170 lines
4.2 KiB
Markdown
170 lines
4.2 KiB
Markdown
# <img src="logo.png" alt="logo" width="50"/> BayesNet
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[](<https://opensource.org/licenses/MIT>)
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[](https://app.codacy.com/gh/Doctorado-ML/BayesNet/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
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[](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
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[](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
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[](https://gitea.rmontanana.es/rmontanana/BayesNet)
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[](https://doi.org/10.5281/zenodo.14210344)
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Bayesian Network Classifiers library
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## Setup
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### Using the vcpkg library
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You can use the library with the vcpkg library manager. In your project you have to add the following files:
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#### vcpkg.json
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```json
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{
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"name": "sample-project",
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"version-string": "0.1.0",
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"dependencies": [
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"bayesnet"
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]
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}
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```
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#### vcpkg-configuration.json
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```json
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{
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"registries": [
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{
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"kind": "git",
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"repository": "https://github.com/rmontanana/vcpkg-stash",
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"baseline": "393efa4e74e053b6f02c4ab03738c8fe796b28e5",
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"packages": [
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"folding",
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"bayesnet",
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"arff-files",
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"fimdlp",
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"libtorch-bin"
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]
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}
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],
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"default-registry": {
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"kind": "git",
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"repository": "https://github.com/microsoft/vcpkg",
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"baseline": "760bfd0c8d7c89ec640aec4df89418b7c2745605"
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}
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}
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```
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#### CMakeLists.txt
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You have to include the following lines in your `CMakeLists.txt` file:
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```cmake
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find_package(bayesnet CONFIG REQUIRED)
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add_executable(myapp main.cpp)
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target_link_libraries(myapp PRIVATE bayesnet::bayesnet)
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```
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After that, you can use the `vcpkg` command to install the dependencies:
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```bash
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vcpkg install
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```
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**Note: In the `sample` folder you can find a sample application that uses the library. You can use it as a reference to create your own application.**
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## Playing with the library
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The dependencies are managed with [vcpkg](https://vcpkg.io/) and supported by a private vcpkg repository in [https://github.com/rmontanana/vcpkg-stash](https://github.com/rmontanana/vcpkg-stash).
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### Getting the code
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```bash
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git clone https://github.com/doctorado-ml/bayesnet
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```
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Once you have the code, you can use the `make` command to build the project. The `Makefile` is used to manage the build process and it will automatically download and install the dependencies.
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### Release
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```bash
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make init # Install dependencies
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make release # Build the release version
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make buildr # compile and link the release version
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```
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### Debug & Tests
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```bash
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make init # Install dependencies
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make debug # Build the debug version
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make test # Run the tests
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```
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### Coverage
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```bash
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make coverage # Run the tests with coverage
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make viewcoverage # View the coverage report in the browser
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```
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### Sample app
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After building and installing the release version, you can run the sample app with the following commands:
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```bash
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make sample
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make sample fname=tests/data/glass.arff
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```
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## Models
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#### - TAN
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#### - KDB
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#### - SPODE
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#### - SPnDE
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#### - AODE
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#### - A2DE
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#### - [BoostAODE](docs/BoostAODE.md)
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#### - XBAODE
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#### - BoostA2DE
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#### - XBA2DE
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### With Local Discretization
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#### - TANLd
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#### - KDBLd
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#### - SPODELd
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#### - AODELd
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## Documentation
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### [Manual](https://rmontanana.github.io/bayesnet/)
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### [Coverage report](https://rmontanana.github.io/bayesnet/coverage/index.html)
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## Diagrams
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### UML Class Diagram
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### Dependency Diagram
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