def test_mean_function_VSGP():
X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function()
Xu = X[::20].clone()
likelihood = Gaussian()
model = VariationalSparseGP(X, y, kernel, Xu, likelihood, mean_function=mean_fn)
model.optimize(optim.Adam({"lr": 0.02}))
_post_test_mean_function(model, Xnew, ynew)
def test_mean_function_VSGP():
X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function()
Xu = X[::20].clone()
likelihood = Gaussian()
model = VariationalSparseGP(X,
y,
kernel,
Xu,
likelihood,
mean_function=mean_fn)
model.optimize(optim.Adam({"lr": 0.02}))
_post_test_mean_function(model, Xnew, ynew)
def test_inference_whiten_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(), whiten=True)
vsgp.optimize(optim.Adam({"lr": 0.01}), num_steps=1000)
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=0.07)
def test_inference_whiten_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(), whiten=True)
vsgp.optimize(optim.Adam({"lr": 0.01}), num_steps=1000)
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=0.07)