gpflow.inducing_variables.inducing_variables#

Classes#

gpflow.inducing_variables.inducing_variables.InducingPointsBase#

class gpflow.inducing_variables.inducing_variables.InducingPointsBase(Z, name=None)[source]#

Bases: InducingVariables

Parameters:

  • Z (Union[int, float, Sequence[Any], ndarray[Any, Any], Tensor, Variable, Parameter]) –

  • name (Optional[str]) –

property num_inducing: Optional[Tensor]#

Returns the number of inducing variables, relevant for example to determine the size of the variational distribution.

Return type:

Optional[Tensor]

property shape: Optional[Tuple[Optional[int], ...]]#

Return the shape of these inducing variables.

Shape should be some variation of [M, D, P], where:

  • M is the number of inducing variables.

  • D is the number of input dimensions.

  • P is the number of output dimensions (1 if this is not a multi-output inducing variable).

Return type:

Optional[Tuple[Optional[int], ...]]

Functions#

gpflow.inducing_variables.inducing_variables.get_scalar_shape#

gpflow.inducing_variables.inducing_variables.get_scalar_shape(shaped, context)[source]#
Parameters:
Return type:

Optional[Tuple[Optional[int], ...]]