idmtools_calibra.algorithms.optim_tools_pspo module#

class idmtools_calibra.algorithms.optim_tools_pspo.OptimToolPSPO(params, constrain_sample_fn, comps_per_iteration=10)[source]#

Bases: NextPointAlgorithm

OptimTool

The basic idea of OptimTool is

cleanup()[source]#

resolve_args(iteration)[source]#

add_samples(samples, iteration)[source]#

get_samples_for_iteration(iteration)[source]#

clamp(X)[source]#

set_results_for_iteration(iteration, results)[source]#

choose_initial_samples()[source]#

stochastic_newton_raphson(iteration)[source]#

choose_and_clamp_samples_for_iteration(iteration)[source]#

sample_simultaneous_perturbation(M, iteration, state)[source]#

end_condition()[source]#

get_final_samples()[source]#

prep_for_dict(df)[source]#: Utility function allowing to transform a DataFrame into a dict removing null values

get_state()[source]#

set_state(state, iteration)[source]#

get_param_names()[source]#