{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **Import Libraries**","metadata":{}},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport os\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nfrom sklearn import svm\nfrom sklearn.linear_model import LinearRegression,LogisticRegression\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.model_selection import StratifiedKFold\nfrom imblearn.over_sampling import RandomOverSampler\nfrom sklearn.metrics import precision_score\nfrom statistics import mean\nfrom sklearn.preprocessing import StandardScaler\nimport joblib\nfrom keras.preprocessing.sequence import TimeseriesGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Conv1D, MaxPooling1D, Flatten, Dense\nfrom sklearn.metrics import RocCurveDisplay","metadata":{"execution":{"iopub.status.busy":"2023-05-16T16:24:54.775618Z","iopub.execute_input":"2023-05-16T16:24:54.775963Z","iopub.status.idle":"2023-05-16T16:24:54.783604Z","shell.execute_reply.started":"2023-05-16T16:24:54.775937Z","shell.execute_reply":"2023-05-16T16:24:54.782808Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":"# **Take all the csv Files from tdcsfog Folder**","metadata":{}},{"cell_type":"code","source":"tdcsfog_path = '/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/train/tdcsfog'\n\ntdcsfog_list = []\n\n# Loop through each file in the directory and read it into a dataframe.\nfor file_name in os.listdir(tdcsfog_path):\n    if file_name.endswith('.csv'):\n        file_path = os.path.join(tdcsfog_path, file_name)\n        file = pd.read_csv(file_path)\n        file.Time = file.Time / (len(file) - 1)\n        tdcsfog_list.append(file)\ntdcsfog = pd.concat(tdcsfog_list, axis = 0)\n\n# Show the concatenated dataframe.\ntdcsfog","metadata":{"execution":{"iopub.status.busy":"2023-05-16T16:25:01.472793Z","iopub.execute_input":"2023-05-16T16:25:01.473159Z","iopub.status.idle":"2023-05-16T16:25:08.591273Z","shell.execute_reply.started":"2023-05-16T16:25:01.47313Z","shell.execute_reply":"2023-05-16T16:25:08.590625Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"          Time      AccV     AccML     AccAP  StartHesitation  Turn  Walking\n0     0.000000 -9.665890  0.042550  0.184744                0     0        0\n1     0.000135 -9.672969  0.049217  0.184644                0     0        0\n2     0.000270 -9.670260  0.033620  0.193790                0     0        0\n3     0.000405 -9.673356  0.035159  0.184369                0     0        0\n4     0.000541 -9.671458  0.043913  0.197814                0     0        0\n...        ...       ...       ...       ...              ...   ...      ...\n5153  0.999224 -9.915920 -0.105897 -1.123455                0     0        0\n5154  0.999418 -9.693752 -0.066892 -1.114903                0     0        0\n5155  0.999612 -9.548118 -0.098315 -1.112123                0     0        0\n5156  0.999806 -9.469803 -0.111004 -1.130814                0     0        0\n5157  1.000000 -9.566318 -0.113865 -1.103353                0     0        0\n\n[7062672 rows x 7 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Time</th>\n      <th>AccV</th>\n      <th>AccML</th>\n      <th>AccAP</th>\n      <th>StartHesitation</th>\n      <th>Turn</th>\n      <th>Walking</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.000000</td>\n      <td>-9.665890</td>\n      <td>0.042550</td>\n      <td>0.184744</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.000135</td>\n      <td>-9.672969</td>\n      <td>0.049217</td>\n      <td>0.184644</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.000270</td>\n      <td>-9.670260</td>\n      <td>0.033620</td>\n      <td>0.193790</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.000405</td>\n      <td>-9.673356</td>\n      <td>0.035159</td>\n      <td>0.184369</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.000541</td>\n      <td>-9.671458</td>\n      <td>0.043913</td>\n      <td>0.197814</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5153</th>\n      <td>0.999224</td>\n      <td>-9.915920</td>\n      <td>-0.105897</td>\n      <td>-1.123455</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5154</th>\n      <td>0.999418</td>\n      <td>-9.693752</td>\n      <td>-0.066892</td>\n      <td>-1.114903</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5155</th>\n      <td>0.999612</td>\n      <td>-9.548118</td>\n      <td>-0.098315</td>\n      <td>-1.112123</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5156</th>\n      <td>0.999806</td>\n      <td>-9.469803</td>\n      <td>-0.111004</td>\n      <td>-1.130814</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5157</th>\n      <td>1.000000</td>\n      <td>-9.566318</td>\n      <td>-0.113865</td>\n      <td>-1.103353</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>7062672 rows × 7 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# **Show the distributions of StartHesitation, Turn and Walking variables**","metadata":{}},{"cell_type":"code","source":"fig, ((ax0, ax1,ax2)) = plt.subplots(nrows=3, ncols=1)\nax0.hist(tdcsfog['StartHesitation'])\nax0.title.set_text('StartHesitation')\nax1.hist(tdcsfog['Turn'])\nax1.title.set_text('Turn')\nax2.hist(tdcsfog['Walking'])\nax2.title.set_text('Walking')\nfig.suptitle('Distribution of StartHesitation, Turn and Walking variables')\nax0.set_xticks([0,1])\nax1.set_xticks([0,1])\nax2.set_xticks([0,1])\nfig.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-05-16T16:25:11.323953Z","iopub.execute_input":"2023-05-16T16:25:11.324802Z","iopub.status.idle":"2023-05-16T16:25:11.896072Z","shell.execute_reply.started":"2023-05-16T16:25:11.324764Z","shell.execute_reply":"2023-05-16T16:25:11.894602Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 3 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# Create a naïve baseline solution","metadata":{}},{"cell_type":"code","source":"def run_model(X,y):\n    skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n    ros = RandomOverSampler(random_state=42)\n    model = LogisticRegression(random_state=42)\n    accuracies = []\n    for train_index, test_index in skf.split(X, y):\n        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n        model.fit(X_train, y_train)\n        y_pred = model.predict(X_test)\n        accuracy = accuracy_score(y_test, y_pred)\n        accuracies.append(accuracy)\n    print('Accuracy:', mean(accuracies))\n    return model\n\nnum_samples = 1000000\nX = tdcsfog.iloc[:num_samples,:4]\ny1 = tdcsfog[\"StartHesitation\"][:num_samples]\ny2 = tdcsfog[\"Turn\"][:num_samples]\ny3 = tdcsfog[\"Walking\"][:num_samples]\n\nmodel1 = run_model(X, y1)\nmodel2 = run_model(X, y2)\nmodel3 = run_model(X, y3)\n","metadata":{"execution":{"iopub.status.busy":"2023-05-16T16:25:15.971304Z","iopub.execute_input":"2023-05-16T16:25:15.971689Z","iopub.status.idle":"2023-05-16T16:25:53.851235Z","shell.execute_reply.started":"2023-05-16T16:25:15.971658Z","shell.execute_reply":"2023-05-16T16:25:53.850353Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Accuracy: 0.932097\nAccuracy: 0.753035\nAccuracy: 0.996623\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Analyzing the model's performance","metadata":{}},{"cell_type":"code","source":"score = model1.score(X, y1)\nprint('Test accuracy:', score)\n\ny_pred = model1.predict(X)\nRocCurveDisplay.from_predictions(y1, y_pred)","metadata":{"execution":{"iopub.status.busy":"2023-05-16T16:25:53.852862Z","iopub.execute_input":"2023-05-16T16:25:53.853351Z","iopub.status.idle":"2023-05-16T16:25:54.211732Z","shell.execute_reply.started":"2023-05-16T16:25:53.853321Z","shell.execute_reply":"2023-05-16T16:25:54.210771Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"Test accuracy: 0.932117\n","output_type":"stream"},{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7d1f403b5240>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"score = model2.score(X, y2)\nprint('Test accuracy:', score)\n\ny_pred = model2.predict(X)\nRocCurveDisplay.from_predictions(y2, y_pred)","metadata":{"execution":{"iopub.status.busy":"2023-05-16T16:25:54.212915Z","iopub.execute_input":"2023-05-16T16:25:54.213252Z","iopub.status.idle":"2023-05-16T16:25:54.548872Z","shell.execute_reply.started":"2023-05-16T16:25:54.213227Z","shell.execute_reply":"2023-05-16T16:25:54.54798Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":"Test accuracy: 0.753039\n","output_type":"stream"},{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7d1f2410d2d0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"score = model3.score(X, y1)\nprint('Test accuracy:', score)\n\ny_pred = model3.predict(X)\nRocCurveDisplay.from_predictions(y3, y_pred)","metadata":{"execution":{"iopub.status.busy":"2023-05-16T16:25:58.48452Z","iopub.execute_input":"2023-05-16T16:25:58.484912Z","iopub.status.idle":"2023-05-16T16:25:58.817423Z","shell.execute_reply.started":"2023-05-16T16:25:58.48488Z","shell.execute_reply":"2023-05-16T16:25:58.815837Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"Test accuracy: 0.930098\n","output_type":"stream"},{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7d1fc1715b10>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# **Save the models on disk**","metadata":{}},{"cell_type":"code","source":"# Save the model to disk.\njoblib.dump(model1, 'model1.joblib')\njoblib.dump(model2, 'model2.joblib')\njoblib.dump(model3, 'model3.joblib')","metadata":{"execution":{"iopub.status.busy":"2023-05-16T16:26:03.325677Z","iopub.execute_input":"2023-05-16T16:26:03.326075Z","iopub.status.idle":"2023-05-16T16:26:03.339794Z","shell.execute_reply.started":"2023-05-16T16:26:03.326044Z","shell.execute_reply":"2023-05-16T16:26:03.338286Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"['model3.joblib']"},"metadata":{}}]},{"cell_type":"markdown","source":"# **Take all the csv files from tdcsfog folder**","metadata":{}},{"cell_type":"code","source":"tdcsfog_test_path = '/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/test/tdcsfog'\ntdcsfog_test_list = []\nfor file_name in os.listdir(tdcsfog_test_path):\n    if file_name.endswith('.csv'):\n        file_path = os.path.join(tdcsfog_test_path, file_name)\n        file = pd.read_csv(file_path)\n        file['Id'] = file_name[:-4] + '_' + file['Time'].apply(str)\n        file.Time = file.Time / (len(file) - 1)\n        tdcsfog_test_list.append(file)\n\ntdcsfog_test = pd.concat(tdcsfog_test_list, axis = 0)\ntdcsfog_test","metadata":{"execution":{"iopub.status.busy":"2023-05-16T16:26:05.438602Z","iopub.execute_input":"2023-05-16T16:26:05.438971Z","iopub.status.idle":"2023-05-16T16:26:05.466864Z","shell.execute_reply.started":"2023-05-16T16:26:05.438943Z","shell.execute_reply":"2023-05-16T16:26:05.465481Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"          Time      AccV     AccML     AccAP               Id\n0     0.000000 -9.533939  0.566322 -1.413525     003f117e14_0\n1     0.000214 -9.536140  0.564137 -1.440621     003f117e14_1\n2     0.000427 -9.529345  0.561765 -1.429332     003f117e14_2\n3     0.000641 -9.531239  0.564227 -1.415490     003f117e14_3\n4     0.000855 -9.540825  0.561854 -1.429471     003f117e14_4\n...        ...       ...       ...       ...              ...\n4677  0.999145 -9.351431  0.370047 -2.286318  003f117e14_4677\n4678  0.999359 -9.341410  0.347721 -2.321991  003f117e14_4678\n4679  0.999573 -9.350667  0.361341 -2.297158  003f117e14_4679\n4680  0.999786 -9.343977  0.365726 -2.292650  003f117e14_4680\n4681  1.000000 -9.348477  0.379439 -2.335422  003f117e14_4681\n\n[4682 rows x 5 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Time</th>\n      <th>AccV</th>\n      <th>AccML</th>\n      <th>AccAP</th>\n      <th>Id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.000000</td>\n      <td>-9.533939</td>\n      <td>0.566322</td>\n      <td>-1.413525</td>\n      <td>003f117e14_0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.000214</td>\n      <td>-9.536140</td>\n      <td>0.564137</td>\n      <td>-1.440621</td>\n      <td>003f117e14_1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.000427</td>\n      <td>-9.529345</td>\n      <td>0.561765</td>\n      <td>-1.429332</td>\n      <td>003f117e14_2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.000641</td>\n      <td>-9.531239</td>\n      <td>0.564227</td>\n      <td>-1.415490</td>\n      <td>003f117e14_3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.000855</td>\n      <td>-9.540825</td>\n      <td>0.561854</td>\n      <td>-1.429471</td>\n      <td>003f117e14_4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4677</th>\n      <td>0.999145</td>\n      <td>-9.351431</td>\n      <td>0.370047</td>\n      <td>-2.286318</td>\n      <td>003f117e14_4677</td>\n    </tr>\n    <tr>\n      <th>4678</th>\n      <td>0.999359</td>\n      <td>-9.341410</td>\n      <td>0.347721</td>\n      <td>-2.321991</td>\n      <td>003f117e14_4678</td>\n    </tr>\n    <tr>\n      <th>4679</th>\n      <td>0.999573</td>\n      <td>-9.350667</td>\n      <td>0.361341</td>\n      <td>-2.297158</td>\n      <td>003f117e14_4679</td>\n    </tr>\n    <tr>\n      <th>4680</th>\n      <td>0.999786</td>\n      <td>-9.343977</td>\n      <td>0.365726</td>\n      <td>-2.292650</td>\n      <td>003f117e14_4680</td>\n    </tr>\n    <tr>\n      <th>4681</th>\n      <td>1.000000</td>\n      <td>-9.348477</td>\n      <td>0.379439</td>\n      <td>-2.335422</td>\n      <td>003f117e14_4681</td>\n    </tr>\n  </tbody>\n</table>\n<p>4682 rows × 5 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# **Load the saved models and use run it on a test set**","metadata":{}},{"cell_type":"code","source":"model1_loaded = joblib.load('model1.joblib')\nmodel2_loaded = joblib.load('model2.joblib')\nmodel3_loaded = joblib.load('model3.joblib')\n\n\nX_test = tdcsfog_test.iloc[:,:4]\ntdcsfog_test[\"StartHesitation\"] = model1_loaded.predict(X_test)\ntdcsfog_test[\"Turn\"] = model2_loaded.predict(X_test)\ntdcsfog_test[\"Walking\"] = model3_loaded.predict(X_test)\n\ntdcsfog_test","metadata":{"execution":{"iopub.status.busy":"2023-05-16T16:26:08.082623Z","iopub.execute_input":"2023-05-16T16:26:08.083031Z","iopub.status.idle":"2023-05-16T16:26:08.110896Z","shell.execute_reply.started":"2023-05-16T16:26:08.082998Z","shell.execute_reply":"2023-05-16T16:26:08.110149Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"          Time      AccV     AccML     AccAP               Id  \\\n0     0.000000 -9.533939  0.566322 -1.413525     003f117e14_0   \n1     0.000214 -9.536140  0.564137 -1.440621     003f117e14_1   \n2     0.000427 -9.529345  0.561765 -1.429332     003f117e14_2   \n3     0.000641 -9.531239  0.564227 -1.415490     003f117e14_3   \n4     0.000855 -9.540825  0.561854 -1.429471     003f117e14_4   \n...        ...       ...       ...       ...              ...   \n4677  0.999145 -9.351431  0.370047 -2.286318  003f117e14_4677   \n4678  0.999359 -9.341410  0.347721 -2.321991  003f117e14_4678   \n4679  0.999573 -9.350667  0.361341 -2.297158  003f117e14_4679   \n4680  0.999786 -9.343977  0.365726 -2.292650  003f117e14_4680   \n4681  1.000000 -9.348477  0.379439 -2.335422  003f117e14_4681   \n\n      StartHesitation  Turn  Walking  \n0                   0     0        0  \n1                   0     0        0  \n2                   0     0        0  \n3                   0     0        0  \n4                   0     0        0  \n...               ...   ...      ...  \n4677                0     0        0  \n4678                0     0        0  \n4679                0     0        0  \n4680                0     0        0  \n4681                0     0        0  \n\n[4682 rows x 8 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Time</th>\n      <th>AccV</th>\n      <th>AccML</th>\n      <th>AccAP</th>\n      <th>Id</th>\n      <th>StartHesitation</th>\n      <th>Turn</th>\n      <th>Walking</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.000000</td>\n      <td>-9.533939</td>\n      <td>0.566322</td>\n      <td>-1.413525</td>\n      <td>003f117e14_0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.000214</td>\n      <td>-9.536140</td>\n      <td>0.564137</td>\n      <td>-1.440621</td>\n      <td>003f117e14_1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.000427</td>\n      <td>-9.529345</td>\n      <td>0.561765</td>\n      <td>-1.429332</td>\n      <td>003f117e14_2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.000641</td>\n      <td>-9.531239</td>\n      <td>0.564227</td>\n      <td>-1.415490</td>\n      <td>003f117e14_3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.000855</td>\n      <td>-9.540825</td>\n      <td>0.561854</td>\n      <td>-1.429471</td>\n      <td>003f117e14_4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4677</th>\n      <td>0.999145</td>\n      <td>-9.351431</td>\n      <td>0.370047</td>\n      <td>-2.286318</td>\n      <td>003f117e14_4677</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4678</th>\n      <td>0.999359</td>\n      <td>-9.341410</td>\n      <td>0.347721</td>\n      <td>-2.321991</td>\n      <td>003f117e14_4678</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4679</th>\n      <td>0.999573</td>\n      <td>-9.350667</td>\n      <td>0.361341</td>\n      <td>-2.297158</td>\n      <td>003f117e14_4679</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4680</th>\n      <td>0.999786</td>\n      <td>-9.343977</td>\n      <td>0.365726</td>\n      <td>-2.292650</td>\n      <td>003f117e14_4680</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4681</th>\n      <td>1.000000</td>\n      <td>-9.348477</td>\n      <td>0.379439</td>\n      <td>-2.335422</td>\n      <td>003f117e14_4681</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>4682 rows × 8 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# analyzing test data","metadata":{}},{"cell_type":"markdown","source":"In the following cell, we show the distribution of StartHesitation, Turn and Walking features values (test set)","metadata":{}},{"cell_type":"code","source":"fig, ((ax0, ax1,ax2)) = plt.subplots(nrows=3, ncols=1)\n\nax0.hist(tdcsfog_test['StartHesitation'], color = \"orange\")\nax0.title.set_text('StartHesitation')\nax0.set_xticks([0, 1])\n\nax1.hist(tdcsfog_test['Turn'], color = \"orange\")\nax1.title.set_text('Turn')\nax1.set_xticks([0, 1])\n\nax2.hist(tdcsfog_test['Walking'], color = \"orange\")\nax2.title.set_text('Walking')\nax2.set_xticks([0, 1])\n\nfig.suptitle('Distribution of StartHesitation, Turn and Walking variables (Test set)')\nfig.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-05-16T16:26:12.108147Z","iopub.execute_input":"2023-05-16T16:26:12.108531Z","iopub.status.idle":"2023-05-16T16:26:12.443633Z","shell.execute_reply.started":"2023-05-16T16:26:12.108501Z","shell.execute_reply":"2023-05-16T16:26:12.442675Z"},"trusted":true},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 3 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# Creat 1D-CNN models and fit these model to a relevant data","metadata":{}},{"cell_type":"markdown","source":"In the following cell, we create 1D-CNN model to predict StartHesitation using relevant features","metadata":{}},{"cell_type":"code","source":"def run_1D_CNN_model(X_train, y_train, X_test, y_test, epochs=3):\n    model = Sequential()\n    convential_layer1 = Conv1D(filters=32, kernel_size=3,padding='same', activation='relu', input_shape=(4,1))\n    model.add(convential_layer1)\n    model.add(MaxPooling1D(pool_size=2))\n    convential_layer2 = Conv1D(filters=64, kernel_size=3, padding='same', activation='relu')\n    model.add(convential_layer2)\n    model.add(MaxPooling1D(pool_size=2))\n    model.add(Flatten())\n    model.add(Dense(128, activation='relu'))\n    model.add(Dense(1, activation='sigmoid'))\n\n    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n    model.fit(X_train, y_train, epochs=epochs, batch_size=32, verbose=1, validation_data=(X_test, y_test))\n    return model","metadata":{"execution":{"iopub.status.busy":"2023-05-16T17:19:11.318368Z","iopub.execute_input":"2023-05-16T17:19:11.318708Z","iopub.status.idle":"2023-05-16T17:19:11.327721Z","shell.execute_reply.started":"2023-05-16T17:19:11.318683Z","shell.execute_reply":"2023-05-16T17:19:11.326166Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"markdown","source":"# Analyzing the model's performance","metadata":{}},{"cell_type":"code","source":"# num_samples = tdcsfog.shape[0]\nnum_samples = 1000000\n\nX_train = tdcsfog.iloc[:num_samples,:4]\nX_train = np.array(X_train)\nX_train = np.reshape(X_train, (num_samples, 4, 1))\n\ny_train = tdcsfog[\"StartHesitation\"][:num_samples]\ny_train = np.array(y_train)\n\n\nX_test = tdcsfog.iloc[:num_samples,:4]\nX_test = np.array(X_test)\nX_test = np.reshape(X_test, (num_samples, 4, 1))\n\ny_test = tdcsfog[\"StartHesitation\"][:num_samples]\ny_test = np.array(y_test)\n\nmodel = run_1D_CNN_model(X_train, y_train, X_test, y_test)\nscore = model.evaluate(X_test, y_test, verbose=0)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])\n\ny_pred = model.predict(X_test)\nRocCurveDisplay.from_predictions(y_test, y_pred)","metadata":{"execution":{"iopub.status.busy":"2023-05-16T17:19:15.107685Z","iopub.execute_input":"2023-05-16T17:19:15.108051Z","iopub.status.idle":"2023-05-16T17:26:08.818869Z","shell.execute_reply.started":"2023-05-16T17:19:15.108022Z","shell.execute_reply":"2023-05-16T17:26:08.817497Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stdout","text":"Epoch 1/3\n31250/31250 [==============================] - 104s 3ms/step - loss: 0.1170 - accuracy: 0.9531 - val_loss: 0.1086 - val_accuracy: 0.9540\nEpoch 2/3\n31250/31250 [==============================] - 106s 3ms/step - loss: 0.0997 - accuracy: 0.9601 - val_loss: 0.0989 - val_accuracy: 0.9602\nEpoch 3/3\n31250/31250 [==============================] - 107s 3ms/step - loss: 0.0949 - accuracy: 0.9621 - val_loss: 0.0895 - val_accuracy: 0.9646\nTest loss: 0.08949283510446548\nTest accuracy: 