How to use

NaiveBayesUpdateable

in

weka.classifiers.bayes

/**
 * 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);
}

/**
 * Returns a string describing this classifier
 * @return a description of the classifier suitable for
 * displaying in the explorer/experimenter gui
 */
public String globalInfo() {
 return "Class for a Naive Bayes classifier using estimator classes. This is the "
  +"updateable version of NaiveBayes.\n"
  +"This classifier will use a default precision of 0.1 for numeric attributes "
  +"when buildClassifier is called with zero training instances.\n\n"
  +"For more information on Naive Bayes classifiers, see\n\n"
  + getTechnicalInformation().toString();
}

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

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

/**
 * Return a textual description of the node
 *
 * @return a <code>String</code> value
 */
public String toString() {
 return m_nb.toString();
}

@Override
public void updateNode(Instance inst) throws Exception {
 super.updateNode(inst);
 try {
  m_bayes.updateClassifier(inst);
 } catch (Exception e) {
  e.printStackTrace();
 }
}

@Override
public double[] getDistribution(Instance inst, Attribute classAtt)
  throws Exception {
 // totalWeight - m_weightSeenAtLastSplitEval is the weight mass
 // observed by this node's NB model
 boolean doNB = m_nbWeightThreshold == 0 ? true : (totalWeight()
   - m_weightSeenAtLastSplitEval > m_nbWeightThreshold);
 if (doNB) {
  return m_bayes.distributionForInstance(inst);
 }
 return super.getDistribution(inst, classAtt);
}

@Override
public void updateNode(Instance inst) throws Exception {
 String trueClass = inst.classAttribute().value((int) inst.classValue());
 int trueClassIndex = (int) inst.classValue();
 if (majorityClass().equals(trueClass)) {
  m_majClassCorrectWeight += inst.weight();
 }
 if (m_bayes.classifyInstance(inst) == trueClassIndex) {
  m_nbCorrectWeight += inst.weight();
 }
 super.updateNode(inst);
}

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

/**
 * Return a textual description of the node
 *
 * @return a <code>String</code> value
 */
public String toString() {
 return m_nb.toString();
}

@Override
public void updateNode(Instance inst) throws Exception {
 super.updateNode(inst);
 try {
  m_bayes.updateClassifier(inst);
 } catch (Exception e) {
  e.printStackTrace();
 }
}

@Override
public double[] getDistribution(Instance inst, Attribute classAtt)
  throws Exception {
 // totalWeight - m_weightSeenAtLastSplitEval is the weight mass
 // observed by this node's NB model
 boolean doNB = m_nbWeightThreshold == 0 ? true : (totalWeight()
   - m_weightSeenAtLastSplitEval > m_nbWeightThreshold);
 if (doNB) {
  return m_bayes.distributionForInstance(inst);
 }
 return super.getDistribution(inst, classAtt);
}

@Override
public void updateNode(Instance inst) throws Exception {
 String trueClass = inst.classAttribute().value((int) inst.classValue());
 int trueClassIndex = (int) inst.classValue();
 if (majorityClass().equals(trueClass)) {
  m_majClassCorrectWeight += inst.weight();
 }
 if (m_bayes.classifyInstance(inst) == trueClassIndex) {
  m_nbCorrectWeight += inst.weight();
 }
 super.updateNode(inst);
}

/**
 * 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();
}

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

@Override
protected void printLeafModels(StringBuffer buff) {
 buff.append("NB adaptive" + m_leafNum).append("\n")
   .append(m_bayes.toString());
}

((NaiveBayesUpdateable)copies[j]).updateClassifier(test.instance(k));

/**
 * Return the probability for a class value
 *
 * @param classIndex the index of the class value
 * @param instance the instance to generate a probability for
 * @param theSubset the subset to consider
 * @return a probability
 * @exception Exception if an error occurs
 */
public double classProb(int classIndex, Instance instance, int theSubset) 
 throws Exception {
 m_disc.input(instance);
 Instance temp = m_disc.output();
 return m_nb.distributionForInstance(temp)[classIndex];
}

/**
 * Returns a string describing this classifier
 * @return a description of the classifier suitable for
 * displaying in the explorer/experimenter gui
 */
public String globalInfo() {
 return "Class for a Naive Bayes classifier using estimator classes. This is the "
  +"updateable version of NaiveBayes.\n"
  +"This classifier will use a default precision of 0.1 for numeric attributes "
  +"when buildClassifier is called with zero training instances.\n\n"
  +"For more information on Naive Bayes classifiers, see\n\n"
  + getTechnicalInformation().toString();
}