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clear all;close all;

% Generate some data
N1 = 100; N2 = 30; % Class sizes
x = [randn(N1,2);randn(N2,2)+3];
t = [repmat(0,N1,1);repmat(1,N2,1)];
N = size(x,1);

% Plot the data
ma = {'ko','ks'};
fc = {[0 0 0],[1 1 1]};
tv = unique(t);
figure(1); hold off
for i = 1:length(tv)
    pos = find(t==tv(i));
    plot(x(pos,1),x(pos,2),ma{i},'markerfacecolor',fc{i});
    hold on
end

%Generate the decision boundaries for various values of k
[Xv Yv] = meshgrid(min(x(:,1)):0.1:max(x(:,1)),min(x(:,2)):0.1:max(x(:,2)));
% Loop over test points

Kvals = [1 2 5 10 20 50 59];
for kv = 1:length(Kvals)
  classes = zeros(size(Xv));
  K = Kvals(kv);
  for i = 1:length(Xv(:))
      this = [Xv(i) Yv(i)];
      dists = sum((x - repmat(this,N,1)).^2,2);
      [d I] = sort(dists,'ascend');
      [a,b] = hist(t(I(1:K)));
      pos = find(a==max(a));
      if length(pos)>1
          order = randperm(length(pos));
          pos = pos(order(1));
      end
      classes(i) = b(pos);
  end
  figure(1); hold off
  for i = 1:length(tv)
      pos = find(t==tv(i));
      plot(x(pos,1),x(pos,2),ma{i},'markerfacecolor',fc{i});
      hold on
  end
  contour(Xv,Yv,classes,[0.5 0.5],'k')
  ti = sprintf('K = %g',K);
  title(ti);
end