Robot Simulator of the Robotics Group for Self-Organization of Control: elman.h Source File

# elman.h

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    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 __ELMAN_H
    00025 #define __ELMAN_H
    00026
    00027 #include <vector>
    00028
    00029 #include "multilayerffnn.h"
    00030
    00031 /// updates for network
    00032 class NetUpdate {
    00033 public:
    00034   NetUpdate(){}
    00035   NetUpdate(int numweights, int numbias, int numothers)
    00036     : weights(numweights), bias(numweights), other(numothers) {}
    00037   std::vector<matrix::Matrix> weights;
    00038   std::vector<matrix::Matrix> bias;
    00039   std::vector<matrix::Matrix> other;
    00040 };
    00041
    00042 /** Multilayer Neural Network with context neurons (after Elman and Jordan)
    00043 Example of 2 hidden layer network with both, elman and jordan context units.
    00044 \pre{
    00045 +--<-----O O O
    00046 |        | | |
    00047 |        H H H
    00048 |        | | |
    00049 |        | | |
    00050 |        | | |
    00051 |        H H H ----->-----+ 1:1 fixed connections (time delayed)
    00052 |   >->-/| | |\-<-<       |
    00053 |  / / / | | | \ \ \      |
    00054 | J J J  I I I  E E E     |
    00055 +-^-^-^         ^-^-^--<--+
    00056 }
    00057  */
    00058 class Elman : public MultiLayerFFNN {
    00059 public:
    00060   /**
    00061      @param eps learning rate
    00062      @param layers Layer description (the input layer is not specified (always linear))
    00063      @param lambda self-recurrent feedback strength of context neurons
    00064   */
    00065   Elman(double eps, const std::vector<Layer>& layers,
    00066         bool useElman, bool useJordan=false, bool useBypass=false)
    00067     : MultiLayerFFNN(eps,layers,useBypass), useElman(useElman), useJordan(useJordan) {
    00068
    00069     initialised = false;
    00070   }
    00071
    00072   virtual ~Elman(){ }
    00073
    00074   /// initialisation of the network with the given number of input and output units
    00075   virtual void init(unsigned int inputDim, unsigned  int outputDim,
    00076                     double unit_map = 0.0, RandGen* randGen = 0);
    00077
    00078   /** passive processing of the input
    00079       (this will be different for every input, since it is a recurrent network)
    00080   */
    00081   virtual const matrix::Matrix process (const matrix::Matrix& input);
    00082
    00083   /// performs learning and returns the network output before learning
    00084   virtual const matrix::Matrix learn (const matrix::Matrix& input,
    00085                                       const matrix::Matrix& nom_output,
    00086                                       double learnRateFactor = 1);
    00087
    00088   /** determines the weight and bias updates
    00089    */
    00090   virtual NetUpdate weightIncrement(const matrix::Matrix& xsi);
    00091
    00092   /** like weightIncrement but with blocked backprop flow for some neurons.
    00093       @param blockedlayer index of layer with blocked neurons
    00094       @param blockfrom index of neuron in blockedlayer to start blocking
    00095       @param blockto index of neuron in blockedlayer to end blocking (if -1 then to end)
    00096       (not included)
    00097    */
    00098   virtual NetUpdate weightIncrementBlocked(const matrix::Matrix& xsi_,
    00099                                            int blockedlayer,
    00100                                            int blockfrom, int blockto);
    00101
    00102
    00103   /** applies the weight increments to the weight (and bias) matrices
    00104       with the learningrate and the learnRateFactor */
    00105   virtual void updateWeights(const NetUpdate& updates);
    00106
    00107
    00108   /* Is implemented in multilayerfnn
    00109      virtual const matrix::Matrix response(const matrix::Matrix& input) const;
    00110    */
    00111
    00112   void damp(double damping);
    00113
    00114   /**************  STOREABLE **********************************/
    00115   /// stores the layer binary into file stream
    00116   bool store(FILE* f) const;
    00117   /// restores the layer binary from file stream
    00118   bool restore(FILE* f);
    00119
    00120
    00121   /************** CONFIGURABLE INTERFACE ************************/
    00122   virtual paramkey getName() const {
    00123     return std::string("elmanNN");
    00124   }
    00125
    00126
    00127
    00128   /************** Inspectable INTERFACE ************************/
    00129   virtual iparamkeylist getInternalParamNames() const;
    00130   virtual iparamvallist getInternalParams() const;
    00131   virtual ilayerlist getStructuralLayers() const;
    00132   virtual iconnectionlist getStructuralConnections() const;
    00133
    00134 protected:
    00135   matrix::Matrix elmanWeights;
    00136   matrix::Matrix elmanContext;
    00137   matrix::Matrix jordanWeights;
    00138   matrix::Matrix jordanContext;
    00139   bool useElman;
    00140   bool useJordan;
    00141
    00142 };
    00143
    00144 #endif