Lodestar.Cluster 0.2.0. 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.

Home › Partitioning

Clusters a row-major sample matrix into a given number of clusters.

public static AgglomerativeClustering Fit(ReadOnlySpan<double> samples, int featureCount, int clusterCount, Linkage linkage = Linkage.Ward)

Parameters — samples is the sample matrix, row-major: featureCount values per row. featureCount is how many values each row carries. clusterCount is how many clusters to cut the tree into. linkage is how the distance between two clusters is measured; Ward by default, as in the reference.

Returns — a fitted AgglomerativeClustering.

Exceptions — ArgumentOutOfRangeException when featureCount or clusterCount is not positive, when clusterCount exceeds the sample count, or when linkage is not a defined value. ArgumentException when samples holds fewer than two rows, a partial one, or a NaN or infinite value.

Example — the same five points cut into three under complete linkage, where the labels show the reference's numbering.

using Lodestar.Cluster;

double[] samples = [0.0, 1.0, 5.0, 6.0, 20.0];

AgglomerativeClustering model = AgglomerativeClustering.Fit(
    samples, featureCount: 1, clusterCount: 3, Linkage.Complete);

// Not 0, 0, 1, 1, 2: a label is a position in the heap the cut walks, not an order of appearance.
int first = model.Labels[0];    // => 2
int middle = model.Labels[2];   // => 0
int far = model.Labels[4];      // => 1

// Complete linkage's last merge is the largest distance between the two final clusters.
double top = model.Distances[3];   // => 20

Remarks — one sample is refused, as the reference refuses it: there is no merge to make and no tree to cut.

Applies to — net10.0, netstandard2.0.

See also — AgglomerativeClustering, AgglomerativeClustering.FitToThreshold, Linkage.