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readout

eigenkit.reservoir.readout

A pure-numpy ridge (Tikhonov) linear readout — the classic QRC head.

Frozen-reservoir QRC trains only the readout, and the canonical readout is a linear layer fit by ridge regression. That has a closed-form solution, so no iterative optimizer (and no torch/sklearn) is needed — this stays framework-free and lives in the numpy domain ring. For a trainable non-linear readout, use the estimator layer (an MLP head), not this.

LinearReadout

Ridge regression in closed form, with a bias term.

It solves W = (XᵀX + λI)⁻¹ XᵀY, and the bias column is not regularised. Y can have one output or several. ridge is the strength λ of the L2 penalty, which adds λ‖W‖² to the squared error of the fit. A small positive value also keeps the solution numerically stable when the reservoir features are collinear.

Parameters:

Name	Type	Description	Default
ridge	float	The strength λ of the L2 penalty. Raise it when the readout overfits or the feature matrix is nearly singular, and lower it when the readout underfits. The default is 1e-06.	1e-06

fit(X, y)

Fit the readout to X and y.

Parameters:

Name	Type	Description	Default
X	ndarray	The samples, of shape (n_samples, n_features).	required
y	ndarray	The targets, of shape (n_samples,) for one output, or (n_samples, n_outputs) for several.	required

Returns:

Type	Description
'LinearReadout'	This readout, so that calls can be chained.

predict(X)

Predict the target for every sample in X.

Parameters:

Name	Type	Description	Default
X	ndarray	The samples, of shape (n_samples, n_features).	required

Returns:

Type	Description
ndarray	One prediction per sample, with one column per output when the readout was fitted on several outputs.