function codebook=lvq1(codebook, data, rlen, alpha);%LVQ1 Trains a codebook with the LVQ1 -algorithm.%%  sM = lvq1(sM, D, rlen, alpha)%%   sM = lvq1(sM,sD,30*length(sM.codebook),0.08);%%  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%%   sM    (struct) map struct, the trained codebook%% NOTE: does not take mask into account.% % For more help, try 'type lvq1', or check out online documentation. % See also LVQ3, SOM_SUPERVISED, SOM_SEQTRAIN.%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% lvq1%% PURPOSE%% Trains codebook with the LVQ1 -algorithm (described below).%% SYNTAX%%  sM = lvq1(sM, D, rlen, alpha)%% DESCRIPTION%% Trains codebook with the LVQ1 -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 the first column of data or map -structs'% .labels -field). For each xj there is defined the nearest codebook% -vector index c by searching the minimum of the euclidean distances% between the current xj and codebook -vectors:%%    c = min{ ||xj - mi|| },  i=[1,..,n], for fixed xj%         i% If xj and mc belong to the same class, mc is updated as follows:%    mc(t+1) = mc(t) + alpha * (xj(t) - mc(t))% If xj and mc belong to different classes, mc is updated as follows:%    mc(t+1) = mc(t) - alpha * (xj(t) - mc(t))% Otherwise updating is not performed.% % Argument 'rlen' tells how many times training sequence is performed.% LVQ1 -algorithm may be stopped after a number of steps, that is% 30-50 times the number of codebook vectors.%% Argument 'alpha' is the learning rate, recommended to be smaller% than 0.1.%% NOTE: does not take mask into account.%% REFERENCES%% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, %    Berlin, 1995, pp. 176-179.%% 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.%%  D                 The data to use in training.%          (struct)  A data struct.%%  rlen    (integer) Running length of LVQ1 -algorithm.%                    %  alpha   (float)   Learning rate used in training.%% OUTPUT ARGUMENTS%%  codebook          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% %  lvq3             Use LVQ3 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) Juha Parhankangas% http://www.cis.hut.fi/projects/somtoolbox/% Juha Parhankangas 310100 juuso 020200cod = codebook.codebook;c_class = class2num(codebook.labels(:,1));dat = data.data;d_class = class2num(data.labels(:,1));x=size(dat,1);y=size(cod,2);ONES=ones(size(cod,1),1);for t=1:rlen  fprintf(1,'\rTraining round: %d',t);  tmp=NaN*ones(x,y);  for j=1:x    no_NaN=find(~isnan(dat(j,:)));    di = sqrt(sum([cod(:,no_NaN)  - ONES*dat(j,no_NaN)].^2,2));    [foo,ind] = min(di);    if d_class(j) & d_class(j) == c_class(ind) % 0 is for unclassified vectors      tmp(ind,:) = cod(ind,:) + alpha * (dat(j,:) - cod(ind,:));    elseif d_class(j)      tmp(ind,:) = cod(ind,:) - alpha*(dat(j,:) - cod(ind,:));    end  end  inds = find(~isnan(sum(tmp,2)));  cod(inds,:) = tmp(inds,:);endcodebook.codebook = cod;sTrain = som_set('som_train','algorithm','lvq1',...		 '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;return;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%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