include/learningalgorithm.h

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/********************************************************************************
 *  Neural Network Framework.                                                   *
 *  Copyright (C) 2005-2008 Gianluca Massera <emmegian@yahoo.it>                *
 *                                                                              *
 *  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.                                         *
 *                                                                              *
 *  This program is distributed in the hope that it will be useful,             *
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of              *
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               *
 *  GNU General Public License for more details.                                *
 *                                                                              *
 *  You should have received a copy of the GNU General Public License           *
 *  along with this program; if not, write to the Free Software                 *
 *  Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA  *
 ********************************************************************************/

#ifndef LEARNINGALGORITHM_H
#define LEARNINGALGORITHM_H

#include "types.h"
#include "neuralnet.h"
#include <map>
#include <cmath>

namespace nnfw {

class BaseNeuralNet;

class NNFW_API Pattern {
public:
    class PatternInfo {
    public:
        RealVec inputs;
        RealVec outputs;
    };

    Pattern() : pinfo(), empty() { /*nothing to do*/ };
    ~Pattern() { /*nothing to do*/ };


    void setInputsOf( Cluster*, const RealVec& );
    void setOutputsOf( Cluster*, const RealVec& );
    void setInputsOutputsOf( Cluster*, const RealVec& inputs, const RealVec& outputs );

    const RealVec& inputsOf( Cluster* ) const;
    const RealVec& outputsOf( Cluster* ) const;

    PatternInfo& operator[]( Cluster* );

private:
    mutable std::map<Cluster*, PatternInfo> pinfo;
    RealVec empty;
};

class NNFW_API PatternSet : public VectorData<Pattern> {
public:

    PatternSet() : VectorData<Pattern>() { };

    PatternSet( u_int size ) : VectorData<Pattern>( size ) { };

    PatternSet( u_int size, Pattern& pat ) : VectorData<Pattern>( size, pat ) { };
    
    PatternSet( PatternSet& src, u_int idS, u_int idE ) : VectorData<Pattern>( src, idS, idE ) { };

    PatternSet( const PatternSet& src ) : VectorData<Pattern>( src ) { };
};

class NNFW_API LearningAlgorithm {
public:

    LearningAlgorithm( BaseNeuralNet* net );
    virtual ~LearningAlgorithm();



    BaseNeuralNet* net() {
        return netp;
    };

    virtual void learn() = 0;

    virtual void learn( const Pattern& ) = 0;

    virtual void learnOnSet( const PatternSet& set ) {
        for( int i=0; i<(int)set.size(); i++ ) {
            learn( set[i] );
        }
    };

    virtual Real calculateMSE( const Pattern& ) = 0;
    
    virtual Real calculateMSEOnSet( const PatternSet& set ) {
        Real mseacc = 0.0;
        int dim = (int)set.size();
        for( int i=0; i<dim; i++ ) {
            mseacc += calculateMSE( set[i] );
        }
        return mseacc/dim;
    };

    Real calculateRMSD( const Pattern& p ) {
        return sqrt( calculateMSE( p ) );
    };

    Real calculateRMSDOnSet( const PatternSet& p ) {
        return sqrt( calculateMSEOnSet( p ) );
    };


private:
    BaseNeuralNet* netp;
};

}

#endif

Copyright © 2005-2008 G. Massera

Contact: emmegian (at) yahoo (dot) it

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