97 lines
No EOL
3 KiB
Rust
97 lines
No EOL
3 KiB
Rust
use unicode_segmentation::UnicodeSegmentation;
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//could also be powers of 2 that are combined using bitwise-or
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// enum Backtrace {
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// LEFT,
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// DOWN,
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// DIAGONAL,
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// }
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struct Element {
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value: usize,
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// backtraces: Vec<Backtrace>,
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}
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impl Element {
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fn new() -> Self {
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Self {
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value: 0,
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// backtraces: Vec::new(),
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}
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}
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}
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pub fn get_levenshtein_distance(word1: &str, word2: &str) -> usize {
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get_edit_distance_table(word1, word2)[word1.len()][word2.len()].value
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}
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// non recursive implementation requires a table
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// my guess is that this is more efficient (should check)
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fn get_edit_distance_table(word1: &str, word2: &str) -> Vec<Vec<Element>> {
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// create table
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let mut table = Vec::new();
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for _ in 0..=word1.len() {
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let mut row = Vec::new();
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for _ in 0..=word2.len() {
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row.push(Element::new())
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}
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table.push(row);
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}
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// set the boundaries
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for i in 0..=word1.len() {
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table[i][0].value = i;
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}
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for i in 1..=word2.len() {
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table[0][i].value = i;
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}
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for (i1, g1) in word1.graphemes(true).enumerate() {
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for (i2, g2) in word2.graphemes(true).enumerate() {
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let d_del = table[i1][i2 + 1].value + 1; //deletion
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let d_ins = table[i1 + 1][i2].value + 1; //insertion
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let d_sub = table[i1][i2].value + (if g1 == g2 { 0 } else { 2 }); // substitution
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let min = usize::min(d_del, usize::min(d_ins, d_sub));
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let element = table[i1 + 1].get_mut(i2 + 1).unwrap();
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element.value = min;
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// if d_del == min {
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// element.backtraces.push(Backtrace::DOWN);
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// }
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// if d_ins == min {
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// element.backtraces.push(Backtrace::LEFT);
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// }
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// if d_sub == min {
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// element.backtraces.push(Backtrace::DIAGONAL);
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// }
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}
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}
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table
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}
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#[cfg(test)]
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mod tests {
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use super::{get_edit_distance_table, get_levenshtein_distance};
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#[test]
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fn test_get_levenshtein_distance() {
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assert_eq!(get_levenshtein_distance("intention", "execution"), 8);
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}
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#[test]
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fn test_get_edit_distance_table() {
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// example from Stanford NLP course: https://youtu.be/kgcEaoM_QJA
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let word1 = "intention";
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let word2 = "execution";
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let outcome: [[usize; 10]; 10] = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 2, 3, 4, 5, 6, 7, 6, 7, 8], [2, 3, 4, 5, 6, 7, 8, 7, 8, 7], [3, 4, 5, 6, 7, 8, 7, 8, 9, 8], [4, 3, 4, 5, 6, 7, 8, 9, 10, 9],
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[5, 4, 5, 6, 7, 8, 9, 10, 11, 10], [6, 5, 6, 7, 8, 9, 8, 9, 10, 11], [7, 6, 7, 8, 9, 10, 9, 8, 9, 10], [8, 7, 8, 9, 10, 11, 10, 9, 8, 9], [9, 8, 9, 10, 11, 12, 11, 10, 9, 8]];
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let tab = get_edit_distance_table(word1, word2);
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for (rowindex, row) in tab.iter().enumerate() {
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for (colindex, element) in row.iter().enumerate() {
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assert_eq!(outcome[rowindex][colindex], element.value);
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}
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}
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}
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} |