





'''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__				:   List[str]      =pd.read_csv('''sample_data.csv''', header=None)
      A__				:   Optional[Any]      =df.shape[:1][0]
      # If you're using some other dataset input the target column
      A__				:   Union[str, Any]      =df.iloc[:, 1:2]
      A__				:   Tuple      =actual_data.values.reshape(len_data, 1)
      A__				:   Dict      =MinMaxScaler().fit_transform(actual_data)
      A__				:   Union[str, Any]      =10
      A__				:   Optional[Any]      =5
      A__				:   List[Any]      =20
      A__				:   Optional[Any]      =len_data - periods * look_back
      A__				:   List[str]      =actual_data[:division]
      A__				:   Tuple      =actual_data[division - look_back :]
      A__				:   List[str]      =[], []
      A__				:   List[Any]      =[], []

      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__				:   Tuple      =np.array(train_x)
      A__				:   Any      =np.array(test_x)
      A__				:   Dict      =np.array([list(i.ravel()) for i in train_y])
      A__				:   str      =np.array([list(i.ravel()) for i in test_y])

      A__				:   Union[str, Any]      =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__				:   List[str]      =model.fit(
          x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
      )
      A__				:   Union[str, Any]      =model.predict(x_test)












import numpy as np




def 			__magic_name__				( A		:					np.array   ):






			'''simple docstring'''







			return 1 / (1 + np.exp(-vector   ))




def 			__magic_name__				( A		:					np.array   ):






			'''simple docstring'''







			return vector * sigmoid(1.7_02 * vector   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






