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 newData = new Instances(data);
    newData.deleteWithMissingClass();


    m_Random = new Random(getSeed());
    
    for (int i = 0; i < m_Classifiers.length; i++) {
      getClassifier(i).buildClassifier(newData);

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


    // remove instances with missing class
    Instances trainData = new Instances(instances);
    trainData.deleteWithMissingClass();
    
    if (!(m_Classifier instanceof OptionHandler)) {
      throw new IllegalArgumentException("Base classifier should be OptionHandler.");
    }
    m_InitOptions = ((OptionHandler)m_Classifier).getOptions();

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        if (data.classAttribute().type() != Attribute.NOMINAL) {
            throw new Exception("Class attribute must be nominal");
        }
        int numClasses = data.numClasses();
        
        data.deleteWithMissingClass();
        if ( data.checkForStringAttributes() ) {
            throw new Exception("Can't handle string attributes!");
        }
        
        // Dataset size must be greater than 2

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    if (data.classAttribute().type() != Attribute.NOMINAL) {
      throw new Exception("Class attribute must be nominal");
    }
    int numClasses = data.numClasses();


    data.deleteWithMissingClass();
    if (data.checkForStringAttributes()) {
      throw new Exception("Can't handle string attributes!");
    }


    if (data.numInstances() < 2 * m_TrainPoolSize) {

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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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    // copy the dataset 
    Instances data = new Instances(instances);


    // new dataset where examples with missing class value are removed
    data.deleteWithMissingClass();
    if (data.numInstances() == 0) {
      throw new IllegalArgumentException
      ("No instances with a class value!");
    }

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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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  public void buildClassifier(Instances data) throws Exception {
    // can classifier handle the data?
    getCapabilities().testWithFail(data);
    // remove instances with missing class
    Instances instances =  new Instances(data);
    instances.deleteWithMissingClass();


    m_binaryClassifiers = new DNBBinary[instances.numClasses()];
    m_numClasses=instances.numClasses();
    m_headerInfo = new Instances(instances, 0);
    for (int i = 0; i < instances.numClasses(); i++) {

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    m_errorMessage = null;
    m_targetSI.setUpper(instances.numAttributes() - 1);
    m_target = m_targetSI.getIndex();
    Instances inst = new Instances(instances);
    inst.setClassIndex(m_target);
    inst.deleteWithMissingClass();


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


    if (inst.attribute(m_target).isNominal()) {

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


    // remove instances with missing class
    Instances newData = new Instances(exs);
    newData.deleteWithMissingClass();
    
    int numegs = newData.numInstances();
    m_Dimension = newData.attribute(1).relation().numAttributes();
    m_Attributes = newData.stringFreeStructure(); 
    m_Change = new double[numegs][m_Dimension];

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