NICE_VisLearning/classifier/kernelclassifier/KCMinimumEnclosingBall.h

/** 
* @file KCGPOneClass.h
* @brief One-Class Gaussian Process Regression for Classification
* @author Erik Rodner + Mi.Ke.
* @date 12/03/2010

*/
#ifndef KCMINIMUMENCLOSINGBALLINCLUDE
#define KCMINIMUMENCLOSINGBALLINCLUDE

#include "vislearning/classifier/classifierbase/KernelClassifier.h"
#include "vislearning/math/kernels/ParameterizedKernel.h"

#undef ROADWORKS
#define ROADWORKS fthrow(Exception, "Persistent interface not implemented!");

#define VARIANCE_DETECTION_MODE 1
#define MEAN_DETECTION_MODE 2

#include "vislearning/optimization/quadprog/QuadProg++.h"
#include "vislearning/optimization/quadprog/Array.h"


namespace OBJREC {
 
/** Minimum Enclosing Ball Algorithm (For stationary kernels equal to 1-SVM)*/
class KCMinimumEnclosingBall : public KernelClassifier
{

    protected:
		int trainingSize;
		double radius;
		double aKa;
		double nu;
		QuadProgPP::Matrix<double> TwoK,Eye,Ones,ones;
		QuadProgPP::Vector<double> alpha,b,minusDiagK,minusOne,zeros;

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

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

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

};


}

#undef ROADWORKS

#endif