def test_geodesic_vectorization(self):
space = Hypersphere(2)
metric = space.metric
initial_point = space.random_uniform(2)
vector = gs.random.rand(2, 3)
initial_tangent_vec = space.to_tangent(vector=vector,
base_point=initial_point)
end_point = space.random_uniform(2)
time = gs.linspace(0, 1, 10)
geo = metric.geodesic(initial_point, initial_tangent_vec)
path = geo(time)
result = path.shape
expected = (2, 10, 3)
self.assertAllClose(result, expected)
geo = metric.geodesic(initial_point, end_point=end_point)
path = geo(time)
result = path.shape
expected = (2, 10, 3)
self.assertAllClose(result, expected)
geo = metric.geodesic(initial_point, end_point=end_point[0])
path = geo(time)
result = path.shape
expected = (2, 10, 3)
self.assertAllClose(result, expected)
initial_tangent_vec = space.to_tangent(vector=vector,
base_point=initial_point[0])
geo = metric.geodesic(initial_point[0], initial_tangent_vec)
path = geo(time)
result = path.shape
expected = (2, 10, 3)
self.assertAllClose(result, expected)
class TestVisualization(geomstats.tests.TestCase):
def setUp(self):
self.n_samples = 10
self.SO3_GROUP = SpecialOrthogonal(n=3, point_type='vector')
self.SE3_GROUP = SpecialEuclidean(n=3, point_type='vector')
self.S1 = Hypersphere(dim=1)
self.S2 = Hypersphere(dim=2)
self.H2 = Hyperbolic(dim=2)
self.H2_half_plane = PoincareHalfSpace(dim=2)
plt.figure()
@staticmethod
def test_tutorial_matplotlib():
visualization.tutorial_matplotlib()
def test_plot_points_so3(self):
points = self.SO3_GROUP.random_uniform(self.n_samples)
visualization.plot(points, space='SO3_GROUP')
def test_plot_points_se3(self):
points = self.SE3_GROUP.random_uniform(self.n_samples)
visualization.plot(points, space='SE3_GROUP')
@geomstats.tests.np_and_pytorch_only
def test_plot_points_s1(self):
points = self.S1.random_uniform(self.n_samples)
visualization.plot(points, space='S1')
def test_plot_points_s2(self):
points = self.S2.random_uniform(self.n_samples)
visualization.plot(points, space='S2')
def test_plot_points_h2_poincare_disk(self):
points = self.H2.random_uniform(self.n_samples)
visualization.plot(points, space='H2_poincare_disk')
def test_plot_points_h2_poincare_half_plane_ext(self):
points = self.H2.random_uniform(self.n_samples)
visualization.plot(points,
space='H2_poincare_half_plane',
point_type='extrinsic')
def test_plot_points_h2_poincare_half_plane_none(self):
points = self.H2_half_plane.random_uniform(self.n_samples)
visualization.plot(points, space='H2_poincare_half_plane')
def test_plot_points_h2_poincare_half_plane_hs(self):
points = self.H2_half_plane.random_uniform(self.n_samples)
visualization.plot(points,
space='H2_poincare_half_plane',
point_type='half_space')
def test_plot_points_h2_klein_disk(self):
points = self.H2.random_uniform(self.n_samples)
visualization.plot(points, space='H2_klein_disk')
def test_extrinsic_to_angle_inverse(self):
space = Hypersphere(1)
point = space.random_uniform()
angle = space.extrinsic_to_angle(point)
result = space.angle_to_extrinsic(angle)
self.assertAllClose(result, point)
space = Hypersphere(1, default_coords_type='intrinsic')
angle = space.random_uniform()
extrinsic = space.angle_to_extrinsic(angle)
result = space.extrinsic_to_angle(extrinsic)
self.assertAllClose(result, angle)
def test_rotate_points(self):
sphere = Hypersphere(2)
end_point = sphere.random_uniform()
north_pole = gs.array([1., 0., 0.])
result = utils.rotate_points(north_pole, end_point)
expected = end_point
self.assertAllClose(result, expected)
points = sphere.random_uniform(10)
result = utils.rotate_points(points, north_pole)
self.assertAllClose(result, points)
points = gs.concatenate([north_pole[None, :], points])
result = utils.rotate_points(points, end_point)
self.assertAllClose(result[0], end_point)
class GeomstatsSphere(Manifold):
"""A simple adapter class which proxies calls by pymanopt's solvers to
`Manifold` subclasses to the underlying geomstats `Hypersphere` class.
"""
def __init__(self, ambient_dimension):
self._sphere = Hypersphere(ambient_dimension - 1)
def norm(self, base_vector, tangent_vector):
return self._sphere.metric.norm(tangent_vector, base_point=base_vector)
def inner(self, base_vector, tangent_vector_a, tangent_vector_b):
return self._sphere.metric.inner_product(
tangent_vector_a, tangent_vector_b, base_point=base_vector)
def proj(self, base_vector, tangent_vector):
return self._sphere.to_tangent(
tangent_vector, base_point=base_vector)
def retr(self, base_vector, tangent_vector):
"""The retraction operator, which maps a tangent vector in the tangent
space at a specific point back to the manifold by approximating moving
along a geodesic. Since geomstats's `Hypersphere` class doesn't provide
a retraction we use the exponential map instead (see also
https://hal.archives-ouvertes.fr/hal-00651608/document).
"""
return self._sphere.metric.exp(tangent_vector, base_point=base_vector)
def rand(self):
return self._sphere.random_uniform()
def test_parallel_transport_trajectory(self, dim, n_samples):
sphere = Hypersphere(dim)
for step in ["pole", "schild"]:
n_steps = 1 if step == "pole" else 50
tol = 1e-6 if step == "pole" else 1e-2
base_point = sphere.random_uniform(n_samples)
tan_vec_a = sphere.to_tangent(gs.random.rand(n_samples, 3),
base_point)
tan_vec_b = sphere.to_tangent(gs.random.rand(n_samples, 3),
base_point)
expected = sphere.metric.parallel_transport(
tan_vec_a, base_point, tan_vec_b)
expected_point = sphere.metric.exp(tan_vec_b, base_point)
ladder = sphere.metric.ladder_parallel_transport(
tan_vec_a,
base_point,
tan_vec_b,
n_rungs=n_steps,
scheme=step,
return_geodesics=True,
)
result = ladder["transported_tangent_vec"]
result_point = ladder["end_point"]
self.assertAllClose(result, expected, rtol=tol, atol=tol)
self.assertAllClose(result_point, expected_point)
def test_parallel_transport(self, dim, n_samples):
sphere = Hypersphere(dim)
base_point = sphere.random_uniform(n_samples)
tan_vec_a = sphere.to_tangent(gs.random.rand(n_samples, 3), base_point)
tan_vec_b = sphere.to_tangent(gs.random.rand(n_samples, 3), base_point)
expected = sphere.metric.parallel_transport(tan_vec_a, base_point,
tan_vec_b)
expected_point = sphere.metric.exp(tan_vec_b, base_point)
base_point = gs.cast(base_point, gs.float64)
base_point, tan_vec_a, tan_vec_b = gs.convert_to_wider_dtype(
[base_point, tan_vec_a, tan_vec_b])
for step, alpha in zip(["pole", "schild"], [1, 2]):
min_n = 1 if step == "pole" else 50
tol = 1e-5 if step == "pole" else 1e-2
for n_rungs in [min_n, 11]:
ladder = sphere.metric.ladder_parallel_transport(
tan_vec_a,
base_point,
tan_vec_b,
n_rungs=n_rungs,
scheme=step,
alpha=alpha,
)
result = ladder["transported_tangent_vec"]
result_point = ladder["end_point"]
self.assertAllClose(result, expected, rtol=tol, atol=tol)
self.assertAllClose(result_point, expected_point)
class TestVisualizationMethods(geomstats.tests.TestCase):
def setUp(self):
self.n_samples = 10
self.SO3_GROUP = SpecialOrthogonal(n=3)
self.SE3_GROUP = SpecialEuclidean(n=3)
self.S1 = Hypersphere(dim=1)
self.S2 = Hypersphere(dim=2)
self.H2 = Hyperbolic(dim=2)
plt.figure()
@geomstats.tests.np_only
def test_plot_points_so3(self):
points = self.SO3_GROUP.random_uniform(self.n_samples)
visualization.plot(points, space='SO3_GROUP')
@geomstats.tests.np_only
def test_plot_points_se3(self):
points = self.SE3_GROUP.random_uniform(self.n_samples)
visualization.plot(points, space='SE3_GROUP')
@geomstats.tests.np_only
def test_plot_points_s1(self):
points = self.S1.random_uniform(self.n_samples)
visualization.plot(points, space='S1')
@geomstats.tests.np_only
def test_plot_points_s2(self):
points = self.S2.random_uniform(self.n_samples)
visualization.plot(points, space='S2')
@geomstats.tests.np_only
def test_plot_points_h2_poincare_disk(self):
points = self.H2.random_uniform(self.n_samples)
visualization.plot(points, space='H2_poincare_disk')
@geomstats.tests.np_only
def test_plot_points_h2_poincare_half_plane(self):
points = self.H2.random_uniform(self.n_samples)
visualization.plot(points, space='H2_poincare_half_plane')
@geomstats.tests.np_only
def test_plot_points_h2_klein_disk(self):
points = self.H2.random_uniform(self.n_samples)
visualization.plot(points, space='H2_klein_disk')
def empirical_frechet_var_bubble(n_samples, theta, dim, n_expectation=1000):
"""Variance of the empirical Fréchet mean for a bubble distribution.
Draw n_sampless from a bubble distribution, computes its empirical
Fréchet mean and the square distance to the asymptotic mean. This
is repeated n_expectation times to compute an approximation of its
expectation (i.e. its variance) by sampling.
The bubble distribution is an isotropic distributions on a Riemannian
hyper sub-sphere of radius 0 < theta < Pi around the north pole of the
sphere of dimension dim.
Parameters
----------
n_samples : int
Number of samples to draw.
theta: float
Radius of the bubble distribution.
dim : int
Dimension of the sphere (embedded in R^{dim+1}).
n_expectation: int, optional (defaults to 1000)
Number of computations for approximating the expectation.
Returns
-------
tuple (variance, std-dev on the computed variance)
"""
if dim <= 1:
raise ValueError(
'Dim > 1 needed to draw a uniform sample on sub-sphere.')
var = []
sphere = Hypersphere(dim=dim)
bubble = Hypersphere(dim=dim - 1)
north_pole = gs.zeros(dim + 1)
north_pole[dim] = 1.0
for _ in range(n_expectation):
# Sample n points from the uniform distribution on a sub-sphere
# of radius theta (i.e cos(theta) in ambient space)
# TODO (nina): Add this code as a method of hypersphere
data = gs.zeros((n_samples, dim + 1), dtype=gs.float64)
directions = bubble.random_uniform(n_samples)
directions = gs.to_ndarray(directions, to_ndim=2)
for i in range(n_samples):
for j in range(dim):
data[i, j] = gs.sin(theta) * directions[i, j]
data[i, dim] = gs.cos(theta)
# TODO (nina): Use FrechetMean here
current_mean = _adaptive_gradient_descent(data,
metric=sphere.metric,
max_iter=32,
init_point=north_pole)
var.append(sphere.metric.squared_dist(north_pole, current_mean))
return gs.mean(var), 2 * gs.std(var) / gs.sqrt(n_expectation)
def empirical_frechet_var_bubble(n_samples, theta, dim, n_expectation=1000):
"""Variance of the empirical Fréchet mean for a bubble distribution.
