A.I.M.S algorithms


lm2gauss.h
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33 
34 
35 #ifndef AIMS_OPTIMIZATION_LM2GAUSS_H
36 #define AIMS_OPTIMIZATION_LM2GAUSS_H
37 
39 
40 
41 template < class T >
42 class LM2Gaussian : public LMFunction< T >
43 {
44 public:
45 
46  LM2Gaussian( T k1=(T)1.0, T m1=(T)0.0, T s1=(T)1.0,
47  T k2=(T)1.0, T m2=(T)0.0, T s2=(T)1.0 );
48 
49  T apply( T );
50  T eval( T );
51 };
52 
53 
54 template< class T > inline
55 LM2Gaussian< T >::LM2Gaussian( T k1, T m1, T s1, T k2, T m2, T s2 )
56  : LMFunction< T >()
57 {
58  this->par.push_back( k1 );
59  this->par.push_back( m1 );
60  this->par.push_back( s1 );
61  this->par.push_back( k2 );
62  this->par.push_back( m2 );
63  this->par.push_back( s2 );
64 
65  this->der = std::vector< T >( 6 );
66 }
67 
68 
69 
70 template< class T > inline
72 {
73  T y = (T)0;
74 
75  for ( int i=0; i<6; i+=3 )
76  {
77  T arg = ( x - this->par[ i + 1 ] ) / this->par[ i + 2 ];
78  T ex = (T)exp( -1.0 * ( arg * arg ) );
79  T fac = (T)2 * this->par[ i ] * ex * arg;
80 
81  y += this->par[ i ] * ex;
82 
83  this->der[ i ] = ex;
84  this->der[ i + 1 ] = fac / this->par[ i + 2 ];
85  this->der[ i + 2 ] = fac * arg / this->par[ i + 2 ];
86  }
87 
88  return y;
89 }
90 
91 
92 template< class T > inline
94 {
95  T y = (T)0;
96 
97  for ( int i=0; i<6; i+=3 )
98  {
99  T arg = ( x - this->par[ i + 1 ] ) / this->par[ i + 2 ];
100 
101  y += this->par[ i ] * (T)exp( -1.0 * ( arg * arg ) );
102  }
103 
104  return y;
105 }
106 
107 #endif
LM2Gaussian(T k1=(T) 1.0, T m1=(T) 0.0, T s1=(T) 1.0, T k2=(T) 1.0, T m2=(T) 0.0, T s2=(T) 1.0)
Definition: lm2gauss.h:55
std::vector< T > der
Definition: lmfunc.h:59
T apply(T)
Definition: lm2gauss.h:93
std::vector< T > par
Definition: lmfunc.h:54
T eval(T)
Definition: lm2gauss.h:71