English
资源说明：lda with Gibbs Sampler
LDA Implementation Using Gibbs Sampler: Input arguments: -f filename The file should have a collection of documents seperated by new line -k number of topics -a hyper-parameter alpha -b hyper-parameter beta -i number of iterations -w top w words to be output in the topic_word distributions -o output folder where all the output files will be saves Output files: Four files in the output folder : (xxx is the input filename) ======= Four files: (xxx is the input filename) 1. doc_topic_input_xxx - document topic distributions of the entire document collection 2. topic_word_xxx - topic word distributions of the k topics 3. vocabinput_xxx - list of vocabulary terms 4. loglike-input_xxx - loglikelihood at each iteration. Example usage:- python lda_gibbs.py -f input.data -k 18 -a 0.05 -b 0.05 -i 100 -w 20 -o output_folder
本源码包内暂不包含可直接显示的源代码文件，请下载源码包。
