new_contents: import string
  import sklearn
  import numpy as np
  from titleParse import *
  import os
  from sklearn import svm
  import re
  from featureHelpers.py import *
  
  #testStringList = getTitles("test_data" + os.sep + "merged.txt")
  #testStringList = getTitles("test.txt")
  """This is a function designed to extract an attribute vector out of the text of
  a Craigslist posting. These attribute vectors will be fed to the SciKit Learn
  module to determine the quality of the posting itself."""
  
  clf = svm.SVC()
  
  def extractVectorsFromListOfPosts(postList):
      
      def extractVectorFromPost(postText):
          upperCaseText = string.upper(postText)
          count = len(postText)
          whiteCount, letterCount, symbolCount, lowerCaseCount = 0, 0 ,0, 0
          for i in xrange(count):
              if postText[i] in string.whitespace: whiteCount += 1
              elif postText[i] in string.ascii_letters: 
                  letterCount += 1
                  lowerCaseCount += (1 - (upperCaseText[i] == postText[i]))
              else: symbolCount += 1
              #Python boolean arithmetic casts True to 1 and 0 to False.
              #If a char was lowercase, the count will increase
          upperCaseRatio = 1 - float(lowerCaseCount)/letterCount
          symbolRatio = float(symbolCount)/count
          whiteRatio = float(whiteCount)/count
          return [upperCaseRatio, symbolRatio, whiteRatio,count]
  
      result = np.array(map(extractVectorFromPost,postList))
      #print result
      np.set_printoptions(precision=3)
      np.savetxt('long_run.txt',result)
      return result
  
  def writeFile(filename, contents, mode="wt"):
      """This is a function taken from the 15-112 website. It writes
      the string contents to the path defined by the string filename"""
      # wt stands for "write text"
      fout = None
      try:
          fout = open(filename, mode)
          fout.write(contents)
      finally:
          if (fout != None): fout.close()
      return True
  
  def predictScoreForArrayOfVectors(vec_arr):
      for vec in vec_arr:
          print clf.predict(vec)
      return
  
  def getLearningModelFromArray(data_array, scores):
      clf.fit(data_array,np.array(scores))
      return True
  
  (scores,titles) = getTitles('output3.txt')
  vectors = extractVectorsFromListOfPosts(titles)
  getLearningModelFromArray(vectors,scores)
  
subject: Revert "Got rid of tuples"