function [P,C,Err]=lbgUpdate(X,C)
% function C=lbgUpdate(X,C)
%  Performs 1 step of the LBG algorithm where X is p by n (data)
%  and C is k by n (k clusters)
% P holds the sorted data 
% C is the new matrix of clusters
% Err is a vector of distortion errors

[m,n]=size(X);
[k,p]=size(C);
if n~=p
    error('Data dimensions do not match in lbgUpdate\n');
end

D=edm(X,C);
Err=sqrt(sum(D.*D,2));
[Vals,Idx]=min(D,[],2);
for j=1:k
    idx=find(Idx==j);
    if length(idx)>0
        P{j}=X(idx,:);
        C(j,:)=mean(P{j});
    end
end