init_shogun num=1000; dist=1; width=2.1 C=1 epsilon=1e-5 traindata_real=[randn(2,num)-dist, randn(2,num)+dist]; testdata_real=[randn(2,num)-dist, randn(2,num)+dist]; trainlab=[-ones(1,num), ones(1,num)]; testlab=[-ones(1,num), ones(1,num)]; feats_train=RealFeatures(traindata_real); feats_test=RealFeatures(testdata_real); feats_test.copy_feature_matrix(testdata_real); kernel=GaussianKernel(feats_train, feats_train, width); labels=Labels(trainlab); svm=LibSVM(C, kernel, labels); svm.parallel.set_num_threads(8); svm.set_epsilon(epsilon); svm.train(); kernel.init(feats_train, feats_test); out=svm.classify().get_labels(); testerr=mean(sign(out)~=testlab)