pyFDN.Match

class pyFDN.Match(target, feature, distance=<pyFDN.train.losses.distances.SquaredError object>, reduction=<pyFDN.train.losses.reductions.Mean object>)

Compares a model’s impulse response to a target using three steps:

1. Feature: Extracts representations from both the model response and the target (e.g., raw waveform, magnitude spectrum, or phase). 2. Distance: Computes the differences between the extracted features (e.g., squared error or circular phase difference). 3. Reduction: Aggregates the elementwise differences into a scalar loss tensor (e.g., mean or sum).

The reference target is aligned in length, shape, device, and dtype using _CachedTarget, and its feature is computed once and cached until the loss is called on a response of a different shape, device, dtype or sample rate.

Without a target (target=None) the distance is called with an all-zeros tensor in place of the reference feature; targetless losses use a distance that compares against a constant of its own and ignores it.

Parameters:

• target (Any | None) – Reference impulse response (NumPy array, PyTorch tensor, or list), or None.

• feature (Feature | Callable[[Tensor, float], Tensor]) – Extracts the representation to compare: (h, fs) -> torch.Tensor.

• distance (Distance | Callable[[Tensor, Tensor], Tensor]) – Calculates error between two features: (pred, target) -> torch.Tensor.

• reduction (Reduction | Callable[[Tensor], Tensor]) – Turns the error tensor into a single scalar: (diff) -> torch.Tensor.

__init__(target, feature, distance=<pyFDN.train.losses.distances.SquaredError object>, reduction=<pyFDN.train.losses.reductions.Mean object>)

Methods

__init__(target, feature[, distance, reduction])
check(model)	Preflight against the model it will train, before the first step.
terms()	Flatten into (weight, loss) leaves.

Attributes

name	Short label, used as the key in pyFDN.TrainLog.loss_log.