vsapy 0.10.0

Vector Symbolic Architecture(VSA) library that allows building VSA apps that use various flavours of VSA vectors.

vsapy - Vector Symbolic Architecture (VSA) library

⚠️ Version 0.10+ Update — Breaking Changes

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Python Version Requirement (Breaking Change)

As of this release, vsapy now requires Python >=3.11 and <3.13.

python = ">=3.11,<3.13"

This restriction allows us to:

• Standardise development across Poetry-managed environments
• Leverage modern typing and performance improvements
• Remove legacy compatibility layers
• Align CI/CD and local development setups

If you are currently using Python 3.9 or 3.10, you will need to upgrade.

Recommended setup:

pyenv install 3.11.x
pyenv local 3.11.x
poetry env use 3.11
poetry install

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NumberLine Refactor & Extensions

This release significantly refactors and extends the NumberLine implementations.

Linear NumberLine Improvements

• Cleaner behaviour across extended ranges
• Improved quantisation handling
• More consistent HD similarity gradients
• Clearer diagnostic plotting support
• Better geometric interpretation of hyperdimensional distance

New Circular Variants

The following circular representations are now included:

• CircularNumberLineFolded
• CircularNumberLineRotational

These allow:

• True rotational similarity (distance depends only on angular difference)
• Periodic encodings
• Geometry-aware embedding behaviour
• Exploration of circular manifolds in hyperdimensional space

These representations are particularly useful for:

• Periodic signals
• Angular encodings
• Cyclic variables (e.g., time-of-day, phase)
• Spatial or geometric modelling
• Investigating rotational invariance in VSA

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Additional & Updated Demos

The demo suite has been expanded and cleaned up:

• Improved hierarchical document demos
• Expanded JSON bundling examples
• NumberLine comparison and diagnostic plots
• Clearer exploration of embedding geometry

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Library Overview

This library implements the common methods used in hyperdimensional computing / Vector Symbolic Architectures. Namely:

• bind
• unbind
• Bundling operations:
  • bag
  • ShiftedBag
  • NamedBag
• Hierarchical bundling method:
  • CSPvec

The main objective of the library is to enable a single API to cater for various flavours of VSA, including:

• Binary Spatter Codes
• Ternary
• TernaryZero
• Laiho
• LaihoX (a simplified Laiho that is faster and supports incremental bundling without catastrophic forgetting)
• HRR

A set of demo test cases are supplied to demonstrate the use of the library calls.

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Installation

Install from PyPI

poetry add vsapy

or

pip install vsapy

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Installing from Source

Clone the repository:

git clone <repo-url>
cd vsapy

Installing Dependencies (Poetry Recommended)

poetry install
poetry shell

Installing with pip

Create and activate a virtual environment:

conda create -n vsapy python=3.11
conda activate vsapy
pip install -r requirements.txt

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Usage

Valid values for VsaType are:

• VsaType.BSC
• VsaType.Laiho
• VsaType.LaihoX (fastest)
• VsaType.Tern
• VsaType.TernZero
• VsaType.HRR

Note: The demos listed below will not run with VsaType.HRR.

For examples of using the vsapy library, see the code examples in the ./tests directory.

There are currently no command-line arguments implemented for the tests.
To change the VSA type, edit the code and modify:

vsa_type = VsaType.BSC

All test cases can be run directly from the command line, for example:

python cspvec_sequence.py

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Demo Files

cspvec_sequence.py

The most straightforward demo.
Demonstrates building a sentence as a VSA sequence and stepping forward & backwards.

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build_docs.py

Combines large documents into a hierarchical VSA code book.

The top-level document vector is a high-level semantic representation of the entire document.

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load_docs.py

Compares document vectors built using build_docs.py at various levels in the document hierarchy.

Modify:

levels_to_extract = [0, 1]

Where:

• 0 = top-level document vectors
• 1 = Act-level vectors
• 2 = Scene-level vectors
• etc.

You may set to any level (e.g., [2] to compare Scene-level only).

Understanding output names:

• OE_ = Old English
• NE_ = New English
• nf_ = NoFear Shakespeare
• tk_ = NLTK Shakespeare
• og_ = Alternate Shakespeare
• ham = Hamlet
• mbeth = Macbeth

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json2vsa.py

Demonstrates creation of a VSA vector from an input JSON file and comparison of multiple JSON structures using VSA.