0.9645640254020691\n31250/31250 [==============================] - 40s 1ms/step\n","output_type":"stream"},{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7d1f25702d40>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# Analyzing the model's performance","metadata":{}},{"cell_type":"markdown","source":"In the following cell, we create 1D-CNN model to predict Turn using relevant features and analyze the output results.","metadata":{}},{"cell_type":"code","source":"y_train = tdcsfog[\"Turn\"][:num_samples]\ny_train = np.array(y_train)\n\ny_test = tdcsfog[\"Turn\"][:num_samples]\ny_test = np.array(y_test)\n\nmodel = run_1D_CNN_model(X_train, y_train, X_test, y_test)\nscore = model.evaluate(X_test, y_test, verbose=0)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])\n\ny_pred = model.predict(X_test)\nRocCurveDisplay.from_predictions(y_test, y_pred)","metadata":{"execution":{"iopub.status.busy":"2023-05-16T17:42:54.509672Z","iopub.execute_input":"2023-05-16T17:42:54.510067Z","iopub.status.idle":"2023-05-16T17:49:37.06775Z","shell.execute_reply.started":"2023-05-16T17:42:54.510037Z","shell.execute_reply":"2023-05-16T17:49:37.066712Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stdout","text":"Epoch 1/3\n31250/31250 [==============================] - 105s 3ms/step - loss: 0.3755 - accuracy: 0.8337 - val_loss: 0.3520 - val_accuracy: 0.8475\nEpoch 2/3\n31250/31250 [==============================] - 105s 3ms/step - loss: 0.3426 - accuracy: 0.8523 - val_loss: 0.3366 - val_accuracy: 0.8550\nEpoch 3/3\n31250/31250 [==============================] - 105s 3ms/step - loss: 0.3366 - accuracy: 0.8551 - val_loss: 0.3311 - val_accuracy: 0.8573\nTest loss: 0.33108383417129517\nTest accuracy: 0.8573489785194397\n31250/31250 [==============================] - 40s 1ms/step\n","output_type":"stream"},{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7d1f9568d600>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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ZGVlwdnZ2dTlkAmVqDXYcjoBnxy4jtz7nYWf6VwP0/s144zCRESPGH2+vzlaimqliKRMzPvpCi7fygIAtKunwKKnWnOuGiIiC2CwcBMVFYVBgwYhNjbWULskMris/GIs/OUqfrx4CwDgZGuNWQNaYmQXJfvUEBFZCIOFG5VKhYSEBEPtjsighBDYfek2Fv8SiXv3h3YP7+iLmQNbwMPJ1sTVERGRIekcbqZPn/7A1+/evfvQxRAZQ1J6PiZvv4BLN0svQTWq64AFg1ujd7O6Jq6MiIiMQedws3r1anTo0KHKTjy5ubkGK4rIEDQaga9OxmHF/usoKFZDbi3FmG4N8E7/5pBbcxQUEZGl0jncNGnSBNOmTcOYMWMqfT0iIgKdO3c2WGFED+PKrSws2H0V4QkZAICufm54b1gbNPN0MnFlRERkbDqHG39/f4SHh1cZbiQSCWrZqHJ6BBUWq/HJwetY/3sshAAcZFaYPaglRnWtz/tAERHVEjqHmxUrVqCoqKjK19u3bw+NRmOQooiq48RfaZj942UkpucDAJ5s541ZA1vCl3PWEBHVKjqHGy8vL2PWQVRteUUl+Hh/NL4+GQ8A8HSWY8mQNniiNT+zRES1ESfxI7MWnpCBGTsvIS4tDwAwOqA+Zg1sCUc5P9pERLUVvwHILBWrNVh98C+sOXoDQpS21nz4THsO7yYiIoYbMj/XUrIxfcclRCZnAwCGd/LF/CdbwcVeZuLKiIjoUcBwQ2ZDrRFY/3ssVh6IRrFawNXeBkuGtsGT7XxMXRoRET1CpKYuYM2aNfDz84OtrS0CAgJw9uzZB66/atUqNG/eHHZ2dlAqlZg2bRoKCwtrqFoylaT0fDy//g98EHYNxWqBoJYe2PdmLwYbIiKqoFotN7///jvs7e3h7++vXXb+/Hnk5+ejV69eOu9nx44dmD59OtatW4eAgACsWrUKwcHBiI6OhoeHR4X1t2/fjpkzZ+Krr75CYGAgrl+/jvHjx0MikWDlypXVeSv0iBNCYMe5JCzdE4WcohI4yKywYHBrPOtfj/PWEBFRpSSiGjPvSaVStGjRApGRkdplLVu2xPXr16FWq3XeT0BAALp06YLPP/8cAKDRaKBUKjFlyhTMnDmzwvqTJ09GVFQUDh06pF321ltv4cyZMzhx4kSlxygqKio3P092djaUSiWysrKqvJUEPRoy81WYsfNPHIxKBQB0qu+CVSM6on4dexNXRkRENS07OxsKhUKn7+9qXZaKi4vDwYMHyy07dOgQYmNjdd6HSqVCeHg4goKC/i5GKkVQUBBOnz5d6TaBgYEIDw/XXrqKjY3F3r17MXDgwCqPs3z5cigUCu1DqVTqXCOZzrn4dDz52QkcjEqFzEqKWQNaYOdrgQw2RET0n6p1WapBgwYVlvn46Nf3IS0tDWq1Gp6enuWWe3p64tq1a5VuM2rUKKSlpeGxxx6DEAIlJSV47bXXMHv27CqPM2vWrHJ3NC9ruaFHk0YjsO73GKw68BdUag2UbnZYO7oz2vgqTF0aERGZCZN3KNbH0aNHsWzZMnzxxRe4cOECfvjhB+zZswdLliypchu5XA5nZ+dyD3o0Zear8Mo34fgwLBoqtQZDOvhg7xs9GWyIiEgvOrXcuLq66tx5Mz09Xaf13N3dYWVlhdTU1HLLU1NTq7zVw7x58zB27Fi89NJLAIC2bdsiLy8Pr7zyCubMmQOp1KyyGv1DeEIGpmy/gNtZhZBZSbHwqdZ4vquSnYaJiEhvOoWbVatWGfzAMpkMnTt3xqFDhzB06FAApR2KDx06hMmTJ1e6TX5+foUAY2VlBQC8I7mZ0mgE1h+Pxcf7olGiEWhQxx6fPd8R7eq5mLo0IiIyUzqFm5CQEKMcfPr06QgJCYG/vz+6du2KVatWIS8vDxMmTAAAjBs3Dr6+vli+fDkAYPDgwVi5ciU6duyIgIAA3LhxA/PmzcPgwYO1IYfMR3ZhMaaFRuDQtTsAgMHtfbBsWBs42dqYuDIiIjJn1epQHBMTg6+//hoxMTFYvXo1PDw88Ntvv6F+/fpo3bq1zvsZMWIE7t69i/nz5yMlJQUdOnRAWFiYtpNxYmJiuZaauXPnQiKRYO7cubh16xbq1q2LwYMHY+nSpdV5G2RCN+7k4pVvziP2bh4vQxERkUHpPc/NsWPHMGDAAPTo0QO///47oqKi0KhRI7z//vs4f/48du3aZaxaDUKfcfJkHGFXkvHWd5eQp1LDy9kWG8b5o209dhomIqKqGXWem5kzZ+K9997DgQMHIJP9faPCvn374o8//tC/Wqo1hBBYc+QGXvu/C8hTqRHQ0A27p/RgsCEiIoPS+7LU5cuXsX379grLPTw8kJaWZpCiyPIUFqvxzq4/sfvSbQDAhB5+mD2wJWysOMKNiIgMS+9w4+LiguTkZDRs2LDc8osXL8LX19dghZHlSM9T4eWt5xGekAErqQQLBrfCuO5+pi6LiIgslN7/bB45ciTeffddpKSkQCKRQKPR4OTJk5gxYwbGjRtnjBrJjF2+mYWnPj+B8IQMONtaY9tLAQw2RERkVHqHm2XLlqFFixZQKpXIzc1Fq1at0KtXLwQGBmLu3LnGqJHM1KGoVIxYfxo3MwpQ380euyYGolujOqYui4iILFy17goOlA7TvnLlCnJzc9GxY0c0bdrU0LUZBUdL1Yytp+OxcPdVaATQo0kdfDGqMxT2nL+GiIiqR5/v72rNcwMA9evX196AknOTUBkhBD7eH401R2IAACO7KLFkaBt2HCYiohpTrW+cTZs2oU2bNrC1tYWtrS3atGmDjRs3Gro2MjMlag3m/3xVG2xmPNEMy4e3ZbAhIqIapXfLzfz587Fy5UpMmTIF3bt3BwCcPn0a06ZNQ2JiIhYvXmzwIunRV1isxsT/C8eR6LsAgCVD22BstwYmroqIiGojvfvc1K1bF59++imef/75csu//fZbTJky5ZGf64Z9bgwvt6gEr35zHidv3IOtjRQfP9seT7bzMXVZRERkQYza56a4uBj+/v4Vlnfu3BklJSX67o7MXE5hMV7cch5n49JhL7PC5gld0bWhm6nLIiKiWkzvzhBjx47F2rVrKyxfv349Ro8ebZCiyDzcyy3CmE1ncTYuHU5ya2x/uRuDDRERmZxOLTfTp0/X/iyRSLBx40bs378f3bp1AwCcOXMGiYmJnMSvFknPU2H0xjO4lpIDZ1trbH0xAB2ULqYui4iISLdwc/HixXLPO3fuDACIiSkdFePu7g53d3dcvXrVwOXRoygrvxhj7gcbDyc5vn2lGxrXdTR1WURERAB0DDdHjhwxdh1kJjLyVHhhyzlEJmfD3VGG7S8z2BAR0aOl2pP4Ue2TlV+MURvPICo5Gwo7G/zfSwFo4sFgQ0REj5ZqhZvz58/ju+++Q2JiIlQqVbnXfvjhB4MURo+WnMJijP2qNNi4O8rwfy8FoIUXh9ITEdGjR+/RUqGhoQgMDERUVBR+/PFHFBcX4+rVqzh8+DAUCoUxaiQTKyxW4/VtF/DnzSy42Ntg+8vdGGyIiOiRVa27gn/yySf45ZdfIJPJsHr1aly7dg3PPfcc6tevb4wayYSEEHhn1584/lca7GVW2BTSBc08nUxdFhERUZX0DjcxMTEYNGgQAEAmkyEvLw8SiQTTpk3D+vXrDV4gmdbKA9ex+9JtWEsl2DDOH50buJq6JCIiogfSO9y4uroiJycHAODr64srV64AADIzM5Gfn2/Y6sikvjkdj88O3wAALBvWFj2auJu4IiIiov+md4fiXr164cCBA2jbti2effZZTJ06FYcPH8aBAwfw+OOPG6NGMoGwK8mYv7t03qI3+jbBc12UJq6IiIhIN3qHm88//xyFhYUAgDlz5sDGxganTp3C008/jblz5xq8QKp55+LTMTU0AkIAowPqY1q/ZqYuiYiISGd63xXc3PGu4A92MyMfw744hbs5RejbwgPrx3aGtZXeVy+JiIgMyuB3Bc/Oztb54AwM5itfVYKXtpzH3ZwiKN3s8OnzHRlsiIjI7OgUblxcXCCRSB64jhACEokEarXaIIVRzRJCYPqOS7iWkgN3Rxl2vNIdjnJOYE1EROaH95YiCCGw6JdIhF1NgcxKinVjOsPHxc7UZREREVWLTuGmd+/exq6DTGjTiThsPhUPAFg+vC38/dxMWxAREdFDYIeKWu5gZCqW7Y0CAMwd1BJPd65n4oqIiIgeDsNNLXY9NQeTv70AjQBGdlHixccamrokIiKih8ZwU0tlFxZj4v+Fo7BYg8eauGPJ0Db/2WmciIjIHDDc1EJqjcDUby8i5m4ePJ3lWDWyA2w45JuIiCxEtb7RSkpKcPDgQXz55Zfa+0zdvn0bubm5Bi2OjGPZ3igcib4LWxsp1o/1h7uj3NQlERERGYzeE5kkJCSgf//+SExMRFFREfr16wcnJyd88MEHKCoqwrp164xRJxnId+eSsOlEHADg42fbo73SxbQFERERGZjeLTdTp06Fv78/MjIyYGf391wow4YNw6FDhwxaHBnW+fh0zP2p9C7uk/o0xpPtfExcERERkeHp3XJz/PhxnDp1CjKZrNxyPz8/3Lp1y2CFkWGl5RZh8vaLUKk1CGrpien9mpu6JCIiIqPQu+VGo9FUeouFmzdvwsnJySBFkWFpNAIzdl5CSnYhGtV1wKqRHWAl5cgoIiKyTHqHmyeeeAKrVq3SPpdIJMjNzcWCBQswcOBAQ9ZGBvLF0Rs4Gn0Xcmsp1ozqxHtGERGRRdP7W27FihUIDg5Gq1atUFhYiFGjRuGvv/6Cu7s7vv32W2PUSA/hVEwaVh64DgBYMqQNWnrzru1ERGTZ9A439erVw6VLlxAaGoo///wTubm5ePHFFzF69OhyHYzJ9NJyi/DGtxHQCODpTvXwXBelqUsiIiIyOr3DTWFhIWxtbTFmzBhj1EMGIoTAzO//RFpuEZp7OmHJ0NamLomIiKhG6N3nxsPDAyEhIThw4AA0Go0xaiID+OHCLRyMugOZlRSrRnaAvYz9bIiIqHbQO9xs2bIF+fn5GDJkCHx9ffHmm2/i/PnzxqiNqikpPR8Ld18FAEwNasp+NkREVKvoHW6GDRuGnTt3IjU1FcuWLUNkZCS6deuGZs2aYfHixcaokfSg1gi8tfMScopK0LmBK17p1cjUJREREdWoat8t0cnJCRMmTMD+/fvx559/wsHBAYsWLTJkbVQN//dHAs7GpcNBZoWVz7XnDTGJiKjWqfY3X2FhIb777jsMHToUnTp1Qnp6Ot5++21D1kZ6upmRjw/DrgEAZg5ogQZ1HExcERERUc3Tu5fpvn37sH37dvz000+wtrbGM888g/3796NXr17GqI90JITAm6ERyFOp0cXPFaMCGpi6JCIiIpPQO9wMGzYMTz75JLZu3YqBAwfCxsbGGHWRnn75MxnnEzJgYyXBe0Pb8vYKRERUa+kdblJTU3kPqUdMvqoE7++NAgCM7eaH5l78/0NERLWXTuEmOzsbzs6lw4mFEMjOzq5y3bL1qOasPvgXbmcVwtfFDm8H827fRERUu+kUblxdXZGcnAwPDw+4uLhAIql4yUMIAYlEUukdw8l4/krNwaYTcQCARU+1hp3MysQVERERmZZO4ebw4cNwc3MDABw5csSoBZF+lv92DSUagaCWnghq5WnqcoiIiExOp3DTu3dv7c8NGzaEUqms0HojhEBSUpJhq6MHOnb9Lg5fuwNrqQSzBrYwdTlERESPBL3nuWnYsCHu3r1bYXl6ejoaNmxokKLovxWrNVj0S+ktFsZ190Pjuo4mroiIiOjRoHe4Ketb82+5ubmwtbU1SFH03749m4jYu3lwc5BhWr+mpi6HiIjokaHzUPDp06cDACQSCebNmwd7e3vta2q1GmfOnEGHDh0MXiBVVFisxmeHbwAA3gxqCidbzjVERERURueWm4sXL+LixYsQQuDy5cva5xcvXsS1a9fQvn17bN68We8C1qxZAz8/P9ja2iIgIABnz5594PqZmZmYNGkSvL29IZfL0axZM+zdu1fv45qz//sjAXdziuCjsMXILvVNXQ4REdEjReeWm7JRUhMmTMDq1asNMp/Njh07MH36dKxbtw4BAQFYtWoVgoODER0dDQ8Pjwrrq1Qq9OvXDx4eHti1axd8fX2RkJAAFxeXh67FXOQVlWDt0RgAwJTHm0JmzRtjEhER/ZNECCFMdfCAgAB06dIFn3/+OQBAo9FAqVRiypQpmDlzZoX1161bh48++gjXrl3T+bYPRUVFKCoq0j7Pzs6GUqlEVlaWWU44+NWJOCz+NRIN6tjj4PTevOs3ERHVCtnZ2VAoFDp9f+vUcjN8+HBs3rwZzs7OGD58+APX/eGHH3QqUqVSITw8HLNmzdIuk0qlCAoKwunTpyvdZvfu3ejevTsmTZqEn3/+GXXr1sWoUaPw7rvvwsqq8snrli9fjkWLFulU06OuqESNL38vbbV5pVcjBhsiIqJK6BRuFAqFdoSUQqEwyIHT0tKgVqvh6Vl+4jlPT09cu3at0m1iY2Nx+PBhjB49Gnv37sWNGzfw+uuvo7i4GAsWLKh0m1mzZmk7QwN/t9yYo58u3kJqdhE8neV4pnM9U5dDRET0SNIp3Hz99deV/lzTNBoNPDw8sH79elhZWaFz5864desWPvrooyrDjVwuh1wur+FKDU+tEfjyWCwA4OWejSC35m0WiIiIKqP3XcELCgoghNAOBU9ISMCPP/6IVq1a4YknntB5P+7u7rCyskJqamq55ampqfDy8qp0G29vb9jY2JS7BNWyZUukpKRApVJBJpPp+3bMxr6rKYhNy4PCzgYju3KEFBERUVX07rQxZMgQbN26FUDpsOyuXbtixYoVGDJkCNauXavzfmQyGTp37oxDhw5pl2k0Ghw6dAjdu3evdJsePXrgxo0b0Gg02mXXr1+Ht7e3RQcbIYR2hFRI9wZwlOudSYmIiGoNvcPNhQsX0LNnTwDArl274OXlhYSEBGzduhWffvqpXvuaPn06NmzYgC1btiAqKgoTJ05EXl4eJkyYAAAYN25cuQ7HEydORHp6OqZOnYrr169jz549WLZsGSZNmqTv2zArJ2/cw+VbWbC1kSIk0M/U5RARET3S9G4CyM/Ph5OTEwBg//79GD58OKRSKbp164aEhAS99jVixAjcvXsX8+fPR0pKCjp06ICwsDBtJ+PExERIpX/nL6VSiX379mHatGlo164dfH19MXXqVLz77rv6vg2zsulEaV+bEf5K1HE0//5DRERExqT3PDft2rXDSy+9hGHDhqFNmzYICwtD9+7dER4ejkGDBiElJcVYtRqEPuPkHwVxaXnou+IohACOzPgfGro7mLokIiKiGqfP97fel6Xmz5+PGTNmwM/PD127dtX2j9m/fz86duxYvYqpSl+fjIMQQN8WHgw2REREOtD7stQzzzyDxx57DMnJyWjfvr12+eOPP45hw4YZtLjaLl9Vgh8v3AIATOjhZ9piiIiIzES1ht14eXnBy8sLN2/eBADUq1cPXbt2NWhhBPx6KRk5RSVoUMcePRq7m7ocIiIis6D3ZSmNRoPFixdDoVCgQYMGaNCgAVxcXLBkyZJyQ7Tp4Qgh8PWpeADAyC71IZVKTFsQERGRmdC75WbOnDnYtGkT3n//ffTo0QMAcOLECSxcuBCFhYVYunSpwYusjS7fykJUcjZk1lKM4qR9REREOtM73GzZsgUbN27EU089pV1WNiz79ddfZ7gxkJ3nSy/5Bbf2gsJetzugExERUTUuS6Wnp6NFixYVlrdo0QLp6ekGKaq2y1eV4KeLpR2Jn/PnDTKJiIj0oXe4ad++PT7//PMKyz///PNyo6eo+g5EpiKnqAT13diRmIiISF96X5b68MMPMWjQIBw8eFA7x83p06eRlJSEvXv3GrzA2uiXS7cBAIPbe7MjMRERkZ70brnp3bs3rl+/juHDhyMzMxOZmZkYPnw4oqOjtfecourLLizG0ei7AIChHXxNXA0REZH50avlJj4+HgcOHIBKpcLIkSPRpk0bY9VVax2OuoMSjUCjug5o6ulk6nKIiIjMjs7h5siRI3jyySdRUFBQuqG1Nb766iuMGTPGaMXVRj9HlHYkHtzOx8SVEBERmSedL0vNmzcP/fr1w61bt3Dv3j28/PLLeOedd4xZW62TnqfCiRtpAEr72xAREZH+dA43V65cwbJly+Dt7Q1XV1d89NFHuHPnDu7du2fM+mqVA5EpKFYLtPZxRhMPXpIiIiKqDp3DTXZ2Ntzd/x6WbG9vDzs7O2RlZRmlsNpoz+UUAED/1l4mroSIiMh86dWheN++fVAoFNrnGo0Ghw4dwpUrV7TL/jlzMekur6gEp2NKL0kNbMdLUkRERNWlV7gJCQmpsOzVV1/V/iyRSKBWqx++qlroSPQdFKsFGtSxRyN3B1OXQ0REZLZ0Dje847dxHY66A6D0XlISCSfuIyIiqi69J/Ejw9NoBI5dL524r09zDxNXQ0REZN50Cjd//PGHzjvMz8/H1atXq11QbRRxMxP38lRwlFvD38/V1OUQERGZNZ3CzdixYxEcHIydO3ciLy+v0nUiIyMxe/ZsNG7cGOHh4QYt0tIdjEwFAPRp4QEbKzamERERPQyd+txERkZi7dq1mDt3LkaNGoVmzZrBx8cHtra2yMjIwLVr15Cbm4thw4Zh//79aNu2rbHrtihl95Lq26KuiSshIiIyfxIhhNBng/Pnz+PEiRNISEhAQUEB3N3d0bFjR/Tp0wdubm7GqtNgsrOzoVAokJWVBWdnZ1OXg7TcIvi/dxAAcG5OEOo6yU1cERER0aNHn+9vvYaCA4C/vz/8/f2rXRyVd/L+7RZaeTsz2BARERkAO3iY2PG/SsPNY03d/2NNIiIi0gXDjYmVtdz0ZLghIiIyCIYbE0pKz0dyViGspRJ0qs8h4ERERIbAcGNC5xPSAQBtfBVwkOvd/YmIiIgq8VDhprCw0FB11EoXEjIBAJ0bsNWGiIjIUPQONxqNBkuWLIGvry8cHR0RGxsLAJg3bx42bdpk8AIt2YXEDADgJSkiIiID0jvcvPfee9i8eTM+/PBDyGQy7fI2bdpg48aNBi3OkuUVleBaSg4AoFMDF9MWQ0REZEH0Djdbt27F+vXrMXr0aFhZWWmXt2/fHteuXTNocZbsUlIm1BoBb4UtvBV2pi6HiIjIYugdbm7duoUmTZpUWK7RaFBcXGyQomqDi0mZAIBO7G9DRERkUHqHm1atWuH48eMVlu/atQsdO3Y0SFG1waX74aaj0sWkdRAREVkavccfz58/HyEhIbh16xY0Gg1++OEHREdHY+vWrfj111+NUaNFuno7GwDQysf097ciIiKyJHq33AwZMgS//PILDh48CAcHB8yfPx9RUVH45Zdf0K9fP2PUaHGyCopxK7MAANDaW2HiaoiIiCxLtWaO69mzJw4cOGDoWmqNG3dKR0l5OdtCYW9j4mqIiIgsi94tN40aNcK9e/cqLM/MzESjRo0MUpSlu3Kr9JJUS28nE1dCRERkefQON/Hx8VCr1RWWFxUV4datWwYpytJdvD95XwclR0oREREZms6XpXbv3q39ed++fVAo/u4rolarcejQIfj5+Rm0OEsVlVx6WaqNLzsTExERGZrO4Wbo0KEAAIlEgpCQkHKv2djYwM/PDytWrDBocZZIVaJBbFouAKCZJy9LERERGZrO4Uaj0QAAGjZsiHPnzsHd3d1oRVmy+Ht5KFYLOMqtUc+VMxMTEREZmt6jpeLi4oxRR61Rdj+pJh6OkEgkJq6GiIjI8lRrKHheXh6OHTuGxMREqFSqcq+98cYbBinMUv2VWhpuWnjxkhQREZEx6B1uLl68iIEDByI/Px95eXlwc3NDWloa7O3t4eHhwXDzH67fDzdN2d+GiIjIKPQeCj5t2jQMHjwYGRkZsLOzwx9//IGEhAR07twZH3/8sTFqtChll6VasuWGiIjIKPQONxEREXjrrbcglUphZWWFoqIiKJVKfPjhh5g9e7YxarQYhcVqJKbnA2DLDRERkbHoHW5sbGwglZZu5uHhgcTERACAQqFAUlKSYauzMDfu5EIIQGFnA3dHmanLISIiskh697np2LEjzp07h6ZNm6J3796YP38+0tLS8M0336BNmzbGqNFiaC9JeTtxpBQREZGR6N1ys2zZMnh7ewMAli5dCldXV0ycOBF3797Fl19+afACLUl8Wh4AoHFdRxNXQkREZLn0brnx9/fX/uzh4YGwsDCDFmTJYu6Wzkzc0N3BxJUQERFZLr1bbqpy4cIFPPnkk4banUW6cac03DT2YMsNERGRsegVbvbt24cZM2Zg9uzZiI2NBQBcu3YNQ4cORZcuXbS3aKCK1BqBhHulI6Wa8LIUERGR0eh8WWrTpk14+eWX4ebmhoyMDGzcuBErV67ElClTMGLECFy5cgUtW7Y0Zq1m7XZmAVRqDWRWUvi48J5SRERExqJzy83q1avxwQcfIC0tDd999x3S0tLwxRdf4PLly1i3bh2DzX8oa7VRutnBSsqRUkRERMaic7iJiYnBs88+CwAYPnw4rK2t8dFHH6FevXpGK86SlE3eV9/N3sSVEBERWTadw01BQQHs7Uu/mCUSCeRyuXZI+MNas2YN/Pz8YGtri4CAAJw9e1an7UJDQyGRSDB06FCD1GFMCemlw8Ab1OFIKSIiImPSayj4xo0b4ehY2hm2pKQEmzdvhru7e7l19L1x5o4dOzB9+nSsW7cOAQEBWLVqFYKDgxEdHQ0PD48qt4uPj8eMGTPQs2dPvY5nKrczCwEA9VzZ34aIiMiYJEIIocuKfn5+/zmrrkQi0Y6i0lVAQAC6dOmCzz//HACg0WigVCoxZcoUzJw5s9Jt1Go1evXqhRdeeAHHjx9HZmYmfvrpJ52Ol52dDYVCgaysLDg7O+tV68MYsuYkLiVlYt2YTujfxjAtXkRERLWFPt/fOrfcxMfHP2xdFahUKoSHh2PWrFnaZVKpFEFBQTh9+nSV2y1evBgeHh548cUXcfz48Qceo6ioCEVFRdrn2dnZD194NdzKKO1zU8+VfW6IiIiMyWCT+FVHWloa1Go1PD09yy339PRESkpKpducOHECmzZtwoYNG3Q6xvLly6FQKLQPpVL50HXrq7BYjbRcFQDAl8PAiYiIjMqk4UZfOTk5GDt2LDZs2FChr09VZs2ahaysLO3DFHcuT84q7W9jZ2MFF3ubGj8+ERFRbaL3vaUMyd3dHVZWVkhNTS23PDU1FV5eXhXWj4mJQXx8PAYPHqxdVjYrsrW1NaKjo9G4ceNy28jlcsjlciNUr7vkzAIAgI+LLe8GTkREZGQmbbmRyWTo3LkzDh06pF2m0Whw6NAhdO/evcL6LVq0wOXLlxEREaF9PPXUU+jTpw8iIiJMcslJF2UtN14KWxNXQkREZPlM2nIDANOnT0dISAj8/f3RtWtXrFq1Cnl5eZgwYQIAYNy4cfD19cXy5ctha2uLNm3alNvexcUFACosf5QkZ5W23Hgr2N+GiIjI2KoVbmJiYvD1118jJiYGq1evhoeHB3777TfUr18frVu31mtfI0aMwN27dzF//nykpKSgQ4cOCAsL03YyTkxMhFRqVl2DKiibnVjJkVJERERGp/M8N2WOHTuGAQMGoEePHvj9998RFRWFRo0a4f3338f58+exa9cuY9VqEKaY52bMxjM4cSMNK55tj6c783YVRERE+tLn+1vvJpGZM2fivffew4EDByCTybTL+/btiz/++EP/amuBJO0cN7wsRUREZGx6h5vLly9j2LBhFZZ7eHggLS3NIEVZErVG4FZGaZ8bJW+aSUREZHR6hxsXFxckJydXWH7x4kX4+voapChLci+vCCUaAYkE8HAy7ZB0IiKi2kDvcDNy5Ei8++67SElJgUQigUajwcmTJzFjxgyMGzfOGDWatdSs0ls/1HWUw9rKvDtGExERmQO9v22XLVuGFi1aQKlUIjc3F61atUKvXr0QGBiIuXPnGqNGs5aaXTrHjacz57ghIiKqCXoPBZfJZNiwYQPmzZuHK1euIDc3Fx07dkTTpk2NUZ/Zu5NT2nLj6cxLUkRERDVB73Bz4sQJPPbYY6hfvz7q169vjJosSlnLTV0nttwQERHVBL0vS/Xt2xcNGzbE7NmzERkZaYyaLErK/VsvePPWC0RERDVC73Bz+/ZtvPXWWzh27BjatGmDDh064KOPPsLNmzeNUZ/ZS825f18p9rkhIiKqEXqHG3d3d0yePBknT55ETEwMnn32WWzZsgV+fn7o27evMWo0a3ey74+W4jBwIiKiGvFQY5MbNmyImTNn4v3330fbtm1x7NgxQ9VlMe7mMtwQERHVpGqHm5MnT+L111+Ht7c3Ro0ahTZt2mDPnj2GrM3sqTUC6XkqAAw3RERENUXv0VKzZs1CaGgobt++jX79+mH16tUYMmQI7O15a4F/S8stglojYCWVwN2R4YaIiKgm6B1ufv/9d7z99tt47rnn4O7uboyaLEZZf5s6DjJYSSUmroaIiKh20DvcnDx50hh1WKSyOW48OIEfERFRjdEp3OzevRsDBgyAjY0Ndu/e/cB1n3rqKYMUZgnKOhN7cAI/IiKiGqNTuBk6dChSUlLg4eGBoUOHVrmeRCKBWq02VG1mr6wzcR0HmYkrISIiqj10CjcajabSn+nB7t6/r1QddiYmIiKqMXoPBd+6dSuKiooqLFepVNi6datBirIUZeHGg8PAiYiIaoze4WbChAnIysqqsDwnJwcTJkwwSFGWIu1+nxt3hhsiIqIao3e4EUJAIqk4rPnmzZtQKBQGKcpSsOWGiIio5uk8FLxjx46QSCSQSCR4/PHHYW3996ZqtRpxcXHo37+/UYo0V+n57FBMRERU03QON2WjpCIiIhAcHAxHR0ftazKZDH5+fnj66acNXqC5KlZrkFVQDABwZbghIiKqMTqHmwULFgAA/Pz8MGLECNjacu6WB7mXq4IQgJVUAjd7hhsiIqKaovcMxSEhIcaow+KUzXHjai+DlLdeICIiqjE6hRs3Nzdcv34d7u7ucHV1rbRDcZn09HSDFWfOysKNm4ONiSshIiKqXXQKN5988gmcnJy0Pz8o3FCpe3mlI6VceUmKiIioRukUbv55KWr8+PHGqsWilLXcuHN2YiIiohql9zw3Fy5cwOXLl7XPf/75ZwwdOhSzZ8+GSqUyaHHmLEN7WYotN0RERDVJ73Dz6quv4vr16wCA2NhYjBgxAvb29ti5cyfeeecdgxdorjLy7w8Dt2efGyIiopqkd7i5fv06OnToAADYuXMnevfuje3bt2Pz5s34/vvvDV2f2dKOlmLLDRERUY2q1u0Xyu4MfvDgQQwcOBAAoFQqkZaWZtjqzFjZfaV4R3AiIqKapXe48ff3x3vvvYdvvvkGx44dw6BBgwAAcXFx8PT0NHiB5kp700xHttwQERHVJL3DzapVq3DhwgVMnjwZc+bMQZMmTQAAu3btQmBgoMELNFdpuRwtRUREZAp6z1Dcrl27cqOlynz00UewsrIySFHmTlXy932lGG6IiIhqlt7hpkx4eDiioqIAAK1atUKnTp0MVpS5y7h/N3ArqQQudhwtRUREVJP0Djd37tzBiBEjcOzYMbi4uAAAMjMz0adPH4SGhqJu3bqGrtHslIUbhZ0N7ytFRERUw/TuczNlyhTk5ubi6tWrSE9PR3p6Oq5cuYLs7Gy88cYbxqjR7Px900y22hAREdU0vVtuwsLCcPDgQbRs2VK7rFWrVlizZg2eeOIJgxZnrjLyyibw40gpIiKimqZ3y41Go4GNTcUWCRsbG+38N7Vdej5vvUBERGQqeoebvn37YurUqbh9+7Z22a1btzBt2jQ8/vjjBi3OXJXdV6oO57ghIiKqcXqHm88//xzZ2dnw8/ND48aN0bhxYzRs2BDZ2dn47LPPjFGj2bl3fwI/ttwQERHVPL373CiVSly4cAGHDh3SDgVv2bIlgoKCDF6cufr7ppkMN0RERDVNr3CzY8cO7N69GyqVCo8//jimTJlirLrMWtlQcIYbIiKimqdzuFm7di0mTZqEpk2bws7ODj/88ANiYmLw0UcfGbM+s3Tv/q0X3NjnhoiIqMbp3Ofm888/x4IFCxAdHY2IiAhs2bIFX3zxhTFrM1uZbLkhIiIyGZ3DTWxsLEJCQrTPR40ahZKSEiQnJxulMHMlhPh7KDjDDRERUY3TOdwUFRXBwcHh7w2lUshkMhQUFBilMHNVUKxGYXHpfD+8LEVERFTz9OpQPG/ePNjb22ufq1QqLF26FAqFQrts5cqVhqvODJXdDdxKKoGDjHdJJyIiqmk6h5tevXohOjq63LLAwEDExsZqn0skvElk5v1h4C52NjwfREREJqBzuDl69KgRy7AcZeFGwZtmEhERmYTeMxTTg2UX3g83dgw3REREpsBwY2A5hSUAACdbhhsiIiJTYLgxsOz7HYqdbfW+swUREREZAMONgZVdlmLLDRERkWkw3BhY7v3LUs52bLkhIiIyhWqFm+PHj2PMmDHo3r07bt26BQD45ptvcOLEiWoVsWbNGvj5+cHW1hYBAQE4e/Zsletu2LABPXv2hKurK1xdXREUFPTA9Wuats+NnOGGiIjIFPQON99//z2Cg4NhZ2eHixcvoqioCACQlZWFZcuW6V3Ajh07MH36dCxYsAAXLlxA+/btERwcjDt37lS6/tGjR/H888/jyJEjOH36NJRKJZ544gltyDK1nCJeliIiIjIlvcPNe++9h3Xr1mHDhg2wsfn7C7xHjx64cOGC3gWsXLkSL7/8MiZMmIBWrVph3bp1sLe3x1dffVXp+tu2bcPrr7+ODh06oEWLFti4cSM0Gg0OHTqk97GNoazlxpEtN0RERCahd7iJjo5Gr169KixXKBTIzMzUa18qlQrh4eEICgr6uyCpFEFBQTh9+rRO+8jPz0dxcTHc3Nwqfb2oqAjZ2dnlHsakDTccLUVERGQSeocbLy8v3Lhxo8LyEydOoFGjRnrtKy0tDWq1Gp6enuWWe3p6IiUlRad9vPvuu/Dx8SkXkP5p+fLlUCgU2odSqdSrRn3lFbHlhoiIyJT0Djcvv/wypk6dijNnzkAikeD27dvYtm0bZsyYgYkTJxqjxiq9//77CA0NxY8//ghbW9tK15k1axaysrK0j6SkJKPWlK9SAwAcGG6IiIhMQu9v4JkzZ0Kj0eDxxx9Hfn4+evXqBblcjhkzZmDKlCl67cvd3R1WVlZITU0ttzw1NRVeXl4P3Pbjjz/G+++/j4MHD6Jdu3ZVrieXyyGXy/Wq62EUFJeGG3veEZyIiMgk9G65kUgkmDNnDtLT03HlyhX88ccfuHv3LpYsWaL3wWUyGTp37lyuM3BZ5+Du3btXud2HH36IJUuWICwsDP7+/nof15jyVaWXpexsGG6IiIhModrXTmQyGVq1avXQBUyfPh0hISHw9/dH165dsWrVKuTl5WHChAkAgHHjxsHX1xfLly8HAHzwwQeYP38+tm/fDj8/P23fHEdHRzg6Oj50PQ9DoxEoLNYAAOzYckNERGQSeoebPn36QCKRVPn64cOH9drfiBEjcPfuXcyfPx8pKSno0KEDwsLCtJ2MExMTIZX+3cC0du1aqFQqPPPMM+X2s2DBAixcuFCvYxtaUYlG+7MtW26IiIhMQu9w06FDh3LPi4uLERERgStXriAkJKRaRUyePBmTJ0+u9LWjR4+Wex4fH1+tY9SEwvv9bQDA1pp3tiAiIjIFvcPNJ598UunyhQsXIjc396ELMmdlnYltrCSwtmK4ISIiMgWDfQOPGTOmylmFa4uycMPOxERERKZjsHBz+vTpKueaqS0K7s9xw/42REREpqP3Zanhw4eXey6EQHJyMs6fP4958+YZrDBzVMg5boiIiExO73CjUCjKPZdKpWjevDkWL16MJ554wmCFmaOyYeBsuSEiIjIdvcKNWq3GhAkT0LZtW7i6uhqrJrNV1nIjZ7ghIiIyGb363FhZWeGJJ57Q++7ftcXfHYo5UoqIiMhU9P4WbtOmDWJjY41Ri9krm8RPbs2WGyIiIlPRO9y89957mDFjBn799VckJycjOzu73KM2Kyq5f1mKE/gRERGZjM59bhYvXoy33noLAwcOBAA89dRT5W7DIISARCKBWq2uahcWjx2KiYiITE/ncLNo0SK89tprOHLkiDHrMWtlHYpt2eeGiIjIZHQON0IIAEDv3r2NVoy5K+QMxURERCanVxPDg+4GTpyhmIiI6FGg1zw3zZo1+8+Ak56e/lAFmTOVumy0FC9LERERmYpe4WbRokUVZiimv6nuDwWXMdwQERGZjF7hZuTIkfDw8DBWLWaP4YaIiMj0dP4WZn+b/1Z0/7KUzIrhhoiIyFR0/hYuGy1FVSu+33Jjw5YbIiIik9H5spRGozFmHRahRFMaAG3YckNERGQy/BY2oOL7l6VsrHgJj4iIyFQYbgzo73DD00pERGQq/BY2oGJ16WUpaylPKxERkanwW9iAeFmKiIjI9BhuDKis5YaXpYiIiEyH38IGVHK/5caaLTdEREQmw3BjQBwKTkREZHr8Fjagsj431lK23BAREZkKw40BcSg4ERGR6fFb2IDUvCxFRERkcvwWNqCyPjdWvCxFRERkMgw3BqTWTuLHcENERGQqDDcGxJYbIiIi02O4MaCyPjec54aIiMh0GG4MqFhTOlqKLTdERESmw3BjIBqNgChtuIGVhOGGiIjIVBhuDKSsvw0AWHMoOBERkcnwW9hANOLvcMPLUkRERKbDcGMg5VpuGG6IiIhMhuHGQMrmuAHYckNERGRKDDcGov7nZSl2KCYiIjIZhhsDKbk/DFwiAaRsuSEiIjIZhhsDKZvAj602REREpsVwYyBl/YnZakNERGRaDDcGormfbhhtiIiITMva1AVYGikvSxHVekIIlJSUQK1Wm7oUIrNiY2MDKyurh94Pw42BlE3ix2xDVLupVCokJycjPz/f1KUQmR2JRIJ69erB0dHxofbDcGMgZSPB2XJDVHtpNBrExcXBysoKPj4+kMlkkPBvApFOhBC4e/cubt68iaZNmz5UCw7DjYFoW25MXAcRmY5KpYJGo4FSqYS9vb2pyyEyO3Xr1kV8fDyKi4sfKtywQ7GBlE3hx3+kEZFUyj+tRNVhqJZO/gYaiND2uWG6ISIiMiWGGwP5u8+NaesgIiKq7RhuDETDDsVERESPBIYbA+FQcCKqDSQSCX766SejH+fo0aOQSCTIzMzULvvpp5/QpEkTWFlZ4c0338TmzZvh4uJitBqio6Ph5eWFnJwcox2jtunWrRu+//57ox+H4cZAyi5Lsc8NEZmrlJQUTJkyBY0aNYJcLodSqcTgwYNx6NChGq8lMDAQycnJUCgU2mWvvvoqnnnmGSQlJWHJkiUYMWIErl+/brQaZs2ahSlTpsDJyanCay1atIBcLkdKSkqF1/z8/LBq1aoKyxcuXIgOHTqUW2aqc75z5060aNECtra2aNu2Lfbu3fuf26xZswYtW7aEnZ0dmjdvjq1bt+q937lz52LmzJnQ3L/ZtLEw3BgIh4ITUWWEEMhXldT4o2yQg67i4+PRuXNnHD58GB999BEuX76MsLAw9OnTB5MmTTLS2amaTCaDl5eX9h+Mubm5uHPnDoKDg+Hj4wMnJyfY2dnBw8PjoY5TXFxc6fLExET8+uuvGD9+fIXXTpw4gYKCAjzzzDPYsmVLtY9tqnN+6tQpPP/883jxxRdx8eJFDB06FEOHDsWVK1eq3Gbt2rWYNWsWFi5ciKtXr2LRokWYNGkSfvnlF732O2DAAOTk5OC3334z2vsDOM+NwbHPDRH9U0GxGq3m76vx40YuDoa9TPc/8a+//jokEgnOnj0LBwcH7fLWrVvjhRdeqHK7d999Fz/++CNu3rwJLy8vjB49GvPnz4eNjQ0A4NKlS3jzzTdx/vx5SCQSNG3aFF9++SX8/f2RkJCAyZMn48SJE1CpVPDz88NHH32EgQMH4ujRo+jTpw8yMjIQERGBPn36AAD69u0LADhy5Aji4+Px5ptvlrt09fPPP2PRokWIjIyEj48PQkJCMGfOHFhbl54LiUSCL774Ar/99hsOHTqEt99+GwsXLqzwvr777ju0b98evr6+FV7btGkTRo0ahd69e2Pq1Kl49913dT7P/1Tdc/6wVq9ejf79++Ptt98GACxZsgQHDhzA559/jnXr1lW6zTfffINXX30VI0aMAAA0atQI586dwwcffIDBgwfrvF8rKysMHDgQoaGhGDRokNHe4yPRcrNmzRr4+fnB1tYWAQEBOHv27APXr05zmrGxzw0Rmav09HSEhYVh0qRJ5b5kyzyoX4uTkxM2b96MyMhIrF69Ghs2bMAnn3yifX306NGoV68ezp07h/DwcMycOVMbfCZNmoSioiL8/vvvuHz5Mj744INKp90PDAxEdHQ0AOD7779HcnIyAgMDK6x3/PhxjBs3DlOnTkVkZCS+/PJLbN68GUuXLi233sKFCzFs2DBcvny5yhBx/Phx+Pv7V1iek5ODnTt3YsyYMejXrx+ysrJw/PjxKs9PVR7mnG/btg2Ojo4PfDyoptOnTyMoKKjcsuDgYJw+fbrKbYqKimBra1tumZ2dHc6ePatt/dJ1v127dq3WOdOHyVtuduzYgenTp2PdunUICAjAqlWrEBwcjOjo6EqbG8uavZYvX44nn3wS27dvx9ChQ3HhwgW0adPGBO+gFG+/QESVsbOxQuTiYJMcV1c3btyAEAItWrTQ+zhz587V/uzn54cZM2YgNDQU77zzDoDSyztvv/22dt9NmzbVrp+YmIinn34abdu2BVDaGlAZmUym/T5wc3ODl5dXpestWrQIM2fOREhIiHZ/S5YswTvvvIMFCxZo1xs1ahQmTJjwwPeVkJBQabgJDQ1F06ZN0bp1awDAyJEjsWnTJvTs2fOB+/u3hznnTz31FAICAh64TmUtTmVSUlLg6elZbpmnp2el/YfKBAcHY+PGjRg6dCg6deqE8PBwbNy4EcXFxUhLS4O3t7fO+/Xx8UFSUhI0Go3RJrw0ebhZuXIlXn75Ze0Hbd26ddizZw+++uorzJw5s8L61WlOqwkaPa9vE1HtIJFI9Lo8ZAr69s/5px07duDTTz9FTEwMcnNzUVJSAmdnZ+3r06dPx0svvYRvvvkGQUFBePbZZ9G4cWMAwBtvvIGJEydi//79CAoKwtNPP4127dpVu5ZLly7h5MmT5Vpq1Go1CgsLkZ+fr70lRmWh5d8KCgoqtFQAwFdffYUxY8Zon48ZMwa9e/fGZ599VmnH46o8zDl3cnLS61iGMG/ePKSkpKBbt24QQsDT0xMhISH48MMP9Q4odnZ20Gg0KCoqgp2dnVHqNellKZVKhfDw8HLNWFKpFEFBQVU2j+nbnFZUVITs7OxyD2Mo+5hy1nUiMjdNmzaFRCLBtWvX9Nru9OnTGD16NAYOHIhff/0VFy9exJw5c6BSqbTrlHVAHTRoEA4fPoxWrVrhxx9/BAC89NJLiI2NxdixY3H58mX4+/vjs88+q/b7yM3NxaJFixAREaF9XL58GX/99Ve5oFLZZaB/c3d3R0ZGRrllkZGR+OOPP/DOO+/A2toa1tbW6NatG/Lz8xEaGqpdz9nZGVlZWRX2mZmZqR39Vd1zDjz8ZSkvLy+kpqaWW5aamlplixhQGki++uor5OfnIz4+HomJifDz84OTkxPq1q2r137T09Ph4OBgtGADmDjcpKWlQa1W69U8pm9z2vLly6FQKLQPpVJpmOIrYWsjha119W/0RURkCm5ubggODsaaNWuQl5dX4fV/dtj9p1OnTqFBgwaYM2cO/P390bRpUyQkJFRYr1mzZpg2bRr279+P4cOH4+uvv9a+plQq8dprr+GHH37AW2+9hQ0bNlT7fXTq1AnR0dFo0qRJhYe+rQsdO3ZEZGRkuWWbNm1Cr169cOnSpXIBavr06di0aZN2vebNmyM8PLzCPi9cuIBmzZoBqP45B0ovS/3z+JU9HtQ61b179wpDzQ8cOIDu3btXuU0ZGxsb1KtXD1ZWVggNDcWTTz6pPbe67vfKlSvo2LHjfx7roQgTunXrlgAgTp06VW7522+/Lbp27VrpNjY2NmL79u3llq1Zs0Z4eHhUun5hYaHIysrSPpKSkgQAkZWVZZg3QUR0X0FBgYiMjBQFBQWmLkVvMTExwsvLS7Rq1Urs2rVLXL9+XURGRorVq1eLFi1aaNcDIH788UchhBA///yzsLa2Ft9++624ceOGWL16tXBzcxMKhUIIIUR+fr6YNGmSOHLkiIiPjxcnTpwQjRs3Fu+8844QQoipU6eKsLAwERsbK8LDw0VAQIB47rnnhBBCHDlyRAAQGRkZQgghMjIyBABx5MgRbS1ff/219lhCCBEWFiasra3FwoULxZUrV0RkZKT49ttvxZw5cyqt/0F2794tPDw8RElJiRBCCJVKJerWrSvWrl1bYd3IyEgBQFy5ckUIIcTJkyeFVCoV7733noiMjBSXL18Ws2fPFtbW1uLy5ct6n3NDO3nypLC2thYff/yxiIqKEgsWLBA2Njblaps5c6YYO3as9nl0dLT45ptvxPXr18WZM2fEiBEjhJubm4iLi9Nrv0II0bt3b7F48eJKa3vQ71BWVpbO398mDTdFRUXCysqqwgdt3Lhx4qmnnqp0G6VSKT755JNyy+bPny/atWun0zH1OTlERPow53AjhBC3b98WkyZNEg0aNBAymUz4+vqKp556qlyg+Hc4ePvtt0WdOnWEo6OjGDFihPjkk0+0gaOoqEiMHDlSKJVKIZPJhI+Pj5g8ebL2/EyePFk0btxYyOVyUbduXTF27FiRlpYmhKheuBGiNOAEBgYKOzs74ezsLLp27SrWr19fZf1VKS4uFj4+PiIsLEwIIcSuXbuEVCoVKSkpla7fsmVLMW3aNO3zffv2iR49eghXV1dRp04d8b///U8cO3aswna6nHNj+O6770SzZs2ETCYTrVu3Fnv27Cn3ekhIiOjdu7f2eWRkpOjQoYP2vA4ZMkRcu3ZN7/3evHlT2NjYiKSkpErrMlS4kQhh2p6wAQEB6Nq1q/Y6q0ajQf369TF58uRKOxSPGDEC+fn55SYOCgwMRLt27XTqUJydnQ2FQoGsrKxynd6IiB5WYWEh4uLi0LBhw0o7o5J5WbNmDXbv3o19+2p+niJL9e677yIjIwPr16+v9PUH/Q7p8/1t8i7806dPR0hICPz9/dG1a1esWrUKeXl52tFT48aNg6+vL5YvXw4AmDp1Knr37o0VK1Zg0KBBCA0Nxfnz56s8UURERNXx6quvIjMzEzk5OTU+OslSeXh4YPr06UY/jsnDzYgRI3D37l3Mnz8fKSkp6NChA8LCwrSdhhMTE8t1BAsMDMT27dsxd+5czJ49G02bNsVPP/1k0jluiIjI8lhbW2POnDmmLsOivPXWWzVyHJNflqppvCxFRMbCy1JED8dQl6U4KwsRkYHVsn8zEhmMoX53GG6IiAyk7J5J+fn5Jq6EyDyVTQBpZfVwc8aZvM8NEZGlsLKygouLC+7cuQMAsLe3h4T3myPSiUajwd27d2Fvb6+9i3t1MdwQERlQ2VTzZQGHiHQnlUpRv379h/5HAcMNEZEBSSQSeHt7w8PDA8XFxaYuh8isyGQyg9wpnOGGiMgIrKysHrrfABFVDzsUExERkUVhuCEiIiKLwnBDREREFqXW9bkpmyAoOzvbxJUQERGRrsq+t3WZ6K/WhZucnBwAgFKpNHElREREpK+cnBwoFIoHrlPr7i2l0Whw+/ZtODk5GXxyrezsbCiVSiQlJfG+VUbE81wzeJ5rBs9zzeG5rhnGOs9CCOTk5MDHx+c/h4vXupYbqVSKevXqGfUYzs7O/MWpATzPNYPnuWbwPNccnuuaYYzz/F8tNmXYoZiIiIgsCsMNERERWRSGGwOSy+VYsGAB5HK5qUuxaDzPNYPnuWbwPNccnuua8Sic51rXoZiIiIgsG1tuiIiIyKIw3BAREZFFYbghIiIii8JwQ0RERBaF4UZPa9asgZ+fH2xtbREQEICzZ88+cP2dO3eiRYsWsLW1Rdu2bbF3794aqtS86XOeN2zYgJ49e8LV1RWurq4ICgr6z/8vVErfz3OZ0NBQSCQSDB061LgFWgh9z3NmZiYmTZoEb29vyOVyNGvWjH87dKDveV61ahWaN28OOzs7KJVKTJs2DYWFhTVUrXn6/fffMXjwYPj4+EAikeCnn376z22OHj2KTp06QS6Xo0mTJti8ebPR64QgnYWGhgqZTCa++uorcfXqVfHyyy8LFxcXkZqaWun6J0+eFFZWVuLDDz8UkZGRYu7cucLGxkZcvny5his3L/qe51GjRok1a9aIixcviqioKDF+/HihUCjEzZs3a7hy86LveS4TFxcnfH19Rc+ePcWQIUNqplgzpu95LioqEv7+/mLgwIHixIkTIi4uThw9elRERETUcOXmRd/zvG3bNiGXy8W2bdtEXFyc2Ldvn/D29hbTpk2r4crNy969e8WcOXPEDz/8IACIH3/88YHrx8bGCnt7ezF9+nQRGRkpPvvsM2FlZSXCwsKMWifDjR66du0qJk2apH2uVquFj4+PWL58eaXrP/fcc2LQoEHllgUEBIhXX33VqHWaO33P87+VlJQIJycnsWXLFmOVaBGqc55LSkpEYGCg2LhxowgJCWG40YG+53nt2rWiUaNGQqVS1VSJFkHf8zxp0iTRt2/fcsumT58uevToYdQ6LYku4eadd94RrVu3LrdsxIgRIjg42IiVCcHLUjpSqVQIDw9HUFCQdplUKkVQUBBOnz5d6TanT58utz4ABAcHV7k+Ve88/1t+fj6Ki4vh5uZmrDLNXnXP8+LFi+Hh4YEXX3yxJso0e9U5z7t370b37t0xadIkeHp6ok2bNli2bBnUanVNlW12qnOeAwMDER4err10FRsbi71792LgwIE1UnNtYarvwVp348zqSktLg1qthqenZ7nlnp6euHbtWqXbpKSkVLp+SkqK0eo0d9U5z//27rvvwsfHp8IvFP2tOuf5xIkT2LRpEyIiImqgQstQnfMcGxuLw4cPY/To0di7dy9u3LiB119/HcXFxViwYEFNlG12qnOeR40ahbS0NDz22GMQQqCkpASvvfYaZs+eXRMl1xpVfQ9mZ2ejoKAAdnZ2RjkuW27Iorz//vsIDQ3Fjz/+CFtbW1OXYzFycnIwduxYbNiwAe7u7qYux6JpNBp4eHhg/fr16Ny5M0aMGIE5c+Zg3bp1pi7Nohw9ehTLli3DF198gQsXLuCHH37Anj17sGTJElOXRgbAlhsdubu7w8rKCqmpqeWWp6amwsvLq9JtvLy89Fqfqneey3z88cd4//33cfDgQbRr186YZZo9fc9zTEwM4uPjMXjwYO0yjUYDALC2tkZ0dDQaN25s3KLNUHU+z97e3rCxsYGVlZV2WcuWLZGSkgKVSgWZTGbUms1Rdc7zvHnzMHbsWLz00ksAgLZt2yIvLw+vvPIK5syZA6mU//Y3hKq+B52dnY3WagOw5UZnMpkMnTt3xqFDh7TLNBoNDh06hO7du1e6Tffu3cutDwAHDhyocn2q3nkGgA8//BBLlixBWFgY/P39a6JUs6bveW7RogUuX76MiIgI7eOpp55Cnz59EBERAaVSWZPlm43qfJ579OiBGzduaMMjAFy/fh3e3t4MNlWoznnOz8+vEGDKAqXgLRcNxmTfg0btrmxhQkNDhVwuF5s3bxaRkZHilVdeES4uLiIlJUUIIcTYsWPFzJkzteufPHlSWFtbi48//lhERUWJBQsWcCi4DvQ9z++//76QyWRi165dIjk5WfvIyckx1VswC/qe53/jaCnd6HueExMThZOTk5g8ebKIjo4Wv/76q/Dw8BDvvfeeqd6CWdD3PC9YsEA4OTmJb7/9VsTGxor9+/eLxo0bi+eee85Ub8Es5OTkiIsXL4qLFy8KAGLlypXi4sWLIiEhQQghxMyZM8XYsWO165cNBX/77bdFVFSUWLNmDYeCP4o+++wzUb9+fSGTyUTXrl3FH3/8oX2td+/eIiQkpNz63333nWjWrJmQyWSidevWYs+ePTVcsXnS5zw3aNBAAKjwWLBgQc0Xbmb0/Tz/E8ON7vQ9z6dOnRIBAQFCLpeLRo0aiaVLl4qSkpIartr86HOei4uLxcKFC0Xjxo2Fra2tUCqV4vXXXxcZGRk1X7gZOXLkSKV/b8vObUhIiOjdu3eFbTp06CBkMplo1KiR+Prrr41ep0QItr8RERGR5WCfGyIiIrIoDDdERERkURhuiIiIyKIw3BAREZFFYbghIiIii8JwQ0RERBaF4YaIiIgsCsMNERERWRSGG6JKbN68GS4uLqYuo9okEgl++umnB64zfvx4DB06tEbqedTMmzcPr7zySo0c6+jRo5BIJMjMzHzgen5+fli1apVRa9H3GIb6PdDl86ivyMhI1KtXD3l5eQbdL1kGhhuyWOPHj4dEIqnwuHHjhqlLw+bNm7X1SKVS1KtXDxMmTMCdO3cMsv/k5GQMGDAAABAfHw+JRIKIiIhy66xevRqbN282yPGqsnDhQu37tLKyglKpxCuvvIL09HS99mPIIJaSkoLVq1djzpw55fZfVqdMJkOTJk2wePFilJSUPPTxAgMDkZycDIVCAaDqwHDu3LkaC1zmYOnSpQgMDIS9vX2l56tVq1bo1q0bVq5cWfPF0SOP4YYsWv/+/ZGcnFzu0bBhQ1OXBQBwdnZGcnIybt68iQ0bNuC3337D2LFjDbJvLy8vyOXyB66jUChqpHWqdevWSE5ORmJiIr7++muEhYVh4sSJRj9uVTZu3IjAwEA0aNCg3PKyz8pff/2Ft956CwsXLsRHH3300MeTyWTw8vKCRCJ54Hp169aFvb39Qx/PUqhUKjz77LMP/KxMmDABa9euNUgIJcvCcEMWTS6Xw8vLq9zDysoKK1euRNu2beHg4AClUonXX38dubm5Ve7n0qVL6NOnD5ycnODs7IzOnTvj/Pnz2tdPnDiBnj17ws7ODkqlEm+88cZ/NpdLJBJ4eXnBx8cHAwYMwBtvvIGDBw+ioKAAGo0GixcvRr169SCXy9GhQweEhYVpt1WpVJg8eTK8vb1ha2uLBg0aYPny5eX2XXYZoCzMdezYERKJBP/73/8AlG8NWb9+PXx8fKDRaMrVOGTIELzwwgva5z///DM6deoEW1tbNGrUCIsWLfrPLxZra2t4eXnB19cXQUFBePbZZ3HgwAHt62q1Gi+++CIaNmwIOzs7NG/eHKtXr9a+vnDhQmzZsgU///yztnXl6NGjAICkpCQ899xzcHFxgZubG4YMGYL4+PgH1hMaGorBgwdXWF72WWnQoAEmTpyIoKAg7N69GwCQkZGBcePGwdXVFfb29hgwYAD++usv7bYJCQkYPHgwXF1d4eDggNatW2Pv3r0Ayl+WOnr0KCZMmICsrCzte1m4cCGA8peMRo0ahREjRpSrr7i4GO7u7ti6dSsAQKPRYPny5drz1r59e+zateuB7/3fdP09+Omnn9C0aVPY2toiODgYSUlJ5V6vzufivyxatAjTpk1D27Ztq1ynX79+SE9Px7Fjxx7qWGR5GG6oVpJKpfj0009x9epVbNmyBYcPH8Y777xT5fqjR49GvXr1cO7cOYSHh2PmzJmwsbEBAMTExKB///54+umn8eeff2LHjh04ceIEJk+erFdNdnZ20Gg0KCkpwerVq7FixQp8/PHH+PPPPxEcHIynnnpK+4X66aefYvfu3fjuu+8QHR2Nbdu2wc/Pr9L9nj17FgBw8OBBJCcn44cffqiwzrPPPot79+7hyJEj2mXp6ekICwvD6NGjAQDHjx/HuHHjMHXqVERGRuLLL7/E5s2bsXTpUp3fY3x8PPbt2weZTKZdptFoUK9ePezcuRORkZGYP38+Zs+eje+++w4AMGPGDDz33HPlWuECAwNRXFyM4OBgODk54fjx4zh58iQcHR3Rv39/qFSqSo+fnp6OyMhI+Pv7/2etdnZ22v2MHz8e58+fx+7du3H69GkIITBw4EAUFxcDACZNmoSioiL8/vvvuHz5Mj744AM4OjpW2GdgYCBWrVqlbbVLTk7GjBkzKqw3evRo/PLLL+WCxr59+5Cfn49hw4YBAJYvX46tW7di3bp1uHr1KqZNm4YxY8bo9UWvy+9Bfn4+li5diq1bt+LkyZPIzMzEyJEjta9X53Pxv//9D+PHj9e5zqrIZDJ06NABx48ff+h9kYUx+n3HiUwkJCREWFlZCQcHB+3jmWeeqXTdnTt3ijp16miff/3110KhUGifOzk5ic2bN1e67YsvviheeeWVcsuOHz8upFKpKCgoqHSbf+//+vXrolmzZsLf318IIYSPj49YunRpuW26dOkiXn/9dSGEEFOmTBF9+/YVGo2m0v0DED/++KMQQoi4uDgBQFy8eLHcOiEhIWLIkCHa50OGDBEvvPCC9vmXX34pfHx8hFqtFkII8fjjj4tly5aV28c333wjvL29K61BCCEWLFggpFKpcHBwELa2tgKAACBWrlxZ5TZCCDFp0iTx9NNPV1lr2bGbN29e7hwUFRUJOzs7sW/fvkr3e/HiRQFAJCYmllv+z/1rNBpx4MABIZfLxYwZM8T169cFAHHy5Ent+mlpacLOzk589913Qggh2rZtKxYuXFjpMY8cOSIAiIyMDCFExf/3ZRo0aCA++eQTIYQQxcXFwt3dXWzdulX7+vPPPy9GjBghhBCisLBQ2Nvbi1OnTpXbx4svviief/75Suv49zEqU9nvAQDxxx9/aJdFRUUJAOLMmTNCCN0+F//8PAohxNixY8XMmTOrrOOfqjpfZYYNGybGjx+v076o9rA2Vagiqgl9+vTB2rVrtc8dHBwAlLZiLF++HNeuXUN2djZKSkpQWFiI/Pz8Svs9TJ8+HS+99BK++eYb7aWVxo0bAyi9ZPXnn39i27Zt2vWFENBoNIiLi0PLli0rrS0rKwuOjo7QaDQoLCzEY489ho0bNyI7Oxu3b99Gjx49yq3fo0cPXLp0CUBpS0K/fv3QvHlz9O/fH08++SSeeOKJhzpXo0ePxssvv4wvvvgCcrkc27Ztw8iRIyGVSrXv8+TJk+X+Ra5Wqx943gCgefPm2L17NwoLC/F///d/iIiIwJQpU8qts2bNGnz11VdITExEQUEBVCoVOnTo8MB6L126hBs3bsDJyanc8sLCQsTExFS6TUFBAQDA1ta2wmu//vorHB0dUVxcDI1Gg1GjRmHhwoU4dOgQrK2tERAQoF23Tp06aN68OaKiogAAb7zxBiZOnIj9+/cjKCgITz/9NNq1a/fA+h/E2toazz33HLZt24axY8ciLy8PP//8M0JDQwEAN27cQH5+Pvr161duO5VKhY4dO+p8HF1+D6ytrdGlSxftNi1atICLiwuioqLQtWvXan0uyi6tGYKdnR3y8/MNtj+yDAw3ZNEcHBzQpEmTcsvi4+Px5JNPYuLEiVi6dCnc3Nxw4sQJvPjii1CpVJX+MV64cCFGjRqFPXv24LfffsOCBQsQGhqKYcOGITc3F6+++ireeOONCtvVr1+/ytqcnJxw4cIFSKVSeHt7w87ODgCQnZ39n++rU6dOiIuLw2+//YaDBw/iueeeQ1BQkN59Lv5p8ODBEEJgz5496NKlC44fP45PPvlE+3pubi4WLVqE4cOHV9i2srBQpmz0EQC8//77GDRoEBYtWoQlS5YAKO0DM2PGDKxYsQLdu3eHk5MTPvroI5w5c+aB9ebm5qJz587lQmWZunXrVrqNu7s7gNI+NP9epywIy2Qy+Pj4wNpa9z+PL730EoKDg7Fnzx7s378fy5cvx4oVKyqEOH2MHj0avXv3xp07d3DgwAHY2dmhf//+AKC9XLVnzx74+vqW2+6/OpKXqc7vQWWq+7kwlPT0dO0/NIjKMNxQrRMeHg6NRoMVK1ZoWyXK+nc8SLNmzdCsWTNMmzYNzz//PL7++msMGzYMnTp1QmRkZIUQ9V+kUmml2zg7O8PHxwcnT55E7969tctPnjyJrl27lltvxIgRGDFiBJ555hn0798f6enpcHNzK7e/sv4tarX6gfXY2tpi+PDh2LZtG27cuIHmzZujU6dO2tc7deqE6Ohovd/nv82dOxd9+/bFxIkTte8zMDAQr7/+unadf7e8yGSyCvV36tQJO3bsgIeHB5ydnXU6duPGjeHs7IzIyEg0a9as3GuVBWEAaNmyJUpKSnDmzBkEBgYCAO7du4fo6Gi0atVKu55SqcRrr72G1157DbNmzcKGDRsqDTeVvZfKBAYGQqlUYseOHfjtt9/w7LPPavt5tWrVCnK5HImJieU+I/rQ9fegpKQE58+f1372oqOjkZmZqW2RNNTnorquXLmCZ555xiTHpkcXOxRTrdOkSRMUFxfjs88+Q2xsLL755husW7euyvULCgowefJkHD16FAkJCTh58iTOnTun/eP+7rvv4tSpU5g8eTIiIiLw119/4eeff9a7Q/E/vf322/jggw+wY8cOREdHY+bMmYiIiMDUqVMBlI5y+fbbb3Ht2jVcv34dO3fuhJeXV6VDuz08PGBnZ4ewsDCkpqYiKyuryuOOHj0ae/bswVdffaXtSFxm/vz52Lp1KxYtWoSrV68iKioKoaGhmDt3rl7vrXv37mjXrh2WLVsGAGjatCnOnz+Pffv24fr165g3bx7OnTtXbhs/Pz/8+eefiI6ORlpaGoqLizF69Gi4u7tjyJAhOH78OOLi4nD06FG88cYbuHnzZqXHlkqlCAoKwokTJ3Sut2nTphgyZAhefvllnDhxApcuXcKYMWPg6+uLIUOGAADefPNN7Nu3D3Fxcbhw4QKOHDlS5eVIPz8/5Obm4tChQ0hLS3vgJZVRo0Zh3bp1OHDgQLn/H05OTpgxYwamTZuGLVu2ICYmBhcuXMBnn32GLVu26PS+dP09sLGxwZQpU3DmzBmEh4dj/Pjx6NatmzbsVOdzMW7cOMyaNeuB9SUmJiIiIgKJiYlQq9WIiIhAREREuU7W8fHxuHXrFoKCgnR6z1SLmLrTD5GxVNYJtczKlSuFt7e3sLOzE8HBwWLr1q1VdvosKioSI0eOFEqlUshkMuHj4yMmT55crrPw2bNnRb9+/YSjo6NwcHAQ7dq1q9Ah+J/+q5OkWq0WCxcuFL6+vsLGxka0b99e/Pbbb9rX169fLzp06CAcHByEs7OzePzxx8WFCxe0r+NfHTg3bNgglEqlkEqlonfv3lWeH7VaLby9vQUAERMTU6GusLAwERgYKOzs7ISzs7Po2rWrWL9+fZXvY8GCBaJ9+/YVln/77bdCLpeLxMREUVhYKMaPHy8UCoVwcXEREydOFDNnziy33Z07d7TnF4A4cuSIEEKI5ORkMW7cOOHu7i7kcrlo1KiRePnll0VWVlaVNe3du1f4+vpqO0pXdS7+KT09XYwdO1YoFArtZ+b69eva1ydPniwaN24s5HK5qFu3rhg7dqxIS0sTQlTsUCyEEK+99pqoU6eOACAWLFgghKi8s29kZKQAIBo0aFCh87hGoxGrVq0SzZs3FzY2NqJu3boiODhYHDt2rMr38e9j6Pp78P3334tGjRoJuVwugoKCREJCQrn9/tfn4t+fx969e4uQkJAq6xSi9P8J7ndA/+ej7P+9EEIsW7ZMBAcHP3A/VDtJhBDCFKGKiMgUhBAICAjQXl4k86RSqdC0aVNs3769Qud7Il6WIqJaRSKRYP369ZzV1swlJiZi9uzZDDZUKbbcEBERkUVhyw0RERFZFIYbIiIisigMN0RERGRRGG6IiIjIojDcEBERkUVhuCEiIiKLwnBDREREFoXhhoiIiCwKww0RERFZlP8HYFY0KNVu6xMAAAAASUVORK5CYII="},"metadata":{}}]},{"cell_type":"markdown","source":"# Analyzing the model's performance","metadata":{}},{"cell_type":"markdown","source":"In the following cell, we create 1D-CNN model to predict Walking using relevant features and analyze the output results.","metadata":{}},{"cell_type":"code","source":"y_train = tdcsfog[\"Walking\"][:num_samples]\ny_train = np.array(y_train)\n\ny_test = tdcsfog[\"Walking\"][:num_samples]\ny_test = np.array(y_test)\n\nmodel = run_1D_CNN_model(X_train, y_train, X_test, y_test)\nscore = model.evaluate(X_test, y_test, verbose=0)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])\n\ny_pred = model.predict(X_test)\nRocCurveDisplay.from_predictions(y_test, y_pred)","metadata":{"execution":{"iopub.status.busy":"2023-05-16T17:50:08.420359Z","iopub.execute_input":"2023-05-16T17:50:08.420691Z","iopub.status.idle":"2023-05-16T17:56:51.721142Z","shell.execute_reply.started":"2023-05-16T17:50:08.420667Z","shell.execute_reply":"2023-05-16T17:56:51.719989Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stdout","text":"Epoch 