============================] - 7s 5ms/step - loss: 0.0015\n",
      "Epoch 9/25\n",
      "1328/1328 [==============================] - 7s 5ms/step - loss: 0.0017\n",
      "Epoch 10/25\n",
      "1328/1328 [==============================] - 6s 5ms/step - loss: 0.0015\n",
      "Epoch 1/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0366\n",
      "Epoch 2/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 0.0241\n",
      "Epoch 3/25\n",
      "1328/1328 [==============================] - 3s 3ms/step - loss: 0.0241\n",
      "Epoch 4/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0240\n",
      "Epoch 5/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0241\n",
      "Epoch 6/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0241\n",
      "Epoch 1/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0395\n",
      "Epoch 2/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0241\n",
      "Epoch 3/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0241\n",
      "Epoch 4/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 0.0241\n"
     ]
    }
   ],
   "source": [
    "search_dict = {}\n",
    "\n",
    "for x in list_n_inputs:\n",
    "    for y in list_n_nodes_list:\n",
    "        for z in list_rate_dropout:\n",
    "            search_dict[f'{x}_{y}_{z}'] = hyper_search(\n",
    "                n_input = x, \n",
    "                n_nodes_list = y, \n",
    "                rate_dropout = z,\n",
    "#                 epochs = 3\n",
    "            )\n",
    "#             print(f'{x}_{y}_{z}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>8_[10]_0.0</td>\n",
       "      <td>0.374171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4_[2]_0.0</td>\n",
       "      <td>0.573326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4_[10]_0.0</td>\n",
       "      <td>0.689814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[10, 5]_0.05</td>\n",
       "      <td>0.708844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[10]_0.05</td>\n",
       "      <td>0.732239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4_[10]_0.05</td>\n",
       "      <td>2.988532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[2]_0.0</td>\n",
       "      <td>12.907594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4_[10, 5]_0.0</td>\n",
       "      <td>13.075160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[2]_0.05</td>\n",
       "      <td>13.197346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[10, 5]_0.0</td>\n",
       "      <td>13.313454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4_[10, 5]_0.05</td>\n",
       "      <td>13.343898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4_[2]_0.05</td>\n",
       "      <td>13.610898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    error\n",
       "8_[10]_0.0       0.374171\n",
       "4_[2]_0.0        0.573326\n",
       "4_[10]_0.0       0.689814\n",
       "8_[10, 5]_0.05   0.708844\n",
       "8_[10]_0.05      0.732239\n",
       "4_[10]_0.05      2.988532\n",
       "8_[2]_0.0       12.907594\n",
       "4_[10, 5]_0.0   13.075160\n",
       "8_[2]_0.05      13.197346\n",
       "8_[10, 5]_0.0   13.313454\n",
       "4_[10, 5]_0.05  13.343898\n",
       "4_[2]_0.05      13.610898"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euclid_errors = pd.DataFrame()\n",
    "\n",
    "for k, v in search_dict.items():\n",
    "    error = np.linalg.norm((test - v[2]))\n",
    "    euclid_errors.loc[k, 'error'] = error\n",
    "    \n",
    "euclid_errors.sort_values('error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16, 7))\n",
    "plt.plot(test, label = 'Actual')\n",
    "\n",
    "# plt.ylim(top = 3.2)\n",
    "# for k, v in search_dict.items():\n",
    "#     plt.plot(v[2], label = k)\n",
    "    \n",
    "# plt.plot(search_dict['4_[10]_0.0'][2], label = '4_[10]_0.0')\n",
    "# plt.plot(search_dict['4_[10]_0.05'][2], label = '4_[10]_0.05')\n",
    "\n",
    "## plt.plot(search_dict['4_[10, 5]_0.0'][2], label = '4_[10, 5]_0.0')\n",
    "## plt.plot(search_dict['4_[10, 5]_0.05'][2], label = '4_[10, 5]_0.05')\n",
    "\n",
    "# plt.plot(search_dict['4_[2]_0.0'][2], label = '4_[2]_0.0')\n",
    "## plt.plot(search_dict['4_[2]_0.05'][2], label = '4_[2]_0.05')\n",
    "\n",
    "plt.plot(search_dict['8_[10]_0.0'][2], label = '8_[10]_0.0')\n",
    "# plt.plot(search_dict['8_[10]_0.05'][2], label = '8_[10]_0.05')\n",
    "\n",
    "## plt.plot(search_dict['8_[10, 5]_0.0'][2], label = '8_[10, 5]_0.0')\n",
