def test_inference_deepGP():
gp1 = GPRegression(
X, None,
RBF(input_dim=3,
variance=torch.tensor(3.),
lengthscale=torch.tensor(2.)))
Z, _ = gp1.model()
gp2 = VariationalSparseGP(Z, y2D, Matern32(input_dim=3), Z.clone(),
Gaussian(torch.tensor(1e-6)))
class DeepGP(torch.nn.Module):
def __init__(self, gp1, gp2):
super(DeepGP, self).__init__()
self.gp1 = gp1
self.gp2 = gp2
def model(self):
Z, _ = self.gp1.model()
self.gp2.set_data(Z, y2D)
self.gp2.model()
def guide(self):
self.gp1.guide()
self.gp2.guide()
deepgp = DeepGP(gp1, gp2)
train(deepgp, num_steps=1)
def test_inference_deepGP():
gp1 = GPRegression(X, None, kernel, name="GPR1")
Z, _ = gp1.model()
gp2 = VariationalSparseGP(Z,
y2D,
Matern32(input_dim=3),
Z.clone(),
likelihood,
name="GPR2")
def model():
Z, _ = gp1.model()
gp2.set_data(Z, y2D)
gp2.model()
def guide():
gp1.guide()
gp2.guide()
svi = SVI(model, guide, optim.Adam({}), Trace_ELBO())
svi.step()
def test_active_dims_disjoint_ok():
k1 = Matern52(2, variance, lengthscale[0], active_dims=[0, 1])
k2 = Matern32(1, variance, lengthscale[0], active_dims=[2])
Sum(k1, k2)
def test_active_dims_overlap_ok():
k1 = Matern52(2, variance, lengthscale[0], active_dims=[0, 1])
k2 = Matern32(2, variance, lengthscale[0], active_dims=[1, 2])
Sum(k1, k2)
lengthscale = torch.tensor([2.0, 1.0, 2.0])
X = torch.tensor([[1.0, 0.0, 1.0], [2.0, 1.0, 3.0]])
Z = torch.tensor([[4.0, 5.0, 6.0], [3.0, 1.0, 7.0], [3.0, 1.0, 2.0]])
TEST_CASES = [
T(Constant(3, variance), X=X, Z=Z, K_sum=18),
T(
Brownian(1, variance),
# only work on 1D input
X=X[:, 0],
Z=Z[:, 0],
K_sum=27),
T(Cosine(3, variance, lengthscale), X=X, Z=Z, K_sum=-0.193233),
T(Linear(3, variance), X=X, Z=Z, K_sum=291),
T(Exponential(3, variance, lengthscale), X=X, Z=Z, K_sum=2.685679),
T(Matern32(3, variance, lengthscale), X=X, Z=Z, K_sum=3.229314),
T(Matern52(3, variance, lengthscale), X=X, Z=Z, K_sum=3.391847),
T(Periodic(3, variance, lengthscale, period=torch.ones(1)),
X=X,
Z=Z,
K_sum=18),
T(Polynomial(3, variance, degree=2), X=X, Z=Z, K_sum=7017),
T(RationalQuadratic(3, variance, lengthscale, scale_mixture=torch.ones(1)),
X=X,
Z=Z,
K_sum=5.684670),
T(RBF(3, variance, lengthscale), X=X, Z=Z, K_sum=3.681117),
T(WhiteNoise(3, variance, lengthscale), X=X, Z=Z, K_sum=0),
T(WhiteNoise(3, variance, lengthscale), X=X, Z=None, K_sum=6),
T(
Coregionalize(3, components=torch.eye(3, 3)),
X=X[:, 0], Z=Z[:, 0], K_sum=27
),
T(
Cosine(3, variance, lengthscale),
X=X, Z=Z, K_sum=-0.193233
),
T(
Linear(3, variance),
X=X, Z=Z, K_sum=291
),
T(
Exponential(3, variance, lengthscale),
X=X, Z=Z, K_sum=2.685679
),
T(
Matern32(3, variance, lengthscale),
X=X, Z=Z, K_sum=3.229314
),
T(
Matern52(3, variance, lengthscale),
X=X, Z=Z, K_sum=3.391847
),
T(
Periodic(3, variance, lengthscale, period=torch.ones(1)),
X=X, Z=Z, K_sum=18
),
T(
Polynomial(3, variance, degree=2),
X=X, Z=Z, K_sum=7017
),
T(