# Text Metrics
[](http://opensource.org/licenses/BSD-3-Clause)
[](https://hackage.haskell.org/package/text-metrics)
[](http://stackage.org/nightly/package/text-metrics)
[](http://stackage.org/lts/package/text-metrics)
[](https://travis-ci.org/mrkkrp/text-metrics)
[](https://coveralls.io/github/mrkkrp/text-metrics?branch=master)
The library provides efficient implementations of various strings metrics.
It works with strict `Text` values and returns either `Natural` numbers
(because the metrics cannot be negative), or `Ratio Natural` values because
returned values are rational non-negative numbers by definition.
The functions provided here are the fastest implementations available for
use in Haskell programs. In fact the functions are implemented in C for
maximal efficiency, but this leads to a minor flaw. When we work with `Text`
values in C, they are represented as UTF-16 encoded strings of two-byte
values. The algorithms treat the strings as if a character corresponds to
one element in such strings, which is true for almost all modern text data.
However, there are characters that are represented by two adjoined elements
in UTF-16: emoji, historic scripts, less used Chinese ideographs, and some
more. If input `Text` of the functions contains such characters, the
functions may return slightly incorrect result. Decide for yourself if this
is acceptable for your use case, but chances are you will never run into
situations when the functions produce incorrect results.
The current version of the package implements:
* [Levenshtein distance](http://en.wikipedia.org/wiki/Levenshtein_distance)
* [Normalized Levenshtein distance](http://en.wikipedia.org/wiki/Levenshtein_distance)
* [Damerau-Levenshtein distance](http://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance)
* [Normalized Damerau-Levenshtein distance](http://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance)
* [Hamming distance](http://en.wikipedia.org/wiki/Hamming_distance)
* [Jaro distance](http://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance)
* [Jaro-Winkler distance](http://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance)
TODO list:
* [Overlap coefficient](http://en.wikipedia.org/wiki/Overlap_coefficient)
* [Jaccard similarity coefficient](http://en.wikipedia.org/wiki/Jaccard_index)
## Comparison with the `edit-distance` package
There
is [`edit-distance`](https://hackage.haskell.org/package/edit-distance)
package whose scope overlaps with the scope of this package. The differences
are:
* `edit-distance` allows to specify costs for every operation when
calculating Levenshtein distance (insertion, deletion, substitution, and
transposition). This is rarely needed though in real-world applications,
IMO.
* `edit-distance` only provides single Levenshtein distance, `text-metrics`
aims to provide implementations of most string metrics algorithms.
* `edit-distance` works on `Strings`, while `text-metrics` works on strict
`Text` values.
* As `README.md` of `edit-distance` states, “[the algorithms] have been
fairly heavily optimized”, which is apparently true, yet the
`text-metrics` is faster for short strings (under 64 characters) and even
faster for longer strings (scales better). How much faster? For short
strings more than ×3, and about ×4 for longer strings.
## Implementation
All “meat” of the algorithms is written in C in a rather straightforward
way. Levenshtein variants are based on the “iterative algorithm with two
matrix rows” from Wikipedia with additional improvement that we do not copy
current row of distances into previous row, but just swap the pointers
(which is OK, since the arrays have equal length and current row will be
overwritten in the next iteration anyway).
Normalized versions are defined as thin (inlined) Haskell wrappers.
## License
Copyright © 2016 Mark Karpov
Distributed under BSD 3 clause license.