--- /dev/null
+//! This library implements string similarity metrics.
+
+#![forbid(unsafe_code)]
+
+use std::char;
+use std::cmp::{max, min};
+use std::collections::HashMap;
+use std::error::Error;
+use std::fmt::{self, Display, Formatter};
+use std::hash::Hash;
+use std::str::Chars;
+
+#[derive(Debug, PartialEq)]
+pub enum StrSimError {
+ DifferentLengthArgs,
+}
+
+impl Display for StrSimError {
+ fn fmt(&self, fmt: &mut Formatter) -> Result<(), fmt::Error> {
+ let text = match self {
+ StrSimError::DifferentLengthArgs => "Differing length arguments provided",
+ };
+
+ write!(fmt, "{}", text)
+ }
+}
+
+impl Error for StrSimError {}
+
+pub type HammingResult = Result<usize, StrSimError>;
+
+/// Calculates the number of positions in the two sequences where the elements
+/// differ. Returns an error if the sequences have different lengths.
+pub fn generic_hamming<Iter1, Iter2, Elem1, Elem2>(a: Iter1, b: Iter2) -> HammingResult
+ where Iter1: IntoIterator<Item=Elem1>,
+ Iter2: IntoIterator<Item=Elem2>,
+ Elem1: PartialEq<Elem2> {
+ let (mut ita, mut itb) = (a.into_iter(), b.into_iter());
+ let mut count = 0;
+ loop {
+ match (ita.next(), itb.next()){
+ (Some(x), Some(y)) => if x != y { count += 1 },
+ (None, None) => return Ok(count),
+ _ => return Err(StrSimError::DifferentLengthArgs),
+ }
+ }
+}
+
+/// Calculates the number of positions in the two strings where the characters
+/// differ. Returns an error if the strings have different lengths.
+///
+/// ```
+/// use strsim::{hamming, StrSimError::DifferentLengthArgs};
+///
+/// assert_eq!(Ok(3), hamming("hamming", "hammers"));
+///
+/// assert_eq!(Err(DifferentLengthArgs), hamming("hamming", "ham"));
+/// ```
+pub fn hamming(a: &str, b: &str) -> HammingResult {
+ generic_hamming(a.chars(), b.chars())
+}
+
+/// Calculates the Jaro similarity between two sequences. The returned value
+/// is between 0.0 and 1.0 (higher value means more similar).
+pub fn generic_jaro<'a, 'b, Iter1, Iter2, Elem1, Elem2>(a: &'a Iter1, b: &'b Iter2) -> f64
+ where &'a Iter1: IntoIterator<Item=Elem1>,
+ &'b Iter2: IntoIterator<Item=Elem2>,
+ Elem1: PartialEq<Elem2> {
+ let a_len = a.into_iter().count();
+ let b_len = b.into_iter().count();
+
+ // The check for lengths of one here is to prevent integer overflow when
+ // calculating the search range.
+ if a_len == 0 && b_len == 0 {
+ return 1.0;
+ } else if a_len == 0 || b_len == 0 {
+ return 0.0;
+ } else if a_len == 1 && b_len == 1 {
+ return if a.into_iter().eq(b.into_iter()) { 1.0} else { 0.0 };
+ }
+
+ let search_range = (max(a_len, b_len) / 2) - 1;
+
+ let mut b_consumed = Vec::with_capacity(b_len);
+ for _ in 0..b_len {
+ b_consumed.push(false);
+ }
+ let mut matches = 0.0;
+
+ let mut transpositions = 0.0;
+ let mut b_match_index = 0;
+
+ for (i, a_elem) in a.into_iter().enumerate() {
+ let min_bound =
+ // prevent integer wrapping
+ if i > search_range {
+ max(0, i - search_range)
+ } else {
+ 0
+ };
+
+ let max_bound = min(b_len - 1, i + search_range);
+
+ if min_bound > max_bound {
+ continue;
+ }
+
+ for (j, b_elem) in b.into_iter().enumerate() {
+ if min_bound <= j && j <= max_bound && a_elem == b_elem &&
+ !b_consumed[j] {
+ b_consumed[j] = true;
+ matches += 1.0;
+
+ if j < b_match_index {
+ transpositions += 1.0;
+ }
+ b_match_index = j;
+
+ break;
+ }
+ }
+ }
+
+ if matches == 0.0 {
+ 0.0
+ } else {
+ (1.0 / 3.0) * ((matches / a_len as f64) +
+ (matches / b_len as f64) +
+ ((matches - transpositions) / matches))
+ }
+}
+
+struct StringWrapper<'a>(&'a str);
+
+impl<'a, 'b> IntoIterator for &'a StringWrapper<'b> {
+ type Item = char;
+ type IntoIter = Chars<'b>;
+
+ fn into_iter(self) -> Self::IntoIter {
+ self.0.chars()
+ }
+}
+
+/// Calculates the Jaro similarity between two strings. The returned value
+/// is between 0.0 and 1.0 (higher value means more similar).
