





'''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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				float:



			"""simple docstring"""


			__snake_case					:      str							      =     np.array([[1, item, train_mtch[i]] for i, item in enumerate(_lowerCamelCase )] )
			__snake_case					:      Optional[Any]							      =     np.array(_lowerCamelCase )
			__snake_case					:      List[str]							      =     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 ) ->				float:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				float:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase ) ->				float:



			"""simple docstring"""


			train_user.sort()
			__snake_case					:      List[str]							      =     np.percentile(_lowerCamelCase    ,	25 )
			__snake_case					:      Optional[int]							      =     np.percentile(_lowerCamelCase    ,	75 )
			__snake_case					:      List[Any]							      =     qa - qa
			__snake_case					:      List[str]							      =     qa - (iqr * 0.1)
			return low_lim







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				bool:



			"""simple docstring"""


			__snake_case					:      Tuple							      =     0
			__snake_case					:      str							      =     0
			for i in list_vote:
						if i > actual_result:
									__snake_case					:      str							      =     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					    =	[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
	__UpperCamelCase					    =	pd.DataFrame(
	    data_input, columns=["total_user", "total_even", "days"]
	)
	__UpperCamelCase					    =	Normalizer().fit_transform(data_input_df.values)
	# split data
	__UpperCamelCase					    =	normalize_df[:, 2].tolist()
	__UpperCamelCase					    =	normalize_df[:, 0].tolist()
	__UpperCamelCase					    =	normalize_df[:, 1].tolist()

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

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

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

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









'''simple docstring'''






from __future__ import annotations







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase = None    ,	_lowerCamelCase = None ) ->				None:



			"""simple docstring"""


			if start is None:
						__snake_case					:      Optional[Any]							      =     0

			if end is None:
						__snake_case					:      Optional[Any]							      =     len(_lowerCamelCase ) - 1

			if start >= end:
						return

			__snake_case					:      Tuple							      =     (start + end) // 2

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )
			slowsort(_lowerCamelCase    ,	mid + 1    ,	_lowerCamelCase )

			if sequence[end] < sequence[mid]:
						__snake_case      ,     __snake_case					:      str							      =     sequence[mid], sequence[end]

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	end - 1 )


if __name__ == "__main__":
	from doctest import testmod

	testmod()


