d1ac7b831ad36cd0e4bdd7980819f83208345148,gpflow/expectations.py,,_expectation,#Any#Any#Any#Any#Any#,92

Before Change

        else:
            lengthscales = tf.zeros((D,), dtype=settings.tf_float) + kern.lengthscales

        vec = tf.expand_dims(Xmu, 2) - tf.expand_dims(tf.transpose(Z), 0)  // NxDxM
        chols = tf.cholesky(tf.expand_dims(tf.matrix_diag(lengthscales ** 2), 0) + Xcov)
        Lvec = tf.matrix_triangular_solve(chols, vec)
        q = tf.reduce_sum(Lvec ** 2, [1])

        chol_diags = tf.matrix_diag_part(chols)  // N x D
        half_log_dets = (tf.reduce_sum(tf.log(chol_diags), 1)
                         - tf.reduce_sum(tf.log(lengthscales)))  // N,

        return kern.variance * tf.exp(-0.5 * q - tf.expand_dims(half_log_dets, 1))


@dispatch(Gaussian, kernels.RBF, InducingPoints, mean_functions.Identity, type(None))
def _expectation(p, rbf_kern, feat, identity_mean, none):

After Change

        all_diffs = tf.transpose(Z) - tf.expand_dims(Xmu, 2)  // NxDxM
        exponent_mahalanobis = tf.matrix_triangular_solve(chol_L_plus_Xcov, all_diffs, lower=True)  // NxDxM
        exponent_mahalanobis = tf.reduce_sum(tf.square(exponent_mahalanobis), 1)  // NxM
        exponent_mahalanobis = tf.exp(-0.5 * exponent_mahalanobis)  // NxM

        sqrt_det_L = tf.reduce_prod(lengthscales)
        sqrt_det_L_plus_Xcov = tf.exp(tf.reduce_sum(tf.log(tf.matrix_diag_part(chol_L_plus_Xcov)), axis=1))
        determinants = sqrt_det_L / sqrt_det_L_plus_Xcov  // N

        return kern.variance * (determinants[:, None] * exponent_mahalanobis)


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

Instances