new_contents: """
  Observe if C varies when we use more examples 
  """
  import logging 
  import numpy 
  import sys 
  import multiprocessing 
  from apgl.util.PathDefaults import PathDefaults 
  from apgl.predictors.AbstractPredictor import computeTestError 
  from exp.modelselect.ModelSelectUtils import ModelSelectUtils 
  from apgl.util.Sampling import Sampling
  from apgl.predictors.LibSVM import LibSVM
  import matplotlib.pyplot as plt 
  
  
  logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
  numpy.seterr(all="raise")
  numpy.random.seed(45)
  dataDir = PathDefaults.getDataDir() 
  dataDir += "modelPenalisation/regression/"
  outputDir = PathDefaults.getOutputDir() + "modelPenalisation/regression/CART/"
  
  figInd = 0 
  
  loadMethod = ModelSelectUtils.loadRegressDataset
  datasets = ModelSelectUtils.getRegressionDatasets(True)
  datasetName = datasets[9][0]
  
  #sampleSizes = numpy.array([50, 100, 200])
  sampleSizes = numpy.array([50, 100, 200])
  foldsSet = numpy.arange(2, 13, 1)
  alpha = 1.0
  
  paramDict = {} 
  paramDict["setC"] = 2.0**numpy.arange(-10, 14, 2, dtype=numpy.float)
  paramDict["setGamma"] = 2.0**numpy.arange(-10, 4, 2, dtype=numpy.float)
  paramDict["setEpsilon"] = numpy.array([2**-2])
  
  sampleMethod = Sampling.crossValidation
  numProcesses = multiprocessing.cpu_count()
  
  
  j = 0 
  trainX, trainY, testX, testY = ModelSelectUtils.loadRegressDataset(dataDir, datasetName, j)
  learner = LibSVM(kernel='gaussian', type="Epsilon_SVR", processes=numProcesses) 
  
  
  for sampleSize in sampleSizes: 
      print("Sample size " +str(sampleSize))
      trainInds = numpy.random.permutation(trainX.shape[0])[0:sampleSize]
      validX = trainX[trainInds,:]
      validY = trainY[trainInds]
      
      folds = 5 
      idx = sampleMethod(folds, sampleSize)
      
      meanErrors = learner.parallelPenaltyGrid(validX, validY, testX, testY, paramDict, computeTestError)
      meanErrors = numpy.squeeze(meanErrors)
      
      for i in range(paramDict["setGamma"].shape[0]): 
          plt.figure(i)
          plt.plot(numpy.arange(paramDict["setC"].shape[0]), meanErrors[i, :], label=str(sampleSize))
          plt.legend(loc="upper left")
          plt.xlabel("C")
          plt.ylabel("Error")
  plt.show()
subject: Test if C changes with more examples.