nnlearner.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 edu.udo.cs.yale.operator.parameter.*;
import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.operator.learner.Learner;
import edu.udo.cs.yale.operator.learner.Model;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.example.ExampleReader;
import edu.udo.cs.yale.example.Example;

import java.util.List;

/** Learns a neural net.
 *  <h4>Parameters:</h4>
 *  <ul>
 *    <li><tt>hidden_layer</tt> number of neurons in the hidden layer
 *    <li><tt>lambda</tt> approximation increment
 *  </ul>
 *  <b>Classname for config-file:</b>NeuralNetLearner
 *  @author simon
 *  @version 22.06.2001
 */
public class NNLearner extends Learner {

    private int hiddenLayer;
    private double lambda;

    public void initApply() throws OperatorException {
	super.initApply();
	hiddenLayer = getParameterAsInt("hidden_layer");
	lambda      = getParameterAsDouble("lambda");
    }

    public Model learn(ExampleSet exampleSet) {
	NeuralNet nn = new NeuralNet(hiddenLayer, exampleSet.getNumberOfAttributes(), 1, lambda);
	ExampleReader i = exampleSet.getExampleReader();
	while (i.hasNext()) {
	    Example e = i.next();
	    double[] s = new double[e.getNumberOfAttributes()];
	    for (int j = 0; j < s.length; j++)
		s[j] = e.getValue(j);
	    nn.learn(s, new double[] { e.getLabel() });
	}
	return nn;
    }
    
    public List getParameterTypes() {
	List types = super.getParameterTypes();
	types.add(new ParameterTypeInt("hidden_layer", "Number of neurons in the hidden layer.", 0, Integer.MAX_VALUE, 10));
	types.add(new ParameterTypeDouble("lambda", "Parameter lambda.", 0, 1, 0.05));
	return types;
    }
    
}