old_contents: from tools.load import LoadMatrix
  lm=LoadMatrix()
  
  traindat = lm.load_numbers('../data/fm_train_real.dat')
  testdat = lm.load_numbers('../data/fm_test_real.dat')
  label_traindat = lm.load_labels('../data/label_train_twoclass.dat')
  
  parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5],[traindat,testdat,label_traindat,2.2,1,1e-5]]
  
  def classifier_libsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
  	from shogun.Features import RealFeatures, Labels
  	from shogun.Kernel import GaussianKernel
  	from shogun.Classifier import LibSVM
  
  	feats_train=RealFeatures(fm_train_real)
  	feats_test=RealFeatures(fm_test_real)
  	
  	kernel=GaussianKernel(feats_train, feats_train, width)
  	labels=Labels(label_train_twoclass)
  
  	svm=LibSVM(C, kernel, labels)
  	svm.set_epsilon(epsilon)
  	svm.train()
  
  	kernel.init(feats_train, feats_test)
  	labels = svm.apply().get_labels()
  	supportvectors = sv_idx=svm.get_support_vectors()
  	alphas=svm.get_alphas()
  	predictions = svm.apply()
  	return predictions, svm, predictions.get_labels()
  
  if __name__=='__main__':
  	print('LibSVM')
  	classifier_libsvm_modular(*parameter_list[0])
  
new_contents: from tools.load import LoadMatrix
  lm=LoadMatrix()
  
  traindat = lm.load_numbers('../data/fm_train_real.dat')
  testdat = lm.load_numbers('../data/fm_test_real.dat')
  label_traindat = lm.load_labels('../data/label_train_twoclass.dat')
  
  parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5],[traindat,testdat,label_traindat,2.2,1,1e-5]]
  
  def classifier_libsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
  	from shogun.Features import RealFeatures, BinaryLabels
  	from shogun.Kernel import GaussianKernel
  	from shogun.Classifier import LibSVM
  
  	feats_train=RealFeatures(fm_train_real)
  	feats_test=RealFeatures(fm_test_real)
  	
  	kernel=GaussianKernel(feats_train, feats_train, width)
  	labels=BinaryLabels(label_train_twoclass)
  
  	svm=LibSVM(C, kernel, labels)
  	svm.set_epsilon(epsilon)
  	svm.train()
  
  	kernel.init(feats_train, feats_test)
  	labels = svm.apply().get_labels()
  	supportvectors = sv_idx=svm.get_support_vectors()
  	alphas=svm.get_alphas()
  	predictions = svm.apply()
  	print predictions.get_labels()
  	return predictions, svm, predictions.get_labels()
  
  if __name__=='__main__':
  	print('LibSVM')
  	classifier_libsvm_modular(*parameter_list[0])
  
subject: fix libsvm py example