layer.java

著名的开源仿真软件yale

/*
 *  YALE - Yet Another Learning Environment
 *  Copyright (C) 2002, 2003
 *      Simon Fischer, Ralf Klinkenberg, Ingo Mierswa, 
 *          Katharina Morik, Oliver Ritthoff
 *      Artificial Intelligence Unit
 *      Computer Science Department
 *      University of Dortmund
 *      44221 Dortmund,  Germany
 *  email: [e-mail]
 *  web:   http://yale.cs.uni-dortmund.de/
 *
 *  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.
 */
package edu.udo.cs.yale.operator.learner.nn;

import java.io.Serializable;

/** A neural net layer. Subclasses have to implement the <tt>calculate</tt>method.
 *  An additional 1 is added to the output because subsequent layers may need an
 *  additional constant input (for additive terms).
 *  @author simon
 *  @version 22.06.2001
 */
public abstract class Layer implements Serializable {

    private Trafo trafo;
    private Layer succ;
    private double e[];
    private double u[];

    public Layer(int neurons, Trafo trafo, Layer succ) {
	this.succ = succ;
	this.trafo = trafo;
	this.e = new double[neurons+1];
	this.u = new double[neurons+1];
	if (trafo == null) e = u;
    }

    public abstract  double[] calculate(double[] s);

    public void input(double[] s) {
	double dummy[] = calculate(s); 
	for (int i = 0; i < dummy.length; i++) {
	    u[i] = dummy[i];
	    if (trafo != null)
		e[i] = trafo.phi(u[i]);
	}
	u[e.length-1] = 1;
	if (trafo != null) e[e.length-1] = trafo.phi(1);
	if (succ != null)
	    succ.input(e);
    }


    public int neurons() { return e.length; }
    public double[] e() { return e; }
    public double[] u() { return u; }
    public Trafo getTrafo() { return trafo; }
}