+///
+/// ```
+/// use strsim::jaro;
+///
+/// assert!((0.392 - jaro("Friedrich Nietzsche", "Jean-Paul Sartre")).abs() <
+/// 0.001);
+/// ```
+pub fn jaro(a: &str, b: &str) -> f64 {
+ generic_jaro(&StringWrapper(a), &StringWrapper(b))
+}
+
+/// Like Jaro but gives a boost to sequences that have a common prefix.
+pub fn generic_jaro_winkler<'a, 'b, Iter1, Iter2, Elem1, Elem2>(a: &'a Iter1, b: &'b Iter2) -> f64
+ where &'a Iter1: IntoIterator<Item=Elem1>,
+ &'b Iter2: IntoIterator<Item=Elem2>,
+ Elem1: PartialEq<Elem2> {
+ let jaro_distance = generic_jaro(a, b);
+
+ // Don't limit the length of the common prefix
+ let prefix_length = a.into_iter()
+ .zip(b.into_iter())
+ .take_while(|&(ref a_elem, ref b_elem)| a_elem == b_elem)
+ .count();
+
+ let jaro_winkler_distance =
+ jaro_distance + (0.1 * prefix_length as f64 * (1.0 - jaro_distance));
+
+ if jaro_winkler_distance <= 1.0 {
+ jaro_winkler_distance
+ } else {
+ 1.0
+ }
+}
+
+/// Like Jaro but gives a boost to strings that have a common prefix.
+///
+/// ```
+/// use strsim::jaro_winkler;
+///
+/// assert!((0.911 - jaro_winkler("cheeseburger", "cheese fries")).abs() <
+/// 0.001);
+/// ```
+pub fn jaro_winkler(a: &str, b: &str) -> f64 {
+ generic_jaro_winkler(&StringWrapper(a), &StringWrapper(b))
+}
+
+/// Calculates the minimum number of insertions, deletions, and substitutions
+/// required to change one sequence into the other.
+///
+/// ```
+/// use strsim::generic_levenshtein;
+///
+/// assert_eq!(3, generic_levenshtein(&[1,2,3], &[1,2,3,4,5,6]));
+/// ```
+pub fn generic_levenshtein<'a, 'b, Iter1, Iter2, Elem1, Elem2>(a: &'a Iter1, b: &'b Iter2) -> usize
+ where &'a Iter1: IntoIterator<Item=Elem1>,
+ &'b Iter2: IntoIterator<Item=Elem2>,
+ Elem1: PartialEq<Elem2> {
+ let b_len = b.into_iter().count();
+
+ if a.into_iter().next().is_none() { return b_len; }
+
+ let mut cache: Vec<usize> = (1..b_len+1).collect();
+
+ let mut result = 0;
+
+ for (i, a_elem) in a.into_iter().enumerate() {
+ result = i + 1;
+ let mut distance_b = i;
+
+ for (j, b_elem) in b.into_iter().enumerate() {
+ let cost = if a_elem == b_elem { 0usize } else { 1usize };
+ let distance_a = distance_b + cost;
+ distance_b = cache[j];
+ result = min(result + 1, min(distance_a, distance_b + 1));
+ cache[j] = result;
+ }
+ }
+
+ result
+}
+
+/// Calculates the minimum number of insertions, deletions, and substitutions
+/// required to change one string into the other.
+///
+/// ```
+/// use strsim::levenshtein;
+///
+/// assert_eq!(3, levenshtein("kitten", "sitting"));
+/// ```
+pub fn levenshtein(a: &str, b: &str) -> usize {
+ generic_levenshtein(&StringWrapper(a), &StringWrapper(b))
+}
+
+/// Calculates a normalized score of the Levenshtein algorithm between 0.0 and
+/// 1.0 (inclusive), where 1.0 means the strings are the same.
