





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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						float:
    SCREAMING_SNAKE_CASE__											=			np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowerCAmelCase_	)]	)
    SCREAMING_SNAKE_CASE__											=			np.array(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						float:
    SCREAMING_SNAKE_CASE__											=			(1, 2, 1)
    SCREAMING_SNAKE_CASE__											=			(1, 1, 0, 7)
    SCREAMING_SNAKE_CASE__											=			SARIMAX(
        lowerCAmelCase_ ,							exog=lowerCAmelCase_ ,							order=lowerCAmelCase_ ,							seasonal_order=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			model.fit(disp=lowerCAmelCase_ ,							maxiter=6_0_0 ,							method='''nm'''	)
    SCREAMING_SNAKE_CASE__											=			model_fit.predict(1 ,							len(lowerCAmelCase_	) ,							exog=[test_match]	)
    return result[0]


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						float:
    SCREAMING_SNAKE_CASE__											=			SVR(kernel='''rbf''' ,							C=1 ,							gamma=0.1 ,							epsilon=0.1	)
    regressor.fit(lowerCAmelCase_ ,							lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			regressor.predict(lowerCAmelCase_	)
    return y_pred[0]


def 							__snake_case							(    lowerCAmelCase_	)				->						float:
    train_user.sort()
    SCREAMING_SNAKE_CASE__											=			np.percentile(lowerCAmelCase_ ,							2_5	)
    SCREAMING_SNAKE_CASE__											=			np.percentile(lowerCAmelCase_ ,							7_5	)
    SCREAMING_SNAKE_CASE__											=			qa - qa
    SCREAMING_SNAKE_CASE__											=			qa - (iqr * 0.1)
    return low_lim


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						bool:
    SCREAMING_SNAKE_CASE__											=			0
    SCREAMING_SNAKE_CASE__											=			0
    for i in list_vote:
        if i > actual_result:
            SCREAMING_SNAKE_CASE__											=			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					:				Tuple				=			[[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					:				str				=			pd.DataFrame(
      data_input, columns=["""total_user""", """total_even""", """days"""]
  )
  _A					:				Optional[int]				=			Normalizer().fit_transform(data_input_df.values)
  # split data
  _A					:				Union[str, Any]				=			normalize_df[:, 2].tolist()
  _A					:				Optional[int]				=			normalize_df[:, 0].tolist()
  _A					:				Any				=			normalize_df[:, 1].tolist()

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

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

  _A					:				Any				=			total_date[len(total_date) - 1 :]
  _A					:				str				=			total_user[len(total_user) - 1 :]
  _A					:				Dict				=			total_match[len(total_match) - 1 :]

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







class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			name
        SCREAMING_SNAKE_CASE__											=			value
        SCREAMING_SNAKE_CASE__											=			weight



    def __repr__(       self     ):





        '''simple docstring'''

        return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.name



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.weight




    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value / self.weight





def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			[]
    for i in range(len(lowerCAmelCase_	)	):
        menu.append(Things(name[i] ,							value[i] ,							weight[i]	)	)
    return menu


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			sorted(lowerCAmelCase_ ,							key=lowerCAmelCase_ ,							reverse=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			0.0, 0.0
    for i in range(len(lowerCAmelCase_	)	):
        if (total_cost + items_copy[i].get_weight()) <= max_cost:
            result.append(items_copy[i]	)
            total_cost += items_copy[i].get_weight()
            total_value += items_copy[i].get_value()
    return (result, total_value)


def 							__snake_case							(    )				->						str:
    pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()
