字符串相似性度量方法

推荐文章：

1. FIVE MOST POPULAR SIMILARITY MEASURES IMPLEMENTATION IN PYTHON
   2.字符串相似算法-Jaro-Winkler Distance

推荐代码实现：

1. python-string-similarity
   A library implementing different string similarity and distance measures. A dozen of algorithms (including Levenshtein edit distance and sibblings, Jaro-Winkler, Longest Common Subsequence, cosine similarity etc.) are currently implemented.

The main characteristics of each implemented algorithm are presented below. The "cost" column gives an estimation of the computational cost to compute the similarity between two strings of length m and n respectively.

		Normalized?	Metric?	Type	Cost	Typical usage
Levenshtein	distance	No	Yes		O(m*n) 1
Normalized Levenshtein	distance similarity	Yes	No		O(m*n) 1
Weighted Levenshtein	distance	No	No		O(m*n) 1	OCR
Damerau-Levenshtein 3	distance	No	Yes		O(m*n) 1
Optimal String Alignment 3	distance	No	No		O(m*n) 1
Jaro-Winkler	similarity distance	Yes	No		O(m*n)	typo correction
Longest Common Subsequence	distance	No	No		O(m*n) 1,2	diff utility, GIT reconciliation
Metric Longest Common Subsequence	distance	Yes	Yes		O(m*n) 1,2
N-Gram	distance	Yes	No		O(m*n)
Q-Gram	distance	No	No	Profile	O(m+n)
Cosine similarity	similarity distance	Yes	No	Profile	O(m+n)
Jaccard index	similarity distance	Yes	Yes	Set	O(m+n)
Sorensen-Dice coefficient	similarity distance	Yes	No	Set	O(m+n)

[1] In this library, Levenshtein edit distance, LCS distance and their sibblings are computed using the dynamic programming