Draw n_sampless from a bubble distribution, computes its empirical
Fréchet mean and the square distance to the asymptotic mean. This
is repeated n_expectation times to compute an approximation of its
expectation (i.e. its variance) by sampling.
The bubble distribution is an isotropic distributions on a Riemannian
hyper sub-sphere of radius 0 < theta < Pi around the north pole of the
sphere of dimension dim.
Parameters
----------
n_samples : int
Number of samples to draw.
theta: float
Radius of the bubble distribution.
dim : int
Dimension of the sphere (embedded in R^{dim+1}).
n_expectation: int, optional (defaults to 1000)
Number of computations for approximating the expectation.
Returns
-------
tuple (variance, std-dev on the computed variance)
"""
if dim <= 1:
raise ValueError(
"Dim > 1 needed to draw a uniform sample on sub-sphere.")
var = []
sphere = Hypersphere(dim=dim)
bubble = Hypersphere(dim=dim - 1)
north_pole = gs.zeros(dim + 1)
north_pole[dim] = 1.0
for _ in range(n_expectation):
# Sample n points from the uniform distribution on a sub-sphere
# of radius theta (i.e cos(theta) in ambient space)
# TODO (nina): Add this code as a method of hypersphere
last_col = gs.cos(theta) * gs.ones(n_samples)
last_col = last_col[:, None] if (n_samples > 1) else last_col
directions = bubble.random_uniform(n_samples)
rest_col = gs.sin(theta) * directions
data = gs.concatenate([rest_col, last_col], axis=-1)
estimator = FrechetMean(sphere.metric,
max_iter=32,
method="adaptive",
init_point=north_pole)
estimator.fit(data)
current_mean = estimator.estimate_
var.append(sphere.metric.squared_dist(north_pole, current_mean))
return gs.mean(var), 2 * gs.std(var) / gs.sqrt(n_expectation)
def empirical_frechet_mean_random_init_s2(data, n_init=1, init_points=[]):
"""Fréchet mean on S2 by gradient descent from multiple starting points"
Parameters
----------
data: empirical distribution on S2
n_init: number of initial points drawn uniformly at random on S2
init_points: list of initial points for the first gradient descent
Returns
-------
frechet mean list
"""
assert n_init >= 1, "Gradient descent needs at least one starting point"
dim = len(data[0]) - 1
sphere = Hypersphere(dimension=dim)
if len(init_points) == 0:
init_points = [sphere.random_uniform()]
# for a noncompact manifold, we need to revise this to a ball
# with a maximal radius
mean = _adaptive_gradient_descent(data,
metric=sphere.metric,
n_max_iterations=64,
init_points=init_points)
sigma_mean = mean_sq_dist_s2(mean, data)
# print ("variance {0} for FM {1}".format(sigFM,FM))
for i in range(n_init - 1):
init_points = sphere.random_uniform()
new_mean = _adaptive_gradient_descent(data,
metric=sphere.metric,
n_max_iterations=64,
init_points=init_points)
sigma_new_mean = mean_sq_dist_s2(new_mean, data)
if sigma_new_mean < sigma_mean:
mean = new_mean
sigma_mean = sigma_new_mean
# print ("new variance {0} for FM {1}".format(sigFM,FM))
return mean
def test_parallel_transport_vectorization(self):
sphere = Hypersphere(2)
n_samples = 4
def is_isometry(tan_a, trans_a, endpoint):
is_tangent = gs.isclose(sphere.metric.inner_product(
endpoint, trans_a),
0.,
atol=1e-6)
is_equinormal = gs.isclose(gs.linalg.norm(trans_a, axis=-1),
gs.linalg.norm(tan_a, axis=-1))
return gs.logical_and(is_tangent, is_equinormal)
base_point = sphere.random_uniform(n_samples)
tan_vec_a = sphere.to_tangent(gs.random.rand(n_samples, 3), base_point)
tan_vec_b = sphere.to_tangent(gs.random.rand(n_samples, 3), base_point)
end_point = sphere.metric.exp(tan_vec_b, base_point)
transported = sphere.metric.parallel_transport(tan_vec_a, tan_vec_b,
base_point)
result = is_isometry(tan_vec_a, transported, end_point)
self.assertTrue(gs.all(result))
base_point = base_point[0]
tan_vec_a = sphere.to_tangent(tan_vec_a, base_point)
tan_vec_b = sphere.to_tangent(tan_vec_b, base_point)
end_point = sphere.metric.exp(tan_vec_b, base_point)
transported = sphere.metric.parallel_transport(tan_vec_a, tan_vec_b,
base_point)
result = is_isometry(tan_vec_a, transported, end_point)
self.assertTrue(gs.all(result))
one_tan_vec_a = tan_vec_a[0]
transported = sphere.metric.parallel_transport(one_tan_vec_a,
tan_vec_b, base_point)
result = is_isometry(one_tan_vec_a, transported, end_point)
self.assertTrue(gs.all(result))
one_tan_vec_b = tan_vec_b[0]
end_point = end_point[0]
transported = sphere.metric.parallel_transport(tan_vec_a,
one_tan_vec_b,
base_point)
result = is_isometry(tan_vec_a, transported, end_point)
self.assertTrue(gs.all(result))
transported = sphere.metric.parallel_transport(one_tan_vec_a,
one_tan_vec_b,
base_point)
result = is_isometry(one_tan_vec_a, transported, end_point)
self.assertTrue(result)
def test_parallel_transport(self):
sphere = Hypersphere(dimension=2)
connection = LeviCivitaConnection(sphere.metric)
n_samples = 10
base_point = sphere.random_uniform(n_samples)
tan_vec_a = sphere.projection_to_tangent_space(
gs.random.rand(n_samples, 3), base_point)
tan_vec_b = sphere.projection_to_tangent_space(
gs.random.rand(n_samples, 3), base_point)
expected = sphere.metric.parallel_transport(
tan_vec_a, tan_vec_b, base_point)
result = connection.pole_ladder_parallel_transport(
tan_vec_a, tan_vec_b, base_point)
self.assertAllClose(result, expected, rtol=1e-7, atol=1e-5)
def test_spherical_to_extrinsic_and_inverse(self):
dim = 2
n_samples = 5
sphere = Hypersphere(dim)
points = gs.random.rand(n_samples, 2) * gs.pi * gs.array([1., 2.
])[None, :]
extrinsic = sphere.spherical_to_extrinsic(points)
result = sphere.extrinsic_to_spherical(extrinsic)
self.assertAllClose(result, points)
points_extrinsic = sphere.random_uniform(n_samples)
spherical = sphere.extrinsic_to_spherical(points_extrinsic)
result = sphere.spherical_to_extrinsic(spherical)
self.assertAllClose(result, points_extrinsic)
def test_circle_mean(self):
space = Hypersphere(1)
points = space.random_uniform(10)
mean_circle = FrechetMean(space.metric)
mean_circle.fit(points)
estimate_circle = mean_circle.estimate_
# set a wrong dimension so that the extrinsic coordinates are used
metric = space.metric
metric.dim = 2
mean_extrinsic = FrechetMean(metric)
mean_extrinsic.fit(points)
estimate_extrinsic = mean_extrinsic.estimate_
self.assertAllClose(estimate_circle, estimate_extrinsic)
def test_ladder_alpha(self, dim, n_samples):
sphere = Hypersphere(dim)
base_point = sphere.random_uniform(n_samples)
tan_vec_a = sphere.to_tangent(gs.random.rand(n_samples, 3), base_point)
tan_vec_b = sphere.to_tangent(gs.random.rand(n_samples, 3), base_point)
with pytest.raises(ValueError):
sphere.metric.ladder_parallel_transport(
tan_vec_a,
base_point,
tan_vec_b,
n_rungs=1,
scheme="pole",
alpha=0.5,
return_geodesics=False,
)
def empirical_frechet_var_bubble(n_samples, theta, dim, n_expectation=1000):
"""Variance of the empirical Fréchet mean for a bubble distribution.
Draw n_sampless from a bubble distribution, computes its empirical
Fréchet mean and the square distance to the asymptotic mean. This
is repeated n_expectation times to compute an approximation of its
expectation (i.e. its variance) by sampling.
The bubble distribution is an isotropic distributions on a Riemannian
hyper sub-sphere of radius 0 < theta = around the north pole of the
hyperbolic space of dimension dim.
Parameters
----------
n_samples: number of samples to draw
theta: radius of the bubble distribution
dim: dimension of the hyperbolic space (embedded in R^{1,dim})
n_expectation: number of computations for approximating the expectation
Returns
-------
tuple (variance, std-dev on the computed variance)
"""
assert dim > 1, "Dim > 1 needed to draw a uniform sample on sub-sphere"
var = []
hyperbole = Hyperbolic(dimension=dim)
bubble = Hypersphere(dimension=dim - 1)
origin = gs.zeros(dim + 1)
origin[0] = 1.0
for k in range(n_expectation):
# Sample n points from the uniform distribution on a sub-sphere
# of radius theta (i.e cos(theta) in ambient space)
data = gs.zeros((n_samples, dim + 1), dtype=gs.float64)
directions = bubble.random_uniform(n_samples)
for i in range(n_samples):
for j in range(dim):
data[i, j + 1] = gs.sinh(theta) * directions[i, j]
data[i, 0] = gs.cosh(theta)
current_mean = _adaptive_gradient_descent(data,
metric=hyperbole.metric,
n_max_iterations=64,
init_points=[origin])
var.append(hyperbole.metric.squared_dist(origin, current_mean))
return np.mean(var), 2 * np.std(var) / np.sqrt(n_expectation)
def main():
"""Plot the result of a KNN classification on the sphere."""
