Levenshtein Distance Algorithm

Levenshtein Distance Function Overview

• Calculates the Levenshtein distance between two strings s1 and s2, representing the minimum number of single-character edits (insertions, deletions, substitutions) required to change one string into the other.
• Useful in areas like spell checking, DNA sequencing, and natural language processing.

Function Parameters

• s1: The first string to compare.
• s2: The second string to compare.

Variable Definitions

• l1: Length of the first string s1.
• l2: Length of the second string s2.
• d(): A 2D array that stores the distances between substrings during computation.
• min1 & min2: Variables used to find the minimum cost of edits.

Initialization

• The distance array d is resized to dimensions (l1 + 1) x (l2 + 1) to accommodate all character comparisons.
• The first column is initialized to represent deletion costs, with d(i, 0) = i.
• The first row is initialized to represent insertion costs, with d(0, j) = j.

Main Logic

• Iterates through each character in both strings using nested loops. Comparisons are made between characters at positions i and j.
• If characters match, the distance value is carried from the diagonal cell d(i - 1, j - 1).
• If characters do not match, the function calculates the minimum edit distance considering:
  • Deletion from s1 (d(i - 1, j) + 1)
  • Insertion into s1 (d(i, j - 1) + 1)
  • Substitution of a character (d(i - 1, j - 1) + 1)
• The smallest value among these is assigned to d(i, j).

Return Value

• The final Levenshtein distance is returned as d(l1, l2), representing the total minimum edits needed to transform s1 into s2.

Levenshtein Distance Function Overview

• Calculates the Levenshtein distance between two strings s1 and s2, representing the minimum number of single-character edits (insertions, deletions, substitutions) required to change one string into the other.
• Useful in areas like spell checking, DNA sequencing, and natural language processing.

Function Parameters

• s1: The first string to compare.
• s2: The second string to compare.

Variable Definitions

• l1: Length of the first string s1.
• l2: Length of the second string s2.
• d(): A 2D array that stores the distances between substrings during computation.
• min1 & min2: Variables used to find the minimum cost of edits.

Initialization

• The distance array d is resized to dimensions (l1 + 1) x (l2 + 1) to accommodate all character comparisons.
• The first column is initialized to represent deletion costs, with d(i, 0) = i.
• The first row is initialized to represent insertion costs, with d(0, j) = j.

Main Logic

• Iterates through each character in both strings using nested loops. Comparisons are made between characters at positions i and j.
• If characters match, the distance value is carried from the diagonal cell d(i - 1, j - 1).
• If characters do not match, the function calculates the minimum edit distance considering:
  • Deletion from s1 (d(i - 1, j) + 1)
  • Insertion into s1 (d(i, j - 1) + 1)
  • Substitution of a character (d(i - 1, j - 1) + 1)
• The smallest value among these is assigned to d(i, j).

Return Value

• The final Levenshtein distance is returned as d(l1, l2), representing the total minimum edits needed to transform s1 into s2.

This quiz focuses on the Levenshtein Distance function implemented in VBA. It covers the algorithm's logic for calculating the minimum edit distance between two strings. Test your understanding of this important string similarity measure with practical coding questions.