


'''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__					(	UpperCAmelCase_    :				list      ,    UpperCAmelCase_    :				list      ,    UpperCAmelCase_    :				list      ,    UpperCAmelCase_    :				list      ,    UpperCAmelCase_    :				list							)     ->		float:
     __lowerCamelCase   :	Optional[int]								=		np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCAmelCase_							)]							)
     __lowerCamelCase   :	Tuple								=		np.array(UpperCAmelCase_							)
     __lowerCamelCase   :	Dict								=		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 			UpperCAmelCase__					(	UpperCAmelCase_    :				list      ,    UpperCAmelCase_    :				list      ,    UpperCAmelCase_    :				list							)     ->		float:
     __lowerCamelCase   :	Any								=		(1, 2, 1)
     __lowerCamelCase   :	Optional[int]								=		(1, 1, 0, 7)
     __lowerCamelCase   :	List[Any]								=		SARIMAX(
         UpperCAmelCase_      ,    exog=UpperCAmelCase_      ,    order=UpperCAmelCase_      ,    seasonal_order=UpperCAmelCase_							)
     __lowerCamelCase   :	Optional[Any]								=		model.fit(disp=UpperCAmelCase_      ,    maxiter=6_00      ,    method='nm'							)
     __lowerCamelCase   :	str								=		model_fit.predict(1      ,    len(UpperCAmelCase_							)      ,    exog=[test_match]							)
     return result[0]



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				list      ,    UpperCAmelCase_    :				list      ,    UpperCAmelCase_    :				list							)     ->		float:
     __lowerCamelCase   :	Any								=		SVR(kernel='rbf'      ,    C=1      ,    gamma=0.1      ,    epsilon=0.1							)
     regressor.fit(UpperCAmelCase_      ,    UpperCAmelCase_							)
     __lowerCamelCase   :	List[Any]								=		regressor.predict(UpperCAmelCase_							)
     return y_pred[0]



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				list							)     ->		float:
     train_user.sort()
     __lowerCamelCase   :	Optional[Any]								=		np.percentile(UpperCAmelCase_      ,    25							)
     __lowerCamelCase   :	Optional[int]								=		np.percentile(UpperCAmelCase_      ,    75							)
     __lowerCamelCase   :	Tuple								=		qa - qa
     __lowerCamelCase   :	Optional[Any]								=		qa - (iqr * 0.1)
     return low_lim



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				list      ,    UpperCAmelCase_    :				float							)     ->		bool:
     __lowerCamelCase   :	List[str]								=		0
     __lowerCamelCase   :	Optional[int]								=		0
     for i in list_vote:
          if i > actual_result:
               __lowerCamelCase   :	str								=		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												=  [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 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'''



import unittest

from knapsack import knapsack as k





class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	int								=		0
         __lowerCamelCase   :	Union[str, Any]								=		[0]
         __lowerCamelCase   :	Any								=		[0]
         __lowerCamelCase   :	Union[str, Any]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     0	)

         __lowerCamelCase   :	List[str]								=		[60]
         __lowerCamelCase   :	Union[str, Any]								=		[10]
         __lowerCamelCase   :	List[Any]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     0	)







    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	Optional[int]								=		3
         __lowerCamelCase   :	int								=		[1, 2, 3]
         __lowerCamelCase   :	str								=		[3, 2, 1]
         __lowerCamelCase   :	Union[str, Any]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     5	)







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	Optional[int]								=		50
         __lowerCamelCase   :	List[str]								=		[60, 1_00, 1_20]
         __lowerCamelCase   :	List[str]								=		[10, 20, 30]
         __lowerCamelCase   :	List[str]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     2_20	)


if __name__ == "__main__":
      unittest.main()






