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1 - NormalizedDistance: 1 when the two are identical, 0 when nothing is shared.

public static double NormalizedSimilarity(ReadOnlySpan<char> a, ReadOnlySpan<char> b, TextElement element = TextElement.Utf16Unit)

Parameters — a and b are the two strings to compare; element says what counts as one character, exactly as it does for NormalizedDistance, which this is computed from.

Returns — double in [0, 1], larger meaning more alike.

Exceptions — none: the two inputs may be any length. Their shared prefix and suffix go first, as rapidfuzz strips them, and the rest keeps three rows of the table rather than all of it, so the memory grows with the length of b alone.

Example — the same pair as above, read the other way round.

using Lodestar.Text.Distances;

double s = DamerauLevenshtein.NormalizedSimilarity("MARTHA", "MARHTA");   // => 0.8333…

Remarks — use this rather than NormalizedDistance wherever a bigger number ought to mean a better match: ranking candidates, sorting descending, or handing a score to something that expects one. Nothing else about the two differs.

The trap is the empty case. NormalizedDistance("", "") is 0, so this returns 1 — two empty strings are reported as a perfect match. If an empty string means "this field was never filled in", that is exactly backwards, and the filtering has to happen before the call.

Applies to — net10.0, netstandard2.0.

See also — DamerauLevenshtein.NormalizedDistance, Osa.NormalizedSimilarity, Levenshtein.NormalizedSimilarity, the Python equivalence table.