old_contents: ###########################################################################
  # linear ridge regression
  ###########################################################################
  from numpy import array
  from numpy.random import seed, rand
  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,1e-6],[traindat,testdat,label_traindat,100]]
  
  def regression_linear_ridge_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,tau=1e-6):
  
  	from shogun.Features import Labels, RealFeatures
  	from shogun.Kernel import GaussianKernel
  	from shogun.Regression import LinearRidgeRegression
  
  	rr=LinearRidgeRegression(tau, RealFeatures(traindat), Labels(label_train))
  	rr.train()
  	out = rr.apply(RealFeatures(fm_test)).get_labels()
  	return out,rr
  
  if __name__=='__main__':
  	print 'LinearRidgeRegression'
  	regression_linear_ridge_modular(*parameter_list[0])
  
new_contents: ###########################################################################
  # linear ridge regression
  ###########################################################################
  from numpy import array
  from numpy.random import seed, rand
  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,1e-6],[traindat,testdat,label_traindat,100]]
  
  def regression_linear_ridge_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,tau=1e-6):
  
  	from shogun.Features import Labels, RealFeatures
  	from shogun.Regression import LinearRidgeRegression
  
  	rr=LinearRidgeRegression(tau, RealFeatures(traindat), Labels(label_train))
  	rr.train()
  	out = rr.apply(RealFeatures(fm_test)).get_labels()
  	return out,rr
  
  if __name__=='__main__':
  	print 'LinearRidgeRegression'
  	regression_linear_ridge_modular(*parameter_list[0])
  
subject: remove unused gaussian kernel from example