onlinehd 0.1.2

Efficient single-pass hyperdimensional classifier

onlinehd

Authors: Alejandro Hernández Cano, Mohsen Imani.

Installation

In order to install the package, simply run the following:

pip install onlinehd

Visit the PyPI project page for more information about releases.

Documentation

Read the documentation of this project.

Quick start

The following code generates dummy data and trains a OnlnineHD classification model with it.

>>> import onlinehd
>>> dim = 10000
>>> n_samples = 1000
>>> features = 100
>>> classes = 5
>>> x = torch.randn(n_samples, features) # dummy data
>>> y = torch.randint(0, classes, [n_samples]) # dummy data
>>> model = onlinehd.OnlineHD(classes, features, dim=dim)
>>> if torch.cuda.is_available():
...     print('Training on GPU!')
...     model = model.to('cuda')
...     x = x.to('cuda')
...     y = y.to('cuda')
...
Training on GPU!
>>> model.fit(x, y, epochs=10)
>>> ypred = model(x)
>>> ypred.size()
torch.Size([1000])

For more examples, see the example.py script. Be aware that this script needs pytorch, sklearn and numpy to run.

Citation Request

If you use onlinehd code, please cite the following paper:

1. Alejandro Hernández-Cano, Namiko Matsumoto, Eric Ping, Mohsen Imani "OnlineHD: Robust, Efficient, and Single-Pass Online Learning Using Hyperdimensional System", IEEE/ACM Design Automation and Test in Europe Conference (DATE), 2021.