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.
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 setup.py 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])) >>> plt.show()
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.
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|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|