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/* SPDX-License-Identifier: MIT */
/* Copyright © 2022-present Max Bachmann */
#pragma once
#include "common.hpp"
#include <algorithm>
#include <cstddef>
#include <cstdint>
#include <limits>
#include <unordered_map>
#include <vector>
namespace rapidfuzz_reference {
template <typename InputIt1, typename InputIt2>
Matrix<size_t> damerau_levenshtein_matrix(InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2)
{
size_t len1 = std::distance(first1, last1);
size_t len2 = std::distance(first2, last2);
size_t infinite = len1 + len2;
std::unordered_map<uint32_t, size_t> da;
Matrix<size_t> matrix(len1 + 2, len2 + 2);
matrix(0, 0) = infinite;
for (size_t i = 0; i <= len1; ++i) {
matrix(i + 1, 0) = infinite;
matrix(i + 1, 1) = i;
}
for (size_t i = 0; i <= len2; ++i) {
matrix(0, i + 1) = infinite;
matrix(1, i + 1) = i;
}
for (size_t pos1 = 0; pos1 < len1; ++pos1) {
size_t db = 0;
for (size_t pos2 = 0; pos2 < len2; ++pos2) {
size_t i1 = da[static_cast<uint32_t>(first2[pos2])];
size_t j1 = db;
size_t cost = 1;
if (first1[pos1] == first2[pos2]) {
cost = 0;
db = pos2 + 1;
}
matrix(pos1 + 2, pos2 + 2) =
std::min({matrix(pos1 + 1, pos2 + 1) + cost, matrix(pos1 + 2, pos2 + 1) + 1,
matrix(pos1 + 1, pos2 + 2) + 1, matrix(i1, j1) + (pos1 - i1) + 1 + (pos2 - j1)
});
}
da[first1[pos1]] = pos1 + 1;
}
return matrix;
}
template <typename InputIt1, typename InputIt2>
size_t damerau_levenshtein_distance(InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2,
size_t score_cutoff = std::numeric_limits<size_t>::max())
{
auto matrix = damerau_levenshtein_matrix(first1, last1, first2, last2);
size_t dist = matrix.back();
return (dist <= score_cutoff) ? dist : score_cutoff + 1;
}
template <typename Sentence1, typename Sentence2>
size_t damerau_levenshtein_distance(const Sentence1& s1, const Sentence2& s2,
size_t score_cutoff = std::numeric_limits<size_t>::max())
{
return damerau_levenshtein_distance(std::begin(s1), std::end(s1), std::begin(s2), std::end(s2),
score_cutoff);
}
} // namespace rapidfuzz_reference
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