+///
+/// ```
+/// use strsim::normalized_levenshtein;
+///
+/// assert!((normalized_levenshtein("kitten", "sitting") - 0.57142).abs() < 0.00001);
+/// assert!((normalized_levenshtein("", "") - 1.0).abs() < 0.00001);
+/// assert!(normalized_levenshtein("", "second").abs() < 0.00001);
+/// assert!(normalized_levenshtein("first", "").abs() < 0.00001);
+/// assert!((normalized_levenshtein("string", "string") - 1.0).abs() < 0.00001);
+/// ```
+pub fn normalized_levenshtein(a: &str, b: &str) -> f64 {
+ if a.is_empty() && b.is_empty() {
+ return 1.0;
+ }
+ 1.0 - (levenshtein(a, b) as f64) / (a.chars().count().max(b.chars().count()) as f64)
+}
+
+/// Like Levenshtein but allows for adjacent transpositions. Each substring can
+/// only be edited once.
+///
+/// ```
+/// use strsim::osa_distance;
+///
+/// assert_eq!(3, osa_distance("ab", "bca"));
+/// ```
+pub fn osa_distance(a: &str, b: &str) -> usize {
+ let a_len = a.chars().count();
+ let b_len = b.chars().count();
+ if a == b { return 0; }
+ else if a_len == 0 { return b_len; }
+ else if b_len == 0 { return a_len; }
+
+ let mut prev_two_distances: Vec<usize> = Vec::with_capacity(b_len + 1);
+ let mut prev_distances: Vec<usize> = Vec::with_capacity(b_len + 1);
+ let mut curr_distances: Vec<usize> = Vec::with_capacity(b_len + 1);
+
+ let mut prev_a_char = char::MAX;
+ let mut prev_b_char = char::MAX;
+
+ for i in 0..(b_len + 1) {
+ prev_two_distances.push(i);
+ prev_distances.push(i);
+ curr_distances.push(0);
+ }
+
+ for (i, a_char) in a.chars().enumerate() {
+ curr_distances[0] = i + 1;
+
+ for (j, b_char) in b.chars().enumerate() {
+ let cost = if a_char == b_char { 0 } else { 1 };
+ curr_distances[j + 1] = min(curr_distances[j] + 1,
+ min(prev_distances[j + 1] + 1,
+ prev_distances[j] + cost));
+ if i > 0 && j > 0 && a_char != b_char &&
+ a_char == prev_b_char && b_char == prev_a_char {
+ curr_distances[j + 1] = min(curr_distances[j + 1],
+ prev_two_distances[j - 1] + 1);
+ }
+
+ prev_b_char = b_char;
+ }
+
+ prev_two_distances.clone_from(&prev_distances);
+ prev_distances.clone_from(&curr_distances);
+ prev_a_char = a_char;
+ }
+
+ curr_distances[b_len]
+
+}
+
+/* Returns the final index for a value in a single vector that represents a fixed
+ 2d grid */
+fn flat_index(i: usize, j: usize, width: usize) -> usize {
+ j * width + i
+}
+
+/// Like optimal string alignment, but substrings can be edited an unlimited
+/// number of times, and the triangle inequality holds.
+///
+/// ```
+/// use strsim::generic_damerau_levenshtein;
+///
+/// assert_eq!(2, generic_damerau_levenshtein(&[1,2], &[2,3,1]));
+/// ```
+pub fn generic_damerau_levenshtein<Elem>(a_elems: &[Elem], b_elems: &[Elem]) -> usize
+ where Elem: Eq + Hash + Clone {
+ let a_len = a_elems.len();
+ let b_len = b_elems.len();
+
+ if a_len == 0 { return b_len; }
+ if b_len == 0 { return a_len; }
+
+ let width = a_len + 2;
+ let mut distances = vec![0; (a_len + 2) * (b_len + 2)];
+ let max_distance = a_len + b_len;
+ distances[0] = max_distance;
+
+ for i in 0..(a_len + 1) {
+ distances[flat_index(i + 1, 0, width)] = max_distance;
+ distances[flat_index(i + 1, 1, width)] = i;
+ }
+
+ for j in 0..(b_len + 1) {
+ distances[flat_index(0, j + 1, width)] = max_distance;
+ distances[flat_index(1, j + 1, width)] = j;
+ }
+
+ let mut elems: HashMap<Elem, usize> = HashMap::with_capacity(64);
+
+ for i in 1..(a_len + 1) {
+ let mut db = 0;
+
+ for j in 1..(b_len + 1) {
+ let k = match elems.get(&b_elems[j - 1]) {
+ Some(&value) => value,
+ None => 0
+ };
+
+ let insertion_cost = distances[flat_index(i, j + 1, width)] + 1;
+ let deletion_cost = distances[flat_index(i + 1, j, width)] + 1;
+ let transposition_cost = distances[flat_index(k, db, width)] +
+ (i - k - 1) + 1 + (j - db - 1);
+
+ let mut substitution_cost = distances[flat_index(i, j, width)] + 1;
+ if a_elems[i - 1] == b_elems[j - 1] {
+ db = j;
+ substitution_cost -= 1;
+ }
+
+ distances[flat_index(i + 1, j + 1, width)] = min(substitution_cost,
+ min(insertion_cost, min(deletion_cost, transposition_cost)));
+ }
+
+ elems.insert(a_elems[i - 1].clone(), i);
+ }
+
+ distances[flat_index(a_len + 1, b_len + 1, width)]
+}
+
+/// Like optimal string alignment, but substrings can be edited an unlimited
+/// number of times, and the triangle inequality holds.
