Metadata-Version: 2.4
Name: tensor-product-binding
Version: 1.1.0
Summary: Structured knowledge representation in neural networks via tensor products
Project-URL: Funding, https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=WXQKYYKPHWXHS
Author-email: Benedict Chen <benedict@benedictchen.com>
Maintainer-email: Benedict Chen <benedict@benedictchen.com>
License: Custom Non-Commercial License with Donation Requirements
License-File: LICENSE
Requires-Python: >=3.9
Requires-Dist: matplotlib>=3.5.0
Requires-Dist: numpy>=1.21.0
Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: scipy>=1.7.0
Provides-Extra: test
Requires-Dist: pytest-cov>=4.0; extra == 'test'
Requires-Dist: pytest>=7.0; extra == 'test'
Description-Content-Type: text/markdown

# 💰 Support This Research - Please Donate!

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---

# Tensor Product Binding

🔗 Compositional neural representations

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

## 📦 Installation

```bash
pip install tensor-product-binding
```

## 🚀 Quick Start

```python
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: benedict@benedictchen.com

## 📖 Citation

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

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

## 📜 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](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=WXQKYYKPHWXHS)**

**Your support helps maintain and expand these research implementations! 🙏**