"""The manifold of symmetric positive definite (SPD) matrices."""
import math
import geomstats.backend as gs
import geomstats.vectorization
from geomstats.geometry.embedded_manifold import EmbeddedManifold
from geomstats.geometry.general_linear import GeneralLinear
from geomstats.geometry.matrices import Matrices
from geomstats.geometry.riemannian_metric import RiemannianMetric
from geomstats.geometry.symmetric_matrices import SymmetricMatrices
[docs]class SPDMatrices(SymmetricMatrices, EmbeddedManifold):
"""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):
super(SPDMatrices, self).__init__(
n=n,
dim=int(n * (n + 1) / 2),
embedding_manifold=GeneralLinear(n=n))
[docs] def belongs(self, mat, atol=gs.atol):
"""Check if a matrix is symmetric and invertible.
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_symmetric = super(SPDMatrices, self).belongs(mat, atol)
eigvalues, _ = gs.linalg.eigh(mat)
is_positive = gs.all(eigvalues > 0, axis=-1)
belongs = gs.logical_and(is_symmetric, is_positive)
return belongs
[docs] def random_point(self, n_samples=1, bound=1.):
"""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)
spd_mat = GeneralLinear.exp(Matrices.to_symmetric(mat))
return spd_mat
[docs] def random_tangent_vec(self, n_samples=1, base_point=None):
"""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.
Optional, default: None.
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)
if base_point is None:
base_point = gs.eye(n)
sqrt_base_point = gs.linalg.sqrtm(base_point)
tangent_vec_at_id = 2 * gs.random.rand(*size) - 1
tangent_vec_at_id += Matrices.transpose(tangent_vec_at_id)
tangent_vec = Matrices.mul(
sqrt_base_point, tangent_vec_at_id, sqrt_base_point)
return tangent_vec
[docs] @staticmethod
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, :])
if power == 0:
numerator = gs.where(
denominator == 0, gs.ones_like(numerator), numerator)
denominator = gs.where(
denominator == 0, eigvalues[..., :, None], denominator)
elif power == math.inf:
numerator = gs.where(
denominator == 0, powered_eigvalues[..., :, None], numerator)
denominator = gs.where(
denominator == 0, gs.ones_like(numerator), denominator)
else:
numerator = gs.where(
denominator == 0,
power * powered_eigvalues[..., :, None],
numerator)
denominator = gs.where(
denominator == 0,
eigvalues[..., :, None],
denominator)
transp_eigvectors = Matrices.transpose(eigvectors)
temp_result = Matrices.mul(transp_eigvectors, tangent_vec, eigvectors)
return (
eigvectors, transp_eigvectors, numerator, denominator, temp_result)
[docs] @classmethod
def differential_power(cls, power, tangent_vec, base_point):
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
----------
power : int
Power.
tangent_vec : array_like, shape=[..., n, n]
Tangent vector at base point.
base_point : array_like, shape=[..., n, n]
Base point.
Returns
-------
differential_power : array-like, shape=[..., n, n]
Differential of the power function.
"""
eigvectors, transp_eigvectors, numerator, denominator, temp_result =\
cls.aux_differential_power(power, tangent_vec, base_point)
power_operator = numerator / denominator
result = power_operator * temp_result
result = Matrices.mul(eigvectors, result, transp_eigvectors)
return result
[docs] @classmethod
def inverse_differential_power(cls, power, tangent_vec, base_point):
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
----------
power : int
Power.
tangent_vec : array_like, shape=[..., n, n]
Tangent vector at base 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.
"""
eigvectors, transp_eigvectors, numerator, denominator, temp_result =\
cls.aux_differential_power(power, tangent_vec, base_point)
power_operator = denominator / numerator
result = power_operator * temp_result
result = Matrices.mul(eigvectors, result, transp_eigvectors)
return result
[docs] @classmethod
def differential_log(cls, tangent_vec, base_point):
"""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.
Returns
-------
differential_log : array-like, shape=[..., n, n]
Differential of the matrix logarithm.
"""
eigvectors, transp_eigvectors, numerator, denominator, temp_result =\
cls.aux_differential_power(0, tangent_vec, base_point)
power_operator = numerator / denominator
result = power_operator * temp_result
result = Matrices.mul(eigvectors, result, transp_eigvectors)
return result
[docs] @classmethod
def inverse_differential_log(cls, tangent_vec, base_point):
"""Compute the inverse of the differential of the matrix logarithm.
Compute the inverse of 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.
Returns
-------
inverse_differential_log : array-like, shape=[..., n, n]
Inverse of the differential of the matrix logarithm.
