Lodestar.Text 0.6.0. This page is frozen at that release. Read the current documentation for what main says now. A link to a decision or a migration page follows main, and leaves the archive.

Counts the positions at which the two differ, then adds the difference in their lengths.

public static int Distance(ReadOnlySpan<char> a, ReadOnlySpan<char> b, TextElement element = TextElement.Utf16Unit)
public static int Distance<T>(ReadOnlySpan<T> a, ReadOnlySpan<T> b) where T : IEquatable<T>

Parameters — a and b are the two strings to compare, and they need not be the same length. element says what counts as one position: TextElement.Utf16Unit by default, or TextElement.CodePoint to count an emoji once instead of twice. The second overload compares any two spans of an IEquatable<T>.

Returns — int, never negative, and 0 only when the two are equal.

Example — nothing can differ position by position here, so only the two missing characters count.

using Lodestar.Text.Distances;

int d = Hamming.Distance("a", "abc");   // => 2

Remarks — this is the right measure for things that are aligned by construction: fixed-width identifiers, ISBNs, hashes, DNA reads, two readings of the same fixed-length field. It is also by far the cheapest thing on this page, a single pass with no matrix behind it.

It is the wrong measure the moment anything can shift. Inserting one character at the front of a string makes every later position disagree, so Hamming.Distance("abcdef", "xabcdef") is 7 where Levenshtein says 1. If insertions are possible at all, you want Levenshtein or Indel.

The textbook definition is undefined for inputs of different lengths; this one is not — it charges the length difference and carries on, so a wrong-length input returns a number instead of throwing, and a length bug will read as a large distance rather than as an error. Against combining marks and mixed scripts the result also deliberately differs from jellyfish.hamming_distance, which diverges from the standard definition there; the measurements are in decision 0005.

Applies to — net10.0, netstandard2.0.

See also — Hamming.NormalizedSimilarity, Levenshtein.Distance, Indel.Distance, the Python equivalence table.