How to use

weka.classifiers.bayes.NaiveBayesUpdateable

constructor

public AggregateableFilteredClassifierUpdateable() {
 m_Classifier = new NaiveBayesUpdateable();
}

/**
 * Construct a new NBNode
 * 
 * @param header the instances structure of the data we're learning from
 * @param nbWeightThreshold the weight mass to see before allowing naive Bayes
 *          to predict
 * @throws Exception if a problem occurs
 */
public NBNode(Instances header, double nbWeightThreshold) throws Exception {
 m_nbWeightThreshold = nbWeightThreshold;
 m_bayes = new NaiveBayesUpdateable();
 m_bayes.buildClassifier(header);
}

/**
 * Default constructor.
 */
public FilteredClassifierUpdateable() {
 super();
 m_Classifier = new weka.classifiers.bayes.NaiveBayesUpdateable();
 m_Filter = new weka.filters.AllFilter();
}

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

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

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

/**
 * Construct a new NBNode
 * 
 * @param header the instances structure of the data we're learning from
 * @param nbWeightThreshold the weight mass to see before allowing naive Bayes
 *          to predict
 * @throws Exception if a problem occurs
 */
public NBNode(Instances header, double nbWeightThreshold) throws Exception {
 m_nbWeightThreshold = nbWeightThreshold;
 m_bayes = new NaiveBayesUpdateable();
 m_bayes.buildClassifier(header);
}

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

protected WekaClassifierMapTask setupIncrementalClassifier() {
 WekaClassifierMapTask task = new WekaClassifierMapTask();
 task.setClassifier(new weka.classifiers.bayes.NaiveBayesUpdateable());
 return task;
}

/**
 * Build the no-split node
 *
 * @param instances an <code>Instances</code> value
 * @exception Exception if an error occurs
 */
public final void buildClassifier(Instances instances) throws Exception {
 m_nb = new NaiveBayesUpdateable();
 m_disc = new Discretize();
 m_disc.setInputFormat(instances);
 Instances temp = Filter.useFilter(instances, m_disc);
 m_nb.buildClassifier(temp);
 if (temp.numInstances() >= 5) {
  m_errors = crossValidate(m_nb, temp, new Random(1));
 }
 m_numSubsets = 1;
}

/**
 * Build the no-split node
 *
 * @param instances an <code>Instances</code> value
 * @exception Exception if an error occurs
 */
public final void buildClassifier(Instances instances) throws Exception {
 m_nb = new NaiveBayesUpdateable();
 m_disc = new Discretize();
 m_disc.setInputFormat(instances);
 Instances temp = Filter.useFilter(instances, m_disc);
 m_nb.buildClassifier(temp);
 if (temp.numInstances() >= 5) {
  m_errors = crossValidate(m_nb, temp, new Random(1));
 }
 m_numSubsets = 1;
}

task.setClassifier(new weka.classifiers.bayes.NaiveBayesUpdateable());
task.setup(new Instances(train, 0));
for (int i = 0; i < train.numInstances(); i++) {

NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
fullModel.buildClassifier(trainingSets[i]);

NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
fullModel.buildClassifier(trainingSets[i]);

trainer.setClassifier(new weka.classifiers.bayes.NaiveBayesUpdateable());
trainer.setTotalNumFolds(10);

trainer.setClassifier(new weka.classifiers.bayes.NaiveBayesUpdateable());
trainer.setTotalNumFolds(10);

NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
fullModel.buildClassifier(trainingSets[i]);

NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
fullModel.buildClassifier(trainingSets[i]);