diff --git "a/LSTM crypto time series-Test different lookback.ipynb" "b/LSTM crypto time series-Test different lookback.ipynb" new file mode 100644--- /dev/null +++ "b/LSTM crypto time series-Test different lookback.ipynb" @@ -0,0 +1,528 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "219b6607", + "metadata": {}, + "outputs": [], + "source": [ + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2b8e0ecb", + "metadata": {}, + "outputs": [], + "source": [ + "os.chdir(r\"C:\\Users\\yozhan\\cryptocurrency\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4c7b21d5", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b327ac53", + "metadata": {}, + "outputs": [], + "source": [ + "from cryptocmd import CmcScraper" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "137760ef", + "metadata": {}, + "outputs": [], + "source": [ + "scraper = CmcScraper(\"BTC\")\n", + "headers, data = scraper.get_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f1ad104e", + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1e8efe7d", + "metadata": {}, + "outputs": [], + "source": [ + "start_date = datetime.strptime(\"01-01-2022\", r\"%d-%m-%Y\")\n", + "end_date = datetime.strptime(\"31-12-2022\", r\"%d-%m-%Y\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9108a101", + "metadata": {}, + "outputs": [], + "source": [ + "bitcoin_price_list = []\n", + "date_list = []\n", + "\n", + "for record in data:\n", + " date = datetime.strptime(record[0], r\"%d-%m-%Y\")\n", + " if(date >= start_date and date <= end_date):\n", + " date_list.append(date)\n", + " \n", + " \n", + " # Note: here we should make each price as a single-element list\n", + " bitcoin_price_list.append([record[1]])\n", + "\n", + " \n", + "date_list.reverse()\n", + "bitcoin_price_list.reverse()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fc689caf", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(date_list, bitcoin_price_list)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d47df64e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min: 15782.301231, Max: 47680.926625\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaler = scaler.fit(bitcoin_price_list)\n", + "print('Min: %f, Max: %f' % (scaler.data_min_, scaler.data_max_))\n", + "normalized = scaler.transform(bitcoin_price_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "56e81293", + "metadata": {}, + "outputs": [], + "source": [ + "# normalized\n", + "\n", + "train_size = int(len(normalized) * 0.75)\n", + "train, test = normalized[:train_size], normalized[train_size:]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c89607e3", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + " \n", + "def create_dataset(dataset, lookback):\n", + " \"\"\"Transform a time series into a prediction dataset\n", + " \n", + " Args:\n", + " dataset: A numpy array of time series, first dimension is the time steps\n", + " lookback: Size of window for prediction\n", + " \"\"\"\n", + " X, y = [], []\n", + " for i in range(len(dataset)-lookback):\n", + " feature = dataset[i:i+lookback]\n", + " target = dataset[i+1:i+lookback+1]\n", + " X.append(feature)\n", + " y.append(target)\n", + " return torch.tensor(X), torch.tensor(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "39904e6b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([243, 30, 1]) torch.Size([243, 30, 1])\n", + "torch.Size([62, 30, 1]) torch.Size([62, 30, 1])\n" + ] + } + ], + "source": [ + "lookback = 30\n", + "X_train, y_train = create_dataset(train, lookback=lookback)\n", + "X_test, y_test = create_dataset(test, lookback=lookback)\n", + "print(X_train.shape, y_train.shape)\n", + "print(X_test.shape, y_test.shape)\n", + "\n", + "# window sample, time steps, features" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a26b8bcb", + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn as nn\n", + " \n", + "class CryptoModel(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " \n", + " #input_size: how many features (currently only 1, the time series from previous days)\n", + " #if more features are used (for example, text features, sentiment scores, then input_size should be larger)\n", + " \n", + " self.lstm = nn.LSTM(input_size=1, hidden_size=50, num_layers=1, batch_first=True)\n", + " self.linear = nn.Linear(50, 1)\n", + " def forward(self, x):\n", + " x = x.float()\n", + " x, _ = self.lstm(x)\n", + " x = x[:, -1:, :]\n", + " x = self.linear(x)\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "de43b258", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: train RMSE 0.2294, test RMSE 0.3820\n", + "Epoch 100: train RMSE 0.0398, test RMSE 0.0316\n", + "Epoch 200: train RMSE 0.0355, test RMSE 0.0275\n", + "Epoch 300: train RMSE 0.0327, test RMSE 0.0200\n", + "Epoch 400: train RMSE 0.0299, test RMSE 0.0235\n", + "Epoch 500: train RMSE 0.0299, test RMSE 0.0203\n", + "Epoch 600: train RMSE 0.0226, test RMSE 0.0211\n", + "Epoch 700: train RMSE 0.0206, test RMSE 0.0372\n", + "Epoch 800: train RMSE 0.0202, test RMSE 0.0477\n", + "Epoch 900: train RMSE 0.0165, test RMSE 0.0383\n", + "Epoch 1000: train RMSE 0.0148, test RMSE 0.0381\n", + "Epoch 1100: train RMSE 0.0145, test RMSE 0.0415\n", + "Epoch 1200: train RMSE 0.0103, test RMSE 0.0359\n", + "Epoch 1300: train RMSE 0.0111, test RMSE 0.0369\n", + "Epoch 1400: train RMSE 0.0071, test RMSE 0.0373\n", + "Epoch 1500: train RMSE 0.0066, test RMSE 0.0425\n", + "Epoch 1600: train RMSE 0.0064, test RMSE 0.0462\n", + "Epoch 1700: train RMSE 0.0093, test RMSE 0.0499\n", + "Epoch 1800: train RMSE 0.0085, test RMSE 0.0397\n", + "Epoch 1900: train RMSE 0.0070, test RMSE 0.0444\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import torch.optim as optim\n", + "import torch.utils.data as data\n", + "\n", + "model = CryptoModel()\n", + "optimizer = optim.Adam(model.parameters())\n", + "loss_fn = nn.MSELoss()\n", + "loader = data.DataLoader(data.TensorDataset(X_train, y_train), shuffle=True, batch_size=8)\n", + "\n", + "n_epochs = 2000\n", + "for epoch in range(n_epochs):\n", + " model.train()\n", + " for X_batch, y_batch in loader: \n", + " \n", + " X_batch, y_batch = X_batch.float(), y_batch.float()\n", + " \n", + " X_batch = X_batch.float()\n", + " y_pred = model(X_batch)\n", + " \n", + " loss = loss_fn(y_pred, y_batch[:, -1:, :])\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + " # Validation\n", + " if epoch % 100 != 0:\n", + " continue\n", + " model.eval()\n", + " with torch.no_grad():\n", + " y_pred = model(X_train)\n", + " train_rmse = np.sqrt(loss_fn(y_pred[:, -1:, :], y_train[:, -1:, :]))\n", + " \n", + " y_pred = model(X_test)\n", + " test_rmse = np.sqrt(loss_fn(y_pred[:, -1:, :], y_test[:, -1:, :]))\n", + " \n", + " \n", + " print(\"Epoch %d: train RMSE %.4f, test RMSE %.4f\" % (epoch, train_rmse, test_rmse))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ef0a74dc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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IMZGYCJU4Z4Tr0KQl7dAVYcQ1HMrpN9pn6CoqoLYWfvihkw0UQgjR1SSMtCA+Xj3W1XnmFHHxFUbOPhsmTFDLe/YEtGnU7ldhxGrx9KlIGJ2IDRN6nDOylZaSWKUmKytOVh1BozSrK6e0m77Beb+bkBASEqCGUPW86S+li9h3dUEYMRhA18IlnvXrO3hQIYQQgSJhpAUREWpEDTSv7LvCiLMoAcDRR8OQIWrZ+cd9wNTnqDBSEZHiXpeapiePVAAaDhyENWsA2M4oatLUkNyOzDXiqozonZWRapwnHaAwUrdd/fKKI4e4un50TAslIO0HCSNCCNHTSBhpgU7nqY40DiPV1WC1quXGA0pGjIChQ9VyoCsjWr4KIzUxnspIfLyamAzA9sA/4M03AVjNcRjiY4COhRG93VMZaRxGHFUBuEyjaZhyVRiJmzKkxeJGZzR8+0OLQUUIIURwSBhpRVycemwcRg6oKx9ERYHF4lmv03kqI3v2BPb7znBIhZGGBE8Y0euhJFSFkbCP34GX1U3zVnMc+rgYoINhxFUZMZuIi/NcpqkuDkBl5NAhTLZqHOgYemJGlxzS1V+mknDqMWIqLYKcAN/oTwghhF8kjLTCVRkpLfWs27dPPWZmwgsvQHi4uwjhDiNlZS1PJd8VzFYVRkjynofj48HX8g0zqA/zdOBczXGEp8UAzllY/QwjBmdlRBdiwmQCe4iqjAQijGh7VFUkh3SOOjakcwf77DO4/HL+cc0eNnIEi1nANpx3APY1REoIIUTQSBhpha/LNK4wMngwnHMOlJfDueeqdWFhkOLsxnH22bBuXWDaFVGuOq0Y07zDSMHI4ziab3hx3nfUxA3kU04melQq0WkqnHTsMo2zMmJR4cARqsJIrbWLLtP8/e9w770AlPyoyk4HGMyUKZ087kknwfLlmEcOZgobuZt7KSZBvSZ3zxNCiB5FwkgrfIUR11xfmZnqUd/kN+jaZ80a+OMfA9OuqGrvCc9cXEOL//TCaC6esZ9T+IRZs1DlGyCMav/DiEOFEZ1Z9eZ1VUYcFV1QGcnOhr/8Be6+G/Lzyd+YB0BV7ECvzsGdYTB4ll39XRq6ou1CCCG6jISRVrRWGXGFkaaOOMKzvG5d18/GWl8PyfWqz0PC5HSv166/Xr3/4cPw7gdGQOcVRsKp8juMGJ2XafRmVRmxh6g+I1pXjKZpPDvcgQPYc1QYqYtP7fyxnY46Sj2azVBvUmEkf7eEESGE6EkkjLSircs0vtx2G/zud57nXT2yJm9nJXHOe67ET/IOIyYT/Oc/nnabTHDccXiFkdpa/97P4KyMGCzOyojZNbS385dpylZ6woj1pwPoC1UYqU/sujAydSp8/rm64aE5RrXdmidhRAghehIJI61oz2WapsaPV4Fg6lT1fPPmrm1T0UZVFSnXR6OPiWr2+pgx8OuvcMst8OSTar6UTlVGHM7KiKvPiKXleUZsNnjttfbfydj25Vr3cvmW/ZhLVBjRpaS0tEuHnHgipKeD3dl2R6WEESGE6EkkjLSicRj5+WeYNcszsqalyojLpEnqsavDiHVLNgDFYYNa3CY+HhYvhrlznSs6EUYMmndlRLOoyzS62uZf6G++CRdfDHfe2Y4Dl5URd9AzqsW26wAR5SqMGAd1XWWkMU3CiBBC9EjGYDegJ3OFkbw8+O1vVcXBJTKy9X0DFUZqd6owUhHbchhppgsqI4ZQVRnRnKNpdLXND5SX5/3Ymuq1PxKGp0ONLvsAMdVqx9ChgQkjrpFAXdLfRQghRJeRykgrXGEkJ0cFkVDnbVlmz25734kT1WNXhxGyVRipT05vY8NGOhNGnJURY6hzbnznMBdDXfMvdNexKyvbPu7eL9UwXgdqmtXYg1sJd6gdo0d17WUaN1eQam8Yqa6GJUtUhxMhhBABI5WRVrhmYHV5800YPhxS2/GH+xjn/Fp5eepL2hVkOiukSPUZ0Wf4XxkJpZbaKjtgaH37Rkyad2VEF6ZORF/XPNX4E0aqdqjzWMMxHMfXxFWq52VEkZgZ0e72+UMX7rzEVNPOMHL22bBqFXz1Fbz7bkDaJIQQooOVkSeffJLMzEwsFgtZWVmscd6UrSWvvPIKEydOJCwsjJSUFK644gpKmt59rgdqHEauvBJOO02FEed3e6tiYjzb5eZ2vi0OB7zyCpjyVWUkdKT/YQSgody/SxRNKyO6CFVdMNqaH8dVcGhPGHEcUOFj94AZXuvzSCUpya8mtps+XLVd76O/S1PW1z9WQQTgf/8LTIOEEEIAHQgjK1asYP78+dx5551s2rSJY445htmzZ5PtvHzQ1Nq1a7nsssu46qqr+OWXX3jjjTdYv349c929K3uukBA1THfmTNUh1B86HaSpW8V0ya1Q1q5VbRmE+j3HTPAjjISGui+HOCqq/HrfELwrIwZnGDHVN/9Cr6+o5fe8QHhZ251GTEUqoUVMHEYenssyhfpUNQIoAPQRLV9iamrfwqfdy5rJBHZ7YBolhBDC/zCyePFirrrqKubOncvo0aNZsmQJ6enpLF261Of23333HYMHD+amm24iMzOTo48+mmuuuYYNGzZ0uvHd4T//UUEgOtr/fdOd3Tq6IoxY95byI5MZhpq4JPEIP/qM6HTuCb/8CiOaRgiqMmIKU5URQ4S61GGqb36Z5tTv/8YLXMlbh45t89BRZeqXknF0OlsZ715fFRrf/vb5yRDpDCM+qjpNJWV7/n3q6uvl5npCCBFAfoURm83Gxo0bmTVrltf6WbNmsa6FG7HMmDGD3NxcVq5ciaZpFBYW8uabb3L66ae3+D51dXWUl5d7/fRGrjDSFZdporesYTKbAbAnJmHMGOjX/g0hzks1VX6EkUbVANdlGmOU+kIPaWj+hT51/38BGKLtafWuxQ4HJNapX8rAI9O4f8C/3a8VxI5uf/v85Gq7r6pOYw0HC0l15OJAx0GcHYS6evY6IYQQbn6FkeLiYux2O0lNLuonJSVRUFDgc58ZM2bwyiuvcMEFFxASEkJycjIxMTH8+9//9rk9wKJFi4iOjnb/pKf7UQXoQbqyMuLqHWrXGTBs/lFNr+qHBrP/YcReY3Mvm8LVZRp3GHHUNpvr3tjgmd61pZle162Dqy6oJBYrACnT0ok4YgTJ5HMdT/DVhJvb3T5/maKdba+vRtPg8svhD39ovl3+BxsB+JWRbGIyANpuCSNCCBEoHerAqtPpvJ5rmtZsncu2bdu46aabuOuuu9i4cSMff/wx+/btY968eS0ef+HChZSVlbl/cnppibwrw4ijWn27bx4wq33DeZposPgfRuqr693Lrss0pqhGw4KaJA6T3fO8pU6sd1++n8w3HwbULLLG2EgWLYJCklnKdVj1cb537AIhzungQ+zV5OXBSy/Bs8+qe/k0Vv6lCiO7o6ewh6EA1Py8O2DtEkKI/s6vob0JCQkYDIZmVZCioqJm1RKXRYsWMXPmTP7ovIXthAkTCA8P55hjjuH+++8nxcfU32azGbPZ7E/TeqRAVEYaTB0bI+wIVWFEV+NHGKmyYXEuh4T7CCPV1TS+va7JUederqyExMTmx7xp942cwQcA5OnTiEJNELdkibqvz2WXtbt5fjPHqraa7dXsy4FRbMeOgYKCEcTGerbTb1ZhpHJUFsW/GsAKNT/voYtuJCyEEKIJvyojISEhZGVlsco15NFp1apVzJgxw+c+1dXV6PXeb2Nw3tdda61jQR/QlaNptBpVdbCbLG1s6ZvrnjL69s6xgacy0oABk1l9hqERBmpxBsUmk4eFONqujLiCCEB0jKeadvPNap/zzmt38/zmCiMWRzUH99SwnTHsZCSFB7wrPHE5PwFgnHoEDYNUZUS3RyojQggRKH5fplmwYAHPPfccy5YtY/v27dxyyy1kZ2e7L7ssXLiQyxr9eXvGGWfw9ttvs3TpUvbu3cs333zDTTfdxLRp00jtwOWG3sRVGbFa2zf3Rqucl0QcHQwjWpiqjBhq218ZaahWfUZshODMj4SGQg3O6kiT6VyNeDq8tnS+h3Seckn8TO/OqoEuhoXGO8MIdZRv9QxFr/tuE3zxBVRUQH09CdXqtaSjh2McPRyAiPxdMrxXCCECxO8ZWC+44AJKSkq49957yc/PZ9y4caxcuZKMjAwA8vPzveYc+f3vf09FRQWPP/44t956KzExMZx44ok89NBDXXcWPVRUlPopL1cjakaN6vixXPeCcZg7OJVreAfCSI2qjNTj6SwbGgrVhKkOqK1Mq+4rjNTXOYjT1GR3ttPOJuTh+9vdlq4QluC50GL7dZ97+ah/XQx/2w/R0Wi3/REDDmqwMDArmZgDDipXhBPRUAU7dsDYsd3aZiGE6A86NB38ddddx3XXXefzteXLlzdbd+ONN3LjjTd25K16vYQEFUaadpL0l67OWRkJ6VhlROcMIyab/5WRel2Ie50rjAB+h5GyfaUkOG+Op3/zvxDq34igzgqL8/zuHLv3upejD+9XC2VlcPddAOwjk8yBOoaNNLCJyRzDWti4UcKIEEIEgNwoL8Bclx7q6lrfri2ue8Folo5VRvRRzg6s1VWtzgHSmKsy0tCoMhIW1iiMNL5M09DgtW9lRfM3qdpXBMBhYj033utG4ZF6qp2XmEw5e31uo3MOV841ZRIaCsOGwUayANA2bHRv9803sH9/YNsrhBD9hYSRAOuyMGJTlRHN3LHKSMxAFUb0tVXt/hJtqTLi7jPSqDKiHbZ67VtdVk9T1QcOAVBiHNDOVncto9ETpOLLvOcNWctMyol0Py+NygRg6FD40RlG6r9XYWTzZjj6aMjM7IZGCyFEPyBhJMC6PIx0sDJiilZhJJwqvv22ffs46pyVEX3zPiPgfdO9ugLv61B11ubTxdflqMpIudnHmN9uUqtXbR+Cd2XkI2Z7TUtfkaiShsUCeSkqjBi2bAK7nSaDyYQQQnSShJEA66owYnDdCya0Y5URVwdWf8KI+zJNC31G6ss9gaNpGLGVNQ8j9gJVGakIDU5lBHyHkSrCeIPz+ImJ7nX1aUPcy/rRI6kkHENtNWzdSmmp53h9fHS6EEJ0CwkjAWZxZofOhhFjvaqM6CydDyP1jz/FActIctfub3UXR626TNO4MmI2ey7T1Jd5KiP1RW2HEa1QVUZqI4JXGbEZVBiJRPWwPZt3SCGfqKwR7Ar1hBH9UM81mKEjDHzOSerJO+9gLfZcgmpp2nshhBDtJ2EkwFyVkc5+aRmdlRFdWOeG9kZTxlNcS0bdTkrufaLVXey16kvX3iiM6HRQ5/xCbyjzDJlpOOQdRhpXTQB44w0G/PIFAHXRwauM2Eze86jmkE4FUaSmQvVwTxixjPaEkeHD4U1+q57cey//WhbBn1BD0ysqAt9mIYTo6ySMBFhXXaZx3YROH9a5ysjxrHavqqpvfUSLqzJi14d4rc8OGQZAyNov3esair3DSOP+JGzaBOefz6D9a9RrscGrjNQ3CSP5qNsRpKeDaeokdjKc1RzLgOHR7m2GDYP3OcP93OSw8RC3o8cuYUQIIbqAhJEA66owYrI7KyPhnauMNGYoLW51F7uzA6vd4B1aVkZdpA757SrIzwdAK/EOI/bKRpWRJvPha4nBq4w0hHjCiKbTce41icTFwcKFMHKihdFs5wS+dE/lD6oyUkYMHxrO8DrW8XxFRYXqN7J3r/QfEUKIjpIwEmBdFkaclRFDJysjjZmtBT429NBaqIwURg5jHdPVnByvvaa2PdxKGGkybbw+KYiVEaMnjDjiEvj3UyaKitR9hIYNAwcGNPQMHOjZZ8gQ0OvhavtTFD20jBdRtzu4hFeoqIBly9QQ4Ace8P2ev979Kr/e/WogT0sIIXo1CSMB1mVhxHkTOkNEBysjrhvlAEWD1FDV8IrWw4hraK+jSWUkNBReQ1VH+PhjAHRNwoijyhNAKg6Web1mSg1eGCmu9oQRfWoygPu+O0cc4dkuLs6zbDbDuHGQTyr37L+C5fwegFP4hIoKuHaujVP5iEV/aT7tbFVeGSPvvYSR915CTXH7Z78VQoj+RMJIgHVVGDE7L9MYwjtYGUlPh/XrYe9eNl/9JABR1W1URuqclRGjd2UkLAx+Zpx64rwPkeFwk0s+jaoh2VuahJGhg/xuflexNRoBpLvkEq/XkpLUhGY7d6qOuo1df716XLoUtjEGgBTyqTxcz73cxUecxhNc3+z9yg5Y3cvVJc1HGAkhhJAwEnBdFUZCNFUZMUZ0MIwATJkCmZlYBquKQJytoNWODppNVUY0H5WRbFSg0LKzQdMIKcn33rlRGNFXqDCyktmM4Rci06IJltiTVFUoJ34i/PGPzV6fOFH1EWnq0ksh0VnQOUQiNkzo0cjbmM/tzpE1l/NSs/1qD3l6uPoa7iyEEELCSMB11TwjZoezMhLZwcs0jYQPSQLApNU3v4OfwwGPPAI//NBiZSQ0FHJRPTx1NTVQUoLlsAoj+UZ1Oai+vAa73XnIwyqMbGIy2xlDTEynT6HDTnzrerb84xMG7PtBdQRpp9BQeOghFUgio/TkkQrAjs8Pem3nvLWNW3We1b1cW9ryjQWFEKI/kzASYF0yz4imYUGlGVNnKiNOMUlmSolVTwqaXKr5+GNVMTjySAw1qg+EZvSujJjNYMNMPqrCwoEDhJar45TGqplLDfU17HHe/kUrLwegDFURSU3t9Cl0mCnSwoTbZmGODGl74yauuAKKitTNfRuSVA/Xii2emVwPE0Nxk6tVdYVW97JURoQQwjcJIwHWJZdpGiUZU1TnKyNxcVDgDBK2bO8wsn/1AffykLUvAuBoUhn5/HP1mIOqgmibNmOwq0s6h6MHAxBGNVu2qO305aoykjIymh9+IKiVka5SGa0qQ7/hM/e6Wiyukc5utiKrZ1nCiBBC+CRhJMC6OoyERHW+MhId7Qkj1fsKvV47tN3zp31SpfOv/iaVkZtuUo+ufiO2td8DUEw89aGq+hFKDT/95Ny9SoWRtLFRTJ3a6eb3CDVxqjJyGivd6+IpIe+gdx8ce4nVvdxsVlohhBCAhJGA64owotWoMNKAAXNE67OmtodeD4dDVL+R2v3elZGGnPxm2ze9TPPnP6urOYcsKow4vlVhJJ8UtFBVuQmlxl0ZMdWoMGKMC17H1a5Wl6DCSBJF7nUh1HNon/fwXkep1b1cXy59RoQQwhcJIwHWFWHE9Rd1LRb38TqrPFRVRhpyvcOIochHGDF5X6aJjIRTToGaRBVGQneq1JFPClg8YeS992DSJDBUqjBiHtB3wkhDcprP9WV7vDuN6Mqs7mV7hVRGhBDCFwkjAdaZMLJlC2zYAPUVqjLSlWGkKlJVRrQC78s0odbmYQST72qMPTXd63k+KRDmCSMAP/0EYfUqjFiS+k4Y0VI9U7Q60OFATUxSeaDEazt9udW97DUrrRBCCDcJIwHW0TBSWKDx1cSb2Dj1GsoL1ZdYDaFdFkYcUWo0jWb1npAspkaFkWoa3VAuxPfIE+MQ78nL8klBF672S0+owUADCRwiCjWaJiyl74QRfbonjPwQdjylaRMAqMtzhhGHA6xWTFVW93YSRoQQwjcJIwHW0XlG3vrrZm7i31zDM1h/VB1Ja7H4MzVG65xDWhpfRjhcqjFAU5dt9sU1mhu9hcqIZcIIavGko3xSiEtVlZETjqxh7TF3cIgBhKBG2kQM7DthxJThGZ+8JXU2Wlw8AA0FxbB0KVp0NMTGMi3nLfd2WrX0GRFCCF8kjARYRyoj1dVQ//IK9/O6PbnqUdf5Yb0u+rgYAIyVVve6/T+WYkZNdFacNsm9XmuhMpIyKpqX+Z37eT4ppA5VbdTV1jB+u+ccHOiISo3ootYHX0S8mU85mT0MYdOUqzEmqTCiFZdQ9a/n0FU2v0+NViWVESGE8EXCSIB1ZNKzLz7XOLPW80WuZecAYNN3fliviylBVSlM1Vb3usLN6hKN1RhPfbLnEowuxHdlJCMDljDf/bzCMoCEdGdg2rWL8OJsz2tEEhvfd/65RUbCKXzKCHYSmxlD2KAEAEIqS6jened7pxoJI0II4Ysx2A3o6zpSGcn930bmsN/93FSgwkidoesqI6EpMQBYaq2s/esn2EorsdZGAVARkeK+oy2AroXKyMiRsMcyjsW1t3AEP1I94Sh0YV+pF7OzvbYtI5r0vlMYITJSPTowkJoKZoOqjCRTQHxDoe+daiWMCCGELxJGAsyfMNLQAKWlYP76U6/1oSUqjNR3YWUkMj0GUCNdJt9/DhZqeXmcuuFbXWwKIYMahxHflZGwMDj1VLj13cUAzJ2AuomLDw70ze6E25u5wghAbCzQoMLIGLahx/fNB/U10mdECCF86Tt18x7KnzByww2QkgLpu77wWh9T7gwjhq4LIzGDYwDQoxFONQYcDNi5BgBHUgphQxqFEXPL93E55xzP8rhxtBhG0sjtdJt7krBGg42iooAEdZlmAmrOlTxDGoUM8NpHXyeVESGE8EXCSIC1N4zU1MDTT4PJUctMvgFg45DfAhBfp/og2Ixdd5kmIc3iNRIGYLptNQD6jHSiR3rCiLGV+tmcOZ7loUOB5GTcJZBGl3eM2Dvd5p5Ep1OnCjBjBhCvKiPRzmHMFRGplBqbhBGbhBEhhPBFwkiAucKIzQaa7+o9AJ8577c2g3WEUkseKRQNP9prG7ux6yojSUlgJcZrXQzOmVInjSZueLx7vbHRXBlNxcbCokVw/vkwaxYwaBCsXAnPPw9bt5LLwBb37e127oTCQmcOSfeeAK4uIZVyi3cYMUgYEUIIn6TPSIBZGuUHm40WJy175x31eCLqEk3RuJPQDUj02qbe1HWVkfh42EUMyTTvbBkzfTQRcY1yaklpq8e6/fYmK0491b34W/MHPFt3KXfyd97rTIN7oMjIRn1HRo3Cbg7F4LwUY0hLpabSDI1G+BrrJYwIIYQvUhkJsMbho6VLNZoGH3yglk9/9CTyz57HmL+eiz7JO4zYTV1XGTEYoNIY4/O1iKyRXp1N90dP7PD7/O1/kzjCuJVZ/z6zw8foFYxG6kd7fk9hw1L5aeT5XpuY6qUDqxBC+CKVkQBrPCq2pTCSmwuHDqm+GWNvOIGQBSeoffM2eW1XGDm8S9tWa4nx+ssdIM84iNSIcACGs5MpbGD27LM6/B6nnAIVFd4Vor7KdNQRsPk7AOInpHLqbf/H/439H6Oj83jg8LWYGqQyIoQQvkhlJMB0Ok8gaWnis59+Uo+jR3uHl9C0BK/t9idO7dK22cJimq3Lix7lXn7l++FMfugiLvld58bk9ocgAmCYmuVejhqZyshROl48fCYX/U39TkPsEkaEEMIXqYx0A7NZ9RdpqTKyRY0GZcIE7/URg73DSH5ikw06yREZA0Xe6w4njXYvT5umfkQ7HdHofj4pKYAa9muJVX19zA4JI0II4YtURrpBW8N7Wwoj0cmeDquHicEQ1kW37HVx3iyvsdrBo5pvJ9pn7FjP8iDPdPqmKPU5WhzSZ0QIIXyRykg3aCuMuC7TTGzSTzQ21rO8j8wWR+J0lOtmeQCvGC/niIbvqTj29K59k/7EZIKff1aTxjQKeq7KiEWTyogQQvgiYaQbtBZGamrUfBXQvDLSeJbPrzm2y8NIyIAY9/L2Sx/gDytS2djxvqoCvKsjTuZY9UGGUkNDQ+uTyAkhRH8kl2m6QWth5PvvweFQs4knJ3u/ptPBFVFv8V/O4y7u9XVVpVMsyZ4D3v90IocPwyi5StPlXJUREw3UlNcHuTVCCNHzyN9o3cA1mqRxGNm9G957D75w3obmrLPweSO55eXnsBx1A5i5c7u2XaljYgAoM8YTbTLR8h1oRGe4wghATWkNkXG+bzwohBD9lYSRbuCrMnLllbBmjef5tdf63nfcONUN4eKLISOja9uVevwINJ2OsOldO0pHeNOFesY215TWAFHBa4wQQvRAEka6gSuMuOYZ2b7dO4hMmwZZWc33A1i+HD7/HG66KQANGzIE3Y4dmJKSAnBw4abTUU0oYdRgK5NOrEII0ZSEkW7QtDLy7LPqcexYmD4drrmm5X2zsloOKl1ixIgAHly41OlDCXPUUGeVMCKEEE1JGOkGjcNIQwP85z/q+UMPwekykrZfqNOHggNsVplrRAghmpLRNN2gcRhZtw6KiyEuTt23RfQPNoPqxFpRJJURIYRoSsJINwh1DqaoqYF331XLc+bIfBP9iS5czTWyb3NZkFsihBA9j4SRbhAXpx6Li+F//1PLZ8nkYv1K7Ug1va7ph7VBbokQQvQ8Eka6QWKievz+e9i7V92Zd9as4LZJdK/QM9UHPjp3FQ0NQW6MEEL0MBJGuoErjPz4o3rMzISIiOC1R3S/1Mt+A8Akx49s//pQkFsjhBA9i4SRbuAKI+Xl6rHptO+i79OnJrMnYgJ6NApf+SzYzRFCiB5Fwkg3cIURl5SU4LRDBFfe+FMBiPzif0FuiRBC9CwSRrpB0zAilZH+SX/BeQCMP/A+VFUFuTVCCNFzSBjpBlIZEQCjLsliN0MJ06qpePX9YDdHCCF6DAkj3SAmxntOEamM9E/xCTpWxV0IQNWLbwa5NUII0XNIGOkGej0kJHieS2Wk/6qafDQA2u7dQW6JEEL0HBJGuknjSzVSGem/MqaqfwgmqwzvFUIIFwkj3aRxGJHKSP81YOwAAKJth0DTgtwaIYToGSSMdBNXGDEaPdPDi/4nepizMqLVQ5ncp0YIIaCDYeTJJ58kMzMTi8VCVlYWa9asaXX7uro67rzzTjIyMjCbzQwdOpRly5Z1qMG9lSuMJCerPiSif0pIs1BOJABaYVGQWyOEED