Important files:
- election.py -- code for HAC, "Election" class which outputs clusters and cluster centers
- util.py -- different distance metrics used in election and CPS model, as well as some code for reading from the sushi dataset
- learncps.py -- code for learning CPS model, including gradient ascent / MLE of theta paramaters
- inference.py -- code for testing sequential / community inference for outputting best ranking
- comparison.py -- prints comparison of different metrics
- requirements.txt -- please run 'pip install -r requirements.txt' before attempting to run any code
- results -- folder of results for learned CPS models, files are named by the target ranking and the number of cluster, each file is a text file that contains the parameters for the CPS models as well as the losses incurred by learning the optimal theta