pyriemann.utils.ajd.rjd¶

pyriemann.utils.ajd.rjd(X, *, init=None, eps=1e-08, n_iter_max=100)¶

Approximate joint diagonalization based on JADE.

This is an implementation of the orthogonal AJD algorithm [1]: joint approximate diagonalization of eigen-matrices (JADE), based on Jacobi angles. The code is a translation of the Matlab code provided on the author’s website.

Parameters:

Xndarray, shape (n_matrices, n, n): Set of symmetric matrices to diagonalize.
initNone | ndarray, shape (n, n), default=None: Initialization for the diagonalizer.
epsfloat, default=1e-8: Tolerance for stopping criterion.
n_iter_maxint, default=100: The maximum number of iterations to reach convergence.

Returns:

Vndarray, shape (n, n): The diagonalizer, an orthogonal matrix.
Dndarray, shape (n_matrices, n, n): Set of quasi diagonal matrices, D = V^T X V.