sigmoid.java

JaNet: Java Neural Network Toolkit resume: A well documented toolkit for designing and training, a

////////////////////////////////////////////////////////////////////////////////////  
//  
//  Copyright (C) 1996 L.  Patocchi & W.Gander
//
//  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., 675 Mass
//  Ave, Cambridge, MA 02139, USA.
//
//  Contacts:
//  
//    Project Supervisor
//      W.Hett       [e-mail]
//  
//    Authors
//      W.Gander     [e-mail]
//      L.Patocchi   [e-mail]
//
//  Documentation can be found at:
//
//      http://www.isbiel.ch/Projects/janet/index.html
//
////////////////////////////////////////////////////////////////////////////////////
//
// File : Sigmoid.java
//
/*

              Sigmoid(x)
                 ^
                 |+1  ,-------
                 |   /
                 |  /
                 | /
                 |/
     ------------/-0----------> x
                /|
               / |
              /  |
             /   |
     -------'    |-1



*/

package jaNet.backprop;

class Sigmoid implements activationFn{
	
	public double activation(double x){
//		double em = Math.exp(-1.0*x);
//		double ep = Math.exp(x);
//		double out = (ep-em)/(ep+em);
//		return out;
		return 2.0/(1.0 + Math.exp((-1.0)*x)) - 1.0;
	}
	
	public double activation1stD(double x){
//		double em = Math.exp(-1.0*x);
//		double ep = Math.exp(x);
//		double out = 1.0 - ((ep-em)*(ep-em)/(em+ep)*(em+ep));
//		return out ;
		double expX = Math.exp(x);
		return 2.0 * expX/( (1+expX) * (1+expX) );
	}
	
}