pybop._classification#

Functions#

classify_using_hessian(result[, dx, cost_tolerance])

A simple check for parameter correlations based on numerical approximation

Module Contents#

pybop._classification.classify_using_hessian(result: pybop.OptimisationResult, dx=None, cost_tolerance: float | None = 1e-05)[source]#

A simple check for parameter correlations based on numerical approximation of the Hessian matrix at the optimal point using central finite differences.

Parameters:

  • result (OptimisationResult) – The PyBOP optimisation results.

  • dx (array-like, optional) – An array of small positive values used to check proximity to the parameter bounds and as the perturbation distance in the finite difference calculations.

  • cost_tolerance (float, optional) – A small positive tolerance used for cost value comparisons (default: 1e-5).