



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

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

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









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				0
			for ch in input_str:
						lowercase__					  =				ord(SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				pow(2						,  SCREAMING_SNAKE_CASE_  )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()




