svmroc.m

用于matlab环境下的支持向量机svm的工具箱

function [xsup,w,w0,pos,timeps,alpha,matriceind]=svmroc(x,y,C,kppv,margin,lambda,kernel,kerneloption,verbose,span,qpsize)

% USAGE
% 
% [xsup,w,w0,pos,timeps,alpha,matriceind]=svmroc(x,y,C,kppv,margin,lambda,kernel,kerneloption,verbose,span,qpsize)
%
%
% SVM ROC Optimizer that can handle LS problem and large neighboorhood. This
% algorithm uses a decomposition procedure.
%
%  x y          the learning data and labels
%  C            penalization parameters
%  kppv         the number of neighboor to consider
%  margin       the margin for ranking
%  lambda       conditioning parameter for the qp problem e.g 1e-7
%  kernel       the kernel type e.g 'gaussian' or 'poly'
%  kerneloption kernel parameters
%  verbose      verbosity of the algo 0 or 1
%  span         type of semi parametric function e.g 1
%  qpsize       size of qp algorithm
%
%
% Outputs as usual for SVM except that xsup is a cell containing
% the couple of positive and negative support vectors.
% 
% see also svmrocval
%
% 

% 30/07/2004 A. Rakotomamonjy



iteration=0;
kkttol=1e-4;
difftol=1e-7;
chunksize=qpsize;

indpos=find(y==1);
indneg=find(y==-1);
nbpos=length(indpos);
nbneg=length(indneg);

timeps=0;


%-------------------------------------------------------------------------
%
% dist in feature space of each positive example to negative examples
%
% the idea here is to select a subset of couple for
% optimizing the ranking
%
% 
% CASE 1 : select only the k-nearest negative neighbor of positive examples

for i=1:nbpos+nbneg
    norme2(i)=svmkernel(x(i,:),kernel,kerneloption);
end;

matriceindneg=[];
% select only the k-nearest positive neighbor of negative xamples
for i=1:nbneg
    aux1=svmkernel(x(indneg(i),:),kernel,kerneloption,x(indpos,:));
    dist=norme2(indneg(i))*ones(1,nbpos) + norme2(indpos) - 2*aux1 ;
    [aux,indicesorted]=sort(dist');
    minim=min(length(indicesorted),kppv);
    matriceindneg=[matriceindneg; i*ones(minim,1) indicesorted(1:minim)];
end;
vect=unique(matriceindneg(:,2)); 
% process only these couples of nn of these positive samples.
dist=[];
matriceind=[];
for i=1:length(vect)
    aux1=svmkernel(x(indpos(vect(i)),:),kernel,kerneloption,x(indneg,:));
    dist=norme2(indpos(vect(i)))*ones(1,nbneg) + norme2(indneg) - 2*aux1 ;
    [aux,indicesorted]=sort(dist');
    minim=min(length(indicesorted),kppv);
    matriceind=[matriceind; vect(i)*ones(minim,1) indicesorted(1:minim)];
end;
taille=length(matriceind);

%--------Matrice stocke la liste des couples de points