multiviewica.permica¶
- multiviewica.permica(X, n_components=None, dimension_reduction='pca', max_iter=1000, random_state=None, tol=1e-07)¶
Performs one ICA per group (ex: subject) and align sources using the hungarian algorithm.
- Parameters
- Xnp array of shape (n_groups, n_features, n_samples)
Training vector, where n_groups is the number of groups, n_samples is the number of samples and n_components is the number of components.
- n_componentsint, optional
Number of components to extract. If None, no dimension reduction is performed
- dimension_reduction: str, optional
if srm: use srm to reduce the data if pca: use group specific pca to reduce the data
- max_iterint, optional
Maximum number of iterations to perform
- random_stateint, RandomState instance or None, optional (default=None)
Used to perform a random initialization. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
- tolfloat, optional
A positive scalar giving the tolerance at which the un-mixing matrices are considered to have converged.
- Returns
- Pnp array of shape (n_groups, n_components, n_features)
K is the projection matrix that projects data in reduced space
- Wnp array of shape (n_groups, n_components, n_components)
Estimated un-mixing matrices
- Snp array of shape (n_components, n_samples)
Estimated source
See also