# BayesNet ![C++](https://img.shields.io/badge/c++-%2300599C.svg?style=flat&logo=c%2B%2B&logoColor=white) [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)]() ![Gitea Release](https://img.shields.io/gitea/v/release/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es:3000) [![Codacy Badge](https://app.codacy.com/project/badge/Grade/cf3e0ac71d764650b1bf4d8d00d303b1)](https://app.codacy.com/gh/Doctorado-ML/BayesNet/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade) ![Gitea Last Commit](https://img.shields.io/gitea/last-commit/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es:3000&logo=gitea) ![Static Badge](https://img.shields.io/badge/Coverage-97,2%25-green) Bayesian Network Classifiers using libtorch from scratch ## Dependencies The only external dependency is [libtorch](https://pytorch.org/cppdocs/installing.html) which can be installed with the following commands: ```bash wget https://download.pytorch.org/libtorch/nightly/cpu/libtorch-shared-with-deps-latest.zip unzip libtorch-shared-with-deps-latest.zips ``` ## Setup ### Release ```bash make release make buildr sudo make install ``` ### Debug & Tests ```bash make debug make test make coverage ``` ### Sample app After building and installing the release version, you can run the sample app with the following commands: ```bash make sample make sample fname=tests/data/glass.arff ``` ## Models ### [BoostAODE](docs/BoostAODE.md)