Fuzzy matching algorithm(s) in Rust!
In your Cargo.toml add the following:
[dependencies]
fuzzy-matcher = "*"
Here are some code example:
use fuzzy_matcher::FuzzyMatcher;
use fuzzy_matcher::skim::SkimMatcherV2;
let matcher = SkimMatcherV2::default();
assert_eq!(None, matcher.fuzzy_match("abc", "abx"));
assert!(matcher.fuzzy_match("axbycz", "abc").is_some());
assert!(matcher.fuzzy_match("axbycz", "xyz").is_some());
let (score, indices) = matcher.fuzzy_indices("axbycz", "abc").unwrap();
assert_eq!(indices, [0, 2, 4]);
fuzzy_match
only return scores whilefuzzy_indices
returns the matching indices as well.- Both function return None if the pattern won't match.
- The score is the higher the better.
echo "axbycz" | cargo run --example fz "abc"
and check what happens.
The skim is currently used by skim, a fuzzy finder.
- Just like fzf v2, the algorithm is based on Smith-Waterman algorithm which is normally used in DNA sequence alignment
- Also checkout https://www.cs.cmu.edu/~ckingsf/bioinfo-lectures/gaps.pdf for more details
- The time complexity is
O(mn)
wherem, n
are the length of the pattern and input line. - Space complexity is
O(mn)
forfuzzy_indices
andO(2n)
forfuzzy_match
which will compress the table for dynamic programming. - V2 matcher has an option to set the max element of the score matrix, if
m*n
exceeded the limit, it will fallback to a linear search.
- It's based on Smith's post Reverse Engineering Sublime Text’s Fuzzy Match
- The implementation here actually has some flaws that don't perform well in certain cases.
- It's recommended to checkout original implementation in C++ and JavaScript
- The algorithm is based on clangd's FuzzyMatch.cpp.
- Also checkout lewang/flx#98 for some variants.
- The algorithm is
O(mn)
wherem, n
are the length of the pattern and input line. - Space complexity is
O(mn)
forfuzzy_indices
andO(2n)
forfuzzy_match
which will compress the table for dynamic programming.