我想你们可能在寻找一种描述字符串之间距离的算法。这里有一些你可以参考的:

汉明距离 Levenshtein距离 Damerau-Levenshtein距离 Jaro-Winkler距离

包装距离包括Levenshtein距离:

import distance
distance.levenshtein("lenvestein", "levenshtein")
# 3

TheFuzz是一个用python实现Levenshtein距离的包，在某些情况下，当你希望两个不同的字符串被认为是相同的时，它带有一些帮助函数来提供帮助。例如:

>>> fuzz.ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
    91
>>> fuzz.token_sort_ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
    100

注意,difflib。SequenceMatcher只找到最长的连续匹配子序列，这通常不是我们想要的，例如:

>>> a1 = "Apple"
>>> a2 = "Appel"
>>> a1 *= 50
>>> a2 *= 50
>>> SequenceMatcher(None, a1, a2).ratio()
0.012  # very low
>>> SequenceMatcher(None, a1, a2).get_matching_blocks()
[Match(a=0, b=0, size=3), Match(a=250, b=250, size=0)]  # only the first block is recorded

寻找两个字符串之间的相似性与生物信息学中成对序列比对的概念密切相关。有许多专门的库，包括生物马拉松。这个例子实现了Needleman Wunsch算法:

>>> from Bio.Align import PairwiseAligner
>>> aligner = PairwiseAligner()
>>> aligner.score(a1, a2)
200.0
>>> aligner.algorithm
'Needleman-Wunsch'

使用biopython或其他生物信息学包比python标准库的任何部分都更灵活，因为有许多不同的评分方案和算法可用。此外，你可以得到匹配的序列来可视化正在发生的事情:

>>> alignment = next(aligner.align(a1, a2))
>>> alignment.score
200.0
>>> print(alignment)
Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-Apple-
|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-|||-|-
App-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-elApp-el

还添加了Spacy NLP库;

@profile
def main():
    str1= "Mar 31 09:08:41  The world is beautiful"
    str2= "Mar 31 19:08:42  Beautiful is the world"
    print("NLP Similarity=",nlp(str1).similarity(nlp(str2)))
    print("Diff lib similarity",SequenceMatcher(None, str1, str2).ratio()) 
    print("Jellyfish lib similarity",jellyfish.jaro_distance(str1, str2))

if __name__ == '__main__':

    #python3 -m spacy download en_core_web_sm
    #nlp = spacy.load("en_core_web_sm")
    nlp = spacy.load("en_core_web_md")
    main()

使用Robert Kern的line_profiler运行

kernprof -l -v ./python/loganalysis/testspacy.py

NLP Similarity= 0.9999999821467294
Diff lib similarity 0.5897435897435898
Jellyfish lib similarity 0.8561253561253562

然而，时间的启示

Function: main at line 32

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
    32                                           @profile
    33                                           def main():
    34         1          1.0      1.0      0.0      str1= "Mar 31 09:08:41  The world is beautiful"
    35         1          0.0      0.0      0.0      str2= "Mar 31 19:08:42  Beautiful is the world"
    36         1      43248.0  43248.0     99.1      print("NLP Similarity=",nlp(str1).similarity(nlp(str2)))
    37         1        375.0    375.0      0.9      print("Diff lib similarity",SequenceMatcher(None, str1, str2).ratio()) 
    38         1         30.0     30.0      0.1      print("Jellyfish lib similarity",jellyfish.jaro_distance(str1, str2))

这是我想到的:

import string

def match(a,b):
    a,b = a.lower(), b.lower()
    error = 0
    for i in string.ascii_lowercase:
            error += abs(a.count(i) - b.count(i))
    total = len(a) + len(b)
    return (total-error)/total

if __name__ == "__main__":
    print(match("pple inc", "Apple Inc."))