TrainNodeGMM.h

// Gaussian Mixture Model training class interface
// Written by Sergey G. Kosov in 2012 - 2014 for Project X
// Refactored by Sergey G. Kosov in 2017 for Project X
#pragma once

#include "TrainNode.h"
#include "KDGauss.h"

namespace DirectGraphicalModels
{
    typedef struct TrainNodeGMMParams {
        word    maxGausses;                 
        size_t  minSamples;                 
        double  dist_Etreshold;             
        double  dist_Mtreshold;             
        double  div_KLtreshold;             

        TrainNodeGMMParams() {}
        TrainNodeGMMParams(word _maxGausses, size_t _minSamples, double _dist_Etreshold, double _dist_Mtreshold, double _div_KLtreshold) : maxGausses(_maxGausses), minSamples(_minSamples), dist_Etreshold(_dist_Etreshold), dist_Mtreshold(_dist_Mtreshold), div_KLtreshold(_div_KLtreshold) {}
    } TrainNodeGMMParams;

    const TrainNodeGMMParams TRAIN_NODE_GMM_PARAMS_DEFAULT = TrainNodeGMMParams(
        64,     // maxGausses
        64,     // min_samples
        64,     // dist_Etreshold
        -16,    // dist_Mtreshold
        -16     // div_KLtreshold
    );

    // ==================== Gaussian Mixture Model Train Class =====================
    class CTrainNodeGMM : public CTrainNode
    {
    public:
        DllExport CTrainNodeGMM(byte nStates, word nFeatures, TrainNodeGMMParams params = TRAIN_NODE_GMM_PARAMS_DEFAULT);
        DllExport CTrainNodeGMM(byte nStates, word nFeatures, byte maxGausses);
        DllExport virtual ~CTrainNodeGMM(void);

        DllExport void  reset(void);

        DllExport void  addFeatureVec(const Mat &featureVector, byte gt);
        DllExport void  train(bool doClean = false);


    protected:
        DllExport void  saveFile(FILE *pFile) const;
        DllExport void  loadFile(FILE *pFile);
        DllExport void calculateNodePotentials(const Mat &featureVector, Mat &potential, Mat &mask) const;


    private:
        static const size_t             MIN_SAMPLES;
        static const long double        MAX_COEFFICIENT;


    private:
        TrainNodeGMMParams              m_params;
        std::vector<GaussianMixture>    m_vGaussianMixtures;                        // block of n-dimensional Gauss function    
        long double                     m_minAlpha = 1;                             // auxilary coefficient for scaling gaussian coefficients
    };
}