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.
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Release files for dphmix 0.2.0
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| File | Size | Uploaded | Provenance | Actions |
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| dphmix-0.2.0.tar.gz | 15.5 kB | Without provenance | Details for dphmix-0.2.0.tar.gz |
Release files / dphmix-0.2.0.tar.gz
| Download URL | dphmix-0.2.0.tar.gz |
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| Size | 15.5 kB |
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