def _pre_test_mean_function():
def f(x):
return 2 * x + 3 + 5 * torch.sin(7 * x)
X = torch.arange(100)
y = f(X)
Xnew = torch.arange(100, 150)
ynew = f(Xnew)
kernel = Cosine(input_dim=1)
def trend(x):
a = pyro.param("a", torch.tensor(0.))
b = pyro.param("b", torch.tensor(1.))
return a * x + b
return X, y, Xnew, ynew, kernel, trend
def _pre_test_mean_function():
def f(x):
return 2 * x + 3 + 5 * torch.sin(7 * x)
X = torch.arange(100, dtype=torch.Tensor().dtype)
y = f(X)
Xnew = torch.arange(100, 150, dtype=torch.Tensor().dtype)
ynew = f(Xnew)
kernel = Cosine(input_dim=1)
class Trend(torch.nn.Module):
def __init__(self):
super(Trend, self).__init__()
self.a = torch.nn.Parameter(torch.tensor(0.))
self.b = torch.nn.Parameter(torch.tensor(1.))
def forward(self, x):
return self.a * x + self.b
trend = Trend()
return X, y, Xnew, ynew, kernel, trend
T = namedtuple("TestGPKernel", ["kernel", "X", "Z", "K_sum"])
variance = torch.tensor([3.0])
lengthscale = torch.tensor([2.0, 1.0, 2.0])
X = torch.tensor([[1.0, 0.0, 1.0], [2.0, 1.0, 3.0]])
Z = torch.tensor([[4.0, 5.0, 6.0], [3.0, 1.0, 7.0], [3.0, 1.0, 2.0]])
TEST_CASES = [
T(Constant(3, variance), X=X, Z=Z, K_sum=18),
T(
Brownian(1, variance),
# only work on 1D input
X=X[:, 0],
Z=Z[:, 0],
K_sum=27),
T(Cosine(3, variance, lengthscale), X=X, Z=Z, K_sum=-0.193233),
T(Linear(3, variance), X=X, Z=Z, K_sum=291),
T(Exponential(3, variance, lengthscale), X=X, Z=Z, K_sum=2.685679),
T(Matern32(3, variance, lengthscale), X=X, Z=Z, K_sum=3.229314),
T(Matern52(3, variance, lengthscale), X=X, Z=Z, K_sum=3.391847),
T(Periodic(3, variance, lengthscale, period=torch.ones(1)),
X=X,
Z=Z,
K_sum=18),
T(Polynomial(3, variance, degree=2), X=X, Z=Z, K_sum=7017),
T(RationalQuadratic(3, variance, lengthscale, scale_mixture=torch.ones(1)),
X=X,
Z=Z,
K_sum=5.684670),
T(RBF(3, variance, lengthscale), X=X, Z=Z, K_sum=3.681117),
T(WhiteNoise(3, variance, lengthscale), X=X, Z=Z, K_sum=0),
lengthscale = torch.tensor([2.0, 1.0, 2.0])
X = torch.tensor([[1.0, 0.0, 1.0], [2.0, 1.0, 3.0]])
Z = torch.tensor([[4.0, 5.0, 6.0], [3.0, 1.0, 7.0], [3.0, 1.0, 2.0]])
TEST_CASES = [
T(
Constant(3, variance),
X=X, Z=Z, K_sum=18
),
T(
Brownian(1, variance),
# only work on 1D input
X=X[:, 0], Z=Z[:, 0], K_sum=27
),
T(
Cosine(3, variance, lengthscale),
X=X, Z=Z, K_sum=-0.193233
),
T(
Linear(3, variance),
X=X, Z=Z, K_sum=291
),
T(
Exponential(3, variance, lengthscale),
X=X, Z=Z, K_sum=2.685679
),
T(
Matern32(3, variance, lengthscale),
X=X, Z=Z, K_sum=3.229314
),
T(