work/git/mixpp/bdm/stat/merger.h

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#ifndef MER_H
#define MER_H

#include <itpp/itbase.h>
#include "libBM.h"
#include "libEF.h"
#include "emix.h"

using namespace itpp;
using std::string;

class merger : public mprod{
protected:      
        enorm<fsqmat> condpdf;
        Array<ivec> overlaps;
public:
        merger (const Array<mpdf*> &in_sources) : mprod(in_sources,true), condpdf(rvc), overlaps(rv.count()) {
                if (rvc.count()>0) {
                        vec mu = zeros(rvc.count());
                        mat R = 100*eye(rvc.count());
                        condpdf.set_parameters(mu,R);
                }
                for (int i=0;i<rv.count();i++){ // cycle over rv
                        ivec tmp(0);
                        for (int j=0;j<n;j++){//cycle over rvinds
                                ivec id=itpp::find(rvinds(j)==i);
                                tmp=concat(tmp,id); // add i to tmp if found
                        }
                        overlaps(i)=tmp;
                }
        }
/*      vec sample(double &w, const vec &smp0){
                // result
                vec smp=smp0;
                vec cond=condpdf.sample(); // Just like in samplecond, here it is not given!
                // temporary
                mat smpi=zeros(rv.count()+rvc.count(), n);
                vec condi;
                vec ll(n);
                
                // Consider arithmetic mean as a proposal density
                ll = 0;
                for ( int i = ( n - 1 );i >= 0;i-- ) {
                        if ( rvcinds ( i ).length() > 0 ) {
                                condi = zeros ( rvcinrv.length() + rvcinds.length() );
                                // copy data in condition
                                set_subvector ( condi,rvcinds ( i ), cond );
                                // copy data in already generated sample
                                set_subvector ( condi,rvcinrv ( i ), smp );

                                mpdfs ( i )->condition ( condi );
                        }
                        smpi.set_col(i, epdfs ( i )->sample());
                        // add ith likelihood contribution
                        ll+=epdfs(i)->evalpdflog(smpi);
                }
                // Now lets analyze samples smpi
                for (int i=0;i<rv.count();i++){
                        // number of components
                        int noc = overlaps(i).length();
                        if (noc<2) { // only one sample in this dimension, take it
                                smp(i) = smpi(i,overlaps(i)(0));
                        }
                        else {
                                //pick dimension
                                double unis = UniRNG.sample();
                                dim = 0;
                                while(unis<(dim+1)/noc) {dim++;} // 
                                //in dim we now have randomly picked dimension
                                smp(i) = smpi(i,overlaps(i)(dim));
                        }
                }
                
                // now we have sample how to evaluate weight?
                return smp;
                
                // copied from mprod.sample
        };*/
//      project 
        virtual ~merger() {};
};


#endif // MER_H

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