def test_computes_exponential_decay_function(self):
a = torch.tensor([1.0, 2.0]).view(2, 1)
b = torch.tensor([2.0, 4.0]).view(2, 1)
lengthscale = 1
power = 1
offset = 1
kernel = ExponentialDecayKernel()
kernel.initialize(lengthscale=lengthscale, power=power, offset=offset)
kernel.eval()
diff = torch.tensor([[4.0, 6.0], [5.0, 7.0]])
actual = offset + torch.tensor([1.0]).div(diff.pow(power))
res = kernel(a, b).evaluate()
self.assertLess(torch.norm(res - actual), 1e-5)
def test_subset_active_compute_exponential_decay_function(self):
a = torch.tensor([1.0, 2.0]).view(2, 1)
a_p = torch.tensor([3.0, 4.0]).view(2, 1)
a = torch.cat((a, a_p), 1)
b = torch.tensor([2.0, 4.0]).view(2, 1)
lengthscale = 1
power = 1
offset = 1
kernel = ExponentialDecayKernel(active_dims=[0])
kernel.initialize(lengthscale=lengthscale, power=power, offset=offset)
kernel.eval()
diff = torch.tensor([[4.0, 6.0], [5.0, 7.0]])
actual = offset + diff.pow(-power)
res = kernel(a, b).evaluate()
self.assertLess(torch.norm(res - actual), 1e-5)
def test_computes_exponential_decay_function_batch(self):
a = torch.tensor([[1.0, 2.0], [3.0, 4.0]]).view(2, 2, 1)
b = torch.tensor([[5.0, 6.0], [7.0, 8.0]]).view(2, 2, 1)
lengthscale = 1
power = 1
offset = 1
kernel = ExponentialDecayKernel(batch_shape=torch.Size([2]))
kernel.initialize(lengthscale=lengthscale, power=power, offset=offset)
kernel.eval()
actual = torch.zeros(2, 2, 2)
diff = torch.tensor([[7.0, 8.0], [8.0, 9.0]])
actual[0, :, :] = offset + diff.pow(-power)
diff = torch.tensor([[11.0, 12.0], [12.0, 13.0]])
actual[1, :, :] = offset + diff.pow(-power)
res = kernel(a, b).evaluate()
self.assertLess(torch.norm(res - actual), 1e-5)