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 $

   * Returns an enumeration describing the available options
   * @return an enumeration of all the available options
   **/
  public Enumeration listOptions () {
    Vector newVector = new Vector(2);
    newVector.addElement(new Option("\ttreat missing values as a seperate " 
                                    + "value.", "M", 0, "-M"));
    newVector.addElement(new Option("\tjust binarize numeric attributes instead \n" 
                                    +"\tof properly discretizing them.", "B", 0, 
                                    "-B"));
    return  newVector.elements();
  }

View Full Code Here

   */
  public Enumeration listOptions() {


    Vector newVector = new Vector(1);


    newVector.addElement(new Option(
       "\tThe full class name of the classifier.\n"
        +"\teg: weka.classifiers.bayes.NaiveBayes", 
       "W", 1, 
       "-W <class name>"));


    if ((m_Template != null) &&
  (m_Template instanceof OptionHandler)) {
      newVector.addElement(new Option(
       "",
       "", 0, "\nOptions specific to classifier "
       + m_Template.getClass().getName() + ":"));
      Enumeration enu = ((OptionHandler)m_Template).listOptions();
      while (enu.hasMoreElements()) {

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   * @return an enumeration of all the available options
   */
  public Enumeration listOptions() {
    Vector result = new Vector();
    
    result.addElement(new Option(
          "\tTurn on debugging output.",
          "D", 0, "-D"));
    
    result.addElement(new Option(
        "\tSet the ridge in the log-likelihood.",
        "R", 1, "-R <ridge>"));


    result.addElement(new Option(
        "\tDefines the type of algorithm:\n"
        + "\t 0. standard MI assumption\n"
        + "\t 1. collective MI assumption, arithmetic mean for posteriors\n"
        + "\t 2. collective MI assumption, geometric mean for posteriors",
        "A", 1, "-A [0|1|2]"));

View Full Code Here

   */
  public Enumeration listOptions() {


    Vector newVector = new Vector(5);


    newVector.addElement(new Option(
              "\tWhether minGroup (-G) and maxGroup (-H) refer to"
              + "\n\tthe number of groups or their size."
              + "\n\t(default: false)",
              "N", 0, "-N"));


    newVector.addElement(new Option(
              "\tMinimum size of a group of attributes:"
              + "\n\t\tif numberOfGroups is true, the minimum number"
              + "\n\t\tof groups."
              + "\n\t\t(default: 3)",
              "G", 1, "-G <num>"));


    newVector.addElement(new Option(
              "\tMaximum size of a group of attributes:"
              + "\n\t\tif numberOfGroups is true, the maximum number" 
              + "\n\t\tof groups."
              + "\n\t\t(default: 3)",
              "H", 1, "-H <num>"));


    newVector.addElement(new Option(
              "\tPercentage of instances to be removed."
              + "\n\t\t(default: 50)",
              "P", 1, "-P <num>"));


    newVector.addElement(new Option(
        "\tFull class name of filter to use, followed\n"
        + "\tby filter options.\n"
        + "\teg: \"weka.filters.unsupervised.attribute.PrincipalComponents-R 1.0\"",
        "F", 1, "-F <filter specification>"));

View Full Code Here

   *
   * @return an enumeration of all the available options
   */
  public Enumeration listOptions() {
    Vector newVector = new Vector(3);
    newVector.addElement(new Option("\tSet number of folds for REP\n" +
            "\tOne fold is used as pruning set.\n" +
            "\t(default 3)","F", 1, "-F <number of folds>"));
    newVector.addElement(new Option("\tSet the minimal weights of instances\n" +
            "\twithin a split.\n" +
            "\t(default 2.0)","N", 1, "-N <min. weights>"));     
    newVector.addElement(new Option("\tSet the number of runs of\n"+
            "\toptimizations. (Default: 2)", "O",
            1,"-O <number of runs>"));
  
    newVector.addElement(new Option("\tSet whether turn on the\n"+
            "\tdebug mode (Default: false)", "D",
            0,"-D"));
  
    newVector.addElement(new Option("\tThe seed of randomization\n"+
            "\t(Default: 1)", "S",
            1,"-S <seed>"));
  
    newVector.addElement(new Option("\tWhether NOT check the error rate>=0.5\n"
            +"\tin stopping criteria "
            +"\t(default: check)", "E", 
            0, "-E")); 
  
    newVector.addElement(new Option("\tWhether NOT use pruning\n"
            +"\t(default: use pruning)", "P", 
            0, "-P")); 
    return newVector.elements();
  }

