The above binder link launches a notebook that demonstrates the agglomerative info-clustering algorithm (AIC) implemented in C++. It is run using the xeus-cling C++11 jupyter kernel.
Simply download the include folder and add it to the include path.
- The source code requires Eigen C++ library, which is included in
includefolder. - To use the finite linear source model, you would need to install
libpari-dev. (See the next section.)
Some examples from the jupyter notebook are also included as .cpp files as follows. See the jupyter notebook for more detailed explanations.
hypergraph_demo.cpp performs the exact and approximate clustering algorithm for hypergraphical source model. Run using
make hypergraph_demo; ./hypergraph_demo.out
gaussian_demo.cpp gives an example of a jointly gaussian source model. Run using
make gaussian_demo; ./gaussian_demo.out
fls_demo.cpp is an example for clustering a finite linear source model. It requires the C library pari, which can be installed on ubuntu using
apt-get install libpari-dev
Run the using
make fls_demo; ./fls_demo.out