gpflow.conditionals.multioutput.conditionals

gpflow.conditionals.multioutput.conditionals.coregionalization_conditional

gpflow.conditionals.multioutput.conditionals.coregionalization_conditional(Xnew, inducing_variable, kernel, f, *, full_cov=False, full_output_cov=False, q_sqrt=None, white=False)[source]

Most efficient routine to project L independent latent gps through a mixing matrix W. The mixing matrix is a member of the LinearCoregionalization and has shape [P, L]. The covariance matrices used to calculate the conditional have the following shape: - Kuu: [L, M, M] - Kuf: [L, M, N] - Kff: [L, N] or [L, N, N]

Parameters
Return type

Tuple[Tensor, Tensor]

gpflow.conditionals.multioutput.conditionals.fallback_independent_latent_conditional

gpflow.conditionals.multioutput.conditionals.fallback_independent_latent_conditional(Xnew, inducing_variable, kernel, f, *, full_cov=False, full_output_cov=False, q_sqrt=None, white=False)[source]

Interdomain conditional with independent latents. In this case the number of latent GPs (L) will be different than the number of outputs (P) The covariance matrices used to calculate the conditional have the following shape: - Kuu: [L, M, M] - Kuf: [M, L, N, P] - Kff: [N, P, N, P], [N, P, P], [N, P]

Parameters
Return type

Tuple[Tensor, Tensor]

gpflow.conditionals.multioutput.conditionals.inducing_point_conditional

gpflow.conditionals.multioutput.conditionals.inducing_point_conditional(Xnew, inducing_variable, kernel, f, *, full_cov=False, full_output_cov=False, q_sqrt=None, white=False)[source]

Multi-output GP with fully correlated inducing variables. The inducing variables are shaped in the same way as evaluations of K, to allow a default inducing point scheme for multi-output kernels. The covariance matrices used to calculate the conditional have the following shape: - Kuu: [M, L, M, L] - Kuf: [M, L, N, P] - Kff: [N, P, N, P], [N, P, P], [N, P]

Parameters
:param f: variational mean, [L, 1]
:param q_sqrt: standard-deviations or cholesky, [L, 1] or [1, L, L]
Parameters

  • Xnew (Tensor) –

  • inducing_variable (InducingPoints) –

  • kernel (MultioutputKernel) –

  • f (Tensor) –

  • full_cov (bool) –

  • full_output_cov (bool) –

  • q_sqrt (Optional[Tensor]) –

  • white (bool) –

Return type

Tuple[Tensor, Tensor]

gpflow.conditionals.multioutput.conditionals.separate_independent_conditional

gpflow.conditionals.multioutput.conditionals.separate_independent_conditional(Xnew, inducing_variable, kernel, f, *, full_cov=False, full_output_cov=False, q_sqrt=None, white=False)[source]
Parameters
Return type

Tuple[Tensor, Tensor]

gpflow.conditionals.multioutput.conditionals.shared_independent_conditional

gpflow.conditionals.multioutput.conditionals.shared_independent_conditional(Xnew, inducing_variable, kernel, f, *, full_cov=False, full_output_cov=False, q_sqrt=None, white=False)[source]

Multioutput conditional for an independent kernel and shared inducing inducing. Same behaviour as conditional with non-multioutput kernels. The covariance matrices used to calculate the conditional have the following shape: - Kuu: [M, M] - Kuf: [M, N] - Kff: N or [N, N]

Parameters
Return type

Tuple[Tensor, Tensor]