本文整理汇总了Java中cc.mallet.types.InfoGain.print方法的典型用法代码示例。如果您正苦于以下问题：Java InfoGain.print方法的具体用法？Java InfoGain.print怎么用？Java InfoGain.print使用的例子？那么恭喜您, 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.types.InfoGain的用法示例。
在下文中一共展示了InfoGain.print方法的1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: run
import cc.mallet.types.InfoGain; //导入方法依赖的package包/类
public void run () {
Alphabet alphabet = dictOfSize(20);
// TRAIN
Clustering training = sampleClustering(alphabet);
Pipe clusterPipe = new OverlappingFeaturePipe();
System.err.println("Training with " + training);
InstanceList trainList = new InstanceList(clusterPipe);
trainList.addThruPipe(new ClusterSampleIterator(training, random, 0.5, 100));
System.err.println("Created " + trainList.size() + " instances.");
Classifier me = new MaxEntTrainer().train(trainList);
ClassifyingNeighborEvaluator eval =
new ClassifyingNeighborEvaluator(me, "YES");
Trial trial = new Trial(me, trainList);
System.err.println(new ConfusionMatrix(trial));
InfoGain ig = new InfoGain(trainList);
ig.print();
// Clusterer clusterer = new GreedyAgglomerative(training.getInstances().getPipe(),
// eval, 0.5);
Clusterer clusterer = new GreedyAgglomerativeByDensity(training.getInstances().getPipe(),
eval, 0.5, false,
new java.util.Random(1));
// TEST
Clustering testing = sampleClustering(alphabet);
InstanceList testList = testing.getInstances();
Clustering predictedClusters = clusterer.cluster(testList);
// EVALUATE
System.err.println("\n\nEvaluating System: " + clusterer);
ClusteringEvaluators evaluators = new ClusteringEvaluators(new ClusteringEvaluator[]{
new BCubedEvaluator(),
new PairF1Evaluator(),
new MUCEvaluator(),
new AccuracyEvaluator()});
System.err.println("truth:" + testing);
System.err.println("pred: " + predictedClusters);
System.err.println(evaluators.evaluate(testing, predictedClusters));
}