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 $


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


    result.addElement(new Option(
                                 "\tThe file name of the file that holds the kernel matrix.\n"
                                 + "\t(default: kernelMatrix.matrix)",
                                 "M", 1, "-M <file name>"));


    return result.elements();

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   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {
    Vector result = new Vector();
    
    result.addElement(new Option(
  "\tThe epsilon parameter in epsilon-insensitive loss function.\n" 
  + "\t(default 1.0e-3)", 
  "L", 1, "-L <double>"));
    
//  result.addElement(new Option(
//      "\tLoss type (L1, L2, Huber, Epsilon insensitive loss)\n",
//      "L", 1, "-L [L1|L2|HUBER|EPSILON]"));
    
    result.addElement(new Option(
  "\tThe random number seed.\n" 
  + "\t(default 1)", 
  "W", 1, "-W <double>"));
    
    return result.elements();

View Full Code Here


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


    result.addElement(new Option(
  "\tThe Omega parameter.\n"
  + "\t(default: 1.0)",
  "O", 1, "-O <num>"));


    result.addElement(new Option(
  "\tThe Sigma parameter.\n"
  + "\t(default: 1.0)",
  "S", 1, "-S <num>"));


    return result.elements();

View Full Code Here

      desc  +=   "\t" + tag.getSelectedTag().getID() 
               + " = " + tag.getSelectedTag().getReadable()
               + "\n";
    }


    result.addElement(new Option(
  "\tThe pruning method to use:\n"
  + desc
  + "\t(default: " + PRUNING_NONE + ")",
  "P", 1, "-P <" + param + ">"));


    result.addElement(new Option(
  "\tThe size of the cache (a prime number).\n"
  + "\t(default: 250007)",
  "C", 1, "-C <num>"));


    result.addElement(new Option(
  "\tThe size of the internal cache (a prime number).\n"
  + "\t(default: 200003)",
  "IC", 1, "-IC <num>"));


    result.addElement(new Option(
  "\tThe lambda constant. Penalizes non-continuous subsequence\n"
  + "\tmatches. Must be in (0,1).\n"
  + "\t(default: 0.5)",
  "L", 1, "-L <num>"));


    result.addElement(new Option(
  "\tThe length of the subsequence.\n"
  + "\t(default: 3)",
  "ssl", 1, "-ssl <num>"));


    result.addElement(new Option(
  "\tThe maximum length of the subsequence.\n"
  + "\t(default: 9)",
  "ssl-max", 1, "-ssl-max <num>"));


    result.addElement(new Option(
  "\tUse normalization.\n"
  + "\t(default: no)",
  "N", 0, "-N"));


    return result.elements();

View Full Code Here

   */
  public Enumeration listOptions() {
  
    Vector newVector = new Vector(1);
  
    newVector.addElement(new Option("\tSpecify the seed of randomization\n"
            + "\tused to randomize the class\n"
            + "\torder (default: 1)",
            "R", 1, "-R <seed>"));
  
    newVector.addElement(new Option("\tSpecify the class order to be\n"
            + "\tsorted, could be 0: ascending\n"
            + "\t1: descending and 2: random.(default: 0)",
            "C", 1, "-C <order>"));
  
    return newVector.elements();

View Full Code Here


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


    result.addElement(new Option(
  "\tFull class name of classifier to use, followed\n"
  + "\tby scheme options. eg:\n"
  + "\t\t\"weka.classifiers.bayes.NaiveBayes -D\"\n"
  + "\t(default: weka.classifiers.rules.ZeroR)",
  "W", 1, "-W <classifier specification>"));


    result.addElement(new Option(
  "\tInstead of training a classifier on the data, one can also provide\n"
  + "\ta serialized model and use that for tagging the data.",
  "serialized", 1, "-serialized <file>"));


    result.addElement(new Option(
  "\tAdds an attribute with the actual classification.\n"
  + "\t(default: off)",
  "classification", 0, "-classification"));


    result.addElement(new Option(
  "\tRemoves the old class attribute.\n"
  + "\t(default: off)",
  "remove-old-class", 0, "-remove-old-class"));


    result.addElement(new Option(
  "\tAdds attributes with the distribution for all classes \n"
        + "\t(for numeric classes this will be identical to the attribute \n"
        + "\toutput with '-classification').\n"
  + "\t(default: off)",
  "distribution", 0, "-distribution"));


    result.addElement(new Option(
  "\tAdds an attribute indicating whether the classifier output \n"
        + "\ta wrong classification (for numeric classes this is the numeric \n"
        + "\tdifference).\n"
  + "\t(default: off)",
  "error", 0, "-error"));

