Examples of weka.core.Option

weka.core.Option
Class to store information about an option.
Typical usage:
Option myOption = new Option("Uses extended mode.", "E", 0, "-E"));
@author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 1.7 $

      optionsText.append("\nOptions specific to "
    + classifier.getClass().getName()
    + ":\n\n");
      Enumeration enu = ((OptionHandler)classifier).listOptions();
      while (enu.hasMoreElements()) {
  Option option = (Option) enu.nextElement();
  optionsText.append(option.synopsis() + '\n');
  optionsText.append(option.description() + "\n");
      }
    }
    
    // Get global information (if available)
    if (globalInfo) {

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      optionsText.append("\nOptions specific to " 
       + clusterer.getClass().getName() + ":\n\n");
      Enumeration enu = ((OptionHandler)clusterer).listOptions();


      while (enu.hasMoreElements()) {
  Option option = (Option)enu.nextElement();
  optionsText.append(option.synopsis() + '\n');
  optionsText.append(option.description() + "\n");
      }
    }
    
    // Get global information (if available)
    if (globalInfo) {

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  public Enumeration listOptions() {
    
    Vector newVector = new Vector(5);


    newVector.
      addElement(new Option("\tSet minimum number of instances per leaf " +
          "(default 2).",
          "M", 1, "-M <minimum number of instances>"));
    newVector.
      addElement(new Option("\tSet minimum numeric class variance proportion\n" +
          "\tof train variance for split (default 1e-3).",
          "V", 1, "-V <minimum variance for split>"));
    newVector.
      addElement(new Option("\tNumber of folds for reduced error pruning " +
          "(default 3).",
          "N", 1, "-N <number of folds>"));
    newVector.
      addElement(new Option("\tSeed for random data shuffling (default 1).",
          "S", 1, "-S <seed>"));
    newVector.
      addElement(new Option("\tNo pruning.",
          "P", 0, "-P"));
    newVector.
      addElement(new Option("\tMaximum tree depth (default -1, no maximum)",
          "L", 1, "-L"));


    return newVector.elements();
  }

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   * @return an enumeration of all the available options
   */
  public Enumeration listOptions() {
    Vector newVector = new Vector(6);
  
    newVector.addElement(new Option("\tSet number of folds for REP\n" +
            "\tOne fold is used as pruning set.\n" +
            "\t(default 3)","N", 1, "-N <number of folds>"));
  
    newVector.addElement(new Option("\tSet if NOT uses randomization\n" +
            "\t(default:use randomization)","R", 0, "-R"));


    newVector.addElement(new Option("\tSet whether consider the exclusive\n" +
            "\texpressions for nominal attributes\n"+
            "\t(default false)","E", 0, "-E"));
  
    newVector.addElement(new Option("\tSet the minimal weights of instances\n" +
            "\twithin a split.\n" +
            "\t(default 2.0)","M", 1, "-M <min. weights>"));
    
    newVector.addElement(new Option("\tSet number of antecedents for pre-pruning\n" +
            "\tif -1, then REP is used\n" +
            "\t(default -1)","P", 1, "-P <number of antecedents>"));
    
    newVector.addElement(new Option("\tSet the seed of randomization\n" +
            "\t(default 1)","S", 1, "-S <seed>"));
    
    return newVector.elements();
  }

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   */
  public Enumeration listOptions() {


    Vector newVector = new Vector(1);


    newVector.addElement(new Option(
        "\tIf set, classifier is run in debug mode and\n"
        + "\tmay output additional info to the console",
        "D", 0, "-D"));
    return newVector.elements();
  }

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   * @return an enumeration of all the available options.
   **/
  public Enumeration listOptions() {
    Vector result = new Vector();
    
    result.addElement(new Option(
  "\tAcuity.\n"
  +"\t(default=1.0)",
  "A", 1,"-A <acuity>"));
    
    result.addElement(new Option(
  "\tCutoff.\n"
  +"\t(default=0.002)",
  "C", 1,"-C <cutoff>"));


    Enumeration en = super.listOptions();

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   */
  public Enumeration listOptions() {


    Vector newVector = new Vector(1);


    newVector.addElement(new Option(
        "\tFull class name of classifier to include, followed\n"
        + "\tby scheme options. May be specified multiple times.\n"
        + "\t(default: \"weka.classifiers.rules.ZeroR\")",
        "B", 1, "-B <classifier specification>"));

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    enm = super.listOptions();
    while (enm.hasMoreElements())
      result.addElement(enm.nextElement());


    result.addElement(new Option(
  "\tThe file containing the serialized model.\n"
  + "\t(required)",
  "model", 1, "-model <filename>"));


    return result.elements();

View Full Code Here

     */
    public Enumeration listOptions() {
        Vector vector = new Vector();


        vector.addElement(
                new Option("\tepsilon (default = 0.9)",
                        "E",
                        1,
                        "-E <double>"));
        vector.addElement(
                new Option("\tminPoints (default = 6)",
                        "M",
                        1,
                        "-M <int>"));
        vector.addElement(
                new Option("\tindex (database) used for DBScan (default = weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase)",
                        "I",
                        1,
                        "-I <String>"));
        vector.addElement(
                new Option("\tdistance-type (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject)",
                        "D",
                        1,
                        "-D <String>"));
        return vector.elements();
    }

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    m_numberOfClustersDetermined = true;
  }


  public Enumeration listOptions() {
    Vector result = new Vector();
    result.addElement(new Option(
  "\tRepulsion\n" + "\t(default " + m_RepulsionDefault + ")",
  "R", 1, "-R <num>"));
    return result.elements();
  }

View Full Code Here

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