geometric_kernels.spaces.su =========================== .. py:module:: geometric_kernels.spaces.su .. autoapi-nested-parse:: This module provides the :class:`SpecialUnitary` space and the respective :class:`~.eigenfunctions.Eigenfunctions` subclass :class:`SUEigenfunctions`. Module Contents --------------- .. py:class:: SUCharacter(n, signature) Bases: :py:obj:`geometric_kernels.spaces.lie_groups.LieGroupCharacter` The class that represents a character of the SU(n) group. Many of the characters on SU(n) are complex-valued. These are polynomials whose coefficients are precomputed and stored in a file. By default, there are 20 precomputed characters for n from 2 to 6. If you want more, use the `utils/compute_characters.py` script. :param n: The order n of the SO(n) group. :param signature: The signature that determines a particular character (and an irreducible unitary representation along with it). .. py:method:: __call__(gammas) Compute the character on `gammas` lying in a maximal torus. :param gammas: [..., rank] where `rank` is the dimension of a maximal torus. :return: An array of shape [...] representing the values of the characters. The values can be complex-valued. .. py:class:: SUEigenfunctions(n, num_levels, compute_characters=True) Bases: :py:obj:`geometric_kernels.spaces.lie_groups.WeylAdditionTheorem` A class for computing the sums of outer products of certain combinations (we call "levels") of Laplace-Beltrami eigenfunctions on compact Lie groups. These are much like the *zonal spherical harmonics* of the :class:`~.SphericalHarmonics` class. However, the key to computing them is representation-theoretic: they are proportional to *characters* of irreducible unitary representations of the group. These characters, in their turn, can be algebraically computed using the *Weyl character formula*. See :cite:t:`azangulov2022` for the mathematical details behind this class. :param n: The order of the Lie group, e.g. for SO(5) this is 5, for SU(3) this is 3. :param num_levels: The number of levels used for kernel approximation. Here, each level corresponds to an irreducible unitary representation of the group. :param compute_characters: Whether or not to actually compute the *characters*. Setting this parameter to False might make sense if you do not care about eigenfunctions (or sums of outer products thereof), but care about eigenvalues, dimensions of irreducible unitary representations, etc. Defaults to True. .. note:: Unlike :class:`~.SphericalHarmonics`, we do not expect the descendants of this class to compute the actual Laplace-Beltrami eigenfunctions (which are similar to (non-zonal) *spherical harmonics* of :class:`~.SphericalHarmonics`), implementing the `__call___` method. In this case, this is not really necessary and computationally harder. .. note:: Unlike in :class:`~.SphericalHarmonics`, here the levels do not necessarily correspond to whole eigenspaces (all eigenfunctions that correspond to the same eigenvalue). Here, levels are defined in terms of the algebraic structure of the group: they are the eigenfunctions that correspond to the same irreducible unitary representation of the group. .. note:: Here we break the general convention that the subclasses of the :class:`~.eigenfunctions.Eigenfunctions` only provide an interface for working with eigenfunctions, not eigenvalues, offering an interface for computing the latter as well. .. py:method:: __call__(X, **kwargs) :abstractmethod: Evaluate the individual eigenfunctions at a batch of input locations. :param X: Points to evaluate the eigenfunctions at, an array of shape [N, ], where N is the number of points and is the shape of the arrays that represent the points in a given space. :param ``**kwargs``: Any additional parameters. :return: An [N, J]-shaped array, where `J` is the number of eigenfunctions. .. py:method:: inverse(X) Should call the group's static :meth:`~.spaces.CompactMatrixLieGroup.inverse` method. :param X: As in :meth:`~.spaces.CompactMatrixLieGroup.inverse`. .. py:class:: SpecialUnitary(n) Bases: :py:obj:`geometric_kernels.spaces.lie_groups.CompactMatrixLieGroup` The GeometricKernels space representing the special unitary group SU(n) consisting of n by n complex unitary matrices with unit determinant. The elements of this space are represented as n x n unitary matrices with complex entries and unit determinant. .. note:: A tutorial on how to use this space is available in the :doc:`SpecialUnitary.ipynb ` notebook. :param n: The order n of the group SU(n). .. note:: We only support n >= 2. Mathematically, SU(1) is trivial, consisting of a single element (the identity), chances are you do not need it. For large values of n, you might need to run the `compute_characters.py` script to precompute the necessary mathematical quantities beyond the ones provided by default. .. admonition:: Citation If you use this GeometricKernels space in your research, please consider citing :cite:t:`azangulov2022`. .. py:method:: get_eigenfunctions(num) Returns the :class:`~.SUEigenfunctions` object with `num` levels and order n. :param num: Number of levels. .. py:method:: get_eigenvalues(num) Eigenvalues of the Laplacian corresponding to the first `num` levels. :param num: Number of levels. :return: (num, 1)-shaped array containing the eigenvalues. .. note:: The notion of *levels* is discussed in the documentation of the :class:`~.kernels.MaternKarhunenLoeveKernel`. .. py:method:: get_repeated_eigenvalues(num) Eigenvalues of the Laplacian corresponding to the first `num` levels, repeated according to their multiplicity within levels. :param num: Number of levels. :return: (J, 1)-shaped array containing the repeated eigenvalues, J is the resulting number of the repeated eigenvalues. .. note:: The notion of *levels* is discussed in the documentation of the :class:`~.kernels.MaternKarhunenLoeveKernel`. .. py:method:: inverse(X) :staticmethod: Inverse of a batch `X` of the elements of the group. A static method. :param X: A batch [..., n, n] of elements of the group. Each element is a n x n matrix. :return: A batch [..., n, n] with each n x n matrix inverted. .. py:method:: random(key, number) Sample uniformly random points in the space. :param key: Either `np.random.RandomState`, `tf.random.Generator`, `torch.Generator` or `jax.tensor` (representing random state). :param number: Number of samples to draw. :return: An array of `number` uniformly random samples on the space. .. py:property:: dimension :type: int The dimension of the space, as that of a Riemannian manifold. :return: floor(n^2-1) where n is the order of the group SU(n). .. py:property:: element_shape :return: [n, n].