1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
//! 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<usize> {
    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<usize> {
    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<usize>,
) -> Option<Symbol> {
    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<usize>,
) -> Option<Symbol> {
    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<usize>,
) -> Option<Symbol> {
    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<Symbol> {
    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("_")
}