function status = svm_classify(options, data, model, predictions)% SVM_CLASSIFY - Interface to SVM light, classification module%   %   STATUS = SVM_CLASSIFY(OPTIONS, DATA, MODEL, PREDICTIONS)%   Call the classification program 'svm_classify' of the SVM light%   package.%   OPTIONS must be a structure generated by SVMLOPT. For SVM_CLASSIFY,%   only the options 'ExecPath' and 'Verbosity' are relevant.%   DATA is the name of the file containing the data to be classified%   (the test data). Use SVMLWRITE to convert a Matlab matrix to the%   appropriate format.%   MODEL is the name of the file holding the trained Support Vector%   Machine, generated by SVM_LEARN.%   PREDICTIONS is the file that will store the predicted classes. There%   is one line per test example in output_file containing the value of%   the decision function on that example. The sign of this value%   determines the predicted class. This file can be read into Matlab%   using SVMLREAD.%   If 'svm_learn' is not on the path, OPTIONS must contain a field%   'ExecPath' with the path of the executable.%   STATUS is the error code returned by SVM light (0 if everything went%   fine)%%   See also SVML, SVMLOPT, SVMLWRITE, SVMLREAD, SVM_LEARN%% % Copyright (c) by Anton Schwaighofer (2001)% $Revision: 1.6 $ $Date: 2002/08/09 20:24:12 $% mailto:anton.schwaighofer@gmx.net% % This program is released unter the GNU General Public License.% error(nargchk(4, 4, nargin));Names = fieldnames(options);[m,n] = size(Names);s = '';for i = 1:m,  field = Names{i,:};  value = getfield(options, field);  switch field,    case 'Verbosity'      s = stroption(s, '-v %i', value);  endendevalstr = [fullfile(options.ExecPath, 'svm_classify') s ' ' ...           data ' ' model ' ' predictions];fprintf('\nCalling SVMlight:\n%s\n\n', evalstr);if isunix,  status = unix(evalstr);else  status = dos(evalstr);endfunction s = stroption(s, formatstr, value, varargin)% STROPTION - Add a new option to string% if ~isempty(value),  s = [s ' ' sprintf(formatstr, value, varargin{:})];end