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This tells us the number of edits needed to turn one string into another. With Levenshtein distance, we measure similarity and match approximate strings with fuzzy logic.

**Info:** Returns the number of character edits (removals, inserts, replacements) that must occur to get from string A to string B.

Levenshtein distance computationsWords:ant, auntLevenshtein distance:1 Note: Only 1 edit is needed. The 'u' must be added at index 2.Words:Samantha, SamLevenshtein distance:5 Note: The final 5 letters must be removed.Words:Flomax, VolmaxLevenshtein distance:3 Note: The first 3 letters must be changed Drug names are commonly confused.

**Example.** First, credit at the conceptual level goes to Vladimir Levenshtein, a Russian scientist. This code uses a two-dimensional array instead of a jagged array because the space required will only have one width and one height.

**Tip:** The two-dimensional array requires fewer allocations upon the managed heap and may be faster in this context.

C# program that implements the algorithmusing System;/// <summary> /// Contains approximate string matching /// </summary>static class LevenshteinDistance {/// <summary> /// Compute the distance between two strings. /// </summary>public static int Compute(string s, string t) { int n = s.Length; int m = t.Length; int[,] d = new int[n + 1, m + 1];// Step 1if (n == 0) { return m; } if (m == 0) { return n; }// Step 2for (int i = 0; i <= n; d[i, 0] = i++) { } for (int j = 0; j <= m; d[0, j] = j++) { }// Step 3for (int i = 1; i <= n; i++) {//Step 4for (int j = 1; j <= m; j++) {// Step 5int cost = (t[j - 1] == s[i - 1]) ? 0 : 1;// Step 6d[i, j] = Math.Min( Math.Min(d[i - 1, j] + 1, d[i, j - 1] + 1), d[i - 1, j - 1] + cost); } }// Step 7return d[n, m]; } } class Program { static void Main() { Console.WriteLine(LevenshteinDistance.Compute("aunt", "ant")); Console.WriteLine(LevenshteinDistance.Compute("Sam", "Samantha")); Console.WriteLine(LevenshteinDistance.Compute("flomax", "volmax")); } }Output1 5 3

**The Levenshtein method is static**—this Compute method doesn't need to store state or instance data, which means you can declare it as static. This can also improve performance, avoiding callvirt instructions.

**Tip:** You can verify the implementation is the standard version of Levenshtein by looking at a computer science textbook.

**This algorithm is stateless,** which means it doesn't store instance data. It therefore can be put in a static class. Static classes are easier to add to new projects than separate methods.

**Example 2.** Continuing on, we call the method. You will often want to compare multiple strings with the Levenshtein algorithm. The example here shows how to compare strings in a loop. We use a List of string arrays.

C# program that calls Levenshtein in loopstatic void Main() { List<string[]> l = new List<string[]> { new string[]{"ant", "aunt"}, new string[]{"Sam", "Samantha"}, new string[]{"clozapine", "olanzapine"}, new string[]{"flomax", "volmax"}, new string[]{"toradol", "tramadol"}, new string[]{"kitten", "sitting"} }; foreach (string[] a in l) { int cost = Compute(a[0], a[1]); Console.WriteLine("{0} -> {1} = {2}", a[0], a[1], cost); } }Outputant -> aunt = 1 Sam -> Samantha = 5 clozapine -> olanzapine = 3 flomax -> volmax = 3 toradol -> tramadol = 3 kitten -> sitting = 3

**Summary.** With the Levenshtein distance algorithm, we implement approximate string matching. The difference between two strings is not represented as true or false, but as the number of steps needed to get from one to the other.