//! Levenshtein distances. //! //! The [Levenshtein distance] is a metric for measuring the difference between two strings. //! //! [Levenshtein distance]: https://en.wikipedia.org/wiki/Levenshtein_distance use crate::symbol::Symbol; use std::cmp; #[cfg(test)] mod tests; /// Finds the Levenshtein distance between two strings. /// /// Returns None if the distance exceeds the limit. pub fn lev_distance(a: &str, b: &str, limit: usize) -> Option { let n = a.chars().count(); let m = b.chars().count(); let min_dist = if n < m { m - n } else { n - m }; if min_dist > limit { return None; } if n == 0 || m == 0 { return (min_dist <= limit).then_some(min_dist); } let mut dcol: Vec<_> = (0..=m).collect(); for (i, sc) in a.chars().enumerate() { let mut current = i; dcol[0] = current + 1; for (j, tc) in b.chars().enumerate() { let next = dcol[j + 1]; if sc == tc { dcol[j + 1] = current; } else { dcol[j + 1] = cmp::min(current, next); dcol[j + 1] = cmp::min(dcol[j + 1], dcol[j]) + 1; } current = next; } } (dcol[m] <= limit).then_some(dcol[m]) } /// Provides a word similarity score between two words that accounts for substrings being more /// meaningful than a typical Levenshtein distance. The lower the score, the closer the match. /// 0 is an identical match. /// /// Uses the Levenshtein distance between the two strings and removes the cost of the length /// difference. If this is 0 then it is either a substring match or a full word match, in the /// substring match case we detect this and return `1`. To prevent finding meaningless substrings, /// eg. "in" in "shrink", we only perform this subtraction of length difference if one of the words /// is not greater than twice the length of the other. For cases where the words are close in size /// but not an exact substring then the cost of the length difference is discounted by half. /// /// Returns `None` if the distance exceeds the limit. pub fn lev_distance_with_substrings(a: &str, b: &str, limit: usize) -> Option { let n = a.chars().count(); let m = b.chars().count(); // Check one isn't less than half the length of the other. If this is true then there is a // big difference in length. let big_len_diff = (n * 2) < m || (m * 2) < n; let len_diff = if n < m { m - n } else { n - m }; let lev = lev_distance(a, b, limit + len_diff)?; // This is the crux, subtracting length difference means exact substring matches will now be 0 let score = lev - len_diff; // If the score is 0 but the words have different lengths then it's a substring match not a full // word match let score = if score == 0 && len_diff > 0 && !big_len_diff { 1 // Exact substring match, but not a total word match so return non-zero } else if !big_len_diff { // Not a big difference in length, discount cost of length difference score + (len_diff + 1) / 2 } else { // A big difference in length, add back the difference in length to the score score + len_diff }; (score <= limit).then_some(score) } /// Finds the best match for given word in the given iterator where substrings are meaningful. /// /// A version of [`find_best_match_for_name`] that uses [`lev_distance_with_substrings`] as the score /// for word similarity. This takes an optional distance limit which defaults to one-third of the /// given word. /// /// Besides the modified Levenshtein, we use case insensitive comparison to improve accuracy /// on an edge case with a lower(upper)case letters mismatch. pub fn find_best_match_for_name_with_substrings( candidates: &[Symbol], lookup: Symbol, dist: Option, ) -> Option { find_best_match_for_name_impl(true, candidates, lookup, dist) } /// Finds the best match for a given word in the given iterator. /// /// As a loose rule to avoid the obviously incorrect suggestions, it takes /// an optional limit for the maximum allowable edit distance, which defaults /// to one-third of the given word. /// /// Besides Levenshtein, we use case insensitive comparison to improve accuracy /// on an edge case with a lower(upper)case letters mismatch. pub fn find_best_match_for_name( candidates: &[Symbol], lookup: Symbol, dist: Option, ) -> Option { find_best_match_for_name_impl(false, candidates, lookup, dist) } #[cold] fn find_best_match_for_name_impl( use_substring_score: bool, candidates: &[Symbol], lookup: Symbol, dist: Option, ) -> Option { let lookup = lookup.as_str(); let lookup_uppercase = lookup.to_uppercase(); // Priority of matches: // 1. Exact case insensitive match // 2. Levenshtein distance match // 3. Sorted word match if let Some(c) = candidates.iter().find(|c| c.as_str().to_uppercase() == lookup_uppercase) { return Some(*c); } let mut dist = dist.unwrap_or_else(|| cmp::max(lookup.len(), 3) / 3); let mut best = None; for c in candidates { match if use_substring_score { lev_distance_with_substrings(lookup, c.as_str(), dist) } else { lev_distance(lookup, c.as_str(), dist) } { Some(0) => return Some(*c), Some(d) => { dist = d - 1; best = Some(*c); } None => {} } } if best.is_some() { return best; } find_match_by_sorted_words(candidates, lookup) } fn find_match_by_sorted_words(iter_names: &[Symbol], lookup: &str) -> Option { iter_names.iter().fold(None, |result, candidate| { if sort_by_words(candidate.as_str()) == sort_by_words(lookup) { Some(*candidate) } else { result } }) } fn sort_by_words(name: &str) -> String { let mut split_words: Vec<&str> = name.split('_').collect(); // We are sorting primitive &strs and can use unstable sort here. split_words.sort_unstable(); split_words.join("_") }