Ruby & C implementation of Jaro-Winkler distance algorithm which supports UTF-8 string.
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Updated
Jun 5, 2024 - Ruby
Ruby & C implementation of Jaro-Winkler distance algorithm which supports UTF-8 string.
A fuzzy matching string distance library for Scala and Java that includes Levenshtein distance, Jaro distance, Jaro-Winkler distance, Dice coefficient, N-Gram similarity, Cosine similarity, Jaccard similarity, Longest common subsequence, Hamming distance, and more..
POSIX-compliant command-line UI (CLI) parser and Hierarchical-configuration operations
A text similarity metric library, e.g. from edit distance's (Levenshtein, Gotoh, Jaro, etc) to other metrics, (e.g Soundex, Chapman). This library is compiled based on the .NET standard with a lot of useful extension methods.
A way to analyse how malware and/or goodware samples vary from each other using Shannon Entropy, Hausdorff Distance and Jaro-Winkler Distance
A collection of string comparisons algorithms
Qwerty-Jaro–Winkler distance is a tweak on top of jaro winkler edit distance where we try to consider distance between keys in keyboard while calculating number of matches.
Calculate various string metrics efficiently in Haskell
Comparison among four spelling correction methods. n-gram, Levenshtein, Jaro, Jaro_winkler
cmdr cxx version, a C++17 header-only command-line parser with hierarchical config data manager here
ABAP SimilaritySearch with HANA and Oracle
A set of assorted helper functions, extensions and classes in .NET Standard
Distance related functions (Damerau-Levenshtein, Jaro-Winkler , longest common substring & subsequence) implemented as SQLite run-time loadable extension. Any UTF-8 strings are supported.
Fuzzy-Matching algorithm using Jaro-Winkler distance for measuring similarities in strings
An R script that uses AI for data analysis on Deezer playlists, like looking for fuzzy duplicates, rank of genre and artists.
This package allow use strings operations to generic sequence
Collection of string metric algorithms in OCaml
The main purpose of this project was to develop a matching algorithm in python to fuzzy classify people from a customer list as positive or negative based on a messy positive and negative database with a confidence score.
Computes the pairwise similarity between multiple words
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