View Full Code Here

   * @return an enumeration of all the available options.
   **/
  public Enumeration listOptions () {
    Vector newVector = new Vector(6);


    newVector.addElement(new Option("\tSpecify a starting set of attributes." 
                                    + "\n\tEg. 1,3,5-7."
                                    +"If supplied, the starting set becomes"
                                    +"\n\tone member of the initial random"
                                    +"\n\tpopulation."
                                    ,"P",1
                                    , "-P <start set>"));
    newVector.addElement(new Option("\tSet the size of the population (even number)."
                                    +"\n\t(default = 20)."
                                    , "Z", 1
                                    , "-Z <population size>"));
    newVector.addElement(new Option("\tSet the number of generations."
                                    +"\n\t(default = 20)" 
                                    , "G", 1, "-G <number of generations>"));
    newVector.addElement(new Option("\tSet the probability of crossover."
                                    +"\n\t(default = 0.6)" 
                                    , "C", 1, "-C <probability of"
                                    +" crossover>"));    
    newVector.addElement(new Option("\tSet the probability of mutation."
                                    +"\n\t(default = 0.033)" 
                                    , "M", 1, "-M <probability of mutation>"));


    newVector.addElement(new Option("\tSet frequency of generation reports."
                                    +"\n\te.g, setting the value to 5 will "
                                    +"\n\treport every 5th generation"
                                    +"\n\t(default = number of generations)" 
                                    , "R", 1, "-R <report frequency>"));
    newVector.addElement(new Option("\tSet the random number seed."
                                    +"\n\t(default = 1)" 
                                    , "S", 1, "-S <seed>"));
    return  newVector.elements();
  }

View Full Code Here


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


    result.addElement(new Option(
  "\tTurns off all checks - use with caution!\n"
  + "\tTurning them off assumes that data is purely numeric, doesn't\n"
  + "\tcontain any missing values, and has a nominal class. Turning them\n"
  + "\toff also means that no header information will be stored if the\n"
  + "\tmachine is linear. Finally, it also assumes that no instance has\n"
  + "\ta weight equal to 0.\n"
  + "\t(default: checks on)",
  "no-checks", 0, "-no-checks"));


    result.addElement(new Option(
          "\tThe complexity constant C. (default 1)",
          "C", 1, "-C <double>"));
    
    result.addElement(new Option(
          "\tWhether to 0=normalize/1=standardize/2=neither.\n" 
          + "\t(default 0=normalize)",
          "N", 1, "-N"));
    
    result.addElement(new Option(
          "\tUse MIminimax feature space. ",
          "I", 0, "-I"));
    
    result.addElement(new Option(
          "\tThe tolerance parameter. (default 1.0e-3)",
          "L", 1, "-L <double>"));
    
    result.addElement(new Option(
          "\tThe epsilon for round-off error. (default 1.0e-12)",
          "P", 1, "-P <double>"));
    
    result.addElement(new Option(
          "\tFit logistic models to SVM outputs. ",
          "M", 0, "-M"));
    
    result.addElement(new Option(
          "\tThe number of folds for the internal cross-validation. \n"
          + "\t(default -1, use training data)",
          "V", 1, "-V <double>"));
    
    result.addElement(new Option(
          "\tThe random number seed. (default 1)",
          "W", 1, "-W <double>"));
    
    result.addElement(new Option(
  "\tThe Kernel to use.\n"
  + "\t(default: weka.classifiers.functions.supportVector.PolyKernel)",
  "K", 1, "-K <classname and parameters>"));


    result.addElement(new Option(
  "",
  "", 0, "\nOptions specific to kernel "
  + getKernel().getClass().getName() + ":"));
    
    enm = ((OptionHandler) getKernel()).listOptions();

View Full Code Here

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


     result.addElement(
         new Option("\tSQL query to execute.",
                    "Q",1,"-Q <query>"));
     
     result.addElement(
         new Option("\tReturn sparse rather than normal instances.", 
                    "S", 0, "-S"));
     
     result.addElement(
         new Option("\tThe username to use for connecting.", 
                    "U", 1, "-U <username>"));
     
     result.addElement(
         new Option("\tThe password to use for connecting.", 
                    "P", 1, "-P <password>"));
     
     result.addElement(
         new Option("\tEnables debug output.", 
                    "D", 0, "-D"));
     
     return result.elements();
   }

View Full Code Here

  Utils.checkForRemainingOptions(args);
      } catch (Exception e) {
  System.err.println("Options for weka.experiment.InstanceQuery:\n");
  Enumeration en = iq.listOptions();
  while (en.hasMoreElements()) {
    Option o = (Option)en.nextElement();
    System.err.println(o.synopsis()+"\n"+o.description());
  }
  System.exit(1);
      }
     
      Instances aha = iq.retrieveInstances();

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   */
  public Enumeration listOptions() {


    Vector newVector = new Vector(2);


    newVector.addElement(new Option(
              "\tSize of each bag, as a percentage of the\n" 
              + "\ttraining set size. (default 100)",
              "P", 1, "-P"));
    newVector.addElement(new Option(
              "\tCalculate the out of bag error.",
              "O", 0, "-O"));


    Enumeration enu = super.listOptions();
    while (enu.hasMoreElements()) {

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