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(...)
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gpflow.covariances.kufs.Kuf_kernel_inducingpoints(inducing_variable, kernel, Xnew)[source]¶ - Parameters
inducing_variable (
InducingPoints) –kernel (
Kernel) –
gpflow.covariances.Kuf( Multiscale, SquaredExponential, object )
# dispatch to -> gpflow.covariances.kufs.Kuf_sqexp_multiscale(...)
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gpflow.covariances.kufs.Kuf_sqexp_multiscale(inducing_variable, kernel, Xnew)[source]¶ - Parameters
inducing_variable (
Multiscale) –kernel (
SquaredExponential) –
gpflow.covariances.Kuf( InducingPatches, Convolutional, object )
# dispatch to -> gpflow.covariances.kufs.Kuf_conv_patch(...)
gpflow.covariances.Kuf( InducingPoints, MultioutputKernel, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.Kuf( SharedIndependentInducingVariables, SharedIndependent, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.Kuf( SeparateIndependentInducingVariables, SharedIndependent, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.Kuf( SharedIndependentInducingVariables, SeparateIndependent, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.Kuf( SeparateIndependentInducingVariables, SeparateIndependent, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.Kuf( FallbackSeparateIndependentInducingVariables, LinearCoregionalization, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.Kuf( FallbackSharedIndependentInducingVariables, LinearCoregionalization, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.Kuf( SharedIndependentInducingVariables, LinearCoregionalization, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
gpflow.covariances.Kuf( SeparateIndependentInducingVariables, LinearCoregionalization, object )
# dispatch to -> gpflow.covariances.multioutput.kufs._Kuf(...)
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(...)
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gpflow.covariances.kuus.Kuu_kernel_inducingpoints(inducing_variable, kernel, *, jitter=0.0)[source]¶ - Parameters
inducing_variable (
InducingPoints) –kernel (
Kernel) –
gpflow.covariances.Kuu( Multiscale, SquaredExponential )
# dispatch to -> gpflow.covariances.kuus.Kuu_sqexp_multiscale(...)
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gpflow.covariances.kuus.Kuu_sqexp_multiscale(inducing_variable, kernel, *, 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.Kuu( InducingPoints, MultioutputKernel )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)
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gpflow.covariances.multioutput.kuus._Kuu(inducing_variable, kernel, *, jitter=0.0)[source]¶ - Parameters
inducing_variable (
FallbackSeparateIndependentInducingVariables) –kernel (
Union[SeparateIndependent,LinearCoregionalization]) –
gpflow.covariances.Kuu( FallbackSharedIndependentInducingVariables, SharedIndependent )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)
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gpflow.covariances.multioutput.kuus._Kuu(inducing_variable, kernel, *, jitter=0.0)[source]¶ - Parameters
inducing_variable (
FallbackSeparateIndependentInducingVariables) –kernel (
Union[SeparateIndependent,LinearCoregionalization]) –
gpflow.covariances.Kuu( FallbackSharedIndependentInducingVariables, SeparateIndependent )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)
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gpflow.covariances.multioutput.kuus._Kuu(inducing_variable, kernel, *, jitter=0.0)[source]¶ - Parameters
inducing_variable (
FallbackSeparateIndependentInducingVariables) –kernel (
Union[SeparateIndependent,LinearCoregionalization]) –
gpflow.covariances.Kuu( FallbackSharedIndependentInducingVariables, IndependentLatent )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)
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gpflow.covariances.multioutput.kuus._Kuu(inducing_variable, kernel, *, jitter=0.0)[source]¶ - Parameters
inducing_variable (
FallbackSeparateIndependentInducingVariables) –kernel (
Union[SeparateIndependent,LinearCoregionalization]) –
gpflow.covariances.Kuu( FallbackSeparateIndependentInducingVariables, SharedIndependent )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)
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gpflow.covariances.multioutput.kuus._Kuu(inducing_variable, kernel, *, jitter=0.0)[source]¶ - Parameters
inducing_variable (
FallbackSeparateIndependentInducingVariables) –kernel (
Union[SeparateIndependent,LinearCoregionalization]) –
gpflow.covariances.Kuu( FallbackSeparateIndependentInducingVariables, SeparateIndependent )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)
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gpflow.covariances.multioutput.kuus._Kuu(inducing_variable, kernel, *, jitter=0.0)[source]¶ - Parameters
inducing_variable (
FallbackSeparateIndependentInducingVariables) –kernel (
Union[SeparateIndependent,LinearCoregionalization]) –
gpflow.covariances.Kuu( FallbackSeparateIndependentInducingVariables, LinearCoregionalization )
# dispatch to -> gpflow.covariances.multioutput.kuus._Kuu(...)