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Refactor Levenshtein distance implementation (#5138)

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* ref: refactor Levenshtein distance implementation
- Rewrite the original levenshtein distance implementation in functional style
- Add optimized version of levenshtein distance

* ref: make `LevenshteinDistance` class a proper utility

* ref: remove duplicated test data

* ref: update tests

---

Co-authored-by: Piotr Idzik <65706193+vil02@users.noreply.github.com>
This commit is contained in:
SOZEL
2024-05-04 16:13:30 +07:00
committed by GitHub
parent b3903f5768
commit dda3c9cb59
2 changed files with 107 additions and 43 deletions
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src/main/java/com/thealgorithms/dynamicprogramming/LevenshteinDistance.java
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package com.thealgorithms.dynamicprogramming;
/**
* @author Kshitij VERMA (github.com/kv19971) LEVENSHTEIN DISTANCE dyamic
* programming implementation to show the difference between two strings
* (https://en.wikipedia.org/wiki/Levenshtein_distance)
*/
public class LevenshteinDistance {
import java.util.stream.IntStream;
private static int minimum(int a, int b, int c) {
if (a < b && a < c) {
return a;
} else if (b < a && b < c) {
return b;
} else {
return c;
}
/**
* Provides functions to calculate the Levenshtein distance between two strings.
*
* The Levenshtein distance is a measure of the similarity between two strings by calculating the minimum number of single-character
* edits (insertions, deletions, or substitutions) required to change one string into the other.
*/
public final class LevenshteinDistance {
private LevenshteinDistance() {
}
public static int calculateLevenshteinDistance(String str1, String str2) {
int len1 = str1.length() + 1;
int len2 = str2.length() + 1;
int[][] distanceMat = new int[len1][len2];
for (int i = 0; i < len1; i++) {
distanceMat[i][0] = i;
/**
* Calculates the Levenshtein distance between two strings using a naive dynamic programming approach.
*
* This function computes the Levenshtein distance by constructing a dynamic programming matrix and iteratively filling it in.
* It follows the standard top-to-bottom, left-to-right approach for filling in the matrix.
*
* @param string1 The first string.
* @param string2 The second string.
* @return The Levenshtein distance between the two input strings.
*
* Time complexity: O(nm),
* Space complexity: O(nm),
*
* where n and m are lengths of `string1` and `string2`.
*
* Note that this implementation uses a straightforward dynamic programming approach without any space optimization.
* It may consume more memory for larger input strings compared to the optimized version.
*/
public static int naiveLevenshteinDistance(final String string1, final String string2) {
int[][] distanceMatrix = IntStream.rangeClosed(0, string1.length()).mapToObj(i -> IntStream.rangeClosed(0, string2.length()).map(j -> (i == 0) ? j : (j == 0) ? i : 0).toArray()).toArray(int[][] ::new);
IntStream.range(1, string1.length() + 1).forEach(i -> IntStream.range(1, string2.length() + 1).forEach(j -> {
final int cost = (string1.charAt(i - 1) == string2.charAt(j - 1)) ? 0 : 1;
distanceMatrix[i][j] = Math.min(distanceMatrix[i - 1][j - 1] + cost, Math.min(distanceMatrix[i][j - 1] + 1, distanceMatrix[i - 1][j] + 1));
}));
return distanceMatrix[string1.length()][string2.length()];
}
/**
* Calculates the Levenshtein distance between two strings using an optimized dynamic programming approach.
*
* This edit distance is defined as 1 point per insertion, substitution, or deletion required to make the strings equal.
*
* @param string1 The first string.
* @param string2 The second string.
* @return The Levenshtein distance between the two input strings.
*
* Time complexity: O(nm),
* Space complexity: O(n),
*
* where n and m are lengths of `string1` and `string2`.
*
* Note that this implementation utilizes an optimized dynamic programming approach, significantly reducing the space complexity from O(nm) to O(n), where n and m are the lengths of `string1` and `string2`.
*
* Additionally, it minimizes space usage by leveraging the shortest string horizontally and the longest string vertically in the computation matrix.
*/
public static int optimizedLevenshteinDistance(final String string1, final String string2) {
if (string1.isEmpty()) {
return string2.length();
}
for (int j = 0; j < len2; j++) {
distanceMat[0][j] = j;
}
for (int i = 1; i < len1; i++) {
for (int j = 1; j < len2; j++) {
if (str1.charAt(i - 1) == str2.charAt(j - 1)) {
distanceMat[i][j] = distanceMat[i - 1][j - 1];
} else {
distanceMat[i][j] = 1 + minimum(distanceMat[i - 1][j], distanceMat[i - 1][j - 1], distanceMat[i][j - 1]);
}
int[] previousDistance = IntStream.rangeClosed(0, string1.length()).toArray();
for (int j = 1; j <= string2.length(); j++) {
int prevSubstitutionCost = previousDistance[0];
previousDistance[0] = j;
for (int i = 1; i <= string1.length(); i++) {
final int deletionCost = previousDistance[i] + 1;
final int insertionCost = previousDistance[i - 1] + 1;
final int substitutionCost = (string1.charAt(i - 1) == string2.charAt(j - 1)) ? prevSubstitutionCost : prevSubstitutionCost + 1;
prevSubstitutionCost = previousDistance[i];
previousDistance[i] = Math.min(deletionCost, Math.min(insertionCost, substitutionCost));
}
}
return distanceMat[len1 - 1][len2 - 1];
}
public static void main(String[] args) {
String str1 = ""; // enter your string here
String str2 = ""; // enter your string here
System.out.print("Levenshtein distance between " + str1 + " and " + str2 + " is: ");
System.out.println(calculateLevenshteinDistance(str1, str2));
return previousDistance[string1.length()];
}
}