From 5a5a07c7fad31641d032a743e4e87ffb58ade17d Mon Sep 17 00:00:00 2001
From: 魏曹先生 <1992414357@qq.com>
Date: Fri, 22 May 2026 22:10:19 +0800
Subject: Initial commit

---
 rola-utils/functions/src/levenshtein_distance.rs | 117 +++++++++++++++++++++++
 rola-utils/functions/src/lib.rs                  |   2 +
 2 files changed, 119 insertions(+)
 create mode 100644 rola-utils/functions/src/levenshtein_distance.rs
 create mode 100644 rola-utils/functions/src/lib.rs

(limited to 'rola-utils/functions/src')

diff --git a/rola-utils/functions/src/levenshtein_distance.rs b/rola-utils/functions/src/levenshtein_distance.rs
new file mode 100644
index 0000000..7b8be6c
--- /dev/null
+++ b/rola-utils/functions/src/levenshtein_distance.rs
@@ -0,0 +1,117 @@
+pub struct LevenshteinDistance;
+
+impl LevenshteinDistance {
+    pub fn filter_similar<'a>(str: &str, slice: &[&'a str], threshold: usize) -> Vec<&'a str> {
+        slice
+            .iter()
+            .filter(|s| levenshtein_distance(str, s) <= threshold)
+            .copied()
+            .collect()
+    }
+
+    pub fn compare(a: impl AsRef<str>, b: impl AsRef<str>) -> usize {
+        let a = a.as_ref();
+        let b = b.as_ref();
+        levenshtein_distance(a, b)
+    }
+}
+
+fn levenshtein_distance(a: &str, b: &str) -> usize {
+    let a_chars: Vec<char> = a.chars().collect();
+    let b_chars: Vec<char> = b.chars().collect();
+    let m = a_chars.len();
+    let n = b_chars.len();
+    if m == 0 {
+        return n;
+    }
+    if n == 0 {
+        return m;
+    }
+
+    let mut prev: Vec<usize> = (0..=n).collect();
+    let mut curr = vec![0; n + 1];
+    for (i, ca) in a_chars.iter().enumerate() {
+        curr[0] = i + 1;
+        for (j, cb) in b_chars.iter().enumerate() {
+            let cost = if ca == cb { 0 } else { 1 };
+            let del = prev[j + 1] + 1;
+            let ins = curr[j] + 1;
+            let rep = prev[j] + cost;
+            curr[j + 1] = del.min(ins).min(rep);
+        }
+        std::mem::swap(&mut prev, &mut curr);
+    }
+    prev[n]
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_levenshtein_distance_identical_strings() {
+        assert_eq!(LevenshteinDistance::compare("hello", "hello"), 0);
+    }
+
+    #[test]
+    fn test_levenshtein_distance_completely_different() {
+        let dist = LevenshteinDistance::compare("abc", "xyz");
+        assert_eq!(dist, 3);
+    }
+
+    #[test]
+    fn test_levenshtein_distance_one_insertion() {
+        assert_eq!(LevenshteinDistance::compare("cat", "cats"), 1);
+    }
+
+    #[test]
+    fn test_levenshtein_distance_one_deletion() {
+        assert_eq!(LevenshteinDistance::compare("dogs", "dog"), 1);
+    }
+
+    #[test]
+    fn test_levenshtein_distance_one_substitution() {
+        assert_eq!(LevenshteinDistance::compare("cat", "cut"), 1);
+    }
+
+    #[test]
+    fn test_levenshtein_distance_empty_strings() {
+        assert_eq!(LevenshteinDistance::compare("", ""), 0);
+    }
+
+    #[test]
+    fn test_levenshtein_distance_empty_vs_nonempty() {
+        assert_eq!(LevenshteinDistance::compare("", "abc"), 3);
+        assert_eq!(LevenshteinDistance::compare("xyz", ""), 3);
+    }
+
+    #[test]
+    fn test_levenshtein_distance_unicode() {
+        // Chinese characters
+        assert_eq!(LevenshteinDistance::compare("你好", "你好"), 0);
+        assert_eq!(LevenshteinDistance::compare("你好", "您好"), 1);
+        assert_eq!(LevenshteinDistance::compare("你好", "您不好"), 2);
+    }
+
+    #[test]
+    fn test_filter_similar_exact_threshold() {
+        let words = vec!["cat", "cart", "ca", "cats", "cut"];
+        let result = LevenshteinDistance::filter_similar("cat", &words, 2);
+        // cat -> cat: 0, cat -> cart: 1, cat -> ca: 1, cat -> cats: 1, cat -> cut: 1
+        assert_eq!(result.len(), 5);
+    }
+
+    #[test]
+    fn test_filter_similar_empty_slice() {
+        let words: Vec<&str> = vec![];
+        let result = LevenshteinDistance::filter_similar("hello", &words, 3);
+        assert!(result.is_empty());
+    }
+
+    #[test]
+    fn test_filter_similar_threshold_zero() {
+        let words = vec!["hello", "hallo", "hello!"];
+        let result = LevenshteinDistance::filter_similar("hello", &words, 0);
+        assert_eq!(result, vec!["hello"]);
+    }
+}
diff --git a/rola-utils/functions/src/lib.rs b/rola-utils/functions/src/lib.rs
new file mode 100644
index 0000000..ff2ee7e
--- /dev/null
+++ b/rola-utils/functions/src/lib.rs
@@ -0,0 +1,2 @@
+mod levenshtein_distance;
+pub use levenshtein_distance::*;
-- 
cgit