82fa2f2cd986414c8c968f3cd15886da3c30eb6c,pymc3/gp/gp.py,MarginalKron,_build_conditional,#MarginalKron#Any#Any#Any#,920

Before Change

            Asq = tt.dot(A.T, A)
            cov = Km - Asq
            if pred_noise:
                cov += sigma * np.eye(cov.shape)
        return mu, cov if pred_noise else stabilize(cov)

    def conditional(self, name, Xnew, pred_noise=False, **kwargs):
        
        Returns the conditional distribution evaluated over new input

After Change

            cov = Km - Asq
            if pred_noise:
                cov += sigma * tt.itentity_like(cov)
        return mu, cov

    def conditional(self, name, Xnew, pred_noise=False, **kwargs):
        
        Returns the conditional distribution evaluated over new input

Instances