Formatting
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2 changed files with 15 additions and 16 deletions
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@ -1,14 +1,13 @@
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#include <iostream>
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#include <vector>
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#include <string>
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#include <algorithm>
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#include <algorithm>
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#include <string>
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#include <vector>
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int edit_distance(const std::string &s1, const std::string &s2) {
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int edit_distance(const std::string &s1, const std::string &s2) {
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size_t m = s1.size();
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size_t m = s1.size();
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size_t n = s2.size();
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size_t n = s2.size();
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// Create a 2D DP table
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// Create a 2D DP table
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std::vector<std::vector<int>> dp(m + 1, std::vector<int>(n + 1));
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std::vector<std::vector<size_t>> dp(m + 1, std::vector<size_t>(n + 1));
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// Fill the base cases
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// Fill the base cases
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for (size_t i = 0; i <= m; ++i)
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for (size_t i = 0; i <= m; ++i)
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@ -23,7 +22,8 @@ int edit_distance(const std::string& s1, const std::string& s2) {
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if (s1[i - 1] == s2[j - 1]) {
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if (s1[i - 1] == s2[j - 1]) {
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dp[i][j] = dp[i - 1][j - 1]; // No operation needed
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dp[i][j] = dp[i - 1][j - 1]; // No operation needed
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} else {
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} else {
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dp[i][j] = 1 + std::min({dp[i - 1][j], // Deletion
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dp[i][j] = 1 + std::min({
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dp[i - 1][j], // Deletion
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dp[i][j - 1], // Insertion
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dp[i][j - 1], // Insertion
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dp[i - 1][j - 1] // Substitution
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dp[i - 1][j - 1] // Substitution
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});
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});
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@ -31,5 +31,5 @@ int edit_distance(const std::string& s1, const std::string& s2) {
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}
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}
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}
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}
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return dp[m][n];
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return static_cast<int>(dp[m][n]);
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}
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}
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@ -1,15 +1,14 @@
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#include <algorithm>
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#include <iostream>
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#include <string>
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#include <string>
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#include <vector>
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/**
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/**
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* @brief Computes the edit distance (Levenshtein distance) between two strings.
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* @brief Computes the edit distance (Levenshtein distance) between two strings.
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*
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*
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* The edit distance is defined as the minimum number of single-character edits
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* The edit distance is defined as the minimum number of single-character edits
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* (insertions, deletions, or substitutions) required to transform one string into the other.
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* (insertions, deletions, or substitutions) required to transform one string
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* into the other.
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*
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*
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* This implementation uses dynamic programming to compute the distance efficiently.
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* This implementation uses dynamic programming to compute the distance
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* efficiently.
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*
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*
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* @param s1 The first string.
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* @param s1 The first string.
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* @param s2 The second string.
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* @param s2 The second string.
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