geometric_kernels.feature_maps.deterministic
============================================

.. py:module:: geometric_kernels.feature_maps.deterministic

.. autoapi-nested-parse::

   This module provides the :class:`DeterministicFeatureMapCompact`, a
   Karhunen-Loève expansion-based feature map for those
   :class:`~.spaces.DiscreteSpectrumSpace`\ s, for which the eigenpairs
   are explicitly known.





Module Contents
---------------

.. py:class:: DeterministicFeatureMapCompact(space, num_levels)

   Bases: :py:obj:`geometric_kernels.feature_maps.base.FeatureMap`


   Deterministic feature map for :class:`~.spaces.DiscreteSpectrumSpace`\ s
   for which the actual eigenpairs are explicitly available.

   :param space:
       A :class:`~.spaces.DiscreteSpectrumSpace` space.
   :param num_levels:
       Number of levels in the kernel approximation.


   .. py:method:: __call__(X, params, normalize = None, **kwargs)

      :param X:
          [N, ...] points in the space to evaluate the map on.
      :param params:
          Parameters of the kernel (length scale and smoothness).
      :param normalize:
          Normalize to have unit average variance (if omitted
          or None, follows the standard behavior of
          :class:`~.kernels.MaternKarhunenLoeveKernel`).
      :param ``**kwargs``:
          Unused.

      :return:
          `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.

      .. 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 :class:`FeatureMap`, this
         first element may be an updated random key.