Package org.fnlp.ml.classifier.struct.inf

Examples of org.fnlp.ml.classifier.struct.inf.HigherOrderViterbi

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    HammingLoss loss = new HammingLoss();
    if (standard) {
      inference = new LinearViterbi(templets, labels.size());
      update = new LinearViterbiPAUpdate((LinearViterbi) inference, loss);
    } else {
      inference = new HigherOrderViterbi(templets, labels.size());
      update = new HigherOrderViterbiPAUpdate(templets, labels.size(), true);
    }

    OnlineTrainer trainer;
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    HammingLoss loss = new HammingLoss();
    if (standard) {
      inference = new LinearViterbi(templets, labels.size());
      update = new LinearViterbiPAUpdate((LinearViterbi) inference, loss);
    } else {
      inference = new HigherOrderViterbi(templets, labels.size());
      update = new HigherOrderViterbiPAUpdate(templets, labels.size(), true);
    }

    OnlineTrainer trainer = new OnlineTrainer(inference, update, loss,
        features.size(), iterNum, c1);
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    seg = new CWSTagger("./models/seg.m");
    cl = seg.getClassifier();
    int ysize = cl.getAlphabetFactory().getLabelSize();
    LinearViterbi vit = (LinearViterbi) cl.getInferencer();
    System.out.println(cl.getAlphabetFactory().getFeatureSize());
    HigherOrderViterbi inferencer = new HigherOrderViterbi(vit.getTemplets(), ysize);
    inferencer.setWeights(vit.getWeights());
    cl.setInferencer(inferencer);


    dict = MyCollection.loadSet("./data/FNLPDATA/all.dict", true);
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Related Classes of org.fnlp.ml.classifier.struct.inf.HigherOrderViterbi

  • org.fnlp.nlp.cn.rl.Seg2
  • org.fnlp.nlp.tag.AbstractTagger
  • org.fnlp.train.tag.addedTagger
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