



from sklearn.neural_network import MLPClassifier

UpperCAmelCase :				Any							=[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCAmelCase :				Optional[int]							=[0, 1, 0, 0]


UpperCAmelCase :				Dict							=MLPClassifier(
    solver="""lbfgs""", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCAmelCase :				int							=[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCAmelCase :				Union[str, Any]							=clf.predict(test)



def  _lowerCAmelCase     (_lowerCAmelCase):
					return list(_lowerCAmelCase)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

import torch
from torch import autograd







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			,			_lowerCAmelCase					:     bool			):
           __snake_case       :				Optional[int] 			=    inputs.numel()
           __snake_case       :				str 			=    int(0.005 * nb_elems			) + 1
           if sigmoid:
               __snake_case       :				Union[str, Any] 			=    (torch.sigmoid(_lowerCAmelCase			) > threshold).type(inputs.type()			)
           else:
               __snake_case       :				Any 			=    (inputs > threshold).type(inputs.type()			)
           if mask.sum() < nb_min:
               # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
               __snake_case       :				Dict 			=    inputs.flatten().kthvalue(max(nb_elems - nb_min			,			1			)			).values
               __snake_case       :				List[str] 			=    (inputs > k_threshold).type(inputs.type()			)
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Tuple			):
           return gradOutput, None, None







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				str 			=    inputs.clone()
           __snake_case			, __snake_case       :				Dict 			=    inputs.flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				Optional[int] 			=    mask.flatten()
           __snake_case       :				Dict 			=    0
           __snake_case       :				Optional[Any] 			=    1
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Optional[Any]			):
           return gradOutput, None







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				List[str] 			=    inputs.clone()
           __snake_case			, __snake_case       :				str 			=    inputs.abs().flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				List[Any] 			=    mask.flatten()
           __snake_case       :				int 			=    0
           __snake_case       :				Optional[int] 			=    1
           return mask



