"""The manifold of symmetric positive definite (SPD) matrices.
Lead authors: Yann Thanwerdas and Olivier Bisson.
"""
import math
import geomstats.backend as gs
from geomstats.geometry.base import VectorSpaceOpenSet
from geomstats.geometry.complex_matrices import ComplexMatrices
from geomstats.geometry.diffeo import Diffeo
from geomstats.geometry.general_linear import GeneralLinear
from geomstats.geometry.hermitian_matrices import apply_func_to_eigvalsh, expmh, powermh
from geomstats.geometry.matrices import Matrices, MatricesMetric
from geomstats.geometry.positive_lower_triangular_matrices import (
InvariantPositiveLowerTriangularMatricesMetric,
PositiveLowerTriangularMatrices,
)
from geomstats.geometry.pullback_metric import PullbackDiffeoMetric
from geomstats.geometry.riemannian_metric import RiemannianMetric
from geomstats.geometry.scalar_product_metric import ScalarProductMetric
from geomstats.geometry.symmetric_matrices import SymmetricMatrices
from geomstats.integrator import integrate
from geomstats.vectorization import repeat_out
[docs]
def logmh(mat):
"""Compute the matrix log for a Hermitian matrix."""
n = mat.shape[-1]
dim_3_mat = gs.reshape(mat, [-1, n, n])
logm = apply_func_to_eigvalsh(dim_3_mat, gs.log, check_positive=True)
return gs.reshape(logm, mat.shape)
def _aux_differential_power(power, tangent_vec, base_point):
"""Compute the differential of the matrix power.
Auxiliary function to the functions differential_power and
inverse_differential_power.
Parameters
----------
power : float
Power function to differentiate.
tangent_vec : array_like, shape=[..., n, n]
Tangent vector at base point.
base_point : array_like, shape=[..., n, n]
Base point.
Returns
-------
eigvectors : array-like, shape=[..., n, n]
transp_eigvectors : array-like, shape=[..., n, n]
numerator : array-like, shape=[..., n, n]
denominator : array-like, shape=[..., n, n]
temp_result : array-like, shape=[..., n, n]
"""
eigvalues, eigvectors = gs.linalg.eigh(base_point)
if power == 0:
powered_eigvalues = gs.log(eigvalues)
elif power == math.inf:
powered_eigvalues = gs.exp(eigvalues)
else:
powered_eigvalues = eigvalues**power
denominator = eigvalues[..., :, None] - eigvalues[..., None, :]
numerator = powered_eigvalues[..., :, None] - powered_eigvalues[..., None, :]
null_denominator = gs.abs(denominator) < gs.atol
if power == 0:
numerator = gs.where(null_denominator, gs.ones_like(numerator), numerator)
denominator = gs.where(null_denominator, eigvalues[..., :, None], denominator)
elif power == math.inf:
numerator = gs.where(
null_denominator, powered_eigvalues[..., :, None], numerator
)
denominator = gs.where(null_denominator, gs.ones_like(numerator), denominator)
else:
numerator = gs.where(
null_denominator, power * powered_eigvalues[..., :, None], numerator
)
denominator = gs.where(null_denominator, eigvalues[..., :, None], denominator)
if gs.is_complex(base_point):
transp_eigvectors = ComplexMatrices.transconjugate(eigvectors)
else:
transp_eigvectors = Matrices.transpose(eigvectors)
temp_result = Matrices.mul(transp_eigvectors, tangent_vec, eigvectors)
if gs.is_complex(base_point):
numerator = gs.cast(numerator, dtype=temp_result.dtype)
denominator = gs.cast(denominator, dtype=temp_result.dtype)
return (eigvectors, transp_eigvectors, numerator, denominator, temp_result)
[docs]
class SymMatrixLog(Diffeo):
"""Matrix logarithm diffeomorphism.
A diffeomorphism from the space of symmetric positive-definite matrices to
the space of symmetric matrices.
"""
[docs]
@classmethod
def diffeomorphism(cls, base_point):
"""Compute the matrix log for a symmetric matrix.
Parameters
----------
base_point : array_like, shape=[..., n, n]
Symmetric matrix.
Returns
-------
log : array_like, shape=[..., n, n]
Matrix logarithm of base_point.
