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.

Partitioning — Lodestar.Cluster

One algorithm, KMeans: it partitions samples into k clusters by Lloyd's algorithm, at sklearn.cluster.KMeans(algorithm="lloyd") parity.

Spans in, arrays out, row-major — the shape Lodestar.Metrics already uses. Labels feeds its silhouette, adjusted Rand, AMI and V-measure without being reshaped, which is the whole reason this package was cheap to make credible: the scoring half shipped first.

The starting centres are an input

k-means begins with a choice, and a choice drawn from a generator is not reproducible across two libraries. KMeansOptions.InitialCentres takes the centres themselves, and when they are given they replace the choice entirely — the move decisions/0072 made for Ω. That is what lets the oracle corpus compare every centre, label and inertia rather than comparing distributions.

Left alone, k-means++ chooses them with this package's own generator. Seed reproduces a run of Lodestar and never a run of scikit-learn, and nothing frozen depends on it.

One measured divergence

A sample exactly equidistant from two centres takes the lowest-indexed one here. decisions/0093 has the two configurations that send the reference's choice both ways, and why neither rule reproduces both.

Types

Type	What it is
KMeans	The fitted clustering: centres, labels, inertia, iterations.
KMeansOptions	Where the fit starts, and when it stops.

See also

• Clustering metrics — how to score what comes out.
• Python → C# equivalence.