



'''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 _lowercase      (     __A							,__A							,__A							,__A							,__A				):



 '''simple docstring'''



 __UpperCamelCase 					=     np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCAmelCase_				)]				)
 __UpperCamelCase 					=     np.array(UpperCAmelCase_				)
 __UpperCamelCase 					=     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 _lowercase      (     __A							,__A							,__A				):



 '''simple docstring'''



 __UpperCamelCase 					=     (1, 2, 1)
 __UpperCamelCase 					=     (1, 1, 0, 7)
 __UpperCamelCase 					=     SARIMAX(
     UpperCAmelCase_							,exog=UpperCAmelCase_							,order=UpperCAmelCase_							,seasonal_order=UpperCAmelCase_				)
 __UpperCamelCase 					=     model.fit(disp=UpperCAmelCase_							,maxiter=600							,method="""nm"""				)
 __UpperCamelCase 					=     model_fit.predict(1							,len(UpperCAmelCase_				)							,exog=[test_match]				)
 return result[0]







def _lowercase      (     __A							,__A							,__A				):



 '''simple docstring'''



 __UpperCamelCase 					=     SVR(kernel="""rbf"""							,C=1							,gamma=0.1							,epsilon=0.1				)
 regressor.fit(UpperCAmelCase_							,UpperCAmelCase_				)
 __UpperCamelCase 					=     regressor.predict(UpperCAmelCase_				)
 return y_pred[0]







def _lowercase      (     __A				):



 '''simple docstring'''



 train_user.sort()
 __UpperCamelCase 					=     np.percentile(UpperCAmelCase_							,25				)
 __UpperCamelCase 					=     np.percentile(UpperCAmelCase_							,75				)
 __UpperCamelCase 					=     qa - qa
 __UpperCamelCase 					=     qa - (iqr * 0.1)
 return low_lim







def _lowercase      (     __A							,__A				):



 '''simple docstring'''



 __UpperCamelCase 					=     0
 __UpperCamelCase 					=     0
 for i in list_vote:
  if i > actual_result:
   __UpperCamelCase 					=     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)
 a__  :	Any								=	[[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]]
 a__  :	str								=	pd.DataFrame(
     data_input, columns=['total_user', 'total_even', 'days']
 )
 a__  :	str								=	Normalizer().fit_transform(data_input_df.values)
 # split data
 a__  :	Any								=	normalize_df[:, 2].tolist()
 a__  :	str								=	normalize_df[:, 0].tolist()
 a__  :	List[Any]								=	normalize_df[:, 1].tolist()

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

 # for linear regression & sarimax
 a__  :	Dict								=	total_date[: len(total_date) - 1]
 a__  :	Any								=	total_user[: len(total_user) - 1]
 a__  :	Tuple								=	total_match[: len(total_match) - 1]

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

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









'''simple docstring'''



A__		:					Any												=  8.3_1_4_4_6_2  # Unit - J mol-1 K-1



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				float      ,    UpperCAmelCase_    :				float      ,    UpperCAmelCase_    :				float							)     ->		float:
     if moles < 0 or kelvin < 0 or volume < 0:
          raise ValueError('Invalid inputs. Enter positive value.'							)
     return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				float      ,    UpperCAmelCase_    :				float      ,    UpperCAmelCase_    :				float							)     ->		float:
     if moles < 0 or kelvin < 0 or pressure < 0:
          raise ValueError('Invalid inputs. Enter positive value.'							)
     return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
      from doctest import testmod

      testmod()