"""
return logmh(base_point)
[docs]
@classmethod
def inverse_diffeomorphism(cls, image_point):
"""Compute the matrix exponential for a symmetric matrix.
Parameters
----------
image_point : array_like, shape=[..., n, n]
Symmetric matrix.
Returns
-------
exponential : array_like, shape=[..., n, n]
Exponential of image_point.
"""
return expmh(image_point)
[docs]
@classmethod
def tangent_diffeomorphism(cls, tangent_vec, base_point=None, image_point=None):
"""Compute the differential of the matrix logarithm.
Compute the differential of the matrix logarithm on SPD
matrices at base_point applied to tangent_vec.
Parameters
----------
tangent_vec : array_like, shape=[..., n, n]
Tangent vector at base point.
base_point : array_like, shape=[..., n, n]
Base point.
image_point : array_like, shape=[..., n, n]
Image base point.
Returns
-------
differential_log : array-like, shape=[..., n, n]
Differential of the matrix logarithm.
"""
if base_point is not None:
(
eigvectors,
transp_eigvectors,
numerator,
denominator,
temp_result,
) = _aux_differential_power(0, tangent_vec, base_point)
power_operator = numerator / denominator
else:
(
eigvectors,
transp_eigvectors,
numerator,
denominator,
temp_result,
) = _aux_differential_power(math.inf, tangent_vec, image_point)
power_operator = denominator / numerator
result = power_operator * temp_result
return Matrices.mul(eigvectors, result, transp_eigvectors)
[docs]
@classmethod
def inverse_tangent_diffeomorphism(
cls, image_tangent_vec, image_point=None, base_point=None
):
"""Compute the differential of the matrix exponential.
Computes the differential of the matrix exponential on SPD
matrices at base_point applied to tangent_vec.
Parameters
----------
image_tangent_vec : array_like, shape=[..., n, n]
Image tangent vector at image point.
image_point : array_like, shape=[..., n, n]
Image point.
base_point : array_like, shape=[..., n, n]
Base point.
Returns
-------
differential_exp : array-like, shape=[..., n, n]
Differential of the matrix exponential.
"""
if image_point is not None:
(
eigvectors,
transp_eigvectors,
numerator,
denominator,
temp_result,
) = _aux_differential_power(math.inf, image_tangent_vec, image_point)
power_operator = numerator / denominator
else:
(
eigvectors,
transp_eigvectors,
numerator,
denominator,
temp_result,
) = _aux_differential_power(0, image_tangent_vec, base_point)
power_operator = denominator / numerator
result = power_operator * temp_result
return Matrices.mul(eigvectors, result, transp_eigvectors)
[docs]
class MatrixPower(Diffeo):
"""Matrix power diffeomorphism.
A diffeomorphism from the space of symmetric positive-definite matrices to
itself.
"""
def __init__(self, power):
self.power = power
[docs]
def diffeomorphism(self, base_point):
"""Compute the matrix power.
Parameters
----------
base_point : array_like, shape=[..., n, n]
Symmetric matrix with non-negative eigenvalues.
Returns
-------
powerm : array_like or list of arrays, shape=[..., n, n]
Matrix power of mat.
"""
return powermh(base_point, self.power)
[docs]
def inverse_diffeomorphism(self, image_point):
"""Compute the inverse matrix power.
Parameters
----------
image_point : array_like, shape=[..., n, n]
Symmetric matrix with non-negative eigenvalues.
Returns
-------
powerm : array_like or list of arrays, shape=[..., n, n]
Matrix power of mat.
"""
return powermh(image_point, 1 / self.power)
[docs]
def tangent_diffeomorphism(self, tangent_vec, base_point=None, image_point=None):
r"""Compute the differential of the matrix power function.
Compute the differential of the power function on SPD(n)
(:math:`A^p=\exp(p \log(A))`) at base_point applied to tangent_vec.
Parameters
----------
tangent_vec : array_like, shape=[..., n, n]
Tangent vector at base point.
base_point : array_like, shape=[..., n, n]
Base point.
image_point : array_like, shape=[..., n, n]
Image base point.
Returns
-------
differential_power : array-like, shape=[..., n, n]
Differential of the power function.
