Note
This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.
3.3.4. fmralign.alignment_methods.RidgeAlignment¶
- class fmralign.alignment_methods.RidgeAlignment(alphas=[0.1, 1.0, 10.0, 100, 1000], cv=4)[source]¶
Compute a scikit-estimator R using a mixing matrix M s.t Frobenius norm || XM - Y ||^2 + alpha * ||M||^2 is minimized with cross-validation
- Parameters:
- Rscikit-estimator from sklearn.linear_model.RidgeCV
with methods fit, predict
- alphanumpy array of shape [n_alphas]
Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to
C^-1
in other models such as LogisticRegression or LinearSVC.- cvint, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy. Possible inputs for cv are: -None, to use the efficient Leave-One-Out cross-validation - integer, to specify the number of folds. - An object to be used as a cross-validation generator. - An iterable yielding train/test splits.