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 $




  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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    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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  public UserClassifierTest(String name) { super(name);  }


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

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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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  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;
      }

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  protected FilteredClassifier getFilteredClassifier() {
    FilteredClassifier   result;
    
    result = super.getFilteredClassifier();
    ((NominalToString) result.getFilter()).setAttributeIndexes("1");
    result.setClassifier(new ZeroR());
    
    return result;
  }

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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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    m_NNSearch.setInstances(m_Train);


    // Invalidate any currently cross-validation selected k
    m_kNNValid = false;
    
    m_defaultModel = new ZeroR();
    m_defaultModel.buildClassifier(instances);
  }

View Full Code Here

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