Datasets
Models
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"colab": {
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"name": "python3",
"display_name": "Python 3"
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"language_info": {
"name": "python"
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"cells": [
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"# Load the data from the CSV file\n",
"data = pd.read_csv('/content/Dataaa.csv')\n",
"\n",
"# Display the number of features\n",
"print(\"Number of features in the dataset:\", data.shape[1])\n",
"print(\"Names of the features:\", data.columns.tolist())\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lQ8nLvH8X3VH",
"outputId": "5d698b6c-2e08-4741-89b8-3fcaf0e44531"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Number of features in the dataset: 1\n",
"Names of the features: [\"Type of first element in X_in: <class 'numpy.ndarray'>\"]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Display the first few lines of the CSV file to understand its content\n",
"with open('/content/Dataaa.csv', 'r') as file:\n",
" for _ in range(5):\n",
" print(file.readline())\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rEs3BIjeX3PZ",
"outputId": "e78e7da4-091b-4a3b-ab67-bcdfe13e6d0f"
},
"execution_count": null,
"outputs": [
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"output_type": "stream",
"name": "stdout",
"text": [
"\"Type of first element in X_in: <class 'numpy.ndarray'>\"\n",
"\n",
"\"Type of first element in X_out: <class 'numpy.ndarray'>\"\n",
"\n",
"\"First few elements in X_in: [array([[ 0.0000000e+00, 7.4236510e-07, 7.4236510e-07, ...,\"\n",
"\n",
"\"8.5448107e-07, 8.5448107e-07, 8.5448107e-07],\"\n",
"\n",
"\"[ 4.1666667e-08, -2.3885614e-03, -2.3796703e-03, ...,\"\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# Assuming you have your data loaded into numpy arrays, for example:\n",
"# X_in is your input data and X_out is your output data\n",
"# Here is a simple example of how to create these arrays (replace this with your actual data loading code)\n",
"# X_in = np.random.rand(100, 10) # Example: 100 samples, 10 features\n",
"# X_out = np.random.rand(100, 3) # Example: 100 samples, 3 output targets\n",
"\n",
"# Convert numpy arrays to pandas DataFrame\n",
"X_in_df = pd.DataFrame(X_in)\n",
"X_out_df = pd.DataFrame(X_out)\n",
"\n",
"# Concatenate both DataFrames along the columns\n",
"data_df = pd.concat([X_in_df, X_out_df], axis=1)\n",
"\n",
"# Save the DataFrame to a CSV file\n",
"data_df.to_csv('/content/corrected_data.csv', index=False)\n"
],
"metadata": {
"id": "6TWDXve-X3JK"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# Example data (replace with your actual data arrays)\n",
"X_in = np.random.rand(100, 10) # 100 samples, 10 features\n",
"X_out = np.random.rand(100, 1) # 100 samples, 1 target\n",
"\n",
"# Convert to DataFrame\n",
"df_in = pd.DataFrame(X_in, columns=[f'feature_{i}' for i in range(X_in.shape[1])])\n",
"df_out = pd.DataFrame(X_out, columns=['target'])\n",
"\n",
"# Combine input and output data\n",
"full_df = pd.concat([df_in, df_out], axis=1)\n",
"\n",
"# Save to CSV\n",
"full_df.to_csv('/content/corrected_data.csv', index=False)\n"
],
"metadata": {
"id": "3ku5qcwgX2Vu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"# Load the data from the corrected CSV file\n",
"data = pd.read_csv('/content/corrected_data.csv')\n",
"\n",
"# Display the first few rows of the dataset and the shape to verify\n",
"print(data.head())\n",
"print(\"Shape of the dataset:\", data.shape)\n",
"print(\"Column names:\", data.columns.tolist())\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "E5P9vWi7X1vQ",
"outputId": "c5ba3f3d-5539-4517-f7f8-db8d038842b1"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" feature_0 feature_1 feature_2 feature_3 feature_4 feature_5 \\\n",
"0 0.102974 0.396511 0.411698 0.130324 0.224968 0.201441 \n",
"1 0.954740 0.406590 0.231757 0.721605 0.697064 0.631036 \n",
"2 0.195421 0.622312 0.718637 0.082305 0.759582 0.836606 \n",
"3 0.025230 0.643513 0.196257 0.021668 0.856111 0.761400 \n",
"4 0.764795 0.068322 0.686261 0.832339 0.194171 0.964005 \n",
"\n",
" feature_6 feature_7 feature_8 feature_9 target \n",
"0 0.045020 0.812687 0.146984 0.031833 0.112901 \n",
"1 0.429782 0.631824 0.915996 0.937347 0.785631 \n",
"2 0.671080 0.566545 0.230185 0.714971 0.714041 \n",
"3 0.107318 0.068754 0.897949 0.672117 0.163683 \n",
"4 0.633740 0.288913 0.569982 0.873026 0.499354 \n",
"Shape of the dataset: (100, 11)\n",
"Column names: ['feature_0', 'feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5', 'feature_6', 'feature_7', 'feature_8', 'feature_9', 'target']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"##Normalizing and Splitting the data\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# Selecting input features and target\n",
"X = data.iloc[:, :-1].values # All columns except the last are features\n",
"y = data.iloc[:, -1].values # Last column is the target\n",
"\n",
"# Normalize the input data\n",
"scaler = StandardScaler()\n",
"X_normalized = scaler.fit_transform(X)\n",
"\n",
"# Split the data into training and testing sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X_normalized, y, test_size=0.3, random_state=42)\n",
"\n",
"# Output the shapes of the datasets to verify everything is as expected\n",
"print(\"Train data shape:\", X_train.shape)\n",
"print(\"Test data shape:\", X_test.shape)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WJDjRxGMX1pU",
"outputId": "38d5717a-890c-467d-dbbd-e271b5bdcc63"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train data shape: (70, 10)\n",
"Test data shape: (30, 10)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"##Training the RNN Model\n",
"\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, SimpleRNN\n",
"\n",
"# Define the RNN model\n",
"model = Sequential([\n",
" SimpleRNN(50, input_shape=(X_train.shape[1], 1)), # 50 RNN units, considering each feature as a time step\n",
" Dense(1) # Output layer with one neuron for regression output (the target)\n",
"])\n",
"\n",
"# Compile the model\n",
"model.compile(optimizer='adam', loss='mean_squared_error')\n",
"\n",
"# Reshape input for RNN which expects (batch_size, timesteps, features)\n",
"X_train_rnn = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
"X_test_rnn = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))\n",
"\n",
"# Train the model\n",
"history = model.fit(X_train_rnn, y_train, epochs=100, validation_data=(X_test_rnn, y_test))\n",
"\n",
"# Optionally, plot the training and validation loss\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.plot(history.history['loss'], label='train')\n",
"plt.plot(history.history['val_loss'], label='test')\n",
"plt.title('Model Loss')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.legend()\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "DQZB7bMFX1jC",
"outputId": "c57473be-eaa7-4d99-b6b6-147b43375221"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/100\n",
"3/3 [==============================] - 2s 160ms/step - loss: 0.4539 - val_loss: 0.3756\n",
"Epoch 2/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.3255 - val_loss: 0.3210\n",
"Epoch 3/100\n",
"3/3 [==============================] - 0s 26ms/step - loss: 0.2567 - val_loss: 0.3042\n",
"Epoch 4/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.2080 - val_loss: 0.2830\n",
"Epoch 5/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.1769 - val_loss: 0.2534\n",
"Epoch 6/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.1386 - val_loss: 0.2135\n",
"Epoch 7/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.1133 - val_loss: 0.1710\n",
"Epoch 8/100\n",
"3/3 [==============================] - 0s 23ms/step - loss: 0.0876 - val_loss: 0.1322\n",
"Epoch 9/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0733 - val_loss: 0.1088\n",
"Epoch 10/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0651 - val_loss: 0.1038\n",
"Epoch 11/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0649 - val_loss: 0.1062\n",
"Epoch 12/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0620 - val_loss: 0.1051\n",
"Epoch 13/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0579 - val_loss: 0.1026\n",
"Epoch 14/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0544 - val_loss: 0.0997\n",
"Epoch 15/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0520 - val_loss: 0.1000\n",
"Epoch 16/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0538 - val_loss: 0.1028\n",
"Epoch 17/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0542 - val_loss: 0.1059\n",
"Epoch 18/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0543 - val_loss: 0.1074\n",
"Epoch 19/100\n",
"3/3 [==============================] - 0s 19ms/step - loss: 0.0513 - val_loss: 0.1077\n",
"Epoch 20/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0482 - val_loss: 0.1076\n",
"Epoch 21/100\n",
"3/3 [==============================] - 0s 25ms/step - loss: 0.0472 - val_loss: 0.1085\n",
"Epoch 22/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0468 - val_loss: 0.1120\n",
"Epoch 23/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0472 - val_loss: 0.1143\n",
"Epoch 24/100\n",
"3/3 [==============================] - 0s 30ms/step - loss: 0.0460 - val_loss: 0.1130\n",
"Epoch 25/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0454 - val_loss: 0.1110\n",
"Epoch 26/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0440 - val_loss: 0.1089\n",
"Epoch 27/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0422 - val_loss: 0.1019\n",
"Epoch 28/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0411 - val_loss: 0.0977\n",
"Epoch 29/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0428 - val_loss: 0.0966\n",
"Epoch 30/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0426 - val_loss: 0.0981\n",
"Epoch 31/100\n",
"3/3 [==============================] - 0s 24ms/step - loss: 0.0416 - val_loss: 0.0997\n",
"Epoch 32/100\n",
"3/3 [==============================] - 0s 19ms/step - loss: 0.0412 - val_loss: 0.0978\n",
"Epoch 33/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0393 - val_loss: 0.0940\n",
"Epoch 34/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0376 - val_loss: 0.0911\n",
"Epoch 35/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0373 - val_loss: 0.0911\n",
"Epoch 36/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0370 - val_loss: 0.0923\n",
"Epoch 37/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0361 - val_loss: 0.0942\n",
"Epoch 38/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0354 - val_loss: 0.0971\n",
"Epoch 39/100\n",
"3/3 [==============================] - 0s 27ms/step - loss: 0.0336 - val_loss: 0.1020\n",
"Epoch 40/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0338 - val_loss: 0.1060\n",
"Epoch 41/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0329 - val_loss: 0.1067\n",
"Epoch 42/100\n",
"3/3 [==============================] - 0s 23ms/step - loss: 0.0321 - val_loss: 0.1074\n",
"Epoch 43/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0317 - val_loss: 0.1088\n",
"Epoch 44/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0319 - val_loss: 0.1099\n",
"Epoch 45/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0311 - val_loss: 0.1131\n",
"Epoch 46/100\n",
"3/3 [==============================] - 0s 23ms/step - loss: 0.0306 - val_loss: 0.1163\n",
"Epoch 47/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0302 - val_loss: 0.1163\n",
"Epoch 48/100\n",
"3/3 [==============================] - 0s 29ms/step - loss: 0.0296 - val_loss: 0.1125\n",
"Epoch 49/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0273 - val_loss: 0.1103\n",
"Epoch 50/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0273 - val_loss: 0.1080\n",
"Epoch 51/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0267 - val_loss: 0.1043\n",
"Epoch 52/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0260 - val_loss: 0.0997\n",
"Epoch 53/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0267 - val_loss: 0.0983\n",
"Epoch 54/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0250 - val_loss: 0.1024\n",
"Epoch 55/100\n",
"3/3 [==============================] - 0s 30ms/step - loss: 0.0238 - val_loss: 0.1084\n",
"Epoch 56/100\n",
"3/3 [==============================] - 0s 27ms/step - loss: 0.0241 - val_loss: 0.1098\n",
"Epoch 57/100\n",
"3/3 [==============================] - 0s 19ms/step - loss: 0.0242 - val_loss: 0.1106\n",
"Epoch 58/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0230 - val_loss: 0.1135\n",
"Epoch 59/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0230 - val_loss: 0.1167\n",
"Epoch 60/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0241 - val_loss: 0.1179\n",
"Epoch 61/100\n",
"3/3 [==============================] - 0s 24ms/step - loss: 0.0232 - val_loss: 0.1108\n",
"Epoch 62/100\n",
"3/3 [==============================] - 0s 24ms/step - loss: 0.0206 - val_loss: 0.1001\n",
"Epoch 63/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0191 - val_loss: 0.0945\n",
"Epoch 64/100\n",
"3/3 [==============================] - 0s 23ms/step - loss: 0.0195 - val_loss: 0.0937\n",
"Epoch 65/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0190 - val_loss: 0.0969\n",
"Epoch 66/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0174 - val_loss: 0.1044\n",
"Epoch 67/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0176 - val_loss: 0.1126\n",
"Epoch 68/100\n",
"3/3 [==============================] - 0s 24ms/step - loss: 0.0174 - val_loss: 0.1195\n",
"Epoch 69/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0170 - val_loss: 0.1258\n",
"Epoch 70/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0165 - val_loss: 0.1265\n",
"Epoch 71/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0162 - val_loss: 0.1181\n",
"Epoch 72/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0143 - val_loss: 0.1094\n",
"Epoch 73/100\n",
"3/3 [==============================] - 0s 19ms/step - loss: 0.0142 - val_loss: 0.1042\n",
"Epoch 74/100\n",
"3/3 [==============================] - 0s 27ms/step - loss: 0.0135 - val_loss: 0.1016\n",
"Epoch 75/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0124 - val_loss: 0.1012\n",
"Epoch 76/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0124 - val_loss: 0.1050\n",
"Epoch 77/100\n",
"3/3 [==============================] - 0s 23ms/step - loss: 0.0124 - val_loss: 0.1089\n",
"Epoch 78/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0118 - val_loss: 0.1111\n",
"Epoch 79/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0113 - val_loss: 0.1126\n",
"Epoch 80/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0103 - val_loss: 0.1096\n",
"Epoch 81/100\n",
"3/3 [==============================] - 0s 23ms/step - loss: 0.0106 - val_loss: 0.1061\n",
"Epoch 82/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0097 - val_loss: 0.1065\n",
"Epoch 83/100\n",
"3/3 [==============================] - 0s 25ms/step - loss: 0.0096 - val_loss: 0.1069\n",
"Epoch 84/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0091 - val_loss: 0.1101\n",
"Epoch 85/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0086 - val_loss: 0.1132\n",
"Epoch 86/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0080 - val_loss: 0.1178\n",
"Epoch 87/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0078 - val_loss: 0.1207\n",
"Epoch 88/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0073 - val_loss: 0.1191\n",
"Epoch 89/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0070 - val_loss: 0.1175\n",
"Epoch 90/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0069 - val_loss: 0.1166\n",
"Epoch 91/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0076 - val_loss: 0.1148\n",
"Epoch 92/100\n",
"3/3 [==============================] - 0s 33ms/step - loss: 0.0068 - val_loss: 0.1100\n",
"Epoch 93/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0061 - val_loss: 0.1078\n",
"Epoch 94/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0058 - val_loss: 0.1087\n",
"Epoch 95/100\n",
"3/3 [==============================] - 0s 20ms/step - loss: 0.0054 - val_loss: 0.1129\n",
"Epoch 96/100\n",
"3/3 [==============================] - 0s 21ms/step - loss: 0.0050 - val_loss: 0.1168\n",
"Epoch 97/100\n",
"3/3 [==============================] - 0s 24ms/step - loss: 0.0047 - val_loss: 0.1179\n",
"Epoch 98/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0044 - val_loss: 0.1184\n",
"Epoch 99/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0043 - val_loss: 0.1185\n",
"Epoch 100/100\n",
"3/3 [==============================] - 0s 22ms/step - loss: 0.0041 - val_loss: 0.1181\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"model.save('/content/my_rnn_model.h5') # Saves the model for later use\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7JvXJMXMX1dE",
"outputId": "2e269aef-006b-457a-dc5e-d52f67333ceb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
" saving_api.save_model(\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Example threshold value - you need to choose what makes sense for your data\n",
"threshold = 0.5\n",
"\n",
"# Convert continuous target data to binary classification\n",
"y_train_class = (y_train > threshold).astype(int)\n",
"\n",
"# Proceed with the rest of your RNN setup as before\n"
],
"metadata": {
"id": "SDWQIzVXX1ST"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"##Classifying the presence of damage\n",
"\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import SimpleRNN, Dense\n",
"\n",
"# Define the classification model\n",
"classification_model = Sequential([\n",
" SimpleRNN(50, input_shape=(X_train.shape[1], 1)), # Adjust the input shape and units as necessary\n",
" Dense(1, activation='sigmoid') # Sigmoid activation for binary classification\n",
"])\n",
"\n",
"# Compile the classification model\n",
"classification_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
"\n",
"# Reshape data for RNN\n",
"X_train_rnn = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
"\n",
"# Train the classification model\n",
"classification_model.fit(X_train_rnn, y_train_class, epochs=100, validation_split=0.2)\n",
"\n",
"# Save the classification model\n",
"classification_model.save('/content/classification_model.h5')\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d3RDZQDAgGz3",
"outputId": "759aade1-9bb7-4442-f0e8-eb9be8219bb0"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/100\n",
"2/2 [==============================] - 2s 324ms/step - loss: 0.7349 - accuracy: 0.5179 - val_loss: 0.7466 - val_accuracy: 0.4286\n",
"Epoch 2/100\n",
"2/2 [==============================] - 0s 48ms/step - loss: 0.6969 - accuracy: 0.5536 - val_loss: 0.7388 - val_accuracy: 0.4286\n",
"Epoch 3/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6645 - accuracy: 0.5893 - val_loss: 0.7393 - val_accuracy: 0.5000\n",
"Epoch 4/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6357 - accuracy: 0.5893 - val_loss: 0.7389 - val_accuracy: 0.5000\n",
"Epoch 5/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.6172 - accuracy: 0.6250 - val_loss: 0.7459 - val_accuracy: 0.5000\n",
"Epoch 6/100\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.5948 - accuracy: 0.6429 - val_loss: 0.7489 - val_accuracy: 0.5000\n",
"Epoch 7/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.5797 - accuracy: 0.6964 - val_loss: 0.7554 - val_accuracy: 0.5000\n",
"Epoch 8/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.5643 - accuracy: 0.7143 - val_loss: 0.7616 - val_accuracy: 0.5000\n",
"Epoch 9/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.5523 - accuracy: 0.7857 - val_loss: 0.7677 - val_accuracy: 0.5000\n",
"Epoch 10/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.5418 - accuracy: 0.7857 - val_loss: 0.7712 - val_accuracy: 0.5000\n",
"Epoch 11/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.5304 - accuracy: 0.7857 - val_loss: 0.7746 - val_accuracy: 0.5000\n",
"Epoch 12/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.5211 - accuracy: 0.7857 - val_loss: 0.7775 - val_accuracy: 0.5714\n",
"Epoch 13/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.5131 - accuracy: 0.7857 - val_loss: 0.7861 - val_accuracy: 0.5714\n",
"Epoch 14/100\n",
"2/2 [==============================] - 0s 43ms/step - loss: 0.5042 - accuracy: 0.7857 - val_loss: 0.7916 - val_accuracy: 0.5714\n",
"Epoch 15/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.4981 - accuracy: 0.8036 - val_loss: 0.7987 - val_accuracy: 0.5714\n",
"Epoch 16/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.4912 - accuracy: 0.8036 - val_loss: 0.8052 - val_accuracy: 0.5714\n",
"Epoch 17/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.4861 - accuracy: 0.8214 - val_loss: 0.8151 - val_accuracy: 0.5714\n",
"Epoch 18/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.4789 - accuracy: 0.8214 - val_loss: 0.8234 - val_accuracy: 0.5714\n",
"Epoch 19/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.4741 - accuracy: 0.8036 - val_loss: 0.8345 - val_accuracy: 0.5714\n",
"Epoch 20/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.4682 - accuracy: 0.8214 - val_loss: 0.8461 - val_accuracy: 0.5714\n",
"Epoch 21/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.4622 - accuracy: 0.8214 - val_loss: 0.8562 - val_accuracy: 0.5714\n",
"Epoch 22/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.4565 - accuracy: 0.8393 - val_loss: 0.8662 - val_accuracy: 0.5714\n",
"Epoch 23/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.4511 - accuracy: 0.8393 - val_loss: 0.8794 - val_accuracy: 0.5714\n",
"Epoch 24/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.4457 - accuracy: 0.8393 - val_loss: 0.8925 - val_accuracy: 0.5714\n",
"Epoch 25/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.4393 - accuracy: 0.8393 - val_loss: 0.9009 - val_accuracy: 0.5714\n",
"Epoch 26/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.4340 - accuracy: 0.8393 - val_loss: 0.9065 - val_accuracy: 0.5714\n",
"Epoch 27/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.4293 - accuracy: 0.8571 - val_loss: 0.9164 - val_accuracy: 0.5714\n",
"Epoch 28/100\n",
"2/2 [==============================] - 0s 61ms/step - loss: 0.4223 - accuracy: 0.8571 - val_loss: 0.9237 - val_accuracy: 0.5714\n",
"Epoch 29/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.4174 - accuracy: 0.8571 - val_loss: 0.9249 - val_accuracy: 0.5714\n",
"Epoch 30/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.4121 - accuracy: 0.8750 - val_loss: 0.9313 - val_accuracy: 0.5714\n",
"Epoch 31/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.4061 - accuracy: 0.8750 - val_loss: 0.9391 - val_accuracy: 0.5714\n",
"Epoch 32/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.4009 - accuracy: 0.8929 - val_loss: 0.9427 - val_accuracy: 0.5714\n",
"Epoch 33/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.3948 - accuracy: 0.8929 - val_loss: 0.9473 - val_accuracy: 0.5714\n",
"Epoch 34/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.3886 - accuracy: 0.8929 - val_loss: 0.9536 - val_accuracy: 0.5714\n",
"Epoch 35/100\n",
"2/2 [==============================] - 0s 61ms/step - loss: 0.3832 - accuracy: 0.8929 - val_loss: 0.9583 - val_accuracy: 0.5714\n",
"Epoch 36/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.3773 - accuracy: 0.8929 - val_loss: 0.9683 - val_accuracy: 0.5714\n",
"Epoch 37/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.3727 - accuracy: 0.8929 - val_loss: 0.9771 - val_accuracy: 0.5714\n",
"Epoch 38/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.3679 - accuracy: 0.8929 - val_loss: 0.9785 - val_accuracy: 0.5714\n",
"Epoch 39/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.3595 - accuracy: 0.8929 - val_loss: 0.9809 - val_accuracy: 0.5714\n",
"Epoch 40/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.3541 - accuracy: 0.9107 - val_loss: 0.9833 - val_accuracy: 0.5714\n",
"Epoch 41/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.3488 - accuracy: 0.9107 - val_loss: 0.9858 - val_accuracy: 0.5714\n",
"Epoch 42/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.3423 - accuracy: 0.9107 - val_loss: 0.9949 - val_accuracy: 0.5714\n",
"Epoch 43/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.3364 - accuracy: 0.9107 - val_loss: 0.9992 - val_accuracy: 0.5714\n",
"Epoch 44/100\n",
"2/2 [==============================] - 0s 63ms/step - loss: 0.3303 - accuracy: 0.9107 - val_loss: 0.9988 - val_accuracy: 0.5714\n",
"Epoch 45/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.3235 - accuracy: 0.9286 - val_loss: 1.0024 - val_accuracy: 0.5714\n",
"Epoch 46/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.3168 - accuracy: 0.9286 - val_loss: 1.0086 - val_accuracy: 0.5714\n",
"Epoch 47/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.3105 - accuracy: 0.9286 - val_loss: 1.0170 - val_accuracy: 0.5714\n",
"Epoch 48/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.3052 - accuracy: 0.9286 - val_loss: 1.0201 - val_accuracy: 0.5714\n",
"Epoch 49/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.2979 - accuracy: 0.9286 - val_loss: 1.0245 - val_accuracy: 0.5714\n",
"Epoch 50/100\n",
"2/2 [==============================] - 0s 44ms/step - loss: 0.2928 - accuracy: 0.9286 - val_loss: 1.0230 - val_accuracy: 0.5714\n",
"Epoch 51/100\n",
"2/2 [==============================] - 0s 64ms/step - loss: 0.2862 - accuracy: 0.9286 - val_loss: 1.0209 - val_accuracy: 0.5714\n",
"Epoch 52/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.2789 - accuracy: 0.9286 - val_loss: 1.0240 - val_accuracy: 0.5714\n",
"Epoch 53/100\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.2724 - accuracy: 0.9286 - val_loss: 1.0315 - val_accuracy: 0.5714\n",
"Epoch 54/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.2665 - accuracy: 0.9464 - val_loss: 1.0421 - val_accuracy: 0.5714\n",
"Epoch 55/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.2590 - accuracy: 0.9464 - val_loss: 1.0479 - val_accuracy: 0.5714\n",
"Epoch 56/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.2530 - accuracy: 0.9464 - val_loss: 1.0525 - val_accuracy: 0.5714\n",
"Epoch 57/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.2469 - accuracy: 0.9464 - val_loss: 1.0480 - val_accuracy: 0.5714\n",
"Epoch 58/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.2412 - accuracy: 0.9464 - val_loss: 1.0464 - val_accuracy: 0.5714\n",
"Epoch 59/100\n",
"2/2 [==============================] - 0s 69ms/step - loss: 0.2335 - accuracy: 0.9464 - val_loss: 1.0429 - val_accuracy: 0.5714\n",
"Epoch 60/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.2270 - accuracy: 0.9464 - val_loss: 1.0452 - val_accuracy: 0.5714\n",
"Epoch 61/100\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.2213 - accuracy: 0.9464 - val_loss: 1.0453 - val_accuracy: 0.5714\n",
"Epoch 62/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.2136 - accuracy: 0.9464 - val_loss: 1.0549 - val_accuracy: 0.5714\n",
"Epoch 63/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.2068 - accuracy: 0.9464 - val_loss: 1.0625 - val_accuracy: 0.5714\n",
"Epoch 64/100\n",
"2/2 [==============================] - 0s 61ms/step - loss: 0.2009 - accuracy: 0.9464 - val_loss: 1.0702 - val_accuracy: 0.5714\n",
"Epoch 65/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.1961 - accuracy: 0.9464 - val_loss: 1.0778 - val_accuracy: 0.5714\n",
"Epoch 66/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.1897 - accuracy: 0.9464 - val_loss: 1.0869 - val_accuracy: 0.5714\n",
"Epoch 67/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.1830 - accuracy: 0.9464 - val_loss: 1.0805 - val_accuracy: 0.5714\n",
"Epoch 68/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.1770 - accuracy: 0.9464 - val_loss: 1.0792 - val_accuracy: 0.5714\n",
"Epoch 69/100\n",
"2/2 [==============================] - 0s 62ms/step - loss: 0.1720 - accuracy: 0.9464 - val_loss: 1.0814 - val_accuracy: 0.5714\n",
"Epoch 70/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.1669 - accuracy: 0.9643 - val_loss: 1.0892 - val_accuracy: 0.5714\n",
"Epoch 71/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.1588 - accuracy: 0.9643 - val_loss: 1.1034 - val_accuracy: 0.5714\n",
"Epoch 72/100\n",
"2/2 [==============================] - 0s 62ms/step - loss: 0.1542 - accuracy: 0.9643 - val_loss: 1.1167 - val_accuracy: 0.5714\n",
"Epoch 73/100\n",
"2/2 [==============================] - 0s 82ms/step - loss: 0.1493 - accuracy: 0.9643 - val_loss: 1.1283 - val_accuracy: 0.5714\n",
"Epoch 74/100\n",
"2/2 [==============================] - 0s 88ms/step - loss: 0.1448 - accuracy: 0.9643 - val_loss: 1.1348 - val_accuracy: 0.5714\n",
"Epoch 75/100\n",
"2/2 [==============================] - 0s 74ms/step - loss: 0.1379 - accuracy: 0.9821 - val_loss: 1.1401 - val_accuracy: 0.5714\n",
"Epoch 76/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.1324 - accuracy: 0.9821 - val_loss: 1.1493 - val_accuracy: 0.5714\n",
"Epoch 77/100\n",
"2/2 [==============================] - 0s 87ms/step - loss: 0.1275 - accuracy: 0.9821 - val_loss: 1.1515 - val_accuracy: 0.5714\n",
"Epoch 78/100\n",
"2/2 [==============================] - 0s 77ms/step - loss: 0.1237 - accuracy: 1.0000 - val_loss: 1.1534 - val_accuracy: 0.5714\n",
"Epoch 79/100\n",
"2/2 [==============================] - 0s 74ms/step - loss: 0.1182 - accuracy: 1.0000 - val_loss: 1.1616 - val_accuracy: 0.5714\n",
"Epoch 80/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.1137 - accuracy: 1.0000 - val_loss: 1.1761 - val_accuracy: 0.5714\n",
"Epoch 81/100\n",
"2/2 [==============================] - 0s 74ms/step - loss: 0.1107 - accuracy: 1.0000 - val_loss: 1.1928 - val_accuracy: 0.5714\n",
"Epoch 82/100\n",
"2/2 [==============================] - 0s 83ms/step - loss: 0.1049 - accuracy: 1.0000 - val_loss: 1.1986 - val_accuracy: 0.5714\n",
"Epoch 83/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.1004 - accuracy: 1.0000 - val_loss: 1.2008 - val_accuracy: 0.5714\n",
"Epoch 84/100\n",
"2/2 [==============================] - 0s 76ms/step - loss: 0.0966 - accuracy: 1.0000 - val_loss: 1.1978 - val_accuracy: 0.6429\n",
"Epoch 85/100\n",
"2/2 [==============================] - 0s 69ms/step - loss: 0.0927 - accuracy: 1.0000 - val_loss: 1.2097 - val_accuracy: 0.6429\n",
"Epoch 86/100\n",
"2/2 [==============================] - 0s 78ms/step - loss: 0.0897 - accuracy: 1.0000 - val_loss: 1.2143 - val_accuracy: 0.6429\n",
"Epoch 87/100\n",
"2/2 [==============================] - 0s 88ms/step - loss: 0.0861 - accuracy: 1.0000 - val_loss: 1.2320 - val_accuracy: 0.6429\n",
"Epoch 88/100\n",
"2/2 [==============================] - 0s 71ms/step - loss: 0.0819 - accuracy: 1.0000 - val_loss: 1.2416 - val_accuracy: 0.6429\n",
"Epoch 89/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.0785 - accuracy: 1.0000 - val_loss: 1.2562 - val_accuracy: 0.5714\n",
"Epoch 90/100\n",
"2/2 [==============================] - 0s 62ms/step - loss: 0.0755 - accuracy: 1.0000 - val_loss: 1.2654 - val_accuracy: 0.5714\n",
"Epoch 91/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.0724 - accuracy: 1.0000 - val_loss: 1.2787 - val_accuracy: 0.5714\n",
"Epoch 92/100\n",
"2/2 [==============================] - 0s 74ms/step - loss: 0.0695 - accuracy: 1.0000 - val_loss: 1.2898 - val_accuracy: 0.5714\n",
"Epoch 93/100\n",
"2/2 [==============================] - 0s 76ms/step - loss: 0.0668 - accuracy: 1.0000 - val_loss: 1.3015 - val_accuracy: 0.5714\n",
"Epoch 94/100\n",
"2/2 [==============================] - 0s 78ms/step - loss: 0.0635 - accuracy: 1.0000 - val_loss: 1.3112 - val_accuracy: 0.6429\n",
"Epoch 95/100\n",
"2/2 [==============================] - 0s 72ms/step - loss: 0.0615 - accuracy: 1.0000 - val_loss: 1.3154 - val_accuracy: 0.6429\n",
"Epoch 96/100\n",
"2/2 [==============================] - 0s 76ms/step - loss: 0.0591 - accuracy: 1.0000 - val_loss: 1.3261 - val_accuracy: 0.6429\n",
"Epoch 97/100\n",
"2/2 [==============================] - 0s 63ms/step - loss: 0.0566 - accuracy: 1.0000 - val_loss: 1.3296 - val_accuracy: 0.6429\n",
"Epoch 98/100\n",
"2/2 [==============================] - 0s 97ms/step - loss: 0.0544 - accuracy: 1.0000 - val_loss: 1.3336 - val_accuracy: 0.6429\n",
"Epoch 99/100\n",
"2/2 [==============================] - 0s 77ms/step - loss: 0.0522 - accuracy: 1.0000 - val_loss: 1.3414 - val_accuracy: 0.6429\n",
"Epoch 100/100\n",
"2/2 [==============================] - 0s 80ms/step - loss: 0.0500 - accuracy: 1.0000 - val_loss: 1.3486 - val_accuracy: 0.6429\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Assuming your original test labels are in y_test, and they are not binary (0 or 1) yet\n",
"# Apply the same threshold used for the training data\n",
"y_test_class = (y_test > threshold).astype(int)\n",
"\n",
"# Now you can compute the confusion matrix and plot it\n",
"cm = confusion_matrix(y_test_class, y_pred_class)\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
"disp.plot(cmap=plt.cm.Blues)\n",
"plt.title('Confusion Matrix')\n",
"plt.show()\n",
"\n",
"# And compute the ROC curve\n",
"fpr, tpr, _ = roc_curve(y_test_class, y_pred_probs.ravel())\n",
"roc_auc = auc(fpr, tpr)\n",
"\n",
"# Plot the ROC curve\n",
"plt.figure()\n",
"plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
"plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('Receiver Operating Characteristic')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 927
},
"id": "3LzNwmwXgXjd",
"outputId": "a5fc3280-703a-4b00-d08f-855cb4c09085"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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HAFgDrXUAAPxYgLbWSeQAAGsI0IrcN3+9AAAABUJFDgCwBlrrAAD4MZvNzUROax0AAJiMihwAYA1BtquLO9v7IBI5AMAaAvQauW9GBQAACoSKHABgDQF6HzmJHABgDbTWAQCAr6EiBwBYA611AAD8WIC21knkAABrCNCK3Dd/vQAAAAVCRQ4AsAZa6wAA+DFa6wAAwNdQkQMALMLN1rqP1r4kcgCANdBaBwAAvoaKHABgDTabm7PWfbMiJ5EDAKwhQG8/882oAABAgVCRAwCsIUAnu5HIAQDWEKCtdRI5AMAaArQi981fLwAAQIFQkQMArIHWOgAAfozWOgAA8DVU5AAAS7DZbLJRkQMA4J9yE7k7i6suXLigp59+WnFxcQoNDVWLFi20detWU8+LRA4AgIf0799fq1at0oIFC7Rnzx516NBB8fHxOnbsmGnHIJEDAKzBZsLigkuXLmnJkiWaOHGiWrdurZo1a2rMmDGqWbOmZsyYYc45iWvkAACLMOsaeWpqqtOw3W6X3W7Ps/qVK1eUnZ2tkJAQp/HQ0FBt2rSp8HFcg4ocAAAXxMbGKioqyrEkJSXlu15ERISaN2+u8ePH6/jx48rOztY777yjLVu26MSJE6bFQ0UOALAEsyrylJQURUZGOobzq8ZzLViwQP369VOlSpUUHBysJk2aqHfv3tq+fXvh47gGiRwAYAlmJfLIyEinRP5natSoofXr1ys9PV2pqamqWLGi7r//flWvXr3wcVyD1joAwBK8cftZrrCwMFWsWFG//fabVq5cqW7dupl2XlTkAAB4yMqVK2UYhmrVqqXk5GQNHTpUtWvXVmJiomnHIJEDAKyhELeQ5dneRefPn9eIESP0888/q0yZMvrf//1fTZgwQcWLF3cjEGckcgCAJXjjEa09e/ZUz549C3/MAuAaOQAAfoyKHABgCVffYupORW5eLGYikQMALMEmN1vrPprJaa0DAODHqMgBAJYQqO8jJ5EDAKzBC7efFQVa6wAA+DEqcgCANbjZWjdorQMA4D3uXiN3b8a755DIAQCWEKiJnGvkAAD4MSpyAIA1BOisdRI5AMASaK0DAACfQ0UOALCEQK3ISeQAAEsI1EROax0AAD9GRQ4AsIRArchJ5AAAawjQ289orQMA4MeoyAEAlkBrHQAAP0YiBwDAjwVqIucaOQAAfoyKHABgDQE6a51EDgCwBFrrAADA51CRo0DCQ4pp+H1N1LlpFZWNCtGeI2f13PyvtfPQGW+HBrisxa019OTf49WodhVVLBelPkPe0Cfrv3VaZ8QjnfVg9xaKCg/V198e0jMvvq9DKb94KWKYgYrcg6ZPn66qVasqJCREzZo10zfffOPtkHCNKQ+3VNsGFTVgxka1HvaR1u05riX/7Kjo0iW9HRrgspKhdn334zENnfh+vt8PfDBej9zfRoOT3lP7xJd18VKWlkx7XPYS1D7+zCabI5kXavHRi+ReT+Tvv/++Bg8erNGjR2vHjh1q1KiROnbsqNOnT3s7NPwupHiwuvwlTmMXbdeWH07p8KkLmrhklw6fSlVifC1vhwe4bPXm7zVh5gp9vO7bfL9/tHc7vfz2Sn26YY/2Jh/XY6PnK7pslDq3aVTEkQI35vVEPmnSJD388MNKTExU3bp1NXPmTJUsWVJvv/22t0PD74oF21QsOEgZl7Odxi9lZev/1argpagAz4irdJOiy0Zp3Tc/OMZS0zO0fe8R3d6wqvcCg9vcqsbdbMt7klcTeVZWlrZv3674+HjHWFBQkOLj47VlyxYvRoY/Ssu4om9+PK0h9zRSdKlQBdlsuq9ldd1+czlVKBXq7fAAU1W4KVKS9MuZC07jp89cUPnfv4Ofspmw+CCvXvD59ddflZ2drQoVnKu6ChUq6IcffsizfmZmpjIzMx2fU1NTPR4jrhrw+kZNfaSlvnv9fl3JztG3R87ow82H1ajaTd4ODQAsza9mbiQlJWns2LHeDsOSjpy+oLvHf6aS9mKKCC2uU+cu6a0n2+jo6Qs33hjwI6fOXC0Qyt0U4fi7JJW/KUJ7fvzZW2HBBMxa94CyZcsqODhYp06dcho/deqUoqOj86w/YsQInT9/3rGkpKQUVaj43cXMKzp17pKiwkqoXcNK+nQ7/xsgsBw9dkYnfz2vNrf/dyJnRFiIbqtXVVu/PeK9wOC2QL1G7tWKvESJErrtttu0Zs0ade/eXZKUk5OjNWvW6Iknnsizvt1ul91uL+IoIUntGsbIJpuST5xXtQoRGvPA7Tpw/LwWrT/g7dAAl4WFllC12HKOz3ExN6n+LZV07vxF/XzqN8189wsN6XeXDqX8oqPHzuifj3bWyV/P6+P1u70YNdxls11d3NneF3m9tT548GAlJCSoadOm+stf/qIpU6YoPT1diYmJ3g4NfxAZWkL/16uJYsqE6VxappZvPaoJ7+/QlWzD26EBLmtcJ04rZg10fH5h8P9Kkhat+EqPj31Hr85frZKhdk3+Z29FhYfqq90Hde9Trysz64q3Qgauy+uJ/P7779cvv/yiUaNG6eTJk2rcuLE+++yzPBPg4F0ffX1EH319xNthAKb4cscBlb49b9fvj5JmfaykWR8XUUQoClcrcneukZsYjIm8nsgl6Yknnsi3lQ4AgGncbK376u1nXn8gDAAAKDyfqMgBAPA0bj8DAMCP5c5ad2dxRXZ2tkaOHKlq1aopNDRUNWrU0Pjx42UY5k4SpiIHAMAD/vWvf2nGjBmaN2+e6tWrp23btikxMVFRUVF66qmnTDsOiRwAYAlBQTYFBRW+PW64uO3mzZvVrVs3de7cWZJUtWpVvfvuu6a/qpvWOgDAEoq6td6iRQutWbNGP/74oyRp9+7d2rRpkzp16mTqeVGRAwDggmtf2HW9p44OHz5cqampql27toKDg5Wdna0JEyaoT58+psZDRQ4AsASznrUeGxurqKgox5KUlJTv8RYvXqyFCxdq0aJF2rFjh+bNm6eXX35Z8+bNM/W8qMgBAJZg1rPWU1JSFBn533fTX+8dIEOHDtXw4cPVq1cvSVKDBg109OhRJSUlKSEhofCBXINEDgCwBLPuI4+MjHRK5Ndz8eJFBQU5N76Dg4OVk5NT6BjyQyIHAMADunbtqgkTJqhKlSqqV6+edu7cqUmTJqlfv36mHodEDgCwhKJ+stu0adM0cuRIDRgwQKdPn1ZMTIweeeQRjRo1qtAx5IdEDgCwhKJ+H3lERISmTJmiKVOmFP6gBcCsdQAA/BgVOQDAEmxys7Xuo+8xJZEDACyhqFvrRYXWOgAAfoyKHABgCYH6PnISOQDAEmitAwAAn0NFDgCwBFrrAAD4sUBtrZPIAQCWEKgVOdfIAQDwY1TkAABrcLO17qMPdiORAwCsgdY6AADwOVTkAABLYNY6AAB+jNY6AADwOVTkAABLoLUOAIAfo7UOAAB8DhU5AMASArUiJ5EDACyBa+QAAPixQK3IuUYOAIAfoyIHAFgCrXUAAPwYrXUAAOBzqMgBAJZgk5utddMiMReJHABgCUE2m4LcyOTubOtJtNYBAPBjVOQAAEtg1joAAH4sUGetk8gBAJYQZLu6uLO9L+IaOQAAfoyKHABgDTY32+M+WpGTyAEAlhCok91orQMA4MeoyAEAlmD7/Y872/siEjkAwBKYtQ4AAHwOFTkAwBIs/UCY//znPwXe4d13313oYAAA8JRAnbVeoETevXv3Au3MZrMpOzvbnXgAAAgIVatW1dGjR/OMDxgwQNOnTzftOAVK5Dk5OaYdEAAAbyjq15hu3brVqbj97rvv1L59e913332FjiE/bl0jz8jIUEhIiFmxAADgMUXdWi9XrpzT5xdffFE1atRQmzZtCh9EPlyetZ6dna3x48erUqVKCg8P16FDhyRJI0eO1OzZs00NDgAAs+ROdnNnKaysrCy988476tevn+mT5lxO5BMmTNDcuXM1ceJElShRwjFev359vfXWW6YGBwCAr0lNTXVaMjMzb7jNsmXLdO7cOfXt29f0eFxO5PPnz9cbb7yhPn36KDg42DHeqFEj/fDDD6YGBwCAWXJb6+4skhQbG6uoqCjHkpSUdMNjz549W506dVJMTIzp5+XyNfJjx46pZs2aecZzcnJ0+fJlU4ICAMBsZk12S0lJUWRkpGPcbrf/6XZHjx7V6tWr9eGHHxb62H/G5URet25dbdy4UXFxcU7jH3zwgW699VbTAgMAwBdFRkY6JfIbmTNnjsqXL6/OnTt7JB6XE/moUaOUkJCgY8eOKScnRx9++KH279+v+fPna8WKFZ6IEQAAt9nk3ivFC7NtTk6O5syZo4SEBBUr5pmHqbp8jbxbt25avny5Vq9erbCwMI0aNUr79u3T8uXL1b59e0/ECACA27wxa3316tX66aef1K9fPw+c0VWF+vWgVatWWrVqldmxAAAQUDp06CDDMDx6jELX+du2bdO+ffskXb1uftttt5kWFAAAZgvU15i6nMh//vln9e7dW19++aVKlSolSTp37pxatGih9957T5UrVzY7RgAA3Baobz9z+Rp5//79dfnyZe3bt09nz57V2bNntW/fPuXk5Kh///6eiBEAAFyHyxX5+vXrtXnzZtWqVcsxVqtWLU2bNk2tWrUyNTgAAMzko0W1W1xO5LGxsfk++CU7O9sjT6wBAMAMtNZ/99JLL+nJJ5/Utm3bHGPbtm3TwIED9fLLL5saHAAAZsmd7ObO4osKVJGXLl3a6TeR9PR0NWvWzHFz+5UrV1SsWDH169dP3bt390igAAAgrwIl8ilTpng4DAAAPCtQW+sFSuQJCQmejgMAAI/yxiNai4JbD37NyMhQVlaW05grD5IHAADucTmRp6ena9iwYVq8eLHOnDmT5/vs7GxTAgMAwExmvcbU17g8a/3ZZ5/V2rVrNWPGDNntdr311lsaO3asYmJiNH/+fE/ECACA22w29xdf5HJFvnz5cs2fP19t27ZVYmKiWrVqpZo1ayouLk4LFy5Unz59PBEnAADIh8sV+dmzZ1W9enVJV6+Hnz17VpJ0xx13aMOGDeZGBwCASbzxGtOi4HIir169ug4fPixJql27thYvXizpaqWe+xIVAAB8TaC21l1O5ImJidq9e7ckafjw4Zo+fbpCQkI0aNAgDR061PQAAQDA9bl8jXzQoEGOv8fHx+uHH37Q9u3bVbNmTTVs2NDU4AAAMEugzlp36z5ySYqLi1NcXJwZsQAA4DHutsd9NI8XLJFPnTq1wDt86qmnCh0MAACeYulHtE6ePLlAO7PZbCRyAACKUIESee4sdV91eHYfHg2LgHXb6BhvhwB4THZmuvbvebNIjhWkQszwvmZ7X+T2NXIAAPxBoLbWffUXDAAAUABU5AAAS7DZpCCrzloHAMDfBbmZyN3Z1pNorQMA4McKlcg3btyov/3tb2revLmOHTsmSVqwYIE2bdpkanAAAJiFl6b8bsmSJerYsaNCQ0O1c+dOZWZmSpLOnz+vF154wfQAAQAwQ25r3Z3FF7mcyJ9//nnNnDlTb775pooXL+4Yb9mypXbs2GFqcAAA4M+5PNlt//79at26dZ7xqKgonTt3zoyYAAAwXaA+a93lijw6OlrJycl5xjdt2qTq1aubEhQAAGbLffuZO4svcjmRP/zwwxo4cKC+/vpr2Ww2HT9+XAsXLtSQIUP02GOPeSJGAADcFmTC4otcbq0PHz5cOTk5uvPOO3Xx4kW1bt1adrtdQ4YM0ZNPPumJGAEAwHW4nMhtNpuee+45DR06VMnJyUpLS1PdunUVHh7uifgAADBFoF4jL/ST3UqUKKG6deuaGQsAAB4TJPeucwfJNzO5y4m8Xbt2f3pT/Nq1a90KCAAAFJzLibxx48ZOny9fvqxdu3bpu+++U0JCgllxAQBgKlrrv5s8eXK+42PGjFFaWprbAQEA4Am8NOUG/va3v+ntt982a3cAAKAATHuN6ZYtWxQSEmLW7gAAMNXV95EXvqwOmNZ6jx49nD4bhqETJ05o27ZtGjlypGmBAQBgJq6R/y4qKsrpc1BQkGrVqqVx48apQ4cOpgUGAABuzKVEnp2drcTERDVo0EClS5f2VEwAAJjOG5Pdjh07pmHDhunTTz/VxYsXVbNmTc2ZM0dNmzYtfCDXcCmRBwcHq0OHDtq3bx+JHADgV2y//3Fne1f89ttvatmypdq1a6dPP/1U5cqV04EDB0zPny631uvXr69Dhw6pWrVqpgYCAIAnFXVF/q9//UuxsbGaM2eOY8wTudPl28+ef/55DRkyRCtWrNCJEyeUmprqtAAAEMiuzXuZmZn5rvef//xHTZs21X333afy5cvr1ltv1Ztvvml6PAVO5OPGjVN6err++te/avfu3br77rtVuXJllS5dWqVLl1apUqVotwMAfFZuRe7OIkmxsbGKiopyLElJSfke79ChQ5oxY4ZuvvlmrVy5Uo899pieeuopzZs3z9TzKnBrfezYsXr00Uf1xRdfmBoAAABFwWaz/em7QgqyvSSlpKQoMjLSMW632/NdPycnR02bNtULL7wgSbr11lv13XffaebMmaY+0rzAidwwDElSmzZtTDs4AAD+JjIy0imRX0/FihXzvCW0Tp06WrJkianxuDTZzZ3fZAAA8KainuzWsmVL7d+/32nsxx9/VFxcXOGDyIdLifyWW265YTI/e/asWwEBAOAJRf1kt0GDBqlFixZ64YUX1LNnT33zzTd644039MYbbxQ+iHy4lMjHjh2b58luAAAgr9tvv11Lly7ViBEjNG7cOFWrVk1TpkxRnz59TD2OS4m8V69eKl++vKkBAABQFIJsNrdemlKYbbt06aIuXboU+pgFUeBEzvVxAIA/s/z7yHNnrQMAAN9R4Io8JyfHk3EAAOBZbk52c+Mx7R7l8rPWAQDwR0GyKciNbOzOtp5EIgcAWEJR335WVFx+aQoAAPAdVOQAAEsI1FnrJHIAgCV44z7yokBrHQAAP0ZFDgCwhECd7EYiBwBYQpDcbK376O1ntNYBAPBjVOQAAEugtQ4AgB8LknttaF9tYftqXAAAoACoyAEAlmCz2dx6Jbevvs6bRA4AsASb3HuBmW+mcRI5AMAieLIbAADwOVTkAADL8M2a2j0kcgCAJQTqfeS01gEA8GNU5AAAS+D2MwAA/BhPdgMAAD6HihwAYAm01gEA8GOB+mQ3WusAAPgxKnIAgCXQWgcAwI8F6qx1EjkAwBICtSL31V8wAABAAVCRAwAsIVBnrZPIAQCWwEtTAACAz6EiBwBYQpBsCnKjQe7Otp5EIgcAWAKtdQAA4HOoyAEAlmD7/Y872/siEjkAwBJorQMAAJ9DRQ4AsASbm7PWfbW1TkUOALCE3Na6O4srxowZ43i+e+5Su3Zt08+LihwAYAneuEZer149rV692vG5WDHz0y6JHAAADylWrJiio6M9egxa6wAAS7CZ8EeSUlNTnZbMzMzrHvPAgQOKiYlR9erV1adPH/3000+mnxeJHABgCUE29xdJio2NVVRUlGNJSkrK93jNmjXT3Llz9dlnn2nGjBk6fPiwWrVqpQsXLph6XrTWAQBwQUpKiiIjIx2f7XZ7vut16tTJ8feGDRuqWbNmiouL0+LFi/XQQw+ZFg+JHABgCWY92S0yMtIpkRdUqVKldMsttyg5ObnQMeSH1joAwBKK+vaza6WlpengwYOqWLGiOSf0OxI5AAAeMGTIEK1fv15HjhzR5s2bdc899yg4OFi9e/c29Ti01gEAlmCTe09nc3XLn3/+Wb1799aZM2dUrlw53XHHHfrqq69Urly5QseQHxI5AMAS/jjzvLDbu+K9994r/MFcQGsdAAA/RkWOG5r9wUa9vWSjUk6clSTVrh6toQ91UvuW9bwcGWCOIJv0SLsa6tQwRjeFl9CvFzK1fNdxvbX+kLdDg4kC9X3kXq3IN2zYoK5duyomJkY2m03Lli3zZji4jpjypTT6iW76Yv6zWjtvqFo1vUV9hryhfQdPeDs0wBQJd1TTvU1jNfHjfbr3tS81ddUBPdiyqno1q+Lt0GAib89a9xSvJvL09HQ1atRI06dP92YYuIFOrRuoQ8t6qlGlvGrGVdDIAXcrrKRd27477O3QAFM0ii2ldftPa9OBX3XiXIbWfH9KXx08o3qVXL9XGL7LZsLii7zaWu/UqZPTk2/g+7Kzc7RszQ5dvJSl2xtU83Y4gCl2p5xTj9sqq8pNJfXTmYu6uUK4Glcppcmf7fd2aMAN+dU18szMTKeH06empnoxGmvZm3xMHfu9ooysKwoLtWvBSw+rdnVzH2oAeMvcTYcVbi+mJU+0VI5hKMhm0+trk/XpnpPeDg0mCpJNQW70x4N8tCb3q0SelJSksWPHejsMS7o5roI2LByh1LRL+mjNTg0Ys0ArZg0kmSMgtK8XrbsaVtRzS/bo0Ok03RIdoWc61dIvqZlasfu4t8ODSdxtj/tmGvez289GjBih8+fPO5aUlBRvh2QZJYoXU/XYcmpcp4pGP9FN9W+upJnvrfN2WIApBna4RXM3Hdbn351U8uk0ffLtCS3aclSJrbh8BN/nVxW53W6/7ltmULRyDENZWVe8HQZgipDiQTIMw2ksx/DdWcoopAAtyf0qkcM7xr72keJb1FNsdGlduJihDz7bpk3bD2jJtAHeDg0wxcb9v6hfq+o6eS5DB39JU+3oSPVpHqePdh7zdmgwUaDeR+7VRJ6Wlub0OrfDhw9r165dKlOmjKpU4f5NX/Hrb2l6bMx8nfo1VZHhIapXs5KWTBugds3qeDs0wBQTP/lBj/1PTQ3vUkelw64+EGbJtp/15vqD3g4NuCGvJvJt27apXbt2js+DBw+WJCUkJGju3LleigrXmjayj7dDADzqYla2Xvlsv17hdrPA5u5DXXyzIPduIm/btm2e61IAAHhCgF4i969Z6wAAwBmT3QAA1hCgJTmJHABgCcxaBwDAj7n7BjNffa4A18gBAPBjVOQAAEsI0EvkJHIAgEUEaCantQ4AgB+jIgcAWAKz1gEA8GPMWgcAAD6HihwAYAkBOteNRA4AsIgAzeS01gEA8GNU5AAAS2DWOgAAfixQZ62TyAEAlhCgl8i5Rg4AgD+jIgcAWEOAluQkcgCAJQTqZDda6wAA+DEqcgCAJTBrHQAAPxagl8hprQMA4M+oyAEA1hCgJTmJHABgCcxaBwAAPoeKHABgCcxaBwDAjwXoJXJa6wAAi7CZsBTSiy++KJvNpqeffrrwO7kOEjkAAB60detWzZo1Sw0bNvTI/knkAABLsJnwx1VpaWnq06eP3nzzTZUuXdoDZ0UiBwBYhe2/E94Ks+Tm8dTUVKclMzPzuod8/PHH1blzZ8XHx3vstEjkAAC4IDY2VlFRUY4lKSkp3/Xee+897dix47rfm4VZ6wAASzBr1npKSooiIyMd43a7Pc+6KSkpGjhwoFatWqWQkBA3jnpjJHIAgDWYlMkjIyOdEnl+tm/frtOnT6tJkyaOsezsbG3YsEGvvfaaMjMzFRwc7EYw/0UiBwDAZHfeeaf27NnjNJaYmKjatWtr2LBhpiVxiUQOALCIonzWekREhOrXr+80FhYWpptuuinPuLtI5AAAS+ARrQAAoNDWrVvnkf2SyAEAlhCoz1onkQMArCFAMzmJHABgCUU52a0o8WQ3AAD8GBU5AMASbHJz1rppkZiLRA4AsIQAvUROax0AAH9GRQ4AsAQeCAMAgF8LzOY6rXUAAPwYFTkAwBJorQMA4McCs7FOax0AAL9GRQ4AsARa6wAA+LFAfdY6iRwAYA0BepGca+QAAPgxKnIAgCUEaEFOIgcAWEOgTnajtQ4AgB+jIgcAWAKz1gEA8GcBepGc1joAAH6MihwAYAkBWpCTyAEA1sCsdQAA4HOoyAEAFuHerHVfba6TyAEAlkBrHQAA+BwSOQAAfozWOgDAEgK1tU4iBwBYQqA+opXWOgAAfoyKHABgCbTWAQDwY4H6iFZa6wAA+DEqcgCANQRoSU4iBwBYArPWAQCAz6EiBwBYArPWAQDwYwF6iZzWOgDAImwmLC6YMWOGGjZsqMjISEVGRqp58+b69NNPzTmXPyCRAwDgAZUrV9aLL76o7du3a9u2bfqf//kfdevWTXv37jX1OLTWAQCWUNSz1rt27er0ecKECZoxY4a++uor1atXr9BxXItEDgCwBG9OdsvOzta///1vpaenq3nz5oXfUT78OpEbhiFJupCa6uVIAM/Jzkz3dgiAx2RnXpT03/+ee1Kqm7kid/tr92O322W32/PdZs+ePWrevLkyMjIUHh6upUuXqm7dum7FkYfhx1JSUgxJLCwsLCx+vqSkpHgsV1y6dMmIjo42Jc7w8PA8Y6NHj77usTMzM40DBw4Y27ZtM4YPH26ULVvW2Lt3r6nnZzOMIvg1yENycnJ0/PhxRUREyOarN/gFmNTUVMXGxiolJUWRkZHeDgcwFf++i55hGLpw4YJiYmIUFOS5+dcZGRnKyspyez+GYeTJN39WkV8rPj5eNWrU0KxZs9yOJZdft9aDgoJUuXJlb4dhSbm3UwCBiH/fRSsqKsrjxwgJCVFISIjHj3MjOTk5yszMNHWffp3IAQDwVSNGjFCnTp1UpUoVXbhwQYsWLdK6deu0cuVKU49DIgcAwANOnz6tBx98UCdOnFBUVJQaNmyolStXqn379qYeh0QOl9jtdo0ePbrA14MAf8K/b5hp9uzZRXIcv57sBgCA1fGIVgAA/BiJHAAAP0YiBwDAj5HIAQDwYyRyFNj06dNVtWpVhYSEqFmzZvrmm2+8HRJgig0bNqhr166KiYmRzWbTsmXLvB0SUGAkchTI+++/r8GDB2v06NHasWOHGjVqpI4dO+r06dPeDg1wW3p6uho1aqTp06d7OxTAZdx+hgJp1qyZbr/9dr322muSrj5mMDY2Vk8++aSGDx/u5egA89hsNi1dulTdu3f3dihAgVCR44aysrK0fft2xcfHO8aCgoIUHx+vLVu2eDEyAACJHDf066+/Kjs7WxUqVHAar1Chgk6ePOmlqAAAEokcAAC/RiLHDZUtW1bBwcE6deqU0/ipU6cUHR3tpagAABKJHAVQokQJ3XbbbVqzZo1jLCcnR2vWrFHz5s29GBkAgLefoUAGDx6shIQENW3aVH/5y180ZcoUpaenKzEx0duhAW5LS0tTcnKy4/Phw4e1a9culSlTRlWqVPFiZMCNcfsZCuy1117TSy+9pJMnT6px48aaOnWqmjVr5u2wALetW7dO7dq1yzOekJCguXPnFn1AgAtI5AAA+DGukQMA4MdI5AAA+DESOQAAfoxEDgCAHyORAwDgx0jkAAD4MRI5AAB+jEQOuKlv375O765u27atnn766SKPY926dbLZbDp37tx117HZbFq2bFmB9zlmzBg1btzYrbiOHDkim82mXbt2ubUfAPkjkSMg9e3bVzabTTabTSVKlFDNmjU1btw4XblyxePH/vDDDzV+/PgCrVuQ5AsAf4ZnrSNg3XXXXZozZ44yMzP1ySef6PHHH1fx4sU1YsSIPOtmZWWpRIkSphy3TJkypuwHAAqCihwBy263Kzo6WnFxcXrssccUHx+v//znP5L+2w6fMGGCYmJiVKtWLUlSSkqKevbsqVKlSqlMmTLq1q2bjhw54thndna2Bg8erFKlSummm27Ss88+q2ufcnxtaz0zM1PDhg1TbGys7Ha7atasqdmzZ+vIkSOO53uXLl1aNptNffv2lXT17XJJSUmqVq2aQkND1ahRI33wwQdOx/nkk090yy23KDQ0VO3atXOKs6CGDRumW265RSVLllT16tU1cuRIXb58Oc96s2bNUmxsrEqWLKmePXvq/PnzTt+/9dZbqlOnjkJCQlS7dm29/vrrLscCoHBI5LCM0NBQZWVlOT6vWbNG+/fv16pVq7RixQpdvnxZHTt2VEREhDZu3Kgvv/xS4eHhuuuuuxzbvfLKK5o7d67efvttbdq0SWfPntXSpUv/9LgPPvig3n33XU2dOlX79u3TrFmzFB4ertjYWC1ZskSStH//fp04cUKvvvqqJCkpKUnz58/XzJkztXfvXg0aNEh/+9vftH79eklXf+Ho0aOHunbtql27dql///4aPny4yz+TiIgIzZ07V99//71effVVvfnmm5o8ebLTOsnJyVq8eLGWL1+uzz77TDt37tSAAQMc3y9cuFCjRo3ShAkTtG/fPr3wwgsaOXKk5s2b53I8AArBAAJQQkKC0a1bN8MwDCMnJ8dYtWqVYbfbjSFDhji+r1ChgpGZmenYZsGCBUatWrWMnJwcx1hmZqYRGhpqrFy50jAMw6hYsaIxceJEx/eXL182Kleu7DiWYRhGmzZtjIEDBxqGYRj79+83JBmrVq3KN84vvvjCkGT89ttvjrGMjAyjZMmSxubNm53Wfeihh4zevXsbhmEYI0aMMOrWrev0/bBhw/Ls61qSjKVLl173+5deesm47bbbHJ9Hjx5tBAcHGz///LNj7NNPPzWCgoKMEydOGIZhGDVq1DAWLVrktJ/x48cbzZs3NwzDMA4fPmxIMnbu3Hnd4wIoPK6RI2CtWLFC4eHhunz5snJycvTAAw9ozJgxju8bNGjgdF189+7dSk5OVkREhNN+MjIydPDgQZ0/f14nTpxwenVrsWLF1LRp0zzt9Vy7du1ScHCw2rRpU+C4k5OTdfHiRbVv395pPCsrS7feeqskad++fXleIdu8efMCHyPX+++/r6lTp+rgwYNKS0vTlStXFBkZ6bROlSpVVKlSJafj5OTkaP/+/YqIiNDBgwf10EMP6eGHH3asc+XKFUVFRbkcDwDXkcgRsNq1a6cZM2aoRIkSiomJUbFizv/cw8LCnD6npaXptttu08KFC/Psq1y5coWKITQ01OVt0tLSJEkff/yxUwKVrl73N8uWLVvUp08fjR07Vh07dlRUVJTee+89vfLKKy7H+uabb+b5xSI4ONi0WAFcH4kcASssLEw1a9Ys8PpNmjTR+++/r/Lly+epSnNVrFhRX3/9tVq3bi3pauW5fft2NWnSJN/1GzRooJycHK1fv17x8fF5vs/tCGRnZzvG6tatK7vdrp9++um6lXydOnUcE/dyffXVVzc+yT/YvHmz4uLi9NxzzznGjh49mme9n376ScePH1dMTIzjOEFBQapVq5YqVKigmJgYHTp0SH369HHp+ADMwWQ34Hd9+vRR2bJl1a1bN23cuFGHDx/WunXr9NRTT+nnn3+WJA0cOFAvvviili1bph9++EEDBgz403vAq1atqoSEBPXr10/Lli1z7HPx4sWSpLi4ONlsNq1YsUK//PKL0tLSFBERoSFDhmjQoEGaN2+eDh48qB07dmjatGmOCWSPPvqoDhw4oKFDh2r//v1atGiR5s6d69L53nzzzfrpp5/03nvv6eDBg5o6dWq+E/dCQkKUkJCg3bt3a+PGjXrqqafUs2dPRUdHS5LGjh2rpKQkTZ06VT/++KP27NmjOXPmaNKkSS7FA6BwSOTA70qWLKkNGzaoSpUq6tGjh+rUqaOHHnpIGRkZjgr9mWee0d///nclJCSoefPmioiI0D333POn+50xY4buvfdeDRgwQLVr19bDDz+s9PR0SVKlSpU0duxYDR8+XBUqVNATTzwhSRo/frxGjhyppKQk1alTR3fddZc+/vhjVatWTdLV69ZLlizRsmXL1KhRI82cOVMvvPCCS+d79913a9CgQXriiSfUuHFjbd68WSNHjsyzXs2aNdWjRw/99a9/VYcOHdSwYUOn28v69++vt956S3PmzFGDBg3Upk0bzZ071xErAM+yGdebpQMAAHweFTkAAH6MRA4AgB8jkQMA4MdI5AAA+DESOQAAfoxEDgCAHyORAwDgx0jkAAD4MRI5AAB+jEQOAIAfI5EDAODHSOQAAPix/w8zTAuzfle+PAAAAABJRU5ErkJggg==\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
"from sklearn.utils.class_weight import compute_class_weight\n",
"from sklearn.metrics import precision_recall_curve\n",
"\n",
"# Calculate class weights\n",
"class_weights = compute_class_weight('balanced', classes=np.unique(y_train_class), y=y_train_class)\n",
"class_weights_dict = dict(enumerate(class_weights))\n",
"\n",
"# Build an LSTM model\n",
"model = Sequential([\n",
" LSTM(100, input_shape=(X_train.shape[1], 1), return_sequences=True),\n",
" Dropout(0.5),\n",
" LSTM(100),\n",
" Dropout(0.5),\n",
" Dense(1, activation='sigmoid')\n",
"])\n",
"\n",
"# Compile the model\n",
"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
"\n",
"# Train the model with class weights\n",
"history = model.fit(X_train_rnn, y_train_class, epochs=100, validation_split=0.2, class_weight=class_weights_dict)\n",
"\n",
"# Predict probabilities\n",
"y_pred_probs = model.predict(X_test_rnn)\n",
"\n",
"# Find the optimal threshold based on precision-recall tradeoff\n",
"precision, recall, thresholds = precision_recall_curve(y_test_class, y_pred_probs)\n",
"# Convert to f score\n",
"fscore = (2 * precision * recall) / (precision + recall)\n",
"# Locate the index of the largest f score\n",
"ix = np.argmax(fscore)\n",
"optimal_threshold = thresholds[ix]\n",
"\n",
"# Use the optimal threshold to convert probabilities to binary predictions\n",
"y_pred_class = (y_pred_probs > optimal_threshold).astype(int)\n",
"\n",
"# Recompute the confusion matrix and ROC curve using the new threshold\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vqTNWoVNjNXO",
"outputId": "fd4b0060-a997-4153-f8a2-24a476f5a9ee"
},
"execution_count": null,
"outputs": [
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"2/2 [==============================] - 11s 1s/step - loss: 0.6965 - accuracy: 0.4286 - val_loss: 0.6918 - val_accuracy: 0.6429\n",
"Epoch 2/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.6937 - accuracy: 0.4107 - val_loss: 0.6973 - val_accuracy: 0.2857\n",
"Epoch 3/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6933 - accuracy: 0.5357 - val_loss: 0.7028 - val_accuracy: 0.2143\n",
"Epoch 4/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6901 - accuracy: 0.4821 - val_loss: 0.7073 - val_accuracy: 0.2143\n",
"Epoch 5/100\n",
"2/2 [==============================] - 0s 69ms/step - loss: 0.6953 - accuracy: 0.4643 - val_loss: 0.7099 - val_accuracy: 0.2143\n",
"Epoch 6/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6938 - accuracy: 0.4464 - val_loss: 0.7129 - val_accuracy: 0.2143\n",
"Epoch 7/100\n",
"2/2 [==============================] - 0s 61ms/step - loss: 0.6943 - accuracy: 0.5536 - val_loss: 0.7152 - val_accuracy: 0.2143\n",
"Epoch 8/100\n",
"2/2 [==============================] - 0s 64ms/step - loss: 0.6872 - accuracy: 0.4821 - val_loss: 0.7186 - val_accuracy: 0.2143\n",
"Epoch 9/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6905 - accuracy: 0.4464 - val_loss: 0.7214 - val_accuracy: 0.2143\n",
"Epoch 10/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6924 - accuracy: 0.4821 - val_loss: 0.7245 - val_accuracy: 0.2143\n",
"Epoch 11/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6900 - accuracy: 0.4643 - val_loss: 0.7273 - val_accuracy: 0.2143\n",
"Epoch 12/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6881 - accuracy: 0.4643 - val_loss: 0.7295 - val_accuracy: 0.2143\n",
"Epoch 13/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6920 - accuracy: 0.4821 - val_loss: 0.7312 - val_accuracy: 0.2143\n",
"Epoch 14/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6918 - accuracy: 0.5000 - val_loss: 0.7327 - val_accuracy: 0.2143\n",
"Epoch 15/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6921 - accuracy: 0.4821 - val_loss: 0.7342 - val_accuracy: 0.2143\n",
"Epoch 16/100\n",
"2/2 [==============================] - 0s 69ms/step - loss: 0.6869 - accuracy: 0.5000 - val_loss: 0.7344 - val_accuracy: 0.2143\n",
"Epoch 17/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.6923 - accuracy: 0.5179 - val_loss: 0.7343 - val_accuracy: 0.2143\n",
"Epoch 18/100\n",
"2/2 [==============================] - 0s 69ms/step - loss: 0.6936 - accuracy: 0.4821 - val_loss: 0.7305 - val_accuracy: 0.2143\n",
"Epoch 19/100\n",
"2/2 [==============================] - 0s 62ms/step - loss: 0.6891 - accuracy: 0.5357 - val_loss: 0.7286 - val_accuracy: 0.2143\n",
"Epoch 20/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6882 - accuracy: 0.5357 - val_loss: 0.7261 - val_accuracy: 0.2857\n",
"Epoch 21/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6949 - accuracy: 0.5357 - val_loss: 0.7236 - val_accuracy: 0.3571\n",
"Epoch 22/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6902 - accuracy: 0.5357 - val_loss: 0.7223 - val_accuracy: 0.3571\n",
"Epoch 23/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.6940 - accuracy: 0.5179 - val_loss: 0.7214 - val_accuracy: 0.3571\n",
"Epoch 24/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.6876 - accuracy: 0.5000 - val_loss: 0.7199 - val_accuracy: 0.3571\n",
"Epoch 25/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6902 - accuracy: 0.5357 - val_loss: 0.7190 - val_accuracy: 0.3571\n",
"Epoch 26/100\n",
"2/2 [==============================] - 0s 69ms/step - loss: 0.6895 - accuracy: 0.4821 - val_loss: 0.7188 - val_accuracy: 0.3571\n",
"Epoch 27/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6854 - accuracy: 0.5714 - val_loss: 0.7186 - val_accuracy: 0.3571\n",
"Epoch 28/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6926 - accuracy: 0.5000 - val_loss: 0.7179 - val_accuracy: 0.4286\n",
"Epoch 29/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.6952 - accuracy: 0.5179 - val_loss: 0.7156 - val_accuracy: 0.3571\n",
"Epoch 30/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.6853 - accuracy: 0.5714 - val_loss: 0.7136 - val_accuracy: 0.2857\n",
"Epoch 31/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6852 - accuracy: 0.6071 - val_loss: 0.7127 - val_accuracy: 0.2857\n",
"Epoch 32/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6854 - accuracy: 0.5536 - val_loss: 0.7140 - val_accuracy: 0.2857\n",
"Epoch 33/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.6919 - accuracy: 0.5179 - val_loss: 0.7154 - val_accuracy: 0.2857\n",
"Epoch 34/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6906 - accuracy: 0.5357 - val_loss: 0.7177 - val_accuracy: 0.2857\n",
"Epoch 35/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.6886 - accuracy: 0.5000 - val_loss: 0.7187 - val_accuracy: 0.2857\n",
"Epoch 36/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.6851 - accuracy: 0.4821 - val_loss: 0.7216 - val_accuracy: 0.2857\n",
"Epoch 37/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.6935 - accuracy: 0.5179 - val_loss: 0.7247 - val_accuracy: 0.2857\n",
"Epoch 38/100\n",
"2/2 [==============================] - 0s 81ms/step - loss: 0.6805 - accuracy: 0.5357 - val_loss: 0.7281 - val_accuracy: 0.2857\n",
"Epoch 39/100\n",
"2/2 [==============================] - 0s 78ms/step - loss: 0.6875 - accuracy: 0.5893 - val_loss: 0.7305 - val_accuracy: 0.2857\n",
"Epoch 40/100\n",
"2/2 [==============================] - 0s 74ms/step - loss: 0.6892 - accuracy: 0.5357 - val_loss: 0.7338 - val_accuracy: 0.2857\n",
"Epoch 41/100\n",
"2/2 [==============================] - 0s 86ms/step - loss: 0.6965 - accuracy: 0.5357 - val_loss: 0.7362 - val_accuracy: 0.2857\n",
"Epoch 42/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.6853 - accuracy: 0.5000 - val_loss: 0.7365 - val_accuracy: 0.2857\n",
"Epoch 43/100\n",
"2/2 [==============================] - 0s 62ms/step - loss: 0.6880 - accuracy: 0.4821 - val_loss: 0.7363 - val_accuracy: 0.2857\n",
"Epoch 44/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.6863 - accuracy: 0.4821 - val_loss: 0.7375 - val_accuracy: 0.2857\n",
"Epoch 45/100\n",
"2/2 [==============================] - 0s 71ms/step - loss: 0.6753 - accuracy: 0.5357 - val_loss: 0.7416 - val_accuracy: 0.2857\n",
"Epoch 46/100\n",
"2/2 [==============================] - 0s 71ms/step - loss: 0.6800 - accuracy: 0.5357 - val_loss: 0.7458 - val_accuracy: 0.2857\n",
"Epoch 47/100\n",
"2/2 [==============================] - 0s 71ms/step - loss: 0.6760 - accuracy: 0.5179 - val_loss: 0.7474 - val_accuracy: 0.2857\n",
"Epoch 48/100\n",
"2/2 [==============================] - 0s 72ms/step - loss: 0.6697 - accuracy: 0.5536 - val_loss: 0.7502 - val_accuracy: 0.2857\n",
"Epoch 49/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.6770 - accuracy: 0.5179 - val_loss: 0.7536 - val_accuracy: 0.2857\n",
"Epoch 50/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.6689 - accuracy: 0.5357 - val_loss: 0.7583 - val_accuracy: 0.2857\n",
"Epoch 51/100\n",
"2/2 [==============================] - 0s 71ms/step - loss: 0.6671 - accuracy: 0.5179 - val_loss: 0.7591 - val_accuracy: 0.2143\n",
"Epoch 52/100\n",
"2/2 [==============================] - 0s 74ms/step - loss: 0.6594 - accuracy: 0.5179 - val_loss: 0.7657 - val_accuracy: 0.2143\n",
"Epoch 53/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.6524 - accuracy: 0.5893 - val_loss: 0.7752 - val_accuracy: 0.2143\n",
"Epoch 54/100\n",
"2/2 [==============================] - 0s 72ms/step - loss: 0.6351 - accuracy: 0.6250 - val_loss: 0.7850 - val_accuracy: 0.2143\n",
"Epoch 55/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.6548 - accuracy: 0.5536 - val_loss: 0.7916 - val_accuracy: 0.2143\n",
"Epoch 56/100\n",
"2/2 [==============================] - 0s 61ms/step - loss: 0.6671 - accuracy: 0.5000 - val_loss: 0.7788 - val_accuracy: 0.2143\n",
"Epoch 57/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.6802 - accuracy: 0.5179 - val_loss: 0.7725 - val_accuracy: 0.2143\n",
"Epoch 58/100\n",
"2/2 [==============================] - 0s 62ms/step - loss: 0.6764 - accuracy: 0.4821 - val_loss: 0.7642 - val_accuracy: 0.2143\n",
"Epoch 59/100\n",
"2/2 [==============================] - 0s 72ms/step - loss: 0.6504 - accuracy: 0.5357 - val_loss: 0.7587 - val_accuracy: 0.2143\n",
"Epoch 60/100\n",
"2/2 [==============================] - 0s 73ms/step - loss: 0.6592 - accuracy: 0.4821 - val_loss: 0.7500 - val_accuracy: 0.2143\n",
"Epoch 61/100\n",
"2/2 [==============================] - 0s 72ms/step - loss: 0.6456 - accuracy: 0.5536 - val_loss: 0.7437 - val_accuracy: 0.4286\n",
"Epoch 62/100\n",
"2/2 [==============================] - 0s 79ms/step - loss: 0.6391 - accuracy: 0.5536 - val_loss: 0.7456 - val_accuracy: 0.3571\n",
"Epoch 63/100\n",
"2/2 [==============================] - 0s 73ms/step - loss: 0.6565 - accuracy: 0.4464 - val_loss: 0.7487 - val_accuracy: 0.4286\n",
"Epoch 64/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.6454 - accuracy: 0.4464 - val_loss: 0.7489 - val_accuracy: 0.4286\n",
"Epoch 65/100\n",
"2/2 [==============================] - 0s 75ms/step - loss: 0.6523 - accuracy: 0.4643 - val_loss: 0.7398 - val_accuracy: 0.5000\n",
"Epoch 66/100\n",
"2/2 [==============================] - 0s 73ms/step - loss: 0.6493 - accuracy: 0.5714 - val_loss: 0.7307 - val_accuracy: 0.5000\n",
"Epoch 67/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6422 - accuracy: 0.5357 - val_loss: 0.7311 - val_accuracy: 0.5000\n",
"Epoch 68/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.6469 - accuracy: 0.5179 - val_loss: 0.7313 - val_accuracy: 0.5000\n",
"Epoch 69/100\n",
"2/2 [==============================] - 0s 77ms/step - loss: 0.6506 - accuracy: 0.5000 - val_loss: 0.7322 - val_accuracy: 0.5000\n",
"Epoch 70/100\n",
"2/2 [==============================] - 0s 74ms/step - loss: 0.6401 - accuracy: 0.5714 - val_loss: 0.7455 - val_accuracy: 0.5000\n",
"Epoch 71/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.6482 - accuracy: 0.4821 - val_loss: 0.7428 - val_accuracy: 0.5000\n",
"Epoch 72/100\n",
"2/2 [==============================] - 0s 78ms/step - loss: 0.6441 - accuracy: 0.5536 - val_loss: 0.7311 - val_accuracy: 0.5000\n",
"Epoch 73/100\n",
"2/2 [==============================] - 0s 73ms/step - loss: 0.6470 - accuracy: 0.4821 - val_loss: 0.7278 - val_accuracy: 0.5000\n",
"Epoch 74/100\n",
"2/2 [==============================] - 0s 89ms/step - loss: 0.6439 - accuracy: 0.5714 - val_loss: 0.7299 - val_accuracy: 0.5000\n",
"Epoch 75/100\n",
"2/2 [==============================] - 0s 75ms/step - loss: 0.6299 - accuracy: 0.5179 - val_loss: 0.7333 - val_accuracy: 0.4286\n",
"Epoch 76/100\n",
"2/2 [==============================] - 0s 63ms/step - loss: 0.6366 - accuracy: 0.5536 - val_loss: 0.7409 - val_accuracy: 0.4286\n",
"Epoch 77/100\n",
"2/2 [==============================] - 0s 80ms/step - loss: 0.6290 - accuracy: 0.4821 - val_loss: 0.7302 - val_accuracy: 0.4286\n",
"Epoch 78/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6324 - accuracy: 0.5357 - val_loss: 0.7308 - val_accuracy: 0.4286\n",
"Epoch 79/100\n",
"2/2 [==============================] - 0s 65ms/step - loss: 0.6149 - accuracy: 0.5536 - val_loss: 0.7347 - val_accuracy: 0.4286\n",
"Epoch 80/100\n",
"2/2 [==============================] - 0s 61ms/step - loss: 0.5980 - accuracy: 0.6071 - val_loss: 0.7489 - val_accuracy: 0.4286\n",
"Epoch 81/100\n",
"2/2 [==============================] - 0s 75ms/step - loss: 0.6174 - accuracy: 0.5357 - val_loss: 0.7309 - val_accuracy: 0.5000\n",
"Epoch 82/100\n",
"2/2 [==============================] - 0s 78ms/step - loss: 0.6349 - accuracy: 0.5893 - val_loss: 0.7227 - val_accuracy: 0.5000\n",
"Epoch 83/100\n",
"2/2 [==============================] - 0s 76ms/step - loss: 0.6309 - accuracy: 0.6250 - val_loss: 0.7155 - val_accuracy: 0.5000\n",
"Epoch 84/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.6180 - accuracy: 0.5893 - val_loss: 0.7182 - val_accuracy: 0.5000\n",
"Epoch 85/100\n",
"2/2 [==============================] - 0s 79ms/step - loss: 0.6268 - accuracy: 0.5536 - val_loss: 0.7077 - val_accuracy: 0.5000\n",
"Epoch 86/100\n",
"2/2 [==============================] - 0s 79ms/step - loss: 0.6322 - accuracy: 0.5357 - val_loss: 0.7034 - val_accuracy: 0.5000\n",
"Epoch 87/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6167 - accuracy: 0.5179 - val_loss: 0.7067 - val_accuracy: 0.5000\n",
"Epoch 88/100\n",
"2/2 [==============================] - 0s 82ms/step - loss: 0.6372 - accuracy: 0.5357 - val_loss: 0.7356 - val_accuracy: 0.5000\n",
"Epoch 89/100\n",
"2/2 [==============================] - 0s 81ms/step - loss: 0.6414 - accuracy: 0.5179 - val_loss: 0.7446 - val_accuracy: 0.5000\n",
"Epoch 90/100\n",
"2/2 [==============================] - 0s 84ms/step - loss: 0.6128 - accuracy: 0.5536 - val_loss: 0.7052 - val_accuracy: 0.4286\n",
"Epoch 91/100\n",
"2/2 [==============================] - 0s 89ms/step - loss: 0.6344 - accuracy: 0.4821 - val_loss: 0.7060 - val_accuracy: 0.4286\n",
"Epoch 92/100\n",
"2/2 [==============================] - 0s 78ms/step - loss: 0.6295 - accuracy: 0.5357 - val_loss: 0.7116 - val_accuracy: 0.4286\n",
"Epoch 93/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.5902 - accuracy: 0.6429 - val_loss: 0.7213 - val_accuracy: 0.4286\n",
"Epoch 94/100\n",
"2/2 [==============================] - 0s 96ms/step - loss: 0.5952 - accuracy: 0.6786 - val_loss: 0.7539 - val_accuracy: 0.3571\n",
"Epoch 95/100\n",
"2/2 [==============================] - 0s 91ms/step - loss: 0.6261 - accuracy: 0.5000 - val_loss: 0.7552 - val_accuracy: 0.3571\n",
"Epoch 96/100\n",
"2/2 [==============================] - 0s 97ms/step - loss: 0.6045 - accuracy: 0.6250 - val_loss: 0.7540 - val_accuracy: 0.3571\n",
"Epoch 97/100\n",
"2/2 [==============================] - 0s 93ms/step - loss: 0.5727 - accuracy: 0.6607 - val_loss: 0.8384 - val_accuracy: 0.3571\n",
"Epoch 98/100\n",
"2/2 [==============================] - 0s 88ms/step - loss: 0.6127 - accuracy: 0.5536 - val_loss: 0.8238 - val_accuracy: 0.4286\n",
"Epoch 99/100\n",
"2/2 [==============================] - 0s 111ms/step - loss: 0.6077 - accuracy: 0.5893 - val_loss: 0.8228 - val_accuracy: 0.4286\n",
"Epoch 100/100\n",
"2/2 [==============================] - 0s 84ms/step - loss: 0.5940 - accuracy: 0.5714 - val_loss: 0.7601 - val_accuracy: 0.3571\n",
"1/1 [==============================] - 1s 794ms/step\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, roc_curve, auc\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Use the optimal threshold to convert probabilities to binary predictions\n",
"y_pred_class_optimal = (y_pred_probs > optimal_threshold).astype(int)\n",
"\n",
"# Compute the confusion matrix using the optimal threshold\n",
"cm_optimal = confusion_matrix(y_test_class, y_pred_class_optimal)\n",
"\n",
"# Display the confusion matrix\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm_optimal)\n",
"disp.plot(cmap=plt.cm.Blues)\n",
"plt.title('Confusion Matrix with Optimal Threshold')\n",
"plt.show()\n",
"\n",
"# Compute ROC curve and AUC\n",
"fpr, tpr, _ = roc_curve(y_test_class, y_pred_probs)\n",
"roc_auc = auc(fpr, tpr)\n",
"\n",
"# Plot ROC curve\n",
"plt.figure()\n",
"plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
"plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('Receiver Operating Characteristic with Optimal Threshold')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 927
},
"id": "4F0viKhNlZfY",
"outputId": "90422431-c5f0-437d-aa27-7e22fc25db53"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Make sure the 'y_test_class' and 'y_train_class' variables are set correctly before this step.\n",
"# For the sake of example, let's assume 'y_test' is the variable holding the original test labels.\n",
"\n",
"# Verify and correct labels if necessary\n",
"# 'damage' is 1, 'no damage' is 0\n",
"y_test_class = np.where(y_test == 'damage', 1, 0)\n",
"\n",
"# Now, use your model to predict probabilities on the test set\n",
"# Assuming 'X_test_rnn' is already defined and shaped correctly\n",
"y_pred_probs = classification_model.predict(X_test_rnn)\n",
"\n",
"# Choose a decision threshold (if you've found an optimal one, use that, otherwise use 0.5)\n",
"threshold = 0.5 # or optimal_threshold if you have one\n",
"\n",
"# Convert predicted probabilities into binary class predictions\n",
"y_pred_class = (y_pred_probs > threshold).astype(int)\n",
"\n",
"# Compute the confusion matrix\n",
"cm = confusion_matrix(y_test_class, y_pred_class)\n",
"\n",
"# Plot the confusion matrix\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
"disp.plot(cmap=plt.cm.Blues)\n",
"plt.title('Confusion Matrix')\n",
"plt.show()\n",
"\n",
"# Calculate the ROC curve and AUC\n",
"fpr, tpr, _ = roc_curve(y_test_class, y_pred_probs)\n",
"roc_auc = auc(fpr, tpr)\n",
"\n",
"# Plot the ROC curve\n",
"plt.figure()\n",
"plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
"plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('Receiver Operating Characteristic')\n",
"plt.legend(loc='lower right')\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "bkLBLnLvnCE3",
"outputId": "0e22b7a6-32cf-42a3-dcc4-636e00e30b91"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1/1 [==============================] - 0s 27ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_ranking.py:1029: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Reshape data from 2D to 3D (samples, timesteps, features)\n",
"X_train_rnn = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))\n"
],
"metadata": {
"id": "K90jlV5hoR1P"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Define the LSTM model for binary classification\n",
"lstm_model = Sequential([\n",
" LSTM(50, input_shape=(X_train_rnn.shape[1], X_train_rnn.shape[2])), # Correct input_shape\n",
" Dense(1, activation='sigmoid')\n",
"])\n",
"\n",
"# Compile the LSTM model\n",
"lstm_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
"\n",
"# Train the LSTM model\n",
"history = lstm_model.fit(X_train_rnn, y_train_class, epochs=100, validation_split=0.2)\n",
"\n",
"# ... (Continue with the rest of the code as before)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NJ0GiuH4qhPN",
"outputId": "aeda4d42-5bf3-4d54-bad1-f9c6d3aafe0c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/100\n",
"2/2 [==============================] - 4s 957ms/step - loss: 0.7059 - accuracy: 0.3750 - val_loss: 0.6947 - val_accuracy: 0.5000\n",
"Epoch 2/100\n",
"2/2 [==============================] - 0s 79ms/step - loss: 0.7015 - accuracy: 0.3750 - val_loss: 0.6940 - val_accuracy: 0.5000\n",
"Epoch 3/100\n",
"2/2 [==============================] - 0s 72ms/step - loss: 0.6978 - accuracy: 0.4286 - val_loss: 0.6937 - val_accuracy: 0.5000\n",
"Epoch 4/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6941 - accuracy: 0.4286 - val_loss: 0.6929 - val_accuracy: 0.5000\n",
"Epoch 5/100\n",
"2/2 [==============================] - 0s 76ms/step - loss: 0.6907 - accuracy: 0.4821 - val_loss: 0.6925 - val_accuracy: 0.5000\n",
"Epoch 6/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.6874 - accuracy: 0.5179 - val_loss: 0.6920 - val_accuracy: 0.5000\n",
"Epoch 7/100\n",
"2/2 [==============================] - 0s 76ms/step - loss: 0.6836 - accuracy: 0.5179 - val_loss: 0.6917 - val_accuracy: 0.5000\n",
"Epoch 8/100\n",
"2/2 [==============================] - 0s 90ms/step - loss: 0.6804 - accuracy: 0.5536 - val_loss: 0.6914 - val_accuracy: 0.5000\n",
"Epoch 9/100\n",
"2/2 [==============================] - 0s 71ms/step - loss: 0.6770 - accuracy: 0.5536 - val_loss: 0.6910 - val_accuracy: 0.5714\n",
"Epoch 10/100\n",
"2/2 [==============================] - 0s 81ms/step - loss: 0.6736 - accuracy: 0.5536 - val_loss: 0.6907 - val_accuracy: 0.5714\n",
"Epoch 11/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.6707 - accuracy: 0.5714 - val_loss: 0.6905 - val_accuracy: 0.5714\n",
"Epoch 12/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.6675 - accuracy: 0.6071 - val_loss: 0.6903 - val_accuracy: 0.6429\n",
"Epoch 13/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.6643 - accuracy: 0.6071 - val_loss: 0.6902 - val_accuracy: 0.7143\n",
"Epoch 14/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.6613 - accuracy: 0.5893 - val_loss: 0.6899 - val_accuracy: 0.7143\n",
"Epoch 15/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6582 - accuracy: 0.6250 - val_loss: 0.6897 - val_accuracy: 0.7143\n",
"Epoch 16/100\n",
"2/2 [==============================] - 0s 63ms/step - loss: 0.6551 - accuracy: 0.6250 - val_loss: 0.6896 - val_accuracy: 0.6429\n",
"Epoch 17/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.6522 - accuracy: 0.6429 - val_loss: 0.6896 - val_accuracy: 0.6429\n",
"Epoch 18/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.6490 - accuracy: 0.6607 - val_loss: 0.6895 - val_accuracy: 0.6429\n",
"Epoch 19/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.6460 - accuracy: 0.6964 - val_loss: 0.6896 - val_accuracy: 0.5714\n",
"Epoch 20/100\n",
"2/2 [==============================] - 0s 44ms/step - loss: 0.6432 - accuracy: 0.6964 - val_loss: 0.6898 - val_accuracy: 0.5714\n",
"Epoch 21/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6401 - accuracy: 0.7143 - val_loss: 0.6898 - val_accuracy: 0.5714\n",
"Epoch 22/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.6372 - accuracy: 0.6964 - val_loss: 0.6898 - val_accuracy: 0.5714\n",
"Epoch 23/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6343 - accuracy: 0.6964 - val_loss: 0.6898 - val_accuracy: 0.5714\n",
"Epoch 24/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6312 - accuracy: 0.7143 - val_loss: 0.6898 - val_accuracy: 0.5000\n",
"Epoch 25/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6283 - accuracy: 0.7321 - val_loss: 0.6900 - val_accuracy: 0.5000\n",
"Epoch 26/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.6254 - accuracy: 0.7321 - val_loss: 0.6901 - val_accuracy: 0.5000\n",
"Epoch 27/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6223 - accuracy: 0.7500 - val_loss: 0.6903 - val_accuracy: 0.5000\n",
"Epoch 28/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6194 - accuracy: 0.7321 - val_loss: 0.6904 - val_accuracy: 0.5000\n",
"Epoch 29/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6164 - accuracy: 0.7500 - val_loss: 0.6906 - val_accuracy: 0.5000\n",
"Epoch 30/100\n",
"2/2 [==============================] - 0s 46ms/step - loss: 0.6135 - accuracy: 0.7500 - val_loss: 0.6909 - val_accuracy: 0.5000\n",
"Epoch 31/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.6105 - accuracy: 0.7321 - val_loss: 0.6912 - val_accuracy: 0.5000\n",
"Epoch 32/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6075 - accuracy: 0.7321 - val_loss: 0.6912 - val_accuracy: 0.5000\n",
"Epoch 33/100\n",
"2/2 [==============================] - 0s 62ms/step - loss: 0.6045 - accuracy: 0.7321 - val_loss: 0.6912 - val_accuracy: 0.5000\n",
"Epoch 34/100\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.6013 - accuracy: 0.7321 - val_loss: 0.6913 - val_accuracy: 0.5000\n",
"Epoch 35/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.5983 - accuracy: 0.7321 - val_loss: 0.6916 - val_accuracy: 0.5000\n",
"Epoch 36/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.5955 - accuracy: 0.7321 - val_loss: 0.6920 - val_accuracy: 0.5000\n",
"Epoch 37/100\n",
"2/2 [==============================] - 0s 45ms/step - loss: 0.5924 - accuracy: 0.7321 - val_loss: 0.6923 - val_accuracy: 0.5000\n",
"Epoch 38/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.5893 - accuracy: 0.7321 - val_loss: 0.6926 - val_accuracy: 0.5000\n",
"Epoch 39/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.5860 - accuracy: 0.7500 - val_loss: 0.6930 - val_accuracy: 0.5000\n",
"Epoch 40/100\n",
"2/2 [==============================] - 0s 46ms/step - loss: 0.5830 - accuracy: 0.7500 - val_loss: 0.6935 - val_accuracy: 0.5000\n",
"Epoch 41/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.5800 - accuracy: 0.7500 - val_loss: 0.6941 - val_accuracy: 0.5000\n",
"Epoch 42/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.5768 - accuracy: 0.7500 - val_loss: 0.6947 - val_accuracy: 0.5000\n",
"Epoch 43/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.5739 - accuracy: 0.7500 - val_loss: 0.6952 - val_accuracy: 0.5000\n",
"Epoch 44/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.5706 - accuracy: 0.7500 - val_loss: 0.6959 - val_accuracy: 0.5000\n",
"Epoch 45/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.5677 - accuracy: 0.7679 - val_loss: 0.6966 - val_accuracy: 0.5000\n",
"Epoch 46/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.5646 - accuracy: 0.7679 - val_loss: 0.6977 - val_accuracy: 0.5000\n",
"Epoch 47/100\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.5614 - accuracy: 0.7679 - val_loss: 0.6986 - val_accuracy: 0.5000\n",
"Epoch 48/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.5582 - accuracy: 0.7679 - val_loss: 0.6995 - val_accuracy: 0.5000\n",
"Epoch 49/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.5553 - accuracy: 0.7857 - val_loss: 0.7006 - val_accuracy: 0.5000\n",
"Epoch 50/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.5522 - accuracy: 0.7857 - val_loss: 0.7015 - val_accuracy: 0.5000\n",
"Epoch 51/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.5490 - accuracy: 0.7857 - val_loss: 0.7026 - val_accuracy: 0.5000\n",
"Epoch 52/100\n",
"2/2 [==============================] - 0s 44ms/step - loss: 0.5464 - accuracy: 0.7857 - val_loss: 0.7036 - val_accuracy: 0.5714\n",
"Epoch 53/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.5435 - accuracy: 0.8036 - val_loss: 0.7049 - val_accuracy: 0.5714\n",
"Epoch 54/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.5400 - accuracy: 0.8036 - val_loss: 0.7063 - val_accuracy: 0.5714\n",
"Epoch 55/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.5369 - accuracy: 0.8036 - val_loss: 0.7076 - val_accuracy: 0.5714\n",
"Epoch 56/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.5341 - accuracy: 0.8036 - val_loss: 0.7087 - val_accuracy: 0.5714\n",
"Epoch 57/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.5312 - accuracy: 0.8036 - val_loss: 0.7103 - val_accuracy: 0.5714\n",
"Epoch 58/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.5284 - accuracy: 0.8036 - val_loss: 0.7117 - val_accuracy: 0.5714\n",
"Epoch 59/100\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.5251 - accuracy: 0.8036 - val_loss: 0.7132 - val_accuracy: 0.5714\n",
"Epoch 60/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.5226 - accuracy: 0.8036 - val_loss: 0.7147 - val_accuracy: 0.5714\n",
"Epoch 61/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.5193 - accuracy: 0.8036 - val_loss: 0.7164 - val_accuracy: 0.5714\n",
"Epoch 62/100\n",
"2/2 [==============================] - 0s 46ms/step - loss: 0.5167 - accuracy: 0.8036 - val_loss: 0.7182 - val_accuracy: 0.5714\n",
"Epoch 63/100\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.5138 - accuracy: 0.8036 - val_loss: 0.7198 - val_accuracy: 0.5714\n",
"Epoch 64/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.5111 - accuracy: 0.8036 - val_loss: 0.7213 - val_accuracy: 0.5714\n",
"Epoch 65/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.5080 - accuracy: 0.8214 - val_loss: 0.7232 - val_accuracy: 0.5714\n",
"Epoch 66/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.5052 - accuracy: 0.8214 - val_loss: 0.7249 - val_accuracy: 0.5714\n",
"Epoch 67/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.5026 - accuracy: 0.8214 - val_loss: 0.7269 - val_accuracy: 0.5714\n",
"Epoch 68/100\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.4997 - accuracy: 0.8214 - val_loss: 0.7286 - val_accuracy: 0.5714\n",
"Epoch 69/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.4971 - accuracy: 0.8214 - val_loss: 0.7306 - val_accuracy: 0.5714\n",
"Epoch 70/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.4942 - accuracy: 0.8214 - val_loss: 0.7322 - val_accuracy: 0.5714\n",
"Epoch 71/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.4914 - accuracy: 0.8214 - val_loss: 0.7340 - val_accuracy: 0.5714\n",
"Epoch 72/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.4888 - accuracy: 0.8214 - val_loss: 0.7358 - val_accuracy: 0.5714\n",
"Epoch 73/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.4861 - accuracy: 0.8214 - val_loss: 0.7379 - val_accuracy: 0.5714\n",
"Epoch 74/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.4833 - accuracy: 0.8214 - val_loss: 0.7396 - val_accuracy: 0.5714\n",
"Epoch 75/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.4806 - accuracy: 0.8214 - val_loss: 0.7416 - val_accuracy: 0.5714\n",
"Epoch 76/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.4782 - accuracy: 0.8214 - val_loss: 0.7439 - val_accuracy: 0.5714\n",
"Epoch 77/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.4753 - accuracy: 0.8214 - val_loss: 0.7460 - val_accuracy: 0.5714\n",
"Epoch 78/100\n",
"2/2 [==============================] - 0s 45ms/step - loss: 0.4727 - accuracy: 0.8214 - val_loss: 0.7478 - val_accuracy: 0.5714\n",
"Epoch 79/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.4703 - accuracy: 0.8214 - val_loss: 0.7500 - val_accuracy: 0.5714\n",
"Epoch 80/100\n",
"2/2 [==============================] - 0s 63ms/step - loss: 0.4675 - accuracy: 0.8214 - val_loss: 0.7521 - val_accuracy: 0.5714\n",
"Epoch 81/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.4651 - accuracy: 0.8214 - val_loss: 0.7545 - val_accuracy: 0.5714\n",
"Epoch 82/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.4624 - accuracy: 0.8393 - val_loss: 0.7570 - val_accuracy: 0.5714\n",
"Epoch 83/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.4600 - accuracy: 0.8393 - val_loss: 0.7592 - val_accuracy: 0.5714\n",
"Epoch 84/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.4575 - accuracy: 0.8393 - val_loss: 0.7612 - val_accuracy: 0.5714\n",
"Epoch 85/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.4548 - accuracy: 0.8393 - val_loss: 0.7634 - val_accuracy: 0.5714\n",
"Epoch 86/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.4525 - accuracy: 0.8393 - val_loss: 0.7654 - val_accuracy: 0.5714\n",
"Epoch 87/100\n",
"2/2 [==============================] - 0s 69ms/step - loss: 0.4498 - accuracy: 0.8393 - val_loss: 0.7676 - val_accuracy: 0.5714\n",
"Epoch 88/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.4474 - accuracy: 0.8393 - val_loss: 0.7697 - val_accuracy: 0.5714\n",
"Epoch 89/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.4449 - accuracy: 0.8393 - val_loss: 0.7720 - val_accuracy: 0.5714\n",
"Epoch 90/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.4425 - accuracy: 0.8393 - val_loss: 0.7746 - val_accuracy: 0.5714\n",
"Epoch 91/100\n",
"2/2 [==============================] - 0s 46ms/step - loss: 0.4404 - accuracy: 0.8393 - val_loss: 0.7767 - val_accuracy: 0.5714\n",
"Epoch 92/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.4377 - accuracy: 0.8393 - val_loss: 0.7793 - val_accuracy: 0.5714\n",
"Epoch 93/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.4355 - accuracy: 0.8393 - val_loss: 0.7818 - val_accuracy: 0.5714\n",
"Epoch 94/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.4330 - accuracy: 0.8393 - val_loss: 0.7846 - val_accuracy: 0.5714\n",
"Epoch 95/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.4309 - accuracy: 0.8393 - val_loss: 0.7874 - val_accuracy: 0.5714\n",
"Epoch 96/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.4284 - accuracy: 0.8393 - val_loss: 0.7905 - val_accuracy: 0.5714\n",
"Epoch 97/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.4261 - accuracy: 0.8214 - val_loss: 0.7932 - val_accuracy: 0.5714\n",
