9cb524ed78749685a3e350e77e374adbaa4bd70d,tests/test_hypersphere.py,TestHypersphereMethods,test_log_vectorization,#TestHypersphereMethods#,210

Before Change

        result = self.metric.log(one_point, one_base_point)
        point_numpy = np.random.uniform(size=(1, dim))
        with self.session():
            // TODO(nina): Fix that this test fails with assertShapeEqual
            self.assertAllClose(point_numpy.shape, gs.shape(result))

        result = self.metric.log(n_points, one_base_point)
        point_numpy = np.random.uniform(size=(n_samples, dim))
        with self.session():
            self.assertShapeEqual(point_numpy, result)

        result = self.metric.log(one_point, n_base_points)
        point_numpy = np.random.uniform(size=(n_samples, dim))
        with self.session():
            self.assertShapeEqual(point_numpy, result)

        result = self.metric.log(n_points, n_base_points)
        point_numpy = np.random.uniform(size=(n_samples, dim))
        with self.session():
            self.assertShapeEqual(point_numpy, result)

    def test_exp_and_log_and_projection_to_tangent_space_general_case(self):
        
        Test that the riemannian exponential
        and the riemannian logarithm are inverse.

After Change

        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_exp_and_log_and_projection_to_tangent_space_general_case(self):
        
        Test that the riemannian exponential

Instances