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JAMME-Bandit

JAMME-Bandit is a web app built for testing thompson sampling multi-armed bandits.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

The app requires Python 3.X as well as Git and the Heroku CLI

Installing

After cloning the resposity you need to install the required python packages

pip install django
pip install gunicorn (If on Windows ignore this)
pip install django-heroku

Additional Packages:
pip install numpy

Detaield Instructions for how to deploy Heroku Python Apps (Free) such as this one can be found at this link. The free-tier can support deployment of this web app.

Usage

There are two custom commands as part of the Django manage.py command interface

python manage.py populatearms 
python manage.py resetarms
  • populatearms will take very .bmp file and create an Arm model and populate the database
  • resetarms will reset the cumulative statistics on the arms for new studies

Built With

Authors

  • Mustafa Haiderbhai
  • Allen Bao
  • Emmy Liu
  • Joe Hoang
  • Molly Sun

License

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

Acknowledgments

  • Big thanks to Dr. Joseph Williams for his inspiration and guidance from his course CSC2558H F

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Facial Feature Optimization Using Thompson Sampling Bandit Algorithm

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