geometric_kernels.feature_maps.rejection_sampling
=================================================

.. py:module:: geometric_kernels.feature_maps.rejection_sampling

.. autoapi-nested-parse::

   This module provides the :class:`RejectionSamplingFeatureMapHyperbolic` and the
   :class:`RejectionSamplingFeatureMapSPD`, rejection sampling-based feature maps
   for :class:`~.spaces.Hyperbolic` and
   :class:`~.spaces.SymmetricPositiveDefiniteMatrices`, respectively.





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

.. py:class:: RejectionSamplingFeatureMapHyperbolic(space, num_random_phases = 3000, shifted_laplacian = True)

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


   Random phase feature map for the :class:`~.spaces.Hyperbolic` space based
   on the rejection sampling algorithm.

   :param space:
       A :class:`~.spaces.Hyperbolic` space.
   :param num_random_phases:
       Number of random phases to use.
   :param shifted_laplacian:
       If True, assumes that the kernels are defined in terms of the shifted
       Laplacian. This often makes Matérn kernels more flexible by widening
       the effective range of the length scale parameter.

       Defaults to True.


   .. py:method:: __call__(X, params, *, key, normalize = True, **kwargs)

      :param X:
          [N, D] points in the space to evaluate the map on.
      :param params:
          Parameters of the feature map (length scale and smoothness).
      :param key:
          Random state, either `np.random.RandomState`,
          `tf.random.Generator`, `torch.Generator` or `jax.tensor` (which
          represents a random state).

          .. note::
              For any backend other than `jax`, passing the same `key` twice
              does not guarantee that the feature map will be the same each
              time. This is because these backends' random state has... a
              state. To evaluate the same (including randomness) feature map
              on different inputs, you can either save/restore state manually
              each time or use the helper function
              :func:`~.utils.make_deterministic` which does this for you.

      :param normalize:
          Normalize to have unit average variance (`True` by default).
      :param ``**kwargs``:
          Unused.

      :return:
          `Tuple(key, features)` where `features` is an [N, O] array, N
          is the number of inputs and O is the dimension of the feature map;
          `key` is the updated random key for `jax`, or the similar random
          state (generator) for any other backends.



.. py:class:: RejectionSamplingFeatureMapSPD(space, num_random_phases = 3000, shifted_laplacian = True)

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


   Random phase feature map for the
   :class:`~.spaces.SymmetricPositiveDefiniteMatrices` space based on the
   rejection sampling algorithm.

   :param space:
       A :class:`~.spaces.SymmetricPositiveDefiniteMatrices` space.
   :param num_random_phases:
       Number of random phases to use.
   :param shifted_laplacian:
       If True, assumes that the kernels are defined in terms of the shifted
       Laplacian. This often makes Matérn kernels more flexible by widening
       the effective range of the length scale parameter.

       Defaults to True.


   .. py:method:: __call__(X, params, *, key, normalize = True, **kwargs)

      :param X:
          [N, D, D] points in the space to evaluate the map on.
      :param params:
          Parameters of the feature map (length scale and smoothness).
      :param key:
          Random state, either `np.random.RandomState`,
          `tf.random.Generator`, `torch.Generator` or `jax.tensor` (which
          represents a random state).

          .. note::
              For any backend other than `jax`, passing the same `key` twice
              does not guarantee that the feature map will be the same each
              time. This is because these backends' random state has... a
              state. To evaluate the same (including randomness) feature map
              on different inputs, you can either save/restore state manually
              each time or use the helper function
              :func:`~.utils.make_deterministic` which does this for you.

      :param normalize:
          Normalize to have unit average variance (`True` by default).
      :param ``**kwargs``:
          Unused.

      :return:
          `Tuple(key, features)` where `features` is an [N, O] array, N
          is the number of inputs and O is the dimension of the feature map;
          `key` is the updated random key for `jax`, or the similar random
          state (generator) for any other backends.