somtoolbox2/som_dmatminima.m
4dbef185
 function minima = som_dmatminima(sM,U,Ne)
 
 %SOM_DMATMINIMA Find clusters based on local minima of U-matrix.
 %
 % minima = som_dmatminima(sM,[U],[Ne])
 %
 %  Input and output arguments ([]'s are optional):
 %   sM         (struct) map struct
 %   U          (matrix) the distance matrix from which minima is
 %                       searched from 
 %                       size msize(1) x ... x msize(end) or 
 %                            2*msize(1)-1 x 2*msize(2)-1 or 
 %                            munits x 1
 %   Ne         (matrix) neighborhood connections matrix
 %
 %   minima     (vector) indeces of the map units where locla minima of
 %                       of U-matrix (or other distance matrix occured)
 %   
 % See also KMEANS_CLUSTERS, SOM_CLLINKAGE, SOM_CLSTRUCT.
 
 % Copyright (c) 2000 by Juha Vesanto
 % Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto
 % http://www.cis.hut.fi/projects/somtoolbox/
  
 % Version 2.0beta juuso 220800
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 % map 
 if isstruct(sM), 
   switch sM.type, 
    case 'som_map',  M = sM.codebook; mask = sM.mask; 
    case 'som_data', M = sM.data; mask = ones(size(M,2),1);
   end
 else
   M = sM; mask = ones(size(M,2),1);
 end
 [munits dim] = size(M);
 
 % distances between map units
 if nargin<2, U = []; end
 
 % neighborhoods 
 if nargin<3, Ne = som_neighbors(sM); end
 
 % distance matrix
 if nargin<2 | isempty(U), U = som_dmat(sM,Ne,'median'); end
 if prod(size(U))>munits, U = U(1:2:size(U,1),1:2:size(U,2)); end
 U = U(:); 
 if length(U) ~= munits, error('Distance matrix has incorrect size.'); end
 
 % find local minima
 minima = []; 
 for i=1:munits, 
   ne = find(Ne(i,:));
   if all(U(i)<=U(ne)) & ~anycommon(ne,minima), minima(end+1)=i; end
 end
 
 return; 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 function t = anycommon(i1,i2)
   if isempty(i1) | isempty(i2), t = 0; 
   else 
     m = max(max(i1),max(i2));
     t = any(sparse(i1,1,1,m,1) & sparse(i2,1,1,m,1)); 
   end
   return;