def testTensorsInSupportsAlwaysFinite(self, support, data):
try:
result_ = data.draw(tfp_hps.tensors_in_support(support))
except NotImplementedError:
# Constraint class doesn't have a constrainer function at all, so this
# test is moot.
return
result = self.evaluate(result_)
self.assertTrue(np.all(np.isfinite(result)))
def variational_gaussian_processes(draw,
kernel_name=None,
batch_shape=None,
event_dim=None,
feature_dim=None,
feature_ndims=None,
enable_vars=False):
# First draw a kernel.
k, _ = draw(kernel_hps.base_kernels(
kernel_name=kernel_name,
batch_shape=batch_shape,
event_dim=event_dim,
feature_dim=feature_dim,
feature_ndims=feature_ndims,
# Disable variables
enable_vars=False))
compatible_batch_shape = draw(
tfp_hps.broadcast_compatible_shape(k.batch_shape))
index_points = draw(kernel_hps.kernel_input(
batch_shape=compatible_batch_shape,
example_ndims=1,
feature_dim=feature_dim,
feature_ndims=feature_ndims,
enable_vars=enable_vars,
name='index_points'))
hp.note('Index points:\n{}'.format(repr(index_points)))
inducing_index_points = draw(kernel_hps.kernel_input(
batch_shape=compatible_batch_shape,
example_ndims=1,
feature_dim=feature_dim,
feature_ndims=feature_ndims,
enable_vars=enable_vars,
name='inducing_index_points'))
hp.note('Inducing index points:\n{}'.format(repr(inducing_index_points)))
num_inducing_points = int(inducing_index_points.shape[-(feature_ndims + 1)])
variational_inducing_observations_loc = draw(kernel_hps.kernel_input(
batch_shape=compatible_batch_shape,
example_ndims=1,
example_dim=num_inducing_points,
feature_dim=0,
feature_ndims=0,
enable_vars=enable_vars,
name='variational_inducing_observations_loc'))
hp.note('Variational inducing observations loc:\n{}'.format(
repr(variational_inducing_observations_loc)))
variational_inducing_observations_scale = draw(tfp_hps.tensors_in_support(
support=tfp_hps.Support.MATRIX_LOWER_TRIL_POSITIVE_DEFINITE,
batch_shape=compatible_batch_shape.as_list(),
event_dim=num_inducing_points,
dtype=np.float64))
hp.note('Variational inducing observations scale:\n{}'.format(
repr(variational_inducing_observations_scale)))
params = draw(broadcasting_params(
'GaussianProcessRegressionModel',
compatible_batch_shape,
event_dim=event_dim,
enable_vars=enable_vars))
hp.note('Params:\n{}'.format(repr(params)))
vgp = tfd.VariationalGaussianProcess(
kernel=k,
index_points=index_points,
inducing_index_points=inducing_index_points,
variational_inducing_observations_loc=(
variational_inducing_observations_loc),
variational_inducing_observations_scale=(
variational_inducing_observations_scale),
observation_noise_variance=params[
'observation_noise_variance'])
return vgp