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

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

'''simple docstring'''







def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):
						_SCREAMING_SNAKE_CASE				= int(_SCREAMING_SNAKE_CASE  )
						if n_element < 1:
												_SCREAMING_SNAKE_CASE				= ValueError("a should be a positive number"  )
												raise my_error

						_SCREAMING_SNAKE_CASE				= [1]
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= (0, 0, 0)
						_SCREAMING_SNAKE_CASE				= 1
						while index < n_element:
												while hamming_list[i] * 2 <= hamming_list[-1]:
																		i += 1
												while hamming_list[j] * 3 <= hamming_list[-1]:
																		j += 1
												while hamming_list[k] * 5 <= hamming_list[-1]:
																		k += 1
												hamming_list.append(
												    min(hamming_list[i] * 2	,			hamming_list[j] * 3	,			hamming_list[k] * 5  )  )
												index += 1
						return hamming_list


if __name__ == "__main__":
						_snake_case				:   int     =				input("""Enter the last number (nth term) of the Hamming Number Series: """)
						print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
						_snake_case				:   str     =				hamming(int(n))
						print("""-----------------------------------------------------""")
						print(F"The list with nth numbers is: {hamming_numbers}")
						print("""-----------------------------------------------------""")