text-metrics-0.3.1: Calculate various string metrics efficiently

Data.Text.Metrics

Description

The module provides efficient implementations of various strings metric algorithms. It works with strict Text values.

Note: before version 0.3.0 the package used C implementations of the algorithms under the hood. Beginning from version 0.3.0, the implementations are written in Haskell while staying almost as fast, see:

https://markkarpov.com/post/migrating-text-metrics.html

Synopsis

# Levenshtein variants

Return the Levenshtein distance between two Text values. The Levenshtein distance between two strings is the minimal number of operations necessary to transform one string into another. For the Levenshtein distance allowed operations are: deletion, insertion, and substitution.

Heads up, before version 0.3.0 this function returned Natural.

Return the normalized Levenshtein distance between two Text values. Result is a non-negative rational number (represented as Ratio Natural), where 0 signifies no similarity between the strings, while 1 means exact match.

Heads up, before version 0.3.0 this function returned Ratio Natural.

Return the Damerau-Levenshtein distance between two Text values. The function works like levenshtein, but the collection of allowed operations also includes transposition of two adjacent characters.

Heads up, before version 0.3.0 this function returned Natural.

Return the normalized Damerau-Levenshtein distance between two Text values. 0 signifies no similarity between the strings, while 1 means exact match.

Heads up, before version 0.3.0 this function returned Ratio Natural.

# Treating inputs like sets

Return the overlap coefficient for two Text values. Returned value is in the range from 0 (no similarity) to 1 (exact match). Return 1 if both Text values are empty.

Since: 0.3.0

Return the Jaccard similarity coefficient for two Text values. Returned value is in the range from 0 (no similarity) to 1 (exact match). Return 1 if both

Since: 0.3.0

# Other

O(n) Return the Hamming distance between two Text values. Hamming distance is defined as the number of positions at which the corresponding symbols are different. The input Text values should be of equal length or Nothing will be returned.

Heads up, before version 0.3.0 this function returned Maybe Natural.

Return the Jaro distance between two Text values. Returned value is in the range from 0 (no similarity) to 1 (exact match).

While the algorithm is pretty clear for artificial examples (like those from the linked Wikipedia article), for arbitrary strings, it may be hard to decide which of two strings should be considered as one having “reference” order of characters (order of matching characters in an essential part of the definition of the algorithm). This makes us consider the first string the “reference” string (with correct order of characters). Thus generally,

jaro a b ≠ jaro b a

This asymmetry can be found in all implementations of the algorithm on the internet, AFAIK.

Heads up, before version 0.3.0 this function returned Ratio Natural.

Since: 0.2.0

Return the Jaro-Winkler distance between two Text values. Returned value is in range from 0 (no similarity) to 1 (exact match).

Heads up, before version 0.3.0 this function returned Ratio Natural.

Since: 0.2.0