SA='成都|上海|北京|南京|深圳'
SV='北京的中国的首都'

我需要一个判定，判断sv中是否有sa的词语，该如何写呢呢？
我最开始写的是

    for k in SA:
        if re.search(k.replace('|', ''), SV):
            print('yes')

import re
SA=r'成都|上海|北京|南京|深圳'
SV='北京的中国的首都'
if re.search(SA,SV):
print('YES')
else:
print('No')

SA='成都|上海|北京|南京|深圳'
SV='京北的中国的首都'

for i  in SA.split("|"):
    if i in SV:
        print("ok")
    else:
        print("no")

SA='成都|上海|北京|南京|深圳'
SV='北京的中国的首都'

for s in SA.split("|"):
if SV.find(s) != -1:
print(s)

https://blog.csdn.net/weixin_33912246/article/details/94517601

#coding=utf-8

import xlrd
import distance
from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer
import numpy as np
from scipy.linalg import norm

workbook = xlrd.open_workbook(u'工程师问答.xls')
sheet_names= workbook.sheet_names()

ls = []
for sheet_name in sheet_names:

sheet1 = workbook.sheet_by_name(sheet_name)
for i in range(1, 3858):
    row = sheet1.row_values(i)
    ls.append(row[0])

print len(ls)

target = u'D90的发动机热效率是多少？'
print u'目标语句：' + target

编辑距离计算

def edit_distance(s1, s2):
return distance.levenshtein(s1, s2)

results = list(filter(lambda x: edit_distance(x, target) <= 5, ls))
print u'1)编辑距离计算，阈值为5'
for i in results:
print i

杰卡德系数计算

def jaccard_similarity(s1, s2):
def add_space(s):
return ' '.join(list(s))

# 将字中间加入空格
s1, s2 = add_space(s1), add_space(s2)
# 转化为TF矩阵
cv = CountVectorizer(tokenizer=lambda s: s.split())
corpus = [s1, s2]
vectors = cv.fit_transform(corpus).toarray()
# 求交集
numerator = np.sum(np.min(vectors, axis=0))
# 求并集
denominator = np.sum(np.max(vectors, axis=0))
# 计算杰卡德系数
return 1.0 * numerator / denominator

results = list(filter(lambda x: jaccard_similarity(x, target) > 0.6, ls))
print u'2)杰卡德系数计算，阈值为0.6'
for i in results:
print i

TF 计算

def tf_similarity(s1, s2):
def add_space(s):
return ' '.join(list(s))

# 将字中间加入空格
s1, s2 = add_space(s1), add_space(s2)
# 转化为TF矩阵
cv = CountVectorizer(tokenizer=lambda s: s.split())
corpus = [s1, s2]
vectors = cv.fit_transform(corpus).toarray()
# 计算TF系数
return np.dot(vectors[0], vectors[1]) / (norm(vectors[0]) * norm(vectors[1]))

results = list(filter(lambda x: tf_similarity(x, target) > 0.7, ls))
print u'3)TF 计算，阈值为0.7'
for i in results:
print i

TFIDF 系数

def tfidf_similarity(s1, s2):
def add_space(s):
return ' '.join(list(s))

# 将字中间加入空格
s1, s2 = add_space(s1), add_space(s2)
# 转化为TF矩阵
cv = TfidfVectorizer(tokenizer=lambda s: s.split())
corpus = [s1, s2]
vectors = cv.fit_transform(corpus).toarray()
# 计算TF系数
return np.dot(vectors[0], vectors[1]) / (norm(vectors[0]) * norm(vectors[1]))

results = list(filter(lambda x: tfidf_similarity(x, target) > 0.6, ls))
print u'4)TFIDF 系数，阈值为0.6'
for i in results:
print i