model.h

该算法是张青复与周爱民的基于RM-MEDA算法的程序

//
//Please find details of the method from
//
//	Q. Zhang, A. Zhou and Y. Jin, "RM-MEDA: A Regularity Model Based Multiobjective Estimation of Distribution Algorithm", IEEE Trans. Evolutionary Computation, Vol. 12, no. 1, pp41-63, 2008.
//
//The source codes are free for reserach work. If you have any problem with the source codes, please contact with 
//	Qingfu Zhang, 
//		Department of Computing and Electronic Systems,
//		University of Essex,
//		Colchester, CO4 3SQ, UK
//		http://cswww.essex.ac.uk/staff/zhang
//		Email: [e-mail]
//    Aimin Zhou
//		Department of Computing and Electronic Systems,
//		University of Essex,
//		Colchester, CO4 3SQ, UK
//		http://privatewww.essex.ac.uk/~azhou/
//		Email: [e-mail] or [e-mail] 
//Programmer:		
//		Aimin Zhou
//Last Update:
//		Feb. 21, 2008
//
//Model.h : Estimation Distribution Models
//

#ifndef	AZ_MODEL_H
#define	AZ_MODEL_H

#include <fstream>
#include <string>
#include <vector>

//!\brief	az namespace, the top namespace
namespace az
{

//!\brief	alg namespace, contains algorithms
namespace alg
{

//!\brief	calcualte the eigenvalues and eigenvectors of a covariance matrix
//!\param	eva		sorted eigenvalues
//!\param	eve		sorted eigenvectors
//!\param	no		the main no-eigens are stored
//!\param	cov		the covariance matrix
//!\return	void
void Eigen(std::vector<double>& eva, std::vector< std::vector<double> >& eve, unsigned int no, std::vector< std::vector<double> >& cov);

//!\brief Model structure, used to store data
class Model
{
public:
	double			mErrTol;		//!< error tolerance
	unsigned int	mNClu,			//!< number of clusters
					mNX,			//!< number of trainning data
					mDX,			//!< dimension of trainning data
					mDLat,			//!< dimension of latent space
					mMaxIter,		//!< maximal trainning steps
					mIter;			//!< real trainning steps
	std::vector< unsigned int >			mvIndex,						//!< cluster index of each data
										mvNo;							//!< the number of points assigned to each cluster
	std::vector< std::vector<double> >	mvX,							//!< trainning data, each row is a data vector
										mvMean,							//!< center of each cluster
										mvEigenvalue,					//!< eigenvalue of ecah cluster
										mvProMin,						//!< minimal projection in primary dimensions
										mvProMax;						//!< maximal projection in primary dimensions
	std::vector< std::vector< std::vector<double> > >	mvEigenvector;	//!< eigenvector of each cluster

public:
	//!\brief	set parameters
	//!\param	nclu	number of cluster
	//!\param	nx		number of trainning data
	//!\param	dx		dimension of trainning data
	//!\param	dlat	dimension of latent space
	//!\param	maxiter	maximal trainning steps
	//!\param	errtol	error tolerance
	//!\return	void
	void Set(unsigned int nclu, unsigned int nx, unsigned int dx, unsigned int dlat, unsigned int maxiter=100, double errtol=1.0E-5);
	//!\brief	write model into file
	//!\param	file file name
	//!\return	void
	void Write(std::string file);

protected:
	//!\brief	calcualte the mean, eigenvalue and eigenvector of a given set of data
	//!\param	mean	mean value of the data
	//!\param	eva		sorted eigenvalues
	//!\param	eve		sorted eigenvectors
	//!\param	index	index of given set of data
	//!\return	void
	void Eigen(std::vector<double>& mean, std::vector<double>& eva, std::vector< std::vector<double> >& eve, std::vector< unsigned int >& index);
};// class Model

} //namespace alg

} //namespace az

#endif //AZ_MODEL_H