def test_independent_single_output(register_posterior_test, q_sqrt_factory,
whiten, full_cov, full_output_cov):
kernel = gpflow.kernels.SquaredExponential()
inducing_variable = inducingpoint_wrapper(
np.random.randn(NUM_INDUCING_POINTS, INPUT_DIMS))
q_mu = np.random.randn(NUM_INDUCING_POINTS, 1)
q_sqrt = q_sqrt_factory(NUM_INDUCING_POINTS, 1)
conditional = create_conditional(
kernel=kernel,
inducing_variable=inducing_variable,
q_mu=q_mu,
q_sqrt=q_sqrt,
whiten=whiten,
)
posterior = create_posterior(
kernel=kernel,
inducing_variable=inducing_variable,
q_mu=q_mu,
q_sqrt=q_sqrt,
whiten=whiten,
)
register_posterior_test(posterior, IndependentPosteriorSingleOutput)
_assert_fused_predict_f_equals_precomputed_predict_f_and_conditional(
posterior, conditional, full_cov, full_output_cov)
def test_linear_coregionalization_shi(
register_posterior_test,
q_sqrt_factory,
full_cov,
full_output_cov,
whiten,
num_latent_gps,
output_dims,
):
"""
Linear coregionalization with shared independent inducing variables.
"""
kernel = gpflow.kernels.LinearCoregionalization(
[gpflow.kernels.SquaredExponential() for _ in range(num_latent_gps)],
W=tf.random.normal((output_dims, num_latent_gps)),
)
inducing_variable = gpflow.inducing_variables.SharedIndependentInducingVariables(
inducingpoint_wrapper(np.random.randn(NUM_INDUCING_POINTS, INPUT_DIMS))
)
q_mu = np.random.randn(NUM_INDUCING_POINTS, num_latent_gps)
q_sqrt = q_sqrt_factory(NUM_INDUCING_POINTS, num_latent_gps)
conditional = create_conditional(
kernel=kernel, inducing_variable=inducing_variable, q_mu=q_mu, q_sqrt=q_sqrt, whiten=whiten,
)
posterior = create_posterior(
kernel=kernel, inducing_variable=inducing_variable, q_mu=q_mu, q_sqrt=q_sqrt, whiten=whiten,
)
register_posterior_test(posterior, LinearCoregionalizationPosterior)
_assert_fused_predict_f_equals_precomputed_predict_f_and_conditional(
posterior, conditional, full_cov, full_output_cov
)
def test_independent_multi_output_sek_shi(
register_posterior_test,
q_sqrt_factory,
full_cov,
full_output_cov,
whiten,
num_latent_gps,
output_dims,
):
"""
Independent multi-output posterior with separate independent kernels and shared inducing points.
"""
kernel = gpflow.kernels.SeparateIndependent(
[gpflow.kernels.SquaredExponential() for _ in range(num_latent_gps)]
)
inducing_variable = gpflow.inducing_variables.SharedIndependentInducingVariables(
inducingpoint_wrapper(np.random.randn(NUM_INDUCING_POINTS, INPUT_DIMS))
)
q_mu = np.random.randn(NUM_INDUCING_POINTS, num_latent_gps)
q_sqrt = q_sqrt_factory(NUM_INDUCING_POINTS, num_latent_gps)
conditional = create_conditional(
kernel=kernel, inducing_variable=inducing_variable, q_mu=q_mu, q_sqrt=q_sqrt, whiten=whiten,
)
posterior = create_posterior(
kernel=kernel, inducing_variable=inducing_variable, q_mu=q_mu, q_sqrt=q_sqrt, whiten=whiten,
)
register_posterior_test(posterior, IndependentPosteriorMultiOutput)
_assert_fused_predict_f_equals_precomputed_predict_f_and_conditional(
posterior, conditional, full_cov, full_output_cov
)
def test_fallback_independent_multi_output_shi(
register_posterior_test, q_sqrt_factory, full_cov, full_output_cov, whiten, output_dims,
):
"""
Fallback posterior with shared independent inducing variables.
The FallbackIndependentLatentPosterior is a subclass of the FullyCorrelatedPosterior which
requires a single latent GP function.
"""
kernel = gpflow.kernels.LinearCoregionalization(
[gpflow.kernels.SquaredExponential()], W=tf.random.normal((output_dims, 1))
)
inducing_variable = gpflow.inducing_variables.FallbackSharedIndependentInducingVariables(
inducingpoint_wrapper(np.random.randn(NUM_INDUCING_POINTS, INPUT_DIMS))
)
q_mu = np.random.randn(NUM_INDUCING_POINTS, 1)
q_sqrt = q_sqrt_factory(NUM_INDUCING_POINTS, 1)
conditional = create_conditional(
kernel=kernel, inducing_variable=inducing_variable, q_mu=q_mu, q_sqrt=q_sqrt, whiten=whiten,
)
posterior = create_posterior(
kernel=kernel, inducing_variable=inducing_variable, q_mu=q_mu, q_sqrt=q_sqrt, whiten=whiten,
)
register_posterior_test(posterior, FallbackIndependentLatentPosterior)
_assert_fused_predict_f_equals_precomputed_predict_f_and_conditional(
posterior, conditional, full_cov, full_output_cov
)
def test_fully_correlated_multi_output(
register_posterior_test, q_sqrt_factory, full_cov, full_output_cov, whiten, output_dims,
):
"""
The fully correlated posterior has one latent GP.
"""
kernel = gpflow.kernels.SharedIndependent(
gpflow.kernels.SquaredExponential(), output_dim=output_dims
)
inducing_variable = inducingpoint_wrapper(np.random.randn(NUM_INDUCING_POINTS, INPUT_DIMS))
q_mu = np.random.randn(output_dims * NUM_INDUCING_POINTS, 1)
q_sqrt = q_sqrt_factory(output_dims * NUM_INDUCING_POINTS, 1)
conditional = create_conditional(
kernel=kernel, inducing_variable=inducing_variable, q_mu=q_mu, q_sqrt=q_sqrt, whiten=whiten,
)
posterior = create_posterior(
kernel=kernel, inducing_variable=inducing_variable, q_mu=q_mu, q_sqrt=q_sqrt, whiten=whiten,
)
register_posterior_test(posterior, FullyCorrelatedPosterior)
_assert_fused_predict_f_equals_precomputed_predict_f_and_conditional(
posterior, conditional, full_cov, full_output_cov
)