"""
eigvectors, transp_eigvectors, numerator, denominator, temp_result =\
cls.aux_differential_power(0, tangent_vec, base_point)
power_operator = denominator / numerator
result = power_operator * temp_result
result = Matrices.mul(eigvectors, result, transp_eigvectors)
return result
[docs] @classmethod
def differential_exp(cls, tangent_vec, base_point):
"""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
----------
tangent_vec : array_like, shape=[..., n, n]
Tangent vector at base point.
base_point : array_like, shape=[..., n, n]
Base point.
Returns
-------
differential_exp : array-like, shape=[..., n, n]
Differential of the matrix exponential.
"""
eigvectors, transp_eigvectors, numerator, denominator, temp_result = \
cls.aux_differential_power(math.inf, tangent_vec, base_point)
power_operator = numerator / denominator
result = power_operator * temp_result
result = Matrices.mul(eigvectors, result, transp_eigvectors)
return result
[docs] @classmethod
def inverse_differential_exp(cls, tangent_vec, base_point):
"""Compute the inverse of the differential of the matrix exponential.
Computes the inverse of the differential of the matrix
exponential 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.
Returns
-------
inverse_differential_exp : array-like, shape=[..., n, n]
Inverse of the differential of the matrix exponential.
"""
eigvectors, transp_eigvectors, numerator, denominator, temp_result = \
cls.aux_differential_power(math.inf, tangent_vec, base_point)
power_operator = denominator / numerator
result = power_operator * temp_result
result = Matrices.mul(eigvectors, result, transp_eigvectors)
return result
[docs] @classmethod
def logm(cls, mat):
"""
Compute the matrix log for a symmetric matrix.
Parameters
----------
mat : array_like, shape=[..., n, n]
Symmetric matrix.
Returns
-------
log : array_like, shape=[..., n, n]
Matrix logarithm of mat.
"""
return cls.apply_func_to_eigvals(mat, gs.log, check_positive=True)
[docs] def is_tangent(self, vector, base_point=None, atol=gs.atol):
"""Check whether the vector is tangent at base_point.
A "vector" is tangent to the manifold of SPD matrices if it is a
symmetric matrix.
Parameters
----------
vector : array-like, shape=[..., n, n]
Matrix.
base_point : array-like, shape=[..., n, n]
Point on the manifold.
Optional, default: None.
atol : float
Absolute tolerance.
Optional, default: 1e-6.
Returns
-------
is_tangent : bool
Boolean denoting if vector is a tangent vector at the base point.
"""
return super(SPDMatrices, self).belongs(vector, atol)
[docs] def to_tangent(self, vector, base_point=None):
"""Project a vector to a tangent space of the manifold.
A "vector" is tangent to the manifold of SPD matrices if it is a
symmetric matrix, so the symmetric component of the input matrix is
returned.
Parameters
----------
vector : array-like, shape=[..., n, n]
Matrix.
base_point : array-like, shape=[..., n, n]
Point on the manifold.
Optional, default: None.
Returns
-------
tangent_vec : array-like, shape=[..., n, n]
Tangent vector at base point.
"""
return super(SPDMatrices, self).projection(vector)
[docs]class SPDMetricAffine(RiemannianMetric):
"""Class for the affine-invariant metric on the SPD manifold."""
def __init__(self, n, power_affine=1):
"""Build the affine-invariant metric.
Parameters
----------
n : int
Integer representing the shape of the matrices: n x n.
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
"""
dim = int(n * (n + 1) / 2)
super(SPDMetricAffine, self).__init__(
dim=dim,
signature=(dim, 0, 0),
default_point_type='matrix')
self.n = n
self.space = SPDMatrices(n)
self.power_affine = power_affine
@staticmethod
def _aux_inner_product(tangent_vec_a, tangent_vec_b, inv_base_point):
"""Compute the inner-product (auxiliary).
Parameters
----------
tangent_vec_a : array-like, shape=[..., n, n]
tangent_vec_b : array-like, shape=[..., n, n]
inv_base_point : array-like, shape=[..., n, n]
Returns
-------
inner_product : array-like, shape=[...]
"""
aux_a = Matrices.mul(inv_base_point, tangent_vec_a)
aux_b = Matrices.mul(inv_base_point, tangent_vec_b)
# Use product instead of matrix product and trace to save time
inner_product = Matrices.trace_product(aux_a, aux_b)
return inner_product
[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.
