Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

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.

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 dpcluster, version 0.104
Filename, size File type Python version Upload date Hashes
Filename, size dpcluster-0.104.linux-x86_64.tar.gz (34.7 kB) File type Dumb Binary Python version any Upload date Hashes View hashes
Filename, size dpcluster-0.104.tar.gz (14.6 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page