hdlib

Hyperdimensional Computing Library for building Vector Symbolic Architectures in Python 3.

Vector-Symbolic Architectures (VSA, a.k.a. Hyperdimensional Computing) is an emergent computing paradigm that works by combining vectors in a high-dimensional space for representing and processing information. This approach recently shown promise in various domains for dealing with different kind of computational problems, including artificial intelligence, cognitive science, robotics, natural language processing, bioinformatics, medical informatics, cheminformatics, and internet of things among other scientific disciplines.

Here we present hdlib, a Python library for designing Vector-Symbolic Architectures. It is distributed under the MIT license as a Python package through PyPI and Conda on the conda-forge channel.

GitHub releases are also available on Zenodo at https://doi.org/10.5281/zenodo.7996502.

Please refer to the official Wiki for any information about the implemented modules and how to use the library.

Here is the table of content:

• Getting started
  • Installing hdlib
  • Dependencies
• Vector-Symbolic Architectures
  • Hyperdimensional Vectors and Space
  • Arithmetic operations
• Examples
  • What is the Dollar of Mexico?
  • Supervised Machine Learning Model
  • Stepwise Feature Selection
• Support and contributions

Credits

Please credit our work in your manuscript by citing:

Cumbo et al., (2023). hdlib: A Python library for designing Vector-Symbolic Architectures. Journal of Open Source Software, 8(89), 5704, https://doi.org/10.21105/joss.05704

Support and contributions

Long-term discussion and bug reports are maintained via GitHub Issues, while code review is managed via GitHub Pull Requests.

Please, (i) be sure that there are no existing issues/PR concerning the same bug or improvement before opening a new issue/PR; (ii) write a clear and concise description of what the bug/PR is about; (iii) specifying the list of steps to reproduce the behavior in addition to versions and other technical details is highly recommended.

For additional information about how to contribute, please visit the CONTRIBUTING section.

Copyright © 2022 Fabio Cumbo. See LICENSE for additional details.