SVMCrossVal.git

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function [t,r] = db_index(D, cl, C, p, q)
 
% DB_INDEX Davies-Bouldin clustering evaluation index.
%
% [t,r] = db_index(D, cl, C, p, q)
%
%  Input and output arguments ([]'s are optional):  
%    D     (matrix) data (n x dim)
%          (struct) map or data struct
%    cl    (vector) cluster numbers corresponding to data samples (n x 1)
%    [C]   (matrix) prototype vectors (c x dim) (default = cluster means)
%    [p]   (scalar) norm used in the computation (default == 2)
%    [q]   (scalar) moment used to calculate cluster dispersions (default = 2)
% 
%    t     (scalar) Davies-Bouldin index for the clustering (=mean(r))
%    r     (vector) maximum DB index for each cluster (size c x 1)    
% 
% See also  KMEANS, KMEANS_CLUSTERS, SOM_GAPINDEX.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% input arguments
if isstruct(D), 
    switch D.type,
    case 'som_map', D = D.codebook; 
    case 'som_data', D = D.data; 
    end
end
% cluster centroids
[l dim] = size(D);
u = unique(cl); 
c = length(u); 
if nargin <3, 
  C = zeros(c,dim); 
  for i=1:c, 
      me = nanstats(D(find(cl==u(i)),:));
      C(i,:) = me';
  end 
end
u2i = zeros(max(u),1); u2i(u) = 1:c; 
D = som_fillnans(D,C,u2i(cl)); % replace NaN's with cluster centroid values
if nargin <4, p = 2; end % euclidian distance between cluster centers
if nargin <5, q = 2; end % dispersion = standard deviation
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% action
% dispersion in each cluster 
 
 
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