kmeanssa-ng 0.2.1

K-means clustering on quantum graphs and metric spaces using simulated annealing

kmeanssa-ng

K-means clustering on quantum graphs and metric spaces using simulated annealing.

kmeanssa-ng provides tools for clustering data points that exist on complex network structures (quantum graphs) or other metric spaces where standard Euclidean distance does not apply. It uses a simulated annealing approach for robust convergence.

Installation

Install the latest version directly from GitLab:

pip install git+https://plmlab.math.cnrs.fr/nicolas.klutchnikoff/kmeanssa-ng.git

Quickstart

Here is a minimal example of clustering points on a quantum graph:

from kmeanssa_ng import generate_sbm, QGSimulatedAnnealing, SimulatedAnnealing

# Generate a graph with two distinct communities
graph = generate_sbm(
    sizes=[40, 40],       # Two communities of 40 nodes each
    p=[[0.8, 0.1],        # High intra-community connectivity  
       [0.1, 0.8]],       # Low inter-community connectivity
)

# Essential: precompute shortest paths
graph.precomputing()

# Sample points uniformly across the graph
points = graph.sample_points(150)

# Run quantum graph specialized simulated annealing
qg_sa = QGSimulatedAnnealing(
    observations=points,
    k=2,                  # We know there are 2 clusters
    lambda_param=1.0,     # Standard temperature
    beta=1.0,            # Standard drift strength
    step_size=0.1        # Standard step size
)

# Get cluster centers as node IDs (more interpretable)
node_centers = qg_sa.run_for_kmeans(robust_prop=0.1)
print(f"Cluster centers near nodes: {node_centers}")

Documentation

The full documentation, including API reference and tutorials, is under construction and will be available soon.

License

This project is licensed under the MIT License. See the LICENSE file for details.