sphere = Hypersphere(dim=2)
sphere_distance = sphere.metric.dist
n_labels = 2
n_samples_per_dataset = 10
n_targets = 200
dataset_1 = sphere.random_von_mises_fisher(kappa=10,
n_samples=n_samples_per_dataset)
dataset_2 = -sphere.random_von_mises_fisher(
kappa=10, n_samples=n_samples_per_dataset)
training_dataset = gs.concatenate((dataset_1, dataset_2), axis=0)
labels_dataset_1 = gs.zeros([n_samples_per_dataset], dtype=gs.int64)
labels_dataset_2 = gs.ones([n_samples_per_dataset], dtype=gs.int64)
labels = gs.concatenate((labels_dataset_1, labels_dataset_2))
target = sphere.random_uniform(n_samples=n_targets)
neigh = KNearestNeighborsClassifier(n_neighbors=2,
distance=sphere_distance)
neigh.fit(training_dataset, labels)
target_labels = neigh.predict(target)
plt.figure(0)
ax = plt.subplot(111, projection="3d")
plt.title("Training set")
sphere_plot = visualization.Sphere()
sphere_plot.draw(ax=ax)
for i_label in range(n_labels):
points_label_i = training_dataset[labels == i_label, ...]
sphere_plot.draw_points(ax=ax, points=points_label_i)
plt.figure(1)
ax = plt.subplot(111, projection="3d")
plt.title("Classification")
sphere_plot = visualization.Sphere()
sphere_plot.draw(ax=ax)
for i_label in range(n_labels):
target_points_label_i = target[target_labels == i_label, ...]
sphere_plot.draw_points(ax=ax, points=target_points_label_i)
plt.show()
def test_fit_init_random_sphere(self):
"""Test fitting data into a GMM."""
space = Hypersphere(2)
gmm_learning = RiemannianEM(
metric=space.metric,
n_gaussians=2,
initialisation_method=self.initialisation_method,
)
means = space.random_uniform(2)
cluster_1 = space.random_von_mises_fisher(mu=means[0], kappa=20, n_samples=140)
cluster_2 = space.random_von_mises_fisher(mu=means[1], kappa=20, n_samples=140)
data = gs.concatenate((cluster_1, cluster_2), axis=0)
means, variances, coefficients = gmm_learning.fit(data)
self.assertTrue((coefficients < 1).all() and (coefficients > 0).all())
self.assertTrue((variances < 1).all() and (variances > 0).all())
self.assertTrue(space.belongs(means).all())
def sectional_curvature_test_data(self):
dim_list = [1]
n_samples_list = random.sample(range(1, 4), 2)
random_data = []
for dim, n_samples in zip(dim_list, n_samples_list):
sphere = Hypersphere(dim)
base_point = sphere.random_uniform()
tangent_vec_a = sphere.to_tangent(
gs.random.rand(n_samples, sphere.dim + 1), base_point)
tangent_vec_b = sphere.to_tangent(
gs.random.rand(n_samples, sphere.dim + 1), base_point)
expected = gs.ones(n_samples) # try shape here
random_data.append(
dict(
dim=dim,
tangent_vec_a=tangent_vec_a,
tangent_vec_b=tangent_vec_b,
base_point=base_point,
expected=expected,
), )
return self.generate_tests(random_data)
def test_sample_von_mises_fisher_arbitrary_mean(self):
"""
Check that the maximum likelihood estimates of the mean and
concentration parameter are close to the real values. A first
estimation of the concentration parameter is obtained by a
closed-form expression and improved through the Newton method.
"""
for dim in [2, 9]:
n_points = 10000
sphere = Hypersphere(dim)
# check mean value for concentrated distribution for different mean
kappa = 1000.0
mean = sphere.random_uniform()
points = sphere.random_von_mises_fisher(mu=mean,
kappa=kappa,
n_samples=n_points)
sum_points = gs.sum(points, axis=0)
result = sum_points / gs.linalg.norm(sum_points)
expected = mean
self.assertAllClose(result, expected, atol=MEAN_ESTIMATION_TOL)
def main():
circle = Hypersphere(dimension=1)
data = circle.random_uniform(n_samples=1000)
n_clusters = 5
clustering = OnlineKMeans(metric=circle.metric, n_clusters=n_clusters)
clustering = clustering.fit(data)
plt.figure(0)
visualization.plot(points=clustering.cluster_centers_, space='S1',
color='red')
plt.show()
plt.figure(1)
ax = plt.axes()
circle_plot = visualization.Circle()
circle_plot.draw(ax=ax)
for i in range(n_clusters):
cluster = data[clustering.labels_ == i, :]
circle_plot.draw_points(ax=ax, points=cluster)
plt.show()
class GeomstatsSphere(EuclideanEmbeddedSubmanifold):
"""A simple adapter class which proxies calls by pymanopt's solvers to
`Manifold` subclasses to the underlying geomstats `Hypersphere` class.
"""
def __init__(self, ambient_dimension):
dim = ambient_dimension - 1
self._sphere = Hypersphere(dim)
super().__init__('{}-dimensional Hypersphere'.format(dim), dim)
def norm(self, base_point, tangent_vector):
return self._sphere.metric.norm(tangent_vector, base_point=base_point)
def inner(self, base_point, tangent_vector_a, tangent_vector_b):
return self._sphere.metric.inner_product(tangent_vector_a,
tangent_vector_b,
base_point=base_point)
def proj(self, base_point, ambient_vector):
return self._sphere.to_tangent(ambient_vector, base_point=base_point)
def retr(self, base_point, tangent_vector):
"""The retraction operator, which maps a tangent vector in the tangent
space at a specific point back to the manifold by approximating moving
along a geodesic. Since geomstats's `Hypersphere` class doesn't provide
a retraction we use the exponential map instead (see also
https://hal.archives-ouvertes.fr/hal-00651608/document).
"""
return self._sphere.metric.exp(tangent_vector, base_point=base_point)
def rand(self):
return self._sphere.random_uniform()
def randvec(self, base_point):
random_point = gs.random.normal(size=self.dim + 1)
random_tangent_vector = self.proj(base_point, random_point)
return random_tangent_vector / gs.linalg.norm(random_tangent_vector)
def zerovec(self, base_point):
return gs.zeros_like(self.rand())
def test_tangent_spherical_and_extrinsic_inverse(self):
dim = 2
n_samples = 5
sphere = Hypersphere(dim)
points = gs.random.rand(n_samples, 2) * gs.pi * gs.array([1., 2.
])[None, :]
tangent_spherical = gs.random.rand(n_samples, 2)
tangent_extrinsic = sphere.tangent_spherical_to_extrinsic(
tangent_spherical, points)
result = sphere.tangent_extrinsic_to_spherical(
tangent_extrinsic, base_point_spherical=points)
self.assertAllClose(result, tangent_spherical)
points_extrinsic = sphere.random_uniform(n_samples)
vector = gs.random.rand(n_samples, dim + 1)
tangent_extrinsic = sphere.to_tangent(vector, points_extrinsic)
tangent_spherical = sphere.tangent_extrinsic_to_spherical(
tangent_extrinsic, base_point=points_extrinsic)
spherical = sphere.extrinsic_to_spherical(points_extrinsic)
result = sphere.tangent_spherical_to_extrinsic(tangent_spherical,
spherical)
self.assertAllClose(result, tangent_extrinsic)
class TestVisualization(geomstats.tests.TestCase):
def setUp(self):
self.n_samples = 10
self.SO3_GROUP = SpecialOrthogonal(n=3, point_type='vector')
self.SE3_GROUP = SpecialEuclidean(n=3, point_type='vector')
self.S1 = Hypersphere(dim=1)
self.S2 = Hypersphere(dim=2)
self.H2 = Hyperbolic(dim=2)
self.H2_half_plane = PoincareHalfSpace(dim=2)
self.M32 = Matrices(m=3, n=2)
self.S32 = PreShapeSpace(k_landmarks=3, m_ambient=2)
self.KS = visualization.KendallSphere()
self.M33 = Matrices(m=3, n=3)
self.S33 = PreShapeSpace(k_landmarks=3, m_ambient=3)
self.KD = visualization.KendallDisk()
plt.figure()
@staticmethod
def test_tutorial_matplotlib():
visualization.tutorial_matplotlib()
def test_plot_points_so3(self):
points = self.SO3_GROUP.random_uniform(self.n_samples)
visualization.plot(points, space='SO3_GROUP')
def test_plot_points_se3(self):
points = self.SE3_GROUP.random_point(self.n_samples)
visualization.plot(points, space='SE3_GROUP')
def test_draw_pre_shape_2d(self):
self.KS.draw()
def test_draw_points_pre_shape_2d(self):
points = self.S32.random_point(self.n_samples)
visualization.plot(points, space='S32')
points = self.M32.random_point(self.n_samples)
visualization.plot(points, space='M32')
self.KS.clear_points()
def test_draw_curve_pre_shape_2d(self):
self.KS.draw()
base_point = self.S32.random_point()
vec = self.S32.random_point()
tangent_vec = self.S32.to_tangent(vec, base_point)
times = gs.linspace(0., 1., 1000)
speeds = gs.array([-t * tangent_vec for t in times])
points = self.S32.ambient_metric.exp(speeds, base_point)
self.KS.add_points(points)
self.KS.draw_curve()
self.KS.clear_points()
def test_draw_vector_pre_shape_2d(self):
self.KS.draw()
base_point = self.S32.random_point()
vec = self.S32.random_point()
tangent_vec = self.S32.to_tangent(vec, base_point)
self.KS.draw_vector(tangent_vec, base_point)
def test_convert_to_spherical_coordinates_pre_shape_2d(self):
points = self.S32.random_point(self.n_samples)
coords = self.KS.convert_to_spherical_coordinates(points)
x = coords[:, 0]
y = coords[:, 1]
z = coords[:, 2]
result = x**2 + y**2 + z**2
expected = .25 * gs.ones(self.n_samples)
self.assertAllClose(result, expected)
def test_rotation_pre_shape_2d(self):
theta = gs.random.rand(1)[0]
phi = gs.random.rand(1)[0]
rot = self.KS.rotation(theta, phi)
result = _SpecialOrthogonalMatrices(3).belongs(rot)
expected = True
self.assertAllClose(result, expected)
def test_draw_pre_shape_3d(self):
self.KD.draw()
def test_draw_points_pre_shape_3d(self):
points = self.S33.random_point(self.n_samples)
visualization.plot(points, space='S33')
points = self.M33.random_point(self.n_samples)
visualization.plot(points, space='M33')
self.KD.clear_points()
def test_draw_curve_pre_shape_3d(self):
self.KD.draw()
base_point = self.S33.random_point()
vec = self.S33.random_point()
tangent_vec = self.S33.to_tangent(vec, base_point)
tangent_vec = .5 * tangent_vec / self.S33.ambient_metric.norm(
tangent_vec)
times = gs.linspace(0., 1., 1000)
speeds = gs.array([-t * tangent_vec for t in times])
points = self.S33.ambient_metric.exp(speeds, base_point)
self.KD.add_points(points)
self.KD.draw_curve()
self.KD.clear_points()
def test_draw_vector_pre_shape_3d(self):
self.KS.draw()
base_point = self.S32.random_point()
vec = self.S32.random_point()
tangent_vec = self.S32.to_tangent(vec, base_point)
self.KS.draw_vector(tangent_vec, base_point)
def test_convert_to_planar_coordinates_pre_shape_3d(self):
points = self.S33.random_point(self.n_samples)
coords = self.KD.convert_to_planar_coordinates(points)
x = coords[:, 0]
y = coords[:, 1]
radius = x**2 + y**2
result = [r <= 1. for r in radius]
self.assertTrue(gs.all(result))
@geomstats.tests.np_and_pytorch_only
def test_plot_points_s1(self):
points = self.S1.random_uniform(self.n_samples)
visualization.plot(points, space='S1')
def test_plot_points_s2(self):
points = self.S2.random_uniform(self.n_samples)
visualization.plot(points, space='S2')
def test_plot_points_h2_poincare_disk(self):
points = self.H2.random_point(self.n_samples)
visualization.plot(points, space='H2_poincare_disk')
def test_plot_points_h2_poincare_half_plane_ext(self):
points = self.H2.random_point(self.n_samples)
visualization.plot(points,
space='H2_poincare_half_plane',
point_type='extrinsic')
def test_plot_points_h2_poincare_half_plane_none(self):
points = self.H2_half_plane.random_point(self.n_samples)
visualization.plot(points, space='H2_poincare_half_plane')
def test_plot_points_h2_poincare_half_plane_hs(self):
points = self.H2_half_plane.random_point(self.n_samples)
visualization.plot(points,
space='H2_poincare_half_plane',
point_type='half_space')
def test_plot_points_h2_klein_disk(self):
points = self.H2.random_point(self.n_samples)
visualization.plot(points, space='H2_klein_disk')
@staticmethod
def test_plot_points_se2():
points = SpecialEuclidean(n=2, point_type='vector').random_point(4)
visu = visualization.SpecialEuclidean2(points, point_type='vector')
ax = visu.set_ax()
visu.draw(ax)
class TestHypersphereMethods(geomstats.tests.TestCase):
def setUp(self):
gs.random.seed(1234)
self.dimension = 4
self.space = Hypersphere(dimension=self.dimension)
self.metric = self.space.metric
self.n_samples = 10
@geomstats.tests.np_and_pytorch_only
def test_random_uniform_and_belongs(self):
"""
Test that the random uniform method samples
on the hypersphere space.
