


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							:	str					      =				df.shape[:1][0]
						# If you're using some other dataset input the target column
						snake_case							:	Tuple					      =				df.iloc[:, 1:2]
						snake_case							:	str					      =				actual_data.values.reshape(len_data, 1)
						snake_case							:	Dict					      =				MinMaxScaler().fit_transform(actual_data)
						snake_case							:	Dict					      =				10
						snake_case							:	Any					      =				5
						snake_case							:	Optional[Any]					      =				20
						snake_case							:	Dict					      =				len_data - periods * look_back
						snake_case							:	Dict					      =				actual_data[:division]
						snake_case							:	int					      =				actual_data[division - look_back :]
						snake_case      ,					snake_case							:	int					      =				[], []
						snake_case      ,					snake_case							:	int					      =				[], []

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

						snake_case							:	Tuple					      =				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''')
						snake_case							:	Optional[Any]					      =				model.fit(
						    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
						)
						snake_case							:	Tuple					      =				model.predict(x_test)








from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    List[str]       ):  # noqa: E741
							while r - l > 1:
														a__					=					(l + r) // 2
														if v[m] >= key:
																					a__					=					m
														else:
																					a__					=					m  # noqa: E741
							return r






def 							__lowercase       (			__lowerCAmelCase							:    list[int]       ):
							if len(__lowerCAmelCase       ) == 0:
														return 0

							a__					=					[0] * len(__lowerCAmelCase       )
							a__					=					1

							a__					=					v[0]

							for i in range(1 ,   len(__lowerCAmelCase       )       ):
														if v[i] < tail[0]:
																					a__					=					v[i]
														elif v[i] > tail[length - 1]:
																					a__					=					v[i]
																					length += 1
														else:
																					a__					=					v[i]

							return length


if __name__ == "__main__":
						import doctest

						doctest.testmod()




