Examples of weka.filters.unsupervised.attribute.ReplaceMissingValues

Package weka.filters.unsupervised.attribute

Examples of weka.filters.unsupervised.attribute.ReplaceMissingValues

weka.filters.unsupervised.attribute.ReplaceMissingValues
Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
Valid options are:
```
 -unset-class-temporarily Unsets the class index temporarily before the filter is applied to the data. (default: no)
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5547 $

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


    //long start = System.currentTimeMillis();


    m_ReplaceMissingFilter = new ReplaceMissingValues();
    m_ReplaceMissingFilter.setInputFormat(data);
    m_instances = Filter.useFilter(data, m_ReplaceMissingFilter);


    initMinMax(m_instances);

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      instances = Filter.useFilter(instances, m_DiscretizeFilter);
    }


    if (bHasMissingValues) {
      System.err.println("Warning: filling in missing values in data set");
      m_MissingValuesFilter = new ReplaceMissingValues();
      m_MissingValuesFilter.setInputFormat(instances);
      instances = Filter.useFilter(instances, m_MissingValuesFilter);
    }
    return instances;
  } // normalizeDataSet

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      // is there a missing value in this instance?
      // this can happen when there is no missing value in the training set
      for (int iAttribute = 0; iAttribute < m_Instances.numAttributes(); iAttribute++) {
  if (iAttribute != instance.classIndex() && instance.isMissing(iAttribute)) {
    System.err.println("Warning: Found missing value in test set, filling in values.");
    m_MissingValuesFilter = new ReplaceMissingValues();
    m_MissingValuesFilter.setInputFormat(m_Instances);
    Filter.useFilter(m_Instances, m_MissingValuesFilter);
    m_MissingValuesFilter.input(instance);
    instance = m_MissingValuesFilter.output();
    iAttribute = m_Instances.numAttributes();

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


    m_Iterations = 0;


    m_ReplaceMissingFilter = new ReplaceMissingValues();
    Instances instances = new Instances(data);
        
    instances.setClassIndex(-1);
    if (!m_dontReplaceMissing) {
      m_ReplaceMissingFilter.setInputFormat(instances);

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    throws Exception {
    
    // can clusterer handle the data?
    getCapabilities().testWithFail(data);


    m_replaceMissing = new ReplaceMissingValues();
    Instances instances = new Instances(data);
    instances.setClassIndex(-1);
    m_replaceMissing.setInputFormat(instances);
    data = weka.filters.Filter.useFilter(instances, m_replaceMissing);
    instances = null;

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    // remove instances with missing class
    insts = new Instances(insts);
    insts.deleteWithMissingClass();
    
    if (!getDoNotReplaceMissingValues()) {
      m_ReplaceMissingValues = new ReplaceMissingValues();
      m_ReplaceMissingValues.setInputFormat(insts);
      insts = Filter.useFilter(insts, m_ReplaceMissingValues);
    }
    
    // can classifier handle the data?

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    data = new Instances(data);
    data.deleteWithMissingClass();
    
    if (data.numInstances() > 0 && !m_dontReplaceMissing) {
      m_replaceMissing = new ReplaceMissingValues();
      m_replaceMissing.setInputFormat(data);
      data = Filter.useFilter(data, m_replaceMissing);
    }
    
    // check for only numeric attributes

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


        if (!getDoNotReplaceMissingValues()) {
            m_ReplaceMissingValues = new ReplaceMissingValues();
            m_ReplaceMissingValues.setInputFormat(insts);
            insts = Filter.useFilter(insts, m_ReplaceMissingValues);
        }


        // can classifier handle the data?

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