本文整理汇总了Java中cc.mallet.classify.Classifier.classify方法的典型用法代码示例。如果您正苦于以下问题：Java Classifier.classify方法的具体用法？Java Classifier.classify怎么用？Java Classifier.classify使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cc.mallet.classify.Classifier的用法示例。
在下文中一共展示了Classifier.classify方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: applyModel
import cc.mallet.classify.Classifier; //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;
}
示例2: classify
import cc.mallet.classify.Classifier; //导入方法依赖的package包/类
/**
*
* @param instance the instance to classify
* @param useOutOfFold whether to check the instance name and use the out-of-fold classifier
* if the instance name matches one in the training data
* @return the token classifier's output
*/
public Classification classify(Instance instance, boolean useOutOfFold)
{
Object instName = instance.getName();
if (! useOutOfFold || ! m_table.containsKey(instName))
return m_tokenClassifier.classify(instance);
Classifier classifier = (Classifier) m_table.get(instName);
return classifier.classify(instance);
}