fit_readout#

torchdyno.optim.ridge_regression.fit_readout(train_loader, preprocess_fn=None, l2=None, weights=None, skip_first_n=0, device='cpu')[source]#

Applies the ridge regression on the training data with all the given l2 values and returns a list of matrices, one for each L2 value.

Parameters:

  • train_loader (DataLoader) – DataLoader of the training data.

  • preprocess_fn (Optional[Callable], optional) – a transformation to be applied to X before the linear transformation. Useful whenever this function is called to learn a Readout of a ESN. Defaults to None.

  • l2_values (List[float]) – List of all the candidate L2 values.

  • weights (Optional[List[float]], optional) – list of weights to be applied to each sample in the batch. Defaults to None.

  • skip_first_n (Optional[int], optional) – number of samples to skip in each batch of the train_loader. Defaults to None.

  • device (Optional[str], optional) – the device on which the function is executed. If None, the function is executed on a CUDA device if available, on CPU otherwise. Defaults to None.

  • l2 (float | List[float] | None)

Returns:

a Tuple containing the best linear matrix, the

corrisponding l2 value and the metric value.

Return type:

Tuple[Tensor, float, float]