information-bottleneck 1.1.0

Optimal compression-prediction tradeoffs for principled feature extraction

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Information Bottleneck

🌟 Optimal compression-prediction tradeoffs for principled feature extraction

Tishby, N., Pereira, F. C., & Bialek, W. (1999) - "The Information Bottleneck Method"

📦 Installation

pip install information-bottleneck

🚀 Quick Start

import information_bottleneck
import numpy as np

# Create sample data
X = np.random.randn(1000, 20)  # Input data
Y = np.random.randint(0, 3, 1000)  # Target labels

# Create Information Bottleneck
ib = information_bottleneck.create_information_bottleneck(
    method='discrete',
    beta=1.0
)

# Fit the model
ib.fit(X, Y)

# Get compressed representations
compressed = ib.transform(X)
print(f"✅ Compressed {X.shape} → {compressed.shape}")

🎓 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:

Tishby, N., Pereira, F. C., & Bialek, W. (1999) - "The Information Bottleneck Method"

📜 License

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

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💰 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! 🙏