def expanded_terms(self, number, normalize = True):
        """Returns the N most important terms in the vectors added so far.
        
        :number: The number of terms to return.
        :normalize: Whether to normalize the weights.
        :*returns*: A list of ("term", weight) tuples.
        """
        
        model = self.model
        tlist = []
        maxweight = 0
        collection_freq = self.collection_freq
        
        for word, weight in self.topN_weight.iteritems():
            score = model.score(weight, collection_freq[word], self.top_total)
            if score > maxweight: maxweight = score
            tlist.append((score, word))
        
        if normalize:
            norm = model.normalizer(maxweight, self.top_total)
        else:
            norm = maxweight
        tlist = [(norm / weight, t) for weight, t in tlist]
        tlist.sort(reverse = True)
        
        return [(t, weight) for weight, t in tlist[:number]]
dataset/ETHPy150Open tallstreet/Whoosh-AppEngine/src/whoosh/classify.py/Expander.expanded_terms/swapped operands of `/`