d1ac7b831ad36cd0e4bdd7980819f83208345148,gpflow/expectations.py,,_expectation,#Any#Any#Any#Any#Any#,547

Before Change

def _expectation(p, kern, feat, none2, none3):
    if not kern.on_separate_dimensions:
        raise NotImplementedError("Product currently needs to be defined on separate dimensions.")  // pragma: no cover
    with tf.control_dependencies([
        tf.assert_equal(tf.rank(p.var), 2,
                        message="Product currently only supports diagonal Xcov.", name="assert_Xcov_diag"),
    ]):
        _expectation_fn = lambda k: _expectation(p, k, feat, None, None)
        return functools.reduce(tf.multiply, [_expectation_fn(k) for k in kern.kern_list])


@dispatch(DiagonalGaussian, kernels.Product, InducingPoints, kernels.Product, InducingPoints)
@quadrature_fallback
def _expectation(p, kern1, feat1, kern2, feat2):
    if feat1 != feat2:

After Change

                                  "different Product kernels is not supported.")

    kern = kern1
    feat = feat1

    if not kern.on_separate_dimensions:
        raise NotImplementedError(
            "Product currently needs to be defined on separate dimensions.")  // pragma: no cover

    return functools.reduce(tf.multiply, [
        expectation(p, (k, feat), (k, feat)) for k in kern.kern_list])


// ============== Conversion to Gaussian from Diagonal or Markov ===============

Instances