"""
if base_point is None:
base_point = self.inverse_diffeomorphism(image_point)
(
eigvectors,
transp_eigvectors,
numerator,
denominator,
temp_result,
) = _aux_differential_power(self.power, tangent_vec, base_point)
power_operator = numerator / denominator
result = power_operator * temp_result
return Matrices.mul(eigvectors, result, transp_eigvectors)
[docs]
def inverse_tangent_diffeomorphism(
self, image_tangent_vec, image_point=None, base_point=None
):
r"""Compute the inverse of the differential of the matrix power.
Compute the inverse of the differential of the power
function on SPD matrices (:math:`A^p=\exp(p \log(A))`) at base_point
applied to tangent_vec.
Parameters
----------
image_tangent_vec : array_like, shape=[..., n, n]
Image tangent vector at image point.
image_base_point : array_like, shape=[..., n, n]
Image point.
base_point : array_like, shape=[..., n, n]
Base point.
Returns
-------
inverse_differential_power : array-like, shape=[..., n, n]
Inverse of the differential of the power function.
"""
if base_point is None:
base_point = self.inverse_diffeomorphism(image_point)
(
eigvectors,
transp_eigvectors,
numerator,
denominator,
temp_result,
) = _aux_differential_power(self.power, image_tangent_vec, base_point)
power_operator = denominator / numerator
result = power_operator * temp_result
return Matrices.mul(eigvectors, result, transp_eigvectors)
[docs]
class CholeskyMap(Diffeo):
"""Cholesky map.
A diffeomorphism from the space of symmetric positive-definite matrices to
the space of positive lower triangular matrices.
"""
[docs]
@classmethod
def diffeomorphism(cls, base_point):
"""Compute cholesky factor.
Compute cholesky factor for a symmetric positive definite matrix.
Parameters
----------
base_point : array_like, shape=[..., n, n]
spd matrix.
Returns
-------
cf : array_like, shape=[..., n, n]
lower triangular matrix with positive diagonal elements.
"""
return gs.linalg.cholesky(base_point)
[docs]
@staticmethod
def inverse_diffeomorphism(image_point):
"""Compute gram matrix of rows.
Gram_matrix is mapping from point to point.point^{T}.
This is diffeomorphism between cholesky space and spd manifold.
Parameters
----------
image_point : array-like, shape=[..., n, n]
element in cholesky space.
Returns
-------
projected: array-like, shape=[..., n, n]
SPD matrix.
"""
return gs.einsum("...ij,...kj->...ik", image_point, image_point)
[docs]
@classmethod
def tangent_diffeomorphism(cls, tangent_vec, base_point=None, image_point=None):
"""Compute the differential of the cholesky factor map.
Parameters
----------
tangent_vec : array_like, shape=[..., n, n]
Tangent vector at base point.
base_point : array_like, shape=[..., n, n]
Base point.
image_point : array_like, shape=[..., n, n]
Image base point.
Returns
-------
differential_cf : array-like, shape=[..., n, n]
Differential of cholesky factor map
lower triangular matrix.
"""
if image_point is None:
image_point = cls.diffeomorphism(base_point)
inv_base_point = gs.linalg.inv(image_point)
inv_transpose_base_point = Matrices.transpose(inv_base_point)
aux = Matrices.to_lower_triangular_diagonal_scaled(
Matrices.mul(inv_base_point, tangent_vec, inv_transpose_base_point)
)
return Matrices.mul(image_point, aux)
[docs]
@classmethod
def inverse_tangent_diffeomorphism(
cls, image_tangent_vec, image_point=None, base_point=None
):
"""Compute differential of gram.
Parameters
----------
image_tangent_vec : array_like, shape=[..., n, n]
Tangent vector at base point.
image_point : array_like, shape=[..., n, n]
Base point.
base_point : array_like, shape=[..., n, n]
Base point.
Returns
-------
differential_gram : array-like, shape=[..., n, n]
Differential of the gram.
"""
if image_point is None:
image_point = cls.diffeomorphism(base_point)
mat1 = gs.einsum("...ij,...kj->...ik", image_tangent_vec, image_point)
mat2 = gs.einsum("...ij,...kj->...ik", image_point, image_tangent_vec)
return mat1 + mat2
[docs]
class SPDMatrices(VectorSpaceOpenSet):
"""Class for the manifold of symmetric positive definite (SPD) matrices.
