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/**
\file wang2012_model.hpp
\brief Jingying Wang's model
\author Jingying Wang
*/
#ifndef WANG2012_MODEL
#define WANG2012_MODEL
#define OPT_HEADER
#include <core/fitter.hpp>
#include <cmath>
namespace opt_utilities
{
template <typename T>
class wang2012_model
:public model<T,T,std::vector<T>,std::string>
{
private:
model<T,T,std::vector<T> >* do_clone()const
{
return new wang2012_model<T>(*this);
}
const char* do_get_type_name()const
{
return "1d power law";
}
public:
wang2012_model()
{
this->push_param_info(param_info<std::vector<T> >("A",5,0,500));
this->push_param_info(param_info<std::vector<T> >("n",1.66,0,10));
this->push_param_info(param_info<std::vector<T> >("xi",0.45,0,1));
this->push_param_info(param_info<std::vector<T> >("a2",1500,0,1e8));
this->push_param_info(param_info<std::vector<T> >("a3",50,0,1e8));
this->push_param_info(param_info<std::vector<T> >("beta",0.49,0.1,0.7));
this->push_param_info(param_info<std::vector<T> >("T0",0,0,10));
}
T do_eval(const T& x,const std::vector<T>& param)
{
T A=param[0];
T n=param[1];
T xi=param[2];
T a2=param[3];
T a3=param[4];
T beta=param[5];
T T0=param[6];
return A*(pow(x,n)+xi*a2)/(pow(x,n)+a2)/pow(1+x*x/a3/a3,beta)+T0;
//return A*(pow(x,n)+a1)/(pow(x,n)+1)/pow(1+x*x/a3/a3,beta)+T0;
}
private:
std::string do_get_information()const
{
return "";
}
};
}
#endif
//EOF
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