Differentiable implementation of the JUNE model.
Project description
GradABM-JUNE
Implementation of the JUNE model using the GradABM framework.
Setup
Install requirements
pip install -r requirements.txt
and install PyTorch geometric, manually for now:
pip install torch-scatter torch-sparse torch-cluster torch-geometric -f https://data.pyg.org/whl/torch-1.13.0+cpu.html
Then install the GradABM-JUNE package
pip install -e .
Usage
See the docs.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
grad_june-0.1.2.tar.gz
(20.6 kB
view details)
File details
Details for the file grad_june-0.1.2.tar.gz
.
File metadata
- Download URL: grad_june-0.1.2.tar.gz
- Upload date:
- Size: 20.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 593a005aa7f89f984ed3fcf4ea9b5717fd8cc283471ea27ef1631fc7f4be598b |
|
MD5 | 1b1a33afa93b83ff2650ff349fa3eba5 |
|
BLAKE2b-256 | e7d1abe73760ebdc6529e74ee79ad64792ac446b53da62982264e587241c8736 |