Optimization

>>> import cr_bayesian_optim as crb
>>> options = crb.Options()
>>> dim, dim_err = crb.optimization.rhs_fractal_dim(options)

callback_diffevol(intermediate_result)

load_optimization_result(path='', add_filename='')

optimization_bayes(cost, bnds, args=(), workers=-1)

optimization_diff_evolution(cost, bnds, args=(), workers=-1)

rhs_fractal_dim(options: Options) → tuple[float, float]

Parameters:

options (Options) – Options to run/load the branching simulation. Be aware that the storage_location should be set to None since otherwise, many disk space would be used (and probably not reused if running optimization again).

Returns:

fractal_dim (float) – The fractal dimension of the last iteration.
fractal_dim_err (float) – The uncertainty of the fractal dimension.

save_optimization_result(res, path='', add_filename='')