msc-dissertation-stacked-lstm-ver-1-opt.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","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":"code","source":"import warnings\nwarnings.filterwarnings('ignore')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-11-02T05:46:13.594105Z","iopub.execute_input":"2021-11-02T05:46:13.594423Z","iopub.status.idle":"2021-11-02T05:46:13.615369Z","shell.execute_reply.started":"2021-11-02T05:46:13.594337Z","shell.execute_reply":"2021-11-02T05:46:13.614749Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nplt.style.use(style='seaborn')\n%matplotlib inline\nimport tensorflow as tf","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:13.616990Z","iopub.execute_input":"2021-11-02T05:46:13.617355Z","iopub.status.idle":"2021-11-02T05:46:17.053012Z","shell.execute_reply.started":"2021-11-02T05:46:13.617318Z","shell.execute_reply":"2021-11-02T05:46:17.052227Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"tf.random.set_seed(47)\nnp.random.seed(47)","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:17.054175Z","iopub.execute_input":"2021-11-02T05:46:17.055177Z","iopub.status.idle":"2021-11-02T05:46:17.059848Z","shell.execute_reply.started":"2021-11-02T05:46:17.055136Z","shell.execute_reply":"2021-11-02T05:46:17.058871Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"# !pip install optuna > /dev/null","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:17.061595Z","iopub.execute_input":"2021-11-02T05:46:17.061878Z","iopub.status.idle":"2021-11-02T05:46:17.069271Z","shell.execute_reply.started":"2021-11-02T05:46:17.061843Z","shell.execute_reply":"2021-11-02T05:46:17.068462Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"import optuna\nfrom optuna.visualization.matplotlib import plot_optimization_history\nfrom optuna.visualization.matplotlib import plot_param_importances","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:17.073063Z","iopub.execute_input":"2021-11-02T05:46:17.073324Z","iopub.status.idle":"2021-11-02T05:46:18.261346Z","shell.execute_reply.started":"2021-11-02T05:46:17.073291Z","shell.execute_reply":"2021-11-02T05:46:18.260644Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"plt.rcParams['figure.figsize'] = (16,6)","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.262735Z","iopub.execute_input":"2021-11-02T05:46:18.263012Z","iopub.status.idle":"2021-11-02T05:46:18.267332Z","shell.execute_reply.started":"2021-11-02T05:46:18.262976Z","shell.execute_reply":"2021-11-02T05:46:18.266574Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv('../input/stockdata/ADBL_data.csv')\ndf['Date'] = pd.to_datetime(df.Date)","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.268988Z","iopub.execute_input":"2021-11-02T05:46:18.269292Z","iopub.status.idle":"2021-11-02T05:46:18.306371Z","shell.execute_reply.started":"2021-11-02T05:46:18.269256Z","shell.execute_reply":"2021-11-02T05:46:18.305780Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.308282Z","iopub.execute_input":"2021-11-02T05:46:18.308738Z","iopub.status.idle":"2021-11-02T05:46:18.325811Z","shell.execute_reply.started":"2021-11-02T05:46:18.308705Z","shell.execute_reply":"2021-11-02T05:46:18.325064Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"        Date   Open   High    Low  Close\n0 2012-01-01  115.0  115.0  113.0  114.0\n1 2012-01-02  114.0  118.0  115.0  118.0\n2 2012-01-03  118.0  116.0  114.0  115.0\n3 2012-01-04  115.0  115.0  113.0  114.0\n4 2012-01-05  114.0  114.0  112.0  113.0","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>Date</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2012-01-01</td>\n      <td>115.0</td>\n      <td>115.0</td>\n      <td>113.0</td>\n      <td>114.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2012-01-02</td>\n      <td>114.0</td>\n      <td>118.0</td>\n      <td>115.0</td>\n      <td>118.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2012-01-03</td>\n      <td>118.0</td>\n      <td>116.0</td>\n      <td>114.0</td>\n      <td>115.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2012-01-04</td>\n      <td>115.0</td>\n      <td>115.0</td>\n      <td>113.0</td>\n      <td>114.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2012-01-05</td>\n      <td>114.0</td>\n      <td>114.0</td>\n      <td>112.0</td>\n      <td>113.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"Using only Date and Close","metadata":{}},{"cell_type":"code","source":"df = df[['Date', 'Close']]","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.327138Z","iopub.execute_input":"2021-11-02T05:46:18.327382Z","iopub.status.idle":"2021-11-02T05:46:18.332720Z","shell.execute_reply.started":"2021-11-02T05:46:18.327350Z","shell.execute_reply":"2021-11-02T05:46:18.332075Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.334220Z","iopub.execute_input":"2021-11-02T05:46:18.334737Z","iopub.status.idle":"2021-11-02T05:46:18.354823Z","shell.execute_reply.started":"2021-11-02T05:46:18.334701Z","shell.execute_reply":"2021-11-02T05:46:18.354211Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"            Close\ncount  1911.00000\nmean    406.92831\nstd     167.13069\nmin     101.00000\n25%     313.00000\n50%     420.00000\n75%     480.00000\nmax    1082.00000","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>Close</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1911.00000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>406.92831</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>167.13069</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>101.00000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>313.00000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>420.00000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>480.00000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1082.00000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.355921Z","iopub.execute_input":"2021-11-02T05:46:18.356176Z","iopub.status.idle":"2021-11-02T05:46:18.367557Z","shell.execute_reply.started":"2021-11-02T05:46:18.356144Z","shell.execute_reply":"2021-11-02T05:46:18.366768Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"        Date  Close\n0 2012-01-01  114.0\n1 2012-01-02  118.0\n2 2012-01-03  115.0\n3 2012-01-04  114.0\n4 2012-01-05  113.0","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>Date</th>\n      <th>Close</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2012-01-01</td>\n      <td>114.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2012-01-02</td>\n      <td>118.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2012-01-03</td>\n      <td>115.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2012-01-04</td>\n      <td>114.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2012-01-05</td>\n      <td>113.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.title('ADBL Stock Price', fontsize=14)\nplt.xlabel('Year', fontsize=14)\nplt.ylabel('Closing Price', fontsize=14)\nplt.plot(df['Date'], df['Close'])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.368854Z","iopub.execute_input":"2021-11-02T05:46:18.369268Z","iopub.status.idle":"2021-11-02T05:46:18.607800Z","shell.execute_reply.started":"2021-11-02T05:46:18.369231Z","shell.execute_reply":"2021-11-02T05:46:18.607181Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x432 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Generating features from target variable","metadata":{}},{"cell_type":"code","source":"# Percentage change is : (Todays price - yesterdays price) / yesterdays price\n# For instance: (658.0 - 661.0) / 661.0\ndf['returns'] = df.Close.pct_change() * 100\ndf.dropna(inplace=True)\ndf.reset_index(drop=True, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.608815Z","iopub.execute_input":"2021-11-02T05:46:18.609171Z","iopub.status.idle":"2021-11-02T05:46:18.622534Z","shell.execute_reply.started":"2021-11-02T05:46:18.609138Z","shell.execute_reply":"2021-11-02T05:46:18.621941Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.626947Z","iopub.execute_input":"2021-11-02T05:46:18.627428Z","iopub.status.idle":"2021-11-02T05:46:18.638546Z","shell.execute_reply.started":"2021-11-02T05:46:18.627399Z","shell.execute_reply":"2021-11-02T05:46:18.637607Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"        Date  Close   returns\n0 2012-01-02  118.0  3.508772\n1 2012-01-03  115.0 -2.542373\n2 2012-01-04  114.0 -0.869565\n3 2012-01-05  113.0 -0.877193\n4 2012-01-08  114.0  0.884956","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>Date</th>\n      <th>Close</th>\n      <th>returns</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2012-01-02</td>\n      <td>118.0</td>\n      <td>3.508772</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2012-01-03</td>\n      <td>115.0</td>\n      <td>-2.542373</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2012-01-04</td>\n      <td>114.0</td>\n      <td>-0.869565</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2012-01-05</td>\n      <td>113.0</td>\n      <td>-0.877193</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2012-01-08</td>\n      <td>114.0</td>\n      <td>0.884956</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data = df[['Close', 'returns']].values\ndata","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.639974Z","iopub.execute_input":"2021-11-02T05:46:18.640463Z","iopub.status.idle":"2021-11-02T05:46:18.648905Z","shell.execute_reply.started":"2021-11-02T05:46:18.640295Z","shell.execute_reply":"2021-11-02T05:46:18.648058Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"array([[118.        ,   3.50877193],\n       [115.        ,  -2.54237288],\n       [114.        ,  -0.86956522],\n       ...,\n       [374.        ,  -1.05820106],\n       [376.        ,   0.53475936],\n       [368.        ,  -2.12765957]])"},"metadata":{}}]},{"cell_type":"markdown","source":"# Train, validation and test split","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.650233Z","iopub.execute_input":"2021-11-02T05:46:18.650619Z","iopub.status.idle":"2021-11-02T05:46:18.656456Z","shell.execute_reply.started":"2021-11-02T05:46:18.650581Z","shell.execute_reply":"2021-11-02T05:46:18.655593Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"X_train, X_data = train_test_split(data, test_size=0.20, shuffle=False)\nX_val, X_test = train_test_split(X_data, test_size=0.50, shuffle=False)\n\nprint(X_train.shape, X_val.shape, X_test.shape, sep='\\n')","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.658069Z","iopub.execute_input":"2021-11-02T05:46:18.658351Z","iopub.status.idle":"2021-11-02T05:46:18.667216Z","shell.execute_reply.started":"2021-11-02T05:46:18.658315Z","shell.execute_reply":"2021-11-02T05:46:18.666412Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"(1528, 2)\n(191, 2)\n(191, 2)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Scaling input data","metadata":{}},{"cell_type":"code","source":"from sklearn.preprocessing import MinMaxScaler","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.668775Z","iopub.execute_input":"2021-11-02T05:46:18.669655Z","iopub.status.idle":"2021-11-02T05:46:18.673837Z","shell.execute_reply.started":"2021-11-02T05:46:18.669614Z","shell.execute_reply":"2021-11-02T05:46:18.672878Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"scaler = MinMaxScaler(feature_range=(0, 1))\nX_train = scaler.fit_transform(X_train)\nX_val = scaler.transform(X_val)\nX_test = scaler.transform(X_test)","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.675238Z","iopub.execute_input":"2021-11-02T05:46:18.675995Z","iopub.status.idle":"2021-11-02T05:46:18.683629Z","shell.execute_reply.started":"2021-11-02T05:46:18.675954Z","shell.execute_reply":"2021-11-02T05:46:18.682951Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":"# Generating input features based on time step","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:18.684848Z","iopub.execute_input":"2021-11-02T05:46:18.685227Z","iopub.status.idle":"2021-11-02T05:46:19.461234Z","shell.execute_reply.started":"2021-11-02T05:46:18.685189Z","shell.execute_reply":"2021-11-02T05:46:19.460495Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"LAG_DAYS = 1\n\n# Generate Training set\ntrain_gen = TimeseriesGenerator(X_train, X_train[:,0], length=LAG_DAYS, batch_size=1)\n\nprint('Train Samples: %d' % len(train_gen))\n\nx_train, y_train = [], []\nfor i in range(len(train_gen)):\n    x, y = train_gen[i]\n    x_train.append(x[0])\n    y_train.append(y[0])\nx_train = np.array(x_train)\ny_train = np.array(y_train)\n\n# Generate Validation set\nval_gen = TimeseriesGenerator(X_val, X_val[:,0], length=LAG_DAYS, batch_size=1)\n\nprint('Validation Samples: %d' % len(val_gen))\n\nx_val, y_val = [], []\nfor i in range(len(val_gen)):\n    x, y = val_gen[i]\n    x_val.append(x[0])\n    y_val.append(y[0])\nx_val = np.array(x_val)\ny_val = np.array(y_val)\n\n# Generate Test set\ntest_gen = TimeseriesGenerator(X_test, X_test[:,0], length=LAG_DAYS, batch_size=1)\n\nprint('Test Samples: %d' % len(test_gen))\n\nx_test, y_test = [], []\nfor i in range(len(test_gen)):\n    x, y = test_gen[i]\n    x_test.append(x[0])\n    y_test.append(y[0])\nx_test = np.array(x_test)\ny_test = np.array(y_test)\n","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:19.462680Z","iopub.execute_input":"2021-11-02T05:46:19.462949Z","iopub.status.idle":"2021-11-02T05:46:19.522081Z","shell.execute_reply.started":"2021-11-02T05:46:19.462914Z","shell.execute_reply":"2021-11-02T05:46:19.521407Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"Train Samples: 1527\nValidation Samples: 190\nTest Samples: 190\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Scaling back output data to original Closing price","metadata":{}},{"cell_type":"code","source":"def prep_data_transform(data, length):\n    '''Prepare data for inverse transform'''\n    a = []\n    a.append(data)\n    [a.append(np.zeros(data.shape)) for i in range(1,length)]\n    return np.c_[a].T","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:19.523699Z","iopub.execute_input":"2021-11-02T05:46:19.523968Z","iopub.status.idle":"2021-11-02T05:46:19.530382Z","shell.execute_reply.started":"2021-11-02T05:46:19.523922Z","shell.execute_reply":"2021-11-02T05:46:19.529630Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"Y_train = np.round(scaler.inverse_transform(prep_data_transform(y_train, X_train.shape[1])))[:,0]\nY_val = np.round(scaler.inverse_transform(prep_data_transform(y_val, X_val.shape[1])))[:,0]\nY_test = np.round(scaler.inverse_transform(prep_data_transform(y_test, X_test.shape[1])))[:,0]\n\nprint(Y_train.shape, Y_val.shape, Y_test.shape)","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:19.531517Z","iopub.execute_input":"2021-11-02T05:46:19.531791Z","iopub.status.idle":"2021-11-02T05:46:19.540533Z","shell.execute_reply.started":"2021-11-02T05:46:19.531733Z","shell.execute_reply":"2021-11-02T05:46:19.539674Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"(1527,) (190,) (190,)\n","output_type":"stream"}]},{"cell_type":"code","source":"x_train[1], y_train[0]","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:19.541659Z","iopub.execute_input":"2021-11-02T05:46:19.542207Z","iopub.status.idle":"2021-11-02T05:46:19.549157Z","shell.execute_reply.started":"2021-11-02T05:46:19.542166Z","shell.execute_reply":"2021-11-02T05:46:19.548272Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"(array([[0.01427115, 0.70251106]]), 0.01427115188583078)"},"metadata":{}}]},{"cell_type":"code","source":"x_val[1], y_val[0]","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:19.550438Z","iopub.execute_input":"2021-11-02T05:46:19.551159Z","iopub.status.idle":"2021-11-02T05:46:19.557911Z","shell.execute_reply.started":"2021-11-02T05:46:19.551121Z","shell.execute_reply":"2021-11-02T05:46:19.556938Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"(array([[0.23445464, 0.7485674 ]]), 