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 $

    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);
    m_Train = Filter.useFilter(m_Train, m_NominalToBinary);

View Full Code Here

    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);

View Full Code Here

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

View Full Code Here

    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();

View Full Code Here

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

View Full Code Here

      insts = data;     
    }


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


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

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

    // Preprocess instances
    if (!m_checksTurnedOff) {
      m_TransformFilter = new NominalToBinary();
      m_TransformFilter.setInputFormat(data);
      data = Filter.useFilter(data, m_TransformFilter);
      m_MissingFilter = new ReplaceMissingValues();
      m_MissingFilter.setInputFormat(data);
      data = Filter.useFilter(data, m_MissingFilter);
      data.deleteWithMissingClass();
    } else {
      m_TransformFilter = null;

View Full Code Here

    // Preprocess instances
    if (!m_checksTurnedOff) {
      m_TransformFilter = new NominalToBinary();
      m_TransformFilter.setInputFormat(data);
      data = Filter.useFilter(data, m_TransformFilter);
      m_MissingFilter = new ReplaceMissingValues();
      m_MissingFilter.setInputFormat(data);
      data = Filter.useFilter(data, m_MissingFilter);
      data.deleteWithMissingClass();
    } else {
      m_TransformFilter = null;

View Full Code Here

    
    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

View Full Code Here

0 1 2 3 4 5 6 7

TOP

Related Classes of weka.filters.unsupervised.attribute.ReplaceMissingValues

weka.attributeSelection.LatentSemanticAnalysis

weka.attributeSelection.PrincipalComponents

weka.classifiers.bayes.BayesNet

weka.classifiers.functions.GaussianProcesses

weka.classifiers.functions.LeastMedSq

weka.classifiers.functions.LibLINEAR

weka.classifiers.functions.LibSVM

weka.classifiers.functions.LinearRegression

weka.classifiers.functions.Logistic

weka.classifiers.functions.SGD

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.