1/3\n31250/31250 [==============================] - 106s 3ms/step - loss: 0.0200 - accuracy: 0.9966 - val_loss: 0.0171 - val_accuracy: 0.9966\nEpoch 2/3\n31250/31250 [==============================] - 103s 3ms/step - loss: 0.0167 - accuracy: 0.9966 - val_loss: 0.0152 - val_accuracy: 0.9966\nEpoch 3/3\n31250/31250 [==============================] - 108s 3ms/step - loss: 0.0151 - accuracy: 0.9966 - val_loss: 0.0142 - val_accuracy: 0.9966\nTest loss: 0.014154487289488316\nTest accuracy: 0.9966089725494385\n31250/31250 [==============================] - 39s 1ms/step\n","output_type":"stream"},{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7d1f9524b2e0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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8OKLL2LGjBlo2rQpTp8+be64qQzY+Vccak7ZpZfYzO3ZCDELuiJmQTe80siDiQ0RERnNoJobe3t7REdHo1KlSgX2ubm5oVOnTujUqRNmzpyJsLAw3L59G61atTJ5sFQ23EvOxMDVfyI6MV1XFtjAHasGt+TEe0RE9Nxk4un2pXIgNTUVTk5OSElJgaMjV4guSXEpmRjz43mc+dfaT5vfewH+NQsmzkRERPmMeX9z2AmViFMxSXjz+xN6ZWM61UZI57qsrSEiIpMyWc/fK1euoGbNmqa6HJUhey7H6yU2rX0q4sKMV/DRK/WY2BARkcmZrOZGrVbj1q1bproclQFp2bl4/bvjiEp4rCvbNjIAzau5SBgVERGVdQYnNyEhIc/c/+DBg+cOhsqO5Aw1/D7dj1ztP126wj5sh/oe7OdERETmZXBys2TJEvj6+hbZiSctLc1kQZFly1DnwnfOPt32y/XdsDrYj01QRERUIgxObmrXro3x48dj4MCBhe6PiIhAy5YtTRYYWabdF+MwYtM53fbYl+sgpHNdCSMiIqLyxuAOxX5+fjh79myR+2UyGcrZqHJ6ysU7KQhYEK6X2Hg52zKxISKiEmdwzc3ChQuRnZ1d5P5mzZpBq9WaJCiyLFHxj9F96VG9MnYcJiIiqRic3Hh4eJgzDrJAKZk5mLrtIn7/K05X9mmvxhjgX439a4iISDKcxI+MlpWjQY+lR3EtQb8T+dohrdCxvptEUREREeVhckNGycrRoONXhxCXkqUrs1bIcGxSJ7g52EgYGRERUR4mN2Sw5Ydu4POwq3plMQu6sgmKiIhKFZMtv0Bl24GrCXqJjWsFFf6e14WJDRERlTqSJzfLli2Dj48PbGxs4O/vj1OnTj3z+MWLF6NevXqwtbWFt7c3xo8fj6ysrGeeQ8/nz+iHeGfdGd32/pAOODMtENYKyX98iIiICihWs9Qff/wBOzs7+Pn56crOnDmDjIwMtG/f3uDrbN68GSEhIVixYgX8/f2xePFiBAUFISoqCm5uBTum/vjjj5g0aRLWrFmDgIAAXLt2DUOGDIFMJsOiRYuK81HoGVIycjBsw2mcvvlIV7Y/pD1qu1WQMCoiIqJnk4lizLwnl8tRv359REZG6soaNGiAa9euQaPRGHwdf39/tGrVCkuXLgUAaLVaeHt7Y8yYMZg0aVKB40ePHo0rV64gPDxcV/bRRx/hzz//xNGjRwscDwDZ2dl68/OkpqbC29sbKSkpRS4lQcD91Cy0nh+uV7ZjdFs0reosTUBERFSupaamwsnJyaD3d7HaFWJiYrB//369svDwcERHRxt8DbVajbNnzyIwMPCfYORyBAYG4sSJE4WeExAQgLNnz+qarqKjo7Fr1y507dq1yPssWLAATk5Oui9vb2+DYyyPNFqBKdsu6iU2rXxccHZaIBMbIiKyCMVqlqpevXqBMk9PT6OukZiYCI1GA3d3d71yd3d3XL16tdBz+vfvj8TERLz44osQQiA3NxcffPABpkyZUuR9Jk+erLeieX7NDRX0+1/3MPrH83plQwJ8MKtHI4kiIiIiMp5FDQU/dOgQ5s+fj++++w7+/v64fv06xo0bh7lz52L69OmFnqNSqaBSqUo4UsuToc7VS2y8nG1xYEIHqKwUEkZFRERkPIOSGxcXF4OH/CYlJRl0nKurKxQKBRISEvTKExISilzqYfr06Rg0aBCGDRsGAGjSpAnS09Px3nvvYerUqZDLOXqnuGb8dln3/ZFPOsK7op2E0RARERWfQcnN4sWLTX5jpVKJli1bIjw8HL169QKQ16E4PDwco0ePLvScjIyMAgmMQpFXs8AVyYvv59O38cvZOwCAwW2qM7EhIiKLZlByExwcbJabh4SEIDg4GH5+fmjdujUWL16M9PR0DB06FAAwePBgeHl5YcGCBQCA7t27Y9GiRWjevLmuWWr69Ono3r27Lskhwwkh8O2B61i075qubDb71xARkYUrVp+bGzduYO3atbhx4waWLFkCNzc37N69G9WqVUOjRoa/HPv164cHDx5gxowZiI+Ph6+vL8LCwnSdjGNjY/VqaqZNmwaZTIZp06bh7t27qFy5Mrp374558+YV52OUa0II9F1xAmdv/TOHzbnpnTnjMBERWTyj57k5fPgwunTpgrZt2+KPP/7AlStXULNmTXz22Wc4c+YMfvnlF3PFahLGjJMvq4QQ6LTwMGIS03Vll2cHwV5lUf3LiYioHDHrPDeTJk3Cp59+in379kGpVOrKO3XqhJMnTxofLZW4refu6hIbuQyInt+ViQ0REZUZRic3Fy9eRO/evQuUu7m5ITEx0SRBkflcvJOCj7ZcAADYKRWIXtANcjmbooiIqOwwOrlxdnZGXFxcgfLz58/Dy8vLJEGReUQ/SEP3pf8sU3F8UicJoyEiIjIPo5Obt956CxMnTkR8fDxkMhm0Wi2OHTuGCRMmYPDgweaIkUzgZmI6Oi08rNveNjIAznbKZ5xBRERkmYxObubPn4/69evD29sbaWlpaNiwIdq3b4+AgABMmzbNHDHSc9JoBV766pBue+WglmhezUW6gIiIiMyoWKuCA3nDtC9duoS0tDQ0b94cderUMXVsZlEeR0u9u+40wq/eBwD8NPwFtKlVSeKIiIiIjGPM+7vYQ2SqVaumW4CSc6OUXl/vu6ZLbF6oWZGJDRERlXnFWozphx9+QOPGjWFjYwMbGxs0btwYq1evNnVs9Jz2RyZgSfjfuu3Q99pIGA0REVHJMLrmZsaMGVi0aBHGjBmDNm3yXpYnTpzA+PHjERsbizlz5pg8SDJefEoWhm04o9s+P72zhNEQERGVHKP73FSuXBnffPMN3n77bb3yn376CWPGjCn1c92Ulz43Q9aewqGoBwCAPz7uiGqVuBgmERFZLrPOUJyTkwM/P78C5S1btkRubq6xlyMzOBR1X5fYzO3ZiIkNERGVK0YnN4MGDcLy5csLlK9cuRIDBgwwSVBUfLeTMjBk7WkAQNOqThjUxkfagIiIiEqYQX1uQkJCdN/LZDKsXr0ae/fuxQsvvAAA+PPPPxEbG8tJ/CQmhMD3f9zQbU/r1lDCaIiIiKRhUHJz/vx5ve2WLVsCAG7cyHuRurq6wtXVFZcvXzZxeGSMbefv4v9OxgIAFr7RDK1rVJQ4IiIiopJnUHJz8OBBc8dBz0kIgUm/XtRt92nBdb6IiKh8KtY8N1T67I1MgFqjBQD8OeVlTqxIRETlVrFmKD5z5gx+/vlnxMbGQq1W6+3bunWrSQIj43wYGgEAqKCygrujjbTBEBERScjompvQ0FAEBATgypUr2LZtG3JycnD58mUcOHAATk5O5oiR/sOJGw+RmaMBAHw3oIXE0RAREUmrWKuCf/311/jf//4HpVKJJUuW4OrVq3jzzTdRrVo1c8RI/2Ha9n/62rSvW1nCSIiIiKRndHJz48YNdOvWDQCgVCqRnp4OmUyG8ePHY+XKlSYPkJ7twNUE3HiQDgCY37uJxNEQERFJz+jkxsXFBY8fPwYAeHl54dKlSwCA5ORkZGRkmDY6eqao+Md4Z13e+lFKhRxvt/aWOCIiIiLpGd2huH379ti3bx+aNGmCN954A+PGjcOBAwewb98+vPzyy+aIkQqh0QqM3HRWt71rXDuOkCIiIkIxkpulS5ciKysLADB16lRYW1vj+PHjeP311zFt2jSTB0iFm/t7pK45atVgP9R2qyBxRERERKWD0clNxYr/zHorl8sxadIkkwZE/23nX3FYd/wmAKBHM090bugubUBERESliEHJTWpqqsEX/K9lyOn55Gq0GP9zhG57cT9fyWIhIiIqjQxKbpydnf+zP4cQAjKZDBqNxiSBUUFCCLz01SGoc/NmIo6cEwS5nP1siIiInsa1pSzI/528hTuPMgEAvZt7wU5ZrAmmiYiIyjSD3o4dOnQwdxz0H249TMf03/JWXXetoMLXbI4iIiIqFBfOtADZuRr0WnZMt71tZICE0RAREZVuTG4sQPdvj+JRRg4AYGn/5vCuaCdxRERERKUXk5tS7uMtF3AtIQ0A8ELNinitqafEEREREZVuTG5KsRM3HmLL2TsAACu5DKHvtZE4IiIiotKvWMlNbm4u9u/fj++//163ztS9e/eQlpZm0uDKs0fpary96iQAwMvZFn/P6yJxRERERJbB6LHEt27dwquvvorY2FhkZ2ejc+fOcHBwwOeff47s7GysWLHCHHGWK+pcLZrP3afb3vJBG64bRUREZCCja27GjRsHPz8/PHr0CLa2trry3r17Izw83KTBlUdCCHT/9qhu+522NeDpbPuMM4iIiOhpRtfcHDlyBMePH4dSqdQr9/Hxwd27d00WWHl17PpDRCXkNfW9VK8yZnRvKHFERERElsXomhutVlvoEgt37tyBg4ODSYIqz77/4wYAoEPdylg3tLXE0RAREVkeo5ObV155BYsXL9Zty2QypKWlYebMmejataspYyt3ktLVOPJ3IgDggw61JI6GiIjIMhndLLVw4UIEBQWhYcOGyMrKQv/+/fH333/D1dUVP/30kzliLDfCLsUDyBsd1aZWJYmjISIiskxGJzdVq1bFhQsXEBoair/++gtpaWl49913MWDAAL0OxmS83ZfiAAA21px+iIiIqLiMTm6ysrJgY2ODgQMHmiOecuvP6Ie6Jqmp3RpIHA0REZHlMrqKwM3NDcHBwdi3bx+0Wq05Yip3NFqBUT+eAwBUtFeiU313iSMiIiKyXEYnN+vXr0dGRgZ69uwJLy8vfPjhhzhz5ow5Yis3jt9IRGKaGgCw+b0XJI6GiIjIshmd3PTu3RtbtmxBQkIC5s+fj8jISLzwwguoW7cu5syZY44YyzQhBBbtuwYACGzghjruHE5PRET0PIrdc9XBwQFDhw7F3r178ddff8He3h6zZ882ZWzlwitf/4HzsckAgNdbVJU2GCIiojLA6A7F+bKysrBjxw78+OOPCAsLg7u7Oz7++GNTxlZm3X+chVV/RGPVkRhdmU8lO3RpUkXCqIiIiMoGo5ObPXv24Mcff8T27dthZWWFvn37Yu/evWjfvr054itzTtx4qFvtO1/zas7YNrKtRBERERGVLUYnN71798Zrr72GDRs2oGvXrrC2tjZHXGVSWnauXmITUKsSRnesjYDarhJGRUREVLYYndwkJCRwDali2n0xTvf94Y9fQvVK9hJGQ0REVDYZlNykpqbC0dERQN7ontTU1CKPzT+OClp1JBoA8GFgHSY2REREZmJQcuPi4oK4uDi4ubnB2dkZMpmswDFCCMhkskJXDCcg/EoCriWkAQDe9POWOBoiIqKyy6Dk5sCBA6hYsSIA4ODBg2YNqKyatPUiAKBFNWd4OnMNLiIiInMxKLnp0KGD7vsaNWrA29u7QO2NEAK3b982bXRlRFxKJh48zgYATO7KdaOIiIjMyehJ/GrUqIEHDx4UKE9KSkKNGjVMElRZk98cVcetAlr5VJQ4GiIiorLN6OQmv2/Nv6WlpcHGxsYkQZU1N+7nJTfsRExERGR+Bg8FDwkJAQDIZDJMnz4ddnZ2un0ajQZ//vknfH19TR5gWXAy+iEAoKEnR5IRERGZm8E1N+fPn8f58+chhMDFixd12+fPn8fVq1fRrFkzrFu3zugAli1bBh8fH9jY2MDf3x+nTp165vHJyckYNWoUqlSpApVKhbp162LXrl1G37ekZKhzcfxGXnLzQg02SREREZmbwTU3+aOkhg4diiVLlphkPpvNmzcjJCQEK1asgL+/PxYvXoygoCBERUXBzc2twPFqtRqdO3eGm5sbfvnlF3h5eeHWrVtwdnZ+7ljM5fj1h0jLzoWbgwr+NStJHQ4REVGZZ/QMxWvXrjXZzRctWoThw4dj6NChAIAVK1Zg586dWLNmDSZNmlTg+DVr1iApKQnHjx/XLfvg4+PzzHtkZ2cjOztbt/2sCQjN4Wp83v38a1aCQl6wrxIRERGZlkHJTZ8+fbBu3To4OjqiT58+zzx269atBt1YrVbj7NmzmDx5sq5MLpcjMDAQJ06cKPScHTt2oE2bNhg1ahR+++03VK5cGf3798fEiROhUCgKPWfBggWYPXu2QTGZQ0xiBgCgVmV2JiYiIioJBiU3Tk5OuhFSTk5OJrlxYmIiNBoN3N3d9crd3d1x9erVQs+Jjo7GgQMHMGDAAOzatQvXr1/HyJEjkZOTg5kzZxZ6zuTJk3WdoYG8mhtv75KbIfjXc3cAALUqVyixexIREZVnBiU3TzdFmbJZylharRZubm5YuXIlFAoFWrZsibt37+LLL78sMrlRqVRQqVQlHGkeda5W9z1HShEREZUMo/vcZGZmQgihGwp+69YtbNu2DQ0bNsQrr7xi8HVcXV2hUCiQkJCgV56QkAAPD49Cz6lSpQqsra31mqAaNGiA+Ph4qNVqKJVKYz+OWZ2+maT7vqYrm6WIiIhKgtGT+PXs2RMbNmwAkDcsu3Xr1li4cCF69uyJ5cuXG3wdpVKJli1bIjw8XFem1WoRHh6ONm3aFHpO27Ztcf36dWi1/9SIXLt2DVWqVCl1iQ0A3HqY199GaSUvdOJDIiIiMj2jk5tz586hXbt2AIBffvkFHh4euHXrFjZs2IBvvvnGqGuFhIRg1apVWL9+Pa5cuYIRI0YgPT1dN3pq8ODBeh2OR4wYgaSkJIwbNw7Xrl3Dzp07MX/+fIwaNcrYj1Ei4lIyAQC9fb0kjoSIiKj8MLpZKiMjAw4ODgCAvXv3ok+fPpDL5XjhhRdw69Yto67Vr18/PHjwADNmzEB8fDx8fX0RFham62QcGxsLufyf/Mvb2xt79uzB+PHj0bRpU3h5eWHcuHGYOHGisR+jRFyJewwA8GGTFBERUYkxOrmpXbs2tm/fjt69e+sSDQC4f/9+sSb2Gz16NEaPHl3ovkOHDhUoa9OmDU6ePGn0faTw9/285KaeB0dKERERlRSjm6VmzJiBCRMmwMfHB61bt9b1j9m7dy+aN29u8gAtVUpmjq7PTbOqztIGQ0REVI4YXXPTt29fvPjii4iLi0OzZs105S+//DJ69+5t0uAsWX5/G2c7a1SqIM1QdCIiovLI6OQGADw8PODh4YE7d/ImqKtatSpat25t0sAsXXxKFgDAw9FG4kiIiIjKF6ObpbRaLebMmQMnJydUr14d1atXh7OzM+bOnas3RLu8e5imBgBUqlD6hqgTERGVZUbX3EydOhU//PADPvvsM7Rt2xYAcPToUcyaNQtZWVmYN2+eyYO0RAmP82puXNkkRUREVKKMTm7Wr1+P1atXo0ePHrqy/GHZI0eOZHLzRFpWLgCgoj1rboiIiEqS0c1SSUlJqF+/foHy+vXrIykpqZAzyqdHGTkAACdba4kjISIiKl+MTm6aNWuGpUuXFihfunSp3uip8i41My+5cbFjzQ0REVFJMrpZ6osvvkC3bt2wf/9+3Rw3J06cwO3bt7Fr1y6TB2ipUrPykpsKqmINSCMiIqJiMrrmpkOHDrh27Rr69OmD5ORkJCcno0+fPoiKitKtOUVAenZenxt7JjdEREQlyqg3782bN7Fv3z6o1Wq89dZbaNy4sbnisnhpT5Ib1twQERGVLIPfvAcPHsRrr72GzMy8mXetrKywZs0aDBw40GzBWbLUzLzkhh2KiYiISpbBzVLTp09H586dcffuXTx8+BDDhw/HJ598Ys7YLFrKkw7FjrasuSEiIipJBic3ly5dwvz581GlShW4uLjgyy+/xP379/Hw4UNzxmeRNFqBzBwNADZLERERlTSDk5vU1FS4urrqtu3s7GBra4uUlBSzBGbJ8hMbgB2KiYiISppRb949e/bAyclJt63VahEeHo5Lly7pyp6eubi8ynoquVFZGT0gjYiIiJ6DUclNcHBwgbL3339f971MJoNGoylwTHmTnZu3gKjSSg6ZTCZxNEREROWLwckNV/w2XPaTmhvW2hAREZU8vn3NIEOdl9zYWiskjoSIiKj8MSi5OXnypMEXzMjIwOXLl4sdUFmQn9xwpBQREVHJMyi5GTRoEIKCgrBlyxakp6cXekxkZCSmTJmCWrVq4ezZsyYN0tLkL71gq2TNDRERUUkzqGohMjISy5cvx7Rp09C/f3/UrVsXnp6esLGxwaNHj3D16lWkpaWhd+/e2Lt3L5o0aWLuuEs1Lr1AREQkHYPevtbW1hg7dizGjh2LM2fO4OjRo7h16xYyMzPRrFkzjB8/Hh07dkTFihXNHa9FyFAzuSEiIpKK0W9fPz8/+Pn5mSOWMiM9O6/PjR2TGyIiohLH0VJmEJOY1y/JjqOliIiIShyTGzOwUuRN3Je/eCYRERGVHCY3ZuTuqJI6BCIionKHyY0ZqJ8sv+Bsp5Q4EiIiovLnuZKbrKwsU8VRpqifWluKiIiISpbRb1+tVou5c+fCy8sLFSpUQHR0NABg+vTp+OGHH0weoCXKXzjThh2KiYiISpzRyc2nn36KdevW4YsvvoBS+U+zS+PGjbF69WqTBmepMnO4thQREZFUjE5uNmzYgJUrV2LAgAFQKP55eTdr1gxXr141aXCWKvPJ2lI21myWIiIiKmlGv33v3r2L2rVrFyjXarXIyeHQZwB4nJX3HBxtrCWOhIiIqPwxOrlp2LAhjhw5UqD8l19+QfPmzU0SlKXLysnrc8OFM4mIiEqe0esDzJgxA8HBwbh79y60Wi22bt2KqKgobNiwAb///rs5YrQ42blsliIiIpKK0W/fnj174n//+x/2798Pe3t7zJgxA1euXMH//vc/dO7c2RwxWpyMJ31uVFasuSEiIippxVrZsV27dti3b5+pYykz8jsU23PhTCIiohJndM1NzZo18fDhwwLlycnJqFmzpkmCsnQcCk5ERCQdo5ObmzdvQqPRFCjPzs7G3bt3TRKUJVPnapGrFQCY3BAREUnB4HaTHTt26L7fs2cPnJycdNsajQbh4eHw8fExaXCWKL/WBgBslOxQTEREVNIMTm569eoFAJDJZAgODtbbZ21tDR8fHyxcuNCkwVmi/JFSMhmgVDC5ISIiKmkGJzdabd7cLTVq1MDp06fh6upqtqAsWfaTOW5UVnLIZDKJoyEiIip/jB7OExMTY444yowsdiYmIiKSVLHGKqenp+Pw4cOIjY2FWq3W2zd27FiTBGap8lcEV1qxSYqIiEgKRic358+fR9euXZGRkYH09HRUrFgRiYmJsLOzg5ubG5Ob3PxmKdbcEBERScHo6oXx48eje/fuePToEWxtbXHy5EncunULLVu2xFdffWWOGC1Kfodi1twQERFJw+g3cEREBD766CPI5XIoFApkZ2fD29sbX3zxBaZMmWKOGC1K/uzEdlw0k4iISBJGJzfW1taQy/NOc3NzQ2xsLADAyckJt2/fNm10FoizExMREUnL6D43zZs3x+nTp1GnTh106NABM2bMQGJiIjZu3IjGjRubI0aLkpHNdaWIiIikZHTNzfz581GlShUAwLx58+Di4oIRI0bgwYMH+P77700eoKVhzQ0REZG0jK5e8PPz033v5uaGsLAwkwZk6fKTG5U1OxQTERFJwWRv4HPnzuG1114z1eUsVn6HYtbcEBERScOo5GbPnj2YMGECpkyZgujoaADA1atX0atXL7Rq1Uq3REN5ptZwnhsiIiIpGdws9cMPP2D48OGoWLEiHj16hNWrV2PRokUYM2YM+vXrh0uXLqFBgwbmjNUi5K8txXluiIiIpGHwG3jJkiX4/PPPkZiYiJ9//hmJiYn47rvvcPHiRaxYsYKJzRNqDSfxIyIikpLBb+AbN27gjTfeAAD06dMHVlZW+PLLL1G1alWzBWeJsp7U3LDPDRERkTQMTm4yMzNhZ2cHAJDJZFCpVLoh4c9r2bJl8PHxgY2NDfz9/XHq1CmDzgsNDYVMJkOvXr1MEocp/DMUnDU3REREUjBqKPjq1atRoUIFAEBubi7WrVsHV1dXvWOMXThz8+bNCAkJwYoVK+Dv74