skapex 0.2.1

Leveraged Affinity Propagation implementation

Features

• Implements Leveraged Affinity Propagation (LAP).

• Maintains compatibility with scikit-learn, enabling seamless integration with existing scikit-learn workflows.

Installation

You can install skapex via pip:

pip install skapex

Usage

Here’s a basic example of how to use skapex LAP for clustering:

from skapex import LeveragedAffinityPropagation
from sklearn.datasets import make_blobs
from sklearn.metrics.pairwise import euclidean_distances

# Generate sample data
X, _ = make_blobs(n_samples=200, centers=3, random_state=0)

# Create and fit the skapex Leveraged Affinity Propagation
model = LeveragedAffinityPropagation(simf=lambda x,y : -euclidean_distances(x, y, squared=True), fraction=0.1, sweeps=5)
model.fit(X)

# Get cluster labels for the input data
labels = model.labels_

# Print the resulting cluster labels
print(labels)

References

B. J. Frey and D. Dueck, “Clustering by Passing Messages Between Data Points,” Science, vol. 315, no. 5814, pp. 972–976, Feb. 16, 2007, issn: 0036- 8075, 1095-9203. doi: 10.1126/science.1136800.

U. Bodenhofer, A. Kothmeier, and S. Hochreiter, “APCluster: An R package for affinity propagation clustering,” Bioinformatics, vol. 27, no. 17, pp. 2463– 2464, Sep. 1, 2011, issn: 1367-4803. doi: 10.1093/bioinformatics/btr406.