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:
Variational inference as described in “Variational Inference for Dirichlet Process Mixtures” by Blei et al. (2006). This is a batch algorithm that requires storing all data in memory.
An experimental on-line inference algorithm that requires only O(log(n)) memory where n is the total number of observations.
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.
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.
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