/** \file robust_chisq.hpp \brief chi-square statistic \author Junhua Gu */ #ifndef ROBUST_CHI_SQ_HPP #define ROBUST_CHI_SQ_HPP #define OPT_HEADER #include #include #include #include #include using std::cerr;using std::endl; namespace opt_utilities { /** \brief chi-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 class robust_chisq :public statistic { private: bool verb; int n; statistic* do_clone()const { // return const_cast*>(this); return new robust_chisq(*this); } const char* do_get_type_name()const { return "chi^2 statistic"; } public: void verbose(bool v) { verb=v; } public: robust_chisq() :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()-this->eval_model(this->get_data_set().get_data(i).get_x(),p))/this->get_data_set().get_data(i).get_y_upper_err(); result+=std::abs(chi); } if(verb) { n++; if(n%10==0) { cerr< class robust_chisq,double,std::string> :public statistic ,double,std::string> { public: typedef double Ty; typedef double Tx; typedef std::vector Tp; typedef double Ts; typedef std::string Tstr; private: bool verb; int n; statistic* do_clone()const { // return const_cast*>(this); return new robust_chisq(*this); } const char* do_get_type_name()const { return "chi^2 statistics (specialized for double)"; } public: void verbose(bool v) { verb=v; } public: robust_chisq() :verb(false) {} Ty do_eval(const Tp& p) { Ty result(0); for(int i=(this->get_data_set()).size()-1;i>=0;--i) { #ifdef HAVE_X_ERROR Tx x1=this->get_data_set().get_data(i).get_x()-this->get_data_set().get_data(i).get_x_lower_err(); Tx x2=this->get_data_set().get_data(i).get_x()+this->get_data_set().get_data(i).get_x_upper_err(); Tx x=this->get_data_set().get_data(i).get_x(); Ty errx1=(this->eval_model(x1,p)-this->eval_model(x,p)); Ty errx2=(this->eval_model(x2,p)-this->eval_model(x,p)); //Ty errx=0; #else Ty errx1=0; Ty errx2=0; #endif Ty y_model=this->eval_model(this->get_data_set().get_data(i).get_x(),p); Ty y_obs=this->get_data_set().get_data(i).get_y(); Ty y_err; Ty errx=0; if(errx10?errx1:-errx1; } else { errx=errx2>0?errx2:-errx2; } } else { if(y_obs0?errx2:-errx2; } else { errx=errx1>0?errx1:-errx1; } } if(y_model>y_obs) { y_err=this->get_data_set().get_data(i).get_y_upper_err(); } else { y_err=this->get_data_set().get_data(i).get_y_lower_err(); } Ty chi=(y_obs-y_model)/std::sqrt(y_err*y_err+errx*errx); // Ty chi=(this->get_data_set().get_data(i).get_y()-this->eval_model(this->get_data_set().get_data(i).get_x(),p)); // cerr<eval_model(this->get_data_set()[i].x,p)<get_data_set()[i].y_upper_err<get_data_set()[i].x<<"\t"<get_data_set()[i].y<<"\t"<eval_model(this->get_data_set()[i].x,p)<