NICE_VisLearning/math/cluster/KMeans.h

/** 
* @file KMeans.h
* @brief K-Means
* @author Erik Rodner
* @date 10/29/2007

*/
#ifndef KMEANSINCLUDE
#define KMEANSINCLUDE

#include "core/vector/VectorT.h"
#include "core/vector/MatrixT.h"
  
#include "ClusterAlgorithm.h"
#include <core/vector/Distance.h>

namespace OBJREC {

/** K-Means */
class KMeans : public ClusterAlgorithm
{

    protected:
	int noClasses;
	std::string distanceType;
	NICE::VectorDistance<double> *distancefunction;
	double vectorDistance(const NICE::Vector &vector1, const NICE::Vector &vector2, uint distancetype);
	double compute_assignments ( const NICE::VVector & features,
				     const NICE::VVector & prototypes,
				     std::vector<int> & assignment );

	double compute_weights ( const NICE::VVector & features,
				 std::vector<double> & weights,
				 std::vector<int>    & assignment );

	double compute_delta ( const NICE::VVector & oldprototypes,
				       const NICE::VVector & prototypes );

	int compute_prototypes ( const NICE::VVector & features,
				  NICE::VVector & prototypes,
				  std::vector<double> & weights,
				  const std::vector<int>    & assignment );

	void initial_guess ( const NICE::VVector & features,
			     NICE::VVector & prototypes );
	
	void print_iteration ( int iterations, 
			       NICE::VVector & prototypes,
			       double delta );

    public:
  
	/** simple constructor */
	KMeans( int noClasses , std::string distanceMode="euclidean");
      
	/** simple destructor */
	virtual ~KMeans();
     
	void cluster ( const NICE::VVector & features,
		       NICE::VVector & prototypes,
		       std::vector<double> & weights,
		       std::vector<int>    & assignment );

};


} // namespace

#endif