function [K,P] = som_estimate_gmm(sM, sD)%SOM_ESTIMATE_GMM Estimate a gaussian mixture model based on map.%% [K,P] = som_estimate_gmm(sM, sD)%%  Input and output arguments:%   sM    (struct) map struct%   sD    (struct) data struct%         (matrix) size dlen x dim, the data to use when estimating%                  the gaussian kernels%%   K     (matrix) size munits x dim, kernel width parametes for %                  each map unit%   P     (vector) size 1 x munits, a priori probability of each map unit%% See also SOM_PROBABILITY_GMM.% Reference: Alhoniemi, E., Himberg, J., Vesanto, J.,%   "Probabilistic measures for responses of Self-Organizing Maps", %   Proceedings of Computational Intelligence Methods and%   Applications (CIMA), 1999, Rochester, N.Y., USA, pp. 286-289.% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Esa Alhoniemi% Copyright (c) by Esa Alhoniemi% http://www.cis.hut.fi/projects/somtoolbox/% ecco 180298 juuso 050100 250400%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%[c, dim] = size(sM.codebook);M = sM.codebook;if isstruct(sD), D = sD.data; else D = sD; enddlen = length(D(:,1));%%%%%%%%%%%%%%%%%%%%%% compute hits & bmus[bmus, qerrs] = som_bmus(sM, D);hits = zeros(1,c);for i = 1:c, hits(i) = sum(bmus == i); end%%%%%%%%%%%%%%%%%%%%% a priori  % neighborhood kernelr  = sM.trainhist(end).radius_fin; % neighborhood radiusif isempty(r) | isnan(r), r=1; endUd = som_unit_dists(sM);Ud = Ud.^2; r = r^2; if r==0, r=eps; end % to get rid of div-by-zero errorsswitch sM.neigh,  case 'bubble',   H = (Ud<=r);  case 'gaussian', H = exp(-Ud/(2*r));  case 'cutgauss', H = exp(-Ud/(2*r)) .* (Ud<=r); case 'ep',       H = (1-Ud/r) .* (Ud<=r);end  % a priori prob. = hit histogram weighted by the neighborhood kernelP = hits*H;P = P/sum(P);              %%%%%%%%%%%%%%%%%%%%% kernel widths (& centers)K = ones(c, dim) * NaN; % kernel widthsfor m = 1:c,  w = H(bmus,m);  w = w/sum(w);  for i = 1:dim,    d = (D(:,i) - M(m,i)).^2;   % compute variance of ith    K(m,i) = w'*d;              % variable of centroid m  endend%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%