self-organizing-maps 1.1.0

Unsupervised topological learning preserving neighborhood relationships, modeling biological cortical organization

💰 Support This Research - Please Donate!

🙏 If this library helps your research or project, please consider donating to support continued development:

💳 DONATE VIA PAYPAL - CLICK HERE

---

Self-Organizing Maps

🗺️ Unsupervised learning and visualization

Kohonen, T. (1982) - "Self-organized formation of topologically correct feature maps"

📦 Installation

pip install self-organizing-maps

🚀 Quick Start

import self_organizing_maps
import numpy as np

# Create sample 2D data
data = np.random.randn(500, 3)

# Create SOM
som = self_organizing_maps.SelfOrganizingMap(
    map_size=(10, 10),
    input_dim=3,
    learning_rate=0.5
)

# Train the SOM
som.train(data, epochs=100)

# Find best matching unit for new data
test_point = np.random.randn(3)
winner = som.find_winner(test_point)
print(f"✅ Best matching unit: {winner}")

# Visualize with built-in tools
visualizer = self_organizing_maps.SOMVisualizer(som)
visualizer.plot_map()

🎓 About the Implementation

Implemented by Benedict Chen - bringing foundational AI research to modern Python.

📧 Contact: [e-mail]

📖 Citation

If you use this implementation in your research, please cite the original paper:

Kohonen, T. (1982) - "Self-organized formation of topologically correct feature maps"

📜 License

Custom Non-Commercial License with Donation Requirements - See LICENSE file for details.

---

💰 Support This Work - Donation Appreciated!

This implementation represents hundreds of hours of research and development. If you find it valuable, please consider donating:

💳 DONATE VIA PAYPAL - CLICK HERE

Your support helps maintain and expand these research implementations! 🙏