"""
power_affine = self.power_affine
spd_space = self.space
if power_affine == 1:
inv_base_point = GeneralLinear.inverse(base_point)
inner_product = self._aux_inner_product(
tangent_vec_a, tangent_vec_b, inv_base_point)
else:
modified_tangent_vec_a = spd_space.differential_power(
power_affine, tangent_vec_a, base_point)
modified_tangent_vec_b = spd_space.differential_power(
power_affine, tangent_vec_b, base_point)
power_inv_base_point = SymmetricMatrices.powerm(
base_point, -power_affine)
inner_product = self._aux_inner_product(
modified_tangent_vec_a,
modified_tangent_vec_b,
power_inv_base_point)
inner_product = inner_product / (power_affine**2)
return inner_product
@staticmethod
def _aux_exp(tangent_vec, sqrt_base_point, inv_sqrt_base_point):
"""Compute the exponential map (auxiliary function).
Parameters
----------
tangent_vec : array-like, shape=[..., n, n]
sqrt_base_point : array-like, shape=[..., n, n]
inv_sqrt_base_point : array-like, shape=[..., n, n]
Returns
-------
exp : array-like, shape=[..., n, n]
"""
tangent_vec_at_id = Matrices.mul(
inv_sqrt_base_point, tangent_vec, inv_sqrt_base_point)
tangent_vec_at_id = GeneralLinear.to_symmetric(tangent_vec_at_id)
exp_from_id = SymmetricMatrices.expm(tangent_vec_at_id)
exp = Matrices.mul(
sqrt_base_point, exp_from_id, sqrt_base_point)
return exp
[docs] def exp(self, tangent_vec, base_point, **kwargs):
"""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.
"""
power_affine = self.power_affine
if power_affine == 1:
powers = SymmetricMatrices.powerm(base_point, [1. / 2, -1. / 2])
exp = self._aux_exp(
tangent_vec, powers[0], powers[1])
else:
modified_tangent_vec = self.space.differential_power(
power_affine, tangent_vec, base_point)
power_sqrt_base_point = SymmetricMatrices.powerm(
base_point, power_affine / 2)
power_inv_sqrt_base_point = GeneralLinear.inverse(
power_sqrt_base_point)
exp = self._aux_exp(
modified_tangent_vec,
power_sqrt_base_point,
power_inv_sqrt_base_point)
exp = SymmetricMatrices.powerm(exp, 1 / power_affine)
return exp
@staticmethod
def _aux_log(point, sqrt_base_point, inv_sqrt_base_point):
"""Compute the log (auxiliary function).
Parameters
----------
point : array-like, shape=[..., n, n]
sqrt_base_point : array-like, shape=[..., n, n]
inv_sqrt_base_point : array-like, shape=[.., n, n]
Returns
-------
log : array-like, shape=[..., n, n]
"""
point_near_id = Matrices.mul(
inv_sqrt_base_point, point, inv_sqrt_base_point)
point_near_id = GeneralLinear.to_symmetric(point_near_id)
log_at_id = SPDMatrices.logm(point_near_id)
log = Matrices.mul(sqrt_base_point, log_at_id, sqrt_base_point)
return log
[docs] def log(self, point, base_point, **kwargs):
"""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.
"""
power_affine = self.power_affine
if power_affine == 1:
powers = SymmetricMatrices.powerm(base_point, [1. / 2, -1. / 2])
log = self._aux_log(point, powers[0], powers[1])
else:
power_point = SymmetricMatrices.powerm(point, power_affine)
powers = SymmetricMatrices.powerm(
base_point, [power_affine / 2, -power_affine / 2])
log = self._aux_log(
power_point, powers[0], powers[1])
log = self.space.inverse_differential_power(
power_affine, log, base_point)
return log
[docs] def parallel_transport(self, tangent_vec_a, tangent_vec_b, base_point):
r"""Parallel transport of a tangent vector.
Closed-form solution for the parallel transport of a tangent vector a
along the geodesic defined by exp_(base_point)(tangent_vec_b).
Denoting `tangent_vec_a` by `S`, `base_point` by `A`, let
`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_a : array-like, shape=[..., dim + 1]
Tangent vector at base point to be transported.
tangent_vec_b : array-like, shape=[..., dim + 1]
Tangent vector at base point, initial speed of the geodesic along
which the parallel transport is computed.
base_point : array-like, shape=[..., dim + 1]
Point on the manifold of SPD matrices.
Returns
-------
transported_tangent_vec: array-like, shape=[..., dim + 1]
Transported tangent vector at exp_(base_point)(tangent_vec_b).
"""
end_point = self.exp(tangent_vec_b, base_point)
inverse_base_point = GeneralLinear.inverse(base_point)
congruence_mat = GeneralLinear.mul(end_point, inverse_base_point)
congruence_mat = gs.linalg.sqrtm(congruence_mat)
return GeneralLinear.congruent(tangent_vec_a, congruence_mat)
[docs]class SPDMetricBuresWasserstein(RiemannianMetric):
"""Class for the Bures-Wasserstein metric on the SPD manifold.
