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#ifndef VDUALGAUSS1D_ESTIMATER
#define VDUALGAUSS1D_ESTIMATER
#include <core/pre_estimater.hpp>
#include <misc/optvec.hpp>
#include <vmodels/dualgauss1d.hpp>
#include <vector>
namespace opt_utilities
{
template <typename T>
class dualgauss1d_estimater
:public pre_estimater<optvec<T>,optvec<T>,optvec<T>,std::string>
{
public:
dualgauss1d_estimater()
{
this->set_model_id("1d dualgaussian");
}
dualgauss1d_estimater<T>* do_clone()const
{
return new dualgauss1d_estimater<T>(*this);
}
void do_estimate(const data_set<optvec<T>,optvec<T> >& d,model<optvec<T>,optvec<T>,optvec<T>,std::string>& m)const
{
int n=d.size();
T xp1st=0;
T xp2nd=0;
T yp1st=d.get_data(0).get_y()[0];
T yp2nd=d.get_data(0).get_y()[0];
for(int i=0;i<n;++i)
{
if(d.get_data(i).get_y()[0]>yp1st)
{
yp1st=d.get_data(i).get_y()[0];
xp1st=d.get_data(i).get_x()[0];
}
}
for(int i=0;i<n;++i)
{
}
T xmean=0;
T x2mean=0;
T wgt=0;
T wgt2=0;
for(int i=0;i<n;++i)
{
T x=d.get_data(i).get_x()[0];
T y=d.get_data(i).get_y()[0];
xmean+=x*y;
x2mean+=x*x*y;
wgt+=y;
}
xmean/=wgt;
x2mean/=wgt;
T sigma=std::sqrt(x2mean-xmean*xmean);
m.set_param_value("x0",xmean);
m.set_param_value("sigma",sigma);
}
};
}
#endif
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