rustpam 0.2.1

High-performance PAM (k-medoids) clustering implemented in Rust with Python bindings

RustPAM

High-performance k-medoids (based on OneBatchPAM) clustering with a Rust core.

Installation

Install from PyPI:

pip install rustpam

For development installation:

# Clone the repository
git clone https://github.com/yourusername/rustpam.git
cd rustpam

# Install in development mode
pip install maturin
maturin develop --release

Quick Start

import numpy as np
from rustpam import OneBatchPAM

# Sample data
X = np.random.randn(1000, 10).astype(np.float32)

# Create and fit model
model = OneBatchPAM(
    n_medoids=3,
    distance="euclidean",
    max_iter=50,
    random_state=42,
    n_threads=2,
)
model.fit(X)

# Results
print("Medoid indices:", model.medoid_indices_)
print("Inertia:", model.inertia_)
print("Iterations:", model.n_iter_)

# Predict new samples
X_new = np.random.randn(20, X.shape[1]).astype(np.float32)
labels = model.predict(X_new)
print("Predicted labels:", labels)

License

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

Acknowledgments

• Rust 🦀 - Memory-safe systems programming language
• PyO3 🐍 - Rust bindings for Python
• Maturin 📦 - Build and publish Rust-based Python packages
• Rayon ⚡ - Data parallelism library for fearless concurrency
• ndarray 🔢 - N-dimensional arrays for numerical computing