Parameters
----------
n : int
Integer representing the shape of the matrices: n x n.
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
"""
def __init__(self, n):
dim = int(n * (n + 1) / 2)
super(SPDMetricBuresWasserstein, self).__init__(
dim=dim,
signature=(dim, 0, 0),
default_point_type='matrix')
self.n = n
self.space = SPDMatrices(n)
[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.
"""
product = gs.matmul(base_point, point)
sqrt_product = gs.linalg.sqrtm(product)
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, axis1=-2, axis2=-1)
trace_b = gs.trace(point_b, axis1=-2, axis2=-1)
trace_prod = gs.trace(sqrt_product, axis1=-2, axis2=-1)
return trace_a + trace_b - 2 * trace_prod
[docs]class SPDMetricEuclidean(RiemannianMetric):
"""Class for the Euclidean metric on the SPD manifold."""
def __init__(self, n, power_euclidean=1):
dim = int(n * (n + 1) / 2)
super(SPDMetricEuclidean, self).__init__(
dim=dim,
signature=(dim, 0, 0),
default_point_type='matrix')
self.n = n
self.space = SPDMatrices(n)
self.power_euclidean = power_euclidean
[docs] def inner_product(self, tangent_vec_a, tangent_vec_b, base_point):
"""Compute the Euclidean inner-product.
Compute the inner-product of tangent_vec_a and tangent_vec_b
at point base_point using the power-Euclidean 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=[...,]
Inner-product.
"""
power_euclidean = self.power_euclidean
spd_space = self.space
if power_euclidean == 1:
inner_product = Matrices.frobenius_product(
tangent_vec_a, tangent_vec_b)
else:
modified_tangent_vec_a = spd_space.differential_power(
power_euclidean, tangent_vec_a, base_point)
modified_tangent_vec_b = spd_space.differential_power(
power_euclidean, tangent_vec_b, base_point)
inner_product = Matrices.frobenius_product(
modified_tangent_vec_a, modified_tangent_vec_b
) / (power_euclidean ** 2)
return inner_product
@staticmethod
@geomstats.vectorization.decorator(['matrix', 'matrix'])
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 = SymmetricMatrices.powerm(base_point, -.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., gs.array(-math.inf), - 1. / max_eig)
inf_value = gs.to_ndarray(inf_value, to_ndim=2)
sup_value = gs.where(
min_eig >= 0., gs.array(-math.inf), - 1. / min_eig)
sup_value = gs.to_ndarray(sup_value, to_ndim=2)
domain = gs.concatenate((inf_value, sup_value), axis=1)
return domain
[docs]class SPDMetricLogEuclidean(RiemannianMetric):
"""Class for the Log-Euclidean metric on the SPD manifold.
Parameters
----------
n : int
Integer representing the shape of the matrices: n x n.
"""
def __init__(self, n):
dim = int(n * (n + 1) / 2)
super(SPDMetricLogEuclidean, self).__init__(
dim=dim,
signature=(dim, 0, 0),
default_point_type='matrix')
self.n = n
self.space = SPDMatrices(n)
[docs] def inner_product(self, tangent_vec_a, tangent_vec_b, base_point):
"""Compute the Log-Euclidean inner-product.
Compute the inner-product of tangent_vec_a and tangent_vec_b
at point base_point using the log-Euclidean 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=[...,]
Inner-product.
"""
spd_space = self.space
modified_tangent_vec_a = spd_space.differential_log(
tangent_vec_a, base_point)
modified_tangent_vec_b = spd_space.differential_log(
tangent_vec_b, base_point)
product = Matrices.trace_product(
modified_tangent_vec_a, modified_tangent_vec_b)
return product
[docs] def exp(self, tangent_vec, base_point):
"""Compute the Log-Euclidean exponential map.
Compute the Riemannian exponential at point base_point
of tangent vector tangent_vec wrt the Log-Euclidean metric.
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.
"""
log_base_point = self.space.logm(base_point)
dlog_tangent_vec = self.space.differential_log(tangent_vec, base_point)
exp = SymmetricMatrices.expm(log_base_point + dlog_tangent_vec)
return exp
[docs] def log(self, point, base_point):
"""Compute the Log-Euclidean logarithm map.
Compute the Riemannian logarithm at point base_point,
of point wrt the Log-Euclidean 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.
"""
log_base_point = SPDMatrices.logm(base_point)
log_point = SPDMatrices.logm(point)
log = self.space.differential_exp(
log_point - log_base_point, log_base_point)
return log