norm is a collection of different distance metrics on stings. This problem is sometimes referred to as "string similarity search", or more colloquially "fuzzy matching".
FzfV1: port of the algorithm used by fzf when launching with--algo=v1;FzfV2: port of the algorithm used by fzf when launching without any extra flags or with--algo=v2;
Performance is a top priority for this crate. Our goal is to have the fastest implementation of every metric algorithm we provide, across all languages. Here you can find a number of benchmarks comparing norm's metrics to each other, as well as to other popular libraries.
use std::ops::Range;
use norm::fzf::{FzfParser, FzfV2};
use norm::Metric;
let mut fzf = FzfV2::new();
let mut parser = FzfParser::new();
let query = parser.parse("aa");
let cities = ["Geneva", "Ulaanbaatar", "New York City", "Adelaide"];
let mut results = cities
.iter()
.copied()
.filter_map(|city| fzf.distance(query, city).map(|dist| (city, dist)))
.collect::<Vec<_>>();
// We sort the results by distance in ascending order, so that the best match
// will be at the front of the vector.
results.sort_by_key(|(_city, dist)| *dist);
assert_eq!(results.len(), 2);
assert_eq!(results[0].0, "Adelaide");
assert_eq!(results[1].0, "Ulaanbaatar");
// We can also find out which sub-strings of each candidate matched the query.
let mut ranges: Vec<Range<usize>> = Vec::new();
let _ = fzf.distance_and_ranges(query, results[0].0, &mut ranges);
assert_eq!(ranges.len(), 2);
assert_eq!(ranges[0], 0..1); // "A" in "Adelaide"
assert_eq!(ranges[1], 4..5); // "a" in "Adelaide"
ranges.clear();
let _ = fzf.distance_and_ranges(query, results[1].0, &mut ranges);
assert_eq!(ranges.len(), 1);
assert_eq!(ranges[0], 2..4); // The first "aa" in "Ulaanbaatar"