geometric_kernels.feature_maps.deterministic

This module provides the DeterministicFeatureMapCompact, a Karhunen-Loève expansion-based feature map for those DiscreteSpectrumSpaces, for which the eigenpairs are explicitly known.

Module Contents

class geometric_kernels.feature_maps.deterministic.DeterministicFeatureMapCompact(space, num_levels)[source]

Bases: geometric_kernels.feature_maps.base.FeatureMap

Deterministic feature map for DiscreteSpectrumSpaces for which the actual eigenpairs are explicitly available.

Parameters:
__call__(X, params, normalize=None, **kwargs)[source]
Parameters:

  • X (lab.Numeric) – [N, …] points in the space to evaluate the map on.

  • params (beartype.typing.Dict[str, lab.Numeric]) – Parameters of the kernel (length scale and smoothness).

  • normalize (beartype.typing.Optional[bool]) – Normalize to have unit average variance (if omitted or None, follows the standard behavior of MaternKarhunenLoeveKernel).

  • **kwargs – Unused.

Returns:

Tuple(None, features) where features is an [N, O] array, N is the number of inputs and O is the dimension of the feature map.

Return type:

beartype.typing.Tuple[None, lab.Numeric]

Note

The first element of the returned tuple is the simple None and should be ignored. It is only there to support the abstract interface: for some other subclasses of FeatureMap, this first element may be an updated random key.