First Example With CoreSGClusterer

The recommended user-facing API is CoreSGClusterer. It follows a scikit-learn-style workflow:

1. configure reusable capacity with k_max in the constructor;

2. call fit(X, k=...) for the current extraction.

from sklearn.datasets import make_blobs
from core_sg import CoreSGClusterer

X, _ = make_blobs(
    n_samples=1000,
    n_features=10,
    centers=5,
    random_state=42,
)

clusterer = CoreSGClusterer(k_max=15, metric="euclidean", p=2)
clusterer.fit(X, k=10)

labels = clusterer.labels_

What Each Step Does

CoreSGClusterer(k_max=15, metric="euclidean", p=2) configures the estimator and the reusable support graph capacity.

clusterer.fit(X, k=10) builds the internal reusable Core-SG object on the first call and exposes labels and hierarchy artifacts for k=10.

Later calls such as clusterer.fit(X, k=8) reuse clusterer.core_sg_ and update labels_, probabilities_, cluster_persistence_, and tree artifacts for the new k.