


'''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__		:					Any												=  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__		:					List[Any]												=  df.iloc[:, 1:2]
      A__		:					Union[str, Any]												=  actual_data.values.reshape(len_data, 1)
      A__		:					Optional[int]												=  MinMaxScaler().fit_transform(actual_data)
      A__		:					Dict												=  10
      A__		:					Optional[Any]												=  5
      A__		:					int												=  20
      A__		:					List[str]												=  len_data - periods * look_back
      A__		:					Union[str, Any]												=  actual_data[:division]
      A__		:					int												=  actual_data[division - look_back :]
      A__						,					A__		:					Any												=  [], []
      A__						,					A__		:					Optional[int]												=  [], []

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

      A__		:					List[str]												=  Sequential()
      model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
      model.add(LSTM(64, input_shape=(128, 1)))
      model.add(Dense(forward_days))
      model.compile(loss="""mean_squared_error""", optimizer="""adam""")
      A__		:					List[str]												=  model.fit(
          x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
      )
      A__		:					Dict												=  model.predict(x_test)










'''simple docstring'''



from numpy import exp, pi, sqrt



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				float = 0.0      ,    UpperCAmelCase_    :				float = 1.0							)     ->		int:
     return 1 / sqrt(2 * pi * sigma**2							) * exp(-((x - mu) ** 2) / (2 * sigma**2)							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






