




'''simple docstring'''







import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
   A     =pd.read_csv('sample_data.csv', header=None)
   A     =df.shape[:1][0]
   # If you're using some other dataset input the target column
   A     =df.iloc[:, 1:2]
   A     =actual_data.values.reshape(len_data, 1)
   A     =MinMaxScaler().fit_transform(actual_data)
   A     =10
   A     =5
   A     =20
   A     =len_data - periods * look_back
   A     =actual_data[:division]
   A     =actual_data[division - look_back :]
   A    ,      A     =[], []
   A    ,      A     =[], []

   for i in range(0, len(train_data) - forward_days - look_back + 1):
      train_x.append(train_data[i : i + look_back])
      train_y.append(train_data[i + look_back : i + look_back + forward_days])
   for i in range(0, len(test_data) - forward_days - look_back + 1):
      test_x.append(test_data[i : i + look_back])
      test_y.append(test_data[i + look_back : i + look_back + forward_days])
   A     =np.array(train_x)
   A     =np.array(test_x)
   A     =np.array([list(i.ravel()) for i in train_y])
   A     =np.array([list(i.ravel()) for i in test_y])

   A     =Sequential()
   model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
   model.add(LSTM(64, input_shape=(1_28, 1)))
   model.add(Dense(forward_days))
   model.compile(loss='mean_squared_error', optimizer='adam')
   A     =model.fit(
       x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
   )
   A     =model.predict(x_test)










'''simple docstring'''







import os
from distutils.util import strtobool





def 						snake_case_							(_a	:							Union[str, Any]    ,   _a	:							List[Any]     ):
    for e in env_keys:
        UpperCAmelCase			       =		int(os.environ.get(_a    ,   -1     )     )
        if val >= 0:
            return val
    return default





def 						snake_case_							(_a	:							Dict    ,   _a	:							Any=False     ):
    UpperCAmelCase			       =		os.environ.get(_a    ,   str(_a     )     )
    return strtobool(_a     ) == 1  # As its name indicates `strtobool` actually returns an int...







def 						snake_case_							(_a	:							str    ,   _a	:							Optional[Any]="no"     ):
    UpperCAmelCase			       =		os.environ.get(_a    ,   str(_a     )     )
    return value




