code: def initialize_dictionaries(self, p_set):
          
          success = False
          if not (hasattr(p_set, '_type')):
              error_message = "needs to be an essay set of the train type."
              log.exception(error_message)
              raise util_functions.InputError(p_set, error_message)
  
          if not (p_set._type == "train"):
              error_message = "needs to be an essay set of the train type."
              log.exception(error_message)
              raise util_functions.InputError(p_set, error_message)
  
          div_length=len(p_set._essay_sets)
          if div_length==0:
              div_length=1
  
          #Ensures that even with a large amount of input textual features, training time stays reasonable
          max_feats2 = int(math.floor(200/div_length))
          for i in xrange(0,len(p_set._essay_sets)):
              self._extractors.append(FeatureExtractor())
              self._extractors[i].initialize_dictionaries(p_set._essay_sets[i], max_feats2=max_feats2)
              self._initialized = True
              success = True
          return success
docs: Initialize dictionaries with the textual inputs in the PredictorSet object
          p_set - PredictorSet object that has had data fed in