function demsvml1()% DEMSVML1 - Demo program for Matlab SVMlight wrapper%   %   This program shows how Thorsten Joachims SVMlight test examples can%   be executed using the Matlab SVMlight wrapper, plus some additional%   features.%   In order to execute this demo, SVMlight must be installed (on the%   execution path or in the current directory) and the two%   subdirectories 'example1' and 'example2' must be present in the%   current directory.%   %   Please check the source code for more info.%%   See also SVML, SVMLTRAIN, SVMLFWD, SVMLWRITE, SVMLREAD, SVM_LEARN,%            SVM_CLASSIFY%% % Copyright (c) by Anton Schwaighofer (2002)% $Revision: 1.3 $ $Date: 2002/08/09 20:27:33 $% mailto:anton.schwaighofer@gmx.net% % This program is released unter the GNU General Public License.% fprintf('In order to execute this demo, SVMlight must be installed (on the\n');fprintf('execution path or in the current directory) and the two\n');fprintf('subdirectories ''example1'' and ''example2'' must be present in the\n');fprintf('current directory.\n');fprintf('Have a look at the source code for details...\n');fprintf('\nPress the Return key to continue, or Control-C to cancel.\n');% We do basically the same as Joachims example2, just that we load% the data into Matlab beforehand and use the SVML structure[Y, X] = svmlread('example1/train.dat');net = svml('example1/model1', 'Kernel', 0, 'C', 1);net = svmltrain(net, X, Y);% Also read out Joachims' test data[Ytest, Xtest] = svmlread('example1/test.dat');% Compute prediction on the test dataYpred = svmlfwd(net, Xtest);% We can also get SVMlight to print out accuracy and precision/recall, by% providing the test targets as well:Ypred = svmlfwd(net, Xtest, Ytest);% The SVMlight wrapper can also directly access data files in SVMlight's% format, both during training and testing. This is of course more% efficient than reading the files into Matlab and writing it back to% disk again, if the data are already available in SVMlights format.net = svml('example1/model2', 'Kernel', 0, 'C', 1);% directly access training datanet = svmltrain(net, 'example1/train.dat');% Compute prediction on the test dataYtest = svmlfwd(net, 'example1/test.dat');% Now for the transductive SVM:net = svml('example2/model1', 'Kernel', 0, 'C', 1, 'TransLabelFile', ...           'example2/trans_predictions');% Transductive learner is automatically invoked% You may just as well load the data into Matlab variables% [Y, X] = svmlread('example2/train_transduction.dat');% and go net = svmltrain(net, X, Y);net = svmltrain(net, 'example2/train_transduction.dat');% Read out the labels for the unclassified training examplestransLabels = svmlread('example2/trans_predictions');% We now use only the simple calling routines SVM_LEARN and% SVM_CLASSIFY. For the options, we only assume that SVMlight is% installed in the current directory '.'% Joachims' first example (inductive SVM):options = svmlopt('ExecPath', '.');svm_learn(options, 'example1/train.dat', 'example1/model');svm_classify(options, 'example1/test.dat', 'example1/model', ...             'example1/predictions');% The file example1/model holds the trained SVM model,% example1/predictions are the labels for the test points% Joachims' second example (transductive SVM)svm_learn(options, 'example2/train_transduction.dat', 'example2/model');svm_classify(options, 'example2/test.dat', 'example2/model', ...             'example2/predictions');