






import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       np.array([[1, item, train_mtch[i]] for i, item in enumerate(__snake_case							)]							)
     __lowerCAmelCase             =       np.array(__snake_case							)
     __lowerCAmelCase             =       np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()       ,			__snake_case							)							)       ,			x.transpose()							)       ,			__snake_case							)
     return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]							)





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       (1, 2, 1)
     __lowerCAmelCase             =       (1, 1, 0, 7)
     __lowerCAmelCase             =       SARIMAX(
         __snake_case       ,			exog=__snake_case       ,			order=__snake_case       ,			seasonal_order=__snake_case							)
     __lowerCAmelCase             =       model.fit(disp=__snake_case       ,			maxiter=600       ,			method="nm"							)
     __lowerCAmelCase             =       model_fit.predict(1       ,			len(__snake_case							)       ,			exog=[test_match]							)
     return result[0]





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       SVR(kernel="rbf"       ,			C=1       ,			gamma=0.1       ,			epsilon=0.1							)
     regressor.fit(__snake_case       ,			__snake_case							)
     __lowerCAmelCase             =       regressor.predict(__snake_case							)
     return y_pred[0]





def    __lowerCAmelCase   (    __snake_case							):
     train_user.sort()
     __lowerCAmelCase             =       np.percentile(__snake_case       ,			25							)
     __lowerCAmelCase             =       np.percentile(__snake_case       ,			75							)
     __lowerCAmelCase             =       qa - qa
     __lowerCAmelCase             =       qa - (iqr * 0.1)
     return low_lim





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       0
     for i in list_vote:
          if i > actual_result:
               __lowerCAmelCase             =       not_safe + 1
          else:
               if abs(abs(__snake_case							) - abs(__snake_case							)							) <= 0.1:
                    safe += 1
               else:
                    not_safe += 1
     return safe > not_safe


if __name__ == "__main__":
 # data_input_df = pd.read_csv("ex_data.csv", header=None)
 lowerCamelCase      :       List[str]    =						[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
 lowerCamelCase      :       Optional[int]    =						pd.DataFrame(
     data_input, columns=['''total_user''', '''total_even''', '''days''']
 )
 lowerCamelCase      :       List[Any]    =						Normalizer().fit_transform(data_input_df.values)
 # split data
 lowerCamelCase      :       Union[str, Any]    =						normalize_df[:, 2].tolist()
 lowerCamelCase      :       Union[str, Any]    =						normalize_df[:, 0].tolist()
 lowerCamelCase      :       Optional[Any]    =						normalize_df[:, 1].tolist()

 # for svr (input variable = total date and total match)
 lowerCamelCase      :       Dict    =						normalize_df[:, [1, 2]].tolist()
 lowerCamelCase      :       Tuple    =						x[: len(x) - 1]
 lowerCamelCase      :       str    =						x[len(x) - 1 :]

 # for linear regression & sarimax
 lowerCamelCase      :       int    =						total_date[: len(total_date) - 1]
 lowerCamelCase      :       Optional[Any]    =						total_user[: len(total_user) - 1]
 lowerCamelCase      :       List[str]    =						total_match[: len(total_match) - 1]

 lowerCamelCase      :       List[Any]    =						total_date[len(total_date) - 1 :]
 lowerCamelCase      :       Optional[int]    =						total_user[len(total_user) - 1 :]
 lowerCamelCase      :       str    =						total_match[len(total_match) - 1 :]

 # voting system with forecasting
 lowerCamelCase      :       List[Any]    =						[
     linear_regression_prediction(
         trn_date, trn_user, trn_match, tst_date, tst_match
     ),
     sarimax_predictor(trn_user, trn_match, tst_match),
     support_vector_regressor(x_train, x_test, trn_user),
 ]

 # check the safety of today's data
 lowerCamelCase      :       Tuple    =						'''''' if data_safety_checker(res_vote, tst_user) else '''not '''
 print('''Today\'s data is {not_str}safe.''')














import numpy as np





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case = 1E-12       ,			__snake_case = 100       ,			):
     assert np.shape(__snake_case							)[0] == np.shape(__snake_case							)[1]
     # Ensure proper dimensionality.
     assert np.shape(__snake_case							)[0] == np.shape(__snake_case							)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(__snake_case							) == np.iscomplexobj(__snake_case							)
     __lowerCAmelCase             =       np.iscomplexobj(__snake_case							)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(__snake_case       ,			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.

     __lowerCAmelCase             =       False
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       1E12

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

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

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

          __lowerCAmelCase             =       lambda_

     if is_complex:
          __lowerCAmelCase             =       np.real(lambda_							)

     return lambda_, vector





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

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

          # Our implementation.
          __lowerCAmelCase      ,			__lowerCAmelCase             =       power_iteration(__snake_case       ,			__snake_case							)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          __lowerCAmelCase      ,			__lowerCAmelCase             =       np.linalg.eigh(__snake_case							)
          # Last eigenvalue is the maximum one.
          __lowerCAmelCase             =       eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          __lowerCAmelCase             =       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(__snake_case							) - np.abs(__snake_case							)							) <= 1E-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_power_iteration()






