Examples of weka.filters.supervised.attribute.Discretize

Package weka.filters.supervised.attribute

Examples of weka.filters.supervised.attribute.Discretize

weka.filters.supervised.attribute.Discretize

i.uni-lj.si/papers/kononenko95-ijcai.ps.gz} }

Valid options are:

 -R <col1,col2-col4,...> Specifies list of columns to Discretize. First and last are valid indexes. (default none)

 -V Invert matching sense of column indexes.

 -D Output binary attributes for discretized attributes.

 -E Use better encoding of split point for MDL.

 -K Use Kononenko's MDL criterion.

@author Len Trigg (trigg@cs.waikato.ac.nz) @author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 6565 $

    // Build classifier
    if (nominalClassValue) {


      FilteredClassifier fclass = new FilteredClassifier();
      fclass.setClassifier(new NaiveBayesSimple());
      fclass.setFilter(new Discretize());
      classifier = fclass;


      /*
       * classifier = new Bagging(); // try also //
       * classifier.setOptions(Utils.splitOptions("-P 10 -S 1 -I 10 -W

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    int classIndex = data.classIndex();
    int numInstances = data.numInstances();
    
    if (!m_Binarize) {
      Discretize disTransform = new Discretize();
      disTransform.setUseBetterEncoding(true);
      disTransform.setInputFormat(data);
      data = Filter.useFilter(data, disTransform);
    } else {
      NumericToBinary binTransform = new NumericToBinary();
      binTransform.setInputFormat(data);
      data = Filter.useFilter(data, binTransform);

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    m_trainInstances = data;
    m_classIndex = m_trainInstances.classIndex();
    m_numAttribs = m_trainInstances.numAttributes();
    m_numInstances = m_trainInstances.numInstances();
    Discretize disTransform = new Discretize();
    disTransform.setUseBetterEncoding(true);
    disTransform.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, disTransform);
    m_numClasses = m_trainInstances.attribute(m_classIndex).numValues();
  }

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    int classIndex = data.classIndex();
    int numInstances = data.numInstances();
    
    if (!m_Binarize) {
      Discretize disTransform = new Discretize();
      disTransform.setUseBetterEncoding(true);
      disTransform.setInputFormat(data);
      data = Filter.useFilter(data, disTransform);
    } else {
      NumericToBinary binTransform = new NumericToBinary();
      binTransform.setInputFormat(data);
      data = Filter.useFilter(data, binTransform);

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      }
    }


    if (bHasNonNominal) {
      System.err.println("Warning: discretizing data set");
      m_DiscretizeFilter = new Discretize();
      m_DiscretizeFilter.setInputFormat(instances);
      instances = Filter.useFilter(instances, m_DiscretizeFilter);
    }


    if (bHasMissingValues) {

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    m_trainInstances = data;
    m_classIndex = m_trainInstances.classIndex();
    m_numAttribs = m_trainInstances.numAttributes();
    m_numInstances = m_trainInstances.numInstances();
    Discretize disTransform = new Discretize();
    disTransform.setUseBetterEncoding(true);
    disTransform.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, disTransform);
    m_numClasses = m_trainInstances.attribute(m_classIndex).numValues();
  }

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    m_trainInstances.deleteWithMissingClass();
    m_classIndex = m_trainInstances.classIndex();
    m_numAttribs = m_trainInstances.numAttributes();
    m_numInstances = m_trainInstances.numInstances();


    m_disTransform = new Discretize();
    m_disTransform.setUseBetterEncoding(true);
    m_disTransform.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, m_disTransform);
  }

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    m_numAttribs = m_trainInstances.numAttributes();
    m_numInstances = m_trainInstances.numInstances();
    m_isNumeric = m_trainInstances.attribute(m_classIndex).isNumeric();


    if (!m_isNumeric) {
      m_disTransform = new Discretize();
      m_disTransform.setUseBetterEncoding(true);
      m_disTransform.setInputFormat(m_trainInstances);
      m_trainInstances = Filter.useFilter(m_trainInstances, m_disTransform);
    }

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    m_trainInstances = data;
    m_classIndex = m_trainInstances.classIndex();
    m_numAttribs = m_trainInstances.numAttributes();
    m_numInstances = m_trainInstances.numInstances();
    Discretize disTransform = new Discretize();
    disTransform.setUseBetterEncoding(true);
    disTransform.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, disTransform);
    m_numClasses = m_trainInstances.attribute(m_classIndex).numValues();
  }

View Full Code Here


    m_trainInstances = data;
    m_classIndex = m_trainInstances.classIndex();
    m_numAttribs = m_trainInstances.numAttributes();
    m_numInstances = m_trainInstances.numInstances();
    Discretize disTransform = new Discretize();
    disTransform.setUseBetterEncoding(true);
    disTransform.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, disTransform);
    m_numClasses = m_trainInstances.attribute(m_classIndex).numValues();
  }

View Full Code Here

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