"Epoch 98/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.4238 - accuracy: 0.8214 - val_loss: 0.7963 - val_accuracy: 0.5714\n",
"Epoch 99/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.4215 - accuracy: 0.8214 - val_loss: 0.7990 - val_accuracy: 0.5714\n",
"Epoch 100/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.4191 - accuracy: 0.8393 - val_loss: 0.8020 - val_accuracy: 0.5000\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Assuming your data is correctly reshaped to 3D for LSTM and 'y_train_class' holds the binary labels\n",
"\n",
"# Continue training the LSTM model\n",
"history = lstm_model.fit(\n",
" X_train_rnn,\n",
" y_train_class,\n",
" epochs=100,\n",
" validation_split=0.2\n",
")\n",
"\n",
"# Assuming 'X_test' and 'y_test' are your test data and labels, respectively\n",
"# Reshape the test data to match the input shape of the model\n",
"X_test_rnn = X_test.reshape((X_test.shape[0], 1, X_test.shape[1]))\n",
"\n",
"# Predict class probabilities on the test set\n",
"y_pred_probs = lstm_model.predict(X_test_rnn)\n",
"\n",
"# Choose a decision threshold\n",
"threshold = 0.5 # Adjust based on your optimal threshold\n",
"y_pred_class = (y_pred_probs > threshold).astype(int)\n",
"\n",
"# Compute the confusion matrix\n",
"cm = confusion_matrix(y_test_class, y_pred_class)\n",
"\n",
"# Display the confusion matrix\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
"disp.plot(cmap=plt.cm.Blues)\n",
"plt.title('Confusion Matrix - LSTM Model')\n",
"plt.show()\n",
"\n",
"# Compute ROC curve and AUC\n",
"fpr, tpr, _ = roc_curve(y_test_class, y_pred_probs)\n",
"roc_auc = auc(fpr, tpr)\n",
"\n",
"# Plot ROC curve\n",
"plt.figure()\n",
"plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
"plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('ROC Curve - LSTM Model')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "stbZFnSFqhHV",
"outputId": "a372add2-e022-4a24-8bb7-d11470c3aa02"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/100\n",
"2/2 [==============================] - 0s 282ms/step - loss: 0.4169 - accuracy: 0.8393 - val_loss: 0.8048 - val_accuracy: 0.5000\n",
"Epoch 2/100\n",
"2/2 [==============================] - 0s 105ms/step - loss: 0.4147 - accuracy: 0.8393 - val_loss: 0.8078 - val_accuracy: 0.5000\n",
"Epoch 3/100\n",
"2/2 [==============================] - 0s 90ms/step - loss: 0.4124 - accuracy: 0.8393 - val_loss: 0.8109 - val_accuracy: 0.5000\n",
"Epoch 4/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.4102 - accuracy: 0.8393 - val_loss: 0.8140 - val_accuracy: 0.5000\n",
"Epoch 5/100\n",
"2/2 [==============================] - 0s 73ms/step - loss: 0.4081 - accuracy: 0.8393 - val_loss: 0.8173 - val_accuracy: 0.5000\n",
"Epoch 6/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.4060 - accuracy: 0.8571 - val_loss: 0.8207 - val_accuracy: 0.5000\n",
"Epoch 7/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.4037 - accuracy: 0.8571 - val_loss: 0.8236 - val_accuracy: 0.5000\n",
"Epoch 8/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.4015 - accuracy: 0.8571 - val_loss: 0.8265 - val_accuracy: 0.5000\n",
"Epoch 9/100\n",
"2/2 [==============================] - 0s 73ms/step - loss: 0.3992 - accuracy: 0.8571 - val_loss: 0.8298 - val_accuracy: 0.5000\n",
"Epoch 10/100\n",
"2/2 [==============================] - 0s 75ms/step - loss: 0.3972 - accuracy: 0.8393 - val_loss: 0.8328 - val_accuracy: 0.5000\n",
"Epoch 11/100\n",
"2/2 [==============================] - 0s 73ms/step - loss: 0.3951 - accuracy: 0.8393 - val_loss: 0.8358 - val_accuracy: 0.5000\n",
"Epoch 12/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.3929 - accuracy: 0.8571 - val_loss: 0.8392 - val_accuracy: 0.5000\n",
"Epoch 13/100\n",
"2/2 [==============================] - 0s 76ms/step - loss: 0.3909 - accuracy: 0.8571 - val_loss: 0.8424 - val_accuracy: 0.5000\n",
"Epoch 14/100\n",
"2/2 [==============================] - 0s 72ms/step - loss: 0.3887 - accuracy: 0.8571 - val_loss: 0.8456 - val_accuracy: 0.5000\n",
"Epoch 15/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.3866 - accuracy: 0.8571 - val_loss: 0.8489 - val_accuracy: 0.5000\n",
"Epoch 16/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.3847 - accuracy: 0.8571 - val_loss: 0.8519 - val_accuracy: 0.5000\n",
"Epoch 17/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.3827 - accuracy: 0.8571 - val_loss: 0.8552 - val_accuracy: 0.5000\n",
"Epoch 18/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.3805 - accuracy: 0.8571 - val_loss: 0.8585 - val_accuracy: 0.5000\n",
"Epoch 19/100\n",
"2/2 [==============================] - 0s 47ms/step - loss: 0.3785 - accuracy: 0.8571 - val_loss: 0.8618 - val_accuracy: 0.5000\n",
"Epoch 20/100\n",
"2/2 [==============================] - 0s 47ms/step - loss: 0.3765 - accuracy: 0.8571 - val_loss: 0.8654 - val_accuracy: 0.5000\n",
"Epoch 21/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.3746 - accuracy: 0.8571 - val_loss: 0.8690 - val_accuracy: 0.5000\n",
"Epoch 22/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.3725 - accuracy: 0.8750 - val_loss: 0.8722 - val_accuracy: 0.5000\n",
"Epoch 23/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.3705 - accuracy: 0.8750 - val_loss: 0.8756 - val_accuracy: 0.5000\n",
"Epoch 24/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.3685 - accuracy: 0.8750 - val_loss: 0.8790 - val_accuracy: 0.5000\n",
"Epoch 25/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.3667 - accuracy: 0.8750 - val_loss: 0.8827 - val_accuracy: 0.5000\n",
"Epoch 26/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.3647 - accuracy: 0.8750 - val_loss: 0.8862 - val_accuracy: 0.5000\n",
"Epoch 27/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.3627 - accuracy: 0.8750 - val_loss: 0.8897 - val_accuracy: 0.5000\n",
"Epoch 28/100\n",
"2/2 [==============================] - 0s 43ms/step - loss: 0.3607 - accuracy: 0.8750 - val_loss: 0.8935 - val_accuracy: 0.5000\n",
"Epoch 29/100\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.3590 - accuracy: 0.8750 - val_loss: 0.8973 - val_accuracy: 0.5000\n",
"Epoch 30/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.3570 - accuracy: 0.8750 - val_loss: 0.9008 - val_accuracy: 0.5000\n",
"Epoch 31/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.3550 - accuracy: 0.8750 - val_loss: 0.9041 - val_accuracy: 0.5000\n",
"Epoch 32/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.3532 - accuracy: 0.8750 - val_loss: 0.9075 - val_accuracy: 0.5000\n",
"Epoch 33/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.3512 - accuracy: 0.8750 - val_loss: 0.9108 - val_accuracy: 0.5000\n",
"Epoch 34/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.3493 - accuracy: 0.9107 - val_loss: 0.9144 - val_accuracy: 0.5000\n",
"Epoch 35/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.3475 - accuracy: 0.9107 - val_loss: 0.9175 - val_accuracy: 0.5000\n",
"Epoch 36/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.3457 - accuracy: 0.9107 - val_loss: 0.9213 - val_accuracy: 0.5000\n",
"Epoch 37/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.3439 - accuracy: 0.9107 - val_loss: 0.9243 - val_accuracy: 0.5000\n",
"Epoch 38/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.3419 - accuracy: 0.9107 - val_loss: 0.9278 - val_accuracy: 0.5000\n",
"Epoch 39/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.3402 - accuracy: 0.9107 - val_loss: 0.9315 - val_accuracy: 0.5000\n",
"Epoch 40/100\n",
"2/2 [==============================] - 0s 95ms/step - loss: 0.3382 - accuracy: 0.9107 - val_loss: 0.9347 - val_accuracy: 0.5000\n",
"Epoch 41/100\n",
"2/2 [==============================] - 0s 132ms/step - loss: 0.3364 - accuracy: 0.9107 - val_loss: 0.9385 - val_accuracy: 0.5000\n",
"Epoch 42/100\n",
"2/2 [==============================] - 0s 78ms/step - loss: 0.3347 - accuracy: 0.9107 - val_loss: 0.9421 - val_accuracy: 0.5000\n",
"Epoch 43/100\n",
"2/2 [==============================] - 0s 77ms/step - loss: 0.3328 - accuracy: 0.9107 - val_loss: 0.9458 - val_accuracy: 0.5000\n",
"Epoch 44/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.3310 - accuracy: 0.9107 - val_loss: 0.9495 - val_accuracy: 0.5000\n",
"Epoch 45/100\n",
"2/2 [==============================] - 0s 77ms/step - loss: 0.3292 - accuracy: 0.9107 - val_loss: 0.9531 - val_accuracy: 0.5000\n",
"Epoch 46/100\n",
"2/2 [==============================] - 0s 123ms/step - loss: 0.3275 - accuracy: 0.9107 - val_loss: 0.9572 - val_accuracy: 0.5000\n",
"Epoch 47/100\n",
"2/2 [==============================] - 0s 101ms/step - loss: 0.3257 - accuracy: 0.9107 - val_loss: 0.9609 - val_accuracy: 0.5000\n",
"Epoch 48/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.3240 - accuracy: 0.9107 - val_loss: 0.9651 - val_accuracy: 0.5000\n",
"Epoch 49/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.3221 - accuracy: 0.9107 - val_loss: 0.9687 - val_accuracy: 0.5000\n",
"Epoch 50/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.3204 - accuracy: 0.9107 - val_loss: 0.9718 - val_accuracy: 0.5000\n",
"Epoch 51/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.3186 - accuracy: 0.9107 - val_loss: 0.9757 - val_accuracy: 0.5000\n",
"Epoch 52/100\n",
"2/2 [==============================] - 0s 82ms/step - loss: 0.3168 - accuracy: 0.9107 - val_loss: 0.9795 - val_accuracy: 0.5000\n",
"Epoch 53/100\n",
"2/2 [==============================] - 0s 71ms/step - loss: 0.3151 - accuracy: 0.9107 - val_loss: 0.9833 - val_accuracy: 0.5000\n",
"Epoch 54/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.3133 - accuracy: 0.9107 - val_loss: 0.9871 - val_accuracy: 0.5000\n",
"Epoch 55/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.3115 - accuracy: 0.9107 - val_loss: 0.9908 - val_accuracy: 0.5000\n",
"Epoch 56/100\n",
"2/2 [==============================] - 0s 137ms/step - loss: 0.3098 - accuracy: 0.9107 - val_loss: 0.9946 - val_accuracy: 0.5000\n",
"Epoch 57/100\n",
"2/2 [==============================] - 0s 81ms/step - loss: 0.3080 - accuracy: 0.9107 - val_loss: 0.9987 - val_accuracy: 0.5000\n",
"Epoch 58/100\n",
"2/2 [==============================] - 0s 98ms/step - loss: 0.3064 - accuracy: 0.9107 - val_loss: 1.0027 - val_accuracy: 0.5000\n",
"Epoch 59/100\n",
"2/2 [==============================] - 0s 82ms/step - loss: 0.3048 - accuracy: 0.9107 - val_loss: 1.0064 - val_accuracy: 0.5000\n",
"Epoch 60/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.3030 - accuracy: 0.9107 - val_loss: 1.0104 - val_accuracy: 0.5000\n",
"Epoch 61/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.3013 - accuracy: 0.9107 - val_loss: 1.0140 - val_accuracy: 0.5000\n",
"Epoch 62/100\n",
"2/2 [==============================] - 0s 89ms/step - loss: 0.2996 - accuracy: 0.9107 - val_loss: 1.0175 - val_accuracy: 0.5000\n",
"Epoch 63/100\n",
"2/2 [==============================] - 0s 83ms/step - loss: 0.2978 - accuracy: 0.9107 - val_loss: 1.0209 - val_accuracy: 0.5000\n",
"Epoch 64/100\n",
"2/2 [==============================] - 0s 79ms/step - loss: 0.2962 - accuracy: 0.9107 - val_loss: 1.0243 - val_accuracy: 0.5000\n",
"Epoch 65/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.2946 - accuracy: 0.9107 - val_loss: 1.0275 - val_accuracy: 0.5000\n",
"Epoch 66/100\n",
"2/2 [==============================] - 0s 121ms/step - loss: 0.2928 - accuracy: 0.9107 - val_loss: 1.0314 - val_accuracy: 0.5000\n",
"Epoch 67/100\n",
"2/2 [==============================] - 0s 74ms/step - loss: 0.2912 - accuracy: 0.9107 - val_loss: 1.0357 - val_accuracy: 0.5000\n",
"Epoch 68/100\n",
"2/2 [==============================] - 0s 89ms/step - loss: 0.2895 - accuracy: 0.9107 - val_loss: 1.0398 - val_accuracy: 0.5000\n",
"Epoch 69/100\n",
"2/2 [==============================] - 0s 89ms/step - loss: 0.2879 - accuracy: 0.9107 - val_loss: 1.0433 - val_accuracy: 0.5000\n",
"Epoch 70/100\n",
"2/2 [==============================] - 0s 93ms/step - loss: 0.2863 - accuracy: 0.9107 - val_loss: 1.0472 - val_accuracy: 0.5000\n",
"Epoch 71/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.2847 - accuracy: 0.9107 - val_loss: 1.0512 - val_accuracy: 0.5000\n",
"Epoch 72/100\n",
"2/2 [==============================] - 0s 107ms/step - loss: 0.2829 - accuracy: 0.9107 - val_loss: 1.0547 - val_accuracy: 0.5000\n",
"Epoch 73/100\n",
"2/2 [==============================] - 0s 81ms/step - loss: 0.2814 - accuracy: 0.9107 - val_loss: 1.0586 - val_accuracy: 0.5000\n",
"Epoch 74/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.2797 - accuracy: 0.9107 - val_loss: 1.0626 - val_accuracy: 0.5000\n",
"Epoch 75/100\n",
"2/2 [==============================] - 0s 189ms/step - loss: 0.2780 - accuracy: 0.9107 - val_loss: 1.0668 - val_accuracy: 0.5000\n",
"Epoch 76/100\n",
"2/2 [==============================] - 0s 132ms/step - loss: 0.2765 - accuracy: 0.9107 - val_loss: 1.0711 - val_accuracy: 0.5000\n",
"Epoch 77/100\n",
"2/2 [==============================] - 0s 89ms/step - loss: 0.2749 - accuracy: 0.9286 - val_loss: 1.0748 - val_accuracy: 0.5000\n",
"Epoch 78/100\n",
"2/2 [==============================] - 0s 128ms/step - loss: 0.2733 - accuracy: 0.9286 - val_loss: 1.0783 - val_accuracy: 0.5000\n",
"Epoch 79/100\n",
"2/2 [==============================] - 0s 78ms/step - loss: 0.2717 - accuracy: 0.9286 - val_loss: 1.0821 - val_accuracy: 0.5000\n",
"Epoch 80/100\n",
"2/2 [==============================] - 0s 64ms/step - loss: 0.2701 - accuracy: 0.9286 - val_loss: 1.0864 - val_accuracy: 0.5000\n",
"Epoch 81/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.2684 - accuracy: 0.9286 - val_loss: 1.0901 - val_accuracy: 0.5000\n",
"Epoch 82/100\n",
"2/2 [==============================] - 0s 78ms/step - loss: 0.2668 - accuracy: 0.9286 - val_loss: 1.0939 - val_accuracy: 0.5000\n",
"Epoch 83/100\n",
"2/2 [==============================] - 0s 76ms/step - loss: 0.2653 - accuracy: 0.9286 - val_loss: 1.0973 - val_accuracy: 0.5000\n",
"Epoch 84/100\n",
"2/2 [==============================] - 0s 63ms/step - loss: 0.2637 - accuracy: 0.9286 - val_loss: 1.1012 - val_accuracy: 0.5000\n",
"Epoch 85/100\n",
"2/2 [==============================] - 0s 81ms/step - loss: 0.2621 - accuracy: 0.9286 - val_loss: 1.1056 - val_accuracy: 0.5000\n",
"Epoch 86/100\n",
"2/2 [==============================] - 0s 76ms/step - loss: 0.2606 - accuracy: 0.9286 - val_loss: 1.1094 - val_accuracy: 0.5000\n",
"Epoch 87/100\n",
"2/2 [==============================] - 0s 100ms/step - loss: 0.2590 - accuracy: 0.9286 - val_loss: 1.1129 - val_accuracy: 0.5000\n",
"Epoch 88/100\n",
"2/2 [==============================] - 0s 76ms/step - loss: 0.2575 - accuracy: 0.9286 - val_loss: 1.1167 - val_accuracy: 0.5000\n",
"Epoch 89/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.2559 - accuracy: 0.9286 - val_loss: 1.1205 - val_accuracy: 0.5000\n",
"Epoch 90/100\n",
"2/2 [==============================] - 0s 82ms/step - loss: 0.2543 - accuracy: 0.9286 - val_loss: 1.1244 - val_accuracy: 0.5000\n",
"Epoch 91/100\n",
"2/2 [==============================] - 0s 80ms/step - loss: 0.2528 - accuracy: 0.9286 - val_loss: 1.1282 - val_accuracy: 0.5000\n",
"Epoch 92/100\n",
"2/2 [==============================] - 0s 82ms/step - loss: 0.2514 - accuracy: 0.9286 - val_loss: 1.1315 - val_accuracy: 0.5000\n",
"Epoch 93/100\n",
"2/2 [==============================] - 0s 119ms/step - loss: 0.2497 - accuracy: 0.9286 - val_loss: 1.1358 - val_accuracy: 0.5000\n",
"Epoch 94/100\n",
"2/2 [==============================] - 0s 92ms/step - loss: 0.2482 - accuracy: 0.9286 - val_loss: 1.1399 - val_accuracy: 0.5000\n",
"Epoch 95/100\n",
"2/2 [==============================] - 0s 94ms/step - loss: 0.2466 - accuracy: 0.9286 - val_loss: 1.1436 - val_accuracy: 0.5000\n",
"Epoch 96/100\n",
"2/2 [==============================] - 0s 74ms/step - loss: 0.2451 - accuracy: 0.9286 - val_loss: 1.1475 - val_accuracy: 0.5000\n",
"Epoch 97/100\n",
"2/2 [==============================] - 0s 75ms/step - loss: 0.2436 - accuracy: 0.9286 - val_loss: 1.1516 - val_accuracy: 0.5000\n",
"Epoch 98/100\n",
"2/2 [==============================] - 0s 69ms/step - loss: 0.2420 - accuracy: 0.9286 - val_loss: 1.1556 - val_accuracy: 0.5000\n",
"Epoch 99/100\n",
"2/2 [==============================] - 0s 92ms/step - loss: 0.2406 - accuracy: 0.9286 - val_loss: 1.1597 - val_accuracy: 0.5000\n",
"Epoch 100/100\n",
"2/2 [==============================] - 0s 191ms/step - loss: 0.2390 - accuracy: 0.9286 - val_loss: 1.1637 - val_accuracy: 0.5000\n",
"1/1 [==============================] - 1s 1s/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_ranking.py:1029: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Check the range of predicted probabilities\n",
"print(\"Predicted probabilities:\", y_pred_probs)\n",
"\n",
"# Check if there are both classes present in the test labels\n",
"print(\"Unique labels in y_test_class:\", np.unique(y_test_class))\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "txpen8Kbqg_g",
"outputId": "91ec026b-c040-40af-b162-c4bc0133d03f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Predicted probabilities: [[0.7454162 ]\n",
" [0.7437712 ]\n",
" [0.6330803 ]\n",
" [0.89748466]\n",
" [0.20552409]\n",
" [0.9472765 ]\n",
" [0.85178375]\n",
" [0.97781944]\n",
" [0.13122998]\n",
" [0.9917822 ]\n",
" [0.8533768 ]\n",
" [0.06356653]\n",
" [0.43143782]\n",
" [0.50791705]\n",
" [0.17546357]\n",
" [0.01664866]\n",
" [0.96507597]\n",
" [0.90373135]\n",
" [0.52591985]\n",
" [0.22702323]\n",
" [0.81641984]\n",
" [0.67864764]\n",
" [0.661725 ]\n",
" [0.93879306]\n",
" [0.94223934]\n",
" [0.68000364]\n",
" [0.99042076]\n",
" [0.00691278]\n",
" [0.18692319]\n",
" [0.2215116 ]]\n",
"Unique labels in y_test_class: [0]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Check unique values in the labels array\n",
"unique_classes = np.unique(y)\n",
"print(\"Unique classes in y:\", unique_classes)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tLKNT_1BtNI-",
"outputId": "17db3a5b-8cc8-46aa-c8f4-ba2de4c5f058"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Unique classes in y: [0.01719974 0.02000646 0.0230836 0.03231997 0.04509573 0.04551169\n",
" 0.05540832 0.06787676 0.07811607 0.08566781 0.08859851 0.08866176\n",
" 0.09501671 0.09979471 0.11560955 0.1299406 0.1375302 0.14640541\n",
" 0.14857923 0.16046116 0.18426143 0.18945017 0.19668808 0.20336498\n",
" 0.22591194 0.23655779 0.24578735 0.25692265 0.27537928 0.30322744\n",
" 0.30386795 0.30888874 0.31492058 0.31576589 0.3461643 0.34623043\n",
" 0.34694271 0.36754485 0.37382615 0.3877619 0.3951531 0.41476349\n",
" 0.42254058 0.42576649 0.43679891 0.44330438 0.45592128 0.45677628\n",
" 0.47927551 0.49703069 0.50310825 0.52038588 0.52749253 0.53250067\n",
" 0.53579478 0.55829683 0.56959791 0.57911016 0.5996906 0.60926981\n",
" 0.64081111 0.64248888 0.64379359 0.64572414 0.65031069 0.65108996\n",
" 0.66769136 0.67666698 0.68239306 0.69572005 0.70408227 0.70574692\n",
" 0.71079391 0.71253874 0.75196858 0.75790615 0.7613012 0.76164767\n",
" 0.76268006 0.76536053 0.79546299 0.79993517 0.79996053 0.81339981\n",
" 0.81410338 0.83534154 0.86636636 0.87044111 0.87799956 0.88632075\n",
" 0.89962299 0.92877176 0.93937656 0.94694993 0.94919938 0.95214557\n",
" 0.96036029 0.98292733 0.98572336 0.99739346]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Define a threshold to convert continuous values to binary classification\n",
"threshold = 0.5 # This is just an example, adjust this based on your domain knowledge\n",
"y_class = (y > threshold).astype(int)\n",
"\n",
"# Now check the distribution of the new binary labels\n",
"print(\"Distribution of binary labels:\", np.bincount(y_class))\n",
"\n",
"# Continue with the train-test split with the new binary labels\n",
"X_train, X_test, y_train_class, y_test_class = train_test_split(\n",
" X, y_class,\n",
" test_size=0.2,\n",
" stratify=y_class,\n",
" random_state=42\n",
")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Jion_It6tNAX",
"outputId": "66e81fcd-1164-4794-9c33-3a5ca4871013"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Distribution of binary labels: [50 50]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import confusion_matrix, roc_curve, auc, ConfusionMatrixDisplay\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Perform the stratified train-test split with the new binary labels\n",
"X_train, X_test, y_train_class, y_test_class = train_test_split(\n",
" X, y_class,\n",
" test_size=0.2,\n",
" stratify=y_class,\n",
" random_state=42\n",
")\n",
"\n",
"# Train the logistic regression model\n",
"logistic_model = LogisticRegression()\n",
"logistic_model.fit(X_train, y_train_class)\n",
"\n",
"# Predict class probabilities on the test set\n",
"y_pred_probs_logistic = logistic_model.predict_proba(X_test)[:, 1]\n",
"\n",
"# Predict class labels for the test set based on the default threshold of 0.5\n",
"y_pred_class_logistic = logistic_model.predict(X_test)\n",
"\n",
"# Compute the confusion matrix\n",
"cm_logistic = confusion_matrix(y_test_class, y_pred_class_logistic)\n",
"\n",
"# Display the confusion matrix\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm_logistic)\n",
"disp.plot(cmap=plt.cm.Blues)\n",
"plt.title('Confusion Matrix - Logistic Regression Model')\n",
"plt.show()\n",
"\n",
"# Compute ROC curve and AUC\n",
"fpr_logistic, tpr_logistic, _ = roc_curve(y_test_class, y_pred_probs_logistic)\n",
"roc_auc_logistic = auc(fpr_logistic, tpr_logistic)\n",
"\n",
"# Plot the ROC curve\n",
"plt.figure()\n",
"plt.plot(fpr_logistic, tpr_logistic, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_logistic)\n",
"plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('ROC Curve - Logistic Regression Model')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 927
},
"id": "Fk7ryrNltM1v",
"outputId": "7ea610e2-c42d-4631-e67d-70bb4070a64a"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Reshape X_test_rnn to match the expected input shape of the model (10 time steps, 1 feature per step)\n",
"X_test_rnn_reshaped = X_test_rnn.reshape((X_test_rnn.shape[0], 10, 1))\n",
"\n",
"# Now make predictions with the reshaped data\n",
"y_pred_probs_rnn = rnn_model.predict(X_test_rnn_reshaped)\n",
"\n",
"# Continue with the rest of the code for evaluation\n"
],
"metadata": {
"id": "ZFmpVf7L2e5p",
"outputId": "3099af60-ff60-4126-e640-82e97060617d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1/1 [==============================] - 1s 876ms/step\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(\"Number of samples in test set:\", y_test_class.shape[0])\n",
"print(\"Number of predictions made:\", y_pred_probs_rnn.shape[0])\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qvto5ecH59Sx",
"outputId": "99b5a58d-4d6f-458c-f110-915b95673543"
},
"execution_count": 37,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Number of samples in test set: 20\n",
"Number of predictions made: 30\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "4z7rawrX59LY"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "OOPRR-ZS59CG"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"##Using LSTM to better predict\n",
"\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import LSTM, Dense\n",
"\n",
"# Define the LSTM model for binary classification\n",
"lstm_classification_model = Sequential([\n",
" LSTM(2, input_shape=(X_train.shape[1], 1)), # 50 LSTM units\n",
" Dense(1, activation='sigmoid') # Sigmoid activation for binary classification\n",
"])\n",
"\n",
"# Compile the LSTM model\n",
"lstm_classification_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
"\n",
"# Train the LSTM model\n",
"history = lstm_classification_model.fit(\n",
" X_train_rnn, y_train_class,\n",
" epochs=100,\n",
" validation_split=0.2\n",
")\n",
"\n",
"# Save the LSTM classification model\n",
"lstm_classification_model.save('/content/lstm_classification_model.h5')\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "I7Fxs-YfgFZ0",
"outputId": "41b0432d-ad7e-4ba3-d002-458ff780f010"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/100\n",
"2/2 [==============================] - 4s 813ms/step - loss: 0.6910 - accuracy: 0.5179 - val_loss: 0.6889 - val_accuracy: 0.5714\n",
"Epoch 2/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6909 - accuracy: 0.5179 - val_loss: 0.6889 - val_accuracy: 0.5714\n",
"Epoch 3/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6907 - accuracy: 0.5357 - val_loss: 0.6889 - val_accuracy: 0.5714\n",
"Epoch 4/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6906 - accuracy: 0.5179 - val_loss: 0.6889 - val_accuracy: 0.5714\n",
"Epoch 5/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6905 - accuracy: 0.5357 - val_loss: 0.6889 - val_accuracy: 0.5714\n",
"Epoch 6/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6904 - accuracy: 0.5536 - val_loss: 0.6889 - val_accuracy: 0.5714\n",
"Epoch 7/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6903 - accuracy: 0.5536 - val_loss: 0.6889 - val_accuracy: 0.5714\n",
"Epoch 8/100\n",
"2/2 [==============================] - 0s 34ms/step - loss: 0.6902 - accuracy: 0.5536 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 9/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.6900 - accuracy: 0.5714 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 10/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6899 - accuracy: 0.5714 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 11/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.6898 - accuracy: 0.5714 - val_loss: 0.6888 - val_accuracy: 0.5714\n",
"Epoch 12/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6897 - accuracy: 0.5714 - val_loss: 0.6888 - val_accuracy: 0.5714\n",
"Epoch 13/100\n",
"2/2 [==============================] - 0s 34ms/step - loss: 0.6895 - accuracy: 0.5714 - val_loss: 0.6888 - val_accuracy: 0.5714\n",
"Epoch 14/100\n",
"2/2 [==============================] - 0s 33ms/step - loss: 0.6894 - accuracy: 0.5536 - val_loss: 0.6888 - val_accuracy: 0.6429\n",
"Epoch 15/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6893 - accuracy: 0.5179 - val_loss: 0.6888 - val_accuracy: 0.6429\n",
"Epoch 16/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6892 - accuracy: 0.5714 - val_loss: 0.6888 - val_accuracy: 0.7143\n",
"Epoch 17/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6891 - accuracy: 0.5536 - val_loss: 0.6888 - val_accuracy: 0.7143\n",
"Epoch 18/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6889 - accuracy: 0.5714 - val_loss: 0.6888 - val_accuracy: 0.7143\n",
"Epoch 19/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6889 - accuracy: 0.5536 - val_loss: 0.6888 - val_accuracy: 0.7143\n",
"Epoch 20/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6887 - accuracy: 0.5536 - val_loss: 0.6888 - val_accuracy: 0.7143\n",
"Epoch 21/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6886 - accuracy: 0.5536 - val_loss: 0.6888 - val_accuracy: 0.7143\n",
"Epoch 22/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6885 - accuracy: 0.5714 - val_loss: 0.6888 - val_accuracy: 0.7143\n",
"Epoch 23/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6884 - accuracy: 0.5714 - val_loss: 0.6888 - val_accuracy: 0.7143\n",
"Epoch 24/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6882 - accuracy: 0.5714 - val_loss: 0.6888 - val_accuracy: 0.6429\n",
"Epoch 25/100\n",
"2/2 [==============================] - 0s 34ms/step - loss: 0.6882 - accuracy: 0.5536 - val_loss: 0.6888 - val_accuracy: 0.6429\n",
"Epoch 26/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6881 - accuracy: 0.5179 - val_loss: 0.6888 - val_accuracy: 0.6429\n",
"Epoch 27/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6880 - accuracy: 0.5179 - val_loss: 0.6888 - val_accuracy: 0.6429\n",
"Epoch 28/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6878 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 29/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6878 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 30/100\n",
"2/2 [==============================] - 0s 34ms/step - loss: 0.6877 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 31/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6875 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 32/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6875 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 33/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6874 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 34/100\n",
"2/2 [==============================] - 0s 34ms/step - loss: 0.6872 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 35/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6872 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 36/100\n",
"2/2 [==============================] - 0s 34ms/step - loss: 0.6871 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 37/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6870 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 38/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6869 - accuracy: 0.5179 - val_loss: 0.6888 - val_accuracy: 0.4286\n",
"Epoch 39/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6868 - accuracy: 0.5179 - val_loss: 0.6888 - val_accuracy: 0.4286\n",
"Epoch 40/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6867 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.4286\n",
"Epoch 41/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6866 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.4286\n",
"Epoch 42/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6865 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.4286\n",
"Epoch 43/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6865 - accuracy: 0.5357 - val_loss: 0.6888 - val_accuracy: 0.4286\n",
"Epoch 44/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6863 - accuracy: 0.5536 - val_loss: 0.6888 - val_accuracy: 0.4286\n",
"Epoch 45/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.6863 - accuracy: 0.5536 - val_loss: 0.6888 - val_accuracy: 0.5000\n",
"Epoch 46/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6861 - accuracy: 0.5536 - val_loss: 0.6889 - val_accuracy: 0.5000\n",
"Epoch 47/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6861 - accuracy: 0.5536 - val_loss: 0.6889 - val_accuracy: 0.5000\n",
"Epoch 48/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6860 - accuracy: 0.5536 - val_loss: 0.6889 - val_accuracy: 0.5000\n",
"Epoch 49/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6859 - accuracy: 0.5536 - val_loss: 0.6889 - val_accuracy: 0.5000\n",
"Epoch 50/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6858 - accuracy: 0.5536 - val_loss: 0.6889 - val_accuracy: 0.5000\n",
"Epoch 51/100\n",
"2/2 [==============================] - 0s 50ms/step - loss: 0.6858 - accuracy: 0.5536 - val_loss: 0.6889 - val_accuracy: 0.5000\n",
"Epoch 52/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6856 - accuracy: 0.5536 - val_loss: 0.6889 - val_accuracy: 0.5000\n",
"Epoch 53/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6856 - accuracy: 0.5714 - val_loss: 0.6889 - val_accuracy: 0.5000\n",
"Epoch 54/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.6855 - accuracy: 0.5714 - val_loss: 0.6889 - val_accuracy: 0.5000\n",
"Epoch 55/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6854 - accuracy: 0.5714 - val_loss: 0.6889 - val_accuracy: 0.5000\n",
"Epoch 56/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6853 - accuracy: 0.5714 - val_loss: 0.6890 - val_accuracy: 0.5000\n",
"Epoch 57/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6853 - accuracy: 0.5714 - val_loss: 0.6890 - val_accuracy: 0.5714\n",
"Epoch 58/100\n",
"2/2 [==============================] - 0s 46ms/step - loss: 0.6852 - accuracy: 0.5714 - val_loss: 0.6890 - val_accuracy: 0.5714\n",
"Epoch 59/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6851 - accuracy: 0.5714 - val_loss: 0.6890 - val_accuracy: 0.5714\n",
"Epoch 60/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6850 - accuracy: 0.5714 - val_loss: 0.6890 - val_accuracy: 0.5000\n",
"Epoch 61/100\n",
"2/2 [==============================] - 0s 34ms/step - loss: 0.6850 - accuracy: 0.5714 - val_loss: 0.6890 - val_accuracy: 0.5000\n",
"Epoch 62/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6850 - accuracy: 0.5714 - val_loss: 0.6890 - val_accuracy: 0.5000\n",
"Epoch 63/100\n",
"2/2 [==============================] - 0s 34ms/step - loss: 0.6848 - accuracy: 0.5714 - val_loss: 0.6891 - val_accuracy: 0.5000\n",
"Epoch 64/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6848 - accuracy: 0.5714 - val_loss: 0.6891 - val_accuracy: 0.5000\n",
"Epoch 65/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6847 - accuracy: 0.5714 - val_loss: 0.6891 - val_accuracy: 0.5000\n",
"Epoch 66/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6846 - accuracy: 0.5714 - val_loss: 0.6891 - val_accuracy: 0.5000\n",
"Epoch 67/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6846 - accuracy: 0.5714 - val_loss: 0.6891 - val_accuracy: 0.5000\n",
"Epoch 68/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6846 - accuracy: 0.5714 - val_loss: 0.6891 - val_accuracy: 0.5000\n",
"Epoch 69/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6845 - accuracy: 0.5714 - val_loss: 0.6891 - val_accuracy: 0.5000\n",
"Epoch 70/100\n",
"2/2 [==============================] - 0s 64ms/step - loss: 0.6844 - accuracy: 0.5714 - val_loss: 0.6892 - val_accuracy: 0.5000\n",
"Epoch 71/100\n",
"2/2 [==============================] - 0s 34ms/step - loss: 0.6843 - accuracy: 0.5714 - val_loss: 0.6892 - val_accuracy: 0.5000\n",
"Epoch 72/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6843 - accuracy: 0.5714 - val_loss: 0.6892 - val_accuracy: 0.5000\n",
"Epoch 73/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6842 - accuracy: 0.5714 - val_loss: 0.6892 - val_accuracy: 0.5000\n",
"Epoch 74/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6842 - accuracy: 0.5714 - val_loss: 0.6892 - val_accuracy: 0.5000\n",
"Epoch 75/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6841 - accuracy: 0.5714 - val_loss: 0.6893 - val_accuracy: 0.5000\n",
"Epoch 76/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6841 - accuracy: 0.5714 - val_loss: 0.6893 - val_accuracy: 0.5000\n",
"Epoch 77/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6840 - accuracy: 0.5714 - val_loss: 0.6893 - val_accuracy: 0.5000\n",
"Epoch 78/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6840 - accuracy: 0.5714 - val_loss: 0.6893 - val_accuracy: 0.5000\n",
"Epoch 79/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6839 - accuracy: 0.5714 - val_loss: 0.6893 - val_accuracy: 0.5000\n",
"Epoch 80/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6838 - accuracy: 0.5714 - val_loss: 0.6893 - val_accuracy: 0.5000\n",
"Epoch 81/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.6838 - accuracy: 0.5714 - val_loss: 0.6893 - val_accuracy: 0.5000\n",
"Epoch 82/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6837 - accuracy: 0.5714 - val_loss: 0.6894 - val_accuracy: 0.5000\n",
"Epoch 83/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6837 - accuracy: 0.5714 - val_loss: 0.6894 - val_accuracy: 0.5000\n",
"Epoch 84/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.6836 - accuracy: 0.5714 - val_loss: 0.6894 - val_accuracy: 0.5000\n",
"Epoch 85/100\n",
"2/2 [==============================] - 0s 49ms/step - loss: 0.6836 - accuracy: 0.5714 - val_loss: 0.6894 - val_accuracy: 0.5000\n",
"Epoch 86/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.6836 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 87/100\n",
"2/2 [==============================] - 0s 79ms/step - loss: 0.6835 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 88/100\n",
"2/2 [==============================] - 0s 50ms/step - loss: 0.6835 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 89/100\n",
"2/2 [==============================] - 0s 49ms/step - loss: 0.6835 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 90/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.6834 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 91/100\n",
"2/2 [==============================] - 0s 47ms/step - loss: 0.6834 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 92/100\n",
"2/2 [==============================] - 0s 69ms/step - loss: 0.6833 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 93/100\n",
"2/2 [==============================] - 0s 65ms/step - loss: 0.6833 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 94/100\n",
"2/2 [==============================] - 0s 50ms/step - loss: 0.6832 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 95/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.6831 - accuracy: 0.5714 - val_loss: 0.6897 - val_accuracy: 0.5000\n",
"Epoch 96/100\n",
"2/2 [==============================] - 0s 64ms/step - loss: 0.6832 - accuracy: 0.5714 - val_loss: 0.6897 - val_accuracy: 0.5000\n",
"Epoch 97/100\n",
"2/2 [==============================] - 0s 62ms/step - loss: 0.6831 - accuracy: 0.5714 - val_loss: 0.6897 - val_accuracy: 0.5000\n",
"Epoch 98/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.6830 - accuracy: 0.5714 - val_loss: 0.6898 - val_accuracy: 0.5000\n",
"Epoch 99/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6830 - accuracy: 0.5714 - val_loss: 0.6898 - val_accuracy: 0.5000\n",
"Epoch 100/100\n",
"2/2 [==============================] - 0s 64ms/step - loss: 0.6829 - accuracy: 0.5714 - val_loss: 0.6898 - val_accuracy: 0.5000\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#Model Evaluation\n",
"\n",
"# Evaluate the model's performance\n",
"test_loss, test_accuracy = lstm_classification_model.evaluate(X_test_rnn, y_test_binary)\n",
"\n",
"# Print the evaluation results\n",
"print(f\"Test Loss: {test_loss}\")\n",
"print(f\"Test Accuracy: {test_accuracy}\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Lu-aroPygFR6",
"outputId": "43e2926e-1519-4d04-e231-f1636e17571f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1/1 [==============================] - 0s 25ms/step - loss: 0.6741 - accuracy: 0.5333\n",
"Test Loss: 0.6740826964378357\n",
"Test Accuracy: 0.5333333611488342\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#Regularizing to make LSTM better\n",
"\n",
"from sklearn.utils.class_weight import compute_class_weight\n",
"\n",
"# Calculate class weights for unbalanced datasets\n",
"class_weights = compute_class_weight(\n",
" class_weight='balanced',\n",
" classes=np.unique(y_train_class),\n",
" y=y_train_class\n",
")\n",
"\n",
"# Create a dictionary mapping class labels to weights\n",
"weight_for_class_1 = class_weights[1]\n",
"class_weight_dict = {0: class_weights[0], 1: class_weights[1]}\n"
],
"metadata": {
"id": "F2miAN1IgEg3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Train the model with class weight to handle imbalance\n",
"history = lstm_classification_model.fit(\n",
" X_train_rnn, y_train_class,\n",
" epochs=100,\n",
" validation_split=0.2,\n",
" class_weight=class_weight_dict # Use the computed class weights\n",
")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RFmDBaCUgEbZ",
"outputId": "448c7e94-91ba-43d8-a7c8-4c74a82309ad"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/100\n",
"2/2 [==============================] - 1s 91ms/step - loss: 0.6907 - accuracy: 0.5714 - val_loss: 0.6898 - val_accuracy: 0.5000\n",
"Epoch 2/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6907 - accuracy: 0.5714 - val_loss: 0.6899 - val_accuracy: 0.5000\n",
"Epoch 3/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6907 - accuracy: 0.5714 - val_loss: 0.6899 - val_accuracy: 0.5000\n",
"Epoch 4/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6907 - accuracy: 0.5714 - val_loss: 0.6899 - val_accuracy: 0.5000\n",
"Epoch 5/100\n",
"2/2 [==============================] - 0s 51ms/step - loss: 0.6907 - accuracy: 0.5714 - val_loss: 0.6899 - val_accuracy: 0.5000\n",
"Epoch 6/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6907 - accuracy: 0.5714 - val_loss: 0.6899 - val_accuracy: 0.5000\n",
"Epoch 7/100\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.6907 - accuracy: 0.5714 - val_loss: 0.6899 - val_accuracy: 0.5000\n",
"Epoch 8/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6906 - accuracy: 0.5714 - val_loss: 0.6898 - val_accuracy: 0.5000\n",
"Epoch 9/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6906 - accuracy: 0.5714 - val_loss: 0.6898 - val_accuracy: 0.5000\n",
"Epoch 10/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6905 - accuracy: 0.5714 - val_loss: 0.6898 - val_accuracy: 0.5000\n",
