COMPR[CH]ESS

Introduction

COMPR[CH]ESS is an innovative approach to lossless chess game compression, seamlessly blending chess engines, machine learning, and Huffman Coding. By training models that predict player moves at a given rating range, this program can adaptively encode/decode chess games efficiently.

Getting Started

Follow these simple steps to get started with COMPR[CH]ESS:

1. Clone the Repository:

   git clone https://github.com/tylerrlin/compr_ch_ess.git
   cd compr_ch_ess

2. Install Package:

   python setup.py install

3. Run a Script:

   python scripts/compress.py <engine_path> <model_path>

   or to build a model (will have to edit the default values in scripts/build_model.py):

   python scripts/build_model.py <engine_path>

How it works

This program utilizes Huffman Coding to encode/decode chess games. Huffman Coding is a technique where more probable characters or strings of characters are assigned lower-length and unique bitstrings. Using a chess engine to evaluate positions and a machine learning model trained to predict a player's next move, every unique move is encoded and appended to a bitstring containing the code of the whole game. For a hard-coded visualization of this, check out https://tylerrlin.github.io/projects/comprchess.