wolffd@0: function dmat = som_dmat(sM,Ne,mode)
wolffd@0: 
wolffd@0: %SOM_DMAT Find distance to neighbors for each map unit.
wolffd@0: %
wolffd@0: % dmat = som_dmat(sM,[Ne],[mode])
wolffd@0: %
wolffd@0: %  Input and output arguments ([]'s are optional):
wolffd@0: %   sM         (struct) map or data struct 
wolffd@0: %              (matrix) data matrix, size n x dim
wolffd@0: %   [Ne]       (matrix) neighborhood connections matrix 
wolffd@0: %              (string) 'Nk' (on map) or 'kNN' (any vector set)
wolffd@0: %                       where k = some number, e.g. 'N1' or '10NN'
wolffd@0: %              (empty)  use default 
wolffd@0: %   [mode]     (string) 'min', 'median', 'mean', 'max', or 
wolffd@0: %                       some arbitrary scalar function of 
wolffd@0: %                       a set of vectors
wolffd@0: %
wolffd@0: %   dmat       (vector) size n x 1, distance associated with each vector
wolffd@0: %   
wolffd@0: % See also KMEANS_CLUSTERS, SOM_CLLINKAGE, SOM_CLSTRUCT.
wolffd@0: 
wolffd@0: % Copyright (c) 2000 by Juha Vesanto
wolffd@0: % Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto
wolffd@0: % http://www.cis.hut.fi/projects/somtoolbox/
wolffd@0:  
wolffd@0: % Version 2.0beta juuso 220800
wolffd@0: 
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: 
wolffd@0: % map 
wolffd@0: if isstruct(sM), 
wolffd@0:   switch sM.type, 
wolffd@0:    case 'som_map',  M = sM.codebook; mask = sM.mask; 
wolffd@0:    case 'som_data', M = sM.data; mask = ones(size(M,2),1);
wolffd@0:   end
wolffd@0: else
wolffd@0:   M = sM; mask = ones(size(M,2),1);
wolffd@0: end
wolffd@0: [n dim] = size(M);
wolffd@0: 
wolffd@0: % neighborhoods 
wolffd@0: if nargin<2 | isempty(Ne), Ne = som_neighbors(sM); 
wolffd@0: elseif ischar(Ne), Ne = som_neighbors(sM,Ne); 
wolffd@0: end
wolffd@0: l = size(Ne,1); Ne([0:l-1]*l+[1:l]) = 0; % set diagonal elements = 0
wolffd@0: 
wolffd@0: % mode
wolffd@0: if nargin<3 | isempty(mode), mode = 'median'; end
wolffd@0: calc = sprintf('%s(x)',mode); 
wolffd@0: 
wolffd@0: % distances
wolffd@0: dmat = zeros(n,1); 
wolffd@0: for i=1:n, 
wolffd@0:   ne = find(Ne(i,:));
wolffd@0:   if any(ne), 
wolffd@0:     [dummy,x] = som_bmus(M(ne,:),M(i,:),[1:length(ne)],mask); 
wolffd@0:     dmat(i) = eval(calc); 
wolffd@0:   else 
wolffd@0:     dmat(i) = NaN; 
wolffd@0:   end
wolffd@0: end
wolffd@0: 
wolffd@0: return; 
wolffd@0: 
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: 
wolffd@0:   
wolffd@0: