code: def sim(self, src, tar, sim_func=sim_levenshtein, symmetric=False):
          
          if src == tar:
              return 1.0
  
          q_src = sorted(QGrams(src).elements())
          q_tar = sorted(QGrams(tar).elements())
  
          if not q_src or not q_tar:
              return 0.0
  
          sum_of_maxes = 0
          for q_s in q_src:
              max_sim = float('-inf')
              for q_t in q_tar:
                  max_sim = max(max_sim, sim_func(q_s, q_t))
              sum_of_maxes += max_sim
          sim_em = sum_of_maxes / len(q_src)
  
          if symmetric:
              sim_em = (sim_em + self.sim(tar, src, sim_func, False)) / 2
  
          return sim_em
docs: Return the Monge-Elkan similarity of two strings.
  
          Parameters
          ----------
          src : str
              Source string for comparison
          tar : str
              Target string for comparison
          sim_func : function
              The internal similarity metric to employ
          symmetric : bool
              Return a symmetric similarity measure
  
          Returns
          -------
          float
              Monge-Elkan similarity
  
          Examples
          --------
          >>> cmp = MongeElkan()
          >>> cmp.sim('cat', 'hat')
          0.75
          >>> round(cmp.sim('Niall', 'Neil'), 12)
          0.666666666667
          >>> round(cmp.sim('aluminum', 'Catalan'), 12)
          0.388888888889
          >>> cmp.sim('ATCG', 'TAGC')
          0.5