The Levenshtein distance between two strings is defined as the minimum number of edits needed to transform one string into the other, with the allowable edit operations being insertion, deletion, or substitution of a single character.
For example, the Levenshtein distance between "kitten" and "sitting" is 3, since the following three edits change one into the other, and there is no way to do it with fewer than three edits:
kitten => sitten (substitution of 's' for 'k')
sitten => sittin (substitution of 'e' for 'i')
sittin => sitting (insert 'g' at the end).
So when
s1 = 'kitten'
and
s2 = 'sitting'
then the distance d is equal to 3.
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Good question
I really like this problem. So far, this is the one I had to think about most. Mostly because the straight-forward recursive implementation is simply not feasible for longer inputs.
This question is a good example of using a bottom-up dynamic programming algorithm.