d1ac7b831ad36cd0e4bdd7980819f83208345148,gpflow/expectations.py,,_expectation,#Any#Any#Any#Any#Any#,560

Before Change

@quadrature_fallback
def _expectation(p, kern1, feat1, kern2, feat2):
    if feat1 != feat2:
        raise NotImplementedError("Different features are not supported")

    if kern1 != kern2:
        raise NotImplementedError("Calculating the expectation over two different Product kernels is not supported")

After Change

    with params_as_tensors_for(mean1), params_as_tensors_for(mean2):
        N = tf.shape(p.mu)[0]
        e_xxt = p.cov + (p.mu[:, :, None] * p.mu[:, None, :])  // NxDxD
        e_xxt_A = tf.matmul(e_xxt, tf.tile(mean2.A[None, ...], (N, 1, 1)))  // NxDxQ
        e_x_bt = p.mu[:, :, None] * mean2.b[None, None, :]  // NxDxQ

        return e_xxt_A + e_x_bt


@dispatch(Gaussian, mean_functions.Linear, type(None), mean_functions.Identity, type(None))
def _expectation(p, mean1, none1, mean2, none2):

Instances