def test_shape(dim):
x = jnp.linspace(-1.0, 1.0, num=10).reshape(-1, 1)
if dim > 1:
x = jnp.hstack([x] * dim)
meanf = Zero()
mu = meanf(x)
assert mu.shape[0] == x.shape[0]
assert mu.shape[1] == 1
def __init__(
self,
kernel: Kernel,
likelihood: Likelihood,
mean_function: Optional[MeanFunction] = None,
num_latent_gps: int = None,
jitter=1e-6,
):
assert (num_latent_gps
is not None), "GP requires specification of num_latent_gps"
self.num_latent_gps = num_latent_gps
self.kernel = kernel
self.likelihood = likelihood
if mean_function is None:
mean_function = Zero()
self.mean_function = mean_function
self.jitter = jitter
def test_initialisers():
params = initialise(Zero())
assert not params
def test_hyperparametr_initialise():
params = _initialise_hyperparams(RBF(), Zero())
assert list(params.keys()) == sorted(["lengthscale", "variance"])
class Datum:
input_dim, output_dim = 3, 2
N, Ntest, M = 20, 30, 10
# Constant(c=jax.random.normal(key, shape=(Datum.output_dim,))),
class Data:
x1 = jnp.linspace(0, 10, 20).reshape(10, 2)
_mean_functions = [
Zero(),
# Linear(
# A=rng.randn(Datum.input_dim, Datum.output_dim),
# b=rng.randn(Datum.output_dim, 1).reshape(-1),
# ),
Constant(c=jax.random.normal(key, shape=(Datum.output_dim,))),
]
@pytest.mark.parametrize("mean_function_1", _mean_functions)
@pytest.mark.parametrize("mean_function_2", _mean_functions)
@pytest.mark.parametrize("operation", ["+", "*"])
def test_mean_functions_output_shape(
mean_function_1, mean_function_2, operation
):
"""