PrincipalComponentAnalysis.h

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#ifndef GRT_PRINCIPAL_COMPONENT_ANALYSIS_HEADER
#define GRT_PRINCIPAL_COMPONENT_ANALYSIS_HEADER

#include "../../Util/GRTCommon.h"
#include "../../CoreModules/MLBase.h"

GRT_BEGIN_NAMESPACE
 
class PrincipalComponentAnalysis : public MLBase{
 public:
 PrincipalComponentAnalysis();
 
 virtual ~PrincipalComponentAnalysis();
 
 bool computeFeatureVector(const MatrixFloat &data,Float maxVariance=0.95,bool normData=false);

 bool computeFeatureVector(const MatrixFloat &data,UINT numPrincipalComponents,bool normData=false);
 
 bool project(const MatrixFloat &data,MatrixFloat &prjData);
 
 bool project(const VectorFloat &data,VectorFloat &prjData);

 virtual bool saveModelToFile( std::fstream &file ) const;
 
 virtual bool loadModelFromFile( std::fstream &file );
 
 bool getTrained() const { return trained; }
 
 bool getNormData() const { return normData; }
 
 UINT getNumInputDimensions() const { return numInputDimensions; }
 
 UINT getNumPrincipalComponents() const { return numPrincipalComponents; }
 
 Float getMaxVariance() const { return maxVariance; }
 
 VectorFloat getMeanVector() const { return mean; }
 
 VectorFloat getStdDevVector() const { return stdDev; }
 
 VectorFloat getComponentWeights() const { return componentWeights; }
 
 VectorFloat getEigenValues() const { return eigenvalues; }
 
 virtual bool print( std::string title="" ) const;
 
 MatrixFloat getEigenVectors() const;

 bool setModel( const VectorFloat &mean, const MatrixFloat &eigenvectors );
 
protected:
 bool computeFeatureVector_(const MatrixFloat &data,UINT analysisMode);

 bool normData;
 UINT numPrincipalComponents;
 Float maxVariance;
 VectorFloat mean;
 VectorFloat stdDev;
 VectorFloat componentWeights;
 VectorFloat eigenvalues;
 Vector< IndexedDouble > sortedEigenvalues;
 MatrixFloat eigenvectors;

 enum AnalysisMode{MAX_VARIANCE=0,MAX_NUM_PCS};
 
};

GRT_END_NAMESPACE

#endif //GRT_PRINCIPAL_COMPONENT_ANALYSIS_HEADER