"""
n_samples = self.n_samples
point = self.space.random_uniform(n_samples)
result = self.space.belongs(point)
expected = gs.array([[True]] * n_samples)
self.assertAllClose(expected, result)
@geomstats.tests.np_and_pytorch_only
def test_random_uniform(self):
point = self.space.random_uniform()
self.assertAllClose(gs.shape(point), (1, self.dimension + 1))
def test_projection_and_belongs(self):
point = gs.array([1., 2., 3., 4., 5.])
proj = self.space.projection(point)
result = self.space.belongs(proj)
expected = gs.array([[True]])
self.assertAllClose(expected, result)
def test_intrinsic_and_extrinsic_coords(self):
"""
Test that the composition of
intrinsic_to_extrinsic_coords and
extrinsic_to_intrinsic_coords
gives the identity.
"""
point_int = gs.array([.1, 0., 0., .1])
point_ext = self.space.intrinsic_to_extrinsic_coords(point_int)
result = self.space.extrinsic_to_intrinsic_coords(point_ext)
expected = point_int
expected = helper.to_vector(expected)
self.assertAllClose(result, expected)
point_ext = (1. / (gs.sqrt(6.)) * gs.array([1., 0., 0., 1., 2.]))
point_int = self.space.extrinsic_to_intrinsic_coords(point_ext)
result = self.space.intrinsic_to_extrinsic_coords(point_int)
expected = point_ext
expected = helper.to_vector(expected)
self.assertAllClose(result, expected)
def test_intrinsic_and_extrinsic_coords_vectorization(self):
"""
Test that the composition of
intrinsic_to_extrinsic_coords and
extrinsic_to_intrinsic_coords
gives the identity.
"""
point_int = gs.array([[.1, 0., 0., .1], [.1, .1, .1, .4],
[.1, .3, 0., .1], [-0.1, .1, -.4, .1],
[0., 0., .1, .1], [.1, .1, .1, .1]])
point_ext = self.space.intrinsic_to_extrinsic_coords(point_int)
result = self.space.extrinsic_to_intrinsic_coords(point_ext)
expected = point_int
expected = helper.to_vector(expected)
self.assertAllClose(result, expected)
point_int = self.space.extrinsic_to_intrinsic_coords(point_ext)
result = self.space.intrinsic_to_extrinsic_coords(point_int)
expected = point_ext
expected = helper.to_vector(expected)
self.assertAllClose(result, expected)
@geomstats.tests.np_and_pytorch_only
def test_log_and_exp_general_case(self):
"""
Test that the riemannian exponential
and the riemannian logarithm are inverse.
Expect their composition to give the identity function.
NB: points on the n-dimensional sphere are
(n+1)-D vectors of norm 1.
"""
# Riemannian Log then Riemannian Exp
# General case
base_point = gs.array([1., 2., 3., 4., 6.])
base_point = base_point / gs.linalg.norm(base_point)
point = gs.array([0., 5., 6., 2., -1.])
point = point / gs.linalg.norm(point)
log = self.metric.log(point=point, base_point=base_point)
result = self.metric.exp(tangent_vec=log, base_point=base_point)
expected = point
expected = helper.to_vector(expected)
self.assertAllClose(result, expected, atol=1e-6)
@geomstats.tests.np_and_pytorch_only
def test_log_and_exp_edge_case(self):
"""
Test that the riemannian exponential
and the riemannian logarithm are inverse.
Expect their composition to give the identity function.
NB: points on the n-dimensional sphere are
(n+1)-D vectors of norm 1.
"""
# Riemannian Log then Riemannian Exp
# Edge case: two very close points, base_point_2 and point_2,
# form an angle < epsilon
base_point = gs.array([1., 2., 3., 4., 6.])
base_point = base_point / gs.linalg.norm(base_point)
point = (base_point + 1e-12 * gs.array([-1., -2., 1., 1., .1]))
point = point / gs.linalg.norm(point)
log = self.metric.log(point=point, base_point=base_point)
result = self.metric.exp(tangent_vec=log, base_point=base_point)
expected = point
expected = helper.to_vector(expected)
self.assertAllClose(result, expected)
@geomstats.tests.np_and_pytorch_only
def test_exp_vectorization(self):
n_samples = self.n_samples
dim = self.dimension + 1
one_vec = self.space.random_uniform()
one_base_point = self.space.random_uniform()
n_vecs = self.space.random_uniform(n_samples=n_samples)
n_base_points = self.space.random_uniform(n_samples=n_samples)
one_tangent_vec = self.space.projection_to_tangent_space(
one_vec, base_point=one_base_point)
result = self.metric.exp(one_tangent_vec, one_base_point)
self.assertAllClose(gs.shape(result), (1, dim))
n_tangent_vecs = self.space.projection_to_tangent_space(
n_vecs, base_point=one_base_point)
result = self.metric.exp(n_tangent_vecs, one_base_point)
self.assertAllClose(gs.shape(result), (n_samples, dim))
one_tangent_vec = self.space.projection_to_tangent_space(
one_vec, base_point=n_base_points)
result = self.metric.exp(one_tangent_vec, n_base_points)
self.assertAllClose(gs.shape(result), (n_samples, dim))
n_tangent_vecs = self.space.projection_to_tangent_space(
n_vecs, base_point=n_base_points)
result = self.metric.exp(n_tangent_vecs, n_base_points)
self.assertAllClose(gs.shape(result), (n_samples, dim))
@geomstats.tests.np_and_pytorch_only
def test_log_vectorization(self):
n_samples = self.n_samples
dim = self.dimension + 1
one_base_point = self.space.random_uniform()
one_point = self.space.random_uniform()
n_points = self.space.random_uniform(n_samples=n_samples)
n_base_points = self.space.random_uniform(n_samples=n_samples)
result = self.metric.log(one_point, one_base_point)
self.assertAllClose(gs.shape(result), (1, dim))
result = self.metric.log(n_points, one_base_point)
self.assertAllClose(gs.shape(result), (n_samples, dim))
result = self.metric.log(one_point, n_base_points)
self.assertAllClose(gs.shape(result), (n_samples, dim))
result = self.metric.log(n_points, n_base_points)
self.assertAllClose(gs.shape(result), (n_samples, dim))
@geomstats.tests.np_and_pytorch_only
def test_exp_and_log_and_projection_to_tangent_space_general_case(self):
"""
Test that the riemannian exponential
and the riemannian logarithm are inverse.
Expect their composition to give the identity function.
NB: points on the n-dimensional sphere are
(n+1)-D vectors of norm 1.
"""
# TODO(nina): Fix that this test fails, also in numpy
# Riemannian Exp then Riemannian Log
# General case
# NB: Riemannian log gives a regularized tangent vector,
# so we take the norm modulo 2 * pi.
base_point = gs.array([0., -3., 0., 3., 4.])
base_point = base_point / gs.linalg.norm(base_point)
vector = gs.array([9., 5., 0., 0., -1.])
vector = self.space.projection_to_tangent_space(vector=vector,
base_point=base_point)
# exp = self.metric.exp(tangent_vec=vector, base_point=base_point)
# result = self.metric.log(point=exp, base_point=base_point)
expected = vector
norm_expected = gs.linalg.norm(expected)
regularized_norm_expected = gs.mod(norm_expected, 2 * gs.pi)
expected = expected / norm_expected * regularized_norm_expected
expected = helper.to_vector(expected)
@geomstats.tests.np_and_pytorch_only
def test_exp_and_log_and_projection_to_tangent_space_edge_case(self):
"""
Test that the riemannian exponential
and the riemannian logarithm are inverse.
Expect their composition to give the identity function.
NB: points on the n-dimensional sphere are
(n+1)-D vectors of norm 1.
"""
# Riemannian Exp then Riemannian Log
# Edge case: tangent vector has norm < epsilon
base_point = gs.array([10., -2., -.5, 34., 3.])
base_point = base_point / gs.linalg.norm(base_point)
vector = 1e-10 * gs.array([.06, -51., 6., 5., 3.])
vector = self.space.projection_to_tangent_space(vector=vector,
base_point=base_point)
exp = self.metric.exp(tangent_vec=vector, base_point=base_point)
result = self.metric.log(point=exp, base_point=base_point)
expected = self.space.projection_to_tangent_space(
vector=vector, base_point=base_point)
expected = helper.to_vector(expected)
self.assertAllClose(result, expected, atol=1e-8)
def test_squared_norm_and_squared_dist(self):
"""
Test that the squared distance between two points is
the squared norm of their logarithm.
"""
point_a = (1. / gs.sqrt(129.) * gs.array([10., -2., -5., 0., 0.]))
point_b = (1. / gs.sqrt(435.) * gs.array([1., -20., -5., 0., 3.]))
log = self.metric.log(point=point_a, base_point=point_b)
result = self.metric.squared_norm(vector=log)
expected = self.metric.squared_dist(point_a, point_b)
expected = helper.to_scalar(expected)
self.assertAllClose(result, expected)
@geomstats.tests.np_and_pytorch_only
def test_squared_dist_vectorization(self):
n_samples = self.n_samples
one_point_a = self.space.random_uniform()
one_point_b = self.space.random_uniform()
n_points_a = self.space.random_uniform(n_samples=n_samples)
n_points_b = self.space.random_uniform(n_samples=n_samples)
result = self.metric.squared_dist(one_point_a, one_point_b)
self.assertAllClose(gs.shape(result), (1, 1))
result = self.metric.squared_dist(n_points_a, one_point_b)
self.assertAllClose(gs.shape(result), (n_samples, 1))
result = self.metric.squared_dist(one_point_a, n_points_b)
self.assertAllClose(gs.shape(result), (n_samples, 1))
result = self.metric.squared_dist(n_points_a, n_points_b)
self.assertAllClose(gs.shape(result), (n_samples, 1))
def test_norm_and_dist(self):
"""
Test that the distance between two points is
the norm of their logarithm.
"""
point_a = (1. / gs.sqrt(129.) * gs.array([10., -2., -5., 0., 0.]))
point_b = (1. / gs.sqrt(435.) * gs.array([1., -20., -5., 0., 3.]))
log = self.metric.log(point=point_a, base_point=point_b)
result = self.metric.norm(vector=log)
expected = self.metric.dist(point_a, point_b)
expected = helper.to_scalar(expected)
self.assertAllClose(result, expected)
def test_dist_point_and_itself(self):
# Distance between a point and itself is 0
point_a = (1. / gs.sqrt(129.) * gs.array([10., -2., -5., 0., 0.]))
point_b = point_a
result = self.metric.dist(point_a, point_b)
expected = 0.
expected = helper.to_scalar(expected)
self.assertAllClose(result, expected)
def test_dist_orthogonal_points(self):
# Distance between two orthogonal points is pi / 2.
point_a = gs.array([10., -2., -.5, 0., 0.])
point_a = point_a / gs.linalg.norm(point_a)
point_b = gs.array([2., 10, 0., 0., 0.])
point_b = point_b / gs.linalg.norm(point_b)
result = gs.dot(point_a, point_b)
result = helper.to_scalar(result)
expected = 0
expected = helper.to_scalar(expected)
self.assertAllClose(result, expected)
result = self.metric.dist(point_a, point_b)
expected = gs.pi / 2
expected = helper.to_scalar(expected)
self.assertAllClose(result, expected)
@geomstats.tests.np_and_pytorch_only
def test_exp_and_dist_and_projection_to_tangent_space(self):
base_point = gs.array([16., -2., -2.5, 84., 3.])
base_point = base_point / gs.linalg.norm(base_point)
vector = gs.array([9., 0., -1., -2., 1.])
tangent_vec = self.space.projection_to_tangent_space(
vector=vector, base_point=base_point)
exp = self.metric.exp(tangent_vec=tangent_vec, base_point=base_point)
result = self.metric.dist(base_point, exp)
expected = gs.linalg.norm(tangent_vec) % (2 * gs.pi)
expected = helper.to_scalar(expected)
self.assertAllClose(result, expected)
@geomstats.tests.np_and_pytorch_only
def test_exp_and_dist_and_projection_to_tangent_space_vec(self):
base_point = gs.array([[16., -2., -2.5, 84., 3.],
[16., -2., -2.5, 84., 3.]])
base_single_point = gs.array([16., -2., -2.5, 84., 3.])
scalar_norm = gs.linalg.norm(base_single_point)
base_point = base_point / scalar_norm
vector = gs.array([[9., 0., -1., -2., 1.], [9., 0., -1., -2., 1]])
tangent_vec = self.space.projection_to_tangent_space(
vector=vector, base_point=base_point)
exp = self.metric.exp(tangent_vec=tangent_vec, base_point=base_point)
result = self.metric.dist(base_point, exp)
expected = gs.linalg.norm(tangent_vec, axis=-1) % (2 * gs.pi)
expected = helper.to_scalar(expected)
self.assertAllClose(result, expected)
@geomstats.tests.np_and_pytorch_only
def test_geodesic_and_belongs(self):
n_geodesic_points = 100
initial_point = self.space.random_uniform()
vector = gs.array([2., 0., -1., -2., 1.])
initial_tangent_vec = self.space.projection_to_tangent_space(
vector=vector, base_point=initial_point)
geodesic = self.metric.geodesic(
initial_point=initial_point,
initial_tangent_vec=initial_tangent_vec)
t = gs.linspace(start=0., stop=1., num=n_geodesic_points)
points = geodesic(t)
result = self.space.belongs(points)
expected = gs.array(n_geodesic_points * [[True]])
self.assertAllClose(expected, result)
def test_inner_product(self):
tangent_vec_a = gs.array([1., 0., 0., 0., 0.])
tangent_vec_b = gs.array([0., 1., 0., 0., 0.])
base_point = gs.array([0., 0., 0., 0., 1.])
result = self.metric.inner_product(tangent_vec_a, tangent_vec_b,
base_point)
expected = gs.array([[0.]])
self.assertAllClose(expected, result)
@geomstats.tests.np_and_pytorch_only
def test_variance(self):
point = gs.array([0., 0., 0., 0., 1.])
points = gs.zeros((2, point.shape[0]))
points[0, :] = point
points[1, :] = point
result = self.metric.variance(points)
expected = helper.to_scalar(0.)
self.assertAllClose(expected, result)
@geomstats.tests.np_and_pytorch_only
def test_mean(self):
point = gs.array([0., 0., 0., 0., 1.])
points = gs.zeros((2, point.shape[0]))
points[0, :] = point
points[1, :] = point
result = self.metric.mean(points)
expected = helper.to_vector(point)
self.assertAllClose(expected, result)
@geomstats.tests.np_only
def test_adaptive_gradientdescent_mean(self):
n_tests = 100
result = gs.zeros(n_tests)
expected = gs.zeros(n_tests)
for i in range(n_tests):
# take 2 random points, compute their mean, and verify that
# log of each at the mean is opposite
points = self.space.random_uniform(n_samples=2)
mean = self.metric.adaptive_gradientdescent_mean(points)
logs = self.metric.log(point=points, base_point=mean)
result[i] = gs.linalg.norm(logs[1, :] + logs[0, :])
self.assertAllClose(expected, result, rtol=1e-10, atol=1e-10)
@geomstats.tests.np_and_pytorch_only
def test_mean_and_belongs(self):
point_a = gs.array([1., 0., 0., 0., 0.])
point_b = gs.array([0., 1., 0., 0., 0.])
points = gs.zeros((2, point_a.shape[0]))
points[0, :] = point_a
points[1, :] = point_b
mean = self.metric.mean(points)
result = self.space.belongs(mean)
expected = gs.array([[True]])
self.assertAllClose(result, expected)
def test_diameter(self):
dim = 2
sphere = Hypersphere(dim)
point_a = gs.array([[0., 0., 1.]])
point_b = gs.array([[1., 0., 0.]])
point_c = gs.array([[0., 0., -1.]])
result = sphere.metric.diameter(gs.vstack((point_a, point_b, point_c)))
expected = gs.pi
self.assertAllClose(expected, result)
@geomstats.tests.np_and_pytorch_only
def test_closest_neighbor_index(self):
"""
Check that the closest neighbor is one of neighbors.
"""
n_samples = 10
points = self.space.random_uniform(n_samples=n_samples)
point = points[0, :]
neighbors = points[1:, :]
index = self.metric.closest_neighbor_index(point, neighbors)
closest_neighbor = points[index, :]
test = gs.sum(gs.all(points == closest_neighbor, axis=1))
result = test > 0
self.assertTrue(result)
@geomstats.tests.np_and_pytorch_only
def test_sample_von_mises_fisher(self):
"""
Check that the maximum likelihood estimates of the mean and
concentration parameter are close to the real values. A first
estimation of the concentration parameter is obtained by a
closed-form expression and improved through the Newton method.
"""
dim = 2
n_points = 1000000
sphere = Hypersphere(dim)
# check mean value for concentrated distribution
kappa = 10000000
points = sphere.random_von_mises_fisher(kappa, n_points)
sum_points = gs.sum(points, axis=0)
mean = gs.array([0., 0., 1.])
mean_estimate = sum_points / gs.linalg.norm(sum_points)
expected = mean
result = mean_estimate
self.assertTrue(gs.allclose(result, expected,
atol=MEAN_ESTIMATION_TOL))
# check concentration parameter for dispersed distribution
kappa = 1
points = sphere.random_von_mises_fisher(kappa, n_points)
sum_points = gs.sum(points, axis=0)
mean_norm = gs.linalg.norm(sum_points) / n_points
kappa_estimate = (mean_norm * (dim + 1. - mean_norm**2) /
(1. - mean_norm**2))
kappa_estimate = gs.cast(kappa_estimate, gs.float64)
p = dim + 1
n_steps = 100
for i in range(n_steps):
bessel_func_1 = scipy.special.iv(p / 2., kappa_estimate)
bessel_func_2 = scipy.special.iv(p / 2. - 1., kappa_estimate)
ratio = bessel_func_1 / bessel_func_2
denominator = 1. - ratio**2 - (p - 1.) * ratio / kappa_estimate
mean_norm = gs.cast(mean_norm, gs.float64)
kappa_estimate = kappa_estimate - (ratio - mean_norm) / denominator
expected = kappa
result = kappa_estimate
self.assertTrue(
gs.allclose(result, expected, atol=KAPPA_ESTIMATION_TOL))
@geomstats.tests.np_and_pytorch_only
def test_spherical_to_extrinsic(self):
"""
Check vectorization of conversion from spherical
to extrinsic coordinates on the 2-sphere.
"""
dim = 2
sphere = Hypersphere(dim)
points_spherical = gs.array([[gs.pi / 2, 0], [gs.pi / 6, gs.pi / 4]])
result = sphere.spherical_to_extrinsic(points_spherical)
expected = gs.array([[1., 0., 0.],
[gs.sqrt(2) / 4,
gs.sqrt(2) / 4,
gs.sqrt(3) / 2]])
self.assertAllClose(result, expected)
@geomstats.tests.np_and_pytorch_only
def test_tangent_spherical_to_extrinsic(self):
"""
Check vectorization of conversion from spherical
to extrinsic coordinates for tangent vectors to the
2-sphere.
"""
dim = 2
sphere = Hypersphere(dim)
base_points_spherical = gs.array([[gs.pi / 2, 0], [gs.pi / 2, 0]])
tangent_vecs_spherical = gs.array([[0.25, 0.5], [0.3, 0.2]])
result = sphere.tangent_spherical_to_extrinsic(tangent_vecs_spherical,
base_points_spherical)
expected = gs.array([[0, 0.5, -0.25], [0, 0.2, -0.3]])
self.assertAllClose(result, expected)
def test_christoffels_vectorization(self):
"""
Check vectorization of Christoffel symbols in
spherical coordinates on the 2-sphere.
"""
dim = 2
sphere = Hypersphere(dim)
points_spherical = gs.array([[gs.pi / 2, 0], [gs.pi / 6, gs.pi / 4]])
christoffel = sphere.metric.christoffels(points_spherical)
result = christoffel.shape
expected = gs.array([2, dim, dim, dim])
self.assertAllClose(result, expected)
class TestConnection(geomstats.tests.TestCase):
def setup_method(self):
warnings.simplefilter("ignore", category=UserWarning)
gs.random.seed(0)
self.dim = 4
self.euc_metric = EuclideanMetric(dim=self.dim)
self.connection = Connection(dim=2)
self.hypersphere = Hypersphere(dim=2)
def test_metric_matrix(self):
base_point = gs.array([0.0, 1.0, 0.0, 0.0])
result = self.euc_metric.metric_matrix(base_point)
expected = gs.eye(self.dim)
self.assertAllClose(result, expected)
def test_parallel_transport(self):
n_samples = 2
base_point = self.hypersphere.random_uniform(n_samples)
tan_vec_a = self.hypersphere.to_tangent(gs.random.rand(n_samples, 3),
base_point)
tan_vec_b = self.hypersphere.to_tangent(gs.random.rand(n_samples, 3),
base_point)
expected = self.hypersphere.metric.parallel_transport(
tan_vec_a, base_point, tan_vec_b)
expected_point = self.hypersphere.metric.exp(tan_vec_b, base_point)
base_point = gs.cast(base_point, gs.float64)
base_point, tan_vec_a, tan_vec_b = gs.convert_to_wider_dtype(
[base_point, tan_vec_a, tan_vec_b])
for step, alpha in zip(["pole", "schild"], [1, 2]):
min_n = 1 if step == "pole" else 50
tol = 1e-5 if step == "pole" else 1e-2
for n_rungs in [min_n, 11]:
ladder = self.hypersphere.metric.ladder_parallel_transport(
tan_vec_a,
base_point,
tan_vec_b,
n_rungs=n_rungs,
scheme=step,
alpha=alpha,
)
result = ladder["transported_tangent_vec"]
result_point = ladder["end_point"]
self.assertAllClose(result, expected, rtol=tol, atol=tol)
self.assertAllClose(result_point, expected_point)
def test_parallel_transport_trajectory(self):
n_samples = 2
for step in ["pole", "schild"]:
n_steps = 1 if step == "pole" else 50
tol = 1e-6 if step == "pole" else 1e-2
base_point = self.hypersphere.random_uniform(n_samples)
tan_vec_a = self.hypersphere.to_tangent(
gs.random.rand(n_samples, 3), base_point)
tan_vec_b = self.hypersphere.to_tangent(
gs.random.rand(n_samples, 3), base_point)
expected = self.hypersphere.metric.parallel_transport(
tan_vec_a, base_point, tan_vec_b)
expected_point = self.hypersphere.metric.exp(tan_vec_b, base_point)
ladder = self.hypersphere.metric.ladder_parallel_transport(
tan_vec_a,
base_point,
tan_vec_b,
n_rungs=n_steps,
scheme=step,
return_geodesics=True,
)
result = ladder["transported_tangent_vec"]
result_point = ladder["end_point"]
self.assertAllClose(result, expected, rtol=tol, atol=tol)
self.assertAllClose(result_point, expected_point)
def test_ladder_alpha(self):
n_samples = 2
base_point = self.hypersphere.random_uniform(n_samples)
tan_vec_a = self.hypersphere.to_tangent(gs.random.rand(n_samples, 3),
base_point)
tan_vec_b = self.hypersphere.to_tangent(gs.random.rand(n_samples, 3),
base_point)
with pytest.raises(ValueError):
self.hypersphere.metric.ladder_parallel_transport(
tan_vec_a,
base_point,
tan_vec_b,
n_rungs=1,
scheme="pole",
alpha=0.5,
return_geodesics=False,
)
def test_exp_connection_metric(self):
point = gs.array([gs.pi / 2, 0])
vector = gs.array([0.25, 0.5])
point_ext = self.hypersphere.spherical_to_extrinsic(point)
vector_ext = self.hypersphere.tangent_spherical_to_extrinsic(
vector, point)
self.connection.christoffels = self.hypersphere.metric.christoffels
expected = self.hypersphere.metric.exp(vector_ext, point_ext)
result_spherical = self.connection.exp(vector,
point,
n_steps=50,
step="rk4")
result = self.hypersphere.spherical_to_extrinsic(result_spherical)
self.assertAllClose(result, expected)
def test_exp_connection_metric_vectorization(self):
point = gs.array([[gs.pi / 2, 0], [gs.pi / 6, gs.pi / 4]])
vector = gs.array([[0.25, 0.5], [0.30, 0.2]])
point_ext = self.hypersphere.spherical_to_extrinsic(point)
vector_ext = self.hypersphere.tangent_spherical_to_extrinsic(
vector, point)
self.connection.christoffels = self.hypersphere.metric.christoffels
expected = self.hypersphere.metric.exp(vector_ext, point_ext)
result_spherical = self.connection.exp(vector,
point,
n_steps=50,
step="rk4")
result = self.hypersphere.spherical_to_extrinsic(result_spherical)
self.assertAllClose(result, expected)
@geomstats.tests.autograd_tf_and_torch_only
def test_log_connection_metric(self):
base_point = gs.array([gs.pi / 3, gs.pi / 4])
point = gs.array([1.0, gs.pi / 2])
self.connection.christoffels = self.hypersphere.metric.christoffels
vector = self.connection.log(point=point,
base_point=base_point,
n_steps=75,
step="rk4",
tol=1e-10)
result = self.hypersphere.tangent_spherical_to_extrinsic(
vector, base_point)
p_ext = self.hypersphere.spherical_to_extrinsic(base_point)
q_ext = self.hypersphere.spherical_to_extrinsic(point)
expected = self.hypersphere.metric.log(base_point=p_ext, point=q_ext)
self.assertAllClose(result, expected)
@geomstats.tests.autograd_tf_and_torch_only
def test_log_connection_metric_vectorization(self):
base_point = gs.array([[gs.pi / 3, gs.pi / 4], [gs.pi / 2, gs.pi / 4]])
point = gs.array([[1.0, gs.pi / 2], [gs.pi / 6, gs.pi / 3]])
self.connection.christoffels = self.hypersphere.metric.christoffels
vector = self.connection.log(point=point,
base_point=base_point,
n_steps=75,
step="rk4",
tol=1e-10)
result = self.hypersphere.tangent_spherical_to_extrinsic(
vector, base_point)
p_ext = self.hypersphere.spherical_to_extrinsic(base_point)
q_ext = self.hypersphere.spherical_to_extrinsic(point)
expected = self.hypersphere.metric.log(base_point=p_ext, point=q_ext)
self.assertAllClose(result, expected, atol=1e-6)
def test_geodesic_and_coincides_exp_hypersphere(self):
n_geodesic_points = 10
initial_point = self.hypersphere.random_uniform(2)
vector = gs.array([[2.0, 0.0, -1.0]] * 2)
initial_tangent_vec = self.hypersphere.to_tangent(
vector=vector, base_point=initial_point)
geodesic = self.hypersphere.metric.geodesic(
initial_point=initial_point,
initial_tangent_vec=initial_tangent_vec)
t = gs.linspace(start=0.0, stop=1.0, num=n_geodesic_points)
points = geodesic(t)
result = points[:, -1]
expected = self.hypersphere.metric.exp(vector, initial_point)
self.assertAllClose(expected, result)
initial_point = initial_point[0]
initial_tangent_vec = initial_tangent_vec[0]
geodesic = self.hypersphere.metric.geodesic(
initial_point=initial_point,
initial_tangent_vec=initial_tangent_vec)
points = geodesic(t)
result = points[-1]
expected = self.hypersphere.metric.exp(initial_tangent_vec,
initial_point)
self.assertAllClose(expected, result)
def test_geodesic_and_coincides_exp_son(self):
n_geodesic_points = 10
space = SpecialOrthogonal(n=4)
initial_point = space.random_uniform(2)
vector = gs.random.rand(2, 4, 4)
initial_tangent_vec = space.to_tangent(vector=vector,
base_point=initial_point)
geodesic = space.bi_invariant_metric.geodesic(
initial_point=initial_point,
initial_tangent_vec=initial_tangent_vec)
t = gs.linspace(start=0.0, stop=1.0, num=n_geodesic_points)
points = geodesic(t)
result = points[:, -1]
expected = space.bi_invariant_metric.exp(initial_tangent_vec,
initial_point)
self.assertAllClose(result, expected)
initial_point = initial_point[0]
initial_tangent_vec = initial_tangent_vec[0]
geodesic = space.bi_invariant_metric.geodesic(
initial_point=initial_point,
initial_tangent_vec=initial_tangent_vec)
points = geodesic(t)
result = points[-1]
expected = space.bi_invariant_metric.exp(initial_tangent_vec,
initial_point)
self.assertAllClose(expected, result)
def test_geodesic_invalid_initial_conditions(self):
space = SpecialOrthogonal(n=4)
initial_point = space.random_uniform(2)
vector = gs.random.rand(2, 4, 4)
initial_tangent_vec = space.to_tangent(vector=vector,
base_point=initial_point)
end_point = space.random_uniform(2)
with pytest.raises(RuntimeError):
space.bi_invariant_metric.geodesic(
initial_point=initial_point,
initial_tangent_vec=initial_tangent_vec,
end_point=end_point,
)
def test_geodesic_vectorization(self):
space = Hypersphere(2)
metric = space.metric
initial_point = space.random_uniform(2)
vector = gs.random.rand(2, 3)
initial_tangent_vec = space.to_tangent(vector=vector,
base_point=initial_point)
end_point = space.random_uniform(2)
time = gs.linspace(0, 1, 10)
geo = metric.geodesic(initial_point, initial_tangent_vec)
path = geo(time)
result = path.shape
expected = (2, 10, 3)
self.assertAllClose(result, expected)
geo = metric.geodesic(initial_point, end_point=end_point)
path = geo(time)
result = path.shape
expected = (2, 10, 3)
self.assertAllClose(result, expected)
geo = metric.geodesic(initial_point, end_point=end_point[0])
path = geo(time)
result = path.shape
expected = (2, 10, 3)
self.assertAllClose(result, expected)
initial_tangent_vec = space.to_tangent(vector=vector,
base_point=initial_point[0])
geo = metric.geodesic(initial_point[0], initial_tangent_vec)
path = geo(time)
result = path.shape
expected = (2, 10, 3)
self.assertAllClose(result, expected)
class TestToTangentSpace(geomstats.tests.TestCase):
_multiprocess_can_split_ = True
def setUp(self):
gs.random.seed(123)
self.sphere = Hypersphere(dim=4)
self.hyperbolic = Hyperboloid(dim=3)
self.euclidean = Euclidean(dim=2)
self.minkowski = Minkowski(dim=2)
self.so3 = SpecialOrthogonal(n=3, point_type='vector')
self.so_matrix = SpecialOrthogonal(n=3, point_type='matrix')
def test_estimate_transform_sphere(self):
point = gs.array([0., 0., 0., 0., 1.])
points = gs.array([point, point])
transformer = ToTangentSpace(geometry=self.sphere)
transformer.fit(X=points)
result = transformer.transform(points)
expected = gs.zeros_like(points)
self.assertAllClose(expected, result)
def test_inverse_transform_no_fit_sphere(self):
point = self.sphere.random_uniform(3)
base_point = point[0]
point = point[1:]
transformer = ToTangentSpace(geometry=self.sphere)
X = transformer.transform(point, base_point=base_point)
result = transformer.inverse_transform(X, base_point=base_point)
expected = point
self.assertAllClose(expected, result)
@geomstats.tests.np_and_tf_only
def test_estimate_transform_so_group(self):
point = self.so_matrix.random_uniform()
points = gs.array([point, point])
transformer = ToTangentSpace(geometry=self.so_matrix)
transformer.fit(X=points)
result = transformer.transform(points)
expected = gs.zeros((2, 6))
self.assertAllClose(expected, result)
def test_estimate_transform_spd(self):
point = spd.SPDMatrices(3).random_uniform()
points = gs.stack([point, point])
transformer = ToTangentSpace(geometry=spd.SPDMetricAffine(3))
transformer.fit(X=points)
result = transformer.transform(points)
expected = gs.zeros((2, 6))
self.assertAllClose(expected, result, atol=1e-5)
def test_fit_transform_hyperbolic(self):
point = gs.array([2., 1., 1., 1.])
points = gs.array([point, point])
transformer = ToTangentSpace(geometry=self.hyperbolic.metric)
result = transformer.fit_transform(X=points)
expected = gs.zeros_like(points)
self.assertAllClose(expected, result)
def test_inverse_transform_hyperbolic(self):
points = self.hyperbolic.random_uniform(10)
transformer = ToTangentSpace(geometry=self.hyperbolic.metric)
X = transformer.fit_transform(X=points)
result = transformer.inverse_transform(X)
expected = points
self.assertAllClose(expected, result)
def test_inverse_transform_spd(self):
point = spd.SPDMatrices(3).random_uniform(10)
transformer = ToTangentSpace(geometry=spd.SPDMetricLogEuclidean(3))
X = transformer.fit_transform(X=point)
result = transformer.inverse_transform(X)
expected = point
self.assertAllClose(expected, result, atol=1e-4)
transformer = ToTangentSpace(geometry=spd.SPDMetricAffine(3))
X = transformer.fit_transform(X=point)
result = transformer.inverse_transform(X)
expected = point
self.assertAllClose(expected, result, atol=1e-4)
@geomstats.tests.np_only
def test_inverse_transform_so(self):
# FIXME: einsum vectorization error for invariant_metric log in tf
point = self.so_matrix.random_uniform(10)
transformer = ToTangentSpace(
geometry=self.so_matrix.bi_invariant_metric)
X = transformer.transform(X=point, base_point=self.so_matrix.identity)
result = transformer.inverse_transform(
X, base_point=self.so_matrix.identity)
expected = point
self.assertAllClose(expected, result)
class TestFrechetMean(geomstats.tests.TestCase):
_multiprocess_can_split_ = True
def setUp(self):
gs.random.seed(123)
self.sphere = Hypersphere(dim=4)
self.hyperbolic = Hyperboloid(dim=3)
self.euclidean = Euclidean(dim=2)
self.minkowski = Minkowski(dim=2)
self.so3 = SpecialOrthogonal(n=3, point_type='vector')
self.so_matrix = SpecialOrthogonal(n=3)
def test_logs_at_mean_default_gradient_descent_sphere(self):
n_tests = 10
estimator = FrechetMean(
metric=self.sphere.metric, method='default', lr=1.)
result = []
for _ in range(n_tests):
# take 2 random points, compute their mean, and verify that
# log of each at the mean is opposite
points = self.sphere.random_uniform(n_samples=2)
estimator.fit(points)
mean = estimator.estimate_
logs = self.sphere.metric.log(point=points, base_point=mean)
result.append(gs.linalg.norm(logs[1, :] + logs[0, :]))
result = gs.stack(result)
expected = gs.zeros(n_tests)
self.assertAllClose(expected, result)
def test_logs_at_mean_adaptive_gradient_descent_sphere(self):
n_tests = 10
estimator = FrechetMean(metric=self.sphere.metric, method='adaptive')
result = []
for _ in range(n_tests):
# take 2 random points, compute their mean, and verify that
# log of each at the mean is opposite
points = self.sphere.random_uniform(n_samples=2)
estimator.fit(points)
mean = estimator.estimate_
logs = self.sphere.metric.log(point=points, base_point=mean)
result.append(gs.linalg.norm(logs[1, :] + logs[0, :]))
result = gs.stack(result)
expected = gs.zeros(n_tests)
self.assertAllClose(expected, result)
def test_estimate_shape_default_gradient_descent_sphere(self):
dim = 5
point_a = gs.array([1., 0., 0., 0., 0.])
point_b = gs.array([0., 1., 0., 0., 0.])
points = gs.array([point_a, point_b])
mean = FrechetMean(
metric=self.sphere.metric, method='default', verbose=True)
mean.fit(points)
result = mean.estimate_
self.assertAllClose(gs.shape(result), (dim,))
def test_estimate_shape_adaptive_gradient_descent_sphere(self):
dim = 5
point_a = gs.array([1., 0., 0., 0., 0.])
point_b = gs.array([0., 1., 0., 0., 0.])
points = gs.array([point_a, point_b])
mean = FrechetMean(metric=self.sphere.metric, method='adaptive')
mean.fit(points)
result = mean.estimate_
self.assertAllClose(gs.shape(result), (dim,))
def test_estimate_and_belongs_default_gradient_descent_sphere(self):
point_a = gs.array([1., 0., 0., 0., 0.])
point_b = gs.array([0., 1., 0., 0., 0.])
points = gs.array([point_a, point_b])
mean = FrechetMean(metric=self.sphere.metric, method='default')
mean.fit(points)
result = self.sphere.belongs(mean.estimate_)
expected = True
self.assertAllClose(result, expected)
def test_estimate_default_gradient_descent_so3(self):
points = self.so3.random_uniform(2)
mean_vec = FrechetMean(
metric=self.so3.bi_invariant_metric, method='default', lr=1.)
mean_vec.fit(points)
logs = self.so3.bi_invariant_metric.log(points, mean_vec.estimate_)
result = gs.sum(logs, axis=0)
expected = gs.zeros_like(points[0])
self.assertAllClose(result, expected)
def test_estimate_and_belongs_default_gradient_descent_so3(self):
point = self.so3.random_uniform(10)
mean_vec = FrechetMean(
metric=self.so3.bi_invariant_metric, method='default')
mean_vec.fit(point)
result = self.so3.belongs(mean_vec.estimate_)
expected = True
self.assertAllClose(result, expected)
@geomstats.tests.np_and_tf_only
def test_estimate_default_gradient_descent_so_matrix(self):
points = self.so_matrix.random_uniform(2)
mean_vec = FrechetMean(
metric=self.so_matrix.bi_invariant_metric, method='default',
lr=1.)
mean_vec.fit(points)
logs = self.so_matrix.bi_invariant_metric.log(
points, mean_vec.estimate_)
result = gs.sum(logs, axis=0)
expected = gs.zeros_like(points[0])
self.assertAllClose(result, expected, atol=1e-5)
@geomstats.tests.np_and_tf_only
def test_estimate_and_belongs_default_gradient_descent_so_matrix(self):
point = self.so_matrix.random_uniform(10)
mean = FrechetMean(
metric=self.so_matrix.bi_invariant_metric, method='default')
mean.fit(point)
result = self.so_matrix.belongs(mean.estimate_)
expected = True
self.assertAllClose(result, expected)
@geomstats.tests.np_and_tf_only
def test_estimate_and_belongs_adaptive_gradient_descent_so_matrix(self):
point = self.so_matrix.random_uniform(10)
mean = FrechetMean(
metric=self.so_matrix.bi_invariant_metric, method='adaptive',
verbose=True, lr=.5)
mean.fit(point)
result = self.so_matrix.belongs(mean.estimate_)
self.assertTrue(result)
@geomstats.tests.np_and_tf_only
def test_estimate_and_coincide_default_so_vec_and_mat(self):
point = self.so_matrix.random_uniform(3)
mean = FrechetMean(
metric=self.so_matrix.bi_invariant_metric, method='default')
mean.fit(point)
expected = mean.estimate_
mean_vec = FrechetMean(
metric=self.so3.bi_invariant_metric, method='default')
point_vec = self.so3.rotation_vector_from_matrix(point)
mean_vec.fit(point_vec)
result_vec = mean_vec.estimate_
result = self.so3.matrix_from_rotation_vector(result_vec)
self.assertAllClose(result, expected)
def test_estimate_and_belongs_adaptive_gradient_descent_sphere(self):
point_a = gs.array([1., 0., 0., 0., 0.])
point_b = gs.array([0., 1., 0., 0., 0.])
points = gs.array([point_a, point_b])
mean = FrechetMean(metric=self.sphere.metric, method='adaptive')
mean.fit(points)
result = self.sphere.belongs(mean.estimate_)
expected = True
self.assertAllClose(result, expected)
def test_variance_sphere(self):
point = gs.array([0., 0., 0., 0., 1.])
points = gs.array([point, point])
result = variance(
points, base_point=point, metric=self.sphere.metric)
expected = gs.array(0.)
self.assertAllClose(expected, result)
def test_estimate_default_gradient_descent_sphere(self):
point = gs.array([0., 0., 0., 0., 1.])
points = gs.array([point, point])
mean = FrechetMean(metric=self.sphere.metric, method='default')
mean.fit(X=points)
result = mean.estimate_
expected = point
self.assertAllClose(expected, result)
def test_estimate_adaptive_gradient_descent_sphere(self):
point = gs.array([0., 0., 0., 0., 1.])
points = gs.array([point, point])
mean = FrechetMean(metric=self.sphere.metric, method='adaptive')
mean.fit(X=points)
result = mean.estimate_
expected = point
self.assertAllClose(expected, result)
def test_estimate_spd(self):
point = SPDMatrices(3).random_point()
points = gs.array([point, point])
mean = FrechetMean(metric=SPDMetricAffine(3), point_type='matrix')
mean.fit(X=points)
result = mean.estimate_
expected = point
self.assertAllClose(expected, result)
def test_variance_hyperbolic(self):
point = gs.array([2., 1., 1., 1.])
