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_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_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_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)
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)