edcbc642ae1518a049471747fd89909c5f841b84,geomstats/special_euclidean_group.py,SpecialEuclideanGroup,exponential_matrix,#SpecialEuclideanGroup#Any#,422

Before Change

        rot_vec = self.rotations.regularize(rot_vec)
        n_rot_vecs = rot_vec.shape[0]
        angle = np.linalg.norm(rot_vec, axis=1)
        if angle.ndim == 1:
            angle = np.expand_dims(angle, axis=1)
        assert angle.shape == (n_rot_vecs, 1), angle.shape
        skew_rot_vec = so_group.skew_matrix_from_vector(rot_vec)

        coef_1 = np.empty_like(angle)
        coef_2 = np.empty_like(coef_1)

        mask_0 = np.equal(angle, 0)
        mask_0 = np.squeeze(mask_0, axis=1)
        mask_close_to_0 = np.isclose(angle, 0)
        mask_close_to_0 = np.squeeze(mask_close_to_0, axis=1)
        mask_else = ~mask_0 & ~mask_close_to_0

        coef_1[mask_close_to_0] = (1. / 2.
                                   - angle[mask_close_to_0] ** 2 / 24.)
        coef_2[mask_close_to_0] = (1. / 6.
                                   - angle[mask_close_to_0] ** 3 / 120.)

        // TODO(nina): check if the discountinuity as 0 is expected.
        coef_1[mask_0] = 0
        coef_2[mask_0] = 0

        coef_1[mask_else] = (angle[mask_else] ** (-2)
                             * (1. - np.cos(angle[mask_else])))
        coef_2[mask_else] = (angle[mask_else] ** (-2)
                             * (1. - (np.sin(angle[mask_else])
                                      / angle[mask_else])))

        term_1 = np.zeros((n_rot_vecs, self.n, self.n))
        term_2 = np.zeros_like(term_1)

        for i in range(n_rot_vecs):
            term_1[i] = np.eye(self.n) + skew_rot_vec[i] * coef_1[i]
            term_2[i] = np.matmul(skew_rot_vec[i], skew_rot_vec[i]) * coef_2[i]

        exponential_mat = term_1 + term_2

After Change

        rot_vec = self.rotations.regularize(rot_vec)
        n_rot_vecs, _ = rot_vec.shape

        angle = np.linalg.norm(rot_vec, axis=1)
        angle = vectorization_utils.expand_dims(angle, to_ndim=2, axis=1)

        skew_rot_vec = so_group.skew_matrix_from_vector(rot_vec)

        coef_1 = np.empty_like(angle)
        coef_2 = np.empty_like(coef_1)

        mask_0 = np.equal(angle, 0)
        mask_0 = np.squeeze(mask_0, axis=1)
        mask_close_to_0 = np.isclose(angle, 0)
        mask_close_to_0 = np.squeeze(mask_close_to_0, axis=1)
        mask_else = ~mask_0 & ~mask_close_to_0

        coef_1[mask_close_to_0] = (1. / 2.
                                   - angle[mask_close_to_0] ** 2 / 24.)
        coef_2[mask_close_to_0] = (1. / 6.
                                   - angle[mask_close_to_0] ** 3 / 120.)

        // TODO(nina): check if the discountinuity as 0 is expected.
        coef_1[mask_0] = 0
        coef_2[mask_0] = 0

        coef_1[mask_else] = (angle[mask_else] ** (-2)
                             * (1. - np.cos(angle[mask_else])))
        coef_2[mask_else] = (angle[mask_else] ** (-2)
                             * (1. - (np.sin(angle[mask_else])
                                      / angle[mask_else])))

        term_1 = np.zeros((n_rot_vecs, self.n, self.n))
        term_2 = np.zeros_like(term_1)

        for i in range(n_rot_vecs):
            term_1[i] = np.eye(self.n) + skew_rot_vec[i] * coef_1[i]
            term_2[i] = np.matmul(skew_rot_vec[i], skew_rot_vec[i]) * coef_2[i]

        exponential_mat = term_1 + term_2

Instances