Nearest-Neighbour-Search-Neural-Networks-Product-Quantization/TEXMEX/Run_WNNetsOverPQ_Fine_Coarse.m

    clear all; close all; clc;

    addpath('./SIFTs Library');

    bVSetMat=double(fvecs_read('../Data/sift/sift_learn.fvecs'));
    bWSetMat=double(fvecs_read('../Data/sift/sift_base.fvecs'));
    bZSetMat=double(fvecs_read('../Data/sift/sift_query.fvecs'));  
    iMinDistExh=ivecs_read('../Data/sift/sift_groundtruth.ivecs')+1;
    
    kkf=256;
    mmf=16;
    
    nn=1e6./[1e3 1.25e3 2e3 2.5e3 5e3 8e3 1e4];
    
    kkc=[1, 10:10:100];
    mmc=2.^[1:4];
    L0factor=0.1;
    kkc=[1 2];mmc=1;nn=1e3;
    
    lnn=length(nn); lmm=length(mmc); lkk=length(kkc); ltt=lnn*lmm*lkk;
    pperc=[]; 
    for inn=1:lnn
        for imm=1:lmm
            for ikk=1:lkk
                fprintf(1,'%s\n',('-')*ones(1,64));
                str=sprintf('Overall Progress: %d simulations running',ltt);        
                fprintf(1,'%s%s ',str,('.')*ones(1,55-length(str)));
                
                pp=((inn-1)*lmm*lkk+(imm-1)*lkk+ikk)/(lnn*lmm*lkk);
                perc=sprintf('%2.2f%%',pp*100);
                fprintf(1,'%s',sprintf('\b')*ones(1,length(pperc)));
                fprintf(1,'%s\n',perc); pperc=perc;
                fprintf(1,'%s\n',('-')*ones(1,64));
                
                [LearnDist(ikk,imm,inn), NNeighPerf(ikk,imm,inn), ScoresCoarseCpxty(ikk,imm,inn), ...
                 ADCfineCpxty(ikk,imm,inn),NormCpxty(ikk,imm,inn), NormPQCpxty(ikk,imm,inn)]=...
                 WNNetsOverPQ_Fine_Coarse_function(bVSetMat,bWSetMat,bZSetMat,iMinDistExh,kkf,mmf,...
                 kkc(ikk),mmc(imm),nn(inn),L0factor);
                
                fprintf(1,'%s\n',('-')*ones(1,64));
                clc;
            end
        end
    end
    fprintf(1,'%s',sprintf('\b')*ones(1,length(pperc)));
    fprintf('Done.\n');
    fprintf(1,'%s\n',('-')*ones(1,64));