



import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def 		UpperCamelCase	(   snake_case__       :    ndarray	)	->     float:
   return np.dot(snake_case__       ,     snake_case__	)



class 				lowerCAmelCase_		:
       def __init__(			self,			*,
           SCREAMING_SNAKE_CASE_ = np.inf,			SCREAMING_SNAKE_CASE_ = "linear",			SCREAMING_SNAKE_CASE_ = 0.0,			)     ->		None:
          UpperCamelCase    :  str							=	regularization
          UpperCamelCase    :  Union[str, Any]							=	gamma
          if kernel == "linear":
             UpperCamelCase    :  Tuple							=	self.__linear
          elif kernel == "rbf":
             if self.gamma == 0:
                raise ValueError('rbf kernel requires gamma'       )
             if not isinstance(self.gamma,			(float, int)       ):
                raise ValueError('gamma must be float or int'       )
             if not self.gamma > 0:
                raise ValueError('gamma must be > 0'       )
             UpperCamelCase    :  Dict							=	self.__rbf
             # in the future, there could be a default value like in sklearn
             # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
             # previously it was 1/(n_features)
          else:
             UpperCamelCase    :  Optional[Any]							=	F"""Unknown kernel: {kernel}"""
             raise ValueError(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		float:
          return np.dot(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		float:
          return np.exp(-(self.gamma * norm_squared(vectora - vectora       ))       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		None:
          UpperCamelCase    :  Optional[Any]							=	observations
          UpperCamelCase    :  Dict							=	classes

          # using Wolfe's Dual to calculate w.
          # Primal problem: minimize 1/2*norm_squared(w)
          #   constraint: yn(w . xn + b) >= 1
          #
          # With l a vector
          # Dual problem: maximize sum_n(ln) -
          #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
          #   constraint: self.C >= ln >= 0
          #           and sum_n(ln*yn) = 0
          # Then we get w using w = sum_n(ln*yn*xn)
          # At the end we can get b ~= mean(yn - w . xn)
          #
          # Since we use kernels, we only need l_star to calculate b
          # and to classify observations

          ((UpperCamelCase)	,     )    :  Optional[int]							=	np.shape(SCREAMING_SNAKE_CASE_       )

          def to_minimize(SCREAMING_SNAKE_CASE_       ) -> float:
             UpperCamelCase    :  Optional[int]							=	0
             ((UpperCamelCase)	,     )    :  List[Any]							=	np.shape(SCREAMING_SNAKE_CASE_       )
             for i in range(SCREAMING_SNAKE_CASE_       ):
                for j in range(SCREAMING_SNAKE_CASE_       ):
                   s += (
                       candidate[i]
                       * candidate[j]
                       * classes[i]
                       * classes[j]
                       * self.kernel(observations[i],			observations[j]       )
                   )
             return 1 / 2 * s - sum(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Any							=	LinearConstraint(SCREAMING_SNAKE_CASE_,			0,			0       )
          UpperCamelCase    :  Optional[int]							=	Bounds(0,			self.regularization       )

          UpperCamelCase    :  Optional[int]							=	minimize(
              SCREAMING_SNAKE_CASE_,			np.ones(SCREAMING_SNAKE_CASE_       ),			bounds=SCREAMING_SNAKE_CASE_,			constraints=[ly_contraint]       ).x
          UpperCamelCase    :  Optional[int]							=	l_star

          # calculating mean offset of separation plane to points
          UpperCamelCase    :  Union[str, Any]							=	0
          for i in range(SCREAMING_SNAKE_CASE_       ):
             for j in range(SCREAMING_SNAKE_CASE_       ):
                s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                    observations[i],			observations[j]       )
          UpperCamelCase    :  Any							=	s / n






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		int:
          UpperCamelCase    :  Optional[int]							=	sum(
              self.optimum[n]
              * self.classes[n]
              * self.kernel(self.observations[n],			SCREAMING_SNAKE_CASE_       )
              for n in range(len(self.classes       )       )       )
          return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()








import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    str	)	->     Optional[int]:
   monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings'       ,     set()	)
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    str	)	->     Any:



   class 				lowerCAmelCase_		:
          def __init__(			self,			SCREAMING_SNAKE_CASE_       )     ->		Optional[int]:
             UpperCamelCase    :  Tuple							=	metric_id






   class 				lowerCAmelCase_		:
          UpperCAmelCase__				:					Optional[int]        =  [MetricMock(a__							) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
          def 					snake_case_       (			self       )     ->		str:
             return self._metrics






   monkeypatch.setattr('datasets.inspect.huggingface_hub'       ,     HfhMock()	)


@pytest.mark.parametrize(
    'func, args'       ,     [(load_metric, ('metrics/mse',)), (list_metrics, ()), (inspect_metric, ('metrics/mse', 'tmp_path'))]	)
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    Any       ,     snake_case__       :    List[str]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   if "tmp_path" in args:
      UpperCamelCase    :  Dict							=	tuple(arg if arg != 'tmp_path' else tmp_path for arg in args	)
   with pytest.warns(snake_case__       ,     match='https://huggingface.co/docs/evaluate'	):
      func(*snake_case__	)