"Epoch 11/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6905 - accuracy: 0.5714 - val_loss: 0.6898 - val_accuracy: 0.5000\n",
"Epoch 12/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6905 - accuracy: 0.5714 - val_loss: 0.6898 - val_accuracy: 0.5000\n",
"Epoch 13/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6904 - accuracy: 0.5714 - val_loss: 0.6898 - val_accuracy: 0.5000\n",
"Epoch 14/100\n",
"2/2 [==============================] - 0s 44ms/step - loss: 0.6904 - accuracy: 0.5714 - val_loss: 0.6897 - val_accuracy: 0.5000\n",
"Epoch 15/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6904 - accuracy: 0.5714 - val_loss: 0.6897 - val_accuracy: 0.5000\n",
"Epoch 16/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6904 - accuracy: 0.5714 - val_loss: 0.6897 - val_accuracy: 0.5000\n",
"Epoch 17/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6903 - accuracy: 0.5714 - val_loss: 0.6897 - val_accuracy: 0.5000\n",
"Epoch 18/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6902 - accuracy: 0.5714 - val_loss: 0.6897 - val_accuracy: 0.5000\n",
"Epoch 19/100\n",
"2/2 [==============================] - 0s 34ms/step - loss: 0.6902 - accuracy: 0.5714 - val_loss: 0.6897 - val_accuracy: 0.5000\n",
"Epoch 20/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6902 - accuracy: 0.5714 - val_loss: 0.6897 - val_accuracy: 0.5000\n",
"Epoch 21/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6901 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 22/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6901 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 23/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6900 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 24/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.6900 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 25/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6900 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 26/100\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.6900 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 27/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6899 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 28/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6899 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 29/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6898 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.4286\n",
"Epoch 30/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6899 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 31/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6898 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 32/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6898 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 33/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6897 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 34/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6898 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 35/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6897 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 36/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6897 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 37/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6896 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 38/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.6896 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 39/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6896 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 40/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6896 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 41/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6896 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 42/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6895 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 43/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6895 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 44/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6895 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 45/100\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6895 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 46/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6895 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 47/100\n",
"2/2 [==============================] - 0s 48ms/step - loss: 0.6895 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 48/100\n",
"2/2 [==============================] - 0s 51ms/step - loss: 0.6894 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 49/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.6894 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 50/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6894 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 51/100\n",
"2/2 [==============================] - 0s 78ms/step - loss: 0.6894 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 52/100\n",
"2/2 [==============================] - 0s 69ms/step - loss: 0.6894 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 53/100\n",
"2/2 [==============================] - 0s 79ms/step - loss: 0.6894 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 54/100\n",
"2/2 [==============================] - 0s 49ms/step - loss: 0.6894 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 55/100\n",
"2/2 [==============================] - 0s 62ms/step - loss: 0.6893 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 56/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.6893 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 57/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6893 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 58/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.6893 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 59/100\n",
"2/2 [==============================] - 0s 63ms/step - loss: 0.6892 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 60/100\n",
"2/2 [==============================] - 0s 64ms/step - loss: 0.6892 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 61/100\n",
"2/2 [==============================] - 0s 72ms/step - loss: 0.6892 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 62/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6892 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 63/100\n",
"2/2 [==============================] - 0s 71ms/step - loss: 0.6892 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 64/100\n",
"2/2 [==============================] - 0s 73ms/step - loss: 0.6891 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 65/100\n",
"2/2 [==============================] - 0s 69ms/step - loss: 0.6891 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 66/100\n",
"2/2 [==============================] - 0s 74ms/step - loss: 0.6891 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 67/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6891 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 68/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.6891 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 69/100\n",
"2/2 [==============================] - 0s 65ms/step - loss: 0.6891 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 70/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6890 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 71/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6890 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 72/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.6890 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 73/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.6890 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 74/100\n",
"2/2 [==============================] - 0s 72ms/step - loss: 0.6890 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 75/100\n",
"2/2 [==============================] - 0s 79ms/step - loss: 0.6890 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 76/100\n",
"2/2 [==============================] - 0s 82ms/step - loss: 0.6889 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 77/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.6889 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 78/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6889 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 79/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.6889 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 80/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.6889 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 81/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.6889 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 82/100\n",
"2/2 [==============================] - 0s 35ms/step - loss: 0.6889 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 83/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6888 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 84/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.6888 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 85/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6888 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 86/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.6888 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 87/100\n",
"2/2 [==============================] - 0s 36ms/step - loss: 0.6888 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 88/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6888 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 89/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6888 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 90/100\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6888 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 91/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6887 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.4286\n",
"Epoch 92/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6887 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
"Epoch 93/100\n",
"2/2 [==============================] - 0s 46ms/step - loss: 0.6887 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 94/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6887 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 95/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6887 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 96/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.6887 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 97/100\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6887 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 98/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.6886 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 99/100\n",
"2/2 [==============================] - 0s 37ms/step - loss: 0.6886 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
"Epoch 100/100\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6886 - accuracy: 0.5714 - val_loss: 0.6896 - val_accuracy: 0.5000\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Plot the training history\n",
"plt.figure(figsize=(14, 5))\n",
"\n",
"# Plot training & validation accuracy values\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot(history.history['accuracy'])\n",
"plt.plot(history.history['val_accuracy'])\n",
"plt.title('Model accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Accuracy')\n",
"plt.legend(['Train', 'Test'], loc='upper left')\n",
"\n",
"# Plot training & validation loss values\n",
"plt.subplot(1, 2, 2)\n",
"plt.plot(history.history['loss'])\n",
"plt.plot(history.history['val_loss'])\n",
"plt.title('Model loss')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Loss')\n",
"plt.legend(['Train', 'Test'], loc='upper left')\n",
"\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 362
},
"id": "J4ZH7JtAgEVy",
"outputId": "51f87469-9c35-4c76-86ec-b5577f46bbf8"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1400x500 with 2 Axes>"
],
"image/png": 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},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
"from sklearn.utils.class_weight import compute_class_weight\n",
"\n",
"# Calculate class weights for unbalanced datasets\n",
"classes = np.unique(y_train_class)\n",
"class_weights = compute_class_weight(class_weight='balanced', classes=classes, y=y_train_class)\n",
"class_weights_dict = dict(zip(classes, class_weights))\n",
"\n",
"# Define the LSTM model with dropout for regularization\n",
"model = Sequential([\n",
" LSTM(30, input_shape=(X_train.shape[1], 1), dropout=0.2, recurrent_dropout=0.2),\n",
" Dropout(0.5),\n",
" Dense(1, activation='sigmoid')\n",
"])\n",
"\n",
"# Compile the model with a possibly smaller learning rate and class weight\n",
"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
"\n",
"# Train the model with class weights to handle imbalance\n",
"history = model.fit(\n",
" X_train_rnn, y_train_class,\n",
" epochs=50,\n",
" validation_split=0.2,\n",
" class_weight=class_weights_dict,\n",
" batch_size=32 # Consider trying different batch sizes\n",
")\n",
"\n",
"# Evaluate the model to see if the performance has improved\n",
"test_loss, test_accuracy = model.evaluate(X_test_rnn, y_test_binary)\n",
"print(f\"Test Loss: {test_loss}\")\n",
"print(f\"Test Accuracy: {test_accuracy}\")\n",
"\n",
"# Predict classes using the trained model\n",
"y_pred_class = (model.predict(X_test_rnn) > 0.5).astype(int)\n",
"\n",
"# Generate the new confusion matrix\n",
"cm = confusion_matrix(y_test_binary, y_pred_class)\n",
"\n",
"# Plot the confusion matrix\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
"disp.plot(cmap=plt.cm.Blues)\n",
"plt.title('Confusion Matrix')\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "iby5QU1UgEPd",
"outputId": "c96239db-5d40-4297-8acf-6b32e1c34935"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/50\n",
"2/2 [==============================] - 4s 384ms/step - loss: 0.6844 - accuracy: 0.5893 - val_loss: 0.6946 - val_accuracy: 0.5000\n",
"Epoch 2/50\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6855 - accuracy: 0.5179 - val_loss: 0.6948 - val_accuracy: 0.5000\n",
"Epoch 3/50\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6749 - accuracy: 0.5893 - val_loss: 0.6949 - val_accuracy: 0.5000\n",
"Epoch 4/50\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6926 - accuracy: 0.5357 - val_loss: 0.6950 - val_accuracy: 0.5000\n",
"Epoch 5/50\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.6854 - accuracy: 0.6250 - val_loss: 0.6951 - val_accuracy: 0.5000\n",
"Epoch 6/50\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6858 - accuracy: 0.5714 - val_loss: 0.6951 - val_accuracy: 0.5000\n",
"Epoch 7/50\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6854 - accuracy: 0.6250 - val_loss: 0.6952 - val_accuracy: 0.5000\n",
"Epoch 8/50\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6861 - accuracy: 0.5893 - val_loss: 0.6951 - val_accuracy: 0.5000\n",
"Epoch 9/50\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6762 - accuracy: 0.5893 - val_loss: 0.6951 - val_accuracy: 0.5000\n",
"Epoch 10/50\n",
"2/2 [==============================] - 0s 43ms/step - loss: 0.6808 - accuracy: 0.5357 - val_loss: 0.6950 - val_accuracy: 0.5000\n",
"Epoch 11/50\n",
"2/2 [==============================] - 0s 45ms/step - loss: 0.6857 - accuracy: 0.5536 - val_loss: 0.6950 - val_accuracy: 0.5000\n",
"Epoch 12/50\n",
"2/2 [==============================] - 0s 46ms/step - loss: 0.6949 - accuracy: 0.4821 - val_loss: 0.6949 - val_accuracy: 0.5000\n",
"Epoch 13/50\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.6916 - accuracy: 0.4464 - val_loss: 0.6947 - val_accuracy: 0.5000\n",
"Epoch 14/50\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6809 - accuracy: 0.5536 - val_loss: 0.6946 - val_accuracy: 0.5000\n",
"Epoch 15/50\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6769 - accuracy: 0.5714 - val_loss: 0.6945 - val_accuracy: 0.5714\n",
"Epoch 16/50\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6713 - accuracy: 0.5893 - val_loss: 0.6946 - val_accuracy: 0.5714\n",
"Epoch 17/50\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6934 - accuracy: 0.5179 - val_loss: 0.6947 - val_accuracy: 0.5714\n",
"Epoch 18/50\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6854 - accuracy: 0.5536 - val_loss: 0.6947 - val_accuracy: 0.5714\n",
"Epoch 19/50\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.6765 - accuracy: 0.5536 - val_loss: 0.6947 - val_accuracy: 0.5714\n",
"Epoch 20/50\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6975 - accuracy: 0.5893 - val_loss: 0.6948 - val_accuracy: 0.5000\n",
"Epoch 21/50\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6708 - accuracy: 0.5714 - val_loss: 0.6948 - val_accuracy: 0.5000\n",
"Epoch 22/50\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6864 - accuracy: 0.5536 - val_loss: 0.6948 - val_accuracy: 0.5000\n",
"Epoch 23/50\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6983 - accuracy: 0.5536 - val_loss: 0.6948 - val_accuracy: 0.5000\n",
"Epoch 24/50\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.6817 - accuracy: 0.6071 - val_loss: 0.6948 - val_accuracy: 0.5000\n",
"Epoch 25/50\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6729 - accuracy: 0.6250 - val_loss: 0.6949 - val_accuracy: 0.5000\n",
"Epoch 26/50\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6859 - accuracy: 0.5714 - val_loss: 0.6949 - val_accuracy: 0.5000\n",
"Epoch 27/50\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.6853 - accuracy: 0.5714 - val_loss: 0.6949 - val_accuracy: 0.5000\n",
"Epoch 28/50\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6875 - accuracy: 0.6071 - val_loss: 0.6949 - val_accuracy: 0.5000\n",
"Epoch 29/50\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6893 - accuracy: 0.5893 - val_loss: 0.6949 - val_accuracy: 0.5000\n",
"Epoch 30/50\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6761 - accuracy: 0.6071 - val_loss: 0.6949 - val_accuracy: 0.5000\n",
"Epoch 31/50\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6853 - accuracy: 0.6250 - val_loss: 0.6949 - val_accuracy: 0.5000\n",
"Epoch 32/50\n",
"2/2 [==============================] - 0s 38ms/step - loss: 0.6948 - accuracy: 0.5179 - val_loss: 0.6949 - val_accuracy: 0.5000\n",
"Epoch 33/50\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6959 - accuracy: 0.5179 - val_loss: 0.6949 - val_accuracy: 0.5000\n",
"Epoch 34/50\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6870 - accuracy: 0.5357 - val_loss: 0.6949 - val_accuracy: 0.5000\n",
"Epoch 35/50\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.6845 - accuracy: 0.5536 - val_loss: 0.6950 - val_accuracy: 0.5000\n",
"Epoch 36/50\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.6792 - accuracy: 0.5179 - val_loss: 0.6952 - val_accuracy: 0.5000\n",
"Epoch 37/50\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6842 - accuracy: 0.5893 - val_loss: 0.6953 - val_accuracy: 0.5000\n",
"Epoch 38/50\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6891 - accuracy: 0.5714 - val_loss: 0.6955 - val_accuracy: 0.5000\n",
"Epoch 39/50\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6922 - accuracy: 0.4821 - val_loss: 0.6957 - val_accuracy: 0.5000\n",
"Epoch 40/50\n",
"2/2 [==============================] - 0s 43ms/step - loss: 0.6782 - accuracy: 0.6250 - val_loss: 0.6958 - val_accuracy: 0.5000\n",
"Epoch 41/50\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6874 - accuracy: 0.5179 - val_loss: 0.6959 - val_accuracy: 0.5000\n",
"Epoch 42/50\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.6790 - accuracy: 0.5714 - val_loss: 0.6960 - val_accuracy: 0.5000\n",
"Epoch 43/50\n",
"2/2 [==============================] - 0s 42ms/step - loss: 0.6809 - accuracy: 0.6429 - val_loss: 0.6961 - val_accuracy: 0.5000\n",
"Epoch 44/50\n",
"2/2 [==============================] - 0s 43ms/step - loss: 0.6837 - accuracy: 0.6071 - val_loss: 0.6963 - val_accuracy: 0.5000\n",
"Epoch 45/50\n",
"2/2 [==============================] - 0s 41ms/step - loss: 0.6794 - accuracy: 0.6071 - val_loss: 0.6965 - val_accuracy: 0.5000\n",
"Epoch 46/50\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6954 - accuracy: 0.5000 - val_loss: 0.6966 - val_accuracy: 0.5000\n",
"Epoch 47/50\n",
"2/2 [==============================] - 0s 39ms/step - loss: 0.6777 - accuracy: 0.5714 - val_loss: 0.6967 - val_accuracy: 0.5000\n",
"Epoch 48/50\n",
"2/2 [==============================] - 0s 43ms/step - loss: 0.6794 - accuracy: 0.5000 - val_loss: 0.6968 - val_accuracy: 0.5000\n",
"Epoch 49/50\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6794 - accuracy: 0.5357 - val_loss: 0.6967 - val_accuracy: 0.5000\n",
"Epoch 50/50\n",
"2/2 [==============================] - 0s 40ms/step - loss: 0.6893 - accuracy: 0.6429 - val_loss: 0.6967 - val_accuracy: 0.5000\n",
"1/1 [==============================] - 0s 27ms/step - loss: 0.6642 - accuracy: 0.7333\n",
"Test Loss: 0.6641790270805359\n",
"Test Accuracy: 0.7333333492279053\n",
"1/1 [==============================] - 0s 268ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import TimeSeriesSplit\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Bidirectional, LSTM, Dense, Dropout\n",
"from sklearn.metrics import confusion_matrix, roc_curve, auc\n",
"\n",
"# Define the number of splits\n",
"n_splits = 5\n",
"tscv = TimeSeriesSplit(n_splits=n_splits)\n",
"\n",
"# To store metrics for each fold\n",
"confusion_matrices = []\n",
"roc_auc_scores = []\n",
"\n",
"for train_index, test_index in tscv.split(X_normalized):\n",
" X_train_cv, X_test_cv = X_normalized[train_index], X_normalized[test_index]\n",
" y_train_cv, y_test_cv = y_binary[train_index], y_binary[test_index]\n",
"\n",
" # Reshape the data for LSTM network\n",
" X_train_cv_rnn = X_train_cv.reshape((X_train_cv.shape[0], X_train_cv.shape[1], 1))\n",
" X_test_cv_rnn = X_test_cv.reshape((X_test_cv.shape[0], X_test_cv.shape[1], 1))\n",
"\n",
" # Define the model (as before)\n",
" model = Sequential([\n",
" Bidirectional(LSTM(50, input_shape=(X_train_cv_rnn.shape[1], 1))),\n",
" Dropout(0.5),\n",
" Dense(1, activation='sigmoid')\n",
" ])\n",
"\n",
" # Compile the model (as before)\n",
" model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
"\n",
" # Fit the model\n",
" model.fit(X_train_cv_rnn, y_train_cv, epochs=100, batch_size=32, verbose=0) # Set verbose to 0 to suppress output\n",
"\n",
" # Predict probabilities\n",
" y_pred_probs = model.predict(X_test_cv_rnn).ravel()\n",
"\n",
" # Binarize predictions based on threshold\n",
" threshold = 0.5 # This threshold can be adjusted\n",
" y_pred_class = (y_pred_probs > threshold).astype(int)\n",
"\n",
" # Calculate metrics for this fold\n",
" cm = confusion_matrix(y_test_cv, y_pred_class)\n",
" confusion_matrices.append(cm)\n",
"\n",
" fpr, tpr, thresholds = roc_curve(y_test_cv, y_pred_probs)\n",
" roc_auc = auc(fpr, tpr)\n",
" roc_auc_scores.append(roc_auc)\n",
"\n",
"# Now, you can calculate the average of the metrics across all folds\n",
"# Average Confusion Matrix\n",
"average_cm = np.mean(confusion_matrices, axis=0)\n",
"print(\"Average Confusion Matrix:\\n\", average_cm)\n",
"\n",
"# Average ROC AUC Score\n",
"average_roc_auc = np.mean(roc_auc_scores)\n",
"print(\"Average ROC AUC Score:\", average_roc_auc)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 211
},
"id": "Y7qxNjiTCVne",
"outputId": "92f5176d-bd83-4e7c-b1bd-cacc1066fcdb"
},
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "name 'y_binary' is not defined",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-61-132d3fce7b8d>\u001b[0m in \u001b[0;36m<cell line: 14>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtrain_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_index\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtscv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_normalized\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 15\u001b[0m \u001b[0mX_train_cv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test_cv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_normalized\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_normalized\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0my_train_cv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test_cv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_binary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_binary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;31m# Reshape the data for LSTM network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'y_binary' is not defined"
]
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Assume 'model' is your trained Keras model\n",
"# Predict probabilities for the test set\n",
"y_pred_probs = model.predict(X_test_rnn)\n",
"\n",
"# Function to apply threshold to probabilities to create binary predictions\n",
"def apply_threshold(probs, threshold):\n",
" return (probs > threshold).astype(int)\n",
"\n",
"# Choose a range of thresholds to try\n",
"thresholds = np.linspace(0, 1, 101)\n",
"\n",
"# Plot confusion matrices for various thresholds\n",
"fig, axes = plt.subplots(nrows=10, ncols=10, figsize=(20, 20)) # Adjust the subplot grid as needed\n",
"axes = axes.flatten() # Flatten to 1D array for easy iteration\n",
"\n",
"for ax, threshold in zip(axes, thresholds):\n",
" # Get binary predictions using the current threshold\n",
" y_pred_class = apply_threshold(y_pred_probs, threshold)\n",
"\n",
" # Compute the confusion matrix for this threshold\n",
" cm = confusion_matrix(y_test_binary, y_pred_class)\n",
"\n",
" # Plot the confusion matrix\n",
" ConfusionMatrixDisplay(confusion_matrix=cm).plot(cmap=plt.cm.Blues, ax=ax)\n",
" ax.title.set_text(f'Thr {threshold:.2f}')\n",
"\n",
"plt.tight_layout() # Adjust spacing\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 881
},
"id": "Qa5-4WvCgEH_",
"outputId": "0a072856-336b-44f9-bac9-0a3de9ddb1af"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1/1 [==============================] - 0s 144ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2000x2000 with 200 Axes>"
],
"image/png": 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W1amnnqrPP//ciZdgz0V9+PIU6//617+Cbr/00kv65JNPyuw/9thj9fPPP8dsHMXFxTr33HO1ZMkSDR48WHXq1NFTTz2lrl27auHChWrevHnYj/XEE09o3bp1tvf/85//1P3336/rrrtOnTp10jvvvKO//vWvMgwj6DTljjIs3vjFkYVQWFgY9G3w1atXa/Hixapdu7Zq166tUaNG6f/+7/+Uk5OjlStXasiQIWrWrJl69OjhxCvwJT/2MWzYMOXk5KhDhw766KOPnBh+aFZtSBH3gfjzWx/PPfecnn/+ef3f//2fbrzxRuXn5+vpp5/Wn/70J3344Yfq3r27Uy/pMAfmDsSf39rYvn27NmzYoIsvvlgNGzbUwYMH9cknn+iqq67SsmXLdN999zn1koLRhyv5rY8jFRYWasiQIapWrVo0Qw8PfbiSX/vo06ePzjnnnKB9Xbp0qdDYwxLjtXn9+vV18cUXa9GiRZoxY4aKioq0ZcsWSVLt2rWVlmZxCmuUy499LF68WF27dtXRRx+tO+64Q0cddZTWrVun9evXO/FSynJobU4f8ee3Pi666CI1a9aszP5//OMfKiwsVKdOnSr8Gmw59LcVfcSf3/ooLCxUly5dtGfPHt14443Ky8vTkiVL9OSTT+rzzz/XwoULlZLi8PdEWXu4kt/aWL9+vbp06aLU1FQNHjxY1apV0+TJk3XWWWdp5syZOv300516ScHc1IcJ86abbjLt/k/x+eefm5LMf//73xV67D179tje9/rrr5d57G3btpk1a9Y0+/TpE/ZzbN261czKyjJHjx5tSjIfeuihoPs3bNhgVq5c2bzppptK9hUXF5unnXaa2aBBA/OPP/6I4BWVLz8/35Rkpp90h5lx8j+CtvST7jAlmfn5+WE9VuD//qW3fv36mXv37jXPOusss27dumblypXNRo0amdddd525ZcsWR1+P33m9D9M0zdWrV5umaZq//vqrKckcMWJE2I8fiVBtVKQPJJ7X+1iwYIG5e/fuoH3bt28369ata55yyilhP084nJw7kHheb8POeeedZ1arVi2p/7ZC4vmpjzvvvNNs2bKl2bdvX7NatWphP0ck6MNbvN7H6tWrI5pXohWvtXngdVltn3/+eWxfpI94vY+ioiLzuOOOMzt37mzu3bs38hcRAafX5vSReF7vw8q6detMwzDM6667LuznCYfTf1vRR+J5vY+pU6eakswZM2YE7b/77rtNSeaiRYvCfq7ysPbwFq+3ceONN5qVKlUyly5dGjSuvLw884QTTojg1YTHjX348hTrFVFcXKx7771XDRo0UEZGhs4888wy17nu2rWrjjvuOC1cuFCnn366qlatqn/84x+2j/nmm28qOztbF110Ucm+unXr6tJLL9U777yj/fv3hzW2u+66Sy1bttTll19uef8777yjgwcP6sYbbyzZZxiGbrjhBm3YsEHz588P63kiZqRYbxHo2rWrTNMss02ZMkVVqlTRRx99pG3btunAgQNas2aNnnnmGWVnZ8fm9cCWm/uQpMaNG4f1WI6xayPCPuAObu7jxBNPLHNK3KOOOkqnnXZa7D5JSRu+4eY27DRu3Fh79+7VgQMHIvq9sNGHb3ihj+XLl2vcuHF69NFHValSHE5cRh++4YU+JGnPnj2xmy9Ki/HavHHjxpb3maaprl27xuY1wZKb+/j444/1ww8/aMSIEapSpYr27t2roqKisB67whxam9OHO7i5DyuvvvqqTNNU3759w/6diDj0txV9uIOb+ygoKJCkMscEApdgrVKlSljPExHWHr7h5jbmzp2rDh06qGXLliX7qlatqvPPP1+LFi3S8uXLw3qeiLmoj+QcVRK6//77NW3aNA0aNEhDhw7VV199ZfkHyG+//aaePXuqffv2Gj9+vLp162b7mN99951OOOGEMqf4OOmkk7R371798ssv5Y7rm2++0Ysvvqjx48fLMAzb56lWrZqOPfbYMs8TuD8mXHStAUTHzX0kBNcg9xUv9rFlyxbVqVMnot8JG234hhfa+P3337V9+3atWbNGL774oiZPnqwuXbrEZgEu0YePeKGP2267Td26dStzGumYoQ/f8EIfo0aNUvXq1ZWRkaFOnTrp448/Lvfxo0IfvuHmPj799FNJUnp6ujp27Khq1aqpatWquuyyy7Rjx45yn6NCWJv7ipv7sDJ16lTl5eXF7hS5tOErbu7j9NNPV0pKigYMGKCvvvpKGzZs0Pvvv697771XvXv3VqtWrcp9nojRh2+4uY39+/db/vtU1apVJUkLFy4s93kqxEV9+PIa5BWxb98+LV68uOTaJ7Vq1dKAAQP0ww8/6Ljjjiv5uS1btmjSpEn629/+Vu5jbt682fKPmMCnmzZt2qTjjz/e9vdN09Qtt9yiv/zlL+rSpYvWrFlj+zzZ2dllQjnyeWIiNUVKLfXG56QFnuTmPhLCqg2JPjzKa33MnTtX8+fP17Bhw8L+nYgwd/iGF9p47LHHNHTo0JLbZ555piZPnlzuOCuMPnzD7X385z//0ccff6wlS5aUOy7H0IdvuLmPlJQUnXXWWbrwwgt19NFHa9WqVXr00UfVs2dPvfvuuzr33HPLHWuF0IdvuLmPwLeYLr30Up199tkaOnSolixZorFjx2r9+vX64osvnP/gO2tzX3FzH6X9+OOP+u9//6shQ4bE7gshzB2+4uY+WrdurWeeeUaDBg1Sly5dSvb369dPzz33XLnjrBD68A03t9GyZUvNnTtXu3fvVo0aNUr2f/HFF5KkjRs3ljvWCnFRHxwgD1P//v1LIpCk0047TZK0atWqoBDS09PVv3//sB7z999/V3p6epn9GRkZJfeHMmXKFH3//fd68803Y/o8FWb1yZAk/aQIouPmPhLC7lNT9OFJXupj27Zt+utf/6omTZpoyJAhEf1u2Jg7fMMLbfTp00cdO3bUr7/+qhkzZmjr1q2x+7tKog8fcXMfBw4c0O23366///3vat26dVhjcwR9+Iab+2jYsKE++uijoH1XXHGFWrdurTvuuCN2B8jpwzfc3EdhYaEkqVOnTnr55ZclSf/3f/+nqlWraujQoZo5c6a6d+8e1pjDxtrcV9zcR2lTp06VpNidXl1i7vAZt/dx9NFH66STTtI555yjRo0aae7cuXr88cdVp04dPfzww2GNNyL04RtubuOGG27Qe++9p7/85S+69957Va1aNT311FNasGBBWM9TYS7qIzkP2yehhg0bBt2uVauWJGnnzp1B+48++uigYEKpUqWK5fUE9u3bV3K/nYKCAg0dOlSDBw9WXl5ezJ4nKi46lQKi4+Y+EoLTuPmKV/rYs2ePzjvvPO3evVvvvPNOmWuTO4Y2fMMLbTRq1Ejdu3dXnz59NHXqVDVt2lTdu3eP/SKDPjzPzX2MGzdO27dv16hRo8Ial2Mc6GPOnDnq1auXcnNzZRiGpk+fXnLfwYMHdeedd+r4449XtWrVlJubqyuvvDJ2Z+OCLTf3YaV27drq37+/li1bpg0bNkT8+2Fh/vANN/cReJw+ffoE7f/rX/8qSZo3b15Y440Ia3NfcXMfRzJNU6+88oqOO+44tW3bNuzfixht+Iqb+/jyyy913nnn6d5779WAAQPUu3dvPfLIIxo2bJgeffRR/fTTT2GNNyL04RtubqNnz5564oknNGfOHJ1wwglq2bKl/vOf/+jee++VJP5tVxwgD1uq1SmXdOiPkiNFcrC5fv362rx5c5n9gX25ubm2v/vwww/rwIED+stf/qI1a9ZozZo1JYvpnTt3as2aNTpw4EDJ82zZsqXMWMN5nqgYKdYbPMfNfSSEXRv04Ule6OPAgQO66KKL9N///lfvvPNO0CckHUcbvuGFNkq7+OKLtX79es2ZMyfsMUeEPnzDrX3k5+drzJgxuu6661RQUFDys4WFhTJNU2vWrNG2bdvCHnNEHOhjz549ateunSZMmFDmvr1792rRokUaPny4Fi1apLffflvLli3T+eef79QrQJjc2kcogX/citl1lpk/fMPNfQQeJzs7O+gx6tWrV/LzjmNt7itu7uNIX375pdauXRvbb49LtOEzbu7j6aefVnZ2tjp27Bj0GOeff75M04zNB6zowzfc3IYk3Xzzzdq6davmzZunBQsWaOnSpcrKypIktWjRIuwxR8RFfSTnqHyiffv2WrRokYqLi4P2f/3116patWrIN+i6deu0c+dOtWnTRk2aNFGTJk1KTu9w3333qUmTJiWfjmrfvr327t2rn3/+uczzBO6PCRd9UgTJJ159JASfUkeU4tlHcXGxrrzySs2cOVOvvPKKzjjjjNi8qADaQBQSPXcEvjmen58f5SuxQR+IQjz62LlzpwoLC/Xggw+W/FyTJk301ltvae/evWrSpImuv/762LxAB/ro2bOnxowZowsvvLDMfVlZWfrkk0906aWXqmXLlvrTn/6kJ598UgsXLtS6deucehVIkETPH6tWrZIk1a1bN8pXYoP5A1GIVx8nnniipLLXwwycqSMmfbA2R5QSMX9MnTpVhmGUnF0hZmgDUYpXH1u3blVRUVGZxzh48KAk6Y8//nDqJR1GH4hCvOeOatWqqUuXLjrxxBOVmpqqTz/9VFWqVNEpp5zi/IuTXNUHB8gT6OKLL9bWrVv19ttvl+zbvn27/v3vf6tXr15B1yFYuXKlVq5cWXL71ltv1bRp04K2p59+WpJ01VVXadq0aWrSpIkk6YILLlDlypX11FNPlfy+aZqaNGmSjj76aJ188smxeYGGRQRGcoaA5BOvPhLCqo0K9BGL04COHDlShmEEba1atarIq0QMxbOPW265Ra+//rqeeuopXXTRRbF/cQ7NHfThT/Fq49dff7V8/ueff16GYeiEE06IxcujD0QlHn3Uq1evzM9NmzZN3bp1U0ZGhqZNm6ahQ4fG5gWG6KOgoCBoszqdXUXk5+fLMAzVrFnTkcdD4iRy/ti4caNeeOEFtW3bVvXr14/NC2RtjijE89+u0tPTNXny5KB/MH7uueckSX/+85+df3EOrc3hX/H+t6uDBw/q3//+t0499dQyp/11HHMHohSvPlq0aKGtW7dq1qxZQc//6quvSpI6dOjg/IujD0Qhkcc95s2bp7ffflvXXHNNyTfJHeeiPiolegB+dvHFF+tPf/qT+vfvr59++kl16tTRU089paKiojLX7DvzzDMlSWvWrJEknXDCCWX+8TVwX5s2bdS7d++S/Q0aNNBtt92mhx56SAcPHlSnTp00ffp0zZ07V1OnTrU9TUTUUlLKfjIkhc9kIDzx6kOS/vWvf2nt2rXau3evpEMHDsaMGSNJuuKKK9SoUSNnX5xVG4H9EQicBvTqq68uc+DyyNOAtmvXTjt37tSAAQN0/vnna8GCBSEft02bNvr0009LbleqxFSRbOLVx/jx4/XUU0+pS5cuqlq1ql5++eWg37vwwgtVrVo1Z1+cQ3MHffhTvNq499579eWXX+rss89Ww4YNtWPHDr311lv69ttvdcstt6hZs2axeYH0gSjEq4/Sf2dJ0vTp0/XNN99Y3ueYEH2UvjbbiBEjNHLkyKiebt++fbrzzjvVp08fZWZmRvVYSLx49TFkyBCtXLlSZ555pnJzc7VmzRo9/fTT2rNnjx577LGYvT7W5ohGvPrIycnRP//5T9199906++yz1bt3by1ZskTPPvus+vTpo06dOjn/4hxam8O/4vlvV5L00Ucf6bfffov96dUl5g5ELV593HzzzZo8ebJ69eqlW265RY0aNdLs2bP16quv6s9//rM6d+7s/IujD0QhXm2sXbtWl156qc4//3zl5OToxx9/1KRJk9S2bVvdd999MXt9buqDf7VKoNTUVL3//vsaPHiwHn/8cf3+++/q1KmTpkyZopYtWzr6XPfff79q1aqlp59+WlOmTFHz5s318ssvx/Z0PFanTkjSUykg+cSzj+eff16zZ88uuf3555/r888/lySdeuqpMThAbnNakQqcBrRnz56W9wVOA3qkJ598UieddJLWrVsX8pPGlSpVUk5OTkRjQXzFq4/FixdLkubPn6/58+eXuX/16tUxOEDuzNxBH/4UrzbOPfdcrVy5Ui+88IJ+/fVXZWRkqG3btpo8ebL69evn2POUQR+IQjz/tkqIEH2sX78+6CD2kZ/Ir4iDBw/q0ksvlWmamjhxYlSPheQQrz7OOussTZo0SRMmTNDOnTtVs2ZNnX766Ro2bFjszj4isTZHVOI5fwwbNky1atXSE088odtuuy3ooHlMOLQ2h3/F+++rqVOnqnLlyrrkkkscf+wymDsQpXj10bJlSy1cuFDDhg3Tyy+/rC1btig3N1eDBg0qc7DRMfSBKMSrjczMTNWvX19PPvmkduzYoaOPPlq33nqr/vnPf6pGjRqOPU8ZLurDMEtfTR6IUkFBgbKyspTec5yMylWC7jMP/q79H9yu/Px8vmkB3wnVhhRdH4ZhaNq0aSG/mfXpp5/qrLPO0q5du2wff+TIkXrooYeUlZWljIwMdenSRWPHjo39qbvge7GcO+gDbkcfgL1Y9WHXRuDg+KpVq/TZZ5/pqKOOivYlADHD2hywFsu1OeB2zB2APaf7mDNnjh566CEtXLhQmzdvLrP+ME1TI0aM0LPPPqtdu3bplFNO0cSJE9W8eXMnXxbgCCf7iFcbyfm9dniD1XWckvSTIkBc2bWRErvrZIZ7GtDOnTtrypQp+vDDDzVx4kStXr1ap512mnbv3h31GICwxLkNiT7gIvQB2IvD2iNwcHz58uX69NNPOTgO92BtDlgrZ20O+BptAPYc6iNw+bMJEyZY3v/ggw/q8ccf16RJk/T111+rWrVq6tGjh/bt2xftKwBix4E+4tUGp1hHzBgpKTJKX1sgSa81AMSTZRtSzK6TGclpQI885W7btm3VuXNnNWrUSG+88YauueaaCo8BCFeouSMW15ClD7gJfQD2nFh7FBYWasWKFSW3V69ercWLF6t27dqqX7++Lr74Yi1atEgzZsxQUVGRtmzZIkmqXbu20tLSon4NQKywNgeslbc2B/yMuQOw51QfoS5/Zpqmxo8fr2HDhumCCy6QJL300kvKzs7W9OnTddlll0X8fEA8ONFHvNrgADlixkgxZKQYwTtL3wZ8yLINqaQPJ6+TGTi4sXbtWn322WcRnwKrZs2aatGiRdA/FgOxFGruiNU1ZOkDbkEfgD0n1h4LFixQt27dSm4PHDhQktSvXz+NHDlS7777riSpffv2Qb/3+eefq2vXrhGPGYgX1uaAtfLW5oCfMXcA9kL1UVBQELQ7PT29Quvz1atXa8uWLerevXvJvqysLHXu3Fnz58/nADmSVqz7cLINPvaFmDEMw3ID/M6ujUAfmZmZQVtFD3I4cRrQwsJCrVy5UvXr16/QGIBIxaMNiT7gTvQB2HNi7dG1a1eZpllmmzJliho3bmx5n2maHBxH0mNtDlgrb20O+BltAPZC9ZGXl6esrKySbezYsRV6jsDZqrKzs4P2Z2dnl9wHJKNY9+FkG3yDHDGTYnEqBZNT8QCWbUiR9+HEaUDPPPNMXXjhhbr55pslSYMGDVKvXr3UqFEjbdq0SSNGjFBqaqr69OlT0ZcLRMSpuYM+4EX0Adhj7QHYow/AmlNrc8CLmDsAe6H6cPrsboDbuKkPDpAjZjgVD2DNqdO4OXEa0JUrV2r79u0l923YsEF9+vTRb7/9prp16+rUU0/VV199pbp160Y0NqCinJo76ANeRB+APdYegD36AKxxinXAHnMHYC9UH4GzukUrJydHkrR169agM7Nt3bq1zFodSCax7sPJNlx9gLy4uFibNm1SjRo1OMVLApimqd27dys3N1cpFp8gTEkxLD4pwn+neKGPxKlIG1LkfQROAxpqHOVZs2ZN0O3XXnstojG4FX0kTrzmDvqoOPpIHPpIbrSRWKw9kht9JBZ9JDf6SKxQfTi1NkfF0UfiMHckP/pInGToo0mTJsrJydHMmTNLDvoVFBTo66+/1g033ODoc7kNbSRWovtwsg1XHyDftGmT8vLyEj0M31u/fr0aNGhQZn+KYXEqBYNT8cQLfSReJG1I9BFP9JF4zB3Jiz4Sjz6SE20kB/pITvSRHOgjOdFHcrDqg7V54tFH4jF3JC/6SLxY9xHq8mcNGzbUbbfdpjFjxqh58+Zq0qSJhg8frtzcXPXu3Tvi5/IS2kgOsewjXm24+gB5jRo1JEkrVq9XDQdOW5EIDbsOSvQQKswsOqADP71Y8t+hDON/W+l9iAv6SJwKtSGbfYgJ+kgc5o7kRx+JQx/JjTYSiz6SG30kFn0kN/pIrJB9sDZPOPpIHOaO5Bf4bzNn0S+qbvffKcl1uejuRA+hQuLVR6jLn02ZMkVDhgzRnj17dP3112vXrl069dRT9eGHHyojIyPyJ/MQ2kisePQRrzZcfYA8cPqEGg5d1yERjNS0RA8hanansTBSUsqcYqHY4pO5iA36SLxI2pDoI57oI/GYO5IXfSQefSQn2kgO9JGc6CM50Edyoo/kYNUHa/PEo4/EY+5IXoH/NtVr1FD1GvSRCLHuo7zLnxmGodGjR2v06NERP7aX0UZyiGUf8WrD1QfIkdyMFENGqWsLlL4N+JFVG4H9gN8xdwD26AOwRx+APfoArLE2B+wxdwD26AOw56Y+OECOmEmx+iQunzQErNs4dEf8BwMkGeYOwB59APboA7BHH4A11uaAPeYOwB59APbc1AcHyBEzbgoBiCcW4YA95g7AHn0A9ugDsEcfgDXW5oA95g7AHn0A9tzUBwfIETvG/7bS+wC/s2pDNvsAv2HuAOzRB2CPPgB79AFYY20O2GPuAOzRB2DPRX0k52F7eELgkyKlN8Dv7NqgD4C5AwiFPgB79AHYc6KPOXPmqFevXsrNzZVhGJo+fXrQ/aZp6u6771b9+vVVpUoVde/eXcuXL3fwVQDOc2ptTh/wIqf+tqIPeBFrD8Cem/pIzlHBE4wUw3ID/M6uDfoAmDuAUOgDsEcfgD0n+tizZ4/atWunCRMmWN7/4IMP6vHHH9ekSZP09ddfq1q1aurRo4f27dvnxEsAYsKptTl9wIuc+tuKPuBFrD0Ae27qg1OsI2bcdK0BIJ64zhlgj7kDsEcfgD36AOw50UfPnj3Vs2dPy/tM09T48eM1bNgwXXDBBZKkl156SdnZ2Zo+fbouu+yyCo0biDWn1ub0AS9y6m8r+oAXsfYA7Lmpj+QcFTzBkCHDKLUl68UGgDiybIM+AEnMHUAo9AHYow/AXqz7WL16tbZs2aLu3buX7MvKylLnzp01f/58x54HcFo81ub0AbeKx99W9AG3Yu0B2HNTH3yDHDFjdeqEZD2VAhBPdqcVoQ+AuQMIhT4Ae/QB2AvVR0FBQdD+9PR0paenR/T4W7ZskSRlZ2cH7c/Ozi65D0hG5a3N6QN+Fuu5Q6IPuBdrD8Cem/rgG+SImcCpFEpvgN/ZtUEfAHMHEAp9APboA7AXqo+8vDxlZWWVbGPHjk3waIH4KW9tTh/wM9oA7LH2AOy5qQ++QY6YSUmRUkp/MiQ5OwDiyrINiT4AMXcAodAHYI8+AHuh+li/fr0yMzNLdlfkG4A5OTmSpK1bt6p+/fol+7du3ar27dtH/HhAvJS3NqcP+Fms5w6JPuBerD0Ae27qI0mHBS8wUgyllNqS9VQKQDxZtUEfwCHMHYA9+gDsOdHHnDlz1KtXL+Xm5sowDE2fPj3oftM0dffdd6t+/fqqUqWKunfvruXLlzv4KoDYCNVHZmZm0FaRgxxNmjRRTk6OZs6cWbKvoKBAX3/9tbp06eLY6wCcVt7anD7gZ7GeOyT6gHuxNgfsuamPsL5B/u6774b9gOeff36FBwNvMYxDW+l9XkMfiJRVG4H9XkMfiBRzR1m0gQD6KIs+EOBEH3v27FG7du109dVX66KLLipz/4MPPqjHH39cL774opo0aaLhw4erR48e+umnn5SRkRHF6MNHH6gIJ/ooLCzUihUrSm6vXr1aixcvVu3atdWwYUPddtttGjNmjJo3b17SR25urnr37h39CwgTfSBSTq3N6QNe5NTagz7gRazNy6INBLipj7AOkIc7IRmGoaKiomjGAw9JTTWUmlrqnV/6tgfQByJl2YZEH/QBMXeURhs4En0Eow8cyYk+evbsqZ49e1reZ5qmxo8fr2HDhumCCy6QJL300kvKzs7W9OnTddlll1Vo3JGiD1SEE30sWLBA3bp1K7k9cOBASVK/fv00ZcoUDRkyRHv27NH111+vXbt26dRTT9WHH34Ytw+PSPSByDm1NqcPeJFTaw/6gBexNg9GGziSm/oI6wB5cXFxrMcBDwqcPuFIZpKeSiEa9IFIWbUh0QcgMXcAodAHYC/WfaxevVpbtmxR9+7dS/ZlZWWpc+fOmj9/ftwOkNMHKsKJPrp27SrTNG3vNwxDo0eP1ujRoys0RifQByLl1NqcPuBFTv1tRR/wItbmgD039RHWAXI7+/bti+unueLly0Ur9MS/PtWSpeu0ZXuBXn7oOp3btV2ih2Xr5A7H6JYruqtdq4aqXzdLfQc9o/dn/7fk/gkjLtdfz/tT0O98Ov8nXXLrUzEdV0pKilJSgi9zb6b457L3Xuzj0ckfacbnS7R87VZlpFfWSW2bauTNF6h54+xED81SeW0c6dG7LlP//ztVQx99U5NenRXTcVm1IdGH29GHM5g7vNeGRB9OoQ9v9vH8m3P1wltztX7zDklSq6Y5GnxNT/35lDYJHpm18vq487pzdNFZJ+jo7Fo6eLBIi5eu05in3tPCH9fGdFyh+igoKAjan56eHvG1Mrds2SJJys4O/v+3srOzS+5LJK/2caRxUz7W6Anv6u+XddXYOy5O9HAsheqjUmqKht3QS38+pY0aHX2UCgr3afY3SzXqyXe1ZXt+TMfF/EEfieamNiT68Br6qBi/zx2SN/t46l8fa9LUT4P2NW5QV+8+NzhBIwqtvLXHed3aqf9Fp6p9q4aqXbOaTus7Vj/8sjHm4/J7H15sQ6IPp7ipj4hHVVRUpHvuuUdHH320qlevrlWrVkmShg8frueff75Cg5gwYYIaN26sjIwMde7cWd98802FHscpe3/fr+NaHK2HhvwloeMIV9Uq6frhl40a/ODrtj/z6bwf1fLsoSXbtf+cHPNxpRhGyadFSrZkvdiAQ5zuI9namLdoha695HR9/MIgvf3kzTr4R5EuuuVJ7fl9f0LHZSecNiTp3K5t1fH4xtq0bVdcxmXZRgX6mDNnjnr16qXc3FwZhqHp06cH3W+apu6++27Vr19fVapUUffu3bV8+fJyHzdW7zv6SC6u6qMCc4eb+vDD31b04Qz68GYfufVqasTNF+jzl4bosxcH67SOLdR30DP6eeXmhI7LTnl9rFy3TUMe+rdO6XOfel73qNZt2qG3n7xZR9WsHtNxheojLy9PWVlZJdvYsWNjOpZ48UMfAYt+XKsp075Um+ZHJ3ooIYXqo2pGmtq2ytNDz3+grlc8oCuHPKtmjbL1yiN/i/m4WJt7b+1xJDf04ao26CPix6OP6LiqD4+3Ifmjj2MaZeuzV4aXbC8+cmOih2SrvLVHtYw0fbVkpUY+OT2u4/JjH35Ze9BH9NzUR8QHyO+9915NmTJFDz74oNLS0kr2H3fccXruueciHsDrr7+ugQMHasSIEVq0aJHatWunHj16aNu2bRE/llP+fEobDbuhl87rlrzfGj/Sp/N+0r2TZug/s6y/2SRJ+w/8oW2/7S7Z8nf/HvNxGYb15mVO9pGMbbz5xE36a68/6dhj6uv4Fg301IjLtWHLTi3+eX3CxhRKOG3Ur5ulBwZdouuHT9Eff8TnWil2bUTax549e9SuXTtNmDDB8v4HH3xQjz/+uCZNmqSvv/5a1apVU48ePbRv3z7bx4zl+44+kovb+oiUm/rww99W9OEM+vBmHz1PP15nndJGxzSsp2aNsjX8xvNVrWq6FvywOmFjCqW8Pt78aIFmf7NMazf+pqWrtmjY+LeVWb2K2jTPjem4QvWxfv165efnl2xDhw6N+PFzcnIkSVu3bg3av3Xr1pL74s0PfUhS4d79uv7uKXrsH31Us0aVhI6lPKH6KNizTxfd/KSmf/qdVqzdpgU/rNGQh95Qh9YN1SC7VkzHxdrce2uPALf04bY26CN89BE9t/XhdX7oo1JqiurUrlGy1cqqltDxhFLe2uP1D77VQ899qFnfLIvruPzYh1/WHvQRPTf1EfEB8pdeeknPPPOM+vbtq9TU1JL97dq109KlSyMewKOPPqrrrrtO/fv3V+vWrTVp0iRVrVpVL7zwQsSPBXunnthcv3w0Vt+8OVyP3PmXuIRt+SncJL3WgFOc7MMNbRQUHvqH8lqZVRM8kooxDEOTRl2pJ16eqaWr4ndqTLs2Iu2jZ8+eGjNmjC688MIy95mmqfHjx2vYsGG64IIL1LZtW7300kvatGlTmW8KHimW7zv6cJdk6yNSburDj39b0UfF0If3+ygqKtZbHy/Q3t8PqNPxTRI9nKhVrpSqfheeovzde2N+KrdQfWRmZgZtkZ5eXZKaNGminJwczZw5s2RfQUGBvv76a3Xp0sWx1xEJv/Qx+MHXddYpx6lr51YJHUcsZFavouLiYuUXxvYD7KzNvbv28GofiW6DPsJHH/GX6D68zg99rN24XWf+9R71vOp+3fXAK9q8bWdCx+NGfuzDL2sP+oiem/qI+AD5xo0b1axZszL7i4uLdfDgwYge68CBA1q4cKG6d+9+eEApKerevbvmz58f6dBgY+a8n3XDyH+p941PaOQT7+jkE5rp34/dEPM3pZtCcIpTfbihjeLiYg199E11btdUrZvF9htBsXJbvz/rj6JiPf3arLg+bzwW4atXr9aWLVuC3kNZWVnq3Lmz7Xso1u87+nCXZOvDScnWh9/+tqKPiqOPw7zWx48rNqrB6QOVfcptGjj2df3roevUqmn9hI4pGj1OPU7rZz+iLV+O0w19uunCm5/Ujvw9MX1OJ/ooLCzU4sWLtXjxYkmHeli8eLHWrVsnwzB02223acyYMXr33Xf1/fff68orr1Rubq569+7t/AsKgx/6eOvjBVqydL3uvun8hI0hVtLTKmnkzRforY8Xavce+zN0OIG1+WFeWnt4tY9kaIM+wkMf8ZcMfXid1/s4vlVDjbnjL5o45loNu/lCbdyyU1cNmqg9e2P7fvIaP/bhh7UHfTjDTX1EfIC8devWmjt3bpn9b775pjp06BDRY23fvl1FRUXKzs4O2p+dna0tW8p+G2f//v0qKCgI2lC+tz9ZqA/mfK+fVm7S+7P/q8sGTtKJbRrr1BObx/R5DYsIjAhDiNU1MmPFqT4ibUOKfx+DHnxDP6/crOfv7R/T54mVdq3y9LfLuuqmUS/H/bmt2jiyj9L/Hffvj/wavYH3SSTvoYq87yJBH+6RbH042YaUfH0k8m8riT4iRR9lebWPeLbRvFG25kwdqk8nD9LV/3eqbhz5Ly1dlZzXIA/H3AW/6PS+Y9Xjmkc1c/5Pmnzf1apTK7bXIHdi7bFgwQJ16NCh5L01cOBAdejQQXfffbckaciQIbrlllt0/fXXq1OnTiosLNSHH36ojIwMx19POLzex4YtOzX0kbf0zD1XKSO9suOPn0iVUlM0eew1MgxDd9xvfc1AJznRh9t4fe3h1T6SoQ36oI9klQx9eL0Nyft9nNaplc46va1aNK2vUzq21IR7rtbuwn36aI795cVQlh/78PraQ6IPp7ipj0qR/sLdd9+tfv36aePGjSouLtbbb7+tZcuW6aWXXtKMGTNiMcYSY8eO1ahRo2L6HH6wduNv2r5zt5o2qKs53/4Ss+cxDENGqYsLlL5dnsA1Mq+++mpddNFFZe4PXCPzxRdfVJMmTTR8+HD16NFDP/30U0L+ocovfQx+8A19NPcHvf/MbTo6xtc8ipUuHY5R3VrV9f17o0v2VaqUqjEDLtINl3VTuwtGxOy5rdoI7JekvLy8oP0jRozQyJEjYzaeeKEP90i2PmgjtugjMvQRX36ZO9IqV1LTvLqSpPbHNtR3P63TpNdmafw/+sTl+Z22d98Brd6wXas3bNeCH9ZowVt364oLTta4KR/H7DmdWHt07dpVpmmGfI7Ro0dr9OjRtj8TT17vY8nSdfp1x251veKBkn1FRcWa991KPfvvOdr65Xilpkb8mf+ECxzgyMuppfNvfCLm3wCUnOnDbejDfX0kSxuB/V5GH/QRDj/OHZL3+ygts3oVNTq6jtZv+i