Metadata-Version: 1.1
Name: dphmix
Version: 0.1.0
Summary: Unsupervised and Semi-supervised Dirichlet Process Heterogeneous Mixtures
Home-page: https://github.com/tahmidmehdi/dphmix
Author: Tahmid Mehdi
Author-email: tfmehdi@cs.toronto.edu
License: GNU GPL v3.0
Download-URL: https://github.com/tahmidmehdi/dphmix/archive/v0.1.tar.gz
Description-Content-Type: UNKNOWN
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.
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
