Package weka.classifiers.bayes

Examples of weka.classifiers.bayes.NaiveBayesUpdateable.buildClassifier()

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   */
  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++ ) {
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  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
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  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++) {
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  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
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  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++) {
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