def test_fails_Stiefel():
with pytest.raises(ValueError):
manifold = geoopt.Stiefel()
manifold.random_naive(())
with pytest.raises(ValueError):
manifold = geoopt.Stiefel()
manifold.random_naive((5, 10))
def test_fails_Stiefel():
with pytest.raises(ValueError):
manifold = geoopt.Stiefel()
manifold.origin(())
with pytest.raises(ValueError):
manifold = geoopt.Stiefel()
manifold.origin((5, 10))
def product_case():
torch.manual_seed(42)
ex = [
torch.randn(10),
torch.randn(3) / 10,
torch.randn(3, 2),
torch.randn(())
]
ev = [
torch.randn(10),
torch.randn(3) / 10,
torch.randn(3, 2),
torch.randn(())
]
manifolds = [
geoopt.Sphere(),
geoopt.PoincareBall(),
geoopt.Stiefel(),
geoopt.Euclidean(),
]
x = [manifolds[i].projx(ex[i]) for i in range(len(manifolds))]
v = [manifolds[i].proju(x[i], ev[i]) for i in range(len(manifolds))]
product_manifold = geoopt.ProductManifold(*((manifolds[i], ex[i].shape)
for i in range(len(ex))))
yield UnaryCase(
manifold_shapes[geoopt.ProductManifold],
product_manifold.pack_point(*x),
product_manifold.pack_point(*ex),
product_manifold.pack_point(*v),
product_manifold.pack_point(*ev),
product_manifold,
)
# + 1 case without stiefel
torch.manual_seed(42)
ex = [torch.randn(10), torch.randn(3) / 10, torch.randn(())]
ev = [torch.randn(10), torch.randn(3) / 10, torch.randn(())]
manifolds = [
geoopt.Sphere(),
geoopt.PoincareBall(),
# geoopt.Stiefel(),
geoopt.Euclidean(),
]
x = [manifolds[i].projx(ex[i]) for i in range(len(manifolds))]
v = [manifolds[i].proju(x[i], ev[i]) for i in range(len(manifolds))]
product_manifold = geoopt.ProductManifold(*((manifolds[i], ex[i].shape)
for i in range(len(ex))))
yield UnaryCase(
manifold_shapes[geoopt.ProductManifold],
product_manifold.pack_point(*x),
product_manifold.pack_point(*ex),
product_manifold.pack_point(*v),
product_manifold.pack_point(*ev),
product_manifold,
)
def test_product():
manifold = geoopt.ProductManifold(
(geoopt.Sphere(), 10),
(geoopt.PoincareBall(), 3),
(geoopt.Stiefel(), (20, 2)),
(geoopt.Euclidean(), 43),
)
sample = manifold.random(20, manifold.n_elements)
manifold.assert_check_point_on_manifold(sample)
def test_stiefel_3d():
tens1 = geoopt.ManifoldTensor(2, 10, 20,
manifold=geoopt.Stiefel()).normal_().proj_()
vect1 = tens1.proju(torch.randn(*tens1.shape))
t = torch.randn(tens1.shape[0])
newt = tens1.retr(vect1, t)
newt_manual = list()
newt_manual.append(tens1.manifold.retr(tens1[0], vect1[0], t[0]))
newt_manual.append(tens1.manifold.retr(tens1[1], vect1[1], t[1]))
newt_manual = torch.stack(newt_manual)
numpy.testing.assert_allclose(newt_manual, newt, atol=1e-5)
numpy.testing.assert_allclose(newt, tens1.manifold.projx(newt), atol=1e-5)
def __init__(self, in_features, out_features):
super().__init__()
assert out_features <= in_features
self.in_features = in_features
self.out_features = out_features
self.shape = (self.in_features, self.out_features)
self.manifold = geoopt.Stiefel()
self.weight = geoopt.ManifoldParameter(torch.empty(*self.shape),
manifold=self.manifold)
self.reset_parameters()
def __init__(self, in_features, out_features,device, bias=False):
super(StiefelLinear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = geoopt.ManifoldParameter(
data=torch.Tensor(out_features, in_features),
manifold=geoopt.Stiefel()
)
if bias:
self.bias = torch.nn.Parameter(torch.Tensor(out_features).to(device))
else:
self.register_parameter('bias', None)
self.reset_parameters()
self.weight.data=self.weight.data.to(device)
def test_random_Stiefel():
manifold = geoopt.Stiefel()
point = manifold.random_naive(3, 10, 10)
manifold.assert_check_point_on_manifold(point)
assert point.manifold is manifold
def __init__(self, mu, sigma):
super().__init__()
self.d = torch.distributions.Normal(mu, sigma)
self.x = geoopt.ManifoldParameter(torch.randn_like(mu),
manifold=geoopt.Stiefel())
def test_stiefel_2d():
tens1 = geoopt.ManifoldTensor(10, 20,
manifold=geoopt.Stiefel()).normal_().proj_()
vect1 = tens1.proju(torch.randn(*tens1.shape))
newt = tens1.retr(vect1, 1.)
numpy.testing.assert_allclose(newt, tens1.manifold.projx(newt), atol=1e-5)
