237e4335ca2927526e00e81456543da624e2ba31,pb_bss/distribution/vmfcacgmm.py,VMFCACGMM,_predict,#VMFCACGMM#,46

Before Change

        )

        affiliation = (
            unsqueeze(np.log(self.weight), self.weight_constant_axis)
            + self.spatial_weight * cacg_log_pdf
            + self.spectral_weight * vmf_log_pdf
        )
        affiliation -= np.max(affiliation, axis=-2, keepdims=True)
        np.exp(affiliation, out=affiliation)
        denominator = np.maximum(
            np.einsum("...kt->...t", affiliation)[..., None, :],
            np.finfo(affiliation.dtype).tiny,

After Change

            np.reshape(vmf_log_pdf, (num_classes, F, T)), (1, 0, 2)
        )

        if inline_permutation_alignment:
            affiliation \
                = log_pdf_to_affiliation_for_integration_models_with_inline_pa(
                    weight=unsqueeze(self.weight, self.weight_constant_axis),
                    spatial_log_pdf=self.spatial_weight * cacg_log_pdf,
                    spectral_log_pdf=self.spectral_weight * vmf_log_pdf,
                )
        else:
            affiliation = log_pdf_to_affiliation(
                weight=unsqueeze(self.weight, self.weight_constant_axis),
                log_pdf=(
                    self.spatial_weight * cacg_log_pdf
                    + self.spectral_weight * vmf_log_pdf
                ),
            )

        return affiliation, quadratic_form


class VMFCACGMMTrainer:

Instances