logistic.java

利用Java实现的神经网络工具箱

/*
 * $RCSfile: Logistic.java,v $
 * $Revision: 1.1 $
 * $Date: 2004/10/17 01:35:30 $
 *
 * NeuralNetworkToolkit
 * Copyright (C) 2004 Universidade de Brasília
 *
 * This file is part of NeuralNetworkToolkit.
 *
 * NeuralNetworkToolkit 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.
 *
 * NeuralNetworkToolkit 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 NeuralNetworkToolkit; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA - 02111-1307 - USA.
 */

package neuralnetworktoolkit.activationfunctions;

/**
 * Logistic activation function.
 * 
 * @version $Revision: 1.1 $ - $Date: 2004/10/17 01:35:30 $
 * 
 * @author <a href="mailto:[e-mail]">Hugo Iver V. Gonçalves</a>
 * @author <a href="mailto:[e-mail]">Rodrigo C. M. Coimbra</a>
 */
public class Logistic extends ActivationFunction {
	/** Dafault steep value. */
	public static final double DEFAULT_STEEP = 1;
	/** Steep parameter value. */
	private double steep;
	/** Auto-reference to provide the unique class instance. */
	private static Logistic function;

	/**
	 * Creates a new Logistic activation function.
	 */
	public Logistic() {
		functionName = "Logistic";
		functionExpression = "1 / (1 + exp(-s * x))";
		functionDerivativeExpression = 
			"(s * exp(-s * s)) / ((1 + exp(-s * x)) * (1 + exp(-s * x)))";
		steep = DEFAULT_STEEP;
		
	} //Logistic()
	
	/**
	 * Returns the unique instance of <code>Logistic</code>.
	 * 
	 * @return Unique instance of <code>Logistic</code>.
	 */
	public static Logistic instantiate() {
		if(function == null) {
			function = new Logistic();
			
		}
		
		return function;
		
	} //instantiate()
	
	/* 
	 * @see neuralnetworktoolkit.activationfunctions.ActivationFunction#functionValue()
	 */
	public double functionValue(double input) {
		return 1 / (1 + Math.exp(-steep * input));
		
	} //functionValue

	/* 
	 * @see neuralnetworktoolkit.activationfunctions.ActivationFunction#functionDerivative()
	 */
	public double functionDerivative(double input) {
		return (steep * Math.exp(-steep * input))
			/ ((1 + Math.exp(-steep * input)) * (1 + Math.exp(-steep * input)));
			
	} //functionDerivative()
	
	/**
	 * Returns the steep parameter value.
	 * 
	 * @return Returns the steep.
	 */
	public double getSteep() {
		return steep;
		
	} //getSteep()
	
	/**
	 * Sets the steep parameter value.
	 * 
	 * @param steep The steep to set.
	 */
	public void setSteep(double steep) {
		this.steep = steep;
		
	} //setSteep()
	
} //Logistic