Package weka.classifiers.trees.j48

Examples of weka.classifiers.trees.j48.Distribution

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    ModelSelection modSelection;

    if (m_binarySplits)
      modSelection = new BinC45ModelSelection(m_minNumObj, instances);
    else
      modSelection = new C45ModelSelection(m_minNumObj, instances);
      m_root = new C45PruneableClassifierTreeG(modSelection,
                              !m_unpruned, m_CF, m_subtreeRaising,
                               m_relabel, !m_noCleanup);
    m_root.buildClassifier(instances);
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    //create ModelSelection object, either for splits on the residuals or for splits on the class value
    ModelSelection modSelection; 
    if (m_splitOnResiduals) {
      modSelection = new ResidualModelSelection(minNumInstances);
    } else {
      modSelection = new C45ModelSelection(minNumInstances, filteredData);
    }
 
    //create tree root
    m_tree = new LMTNode(modSelection, m_numBoostingIterations, m_fastRegression,
       m_errorOnProbabilities, m_minNumInstances, m_weightTrimBeta, m_useAIC);
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    //create ModelSelection object, either for splits on the residuals or for splits on the class value
    ModelSelection modSelection; 
    if (m_splitOnResiduals) {
      modSelection = new ResidualModelSelection(minNumInstances);
    } else {
      modSelection = new C45ModelSelection(minNumInstances, filteredData);
    }
 
    //create tree root
    m_tree = new LMTNode(modSelection, m_numBoostingIterations, m_fastRegression,
       m_errorOnProbabilities, m_minNumInstances, m_weightTrimBeta, m_useAIC);
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    ModelSelection modSelection;

    if( m_binarySplits ) {
      modSelection = new BinC45ModelSelection( m_minNumObj, instances );
    } else {
      modSelection = new C45ModelSelection( m_minNumObj, instances );
    }
    if( !m_reducedErrorPruning ) {
      m_root = new C45PruneableClassifierTree( modSelection, !m_unpruned, m_CF,
          m_subtreeRaising, !m_noCleanup );
    } else {
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        // Computes constructor error
        if (data.instance(j).classValue()!=getConstError(probsConst)) m_constError=m_constError +1;
      }
       
    //to choose split point on the node data
    m_modelSelection=new  C45ModelSelection(m_minNumInstances, data);
    m_localModel = m_modelSelection.selectModel(data);
      
    //split node if more than minNumInstances...
    if (m_numInstances > m_minNumInstances) {
      grow = (m_localModel.numSubsets() > 1);
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          data.instance(j).setValue(i,probsConst[i]);
      }
 
       
    //needed by dynamic data
    m_modelSelection=new  C45ModelSelection(m_minNumInstances, data);
     
    m_localModel = m_modelSelection.selectModel(data);
      
    //split node if more than minNumInstances...
    if (m_numInstances > m_minNumInstances) {
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        for (int i = 0; i<data.classAttribute().numValues() ; i++)
          data.instance(j).setValue(i,probsConst[i]);
      }
       
    // needed by dynamic data
    m_modelSelection=new  C45ModelSelection(m_minNumInstances, data);
    m_localModel = m_modelSelection.selectModel(data);
    //split node if more than minNumInstances...
    if (m_numInstances > m_minNumInstances) {
      grow = (m_localModel.numSubsets() > 1);
    } else {
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    ModelSelection modSelection;  

    if (m_binarySplits)
      modSelection = new BinC45ModelSelection(m_minNumObj, instances);
    else
      modSelection = new C45ModelSelection(m_minNumObj, instances);
    if (m_unpruned)
      m_root = new MakeDecList(modSelection, m_minNumObj);
    else if (m_reducedErrorPruning)
      m_root = new MakeDecList(modSelection, m_numFolds, m_minNumObj, m_Seed);
    else
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    ModelSelection modSelection;

    if (m_binarySplits)
      modSelection = new BinC45ModelSelection(m_minNumObj, instances);
    else
      modSelection = new C45ModelSelection(m_minNumObj, instances);
      m_root = new C45PruneableClassifierTreeG(modSelection,
                              !m_unpruned, m_CF, m_subtreeRaising,
                               m_relabel, !m_noCleanup);
    m_root.buildClassifier(instances);
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    ModelSelection modSelection;  

    if (m_binarySplits)
      modSelection = new BinC45ModelSelection(m_minNumObj, instances, m_useMDLcorrection);
    else
      modSelection = new C45ModelSelection(m_minNumObj, instances, m_useMDLcorrection);
    if (m_unpruned)
      m_root = new MakeDecList(modSelection, m_minNumObj);
    else if (m_reducedErrorPruning)
      m_root = new MakeDecList(modSelection, m_numFolds, m_minNumObj, m_Seed);
    else
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