b800ecab894566a04000eaad52f0b9bf6327cdf9,dc_integration/distribution/cwmm.py,ComplexWatsonMixtureModelParameters,predict,#ComplexWatsonMixtureModelParameters#Any#,13

Before Change

            Y_normalized: Mix with shape (..., T, D).
        Returns: Affiliations with shape (..., K, T).
        
        affiliations = self.pi[..., None] * ComplexWatson.pdf(
            Y_normalized[..., None, :, :],
            np.copy(self.W[..., None, :], "C"),
            self.kappa[..., None]
        )
        affiliations /= np.sum(affiliations, axis=-2, keepdims=True)
        return affiliations

After Change

    def predict(self, Y):

        *independent, T, D = Y.shape
        assert D < 20, (D, "Sure?")

        Y = _unit_norm(

Instances