


from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

lowerCAmelCase__  :Optional[int]             =							TypeVar('''T''')

class 		__a				(    Generic[T]      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      data
									_UpperCAmelCase					=      None






		def __str__(      self						)			->	Any:




									"""simple docstring"""





									return f'''{self.data}'''



class 		__a				(    Generic[T]      ):
		def __init__(      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      None
		def __iter__(      self						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.top
									while node:
																yield node.data
																_UpperCAmelCase					=      node.next
		def __str__(      self						)			->	List[Any]:




									"""simple docstring"""





									return "->".join([str(_lowercase						) for item in self]						)
		def __len__(      self						)			->	int:




									"""simple docstring"""





									return len(tuple(iter(self						)						)						)
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									return self.top is None
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      Node(_lowercase						)
									if not self.is_empty():
																_UpperCAmelCase					=      self.top
									_UpperCAmelCase					=      node
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									if self.is_empty():
																raise IndexError('pop from empty stack'						)
									assert isinstance(self.top      ,   _lowercase						)
									_UpperCAmelCase					=      self.top
									_UpperCAmelCase					=      self.top.next
									return pop_node.data
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									if self.is_empty():
																raise IndexError('peek from empty stack'						)

									assert self.top is not None
									return self.top.data






		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      None


if __name__ == "__main__":
		from doctest import testmod

		testmod()




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__":
		lowerCAmelCase__  :Dict             =							pd.read_csv('''sample_data.csv''', header=None)
		lowerCAmelCase__  :int             =							df.shape[:1][0]
		# If you're using some other dataset input the target column
		lowerCAmelCase__  :Union[str, Any]             =							df.iloc[:, 1:2]
		lowerCAmelCase__  :Optional[int]             =							actual_data.values.reshape(len_data, 1)
		lowerCAmelCase__  :Tuple             =							MinMaxScaler().fit_transform(actual_data)
		lowerCAmelCase__  :str             =							1_0
		lowerCAmelCase__  :Optional[Any]             =							5
		lowerCAmelCase__  :List[str]             =							2_0
		lowerCAmelCase__  :Any             =							len_data - periods * look_back
		lowerCAmelCase__  :Union[str, Any]             =							actual_data[:division]
		lowerCAmelCase__  :Tuple             =							actual_data[division - look_back :]
		lowerCAmelCase__      ,  lowerCAmelCase__  :Optional[int]             =							[], []
		lowerCAmelCase__      ,  lowerCAmelCase__  :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])
		lowerCAmelCase__  :Optional[Any]             =							np.array(train_x)
		lowerCAmelCase__  :Any             =							np.array(test_x)
		lowerCAmelCase__  :Dict             =							np.array([list(i.ravel()) for i in train_y])
		lowerCAmelCase__  :Tuple             =							np.array([list(i.ravel()) for i in test_y])

		lowerCAmelCase__  :Optional[int]             =							Sequential()
		model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
		model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
		model.add(Dense(forward_days))
		model.compile(loss='''mean_squared_error''', optimizer='''adam''')
		lowerCAmelCase__  :List[Any]             =							model.fit(
		    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
		)
		lowerCAmelCase__  :Optional[Any]             =							model.predict(x_test)
