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 $


        processed_InstanceID = 0;
        numberOfGeneratedClusters = 0;
        clusterID = 0;


        replaceMissingValues_Filter = new ReplaceMissingValues();
        replaceMissingValues_Filter.setInputFormat(instances);
        Instances filteredInstances = Filter.useFilter(instances, replaceMissingValues_Filter);


        database = databaseForName(getDatabase_Type(), filteredInstances);
        for (int i = 0; i < database.getInstances().numInstances(); i++) {

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    // 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) {

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      }
      insts = data;
    }


    if (!m_checksTurnedOff) {
      m_Missing = new ReplaceMissingValues();
      m_Missing.setInputFormat(insts);
      insts = Filter.useFilter(insts, m_Missing); 
    } else {
      m_Missing = null;
    }

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


  //replace missing values
  m_ReplaceMissingValues = new ReplaceMissingValues();
  m_ReplaceMissingValues.setInputFormat(data);
  data = Filter.useFilter(data, m_ReplaceMissingValues);
  
  //convert nominal attributes
  m_NominalToBinary = new NominalToBinary();

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    // remove instances with missing class
    train = new Instances(train);
    train.deleteWithMissingClass();
    
    // Replace missing values  
    m_ReplaceMissingValues = new ReplaceMissingValues();
    m_ReplaceMissingValues.setInputFormat(train);
    train = Filter.useFilter(train, m_ReplaceMissingValues);


    // Remove useless attributes
    m_AttFilter = new RemoveUseless();

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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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      }
      insts = data;
    }


    if (!m_checksTurnedOff) {
      m_Missing = new ReplaceMissingValues();
      m_Missing.setInputFormat(insts);
      insts = Filter.useFilter(insts, m_Missing); 
    } else {
      m_Missing = null;
    }

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    m_Data = data;
    m_TransformFilter = new NominalToBinary();
    m_TransformFilter.setInputFormat(m_Data);
    m_Data = Filter.useFilter(m_Data, m_TransformFilter);
    m_MissingFilter = new ReplaceMissingValues();
    m_MissingFilter.setInputFormat(m_Data);
    m_Data = Filter.useFilter(m_Data, m_MissingFilter);
    m_Data.deleteWithMissingClass();
  }

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    // 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) {

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