How to use

NaiveBayes

in

weka.classifiers.bayes

 Classifier cModel = (Classifier)new NaiveBayes();  
cModel.buildClassifier(isTrainingSet);  

weka.core.SerializationHelper.write("/some/where/nBayes.model", cModel);

Classifier cls = (Classifier) weka.core.SerializationHelper.read("/some/where/nBayes.model");

// Test the model
Evaluation eTest = new Evaluation(isTrainingSet);
eTest.evaluateModel(cls, isTrainingSet);

/**
 * Returns the Capabilities of this filter.
 *
 * @return the capabilities of this object
 * @see Capabilities
 */
@Override
public Capabilities getCapabilities() {
 return new NaiveBayes().getCapabilities();
}

 /**
  * Main method for testing this class.
  * 
  * @param argv the options
  */
 public static void main(String[] argv) {
  runClassifier(new NaiveBayes(), argv);
 }
}

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

@Override
protected Instances process(Instances instances) throws Exception {
 if (m_estimator == null) {
  m_estimator = new NaiveBayes();
   trainingData = Filter.useFilter(instances, m_remove);
  m_estimator.buildClassifier(trainingData);
  Estimator[][] estimators = m_estimator.getConditionalEstimators();
  Instances header = m_estimator.getHeader();
  int index = 0;
  for (int i = 0; i < header.numAttributes(); i++) {

 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));
  }
}

getCapabilities().testWithFail(instances);
while (enumInsts.hasMoreElements()) {
 Instance instance = enumInsts.nextElement();
 updateClassifier(instance);

m_NB.updateClassifier(instance);
double[] nbDist = m_NB.distributionForInstance(instance);
instance.setWeight(-instance.weight());
m_NB.updateClassifier(instance);

m_NB = new NaiveBayes();
m_NB.buildClassifier(m_theInstances);

@Override
protected Instances process(Instances instances) throws Exception {
 if (m_estimator == null) {
  m_estimator = new NaiveBayes();
   trainingData = Filter.useFilter(instances, m_remove);
  m_estimator.buildClassifier(trainingData);
  Estimator[][] estimators = m_estimator.getConditionalEstimators();
  Instances header = m_estimator.getHeader();
  int index = 0;
  for (int i = 0; i < header.numAttributes(); i++) {

 /**
  * Main method for testing this class.
  * 
  * @param argv the options
  */
 public static void main(String[] argv) {
  runClassifier(new NaiveBayes(), argv);
 }
}

getCapabilities().testWithFail(instances);
while (enumInsts.hasMoreElements()) {
 Instance instance = enumInsts.nextElement();
 updateClassifier(instance);

  class_distribs[i][(int) inst.classValue()] -= inst.weight();
  inst.setWeight(-inst.weight());
  m_NB.updateClassifier(inst);
  inst.setWeight(-inst.weight());
 } else {
 double[] nbDist = m_NB.distributionForInstance(inst);
 m_NB.updateClassifier(inst);
} else {
 class_distribs[i][0] += (inst.classValue() * inst.weight());

public AggregateableFilteredClassifier() {
 m_Classifier = new NaiveBayes();
}

@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);
 }
}

/**
 * Returns the Capabilities of this filter.
 *
 * @return the capabilities of this object
 * @see Capabilities
 */
@Override
public Capabilities getCapabilities() {
 return new NaiveBayes().getCapabilities();
}

/** Creates a default NaiveBayes */
public Classifier getClassifier() {
 return new NaiveBayes();
}

m_NB = new NaiveBayes();
m_NB.buildClassifier(m_theInstances);

 Classifier Mode; // a parent class
if(alg.equals("DecisionStump")) {
 Mode = new DecisionStump();
} else if(alg.equals("NaiveBayes")) {
 Mode = new NaiveBayes();
}

@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);
 }
}