




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_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	List[Any]								=np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowerCamelCase   )]   )
				__magic_name__	:	Optional[int]								=np.array(lowerCamelCase   )
				__magic_name__	:	Tuple								=np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()	,     lowerCamelCase   )   )	,     x.transpose()   )	,     lowerCamelCase   )
				return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]   )




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Dict								=(1, 2, 1)
				__magic_name__	:	Any								=(1, 1, 0, 7)
				__magic_name__	:	List[Any]								=SARIMAX(
				    lowerCamelCase	,     exog=lowerCamelCase	,     order=lowerCamelCase	,     seasonal_order=lowerCamelCase   )
				__magic_name__	:	int								=model.fit(disp=lowerCamelCase	,     maxiter=600	,     method="""nm"""   )
				__magic_name__	:	Any								=model_fit.predict(1	,     len(lowerCamelCase   )	,     exog=[test_match]   )
				return result[0]




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	int								=SVR(kernel="""rbf"""	,     C=1	,     gamma=0.1	,     epsilon=0.1   )
				regressor.fit(lowerCamelCase	,     lowerCamelCase   )
				__magic_name__	:	Any								=regressor.predict(lowerCamelCase   )
				return y_pred[0]




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				train_user.sort()
				__magic_name__	:	Any								=np.percentile(lowerCamelCase	,     25   )
				__magic_name__	:	Optional[Any]								=np.percentile(lowerCamelCase	,     75   )
				__magic_name__	:	Optional[Any]								=qa - qa
				__magic_name__	:	List[str]								=qa - (iqr * 0.1)
				return low_lim




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	int								=0
				__magic_name__	:	Tuple								=0
				for i in list_vote:
								if i > actual_result:
												__magic_name__	:	Dict								=not_safe + 1
								else:
												if abs(abs(lowerCamelCase   ) - abs(lowerCamelCase   )   ) <= 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)
							UpperCAmelCase_		:				Union[str, Any]									=     [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
							UpperCAmelCase_		:				Tuple									=     pd.DataFrame(
							    data_input, columns=["total_user", "total_even", "days"]
							)
							UpperCAmelCase_		:				List[Any]									=     Normalizer().fit_transform(data_input_df.values)
							# split data
							UpperCAmelCase_		:				Optional[int]									=     normalize_df[:, 2].tolist()
							UpperCAmelCase_		:				Dict									=     normalize_df[:, 0].tolist()
							UpperCAmelCase_		:				Any									=     normalize_df[:, 1].tolist()

							# for svr (input variable = total date and total match)
							UpperCAmelCase_		:				List[Any]									=     normalize_df[:, [1, 2]].tolist()
							UpperCAmelCase_		:				List[Any]									=     x[: len(x) - 1]
							UpperCAmelCase_		:				Union[str, Any]									=     x[len(x) - 1 :]

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

							UpperCAmelCase_		:				Tuple									=     total_date[len(total_date) - 1 :]
							UpperCAmelCase_		:				Dict									=     total_user[len(total_user) - 1 :]
							UpperCAmelCase_		:				Optional[int]									=     total_match[len(total_match) - 1 :]

							# voting system with forecasting
							UpperCAmelCase_		:				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
							UpperCAmelCase_		:				List[Any]									=     "" if data_safety_checker(res_vote, tst_user) else "not "
							print("Today's data is {not_str}safe.")










import numpy as np




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

				__magic_name__	:	List[Any]								=False
				__magic_name__	:	List[Any]								=0
				__magic_name__	:	List[str]								=0
				__magic_name__	:	str								=1E12

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

								# Check convergence.
								__magic_name__	:	List[str]								=np.abs(lambda_ - lambda_previous   ) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												__magic_name__	:	int								=True

								__magic_name__	:	Optional[Any]								=lambda_

				if is_complex:
								__magic_name__	:	Union[str, Any]								=np.real(lambda_   )

				return lambda_, vector




def 		lowerCAmelCase_   (	):
				__magic_name__	:	Optional[int]								=np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]   )
				__magic_name__	:	Any								=np.array([41, 4, 20]   )
				__magic_name__	:	Optional[Any]								=real_input_matrix.astype(np.complexaaa   )
				__magic_name__	:	str								=np.triu(1J * complex_input_matrix	,     1   )
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__magic_name__	:	List[Any]								=np.array([41, 4, 20]   ).astype(np.complexaaa   )

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												__magic_name__	:	Union[str, Any]								=real_input_matrix
												__magic_name__	:	List[Any]								=real_vector
								elif problem_type == "complex":
												__magic_name__	:	Optional[Any]								=complex_input_matrix
												__magic_name__	:	Tuple								=complex_vector

								# Our implementation.
								__magic_name__				, __magic_name__	:	Any								=power_iteration(lowerCamelCase	,     lowerCamelCase   )

								# Numpy implementation.

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




