gpflow.covariances

gpflow.covariances.Kuf

This function uses multiple dispatch, which will depend on the type of argument passed in:

gpflow.covariances.Kuf( InducingPoints, Kernel, object )
# dispatch to -> gpflow.covariances.kufs.Kuf_kernel_inducingpoints(...)
gpflow.covariances.kufs.Kuf_kernel_inducingpoints(inducing_variable: gpflow.inducing_variables.inducing_variables.InducingPoints, kernel: gpflow.kernels.base.Kernel, Xnew)[source]
Parameters

  • inducing_variable (InducingPoints) –

  • kernel (Kernel) – 

gpflow.covariances.Kuf( Multiscale, SquaredExponential, object )
# dispatch to -> gpflow.covariances.kufs.Kuf_sqexp_multiscale(...)
gpflow.covariances.kufs.Kuf_sqexp_multiscale(inducing_variable: gpflow.inducing_variables.inducing_variables.Multiscale, kernel: gpflow.kernels.stationaries.SquaredExponential, Xnew)[source]
Parameters

  • inducing_variable (Multiscale) –

  • kernel (SquaredExponential) – 

gpflow.covariances.Kuf( InducingPatches, Convolutional, object )
# dispatch to -> gpflow.covariances.kufs.Kuf_conv_patch(...)
gpflow.covariances.kufs.Kuf_conv_patch(feat, kern, Xnew)[source]
gpflow.covariances.Kuf( InducingPoints, MultioutputKernel, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.multioutput.kufs._Kuf(inducing_variable, kernel, Xnew)[source]
gpflow.covariances.Kuf( SharedIndependentInducingVariables, SharedIndependent, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.multioutput.kufs._Kuf(inducing_variable, kernel, Xnew)[source]
gpflow.covariances.Kuf( SeparateIndependentInducingVariables, SharedIndependent, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.multioutput.kufs._Kuf(inducing_variable, kernel, Xnew)[source]
gpflow.covariances.Kuf( SharedIndependentInducingVariables, SeparateIndependent, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.multioutput.kufs._Kuf(inducing_variable, kernel, Xnew)[source]
gpflow.covariances.Kuf( SeparateIndependentInducingVariables, SeparateIndependent, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.multioutput.kufs._Kuf(inducing_variable, kernel, Xnew)[source]
gpflow.covariances.Kuf( FallbackSeparateIndependentInducingVariables, LinearCoregionalization, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.multioutput.kufs._Kuf(inducing_variable, kernel, Xnew)[source]
gpflow.covariances.Kuf( FallbackSharedIndependentInducingVariables, LinearCoregionalization, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.multioutput.kufs._Kuf(inducing_variable, kernel, Xnew)[source]
gpflow.covariances.Kuf( SharedIndependentInducingVariables, LinearCoregionalization, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.multioutput.kufs._Kuf(inducing_variable, kernel, Xnew)[source]
gpflow.covariances.Kuf( SeparateIndependentInducingVariables, LinearCoregionalization, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.multioutput.kufs._Kuf(inducing_variable, kernel, Xnew)[source]

gpflow.covariances.Kuu

This function uses multiple dispatch, which will depend on the type of argument passed in:

gpflow.covariances.Kuu( InducingPoints, Kernel )
# dispatch to -> gpflow.covariances.kuus.Kuu_kernel_inducingpoints(...)
gpflow.covariances.kuus.Kuu_kernel_inducingpoints(inducing_variable: gpflow.inducing_variables.inducing_variables.InducingPoints, kernel: gpflow.kernels.base.Kernel, *, jitter=0.0)[source]
Parameters

  • inducing_variable (InducingPoints) –

  • kernel (Kernel) – 

gpflow.covariances.Kuu( Multiscale, SquaredExponential )
# dispatch to -> gpflow.covariances.kuus.Kuu_sqexp_multiscale(...)
gpflow.covariances.kuus.Kuu_sqexp_multiscale(inducing_variable: gpflow.inducing_variables.inducing_variables.Multiscale, kernel: gpflow.kernels.stationaries.SquaredExponential, *, jitter=0.0)[source]
Parameters

  • inducing_variable (Multiscale) –

  • kernel (SquaredExponential) – 

gpflow.covariances.Kuu( InducingPatches, Convolutional )
# dispatch to -> gpflow.covariances.kuus.Kuu_conv_patch(...)
gpflow.covariances.kuus.Kuu_conv_patch(feat, kern, jitter=0.0)[source]
gpflow.covariances.Kuu( InducingPoints, MultioutputKernel )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)
gpflow.covariances.multioutput.kuus._Kuu(inducing_variable: gpflow.inducing_variables.multioutput.inducing_variables.FallbackSeparateIndependentInducingVariables, kernel: Union[gpflow.kernels.multioutput.kernels.SeparateIndependent, gpflow.kernels.multioutput.kernels.LinearCoregionalization], *, jitter=0.0)[source]
Parameters

