- tedica(data, n_components, fixed_seed, maxit=500, maxrestart=10)
Perform ICA on data and returns mixing matrix
data ((S x T)
numpy.ndarray) – Dimensionally reduced optimally combined functional data, where S is samples and T is time
int) – Number of components retained from PCA decomposition
int) – Seed for ensuring reproducibility of ICA results
int, optional) – Maximum number of iterations for ICA. Default is 500.
int, optional) – Maximum number of attempted decompositions to perform with different random seeds. ICA will stop running if there is convergence prior to reaching this limit. Default is 10.
Uses sklearn implementation of FastICA for decomposition