code: def isObjectClassified(self, objectName, minOverlap=None, maxL2Size=None):
      
      L2Representation = self.getL2Representations()
      objectRepresentation = self.objectL2Representations[objectName]
      sdrSize = self.config["L2Params"]["sdrSize"]
      if minOverlap is None:
        minOverlap = sdrSize / 2
      if maxL2Size is None:
        maxL2Size = 1.5*sdrSize
  
      numCorrectClassifications = 0
      for col in xrange(self.numColumns):
        overlapWithObject = len(objectRepresentation[col] & L2Representation[col])
  
        if ( overlapWithObject >= minOverlap  and
             len(L2Representation[col]) <= maxL2Size ):
          numCorrectClassifications += 1
  
      return numCorrectClassifications == self.numColumns
docs: Return True if objectName is currently unambiguously classified by every L2
      column. Classification is correct and unambiguous if the current L2 overlap
      with the true object is greater than minOverlap and if the size of the L2
      representation is no more than maxL2Size
  
      :param minOverlap: min overlap to consider the object as recognized.
                         Defaults to half of the SDR size
  
      :param maxL2Size: max size for the L2 representation
                         Defaults to 1.5 * SDR size
  
      :return: True/False