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

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

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    // viterbi解码
    Inferencer inference;

    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);
    }
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      dictPipe = new DictLabel(dict, labels);

    oldfeaturePipe = featurePipe;
    featurePipe = new SeriesPipes(new Pipe[] { dictPipe, featurePipe });

    LinearViterbi dv = new ConstraintViterbi(
        (LinearViterbi) getClassifier().getInferencer());
    getClassifier().setInferencer(dv);
  }
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   */
  public void removeDictionary()  {
    if(oldfeaturePipe != null){
      featurePipe = oldfeaturePipe;
    }
    LinearViterbi dv = new LinearViterbi(
        (LinearViterbi) getClassifier().getInferencer());
    getClassifier().setInferencer(dv);

    dictPipe = null;
    oldfeaturePipe = null;
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    labels.setStopIncrement(true);


    // viterbi解码
    HammingLoss loss = new HammingLoss();
    Inferencer inference = new LinearViterbi(templets, labels.size());
    Update update = new LinearViterbiPAUpdate((LinearViterbi) inference, loss);


    OnlineTrainer trainer = new OnlineTrainer(inference, update, loss,
        features.size(), 50,0.1f);
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      cws.setDictionary(dict);
    dictPipe = null;
    dictPipe = new DictPOSLabel(dict, labels);
    oldfeaturePipe = featurePipe;
    featurePipe = new SeriesPipes(new Pipe[] { dictPipe, featurePipe });
    LinearViterbi dv = new ConstraintViterbi(
        (LinearViterbi) getClassifier().getInferencer(),labels.size());
    getClassifier().setInferencer(dv);
  }
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      cws.removeDictionary();

    if(oldfeaturePipe != null){
      featurePipe = oldfeaturePipe;
    }
    LinearViterbi dv = new LinearViterbi(
        (LinearViterbi) getClassifier().getInferencer());
    getClassifier().setInferencer(dv);

    dictPipe = null;
    oldfeaturePipe = null;
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    // viterbi解码
    Inferencer inference;
    boolean standard = true;
    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);
    }
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   */
  public static void main(String[] args) throws Exception {
    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.LinearViterbi

  • org.fnlp.demo.ml.SequenceLabeling
  • org.fnlp.nlp.cn.rl.Seg2
  • org.fnlp.nlp.cn.tag.CWSTagger
  • org.fnlp.nlp.cn.tag.POSTagger
  • org.fnlp.nlp.tag.AbstractTagger
  • org.fnlp.train.tag.addedTagger
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