Lodestar.Cluster 0.1.0. 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.

KMeansOptions

Where KMeans.Fit starts, and when it stops.

public sealed record KMeansOptions

Properties — MaxIterations caps the Lloyd loop (max_iter, default 300). Tolerance is the convergence threshold before scaling (tol, default 1e-4). Seed drives this package's own generator when no centres are given. InitialCentres is the starting block itself, row-major and clusterCount × featureCount, or null to choose one.

Example — the same data, started two ways.

using Lodestar.Cluster;

double[] samples = [0.0, 0.0, 0.0, 1.0, 10.0, 10.0, 10.0, 11.0, 5.0, 5.0];

// Given centres: reproducible anywhere, and comparable against Python.
KMeans given = KMeans.Fit(samples, featureCount: 2, clusterCount: 3,
    new KMeansOptions { InitialCentres = [0.0, 0.0, 10.0, 10.0, 5.0, 5.0] });

// Drawn centres: reproducible run to run here, and nowhere else.
KMeans drawn = KMeans.Fit(samples, featureCount: 2, clusterCount: 3,
    new KMeansOptions { Seed = 11 });

double reachedTheSamePartition = drawn.Inertia - given.Inertia;  // => 0

Remarks — Seed and InitialCentres answer different questions, and only one of them travels. A seed reproduces a run of Lodestar: the two libraries draw from different generators, so a shared seed shares nothing. InitialCentres is what the oracle corpus passes, and what a caller comparing against Python must pass too.

Tolerance is multiplied by the mean feature variance before use, so it is scale-free; 0 removes the shift test and iterates until the labels settle.

Applies to — net10.0, netstandard2.0.

See also — KMeans.Fit, KMeans, decisions/0072.