0.23445463812436293)"},"metadata":{}}]},{"cell_type":"code","source":"x_test[1], y_test[0]","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:19.559368Z","iopub.execute_input":"2021-11-02T05:46:19.560087Z","iopub.status.idle":"2021-11-02T05:46:19.566621Z","shell.execute_reply.started":"2021-11-02T05:46:19.560047Z","shell.execute_reply":"2021-11-02T05:46:19.565807Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"(array([[0.32008155, 0.75711126]]), 0.32008154943934763)"},"metadata":{}}]},{"cell_type":"markdown","source":"# LSTM Model","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import LSTM, Dense, Dropout\nfrom tensorflow.keras.utils import plot_model\nfrom sklearn.metrics import mean_squared_error\nfrom tensorflow.keras import optimizers","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:19.567813Z","iopub.execute_input":"2021-11-02T05:46:19.568657Z","iopub.status.idle":"2021-11-02T05:46:19.575883Z","shell.execute_reply.started":"2021-11-02T05:46:19.568611Z","shell.execute_reply":"2021-11-02T05:46:19.574915Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"markdown","source":"## Hyperparameter Search","metadata":{}},{"cell_type":"code","source":"early_stopping_rounds = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=200)","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:19.577128Z","iopub.execute_input":"2021-11-02T05:46:19.577711Z","iopub.status.idle":"2021-11-02T05:46:19.584383Z","shell.execute_reply.started":"2021-11-02T05:46:19.577659Z","shell.execute_reply":"2021-11-02T05:46:19.583612Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"# Objective function\ndef run(trial):\n    space = {\n        'optimizer':  trial.suggest_categorical('optimizer', [optimizers.Adam, optimizers.Adagrad, optimizers.Adamax, optimizers.Nadam, optimizers.RMSprop, optimizers.SGD]),\n        'units_1': trial.suggest_int('units_1', 50, 4000, 50),\n        'dropout_rate_1': trial.suggest_float('dropout_rate_1', 0.1, 0.6),\n        'units_2': trial.suggest_int('units_2', 50, 4000, 50),\n        'dropout_rate_2': trial.suggest_float('dropout_rate_2', 0.1, 0.6),\n        'units_3': trial.suggest_int('units_3', 50, 4000, 50),\n        'dropout_rate_3': trial.suggest_float('dropout_rate_3', 0.1, 0.6),\n        'units_4': trial.suggest_int('units_4', 50, 4000, 50),\n        'dropout_rate_4': trial.suggest_float('dropout_rate_4', 0.1, 0.6),\n        'epochs': trial.suggest_int('epochs', 100, 5000, 50),\n        'batch_size': trial.suggest_categorical('batch_size', [16, 32, 64, 128, 256, 512]),\n        'learning_rate': trial.suggest_loguniform('learning_rate', 1e-3, 8e-1)\n    }\n\n    model = Sequential()\n    # Adding the first LSTM layer and some Dropout regularization\n    model.add(LSTM(units=space['units_1'], return_sequences = True, input_shape=(x_train.shape[1], x_train.shape[2])))\n    model.add(Dropout(rate=space['dropout_rate_1']))\n    # Adding a second LSTM layer and some Dropout regularisation\n    model.add(LSTM(units=space['units_2'], return_sequences = True))\n    model.add(Dropout(rate=space['dropout_rate_2']))\n    # Adding a third LSTM layer and some Dropout regularisation\n    model.add(LSTM(units=space['units_3'], return_sequences = True))\n    model.add(Dropout(rate=space['dropout_rate_3']))\n    # Adding a fourth LSTM layer and some Dropout regularisation\n    model.add(LSTM(units=space['units_4']))\n    model.add(Dropout(rate=space['dropout_rate_4']))\n    # Adding the output layer\n    model.add(Dense(units=1))\n\n    # Compiling the RNN\n    model.compile(loss='mean_squared_error', optimizer=space['optimizer'](learning_rate=space['learning_rate']), metrics=['accuracy'])\n\n    model.fit(x_train, y_train, epochs=space['epochs'], validation_data=(x_val, y_val), batch_size=space['batch_size'], callbacks=[early_stopping_rounds], verbose=0)\n\n    yhat_train = np.squeeze(model.predict(x_train))\n    yhat_val = np.squeeze(model.predict(x_val))\n\n    yhat_train = scaler.inverse_transform(prep_data_transform(yhat_train, X_train.shape[1]))[:,0]\n    yhat_val = scaler.inverse_transform(prep_data_transform(yhat_val, X_val.shape[1]))[:,0]\n\n    nan_check = [np.isnan(yhat_train).any(), np.isnan(yhat_val).any()]\n    print(nan_check)\n    if True in nan_check:\n        rmse = 99999\n    else:\n        rmse = mean_squared_error(Y_val, yhat_val, squared=False)\n    return rmse","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:19.586024Z","iopub.execute_input":"2021-11-02T05:46:19.586534Z","iopub.status.idle":"2021-11-02T05:46:19.604178Z","shell.execute_reply.started":"2021-11-02T05:46:19.586491Z","shell.execute_reply":"2021-11-02T05:46:19.603474Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"%%time\nstudy = optuna.create_study(direction='minimize')\nstudy.optimize(run, n_trials=30)","metadata":{"execution":{"iopub.status.busy":"2021-11-02T05:46:19.605518Z","iopub.execute_input":"2021-11-02T05:46:19.605813Z","iopub.status.idle":"2021-11-02T13:24:00.482729Z","shell.execute_reply.started":"2021-11-02T05:46:19.605761Z","shell.execute_reply":"2021-11-02T13:24:00.481583Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stderr","text":"\u001b[32m[I 2021-11-02 05:46:19,612]\u001b[0m A new study created in memory with name: no-name-e435421c-b73a-4985-aee6-22de439c5e08\u001b[0m\n2021-11-02 05:46:19.689673: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-11-02 05:46:19.772915: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-11-02 05:46:19.773609: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-11-02 05:46:19.775284: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2021-11-02 05:46:19.776351: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-11-02 05:46:19.777071: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-11-02 05:46:19.777698: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-11-02 05:46:21.732475: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-11-02 05:46:21.733324: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-11-02 05:46:21.733983: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-11-02 05:46:21.734580: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n2021-11-02 05:47:21.675952: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n2021-11-02 05:47:27.808848: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005\n\u001b[32m[I 2021-11-02 05:56:16,646]\u001b[0m Trial 0 finished with value: 17.89917567899922 and parameters: {'optimizer': <class 'keras.optimizer_v2.nadam.Nadam'>, 'units_1': 1700, 'dropout_rate_1': 0.5471277862585731, 'units_2': 2400, 'dropout_rate_2': 0.2255530158322935, 'units_3': 2400, 'dropout_rate_3': 0.12085440729454425, 'units_4': 3500, 'dropout_rate_4': 0.12288369960885157, 'epochs': 2750, 'batch_size': 128, 'learning_rate': 0.0015576383622295106}. Best is trial 0 with value: 17.89917567899922.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 06:38:01,696]\u001b[0m Trial 1 finished with value: 53.00853413673788 and parameters: {'optimizer': <class 'keras.optimizer_v2.nadam.Nadam'>, 'units_1': 3600, 'dropout_rate_1': 0.44915389226354374, 'units_2': 3850, 'dropout_rate_2': 0.12992302763527128, 'units_3': 2450, 'dropout_rate_3': 0.2879523604033739, 'units_4': 3050, 'dropout_rate_4': 0.15772563127195413, 'epochs': 2300, 'batch_size': 32, 'learning_rate': 0.006647865251760402}. Best is trial 0 with value: 17.89917567899922.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 06:41:24,416]\u001b[0m Trial 2 finished with value: 74.4361307111165 and parameters: {'optimizer': <class 'keras.optimizer_v2.adagrad.Adagrad'>, 'units_1': 3400, 'dropout_rate_1': 0.48731777999862913, 'units_2': 1950, 'dropout_rate_2': 0.2842075038222517, 'units_3': 1800, 'dropout_rate_3': 0.4449297195097748, 'units_4': 2550, 'dropout_rate_4': 0.5712177751257099, 'epochs': 3500, 'batch_size': 256, 'learning_rate': 0.08041909572538646}. Best is trial 0 with value: 17.89917567899922.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 06:45:29,030]\u001b[0m Trial 3 finished with value: 74.1042484516753 and parameters: {'optimizer': <class 'keras.optimizer_v2.adagrad.Adagrad'>, 'units_1': 750, 'dropout_rate_1': 0.557559180001156, 'units_2': 2600, 'dropout_rate_2': 0.4720172897476538, 'units_3': 650, 'dropout_rate_3': 0.4952235006231197, 'units_4': 2700, 'dropout_rate_4': 0.31913997875978406, 'epochs': 950, 'batch_size': 64, 'learning_rate': 0.021408545647213457}. Best is trial 0 with value: 17.89917567899922.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 06:48:43,666]\u001b[0m Trial 4 finished with value: 7.63890552401081 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 2500, 'dropout_rate_1': 0.14951357261005424, 'units_2': 900, 'dropout_rate_2': 0.3749070634801126, 'units_3': 1950, 'dropout_rate_3': 0.26509214469048625, 'units_4': 3150, 'dropout_rate_4': 0.4368722363568167, 'epochs': 4100, 'batch_size': 256, 'learning_rate': 0.002053577478753283}. Best is trial 4 with value: 7.63890552401081.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 06:57:44,979]\u001b[0m Trial 5 finished with value: 2231.728710016807 and parameters: {'optimizer': <class 'keras.optimizer_v2.rmsprop.RMSprop'>, 'units_1': 3950, 'dropout_rate_1': 0.44440968664717806, 'units_2': 2300, 'dropout_rate_2': 0.10084734499888529, 'units_3': 3250, 'dropout_rate_3': 0.4864157435950789, 'units_4': 1500, 'dropout_rate_4': 0.23395960172961358, 'epochs': 3000, 'batch_size': 64, 'learning_rate': 0.013587506527680775}. Best is trial 4 with value: 7.63890552401081.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 07:10:35,214]\u001b[0m Trial 6 finished with value: 11.224712430224507 and parameters: {'optimizer': <class 'keras.optimizer_v2.rmsprop.RMSprop'>, 'units_1': 2350, 'dropout_rate_1': 0.4780547942843134, 'units_2': 900, 'dropout_rate_2': 0.4423033079264983, 'units_3': 2400, 'dropout_rate_3': 0.3222128091778972, 'units_4': 1800, 'dropout_rate_4': 0.19487551437287962, 'epochs': 2750, 'batch_size': 128, 'learning_rate': 0.005701804817869916}. Best is trial 4 with value: 7.63890552401081.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 07:36:27,904]\u001b[0m Trial 7 finished with value: 133695.2883776371 and parameters: {'optimizer': <class 'keras.optimizer_v2.rmsprop.RMSprop'>, 'units_1': 3250, 'dropout_rate_1': 0.4689409890453491, 'units_2': 2050, 'dropout_rate_2': 0.19215090164900675, 'units_3': 1300, 'dropout_rate_3': 0.12262702277331175, 'units_4': 2950, 'dropout_rate_4': 0.1479665358402619, 'epochs': 1100, 'batch_size': 16, 'learning_rate': 0.3013197490058176}. Best is trial 4 with value: 7.63890552401081.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 08:02:33,625]\u001b[0m Trial 8 finished with value: 9043.19049771222 and parameters: {'optimizer': <class 'keras.optimizer_v2.nadam.Nadam'>, 'units_1': 300, 'dropout_rate_1': 0.2482344637616665, 'units_2': 1650, 'dropout_rate_2': 0.1192333438083141, 'units_3': 1500, 'dropout_rate_3': 0.5162512278507445, 'units_4': 3300, 'dropout_rate_4': 0.575745642849186, 'epochs': 4950, 'batch_size': 32, 'learning_rate': 0.2316295834015568}. Best is trial 4 with value: 7.63890552401081.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 09:50:54,753]\u001b[0m Trial 9 finished with value: 6.859358971528525 and parameters: {'optimizer': <class 'keras.optimizer_v2.rmsprop.RMSprop'>, 'units_1': 2300, 'dropout_rate_1': 0.14392211465515845, 'units_2': 700, 'dropout_rate_2': 0.1030063385730394, 'units_3': 3150, 'dropout_rate_3': 0.4806478807165333, 'units_4': 3050, 'dropout_rate_4': 0.38305785950651283, 'epochs': 3300, 'batch_size': 16, 'learning_rate': 0.0020818425666182428}. Best is trial 9 with value: 6.859358971528525.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 09:58:32,609]\u001b[0m Trial 10 finished with value: 89.16115284997365 and parameters: {'optimizer': <class 'keras.optimizer_v2.gradient_descent.SGD'>, 'units_1': 1550, 'dropout_rate_1': 0.10489083299806196, 'units_2': 100, 'dropout_rate_2': 0.5943547319718743, 'units_3': 4000, 'dropout_rate_3': 0.5962329202603471, 'units_4': 300, 'dropout_rate_4': 0.4298244989652299, 'epochs': 1650, 'batch_size': 16, 'learning_rate': 0.05326883523154378}. Best is trial 9 with value: 6.859358971528525.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 10:04:01,985]\u001b[0m Trial 11 finished with value: 7.136764084016272 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 2600, 'dropout_rate_1': 0.11202108302890386, 'units_2': 850, 'dropout_rate_2': 0.37856407393006025, 'units_3': 3250, 'dropout_rate_3': 0.2435275719861279, 'units_4': 3900, 'dropout_rate_4': 0.41413353830554167, 'epochs': 4200, 'batch_size': 512, 'learning_rate': 0.0010352204405657353}. Best is trial 9 with value: 6.859358971528525.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 10:07:51,412]\u001b[0m Trial 12 finished with value: 6.579238373687748 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 2750, 'dropout_rate_1': 0.22792165292975838, 'units_2': 50, 'dropout_rate_2': 0.3379162398571884, 'units_3': 3200, 'dropout_rate_3': 0.3967512484540226, 'units_4': 3800, 'dropout_rate_4': 0.3959732343048801, 'epochs': 4250, 'batch_size': 512, 'learning_rate': 0.0010512100721296591}. Best is trial 12 with value: 6.579238373687748.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 10:11:43,334]\u001b[0m Trial 13 finished with value: 15.950177672443713 and parameters: {'optimizer': <class 'keras.optimizer_v2.adam.Adam'>, 'units_1': 2900, 'dropout_rate_1': 0.24716951069434506, 'units_2': 100, 'dropout_rate_2': 0.2945153013951589, 'units_3': 3450, 'dropout_rate_3': 0.4061433092788408, 'units_4': 3900, 'dropout_rate_4': 0.3385683080642317, 'epochs': 4950, 'batch_size': 512, 'learning_rate': 0.0041775270934702015}. Best is trial 12 with value: 6.579238373687748.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 10:25:51,036]\u001b[0m Trial 14 finished with value: 9.717824075817244 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 1800, 'dropout_rate_1': 0.2218500555348572, 'units_2': 500, 'dropout_rate_2': 0.5962500520011372, 'units_3': 2900, 'dropout_rate_3': 0.3884346689527454, 'units_4': 2200, 'dropout_rate_4': 0.5113136794028218, 'epochs': 3750, 'batch_size': 16, 'learning_rate': 0.0029877654123053425}. Best is trial 12 with value: 6.579238373687748.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 10:30:12,952]\u001b[0m Trial 15 finished with value: 74.38458102433925 and parameters: {'optimizer': <class 'keras.optimizer_v2.adam.Adam'>, 'units_1': 1050, 'dropout_rate_1': 0.3138771486171533, 'units_2': 1350, 'dropout_rate_2': 0.5026417665665256, 'units_3': 3850, 'dropout_rate_3': 0.5935666149337672, 'units_4': 1150, 'dropout_rate_4': 0.28280456844526675, 'epochs': 2000, 'batch_size': 512, 'learning_rate': 0.010154388560950144}. Best is trial 12 with value: 6.579238373687748.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 10:39:20,703]\u001b[0m Trial 16 finished with value: 73.28709992163908 and parameters: {'optimizer': <class 'keras.optimizer_v2.gradient_descent.SGD'>, 'units_1': 2100, 'dropout_rate_1': 0.17820549914125935, 'units_2': 3150, 'dropout_rate_2': 0.2085509095457293, 'units_3': 2800, 'dropout_rate_3': 0.3537930109668632, 'units_4': 4000, 'dropout_rate_4': 0.3839695325131772, 'epochs': 3400, 'batch_size': 512, 'learning_rate': 0.0012439769851314269}. Best is trial 12 with value: 6.579238373687748.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 11:40:44,505]\u001b[0m Trial 17 finished with value: 913.3709296690477 and parameters: {'optimizer': <class 'keras.optimizer_v2.rmsprop.RMSprop'>, 'units_1': 2850, 'dropout_rate_1': 0.3379899972730812, 'units_2': 550, 'dropout_rate_2': 0.29679544925578616, 'units_3': 100, 'dropout_rate_3': 0.5343426712790251, 'units_4': 3500, 'dropout_rate_4': 0.4982162499656346, 'epochs': 4300, 'batch_size': 16, 'learning_rate': 0.7519016885728478}. Best is trial 12 with value: 6.579238373687748.