Method get_projected_pattern_matrix

hflayers/__init__.py:257–272

Fetch Hopfield projected pattern matrix gathered by passing through the specified data. :param input: data to be passed through the Hopfield association :param stored_pattern_padding_mask: mask to be applied on stored patterns :param association_mask: mask to be app

(self, input: Union[Tensor, Tuple[Tensor, Tensor, Tensor]],
                                     stored_pattern_padding_mask: Optional[Tensor] = None,
                                     association_mask: Optional[Tensor] = None)

                association_mask=association_mask)[2]

    def get_projected_pattern_matrix(self, input: Union[Tensor, Tuple[Tensor, Tensor, Tensor]],
                                     stored_pattern_padding_mask: Optional[Tensor] = None,
                                     association_mask: Optional[Tensor] = None) -> Tensor:
        """
        Fetch Hopfield projected pattern matrix gathered by passing through the specified data.

        :param input: data to be passed through the Hopfield association
        :param stored_pattern_padding_mask: mask to be applied on stored patterns
        :param association_mask: mask to be applied on inner association matrix
        :return: pattern projection matrix as computed by the Hopfield core module
        """
        with torch.no_grad():
            return self._associate(
                data=input, return_projected_patterns=True,
                stored_pattern_padding_mask=stored_pattern_padding_mask,
                association_mask=association_mask)[3]

    @property
    def batch_first(self) -> bool: