def test_log_and_exp_general_case_general_dim(self):
"""
Test that the Riemannian exponential
and the Riemannian logarithm are inverse.
Expect their composition to give the identity function.
"""
# Riemannian Log then Riemannian Exp
dim = 5
n_samples = self.n_samples
h5 = Hyperboloid(dim=dim)
h5_metric = h5.metric
base_point = h5.random_point()
point = h5.random_point()
point = gs.cast(point, gs.float64)
base_point = gs.cast(base_point, gs.float64)
one_log = h5_metric.log(point=point, base_point=base_point)
result = h5_metric.exp(tangent_vec=one_log, base_point=base_point)
expected = point
self.assertAllClose(result, expected)
# Test vectorization of log
base_point = gs.stack([base_point] * n_samples, axis=0)
point = gs.stack([point] * n_samples, axis=0)
expected = gs.stack([one_log] * n_samples, axis=0)
log = h5_metric.log(point=point, base_point=base_point)
result = log
self.assertAllClose(gs.shape(result), (n_samples, dim + 1))
self.assertAllClose(result, expected)
result = h5_metric.exp(tangent_vec=log, base_point=base_point)
expected = point
self.assertAllClose(gs.shape(result), (n_samples, dim + 1))
self.assertAllClose(result, expected)
# Test vectorization of exp
tangent_vec = gs.stack([one_log] * n_samples, axis=0)
exp = h5_metric.exp(tangent_vec=tangent_vec, base_point=base_point)
result = exp
expected = point
self.assertAllClose(gs.shape(result), (n_samples, dim + 1))
self.assertAllClose(result, expected)
class TestToTangentSpace(geomstats.tests.TestCase):
_multiprocess_can_split_ = True
def setup_method(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, 0.0, 0.0, 1.0])
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_autograd_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_point()
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([2.0, 1.0, 1.0, 1.0])
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_point(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_point(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_autograd_and_tf_only
def test_inverse_transform_so(self):
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)
def main():
"""Perform tangent PCA at the mean on H2."""
fig = plt.figure(figsize=(15, 5))
hyperbolic_plane = Hyperboloid(dim=2)
data = hyperbolic_plane.random_point(n_samples=140)
mean = FrechetMean(metric=hyperbolic_plane.metric)
mean.fit(data)
mean_estimate = mean.estimate_
tpca = TangentPCA(metric=hyperbolic_plane.metric, n_components=2)
tpca = tpca.fit(data, base_point=mean_estimate)
tangent_projected_data = tpca.transform(data)
geodesic_0 = hyperbolic_plane.metric.geodesic(
initial_point=mean_estimate, initial_tangent_vec=tpca.components_[0])
geodesic_1 = hyperbolic_plane.metric.geodesic(
initial_point=mean_estimate, initial_tangent_vec=tpca.components_[1])
n_steps = 100
t = np.linspace(-1, 1, n_steps)
geodesic_points_0 = geodesic_0(t)
geodesic_points_1 = geodesic_1(t)
logging.info(
'Coordinates of the Log of the first 5 data points at the mean, '
'projected on the principal components:')
logging.info('\n{}'.format(tangent_projected_data[:5]))
ax_var = fig.add_subplot(121)
xticks = np.arange(1, 2 + 1, 1)
ax_var.xaxis.set_ticks(xticks)
ax_var.set_title('Explained variance')
ax_var.set_xlabel('Number of Principal Components')
ax_var.set_ylim((0, 1))
ax_var.plot(xticks, tpca.explained_variance_ratio_)
ax = fig.add_subplot(122)
visualization.plot(mean_estimate,
ax,
space='H2_poincare_disk',
color='darkgreen',
s=10)
visualization.plot(geodesic_points_0,
ax,
space='H2_poincare_disk',
linewidth=2)
visualization.plot(geodesic_points_1,
ax,
space='H2_poincare_disk',
linewidth=2)
visualization.plot(data,
ax,
space='H2_poincare_disk',
color='black',
alpha=0.7)
plt.show()
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 TestHyperbolic(geomstats.tests.TestCase):
def setup_method(self):
gs.random.seed(1234)
self.dimension = 3
self.space = Hyperboloid(dim=self.dimension)
self.metric = self.space.metric
self.ball_manifold = PoincareBall(dim=2)
self.n_samples = 10
def test_belongs_intrinsic(self):
self.space.coords_type = "intrinsic"
point = gs.random.rand(self.n_samples, self.dimension)
result = self.space.belongs(point)
self.assertTrue(gs.all(result))
def test_regularize_intrinsic(self):
self.space.coords_type = "intrinsic"
point = gs.random.rand(self.n_samples, self.dimension)
regularized = self.space.regularize(point)
self.space.coords_type = "extrinsic"
result = self.space.belongs(regularized)
self.assertTrue(gs.all(result))
def test_regularize_zero_norm(self):
point = gs.array([-1.0, 1.0, 0.0, 0.0])
with pytest.raises(ValueError):
self.space.regularize(point)
with pytest.raises(NameError):
self.space.extrinsic_to_intrinsic_coords(point)
def test_random_uniform_and_belongs(self):
point = self.space.random_point()
result = self.space.belongs(point)
expected = True
self.assertAllClose(result, expected)
def test_random_uniform(self):
result = self.space.random_point()
self.assertAllClose(gs.shape(result), (self.dimension + 1,))
def test_projection_to_tangent_space(self):
base_point = gs.array([1.0, 0.0, 0.0, 0.0])
belongs = self.space.belongs(base_point)
self.assertTrue(belongs)
tangent_vec = self.space.to_tangent(
vector=gs.array([1.0, 2.0, 1.0, 3.0]), base_point=base_point
)
result = self.metric.inner_product(tangent_vec, base_point)
expected = 0.0
self.assertAllClose(result, expected)
result = self.space.to_tangent(
vector=gs.array([1.0, 2.0, 1.0, 3.0]), base_point=base_point
)
expected = tangent_vec
self.assertAllClose(result, expected)
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.ones(self.dimension)
point_ext = self.space.intrinsic_to_extrinsic_coords(point_int)
result = self.space.extrinsic_to_intrinsic_coords(point_ext)
expected = point_int
self.assertAllClose(result, expected)
point_ext = gs.array([2.0, 1.0, 1.0, 1.0])
point_int = self.space.to_coordinates(point_ext, "intrinsic")
result = self.space.from_coordinates(point_int, "intrinsic")
expected = point_ext
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(
[
[0.1, 0.0, 0.0, 0.1, 0.0, 0.0],
[0.1, 0.1, 0.1, 0.4, 0.1, 0.0],
[0.1, 0.3, 0.0, 0.1, 0.0, 0.0],
[-0.1, 0.1, -0.4, 0.1, -0.01, 0.0],
[0.0, 0.0, 0.1, 0.1, -0.08, -0.1],
[0.1, 0.1, 0.1, 0.1, 0.0, -0.5],
]
)
point_ext = self.space.from_coordinates(point_int, "intrinsic")
result = self.space.to_coordinates(point_ext, "intrinsic")
expected = point_int
expected = helper.to_vector(expected)
self.assertAllClose(result, expected)
point_ext = gs.array(
[
[2.0, 1.0, 1.0, 1.0],
[4.0, 1.0, 3.0, math.sqrt(5.0)],
[3.0, 2.0, 0.0, 2.0],
]
)
point_int = self.space.to_coordinates(point_ext, "intrinsic")
result = self.space.from_coordinates(point_int, "intrinsic")
expected = point_ext
expected = helper.to_vector(expected)
self.assertAllClose(result, expected)
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.
"""
# Riemannian Log then Riemannian Exp
# General case
base_point = gs.array([4.0, 1.0, 3.0, math.sqrt(5.0)])
point = gs.array([2.0, 1.0, 1.0, 1.0])
log = self.metric.log(point=point, base_point=base_point)
result = self.metric.exp(tangent_vec=log, base_point=base_point)
expected = point
self.assertAllClose(result, expected)
def test_log_and_exp_general_case_general_dim(self):
"""
Test that the Riemannian exponential
and the Riemannian logarithm are inverse.
Expect their composition to give the identity function.
"""
# Riemannian Log then Riemannian Exp
dim = 5
n_samples = self.n_samples
h5 = Hyperboloid(dim=dim)
h5_metric = h5.metric
base_point = h5.random_point()
point = h5.random_point()
point = gs.cast(point, gs.float64)
base_point = gs.cast(base_point, gs.float64)
one_log = h5_metric.log(point=point, base_point=base_point)
result = h5_metric.exp(tangent_vec=one_log, base_point=base_point)
expected = point
self.assertAllClose(result, expected)
# Test vectorization of log
base_point = gs.stack([base_point] * n_samples, axis=0)
point = gs.stack([point] * n_samples, axis=0)
expected = gs.stack([one_log] * n_samples, axis=0)
log = h5_metric.log(point=point, base_point=base_point)
result = log
self.assertAllClose(gs.shape(result), (n_samples, dim + 1))
self.assertAllClose(result, expected)
result = h5_metric.exp(tangent_vec=log, base_point=base_point)
expected = point
self.assertAllClose(gs.shape(result), (n_samples, dim + 1))
self.assertAllClose(result, expected)
# Test vectorization of exp
tangent_vec = gs.stack([one_log] * n_samples, axis=0)
exp = h5_metric.exp(tangent_vec=tangent_vec, base_point=base_point)
result = exp
expected = point
self.assertAllClose(gs.shape(result), (n_samples, dim + 1))
self.assertAllClose(result, expected)
def test_exp_and_belongs(self):
H2 = Hyperboloid(dim=2)
METRIC = H2.metric
base_point = gs.array([1.0, 0.0, 0.0])
self.assertTrue(H2.belongs(base_point))
tangent_vec = H2.to_tangent(
vector=gs.array([1.0, 2.0, 1.0]), base_point=base_point
)
exp = METRIC.exp(tangent_vec=tangent_vec, base_point=base_point)
self.assertTrue(H2.belongs(exp))
def test_exp_small_vec(self):
H2 = Hyperboloid(dim=2)
METRIC = H2.metric
base_point = H2.regularize(gs.array([1.0, 0.0, 0.0]))
self.assertTrue(H2.belongs(base_point))
tangent_vec = 1e-9 * H2.to_tangent(
vector=gs.array([1.0, 2.0, 1.0]), base_point=base_point
)
exp = METRIC.exp(tangent_vec=tangent_vec, base_point=base_point)
self.assertTrue(H2.belongs(exp))
def test_exp_vectorization(self):
n_samples = 3
dim = self.dimension + 1
one_vec = gs.array([2.0, 1.0, 1.0, 1.0])
one_base_point = gs.array([4.0, 3.0, 1.0, math.sqrt(5)])
n_vecs = gs.array(
[
[2.0, 1.0, 1.0, 1.0],
[4.0, 1.0, 3.0, math.sqrt(5.0)],
[3.0, 2.0, 0.0, 2.0],
]
)
n_base_points = gs.array(
[
[2.0, 0.0, 1.0, math.sqrt(2)],
[5.0, math.sqrt(8), math.sqrt(8), math.sqrt(8)],
[1.0, 0.0, 0.0, 0.0],
]
)
one_tangent_vec = self.space.to_tangent(one_vec, base_point=one_base_point)
result = self.metric.exp(one_tangent_vec, one_base_point)
self.assertAllClose(gs.shape(result), (dim,))
n_tangent_vecs = self.space.to_tangent(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))
expected = []
for i in range(n_samples):
expected.append(self.metric.exp(n_tangent_vecs[i], one_base_point))
expected = gs.stack(expected, axis=0)
expected = helper.to_vector(gs.array(expected))
self.assertAllClose(result, expected, atol=1e-2)
one_tangent_vec = self.space.to_tangent(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))
expected = []
for i in range(n_samples):
expected.append(self.metric.exp(one_tangent_vec[i], n_base_points[i]))
expected = gs.stack(expected, axis=0)
expected = helper.to_vector(gs.array(expected))
self.assertAllClose(result, expected)
n_tangent_vecs = self.space.to_tangent(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))
expected = []
for i in range(n_samples):
expected.append(self.metric.exp(n_tangent_vecs[i], n_base_points[i]))
expected = gs.stack(expected, axis=0)
expected = helper.to_vector(gs.array(expected))
self.assertAllClose(result, expected)
def test_log_vectorization(self):
n_samples = 3
dim = self.dimension + 1
one_point = gs.array([2.0, 1.0, 1.0, 1.0])
one_base_point = gs.array([4.0, 3.0, 1.0, math.sqrt(5)])
n_points = gs.array(
[[2.0, 1.0, 1.0, 1.0], [4.0, 1.0, 3.0, math.sqrt(5)], [3.0, 2.0, 0.0, 2.0]]
)
n_base_points = gs.array(
[
[2.0, 0.0, 1.0, math.sqrt(2)],
[5.0, math.sqrt(8), math.sqrt(8), math.sqrt(8)],
[1.0, 0.0, 0.0, 0.0],
]
)
result = self.metric.log(one_point, one_base_point)
self.assertAllClose(gs.shape(result), (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))
def test_inner_product(self):
"""
Test that the inner product between two tangent vectors
is the Minkowski inner product.
"""
minkowski_space = Minkowski(self.dimension + 1)
base_point = gs.array([1.16563816, 0.36381045, -0.47000603, 0.07381469])
tangent_vec_a = self.space.to_tangent(
vector=gs.array([10.0, 200.0, 1.0, 1.0]), base_point=base_point
)
tangent_vec_b = self.space.to_tangent(
vector=gs.array([11.0, 20.0, -21.0, 0.0]), base_point=base_point
)
result = self.metric.inner_product(tangent_vec_a, tangent_vec_b, base_point)
expected = minkowski_space.metric.inner_product(
tangent_vec_a, tangent_vec_b, base_point
)
self.assertAllClose(result, expected)
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 = gs.array([2.0, 1.0, 1.0, 1.0])
point_b = gs.array([4.0, 1.0, 3.0, math.sqrt(5)])
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)
self.assertAllClose(result, expected)
def test_norm_and_dist(self):
"""
Test that the distance between two points is
the norm of their logarithm.
"""
point_a = gs.array([2.0, 1.0, 1.0, 1.0])
point_b = gs.array([4.0, 1.0, 3.0, math.sqrt(5)])
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)
self.assertAllClose(result, expected)
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.
"""
# Riemannian Log then Riemannian Exp
# Edge case: two very close points, base_point_2 and point_2,
# form an angle < epsilon
base_point_intrinsic = gs.array([1.0, 2.0, 3.0])
base_point = self.space.from_coordinates(base_point_intrinsic, "intrinsic")
point_intrinsic = base_point_intrinsic + 1e-12 * gs.array([-1.0, -2.0, 1.0])
point = self.space.from_coordinates(point_intrinsic, "intrinsic")
log = self.metric.log(point=point, base_point=base_point)
result = self.metric.exp(tangent_vec=log, base_point=base_point)
expected = point
self.assertAllClose(result, expected)
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.
"""
# Riemannian Exp then Riemannian Log
# General case
base_point = gs.array([4.0, 1.0, 3.0, math.sqrt(5)])
vector = gs.array([2.0, 1.0, 1.0, 1.0])
vector = self.space.to_tangent(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
self.assertAllClose(result, expected)
def test_dist(self):
# Distance between a point and itself is 0.
point_a = gs.array([4.0, 1.0, 3.0, math.sqrt(5)])
point_b = point_a
result = self.metric.dist(point_a, point_b)
expected = 0
self.assertAllClose(result, expected)
def test_exp_and_dist_and_projection_to_tangent_space(self):
base_point = gs.array([4.0, 1.0, 3.0, math.sqrt(5)])
vector = gs.array([0.001, 0.0, -0.00001, -0.00003])
tangent_vec = self.space.to_tangent(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)
sq_norm = self.metric.embedding_metric.squared_norm(tangent_vec)
expected = sq_norm
self.assertAllClose(result, expected, atol=1e-2)
def test_geodesic_and_belongs(self):
initial_point = gs.array([4.0, 1.0, 3.0, math.sqrt(5)])
n_geodesic_points = 100
vector = gs.array([1.0, 0.0, 0.0, 0.0])
initial_tangent_vec = self.space.to_tangent(
vector=vector, base_point=initial_point
)
geodesic = self.metric.geodesic(
initial_point=initial_point, initial_tangent_vec=initial_tangent_vec
)
t = gs.linspace(start=0.0, stop=1.0, num=n_geodesic_points)
points = geodesic(t)
result = gs.all(self.space.belongs(points))
self.assertTrue(result)
def test_geodesic_and_belongs_large_initial_velocity(self):
initial_point = gs.array([4.0, 1.0, 3.0, math.sqrt(5)])
n_geodesic_points = 100
vector = gs.array([2.0, 0.0, 0.0, 0.0])
initial_tangent_vec = self.space.to_tangent(
vector=vector, base_point=initial_point
)
geodesic = self.metric.geodesic(
initial_point=initial_point, initial_tangent_vec=initial_tangent_vec
)
t = gs.linspace(start=0.0, stop=1.0, num=n_geodesic_points)
points = geodesic(t)
result = gs.all(self.space.belongs(points, atol=gs.atol * 1e4))
self.assertTrue(result)
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.
"""
# Riemannian Exp then Riemannian Log
# Edge case: tangent vector has norm < epsilon
base_point = gs.array([2.0, 1.0, 1.0, 1.0])
vector = 1e-10 * gs.array([0.06, -51.0, 6.0, 5.0])
exp = self.metric.exp(tangent_vec=vector, base_point=base_point)
result = self.metric.log(point=exp, base_point=base_point)
expected = self.space.to_tangent(vector=vector, base_point=base_point)
self.assertAllClose(result, expected)
def test_scaled_inner_product(self):
base_point_intrinsic = gs.array([1.0, 1.0, 1.0])
base_point = self.space.from_coordinates(base_point_intrinsic, "intrinsic")
tangent_vec_a = gs.array([1.0, 2.0, 3.0, 4.0])
tangent_vec_b = gs.array([5.0, 6.0, 7.0, 8.0])
tangent_vec_a = self.space.to_tangent(tangent_vec_a, base_point)
tangent_vec_b = self.space.to_tangent(tangent_vec_b, base_point)
scale = 2
default_space = Hyperboloid(dim=self.dimension)
scaled_space = Hyperboloid(dim=self.dimension, scale=2)
inner_product_default_metric = default_space.metric.inner_product(
tangent_vec_a, tangent_vec_b, base_point
)
inner_product_scaled_metric = scaled_space.metric.inner_product(
tangent_vec_a, tangent_vec_b, base_point
)
result = inner_product_scaled_metric
expected = scale**2 * inner_product_default_metric
self.assertAllClose(result, expected)
def test_scaled_squared_norm(self):
base_point_intrinsic = gs.array([1.0, 1.0, 1.0])
base_point = self.space.from_coordinates(base_point_intrinsic, "intrinsic")
tangent_vec = gs.array([1.0, 2.0, 3.0, 4.0])
tangent_vec = self.space.to_tangent(tangent_vec, base_point)
scale = 2
default_space = Hyperboloid(dim=self.dimension)
scaled_space = Hyperboloid(dim=self.dimension, scale=2)
squared_norm_default_metric = default_space.metric.squared_norm(
tangent_vec, base_point
)
squared_norm_scaled_metric = scaled_space.metric.squared_norm(
tangent_vec, base_point
)
result = squared_norm_scaled_metric
expected = scale**2 * squared_norm_default_metric
self.assertAllClose(result, expected)
def test_scaled_distance(self):
point_a_intrinsic = gs.array([1.0, 2.0, 3.0])
point_b_intrinsic = gs.array([4.0, 5.0, 6.0])
point_a = self.space.from_coordinates(point_a_intrinsic, "intrinsic")
point_b = self.space.from_coordinates(point_b_intrinsic, "intrinsic")
scale = 2
scaled_space = Hyperboloid(dim=self.dimension, scale=2)
distance_default_metric = self.space.metric.dist(point_a, point_b)
distance_scaled_metric = scaled_space.metric.dist(point_a, point_b)
result = distance_scaled_metric
expected = scale * distance_default_metric
self.assertAllClose(result, expected)
def test_is_tangent(self):
base_point = gs.array([4.0, 1.0, 3.0, math.sqrt(5.0)])
point = gs.array([2.0, 1.0, 1.0, 1.0])
log = self.metric.log(point=point, base_point=base_point)
result = self.space.is_tangent(log, base_point)
self.assertTrue(result)
@geomstats.tests.np_autograd_and_tf_only
def test_parallel_transport_vectorization(self):
space = self.space
shape = (4, space.dim + 1)
metric = space.metric
results = helper.test_parallel_transport(space, metric, shape)
for res in results:
self.assertTrue(res)
def test_projection_and_belongs(self):
shape = (self.n_samples, self.dimension + 1)
result = helper.test_projection_and_belongs(
self.space, shape, atol=gs.atol * 100
)
for res in result:
self.assertTrue(res)
point = gs.array([0.0, 1.0, 0.0, 0.0])
projected = self.space.projection(point)
result = self.space.belongs(projected)
self.assertTrue(result)
class TestFrechetMean(geomstats.tests.TestCase):
_multiprocess_can_split_ = True
def setup_method(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)
self.curves_2d = DiscreteCurves(R2)
self.elastic_metric = ElasticMetric(a=1, b=1, ambient_manifold=R2)
def test_logs_at_mean_curves_2d(self):
n_tests = 10
metric = self.curves_2d.srv_metric
estimator = FrechetMean(metric=metric, init_step_size=1.0)
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.curves_2d.random_point(n_samples=2)
estimator.fit(points)
mean = estimator.estimate_
# Expand and tile are added because of GitHub issue 1575
mean = gs.expand_dims(mean, axis=0)
mean = gs.tile(mean, (2, 1, 1))
logs = metric.log(point=points, base_point=mean)
logs_srv = metric.aux_differential_srv_transform(logs, curve=mean)
# Note that the logs are NOT inverse, only the logs_srv are.
result.append(gs.linalg.norm(logs_srv[1, :] + logs_srv[0, :]))
result = gs.stack(result)
expected = gs.zeros(n_tests)
self.assertAllClose(expected, result, atol=1e-5)
def test_logs_at_mean_default_gradient_descent_sphere(self):
n_tests = 10
estimator = FrechetMean(metric=self.sphere.metric,
method="default",
init_step_size=1.0)
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, 0.0, 0.0, 0.0])
point_b = gs.array([0.0, 1.0, 0.0, 0.0, 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, 0.0, 0.0, 0.0])
point_b = gs.array([0.0, 1.0, 0.0, 0.0, 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_shape_elastic_metric(self):
points = self.curves_2d.random_point(n_samples=2)
mean = FrechetMean(metric=self.elastic_metric)
mean.fit(points)
result = mean.estimate_
self.assertAllClose(gs.shape(result), (points.shape[1:]))
def test_estimate_shape_metric(self):
points = self.curves_2d.random_point(n_samples=2)
mean = FrechetMean(metric=self.curves_2d.srv_metric)
mean.fit(points)
result = mean.estimate_
self.assertAllClose(gs.shape(result), (points.shape[1:]))
def test_estimate_and_belongs_default_gradient_descent_sphere(self):
point_a = gs.array([1.0, 0.0, 0.0, 0.0, 0.0])
point_b = gs.array([0.0, 1.0, 0.0, 0.0, 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_and_belongs_curves_2d(self):
points = self.curves_2d.random_point(n_samples=2)
mean = FrechetMean(metric=self.curves_2d.srv_metric)
mean.fit(points)
result = self.curves_2d.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",
init_step_size=1.0)
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_autograd_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",
init_step_size=1.0,
)
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_autograd_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_autograd_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",
init_step_size=0.5,
verbose=True,
)
mean.fit(point)
result = self.so_matrix.belongs(mean.estimate_)
self.assertTrue(result)
@geomstats.tests.np_autograd_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, 0.0, 0.0, 0.0])
point_b = gs.array([0.0, 1.0, 0.0, 0.0, 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, 0.0, 0.0, 1.0])
points = gs.array([point, point])
result = variance(points, base_point=point, metric=self.sphere.metric)
expected = gs.array(0.0)
self.assertAllClose(expected, result)
def test_estimate_default_gradient_descent_sphere(self):
point = gs.array([0.0, 0.0, 0.0, 0.0, 1.0])
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_elastic_metric(self):
point = self.curves_2d.random_point(n_samples=1)
points = gs.array([point, point])
mean = FrechetMean(metric=self.elastic_metric)
mean.fit(X=points)
result = mean.estimate_
expected = point
self.assertAllClose(expected, result)
def test_estimate_curves_2d(self):
point = self.curves_2d.random_point(n_samples=1)
points = gs.array([point, point])
mean = FrechetMean(metric=self.curves_2d.srv_metric)
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, 0.0, 0.0, 1.0])
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_estimate_spd_two_samples(self):
space = SPDMatrices(3)
metric = SPDMetricAffine(3)
point = space.random_point(2)
mean = FrechetMean(metric)
mean.fit(point)
result = mean.estimate_
expected = metric.exp(metric.log(point[0], point[1]) / 2, point[1])
self.assertAllClose(expected, result)
def test_variance_hyperbolic(self):
point = gs.array([2.0, 1.0, 1.0, 1.0])
points = gs.array([point, point])
result = variance(points,
base_point=point,
metric=self.hyperbolic.metric)
expected = gs.array(0.0)
self.assertAllClose(result, expected)
def test_estimate_hyperbolic(self):
point = gs.array([2.0, 1.0, 1.0, 1.0])
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([1.0, 4.0])
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([1.0, 4.0])
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([[1.0, 2.0], [2.0, 3.0], [3.0, 4.0], [4.0, 5.0]])
weights = [1.0, 2.0, 1.0, 2.0]
mean = FrechetMean(metric=self.euclidean.metric)
mean.fit(points, weights=weights)
result = mean.estimate_
expected = gs.array([16.0 / 6.0, 22.0 / 6.0])
self.assertAllClose(result, expected)
def test_variance_euclidean(self):
points = gs.array([[1.0, 2.0], [2.0, 3.0], [3.0, 4.0], [4.0, 5.0]])
weights = gs.array([1.0, 2.0, 1.0, 2.0])
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 * 5.0 + 2 * 13.0 + 1 * 25.0 + 2 * 41.0) / 6.0)
self.assertAllClose(result, expected)
def test_mean_matrices_shape(self):
m, n = (2, 2)
point = gs.array([[1.0, 4.0], [2.0, 3.0]])
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([[1.0, 4.0], [2.0, 3.0]])
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([2.0, -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([2.0, -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, 0.0], [2.0, math.sqrt(3)],
[3.0, math.sqrt(8)], [4.0, math.sqrt(24)]])
weights = gs.array([1.0, 2.0, 1.0, 2.0])
mean = FrechetMean(metric=self.minkowski.metric)
mean.fit(points, weights=weights)
result = self.minkowski.belongs(mean.estimate_)
self.assertTrue(result)
def test_variance_minkowski(self):
points = gs.array([[1.0, 0.0], [2.0, math.sqrt(3)],
[3.0, math.sqrt(8)], [4.0, math.sqrt(24)]])
weights = gs.array([1.0, 2.0, 1.0, 2.0])
base_point = gs.array([-1.0, 0.0])
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, 0.0, 0.0, 1.0])
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="default")
mean.fit(X=point)
result = mean.estimate_
expected = point
self.assertAllClose(expected, result)
def test_batched(self):
space = SPDMatrices(3)
metric = SPDMetricAffine(3)
point = space.random_point(4)
mean_batch = FrechetMean(metric, method="batch", verbose=True)
data = gs.stack([point[:2], point[2:]], axis=1)
mean_batch.fit(data)
result = mean_batch.estimate_
mean = FrechetMean(metric)
mean.fit(data[:, 0])
expected_1 = mean.estimate_
mean.fit(data[:, 1])
expected_2 = mean.estimate_
expected = gs.stack([expected_1, expected_2])
self.assertAllClose(expected, result)
@geomstats.tests.np_and_autograd_only
def test_stiefel_two_samples(self):
space = Stiefel(3, 2)
metric = space.metric
point = space.random_point(2)
mean = FrechetMean(metric)
mean.fit(point)
result = mean.estimate_
expected = metric.exp(metric.log(point[0], point[1]) / 2, point[1])
self.assertAllClose(expected, result)
@geomstats.tests.np_and_autograd_only
def test_stiefel_n_samples(self):
space = Stiefel(3, 2)
metric = space.metric
point = space.random_point(2)
mean = FrechetMean(metric,
method="default",
init_step_size=0.5,
verbose=True)
mean.fit(point)
result = space.belongs(mean.estimate_)
self.assertTrue(result)
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)
