Examples of weka.core.Instances.deleteWithMissingClass()

weka.core.Instances.deleteWithMissingClass()
Removes all instances with a missing class value from the dataset. @throws UnassignedClassException if class is not set

    // can classifier handle the data?
    getCapabilities().testWithFail(data);


    // remove instances with missing class
    Instances train = new Instances(data);
    train.deleteWithMissingClass();
    
    int numAttributes = train.attribute(1).relation().numAttributes();  
    m_Center = new double[numAttributes];


    if (getDebug())

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    Instances te = new Instances(i, i.numInstances());
    for (int noa = 0; noa < i.numInstances(); noa++) {
      te.add(i.instance(noa));
    }
    
    te.deleteWithMissingClass(); //remove all instances with a missing class
    //from training
    
    m_top = new TreeClass(null, 0, 0, m_nextId, 1, te, null);
    m_focus = m_top;
    //System.out.println(graph());

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    // can classifier handle the data?
    getCapabilities().testWithFail(instances);


    // remove instances with missing class
    Instances data = new Instances(instances);
    data.deleteWithMissingClass();


    // only class? -> build ZeroR model
    if (data.numAttributes() == 1) {
      System.err.println(
    "Cannot build model (only class attribute present in data!), "

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    // can classifier handle the data?
    getCapabilities().testWithFail(data);


    // remove instances with missing class
    Instances filteredData = new Instances(data);
    filteredData.deleteWithMissingClass();
    
    //replace missing values
    m_replaceMissing = new ReplaceMissingValues();
    m_replaceMissing.setInputFormat(filteredData);  
    filteredData = Filter.useFilter(filteredData, m_replaceMissing);

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    // can classifier handle the data?
    getCapabilities().testWithFail(data);


    // remove instances with missing class
    Instances filteredData = new Instances(data);
    filteredData.deleteWithMissingClass();
    
    //replace missing values
    m_replaceMissing = new ReplaceMissingValues();
    m_replaceMissing.setInputFormat(filteredData);  
    filteredData = Filter.useFilter(filteredData, m_replaceMissing);

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    // can classifier handle the data?
    getCapabilities().testWithFail(data);


    // remove instances with missing class
    Instances newData = new Instances(data);
    newData.deleteWithMissingClass();
    
    Random random = new Random(m_Seed);
    newData.randomize(random);
    if (newData.classAttribute().isNominal() && (m_NumXValFolds > 1)) {
      newData.stratify(m_NumXValFolds);

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    // can classifier handle the data?
    getCapabilities().testWithFail(data);


    // remove instances with missing class
    Instances newData = new Instances(data);
    newData.deleteWithMissingClass();


    double sum = 0;
    double temp_sum = 0;
    // Add the model for the mean first
    m_zeroR = new ZeroR();

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    // can classifier handle the data?
    getCapabilities().testWithFail(instances);


    // remove instances with missing class
    Instances data = new Instances(instances);
    data.deleteWithMissingClass();
    
    if(data.numInstances() < m_Folds)
      throw new Exception("Not enough data for REP.");


    m_ClassAttribute = data.classAttribute();

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    // can classifier handle the data?
    getCapabilities().testWithFail(instances);


    // remove instances with missing class
    Instances data = new Instances(instances);
    data.deleteWithMissingClass();
    
    int numCl = data.numClasses();
    m_Root = new Ridor_node();
    m_Class = instances.classAttribute();     // The original class label

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      Instances data = new Instances(instances);
      if(Utils.eq(data.sumOfWeights(),0))
  throw new Exception(" No training data.");
      
      data.deleteWithMissingClass();
      if(Utils.eq(data.sumOfWeights(),0))
  throw new Exception(" The class labels of all the training data are missing.");  
      
      if(data.numInstances() < m_Folds)
  throw new Exception(" Not enough data for REP.");

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