Function _sample_conditional

gpflow/conditionals/multioutput/sample_conditionals.py:42–77

`sample_conditional` will return a sample from the conditional distribution. In most cases this means calculating the conditional mean m and variance v and then returning m + sqrt(v) * eps, with eps ~ N(0, 1). However, for some combinations of Mok and Mof, more efficient samplin

(
    Xnew: tf.Tensor,
    inducing_variable: SharedIndependentInducingVariables,
    kernel: LinearCoregionalization,
    f: tf.Tensor,
    *,
    full_cov: bool = False,
    full_output_cov: bool = False,
    q_sqrt: Optional[tf.Tensor] = None,
    white: bool = False,
    num_samples: Optional[int] = None,
)

    "return[2]: [batch..., N, P]",
)
def _sample_conditional(
    Xnew: tf.Tensor,
    inducing_variable: SharedIndependentInducingVariables,
    kernel: LinearCoregionalization,
    f: tf.Tensor,
    *,
    full_cov: bool = False,
    full_output_cov: bool = False,
    q_sqrt: Optional[tf.Tensor] = None,
    white: bool = False,
    num_samples: Optional[int] = None,
) -> SamplesMeanAndVariance:
    """
     `sample_conditional` will return a sample from the conditional distribution.
     In most cases this means calculating the conditional mean m and variance v and then
     returning m + sqrt(v) * eps, with eps ~ N(0, 1).
     However, for some combinations of Mok and Mof, more efficient sampling routines exist.
     The dispatcher will make sure that we use the most efficent one.

    :return: samples, mean, cov
    """
    if full_cov:
        raise NotImplementedError("full_cov not yet implemented")
    if full_output_cov:
        raise NotImplementedError("full_output_cov not yet implemented")

    ind_conditional = conditional.dispatch_or_raise(
        object, SeparateIndependentInducingVariables, SeparateIndependent, object
    )
    g_mu, g_var = ind_conditional(
        Xnew, inducing_variable, kernel, f, white=white, q_sqrt=q_sqrt
    )  # [..., N, L], [..., N, L]
    g_sample = sample_mvn(g_mu, g_var, full_cov, num_samples=num_samples)  # [..., (S), N, L]
    f_mu, f_var = mix_latent_gp(kernel.W, g_mu, g_var, full_cov, full_output_cov)
    f_sample = tf.tensordot(g_sample, kernel.W, [[-1], [-1]])  # [..., N, P]
    return f_sample, f_mu, f_var