KappaFilter

class braintrace.KappaFilter(init_value, kappa)

Low-pass filter helper state.

EProp no longer uses this class directly — it filters the eligibility trace internally instead. KappaFilter remains public and available for user-side filtering of an output-side (or any other) signal outside the algorithm’s own hooks.

The filter smooths the signal following \(x_{\mathrm{filt}} \leftarrow (1-\kappa) \cdot x + \kappa \cdot x_{\mathrm{filt}}\).

Parameters:

• init_value (Any) – Initial value; also dictates the shape and dtype of the filtered state.

• kappa (float) – Decay factor \(\kappa\) in [0, 1). A value of 0 disables filtering.

Raises:

ValueError – If kappa is not inside the half-open interval [0, 1).

Examples

>>> import jax.numpy as jnp
>>> import braintrace
>>> filt = braintrace.KappaFilter(jnp.zeros(3), kappa=0.5)
>>> out = filt.update(jnp.ones(3))
>>> print(out)
[0.5 0.5 0.5]
>>> out = filt.update(jnp.ones(3))
>>> print(out)
[0.75 0.75 0.75]

update(x)

Apply one low-pass step \(x_{\mathrm{filt}} \leftarrow (1-\kappa) x + \kappa\, x_{\mathrm{filt}}\).

Parameters:

x (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – The new input mixed into the filtered state.

Returns:

The updated, filtered value.

Return type:

Any