Examples of weka.classifiers.rules.ZeroR

weka.classifiers.rules.ZeroR
Class for building and using a 0-R classifier. Predicts the mean (for a numeric class) or the mode (for a nominal class).
Valid options are:
```
 -D If set, classifier is run in debug mode and may output additional info to the console
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5529 $

    insts.deleteWithMissingClass();
    
    if (m_Classifier == null) {
      throw new Exception("No base classifier has been set!");
    }
    m_ZeroR = new ZeroR();
    m_ZeroR.buildClassifier(insts);


    int numClassifiers = insts.numClasses() - 1;


    numClassifiers = (numClassifiers == 0) ? 1 : numClassifiers;

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    newData.deleteWithMissingClass();


    double sum = 0;
    double temp_sum = 0;
    // Add the model for the mean first
    m_zeroR = new ZeroR();
    m_zeroR.buildClassifier(newData);
    
    // only class? -> use only ZeroR model
    if (newData.numAttributes() == 1) {
      System.err.println(

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  protected void resetOptions () {
    m_trainInstances = null;
    m_Evaluation = null;
    m_BaseClassifier = new ZeroR();
    m_folds = 5;
    m_seed = 1;
    m_threshold = 0.01;
  }

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   * reset to defaults
   */
  protected void resetOptions () {
    m_trainingInstances = null;
    m_Evaluation = null;
    m_Classifier = new ZeroR();
    m_holdOutFile = new File("Click to set hold out or test instances");
    m_holdOutInstances = null;
    m_useTraining = false;
  }

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    insts.deleteWithMissingClass();
    
    if (m_Classifier == null) {
      throw new Exception("No base classifier has been set!");
    }
    m_ZeroR = new ZeroR();
    m_ZeroR.buildClassifier(insts);


    m_TwoClassDataset = null;


    int numClassifiers = insts.numClasses();

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    clusterData.deleteAttributeAt(m_OriginalHeader.classIndex());
    m_ClusteringHeader = new Instances(clusterData, 0);


    if (m_ClusteringHeader.numAttributes() == 0) {
      System.err.println("Data contains only class attribute, defaulting to ZeroR model.");
      m_ZeroR = new ZeroR();
      m_ZeroR.buildClassifier(data);
    }
    else {
      m_ZeroR = null;

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    Random random = new Random(m_Seed);


    m_zeroR = null;
    if (data.numAttributes() == 1) {
      m_zeroR = new ZeroR();
      m_zeroR.buildClassifier(data);
      return;
    }


    // Randomize and stratify

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      filter.setInputFormat(data);
      FilteredClassifier classifier = new FilteredClassifier();
      if (data.numInstances() > 0) {
  classifier.setClassifier(Classifier.makeCopies(m_Classifier, 1)[0]);
      } else {
  classifier.setClassifier(new ZeroR());
      }
      classifier.setFilter(filter);
      
      if (!m_classifiers.containsKey(node.m_left.getString() + "|" + node.m_right.getString())) {
  classifier.buildClassifier(data);

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  public double[] distributionForInstance(Instance instance) throws Exception {


    if (m_bestCommittee != null) return m_bestCommittee.distributionForInstance(instance);
    else {
      if (m_validationSetChanged || m_zeroR == null) {
  m_zeroR = new ZeroR();
  m_zeroR.buildClassifier(m_validationSet);
  m_validationSetChanged = false;
      }
      return m_zeroR.distributionForInstance(instance);
    }

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    if (m_bestCommittee != null) {
      return m_bestCommittee.toString();
    } else {
      if ((m_validationSetChanged || m_zeroR == null) && m_validationSet != null
    && m_validationSet.numInstances() > 0) {
  m_zeroR = new ZeroR();
  try {
    m_zeroR.buildClassifier(m_validationSet);
  } catch (Exception e) {}
  m_validationSetChanged = false;
      }

View Full Code Here

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