oaoclass.m

支持向量机工具箱

function [labels,votes] = oaoclass(data,model)
% OAOCLASS One-Against-One SVM classifier.
% [labels,votes] = oaoclass(data,model)
%
% Inputs:
%  data [dim x num_data] data to be classified.
%  Model [struct] classification rules.
%
% Outputs:
%  labels [1,num_data] predicted labels.
%  votes [1,num_data] number of votes for the winner class.
%

%  Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac
%  (c) Czech Technical University Prague, http://cmp.felk.cvut.cz
%  Written Vojtech Franc (diploma thesis) 02.11.1999, 13.4.2000
%
%  Modifications
%   3-Jun-2002, V.Franc

[dim,num_data] = size(data);

discr_fce=zeros(model.num_classes, num_data);
inx = zeros(1, num_data);

for i=1:model.num_rules,
  
  labels12 = svmclass2(data,model.trn_data(:,model.rule{i}.data_inx),...
      model.rule{i}.labels, model.rule{i}.Alpha, model.rule{i}.bias, ...
      model.SVM.kernel, model.SVM.arg);

  % voting
  vote_class1 = zeros(1,num_data);
  vote_class1(find(labels12 == 1 ))=1;
  vote_class2 = zeros(1,num_data);
  vote_class2(find(labels12 == 2 ))=1;

  discr_fce(model.rule{i}.class1,:) = discr_fce(model.rule{i}.class1,:)+...
      vote_class1;
  discr_fce(model.rule{i}.class2,:) = discr_fce(model.rule{i}.class2,:)+...
      vote_class2;
  
  
end

[votes,labels] = max(discr_fce);

return;

% EOF