6c35a7a8544dfecfe92ef9b559b90fcb40b364eb,gpflow/models/sgpr.py,SGPR,_build_predict,#SGPR#Any#Any#,164

Before Change

        notebook.
        
        jitter_level = settings.numerics.jitter_level
        num_inducing = tf.shape(self.Z)[0]
        err = self.Y - self.mean_function(self.X)
        Kuf = self.kern.K(self.Z, self.X)
        Kuu = self.kern.K(self.Z) + tf.eye(num_inducing, dtype=settings.tf_float) * jitter_level
        Kus = self.kern.K(self.Z, Xnew)
        sigma = tf.sqrt(self.likelihood.variance)
        L = tf.cholesky(Kuu)
        A = tf.matrix_triangular_solve(L, Kuf, lower=True) / sigma

After Change

        return bound

    @params_as_tensors
    def _build_predict(self, Xnew, full_cov=False):
        
        Compute the mean and variance of the latent function at some new points
        Xnew. For a derivation of the terms in here, see the associated SGPR
        notebook.
        
        num_inducing = len(self.feature)
        err = self.Y - self.mean_function(self.X)
        Kuf = self.feature.Kuf(self.kern, self.X)
        Kuu = self.feature.Kuu(self.kern, jitter=settings.numerics.jitter_level)
        Kus = self.feature.Kuf(self.kern, Xnew)
        sigma = tf.sqrt(self.likelihood.variance)
        L = tf.cholesky(Kuu)
        A = tf.matrix_triangular_solve(L, Kuf, lower=True) / sigma

Instances