    "# plt.plot(search_dict['8_[10, 5]_0.05'][2], label = '8_[10, 5]_0.05')\n",
    "\n",
    "## plt.plot(search_dict['8_[2]_0.0'][2], label = '8_[2]_0.0')\n",
    "## plt.plot(search_dict['8_[2]_0.05'][2], label = '8_[2]_0.05')\n",
    "    \n",
    "plt.legend(loc = 'best')\n",
    "plt.grid(axis = 'y');\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(260, 1)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try more hyperparameters..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18\n",
      "Epoch 1/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 0.0027\n",
      "Epoch 2/25\n",
      "1328/1328 [==============================] - 5s 3ms/step - loss: 8.5137e-04\n",
      "Epoch 3/25\n",
      "1328/1328 [==============================] - 4s 3ms/step - loss: 6.3822e-04\n",
      "Epoch 4/25\n",
      "1328/1328 [==============================] - 5s 3ms/step - loss: 5.9154e-04\n",
      "Epoch 5/25\n",
      "1328/1328 [==============================] - 6s 4ms/step - loss: 5.0555e-04\n",
      "Epoch 6/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 4.1057e-04\n",
      "Epoch 7/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 4.0336e-04\n",
      "Epoch 8/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.7170e-04\n",
      "Epoch 9/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.3358e-04\n",
      "Epoch 10/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.5283e-04\n",
      "Epoch 11/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.1359e-04\n",
      "Epoch 12/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.0617e-04\n",
      "Epoch 13/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.3079e-04\n",
      "Epoch 14/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.0047e-04\n",
      "Epoch 15/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.2899e-04\n",
      "Epoch 16/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 2.9901e-04\n",
      "Epoch 17/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 2.7376e-04\n",
      "Epoch 18/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 2.6602e-04\n",
      "Epoch 19/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 2.9621e-04\n",
      "Epoch 20/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 2.9626e-04\n",
      "Epoch 1/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 0.0025\n",
      "Epoch 2/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 9.2306e-04\n",
      "Epoch 3/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 8.1997e-04\n",
      "Epoch 4/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 7.5972e-04\n",
      "Epoch 5/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 5.3645e-04\n",
      "Epoch 6/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 4.6907e-04\n",
      "Epoch 7/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 4.8016e-04\n",
      "Epoch 8/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 4.2196e-04\n",
      "Epoch 9/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.9281e-04\n",
      "Epoch 10/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.5559e-04\n",
      "Epoch 11/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.4707e-04\n",
      "Epoch 12/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.2535e-04\n",
      "Epoch 13/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.4564e-04\n",
      "Epoch 14/25\n",
      "1328/1328 [==============================] - 5s 4ms/step - loss: 3.3002e-04\n",
      "Epoch 1/25\n",
      "1328/1328 [==============================] - 8s 6ms/step - loss: 0.0037\n",
      "Epoch 2/25\n",
      "1328/1328 [==============================] - 7s 6ms/step - loss: 0.0013\n",
      "Epoch 3/25\n",
      "1328/1328 [==============================] - 7s 5ms/step - loss: 0.0011\n",
      "Epoch 4/25\n",
      "1328/1328 [==============================] - 7s 5ms/step - loss: 8.1342e-04\n",
      "Epoch 5/25\n",
      "1328/1328 [==============================] - 7s 5ms/step - loss: 7.3875e-04\n",
      "Epoch 6/25\n",
      "1328/1328 [==============================] - 7s 5ms/step - loss: 5.8572e-04\n",
      "Epoch 7/25\n",
      "1328/1328 [==============================] - 7s 5ms/step - loss: 4.9820e-04\n",
      "Epoch 8/25\n",
      "1328/1328 [==============================] - 7s 5ms/step - loss: 4.4988e-04\n",
      "Epoch 9/25\n",
      "1328/1328 [==============================] - 7s 5ms/step - loss: 4.6155e-04\n",
      "Epoch 10/25\n",
      "1328/1328 [==============================] - 7s 5ms/step - loss: 3.4527e-04\n",
