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

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

					lowercase    :							Optional[int]      =					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")
					lowercase    :							int      =					model.fit(
					    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
					)
					lowercase    :							List[Any]      =					model.predict(x_test)



'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A = 100	)       ->	int:
	_snake_case   						=		n * (n + 1) * (2 * n + 1) / 6
	_snake_case   						=		(n * (n + 1) / 2) ** 2
	return int(square_of_sum - sum_of_squares	)


if __name__ == "__main__":
					print(F'''{solution() = }''')

