diff options
| -rw-r--r-- | interface/optimize.hpp | 55 | ||||
| -rw-r--r-- | statistics/robust_chisq.hpp | 219 |
2 files changed, 274 insertions, 0 deletions
diff --git a/interface/optimize.hpp b/interface/optimize.hpp new file mode 100644 index 0000000..f56642f --- /dev/null +++ b/interface/optimize.hpp @@ -0,0 +1,55 @@ +#ifndef OPTIMIZE_FUNC_HPP +#define OPTIMIZE_FUNC_HPP + +#if __cplusplus<201103L +#error This header must be used with C++ 11(0x) or newer +#endif + +#include <vector> +#include <functional> +#include <cmath> +#include <utility> +#include "../core/optimizer.hpp" +#include "../methods/powell/powell_method.hpp" + +namespace opt_utilities +{ + + template <typename Ty,typename Tx> + Tx optimize(const std::function<Ty(Tx)>& func,const Tx& start_point,const opt_utilities::opt_method<Ty,Tx>& opm=opt_utilities::powell_method<double,std::vector<double> >()) + { + class func_wrapper + :public opt_utilities::func_obj<Ty,Tx> + { + std::function<Ty(Tx)> f; + public: + func_wrapper(const std::function<Ty(Tx)>& f1) + :f(f1) + {}; + + func_wrapper* do_clone()const + { + return const_cast<func_wrapper*>(this); + } + + void do_destroy() + { + //do nothing + } + + Ty do_eval(const Tx& x) + { + return f(x); + } + }foo(func); + opt_utilities::optimizer<Ty,Tx> opt; + opt.set_opt_method(opm); + opt.set_func_obj(foo); + opt.set_start_point(start_point); + return opt.optimize(); + } + +} + +#endif +//EOF diff --git a/statistics/robust_chisq.hpp b/statistics/robust_chisq.hpp new file mode 100644 index 0000000..6cf5b96 --- /dev/null +++ b/statistics/robust_chisq.hpp @@ -0,0 +1,219 @@ +/**
+ \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 <core/fitter.hpp>
+#include <iostream>
+#include <vector>
+#include <misc/optvec.hpp>
+#include <cmath>
+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<typename Ty,typename Tx,typename Tp,typename Ts,typename Tstr>
+ class robust_chisq
+ :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 robust_chisq<Ty,Tx,Tp,Ts,Tstr>(*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<<result<<"\t";
+ for(size_t i=0;i<get_size(p);++i)
+ {
+ cerr<<get_element(p,i)<<",";
+ }
+ cerr<<endl;
+ }
+
+ }
+
+ return result;
+ }
+ };
+
+#if 1
+
+ template<>
+ class robust_chisq<double,double,std::vector<double>,double,std::string>
+ :public statistic<double,double,std::vector<double> ,double,std::string>
+ {
+ public:
+ typedef double Ty;
+ typedef double Tx;
+ typedef std::vector<double> Tp;
+ typedef double Ts;
+ typedef std::string 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 robust_chisq<Ty,Tx,Tp,Ts,Tstr>(*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(errx1<errx2)
+ {
+ if(y_obs<y_model)
+ {
+ errx=errx1>0?errx1:-errx1;
+ }
+ else
+ {
+ errx=errx2>0?errx2:-errx2;
+ }
+ }
+ else
+ {
+ if(y_obs<y_model)
+ {
+ errx=errx2>0?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<<chi<<"\n";
+ result+=std::abs(chi);
+ //std::cerr<<chi<<std::endl;
+ //cerr<<this->eval_model(this->get_data_set()[i].x,p)<<endl;
+ //cerr<<this->get_data_set()[i].y_upper_err<<endl;
+ // cerr<<this->get_data_set()[i].x<<"\t"<<this->get_data_set()[i].y<<"\t"<<this->eval_model(this->get_data_set()[i].x,p)<<endl;
+ }
+ if(verb)
+ {
+ n++;
+ if(n%10==0)
+ {
+ cerr<<result<<"\t";
+ for(int i=0;i<(int)get_size(p);++i)
+ {
+ cerr<<get_element(p,i)<<",";
+ }
+ cerr<<endl;
+ }
+
+ }
+
+ return result;
+ }
+ };
+#endif
+
+
+}
+
+#endif
+//EOF
+
+
|