'''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 __UpperCamelCase( _A       :					list	,      _A       :					list	,      _A       :					list	,      _A       :					list	,      _A       :					list     ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	np.array([[1, item, train_mtch[i]] for i, item in enumerate(_A     )]     )
   UpperCAmelCase__				:					Optional[Any] 					=	np.array(_A     )
   UpperCAmelCase__				:					Optional[Any] 					=	np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()	,      _A     )     )	,      x.transpose()     )	,      _A     )
   return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]     )







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







def __UpperCamelCase( _A       :					list	,      _A       :					list	,      _A       :					list     ):
   '''simple docstring'''
   UpperCAmelCase__				:					int 					=	SVR(kernel='''rbf'''	,      C=1	,      gamma=0.1	,      epsilon=0.1     )
   regressor.fit(_A	,      _A     )
   UpperCAmelCase__				:					Tuple 					=	regressor.predict(_A     )
   return y_pred[0]







def __UpperCamelCase( _A       :					list     ):
   '''simple docstring'''
   train_user.sort()
   UpperCAmelCase__				:					Optional[int] 					=	np.percentile(_A	,      25     )
   UpperCAmelCase__				:					List[str] 					=	np.percentile(_A	,      75     )
   UpperCAmelCase__				:					Optional[Any] 					=	qa - qa
   UpperCAmelCase__				:					int 					=	qa - (iqr * 0.1)
   return low_lim







def __UpperCamelCase( _A       :					list	,      _A       :					float     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Union[str, Any] 					=	0
   UpperCAmelCase__				:					int 					=	0
   for i in list_vote:
      if i > actual_result:
         UpperCAmelCase__				:					Tuple 					=	not_safe + 1
      else:
         if abs(abs(_A     ) - abs(_A     )     ) <= 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__  :						int					=      [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
  UpperCamelCase__  :						str					=      pd.DataFrame(
      data_input, columns=['total_user', 'total_even', 'days']
  )
  UpperCamelCase__  :						int					=      Normalizer().fit_transform(data_input_df.values)
  # split data
  UpperCamelCase__  :						str					=      normalize_df[:, 2].tolist()
  UpperCamelCase__  :						Dict					=      normalize_df[:, 0].tolist()
  UpperCamelCase__  :						Optional[Any]					=      normalize_df[:, 1].tolist()

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

  # for linear regression & sarimax
  UpperCamelCase__  :						Optional[int]					=      total_date[: len(total_date) - 1]
  UpperCamelCase__  :						Union[str, Any]					=      total_user[: len(total_user) - 1]
  UpperCamelCase__  :						Dict					=      total_match[: len(total_match) - 1]

  UpperCamelCase__  :						int					=      total_date[len(total_date) - 1 :]
  UpperCamelCase__  :						List[Any]					=      total_user[len(total_user) - 1 :]
  UpperCamelCase__  :						List[Any]					=      total_match[len(total_match) - 1 :]

  # voting system with forecasting
  UpperCamelCase__  :						Union[str, Any]					=      [
      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__  :						int					=      '' if data_safety_checker(res_vote, tst_user) else 'not '
  print('Today\'s data is {not_str}safe.')





'''simple docstring'''

import baseaa







def __UpperCamelCase( _A       :					str     ):
   '''simple docstring'''
   return baseaa.baaencode(string.encode('''utf-8'''     )     )







def __UpperCamelCase( _A       :					bytes     ):
   '''simple docstring'''
   return baseaa.baadecode(_A     ).decode('''utf-8'''     )


if __name__ == "__main__":
  UpperCamelCase__  :						List[str]					=      'Hello World!'
  UpperCamelCase__  :						str					=      baseaa_encode(test)
  print(encoded)

  UpperCamelCase__  :						int					=      baseaa_decode(encoded)
  print(decoded)




