

'''simple docstring'''



def       snake_case   (	snake_case      : str					,		snake_case      : str			) ->  Dict:


    """simple docstring"""


    lowerCAmelCase 						=							len(snake_case			)
    lowerCAmelCase 						=							len(snake_case			)
    lowerCAmelCase 						=							[[False for _ in range(m + 1			)] for _ in range(n + 1			)]
    lowerCAmelCase 						=							True
    for i in range(snake_case			):
        for j in range(m + 1			):
            if dp[i][j]:
                if j < m and a[i].upper() == b[j]:
                    lowerCAmelCase 						=							True
                if a[i].islower():
                    lowerCAmelCase 						=							True
    return dp[n][m]


if __name__ == "__main__":
     import doctest

     doctest.testmod()












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

							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])
							_UpperCamelCase					      =   np.array(train_x)
							_UpperCamelCase					      =   np.array(test_x)
							_UpperCamelCase					      =   np.array([list(i.ravel()) for i in train_y])
							_UpperCamelCase					      =   np.array([list(i.ravel()) for i in test_y])

							_UpperCamelCase					      =   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')
							_UpperCamelCase					      =   model.fit(
							    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
							)
							_UpperCamelCase					      =   model.predict(x_test)




