Source Code of Clustering pipeline / ACA optimization / Inference labeling
$ git clone https://github.com/xiaozhuchacha/AtomicAction.git
- use 'main.m' as the entry in 'Clustering' folder to start clustering pipeline
- sample hand data is placed in 'Clustering/hand_data'
- use 'ACA.cpp' as the entry in 'ACA' folder to start clustering optimization
- to compile: g++ ACA.cpp -o ACA
- execute: ./ACA [NC] [data_name], eg. ./ACA 9 sample_hand_data
- use 'AnnealGibbs.py' as the entry in 'Inference' folder to start Gibbs annealing
- parser is placed in 'Inference/induced_grammar' to calculate prior
- gaussians are placed in 'Inference/Gaussians' to calculate likelihood
- use 'main.m' as the entry in 'Inference/Gaussians' to compute gauusian parameters w.r.t ground truth labeled data
- labeled motion sequence as input can be found either in 'Clustering/hcluster' as hierarchical clustering result or 'ACA/ACAbin' as ACA optimization result
- execute: python AnnealGibbs.py [--nlabel] [--data-name], eg. python AnnealGibbs.py --nlabel 6 --data-name sample_hand_data
- see gloveaction2018icra.pdf