text: #!/usr/bin/env python
  traindat = '../data/fm_train_real.dat'
  testdat = '../data/fm_test_real.dat'
  label_traindat = '../data/label_train_twoclass.dat'
  
  parameter_list = [[traindat,testdat,label_traindat,0.9,1e-5,1],[traindat,testdat,label_traindat,0.8,1e-5,1]]
  
  def classifier_svmocas_modular (train_fname=traindat,test_fname=testdat,label_fname=label_traindat,C=0.9,epsilon=1e-5,num_threads=1):
  	from modshogun import RealFeatures, BinaryLabels
  	from modshogun import SVMOcas, CSVFile
  
  	feats_train=RealFeatures(CSVFile(train_fname))
  	feats_test=RealFeatures(CSVFile(test_fname))
  	labels=BinaryLabels(CSVFile(label_fname))
  
  	svm=SVMOcas(C, feats_train, labels)
  	svm.set_epsilon(epsilon)
  	svm.parallel.set_num_threads(num_threads)
  	svm.set_bias_enabled(False)
  	svm.train()
  
  	bias=svm.get_bias()
  	w=svm.get_w()
  	predictions = svm.apply(feats_test)
  	return predictions, svm, predictions.get_labels()
  
  if __name__=='__main__':
  	print('SVMOcas')
  	classifier_svmocas_modular(*parameter_list[0])
  