Skip to main content

A library for crowdflow prediction!

Project description

Crowd framework paper code

Paper name TBD

Installation

git clone https://github.com/vivian-wong/crowd-framework
cd crowd-framework
# create conda virtual environment
conda create --name crowd-framework python=3.10 
conda activate crowd-framework
# install prerequisites
pip install -r requirements.txt
# install pytorch geometric and pytorch geometric temporal
python install_pyg.py

Usage

Examples

Check the examples/ directory for simplified demo notebooks.

Reproducing paper experiments

To run all experiments as detailed in the paper, run

bash reproduce_paper_experiments.sh

and generate plots with the jupyter notebook experiments/plot_results.ipynb

Contributing

Contributions are welcome! Please read the CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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

st_dif-0.1.1.tar.gz (11.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

st_dif-0.1.1-py3-none-any.whl (11.1 kB view details)

Uploaded Python 3

File details

Details for the file st_dif-0.1.1.tar.gz.

File metadata

  • Download URL: st_dif-0.1.1.tar.gz
  • Upload date:
  • Size: 11.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.0

File hashes

Hashes for st_dif-0.1.1.tar.gz
Algorithm Hash digest
SHA256 ac031fbe760d099a58f3b641073664d28b86be1acd3bd61ba079d9637c679ea2
MD5 69ba268abaa60d957c66aa956f9476b2
BLAKE2b-256 7d289b74e5caa1e29a6a1cef51c46ece9dabc9e6569efbeb49a4e4e45372194a

See more details on using hashes here.

File details

Details for the file st_dif-0.1.1-py3-none-any.whl.

File metadata

  • Download URL: st_dif-0.1.1-py3-none-any.whl
  • Upload date:
  • Size: 11.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.0

File hashes

Hashes for st_dif-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 1f819181e18bfa121547ab69b9ff6af4cd4a766c727df8db328886b76a70e967
MD5 6ab88dab47c7e208de53be88b2f0ff94
BLAKE2b-256 96114f960ce58a33f1059b7d1659a5b119e06af683610782f3979ea04b38a4dd

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page