%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% function kNN()
%   z.li, 04-28-2004
%   k-NN search
% function dependency:
%   - n/a
% input:
%   K - number of samples desired
%   model - data: (dim+1) x n, model(dim+1,:) is label
%   x0 - dim x 1, search against
%   R - cut off radius
% output:
%   x - results: dim x K. 
%   y - labels:  1 x K
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%function [x, y]=kNN(model, x0, K, R)
function [x, y]=kNN(model, x0, K, R)
 dbg='n';
 if dbg == 'y'
    model = prj1(1:12,:);
    x0 = model(:, 200);
    K  = 40;
    R  = 30;
 end

 [dim,n]=size(model);
 dim=dim-1;
 nFace=40; nPose = 10;
 
 % compute distance vector
 for k=1:n
   d(k) = norm(x0-model(1:dim, k), 2);
 end

 [d2, indx]=sort(d);
 x = model(1:dim, indx(1:K));
 y = model(dim+1, indx(1:K));