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+# <img src="logo.png" alt="logo" width="50"/>  BayesNet
+
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+
+Bayesian Network Classifiers library
+
+## Setup
+
+### Using the vcpkg library
+
+You can use the library with the vcpkg library manager. In your project you have to add the following files:
+
+#### vcpkg.json
+
+```json
+{
+  "name": "sample-project",
+  "version-string": "0.1.0",
+  "dependencies": [
+    "bayesnet"
+  ]
+}
+```
+
+#### vcpkg-configuration.json
+
+```json
+{
+  "registries": [
+    {
+      "kind": "git",
+      "repository": "https://github.com/rmontanana/vcpkg-stash",
+      "baseline": "393efa4e74e053b6f02c4ab03738c8fe796b28e5",
+      "packages": [
+        "folding",
+        "bayesnet",
+        "arff-files",
+        "fimdlp",
+        "libtorch-bin"
+      ]
+    }
+  ],
+  "default-registry": {
+    "kind": "git",
+    "repository": "https://github.com/microsoft/vcpkg",
+    "baseline": "760bfd0c8d7c89ec640aec4df89418b7c2745605"
+  }
+}
+```
+
+#### CMakeLists.txt
+
+You have to include the following lines in your `CMakeLists.txt` file:
+
+```cmake
+find_package(bayesnet CONFIG REQUIRED)
+
+add_executable(myapp main.cpp)
+
+target_link_libraries(myapp PRIVATE bayesnet::bayesnet)
+```
+
+After that, you can use the `vcpkg` command to install the dependencies:
+
+```bash
+vcpkg install
+```
+
+**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.**
+
+## Playing with the library
+
+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).
+
+### Getting the code
+
+```bash
+git clone https://github.com/doctorado-ml/bayesnet
+```
+
+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.
+
+### Release
+
+```bash
+make init # Install dependencies
+make release # Build the release version
+make buildr # compile and link the release version
+```
+
+### Debug & Tests
+
+```bash
+make init # Install dependencies
+make debug # Build the debug version
+make test # Run the tests
+```
+
+### Coverage
+
+```bash
+make coverage # Run the tests with coverage
+make viewcoverage # View the coverage report in the browser
+```
+
+### 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
+
+#### - A2DE
+
+#### - [BoostAODE](docs/BoostAODE.md)
+
+#### - XBAODE
+
+#### - BoostA2DE
+
+#### - XBA2DE
+
+### 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)