bool NMfit::termination_criteria(int iter, realt convergence)
{
if (F_->get_verbosity() >= 1)
F_->ui()->mesg("#" + S(iter_nr_) + " (ev:" + S(evaluations_) + "):"
" best:" + S(best->wssr) +
" worst:" + S(worst->wssr) + ", " + S(s_worst->wssr) +
" [V * |" + S(volume_factor) + "|]");
bool stop = false;
if (volume_factor == 1. && iter != 0) {
F_->msg ("Simplex got stuck.");
stop = true;
}
volume_factor = 1.;
/*DEBUG - BEGIN*
string s = "WSSR:";
for (vector<Vertex>::iterator i = vertices.begin(); i!=vertices.end(); ++i)
s += " " + S(i->wssr);
F_->msg (s);
*DEBUG - END*/
//checking stop conditions
if (common_termination_criteria(iter))
stop = true;
if (is_zero(worst->wssr)) {
F_->msg ("All vertices have WSSR < epsilon=" + S(epsilon));
return true;
}
realt r_diff = 2 * (worst->wssr - best->wssr) / (best->wssr + worst->wssr);
if (r_diff < convergence) {
F_->msg ("Relative difference between worst and best vertex is only "
+ S(r_diff) + ". Stop");
stop = true;
}
return stop;
}
void LMfit::autoiter()
{
wssr_before_ = chi2;
realt prev_chi2 = chi2;
const SettingsMgr *sm = F_->settings_mgr();
if (F_->get_verbosity() >= 1) {
F_->ui()->mesg("\t === Levenberg-Marquardt method ===");
F_->ui()->mesg("Initial values: lambda=" + S(lambda) +
" WSSR=" + sm->format_double(chi2));
F_->ui()->mesg("Max. number of iterations: " + max_iterations_);
}
realt stop_rel = F_->get_settings()->lm_stop_rel_change;
realt max_lambda = F_->get_settings()->lm_max_lambda;
if (stop_rel > 0 && F_->get_verbosity() >= 1)
F_->ui()->mesg("Will stop when relative change of WSSR is "
"twice in row below " + S (stop_rel * 100.) + "%");
int small_change_counter = 0;
for (int iter = 0; !common_termination_criteria(iter); iter++) {
bool better_fit = do_iteration();
if (better_fit) {
realt d = prev_chi2 - chi2;
if (F_->get_verbosity() >= 1)
F_->ui()->mesg("#" + S(iter_nr_) + ":"
" WSSR=" + sm->format_double(chi2) +
" lambda=" + S(lambda) + " d(WSSR)=" + S(-d) +
" (" + S (d / prev_chi2 * 100) + "%)");
// another termination criterium: negligible change of chi2
if (d / prev_chi2 < stop_rel || chi2 == 0) {
small_change_counter++;
if (small_change_counter >= 2 || chi2 == 0) {
F_->msg("... converged.");
break;
}
}
else
small_change_counter = 0;
prev_chi2 = chi2;
}
else { // no better fit
if (F_->get_verbosity() >= 1)
F_->ui()->mesg("#" + S(iter_nr_) + ":"
" (WSSR=" + sm->format_double(chi2_) + ")"
" lambda=" + S(lambda));
if (lambda > max_lambda) { // another termination criterium
F_->msg("In L-M method: lambda=" + S(lambda) + " > "
+ S(max_lambda) + ", stopped.");
break;
}
}
iteration_plot(a, chi2);
}
post_fit (a, chi2);
}
double NLfit::calculate(int n, const double* par, double* grad)
{
assert(n == na_);
vector<realt> A(par, par+n);
if (F_->get_verbosity() >= 1)
output_tried_parameters(A);
bool stop = common_termination_criteria();
if (stop)
nlopt_force_stop(opt_);
double wssr;
if (!grad || stop)
wssr = compute_wssr(A, fitted_datas_);
else
wssr = compute_wssr_gradient(A, fitted_datas_, grad);
if (F_->get_verbosity() >= 1)
F_->ui()->mesg(iteration_info(wssr));
return wssr;
}
