tanh.java

利用Java实现的神经网络工具箱

/*
 * $RCSfile: Tanh.java,v $
 * $Revision: 1.2 $
 * $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;

/**
 * Hyperbolic Tangent activation function.
 * 
 * @version $Revision: 1.2 $ - $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 Tanh extends ActivationFunction {
	/** Auto-reference to provide the unique class instance. */
	private static Tanh function;

	/**
	 * Creates a new Hyperbolic Tangent activation function.
	 */
	public Tanh() {
		functionName = "Tanh";
		functionExpression = "(exp(2 * x) - 1) / (exp(2 * x) + 1)";
		functionDerivativeExpression = "1 - Tanh(x)^2";
		
	} //Tanh()
	
	/**
	 * Returns the unique instance of <code>Tanh</code>.
	 * 
	 * @return Unique instance of <code>Tanh</code>.
	 */
	public static Tanh instantiate() {
		if(function == null) {
			function = new Tanh();
			
		}
		
		return function;
		
	} //instantiate()
	
	/* 
	 * @see neuralnetworktoolkit.activationfunctions.IActivationFunction#functionValue(double)
	 */
	public double functionValue(double input) {
		return (Math.exp(2*input)-1)/(Math.exp(2*input)+1);
		
	} //functionValue()

	/* 
	 * @see neuralnetworktoolkit.activationfunctions.IActivationFunction#functionDerivative(double)
	 */
	public double functionDerivative(double input) {
		return 1-functionValue(input)*functionValue(input);
		
	} //functionDerivative()

} //Tanh