comparison toolboxes/MIRtoolbox1.3.2/somtoolbox/lvq1.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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 020200
+
+cod = 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,:);
+end
+
+codebook.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));
+  end
+end
+
+tmp_nos = (1:length(names))';
+
+for i=1:length(class)
+  if ~isempty(class{i})
+    nos(i,1) = find(strcmp(class{i},names));    
+  end
+end
+
+
+