Java LabelVector.numLocations方法代码示例

import cc.mallet.types.LabelVector; //导入方法依赖的package包/类
@Override
public List<ModelApplication> applyModel(
        AnnotationSet instanceAS, AnnotationSet inputAS, AnnotationSet sequenceAS, String parms) {
  // NOTE: the crm should be of type CorpusRepresentationMalletClass for this to work!
  if(!(corpusRepresentation instanceof CorpusRepresentationMalletTarget)) {
    throw new GateRuntimeException("Cannot perform classification with data from "+corpusRepresentation.getClass());
  }
  CorpusRepresentationMalletTarget data = (CorpusRepresentationMalletTarget)corpusRepresentation;
  data.stopGrowth();
  List<ModelApplication> gcs = new ArrayList<ModelApplication>();
  LFPipe pipe = (LFPipe)data.getRepresentationMallet().getPipe();
  Classifier classifier = (Classifier)model;
  // iterate over the instance annotations and create mallet instances 
  for(Annotation instAnn : instanceAS.inDocumentOrder()) {
    Instance inst = data.extractIndependentFeatures(instAnn, inputAS);
    inst = pipe.instanceFrom(inst);
    Classification classification = classifier.classify(inst);
    Labeling labeling = classification.getLabeling();
    LabelVector labelvec = labeling.toLabelVector();
    List<String> classes = new ArrayList<String>(labelvec.numLocations());
    List<Double> confidences = new ArrayList<Double>(labelvec.numLocations());
    for(int i=0; i<labelvec.numLocations(); i++) {
      classes.add(labelvec.getLabelAtRank(i).toString());
      confidences.add(labelvec.getValueAtRank(i));
    }      
    ModelApplication gc = new ModelApplication(instAnn, labeling.getBestLabel().toString(), 
            labeling.getBestValue(), classes, confidences);
    //System.err.println("ADDING GC "+gc);
    // now save the class in our special class feature on the instance as well
    instAnn.getFeatures().put("gate.LF.target",labeling.getBestLabel().toString());
    gcs.add(gc);
  }
  data.startGrowth();
  return gcs;
}
 

import cc.mallet.types.LabelVector; //导入方法依赖的package包/类
/**
 *
 * @param neighbors
 * @return An array containing a score for each of the elements of <code>neighbors</code>.
 */
public double[] evaluate (Neighbor[] neighbors) {
	double[] scores = new double[neighbors.length];
	LabelVector ranks = classifier.classify(neighbors).getLabelVector();
	for (int i = 0; i < ranks.numLocations(); i++) {
		int idx = ((Integer)ranks.getLabelAtRank(i).getEntry()).intValue();
		scores[idx] = ranks.getValueAtRank(i);
	}
	return scores;
}
 

import cc.mallet.types.LabelVector; //导入方法依赖的package包/类
@SuppressWarnings("unchecked")
@Override
public <T extends Comparable<?>> Map<T, Double> getPredictionProbabilities(String instanceId) {
  Map<T, Double> res = new TreeMap<T, Double>();
  Integer in = data.instanceIds.get(instanceId);
  LabelVector vec = prediction.get(in).getLabelVector();
  for (int i = 0; i < vec.numLocations(); ++i)
    res.put((T) vec.labelAtLocation(i).getEntry(), vec.valueAtLocation(i));
  return res;
}
 

import cc.mallet.types.LabelVector; //导入方法依赖的package包/类
public Classification classify (Instance instance)
	{
		Classification c = underlyingClassifier.classify (instance);
		Classification cpc = confidencePredictingClassifier.classify (c);
		LabelVector lv = c.getLabelVector();
		int bestIndex = lv.getBestIndex();
		double [] values = new double[lv.numLocations()];
		//// Put score of "correct" into score of the winning class...
		// xxx Can't set lv - it's immutable.
		//     Must create copy and new classification object
		// lv.set (bestIndex, cpc.getLabelVector().value("correct"));
		//for (int i = 0; i < lv.numLocations(); i++)
		//	if (i != bestIndex)
		//		lv.set (i, 0.0);

		// Put score of "correct" in winning class and
		// set rest to 0
		for (int i = 0; i < lv.numLocations(); i++) {
			if (i != bestIndex)
				values[i] = 0.0;
			else values[i] = cpc.getLabelVector().value("correct");
		}
		//return c;
		
		if(c.bestLabelIsCorrect()){
			numCorrectInstances++;
			totalCorrect+=cpc.getLabelVector().value("correct");
			totalIncorrectCorrect+=cpc.getLabelVector().value("incorrect");
			String correct = new String("correct");
			if(correct.equals(cpc.getLabelVector().getBestLabel().toString()))
				numConfidenceCorrect++;
			else numFalseNegative++;
		}
		
		else{
			numIncorrectInstances++;
			totalIncorrect+=cpc.getLabelVector().value("correct");
			totalIncorrectIncorrect+=cpc.getLabelVector().value("incorrect");
			if((new String("incorrect")).equals(cpc.getLabelVector().getBestLabel().toString())) 
				numConfidenceCorrect++;
			else numFalsePositive++;
		}
		
		return new Classification(instance, this, new LabelVector(lv.getLabelAlphabet(), values));
//		return cpc;
	}