class TestLieAlgebraMethods(geomstats.tests.TestCase):
def setUp(self):
self.n = 4
self.dim = int(self.n * (self.n - 1) / 2)
self.algebra = SkewSymmetricMatrices(n=self.n)
def test_dimension(self):
result = self.algebra.dim
expected = self.dim
self.assertAllClose(result, expected)
@geomstats.tests.np_only
def test_matrix_representation_and_belongs(self):
n_samples = 2
point = gs.random.rand(n_samples * self.dim)
point = gs.reshape(point, (n_samples, self.dim))
mat = self.algebra.matrix_representation(point)
result = self.algebra.belongs(mat).all()
expected = True
self.assertAllClose(result, expected)
@geomstats.tests.np_only
def test_basis_and_matrix_representation(self):
n_samples = 2
expected = gs.random.rand(n_samples * self.dim)
expected = gs.reshape(expected, (n_samples, self.dim))
mat = self.algebra.matrix_representation(expected)
result = self.algebra.basis_representation(mat)
self.assertAllClose(result, expected)
class TestLieAlgebra(geomstats.tests.TestCase):
def setUp(self):
self.n = 4
self.dim = int(self.n * (self.n - 1) / 2)
self.algebra = SkewSymmetricMatrices(n=self.n)
def test_dimension(self):
result = self.algebra.dim
expected = self.dim
self.assertAllClose(result, expected)
def test_matrix_representation_and_belongs(self):
n_samples = 2
point = gs.random.rand(n_samples * self.dim)
point = gs.reshape(point, (n_samples, self.dim))
mat = self.algebra.matrix_representation(point)
result = gs.all(self.algebra.belongs(mat))
expected = True
self.assertAllClose(result, expected)
@geomstats.tests.np_and_pytorch_only
def test_basis_and_matrix_representation(self):
n_samples = 2
expected = gs.random.rand(n_samples * self.dim)
expected = gs.reshape(expected, (n_samples, self.dim))
mat = self.algebra.matrix_representation(expected)
result = self.algebra.basis_representation(mat)
self.assertAllClose(result, expected)
def test_orthonormal_basis(self):
group = SpecialOrthogonal(3)
lie_algebra = SkewSymmetricMatrices(3)
metric = InvariantMetric(group=group, algebra=lie_algebra)
basis = lie_algebra.orthonormal_basis(metric.metric_mat_at_identity)
result = metric.inner_product_at_identity(basis[0], basis[1])
self.assertAllClose(result, 0.)
result = metric.inner_product_at_identity(basis[1], basis[1])
self.assertAllClose(result, 1.)
metric_mat = from_vector_to_diagonal_matrix(gs.array([1., 2., 3.]))
metric = InvariantMetric(group=group,
algebra=lie_algebra,
metric_mat_at_identity=metric_mat)
basis = lie_algebra.orthonormal_basis(metric.metric_mat_at_identity)
result = metric.inner_product_at_identity(basis[0], basis[1])
self.assertAllClose(result, 0.)
result = metric.inner_product_at_identity(basis[1], basis[1])
self.assertAllClose(result, 1.)
class SpecialEuclideanMatrixLieAlgebra(MatrixLieAlgebra):
r"""Lie Algebra of the special Euclidean group.
This is the tangent space at the identity. It is identified with the
:math:`n + 1 \times n + 1` block matrices of the form:
.. math:
((A, t), (0, 0))
where A is an :math:`n \times n` skew-symmetric matrix, :math: `t` is an
n-dimensional vector.
Parameters
----------
n : int
Integer dimension of the underlying Euclidean space. Matrices will
be of size: (n+1) x (n+1).
"""
def __init__(self, n):
dim = int(n * (n + 1) / 2)
super(SpecialEuclideanMatrixLieAlgebra, self).__init__(dim, n + 1)
self.skew = SkewSymmetricMatrices(n)
self.n = n
def _create_basis(self):
"""Create the canonical basis."""
n = self.n
basis = homogeneous_representation(
self.skew.basis,
gs.zeros((self.skew.dim, n)),
(self.skew.dim, n + 1, n + 1),
0.0,
)
basis = list(basis)
for row in gs.arange(n):
basis.append(
gs.array_from_sparse([(row, n)], [1.0], (n + 1, n + 1)))
return gs.stack(basis)
def belongs(self, mat, atol=ATOL):
"""Evaluate if the rotation part of mat is a skew-symmetric matrix.
Parameters
----------
mat : array-like, shape=[..., n + 1, n + 1]
Square matrix to check.
atol : float
Tolerance for the equality evaluation.
Optional, default: backend atol.
Returns
-------
belongs : array-like, shape=[...,]
Boolean evaluating if rotation part of matrix is skew symmetric.
