def test_active_dims_disjoint_ok():
k1 = Matern52(2, variance, lengthscale[0], active_dims=[0, 1])
k2 = Matern32(1, variance, lengthscale[0], active_dims=[2])
Sum(k1, k2)
def test_active_dims_overlap_ok():
k1 = Matern52(2, variance, lengthscale[0], active_dims=[0, 1])
k2 = Matern32(2, variance, lengthscale[0], active_dims=[1, 2])
Sum(k1, k2)
from collections import namedtuple
import pytest
import torch
import pyro
from pyro.contrib.gp.kernels import Matern52, WhiteNoise
from pyro.contrib.gp.util import conditional
from tests.common import assert_equal
T = namedtuple("TestConditional",
["Xnew", "X", "kernel", "f_loc", "f_scale_tril", "loc", "cov"])
Xnew = torch.tensor([[2., 3.], [4., 6.]])
X = torch.tensor([[1., 5.], [2., 1.], [3., 2.]])
kernel = Matern52(input_dim=2)
Kff = kernel(X) + torch.eye(3) * 1e-6
Lff = Kff.potrf(upper=False)
pyro.set_rng_seed(123)
f_loc = torch.rand(3)
f_scale_tril = torch.rand(3, 3).tril(-1) + torch.rand(3).exp().diag()
f_cov = f_scale_tril.matmul(f_scale_tril.t())
TEST_CASES = [
T(Xnew, X, kernel, torch.zeros(3), Lff, torch.zeros(2), None),
T(Xnew, X, kernel, torch.zeros(3), None, torch.zeros(2), None),
T(Xnew, X, kernel, f_loc, Lff, None, kernel(Xnew)),
T(X, X, kernel, f_loc, f_scale_tril, f_loc, f_cov),
T(X, X, kernel, f_loc, None, f_loc, torch.zeros(3, 3)),
T(Xnew, X, WhiteNoise(input_dim=2), f_loc, f_scale_tril, torch.zeros(2),
torch.eye(2)),
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),
T(WhiteNoise(3, variance, lengthscale), X=X, Z=None, K_sum=6),
T(
Coregionalize(3, components=torch.eye(3, 3)),
X=torch.tensor([[1., 0., 0.], [0.5, 0., 0.5]]),
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(