feedforwardnn.h

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00001 /*************************************************************************** 00002 * Copyright (C) 2005 by Robot Group Leipzig * 00003 * martius@informatik.uni-leipzig.de * 00004 * fhesse@informatik.uni-leipzig.de * 00005 * der@informatik.uni-leipzig.de * 00006 * * 00007 * This program is free software; you can redistribute it and/or modify * 00008 * it under the terms of the GNU General Public License as published by * 00009 * the Free Software Foundation; either version 2 of the License, or * 00010 * (at your option) any later version. * 00011 * * 00012 * This program is distributed in the hope that it will be useful, * 00013 * but WITHOUT ANY WARRANTY; without even the implied warranty of * 00014 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * 00015 * GNU General Public License for more details. * 00016 * * 00017 * You should have received a copy of the GNU General Public License * 00018 * along with this program; if not, write to the * 00019 * Free Software Foundation, Inc., * 00020 * 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. * 00021 *************************************************************************** 00022 * * 00023 * DESCRIPTION * 00024 * * 00025 * $Log: feedforwardnn.h,v $ 00026 * Revision 1.5 2006/07/20 17:14:34 martius 00027 * removed std namespace from matrix.h 00028 * storable interface 00029 * abstract model and invertablemodel as superclasses for networks 00030 * 00031 * Revision 1.4 2006/07/18 14:49:48 martius 00032 * invertable networks provide the linear response function (jacobian) 00033 * 00034 * * 00035 ***************************************************************************/ 00036 #ifndef __FEEDFORWARDNN_H 00037 #define __FEEDFORWARDNN_H 00038 00039 #include "abstractmodel.h" 00040 #include <math.h> 00041 00042 typedef double (*ActivationFunction) (double); 00043 00044 /// abstract class (interface) for feed forward rate based neural networks 00045 class FeedForwardNN : public AbstractModel { 00046 public: 00047 FeedForwardNN(){}; 00048 virtual ~FeedForwardNN(){}; 00049 00050 /// damps the weights and the biases by multiplying (1-damping) 00051 virtual void damp(double damping) =0 ; 00052 00053 static double linear(double x) { return x;} 00054 static double dlinear(double ) { return 1;} 00055 static double tanh(double x) { return ::tanh(x); } 00056 static double dtanh(double x) { double k = ::tanh(x); return 1.01-k*k; } 00057 static double sigmoid(double x) { return 1/(1+exp(-x)); } 00058 static double dsigmoid(double x) { double k = sigmoid(x); return k*(1-k); } 00059 00060 }; 00061 00062 00063 #endif

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