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    function model = oaasvm(data,options)
% OAASVM Multi-class SVM using One-Agains-All decomposition.
% 
% Synopsis:
%  model = oaasvm( data )
%  model = oaasvm( data, options)
%
% Description:
%  model = oaasvm( data ) uses one-agains-all deconposition
%   to train the multi-class Support Vector Machines (SVM)
%   classifier. The classification into nclass classes 
%   is decomposed to nclass binary problems.
%
%  model = oaasvm( data, options) allows to specify the
%   binary SVM solver and its paramaters.
%
% Input:
%  data [struct] Training data:
%   .X [dim x num_data] Training vectors.
%   .y [1 x num_data] Labels of training data (1,2,...,nclass). 
%
%  options [struct] Control parameters:
%   .solver [string] Function which implements the binary SVM 
%     solver; (default 'smo').
%   .verb [1x1] If 1 then a progress info is displayed (default 0).
%  The other fileds of options specifies the options of the binary
%  solver (e.g., ker, arg, C). See help of the selected solver.
%  
% Output:
%  model [struct] Multi-class SVM classifier:
%   .Alpha [nsv x nclass] Weights (Lagrangians).
%   .b [nclass x 1] Biases of discriminant functions.
%   .sv.X [dim x nsv] Support vectors.
%   .nsv [1x1] Number of support vectors.
%   .trnerr [1x1] Training error.
%   .kercnt [1x1] Number of kernel evaluations.
%   .options [struct[ Copy of input argument options.
%
% Example:
%  data = load('pentagon');
%  options = struct('ker','rbf','arg',1,'C',10,'verb',1);
%  model = oaasvm(data,options);
%  figure; 
%  ppatterns(data); ppatterns( model.sv.X, 'ok',13);
%  pboundary( model );
%
% See also 
%  SVMCLASS, OAOSVM.
%

% About: Statistical Pattern Recognition Toolbox
% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac
% <a href="http://www.cvut.cz">Czech Technical University Prague</a>
% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a>
% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>

% Modifications:
% 27-may-2004, VF, completely re-programed
% 18-sep-2001, V. Franc, created

% Process inputs
%-----------------------------
if nargin < 2, options = []; else options=c2s(options); end
if ~isfield(options,'verb'), options.verb = 0; end
if ~isfield(options,'solver'), options.solver = 'smo'; end
if ~isfield(options,'ker'), options.ker = 'linear'; end
if ~isfield(options,'arg'), options.arg = 1; end
if ~isfield(options,'C'), options.C = inf; end

[dim,num_data] = size(data.X);
nclass = max(data.y);

% display info
%---------------------
if options.verb == 1,
  fprintf('Binary rules: %d\n', nclass);
  fprintf('Training data: %d\n', num_data);
  fprintf('Dimension: %d\n', dim);
  if isfield( options, 'ker'), fprintf('Kernel: %s\n', options.ker); end
  if isfield( options, 'arg'), fprintf('arg: %f\n', options.arg(1)); end
  if isfield( options, 'C'), fprintf('C: %f\n', options.C); end
end

%----------------------------------------
Alpha = zeros(num_data,nclass);
b = zeros(nclass,1);
orig_labels = data.y;
kercnt = 0;

% One-Against-All decomposition
%----------------------------------------
for i=1:nclass,

   if options.verb==1,
     fprintf('Training rule %d', i);
   end

   % set binary subtask
   %---------------------------------------------
   bin_labels = zeros(1,num_data);
   bin_labels(find( orig_labels==i)) = 1;
   bin_labels(find( orig_labels~=i)) = 2;   
   data.y = bin_labels;

   % solve binary subtask
   %-------------------------------------
   bin_model = feval( options.solver, data, options );

   Alpha(bin_model.sv.inx,i) = bin_model.Alpha(:);
   b(i) = bin_model.b;
   kercnt = kercnt + bin_model.kercnt;
   
   % progress info 
   %----------------------------
   if options.verb ==1,
     if isfield(bin_model, 'trnerr'),
       fprintf(': trnerr = %.4f', bin_model.trnerr);
     end
     if isfield(bin_model, 'margin'),
       fprintf(', margin = %f', bin_model.margin );
     end
     fprintf('\n');
   end
end

% set output model
%---------------------------------

% indices of all support vectors
inx = find(sum(abs(Alpha),2)~= 0);

model.Alpha = Alpha(inx,:);
model.b = b;
model.sv.X = data.X(:,inx);
model.sv.y = orig_labels(inx);
model.sv.inx = inx;
model.nsv = length(inx);
model.kercnt = kercnt;
model.options = options;
model.fun = 'svmclass';
model.trnerr = cerror( svmclass(data.X, model), orig_labels );

if strcmp(options.ker,'linear') == 1,
  model.W = model.sv.X*model.Alpha;
end

% display info
%--------------------
if options.verb == 1,
  fprintf('Total training error = %.4f\n', model.trnerr);
end

return;
% EOF