Hyperdimensional computing for machine learning
pip install pyhd==0.1
Hyperdimensionality computing library for machine learning. This package aims to contain everything needed for training HD models with different settings using various datasets; there are some included in the package.
In order to test the code, please make sure you have python 3 installed and the python libraries required:
python3 -m pip install xlsxwriter
python3 -m pip install sklearn
python3 -m pip install numpy
python3 -m pip install tqdm
python3 -m pip install multiprocess
If you plan on contribute to the project and you are confused, please read the documentation under ./doc. It describes the code layout and relationships between files and directories. It also contains several examples on how to use the package.
If you only plan to test the existing code, make sure you have the following directories created in the project source:
mkdir ./encoded/
mkdir ./models/
mkdir ./out/
mkdir ./out/ssl/
mkdir ./out/sup/
mkdir ./out/recovered/
To run, depending what is the script you want to run, just do:
python3 src/hd-lib/supervised.py path/to/dataset/dataset_name
python3 src/hd-lib/semi_supervised.py path/to/dataset/
Package started by Alejandro Hernández Cano. If you are interested in expanding the package but are getting stuck trying to figuring out the code, please feel free to email me any question at ale.hdz333@ciencias.unam.mx