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 11:47:06,257]\u001b[0m Trial 18 finished with value: 7.95103287195589 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 1350, 'dropout_rate_1': 0.2904728613022567, 'units_2': 1350, 'dropout_rate_2': 0.5295622413943684, 'units_3': 3550, 'dropout_rate_3': 0.19591719485069242, 'units_4': 2300, 'dropout_rate_4': 0.2836225515499431, 'epochs': 4500, 'batch_size': 512, 'learning_rate': 0.0027742040822562467}. Best is trial 12 with value: 6.579238373687748.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 11:56:11,637]\u001b[0m Trial 19 finished with value: 47.82565841415889 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 3000, 'dropout_rate_1': 0.39353558817754986, 'units_2': 50, 'dropout_rate_2': 0.4321140695499215, 'units_3': 2700, 'dropout_rate_3': 0.4230231851656036, 'units_4': 800, 'dropout_rate_4': 0.47751826871592684, 'epochs': 250, 'batch_size': 16, 'learning_rate': 0.043564756445630105}. Best is trial 12 with value: 6.579238373687748.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 12:30:53,335]\u001b[0m Trial 20 finished with value: 40.086488039498335 and parameters: {'optimizer': <class 'keras.optimizer_v2.rmsprop.RMSprop'>, 'units_1': 2100, 'dropout_rate_1': 0.18391779427009208, 'units_2': 1300, 'dropout_rate_2': 0.17057980439036327, 'units_3': 3050, 'dropout_rate_3': 0.4575069462198438, 'units_4': 2750, 'dropout_rate_4': 0.37795046630130397, 'epochs': 3300, 'batch_size': 32, 'learning_rate': 0.010163466940185849}. Best is trial 12 with value: 6.579238373687748.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 12:38:19,485]\u001b[0m Trial 21 finished with value: 7.301473724494618 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 2550, 'dropout_rate_1': 0.10866898002079309, 'units_2': 600, 'dropout_rate_2': 0.37441912591489696, 'units_3': 3600, 'dropout_rate_3': 0.20733700321184836, 'units_4': 3800, 'dropout_rate_4': 0.39007697058640456, 'epochs': 3950, 'batch_size': 512, 'learning_rate': 0.0010323246614424646}. Best is trial 12 with value: 6.579238373687748.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 12:45:08,348]\u001b[0m Trial 22 finished with value: 6.321012135097071 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 2550, 'dropout_rate_1': 0.14865075581332382, 'units_2': 850, 'dropout_rate_2': 0.342209947650187, 'units_3': 3250, 'dropout_rate_3': 0.35830998318164287, 'units_4': 3600, 'dropout_rate_4': 0.43561629301784605, 'epochs': 4550, 'batch_size': 512, 'learning_rate': 0.0010519882014581917}. Best is trial 22 with value: 6.321012135097071.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 12:49:47,107]\u001b[0m Trial 23 finished with value: 6.662762321310076 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 2200, 'dropout_rate_1': 0.16912092992377784, 'units_2': 500, 'dropout_rate_2': 0.32496431124171815, 'units_3': 3150, 'dropout_rate_3': 0.37421219457767696, 'units_4': 3500, 'dropout_rate_4': 0.46461221003463404, 'epochs': 4600, 'batch_size': 512, 'learning_rate': 0.0022121763975155796}. Best is trial 22 with value: 6.321012135097071.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 12:55:40,422]\u001b[0m Trial 24 finished with value: 7.213787778578423 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 1900, 'dropout_rate_1': 0.21003668314873752, 'units_2': 350, 'dropout_rate_2': 0.3306156761306487, 'units_3': 3750, 'dropout_rate_3': 0.3618973985616098, 'units_4': 3500, 'dropout_rate_4': 0.47132451173221995, 'epochs': 4550, 'batch_size': 512, 'learning_rate': 0.0039153496780480685}. Best is trial 22 with value: 6.321012135097071.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 13:01:26,631]\u001b[0m Trial 25 finished with value: 8.529141927195429 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 3100, 'dropout_rate_1': 0.1744037467077592, 'units_2': 1250, 'dropout_rate_2': 0.24983401249063486, 'units_3': 2650, 'dropout_rate_3': 0.31107826035663916, 'units_4': 3600, 'dropout_rate_4': 0.5308576208733876, 'epochs': 4700, 'batch_size': 512, 'learning_rate': 0.0018943208508435793}. Best is trial 22 with value: 6.321012135097071.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 13:06:34,349]\u001b[0m Trial 26 finished with value: 8.459917808010909 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 3800, 'dropout_rate_1': 0.2715129106121434, 'units_2': 350, 'dropout_rate_2': 0.3278732370849596, 'units_3': 2150, 'dropout_rate_3': 0.37416921522182595, 'units_4': 3300, 'dropout_rate_4': 0.4548573937824818, 'epochs': 3850, 'batch_size': 512, 'learning_rate': 0.005600140452775659}. Best is trial 22 with value: 6.321012135097071.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 13:14:36,746]\u001b[0m Trial 27 finished with value: 8.329456127473852 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 2800, 'dropout_rate_1': 0.21803607637772987, 'units_2': 1100, 'dropout_rate_2': 0.40580100621913584, 'units_3': 3350, 'dropout_rate_3': 0.32746804243523564, 'units_4': 3700, 'dropout_rate_4': 0.30816196057750744, 'epochs': 5000, 'batch_size': 512, 'learning_rate': 0.0026732047893599175}. Best is trial 22 with value: 6.321012135097071.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 13:18:41,637]\u001b[0m Trial 28 finished with value: 6.869384687779228 and parameters: {'optimizer': <class 'keras.optimizer_v2.adamax.Adamax'>, 'units_1': 1300, 'dropout_rate_1': 0.14289009466939026, 'units_2': 300, 'dropout_rate_2': 0.2564776494592856, 'units_3': 2950, 'dropout_rate_3': 0.42255821337453686, 'units_4': 2450, 'dropout_rate_4': 0.5389075576181861, 'epochs': 4550, 'batch_size': 512, 'learning_rate': 0.0010083905252681374}. Best is trial 22 with value: 6.321012135097071.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[32m[I 2021-11-02 13:24:00,476]\u001b[0m Trial 29 finished with value: 73.39329202697496 and parameters: {'optimizer': <class 'keras.optimizer_v2.adagrad.Adagrad'>, 'units_1': 2200, 'dropout_rate_1': 0.37086800738397796, 'units_2': 1650, 'dropout_rate_2': 0.33821948594257845, 'units_3': 2150, 'dropout_rate_3': 0.3434916316829974, 'units_4': 1950, 'dropout_rate_4': 0.3548362417133639, 'epochs': 2950, 'batch_size': 128, 'learning_rate': 0.0015900460982415199}. Best is trial 22 with value: 6.321012135097071.\u001b[0m\n","output_type":"stream"},{"name":"stdout","text":"[False, False]\nCPU times: user 2h 51min 3s, sys: 15min 57s, total: 3h 7min\nWall time: 7h 37min 40s\n","output_type":"stream"}]},{"cell_type":"code","source":"best_params = study.best_params\nbest_params","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:24:00.484143Z","iopub.execute_input":"2021-11-02T13:24:00.484520Z","iopub.status.idle":"2021-11-02T13:24:00.491256Z","shell.execute_reply.started":"2021-11-02T13:24:00.484478Z","shell.execute_reply":"2021-11-02T13:24:00.490530Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"{'optimizer': keras.optimizer_v2.adamax.Adamax,\n 'units_1': 2550,\n 'dropout_rate_1': 0.14865075581332382,\n 'units_2': 850,\n 'dropout_rate_2': 0.342209947650187,\n 'units_3': 3250,\n 'dropout_rate_3': 0.35830998318164287,\n 'units_4': 3600,\n 'dropout_rate_4': 0.43561629301784605,\n 'epochs': 4550,\n 'batch_size': 512,\n 'learning_rate': 0.0010519882014581917}"},"metadata":{}}]},{"cell_type":"code","source":"# best_params = {'optimizer': optimizers.Adagrad,\n#  'units_1': 3800,\n#  'dropout_rate_1': 0.3882048176996995,\n#  'units_2': 3650,\n#  'dropout_rate_2': 0.4604730799562804,\n#  'units_3': 3150,\n#  'dropout_rate_3': 0.5312364086754949,\n#  'units_4': 3950,\n#  'dropout_rate_4': 0.5937661207246528,\n#  'epochs': 4900,\n#  'batch_size': 64,\n#  'learning_rate': 0.013971482413425227}","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:24:00.492616Z","iopub.execute_input":"2021-11-02T13:24:00.493443Z","iopub.status.idle":"2021-11-02T13:24:00.503973Z","shell.execute_reply.started":"2021-11-02T13:24:00.493396Z","shell.execute_reply":"2021-11-02T13:24:00.503203Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"plot_optimization_history(study);","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:24:00.505556Z","iopub.execute_input":"2021-11-02T13:24:00.506290Z","iopub.status.idle":"2021-11-02T13:24:00.794083Z","shell.execute_reply.started":"2021-11-02T13:24:00.506253Z","shell.execute_reply":"2021-11-02T13:24:00.793348Z"},"trusted":true},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x432 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot_param_importances(study);","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:24:00.795216Z","iopub.execute_input":"2021-11-02T13:24:00.796896Z","iopub.status.idle":"2021-11-02T13:24:01.937276Z","shell.execute_reply.started":"2021-11-02T13:24:00.796853Z","shell.execute_reply":"2021-11-02T13:24:01.936592Z"},"trusted":true},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure 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fqiYmBj98MMPJdJ2WFiYatWqpUmTJpVI/bDMqFGjlJCQUOB72QEAKGmMuAMAcAcmk0lXr17Vl19+qczMTD377LPWDgkAAJQzJO4AANxBfHy8unTpoqpVq2ry5MnmRckAAAAeFKbKAwAAAABgw1hVHgAAAAAAG0biDgAAAACADSNxBwAAAADAhrE4XSkTHx9v7RCAYuXr60u/RplE30ZZRd9GWUXfhrX5+vre9hgj7gAAAAAA2DASdwAAAAAAbBiJOwAAAAAANozEHQAAAAAAG0biDgAAAACADSNxBwAAAADAhpG4AwAAAABgw0jcAQAAAACwYSTuAAAAAADYMBJ3AAAAAABsGIk7AAAAAAA2jMQdAAAAAAAbZmcYhmHtIGC5ZhM3WDsEAAAAACg1vu0faO0QLOLr63vbY4y4AwAAAABgw0jcAQAAAACwYSTuAAAAAADYMBJ3AAAAAABsGIk7AAAAAAA2jMQdAAAAAAAbRuIOAAAAAIANs5nEPSwsrMTbWL9+vTZt2lTi7RRm48aNSklJsUrbAAAAAIDSy9HaARQ3k8kke/vCn0c88cQTVmt748aNqlmzpry8vEo0BgAAAABA2WKTiXtMTIy2b9+unJwchYaGqlevXpKk6dOn68qVK8rJyVG3bt30+OOPS7o5Wt+1a1cdPnxY/fv316RJk9StWzft27dPzs7OioyMVKVKlbRixQq5uLioR48eeuedd1SnTh0dPXpUGRkZGjhwoOrXr68bN24oKipKcXFx8vX11dWrV9W/f3/5+/sXGuvv2z5y5Ij27t2r7OxsBQQEaMCAAdq5c6d++uknzZ07V87Ozpo0aZIuXryoJUuWKCsrSx4eHoqIiFDlypUf2D0GAAAAAJQONpe4Hzx4UJcuXdLkyZNlGIamT5+uY8eOqUGDBoqIiJCbm5uys7M1evRotWzZUu7u7rpx44bq1Kmjl156SZJ048YN1a1bVy+88IL+8Y9/6L///a+effbZAm2ZTCZNmTJF+/bt08qVKzVu3DitW7dObm5uev/993XhwgWNGDHijvH+vu0aNWroueeekyR9+OGH2rt3r1q1aqXvv/9eYWFh8vf3V25urqKjozVixAh5eHho27Zt+uKLLxQREVHMdxMAAAAAyjdfX19rh3DfbDJxP3TokDlhzsrKUkJCgho0aKDvvvtOu3fvliQlJyfr0qVLcnd3l729vVq1amWuw9HRUSEhIZIkPz8/HTp0qNC2QkNDzWUSExMlSSdOnFC3bt0kSbVq1dKjjz56x3h/3/aRI0cUExOjGzdu6Pr166pZs6aaN2+e75z4+HjFxcVp4sSJkm4+QGC0HQAAAACKX3x8vLVDsMidHjDYXOIuST179lTXrl3z7Tt69KgOHz6s9957TxUqVNA777yjnJwcSZKTk1O+d8sdHBxkZ2cn6WZinZeXV2g7Tk5O5jImk+meYv1t29nZ2Vq0aJGmTJkib29vrVixQtnZ2YWeV6NGDU2aNOme2gQAAAAAlB82s6r8LU2aNNGPP/6orKwsSVJKSop+/fVXZWRk6KGHHlKFChX0yy+/6PTp0yXSfmBgoLZv3y5Junjxoi5cuGDxubceJHh4eCgrK0s7d+40H3NxcVFmZqakm09SUlNTderUKUlSbm6u4uLiiusSAAAAAABliM2NuDdp0kS//PKLxo4dK+lmwjt48GAFBwfrhx9+0JtvvqlHHnlEdevWLZH2n3jiCUVFRenNN99U9erVVaNGDbm6ulp07kMPPaQuXbpo2LBhqlSpUr4F7Tp16qSFCxeaF6cbNmyYPvvsM2VkZCgvL0/dunVTzZo1S+SaAAAAAACll51hGIa1g7AlJpNJubm5cnZ2VkJCgiZOnKg5c+bI0dE2nnE0m7jB2iEAAAAAQKnxbf9Aa4dgkVL3jrs13bhxQxMmTFBeXp4Mw9Arr7xiM0k7AAAAAKD8ISP9nYoVK2rq1KkF9o8ZM8b8DvstgwcPVq1atR5UaAAAAACAcojE3UKTJ0+2dggAAAAAgHLI5laVBwAAAAAA/8PidKVMfHy8tUMAipWvry/9GmUSfRtlFX0bZRV9G9Z2p8XpGHEHAAAAAMCGkbgDAAAAAGDDSNwBAAAAALBhJO4AAAAAANgwFqcrZZpN3GDtEAAAAIBy59v+gdYOAWUci9MBAAAAAFBKkbgDAAAAAGDDSNwBAAAAALBhJO4AAAAAANgwEncAAAAAAGwYiTsAAAAAADbMKon7ihUrFBMT88DbTUxMVGxsbLHUlZ6ernXr1t1XHSaTSSNGjNDUqVOLJSYAAAAAQNljMyPueXl5Jd5GUlJSkRL3O8WUnp6u9evX31c83333napXr35fdQAAAAAAyjbHB9XQqlWrtGnTJnl4eKhKlSry8/PTO++8o9q1a+vEiRNq27atateuraVLlyovL0/+/v569dVX5eTkpEGDBql169bav3+/nJ2dNWTIEFWrVk2JiYlasGCB0tLS5OHhoYiICHl7eysqKkohISFq1aqVJCksLExLly7V8uXLdfHiRUVGRqpjx4566qmnCsS5ceNG7dy5U1lZWTKZTBo9erSmT5+u9PR05ebmqk+fPmrRooWWL1+uhIQERUZGKigoSGFhYYqJidH27duVk5Oj0NBQ9erV67b348qVK9q3b5+eeeYZffvttyV23wEAAAAApdsDSdzPnj2rrVu3avr06crLy9PIkSPl5+cnScrNzdXUqVOVnZ2tIUOGaNy4cfL19dW8efO0fv16de/eXZLk6uqqWbNmadOmTVq8eLFGjRql6OhodezYUZ06ddKGDRsUHR2tESNG3DaOvn37as2aNRo1atQd4z137pxmzpwpNzc35eXlafjw4XJ1dVVqaqrGjh2r5s2bq2/fvoqLi9OMGTMkSQcPHtSlS5c0efJkGYah6dOn69ixY2rQoEGhbSxevFgvvviiMjMz7+WWAgAAAHiAfH19rR0CyrEHkrgfP35coaGhqlChgiSpefPm5mNt2rSRJMXHx8vHx8f8P0THjh21bt06c+Letm1b888lS5ZIkk6fPq3hw4dLkjp06KBly5YVS7xBQUFyc3OTJBmGoS+++ELHjx+XnZ2dUlJS9OuvvxY45+DBgzp06JD5wUFWVpYSEhIKTdz37t0rT09P+fn56ejRo8USMwAAAICSEx8fb+0QUMbd6eHQA5sqfzu3kvm7sbOzK/RzYRwcHGQymSTdXAAuNzf3nmOKjY1Vamqqpk6dKkdHRw0aNEjZ2dmFntezZ0917dr1rvWfPHlSe/bs0f79+5Wdna3MzEzNnTtXb7zxRpHiBAAAAACUfQ9kcbr69etr9+7d5iR17969Bcr4+voqMTFRCQkJkqTNmzfnG63etm2b+WfdunUlSQEBAeb9sbGxCgwMlCRVrVpVZ8+elSTt2bPHvMhcxYoVizw1PSMjQ56ennJ0dNSRI0eUlJRUaF1NmjTRjz/+qKysLEm67ci8dHPK/kcffaSoqCgNHTpUjRo1ImkHAAAAABTqgYy4+/n5qU2bNoqMjJSHh4f8/f0LlHF2dlZERIRmz55tXpzut6PX169f1/Dhw+Xk5KQhQ4ZIksLDwzV//nzFxMSYF6eTpC5dumjGjBmKjIxUkyZNzCPotWrVkr29/R0Xp/u9du3aadq0aRo2bJj8/f3Nq8C7u7urXr16GjZsmIKDgxUWFqZffvlFY8eOlSS5uLho8ODB8vT0vL+bBwAAAAAo1+wMwzCsHcTdDBo0SFOmTJGHh4e1Q7G6ZhM3WDsEAAAAoNz5tn+gtUNAGXend9xt5nvcAQAAAABAQVZfnM4SUVFRxV7ngQMHCqxC7+Pjo8jIyGJrIy0tTe+++26B/W+//bbc3d2LrR0AAAAAQNlVKqbK43+YKg8AAAA8eEyVR0ljqjwAAAAAAKUUI+6lTHx8vLVDAIqVr68v/RplEn0bZRV9G2UVfRvWxog7AAAAAAClFIk7AAAAAAA2jMQdAAAAAAAbRuIOAAAAAIANKxXf447/eWrRCWuHABQz+jTKKvo2yqr779t8rRYAFA0j7gAAAAAA2DASdwAAAAAAbBiJOwAAAAAANozEHQAAAAAAG0biDgAAAACADSNxBwAAAADAhlnl6+BWrFghFxcX9ejR44G2m5iYqFOnTqldu3b3XVd6erpiY2P15JNPFvnc7OxsjR8/Xrm5ucrLy1OrVq3Uq1ev+44JAAAAAFD22MyIe15eXom3kZSUpNjYWIvL3ymm9PR0rV+//p7icHJy0vjx4zVjxgxNnz5dBw4c0KlTp+6pLgAAAABA2fbARtxXrVqlTZs2ycPDQ1WqVJGfn5/eeecd1a5dWydOnFDbtm1Vu3ZtLV26VHl5efL399err74qJycnDRo0SK1bt9b+/fvl7OysIUOGqFq1akpMTNSCBQuUlpYmDw8PRUREyNvbW1FRUQoJCVGrVq0kSWFhYVq6dKmWL1+uixcvKjIyUh07dtRTTz1VIM6NGzdq586dysrKkslk0ujRozV9+nSlp6crNzdXffr0UYsWLbR8+XIlJCQoMjJSQUFBCgsLU0xMjLZv366cnByFhobedhTdzs5OLi4ukm4+HMjLy5OdnV3J3XwAAAAAQKn1QBL3s2fPauvWrZo+fbry8vI0cuRI+fn5SZJyc3M1depUZWdna8iQIRo3bpx8fX01b948rV+/Xt27d5ckubq6atasWdq0aZMWL16sUaNGKTo6Wh07dlSnTp20YcMGRUdHa8SIEbeNo2/fvlqzZo1GjRp1x3jPnTunmTNnys3NTXl5eRo+fLhcXV2VmpqqsWPHqnnz5urbt6/i4uI0Y8YMSdLBgwd16dIlTZ48WYZhaPr06Tp27JgaNGhQaBsmk0kjR45UQkKCnnzySdWtW/debi0AAECp4+vra+0QgELRN2GrHkjifvz4cYWGhqpChQqSpObNm5uPtWnTRpIUHx8vHx8f8/8sHTt21Lp168yJe9u2bc0/lyxZIkk6ffq0hg8fLknq0KGDli1bVizxBgUFyc3NTZJkGIa++OILHT9+XHZ2dkpJSdGvv/5a4JyDBw/q0KFD5gcHWVlZSkhIuG3ibm9vrxkzZig9PV0zZ87UhQsXVKtWrWKJHwAAwJbFx8dbOwSgAF9fX/omrOpOD46ssjjdb91K5u/mt1PJ7zat3MHBQSaTSdLNke3c3Nx7jik2NlapqamaOnWqHB0dNWjQIGVnZxd6Xs+ePdW1a9citfXQQw+pYcOGOnDgAIk7AAAAAKCAB7I4Xf369bV7925lZ2crMzNTe/fuLVDG19dXiYmJSkhIkCRt3rw532j1tm3bzD9vTSsPCAgw74+NjVVgYKAkqWrVqjp79qwkac+ePeZF5ipWrKjMzMwixZ6RkSFPT085OjrqyJEjSkpKKrSuJk2a6Mcff1RWVpYk3XZkXpJSU1OVnp4u6eYK84cOHVL16tWLFBcAAAAAoHx4ICPufn5+atOmjSIjI+Xh4SF/f/8CZZydnRUREaHZs2ebF6f77ej19evXNXz4cDk5OWnIkCGSpPDwcM2fP18xMTHmxekkqUuXLpoxY4YiIyPVpEkT8wh6rVq1ZG9vf8fF6X6vXbt2mjZtmoYNGyZ/f39zgu3u7q569epp2LBhCg4OVlhYmH755ReNHTtWkuTi4qLBgwfL09OzQJ1Xr15VVFSUTCaTDMNQ69atFRISUsS7CgAAAAAoD+wMwzCsHcTdDBo0SFOmTJGHh4e1Q7G6ZhM3WDsEAACA+/Jt/0BrhwAUwDvusLY7veNuM9/jDgAAAAAACrL64nSWiIqKKvY6Dxw4UGAVeh8fH0VGRhZbG2lpaXr33XcL7H/77bfl7u5ebO0AAAAAAMquUpG4l4Tg4GAFBweXaBvu7u7m73kHAAAAAOBeMFUeAAAAAAAbVm5H3EsrFnNBWcNCMCir6Nsoq+jbAPDgMeIOAAAAAIANI3EHAAAAAMCGkbgDAAAAAGDDSNwBAAAAALBhLE5Xyjy16IS1QwCKGX0aZRV9G7aNBW8BoPRgxB0AAAAAABtG4g4AAAAAgA0jcQcAAAAAwIaRuAMAAAAAYMNI3AEAAAAAsGEk7gAAAAAA2DAS9yL66aefFB0dLUk6evSoTp48WeQ60tLSNGHCBIWFhWnRokXFHSIAAAAAoAzhe9yLyN/fX/7+/pJuJu4uLi6qV69ekepwcnJS7969deHCBcXFxZVEmAAAAACAMqLcJ+6JiYmaNm2aZs2aJUmKiYlRVlaWjh07pjp16ujo0aPKyMjQwIEDVb9+fR09elRr1qxReHi4fvjhB9nb22vLli0KDw/XtWvXtHLlStnb28vV1VUTJkwotE0XFxcFBgYqISHhQV4qAAAAAKAUKveJ+52YTCZNmTJF+/bt08qVKzVu3DjzMR8fH3Xt2lUuLi7q0aOHJGnYsGEaO3asvLy8lJ6ebq2wAQAA7srX19cq5wK2jL4NW0XifgehoaGSJD8/PyUmJt61fL169RQVFaXWrVurZcuWJR0eAADAPYuPj7+n83x9fe/5XMCW0bdhbXd6cFTuF6dzcHCQyWQyb+fk5Jg/Ozk5SZLs7e3zlbmdAQMGqE+fPrpy5YpGjRqltLS04g8YAAAAAFCulPvE3dPTU6mpqUpLS1NOTo727dtn8bkVK1ZUVlaWeTshIUF169ZV79695eHhoStXrpREyAAAAACAcqTcT5V3dHTUs88+qzFjxsjLy6tI77WEhIRo9uzZ2r17t8LDw7V27VpdunRJktSoUSM9+uijtz130KBBysjIUG5urnbv3q2///3vqlGjxn1fDwAAAACgbLEzDMOwdhCwXLOJG6wdAgAAKAO+7R94T+fxHjDKKvo2rI133AEAAAAAKKXK/VT5knTgwAEtW7Ys3z4fHx9FRkZaKSIAAAAAQGlD4l6CgoODFRwcbO0wAAAAAAClGFPlAQAAAACwYYy4lzL3upAMYKtYCAZlFX0bAAAUF0bcAQAAAACwYSTuAAAAAADYMBJ3AAAAAABsGIk7AAAAAAA2zOLF6Uwmk+ztyfOt7alFJ6wdAlDM6NMofVgoFAAAPEgWZeImk0lhYWHKyckp6XgAAAAAAMBvWJS429vby9fXV2lpaSUdDwAAAAAA+A2Lp8q3a9dO06ZN05/+9CdVqVJFdnZ25mONGjUqkeAAAAAAACjvLE7c169fL0n6+uuv8+23s7PTvHnzijcqAAAAAAAgqQiJe1RUVEnGAQAAAAAAClGkZeJzc3N1/Phxbdu2TZKUlZWlrKysEgkMAAAAAAAUYcT9woULmjZtmpycnHTlyhW1adNGx44d06ZNm/Tmm2+WZIw25aefftKmTZsUHh6uo0ePytHRUfXq1StSHWfOnNHHH39s3n7++ecVGhpa3KECAAAAAMoAixP3hQsXqnfv3urQoYNefvllSVKDBg3yJaDlgb+/v/z9/SVJR48elYuLS5ET95o1a2rq1KlycHDQ1atXFRkZqZCQEDk4OJREyAAAAACAUszixP3ixYtq3759vn0uLi7Kzs4u9qAepMTERE2bNk2zZs2SJMXExCgrK0vHjh1TnTp1dPToUWVkZGjgwIGqX7++jh49qjVr1ig8PFw//PCD7O3ttWXLFoWHh+vatWtauXKl7O3t5erqqgkTJhTaZoUKFcyfc3Jy8q3QDwAAAADAb1mcuFetWlVnz541jzZLN6d8V6tWrUQCswUmk0lTpkzRvn37tHLlSo0bN858zMfHR127dpWLi4t69OghSRo2bJjGjh0rLy8vpaen37Hu06dPa8GCBUpKStLgwYMZbQeAUsTX17dYywGlDX0bZRV9G7bK4sS9d+/emjp1qrp27arc3FytXr1aP/zwg1577bWSjM+qbr137ufnp8TExLuWr1evnqKiotS6dWu1bNnyjmXr1q2r2bNn6+LFi4qKilJwcLCcnZ2LJW4AQMmKj4+/axlfX1+LygGlDX0bZRV9G9Z2pwdHFq8qHxISojFjxig1NVUNGjRQUlKShg8friZNmhRLkNbi4OAgk8lk3s7JyTF/dnJykiTZ29vnK3M7AwYMUJ8+fXTlyhWNGjVKaWlpdz2nRo0acnFxUVxc3D1EDwAAAAAo6ywecd++fbtat26tV155Jd/+HTt2qFWrVsUe2IPi6emp1NRUpaWlycXFRfv27bP4YUTFihWVmZlp3k5ISFDdunVVt25dHThwQFeuXJG7u3uB8xITE1WlShU5ODgoKSlJ8fHxqlq1arFdEwAAAACg7LA4cf/oo4/UunXrAvs//vjjUp24Ozo66tlnn9WYMWPk5eVVpPdaQkJCNHv2bO3evVvh4eFau3atLl26JElq1KiRHn300ULPO3HihL755hs5ODjI3t5e/fv3l4eHR7FcDwAAAACgbLlr4n758mVJNxdqS0xMlGEY+Y6Vhfeyu3Xrpm7dut32uIeHh6KioiRJDRs2VMOGDSXdfAdh5syZ5nL169e3qL0OHTqoQ4cO9xExAAAAAKC8uGvi/sYbb5g/Dx48ON+xSpUq6fnnny/+qAAAAAAAgCQLEvevvvpKkjR+/Pjbfi85CnfgwAEtW7Ys3z4fHx9FRkZaKSIAAAAAQGlj8Tvut5L25ORkpaSkKCAgoMSCKiuCg4MVHBxs7TAAAAAAAKWYxYl7cnKy5syZo/Pnz0uSli5dqh07dujAgQMaOHBgScUHAAAAAEC5ZnHi/sknn6hp06aaMGGC+vfvL0kKCgrS559/XmLBoaBv+wdaOwSgWPn6+io+Pt7aYQAAAAA2y97SgmfOnFHPnj1lb/+/U1xdXZWRkVEigQEAAAAAgCIk7p6enkpISMi37+LFi/L29i72oAAAAAAAwE0WT5V/+umnNW3aNPXs2VMmk0mxsbFavXq1evbsWYLhAQAAAABQvlmcuHfu3Fnu7u76z3/+oypVqmjTpk3q3bu3QkNDSzI+AAAAAADKNYsTd0lq0aKFWrRoUVKxwAJPLTph7RCAYlayfZoFHQEAAFDaFSlxP378uM6dO6esrKx8+5955pliDQoAAAAAANxkceIeHR2t7du3KzAwUM7Ozub9dnZ2JRIYAAAAAAAoQuK+ZcsWzZo1S15eXiUZDwAAAAAA+A2Lvw7O29tbTk5OJRkLAAAAAAD4HYtH3AcOHKiPP/5Ybdu2laenZ75jDRo0KPbAAAAAAABAERL3s2fPav/+/Tp+/Hi+d9wlacGCBcUeGAAAAAAAKELi/sUXX2jkyJEKCgoqyXgAAAAAAMBvWJy4V6hQodimxK9YsUIuLi7q0aNHsdRnqcTERJ06dUrt2rW777rS09MVGxurJ598ssjnJicnKyoqSteuXZOdnZ0ef/xxdevW7b5jAgAAAACUPRYvTte7d28tXrxY165dk8lkyvdfccjLyyuWeu4kKSlJsbGxFpe/U0zp6elav379PcXh4OCgsLAwvf/++5o0aZLWrVunixcv3lNdAAAAAICyzc4wDMOSgr17977tsa+++uqu569atUqbNm2Sh4eHqlSpIj8/P+3bt0+1a9fWiRMn1LZtW9WuXVtLly5VXl6e/P399eqrr8rJyUmDBg1S69attX//fjk7O2vIkCGqVq2aEhMTtWDBAqWlpcnDw0MRERHy9vZWVFSUQkJC1KpVK0lSWFiYli5dqrFjx+rixYvy8fFRx44d9dRTTxWIc+PGjdq5c6eysrJkMpk0evRoTZ8+Xenp6crNzVWfPn3UokULffDBB9q9e7d8fX0VFBSksLAwxcTEaPv27crJyVFoaKh69eplya3V9OnT9cc//tGi1xCaTdxgUZ0Abto3rrO1QwAAAADui8VT5efNm3fPjZw9e1Zbt27V9OnTlZeXp5EjR8rPz0+SlJubq6lTpyo7O1tDhgzRuHHj5Ovrq3nz5mn9+vXq3r27JMnV1VWzZs3Spk2btHjxYo0aNUrR0dHq2LGjOnXqpA0bNig6OlojRoy4bRx9+/bVmjVrNGrUqDvGe+7cOc2cOVNubm7Ky8vT8OHD5erqqtTUVI0dO1bNmzdX3759FRcXpxkzZkiSDh48qEuXLmny5MkyDEPTp0/XsWPH7vp6QWJios6dO6c6deoU5ZYCsFB8fLy1Q0A55evrS/9DmUTfRllF34a1+fr63vaYxYl71apV7zmA48ePKzQ0VBUqVJAkNW/e3HysTZs2km7+49rHx8ccbMeOHbVu3Tpz4t62bVvzzyVLlkiSTp8+reHDh0uSOnTooGXLlt1zjL8VFBQkNzc3SZJhGPriiy90/Phx2dnZKSUlRb/++muBcw4ePKhDhw6ZHxxkZWUpISHhjol7VlaWZs2apX79+snV1bVYYgcAAAAAlC0WJ+6StGfPHh07dkypqan59r/++uv3HMCtZP5u7OzsCv1cGAcHB/O79yaTSbm5ufccU2xsrFJTUzV16lQ5Ojpq0KBBys7OLvS8nj17qmvXrha1kZubq1mzZql9+/Zq2bJlkeIDAAAAAJQfFi9O9/XXX+uTTz6RyWTSjh075ObmpoMHD1o0Uly/fn3t3r1b2dnZyszM1N69ewuU8fX1VWJiohISEiRJmzdvzjdavW3bNvPPunXrSpICAgLM+2NjYxUYGCjp5uyAs2fPSrr5sOHWInMVK1ZUZmampZcsScrIyJCnp6ccHR115MgRJSUlFVpXkyZN9OOPPyorK0uSbjsyL90cxf/oo49UvXr1Qt+zBwAAAADgFotH3H/88Uf9/e9/V61atbRx40b169dP7dq10z//+c+7nuvn56c2bdooMjJSHh4e8vf3L1DG2dlZERERmj17tnlxut+OXl+/fl3Dhw+Xk5OThgwZIkkKDw/X/PnzFRMTY16cTpK6dOmiGTNmKDIyUk2aNDGPoNeqVUv29vaKjIy87eJ0v9euXTtNmzZNw4YNk7+/v6pXry5Jcnd3V7169TRs2DAFBwcrLCxMv/zyi8aOHStJcnFx0eDBg+Xp6VmgzpMnT2rz5s2qVauWIiMjJUkvvPCCmjVrdtd4AAAAAADli8Wryv/f//2f+d3yV155RR999JEcHR3z7S8pgwYN0pQpU+Th4VGi7ZQGrCoPFM23/QOtHQLKKRY5QllF30ZZRd+GtRXL4nTVqlVTXFycatasqZo1a2r9+vVyc3MzL+IGAAAAAACKn8WJe+/evZWWliZJ+utf/6o5c+YoKytLr7zySokFd0tUVFSx13ngwIECq9D7+PiYp64Xh7S0NL377rsF9r/99ttyd3cvtnYAAAAAAGWXRYm7yWSSs7OzAgICJEl16tTRhx9+WKKBlbTg4GAFBweXaBvu7u7m73kHAAAAAOBeWJS429vba/r06fr8889LOh7cBe/roqzhfTIAAADgziz+Orj69evr1KlTJRkLAAAAAAD4HYvfca9ataqmTJmi5s2bq0qVKrKzszMf6927d4kEBwAAAABAeWdx4p6dna0WLVpIklJSUkosIAAAAAAA8D8WJ+4RERElGQcAAAAAACiExYn7LZmZmUpLS5NhGOZ9Dz/8cLEGhdt7atEJa4eAUoYFDQEAAIDSzeLE/eLFi5o7d65+/vnnAse++uqrYg0KAAAAAADcZPGq8p9++qkaNmyo6Ohoubq66rPPPlPXrl01aNCgkowPAAAAAIByzeLE/eeff9Zf//pXPfTQQzIMQ66urnrxxRcZbQcAAAAAoARZnLg7OTkpLy9PkuTu7q7k5GQZhqHr16+XWHAAAAAAAJR3Fr/jHhgYqO3bt6tTp05q1aqVJk+eLCcnJzVs2LAk4wMAAAAAoFyzOHF/6623zJ9feOEF1axZU1lZWerQoUOJBAYAAAAAAIowVf6WW9Pj27dvryeeeEIuLi4lEVeZcfToUU2dOtXaYQAAAAAASimLR9zT09MVHR2tHTt2KDc3V46OjmrVqpVefvllubm5lWSMAAAAAACUWxYn7vPnz5e9vb2mTZumqlWrKikpSStWrND8+fM1YsSIkozxgdi8ebP+/e9/Kzc3V3Xr1tUrr7yi//u//1OXLl106NAhVapUSUOHDpWHh4fOnz+vhQsX6saNG3r44Yf1t7/9TW5ubkpISNDChQuVmpoqe3t7vfnmm5KkrKwszZo1S3FxcfLz89PgwYNlZ2enZcuWac+ePXJwcFBQUJBeeuklK98FAAAAAICtsThxP3LkiBYuXChnZ2dJUo0aNTRo0CC99tprJRbcg3Lx4kVt27ZNEydOlKOjoz799FNt2bJFN27ckL+/v/r166eVK1fq66+/Vv/+/TVv3jyFh4erQYMG+uqrr7Ry5Ur169dPc+fOVc+ePRUaGqrs7GwZhqErV67o3Llzmj17tipXrqxx48bp5MmTql69unbt2qUPPvhAdnZ2Sk9Pt/ZtQBnl6+tr7RDuqjTECNwL+jbKKvo2yir6NmyVxYl79erVlZiYqBo1apj3JScnl4nOfeTIEZ07d06jR4+WJGVnZ8vDw0N2dnZq06aNJKl9+/aaOXOmMjIylJ6ergYNGkiSOnbsqPfff1+ZmZlKSUlRaGioJJkfcEhSnTp1VKVKFUlS7dq1lZiYqLp168rZ2VkLFixQSEiIQkJCHuQloxyJj4+3dgh35Ovra/MxAveCvo2yir6Nsoq+DWu7U25tceLeqFEjTZo0Se3bt5e3t7eSk5O1ZcsWdejQQRs2bDCX69y58/1FawWGYahjx47q27dvvv3//Oc/823b2dndU/1OTk7mz/b29jKZTHJwcNDkyZN1+PBh7dixQ99//73Gjx9/T/UDAAAAAMoui1eVP336tKpVq6bTp09r+/bt5u1Tp05py5Yt5v9Ko8aNG2vHjh369ddfJUnXr19XUlKSDMPQjh07JEmxsbEKDAyUq6ur3NzcdPz4cUk3342vX7++KlasqCpVqmjXrl2SpJycHN24ceO2bWZlZSkjI0PNmjVTv3799PPPP5fwVQIAAAAASiOLRtwNw9DAgQPl7e0tBweHko7pgatRo4b69Omj9957T4ZhyMHBQf3791eFChV05swZrVq1Sh4eHubF5gYNGmRenM7Hx0cRERGSpNdff12ffPKJVqxYIQcHB7311lu3bTMzM1PTp09XTk6ODMNgYToAAAAAQKHsDMMwLCkYFhamJUuWyN6+yF/9XmqFhYVp6dKl1g4jn2YTN9y9EPAb3/YPtHYId8T7ZCir6Nsoq+jbKKvo27C2O73jbnEWXrt2bV26dKlYAgIAAAAAAJaxeHG6hg0bavLkyerYsaO8vb3zHSuNC9JZwtZG2wEAAAAA5Y/FifvJkyfl4+NjXpTtt8pq4g4AAAAAgLVZnLjzVWUAAAAAADx4FifukpSWlqb9+/fr2rVr6tGjh1JSUmQYhqpUqVJS8eF3bH2hMQAAAABA8bJ4cbpjx45p6NCh2rJli1auXClJSkhI0MKFC0ssOAAAAAAAyjuLE/fFixdr6NChGjt2rPm73OvUqaOffvqpxIIDAAAAAKC8szhxT0pKUuPGjfPtc3R0VF5eXrEHBQAAAAAAbrI4ca9Ro4YOHDiQb9/hw4dVq1at4o4JAAAAAAD8fxYvThcWFqZp06apadOmys7O1ieffKK9e/cqMjKyJOPD7zy16IS1Q8ADxGKEAAAAACxO3AMCAjRjxgxt2bJFLi4u8vb21uTJk1lRHgAAAACAElSkr4Pz8vJSjx49lJaWJnd3d9nZ2ZVUXAAAAAAAQEVI3NPT0xUdHa0dO3YoNzdXjo6OatWqlV5++WW5ubmVZIwAAAAAAJRbFi9ON3/+fGVnZ2vatGn6/PPPNW3aNOXk5Gj+/PklGR8AAAAAAOWaxYn7kSNHNHjwYNWoUUMVKlRQjRo1NGjQIB07dqwk4wMAAAAAoFyzOHGvXr26EhMT8+1LTk6Wr69vsQcFAAAAAABusvgd90aNGmnSpElq3769vL29lZycrC1btqhDhw7asGGDuVznzp1LJFAAAAAAAMoji0fcT58+rWrVqun06dPavn27efvUqVPasmWL+b8HLTExUcOGDbO4/MaNG5WSknLXMosWLbqvuL766isdOnTovuoAAAAAAMDiEffx48eXZBwPzMaNG1WzZk15eXmVaDu9e/cu0foBAAAAAOWDxYn74sWL1alTJ9WuXbsEw7k3eXl5mjt3rs6dO6caNWro9ddf15o1a7R3715lZ2crICBAAwYM0M6dO/XTTz9p7ty5cnZ21qRJk3ThwgUtXrxYN27ckKOjo95++21J0tWrVzVp0iRdvnxZoaGhevHFFwtt22QyacGCBTp79qwk6bHHHtNTTz2lqKgohYSEqGrVqvroo4/MZePi4rRixQolJCRo0aJFSk1NVYUKFfTaa6+pevXqD+aGodQoL2tIlJfrRPlD30ZZRd9GWUXfhq2yOHE3mUyaNGmSPDw81L59e7Vv315VqlQpydgsFh8fr4EDByowMFDz58/XunXr9Mc//lHPPfecJOnDDz/U3r171apVK33//fcKCwuTv7+/cnNz9cEHH2jo0KGqU6eOMjIy5OzsLEk6f/68pk+fLkdHRw0dOlR//OMf5e3tXaDt8+fPKyUlRbNmzZJ08/vuf8vf318zZsyQJC1dulTBwcGSpE8++USvvvqqHnnkEZ0+fVqffvppmZnVgOITHx9v7RBKnK+vb7m4TpQ/9G2UVfRtlFX0bVjbnR4cWZy4h4eHq1+/ftq/f7+2bNmiVatWqW7duurQoYNatmwpFxeXYgn2XlSpUkWBgYGSpA4dOui7776Tj4+PYmJidOPGDV2/fl01a9ZU8+bN850XHx+vypUrq06dOpIkV1dX87FGjRqZt2vUqKHk5ORCE3cfHx8lJiYqOjpazZo1U1BQUKExbtu2TefOndPf//53ZWVl6eTJk5o9e7b5eG5u7v3dBAAAAABAmWRx4i5J9vb2CgkJUUhIiOLi4jR37lzNnz9fn376qdq2batevXqV+LvjhbGzsyuwvWjRIk2ZMkXe3t5asWKFsrOzi1Snk5OT+bO9vb3y8vIKLefm5qYZM2bowIEDWr9+vbZt26aIiIh8ZS5cuKCvv/5aEyZMkL29vUwmkx566CHzSDwAAAAAALdj8arykpSRkaENGzZowoQJGj9+vOrUqaMJEybo/fffl4uLiyZPnlxScd5RcnKyTp06JUmKjY01j757eHgoKytLO3fuNJd1cXFRZmampJtTEa5evaozZ85IkjIzM2+boN9OamqqTCaTWrVqpT59+ujcuXP5jqenp2vOnDkaNGiQPDw8JN0c2ffx8dH27dslSYZh6Pz580W/cAAAAABAmWfxiPusWbN04MABNWjQQF27dlWLFi3yjUq/9NJL6tevX0nEeFe+vr76/vvvtWDBAlWvXl1PPPGE0tPTNWzYMFWqVEn+/v7msp06ddLChQvNi9MNHTpUn332mbKzs+Xs7Kxx48YVqe2UlBQtWLBAJpNJktS3b998x3fv3q3k5GR9/PHH5n0zZszQG2+8oYULF2rVqlXKzc1V27ZtbXLhPwAAAACAddkZhmFYUjAmJkYdOnRQpUqVblvmxo0bqlChQnHFhkI0m7jB2iHgAfq2f6C1QyhxLASDsoq+jbKKvo2yir4Na7uvxenefvtt8zvke/fuLbTMhAkTJImkHQAAAACAYnbXxL1z5875thctWqT+/fuXWEC2bMyYMcrJycm3b/DgwapVq5aVIgIAAAAAlHV3Tdw7deqUb3vJkiUF9pUX1lp8DwAAAABQfhXp6+BgfeXhnWcAAAAAwP8U6evgAAAAAADAg3XXEfcjR47k2zaZTAX2NWrUqHijAgAAAAAAkixI3BcsWJBv283NLd8+Ozs7zZs3r/gjAwAAAAAAd0/co6KiHkQcAAAAAACgECxOV8o8teiEtUMoFVjEDwAAAEBZweJ0AAAAAADYMBJ3AAAAAABsGIk7AAAAAAA2jMQdAAAAAAAbRuIOAAAAAIANI3EHAAAAAMCGkbgDAAAAAGDDrJK4r1ixQjExMQ+83cTERMXGxhZLXenp6Vq3bt09nz9//ny98sorGjZsWLHEAwAAAAAom2xmxD0vL6/E20hKSipS4n6nmNLT07V+/fp7jqVTp04aM2bMPZ8PAAAAACgfHB9UQ6tWrdKmTZvk4eGhKlWqyM/PT++8845q166tEydOqG3btqpdu7aWLl2qvLw8+fv769VXX5WTk5MGDRqk1q1ba//+/XJ2dtaQIUNUrVo1JSYmasGCBUpLS5OHh4ciIiLk7e2tqKgohYSEqFWrVpKksLAwLV26VMuXL9fFixcVGRmpjh076qmnnioQ58aNG7Vz505lZWXJZDJp9OjRmj59utLT05Wbm6s+ffqoRYsWWr58uRISEhQZGamgoCCFhYUpJiZG27dvV05OjkJDQ9WrV6/b3o8GDRooMTGxxO43AAAAAKBseCCJ+9mzZ7V161ZNnz5deXl5GjlypPz8/CRJubm5mjp1qrKzszVkyBCNGzdOvr6+mjdvntavX6/u3btLklxdXTVr1ixt2rRJixcv1qhRoxQdHa2OHTuqU6dO2rBhg6KjozVixIjbxtG3b1+tWbNGo0aNumO8586d08yZM+Xm5qa8vDwNHz5crq6uSk1N1dixY9W8eXP17dtXcXFxmjFjhiTp4MGDunTpkiZPnizDMDR9+nQdO3ZMDRo0KKa7iKLw9fW1dggoAn5fKKvo2yir6Nsoq+jbsFUPJHE/fvy4QkNDVaFCBUlS8+bNzcfatGkjSYqPj5ePj4/5f5aOHTtq3bp15sS9bdu25p9LliyRJJ0+fVrDhw+XJHXo0EHLli0rlniDgoLk5uYmSTIMQ1988YWOHz8uOzs7paSk6Ndffy1wzsGDB3Xo0CHzg4OsrCwlJCSQuFtJfHy8tUOAhXx9ffl9oUyib6Osom+jrKJvw9ru9ODogU2Vv51byfzd2NnZFfq5MA4ODjKZTJIkk8mk3Nzce44pNjZWqampmjp1qhwdHTVo0CBlZ2cXel7Pnj3VtWvXIrUFAAAAAMCdPJDF6erXr6/du3crOztbmZmZ2rt3b4Eyvr6+SkxMVEJCgiRp8+bN+Uart23bZv5Zt25dSVJAQIB5f2xsrAIDAyVJVatW1dmzZyVJe/bsMS8yV7FiRWVmZhYp9oyMDHl6esrR0VFHjhxRUlJSoXU1adJEP/74o7KysiTptiPzAAAAAAAUxQMZcffz81ObNm0UGRkpDw8P+fv7Fyjj7OysiIgIzZ4927w43W9Hr69fv67hw4fLyclJQ4YMkSSFh4dr/vz5iomJMS9OJ0ldunTRjBkzFBkZqSZNmphH0GvVqiV7e/s7Lk73e+3atdO0adM0bNgw+fv7q3r16pIkd3d31atXT8OGDVNwcLDCwsL0yy+/aOzYsZIkFxcXDR48WJ6enoXW+8EHH+jYsWNKS0vTwIED1atXL3Xu3LkIdxUAAAAAUB7YGYZhWDuIuxk0aJCmTJkiDw8Pa4didc0mbrB2CKXCt/0DrR0CLMT7ZCir6Nsoq+jbKKvo27C2O73jbjPf4w4AAAAAAAqy+uJ0loiKiir2Og8cOFBgFXofHx9FRkYWWxtpaWl69913C+x/++235e7uXmztAAAAAADKrlKRuJeE4OBgBQcHl2gb7u7u5u95BwAAAADgXjBVHgAAAAAAG1ZuR9xLKxZdAwAAAIDyhRF3AAAAAABsGIk7AAAAAAA2jMQdAAAAAAAbRuIOAAAAAIANY3G6UuapRSesHUKxYrE9AAAAALgzRtwBAAAAALBhJO4AAAAAANgwEncAAAAAAGwYiTsAAAAAADaMxB0AAAAAABtG4g4AAAAAgA0jcS+in376SdHR0ZKko0eP6uTJk/dcV3JyssLCwhQTE1Nc4QEAAAAAyhi+x72I/P395e/vL+lm4u7i4qJ69erdU11LlixR06ZNizM8AAAAAEAZU+4T98TERE2bNk2zZs2SJMXExCgrK0vHjh1TnTp1dPToUWVkZGjgwIGqX7++jh49qjVr1ig8PFw//PCD7O3ttWXLFoWHh+vatWtauXKl7O3t5erqqgkTJty23V27dsnHx0cVKlR4UJcKAAAAACiFyn3ificmk0lTpkzRvn37tHLlSo0bN858zMfHR127dpWLi4t69OghSRo2bJjGjh0rLy8vpaen37berKws/etf/9K4cePK/TR5X19fa4cAG0A/QFlF30ZZRd9GWUXfhq0icb+D0NBQSZKfn58SExPvWr5evXqKiopS69at1bJly9uWW7Fihbp37y4XF5dii7W0io+Pt3YIsDJfX1/6Acok+jbKKvo2yir6NqztTg+Oyn3i7uDgIJPJZN7Oyckxf3ZycpIk2dvb5ytzOwMGDNDp06e1b98+jRo1SlOnTpW7u3uBcmfOnNHOnTu1bNkypaeny87OTs7OzvrjH/9YDFcEAAAAAChLyn3i7unpqdTUVKWlpcnFxUX79u1TkyZNLDq3YsWKyszMNG8nJCSobt26qlu3rg4cOKArV64Umri/++675s8rVqyQi4sLSTsAAAAAoFDlPnF3dHTUs88+qzFjxsjLy6tI77WEhIRo9uzZ2r17t8LDw7V27VpdunRJktSoUSM9+uijJRU2AAAAAKCcsDMMw7B2ELBcs4kbrB1Csfq2f6C1Q4CV8T4Zyir6Nsoq+jbKKvo2rO1Og8j2DzAOAAAAAABQROV+qnxJOnDggJYtW5Zvn4+PjyIjI60UEQAAAACgtCFxL0HBwcEKDg62dhgAAAAAgFKMqfIAAAAAANgwRtxLGRZzAwAAAIDyhRF3AAAAAABsGIk7AAAAAAA2jMQdAAAAAAAbRuIOAAAAAIANY3G6UuapRSfMn1moDgAAAADKPkbcAQAAAACwYSTuAAAAAADYMBJ3AAAAAABsGIk7AAAAAAA2jMQdAAAAAAAbRuIOAAAAAIAN4+vgiuinn37Spk2bFB4erqNHj8rR0VH16tUrUh2HDh3SsmXLlJubK0dHR4WFhalRo0YlFDEAAAAAoDQjcS8if39/+fv7S5KOHj0qFxeXIifu7u7uGjlypLy8vHThwgVNmjRJH3/8cUmECwAAAAAo5cp94p6YmKhp06Zp1qxZkqSYmBhlZWXp2LFjqlOnjo4ePaqMjAwNHDhQ9evX19GjR7VmzRqFh4frhx9+kL29vbZs2aLw8HBdu3ZNK1eulL29vVxdXTVhwoRC2/zDH/5g/lyzZk1lZ2crJydHTk5OD+SaAQAAAAClR7lP3O/EZDJpypQp2rdvn1auXKlx48aZj/n4+Khr165ycXFRjx49JEnDhg3T2LFj5eXlpfT0dIva2Llzp/z8/O4paff19S3yOYAtoi+jrKJvo6yib6Osom/DVpG430FoaKgkyc/PT4mJiXctX69ePUVFRal169Zq2bLlXcvHxcVp2bJlGjt27D3FFx8ff0/nAbbE19eXvowyib6Nsoq+jbKKvg1ru9ODo3K/qryDg4NMJpN5Oycnx/z51ii4vb19vjK3M2DAAPXp00dXrlzRqFGjlJaWdtuyV65c0cyZMzVo0CBVq1btPq4AAAAAAFCWlfvE3dPTU6mpqUpLS1NOTo727dtn8bkVK1ZUVlaWeTshIUF169ZV79695eHhoStXrhR6Xnp6uqZOnaq+ffsqMDDwvq8BAAAAAFB2lfup8o6Ojnr22Wc1ZswYeXl5Fem9lpCQEM2ePVu7d+9WeHi41q5dq0uXLkmSGjVqpEcffbTQ877//nslJCRo5cqVWrlypSTp73//uzw9Pe//ggAAAAAAZYqdYRiGtYOA5ZpN3GD+/G1/RutR+vE+Gcoq+jbKKvo2yir6NqyNd9wBAAAAACilyv1U+ZJ04MABLVu2LN8+Hx8fRUZGWikiAAAAAEBpQ+JegoKDgxUcHGztMAAAAAAApRhT5QEAAAAAsGGMuJcyLEgHAAAAAOULI+4AAAAAANgwEncAAAAAAGwYiTsAAAAAADaMxB0AAAAAABtG4g4AAAAAgA0jcQcAAAAAwIaRuAMAAAAAYMNI3AEAAAAAsGEk7gAAAAAA2DASdwAAAAAAbBiJu6S1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return_sequences=True))\nmodel.add(Dropout(rate=best_params['dropout_rate_3']))\nmodel.add(LSTM(units=best_params['units_4']))\nmodel.add(Dropout(rate=best_params['dropout_rate_4']))\nmodel.add(Dense(units=1))\n\nmodel.compile(loss='mean_squared_error', optimizer=best_params['optimizer'](learning_rate=best_params['learning_rate']), metrics=['accuracy'])\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:24:02.430446Z","iopub.execute_input":"2021-11-02T13:24:02.430712Z","iopub.status.idle":"2021-11-02T13:25:21.120153Z","shell.execute_reply.started":"2021-11-02T13:24:02.430677Z","shell.execute_reply":"2021-11-02T13:25:21.119414Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stdout","text":"Model: \"sequential_30\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nlstm_120 (LSTM)              (None, 1, 2550)           26040600  \n_________________________________________________________________\ndropout_120 (Dropout)        (None, 1, 2550)           0         \n_________________________________________________________________\nlstm_121 (LSTM)              (None, 1, 850)            11563400  \n_________________________________________________________________\ndropout_121 (Dropout)        (None, 1, 850)            0         \n_________________________________________________________________\nlstm_122 (LSTM)              (None, 1, 3250)           53313000  \n_________________________________________________________________\ndropout_122 (Dropout)        (None, 1, 3250)           0         \n_________________________________________________________________\nlstm_123 (LSTM)              (None, 3600)              98654400  \n_________________________________________________________________\ndropout_123 (Dropout)        (None, 3600)              0         \n_________________________________________________________________\ndense_30 (Dense)             (None, 1)                 3601      \n=================================================================\nTotal params: 189,575,001\nTrainable params: 189,575,001\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"plot_model(model, show_shapes=True)","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:25:21.121374Z","iopub.execute_input":"2021-11-02T13:25:21.121633Z","iopub.status.idle":"2021-11-02T13:25:21.991756Z","shell.execute_reply.started":"2021-11-02T13:25:21.121597Z","shell.execute_reply":"2021-11-02T13:25:21.990918Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"image/png":"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\n","text/plain":"<IPython.core.display.Image object>"},"metadata":{}}]},{"cell_type":"code","source":"%%time\nhistory = model.fit(x_train, y_train, epochs=best_params['epochs'], validation_data=(x_val, y_val), batch_size=best_params['batch_size'], verbose=0)","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:25:21.993158Z","iopub.execute_input":"2021-11-02T13:25:21.993828Z","iopub.status.idle":"2021-11-02T13:55:57.901170Z","shell.execute_reply.started":"2021-11-02T13:25:21.993783Z","shell.execute_reply":"2021-11-02T13:55:57.900408Z"},"trusted":true},"execution_count":38,"outputs":[{"name":"stdout","text":"CPU