learningalgorithm.h

/********************************************************************************
 *  Neural Network Framework.                                                   *
 *  Copyright (C) 2005-2011 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 "nnfwconfig.h"
#include "neuralnet.h"
#include <QMap>
#include <QVector>
#include <cmath>
#include <parametersettable.h>
#include <configurationparameters.h>

namespace farsa {

class NeuralNet;

class FARSA_NNFW_API Pattern : public ParameterSettableWithConfigureFunction {
public:
    class PatternInfo {
    public:
        DoubleVector inputs;
        DoubleVector outputs;
    };
    Pattern() : ParameterSettableWithConfigureFunction(), pinfo() { /*nothing to do*/ };
    ~Pattern() { /*nothing to do*/ };
    void setInputsOf( Cluster*, const DoubleVector& );
    void setOutputsOf( Cluster*, const DoubleVector& );
    void setInputsOutputsOf( Cluster*, const DoubleVector& inputs, const DoubleVector& outputs );
    const DoubleVector& inputsOf( Cluster* ) const;
    const DoubleVector& outputsOf( Cluster* ) const;
    PatternInfo& operator[]( Cluster* );
    virtual void configure(ConfigurationParameters& params, QString prefix);
    virtual void save(ConfigurationParameters& params, QString prefix);
    static void describe( QString type );
private:
    mutable QMap<Cluster*, PatternInfo> pinfo;
};

typedef QVector<Pattern> PatternSet;

class FARSA_NNFW_API LearningAlgorithm : public ParameterSettableWithConfigureFunction {
public:
    LearningAlgorithm( NeuralNet* net );
    LearningAlgorithm();
    virtual ~LearningAlgorithm();
    void setNeuralNet( NeuralNet* net ) {
        netp = net;
        this->neuralNetChanged();
    };
    NeuralNet* neuralNet() {
        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 double calculateMSE( const Pattern& ) = 0;
    virtual double calculateMSEOnSet( const PatternSet& set ) {
        double mseacc = 0.0;
        int dim = (int)set.size();
        for( int i=0; i<dim; i++ ) {
            mseacc += calculateMSE( set[i] );
        }
        return mseacc/dim;
    };
    double calculateRMSD( const Pattern& p ) {
        return sqrt( calculateMSE( p ) );
    };
    double calculateRMSDOnSet( const PatternSet& p ) {
        return sqrt( calculateMSEOnSet( p ) );
    };
    PatternSet loadPatternSet( ConfigurationParameters& params, QString path, QString prefix );
    void savePatternSet( PatternSet& set, ConfigurationParameters& params, QString prefix );
protected:
    virtual void neuralNetChanged() = 0;
private:
    NeuralNet* netp;
};

}

#endif