Kongming HV

Kongming is a hyperdimensional computing library implementing sparse binary hypervectors for cognitive computing applications.

The core engine is implemented in Rust for maximum efficiency, while ergonomic APIs are open-sourced in Python for better usability.

See Hypervectors for an introduction to hyperdimensional computing and the sparse binary representation.

License

The Python source code, examples, and documentation in this repository are licensed under the MIT License.

The compiled engine distributed via PyPI (kongming-rs-hv) is proprietary.

Install

pip install kongming-rs-hv

See Installation for supported platforms and verification steps.

Published notebooks

See Notebook Platforms for all available notebooks and platform details.

Guides

Language Support

This documentation covers code snippets in multiple languages (if available) side by side.

• Python: bindings to the underlying Rust implementation (public kongming-rs-hv on PyPI);
• Go: canonical / reference implementation in proprietary package;
• Rust: parallel implementation, carefully maintained in feature parity;

Docs versioning

The documentation on yangzh.github.io/hv is deployed from release tags (v*) and stays in lockstep with the latest kongming-rs-hv release on PyPI. Whatever you read there matches what pip install kongming-rs-hv gives you.

The main branch of this repository is the working head — it may describe APIs or examples that haven’t been released yet. If you browse the raw markdown on GitHub, expect it to occasionally be ahead of the published site.

Reference

The work was initially outlined in this arxiv paper, built on top of the work from many others, and here is the citation:

Yang, Zhonghao (2023). Cognitive modeling and learning with sparse binary hypervectors. arXiv:2310.18316v1 [cs.AI]

Feedback

Found a bug, have a question, or want to suggest an improvement? Open an issue on GitHub.