View Full Code Here

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


    result.addElement(new Option(
  "\tSpecify the random number seed (default 1)",
  "S", 1, "-S <num>"));


    result.addElement(new Option(
  "\tThe size of the output dataset, as a percentage of\n"
  +"\tthe input dataset (default 100)",
  "Z", 1, "-Z <num>"));


    result.addElement(new Option(
  "\tBias factor towards uniform class distribution.\n"
  +"\t0 = distribution in input data -- 1 = uniform distribution.\n"
  +"\t(default 0)",
  "B", 1, "-B <num>"));


    result.addElement(new Option(
  "\tDisables replacement of instances\n"
  +"\t(default: with replacement)",
  "no-replacement", 0, "-no-replacement"));


    result.addElement(new Option(
  "\tInverts the selection - only available with '-no-replacement'.",
  "V", 0, "-V"));


    return result.elements();
  }

View Full Code Here

   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {
    Vector newVector = new Vector();
    
    newVector.addElement(new Option(
  "\tSpecifies the random number seed\n"
  + "\t(default 1)",
  "S", 1, "-S <num>"));
    
    newVector.addElement(new Option(
  "\tSpecifies percentage of SMOTE instances to create.\n"
  + "\t(default 100.0)\n",
  "P", 1, "-P <percentage>"));
    
    newVector.addElement(new Option(
  "\tSpecifies the number of nearest neighbors to use.\n"
  + "\t(default 5)\n",
  "K", 1, "-K <nearest-neighbors>"));
    
    newVector.addElement(new Option(
  "\tSpecifies the index of the nominal class value to SMOTE\n"
  +"\t(default 0: auto-detect non-empty minority class))\n",
  "C", 1, "-C <value-index>"));


    return newVector.elements();

View Full Code Here

   */
  public Enumeration listOptions() {


    Vector newVector = new Vector(6);


    newVector.addElement(new Option(
        "\tSpecifies if inverse of selection is to be output.\n",
        "V", 0, "-V"));


    newVector.addElement(new Option(
              "\tSpecifies number of folds dataset is split into. \n"
        + "\t(default 10)\n",
              "N", 1, "-N <number of folds>"));


    newVector.addElement(new Option(
        "\tSpecifies which fold is selected. (default 1)\n",
        "F", 1, "-F <fold>"));


    newVector.addElement(new Option(
        "\tSpecifies random number seed. (default 0, no randomizing)\n",
        "S", 1, "-S <seed>"));


    return newVector.elements();
  }

View Full Code Here

   */
  public Enumeration listOptions() {


    Vector newVector = new Vector(4);


    newVector.addElement(new Option(
              "\tSpecify the random number seed (default 1)",
              "S", 1, "-S <num>"));
    newVector.addElement(new Option(
              "\tThe maximum class distribution spread.\n"
              +"\t0 = no maximum spread, 1 = uniform distribution, 10 = allow at most\n"
        +"\ta 10:1 ratio between the classes (default 0)",
              "M", 1, "-M <num>"));
    newVector.addElement(new Option(
              "\tAdjust weights so that total weight per class is maintained.\n"
              +"\tIndividual instance weighting is not preserved. (default no\n"
              +"\tweights adjustment",
              "W", 0, "-W"));
    newVector.addElement(new Option(
        "\tThe maximum count for any class value (default 0 = unlimited).\n",
              "X", 0, "-X <num>"));


    return newVector.elements();
  }

View Full Code Here

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