function codebook = lvq3(codebook,data,rlen,alpha,win,epsilon)%LVQ3 trains codebook with LVQ3 -algorithm%% sM = lvq3(sM,D,rlen,alpha,win,epsilon)%%   sM = lvq3(sM,sD,50*length(sM.codebook),0.05,0.2,0.3);%%  Input and output arguments: %   sM      (struct) map struct, the class information must be %                    present on the first column of .labels field%   D       (struct) data struct, the class information must%                    be present on the first column of .labels field%   rlen    (scalar) running length%   alpha   (scalar) learning parameter, e.g. 0.05%   win     (scalar) window width parameter, e.g. 0.25%   epsilon (scalar) relative learning parameter, e.g. 0.3%%   sM      (struct) map struct, the trained codebook%% NOTE: does not take mask into account.%% For more help, try 'type lvq3', or check out online documentation.% See also LVQ1, SOM_SUPERVISED, SOM_SEQTRAIN.%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% lvq3%% PURPOSE%% Trains codebook with the LVQ3 -algorithm (described below).%% SYNTAX%% sM = lvq3(sM, data, rlen, alpha, win, epsilon)%% DESCRIPTION%% Trains codebook with the LVQ3 -algorithm. Codebook contains a number% of vectors (mi, i=1,2,...,n) and so does data (vectors xj, j=1,2,...k).% Both vector sets are classified: vectors may have a class (classes are% set to data- or map -structure's 'labels' -field. For each xj the two % closest codebookvectors mc1 and mc2 are searched (euclidean distances% d1 and d2). xj must fall into the zone of window. That happens if:%%    min(d1/d2, d2/d1) > s, where s = (1-win) / (1+win).%% If xj belongs to the same class of one of the mc1 and mc1, codebook% is updated as follows (let mc1 belong to the same class as xj):%    mc1(t+1) = mc1(t) + alpha * (xj(t) - mc1(t))%    mc2(t+1) = mc2(t) - alpha * (xj(t) - mc2(t))% If both mc1 and mc2 belong to the same class as xj, codebook is% updated as follows:%    mc1(t+1) = mc1(t) + epsilon * alpha * (xj(t) - mc1(t))%    mc2(t+1) = mc2(t) + epsilon * alpha * (xj(t) - mc2(t))% Otherwise updating is not performed.%% Argument 'rlen' tells how many times training -sequence is performed.%% Argument 'alpha' is recommended to be smaller than 0.1 and argument% 'epsilon' should be between 0.1 and 0.5.%% NOTE: does not take mask into account.%% REFERENCES%% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, %    Berlin, 1995, pp. 181-182.%% See also LVQ_PAK from http://www.cis.hut.fi/research/som_lvq_pak.shtml% % REQUIRED INPUT ARGUMENTS%%  sM                The data to be trained.%          (struct)  A map struct.%%  data              The data to use in training.%          (struct)  A data struct.%%  rlen    (integer) Running length of LVQ3 -algorithm.%                    %  alpha   (float)   Learning rate used in training, e.g. 0.05%%  win     (float)   Window length, e.g. 0.25%  %  epsilon (float)   Relative learning parameter, e.g. 0.3%% OUTPUT ARGUMENTS%%  sM          Trained data.%          (struct)  A map struct.%% EXAMPLE%%   lab = unique(sD.labels(:,1));         % different classes%   mu = length(lab)*5;                   % 5 prototypes for each    %   sM = som_randinit(sD,'msize',[mu 1]); % initial prototypes%   sM.labels = [lab;lab;lab;lab;lab];    % their classes%   sM = lvq1(sM,sD,50*mu,0.05);          % use LVQ1 to adjust%                                         % the prototypes      %   sM = lvq3(sM,sD,50*mu,0.05,0.2,0.3);  % then use LVQ3 % % SEE ALSO% %  lvq1             Use LVQ1 algorithm for training.%  som_supervised   Train SOM using supervised training.%  som_seqtrain     Train SOM with sequential algorithm.% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas% Copyright (c) by Juha Parhankangas% http://www.cis.hut.fi/projects/somtoolbox/% Juha Parhankangas 310100 juuso 020200NOTFOUND = 1;cod = codebook.codebook;dat = data.data;c_class = codebook.labels(:,1);d_class = data.labels(:,1);s = (1-win)/(1+win);x = size(dat,1);y = size(cod,2);c_class=class2num(c_class);d_class=class2num(d_class);ONES=ones(size(cod,1),1);for t=1:rlen  fprintf('\rTraining round: %d/%d',t,rlen);  tmp = NaN*ones(x,y);   for j=1:x    flag = 0;    mj = 0;    mi = 0;    no_NaN=find(~isnan(dat(j,:)));    di=sqrt(sum([cod(:,no_NaN) - ONES*dat(j,no_NaN)].^2,2));    [foo, ind1] = min(di);    di(ind1)=Inf;    [foo,ind2] =  min(di);          %ind2=ind2+1;    if d_class(j) & d_class(j)==c_class(ind1)      mj = ind1;      mi = ind2;      if d_class(j)==c_class(ind2)        flag = 1;      end    elseif d_class(j) & d_class(j)==c_class(ind2)      mj = ind2;      mi = ind1;      if d_class(j)==c_class(ind1)        flag = 1;      end    end    if mj & mi      if flag        tmp([mj mi],:) = cod([mj mi],:) + epsilon*alpha*...                       (dat([j j],:) - cod([mj mi],:));      else        tmp(mj,:) = cod(mj,:) + alpha * (dat(j,:)-cod(mj,:));        tmp(mi,:) = cod(mi,:) - alpha * (dat(j,:)-cod(mj,:));      end    end    end      inds = find(~isnan(sum(tmp,2)));  cod(inds,:) = tmp(inds,:);endfprintf(1,'\n');sTrain = som_set('som_train','algorithm','lvq3',...		 'data_name',data.name,...		 'neigh','',...		 'mask',ones(y,1),...		 'radius_ini',NaN,...		 'radius_fin',NaN,...		 'alpha_ini',alpha,...		 'alpha_type','constant',...		 'trainlen',rlen,...		 'time',datestr(now,0));codebook.trainhist(end+1) = sTrain;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%function nos = class2num(class)names = {};nos = zeros(length(class),1);for i=1:length(class)  if ~isempty(class{i}) & ~any(strcmp(class{i},names))    names=cat(1,names,class(i));  endendtmp_nos = (1:length(names))';for i=1:length(class)  if ~isempty(class{i})    nos(i,1) = find(strcmp(class{i},names));      endend