function [sM,sTrain] = som_prototrain(sM, D)%SOM_PROTOTRAIN  Use sequential algorithm to train the Self-Organizing Map.%% [sM,sT] = som_prototrain(sM, D)% %  sM = som_prototrain(sM,D);%%  Input and output arguments: %   sM      (struct) map struct, the trained and updated map is returned%           (matrix) codebook matrix of a self-organizing map%                    size munits x dim or  msize(1) x ... x msize(k) x dim%                    The trained map codebook is returned.%   D       (struct) training data; data struct%           (matrix) training data, size dlen x dim%% This function is otherwise just like SOM_SEQTRAIN except that% the implementation of the sequential training algorithm is very % straightforward (and slower). This should make it easy for you % to modify the algorithm, if you want to. %% For help on input and output parameters, try % 'type som_prototrain' or check out the help for SOM_SEQTRAIN.% See also SOM_SEQTRAIN, SOM_BATCHTRAIN.% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto% http://www.cis.hut.fi/projects/somtoolbox/% Version 2.0beta juuso 080200 130300 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Check input arguments% map struct_mode = isstruct(sM);if struct_mode,   M = sM.codebook;   sTopol = sM.topol;   mask = sM.mask;   msize = sTopol.msize;  neigh = sM.neigh;else    M = sM; orig_size = size(M);  if ndims(sM) > 2,     si = size(sM); dim = si(end); msize = si(1:end-1);    M = reshape(sM,[prod(msize) dim]);  else    msize = [orig_size(1) 1]; dim = orig_size(2);  end  sM = som_map_struct(dim,'msize',msize); sTopol = sM.topol;  mask = ones(dim,1);  neigh = 'gaussian';end[munits dim] = size(M); % dataif isstruct(D), data_name = D.name; D = D.data; else data_name = inputname(2); endD = D(find(sum(isnan(D),2) < dim),:); % remove empty vectors from the data[dlen ddim] = size(D);                % check input dimensionif dim ~= ddim, error('Map and data input space dimensions disagree.'); end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% initialize (these are default values, change as you will)% training lengthtrainlen = 20*dlen; % 20 epochs by default% neighborhood radiusradius_type = 'linear';rini = max(msize)/2;rfin = 1;% learning ratealpha_type = 'inv'; alpha_ini = 0.2;% initialize random number generatorrand('state',sum(100*clock));% tracking start = clock; trackstep = 100;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ActionUd = som_unit_dists(sTopol); % distance between map units on the gridmu_x_1 = ones(munits,1);     % this is used pretty oftenfor t = 1:trainlen,   %% find BMU  ind = ceil(dlen*rand(1)+eps);       % select one vector  x = D(ind,:);                       % pick it up  known = ~isnan(x);                  % its known components  Dx = M(:,known) - x(mu_x_1,known);  % each map unit minus the vector  dist2 = (Dx.^2)*mask(known);        % squared distances    [qerr bmu] = min(dist2);            % find BMU  %% neighborhood    switch radius_type, % radius   case 'linear', r = rini+(rfin-rini)*(t-1)/(trainlen-1);  end  if ~r, r=eps; end % zero neighborhood radius may cause div-by-zero error    switch neigh, % neighborhood function   case 'bubble',   h = (Ud(:,bmu) <= r);  case 'gaussian', h = exp(-(Ud(:,bmu).^2)/(2*r*r));   case 'cutgauss', h = exp(-(Ud(:,bmu).^2)/(2*r*r)) .* (Ud(:,bmu) <= r);  case 'ep',       h = (1 - (Ud(:,bmu).^2)/(r*r)) .* (Ud(:,bmu) <= r);  end    %% learning rate  switch alpha_type,   case 'linear', a = (1-t/trainlen)*alpha_ini;   case 'inv',    a = alpha_ini / (1 + 99*(t-1)/(trainlen-1));   case 'power',  a = alpha_ini * (0.005/alpha_ini)^((t-1)/trainlen);   end    %% update  M(:,known) = M(:,known) - a*h(:,ones(sum(known),1)).*Dx;			   %% tracking  if t==1 | ~rem(t,trackstep),    elap_t = etime(clock,start); tot_t = elap_t*trainlen/t;     fprintf(1,'\rTraining: %3.0f/ %3.0f s',elap_t,tot_t)  end  end; % for t = 1:trainlenfprintf(1,'\n');% outputssTrain = som_set('som_train','algorithm','proto',...		 'data_name',data_name,...		 'neigh',neigh,...		 'mask',mask,...		 'radius_ini',rini,...		 'radius_fin',rfin,...		 'alpha_ini',alpha_ini,...		 'alpha_type',alpha_type,...		 'trainlen',trainlen,...		 'time',datestr(now,0));if struct_mode,   sM = som_set(sM,'codebook',M,'mask',mask,'neigh',neigh);  sM.trainhist(end+1) = sTrain;else  sM = reshape(M,orig_size);end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%