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)[source]#

Bases: NextPointAlgorithm

The basic idea of OptimTool is

cleanup()[source]#

verify_param()[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]#

choose_samples_via_gradient_ascent(iteration)[source]#

choose_and_clamp_hypersphere_samples_for_iteration(iteration)[source]#

sample_hypersphere(N, state)[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]#

static get_r(num_params, volume_fraction)[source]#