


'''simple docstring'''






import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize





def lowerCamelCase__      (						__lowerCamelCase	:							ndarray							):
   return np.dot(__lowerCamelCase	,   __lowerCamelCase							)






class  __magic_name__							:






       def __init__(      self  ,							*,
           snake_case = np.inf  ,							snake_case = "linear"  ,							snake_case = 0.0  ,							)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =regularization
          _UpperCAmelCase       :       Optional[int]							   =gamma
          if kernel == "linear":
             _UpperCAmelCase       :       Union[str, Any]							   =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       :       List[str]							   =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       :       Any							   =f"Unknown kernel: {kernel}"
             raise ValueError(__A)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  Union[str, Any]:




          '''simple docstring'''
          return np.dot(__A  ,							__A)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  int:




          '''simple docstring'''
          return np.exp(-(self.gamma * norm_squared(vectora - vectora)))






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =observations
          _UpperCAmelCase       :       Optional[int]							   =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						)       :       str							   =np.shape(__A)

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

          _UpperCAmelCase       :       List[str]							   =LinearConstraint(__A  ,							0  ,							0)
          _UpperCAmelCase       :       Any							   =Bounds(0  ,							self.regularization)

          _UpperCAmelCase       :       str							   =minimize(
              __A  ,							np.ones(__A)  ,							bounds=__A  ,							constraints=[ly_contraint]).x
          _UpperCAmelCase       :       List[str]							   =l_star

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






       def       lowerCAmelCase		(      self  ,							snake_case)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =sum(
              self.optimum[n]
              * self.classes[n]
              * self.kernel(self.observations[n]  ,							__A)
              for n in range(len(self.classes)))
          return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase							={
    'configuration_groupvit': [
        'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'GroupViTConfig',
        'GroupViTOnnxConfig',
        'GroupViTTextConfig',
        'GroupViTVisionConfig',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase							=[
     'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GroupViTModel',
     'GroupViTPreTrainedModel',
     'GroupViTTextModel',
     'GroupViTVisionModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase							=[
     'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFGroupViTModel',
     'TFGroupViTPreTrainedModel',
     'TFGroupViTTextModel',
     'TFGroupViTVisionModel',
 ]

if TYPE_CHECKING:
 from .configuration_groupvit import (
     GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     GroupViTConfig,
     GroupViTOnnxConfig,
     GroupViTTextConfig,
     GroupViTVisionConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_groupvit import (
      GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      GroupViTModel,
      GroupViTPreTrainedModel,
      GroupViTTextModel,
      GroupViTVisionModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_groupvit import (
      TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFGroupViTModel,
      TFGroupViTPreTrainedModel,
      TFGroupViTTextModel,
      TFGroupViTVisionModel,
  )

else:
 import sys

 lowercase							=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

