





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

			a__					:			Optional[Any]          =     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__					:			List[str]          =     model.predict(x_test)










'''simple docstring'''




from math import loga




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	if a < 0:
		raise ValueError('Input value must be a positive integer'							)
	elif isinstance(UpperCAmelCase_					,				UpperCAmelCase_							):
		raise TypeError('Input value must be a \'int\' type'							)
	return 0 if (a == 0) else int(loga(a & -a							)							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



