% In this example a multi-class support vector machine is trained on a toy data
% set and the trained classifier is used to predict labels of test examples.
% The training algorithm is based on BSVM formulation (L2-soft margin
% and the bias added to the objective function) which is solved by the Improved
% Mitchell-Demyanov-Malozemov algorithm. The training algorithm uses the Gaussian
% kernel of width 2.1 and the regularization constant C=1.2. The bias term of the
% classification rule is not used. The solver stops if the relative duality gap
% falls below 1e-5 and it uses 10MB for kernel cache.
% 
% For more details on the used SVM solver see
%  V.Franc: Optimization Algorithms for Kernel Methods. Research report.
%  CTU-CMP-2005-22. CTU FEL Prague. 2005.
%  ftp://cmp.felk.cvut.cz/pub/cmp/articles/franc/Franc-PhD.pdf .
% 

% GMNPSVM
print GMNPSVM

set_kernel GAUSSIAN REAL 10 1.2
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_multiclass.dat
new_classifier GMNPSVM
svm_epsilon 1e-5
svm_use_bias 0
c 0.017

train_classifier

set_features TEST ../data/fm_test_real.dat
out.txt = classify
! rm out.txt