points = gs.array([point, point])
result = variance(
points, base_point=point, metric=self.hyperbolic.metric)
expected = gs.array(0.)
self.assertAllClose(result, expected)
def test_estimate_hyperbolic(self):
point = gs.array([2., 1., 1., 1.])
points = gs.array([point, point])
mean = FrechetMean(metric=self.hyperbolic.metric)
mean.fit(X=points)
expected = point
result = mean.estimate_
self.assertAllClose(result, expected)
def test_estimate_and_belongs_hyperbolic(self):
point_a = self.hyperbolic.random_point()
point_b = self.hyperbolic.random_point()
point_c = self.hyperbolic.random_point()
points = gs.stack([point_a, point_b, point_c], axis=0)
mean = FrechetMean(metric=self.hyperbolic.metric)
mean.fit(X=points)
result = self.hyperbolic.belongs(mean.estimate_)
expected = True
self.assertAllClose(result, expected)
def test_mean_euclidean_shape(self):
dim = 2
point = gs.array([1., 4.])
mean = FrechetMean(metric=self.euclidean.metric)
points = [point, point, point]
mean.fit(points)
result = mean.estimate_
self.assertAllClose(gs.shape(result), (dim,))
def test_mean_euclidean(self):
point = gs.array([1., 4.])
mean = FrechetMean(metric=self.euclidean.metric)
points = [point, point, point]
mean.fit(points)
result = mean.estimate_
expected = point
self.assertAllClose(result, expected)
points = gs.array([
[1., 2.],
[2., 3.],
[3., 4.],
[4., 5.]])
weights = [1., 2., 1., 2.]
mean = FrechetMean(metric=self.euclidean.metric)
mean.fit(points, weights=weights)
result = mean.estimate_
expected = gs.array([16. / 6., 22. / 6.])
self.assertAllClose(result, expected)
def test_variance_euclidean(self):
points = gs.array([
[1., 2.],
[2., 3.],
[3., 4.],
[4., 5.]])
weights = gs.array([1., 2., 1., 2.])
base_point = gs.zeros(2)
result = variance(
points, weights=weights, base_point=base_point,
metric=self.euclidean.metric)
# we expect the average of the points' sq norms.
expected = gs.array((1 * 5. + 2 * 13. + 1 * 25. + 2 * 41.) / 6.)
self.assertAllClose(result, expected)
def test_mean_matrices_shape(self):
m, n = (2, 2)
point = gs.array([
[1., 4.],
[2., 3.]])
metric = MatricesMetric(m, n)
mean = FrechetMean(metric=metric, point_type='matrix')
points = [point, point, point]
mean.fit(points)
result = mean.estimate_
self.assertAllClose(gs.shape(result), (m, n))
def test_mean_matrices(self):
m, n = (2, 2)
point = gs.array([
[1., 4.],
[2., 3.]])
metric = MatricesMetric(m, n)
mean = FrechetMean(metric=metric, point_type='matrix')
points = [point, point, point]
mean.fit(points)
result = mean.estimate_
expected = point
self.assertAllClose(result, expected)
def test_mean_minkowski_shape(self):
dim = 2
point = gs.array([2., -math.sqrt(3)])
points = [point, point, point]
mean = FrechetMean(metric=self.minkowski.metric)
mean.fit(points)
result = mean.estimate_
self.assertAllClose(gs.shape(result), (dim,))
def test_mean_minkowski(self):
point = gs.array([2., -math.sqrt(3)])
points = [point, point, point]
mean = FrechetMean(metric=self.minkowski.metric)
mean.fit(points)
result = mean.estimate_
expected = point
self.assertAllClose(result, expected)
points = gs.array([
[1., 0.],
[2., math.sqrt(3)],
[3., math.sqrt(8)],
[4., math.sqrt(24)]])
weights = gs.array([1., 2., 1., 2.])
mean = FrechetMean(metric=self.minkowski.metric)
mean.fit(points, weights=weights)
result = mean.estimate_
result = self.minkowski.belongs(result)
expected = gs.array(True)
self.assertAllClose(result, expected)
def test_variance_minkowski(self):
points = gs.array([
[1., 0.],
[2., math.sqrt(3)],
[3., math.sqrt(8)],
[4., math.sqrt(24)]])
weights = gs.array([1., 2., 1., 2.])
base_point = gs.array([-1., 0.])
var = variance(
points, weights=weights, base_point=base_point,
metric=self.minkowski.metric)
result = var != 0
# we expect the average of the points' Minkowski sq norms.
expected = True
self.assertAllClose(result, expected)
def test_one_point(self):
point = gs.array([0., 0., 0., 0., 1.])
mean = FrechetMean(metric=self.sphere.metric, method='default')
mean.fit(X=point)
result = mean.estimate_
expected = point
self.assertAllClose(expected, result)
mean = FrechetMean(
metric=self.sphere.metric, method='frechet-poincare-ball')
mean.fit(X=point)
result = mean.estimate_
expected = point
self.assertAllClose(expected, result)
class TestFrechetMean(geomstats.tests.TestCase):
_multiprocess_can_split_ = True
def setUp(self):
self.sphere = Hypersphere(dimension=4)
self.hyperbolic = Hyperbolic(dimension=3)
self.euclidean = Euclidean(dimension=2)
self.minkowski = Minkowski(dimension=2)
@geomstats.tests.np_only
def test_adaptive_gradient_descent_sphere(self):
n_tests = 100
result = gs.zeros(n_tests)
expected = gs.zeros(n_tests)
for i in range(n_tests):
# take 2 random points, compute their mean, and verify that
# log of each at the mean is opposite
points = self.sphere.random_uniform(n_samples=2)
mean = _adaptive_gradient_descent(points=points,
metric=self.sphere.metric)
logs = self.sphere.metric.log(point=points, base_point=mean)
result[i] = gs.linalg.norm(logs[1, :] + logs[0, :])
self.assertAllClose(expected, result, rtol=1e-10, atol=1e-10)
@geomstats.tests.np_and_pytorch_only
def test_estimate_and_belongs_sphere(self):
point_a = gs.array([1., 0., 0., 0., 0.])
point_b = gs.array([0., 1., 0., 0., 0.])
points = gs.zeros((2, point_a.shape[0]))
points[0, :] = point_a
points[1, :] = point_b
mean = FrechetMean(metric=self.sphere.metric)
mean.fit(points)
result = self.sphere.belongs(mean.estimate_)
expected = gs.array([[True]])
self.assertAllClose(result, expected)
@geomstats.tests.np_and_pytorch_only
def test_variance_sphere(self):
point = gs.array([0., 0., 0., 0., 1.])
points = gs.zeros((2, point.shape[0]))
points[0, :] = point
points[1, :] = point
result = variance(points, base_point=point, metric=self.sphere.metric)
expected = helper.to_scalar(0.)
self.assertAllClose(expected, result)
@geomstats.tests.np_and_pytorch_only
def test_estimate_sphere(self):
point = gs.array([0., 0., 0., 0., 1.])
points = gs.zeros((2, point.shape[0]))
points[0, :] = point
points[1, :] = point
mean = FrechetMean(metric=self.sphere.metric)
mean.fit(X=points)
result = mean.estimate_
expected = helper.to_vector(point)
self.assertAllClose(expected, result)
@geomstats.tests.np_and_tf_only
def test_variance_hyperbolic(self):
point = gs.array([2., 1., 1., 1.])
points = gs.array([point, point])
result = variance(points,
base_point=point,
metric=self.hyperbolic.metric)
expected = helper.to_scalar(0.)
self.assertAllClose(result, expected)
@geomstats.tests.np_and_tf_only
def test_estimate_hyperbolic(self):
point = gs.array([2., 1., 1., 1.])
points = gs.array([point, point])
mean = FrechetMean(metric=self.hyperbolic.metric)
mean.fit(X=points)
result = mean.estimate_
expected = helper.to_vector(point)
self.assertAllClose(result, expected)
@geomstats.tests.np_and_tf_only
def test_estimate_and_belongs_hyperbolic(self):
point_a = self.hyperbolic.random_uniform()
point_b = self.hyperbolic.random_uniform()
point_c = self.hyperbolic.random_uniform()
points = gs.concatenate([point_a, point_b, point_c], axis=0)
mean = FrechetMean(metric=self.hyperbolic.metric)
mean.fit(X=points)
result = self.hyperbolic.belongs(mean.estimate_)
expected = gs.array([[True]])
self.assertAllClose(result, expected)
def test_mean_euclidean(self):
point = gs.array([[1., 4.]])
mean = FrechetMean(metric=self.euclidean.metric)
points = [point, point, point]
mean.fit(points)
result = mean.estimate_
expected = point
expected = helper.to_vector(expected)
self.assertAllClose(result, expected)
points = gs.array([[1., 2.], [2., 3.], [3., 4.], [4., 5.]])
weights = gs.array([1., 2., 1., 2.])
mean = FrechetMean(metric=self.euclidean.metric)
mean.fit(points, weights=weights)
result = mean.estimate_
expected = gs.array([16. / 6., 22. / 6.])
expected = helper.to_vector(expected)
self.assertAllClose(result, expected)
def test_variance_euclidean(self):
points = gs.array([[1., 2.], [2., 3.], [3., 4.], [4., 5.]])
weights = gs.array([1., 2., 1., 2.])
base_point = gs.zeros(2)
result = variance(points,
weights=weights,
base_point=base_point,
metric=self.euclidean.metric)
# we expect the average of the points' sq norms.
expected = (1 * 5. + 2 * 13. + 1 * 25. + 2 * 41.) / 6.
expected = helper.to_scalar(expected)
self.assertAllClose(result, expected)
def test_mean_minkowski(self):
point = gs.array([[2., -math.sqrt(3)]])
points = [point, point, point]
mean = FrechetMean(metric=self.minkowski.metric)
mean.fit(points)
result = mean.estimate_
expected = point
expected = helper.to_vector(expected)
self.assertAllClose(result, expected)
points = gs.array([[1., 0.], [2., math.sqrt(3)], [3., math.sqrt(8)],
[4., math.sqrt(24)]])
weights = gs.array([1., 2., 1., 2.])
mean = FrechetMean(metric=self.minkowski.metric)
mean.fit(points, weights=weights)
result = mean.estimate_
result = self.minkowski.belongs(result)
expected = gs.array([[True]])
self.assertAllClose(result, expected)
def test_variance_minkowski(self):
points = gs.array([[1., 0.], [2., math.sqrt(3)], [3., math.sqrt(8)],
[4., math.sqrt(24)]])
weights = gs.array([1., 2., 1., 2.])
base_point = gs.array([-1., 0.])
var = variance(points,
weights=weights,
base_point=base_point,
metric=self.minkowski.metric)
result = helper.to_scalar(var != 0)
# we expect the average of the points' Minkowski sq norms.
expected = helper.to_scalar(gs.array([True]))
self.assertAllClose(result, expected)