Parameters
----------
n : int
Integer representing the shape of the matrices: n x n.
"""
def __init__(self, n, equip=True):
super().__init__(
dim=int(n * (n + 1) / 2),
embedding_space=SymmetricMatrices(n),
equip=equip,
)
self.n = n
[docs]
@staticmethod
def default_metric():
"""Metric to equip the space with if equip is True."""
return SPDAffineMetric
[docs]
def belongs(self, point, atol=gs.atol):
"""Check if a matrix is symmetric with positive eigenvalues.
Parameters
----------
mat : array-like, shape=[..., n, n]
Matrix to be checked.
atol : float
Tolerance.
Optional, default: backend atol.
Returns
-------
belongs : array-like, shape=[...,]
Boolean denoting if mat is an SPD matrix.
"""
is_sym = self.embedding_space.belongs(point, atol)
is_pd = Matrices.is_pd(point)
return gs.logical_and(is_sym, is_pd)
[docs]
def projection(self, point):
"""Project a matrix to the space of SPD matrices.
First the symmetric part of point is computed, then the eigenvalues
are floored to gs.atol.
Parameters
----------
point : array-like, shape=[..., n, n]
Matrix to project.
Returns
-------
projected: array-like, shape=[..., n, n]
SPD matrix.
"""
sym = Matrices.to_symmetric(point)
eigvals, eigvecs = gs.linalg.eigh(sym)
regularized = gs.where(eigvals < gs.atol, gs.atol, eigvals)
reconstruction = gs.einsum("...ij,...j->...ij", eigvecs, regularized)
return Matrices.mul(reconstruction, Matrices.transpose(eigvecs))
[docs]
def random_point(self, n_samples=1, bound=1.0):
"""Sample in SPD(n) from the log-uniform distribution.
Parameters
----------
n_samples : int
Number of samples.
Optional, default: 1.
bound : float
Bound of the interval in which to sample in the tangent space.
Optional, default: 1.
Returns
-------
samples : array-like, shape=[..., n, n]
Points sampled in SPD(n).
"""
n = self.n
size = (n_samples, n, n) if n_samples != 1 else (n, n)
mat = bound * (2 * gs.random.rand(*size) - 1)
return GeneralLinear.exp(Matrices.to_symmetric(mat))
[docs]
def random_tangent_vec(self, base_point, n_samples=1):
"""Sample on the tangent space of SPD(n) from the uniform distribution.
Parameters
----------
n_samples : int
Number of samples.
Optional, default: 1.
base_point : array-like, shape=[..., n, n]
Base point of the tangent space.
Returns
-------
samples : array-like, shape=[..., n, n]
Points sampled in the tangent space at base_point.
"""
n = self.n
size = (n_samples, n, n) if n_samples != 1 else (n, n)
sqrt_base_point = gs.linalg.sqrtm(base_point)
tangent_vec_at_id_aux = 2 * gs.random.rand(*size) - 1
tangent_vec_at_id = tangent_vec_at_id_aux + Matrices.transpose(
tangent_vec_at_id_aux
)
return Matrices.mul(sqrt_base_point, tangent_vec_at_id, sqrt_base_point)
from_vector = SymmetricMatrices.__dict__["from_vector"]
to_vector = SymmetricMatrices.__dict__["to_vector"]
[docs]
class SPDAffineMetric(RiemannianMetric):
"""Class for the affine-invariant metric on the SPD manifold.
Parameters
----------
power_affine : int
Power transformation of the classical SPD metric.
Optional, default: 1.
References
----------
.. [TP2019] Thanwerdas, Pennec. "Is affine-invariance well defined on
SPD matrices? A principled continuum of metrics" Proc. of GSI,
2019. https://arxiv.org/abs/1906.01349
"""
[docs]
def inner_product(self, tangent_vec_a, tangent_vec_b, base_point):
"""Compute the affine-invariant inner-product.
Compute the inner-product of tangent_vec_a and tangent_vec_b
at point base_point using the affine invariant Riemannian metric.
Parameters
----------
tangent_vec_a : array-like, shape=[..., n, n]
Tangent vector at base point.
tangent_vec_b : array-like, shape=[..., n, n]
Tangent vector at base point.
base_point : array-like, shape=[..., n, n]
Base point.
Returns
-------
inner_product : array-like, shape=[..., n, n]
Inner-product.
