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| author | astrojhgu <astrojhgu@ed2142bd-67ad-457f-ba7c-d818d4011675> | 2012-09-02 15:16:24 +0000 |
|---|---|---|
| committer | astrojhgu <astrojhgu@ed2142bd-67ad-457f-ba7c-d818d4011675> | 2012-09-02 15:16:24 +0000 |
| commit | b41a10e36629611fea88efe354e6e7deb66cd697 (patch) | |
| tree | df9b7d0061c2fd8fb66e8cdbf54b02cac956ad2f | |
| parent | 19f2b2cbd127a15034af6a1d1256b47c38c57543 (diff) | |
| download | opt-utilities-b41a10e36629611fea88efe354e6e7deb66cd697.tar.bz2 | |
Added the log-chisq statistic, which can be used to treat the log-space fitting.
git-svn-id: file:///home/svn/opt_utilities@242 ed2142bd-67ad-457f-ba7c-d818d4011675
| -rw-r--r-- | statistics/logchisq.hpp | 201 |
1 files changed, 201 insertions, 0 deletions
diff --git a/statistics/logchisq.hpp b/statistics/logchisq.hpp new file mode 100644 index 0000000..57cf7aa --- /dev/null +++ b/statistics/logchisq.hpp @@ -0,0 +1,201 @@ +/**
+ \file logchisq.hpp
+ \brief chi-square statistic
+ \author Junhua Gu
+ */
+
+#ifndef LOG_CHI_SQ_HPP
+#define LOG_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
+{
+ class negative_data_value
+ :public opt_exception
+ {
+ public:
+ negative_data_value()
+ :opt_exception("log chisq statistics cannot be used when has negative data!")
+ {}
+ };
+
+
+
+ /**
+ \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 logchisq
+ :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 logchisq<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:
+ logchisq()
+ :verb(false)
+ {}
+
+
+
+ Ts do_eval(const Tp& p)
+ {
+ Ts result(0);
+ for(int i=(this->get_data_set()).size()-1;i>=0;--i)
+ {
+ Ty y=std::log(this->get_data_set().get_data(i).get_y());
+ Ty ym=std::log(this->eval_model(this->get_data_set().get_data(i).get_x(),p));
+ Ty ye1=std::log(1+this->get_data_set().get_data(i).get_y_upper_err()/this->get_data_set().get_data(i).get_y());
+ Ty chi=(y-ym)/ye1;
+ 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;
+ }
+ };
+
+#if 1
+
+ template<>
+ class logchisq<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 logchisq<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:
+ logchisq()
+ :verb(false)
+ {}
+
+
+
+ Ty do_eval(const Tp& p)
+ {
+ Ty result(0);
+ for(int i=0;i!=(this->get_data_set()).size();++i)
+ {
+
+ 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;
+
+ if(y_model>y_obs)
+ {
+ y_err=std::abs(this->get_data_set().get_data(i).get_y_upper_err());
+ }
+ else
+ {
+ y_err=-std::abs(this->get_data_set().get_data(i).get_y_lower_err());
+ }
+ if(y_obs+y_err<0)
+ {
+ throw negative_data_value();
+ }
+ Ty logy=std::log(y_obs);
+ Ty logym=std::log(y_model);
+ Ty logerr=std::log(y_obs+y_err)-log(y_obs);
+
+
+ Ty chi=(logy-logym)/logerr;
+
+ // 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+=chi*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
+
+
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