This repository contains the code for the Markov Chains and Algorithimic Applications class given at EPFL. In short, it is an implementation of simulated annealing for the Ising perceptron.
Code is directly commented, there is no additional documentation.
The report and its figures are in the report folder.
The code base is written in C++ using Eigen for all linear algebra related operations. This is then built into a shared library which is accessible as Python module thanks to Pybind11. Finally a Jupyter Notebook is used to run the previous Python code.
On a Linux or equivalent system using gcc, you can simply:
git clone git@github.com:njroussel/MCAA.git --recursive
pip install ./MCAA
The whole build system uses CMake, it is therefore a requirement for building and running this project.
Note that the Jupyter notebooks will need specific packages which can be installed through pip.
In the python folder, there are several notebooks which contain examples of how to run code. They should already have the cells rendered, you can also simply just run them again if you change their configuration.