According to Wallach’s paper, asymmetric, hierarchical Dirichlet prior over the document–topic distributions and a symmetric Dirichlet prior over the topic–word distributions results in significantly better model
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.