# import numpy as np
# from scipy.spatial.distance import pdist#直接调包可以计算JC值 :需要两个句子长度一样；所以暂时不用
import jieba


def Jaccrad(model,reference):   terms_reference为源句子，terms_model为候选句子
    terms_reference = jieba.cut(reference)   默认精准模式
    terms_model = jieba.cut(model)

    grams_reference = set(terms_reference)   去重；如果不需要就改为list
    grams_model = set(terms_model)


    temp = 0
    for i in grams_reference:   遍历传进来的list
        print("传进来对比的值",i)
        if i in grams_model:
            temp = temp + 1

    fenmu = len(grams_model) + len(grams_reference) - temp   并集 计算并集数量
    jaccard_coefficient = float(temp / fenmu)   交集
    return jaccard_coefficient


if __name__ == '__main__':

    a = 香农在信息论中提出的信息熵定义为自信息的期望
    b = 
    jaccard_coefficient = Jaccrad(a,b)
    print(jaccard_coefficient)