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/**
\file bootstrap.hpp
*/
#ifndef BOOT_STRIP
#define BOOT_STRIP
#define OPT_HEADER
#include <core/fitter.hpp>
#include <vector>
#include <cstdlib>
#include <iostream>
#include <utility>
#include <algorithm>
#include <data_sets/default_data_set.hpp>
using std::cout;
namespace opt_utilities
{
/**
\brief using bootstrap method to estimate confidence interval
\tparam Ty the return type of a model
\tparam Tx the type of self-var
\tparam Tp the type of model parameters
\tparam Ts the type of statistic
\tparam Tstr the type of string used
*/
template <typename Ty,typename Tx,typename Tp,typename Ts,typename Tstr=std::string>
class bootstrap
{
private:
Ty rand_norm(Ty y0,Ty y_err)const
{
Ty y;
do
{
y=(rand()/(Ty)RAND_MAX-(Ty).5)*(10*y_err)+y0;
}
while(rand()/(Ty)RAND_MAX>exp(-(y-y0)*(y-y0)/(y_err*y_err)));
return y;
}
public:
std::vector<Tp> param_pool;
default_data_set<Ty,Tx> current_data_set;
default_data_set<Ty,Tx> origin_data_set;
fitter<Ty,Tx,Tp,Ts,Tstr>* p_fitter;
Tp origin_param;
public:
/**
default construct
*/
bootstrap()
:p_fitter(NULL)
{}
/**
destructure
*/
~bootstrap()
{
reset();
}
/**
set a fitter
\param pf the fitter, the confidence interval of which will be estimated
*/
void set_fitter(fitter<Ty,Tx,Tp,Ts,Tstr>& pf)
{
param_pool.clear();
p_fitter=&pf;
origin_data_set=dynamic_cast<const default_data_set<Ty,Tx>&>(pf.get_data_set());
origin_param=pf.get_all_params();
}
/**
reset the estimation
reload the data
restore the parameters
*/
void reset()
{
if(p_fitter!=NULL)
{
p_fitter->load_data(origin_data_set);
p_fitter->set_param_value(origin_param);
}
}
/**
resample n times
\param n the times to sample
*/
void sample(int n)
{
if(p_fitter!=NULL)
{
for(int i=0;i<n;++i)
{
sample();
}
p_fitter->load_data(origin_data_set);
p_fitter->set_param_value(origin_param);
}
else
{
throw opt_exception("Fitter unset");
}
}
/**
get the param of i-th sampling
\param i the order of parameter
\return the parameter
*/
const Tp& get_param(int i)const
{
return param_pool.at(i);
}
private:
Tp sample()
{
if(p_fitter==NULL)
{
throw fitter_unset();
}
current_data_set=default_data_set<Ty,Tx>();
for(size_t i=0;i<origin_data_set.size();++i)
{
data<Ty,Tx> d;
d=origin_data_set.get_data(i);
d.set_y(rand_norm(d.get_y(),(d.get_y_upper_err()+d.get_y_lower_err())/2));
current_data_set.add_data(d);
}
p_fitter->load_data(current_data_set);
p_fitter->set_param_value(origin_param);
param_pool.push_back(p_fitter->fit());
for(size_t i=0;i<(param_pool.back()).size();++i)
{
cout<<param_pool.back()[i]<<",";
}
std::cout<<std::endl;
return param_pool.back();
}
public:
/**
Calculate the confdence interval for one parameter under given level
\param param_name the name of the parameter to be estimated
\param level the confidence level
\return a std::pair containing the lower and upper boundaries of the confidence interval
*/
std::pair<typename element_type_trait<Tp>::element_type,typename element_type_trait<Tp>::element_type>
interval(const Tstr& param_name,double level)
{
if(p_fitter==NULL)
{
throw opt_exception("Fitter unset");
}
if(param_pool.empty())
{
throw opt_exception("Bootstrap not done");
}
//sample();
std::vector<typename element_type_trait<Tp>::element_type> _tmp;
int order=p_fitter->get_param_order(param_name);
for(typename std::vector<Tp>::iterator i=param_pool.begin();
i!=param_pool.end();++i)
{
_tmp.push_back((*i)[order]);
}
sort(_tmp.begin(),_tmp.end());
std::pair<typename std::vector<typename element_type_trait<Tp>::element_type>::iterator,
typename std::vector<typename element_type_trait<Tp>::element_type>::iterator>
itv=equal_range(_tmp.begin(),_tmp.end(),origin_param[order]);
int current_param_position=itv.second-_tmp.begin();
std::cout<<_tmp.size()<<std::endl;
return std::pair<typename element_type_trait<Tp>::element_type,
typename element_type_trait<Tp>::element_type>(
_tmp.at((int)((1-level)*current_param_position)),
_tmp.at((int)(current_param_position+level*(_tmp.size()-current_param_position)))
);
}
};
}
#endif
//EOF
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