


import unittest

import numpy as np

def 	lowerCAmelCase__			(      a__:		np.ndarray      ,  a__:		np.ndarray      ,  a__:		np.ndarray      ,  a__:		np.ndarray | None = None      ,  )  ->		np.ndarray:


							'''simple docstring'''





							_UpperCAmelCase					=      np.shape(a__	)
							_UpperCAmelCase					=      np.shape(a__	)
							_UpperCAmelCase					=      np.shape(a__	)

							if shape_a[0] != shape_b[0]:
														_UpperCAmelCase					=      (
														    'Expected the same number of rows for A and B. '
														    F'''Instead found A of size {shape_a} and B of size {shape_b}'''
														)
														raise ValueError(a__	)

							if shape_b[1] != shape_c[1]:
														_UpperCAmelCase					=      (
														    'Expected the same number of columns for B and C. '
														    F'''Instead found B of size {shape_b} and C of size {shape_c}'''
														)
														raise ValueError(a__	)

							_UpperCAmelCase					=      pseudo_inv
							if a_inv is None:
														try:
																					_UpperCAmelCase					=      np.linalg.inv(a__	)
														except np.linalg.LinAlgError:
																					raise ValueError(
																					    'Input matrix A is not invertible. Cannot compute Schur complement.'	)

							return mat_c - mat_b.T @ a_inv @ mat_b

class 		__a				(    unittest.TestCase      ):
		def      UpperCAmelCase__    (      self						)			->	None:




									"""simple docstring"""





									_UpperCAmelCase					=      np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]						)
									_UpperCAmelCase					=      np.array([[0, 3], [3, 0], [2, 3]]						)
									_UpperCAmelCase					=      np.array([[2, 1], [6, 3]]						)

									_UpperCAmelCase					=      schur_complement(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      np.block([[a, b], [b.T, c]]						)

									_UpperCAmelCase					=      np.linalg.det(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      np.linalg.det(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      np.linalg.det(_SCREAMING_SNAKE_CASE						)

									self.assertAlmostEqual(_SCREAMING_SNAKE_CASE      ,   det_a * det_s						)
		def      UpperCAmelCase__    (      self						)			->	None:




									"""simple docstring"""





									_UpperCAmelCase					=      np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]						)
									_UpperCAmelCase					=      np.array([[0, 3], [3, 0], [2, 3]]						)
									_UpperCAmelCase					=      np.array([[2, 1], [6, 3]]						)

									with self.assertRaises(_SCREAMING_SNAKE_CASE						):
																schur_complement(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self						)			->	None:




									"""simple docstring"""





									_UpperCAmelCase					=      np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]						)
									_UpperCAmelCase					=      np.array([[0, 3], [3, 0], [2, 3]]						)
									_UpperCAmelCase					=      np.array([[2, 1, 3], [6, 3, 5]]						)

									with self.assertRaises(_SCREAMING_SNAKE_CASE						):
																schur_complement(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		unittest.main()




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)
