d1ac7b831ad36cd0e4bdd7980819f83208345148,gpflow/expectations.py,,_expectation,#Any#Any#Any#Any#Any#,403

Before Change

    ]):
        Xmu = tf.identity(Xmu)

    with params_as_tensors_for(lin_kern), \
         params_as_tensors_for(identity_mean), \
         params_as_tensors_for(feat):

        N = tf.shape(Xmu)[0]
        op = tf.expand_dims(Xmu, 2) * tf.expand_dims(Xmu, 1) + Xcov  // NxDxD
        return lin_kern.variance * tf.matmul(tf.tile(tf.expand_dims(feat.Z, 0), (N, 1, 1)), op)


@dispatch(Gaussian, (mean_functions.Linear, mean_functions.Constant), type(None), type(None), type(None))
def _expectation(p, mean, none1, none2, none3):
    
    It computes the expectation:

After Change

    with params_as_tensors_for(kern), params_as_tensors_for(feat):
        N = tf.shape(Xmu)[0]
        var_Z = kern.variance * feat.Z  // MxD
        tiled_Z = tf.tile(tf.expand_dims(var_Z, 0), (N, 1, 1))  // NxMxD
        return tf.matmul(tiled_Z, Xcov + (Xmu[..., None] * Xmu[:, None, :]))


@dispatch(MarkovGaussian, kernels.Linear, InducingPoints, mean_functions.Identity, type(None))
def _expectation(p, kern, feat, mean, none):

Instances