import numpy as np







def  __UpperCamelCase    (						A ,							A ,							A = 1e-12 ,							A = 100 ,							):
     assert np.shape(A							)[0] == np.shape(A							)[1]
     # Ensure proper dimensionality.
     assert np.shape(A							)[0] == np.shape(A							)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(A							) == np.iscomplexobj(A							)
     UpperCamelCase__										=      np.iscomplexobj(A							)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(A ,							input_matrix.conj().T							)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     UpperCamelCase__										=      False
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     UpperCamelCase__										=      1e12

     while not convergence:
          # Multiple matrix by the vector.
          UpperCamelCase__										=      np.dot(A ,							A							)
          # Normalize the resulting output vector.
          UpperCamelCase__										=      w / np.linalg.norm(A							)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          UpperCamelCase__										=      vector.conj().T if is_complex else vector.T
          UpperCamelCase__										=      np.dot(A ,							np.dot(A ,							A							)							)

          # Check convergence.
          UpperCamelCase__										=      np.abs(lambda_ - lambda_previous							) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               UpperCamelCase__										=      True

          UpperCamelCase__										=      lambda_

     if is_complex:
          UpperCamelCase__										=      np.real(lambda_							)

     return lambda_, vector







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
     UpperCamelCase__										=      np.array([41, 4, 20]							)
     UpperCamelCase__										=      real_input_matrix.astype(np.complexaaa							)
     UpperCamelCase__										=      np.triu(1j * complex_input_matrix ,							1							)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     UpperCamelCase__										=      np.array([41, 4, 20]							).astype(np.complexaaa							)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               UpperCamelCase__										=      real_input_matrix
               UpperCamelCase__										=      real_vector
          elif problem_type == "complex":
               UpperCamelCase__										=      complex_input_matrix
               UpperCamelCase__										=      complex_vector

          # Our implementation.
          UpperCamelCase__						,       UpperCamelCase__										=      power_iteration(A ,							A							)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          UpperCamelCase__						,       UpperCamelCase__										=      np.linalg.eigh(A							)
          # Last eigenvalue is the maximum one.
          UpperCamelCase__										=      eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          UpperCamelCase__										=      eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max							) <= 1e-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(A							) - np.abs(A							)							) <= 1e-6


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     test_power_iteration()




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

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

     __magic_name__								=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''')
     __magic_name__								=model.fit(
         x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
     )
     __magic_name__								=model.predict(x_test)



