def test_computes_cubic_kernel(self):
a = torch.tensor([[4, 1], [2, 2], [8, 0]], dtype=torch.float)
b = torch.tensor([[0, 0], [2, 1], [1, 0]], dtype=torch.float)
kernel = PolynomialKernel(power=3)
kernel.eval()
actual = torch.zeros(3, 3)
for i in range(3):
for j in range(3):
actual[i, j] = (a[i].matmul(b[j]) + kernel.offset).pow(
kernel.power)
res = kernel(a, b).evaluate()
self.assertLess(torch.norm(res - actual), 1e-5)
# diag
res = kernel(a, b).diag()
actual = actual.diag()
self.assertLess(torch.norm(res - actual), 1e-5)
# batch_dims
actual = torch.zeros(2, 3, 3)
for l in range(2):
actual[l] = kernel(a[:, l].unsqueeze(-1),
b[:, l].unsqueeze(-1)).evaluate()
res = kernel(a, b, last_dim_is_batch=True).evaluate()
self.assertLess(torch.norm(res - actual), 1e-5)
# batch_dims + diag
res = kernel(a, b, last_dim_is_batch=True).diag()
actual = torch.cat(
[actual[i].diag().unsqueeze(0) for i in range(actual.size(0))])
self.assertLess(torch.norm(res - actual), 1e-5)
def test_quadratic_kernel_batch(self):
a = torch.tensor([[4, 2, 8], [1, 2, 3]],
dtype=torch.float).view(2, 3, 1)
b = torch.tensor([[0, 2, 1], [-1, 2, 0]],
dtype=torch.float).view(2, 3, 1)
kernel = PolynomialKernel(power=2, batch_shape=torch.Size(
[2])).initialize(offset=torch.rand(2, 1))
kernel.eval()
actual = torch.zeros(2, 3, 3)
for k in range(2):
for i in range(3):
for j in range(3):
actual[k, i, j] = (a[k, i].matmul(b[k, j]) +
kernel.offset[k]).pow(kernel.power)
res = kernel(a, b).evaluate()
self.assertLess(torch.norm(res - actual), 1e-5)