Skip to main content

Unsupervised and Semi-supervised Dirichlet Process Heterogeneous Mixtures

Project description

Implements Dirichlet Process Heterogeneous Mixtures of exponential family distributions for clustering heterogeneous data without choosing the number of clusters. Inference can be performed with Gibbs sampling or coordinate ascent mean-field variational inference. For semi-supervised learning, Gibbs sampling supports must-link and cannot-link constraints. A novel variational inference algorithm was derived to handle must-link constraints.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for dphmix, version 0.2.0
Filename, size File type Python version Upload date Hashes
Filename, size dphmix-0.2.0.tar.gz (15.5 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page