// inference new model ~ getting data from a specified dataset
public Model inference(LDADataset newData) {
System.out.println("init new model");
Model newModel = new Model();
newModel.initNewModel(option, newData, trnModel);
this.newModel = newModel;
System.out.println("Sampling " + niters + " iteration for inference!");
for (newModel.liter = 1; newModel.liter <= niters; newModel.liter++) {
// System.out.println("Iteration " + newModel.liter + " ...");
// for all newz_i
for (int m = 0; m < newModel.M; ++m) {
for (int n = 0; n < newModel.data.docs[m].length; n++) {
// (newz_i = newz[m][n]
// sample from p(z_i|z_-1,w)
int topic = infSampling(m, n);
newModel.z[m].set(n, topic);
}
} // end foreach new doc
} // end iterations
System.out.println("Gibbs sampling for inference completed!");
computeNewTheta();
computeNewPhi();
newModel.liter--;
return this.newModel;
}
// inference new model ~ getting dataset from file specified in option
public Model inference() {
System.out.println("inference - new");
newModel = new Model();
if (!newModel.initNewModel(option, trnModel)) return null;
System.out.println("Sampling " + niters + " iteration for inference!");
for (newModel.liter = 1; newModel.liter <= niters; newModel.liter++) {
// System.out.println("Iteration " + newModel.liter + " ...");
// for all newz_i
for (int m = 0; m < newModel.M; ++m) {
for (int n = 0; n < newModel.data.docs[m].length; n++) {
// (newz_i = newz[m][n]
// sample from p(z_i|z_-1,w)
int topic = infSampling(m, n);
newModel.z[m].set(n, topic);
}
} // end foreach new doc
} // end iterations
System.out.println("Gibbs sampling for inference completed!");
System.out.println("Saving the inference outputs!");
computeNewTheta();
computeNewPhi();
newModel.liter--;
newModel.saveModel(newModel.dfile + "." + newModel.modelName);
return newModel;
}
// -----------------------------------------------------
// Init method
// -----------------------------------------------------
public boolean init(LDACmdOption option) {
this.option = option;
trnModel = new Model();
if (!trnModel.initEstimatedModel(option)) return false;
globalDict = trnModel.data.localDict;
computeTrnTheta();
computeTrnPhi();
return true;
}