基于编辑距离的单词纠错算法

class Candidate(object):

    #  WORDS_dict={word:freq}
    def __init__(self,WORDS_dict):
        self.WORDS=WORDS_dict

    def P(self,word):
        "Probability of `word`."
        # print(word,WORDS[word]/N)
        return self.WORDS[word] / sum(self.WORDS.values())

    def correction(self,word):
        "Most probable spelling correction for word."
        return max(self.candidates(word), key=self.P)

    def candidates(self,word):
        "Generate possible spelling corrections for word."
        return (self.known([word]) or self.known(self.edits1(word)) or self.known(self.edits2(word)) or [word])

    def known(self,words):
        "The subset of `words` that appear in the dictionary of WORDS."
        # print("word_list===>",set(w for w in words if w in WORDS))
        return set(w for w in words if w in self.WORDS)

    def edits1(self,word):
        "All edits that are one edit away from `word`. "
        # todo
        letters = 'abcdefghijklmnopqrstuvwxyz'
        splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
        deletes = [L + R[1:] for L, R in splits if R]
        transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R) > 1]
        replaces = [L + c + R[1:] for L, R in splits if R for c in letters]
        inserts = [L + c + R for L, R in splits for c in letters]
        return set(deletes + transposes + replaces + inserts)

    def edits2(self,word):
        "All edits that are two edits away from `word`."
        return (e2 for e1 in self.edits1(word) for e2 in self.edits1(e1))