

'''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__":
    lowerCAmelCase     :   str						     =  pd.read_csv('sample_data.csv', header=None)
    lowerCAmelCase     :   str						     =  df.shape[:1][0]
    # If you're using some other dataset input the target column
    lowerCAmelCase     :   str						     =  df.iloc[:, 1:2]
    lowerCAmelCase     :   Optional[int]						     =  actual_data.values.reshape(len_data, 1)
    lowerCAmelCase     :   Dict						     =  MinMaxScaler().fit_transform(actual_data)
    lowerCAmelCase     :   Tuple						     =  10
    lowerCAmelCase     :   Tuple						     =  5
    lowerCAmelCase     :   Dict						     =  20
    lowerCAmelCase     :   Optional[Any]						     =  len_data - periods * look_back
    lowerCAmelCase     :   Union[str, Any]						     =  actual_data[:division]
    lowerCAmelCase     :   Union[str, Any]						     =  actual_data[division - look_back :]
    lowerCAmelCase     ,       lowerCAmelCase     :   Optional[int]						     =  [], []
    lowerCAmelCase     ,       lowerCAmelCase     :   Union[str, 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])
    lowerCAmelCase     :   Any						     =  np.array(train_x)
    lowerCAmelCase     :   List[Any]						     =  np.array(test_x)
    lowerCAmelCase     :   str						     =  np.array([list(i.ravel()) for i in train_y])
    lowerCAmelCase     :   Any						     =  np.array([list(i.ravel()) for i in test_y])

    lowerCAmelCase     :   List[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')
    lowerCAmelCase     :   Any						     =  model.fit(
        x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
    )
    lowerCAmelCase     :   Optional[Any]						     =  model.predict(x_test)









def    A_  (							a					,   a  ):

      """simple docstring"""



      return numa ^ numa < 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()






