function [C,V,L]=initoakmr(patterns,numcenters,variation)
%INITOAKMR Initialize centers, variations and learning rate from input patterns
%
%  [C,V,L]=INITOAKM(patterns,numcenters,variation=1.0e-10)
%
%    C - Initial centers selected randomly from input patterns
%    V - Initial variations for each cluster (close to 0.0 ; default is 1.0e-10)
%    L - Initial learning rate
%
%    patterns - Input patterns
%    numcenters - Number of clusters
%    variation - Initial cluster variations
%
%  Computes initial values for the centers and cluster variation, associated with
%  each cluster, given the input patterns. Computes also the initial learning rate
%
%  References:
%
%    Chinrungrueng, C., and C.H. Séquin (1995).
%    "Optimal Adaptive k-means Algorithm With Dynamic Adjustment of Learning Rate"
%    IEEE Transactions on Neural Networks, vol.6, Nº 1, pp. 157-169
%
%  04/06/1999 ; Pedro Frazão Ferreira
%

if (nargin<2) | (nargin>3),
   error('Wrong number of input arguments. Try "help initoakmr".');
end
if nargin==2,
   variation=1.0e-10;
end

rand('state',sum(100*clock));
dim=size(patterns);
C=zeros(numcenters,dim(2));
while ~diffrows(C),
   shuff=randperm(dim(1));
   for i=1:numcenters,
      C(i,:)=patterns(shuff(i),:);
   end
end
V=ones(numcenters,1)*variation;
vknorm=V/sum(V);
quality=sum(-vknorm.*log(vknorm));
L=1-(quality/log(dim(1)));