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)

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.

__init__(alphas=[0.1, 1.0, 10.0, 100, 1000], cv=4)

fit(X, Y)

Fit R s.t. || XR - Y ||^2 + alpha ||R||^2 is minimized with cv

Parameters:

X: (n_samples, n_features) nd array

source data

Y: (n_samples, n_features) nd array

target data

transform(X)

Transform X using optimal transform computed during fit.