Package weka.classifiers.trees.j48

Examples of weka.classifiers.trees.j48.C45PruneableClassifierTree

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  public Capabilities getCapabilities() {
    Capabilities      result;
   
    try {
      if (!m_reducedErrorPruning)
        result = new C45PruneableClassifierTree(null, !m_unpruned, m_CF, m_subtreeRaising, !m_noCleanup, m_collapseTree).getCapabilities();
      else
        result = new PruneableClassifierTree(null, !m_unpruned, m_numFolds, !m_noCleanup, m_Seed).getCapabilities();
    }
    catch (Exception e) {
      result = new Capabilities(this);
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    if (m_binarySplits)
      modSelection = new BinC45ModelSelection(m_minNumObj, instances, m_useMDLcorrection);
    else
      modSelection = new C45ModelSelection(m_minNumObj, instances, m_useMDLcorrection);
    if (!m_reducedErrorPruning)
      m_root = new C45PruneableClassifierTree(modSelection, !m_unpruned, m_CF,
                                              m_subtreeRaising, !m_noCleanup, m_collapseTree);
    else
      m_root = new PruneableClassifierTree(modSelection, !m_unpruned, m_numFolds,
             !m_noCleanup, m_Seed);
    m_root.buildClassifier(instances);
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   */
  public Capabilities getCapabilities() {
    Capabilities      result;

    try {
     result = new C45PruneableClassifierTreeG(null, !m_unpruned, m_CF, m_subtreeRaising, m_relabel, !m_noCleanup).getCapabilities();
    }
    catch (Exception e) {
      result = new Capabilities(this);
      result.disableAll();
    }
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    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);

    if (m_binarySplits) {
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   */
  public Capabilities getCapabilities() {
    Capabilities      result;

    try {
     result = new C45PruneableClassifierTreeG(null, !m_unpruned, m_CF, m_subtreeRaising, m_relabel, !m_noCleanup).getCapabilities();
    }
    catch (Exception e) {
      result = new Capabilities(this);
    }

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    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);

    if (m_binarySplits) {
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    m_isLeaf = false;
    m_isEmpty = false;
    m_sons = null;
    indeX = 0;
    sumOfWeights = data.sumOfWeights();
    noSplit = new NoSplit (new Distribution((Instances)data));
    if (leaf)
      m_localModel = noSplit;
    else
      m_localModel = m_toSelectModel.selectModel(data);
    if (m_localModel.numSubsets() > 1) {
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    m_isLeaf = false;
    m_isEmpty = false;
    m_sons = null;
    indeX = 0;
    sumOfWeights = train.sumOfWeights();
    noSplit = new NoSplit (new Distribution((Instances)train));
    if (leaf)
      m_localModel = noSplit;
    else
      m_localModel = m_toSelectModel.selectModel(train, test);
    m_test = new Distribution(test, m_localModel);
    if (m_localModel.numSubsets() > 1) {
      localTrain = m_localModel.split(train);
      localTest = m_localModel.split(test);
      train = null;
      test = null;
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  /**
   * Computes error estimate for tree.
   */
  private double errorsForTree() throws Exception {

    Distribution test;

    if (m_isLeaf)
      return errorsForLeaf();
    else {
      double error = 0;
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    m_isLeaf = false;
    m_isEmpty = false;
    m_sons = null;
    indeX = 0;
    sumOfWeights = data.sumOfWeights();
    noSplit = new NoSplit (new Distribution((Instances)data));
    if (leaf)
      m_localModel = noSplit;
    else
      m_localModel = m_toSelectModel.selectModel(data);
    if (m_localModel.numSubsets() > 1) {
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