00001 /*************************************************************************** 00002 * Copyright (C) 2005-2011 LpzRobots development team * 00003 * Georg Martius <georg dot martius at web dot de> * 00004 * Frank Guettler <guettler at informatik dot uni-leipzig dot de * 00005 * Frank Hesse <frank at nld dot ds dot mpg dot de> * 00006 * Ralf Der <ralfder at mis dot mpg dot de> * 00007 * * 00008 * This program is free software; you can redistribute it and/or modify * 00009 * it under the terms of the GNU General Public License as published by * 00010 * the Free Software Foundation; either version 2 of the License, or * 00011 * (at your option) any later version. * 00012 * * 00013 * This program is distributed in the hope that it will be useful, * 00014 * but WITHOUT ANY WARRANTY; without even the implied warranty of * 00015 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * 00016 * GNU General Public License for more details. * 00017 * * 00018 * You should have received a copy of the GNU General Public License * 00019 * along with this program; if not, write to the * 00020 * Free Software Foundation, Inc., * 00021 * 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. * 00022 * * 00023 ***************************************************************************/ 00024 #ifndef __ABSTRACTMODEL_H 00025 #define __ABSTRACTMODEL_H 00026 00027 #include "matrix.h" 00028 #include "configurable.h" 00029 #include "storeable.h" 00030 #include "inspectable.h" 00031 #include "randomgenerator.h" 00032 00033 /// abstract class (interface) for a model that can be used by a controller 00034 class AbstractModel : public Configurable, public Storeable, public Inspectable { 00035 public: 00036 // 20110317, guettler: disabled default constructor since it is not needed and would cause difficulties 00037 //AbstractModel() {}; 00038 AbstractModel(const std::string& name, const std::string& revision) 00039 : Configurable(name, revision), Inspectable(name) {} 00040 virtual ~AbstractModel(){}; 00041 00042 /** initialisation of the network with the given number of input and output units 00043 @param inputDim length of input vector 00044 @param outputDim length of output vector 00045 @param unit_map if 0 the parametes are choosen randomly. 00046 Otherwise the model is initialised to represent a unit_map 00047 with the given response strength. 00048 @param randGen pointer to random generator, if 0 an new one is used 00049 */ 00050 virtual void init(unsigned int inputDim, unsigned int outputDim, 00051 double unit_map = 0.0, RandGen* randGen = 0) = 0; 00052 00053 /** passive processing of the input 00054 (this function is not constant since a recurrent network 00055 for example might change internal states 00056 */ 00057 virtual const matrix::Matrix process (const matrix::Matrix& input) = 0; 00058 00059 /* performs learning and returns the network output before learning. 00060 Neural networks process the input before. (no need to call process before) 00061 \param learnRateFactor can be given to modify eps for this learning step. 00062 */ 00063 virtual const matrix::Matrix learn (const matrix::Matrix& input, 00064 const matrix::Matrix& nom_output, 00065 double learnRateFactor = 1) = 0; 00066 00067 /// damps the weights and the biases by multiplying (1-damping) 00068 virtual void damp(double damping) = 0; 00069 00070 /// returns the number of input neurons 00071 virtual unsigned int getInputDim() const = 0; 00072 /// returns the number of output neurons 00073 virtual unsigned int getOutputDim() const = 0; 00074 00075 }; 00076 00077 00078 #endif