pyriemann.utils.mean.mean_ale¶
- pyriemann.utils.mean.mean_ale(covmats, tol=1e-06, maxiter=50, sample_weight=None)¶
AJD-based log-Euclidean (ALE) mean of SPD matrices.
Return the mean of a set of SPD matrices using the approximate joint diagonalization (AJD) based log-Euclidean (ALE) mean [1].
- Parameters:
- covmatsndarray, shape (n_matrices, n, n)
Set of SPD matrices.
- tolfloat, default=10e-7
The tolerance to stop the gradient descent.
- maxiterint, default=50
The maximum number of iterations.
- sample_weightNone | ndarray, shape (n_matrices,), default=None
Weights for each matrix. If None, it uses equal weights.
- Returns:
- Cndarray, shape (n, n)
ALE mean.
See also
Notes
New in version 0.2.4.
References
[1]Approximate Joint Diagonalization and Geometric Mean of Symmetric Positive Definite Matrices M. Congedo, B. Afsari, A. Barachant, M. Moakher. PLOS ONE, 2015