def test_mean_function_SGPR_FITC():
X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function()
Xu = X[::20].clone()
model = SparseGPRegression(X,
y,
kernel,
Xu,
mean_function=mean_fn,
approx="FITC")
model.optimize(optim.Adam({"lr": 0.01}))
_post_test_mean_function(model, Xnew, ynew)
def test_inference_sgpr():
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)
sgpr = SparseGPRegression(X, y, kernel, Xu)
sgpr.optimize(optim.Adam({"lr": 0.01}), num_steps=1000)
Xnew = torch.arange(0, 5.05, 0.05)
loc, var = sgpr(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_sgpr():
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)
sgpr = SparseGPRegression(X, y, kernel, Xu)
sgpr.optimize(optim.Adam({"lr": 0.01}), num_steps=1000)
Xnew = torch.arange(0, 5.05, 0.05)
loc, var = sgpr(Xnew, full_cov=False)
target = 0.5 * torch.sin(3 * Xnew)
assert_equal((loc - target).abs().mean().item(), 0, prec=0.07)
def test_mean_function_SGPR_FITC():
X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function()
Xu = X[::20].clone()
model = SparseGPRegression(X, y, kernel, Xu, mean_function=mean_fn, approx="FITC")
model.optimize(optim.Adam({"lr": 0.01}))
_post_test_mean_function(model, Xnew, ynew)