"""
inv_base_point = GeneralLinear.inverse(base_point)
aux_a = Matrices.mul(inv_base_point, tangent_vec_a)
aux_b = Matrices.mul(inv_base_point, tangent_vec_b)
return Matrices.trace_product(aux_a, aux_b)
[docs]
def exp(self, tangent_vec, base_point):
"""Compute the affine-invariant exponential map.
Compute the Riemannian exponential at point base_point
of tangent vector tangent_vec wrt the metric defined in inner_product.
This gives a symmetric positive definite matrix.
Parameters
----------
tangent_vec : array-like, shape=[..., n, n]
Tangent vector at base point.
base_point : array-like, shape=[..., n, n]
Base point.
Returns
-------
exp : array-like, shape=[..., n, n]
Riemannian exponential.
"""
sqrt_base_point, inv_sqrt_base_point = powermh(base_point, [1.0 / 2, -1.0 / 2])
tangent_vec_at_id = Matrices.mul(
inv_sqrt_base_point, tangent_vec, inv_sqrt_base_point
)
tangent_vec_at_id = Matrices.to_symmetric(tangent_vec_at_id)
exp_from_id = expmh(tangent_vec_at_id)
return Matrices.mul(sqrt_base_point, exp_from_id, sqrt_base_point)
[docs]
def log(self, point, base_point):
"""Compute the affine-invariant logarithm map.
Compute the Riemannian logarithm at point base_point,
of point wrt the metric defined in inner_product.
This gives a tangent vector at point base_point.
Parameters
----------
point : array-like, shape=[..., n, n]
Point.
base_point : array-like, shape=[..., n, n]
Base point.
Returns
-------
log : array-like, shape=[..., n, n]
Riemannian logarithm of point at base_point.
"""
sqrt_base_point, inv_sqrt_base_point = powermh(base_point, [1.0 / 2, -1.0 / 2])
point_near_id = Matrices.mul(inv_sqrt_base_point, point, inv_sqrt_base_point)
point_near_id = Matrices.to_symmetric(point_near_id)
log_at_id = logmh(point_near_id)
return Matrices.mul(sqrt_base_point, log_at_id, sqrt_base_point)
[docs]
def parallel_transport(
self, tangent_vec, base_point, direction=None, end_point=None
):
r"""Parallel transport of a tangent vector.
Closed-form solution for the parallel transport of a tangent vector
along the geodesic between two points `base_point` and `end_point`
or alternatively defined by :math:`t \mapsto exp_{(base\_point)}(
t*direction)`.
Denoting `tangent_vec_a` by `S`, `base_point` by `A`, and `end_point`
by `B` or `B = Exp_A(tangent_vec_b)` and :math:`E = (BA^{- 1})^{( 1
/ 2)}`. Then the parallel transport to `B` is:
.. math::
S' = ESE^T
Parameters
----------
tangent_vec : array-like, shape=[..., n, n]
Tangent vector at base point to be transported.
base_point : array-like, shape=[..., n, n]
Point on the manifold of SPD matrices. Point to transport from
direction : array-like, shape=[..., n, n]
Tangent vector at base point, initial speed of the geodesic along
which the parallel transport is computed. Unused if `end_point` is given.
Optional, default: None.
end_point : array-like, shape=[..., n, n]
Point on the manifold of SPD matrices. Point to transport to.
Optional, default: None.
Returns
-------
transported_tangent_vec: array-like, shape=[..., n, n]
Transported tangent vector at exp_(base_point)(tangent_vec_b).
"""
if end_point is None:
end_point = self.exp(direction, base_point)
# compute B^1/2(B^-1/2 A B^-1/2)B^-1/2 instead of sqrtm(AB^-1)
sqrt_bp, inv_sqrt_bp = powermh(base_point, [1.0 / 2, -1.0 / 2])
pdt = powermh(Matrices.mul(inv_sqrt_bp, end_point, inv_sqrt_bp), 1.0 / 2)
congruence_mat = Matrices.mul(sqrt_bp, pdt, inv_sqrt_bp)
return Matrices.congruent(tangent_vec, congruence_mat)
[docs]
def injectivity_radius(self, base_point):
"""Radius of the largest ball where the exponential is injective.
