f_var = []
for d in range(self.num_latent):
b = self.q_lambda[:, d]
A = K + tf.diag(1./tf.square(b))
L = tf.cholesky(A)
LiKx = tf.matrix_triangular_solve(L, tf.transpose(Kx), lower=True)
if full_cov:
f_var.append(self.kern.K(Xnew) -
After Change
// predictive var
A = K + tf.batch_matrix_diag(tf.transpose(1./tf.square(self.q_lambda)))
L = tf.batch_cholesky(A)
Kx_tiled = tf.tile(tf.expand_dims(Kx, 0), [self.num_latent, 1, 1])
LiKx = tf.batch_matrix_triangular_solve(L, Kx_tiled)
if full_cov:
f_var = self.kern.K(Xnew) - tf.batch_matmul(LiKx, LiKx, adj_x=True)
