idmtools_calibra.algorithms.optim_tool module

class idmtools_calibra.algorithms.optim_tool.OptimTool(params, constrain_sample_fn=<function OptimTool.<lambda>>, mu_r=0.1, sigma_r=0.02, center_repeats=2, samples_per_iteration=100.0, rsquared_thresh=0.5)

Bases: NextPointAlgorithm

The basic idea of OptimTool is

cleanup()

verify_param()

resolve_args(iteration)

add_samples(samples, iteration)

get_samples_for_iteration(iteration)

clamp(X)

set_results_for_iteration(iteration, results)

choose_initial_samples()

choose_samples_via_gradient_ascent(iteration)

choose_and_clamp_hypersphere_samples_for_iteration(iteration)

sample_hypersphere(N, state)

end_condition()

get_final_samples()

Resample Stage:

prep_for_dict(df)

Utility function allowing to transform a DataFrame into a dict removing null values

get_state()

set_state(state, iteration)

get_param_names()

static get_r(num_params, volume_fraction)