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 $

    m_onlyNumeric = false;
    break;
  }
      }
    }
    m_Missing = new ReplaceMissingValues();
    m_Missing.setInputFormat(instances);
    instances = Filter.useFilter(instances, m_Missing);
    
    if (!m_onlyNumeric) {
      m_NominalToBinary = new NominalToBinary();

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    insts = new Instances(insts);
    insts.deleteWithMissingClass();
    
    // Filter data
    m_Train = new Instances(insts);
    m_ReplaceMissingValues = new ReplaceMissingValues();
    m_ReplaceMissingValues.setInputFormat(m_Train);
    m_Train = Filter.useFilter(m_Train, m_ReplaceMissingValues);
    
    m_NominalToBinary = new NominalToBinary();
    m_NominalToBinary.setInputFormat(m_Train);

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   */
  public PLSFilter() {
    super();
    
    // setup pre-processing
    m_Missing = new ReplaceMissingValues();
    m_Filter  = new Center();
  }

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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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      insts = data;     
    }


    // filter data
    if (!m_checksTurnedOff) 
      m_Missing = new ReplaceMissingValues();
    else 
      m_Missing = null;


    if (getCapabilities().handles(Capability.NUMERIC_ATTRIBUTES)) {
      boolean onlyNumeric = true;

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        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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        resultVector = new FastVector();
        long time_1 = System.currentTimeMillis();


        numberOfGeneratedClusters = 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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    m_NumSplits = 0;
    m_NumSplitsDone = 0;
    m_NumSplitsStillDone = 0;


    // replace missing values
    m_ReplaceMissingFilter = new ReplaceMissingValues();
    m_ReplaceMissingFilter.setInputFormat(data);
    m_Instances = Filter.useFilter(data, m_ReplaceMissingFilter);
    
    // initialize random function
    Random random0 = new Random(m_Seed);

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    // 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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    // make a copy of the training data so that we can get the class
    // column to append to the transformed data (if necessary)
    m_trainHeader = new Instances(m_trainInstances, 0);
    
    m_replaceMissingFilter = new ReplaceMissingValues();
    m_replaceMissingFilter.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, 
                                        m_replaceMissingFilter);


    /*if (m_normalize) {

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