This library provides efficient and easy-to-use implementation for managing Bayesian Networks and executing various types of inferences.
This library features the implementation of the following types of inferences on Bayesian networks:
- Exact Inference
- Approximate Inference
- Gibbs Inference
You'll need to have a modern C++ compiler (supporting C++17) and cmake (version 3.5 or newer) installed on your system. We also use Google Test for our testing.
You can install the dependencies on a Ubuntu system using the following command:
sudo apt-get install cmake libgtest-devFor other operating systems, please check the official installation instructions of the respective dependencies.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
To clone this repository, run the following command in your terminal:
git clone https://github.com/muhamm-ad/BayeNet.gitNavigate to the root directory of the project, and run the provided bash script run.sh. It automates the process of creating a build directory, running cmake and make.
./run.shThe resulting executable and the test binaries will be in the build directory.
After building the project, you can run the tests for each class separately.
To run the tests for the Bayesian Network class, execute:
./build/tests_BayeNetTo run the tests for the Variable class, execute:
./build/tests_VariableYou can also create your own tests by adding them to the respective test source files located in the test directory. Follow the same structure as the existing tests. After adding new tests, rebuild the project using the run.sh script and run the test binaries as described above.
Include the library in your project, create instances of the BayesianNetwork class, add variables and dependencies to the network, and use the inference methods as needed.
Contributions to improve the library are welcome. Please feel free to open issues or submit pull requests.