% TUNE_OCR Tunes SVM classifier for OCR problem.
%
% Description:
% The following steps are performed:
% - Training set is created from data in directory ExamplesDir.
% - Multi-class SVM is trained for a set of arguments and
% regularization constants. The best model is selected
% based on the cross-validation error.
%
% (c) Statistical Pattern Recognition Toolbox, (C) 1999-2003,
% Written by Vojtech Franc and Vaclav Hlavac,
% <a href="http://www.cvut.cz">Czech Technical University Prague</a>,
% <a href="http://www.feld.cvut.cz">Faculty of Electrical engineering</a>,
% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>
% Modifications:
% 04-jun-2004, VF
% 09-sep-2003, VF
cd /home.dokt/xfrancv/work/new_stprtool/;
stprpath;
cd /home.dokt/xfrancv/work/new_stprtool/demos/ocr;
ExamplesDir = '../../data/ocr_numerals/';
OCRTuningFileName = 'ocrtuning';
options.ker = 'rbf';
options.arg = [1 2.5 5 7.5 10] ;
options.C = [inf];
options.verb = 1;
options.solver ='oaosvm';
options.num_fold = 5;
options.svm_options.solver = 'svmlight';
fprintf('Creating training set:\n');
TrainingDataFile = [ExamplesDir 'OcrTrndata.mat'];
mergesets( ExamplesDir, TrainingDataFile );
data = load(TrainingDataFile );
fprintf('Tuning multi-class SVM classifier.\n');
[model,Error] = evalsvm(data,options);
fprintf('\nSaving results to: %s\n',OCRTuningFileName);
save(OCRTuningFileName,'model','Error','options');