# mk-string-metrics

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

## Installation

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 (recommended):

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

## Documentation

`damerau-levenshtein x y`

Calculate Damerau-Levenshtein distance between two given strings `x`

and `y`

.

`hamming x y`

Calculate Hamming distance between two given strings `x`

and `y`

, they have to be of the same length.

`jaccard x y`

Calculate Jaccard similarity coefficient for two strings `x`

and `y`

. Returned value is in range from `0`

(no similarity) to `1`

(exact match).

`jaro x y`

Calculate Jaro distance between two strings `x`

and `y`

. Returned value is in range from `0`

(no similarity) to `1`

(exact match).

`jaro-winkler x y`

Calculate Jaro-Winkler distance between two strings `x`

and `y`

. Returned value is in range from `0`

(no similarity) to `1`

(exact match).

`levenshtein x y`

Calculate Levenshtein distance between two given strings `x`

and `y`

.

`norm-damerau-levenshtein x y`

Return 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`

Return 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 `x`

and `y`

. Returned value is in range from `0`

(no similarity) to `1`

(exact match).

## License

Copyright ? 2014?2017 Mark Karpov

Distributed under MIT License.