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SVMCrossVal.git
somtoolbox2
som_dmat.m
starting som prediction fine-tuned class-performance visualisation
Christoph Budziszewski
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at 2009-01-21 16:34:25
som_dmat.m
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function dmat = som_dmat(sM,Ne,mode) %SOM_DMAT Find distance to neighbors for each map unit. % % dmat = som_dmat(sM,[Ne],[mode]) % % Input and output arguments ([]'s are optional): % sM (struct) map or data struct % (matrix) data matrix, size n x dim % [Ne] (matrix) neighborhood connections matrix % (string) 'Nk' (on map) or 'kNN' (any vector set) % where k = some number, e.g. 'N1' or '10NN' % (empty) use default % [mode] (string) 'min', 'median', 'mean', 'max', or % some arbitrary scalar function of % a set of vectors % % dmat (vector) size n x 1, distance associated with each vector % % 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 [n dim] = size(M); % neighborhoods if nargin<2 | isempty(Ne), Ne = som_neighbors(sM); elseif ischar(Ne), Ne = som_neighbors(sM,Ne); end l = size(Ne,1); Ne([0:l-1]*l+[1:l]) = 0; % set diagonal elements = 0 % mode if nargin<3 | isempty(mode), mode = 'median'; end calc = sprintf('%s(x)',mode); % distances dmat = zeros(n,1); for i=1:n, ne = find(Ne(i,:)); if any(ne), [dummy,x] = som_bmus(M(ne,:),M(i,:),[1:length(ne)],mask); dmat(i) = eval(calc); else dmat(i) = NaN; end end return; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%