https://github.com/mrkkrp/mk-string-metrics.git

```
git clone 'https://github.com/mrkkrp/mk-string-metrics.git'
```

(ql:quickload :mk-string-metrics)

★1

This library implements efficient algorithms that calculate various string metrics in Common Lisp:

- Damerau-Levenshtein distance;
- Hamming distance;
- Jaccard similarity coefficient;
- Jaro distance;
- Jaro-Winkler distance;
- Levenshtein distance;
- Normalized Damerau-Levenshtein distance;
- Normalized Levenshtein distance;
- Overlap coefficient.

Copy files of this library in any place where ASDF can find them. Then you can use it in system definitions and ASDF will take care of the rest.

Via Quicklisp:

`(ql:quickload "mk-string-metrics")`

`damerau-levenshtein x y`

This function calculates Damerau-Levenshtein distance between two given strings.

`hamming x y`

Calculates Hamming distance between two given strings, they have to be of the same length.

`jaccard x y`

Calculates Jaccard similarity coefficient for two strings. Returned value is in range from 0 (no similarity) to 1 (exact match).

`jaro x y`

Calculates Jaro distance between two strings. Returned value is in range from 0 (no similarity) to 1 (exact match).

`jaro-winkler x y`

Calculates Jaro-Winkler distance between two strings. Returned value is in range from 0 (no similarity) to 1 (exact match).

`levenshtein x y`

This function calculates Levenshtein distance between two given strings.

`norm-damerau-levenshtein x y`

Returns normalized Damerau-Levenshtein distance between X and Y. Result is a real number from 0 to 1, where 0 signifies no similarity between the strings, while 1 means exact match.

`norm-levenshtein x y`

Returns normalized Levenshtein distance between X and Y. Result is a real number from 0 to 1, where 0 signifies no similarity between the strings, while 1 means exact match.

`overlap x y`

This function calculates overlap coefficient between two given strings.

Let me know if you would like to see more functions in this library. Open an issue and tell me what's missing.

Copyright © 2014 Mark Karpov

Distributed under MIT License.