

'''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 						__lowerCamelCase	(       __snake_case					:		list,		__snake_case					:		list,		__snake_case					:		list,		__snake_case					:		list,		__snake_case					:		list	)	->							float:






      """simple docstring"""





      A__					:       Dict     =np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCamelCase	)]	)
      A__					:       Union[str, Any]     =np.array(_UpperCamelCase	)
      A__					:       Tuple     =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	(       __snake_case					:		list,		__snake_case					:		list,		__snake_case					:		list	)	->							float:






      """simple docstring"""





      A__					:       str     =(1, 2, 1)
      A__					:       Any     =(1, 1, 0, 7)
      A__					:       Union[str, Any]     =SARIMAX(
          _UpperCamelCase,		exog=_UpperCamelCase,		order=_UpperCamelCase,		seasonal_order=_UpperCamelCase	)
      A__					:       List[Any]     =model.fit(disp=_UpperCamelCase,		maxiter=600,		method="""nm"""	)
      A__					:       List[str]     =model_fit.predict(1,		len(_UpperCamelCase	),		exog=[test_match]	)
      return result[0]
def 						__lowerCamelCase	(       __snake_case					:		list,		__snake_case					:		list,		__snake_case					:		list	)	->							float:






      """simple docstring"""





      A__					:       int     =SVR(kernel="""rbf""",		C=1,		gamma=0.1,		epsilon=0.1	)
      regressor.fit(_UpperCamelCase,		_UpperCamelCase	)
      A__					:       str     =regressor.predict(_UpperCamelCase	)
      return y_pred[0]
def 						__lowerCamelCase	(       __snake_case					:		list	)	->							float:






      """simple docstring"""





      train_user.sort()
      A__					:       List[str]     =np.percentile(_UpperCamelCase,		25	)
      A__					:       List[Any]     =np.percentile(_UpperCamelCase,		75	)
      A__					:       Tuple     =qa - qa
      A__					:       str     =qa - (iqr * 0.1)
      return low_lim
def 						__lowerCamelCase	(       __snake_case					:		list,		__snake_case					:		float	)	->							bool:






      """simple docstring"""





      A__					:       Tuple     =0
      A__					:       Optional[int]     =0
      for i in list_vote:
            if i > actual_result:
                  A__					:       Optional[Any]     =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)
  __snake_case			:     Dict										=      [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
  __snake_case			:     int										=      pd.DataFrame(
      data_input, columns=['total_user', 'total_even', 'days']
  )
  __snake_case			:     Union[str, Any]										=      Normalizer().fit_transform(data_input_df.values)
  # split data
  __snake_case			:     int										=      normalize_df[:, 2].tolist()
  __snake_case			:     List[Any]										=      normalize_df[:, 0].tolist()
  __snake_case			:     Optional[int]										=      normalize_df[:, 1].tolist()

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

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

  __snake_case			:     Union[str, Any]										=      total_date[len(total_date) - 1 :]
  __snake_case			:     List[Any]										=      total_user[len(total_user) - 1 :]
  __snake_case			:     Union[str, Any]										=      total_match[len(total_match) - 1 :]

  # voting system with forecasting
  __snake_case			:     Dict										=      [
      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
  __snake_case			:     Union[str, Any]										=      "" 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 						__lowerCamelCase	(       __snake_case					:		list[int]	)	->							bool:






						"""simple docstring"""





						return len(set(__snake_case	)	) == len(__snake_case	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

