主题模型 LDA 源码分享
转载请注明来源：http://blog.csdn.net/yihucha166/article/details/9046835
Latent Dirichlet Allocation（LDA）是目前业界最为流行的机器学习方法之一，这里用C++实现了一个as-lda版本，使用了非对称的先验设置，随着主题数的增加，主题分布上比传统模型更加稳定，减少因为主题数量大而导致大量小众主题，参考文献《Rethinking LDA:Why Priors Matter》，代码目录中包含了中文测试数据
代码地址：https://code.google.com/p/as-lda/
asymmetric prior Latent Dirichlet Allocation (LDA) by c++
Usually, symmetric dirichlet prior is used in the implementation of lda. in "Rethinking LDA:Why Priors Matter" , they have showed that asymmetric prior can generate better result and stable topic distribution under the increment of topic number. So, in this project, we adopt this algorithm.
other features：
#easy to use, easy to understand
#small memory used
ML tools source code:
as-lda: https://code.google.com/p/as-lda/
gbdt: http://code.google.com/p/simple-gbdt/
adaboost: http://code.google.com/p/simple-adaboost/
--------how to use it-----------
Usage: -c corpus file, default './corpus.txt' -v vocab file, default './vocab.txt' -e or -i act type(e for estimate,i for inference) -m model files dir, default './models' -z pre model assignment file ( inference ) -a hyperparameter alpha, default 500/topic_num -b hyperparameter beta, default 0.1 -k topic number, default 100 -n max iteration number, default 1000
Examples:
extimate: ./as_lda -e -c ./corpus.txt -v ./vocab.txt -n 2000 inference: ./as_lda -i -n 100 -c corpus.txt.test -v vocab.txt -z ./models/model.z
--------input format------------
For corpus:
one line one doc, the number stands for word id
example:
2699\t10608\t52656\t17781\t17781\t7900\t24007
For vocab：
one line one word，word id is the line number