kerdensom.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'
 ***************************************************************************/

//-----------------------------------------------------------------------------
// KerDenSOM.hh
// Implements Smoothly Distributed Kernel Probability Density Estimator Self-Organizing Map
// This is an abstract base class for different variants of the KerDenSOM algorithm
//-----------------------------------------------------------------------------

#ifndef XMIPPKERDENSOM_H
#define XMIPPKERDENSOM_H

#include "base_algorithm.h"
#include "map.h"

class KerDenSOM : public ClassificationAlgorithm<FuzzyMap>
{
public:

    KerDenSOM(double _reg0, double _reg1, unsigned long _annSteps,
                   double _epsilon, unsigned long _nSteps)
            : ClassificationAlgorithm<FuzzyMap>(), annSteps(_annSteps), reg0(_reg0), reg1(_reg1),
            epsilon(_epsilon), somNSteps(_nSteps)
    {};

    virtual ~KerDenSOM()
    {};

    void nSteps(const unsigned long& _nSteps);

#ifdef UNUSED // detected as unused 29.6.2018

    virtual double getSigma();
#endif

    void setAnnSteps(const unsigned long& _annSteps);


    virtual void train(FuzzyMap& _som, TS& _examples, FileName& _fn_vectors,
                       bool _update = false, double _sigma = 0, bool _saveIntermediate=false) = 0;

    virtual double test(const FuzzyMap& _som, const TS& _examples) const;


    virtual double functional(const TS* _examples, const FuzzyMap* _som, double _sigma, double _reg, double& _likelihood, double& _penalty) = 0;

#ifdef UNUSED // detected as unused 29.6.2018

    virtual double randApproxGVC(const TS* _examples, const FuzzyMap* _som, double _dataSD, double _reg);
#endif

protected:

    double sigma;        // Optimum Kernel Width
    size_t annSteps;    // number of deterministic annealing steps
    double reg0;
    double reg1;   // Regularization factors
    double epsilon;      // Stopping criterion Error < epsilon
    size_t somNSteps;   // number of steps


    // Internal Scratch

    size_t numNeurons;
    size_t numVectors;
    size_t dim;
    std::vector < std::vector<double> > tmpMap;
    std::vector<double> tmpD;
    std::vector<double> tmpD1;
    std::vector<double> tmpDens;
    std::vector<double> tmpV;


    virtual void train(FuzzyMap& _som, const TS& _examples) const
        {};

    /* Declaration of abstract methods */

    // Update Us
    virtual double updateU(FuzzyMap* _som, const TS* _examples, const double& _sigma, double& _alpha) = 0;

    // Estimate Sigma II
    virtual double updateSigmaII(FuzzyMap* _som, const TS* _examples, const double& _reg, const double& _alpha) = 0;

    // Estimate the PD (Method 1: Using the code vectors)
    virtual double codeDens(const FuzzyMap* _som, const FeatureVector* _example, double _sigma) const = 0;

#ifdef UNUSED // detected as unused 29.6.2018
    // Estimate the PD (Method 2: Using the data)
    virtual double dataDens(const TS* _examples, const FeatureVector* _example, double _sigma) const = 0;
#endif

    /* Some other common methods */

    // Update Code vectors
    virtual void updateV(FuzzyMap* _som, const TS* _examples, const double& _sigma);

    // Main iterations
    virtual double mainIterations(FuzzyMap* _som, const TS* _examples, double& _sigma, const double& _reg);

    // Special Initialization of the Us
    virtual void initU(FuzzyMap* _som);

    // Special initialization of Code vectors
    virtual void updateV1(FuzzyMap* _som, const TS* _examples);

    // Special initialization of Membership Matrix
    virtual void updateU1(FuzzyMap* _som, const TS* _examples);

    // Estimate Sigma I
    virtual double updateSigmaI(FuzzyMap* _som, const TS* _examples);

    // Some printing methods.
    void showX(const TS* _ts);
    void showV(FuzzyMap* _som);
    void showU(FuzzyMap* _som, const TS* _ts);
#ifdef UNUSED // detected as unused 29.6.2018
    void printV(FuzzyMap* _som, const TS* _ts, FileName& _fname);
#endif

};

#endif