      "Epoch 11/25\n",
      "1328/1328 [==============================] - 7s 5ms/step - loss: 3.7237e-04\n",
      "Epoch 12/25\n",
      "1328/1328 [==============================] - 7s 5ms/step - loss: 3.5949e-04\n",
      "Epoch 1/25\n",
      "1328/1328 [==============================] - 8s 6ms/step - loss: 0.0035\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'euclid_errors' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-110-9c188d903cec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msearch_dict2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0meuclid_errors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'error'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0meuclid_errors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'euclid_errors' is not defined"
     ]
    }
   ],
   "source": [
    "list_n_inputs = [8, 52, 104]\n",
    "list_n_nodes_list = [[200], [100, 100], [100, 100, 100]]\n",
    "list_rate_dropout = [0.0, 0.01]\n",
    "\n",
    "print(len(list_n_inputs) * len(list_n_nodes_list) * len(list_rate_dropout))\n",
    "\n",
    "search_dict2 = {}\n",
    "\n",
    "for x in list_n_inputs:\n",
    "    for y in list_n_nodes_list:\n",
    "        for z in list_rate_dropout:\n",
    "            search_dict2[f'{x}_{y}_{z}'] = hyper_search(\n",
    "                n_input = x, \n",
    "                n_nodes_list = y, \n",
    "                rate_dropout = z,\n",
    "            )\n",
    "            \n",
    "## This took aaaagggggeeeeeessss......"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>52_[200]_0.0</td>\n",
       "      <td>0.368613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[10]_0.0</td>\n",
       "      <td>0.374171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104_[100, 100]_0.01</td>\n",
       "      <td>0.407671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52_[100, 100]_0.0</td>\n",
       "      <td>0.409768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[100, 100]_0.0</td>\n",
       "      <td>0.457718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104_[100, 100, 100]_0.01</td>\n",
       "      <td>0.465942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52_[200]_0.01</td>\n",
       "      <td>0.476495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52_[100, 100, 100]_0.0</td>\n",
       "      <td>0.504334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4_[2]_0.0</td>\n",
       "      <td>0.573326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104_[200]_0.0</td>\n",
       "      <td>0.590392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[100, 100, 100]_0.0</td>\n",
       "      <td>0.616396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52_[100, 100, 100]_0.01</td>\n",
       "      <td>0.669168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4_[10]_0.0</td>\n",
       "      <td>0.689814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52_[100, 100]_0.01</td>\n",
       "      <td>0.690654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[10, 5]_0.05</td>\n",
       "      <td>0.708844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[10]_0.05</td>\n",
       "      <td>0.732239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104_[100, 100]_0.0</td>\n",
       "      <td>0.787318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[200]_0.0</td>\n",
       "      <td>0.883255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[100, 100, 100]_0.01</td>\n",
       "      <td>0.891277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104_[100, 100, 100]_0.0</td>\n",
       "      <td>0.925179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[200]_0.01</td>\n",
       "      <td>0.953138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[100, 100]_0.01</td>\n",
       "      <td>0.960651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104_[200]_0.01</td>\n",
       "      <td>1.193087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4_[10]_0.05</td>\n",
       "      <td>2.988532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[2]_0.0</td>\n",
       "      <td>12.907594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4_[10, 5]_0.0</td>\n",
       "      <td>13.075160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[2]_0.05</td>\n",
       "      <td>13.197346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8_[10, 5]_0.0</td>\n",
       "      <td>13.313454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4_[10, 5]_0.05</td>\n",