Because of the negative curvature of this space, the injectivity radius
is infinite everywhere.
Parameters
----------
base_point : array-like, shape=[..., n, n]
Point on the manifold.
Returns
-------
radius : array-like, shape=[...,]
Injectivity radius.
"""
radius = gs.array(math.inf)
return repeat_out(self._space.point_ndim, radius, base_point)
[docs]
class SPDBuresWassersteinMetric(RiemannianMetric):
"""Class for the Bures-Wasserstein metric on the SPD manifold.
References
----------
.. [BJL2017] Bhatia, Jain, Lim. "On the Bures-Wasserstein distance between
positive definite matrices" Elsevier, Expositiones Mathematicae,
vol. 37(2), 165-191, 2017. https://arxiv.org/pdf/1712.01504.pdf
.. [MMP2018] Malago, Montrucchio, Pistone. "Wasserstein-Riemannian
geometry of Gaussian densities" Information Geometry, vol. 1, 137-179,
2018. https://arxiv.org/pdf/1801.09269.pdf
"""
[docs]
def inner_product(self, tangent_vec_a, tangent_vec_b, base_point):
r"""Compute the Bures-Wasserstein inner-product.
Compute the inner-product of tangent_vec_a :math:`A` and tangent_vec_b
:math:`B` at point base_point :math:`S=PDP^\top` using the
Bures-Wasserstein Riemannian metric:
.. math::
\frac{1}{2}\sum_{i,j}\frac{[P^\top AP]_{ij}[P^\top BP]_{ij}}{d_i+d_j}
Parameters
----------
tangent_vec_a : array-like, shape=[..., n, n]
Tangent vector at base point.
tangent_vec_b : array-like, shape=[..., n, n]
Tangent vector at base point.
base_point : array-like, shape=[..., n, n]
Base point.
Returns
-------
inner_product : array-like, shape=[...,]
Inner-product.
"""
eigvals, eigvecs = gs.linalg.eigh(base_point)
transp_eigvecs = Matrices.transpose(eigvecs)
rotated_tangent_vec_a = Matrices.mul(transp_eigvecs, tangent_vec_a, eigvecs)
rotated_tangent_vec_b = Matrices.mul(transp_eigvecs, tangent_vec_b, eigvecs)
coefficients = 1 / (eigvals[..., :, None] + eigvals[..., None, :])
result = (
Matrices.frobenius_product(
coefficients * rotated_tangent_vec_a, rotated_tangent_vec_b
)
/ 2
)
return result
[docs]
def exp(self, tangent_vec, base_point):
"""Compute the Bures-Wasserstein exponential map.
Parameters
----------
tangent_vec : array-like, shape=[..., n, n]
Tangent vector at base point.
base_point : array-like, shape=[..., n, n]
Base point.
Returns
-------
exp : array-like, shape=[...,]
Riemannian exponential.
"""
eigvals, eigvecs = gs.linalg.eigh(base_point)
transp_eigvecs = Matrices.transpose(eigvecs)
rotated_tangent_vec = Matrices.mul(transp_eigvecs, tangent_vec, eigvecs)
coefficients = 1 / (eigvals[..., :, None] + eigvals[..., None, :])
rotated_sylvester = rotated_tangent_vec * coefficients
rotated_hessian = gs.einsum("...ij,...j->...ij", rotated_sylvester, eigvals)
rotated_hessian = Matrices.mul(rotated_hessian, rotated_sylvester)
hessian = Matrices.mul(eigvecs, rotated_hessian, transp_eigvecs)
return base_point + tangent_vec + hessian
[docs]
def log(self, point, base_point):
"""Compute the Bures-Wasserstein logarithm map.
Compute the Riemannian logarithm at point base_point,
of point wrt the Bures-Wasserstein metric.
This gives a tangent vector at point base_point.
Parameters
----------
point : array-like, shape=[..., n, n]
Point.
base_point : array-like, shape=[..., n, n]
Base point.
Returns
-------
log : array-like, shape=[..., n, n]
Riemannian logarithm.