+///
+/// ```
+/// use strsim::damerau_levenshtein;
+///
+/// assert_eq!(2, damerau_levenshtein("ab", "bca"));
+/// ```
+pub fn damerau_levenshtein(a: &str, b: &str) -> usize {
+ let (x, y): (Vec<_>, Vec<_>) = (a.chars().collect(), b.chars().collect());
+ generic_damerau_levenshtein(x.as_slice(), y.as_slice())
+}
+
+/// Calculates a normalized score of the Damerau–Levenshtein algorithm between
+/// 0.0 and 1.0 (inclusive), where 1.0 means the strings are the same.
+///
+/// ```
+/// use strsim::normalized_damerau_levenshtein;
+///
+/// assert!((normalized_damerau_levenshtein("levenshtein", "löwenbräu") - 0.27272).abs() < 0.00001);
+/// assert!((normalized_damerau_levenshtein("", "") - 1.0).abs() < 0.00001);
+/// assert!(normalized_damerau_levenshtein("", "flower").abs() < 0.00001);
+/// assert!(normalized_damerau_levenshtein("tree", "").abs() < 0.00001);
+/// assert!((normalized_damerau_levenshtein("sunglasses", "sunglasses") - 1.0).abs() < 0.00001);
+/// ```
+pub fn normalized_damerau_levenshtein(a: &str, b: &str) -> f64 {
+ if a.is_empty() && b.is_empty() {
+ return 1.0;
+ }
+ 1.0 - (damerau_levenshtein(a, b) as f64) / (a.chars().count().max(b.chars().count()) as f64)
+}
+
+/// Returns an Iterator of char tuples.
+fn bigrams(s: &str) -> impl Iterator<Item=(char, char)> + '_ {
+ s.chars().zip(s.chars().skip(1))
+}
+
+
+/// Calculates a Sørensen-Dice similarity distance using bigrams.
+/// See http://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient.
+///
+/// ```
+/// use strsim::sorensen_dice;
+///
+/// assert_eq!(1.0, sorensen_dice("", ""));
+/// assert_eq!(0.0, sorensen_dice("", "a"));
+/// assert_eq!(0.0, sorensen_dice("french", "quebec"));
+/// assert_eq!(1.0, sorensen_dice("ferris", "ferris"));
+/// assert_eq!(1.0, sorensen_dice("ferris", "ferris"));
+/// assert_eq!(0.8888888888888888, sorensen_dice("feris", "ferris"));
+/// ```
+pub fn sorensen_dice(a: &str, b: &str) -> f64 {
+ // implementation guided by
+ // https://github.com/aceakash/string-similarity/blob/f83ba3cd7bae874c20c429774e911ae8cff8bced/src/index.js#L6
+
+ let a: String = a.chars().filter(|&x| !char::is_whitespace(x)).collect();
+ let b: String = b.chars().filter(|&x| !char::is_whitespace(x)).collect();
+
+ if a.len() == 0 && b.len() == 0 {
+ return 1.0;
+ }
+
+ if a.len() == 0 || b.len() == 0 {
+ return 0.0;
+ }
+
+ if a == b {
+ return 1.0;
+ }
+
+ if a.len() == 1 && b.len() == 1 {
+ return 0.0;
+ }
+
+ if a.len() < 2 || b.len() < 2 {
+ return 0.0;
+ }
+
+ let mut a_bigrams: HashMap<(char, char), usize> = HashMap::new();
+
+ for bigram in bigrams(&a) {
+ *a_bigrams.entry(bigram).or_insert(0) += 1;
+ }
+
+ let mut intersection_size = 0;
+
+ for bigram in bigrams(&b) {
+ a_bigrams.entry(bigram).and_modify(|bi| {
+ if *bi > 0 {
+ *bi -= 1;
+ intersection_size += 1;
+ }
+ });
+ }
+
+ (2 * intersection_size) as f64 / (a.len() + b.len() - 2) as f64
+}
+
+
+#[cfg(test)]
+mod tests {
+ use super::*;
+
+ #[test]
+ fn bigrams_iterator() {
+ let mut bi = bigrams("abcde");
+
+ assert_eq!(Some(('a', 'b')), bi.next());
