本文整理了Java中cc.mallet.types.InstanceList.subList()方法的一些代码示例，展示了InstanceList.subList()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。InstanceList.subList()方法的具体详情如下：
包路径：cc.mallet.types.InstanceList
类名称：InstanceList
方法名：subList

InstanceList.subList介绍

暂无

代码示例

代码示例来源：origin: cc.mallet/mallet

public void testEvaluators ()
{
  InstanceList instances = new InstanceList(new Randoms(1), 100, 2).subList(0,12);
  System.err.println(instances.size() + " instances");
  Clustering truth = generateTruth(instances);
  System.err.println("truth=" + truth);
  Clustering[] predicted = generatePredicted(instances);
  ClusteringEvaluator pweval = new PairF1Evaluator();
  ClusteringEvaluator bceval = new BCubedEvaluator();
  ClusteringEvaluator muceval = new MUCEvaluator();
  for (int i = 0; i < predicted.length; i++) {
    System.err.println("\npred" + i + "=" + predicted[i]);
    System.err.println("pairs: " + pweval.evaluate(truth, predicted[i]));
    System.err.println("bcube: " + bceval.evaluate(truth, predicted[i]));
    System.err.println("  muc: " + muceval.evaluate(truth, predicted[i]));
  }
  System.err.println("totals:");
  System.err.println("pairs: " + pweval.evaluateTotals());
  System.err.println("bcube: " + bceval.evaluateTotals());
  System.err.println("  muc: " + muceval.evaluateTotals());
  assertTrue(pweval.evaluateTotals().matches(".*f1=0\\.5550.*"));
  assertTrue(bceval.evaluateTotals().matches(".*f1=0\\.7404.*"));
  assertTrue(muceval.evaluateTotals().matches(".*f1=0\\.8059.*"));
}

代码示例来源：origin: com.github.steveash.mallet/mallet

public void testEvaluators ()
{
  InstanceList instances = new InstanceList(new Randoms(1), 100, 2).subList(0,12);
  System.err.println(instances.size() + " instances");
  Clustering truth = generateTruth(instances);
  System.err.println("truth=" + truth);
  Clustering[] predicted = generatePredicted(instances);
  ClusteringEvaluator pweval = new PairF1Evaluator();
  ClusteringEvaluator bceval = new BCubedEvaluator();
  ClusteringEvaluator muceval = new MUCEvaluator();
  for (int i = 0; i < predicted.length; i++) {
    System.err.println("\npred" + i + "=" + predicted[i]);
    System.err.println("pairs: " + pweval.evaluate(truth, predicted[i]));
    System.err.println("bcube: " + bceval.evaluate(truth, predicted[i]));
    System.err.println("  muc: " + muceval.evaluate(truth, predicted[i]));
  }
  System.err.println("totals:");
  System.err.println("pairs: " + pweval.evaluateTotals());
  System.err.println("bcube: " + bceval.evaluateTotals());
  System.err.println("  muc: " + muceval.evaluateTotals());
  assertTrue(pweval.evaluateTotals().matches(".*f1=0\\.5550.*"));
  assertTrue(bceval.evaluateTotals().matches(".*f1=0\\.7404.*"));
  assertTrue(muceval.evaluateTotals().matches(".*f1=0\\.8059.*"));
}

代码示例来源：origin: cc.mallet/mallet

public void testEvaluators ()
  InstanceList instances = new InstanceList(random, 100, 2).subList(0,10);
  System.err.println(instances.size() + " instances");
  Clustering clustering = generateClustering(instances);

代码示例来源：origin: com.github.steveash.mallet/mallet

public void testEvaluators ()
  InstanceList instances = new InstanceList(random, 100, 2).subList(0,10);
  System.err.println(instances.size() + " instances");
  Clustering clustering = generateClustering(instances);

代码示例来源：origin: de.julielab/jcore-mallet-2.0.9

public void testEvaluators ()
  InstanceList instances = new InstanceList(random, 100, 2).subList(0,10);
  System.err.println(instances.size() + " instances");
  Clustering clustering = generateClustering(instances);

代码示例来源：origin: com.github.steveash.mallet/mallet

alph,
                   new String[]{"foo", "bar"},
                   30).subList(0, 20);
Clustering singletons = ClusterUtils.createSingletonClustering(instances);

代码示例来源：origin: cc.mallet/mallet

alph,
                   new String[]{"foo", "bar"},
                   30).subList(0, 20);
Clustering singletons = ClusterUtils.createSingletonClustering(instances);

代码示例来源：origin: de.julielab/jcore-mallet-2.0.9

alph,
                   new String[]{"foo", "bar"},
                   30).subList(0, 20);
Clustering singletons = ClusterUtils.createSingletonClustering(instances);