InfoTorch

Advanced statistical modeling, analyses, and tests in PyTorch.

With easy hardware acceleration on GPU and TPU.

Installing

• Clone this repository wherever you want

• Create a vitual environment and active it (if you're radical like me and addicted to danger you can skip this step)

cd /path/to/this/repository
python3 -m venv venv
source venv/bin/activate

• Install the requirements

pip3 install -r requirements.txt

• That's it!

Using

Functions:

Calculations include:

• skewness
• kurtosis
• bimodality index
• kernel density estimate
• MLE Fit (fit a model by maximum likelihood estimation)
• ECDF (empirical cumulative distribution function)
• Metalog Fit (Closed-form)
• Polynomial Fit (Closed-form, with or without weights)
• Mutual Information

Classes:

• Normal Model (for example)
• Unbounded Metalog Model

See our Documentation for details.

ToDo:

• Mutual information estimation.
• Interaction information estimation.

Contributing

For contributors to the project; do this before making your first commit:

• Install pre-commit

cd /path/to/this/repository/
sudo apt install pre-commit
pre-commit install

(we do all of our development on linux for now)

• Make sure to update the documentation to include your changes before commiting:

pdoc --html infotorch.py --force

• Also Make sure to include any new dependencies in the requirements.txt before commiting with:

pip3 freeze > requirements.txt

• To test updates to the readme and other GitHub flavored markdown, simply install Grip and feed it your desired file.

pip3 install grip
python3 -m grip README.md

• Then follow the link provided by the Grip sever for a live preview of your work.

• When satisfied with your changes you can compile to an html file with:

python3 -m grip README.md --export README.html

Authors

• Gabe M. LaFond - Initial work - ExamDay

See also the list of contributors who participated in this project.

License

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