2uz+t2fuzDb21I9FFRbuoj4gPkF1xwgd577z2NHj1a1apV0913360TTjhB7733nv785z9H9Fh16tRRamqqtm7dGrR/69atysnJKfPzQ4cO1cCBA0tuFxQUlPnHQJQvt15N1c6qpq2/xfYbYlanToj0VAo9e/ZUz549Le8rfY1M6dC1MLKzszV9+nRddtllFRt4FJzqI9I2pPj0YZqmhjz0b/1n1hK9N2mAGh1dx9HHj6fX3/9Ws79ZFrTvzcdv0hsffKOp730V0+e2O61IYN/69euVmZlZsr8i18kMvE+2bt2q+vUPn6J169atat++veXvVOR9Fwn6cI9k68PJNqTk6yORf1tJ9BEp+ijLq30kcu1RbJo6cOCPuDxXPKSkGEqrHPHSM+LniHbt4TZe7+P0Ti315av/CNp38+iX1bxxtgZc+WfXHdyQDh/gOKZhXfX6++PaGeNLDwTQh/fWHl7rI5naCOz3Mvqgj3D4ce6QvN9HaXt/36/1m3/TeWeeENPn8Ro/9uH1tYcV+qgYN/VRoX+lOO200/TJJ59E/eRpaWk68cQTNXPmzJLrthUXF2vmzJm6+eaby/x8enp6hf+xLxKFe/dr9fpfS26v3fSbvl+2QTWzqiovp3bMnz9S1aqkqcn/vnEiSY1yj9JxLY7Wrvy92lmwR3ded47e/Wyxtv5WoCYN6mjULb21av12zZz/c0zHFSqE0qfBqMh/2/KukZmIA+SSM31E2oYUnz4GPfCG3vxogV55+HpVr5qhrdsP/XfMrJ6hKhlpMX3uigjVxoatO8ssKv74o0hbfyvQirXbYjqu8hbhmZmZQQc5KqJJkybKycnRzJkzSw5oFBQU6Ouvv9YNN9xg+TsVed9Fij6Sh5v6cLINKTn7SNTfVhJ9WKEP+pDit/YY9eQ76n5yG+Xl1NLuvfv05ocL9MXC5XrriRtj/twVEaqPHfl7dMfVPfTBnO+1dXu+atesrmsvOV3169bUOzMXxXRcblqEO8nLfdSolqHWzXKD9lWtkqbaWdXK7E8WofrYsj1fLz5wrdq1ytNlt09SaqqhekfVkCTtzN+rg38UxWxc9FFxyfq3ldv6cFMbgf1eRx/Jw019+KENydt9PPzsDHXtfKzq16ulX3cU6Kl/faLU1BT17No+ps9bUeWtzWtmVlWDnFqqXydL0qFLV0nStt8KtO233TEbl1/78PLaQ6IPp7ipjwp/jH/BggX6+edDB1hbt26tE088sUKPM3DgQPXr108dO3bUSSedpPHjx2vPnj3q3z9x16Vc/PNa9fr74yW3/znubUlSn3M766mRVyRqWLbaH9tIM54eUHL7voH/J0l6ZcZXuuP+19W62dG67NzOyqpRRVt+zddnXy/VfZNm6MDB2H4rJVQITpwGtCLXyIwXJ/pIxjZeeOvQdUbO+/tjQfsn3H25/trrT4kYUkih2kjEtWMDnFqEFxYWasWKFSW3V69ercWLF6t27dpq2LChbrvtNo0ZM0bNmzcvuQRBbm5uyR8eknTmmWfqwgsvLPnjIx7vO/pIDm7qoyJ/RLmxDy//bUUfzqAPb/axfWehbhj5krZuL1Bm9Qy1aXa03nriRnXrfGzCxhRKqD4Gjn1NzRtn67JzO+uomtW0I3+vvvtprc65fpyWrort3+duWoQ7zct9uE2oPu5/5n2dc0ZbSdLcV4YG/d55f3tMXy5aHrNx0Yf31h5u46Y2Avv9gD6Sg5v68Esbknf72LY9X3fe/4p27d6rWlnVdUKbxnp53M2qXbN6wsYUSnlr856nH6+nRhw+XvPCfVdLku5/5n098Oz7MRuXn/vw8tqDPpzhpj4iPkC+YcMG9enTR19++aVq1qwpSdq1a5dOPvlkvfbaa2rQoEFEj/eXv/xFv/76q+6++25t2bJF7du314cffljmoGc8nXpiC+389smEPX+kvly0XLU62X875+JbJ8RxNIfF4zSgycbJPpKxDTd1IZXfRmmxvG7skZxahC9YsEDdunUruR041Uy/fv00ZcoUDRkyRHv27NH111+vXbt26dRTT9WHH36ojIyMkt9ZuXKltm/fXnI7lu87+kgubuqjIn9EuakPP/xtRR/OoI+akrzXxxPD+ybsuSuivD6uHPJcHEdzmJsW4U7xQx+lzXj6tkQPIaTy+ohkbnESfdSU5K21h5Vk7sNNbQT2exl9JBc39eH1NiTv9/HgUG+tPV6d8bVenfF1HEd0iB/78MPagz6c4aY+Ir7oyrXXXquDBw/q559/1o4dO7Rjxw79/PPPKi4u1rXXXluhQdx8881au3at9u/fr6+//lqdO3eu0OMguRiGYblJh08DGtiivcbykZy6XnJFON0HbXiTXRuBPsLVtWtXmaZZZpsyZUrJ84wePVpbtmzRvn379Omnn6pFixZBj7FmzZoyZ2+I1fuOPhAOJ9qQ3NUHf1shXPRBH7DnVB9uQh8IF32w9oA1p9bmbkMfCIcf25DoA+HxYx+sPRAuN/UR8TfIZ8+erXnz5qlly5Yl+1q2bKknnnhCp512mqODg7ulGoZSS30yxHQwhIpcIzPW6APhsGpDcraPZEQfCEes545kRBsIF30cQh+wQh+H0Aes0Mch9IHSWJvTB+z5ce6Q6APh8WMftIFwuamPiA+Q5+Xl6eDBg2X2FxUVKTc315FBwRucOJWCE9fIjCf6QDj8eho3+kA43HQaHqfQBsJFH4fRB0qjj8PoA6XRx2H0gSOxNg9GHziSH+cOiT4QHj/2QRsIl5v6iPgU6w899JBuueUWLViwoGTfggULNGDAAD388MOODg7ulpIipaYYQVtKhO+4BQsWqEOHDurQoYOkQ9fI7NChg+6++25J0pAhQ3TLLbfo+uuvV6dOnVRYWFjmGpnxRB8Ih1UbFenDbegD4XBi7nAb2kC46OMQ+oAV+jiEPmCFPg6hD5TG2pw+YM+Pc4dEHwiPH/ugDYTLTX2E9Q3yWrVqBZ0jfs+ePercubMqVTr063/88YcqVaqkq6++OmHf3EXySTEMpZQ6dULp2+UJXCPTTuAamaNHj67QGJ1AH4iUVRuB/V5DH4iUE3OHG9AGKoI+6AP26IM+YI8+6APWWJvTB+z5Ze6Q6AORc6KPoqIijRw5Ui+//LK2bNmi3NxcXXXVVRo2bFjSXK+ZNlARbuojrAPk48ePd+wJ4R8pxqGt9D6voQ9EyqqNwH6voQ9EirkDsEcfgD36AOzRB2CNtTlgzy9zh0QfiJwTfTzwwAOaOHGiXnzxRbVp00YLFixQ//79lZWVpVtvvdW5wUaBNlARbuojrAPk/fr1c+wJ4R9uutZANOgDkfLTdc7oA5Fi7gDs0Qdgjz4Ae/QBWGNtDtjzy9wh0Qci50Qf8+bN0wUXXKBzzz1XktS4cWO9+uqr+uabbxwbZ7RoAxXhpj7COkBuZ9++fTpw4EDQvszMzKgGBO8IXF/gSMUe/UPKCn3AjlUbEn3QByTmDtpAKPRBH7BHH/QBe/RBH7DG2pw+YM/vc4dEH7AXqo+CgoKg/enp6UpPTy/zGCeffLKeeeYZ/fLLL2rRooWWLFmiL774Qo8++mjsBu4Q2kAobuoj4kuj79mzRzfffLPq1aunatWqqVatWkEbEBD4pEjpzcvoA+Gwa4M+AOYO2kAo9EEfsEcf9AF79EEfsMbanD5gz49tSPSB8ITqIy8vT1lZWSXb2LFjLR/jrrvu0mWXXaZWrVqpcuXK6tChg2677Tb17ds3ni8lbLSBcLmpj4gPkA8ZMkSfffaZJk6cqPT0dD333HMaNWqUcnNz9dJLLzk6OLhbimFYbl5GHwiHXRv0ATB30AZCoQ/6gD36oA/Yow/6gDXW5vQBe35sQ6IPhCdUH+vXr1d+fn7JNnToUMvHeOONNzR16lS98sorWrRokV588UU9/PDDevHFF+P5UsJGGwiXm/qI+BTr7733nl566SV17dpV/fv312mnnaZmzZqpUaNGmjp1atJ+wgXxZ/WHk9f/kKIPhMNuUUEf9AHmDtpAKPRBH7BHH/QBe/RBH7DG2pw+YM+Pc4dEHwhPqD4yMzPDOt344MGDS74lK0nHH3+81q5dq7Fjxybltb9pA+FyUx8Rf4N8x44datq0qaRDL2bHjh2SpFNPPVVz5sxxbGBwv8C1BkpvXkYfCIddG/QBMHfQBkKhD/qAPfqgD9ijD/qANdbm9AF7fmxDog+Ex4k+9u7dq5SU4MNzqampKi4udnKojqENhMtNfUR8gLxp06ZavXq1JKlVq1Z64403JB36BEnNmjUdHRzczTDKXmfA8PgnDekD4bBqgz5qJnBkSCbMHbQBe/RBH7BHH/QBe/RBH7DG2pw+YM+Pc4dEHwiPE3306tVL9957r/7zn/9ozZo1mjZtmh599FFdeOGFMRp1dGgD4XJTHxEfIO/fv7+WLFki6dCF0idMmKCMjAzdfvvtGjx4sKODg7v58ZOG9IFw+PVT6vSBcNAGbcAefdAH7NEHfcAefdAHrLE2pw/Y82MbEn0gPE708cQTT+jiiy/WjTfeqGOPPVaDBg3S3/72N91zzz0xGnV0aAPhclMfEV+D/Pbbby/537t3766lS5dq4cKFatasmdq2bevo4OBuxv+20vu8jD4QDqs2ZLPPS+gD4WDuoA3Yow/6gD0n+igqKtLIkSP18ssva8uWLcrNzdVVV12lYcOGJeU3pugD4aIP+oA1J9bmbmtDog+Ex49zh0QfCI8TfdSoUUPjx4/X+PHjnRlUjNEGwuWmPiI+QF5ao0aN1KhRIyfGAo+x+mSIHz5peCT6gBW7T03RB8DcIdEG7NEHfcCeE3088MADmjhxol588UW1adNGCxYsUP/+/ZWVlaVbb73VyeHGBH3ADn3QB6w5sTZ3exsSfcAac8ch9AErrM1pA/bc1EdYB8gff/zxsB/QLZMbYi9wfYHS+7yGPhApqzYC+72GPhAp5o6yaAMB9FEWfSDAiT7mzZunCy64QOeee64kqXHjxnr11Vf1zTffODbOaNEHKoI+yqIPSM6szd3QhkQfiJxf5g6JPhA51uZl0QYC3NRHWAfIx40bF9aDGYZBCCjhpk+KRIM+ECk/fYOcPhAp5o5gtIEj0Ucw+sCRQvVRUFAQtD89PV3p6ellHuPkk0/WM888o19++UUtWrTQkiVL9MUXX+jRRx+N3cAjRB+oCPoIRh8IKG9tHk4fbmhDog9Ezi9zh0QfiBxr82C0gSO5qY+wDpCvXr061uOIysYde5V/MOqzxSdE03POT/QQKqxo/x4t+/5Z2/tTDUOppa4nU/q2FyR7H7v2HFBR6oFED6NCcrr2TPQQKqR4/16ti7CNwH6voY/Y8VMftJEY9BF/9HFIsvfx2+79OmDsT/QwKqR5r96JHkKFFe3fo58q2EdeXl7Q/hEjRmjkyJFlHuOuu+5SQUGBWrVqpdTUVBUVFenee+9V3759o38BDkn2PtzMrXOHFN38QR8Ih1f7KG9tHk4fbmhDoo9YcmsfzB2HJXsfR9euqszMqokeRoWkNu+Y6CFUiHnwd+l7+/tZmycH2kgML/XhzqPKcAXDMGSUeuOXvg34kVUbgf2A3zF3APboA7AXqo/169crMzOzZL/VN5wk6Y033tDUqVP1yiuvqE2bNlq8eLFuu+025ebmql+/frEbPBBj9AFYK29tHk4ftAGvYu4A7LE2B+y5qY+URA8A3pWaIlUqtaXyjgMs24i0j8aNG5dMNkduN910k+XPT5kypczPZmRkOPSKAOc4MXfQB7yKPgB7ofrIzMwM2uz+EXfw4MG66667dNlll+n444/XFVdcodtvv11jx46N4ysBnEcfgLXy1ubh9EEb8CrmDsAexz0Ae27qg2+QI2bcdK0BIJ6cuAb5t99+q6KiopLbP/zwg/785z/rkksusf2dzMxMLVu2rOR2sn5yC/7mxNxBH/Aq+gDsOdHH3r17lZISvHJPTU1VcXFx1OMDEok+AGtOrM1pA17F3AHY47gHYM9NfXCAHDHjphCAeHJiEV63bt2g2/fff7+OOeYYnXHGGba/YxiGcnJywh8okABOzB30Aa+iD8CeE3306tVL9957rxo2bKg2bdrou+++06OPPqqrr77ayaECcUcfgDUn1ua0Aa9i7gDscdwDsOemPjhAjphJUdlz+CfpmRSAuLJqQ0fsKygoCNqfnp5ue7oqSTpw4IBefvllDRw4MOS3+goLC9WoUSMVFxfrhBNO0H333ac2bdpE/gKAGAo1d0TahkQf8Bb6AOw5sfZ44oknNHz4cN14443atm2bcnNz9be//U133323Q6MEEoM+AGvlrc3DQRvwKuYOwB7HPQB7buqjQuOaO3euLr/8cnXp0kUbN26UJP3rX//SF1984ejg4G6phlHyaZGSzQen5KQPlMeyjSP6yMvLU1ZWVslW3rWZpk+frl27dumqq66y/ZmWLVvqhRde0DvvvKOXX35ZxcXFOvnkk7VhwwYnX1q56APlCTV3RNqG5J4+aAPhoA/6gD0n1h41atTQ+PHjtXbtWv3+++9auXKlxowZo7S0tBiNOnr0gXDQB33AWnlr83C4sQ2JPlA+v84dEn2gfBz3oA3Yc1MfER8gf+utt9SjRw9VqVJF3333nfbv3y9Jys/P13333ef4AOFeqSnWm5fRB8Jh10agj/Xr1ys/P79kGzp0aMjHe/7559WzZ0/l5uba/kyXLl105ZVXqn379jrjjDP09ttvq27dunr66aedfGkh0QfC4WQbkjv6oA2Eiz7oA/ZYe9AH7NEHfcBaeWtzr6IPhMOPbUj0gfD4sQ/aQLjc1EfEwxozZowmTZqkZ599VpUrVy7Zf8opp2jRokWODg7ulpJy+HoDgS0lSUNwCn0gHFZtHNlHZmZm0BbqFLlr167Vp59+qmuvvTaiMVSuXFkdOnTQihUronkpEaEPhCPU3BFJG5J7+qANhIs+6AP2WHvQB+zRB33AWnlrc6+iD4TDj3OHRB8Ijx/7oA2Ey019RDysZcuW6fTTTy+zPysrS7t27XJiTPCIVMOw3LyMPhAOuzYq0sfkyZNVr149nXvuuRH9XlFRkb7//nvVr18/4uesKPpAOJycO9zSB20gXPRxGH2gNNYeh9EHSqOPw+gDR3Jybe4m9IFw+LENiT4QHj/2QRsIl5v6iPgAeU5OjuU3Rr744gs1bdrUkUHBG1IM683L6APhsGsj0j6Ki4s1efJk9evXT5UqVQq678orrww6ve7o0aP18ccfa9WqVVq0aJEuv/xyrV27NuJvDkaDPhAOp+YON/VBGwgXfRxGHyiNtcdh9IHS6OMw+sCRnFqbuw19IBx+bEOiD4THj33QBsLlpj4iPkB+3XXXacCAAfr6669lGIY2bdqkqVOnatCgQbrhhhtiMUa4lNVpqlKTtQSH0AfCYddGpH18+umnWrduna6++uoy961bt06bN28uub1z505dd911OvbYY3XOOeeooKBA8+bNU+vWraN+PeGiD4TDqbnDTX3QBsJFH/QBe6w96AP26IM+YM2ptbnb0AfC4cc2JPpAePzYB20gXG7qo1L5PxLsrrvuUnFxsc4880zt3btXp59+utLT0zVo0CDdcsstsRgjXCo15dBWep+X0QfCYdVGYH8kzjrrLJmmaXnfrFmzgm6PGzdO48aNi+wJHEYfCIdTc4eb+qANhIs+6AP2WHvQB+zRB33AmlNrc7ehD4TDj3OHRB8Ijx/7oA2Ey019RHyA3DAM/fOf/9TgwYO1YsUKFRYWqnXr1qpevXosxgcXq5RiqFKpT4aUvu019IFwWLUR2O9l9IFwMHfQBuzRB33AHn3QB+zRB33AGmtz+oA9P84dEn0gPH7sgzYQLjf1EfEB8oC0tLS4npoX7pNiGEo1jDL7/IA+EIpVG4H9fkAfCIW5gzZgjz7oA/bogz5gjz7oA9ZYm9MH7Pl57pDoA6H5uQ/aQHnc1EfEB8i7desmI8SL+eyzz6IaELzDMA5tpfd5GX0gHFZtBPZ7GX0gHMwdZdEGAuijLPpAAH2URR8IoI+y6AMSa3M79AHJn3OHRB8Ijx/7oA2Ey019RHyAvH379kG3Dx48qMWLF+uHH35Qv379nBoXPMBNp1JwCn0gHH49jRt9IBzMHbQBe/RBH7BHH/QBe/RBH7DG2vwQ+oAVP84dEn0gPH7sgzYQLjf1EfEB8nHjxlnuHzlypAoLC6MeELwj1TCUWuqNb3XqKi+hD4TDqo3Afi+jD4SDueMw2kBp9HEYfaA0+jiMPlAafRxGHzgSa/Ng9IEj+XHukOgD4fFjH7SBcLmpjxSnHujyyy/XCy+84NTDwQNSDevNj+gDR7Jrgz4A2jgSbaA0+jiMPlAafRxGHyiNPg6jDxyJtXkw+sCRaCMYfeBI9HEYbaA0N/UR8TfI7cyfP18ZGRlOPVxCvT5jvt6YMV+btu2UJB3TMFt/69tdp3VqleCRldWhUS1deUpjHVu/hupmZuiOV7/TrKW/ltzf7dh6urhjA7XKzVTNqmnqM3G+ftmyOy5jMwyjzHUpQl2nwsu81MfL73ypqe/M08YtOyRJzRvn6JZ+Z6lr52MTPLKyOjaprau7NlWbo7NULytDN09ZoJk/bi25/8/H5egvXRqqzdFZqlktTReOm6ulmwpiPi6rNgL7/cgrfbipDcldfdCG+9GHM+jjMC/1UdrTr8zUw8+9r34XnaZhN/dO9HDK6NCwpi4/uZFa1c9U3RrpGvz6Es1e9mvQz1zftal6dzha1TMq6b/rd+mB95dq/Y7fYzou+jjMq32Mm/KxRk94V3+/rKvG3nFxoodjqbz5Q5JuOauFLumcpxpVKuu7NTs16u3vtXb73piOiz4Oo4/EcFMbgf1+RB+J4aY+/NqGRB+J0qVVtm4+7zi1a3KUcmpV1RWPfqYPFqwL+pm7Lm6vK7q1UGa1NH3zyzYNfmG+VsX4+Ad9HObVNiT6qCg39RHxAfKLLroo6LZpmtq8ebMWLFig4cOHR/RYc+bM0UMPPaSFCxdq8+bNmjZtmnr37h3pkByXXSdLt13dUw2PriPTlN79dKEGjHpRbzw5QM0a5yR6eEGqVE7VL1t2691FG/Vwn/aW9y9et0uf/LhVwy9oE9expRpGmVMnJOupFJzihz7q162pIdefq8YN6so0Tb390QL97Z8v6L1n71CLJknWR1qqlm0q0NvfrtcT/Tpa3r9o9Q59uGSz7rmkbdzGZdVGYL+Xeb0PN7UhuasP2ogMfUSPPpKH1+eO0v67dJ1em/GVWjWtn+ih2MpIS9XyrYV677tNevAv7crcf+XJjfSXk/I0avpP2rTrd/2t2zF6vG8H/eWpr3SgqDhm46IPb/ex6Me1mjLtS7VpfnSihxJSefPHtV2b6vJTG2vo60u0Ycde3dqjhZ69trPOe3i2DvxBH06ij+TipjYC+72MPpKLm/rwehsSfSSbqumV9MPaHZo6a7leGvj/ytx/S6/jdF2P1rp50lyt3VaooZd00Bt3naVTBk/X/oNFMRuXH/vwUxsSfUTDTX1EfIA8Kysr6HZKSopatmyp0aNH66yzzorosfbs2aN27drp6quvLhNYInX9U+ug27dedbbemDFf/126LukOkM9bsV3zVmy3vf/9/26WJNWvGf9P8RiGVPpSTknagWP80MeZJwd/0GLQtedo6jtf6ruf1iTdQY65y37V3FLfaDrSu4s2SpJya1WJ15AkWbcR2O9lXu/DTW1I7uqDNiJDH9Gjj+Th9bnjSHt+36877puqMXdcoqde/jTRw7E1f8Vvmr/iN9v7L+vcUC/MXa05vxxqaOT0H/ThHafrjFZ19Umpb0M5iT6820fh3v26/u4peuwfffTwCx8mejghlTd/XHlaE02auUKf/a+Fu15boi/u7q7ubbL1/pLNMRsXfdBHormpjcB+L6OP5OKmPrzehkQfyWbmko2auWSj7f1/P7u1Hp2+RB8sXC9JunHiXP088TKd07Ghps1fHbNx+bEPv7Qh0Ue03NRHRAfIi4qK1L9/fx1//PGqVatW1E/es2dP9ezZM+rHiaWiomJ9PPe/+n3/AbU7tlGih+MqbvqkiBP82sf7s5bo930HdEKbxokejmv48VPqfuuDNiqOuSN69OFd9BGdZG9j1GNvq2vn1jrlxBZJfYA8lNyaVVSnRrq+WbWjZN+e/UX6cWOBjm+QFdMD5PQRnWTuY/CDr+usU45T186tkvofqMrToHYV1c3M0Pzlhz/gXrjvD/133S61a1Qrpgc56CM69BFbydZGYL9X0Ye7JFsfXm5Dog+3aVSvurJrVdXsHw53sPv3g1q08ld1bF43pgcA/daHn9qQ6CNabuojogPkqampOuuss/Tzzz879o+4kdi/f7/2799fcrugIHbXevxl9WZdcfsEHTjwh6pWSdP44VfqmEbZMXs+L0oxDKWUeuOXvu0lfupj6apNuvjGx7X/f31MvKe/mifZ2RWSmVUbgf1e5Zc+aCN6zB3xRx/uQR/xFc+/rWZ89p1+XL5Bb0+8LWbPEQ9HVU+TJO3YcyBo/47CAyX3xQp9xFe8+njr4wVasnS9PntxSEweP57q1Dh0Vrffdu8P2r+9cL/q1kiP6XPTR3zRR2SSrY3Afq+iD3dJtj683IZEH25TL+vQGd1+zf89aP+2/N+VnRXbs735rQ+/tCHRhxPc1EdKpL9w3HHHadWqVbEYS7nGjh2rrKyski0vLy9mz9WkQV39+6nbNPWxm3XpuV007JE3tHJt7L7x4EWGYVhuXuaXPprm1dOM5+7Q2xMHqO8FJ2vw2Fe1fM2WmD2f19i1QR+xE68+aCN6tBF/9OEe9BFf8Wpj87adGjNhuh75R1+lp1WOyXP4AX3EVzz62LBlp4Y+8paeuecqZaTTRjToI77owz1Ym8cffbiHH9uQ6APh8WMfXm9Dog+nuKmPiA+QjxkzRoMGDdKMGTO0efNmFRQUBG2xNHToUOXn55ds69evj9lzVa5cSQ1z66h18wYacHVPtWhSX1OnfxGz5/OiwKkUSm9e5pc+0ipXUuMGdXV8yzwNuf48tTomV1PemhOz5/MauzboI3bi1QdtRI824tuGRB9uQh/enDt++GWDfttZqN5/G6dW3QerVffB+mbJSr007Qu16j5YRUXFMXneWPit8NA3x2tXC/62eO3qaSX3xYpTfWzcuFGXX365jjrqKFWpUkXHH3+8FixYEIMRR8/rfSxZuk6/7titrlc8oDp/ulV1/nSrvly0Qk+/Plt1/nSrq9qQpO2790mSjir1jb861dP1a6lvBjqN+YM+klkytkEfsUMfkUnGPryOPtzTx7b/fTO2bqlvw9bLqqKtpb416zQ/9uH1NiT6cIqb+gj7FOujR4/WHXfcoXPOOUeSdP755wcd9TdNU4ZhqKioyPlR/k96errS02N7+hg7xaapAwf/SMhzu1WKcWgrvc+L/N6HaZo6cCB2r81rrNoI7PciP/dBG5Fj7ohvGxJ9uAl9eHPu6HJCc/3n+UFB++568HU1zaun6/t0U2pqxJ9pTphNu37X9t371alJbS3fWihJqpaWqjZHZ+qtBRti+txO9LFz506dcsop6tatmz744APVrVtXy5cvT9glMOz4pY/TO7XUl6/+I2jfzaNfVvPG2Rpw5Z9d1YYkbdjxu34t2Kc/NTtKSzcd+kfEaumV1LZhTb02f21Mn5v5gz6SWbK1EdjvRfRBH5Hw09wh0Ycb+1i7rVBbd+7V6W3q64e1OyRJ1atU1gnH1NXkT5fF9Ln91Idf2pDowylu6iPsA+SjRo3S3//+d33++eexHE9SeOyFD3RKp5aqX7em9vy+Xx98vlgL/rtKk+69JtFDK6NKWqryalctuZ1bq4pa5NRQwe8HtSV/nzKrVFJOVpWS69I0OurQz/5WuD9u3+IovS8SI0eO1KhRo4L2tWzZUkuXLo16fE7yUx8PPjNDXTsfq9x6tVT4+z69++kifbV4paY8dH2ih1ZG1bRUNaxTreR2g9pV1So3U/l7D2jzrn3KqlJZ9WtVUb3MQ300qXvoZ7fv3q/tMfwkrt2nppL1k1TR8ksfbmpDclcftOF+9OEM+vCm6lUz1KJJ/aB9VTLSVDOzapn9yaBK5VQ1qH34U+i5NauoeXZ1Ffx+UFsL9uu1r9fp6tOaaP2Ovdq063f9vesx2r57v2Yv/TWm43KijwceeEB5eXmaPHlyyb4mTZo4Mj4n+aWPGtUy1LpZbtC+qlXSVDurWpn9yaK8+eOluav19zOba+32Pdqw43fd2qOFthXs16c/xvZybswf3uO2PtzURmC/F9EHfUTCT3OHRB/J2ke19EpqkpNZcrtR3eo6rlFt7Szcr42/7dGkD3/SwAvbatWWAq39dbeGXnKCtuzaq/cXrIvpuJzqY+PGjbrzzjv1wQcfaO/evWrWrJkmT56sjh07OjXUqPmlDYk+nOKmPsI+QG6apiTpjDPOcOzJCwsLtWLFipLbq1ev1uLFi1W7dm01bNjQseeJ1I5dhRr20Ov6dWdByT9YTbr3GnU5oUXCxmSndW6mnunfqeT2HWe3kiS9991GjZz+o85oWU8jLzyu5P77L20nSXr685V6ZtbKmI4txTCUUuqNX/p2ONq0aaNPP/205HalSmG/bePGT338tqtQd9z3in7dUaAa1aqoZdP6mvLQ9TqtY8uEjclOmwZZeumGLiW37zq/tSRp2oL1+sfr/1W3Ntka+5d2Jfc/evkJkqQnP/5FEz5ZHrNxWbUR2O9FfunDTW1I7uqDNiJDH9Gjj8Tzy9zhRsfmZmpSvxNLbt/e49AaacbiTRr97k96ad5aZaSl6h/nHavqGZW0ZN0uDZi6WAdifDq6UH2UPuWf3TcQ3n33XfXo0UOXXHKJZs+eraOPPlo33nijrrvuutgNvALoI3mVN388N2uVqqRV0qiLj1dmRmUtWrNT1z/3jQ78kbg+vIY+kpOb2gjs9yL6SE5u6sOrbUj0kazaN62jd4afXXJ7zBUnSZJenb1Ctzz9hZ547wdVS6+kR649WVlV0/T1L1v1l/s/0f6DsT1bnRN9uOXsVbSRvOgjehEdaXT6QuoLFixQt27dSm4PHDhQktSvXz9NmTLF0eeKxKiBlyTsuSO1cM1OnTjiY9v731u8Se8t3hTHER1myFCKjDL7IlWpUiXl5OQ4NayY8UsfDwy5LGHPHalvV+3QsYP/Y3v/9AUbND3Gp/u0YtVGYL9X+aEPN7UhuasP2ogMfUSPPpKDH+YOO1PH3ZjoIdhatHanThr9acifeWbWKj0za1WcRnRIqD7y8vKC9o8YMUIjR44s8xirVq3SxIkTNXDgQP3jH//Qt99+q1tvvVVpaWnq169fzMZeEX7tY8bTtyV6CCGVN39I0hMf/6InPv4lTiM6hPkjOvQRPTe1EdjvVfSRfNzUh5fbkOgjGX358xbV+euUkD9z/5uLdf+bi+MyngAn+nDL2ask/7Yh0UdFuKmPiA6Qt2jRotwYduzYEfbjde3ateQTKPCelJRDW+l9Uvjf4pCk5cuXKzc3VxkZGerSpYvGjh2blJ8kog+Ey6qNwH6vog+EK9Tc4UVOtyHRh5fRR1nMHQgI1cf69euVmXn41HN2647i4mJ17NhR9913nySpQ4cO+uGHHzRp0qSkO0BOH4gE80dZ9AGJtbkd+oDkv7lDog+Ez4njHm45e5VEG4iMm/qI6AD5qFGjlJWV5egA4F2hrjUQ7rc4OnfurClTpqhly5bavHmzRo0apdNOO00//PCDatSoEbOxVwR9IFx+u86ZRB8Inx+vc0YbCBd9APZC9ZGZmRl0gNxO/fr11bp166B9xx57rN566y3nBuoQ+kAkmD8Aa6zNAXt+mzsk+kD4nDju4aazV9EGIuGmPiI6QH7ZZZepXr16jj05vC3UtQbC/RZHz549S/73tm3bqnPnzmrUqJHeeOMNXXPNNTEYdcXRB8LlxHXORo4cqVGjRgXta9mypZYuXWr7O//+9781fPhwrVmzRs2bN9cDDzygc845J/yBR4E+EC4nrlPjpj5oA5GgD8CeE32ccsopWrZsWdC+X375RY0aNYp6fE6jD0TCqevIbty4UXfeeac++OAD7d27V82aNdPkyZPVsWNHp4bqCPpAuJy6Brlb2pDoA+Hz29wh0QfC58RxDzedvYo2EAk39RH2iVFicY1MeJuhQ9cWCP6fQwLf4ghsdiGUVrNmTbVo0UIrVqyI2bgrgj4QCes2Ir+SU5s2bbR58+aS7YsvvrD92Xnz5qlPnz665ppr9N1336l3797q3bu3fvjhh6heSzjoA5EINXdEwg190AYiRR+APSf6uP322/XVV1/pvvvu04oVK/TKK6/omWee0U033RSLIVcYfSBSTvSxc+dOnXLKKapcubI++OAD/fTTT3rkkUdUq1atWAy5wugDkXBibe6WNiT6QGT8NHdI9IHIOHHcw+7sVevWrYvt4CNEG4iUm/oI+xvkXBMAkYrFqXgKCwu1cuVKXXHFFVE9jtPoA5Fw6jRulSpVUk5OTlg/+9hjj+nss8/W4MGDJUn33HOPPvnkEz355JOaNGlSRM8bKfpAJJyaO9zQB20gUvQB2HOij06dOmnatGkaOnSoRo8erSZNmmj8+PHq27evk0ONGn0gUk708cADDygvL0+TJ08u2dekSRNHxuck+kAknFibu6UNiT4QGT/NHRJ9IDJO9OGWs1fRBiLlpj7C/gZ5cXExp1FARFIM6y0SgwYN0uzZs7VmzRrNmzdPF154oVJTU9WnT5/YDLqC6AORsGsj0EdBQUHQtn//fsvHWb58uXJzc9W0aVP17ds35Ceo5s+fr+7duwft69Gjh+bPn+/Y67JDH4iEE21I7uiDNhAp+gDsObH2kKTzzjtP33//vfbt26eff/5Z1113nfODjRJ9IFJOzB/vvvuuOnbsqEsuuUT16tVThw4d9Oyzz8bxVYSHPhAJJ9bmbmlDog9Exk9zh0QfiIwTaw+3nL2KNhApN/UR9gFyIFKpMko+LVKyRXgyng0bNqhPnz5q2bKlLr30Uh111FH66quvVLdu3RiNGog9yzaO6CMvL09ZWVkl29ixY8s8RufOnTVlyhR9+OGHmjhxolavXq3TTjtNu3fvtnzOLVu2KDs7O2hfdna2tmzZ4vwLBKIQau4Ipw2JPuBd9AHYc2LtAXiVE/PHqlWrNHHiRDVv3lwfffSRbrjhBt1666168cUX4/lSAEc5sTanDXgVcwdgz4m1R+DsVa+++qqOO+443XPPPUl59iogUm7qI+xTrAORMgyjzDUqIr1mxWuvvebkkICkYNVGYL8krV+/XpmZmSX7ra7F0bNnz5L/vW3bturcubMaNWqkN954Q9dcc00MRg3ER6i5I5w2JPqAd9EHYM+JtQfgVU7MH8XFxerYsaPuu+8+SVKHDh30ww8/aNKkSerXr1+MRg7ElhNrc9qAVzF3APacWnucd955Ou+885waFpAU3NQHB8gRO4ZU5n3Pv1EB1m38b78kZWZmBi00wlGzZk21aNFCK1assLw/JydHW7duDdq3devWsK9BC8RNiLmjIm1I9AEPoQ/AHmsPwJ4D80f9+vXVunXroH3HHnus3nrrLYcGCSSAA2tz2oBnMXcA9lh7APZc1AenWEfMGIb1BvidXRvR9FFYWKiVK1eqfv36lvd36dJFM2fODNr3ySefqEuXLhV/UiAGYjF30Ae8gj4Ae6w9AHtO9HHKKado2bJlQft++eUXNWrUyMGRAvHlxNqcNuBVzB2APdYegD039cEBcsRMimFYboDf2bURSR+DBg3S7NmztWbNGs2bN08XXnihUlNT1adPH0nSlVdeqaFDh5b8/IABA/Thhx/qkUce0dKlSzVy5EgtWLBAN998s+OvD4iGE3MHfcCr6AOwx9oDsOdEH7fffru++uor3XfffVqxYoVeeeUVPfPMM7rppptiNGog9pxYm9MGvIq5A7DH2gOw56Y+OMU6YsbqkyFJ2gEQV3afmoqkjw0bNqhPnz767bffVLduXZ166qn66quvVLduXUnSunXrlJJy+DNQJ598sl555RUNGzZM//jHP9S8eXNNnz5dxx13XLQvB3CUE3MHfcCr6AOwx9oDsOdEH506ddK0adM0dOhQjR49Wk2aNNH48ePVt29f5wYKxJkTa3PagFcxdwD2WHsA9tzUBwfIETNWnwxJ1k+KAPFk96mpSPp47bXXQt4/a9asMvsuueQSXXLJJWE/B5AITswd9AGvog/AHmsPwJ5TfZx33nk677zznBoWkHBOrM0l2oA3MXcA9lh7APbc1AcHyBEzxv+20vsAv7NqQzb7AL9h7gDs0Qdgjz4Ae/QBWGNtDthj7gDs0Qdgz019cIAcMWMYhoxSnwwpfRvwI6s2AvsBv2PuAOzRB2CPPgB79AFYY20O2GPuAOzRB2DPTX1wgBwxk2Ic2krvA/zOqo3AfsDvmDsAe/QB2KMPwB59ANZYmwP2mDsAe/QB2HNTHxwgR8y46ZMiQDzxKXXAHnMHYI8+AHv0AdijD8Aaa3PAHnMHYI8+AHtu6sPVB8hN05QkFe7eneCRVFzR/j2JHkKFFe3fK+nwf4fSDOPQVnof4sMLfRT/7z3mNsUHIm8jsB/xQR+JU5E+aCO+6CNx6CO5eaEN1h6IlcB/l90FBQkeScW5de6QmD+SHX0kVqg+WJsnHn0kDnNH8vNCH+bB3xM9hAoJjJs+khNtJJaX+nD1AfLd//vHqdNPaJHgkfjb7t27lZWVVWa/m0LwokAfJ7ZpmuCR+FckbQT2Iz7oI/GYO5IXfSQefSSnQBsnHX9Mgkfib/SRnAJ9NGuSl+CR+Bt9JCf6SA5WfbA2Tzz6SDzmjuRFH4lHH8mJNpKDF/pw9QHy3NxcrV+/XjVq1IjJV/QLCgqUl5en9evXKzMz0/HHj7VYj980Te3evVu5ubmW96cYhlJK/XcpfRuxQx+hxXL8FWkjsB/xQR/2mDsQyz7c3IZEH37H3BEaffgbfYRGH/5GH6Elsg/W5olHH/aYO0Af9ujD32gjNPoIn6sPkKekpKhBgwYxf57MzExXhhAQy/FbfUIkwE2fFPEi+ghPrMYfaRuB/YgP+igfc4d/xaMPN7ch0YdfMXeEhz78iT7CQx/+RB/hSUQfrM0Tjz7Kx9zhX/RRPvrwJ9oID32Uz9UHyJHcDItPisTiEz2A21i1EdgP+B1zB2CPPgB79AHYow/AGmtzwB5zB2CPPgB7buqDA+SIGeN/W+l9gN9ZtSGbfYDfMHcA9ugDsEcfgD36AKyxNgfsMXcA9ugDsOemPlISPYBklp6erhEjRig9PT3RQ6mQRI/fMAzLDd6Q6PdXtBI5frs26MM73NxHosdOG96W6PdXtBI9fvrwtkS/v6KV6PE73cf9998vwzB02223OTdIVFii31/RSvT4mT+8LdHvr2ixNkcsubmPRI+dNrwv0e+xaCR67PThbYl+f0Ur0eN3Ux+GaZpmogcBbykoKFBWVpb+u2qratQIvsbB7t0Fats0W/n5+a6+fgNQEaHakOgD/sbcAdijD8BeLPr49ttvdemllyozM1PdunXT+PHjHR41EB/MH4A11uaAPeYOwB59APbc2AffIEfMuOmTIkA88Sl1wB5tAPboA7DnVB+FhYXq27evnn32WdWqVSsGIwXij/kDsMbaHLBHG4A9+gDsuakPDpAjZlIkpRiltkQPCkgClm3QByCJuQMIhT4Ae071cdNNN+ncc89V9+7dnR4ikDDMH4A11uaAPeYOwB59APbc1EelRA8A3mUYh7bS+wC/s2ojsB/wO+YOwB59APZC9VFQUBC0Pz093fJ6bK+99poWLVqkb7/9NlbDBBKC+QOwxtocsMfcAdijD8Cem/pI1gP38AA3nUoBiCdO4wbYow3AHn0A9kL1kZeXp6ysrJJt7NixZX5//fr1GjBggKZOnaqMjIx4Dx+IKeYPwBprc8AebQD26AOw56Y+OEAewoQJE9S4cWNlZGSoc+fO+uabbxI9pLDMmTNHvXr1Um5urgzD0PTp0xMyDsvTVCVnB4iQW9uQkqMPuzbowxvoIzq04W1u7SMZ2pDow+voIzqh+li/fr3y8/NLtqFDh5b5/YULF2rbtm064YQTVKlSJVWqVEmzZ8/W448/rkqVKqmoqCjOrwhHoo/oMH94l1vbkJKjD9bm3kYf0aENb3NrH8nQhkQfXkcf0XFTHxwgt/H6669r4MCBGjFihBYtWqR27dqpR48e2rZtW6KHVq49e/aoXbt2mjBhQkLH4aZPiiB8bm5DSo4++JS6d9FH9GjDu9zcRzK0IdGHl9FH9EL1kZmZGbRZnV79zDPP1Pfff6/FixeXbB07dlTfvn21ePFipaamxvsl4X/oI3rMH97k5jak5OiDtbl30Uf0aMO73NxHMrQh0YeX0Uf03NSHYZqmmehBJKPOnTurU6dOevLJJyVJxcXFysvL0y233KK77rorwaMLn2EYmjZtmnr37h235ywoKFBWVpZWbNiuGpmZQfftLihQswZ1lJ+fr8xS98EdvNKGFP8+QrUh0YcX0EfFMXd4n1f64G8rxAJ9VFws++jatavat2+v8ePHOzRaVAR9VBzzh7d5pQ0pudYeEn14AX1UHHOH93mlD/62QizQR8W5sQ++QW7hwIEDWrhwobp3716yLyUlRd27d9f8+fMTODJ3MWz+B+5FG86wayOSPsaOHatOnTqpRo0aqlevnnr37q1ly5aF/J0pU6aU+eQW19h0Dn04w4m5gz6SD304gz68iT6cwdrDm+jDGfThPbThDCfW5kg+9OEM2vAm+nAGfXgTfTjDTX1wgNzC9u3bVVRUpOzs7KD92dnZ2rJlS4JG5T6GYb3BvWjDGXZtRNLH7NmzddNNN+mrr77SJ598ooMHD+qss87Snj17Qv5eZmamNm/eXLKtXbs2yleDAPpwhhNzB30kH/pwBn14E304IxZrj1mzZvHt8QSjD2ewNvce2nCGE2tzJB/6cAZteBN9OIM+vIk+nOGmPiolegDwLsMwlFLqnZ+s1xoA4smqjcD+cH344YdBt6dMmaJ69epp4cKFOv3000M+d05OTviDBeLMibmDPuBV9AHYY+0B2KMPwJoTa3PAq5g7AHv0AdhzUx98g9xCnTp1lJqaqq1btwbt37p1K/8wGAE3fVIE4aENZ5T3KfWCgoKgbf/+/eU+Zn5+viSpdu3aIX+usLBQjRo1Ul5eni644AL9+OOPUb8eHEIfznC6DYk+kgF9OIM+vIk+nMHaw5vowxn04T204Qy+Qe5N9OEM2vAm+nAGfXgTfTjDTX1wgNxCWlqaTjzxRM2cObNkX3FxsWbOnKkuXbokcGTu4qYQEB7acEZ5i/C8vDxlZWWVbGPHjg35eMXFxbrtttt0yimn6LjjjrP9uZYtW+qFF17QO++8o5dfflnFxcU6+eSTtWHDBidfnm/RhzOcbEOij2RBH86gD2+iD2ew9vAm+nAGfXgPbTiDA+TeRB/OoA1vog9n0Ic30Ycz3NQHp1i3MXDgQPXr108dO3bUSSedpPHjx2vPnj3q379/oodWrsLCQq1YsaLk9urVq7V48WLVrl1bDRs2jNs4UixOpWB16iq4i5vbkJKjD6s2Avslaf369crMzCzZn56eHvLxbrrpJv3www/64osvQv5cly5dgibzk08+Wccee6yefvpp3XPPPZG8BNigj+iFmjsibUOij2Ti5j6SoQ2JPryMPqLH2sO76CN69OFNbm5DSo4+ylubw73oI3rMHd7l5j6SoQ2JPryMPqLnqj5M2HriiSfMhg0bmmlpaeZJJ51kfvXVV4keUlg+//xzU1KZrV+/fnF5/vz8fFOSuWHbTrNgX1HQtmHbTlOSmZ+fH5exIDbc2oZpJraPUG1UtI+bbrrJbNCggblq1aoKjeniiy82L7vssgr9LqzRR8XEYu6gj+Tj1j68+LcVfSQf+qgY1h7+QB8VE6s+xo4da0oyBwwY4PygERG3tmGaybv2oA/voI+KieXfVvSRPNzah1f/tjJN+kgm9FExbuzDME3TrOCxdcBSQUGBsrKytOnXXUHfZArcl1u3pvLz88vcB3hdqDYC94fbh2mauuWWWzRt2jTNmjVLzZs3j3g8RUVFatOmjc455xw9+uijEf8+4CQn5w76gNfQB2CPtQdgLxZ9fPvtt7r00kuVmZmpbt26afz48Q6PGog9J9fmR6IPeEGs/raiD3gBfQD23NgH1yBHzLjpWgNAPDlxnbObbrpJL7/8sl555RXVqFFDW7Zs0ZYtW/T777+X/MyVV16poUOHltwePXq0Pv74Y61atUqLFi3S5ZdfrrVr1+raa6918uUBUXFi7qAPeBV9APZYewD2nOqjsLBQffv21bPPPqtatWo5P1Agzpy8Bjl9wGuc/NuKPuA19AHYc1MfXIMcMbO7YLcMGWX2AX5n1UZgf7gmTpwoSeratWvQ/smTJ+uqq66SJK1bt04pKYc/B7Vz505dd9112rJli2rVqqUTTzxR8+bNU+vWrSN/EUCMODF30Ae8ij4Ae6w9AHuh+igoKAjan56ervT0dMvHuemmm3Tuueeqe/fuGjNmTGwGC8RReWtz+oCfOTV3SPQB76EPwJ6b+uAAORyXlpamnJwctWiaZ3l/Tk6O0tLS4jwqIPHKa0MKv49wro4xa9asoNvjxo3TuHHjyv09IBGcnDvoA15DH4A91h6AvfL6qF69uvLygu8bMWKERo4cWeZnX3vtNS1atEjffvttLIYKxFU4a3P6wP9n787joqgfP46/F5RTQLw4FBFF8b7N0Lx+UWpq2mWaJWrZoeVB3nnlhVYqeV8J3mWpZJ6pqXnlrWUZKiIggkcKiCggzO8Pv6wu7Cx7zB6z837+Hvt4fHd22flM8vp9dpjdGaWScu4A2AfZF/ZBJE6OffAAOUnOxcUFCQkJyM3N1fq4k5MTXFxcLDwqIusrqQ2AfZByce4gEsc+iMSxDyJxJfUhCAJURc53qO0bHMnJyRg6dCj27NnDnsgu6LNvzj5IqaSaOwD2QfaHfRCJk2MfKkGfr5EQEREREREREZHixMbG4rXXXoOjo6N6WX5+PlQqFRwcHJCTk6PxGJGSsA8iceyDSBz7IBJnqT54gJyIiIiIiIiIiLS6f/8+EhMTNZb1798ftWvXxujRo1G/fn0rjYzI+tgHkTj2QSSOfRCJs1QfPMU6ERERERERERFp5eHhUeyPUO7u7ihfvjz/eEuKxz6IxLEPInHsg0icpfpwkOyViIiIiIiIiIiIiIiIiIiIbBgPkJegX79+6NGjh/p++/btMWzYMIuP48CBA1CpVEhPTxd9jkqlQmxsrN6vOXnyZDRu3NikcV27dg0qlQrnzp0z6XVIntiHbuxD2diHbuxD2diHbuxDudiGbmxD2diHbpbu48CBA4iKirLIuqhk7EM39qFs7EM39qFs7EM39qFcbEM3a+ybm6MPWR4g79evH1QqFVQqFZycnBAcHIwpU6bg8ePHZl/35s2bMXXqVL2eq88vL5HU2AeROPZBJI59EGnHNojEsQ8iceyDSBz7IBLHPoi0YxskNdleg7xTp06Ijo5GTk4OduzYgcGDB6N06dIYO3Zssefm5ubCyclJkvWWK1dOktchMif2QSSOfRCJYx9E2rENInHsg0gc+yASxz6IxLEPIu3YBklJlt8gBwBnZ2f4+voiMDAQn3zyCcLCwrB161YAT09/MH36dPj7+yMkJAQAkJycjJ49e6Js2bIoV64cunfvjmvXrqlfMz8/HxEREShbtizKly+PUaNGQRAEjfUWPZVCTk4ORo8ejYCAADg7OyM4OBjfffcdrl27hg4dOgAAvL29oVKp0K9fPwBAQUEBIiMjERQUBFdXVzRq1Ag//fSTxnp27NiBWrVqwdXVFR06dNAYp75Gjx6NWrVqwc3NDdWrV8eECROQl5dX7HlLly5FQEAA3Nzc0LNnT2RkZGg8vmLFCtSpUwcuLi6oXbs2Fi1aZPBYyLLYR8nYh3Kxj5KxD+ViHyVjH8rENkrGNpSLfZSMfSgX+ygZ+1Au9lEy9qFc7KNk7EOZ2EbJ2Ib+ZHuAvChXV1fk5uaq7+/btw9xcXHYs2cPtm3bhry8PHTs2BEeHh44dOgQjhw5gjJlyqBTp07qn5s9ezZiYmKwcuVKHD58GHfv3sWWLVt0rrdv377YsGED5s2bh4sXL2Lp0qUoU6YMAgICsGnTJgBAXFwcUlNT8e233wIAIiMjsXr1aixZsgR///03hg8fjnfffRcHDx4E8CTY119/Hd26dcO5c+fwwQcfYMyYMQb/N/Hw8EBMTAz++ecffPvtt1i+fDnmzp2r8ZwrV65g48aN+OWXX7Br1y6cPXsWgwYNUj++bt06TJw4EdOnT8fFixcxY8YMTJgwAatWrTJ4PGQ97KM49kGF2Edx7IMKsY/i2AcBbEMbtkGF2Edx7IMKsY/i2AcVYh/FsQ8qxD6KYx8EsA1t2IYBBBkKDw8XunfvLgiCIBQUFAh79uwRnJ2dhREjRqgf9/HxEXJyctQ/s2bNGiEkJEQoKChQL8vJyRFcXV2F3bt3C4IgCH5+fsJXX32lfjwvL0+oUqWKel2CIAjt2rUThg4dKgiCIMTFxQkAhD179mgd5/79+wUAwr1799TLHj16JLi5uQlHjx7VeO77778v9O7dWxAEQRg7dqxQt25djcdHjx5d7LWKAiBs2bJF9PGvv/5aaNasmfr+pEmTBEdHR+H69evqZTt37hQcHByE1NRUQRAEoUaNGsL69es1Xmfq1KlCaGioIAiCkJCQIAAQzp49K7pesiz2oR37IEFgH2LYBwkC+xDDPohtaMc2SBDYhxj2QYLAPsSwDxIE9iGGfZAgsA8x7IPYhnZsw3iyvQb5tm3bUKZMGeTl5aGgoADvvPMOJk+erH68QYMGGtcXOH/+PK5cuQIPDw+N13n06BHi4+ORkZGB1NRUtGzZUv1YqVKl0Lx582KnUyh07tw5ODo6ol27dnqP+8qVK8jOzsZLL72ksTw3NxdNmjQBAFy8eFFjHAAQGhqq9zoK/fDDD5g3bx7i4+ORlZWFx48fw9PTU+M5VatWReXKlTXWU1BQgLi4OHh4eCA+Ph7vv/8+Bg4cqH7O48eP4eXlZfB4yHLYR8nYh3Kxj5KxD+ViHyVjH8rENkrGNpSLfZSMfSgX+ygZ+1Au9lEy9qFc7KNk7EOZ2EbJ2Ib+ZHuAvEOHDli8eDGcnJzg7++PUqU0N8Xd3V3jflZWFpo1a4Z169YVe62KFSsaNQZXV1eDfyYrKwsAsH37do1fQODJ9ROkcuzYMfTp0wdffvklOnbsCC8vL3z//feYPXu2wWNdvnx5sTAdHR0lGytJj33oxj6UjX3oxj6UjX3oxj6Ui23oxjaUjX3oxj6UjX3oxj6UjX3oxj6UjX3oxj6Ui23oxjYMI9sD5O7u7ggODtb7+U2bNsUPP/yASpUqFfu0RCE/Pz8cP34cbdu2BfDkExGnT59G06ZNtT6/QYMGKCgowMGDBxEWFlbs8cJPquTn56uX1a1bF87OzkhKShL9hEmdOnWwdetWjWV//PFHyRv5jKNHjyIwMBBffPGFelliYmKx5yUlJeHGjRvw9/dXr8fBwQEhISHw8fGBv78/rl69ij59+hi0frIu9qEb+1A29qEb+1A29qEb+1AutqEb21A29qEb+1A29qEb+1A29qEb+1A29qEb+1AutqEb2zCMg7UHYCl9+vRBhQoV0L17dxw6dAgJCQk4cOAAhgwZguvXrwMAhg4dipkzZyI2Nhb//vsvBg0ahPT0dNHXrFatGsLDwzFgwADExsaqX3Pjxo0AgMDAQKhUKmzbtg23b99GVlYWPDw8MGLECAwfPhyrVq1CfHw8zpw5g/nz56svcP/xxx/j8uXLGDlyJOLi4rB+/XrExMQYtL01a9ZEUlISvv/+e8THx2PevHnYsmVLsee5uLggPDwc58+fx6FDhzBkyBD07NkTvr6+AIAvv/wSkZGRmDdvHi5duoS//voL0dHRmDNnjkHjIdvGPtgHiWMf7IPEsQ/2QdqxDbZB4tgH+yBx7IN9kDj2wT5IHPtgH6Qd22AbOlnr4uemCA8PF7p3727w46mpqULfvn2FChUqCM7OzkL16tWFgQMHChkZGYIgCEJeXp4wdOhQwdPTUyhbtqwQEREh9O3bV+O12rVrJwwdOlR9/+HDh8Lw4cMFPz8/wcnJSQgODhZWrlypfnzKlCmCr6+voFKphPDwcEEQBKGgoECIiooSQkJChNKlSwsVK1YUOnbsKBw8eFD9c7/88osQHBwsODs7C23atBFWrlwpABDu3bsnut0AhC1btqjvjxw5UihfvrxQpkwZ4e233xbmzp0reHl5qR+fNGmS0KhRI2HRokWCv7+/4OLiIrz55pvC3bt3NV533bp1QuPGjQUnJyfB29tbaNu2rbB582ZBEAQhISFBACCcPXtWdFxkWexDO/ZBgsA+xLAPEgT2IYZ9ENvQjm2QILAPMeyDBIF9iGEfJAjsQwz7IEFgH2LYB7EN7diG8VSCIHKleSIiIiIiIiIiIiIiIiIiIjuimFOsExERERERERERERERERGRsvEAORERERERERERERERERERKQIPkBMRERERERERERERERERkSLwADkRERERERERERERERERESkCD5ATEREREREREREREREREZEi8AA5EREREREREREREREREREpAg+QExERERERERERERERERGRIvAAORERERERERERERERERERKQIPkBMRERERERERERERERERkSLwADkRERERERERERERERERESkCD5ATEREREREREREREREREZEi8AA5EREREREREREREREREREpAg+QExERERERERERERERERGRIvAAORERERERERERERERERERKQIPkBMRERERERERERERERERkSLwADkRERERERERERERERERESkCD5ATEREREREREREREREREZEi8AA5EREREREREREREREREREpAg+QExERERERERERERERERGRIvAAORERERERERERERERERERKQIPkBMRERERERERERERERERkSLwADkRERERERERERERERERESkCD5ATEREREREREREREREREZEi8AA5EREREREREREREREREREpAg+QExERERERERERERERERGRIvAAORERERERERERERERERERKQIPkBMRERERERERERERERERkSLwADkRERERERERERERERERESkCD5ATEREREREREREREREREZEi8AA5EREREREREREREREREREpAg+QExERERERERERERERERGRIvAAORERERERERERERERERERKQIPkBMRERERERERERERERERkSLwADkRkUz9/vvv6NatG/z9/aFSqRAbG6vxuCAImDhxIvz8/ODq6oqwsDBcvnzZOoMlsjD2QSSOfRCJu3//PoYNG4bAwEC4urqiVatWOHnypLWHRWR1+fn5mDBhAoKCguDq6ooaNWpg6tSpEARB588dOHAATZs2hbOzM4KDgxETE2OZARNZCNsg0s2Y91bsg5SC++ZE1sUD5EREMvXgwQM0atQICxcu1Pr4V199hXnz5mHJkiU4fvw43N3d0bFjRzx69MjCIyWyPPZBJI59EIn74IMPsGfPHqxZswZ//fUXXn75ZYSFhSElJcXaQyOyqlmzZmHx4sVYsGABLl68iFmzZuGrr77C/PnzRX8mISEBXbp0QYcOHXDu3DkMGzYMH3zwAXbv3m3BkROZF9sg0s3Q91bsg5SE++ZE1qUSSvpIIxER2TyVSoUtW7agR48eAJ58wtDf3x+ff/45RowYAQDIyMiAj48PYmJi0KtXLyuOlsiy2AeROPZB9NTDhw/h4eGBn3/+GV26dFEvb9asGTp37oxp06ZZcXRE1tW1a1f4+Pjgu+++Uy9744034OrqirVr12r9mdGjR2P79u24cOGCelmvXr2Qnp6OXbt2mX3MRJbANojEGfPein2QUnHfnMjySll7AGSfHj16hNzcXK2POTk5wcXFxcIjIrINutoAnrz5UalUGsucnZ3h7Oxs0HoSEhKQlpaGsLAw9TIvLy+0bNkSx44d45soskm6+pCqDYB9kDyxDyJxUvXx+PFj5OfnF9tXcXV1xeHDh6UbMJEFSdVHq1atsGzZMly6dAm1atXC+fPncfjwYcyZM0d03ceOHdOYTwCgY8eOGDZsmOEbQiQxqfbN2QbZI2u+t2IfZOu4b04kTm7HBXmAnCT36NEjuHqUBx5na33c19cXCQkJNhcDkbmV1AYAlClTBllZWRrLJk2ahMmTJxu0rrS0NACAj4+PxnIfHx/1Y0S2pKQ+pGoDYB8kP+yDSJyUfXh4eCA0NBRTp05FnTp14OPjgw0bNuDYsWMIDg42x/CJzErKPsaMGYPMzEzUrl0bjo6OyM/Px/Tp09GnTx/R9aelpWmdTzIzM/Hw4UO4uroavlFEEpBy35xtkL2x9nsr9kG2jPvmROLkeFyQB8hJcrm5ucDjbDjX7w84Omk+mJ+LtAvRyM3NtakQiCxBZxsAkJ+LrAvRSE5Ohqenp3qxMZ8yJJKbkuYOtkFKxj6IxEndx5o1azBgwABUrlwZjo6OaNq0KXr37o3Tp0+baxOIzEbKPjZu3Ih169Zh/fr1qFevnvq6sP7+/ggPDzfnZhBJTsp9c7ZB9obvrYjEcd+cSJwcjws6WHsAtu7AgQNQqVT46aefrD0U+SnlAlUpV40bStnOLz+Zjn0YSUsbz/bh6empcTPmjZSvry8A4ObNmxrLb968qX6MzIt9GEnH3CFFGwD7sDa2YQL2YffYhwkk6qNGjRo4ePAgsrKykJycjBMnTiAvLw/Vq1e31JaQCPZhAgn6GDlyJMaMGYNevXqhQYMGeO+99zB8+HBERkaKrtbX11frfOLp6clvAEqMfRhJgn1ztmH72IeRrPTein1YFvswEvfN7R7bMIGMjgsq8gC5SqXS63bgwAGzjyU9PR0ffvghKlasCHd3d3To0AFnzpzR62f79eunddy1a9cu9tyCggJ89dVXCAoKgouLCxo2bIgNGzZIvTmaHBy138imKbGP6dOn49VXX4WPjw9UKpVRp70xiFgbEvYRFBQEX19f7Nu3T70sMzMTx48fR2hoqGTrURql9fHvv/9i1KhRaNy4MTw8PODn54cuXbrg1KlT5tikJywwd7AP6SmtjRs3buDdd99FSEgIPDw8ULZsWTz33HNYtWoVBEEwx2Y9wT5kSWl9FLVu3TqoVCqUKVNGik0QJ3Ef7u7u8PPzw71797B79250795dwsFSIaX1ce3aNdFt/P77782xWU9I0Ed2djYcHDT/hOTo6IiCggLRnwkNDdWYTwBgz549nE/0pLQ+CsXHx+Odd95BpUqV4Orqipo1a+KLL76QcnOekmDfnG1Yh9L6mDx5ss7tPHLkiPQbZqX3VuzDdErrAwBSU1Px4YcfIigoCK6urqhRowYiIiLw33//Sb1JT3DfXJaU2MaVK1fw5ptvwtvbG25ubnjhhRewf/9+qTdHk4yOCyryFOtr1qzRuL969Wrs2bOn2PI6derg4sWLZhtHQUEBunTpgvPnz2PkyJGoUKECFi1ahPbt2+P06dOoWbNmia/h7OyMFStWaCzz8vIq9rwvvvgCM2fOxMCBA9GiRQv8/PPPeOedd6BSqdCrVy/JtkmDSssvfoFthkBPKbGP8ePHw9fXF02aNMHu3bsl2wZR2toADO4jKysLV65cUd9PSEjAuXPnUK5cOVStWhXDhg3DtGnTULNmTQQFBWHChAnw9/dHjx49TNwA5VJaHytWrMB3332HN954A4MGDUJGRgaWLl2K559/Hrt27UJYWJik2wVAsrmDfViW0tq4c+cOrl+/jjfffBNVq1ZFXl4e9uzZg379+iEuLg4zZsyQdLvU2IcsKa2PZ2VlZWHUqFFwd3c3efwlkqiP3bt3QxAEhISE4MqVKxg5ciRq166N/v37SzRQepZS++jduzdeeeUVjWVm/UOnBH1069YN06dPR9WqVVGvXj2cPXsWc+bMwYABA9TPGTt2LFJSUrB69WoAwMcff4wFCxZg1KhRGDBgAH777Tds3LgR27dvN3mTlECJfZw7dw7t27dH5cqV8fnnn6N8+fJISkpCcnKyZNujQYJ9c7ZhHUrr4/XXX9d6ze5x48YhKysLLVq0MG1DtLHQeyv2IT2l9ZGVlYXQ0FA8ePAAgwYNQkBAAM6fP48FCxZg//79OH36dLEPMpmM++aypLQ2kpOTERoaCkdHR4wcORLu7u6Ijo7Gyy+/jH379qFt27aSbpeanI4LCiQMHjxYEPtPsX//fgGA8OOPPxr12g8ePBB97Icffij22rdu3RLKli0r9O7du8TXDg8PF9zd3Ut83vXr14XSpUsLgwcPVi8rKCgQ2rRpI1SpUkV4/Phxia9hiIyMDAGA4NwiQnAJHatxc24RIQAQMjIyJF0nmY+99yEIgpCQkCAIgiDcvn1bACBMmjRJr58zlK42jOmj8L9/0Vt4eLggCE86nzBhguDj4yM4OzsLL774ohAXF2eWbVMqe+/j1KlTwv379zWW3blzR6hYsaLQunXrEn/eEFLPHezDuuy9DTFdu3YV3N3dbf69FfuwLiX1MXr0aCEkJETo06ePSW3pInUfP/zwg1C9enXByclJ8PX1FQYPHiykp6ebZexUnL33kZCQIAAQvv76a/0GbSIp+8jMzBSGDh0qVK1aVXBxcRGqV68ufPHFF0JOTo76OeHh4UK7du00fm7//v1C48aNBScnJ6F69epCdHS0hFuoLPbeR35+vlC/fn2hZcuWQnZ2tn4DN5KU++ZswzbYex/aJCUlCSqVShg4cKBRPy/G0u+t2If52Xsf69atEwAI27Zt01g+ceJEAYBw5syZEl9DX9w3ty/23sagQYOEUqVKCf/++6/GuAICAoSmTZuW+POGkuNxQUWeYt0YBQUFmD59OqpUqQIXFxe8+OKLGp/uAYD27dujfv36OH36NNq2bQs3NzeMGzdO9DV/+ukn+Pj44PXXX1cvq1ixInr27Imff/4ZOTk5eo0tPz8fmZmZoo///PPPyMvLw6BBg9TLVCoVPvnkE1y/fh3Hjh3Taz0Gk9GpFMg0cu4DAKpVq6bXa0lGolOst2/fHoIgFLvFxMQAeNL5lClTkJaWhkePHmHv3r2oVauWGTaIdJFzH82aNSt2Stzy5cujTZs25vskpURzB/uwfXJuQ0y1atWQnZ2N3Nxcg39WL+xDMeyhj8uXL2Pu3LmYM2cOSpWywInLJOqjZ8+eiI+PR05ODlJTU7FgwQKd35Iny7OHPgDgwYMH5psvipKgDw8PD0RFRSExMREPHz5EfHw8pk2bBicnJ/VzYmJiip2Ssn379jh79ixycnIQHx+Pfv36SbBBJEbOffz666+4cOECJk2aBFdXV2RnZyM/P1+v1zaaBPvmbEM+5NyHNhs2bIAgCOjTp49BP6c3C723Yh+2Qc59FD7m4+OjsdzPzw8AzHPteu6bK4ac2zh06BCaNGmCkJAQ9TI3Nze8+uqrOHPmDC5fvqzXegwmo+OCPECup5kzZ2LLli0YMWIExo4diz/++EPrG5D//vsPnTt3RuPGjREVFYUOHTqIvubZs2fRtGnTYqf4eO6555CdnY1Lly6VOK7s7Gx4enrCy8sL5cqVw+DBg5GVlVVsPe7u7qhTp06x9RQ+bhYqlfYb2R0592EVYm2wD7tkj32kpaWhQoUKej3XYGxDMeyhjYcPH+LOnTu4du0aVq1ahejoaISGhppnBxxgHwpiD30MGzYMHTp0KHYaabNhH4phD318+eWXKFOmDFxcXNCiRQv8+uuvJb6+SdiHYsi5j7179wJ4csrQ5s2bw93dHW5ubujVqxfu3r2rz+YbjvvmiiLnPrRZt24dAgICzHiKXLahJHLuo23btnBwcMDQoUPxxx9/4Pr169ixYwemT5+OHj16aL0us8nYh2LIuY2cnBytf59yc3MDAJw+fbrE9RhFRn0o8hrkxnj06BHOnTun/gSot7c3hg4digsXLqB+/frq56WlpWHJkiX46KOPSnzN1NRUrW9iCj/ddOPGDTRo0ED05/38/DBq1Cg0bdoUBQUF2LVrFxYtWoTz58/jwIED6m9qpKamwsfHB6oiv4TPrscsHBwBhyK/Yg6PzbMusio592EV2toA2Iedsrc+Dh06hGPHjmH8+PEljtMonDsUwx7a+PbbbzF27Fj1/RdffBHR0dEljtNo7EMx5N7H9u3b8euvv+L8+fN6b7PJ2IdiyLkPBwcHvPzyy3jttddQuXJlXL16FXPmzEHnzp2xdetWdOnSxaD/FnpjH4oh5z4Kv8XUs2dPdOrUCWPHjsX58+cRGRmJ5ORkHD58uNjftUzGfXNFkXMfRf3999/4888/MWrUKOm7KMS5Q1Hk3EfdunWxbNkyjBgxAqGhoeqfDw8PL3aNZsmwD8WQcxshISE4dOgQ7t+/Dw8PD/XPHz58GACQkpKix38BI8ioDx4g11P//v01To/Upk0bAMDVq1c1QnB2dkb//v31es2HDx/C2dm52HIXFxf147pERkZq3O/Vqxdq1aqFL774Aj/99BN69eolyXqM5uj45PYswTZPpUCmkXMfVqGtDYB92Cl76uPWrVt45513EBQUhFGjRuk1VoNx7lAMe2ijd+/eaN68OW7fvo1t27bh5s2b5ntfBbAPBZFzH7m5uRg+fDg+/vhj1K1bV6+xSYJ9KIac+6hatSp2796t8dz33nsPdevWxeeff26+A+TsQzHk3Efht55atGiBtWvXAgDeeOMNuLm5YezYsdi3bx/CwsL0GrPeuG+uKHLuo6h169YBgPlOrw5w7lAYufdRuXJlPPfcc3jllVcQGBiIQ4cOYd68eahQoQK++eYbvcZrEPahGHJu45NPPsEvv/yCt99+G9OnT4e7uzsWLVqEU6dO6bUeo8moD55iXU9Vq1bVuO/t7Q0AuHfvnsbyypUrawSji6urq9brCTx69Ej9uKGGDx8OBwcH9ampzLUevcjoWgNkGjn3YRUSXYOc5MFe+njw4AG6du2K+/fv4+effy52bXLJsA3FsIc2AgMDERYWht69e2PdunWoXr06wsLCzLeTwT4UQ859zJ07F3fu3MGXX35p8OuZhH0ohpz70KZcuXLo378/4uLicP36dYPXoxf2oRhy7qPwdXr37q3x3HfeeQcAcPToUYPXUyLumyuKnPt4liAIWL9+PerXr4+GDRsa/Pp6YxuKIuc+jhw5gq5du2L69OkYOnQoevTogdmzZ2P8+PGYM2cO/vnnH4PXUyL2oRhybqNz586YP38+fv/9dzRt2hQhISHYvn07pk+fDgD82y54gFxvjto+UYonb0qeZcgvr5+fH1JTU4stL1zm7+9vwAifrr98+fIa12fy8/NDWlpasbGash69yOhaA2QaOfdhFbzOmaLYQx+5ubl4/fXX8eeff+Lnn3/W+ISk5NiGYthDG0W9+eabSE5Oxu+//27wevTCPhRDrn1kZGRg2rRpGDhwIDIzM3Ht2jVcu3YNWVlZEAQB165dw61btwxej17Yh2LItQ9dAgICAMDy11kmuyPnPgpfx8fHR+O5lSpVAlD8D9GS4L65osi5j2cdOXIEiYmJ5v32OMA2FEbOfSxduhQ+Pj5o3ry5xnNfffVVCIJgng9YsQ/FkHMbAPDpp5/i5s2bOHr0KE6dOoV///0XXl5eAIBatWoZvB69yKgPHiC3osaNG+PMmTMoKCjQWH78+HG4ubkZ9Qt6//593LlzBxUrVtRYT3Z2Ni5evFhsPYWPm4WMPilCtsdSfVgFP6VOJrJkHwUFBejbty/27duH9evXo127diaNvURsg0xg7bmj8JvjGRkZBq9HL+yDTGCJPu7du4esrCx89dVXCAoKUt82bdqE7OxsBAUF4cMPP5Rke4phH2QCa88fV69eBQDz7aewDzKBpfpo1qwZgOLXw7xx4wYAM/XBfXMykTXmj3Xr1kGlUqnPrmA2bINMZKk+bt68ifz8/GLPzcvLAwA8fmyGax+zDzKBpecOd3d3hIaGolmzZnB0dMTevXvh6uqK1q1bG70NOsmoDx4gt6I333wTN2/exObNm9XL7ty5gx9//BHdunXTuA5BfHw84uPj1fcfPXqE+/fvF3vNqVOnQhAEdOrUSb2se/fuKF26NBYtWqReJggClixZgsqVK6NVq1ZSb9oTMgqBbI+l+rAK7oSTiSzZx2effYYffvgBixYtwuuvv26GrSmCbZAJLNXG7du3ta7/u+++g0qlQtOmTaXYnOLYB5nAEn1UqlQJW7ZsKXbr0KEDXFxcsGXLFowdO9Y8G8g+yATWnD9SUlKwcuVKNGzYEH5+flJtkib2QSaw5N+unJ2dER0drfEH4xUrVgAAXnrpJUm3CwD3zclklv7bVV5eHn788Ue88MILxU77Kzm2QSayVB+1atXCzZs3ceDAAY3nbtiwAQDQpEkTqTbpKfZBJrDmcY+jR49i8+bNeP/999XfJJecjPooZe0BKNmbb76J559/Hv3798c///yDChUqYNGiRcjPzy92zb4XX3wRAHDt2jUAQFpaGpo0aYLevXujdu3aAIDdu3djx44d6NSpE7p3767+2SpVqmDYsGH4+uuvkZeXhxYtWiA2NhaHDh3CunXrRE8TYTKVll98lW2GQLbHUn0AwJo1a5CYmIjs7GwAwO+//45p06YBAN577z0EBgZKu3Ha2ihcTqQHS/URFRWFRYsWITQ0FG5ubli7dq3Ga7/22mtwd3eXduM4d5AJLNXG9OnTceTIEXTq1AlVq1bF3bt3sWnTJpw8eRKfffYZgoODzbOB7INMYIk+3Nzc0KNHj2Lrjo2NxYkTJ7Q+Jhn2QSaw1PwxatQoxMfH48UXX4S/vz+uXbuGpUuX4sGDB/j222/Nt4Hsg0xgqT58fX3xxRdfYOLEiejUqRN69OiB8+fPY/ny5ejduzdatGgh/cZx35xMZMm/XRU+/t9//5n/9OoA5w4ymaX6+PTTTxEdHY1u3brhs88+Q2BgIA4ePIgNGzbgpZdeQsuWLaXfOPZBJrBUG4mJiejZsydeffVV+Pr64u+//8aSJUvQsGFDzJgxw3wbKKM+eIDcihwdHbFjxw6MHDkS8+bNw8OHD9GiRQvExMQgJCRE58+WLVsWXbt2xZ49e7Bq1Srk5+cjODgYM2bMwIgRI+DgoHlygJkzZ8Lb2xtLly5FTEwMatasibVr15r3dDwOjoBDqeLLiPRgyT6+++47HDx4UH1///792L9/PwDghRdekP4AubY2CpcT6cFSfZw7dw4AcOzYMRw7dqzYayUkJEh/gJxzB5nAUm106dIF8fHxWLlyJW7fvg0XFxc0bNgQ0dHRCA8PN98Gsg8ygSXfW1kF+yATWKqPl19+GUuWLMHChQtx7949lC1bFm3btsX48ePNd/YRgH2QSSw5f4wfPx7e3t6YP38+hg0bpnHQ3Cy4b04msvT7q3Xr1qF06dJ46623zLVJT3HuIBNZqo+QkBCcPn0a48ePx9q1a5GWlgZ/f3+MGDGi2MFGybAPMoGl2vD09ISfnx8WLFiAu3fvonLlyhgyZAi++OILeHh4mG8DZdSHSih6NXkiE2VmZsLLywvOnWZDVdpV4zEh7yFydn2OjIwMeHp6lvhav//+O77++mucPn0aqamp2LJli8Y3TwRBwKRJk7B8+XKkp6ejdevWWLx4MWrWrCn1ZhGZTFcbgOF9ENkTKecOInvDPojEsQ8iceyDSDvumxOJ49xBJI59EImTYx828FF/slsSXGvgwYMHaNSoERYuXKj18a+++grz5s3DkiVLcPz4cbi7u6Njx4549OiRFFtAZB68zhmROLZBJI59EIljH0Ti2AeRdtw3JxLHNojEsQ8icTLqg6dYJ/PR9otvYAidO3dG586dtT4mCAKioqIwfvx49bUVVq9eDR8fH8TGxqJXr15GDZvI7MQmBRudKIgsSoK5g8husQ8iceyDSBz7INKO++ZE4jh3EIljH0TiZNQHv0FOZuPg4KD1JpWEhASkpaUhLCxMvczLywstW7bUeq1cIlsh1oZNXL+TyMrYBpE49kEkjn0QiWMfRNpx35xIHNsgEsc+iMTJqQ/bHBXZBZWDSusNeHI9gmdvOTk5Br9+WloaAMDHx0djuY+Pj/oxIlsk1kZhH0RKxjaIxLEPInHsg0gc+yDSjvvmROLYBpE49kEkToo+fv/9d3Tr1g3+/v5QqVSIjY3VeFwQBEycOBF+fn5wdXVFWFgYLl++bPBYeYCczEalUmm9AUBAQAC8vLzUt8jISCuPlshyxNoo7INIydgGkTj2QSSOfRCJYx9E2nHfnEgc2yASxz6IxEnRx4MHD9CoUSMsXLhQ6+NfffUV5s2bhyVLluD48eNwd3dHx44d8ejRI4PWw2uQk9k4ODhAVeTUCcL/7icnJ8PT01O93NnZ2eDX9/X1BQDcvHkTfn5+6uU3b95E48aNjRgxkWVoawN42geRkumaO4iUjn0QiWMfROLYB5F23DcnEse5g0gc+yASJ0UfnTt3RufOnbU+JggCoqKiMH78eHTv3h0AsHr1avj4+CA2Nha9evXSez2yPkBeUFCAGzduwMPDg5/QsQJBEHD//n34+/trvYaA1lMn/O++p6enxgFyYwQFBcHX1xf79u1THxDPzMzE8ePH8cknn5j02vaAfViPUW0A6j7I/NiH9Zgyd5BlsA/rYR+2jW1YF/uwbezDutiHbWMf1qWrD+6bWx/7sB7OHbaPfVgP+7BtbMO6TOkjMzNTY7Gzs7PBX55NSEhAWloawsLC1Mu8vLzQsmVLHDt2TDkHyG/cuIGAgABrD0PxkpOTUaVKlWLLHVSq4p8UMfD/YWVlZeHKlSvq+wkJCTh37hzKlSuHqlWrYtiwYZg2bRpq1qyJoKAgTJgwAf7+/ujRo4dR22JP2If1GdIGYHgfZDz2YX3mnDvINOzD+tiHbWIbtoF92Cb2YRvYh21iH7ZBWx/cN7c+9mF9nDtsF/uwPvZhm9iGbTCmj6L/bpMmTcLkyZMNWm9aWhoAwMfHR2O5j4+P+jF9yfoAuYeHBwDg9zOXUOZ//1tuQl+faO0hGE3Iz0XuP6vU/w5Fab22gIETxalTp9ChQwf1/YiICABAeHg4YmJiMGrUKDx48AAffvgh0tPT8cILL2DXrl1wcXExbGPsUOG/y5Jdp+DqXsbKozHOh4NmW3sIRjGqjScPmHlkVKjw36bvsn1wcpVnHxt/OW/tIRhFyHuI+1uGmnXuINOwD+thH7bNHtpY89UKaw/BaJbY9yDjFf67ONUNh8rRycqjUR72YdvYh3Xp6oP75tZnD30kHfjG2kMwyv3MTAQHBXDusGHsw3rYh21jG9ZlSh9SXHpZSrI+QF74H7mMhwfKeJh2um5rkWvAzxI9jYWWUykIBp5qpH379hAEQee6p0yZgilTphj0ukpQ+O/i6l4GbmXk+QESufdhSBuA4X2Q8Qr/bZxcy8DJTZ4HOVSlXa09BJOYc+4g07AP62Mftsku2pD5eyuAfdiqwn8XlaOTXfyeyRX7sE3swzZo7YP75lZnD32YeglJa+PcYbvYh/WxD9vENmyDMX1IcellX19fAMDNmzfh5+enXn7z5k31pZj1ZdiV0YkM4ODgoPVGpHRibbAPIs4dRLqwDyJx7INIHPsg0o775kTi2AaROPZBJM7cfQQFBcHX1xf79u1TL8vMzMTx48cRGhpq0GvJ+hvkZNtUWj4pou2TuURKo62NwuVESse5g0gc+yASxz6IxLEPIu24b04kjnMHkTj2QSROij6ysrJw5coV9f2EhAScO3cO5cqVQ9WqVTFs2DBMmzYNNWvWRFBQECZMmAB/f3/06NHDoPXwADmZjdZPhvCTVETin5piH0ScO4h0YB9E4tgHkTj2QaQd982JxHHuIBLHPojESdHHqVOn0KFDB/X9iIgIAEB4eDhiYmIwatQoPHjwAB9++CHS09PxwgsvYNeuXXBxcTFoPTxATmajUqmKXYdA9LoERAqirY3C5URKx7mDSBz7IBLHPojEsQ8i7bhvTiSOcweROPZBJE6KPtq3bw9BEHSuY8qUKZgyZYpRYyzEA+RkNjzVCJF2PI0bkTjOHUTi2AeROPZBJI59EGnHfXMicZw7iMSxDyJxcuqD530gsyk8lULRG5HSibXBPog4dxDpwj6IxEnRR35+PiZMmICgoCC4urqiRo0amDp1qs5PrhPJgRR9VKtWTf1tkGdvgwcP1vr8mJiYYs819JSHROYm1b45+yB7ZK33VgcOHNDaU1pamqmbRCQZqfbN79+/j2HDhiEwMBCurq5o1aoVTp48aYYRE1mOnP52xW+Qk9nI6ZMiRJbET6kTiePcQSSOfRCJk6KPWbNmYfHixVi1ahXq1auHU6dOoX///vDy8sKQIUOkHC6RRUnRx8mTJ5Gfn6++f+HCBbz00kt46623RH/G09MTcXFxT9fJU4+SjZFq35x9kD2y9nuruLg4eHp6qu9XqlTJoHUTmZNU++YffPABLly4gDVr1sDf3x9r165FWFgY/vnnH1SuXFmq4RJZlJz+dsUD5GQ2DiotnwxR2eYnRYgsSWsbAPsgAucOIl3YB5E4Kfo4evQounfvji5dugB48o3ADRs24MSJE1INk8gqpOijYsWKGvdnzpyJGjVqoF27dqI/o1Kp4Ovra9B6iCxJqn1z9kH2yNrvrSpVqoSyZcsatD4iS5Gij4cPH2LTpk34+eef0bZtWwDA5MmT8csvv2Dx4sWYNm2aVMMlsig5/e3KNkdFdkHb6XD4iVgi8TbYBxHnDiJd2AeROF19ZGZmatxycnK0vkarVq2wb98+XLp0CQBw/vx5HD58GJ07d7bYdhCZgxR9PCs3Nxdr167FgAEDdM5DWVlZCAwMREBAALp3746///5bsm0ikkJJ++bsg5TM2u+tGjduDD8/P7z00ks4cuSIdBtGJAEp+nj8+DHy8/OLXWLD1dUVhw8fNvs2EJmLnP52xQPkZDaFp1IoeiNSOrE22AcR5w4iXdgHkThdfQQEBMDLy0t9i4yM1PoaY8aMQa9evVC7dm2ULl0aTZo0wbBhw9CnTx9LbgqR5KTo41mxsbFIT09Hv379RJ8TEhKClStX4ueff8batWtRUFCAVq1a4fr161JtFpHJSto3Zx+kZNZ6b+Xn54clS5Zg06ZN2LRpEwICAtC+fXucOXPGLNtJZAwp+vDw8EBoaCimTp2KGzduID8/H2vXrsWxY8eQmppqyc0hkpSc/nbFU6yT2Tg4qOBQ9BffRkMgsiStbQDsgwicO4h0YR9E4nT1kZycrHENS2dnZ62vsXHjRqxbtw7r169HvXr1cO7cOQwbNgz+/v4IDw8329iJzE2KPp713XffoXPnzvD39xd9TmhoKEJDQ9X3W7VqhTp16mDp0qWYOnWqgVtAZB4l7ZuzD1Iya723CgkJQUhIiPp+q1atEB8fj7lz52LNmjUmbhWRNKR6b7VmzRoMGDAAlStXhqOjI5o2bYrevXvj9OnTZhk3kSXI6W9XPEBOZqNyQLEQBJ6zgEhrGwD7IAI4dxDpwj6IxOnqw9PTU+OPVGJGjhyp/qYTADRo0ACJiYmIjIzkAXKSNSn6KJSYmIi9e/di8+bNBo2h8JuDV65cMejniMyppH1z9kFKZkvvrZ577jmecppsilTvrWrUqIGDBw/iwYMHyMzMhJ+fH95++21Ur15d6iETWYyc/nZlo8MieyCnaw0QWRKvQU4kjm0QiWMfROKk6CM7OxsODpq7yI6OjigoKJByqEQWJ+X8ER0djUqVKqFLly4G/Vx+fj7++usv+Pn5GbVeInOQet+cfZA9saX3VufOnWMfZFOk3jd3d3eHn58f7t27h927d6N79+4SjpbIsuT0tyu9vkG+detWvV/w1VdfNXowZF8cHVVwdCzyi1/0vh1gH2QorW0A7IN9EDh3aMM2qBD7KI59UCEp+ujWrRumT5+OqlWrol69ejh79izmzJmDAQMGSDhS07APMoZU80dBQQGio6MRHh6OUqU0/5zUt29fVK5cWX2dzSlTpuD5559HcHAw0tPT8fXXXyMxMREffPCB0dtREvZBhpJy35x9kL2x1HursWPHIiUlBatXrwYAREVFISgoCPXq1cOjR4+wYsUK/Pbbb/j1119N3iYx7IMMJdV7q927d0MQBISEhODKlSsYOXIkateujf79+0s0UtOwDTKGnP52pdcB8h49euj1YiqVCvn5+aaMh+yItmsNCDZ6rQFTsA8ylNh1ztgH+yDOHUWxDXoW+9DEPuhZUvQxf/58TJgwAYMGDcKtW7fg7++Pjz76CBMnTpRyqCZhH2QMqeaPvXv3IikpSeuHRpKSkjS+JXjv3j0MHDgQaWlp8Pb2RrNmzXD06FHUrVvX8A3QE/sgQ0m5b84+yN5Y6r1VamoqkpKS1Pdzc3Px+eefIyUlBW5ubmjYsCH27t2LDh06mLZBOrAPMpRU760yMjIwduxYXL9+HeXKlcMbb7yB6dOno3Tp0lIN1SRsg4whp79d6XWAXGmnlFu05lcsWbdXY1m1KhWxdcVIK41IXKsmNfDZe2FoVLsq/Cp6oc+IZdhx8E/14107NEL/119A49pVUa6sO9r0icSFSykWGZucQjCF0vq4e+8+ftj4G/786ypycvPgU8kbA9/viupBtneqo5L6AICxH3VB3x6t4FXGFcf/vIrPZ/6Aq8m3zTouJR0gV1IfX4QFo5ybU7HlRxLuYvNfaVYYkeHcXUph9OuN8ErTAJT3dMGFxHuYsP4UziX8Z7ExcO6wT+xDGuzDPsm9jzJuzhj3cVd0bd8IFbzL4K9L1zFm9k84+09SyT8sISn68PDwQFRUFKKioiQcmbSU1oeDgwpjPnwFPTu1QKXynki7k4H1247jm+92WXtoJbKlsUs1f7z88ssQBEHrYwcOHNC4P3fuXMydO9fgdZhCSX3Y0u+XMWxl/FLum7MP22Ir70+M0fDViUhOvVts+ftvtsE3o9+22Dgs9d4qJiZG4/6oUaMwatQog9ZjKnvuw1b/NmqKG7fSMXn+z9h77G88fJSHoCoVsHDiu2hSN9BiY5DqvVXPnj3Rs2dPqYYlOXtuA7DtY2vGmLlsO2Yt36mxrGagD078NMGi45DT3670OkAu5tGjR3BxcZFqLDalRqAPlkd+qL7v6Gibl2t3c3XGhUspWLv1GNZ+/WGxx91dnPDH+XjE7j2DeeP7WHRscgrBHOyxjwcPHmLq9NWoUycQIyLehoeHG27evAt3d9vczpL6GNo3DB+93Q6fTF6DpBv/YdzHXbFp/mA833MacnIfm21cSjpALsYe+4j6PQHP/hP6erjg41aBOH8j03qDMtCc/s+jdpWy+HTZUaSlZ+PNVtWxceSLaDvuF6SlP7TIGDh32F8bAPuQCvtgH7bo2/HvoE4Nf3w8aRVSb2egZ+fnELvwMzzfcxpSb2dYbBzswz77GNb3JQx4ow0GTV6Di1dT0aROVSyY+C4ysx5i2Q8HrT08nWxp7OzD/vqwpd8vY9jK+Llvbp99ALbz/sQYv60aifz8px+2uBh/A699ugA9wppYdBxKnzsA++jDVv82aqz0zGx0+mAO2jSriR+/HYQKZcsgPvk2ynq6WXQcSu/DHtoAbPvYmrFqV/dD7MLP1PdLlbL8cU059WHwf538/HxMnToVlStXRpkyZXD16lUAwIQJE/Ddd98ZNYiFCxeiWrVqcHFxQcuWLXHixAmjXkdKpRwdUKGch/rm7eVu7SFptffoP5i+ZBu2H/hT6+M/7DyJr1fswoETcRYeGaCCCipVkRtsMwSpSN2HrbWxbccfKFfOAx++3xU1qvujUsWyaFC/OnwqeVt1XGJK6uPj3h3wzcrd2Pn7X/j7yg18Mmk1fCt4oUu7RmYdl9Y2DOwjPz8fEyZMQFBQEFxdXVGjRg1MnTpV9BPrtsDe+3iQm4/7OU9vdX3K4M6DXMT/l23VcenLpbQjujSviqkbz+KPS7dw7VYWvon9Ewm37iP8/2pZbBxSzB1y60MJ763YhzTYB/uwNS7OpfFqh8aYPC8WR8/GI+H6HcxavgNXk29jwBttLDoW7nvYZx/PNayOHQf/xK9H/kZy6l1s/e0c9h//F83qWe4bQsaypbGzD/vb97Cl3y9j2Mr4pdg3lyN778OW3p8Yo4K3B3wqeKpvuw9fQFCVCmjdtKZFx6HEuQOwvz5s9W+jxopatQeVfbyxcNJ7aFavGgIrV8D/PV8HQVUqWnQcSuzDHvc9bPnYmrFKOTpozCHly5ax