findsv.m

一些自已编写的SVM应用程序,对初学者很有启发意义,

function [net, SVthresh, SV, SVbound, SVnonbound] = findSV(net, C)
% FINDSV - Select the Support Vectors from the current coefficients NET.alpha

% Threshold for selecting Support Vectors
maxalpha = max(net.alpha);
if maxalpha > net.alphatol,
  % For most cases, net.alphatol is a reasonable choice (approx 1e-2)
  SVthresh = net.alphatol;
else
  % For complex kernel on small data sets: all alphas will be very small.
  % Use the mean between the minimum and maximum logarithm of values
  % NET.alpha as a threshold.
  SVthresh = exp((log(max(eps,maxalpha))+log(eps))/2);
end
% All examples that have a value of NET.alpha above this threshold are
% assumed to be Support Vectors.
SV = logical(uint8(net.alpha>=SVthresh));
% All Support Vectors that have a value at their upper bound C
if net.use2norm,
  % There is no such thing in the 2norm case!
  SVbound = logical(repmat(uint8(0), size(net.alpha)));
else
  SVbound = logical(uint8(net.alpha>(C-net.alphatol)));
end
% The Support Vectors not at the upper bound
SVnonbound = SV & (~SVbound);
% The actual indices of the Support Vectors in the training set
net.svind = find(SV);