+ assert_eq!(Some(('b', 'c')), bi.next());
+ assert_eq!(Some(('c', 'd')), bi.next());
+ assert_eq!(Some(('d', 'e')), bi.next());
+ assert_eq!(None, bi.next());
+ }
+
+ fn assert_hamming_dist(dist: usize, str1: &str, str2: &str) {
+ assert_eq!(Ok(dist), hamming(str1, str2));
+ }
+
+ #[test]
+ fn hamming_empty() {
+ assert_hamming_dist(0, "", "")
+ }
+
+ #[test]
+ fn hamming_same() {
+ assert_hamming_dist(0, "hamming", "hamming")
+ }
+
+ #[test]
+ fn hamming_numbers() {
+ assert_eq!(Ok(1), generic_hamming(&[1, 2, 4], &[1, 2, 3]));
+ }
+
+ #[test]
+ fn hamming_diff() {
+ assert_hamming_dist(3, "hamming", "hammers")
+ }
+
+ #[test]
+ fn hamming_diff_multibyte() {
+ assert_hamming_dist(2, "hamming", "h香mmüng");
+ }
+
+ #[test]
+ fn hamming_unequal_length() {
+ assert_eq!(
+ Err(StrSimError::DifferentLengthArgs),
+ generic_hamming("ham".chars(), "hamming".chars())
+ );
+ }
+
+ #[test]
+ fn hamming_names() {
+ assert_hamming_dist(14, "Friedrich Nietzs", "Jean-Paul Sartre")
+ }
+
+ #[test]
+ fn jaro_both_empty() {
+ assert_eq!(1.0, jaro("", ""));
+ }
+
+ #[test]
+ fn jaro_first_empty() {
+ assert_eq!(0.0, jaro("", "jaro"));
+ }
+
+ #[test]
+ fn jaro_second_empty() {
+ assert_eq!(0.0, jaro("distance", ""));
+ }
+
+ #[test]
+ fn jaro_same() {
+ assert_eq!(1.0, jaro("jaro", "jaro"));
+ }
+
+ #[test]
+ fn jaro_multibyte() {
+ assert!((0.818 - jaro("testabctest", "testöঙ香test")) < 0.001);
+ assert!((0.818 - jaro("testöঙ香test", "testabctest")) < 0.001);
+ }
+
+ #[test]
+ fn jaro_diff_short() {
+ assert!((0.767 - jaro("dixon", "dicksonx")).abs() < 0.001);
+ }
+
+ #[test]
+ fn jaro_diff_one_character() {
+ assert_eq!(0.0, jaro("a", "b"));
+ }
+
+ #[test]
+ fn jaro_same_one_character() {
+ assert_eq!(1.0, jaro("a", "a"));
+ }
+
+ #[test]
+ fn generic_jaro_diff() {
+ assert_eq!(0.0, generic_jaro(&[1, 2], &[3, 4]));
+ }
+
+ #[test]
+ fn jaro_diff_one_and_two() {
+ assert!((0.83 - jaro("a", "ab")).abs() < 0.01);
+ }
+
+ #[test]
+ fn jaro_diff_two_and_one() {
+ assert!((0.83 - jaro("ab", "a")).abs() < 0.01);
+ }
+
+ #[test]
+ fn jaro_diff_no_transposition() {
+ assert!((0.822 - jaro("dwayne", "duane")).abs() < 0.001);
+ }
+
+ #[test]
+ fn jaro_diff_with_transposition() {
+ assert!((0.944 - jaro("martha", "marhta")).abs() < 0.001);
+ }
+
+ #[test]
+ fn jaro_names() {
+ assert!((0.392 - jaro("Friedrich Nietzsche",
+ "Jean-Paul Sartre")).abs() < 0.001);
+ }
+
+ #[test]
+ fn jaro_winkler_both_empty() {
+ assert_eq!(1.0, jaro_winkler("", ""));
+ }
+
+ #[test]
+ fn jaro_winkler_first_empty() {
+ assert_eq!(0.0, jaro_winkler("", "jaro-winkler"));
+ }
+
+ #[test]
+ fn jaro_winkler_second_empty() {
+ assert_eq!(0.0, jaro_winkler("distance", ""));
+ }
+
+ #[test]
+ fn jaro_winkler_same() {
+ assert_eq!(1.0, jaro_winkler("Jaro-Winkler", "Jaro-Winkler"));
+ }
+
+ #[test]
+ fn jaro_winkler_multibyte() {
+ assert!((0.89 - jaro_winkler("testabctest", "testöঙ香test")).abs() <
+ 0.001);
+ assert!((0.89 - jaro_winkler("testöঙ香test", "testabctest")).abs() <
+ 0.001);
+ }
+
+ #[test]
+ fn jaro_winkler_diff_short() {
