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fix: constrain PoweredExponential.power to (0, 2] - #816
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The powered exponential kernel is positive definite in every dimension only for 0 < power <= 2, but any value was accepted: power=3.0 gave a Gram matrix with a minimum eigenvalue of -3.3. A float power in (0, 2) is now a trainable SigmoidBounded parameter, so fit learns it and cannot leave the interval. Before, a float power was a plain Python float that fit did not train, unlike Periodic.period and RationalQuadratic.alpha. power=2.0 (the RBF case) stays fixed, because a bounded parameter cannot sit on its bound. A value outside the interval, or a parameter whose value is outside it, raises ValueError. Fixes #813 Co-Authored-By: Claude Opus 5.5 <[email protected]>
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Summary
PoweredExponential.poweris now constrained toEvidence
tests/test_kernels/test_stationary.py:power=2.0is fixedPositiveReal(3.0)raiseValueErrorpower=2.0still passes.uv run poe test: 3245 passed, 1 skipped. Lint, doctests and the CI docs build (-E -W) pass.Merge Danger
Door: two-way
Blast Radius: small
powerinfit. Before this change,fitdid not train it, because it was a plain Python float. This makes it consistent withPeriodic.periodandRationalQuadratic.alpha, but a model that used to keep its initial power will now learn it. To keep the old behaviour, passparamax.non_trainable(jnp.array(power)).kernel.poweras a float must now useval(kernel.power). Apoweroutsidekernel.power.🤖 Generated with Claude Code