A Java implemention of LDA(Latent Dirichlet Allocation). Inference topics from a set of documents with few lines of Java code.
- code
public static void main(String[] args)
{
// 1. Load corpus from disk
Corpus corpus = Corpus.load("data/mini");
// 2. Create a LDA sampler
LdaGibbsSampler ldaGibbsSampler = new LdaGibbsSampler(corpus.getDocument(), corpus.getVocabularySize());
// 3. Train it
ldaGibbsSampler.gibbs(10);
// 4. The phi matrix is a LDA model, you can use LdaUtil to explain it.
double[][] phi = ldaGibbsSampler.getPhi();
Map<String, Double>[] topicMap = LdaUtil.translate(phi, corpus.getVocabulary(), 10);
LdaUtil.explain(topicMap);
}- output
topic 0 :
公司=0.009538408630174017
市场=0.008848009751698062
**=0.008756489189917975
企业=0.0068280510303913395
发展=0.005991900977658479
目前=0.004408401842957633
产品=0.0041981128106208625
服务=0.003756081561227181
已经=0.003410105744626914
记者=0.003289155629929911
topic 1 :
专业=0.00872496522205349
工作=0.008108171408190876
学生=0.00793944661866665
学校=0.006307480899983371
考生=0.005295205701518912
大学=0.0052671267600129445
教育=0.0051547106121291805
考试=0.00507254577329609
人才=0.004037747449851247
招聘=0.003913811857165103
topic 2 :
医院=0.006197066939127888
治疗=0.0048149451145789455
患者=0.0032264139617756145
健康=0.0026521203697810374
手术=0.0025525793863978826
女性=0.0023724111474892357
专家=0.0021711200905248276
发现=0.0021645199996586885
病人=0.0021567877663232846
医生=0.002155356316589454
topic 3 :
没有=0.008818728535385055
问题=0.00476170232225101
**=0.00476161560515722
工作=0.004610303190509696
生活=0.004283310385880329
文化=0.0036558079614339278
孩子=0.003327977201447208
不能=0.0032901108349775716
知道=0.003127437274214269
已经=0.0030419673256694545
topic 4 :
公司=0.018241005428669386
股东=0.009281048036676322
股份=0.0078638937643388
搜狐=0.0065617441267974705
有限公司=0.006139808167975946
直播员=0.005439495997416965
股权=0.005353954615162839
项目=0.004984451830097043
发行=0.004511099443364358
改革=0.004489038403046334
topic 5 :
旅游=0.013331508385667979
游客=0.004296589238778804
城市=0.0032312276892446116
文化=0.0026831367778820704
旅行社=0.002242817493567529
世界=0.0021001546909288965
成都=0.001991337289815279
活动=0.001894687770595843
北京=0.0017106388886854072
公园=0.0016134766410937638
topic 6 :
美国=0.007679518424242107
日本=0.004777746687572576
训练=0.003947682941243526
系统=0.003926562149803556
飞机=0.0038757503504304267
部队=0.00365041154980242
进行=0.003644226666909795
军事=0.003637873811678725
作战=0.003407296869780034
装备=0.003319112427162246
topic 7 :
比赛=0.0092171879571152
队员=0.0036851386114063237
联赛=0.0032845199043377146
球队=0.0029432131822116707
冠军=0.0024090127058022104
俱乐部=0.002348957542679953
球员=0.0022159606741087795
决赛=0.002192739194333911
赛季=0.0020352324832133267
对手=0.001974829226645783
topic 8 :
The=0.002190604616155811
意思=0.001186435720799536
It=0.0011515962078723501
理解=0.0010433831740419728
What=9.560997173453189E-4
They=9.345962358267594E-4
听力=8.362275772461826E-4
In=8.166984660263638E-4
阅读=7.775969918239417E-4
译文=7.568900132152651E-4
topic 9 :
***=0.002633793448326645
曹操=0.0018832599387516155
曹丕=0.0016567353952110328
皇帝=0.001629990508040292
甄洛=0.0012930147890964736
**=0.0012783947883529055
蒋介石=0.0010732052837016102
曹睿=8.511476483731437E-4
女王=8.125680914406854E-4
皇后=8.013815303127338E-4
- corpus The data/mini is some documents included in this project, which use space to segment words. Feel free to replace it with yours.
- algorithm Mainly depend on Gregor Heinrich's great work. Read more about this implementation on《LDA入门与Java实现》
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