"""
point_dim1, point_dim2 = mat.shape[-2:]
belongs = point_dim1 == point_dim2 == self.n + 1
rotation = mat[..., :self.n, :self.n]
rot_belongs = self.skew.belongs(rotation, atol=atol)
belongs = gs.logical_and(belongs, rot_belongs)
last_line = mat[..., -1, :]
all_zeros = ~gs.any(last_line, axis=-1)
belongs = gs.logical_and(belongs, all_zeros)
return belongs
def random_point(self, n_samples=1, bound=1.0):
"""Sample in the lie algebra with a uniform distribution in a box.
Parameters
----------
n_samples : int
Number of samples.
Optional, default: 1.
bound : float
Side of hypercube support of the uniform distribution.
Optional, default: 1.0
Returns
-------
point : array-like, shape=[..., n + 1, n + 1]
Sample.
"""
point = super(SpecialEuclideanMatrixLieAlgebra,
self).random_point(n_samples, bound)
return self.projection(point)
def projection(self, mat):
"""Project a matrix to the Lie Algebra.
Compute the skew-symmetric projection of the rotation part of matrix.
Parameters
----------
mat : array-like, shape=[..., n + 1, n + 1]
Matrix.
Returns
-------
projected : array-like, shape=[..., n + 1, n + 1]
Matrix belonging to Lie Algebra.
"""
rotation = mat[..., :self.n, :self.n]
skew = SkewSymmetricMatrices.projection(rotation)
return homogeneous_representation(skew, mat[..., :self.n, self.n],
mat.shape, 0.0)
def basis_representation(self, matrix_representation):
"""Calculate the coefficients of given matrix in the basis.
Compute a 1d-array that corresponds to the input matrix in the basis
representation.
Parameters
----------
matrix_representation : array-like, shape=[..., n + 1, n + 1]
Matrix.
Returns
-------
basis_representation : array-like, shape=[..., dim]
Representation in the basis.
"""
skew_part = self.skew.basis_representation(
matrix_representation[..., :self.n, :self.n])
translation_part = matrix_representation[..., :-1, self.n]
return gs.concatenate([skew_part, translation_part[..., :]], axis=-1)
class TestLieAlgebra(geomstats.tests.TestCase):
def setup_method(self):
self.n = 4
self.dim = int(self.n * (self.n - 1) / 2)
self.algebra = SkewSymmetricMatrices(n=self.n)
def test_dimension(self):
result = self.algebra.dim
expected = self.dim
self.assertAllClose(result, expected)
def test_matrix_representation_and_belongs(self):
n_samples = 2
point = gs.random.rand(n_samples * self.dim)
point = gs.reshape(point, (n_samples, self.dim))
mat = self.algebra.matrix_representation(point)
result = gs.all(self.algebra.belongs(mat))
self.assertTrue(result)
def test_basis_and_matrix_representation(self):
n_samples = 2
expected = gs.random.rand(n_samples * self.dim)
expected = gs.reshape(expected, (n_samples, self.dim))
mat = self.algebra.matrix_representation(expected)
result = self.algebra.basis_representation(mat)
self.assertAllClose(result, expected)
def test_orthonormal_basis(self):
group = SpecialOrthogonal(3)
lie_algebra = SkewSymmetricMatrices(3)
metric = InvariantMetric(group=group)
basis = metric.normal_basis(lie_algebra.basis)
result = metric.inner_product_at_identity(basis[0], basis[1])
self.assertAllClose(result, 0.0)
result = metric.inner_product_at_identity(basis[1], basis[1])
self.assertAllClose(result, 1.0)
metric_mat = from_vector_to_diagonal_matrix(gs.array([1.0, 2.0, 3.0]))
metric = InvariantMetric(group=group, metric_mat_at_identity=metric_mat)
basis = metric.normal_basis(lie_algebra.basis)
result = metric.inner_product_at_identity(basis[0], basis[1])
self.assertAllClose(result, 0.0)
result = metric.inner_product_at_identity(basis[1], basis[1])
self.assertAllClose(result, 1.0)
def test_orthonormal_basis_se3(self):
group = SpecialEuclidean(3)
lie_algebra = group.lie_algebra
metric = InvariantMetric(group=group)
basis = metric.normal_basis(lie_algebra.basis)
for i, x in enumerate(basis):
for y in basis[i:]:
result = metric.inner_product_at_identity(x, y)
expected = 0.0 if gs.any(x != y) else 1.0
self.assertAllClose(result, expected)
metric_mat = from_vector_to_diagonal_matrix(
gs.cast(gs.arange(1, group.dim + 1), gs.float32)
)
metric = InvariantMetric(group=group, metric_mat_at_identity=metric_mat)
basis = metric.normal_basis(lie_algebra.basis)
for i, x in enumerate(basis):
for y in basis[i:]:
result = metric.inner_product_at_identity(x, y)
expected = 0.0 if gs.any(x != y) else 1.0
self.assertAllClose(result, expected)
