真damerau-levenshtein算法的cython实现。
fastDamerauLevenshtein的Python项目详细描述
FastDamerauleVenshtein
cython实现了真正的damerau levenshtein编辑距离，允许一个项目被多次编辑。 更多信息来自Wikipedia：
In information theory and computer science, the Damerau-Levenshtein distance (named after Frederick J. Damerau and Vladimir I. Levenshtein) is a string metric for measuring the edit distance between two sequences. Informally, the Damerau-Levenshtein distance between two words is the minimum number of operations (consisting of insertions, deletions or substitutions of a single character, or transposition of two adjacent characters) required to change one word into the other.
The Damerau-Levenshtein distance differs from the classical Levenshtein distance by including transpositions among its allowable operations in addition to the three classical single-character edit operations (insertions, deletions and substitutions).
该实现基于James M. Jensen II解释，它允许指定每个操作的成本。
要求
这段代码需要Python2.7或3.4+和一个C编译器，比如GCC。
安装
fastdameraulevenshtein可在pypi上的https://pypi.python.org/pypi/fastDamerauLevenshtein找到。
使用pip：
安装pip install fastDamerauLevenshtein
从源安装：
python setup.py install
或
pip install .
用法
它被称为damerauLevenshtein的可用方法，可以计算两个可散列对象（字符串、字符串列表等）上的距离。该方法提供以下参数：
firstobject
secondobject
相似性
- 如果这个参数值是
False，它将返回编辑的总成本，否则它将返回一个从0.0到1.0的分数，表示两个对象有多相似。默认为True。
- 如果这个参数值是
deleteWeight
- 删除操作的成本。
insertweight
- 插入操作的成本。
replaceWeight
- 更换操作的成本。
swapweight
- 交换操作的成本。
提供的操作权重必须是int值。默认情况下，所有这些值都是1。
基本用途：
fromfastDamerauLevenshteinimportdamerauLevenshteindamerauLevenshtein('ca','abc',similarity=False)# expected result: 2.0damerauLevenshtein('car','cars',similarity=True)# expected result: 0.75damerauLevenshtein(['ab','bc'],['ab'],similarity=False)# expected result: 1.0damerauLevenshtein(['ab','bc'],['ab'],similarity=True)# expected result: 0.5
基准
其他python damerau levenshtein和osa实现：
- pyxDamerauLevenshtein（编辑距离受限，无自定义权重）
- jellyfish（真正的damerau levenshtein，但没有自定义权重）
- editdistance（编辑距离受限，无自定义权重）
- textdistance（真正的damerau levenshtein，但没有自定义权重）
Python 3.7（在Intel i5 6500上）：
>>> import timeit
>>> #fastDamerauLevenshtein:
... timeit.timeit(setup="import fastDamerauLevenshtein; text1='afwafghfdowbihgp'; text2='goagumkphfwifawpte'", stmt="fastDamerauLevenshtein.damerauLevenshtein(text1, text2)", number=100000)
0.43
>>> #pyxDamerauLevenshtein:
... timeit.timeit(setup="from pyxdameraulevenshtein import normalized_damerau_levenshtein_distance; text1='afwafghfdowbihgp'; text2='goagumkphfwifawpte'", stmt="normalized_damerau_levenshtein_distance(text1, text2)", number=100000)
2.44
>>> #jellyfish
... timeit.timeit(setup="import jellyfish; text1='afwafghfdowbihgp'; text2='goagumkphfwifawpte'", stmt="jellyfish.damerau_levenshtein_distance(text1, text2)", number=100000)
0.20
>>> #editdistance
... timeit.timeit(setup="import editdistance; text1='afwafghfdowbihgp'; text2='goagumkphfwifawpte'", stmt="editdistance.eval(text1, text2)", number=100000)
0.22
>>> #textdistance
... timeit.timeit(setup="import textdistance; text1='afwafghfdowbihgp'; text2='goagumkphfwifawpte'", stmt="textdistance.damerau_levenshtein.distance(text1, text2)", number=100000)
0.70
许可证
它是根据麻省理工学院的许可证发行的。
Copyright (c) 2019 Robert Grigoroiu
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