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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   */
  public void setIgnoredAttributeIndices(String rangeList) {
    if ((rangeList == null) || (rangeList.length() == 0)) {
      m_IgnoreAttributesRange = null;
    } else {
      m_IgnoreAttributesRange = new Range();
      m_IgnoreAttributesRange.setRanges(rangeList);
    }
  }

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    outputFormat.insertAttributeAt(newAttribute, m_Insert.getIndex());
    setOutputFormat(outputFormat);
    
    // all attributes, except index of added attribute
    // (otherwise the length of the input/output indices differ)
    Range atts = new Range(m_Insert.getSingleIndex());
    atts.setInvert(true);
    atts.setUpper(outputFormat.numAttributes() - 1);
    initOutputLocators(outputFormat, atts.getSelection());
    
    return true;
  }

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    String trainFileName, testFileName, seedString, foldsString;
    String objectInputFileName, objectOutputFileName, attributeRangeString;
    String graphFileName;
    String[] savedOptions = null;
    boolean printClusterAssignments = false;
    Range attributesToOutput = null;
    StringBuffer text = new StringBuffer();
    int theClass = -1; // class based evaluation of clustering
    boolean updateable = (clusterer instanceof UpdateableClusterer);
    DataSource source = null;
    Instance inst;


    if (Utils.getFlag('h', options) || Utils.getFlag("help", options)) {
      
      // global info requested as well?
      boolean globalInfo = Utils.getFlag("synopsis", options) ||
        Utils.getFlag("info", options);
      
      throw  new Exception("Help requested." 
          + makeOptionString(clusterer, globalInfo));
    }
    
    try {
      // Get basic options (options the same for all clusterers
      //printClusterAssignments = Utils.getFlag('p', options);
      objectInputFileName = Utils.getOption('l', options);
      objectOutputFileName = Utils.getOption('d', options);
      trainFileName = Utils.getOption('t', options);
      testFileName = Utils.getOption('T', options);
      graphFileName = Utils.getOption('g', options);


      // Check -p option
      try {
  attributeRangeString = Utils.getOption('p', options);
      }
      catch (Exception e) {
  throw new Exception(e.getMessage() + "\nNOTE: the -p option has changed. " +
          "It now expects a parameter specifying a range of attributes " +
          "to list with the predictions. Use '-p 0' for none.");
      }
      if (attributeRangeString.length() != 0) {
  printClusterAssignments = true;
  if (!attributeRangeString.equals("0")) 
    attributesToOutput = new Range(attributeRangeString);
      }


      if (trainFileName.length() == 0) {
        if (objectInputFileName.length() == 0) {
          throw  new Exception("No training file and no object "

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  public void setOptions(String[] options) throws Exception {
    String  tmpStr;
    String  classname;
    String[]  options2;
    Vector  objects;
    Range  range;


    super.setOptions(options);


    setRemoveUnused(Utils.getFlag("U", options));


    objects = new Vector();
    while ((tmpStr = Utils.getOption("F", options)).length() != 0) {
      options2    = Utils.splitOptions(tmpStr);
      classname      = options2[0];
      options2[0] = "";
      objects.add(Utils.forName(Filter.class, classname, options2));
    }


    // at least one filter
    if (objects.size() == 0)
      objects.add(new AllFilter());


    setFilters((Filter[]) objects.toArray(new Filter[objects.size()]));


    objects = new Vector();
    while ((tmpStr = Utils.getOption("R", options)).length() != 0) {
      if (tmpStr.startsWith("inv(") && tmpStr.endsWith(")")) {
  range = new Range(tmpStr.substring(4, tmpStr.length() - 1));
  range.setInvert(true);
      }
      else {
  range = new Range(tmpStr);
      }
      objects.add(range);
    }


    // at least one Range
    if (objects.size() == 0)
      objects.add(new Range("first-last"));


    setRanges((Range[]) objects.toArray(new Range[objects.size()]));


    // is number of filters the same as ranges?
    checkDimensions();

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    for (int i = 0; i < getRanges().length; i++) {
      Instances newi = new Instances(instanceInfo, 0);
      if (instanceInfo.size() > 0){
  newi.add((Instance)instanceInfo.get(0).copy());
      }
      Range range = getRanges()[i];
      range.setUpper(instanceInfo.numAttributes() - 1);
      Instances subset = generateSubset(newi, range);
      getFilters()[i].setInputFormat(subset);
    }
  }

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  /**
   * Constructor
   */
  public MakeIndicator() {


      m_ValIndex = new Range("last");
  }

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   * Set the range of attributes to process.
   *
   * @param value   the new range.
   */
  public void setAttributeIndices(String value) {
    m_AttributeIndices = new Range(value);
  }

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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

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