Kohonen.h

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#ifndef KOHONEN_H
#define KOHONEN_H

#include "Exception.h"
#include "Network.h"
#include "SimpleNeuron.h"
#include "InputNeuron.h"
#include "InputLayer.h"
#include <string>

namespace annie
{
class TrainingSet;
/*
OPT: this could be much more tuned... no activationFunction, .., hasBias..
TODO: think about labels..
 the neuron output gives DISTANCE of the neuron from the input.
 TODO activation == output
*/
class KohonenNeuron : public SimpleNeuron   {
public:
    KohonenNeuron(int label);
    static const real RANDOM_WEIGHTS_DEV;   //devation of ramdom weights from mid
    void adaptTowards(const Vector &input, real nbWeight, real learningParam);
    void randomizeWeights();
protected:
    virtual void _recacheOutput() const;
    void setWeights(const Vector &w);
};


#if 0
class Topology  {
public:
    real getNeighborWeight(uint srcNeuron, destNeuron, real nbSize);

};
#endif

class Topology  {
public:
    virtual real getNeighborWeight(uint srcNeuron, uint destNeuron, real nbSize) const = 0;

    virtual bool isGridNeighbor(uint srcNeuron, uint destNeuron) const = 0;
    
    uint getTotalCount() const { return totalCount; }

    real getMaxDim() const  { return maxDim; }
protected:
    uint totalCount;
    real maxDim;
};

class KohonenParameters {
public:
    static const char * const NB, *const LP;
    KohonenParameters(real initNB, real initLP, PublicValues &ctrl=defaultControl) : control(ctrl)  { setNB(initNB); setLP(initLP); }

    virtual void nextIteration()    = 0;

    real getNeighborhoodSize() const;

    real getLearningParam() const;
protected:
    void setNB(real nb);
    void setLP(real lp);

    PublicValues &control;
};

class StaticKohonenParameters   : public KohonenParameters {
public:
    StaticKohonenParameters(real initNB, real initLP) : KohonenParameters(initNB, initLP)   {}
    virtual void nextIteration()    {}
};

/*abstract*/ class DynamicKohonenParameters : public KohonenParameters {
public:
    /* setting 'em initially to 0, because they'll be replaced in  nextIteration()*/
    virtual void nextIteration()    { ++_step; recompute(); }
    virtual void recompute() = 0;
    uint getStep() const { assert(_step); return _step; }
protected:
    DynamicKohonenParameters() : KohonenParameters(1., 1.), _step(1)    { }
private:
    uint _step; //time step; \in [1, ..]
};

class StandardKohonenParameters : public DynamicKohonenParameters {
public:
    StandardKohonenParameters(real maxNB, real slope) : _slope(-slope), _maxNB(maxNB) {  }
    virtual void recompute();
protected:
    real _slope, _maxNB;
};

/*
a ~ t^-p

--> e^-p*log^2(t)
*/

//template <class Topology> ???give + and -ses for templatization..

class KohonenNetwork : public Network
{
public:
    enum { INPUT_LAYER, OUTPUT_LAYER };
    static const real MAX_OUTPUT, MIN_OUTPUT;
    static const real TERMINATING_LP;   

    KohonenNetwork(uint inputs, const Topology &topology, KohonenParameters &params, PublicValues &ctrl=defaultControl);
    void train(TrainingSet &T, real terminatingLP=TERMINATING_LP);

    void trainExample(const Vector &input);
    
    //void train(TrainingSet &T, uint epochs);

    uint getWinnerOutput(const Vector &input);
    
    uint getWinner() const;

    virtual Vector getOutput(const Vector &input);
    const Layer &getOutputLayer() const { return _outputLayer; }
    virtual const char *getClassName() const    { return "KohonenNetwork"; }

    virtual Vector getCluster(uint cluserNum) const;

    virtual void save(const std::string &filename)  { throw Exception("not implemented"); } //TODO

    const Topology &getTopology() const { return topology; }
protected:
    void connectLayers();
    void randomizeWeights();
    real getNBSize() const  { return parameters.getNeighborhoodSize(); }
    real getLearningParam() const   { return parameters.getLearningParam(); }
    void _setInput(const Vector &input);

    InputLayer _inputLayer;
    Layer _outputLayer;
    const Topology &topology;
    KohonenParameters &parameters;
    PublicValues &control;
    mutable uint _winner;   //<current winner
    mutable bool _winnerValid;
};

}   //annie
#endif //_H

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