KDGauss.h

// K-Dimensional Gauss function 
// Written by Sergey G. Kosov in 2012 - 2014 for Project X
#pragma once

#include "types.h"

namespace DirectGraphicalModels
{
    // ================================ KDGauss Class ==============================
    class CKDGauss
    {
    public:
        DllExport CKDGauss(dword k);
        DllExport CKDGauss(const Mat &mu);
        DllExport CKDGauss(const CKDGauss &rhs);

        DllExport CKDGauss & operator=  (const CKDGauss & rhs);
        DllExport CKDGauss & operator+= (const CKDGauss &rhs);
        DllExport CKDGauss & operator+= (const Mat &point);

        DllExport void          clear(void);
        DllExport bool          empty(void) const { return (m_nPoints == 0); }
        

        DllExport void          setNumPoints(long nPoints) { m_nPoints = nPoints; }
        DllExport size_t        getNumPoints(void) const { return m_nPoints; }
        DllExport void          setMu(Mat &mu);
        DllExport Mat           getMu(void) const { return m_mu.clone(); }
        DllExport void          setSigma(Mat &sigma);
        DllExport Mat           getSigma(void) const { return m_sigma.clone(); }
        

        DllExport void          addPoint(const Mat &point, bool approximate = false);
        DllExport long double   getAlpha(void) const;
        DllExport double        getValue(Mat &x, Mat &aux1 = EmptyMat, Mat &aux2 = EmptyMat, Mat &aux3 = EmptyMat) const;
        DllExport Mat           getSample(void) const;


        DllExport double        getEuclidianDistance(const Mat &x) const;
        DllExport double        getMahalanobisDistance(const Mat &x) const;
        DllExport double        getKullbackLeiberDivergence(const CKDGauss &x) const;


    private:
        static const bool USE_SAFE_SIGMA;

    
    private:
        size_t                m_nPoints;                // number of samples
        Mat                   m_mu;                     // The mathematical expectation \f$mu\f$: (size: k x 1; type: CV_64FC1)
        Mat                   m_sigma;                  // The covariance matrix \f$\Sigma\f$: (size: k x k; type: CV_64FC1)
        mutable Mat           m_sigmaInv    = Mat();    // the inverse to the <sigma> matrix
        mutable Mat           m_Q           = Mat();    // aux Mat for getSample()
        mutable long double   m_alpha       = -1;       // gaussian coefficient

    
    private:        
        Mat             getSigmaInv(void) const;
        inline void     reset_SigmaInv_Q_Alpha(void);
        Mat             calculateQ(void) const;
    };

    using GaussianMixture = std::vector<CKDGauss>;
}