NICE_VisLearning/math/cluster/KMeansHeuristic.h

/** 
 * @file KMeansHeuristic.h
 * @brief K-Means
 * @author Erik Rodner, Michael Koch, Michael Trummer
 * @date 02/04/2011

 */
#ifndef KMeansHeuristicINCLUDE
#define KMeansHeuristicINCLUDE

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


namespace OBJREC
{

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

protected:
	int noClasses;
	std::string distanceType;
	NICE::VectorDistance<double> *distancefunction;
	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);


	void initial_guess(const NICE::VVector & features, NICE::VVector & prototypes);

	void print_iteration(int iterations, NICE::VVector & prototypes, double delta);
	int robust_prototypes(const NICE::VVector &features, NICE::VVector &prototypes, std::vector<
			double> & weights, const std::vector<int> & assignment);
public:

	/** simple constructor */
	KMeansHeuristic(int noClasses, std::string distanceMode = "euclidean");

	/** simple destructor */
	virtual ~KMeansHeuristic();

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

};

} // namespace

#endif