root/bdm/stat/emix.h @ 115

/*!
  \file
  \brief Probability distributions for Mixtures of pdfs
  \author Vaclav Smidl.

  -----------------------------------
  BDM++ - C++ library for Bayesian Decision Making under Uncertainty

  Using IT++ for numerical operations
  -----------------------------------
*/

#ifndef MX_H
#define MX_H

#include "libBM.h"
#include "libEF.h"
//#include <std>

using namespace itpp;

/*!
* \brief Mixture of epdfs

Density function:
\f[
f(x) = \sum_{i=1}^{n} w_{i} f_i(x), \quad \sum_{i=1}^n w_i = 1.
\f]
where \f$f_i(x)\f$ is any density on random variable \f$x\f$, called \a component,

*/
class emix : public epdf {
protected:
        //! weights of the components
        vec w;
        //! Component (epdfs)
        Array<epdf*> Coms;
public:
        //!Default constructor
        emix ( RV &rv ) : epdf ( rv ) {};
        //! Set weights \c w and components \c R
        void set_parameters ( const vec &w, const Array<epdf*> &Coms );

        vec sample() const;
        vec mean() const {
                int i; vec mu=zeros ( rv.count() );
                for ( i=0;i<w.length();i++ ) {mu+=w ( i ) *Coms ( i )->mean(); }
                return mu;
        }
        double evalpdflog ( const vec &val ) const {
                int i;
                double sum=0.0;
                for ( i=0;i<w.length();i++ ) {sum+=w ( i ) *Coms ( i )->evalpdflog ( val );}
                return log ( sum );
        };

//Access methods
        //! returns a pointer to the internal mean value. Use with Care!
        vec& _w() {return w;}
};

/*! \brief Chain rule decomposition of epdf


*/
class eprod: public epdf {
protected:
        Array<epdf*> epdfs;
        Array<mpdf*> mpdfs;
public:
        
        
};

#endif //MX_H