  • inducing_variable (FallbackSeparateIndependentInducingVariables) –

  • kernel (Union[SeparateIndependent, LinearCoregionalization]) – 

gpflow.covariances.Kuu( FallbackSharedIndependentInducingVariables, SharedIndependent )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)
gpflow.covariances.multioutput.kuus._Kuu(inducing_variable: gpflow.inducing_variables.multioutput.inducing_variables.FallbackSeparateIndependentInducingVariables, kernel: Union[gpflow.kernels.multioutput.kernels.SeparateIndependent, gpflow.kernels.multioutput.kernels.LinearCoregionalization], *, jitter=0.0)[source]
Parameters

  • inducing_variable (FallbackSeparateIndependentInducingVariables) –

  • kernel (Union[SeparateIndependent, LinearCoregionalization]) – 

gpflow.covariances.Kuu( FallbackSharedIndependentInducingVariables, SeparateIndependent )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)
gpflow.covariances.multioutput.kuus._Kuu(inducing_variable: gpflow.inducing_variables.multioutput.inducing_variables.FallbackSeparateIndependentInducingVariables, kernel: Union[gpflow.kernels.multioutput.kernels.SeparateIndependent, gpflow.kernels.multioutput.kernels.LinearCoregionalization], *, jitter=0.0)[source]
Parameters

  • inducing_variable (FallbackSeparateIndependentInducingVariables) –

  • kernel (Union[SeparateIndependent, LinearCoregionalization]) – 

gpflow.covariances.Kuu( FallbackSharedIndependentInducingVariables, IndependentLatent )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)
gpflow.covariances.multioutput.kuus._Kuu(inducing_variable: gpflow.inducing_variables.multioutput.inducing_variables.FallbackSeparateIndependentInducingVariables, kernel: Union[gpflow.kernels.multioutput.kernels.SeparateIndependent, gpflow.kernels.multioutput.kernels.LinearCoregionalization], *, jitter=0.0)[source]
Parameters

  • inducing_variable (FallbackSeparateIndependentInducingVariables) –

  • kernel (Union[SeparateIndependent, LinearCoregionalization]) – 

gpflow.covariances.Kuu( FallbackSeparateIndependentInducingVariables, SharedIndependent )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)
gpflow.covariances.multioutput.kuus._Kuu(inducing_variable: gpflow.inducing_variables.multioutput.inducing_variables.FallbackSeparateIndependentInducingVariables, kernel: Union[gpflow.kernels.multioutput.kernels.SeparateIndependent, gpflow.kernels.multioutput.kernels.LinearCoregionalization], *, jitter=0.0)[source]
Parameters

  • inducing_variable (FallbackSeparateIndependentInducingVariables) –

  • kernel (Union[SeparateIndependent, LinearCoregionalization]) – 

gpflow.covariances.Kuu( FallbackSeparateIndependentInducingVariables, SeparateIndependent )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)
gpflow.covariances.multioutput.kuus._Kuu(inducing_variable: gpflow.inducing_variables.multioutput.inducing_variables.FallbackSeparateIndependentInducingVariables, kernel: Union[gpflow.kernels.multioutput.kernels.SeparateIndependent, gpflow.kernels.multioutput.kernels.LinearCoregionalization], *, jitter=0.0)[source]
Parameters

  • inducing_variable (FallbackSeparateIndependentInducingVariables) –

  • kernel (Union[SeparateIndependent, LinearCoregionalization]) – 

gpflow.covariances.Kuu( FallbackSeparateIndependentInducingVariables, LinearCoregionalization )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)
gpflow.covariances.multioutput.kuus._Kuu(inducing_variable: gpflow.inducing_variables.multioutput.inducing_variables.FallbackSeparateIndependentInducingVariables, kernel: Union[gpflow.kernels.multioutput.kernels.SeparateIndependent, gpflow.kernels.multioutput.kernels.LinearCoregionalization], *, jitter=0.0)[source]
Parameters

  • inducing_variable (FallbackSeparateIndependentInducingVariables) –

  • kernel (Union[SeparateIndependent, LinearCoregionalization]) –

gpflow.covariances.kufs

gpflow.covariances.kuus

gpflow.covariances.multioutput