





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_	(		A__		,      A__		,      A__		,      A__		,      A__				)  ->		float:
       a__						:	Optional[Any] =				np.array([[1, item, train_mtch[i]] for i, item in enumerate(A__				)]				)
       a__						:	Dict =				np.array(A__				)
       a__						:	List[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 A_	(		A__		,      A__		,      A__				)  ->		float:
       a__						:	List[Any] =				(1, 2, 1)
       a__						:	Union[str, Any] =				(1, 1, 0, 7)
       a__						:	str =				SARIMAX(
           A__		,      exog=A__		,      order=A__		,      seasonal_order=A__				)
       a__						:	List[str] =				model.fit(disp=A__		,      maxiter=600		,      method='nm'				)
       a__						:	List[Any] =				model_fit.predict(1		,      len(A__				)		,      exog=[test_match]				)
       return result[0]



def A_	(		A__		,      A__		,      A__				)  ->		float:
       a__						:	Optional[Any] =				SVR(kernel='rbf'		,      C=1		,      gamma=0.1		,      epsilon=0.1				)
       regressor.fit(A__		,      A__				)
       a__						:	Optional[int] =				regressor.predict(A__				)
       return y_pred[0]



def A_	(		A__				)  ->		float:
       train_user.sort()
       a__						:	Union[str, Any] =				np.percentile(A__		,      25				)
       a__						:	Dict =				np.percentile(A__		,      75				)
       a__						:	Union[str, Any] =				qa - qa
       a__						:	str =				qa - (iqr * 0.1)
       return low_lim



def A_	(		A__		,      A__				)  ->		bool:
       a__						:	Optional[Any] =				0
       a__						:	List[Any] =				0
       for i in list_vote:
              if i > actual_result:
                     a__						:	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)
    lowercase      :       Optional[int]									=  [[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
    lowercase      :       List[str]									=  pd.DataFrame(
        data_input, columns=["""total_user""", """total_even""", """days"""]
    )
    lowercase      :       str									=  Normalizer().fit_transform(data_input_df.values)
    # split data
    lowercase      :       List[str]									=  normalize_df[:, 2].tolist()
    lowercase      :       int									=  normalize_df[:, 0].tolist()
    lowercase      :       int									=  normalize_df[:, 1].tolist()

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

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

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

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





'''simple docstring'''
import copy
import re







class 							A__    :
  A__		 =				'hp'
  A__		 =				{}
  A__		 =				None





  @classmethod
  def 						A  (		cls      :	Optional[Any]							,					_a      :	Optional[Any]							,					_a      :	Any )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =prefix
       _SCREAMING_SNAKE_CASE							    =defaults
       cls.build_naming_info()





  @staticmethod
  def 						A  (		_a      :	Optional[Any]							,					_a      :	List[Any] )	->    Any:





       '''simple docstring'''






       if len(_a ) == 0:
            return ""
       _SCREAMING_SNAKE_CASE							    =None
       if any(char.isdigit() for char in word ):
            raise Exception(f"Parameters should not contain numbers: '{word}' contains a number" )
       if word in info["short_word"]:
            return info["short_word"][word]
       for prefix_len in range(1							,					len(_a ) + 1 ):
            _SCREAMING_SNAKE_CASE							    =word[:prefix_len]
            if prefix in info["reverse_short_word"]:
                 continue
            else:
                 _SCREAMING_SNAKE_CASE							    =prefix
                 break

       if short_word is None:
            # Paranoid fallback
            def int_to_alphabetic(_a      :	str ):
                 _SCREAMING_SNAKE_CASE							    =''
                 while integer != 0:
                      _SCREAMING_SNAKE_CASE							    =chr(ord('A' ) + integer % 10 ) + s
                      integer //= 10
                 return s

            _SCREAMING_SNAKE_CASE							    =0
            while True:
                 _SCREAMING_SNAKE_CASE							    =word + '#' + int_to_alphabetic(_a )
                 if sword in info["reverse_short_word"]:
                      continue
                 else:
                      _SCREAMING_SNAKE_CASE							    =sword
                      break