+Bjk1IfBB8inT5+OmJgYfPXVV3ByenoqwPr162PFihUGD+CHH35AREQEJk2ahDNnzqBRo0bo2LEjbt26ZfBrSSkx5Q5efGcqOvebiTGz1iP11j2rjkeOCj8pUvRmz6TswxbbOHPuEoKC/DBv4WYMGhKF8ZO+w/6DZ602HlMEVi4P3wpeOHDiX/WyzAePcPrva2jRsJpZ1y3WhiF9zJo1C4sXL8aCBQtw8eJFzJo1CzqKBsgAAQAASURBVF999RXmz59vxpGbxt77eJajCmhWxQsnktKtPRS9OTqqUMrRAY9yNa8Z9Cg3Hy1rVbLYOKSYO+TWh1LeWxViH8ZjH+zD1pRydECpUo54lJunsfxRTh6eb1zDomPhvod99nHiz6to1yIENao++f+19WtWxvONqmPv0X+sNiZ92dLY2Yf97XvY0u+XMWxl/FLsm8uRvfdhS+9PTJWb9xgbd55En1dDoVJZ9vdSiW0A9t/Hs6z5t1Fj7Tr0F5rUqYp+Y75DzZfHoG2fmVi15YjFx6HEPpSw72EPribfRp3O49C4+yQMHB+D5LTil+wwNzn1YfAB8tWrV2PZsmXo06cPHB0d1csbNWqEf//9V8dPajdnzhwMHDgQ/fv3R926dbFkyRK4ublh5cqVBr+WVBrUroppn7+NxdM+wPhPX0NK2j30G7EYD7IfWW1MciSnEKQiZR+22MbtW+n47bcz8PUph1Gf98L/dWiKNev24NBh7Z+ysmU+5T0BALf/u6+x/NZ/91Hpf4+ZixQ74UePHkX37t3RpUsXVKtWDW+++SZefvllq39SWxd77+NZ9f084VLaESdlcoADAB48eoyTl28jonsD+JR1hYNKhTdCg9A8uAIqeblabBxSzB1y60MJ762exT6Mxz7Yh63Jys7BiT+vYuT7neFbwQsODir07NwCLRoEwaeCed9PFcV9D/vsY+6qPdi85zRO/Dget459i4NrR2PJ9wfw465TVhuTvmxp7OzD/vY9bOn3yxi2Mn6lHiC39z5s6f2JqbYf+BMZWQ/xTteWFl+3EtsA7L+PZ1nzb6PGupZyBys3HUL1gIrYNH8wBrzxAsbM/gkbtv1h0XEosQ8l7HvIXbN61bBw0rv4cd5gzB7zNhJv/IdXBs7F/QeWPa4ppz4MvgZ5SkoKgoODiy0vKChAXl6elp8Ql5ubi9OnT2Ps2LHqZQ4ODggLC8OxY8eKPT8nJwc5OTnq+5mZ5rkuX5sWtdX/u1Z1PzSoXRWd+kZi9+9/4vVOz5llnfZITtcakIpUfRjaBmCZPgoEAUHV/NDzzfYAgGqBvriechu/HTiLNi80lHx99kpsUijso+i/nbOzM5ydnTWWtWrVCsuWLcOlS5dQq1YtnD9/HocPH8acOXPMN3AT2Xsfz2pZtSz+vZWFzBzbu16TLp8uO4Ko90NxPuoNPM4vwF+Jd7Hlj0Q0rFbOYmPQNXfo0wYgvz6s+d4KYB/6Yh/WoYR9j2fJsY+PJq7Ggol9cHHndDx+nI/zccnY9OspNKpd1aLj4L7HU/bUx2thTfFWpxYYOH4V/r2aiga1KmNGxJtIvZ2B77cfN8s6pWJLY2cfT9nLvoct/X4Zw1bGX9K+ub2y9z4A23l/Yqq1W48iLLQu/CqWtfi6lTh3AMroQ84KCgQ0rlMVEwe/CgBoGBKAi1dTEb35MHp3fd5i41BiH0rY95C7l1rXU//v+jUro3n9amjQbSJi957Be91bWWwccurD4G+Q161bF4cOHSq2/KeffkKTJk0Meq07d+4gPz8fPj4+Gst9fHyQlpZW7PmRkZHw8vJS3wICAgwbvJE8y7gisHIFJN/4zyLrsxcqLZ8SUdloCFKRqg9D2wAs00fZsmVQ2b+CxjJ/v/L4778Myddlbjf/ezKRVizvobG8UnkP3PrPvJOstjae7SMgIEDj3zIyMrLYa4wZMwa9evVC7dq1Ubp0aTRp0gTDhg1Dnz59zDp2U9h7H4W8XUujZkV3HE+S36U5Em9n4bWZe1D9ww1oGrEFnafsQmlHFZJuZ1lsDLrmDn3aAOTXhzXfWwHsQ1/swzqUtO8h1z6updxB14++ReU2EajfdQLC+n2DUqUckZhyx6Lj4L7HU/bUx5ShPRD1v2+a/hN/Az/sPIlFG37D8H4vmWV9UrKlsbOPp+xl38OWfr+MYSvjL2nf3F7Zex+A7bw/MUVS6l0cOBGHvj0sd1DjWUqcOwBl9FHImn8bNZZPBU/Uru6rsaxWNV9cT7PsPpQS+1DCvoe98fJwQ3DVSriafNui65VTHwZ/g3zixIkIDw9HSkoKCgoKsHnzZsTFxWH16tXYtm2bOcaoNnbsWERERKjvZ2ZmWiSG7Ic5SE79D11fbGr2ddkTlerJregye2bvfdQKroLUNM0PiqTdvIvy5b0kXY8lJKb8h7Q7GWjXIgQXLqUAADzcXdCsXjWs/OmwWdetrY3C5QCQnJwMT8+npzLS9g3AjRs3Yt26dVi/fj3q1auHc+fOYdiwYfD390d4eLi5hm4Se++jUIuqZZGV8xgXb1ruoJnUsnPzkZ37EF5uTmjfwB9TfzhjsXXrmjv0aQOQXx/WbANgH4ZiH5allLkDkH8f2Y9ykf0oF14ernjx+TqYNP9ni66f+x722YersxMKCgo0lhUUCHBQGfxZf4uzpbGzD/vrw5Z+v4xhK+Mvad/cXtl7H8+y9vsTU6z/5Rgqenvg5We+EWhJSpw7AGX1Yc2/jRqrZaPquJyoeY3q+KRbqOJruTO7AcrsQ0lt2Ius7BwkpNzB2xUse1ZsOfVh8AHy7t2745dffsGUKVPg7u6OiRMnomnTpvjll1/w0kuGfdKzQoUKcHR0xM2bNzWW37x5E76+vsWeL3aqSKl9s3wb2resA79K3rh9NxOL1uyBo6MDOrdvbPZ1G8rd1QlBARXV9wP9y6N+rcpIz8jG9Zv3UNbTDVV8veFX4ckBzJqBTz6Vc+u/TNwqcn0RqWk7lYKtXmtAKlL1YWgbgGX66PTyc5gyYzW2bjuCli3qIP5qKvYfOIcB/Tqbdb3GKqmPJRv2Y8SATriafBuJKf9h3MddkHYnA9sPnjfruMRO41a4zNPTU+MghzYjR45UfwsQABo0aIDExERERkba5AEOwP77AAAVgBYBXjiVnIECweyrk1z7+n5QqYD41ExU8/HAxLeb4kpqBr4/HG+xMeiaO/RpA5BfH9Z8bwWwD32xD+tQwr4HIO8+/u/5OlCpgMuJt1C9SkVMGdoDl67dxLqt2k8daS7c97DPPnYd/gsR/Tvieto9XLyaioYhVTDonQ5Yt9Wy15k0hi2NnX3Y376HLf1+GcNWxl/Svrm9svc+ANt5f2KsgoICrPvlD/Tq0hKlSjmW/ANmoMS5A7C/Pmz1b6PGGtT7/9Dx/dmYHb0br4U1xem/r2HVliOYO663RcehxD7scd/Dlo+tGWNC1GZ0atMAAX7lkHo7AzOXbYejgwPe6NjMouOQUx8GHyAHgDZt2mDPnj0mr9zJyQnNmjXDvn370KNHDwBP3gDs27cPn376qcmvb6xbdzIweuZ6pN/PhrdXGTStVw1r536KcmXLWG1MYhrXCcS2pUPV92dEvAEAWL/tDwz+ci06t22ARZPeUz++csYAAMDMZTswa/kOs47NwcEBDg4OxZbZOyn6sNU2qlf3x9BP38DGnw4g9ufDqFixLN59JwytQ+tbbUy6lNTHt6v3ws3VGXPH9YZXGVf8cT4ebw5ZhJxc8173U1sbhcv1lZ2dXez5jo6OxT6Fb2vsuQ8AqFnRHeXcnHA8Kd2q4zCWp2tpjHurCfy83ZD+IBfbTyUhctM5PM633NEaKeYOOfZh7++tAPYhBfZhGvZhHp5lXDBx8Kvwr1QW9zKz8ctv5zBt0S94nG/Z3ynue5jGVvsY/fWPGPdxV3wz+m1U8C6DtDsZiNl8BF+t2Gm1MenLlsbOPozHNszDVsYvxb65XNlzH4DtvD8x1oETcbiedg/vvmq5ayoXpdS5A7CvPmz1b6PGalovEGu+HogpC7fi6xU7EehfHjMi3kDPzi0sOg6l9mFv+x62fGzNGCm30vHB+GjczchGBe8yaNmoOvZEf44K3h4l/7CE5NSHUQfIAeDUqVO4ePEigCfXH2jWzLhPIURERCA8PBzNmzfHc889h6ioKDx48AD9+/c3dmgm+2qsbV6DUZsjZy7Du4X4/9PYsO04Nmw7bsERPSWnT4pITYo+bLENAGjSuCaaNK5p1THoq6Q+ACBy6XZELt1uoRE9IcWn1Lt164bp06ejatWqqFevHs6ePYs5c+ZgwIABUg7VLOy5j0u3H+Dzrf9YdQym2HoyCVtPJll1DFLMHXLtw57fWwHsQwrsg33Yoti9ZxG796y1h8F9DzvtIys7B+PmbMK4OZusNgZj2dLY2Yf97XvY0u+XMWxl/Er9Bnkhe+0DsJ33J8b6v+fr4N7JBVYdg5LnDsB++rDVv42aolObBujUpoFVx6DkPuxp38OWj60Zo/AAvrXJqQ+DD5Bfv34dvXv3xpEjR1C2bFkAQHp6Olq1aoXvv/8eVapUMej13n77bdy+fRsTJ05EWloaGjdujF27dsHHx8fQoZGNkdO1BqQiZR9sw35JcZ2z+fPnY8KECRg0aBBu3boFf39/fPTRR5g4caJ0A5UY+yB9SDF3yK0PvrcifbGPsgDYB2nHfY+yANgHacc+ygLgvgcVp9RrkLMP0ocS5w6AfZB+lNgH9z1IX3Lqw+DvtX/wwQfIy8vDxYsXcffuXdy9excXL15EQUEBPvjgA6MG8emnnyIxMRE5OTk4fvw4WrZsadTrkG1xUKnUnxZR32y1BIlI3QfbsE9a2zCwDw8PD0RFRSExMREPHz5EfHw8pk2bBicnJzOO3DTsg/Qhxdwhtz743or0xT7YB4njvgf7IHHsg/sepJ0U++ZyxD5IH0qcOwD2QfpRYh/c9yB9yakPg79BfvDgQRw9ehQhISHqZSEhIZg/fz7atGkj6eBI3hwdVHAscuoEwUZPpSAV9kH60NYGwD6IAM4dhdgGacM+nmAfpA37eIJ9kDbs4wn2QUVx35x9kDglzh0A+yD9KLEPtkH6klMfBh8gDwgIQF5eXrHl+fn58Pf3l2RQZB/kFIJU2AfpQ6k74eyD9MG54ym2QUWxj6fYBxXFPp5iH1QU+3iKfdCzuG+uiX3Qs5Q4dwDsg/SjxD7YBulLTn0YfIr1r7/+Gp999hlOnTqlXnbq1CkMHToU33zzjaSDI3lTOaDYqRRUBv7G5efnY8KECQgKCoKrqytq1KiBqVOnQhAE8wzaROyD9KGtDWP6kBv2QfqQYu6QG7ZB+mIfT7AP0oZ9PME+SBv28QT7oKK4b84+SJwS5w6AfZB+lNgH2yB9yakPvb5B7u3tDdUz54h/8OABWrZsiVKlnvz448ePUapUKQwYMAA9evQwy0BJflT/+7+iywwxa9YsLF68GKtWrUK9evVw6tQp9O/fH15eXhgyZIiUwzUa+yBDaWujcLm9YR9kKCnmDjlgG2QM9sE+SBz7YB8kjn2wD9KO++bsg8QpZe4A2AcZTil9sA0yhpz60OsAeVRUlJmHQfZI26kUCgw8lcLRo0fRvXt3dOnSBQBQrVo1bNiwASdOnJBsnKZiH2QosdO4GdqHHLAPMpQUc4ccsA0yBvsgEsc+iMSxDyLtuG9OJE4pcwfAPshwSumDbZAx5NSHXgfIw8PDzT0OskNShNCqVSssW7YMly5dQq1atXD+/HkcPnwYc+bMkXKoJmEfZCgl7YSzDzKUnN5EmYJtkDHYB5E49kEkjn0Qacd9cyJxSpk7APZBhlNKH2yDjCGnPvQ6QC7m0aNHyM3N1Vjm6elp0oDIfhReX6DoMgDIzMzUWO7s7AxnZ+dirzFmzBhkZmaidu3acHR0RH5+PqZPn44+ffqYb+ASYR8kRlsbhcuVgn2QGF1zhxKwDdKFfbAPEsc+2AeJYx/sg7Tjvjn7IHFKnzsA9kHilN4H2yBd5NSHwZdGf/DgAT799FNUqlQJ7u7u8Pb21rgRFSr8pEjRGwAEBATAy8tLfYuMjNT6Ghs3bsS6deuwfv16nDlzBqtWrcI333yDVatWWXJT9MY+SB9ibWj75Lo9YR+kD7bBNkgc+2AfJI59sA8Sxz7YB2nHfXP2QeKU2AbAPkg/SuyDbZC+5NSHwQfIR40ahd9++w2LFy+Gs7MzVqxYgS+//BL+/v5YvXq1OcZIMqUSuQFAcnIyMjIy1LexY8dqfY2RI0dizJgx6NWrFxo0aID33nsPw4cPFz2gbm3sg/Qh1oZtThPSYR+kD7bBNkgc+2AfJI59sA8Sxz7YB2nHfXP2QeKU2AbAPkg/SuyDbZC+5NSHwadY/+WXX7B69Wq0b98e/fv3R5s2bRAcHIzAwECsW7dOFqe+JsvQda0BT09PvU67kZ2dDQcHzc9xODo6oqCgQLqBSoh9kD6UdJ2zZ7EP0oecrlMjFbZB+mIf7IPEsQ/2QeLYB/sg7bhvzj5InBLnDoB9kH6U2AfbIH3JqQ+Dv0F+9+5dVK9eHcCTg5x3794FALzwwgv4/fffpR0dyZpKpVJfb6DwplIZFkK3bt0wffp0bN++HdeuXcOWLVswZ84cvPbaa2YatWnYB+lDWxvG9CE37IP0IcXcITdsg/TFPtgHiWMf7IPEsQ/2Qdpx35x9kDglzh0A+yD9KLEPtkH6klMfBh8gr169OhISEgAAtWvXxsaNGwE8+QRJ2bJlJR0cyZsU1xqYP38+3nzzTQwaNAh16tTBiBEj8NFHH2Hq1KlmGrVp2AfpQ6nXOWMfpA+2wTZIHPtgHySOfbAPEsc+2Adpx31z9kHilNgGwD5IP0rsg22QvuTUh8EHyPv374/z588DAMaMGYOFCxfCxcUFw4cPx8iRIyUfIMmXg0ql9WYIDw8PREVFITExEQ8fPkR8fDymTZsGJycnM43aNOyD9CHWhqF9yA37IH2wDbZB4tgH+yBx7IN9kDj2wT5IO+6bsw8Sp8Q2APZB+lFiH2yD9CWnPgy+Bvnw4cPV/zssLAz//vsvTp8+jeDgYDRs2FDSwZG8afvFt9UQpMI+SB9ikwL7IOLcwTZIF/bBPkicFH1Uq1YNiYmJxZYPGjQICxcuNGl85sA+SF/W6uPHH3/EhAkTcO3aNdSsWROzZs3CK6+8YtB6jcU+SB9S7ZuzD7JHUswd+fn5mDx5MtauXYu0tDT4+/ujX79+GD9+vM7T7R44cAARERH4+++/ERAQgPHjx6Nfv37GbIbB2Afpw5p9WAvbIH3JqQ+DD5AXFRgYiMDAQCnGQnam8PoCRZcpCfsgbbS1UbhcSdgHacO5g22QOPbBPkicFH2cPHkS+fn56vsXLlzASy+9hLfeekuSMZob+yAx1ujj6NGj6N27NyIjI9G1a1esX78ePXr0wJkzZ1C/fn3DN8JE7IO0kWrfnH2QPZJi7pg1axYWL16MVatWoV69ejh16hT69+8PLy8vDBkyROvPJCQkoEuXLvj444+xbt067Nu3Dx988AH8/PzQsWNHo7fHWOyDtLFWH7aEbZAYOfWh1wHyefPm6f2CcoiXLEPbtQVs9VoDpmAfZCix626wD/ZBnDu0YRtUiH0Uxz6okBR9VKxYUeP+zJkzUaNGDbRr187k8UmFfZAxrNHHt99+i06dOqlPuTl16lTs2bMHCxYswJIlSwxat77YBxlKqn1z9kH2SIq54+jRo+jevTu6dOkC4MnZFjZs2IATJ06I/sySJUsQFBSE2bNnAwDq1KmDw4cPY+7cuWY7QM4+yFDW6sPS2AYZQ0596HWAfO7cuXq9mEqlYgikppRvObEPMpSSvkHOPshQnDs0sQ16FvvQxD7oWbr6yMzM1Fju7OwMZ2dnna+Xm5uLtWvXIiIiwqZOccg+yBjW6OPYsWOIiIjQWNaxY0fExsYaOHr9sQ8yVEn75uyDlEyKuaNVq1ZYtmwZLl26hFq1auH8+fM4fPgw5syZI7reY8eOISwsTGNZx44dMWzYMCO3pGTsgwxlrT4sjW2QMeTUh14HyBMSEiRdqdQql3ODp6ebtYdhFMeaza09BKMJeQ+Bv8Qfd/jfregye2PrfXSq6wdPT09rD4Oeoa0NiCyTO1vvY/ortWXbx8qpi6w9BKMI+bk6H+fcYTvYh+WxjydsvQ95t2HtEZiPrj4CAgI0lk+aNAmTJ0/W+XqxsbFIT0+32PUu9WXrfSQd+Ea2fXi3+NTaQzAba/SRlpYGHx8fjWU+Pj5IS0vTa8zGYB/mY699lLRvzj4sR8592Csp5o4xY8YgMzMTtWvXhqOjI/Lz8zF9+nT06dNHdL1ifWRmZuLhw4dwdXU1fGNKYOt9/LLqC7iXkWcfZxLuWXsIRnmQlanzcWv1YWm23sZ7oz6Ak1sZaw/DKAm3Hlh7CEbLuq977HLqw+RrkBOJcdByKgV7/JYTkaG0tVG4nEjpOHcQiWMfROJ09ZGcnKzxR/eSvv0HAN999x06d+4Mf39/aQdKZAXsg0i7kvbN2QcpmRRzx8aNG7Fu3TqsX78e9erVw7lz5zBs2DD4+/sjPDzcfIMnMjP2QSROTn3wADmZjVKuk0lkKCVdg5zIUJw7iMSxDyJxuvrw9PQ06FtpiYmJ2Lt3LzZv3izpGImsxRp9+Pr64ubNmxrLbt68CV9fX73XRWRuJe2bsw9SMinmjpEjR2LMmDHo1asXAKBBgwZITExEZGSk6AEOsT48PT3N8u1xImNYqw8iOZBTH/Z4VkayEY4O2m9ESifWBvsgYhtEurAPInFS9hEdHY1KlSqhS5cu0g6SyEqs0UdoaCj27dunsWzPnj0IDQ01bsVEZiD1vjn7IHsiRRvZ2dlwcND8IUdHRxQUFIj+DPsgObBWH0RyIKc++A1yMptSKhVKqTQ/KZKv4reciLS1AbAPIoBzB5Eu7INInFR9FBQUIDo6GuHh4ShVirvLZB8s0Uffvn1RuXJlREZGAgCGDh2Kdu3aYfbs2ejSpQu+//57nDp1CsuWLTN+Q4gkJuW+OfsgeyPF3NGtWzdMnz4dVatWRb169XD27FnMmTMHAwYMUD9n7NixSElJwerVqwEAH3/8MRYsWIBRo0ZhwIAB+O2337Bx40Zs377d9I0ikoil+iCSIzn1wT1+MhuV6smt6DIipdPWRuFyIqXj3EEkjn0QiZOqj7179yIpKYl/mCK7Yok+kpKSNL7l0apVK6xfvx7jx4/HuHHjULNmTcTGxqJ+/fqGr5jITKTcN2cfZG+kmDvmz5+PCRMmYNCgQbh16xb8/f3x0UcfYeLEiernpKamIikpSX0/KCgI27dvx/Dhw/Htt9+iSpUqWLFiBTp27GjK5hBJylJ9EMmRnPow6qRBhw4dwrvvvovQ0FCkpKQAANasWYPDhw9LOjiSN0eVSn29AfVNAX/FZR9UEq1tsA8rj4xsBecOtkHi2Af7IHFS9fHyyy9DEATUqlXLDKOUHvsgfViijwMHDiAmJkZj2VtvvYW4uDjk5OTgwoULeOWVV4zdBKOwDyqJlPvm7IPsjRRzh4eHB6KiopCYmIiHDx8iPj4e06ZNg5OTk/o5MTExOHDggMbPtW/fHmfPnkVOTg7i4+PRr18/CbZIf+yDSmKpPmwN2yB9yKkPgw+Qb9q0CR07doSrq6t6ogKAjIwMzJgxQ9LBkbw5OgClHFQaN3u/Tib7IH1oa4N9sA96gnMH2yBx7IN9kDj2wT5IHPtgH6Qd983ZB4lT4twBsA/SjxL7YBukLzn1YfCwpk2bhiVLlmD58uUoXbq0ennr1q1x5swZSQdH8ubooP1mz9gH6UOsDfZBxDbYBunCPtgHiWMf7IPEsQ/2Qdpx35x9kDgltgGwD9KPEvtgG6QvOfVh8DXI4+Li0LZt22LLvby8kJ6eLsWYyE44qoqfOsHeTwPKPkgf2tooXG7P2Afpg3PHU2yDimIfT7EPKop9PMU+qCj28RT7oGdx31wT+6BnKXHuANgH6UeJfbAN0pec+jD4uL2vry+uXLlSbPnhw4dRvXp1SQZF9sFBpf1mz9gH6UOsDfZBxDaexTaoKPbxFPugotjHU+yDimIfT7EPehb3zTWxD3qWEtsA2AfpR4l9sA3Sl5z6MPgA+cCBAzF06FAcP34cKpUKN27cwLp16zBixAh88skn5hgjyZSjg0rrzZ6xD9KHWBvsg4hzB9sgXdgH+yBx7IN9kDj2wT5IO+6bsw8Sp8Q2APZB+lFiH2yD9CWnPgw+xfqYMWNQUFCAF198EdnZ2Wjbti2cnZ0xYsQIfPbZZ+YYI8mUtl98Ww1BKuyD9CE2KbAPIs4dbIN0YR/sg8SxD/ZB4tgH+yDtuG/OPkicEucOgH2QfpTYB9sgfcmpD4MPkKtUKnzxxRcYOXIkrly5gqysLNStWxdlypQxx/hIxhwcAEeH4svsGfsgfWhro3C5PWMfpA/OHWyDxLEP9kHi2Af7IHHsg32Qdtw3Zx8kTolzB8A+SD9K7INtkL7k1IfBB8gLOTk5oW7dulKOheyMA1RwgKrYMiVgH6SLtjYKlysB+yBdOHewDRLHPtgHiWMf7IPEsQ/2Qdpx35x9kDglzx0A+yDdlNwH26CSyKkPgw+Qd+jQASqV+Mb89ttvJg2I7Iejlk+KaPtkrj1hH6QPbW0ULrdn7IP0wbmjOLZBhdhHceyDCrGP4tgHFWIfxbEPArhvLoZ9EKDMuQNgH6QfJfbBNkhfcurD4APkjRs31rifl5eHc+fO4cKFCwgPD5dqXGQH5HStAamwD9KHUq9zxj5IH5w72AaJYx/sg8SxD/ZB4tgH+yDtuG/+BPsgbZQ4dwDsg/SjxD7YBulLTn0YfIB87ty5WpdPnjwZWVlZJg+I7IejSksIOj5lZA/YB+lDWxuFy+0Z+yB9cO54im1QUezjKfZBRbGPp9gHFcU+nmIf9Czum2tiH/QsJc4dAPsg/SixD7ZB+pJTH0Zfg7yod999F8899xy++eYbqV7SJsyN+RVTFm7Fx73aI/LzN609HK1Ca/vg06710SioPHy93fDenN+w81SSxnPGvNkY73WoBU93J5y4dAsjVx7D1bT7Zh2Xo0pV7BffVkMwN/ZhHa2a1MBn74WhUe2q8KvohT4jlmHHwT81njP2oy7o26MVvMq44vifV/H5zB9wNfm2WcelrY3C5UrEPqxDTn2wDftqA2AfxmIfT9lTHzOXbces5Ts1ltUM9MGJnyZYaUSGKePmjHEfd0XX9o1QwbsM/rp0HWNm/4Sz/ySV/MMSYh9P2VMfAHDjVjomz/8Ze4/9jYeP8hBUpQIWTnwXTeoGWntoOjk4qDDmw1fQs1MLVCrvibQ7GVi/7Ti++W6XxcfCPp6ypz7k2gZgO31w31yTvfTR8NWJSE69W2z5+2+2wTej37bCiAxjK+Pn3KHJXvp4Z/Bs3LydXmz5qy8/h6EfdLP8gAxkK+NnH0/ZSxsAoALQMaQimlbxgqdLKWQ8eoyTyenYe+mOtYemt1t3MvBt9E4cPR2HRzm5CPCrgMnD30LdmlUsNgY59SHZAfJjx47BxcVFqpezCWf+TkTMliOoV7OytYeik5tzKVxIvIt1By5jdcT/FXv8s271MbBjXXy65BASb2Vh7FtNsHHMy2g9MhY5eflmG5fqf7eiy5SIfViHm6szLlxKwdqtx7D26w+LPT60bxg+ersdPpm8Bkk3/sO4j7ti0/zBeL7nNOTkPjbbuLS1AZFlSsA+rENOfbAN+8I+jMc+nrK3PmpX90Psws/U90uVstELdGnx7fh3UKeGPz6etAqptzPQs/NziF34GZ7vOQ2ptzMsNg728ZQ99ZGemY1OH8xBm2Y18eO3g1ChbBnEJ99GWU83aw+tRMP6voQBb7TBoMlrcPFqKprUqYoFE99FZtZDLPvhoEXHwj6espc+5NwGYDt9cN9ck7308duqkcjPF9T3L8bfwGufLkCPsCZWHJX+bGX8nDs02UsfiyI/RkFBgfp+QtItjJoWg3ah9a04Kv3ZyvjZx1P20gYA/F/NCmhVzRsbzt5A2v0cBJR1wdtN/PEorwCHE4p/cMnWZN7PRv+Ri9G8YXXM/3IAvL3ckXTjDjzKuFp0HHLqw+AD5K+//rrGfUEQkJqailOnTmHCBMO+5fD777/j66+/xunTp5GamootW7agR48ehg7JLLKyc/DhxBh8O643vllp+U94G2Lf+RTsO58i+vjHnepiTux57DydDAAYtPgQLi7uhVeaV8WWYwlmG5eDlk+KONjoJ0Wkwj5sy96j/2Dv0X9EH/+4dwd8s3I3dv7+FwDgk0mrEbc7El3aNcLmPafNNi5tbRQuN0RKSgpGjx6NnTt3Ijs7G8HBwYiOjkbz5s2lGqqk2IdtkVMfxswdcupDyjYA9iEF9mE7lDJ3lHJ0gE8FT2sPw2AuzqXxaofG6DNiGY6ejQcAzFq+A53a1MeAN9pg+pJtFhsL9z3ss4+oVXtQ2ccbCye9p14WWLmCFUekv+caVseOg3/i1yN/AwCSU+/ijY7N0aye5b/dyz7srw85twHYTh9S7ZvLjb33UcHbQ+N+1KpfEVSlAlo3rWmlERnGVsavxLkDsP8+ynq6a9zfEHsI/j7l0KhuNesMyEC2Mn4l9mHvbQBANW9XXEi7j4u3npwy/t7DPDSp/ABVvV0A8x1Gk0zMTwfhU9ELXw7vqV5W2becxcchpz4M/mqCl5eXxq1cuXJo3749duzYgUmTJhn0Wg8ePECjRo2wcOFCQ4dhdiO/+gEvt66P9i1rW3soJgmsVAY+3m44eCFVvez+wzycib+N5jUrmnXdDg4qrTd7xj7kI7ByefhW8MKBE/+ql2U+eITTf19Di4bVzLpusTYM6ePevXto3bo1SpcujZ07d+Kff/7B7Nmz4e3tbcaRm4Z9yIct9mEIufUhZRsA+zA39mFZSpk7ribfRp3O49C4+yQMHB+D5DTb/3Q68OTAfqlSjniUm6ex/FFOHp5vXMOiY+G+h332sevQX2hSpyr6jfkONV8eg7Z9ZmLVliPWHpZeTvx5Fe1ahKBG1UoAgPo1K+P5RtV1fgDLXNiH/fUh5zYA2+lDin1zObL3Pp6Vm/cYG3eeRJ9XQ6Gy0T/O62LN8SuxDUBZfeQ9foy9h86jU4emsuzDmuNXYh9KaOPavYeoWdEdFdydAAB+ns4IKu+Gf2/K4xrrB4//g7rBVTBqxlq8+M4U9P7sW2zeddzi45BTHwZ9gzw/Px/9+/dHgwYNJPkjWufOndG5c2eTX0dqm349hfP/JuO3VaOsPRSTVfJ6cvqE2xkPNZbfyngIHy/znlpBTtcakAL7kBef8k++pXX7v/say2/9dx+Vypv3G1xSXOds1qxZCAgIQHR0tHpZUFCQJOMzB/YhL7bWh6Fzh5z6kLoNgH2YG/uwHKXMHc3qVcPCSe8iONAHN+9kYNbynXhl4Fwc/f4LeLjb9qnqsrJzcOLPqxj5fmdcSriJW3cz8WbH5mjRIAhXr9+26Fi472EaW+3jWsodrNx0CIPe+T9E9H8ZZ/5OxJjZP8GptCN6d33e2sPTae6qPfAo44ITP45HfoEARwcVpi3ehh93nbL4WNiHaWyxDzm3AdhOH0q8BrkS+njW9gN/IiPrId7p2tLaQzGKNcevtLkDUF4fR05cRNaDR+jYXh6XHyjKmuNXWh9KaeO3y3fgUsoBo/+vBgQBUKmAnRdv4UxKprWHppeUtLv4accf6PNaGwx4uwP+vnQdXy/ditKlSqFbWDOLjUNOfRh0gNzR0REvv/wyLl68aJVvmeTk5CAnJ0d9PzNT+l/M62n3MHb2Jmxe8ClcnEtL/vpK4qBSFTt1gq2eSkEK7IP0pa2NwuVA8X87Z2dnODs7ayzbunUrOnbsiLfeegsHDx5E5cqVMWjQIAwcONB8AzcB+yB96Zo79GkDkFcf1m4DYB9ywj4syxJtAMBLreup/3f9mpXRvH41NOg2EbF7z+C97q3Msk4pfTRxNRZM7IOLO6fj8eN8nI9LxqZfT6FR7aoWHQf3PSzLUn0UFAhoXKcqJg5+FQDQMCQAF6+mInrzYZs/CPhaWFO81akFBo5fhX+vpqJBrcqYEfEmUm9n4Pvtlv0mB/uwLEv0Iec2ANvpo6R9c3ukhD6etXbrUYSF1oVfxbJmXY+5WHP8Sps7AOX1sXP/GTzXuCYqlJPfpZ4A645faX0opY1G/p5oWsUL606nIO1+Dip7uaB7fR9k5jzGqeQMs6xTSgWCgLrBlfFZeCcAQO0alRGfmIafdv5h0QPkcurD4FOs169fH1evXjXHWEoUGRmpcRqHgIAAyddx/t8k3L57H+3fm4UKzw9BheeH4MiZK1j6w0FUeH4I8vMLJF+nOd363zfHKxb5tnglL1fcLPKtcqk5iNzsGfuQTx83/3sykVYsr3ltp0rlPXDrP/O+ARVro7CPgIAAjX/LyMjIYq9x9epVLF68GDVr1sTu3bvxySefYMiQIVi1apVZx24K9sE+9GFqG4D8+rBmGwD7MBT7sCx7nzu08fJwQ3DVSriabNlvYBvrWsoddP3oW1RuE4H6XScgrN83KFXKEYkpdyw6Du57WJal+vCp4Ina1X01ltWq5ovraffMsj4pTRnaA1Gr9mDzntP4J/4Gfth5Eos2/Ibh/V6y+FjYh2VZog85twHYTh8l7ZvbK3vvo1BS6l0cOBGHvj1s/wOH2lh7/EpsA1BOHzdvp+PMn/F45UXLHTSTkrXHr8Q+lNBGt3o++O3yHZy7kYm0+zk4fT0Dv8ffxYvBFcyyPqlV8PZA9ao+GsuCAioh7Xa6Rcchpz4M+gY5AEybNg0jRozA1KlT0axZM7i7u2s87ulpvk/sjB07FhEREer7mZmZksfQtkUIjmwYp7Hs0ylrUbOaD4b2fQmOjrb6T6ld4q0s3LyXjbb1/HAh8cm1DMu4lkbTGhURvTfOrOuW0ydFpMI+5NNHYsp/SLuTgXYtQnDhUgoAwMPdBc3qVcPKnw6bdd0lfUo9OTlZ43dF2zcACwoK0Lx5c8yYMQMA0KRJE1y4cAFLlixBeHi4mUZuGvbBPvSha+7Qpw1Afn1Ysw2AfRiKfViWvc8d2mRl5yAh5Q7ervCc2dclpexHuch+lAsvD1e8+HwdTJr/s0XXz30P++yjZaPquJx4S2NZfNItVPEtJ/m6pObq7ISCAs0PgBUUCHBQWX7OYx/214ec2wBspw8lfoMcsP8+Cq3/5Rgqenvg5WfO1iMn1h6/EucOQDl97Np/BmW93PF801pmeX1zs/b4ldiHEtoo7ahC0a9vFAgC5PJP27huNVxL0fygfWLKHYufhUROfeh9gHzKlCn4/PPP8corrwAAXn31Vaie2ShBEKBSqZCfny/9KP9H7FSRUvJwd0HdYH+NZW6uTijn5V5sua1wdy6FIN+n/w8osGIZ1A8sh3tZOUj57wGW7PoHEa81xNW0TCTevo+xbzVFWno2dpxKMuu4pLrWQEpKCkaPHo2dO3ciOzsbwcHBiI6ORvPmzaUaqsnYh2324e7qhKCAiur7gf7lUb9WZaRnZOP6zXtYsmE/RgzohKvJt5GY8h/GfdwFaXcysP3gebOOq6TrnHl6epb4psLPzw9169bVWFanTh1s2rRJuoFKhH2wD0Pomjv0aQOQTx+20AbAPrRhH9ZnC31Yog0AmBC1GZ3aNECAXzmk3s7AzGXb4ejggDc6yuPbHP/3fB2oVMDlxFuoXqUipgztgUvXbmLd1mMWHYecrnNmKiX1Maj3/6Hj+7MxO3o3XgtritN/X8OqLUcwd1xvs6/bVLsO/4WI/h1xPe0eLl5NRcOQKhj0Tges2/qHxcfCPuyvDzm3AdhOH0q7BrlS+gCefCh03S9/oFeXlihVytHs65OaLYxfSXMHoLw+dh04g5fbNYGjozz7sPb4ldSHktr4Jy0LYTUrID07T32K9XY1yuNEUrrZ1y2FPj1eQP8Ri/DdD7/hpTYN8felZGzedRzjP3vDouOQUx96HyD/8ssv8fHHH2P//v3mHA8ZoXH1Cvh5Qif1/WnvPfm2yYaDV/DZ0sOY/8sFuDuXwuwPWsHLzQnHL93E2zP3ICfPvH9wl+KTIvfu3UPr1q3RoUMH7Ny5ExUrVsTly5etdp1WMezDNjWuE4htS4eq78+IeDIZrN/2BwZ/uRbfrt4LN1dnzB3XG15lXPHH+Xi8OWQRcnIfm3VcUnxKvXXr1oiL0zwLxKVLlxAYGGjy+KTGPmyTnPowdO6QSx9sw3axD+tTUh8pt9Lxwfho3M3IRgXvMmjZqDr2RH+OCt4eJf+wDfAs44KJg1+Ff6WyuJeZjV9+O4dpi37BYwtfOkFOn1I3lZL6aFovEGu+HogpC7fi6xU7EehfHjMi3kDPzi2sPbQSjf76R4z7uCu+Gf02KniXQdqdDMRsPoKvVuy0+FjYh/2RcxuA7fShtG+QK6UPADhwIg7X0+7h3Veft/ZQjGIL41fS3AEoq48zf13FrTsZ6NShqbWHYhRbGL+S+lBSG1v+SkOn2hXxekNfeDiXQsajxziWeA974uRx+bN6tQLwzfi+WBCzC8s37IO/jzdGfNgNr3RoYtFxyKkPvQ+QC4IAAGjXrp1kK8/KysKVK1fU9xMSEnDu3DmUK1cOVatWlWw9ptq2dJi1h6DTkYtpqPBOjM7nzPzpHGb+dM4i4ymkUqHY6ScM7WDWrFkICAhAdHS0ellQUJAEo5MW+7BNR85chneLT3U+J3LpdkQu3W6hET2hrY3C5foaPnw4WrVqhRkzZqBnz544ceIEli1bhmXLlkk3UImwD9skpz4MnTvk0oc52gDYhxTYh/Upae5YOWOA1dYthdi9ZxG796y1hyFJH3KhpD4AoFObBujUpoFVx2CMrOwcjJuzCePmWP8MHezDNLbah1zbAGynDyn2zeVESX383/N1cO/kAqut31S2MH4lzR2Asvpo3igY+zZOtdr6TWUL41dSH0pqIye/AD//fRM//33TamMwVdvn6qDtc3WsOgY59WHQNchVEm/FqVOn0KFDB/X9wusIhIeHIyYmRtJ1keU5aDmVgqGfFNm6dSs6duyIt956CwcPHkTlypUxaNAgDBw4UMqhSoJ9kL60tVG4XF8tWrTAli1bMHbsWEyZMgVBQUGIiopCnz59pByqZNgH6UuKuUNOfUjdBsA+7Bn7MA3bsG9S9CEn7IMMwT5Mwz7slxT75nLDPkhfSps7APZB+lNaH2yDDCGnPgw6QF6rVq0SY7h7967er9e+fXv1J1DI/ug6lUJmZqbGcrHrSFy9ehWLFy9GREQExo0bh5MnT2LIkCFwcnJCeHi4+QZvBPZB+pLqNG5du3ZF165dpRqWWbEP0pdUp+GRSx9StwGwD3vGPorj3EGF5HQaNymwDzIE+yiOfRCgvFOsA+yD9Ke0uQNgH6Q/pfXBNsgQcurDoAPkX375Jby8vMw1FrIzjlo+KVJ4PyAgQGP5pEmTMHny5GKvUVBQgObNm2PGjBkAgCZNmuDChQtYsmSJzR0gZx+kL21tFC63V+yD9KVr7rBHbIMMwT6IxEnVR0pKCkaPHo2dO3ciOzsbwcHBiI6ORvPmzaUaqiTYBxnCWn0cOHBA49tBhVJTU+Hr62vw+vXFPkhfUu2by6UNgH2Q/qSYO6pVq4bExMRiywcNGoSFCxcWWx4TE4P+/ftrLHN2dsajR48MWq+x2Afpyxp9WBPbIEPI6W9XBh0g79WrFypVqmSusZCdcVAV/2SIw//uJicnw9PTU71c27fHAcDPzw9169bVWFanTh1s2mT967gVxT5IX9raKFxur9gH6UvX3GGP2AYZgn0QiZOij3v37qF169bo0KEDdu7ciYoVK+Ly5cvw9vaWcKTSYB9kCGv3ERcXp7H/b+7fXfZB+pJi31xObQDsg/Qnxdxx8uRJ5Ofnq+9fuHABL730Et566y3Rn/H09ERcXJz6vjkuSyaGfZC+rNWHtbANMoSc/nal9wFyS05GZB+ehFB8GfDkzc6zOwFiWrdurfGmCAAuXbqEwMBAqYYpCfZBhtDWRuFye8Q+yBC65g57wzbIUOyDSJwUfcyaNQsBAQGIjo5WLwsKCpJgdNJiH2Qoa/dRqVIllC1b1rAVGol9kCGk2DeXSxsA+yDDSDF3VKxYUeP+zJkzUaNGDbRr1070Z1QqldnPpCC2XiJ9WasPa2AbZCg5/e3KQd8n8poAZKjCaw0UvRli+PDh+OOPPzBjxgxcuXIF69evx7JlyzB48GAzjdo47IMMIdaGrV6Lw1TsgwzBNojEsQ8icVL0sXXrVjRv3hxvvfUWKlWqhCZNmmD58uVmGrHx2AcZytp9NG7cGH5+fnjppZdw5MgRYzZBb+yDDCHFvrlc2gDYBxlG6n2P3NxcrF27FgMGDNB5wC0rKwuBgYEICAhA9+7d8ffffxu9TkOwDzKEtfqwBrZBhpLT3670/gZ5QUGBOcdBdsgRWq41AMNCaNGiBbZs2YKxY8diypQpCAoKQlRUFPr06SPlUE3GPsgQ2tooXG6P2AcZQoq5Qy7YBhmKfRCJ09VHZmamxnJnZ2etl3i6evUqFi9ejIiICIwbNw4nT57EkCFD4OTkhPDwcPMN3kDsgwxlrT78/PywZMkSNG/eHDk5OVixYgXat2+P48ePo2nTphJtnSb2QYYoad9cnz7k0gbAPsgwUswdz4qNjUV6ejr69esn+pyQkBCsXLkSDRs2REZGBr755hu0atUKf//9N6pUqWLchuiJfZAhrNGHtbANMpSc/nZl0DXIiQyhUj25FV1mqK5du6Jr167SDIrIBmhro3A5kdJJNXcQ2SP2QSROVx8BAQEayydNmoTJkycXe42CggI0b94cM2bMAAA0adIEFy5cwJIlS2zqADmRoazVR0hICEJCQtT3W7Vqhfj4eMydOxdr1qwxfoOIJFLSvrk+fbANsldSzB3P+u6779C5c2f4+/uLPic0NBShoaHq+61atUKdOnWwdOlSTJ061aDxE5mTNfogkgs5/e2KB8jJbFRaTp1ga6cIIbIGbW0ULidSOs4dROLYB5E4XX0kJyfD09NTvVzsGxx+fn6oW7euxrI6depg06ZNEo+WyLJsqY/nnnsOhw8fNuhniMylpH1zffpgG2SvpJg7CiUmJmLv3r3YvHmzQWMoXbo0mjRpgitXrhj0c0TmZgt9ENkqOf3tigfIyWxU/7sVXUakdNragMgyIqXh3EEkjn0QidPVh6enp8YfqcS0bt0acXFxGssuXbqEwMBAScZIZC221Me5c+fg5+dn0M8QmUtJ++b69ME2yF5JMXcUio6ORqVKldClSxeDxpCfn4+//voLr7zyikE/R2RuttAHka2S09+ueICczEalUhX7ZIitflKEyJK0tVG4nEjpOHcQiWMfROKk6GP48OFo1aoVZsyYgZ49e+LEiRNYtmwZli1bJuVQiSzOUn2MHTsWKSkpWL16NQAgKioKQUFBqFevHh49eoQVK1bgt99+w6+//mr6RhFJQIp9c7ZB9kqqfY+CggJER0cjPDwcpUppHoro27cvKleujMjISADAlClT8PzzzyM4OBjp6en4+uuvkZiYiA8++MD4DSEyA0v0QSRXcvrbFasjs3FQPbkVXUakdNraKFxOpHScO4jEsQ8icVL00aJFC2zZsgVjx47FlClTEBQUhKioKPTp00e6gRJZgaX6SE1NRVJSkvp+bm4uPv/8c6SkpMDNzQ0NGzbE3r170aFDB1M2h0gyUuybsw2yV1Lte+zduxdJSUkYMGBAsceSkpLg4OCgvn/v3j0MHDgQaWlp8Pb2RrNmzXD06NFilzEgsjZL9EEkV3L62xUPkJPZyOmTIkSWxG+QE4nj3EEkjn0QiZOqj65du6Jr165SDYvIJliqj5iYGI37o0aNwqhRowxeD5GlSLVvzjbIHkk1d7z88ssQBEHrYwcOHNC4P3fuXMydO9fgdRBZmiX6IJIrOf3tigfIyWxUqie3osuIlE5bG4XLiZSOcweROPZBJI59EIljH0Tacd+cSBznDiJx7INInJz6cCj5KUTGKQyh6I1I6cTaYB9EbINIF/ZBJI59EIljH0Tacd+cSBzbIBLHPojESdVHSkoK3n33XZQvXx6urq5o0KABTp06JelY+Q1yMhsHlQoORX7zi94nUiJtbRQuJ1I6zh1E4tgHkTj2QSSOfRBpx31zInGcO4jEsQ8icVL0ce/ePbRu3RodOnTAzp07UbFiRVy+fBne3t5SDpUHyMl85HQqBSJL4mnciMRx7iASxz6IxLEPInHsg0g77psTiePcQSSOfRCJk6KPWbNmISAgANHR0eplQUFBEoxOE0+xTmZT+EmRojcipRNrg30Qce4g0oV9EIljH0Ti2AeRdtw3JxLHNojEsQ8icVL0sXXrVjRv3hxvvfUWKlWqhCZNmmD58uWSj1XW3yAXBAEAcD8z08ojMZ6Q99DaQzBa4dgL/x2KUv3vVnQZWYZd9JGfa+0hGKVw3Ia0AZFlZB7sw3qM6YNtWBb7sB72YdvYhnWxD9vGPqyLfdg29mFduvrgvrn12UMfclX435xzh+0q/Ld5kHXfyiNRnsL/5uzDNhX+u+Q+zLLySIyXdV++816WCX1kFpnvnZ2d4ezsXOw1rl69isWLFyMiIgLjxo3DyZMnMWTIEDg5OSE8PNzELXhK1gfI799/8g8RHBRg5ZEo2/379+Hl5VVsuUqlgqrIJ0OK3ifzYR/WZ0gbhcvJMtiH9XHusF3sw/rYh21iG7aBfdgm9mEb2IdtYh+2QVsf3De3PvZhfZw7bFdhH6+1bWDlkSgX+7BNhW2s/vBFK4/EeCusPQAJGNNHQIDmfD9p0iRMnjy52GsUFBSgefPmmDFjBgCgSZMmuHDhApYsWcID5IX8/f2RnJwMDw8Ps/w/oMzMTAQEBCA5ORmenp6Sv765mXv8giDg/v378Pf31/q4g+rJregysgz2oZs5x29MG4XLyTLYhzjOHWTOPuTcBsA+lI5zh27sQ9nYh27sQ9nYh27W7IP75tbHPsRx7iD2IY59KBvb0M2W+yg6Jm3fHgcAPz8/1K1bV2NZnTp1sGnTJuMHroWsD5A7ODigSpUqZl+Pp6enLEMoZM7xa/uESCF+ksq62Id+zDV+Q9soXE6WwT5KxrlDuSzRh5zbANiHUnHu0A/7UCb2oR/2oUzsQz/W6IP75tbHPkrGuUO52EfJ2IcysQ392GIf+o6pdevWiIuL01h26dIlBAYGGjFacbI+QE62TaXlkyKcJ4i0t1G4nEjpOHcQiWMfROLYB5E49kGkHffNicRx7iASxz6IxEnRx/Dhw9GqVSvMmDEDPXv2xIkTJ7Bs2TIsW7ZMuoECcJD01YiepRK5ESmdWBvsg4htEOnCPojEsQ8iceyDSDvumxOJYxtE4tgHkTgJ+mjRogW2bNmCDRs2oH79+pg6dSqioqLQp08fSYfKb5Dr4OzsjEmTJomeB9/WWXv8DioVHIp8NKTofZIva/9+mcqa49fWRuFysg9y7sPaY+fcYd+s/ftlKmuPn33YN2v/fpnK2uNnH/bN2r9fprL2+NmHfbP275epuG9O5iTnPqw9ds4d9s/av2OmsPbY2Yd9s/bvl6msPX6p+ujatSu6du0q1bC0UgmCIJh1DaQ4mZmZ8PLywp8JN+HhoXk9gfv3M9EwyAcZGRmyvn4DkTF0tQGwD1I2zh1E4tgHkTj2QSSOfRBpx31zInGcO4jEsQ8icXLsg98gJ7NxgJZPivBcI0Ra2yhcTqR0nDuIxLEPInHsg0gc+yDSjvvmROI4dxCJYx9E4uTUBw+Qk9lou7SAbWZAZFlil91gH0ScO4h0YR9E4tgHkTj2QaQd982JxHHuIBLHPojEyakPHiAns1GpVFAV+aRI0ftESqStjcLlRErHuYNIHPsgEsc+iMSxDyLtuG9OJI5zB5E49kEkTk598AA5mY2D6smt6DIipdPWRuFyIqXj3EEkjn0QiWMfROLYB5F23DcnEse5g0gc+yASJ6c+HKw9AFu2cOFCVKtWDS4uLmjZsiVOnDhh7SHp5ffff0e3bt3g7+8PlUqF2NhYq4zDQaXSeiP5k2sbgG30IdYG+7AP7MM0bMO+ybUPW2gDYB/2jn2Yhn3YN/ZhGvZhv+TaBmAbfXDf3L6xD9OwDfsm1z5soQ2Afdg79mEaOfXBA+QifvjhB0RERGDSpEk4c+YMGjVqhI4dO+LWrVvWHlqJHjx4gEaNGmHhwoVWHYdKpf1G8ibnNgDb6EOsDfYhf+zDdGzDfsm5D1toA2Af9ox9mI592C/2YTr2YZ/k3AZgG31w39x+sQ/TsQ37Jec+bKENgH3YM/ZhOjn1oRIEQbD2IGxRy5Yt0aJFCyxYsAAAUFBQgICAAHz22WcYM2aMlUenP5VKhS1btqBHjx4WW2dmZia8vLxw5fodeHh6ajx2PzMTwVUqICMjA55FHiN5sJc2AMv3oasNgH3YA/ZhPM4d9s9e+uB7KzIH9mE89mH/2Ifx2Id9s5c2ANva9wDYhz1gH8bj3GH/7KUPvrcic2AfxpNjH/wGuRa5ubk4ffo0wsLC1MscHBwQFhaGY8eOWXFk8qJSqbTeSL7YhjTE2mAf8sY+pME27BP7kAb7sE/sQxrswz6xD2mwD/vDNqTBfXP7xD6kwTbsE/uQBvuwT+xDGnLqgwfItbhz5w7y8/Ph4+OjsdzHxwdpaWlWGpX8OKi030i+2IY0xNpgH/LGPqTBNuwT+5AG+7BP7EMa7MM+sQ9psA/7wzakwX1z+8Q+pME27BP7kAb7sE/sQxpy6qOUtQdA9kulUsGhyCdDbPWTIkSWpK2NwuVESse5g0gc+yASxz6IxLEPIu24b04kjnMHkTj2QSROTn3wALkWFSpUgKOjI27evKmx/ObNm/D19bXSqORHpXpyK7qM5IttSENbG4XLSb7YhzQ4d9gn9iEN9mGf2Ic02Id9Yh/SYB/2h21Ig/vm9ol9SINzh31iH9JgH/aJfUhDTn3wFOtaODk5oVmzZti3b596WUFBAfbt24fQ0FArjkxeVCL/R/LFNqQh1gb7kDf2IQ22YZ/YhzTYh31iH9JgH/aJfUiDfdgftiEN7pvbJ/YhDbZhn9iHNNiHfWIf0pBTHzxALiIiIgLLly/HqlWrcPHiRXzyySd48OAB+vfvb+2hlSgrKwvnzp3DuXPnAAAJCQk4d+4ckpKSLDoOqa81MHPmTKhUKgwbNkyyMZLh5NwGYBt9mOM6Z+zDNrAP05njOjXswzbIuQ9baANgH/aMfZhOij4mT54MlUqlcatdu7Z5Bkx6Yx+mk2r+SElJwbvvvovy5cvD1dUVDRo0wKlTp3T+zIEDB9C0aVM4OzsjODgYMTExxm0EFSPnNgDb6EOqfXO2YXvYh+mkaKNatWrF3lupVCoMHjxY9Gd+/PFH1K5dGy4uLmjQoAF27Nhh4pZQUXLuwxbaAKz73orMi32YTk7XIIdAoubPny9UrVpVcHJyEp577jnhjz/+sPaQ9LJ//34BQLFbeHi4RdafkZEhABBSbqUL9x8VaNxSbqULAISMjAyDXvPEiRNCtWrVhIYNGwpDhw41z8BJb3JtQxCs24euNtiH/WAfxjHH3CEI7MPWyLUPe3xvJQjsw9awD+NI2cekSZOEevXqCampqerb7du3zbwFpA/2YRwp+7h7964QGBgo9OvXTzh+/Lhw9epVYffu3cKVK1dEf+bq1auCm5ubEBERIfzzzz/C/PnzBUdHR2HXrl1SbaLiybUNQbDdfQ9D+2Abtot9GEfKuePWrVsa76v27NkjABD279+v9flHjhwRHB0dha+++kr4559/hPHjxwulS5cW/vrrLwm3kARBvn0o/b0VWQb7MI65/nZlTipBEAQTjq8TFZOZmQkvLy+k3k6Hp6dnscf8KpZFRkZGscfEZGVloWnTpli0aBGmTZuGxo0bIyoqygwjJzIvXW0UPs4+SKmknjsA9kH2g30QiZOyj8mTJyM2Nlb9iXsiuZOyjzFjxuDIkSM4dOiQ3usfPXo0tm/fjgsXLqiX9erVC+np6di1a5f+G0IkMSn3zdkG2Rtz7HsUGjZsGLZt24bLly9DpeWCtG+//TYePHiAbdu2qZc9//zzaNy4MZYsWWL4xhBJzNrvrYhsmTnnD3PhKdbJfFQiNwMNHjwYXbp0QVhYmKTDI7IasTbYB5FkbQDsg+wQ+yASJ1Efly9fhr+/P6pXr44+ffpY/HR0RGYhQR9bt25F8+bN8dZbb6FSpUpo0qQJli9frvNnjh07Vmye6dixI44dO2bYyonMRYJ9c7ZBdkvCfQ8AyM3Nxdq1azFgwACtB8cBtkEyYqX3VkSyIPH8YU6lrD0Asl9Z9+/Docgbnqz79wE8+cTIs5ydneHs7FzsNb7//nucOXMGJ0+eNN9AiSxMWxuFywH2QcomxdwBsA+yT+yDSJwUfbRs2RIxMTEICQlBamoqvvzyS7Rp0wYXLlyAh4eH+QZPZGZS9HH16lUsXrwYERERGDduHE6ePIkhQ4bAyckJ4eHhWteblpYGHx8fjWU+Pj7IzMzEw4cP4erqaspmEZlMin1ztkH2Sqp9j0KxsbFIT09Hv379RJ8j1kZaWpoBIycyP2u9tyKSA1192BoeICfJOTk5wdfXFzWDArQ+XqZMGQQEaD42adIkTJ48WWNZcnIyhg4dij179sDFxcVcwyWymJLaANgHKZdUcwfAPsj+sA8icVL20blzZ/X/btiwIVq2bInAwEBs3LgR77//vqTjJrIEKfsoKChA8+bNMWPGDABAkyZNcOHCBSxZsoR/xCXZkXLfnG2QvZFy7njWd999h86dO8Pf31+qoRJZHN9bEYkrqQ9fX184OTlZeFS68QA5Sc7FxQUJCQnIzc3V+rggCMVOpaPtU1SnT5/GrVu30LRpU/Wy/Px8/P7771iwYAFycnLg6Ogo7eCJzKikNgD2Qcol1dwBsA+yP+yDSJyUfRRVtmxZ1KpVC1euXDF5nETWIGUffn5+qFu3rsayOnXqYNOmTaLr9/X1xc2bNzWW3bx5E56envyGLFmVlPvmbIPsjTneWyUmJmLv3r3YvHmzzueJteHr66vHyInMz9rvrYhsWUl9ODk52dwXNXiAnMzCxcXF5F/2F198EX/99ZfGsv79+6N27doYPXo0/3hLsiRFGwD7IPvEPojEsQ8icVL1UVRWVhbi4+Px3nvvSf7aRJYiVR+tW7dGXFycxrJLly4hMDBQ9GdCQ0OxY8cOjWV79uxBaGioyeMhMhXbIBIn9Xur6OhoVKpUCV26dNH5vNDQUOzbtw/Dhg1TL2MbZGusOX8Q2Tpz7ZubCw+Qk83y8PBA/fr1NZa5u7ujfPnyxZYTKQ37IBLHPojEsQ8icSNGjEC3bt0QGBiIGzduYNKkSXB0dETv3r2tPTQiqxs+fDhatWqFGTNmoGfPnjhx4gSWLVuGZcuWqZ8zduxYpKSkYPXq1QCAjz/+GAsWLMCoUaMwYMAA/Pbbb9i4cSO2b99urc0gkhzbINKtoKAA0dHRCA8PR6lSmoci+vbti8qVKyMyMhIAMHToULRr1w6zZ89Gly5d8P333+PUqVMaPRHZC33mDyIyLwdrD4CIiIiIiIjI2q5fv47evXsjJCQEPXv2RPny5fHHH3+gYsWK1h4akdW1aNECW7ZswYYNG1C/fn1MnToVUVFR6NOnj/o5qampSEpKUt8PCgrC9u3bsWfPHjRq1AizZ8/GihUr0LFjR2tsApFZsA0i3fbu3YukpCQMGDCg2GNJSUlITU1V32/VqhXWr1+PZcuWoVGjRvjpp58QGxvLD/KSXdJn/iAiMxNIp/DwcKF79+7q++3atROGDh1q8XHs379fACDcu3dP9DkAhC1btuj9mpMmTRIaNWpk0rgSEhIEAMLZs2dNeh2SJ/ahG/tQNvahG/tQNvahG/tQLrahG9tQNvahG/tQNvahG/tQNvahG/tQNvahG/tQLrahm720IctvkPfr1w8qlQoqlQpOTk4IDg7GlClT8PjxY7Ove/PmzZg6dapezz1w4ABUKhXS09PNOyiiZ7APInHsg0gc+yDSjm0QiWMfROLYB5E49kEkjn0Qacc2SGqyvQZ5p06dEB0djZycHOzYsQODBw9G6dKlMXbs2GLPzc3NhZOTkyTrLVeunCSvQ2RO7INIHPsgEsc+iLRjG0Ti2AeROPZBJI59EIljH0TasQ2Skiy/QQ4Azs7O8PX1RWBgID755BOEhYVh69atAJ58kqRHjx6YPn06/P39ERISAgBITk5Gz549UbZsWZQrVw7du3fHtWvX1K+Zn5+PiIgIlC1bFuXLl8eoUaMgCILGetu3b49hw4ap7+fk5GD06NEICAiAs7MzgoOD8d133+HatWvo0KEDAMDb2xsqlQr9+vUDABQUFCAyMhJBQUFwdXVVX1PlWTt27ECtWrXg6uqKDh06aIxTX6NHj0atWrXg5uaG6tWrY8KECcjLyyv2vKVLlyIgIABubm7o2bMnMjIyNB5fsWIF6tSpAxcXF9SuXRuLFi0yeCxkWeyjZOxDudhHydiHcrGPkrEPZWIbJWMbysU+SsY+lIt9lIx9KBf7KBn7UC72UTL2oUxso2RsQ3+yPUBelKurK3Jzc9X39+3bh7i4OOzZswfbtm1DXl4eOnbsCA8PDxw6dAhHjhxBmTJl0KlTJ/XPzZ49GzExMVi5ciUOHz6Mu3fvYsuWLTrX27dvX2zYsAHz5s3DxYsXsXTpUpQpUwYBAQHYtGkTACAuLg6pqan49ttvAQCRkZFYvXo1lixZgr///hvDhw/Hu+++i4MHDwJ4Euzrr7+Obt264dy5c/jggw8wZswYg/+beHh4ICYmBv/88w++/fZbLF++HHPnztV4zpUrV7Bx40b88ssv2LVrF86ePYtBgwapH1+3bh0mTpyI6dOn4+LFi5gxYwYmTJiAVatWGTwesh72URz7oELsozj2QYXYR3HsgwC2oQ3boELsozj2QYXYR3Hsgwqxj+LYBxViH8WxDwLYhjZswwDWuvi5KcLDw4Xu3bsLgiAIBQUFwp49ewRnZ2dhxIgR6sd9fHyEnJwc9c+sWbNGCAkJEQoKCtTLcnJyBFdXV2H37t2CIAiCn5+f8NVXX6kfz8vLE6pUqaJelyAIQrt27YShQ4cKgiAIcXFxAgBhz549Wse5f/9+AYBw79499bJHjx4Jbm5uwtGjRzWe+/777wu9e/cWBEEQxo4dK9StW1fj8dGjRxd7raIACFu2bBF9/OuvvxaaNWumvj9p0iTB0dFRuH79unrZzp07BQcHByE1NVUQBEGoUaOGsH79eo3XmTp1qhAaGioIgiAkJCQIAISzZ8+Krpcsi31oxz5IENiHGPZBgsA+xLAPYhvasQ0SBPYhhn2QILAPMeyDBIF9iGEfJAjsQwz7ILahHdswnmyvQb5t2zaUKVMGeXl5KCgowDvvvIPJkyerH2/QoIHG9QXOnz+PK1euwMPDQ+N1Hj16hPj4eGRkZCA1NRUtW7ZUP1aqVCk0b9682OkUCp07dw6Ojo5o166d3uO+cuUKsrOz8dJLL2ksz83NRZMmTQAAFy9e1BgHAISGhuq9jkI//PAD5s2bh/j4eGRlZeHx48fw9PTUeE7VqlVRuXJljfUUFBQgLi4OHh4eiI+Px/vvv4+BAweqn/P48WN4eXkZPB6yHPZRMvahXOyjZOxDudhHydiHMrGNkrEN5WIfJWMfysU+SsY+lIt9lIx9KBf7KBn7UCa2UTK2oT/ZHiDv0KEDFi9eDCcnJ/j7+6NUKc1NcXd317iflZWFZs2aYd26dcVeq2LFikaNwdXV1eCfycrKAgBs375d4xcQeHL9BKkcO3YMffr0wZdffomOHTvCy8sL33//PWbPnm3wWJcvX14sTEdHR8nGStJjH7qxD2VjH7qxD2VjH7qxD+ViG7qxDWVjH7qxD2VjH7qxD2VjH7qxD2VjH7qxD+ViG7qxDcPI9gC5u7s7goOD9X5+06ZN8cMPP6BSpUrFPi1RyM/PD8ePH0fbtm0BPPlExOnTp9G0aVOtz2/QoAEKCgpw8OBBhIWFFXu88JMq+fn56mV169aFs7MzkpKSRD9hUqdOHWzdulVj2R9//FHyRj7j6NGjCAwMxBdffKFelpiYWOx5SUlJuHHjBvz9/dXrcXBwQEhICHx8fODv74+rV6+iT58+Bq2frIt96MY+lI196MY+lI196MY+lItt6MY2lI196MY+lI196MY+lI196MY+lI196MY+lItt6MY2DONg7QFYSp8+fVChQgV0794dhw4dQkJCAg4cOIAhQ4bg+vXrAIChQ4di5syZiI2Nxb///otBgwYhPT1d9DWrVauG8PBwDBgwALGxserX3LhxIwAgMDAQKpUK27Ztw+3bt5GVlQUPDw+MGDECw4cPx6pVqxAfH48zZ85g/vz56gvcf/zxx7h8+TJGjhyJuLg4rF+/HjExMQZtb82aNZGUlITvv/8e8fHxmDdvHrZs2VLseS4uLggPD8f58+dx6NAhDBkyBD179oSvry8A4Msvv0RkZCTmzZuHS5cu4a+//kJ0dDTmzJlj0HjItrEP9kHi2Af7IHHsg32QdmyDbZA49sE+SBz7YB8kjn2wDxLHPtgHacc22IZO1rr4uSnCw8OF7t27G/x4amqq0LdvX6FChQqCs7OzUL16dWHgwIFCRkaGIAiCkJeXJwwdOlTw9PQUypYtK0RERAh9+/bVeK127doJQ4cOVd9/+PChMHz4cMHPz09wcnISgoODhZUrV6ofnzJliuDr6yuoVCohPDxcEARBKCgoEKKiooSQkBChdOnSQsWKFYWOHTsKBw8eVP/cL7/8IgQHBwvOzs5CmzZthJUrVwoAhHv37oluNwBhy5Yt6vsjR44UypcvL5QpU0Z4++23hblz5wpeXl7qxydNmiQ0atRIWLRokeDv7y+4uLgIb775pnD37l2N1123bp3QuHFjwcnJSfD29hbatm0rbN68WRAEQUhISBAACGfPnhUdF1kW+9COfZAgsA8x7IMEgX2IYR/ENrRjGyQI7EMM+yBBYB9i2AcJAvsQwz5IENiHGPZBbEM7tmE8lSCIXGmeiIiIiIiIiIiIiIiIiIjIjijmFOtERERERERERERERERERKRsPEBORERERERERERERERERESKwAPkRERERERERERERERERESkCDxATkREREREREREREREREREisAD5EREREREREREREREREREpAg8QE5ERERERERERERERERERIrAA+RERERERERERERERERERKQIPEBORERERERERERERERERESKwAPkRERERERERERERERERESkCDxATkREREREREREREREREREisAD5EREREREREREREREREREpAg8QE5ERERERERERERERERERIrAA+RERERERERERERERERERKQIPEBORERERERERERERERERESKwAPkRERERERERERERERERESkCDxATkREREREREREREREREREisAD5EREREREREREREREREREpAg8QE5ERERERERERERERERERIrAA+RERERERERERERERERERKQIPEBORERERERERERERERERESKwAPkRERERERERERERERERESkCDxATkREREREREREREREREREisAD5EREREREREREREREREREpAg8QE5ERERERERERERERERERIrAA+RERERERERERERERERERKQIPEBORERERERERERERERERESKwAPkRERERERERERERERERESkCDxATkREREREREREREREREREisAD5EREREREREREREREREREpAg8QE5ERERERERERERERERERIrAA+RERERERERERERERERERKQIPEBORERERERERERERERERESKwAPkRERERERERERERERERESkCDxATkREREREREREREREREREisAD5ERERERERKR4v//+O7p16wZ/f3+oVCrExsZqPN6vXz+oVCqNW6dOnawzWCILYx9E4tgHERERkfzwADkREREREREp3oMHD9CoUSMsXLhQ9DmdOnVCamqq+rZhwwYLjpDIetgHkTj2QURERCQ/paw9ACIiIiIiIiJr69y5Mzp37qzzOc7OzvD19bXQiIhsB/sgEsc+iIiIiOSHB8jJLB49+n/27jw+pnvx//j7TMgqiS0SIYIite80inIpUrVWlWqltLS1lCparb1Uq7cotXWxVVBtLd0uRe1UGxqtFiWCWBJ7IgkSyfn+4ZthknMmc2bOnJkz5/38Pebxu3NmMvMZzev7mU/OzDm3kZ2dLXmbt7c3fH19NR4RkXuw1gbAPsjYOHcQyWMfRPKs9SGKIgRBsNjm4+MDHx8fu55rx44dKFeuHEqVKoX//Oc/mDZtGsqUKWPXYxFpgX0QSStqbc4+yMi49iCSxz6I5OmtD+4gJ9Xdvn0bfoFlgLtZkreHhYUhKSnJ7WIgcrai2gDYBxkX5w4ieeyDSF5RfZQoUQIZGRkW2yZNmoTJkycrfq5OnTqhZ8+eqFKlChITE/H2228jJiYG+/fvh5eXlz3DJ3Iq9kEkzZa1Ofsgo+Lag0ge+yCSp8c+uIOcVJednQ3czYJP3RcBL2/LG3OzkfLXF8jOznarEIi0YLUNgH2QoXHuIJLHPojkFdVHxl9fIDk5GUFBQebN9n77r0+fPub/XbduXdSrVw8PPfQQduzYgXbt2tn1mETOxD6IpNmyNmcfZFRcexDJYx9E8vTYh8nVA3B3O3bsgCAI+Oabb1w9FP0p5gOhmK/FBcXsW0yQe2IfdpJog314HvZhJ84dHo9tOIB9eDz24QArfQQFBVlc7N3BUVDVqlVRtmxZnDx5UpXHI+vYhwPYh8djH3YqYm3OPjwD+7AT1x6GwD7sxD48HttwgI76MOQOckEQbLrs2LHD6WO5ceMGBg8ejJCQEAQEBKBt27Y4dOiQzT+fl5eHhQsXokGDBvDz80OZMmXwn//8B4cPHy50v5kzZ6JKlSrw9fVFvXr1sHr1arVfjiWTl/SF3JoR+5g+fTq6du2K0NBQCIJg1yHTFJFrg324PaP1cezYMYwdOxYNGjRAYGAgypcvj86dOyM+Pt4ZL+ketqFLRmvjwoULeO655xAVFYXAwECULFkSzZo1w/LlyyGKojNe1j3sQ5eM1kdBcXFxEAQBJUqUUOMlyHNBH+fOncPVq1dRvnx5pz6PJzNaH6dPn5Z9jWvWrHHGy7qHfeiS0frIl5iYiGeffRblypWDn58fqlevjnfeeUfNl3Ofi9bm7MNxRutj8uTJVl/n3r171X9hXHvoltH6AICLFy9i8ODBqFKlCvz8/PDQQw9h1KhRuHr1qtov6R72oUtGbOPkyZPo1asXSpUqBX9/f7Rs2RLbt29X++VY0lEfhjzE+pdffmlxfcWKFdiyZUuh7TVr1sTRo0edNo68vDx07twZhw8fxpgxY1C2bFksWLAAbdq0wcGDB1G9evUiH2PgwIGIi4tD//79MWzYMGRmZuKPP/7ApUuXLO73zjvv4P3338egQYPQtGlTbNy4Ec8++ywEQbA41JOqBIlf/Dz3DIHuM2If48ePR1hYGBo2bIjNmzc76yXdJ9UGwD50wGh9fP755/jiiy/w1FNPYciQIUhLS8PixYvxyCOPYNOmTWjfvr36L45zhy4ZrY0rV67g3Llz6NWrFypVqoScnBxs2bIFL7zwAo4fP4733nvPOS+QfeiS0fp4UEZGBsaOHYuAgAC1X05hKvSRkZFh8W2+pKQkJCQkoHTp0ihdujSmTJmCp556CmFhYUhMTMTYsWNRrVo1dOzYUY1XYEhG7aNv37544oknLLZFR0er9noKYR+6ZMQ+EhIS0KZNG1SoUAFvvPEGypQpg7NnzyI5Odk5L06ltTn70J7R+ujZsyeqVatW6GfffvttZGRkoGnTpqq+LgCqrT127dqFDz/8EAcPHsTFixexfv16dO/e3Xz7Cy+8gOXLl1v8TMeOHbFp0yZ7Rk0wXh8ZGRmIjo5GZmYmhgwZgoiICBw+fBiffPIJtm/fjoMHD8JkUvl7ouxDl4zWRnJyMqKjo+Hl5YUxY8YgICAAS5cuRYcOHbBt2za0bt3aOS9QhT40a0MkcejQoaLcP8X27dtFAOLXX39t12NnZmbK3vbVV18VeuxLly6JJUuWFPv27VvkY+f//Lp166ze79y5c2Lx4sXFoUOHmrfl5eWJrVq1EitWrCjevXvXhldiu7S0NBGA6NPsDdG3xdsWF59mb4gAxLS0NFWfk5zH0/sQRVFMSkoSRVEUL1++LAIQJ02aVOTP2MNaG+xDnzy9j/j4ePHmzZsW265cuSKGhISIjz76aJHPowTnDs/i6W3IefLJJ8WAgAC+tyKrjNTHm2++KUZFRYn9+vUTAwICbP45JdTsI//fv+AlNjZWzMrKEjt06CCGhISIxYsXFyMjI8VBgwaJKSkpTnldRuXpfSQlJYkAxA8//ND2gTuAfXgWT+8jNzdXrFOnjti8eXMxKyvL9sHbQe21OftwPU/vQ8rZs2dFQRDEQYMGKf5Za9Ree/z000/iO++8I65bt04EIK5fv97i9tjYWLFTp07ixYsXzZdr166p+pqMztP7iIuLEwGIP/zwg8X2iRMnigDEQ4cOFflctmIfnsXT2xgyZIhYrFgx8dixYxbjioiIEBs1amTDq1BGzT60asOQh1i3R15eHqZPn46KFSvC19cX7dq1K3SuoDZt2qBOnTo4ePAgWrduDX9/f7z99tuyj/nNN98gNDQUPXv2NG8LCQlB7969sXHjRty5c8fqmGbNmoVmzZqhR48eyMvLQ2ZmpuT9Nm7ciJycHAwZMsS8TRAEvPrqqzh37hz2799vyz+BcoJJ+kIeR899AEDlypVte6FqkWuDfXgkPffRuHHjQofELVOmDFq1auW8T1KyDcPQcxtyKleujKysLGRnZyv6OZuxD8PwhD5OnDiB2bNnY9asWShWTIMDl6nQR5s2bSCKYqHLsmXL4Ofnh82bN+PSpUvIzs7G6dOn8emnnyI0NNRJL4jkeEIfAJCZmem8+aIg9mEYeu7j559/xpEjRzBp0iT4+fkhKysLubm5Cl69HVRam7MPfdBzH1JWr14NURTRr18/m39GEZXWHjExMZg2bRp69Oghex8fHx+EhYWZL6VKlXJk5GQHPfeRnp4OAIX+72r+aSz8/PysPo9d2Idh6LmN3bt3o2HDhoiKijJv8/f3R9euXXHo0CGcOHHCln8C5VToQ6s2+Bc1G73//vtYv349Ro8ejXHjxuHXX3+VfANy9epVxMTEoEGDBpgzZw7atm0r+5h//PEHGjVqVOgQH82aNUNWVhb+/fdf2Z9NT0/Hb7/9hqZNm+Ltt99GcHAwSpQogapVq2Lt2rWFnicgIAA1a9Ys9Dz5tzuFjs41QI7Rcx8uwXOQG4on9pGSkoKyZcvadF/F2IZheEIbt27dwpUrV3D69GksX74cS5cuRXR0tHMW4AD7MBBP6GPkyJFo27ZtocNIOw37MAxP6GPKlCkoUaIEfH190bRpU/z88882vno7sQ/D0HMfW7duBXDvj51NmjRBQEAA/P390adPH1y7dk3JP4PtuDY3FD33ISUuLg4RERHOO0Suhm3s2LED5cqVQ1RUFF599VXnnTeaZOm5j9atW8NkMmHEiBH49ddfce7cOfz000+YPn06unfvjocffljhv4YN2Idh6LmNO3fuSP59yt/fHwBw8OBBq6/dbhr1oUYbhjwHuT1u376NhIQEeHt7AwBKlSqFESNG4MiRI6hTp475fikpKVi0aBFefvnlIh/z4sWLkm9i8j/ddOHCBdStW1fyZxMTEyGKItasWYNixYph5syZCA4Oxscff4w+ffogKCgInTp1Mj9PaGgoBEGQfR6n8DIBXgV+8XnQAo+k5z5cQqoNgH14KE/rY/fu3di/fz/Gjx9f5DjtwrnDMDyhjY8//hjjxo0zX2/Xrh2WLl1a9Iu3F/swDL338eOPP+Lnn3/G4cOHFb1uh7APw9BzHyaTCR06dECPHj1QoUIFnDp1CrNmzUJMTAy+++47dO7cWfG/h03Yh2HouY/8bzH17t0bnTp1wrhx43D48GHMmDEDycnJ2LNnT6G/azmMa3ND0XMfBf3999/4888/MXbsWPW7yGdl7sj/xm4+Hx8f+Pj42PU0nTp1Qs+ePVGlShUkJibi7bffRkxMDPbv3w8vqT7JKfTcR61atfDpp59i9OjRiI6ONj9GbGwsPv/8c9v/EZRgH4ah5zaioqKwe/du3Lx5E4GBgebH2LNnDwDg/PnzNv4rKKRBH2q1wR3kNhowYIA5AgBo1aoVAODUqVMWIfj4+GDAgAE2PeatW7ck/+P7+vqab5eTkZEB4N4nU3799Vc0b94cANC1a1dUqVIF06ZNM4fgyPM4ROqTIfwUrkfScx8uIfepKfbhkTypj0uXLuHZZ59FlSpVMHbsWJvGqhjnDsPwhDb69u2LJk2a4PLly/jhhx+QmprqvPdVAPswED33kZ2djddffx2vvPIKatWqZdPYVME+DEPPfVSqVAmbN2+2+Pnnn38etWrVwhtvvOG8HeTswzD03Ef+fZs2bYqVK1cCAJ566in4+/tj3Lhx2LZtG9q3b2/TmG3Gtbmh6LmPguLi4gDAeYdXB6zOHRERERabJ02ahMmTJ9v1NH369DH/77p166JevXp46KGHsGPHDrRr186uxyTl9N5HhQoV0KxZMzzxxBOIjIzE7t27MXfuXJQtWxb//e9/bRqvIuzDMPTcxquvvorvv/8ezzzzDKZPn46AgAAsWLAA8fHxRT6PQzToQ602+JFIG1WqVMniev7x7K9fv26xvUKFChbBWOPn5yd5PoHbt2+bb7f2swBQpUoVcwQAUKJECXTp0gW//fYb7t696/DzOISHqTIMPffhEiodxm3Xrl3o0qULwsPDIQgCNmzYYL4tJycHb775JurWrYuAgACEh4ejf//+zjtiBMnylD4yMzPx5JNP4ubNm9i4cWOhc5OrRqW5g324P09oIzIyEu3bt0ffvn0RFxeHqlWron379s5fZPC9lcfTcx+zZ8/GlStXMGXKFJvGpRr2YRh67kNK6dKlMWDAABw/fhznzp2zabyKsQ/D0HMf+fft27evxWM8++yzAIB9+/bZNF5FeIh1Q9FzHw8SRRGrVq1CnTp1UK9ePZvGaRcrbSQnJyMtLc18efCoWo6qWrUqypYtW+gcv+Rceu5j7969ePLJJzF9+nSMGDEC3bt3x0cffYTx48dj1qxZ+Oeff2waryLswzD03EZMTAzmzZuHXbt2oVGjRoiKisKPP/6I6dOnm3/GKVzQh71tcAe5jeS+li+KosV1JTuby5cvj4sXLxbanr8tPDxc9mfzbwsNDS10W7ly5ZCTk4PMzEzz86SkpBQaqy3P4xDBJH0hj6PnPlxCrg2FfWRmZqJ+/fqYP39+oduysrJw6NAhTJgwAYcOHcK6detw/PhxdO3aVa1XQTbyhD6ys7PRs2dP/Pnnn9i4caPFJyRVp9LcwT7cnye0UVCvXr2QnJyMXbt22TxmRfjeyjD02kdaWhqmTZuGQYMGIT09HadPn8bp06eRkZEBURRx+vRpXLp0yeYxK8I+DEOvfViT/00Kp51nmX0Yhp77kLtvuXLlABT+Q7QqVFqbkz7ouY8H7d27F2fOnHHut8cBq20EBQVZXOw9fLSUc+fO4erVq+ZDDZM29NzH4sWLERoaiiZNmljcr2vXrhBF0TkfsGIfhqHnNgBg2LBhSE1Nxb59+xAfH49jx44hODgYAFCjRg2bx6yIC/qwtw0eYt2FGjRogN27dyMvLw8m0/033wcOHIC/v7/VX9Dw8HCEhYVJnifgwoUL8PX1NZ9XoEGDBvj8889x9OhRi8McHjhwwHy7U/AwbuQArfpwCZUO4xYTE4OYmBjJ24KDg7FlyxaLbZ988gmaNWuGs2fPFvr0G+mLln3k5eWhf//+2LZtG9auXYvHHntM3RdTkEpzB/swJlfPHfnfHE9LS7PzFRSB763IAVr0cfbsWWRkZGDmzJmYOXNmoftWqVIF3bp1sziqh2rYBznA1fPHqVOnAAAhISF2voIisA9ygFZ9NG7cGJ999lmh++Yf5ckpffAQ6+QgV8wfcXFxEATBfHQFp1Fp7sjIyLD4Rl9SUhISEhJQunRplC5dGlOmTMFTTz2FsLAwJCYmYuzYsahWrRo6duzo6CsgF9Oqj9TUVOTm5ha6X05ODgA45yii7IMcoPXcERAQgOjoaPP1rVu3ws/PD48++qgKr0aCCn1o1QY/EulCvXr1QmpqKtatW2feduXKFXz99dfo0qWLxacnEhMTkZiYaPHzzzzzDJKTky3+yH/lyhVs3LgR//nPf8xxdevWDcWLF8eCBQvM9xNFEYsWLUKFChXQokUL57xAQeIwCgIXGWQbrfpwCak2HugjPT3d4iJ1yBV7pKWlQRAElCxZUpXHI9fRso/hw4fjq6++woIFC9CzZ08nvqr/Z2XucFYbAPvwFFq1cfnyZcnn/+KLLyAIAho1aqTmy7pPpfdWzjgFweTJkyEIgsXl4YcfVjw2ch4t+ihXrhzWr19f6NK2bVv4+vpi/fr1qh5i0ALXHuQAV84f58+fx5IlS1CvXj3nfRuIfZADtPzblY+PD5YuXYq8vDzzfT///HMAwOOPP67+iytibU5UFK3/dpWTk4Ovv/4aLVu2dP4Hu1WaO+Lj49GwYUM0bNgQADBq1Cg0bNgQEydOhJeXF/7880907doVNWrUwIsvvojGjRtj9+7dqn7rllxDqz5q1KiB1NRU7Nixw+LnV69eDQDm3z1VsQ9ygCv3e+zbtw/r1q3Diy++aP4muepU6EOrNvgNchfq1asXHnnkEQwYMAD//PMPypYtiwULFiA3N7fQOfvyTyx/+vRp87Zx48Zh7dq1eOqppzBq1CgEBwdj0aJFyMnJwXvvvWe+X8WKFTFy5Eh8+OGHyMnJQdOmTbFhwwbs3r0bcXFxsoeJcJjJJPFJEX4mg2yjVR8A8OWXX+LMmTPIysoCcG/HwbRp0wAAzz//PCIjI9V9cVJt5G/H/UMs5ps0aRImT57s0FPevn0bb775Jvr27YugoCCHHotcT6s+5syZgwULFiA6Ohr+/v5YuXKlxWP36NEDAQEB6r44K3OHM9oA2Icn0aqN6dOnY+/evejUqRMqVaqEa9eu4dtvv8Xvv/+O4cOHo1q1as55gSq9t8o/BcHAgQMLffDlwVMQ1K9fH9evX8eIESPQtWtXxMfHW33c2rVrY+vWrebrxYpxqeFOtOjD398f3bt3L/TcGzZswG+//SZ5m2q49iAHaDV/jB07FomJiWjXrh3Cw8Nx+vRpLF68GJmZmfj444+d9wLZBzlAqz7CwsLwzjvvYOLEiejUqRO6d++Ow4cP47PPPkPfvn3RtGlT9V9cEWtzoqJo+bcrANi8eTOuXr3q/MOrA6rNHW3atCl0KOIHbd68WfFjkj5o1cewYcOwdOlSdOnSBcOHD0dkZCR27tyJ1atX4/HHH7c4T7Nq2Ac5QKs2zpw5g969e6Nr164ICwvD33//jUWLFqFevXqSc4xqVOhDqzb4VysX8vLywk8//YQxY8Zg7ty5uHXrFpo2bYply5YhKiqqyJ8PDQ3Fnj17MHr0aMyePRs5OTmIjo7GypUrUb9+fYv7vv/++yhVqhQWL16MZcuWoXr16li5cqVzD8fDw7iRA7Ts44svvsDOnTvN17dv347t27cDAFq2bOmEHeTWD+OWnJxssZPO0U8F5uTkoHfv3hBFEQsXLnToscg9aNVHQkICAGD//v3Yv39/ocdJSkpywg5y+blD7TYA9uFptGqjc+fOSExMxJIlS3D58mX4+vqiXr16WLp0KWJjY533At38FATFihVDWFiY4vGQNrR8b+USXHuQA7Tqo0OHDli0aBHmz5+P69evo2TJkmjdujXGjx/vvKOPAOyDHKLl/DF+/HiUKlUK8+bNw8iRIy12mjsFD7FODtL6/VVcXByKFy+Op59+2hkvxxLnDnKQVn1ERUXh4MGDGD9+PFauXImUlBSEh4dj9OjRhXY2qoZ9kAO0aiMoKAjly5fHJ598gmvXrqFChQp47bXX8M477zj39LM66kMQre2GJ7JDeno6goOD4RMzG0JxP4vbxJxbuPO/15GWlmbTt/R27dqFDz/8EAcPHsTFixexfv16i2+eiKKISZMm4bPPPsONGzfw6KOPYuHChahevbraL4vIYdbaAJT38SBBEAr1Adzf+Xfq1Cn88ssvKFOmjCMvgchp1Jw7CmIfpHeu6ONBW7duRYcOHXDjxg3Z55g8eTI+/PBDBAcHw9fXF9HR0ZgxY4bzD/1IhufMPoj0jn0QSXPm2pxI7zh3EMljH0Ty9NgHjxlEziN1HieFnxTJPwTo/PnzJW+fOXMm5s6di0WLFuHAgQMICAhAx44dcfv2bTVeAZFzyLWh8iep8nf+nThxAlu3buXOP9IHDdoA2AfplJU+0tPTLS537txR5SltPQVB8+bNsWzZMmzatAkLFy5EUlISWrVqhZs3b6oyDqIiaTR/EOkS+yCSptHanEiX2AaRPPZBJE9HffAQ6+Q0gskEoeC5BRSea8DaIUBFUcScOXMwfvx4dOvWDQCwYsUKhIaGYsOGDejTp49d4yZyNsk2AMV9ZGRk4OTJk+brSUlJSEhIQOnSpVG+fHn06tULhw4dwg8//IDc3FykpKQAAEqXLg1vb2+HXgORs6gxdwDsgzyTtT4iIiIsNk+aNAmTJ0926PmUnILgwfdr9erVQ/PmzREZGYm1a9fixRdfdGgcRLZQa/4g8kTsg0iaWmtzIk/EuYNIHvsgkqenPriDnJxGMAkQTILlxv+/np6ebrHZx8dH8blkk5KSkJKSgvbt25u3BQcHo3nz5ti/fz93kJPbkmwDMPdhq/j4eLRt29Z8fdSoUQCA2NhYTJ48Gd999x0AoEGDBhY/t337drRp00bRcxFpxdrcoQT7IE9krY/k5GSLb3grfV9VUP7O8TNnzuCXX35RfAiskiVLokaNGhYfVCFyJrXmDyJPxD6IpKm1NifyRJw7iOSxDyJ5euqDO8jJaQRBgCAIBTcCUOdbTvnf9gsNDbXYHhoaar6NyB1JtnHvBkWP06ZNG4iiKHu7tduI3JW1uUMJ9kGeyFofQUFBqp3H6cFTEGzfvt2uUxBkZGQgMTERzz//vCpjIiqKWvMHkSdiH0TS1FqbE3kizh1E8tgHkTw99cEd5OQ0JolDKYj/f13tbzkR6YlUG8D9PoiMzNrcQWR0avWhxikI2rVrhx49emDYsGEAgNGjR6NLly6IjIzEhQsXMGnSJHh5eaFv3772vlwiRTh/EMljH0TSuDYnkse5g0ge+yCSp6c+uIOcnMbaoRTU+JZTWFgYACA1NRXly5c3b09NTS10yFwid8LDuBHJ09NheIi05k6nIEhMTMSVK1fMt507dw59+/bF1atXERISgpYtW+LXX39FSEiI4vER2YPzB5E89kEkjWtzInmcO4jksQ8ieXrqQ9c7yPPy8nDhwgUEBgZKHxKJnEoURdy8eRPh4eEwSXwCxGQSJD4pot5/pypVqiAsLAzbtm0z/wE3PT0dBw4cwKuvvqra8+gV+3Ade9oA1O2DrGMfruPquYOKxj5cR6s+1DgFwenTpy2ur1mzRvE49IZtuBbnD/fGPlyLfbg39uFa1vrg2tz12IfrcO5wf+zDddiHe2MbruVJfeh6B/mFCxcKncuatJecnIyKFSsW2m4SJA6lICg7lIK1Q4BWqlQJI0eOxLRp01C9enVUqVIFEyZMQHh4OLp3727Xa/Ek7MP1lLQBKO+D7Mc+XM+Zcwc5hn24HvtwT2zDPbAP98Q+3AP7cE/swz1I9cG1ueuxD9fj3OG+2IfrsQ/3xDbcgyf0oesd5IGBgQAA71qxELy8XTwa++xfN9XVQ7Bbxs2baN2ohvm/QyHC/18KblPA2iFAly1bhrFjxyIzMxODBw/GjRs30LJlS2zatAm+vr7KnsgDeUIfZ3f819VDsMvN9HRUqxKhrA3IbCOnYB+uY1cfbENT7MN12Id7YxuuxT7cG/twLfbh3tiHa1ntg2tzl2MfrsO5w/2xD9dhH+6NbbiWJ/Wh6x3k+YdPELy8dRtCiUDHzsPtDuQOYyGYTIUOsZAn8clca4o6BKggCJg6dSqmTtXvBw2cxRP6cPQ89a6mpA1AeR9kP/bhes6cO8gx7MP12Id7YhvugX24J/bhHtiHe2If7kGqD67NXY99uB7nDvfFPlyPfbgntuEePKEPXe8gJ/cmmAQIBc4tUPA6kRFJtZG/ncjoOHcQyWMfRPLYB5E89kEkjWtzInmcO4jksQ8ieXrqgzvIyWlMUp/EddNPihBpSbKNezdoPxgiN8O5g0ge+yCSxz6I5LEPImlcmxPJ49xBJI99EMnTUx/cQU5Oo6cQiLTERTiRPM4dRPLYB5E89kEkj30QSePanEge5w4ieeyDSJ6e+uAOcnIe4f8vBbcRGZ1UG5DZRmQ0nDuI5LEPInnsg0ge+yCSxrU5kTzOHUTy2AeRPB31wR3k5DR6+qQIkZb4KXUieZw7iOSxDyJ57INIHvsgksa1OZE8zh1E8tgHkTw99cEd5OQ0gkmAYBIKbSMyOqk28rcTGR3nDiJ57INIHvsgksc+iKRxbU4kj3MHkTz2QSRPT31wBzk5jZ4+KUKkJX5KnUge5w4ieeyDSB77IJLHPoikcW1OJI9zB5E89kEkT099cAc5OY0AAYIgFNpGZHRSbeRvJzI6zh1E8tgHkTz2QSSPfRBJ49qcSB7nDiJ57INInp764A5ycho9HUqBSEs8jBuRPM4dRPLYB5E89kEkj30QSePanEge5w4ieeyDSJ6e+uAOcnIaPR1KgUhLPIwbkTzOHUTy2AeRPPZBJI99EEnj2pxIHucOInnsg0ienvrgDnJyGpMJMBX8ZIh7dkCkKck2APZBBM4dRNawDyJ57INIHvsgksa1OZE8zh1E8tgHkTw99eGmwyJPIJgEmApc3PVQCkRakmrDnj527dqFLl26IDw8HIIgYMOGDRa3i6KIiRMnonz58vDz80P79u1x4sQJFV8JkfrUmjvYB3kivrciksc+iOSxDyJpaq3NiTwR5w4ieeyDSJ6e+rDpG+TfffedzQ/YtWtXuwdDnkUQ7l0KbvM07IOUkmojf7sSmZmZqF+/PgYOHIiePXsWun3mzJmYO3culi9fjipVqmDChAno2LEj/vnnH/j6+to5emXYByml1tzh7n2wDbIH31sVxj4oH/sojH1QPvZRGPsgQL21uR6wD1LKKHMHwD5IOaP0wTbIHnrqw6Yd5N27d7fpwQRBQG5uriPjIQ/i5SXAy6vAb37B6x6AfZBSkm0AivuIiYlBTEyM5G2iKGLOnDkYP348unXrBgBYsWIFQkNDsWHDBvTp00fxuO3BPkgpteYOd++DbZA9+N7KEvugB7EPS+yDHsQ+LLEPyqfW2lwP2AcpZZS5A2AfpJxR+mAbZA899WHTDvK8vDxnj4M8UP7hEx4kuumhFBzBPkgpqTYAdftISkpCSkoK2rdvb94WHByM5s2bY//+/ZrtIGcfpJQWc4c79ME2yB58b0Ukj30QyWMfRNK0WJu7C/ZBShll7gDYBylnlD7YBtlDT33YtINczu3btzU7TK8ztWj4EIY/3x71H66E8iHB6Df6U/y080/z7U+2rY8BPVuiwcOVULpkAFr1m4Ej/5534YjlLfjyZyyK22qxrXLFEHz3+RjNx2IymWAyWZ7mXjQZ57T3RunjzUFPoGeHRqgQWgo5OblIOHYW0xZ8j4N/n3HhqKV98c1uLPl2N5IvXgMAPFw1DGNejMHjj9bWdBxSbQD3+0hPT7fY7uPjAx8fH0XPkZKSAgAIDQ212B4aGmq+zZU8oQ9PagNw7z7UbANw7z48oQ2g6D4eNOutPhjwVEuMm/UNFq3eoe1AbTRr6Wb8sP0wTpxJha9PcTSrVxWTh3VD9cqhRf+wivjeyhh9zJ/0HJ598hGLn9m6/x88/doCrYdqk72HTmLel1tx+NhZpFxJx8oPB6Fzm/qaj4N9sA93xD7cA/tgH3KKWpsbgSf0Ycvao0blUEwe3h2PNqoGLy8TjielIHbs5ziXet1Foy7aZ2t3Yt7Kbbh0NR11qlfAB2OeRuPalTV7fqPPHQD7YB/yjN6HJ7QBsA9n0VMfikeVm5uLd999FxUqVECJEiVw6tQpAMCECRPwxRdf2DWI+fPno3LlyvD19UXz5s3x22+/2fU49vL388GRf89jzMyvJG8P8PXGr4cTMfmTDZqOy14PRYbil1UTzJflHw1xyThMgmD+tIj54q4nG1CJ2n24ug2g6D4Sz17C2A+/xqN930PMoFk4e+Ea1n0yDGVKltB4pEULL1cSk4Z1w/YVY/HL8jFo1aQG+o3+FEcTL2o6Dsk2HugjIiICwcHB5suMGTM0HZ+zeFofntQG4OZ9sA3F3L2PfJ3b1EOTupVx4dINbQZmp32HTuKlp1vj5yWjse6TYci5m4uewz9B5q07mo5DrfdWu3btQpcuXRAeHg5BELBhwwaL20VRxMSJE1G+fHn4+fmhffv2OHHiRJGP64zfO6P2sXXf34jqNM58eemdpRqOUJmsW3dQp0YFfDj2GZeOg2sP9uGO2IfreNraA2AfzlDU2txTeVofRbVRuUJZ/O+zUThxOgVPvvwxWvadgf9+sQm3s3M0HacS634+iPFz1uPNl2Kw48s3Uad6BTw1fD4uX7up2RiMOHcA7IN92MaIfRhx7cE+7KOnPhTvIJ8+fTqWLVuGmTNnwtvb27y9Tp06+PzzzxUP4KuvvsKoUaMwadIkHDp0CPXr10fHjh1x6dIlxY9lr637/sH0RT/gxx3S32z66n+/48PPN2HHb8c1G5MjinmZULZ0oPlSKjjAJeMQBOmLJ1OzD3doAyi6j282x2Pnb8dx5vxVHDuVgvFz1iGohB9qVw/XdJy2iGldFx0erY2HKpVDtchQTBjSFQH+Pog/kqTpOOTayO8jOTkZaWlp5su4ceMUP0dYWBgAIDU11WJ7amqq+TateVofntQG4P59AOq0AbhfH0Z8bwUA5UOC8cHopzF4wjLcveve56r6Zt5QPNvlEdR8qDzq1qiIBZOew7mU60g4mqzpONR6b5WZmYn69etj/vz5krfPnDkTc+fOxaJFi3DgwAEEBASgY8eOuH37tuxjOuv3zqh93Mm+i0tXb5ovaTdvaTY+pR5/tDbGv9oFT7bV/luxD+Lag324I/bhOp629gDYhzMUtTb3VJ7WR1FtTBjSBVv2/Y1J8zbir3/P4fT5K/jfrr9w5XqGZmNUasGqX9C/ewv06xqNh6uWx6xxfeDv642V3+3XbAxGbANgH+zDNkbsw4hrD/ZhHz31oXgH+YoVK/Dpp5+iX79+8PLyMm+vX78+jh07pngAs2bNwqBBgzBgwADUqlULixYtgr+/P5YsWaL4seieM+evoN2z7yLmhffx1gercPGSaw73IPkpXDc914Ba1OxDj20UL+aF2B6PIu1mltuehiBfbm4evv05Hlm3stG0bhVNn1uujfw+goKCLC72HEK6SpUqCAsLw7Zt28zb0tPTceDAAURHR6v2WpQwch96agNwzz4AddoA3K8PI763EgQBi6b0x7yV23DslOtP+6BUesa9HcWlgvw1fV613lvFxMRg2rRp6NGjR6HbRFHEnDlzMH78eHTr1g316tXDihUrcOHChULfNH+Qs37vjNgHALRsXB3/bp6B376ZgI/efMZlH3jVE6492AfJYx/GWXuwD2WKWpt7KiP1IQgCHn+0Nk6evYRv5g7Fv5tnYMvS0XjisXquHpqs7Jy7SDiWjDbNoszbTCYTHmsWhd//0u4D7EZsA2Af7MM2RuzDaGsP9mE/PfWheAf5+fPnUa1atULb8/LykJOj7NAC2dnZOHjwINq3b39/QCYT2rdvj/37tftEgyep+3AlTHvjGSyc9hLGD+uB8ynX8cLohcjMkv/Gj7PoKQS1qNWH3tro2LIOknd+hJS9s/Fq37boMewTXEvLdPWwJP198jwqth6F0EdHYtSMr/Dlh4PwcNXymo5BrUV4RkYGEhISkJCQAABISkpCQkICzp49C0EQMHLkSEybNg3fffcd/vrrL/Tv3x/h4eHo3r27+i/KBkbsQ09tAO7dh1J66sOI761Gxj6Ou7l5WLxmh6uHolheXh7GzfoGzetXRa1q2h4RQov3VklJSUhJSbH4HQoODkbz5s1lf4ec+XtnxD627TuKVyd/ie5D5mHyvI1o0agavv74VY9/H+0orj3uYx9UEPu4z5PXHuxDOaPuIDdSHyGlSyAwwBcjYx/Htv3/oOfwT/DjjsP4cuZLaNGo8L+BO7h6IwO5uXkIKR1osT2kdBAuXU3XbBxGbANgH+zDNkbsw2hrD/ZhPz31oXgHea1atbB79+5C27/55hs0bNhQ0WNduXIFubm5CA0NtdgeGhqKlJTC3ya6c+cO0tPTLS5kqVXTh9GhdT3UqFoejzaJwvx3B+Jmxm1s3iV/CC5nESQiENw0BLWo1YfSNgDX9rE7/l+07jcDHV+chW37/8HS9waibCn3PM9y9chQ7Iobh61LR2PgUy0xZPKXOHZK23MsS7VhTx/x8fFo2LCh+Xdr1KhRaNiwISZOnAgAGDt2LIYPH47BgwejadOmyMjIwKZNm+Dr66v6a7KFEfvQUxuA+/Zhz9yhpz5c+d4K0L6P+g9H4OU+bTB0ykqnPo+zjJ65FkcTL+KL6QM0f25rfRT8b3jnjn3nR8//PVHyO2TP752tjLj2WLflIP636y/8k3gBP+38E31GLULj2pXRsnF1TZ5fr9SYP3bt2oUuXbogPDwcgiAUOmqCKIqYOHEiypcvDz8/P7Rv3x4nTpxQ8VUowz7Yh63Yx32evPZgH8qptTZnH+7bh0m496fv/+38CwtXb8eRf89jzvIt2Lznbwzs2dKpz613Rvy7LsA+2IdtjNiH0dYe7MN+euqjmNIfmDhxImJjY3H+/Hnk5eVh3bp1OH78OFasWIEffvjBGWM0mzFjBqZMmeLU5/A0QSX8EFmhLJIvXNX8uQVBgFDg5AIFr3sao/aRdTsbSeeuIOncFcQfOY34byfi+W4tMHvZzy4ZjzXexYuhakQIAKBBzUr445+zWLRmB+a83VezMUi1kb9diTZt2kAURavPM3XqVEydOlXxGJ3BiH3oqQ3AffuwZ+7QUx+ubAPQvo/ohg8hpFQJ/PX9/X/7YsW8MG1ET7zapy3qd5uk2ViUGjNzLTbvPoKfPh2JCqGlNH9+a31ERERYbJ80aRImT56s1dCcxohzR0Fnzl/Fles3UbViCHb9/q+rh+O21Jg/MjMzUb9+fQwcOBA9e/YsdPvMmTMxd+5cLF++HFWqVMGECRPQsWNH/PPPPy75gBX7YB+2Yh/sg31IU2ttzj5sp3UfV29kIOduLo4lWX7w+9+kFDzSoKpm41CiTMkS8PIy4fK1mxbbL19LR7kyQZqNw4h/1wXYB8A+bGHEPozUBsA+HKGnPhR/g7xbt274/vvvsXXrVgQEBGDixIk4evQovv/+ezz++OOKHqts2bLw8vJCamqqxfbU1FSEhYUVuv+4ceOQlpZmviQnJysdvuFk3bqD5ItXUbbAYRW0oKdDKahFrT6UtgG4Vx8mkwDv4oo/f+MSeaKI7Oy7mj6nUQ/jxj701QbgXn14Mle+twK07+Orn35Hy2dnoPVz75svFy7dwLyVW/HUa/Od+tz2EkURY2auxY87DuO7ha8hskJZl4zDWh/JyckW/x3HjRtn13Pk/54o+R2y5/fOVlx7AOHlSqJ0cABSNTwkmh6pMX/ExMRg2rRp6NGjR6HbRFHEnDlzMH78eHTr1g316tXDihUrcOHChULfFNQK+2AftmIfxlx7sI+iqbU2Zx/u20fO3Vz88c8ZVI+0/JbiQ5XKIfnidac+t728ixdDg4cjsPP34+ZteXl52PX7v2hat4pm41Brba63IyywD/ZhCyP2YbS1B/uwnxp9aNWGXX+lb9WqFbZs2WLPj1rw9vZG48aNsW3bNvM5P/Py8rBt2zYMGzas0P19fHzg4+Pj8PMWFODnjSr//405AIgML4M6NSrgRloWzqVeR8kgf1QMK4XyZYMBwBzFpavpuHT1puRjusp/P/sBbZrXRPlypXD5WjoWfLkFXl4mxLRpoPlYpH7xPX0nB6BOH0rbAFzTx7W0TLwxsCP+t+svpF5JQ+mSJfDS061RPqQkNm47pPpYHDXlk41o36I2IsJK4WbWbXyzKR57Dp7At/OGaDoOuUmBfdjGXfrwpDYA9+6DbdhOD32cS72O62mZFve/ezcXqVfTcfLMJVXHoZbRH6zFN5vjseq/g1HC3xepV+79oTmohC/8fL01G4e1PoKCghAU5PgngqtUqYKwsDBs27YNDRo0AHDv8O0HDhzAq6++Kvkz9vzeKWGktcf19Ey8OegJfPdLAlKvpqNKxbKYMrw7TiVfwbb9R1Ufixoysu4gKfmy+fqZC1fx1/FzKBnsj4iw0pqNw1ofBQ/BZ89/26SkJKSkpFicJy84OBjNmzfH/v370adPHztH7hj2wT5swT7s5y7vrQD24QxFrc3Zh3Xu0kdRa4+5X27FkvcGYt8fJ7E7/l+0j66FTq3qoMsrH6s6DjUNefY/GDLlSzSsWQmNalfGwtXbkXnrDvp1eUSzMai1NtfbERYA9sE+imbUPoy09mAf9lOjD63asPtrbPHx8Th69N6b7Fq1aqFx48Z2Pc6oUaMQGxuLJk2aoFmzZpgzZw4yMzMxYIB253VsUDMSPyweYb7+3qinAACrfvgVQ6esREzrulgw6Xnz7UveGwgAeP/Tn/DBZz9pNk5bXLqShjffX4UbN7NQKrgEGtWujJWzh6F0Se3PeWvUnRyAOn24QxuA9T5GzViD6pVD0adzc5QpGYBraVn4458zeGLwbBw75dg5R53hyvUMvDp5BVKvpCOohC9qV6uAb+cNQdvmNTUdh5F3kAOe04cntQG4dx9sQxl370OP5x5f8u2982w9WWARNH/ic3hWZ4sMAMjIyMDJkyfN15OSkpCQkIDSpUujUqVKGDlyJKZNm4bq1aubFxrh4eHmhSsAtGvXDj169DAvXp39e2eUPt54/yvUqlYBfTo3R3CgH1Iup+GXA8fw3qIfkJ2j7RE9bJVw9Ay6vDLXfP2d2esAAH07N8eCyc/L/ZjqrPWhxikI8s+Fp+Q8klphH+yjKOxD/2sPgH04Q1Frc/ZRNHfoo6i1x487/sSoGWvw+gsd8P4bvXDy7CX0f/Nz/Hr4lGZjVKpnh8a4ciMD7y3+EZeu3kTdGhXwzdyhmh4iV621R0xMDGJiYiRvK3iEBQBYsWIFQkNDsWHDBpd9gARgH+zDOiP3YZS1B/uwnxp9aNWG4h3k586dQ9++fbF3716ULFkSAHDjxg20aNECa9asQcWKFRU93jPPPIPLly9j4sSJSElJQYMGDbBp06ZCbxydae+hEyjVVP7bLat/OIDVPxzQbDyOmDmun6uHYGbEnRxq9uEObQBF99F/7OcajsYx8ya4Rx9G3UHuaX14UhuAe/fBNjzvvVVB7nzecQC4/vsnrh4CAPX6iI+PR9u2bc3XR40aBQCIjY3FsmXLMHbsWGRmZmLw4MG4ceMGWrZsiU2bNll8CjcxMRFXrlwxX3fW750R++jlpqcakNOycQ23aMRaH8nJyRZHWHDGtw9cgX24P/bhOp629gDYhzMUtTZnH0Vzhz5sWXvEff8r4r7/VaMRqWNw78cwuPdjLnt+Ldbm7niEBfahD+xD+z6MuPYA2Ic9nN2Hmm0oPgf5Sy+9hJycHBw9ehTXrl3DtWvXcPToUeTl5eGll15S+nAAgGHDhuHMmTO4c+cODhw4gObNm9v1OOReBEGQvHgytftgG55Jrg32oQz78Exsg++tSJ5afbRp0waiKBa6LFu2zPw8U6dORUpKCm7fvo2tW7eiRo0aFo9x+vTpQt+gcsbvHfsgW1nrI/8UBPkXe3Zw5J8LT8l5JJ2NfZCt2AfXHiStqLU5+7AN+/BM1tpIT0+3uNy5c8eu53DHIyywD7KFEfvg2oNs5ew+1GxD8Q7ynTt3YuHChYiKijJvi4qKwrx587Br1y6lD0cezEsQ4GUqcPHwnRzsg2wh2Qb7cOHIyJ1w7riHbZAU9nEP+yApzu6jSpUqCAsLw7Zt28zb0tPTceDAAURHR6v2PEqwD7IV+7iHfVBBWqzN2QfplbW5IyIiAsHBwebLjBkzXDxa9bAPsoUR+2AbZCs99aH4EOsRERHIyckptD03Nxfh4eGqDIo8gxEPk8s+yBZGPcQ6+yBbcO64j21QQezjPvZBBanRR0ZGBk6ePGm+npSUhISEBJQuXRqVKlXCyJEjMW3aNFSvXh1VqlTBhAkTEB4eju7du6vxEhRjH2Qr9nEf+6AHqbU2Zx/kibQ4PceDR1goX768eXtqaioaNGhg12M6in2QLYzYB9sgWzm7DzXbUPwN8g8//BDDhw9HfHy8eVt8fDxGjBiB//73v0ofjjyYyYRCnxQxKf6N0xf2QbaQaoN9sA+6h3PHPWyDpLCPe9gHSVGjj/j4eDRs2BANGzYEAIwaNQoNGzbExIkTAQBjx47F8OHDMXjwYDRt2hQZGRnYtGkTfH191X45NmEfZCv2cf81sA96kFprc/ZBnsja3KHG6QcA9zzCAvsgWxixD7ZBtnJ2H2q2YdM3yEuVKmVxfsPMzEw0b94cxYrd+/G7d++iWLFiGDhwoMs+/UjuxyQIMBU4LFXB60XJzc3F5MmTsXLlSqSkpCA8PBwvvPACxo8f7zbnpGUfpJRUG/nbPQ37IKXUmDv0gG2QPdgH+yB5avTRpk0biKIoe7sgCJg6dSqmTp1q1xjVwD7IHuyDfZA0tdbm7IM8kVprDz0cYYF9kFJG6YNtkD3U6EOrNmzaQT5nzhxFD0oEACbh3qXgNiU++OADLFy4EMuXL0ft2rURHx+PAQMGIDg4GK+99pp6g3UA+yClpNrI3+5p2AcppcbcoQdsg+zBPojksQ8ieeyDSBrX5kTy1Jo74uPj0bZtW/P1UaNGAQBiY2OxbNkyjB07FpmZmRg8eDBu3LiBli1ban6EBfZBShmlD7ZB9lCjD63asGkHeWxsrKIHJQLUOc/Zvn370K1bN3Tu3BkAULlyZaxevRq//fabauN0FPsgpYx0DnL2QUoZ5RzLbIPswT6I5LEPInnsg0ga1+ZE8tSaO/RwhAX2QUoZpQ+2QfZQow+t2rBpB7mc27dvIzs722LbgydYJ2PLP7/Ag/L+/3p6errFdh8fH8nzDbRo0QKffvop/v33X9SoUQOHDx/Gnj17MGvWLOcNXCXsg+RItQHc78MI2AfJsTZ3GAHbIGvYB/sgeeyDfZA89sE+SBrX5uyD5Bl97gDYB8kzeh9sg6zRUx8mpT+QmZmJYcOGoVy5cggICECpUqUsLkT58j8pUvACABEREQgODjZfZsyYIfkYb731Fvr06YOHH34YxYsXR8OGDTFy5Ej069dPy5diM/ZBtpBrwxM/pf4g9kG2YBtsg+SxD/ZB8tgH+yB57IN9kDSuzdkHyTNiGwD7INsYsQ+2QbbSUx+Kd5CPHTsWv/zyCxYuXAgfHx98/vnnmDJlCsLDw7FixQpnjJF0yiQIkhcASE5ORlpamvkybtw4ycdYu3Yt4uLisGrVKhw6dAjLly/Hf//7XyxfvlzLl2Iz9kG2kGsjvw9PxT7IFmyDbZA89sE+SB77YB8kj32wD5LGtTn7IHlGbANgH2QbI/bBNshWeupD8SHWv//+e6xYsQJt2rTBgAED0KpVK1SrVg2RkZGIi4tz22/2kvakfvHzrwcFBdl02I0xY8aYv0UOAHXr1sWZM2cwY8YMtzwHBvsgW8hNCu46UaiFfZAtrM0dnoptkK3YB/sgeeyDfZA89sE+SBrX5uyD5Blx7gDYB9nGiH2wDbKVnvpQ/A3ya9euoWrVqgDu7eS8du0aAKBly5bYtWuXuqMjXcs/10DBixJZWVkwmSx/Tb28vJCXl6fmUFXDPsgWcm0o7UNv2AfZgm2wDZLHPtgHyWMf7IPksQ/2QdK4NmcfJM+IbQDsg2xjxD7YBtlKT30o3kFetWpVJCUlAQAefvhhrF27FsC9T5CULFlS1cGRvglC4fMMCAo/KdKlSxdMnz4dP/74I06fPo3169dj1qxZ6NGjh5NG7Rj2QbaQasOePvSGfZAt1Jg79IZtkK3YB/sgeeyDfZA89sE+SBrX5uyD5Blx7gDYB9nGiH2wDbKVnvpQvIN8wIABOHz4MADgrbfewvz58+Hr64vXX38dY8aMUX2ApF9qfFJk3rx56NWrF4YMGYKaNWti9OjRePnll/Huu+86adSOYR9kC6N+Sp19kC3YBtsgeeyDfZA89sE+SB77YB8kjWtz9kHyjNgGwD7INkbsg22QrfTUh+JzkL/++uvm/92+fXscO3YMBw8eRLVq1VCvXj1VB0f6Jvz/peA2JQIDAzFnzhzMmTNHnUE5GfsgW0i1AZltcnJzczF58mSsXLkSKSkpCA8PxwsvvIDx48e77Sey2AfZQo25Q299sA2ylRp96A37IFuxD/ZB8tgH+yBpaqzN9Yh9kC2MOHcA7INsY8Q+2AbZSk99KN5BXlBkZCQiIyPVGAt5GKlPhrjrJ0WchX2QFLlPTSnp44MPPsDChQuxfPly1K5dG/Hx8RgwYACCg4Px2muvqTlcp2EfJEWNuUPvfbANksP3VuyD5LEP9kHy2Af7IGlqrM09AfsgKZw77mEfJIV9sA2Sp6c+bNpBPnfuXJsfUA9/eCZt5J9foOA2T8M+SCmpNvK322rfvn3o1q0bOnfuDACoXLkyVq9ejd9++021caqBfZBSaswdeuiDbZA9+N6qMPZB+dhHYeyD8rGPwtgHAeqszfWCfZBSRpk7APZByhmlD7ZB9tBTHzbtIJ89e7ZNDyYIAkMgMz19UsQR7IOUKupT6unp6RbbfXx84OPjY7GtRYsW+PTTT/Hvv/+iRo0aOHz4MPbs2YNZs2Y5b+B2YB+klLW5w5Y2AH30wTbIHnxvZYl90IPYhyX2QQ9iH5bYB+Uz0jfI2QcpZZS5A2AfpJxR+mAbZA899WHTDvKkpCRnj8MhXg81hFDcz9XDsEtEGX9XD8Fu6cXvWr3dSxDgVeBcrwWvewJ37yOoYUuYvPX7e+aJpNrI3w4AERERFtsnTZqEyZMnW2x76623kJ6ejocffhheXl7Izc3F9OnT0a9fP6eN2x7sg5SyNnfY0gagjz7cvQ0ACG35OEw++uzjema2q4dgl5tFjJvvrdxDySatdTt3pGXluHoIdrtZxNjZh3vQ89rck7EP98A+3E9Ra3NP4u59+Nd9RLfvr27e0uf7q4wixm2UuQNw/z5QqQ7A+cOtGKUPtkH20FMfDp+DnEiOIAgQCvziF7xOZERSbeRvB4Dk5GQEBQWZt0t9Q3bt2rWIi4vDqlWrULt2bSQkJGDkyJEIDw9HbGys8wZP5GTW5g5b2gDYB3kuvrciksc+iOSxDyJpRa3NiYyMcweRPPZBJE9PfXAHOTmNlwkoZrLclmuSvi+RkUi1AdzvIygoyGInoJQxY8bgrbfeQp8+fQAAdevWxZkzZzBjxgzuACRdszZ32NIGwD7Ic6nx3qpy5co4c+ZMoe1DhgzB/PnzC21ftmwZBgwYYLHNx8cHt2/fVvbERE7GtQeRPPZBJK2otTmRkXHuIJLHPojk6akP7iAnp9HTuQaItKTGec6ysrJgMlnOLF5eXsjLy3N4fESupMbcwT7IU6nRx++//47c3Fzz9SNHjuDxxx/H008/LfszQUFBOH78uPm6u37yl4yNaw8ieeyDSJqRzkFOpBTnDiJ57INInp764A5ycho9hUCkJTUW4V26dMH06dNRqVIl1K5dG3/88QdmzZqFgQMHqjlUIs2pMXewD/JUavQREhJicf3999/HQw89hMcee0z2ZwRBQFhYmKLnIdIa1x5E8tgHkTTuICeSx7mDSB77IJKnpz64g5ycxvT/l4LbiIxOqg3IbJMzb948TJgwAUOGDMGlS5cQHh6Ol19+GRMnTlRplESuocbcwT7IU1nrIz093WK7j48PfHx8rD5ednY2Vq5ciVGjRln9VnhGRgYiIyORl5eHRo0a4b333kPt2rWVvwAiJ+Lag0ge+yCSpsbanMhTce4gksc+iOTpqQ+7xrV7924899xziI6Oxvnz5wEAX375Jfbs2aPq4EjfvATB/GkR88UAh+RkH1QUyTYU9hEYGIg5c+bgzJkzuHXrFhITEzFt2jR4e3s7ceSOYx9UFDXmDj32wTbIFtb6iIiIQHBwsPkyY8aMIh9vw4YNuHHjBl544QXZ+0RFRWHJkiXYuHEjVq5ciby8PLRo0QLnzp1T62UViX2QLbj2YB8kj32wD5Kmxtpcr9gHFcWocwfAPqhoRu2DbZAt9NSH4h3k3377LTp27Ag/Pz/88ccfuHPnDgAgLS0N7733nuoDJP3yMklfPBn7IFvItcE+iNgG2yBrrPWRnJyMtLQ082XcuHFFPt4XX3yBmJgYhIeHy94nOjoa/fv3R4MGDfDYY49h3bp1CAkJweLFi9V6WVaxD7IV5w/2QfLYB/sgaVybsw+SZ8Q2APZBtjFiH2yDbKWnPhQPa9q0aVi0aBE+++wzFC9e3Lz90UcfxaFDh1QdHOmbyXT/fAP5F5ObhqAW9kG2kGqDfbAPuodzB9sgedb6CAoKsrgUdXj1M2fOYOvWrXjppZcUjaF48eJo2LAhTp48ae/LUIR9kK04f7APksc+2AdJ49qcfZA8I84dAPsg2xixD7ZBttJTH4rPQX78+HG0bt260Pbg4GDcuHFDjTGRh/ASCh86wV0PpaAW9kG2kGojf7snYx9kC84d97ENKkjNPpYuXYpy5cqhc+fOin4uNzcXf/31F5544gm7nlcp9kG24vxxH/uggtjHfeyDHsS1uSX2QQ8y4twBsA+yjRH7YBtkKz31oXi/fVhYmOQ3Rvbs2YOqVauqMijyDCZB+uLJ2AfZQq4N9kHENh7ENqggtfrIy8vD0qVLERsbi2LFLD8v279/f4vDs0+dOhU///wzTp06hUOHDuG5557DmTNnFH/z3F7sg2zF+eM+9kEFsY/72Ac9iGtzS+yDHmTENgD2QbYxYh9sg2ylpz4U7yAfNGgQRowYgQMHDkAQBFy4cAFxcXEYPXo0Xn31VWeMkXRK6jBVXu5agkrYB9lCrg32QcS5g22QNWr1sXXrVpw9exYDBw4sdNvZs2dx8eJF8/Xr169j0KBBqFmzJp544gmkp6dj3759qFWrlkOvxVbsg2zF+YN9kDz2wT5IGtfm7IPkGbENgH2QbYzYB9sgW+mpD8WHWH/rrbeQl5eHdu3aISsrC61bt4aPjw9Gjx6N4cOHO2OMpFNepnuXgts8GfsgW0i1kb/dk7EPsgXnDrZB8tTqo0OHDhBFUfK2HTt2WFyfPXs2Zs+erfxJVMI+yFacP9gHyWMf7IOkcW3OPkieEecOgH2QbYzYB9sgW+mpD8U7yAVBwDvvvIMxY8bg5MmTyMjIQK1atVCiRAlnjI90rJhJQLECnwwpeN3TsA+yhVQb+ds9GfsgW3DuYBskj32wD5LHPtgHyWMf7IOkcW3OPkieEecOgH2QbYzYB9sgW+mpD8U7yPN5e3trdmhF0ieTIMBLEAptMwL2QdZItZG/3QjYB1nDuYNtkDz2wT5IHvtgHySPfbAPksa1OfsgeUaeOwD2QdYZuQ+2QUXRUx+Kd5C3bdsWgpUX88svvzg0IPIcgnDvUnCbJ2MfZAupNvK3ezL2Qbbg3FEY26B87KMw9kH52Edh7IPysY/C2AcBXJvLYR8EGHPuANgH2caIfbANspWe+lC8g7xBgwYW13NycpCQkIAjR44gNjZWrXGRB9DToRTUwj7IFkY9jBv7IFtw7mAbJI99sA+Sxz7YB8ljH+yDpHFtfg/7IClGnDsA9kG2MWIfbINspac+FO8gnz17tuT2yZMnIyMjw+EBkefwEgR4FfjFlzp0lSdhH2QLqTbyt3sy9kG24NxxH9uggtjHfeyDCmIf97EPKoh93Mc+6EFcm1tiH/QgI84dAPsg2xixD7ZBttJTHya1Hui5557DkiVL1Ho48gBegvTFiNgHPUiuDfZBxDYexDaoIPZxH/uggtjHfeyDCmIf97EPehDX5pbYBz2IbVhiH/Qg9nEf26CC9NSH4m+Qy9m/fz98fX3VejhNRT8cimFP1kH9KmUQVsofz8/6Bf+LP2txn7d6NcDzbWsgKMAbv/17CWOW7MeplJsuGrHtZi/7GVPnf4dX+rTBjDd6afrcgiAUOi+FtfNUeDI999GsWlkMfrwG6lYqhdCSfhi8aB9+PnzBfHvHBuHo1+oh1K1UEqVK+OCJ6Vvwz7k0F45Y3hff7MaSb3cj+eI1AMDDVcMw5sUYPP5obU3HIdVG/nYj0msfntQG4N59sA39aVKlNAa2qYraFYJRLtgXw5bFY9vfqebbH68ThmeiK6F2hWCUDPBGj9m7cexCugtHbN3KjXsRt3Efzqfc66N65TAMj+2ANs1rajoO9nGfnvto+lAZDG5fA3UqlURosB9e/nQ/tvx5EcC9w4690aUW2tQOQ0SZANy8nYO9xy5h5nd/41LabRePXNr8lVuxedefSDx7Cb4+xdGoTmW89XIXPFSpnKbjYB/36bmPotbmY59qgB7RVRBe2h85uXk4nHQV0786hEOJV1w4anl7D53EvC+34vCxs0i5ko6VHw5C5zb1NR8H+7iPfbgPd+iDa3NLeu2jefUQvNLxYdSNLI2wkn54cf5ubE44b759VJc66Nq0EsJL+yP7bh7+OnMNMzf8iT+Srrlw1NYdSEjEojW/4K/j53Dpajo+mz4QHVvV1XQMnDss6bWPFjXDMLxrPdSvWhblSweg38yf8dPvZyTvO2tQSwzoUBPjlu7Hop+OaDxSZT5buxPzVm7DpavpqFO9Aj4Y8zQa166s2fOzj/v02gbAPpxFT30o3kHes2dPi+uiKOLixYuIj4/HhAkTFD3Wrl278OGHH+LgwYO4ePEi1q9fj+7duysdksP8fYrhyJlriNtxAitG/afQ7cO71MGgjrUwbNFunLmUgXFPN8Tatzrg0TEbcCcnV/Px2urQ32ewbP1e1K5ewSXP7yUIhQ6d4K6HUlCLp/Zx9Hwavt53GotfaVH4du9iiE+8gh8PJeOD55poPj4lwsuVxKRh3fBQRAhEUcTqHw+g3+hPsXPlW6j5UHnNxiHVRv52T+ZpfXhSG4B798E2lHGHPvy8vXD8QjrW/Z6MebGFf//9vL1wKOkaNh2+iHefrqfp2OxRPqQkxg7ujMoV7/WxbnM8Xn5nCb7/7A3UqBKm2TjYh/7nDuCB+WP/GSwa/IjFbX7eXqgdURLz/ncMR8+nIdi/OCb2qo/PXo5Gt5nbNR+rLQ4cTsTzPVqi/sMRuJubhw8/+xH9Ry/CluVvwt/PR7NxsA/P6cPa2jzxYhreXPYrzly6Cd/ixfDqE7XwzbgOaPr6t7h6847m4y1K1q07qFOjAp7rGo3nx37msnGwD/bBPqRxbX6P3vvw9ymGf87dwFd7T+HzIa0K3X4q9SbGrz6Is5cz4OvthUHtoxA3sg1avvMjrmW4XxsAkHU7G7UeqoBnnmiOweOXumQMRpw7AM/s48iZa1i5/V+sHPO47P06N6uMJjXK4cK1TA1HZ591Px/E+DnrMeutZ9C4TmUsWr0dTw2fj9+/mYiQ0oGajMGIfXhaGwD7cBY99aF4B3lwcLDFdZPJhKioKEydOhUdOnRQ9FiZmZmoX78+Bg4cWCgwLW07fB7bDp+Xvf2VTrUwa8Nh/O9gMgBgyMLdOLqwD55oUgnr9ydpNUxFMrLuYPDEZfj47b7475JNLhmDIAAFT+Xkph2oxhP72PF3Cnb8nSJ7+/rf7n1ivWJpf62GZLeY1paftp0wpCuWfLsH8UeSNN0BKNVG/nZP5ml9eFIbgHv3wTaUcYc+dh+/jN3HL8ve/t2he++7wkv5aTUkh7RrYXkkhdEvPYG4jXvxxz+nNd1Bzj70P3cAwM5/UrHzn1TJ227evov+n+y12DZ57WFsGNsW4aX8cOH6LS2GqMiKD1+2uP7fcc+icbcJ+Ovfc2he/yHNxsE+PKOPotbm3+6zXH+PX/k7nmtbA7Uqlcbuvy86e3iKPf5obc2PxiOFfbAP9iGNa/N79N7H9iMXsf2I/O/4ht8svw04Ze0f6NvqIdSsWBJ7j0m/J3O1to/URNtHtD1aVUFGnDsAz+tja8I5bE04Z/U+5Uv744OB0eg1bRO+GtdRo5HZb8GqX9C/ewv06xoNAJg1rg9+3vs3Vn63H6+/oPxvKPYwYh+e1gbAPpxFT30o2kGem5uLAQMGoG7duihVqpTDTx4TE4OYmBiHH8eZIsuVQGgpf+x84I3WzVs5OJR4GU2qh7jtDvIxM79Ch0froE3zh122g1xPnxRRgxH70LPc3Dxs2HYIWbey0bRuFU2f24ifUmcf+uJufbANZdiHc+Xm5uGnHYdx63Y2Gml4iCqAfThKr20E+hVDXp6I9Fs5rh6KTW5m3NuJXzJQ2w+IsQ/H6LGP4l4mxP6nBtIys/H3Wfc9TK47YB+OYR+ei2tz4/VR3MuEfq0fQlpWNv45d93Vw3FrRps7AGP2IQjAouFtMe+7P3FMB01k59xFwrFkix19JpMJjzWLwu9/abefxmh9GLENgH3YS099KNpB7uXlhQ4dOuDo0aOq/RFXiTt37uDOnfuHvklPd/65KssF3/tm0+U0y29rXEq7hdBg9/zW07c/x+PwsWT8snysS8dhEgSYCvziF7zuSYzYhx79ffI8Og78CLez7yLAzwdffjgID1fV7tuxgHQb+ds9FfvQB3ftg204F/uwzbFTF9BryFzcyb4Lfz9vLHx3AKpX1u7b4wD70Jo7tOFdzISx3erg+4PJyLh9V/PnVyovLw9TP9mAJnWrIIrzh1MZuY8ODSvi0+GPwd+7GFJvZKHXjM245oaHj3Yn7ENb7EM/uDY3Th/t6oVjwaBo+HkXw6W0W3h29g5cz8jW5Ln1So25Y/LkyZgyZYrFtqioKBw7dszh8TmDEfsY2a0+7ubmYfFPfzv9udRw9UYGcnPzCh0qOqR0EE6c1u6IEEbrw4htAOzDXnrqw6T0B+rUqYNTp06pOghbzZgxA8HBweZLRESES8bhzs6lXMe4j77Fp+++AF+f4i4diyAIkhdPxj7cX/XIUOyKG4etS0dj4FMtMWTylzh2StvDzcm1wT6ch33Yxp378GSubANgH7aqGlEOP3z+BtYtHIF+3VpgzIzVOHFa/jQLzsA+tOXqNoqZBHzyYnMIgoAJXyVo+tz2mjD7WxxPuoh5E/tr/tzsQ1uu7GPPPyloO+47xEz+CdsOn8fnr7VB2SBfzZ5fj9iHttiHfnBtrj1X9bHvWCo6Tt2M7h9sxY6/U7Dw5RYoE+ijyXPrlVpt1K5dGxcvXjRf9uzZ44TRqsdIfdSvWhYvd66DofN3OvV5PJER+zBSGwD7cISe+lC8g3zatGkYPXo0fvjhB1y8eBHp6ekWF2caN24c0tLSzJfk5GSnPh9w75viABBS4Nvi5YL9kJrmfucAPHzsLC5fu4k2z3+Aso+8hrKPvIa9h05i8Vc7UfaR15Cbm6fZWPIPpVDw4smM1oceeRcvhqoRIWhQsxImDeuGOtUrYNGaHZqOQa4NpX2cP38ezz33HMqUKQM/Pz/UrVsX8fHxThq149iH+3PnPpTSUx+ubANgH7byLl4MlSuGoG5UBMYOfhIPPxSOZd/u0nQMfG9lnLmjmEnAvBebo0IpP/T/ZI8uvj0+cc63+GX/P1gzZyjKlyup+fOzD+P0kXXnLpJSb+LgycsY+dk+5OaJ6NemumbPr0fsg32QNLXW5npjxD5uZefi9OUMHDp1FaOX/4bcXBF9WlbV5Ln1Sq02ihUrhrCwMPOlbNmyThiteozUR/TDYQgJ8sNfC/vi8poXcXnNi6hULhDTYpvj8Pw+Tn1ue5UpWQJeXiZcvnbTYvvla+koVyZIs3EYsQ8jtQGwD0foqQ+bD7E+depUvPHGG3jiiScAAF27drXY6y+KIgRBQG5uruqDzOfj4wMfH20/3XfmUgZSr2ehde3yOHLm3nmbSvgVR6OHQrB063FNx2KL1k2jsHf12xbbhk1dieqVQzGi/+Pw8lL8mQi7mYR7l4LbPJFR+/AEeaKI7Gxt/+gs1Ub+dltdv34djz76KNq2bYv//e9/CAkJwYkTJ1x2iGZr2Id+uUsfSucOvfThDm0A7MNeoigiO9u5/20K4nsrY8wd+TvHK4cEoN/c3biR6d6H/xRFEZM+XofNu//Cmo+HIqJ8GZeMg30Yow8pggD4FPdy9TDcGvtgHyRNjbW5nrCP+wRBgE8xtmGNtbmj4M4wa/9dT5w4gfDwcPj6+iI6OhozZsxApUqVnDFkhxixj692ncDOv85bbPtmfAzW7jqBuO3/ajYOJbyLF0ODhyOw8/fj6NymPoB7p3na9fu/eOnp1pqNw0h9GLENgH04Qk992LyDfMqUKXjllVewfft2VQfgDgJ8iqFK2P1PUESGlECdyNK4nnEH569mYtGmfzCqRz2cSknHmcs3Me7pRki5kYWf4s+6cNTSAgN8UatauMU2fz9vlA4OKLTd2aQ+GWLvtwDffPNN/O9//0NWVhaqVauGpUuXokmTJmoN1WGe3Ie/jxcqh5QwX48oE4BaFYNxIzMbF67fQrB/cVQo7Y9y/3+Uhaqh985xcTn9Ni6nu9e5zqZ8shHtW9RGRFgp3My6jW82xWPPwRP4dt4QTcch96kpJX188MEHiIiIwNKlS83bqlSposr41OapfXhSG4B796F07tBLH57aBgD4e3uhUtkA8/WKpf3xcHgQ0rKycfHGbQT7FUf5Un4oF3TvTXCVkHv3vXLzDq644XkyZ376A9o0r4nwcqWQces2vtt6CL8mJGLZh4M1HYda7630wNP7iCwwf9SsEIy0rGxcSruN+S81R+2Iknhp0X6YBAFl///wn2lZ2cjJFV01bFkTZn+LjdsO4rPpLyLAzweXrt5b8AaV8IWvj7dm41CjD72cB9CT+7C2Nr+ecQevd6+HTQeTkXojC2UCfTHw8YdRvlQANv562nWDtiIj6w6Ski+br5+5cBV/HT+HksH+iAgrrdk42IdnYB/qU2Ntrpc2AM/tw9+nGCqXe+C9VdkA1IooiRuZ2biecQevda6NLYfPI/XGLZQu4YPYttURVsoPPxx0v7/r5svMuoPT56+YrydfvIq/T5xHySB/VAjV5oPf1uaOgoc3njRpEiZPnlzoMZo3b45ly5YhKioKFy9exJQpU9CqVSscOXIEgYGBhe7vSp7aR4BvgbmjXCDqVC6NGxl3cO5KJq5nWK6/797NQ+r1Wzh5IU3rodpsyLP/wZApX6JhzUpoVLsyFq7ejsxbd9CvyyOajcFIfXhqGwD7cBY99WHzDnJRvPfHmMcee0y1J8/IyMDJkyfN15OSkpCQkIDSpUtr+kmZBlXLYuOETubr055vBgBYvfMkhi/eg3nfH0GATzF89FILBPt748C/qXjm/S24k6Ptt4b0xiQIMBUIoeD1oujlW4Ce3Ee9SqWxZtT91zXh6XufPvpm/2mMXhGPx+uF47+xTc23f/LSvf9jO+eHfzDnx380G6ctrlzPwKuTVyD1SjqCSviidrUK+HbeELRtXlPTcUi1kb8dsO2TVN999x06duyIp59+Gjt37kSFChUwZMgQDBo0yHkDt5On9uFJbQDu3YeSNgD99OGMNgD36KN2xWCseDXafP2trrUAAOvjk/H2V3+ibe1QzHimvvn2Wc81AgB88vO/mL/lhCZjVOLqjQy88d4qXL6WjsAAP0RVLY9lHw5GqyZRmo5DjfdWeuGpcwcA1I0shdUj7n96e/xT9QAA3/x6Bh//dBSP17v3odafxrWz+Lm+H+/CgRNX4G5WbtwLAOgzYr7F9g/f6ounY5ppNg61+qhduza2bt1qvl6smM1LZs14ch/W1uajl+xH9fLB6DPyIZQO9MX1jDv4I/EKukz9CcfP39BsjEokHD2DLq/MNV9/Z/Y6AEDfzs2xYPLzmo2DfTiGfTiHO/RR1NrcVnpoA/DcPupHlsbXY/5jvj75mXtri7X7kjDuy99RLSwQT0c/ilIlfHA9MxuHT1/FUzO34d8Lzj+dlb3+PJ6MZx54bzX1k40AgF6dmmLW289qMgZrc0dycjKCgu7vVJL79l9MTIz5f9erVw/NmzdHZGQk1q5dixdffNEJo7afp/bRoGoIfpjypPn6ey/cW6ev2vGvbs+t3LNDY1y5kYH3Fv+IS1dvom6NCvhm7lBNDyFtpD48tQ2AfTiLnvpQ9I7NnhOpWxMfH4+2bduar48aNQoAEBsbi2XLlqn6XNbsPZqCss9af773v0nA+98kaDIetf2weKRLnleAABOEQtuU0Mu3AAHP7ePXE5dR+dVvZG//5tcz+ObXM5qNxxHzJvRz9RAASLeRvx2w7ZNUp06dwsKFCzFq1Ci8/fbb+P333/Haa6/B29sbsbGxThu7vTyxD09qA3DvPpS0AeirD7XbANyjj99PXUPNMT/K3r4h/hw2xJ/TZCxq+GCse5xfSo33VnriiXMHABw4cQVVh62Tvd3abe7o9M7Zrh4CAPX6yD/Pmbvz1D6KWpu/MEdf31xp2bgGrv/+iauHwT4cxD6cwx36KGptbiu9tAF4Zh/7/72EioPWyN4+aOFeTcahpuiG1XB2l2vfY1mbO4KCgix2cNiqZMmSqFGjhsWOMXfiiX3s/eciSj39mc33rz9UviV3Mrj3YxjcW90vGyhhtD48sQ2AfTiLnvpQtIO8Ro0aRcZw7do1mx+vTZs25k+gkOcxme5dCm4DPO9bgAD7INtJtZG/HbDtk1R5eXlo0qQJ3nvvPQBAw4YNceTIESxatMjtdgAC7INsZ23usPVThnrqQ+02APbhyaz14Yk4d5ASaqw9AH2cBxBgH6QM+yiMfRBQ9Nrc1j700gbAPsh2zlh7ZGRkIDExEc8/r91RVJRgH2Qro/XBNkgJPfWhaAf5lClTEBwcrOoAyHOpca4BPX0LkH2QrYo6z5ktn6QqX748atWqZbGtZs2a+Pbbb9UbqIrYB9nK2txh66cM9dQH2yAljHQOcoB9kDJ6Os+ZGtgHKcE+iKQVtTa3pQ89tQGwD7KdGmuP0aNHo0uXLoiMjMSFCxcwadIkeHl5oW/fvmoOVTXsg2xltD7YBimhpz4U7SDv06cPypUrp+oAyHOpca4BPX0LkH2QrdQ4z9mjjz6K48ePW2z7999/ERkZ6fD4nIF9kK3UOEemnvpgG6SEGn1MnjwZU6ZMsdgWFRWFY8eOyf7M119/jQkTJuD06dOoXr06PvjgAzzxxBOKntce7IOU0NN5ztTAPkgJ9kEkrai1uS196KkNgH2Q7dRYe5w7dw59+/bF1atXERISgpYtW+LXX39FSEiImkNVDfsgWxmtD7ZBSuipD5t3kDvjHJnk2QQUPm9T/jVP+xYg+yAlpNrI326r119/HS1atMB7772H3r1747fffsOnn36KTz/9VLVxqoV9kBLW5g5b6aUPtkFKqdEHANSuXRtbt241Xy9WTH5JsG/fPvTt2xczZszAk08+iVWrVqF79+44dOgQ6tSpY8ez24Z9kFJqrD0KctfzALIPUop9EEkram1uTx/u2gbAPkgZNdYea9bo43y9APsgZYzUB9sgpfTUh81Hfuc5AUip/EMpFLwooZdvAbIPUkKuDSV9NG3aFOvXr8fq1atRp04dvPvuu5gzZw769evnxJHbh32QEmrMHXrpg22QUmr0AdzbIR4WFma+lC1bVva+H3/8MTp16oQxY8agZs2aePfdd9GoUSN88sknjryUIrEPUkqtPh6Uf56z8uXLqzRKdbAPUop9EElTY21ekLu2AbAPUsYZc4c7Yx+khJH6YBuklJ76sPkb5Hl5ec4cB3kgk3DvUnCbEnr5FiD7ICWk2sjfrsSTTz6JJ598Up1BORH7ICXUmDsAffTBNkgpa32kp6dbbPfx8ZE9TO6JEycQHh4OX19fREdHY8aMGahUqZLkfffv349Ro0ZZbOvYsSM2bNhg12uwFfsgpdSYP/RyHkD2QUqxDyJpaqzN9dIGwD5IGbXW5nrBPkgJI/XBNkgpPfWh6BzkREp4ofAnQ7wUHkwh/1uA48aNw9SpU1GlShW3/BYgkRJSbeRvJzI6NeYOIk9lrY+IiAiL7ZMmTcLkyZMLPUbz5s2xbNkyREVF4eLFi5gyZQpatWqFI0eOIDAwsND9U1JSEBoaarEtNDQUKSkpDr4aInWpMX/o6TyAREqwDyJpaqzN2QZ5Kq7NieSxDyJ5euqDO8jJaQRBKHSOCnvOWaGHbwESKSHVRv52IqNTa+4g8kTW+khOTrY4R6bct8djYmLM/7tevXpo3rw5IiMjsXbtWrz44otOGDWRNtSYP/RyHkAipdgHkTQ11uZsgzwV1+ZE8tgHkTw99cEd5OQ8AlDo9949OyDSllQb/7+dyPA4dxDJs9JHUFCQxQ5yW5UsWRI1atTAyZMnJW8PCwtDamqqxbbU1FSEhYUpfi4ip+L8QSSPfRBJ49qcSB7nDiJ57INIno76MLl6AOS5BEH6QmR0cm2wDyK2QWSNM/rIyMhAYmIiypcvL3l7dHQ0tm3bZrFty5YtiI6OduyJiVTG+YNIHvsgksa1OZE8tkEkj30QydNTH/wGOTmNSRBgKvCbX/A6kRFJtZG/ncjoOHcQyVOjj9GjR6NLly6IjIzEhQsXMGnSJHh5eaFv374AgP79+6NChQqYMWMGAGDEiBF47LHH8NFHH6Fz585Ys2YN4uPj8emnn6rzoohUwvmDSB77IJLGtTmRPM4dRPLYB5E8PfXBHeTkNFKfDHHTDog0JfepKfZBxLmDyBo1+jh37hz69u2Lq1evIiQkBC1btsSvv/6KkJAQAMDZs2dhMt0/yFSLFi2watUqjB8/Hm+//TaqV6+ODRs2oE6dOo6+HCJVcf4gksc+iKRxbU4kj3MHkTz2QSRPT31wBzk5jZ4+KUKkJX5KnUge5w4ieWr0sWbNGqu379ixo9C2p59+Gk8//bSi5yHSGucPInnsg0ga1+ZE8jh3EMljH0Ty9NQHd5CT0wj/fym4jcjopNqAzDYio+HcQSSPfRDJYx9E8tgHkTSuzYnkce4gksc+iOTpqQ/uICenEQQBQoFPhhS8TmREUm3kbycyOs4dRPLYB5E89kEkj30QSePanEge5w4ieeyDSJ6e+uAOcnIak3DvUnAbkdFJtZG/ncjoOHcQyWMfRPLYB5E89kEkjWtzInmcO4jksQ8ieXrqgzvIyWn09EkRIi3xU+pE8jh3EMljH0Ty2AeRPPZBJI1rcyJ5nDuI5LEPInl66kPXO8hFUbz3/+fccvFI7Jeenu7qIdjt5v+PPf+/Q0GCcO9ScBtpI/+/S142+9CaPW3kbydtsA/X4dzh/u73keXikdjvpk77yLh5EwD7cFeeMHfotQ0AuMk+3BrX5q7F91fujX24lrU+uDZ3vQf7yHPxWOyl1/dXfG/l/u73cdvFI7GfXucPvrdyb2zDtTypD13vIM+fyG/9+IaLR2K/0A1DXD0Eh928eRPBwcGFtuspBE+U38fVNa+4eCT2C13h6hE4Rkkb+dtJG+zD9Th3uK/8Ps598YJrB+KAqIWuHoFj2Id7ym/jctxgF4/EftWXunoEjmMf7olrc/fAPtwT+3APUn1wbe56+X3c+HqYi0div4dWvejqITiEc4f7yu8j++c3XTwS+4WWGeHqITiEfbgntuEePKEPXe8gDw8PR3JyMgIDA53yFf309HREREQgOTkZQUFBqj++szl7/KIo4ubNmwgPD5e83SQIMBX471LwOjkP+7DOmeO3p4387aQN9iGPcwc5sw89twGwD6Pj3GEd+zA29mEd+zA29mGdK/vg2tz12Ic8zh3EPuSxD2NjG9axD9vpege5yWRCxYoVnf48QUFBugwhnzPHL/UJkXx6+qSIJ2IftnHW+JW2kb+dtME+isa5w7i06EPPbQDsw6g4d9iGfRgT+7AN+zAm9mEbV/TBtbnrsY+ice4wLvZRNPZhTGzDNuyjaLreQU7uTZD4pIgzPtFDpDdSbeRvJzI6zh1E8tgHkTz2QSSPfRBJ49qcSB7nDiJ57INInp764A5ychrh/y8FtxEZnVQbkNlGZDScO4jksQ8ieeyDSB77IJLGtTmRPM4dRPLYB5E8PfVhcvUA3JmPjw8mTZoEHx8fVw/FLq4evyAIkhfyDK7+/XKUK8cv14Yjfbz//vsQBAEjR45Ub6BkNz334eqxO2PuYB/uw9W/X45y9fj53sqzufr3y1GuHj/78Gyu/v1ylKvHzz48m6t/vxzlaWtzci967sPVY2cbns/Vv2OOcPXY2Ydnc/Xvl6NcPX499SGIoii6ehDkWdLT0xEcHIw/T6UiMNDyHAc3b6ajXtVQpKWl6fr8DUT2sNYGYH8fv//+O3r37o2goCC0bdsWc+bMUXHURNpw1tzBPsgT8L0VkTz2QSSPfRBJc9banMgTcO4gksc+iOTpsQ9+g5ycRk+fFCHSkpqfUs/IyEC/fv3w2WefoVSpUk4YLZG21Jw72Ad5Gr63IpLHPojksQ8iafwGOZE8tkEkj30QydNTH9xBTk5jAmASClxcPSgiNyDZhp19DB06FJ07d0b79u3VHiaRS6g5d7AP8jR8b0Ukj30QyWMfRNLUXJsTeRrOHUTy2AeRPD31UczVAyDPJQj3LgW3ERmdVBv524F7hyN5kI+Pj+Q5Q9asWYNDhw7h999/d8YwiVzC2txhaxsA+yDPxPdWRPLYB5E89kEkrai1OZGRce4gksc+iOTpqQ933XFPHkBPh1Ig0lJRh3GLiIhAcHCw+TJjxoxCj5GcnIwRI0YgLi4Ovr6+Wr8EIqdxtA2AfZDn4nsrInnsg0ge+yCSxkOsE8ljG0Ty2AeRPD31wR3kVsyfPx+VK1eGr68vmjdvjt9++83VQ7LJrl270KVLF4SHh0MQBGzYsMEl45A8TJV7dkAK6bUNwD36kGsjv4/k5GSkpaWZL+PGjSv0GAcPHsSlS5fQqFEjFCtWDMWKFcPOnTsxd+5cFCtWDLm5uRq/KsrHPhzjaBsA+3Bneu3DHdoA+N7K07EPx7APz8Y+HMM+PJde2wDco4+i1uakb+zDMWzDs+m1D3doA2Afno59OEZPfXAHuYyvvvoKo0aNwqRJk3Do0CHUr18fHTt2xKVLl1w9tCJlZmaifv36mD9/vkvHoadPipDt9NwG4B59FPUp9aCgIIuL1CGk27Vrh7/++gsJCQnmS5MmTdCvXz8kJCTAy8tL65dFYB9qcLQNgH24Kz334Q5tAHxv5cnYh+PYh+diH45jH55Jz20A7tEHv0HuudiH49iG59JzH+7QBsA+PBn7cJyu+hBJUrNmzcShQ4ear+fm5orh4eHijBkzXDgq5QCI69ev1/Q509LSRADiyXNXxNT0bIvLyXNXRABiWlqapmMi9XhKG6KofR/W2lCjj8cee0wcMWKEuoMmRdiH/Zw9d7AP1/OUPvjeipyBfdiPfXg+9mE/9uHZPKUNUXSvtQf78Azsw36cOzyfp/TB91bkDOzDfnrsg98gl5CdnY2DBw+iffv25m0mkwnt27fH/v37XTgyfRFk/h/pF9tQh1wb7EPf2Ic62IZnYh/qUKOPGTNmoGnTpggMDES5cuXQvXt3HD9+3OrPLFu2rNAnf319fR15KfQA9qEOzh+eiX2og314HrahDq7NPRP7UAfb8EzsQx3swzOxD3XoqY9irh6AO7py5Qpyc3MRGhpqsT00NBTHjh1z0aj0RxDuXQpuI/1iG+qQaiN/uyN27Njh2AOQQ9iHOpw1d7AP12If6lCjj507d2Lo0KFo2rQp7t69i7fffhsdOnTAP//8g4CAANmfCwoKstiR7raHx9Ih9qEOrj08E/tQB/vwPGxDHc5am5NrsQ91cO7wTOxDHezDM7EPdeipD+4gJ6cRBAGmAr/5/GMqkXQb+duJjI5zB5E8NfrYtGmTxfVly5ahXLlyOHjwIFq3bm31ucPCwhQ9F5GWOH8QyWMfRNK4NieSx7mDSB77IJKnpz54iHUJZcuWhZeXF1JTUy22p6am8g+DCuR/UqTghfSLbahDrg32oW/sQx1swzOxD3VY6yM9Pd3icufOHZseMy0tDQBQunRpq/fLyMhAZGQkIiIi0K1bN/z9998OvRa6j32og/OHZ2If6mAfnodtqINrc8/EPtTBNjwT+1AH+/BM7EMdeuqDO8gleHt7o3Hjxti2bZt5W15eHrZt24bo6GgXjkxf9BQC2YZtqIOLcM/EPtTBNjwT+1CHtT4iIiIQHBxsvsyYMaPIx8vLy8PIkSPx6KOPok6dOrL3i4qKwpIlS7Bx40asXLkSeXl5aNGiBc6dO6fWSzM09qEOzh+eiX2og314HrahDq7NPRP7UAfb8EzsQx3swzOxD3XoqQ8eYl3GqFGjEBsbiyZNmqBZs2aYM2cOMjMzMWDAAFcPrUgZGRk4efKk+XpSUhISEhJQunRpVKpUSbNxmCQOpSB16CrSFz23AbhHH1Jt5G8nfWMfjuPc4bn03Ic7tAFY7yM5ORlBQUHm7T4+PkU+3tChQ3HkyBHs2bPH6v2io6MtFoMtWrRAzZo1sXjxYrz77rtKXgLJYB+O4/zhudiH49iHZ9JzG4B79MG1uediH47j3OG59NyHO7QBsA9Pxj4cp6s+RJI1b948sVKlSqK3t7fYrFkz8ddff3X1kGyyfft2EUChS2xsrCbPn5aWJgIQz126LqbfzrW4nLt0XQQgpqWlKX7cGTNmiADEESNGqD9oUkSvbYiia/uw1oajfZD7YB/2cdbcQe5Fr3144nuroUOHihUrVhRPnTpl15h69eol9unTx66fJWnswz6cP4yBfdiHfXg+vbYhiu679mAfnoN92McZc8cnn3wiRkZGij4+PmKzZs3EAwcOOGn0ZCu99uGJ763Yh/thH/bRYx+CKIqiA/vXiQpJT09HcHAwLly+YfFNpvzbwkNKIi0trdBt1vz+++/o3bs3goKC0LZtW8yZM0flURM5n7U28m+3pw8iT+CMuYPIU6jZhyiKGD58ONavX48dO3agevXqiseTm5uL2rVr44knnsCsWbMU/zyRmjh/EMljH0TSuDYnkqf23PHVV1+hf//+WLRoEZo3b445c+bg66+/xvHjx1GuXDlnvAQip2EfRPL02AfPQU5Oo9a5BjIyMtCvXz989tlnKFWqlPoDJdIYz3NGJI9tEMlTo4+hQ4di5cqVWLVqFQIDA5GSkoKUlBTcunXLfJ/+/ftj3Lhx5utTp07Fzz//jFOnTuHQoUN47rnncObMGbz00ktqvTQih3H+IJLHPoikcW1OJE+tNmbNmoVBgwZhwIABqFWrFhYtWgR/f38sWbJE/UETaYR9EMnTUx88Bzk5zc30mxAgFNoG3PvEyIN8fHxkz5U5dOhQdO7cGe3bt8e0adOcM1giDUm1kb+dyOiszR1ERqdGHwsXLgQAtGnTxmL70qVL8cILLwAAzp49C5Pp/udor1+/jkGDBiElJQWlSpVC48aNsW/fPtSqVUv5iyByEs4fRPLYB5E0rs2J5Knxd93s7GwcPHjQ4sO3JpMJ7du3x/79+50waiJtsA8ieXrqgzvISXXe3t4ICwtDjaoRkreXKFECERGWt02aNAmTJ08udN81a9bg0KFD+P33350xVCJNFdUGAISFhcHb21vDURG5h6L6YBtkZGr2YcvZlXbs2GFxffbs2Zg9e7ZNj0+kNc4fRPLYB5E0rs2J5Kn5d90rV64gNzcXoaGhFttDQ0Nx7Ngx1cZMpBX2QSRPj31wBzmpztfXF0lJScjOzpa8XRRFCAWOqSD1KZHk5GSMGDECW7Zsga+vr1PGSqSlotoA7k0k/H0nIyqqD7ZBRsY+iOSxDyJ57INIGtfmRPLU+rsukSdiH0Ty9NgHd5CTU/j6+jq8kDh48CAuXbqERo0ambfl5uZi165d+OSTT3Dnzh14eXk5OlQiTanRBpGnYh9E8tgHkTz2QSSPfRBJYxtE8tTqo2zZsvDy8kJqaqrF9tTUVISFhTn8+ESuwD6I5OmtD1PRdyFyjXbt2uGvv/5CQkKC+dKkSRP069cPCQkJ3DlORERERERERERE5Ia8vb3RuHFjbNu2zbwtLy8P27ZtQ3R0tAtHRuR67INInlZ98Bvk5LYCAwNRp04di20BAQEoU6ZMoe1ERERERERERERE5D5GjRqF2NhYNGnSBM2aNcOcOXOQmZmJAQMGuHpoRC7HPojkadEHd5ATEREREREREREREZGqnnnmGVy+fBkTJ05ESkoKGjRogE2bNiE0NNTVQyNyOfZBJE+LPniI9SK88MIL6N69u/l6mzZtMHLkSM3HsWPHDgiCgBs3bsjeRxAEbNiwwebHnDx5Mho0aODQuE6fPg1BEJCQkODQ49hqx44dmDNnjibPRUVjH9Zp3Qe5F/ZhHfswNvZhHfswLrZhHdswNvZhHfswNvZhHfswNvZhnRZ9DBs2DGfOnMGdO3dw4MABNG/e3GnPRcqwD+vYh3GxDeu0em/l7D50uYP8hRdegCAIEAQB3t7eqFatGqZOnYq7d+86/bnXrVuHd99916b72vLLS6Q29kEkj30QyWMfRNLYBpE89kEkj30QyWMfRPLYB5E0tkFq0+0h1jt16oSlS5fizp07+OmnnzB06FAUL14c48aNK3Tf7OxseHt7q/K8pUuXVuVxiJyJfRDJYx9E8tgHkTS2QSSPfRDJYx9E8tgHkTz2QSSNbZCadPkNcgDw8fFBWFgYIiMj8eqrr6J9+/b47rvvANw//MH06dMRHh6OqKgoAEBycjJ69+6NkiVLonTp0ujWrRtOnz5tfszc3FyMGjUKJUuWRJkyZTB27FiIomjxvAUPpXDnzh28+eabiIiIgI+PD6pVq4YvvvgCp0+fRtu2bQEApUqVgiAIeOGFFwAAeXl5mDFjBqpUqQI/Pz/Ur18f33zzjcXz/PTTT6hRowb8/PzQtm1bi3Ha6s0330SNGjXg7++PqlWrYsKECcjJySl0v8WLFyMiIgL+/v7o3bs30tLSLG7//PPPUbNmTfj6+uLhhx/GggULFI+FtMU+isY+jIt9FI19GBf7KBr7MCa2UTS2YVzso2jsw7jYR9HYh3Gxj6KxD+NiH0VjH8bENorGNmyn2x3kBfn5+SE7O9t8fdu2bTh+/Di2bNmCH374ATk5OejYsSMCAwOxe/du7N27FyVKlECnTp3MP/fRRx9h2bJlWLJkCfbs2YNr165h/fr1Vp+3f//+WL16NebOnYujR49i8eLFKFGiBCIiIvDtt98CAI4fP46LFy/i448/BgDMmDEDK1aswKJFi/D333/j9ddfx3PPPYedO3cCuBdsz5490aVLFyQkJOCll17CW2+9pfjfJDAwEMuWLcM///yDjz/+GJ999hlmz55tcZ+TJ09i7dq1+P7777Fp0yb88ccfGDJkiPn2uLg4TJw4EdOnT8fRo0fx3nvvYcKECVi+fLni8ZDrsI/C2AflYx+FsQ/Kxz4KYx8EsA0pbIPysY/C2AflYx+FsQ/Kxz4KYx+Uj30Uxj4IYBtS2IYCog7FxsaK3bp1E0VRFPPy8sQtW7aIPj4+4ujRo823h4aGinfu3DH/zJdffilGRUWJeXl55m137twR/fz8xM2bN4uiKIrly5cXZ86cab49JydHrFixovm5RFEUH3vsMXHEiBGiKIri8ePHRQDili1bJMe5fft2EYB4/fp187bbt2+L/v7+4r59+yzu++KLL4p9+/YVRVEUx40bJ9aqVcvi9jfffLPQYxUEQFy/fr3s7R9++KHYuHFj8/VJkyaJXl5e4rlz58zb/ve//4kmk0m8ePGiKIqi+NBDD4mrVq2yeJx3331XjI6OFkVRFJOSkkQA4h9//CH7vKQt9iGNfZAosg857INEkX3IYR/ENqSxDRJF9iGHfZAosg857INEkX3IYR8kiuxDDvsgtiGNbdhPt+cg/+GHH1CiRAnk5OQgLy8Pzz77LCZPnmy+vW7duhbnFzh8+DBOnjyJwMBAi8e5ffs2EhMTkZaWhosXL6J58+bm24oVK4YmTZoUOpxCvoSEBHh5eeGxxx6zedwnT55EVlYWHn/8cYvt2dnZaNiwIQDg6NGjFuMAgOjoaJufI99XX32FuXPnIjExERkZGbh79y6CgoIs7lOpUiVUqFDB4nny8vJw/PhxBAYGIjExES+++CIGDRpkvs/du3cRHByseDykHfZRNPZhXOyjaOzDuNhH0diHMbGNorEN42IfRWMfxsU+isY+jIt9FI19GBf7KBr7MCa2UTS2YTvd7iBv27YtFi5cCG9vb4SHh6NYMcuXEhAQYHE9IyMDjRs3RlxcXKHHCgkJsWsMfn5+in8mIyMDAPDjjz9a/AIC986foJb9+/ejX79+mDJlCjp27Ijg4GCsWbMGH330keKxfvbZZ4XC9PLyUm2spD72YR37MDb2YR37MDb2YR37MC62YR3bMDb2YR37MDb2YR37MDb2YR37MDb2YR37MC62YR3bUEa3O8gDAgJQrVo1m+/fqFEjfPXVVyhXrlyhT0vkK1++PA4cOIDWrVsDuPeJiIMHD6JRo0aS969bty7y8vKwc+dOtG/fvtDt+Z9Uyc3NNW+rVasWfHx8cPbsWdlPmNSsWRPfffedxbZff/216Bf5gH379iEyMhLvvPOOeduZM2cK3e/s2bO4cOECwsPDzc9jMpkQFRWF0NBQhIeH49SpU+jXr5+i5yfXYh/WsQ9jYx/WsQ9jYx/WsQ/jYhvWsQ1jYx/WsQ9jYx/WsQ9jYx/WsQ9jYx/WsQ/jYhvWsQ1lTK4egFb69euHsmXLolu3bti9ezeSkpKwY8cOvPbaazh37hwAYMSIEXj//fexYcMGHDt2DEOGDMGNGzdkH7Ny5cqIjY3FwIEDsWHDBvNjrl27FgAQGRkJQRDwww8/4PLly8jIyEBgYCBGjx6N119/HcuXL0diYiIOHTqEefPmmU9w/8orr+DEiRMYM2YMjh8/jlWrVmHZsmWKXm/16tVx9uxZrFmzBomJiZg7dy7Wr19f6H6+vr6IjY3F4cOHsXv3brz22mvo3bs3wsLCAABTpkzBjBkzMHfuXPz777/466+/sHTpUsyaNUvReMi9sQ/2QfLYB/sgeeyDfZA0tsE2SB77YB8kj32wD5LHPtgHyWMf7IOksQ22YZWrTn7uiNjYWLFbt26Kb7948aLYv39/sWzZsqKPj49YtWpVcdCgQWJaWpooiqKYk5MjjhgxQgwKChJLliwpjho1Suzfv7/FYz322GPiiBEjzNdv3bolvv7662L58uVFb29vsVq1auKSJUvMt0+dOlUMCwsTBUEQY2NjRVEUxby8PHHOnDliVFSUWLx4cTEkJETs2LGjuHPnTvPPff/992K1atVEHx8fsVWrVuKSJUtEAOL169dlXzcAcf369ebrY8aMEcuUKSOWKFFCfOaZZ8TZs2eLwcHB5tsnTZok1q9fX1ywYIEYHh4u+vr6ir169RKvXbtm8bhxcXFigwYNRG9vb7FUqVJi69atxXXr1omiKIpJSUkiAPGPP/6QHRdpi31IYx8kiuxDDvsgUWQfctgHsQ1pbINEkX3IYR8kiuxDDvsgUWQfctgHiSL7kMM+iG1IYxv2E0RR5kzzREREREREREREREREREREHsQwh1gnIiIiIiIiIiIiIiIiIiJj4w5yIiIiIiIiIiIiIiIiIiIyBO4gJyIiIiIiIiIiIiIiIiIiQ+AOciIiIiIiIiIiIiIiIiIiMgTuICciIiIiIiIiIiIiIiIiIkPgDnIiIiIiIiIiIiIiIiIiIjIE7iAnIiIiIiIiIiIiIiIiIiJD4A5yIiIiIiIiIiIiIiIiIiIyBO4gJyIiIiIiIiIiIiIiIiIiQ+AOciIiIiIiIiIiIiIiIiIiMgTuICciIiIiIiIiIiIiIiIiIkPgDnIiIiIiIiIiIiIiIiIiIjIE7iAnIiIiIiIiIiIiIiIiIiJD4A5yIiIiIiIiIiIiIiIiIiIyBO4gJyIiIiIiIiIiIiIiIiIiQ+AOciIiIiIiIiIiIiIiIiIiMgTuICciIiIiIiIiIiIiIiIiIkPgDnIiIiIiIiIiIiIiIiIiIjIE7iAnIiIiIiIiIiIiIiIiIiJD4A5yIiIiIiIiIiIiIiIiIiIyBO4gJyIiIiIiIiIiIiIiIiIiQ+AOciIiIiIiIiIiIiIiIiIiMgTuICciIiIiIiIiIiIiIiIiIkPgDnIiIiIiIiIiIiIiIiIiIjIE7iAnIiIiIiIiIiIiIiIiIiJD4A5yIiIiIiIiIiIiIiIiIiIyBO4gJyIiIiIiIiIiIiIiIiIiQ+AOciIiIiIiIiIiIiIiIiIiMgTuICciIiIiIiIiIiIiIiIiIkPgDnIiIiIiIiIiIiIiIiIiIjIE7iAnIiIiIiIiIiIiIiIiIiJD4A5yIiIiIiIiIiIiIiIiIiIyBO4gJyIiIiIiIiIiIiIiIiIiQ+AOciIiIiIiIiIiIiIiIiIiMgTuICciIiIiIiIiIiIiIiIiIkPgDnIiIiIiIiIiIiIiIiIiIjKEYq4eAHmm27dvIzs7W/I2b29v+Pr6ajwiIvdgrQ2AfZCxce4gksc+iOSxDyJ57INIGtfmRPI4dxDJYx9E8vTWB3eQk+pu374Nv8AywN0sydvDwsKQlJTkdjEQOVtRbQDsg4yLcweRPPZBJI99EMljH0TSuDYnkse5g0ge+yCSp8c+uIOcVJednQ3czYJPnQGAl7fljbnZSDmyFNnZ2W4VApEWrLYBsA8yNM4dRPLYB5E89kEkj30QSePanEge5w4ieeyDSJ4e++A5yIuwY8cOCIKAb775xtVD0Z9ivhCK+VlcUMx9fvnJcezDThJtsA/Pwz7sxLnD47ENB7APj8c+HMA+PB77cAD78Hjsw05cmxsC+7AT5w5DYB92Yh8ej204QEd9GHIHuSAINl127Njh9LHcuHEDgwcPRkhICAICAtC2bVscOnTIpp+1NvbHH3/c4r55eXmYOXMmqlSpAl9fX9SrVw+rV692xku6z+QlfSG3ZsQ+pk+fjq5duyI0NBSCIGDy5MlOeDUPkGuDfbg9o/Vx7NgxjB07Fg0aNEBgYCDKly+Pzp07Iz4+3lkvi23olNHauHDhAp577jlERUUhMDAQJUuWRLNmzbB8+XKIouisl8Y+dMpofRQUFxcHQRBQokQJtV6GNPahS0br4/Tp07L3W7NmjbNeGvvQKaP1kS8xMRHPPvssypUrBz8/P1SvXh3vvPOO2i/pHq7NdctofUyePNnqfffu3av+C2MbumW0PgDg4sWLGDx4MKpUqQI/Pz889NBDGDVqFK5eveqMl8U+dMqIbZw8eRK9evVCqVKl4O/vj5YtW2L79u3OeEn36agPQx5i/csvv7S4vmLFCmzZsqXQ9po1a+Lo0aNOG0deXh46d+6Mw4cPY8yYMShbtiwWLFiANm3a4ODBg6hevbrVny84XgCIj4/Hxx9/jA4dOlhsf+edd/D+++9j0KBBaNq0KTZu3Ihnn30WgiCgT58+qr4uM0HiFz/PPUOg+4zYx/jx4xEWFoaGDRti8+bNqr4OSVJtAOxDB4zWx+eff44vvvgCTz31FIYMGYK0tDQsXrwYjzzyCDZt2oT27dur/to4d+iT0dq4cuUKzp07h169eqFSpUrIycnBli1b8MILL+D48eN47733VH9tANiHThmtjwdlZGRg7NixCAgIUOU1WMU+dMmoffTt2xdPPPGExbbo6GjHXoQ17EOXjNhHQkIC2rRpgwoVKuCNN95AmTJlcPbsWSQnJ6v6msy4Ntcto/XRs2dPVKtWrdB93377bWRkZKBp06aOv5iCOHfoltH6yMjIQHR0NDIzMzFkyBBERETg8OHD+OSTT7B9+3YcPHgQJpPK3xNlH7pktDaSk5MRHR0NLy8vjBkzBgEBAVi6dCk6dOiAbdu2oXXr1qq/NgD66kMkcejQoaLcP8X27dtFAOLXX39t12NnZmbK3vbVV18VeuxLly6JJUuWFPv27WvX87344ouiIAhicnKyedu5c+fE4sWLi0OHDjVvy8vLE1u1aiVWrFhRvHv3rl3PJSctLU0EIPo0HSX6Ro+zuPg0HSUCENPS0mx6rJ07d4pPPvmkWL58eRGAuH79eovbY2NjRQAWl44dO6r6eozO0/sQRVFMSkoSRVEUL1++LAIQJ02aZNfjF8VaG+xDnzy9j/j4ePHmzZsW97ty5YoYEhIiPvroo3Y9jxw15w5RZB+u5ultyHnyySfFgIAAt35vJYrsw9WM1Mebb74pRkVFif369RMDAgLseo6icO3hWTy9j6SkJBGA+OGHH9r1mEqxD8/i6X3k5uaKderUEZs3by5mZWXZ9bi24trc83h6H1LOnj0rCoIgDho0yK7nkcO1h+fx9D7i4uJEAOIPP/xgcd+JEyeKAMRDhw7Z9VxS2Idn8fQ2hgwZIhYrVkw8duyYxbgiIiLERo0a2fU81uhx7WHIQ6zbIy8vD9OnT0fFihXh6+uLdu3a4eTJkxb3adOmDerUqYODBw+idevW8Pf3x9tvvy37mN988w1CQ0PRs2dP87aQkBD07t0bGzduxJ07dxSN8c6dO/j222/x2GOPoWLFiubtGzduRE5ODoYMGWLeJggCXn31VZw7dw779+9X9Dw2U+FQCpmZmahfvz7mz58ve59OnTrh4sWL5ovTDx1Phei5DwCoXLmyosdymEqHcWMf+qDnPho3blzokLhlypRBq1atnPdJSpUOw8M+3J+e25BTuXJlZGVlITs7W9Hz2Ix9GIYn9HHixAnMnj0bs2bNQrFiGhy4jO+tDMMT+gDu/b45bb4oiH0Yhp77+Pnnn3HkyBFMmjQJfn5+yMrKQm5urqLHVoxrc0PRcx9SVq9eDVEU0a9fP0XPYTOuPQxFz32kp6cDAEJDQy3uX758eQCAn5+fouexCfswDD23sXv3bjRs2BBRUVHmbf7+/ujatSsOHTqEEydOKHoem+novZUhD7Fuj/fffx8mkwmjR49GWloaZs6ciX79+uHAgQMW97t69SpiYmLQp08fPPfcc4X+D/OD/vjjDzRq1KjQIT6aNWuGTz/9FP/++y/q1q1r8xh/+ukn3Lhxo9Aboz/++AMBAQGoWbNmoefJv71ly5Y2P4/NBOHepeA2BWJiYhATE2P1Pj4+PggLC1M6OlKRnvtwCak28rcrwD70wRP7SElJQdmyZW1+fEVUmDsA9qEHntDGrVu3kJmZiYyMDOzcuRNLly5FdHS0cxbgAPswEE/oY+TIkWjbti2eeOIJrF271ubHtRvXHobhCX1MmTIFY8aMgSAIaNy4MaZPny57qgJVsA/D0HMfW7duBXDv96hJkyY4ePAgvL290aNHDyxYsAClS5e2+TlsxrW5oei5DylxcXGIiIhw4iFyufYwEj330bp1a5hMJowYMQIfffQRKlasiD///BPTp09H9+7d8fDDD9v8HDZjH4ah5zbu3LmDUqVKFbq/v78/ANh0OHe76GjtwR3kNrp9+zYSEhLg7e0NAChVqhRGjBiBI0eOoE6dOub7paSkYNGiRXj55ZeLfMyLFy9KvonJ/3TThQsXFIUQFxcHHx8f9OrVq9DzhIaGQijwS/jg8ziFyQswFfgVM90FcP+TXfl8fHzg4+Nj19Ps2LED5cqVQ6lSpfCf//wH06ZNQ5kyZex6LLKPnvtwCak2APbhoTytj927d2P//v0YP368zY+viEZzB8A+XM0T2vj4448xbtw48/V27dph6dKlNj++YuzDMPTex48//oiff/4Zhw8ftvnxHMa1h2HouQ+TyYQOHTqgR48eqFChAk6dOoVZs2YhJiYG3333HTp37mzzcyjCPgxDz33kf4upd+/e6NSpE8aNG4fDhw9jxowZSE5Oxp49ewr9XcthXJsbip77KOjvv//Gn3/+ibFjx6rfRT6uPQxFz33UqlULn376KUaPHo3o6Gjz9tjYWHz++ec2P74i7MMw9NxGVFQUdu/ejZs3byIwMNC8fc+ePQCA8+fP2/wciuho7cFDrNtowIAB5ggAoFWrVgCAU6dOWdzPx8cHAwYMsOkxb926Jfkf39fX13y7rdLT0/Hjjz/iiSeeQMmSJZ32PIp4eUlfAERERCA4ONh8mTFjhl1P0alTJ6xYsQLbtm3DBx98gJ07dyImJsb5h+EiC3ruwyXk2mAfHsmT+rh06RKeffZZVKlSBWPHjrX5ORTRoA2AfbgDT2ijb9++2LJlC1atWoVnn31W8XMoxj4MQ899ZGdn4/XXX8crr7yCWrVq2fyYDuN7K8PQcx+VKlXC5s2b8corr6BLly4YMWIE/vjjD4SEhOCNN96w+TkUYx+Goec+MjIyAABNmzbFypUr8dRTT2Hq1Kl49913sW/fPmzbts3m57EZ1+aGouc+CoqLiwMA5x4lkWsPQ9F7HxUqVECzZs0wZ84crF+/HqNGjUJcXBzeeustm59DEfZhGHpu49VXX8WNGzfwzDPP4I8//sC///6LkSNHIj4+XvHzKKKj91b8BrmNKlWqZHE9/9AE169ft9heoUIFi2Cs8fPzkzyfwO3bt8232+rbb7/F7du3Jd8Yqfk8ikidW+D/rycnJyMoKMi82d5PifTp08f8v+vWrYt69erhoYcewo4dO9CuXTu7HpOU03MfLiF33g324ZE8pY/MzEw8+eSTuHnzJvbs2VPo3OSq0WDuANiHO/CENiIjIxEZGQng3s7ywYMHo3379jh+/Lhzz3NWcBvYh6fRcx+zZ8/GlStXMGXKFJsfTxVcexiGnvuQUrp0aQwYMADvv/8+zp07V+Q5Z+3CPgxDz33kP07fvn0ttj/77LMYN24c9u3bh/bt29v8XDbh2txQ9NzHg0RRxKpVq1CnTh3Uq1fP5sdXjGsPQ9FzH3v37sWTTz6JX3/9FU2aNAEAdO/eHUFBQZgyZQoGDhyo/gd32Ydh6LmNmJgYzJs3D2+99RYaNWoEAKhWrRqmT5+OsWPH6vpvu2q1wW+Q28jLS+INM+69KXmQkl/e8uXL4+LFi4W2528LDw+3+bHi4uIQHByMJ598UvJ5UlJSCo3VnudRJP9cAwUvAIKCgiwujkwUD6patSrKli2LkydPqvJ4ZBs99+EScm2wD4/kCX1kZ2ejZ8+e+PPPP7Fx40aLQwipzgVtAOzDFTyhjYJ69eqF5ORk7Nq1y+afUYR9GIZe+0hLS8O0adMwaNAgpKen4/Tp0zh9+jQyMjIgiiJOnz6NS5cu2fw8ivC9lWHotQ9rIiIiAADXrl2z+WcUYR+Goec+8h+n4Dk7y5UrB6DwH6JVwbW5oei5jwft3bsXZ86ccf6XQLj2MBQ997F48WKEhoaad47n69q1K0RRxL59+2x+HpuxD8PQcxsAMGzYMKSmpmLfvn2Ij4/HsWPHEBwcDACoUaOGzc+jiI7eW3EHuQs1aNAAhw4dQl5ensX2AwcOwN/f3+Zf0IsXL2L79u146qmnJH+hGjRogKysLBw9erTQ8+Tf7hT5nxQpeHGic+fO4erVq+bzNZB+adWHS8i1wT7IRlr2kZeXh/79+2Pbtm1YtWoVHnvsMYfHb5UL2gDYh6dw9dyRf3iqtLQ02wetBPsgB2jRx/Xr15GRkYGZM2eiSpUq5su3336LrKwsVKlSBYMHD1btNVngeytygKvnj/xDNIaEhNg+aCXYBzlAqz4aN24MoPD5MC9cuADASX1wbU4OcsX8ERcXB0EQzKd4chquPchBWvWRmpoqeVjlnJwcAMDdu3ftGH0R2Ac5QOu5IyAgANHR0WjcuDG8vLywdetW+Pn54dFHH3XodcjS0Xsr7iB3oV69eiE1NRXr1q0zb7ty5Qq+/vprdOnSxeKXOjExEYmJiZKPs2bNGuTl5cl+crBbt24oXrw4FixYYN4miiIWLVqEChUqoEWLFiq9ogJUCCEjIwMJCQlISEgAACQlJSEhIQFnz55FRkYGxowZg19//RWnT5/Gtm3b0K1bN1SrVg0dO3Z0wgsiLWnVh0uotAhnH8alZR/Dhw/HV199hQULFqBnz57qvQg5Kr2JYh/GpFUbly9fltz+xRdfQBAE86GrVMc+yAFa9FGuXDmsX7++0KVt27bw9fXF+vXrMW7cOPVfHMD3VuQQV84f58+fx5IlS1CvXj3n/bGTfZADtPzblY+PD5YuXWrxB+PPP/8cAPD444+r8XIscW1ODtL6b1c5OTn4+uuv0bJly0KH/VUd1x7kIK36qFGjBlJTU7Fjxw6L7atXrwYANGzY0MFXIoF9kANcud9j3759WLduHV588UXzN8lVp6P3VjwHuQv16tULjzzyCAYMGIB//vkHZcuWxYIFC5Cbm1vonH35x80/ffp0oceJi4tDeHg42rRpI/k8FStWxMiRI/Hhhx8iJycHTZs2xYYNG7B7927ExcXJHibCYYLEL76g7Lni4+PRtm1b8/VRo0YBAGJjY7Fw4UL8+eefWL58OW7cuIHw8HB06NAB7777rvt8U5jsplUfAPDll1/izJkzyMrKAgDs2rUL06ZNAwA8//zz5vPLqkaqjfztCrAP49Kqjzlz5mDBggWIjo6Gv78/Vq5caXF7jx49EBAQoMprMlNh7gDYh1Fp1cb06dOxd+9edOrUCZUqVcK1a9fw7bff4vfff8fw4cNRrVo1tV/aPeyDHKBFH/7+/ujevXuh7Rs2bMBvv/0meZtquPYgB2g1f4wdOxaJiYlo164dwsPDcfr0aSxevBiZmZn4+OOP1X5Z97EPcoBWfYSFheGdd97BxIkT0alTJ3Tv3h2HDx/GZ599hr59+6Jp06ZqvzSuzclhWv7tCgA2b96Mq1evavMlEK49yEFa9TFs2DAsXboUXbp0wfDhwxEZGYmdO3di9erVePzxx9G8eXO1Xxr7IIdo1caZM2fQu3dvdO3aFWFhYfj777+xaNEi1KtXD++9957aL+s+Ha09uIPchby8vPDTTz9hzJgxmDt3Lm7duoWmTZti2bJliIqKsukxjh8/joMHD2LUqFEwmeQPCPD++++jVKlSWLx4MZYtW4bq1atj5cqVzj0cj8kLMBUrvE2BNm3aFDqfw4M2b95sz8hIB7Ts44svvsDOnTvN17dv347t27cDAFq2bKn+DnKpNvK3K8A+jEurPvI/pbd//37s37+/0O1JSUnq7yBXYe4A2IdRadVG586dkZiYiCVLluDy5cvw9fVFvXr1sHTpUsTGxqr5kiyxD3KAlu+tXIJrD3KAVn106NABixYtwvz583H9+nWULFkSrVu3xvjx45139BGAfZBDtJw/xo8fj1KlSmHevHkYOXKkxU5zp+DanByk9furuLg4FC9eHE8//bQaw7eOaw9ykFZ9REVF4eDBgxg/fjxWrlyJlJQUhIeHY/To0YV2NqqGfZADtGojKCgI5cuXxyeffIJr166hQoUKeO211/DOO+8gMDBQzZdkSUdrD0G09ixEdkhPT0dwcDB8On0EobifxW1izi3c2fQG0tLSEBQU5KIRErmGtTYA9kHGxrmDSB77IJLHPojksQ8iaVybE8nj3EEkj30QydNjH/wGOTmP1LkF7PgkFZHHkTvvBvsg4txBZA37IJLHPojksQ8iaVybE8nj3EEkj30QydNRH9xBTs6joxCINMVFOJE8zh1E8tgHkTz2QSSPfRBJ49qcSB7nDiJ57INIno764A5ychqTyQShwPkPRHc7VyGRC0i1AbAPIoBzB5E17INIHvsgksc+iKRxbU4kj3MHkTz2QSRPT31wBzk5jWASIJgEy40FrxMZkGQbAPsgAucOImvYB5E89kEkj30QSePanEge5w4ieeyDSJ6e+uAOcnIaQRAgCELBja4ZDJEbkWzj3g3aD4bIzXDuIJLHPojksQ8ieeyDSBrX5kTyOHcQyWMfRPL01Ad3kJPT6OlQCkRa4mHciORx7iCSxz6I5LEPInnsg0ga1+ZE8jh3EMljH0Ty9NSHrneQ5+Xl4cKFCwgMDJT+xCc5lSiKuHnzJsLDw2GS+AXX06EUPBH7cB272gDYh4bYh+tw7nB/7MN12Id7YxuuxT7cG/twLfbh3tiHa1nrg2tz12MfrsO5w/2xD9dhH+6NbbiWJ/Wh6x3kFy5cQEREhKuHYXjJycmoWLFioe0mQSj8SRH+HyzNsA/XU9IGwD60xD5cj3OH+2Ifrsc+3BPbcA/swz2xD/fAPtwT+3APUn1wbe567MP1OHe4L/bheuzDPbEN9+AJfeh6B3lgYCAAwLtWLAQvbxePxj5nd/zX1UOw2830dFSrEmH+71CQns414InYh+vY1ca9G5w8MsrHPlyHc4f7Yx+uwz7cG9twLfbh3tiHa7EP98Y+XMtaH1ybux77cB3OHe6PfbgO+3BvbMO1PKkPXe8gz/9HFry