training_vector.h

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/***************************************************************************
 *
 * Authors:     Alberto Pascual Montano (pascual@cnb.csic.es)
 *
 * Unidad de  Bioinformatica of Centro Nacional de Biotecnologia , CSIC
 *
 * 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., 59 Temple Place, Suite 330, Boston, MA
 * 02111-1307  USA
 *
 *  All comments concerning this program package may be sent to the
 *  e-mail address 'xmipp@cnb.csic.es'
 ***************************************************************************/

//-----------------------------------------------------------------------------
// ClassicTrainingVectors.hh
//-----------------------------------------------------------------------------

#ifndef XMIPPCTVECTORS_H
#define XMIPPCTVECTORS_H

#include <sstream>
#include <core/xmipp_filename.h>
#include "data_types.h"
#include "training_set.h"
#include "vector_ops.h"

class ClassicTrainingVectors : public ClassificationTrainingSet<FeatureVector, Label>
{
public:

    typedef struct stats
    {
        floatFeature mean;
        floatFeature sd;
    }
    statsStruct;


    ClassicTrainingVectors(unsigned _vecSize = 0, bool _calib = true)
            : ClassificationTrainingSet<FeatureVector, Label>(_calib),  /*varStats(_vecSize),*/
            normalized(false)
    {}
    ;

    ClassicTrainingVectors(std::istream & _is);

    ClassicTrainingVectors(const ClassicTrainingVectors &op1);

    virtual ~ClassicTrainingVectors()
    {}
    ;

#ifdef UNUSED // detected as unused 29.6.2018

    unsigned featureSize() const;
#endif

    unsigned dimension() const;

    void clear();

    virtual void printSelf(std::ostream& _os) const;

    virtual void readSelf(std::istream& _is);

    void read(const FileName & fnIn);

    virtual void saveObject(std::ostream& _os) const;


    virtual void loadObject(std::istream& _is);

#ifdef UNUSED // detected as unused 29.6.2018

    void deleteVariable(int _var);
#endif

    ClassicTrainingVectors& operator= (const ClassicTrainingVectors &op1);

#ifdef UNUSED // detected as unused 29.6.2018

    bool copyStructure(ClassicTrainingVectors& _ts);


    bool insertRowFrom(ClassicTrainingVectors& _ts, unsigned int _idx);

    bool deleteRow(unsigned int _idx);
#endif

    virtual void normalizeFeature(unsigned _i);


    void normalize();

    virtual void unNormalize();

#ifdef UNUSED // detected as unused 29.6.2018

    virtual double getUnormalizedVar(unsigned _item, unsigned _var) const;
#endif

    bool isNormalized() const;

    virtual const std::vector<statsStruct>& getNormalizationInfo() const
    {
        return varStats;
    };

    void getFeatureStats(unsigned _i, floatFeature& _mean, floatFeature& _sd);

#ifdef UNUSED // detected as unused 29.6.2018

    ClassicTrainingVectors getStatVector();
#endif

protected:
    std::vector <statsStruct>  varStats;
    bool    normalized;

};
#endif//XMIPPCTVECTORS_H