+ assert!((0.813 - jaro_winkler("dixon", "dicksonx")).abs() < 0.001);
+ assert!((0.813 - jaro_winkler("dicksonx", "dixon")).abs() < 0.001);
+ }
+
+ #[test]
+ fn jaro_winkler_diff_one_character() {
+ assert_eq!(0.0, jaro_winkler("a", "b"));
+ }
+
+ #[test]
+ fn jaro_winkler_same_one_character() {
+ assert_eq!(1.0, jaro_winkler("a", "a"));
+ }
+
+ #[test]
+ fn jaro_winkler_diff_no_transposition() {
+ assert!((0.840 - jaro_winkler("dwayne", "duane")).abs() < 0.001);
+ }
+
+ #[test]
+ fn jaro_winkler_diff_with_transposition() {
+ assert!((0.961 - jaro_winkler("martha", "marhta")).abs() < 0.001);
+ }
+
+ #[test]
+ fn jaro_winkler_names() {
+ assert!((0.562 - jaro_winkler("Friedrich Nietzsche",
+ "Fran-Paul Sartre")).abs() < 0.001);
+ }
+
+ #[test]
+ fn jaro_winkler_long_prefix() {
+ assert!((0.911 - jaro_winkler("cheeseburger", "cheese fries")).abs() <
+ 0.001);
+ }
+
+ #[test]
+ fn jaro_winkler_more_names() {
+ assert!((0.868 - jaro_winkler("Thorkel", "Thorgier")).abs() < 0.001);
+ }
+
+ #[test]
+ fn jaro_winkler_length_of_one() {
+ assert!((0.738 - jaro_winkler("Dinsdale", "D")).abs() < 0.001);
+ }
+
+ #[test]
+ fn jaro_winkler_very_long_prefix() {
+ assert!((1.0 - jaro_winkler("thequickbrownfoxjumpedoverx",
+ "thequickbrownfoxjumpedovery")).abs() <
+ 0.001);
+ }
+
+ #[test]
+ fn levenshtein_empty() {
+ assert_eq!(0, levenshtein("", ""));
+ }
+
+ #[test]
+ fn levenshtein_same() {
+ assert_eq!(0, levenshtein("levenshtein", "levenshtein"));
+ }
+
+ #[test]
+ fn levenshtein_diff_short() {
+ assert_eq!(3, levenshtein("kitten", "sitting"));
+ }
+
+ #[test]
+ fn levenshtein_diff_with_space() {
+ assert_eq!(5, levenshtein("hello, world", "bye, world"));
+ }
+
+ #[test]
+ fn levenshtein_diff_multibyte() {
+ assert_eq!(3, levenshtein("öঙ香", "abc"));
+ assert_eq!(3, levenshtein("abc", "öঙ香"));
+ }
+
+ #[test]
+ fn levenshtein_diff_longer() {
+ let a = "The quick brown fox jumped over the angry dog.";
+ let b = "Lorem ipsum dolor sit amet, dicta latine an eam.";
+ assert_eq!(37, levenshtein(a, b));
+ }
+
+ #[test]
+ fn levenshtein_first_empty() {
+ assert_eq!(7, levenshtein("", "sitting"));
+ }
+
+ #[test]
+ fn levenshtein_second_empty() {
+ assert_eq!(6, levenshtein("kitten", ""));
+ }
+
+ #[test]
+ fn normalized_levenshtein_diff_short() {
+ assert!((normalized_levenshtein("kitten", "sitting") - 0.57142).abs() < 0.00001);
+ }
+
+ #[test]
+ fn normalized_levenshtein_for_empty_strings() {
+ assert!((normalized_levenshtein("", "") - 1.0).abs() < 0.00001);
+ }
+
+ #[test]
+ fn normalized_levenshtein_first_empty() {
+ assert!(normalized_levenshtein("", "second").abs() < 0.00001);
+ }
+
+ #[test]
+ fn normalized_levenshtein_second_empty() {
+ assert!(normalized_levenshtein("first", "").abs() < 0.00001);
+ }
+
+ #[test]
+ fn normalized_levenshtein_identical_strings() {
+ assert!((normalized_levenshtein("identical", "identical") - 1.0).abs() < 0.00001);
+ }
+
+ #[test]
+ fn osa_distance_empty() {
+ assert_eq!(0, osa_distance("", ""));
+ }
+
+ #[test]
+ fn osa_distance_same() {
+ assert_eq!(0, osa_distance("damerau", "damerau"));
+ }
+
+ #[test]
