Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (
Help us improve Python packaging - Donate today!

dpcluster is a package for grouping together (clustering) vectors. It automatically chooses the number of clusters that fits the data best based on the underlying Dirichlet Process mixture model.

Project Description


dpcluster is a package for grouping together (clustering) vectors. It automatically chooses the number of clusters that fits the data best. Specifically, it models the data as a Dirichlet Process mixture in the exponential family. For a tutorial see “Dirichlet Process” by Y.W. Teh (2010). Currently the only distribution implemented is the multivariate Gaussian with a Normal-Inverse-Wishart conjugate prior but extensions to other distributions are possible.

Two inference algorithms are implemented:

To install locally run:

python install --user


Here is a simple example to demonstrate clustering a number of random points in the plane:

>>> from dpcluster import *
>>> n = 10
>>> data = np.random.normal(size=2*n).reshape(-1,2)
>>> vdp = VDP(GaussianNIW(2))
>>> vdp.batch_learn(vdp.distr.sufficient_stats(data))
>>> plt.scatter(data[:,0],data[:,1])
>>> vdp.plot_clusters(slc=np.array([0,1]))

Running this might produce 2-3 clusters depending on the randomly generated data. The adaptive nature of the Dirichlet Process mixture model becomes apparent when we increase the number of data points from n = 10 to n = 500. In this case the clustering algorithm will likely explain the data using only one cluster.


  • Implement more clustering algorithms e.g. based on Gibbs sampling, expectation propagation, stochastic gradient descent.
  • Implement more clustering distributions.
  • Re-implement algorithms to take advantage of multi-core or GPU computing.
Release History

Release History

This version
History Node


History Node


History Node


History Node


History Node


Download Files

Download Files

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

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
dpcluster-0.104.linux-x86_64.tar.gz (34.7 kB) Copy SHA256 Checksum SHA256 any Dumb Binary Jun 21, 2013
dpcluster-0.104.tar.gz (14.6 kB) Copy SHA256 Checksum SHA256 Source Jun 21, 2013

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting