PeriodicKernel

class pmrf.discrepancy_models.kernels.PeriodicKernel(period: Any = 1.0, length_scale: Any = 1.0, *, name: str | None = None)

Bases: CovarianceKernel

Periodic (Exp-Sine-Squared) kernel.

Models functions that repeat over a specific period.

Variables:

  • period (prx.Parameter) – The period of the kernel, dictating the distance between repetitions (default 1.0).

  • length_scale (prx.Parameter) – Characteristic length scale of the correlation (default 1.0).

__call__(x1, x2, key=None)

Evaluate the kernel between two points.

Parameters:

  • x1 (jnp.ndarray) – First input point.

  • x2 (jnp.ndarray) – Second input point.

  • key (jax.random.PRNGKey, optional) – Random key for stochastic kernels.

Returns:

Kernel covariance scalar.

Return type:

jnp.ndarray

length_scale: Parameter = 1.0
period: Parameter = 1.0