pyriemann.utils.mean.mean_logdet¶
- pyriemann.utils.mean.mean_logdet(covmats, tol=0.0001, maxiter=50, init=None, sample_weight=None)¶
Mean of SPD matrices according to the log-det metric.
Log-det mean is obtained by an iterative procedure where the update is:
\[\mathbf{C} = \left(\sum_i \left( 0.5 \mathbf{C} + 0.5 \mathbf{C}_i \right)^{-1} \right)^{-1}\]- Parameters
- covmatsndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- tolfloat, default=10e-5
The tolerance to stop the gradient descent.
- maxiterint, default=50
The maximum number of iterations.
- initNone | ndarray, shape (n_channels, n_channels), default=None
A SPD matrix used to initialize the gradient descent. If None, the weighted Euclidean mean is used.
- sample_weightNone | ndarray, shape (n_matrices,), default=None
Weights for each matrix. If None, it uses equal weights.
- Returns
- Cndarray, shape (n_channels, n_channels)
Log-det mean.