/*	Copyright 2006, 2007 Brian Greer	This file is part of the Java NN Trainer.	Java NN Trainer 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.	Java NN Trainer 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 Java NN Trainer; if not, write to the Free Software	Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA*/package problems;import javax.swing.DefaultListModel;import java.util.HashMap;import algorithms.Trainer;import algorithms.TrainerListener;import algorithms.NeuralNetwork;public class XOR extends Problem{	public XOR(int numHidden, double minError, HashMap outputLists){		super(numHidden, minError, outputLists);		this.outputLists = outputLists;		numPatterns = 1000;		inputs = new double[numPatterns][2];		outputs = new double[numPatterns][1];		for(int i = 0; i < numPatterns; i++){			inputs[i][0] = random.nextInt(2);			inputs[i][1] = random.nextInt(2);			outputs[i][0] = (int)inputs[i][0] ^ (int)inputs[i][1];		}	}	public String testPattern(double [] input, NeuralNetwork nn){		int result = (int)input[0] ^ (int)input[1];		double [] output = new double[1];		nn.activate(input, output);		double nnResult = 0;		if(output[0] > 0.7)			nnResult = 1;		else if(output[0] > 0.3)			nnResult = 0.5;		String outputText = "[" + input[0] + ", " + input[1] + ", " + result + "] -> " + output[0] + " (" + nnResult + ")";		return outputText;	}	public void trainingGenerationComplete(NeuralNetwork nn, Trainer trainer){		if(nn != null && trainer != null){			DefaultListModel listModel = (DefaultListModel)outputLists.get(new Integer(trainer.getType()));			listModel.setSize(6);			double [] input = new double[2];			input[0] = 0; input[1] = 0;			listModel.setElementAt(testPattern(input, nn), 0);			input[0] = 0; input[1] = 1;			listModel.setElementAt(testPattern(input, nn), 1);			input[0] = 1; input[1] = 0;			listModel.setElementAt(testPattern(input, nn), 2);			input[0] = 1; input[1] = 1;			listModel.setElementAt(testPattern(input, nn), 3);			listModel.setElementAt(Double.toString(nn.getFitness()), 4);			if(trainer != null)				listModel.setElementAt(Integer.toString(trainer.getNumGenerations()), 5);			else				listModel.setElementAt("0", 5);		}	}}// vim:noet:ts=3:sw=3