python 相似语句匹配（非机器学习）

#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