Examples of weka.classifiers.bayes.NaiveBayesUpdateable

weka.classifiers.bayes.NaiveBayesUpdateable
Class for a Naive Bayes classifier using estimator classes. This is the updateable version of NaiveBayes.
This classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances.

For more information on Naive Bayes classifiers, see

George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
BibTeX:
```
 @inproceedings{John1995, address = {San Mateo}, author = {George H. John and Pat Langley}, booktitle = {Eleventh Conference on Uncertainty in Artificial Intelligence}, pages = {338-345}, publisher = {Morgan Kaufmann}, title = {Estimating Continuous Distributions in Bayesian Classifiers}, year = {1995} } 
```
Valid options are:
```
 -K Use kernel density estimator rather than normal distribution for numeric attributes
```
```
 -D Use supervised discretization to process numeric attributes 
```
```
 -O Display model in old format (good when there are many classes) 
```
@author Len Trigg (trigg@cs.waikato.ac.nz) @author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 1.11 $

   * Train naive bayes.
   *
   * @param data 
   */
  public void trainNaiveBayes(Instances data) { 
      NaiveBayesUpdateable nb = new NaiveBayesUpdateable();
      
      try {
      nb.buildClassifier(data);
    } catch (Exception e) {
      e.printStackTrace();
    }
    
      for( int n = 0; n < data.size(); n++ ) {
           try {
        nb.updateClassifier(data.get(n));
      } catch (Exception e) {
        e.printStackTrace();
      }
      }

View Full Code Here

  disc.setInputFormat(trainingSets[i]);
  trainingSets[i] = Filter.useFilter(trainingSets[i], disc);


  trainingSets[i].randomize(r);
  trainingSets[i].stratify(5);
  NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
  fullModel.buildClassifier(trainingSets[i]);


  // add the errors for this branch of the split
  m_errors += NBTreeNoSplit.crossValidate(fullModel, trainingSets[i], r);
      } else {
  // if fewer than min obj then just count them as errors

View Full Code Here

  disc.setInputFormat(trainingSets[i]);
  trainingSets[i] = Filter.useFilter(trainingSets[i], disc);


  trainingSets[i].randomize(r);
  trainingSets[i].stratify(5);
  NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
  fullModel.buildClassifier(trainingSets[i]);


  // add the errors for this branch of the split
  m_errors += NBTreeNoSplit.crossValidate(fullModel, trainingSets[i], r);
      } else {
  for (int j = 0; j < trainingSets[i].numInstances(); j++) {

View Full Code Here

   *
   * @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) {

View Full Code Here

  disc.setInputFormat(trainingSets[i]);
  trainingSets[i] = Filter.useFilter(trainingSets[i], disc);


  trainingSets[i].randomize(r);
  trainingSets[i].stratify(5);
  NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
  fullModel.buildClassifier(trainingSets[i]);


  // add the errors for this branch of the split
  m_errors += NBTreeNoSplit.crossValidate(fullModel, trainingSets[i], r);
      } else {
  // if fewer than min obj then just count them as errors

View Full Code Here

  disc.setInputFormat(trainingSets[i]);
  trainingSets[i] = Filter.useFilter(trainingSets[i], disc);


  trainingSets[i].randomize(r);
  trainingSets[i].stratify(5);
  NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
  fullModel.buildClassifier(trainingSets[i]);


  // add the errors for this branch of the split
  m_errors += NBTreeNoSplit.crossValidate(fullModel, trainingSets[i], r);
      } else {
  for (int j = 0; j < trainingSets[i].numInstances(); j++) {

View Full Code Here

   *
   * @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) {

View Full Code Here

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