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

function [iCminDist]=QuantHammingAllOfTheCanonicalIndices(CWidx,AllCentSet)

N=size(CWidx,1);
M=size(AllCentSet,1);
iCminDist=zeros(1,N);
idxs=1:M;
pperc=[];

for ii=1:N
        
        % Approximation of half of the hamming distance
        dH=sum(bsxfun(@ne,AllCentSet,repmat(CWidx(ii,:),M,1))');
        allOfTheMins=idxs(dH==min(dH));
        iCminDist(ii)=allOfTheMins(randi(length(allOfTheMins)));
        %[~,iCminDist(ii)]=min(dH);
        
        perc=sprintf('%2.2f%%',ii/N*100);
        fprintf(1,'%s',sprintf('\b')*ones(1,length(pperc)));
        fprintf(1,'%s',perc);pperc=perc;  
end
fprintf(1,'%s',sprintf('\b')*ones(1,length(pperc)));
end