/*	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 algorithms;import java.util.Random;import java.io.FileOutputStream;import java.io.FileInputStream;import java.io.ObjectOutputStream;import java.io.ObjectInputStream;public class NeuralNetwork{	private double fitness = 0.0;	private int numInput = 4;	private int numHidden = 20;	private int numOutput = 5;	private double inWeights[][] = null;	private double outWeights[][] = null;	private static Random random = new Random(System.currentTimeMillis());	public NeuralNetwork(int numInput, int numHidden, int numOutput){		this.numInput = numInput;		this.numHidden = numHidden;		this.numOutput = numOutput;		reset();	}	private void reset(){		inWeights = new double[numInput][numHidden];		outWeights = new double[numHidden][numOutput];		randomNet();	}	public NeuralNetwork copy(){		NeuralNetwork nn = new NeuralNetwork(numInput, numHidden, numOutput);		double [][] weights = nn.getInWeights();		for(int i = 0; i < numInput; i++)			for(int j = 0; j < numHidden; j++)				weights[i][j] = inWeights[i][j];		weights = nn.getOutWeights();		for(int i = 0; i < numHidden; i++)			for(int j = 0; j < numOutput; j++)				weights[i][j] = outWeights[i][j];		return nn;	}	public double[][] getInWeights(){		return inWeights;	}	public double[][] getOutWeights(){		return outWeights;	}	public int getNumInput(){		return numInput;	}	public int getNumHidden(){		return numHidden;	}	public int getNumOutput(){		return numOutput;	}	private void randomNet(){		for(int i = 0; i < numInput; i++)			for(int j = 0; j < numHidden; j++)				inWeights[i][j] = random.nextGaussian();		for(int i = 0; i < numHidden; i++)			for(int j = 0; j < numOutput; j++)				outWeights[i][j] = random.nextGaussian();	}	public void save(String fileName){		try{			ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(fileName));			oos.writeObject(inWeights);			oos.writeObject(outWeights);			oos.close();		}		catch(Exception e){			e.printStackTrace();		}	}	public void load(String fileName){		try{			ObjectInputStream ois = new ObjectInputStream(new FileInputStream(fileName));			inWeights = (double[][])ois.readObject();			outWeights = (double[][])ois.readObject();			numInput = inWeights.length;			numHidden = outWeights.length;			numOutput = outWeights[0].length;			ois.close();		}		catch(Exception e){			e.printStackTrace();		}	}	private double sigmoid(double x){		if(x > 15.0)			return 1.0;		else if(x < -15.0)			return 0.0;		else			return (1.0 / (1.0 + Math.exp(-x)));	}	private double symmetricSigmoid(double x){		if(x > 15.0)			return 0.5;		else if(x < -15.0)			return -0.5;		else			return (1.0 / (1.0 + Math.exp(-x)) - 0.5);	}	public void activate(double[] inputs, double[] outputs){		double hidden[] = new double[numHidden];		activate(inputs, hidden, outputs);	}	public void activate(double[] inputs, double [] hidden, double[] outputs){		for(int j = 0; j < numHidden; j++){			hidden[j] = 0;			for(int i = 0; i < numInput; i++)				hidden[j] += inputs[i] * inWeights[i][j];			hidden[j] = sigmoid(hidden[j]);		}		for(int j = 0; j < numOutput; j++){			outputs[j] = 0;			for(int i = 0; i < numHidden; i++)				outputs[j] += hidden[i] * outWeights[i][j];			outputs[j] = sigmoid(outputs[j]);		}	}	public double getFitness(){		return fitness;	}	public void setFitness(double fitness){		this.fitness = fitness;	}	public static double sumSquaredError(double [] outputs, double [] targets){		double error = 0.0;		int numOutput = outputs.length;		for(int i = 0; i < numOutput; i++){			double diff = outputs[i] - targets[i];			error += diff * diff;		}		return Math.sqrt(error);	}	public double evaluate(double [] inputs, double [] targets){		double [] outputs = new double[numOutput];		activate(inputs, outputs);		fitness = sumSquaredError(outputs, targets);		return fitness;	}	public double evaluate(double [][] inputs, double [][] targets){		int numPatterns = inputs.length;		fitness = 0;		if(numPatterns > 0){			double [] outputs = new double[numOutput];			for(int i = 0; i < numPatterns; i++){				activate(inputs[i], outputs);				fitness += sumSquaredError(outputs, targets[i]);			}			fitness /= numPatterns;		}		return fitness;	}}// vim:noet:ts=3:sw=3