Preprocessing knnimputer transform

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KnnImputer.Transform

Fills every missing value from its nearest donors among the fitted rows.

public double[] Transform(ReadOnlySpan<double> samples)

Parameters — samples is the matrix to fill, row-major, FeatureCount values per row, where a NaN is a missing value.

Returns — a new matrix, OutputFeatureCount values per row; the input is never written to.

Exceptions — ArgumentException when samples holds a partial row or an infinity. ArgumentOutOfRangeException when the number of distances needed exceeds the ceiling below.

Example — the same two gaps, weighted two ways.

using Lodestar.Preprocessing;

double[] rows =
[
    1.0, 2.0, double.NaN,
    3.0, 4.0, 3.0,
    double.NaN, 6.0, 5.0,
    8.0, 8.0, 7.0,
];

KnnImputer uniform = KnnImputer.Fit(
    rows, 3, new KnnImputerOptions { NeighbourCount = 2 });

KnnImputer weighted = KnnImputer.Fit(
    rows, 3, new KnnImputerOptions { NeighbourCount = 2, Weights = NeighbourWeights.Distance });

double byMean = uniform.Transform(rows)[2];     // => 4
double byDistance = Math.Round(weighted.Transform(rows)[2], 4);   // => 3.6667

Remarks — the call is refused past 100 million distance terms. The cost is rows × fitted rows × features, and on a Ryzen 7 8700G a hundred million terms over one feature take about 0.35 s and the same count over ten features about 1.6 s (measured 2026-09-23). Past that the call throws rather than running for however long it would take — the same stance MannWhitney takes on its exact table. Split the receiving rows into batches to go further; the fitted donors are the expensive half and they are shared.

A donor is a fitted row that has the missing feature, not simply a near row. The nearest rows are ranked by nan_euclidean distance, and the first NeighbourCount of them that actually carry the feature being filled are the ones averaged — so a gap in a rare feature can draw on donors further away than the nominal neighbours.

A value with no donor at all falls back to the feature's mean over the fitted rows, which is the reference's behaviour. A feature with no value anywhere in the fitted matrix was already dropped at Fit.

Applies to — net10.0, netstandard2.0.

See also — KnnImputer.Fit, NeighbourWeights, KnnImputer.