example_ceo_model_fusion2.cpp

 
#include <Pds/Ra>
#include <Pds/It>
 
int main(void)
{
    unsigned int L=100000;
    
    // Dados do canal
    double ps=0.5;
    Pds::Vector f0={0.2,0.4,0.6,0.8,1};
    Pds::Vector p;
    Pds::Vector p_exp;
    
    unsigned int N=24;
    Pds::Vector Rho=Pds::GeomSpace(0.02,0.5,N);
    Pds::Vector BER(N);
    Pds::Vector BERhcond(N);
    
    for(unsigned int n=0;n<N;n++)
    {
        if(n==0)    L=200/1.0e-5;
        else        L=200/BER[n-1];
        
        p=f0*Rho[n];
        
        // Source Us
        Pds::Vector Us(L);
        Us.FillRandC(ps);
        
        // Input channel U
        Pds::Matrix U=Pds::ChannelModel::Bsc(Us,p,p_exp);
        
        // Second fusion method: NaiveBayes 
        Pds::Vector Unb=Pds::Ceo::Binary::Fusion::NaiveBayes(ps,p_exp,U);
        U.MakeEmpty();
        
        // BER
        BER.SetRaw(n,Us.Xor(Unb).Mean());
        Unb.MakeEmpty();
        
        // BERhcond
        double H=Pds::Ceo::Binary::Entropy::HcUsOmegaM(p_exp,ps);
        BERhcond.SetRaw(n,Pds::HbInv(H));
        
        std::cout<<"p: "<<Rho[n]<<"\tBERm: "<<BER[n]<<"\n";
    }
    
    Pds::Octave::XLabel="Rho";
    Pds::Octave::YLabel="BER";
    Pds::Octave::Legend={"P(Hcond)","BER"};
    Pds::Octave::Plot::LogLogXYXY(Rho,BERhcond,Rho,BER,"temporal.m","example_ceo_model_fusion2.png");
    
    return 0;
}