aimsalgo
6.0.0
Neuroimaging image processing
lmgauss.h
Go to the documentation of this file.
1
/* This software and supporting documentation are distributed by
2
* Institut Federatif de Recherche 49
3
* CEA/NeuroSpin, Batiment 145,
4
* 91191 Gif-sur-Yvette cedex
5
* France
6
*
7
* This software is governed by the CeCILL-B license under
8
* French law and abiding by the rules of distribution of free software.
9
* You can use, modify and/or redistribute the software under the
10
* terms of the CeCILL-B license as circulated by CEA, CNRS
11
* and INRIA at the following URL "http://www.cecill.info".
12
*
13
* As a counterpart to the access to the source code and rights to copy,
14
* modify and redistribute granted by the license, users are provided only
15
* with a limited warranty and the software's author, the holder of the
16
* economic rights, and the successive licensors have only limited
17
* liability.
18
*
19
* In this respect, the user's attention is drawn to the risks associated
20
* with loading, using, modifying and/or developing or reproducing the
21
* software by the user in light of its specific status of free software,
22
* that may mean that it is complicated to manipulate, and that also
23
* therefore means that it is reserved for developers and experienced
24
* professionals having in-depth computer knowledge. Users are therefore
25
* encouraged to load and test the software's suitability as regards their
26
* requirements in conditions enabling the security of their systems and/or
27
* data to be ensured and, more generally, to use and operate it in the
28
* same conditions as regards security.
29
*
30
* The fact that you are presently reading this means that you have had
31
* knowledge of the CeCILL-B license and that you accept its terms.
32
*/
33
34
35
#ifndef AIMS_OPTIMIZATION_LMGAUSS_H
36
#define AIMS_OPTIMIZATION_LMGAUSS_H
37
38
#include <
aims/optimization/lmfunc.h
>
39
40
41
template
<
class
T >
42
class
LMGaussian
:
public
LMFunction
< T >
43
{
44
public
:
45
46
LMGaussian
( T k=(T)1.0, T m=(T)0.0, T s=(T)1.0 );
47
48
T
apply
( T );
49
T
eval
( T );
50
};
51
52
53
template
<
class
T >
inline
54
LMGaussian< T >::LMGaussian
( T k, T m, T s ) :
LMFunction
< T >()
55
{
56
this->
par
.push_back( k );
57
this->
par
.push_back( m );
58
this->
par
.push_back( s );
59
60
this->
der
= std::vector< T >( 3 );
61
}
62
63
64
65
template
<
class
T >
inline
66
T
LMGaussian< T >::eval
( T x )
67
{
68
T arg = ( x - this->
par
[ 1 ] ) / this->
par
[ 2 ];
69
T ex = (T)exp( -1.0 * (arg * arg ) );
70
T fac = (T)2 * this->
par
[ 0 ] * ex * arg;
71
72
this->
der
[ 0 ] = ex;
73
this->
der
[ 1 ] = fac / this->
par
[ 2 ];
74
this->
der
[ 2 ] = fac * arg / this->
par
[ 2 ];
75
76
return
this->
par
[ 0 ] * ex;
77
}
78
79
80
template
<
class
T >
inline
81
T
LMGaussian< T >::apply
( T x )
82
{
83
T arg = ( x - this->
par
[ 1 ] ) / this->
par
[ 2 ];
84
85
return
this->
par
[ 0 ] * (T)exp( -1.0 * ( arg * arg ) );
86
}
87
88
#endif
LMFunction::LMFunction
LMFunction()
Definition
lmfunc.h:46
LMFunction::der
std::vector< T > der
Definition
lmfunc.h:59
LMFunction::par
std::vector< T > par
Definition
lmfunc.h:58
LMGaussian::eval
T eval(T)
Definition
lmgauss.h:66
LMGaussian::apply
T apply(T)
Definition
lmgauss.h:81
LMGaussian::LMGaussian
LMGaussian(T k=(T) 1.0, T m=(T) 0.0, T s=(T) 1.0)
Definition
lmgauss.h:54
lmfunc.h
aims
optimization
lmgauss.h
Generated by
1.13.2