| 1 | /** |
| 2 | * `levenshtein-rs` - levenshtein |
| 3 | * |
| 4 | * MIT licensed. |
| 5 | * |
| 6 | * Copyright (c) 2016 Titus Wormer <tituswormer@gmail.com> |
| 7 | */ |
| 8 | #[must_use ] |
| 9 | pub fn levenshtein(a: &str, b: &str) -> usize { |
| 10 | let mut result = 0; |
| 11 | |
| 12 | /* Shortcut optimizations / degenerate cases. */ |
| 13 | if a == b { |
| 14 | return result; |
| 15 | } |
| 16 | |
| 17 | let length_a = a.chars().count(); |
| 18 | let length_b = b.chars().count(); |
| 19 | |
| 20 | if length_a == 0 { |
| 21 | return length_b; |
| 22 | } |
| 23 | |
| 24 | if length_b == 0 { |
| 25 | return length_a; |
| 26 | } |
| 27 | |
| 28 | /* Initialize the vector. |
| 29 | * |
| 30 | * This is why it’s fast, normally a matrix is used, |
| 31 | * here we use a single vector. */ |
| 32 | let mut cache: Vec<usize> = (1..).take(length_a).collect(); |
| 33 | let mut distance_a; |
| 34 | let mut distance_b; |
| 35 | |
| 36 | /* Loop. */ |
| 37 | for (index_b, code_b) in b.chars().enumerate() { |
| 38 | result = index_b; |
| 39 | distance_a = index_b; |
| 40 | |
| 41 | for (index_a, code_a) in a.chars().enumerate() { |
| 42 | distance_b = if code_a == code_b { |
| 43 | distance_a |
| 44 | } else { |
| 45 | distance_a + 1 |
| 46 | }; |
| 47 | |
| 48 | distance_a = cache[index_a]; |
| 49 | |
| 50 | result = if distance_a > result { |
| 51 | if distance_b > result { |
| 52 | result + 1 |
| 53 | } else { |
| 54 | distance_b |
| 55 | } |
| 56 | } else if distance_b > distance_a { |
| 57 | distance_a + 1 |
| 58 | } else { |
| 59 | distance_b |
| 60 | }; |
| 61 | |
| 62 | cache[index_a] = result; |
| 63 | } |
| 64 | } |
| 65 | |
| 66 | result |
| 67 | } |
| 68 | |