https://github.com/yangzh/hv
Sparse binary hypervectors and associated learners.
https://github.com/yangzh/hv
cognitive-computing go golang hypervectors machine-learning python rust sparse-binary vector-symbolic-architecture
Last synced: about 1 month ago
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Sparse binary hypervectors and associated learners.
- Host: GitHub
- URL: https://github.com/yangzh/hv
- Owner: yangzh
- License: mit
- Created: 2023-09-15T17:39:58.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2026-04-30T20:53:29.000Z (about 1 month ago)
- Last Synced: 2026-04-30T21:17:48.239Z (about 1 month ago)
- Topics: cognitive-computing, go, golang, hypervectors, machine-learning, python, rust, sparse-binary, vector-symbolic-architecture
- Language: Jupyter Notebook
- Homepage: https://yangzh.github.io/hv/
- Size: 38.1 MB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Security: SECURITY.md
Awesome Lists containing this project
README
# hv
[](https://yangzh.github.io/hv/)
[](https://pypi.org/project/kongming-rs-hv/)
[](https://pypi.org/project/kongming-rs-hv/)
[](LICENSE)
Public release of sparse binary hypervectors and associated learners, powered by the Rust-backed `kongming-rs-hv` package.
## Installation
```bash
pip install kongming-rs-hv
```
Supports Linux, macOS, and Windows on Python 3.10–3.14.
## Documentation
Full documentation is available at **[yangzh.github.io/hv](https://yangzh.github.io/hv/)**, including:
- [Concepts](https://yangzh.github.io/hv/concepts/hypervectors.html) — hypervectors, models, composites, operators, constant-time near-neighbor search
- [API Reference](https://yangzh.github.io/hv/api/sparkle.html) — Python, Go, and Rust side by side
- [Python Quick Start](https://yangzh.github.io/hv/guides/python/quick-start.html) — installation, examples, and notebooks
- [Notebook Quick Start](https://yangzh.github.io/hv/guides/notebook/quick-start.html) — cell-by-cell Jupyter walkthrough
- [PDF download](https://yangzh.github.io/hv/kongming-hv.pdf)
> The published site is deployed from release tags (`v*`) and tracks the
> latest `kongming-rs-hv` release on PyPI. The `main` branch of this repo is
> the working head — it may describe APIs or examples that haven't been
> released yet.
## Try Online
| Platform | Link |
|----------|------|
| Colab — tutorial walkthrough | 
|
| Colab — hypervector storage | |
| Binder | 
|
## Applications
Runnable scripts under [`examples/`](examples/) including:
* [Mexican Dollar](https://yangzh.github.io/hv/examples/mexican_dollar/index.html): analogical reasoning;
* [Word Indexer](https://yangzh.github.io/hv/examples/word_indexer/index.html): suffix-queryable word indexing;
* [Bulk Storage](https://yangzh.github.io/hv/examples/bulk_storage/index.html): storage benchmarks;
* [Operators from Scratch](https://yangzh.github.io/hv/examples/operators/index.html): the math underneath the library;
* [LISP Interpreter](https://yangzh.github.io/hv/examples/pylisp/index.html): where every value is a hypervector.
See the [examples index](https://yangzh.github.io/hv/examples/index.html) for walkthroughs.
## Community
Questions, ideas, or feedback? Visit [GitHub Discussions](https://github.com/yangzh/hv/discussions) for announcements, Q&A, and general conversation. For bugs, please use [Issues](https://github.com/yangzh/hv/issues).
For private inquiries, use the [contact form](https://docs.google.com/forms/d/e/1FAIpQLSeAPEzqIgcf1CYJZJepN2jtNy1QTPKtpQYkdBBs1-rNeIuoGQ/viewform).
## References
> Yang, Zhonghao (2023). Cognitive modeling and learning with sparse binary hypervectors. [arXiv:2310.18316](https://arxiv.org/abs/2310.18316) [cs.AI]
## License
The Python source code, examples, and documentation in this repository are licensed under the [MIT License](LICENSE).
The underlying engine distributed via PyPI (`kongming-rs-hv`), however, is proprietary.