NEW ANSWER Given the recent update, you could try A* with the Hamming distance as a heuristic. It’s an admissible heuristic since it’s not going to overestimate the distance OLD ANSWER You can modify the dynamic-program used to compute the Levenshtein distance to obtain the sequence of operations. EDIT: If there are a constant number … Read more
I realize it’s not the same thing, but this is close enough: >>> import difflib >>> a=”Hello, All you people” >>> b = ‘hello, all You peopl’ >>> seq=difflib.SequenceMatcher(a=a.lower(), b=b.lower()) >>> seq.ratio() 0.97560975609756095 You can make this as a function def similar(seq1, seq2): return difflib.SequenceMatcher(a=seq1.lower(), b=seq2.lower()).ratio() > 0.9 >>> similar(a, b) True >>> similar(‘Hello, world’, … Read more
The function adist computes the Levenshtein edit distance between two strings. This can be transformed into a similarity metric as 1 – (Levenshtein edit distance / longer string length). The levenshteinSim function in the RecordLinkage package also does this directly, and might be faster than adist. library(RecordLinkage) > levenshteinSim(“apple”, “apple”) [1] 1 > levenshteinSim(“apple”, “aaple”) … Read more
I implemented the standard Levenshtein edit distance function in TSQL with several optimizations that improves the speed over the other versions I’m aware of. In cases where the two strings have characters in common at their start (shared prefix), characters in common at their end (shared suffix), and when the strings are large and a … Read more