universal-learning 1.1.0

Theoretically optimal learning via algorithmic information theory and Solomonoff induction

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Universal Learning

🧠 AIXI theoretical framework

Hutter, M. (2005) - "Universal Artificial Intelligence"

📦 Installation

pip install universal-learning

🚀 Quick Start

import universal_learning
import numpy as np

# Create universal learner
learner = universal_learning.UniversalLearner(
    alphabet_size=2,
    max_program_length=100
)

# Simple binary sequence learning
sequence = [0, 1, 0, 1, 0, 1]  # Alternating pattern

# Learn from sequence
learner.observe_sequence(sequence)

# Predict next symbols
prediction = learner.predict_next(sequence[-3:])
print(f"✅ Predicted next symbol: {prediction.symbol}")
print(f"✅ Confidence: {prediction.probability:.4f}")

# Use Solomonoff induction directly
inductor = universal_learning.SolomonoffInductor()
inductor.update(sequence)
next_prob = inductor.predict_next()
print(f"✅ Solomonoff prediction probabilities: {next_prob}")

🎓 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:

Hutter, M. (2005) - "Universal Artificial Intelligence"

📜 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! 🙏