"""
# compute B^1/2(B^-1/2 A B^-1/2)B^-1/2 instead of sqrtm(AB^-1)
sqrt_bp, inv_sqrt_bp = powermh(base_point, [0.5, -0.5])
pdt = powermh(Matrices.mul(sqrt_bp, point, sqrt_bp), 0.5)
sqrt_product = Matrices.mul(sqrt_bp, pdt, inv_sqrt_bp)
transp_sqrt_product = Matrices.transpose(sqrt_product)
return sqrt_product + transp_sqrt_product - 2 * base_point
[docs]
def squared_dist(self, point_a, point_b):
"""Compute the Bures-Wasserstein squared distance.
Compute the Riemannian squared distance between point_a and point_b.
Parameters
----------
point_a : array-like, shape=[..., n, n]
Point.
point_b : array-like, shape=[..., n, n]
Point.
Returns
-------
squared_dist : array-like, shape=[...]
Riemannian squared distance.
"""
product = gs.matmul(point_a, point_b)
sqrt_product = gs.linalg.sqrtm(product)
trace_a = gs.trace(point_a)
trace_b = gs.trace(point_b)
trace_prod = gs.trace(sqrt_product)
squared_dist = trace_a + trace_b - 2.0 * trace_prod
return gs.where(squared_dist < 0.0, 0.0, squared_dist)
[docs]
def parallel_transport(
self,
tangent_vec,
base_point,
direction=None,
end_point=None,
n_steps=10,
step="rk4",
):
r"""Compute the parallel transport of a tangent vec along a geodesic.
Approximation of the solution of the parallel transport of a tangent
vector a along the geodesic defined by :math:`t \mapsto exp_{(
base\_point)}(t* tangent\_vec\_b)`. The parallel transport equation is
formulated in this case in [TP2021]_.
Parameters
----------
tangent_vec : array-like, shape=[..., n, n]
Tangent vector at `base_point` to transport.
base_point : array-like, shape=[..., n, n]
Initial point of the geodesic.
direction : array-like, shape=[..., n, n]
Tangent vector at `base_point`, initial velocity of the geodesic to
transport along.
end_point : array-like, shape=[..., n, n]
Point to transport to.
Optional, default: None.
n_steps : int
Number of steps to use to approximate the solution of the
ordinary differential equation.
Optional, default: 100
step : str, {'euler', 'rk2', 'rk4'}
Scheme to use in the integration scheme.
Optional, default: 'rk4'.
Returns
-------
transported : array-like, shape=[..., n, n]
Transported tangent vector at `exp_(base_point)(tangent_vec_b)`.
References
----------
.. [TP2021] Yann Thanwerdas, Xavier Pennec. O(n)-invariant Riemannian
metrics on SPD matrices. 2021. ⟨hal-03338601v2⟩
See Also
--------
Integration module: geomstats.integrator
"""
if end_point is None:
end_point = self.exp(direction, base_point)
horizontal_lift_a = gs.linalg.solve_sylvester(
base_point, base_point, tangent_vec
)
square_root_bp, inverse_square_root_bp = powermh(base_point, [0.5, -0.5])
end_point_lift = Matrices.mul(square_root_bp, end_point, square_root_bp)
square_root_lift = powermh(end_point_lift, 0.5)
horizontal_velocity = gs.matmul(inverse_square_root_bp, square_root_lift)
partial_horizontal_velocity = Matrices.mul(horizontal_velocity, square_root_bp)
partial_horizontal_velocity = partial_horizontal_velocity + Matrices.transpose(
partial_horizontal_velocity
)
def force(state, time):
horizontal_geodesic_t = (
1 - time
) * square_root_bp + time * horizontal_velocity
geodesic_t = (
(1 - time) ** 2 * base_point
+ time * (1 - time) * partial_horizontal_velocity
+ time**2 * end_point
)
align = Matrices.mul(
horizontal_geodesic_t,
Matrices.transpose(horizontal_velocity - square_root_bp),
state,
)
right = align + Matrices.transpose(align)
return gs.linalg.solve_sylvester(geodesic_t, geodesic_t, -right)
flow = integrate(force, horizontal_lift_a, n_steps=n_steps, step=step)
final_align = Matrices.mul(end_point, flow[-1])
return final_align + Matrices.transpose(final_align)
[docs]
def injectivity_radius(self, base_point):
"""Compute the upper bound of the injectivity domain.
This is the smallest eigen value of the base point.
Parameters
----------
base_point : array-like, shape=[..., n, n]
Point on the manifold.
