Examples of cc.mallet.types.Labeling

cc.mallet.types.Labeling
A distribution over possible labels for an instance.

    return (labeling.getBestLabel().equals (correctLabeling.getBestLabel()));
  }


  public double valueOfCorrectLabel ()
  {
    Labeling correctLabeling = instance.getLabeling();
    int correctLabelIndex = correctLabeling.getBestIndex();
    return labeling.value (correctLabelIndex);
  }

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      // Initialize the constraints
      logger.fine("Number of instances in training list = " + trainingList.size());
      for (Instance inst : trainingList) {
        double instanceWeight = trainingList.getInstanceWeight(inst);
        Labeling labeling = inst.getLabeling ();
        //logger.fine ("Instance "+ii+" labeling="+labeling);
        FeatureVector fv = (FeatureVector) inst.getData ();
        Alphabet fdict = fv.getAlphabet();
        assert (fv.getAlphabet() == fd);
        int li = labeling.getBestIndex();
        // The "2*" below is because there is one copy for the p(y|x)and another for the p(x|y).
        MatrixOps.rowPlusEquals (constraints, numFeatures, li, fv, 2*instanceWeight);
        // For the default feature, whose weight is 1.0
        assert(!Double.isNaN(instanceWeight)) : "instanceWeight is NaN";
        assert(!Double.isNaN(li)) : "bestIndex is NaN";

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        }


        while (iter.hasNext()) {
          Instance instance = iter.next();
          double instanceWeight = trainingList.getInstanceWeight(instance);
          Labeling labeling = instance.getLabeling ();
          //System.out.println("L Now "+inputAlphabet.size()+" regular features.");


          this.theClassifier.getClassificationScores (instance, scores);
          FeatureVector fv = (FeatureVector) instance.getData ();
          int li = labeling.getBestIndex();
          value = - (instanceWeight * Math.log (scores[li]));
          if(Double.isNaN(value)) {
            logger.fine ("MCMaxEntTrainer: Instance " + instance.getName() +
                         "has NaN value. log(scores)= " + Math.log(scores[li]) +
                         " scores = " + scores[li] +

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    return labeling.toLabelVector();
  }


  public boolean bestLabelIsCorrect ()
  {
    Labeling correctLabeling = instance.getLabeling();
    if (correctLabeling == null)
      throw new IllegalStateException ("Instance has no label.");
    return (labeling.getBestLabel().equals (correctLabeling.getBestLabel()));
  }

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    for (int iter = 0; iter < m_maxIterations; iter++) {


      // loop through all instances
      for (int ii = 0; ii < trainingList.size(); ii++) {
        Instance inst = trainingList.get(ii);
        Labeling labeling = inst.getLabeling ();
        FeatureVector fv = (FeatureVector) inst.getData();
        int fvisize = fv.numLocations();
        int correctIndex = labeling.getBestIndex();
        Arrays.fill(results, 0);


        // compute dot(x, wi) for each class i
        for(int lpos = 0; lpos < numLabels; lpos++) {
          for(int fvi = 0; fvi < fvisize; fvi++) {

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    double[][] featureCounts = new double[labeledFeatures.size()][numLabels];
    for (int ii = 0; ii < trainingData.size(); ii++) {
      Instance instance = trainingData.get(ii);
      FeatureVector fv = (FeatureVector)instance.getData();
      Labeling labeling = trainingData.get(ii).getLabeling();
      double[] labelDist = new double[numLabels];
      
      if (labeling == null) {
        labelByVoting(labeledFeatures,instance,labelDist);
      } else {
        int li = labeling.getBestIndex();
        labelDist[li] = 1.;
      }
  
      keyIter = labeledFeatures.keySet().iterator();
      int i = 0;

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    // Initialize the constraints
    logger.fine("Number of instances in training list = " + trainingList.size());
    for (Instance inst : trainingList) {
      double instanceWeight = trainingList.getInstanceWeight(inst);
      Labeling labeling = inst.getLabeling ();
      if (labeling == null)
        continue;
      //logger.fine ("Instance "+ii+" labeling="+labeling);
      FeatureVector fv = (FeatureVector) inst.getData ();
      Alphabet fdict = fv.getAlphabet();
      assert (fv.getAlphabet() == fd);


      // Here is the difference between this code and the single label 
      //  version: rather than only picking out the "best" index, 
      //  loop over all label indices.
      
      for (int pos = 0; pos < labeling.numLocations(); pos++){
        MatrixOps.rowPlusEquals (constraints, numFeatures,
                     labeling.indexAtLocation(pos),
                     fv,
                     instanceWeight*labeling.valueAtLocation(pos));
      }


      assert(!Double.isNaN(instanceWeight)) : "instanceWeight is NaN";


      boolean hasNaN = false;
      for (int i = 0; i < fv.numLocations(); i++) {
        if (Double.isNaN(fv.valueAtLocation(i))) {
          logger.info("NaN for feature " + fdict.lookupObject(fv.indexAtLocation(i)).toString()); 
          hasNaN = true;
        }
      }
      if (hasNaN)
        logger.info("NaN in instance: " + inst.getName());


      // For the default feature, whose weight is 1.0
      for (int pos = 0; pos < labeling.numLocations(); pos++) {
        constraints[labeling.indexAtLocation(pos)*numFeatures + defaultFeatureIndex] +=
          1.0 * instanceWeight * labeling.value(labeling.indexAtLocation(pos));
      }
    }
  }

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      int ii=0;
      while (iter.hasNext()) {
        ii++;
        Instance instance = iter.next();
        double instanceWeight = trainingList.getInstanceWeight(instance);
        Labeling labeling = instance.getLabeling ();
        if (labeling == null)
          continue;
        //System.out.println("L Now "+inputAlphabet.size()+" regular features.");


        this.theClassifier.getClassificationScores (instance, scores);
        FeatureVector fv = (FeatureVector) instance.getData ();


            value = 0.0;
        for(int pos = 0; pos < labeling.numLocations(); pos++) { //loop, added by Limin Yao
          int ll = labeling.indexAtLocation(pos);
          value -= (instanceWeight * labeling.valueAtLocation(pos) * Math.log (scores[ll]));          
        }      


        if (Double.isNaN(value)) {
          logger.fine ("MaxEntOptimizableByLabelDistribution: Instance " + instance.getName() +
                 "has NaN value.");

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