idmtools_calibra.algorithms.optim_tools_spsa module

class idmtools_calibra.algorithms.optim_tools_spsa.OptimToolSPSA(params, constrain_sample_fn, comps_per_iteration=10)[source]

Bases: NextPointAlgorithm

The basic idea of OptimToolSPSA 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, resolution=None)[source]
end_condition()[source]
get_final_samples()[source]

Resample Stage:

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]