def test_compute_linear_truncated_kernel_no_batch(self):
x1 = torch.tensor([1, 0.1, 0.2, 2, 0.3, 0.4],
dtype=torch.float).view(2, 3)
x2 = torch.tensor([3, 0.5, 0.6, 4, 0.7, 0.8],
dtype=torch.float).view(2, 3)
t_1 = torch.tensor([0.3584, 0.1856, 0.2976, 0.1584],
dtype=torch.float).view(2, 2)
for nu in {0.5, 1.5, 2.5}:
for train_data_fidelity in {False, True}:
kernel = LinearTruncatedFidelityKernel(
nu=nu,
dimension=3,
train_data_fidelity=train_data_fidelity)
kernel.power = 1
if train_data_fidelity:
active_dimsM = [0]
t_2 = torch.tensor([0.4725, 0.2889, 0.4025, 0.2541],
dtype=torch.float).view(2, 2)
t_3 = torch.tensor([0.1685, 0.0531, 0.1168, 0.0386],
dtype=torch.float).view(2, 2)
t = 1 + t_1 + t_2 + t_3
else:
active_dimsM = [0, 1]
t = 1 + t_1
matern_ker = MaternKernel(nu=nu, active_dims=active_dimsM)
matern_term = matern_ker(x1, x2).evaluate()
actual = t * matern_term
res = kernel(x1, x2).evaluate()
self.assertLess(torch.norm(res - actual), 1e-4)
def test_compute_linear_truncated_kernel_with_batch(self):
x1 = torch.tensor([1, 0.1, 0.2, 3, 0.3, 0.4, 5, 0.5, 0.6, 7, 0.7, 0.8],
dtype=torch.float).view(2, 2, 3)
x2 = torch.tensor([2, 0.8, 0.7, 4, 0.6, 0.5, 6, 0.4, 0.3, 8, 0.2, 0.1],
dtype=torch.float).view(2, 2, 3)
t_1 = torch.tensor(
[0.2736, 0.44, 0.2304, 0.36, 0.3304, 0.3816, 0.1736, 0.1944],
dtype=torch.float,
).view(2, 2, 2)
batch_shape = torch.Size([2])
dimension = 3
for nu in {0.5, 1.5, 2.5}:
for train_data_fidelity in {False, True}:
kernel = LinearTruncatedFidelityKernel(
nu=nu,
dimension=dimension,
train_data_fidelity=train_data_fidelity,
batch_shape=batch_shape,
)
kernel.power = 1
kernel.train_data_fidelity = train_data_fidelity
if train_data_fidelity:
active_dimsM = [0]
t_2 = torch.tensor(
[
0.0527, 0.167, 0.0383, 0.1159, 0.1159, 0.167,
0.0383, 0.0527
],
dtype=torch.float,
).view(2, 2, 2)
t_3 = torch.tensor(
[
0.1944, 0.3816, 0.1736, 0.3304, 0.36, 0.44, 0.2304,
0.2736
],
dtype=torch.float,
).view(2, 2, 2)
t = 1 + t_1 + t_2 + t_3
else:
active_dimsM = [0, 1]
t = 1 + t_1
matern_ker = MaternKernel(nu=nu,
active_dims=active_dimsM,
batch_shape=batch_shape)
matern_term = matern_ker(x1, x2).evaluate()
actual = t * matern_term
res = kernel(x1, x2).evaluate()
self.assertLess(torch.norm(res - actual), 1e-4)