       "      <td>13.343898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4_[2]_0.05</td>\n",
       "      <td>13.610898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              error\n",
       "52_[200]_0.0               0.368613\n",
       "8_[10]_0.0                 0.374171\n",
       "104_[100, 100]_0.01        0.407671\n",
       "52_[100, 100]_0.0          0.409768\n",
       "8_[100, 100]_0.0           0.457718\n",
       "104_[100, 100, 100]_0.01   0.465942\n",
       "52_[200]_0.01              0.476495\n",
       "52_[100, 100, 100]_0.0     0.504334\n",
       "4_[2]_0.0                  0.573326\n",
       "104_[200]_0.0              0.590392\n",
       "8_[100, 100, 100]_0.0      0.616396\n",
       "52_[100, 100, 100]_0.01    0.669168\n",
       "4_[10]_0.0                 0.689814\n",
       "52_[100, 100]_0.01         0.690654\n",
       "8_[10, 5]_0.05             0.708844\n",
       "8_[10]_0.05                0.732239\n",
       "104_[100, 100]_0.0         0.787318\n",
       "8_[200]_0.0                0.883255\n",
       "8_[100, 100, 100]_0.01     0.891277\n",
       "104_[100, 100, 100]_0.0    0.925179\n",
       "8_[200]_0.01               0.953138\n",
       "8_[100, 100]_0.01          0.960651\n",
       "104_[200]_0.01             1.193087\n",
       "4_[10]_0.05                2.988532\n",
       "8_[2]_0.0                 12.907594\n",
       "4_[10, 5]_0.0             13.075160\n",
       "8_[2]_0.05                13.197346\n",
       "8_[10, 5]_0.0             13.313454\n",
       "4_[10, 5]_0.05            13.343898\n",
       "4_[2]_0.05                13.610898"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for k, v in search_dict2.items():\n",
    "    error = np.linalg.norm((test - v[2]))\n",
    "    euclid_errors.loc[k, 'error'] = error\n",
    "    \n",
    "euclid_errors.sort_values('error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16, 7))\n",
    "plt.plot(test, label = 'Actual')\n",
    "\n",
    "# for combo in euclid_errors.index[:3]:\n",
    "#     plt.plot(search_dict2[combo][2], label = combo)\n",
    "\n",
    "plt.plot(search_dict2['52_[200]_0.0'][2], label = '52_[200]_0.0')\n",
    "plt.plot(search_dict['8_[10]_0.0'][2], label = '8_[10]_0.0')\n",
    "plt.plot(search_dict2['104_[100, 100]_0.01'][2], label = '104_[100, 100]_0.01')\n",
    "    \n",
    "plt.legend(loc = 'best')\n",
    "plt.grid(axis = 'y');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results:\n",
    "\n",
    "The top performing combination of hyperparameters are: \n",
    "\n",
    "    1. 52 weeks of input data, one hidden layer of 200 nodes, 0% drop out \n",
    "    2. 8 weeks of input data, one hidden layer of 10 nodes, 0% drop out\n",
    "    3. 104 weeks of input data, two hidden layers of 100 nodes each, 1% drop out\n",
    "\n",
    "The mix of results show there is no dominant or consistent hyperparameter. That said the input of 52 weeks appears more frequently in the top of the table, suggesting there may be some seasonal non-linearity the model is identifying. \n",
    "\n",
    "Also notable was that combinations including some rate of dropout tended to perform worse. Its interesting because dropout is usually included to improve validation testing, where as the opposite happened here. \n",
    "\n",
    "Visually, it looks like there is a lagged response in the predicted values. A variation of the model could be to predict whether the return will be positive or negative (classification). Could then build a trading strading that generates buy/sell signals depending on the accuracy of the model..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_dict.update(search_dict2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['4_[10]_0.0', '4_[10]_0.05', '4_[10, 5]_0.0', '4_[10, 5]_0.05', '4_[2]_0.0', '4_[2]_0.05', '8_[10]_0.0', '8_[10]_0.05', '8_[10, 5]_0.0', '8_[10, 5]_0.05', '8_[2]_0.0', '8_[2]_0.05', '8_[200]_0.0', '8_[200]_0.01', '8_[100, 100]_0.0', '8_[100, 100]_0.01', '8_[100, 100, 100]_0.0', '8_[100, 100, 100]_0.01', '52_[200]_0.0', '52_[200]_0.01', '52_[100, 100]_0.0', '52_[100, 100]_0.01', '52_[100, 100, 100]_0.0', '52_[100, 100, 100]_0.01', '104_[200]_0.0', '104_[200]_0.01', '104_[100, 100]_0.0', '104_[100, 100]_0.01', '104_[100, 100, 100]_0.0', '104_[100, 100, 100]_0.01'])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "f = open('../Data/Data/qqq_spx_search_dict.pkl', 'wb')\n",
    "pickle.dump(search_dict, f)\n",
    "f.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}