wolffd@0: function codebook=lvq1(codebook, data, rlen, alpha);
wolffd@0: 
wolffd@0: %LVQ1 Trains a codebook with the LVQ1 -algorithm.
wolffd@0: %
wolffd@0: %  sM = lvq1(sM, D, rlen, alpha)
wolffd@0: %
wolffd@0: %   sM = lvq1(sM,sD,30*length(sM.codebook),0.08);
wolffd@0: %
wolffd@0: %  Input and output arguments: 
wolffd@0: %   sM    (struct) map struct, the class information must be 
wolffd@0: %                  present on the first column of .labels field
wolffd@0: %   D     (struct) data struct, the class information must
wolffd@0: %                  be present on the first column of .labels field
wolffd@0: %   rlen  (scalar) running length
wolffd@0: %   alpha (scalar) learning parameter
wolffd@0: %
wolffd@0: %   sM    (struct) map struct, the trained codebook
wolffd@0: %
wolffd@0: % NOTE: does not take mask into account.
wolffd@0: % 
wolffd@0: % For more help, try 'type lvq1', or check out online documentation. 
wolffd@0: % See also LVQ3, SOM_SUPERVISED, SOM_SEQTRAIN.
wolffd@0: 
wolffd@0: %%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: %
wolffd@0: % lvq1
wolffd@0: %
wolffd@0: % PURPOSE
wolffd@0: %
wolffd@0: % Trains codebook with the LVQ1 -algorithm (described below).
wolffd@0: %
wolffd@0: % SYNTAX
wolffd@0: %
wolffd@0: %  sM = lvq1(sM, D, rlen, alpha)
wolffd@0: %
wolffd@0: % DESCRIPTION
wolffd@0: %
wolffd@0: % Trains codebook with the LVQ1 -algorithm. Codebook contains a number
wolffd@0: % of vectors (mi, i=1,2,...,n) and so does data (vectors xj,
wolffd@0: % j=1,2,...,k).  Both vector sets are classified: vectors may have a
wolffd@0: % class (classes are set to the first column of data or map -structs'
wolffd@0: % .labels -field). For each xj there is defined the nearest codebook
wolffd@0: % -vector index c by searching the minimum of the euclidean distances
wolffd@0: % between the current xj and codebook -vectors:
wolffd@0: %
wolffd@0: %    c = min{ ||xj - mi|| },  i=[1,..,n], for fixed xj
wolffd@0: %         i
wolffd@0: % If xj and mc belong to the same class, mc is updated as follows:
wolffd@0: %    mc(t+1) = mc(t) + alpha * (xj(t) - mc(t))
wolffd@0: % If xj and mc belong to different classes, mc is updated as follows:
wolffd@0: %    mc(t+1) = mc(t) - alpha * (xj(t) - mc(t))
wolffd@0: % Otherwise updating is not performed.
wolffd@0: % 
wolffd@0: % Argument 'rlen' tells how many times training sequence is performed.
wolffd@0: % LVQ1 -algorithm may be stopped after a number of steps, that is
wolffd@0: % 30-50 times the number of codebook vectors.
wolffd@0: %
wolffd@0: % Argument 'alpha' is the learning rate, recommended to be smaller
wolffd@0: % than 0.1.
wolffd@0: %
wolffd@0: % NOTE: does not take mask into account.
wolffd@0: %
wolffd@0: % REFERENCES
wolffd@0: %
wolffd@0: % Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, 
wolffd@0: %    Berlin, 1995, pp. 176-179.
wolffd@0: %
wolffd@0: % See also LVQ_PAK from http://www.cis.hut.fi/research/som_lvq_pak.shtml
wolffd@0: %   
wolffd@0: % REQUIRED INPUT ARGUMENTS
wolffd@0: %
wolffd@0: %  sM                The data to be trained.
wolffd@0: %          (struct)  A map struct.
wolffd@0: %
wolffd@0: %  D                 The data to use in training.
wolffd@0: %          (struct)  A data struct.
wolffd@0: %
wolffd@0: %  rlen    (integer) Running length of LVQ1 -algorithm.
wolffd@0: %                    
wolffd@0: %  alpha   (float)   Learning rate used in training.
wolffd@0: %
wolffd@0: % OUTPUT ARGUMENTS
wolffd@0: %
wolffd@0: %  codebook          Trained data.
wolffd@0: %          (struct)  A map struct.
