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)