new_f_svs.m

模式识别工具箱,希望对大家有用!

function [frac_SV2,alf,b,svx] = new_f_svs(sigma,x,labx,frac_error,fracsv,thiseps);
% Support function for the training of the SVDD in the optimization
% routine for optimizing sigma.

% Copyright: D. Tax, R.P.W. Duin, [e-mail]
% Faculty of Applied Physics, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands

if (nargin<6)
  thiseps = 1e-4;
end
if (nargin<5)
  fracsv = 0;
end
num_points = size(x,1);
if (nargin<4) | (frac_error<=0)
  frac_error = 1/num_points;
end

if (size(labx,2)>1)
  error('svs: Please make labx a column vector! (containing 1/-1)');
end

% OC support vector, RBF kernel:
[svx,alf,b]=m_svm(x,labx,2,2,sigma,frac_error,thiseps);
%[dummy,J,Jb] = intersect(x,svx,'rows');
%alf = zeros(size(labx));
%alf(J)=newalf;


if (isempty(alf))
  warning('No solution for the SVDD could be found!');
  svx = x;
  alf = ones(num_points,1)/num_points;
end

% difference between fraction SV real and desired
%I = find(alf<1.0);
diff = fracsv + frac_error(1) - size(alf,1)/size(x,1);
%diff = fracsv - size(alf,1)/size(x,1);
frac_SV2 = diff.*diff;

%fprintf('new_f_svs: s=%f, frac=%f, err=%f\n',...
%  sigma,size(I,1)/size(x,1),frac_SV2);

return