+ fn osa_distance_first_empty() {
+ assert_eq!(7, osa_distance("", "damerau"));
+ }
+
+ #[test]
+ fn osa_distance_second_empty() {
+ assert_eq!(7, osa_distance("damerau", ""));
+ }
+
+ #[test]
+ fn osa_distance_diff() {
+ assert_eq!(3, osa_distance("ca", "abc"));
+ }
+
+ #[test]
+ fn osa_distance_diff_short() {
+ assert_eq!(3, osa_distance("damerau", "aderua"));
+ }
+
+ #[test]
+ fn osa_distance_diff_reversed() {
+ assert_eq!(3, osa_distance("aderua", "damerau"));
+ }
+
+ #[test]
+ fn osa_distance_diff_multibyte() {
+ assert_eq!(3, osa_distance("öঙ香", "abc"));
+ assert_eq!(3, osa_distance("abc", "öঙ香"));
+ }
+
+ #[test]
+ fn osa_distance_diff_unequal_length() {
+ assert_eq!(6, osa_distance("damerau", "aderuaxyz"));
+ }
+
+ #[test]
+ fn osa_distance_diff_unequal_length_reversed() {
+ assert_eq!(6, osa_distance("aderuaxyz", "damerau"));
+ }
+
+ #[test]
+ fn osa_distance_diff_comedians() {
+ assert_eq!(5, osa_distance("Stewart", "Colbert"));
+ }
+
+ #[test]
+ fn osa_distance_many_transpositions() {
+ assert_eq!(4, osa_distance("abcdefghijkl", "bacedfgihjlk"));
+ }
+
+ #[test]
+ fn osa_distance_diff_longer() {
+ let a = "The quick brown fox jumped over the angry dog.";
+ let b = "Lehem ipsum dolor sit amet, dicta latine an eam.";
+ assert_eq!(36, osa_distance(a, b));
+ }
+
+ #[test]
+ fn osa_distance_beginning_transposition() {
+ assert_eq!(1, osa_distance("foobar", "ofobar"));
+ }
+
+ #[test]
+ fn osa_distance_end_transposition() {
+ assert_eq!(1, osa_distance("specter", "spectre"));
+ }
+
+ #[test]
+ fn osa_distance_restricted_edit() {
+ assert_eq!(4, osa_distance("a cat", "an abct"));
+ }
+
+ #[test]
+ fn damerau_levenshtein_empty() {
+ assert_eq!(0, damerau_levenshtein("", ""));
+ }
+
+ #[test]
+ fn damerau_levenshtein_same() {
+ assert_eq!(0, damerau_levenshtein("damerau", "damerau"));
+ }
+
+ #[test]
+ fn damerau_levenshtein_first_empty() {
+ assert_eq!(7, damerau_levenshtein("", "damerau"));
+ }
+
+ #[test]
+ fn damerau_levenshtein_second_empty() {
+ assert_eq!(7, damerau_levenshtein("damerau", ""));
+ }
+
+ #[test]
+ fn damerau_levenshtein_diff() {
+ assert_eq!(2, damerau_levenshtein("ca", "abc"));
+ }
+
+ #[test]
+ fn damerau_levenshtein_diff_short() {
+ assert_eq!(3, damerau_levenshtein("damerau", "aderua"));
+ }
+
+ #[test]
+ fn damerau_levenshtein_diff_reversed() {
+ assert_eq!(3, damerau_levenshtein("aderua", "damerau"));
+ }
+
+ #[test]
+ fn damerau_levenshtein_diff_multibyte() {
+ assert_eq!(3, damerau_levenshtein("öঙ香", "abc"));
+ assert_eq!(3, damerau_levenshtein("abc", "öঙ香"));
+ }
+
+ #[test]
+ fn damerau_levenshtein_diff_unequal_length() {
+ assert_eq!(6, damerau_levenshtein("damerau", "aderuaxyz"));
+ }
+
+ #[test]
+ fn damerau_levenshtein_diff_unequal_length_reversed() {
+ assert_eq!(6, damerau_levenshtein("aderuaxyz", "damerau"));
+ }
+
+ #[test]
+ fn damerau_levenshtein_diff_comedians() {
+ assert_eq!(5, damerau_levenshtein("Stewart", "Colbert"));
+ }
+
+ #[test]
+ fn damerau_levenshtein_many_transpositions() {
+ assert_eq!(4, damerau_levenshtein("abcdefghijkl", "bacedfgihjlk"));
+ }
+
