pyriemann_qiskit.optimization.mean.qmean_logeuclid

pyriemann_qiskit.optimization.mean.qmean_logeuclid(X, sample_weight=None, optimizer=<pyriemann_qiskit.optimization.docplex.ClassicalOptimizer object>)[source]

Log-Euclidean mean with Constraint Programming Model.

Constraint Programming Model (CPM) [2] formulation of the mean with Log-Euclidean distance [1].

Parameters:

  • X (ndarray, shape (n_matrices, n_channels, n_channels)) – Set of SPD matrices.

  • sample_weights (None | ndarray, shape (n_matrices,), default=None) – Weights for each matrix. Never used in practice. It is kept only for standardization with pyRiemann.

  • optimizer (pyQiskitOptimizer, default=ClassicalOptimizer()) – An instance of pyriemann_qiskit.optimization.docplex.pyQiskitOptimizer.

Returns:

mean – CPM-optimized Log-Euclidean mean.

Return type:

ndarray, shape (n_channels, n_channels)

Notes

Added in version 0.2.0.

References

[1]

Geometric means in a novel vector space structure on symmetric positive-definite matrices V. Arsigny, P. Fillard, X. Pennec, and N. Ayache. SIAM Journal on Matrix Analysis and Applications. Volume 29, Issue 1 (2007).