GMM-MetropolisHastings

Implementation of a Gaussian mixture model with Metropolis-Hastings algorithm.
メトロポリスヘイスティングス法を適用したガウス混合モデルの実装例

How to run

1. The first step is to create the observation data using make_data.py. Then, create data1.txt. true_label.txt is the label data for calculating ARI.
2. After that, you can use gmm_mh.py to run the clustering.

The image below shows the actual generated observables using make_data.py.　　

An example of the results

The image below shows the actual ARI measured by gmm_mh.py, where a value close to 1 means high cluster performance and a value close to 0 means low cluster performance.

The image below shows the number of acceptances per iteration.

Appendix

Other examples of implementations using GMM are as follows

1. GMM with Gibbssampling
2. GMM with Metropolis-Hastings Algorithm
3. Multimodal-GMM
4. Multimodal-GMM with Metropolis-Hastings Algorithm
5. VAE and GMM mutual learning model