blob: 68114a3b68532397c624f102fd74628318f06a06 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
|
/**
\file normed_gauss1d.hpp
\brief normalized guassian distribution
\author Junhua Gu
*/
#ifndef NGAUSS_MODEL_H_
#define NGAUSS_MODEL_H_
#define OPT_HEADER
#include <core/fitter.hpp>
#include <cmath>
#include <misc/optvec.hpp>
#include <limits>
namespace opt_utilities
{
template <typename T>
class normed_gauss1d
:public model<optvec<T>,optvec<T>,optvec<T>,std::string>
{
private:
normed_gauss1d* do_clone()const
{
return new normed_gauss1d<T>(*this);
}
const char* do_get_type_name()const
{
return "1d normed gaussian";
}
public:
normed_gauss1d()
{
this->push_param_info(param_info<optvec<T> >("x0",0));
this->push_param_info(param_info<optvec<T> >("sigma",1));
}
public:
optvec<T> do_eval(const optvec<T>& x,const optvec<T>& param)
{
const double pi=3.14159265358979323846;
T x0=get_element(param,0);
T sigma=get_element(param,1);
if(sigma*sigma<std::numeric_limits<double>::epsilon())
{
sigma=std::numeric_limits<double>::epsilon();
}
T N=1/sqrt(sigma*sigma*pi*2);
optvec<T> y=(x-x0)/sigma;
return N*exp(-y*y/2.);
}
private:
std::string do_get_information()const
{
#include <model_doc/normed_gauss1d.info>
return "";
}
};
}
#endif
//EOF
|
