LDA parser and predictor

Before using this program, you should first install boost C++ library to support regex and mysql to support database access.

preprocess the files

./lda -pprocess info 1 // generate training files
./lda -pprocess test2 2 // generate predict files

training

./lda -est -alpha 0.7912 -beta 0.00464 -ntopics 50 -niters 1000 -savestep 10 -twords 20 -dfile model/trndata.txt

prediction

./lda -inf -dir model -model model-final -niters 30 -twords 20 -dfile model/predata.txt

ranking

./lda -ranking 78 -disp 10 -dir model -model model-final

classification

./lda -class -dir model -model model-final

server

./lda -server 6000 -disp 12 -dir model -model model-final -niters 5 -twords 20 -dfile predata.txt

1000 group 10000 iterations, topics size 100 init: alpha = 0.8, beta = 0.1 thinning interval = 10 iterations posterior mean for α is 0.050982006. posterior mean for β is 0.002609193. 95% credential interval for α is [0.0502137 0.0519432]. 95% credential interval for β is [0.00259244 0.00262603].

5600 group 10000 iterations, topics size 100 init: alpha = 0.8, beta = 0.1 thinning interval = 10 iterations posterior mean for α is 0.026575741. posterior mean for β is 0.002815483. 95% credential interval for α is [0.0262913 0.0268430]. 95% credential interval for β is [0.00280798 0.00282316].