aimsalgo  5.1.2
Neuroimaging image processing
pca.h
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33 
34 
35 #ifndef AIMS_MATH_PCA_H
36 #define AIMS_MATH_PCA_H
37 
38 #include <aims/def/general.h>
40 #include <vector>
41 
42 
43 class AimsPCA
44 {
45 public:
46  AimsPCA( int significantNumberOfVp, bool normalize = false, bool center = true ) ;
47  ~AimsPCA() {}
48 
49  template <class T>
50  void doIt( const carto::rc_ptr<carto::Volume<T> > & individuals ) ;
51 
52  template <class T>
53  void doIt( const std::list< Point3d>& selectedPoints, const carto::rc_ptr<carto::Volume<T> > & data ) ;
54 
55 
56  float noiseVariance( float& meanNorm ) ;
57  double totalVariance( float& meanNorm ) const ;
58 
59  float relativeUnreconstructedVariance( float& meanNorm ) ;
61  void setMinimalInertia( float inertiaLimit ) ;
62 
63  float minimalInertia() const ;
64  float significantNumberOfVp() const ;
65 
67  float noiseInertia() ;
71  const carto::rc_ptr<carto::Volume<float> > & individual ) ;
73  const carto::rc_ptr<carto::Volume<float> > & individual ) ;
74 
77 
78  const std::vector<float>& eigenValues() const ;
81 
82  const std::vector<float>& mean() const ;
83  const std::vector<float>& var() const ;
84 
85  bool valid() const { return _validPca ; }
86 
87 protected:
89  bool _validPca ;
90  bool _computed ;
92  bool _center ;
93  bool _normalize ;
94  std::vector<float> _mean ;
95  std::vector<float> _var ;
96 
97  std::vector<float> _projectionVector ;
100  std::vector<float> _eigenValues ;
104 
107 };
108 
109 #endif
Definition: pca.h:44
bool _matricesComputed
Definition: pca.h:91
const std::vector< float > & mean() const
float _minimalInertia
Definition: pca.h:106
void setSignificantNumberOfVp(int significantNumberOfVp)
carto::VolumeRef< float > _eigenVectors
Definition: pca.h:101
std::vector< float > _mean
Definition: pca.h:94
float noiseInertia()
float relativeUnreconstructedVariance(float &meanNorm)
bool _validPca
Definition: pca.h:89
const std::vector< float > & eigenValues() const
std::vector< float > _var
Definition: pca.h:95
bool _computed
Definition: pca.h:90
void setMinimalInertia(float inertiaLimit)
AimsPCA(int significantNumberOfVp, bool normalize=false, bool center=true)
bool valid() const
Definition: pca.h:85
float relativeUnreconstructedVariance()
float significantInertia()
float unreconstructedVariance()
const carto::VolumeRef< float > & reconstructionErrorMatrix()
const std::vector< float > & var() const
void computeErrorAndProjMatrices()
float noiseVariance(float &meanNorm)
const carto::VolumeRef< float > & eigenVectors() const
carto::VolumeRef< float > selectedEigenVectors() const
double totalVariance(float &meanNorm) const
void doIt(const carto::rc_ptr< carto::Volume< T > > &individuals)
Definition: pca_d.h:60
std::vector< float > _projectionVector
Definition: pca.h:97
float minimalInertia() const
const carto::VolumeRef< float > & projectionMatrix()
carto::VolumeRef< float > _selectedEigenVectorsTr
Definition: pca.h:103
~AimsPCA()
Definition: pca.h:47
carto::VolumeRef< float > _selectedEigenVectors
Definition: pca.h:102
carto::VolumeRef< float > _projectionMatrix
Definition: pca.h:98
float reconstructionError2(const carto::rc_ptr< carto::Volume< float > > &individual)
bool _center
Definition: pca.h:92
carto::VolumeRef< float > _errorMatrix
Definition: pca.h:99
float significantNumberOfVp() const
std::vector< float > _eigenValues
Definition: pca.h:100
carto::VolumeRef< float > projection(const carto::rc_ptr< carto::Volume< float > > &individual)
int _significantNumberOfVp
Definition: pca.h:105
bool _normalize
Definition: pca.h:93