msvmmot.m

很好用的统计模式识别工具箱

function [malpha,mbias,mnsv,mexitflag,mmargin] = ...
   msvmmot(X,I,ker,arg,C,info)
% MSVMMOT multiclass version of SVMMOT.
% [malpha,mbias,mnsv,mexitflag,mmargin]=msvmmot(X,I,ker,arg,C,info)
%
% MSVMMOT decomposes input K-class problem is decomposed onto 
%  K binary problems: i-th class against remaining (K-1)
%  classes. The binary problems are solved by SVMMOT 
%  (see 'help svmmot') which results to the K decision rules.
%
%  For classification use MSVMCLASS.
%
% Mandatory inputs:
%  X [2xM] set of training patterns.
%  I [1xM] labels (1,2,...K).
%  ker [string] kernel identifier (see 'help kernel').
%  arg [...] arguments of given kernel (see 'help kernel').
%  C [1x1] tradeoff between maximal margin and classification error.
%
% Optional inputs:
%  info [1x1] if == 1 then an info is printed. Default is 0.
%
% Output:
%  malpha [KxL] found Lagrange multipliers.
%  mbias [Kx1][real] found biases.
%  mnsv [Kx1] numbers of Supprot Vectors.
%  mexitflag [Kx1] exit flag returned by QP (see 'help qp').
%  mmargin [Kx1] margins.
%
% See also  MSVMCLASS, SVMMOT, SVM.
%

% Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac
% (c) Czech Technical University Prague, http://cmp.felk.cvut.cz
% Modifications
% 19-September-2001, V. Franc, renamed to msvmmot.

if nargin < 5,
   error('Not enough input arguments.');
end

if nargin < 6,
   info = 0;
end

% maximal class label
maxclass=max(I);
L = size(X,2);

% preallocate variables
malpha=zeros(maxclass,L);
mbias=zeros(maxclass,1);
mnsv=zeros(maxclass,1);
mexitflag=cell(maxclass,1);
mmargin=zeros(maxclass,1);


% decompose multi-class problem into maxclass dichotomi
% problems.
for class=1:maxclass,
   
   if info==1,
     disp(sprintf('Building rule %d of %d,', class,maxclass ));
     drawnow;
   end
   
   Idich=multi2dicho(I,class);
   
   if nargout >= 5, 
      [malpha(class,:),mbias(class),mnsv(class),mexitflag{class},flps,...
       mmargin(class)] = svmmot(X,Idich,ker,arg,C);
   else
      [malpha(class,:),mbias(class),mnsv(class),mexitflag{class},flps]...
       = svmmot(X,Idich,ker,arg,C);
   end
end

return;