




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

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

     A_         =							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''')
     A_         =							model.fit(
         x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
     )
     A_         =							model.predict(x_test)








def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			bool:





				"""simple docstring"""


				__lowerCamelCase									=       0
				for ch in input_str:
								__lowerCamelCase									=       ord(UpperCamelCase__ )
								__lowerCamelCase									=       pow(2					,	UpperCamelCase__ )

								# 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()


