Support asymmetric Dirichlet prior optimization about lightlda HOT 4 OPEN

Hi, guys. Thank you for your amazing work on large scale LDA.
On the other hand, I think model quality is as important as scalability. So I am very intresting in improving it. It is exciting to know asymmetric Dirichlet prior could help. Would you please to share some experience on this? I will try my best to contribute

from lightlda.

Hi, guys,
I finished to try to add this new feature in PR#22
This PR supports asymmetric alpha in following steps:

1. Add two extra tables to Multiverso. One is topic frequency table, a matrix to count each topics’ frequency. The other one is doc length table, a row to count how many document is with length k.
2. Initialize the two extra tables with random initialized documents
3. Learn alpha distribution with the two extra table every 5 iterations
4. Build alias table for leanred alpha distribution
5. Sample topics with learned alpha distribution and alias table. Meanwhile, update countings of topic frequency table if necessary

To use this new feature, please just run with an extra option "-num_alpha_iterations".

Please notice that there are two TODOs. One is Evaluation in asymmetric prior mode, the other is Inference with asymmetric prior.

from lightlda.

Thanks, Jianyi! I will review the code.

from lightlda.

@feiga , I am sorry that I made a mistake when updating topic-frequency-table. I fixed it and commit to PR#22.

from lightlda.