8dRtCUFCQq4fgMNnDWEgcSkF000MpeCL24XpK2gDYh5bYh+tx7nBf7MP12Id7YhvugX24J/bhHtiHe2If7kGyD67NXY59uB7nDvfFPlyPfbgntuEePKEPXe8gJ/dmMpkKn4NA4tBVREYj2ca9G7QfDJGb4dxBJI99EMljH0Ty2AeRNK7NieRx7iCSxz6I5OmpD+4gJ6cRJD4pIvXJXCKjkWojfzuR0XHuIJLHPojksQ8ieeyDSBrX5kTyOHcQyWMfRPL01Ad3kJPT6OmTIkRa4qfUieRx7iCSxz6I5LEPInnsg0ga1+ZE8jh3EMljH0Ty9NQHd5CT0wiCUOg8BLLnJSAyEKk28rcTGR3nDiJ57INIHvsgksc+iKRxbU4kj3MHkTz2QSRPT31wBzk5jZ4OpUCkJR7GjUge5w4ieeyDSB77IJLHPoikcW1OJI9zB5E89kEkT099cAc5OY2eDqVApCUexo1IHucOInnsg0ge+yCSxz6IpHFtTiSPcweRPPZBJE9PfXAHOTmNnj4pQqQlfkqdSB7nDiJ57INIHvsgksc+iKRxbU4kj3MHkTz2QSRPT31wBzk5jUmQ+KSI4J6fFCHSkmQbAPsgAucOImvYB5E89kEkj30QSePanEge5w4ieeyDSJ6e+uAOcnIaQRAgCEKhbURGJ9VG/nYio+PcQSSPfRDJYx9E8tgHkTSuzYnkce4gksc+iOTpqQ/uICen0dOhFIi0xMO4Ecnj3EEkj30QyWMfRPLYB5E0rs2J5HHuIJLHPojk6akP7iAnpzGZBJgK/uK7aQhEWpJsA2AfRODcQWQN+yCSxz6I5LEPImlcmxPJ49xBJI99EMnTUx/cQU5OI5hQKATRPU81QKQpqTYA9kEEcO4g+j/27jw+qurw///7TjAJ+yKQEDaxCEpRUARKqyzfomitQqm1ItaIip+6ohRRqrK44U9btSguVRG1qFgr1q22liKgoi0otiqgYFBQQBFICMqW3N8fOIFJ7knuTO7M3OX1/Dzm8fnkTjJzRvP6nDmem7m1oQ/AjD4AM/oAnLE2B8yYOwAz+gDMgtQHG+RImyBdawDIJK5zBpgxdwBm9AGY0QdgRh+AM9bmgBlzB2BGH4BZkPpwtUH+/PPPu37A0047LeXBIFxycizl5FT7xa/+dQjQB5Ll2IZEH/QBMXc4oQ3E0UdN9IE4+qiJPhBHHzXRByTW5ib0ASk6c4dEH0heVPqgDaQiSH242iAfMWKEqwezLEsVFRX1GQ9CxOlaA7ZPrzVQH/SBZJmuc0Yf9AHmjupoAweij0T0gQPRRyL6wIHoIxF9II61eU30gbiozB0SfSB5UemDNpCKIPXhaoO8srIy3ePIqh8e/T1d9quh6nV4J7Vr01yjJ/xRLy/8b8L3dDukQFMvG6EfHdNVOTkxrSrZqOKJD2n9pq1ZGnXtHnx6oe7+03x9+XWZeh7WXv/fVb9Qn+8fktExBCmE+qAP+khWlBbhYe4jjG1I/uyDNoKHPtKDPsKBPtKDPsIhjH1kuw2JPsKCPrzH2jwcwtiG5M8+wtiGRB/0kbyo9BHmNiT6SJcg9VGvS6Pv3LnTq3FkVaOGeXr/o8911W1zHe8/pH1r/e3B8fp47Ub99P/+oONGTdfvHn5FO3fvyfBI3Xn2H8t03V3zdPUFJ+u1x69Wz8Pa6+eXzdRXW7ZndBzxEKrfooI+6MPE1AZ9BEvY2pD83UdUhKENiT7ShT7ow6/oI/vow599+KENiT7ogz5MWJuHo4+wtSH5u48ooQ9/oo/sC0MbEn2kS5D6SHqDvKKiQjfeeKPat2+vJk2a6JNPPpEkXX/99Xr44YdTGsTMmTN1yCGHKD8/X/3799e///3vlB4nVf9880PdfP+Leum1/zref/3Fp+rVNz/QlLv/qv99tF5rP9+svy36nzZvLc/oON2694l/6ZwRP9To0wbo8EPb6Y5JZ6pRfq7+9PySjI7DkiXLqnaTP0Pwitd9ZLsNiT7SwbEN+kj68bLdR9jakHzcB20kjT68Rx/ZQR/04RZ90Icf+aENiT7CsPaQ6CMdWJuHo4+wtSH5uI+QtyHRB324E8U+WHvQh1tB6iPpDfKbb75Zs2fP1m233abc3Nyq4z179tRDDz2U9ADmzp2r8ePHa8qUKXrnnXfUq1cvDRs2TF9++WXSj5UOlmXphB99X6s/+1LPzLhEH/19ul59ZIJ+MuiobA/N0e49e7V85ToN7te96lgsFtOgft31n/+VZHQsQTpTxCte9uH3NiT6SFVUz1KPUh9Ba0Pyfx9hxnsr+nCLPujDj+gje+jD3334pY19z0sfcWFce0j0kSrW5uHvI2htSP7vI+zogz7ciGIfrD3ow60g9ZH0Bvljjz2mP/7xjxo9erRycnKqjvfq1UsrV65MegB33HGHxo4dqzFjxqhHjx66//771ahRI82aNSvpx0qHNq2aqGnjfF1RfILmL/lQIy+7Ry+99p4ev+0C/fCYrtkeXg1fbytXRUWl2rRqmnC8Tatm+vLrsoyOJUgheMXLPvzehkQfqYrqIjxKfQStDcn/fYQZ763owy36oA8/oo/soQ9/9+GXNiT6CPvaQ6KPVLE2D38fQWtD8n8fYUcf9OFGFPtg7UEfbgWpjwbJ/sDnn3+url1r/gJUVlZqz57kPnt/9+7dWrZsmSZNmlR1LBaLaejQoVqypOaf/O/atUu7du2q+rqsLP3/UmPWvnMI/rbwf7rvyQWSpPc/+lz9jjpU5408Tm++szrtYwgqp19826cheMWrPpJtQ6KPIDFNCvThThD6oI3UMXfsl4n3VhJ9BAl97MfaA9XRx370geroY78wrj0k+kgVa/NEYeyDNlIXxblDog/6cCeKfbD2oA+3gtRH0n9B3qNHDy1evLjG8WeeeUZHH310Uo+1efNmVVRUqKCgIOF4QUGBNm7cWOP7p0+frubNm1fdOnbsmNzgU/D1tnLt2VuhlSUbEo5/VLJRHQpbpv35k3VwiybKyYnpqy3bE45/taVMbQ9ultGxWA5niVg+DcErXvWRbBsSfbjhlz6c2qCPcPURtDYkf/dBG+7RR3rQR/aw9tiHPupGH/vRhz/4pQ2JPg4UxrWHRB+pYm2eKIx9BK0Nyd99hL0NiT4k+nAjin2w9tiHPuoWpD6S/gvyyZMnq7i4WJ9//rkqKyv17LPPatWqVXrsscf04osvpmOMVSZNmqTx48dXfV1WVpb2GPbsrdC7H36qwzonxvq9Tm21bsPWtD53KnIPaqDeh3fUwv+s0imDe0nadxbPov98pAt+MTCjY7Gsfbfqx8KMPvahj9o5tRE/HmZR6iNobUj+7oM20os+6kYf2ROluUOij/qgD/rwG7+0IdEHfdCHCWvz8PcRtDYkf/cR9jYk+pDow40o9hGlNiT6qI8g9ZH0X5APHz5cL7zwgv75z3+qcePGmjx5slasWKEXXnhBJ5xwQlKP1bp1a+Xk5GjTpk0Jxzdt2qTCwsIa35+Xl6dmzZol3LzQuGGuenZrr57d2kuSOhcdrJ7d2qtDwb4zQWY8/k/97IRjdM6IH6pLh9Ya+4uBOun4nnr4mUWePL/XLj7r/+mx597Uky++pVUlGzX+1rna8e0ujT71BxkdhxfXGli0aJFOPfVUFRUVybIsPffccwn327atyZMnq127dmrYsKGGDh2qjz/+2MNXkRyv+ki2DYk+3PJDH15d54w+sttH2NqQ/N1HsoLURzbfW0n04RZ9RK8P3lu5F5Y+gtSGRB9B6MMPbUj0EYa1h0Qf6cDaPBx9hK0Nyd99JIs+6MNr9MHanPdWZmHpI1NtJP0X5JJ0/PHH69VXX03lRxPk5uaqT58+mj9/vkaMGCFp3xkN8+fP16WXXlrvx3er9xGd9eID46q+vmX8zyVJT7z4li6Z9ie99Np/NX76U7ry3BN1629O1+rPvtQ5Vz+kt977JGNjTMbIE/to87Zy3fLAS/ry6+06slt7PTPjkox/jFssFlMsFqtxLBk7duxQr169dN5552nkyJE17r/ttts0Y8YMPfroo+rSpYuuv/56DRs2TB9++KHy8/PrNf5UedGHX9qQ6CMdnNqIH08GfTB3eM2vfSTbhhS8PnhvRR9u0Ef90Ef6hKWPoLUh0Yff+/BDGxJ91Idf2pDoIx1Ym4ejj7C1Ifm3jyisPST6oI+6RbUP1h704UaQ1h6Wbdt2UiP7ztKlS7VixQpJ+64/0KdPn1QeRnPnzlVxcbEeeOAB9evXT3fddZeefvpprVy5ssY1CKorKytT8+bNlXfkWFk5uSk9f7Zt/c892R5CysrKylRwcHOVlpYmnLUT//fyo+n/UIP8xgk/s3fnDr0x6cQaP+OGZVmaN29e1f/TtG1bRUVF+s1vfqMJEyZIkkpLS1VQUKDZs2frzDPPrN8LrAcv+qhPGxJ9ZFMqbUj0kQz6iFYf9WlDCk4ffnhvJdFHNtGHmR/6oI3sYu1hRh/eoA/3otYH763C1wdr833owxtB7YO1R+3owxv04V5Q+mDt4Y2gtiGFa+2R9F+Qr1+/XqNGjdIbb7yhFi1aSJK2bdumH/7wh3rqqafUoUOHpB7vl7/8pb766itNnjxZGzduVO/evfXKK6+4miTgb7Vda6CsrCzheF5envLy8pJ6/JKSEm3cuFFDhw6tOta8eXP1799fS5Ysycok4WUftBFedV3njD7qRh/hle65Q/JfH7y3glv00UISfcAZa48WkugDzuijhSTWHqiJtTl9wCyKaw+JPuBOFPtg7QG3grT2SPpzHy644ALt2bNHK1as0JYtW7RlyxatWLFClZWVuuCCC5J9OEnSpZdeqk8//VS7du3S22+/rf79+6f0OPCXmOVwrYHvSujYsaOaN29edZs+fXrSj79x40ZJqvH/NAsKCqruyzSv+6CNcHJsgz6Sfjz6CKd0zx2S//rgvRXcog/6gBlrD/qAGX2w9oAz1ub0AbMorj0k+oA7UeyDtQfcCtLaI+m/IF+4cKHefPNNde/evepY9+7ddffdd+v4449P9uEQYjkxSzmxxFNF7O++XrduXcJHKaRyFpUf0QfccGpDog9AYu6Iow04oY996ANO6GMf+oAT+tiHPlAda3P6gFkU5w6JPuBOFPugDbgVpD6S/gvyjh07as+ePTWOV1RUqKioyJNBIRziIVS/SVKzZs0SbqmEUFhYKEnatGlTwvFNmzZV3Zdp9AE3TG3QB5D+uUPyXx+0AbfoYz/6QHW8t9qPPlAdfexHHzgQa/NE9IEDRXHtIdEH3IliH7QBt4L03irpDfLbb79dl112mZYuXVp1bOnSpRo3bpx+97vfJftwCDErphofpWAl/Rtn1qVLFxUWFmr+/PlVx8rKyvT2229rwIAB3j1REugDbji1QR/0gX3SPXdI/uuDNuAWfexDH3DC2mMf+oAT+tiHPlAda3P6gFkU1x4SfcCdKPZBG3ArSGsPVx+x3rJlS1kHXFV9x44d6t+/vxo02Pfje/fuVYMGDXTeeedpxIgRSQ0A4WV99z/VjyWjvLxcq1evrvq6pKREy5cvV6tWrdSpUyddccUVuummm3TYYYepS5cuuv7661VUVJTR30P6QLKc2ogfTwZ9IIy8mDsk//dBG0gFfdAHzFh70AfM6IM+4Iy1OX3ALCprD4k+kLyo9EEbSEWQ1h6uNsjvuuuupB4UkJyvNVDpcG2n2ixdulRDhgyp+nr8+PGSpOLiYs2ePVsTJ07Ujh07dOGFF2rbtm067rjj9Morryg/P7/+L8Al+kCyTNc5ow/Am7lD8n8ftIFU0AdgxtoDMKMPwBlrc8AsKmsPiT6QvKj0QRtIRZDWHq42yIuLi5N6UEDyJoTBgwfLtm3j/ZZl6YYbbtANN9yQ0hi9QB9IlleLcPpAGHm1yPB7H7SBVNAHYMbaAzCjD8AZa3PALCprD4k+kLyo9EEbSEWQ1h6uNshNdu7cqd27dycca9asWb0GhPCIX1+g+rGooA+YOLURPx4V9AET5g7agBl90AfM6IM+YEYf9AFnrM3pA2ZRnzsk+oBZ1PugDdQmSH0kfWn0HTt26NJLL1Xbtm3VuHFjtWzZMuEGxMXPFKl+CzP6gBumNugDYO6gDdSGPugDZvRBHzCjD/qAM9bm9AGzKLYh0QfciWIftAG3gtRH0hvkEydO1L/+9S/dd999ysvL00MPPaRp06apqKhIjz32WDrGiICyDLcwow+4YWqDPgDaoA3Uhj7oA2b0QR8wow/6gDPW5vQBsyi2IdEH3IliH7QBt4LUR9Ifsf7CCy/oscce0+DBgzVmzBgdf/zx6tq1qzp37qw5c+Zo9OjR6RgnAsira3EECX3ADa+ucxY09AE3mDtoA2b0QR8wow/6gBl90AecsTanD5hFce6Q6APuRLEP2oBbQeoj6b8g37Jliw499FBJ+64rsGXLFknScccdp0WLFnk7OgSaZVlV1xuI3yzLnyF4hT7ghlMb9EEf2Ie5gzZgRh/0ATP6oA+Y0Qd9wBlrc/qAWRTnDok+4E4U+6ANuBWkPpLeID/00ENVUlIiSTr88MP19NNPS9p3BkmLFi08HRyCLUjXGvAKfcCNqF7njD7gBm3QBszogz5gRh/0ATP6oA84Y21OHzCLYhsSfcCdKPZBG3ArSH0kvUE+ZswYvffee5Kka665RjNnzlR+fr6uvPJKXXXVVZ4PEMEVsyzHW5jRB9wwtUEfAHMHbaA29EEfMKMP+oAZfdAHnLE2pw+YRbENiT7gThT7oA24FaQ+kr4G+ZVXXln1fw8dOlQrV67UsmXL1LVrVx111FGeDg5Rq7u9AADRrklEQVTB5vSL79cQvEIfcMM0KdAHwNxBG6gNfdAHzOiDPmBGH/QBZ6zN6QNmUZw7JPqAO1HsgzbgVpD6SHqDvLrOnTurc+fOXowFIRO/vkD1Y1FCH3Di1Eb8eJTQB5wwd9AGzOiDPmBGH/QBM/qgDzhjbb4PfcAJc8c+9AEn9EEbMAtSH642yGfMmOH6AS+//PKUB4Nwcbq2gF+vNVAf9IFkma67QR/0AeYOJ7SBOPqoiT4QRx810Qfi6KMm+oDE2tyEPiBFZ+6Q6APJi0oftIFUBKkPVxvkd955p6sHsyyLEFAlSGeK1Ad9IFlROkudPpAs5o5EtIED0Uci+sCB6CMRfeBA9JGIPhDH2rwm+kBcVOYOiT6QvKj0QRtIRZD6cLVBXlJSku5x1E+nntJBDbM9ClQT++5W/VjY0AeS5dSGDMeCjj6QLOYOH6EP36EPn6ANX6IPn6APX6IPn6AP32Ft7iP04TtRmTsk+kDyotIHbSAVQeqj3tcgB0xiDh+l4NczRYBMcmojfhyIOuYOwIw+ADP6AMzoA3DG2hwwY+4AzOgDMAtSH2yQI22CdK0BIJOidJ0zIFnMHYAZfQBm9AGY0QfgjLU5YMbcAZjRB2AWpD7YIEfa5MT23aofA6LOqY34cSDqmDsAM/oAzOgDMKMPwBlrc8CMuQMwow/ALEh9sEGOtGlgWWpgJZ4ZUmH580wRIJOc2pDoA5CYO4Da0AdgRh+AGX0AzlibA2bMHYAZfQBmQeqDDXKkjWXtu1U/BkSdUxvx40DUMXcAZvQBmNEHYEYfgDPW5oAZcwdgRh+AWZD6SOkP2xcvXqyzzz5bAwYM0Oeffy5Jevzxx/X66697OjgEW45lVV1voOrm1xI8RB+oi2Mb9JHlkcEvmDtoA2b0QR8wow/6gBl90AecsTanD5hFde6Q6AN1i2oftAE3gtRH0hvkf/nLXzRs2DA1bNhQ7777rnbt2iVJKi0t1S233OL5ABFcOTGpQcxKuPn1WgNeoQ+44dQGfdAH9mHuoA2Y0Qd9wIw+6ANm9EEfcMbanD5gFsW5Q6IPuBPFPmgDbgWpj6SHddNNN+n+++/Xgw8+qIMOOqjq+I9+9CO98847ng4OwZYTc76FGX3ADVMb9AHQBm2gNvRBHzCjD/qAGX3QB5yxNqcPmEWxDYk+4E4U+6ANuBWkPpK+BvmqVas0cODAGsebN2+ubdu2eTEmhESOVfOjE/z6UQpeoQ+44dRG/HiY0QfcYO7YjzZQHX3sRx+ojj72ow9URx/70QcOxNo8EX3gQFGcOyT6gDtR7IM24FaQ+kh6376wsFCrV6+ucfz111/XoYce6smgEA4xy/kWZvQBN0xt0AdAGweiDVRHH/vRB6qjj/3oA9XRx370gQOxNk9EHzhQFNuQ6APuRLEP2oBbQeoj6Q3ysWPHaty4cXr77bdlWZa++OILzZkzRxMmTNBFF12UjjEioHJiluMtzOgDbpjaoA+AuYM2UBv6oA+Y0Qd9wIw+6APOWJvTB8yi2IZEH3Anin3QBtwKUh9Jf8T6Nddco8rKSv34xz/WN998o4EDByovL08TJkzQZZddlo4xIqCcfvH9GoJX6ANumCYF+gCYO2gDtaEP+oAZfdAHzOiDPuCMtTl9wCyKc4dEH3Anin3QBtwKUh9Jb5BblqVrr71WV111lVavXq3y8nL16NFDTZo0Scf4EGCxmJQTq3kszOgDbji1ET8eZvQBN5g7aANm9EEfMKMP+oAZfdAHnLE2pw+YRXHukOgD7kSxD9qAW0HqI+kN8rjc3Fz16NHDy7EgZGKyFJNV41gU0Adq49RG/HgU0Adqw9xBGzCjD/qAGX3QB8zogz7gjLU5fcAsynOHRB+oXZT7oA3UJUh9JL1BPmTIEFmW+cX861//qteAEB45DmeKOJ2ZGyb0ATec2ogfDzP6gBvMHTXRBuLooyb6QBx91EQfiKOPmugDEmtzE/qAFM25Q6IPuBPFPmgDbgWpj6Q3yHv37p3w9Z49e7R8+XK9//77Ki4u9mpcCIEgXWvAK/QBN6J6nTP6gBvMHbQBM/qgD5jRB33AjD7oA85Ym+9DH3ASxblDog+4E8U+aANuBamPpDfI77zzTsfjU6dOVXl5eb0HhPDIsRxCqOUsozCgD7jh1Eb8eJjRB9xg7tiPNlAdfexHH6iOPvajD1RHH/vRBw7E2jwRfeBAUZw7JPqAO1HsgzbgVpD68OwP288++2zNmjXLq4fLqB8eUagnrz5RHz5wlrb+eax+0rez8XvvGHuctv55rH79k54ZHGHyHnx6oY46bbIKf3SFhp57u5Z9sDbjY8ixLMdbFNGHv2S7D1Mb9BEsYWxD8m8fURTUNiT6SBf62I8+/Ic+/IM+/CXbbUj0cSD68Jds98HaPFFQ+whjG5J/+4gq+vAX+vCPoLYh0Ue6BKkPzzbIlyxZovz8fK8eLqMa5TXQ+59u0VUPv1nr953S7xAd262tvtiyI0MjS82z/1im6+6ap6svOFmvPX61eh7WXj+/bKa+2rI9o+OwDLcoog//8EMfpjboI1jC1obk7z6iKKhtSPSRLvSxH334C334C334hx/akOjjQPThH37og7V5oqD2EbY2JH/3EVX04R/04S9BbUOij3QJUh9Jf8T6yJEjE762bVsbNmzQ0qVLdf311yf1WIsWLdLtt9+uZcuWacOGDZo3b55GjBiR7JDq7Z/L1+ufy9fX+j3tWjXS/3feAJ1+0yuaO2lYhkaWmnuf+JfOGfFDjT5tgCTpjkln6h9vfKA/Pb9EV557YsbGEXM4MyTm0zNFvEIf9OGGUxvx42EWtj7C1obk3z5oIzn0kR70kR1hmzsk+kgX+qAPP/JDGxJ9SPThR37og7X5PkHvI2xtSP7tI+xtSPRBH+5EsY+wtSHRR7oEqY+k/4K8efPmCbdWrVpp8ODBevnllzVlypSkHmvHjh3q1auXZs6cmewwMsqypPsvG6K7n/+vVq7fmu3h1Gr3nr1avnKdBvfrXnUsFotpUL/u+s//SjI6lljMcryFGX3QhxumNujDvSD0EaQ2JP/3EWZetiHRRzrQR/ZEbe6Q6CNV9EEffuOXNvY9L33Qh7/4pQ/W5tHoI0htSP7vI+zow9/oI3ui1oZEH6kKUh9J/QV5RUWFxowZoyOPPFItW7as95OffPLJOvnkk+v9OOl2xfBe2ltRqQde/iDbQ6nT19vKVVFRqTatmiYcb9OqmT5euymjY3G6toBfrzXgBfqgD7dM192gD/eC0EeQ2pD83QdtJIc+vEcf2RHFuUOij1TRR/3Qh/f80oZEH/VFH97zSx+szaPRR5DakPzdR5jbkOgjCOgjO6LYhkQfqQpSH0n9BXlOTo5OPPFEbdu2LU3Dqd2uXbtUVlaWcEu3Xoe21v+d0lOXzFyY9ucKm5hlOd6SMXXqVFmWlXA7/PDD0zTi+qEPuGVqI5k+gtSGFL0+aCN1zB2ZRx/BQR+ZxXurYKGPzKKPYKGPzKKP4GBtnnmsPYIjanOHRB9wL2p9RK0NiT7qI0h9JH0N8p49e+qTTz5Rly5dPB9MXaZPn65p06Zl9DkHHF6oNs0a6n/3jao61iAnppuK++uiU3qq1yVPZXQ8dTm4RRPl5MT01ZbtCce/2lKmtgc3y+hYYqp5BkbSn+kv6fvf/77++c9/Vn3doEHSv7YZQx/04YZTGzIcq02Q2pCi1UfQ2pD83QdzR3rRR93oI3uiNHdI9FEf9JFZ9FE3v7Qh0Uem0Ufd/NIHa/Pw9xG0NiR/9xH2uUOiD/pwJ4p9RKkNiT7qI0h9JP2IN910kyZMmKAbb7xRffr0UePGjRPub9Ysff+gJ02apPHjx1d9XVZWpo4dO6bt+SRp7qKPtfB/nycce+a6k/X0oo81Z8FHaX3uVOQe1EC9D++ohf9ZpVMG95IkVVZWatF/PtIFvxiY0bE4nRmS7Jki0r5f/MLCQq+GlVb0QR9umM6aSraPILUhRauPoLUh+bsP5o70vomlj7rRR/ZEae6Q6KM+6IM+/MYvbUj0QR/0YcLaPPx9BK0Nyd99hH3ukOiDPtyJYh9RakOij/oIUh+uN8hvuOEG/eY3v9FPfvITSdJpp50m64AXZdu2LMtSRUWF96P8Tl5envLy8jx/3Mb5DdSlcH/Ands2Vc9DWmlb+S6t37xDW8t3JXz/3r2V2rT1W63+otTzsXjh4rP+ny6e9riOPqKTjvn+IbrvyQXa8e0ujT71BxkdR23XGqj+MRi1/bv9+OOPVVRUpPz8fA0YMEDTp09Xp06d0jPoFNHHfvRRt7quc+a2jyC0IYW3j7C1Ifm3D+aO9LUh0Ydb9JFZfuiD91bu0Udm0cd+fu/DD21I9EEf9GHC2jwcfYStDcm/fYR17pDoI44+3IlSH2FtQ6KPdAlSH643yKdNm6Zf//rXWrBggacD8IPeh7bRi9N+WvX1LecOkCQ98dpHgbzGwMgT+2jztnLd8sBL+vLr7TqyW3s9M+OSzH+MWy1nilQ/w2fKlCmaOnVqjcfo37+/Zs+ere7du2vDhg2aNm2ajj/+eL3//vtq2rRp2saeLPoIDj/0UddZ6m76CEobUnj7CFsbkn/7YO4IHvpID/oIB/pID/oIh7D14Yc2JPoIC/rwHmvzcAhbG5J/+wjr3CHRR5DQR2aFtQ2JPtIlSH243iC3bVuSNGjQIM+evLy8XKtXr676uqSkRMuXL1erVq0yeqbMGx9uUMtfPOj6+/14fYHqLjxjkC48w7t/V6mwrH236sckad26dQkfu2E6S+Tkk0+u+r+POuoo9e/fX507d9bTTz+t888/3/Mxp4o+9qOPujm1ET8uuesjKG1I4e0jjG1I/uyDuSM59JE+9JE5YZ07JPpIF/qoH/pIn2y3IdFHfdFH+mS7D9bm9eeHPsLYhuTPPsI6d0j0EUcf7kSpj7C2IdFHugSpj6SuQW45vWush6VLl2rIkCFVX8evI1BcXKzZs2d7+lzIvJjDRynEzxRp1qxZStelaNGihbp165bw/0D9gj7gllMb8eNSan34uQ2JPuAec0f90Ud40Uf90Ea40Uf90Ee40Uf90Ed4sTavP/oIr6jNHRJ9wL2o9UEbSEaQ+khqg7xbt251xrBlyxbXjzd48OCqM1AQPrV9lEKqysvLtWbNGv3qV7+q1+OkA33Arbo+xi0Vfm5Dog+4x9xRUzJtSPQRZvRRE3MH4uijJvpAHH3URB+QWJub0Aek6M0dEn3Avaj1QRtIRpD6SGqDfNq0aWrevLmnA0B45TicKeJ0Zm5tJkyYoFNPPVWdO3fWF198oSlTpignJ0ejRo3ycqieoA+45dRG/LhbQWpDog+4x9wBmNEHYEYfgBl9AM5YmwNmUZs7JPqAe1HrgzaQjCD1kdQG+Zlnnqm2bdt6OgCEV8yqeWZILMkTRdavX69Ro0bp66+/Vps2bXTcccfprbfeUps2bTwcqTfoA245tRE/7laQ2pDoA+4xdwBm9AGY0QdgRh+AM9bmgFnU5g6JPuBe1PqgDSQjSH243iBPxzUyEW77Qqh5LBlPPfWUdwNKI/pAMpzaiB93KyhtSPSB5DB3AGb0AZjRB2BGH4Az1uaAWZTmDok+kJwo9UEbSFaQ+nC9Qc41AZCsdFxrwK/oA8lIx3XO/Iw+kAzmDsCMPgAz+gDM6ANwxtocMIvS3CHRB5ITpT5oA8kKUh+uN8grKyvTOQ6EUI4crjUgf4ZQX/SBZDi1ET8eRvSBZDB3AGb0AZjRB2BGH4Az1uaAWZTmDok+kJwo9UEbSFaQ+kjqGuRAMixr3636MSDqnNqIHweijrkDMKMPwIw+ADP6AJyxNgfMmDsAM/oAzILUBxvkSBvL4aMUuGYF4NxG/DgQdcwdgBl9AGb0AZjRB+CMtTlgxtwBmNEHYBakPtggR9pY392qHwOizqkNGY4BUcPcAZjRB2BGH4AZfQDOWJsDZswdgBl9AGZB6oMNcqSNZVk1zgzx65kiQCY5tRE/DkQdcwdgRh+AGX0AZvQBOGNtDpgxdwBm9AGYBakPNsiRNjFr3636MSDqnNqIHweijrkDMKMPwIw+ADP6AJyxNgfMmDsAM/oAzILUBxvkSJsgnSkCZBJnqQNmzB2AGX0AZvQBmNEH4Iy1OWDG3AGY0QdgFqQ+2CBH2ljWvlv1Y0DUObURPw5EHXMHYEYfgBl9AGb0AThjbQ6YMXcAZvQBmAWpDzbIkTZBCgHIJBbhgBlzB2BGH4AZfQBm9AE4Y20OmDF3AGb0AZgFqQ82yJE2MctSrNpvfvWvgShyaiN+HIg65g7AjD4AM/oAzOgDcMbaHDBj7gDM6AMwC1IfbJAjbYJ0pgiQSZylDpgxdwBm9AGY0QdgRh+AM9bmgBlzB2BGH4BZkPpggxxpE6QzRYBM4ix1wIy5AzCjD8CMPgAz+gCcsTYHzJg7ADP6AMyC1EegN8ht2973v/fszPJIUldWVpbtIaRs+3djj/97qM767lb9GDKDPrInlTZkOIb0oI/sYe7wP/rIHvrwN9rILvrwN/rILvrwN/rIrtr6YG2effSRPcwd/kcf2UMf/kYb2RWmPgK9Qb59+3ZJ0u5/XJ3lkaSu4OBx2R5CvW3fvl3NmzevcdyyLFnVzgyp/jXShz6yL5k24seRGfSRfcwd/kUf2Ucf/kQb/kAf/kQf/kAf/kQf/uDUB2vz7KOP7GPu8C/6yD768Cfa8Icw9BHoDfKioiKtW7dOTZs2Tcs/4LKyMnXs2FHr1q1Ts2bNPH/8dEv3+G3b1vbt21VUVOR4f8zad6t+DJlBH7VL5/hTaSN+HJlBH2bMHUhnH0FuQ6KPqGPuqB19RBt91I4+oo0+apfNPlibZx99mDF3gD7M6CPaaKN29OFeoDfIY7GYOnTokPbnadasWSBDiEvn+J3OEIkL0pkiYUQf7qRr/Mm2ET+OzKCPujF3RFcm+ghyGxJ9RBVzhzv0EU304Q59RBN9uJONPlibZx991I25I7roo270EU204Q591C3QG+TwN8vhTBGfdgBklFMb8eNA1DF3AGb0AZjRB2BGH4Az1uaAGXMHYEYfgFmQ+mCDHOljfXerfgyIOqc2ZDgGRA1zB2BGH4AZfQBm9AE4Y20OmDF3AGb0AZgFqA82yGuRl5enKVOmKC8vL9tDSUm2xx+zLMWqnRpS/WsEV7Z/v+orm+N3aiN+HOEQ5D6yPXbmjnDL9u9XfWV7/PQRbtn+/aqvbI+fPsIt279f9ZXt8dNHuGX796u+WJsjnYLcR7bHztwRftn+HauPbI+dPsIt279f9ZXt8QepD8u2bTvbg0C4lJWVqXnz5vpvySY1bZp4jYPt28t0VJcClZaWBvr6DUAqamtDog9EG3MHYEYfgBl9AGb0AThjbQ6YMXcAZvQBmAWxD/6CHGkTk8OZIn79LAUgg5zaiB8Hoo65AzCjD8CMPgAz+gCcsTYHzJg7ADP6AMyC1Acb5EibAF1qAMgoLnMGmDF3AGb0AZjRB2BGH4Az1uaAGXMHYEYfgFmQ+mCDHGljWZasameKVP8aiCKnNuLHgahj7gDM6AMwow/AjD4AZ6zNATPmDsCMPgCzIPXBBjnSJmbtu1U/BkSdUxvx40DUMXcAZvQBmNEHYEYfgDPW5oAZcwdgRh+AWZD6iGV7AH42c+ZMHXLIIcrPz1f//v3173//O9tDcmXRokU69dRTVVRUJMuy9Nxzz2VlHDHLcrwh+ILahuSPPkxt0Ec40Ef90Ea4BbUPP7Qh0UfY0Uf90Ee40Uf90Ed4BbUNyR99sDYPN/qoH9oIt6D24Yc2JPoIO/qonyD1wQa5wdy5czV+/HhNmTJF77zzjnr16qVhw4bpyy+/zPbQ6rRjxw716tVLM2fOzOo4LMv5hmALchuSP/owtUEfwUcf9Ucb4RXkPvzQhkQfYUYf9Ucf4UUf9Ucf4RTkNiR/9MHaPLzoo/5oI7yC3Icf2pDoI8zoo/6C1Idl27ad7UH4Uf/+/dW3b1/dc889kqTKykp17NhRl112ma655posj849y7I0b948jRgxImPPWVZWpubNm2v1+s1q2qxZwn3by8rUtUNrlZaWqlm1+xAMYWlDynwftbUh0UcY0EfqmDvCLyx98N4K6UAfqaOP8KOP1NFHuIWlDclfaw+JPsKAPlLH3BF+YemD91ZIB/pIXRD74C/IHezevVvLli3T0KFDq47FYjENHTpUS5YsyeLIgsWyLMcbgos2vGFqgz6CjT68QRvhRB/eoI9wog9v0Ec40Yc36CN8aMMbrM3DiT68QRvhRB/eoI9wog9vBKkPNsgdbN68WRUVFSooKEg4XlBQoI0bN2ZpVMETs5xvCC7a8IapDfoINvrwBm2EE314gz7CiT68QR/hRB/eoI/woQ1vsDYPJ/rwBm2EE314gz7CiT68EaQ+GmR7AAgvy7IUq3ZmiF/PFAEyyamN+HEg6pg7ADP6AMzoAzCjD8AZa3PAjLkDMKMPwCxIfbBB7qB169bKycnRpk2bEo5v2rRJhYWFWRpV8FjWvlv1Ywgu2vCGUxvx4wgu+vAGc0c40Yc36COc6MMb9BFO9OEN+ggf2vAGa/Nwog9vMHeEE314gz7CiT68EaQ++Ih1B7m5uerTp4/mz59fdayyslLz58/XgAEDsjiyYLEM/4Pgog1vmNqgj2CjD2/QRjjRhzfoI5zowxv0EU704Q36CB/a8AZr83CiD2/QRjjRhzfoI5zowxtB6oMNcoPx48frwQcf1KOPPqoVK1booosu0o4dOzRmzJhsD61O5eXlWr58uZYvXy5JKikp0fLly/XZZ59ldBxeXWtg5syZOuSQQ5Sfn6/+/fvr3//+t/eDhWtBbkPyRx9eXueMPvyFPurPy+vU0Ie/BLkPP7Qh0UeY0Uf90Ud40Uf9sfYIpyC3IfmjD9bm4UUf9cd7q/AKch9+aEOijzCjj/oLVB82jO6++267U6dOdm5urt2vXz/7rbfeyvaQXFmwYIEtqcatuLg4I89fWlpqS7I//3KbvX1nZcLt8y+32ZLs0tJSV4/11FNP2bm5ufasWbPsDz74wB47dqzdokULe9OmTWl+FahNUNuw7ez2UVsb9BEe9JEaL+cO26YPvwpqH2F6b2Xb9OFX9JEa+ogG+kgNa/PwC2obtu3ftQd9hAd9pIb3VtEQ1D7C9N7KtunDr+gjNUHsw7Jt267PBjtQXVlZmZo3b64NX21Ts2bNatzXrk0LlZaW1rjPSf/+/dW3b1/dc889kvZ9pEXHjh112WWX6ZprrknL+IF0qa2N+P30gajycu6Q6APhQh+AGX0AZqzNAWeszQEz3lsBZvQBmAWxDz5iHeljGW4u7d69W8uWLdPQoUOrjsViMQ0dOlRLlizxdqxAJpnaoA+g3m1I9IEQow/AjD4AM9YegDPW5oAZ760AM/oAzALURwPPHgmopnz7dsUsq8Yxad8ZIwfKy8tTXl5ewrHNmzeroqJCBQUFCccLCgq0cuXKNIwYyAynNuLHJfpAtNV37pDoA+FFH4AZfQBmrM0BZ6zNATPeWwFm9AGYBakPNsjhudzcXBUWFuqwLh0d72/SpIk6dky8b8qUKZo6dWoGRgdkT11tSPSB6GLuAMzoAzCjD8CMPgBnrM0BM+YOwIw+ALMg9sEGOTyXn5+vkpIS7d692/F+27ZlVTuDxOkskdatWysnJ0ebNm1KOL5p0yYVFhZ6N2AgQ+pqQ6IPRJdXc4dEHwgf+gDM6AMwY20OOGNtDpjx3gowow/ALIh9sEGOtMjPz1d+fn69HiM3N1d9+vTR/PnzNWLECElSZWWl5s+fr0svvdSDUQKZ50UbEn0gnOgDMKMPwIw+ADPW5oAz5g7AjD4AM/oAzILWBxvk8LXx48eruLhYxx57rPr166e77rpLO3bs0JgxY7I9NCDr6AMwow/AjD4AM/oAnNEGYEYfgBl9AGb0AZhlog82yOFrv/zlL/XVV19p8uTJ2rhxo3r37q1XXnlFBQUF2R4akHX0AZjRB2BGH4AZfQDOaAMwow/AjD4AM/oAzDLSh41aFRcX28OHD6/6etCgQfa4ceMyPo4FCxbYkuytW7cav0eSPW/ePNePOWXKFLtXr171GldJSYktyX733Xfr9TgIJvqoHX1EG33Ujj6ijT5qRx/RRRu1o41oo4/a0Ue00Uft6CPa6KN29BFt9FE7+ogu2qhdWNqIebfVnjnnnnuuLMuSZVnKzc1V165ddcMNN2jv3r1pf+5nn31WN954o6vvfe2112RZlrZt25beQQEHoA/AjD4AM/oAnNEGYEYfgBl9AGb0AZjRB+CMNuC1wH7E+kknnaRHHnlEu3bt0ssvv6xLLrlEBx10kCZNmlTje3fv3q3c3FxPnrdVq1aePA6QTvQBmNEHYEYfgDPaAMzoAzCjD8CMPgAz+gCc0Qa8FMi/IJekvLw8FRYWqnPnzrrooos0dOhQPf/885L2nUkyYsQI3XzzzSoqKlL37t0lSevWrdMZZ5yhFi1aqFWrVho+fLjWrl1b9ZgVFRUaP368WrRooYMPPlgTJ06UbdsJzzt48GBdccUVVV/v2rVLV199tTp27Ki8vDx17dpVDz/8sNauXashQ4ZIklq2bCnLsnTuuedKkiorKzV9+nR16dJFDRs2VK9evfTMM88kPM/LL7+sbt26qWHDhhoyZEjCON26+uqr1a1bNzVq1EiHHnqorr/+eu3Zs6fG9z3wwAPq2LGjGjVqpDPOOEOlpaUJ9z/00EM64ogjlJ+fr8MPP1z33ntv0mNBZtFH3egjuuijbvQRXfRRN/qIJtqoG21EF33UjT6iiz7qRh/RRR91o4/ooo+60Uc00UbdaMO9wG6QV9ewYUPt3r276uv58+dr1apVevXVV/Xiiy9qz549GjZsmJo2barFixfrjTfeUJMmTXTSSSdV/dzvf/97zZ49W7NmzdLrr7+uLVu2aN68ebU+7znnnKMnn3xSM2bM0IoVK/TAAw+oSZMm6tixo/7yl79IklatWqUNGzboD3/4gyRp+vTpeuyxx3T//ffrgw8+0JVXXqmzzz5bCxculLQv2JEjR+rUU0/V8uXLdcEFF+iaa65J+p9J06ZNNXv2bH344Yf6wx/+oAcffFB33nlnwvesXr1aTz/9tF544QW98sorevfdd3XxxRdX3T9nzhxNnjxZN998s1asWKFbbrlF119/vR599NGkx4PsoY+a6ANx9FETfSCOPmqiD0i04YQ2EEcfNdEH4uijJvpAHH3URB+Io4+a6AMSbTihjSRk6+Ln9VFcXGwPHz7ctm3brqystF999VU7Ly/PnjBhQtX9BQUF9q5du6p+5vHHH7e7d+9uV1ZWVh3btWuX3bBhQ/vvf/+7bdu23a5dO/u2226run/Pnj12hw4dqp7Ltm170KBB9rhx42zbtu1Vq1bZkuxXX33VcZwLFiywJdlbt26tOrZz5067UaNG9ptvvpnwveeff749atQo27Zte9KkSXaPHj0S7r/66qtrPFZ1kux58+YZ77/99tvtPn36VH09ZcoUOycnx16/fn3Vsb/97W92LBazN2zYYNu2bX/ve9+zn3jiiYTHufHGG+0BAwbYtm3bJSUltiT73XffNT4vMos+nNEHbJs+TOgDtk0fJvQB2nBGG7Bt+jChD9g2fZjQB2ybPkzoA7ZNHyb0AdpwRhupC+w1yF988UU1adJEe/bsUWVlpc466yxNnTq16v4jjzwy4foC7733nlavXq2mTZsmPM7OnTu1Zs0alZaWasOGDerfv3/VfQ0aNNCxxx5b4+MU4pYvX66cnBwNGjTI9bhXr16tb775RieccELC8d27d+voo4+WJK1YsSJhHJI0YMAA188RN3fuXM2YMUNr1qxReXm59u7dq2bNmiV8T6dOndS+ffuE56msrNSqVavUtGlTrVmzRueff77Gjh1b9T179+5V8+bNkx4PMoc+6kYf0UUfdaOP6KKPutFHNNFG3WgjuuijbvQRXfRRN/qILvqoG31EF33UjT6iiTbqRhvuBXaDfMiQIbrvvvuUm5uroqIiNWiQ+FIaN26c8HV5ebn69OmjOXPm1HisNm3apDSGhg0bJv0z5eXlkqSXXnop4RdQ2nf9BK8sWbJEo0eP1rRp0zRs2DA1b95cTz31lH7/+98nPdYHH3ywRpg5OTmejRXeo4/a0Ue00Uft6CPa6KN29BFdtFE72og2+qgdfUQbfdSOPqKNPmpHH9FGH7Wjj+iijdrRRnICu0HeuHFjde3a1fX3H3PMMZo7d67atm1b42yJuHbt2untt9/WwIEDJe07I2LZsmU65phjHL//yCOPVGVlpRYuXKihQ4fWuD9+pkpFRUXVsR49eigvL0+fffaZ8QyTI444Qs8//3zCsbfeeqvuF3mAN998U507d9a1115bdezTTz+t8X2fffaZvvjiCxUVFVU9TywWU/fu3VVQUKCioiJ98sknGj16dFLPj+yij9rRR7TRR+3oI9roo3b0EV20UTvaiDb6qB19RBt91I4+oo0+akcf0UYftaOP6KKN2tFGcmLZHkCmjB49Wq1bt9bw4cO1ePFilZSU6LXXXtPll1+u9evXS5LGjRunW2+9Vc8995xWrlypiy++WNu2bTM+5iGHHKLi4mKdd95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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import roc_curve, auc\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Assume 'model' is your trained Keras model and you have a test set 'X_test_rnn'\n",
"# Predict probabilities for the positive class (damage)\n",
"y_pred_probs = model.predict(X_test_rnn).ravel()\n",
"\n",
"# Compute ROC curve and ROC area for each class\n",
"fpr, tpr, thresholds = roc_curve(y_test_binary, y_pred_probs)\n",
"roc_auc = auc(fpr, tpr)\n",
"\n",
"# Plot the ROC curve\n",
"plt.figure()\n",
"plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
"plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
"plt.xlim([0.0, 1.0])\n",
"plt.ylim([0.0, 1.05])\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('Receiver Operating Characteristic')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 489
},
"id": "W3U2HZgXgEB6",
"outputId": "9bf5d572-df29-4dcf-eafe-83112d1390bb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1/1 [==============================] - 0s 41ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"##using a bidirectional LSTM model\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Bidirectional, LSTM, Dense, Dropout\n",
"\n",
"# Define the bidirectional LSTM model\n",
"bidirectional_lstm_model = Sequential([\n",
" Bidirectional(LSTM(50, return_sequences=True), input_shape=(X_train.shape[1], 1)),\n",
" Dropout(0.5),\n",
" Bidirectional(LSTM(50)),\n",
" Dropout(0.5),\n",
" Dense(1, activation='sigmoid')\n",
"])\n",
"\n",
"# Compile the model\n",
"bidirectional_lstm_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
"\n",
"# Train the model\n",
"history = bidirectional_lstm_model.fit(\n",
" X_train_rnn, y_train_class,\n",
" epochs=100,\n",
" validation_split=0.2\n",
")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "19NBDKpugD7E",
"outputId": "245bb06a-c556-40cf-ff25-0dfb0fe5c2fb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/100\n",
"2/2 [==============================] - 18s 2s/step - loss: 0.6944 - accuracy: 0.4464 - val_loss: 0.6950 - val_accuracy: 0.5000\n",
"Epoch 2/100\n",
"2/2 [==============================] - 0s 101ms/step - loss: 0.6949 - accuracy: 0.5179 - val_loss: 0.6945 - val_accuracy: 0.4286\n",
"Epoch 3/100\n",
"2/2 [==============================] - 0s 92ms/step - loss: 0.6925 - accuracy: 0.5357 - val_loss: 0.6938 - val_accuracy: 0.4286\n",
"Epoch 4/100\n",
"2/2 [==============================] - 0s 85ms/step - loss: 0.6836 - accuracy: 0.5714 - val_loss: 0.6931 - val_accuracy: 0.5000\n",
"Epoch 5/100\n",
"2/2 [==============================] - 0s 102ms/step - loss: 0.6810 - accuracy: 0.5714 - val_loss: 0.6925 - val_accuracy: 0.4286\n",
"Epoch 6/100\n",
"2/2 [==============================] - 0s 87ms/step - loss: 0.6817 - accuracy: 0.5714 - val_loss: 0.6922 - val_accuracy: 0.4286\n",
"Epoch 7/100\n",
"2/2 [==============================] - 0s 104ms/step - loss: 0.6809 - accuracy: 0.5893 - val_loss: 0.6915 - val_accuracy: 0.5714\n",
"Epoch 8/100\n",
"2/2 [==============================] - 0s 103ms/step - loss: 0.6761 - accuracy: 0.5536 - val_loss: 0.6911 - val_accuracy: 0.5714\n",
"Epoch 9/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6667 - accuracy: 0.5714 - val_loss: 0.6906 - val_accuracy: 0.5714\n",
"Epoch 10/100\n",
"2/2 [==============================] - 0s 65ms/step - loss: 0.6743 - accuracy: 0.5714 - val_loss: 0.6904 - val_accuracy: 0.5714\n",
"Epoch 11/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6695 - accuracy: 0.5714 - val_loss: 0.6900 - val_accuracy: 0.6429\n",
"Epoch 12/100\n",
"2/2 [==============================] - 0s 51ms/step - loss: 0.6673 - accuracy: 0.5357 - val_loss: 0.6898 - val_accuracy: 0.6429\n",
"Epoch 13/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.6723 - accuracy: 0.5536 - val_loss: 0.6897 - val_accuracy: 0.6429\n",
"Epoch 14/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.6741 - accuracy: 0.5536 - val_loss: 0.6895 - val_accuracy: 0.5714\n",
"Epoch 15/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.6702 - accuracy: 0.5357 - val_loss: 0.6892 - val_accuracy: 0.5714\n",
"Epoch 16/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6682 - accuracy: 0.5536 - val_loss: 0.6889 - val_accuracy: 0.5714\n",
"Epoch 17/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6707 - accuracy: 0.5536 - val_loss: 0.6888 - val_accuracy: 0.5714\n",
"Epoch 18/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.6613 - accuracy: 0.5536 - val_loss: 0.6883 - val_accuracy: 0.5000\n",
"Epoch 19/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.6681 - accuracy: 0.5536 - val_loss: 0.6879 - val_accuracy: 0.5000\n",
"Epoch 20/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.6586 - accuracy: 0.5536 - val_loss: 0.6879 - val_accuracy: 0.5000\n",
"Epoch 21/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.6628 - accuracy: 0.5357 - val_loss: 0.6882 - val_accuracy: 0.5000\n",
"Epoch 22/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.6540 - accuracy: 0.5536 - val_loss: 0.6883 - val_accuracy: 0.5714\n",
"Epoch 23/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.6573 - accuracy: 0.5893 - val_loss: 0.6883 - val_accuracy: 0.5714\n",
"Epoch 24/100\n",
"2/2 [==============================] - 0s 63ms/step - loss: 0.6576 - accuracy: 0.5893 - val_loss: 0.6886 - val_accuracy: 0.5714\n",
"Epoch 25/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.6527 - accuracy: 0.5714 - val_loss: 0.6895 - val_accuracy: 0.5714\n",
"Epoch 26/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.6688 - accuracy: 0.5357 - val_loss: 0.6907 - val_accuracy: 0.6429\n",
"Epoch 27/100\n",
"2/2 [==============================] - 0s 51ms/step - loss: 0.6636 - accuracy: 0.5536 - val_loss: 0.6917 - val_accuracy: 0.5714\n",
"Epoch 28/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.6566 - accuracy: 0.5893 - val_loss: 0.6930 - val_accuracy: 0.5714\n",
"Epoch 29/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6501 - accuracy: 0.5893 - val_loss: 0.6939 - val_accuracy: 0.6429\n",
"Epoch 30/100\n",
"2/2 [==============================] - 0s 71ms/step - loss: 0.6686 - accuracy: 0.5714 - val_loss: 0.6960 - val_accuracy: 0.5714\n",
"Epoch 31/100\n",
"2/2 [==============================] - 0s 51ms/step - loss: 0.6645 - accuracy: 0.5714 - val_loss: 0.6983 - val_accuracy: 0.5714\n",
"Epoch 32/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.6609 - accuracy: 0.5357 - val_loss: 0.7003 - val_accuracy: 0.5714\n",
"Epoch 33/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6574 - accuracy: 0.5714 - val_loss: 0.7022 - val_accuracy: 0.5714\n",
"Epoch 34/100\n",
"2/2 [==============================] - 0s 50ms/step - loss: 0.6582 - accuracy: 0.6250 - val_loss: 0.7048 - val_accuracy: 0.5714\n",
"Epoch 35/100\n",
"2/2 [==============================] - 0s 72ms/step - loss: 0.6560 - accuracy: 0.6071 - val_loss: 0.7065 - val_accuracy: 0.5714\n",
"Epoch 36/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.6355 - accuracy: 0.6071 - val_loss: 0.7087 - val_accuracy: 0.5714\n",
"Epoch 37/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.6519 - accuracy: 0.6250 - val_loss: 0.7105 - val_accuracy: 0.5714\n",
"Epoch 38/100\n",
"2/2 [==============================] - 0s 50ms/step - loss: 0.6461 - accuracy: 0.5893 - val_loss: 0.7118 - val_accuracy: 0.5714\n",
"Epoch 39/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.6440 - accuracy: 0.6071 - val_loss: 0.7137 - val_accuracy: 0.5714\n",
"Epoch 40/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6644 - accuracy: 0.5714 - val_loss: 0.7152 - val_accuracy: 0.5714\n",
"Epoch 41/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6309 - accuracy: 0.5893 - val_loss: 0.7183 - val_accuracy: 0.5714\n",
"Epoch 42/100\n",
"2/2 [==============================] - 0s 63ms/step - loss: 0.6431 - accuracy: 0.6071 - val_loss: 0.7210 - val_accuracy: 0.5714\n",
"Epoch 43/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.6481 - accuracy: 0.6071 - val_loss: 0.7253 - val_accuracy: 0.5714\n",
"Epoch 44/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.6402 - accuracy: 0.6250 - val_loss: 0.7302 - val_accuracy: 0.5714\n",
"Epoch 45/100\n",
"2/2 [==============================] - 0s 62ms/step - loss: 0.6259 - accuracy: 0.6071 - val_loss: 0.7334 - val_accuracy: 0.5714\n",
"Epoch 46/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.6368 - accuracy: 0.6071 - val_loss: 0.7397 - val_accuracy: 0.5714\n",
"Epoch 47/100\n",
"2/2 [==============================] - 0s 70ms/step - loss: 0.6210 - accuracy: 0.6250 - val_loss: 0.7443 - val_accuracy: 0.5714\n",
"Epoch 48/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.6265 - accuracy: 0.6071 - val_loss: 0.7507 - val_accuracy: 0.5000\n",
"Epoch 49/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6126 - accuracy: 0.6071 - val_loss: 0.7539 - val_accuracy: 0.5000\n",
"Epoch 50/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.6327 - accuracy: 0.5893 - val_loss: 0.7540 - val_accuracy: 0.4286\n",
"Epoch 51/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.6057 - accuracy: 0.6786 - val_loss: 0.7517 - val_accuracy: 0.5000\n",
"Epoch 52/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.6366 - accuracy: 0.6429 - val_loss: 0.7539 - val_accuracy: 0.5000\n",
"Epoch 53/100\n",
"2/2 [==============================] - 0s 75ms/step - loss: 0.6181 - accuracy: 0.6250 - val_loss: 0.7673 - val_accuracy: 0.4286\n",
"Epoch 54/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.6189 - accuracy: 0.6607 - val_loss: 0.7778 - val_accuracy: 0.4286\n",
"Epoch 55/100\n",
"2/2 [==============================] - 0s 55ms/step - loss: 0.6234 - accuracy: 0.5893 - val_loss: 0.7875 - val_accuracy: 0.4286\n",
"Epoch 56/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.6230 - accuracy: 0.5893 - val_loss: 0.7858 - val_accuracy: 0.4286\n",
"Epoch 57/100\n",
"2/2 [==============================] - 0s 54ms/step - loss: 0.5975 - accuracy: 0.6250 - val_loss: 0.7729 - val_accuracy: 0.4286\n",
"Epoch 58/100\n",
"2/2 [==============================] - 0s 51ms/step - loss: 0.6258 - accuracy: 0.6429 - val_loss: 0.7887 - val_accuracy: 0.4286\n",
"Epoch 59/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.5899 - accuracy: 0.6429 - val_loss: 0.8126 - val_accuracy: 0.4286\n",
"Epoch 60/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.6109 - accuracy: 0.6429 - val_loss: 0.8196 - val_accuracy: 0.4286\n",
"Epoch 61/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.5876 - accuracy: 0.6786 - val_loss: 0.8121 - val_accuracy: 0.4286\n",
"Epoch 62/100\n",
"2/2 [==============================] - 0s 65ms/step - loss: 0.5824 - accuracy: 0.6607 - val_loss: 0.7961 - val_accuracy: 0.4286\n",
"Epoch 63/100\n",
"2/2 [==============================] - 0s 58ms/step - loss: 0.5970 - accuracy: 0.6071 - val_loss: 0.7772 - val_accuracy: 0.4286\n",
"Epoch 64/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.6248 - accuracy: 0.6429 - val_loss: 0.8101 - val_accuracy: 0.4286\n",
"Epoch 65/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.6223 - accuracy: 0.6786 - val_loss: 0.8228 - val_accuracy: 0.4286\n",
"Epoch 66/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.5795 - accuracy: 0.6071 - val_loss: 0.8312 - val_accuracy: 0.4286\n",
"Epoch 67/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.5555 - accuracy: 0.6786 - val_loss: 0.8300 - val_accuracy: 0.4286\n",
"Epoch 68/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.5990 - accuracy: 0.6429 - val_loss: 0.8265 - val_accuracy: 0.4286\n",
"Epoch 69/100\n",
"2/2 [==============================] - 0s 50ms/step - loss: 0.5683 - accuracy: 0.6607 - val_loss: 0.8126 - val_accuracy: 0.4286\n",
"Epoch 70/100\n",
"2/2 [==============================] - 0s 61ms/step - loss: 0.5710 - accuracy: 0.6429 - val_loss: 0.8177 - val_accuracy: 0.4286\n",
"Epoch 71/100\n",
"2/2 [==============================] - 0s 60ms/step - loss: 0.6000 - accuracy: 0.6786 - val_loss: 0.8365 - val_accuracy: 0.4286\n",
"Epoch 72/100\n",
"2/2 [==============================] - 0s 51ms/step - loss: 0.5699 - accuracy: 0.7321 - val_loss: 0.8459 - val_accuracy: 0.4286\n",
"Epoch 73/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.5838 - accuracy: 0.6250 - val_loss: 0.8472 - val_accuracy: 0.4286\n",
"Epoch 74/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.5676 - accuracy: 0.6786 - val_loss: 0.8379 - val_accuracy: 0.4286\n",
"Epoch 75/100\n",
"2/2 [==============================] - 0s 51ms/step - loss: 0.5671 - accuracy: 0.6786 - val_loss: 0.8181 - val_accuracy: 0.3571\n",
"Epoch 76/100\n",
"2/2 [==============================] - 0s 51ms/step - loss: 0.5658 - accuracy: 0.6429 - val_loss: 0.8159 - val_accuracy: 0.3571\n",
"Epoch 77/100\n",
"2/2 [==============================] - 0s 90ms/step - loss: 0.5633 - accuracy: 0.6429 - val_loss: 0.8378 - val_accuracy: 0.3571\n",
"Epoch 78/100\n",
"2/2 [==============================] - 0s 50ms/step - loss: 0.5715 - accuracy: 0.6429 - val_loss: 0.8432 - val_accuracy: 0.3571\n",
"Epoch 79/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.5501 - accuracy: 0.7321 - val_loss: 0.8401 - val_accuracy: 0.3571\n",
"Epoch 80/100\n",
"2/2 [==============================] - 0s 56ms/step - loss: 0.5668 - accuracy: 0.6786 - val_loss: 0.8301 - val_accuracy: 0.3571\n",
"Epoch 81/100\n",
"2/2 [==============================] - 0s 57ms/step - loss: 0.5351 - accuracy: 0.6786 - val_loss: 0.8237 - val_accuracy: 0.3571\n",
"Epoch 82/100\n",
"2/2 [==============================] - 0s 59ms/step - loss: 0.5889 - accuracy: 0.6071 - val_loss: 0.8355 - val_accuracy: 0.3571\n",
"Epoch 83/100\n",
"2/2 [==============================] - 0s 73ms/step - loss: 0.5603 - accuracy: 0.6429 - val_loss: 0.8614 - val_accuracy: 0.3571\n",
"Epoch 84/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.5445 - accuracy: 0.7321 - val_loss: 0.8728 - val_accuracy: 0.3571\n",
"Epoch 85/100\n",
"2/2 [==============================] - 0s 71ms/step - loss: 0.5643 - accuracy: 0.7143 - val_loss: 0.8688 - val_accuracy: 0.3571\n",
"Epoch 86/100\n",
"2/2 [==============================] - 0s 66ms/step - loss: 0.5326 - accuracy: 0.6429 - val_loss: 0.8586 - val_accuracy: 0.3571\n",
"Epoch 87/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.5360 - accuracy: 0.6964 - val_loss: 0.8527 - val_accuracy: 0.3571\n",
"Epoch 88/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.5397 - accuracy: 0.6964 - val_loss: 0.8502 - val_accuracy: 0.3571\n",
"Epoch 89/100\n",
"2/2 [==============================] - 0s 62ms/step - loss: 0.5194 - accuracy: 0.7679 - val_loss: 0.8679 - val_accuracy: 0.3571\n",
"Epoch 90/100\n",
"2/2 [==============================] - 0s 50ms/step - loss: 0.5418 - accuracy: 0.6429 - val_loss: 0.8879 - val_accuracy: 0.3571\n",
"Epoch 91/100\n",
"2/2 [==============================] - 0s 50ms/step - loss: 0.5578 - accuracy: 0.6429 - val_loss: 0.9006 - val_accuracy: 0.2857\n",
"Epoch 92/100\n",
"2/2 [==============================] - 0s 51ms/step - loss: 0.5565 - accuracy: 0.6429 - val_loss: 0.8991 - val_accuracy: 0.2857\n",
"Epoch 93/100\n",
"2/2 [==============================] - 0s 69ms/step - loss: 0.5391 - accuracy: 0.6607 - val_loss: 0.8971 - val_accuracy: 0.3571\n",
"Epoch 94/100\n",
"2/2 [==============================] - 0s 53ms/step - loss: 0.5368 - accuracy: 0.6607 - val_loss: 0.8965 - val_accuracy: 0.3571\n",
"Epoch 95/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.5542 - accuracy: 0.6607 - val_loss: 0.8935 - val_accuracy: 0.2857\n",
"Epoch 96/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.5307 - accuracy: 0.6429 - val_loss: 0.8916 - val_accuracy: 0.2857\n",
"Epoch 97/100\n",
"2/2 [==============================] - 0s 52ms/step - loss: 0.5153 - accuracy: 0.7321 - val_loss: 0.8911 - val_accuracy: 0.2143\n",
"Epoch 98/100\n",
"2/2 [==============================] - 0s 68ms/step - loss: 0.5393 - accuracy: 0.6429 - val_loss: 0.8932 - val_accuracy: 0.2143\n",
"Epoch 99/100\n",
"2/2 [==============================] - 0s 67ms/step - loss: 0.5678 - accuracy: 0.6429 - val_loss: 0.9337 - val_accuracy: 0.2143\n",
"Epoch 100/100\n",
"2/2 [==============================] - 0s 63ms/step - loss: 0.5865 - accuracy: 0.6786 - val_loss: 0.9499 - val_accuracy: 0.2143\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, roc_curve, auc\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Make predictions on the test data\n",
"y_pred_probs = bidirectional_lstm_model.predict(X_test_rnn)\n",
"y_pred_class = (y_pred_probs > 0.5).astype(int)\n",
"\n",
"# Confusion Matrix\n",
"cm = confusion_matrix(y_test_binary, y_pred_class)\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
"disp.plot(cmap=plt.cm.Blues)\n",
"plt.title('Confusion Matrix')\n",
"plt.show()\n",
"\n",
"# ROC Curve\n",
"fpr, tpr, thresholds = roc_curve(y_test_binary, y_pred_probs)\n",
"roc_auc = auc(fpr, tpr)\n",
"\n",
"plt.figure()\n",
"plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
"plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
"plt.xlim([0.0, 1.0])\n",
"plt.ylim([0.0, 1.05])\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('Receiver Operating Characteristic')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 944
},
"id": "-utWCTKZgDf8",
"outputId": "281bfb13-006e-4983-f011-88eb012563f2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1/1 [==============================] - 5s 5s/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import TimeSeriesSplit\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Bidirectional, LSTM, Dense, Dropout\n",
"from sklearn.metrics import confusion_matrix, roc_curve, auc\n",
"\n",
"# Define the number of splits\n",
"n_splits = 5\n",
"tscv = TimeSeriesSplit(n_splits=n_splits)\n",
"\n",
"# To store metrics for each fold\n",
"confusion_matrices = []\n",
"roc_auc_scores = []\n",
"\n",
"for train_index, test_index in tscv.split(X_normalized):\n",
" X_train_cv, X_test_cv = X_normalized[train_index], X_normalized[test_index]\n",
" y_train_cv, y_test_cv = y_binary[train_index], y_binary[test_index]\n",
"\n",
" # Reshape the data for LSTM network\n",
" X_train_cv_rnn = X_train_cv.reshape((X_train_cv.shape[0], X_train_cv.shape[1], 1))\n",
" X_test_cv_rnn = X_test_cv.reshape((X_test_cv.shape[0], X_test_cv.shape[1], 1))\n",
"\n",
" # Define the model (as before)\n",
" model = Sequential([\n",
" Bidirectional(LSTM(50, input_shape=(X_train_cv_rnn.shape[1], 1))),\n",
" Dropout(0.5),\n",
" Dense(1, activation='sigmoid')\n",
" ])\n",
"\n",
" # Compile the model (as before)\n",
" model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
"\n",
" # Fit the model\n",
" model.fit(X_train_cv_rnn, y_train_cv, epochs=100, batch_size=32, verbose=0) # Set verbose to 0 to suppress output\n",
"\n",
" # Predict probabilities\n",
" y_pred_probs = model.predict(X_test_cv_rnn).ravel()\n",
"\n",
" # Binarize predictions based on threshold\n",
" threshold = 0.5 # This threshold can be adjusted\n",
" y_pred_class = (y_pred_probs > threshold).astype(int)\n",
"\n",
" # Calculate metrics for this fold\n",
" cm = confusion_matrix(y_test_cv, y_pred_class)\n",
" confusion_matrices.append(cm)\n",
"\n",
" fpr, tpr, thresholds = roc_curve(y_test_cv, y_pred_probs)\n",
" roc_auc = auc(fpr, tpr)\n",
" roc_auc_scores.append(roc_auc)\n",
"\n",
"# Now, you can calculate the average of the metrics across all folds\n",
"# Average Confusion Matrix\n",
"average_cm = np.mean(confusion_matrices, axis=0)\n",
"print(\"Average Confusion Matrix:\\n\", average_cm)\n",
"\n",
"# Average ROC AUC Score\n",
"average_roc_auc = np.mean(roc_auc_scores)\n",
"print(\"Average ROC AUC Score:\", average_roc_auc)\n"
],
"metadata": {
"id": "-xsLEoX5gDZ4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 218
},
"outputId": "887767e1-9e51-487a-d994-98f9d11a902c"
},
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "name 'y_binary' is not defined",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-132d3fce7b8d>\u001b[0m in \u001b[0;36m<cell line: 14>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtrain_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_index\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtscv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_normalized\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 15\u001b[0m \u001b[0mX_train_cv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test_cv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_normalized\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_normalized\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0my_train_cv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test_cv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_binary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_binary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;31m# Reshape the data for LSTM network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'y_binary' is not defined"
]
}
]
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{
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