def test_mean_function_VSGP():
X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function()
Xu = X[::20].clone()
likelihood = Gaussian()
gpmodule = VariationalSparseGP(X, y, kernel, Xu, likelihood, mean_function=mean_fn)
optimizer = torch.optim.Adam(gpmodule.parameters(), lr=0.02)
train(gpmodule, optimizer)
_post_test_mean_function(gpmodule, Xnew, ynew)
def test_inference_vsgp():
N = 1000
X = dist.Uniform(torch.zeros(N), torch.ones(N)*5).sample()
y = 0.5 * torch.sin(3*X) + dist.Normal(torch.zeros(N), torch.ones(N)*0.5).sample()
kernel = RBF(input_dim=1)
Xu = torch.arange(0., 5.5, 0.5)
vsgp = VariationalSparseGP(X, y, kernel, Xu, Gaussian())
optimizer = torch.optim.Adam(vsgp.parameters(), lr=0.03)
train(vsgp, optimizer)
Xnew = torch.arange(0., 5.05, 0.05)
loc, var = vsgp(Xnew, full_cov=False)
target = 0.5 * torch.sin(3*Xnew)
assert_equal((loc - target).abs().mean().item(), 0, prec=0.06)