How to use

buildClassifier

method

in

weka.classifiers.bayes.NaiveBayes

nB.buildClassifier(train);

m_NB.buildClassifier(m_theInstances);

@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);
 }
}

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

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

 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));
  }
}

@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);
 }
}

m_NB.buildClassifier(m_theInstances);