






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     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowerCamelCase_							)]							)
				lowercase__  					=     np.array(lowerCamelCase_							)
				lowercase__  					=     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     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     (1, 2, 1)
				lowercase__  					=     (1, 1, 0, 7)
				lowercase__  					=     SARIMAX(
				    lowerCamelCase_						,  exog=lowerCamelCase_						,  order=lowerCamelCase_						,  seasonal_order=lowerCamelCase_							)
				lowercase__  					=     model.fit(disp=lowerCamelCase_						,  maxiter=600						,  method='''nm'''							)
				lowercase__  					=     model_fit.predict(1						,  len(lowerCamelCase_							)						,  exog=[test_match]							)
				return result[0]






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     SVR(kernel='''rbf'''						,  C=1						,  gamma=0.1						,  epsilon=0.1							)
				regressor.fit(lowerCamelCase_						,  lowerCamelCase_							)
				lowercase__  					=     regressor.predict(lowerCamelCase_							)
				return y_pred[0]






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				train_user.sort()
				lowercase__  					=     np.percentile(lowerCamelCase_						,  25							)
				lowercase__  					=     np.percentile(lowerCamelCase_						,  75							)
				lowercase__  					=     qa - qa
				lowercase__  					=     qa - (iqr * 0.1)
				return low_lim






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     0
				lowercase__  					=     0
				for i in list_vote:
								if i > actual_result:
												lowercase__  					=     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)
							A__      :     str       =					[[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
							A__      :     List[Any]       =					pd.DataFrame(
							    data_input, columns=['total_user', 'total_even', 'days']
							)
							A__      :     int       =					Normalizer().fit_transform(data_input_df.values)
							# split data
							A__      :     Optional[Any]       =					normalize_df[:, 2].tolist()
							A__      :     str       =					normalize_df[:, 0].tolist()
							A__      :     Optional[Any]       =					normalize_df[:, 1].tolist()

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

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

							A__      :     Any       =					total_date[len(total_date) - 1 :]
							A__      :     int       =					total_user[len(total_user) - 1 :]
							A__      :     Dict       =					total_match[len(total_match) - 1 :]

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












def     a     (		lowerCamelCase_							):






				'''simple docstring'''

				if p < 2:
								raise ValueError('''p should not be less than 2!'''							)
				elif p == 2:
								return True

				lowercase__  					=     4
				lowercase__  					=     (1 << p) - 1
				for _ in range(p - 2							):
								lowercase__  					=     ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
							print(lucas_lehmer_test(7))
							print(lucas_lehmer_test(11))