       _SCREAMING_SNAKE_CASE							    =short_word
       _SCREAMING_SNAKE_CASE							    =word
       return short_word





  @staticmethod
  def 						A  (		_a      :	Optional[Any]							,					_a      :	int )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =param_name.split('_' )

       _SCREAMING_SNAKE_CASE							    =[TrialShortNamer.shortname_for_word(_a							,					_a ) for word in words]

       # We try to create a separatorless short name, but if there is a collision we have to fallback
       # to a separated short name
       _SCREAMING_SNAKE_CASE							    =['', '_']

       for separator in separators:
            _SCREAMING_SNAKE_CASE							    =separator.join(_a )
            if shortname not in info["reverse_short_param"]:
                 _SCREAMING_SNAKE_CASE							    =shortname
                 _SCREAMING_SNAKE_CASE							    =param_name
                 return shortname

       return param_name





  @staticmethod
  def 						A  (		_a      :	Dict							,					_a      :	int )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =TrialShortNamer.shortname_for_key(_a							,					_a )
       _SCREAMING_SNAKE_CASE							    =short_name
       _SCREAMING_SNAKE_CASE							    =param_name





  @classmethod
  def 						A  (		cls      :	Optional[int] )	->    Tuple:





       '''simple docstring'''






       if cls.NAMING_INFO is not None:
            return

       _SCREAMING_SNAKE_CASE							    ={
           'short_word': {},
           'reverse_short_word': {},
           'short_param': {},
           'reverse_short_param': {},
       }

       _SCREAMING_SNAKE_CASE							    =list(cls.DEFAULTS.keys() )

       for k in field_keys:
            cls.add_new_param_name(_a							,					_a )

       _SCREAMING_SNAKE_CASE							    =info





  @classmethod
  def 						A  (		cls      :	List[Any]							,					_a      :	int )	->    int:





       '''simple docstring'''






       cls.build_naming_info()
       assert cls.PREFIX is not None
       _SCREAMING_SNAKE_CASE							    =[copy.copy(cls.PREFIX )]

       for k, v in params.items():
            if k not in cls.DEFAULTS:
                 raise Exception(f"You should provide a default value for the param name {k} with value {v}" )
            if v == cls.DEFAULTS[k]:
                 # The default value is not added to the name
                 continue

            _SCREAMING_SNAKE_CASE							    =cls.NAMING_INFO['short_param'][k]

            if isinstance(_a							,					_a ):
                 _SCREAMING_SNAKE_CASE							    =1 if v else 0

            _SCREAMING_SNAKE_CASE							    ='' if isinstance(_a							,					(int, float) ) else '-'
            _SCREAMING_SNAKE_CASE							    =f"{key}{sep}{v}"
            name.append(_a )

       return "_".join(_a )





  @classmethod
  def 						A  (		cls      :	Optional[Any]							,					_a      :	List[Any] )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =repr[len(cls.PREFIX ) + 1 :]
       if repr == "":
            _SCREAMING_SNAKE_CASE							    =[]
       else:
            _SCREAMING_SNAKE_CASE							    =repr.split('_' )

       _SCREAMING_SNAKE_CASE							    ={}

       for value in values:
            if "-" in value:
                 _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =value.split('-' )
            else:
                 _SCREAMING_SNAKE_CASE							    =re.sub('[0-9.]'							,					''							,					_a )
                 _SCREAMING_SNAKE_CASE							    =float(re.sub('[^0-9.]'							,					''							,					_a ) )

            _SCREAMING_SNAKE_CASE							    =cls.NAMING_INFO['reverse_short_param'][p_k]

            _SCREAMING_SNAKE_CASE							    =p_v

       for k in cls.DEFAULTS:
            if k not in parameters:
                 _SCREAMING_SNAKE_CASE							    =cls.DEFAULTS[k]

       return parameters
