The code implements DeltaLDA as a Python C extension module, combining the speed of Python with the flexibility and ease-of-use of raw C ;)

from numpy import * from deltaLDA import deltaLDA alpha = .1 * ones((1,3)) beta = ones((3,5)) docs = [[1,1,2], [1,1,1,1,2], [3,3,3,4], [3,3,4,4,3,3], [0,0,0,0,0], [0,0,0,0]] numsamp = 50 randseed = 1 (phi,theta,sample) = deltaLDA(docs,alpha,beta,numsamp,randseed)

'@'.join(['andrzeje','.'.join(['cs','wisc','edu'])])

Andrzejewski, D., Mulhern, A., Liblit, B., and Zhu, X.

In Proceedings of the 18th European Conference on Machine Learning (ECML 2007), 6-17.

(pdf,slides)

[2] Latent Dirichlet Allocation

Blei, D. M., Ng, A. Y., and Jordan, M. I.

Journal of Machine Learning Research (JMLR), 3, Mar. 2003, 993-1022.

[3] Finding Scientific Topics

Griffiths, T., and Steyvers, M.

Proceedings of the National Academy of Sciences (PNAS), 101, 5228-5235.

- lda-c (C)
- GibbsLDA++ (C++)
- Matlab Topic Modeling Toolbox 1.3.2 (Matlab/C via MEX)
- lda-j (Java)
- lda (C and Matlab)