NICE_VisLearning/classifier/kernelclassifier/KCGPApproxOneClass.h

/** 
* @file KCGPApproxOneClass.h
* @brief One-Class Gaussian Process Regression for Classification: we approximate the inverse of the regularized kernel matrix using a diagonal matrix
* @author Alexander Lütz
* @date 22-05-2012 (dd-mm-yyyy)

*/
#ifndef KCGPAPPROXONECLASSINCLUDE
#define KCGPAPPROXONECLASSINCLUDE

#include "vislearning/cbaselib/ClassificationResult.h"

#include "vislearning/classifier/classifierbase/KernelClassifier.h"

#include "vislearning/math/kernels/Kernel.h"
#include "vislearning/math/kernels/ParameterizedKernel.h"

#include "vislearning/regression/regressionbase/RegressionAlgorithmKernel.h"


#define VARIANCE_DETECTION_MODE 1
#define MEAN_DETECTION_MODE 2

namespace OBJREC {
 
class KCGPApproxOneClass : public KernelClassifier
{

    protected:
    NICE::Vector matrixDInv;
    NICE::Vector InvDY;
    int mode;
    double staticNoise;
    

    public:
  
  /** simple constructor */
  KCGPApproxOneClass( const NICE::Config *conf, Kernel *kernel = NULL, const std::string & section = "OneClassGP" );

  /** copy constructor */
  KCGPApproxOneClass( const KCGPApproxOneClass & src );
      
  /** simple destructor */
  virtual ~KCGPApproxOneClass();
    
  /** teach the classifier with a kernel matrix and the corresponding class labels @param y ! */
  void teach ( KernelData *kernelData, const NICE::Vector & y );
  
  void teach (const LabeledSetVector &teachSet);
    
  /** classify an example by using its kernel values with the training set,
    be careful with the order in @param kernelVector */
//   ClassificationResult classifyKernel ( const NICE::Vector & kernelVector, double kernelSelf ) const;
  virtual ClassificationResult classifyKernel ( const NICE::Vector & kernelVector, double kernelSelf ) const;
   
    /** clone this object */
  KCGPApproxOneClass *clone() const;

  void restore(std::istream&, int);
  void store(std::ostream&, int) const;
  void clear(){};
  
};


}

#endif