2D3/eNXbFiBfPnz+fJJ59k5syZPP3008yePZtt27YxaNAgn/ucf/75FBYW8vzzzzNs2DCKiopo6Gc36GgcRkT/lZgIuSQSRQUVew8RNXJEsJskhBBB53cYWbx4MVdddRVz584FYMmSJXzyyScsXbqURYsWNdv+448/ZvXq1ezdu5c45/WJwYMHd67VvVBamnrMyAhuO0Rwmc1Qoh/AUMdeKvcWERXsBgkhRA/g1wUDm83Gxo0bmdXklrOzZs1i3bp1Pvd57733mDJlCg8//DADBw5kxIgR3HbbbdTU1LT4PnV1dZSXl3v99Hbnnw/33gv33RfslohgK7eoMln1ARlRI4QQ4GdlpLi4GLvdTlJSktf6pKQkCgoKfO6zd+9e1q5di8Vi4Z133qG4uJjrrruO0tLSFvuNLFq0iL/97W/+NK3Hi4iAv/412K0QPUF1eCJUg+2g9BkRQgjoYAdWnU7n9VzTtGbrXBwOBzqdjldeeYVp06Zx2mmnsXjxYpYvX95idWThwoWUlZW5f3JycjrSTCF6pLooNbzXUSCVESGEAD8rIwkJCRgMhmZVkKKiombVEpeUlBQGDhxIdHS0e93o0aPRNI3c3FyGDx/ebB+z2YzZbPanaUL0Gg1xibAHdMUSRoQQAvysjISEhJCVlcWqVau81q9atYoZM2b43GfmzJnk5eVRWVnpXrdz5070ej1prl6dQvQniaoyYjosl2mEEAI6cJlmwYIFPPfccyxbtozt27dzyy23kJ2dzbx58wB1ieWyyy5zb3/xxRcTHx/PFVdcwbZt2/j666/54x//yJVXXkloaGjXnYkQvYQxRXVgtVRIZUQIIaADQ3svuOACSkpKuPfee8nPz2fcuHGsXLmSDOeY1fz8fLKzs93bR0REsGrVKm688UamTJlCfHw8559/Pvfff3/XnYUQvYg5XVVGIqqlMiKEEAA6Tev5N8goLy8nOjqasrIyoqJkZgbRu33+Yi4n/T6dBowYHTZoofO3EEL0du39/paJyYXoZjFpEQAYaYD6+iC3Rgghgk/CiBDdLC7V4nlSWxu8hgghRA8hYUSIbhaV6Bm23lApYUQIISSMCNHNLKE6alGBxFYuYUQIISSMCNHNzGaoRV2qqSuTMCKEEBJGhOhmRqMnjEhlRAghJIwIERR1OhVG6iskjAghhIQRIYLAppcwIoQQLhJGhAgCdxiR0TRCCCFhRIhgqHeGEbuEESGEkDAiRDDUG1QYkXlGhBBCwogQQdFgdFZGqiSMCCGEhBEhgsAVRhzVEkaEEELCiBBB0GAKBSSMCCEESBgRIijsJqmMCCGEi4QRIYLAEeK8c29NTXAbIoQQPYCEESGCwBVGtFqpjAghhIQRIYLAYVZhRCdhRAghJIwIERTOMEKdhBEhhJAwIkQQaK7KiIQRIYSQMCJEUISqMKKXMCKEEBJGhAgGncUZRmwSRoQQQsKIEEGgc1VG6iWMCCGEhBEhgsAVRgwSRoQQQsKIEMGgD1dhxNjQPWHEfqiUwlc+g4aGbnk/IYTwh4QRIYLAENZ9YeTAnc9gGBBP0u9OZv01zwX8/YQQwl8SRoQIAkOEulGeqRvCiPboYvdy9S/7Av5+QgjhLwkjQgSBMUJVRkz2wIeR0Poy97K+whrw9xNCCH9JGBEiCNxhxBH4MBLuqHAvmyqtAX8/IYTwl4QRIYLAFUZCWgkjdnvn30drsBNBlft5SLW1+TYa3Dm3kKceKmv2mhBCdAcJI0IEQUiUCiNmR43P17//HqKj4bHHOvc+1UWVXs8ttdZm22xbU8Kfnx/OybcfwaYNXZCAhBDCTxJGhAgCU6QKIxbqVGmiiU2v/8qaqslULn+jU+9TlV/u9bxx/xGX6g3biKKCoezl7Xmfdur9hBCiIySMCBEErsoIgHbTzVBY6PV68g//YzKbOXbfi516n5qiCq/n4fXW5tvklriXp2x8ij17OvWWQgjhNwkjQgSBOdoTRnSP/5v8K+/wet1YlAdAZE1Rp97HFUbszv/UoxzWZtvU5x1yL8/hAw6s79x7CiGEvySMCBEElkgTDnTu58VfbPF6PfSwCiNx9d4VE3/VHVKXaQ7q09X7Uoej2rvTrKPQE0YMOKhYv6NT7ymEEP6SMCJEEFhCdejx9BU5EDrK6/XoShVGErUiqqua9ylpL1uJqoyUWlLd4afqoNVrG33JIa/nNTtzOvx+QgjRERJGhAgCi8X7eXiow72saRBvU2EklFqK93uPiPFHQ6mqjNgs0ZQRDUBFjtVrmxCr92UZ7UB2h99PCCE6QsKIEEFgNHo/j3R4Rr2UWTVStDzP850dv1RjL1OVkXpLJOX6GACq871H1FgqVWXkUNggAEIKpTIihOheEkaE6AFCaj1hpHDHYTXk16lyb8c7lGquMBIWRZUxBoCDv1gpLfVsE1GjwsjhIVkARFmlMiKE6F4SRoQIkiXc7F422zxhxLotz2u7XYve5OnjX/M1HUnbytVxHWGRVIfEAHDcolN5e+gf3ceLtqkwYpp2BABJtmxqfM/FJoQQASFhRIgguYUlzGQtAKH1njBStfOg13aXlfyTa1ZfzJb73iUvNYv851cCsHkzLFoEdXW0SFepKiNaRCS1lhj3+rnWRziUXYPDrhHvUGEk6sQpAKSTw/79nTw5IYTwg4QRIYLI1ak03O4JI7b9eT63nXj3/5Ga/yMpc08H4Lb5DfzjjlI+/LDl4xuq1HG1qChsYdFer2V/sRtrdjlmbABEHqcqI7FYyf7Fe7I0IYQIJAkjQgTJI49AVJoKCBGNOrA6DvoOI01duX4eh0ik6tstLW5jrFGhQh8dSUN4jNdrh7/fiXWXqopUEk5I2gAqTWqbJbfmkC1dR4QQ3UTCiBBBcuut8P7qKADM2GioUtdbjIUqjJTro1vct6agjIurn8eAg4FrXmtxO1OtCiOGmCgcUTFer9l+2UnFXmfnVWOi2n6ImhyN7AP8/e/+n5MQQnSEhBEhgigyJcK9XJmnqiNhh/YDkJN4RIv7WZd6AkhZtanF7VwdY42xkUTifeklZP9OarJVGCk3D1DbjxkGwGIWULZ5X3tPQwghOkXCiBBBFBJqoAIVSCrzytE0SKvYDkD5iCkt7hfx1D/cy+aSli/rWOpVAAmJj6TcHub12sm5yxn58JUAVEeoMMI991CXkMpodvB/26U0IoToHhJGhAiySr26VFNdUE5JdhWDHPsByPzT+S3uE1m0172cYN0DF14IL7zQbLuwBlUZCUmI4svxN/NfzuNO7ne/HmsvoVCfTNSfr1UrJkyg4u5HAUip3IXD0eyQQgjR5SSMCBFk1QYVRmoLy8j78lf0aBTrE0meM4XSj39gw5HXt7r/tOqvYMUKuPJKtCLv+8yEO1RlJCwpkhvuSWDRpP8y7F+e+U2sulgM2fsZectp7nUx41W/kTQthyK5ga8QohtIGBEiyGpMKozUFZdT9u02AHKjxgAQd8pU6kZP8rnfJpqv/3D2vz2TozkcRGjqvjahSVEMGgSbNsEVN0ZQHRYPgP6hRSQMNHsdwzg4DYCBHCTngJRGhBCBJ2FEiCCrM6swUl9cjuNnFUasqWPcr5uS4nzu9zoXNls3/ccn2PSdGpVjK/XcYC88OdJru7AvPoQXXyTqtj80P3CqusOvGRuFPx9q/roQQnQxCSNCBJnNosKI/XA5oQdU51X78NHu1y2pvsPIu5xNHSFe6+IpZe3D6wAo/XE/AFaiiUxscpvgI4+Eyy4Dna75gU0mrJZkAMp/kZvmCSECT8KIEEHWEKbCiFZSyqCijQBYjvBURsLSfIeR0AkjKA1Jdj/PG3SkOs7Hn1BXBzvf/hmA/RHjMJp8hI5WVMaofiN1uyWMCCECT8KIEEFmj1BhZMqXD5Ncn0Mx8aSePc39esQgTxj5kclcy5PMZC33/E2HzmF3vzbgr/MAOKb2Uz75BA6vUWHENnyc322qS1JhxJGT6/8JCSGEnySMCBFkmjOMhDWokS+vj7mPzAmePh7Rg2Pdy7Xh8bwYei3GY2dy1lmeobsAxjmnAnAEm1j9RhEhu38BIP7YsX63SZemOrGaC6UyIoQIPAkjQgSZLjrKvfwTExj32NVer4fGh3n6hsTFUVAAn36qunuEhDeafTU5mbKhkwGwvPwsI2yqMpJxuv+VEctwVRkJK5UwIoQIPAkjQgRZSJjBvbxs+nMcd5LRewOdDqteXappiIonKgrMztG45v++jCM8EsfLrwJgueNWAP7G3QxFTYxmnOR/GEmYpCojiXW5FBf7vbsQQvilQ2HkySefJDMzE4vFQlZWFmvWrGnXft988w1Go5FJkyZ15G2F6JNS551FrjGD92Y+xD++mupzgEuFSYURR4x3Z1bdabPRl1vRX3IRAObfX8R2y2SMqL4kjrh4SEz0u02WEYMAGMoetm3ze3chhPCL32FkxYoVzJ8/nzvvvJNNmzZxzDHHMHv2bLLbuN94WVkZl112GSeddFKHGytEXzT4pKGk1e/nzLV/IiTE9zbVZhVCdAnxzV/U672WSx99gT2mkerpKbM61qgJE7DrDKSSz4G1cqlGCBFYfoeRxYsXc9VVVzF37lxGjx7NkiVLSE9PZ+nSpa3ud80113DxxRczffr0DjdWiP6qauBwAGKmDm9z25nXTWRo3XbYtg2efbZjbxgeTt6ASQA0rP6mY8cQQoh28iuM2Gw2Nm7cyKxZ3n9tzZo1i3Xr1rW43wsvvMCePXu4++672/U+dXV1lJeXe/0I0Z9NXfNP8l75kokLT2t7Y1C9W0ePhvDwDr9nxfgZAERva/m/bSGE6Ap+hZHi4mLsdjtJSUle65OSkigoKPC5z65du7j99tt55ZVXMBqNPrdpatGiRURHR7t/0tPT/WmmEH2OMT6a1IuP974kE2Ahx88EYGiBhBEhRGB16P9suiY97DRNa7YOwG63c/HFF/O3v/2NESNGtPv4CxcupKyszP2TkyPXrIXobsnnqMrI2IbNlOZUBbk1Qoi+rH2lCqeEhAQMBkOzKkhRUVGzaglARUUFGzZsYNOmTdxwww0AOBwONE3DaDTy6aefcuKJJzbbz2w2Yzabm60XQnSfiNHplBNFFOUc3ppLXPrIYDdJCNFH+VUZCQkJISsri1WrVnmtX7VqFTNmzGi2fVRUFFu3bmXz5s3un3nz5jFy5Eg2b97MkUce2bnWCyECympUo3cqs0uD3BIhRF/mV2UEYMGCBVx66aVMmTKF6dOn88wzz5Cdnc28eeq+GAsXLuTgwYO89NJL6PV6xo3znnBpwIABWCyWZuuFED1PpTkeGvZRe7Ak2E0RQvRhfoeRCy64gJKSEu69917y8/MZN24cK1euJCMjA4D8/Pw25xwRQvQONZY4qAJbgVRGhBCBo9M0TQt2I9pSXl5OdHQ0ZWVlREVFtb2DEKJLrBt8MTMOvMaXZy7mhP/dEuzmCCF6mfZ+f8u9aYQQLWqIck4/XyKVESFE4EgYEUK0yBGrOrDqD0ufESFE4EgYEUK0SJ+gKiOmcgkjQojAkTAihGiRYYCqjJir5DKNECJwJIwIIVpkTlGVkbBaqYwIIQJHwogQokWhaaoyEmGTyogQInAkjAghWhSRocJIjF0qI0KIwJEwIoRoUXSmukwTSSV1FbYgt0YI0VdJGBFCtChqUAwO1B25rXvlUo0QIjAkjAghWqQ36rHqYgGoOCBhRAgRGBJGhBCtKnPeubcqW/qNCCECQ8KIEKJVVWbVb0Tu3CuECBQJI0KIVtWaowGoP1wR5JYIIfoqCSNCiFbZjWYAtJq6ILdECNFXSRgRQrTK4Q4jtUFuiRCir5IwIoRolcOkwohDKiNCiACRMCKEaJU9xKIW6iSMCCECQ8KIEKJVmrMyImFECBEoEkaEEK3SQlxhRPqMCCECQ8KIEKJVmlldptFJZUQIESASRoQQrTOryojOJmFECBEYEkaEEK1zhhF9vYQRIURgSBgRQrTO4gwjNukzIoQIDAkjQohW6UJVnxF9g1RGhBCBIWFECNEqvbMyYpDLNEKIAJEwIoRolT7UGUakMiKECBAJI0KIVrnCiLFB+owIIQJDwogQolX6MNVnxGCXyogQIjAkjAghWmUIU5URk4QRIUSASBgRQrTKFUaMDgkjQojAkDAihGiVMVyFkRCH9BkRQgSGhBEhRKuMEarPiEkqI0KIAJEwIoRolbsyokkYEUIEhoQRIUSrTBESRoQQgSVhRAjRqpBIFUYsSJ+RdquthYqKYLdCiF5DwogQolUhUc55RnCg1TcEuTU9nKaRe+Ft1EYnUZuYRv3nXwe7RUL0ChJGhBCtclVGAGwVcqmmNTlvfk/aikex2Mqx1JVjP2U2DfNvkyqJEG2QMCKEaJU5qlEYKZdLNa058No6ANZYTuYT/Wws9mqMjz0K8+cHt2FC9HASRoQQrQoJM9KAAYC6cqmMtEb3/bcANBx3IjX/fZ+5PKuev/UuNMglLiFaImFECNEqvR7qUNURuUzTsoZ6jcx8VRlJ/r8ZnH2ugdIzr6CEOIxlpfDdd0FuoRA9l4QRIUSbXGGkvrIPh5HcXFixAqqr/d519264YEYOqVoe9RgZcfEUAB5+1MBHnAZA4fPvd2lzhehLJIwIIdpUr1NhpKGyb/YZKb1rCY5BGXDhhXDrre3er74e7Ha49846Zm34OwDZsRMxRIYBMGwYlM6YA4Dt7Q+7vuFC9BESRoQQbarTq+G9fbUyYl2yHL3mAKD+hf+0a/RLZaUKG8cfD8d/eTfX8AwAA/7yB6/tjr33JADSy3+hobCkaxsuRB8hYUQI0aZ6vbMyUtUHw4imMaByj/upqa6KX+95rc3d1q+HrOy30a9dzdRDKwEo+uu/iVzgHUYmnJDAr/pRAOSsWNeFDRei75AwIoRokyuM2Kv7Xhix5RYRoVXiQMfzQx9QKx9/HM3uaHW/7E+28zbnspLTGMMvAMRedU6z7fR62JN8NADWld90beOF6CMkjAgh2tTgCiNVfa/PSNG3qiqSq0vnvM+uoZxIRtq2svPBt1rdz/yZ6gMSTjUGHOQb0zBlpPrctiZLhZHwTWu7sOVC9B0SRoQQbao3qj4jvaky8p//wMJ5h7H9912w2VrczrpRhZG80KFEDY7jiwm3ABD1yF3qHjMtGPLrSq/nexOPbHHb2DkzARh8aD1Yre08AyH6DwkjQog22Q2qMuKo6T1h5NrLKrn46WMJueD/4NJLweH7skvdNhVGrPFDAYi7fwGHSCDFuoP3Mm6kuNiz7f7zbuPQgLHUbtnJpErvKsfh4S2HkfFnD2U7owjRbJRccB1oWifPToi+RcKIEKJNDcbeFUbKyuAJrmc8P6sV//0vdQ886nNb/T4VRuoGqjBy9OnRLD7iFezoObPoOTZeo0bJUFND8puPk3hoG4UnXEgI9ewlkzKiANCOPKrF9iQO0LHs6BdowED8p69R8u6aLjpTIfoGCSNCiDY5nGFEq+kdfUb27XFwHm8AsJzLASj/x1M+KxLhBSqM6IerMKLXw6KNs1h/lpo35MR3boA77qD6xTewoMJYRukmAFYPuoyrQl/jjzxM1GlHt9qmu1YexTdhswDY+eGuzp6iEH2KhBEhRJvsJtVnpLdURgo35hJGDQ06IyFPPkYNFhLL96L9tKXZtgllKoyETRjmtX7ksj/ztu5cTFo9LFpE2LWXe71+mBjC7pjPyf88jUOX/5GZR+tabVNkJOjiYwGwW8s7c3pC9DkSRoQQbbKbnJWR2t4RRip/3AlAYcRQzrkimk/1pwJQ8kyTETL19cQ1FAGQmDXI66XYOB2vn/Ual/ISpSFJ7vXbUXOGvJh6B+ddHcM118Dy5WA0tt2uhlB1SYcyCSNCNCZhRAjRJs0ZRqjrHWHEsUOFEWvSSCwW+GXkuQAY33zN6+659QVqRlQ7elLHxjY7zp33mHgn/FJutv3Dve6Wsau4dOyPHP3ubej9/D+oPcIZRiokjAjRWDuyvBCiv9NCnGGklaGuPYn5wK8A1A8eAYDxnDMp+Xsc8Yd2wwsvwNVXA1C+r4R44DCxxMY3TxYTJ6p7582Z8zsyOMBhYrl0YRqXXJLWoXZpzjCir5QwIkRjUhkRQrTJEaL6jOhsddTX9/yRqXGHVGXEOHYkAEefFsW93AVAw8K/8u6TeTQ0QFW2qoxY9XEYDL6PdfrpcOaZOv7OX3iS6xk/vhMNi1JhxFglYUSIxiSMCCHapJlVGNFXVTBtGowd63W1o0fRNBhYpSojUVNUZeSoo+DrMdeynVEYSwoZcf1v+PDVMmpyVRgpN8W3esy/q4E1REfDqFEdb5s+xhlGatu+EZ8Q/UmHwsiTTz5JZmYmFouFrKws1qxpecz822+/zcknn0xiYiJRUVFMnz6dTz75pMMNFkJ0v9LUcQAM2P0Nxs3ridm+jqKiIDeqBSV5dWRo+wEYcIyqjOj18Oe/hnAaK8kjhTFsR//OW9TlqTBSZWk9jIwbBz/8AKtXQ0hIx9tmiFVhxFwrlREhGvM7jKxYsYL58+dz5513smnTJo455hhmz55Ndna2z+2//vprTj75ZFauXMnGjRs54YQTOOOMM9i0aVOnGy+E6B6RZ5+EHT3DbNtZzzTWMZPKwqpgN8un0s3Z6NGoJBzLoAHu9eedBwlTMnmfMwAw5R2goVCFkdqw1sMIwNSpqg9JZ7jDiK37w8hDD8Hw4ZCb2+1vLUSb/A4jixcv5qqrrmLu3LmMHj2aJUuWkJ6eztKlS31uv2TJEv70pz8xdepUhg8fzgMPPMDw4cN5//33O914IUT3mHVBLJtM3tOd1+QUt7B1cFUdUO06bBoAOs/cHwYDfPstjP6N6nwacuggjmIVRmyRbYeRrhCSoMJIaH33h5Hbb4fdu+Gpp7r9rYVok19hxGazsXHjRmbNmuW1ftasWaxbt65dx3A4HFRUVBAXF9fiNnV1dZSXl3v9CCGCx2iE6mnHe62rKeyZ/13WHlQBozKk+f9jjEawDFNhJMKai77UObQ3pnvCiDlRhZFwe/f+7kpLPcvR0d361kK0i19hpLi4GLvdTlJSktf6pKQkCgoK2nWMRx99lKqqKs4///wWt1m0aBHR0dHun/T0dH+aKYQIgDH3X0yDzjMbgO1QWRBb07L6QvXNWx3qO2BYhg4EILrqIMYyFUa0uO4JI6FJKoxEOMq7dUjSpk1wLKt5lAUYHS3fwbi/yS3PZc6rc/ho10fBbkq/16EOrDqd97THmqY1W+fLa6+9xj333MOKFSsYMGBAi9stXLiQsrIy909OTk5HmimE6EIJx4/DuGcnuWFqhEp9Sc+sjNiLVMCoi/AdMCJHq8pIki0Xc6XaVp/YPWEkLCkSACN2qKnplvcE2PyDjde5kAX8k6RN8sXrctFbF/Hhrg857dXTgt2Ufs+vSc8SEhIwGAzNqiBFRUXNqiVNrVixgquuuoo33niD3/zmN61uazabMZvN/jRNCNEdMjOxhg8krXonDSU9szJCiQoYDVG+A0bceFUZicFKbbn6Q8eU3D1hJCIpHAc69GjUl5RjCgvrlvc1vf82Kaj/bxtKe2Zfn+5Wb69nbfZa9/P2/lEtAsOvykhISAhZWVmsWrXKa/2qVauYMWNGi/u99tpr/P73v+fVV1/l9NNP71hLhRA9gi1MdTpwdOPN3vyZ08RgVWHEEeO7X1pUWhQVRACQXHcAAHNq94SRyCgd5ahLNdUF3fP7czhgxqbHPSvKemiI7Gaf7vnU63luuQwzCia/L9MsWLCA5557jmXLlrF9+3ZuueUWsrOzmTdvHqAusVx22WXu7V977TUuu+wyHn30UY466igKCgooKCigTP6DEKJXsoepL1OtGzqWV1TAH/4A4eFw773t28dYofqM6BJ8BwydXkeRcaDXurD07gkjJhNUOMOIzw7AZWXw4IPw/PNd8n42G9x87Cam1H7jXqevlP/3Aryz4x2v51uLtgapJQI6EEYuuOAClixZwr333sukSZP4+uuvWblyJRkZGQDk5+d7zTny9NNP09DQwPXXX09KSor75+abb+66sxBCdBt7pKqM6MoD/6V2883w7LPqS/Xuu9s3LNVSpSojhgEtB4zSMO97y0QO7p4wAlBpUGGktqichgbP7X7sP2xEGzwYFi7EcfU1XdKn5LXXYOI3T3itM0oYAeDXEjVLrw51aWZL4ZZgNqff61AH1uuuu479+/dTV1fHxo0bOfbYY92vLV++nK+++sr9/KuvvkLTtGY/y5cv72zbhRDBEKm+TA3tvNnbDz/AhRfCwYP+v5VrxoCTT1aPS5a0vU9YjQoj5pSWpw+ojPZURqoJJTY11P/GdVC10VkZKSjj4/iLyYkczU8jz8d65CnorFYA9Jqdqr2FnX6v/y4t4RJeAaAgS10iD6mRMAKw7/A+AM4edTYgYSTY5N40Qgi/6GJUZcRY1faXmqbBzCPr2bNiPbf/2b+hrJrmmS30jjvASD17djmwtTEyNdKmwohlYMvVjrpET2WkhHhiY/1qWqfUmlQYOfTet8wpf43hDTuYuPMN4ilhPVMoQYUo6472TZfQkh074MjvHyOUWmzjJmOdoUaMmOskjNQ21HKwQqXjs0aeBUgYCTYJI0IIv7imNA9px/1VVq2Cf3IL65nGwE9f8Ot9ysvhlKq3+JhTOPqx31KLhScc89i9u4Ud3nkHhg9noF2NkInIaDmM5GWdQSEDKCWWZ/gDFotfTeuUWrP6/Q3d8DoADnTM558sGvw0v+EzdjEcgIpdnQsjn6ywcjOPARByz52YElWIDJUwwv9Wq47LESERHJuhKvu7Snfh0BzBbFa/JmFECOEXQ5z6UmvrL2yHAx675zBXsgyA0w+94NdAjtxc+Av3cwqfYnz3LQw4uIIX2PGz76E1tr8tonFSaa0fyPG3H0UyhcRTyv38tf2N6gL1FhVGXKHpx8v/xe/Wz+f2vX9gxUfRlFmSAag90LnLNIO+fployimIHwv/93+YnWEkrKF/h5Hvv4cL5+0FYIApk7SoNHTosNltHKo6FOTW9V8SRoQQfnHdX8XS+GZv338PP/7otd19d9Uz+dsnCEN1xJzJN/zwbl673yc3R2MI6kuDOXMAMNFA6Zc/Nd84L4+Qn9a7n9rREzMoqsVjDxkCmzfDyJFw663tblKXaAjzbtf4P81myhR1G51TTwVtgJqzqSG3c5WRsBIVdvaPmAV6PZbkGAAi7GXdOflrUGiaxgNrHmBH8Q6v9XV1cM45QKzqL+IoycRkMJEamQpAdpnvG76KwJMwIoTwi/v+Kq6/sKuq4KijICvLPQKkJK+OC/8+zl11aNCb0KNR8eLb7X6fol8PE40z8KxYwZ4RswEI+WFts21r3/rQ67kODWNI6/97mzhR9at45JF2N6lL1EZ5Zp8+GD0a85ihXq/Xx6vKCIWdCyOGCisAWnQMAGEpqjISTVl3Tv4aFA9/8zB3fnEnp7x8CnkVngC8Ywfk5QGxKuQW/ToEux0GRQ8CJIwEk4QRIYRfLEnqSy3CdbO3Q41K2zt3ApD/5Q5GopY5+2y2n3c3AGPWPo29vn3X5Wu3qS8Ma2gyhIVRN/VoAJL3ftNs29IXve8Crqfn/un/xZC53M+d3MtfKXr8jWavOwaoMGIq6VwYMVWrsKiPiwEgNNkTRvr6vUevnHwlw+OGk12WzVmvn4XmLAXt369ejx6s/m1V52XyzTcSRnoCCSNCCL+Ep6jKSCRqngyvjiA//wxA1Vb1P/tfwqfCO+8wbPF1lBHNqPqf2XpX8y9gXxx7VCm9Ij5Tvd+pMwEYb12Lw94obNTUEL/5s86cUrcqaEjgr9zP3dzLpEvGNnvdMFCFkVBr58KIudaqjhcfA3hGQZmxUXGotlPH7mkaGsBuV8sHD8Li+xNZ/ptPsBgtbMjb4L5cs0/9k0LnvEzD4SG8/rqEkZ5AwogQwi/hqc5RGdRSUWID59wYAGXrfuGBB6Bskwojh2PVJYjQ1Fi+OXIBAHFP3teu9zHlqi+MuoFDAEg5cyoOdKSQT8FWTzXm8JufY7bXkE06N5rUrGjvcUYnzjCw7rkHpk6Fzz5T/USasgxWYSSyqnNhJLTOCoApMUatiIzE4Zzgqzq/73RizcmBuDi46ir1fO5cNYntH+dmMiNN3abkq/1fAc7KiMFGhWW72rh4JCtWQGq4M4yUSxgJFgkjQgi/hMRHuper8svh8GH38x+eXM/6O9/hwKfqL9Gq5CHu1wY9ejM2TAwq/4Wan3a2+T6Rh5ydV4eoyogxKoxCg+poWPjdPvd2u//5HgDrk87gjv1/4Papn2N89T8dPLvAmzBBTQR30km+Xw8fqsJIjK2QzvQ0Da+3AmAeoMIjej1VevXZ1RT0nTDy+efqtgGvv66Gkn/8sVq/bh2EFh4PwFcHvgKcYSRpC3ZdHbGWWJLNwygthUO7pTISbBJGhBD+MRio1KkbzVXnl6EdtrpfOpnPeIdzuJrnAHBkeMLI2BnRfGc+DoDsJz9o823iy1XgsIz2HONQpFou/0m9VlvtIH2zOtaAuWeSkqrjwR9O5LSLojt6dkEXM1KNpgnVatS3bAdF2q0AWFJi3OuqjOr3UlfUd8LItm3qsa4OTlPzujHU2Sf4k2eOB+DLfWom8P37gYHfAzBt4DQuuVhVin76WsJIsEkYEUL4rVKv+o1ccFo5Gz6ztridaaQnSOh0kDNBDdE1fPJhS7sAsGcPpDeoykjsEZme9x2gjlf/q3rtu2e3kqzlU6ULZ8Ydx/t9Hj1RYkYY5agKhi27Y5dqHA6I1qwAhKfGuNfXhKgwYjvU98IIqL4jOp2qlpxzDjTsnwb1Fg5VF7Fy4xbVZyRNhZEjBx7J5Mlqv4pcFUaKqoqoqe/jQ416KAkjQgi/VepUGLmQ19m2tqTF7cLHD/F+fr66P8rg7K+9Lu809d5LVjI40OwY9kFq2Zitwkj+f1TH1f0Zx2EIM/t7Gj1SXBwUoC7VlP+i5gppWL+Jhl9+bfcxKg7VYqEOgMj0GPf6OrMKIw0lgQ0jVis89pj3QKtAaRxGAE45BTIy4D//gd/+nxn9XjUk/Ka37lejiAb+AMCRaUcS7SygVZXEEm4KByC3PDfwjRbNSBgRQvjNkKrmyvgzD3N59v3u9doRR3htlzjZ++640y4exhbGY9QaqPz7Y82O29CgrvsfWvQcIdRTmjoW0tPdr4eMUmEk8tBebDaI/+lzACxzftM1J9YD6PWwzaJ+j/VfrKH6wCGM047AOG4UWlV1u45ReVCFDQc6zAmePj51oerb9/O3y/jlly5ueCNLlsD8+ap/jGuUSyBUVXmG67pccYV6DAuDN96Ah2ffCw49ey1vwnnnQYIKddMGTnOHkYpyHQlhCQCU1LQcrkXgSBgRQvgt883mM4U9PughdBs3stc00r1u4CCD1zapqfDq0LsAMDz2KNqhYq/Xl7+gcf+s1Vxbr4KK6U8LvIacxExWl2wGVO5l7Rc2ZjasBmDwlS30Bu2ldgxU4cq28jP2v7nBvb7mhNlo0dHUZIyk7rWWJ5CrOmgFoEIXhc7g+d98Q5j69n3k8FV8d9Kdal15Nb/+/U3slV13eWLlSvVYUKAqJIHy66+qj298PBx9NBxxBJx1lvc21583jpCfr1ZPxr4JwBWTriAhLIEo52S4ZWUQG6rulmh1DokW3UvCiBDCf1OnsvXPL3uvc870uTTt7wC8zf8RGtp81/NfP4dNTCa0oZLttz7r9Zrp1RdZzfGkk0u5ZQCR11zs9XryDFUZSXXkkP/mN4RTjdU8AMPEcV1zXj3EpNtUGEnN/Z6S99e514et/xpdeTmh2TvRfvc76n/xPSqpJt8KQIUhxmt9fJxndM5VhQ9QcbiBA6NPYeRfzuOHS7ouNZSWepbvukvN/REIrks048bBmjWwcSOYm1yts1jg6oFPwBsr0K/9CzfGfszzZz4P4K6MlJVBjCUGkDASLBJGhBAdYhnmfQnGNdPnhoxzmcJ69w3ymjpiip5dp1yvjvH2q16vxe9RnQuLhhxJ1PeraHo73eiRydRgwYCD8E9UZSA3fYa6ttGHnHLNYHLNQzDRwITV//J67XumsYrfYHHUkHPKVby4XGPGFBvbPstzDwW2FVkBqDLFeO07+NRRXs8PX7mAoXlqev2wD1Z0SdtramCvc1R2Zqa6lHL77V1y6GZcl5rGjGl9u0ceNvD+g+dT+Np9/OumU9A5q22uMFJbC1EhMYCEkWDpW/8FCyG6TcTIgV7PDQmqzH333bCRKZx+cUyL+x718LnUEcKQqp/Z/94W9/rYEnXX3ZLfzlMdDprS6ci3qOpIVu7/AKgdMb4zp9Ej6XRQedTJAJ778wBWorlav4y9dy6jFjNDDq6l/so/8OXGSMacPJD6ex8AwHbICkCN8wvW7dprYdEidsUdCcCgd//tfmmgLq9T85q4uC6dxMXBCme+eeUVyM/v9KGbcVVG2gojFou612JCgvf6SE93GsL0MYCEkWCRMCKE6JDYcd5hxJwUA8Dxx6u/jJcvb3nfQRNi2JisRtbs+7uqjjgcMLBahZHorGEt7luYqsZjpqNGmpgmNp9SvS+Iu+Vyr+eDOMBADpJ+6liuuT+dbRMuAmCu9hxmbADkvvQFAPZiKwB1oTHeBx0wAG6/nZwTLnOvykZ1EE6wF9Gwv/MjSRoHhKlTYcoUFU4++aTTh271vTrCaIRwNYiGMJ30GQkmCSNCiA6xxIZSQrz7eWijybUyM8Fkan1/3W9/C0DKz58CUJBtIx016VTi9JbDSO2ko7yexx7Tt/qLuAw40/s8M2amowsP5x//UM8nPX+j+7WqcDW6yZy/HwDNqkbT2MJifB477jxPh9+b+BebmQhAwQcbfG7fou++g+uug8pK96qml05mq5G1fPSRf4duS+PLQWM7kUddl2pCHDEAHK5peci5CBwJI0KIDis2JbuXI9Ji/No37dITABhRvZmafCsF3+3HgIMqXTimtKQW94uYNd29XI+R5GOG+9fo3kKn4/G0BwH4kuP5dJWO/fs9X/L6KUfAZZfBpEn8/LAavpJYk63G0jrvF2SP8D0T7eizRvDP8L/wD+NCjn3kLPbHTwHA8PgSKvPaeUvfqirqL7oUli6l4S93u1e7qhWugOAKI59+qoZut9e2bVBf7/u1zZth2TJVTYuLUwWfjnKNqDE2xABgdd7TR3QvCSNCiA6rM0e5l6MGxfi1b9rUFHYbRqBHY/9/1lC2UV2iyQ8b6vsOck5DzppADapj617TSEIiQvxveC8x490/cbFhBWuvfonQ0OZ9HnjxRdi0ibgTJ2HDhIkGtJxc9OVWABxRMT6Pa7bouDz7Pq4qfIAFt+qwDlNhJGXn15QOnoxmd6gNNU2VNLZsaXaM6hv/jGn/bnIZyLW5f3F3N9m8WT2Ocxaspk1TgcFqVc0tKmp77pEVK1SYufzy5q9VValLgTfcoJ6PGdPqP5c2uSojhvoYQC7TBIuEESFEh9nN4e7l+OQ2rss0odPBnoHqXjVVH63Gtl2FEWtCy5doXO+zJUR9eebF9s3+Ii5HZOl4vuJ87nwqvdXt0gcbOEAGoO7bY6y0qhdiYlrcJy5O/QCUzTqPT1EdZgfV7+Xg13soPFDL+uQ56oYvxx6Lmr7UKTcX83J1h+QreIHn3orlrruguNgzCVlWlno0GDzBYe5cSEry3GEXVLVk2TLPfpqG+1LUa6/B1197t/vjj9VQXJeO9hdxcYURXV0M4AkjNfU1aF3QoVe0j4QRIUSHaWGNwkh8Kxu2oObI4wFI++FtIrao+TTq0loPIwDbM1Xn15zhJ/r/pr1MaGjbI5ctFjgYoiaEW//cT6TkrgdAFxfbrveYcUY8p/Ap36FG2RR+sJ6td77O1CLn7GVlZV49kovuexqDZucr3fFMXahCzP33w4UXqtdHjPB8yYMaYXXppZ7nL7+sJkQDeOQRFU5mzwabTd3ReONGz7bz53tXUt5807vtad4jzP3mukyj1Xg6sBZXFzNw8UDOfP3Mzh08wPYe3ovNbgt2M7qEhBEhRIdpERHuZddf2f6IvehUikgkuXofM3PUOFDT6LbDSMW8P5LFBhxXXu3/m/ZRh6NVGPnNB/MZ3LCbfJKJOP+0du07dSpkZ0PZ8KkA1H+3Af0O1RPVhqp4OR77N9l76nl8/NMYn1NVkR+yruOBB+Dhh9VxPlez8zNlivfx9Xp1iWbLFpg4UYWLl19Wl24eekhts2OHmq3Vdaw5c1RQ2LQJXnrJc8XoA+cNn886S92D5ne/8/MX1YQrNDmqYwAVRr7Y9wWHaw/zwc4PqK5v3xT83amuoY55H8xj6L+GMufVOX2igiNhRAjRYVt/swCAD4xntzl6xpeZZ8Sx+Jxv2KkfSS1mto38Pybde06b+91ws4H3D2bx+yvlf2Eu1cmeuxvXYqbuzQ+Yenr7e3amp4N9skoRUTs3EHZwFwB/5T6sRKPfuxvd0TO44ed5xDmKyWUgw247G4DbblNTsbs0DSOgLsuNHw/Xq/nuePJJ+MMfVCBxZdo77oC331bh5YEH1OytAAsXwsUXqytGlZUqhLzzjrq0k5nZ/L384aqM1FfEAGo0jQ5PJ5SfCn7y2v6GlTdw6TuXBiQAbDu0jZnLZvL53s9b3e6uL+/i6Y1PA7Bq7ypW/NI1E9YFk/yXLIToMMekI0gmn1sHvdGh/Y1GePCt4Qyt/hlzTRljdrxNSErb13t0OnWfG9HIoAz34mejbmDwuVl+HyLqJFUZySj5kaTSHQBUDp3EP/gjAOkFaujvQ/yJs6O+5PSzVQLV6eCWWzzHyWrlrS+4QI1+2bdP3cjOYFCP553nGW1z7bUquNx4I4weDYWF8PrrKqRccw28/37nOq025qqM2MpjAKh31JNTnuN+fUOeZ7iztdbKE+uf4OUtL5Ndlt01DWjkH+v+wbqcdTzybfN7P7lU2ip5aqOqTI2MV/eB+tOqP9HgaN9QpcLKQn7M/7Hzje1iEkaEEB2WkQGFJJM6yNip4xjMRnQWc9sbihbpx3imei+75s8dOsbQ00ZSSTjhWhWZNnV329k3DmMxC9yTo73PHIyPPMS/Px7udR+Y889XI2DS0nxXRlyiomDdOpg+HWJj4b334NRT1SytN92k+o7cd5/aNiQEVq+Gs89W1ZP//AeeekoFla7iCiPV1ggMOnVjx92lu92vb8j3hJF9h/e5l8vqGvWi7QJ2h50Pd34IwHe53+HQHD63e3Hzi5TXlTM8bjg/XvMjiWGJ5JTn8Mnu1meV0zSNaz+4lpRHU8h6Jovnf3y+S9vfWRJGhBAddsIJ8OyzquQugkubNJk/8DTH8RUzz07s0DGSBxpYb5rpfl6PkVlXZ5A+PJSLeZWXuJTHxz7FrbeqMNFYSAisXw+7dkFYWOvvM3SoCiRFRerSC6hJ8h57TN3xN7ZRv9vERHVJxmpVl2q6musyTXmZzn2zvD2H97hfb1wZ2Xt4r3u5tKbR3QC7wA8Hf+BQ9SFAVWB+Lf612TYVdRU89I3qZHPjtBsJM4VxyfhLAHjxpxdbPf4HOz/gqY1PoaEuLy38fCFltV0bqDpDwogQosP0ejVcc/ToYLdEzJgBz/IHtsYex+DBHTuGTgf7x81xP882DiEkzMhrr8E3HM3lvMRR5w5scf/Q0Gb3NmyV0Y+CmsHQ/m394aqMlJd77tzbuDKyo3gHlTY1w2zjMFJSXdKl7Xjv1/e8nn+X+x0AtQ21rN6/mh3FO7jp45vIKc8hMyaTKydfCcDlk9RkLP/79X/klvuezr+2oZbbVt0GwIKjFjAqYRSHqg/x4NoHu/QcOkPCiBBC9AGDB6tZS7dv79xxkq863b1cE6ZmWcvKgjVr4OqrVT+OvsQVRsrKPGGkcehwaA73l7xXGKnp2jDyTc43AKRHqcthi79bzCtbXmHy05M5/sXjGf3EaJZvXg7As2c8S3iIGlY/KXkSU1OnYrPbmP78dCY+NZEXNr3gPq6macx9by47S3YyIHwAdx9/Nw+epELI0g1Lqair6NLz6CgJI0II0UeMHq0mFeuM464Y4l421te4l48+Gp55xscssL2c6zJNWRnEhvqel8V1v5q91sBVRlzVmEsnqAlZfi76md+98zt2FO8gyhyFUW9kZPxIXjz7RU4acpLXvit+u4JB0YPILc9lS+EWFny6wB0ylm5YyitbX8GgM/DqOa8SZY7ijJFnMDJ+JGV1ZSzbtKxLz6OjJIwIIYRwCwuDewY8iR09357b8qiOvqLxZZqEMN9J63CtM4x0YWXkgPUA571xHl/u+5JKWyX5lfkA3DDtBs4bcx4j4kdwRMoR3HH0Hey/eT91f6ljxw07uGziZc2OlRmbyTdXfuOueFhrrTz343PsO7yPP636EwCPzHrEHWL0Oj23HKWGPz367aPUNtR26ly6Que6wAshhOhzbvjlWp567RqunNv3/14Nd04iXFXlGSrrMih6ENll2RyuOUyDo4H91v3u1zoTRjRN45oPruGTPZ+wNnst//3tfwGID40nJTKF/573X7+PmRaVxp+P/jPxYfFc/f7VPPTNQ7y5/U2q6qs4NuNYbjryJq/tL5t4GfevuZ+c8hwe/+FxbptxW4fPpyv0/X9pQggh/JKQANffqCc0NNgtCTxXGKmvh+Ex3j2xh8epO0Ifrj1Mbnmu11wenblM8+GuD/lkjxqKW1BZwGXvqmrHsLi2Zx9uy6UTLmV0wmgKqwpZl7OOiJAIlp25DL3O++s+1BTKfSeoMdR/XPVHBi4eyIqfgzd5moQRIYQQ/Va45/ZKZIR7wohep2dwzGBADeNtOslZZyojD3+j5rw/NuNYAHfFZXj88A4f08VsNPPuhe8S5byj9lOnP8XQuKE+t710wqWcNfIsAPIq8ggztTEmO4DkMo0QQoh+KyREzXFSXw/JphHu9Q7NQXyomg34cM3hZnNydLQyklOWw5rsNejQ8co5r3D88uPd85oMi+18ZQRgRPwItszbQk55DkcPOrrF7Qx6A+9e+C6lNaXsLt3NiPgRLW4baFIZEUII0a+5qiP2Ou9JUuJC1d0fD9cepsKmRqcMCFf3+ympKUHTNF7/+XX2lO6hvVz3kTkm4xjSotK4cNyF7te6ojLikhGT0WoQaSwuNI5pA6e5hzYHg4QRIYQQ/ZrrRn2Vld7rXUN9D9cedg+VzYhW9wAqrSnl832fc9FbF3H6q6e3OH17Yy/99BKPrFMjlC4ad5HXI8CQ2CE+9+sPJIwIIYTo1xqPqEmJSHGvj7U4w0iNpzKSEaPCiENz8OW+LwH4teTXNu+0+8KmF7j83csprCpkSOwQLhh7AQBjB4zlkvGXcPSgo5mcPLnNtn7+OZx0EgwaBF9+6d959mQSRoQQQvRrjcPIm+e/SbQ5mmfmPOOzMpIQmkC4Se3gmjUV4MkNLd+g6X87/sd1K68DYP6R89l67VavCdZePudl1lyxBrOx7ZtF3nQTfPEF5OT0rXtCSQdWIYQQ/ZrrMs3WrWDaMYP8mw8TGqpjY95GwLsyEmmOJD4snqqyKtZmr3Uf471f3yO7LJtB0YMorSkl2hyNQW/g1a2vcsnb6mZ2c0bM4dFTHm02zLa9ysu9p/v/8ktwONQ9onq7PnAKQgghRMe5KiN//jMsWAB//rMO8N1nJDIk0j3Kxq7ZATXhmENz8MzGZ/j6wNekPJrCzR/fDMA7O94B4OLxF/PmeW92OIgA/PgjaBqkpqoAVVICP/3U4cP1KBJGhBBC9GuN5xoBWLpUPbr6jFTXV7vnFYk0R3r17dCh44ETHwDg2R+f5bjlx2Gz23hi/RMAHKo6BMAZI85o12WY1mzYoB6nT4fjjlPLn7feVaXXkDAihBCiX3NdpnFpcE60Gm2JRoeqkrgmPYsMieTi8Re7t02NTOXCcReSGplKUVVRs2O71iWGJXa6na4wMmWK6sQKfacTq4QRIYQQ/VrTyghAcbGahdU194Y7jJgjOX7w8e7tDlYcxGQwsfys5c2OYbPbOFStKiOu+UnsdqioaLtNt94KF1yg2gHq8sz69Wp5yhQ48ki1vGVL28fqDSSMCCGE6Nd8hZEfflCPrn4jrlARGRKJQW9wzw9y23R1g7mTh57MlnlbWHj0Qvcx8iry3DO1Joarysgpp0BGBuzYAd99BzYbLF6sRsnU1KhKxy+/qHX//S+MHQvHHw+nnw5794LRCFlZMNo5c31ururY2tvJaBohhBD9WtPLNKCCwmmnefqNuESaIwF44awXOGvkWZw+4nT3a+OTxjM+aTwvb3mZnPIcfi76GQ0NgISwBGpqPH08XGFi0iTYvFktv/UW5OV5t6OoSP0A6HTwr39BrLNJKSmQn69G2LgqJb2VVEaEEEL0a74qIy+/rO5X45oS3iUyRIURs9HMBeMuICKkeZJJiVQTp/1UoIa6xIfGY9Qb2bmz+fu4ggg0DyL33w8ffwwvvQS33AIffgjXXut5fcwY9bhtm/d+332nqiu+/PgjPP441Nb6fj1YpDIihBCiX2scRiwWiIyEfftUIEmNTfXa1lUZaU1qpNrnp0IVRlyXaBqHhpQUmDYN/vc/dbO+G2+Ejz7y3uaii2CIc4b4Sy9t/j5jxqhKy/ffqxE2I0eqyzbHHKM64V59tTq3UaNUqCkqUkHF4YD9++GRR9r+3XQXCSNCCCH6tcZhJD1dfYn/6U/qy/qsxwZ6bVuUE4lWCgcOqI6os2ZBaKj38VxTym8u2Ax4Oq+6JiybOxeeeUZ1Zv3731WoOO889X7ffqvCxNSpniDSEldl5Omn1c/QoXDOOZ7RQM8+2/K+jz2mznPkyNbfo7tIGBFCCNGvNe4zEhurvqRvv11VKcZvTvPadnpWJDR4nv/lL3DffWrZZoN168BuVZWRXaW7AM+wXlfVY8wY1f/DaIS77/Zuy/Tp6hJLYjtGArvCiMuePfCPf6jlESPg1FNVFWTzZlWFOeoodU+b++5Tl3yuvhouuQQOHoQzzlCjdHS6tt83ECSMCCGE6NcaV0bi4iAmRlUmvv8e3n5xIPzW+aLdCA1mLBa1XV4evPuuJ4z8/e9w773A5BQ4y3NMV2XEFUZcnVdb0t5qxfjx6rJSbS089RTMm+d57Ykn4De/8b3fv/4Fq1fDmjXqB9Q5PPigmoU2GKQDqxBCiH6taRgBz6Ri9SWeykhMWCSFhTqqq9X8Hjod/PyzqiwAvPmmc8MK734mOzclUlkJu1ShpFlFo6NiY+Hrr9WU8H/4gwonACYTzJjR8n5DhnhushcSAnPmqN/B7Nld066OkDAihBCiX2t6mQY8YYRyT5+RUJOFAQNUCImPV9UTgE8+gexsVfnQ6+H911K8jv/5ewM48UTVlyMqSvVL6SpTp8KECapNv/+9WjdzJoSFtb7fpZeqOU1+/hnefx8OHfKEmWCQyzRCCCH6NV+VkRkz1KiaikpP541KW6XXfqeeqiZH++QTT6fR6dPh6HGDMX1kot5Rr1ZWJ7LeOdT26qsD1y/jhhvU42mntW/744/3LDfthNvdpDIihBCiX2scRlyVEYtFDbV973+er8kKm/c87iefrB6/+kptCyqgxFhieGSWZ9xs1qgk9/sEsk9GSIi66/CoUYF7j0DpUBh58sknyczMxGKxkJWVxRpXD5gWrF69mqysLCwWC0OGDOGpp57qUGOFEEKIrtb4Mk3jCsHMmWqUSUumTFEBoKgIPvhArXP1u7jpyJv45yn/5OLxF/Pc3TMYO1YN3W3PKJn+yO/LNCtWrGD+/Pk8+eSTzJw5k6effprZs2ezbds2Bg0a1Gz7ffv2cdppp3H11Vfz8ssv880333DdddeRmJjIueee2yUnIYQQQnRU4/4VISHt389iUYFk3Tp1mWbAAJg82fP6/KPmu5d//rnz7ezL/K6MLF68mKuuuoq5c+cyevRolixZQnp6OkuXLvW5/VNPPcWgQYNYsmQJo0ePZu7cuVx55ZU80pOmfhNCCNFv6Rt9E5rN/u07c6Zn+ZRTvI8l2s+vX5vNZmPjxo3MmjXLa/2sWbNYt26dz32+/fbbZtufcsopbNiwgfr6ep/71NXVUV5e7vUjhBBCBNqECc3XfXjxh5j0Jp49o/mUpo3DSDCHxvZ2fl2mKS4uxm63k5SU5LU+KSmJgoICn/sUFBT43L6hoYHi4mJSUlKa7bNo0SL+9re/+dM0IYQQosN++knd18XX8NbThp9G5R2VhBiaX8OZOVPN6wGeDq3Cfx0qKOmajEvSNK3Zura297XeZeHChZSVlbl/cnJyOtJMIYQQol0mTGh9SKyvIAKQkAArV6rhvQkJAWpcP+BXZSQhIQGDwdCsClJUVNSs+uGSnJzsc3uj0Uh8fLzPfcxmM2Z/L9wJIYQQQdDStOui/fyqjISEhJCVlcWqVau81q9atYoZLcw9O3369Gbbf/rpp0yZMgWTq7YlhBBCiH7L78s0CxYs4LnnnmPZsmVs376dW265hezsbOY579CzcOFCLrvsMvf28+bN48CBAyxYsIDt27ezbNkynn/+eW677bauOwshhBBC9Fp+zzNywQUXUFJSwr333kt+fj7jxo1j5cqVZGRkAJCfn092drZ7+8zMTFauXMktt9zCE088QWpqKv/6179kjhEhhBBCAKDTXL1Je7Dy8nKio6MpKysjKioq2M0RQgghRDu09/tbpmcRQgghRFBJGBFCCCFEUEkYEUIIIURQSRgRQgghRFBJGBFCCCFEUEkYEUIIIURQSRgRQgghRFBJGBFCCCFEUEkYEUIIIURQ+T0dfDC4JoktLy8PckuEEEII0V6u7+22JnvvFWGkoqICgPT09CC3RAghhBD+qqioIDo6usXXe8W9aRwOB3l5eURGRqLT6brsuOXl5aSnp5OTk9Ov7nkj5y3n3R/Iect59wc9/bw1TaOiooLU1FT0+pZ7hvSKyoheryctLS1gx4+KiuqRH2KgyXn3L3Le/Yucd//Sk8+7tYqIi3RgFUIIIURQSRgRQgghRFD16zBiNpu5++67MZvNwW5Kt5LzlvPuD+S85bz7g75y3r2iA6sQQggh+q5+XRkRQgghRPBJGBFCCCFEUEkYEUIIIURQSRgRQgghRFD16zDy5JNPkpmZicViISsrizVr1gS7SV3qnnvuQafTef0kJye7X9c0jXvuuYfU1FRCQ0M5/vjj+eWXX4LY4o75+uuvOeOMM0hNTUWn0/Huu+96vd6e86yrq+PGG28kISGB8PBwzjzzTHJzc7vxLPzT1jn//ve/b/bZH3XUUV7b9LZzBli0aBFTp04lMjKSAQMGcPbZZ/Prr796bdMXP+/2nHdf/MyXLl3KhAkT3BN6TZ8+nY8++sj9el/8rKHt8+6Ln3W/DSMrVqxg/vz53HnnnWzatIljjjmG2bNnk52dHeymdamxY8eSn5/v/tm6dav7tYcffpjFixfz+OOPs379epKTkzn55JPd9wLqLaqqqpg4cSKPP/64z9fbc57z58/nnXfe4fXXX2ft2rVUVlYyZ84c7HZ7d52GX9o6Z4BTTz3V67NfuXKl1+u97ZwBVq9ezfXXX893333HqlWraGhoYNasWVRVVbm36Yufd3vOG/reZ56WlsaDDz7Ihg0b2LBhAyeeeCJnnXWWO3D0xc8a2j5v6HufNVo/NW3aNG3evHle60aNGqXdfvvtQWpR17v77ru1iRMn+nzN4XBoycnJ2oMPPuheV1tbq0VHR2tPPfVUN7Ww6wHaO++8437envO0Wq2ayWTSXn/9dfc2Bw8e1PR6vfbxxx93W9s7quk5a5qmXX755dpZZ53V4j69/ZxdioqKNEBbvXq1pmn94/PWtObnrWn95zOPjY3VnnvuuX7zWbu4zlvT+uZn3S8rIzabjY0bNzJr1iyv9bNmzWLdunVBalVg7Nq1i9TUVDIzM7nwwgvZu3cvAPv27aOgoMDrd2A2mznuuOP61O+gPee5ceNG6uvrvbZJTU1l3Lhxvfp38dVXXzFgwABGjBjB1VdfTVFRkfu1vnLOZWVlAMTFxQH95/Nuet4uffkzt9vtvP7661RVVTF9+vR+81k3PW+XvvZZ94ob5XW14uJi7HY7SUlJXuuTkpIoKCgIUqu63pFHHslLL73EiBEjKCws5P7772fGjBn88ssv7vP09Ts4cOBAMJobEO05z4KCAkJCQoiNjW22TW/99zB79mzOO+88MjIy2LdvH3/961858cQT2bhxI2azuU+cs6ZpLFiwgKOPPppx48YB/ePz9nXe0Hc/861btzJ9+nRqa2uJiIjgnXfeYcyYMe4v1b76Wbd03tA3P+t+GUZcdDqd13NN05qt681mz57tXh4/fjzTp09n6NChvPjii+7OTn39d+DSkfPszb+LCy64wL08btw4pkyZQkZGBh9++CHnnHNOi/v1pnO+4YYb2LJlC2vXrm32Wl/+vFs67776mY8cOZLNmzdjtVp56623uPzyy1m9erX79b76Wbd03mPGjOmTn3W/vEyTkJCAwWBolhCLioqapey+JDw8nPHjx7Nr1y73qJq+/jtoz3kmJydjs9k4fPhwi9v0dikpKWRkZLBr1y6g95/zjTfeyHvvvceXX35JWlqae31f/7xbOm9f+spnHhISwrBhw5gyZQqLFi1i4sSJPPbYY33+s27pvH3pC591vwwjISEhZGVlsWrVKq/1q1atYsaMGUFqVeDV1dWxfft2UlJSyMzMJDk52et3YLPZWL16dZ/6HbTnPLOysjCZTF7b5Ofn8/PPP/eZ30VJSQk5OTmkpKQAvfecNU3jhhtu4O233+aLL74gMzPT6/W++nm3dd6+9JXPvClN06irq+uzn3VLXOftS5/4rLu9y2wP8frrr2smk0l7/vnntW3btmnz58/XwsPDtf379we7aV3m1ltv1b766itt79692nfffafNmTNHi4yMdJ/jgw8+qEVHR2tvv/22tnXrVu2iiy7SUlJStPLy8iC33D8VFRXapk2btE2bNmmAtnjxYm3Tpk3agQMHNE1r33nOmzdPS0tL0z777DPtxx9/1E488URt4sSJWkNDQ7BOq1WtnXNFRYV26623auvWrdP27dunffnll9r06dO1gQMH9upz1jRNu/baa7Xo6Gjtq6++0vLz890/1dXV7m364ufd1nn31c984cKF2tdff63t27dP27Jli3bHHXdoer1e+/TTTzVN65uftaa1ft599bPut2FE0zTtiSee0DIyMrSQkBDtiCOO8Bom1xdccMEFWkpKimYymbTU1FTtnHPO0X755Rf36w6HQ7v77ru15ORkzWw2a8cee6y2devWILa4Y7788ksNaPZz+eWXa5rWvvOsqanRbrjhBi0uLk4LDQ3V5syZo2VnZwfhbNqntXOurq7WZs2apSUmJmomk0kbNGiQdvnllzc7n952zpqm+TxnQHvhhRfc2/TFz7ut8+6rn/mVV17p/n90YmKidtJJJ7mDiKb1zc9a01o/7776Wes0TdO6rw4jhBBCCOGtX/YZEUIIIUTPIWFECCGEEEElYUQIIYQQQSVhRAghhBBBJWFECCGEEEElYUQIIYQQQSVhRAghhBBBJWFECCGEEEElYUQIIYQQQSVhRAghhBBBJWFECCGEEEElYUQIIYQQQfX/pGxi48SZSTUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with torch.no_grad():\n", + " # shift train predictions for plotting\n", + " train_plot = np.ones_like(bitcoin_price_list) * np.nan\n", + " y_pred = model(X_train)\n", + " y_pred = y_pred[:, -1, :]\n", + " train_plot[lookback:train_size] = model(X_train)[:, -1, :]\n", + " # shift test predictions for plotting\n", + " test_plot = np.ones_like(bitcoin_price_list) * np.nan\n", + " test_plot[train_size+lookback:len(bitcoin_price_list)] = model(X_test)[:, -1, :]\n", + "# plot\n", + "plt.plot(normalized, c='b')\n", + "plt.plot(train_plot, c='r')\n", + "plt.plot(test_plot, c='g')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "90d2555f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with torch.no_grad():\n", + " \n", + " # shift train predictions for plotting\n", + " train_plot = np.ones_like(bitcoin_price_list) * np.nan\n", + " y_pred = model(X_train)\n", + " y_pred = y_pred[:, -1, :]\n", + " train_inversed = scaler.inverse_transform(model(X_train)[:, -1, :])\n", + " train_plot[lookback:train_size] = train_inversed\n", + " # shift test predictions for plotting\n", + " test_plot = np.ones_like(bitcoin_price_list) * np.nan\n", + " test_inversed = scaler.inverse_transform(model(X_test)[:, -1, :])\n", + " test_plot[train_size+lookback:len(bitcoin_price_list)] = test_inversed\n", + "\n", + "# plot\n", + "plt.plot(bitcoin_price_list, c='b')\n", + "plt.plot(train_plot, c='r')\n", + "plt.plot(test_plot, c='g')\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "e8fa03dd", + "metadata": {}, + "outputs": [], + "source": [ + "from math import sqrt\n", + "from sklearn.metrics import mean_squared_error" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8ef85b1e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test RMSE: 1613.504\n" + ] + } + ], + "source": [ + "rmse = sqrt(mean_squared_error(bitcoin_price_list[train_size+lookback:], test_inversed))\n", + "print('Test RMSE: %.3f' % rmse)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d9b54f95", + "metadata": {}, + "outputs": [], + "source": [ + "bitcoin_train, bitcoin_test = bitcoin_price_list[:train_size], bitcoin_price_list[train_size:]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fc04aebb", + "metadata": {}, + "outputs": [], + "source": [ + "# get previous day's opening price\n", + "# because of the lookback (currently=4), the previous price set starts from position 3\n", + "\n", + "bitcoin_test_previous_price = bitcoin_test.copy()[lookback-1:][:-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e173b3a1", + "metadata": {}, + "outputs": [], + "source": [ + "bitcoin_true_label = []\n", + "\n", + "\n", + "bitcoin_test = bitcoin_test.copy()[lookback:]\n", + "for i, _ in enumerate(bitcoin_test):\n", + " if(bitcoin_test[i] > bitcoin_test_previous_price[i]):\n", + " bitcoin_true_label.append(\"Increase\")\n", + " else:\n", + " bitcoin_true_label.append(\"Decrease\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "df069e2c", + "metadata": {}, + "outputs": [], + "source": [ + "bitcoin_predict_label = []\n", + "for i, _ in enumerate(test_inversed):\n", + " if(test_inversed[i] > bitcoin_test_previous_price[i]):\n", + " bitcoin_predict_label.append(\"Increase\")\n", + " else:\n", + " bitcoin_predict_label.append(\"Decrease\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "71a0b73b", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import precision_recall_fscore_support\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import classification_report" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c454b0bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " Decrease 0.67 0.11 0.19 36\n", + " Increase 0.43 0.92 0.59 26\n", + "\n", + " accuracy 0.45 62\n", + " macro avg 0.55 0.52 0.39 62\n", + "weighted avg 0.57 0.45 0.36 62\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(bitcoin_true_label, bitcoin_predict_label))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}