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Human like chess
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analysis
blunder_2
blunder_explore
blunder_models
blunder_prediction
data
data_generators
data_proccesing
haibrid_chess_utils
human_detector
maia
models
networks
notebooks
plotly_dashboards
provisioning
reports
results
torch_training
training
.gitignore
.gitmodules
README.md
init.sh
open_share.sh
setup.py

README.md

haibrid-chess

Setup

Most of this code uses haibrid_chess_utils which can be installed with setup.py. Theres a few dependancies that should also be installed, and Pytorch and Tensorflow are needed too.

Also, you may also want lczero_tools which does low level Leela stuff. The linked repo has been forked from Leela and updated for the new version of Python-chess.

I've only tested this code with Python3.6+ on Unix machines.

Results

Our main results are in results which has a series of Jupyter notebooks showing how the final plots were created, along with some plots. These are usually cleaned up versions of other notebooks.

Data

Most of the raw data is stored on Ada the lab's server and it is converted with the scripts in data_generators. Then some is transferred to the Azure VM, haibridgpu2.eastus2.cloudapp.azure.com which I can add RSA keys to if you want.

Training

The maias are trained in training and use lots for code from lczero-training. The blunder prediction stuff mean while is in blunder_prediction and is mostly home grown, thus it's in Pytorch instead of Tensorflow.

Analysis

Most of the plots were generated by Jupyter notebooks found in notebooks, although there's a bunch more I haven't added to the repo so there's probably a plot or two with no source there.

Other

There's a few other directories I don't think there's anything useful in any of them though.

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