def test_obs(x):
prior = Measure()
f = GP(EQ(), measure=prior)
noise = 0.1
# Generate some data.
w = B.rand(B.shape(x)[0]) + 1e-2
y = f(x, 0.1).sample()
# Set some observations to be missing.
y_missing = y.copy()
y_missing[::2] = np.nan
# Check dense case.
gpar = GPAR()
obs = gpar._obs(x, None, y_missing, w, f, noise)
assert isinstance(obs, Obs)
approx(
prior.logpdf(obs),
f(x[1::2], noise / w[1::2]).logpdf(y[1::2]),
atol=1e-6,
)
# Check sparse case.
gpar = GPAR(x_ind=x)
obs = gpar._obs(x, x, y_missing, w, f, noise)
assert isinstance(obs, SparseObs)
approx(
prior.logpdf(obs),
f(x[1::2], noise / w[1::2]).logpdf(y[1::2]),
atol=1e-6,
)
def test_obs():
graph = Graph()
f = GP(EQ(), graph=graph)
e = GP(1e-8 * Delta(), graph=graph)
# Check that it produces the correct observations.
x = B.linspace(0, 0.1, 10, dtype=torch.float64)
y = f(x).sample()
# Set some observations to be missing.
y_missing = y.clone()
y_missing[::2] = np.nan
# Check dense case.
gpar = GPAR()
obs = gpar._obs(x, None, y_missing, f, e)
yield eq, type(obs), Obs
yield approx, y, (f | obs).mean(x)
# Check sparse case.
gpar = GPAR(x_ind=x)
obs = gpar._obs(x, x, y_missing, f, e)
yield eq, type(obs), SparseObs
yield approx, y, (f | obs).mean(x)