Package com.clearnlp.classification.instance

Examples of com.clearnlp.classification.instance.StringInstance

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    StringPrediction p = null;
   
    if (isTrain())
    {
      p = new StringPrediction(getGoldLabel(state), 1d);
      s_spaces[idx].addInstance(new StringInstance(p.label, vector));
    }
    else if (isDevelopOrDecode())
    {
      p = getAutoLabel(idx, vector, state);
    }
    else if (isBootstrap())
    {
      p = getAutoLabel(idx, vector, state);
      s_spaces[idx].addInstance(new StringInstance(getGoldLabel(state), vector));
    }

    return p;
  }
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    String label = null;
   
    if (isTrain())
    {
      label = AbstractModel.getBooleanLabel(state.getGoldLabel() != null);
      insts.add(new StringInstance(label, vector));
    }
    else if (isDevelopOrDecode())
    {
      label = getAutoLabel(vector);
    }
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    String label = null;
   
    if (isTrain())
    {
      label = state.getGoldLabel();
      if (vector.size() > 0) insts.add(new StringInstance(label, vector));
    }
    else if (isDevelopOrDecode())
    {
      label = getAutoLabel(vector, state);
    }
    else if (isBootstrap())
    {
      label = getAutoLabel(vector, state);
      if (vector.size() > 0) insts.add(new StringInstance(state.getGoldLabel(), vector));
    }
   
    return label;
  }
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    String label = null;
   
    if (isTrain())
    {
      label = state.getGoldLabel();
      s_spaces[modelId].addInstance(new StringInstance(label, vector));
    }
    else if (isDevelopOrDecode())
    {
      label = getAutoLabel(vector, modelId);
    }
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        r = pModel.predictBest(sp.o2);
        label = sp.o1;
      }
      else
      {
        StringInstance ss = StringTrainSpace.toInstance(line, hasWeight);
        r = sModel.predictBest(ss.getFeatureVector());
        label = ss.getLabel();
      }
     
      fout.println(r.label+" "+r.score);
      if (r.label.equals(label))  correct++;
      total++;
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// ================================ BUILD ================================

  public void build(int labelCutoff, int featureCutoff)
  {
    SparseFeatureVector vector;
    StringInstance instance;
    int label;
   
    l_instances = Lists.newArrayList();
    buildLabels  (labelCutoff);
    buildFeatures(featureCutoff);
   
    while ((instance = i_collector.pollInstance()) != null)
    {
      if ((label = getLabelIndex(instance.getLabel())) < 0)
        continue;
     
      vector = toSparseFeatureVector(instance.getFeatureVector());
      if (!vector.isEmpty()) l_instances.add(new IntInstance(label, vector));
    }
  }
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Related Classes of com.clearnlp.classification.instance.StringInstance

  • com.clearnlp.classification.model.StringModelAD
  • com.clearnlp.classification.model.StringOnlineModel
  • com.clearnlp.classification.train.StringTrainSpace
  • com.clearnlp.component.dep.AbstractDEPParser
  • com.clearnlp.component.online.OnlinePOSTagger
  • com.clearnlp.component.pos.AbstractPOSTagger
  • com.clearnlp.component.pred.AbstractPredicateIdentifier
  • com.clearnlp.component.role.AbstractRolesetClassifier
  • com.clearnlp.component.srl.AbstractSRLabeler
  • com.clearnlp.component.tagger.EnglishOnlinePOSTagger
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