/**
* Generates the LDA model using the documents saved Uses alpha = 0.5 and beta=0.1 ntopics = 3 and
* 5 words pr topic.
*/
public void LDAModel() {
// TODO Auto-generated method stub
LDACmdOption option = new LDACmdOption();
CmdLineParser parser = new CmdLineParser(option);
System.out.println("Working Directory = " + System.getProperty("user.dir"));
String[] inputSplits =
("-est -alpha 0.5 -beta 0.1 -ntopics 3 -niters 1000 -savestep 100 -twords 5 -dfile newdocs.dat -inf -dir "
+ System.getProperty("user.dir")
+ "/model")
.split(" ");
String[] args = inputSplits;
try {
if (args.length == 0) {
System.out.println("No arguments");
return;
}
parser.parseArgument(args);
if (option.est || option.estc) {
Estimator estimator = new Estimator();
estimator.init(option);
estimator.estimate();
} else if (option.inf) {
Inferencer inferencer = new Inferencer();
inferencer.init(option);
Model newModel = inferencer.inference();
for (int i = 0; i < newModel.phi.length; ++i) {
// phi: K * V
System.out.println("-----------------------\ntopic" + i + " : ");
for (int j = 0; j < 10; ++j) {
System.out.println(inferencer.globalDict.id2word.get(j) + "\t" + newModel.phi[i][j]);
}
}
}
} catch (CmdLineException cle) {
System.out.println("Command line error: " + cle.getMessage());
return;
} catch (Exception e) {
System.out.println("Error in main: " + e.getMessage());
e.printStackTrace();
return;
}
}
