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1 function [K,P] = som_estimate_gmm(sM, sD)
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2
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3 %SOM_ESTIMATE_GMM Estimate a gaussian mixture model based on map.
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4 %
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5 % [K,P] = som_estimate_gmm(sM, sD)
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6 %
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7 % Input and output arguments:
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8 % sM (struct) map struct
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9 % sD (struct) data struct
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10 % (matrix) size dlen x dim, the data to use when estimating
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11 % the gaussian kernels
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12 %
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13 % K (matrix) size munits x dim, kernel width parametes for
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14 % each map unit
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15 % P (vector) size 1 x munits, a priori probability of each map unit
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16 %
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17 % See also SOM_PROBABILITY_GMM.
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18
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19 % Reference: Alhoniemi, E., Himberg, J., Vesanto, J.,
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20 % "Probabilistic measures for responses of Self-Organizing Maps",
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21 % Proceedings of Computational Intelligence Methods and
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22 % Applications (CIMA), 1999, Rochester, N.Y., USA, pp. 286-289.
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23
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24 % Contributed to SOM Toolbox vs2, February 2nd, 2000 by Esa Alhoniemi
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25 % Copyright (c) by Esa Alhoniemi
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26 % http://www.cis.hut.fi/projects/somtoolbox/
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27
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28 % ecco 180298 juuso 050100 250400
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29
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30 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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31
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32 [c, dim] = size(sM.codebook);
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33 M = sM.codebook;
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34
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35 if isstruct(sD), D = sD.data; else D = sD; end
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36 dlen = length(D(:,1));
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37
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38 %%%%%%%%%%%%%%%%%%%%%
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39 % compute hits & bmus
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40
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41 [bmus, qerrs] = som_bmus(sM, D);
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42 hits = zeros(1,c);
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43 for i = 1:c, hits(i) = sum(bmus == i); end
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44
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45 %%%%%%%%%%%%%%%%%%%%
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46 % a priori
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47
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48 % neighborhood kernel
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49 r = sM.trainhist(end).radius_fin; % neighborhood radius
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50 if isempty(r) | isnan(r), r=1; end
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51 Ud = som_unit_dists(sM);
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52 Ud = Ud.^2;
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53 r = r^2;
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54 if r==0, r=eps; end % to get rid of div-by-zero errors
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55 switch sM.neigh,
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56 case 'bubble', H = (Ud<=r);
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57 case 'gaussian', H = exp(-Ud/(2*r));
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58 case 'cutgauss', H = exp(-Ud/(2*r)) .* (Ud<=r);
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59 case 'ep', H = (1-Ud/r) .* (Ud<=r);
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60 end
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61
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62 % a priori prob. = hit histogram weighted by the neighborhood kernel
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63 P = hits*H;
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64 P = P/sum(P);
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65
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66 %%%%%%%%%%%%%%%%%%%%
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67 % kernel widths (& centers)
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68
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69 K = ones(c, dim) * NaN; % kernel widths
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70 for m = 1:c,
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71 w = H(bmus,m);
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72 w = w/sum(w);
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73 for i = 1:dim,
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74 d = (D(:,i) - M(m,i)).^2; % compute variance of ith
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75 K(m,i) = w'*d; % variable of centroid m
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76 end
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77 end
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78
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79 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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