def test_mean_wrong_input_shape_raises_error():
with pytest.raises(ValueError):
randprocs.GaussianProcess(
mean=mean_fns.Zero(input_shape=(2, 2), output_shape=(1, )),
cov=kernels.ExpQuad(input_shape=(2, )),
)
with pytest.raises(ValueError):
randprocs.GaussianProcess(
mean=mean_fns.Zero(input_shape=(2, ), output_shape=(2, 1)),
cov=kernels.ExpQuad(input_shape=(2, )),
)
def test_nu_large_recovers_rbf_kernel(x0: np.ndarray, x1: np.ndarray,
input_shape: ShapeType):
"""Test whether a Matern kernel with nu large is close to an RBF kernel."""
lengthscale = 1.25
rbf = kernels.ExpQuad(input_shape=input_shape, lengthscale=lengthscale)
matern = kernels.Matern(input_shape=input_shape,
lengthscale=lengthscale,
nu=15)
np.testing.assert_allclose(
rbf.matrix(x0, x1),
matern.matrix(x0, x1),
err_msg=
"RBF and Matern kernel are not sufficiently close for nu->infty.",
rtol=0.05,
atol=0.01,
)
def get_kernel(kernel_name, input_shape):
"""Return a kernel for a given name."""
if kernel_name == "white_noise":
kernel = kernels.WhiteNoise(input_shape=input_shape)
elif kernel_name == "linear":
kernel = kernels.Linear(input_shape=input_shape)
elif kernel_name == "polynomial":
kernel = kernels.Polynomial(input_shape=input_shape)
elif kernel_name == "exp_quad":
kernel = kernels.ExpQuad(input_shape=input_shape)
elif kernel_name == "rat_quad":
kernel = kernels.RatQuad(input_shape=input_shape)
elif kernel_name == "matern12":
kernel = kernels.Matern(input_shape=input_shape, nu=0.5)
elif kernel_name == "matern32":
kernel = kernels.Matern(input_shape=input_shape, nu=1.5)
elif kernel_name == "matern52":
kernel = kernels.Matern(input_shape=input_shape, nu=2.5)
elif kernel_name == "matern72":
kernel = kernels.Matern(input_shape=input_shape, nu=3.5)
else:
raise ValueError(f"Kernel name '{kernel_name}' not recognized.")
return kernel
def case_kernel_matrix(n: int, rng: np.random.Generator) -> np.ndarray:
"""Kernel Gram matrix."""
x_min, x_max = (-4.0, 4.0)
X = rng.uniform(x_min, x_max, (n, 1))
kern = kernels.ExpQuad(input_shape=1, lengthscale=1)
return kern(X)
def test_mean_not_function_raises_error():
with pytest.raises(TypeError):
randprocs.GaussianProcess(
mean=np.zeros_like,
cov=kernels.ExpQuad(input_shape=(1, )),
)
def test_product_kernel_contracts():
input_shape = ()
k = kernels.ExpQuad(input_shape=input_shape)
k_prod = ProductKernel(k, ProductKernel(k, k))
assert all(not isinstance(factor, ProductKernel)
for factor in k_prod._factors)
def test_sum_kernel_contracts():
input_shape = ()
k = kernels.ExpQuad(input_shape=input_shape)
k_sum = SumKernel(k, SumKernel(k, k))
assert all(not isinstance(summand, SumKernel)
for summand in k_sum._summands)