/FixcjKCgIUVFRcHNzK/K8mzdvYsKECWjXrp1R9zM33WgpLr9AREQkCZkQQhhyoI+PD2Qy2bMvJpPpRlEZyt/fH61atcLSpUsBAFqtFt7e3hgzZgwmTZpU6DkajQbt27fHO++8gyNHjiA5ORnbt2836H6pqalwcnJCSkoKHB0djYrVEP2+P4E/Y5KwtH9zvNbU0+TXJyIiKo+MeX8bXHNz8+bN542rALVajbNnz2Ly5Mm6MrlcjsDAQJw4caLI8+bMmQM3Nze8++67OHLkyDPvkZ2djezsbN12amrq8wf+DOnqXACAvZLLLxAREUlB0o4hiYmJ0Gg0cHd31yt3d3dHfHx8oeccPXoUP/zwA1atWmXQPRYsWAAnJyfdl7e393PH/SwpmTkAAEdbJjdERERSsKher48fP8agQYOwatWqAn19ijJ58mSkpKTovsy9cnlyel5y42SrNOt9iIiIqHCSVi+4urpCoVAgISFBrzwhIQEeHh4Fjr9x4wZu3ryJ7t2768ryZ0W2srJCVFQUatWqpXeOSqWCSqUyQ/QFCSGQ9qRZijU3RERE0pC05kapVKJly5YIDw/XlWm1WoSHh6NNmzYFjq9fvz4uXryIiIgI3VePHj3QsWNHREREmL3J6b+oNVrkd8+24Tw3REREkpC8eiEkJATBwcHw8/ND69atsXjxYqSnp2Po0KEAgMGDB8PLywsLFiyAjY0NGjdurHe+s7MzABQol0L+BH4AYMO1pYiIiCRRrOTmxo0bWLt2LW7cuIElS5bAzc0Nu3fvRrVq1dCoUSOjrtWvXz88ePAAM2bMQHx8PHx9fREWFqbrZBwbGwu53DK6BmU/mcBPIZdx+QUiIiKJGDzPTb7Dhw+jS5cuaNu2Lf744w9cuXIFNWvWxGeffYYzZ87gl19+MVesJmHOeW5uJ2Wg3RcHYWutwJW5r5r02kREROWZMe9vo6sXJk2ahE8//RT79u2DUvnPiKBOnTrh5MmTxkdbhmTn5tXcqLj0AhERkWSMfgtfvHgRvXv3LlDu5uaGxMREkwRlqTLyl15gZ2IiIiLJGJ3cODs7Iy4urkD5+fPn4eXlZZKgLFV+cmPHdaWIiIgkY3Ry89Zbb2HixImIj4+HTCaDVqvFsWPHMGHCBAwePNgcMVoM3YrgTG6IiIgkY3RyM3/+fNSvXx/e3t5IS0tDw4YN0b59ewQEBGDatGnmiNFi5I+W4jBwIiIi6Rg9FFypVGLVqlWYPn06Ll26hLS0NDRv3hx16tQxR3wWRa3JG3hmrWCHYiIiIqkYndwcPXoUL774IqpVq4Zq1aqZIyaLlZObN4mfNee4ISIikozRb+FOnTqhRo0amDJlCiIjI80Rk8XKfbLOlVIhkzgSIiKi8svo5ObevXv46KOPcPjwYTRu3Bi+vr748ssvcefOHXPEZ1HYLEVERCQ9o9/Crq6uGD16NI4dO4YbN27gjTfewPr16+Hj44NOnTqZI0aLoX7SLGXF5IaIiEgyz/UWrlGjBiZNmoTPPvsMTZo0weHDh00Vl0XSzVDMPjdERESSKfZb+NixYxg5ciSqVKmC/v37o3Hjxti5c6cpY7M42U9WBbfh8gtERESSMXq01OTJkxEaGop79+6hc+fOWLJkCXr27Ak7OztzxGdRsp80S6k4zw0REZFkjE5u/vjjD3z88cd488034erqao6YLFZ+nxslm6WIiIgkY3Ryc+zYMXPEUSbkDwW3lnMoOBERkVQMSm527NiBLl26wNraGjt27HjmsT169DBJYJYo58lQcI6WIiIiko5ByU2vXr0QHx8PNzc39OrVq8jjZDIZNBqNqWKzOLma/KHgrLkhIiKSikHJjfZJc8u/vyd9udonk/jJWXNDREQkFaPfwhs2bEB2dnaBcrVajQ0bNpgkKEuVw5obIiIiyRmd3AwdOhQpKSkFyh8/foyhQ4eaJChLpXlSc2PFDsVERESSMTq5EUJAJiv48r5z5w6cnJxMEpSlyk9u5ExuiIiIJGPwUPDmzZtDJpNBJpPh5ZdfhpXVP6dqNBrExMTg1VdfNUuQloI1N0RERNIzOLnJHyUVERGBoKAgVKhQQbdPqVTCx8cHr7/+uskDtCQa8aTmppCaLSIiIioZBic3M2fOBAD4+PigX79+sLGxMVtQlkpXc8MOxURERJIxeobi4OBgc8RRJuj63LDmhoiISDIGJTcVK1bEtWvX4OrqChcXl0I7FOdLSkoyWXCWJj+5UbDPDRERkWQMSm6+/vprODg46L5/VnJTnumSGz4fIiIiyRiU3DzdFDVkyBBzxWLx8jsUc20pIiIi6Rj9Fj537hwuXryo2/7tt9/Qq1cvTJkyBWq12qTBWZonFTdgqxQREZF0jE5u3n//fVy7dg0AEB0djX79+sHOzg5btmzBJ598YvIALYmWHYqJiIgkZ3Ryc+3aNfj6+gIAtmzZgg4dOuDHH3/EunXr8Ouvv5o6PouiFZyhmIiISGrFWn4hf2Xw/fv3o2vXrgAAb29vJCYmmjY6C/PPUHCJAyEiIirHjE5u/Pz88Omnn2Ljxo04fPgwunXrBgCIiYmBu7u7yQO0JELX54bZDRERkVSMTm4WL16Mc+fOYfTo0Zg6dSpq164NAPjll18QEBBg8gAtiZbLLxAREUnO6BmKmzZtqjdaKt+XX34JhUJhkqAs1T9rS0kcCBERUTlmdHKT7+zZs7hy5QoAoGHDhmjRooXJgrJU+c1SnKGYiIhIOkYnN/fv30e/fv1w+PBhODs7AwCSk5PRsWNHhIaGonLlyqaO0WLkN0txBmciIiLpGN3nZsyYMUhLS8Ply5eRlJSEpKQkXLp0CampqRg7dqw5YrQYHC1FREQkPaNrbsLCwrB//340aNBAV9awYUMsW7YMr7zyikmDszR3HmUCYLMUERGRlIyuudFqtbC2ti5Qbm1trZv/przycLQBAORoyvdzICIikpLRyU2nTp0wbtw43Lt3T1d29+5djB8/Hi+//LJJg7M0AnnNUjbW5XvUGBERkZSMTm6WLl2K1NRU+Pj4oFatWqhVqxZq1KiB1NRUfPvtt+aI0WLkL5wpA5uliIiIpGJ0nxtvb2+cO3cO4eHhuqHgDRo0QGBgoMmDszS6GYqNThmJiIjIVIxKbjZv3owdO3ZArVbj5ZdfxpgxY8wVl0USnKGYiIhIcgYnN8uXL8eoUaNQp04d2NraYuvWrbhx4wa+/PJLc8ZnUXTz3EgcBxERUXlmcAPK0qVLMXPmTERFRSEiIgLr16/Hd999Z87YLM6TVilO4kdERCQhg5Ob6OhoBAcH67b79++P3NxcxMXFmSUwS6TV5s9QLHEgRERE5ZjByU12djbs7e3/OVEuh1KpRGZmplkCs0T5NTfsc0NERCQdozoUT58+HXZ2drpttVqNefPmwcnJSVe2aNEi00VnYXSjpZjbEBERScbg5KZ9+/aIiorSKwsICEB0dLRuu7z3NfmnQ3H5fg5ERERSMji5OXTokBnDKBvya27KeY5HREQkKU43Z0L5NTdytksRERFJhsmNCelqbqQNg4iIqFxjcmNC+QtncrQUERGRdJjcmJCWo6WIiIgkx+TGhLRslyIiIpJcsZKbI0eOYODAgWjTpg3u3r0LANi4cSOOHj1arCCWLVsGHx8f2NjYwN/fH6dOnSry2FWrVqFdu3ZwcXGBi4sLAgMDn3l8SfpnnhtmN0RERFIxOrn59ddfERQUBFtbW5w/fx7Z2dkAgJSUFMyfP9/oADZv3oyQkBDMnDkT586dQ7NmzRAUFIT79+8XevyhQ4fw9ttv4+DBgzhx4gS8vb3xyiuv6JIsqeSvCA6w4oaIiEhKRic3n376KVasWIFVq1bB2tpaV962bVucO3fO6AAWLVqE4cOHY+jQoWjYsCFWrFgBOzs7rFmzptDjN23ahJEjR8LX1xf169fH6tWrodVqER4ebvS9Temp3KbcT2ZIREQkJaOTm6ioKLRv375AuZOTE5KTk426llqtxtmzZxEYGPhPQHI5AgMDceLECYOukZGRgZycHFSsWLHQ/dnZ2UhNTdX7MoenchvW3BAREUnI6OTGw8MD169fL1B+9OhR1KxZ06hrJSYmQqPRwN3dXa/c3d0d8fHxBl1j4sSJ8PT01EuQnrZgwQI4OTnpvry9vY2K0VB6zVLMboiIiCRjdHIzfPhwjBs3Dn/++SdkMhnu3buHTZs2YcKECRgxYoQ5YizSZ599htDQUGzbtg02NjaFHjN58mSkpKTovm7fvm2WWPRrbpjdEBERScWoVcEBYNKkSdBqtXj55ZeRkZGB9u3bQ6VSYcKECRgzZoxR13J1dYVCoUBCQoJeeUJCAjw8PJ557ldffYXPPvsM+/fvR9OmTYs8TqVSQaVSGRVXcQi2SxEREZUKRtfcyGQyTJ06FUlJSbh06RJOnjyJBw8eYO7cuUbfXKlUomXLlnqdgfM7B7dp06bI87744gvMnTsXYWFh8PPzM/q+5iDAZikiIqLSwOiam3xKpRINGzZ87gBCQkIQHBwMPz8/tG7dGosXL0Z6ejqGDh0KABg8eDC8vLywYMECAMDnn3+OGTNm4Mcff4SPj4+ub06FChVQoUKF546nuPRGS0kWBRERERmd3HTs2PGZQ50PHDhg1PX69euHBw8eYMaMGYiPj4evry/CwsJ0nYxjY2Mhl/9TwbR8+XKo1Wr07dtX7zozZ87ErFmzjLo3ERERlT1GJze+vr562zk5OYiIiMClS5cQHBxcrCBGjx6N0aNHF7rv0KFDets3b94s1j1KEue5ISIiko7Ryc3XX39daPmsWbOQlpb23AFZKjZLERERlQ4mWzhz4MCBRc4qXB6wQzEREVHpYLLk5sSJE0XONVMe6NfcMLshIiKSitHNUn369NHbFkIgLi4OZ86cwfTp000WmKXRm+aGuQ0REZFkjE5unJyc9Lblcjnq1auHOXPm4JVXXjFZYJZG6M3iR0RERFIxKrnRaDQYOnQomjRpAhcXF3PFZJFYc0NERFQ6GNXnRqFQ4JVXXjF69e/ygH1uiIiISgejOxQ3btwY0dHR5ojFsj2d3DC3ISIikozRyc2nn36KCRMm4Pfff0dcXBxSU1P1vsorvaHgEsZBRERU3hnc52bOnDn46KOP0LVrVwBAjx499GbiFUJAJpNBo9GYPkoLwxmKiYiIpGNwcjN79mx88MEHOHjwoDnjsVgcLEVERFQ6GJzc5A917tChg9mCsWR6o6Uki4KIiIiM6nPD5paiPT3PDR8TERGRdIya56Zu3br/meAkJSU9V0CWSn+eG2Y3REREUjEquZk9e3aBGYopD/vcEBERlQ5GJTdvvfUW3NzczBWLRcsfCs5KGyIiImkZ3OeGTS3/4UnNDZ8SERGRtAxObrgw5LPlPx0mgURERNIyuFlKq9WaMw6LJ1hzQ0REVCoYvfwCFU7zJLth/RYREZG0mNyYSH6NjUbL9IaIiEhKTG5MJD+lUSr4SImIiKTEN7GJCHa6ISIiKhWY3JgYcxsiIiJpMbkxEY6UJyIiKh2Y3JgYp7khIiKSFpMbE/mnyw2zGyIiIikxuTEx1twQERFJi8mNiQhO30dERFQqMLkxEY4EJyIiKh2Y3JgYF84kIiKSFpMbE2GjFBERUenA5MZE8mcoZr0NERGRtJjcmBqzGyIiIkkxuTERNksRERGVDkxuTISjpYiIiEoHJjcm86TPDUdLERERSYrJjYkxtyEiIpIWkxsT4argREREpQOTGxPJz21YcUNERCQtJjcmxj43RERE0mJyYyJsliIiIiodmNyYSP6q4Ky3ISIikhaTGxPRzXPD7IaIiEhSVlIHUPYwuyEq74QQyM3NhUajkToUIotibW0NhULx3NdhcmMi7HNDRACgVqsRFxeHjIwMqUMhsjgymQxVq1ZFhQoVnus6TG5MRNfnhhU3ROWWVqtFTEwMFAoFPD09oVQqOYKSyEBCCDx48AB37txBnTp1nqsGh8mNifHPGFH5pVarodVq4e3tDTs7O6nDIbI4lStXxs2bN5GTk/NcyQ07FJsIm6WIKJ9czj+tRMVhqppO/gaaGGugiYiIpMXkxsRkbJgiIiKSFJMbE2GzFBERUenA5MZEOFqKiMoDmUyG7du3m/0+hw4dgkwmQ3Jysq5s+/btqF27NhQKBT788EOsW7cOzs7OZoshKioKHh4eePz4sdnuUd688MIL+PXXX81+HyY3JqKboVjaMIiIii0+Ph5jxoxBzZo1oVKp4O3tje7duyM8PLzEYwkICEBcXBycnJx0Ze+//z769u2L27dvY+7cuejXrx+uXbtmthgmT56MMWPGwMHBocC++vXrQ6VSIT4+vsA+Hx8fLF68uED5rFmz4Ovrq1cm1TPfsmUL6tevDxsbGzRp0gS7du36z3OWLVuGBg0awNbWFvXq1cOGDRv09m/duhV+fn5wdnaGvb09fH19sXHjRr1jpk2bhkmTJkGr1Zr08/wbh4KbGOe0IKKnCSGQmVPyMxXbWiuM+nt08+ZNtG3bFs7Ozvjyyy/RpEkT5OTkYM+ePRg1ahSuXr1qxmgLUiqV8PDw0G2npaXh/v37CAoKgqenp67c1tb2ue6Tk5MDa2vrAuWxsbH4/fff8e233xbYd/ToUWRmZqJv375Yv349Jk6cWKx7S/XMjx8/jrfffhsLFizAa6+9hh9//BG9evXCuXPn0Lhx40LPWb58OSZPnoxVq1ahVatWOHXqFIYPHw4XFxd0794dAFCxYkVMnToV9evXh1KpxO+//46hQ4fCzc0NQUFBAIAuXbpg2LBh2L17N7p162aWzwcwuTEZdrkhosJk5mjQcMaeEr9v5Jwg2CkN/xM/cuRIyGQynDp1Cvb29rryRo0a4Z133inyvIkTJ2Lbtm24c+cOPDw8MGDAAMyYMUOXMFy4cAEffvghzpw5A5lMhjp16uD777+Hn58fbt26hdGjR+Po0aNQq9Xw8fHBl19+ia5du+LQoUPo2LEjHj16hIiICHTs2BEA0KlTJwDAwYMHcfPmTXz44Yd6TVe//fYbZs+ejcjISHh6eiI4OBhTp06FlVXes5DJZPjuu++we/duhIeH4+OPP8asWbMKfK6ff/4ZzZo1g5eXV4F9P/zwA/r3748OHTpg3LhxxU5uivvMn9eSJUvw6quv4uOPPwYAzJ07F/v27cPSpUuxYsWKQs/ZuHEj3n//ffTr1w8AULNmTZw+fRqff/65Lrl56aWX9M4ZN24c1q9fj6NHj+qSG4VCga5duyI0NNSsyU2paJZatmwZfHx8YGNjA39/f5w6deqZxxenOs3cBHsUE5GFSkpKQlhYGEaNGqX3ks33rH4tDg4OWLduHSIjI7FkyRKsWrUKX3/9tW7/gAEDULVqVZw+fRpnz57FpEmTdInPqFGjkJ2djT/++AMXL17E559/Xui0+wEBAYiKigIA/Prrr4iLi0NAQECB444cOYLBgwdj3LhxiIyMxPfff49169Zh3rx5esfNmjULvXv3xsWLF4tMIo4cOQI/P78C5Y8fP8aWLVswcOBAdO7cGSkpKThy5EiRz6coz/PMN23ahAoVKjzz61kxnThxAoGBgXplQUFBOHHiRJHnZGdnw8bGRq/M1tYWp06dQk5OToHjhRAIDw9HVFQU2rdvr7evdevWxXpmxpC85mbz5s0ICQnBihUr4O/vj8WLFyMoKAhRUVFwc3MrcHxxqtNKEluliOhpttYKRM4JkuS+hrp+/TqEEKhfv77R95k2bZruex8fH0yYMAGhoaH45JNPAOQ173z88ce6a9epU0d3fGxsLF5//XU0adIEQF5tQGGUSqXufVCxYkW95qqnzZ49G5MmTUJwcLDuenPnzsUnn3yCmTNn6o7r378/hg4d+szPdevWrUKTm9DQUNSpUweNGjUCALz11lv44Ycf0K5du2de79+e55n36NED/v7+zzymsBqnfPHx8XB3d9crc3d3L7T/UL6goCCsXr0avXr1QosWLXD27FmsXr0aOTk5SExMRJUqVQAAKSkp8PLyQnZ2NhQKBb777jt07txZ71qenp64ffs2tFqt2Sa8lDy5WbRoEYYPH677QVuxYgV27tyJNWvWYNKkSQWOL051WklgvQ0RFUYmkxnVPCSF56l53rx5M7755hvcuHEDaWlpyM3NhaOjo25/SEgIhg0bho0bNyIwMBBvvPEGatWqBQAYO3YsRowYgb179yIwMBCvv/46mjZtWuxYLly4gGPHjunV1Gg0GmRlZSEjI0O3JEZhScu/ZWZmFqipAIA1a9Zg4MCBuu2BAweiQ4cO+PbbbwvteFyU53nmDg4ORt3LFKZPn474+Hi88MILEELA3d0dwcHB+OKLL/QSFAcHB0RERCAtLQ3h4eEICQlBzZo19ZqsbG1todVqkZ2d/dx9pooiabOUWq3G2bNn9arH5HI5AgMDi6weM7Y6LTs7G6mpqXpf5qAbLcWaGyKyMHXq1IFMJjO6A+uJEycwYMAAdO3aFb///jvOnz+PqVOnQq1W646ZNWsWLl++jG7duuHAgQNo2LAhtm3bBgAYNmwYoqOjMWjQIFy8eBF+fn6FduA1VFpaGmbPno2IiAjd18WLF/H333/rJSqFNQP9m6urKx49eqRXFhkZiZMnT+KTTz6BlZUVrKys8MILLyAjIwOhoaG64xwdHZGSklLgmsnJybrRX8V95sDzN0t5eHggISFBrywhIaHIGjEgLyFZs2YNMjIycPPmTcTGxsLHxwcODg6oXLmy7ji5XI7atWvD19cXH330Efr27YsFCxboXSspKQn29vZmS2wAiZObxMREaDQao6rHjK1OW7BgAZycnHRf3t7epgn+X+QywMZaDhur4i/0RUQkhYoVKyIoKAjLli1Denp6gf1Pd9h92vHjx1G9enVMnToVfn5+qFOnDm7dulXguLp162L8+PHYu3cv+vTpg7Vr1+r2eXt744MPPsDWrVvx0UcfYdWqVcX+HC1atEBUVBRq165d4MvY5o/mzZsjMjJSr+yHH35A+/btceHCBb0EKiQkBD/88IPuuHr16uHs2bMFrnnu3DnUrVsXQPGfOZDXLPX0/Qv7elbtVJs2bQoMNd+3bx/atGlT5Dn5rK2tUbVqVSgUCoSGhuK111575rPNr6F52qVLl9C8efP/vNfzKN11pSYwefJkhISE6LZTU1PNkuA0r+aCq3O7mPy6REQlYdmyZWjbti1at26NOXPmoGnTpsjNzcW+ffuwfPlyXLlypcA5derUQWxsLEJDQ9GqVSvs3LlTVysD5DXtfPzxx+jbty9q1KiBO3fu4PTp03j99dcBAB9++CG6dOmCunXr4tGjRzh48CAaNGhQ7M8wY8YMvPbaa6hWrRr69u0LuVyOCxcu4NKlS/j000+NulZQUBCGDRsGjUYDhUKBnJwcbNy4EXPmzCnQv3PYsGFYtGgRLl++jEaNGmH8+PFo164d5s2bhz59+kCj0eCnn37CiRMn8N133+nOK84zB56/WWrcuHHo0KEDFi5ciG7duiE0NBRnzpzBypUrdcdMnjwZd+/e1c1lc+3aNZw6dQr+/v549OgRFi1ahEuXLmH9+vW6cxYsWAA/Pz/UqlUL2dnZ2LVrFzZu3Ijly5fr3f/IkSN45ZVXih2/QYSEsrOzhUKhENu2bdMrHzx4sOjRo0eh53h7e4uvv/5ar2zGjBmiadOmBt0zJSVFABApKSnFCZmIqEiZmZkiMjJSZGZmSh1Ksdy7d0+MGjVKVK9eXSiVSuHl5SV69OghDh48qDsGgN7f7I8//lhUqlRJVKhQQfTr1098/fXXwsnJSQiR9zf+rbfeEt7e3kKpVApPT08xevRo3fMZPXq0qFWrllCpVKJy5cpi0KBBIjExUQghxMGDBwUA8ejRIyGEEI8ePRIA9GJZu3at7l75wsLCREBAgLC1tRWOjo6idevWYuXKlUXGX5ScnBzh6ekpwsLChBBC/PLLL0Iul4v4+PhCj2/QoIEYP368bnvPnj2ibdu2wsXFRVSqVEm89NJL4vDhwwXOM+SZm8PPP/8s6tatK5RKpWjUqJHYuXOn3v7g4GDRoUMH3XZkZKTw9fXVPdeePXuKq1ev6p0zdepUUbt2bWFjYyNcXFxEmzZtRGhoqN4xd+7cEdbW1uL27duFxvWs3yFj3t8yIaQdw+zv74/WrVvr2lm1Wi2qVauG0aNHF9qhuF+/fsjIyMD//vc/XVlAQACaNm1qUIfi1NRUODk5ISUlRa/TGxHR88rKykJMTAxq1KhRaGdUsizLli3Djh07sGdPyc9TVFZNnDgRjx490qsletqzfoeMeX9L3iwVEhKC4OBg+Pn5oXXr1li8eDHS09N1o6cGDx4MLy8vXYckQ6rTiIiIntf777+P5ORkPH78uMRHJ5VVbm5uel1FzEXy5KZfv3548OABZsyYgfj4ePj6+iIsLEzXaTg2Nlavs1JAQAB+/PFHTJs2DVOmTEGdOnWwffv2UjHHDRERlR1WVlaYOnWq1GGUKR999FGJ3EfyZqmSxmYpIjIXNksRPR9TNUuViuUXiIjKknL2/4xEJmOq3x0mN0REJpK/ZlJGRobEkRBZpvwJIBWK55szTvI+N0REZYVCoYCzszPu378PALCzs4OM05YTGUSr1eLBgwews7PTreJeXExuiIhMKH8K+/wEh4gMJ5fLUa1atef+nwImN0REJiSTyVClShW4ubkhJydH6nCILIpSqTTJSuFMboiIzEChUDx3vwEiKh52KCYiIqIyhckNERERlSlMboiIiKhMKXd9bvInCEpNTZU4EiIiIjJU/nvbkIn+yl1y8/jxYwCAt7e3xJEQERGRsR4/fgwnJ6dnHlPu1pbSarW4d+8eHBwcTD65VmpqKry9vXH79m2uW2VGfM4lg8+5ZPA5lxw+65JhrucshMDjx4/h6en5n8PFy13NjVwuR9WqVc16D0dHR/7ilAA+55LB51wy+JxLDp91yTDHc/6vGpt87FBMREREZQqTGyIiIipTmNyYkEqlwsyZM6FSqaQOpUzjcy4ZfM4lg8+55PBZl4zS8JzLXYdiIiIiKttYc0NERERlCpMbIiIiKlOY3BAREVGZwuSGiIiIyhQmN0ZatmwZfHx8YGNjA39/f5w6deqZx2/ZsgX169eHjY0NmjRpgl27dpVQpJbNmOe8atUqtGvXDi4uLnBxcUFgYOB//rtQHmN/nvOFhoZCJpOhV69e5g2wjDD2OScnJ2PUqFGoUqUKVCoV6taty78dBjD2OS9evBj16tWDra0tvL29MX78eGRlZZVQtJbpjz/+QPfu3eHp6QmZTIbt27f/5zmHDh1CixYtoFKpULt2baxbt87scUKQwUJDQ4VSqRRr1qwRly9fFsOHDxfOzs4iISGh0OOPHTsmFAqF+OKLL0RkZKSYNm2asLa2FhcvXizhyC2Lsc+5f//+YtmyZeL8+fPiypUrYsiQIcLJyUncuXOnhCO3LMY+53wxMTHCy8tLtGvXTvTs2bNkgrVgxj7n7Oxs4efnJ7p27SqOHj0qYmJixKFDh0REREQJR25ZjH3OmzZtEiqVSmzatEnExMSIPXv2iCpVqojx48eXcOSWZdeuXWLq1Kli69atAoDYtm3bM4+Pjo4WdnZ2IiQkRERGRopvv/1WKBQKERYWZtY4mdwYoXXr1mLUqFG6bY1GIzw9PcWCBQsKPf7NN98U3bp10yvz9/cX77//vlnjtHTGPud/y83NFQ4ODmL9+vXmCrFMKM5zzs3NFQEBAWL16tUiODiYyY0BjH3Oy5cvFzVr1hRqtbqkQiwTjH3Oo0aNEp06ddIrCwkJEW3btjVrnGWJIcnNJ598Iho1aqRX1q9fPxEUFGTGyIRgs5SB1Go1zp49i8DAQF2ZXC5HYGAgTpw4Ueg5J06c0DseAIKCgoo8nor3nP8tIyMDOTk5qFixornCtHjFfc5z5syBm5sb3n333ZII0+IV5znv2LEDbdq0wahRo+Du7o7GjRtj/vz50Gg0JRW2xSnOcw4ICMDZs2d1TVfR0dHYtWsXunbtWiIxlxdSvQfL3cKZxZWYmAiNRgN3d3e9cnd3d1y9erXQc+Lj4ws9Pj4+3mxxWrriPOd/mzhxIjw9PQv8QtE/ivOcjx49ih9++AERERElEGHZUJznHB0djQMHDmDAgAHYtWsXrl+/jpEjRyInJwczZ84sibAtTnGec//+/ZGYmIgXX3wRQgjk5ubigw8+wJQpU0oi5HKjqPdgamoqMjMzYWtra5b7suaGypTPPvsMoaGh2LZtG2xsbKQOp8x4/PgxBg0ahFWrVsHV1VXqcMo0rVYLNzc3rFy5Ei1btkS/fv0wdepUrFixQurQypRDhw5h/vz5+O6773Du3Dls3boVO3fuxNy5c6UOjUyANTcGcnV1hUKhQEJCgl55QkICPDw8Cj3Hw8PDqOOpeM8531dffYXPPvsM+/fvR9OmTc0ZpsUz9jnfuHEDN2/eRPfu3XVlWq0WAGBlZYWoqCjUqlXLvEFboOL8PFepUgXW1tZQKBS6sgYNGiA+Ph5qtRpKpdKsMVui4jzn6dOnY9CgQRg2bBgAoEmTJkhPT8d7772HqVOnQi7n//ubQlHvQUdHR7PV2gCsuTGYUqlEy5YtER4erivTarUIDw9HmzZtCj2nTZs2escDwL59+4o8nor3nAHgiy++wNy5cxEWFgY/P7+SCNWiGfuc69evj4sXLyIiIkL31aNHD3Ts2BERERHw9vYuyfAtRnF+ntu2bYvr16/rkkcAuHbtGqpUqcLEpgjFec4ZGRkFEpj8hFJwyUWTkew9aNbuymVMaGioUKlUYt26dSIyMlK89957wtnZWcTHxwshhBg0aJCYNGmS7vhjx44JKysr8dVXX4krV66ImTNncii4AYx9zp999plQKpXil19+EXFxcbqvx48fS/URLIKxz/nfOFrKMMY+59jYWOHg4CBGjx4toqKixO+//y7c3NzEp59+KtVHsAjGPueZM2cKBwcH8dNPP4no6Gixd+9eUatWLfHmm29K9REswuPHj8X58+fF+fPnBQCxaNEicf78eXHr1i0hhBCTJk0SgwYN0h2fPxT8448/FleuXBHLli3jUPDS6NtvvxXVqlUTSqVStG7dWpw8eVK3r0OHDiI4OFjv+J9//lnUrVtXKJVK0ahRI7Fz584SjtgyGfOcq1evLgAU+Jo5c2bJB25hjP15fhqTG8MZ+5yPHz8u/P39hUqlEjVr1hTz5s0Tubm5JRy15THmOefk5IhZs2aJWrVqCRsbG+Ht7S1GjhwpHj16VPKBW5CDBw8W+vc2/9kGBweLDh06FDjH19dXKJVKUbNmTbF27VqzxykTgvVvREREVHawzw0RERGVKUxuiIiIqExhckNERERlCpMbIiIiKlOY3BAREVGZwuSGiIiIyhQmN0RERFSmMLkhIiKiMoXJDVEh1q1bB2dnZ6nDKDaZTIbt27c/85ghQ4agV69eJRJPaTN9+nS89957JXKvQ4cOQSaTITk5+ZnH+fj4YPHixWaNxdh7mOr3wJCfR2NFRkaiatWqSE9PN+l1qWxgckNl1pAhQyCTyQp8Xb9+XerQsG7dOl08crkcVatWxdChQ3H//n2TXD8uLg5dunQBANy8eRMymQwRERF6xyxZsgTr1q0zyf2KMmvWLN3nVCgU8Pb2xnvvvYekpCSjrmPKRCw+Ph5LlizB1KlT9a6fH6dSqUTt2rUxZ84c5ObmPvf9AgICEBcXBycnJwBFJwynT58usYTLEsybNw8BAQGws7Mr9Hk1bNgQL7zwAhYtWlTywVGpx+SGyrRXX30VcXFxel81atSQOiwAgKOjI+Li4nDnzh2sWrUKu3fvxqBBg0xybQ8PD6hUqmce4+TkVCK1U40aNUJcXBxiY2Oxdu1ahIWFYcSIEWa/b1FWr16NgIAAVK9eXa88/2fl77//xkcffYRZs2bhyy+/fO77KZVKeHh4QCaTPfO4ypUrw87O7rnvV1ao1Wq88cYbz/xZGTp0KJYvX26SJJTKFiY3VKapVCp4eHjofSkUCixatAhNmjSBvb09vL29MXLkSKSlpRV5nQsXLqBjx45wcHCAo6MjWrZsiTNnzuj2Hz16FO3atYOtrS28vb0xduzY/6wul8lk8PDwgKenJ7p06YKxY8di//79yMzMhFarxZw5c1C1alWoVCr4+voiLCxMd65arcbo0aNRpUoV2NjYoHr16liwYIHetfObAfKTuebNm0Mmk+Gll14CoF8bsnLlSnh6ekKr1erF2LNnT7zzzju67d9++w0tWrSAjY0NatasidmzZ//ni8XKygoeHh7w8vJCYGAg3njjDezbt0+3X6PR4N1330WNGjVga2uLevXqYcmSJbr9s2bNwvr16/Hbb7/palcOHToEALh9+zbefPNNODs7o2LFiujZsydu3rz5zHhCQ0PRvXv3AuX5PyvVq1fHiBEjEBgYiB07dgAAHj16hMGDB8PFxQV2dnbo0qUL/v77b925t27dQvfu3eHi4gJ7e3s0atQIu3btAqDfLHXo0CEMHToUKSkpus8ya9YsAPpNRv3790e/fv304svJyYGrqys2bNgAANBqtViwYIHuuTVr1gy//PLLMz/7vxn6e7B9+3bUqVMHNjY2CAoKwu3bt/X2F+fn4r/Mnj0b48ePR5MmTYo8pnPnzkhKSsLhw4ef615U9jC5oXJJLpfjm2++weXLl7F+/XocOHAAn3zySZHHDxgwAFWrVsXp06dx9uxZTJo0CdbW1gCAGzdu4NVXX8Xrr7+Ov/76C5s3b8bRo0cxevRoo2KytbWFVqtFbm4ulixZgoULF+Krr77CX3/9haCgIPTo0UP3Qv3mm2+wY8cO/Pzzz4iKisKmTZvg4+NT6HVPnToFANi/fz/i4uKwdevWAse88cYbePjwIQ4ePKgrS0pKQlhYGAYMGAAAOHLkCAYPHoxx48YhMjIS33//PdatW4d58+YZ/Blv3ryJPXv2QKlU6sq0Wi2qVq2KLVu2IDIyEjNmzMCUKVPw888/AwAmTJiAN998U68WLiAgADk5OQgKCoKDgwOOHDmCY8eOoUKFCnj11VehVqsLvX9SUhIiIyPh5+f3n7Ha2trqrjNkyBCcOXMGO3bswIkTJyCEQNeuXZGTkwMAGDVqFLKzs/HHH3/g4sWL+Pzzz1GhQoUC1wwICMDixYt1tXZxcXGYMGFCgeMGDBiA//3vf3qJxp49e5CRkYHevXsDABYsWIANGzZgxYoVuHz5MsaPH4+BAwca9aI35PcgIyMD8+bNw4YNG3Ds2DEkJyfjrbfe0u0vzs/FSy+9hCFDhhgcZ1GUSiV8fX1x5MiR574WlTFmX3ecSCLBwcFCoVAIe3t73Vffvn0LPXbLli2iUqVKuu21a9cKJycn3baDg4NYt25doee+++674r333tMrO3LkiJDL5SIzM7PQc/59/WvXrom6desKPz8/IYQQnp6eYt68eXrntGrVSowcOVIIIcSYMWNEp06dhFarLfT6AMS2bduEEELExMQIAOL8+fN6xwQHB4uePXvqtnv27Cneeecd3fb3338vPD09hUajEUII8fLLL4v58+frXWPjxo2iSpUqhcYghBAzZ84Ucrlc2NvbCxsbGwFAABCLFi0q8hwhhBg1apR4/fXXi4w1/9716tXTewbZ2dnC1tZW7Nmzp9Drnj9/XgAQsbGxeuVPX1+r1Yp9+/YJlUolJkyYIK5duyYAiGPHjumOT0xMFLa2tuLnn38WQgjRpEkTMWvWrELvefDgQQFAPHr0SAhR8N8+X/Xq1cXXX38thBAiJydHuLq6ig0bNuj2v/3226Jfv35CCCGysrKEnZ2dOH78uN413n33XfH2228XGse/71GYwn4PAIiTJ0/qyq5cuSIAiD///FMIYdjPxdM/j0IIMWjQIDFp0qQi43haUc8rX+/evcWQIUMMuhaVH1ZSJVVEJaFjx45Yvny5btve3h5AXi3GggULcPXqVaSmpiI3NxdZWVnIyMgotN9DSEgIhg0bho0bN+qaVmrVqgUgr8nqr7/+wqZNm3THCyGg1WoRExODBg0aFBpbSkoKKlSoAK1Wi6ysLLz44otYvXo1UlNTce/ePbRt21bv+LZt2+LChQsA8moSOnfujHr16uHVV1/Fa6+9hldeeeW5ntWAAQMwfPhwfPfdd1CpVNi0aRPeeustyOVy3ec8duyY3v+RazSaZz43AKhXrx527NiBrKws/N///R8iIiIwZswYvWOWLVuGNWvWIDY2FpmZmVCr1fD19X1mvBcuXMD169fh4OCgV56VlYUbN24Uek5mZiYAwMbGpsC+33//HRUqVEBOTg60Wi369++PWbNmITw8HFZWVvD399cdW6lSJdSrVw9XrlwBAIwdOxYjRozA3r17ERgYiNdffx1NmzZ9ZvzPYmVlhTfffBObNm3CoEGDkJ6ejt9++w2hoaEAgOvXryMjIwOdO3fWO0+tVqN58+YG38eQ3wMrKyu0atVKd079+vXh7OyMK1euoHXr1sX6uchvWjMFW1tbZGRkmOx6VDYwuaEyzd7eHrVr19Yru3nzJl577TWMGDEC8+bNQ8WKFXH06FG8++67UKvVhf4xnjVrFvr374+dO3di9+7dmDlzJkJDQ9G7d2+kpaXh/fffx9ixYwucV61atSJjc3BwwLlz5yCXy1GlShXY2toCAFJTU//zc7Vo0QIxMTHYvXs39u/fjzfffBOBgYFG97l4Wvfu3SGEwM6dO9GqVSscOXIEX3/9tW5/WloaZs+ejT59+hQ4t7BkIV/+6CMA+Oyzz9CtWzfMnj0bc+fOBZDXB2bChAlYuHAh2rRpAwcHB3z55Zf4888/nxlvWloaWrZsqZdU5qtcuXKh57i6ugLI60Pz72PyE2GlUglPT09YWRn+53HYsGEICgrCzp07sXfvXixYsAALFy4skMQZY8CAAejQoQPu37+Pffv2wdbWFq+++ioA6Jqrdu7cCS8vL73z/qsjeb7i/B4Uprg/F6aSlJSk+x8NonxMbqjcOXv2LLRaLRYuXKirlcjv3/EsdevWRd26dTF+/Hi8/fbbWLt2LXr37o0WLVogMjKyQBL1X+RyeaHnODo6wtPTE8eOHUOHDh105ceOHUPr1q31juvXrx/69euHvn374tVXX0VSUhIqVqyod738/i0ajeaZ8djY2KBPnz7YtGkTrl+/jnr16qFFixa6/S1atEBUVJTRn/Pfpk2bhk6dOmHEiBG6zxkQEICRI0fqjvl3zYtSqSwQf4sWLbB582a4ubnB0dHRoHvXqlULjo6OiIyMRN26dfX2FZYIA0CDBg2Qm5uLP//8EwEBAQCAhw8fIioqCg0bNtQd5+3tjQ8++AAffPABJk+ejFWrVhWa3BT2WQoTEBAAb29vbN68Gbt378Ybb7yh6+fVsGFDqFQqxMbG6v2MGMPQ34Pc3FycOXNG97MXFRWF5ORkXY2kqX4uiuvSpUvo27evJPem0osdiqncqV27NnJycvDtt98iOjoaGzduxIoVK4o8PjMzE6NHj8ahQ4dw69YtHDt2DKdPn9b9cZ84cSKOHz+O0aNHIyIiAn///Td+++03ozsUP+3jjz/G559/js2bNyMqKgqTJk1CREQExo0bByBvlMtPP/2Eq1ev4tq1a9iyZQs8PDwKHdrt5uYGW1tbhIWFISEhASkpKUXed8CAAdi5cyfWrFmj60icb8aMGdiwYQNmz56Ny5cv48qVKwgNDcW0adOM+mxt2rRB06ZNMX/+fABAnTp1cObMGezZswfXrl3D9OnTcfr0ab1zfHx88NdffyEqKgqJiYnIycnBgAED4Orqip49e+LIkSOIiYnBoUOHMHbsWNy5c6fQe8vlcgQGBuLo0aMGx1unTh307NkTw4cPx9GjR3HhwgUMHDgQXl5e6NmzJwDgww8/xJ49exATE4Nz587h4MGDRTZH+vj4IC0tDeHh4UhMTHxmk0r//v2xYsUK7Nu3T+/fw8HBARMmTMD48eOxfv163LhxA+fOncO3336L9evXG/S5DP09sLa2xpgxY/Dnn3/i7NmzGDJkCF544QVdslOcn4vBgwdj8uTJz4wvNjYWERERiI2NhUajQUREBCIiIvQ6Wd+8eRN3795FYGCgQZ+ZyhGpO/0QmUthnVDzLVq0SFSpUkXY2tqKoKAgsWHDhiI7fWZnZ4u33npLeHt7C6VSKTw9PcXo0aP1OgufOnVKdO7cWVSoUEHY29uLpk2bFugQ/LT/6iSp0WjErFmzhJeXl7C2thbNmjUTu3fv1u1fuXKl8PX1Ffb29sLR0VG8/PLL4ty5c7r9+FcHzlWrVglvb28hl8tFhw4dinw+Go1GVKlSRQAQN27cKBBXWFiYCAgIELa2tsLR0VG0bt1arFy5ssjPMXPmTNGsWbMC5T/99JNQqVQiNjZWZGVliSFDhggnJyfh7OwsRowYISZNmqR33v3793XPF4A4ePCgEEKIuLg4MXjwYOHq6ipUKpWoWbOmGD58uEhJSSkypl27dgkvLy9dR+minsXTkpKSxKBBg4STk5PuZ+batWu6/aNHjxa1atUSKpVKVK5cWQwaNEgkJiYKIQp2KBZCiA8++EBUqlRJABAzZ84UQhTe2TcyMlIAENWrVy/QeVyr1YrFixeLevXqCWtra1G5cmURFBQkDh8+XOTn+Pc9DP09+PXXX0XNmjWFSqUSgYGB4tatW3rX/a+fi3//PHbo0EEEBwcXGacQef8meNIB/emv/H97IYSYP3++CAoKeuZ1qHySCSGEFEkVEZEUhBDw9/fXNS+SZVKr1ahTpw5+/PHHAp3vidgsRUTlikwmw8qVKzmrrYWLjY3FlClTmNhQoVhzQ0RERGUKa26IiIioTGFyQ0RERGUKkxsiIiIqU5jcEBERUZnC5IaIiIjKFCY3REREVKYwuSEiIqIyhckNERERlSlMboiIiKhM+X+HTtX8wqD+wQAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"markdown","source":"# Improve the model and analyze the new model performance","metadata":{}},{"cell_type":"markdown","source":"In the following cell, we create 1D-CNN model to predict StartHesitation and analyze the output results after increase the number of epochs to 10.","metadata":{}},{"cell_type":"code","source":"y_train = tdcsfog[\"Turn\"][:num_samples]\ny_train = np.array(y_train)\n\ny_test = tdcsfog[\"Turn\"][:num_samples]\ny_test = np.array(y_test)\n\nepochs = 10\nmodel = run_1D_CNN_model(X_train, y_train, X_test, y_test, epochs)\n\nscore = model.evaluate(X_test, y_test, verbose=0)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])\ny_pred = model.predict(X_test)\nRocCurveDisplay.from_predictions(y_test, y_pred)","metadata":{"execution":{"iopub.status.busy":"2023-05-16T18:08:48.742281Z","iopub.execute_input":"2023-05-16T18:08:48.743348Z","iopub.status.idle":"2023-05-16T18:27:04.236168Z","shell.execute_reply.started":"2023-05-16T18:08:48.743303Z","shell.execute_reply":"2023-05-16T18:27:04.235152Z"},"trusted":true},"execution_count":38,"outputs":[{"name":"stdout","text":"Epoch 1/10\n31250/31250 [==============================] - 109s 3ms/step - loss: 0.3767 - accuracy: 0.8329 - val_loss: 0.3486 - val_accuracy: 0.8493\nEpoch 2/10\n31250/31250 [==============================] - 107s 3ms/step - loss: 0.3436 - accuracy: 0.8514 - val_loss: 0.3361 - val_accuracy: 0.8558\nEpoch 3/10\n31250/31250 [==============================] - 111s 4ms/step - loss: 0.3376 - accuracy: 0.8543 - val_loss: 0.3400 - val_accuracy: 0.8528\nEpoch 4/10\n31250/31250 [==============================] - 110s 4ms/step - loss: 0.3343 - accuracy: 0.8554 - val_loss: 0.3318 - val_accuracy: 0.8573\nEpoch 5/10\n31250/31250 [==============================] - 96s 3ms/step - loss: 0.3320 - accuracy: 0.8569 - val_loss: 0.3292 - val_accuracy: 0.8574\nEpoch 6/10\n31250/31250 [==============================] - 96s 3ms/step - loss: 0.3301 - accuracy: 0.8579 - val_loss: 0.3384 - val_accuracy: 0.8526\nEpoch 7/10\n31250/31250 [==============================] - 101s 3ms/step - loss: 0.3285 - accuracy: 0.8586 - val_loss: 0.3258 - val_accuracy: 0.8595\nEpoch 8/10\n31250/31250 [==============================] - 93s 3ms/step - loss: 0.3273 - accuracy: 0.8591 - val_loss: 0.3237 - val_accuracy: 0.8607\nEpoch 9/10\n31250/31250 [==============================] - 98s 3ms/step - loss: 0.3259 - accuracy: 0.8598 - val_loss: 0.3217 - val_accuracy: 0.8614\nEpoch 10/10\n31250/31250 [==============================] - 95s 3ms/step - loss: 0.3248 - accuracy: 0.8602 - val_loss: 0.3213 - val_accuracy: 0.8614\nTest loss: 0.3212824761867523\nTest accuracy: 0.8613989949226379\n31250/31250 [==============================] - 36s 1ms/step\n","output_type":"stream"},{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7d1f180670a0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]}]}