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AK: Move heavyweight fuzzy matching to own compilation unit
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| /* | ||
| * Copyright (c) 2021, Spencer Dixon <[email protected]> | ||
| * | ||
| * SPDX-License-Identifier: BSD-2-Clause | ||
| */ | ||
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| #include <AK/CharacterTypes.h> | ||
| #include <AK/FuzzyMatch.h> | ||
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| namespace AK { | ||
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| static constexpr int const RECURSION_LIMIT = 10; | ||
| static constexpr int const MAX_MATCHES = 256; | ||
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| // Bonuses and penalties are used to build up a final score for the match. | ||
| static constexpr int const SEQUENTIAL_BONUS = 15; // bonus for adjacent matches (needle: 'ca', haystack: 'cat') | ||
| static constexpr int const SEPARATOR_BONUS = 30; // bonus if match occurs after a separator ('_' or ' ') | ||
| static constexpr int const CAMEL_BONUS = 30; // bonus if match is uppercase and prev is lower (needle: 'myF' haystack: '/path/to/myFile.txt') | ||
| static constexpr int const FIRST_LETTER_BONUS = 20; // bonus if the first letter is matched (needle: 'c' haystack: 'cat') | ||
| static constexpr int const LEADING_LETTER_PENALTY = -5; // penalty applied for every letter in str before the first match | ||
| static constexpr int const MAX_LEADING_LETTER_PENALTY = -15; // maximum penalty for leading letters | ||
| static constexpr int const UNMATCHED_LETTER_PENALTY = -1; // penalty for every letter that doesn't matter | ||
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| static int calculate_score(String const& string, u8* index_points, size_t index_points_size) | ||
| { | ||
| int out_score = 100; | ||
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| int penalty = LEADING_LETTER_PENALTY * index_points[0]; | ||
| if (penalty < MAX_LEADING_LETTER_PENALTY) | ||
| penalty = MAX_LEADING_LETTER_PENALTY; | ||
| out_score += penalty; | ||
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| int unmatched = string.length() - index_points_size; | ||
| out_score += UNMATCHED_LETTER_PENALTY * unmatched; | ||
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| for (size_t i = 0; i < index_points_size; i++) { | ||
| u8 current_idx = index_points[i]; | ||
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| if (current_idx == 0) | ||
| out_score += FIRST_LETTER_BONUS; | ||
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| if (i == 0) | ||
| continue; | ||
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| u8 previous_idx = index_points[i - 1]; | ||
| if (current_idx - 1 == previous_idx) | ||
| out_score += SEQUENTIAL_BONUS; | ||
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| u32 current_character = string[current_idx]; | ||
| u32 neighbor_character = string[current_idx - 1]; | ||
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| if (neighbor_character != to_ascii_uppercase(neighbor_character) && current_character != to_ascii_lowercase(current_character)) | ||
| out_score += CAMEL_BONUS; | ||
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| if (neighbor_character == '_' || neighbor_character == ' ') | ||
| out_score += SEPARATOR_BONUS; | ||
| } | ||
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| return out_score; | ||
| } | ||
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| FuzzyMatchResult fuzzy_match_recursive(String const& needle, String const& haystack, size_t needle_idx, size_t haystack_idx, | ||
| u8 const* src_matches, u8* matches, int next_match, int& recursion_count) | ||
| { | ||
| int out_score = 0; | ||
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| ++recursion_count; | ||
| if (recursion_count >= RECURSION_LIMIT) | ||
| return { false, out_score }; | ||
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| if (needle.length() == needle_idx || haystack.length() == haystack_idx) | ||
| return { false, out_score }; | ||
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| bool had_recursive_match = false; | ||
| constexpr size_t recursive_match_limit = 256; | ||
| u8 best_recursive_matches[recursive_match_limit]; | ||
| int best_recursive_score = 0; | ||
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| bool first_match = true; | ||
| while (needle_idx < needle.length() && haystack_idx < haystack.length()) { | ||
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| if (to_ascii_lowercase(needle[needle_idx]) == to_ascii_lowercase(haystack[haystack_idx])) { | ||
| if (next_match >= MAX_MATCHES) | ||
| return { false, out_score }; | ||
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| if (first_match && src_matches) { | ||
| memcpy(matches, src_matches, next_match); | ||
| first_match = false; | ||
| } | ||
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| u8 recursive_matches[recursive_match_limit] {}; | ||
| auto result = fuzzy_match_recursive(needle, haystack, needle_idx, haystack_idx + 1, matches, recursive_matches, next_match, recursion_count); | ||
| if (result.matched) { | ||
| if (!had_recursive_match || result.score > best_recursive_score) { | ||
| memcpy(best_recursive_matches, recursive_matches, recursive_match_limit); | ||
| best_recursive_score = result.score; | ||
| } | ||
| had_recursive_match = true; | ||
| } | ||
| matches[next_match++] = haystack_idx; | ||
| needle_idx++; | ||
| } | ||
| haystack_idx++; | ||
| } | ||
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| bool matched = needle_idx == needle.length(); | ||
| if (!matched) | ||
| return { false, out_score }; | ||
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| out_score = calculate_score(haystack, matches, next_match); | ||
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| if (had_recursive_match && (best_recursive_score > out_score)) { | ||
| memcpy(matches, best_recursive_matches, MAX_MATCHES); | ||
| out_score = best_recursive_score; | ||
| } | ||
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| return { true, out_score }; | ||
| } | ||
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| // This fuzzy_match algorithm is based off a similar algorithm used by Sublime Text. The key insight is that instead | ||
| // of doing a total in the distance between characters (I.E. Levenshtein Distance), we apply some meaningful heuristics | ||
| // related to our dataset that we're trying to match to build up a score. Scores can then be sorted and displayed | ||
| // with the highest at the top. | ||
| // | ||
| // Scores are not normalized between any values and have no particular meaning. The starting value is 100 and when we | ||
| // detect good indicators of a match we add to the score. When we detect bad indicators, we penalize the match and subtract | ||
| // from its score. Therefore, the longer the needle/haystack the greater the range of scores could be. | ||
| FuzzyMatchResult fuzzy_match(String const& needle, String const& haystack) | ||
| { | ||
| int recursion_count = 0; | ||
| u8 matches[MAX_MATCHES] {}; | ||
| return fuzzy_match_recursive(needle, haystack, 0, 0, nullptr, matches, 0, recursion_count); | ||
| } | ||
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| } |
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