Examples of weka.core.Range

weka.core.Range
Class representing a range of cardinal numbers. The range is set by a string representation such as:
first-last 1,2,3,4
or combinations thereof. The range is internally converted from 1-based to 0-based (so methods that set or get numbers not in string format should use 0-based numbers). @author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 1.18 $

   * Set the value of m_SelectedRange.
   *
   * @param newSelectedRange Value to assign to m_SelectedRange.
   */
  public void setSelectedRange(String newSelectedRange) {
    m_SelectedRange = new Range(newSelectedRange);
  }

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      StringBuffer fields = new StringBuffer();
      for (int j = 0; j < inputFormat.numAttributes(); j++) { 
  if (inputFormat.attribute(j).type() == Attribute.STRING)
    fields.append((j + 1) + ",");
      }
      m_SelectedRange = new Range(fields.toString());
    }
    m_SelectedRange.setUpper(inputFormat.numAttributes() - 1);


    // Prevent the user from converting non-string fields
    StringBuffer fields = new StringBuffer();

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    m_forwardSearchMethod = SEARCH_METHOD_FORWARD;
    m_performRanking = true;
    m_numUsedAttributes = 50;
    m_linearSelectionType = TYPE_FIXED_SET;
    m_starting = null;
    m_startRange = new Range();
    m_classIndex = -1;
    m_totalEvals = 0;
    m_cacheSize = 1;
    m_verbose = false;
  }

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      setResultMatrix(new ResultMatrixHTML());
    if (Utils.getFlag("significance", options))
      setResultMatrix(new ResultMatrixSignificance());


    String datasetList = Utils.getOption('D', options);
    Range datasetRange = new Range();
    if (datasetList.length() != 0) {
      datasetRange.setRanges(datasetList);
    }
    setDatasetKeyColumns(datasetRange);


    String indexStr = Utils.getOption('R', options);
    if (indexStr.length() != 0) {
      if (indexStr.equals("first")) {
  setRunColumn(0);
      } else if (indexStr.equals("last")) {
  setRunColumn(-1);
      } else {
  setRunColumn(Integer.parseInt(indexStr) - 1);
      }    
    } else {
      setRunColumn(-1);
    }


    String foldStr = Utils.getOption('F', options);
    if (foldStr.length() != 0) {
      setFoldColumn(Integer.parseInt(foldStr) - 1);
    } else {
      setFoldColumn(-1);
    }


    String sigStr = Utils.getOption('S', options);
    if (sigStr.length() != 0) {
      setSignificanceLevel((new Double(sigStr)).doubleValue());
    } else {
      setSignificanceLevel(0.05);
    }
    
    String resultsetList = Utils.getOption('G', options);
    Range generatorRange = new Range();
    if (resultsetList.length() != 0) {
      generatorRange.setRanges(resultsetList);
    }
    setResultsetKeyColumns(generatorRange);
  }

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      for(int i = 0; i < m_ClassFilters.length; i++) {
  if (m_Classifiers[i] != null) {
    Instance tempInst = (Instance)inst.copy(); 
    tempInst.setDataset(m_TwoClassDataset);
    double [] current = m_Classifiers[i].distributionForInstance(tempInst);  
    Range range = new Range(((RemoveWithValues)m_ClassFilters[i])
          .getNominalIndices());
    range.setUpper(m_ClassAttribute.numValues());
    int[] pair = range.getSelection();
          if (m_pairwiseCoupling && inst.numClasses() > 2) {
            r[pair[0]][pair[1]] = current[0];
            n[pair[0]][pair[1]] = m_SumOfWeights[i];
          } else {
            if (current[0] > current[1]) {

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    for (int i = 0; i < m_Classifiers.length; i++) {
      text.append("Classifier ").append(i + 1);
      if (m_Classifiers[i] != null) {
        if ((m_ClassFilters != null) && (m_ClassFilters[i] != null)) {
    if (m_ClassFilters[i] instanceof RemoveWithValues) {
      Range range = new Range(((RemoveWithValues)m_ClassFilters[i])
            .getNominalIndices());
      range.setUpper(m_ClassAttribute.numValues());
      int[] pair = range.getSelection();
      text.append(", " + (pair[0]+1) + " vs " + (pair[1]+1));
    } else if (m_ClassFilters[i] instanceof MakeIndicator) {
      text.append(", using indicator values: ");
      text.append(((MakeIndicator)m_ClassFilters[i]).getValueRange());
    }

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   * Constructor
   */
  public GreedyStepwise () {
    m_threshold = -Double.MAX_VALUE;
    m_doneRanking = false;
    m_startRange = new Range();
    m_starting = null;
    resetOptions();
  }

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   */
  protected void resetOptions () {
    m_maxStale = 5;
    m_searchDirection = SELECTION_FORWARD;
    m_starting = null;
    m_startRange = new Range();
    m_classIndex = -1;
    m_totalEvals = 0;
    m_cacheSize = 1;
    m_debug = false;
  }

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  /**
   * Resets stuff to default values
   */
  protected void resetOptions () {
    m_starting = null;
    m_startRange = new Range();
    m_attributeList = null;
    m_attributeMerit = null;
    m_threshold = -Double.MAX_VALUE;
  }

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  /**
   * resets to defaults
   */
  private void resetOptions() {
    m_starting = null;
    m_startRange = new Range();
    m_searchSize = 0.25;
    m_seed = 1;
    m_onlyConsiderBetterAndSmaller = false;
    m_verbose = false;
  }

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Related Classes of weka.core.Range

weka.attributeSelection.BestFirst

weka.attributeSelection.FCBFSearch

weka.attributeSelection.GeneticSearch

weka.attributeSelection.GreedyStepwise

weka.attributeSelection.LinearForwardSelection

weka.attributeSelection.RandomSearch

weka.attributeSelection.Ranker

weka.classifiers.Evaluation

weka.classifiers.evaluation.output.prediction.AbstractOutput

weka.classifiers.meta.MultiClassClassifier

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