48270681afc13081094f7f398a1e194c6b07ba9b,gpflow/models/gplvm.py,BayesianGPLVM,_build_predict,#BayesianGPLVM#Any#Any#,149

Before Change

        there are notes in the SGPR notebook.
        :param Xnew: Point to predict at.
        
        num_inducing = tf.shape(self.Z)[0]
        psi1 = self.kern.eKxz(self.Z, self.X_mean, self.X_var)
        psi2 = tf.reduce_sum(self.kern.eKzxKxz(self.Z, self.X_mean, self.X_var), 0)
        Kuu = self.kern.K(self.Z) + tf.eye(num_inducing, dtype=settings.float_type) * settings.numerics.jitter_level
        Kus = self.kern.K(self.Z, Xnew)
        sigma2 = self.likelihood.variance
        sigma = tf.sqrt(sigma2)
        L = tf.cholesky(Kuu)

After Change

        
        pX = DiagonalGaussian(self.X_mean, self.X_var)

        num_inducing = len(self.feature)
        psi1 = expectation(pX, (self.feature, self.kern))
        psi2 = tf.reduce_sum(expectation(pX, (self.feature, self.kern), (self.feature, self.kern)), axis=0)
        Kuu = self.feature.Kuu(self.kern, jitter=settings.numerics.jitter_level)
        Kus = self.feature.Kuf(self.kern, Xnew)
        sigma2 = self.likelihood.variance
        sigma = tf.sqrt(sigma2)
        L = tf.cholesky(Kuu)

Instances