本文整理了Java中weka.classifiers.bayes.NaiveBayes.buildClassifier()方法的一些代码示例，展示了NaiveBayes.buildClassifier()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。NaiveBayes.buildClassifier()方法的具体详情如下：
包路径：weka.classifiers.bayes.NaiveBayes
类名称：NaiveBayes
方法名：buildClassifier

NaiveBayes.buildClassifier介绍

[英]Generates the classifier.
[中]生成分类器。

代码示例

代码示例来源：origin: stackoverflow.com

nB.buildClassifier(train);

代码示例来源：origin: nz.ac.waikato.cms.weka/DTNB

m_NB.buildClassifier(m_theInstances);

代码示例来源：origin: nz.ac.waikato.cms.weka/distributedWekaBase

@Test
public void testScoreWithClassifier() throws Exception {
 Instances train = new Instances(new BufferedReader(new StringReader(
  CorrelationMatrixMapTaskTest.IRIS)));
 train.setClassIndex(train.numAttributes() - 1);
 NaiveBayes bayes = new NaiveBayes();
 bayes.buildClassifier(train);
 WekaScoringMapTask task = new WekaScoringMapTask();
 task.setModel(bayes, train, train);
 assertEquals(0, task.getMissingMismatchAttributeInfo().length());
 assertEquals(3, task.getPredictionLabels().size());
 for (int i = 0; i < train.numInstances(); i++) {
  assertEquals(3, task.processInstance(train.instance(i)).length);
 }
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

trainingData = Filter.useFilter(instances, m_remove);
m_estimator.buildClassifier(trainingData);

代码示例来源：origin: Waikato/weka-trunk

trainingData = Filter.useFilter(instances, m_remove);
m_estimator.buildClassifier(trainingData);

代码示例来源：origin: stackoverflow.com

public class Run {
  public static void main(String[] args) throws Exception {

    ConverterUtils.DataSource source1 = new ConverterUtils.DataSource("./data/train.arff");
    Instances train = source1.getDataSet();
    // setting class attribute if the data format does not provide this information
    // For example, the XRFF format saves the class attribute information as well
    if (train.classIndex() == -1)
      train.setClassIndex(train.numAttributes() - 1);

    ConverterUtils.DataSource source2 = new ConverterUtils.DataSource("./data/test.arff");
    Instances test = source2.getDataSet();
    // setting class attribute if the data format does not provide this information
    // For example, the XRFF format saves the class attribute information as well
    if (test.classIndex() == -1)
      test.setClassIndex(train.numAttributes() - 1);

    // model

    NaiveBayes naiveBayes = new NaiveBayes();
    naiveBayes.buildClassifier(train);

    // this does the trick  
    double label = naiveBayes.classifyInstance(test.instance(0));
    test.instance(0).setClassValue(label);

    System.out.println(test.instance(0).stringValue(4));
  }
}

代码示例来源：origin: nz.ac.waikato.cms.weka/distributedWekaBase

@Test
public void testScoreWithClassifierSomeMissingFields() throws Exception {
 Instances train = new Instances(new BufferedReader(new StringReader(
  CorrelationMatrixMapTaskTest.IRIS)));
 train.setClassIndex(train.numAttributes() - 1);
 NaiveBayes bayes = new NaiveBayes();
 bayes.buildClassifier(train);
 WekaScoringMapTask task = new WekaScoringMapTask();
 Remove r = new Remove();
 r.setAttributeIndices("1");
 r.setInputFormat(train);
 Instances test = Filter.useFilter(train, r);
 task.setModel(bayes, train, test);
 assertTrue(task.getMissingMismatchAttributeInfo().length() > 0);
 assertTrue(task.getMissingMismatchAttributeInfo().equals(
  "sepallength missing from incoming data\n"));
 assertEquals(3, task.getPredictionLabels().size());
 for (int i = 0; i < test.numInstances(); i++) {
  assertEquals(3, task.processInstance(test.instance(i)).length);
 }
}

代码示例来源：origin: nz.ac.waikato.cms.weka/DTNB

m_NB.buildClassifier(m_theInstances);