tensor-product-binding 1.1.0

Structured knowledge representation in neural networks via tensor products

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Tensor Product Binding

🔗 Compositional neural representations

Smolensky, P. (1990) - "Tensor product variable binding"

📦 Installation

pip install tensor-product-binding

🚀 Quick Start

import tensor_product_binding
import numpy as np

# Create tensor product binding system
binding = tensor_product_binding.TensorProductBinding(
    role_dim=50,
    filler_dim=50
)

# Create symbolic structures
sentence = binding.encode_structure({
    'subject': 'John',
    'verb': 'loves', 
    'object': 'Mary'
})

# Query the structure
subject = binding.query(sentence, 'subject')
print(f"✅ Subject: {binding.decode_filler(subject)}")

# Create neural binding network
neural_net = tensor_product_binding.create_neural_binding_network(
    role_dim=50,
    filler_dim=50,
    backend='numpy'
)

🎓 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:

Smolensky, P. (1990) - "Tensor product variable binding"

📜 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! 🙏