def test_computes_diag_eval(self):
a = torch.tensor([4, 2, 8], dtype=torch.float).view(3, 1)
variances = torch.randn(3)
kernel = WhiteNoiseKernel(variances=variances)
kernel.eval()
actual = torch.diag(variances)
res = kernel(a).evaluate()
self.assertLess(torch.norm(res - actual), 1e-5)
def test_computes_diag_eval_batch(self):
a = torch.Tensor([[4, 2, 8], [4, 2, 8]]).view(2, 3, 1)
variances = torch.randn(2, 3, 1)
kernel = WhiteNoiseKernel(variances=variances)
kernel.eval()
actual = torch.cat((torch.diag(variances[0].squeeze(-1)).unsqueeze(0),
torch.diag(variances[1].squeeze(-1)).unsqueeze(0)))
res = kernel(a).evaluate()
self.assertLess(torch.norm(res - actual), 1e-5)
def test_computes_zero_eval(self):
a = torch.tensor([4, 2, 8], dtype=torch.float).view(3, 1)
b = torch.tensor([3, 7], dtype=torch.float).view(2, 1)
variances = torch.randn(3)
kernel = WhiteNoiseKernel(variances=variances)
kernel.eval()
actual_one = torch.zeros(3, 2)
actual_two = torch.zeros(2, 3)
res_one = kernel(a, b).evaluate()
res_two = kernel(b, a).evaluate()
self.assertLess(torch.norm(res_one - actual_one), 1e-5)
self.assertLess(torch.norm(res_two - actual_two), 1e-5)
