
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       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		List[Any]		    =   np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCamelCase	)]	)
	__lowerCAmelCase    :		Optional[Any]		    =   np.array(_UpperCamelCase	)
	__lowerCAmelCase    :		List[str]		    =   np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() ,						_UpperCamelCase	)	) ,						x.transpose()	) ,						_UpperCamelCase	)
	return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		str		    =   (1, 2, 1)
	__lowerCAmelCase    :		int		    =   (1, 1, 0, 7)
	__lowerCAmelCase    :		List[Any]		    =   SARIMAX(
	    _UpperCamelCase ,						exog=_UpperCamelCase ,						order=_UpperCamelCase ,						seasonal_order=_UpperCamelCase	)
	__lowerCAmelCase    :		Dict		    =   model.fit(disp=_UpperCamelCase ,						maxiter=600 ,						method='nm'	)
	__lowerCAmelCase    :		Tuple		    =   model_fit.predict(1 ,						len(_UpperCamelCase	) ,						exog=[test_match]	)
	return result[0]
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		str		    =   SVR(kernel='rbf' ,						C=1 ,						gamma=0.1 ,						epsilon=0.1	)
	regressor.fit(_UpperCamelCase ,						_UpperCamelCase	)
	__lowerCAmelCase    :		Dict		    =   regressor.predict(_UpperCamelCase	)
	return y_pred[0]
def 					__lowerCAmelCase       (_UpperCamelCase	):
	train_user.sort()
	__lowerCAmelCase    :		List[Any]		    =   np.percentile(_UpperCamelCase ,						25	)
	__lowerCAmelCase    :		List[Any]		    =   np.percentile(_UpperCamelCase ,						75	)
	__lowerCAmelCase    :		int		    =   qa - qa
	__lowerCAmelCase    :		Tuple		    =   qa - (iqr * 0.1)
	return low_lim
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Any		    =   0
	__lowerCAmelCase    :		List[str]		    =   0
	for i in list_vote:
		if i > actual_result:
			__lowerCAmelCase    :		List[str]		    =   not_safe + 1
		else:
			if abs(abs(_UpperCamelCase	) - abs(_UpperCamelCase	)	) <= 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__           =	[[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
							lowerCamelCase__           =	pd.DataFrame(
							    data_input, columns=["""total_user""", """total_even""", """days"""]
							)
							lowerCamelCase__           =	Normalizer().fit_transform(data_input_df.values)
							# split data
							lowerCamelCase__           =	normalize_df[:, 2].tolist()
							lowerCamelCase__           =	normalize_df[:, 0].tolist()
							lowerCamelCase__           =	normalize_df[:, 1].tolist()

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

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

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

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

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   len(_UpperCamelCase	)
	__lowerCAmelCase    :		Tuple		    =   [[0] * n for i in range(_UpperCamelCase	)]
	for i in range(_UpperCamelCase	):
		__lowerCAmelCase    :		Any		    =   y_points[i]

	for i in range(2 ,						_UpperCamelCase	):
		for j in range(_UpperCamelCase ,						_UpperCamelCase	):
			__lowerCAmelCase    :		int		    =   (
			    (xa - x_points[j - i + 1]) * q[j][i - 1]
			    - (xa - x_points[j]) * q[j - 1][i - 1]
			) / (x_points[j] - x_points[j - i + 1])

	return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()