jsomlabeling.java

Kohonen网络的学习过程可描述为：对于每一个网络的输入

package fi.javasom.jsom;
/**
 * This is JSomLabeling class that labels the weight vectors.
 *
 *  Copyright (C) 2001  Tomi Suuronen
 *
 *  @version 1.0
 *
 *  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., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
*/

import fi.javasom.jsom.*;
import java.io.*; //this line for debugging

public class JSomLabeling
{
	private WeightVectors wVector;
	private InputVectors iVector;
	private double distCache;
	private double length;
	private double lcache;
	private int	distCacheSize;
	private int iSize; //the number of input vectors
	private int wSize; //the number of weight vectors
	private int index;

	/**
	 * Constructor.
	 *
	 * @param WeightVectors wVector - weight vectors.
	 * @param InputVectors iVector - input vectors.
	*/
	public JSomLabeling(WeightVectors wVector,InputVectors iVector)
	{
		this.wVector = wVector;
		this.iVector = iVector;
		distCacheSize = wVector.getDimensionalityOfNodes();
		iSize = iVector.getCount();
		wSize = wVector.getCount();
	}

	/**
	 * Does the labeling phase.
	 *
	 * @return WeightVectors - Returns the labeled weight vectors.
	*/
	public WeightVectors doLabeling()
	{
		for(int i=0;i<iSize;i++)
		{
			wVector.setNodeLabelAt(resolveIndexOfWinningNeuron(iVector.getNodeValuesAt(i)),iVector.getNodeLabelAt(i));
		}
		return wVector;
	}

	/*
	 * Finds the winning neuron for this input vector.
	 *
	 * @param double[] values - values of an input vector.
	 * @return int - index of the winning neuron.
	*/
	private int resolveIndexOfWinningNeuron(double[] values)
	{
		length = getDistance(values,wVector.getNodeValuesAt(0));
		index = 0;
		for(int i=1;i<wSize;i++)
		{
			lcache = getDistance(values,wVector.getNodeValuesAt(i));
			if(lcache<length)
			{
				index = i;
				length = lcache;
			}
		}
		return index;
	}

	/**
	 * Calculates the Euclidean distance between two vectors.
	 *
	 * @param double[] x - 1st vector.
	 * @param double[] y - 2nd vector.
	 * @return double - returns the distance between two vectors, x and y
	*/
	private double getDistance(double[] x, double[] y)
	{
		distCache = 0.0;
		for(int i=0;i<distCacheSize;i++)
		{
			distCache += Math.pow((x[i]-y[i]),2.0);
		}
		return Math.sqrt(distCache);
	}
}