Implementation of the Neural Clustering Process (NCP) algorithm. Based on the papers:

• Pakman and Paninski, Amortized Bayesian Inference for Clustering Models, BNP@NeurIPS 2018 Workshop
• Pakman, Wang, Mitelut, Lee and Paninski, Neural Clustering Processes, ICML 2020

pip3 install -r requirements.txt
python3 main.py --model Gauss2D
python3 main.py --model MNIST

The code contains two implementations of the NCP algorithm, which differ in the way GPU parallelism is handled:

1. In ncp.py, used at train time, parallelization is over a minibatch of datasets, all with the same size and cluster structure.
2. In ncp_sampler.py, used at test time, only one dataset is used, and samples with different cluster structures are generated in parallel. The use of ncp_sampler.py is illustrated in plot_functions.py.

For an application of NCP to spike sorting, see NCP-Sort

Given the observations in the leftmost panel, we show samples from the posterior of a Dirichlet Process Mixture Model of 2D Gaussians, indicating in each case the number of clusters and posterior probability.