Smart Hyperdimensional Clustering algorithm: FebHD
Project description
hd-clustering
Authors: Alejandro Hernández Cano, Mohsen Imani.
Installation
In order to install the package, simply run the following:
pip install febhd_clustering
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 FebHD clustering model with it.
>>> import febhd_clustering
>>> dim = 10000
>>> n_samples = 1000
>>> features = 100
>>> clusters = 5
>>> x = torch.randn(n_samples, features) # dummy data
>>> model = febhd_clustering.FebHD(clusters, features, dim=dim)
>>> if torch.cuda.is_available():
... print('Training on GPU!')
... model = model.to('cuda')
... x = x.to('cuda')
...
Training on GPU!
>>> model.fit(x, epochs=10)
>>> ypred = model(x)
>>> ypred.size()
torch.Size([1000])
For more examples, see the examples/
directory.
Citation request
If you use hd-clustering, please cite the following papers:
-
Alejandro Hernández-Cano, Yeseong Kim, Mohsen Imani. "A Framework for Efficient and Binary Clustering in High-Dimensional Space". IEEE/ACM Design Automation and Test in Europe Conference (DATE), 2021.
-
Mohsen Imani, et al. "DUAL: Acceleration of Clustering Algorithms using Digital-based Processing In-Memory"r IEEE/ACM International Symposium on Microarchitecture (MICRO), 2020.
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