fuzzykmeanclusteringengine.java

一个用于排队系统仿真的开源软件,有非常形象的图象仿真过程!

/**    
  * Copyright (C) 2006, Laboratorio di Valutazione delle Prestazioni - Politecnico di Milano

  * This program is free software; you can redistribute it and/or modify
  * it under the terms of the GNU General Public License as published by
  * the Free Software Foundation; either version 2 of the License, or
  * (at your option) any later version.

  * This program is distributed in the hope that it will be useful,
  * but WITHOUT ANY WARRANTY; without even the implied warranty of
  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  * GNU General Public License for more details.

  * You should have received a copy of the GNU General Public License
  * along with this program; if not, write to the Free Software
  * Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
  */
  
package jmt.engine.jwat.workloadAnalysis.clustering.fuzzyKMean;

import jmt.engine.jwat.MatrixOsservazioni;
import jmt.engine.jwat.Observation;
import jmt.engine.jwat.TimeConsumingWorker;
import jmt.engine.jwat.VariableNumber;

public class FuzzyKMeanClusteringEngine {
	
	class SFKMClust{
		String centri;			// contiene i centri dei cluster
		String log;			// informazioni generali
/** DA SISTEMARE **/		String[] clus_log; /*= new String[37];	// log dei valori del Cluster = new String[MAXCLU]*/
	}
	
	public String m_strFKMLog;
	public SFKMClust m_arrClust[];	
	public double clus_entropy[];	// entropie finali dei Cluster
	public int m_nMaxClust;
	private VariableNumber[] listOfVars; //Solo quelle selezionate
	
	private Observation[] obsSel;
	private int[] varS;
	
	private double[][] U = null;//0
	private double[][] newU = null;//1
	private double[][] centers = null;//2
	private double[][] mrow = null;//3
	private double[][] singularity = null;//4
	private double[][] distance = null;//5
	
	private int m_nNumNorm;
	private int m_nFLev;
	private int m_nIter;
	
	private FuzzyKMean fuzzy = null;
	private TimeConsumingWorker worker;
	
	public FuzzyKMeanClusteringEngine(FuzzyKMean f,TimeConsumingWorker worker){
		this.worker=worker;
		fuzzy = f;
	}
		
	public void PrepFClustering(MatrixOsservazioni m,int indVarSel[],int nMaxClust,int nFLev,int nIter){
		m_nNumNorm	= indVarSel.length; 
		m_nMaxClust = nMaxClust;		// Num.massimo di clusters da considerare
		m_nFLev = nFLev;				// Livello di fuzzyness
		m_nIter = nIter;				// Valore di tollerabilit