wolffd@0: %
wolffd@0: % EXAMPLE
wolffd@0: %
wolffd@0: %   lab = unique(sD.labels(:,1));         % different classes
wolffd@0: %   mu = length(lab)*5;                   % 5 prototypes for each    
wolffd@0: %   sM = som_randinit(sD,'msize',[mu 1]); % initial prototypes
wolffd@0: %   sM.labels = [lab;lab;lab;lab;lab];    % their classes
wolffd@0: %   sM = lvq1(sM,sD,50*mu,0.05);          % use LVQ1 to adjust
wolffd@0: %                                         % the prototypes      
wolffd@0: %   sM = lvq3(sM,sD,50*mu,0.05,0.2,0.3);  % then use LVQ3 
wolffd@0: %
wolffd@0: % SEE ALSO
wolffd@0: % 
wolffd@0: %  lvq3             Use LVQ3 algorithm for training.
wolffd@0: %  som_supervised   Train SOM using supervised training.
wolffd@0: %  som_seqtrain     Train SOM with sequential algorithm.
wolffd@0: 
wolffd@0: % Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas
wolffd@0: % Copyright (c) Juha Parhankangas
wolffd@0: % http://www.cis.hut.fi/projects/somtoolbox/
wolffd@0: 
wolffd@0: % Juha Parhankangas 310100 juuso 020200
wolffd@0: 
wolffd@0: cod = codebook.codebook;
wolffd@0: c_class = class2num(codebook.labels(:,1));
wolffd@0: 
wolffd@0: dat = data.data;
wolffd@0: d_class = class2num(data.labels(:,1));
wolffd@0: 
wolffd@0: x=size(dat,1);
wolffd@0: y=size(cod,2);
wolffd@0: 
wolffd@0: ONES=ones(size(cod,1),1);
wolffd@0: 
wolffd@0: for t=1:rlen
wolffd@0: 
wolffd@0:   fprintf(1,'\rTraining round: %d',t);
wolffd@0:   tmp=NaN*ones(x,y);
wolffd@0: 
wolffd@0:   for j=1:x
wolffd@0:     no_NaN=find(~isnan(dat(j,:)));
wolffd@0:     di = sqrt(sum([cod(:,no_NaN)  - ONES*dat(j,no_NaN)].^2,2));
wolffd@0: 
wolffd@0:     [foo,ind] = min(di);
wolffd@0: 
wolffd@0:     if d_class(j) & d_class(j) == c_class(ind) % 0 is for unclassified vectors
wolffd@0:       tmp(ind,:) = cod(ind,:) + alpha * (dat(j,:) - cod(ind,:));
wolffd@0:     elseif d_class(j)
wolffd@0:       tmp(ind,:) = cod(ind,:) - alpha*(dat(j,:) - cod(ind,:));
wolffd@0:     end
wolffd@0:   end
wolffd@0: 
wolffd@0:   inds = find(~isnan(sum(tmp,2)));
wolffd@0:   cod(inds,:) = tmp(inds,:);
wolffd@0: end
wolffd@0: 
wolffd@0: codebook.codebook = cod;
wolffd@0: 
wolffd@0: sTrain = som_set('som_train','algorithm','lvq1',...
wolffd@0: 		 'data_name',data.name,...
wolffd@0: 		 'neigh','',...
wolffd@0: 		 'mask',ones(y,1),...
wolffd@0: 		 'radius_ini',NaN,...
wolffd@0: 		 'radius_fin',NaN,...
wolffd@0: 		 'alpha_ini',alpha,...
wolffd@0: 		 'alpha_type','constant',...
wolffd@0: 		 'trainlen',rlen,...
wolffd@0: 		 'time',datestr(now,0));
wolffd@0: codebook.trainhist(end+1) = sTrain;
wolffd@0: 
wolffd@0: return;
wolffd@0: 
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: 
wolffd@0: function nos = class2num(class)
wolffd@0: 
wolffd@0: names = {};
wolffd@0: nos = zeros(length(class),1);
wolffd@0: 
wolffd@0: for i=1:length(class)
wolffd@0:   if ~isempty(class{i}) & ~any(strcmp(class{i},names))
wolffd@0:     names=cat(1,names,class(i));
wolffd@0:   end
wolffd@0: end
wolffd@0: 
wolffd@0: tmp_nos = (1:length(names))';
wolffd@0: 
wolffd@0: for i=1:length(class)
wolffd@0:   if ~isempty(class{i})
wolffd@0:     nos(i,1) = find(strcmp(class{i},names));    
wolffd@0:   end
wolffd@0: end
wolffd@0: 
wolffd@0: 
wolffd@0: