class TestProductManifold(ManifoldTestCase):
def setUp(self):
self.m = m = 100
self.n = n = 50
self.euclidean = Euclidean(m, n)
self.sphere = Sphere(n)
self.manifold = Product([self.euclidean, self.sphere])
point = self.manifold.random_point()
@pymanopt.function.autograd(self.manifold)
def cost(*x):
return np.sum([np.linalg.norm(a - b)**2 for a, b in zip(x, point)])
self.cost = cost
def test_dim(self):
np_testing.assert_equal(self.manifold.dim,
self.m * self.n + self.n - 1)
def test_typical_dist(self):
np_testing.assert_equal(self.manifold.typical_dist,
np.sqrt((self.m * self.n) + np.pi**2))
def test_dist(self):
X = self.manifold.random_point()
Y = self.manifold.random_point()
np_testing.assert_equal(
self.manifold.dist(X, Y),
np.sqrt(
self.euclidean.dist(X[0], Y[0])**2 +
self.sphere.dist(X[1], Y[1])**2),
)
def test_tangent_vector_multiplication(self):
# Regression test for https://github.com/pymanopt/pymanopt/issues/49.
manifold = Product((Euclidean(12), Grassmann(12, 3)))
x = manifold.random_point()
eta = manifold.random_tangent_vector(x)
np.float64(1.0) * eta
# def test_inner_product(self):
# def test_projection(self):
# def test_euclidean_to_riemannian_hessian(self):
# def test_retraction(self):
def test_first_order_function_approximation(self):
self.run_gradient_approximation_test()
def test_second_order_function_approximation(self):
self.run_hessian_approximation_test()
# def test_norm(self):
# def test_random_point(self):
# def test_random_tangent_vector(self):
# def test_transport(self):
def test_exp_log_inverse(self):
s = self.manifold
X = s.random_point()
Y = s.random_point()
Yexplog = s.exp(X, tangent_vector=s.log(X, Y))
np_testing.assert_almost_equal(s.dist(point_a=Y, point_b=Yexplog), 0)
def test_log_exp_inverse(self):
s = self.manifold
X = s.random_point()
U = s.random_tangent_vector(X)
Ulogexp = s.log(X, s.exp(X, U))
np_testing.assert_array_almost_equal(U[0], Ulogexp[0])
np_testing.assert_array_almost_equal(U[1], Ulogexp[1])
def test_pair_mean(self):
s = self.manifold
X = s.random_point()
Y = s.random_point()
Z = s.pair_mean(X, Y)
np_testing.assert_array_almost_equal(s.dist(X, Z), s.dist(Y, Z))
class TestProblemBackendInterface(TestCase):
def setUp(self):
self.m = m = 20
self.n = n = 10
self.rank = rank = 3
A = np.random.normal(size=(m, n))
self.manifold = Product([FixedRankEmbedded(m, n, rank), Euclidean(n)])
@pymanopt.function.autograd(self.manifold)
def cost(u, s, vt, x):
return np.linalg.norm(((u * s) @ vt - A) @ x) ** 2
self.cost = cost
self.gradient = self.cost.get_gradient_operator()
self.hessian = self.cost.get_hessian_operator()
self.problem = pymanopt.Problem(self.manifold, self.cost)
def test_cost_function(self):
(u, s, vt), x = self.manifold.random_point()
self.cost(u, s, vt, x)
def test_gradient_operator_shapes(self):
(u, s, vt), x = self.manifold.random_point()
gu, gs, gvt, gx = self.gradient(u, s, vt, x)
self.assertEqual(gu.shape, (self.m, self.rank))
self.assertEqual(gs.shape, (self.rank,))
self.assertEqual(gvt.shape, (self.rank, self.n))
self.assertEqual(gx.shape, (self.n,))
def test_hessian_operator_shapes(self):
(u, s, vt), x = self.manifold.random_point()
(a, b, c), d = self.manifold.random_point()
hu, hs, hvt, hx = self.hessian(u, s, vt, x, a, b, c, d)
self.assertEqual(hu.shape, (self.m, self.rank))
self.assertEqual(hs.shape, (self.rank,))
self.assertEqual(hvt.shape, (self.rank, self.n))
self.assertEqual(hx.shape, (self.n,))
def test_problem_cost(self):
cost = self.problem.cost
X = self.manifold.random_point()
(u, s, vt), x = X
np_testing.assert_allclose(cost(X), self.cost(u, s, vt, x))
def test_problem_gradient_operator(self):
X = self.manifold.random_point()
(u, s, vt), x = X
G = self.problem.euclidean_gradient(X)
(gu, gs, gvt), gx = G
for ga, gb in zip((gu, gs, gvt, gx), self.gradient(u, s, vt, x)):
np_testing.assert_allclose(ga, gb)
def test_problem_hessian_operator(self):
ehess = self.problem.euclidean_hessian
X = self.manifold.random_point()
U = self.manifold.random_point()
H = ehess(X, U)
(u, s, vt), x = X
(a, b, c), d = U
(hu, hs, hvt), hx = H
for ha, hb in zip(
(hu, hs, hvt, hx), self.hessian(u, s, vt, x, a, b, c, d)
):
np_testing.assert_allclose(ha, hb)
def test_tangent_vector_multiplication(self):
# Regression test for https://github.com/pymanopt/pymanopt/issues/49.
manifold = Product((Euclidean(12), Grassmann(12, 3)))
x = manifold.random_point()
eta = manifold.random_tangent_vector(x)
np.float64(1.0) * eta
