%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% function knnClassify()
%   z.li, 10-20-2008
%   kNN classifier
% function dependency:
%	- n/a
% input:
%	q - query vector, m x d
%	x - known vector, n x d
%	y - known labels, n x 1
% output:
%   id - m x 1, recognized ids
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%function [id]=knnClassify(q, x, y)
function [id]=knnClassify(q, x, y)
dbg = 0;
if dbg
    x = [  19.0716   -6.6704   38.0986
       19.0710   -6.6231   38.0007
       19.0650   -6.6451   37.9860
       19.0691   -6.6377   37.9813
       17.2515   -4.0391   28.6389
       17.2501   -4.0056   28.6514
       17.2565   -4.0477   28.7316
       17.2462   -4.0587   28.7206
       10.7764   -6.7628   37.3780
       10.7864   -6.8147   37.3535
       10.7827   -6.7286   37.4516
       10.7873   -6.7713   37.3364];
    y = [1 1 1 1 2 2 2 2 3 3 3 3]';
    q = [
       19.0669   -6.6518   38.0185
       17.2558   -4.0390   28.6926
       10.7806   -6.7540   37.5014];
end
[m, d]=size(q); 
[n,d]=size(x); 

for k=1:m
    dv = x - repmat(q(k,:), n, 1);
    for j=1:n
        d(j)=norm(dv(j,:));  
    end
    [dmin, offs] = min(d); 
    id(k) = y(offs); 
end
return;