mo_sample_conditionals.py
import tensorflow as tf
from ..inducing_variables import SeparateIndependentInducingVariables, SharedIndependentInducingVariables
from ..kernels import SeparateIndependent, LinearCoregionalization
from .dispatch import conditional, sample_conditional
from .util import sample_mvn, mix_latent_gp
@sample_conditional.register(object, SharedIndependentInducingVariables, LinearCoregionalization, object)
def _sample_conditional(Xnew,
inducing_variable,
kernel,
f,
*,
full_cov=False,
full_output_cov=False,
q_sqrt=None,
white=False,
num_samples=None):
"""
`sample_conditional` will return a sample from the conditinoal 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 exists.
The dispatcher will make sure that we use the most efficent one.
:return: [N, P] (full_output_cov = False) or [N, P, P] (full_output_cov = True)
"""
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(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, "diag",
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