svmclass.m

支持向量机工具箱

function [Ipred,fpred] = svmclass(Xtst,Xtrn,Itrn,alpha,bias,ker,arg,info)
% SVMCLASS classifies patterns using SVM decision rule.
%  [Ipred,fpred] = svmclass(Xtst,Xtrn,Itrn,Alpha,bias,ker,arg)
% 
% Input:
%  Xtst [DxN] test points.
%  Xtrn [DxM] training points.
%  Itrn [1xM] labels of trainign points.
%  Alpha [1xM] Lagrange multipliers.
%  bias [1x1] bias.
%  ker [string] kernel identifier (see help kernel).
%  arg [] arguments of given kernel (see help kernel).
%
% Output:
%  Ipred [1xN] labels of test points (1,2).
%  fpred [1xN] value of output function for each point (If greater or
%    equal to 0 then class is 1 else class is 2).
%
% See also SVMCLASSM, SVM.
%

% Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac
% (c) Czech Technical University Prague, http://cmp.felk.cvut.cz
% Written Vojtech Franc (diploma thesis) 23.12.1999, 5.4.2000
% Modifications
% 19-September-2001, V. Franc, comments changed.


if nargin < 8,
  info =0;
end

if nargin < 7,
  arg=[];
end

%inx=find(alpha ~=0);
%Itrn=Itrn(inx);
%Xtrn=Xtrn(:,inx);

Ytrn=itosgn(Itrn);

Ipred=zeros(1,size(Xtst,2));
fpred=zeros(1,size(Xtst,2));

for i = 1:size(Xtst,2),
  
  xtst = Xtst(:,i);
  
  K=kernel(xtst,Xtrn,ker,arg).*Ytrn;

  fpred(i) = (K*alpha' + bias)';

  if fpred(i) >= 0,
    Ipred(i) = 1;
  else
    Ipred(i) = 2;
  end
  
  if info & mod(i*100/size(Xtst,2),5) == 0, 
    disp(sprintf('Predicted %.2f %%',i*100/size(Xtst,2) )); 
  end
end

return;