Rapid fuzzy string matching in Python using various string metrics
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Updated
Jun 27, 2024 - C++
Rapid fuzzy string matching in Python using various string metrics
The Levenshtein Python C extension module contains functions for fast computation of Levenshtein distance and string similarity
Fast fuzzy regex matcher: specify max edit distance to find approximate matches
Fast fuzzy string search
STL and Boost compatible edit distance functions for C++
A collection of commonly used math routines, so I don't have to write them again. Implementations in C, C++, Rust, and Python (though not so much in Python as it already has a pretty good set of libraries).
A Levenshtein Distance implementation using C++ with a dynamic programming approach.
This application is an implementation of a spell checker for English text. The spell checker incorporates a database of known words, which is built from a simple word list. The Burkhard-Keller Tree (BK-Tree for short) organizes the word list for efficient searching.
Levenshtein distance implementation using an automaton and a trie for fast string similarity searching
A program that checks spelling of given word, if wrong then recommends words.
A blazing fast dictionary for the Terminal.
A C++11 port of Buffer, μ-framework for efficient array diffs, collection observation and data source implementation.
dynamic time warping implemented in C++ with bindings in python
Keyboard aware fuzzy text matching
A single header C++ library for compute edit distance (Levenshtein distance), supporting wstring ( and Chinese string).
Grep-like command line utlity for fuzzy string matching
A library for fuzzy-string-searching using suffix trees and levenschtein automatons to perform extremely fast search queries on large data sets. Serialize and deserialize functions allow suffix trees to persist (and drastically reduce preprocessing time).
MAG for approximate pattern matching (MAGA)
Calculation Of Levenshtein Distance
Levenshtein Algorithm based approach, Levenshtein Algorithm (Edit distance algorithm) is used to find the similarity between two strings.
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