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Bugfix powered_exponential.py #279

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merged 4 commits into from
May 30, 2023
Merged

Bugfix powered_exponential.py #279

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9fa0e6e
Bugfix powered_exponential.py
ingmarschuster May 25, 2023
b983e0b
Fixed test
ingmarschuster May 27, 2023
5e6b3cb
Fix focs
thomaspinder May 30, 2023
2b72201
Merge branch 'main' into patch-1
thomaspinder May 30, 2023
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8 changes: 5 additions & 3 deletions gpjax/kernels/stationary/powered_exponential.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,17 +31,19 @@

@dataclass
class PoweredExponential(AbstractKernel):
r"""The powered exponential family of kernels.
r"""The powered exponential family of kernels. This also equivalent to the symmetric generalized normal distribution.

Key reference is Diggle and Ribeiro (2007) - "Model-based Geostatistics".
See Diggle and Ribeiro (2007) - "Model-based Geostatistics".
and
https://en.wikipedia.org/wiki/Generalized_normal_distribution#Symmetric_version

"""

lengthscale: Union[ScalarFloat, Float[Array, " D"]] = param_field(
jnp.array(1.0), bijector=tfb.Softplus()
)
variance: ScalarFloat = param_field(jnp.array(1.0), bijector=tfb.Softplus())
power: ScalarFloat = param_field(jnp.array(1.0))
power: ScalarFloat = param_field(jnp.array(1.0), bijector=tfb.Sigmoid())
name: str = "Powered Exponential"

def __call__(self, x: Float[Array, " D"], y: Float[Array, " D"]) -> ScalarFloat:
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4 changes: 2 additions & 2 deletions tests/test_kernels/test_stationary.py
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Expand Up @@ -108,7 +108,7 @@ def test_initialization(self, fields: dict, dim: int) -> None:
if field in ["variance", "lengthscale", "period", "alpha"]:
assert isinstance(meta[field]["bijector"], tfb.Softplus)
if field in ["power"]:
assert isinstance(meta[field]["bijector"], tfb.Identity)
assert isinstance(meta[field]["bijector"], tfb.Sigmoid)

# Trainability state
assert meta[field]["trainable"] is True
Expand Down Expand Up @@ -225,7 +225,7 @@ class TestPeriodic(BaseTestKernel):
class TestPoweredExponential(BaseTestKernel):
kernel = PoweredExponential
fields = prod(
{"lengthscale": [0.1, 1.0], "variance": [0.1, 1.0], "power": [0.1, 2.0]}
{"lengthscale": [0.1, 1.0], "variance": [0.1, 1.0], "power": [0.1, 0.9]}
)
params = {"test_initialization": fields}
default_compute_engine = DenseKernelComputation
Expand Down