def test_fit_inverse_transform_matrix(self):
X = self.spd.random_point(n_samples=5)
tpca = TangentPCA(metric=self.spd_metric)
tangent_projected_data = tpca.fit_transform(X)
result = tpca.inverse_transform(tangent_projected_data)
expected = X
self.assertAllClose(result, expected, atol=1e-6)
def test_fit_matrix_se(self):
se_mat = SpecialEuclidean(n=3, default_point_type='matrix')
X = se_mat.random_uniform(self.n_samples)
estimator = ExponentialBarycenter(se_mat)
estimator.fit(X)
mean = estimator.estimate_
tpca = TangentPCA(metric=se_mat, point_type='matrix')
tangent_projected_data = tpca.fit_transform(X, base_point=mean)
result = tpca.inverse_transform(tangent_projected_data)
expected = X
self.assertAllClose(result, expected)
def test_fit_inverse_transform_vector(self):
tpca = TangentPCA(metric=self.metric, point_type='vector')
tangent_projected_data = tpca.fit_transform(self.X)
result = tpca.inverse_transform(tangent_projected_data)
expected = self.X
self.assertAllClose(result, expected)
