old_contents: 
  """
  Run some experiments using the Netflix dataset 
  """
  import os
  import sys
  import errno
  import logging
  import numpy
  import argparse
  import time
  from datetime import datetime
  from exp.recommendexp.RecommendExpHelper import RecommendExpHelper
  from exp.recommendexp.NetflixDataset import NetflixDataset
  
  #Uncomment this for the final run 
  if __debug__: 
      raise RuntimeError("Must run python with -O flag")
  
  numpy.random.seed(21)
  logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
  numpy.set_printoptions(suppress=True, linewidth=60)
  numpy.seterr("raise", under="ignore")
  
  # Arguments related to the dataset
  dataArgs = argparse.Namespace()
  dataArgs.maxIter = 40 
  #Set iterStartTimeStamp to None for all iterations 
  dataArgs.iterStartTimeStamp = time.mktime(datetime(2000,1,1).timetuple())
  
  # Arguments related to the algorithm
  defaultAlgoArgs = argparse.Namespace()
  defaultAlgoArgs.k = 100
  defaultAlgoArgs.svdAlg = "arpack"
  
  # init (reading/writting command line arguments)
  # data args parser #
  dataParser = argparse.ArgumentParser(description="", add_help=False)
  dataParser.add_argument("-h", "--help", action="store_true", help="show this help message and exit")
  devNull, remainingArgs = dataParser.parse_known_args(namespace=dataArgs)
  if dataArgs.help:
      helpParser  = argparse.ArgumentParser(description="", add_help=False, parents=[dataParser, RecommendExpHelper.newAlgoParser(defaultAlgoArgs)])
      helpParser.print_help()
      exit()
  
  dataArgs.extendedDirName = ""
  dataArgs.extendedDirName += "NetflixDataset"
  
  # print args #
  logging.info("Running on NetflixDataset")
  logging.info("Data params:")
  keys = list(vars(dataArgs).keys())
  keys.sort()
  for key in keys:
      logging.info("    " + str(key) + ": " + str(dataArgs.__getattribute__(key)))
  
  # data
  generator = NetflixDataset(maxIter=dataArgs.maxIter, iterStartTimeStamp=dataArgs.iterStartTimeStamp)
  
  # run
  logging.info("Creating the exp-runner")
  recommendExpHelper = RecommendExpHelper(generator.getTrainIteratorFunc, generator.getTestIteratorFunc, remainingArgs, defaultAlgoArgs, dataArgs.extendedDirName)
  recommendExpHelper.printAlgoArgs()
  #    os.makedirs(resultsDir, exist_ok=True) # for python 3.2
  try:
      os.makedirs(recommendExpHelper.resultsDir)
  except OSError as err:
      if err.errno != errno.EEXIST:
          raise
  
  recommendExpHelper.runExperiment()
  
new_contents: 
  """
  Run some experiments using the Netflix dataset 
  """
  import os
  import sys
  import errno
  import logging
  import numpy
  import argparse
  import time
  from datetime import datetime
  from exp.recommendexp.RecommendExpHelper import RecommendExpHelper
  from exp.recommendexp.NetflixDataset import NetflixDataset
  
  #Uncomment this for the final run 
  if __debug__: 
      raise RuntimeError("Must run python with -O flag")
  
  numpy.random.seed(21)
  logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
  numpy.set_printoptions(suppress=True, linewidth=60)
  numpy.seterr("raise", under="ignore")
  
  # Arguments related to the dataset
  dataArgs = argparse.Namespace()
  dataArgs.maxIter = 40 
  #Set iterStartTimeStamp to None for all iterations 
  dataArgs.iterStartTimeStamp = time.mktime(datetime(2005,12,31).timetuple())
  
  # Arguments related to the algorithm
  defaultAlgoArgs = argparse.Namespace()
  defaultAlgoArgs.k = 100
  defaultAlgoArgs.svdAlg = "arpack"
  
  # init (reading/writting command line arguments)
  # data args parser #
  dataParser = argparse.ArgumentParser(description="", add_help=False)
  dataParser.add_argument("-h", "--help", action="store_true", help="show this help message and exit")
  devNull, remainingArgs = dataParser.parse_known_args(namespace=dataArgs)
  if dataArgs.help:
      helpParser  = argparse.ArgumentParser(description="", add_help=False, parents=[dataParser, RecommendExpHelper.newAlgoParser(defaultAlgoArgs)])
      helpParser.print_help()
      exit()
  
  dataArgs.extendedDirName = ""
  dataArgs.extendedDirName += "NetflixDataset"
  
  # print args #
  logging.info("Running on NetflixDataset")
  logging.info("Data params:")
  keys = list(vars(dataArgs).keys())
  keys.sort()
  for key in keys:
      logging.info("    " + str(key) + ": " + str(dataArgs.__getattribute__(key)))
  
  # data
  generator = NetflixDataset(maxIter=dataArgs.maxIter, iterStartTimeStamp=dataArgs.iterStartTimeStamp)
  
  # run
  logging.info("Creating the exp-runner")
  recommendExpHelper = RecommendExpHelper(generator.getTrainIteratorFunc, generator.getTestIteratorFunc, remainingArgs, defaultAlgoArgs, dataArgs.extendedDirName)
  recommendExpHelper.printAlgoArgs()
  #    os.makedirs(resultsDir, exist_ok=True) # for python 3.2
  try:
      os.makedirs(recommendExpHelper.resultsDir)
  except OSError as err:
      if err.errno != errno.EEXIST:
          raise
  
  recommendExpHelper.runExperiment()
  
subject: Change to last matrix