abstractmodel.h

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/***************************************************************************
 *   Copyright (C) 2005 by Robot Group Leipzig                             *
 *    martius@informatik.uni-leipzig.de                                    *
 *    fhesse@informatik.uni-leipzig.de                                     *
 *    der@informatik.uni-leipzig.de                                        *
 *                                                                         *
 *   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.                                   *
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 *   This program is distributed in the hope that it will be useful,       *
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 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the         *
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 ***************************************************************************
 *                                                                         *
 *  DESCRIPTION                                                            *
 *                                                                         *
 *   $Log: abstractmodel.h,v $
 *   Revision 1.2  2006/07/27 15:27:20  martius
 *   processing should be called before learning
 *
 *   Revision 1.1  2006/07/20 17:14:34  martius
 *   removed std namespace from matrix.h
 *   storable interface
 *   abstract model and invertablemodel as superclasses for networks
 *
 *
 *                                                                 *
 ***************************************************************************/
#ifndef __ABSTRACTMODEL_H
#define __ABSTRACTMODEL_H

#include "matrix.h"
#include "configurable.h"
#include "storeable.h"

/// abstract class (interface) for a model that can be used by a controller
class AbstractModel : public Configurable, public Storeable {
 public: 
  AbstractModel(){};
  virtual ~AbstractModel(){};

  /** initialisation of the network with the given number of input and output units
      @param inputDim length of input vector
      @param outputDim length of output vector
      @param unit_map if 0 the parametes are choosen randomly. 
             Otherwise the model is initialised to represent a unit_map with the given response strength.
   */
  virtual void init(unsigned int inputDim, unsigned  int outputDim, double unit_map = 0.0) = 0;

  /** passive processing of the input
     (this function is not constant since a recurrent network for example might change internal states
  */
  virtual const matrix::Matrix process (const matrix::Matrix& input) = 0;

  /* performs learning and returns the network output before learning
     learnRateFactor can be given to modify eps for this learning step
     (process should be called before)
  */
  virtual const matrix::Matrix learn (const matrix::Matrix& input, 
                                      const matrix::Matrix& nom_output, 
                                      double learnRateFactor = 1) = 0;

  /// returns the number of input neurons
  virtual unsigned int getInputDim() const = 0;
  /// returns the number of output neurons
  virtual unsigned int getOutputDim() const = 0;

};


#endif

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