aimsalgo 6.0.0
Neuroimaging image processing
deterministic.h
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33
34
35#ifndef AIMS_OPTIMIZATION_DETERMINISTIC_H
36#define AIMS_OPTIMIZATION_DETERMINISTIC_H
37
38#include <cstdlib>
39#include <aims/math/mathelem.h>
40#include <aims/math/random.h>
41#include <aims/vector/vector.h>
42#include <aims/def/assert.h>
45
46
47//
48// class DetermOptimizer
49//
50template <class T, int D>
51class DetermOptimizer : public Optimizer<T, D>
52{
53 public:
54 DetermOptimizer( const ObjectiveFunc<T,D>& func, T error,
55 int maxIter = 100000, int stability = 1,
56 bool verbose = false )
57 : Optimizer< T, D >( func, error ),
58 _maxIter( maxIter ), _stability( stability ),
59 _verbose( verbose )
60 { }
61 virtual ~DetermOptimizer() { }
62
64 const AimsVector<T,D> & deltaP );
65
66 private:
67 int _maxIter;
68 int _stability;
69 bool _verbose;
70};
71
72
73template <class T,int D> inline
76 const AimsVector<T,D> & deltaP )
77{
78 AimsVector<T,D> p( pinit ), new_p( pinit ), dP( deltaP );
79 T eval, new_eval, old_eval, err;
80
81 old_eval = eval = new_eval = this->_func.eval( p );
82 int iter=0, cntStab = 0, k;
83 do
84 {
85 for ( k = 0; k < D; k++ )
86 new_p[ k ] = p[k] + (T)UniformRandom(-1.0,+1.0) * dP[ k ];
87 new_eval = this->_func.eval( new_p );
88 if ( new_eval < eval )
89 {
90 old_eval = eval;
91 p = new_p;
92 eval = new_eval;
93 }
94 if ( ( err = fabs( eval - old_eval ) ) < this->_error )
95 {
96 cntStab++;
97 }
98 else if ( cntStab )
99 {
100 cntStab = 0;
101 }
102 if ( _verbose )
103 std::cout
104 << "it=" << iter
105 << " param=" << p
106 << " stab=" << cntStab
107 << " objective=" << eval
108 << " error=" << err
109 << std::endl;
110 iter++;
111 dP *= (T)0.995f;
112 ASSERT( iter != _maxIter );
113 }
114 while ( cntStab != _stability );
115
116 return p;
117}
118
119#endif
#define ASSERT(EX)
virtual ~DetermOptimizer()
AimsVector< T, D > doit(const AimsVector< T, D > &pinit, const AimsVector< T, D > &deltaP)
DetermOptimizer(const ObjectiveFunc< T, D > &func, T error, int maxIter=100000, int stability=1, bool verbose=false)
const ObjectiveFunc< T, D > & _func
Definition optimizer.h:88
Optimizer(const ObjectiveFunc< T, D > &func, T error, OptimizerProbe< T, D > *probe=0)
Definition optimizer.h:68
double UniformRandom()
Uniform distribution between 0.0 and 1.0.