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]#