Returns
-------
radius : float
Injectivity radius.
"""
eigen_values = gs.linalg.eigvalsh(base_point)
return eigen_values[..., 0] ** 0.5
[docs]
class SPDEuclideanMetric(MatricesMetric):
"""Class for the Euclidean metric on the SPD manifold."""
[docs]
@staticmethod
def exp_domain(tangent_vec, base_point):
"""Compute the domain of the Euclidean exponential map.
Compute the real interval of time where the Euclidean geodesic starting
at point `base_point` in direction `tangent_vec` is defined.
Parameters
----------
tangent_vec : array-like, shape=[..., n, n]
Tangent vector at base point.
base_point : array-like, shape=[..., n, n]
Base point.
Returns
-------
exp_domain : array-like, shape=[..., 2]
Interval of time where the geodesic is defined.
"""
invsqrt_base_point = powermh(base_point, -0.5)
reduced_vec = gs.matmul(invsqrt_base_point, tangent_vec)
reduced_vec = gs.matmul(reduced_vec, invsqrt_base_point)
eigvals = gs.linalg.eigvalsh(reduced_vec)
min_eig = gs.amin(eigvals, axis=-1)
max_eig = gs.amax(eigvals, axis=-1)
inf_value = gs.where(max_eig <= 0.0, gs.array(-math.inf), -1.0 / max_eig)
sup_value = gs.where(min_eig >= 0.0, gs.array(-math.inf), -1.0 / min_eig)
return gs.stack((inf_value, sup_value), axis=-1)
[docs]
def injectivity_radius(self, base_point):
"""Compute the upper bound of the injectivity domain.
This is the smallest eigen value of the base point.
Parameters
----------
base_point : array-like, shape=[..., n, n]
Point on the manifold.
Returns
-------
radius : float
Injectivity radius.
"""
eigen_values = gs.linalg.eigvalsh(base_point)
return eigen_values[..., 0]
[docs]
class SPDLogEuclideanMetric(PullbackDiffeoMetric):
"""Class for the Log-Euclidean metric on the SPD manifold."""
def __init__(self, space, image_space=None):
if image_space is None:
image_space = SymmetricMatrices(n=space.n)
diffeo = SymMatrixLog()
super().__init__(space, diffeo, image_space)
[docs]
class SPDPowerMetric(PullbackDiffeoMetric):
r"""Pullback metric induced by the power diffeomorphism.
Given an equipped image space, the pullback metric is given by
.. math::
g_{\Sigma}^{(p)}(X, X)=
\frac{1}{p^2} g_{\Sigma^p}\left(d_{\Sigma}
\operatorname{pow}_p(X), d_{\Sigma} \operatorname{pow}_p(X)\right)
The image space must be equipped with a `ScalarProductMetric`. The scale
:math:`s` relates with power :math:`p` by
.. math::
s = 1 / power^2
Check section 5.3 of [T2022]_ for more details.
References
----------
.. [T2022] Thanwerdas, Y. (2022) Riemannian and stratified
geometries of covariance and correlation matrices. Theses.
Université Côte d’Azur. Available at:
https://hal.archives-ouvertes.fr/tel-03698752 (Accessed: 29 September 2022).
"""
def __init__(self, space, image_space):
if not isinstance(image_space.metric, ScalarProductMetric):
raise ValueError(
"`image-space` must be equipped with a `ScalarProductMetric`"
)
power = 1 / gs.sqrt(image_space.metric.scale)
diffeo = MatrixPower(power)
super().__init__(space, diffeo, image_space)
[docs]
class LieCholeskyMetric(PullbackDiffeoMetric):
"""Pullback metric via a diffeomorphism.
Diffeormorphism between SPD matrices and PLT matrices equipped with
left invariant metric (see chapter 7 [TP2022]_).
References
----------
.. [T2022] Yann Thanwerdas. Riemannian and stratified
geometries on covariance and correlation matrices. Differential
Geometry [math.DG]. Université Côte d'Azur, 2022.
"""
def __init__(self, space):
image_space = PositiveLowerTriangularMatrices(space.n, equip=False)
image_space.equip_with_metric(InvariantPositiveLowerTriangularMatricesMetric)
diffeo = CholeskyMap()
super().__init__(space=space, diffeo=diffeo, image_space=image_space)