pyriemann_qiskit.optimization.distance.weights_logeuclid_to_convex_hull¶
- pyriemann_qiskit.optimization.distance.weights_logeuclid_to_convex_hull(A, B, optimizer=<pyriemann_qiskit.optimization.docplex.ClassicalOptimizer object>)[source]¶
Weights for Log-Euclidean distance to a convex hull of SPD matrices.
Weights for Log-Euclidean distance between a SPD matrix B and the convex hull of a set of SPD matrices A [1], formulated as a Constraint Programming Model (CPM) [2].
- Parameters:
A (ndarray, shape (n_matrices, n_channels, n_channels)) – Set of SPD matrices.
B (ndarray, shape (n_channels, n_channels)) – SPD matrix.
optimizer (pyQiskitOptimizer, default=ClassicalOptimizer()) – An instance of
pyriemann_qiskit.optimization.docplex.pyQiskitOptimizer.
- Returns:
weights – Optimized weights for the set of SPD matrices A. Using these weights, the weighted Log-Euclidean mean of set A provides the matrix of the convex hull closest to matrix B.
- Return type:
ndarray, shape (n_matrices,)
Notes
Added in version 0.0.4.
Changed in version 0.2.0: Rename from logeucl_dist_convex to weights_logeuclid_to_convex_hull. Add linear constraint on weights (sum = 1).
References
[1]K. Zhao, A. Wiliem, S. Chen, and B. C. Lovell, ‘Convex Class Model on Symmetric Positive Definite Manifolds’, Image and Vision Computing, 2019.