code: def getCurrentClassification(self, minOverlap=None, includeZeros=True):
      
      results = {}
      l2sdr = self.getL2Representations()
      sdrSize = self.config["L2Params"]["sdrSize"]
      if minOverlap is None:
        minOverlap = sdrSize / 2
  
      for objectName, objectSdr in self.objectL2Representations.iteritems():
        count = 0
        score = 0.0
        for col in xrange(self.numColumns):
          # Ignore inactive column
          if len(l2sdr[col]) == 0:
            continue
  
          count += 1
          overlap = len(l2sdr[col] & objectSdr[col])
          if overlap >= minOverlap:
            score += 1
  
        if count == 0:
          if includeZeros:
            results[objectName] = 0
        else:
          if includeZeros or score>0.0:
            results[objectName] = score / count
  
      return results
docs: Return the current classification for every object.  Returns a dict with a
      score for each object. Score goes from 0 to 1. A 1 means every col (that has
      received input since the last reset) currently has overlap >= minOverlap
      with the representation for that object.
  
      :param minOverlap: min overlap to consider the object as recognized.
                         Defaults to half of the SDR size
  
      :param includeZeros: if True, include scores for all objects, even if 0
  
      :return: dict of object names and their score