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 $

    // 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


    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();
  }

View Full Code Here

    // 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?

View Full Code Here

    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

      // 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();

View Full Code Here

      }
      insts = data;
    }


    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

    // 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();

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

    // 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?

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.