times: user 11min 30s, sys: 36.8 s, total: 12min 7s\nWall time: 30min 35s\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Model Evaluation","metadata":{}},{"cell_type":"code","source":"plt.plot(history.history['loss'], label='loss train')\nplt.plot(history.history['val_loss'], label='loss validation')\nplt.xlabel('epoch', fontsize=14)\nplt.ylabel('loss', fontsize=14)\nplt.legend(fontsize=14)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:55:57.902581Z","iopub.execute_input":"2021-11-02T13:55:57.902861Z","iopub.status.idle":"2021-11-02T13:55:58.132774Z","shell.execute_reply.started":"2021-11-02T13:55:57.902822Z","shell.execute_reply":"2021-11-02T13:55:58.131941Z"},"trusted":true},"execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x432 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"model.evaluate(x_test, y_test, batch_size=best_params['batch_size'])","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:55:58.133899Z","iopub.execute_input":"2021-11-02T13:55:58.134894Z","iopub.status.idle":"2021-11-02T13:55:58.217395Z","shell.execute_reply.started":"2021-11-02T13:55:58.134852Z","shell.execute_reply":"2021-11-02T13:55:58.216606Z"},"trusted":true},"execution_count":40,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 0s 45ms/step - loss: 6.1913e-05 - accuracy: 0.0000e+00\n","output_type":"stream"},{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"[6.191290594870225e-05, 0.0]"},"metadata":{}}]},{"cell_type":"markdown","source":"## Prediction","metadata":{}},{"cell_type":"code","source":"yhat_train = np.squeeze(model.predict(x_train))\nyhat_test = np.squeeze(model.predict(x_test))","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:55:58.222697Z","iopub.execute_input":"2021-11-02T13:55:58.222933Z","iopub.status.idle":"2021-11-02T13:56:00.613504Z","shell.execute_reply.started":"2021-11-02T13:55:58.222907Z","shell.execute_reply":"2021-11-02T13:56:00.612754Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"yhat_train = scaler.inverse_transform(prep_data_transform(yhat_train, X_train.shape[1]))[:,0]\nyhat_test = scaler.inverse_transform(prep_data_transform(yhat_test, X_test.shape[1]))[:,0]\nprint(yhat_train.shape, yhat_test.shape)","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:56:00.614966Z","iopub.execute_input":"2021-11-02T13:56:00.615237Z","iopub.status.idle":"2021-11-02T13:56:00.622922Z","shell.execute_reply.started":"2021-11-02T13:56:00.615201Z","shell.execute_reply":"2021-11-02T13:56:00.622167Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stdout","text":"(1527,) (190,)\n","output_type":"stream"}]},{"cell_type":"code","source":"print('Train Predicted: ', yhat_train[:5])\nprint('Train Actual: ', Y_train[:5])\n\nprint('Test Predicted: ', yhat_test[:5])\nprint('Test Actual: ', Y_test[:5])","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:56:00.624602Z","iopub.execute_input":"2021-11-02T13:56:00.625113Z","iopub.status.idle":"2021-11-02T13:56:00.636053Z","shell.execute_reply.started":"2021-11-02T13:56:00.625077Z","shell.execute_reply":"2021-11-02T13:56:00.635284Z"},"trusted":true},"execution_count":43,"outputs":[{"name":"stdout","text":"Train Predicted:  [122.20814353 118.52097705 117.69803261 116.70519001 117.89270104]\nTrain Actual:  [115. 114. 113. 114. 111.]\nTest Predicted:  [418.92220744 417.39459312 413.09990093 405.82345036 405.43408427]\nTest Actual:  [415. 411. 404. 403. 402.]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Loss Analysis","metadata":{}},{"cell_type":"code","source":"# calculate root mean squared error\ntrainScore = mean_squared_error(Y_train, yhat_train, squared=False)\nprint('Train Score: %.2f RMSE' % (trainScore))\n\ntestScore = mean_squared_error(Y_test, yhat_test, squared=False)\nprint('Test Score: %.2f RMSE' % (testScore))","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:56:00.637514Z","iopub.execute_input":"2021-11-02T13:56:00.637814Z","iopub.status.idle":"2021-11-02T13:56:00.645899Z","shell.execute_reply.started":"2021-11-02T13:56:00.637775Z","shell.execute_reply":"2021-11-02T13:56:00.644965Z"},"trusted":true},"execution_count":44,"outputs":[{"name":"stdout","text":"Train Score: 14.58 RMSE\nTest Score: 7.72 RMSE\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Plotting Predictions","metadata":{}},{"cell_type":"code","source":"date_train = df['Date'][LAG_DAYS:len(Y_train) + LAG_DAYS]\ndate_test = df['Date'][-len(Y_test):]","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:56:00.647178Z","iopub.execute_input":"2021-11-02T13:56:00.647712Z","iopub.status.idle":"2021-11-02T13:56:00.655029Z","shell.execute_reply.started":"2021-11-02T13:56:00.647674Z","shell.execute_reply":"2021-11-02T13:56:00.654191Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"plt.title('Prediction on Train set', fontsize=14)\nplt.xlabel('Year', fontsize=14)\nplt.ylabel('Price', fontsize=14)\nplt.plot(date_train, Y_train, label='actual')\nplt.plot(date_train, yhat_train, label='predicted')\nplt.legend(fontsize=14)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:56:00.656289Z","iopub.execute_input":"2021-11-02T13:56:00.656567Z","iopub.status.idle":"2021-11-02T13:56:01.259275Z","shell.execute_reply.started":"2021-11-02T13:56:00.656532Z","shell.execute_reply":"2021-11-02T13:56:01.258616Z"},"trusted":true},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x432 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA70AAAGICAYAAACN9YebAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAACoVklEQVR4nOzdd3hUZdrH8e+Zll5IhdCrKChFFBAVxV6w19V1bWtbC+quoq7tdVddXXvZXRuWdde1u/bGChZQmtIhdEJI78n0Oe8fk8xkSAJJyCSB/D7X5eXMmTPnPGFSzn3u+7kfwzRNExEREREREZG9kKWrByAiIiIiIiISLQp6RUREREREZK+loFdERERERET2Wgp6RUREREREZK+loFdERERERET2Wgp6RUREREREZK+loFdERKSRzz77jH322Sf0/N1332XcuHG7dcwff/yRffbZh7Kyst0d3l7jqaee4uSTT+7qYYiISA9gaJ1eERHp7mbOnMl7770HgM1mo3fv3hx77LFcd911xMfHd+i5PvvsM2644QbWrFkDgMvlora2lvT09Fa9f9q0aVxwwQVcdtlloW0ej4fKykoyMjIwDKNDxxtt06ZNY9u2bS2+fvDBB/Paa6+1+bi1tbV4PB569eq1O8PbLT/++CMXXXQR8+bNIy0trcvGISIi0WXr6gGIiIi0xiGHHMJDDz2Ez+dj4cKF/PGPf6Suro577723yb4+nw+r1dohAWZsbCyxsbG7dQyHw0FmZuZuj6UrvP322/j9fgBWr17N5ZdfzltvvUWfPn0AsNvtEft7PB4cDscuj5uQkEBCQkLHD1hERGQHKm8WEZE9QkPg2KdPH6ZPn8706dP5+uuvgXCp7LvvvsvRRx/N/vvvT11dHdXV1dx5551MnjyZcePGceGFF7Js2bKI477//vsceeSRjBkzhiuvvJLS0tKI15srb54zZw5nn302BxxwABMnTuSqq67C7Xbz61//mm3btvHQQw+xzz77hMqkmytv/uKLL5g+fTqjR49m6tSp/O1vf6Nx8dW0adN49tlnueuuuxg/fjyHH344L7zwwi7/nd544w2OOeYYRo8ezTHHHMObb74Z8fo+++zDf/7zH66//nrGjh3LUUcdxQcffNDi8dLS0sjMzCQzM5PU1NQm2yZOnMjrr7/Otddey9ixY3nsscfw+/3cfvvtTJs2jQMOOIBjjz2W559/nkAgEDrujuXNM2fO5Morr+SVV17hsMMO46CDDuK2227D6XS2ODav18uf/vQnDj300NC/41//+tfQ6x6Ph4cffpjDDz+cMWPGcOaZZ/Ltt98CkJeXx0UXXQTA5MmT2WeffZg5c+Yu/31FRGTPo0yviIjskWJjY/F6vaHneXl5fPTRRzzxxBPY7XYcDgcXXXQRSUlJ/OMf/yAlJYX33nuP3/zmN3z22WdkZWXxyy+/MHPmTG644QaOP/54fvzxRx577LGdnnfu3LlcffXV/Pa3v+WBBx7A5/Px/fffEwgEeOqppzj11FM588wzOf/881s8xvLly7nhhhu4+uqrmT59OsuWLePuu+8mMTGRX//616H9XnnlFa677jree+895s6dy5/+9CcOPPDAFucYf/nll9x3333cdtttTJkyhe+++457772XjIwMpk2bFtrvmWee4eabb+bmm2/m7bff5o477uCggw4iJyentf/8EZ5++mluuukmbr31VgACgQDZ2dk8/vjjpKWlsXTpUu666y5SU1M5++yzWzzOwoULyczM5OWXX2b79u3MmDGDQYMGceWVVza7/2uvvcaXX37JY489Rt++fSkoKGDjxo2h12+77Ta2bt3KI488Qu/evZkzZw5XX301b7/9NsOHD+epp57iuuuu4+OPPyYlJWW3M/oiItI9KegVEZE9ztKlS/nwww+ZPHlyaJvX6+Whhx4iIyMDgHnz5rF69WrmzZsXCmZmzJjB//73Pz744AN++9vf8uqrrzJ58mSuvvpqAAYPHsyyZct4++23Wzz3s88+y3HHHceNN94Y2jZy5EgA4uLisFqtJCQk7LScedasWRx00EFcf/31ofNu3ryZ559/PiLonTJlChdeeCEAv/71r3nttdeYN29ei0Hviy++yCmnnBJ6z+DBg1mxYgXPP/98RNB76qmncuqppwJwww038Oqrr7JgwYLQtrY68cQTmwSzN9xwQ+hxv379WLlyJR9//PFOg97ExETuvfderFYrQ4cO5fjjj2fevHktBr35+fkMGjSICRMmYBgGOTk5jB8/HoAtW7bw8ccfM3v27FAwf+GFF/LDDz/wxhtvcM8995CSkgIEM9ea0ysisvdS0CsiInuEb7/9lnHjxuHz+fD5fBx11FHceeedodezs7NDAS/AihUrcDqdEYExgNvtZuvWrQCsX7+eI488MuL1sWPH7jToXbVqFWecccZufS0bNmxg6tSpEdsOPPBAnn76aWpqakhMTASI6CINkJWVtdMO0Bs2bODMM89sctzZs2dHbGt8XJvNRlpa2m51lh49enSTbf/+97956623yM/Px+124/V66du3706PM2zYMKxWa+h5Qza+JaeffjqXXnopxx13HFOmTGHq1KkcfvjhWCwWVqxYgWmanHTSSRHv8Xg8TJo0qY1foYiI7MkU9IqIyB5hwoQJ3HfffdhsNrKyspo0UNqxi3MgECAjI4PXX3+9ybEagsruzmaL/DNtGEbEvNjW2rGhV0cdt0FcXFzE808++YT777+fW2+9lXHjxpGYmMjrr7/OV199tdPjNDeunS0yMWrUKL7++mu+++475s2bx6233srIkSOZNWsWpmliGAZvv/12k+OqjFlEpGdR0CsiInuEuLg4Bg4c2Or9R40aRUlJCRaLhf79+ze7z9ChQ5tkEneWWQTYd999mTdvHuecc06zr9vt9lC345YMGTKExYsXR2xbtGgRvXv33q2AvOG4jUuIFy1axNChQ9t9zPZYtGgRY8aMCZVZQ7DcOBoSExM5/vjjOf744znjjDM455xz2Lx5M/vuuy+maVJcXNxiZrfhxsnuBPwiItL9qXuziIjslQ455BDGjx/PNddcw5w5c9i6dStLlizhySefZOHChUBwnuwPP/zAP/7xDzZt2sSbb77Jl19+udPjXn311Xz22Wc89thjrFu3jtzcXF5++eVQl+G+ffuyaNEiCgsLWywZvvTSS1mwYAFPPfUUGzdu5L///S8vvfQSl19++W59zZdffjn//e9/ef3119m0aROvvfYaH3744W4ft60GDRrEihUrmDNnDps2beKZZ55hwYIFHX6eWbNm8dFHH7F+/Xo2b97Mhx9+SGJiIr1792bw4MFMnz6d2267jc8++4ytW7eybNkyXnzxRb744gsg+FkZhsE333xDWVkZtbW1HT5GERHpegp6RURkr2QYBs899xwTJ07kzjvv5IQTTmDGjBls3LiRrKwsIDh/989//jP//ve/OeWUU/jiiy+47rrrdnrcqVOn8vTTT/Ptt99y2mmnceGFFzJ//nwsluCf1Ouvv57t27dz9NFHN5lP3GDUqFE88cQToWWLHnnkEa644oqIzGh7HH300fzxj3/k5Zdf5qSTTuLVV1/l7rvvjmhi1RnOPfdcTjjhBH7/+99z1llnsW3bNi655JIOP09CQgIvvvgiZ511FqeffjqrV6/m+eefD5VbP/DAA5xxxhk8/PDDnHDCCVx11VUsWLAg1NgqOzub6667jscff5xDDjmE++67r8PHKCIiXc8wdzZZRkRERERERGQPpkyviIiIiIiI7LUU9IqIiIiIiMheS0GviIiIiIiI7LUU9IqIiIiIiMheS0GviIiIiIiI7LUU9IqIiIiIiMhey9bVA+gs+fn5XT2EFuXk5HTr8UnH0WfdM+hz7hn0OfcM+px7Bn3OPYM+571bwxrszVGmV0RERERERPZaCnpFRERERERkr6WgV0RERERERPZaCnpFRERERERkr6WgV0RERERERPZaCnpFRERERERkr6WgV0RERERERPZaCnpFRERERERkr6WgV0RERERERPZaCnpFRERERERkr6WgV0RERERERPZaCnpFREREpFXM/C2YG9Z09TBEpIv8/PPPHHnkkVRWVnb1UNpEQa+IiIiItErg+b8SeOAPmIX5XT0UEWnByy+/zCWXXNLVw+hWFPSKiIiISKvkVvq4f/TFVOQp6BWRPYetqwcgIiIiInuGV4aezMrUIbyQ5+IPB3b1aET2Xj/99BP//Oc/2bhxI4ZhsM8++3DttdcycOBAAEpKSvj73//OggULcLvd9OvXj9/97ncUFhbyyiuvAHDkkUcCcOutt3L88cdz5JFHcs899zB16tTQec477zxOP/10zj33XADefPNNPv/8c/Lz80lMTOTggw/m6quvJjExsZP/BTqWgl4RERER2SUzEMBiBgDY6laxoOyZAm/Nwlz0faee0zhwCpaz21Zu7HQ6OfPMMxk6dChut5vXXnuN22+/nZdffhmfz8eMGTNITU3lvvvuIz09nfXr1wPBQHfjxo3MmzePxx9/HICEhIRWn9disfC73/2OnJwcCgoKeOqpp3jyySe5/fbb2zT+7kZBr4iIiIjsmsuJ0xoDQKnPimmaGIbRqreaAT8UFWD07hvNEYrsNRpnYyGYrT355JNZvXo1mzdvpqysjGeeeYaUlBQA+vYN/2zFxcVhtVpJS0tr83nPOuus0OPevXtz5ZVX8sc//pGZM2disey5N7sU9IqIiIjIrrnqcFsdANSYVopqvWQnOlr1VvOTt5j3/VKGTj+Z3occEs1RiuyU5exLoI1Z166wbds2XnrpJVavXk1FRQWBQIBAIEBhYSG5ubkMGTIkFPB2pMWLF/Ovf/2LzZs3U1tbSyAQwOv1UlZWRkZGRoefr7Mo6BURERGRXXPWETDCmZ71Za5WB73Fixbz0OhLYSN8oJhXZJduv/12MjMzuemmm8jIyMBqtXLxxRfj8/nafUzDMDBNM2Kb3+8PPS4oKOC2227jpJNO4pJLLiE5OZnc3Fzuu+++3Tpvd7Dn5qhFREREpPM46/BHBL3uVr/VkxjOSO140S0ikSorK9myZQsXXHABBx54IAMHDqSuri4UoA4fPpwNGza0uFauzWYjEAg02Z6amkppaWnoeVlZWcTzNWvW4PP5+N3vfseoUaPo378/JSUlHfzVdQ0FvSIiIiKya646AoZBnM8FwLoyV6vf6o1LCj0ude7ZGSORaEtKSiIlJYWPPvqIbdu28fPPP/PYY49htVoBOOqoo+jVqxd33HEHS5cuJT8/n++//54lS5YAwbm4hYWFrF27lsrKSjweDwDjxo3jgw8+YM2aNeTm5vKXv/wFhyNcrdGvXz8CgQDvvPMO27dv5+uvv+add97p/H+AKFDQKyIiIiK7ZDrrCGAh2VtLlquczeWtD3r9KeGGOqtfeAH/I38k8PbLURilyJ7PYrFw1113sWHDBi655BKeeOIJLrnkEux2OxBsVPX444+TmZnJ7bffzqWXXsrLL78caix3+OGHM3HiRG6++WZOO+00Zs+eDcDVV19Nnz59mDFjBvfccw8nnngiqampofMOHTqUa6+9lrfeeouLL76Yjz/+mKuuuqrTv/5oMMweUmOSn999F1HPycnp1uOTjqPPumfQ59wz6HPuGfQ5hwXmfs4l63oR53cT73OxNbU/b50/slXvXfnaP7nNMgGA07Z8w0UbPgHAcveTGP0GRWvIrabPuWfQ57x3y8nJafE1NbISERERkV1z1hEw0rHYrMR4vHgCtHrZIs/SRTA2GPS+P+AIvhh0OBa3i/O/X8XJ5w6K8sBFpKdTebOIiIiI7Fr9nF6LzY4j4AXA4991waCZt5GF6fuGnmcn2slKdFBri+U7V9JO3iki0jGU6RURERGRXatfssjqsBHjDzbGcftNYnZ1Nen18mH/wwG4eFQKp4/tg+nz8btZP7AltuPXGRUR2ZEyvSIiIiKya/VBryUmBkcg2IHZ42+6LEoTjdYBDViCl56GzUa6p4paSwzeVmSLRUR2h4JeEREREdkl0xXs3my124kxgoGq29eKgNUd7vLsNsPzf+MJBsNOr7/JW0REOpKCXhERERHZNWf9nF6rBYcjWNPcqkyvyxl66G20ezzBbHGdtxXHEBHZDQp6RURERGTXGub0WizE1F9BtibTa7obBb2NSpnjLMHHtW5vx45TRGQHCnpFREREZJfMhjm9BqHyZpfbi1lWTOCtWZje5oNX76J5ocd2a6Py5vhYAOqKS6I4ahERBb0iIiIi0gqB+jJli2GQYAmWJNc4PQQeuxvnVx8x99O5BMymmd/VtdbQ4zP2Sw89TkgJLldUkF8czWGLiCjoFREREZFdCwe9kGwJNp+qdrqhII8/jruaR6v7sji/tsn7PkrcL/Q4KSYcAB/YJx57wMurhbFUuHxRHr2INOe2227jwQcfDD2fMWMGTzzxRKePo7KykiOPPJKff/45KsdX0CsiIiIiO2X6fPi9wcDUajFIsgYzutVOL37DwoakfgBNlh8yvV76VW0D4KqDsiNe698/m9O3fEOVaWN5YV20vwQRaYX/+7//47e//W2r9v3555858sgjqaysjPKodt+ulhPvMM8++yyLFy8mJSWFRx55BICamhoee+wxiouLyczM5MYbbyQxMRHTNJk1axZLliwhJiaGa665hiFDhgDwzTff8O677wJwxhlncMQRR3TWlyAiIiLSM7mdBIxgrsRiQLI9uLnK5eOXXsNafl9VOW6rA4CRmXGRr2Vkk+UqA8DlUwdnkfbyer3Y7fYOOVZycnKHHKe76bSg94gjjuD444/nmWeeCW17//332X///TnttNN4//33ef/997nwwgtZsmQJBQUFPPnkk+Tm5vLCCy9w//33U1NTw9tvvx1Kwc+cOZMJEyaQmJjYWV+GiIiISM9TV9so6DVIshngh2q3n196jQjt5gvsMKe3ogxXfdAba4ssMDQcMcTGxgAKekUamzFjBgMGDMBut/PFF18AcNJJJ3HFFVdgsVg477zzOP744yksLOTbb79lwoQJ3HPPPSxfvpznn3+eNWvWkJSUxCGHHMIVV1xBQkICAC6Xi8cff5w5c+YQFxfHGWec0ey5Bw8ezA033AAEA+qXX36Zr7/+mrKyMjIyMjjzzDOZMmUKN954IwCnnXYaAMcddxwzZ87ENE3eeOMNPvroI0pKSujbty/nn38+xxxzTOg8q1ev5tFHH2XTpk0MHDiQSy+9NJr/pJ0X9O63334UFRVFbFuwYAH33HMPAFOnTuWee+7hwgsvZOHChRx++OEYhsGIESOora2lvLycFStWcMABB4SC3AMOOICff/6ZQw89tLO+DBEREZGex+UkYAQ7L1sMgySHBZxQ7fFTkpgT2s274DvMzMMwEoJNqsxffsRpDXZp3jHoBYhNCl7TOd2a0yudY9biIn7YUtWp5zxkQDKXjM9q03u++uqrUMJw/fr1PPLII6SlpXHOOecA8NZbb3HhhRfyj3/8A9M02bBhA3/4wx+45JJL+MMf/kBVVRXPPPMMDz30EPfeey8Af/vb31i0aBH33nsvmZmZvPLKKyxdupTDDjusxXE8+OCDLF26lGuvvZbhw4dTWFhIUVERmZmZ3Hvvvdx9993MmjWL5ORkHI7gDa4XX3yRuXPncsMNN9C/f39WrFjBI488QmJiIpMnT8bpdHLbbbcxZswYbrvtNoqLiyMSo9HQaUFvcyorK+nVqxcAqampoXrwhrsIDdLT0ykrK6OsrIz09HDXv7S0NMrKylp1rpycnF3v1IW6+/ik4+iz7hn0OfcM+px7Bn3O4CoroLQ+05sQH0dWZga2TT7qfBbq7PGh/dzLlvBcXhknX/Ubxqfb+f7bH/hx/PUADO6fQ7wj8tIzNSsL/GD1B7r837mrzy+dIzExEau1c+eQJyYmtun7KyYmhuzsbB544AEMw2DSpElUVlbyxhtvMGPGDGw2GxMnTuTmm28OveeWW27hpJNOYsaMGaFtffv25bTTTiMmJobY2Fg+/fRT7r//fk455RQAxo4dy9SpU4mPjw+NLyYmhoSEBHJycti0aROzZ8/m+eef5/DDD28yzsGDBwMwcuRI0tLSAKirq+Ptt9/mpZdeYsKECQAceOCBbN26lc8++4wzzzyT//znP/j9fh577LFQFtowDG655RbS09Oj8rPYpUFvY4ZhYBjGrndsp/z8/Kgde3fl5OR06/FJx9Fn3TPoc+4Z9Dn3DPqcg8ytWwnU9z/1uFxUmwGSvHWUO2OwG+EM7g+Z+7M0bQSfvLuC9w9zcN/+l+G3BDs2lxUXUrHjtV5cHNRAWWFRl/4763PuGXJycjh7RDxnjxjU6eduy/eX2+1m+PDhbN++PbStf//+FBYWkpubi8/nY+DAgRHH/Pnnn8nPz+fjjz9ucrzFixcTExOD1+tt8r0+aNAg6urqQtvcbje1tbXk5+fz/fffY7FYGDBgQLPjLy0tBaCwsBCXywUEy5bdbjeXXXZZxL5+v5/evXuTn5/P0qVLGTx4MJWVlaGkZ0OgW1pa2u6fxZ0Fy10a9KakpFBeXk6vXr0oLy8PTZxOS0ujpCS8UHlpaSlpaWmkpaWxcuXK0PaysjL222+/JscVERERkY5jOmsjGlkZCckke2soioknjXAguzQtPL8XZy0VMcFru6FpsViaSW7ExjqgRnN6RdoqNjY24rlpmpx44omcffbZTfbNyMhg69atnTKuQCD4s/znP/+Z7OzIju1Wq7W5t3SKLl2yaMKECcyZMweAOXPmcNBBB4W2z507F9M0Wbt2LfHx8fTq1YuxY8fyyy+/UFNTQ01NDb/88gtjx47twq9AREREpAdw1eFvCHotBiQmke6uwomN2kblzQ1sZgBqa0jy1tLP6ubREwY1e9gYS/AC2WVGr9pPZE+0evVqTDPcGG7VqlVkZGSEyoF3NHz4cDZt2kTfvn2b/BcTE0Pfvn2x2WwRCUSn08nGjRtbHMOwYcMIBAItrp1rswXzp36/P7Rt0KBB2O12CgsLm4yjd+/eAAwYMIANGzbgdDpD72s8rmjotEzv448/zsqVK6muruaqq67inHPO4bTTTuOxxx5j9uzZoSWLAMaNG8fixYu5/vrrcTgcXHPNNUCwHv7MM8/ktttuA+Css85S52YRERGRKPPWuViX1B8IZnpJSCLdXQFAhSOpyf4+w4K/toZaWw59bS03qWq4EN1heV+RHq+kpISnn36aU089lY0bN/LGG2/w61//usX9zz//fH73u9/x6KOPMn36dOLj49myZQs//PADN998M3FxcZx44ok899xzpKamkp6ezquvvhrKzDanf//+HHHEETz88MNce+21jBgxguLiYgoKCjj22GPJzs7GMAzmz5/PIYccQkxMDPHx8Zx77rn8/e9/xzRNxowZg9PpZOXKlRiGwfTp0zn66KN58cUXeeihh7jooosoLS3l9ddfj8Y/Y0inBb2NJ1U3dtdddzXZZhgGl19+ebP7T5s2jWnTpnXk0ERERERkJ76qcPDcqAsAsBrBTG+mq2Kn76mpdRIwrCTYd1K63FDyrKBXJMLRRx9NIBDgmmuuwTAMTjzxRM4666wW9x86dChPPPEEL774IjNmzCAQCNCnT5+IVW6uuuoqnE4nd955J7GxsZx++umhubgtue2223jppZd4+umnqaysJDMzMzSOzMxMLr74Yl588UX++te/cuyxxzJz5kwuvfRSevXqxZtvvsnjjz9OfHw8w4YN47zzzgMgLi6O+++/n8cff5wrr7yS/v37c8UVV3DHHXd0wL9c8wyzcd58L9admxOoeULPoc+6Z9Dn3DPoc+4Z9DkHvfb8u7wdH+yjcsthORzSJ5av772fp/Y9r8m+qZ5qKhxJPB63ghnOUUxNC3DTCc33YCn76jMuKRzE5EQ3M08dE9WvYWf0OfcMe8rnvONaudI6O2tk1aVzekVERESk+wu4gnPvHjxmAFMGJGPYHWT4m1/2Jc1bDUC5M1jWnBDbcmFhuLdVj8jBiEgXUdArIiIiIjsVCASDUosl3HAqw1Xe7L5pzgoAygqDK3EkxMW0eFyjvvNzz6g7FJGu0m3W6RURERGR7qlhVm6jmJf0/s2XEvbyBDO9T2UfBUBCfMtBb0OqVzGvSNjjjz/e1UPY6yjTKyIiIiI75a/PyFobrbUbe83MZvctiUmJeO6IcbR43GaW7hUR6XAKekVERERkp5rL9Bqxcc3uW5neN+J5tbvl7s2Gol4R6QQKekVERERkpwJmMDi17BCkXrDh04jnWQk2quJ7AdAnyc7gXjEcMywy8xuhYcUi1TeLSBRpTq+IiIiI7FRzmV6AOL879Pgvxw6kb7KDjeUuHvk+n7uO6E9OcsulzRCKeTWnV0SiSkGviIiIiOxUgOYzvVYzXLo8MjNY7nxA7wReOXN45w1ORGQXVN4sIiIiIjvVUqbXYrY8X7c1Gub0mqpvFpEoUtArIiIiIjvVkOm1WnbM9Pp367ihoBc1tBKR6FHQKyIiIiI7FS5vjtxu2e0MrWb1ikj0KegVERERkZ3ytzCnd/fLm3fr7SIiraKgV0RERER2qqVM7+7HrA1zenf7QCIiLVLQKyIiIiItMk2zxe7Nu6vhcIp5RSSaFPSKiIiISMvMAAEjeMnY4ZlelTeLSCdQ0CsiIiIiLQsECBjNd282djNHqzZWItIZFPSKiIiISMsC0cv0htfp3c0DiYjshIJeEREREWlZRNC7Y9S7m2Gv2jeLSCdQ0CsiIiIiLQsECNBSplcpWhHp/hT0ioiIiEjLAv7QnN4dM70dVt6s4FlEokhBr4iIiIi0LBDAb1iwmIEmL3XcnF6VOYtI9CjoFREREZGW1XdvtjSTjTWs1t08eEOmV0QkehT0ioiIiEjLAgEChhVrM6Gp5ZTzd+vQ4TnCCntFJHoU9IqIiIhIy8wAAYxmLxot2X1369AqbxaRzqCgV0RERERa5vcTMCzNljdnJtgA6J1ob9ehtWKRiHQGW1cPQERERES6sZ3M6R3UK5b7jurPoNSYdh5c3ZtFJPoU9IqIiIhIy8wA/hbm9AIc0Duh/cdWpldEOoHKm0VERESkZX4/fsOC1YhGNtbAMAPK84pIVCnoFREREZGW1Qe9USkPNAwM1LtZRKJLQa+IiIiItMzvw2dYsUQj02sAmIp6RSSqFPSKiIiISMt8PgLRyvRiYCjmFZEoU9ArIiIiIi3z+/BZrNGZ02sYGAp5RSTKFPSKiIiISMv8/mD35qh0Wm5YskhEJHoU9IqIiIhIy0Ldm7t6ICIi7aOgV0RERERa5vcFuzdHI+g1wMBUpldEokpBr4iIiIi0yO/zYxoWLEYUol6jvpGVol4RiSIFvSIiIiLSIr/fC4AtKleNWqVXRKJPQa+IiIiItGhpjR2AuoC14w9uNLSyEhGJHgW9IiIiItKiPxVlALDW44jC0dW9WUSiT0GviIiIiHSN+nV6FfSKSDQp6BURERGRrtEwpVdRr4hEkYJeEREREWmRxfRH8ejK9IpI9CnoFREREZEW2QKBqB4/GPSqnZWIRI+CXhERERFpkc30Re/g0Vj7V0RkBwp6RURERKRZps/HsKqtAAxOjYnKOQxTxc0iEl0KekVERESkeR4X2a5yAH5/aE7HH9/QkkUiEn0KekVERESkeW43fiN4uWi1RKEU2TBCDZxFRKJFQa+IiIiINM/tJFCfjbVGZf5tMORV0Csi0aSgV0RERESa53bjN6wAWKJx1WgEw15FvSISTQp6RURERKR5blfUM72GqUyviESXrasHAPDRRx8xe/ZsDMOgf//+XHPNNVRUVPD4449TXV3NkCFDuO6667DZbHi9Xp5++mk2bNhAUlISM2bMICsrq6u/BBEREZG9j8dFoD5HEo0pvVqySEQ6Q5dnesvKyvj000958MEHeeSRRwgEAvzwww/885//5KSTTuKpp54iISGB2bNnAzB79mwSEhJ46qmnOOmkk3j99de7+CsQERER2Us1amRliWKAqkyviERTlwe9AIFAAI/Hg9/vx+PxkJqayooVK5g0aRIARxxxBAsWLABg4cKFHHHEEQBMmjSJ5cuXY2p9NxEREZEOZ7pdBELdm6NwAgMMhbwiEmVdXt6clpbG9OnTufrqq3E4HIwZM4YhQ4YQHx+P1WoN7VNWVgYEM8Pp6ekAWK1W4uPjqa6uJjk5ucu+BhEREZG9UqOgNzqZXi1ZJCLR1+VBb01NDQsWLOCZZ54hPj6eRx99lJ9//rnDz5OTE4UF1TtQdx+fdBx91j2DPueeQZ9zz9CTP+eqWAcBwwtAv5wcHLaOTfe6K0vArMYwLF3+79zV55fOoc+5Z+ryoHfZsmVkZWWFMrUTJ05kzZo11NXV4ff7sVqtlJWVkZaWBgSzvqWlpaSnp+P3+6mrqyMpKWmX58nPz4/q17E7cnJyuvX4pOPos+4Z9Dn3DPqce4ae/jkHiosIGMHrrMKC7Vg7uJuVWVKCAQTMQJf+O/f0z7mn0Oe8d9vZDY0un9ObkZFBbm4ubrcb0zRZtmwZ/fr1Y9SoUcyfPx+Ab775hgkTJgBw4IEH8s033wAwf/58Ro0ahaHOfyIiIiIdL6KRVRSObxgYmJimruVEJHq6PNM7fPhwJk2axK233orVamXQoEEcffTRjB8/nscff5w33niDwYMHM23aNACmTZvG008/zXXXXUdiYiIzZszo2i9AREREZG9Vv2SRBaKTZDA0o1dEoq/Lg16Ac845h3POOSdiW3Z2Ng888ECTfR0OBzfddFNnDU1ERESk53K78BuW6GR5AQgeWGGviERTtwh6RURERKR72VJUyX/9w3BbHViiNSHOAEMRr4hEmYJeEREREWnizo/XUhE/HIDYKPZPMTCV6RWRqOryRlYiIiIi0v1UOMKrY1ijFfQaKm8WkehT0CsiIiIiEUzTxBrwh55bojapN9i9WUQkmhT0iohIhzJNkzUlTrx+XciK7LE8bqxmIPQ0Iz5KM+Lq5/Tqt4WIRJOCXhER6VDfba7mls8388Kiwq4eioi0g+mso/Lb2Xis9tC2wwYmR+lsyvSKSPQp6BURkQ61odwFwFfrK7p2ICLSLubXH/LTnIUR26IW9Ibm9EavUZaIiLo3i4hIh0os2gIk4gvsclcR6Y4qSvk5bQQA4yhjxMBMshLtu3hTOynWFZFOoKBXREQ6VOJ3n8LIs7t6GCLSTv6aan7pNZH0WAt3nzEZI4rLFTWUN6vAWUSiSeXNIiLSoeymr6uHICK7Yb0nhhp7AuP6JEQ54AUMQ42sRCTqFPSKiEiH8hv60yKyJ1tGKgDj+ybtfMcOo5BXRKJLVyYiItKhAo3+tPxnWQmmqQtake7O4w9w/5w8lhbUUkIMAH2THZ1wZkPTekUk6hT0iohIh2qc6f3X0hI2lLu7cDQi0hqLlm3gx7wa7vx6K1VmsGlVSmwntH6pj3h1b0xEoklBr4iIdKjADuXN/oCuZkW6u9gv3w09rrLGApAUY43+iY2GRlbK94pI9CjoFRGRDrXjnF6FvCLdn88WLmWusicQb3qxWTojEDXAVPdmEYkuBb0iItKh/EZkdsjj14K9It2dNykt9LgwLp0UvJ1zYkNL9YpI9CnoFRGRDuWPiYt47vYphyPS3QRMk2q3P/TcY4Tn77qtDpKsnXWzSuv0ikj0KegVEZEOFUhIjnju9vpb2FNEusp7i7Zy4du5rC528nluBe/bBke8HmPvhPm8APXrACvoFZFo6oS2fCIi0pME6i9fD3Nt5tvYgbg9nVQmKSKt9tGKIrAl8saCrSwpD4CtV8Tref6YzhmIAYZaN4tIlCnTKyIiHcpvBjM38ZZgeaTH5enK4YhIM/YrXQcQDHgb+R2rGFKTz+Wjk5t7WxQYO/xfRKTjKdMrIiIdyl+ftIm3By9inbW1XTgaEWnOjl3WG2QfdSyPZsViWDqpvJlguKtcr4hEkzK9IiLSoRryRoPTEwBYuE1Br0hzft5ew3MLCzE7ubzX9PlwWR3NvhZrs3RqwBuc06tGViISXQp6RUSkQ/nryxT79svmgLK1LHfHsq7U1cWjEuleTNPk7tl5fLymnK1VnTwFwOPCZW1+zm5aXOcXAaqwWUSiTUGviIh0qPWxWQBYMrI4bescAN5bWdqVQxLpfrZvDT20dHbU53LisjqI87ubvNSrs4New8AwTUyFviISRQp6RUSkw/yQW8zKxAEA2OLiGJPpoG9dET9tqcTl66x1P0W6v8DalaHHPn8nF/e6XbisDmItJpmusoiXbJ0dgRsKdkUk+hT0iohIh1n+5f9Cjy0WsP729xxWthIPFt5XtlckxLd+deixN9DJQa8rWN4ca5jckVXKydbt3D81i4eOG9i54wAaips1p1dEoklBr4iIdJi4iuLQY6thYKSmc0qaC4ffw7yN5V04MpHuxblhXeixJ0qZ3jqvnxVFdU1fcNUFy5stJoOnT+e35x3JqH5p7JMRF5Vx7JQBhkJeEYkyBb0iItIhzEAAhz/ckCfGFvwTE5eTQ6qnhmq3r6uGJtKtmD4fzqrq0HNvlILe+/7zE7d/uYXcUmfk+V3B8uaYbnEVaGjJIhGJum7x605ERPYCFaV4LXYAxjjqwl1gM3uT6KujRjGvSJCzFmejJYN8LZQ3f7+hnCXb277klz9gsmBdISuNXgAUVEd2h3a7XJiGhdhOXJmoRYYBLTSyqvH42VTuwt/Z5d8istfp/L70IiKyd9qeh7N+GZTfHLVfaLOR2YdE7yrcpgWvP4Ddqvut0sPVVkcsGeTxN23yVrl+HQ/ND94pumNqXw7ul9Tqwz/5+my+sfQNPS8pr4VBKaHnLlewa3OsrXv8LDaUN68vc/HV3F8oKipnkyOdEnvwaz55YAy/PXRwVw5RRPZw3eO3nYiI7PHMgjyctuCFfFzji+msPiT6guWV1R51cBahtiZ0gwjA6/U32WX1tz+GHj/8XT5V7qb7NMcsLWahv1fEtpdXVvHc3A0ALC2o5b/l8QDE2btB52SDUHnzTZ9u4pPaFBYmDMJnwujK4Jg3F1Z26RBFZM+noFdERDpGQR558VlYgLT4cCGRkZBIX2/wonVpQdtLNUX2OrXVuBqVN7+4pDhUwvvf1WXM3VRFmTcYkKZ6qvH4TbZVNl1TtzmfLt9OjT2eoc5Cziz4nsty3wfgm611mJXlPPj5Wt51ZgCwxWPvwC+qvepDXjPyhtgrlxzEnw7NJM7notqr8mYR2T0KekVEpEN4KivYkNSXQSn2JmWTR5r5AHyeqw7OIuYOmd5Kd4BF+TUEqip5cVERj3yfT5k7GOiNqNwMQHHdrifFez96k3/kBYPplNRELrr5Mqb/5gz2qdyE07RQvWY1tbZwh+bj+se0dKjOY3ewPS4j1A8gQmIyib46NvnjCJgKfEWk/RT0iohIh9jkjcFrsTMiM77Ja33SEjmgPJeVxS5K67xdMDqRbmSHOb0AK4qcuBd+H3r+v/ihAAyzBpccKimrZle2fflF6HGMrb5LVc4Akr21BAwLl6xNBmB6Sg3vHOjlmAOH7NaX0RGMhERq7AnNv5iYTHFsGgCn/2sNr/9S3Px+IiK7oKBXREQ6xFozeEG9TzNBL0NGMqxqKwCFNQp6pYerrYkobwZYnF9DXUFh6HlxTCoA2anBzKzXvfPyZjMQYHNin9BzpxGcYmDExJIUCL7XZwQD4aPGDsY2cn8MoxvM6W1kcPU2ACbH1gQ3pKYR6wt/3W8tL+2KYYnIXkBBr4iIdIgN1lQARqTHNnnNGDWWNE8VACX1ZZrvrSzlw9VlnTY+kW6jtjqivBlgS6WHLcVVTXZNMYI/L7ss73U52ZQQDnoNa3g9ol5GuDT6rD4BBvdLb8+oo+6msybyYv8CZpw8BgDDEcOzMb+EXh+Y6mjprSIiO6WgV0REOoSz/k9Kcmwzq+Fl9yW9/nr1reUlmDVVvLykmBcWFSnwlZ6ntiZU3nxX6lYu3PAJAN/70prsaq1PxgZ2WKu2zOmLXL/WWcum+kzvoZVruGRC79BL/S3O0ONxowZ1xFcQFYkJsWQcfgSxMeH5vWlnX8Cra/5OL3cVtXWta+YlIrIjBb0iIrLbTNPETTCzFGNtWjJpGAbpfbOBYEbrr1/mhl57cVER5c5dN+kR2VuYdeHuzb0Pm8r4/qkAfNn7IABO3vpt8LVEOxZL8FKtcdC7fGsZl7y7jn8tLQkftK6WrQnZ9MLNH645lYG9w8sWjXCEg8URGU0rMbqLREfTy1LDaiVp+lmkeqqp8qiZlYi0j4JeERHZfX4f7vruq45mgl6A9MTwxfZ3deHGNSZQ6VLQKz1IbQ1Oe3CubpzdQt9e4Y7KVkwmTJvE3ybYuP+YAVjq592ajcqbF3z0FQDvrwzPca2qqqU4No0MS9M58zknT+esTV/xq5RKHNbud+l3+YFZHDEoucWxGcP3o5enCjcWVhfXdfLoRGRv0EwNmoiISBt5PLgtdmJMX4vNcVJMT4tv9wVafElk71NbjSs12PAt1mbgSE6G+t5NM8YkMW50v9CuxfU/T41Lmf2VFZAMPjO43WoxuONnF5BInhkOoBsY/QZz4cyrI+b5difTRzYt627MSE1jor2KxcDKvHJGNtcsT0RkJ7rf7T4REdnzeD14rHZi8Le4i+2YU5i+dS4Aw6q2RLzm1xqc0pPU1uCqz/TG2iyQmEyyJxj1DuqXGbGrxRIMehs3svIb4cu3z3IrANhKIgBOs/nAtrsGvK01PC04B7qotBrT56Nu1Qpu+XwT761UR2cR2TUFvSIisvs8btwWBzG0nLI1Unpx+cwreP9X+3CWd13Eaz6/gl7pGcxAAOpqcNpiiLUZWAwDY/wh/LV/KbdNTGdAamRX510FvW8uL2FLuZO9Xe+sXlgDfhYUe9nytyd4593/sabExctLtHaviOyagl4REdl99Zlex06C3gaGYTDpkgt5ePv7nL0pODfRG1DQKz2Esw5ME5c1JpjlJZiFzT76OCYNy2yye8Oc3saNrNyN1vitcPm57pPNoeeXDN07Z67Fj9qfY7b/SEnAzoxeJ/D+gCMA6J+iZYxEZNcU9IqIyO7z1s/pNVoXvBpJyQybOJ5Yf7CrrE9Br/QUtdUAOC124uy7vgzbsZGVWVJIcWyvZvdN9Ls4deLQDhpo92Jk5fDbuiXMXPYyfc1afJa9M7gXkehoc9Dr8XiYP38+77//PrW1tQAUFBRQU1PT4YMTEZE9hMeDx2KnFdfwIUZKL2xmcA6wX0Gv9BS1wesll2ELZXp3JlTeXP8zYi76gaL6oPePces4vGp1aN9sW8uN5PYG1stvYuLpJ/D4rw7kqcJ3yXSV4fS03EdARKRBm26TFRQUcN999+FyuaitrWXy5MkkJCTwxRdfUFdXx1VXXRWtcYqISDdmetz4LXG0sFpR81LSsAWCF6zK9EqPUVtNQWwaTmykx+36MswIzekNPvct+oHSARezb5qDg044mew3Xuc7XwCLGeD8A5rPAO8tjH6DMfoNxgL0G5BDXKmbMo+WOxORXWtTpvfll1/mgAMO4Pnnn8fhCM+hmDBhAitWrOjwwYmIyJ4h4A2uDWqztCHqbZTp1Zxe6SnM/M3MzR4PwJSBybvcf8dGVqWllQQMK9nJwXWv+59yKi+PqOSN0wdy0Ji9s7S5OcbQkcT6PTj9kWsYi4g0p01B75o1a5g+fToWS+TbMjIyKCsr69CBiYjInsPvDWZb2pTpTU7BZgYbXynTKz1FYPUy5mSPw2GBSf0Td7m/tf6aK2CamIEAhdYEALIS7QAY8YmkHDwZe2JS9AbdHQ0eQZzfjR9DN81EZJfaPKfX7286d6KkpIT4eC0ULiLSU/nq/zZY2zKn12LFFhtcnkVBr/QEpt/P+vxytsdnMrF/EvH2Xa+dG+rebAJeD0UxwRLmrAR7NIfa/aX0IsHvAqDarXm9IrJzbQp6x4wZw0cffRR6bhgGdXV1vPXWW4wfP77DByciInsGn68+6G1jEx1bXFzw/VqnV3qCzetYE9sbgAl9d53lhcjy5urqOr7NGguEM709lWEYJJseQEGviOxam4Leiy66iNWrV3PDDTfg9Xp57LHH+N3vfkdFRQUXXHBBtMYoIiLdnL8dmV4IB711TndHD0mk2zGLC9iU2AeAIb1iW/Ueo6G8OQB/nFPA0rQRAGT39EwvkGwJTo+oUtArIrvQpu7NaWlpPPzww3z33Xds3LgR0zQ5+uijOeywwyIaW4mISM/i9wcvPm1tXS4lOQWA11dWcs64Ph09LJFuodrtx241cNRWszmhNzbDJCe5dddN4fJmk82u8F2l9HgFvUnW+qDXpQ7OIrJzbV7Z2+FwMG3atA4dRG1tLX//+9/ZunUrhmFw9dVXk5OTw2OPPUZxcTGZmZnceOONJCYmYpoms2bNYsmSJcTExHDNNdcwZMiQDh2PiIi0jb+hvLkt3ZuBnP1Hw7LgY7cvQEwr1i0V2ZOYpsmFb+eSnWDnGUsNWxL2o3+s2epO55ZGjawas7epa9zeKTUm+G9TXlEDpHTtYESkW2vT1cW///1vvvjiiybbv/jiC9544412D2LWrFmMHTuWxx9/nIcffpi+ffvy/vvvs//++/Pkk0+y//778/777wOwZMkSCgoKePLJJ7niiit44YUX2n1eERHpGA2Z3rYGvf33G8ERRUsAmLW4CL8aWskeZEVhHdurPTvdx7noRwAKa70U1PrwWB0MStp1A6sGlvrgNtBMI9GeLjMxmC3fWFLDxnKXli4SkRa1KeidO3cugwcPbrJ9yJAhzJ07t10DqKurY9WqVaHssc1mIyEhgQULFjB16lQApk6dyoIFCwBYuHAhhx9+OIZhMGLECGpraykvL2/XuUVEpGM0dG9u0zq9gGGzcYlvFfaAl09zK/h6nX6fy57B7fVy+1dbuOq/G3a6X9UvS0KP89zBn48Bqa2bzwuNMr0+lfDuKDMluHLI14UmMz7ZxPdbqrt4RK23cFsNd3y5Gac30NVDEekR2lTeXFVVRXJy04XUk5KSqKysbNcAioqKSE5O5tlnn2Xz5s0MGTKEiy++mMrKSnr1CrblT01NDR2/rKyMjIyM0PvT09MpKysL7duSnJycdo2vs3T38UnH0WfdM/S0z7kwNg4qIT4+vs1fe9KRR3PeJ1/w2tCTqKlx7VH/dnvSWKX9dvycN5TU8pt/zaMhd2BPTiczMYZNhRXM/HAlM4/fj7H9UgHYlpIO9X3aqs3gPNxBA/q0+nunNC0NNoPfDN9Qumhkkr73gLTJkzjy5a9YnjyI4tg0vlpdwtmT9sFoa2+Bep35b3rf67MBWFVt5eTR6mfQmfSz0zO1KejNyMhg1apVZGdnR2xftWoVaWlp7RqA3+9n48aNXHrppQwfPpxZs2aFSpkbGIbR7l9gDfLz83fr/dGUk5PTrccnHUefdc/QEz/nqupghsXncbf9a59wOKPX5AFQWVWzx/zb9cTPuSdq7nN+8as1uMxwsdzbP6zi1NFZ/Ovvb7E+aX9+++/F3DC5D0/M285kwgHNa9bhAPi9rf85cVdVArH8HAjOWd3PX8rp4/fR9x5AryxuuP5c/D/N5fplhSwhmyVrN9M7qe3NVbvq57miooL8fJVldxb93t677eyGRpvKm48++mheeeUVvvrqKwoKCigoKOCrr77i1Vdf5eijj27X4NLT00lPT2f48OAfgkmTJrFx40ZSUlJCZcvl5eWhDHNaWholJSWh95eWlrY74BYRkY7hr19nt6EUs63sMcEMmM+vUj/p/nr/8FHE8/LyasyVS9juCgcvT8zbDsA8MkPbqqzBJboSY9owp9cSue9RYwaEOjoLGFYr1olTOaogOA1ufZmri0fUNm2cESIi7dSmTO/06dOprq5m1qxZ+OrnlthsNk488UROPfXUdg0gNTWV9PR08vPzycnJYdmyZfTr149+/foxZ84cTjvtNObMmcNBBx0EwIQJE/jss8+YMmUKubm5xMfH77K0WUREossXqF+yqJ0dZW3W4IW9T816ZA+wY15uc7mLx9aXsCB9VKven+BofdBr3eFGUlqmrnl2ZFgsDPNXAJBb6mLKwKZT8boT0+cNPVbMK9I52rxk0a9+9SvOOOMM8vKCpWj9+vUjNrb1DRmac+mll/Lkk0/i8/nIysrimmuuwTRNHnvsMWbPnh1asghg3LhxLF68mOuvvx6Hw8E111yzW+cWEZHdF6jP9O54gd5aNlswCPD6VOYn3d+qlMimnourrWDvD8DYinUUxaSSHxfsP5Lur+PZja9wY85Z5McHs74Z8a2//DKsFgwzgGkEf7ZSY9t86dYjDDFqAFi3J2R662pCD3d3+p6ItE67fnPGxsYybNiwDhvEoEGDePDBB5tsv+uuu5psMwyDyy+/vMPOLSIiu68h02u1trO8uT7obTiOSHe1taCMhRn7hZ5fsu4jZg07OfR8WJLBkLylvDswuCpFXEIcMZffxEO5ayhyVNI3K5VYe+szvVgsWEwTf31slBqnoLc58fGx9K0rYn1ZbwKmicUweO3nYnKS7Bw1NLWrhxeptjb00K/feSKdYpe/Of/yl79w3XXXER8fz1/+8ped7nvrrbd22MBERGTP4Q+YYAFbO4PeUKbXr0yvdG+r/vsxJASnXD150mD6/dPFrEavDxvSF9uKb3i3YYPFgtF3IEl9B5LUnhNaLBj1BdUGkNKG+cA9iZHci5y6YrbFZ1HrCWAPeHh7RWnwNcNg2pCULh5hI40yvR+tLsOkm41PZC+0y6uTpKSkUOlFYmIiSUlJLf4nIiI9U0OGtt2NrOozX96Agl7p3jyNmmk6rAaWw4+LeH3I/iNIN9yh56eP2M1gxmrDYgZ/vpLtBlZ1PmpeZm/i/MF/d5cvgPPnxaGXvlxX0UWDakGjoHdduSfU9ExEomeXmd7Gc2Yvv/xy7HZ7uy9qRERk7+QOBP8uxLWlbLMRmy3458inoFe6OXej9XIdVgNGj+eRec/xQ2mAoWNHkZ0ykup99gntc/So3rt3wvgELGYdAKmxyvK2KLM3MRuCwaPLF8BeWwukA1Dj6V4N8szaGiCxq4ch0qO0emJIIBDg4osv5uGHH6Zfv37RHJOIiOxh6urLkmNj7e16v80RfJ9X09ukm/M0KpKLsVkwLFaGXXk1jTudJBw6jePfn8/QSQfu/gkTkrCbVbiIITW+fT9fPYGR2ZtY/2YgGPT6PeEOydUuX1cNq3mNMr0i0jlaHfRaLBYyMjJCSxWJiIg0cNZ3XY6La183f5s9eDG/3BPHyqI69suK77CxiXQU0+fFYwQvnc7O9pLYwtJDluH7ctXNIzAsHZCZjU8k01VOtT0BNTffiawcYv0eoD7odYevV6s9fkzT7D6dkncIelOUwReJujbVKZ955pm8/vrrVFVVRWs8IiKyB3LVVw/GtTPTiy38vtu+3IJp6upeuqHyUjyW4Pfq5PHDd7prhwS8AHHx9HYGGzIV1nh3sXMP1iuNWDP47+P2mXi84aDXZxq4u1GTPH9NbcTzgKZ1iERdm/ref/jhhxQVFXHVVVeRnp5OTExMxOt//etfO3RwIiKyZ3AGghmUOHs7ez7YIv8crS52sq+yvdLdlJfisQaDXoe1c7KGhsXCedvmsDx9BFcfvPNAuyczLFYc8XEAfL+lisneyHm81W4/sbbu0ZPG43SCAw4sXUVpYibF9qyuHpLIXq9NQe+kSZMwDEN34EVEJILLrG9k1d6LSkfkTdSF+bW7DHp/yqtmdHY88e1sniXSVmZ5CR5L8NKps4JegAEPP8Or0H3Kc7uplOQEAGZvqGK81w92MMwApmEht9RJZkL3mBPtcrrBATGmH6vfh1/X1SJR16qg1+1289prr7FgwQL8fj+jR4/m0ksvJTk5OdrjExGRPUBN/Z+ThBbmOO5STCzgCj0tc+68f8TGomr+PGcbJ41I5YqDdrM7rkhrlZeEyptj2rkmdXso2G2dSZMPgJ+Dj6t9gB2SvbVUOpL4y7f5vHZWAsnNrHNsmia/5FWQhtkpS0J53MGllWLsViwBH3418BOJulb9xn7zzTeZM2cO48ePZ8qUKSxbtowXXngh2mMTEZE9gFlWQok9EZvpb39Dlh0yvRU1rhZ2DCp59w0AFmyuUPWRdJ7yUpzW4Peqw6ZAtLtxDB/JpOKlAJQHgjfiGm5SACzaFmwgVb7DTbXF+bVc/u/FPPJ9fqeM0+0KNtxy2AwsyvSKdIpWZXp/+uknrrrqKqZMmQLAYYcdxp133kkgENCavSIiPdj2ag8xC+ZTEpNFui2Apb0ZqZhYoDL0dFtZLf5Ay1mX2q1bYT8ocsO2ag/9kmOa3U+kI5nlJWxJOIBeMRaV1XdDhiOG2EAwoK00g5e4Q6vzWN4ruKDUgm01pODm3nllXDepN0cPTQVg+7dzgSF8v6WapQW1fLSymAAWxuQkMn1kWoeP012/nFKM1YLVGyBg0r26S4vshVoVsZaUlLDvvvuGng8bNgyLxUJZWVnUBiYiIt3fVf/dwCUlwyiPSaZ3r4T2H6hRpjfZU0Ohz8aX6yta3L3WFl4aaXF+bYv7iXSkijoPpbGpDE2P6+qhSAtiCNYKV+IA4OzJQ7l5xT9Jd1ewOK+KDz6eB8DLi4tC7/GuWRF6/OycDfy43cWC7XX8e2lJVMboru8sHWOzYDWDDbfUwFkkuloV9AYCAWw7dNa0Wq0EApqEICLSUwV2KMkblpXY/oM1CnpPL1kAwMqCmpb25rkRZ4QeK+jtWdy+AAvyaqJS1l7m9PHCwkKqXM3PKd8QCN7YGZrevvWoJfpijOC1aZUR/J2SMGgQhw5LZ//ydTgDBstThwKQEXACYAb81DW6iZZcui302OUL4O/gaNQMBHB7g2OMsVux1H8f7/j7VEQ6Vqu7Nz/11FMRga/X6+Uf//gHDocjtO3WW2/t2NGJiMhOvbOilDUlTm47vG+nl8b5Nm+IeD48o/3ZL8MaLhXNHtSPGKeHzQU+oH/EfqZp4pr7VWi7gcnywjrcvgAx3WQ5EomuR77P58e8Gm46pA9TB6d06LH/saCA+VtrqPH4mXFITpPXN1iCDTyHpino7a5ijWDwWGUJfkYxVgvGmIlkfL4EAF99921PQzBbVECtLfy7y2ex4fB7mVy8lDm9D6S41kvvJAcdxlUXbobmsGE1g02tlOkVia5WXSFMnTqVXr16kZSUFPrvsMMOIz09PWKbiIh0rp/yavgxr4Zqt3/XO3cwb97miOfDOyj7ZR84lP61BeR5LPh2vBLcsp4N//1v6OkpiVV4A8HAV7q35xcW8tfvtu1W5sysruLHvGAFQEGNt6OGFlJZFzxmaV0LmV57OqCgtztzWILfX3lxGQDE2AwYfSDpvsjKEVf92uLkb46YLrE+qR8Bi5XevmoA3lxeyv1z8nD7Oqi6sa4Wt7Uh6LVjMYPHVTMrkehqVab3mmuuifY4RESkjUyfF+/WjRDXm8JaL8mxbVp6fbf5asIXkb38daTH7d75/zygkq+qExjXN5n5cwtYlzyA/CoPA1IbNamqqSY3OZjlvXHlv0ibfCgfkMKi7bUc2DdYXr2+zMWHq8u4ZmJvHJ24rIy0zKyt5qM15QD0SSrhgjGZ7TvOnE+BMQAkNbP0zO6ybloDSYPwNrOGzJbiKuZnjAbY7e91iZ5YrzPyuc2CEWNjxA7TL5xm8PvHzNtMhSMyceMzLPSOCwbFX28INtj7Ma+Gwwd1wFKddbW4LcHMcUyMIzSnV8sWiUSXrgZERLrYulIXd329hdK6NmauCvPx1V8pFUYh67UrvrrwXNqTYkt3u7x69GETmXHiaGwJCQyoLQBgU4U7cie3k9ykAQAMr9rCPmYFsTYLP2ypDs3x/MOnG/nfxiq+Wl+JdA9rl64NPf5ydz6X8tLQw7golLPbvMHvN5+vaeXEXz5ZFXqsLrvdl1FVEfE8tv77ZEh2CvtUbsIa8DOwZjt1hi34O6NwG6UxKSTZ4LVv7wq9b7/0yJLmtds6qHmrs1GmNzYmNKdXmV6R6FLQKyLSxWZ9v5FfCup49seCtr3RMELz04q6IuitDZYUT42t5KzTj+i4A8clMrAm+G+xeYeg13S5yE3uT6LpIdtVhu3TNzmgeiPl9Q2ITI8bP8GApElptESVyxdosQT0/zaFy0frPO0vxTerykOPoxEk2OuXu2kIegOmya0fLOOz3HLK6rsB28zOn0ogrTfhoFFYA+HPyGEN/j4wjjmF/0vayPNTEtmc2AeAOZuqMF1OSmNSyEqOI+namdy7/g0e39dD9tixEcfdXFTdMQOsrcHdMKc3LgZrfXmzfl2JRJeCXhGRLpaRuwiAXwra2IXYBJ8RLNErrO26TK8tMxvDbu+4AzscDHAFlwp5e0Upd8/eSlX9nOWqOg+FcekMjw9gOfZ06N2X01d/DMAna8vZ+MvK8HH8Ck4607n/Wcu5/1nb7GvDjfD3tttv8vgP+e06h1kZDnq9/o6PEmz1wVJDBUVR7npmry3mbz8V4jOCN5heOEw9TLqz7COncc8vz4WeN2TljcRkYn99NWmD+jO4OtihecnmMsp9BnW2OLKSYzFGj2fsXfcwePwBsM9o7l/8DOds+pIkby2lro75fWI6a/FY68ub42IblTcr6hWJJgW9IiJdrCG75A3AxnIXAFsr3Wyv9uz8jT4vTmtwvmuXlDc7g3PnbI4O7GxK8CI11W6S6A8e/+fttby5PBgE57uCF4YDEyxYzr4E611PsO+FFzBz2csEMPgyNxwUOWvV3KqztXTZPsisAoJrMAP8b2NVu45fXeMKPY5GJr8hi7vVSTC7+/LfQ695rHZOiK+g18ABHX5e6ThGQiKx/pZ/dxoWC7+P3QjAN9vcXJZ1FgBZSZHNyQybnf3+7wF+dc5R9HJXURHooDnkdbU464NeR0I8FrRkkUhnUNArItLFXPbwchmv/1JMQUEJN3y0gav+u4EH5+ZR3EIWd06BlypHsDlLVwS9XmcwALHZOr6hEInJJLnDjbI+XVNOudOHzxP8OmPsjc45fD8G1m4H4BMzvMxMWZWTD1aV8cayko4fXys8890Wfvfh+h5RZm26XTvfIRDMnDYuDXZ629a5xzRNyht1KY9GprfhBhTA334qZIstNeL1YQeM7PBzSseLCez892FG7/Qm27KSYppsMxISITObFG8NtdibbXDWZnW1FMYGz5/VKzHUvbkH/JoQ6VIKekVEOtHqYid3fLmZCqePMqcP0zRx1nfyzHaVsSi/lm/e+iQ0L3Xe1hru+GpLqElTY29uCAcaxbXeqGcK6rx+Zi0uYkNZ8Lz++qDXbolCU5/kVOL94fm8PjM4v9fnCWZwrI3WjTcSkkhID1/EpnqCc+9+KPLx0uIi/r20hJK2NgnbTd7KSr7YXEdelZcVRXt/xtn8/qvQ42aD0fqy4fJGXXIrXM0vC9SiulrKrAmhp9G4meDYIVj6qs/BEc+1VNGewRGz8+qTmPETuWHVv+lbWxjatmOmNyQxhdT6CoXKjlgazlnL1oRsUuyQnJIYmtOr8maR6FLQKyISRb6AycuLi1hVXMe6Uhd/+t8Wlhc5+c2767jk3XWs3l6Fqz7oHVa1hYAJ/047mARvHa98dzfj3fkU1ngpczYTIFSHu+B6Ayblze3TgR7+dDXvryrj7q+C6/P66rN71ijEvEZiMnG+yOzhgm01/OBPA5pmlxOGDAk9HkY1hxUuocoMB8Zz2llO216VGzeEHv+4tYMa4HRnjRoH1XmbCQzqM70HlK8LbXpnRWlE46sVRXXc8dVOuphXllEeEw6avfVBQmmdl7wqd/PvaaOAEXlZlJscWcrcP6VpNlC6n5j4uJ2+bmTlcMSUAzis6JfQtszE5j9bw2YjgeDv1lrP7md6XXVOCuPSGZBkw7BYsNYvqxaFwgURaURBr4hIFH3/yTe8t6qMmV9s4ebPNlHtjbyymfm/7SzvNRSb6SfbGV4S44wxfUjadxSDC4LLpGyr2sX8XqLfwdmyfQsAVV6Tr9eWhDJttmhEvckpTUoUP1pTzpcEy5d3DHotQ/cNPc7OTOWm3lX8cekLoW0frylvV2awzutn9obKNmfRncXhkur5eTXNZuqbs6XCzU2fbmzStbq7Mwl/HnXNlS3XNxU748D+3LZsFv1rC/hyfSU3f7ye3FIn98/J49V5m1leWMdjLTW5qiyPWE/1zeWlPPDxCq77eCO3f9F8NURbNTSGa+ywwiWhx7ZoVDVIh4vp23+X+xijxpHgC6/pm91MeXODeEvwe7p2NzqPN9jqDn6P9U8NZpYttmATQGV6RaJLQa+ISDt9vKacd1eUsr6s+fmMpmmyeM22Vh1rf0sVIxzhQGf6/tlYLr+JbEsw2C2uaRr07piV+iKK69KapknAGw5Cn1xQwrdZ44AoBQJJqaH5lUl+Z5OXbTus0WoMCwe9w7ISMc6/ggOPmMTdvzzH1IJFlDp9fLup7dnex79YwxPztrd5zV9XXfh7orTO13ymvhnPfbuR9WVunlvQxuWrupjPFf6Mmg1660s4YwcM5ODDDuTen/8BwNYaP7//bDM/5tWwuib4fbShpPlycLOqgmpbQsS2+RVWaj0BKt1+ajogC+ff4WdqgiuPP0zpx+lb/sfM3hW7fXzpHPG/voIzrduYeXDTubsh/QYRbwv/7uqT0nLpekPQ2+z3dhtt9QcrewakxQOQHBP8nluW37nVKCI9jYJeEZF2em5hIa/8XMxNn25qEvh+v6WKc/6zlm96T9jlcS6v/Ikrjt6Xgw4excXrP+LRUQFibBaM+ETSM1IB+Cq3rMn7vBZbxPPZGyqbLy3tCB43vvpExH1Lgh1tv8yZCIDNiEbQmxIKehONpl+TdcfmWVl9+MPyVxlRuZkD9x+EYRhYTjiLcZddwulbvgHg3ZWlrChs2/zaHyuCfya3lrXtfU5PZJa6tZlbc/N6APwd0TCnEzUsXwUtZMMavh6bDeO4M0i76U7OzJvb7LFq/UbzWdvKcmrsTctWG24WdcS87cY/U29kr+OPZ0+gz+nn8pvLTmXStIm7fXzpHEZsPBeddxSTh2e2vI/Fwr6pVmL8Hia5t5LgsLW4b0J9cFzj3v3vsa1m8MbNgF7BIPv42HLifU4+WF3OhjJXqGeCiHQsBb0iIu1gVkdm/nZcXujf8zbhaeUkrZOvupCcrFSshx3DaTdfxdCx+4VeS08IZgVWlnmblDj7mynF3FTecnDl9QfaX0JXXYmvPiAYkRh5DIet44NeIzkFW8NSTrGJXL3m7YjXbdbIC1TDMJhy1WU8dOIQUuIaNbEZvh+9Y4IB15ZKD7d/tYX8VpSKA5g+LzH1S59UlFY0eb3G7ePjNeUR/6a/FNTyxboKXJ7g2IdXBec/byrZ9RrMZnkpBsGxmv7ozs/uaP7anQe9ZsOcX4sVw2bDGLYfWXEtf99UN5e1dTmptUUGvS99fy8Hl6wAoKR29//N/Jbgz9TjhyQRd/TJGMmpQHAOqBGNmzvSpXJ+dREv133OzcftvCt3Qn1lSW3t7k872GJLAWBA/fzw5KxMxpatpdRtcuOnm7jx0027fQ4RaUpBr4hIOwTeeTni+Y4NTobkLQs9nlCyklR3ZOnaeUU/0KeuhCsGBjAswV/FhmEEl8hoJKd3r9Djt5aH54magUCoS/GkyrWhAPGNpcWYpklepZv7/reVkjovH6wqY+YXmznrjbU89O02bvl8M7d/uZmqtnQiranCZ1ixESDm11eHv06Hh0MHJLf+OK2VmIK9fnkbLwbHTD+C07f8L/Ryc8skGQOHYgwaHrnNYiXm4MMi/v1fXFS441ubV7SdJG8wmNtaHyhvKnfxp2+2sr3awx1vzOO5hYXMWlzE338qYFuVh7u+3sozPxZQ4g1+piNigp/Lxm2luzxdzdLFNIRV5h6W6fXWhTPhdc6mNxUa7v/YG31uWfEtZ9bWljQtacfvo6Y+6L37yH68dtZwUgcOJGPbGiAy07utysMj3+W3KftrBgL46y+Lsvv1bvX7ZM9lpGUSf9UfcOxiDnBycNotlbUuHvk+n8d+yMfla/vPqOnzss2eSqrfSVJM8GfB6DuAURUbIvfTmr0iHU5Br4hIO3hqIjN3le7ILJPPEr6433dwNv/oF56jacHk3Ot/w99OGsBJh+7HzjgOP463v7mVvs4S/rexijqvH6/f5IaPN7IxqS9W08+tV57Ef7a9yvjS1fxS6OTzlYXM/GAlC/Nr+WxtBS8tLmJVcTCImJ9Xw5oSJyuKnHy9vqL1X3BNFT6LFRsm9B/MJes+ZHD1Nh44dV9S41oOXtotOVze7DUNrAcdytmO8L+h1d76cxoHHRbRsGZlcWRA9eKiQh76tpm51wV5eCzBq93NXge5pU7mri5gwbZabvtiM79sC2b7P1xTzqe5FVzzYfjCdSXBbM6Q3imkuStYXB7AFzBxenzc/uVmPlwdWa7+wZI8Lszrz9Je9UF7feOngGmycFtNi2vSmqYZ9aWqWsPnbDSntzYcAHv8Abz+QKg0vnHPs6zk5udQWswAT8/f3rSLs89HjT2eOCuMz0kkOcaKMfFwMlzlAJTUhX8G568tZO7mKp5b0MobHAB+f+jnVg2rpLHe9uD3VkG1l7mbqvhmYxXP/NiOefclRVTb40k1Gn1v5wxgdMX6iN06Yn66iERS0Csi0g6uXtkAGPUNeiprwmVvpmlSZw1e0E+35HPyCZOIycwKvZ5gt2CxWjFyIpdDaY5htWI97BhS3cEA666vt1BcVcfmquBFU7LpxWK1Yj3hLC5e/yGGafK3nyuotgYzYh+tKaO3s6TZY68vav1SOmZDptcChiOGU885lsemDyfWYW/1MdqkUSOrhq7LMb3CWe/mMr0t6t2XgBHZXbhxSfJ/V5fz/Zam/xbm9jyctmAJYsCw8MyPBSTM+wKAcpefWnt8i6dcbA1+f6QMGsjk4uXUmDZu+WQD5721jhVFTl5YVBQKfKtr6nhpZU3E+71+P6bPxwsfL+G+b/L4Yl0Fbl8gIsANmCaXv7eOh5sL2DtZRNBbF/5ZuPCtXH7zzjoakmKNO31nJdiwBiKrDWIDXn6z/mPKXX4en7d9h5N4qbHFkdjofodx4BQy6iseSmrDgYTzyw8B+DGvhoXbIv9tW+T34rTGYDUD0Vl7WvZYGXYTW8DH1rLwzc7c0maqEXbBrCjDbXEQ27gRX1om/fzV2Bt1q29t4zsRaT0FvSIi7eCsT12N8RUDUFkenuNrupzU2WKxmAEuO+9IYu1WSOlFmrsCgHMOaLm5SnOMI04kUP/rOrfUje+jN0OvNcynNcYcTP9TTueOZS8xqCafbGdpaJwFcRnNHrespA0diWuq8FlsoaZVxr5jMPruOmhvt/iE8Jze+iynJT1848Bubf2fLyMmliE1eRHbGrqwmutWhbbtuKSRr2AbXoud/Ss3MrJyExvL3c2W7jantn7t5f79sznCUgTA+srIzOULi4qYt7WaVe+8H9rWUIa9wW3n8fcW8HFlMLBeX1DJr9/OjcguFf/0IyVOPz9srWn1POVo8bnCzXdqnMGgN5C/BbffpNYbwFdfuN04mLTFx5PtCme8U91VTHRUc3Let/SL8ZNbskNDH5+PWlscCfbwZ28kpZA2sB+GGaC4Ory/ywhHxs8vLMTTmnLxshJq7PEk4tX8XYlgdcSQ7Sxlky+8rFG1q+2Bqc/pwm+xEtPonp1hsWCMGEWKJ3xzZn4Xru39zcbKDlv3WqQ7UdArItIOzvoEVVb9NVBRnZ+v11dQ4fQRqK2myp5IPL7wxXPfQdxc9QMzs8o4ZWRam85lDBjCtWvCge6mwvD81ErCTZssR53MhN9ewmOZefxtQAlHbl8Qei3O9HL8th9Cz+0BLzW+NpTFVlcHM73RWJO3GYZhkF6f3c5Jrv8a08LBu7WNmbgr+vu5YfNHHFmwEID8+sZj5rbNoX3qGjVgcvsCXG07DIDY5ERc1uAY3h50dGif8aWrd3nerEQ7w3J60avRnOL9y3M5Jn8+MQR47Ls8XnH3BeD+3H/y6CHJ/GrzlwB84wlntr/Oc+H2mxFLJ+V98H7o8dUfbuiydT5N08TbKOitdAX/HasXLwxt85nBzyvic4uJI6cueNOoj7+aWaNd3DgqFgNI8tbh8gUi5jb6/D6ctliS7JGfvX3YSFI9NZRUu0IX605r8AfzwNJVFNR4eWdF83Oq3b4A32+pwh8wMTflUm2LJ9GugFd24HDQx1lKgPD3Ro3XbPPPXF19RUTsDr9HjQOnEO8L/wx9tja4rvizPxbwxLwW1q2Ogm1VHh77YTu/+3Bjp51TpLMo6BURaYeGoDcpPhgMrXHZeXJ+AS8uKmLxqi1sj89gpBG+W2/ExDB6xk1MPuaQdp2v78VXhB4/0ue40GNXYIeLp2H7YTnjIizHnML1+9g5f+NnwfEaduL84bv3ae4qavxt+BNQUxmc09uGDOvuOmbmjZy3Xyp3H9kPAKNRptfWxmGknnUBR864ikRvcL7pLZ9vDpYn1oSD0dpGa3AWllRRYgs2FauOSQ4FUQ2mW/J54tIjuGX5qxxWuASA02pW8Je1r/D0jw8xpmwtV2dVYzEM6D+Y3694jZy6Ym4pmc190/fl6tz3ObhiDe6AQV5CNsek+Rh1z59IHzmSs6cM57l5f27267AZJqZpYhbkkRsf2WypuRLtTuF2hoJagAp38N+x8Zzchkxv47myRlw8GfXVD5UxSVgOPhyG7AOGQUxdFSZEdECv9QXfm+CILG03howk01VOocfC7z7cyLyt1dTEJgHwm7qlpLqreGNZabNlzp+sLeehb/N5Yt52zI251NriSGzc/VsEwBFDn2amidQ0tzzXTrhdwZttsTt0vDfGToxoZlXm8rNqbR6fr6tg9oYqZm+I3hrsEeNbvaJTziPSFRT0ioi0g7M+2IxLTGBkZfiu+Py8av79Qy4Apw1tec5nmzUK+FrDMAyMk85m38pNAMTip8oeXB8yyWqS6K2j2tz5vNh5W6v5x4KCYJDVMKfX2oa5tLvJHh/P+eN6k51YH4Q0+jewtKP81IhPZJg13GTp/ZVleIvDjY4aSp5L67zMmrM2tP3oQQn4jfCfyzFZsfzmnCOJHbEfh9x4LTefvD/vj67iN5efysh7H6DftTdx77kTOP6Yg4LnnXIMoy76Nc/+egJTbrgGY+AwjKnHkV4VPvfxY/uFv7apx5M14zbuXf4ity99KeJr8JkG/1hQyNuLtrEiZQgAN618HYCvO+nCuImaalyNbgpU1Aenm6rC5Z/O+oZgEQ2iYuNIrS/pbOhBZcQnQN+BGM7g3MkPV5eHT1MfXyTG7PA9OGQEvTzhmxcPzt3G/LRgg7iMk07llhWvYQ34+ftP2yM67n63uYqXlwQzzXM2VVG4rQC/xUpiYgf+3Mrewe5oNuitbksHfMBVv85vnH2HGzcJiVw0PI7bl83iyrXvAvDQwnDp/xPztjfJ+C7Or2HW4qIWm9y1h/fz9zrsWCLdjYJeEZF2cNVntuKys7k9u4wXF/yFU7fMweM3+c7SmyS/i1EHj+m4E6akMrRqa5PN4/sktPgWw2Jl9FFTuTz3ff5vmAdHfaOUUWl2Urw1eAwrbyxrvsmVGQjw4NxtfLK2glKnD39NdXBOb1tTrB2pUXlzlattF5sNpp5/Km99cysDarbzw9Zq8hs1ptlQ5sIsL+X3769isS+4DNPZiWUcPX4wZqOyxjunDcReX55oZOVgjBiNMeZgLPVrBxsjRmNk5YT2N6xWjDEHY8SG15g1jppOL084MzswKyVinMaw/Rjz579w0HlnMrp8HbE+dyij/GluBf+sSGFp2ggGxPg5tGIVjoCv2fVxO0VtDSUxqaGnBX47vpJCVpeF5xnXWoKN3SIaRJkBMus7Lzf+tjKG7UtR/fFe+6WYTeUu/AGT6kAwUEjcMdObkMQx239qdmixo8aw79h9OXHb9xTX+VmSH/6835oXuUzMe5bBAPRNiczqi+Bw0NvZtES+7UFv8Gcixt705mH8Gb/i4D/czMj6G5VVRvD7cGzZGjI8lczeUMVby0vILaph5Rtvcu//8nh/VRnLCne9BnirxxcT/nuyoqgOrz/At5uqumzqhEhHUtArItIOoUxvjJ2UMy8g/U9PMKp2S+j1PskxWCwd9yvWSE3n/w6w89L2N0Lbzs0JMPPwvjt9n/X40zn5ussYcfBYLpgyjDPZzPVTB3Ju6QLSPFX8e2lJsxdu3u3hjsC1ngAz0k8ONufqwgY/ht3BZbkf4PB7GZUVt+s3NHeMfoOxnXg2hxcuIWDCU4kTQ6+9uKiQTf96jTIzXN4al5mJYbVyS+Vchldt5qVTwgHvbn0tvfsxsbeDqQWLeDx9S7PHNCwWjLEH86czDuCNU/uHSrMbG9XLCilp2APeDs34tImzlqLY4PzjNHclbqw89/UaPu87ObRLrS0Y9EbM6R0+msP6xnBChp8HjhkY3j50X5zW8Gfw7I8F3PbFJm5LngZAQmzTjuETLr+Ee3/+R5PtVouBMWosw6uDN4zK65sPmaaJpSLyhs/89FHB06c1v5SS9FyGI4acRpnehPqfxTatdQ5U1y9FlBDT/JJrRnoWOUnh7+/jEmu4O6OQY/OC/Rj++UsJv/8yj9v8B4T2ady1fHc1Dnpv/3ILZ72xlr9+n991VSQiHUhBr4hIOzjN4K/PWEd9di8xmZwRQ0OvZ6Ynd/g5E6ccQdr5l4Se21J7EdOKzKuRnolhGCRNmcpFFxxHQoydEfsN5aDi4Pyt8maWx3hqYfgCLy93M9tigs23NpR3bVfP6Zefw5tHpZAc2/61gY0DD6GvM1jWuj6hDwCJ3jqcPpMZqcdH7OuoX5Jpnxtu5uErjiY9qX3BdnN6X34tN/72JAYff2zLYzUMjJwBGKlpjHMGA7fkRl1eh2XEQ6907H4vHl8Xre3pdlNcH/ROLFkOwOe+yHL8UNDbKOY17HZirp7JVceNYkRGoyz4sH0jyqXXlLpYUxr+vktq5rM3hu9HfCCyg/XvB9UHAyNGk1QfpPxjQSGldV5O+9caNiT1i9i/yhGcwz0sXUGv7CA2nnRXRehpli9YpfHNhgoCpsnHa8pZX+Zq4c1hZc7gz2h6YsvfY45RY0OPh/TPxDjuDA4t+iXUzX5H36wLlkEvL6zjwbl5VLn9BEyTV5cUsbakbcsqueMSm93+eW4Fq4rq2rVMU0vamiUX2V0KekVE2sFJsDwtvlGpZVxGeuhxVkJ01q81+g7klK1zABiY3v65h8YBE0itL6+97uONoYsZ0zTxPPMAc2vCQcgra8PlcxeMaX75o85i9Bu820slGQOGkJURLifOcJXz8KInGGZvetEaGxPMOBpWK0YHz2c2rFaM5NRW73/QBefwbMpaLt3+v9C23pkpGKnpOAI+PL7du4g0TTNiHeBWv8/jCmV6DypZGfFaQwl3rS0Om+lv3VJA6VkMrmm5Y21iTPM/W2lDBoUe/+eELA6bsj8QvCGV3Cv8eb+yoOVjx+EnJ0mNrGQHg4ZhJfyz0Zvg78sf8mpZurWC5xYWctOnm1i0izWhS+unZaRnpLS4jzFs39Bje0IcRloGfS66jFcSfuGW5a+EXrvLvgpbwMeKMi9vvfE5f/5yPfO21vDiwkJyN2znnZVl/OHzzbjbcDPMVX9z6oINn0ZsX1fmYuaXW/j9Z5t5ZUkR3tYsAVbP7Qs0WQ5u8YYiLnw7l282RmaQTdNke7WnzRl0kdZQ0Csi0g7uhkxvoy6csbHh7FRmlIJegIsP6sujLODg/ruRTR6+H6mB8F3733+2mZ+310JdLSs3FUXsWkgwAL48tYxzRndt0NtREofvE3p8xf4pZPtrSc9f12S/2NjuEwBZhu9H35NPYfiA8GeQnRIPqWk4Al68u5npfWlxEaf/aw0VzWT+d8rlpDi2F72sfka4I793xriCZfI+iw0brQuoDcPg1iMGcbVvBcc1WmarQZNGVvXSL76a5xOW85/jMohNi1wWLH1w+EbJ0i1lO741lMUbaPd0aQm/dE9GYjL0DZfgH5IRvnxe//2PoceLt0fOr31wbh5Pz98OwAeryngjMViWnJmykxuWQ0aGHrrr15u2HHQYiWdcwMQJI7lgw6c8c0CA8UdM5M9L/oYj4OOf/oHUGcG/Od9squKW+eHGbv9YUEhrNTR6yznmWPrULyfWYGzZGgDeXVnGlR9saFUw7fWb/Oaddfzf/7ZimiYvLy7igfeW8NB3wX+TFxaGx/afZSVc9t56rvrvBn79di53fLWFT9aWt3RokTZT0Csi0g7e+kZWjkZL+MQ6woFuZkL7y293xXrUyQy94Nety5q1wLDZGWFG3mXfWO6C6kp+zBjV7Huy9hne7vN1NzGNgtnkgQMwDj2GRF+wBDbJF74ZYHVE7+ZFe/UZPpQT877jnKolwZsrqenYAz48u9ls5r/1nZJXt7Ek0u92UxKTSpbdJH6HzsfDLeEgoC2jSxk1iuMuOJWDajeR7SxlRGV4PeVesc0HvUZCIlmnnUVsRtMbM6lTj+L2ZbNId1VQbglXMRy3/Uf+z7I81OQttpkGQyIQbFDXIPmgyVyzLtjp+NXAoND2wupwGb5r62bmba3hy/WVzPppGy8tDt8Q6r2TagIjLYPbls1iSHUehw+MzAhbz7iIs2/+Lf323w8jK4fhI/pz/ap/AzCl6Odmj/f1hkpWFTXtB9CcukDw71l8//78fuU/GVG5mZO3fcdDLOLu7BJuqD9XqdPHYz/kh0qUX5q/lfP+s4YPV0feUCpduACnL8AvBXVs2JjPe6vKmF8XF1oCrtoTwOsPYAYCvPFLEaWNbrgtL6xT0CsdSkGviEg7NKxL2ngJlsZ9q6JV3tyRBk89nAs3fMI5m74EgmuWzt9cySf9DiXB7+ZffMvr8/8vtH9WelJXDbXDxcSELzrjY+wYx51ObP06xn1iTP649EUGV29jZFbL3bG7imXCFK44djS/OveY4Ib6TK8nAE7v7s/r9bSxIVa504ffYiUzFixnXswNK/8dem14XLhMsSFr1VqGzcaEa67i7/v7uDE23Gm5V1zbbygZffpz8M038tdFT4S2nZtYxtU3X8SY888KdX8+dGDHz8WXvYMxInwz0GK1MC22kn0bLVcHkF9cRXGtlwqnj21z54a2v58b7tQ+PMGMXLqrGRNvvpFHzxzdpKrBMAyM+PC8W8tv/8CUaQfzSO4rXHtIPwbUbG/2eI9/v428qpb7MczdVMX2ak9oWbCkWDtD776fh07fj8v/cBn7XHABlnMu44jjD+Xv8x8AYN7WGi58O5d73l/Gx7mVOH0m328K30g13W6K3/5X6PmffwwGxCeULOZR97dMLF4GwPIiJ1SUkuQN3yCbVLyMgbUFFFR7qPP62VYVOV9fpD0U9IqItEPDnKbIi5fw42iWN3cUy9TjOOvSszjCHyxBLar18eC64J+FcfFuEi74LQk33Mmk4mXE+Vz02YvmOjoalaLH2SwY6Vmc4VrLhJKVnN0XJlx8IY8dHEdaOwKsaDOsVoxxkzDql3AyUtOxB/z4MTjvzbVc/9FGnl9YyI951dS0chkj0xu+qPS0Yb4eQGH9tXRWnBVj/GSmpoffP/yAfbl6zdttOl5jRnYOlolTyR4cLi1NaqG8eZcysom3hMcWl5UVqpY4bco+/NX1LUeN3b354rIXaxT0xtutWAcPZ0RluGN/rN/NNq+Ny99fz61fbCavpunP3tSKVdx9wohdnsrI7I2R2XvX+1mtWI4+lWH3PED8xEO5I/8Tztr8Nfct+TvjS1fxl0VPAlBQ5+d3H25sthHVylWbeeT7fO74cjO19ZneRIcVIzUdo0//iIoiy6HHkH3QQRHvX1Jrx2cJ/p7cXuliVVEdj3yfT2lFNSWO8E2k0kDwb+KIKRMZ3D+LE+unLizIq8a5fTuVjiSGVudx7gArV+6fxICa7XgDcP6buVzz4QZqdjLP1+kNsLnC3XXLtskeofv9NRcR6eZMnxdfoCHoDW9vvA5rgqP7l0kahgF9B5CSlhraZjH9OAI+jh9cn00YOpLf9/4B34ByYrtyjd4OZo0Nd0+NtQe/rvQ/3MMdP8/HmDQWowOXm4q61DQ2JIWzONtrPGxe4+ajNeVYjOASPAdkx3NA7wT2zYxr0vF74bYavl4Tnr9XWuUEUlt1atPjZtu2YsjYl74pwX9Ty2+u41fPvo5l/wOxjD2cY501DHvnaTj+DGDkzg/YAsuQfbhszttUZg/EYrTvGIZhYOsVbjYX26ghlvWok9l7ivclGozkXjwV/zUrY3MYlj6SwOB9cOT+HHr9qOIlfNx7EgAFNV5+dMZD/a/RfnVF9HUWc9n5R7T/pk0rZN/+Jy7EhII89ne7MMvToFGrgg1lboanh/9OmdVVLHv7PRhyPKVOP7PTgnOOd/b3yzjtAp645zZKbIk8se95VDkSecJYyItlKSztNZyZXwZvBJRWWhkfEyzPvn7VG8zNHkd+Ym8m7jcWVuSzb+VGbARYU+KiwFEK9GFYZgK/Omw4ZmAoIxY+y7eNzltU6212Pv/G4hr+8MVWvBgM7hXDQX0T6ZPkYNqQlpuFSc+koFdEpK1qqvDVl2raG83pZczB/N+ad0iYOLWLBtY+CdPP4uoX36BfrMl+BwwHDIzx5wDBQMF+3mV0/7x1GzXKXsTVB4FGfALGIUd11YjaLzWNOlsFADeteJ1D7ruXNSUulhbWsqygjjUlTnJLXbyzsgybxWBkRixjeidw0j69SHBYue+bvIjDrSmqbeYkTZmmifnKU6z3BDPO/QdkA8G1Rs+988bQfpbJRzL0oMMwbLuxzNTAoUy/8BTo02/XO+/sOBnhpZRiulGTMtkzDDj9LBpqAYzBwwkYv4Re+01yGR832vf7zAOIDXi4s18VI4f1w2objJGZTjQZMfUVLAOHBf+/MZf+v2xja0Iwa1zpimxSZ86bzS9pTW/3JNhbvulnJPdi4MPPMNDl5IlH76V65AQGnHYuv/nzXXxZV8xnfQ8BYEW5n4H1Qe/A8WO4O85H4LB9scXHYB5wMLaUVPrUFbOObLbag79z+tR3tTYsFg5PcfNio/OWO32UO308NX87vxmXxcDUGIprvcz4Io+GKquN5W421i+rN6l/IvGaoy+NKOgVEWmr6ip8luAf08blzYbVypjzzyEnJ4f8/JaXRelujIHDOP7//tjVw+hc/nAZnN26Z3frNewO7swsYtGStUwuWYatspTR2ZmMzo6HA4Klf6uK61haUMfSwjpWFDlZXuQkzm5h+si0JsdbWebFHzCx7mLeofndl3gWfM+8Q+8mNdbKsPSW1zDenYA3dIxGS7m0+xhp4aA3Pl7r8cpuSM/CbwSDQxsmjiOPZ8asf7EtPou3Bh0NwG/HZTB69AFdN8a+A7i+6m1Wbrcxa9h0CqrCy7LN3VTF2pJkVqZEllEf59uM1bLzagrDaoWERNLufJiG3yBDL7qYgQ/eGgp6ATYl5gCQdeJ0LLG20JxKIyYG47QLyVlQxNb4bB71BoP0Yf3DNwVSzr+Ux+6+lb+POIM1KYP4cn0liT+tZFFdMhXObTx64hDmfvUjELzpduH6T/jn0BND7//t++t59czhu/w9Jj3HHlS/JSLSTVRX4jMagt4uHou0T9resfRSgwnHHs5vJ/fDagYIPP0nTFe4W2uc3cL4nEQuHp/FoycM4o9HBLOlDU2v+tZGLmniDBic8e81/LClip3asIYlaftQbY3lsEHJe8bFZaNMb3K8Mr3SfoZh0L/+Z2d8LwNj2H4cceuNHBUf/rmZODy7q4YHgOGIYcTvb+PEMTkke2pYsLUKr9fH+pdn8cj3+Xxo5mAaFs72rePVlU/zTtLPXH3c6F0fuLlzDdkH65Encnnu+6FtK1OHYCfQbEm3MflIzvLkhp4PMKsZOTAz/HpKLwb/+VF+51kKwJbCCrZvC3bA3l7pZsWWMl6tCf4eP8S9hdPG9OGqNe/wm3UfAVDjCXDGv9fw5LzI5l73frqSFxYV8sW6Cv72UwEvLiqksEaNsnoCZXpFRNrIrK7EW9+4w74nzf2UEGPgMJ4pXoyn997TuMg47DjYvAFz7mcEnvsrlt/dEczI7KChnNsbMDEDAdLdlWxLCF6cH164mLnZ4wH4y7f5/POsBBoq+GNtlog1bM26GuZmjwPgiEF7yPy5tEyoX1UlOUaXQLJ7pl18HvGL1jD2qJOAYOlvSmq4eVOio3v8fbAfdChT/vkVn/abwu8/WMMm++SI1w84+nBSfnPybp/HOPsSTnr/nxz0zf1cO/EPeC12HIbZ7PJ6hsXKsCMP47zPvyAjOZajrroYizXy38tISGTA0Uezz9xN5CYPpDBlEAB1AYPbvw0GwI6Aj6vOmIwtJZHjT/JjfvYOzl++5M1Bwe72X2+oZJ+MODLibYxLNfloeUGTsZQ5ffzh0L74AiYWA/wBM3LqkuwV9BtfRKStqisblTd38Vik3fpNGN/VQ+hQhmHA+VdglhTCsoWYb76Icf4VTfaz1Zdz+wImeL2hzqsAQ6u3hYJegAvfDmZiDEwm5CRw4j5pOKwWRmfHU+v0sCBzP/ol2xmaFsOewEjPDDX2iWZDIekZLMNGcsiwyFLg2FgH1DcJ35211DtUzgCOcG3kU6awyR38vj+ybBn/S9sfgKGZHbM0m2GzY5x1CVkDh3P6F9/w5qBjSLP6Wt7/wCmc1ysdBo1oeQrE0JH0/3QZa1IGETBsJHrrqLEH1wM/rGQpv73oeFJS65fTs9ngpHM4v+Qpps27n2sn3oLPYuPZn4KB7uXplUD4Bt2F6z9hTv/JzN8K7y4t5JVlwXWBY6wGfztlCOnxe103ix5NQa+ISFtVV+EzMrC1cAdbpKsYNhuWK28h8NBMzNkfEcjqg+Wo6RH72OvLkL1+E7zuUNVCL7vJOPc2ZjVzXBODJfk1LMgPlk1/cMFIfrBk47XYOXJw6p7zc5CWCZQCwWVZRDqaMeVoLnr5PRIOPJj2divvaIZhMGLKwfzh61epSUpnXIKHrCOOYsQ771CaNZgER8eO03LQoZz22XtU533P2INGtbifYbHAsP12Pva4ePoTbq53RMEiSmJTiDN9XHX+kcT2ilxb2zAMjIuvp/fUXN78199ZXebhuRGnsykxh/+VBX/mc+qKuTiljIP6mMRsnsuLw08NBbwAbr/JiiInhw9S0Ls3UdArItJG22q8bEzM2WGNXpHuwYhPwHLdXQQe+D3mf17AzMjGGHNw6PWGTK83YILHg8diI8Hn5OULxuHekhjar39tQajrK4CvURuQMqePf6UG1+ucMjAp2l9ShzHSMvnb/oVUJmbsGXOQZY9jDBjCGbddH8w6diPGUdOZMuFQSE4NLcl2gt0OOQN38c72if/Dn7hy+SIYs/sVNftZqkOPhx1+CJc5N2CMPACjd58W32MMHo7ltofYd+H3PPLLAi72prDeHvz9NmVAEhOPOwzT62HaX+/m65r8UNOtVE81FY4ktpTWwqDkFo8vex4V5omItMKibTUs3FYDwC3meDxWBy7/Lt4k0kWM9Ews1/4R7HYCzz2MuXl96LXITK8nOO+ufv6afb8x2AI++tYW8peST7lu1X84fescrtr+dcTxL393LZX2YElk78Q9KxuSc8Bo9h3Se9c7irSTYbd3u+oHwzAwUtMi1iA39huHkdq0g3uHnC82DmPCoRj23W8YN/zE43l1/Qs8PdrH1Mn7YTlqOkbfXQfrhsWK5eDDsR53Gudt/CK0vaEc2rA7SDh2Ovf9/HcmlKzkJLZxR/V3ALirdtHIT/Y43eY2VCAQYObMmaSlpTFz5kyKiop4/PHHqa6uZsiQIVx33XXYbDa8Xi9PP/00GzZsICkpiRkzZpCVlbXrE4iI7Ib/q1/L9L3DY6iz7hnzF6VnMwYNx3LZzQT+/iCBp+/DcttfMdIyQhUKwTm9brwWK3YjOAnROO4M/vXNp1gmHIo9ZSpHVVeB1ULpG6/x90bH9tffM7/IugnD6B4lnCKydzL2HUPKXWNod7u8voM4rvRnnud0AFJ7hStaGDuRxNN/xR8PPAQjPYuNbxaDt74SRvYq3SbT+8knn9C3b9/Q83/+85+cdNJJPPXUUyQkJDB79mwAZs+eTUJCAk899RQnnXQSr7/+elcNWUR6CNPrDT3+ZFVp6PHk/ntOWaf0TMb4yRhnXQwVZQSeug/TVRdal3hFUR1el5tyR3LoDrhhtRJz1MnYU1KDz5OSMeITSRs6mHMbZUpOKP+Fd+o+4owTJ3buFyQi0kaG1Yp1Qnj94Nj4+IjXLMeehpEeTKDZ6yMjr19B796mWwS9paWlLF68mKOOOgoA0zRZsWIFkyZNAuCII45gwYIFACxcuJAjjjgCgEmTJrF8+XJMU9+YIhJFKxaFHj5fHLxDfIJrHTMP79vSO0S6DeOY0zCmHg95Gwk891dsZjCrW1Ln46af/fgsNhyWnf8dNQ44iHNcqzhty/8YUbmZC849Gttvf4+R3KszvgQRkd1iHHEi+1RuAiA7qeWS64ZlCJXp3ft0i/Lml19+mQsvvBCn0wlAdXU18fHxWOvXF0xLS6OsLLiwXllZGenp6QBYrVbi4+Oprq4mOVmTzUUkOkx703Lm4fsN6YKRiLRdcCmjK0NLGVnffRk4HICt9cuXHBJbs/Nj9ErH9qe/cXFFKVhtGEl7yLq8IiIEp3vcnTSbyiwbOSktT8kI9TxQ0LvX6fKgd9GiRaSkpDBkyBBWrFgRtfPk5ORE7dgdobuPTzqOPus9T/WmtUCAA8pz2ZzQm0pHEidMO5iMXi3fbNPn3DPsSZ9z4J7HKPrD5QS++RiOODy0Pdbv5ncXHo+jvqR5p/agr7cj7Umfs7SfPue93K337nqf1GSoBYvVoe+HvUyXB71r1qxh4cKFLFmyBI/Hg9Pp5OWXX6aurg6/34/VaqWsrIy0tGB3ubS0NEpLS0lPT8fv91NXV0dS0q7n1eXn50f7S2m3nJycbj0+6Tj6rPdMNQVFQAaOXmn8ed07mOMm43GOJN/ZfHZMn3PPsCd+zuZVt2F54PcR2wbGQ0ltHdTWddGourc98XOWttPn3DPs6nN21gX/rte63Pp+2APt7EZFlwe9v/rVr/jVr34FwIoVK/jwww+5/vrrefTRR5k/fz5Tpkzhm2++YcKECQAceOCBfPPNN4wYMYL58+czatSobtcWXkT2Lh6PBwB7fDz9736wi0cj0n5GeiaW86+A1eFtg/tldt2ARES6kdCcXlU373W6RSOr5lxwwQV89NFHXHfdddTU1DBt2jQApk2bRk1NDddddx0fffQRF1xwQRePVET2dh53sHtzjE032GQvsEPzqcHpcV00EBGR7sVis2AL+PAGunok0tG6PNPb2KhRoxg1ahQA2dnZPPDAA032cTgc3HTTTZ09NBHpwbyeYNBrt3bb+4QirRcTA4SX4RrcK7brxiIi0p1YrNgCfrwBa1ePRDqYruBERHbB4/EBEGPXH0HZCzgiu5EPSm3anVxEpEeyWLCbPpU374UU9IqI7ILbGwx6HTYFvbIXcERmdmNsuhQQEQHAYsXeyeXNmyvc1Hn9nXfCHkp/6UREdsFbv4a4PablBe1F9hgx4czu5ftoPq+ISIi1PujtpExvQbWH6z/eyJ1fbe2cE/ZgCnpFRHbBXb+US0xCfBePRKQDNCpvHjZAnZtFREIs1mAjK7NzGlcW564HYF2Zq1PO15Mp6BUR2YWGTK9DmV7ZG9jsoYd+zVsTEQmzWHAEfPg6Kej1vzWrU84jCnpFRHbJ7XQD4FD3ZtkLGIbBr/JmExfwMiRNTaxEREIsVmyd2MjKaYavKzSvN7q61ZJFIiLdjen3N1qySOv0yt7hnD9cxTmGgWHoe1pEpIFhtWAP+PFiwTTNqP6ONAMBam3hxoKL82s5dGBy1M7X0yltISKyM9UVeCzB+4MxCnplL2FYLAp4RUR2VN+9GaDOG8DtC7dx9vgDrClxEjA7KA1cUoizUdCbX17XMceVZinoFRHZmcpyPJbgHEhlekVERPZiFkso6L3wrVzO+c9a/AET0zT59/xN3PL5Zh7+Lr9jzrV1I8tTh4SefrOhghq3SpyjRUGviMjOVJSHMr2a0ysiIrIXq5/TC9CQ431pcRGBL//L96u2A7Agr7pDTrViczHzMw9ghL+Mk7d+yzYnPPjtNp79sYA/fZPHtioP/kDXdxv0+gN8uLpsj59zrDm9IiI7saWkmjcGHweovFlERGSvZrWyPS4jYtNHa8qp88VTGJcOgDcQbDoVb7e2+fDVbj/+gElqnI1FlRZwwHmj0tj/+08oTszkR0ayrDBY5rxgWw0JDgvHDUvl9P3SSY4Jnq/M6SPRYYn6jXh/wKTM6eO73GJeXlHFwvxa7p3WP6rnjCYFvSIiO/FlafiPWlaifSd7ioiIyB7NYqUwNr3J5tm2yGDv/Ddz2T87nhsP6UN6fOuvDX77/jqcPpMbJ/fmXcdwAPqPGIztvMuZ8cZLnH/4n0P7ZrrKKCaNd1eW8e7KMmKsBnce0Y8/fr0VgIn9ErluUh+SYtoefLfGCwsL+CS3MvT85+21UTlPZ1HQKyKyEzVl5ZCUzSNTUtv0h01ERET2MFYLLlswPDq1fAm9izaQ5qlifsZoyuPTGFuxjlcHHEPAsLCssI5L31vPS6cPJT3ejj9g8snactx+k6mDkslMiLxmMIsLcPqC5cqPzSsIbU9PcGA58iRiqyu599t/UO5I4vDYatiyns9yJvHciDMAcPvNUMAL8GNeDT++nctFYzM5c1TTQH13mKbJ1hVrwNE7YntpnXePvRZS0CsishPO+sX6Mntn7GJPERER2aMN2Yf9Fy5mmT+JCWedwv7Z8bBtEwfP/hhjv8GY5oGc+N1/+KuxPz9ljgbgr9/l88CxA/lq8UZeWOMB4LuNFTx+8tCIQ9fN/QqYEHqe5q7gRGM7VstIACyn/IoxYydBZm+MuHhM0+SEWY9z6Hd3Yyancs2+V1Jrj28y5Fd/Lub0/dKwdGRHfrcz2FTLEbl5ZZGTwwYp6BUR2auYponTCJYNxdvVxEpERGRvZliszDxjPJvK3YzOrg8w+w3GuOja4OuAY8IUbrj6bHK3DeBf+53OyuJM/jwnj5/yPKHjbK4MNqGyWoKBqGmaLMgthPoq6Qf9P7HPsCyMkZMizz8g3M3ZMAyMS28k5VIw8zbxu2ee46HRFwFw4YZP+OeQE0P7rit1MSIjbre+dtM0WVHkpHeSnfS6aqrt8WR4qjhx67d8lzWGiSUrOGDc0cCo3TpPV1HQKyLSgrXbK/ml1wgA7OrcLCIistdLdFjDAW8zDMMg4el/c8BT9/Fj0VrW9svkp7ya0OuHFS7h2+xxlDl9oRJn95ZNvJJ1KA7Tx1NDq8k+6FcYttaHYUa/QRxy3mm88/aTbE7sTc6Q3nzoqabSkQQES513J+g1TZPPf1jF3zYFr3XOG2ChJLYXg6nh9DG9Ob1uA9j9GJkdW0bdmRT0ioi04Jb/Fex6JxEREelRDJsdy+W/Z9hfHg1tswe8nGkvwOMPBsDLCuuYNiQFgK0b8iiLyeTY+Ep6T57cvnOOORjbmIMZYppQVsxzD99BRY2bKyffztsrShmZEUd2op0BqTFtPnbxgp94ZZ0DbMHA+Y0twQWbkqwmllMvaNd4uxulLkRERERERNrASEpmSp9Yjtv2A3+pncur5f/l3OPGM9lSBsDijSVUuHzkljp5sSAWgD5pibt/XsPASM8i5oHnyEyODW3/05w8rvt4I++uKG3ynqUFtfx3dVnouS9gYprBniVef4C/rIE6WxxXrH2XRxY+Ftovve3xc7elTK+ISDPMmqquHoKIiIh0Y7EXX8vVq5fC+MkY9Y2khozZD/t2L78UBfjNO+vq9wyWIaf1SuqwcxuGgTFhCjgjt3+xqogpA5NIibURa7MQ2L6VO2cHlxs6IDue5Fgbv/9kA+kJDm48JIevFqxjnSWFiRVrOOGOm2HuZwzN3cr65P5YYndvnnB3okyviEhztm7s6hGIiIhIN2YkJGIceEgo4AWw9h3A0Oo8qgJNw6zeWb069vzjDuG0Lf8jwVvHH5e+wEElK9juNrjigw1c8FYu76wo5fP3/xfa/4ZPNnH/RysodQVYW+ri6g838E6BhVi/mysPHYjFEYNx1Cn8YeU/mVCykrP2z+rQ8XYlBb0iIs0w8zaR4SoH4O7Bri4ejYiIiOwRBo/gjqWzOHPz1xGbjy//hX2yd7+8OcKgYfzm8OG8Ps7PhLvuYXzlhtBLvoDJqz8X8/fkgyPekuuJoX9tAVeufZd0VwVHFi3moZE+0seMBYIZ5N633MMdB6WQ02/vCXpV3iwi0pytG/AZfci2eBh/yNiuHo2IiIjsAYzYOBJPP5++3/4S2vZ6zScknHVuREa4Q85lGBhTjg49PzrdQ82GT/m6z0EMqd7GD1ljAHD4veQ4iyl3JJHhquDSjGr26zuM49Z+iuWU8zGGjow8bv/BGP0Hd+hYu5qCXhGRZlRsy6diSDIH9m552QIRERGRHRmjxmGduyT0PPHKmzrlvPbzr+Ssrz7g7HFpBGa9yp2ORFakDmWsv4iZvh/BG8A44CCMQ0/DsFjgpE4ZVregoFdEZAfmulVsrA626x/Sa+9p4iAiIiKdoO9AjPruyJ3JSMvAOOcyACy3PMhlf76LNXF9mHLqsVgPvL3Tx9OdKOgVkW7r89wK0uNtTOjbwXNgdiHwyVtsSOwLwJC0vahfv4iIiESdYRgMchUBcGB81/QFMVLTGHLH/zHE5YTsnC4ZQ3eiRlYi0i1V1rp49qcC7vsmj4XbajrtvObm9bBsIRtz9gVgaFrsLt4hIiIiEmnALXfxLPO55fh9u2wMRmoaRu++HT6XeE+koFdEuqXcL8JdD//y7Ta2VLh363hlTh/fb6ni07XlVLl8Le4X+ORNlqYOY2niIBLsFrIS7Lt1XhEREel5jN596XvBxcTGqWKsO1B5s4h0S2UlFRAPI6u3sjqpPwu31TAgte1/OEyvF9Mw+MvcbawuCa7gPndTFQ8cO7DJvr6CbbxQlcWnY0/HEoDLxmXo7qiIiIjIHk5Br4h0SzVOD8TDfmW5rE7qT7XH367jBO7/PT/E9Gd1/5ND21YWO5vd98vFG/m03xT62TzMOHo4w9PVxEpERERkT6fyZpE9xPyt1Xy3uaqrh9EpTI+bmtpg44fezlIAatoR9JoBP978rbyWMRlbILKkuaDa02T/3MLg3OFbxiUr4BURERHZSyjoFdlDPDB3Gw9/l9/Vw+gcWzdSYw02kOrjLgOg2h1o+3Fqqvm47yEUxqVzfOFC/rTk2dBL/15a3GT3ytpgIJw2sF87Bi0iIiIi3ZGCXpEuZLZyDbfAhrW73Kekzos/0PlrwkWDuSmX/PgMAHL69wGgurqWr9ZX8PvPNrG1snVNrcyqCj7oP5UEvJx7wfGM/vWveWf7a/SpK+GHLdXUevy4fQHyKt24XG6qLDHYTD+JDmvUvjYRERER6Vya0yvShWZ+sYUEh4W7juy/0/1cP8wG4wgAvH4TuzWyuVJpnZfL3lvPgTkJuzzWnqBkUx7LU6cyMsVC2tCDSF5Vw/KKRJbPLwDg2o82sk9GHPdM60e8veUA9Zv15VQ6kphkKye5z/7QpzeWsmKO/G4B/xpyAr96Kzdif2tSP5JNr5pXiYiIiOxFlOkV6SL524pYXeJkUX4tXv/OS3crS8NzeZ2+pvvmLVkGwKL8WrY3M1d1T/M/dzKmYWHaiAyM3v2ociQCEOsLZ3jXlDj5ZmPzc5zdvgAvLSrkibzgvNzBWSmh14zefTku/8fQ80xXGQeU55LpKiPNU8mR1qZlzyIiIiKy51LQK9JFir//NvT4s9wKlhfWtbhvZVVN6LHT27ShU9kn74cef7SmvGMG2IXWGKkATB6QDGn/396dBkZVpXkYf25VkspG9oSE7BCQHUE2ZVNZtAGXRgVRVFzGBboZdVqlnRZxHLHbFuMI2Nhja2tv025DK20rowioqAQEiUYSlhASkpB9TyWpqjsfSgsjaINUpUL4/z5V3Tr31HvqtTBvnXPPjSO8w/3Z/DRgL6s++TVXF24A4L09Fcc9P6egnL/tOfo5zDpvwNEXBwwl4vK5XHXwHQDu69jJcusXPFP1N56p/TvXn3vsrYxERERE5PSl5c0iftLW2AzuvZp4dkcFNqvBby/rR1RI56+l6XLS2HJ0hrO1o/NMb+6RZp4cfI3n+fr8WjKibEzPivJZ7L5U39LOjoh+BJhOetmsEBTJv+c+x+74wZx33x0Yk0Yx78td7Nx/kL2k09LhPGaJc8WGf0D8BAAuiHXRy3b0MzWsVoyL5jB33wpmNr9N9G1LtJxZREREpAfTTK+In7Q5zWOev5pXfWzD2mrqrUdvn1PS0M5be2txmSaff5DD8g2FAGQ2Hva0Wf1JuW+C9jGny+ShN/MBcBhWDMPAsFgY9O/LmffT67AYBkZaX4yJMxhSX4gLgzvfPEh5YzuVzR38ZP0Bth9uorTDXQQPtJdz1XlZx32voMX3E3Prv6rgFREREenhVPSKdIEOp4uShs47DtuPc23uW3vrqG7p6HywspyGwHDP019/UMpvth1hy8EG3iqowWEJ4P6Kt3liVCDXHPiHp92J7gzdnbz5p9fZ3xYIgNU8+vkYMXEYYb2OPg8LZ5SlHoAjTR28va+O9z7ZQ3F9Ow9vKmFX7EBCHa08PO8ckiOCunYQIiIiItKtqOgV6QIPbyph8RuFlNS1eo61tXXecCrWXke70+QfBXWdjv/HF05eyJp9TJ/ZW8t435ZObEcjY+9cgmXsZOa0fklqs3uW95HNhyltOH02tXLW1/AXZyrhHS3c/cWfyE44/L3th4zoz+PbnwTgtbwa/lQW6Hmt0hbF2UYdQeG9vuNsERERETlT6JpeER+z/34VnwVOB+Av24spqa7nznOTaGt3dGo3vWwbL2VOZ/c3NrRqr6vhU/vRpc0W04nLcC/djWpvJNDVwTVhVZ4lugG/eILlSxdxz6gl5ByGnMNNZEbbiLBZ6dMriMZ2JyOTwoiwWRmTHN6tlvaW7D1Ec2Ao51prmHz7QkjJ+N72louvoF9dDXxrX6/MxsM0BoZy0YAwn8UqIiIiIqcPFb0iPmSaJtv3VcIg9/OXdpYAMCjWxospUwGYVvoJX0T1ZUbpR+zoPYz8qiS+qGhhX7Wd2NwPgf4AXBvfyvQdL2M7mI/N1QEpmRAWjuWKf/O8nxEaTsw5YxhZmce7SWMBKKx1L6v+rNxdTH9Q1AiAxYAnZ2aSHmXz+edwIpbku3f1cgWHYqT1O6FzjAnTYIt7fNNLPyGz6TAXB1RAVCyWcff6LFYREREROX2o6BXxpdZmNscPP+bw73ZWeR5PHZLE4tAmCJ3M8KI89qYncf//HfrqVXfBO6FiF1fMn4d1xsh/+pbGlTdy7c8XkdFURlxbHb8aegMAqc3l1Ab1YlrZNtalXYDLhKUbirj7vD4MjA9x75TsJ6br6HRtHSdRhMf3ZmrZK7ybNJY5F48hKe5CjKRUH0QoIiIiIqcrFb0iPmTW15IbdfzdgwF6my2kTZ2KxWbFrKli7rKf8F7iaGpskZ425ztLuPP6qRiWE1uKbISFE7P8CS5ps1P/y58T0d7Ejw5v5eo0CwSH4KjZhtOw8kHC2dQSwX9uLuHc1F4snZx8TF8fFTeSHmljfUEto5LCGJ0cfpx39IK6o/fUvXHwiS9LNoJDuS2ymmtCPyNu2DxfRCYiIiIipzkVvSI+1FhZRWtACGPrCph8+BMikpPJ2v0u7yWOZlzTAeJuXYLx1QyrERNH0DX/wu2vv8aKYTeS0VTKrLrdTPvZTzGsJzcLa8TEARC5+D6ef/0vWG7/VyxxCQAETruUG3+XzYW7c7hrjHtp9EfFjTS3OwkLOvo+da0Ofrnl6GZSf8+v5W/XDjylz+O7HCyuACyMqClgYNaskzrX9i93nczcsIiIiIicYVT0ivhQeWEJ0J/emWlMPDeOpPMvouzQPGbt3wOjlh6zkZQx+GxGv/Qcq6L20fusGIIG3nTSBW+n/gYMJeBnj3Q+FhOP9Z4VZPz+v1i85yXe7z2S3dH9WfjaPob1DuWB81MwDIPq3N1AaKdz250ugqze3fTdVV3Jsx8XQ3g6lw6KxbD4b5m1iIiIiPQ8umWRiA+Vl7uv3e2dFIcx6jws4b0womIxzplw3J2TjahYLNl/JG3WbGyjz8MIj/BZbMZ1P2HaoN7cnv8qAO1Okx2lzeyrsQNQv2fPMef8ZXcVr+VVs3xjMW/sqaGpzXlMm5P12Uc7+Tw8ndFmFedcMP6U+xMRERER+SYVvSI+VNHg3lm4d3zkP2l5VFfdRsiwWjGuupHE8EAG1x3wHN96yL27c6nl2GtrX8ur4YWdlewsa+bZHRXct6EIu8N1SnF8XuX+jGaNSjulWW0RERERkeNR0SviA2WN7VQ2t3PEcN9jNzE8yM8RHZ8RGITlJ7/g/tznWfvRCoLNDk/R+5Ej+rjnpDQf8TwuaWjn8Q9KMU3zhN9zd3kz2R+Wctmf9nDDq3t5JbA/hmnSv1+fUxuMiIiIiMhx6JpeES9r7XBx++vumdPY6LMASAgP9GdI38tITif86f8h9IFFDKwvYpeRxaoPDrHPEklMWz13F7xEWWAkawbOBeBe6x4SN2fjMiz8bPSd5ByGiuYOeh+nsP9tTjkfFDXSJyKIrJhg5g6N5YF3iz2v19ndy6Mvad1DL9ugrhmwiIiIiJxRNNMr8gMVVLXicB07w5m77TPP4+rgKCLNNoIDuvdXzbBYMYaNJqHFfQ3yO0Ut2K02hoR0MPSe+5gaWM31+9cz0VpN2k23YvvtOmxX38yFR3YAsPrjcs9sb12rg6c/KaestoW/F9RR3+bky8pW3siv5bpX93neM8jZzqvv38/q7U9w4/lndf2gRUREROSMoJlekR9gW3EDj2wppU+vIB6dkUZrh4sAi0F8WCDFu3IhaqynbUDXXKJ7yozRE0jY+1anY8mJ0RixCVhuupM5FeUYo871vGa5YBaTP/2UF4HdR1rIPdLC8MQwXt5+iLcPtfNxYQ3f9bva6Ko8rr30XKxz/0RKux0j4vhLqUVERERETpWKXpGT1GB38Lst+4BQShvbueEbs5eXplh5L2wIALNKPuDvKRMZGXHqOxx3ib4Dibf/j+dpkKuDscOzADBSMiEl85hT4qZfzMMvruWBkbfzwLvFPHRhKh27d0DUMOqd7oL38kObyGgqJbX5CC/2m8XYqi+4+JarCUhNcncSHOL7sYmIiIjIGcvvRW9VVRVr1qyhrq4OwzCYNm0aM2fOpKmpiezsbCorK4mPj+euu+4iPDwc0zR5/vnn2blzJzabjUWLFtG3b19/D0POEM3tTv7t9XwqvnX/2q+9XuKEwDBGBjRy077XGRYTwPCrFnRxlD+MYbEQ6Wz1PP/LBREExH5/QWoMH8PgoP/mrPqD5Edm8ODGYoga1qlNwsRJnJ8ahLl7Ow8lx0LiNRjJ6T4Zg4iIiIjIt/m96LVarVx33XX07duX1tZWli5dyvDhw9m0aRPDhg3j8ssvZ926daxbt44FCxawc+dOysvLeeqpp9i7dy/PPvssK1as8PcwxEt2ljVT3tjOjwZ0z+Wu723cTkVHJOMrc9kTkU6d7eh9dEfUFPBZzAAAbpk+GOv05xgfHdtltyDyhhRXAwAjzGoCUiac0DmW/kO4L+dFbpqwzHOsb2MJER3NAIydPgkjIQYjo7/3AxYRERER+Sf8XvRGR0cTHe0ucEJCQkhOTqampoacnByWL18OwJQpU1i+fDkLFixg+/btTJ48GcMwGDBgAM3NzdTW1nr6kNOXy25n+Ub3zr5TMiMIDfT/PVudLpM3C2oZkhBKw4ebeavMhhEawb+MjCNix3rse/ewLnUKvVxtTC35mBf6zeJHDV+Qcu1jwLH3ue3u4q6/jf9+7nEi7n7whM8xrv8JMaPO45XV99EUGMIXkX0ZXrePsLMGYaRkYEmI8WHEIiIiIiLfz+9F7zdVVFRQWFhIVlYW9fX1nkI2KiqK+vp6AGpqaoiLi/OcExsbS01NzT8tevv06d73AO3u8XWFnSsegsBJALhCouiT0MvPEcE7uw/x7I6Kr56lQxhMTQ1l+NXXwvxrcbU0c/tfnyP84h/jsrfy823v0+uq+zAs371bc7fOdZ8+JM+YffLnpaZizpiN/dOP6GsYBI8cj2H1/48W/tSt8yxeozyfGZTnM4PyfGZQns9M3abotdvtrFy5koULFxIa2vl6ScMwTnmJaGlp6Smd70t9+vTp1vF1la1FdeDeN4lH3vycFdPT/L40eONbm8FIBuCimt1MuGgiQ7OSOufroitoMQFbGEy6mKby8u/sr8fnOvmr6+uPHPFvHH7W4/MsgPJ8plCezwzK85lBee7Zvu8HjW5R9DocDlauXMmkSZMYN24cAJGRkZ5ly7W1tUREuK+djImJoaqqynNudXU1MTFaPtkT7I4b6HmcV9lKvd1JVIj//hOtbung/74qeNdUv07y5VdipOnXQRERERGR08l3r8HsIqZpsnbtWpKTk5k9++iyytGjR7N582YANm/ezJgxYzzHt2zZgmmaFBQUEBoaqut5ewDT5aImIIxQRytXFL0LwMG6NgBcpklj2w+77U9ZYztbDzXgMs2TOu/JraXc9L/7ARjbdICUJfdipGmXcBERERGR043fZ3rz8/PZsmULaWlp3HPPPQDMnz+fyy+/nOzsbDZu3Oi5ZRHAyJEj+fTTT1myZAlBQUEsWrTIn+GLt7Q00RQQQrjhYgh1vAq8s7+OpF6BPLKphEP17fx4cAwJYYEnvLOz02Xy0IYDlNkhMTyQX5yfQmqk7Zh2pmlSUG2nzeHC6nLQlp/He6Xu9zBMFzefHXfMOSIiIiIicnrwe9E7cOBAXnrppeO+tmzZsmOOGYbBLbfc4uuwpKs1NtAUGEofSxsjIg3Sm0p5v6gP7xc1epq8llcDwOSMCMKC3Jsk2R0ulm8sJis2mJtHJXS6Bnhn/mHK7O7H5U0d/GR9IQBnxYUwPjWckAALW4sb2V3e8q1g3AVvSvMR7siykjhuoo8GLSIiIiIivub3olcEoKO+HrvVRi+LHUtyGjN2fsJ/D/gxAHOKNvJG6iQ6LIEAVDZ38F5hPUV1bYS0NvJlpYsvK1vJiLIxtW8kR5o6+Ki4kbd3VgIhPLbjKVYOvpYjIbEA5Fe1kl/VekwMFx/eSrgVXCkZWHtFMHtUPFGDBnfZZyAiIiIiIt6nole6hYb6BiCa8EADI3MAvT/6zPPaJZMGc/4r/8Wm3qN4Lf1C1u8q4f9KHQDEOJvB6r4f7qqPy/nDrkoa25w4TYAQZlTvYsDd93L/r/+T/Ih0gp3t5EVlktRaRYcRQKDpJLqtgbPjg4iYOQsGj8CwnNm32hERERER6UlU9Eq3UFnvXoccF2yBgcOIdr3oeS1m0vlE9+3LyDWreQ08BS9AjTWM5JYKLi3ewvqUSZS5Yglz2ElvLmNi60FmXHUxRkoG6TffRroJZnkJEwvzMQZnQHUl9OmHMXoiRMX4/fZIIiIiIiLifSp6pVuorHdfVxsfEYJhsxGTngpAAC4AjOQ0howeRkZlKQfDO982KDHUyvQEmL73eWhsgIwsjIQkjGsWYMTEu88f7t792xgxpquGJCIiIiIi3YCKXukWypodEAi949z3Y44aM56HX15L0oKFnjaWKRdz5conWTn4Wu4t/QeNjS00xPbh3BsXYI2c4KfIRURERESkO1PRK93CgY4gCISMFPftgSzjpjBswFCM6FhPGyM2nglpEYz4YhW9fr4C86P3MAaPxDjObYhERERERERARa90A58UN/BxaCbhzlbiewV7jn+z4PUcu/Vn9AIMixXjojldGKWIiIiIiJyOLP4OQLq3DfvqWPbuIRrbnN/brrK5g5KGtpPuf0dJIyu2lAKQabT8082kDItVuyuLiIiIiMgJ00yvfCdHawtrPikHYEdpE+dnRh63XbvTxS3r9gPwt2sHnlDfZY3t/PGzSj4oagRgcN0B5k0f7oWoRUREREREjtJMr3yng7//nefxvmo7JfVt/Hb7Eepa3bcMcrhMXvm8mlv+mudpV2d3eF4H6HC6jum3zeHiwQ0HPAVvYmsVj4yNYNiAFF8NRUREREREzlCa6ZXvlFfnAvdmyuyraubvdjtvFrXy9t5arh0Rz2t5NV8tez76n9ENr+4D4A9XZPGLd4spqW9j2QWpnJ0Uxp7KVu7bUNTpPUbUFDAjJQjLmIu7algiIiIiInIG0UyvdPJmQS1/+7IG0+Xky+AkAAJcDorr26nJ+wIAhwte2Fn5vdf5rtteRFFdG04Tnt5WTnVLBz/fcLBTm2sO/IMHWz5kwuRRPhuPiIiIiIic2TTTK508k3MEgFnJFgoiUolqbyS1uZzc6P4csERhMV24DPdvJZcWb+b11CnH7efVog4AxlZ+zjaGctP/7gfcm1SdXZPPsup3sPz4OowRY30/KBEREREROWNpplc8nBVlnseFh2uoCo4mo62KqPYmACpCYriwLMfTJm7IEK46+A4Agxs6L1sGCHR18NP8l0hsrQJgRunHPL79SZaOj8e6fJUKXhERERER8TnN9IpH08GDQDQA7x+oA0JJj7LhqnNvOJXZVMotiS2881V7IymVKz/+I+fl7KY4cyR5pHfq74J4g/CLL2PlP/6L/Ig0Ri5ZgmFciJGc1lVDEhERERGRM5yKXvGosx/ddflvdaEAZKQm0DvnfzkcmsBtV5xHcNwEEv+QQ3lIHEFhodiWP0Wmy6Tu6d8c09/Vk8/CEjqUsLPHMaqlGSMl/Zg2IiIiIiIivqSitxsoqW2hodVBVMjRdDS1Oymua+Os+BAshtElcVTXt3Z6HuxsZ+jQ/sQFXMkDba1YkhMA+I9dz7AhaRzT5i/GsLhjC2+sAvfL/PajR2jvP4zY0HsBMNKzuiR+ERERERGRb1PR2w3c/tedRNkMHrsow3PshZ0VbNhXTy+blXsn9mF4YpjP46iorANbGrcVvEZ0WwOZY0aREDkcxnXerCph0b+xoM3uKXgBUhx1nFV/kBnRHST84pcQHevzeEVERERERP4ZFb3dQHpUMNuK67nsT3uItFm5fmQ8G/bVA9DY5uSBd4s5PzOCyekR9IsJJjTIQpDVvQdZS4eTkAALxinOBputLRyqboI+kDJxAsMSQmHYOcdtaww++5hjIbf+jEff+AuWS3+KERZ+SrGIiIiIiIh4i4rebmD+x89SGTWRwrAk6tucrPq4HHDf2qc0JJ6KkBg2FTawqbDBc05mtI240AByDjczNCGE4YlhmIDTZZIcEUS702R8SjgRwSeW4tacrWyKP5tIo4NBF07CsJ7cxt5GSgbWO35+UueIiIiIiIj4morebmDYqGGs/Gs2W+OHsSVhJEPqCzkcGs+llsMEFBSQG9WPIyGxvNVnPOHONsqDYyisbaOwtg2Azyta+byi9Zh+X8sLZGhCKH0igrhsYAxWi0Gbw8XKD0v5pKSJUUlhlDa2M7R3KCVFYTQHhjIvLYDAkyx4RUREREREuisVvd1AxHV30DRkNBPefZ0J9iJMmwMcxVhuugvz/bfpXVOJeXAf8z98GwMwgdqgCDYmnkNKSyUW00VudD/A4O0+4xlYf5DPo7Moa+ygrNG9THrroUaSewVRUddMXp0TgE/LmgEob6oHSzTRbQ386Oxh/vkQREREREREfEBFbzdgGAZGUgrGgkXHvnbxFQCYpgntbWCaUHWE2C8/48q8XdBQinH2OMZFRmB+tJGb970OCUnwWRlFYb3ZmDiGN1Ins7fazt5qe6e+R1V/SVNAKNPKtnFWQxEJqX0IDR/bFUMWERERERHpEip6TxOGYYAt2P0kJQMjJQOmX9apjTlrrqet6/0NZJQc5MbDRVy/eSn1gWG8kzQOS1AQl+17G7s1iIizz8EsPYRx4UyMyKlwnA2qRERERERETmcqenuQb+7gbJk04+jjw4eILS1i7gfvYJl5JSRfgw0wwiP8EKWIiIiIiEjXUdF7BjCS0zCS02DMJH+HIiIiIiIi0qW0Ta+IiIiIiIj0WCp6RUREREREpMdS0SsiIiIiIiI9lopeERERERER6bFU9IqIiIiIiEiPpaJXREREREREeiwVvSIiIiIiItJjqegVERERERGRHktFr4iIiIiIiPRYKnpFRERERESkx1LRKyIiIiIiIj2Wil4RERERERHpsVT0ioiIiIiISI9lmKZp+jsIEREREREREV/QTK+IiIiIiIj0WCp6RUREREREpMdS0SsiIiIiIiI9lopeERERERER6bFU9IqIiIiIiEiPpaJXREREREREeqwAfwfQE1VVVbFmzRrq6uowDINp06Yxc+ZMmpqayM7OprKykvj4eO666y7Cw8M5fPgwTz/9NIWFhVx99dVceuml39uPdB/eynV7ezsPPvggDocDp9PJ+PHjmTt3rp9HJ1/zVp6/5nK5WLp0KTExMSxdutRPo5Jv82aeFy9eTHBwMBaLBavVyi9/+Us/jky+yZt5bm5uZu3atRQXF2MYBnfccQcDBgzw4+jka97Kc2lpKdnZ2Z5+KyoqmDt3LrNmzfLX0OQbvPl9Xr9+PRs3bsQwDFJTU1m0aBFBQUF+HJ14k+7T6wO1tbXU1tbSt29fWltbWbp0Kffccw+bNm0iPDycyy+/nHXr1tHU1MSCBQuor6+nsrKSnJwcwsLCPF/A7+onJSXFzyOUr3kr16Zp0tbWRnBwMA6Hg2XLlrFw4UL98dRNeCvPX1u/fj379+/39CXdgzfzvHjxYh599FEiIiL8OCI5Hm/mefXq1QwaNIipU6ficDhoa2sjLCzMj6OTr3n7321w/2B52223sWLFCuLj4/0wKvk2b+W5pqaGBx54gOzsbIKCgnjiiScYNWoU559/vn8HKF6j5c0+EB0dTd++fQEICQkhOTmZmpoacnJymDJlCgBTpkwhJycHgMjISLKysrBarSfUj3Qf3sq1YRgEBwcD4HQ6cTqdGIbRhSOR7+OtPANUV1fz6aefMnXq1K4bgJwQb+ZZui9v5bmlpYUvv/ySCy+8EICAgAAVvN2IL77Pubm5JCYmquDtRryZZ5fLRXt7O06nk/b2dqKjo7tuIOJzWt7sYxUVFRQWFpKVlUV9fb3nCxQVFUV9ff0P6ke6p1PNtcvl4r777qO8vJyLLrqI/v37+zpk+QFONc+///3vWbBgAa2trb4OVU6BN/7tfuSRRwCYPn0606ZN81ms8sOdSp4rKiqIiIjg6aefpqioiL59+7Jw4ULPD5jSfXjrb7EPP/yQCRMm+CpMOUWnkueYmBguueQS7rjjDoKCghgxYgQjRozoirCli2im14fsdjsrV65k4cKFhIaGdnrNMIwTnsn7vn6ke/BGri0WC7/+9a9Zu3Yt+/fv59ChQ74KV36gU83zjh07iIyM9PwqLd2TN77PDz/8ML/61a+4//77efvtt8nLy/NVuPIDnWqenU4nhYWFzJgxg8ceewybzca6det8GLH8EN76W8zhcLBjxw7Gjx/vizDlFJ1qnpuamsjJyWHNmjU888wz2O12tmzZ4suQpYup6PURh8PBypUrmTRpEuPGjQPcSypqa2sB9zUIJ3Kt1/H6ke7FW7n+WlhYGEOGDGHXrl2+CFd+IG/kOT8/n+3bt7N48WKefPJJPv/8c5566imfxy4nzlvf55iYGM+5Y8aMYd++fb4LWk6aN/IcGxtLbGysZ1XO+PHjKSws9G3gclK8+f/nnTt3kpmZSVRUlK/ClR/IG3nOzc0lISGBiIgIAgICGDduHAUFBT6PXbqOil4fME2TtWvXkpyczOzZsz3HR48ezebNmwHYvHkzY8aM+UH9SPfhrVw3NDTQ3NwMuHdy3r17N8nJyb4LXE6Kt/J8zTXXsHbtWtasWcOdd97J0KFDWbJkiU9jlxPnrTzb7XbP8nW73c7u3btJS0vzXeByUryV56ioKGJjYyktLQXcfzRro8nuw1t5/pqWNndP3spzXFwce/fupa2tDdM0yc3N1d9hPYx2b/aBPXv2sGzZMtLS0jzLKebPn0///v3Jzs6mqqqq0/bpdXV1LF26lNbWVs+GRk888QSHDh06bj+jRo3y5/DkG7yV68rKStasWYPL5cI0Tc4991yuvPJKP49OvuatPH9zydUXX3zBG2+8od2buxFv5bmxsZHHH38ccC+BnThxInPmzPHn0OQbvPl9PnjwIGvXrsXhcJCQkMCiRYsIDw/38wgFvJtnu93OokWLWL16tS4z62a8meeXXnqJrVu3YrVaycjI4PbbbycwMNDPIxRvUdErIiIiIiIiPZaWN4uIiIiIiEiPpaJXREREREREeiwVvSIiIiIiItJjqegVERERERGRHktFr4iIiIiIiPRYKnpFRERERESkx1LRKyIi0k099dRT3HPPPTgcjk7Hc3NzmT9/Pvn5+X6KTERE5PSholdERKSbuvnmm2lqauLll1/2HGtpaeE3v/kNl1xyCWeddZZX3+/bxbWIiEhPEODvAEREROT4wsLCuOOOO3j00UcZM2YMWVlZvPDCC4SFhTF9+nSefPJJPvvsMwAGDBjAwoULSUpKAqC8vJwXX3yRvXv3Yrfb6dOnD3PnzuWcc87x9L948WKmTJlCVVUV27ZtY/jw4dx9991+GauIiIivGKZpmv4OQkRERL7bc889R25uLvPmzWPVqlU88sgjZGdnM2DAAGbPnk1AQABvvPEGu3fvJjs7G5vNxsGDBykoKGDgwIEEBQWxdetWXn75ZR5//HGSk5MBd9Hb1NTEnDlzGDduHKZpeopmERGRnkLLm0VERLq5BQsWYJom2dnZzJs3jwMHDmCaJosWLSI9PZ3k5GRuvfVW7HY7O3bsACAjI4MZM2aQlpZGYmIic+bMoW/fvnz88ced+h40aBCXXXYZiYmJKnhFRKRH0vJmERGRbi4oKIhLLrmE559/ntmzZ/Pcc89RUVHB9ddf36lde3s7R44cAcBut/PKK6+wY8cO6urqcDgcdHR0kJaW1umcfv36ddk4RERE/EFFr4iIyGnAarViGAYWiwXTNMnIyODOO+88pl14eDgAf/jDH9i1axfXXXcdSUlJ2Gw2Vq9efcxmVTabrSvCFxER8RsVvSIiIqeZzMxMPvzwQ3r16kVYWNhx2+zZs4cpU6Ywfvx44OgssJYwi4jImUbX9IqIiJxmJk2aRGRkJI899hh5eXlUVFSQl5fHiy++SFlZGQBJSUls27aNAwcOcOjQIVatWkV7e7ufIxcREel6mukVERE5zdhsNh566CH+/Oc/88QTT9DS0kJ0dDRDhgzxzPzecMMNrF27lgcffJCwsDBmzpxJR0eHnyMXERHperplkYiIiIiIiPRYWt4sIiIiIiIiPZaKXhEREREREemxVPSKiIiIiIhIj6WiV0RERERERHosFb0iIiIiIiLSY6noFRERERERkR5LRa+IiIiIiIj0WCp6RUREREREpMdS0SsiIiIiIiI91v8DfYFfE/ofZVYAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":"plt.title('Prediction on Test set', fontsize=14)\nplt.xlabel('Year', fontsize=14)\nplt.ylabel('Price', fontsize=14)\nplt.plot(date_test, Y_test, label='actual')\nplt.plot(date_test, yhat_test, label='predicted')\nplt.legend(fontsize=14)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-11-02T13:56:01.261763Z","iopub.execute_input":"2021-11-02T13:56:01.262022Z","iopub.status.idle":"2021-11-02T13:56:01.530915Z","shell.execute_reply.started":"2021-11-02T13:56:01.261988Z","shell.execute_reply":"2021-11-02T13:56:01.530276Z"},"trusted":true},"execution_count":47,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x432 with 1 Axes>","image/png":"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\n"},"metadata":{}}]}]}