GMM-Gibbs

Implementation of a Gaussian mixture model with Gibbs sampling.
ギブスサンプリング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_gibbs.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_gibbs.py, where a value close to 1 means high cluster performance and a value close to 0 means low cluster performance.

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