void report_distances(const valarray<double>& distances,
const string& name,
variables_map& args
)
{
if (not distances.size()) return;
bool show_mean = args.count("mean");
bool show_median = args.count("median");
bool show_minmax = args.count("minmax");
if (not show_mean and not show_median and not show_minmax)
show_median = true;
if (show_minmax)
cout<<" "<<name<<" in ["<<min(distances)<<", "<<max(distances)<<"]"<<endl;
if (show_mean){
cout<<" E "<<name<<" = "<<distances.sum()/distances.size();
cout<<" [+- "<<sqrt(Var(distances))<<"]"<<endl;
}
if (show_median) {
double P = args["CI"].as<double>();
pair<double,double> interval = central_confidence_interval(distances,P);
cout<<" "<<name<<" ~ "<<median(distances);
cout<<" ("<<interval.first<<", "<<interval.second<<")"<<endl;
}
}
double CMinimizerThread::ComputeChi2r(valarray<double> & chis, unsigned int n_params)
{
// Square the numerator, divide by the uncertainties
chis *= chis;
double chi2_sum = chis.sum();
// Now compute the sum dividied by (n_data - n_params - 1)
return chi2_sum / (chis.size() - n_params - 1);
}
log_double_t dirichlet_pdf(const valarray<double>& p,const valarray<double>& n)
{
assert(p.size() == n.size());
log_double_t Pr = 1;
for(int i=0; i<p.size(); i++)
Pr *= pow(log_double_t(p[i]),n[i]-1.0);
// This term is constant in p
Pr.log() += log_gamma(n.sum());
for(int i=0; i<p.size(); i++)
Pr.log() -= log_gamma(n[i]);
return Pr;
}
inline double sum(const valarray<double>& v) {
return v.sum();
}
double getsum(const valarray<double>& v) {
return v.sum();
}