Basic Estimator Usage

Create a CoreSGClusterer instance, fit it for a target k, and read the current HDBSCAN-style outputs.

from core_sg import CoreSGClusterer

clusterer = CoreSGClusterer(k_max=30, metric="euclidean", p=2)
clusterer.fit(X, k=15)

labels = clusterer.labels_
probabilities = clusterer.probabilities_

The first fit(...) call builds the internal reusable graph support. Later fit(X, k=...) calls reuse that support and update the current outputs in place.

The value passed to k must satisfy 2 <= k <= k_max.