Cluster Lodestar.Cluster - CyrilB1531/lodestar GitHub Wiki

Development build. This page describes main, not a released package. The latest published Lodestar.Cluster is 0.2.0 — read its documentation.

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Lodestar.Cluster

Clustering over a row-major span: k-means by Lloyd's algorithm, with the initial centres accepted as an input so a run is reproducible against scikit-learn's own start, plus DBSCAN and agglomerative clustering. Labels, centres and inertia are the ones scikit-learn returns.

Install

dotnet add package Lodestar.Cluster

Example

using Lodestar.Cluster;

// Five points of two features, row-major.
double[] samples = [0.0, 0.0, 0.0, 1.0, 10.0, 10.0, 10.0, 11.0, 5.0, 5.0];

KMeans model = KMeans.Fit(samples, featureCount: 2, clusterCount: 3,
    new KMeansOptions { InitialCentres = [0.0, 0.0, 10.0, 10.0, -500.0, -500.0] });

int middle = model.Labels[4];   // 2

Parity

Replayed against sklearn.cluster. docs/equivalence.md maps each Python call to its C# counterpart, with every deliberate divergence.

Dependencies

A core package (decision 0003), built for net10.0 and netstandard2.0:

  • Lodestar.Abstractions 0.2.1 or later

Documentation