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/*
* Copyright (c) 2021, Spencer Dixon <spencercdixon@gmail.com>
*
* SPDX-License-Identifier: BSD-2-Clause
*/
#pragma once
#include <AK/CharacterTypes.h>
#include <AK/String.h>
namespace AK {
struct FuzzyMatchResult {
bool matched { false };
int score { 0 };
};
static constexpr int const RECURSION_LIMIT = 10;
static constexpr int const MAX_MATCHES = 256;
// 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
static int calculate_score(String const& string, u8* index_points, size_t index_points_size)
{
int out_score = 100;
int penalty = LEADING_LETTER_PENALTY * index_points[0];
if (penalty < MAX_LEADING_LETTER_PENALTY)
penalty = MAX_LEADING_LETTER_PENALTY;
out_score += penalty;
int unmatched = string.length() - index_points_size;
out_score += UNMATCHED_LETTER_PENALTY * unmatched;
for (size_t i = 0; i < index_points_size; i++) {
u8 current_idx = index_points[i];
if (current_idx == 0)
out_score += FIRST_LETTER_BONUS;
if (i == 0)
continue;
u8 previous_idx = index_points[i - 1];
if (current_idx - 1 == previous_idx)
out_score += SEQUENTIAL_BONUS;
u32 current_character = string[current_idx];
u32 neighbor_character = string[current_idx - 1];
if (neighbor_character != to_ascii_uppercase(neighbor_character) && current_character != to_ascii_lowercase(current_character))
out_score += CAMEL_BONUS;
if (neighbor_character == '_' || neighbor_character == ' ')
out_score += SEPARATOR_BONUS;
}
return out_score;
}
static 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;
++recursion_count;
if (recursion_count >= RECURSION_LIMIT)
return { false, out_score };
if (needle.length() == needle_idx || haystack.length() == haystack_idx)
return { false, out_score };
bool had_recursive_match = false;
constexpr size_t recursive_match_limit = 256;
u8 best_recursive_matches[recursive_match_limit];
int best_recursive_score = 0;
bool first_match = true;
while (needle_idx < needle.length() && haystack_idx < haystack.length()) {
if (to_ascii_lowercase(needle[needle_idx]) == to_ascii_lowercase(haystack[haystack_idx])) {
if (next_match >= MAX_MATCHES)
return { false, out_score };
if (first_match && src_matches) {
memcpy(matches, src_matches, next_match);
first_match = false;
}
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++;
}
bool matched = needle_idx == needle.length();
if (!matched)
return { false, out_score };
out_score = calculate_score(haystack, matches, next_match);
if (had_recursive_match && (best_recursive_score > out_score)) {
memcpy(matches, best_recursive_matches, MAX_MATCHES);
out_score = best_recursive_score;
}
return { true, out_score };
}
// 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.
static 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);
}
}
using AK::fuzzy_match;
using AK::FuzzyMatchResult;
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