holographic-memory 1.1.0

Compositional memory with fixed-size vectors via circular convolution (Holographic Reduced Representations)

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Holographic Memory

🌀 Holographic Reduced Representations - Vector symbolic architecture

Plate, T. A. (1995) - "Holographic Reduced Representations"

📦 Installation

pip install holographic-memory

🚀 Quick Start

import holographic_memory
import numpy as np

# Create holographic memory
memory = holographic_memory.create_holographic_memory(
    vector_size=512,
    num_items=1000
)

# Store associations
memory.bind("cat", "animal")
memory.bind("dog", "animal") 
memory.bind("car", "vehicle")

# Retrieve and test
result = memory.probe("cat")
print(f"✅ 'cat' associated with: {result}")

# Clean up noisy retrieval
cleanup = holographic_memory.AssociativeCleanup(memory.get_vocabulary())
cleaned = cleanup.cleanup(result)
print(f"✅ Cleaned result: {cleaned}")

🎓 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:

Plate, T. A. (1995) - "Holographic Reduced Representations"

📜 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! 🙏