+ #[test]
+ fn damerau_levenshtein_diff_longer() {
+ let a = "The quick brown fox jumped over the angry dog.";
+ let b = "Lehem ipsum dolor sit amet, dicta latine an eam.";
+ assert_eq!(36, damerau_levenshtein(a, b));
+ }
+
+ #[test]
+ fn damerau_levenshtein_beginning_transposition() {
+ assert_eq!(1, damerau_levenshtein("foobar", "ofobar"));
+ }
+
+ #[test]
+ fn damerau_levenshtein_end_transposition() {
+ assert_eq!(1, damerau_levenshtein("specter", "spectre"));
+ }
+
+ #[test]
+ fn damerau_levenshtein_unrestricted_edit() {
+ assert_eq!(3, damerau_levenshtein("a cat", "an abct"));
+ }
+
+ #[test]
+ fn normalized_damerau_levenshtein_diff_short() {
+ assert!((normalized_damerau_levenshtein("levenshtein", "löwenbräu") - 0.27272).abs() < 0.00001);
+ }
+
+ #[test]
+ fn normalized_damerau_levenshtein_for_empty_strings() {
+ assert!((normalized_damerau_levenshtein("", "") - 1.0).abs() < 0.00001);
+ }
+
+ #[test]
+ fn normalized_damerau_levenshtein_first_empty() {
+ assert!(normalized_damerau_levenshtein("", "flower").abs() < 0.00001);
+ }
+
+ #[test]
+ fn normalized_damerau_levenshtein_second_empty() {
+ assert!(normalized_damerau_levenshtein("tree", "").abs() < 0.00001);
+ }
+
+ #[test]
+ fn normalized_damerau_levenshtein_identical_strings() {
+ assert!((normalized_damerau_levenshtein("sunglasses", "sunglasses") - 1.0).abs() < 0.00001);
+ }
+
+ #[test]
+ fn sorensen_dice_all() {
+ // test cases taken from
+ // https://github.com/aceakash/string-similarity/blob/f83ba3cd7bae874c20c429774e911ae8cff8bced/src/spec/index.spec.js#L11
+
+ assert_eq!(1.0, sorensen_dice("a", "a"));
+ assert_eq!(0.0, sorensen_dice("a", "b"));
+ assert_eq!(1.0, sorensen_dice("", ""));
+ assert_eq!(0.0, sorensen_dice("a", ""));
+ assert_eq!(0.0, sorensen_dice("", "a"));
+ assert_eq!(1.0, sorensen_dice("apple event", "apple event"));
+ assert_eq!(0.9090909090909091, sorensen_dice("iphone", "iphone x"));
+ assert_eq!(0.0, sorensen_dice("french", "quebec"));
+ assert_eq!(1.0, sorensen_dice("france", "france"));
+ assert_eq!(0.2, sorensen_dice("fRaNce", "france"));
+ assert_eq!(0.8, sorensen_dice("healed", "sealed"));
+ assert_eq!(
+ 0.7878787878787878,
+ sorensen_dice("web applications", "applications of the web")
+ );
+ assert_eq!(
+ 0.92,
+ sorensen_dice(
+ "this will have a typo somewhere",
+ "this will huve a typo somewhere"
+ )
+ );
+ assert_eq!(
+ 0.6060606060606061,
+ sorensen_dice(
+ "Olive-green table for sale, in extremely good condition.",
+ "For sale: table in very good condition, olive green in colour."
+ )
+ );
+ assert_eq!(
+ 0.2558139534883721,
+ sorensen_dice(
+ "Olive-green table for sale, in extremely good condition.",
+ "For sale: green Subaru Impreza, 210,000 miles"
+ )
+ );
+ assert_eq!(
+ 0.1411764705882353,
+ sorensen_dice(
+ "Olive-green table for sale, in extremely good condition.",
+ "Wanted: mountain bike with at least 21 gears."
+ )
+ );
+ assert_eq!(
+ 0.7741935483870968,
+ sorensen_dice("this has one extra word", "this has one word")
+ );
+ }
+}
projects / platform / upstream / rust-strsim.git / commitdiff