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【答案】应助回帖
别的地方看到一个，不知道符合你要求不？
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function classifier=tsvm(X,Y,Kernel,KernelParam,lambda)
% TSVM Implements Transductive SVMs
% [alpha,b]=tsvm(X,Y,Kernel,KernelParam,C)
% C = 1/(2*l*lambda)
%
% Inputs:
% X : (num x dim) examples are rows
% Y : column vector of labels [-1,0,+1] 0:unlabeled points
% YU: Labels of unlabeled data if known
% options: structure with fields
% options.lambda1 (1/(2*l*C))
% options.Kernel = 'rbf' | 'poly' | 'linear'
% options.KernelParam = sigma | degree | 0
%
% Outputs:
% alpha : column vector containing coefficients of expansion
% b : bias
% svs : support vectors
% labels : structure labels.train labels.unlab - labels on training/unlabeled data
% error : structure error.train error.unlab
%
% Requires: SVMLight
% Set SVMLightPath to the path to SVMLight binaries
%
%
% Notes : This code has been tested for correctness:
% /home/vikas/research/data/text/joachims-examples/example2
%
% Author: Vikas Sindhwani vikass@cs.uchicago.edu
% ManifoldLearn : Machine Learning Toolbox
% May 2004
%------------------------------------------------------------------------------%
% Set path here
SVMLightPath='/home/vikas/software/svm_light/';
% for RBF kernel change sigma to gamma
switch Kernel
case 'rbf'
kerparam=1/(2*KernelParam*KernelParam);
case 'poly'
kerparam=[KernelParam 1 0];
otherwise
kerparam=KernelParam;
end
% SVMLight will train and write a model file to 'junk'
% and then read alphas , b svs, from it
C=1/(2*lambda);
optsvml = ...
svmlopt('C',C,'Kernel',KERNELS(Kernel),'KernelParam',kerparam);
optsvml.Verbosity=0;
optsvml.ExecPath=SVMLightPath;
net=svml('junk',optsvml);
net=svmltrain(net,X,Y);
[alpha,svs]=svmlread('junk');
delete('junk');
delete('junk.transduction');
b=alpha(9);
alpha=alpha(10:end);
svs=svs(10:end, ;
disp('tsvm: b set to 0');
%
classifier=saveclassifier('tsvm',Kernel,KernelParam,alpha,svs,b,lambda);
%----------------------------------------------------------------------------------%
% switch to numerical parameter for kernel
function k=KERNELS(kerneltype)
switch kerneltype
case 'linear'
k=0;
case 'poly'
k=1;
case 'rbf'
k=2
otherwise
error('Unknown Kernel Type');
end
%----------------------------------------------------------------------------------%
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