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);


    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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  /** Creates a configured MultiFilter (variant) */
  public Filter getConfiguredFilterVariant() {
    MultiFilter result = new MultiFilter();
    
    Filter[] filters = new Filter[2];
    filters[0] = new ReplaceMissingValues();
    filters[1] = new Center();
    
    result.setFilters(filters);
    
    return result;

View Full Code Here

      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();

View Full Code Here

  if (counter > 2) {
    throw ex;
  }
      } catch (NoSupportForMissingValuesException ex2) {
  System.err.println("\nReplacing missing values.");
  ReplaceMissingValues rmFilter = new ReplaceMissingValues();
  rmFilter.setInputFormat(train);
  train = Filter.useFilter(train, rmFilter);
  rmFilter.batchFinished();
  test = Filter.useFilter(test, rmFilter);
      } catch (IllegalArgumentException ex3) {
  String msg = ex3.getMessage();
  if (msg.indexOf("Not enough instances") != -1) {
    System.err.println("\nInflating training data.");

View Full Code Here

   */
  public void buildClusterer(Instances data) 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;

View Full Code Here

    m_trainHeader = null;
    
    m_attributeFilter = null;
    m_nominalToBinaryFilter = null;
    
    m_replaceMissingFilter = new ReplaceMissingValues();
    m_replaceMissingFilter.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, m_replaceMissingFilter);
    
    // vector to hold indices of attributes to delete (class attribute, 
    // attributes that are all missing, or attributes with one distinct value)

View Full Code Here

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


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


    m_nominalToBinary = new NominalToBinary();
    m_nominalToBinary.setInputFormat(m_instances);

View Full Code Here

   */  
  public void buildClusterer(Instances data) throws Exception {
    // can clusterer handle the data?
    getCapabilities().testWithFail(data);


    m_replaceMissing = new ReplaceMissingValues();
    m_replaceMissing.setInputFormat(data);
    data = weka.filters.Filter.useFilter(data, m_replaceMissing);


    m_theInstances = new Instances(data, 0);
    if (m_wrappedClusterer == null) {

View Full Code Here

    // 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);  
  
    //possibly convert nominal attributes globally
    if (m_convertNominal) {

View Full Code Here

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