# <img src="logo.png" alt="logo" width="50"/>  BayesNet

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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

### Getting the code

```bash
git clone --recurse-submodules https://github.com/doctorado-ml/bayesnet
```

### Release

```bash
make release
make buildr
sudo make install
```

### Debug & Tests

```bash
make debug
make test
```

### Coverage

```bash
make coverage
make viewcoverage
```

### 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

#### - TAN

#### - KDB

#### - SPODE

#### - SPnDE

#### - AODE

#### - [BoostAODE](docs/BoostAODE.md)

#### - BoostA2DE

### With Local Discretization

#### - TANLd

#### - KDBLd

#### - SPODELd

#### - AODELd

## Documentation

### [Manual](https://rmontanana.github.io/bayesnet/)

### [Coverage report](https://rmontanana.github.io/bayesnet/coverage/index.html)

## Diagrams

### UML Class Diagram

![BayesNet UML Class Diagram](diagrams/BayesNet.svg)

### Dependency Diagram

![BayesNet Dependency Diagram](diagrams/dependency.svg)