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/**
\file leastsq.hpp
\brief least square statistic
\author Junhua Gu
*/
#ifndef LEAST_SQ_HPP
#define LEAST_SQ_HPP
#define OPT_HEADER
#include <core/fitter.hpp>
#include <misc/optvec.hpp>
#include <iostream>
#include <vector>
#include <cmath>
using std::cerr;using std::endl;
namespace opt_utilities
{
/**
\brief least-square statistic
\tparam Ty the return type of model
\tparam Tx the type of the self-var
\tparam Tp the type of model parameter
\tparam Ts the type of the statistic
\tparam Tstr the type of the string used
*/
template<typename Ty,typename Tx,typename Tp,typename Ts,typename Tstr>
class leastsq
:public statistic<Ty,Tx,Tp,Ts,Tstr>
{
private:
bool verb;
int n;
statistic<Ty,Tx,Tp,Ts,Tstr>* do_clone()const
{
// return const_cast<statistic<Ty,Tx,Tp>*>(this);
return new leastsq<Ty,Tx,Tp,Ts,Tstr>(*this);
}
const char* do_get_type_name()const
{
return "least square statistic";
}
public:
void verbose(bool v)
{
verb=v;
}
public:
leastsq()
:verb(false)
{}
Ts do_eval(const Tp& p)
{
Ts result(0);
for(int i=(this->get_data_set()).size()-1;i>=0;--i)
{
Ty chi=(this->get_data_set().get_data(i).get_y()-eval_model(this->get_data_set().get_data(i).get_x(),p));
result+=chi*chi;
}
if(verb)
{
n++;
if(n%10==0)
{
cerr<<result<<"\t";
for(size_t i=0;i<get_size(p);++i)
{
cerr<<get_element(p,i)<<",";
}
cerr<<endl;
}
}
return result;
}
};
template<typename T,typename Ts,typename Tstr>
class leastsq<optvec<T>,optvec<T>,optvec<T>,Ts,Tstr>
:public statistic<optvec<T>,optvec<T>,optvec<T>,Ts,Tstr>
{
private:
bool verb;
int n;
typedef optvec<T> Tx;
typedef optvec<T> Ty;
typedef optvec<T> Tp;
statistic<Ty,Tx,Tp,Ts,Tstr>* do_clone()const
{
// return const_cast<statistic<Ty,Tx,Tp>*>(this);
return new leastsq<Ty,Tx,Tp,Ts,Tstr>(*this);
}
const char* do_get_type_name()const
{
return "least square statistic";
}
public:
void verbose(bool v)
{
verb=v;
}
public:
leastsq()
:verb(false)
{}
Ts do_eval(const Tp& p)
{
Ts result(0);
for(int i=(this->get_data_set()).size()-1;i>=0;--i)
{
Ty chi(this->get_data_set().get_data(0).get_y().size());
for(int j=0;j<chi.size();++j)
{
Ty model_y(eval_model(this->get_data_set().get_data(i).get_x(),p));
if(model_y[j]>this->get_data_set().get_data(i).get_y()[j])
{
chi[j]=(this->get_data_set().get_data(i).get_y()[j]-model_y[j]);
}
else
{
chi[j]=(this->get_data_set().get_data(i).get_y()[j]-model_y[j]);
}
}
result+=sum(chi*chi);
}
return result;
}
};
}
#endif
//EOF
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