% MAXDISS maximize dissimilarity
%    [Y] = maxdiss(X,m)
%       X : n x p data matrix
%       m : size of subset
%       Y : X, reorderred, according to dissimilarity
%       Heuristic algorithm for ranking dissimilarity among
%       a set of vectors
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function [Y] = maxdiss(X,m)

n = size(X,1);
p = size(X,2);
Y = zeros(m,p);

% Fill up Y
for j = 1:m
   % Choose most dissimilar vector; if this is the first vector,
   % pick a random one
   if j == 1
      i = ceil(rand*n);
   else
      % Calculate dissimilarity score for remaining X's
      % with respect to already-picked Y's
      D = zeros(size(X,1),1);
      % Iterate over all remaining X's
      for l = 1:size(X,1)
         l_sum = 0;
         % Iterate over all selected Y's
         for k = 1:j
            l_sum = l_sum + sum((Y(k,:)-X(l,:))'.^2);
         end
         D(l) = l_sum;
      end
      [D,i] = max(D);
   end
   % Add new vector to Y
   Y(j,:) = X(i,:);
   % Remove vector from X
   X(i,:) = [];
end