






'''simple docstring'''






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        _SCREAMING_SNAKE_CASE  (				A  :       Any  ,		A  :       int  ,		A  :       List[str]  ,		A  :       Tuple  ,		A  :       Optional[Any]  )    -> float:







			"""simple docstring"""
			__snake_case				:      Union[str, Any]     							= np.array([[1, item, train_mtch[i]] for i, item in enumerate(_lowercase  )]  )
			__snake_case				:      List[str]     							= np.array(_lowercase  )
			__snake_case				:      Dict     							= np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()  ,		_lowercase  )  )  ,		x.transpose()  )  ,		_lowercase  )
			return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]  )




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       Dict  ,		A  :       Any  )    -> float:







			"""simple docstring"""
			__snake_case				:      List[str]     							= (1, 2, 1)
			__snake_case				:      Dict     							= (1, 1, 0, 7)
			__snake_case				:      Union[str, Any]     							= SARIMAX(
			    _lowercase  ,		exog=_lowercase  ,		order=_lowercase  ,		seasonal_order=_lowercase  )
			__snake_case				:      str     							= model.fit(disp=_lowercase  ,		maxiter=6_00  ,		method='nm'  )
			__snake_case				:      int     							= model_fit.predict(1  ,		len(_lowercase  )  ,		exog=[test_match]  )
			return result[0]




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       Any  ,		A  :       str  )    -> float:







			"""simple docstring"""
			__snake_case				:      Any     							= SVR(kernel='rbf'  ,		C=1  ,		gamma=0.1  ,		epsilon=0.1  )
			regressor.fit(_lowercase  ,		_lowercase  )
			__snake_case				:      int     							= regressor.predict(_lowercase  )
			return y_pred[0]




def        _SCREAMING_SNAKE_CASE  (				A  :       List[str]  )    -> float:







			"""simple docstring"""
			train_user.sort()
			__snake_case				:      str     							= np.percentile(_lowercase  ,		25  )
			__snake_case				:      Union[str, Any]     							= np.percentile(_lowercase  ,		75  )
			__snake_case				:      Optional[int]     							= qa - qa
			__snake_case				:      Optional[Any]     							= qa - (iqr * 0.1)
			return low_lim




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  ,		A  :       Dict  )    -> bool:







			"""simple docstring"""
			__snake_case				:      Any     							= 0
			__snake_case				:      List[str]     							= 0
			for i in list_vote:
						if i > actual_result:
									__snake_case				:      Optional[Any]     							= not_safe + 1
						else:
									if abs(abs(_lowercase  ) - abs(_lowercase  )  ) <= 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    	=   [[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
						__A    	=   pd.DataFrame(
						    data_input, columns=['''total_user''', '''total_even''', '''days''']
						)
						__A    	=   Normalizer().fit_transform(data_input_df.values)
						# split data
						__A    	=   normalize_df[:, 2].tolist()
						__A    	=   normalize_df[:, 0].tolist()
						__A    	=   normalize_df[:, 1].tolist()

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

						# for linear regression & sarimax
						__A    	=   total_date[: len(total_date) - 1]
						__A    	=   total_user[: len(total_user) - 1]
						__A    	=   total_match[: len(total_match) - 1]

						__A    	=   total_date[len(total_date) - 1 :]
						__A    	=   total_user[len(total_user) - 1 :]
						__A    	=   total_match[len(total_match) - 1 :]

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







'''simple docstring'''






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__A    	=   abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  )    -> str:







   """simple docstring"""
   from diffusers.utils.testing_utils import pytest_addoption_shared

   pytest_addoption_shared(A  )





def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> Optional[int]:







   """simple docstring"""
   from diffusers.utils.testing_utils import pytest_terminal_summary_main

   __snake_case				:      Any     							= terminalreporter.config.getoption('--make-reports'  )
   if make_reports:
      pytest_terminal_summary_main(A  ,		id=A  )