/** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(4); newVector.addElement(new Option("\tTurn on debugging output.", "D", 0, "-D")); newVector.addElement( new Option( "\tFull class name of classifier to include, followed\n" + "\tby scheme options. May be specified multiple times,\n" + "\trequired at least twice.\n" + "\teg: \"weka.classifiers.bayes.NaiveBayes -D\"", "B", 1, "-B <classifier specification>")); newVector.addElement( new Option( "\tSets the random number seed (default 1).", "S", 1, "-S <random number seed>")); newVector.addElement( new Option( "\tUse cross validation for model selection using the\n" + "\tgiven number of folds. (default 0, is to\n" + "\tuse training error)", "X", 1, "-X <number of folds>")); return newVector.elements(); }
/** * Returns an enumeration of the additional measure names * * @return an enumeration of the measure names */ public Enumeration enumerateMeasures() { Vector newVector = new Vector(3); newVector.addElement("measureTreeSize"); newVector.addElement("measureNumLeaves"); newVector.addElement("measureNumRules"); return newVector.elements(); }
/** * Returns an enumeration describing the available options. * * <p>Valid options are: * * <p>-U <br> * Use unpruned tree. * * <p>-C confidence <br> * Set confidence threshold for pruning. (Default: 0.25) * * <p>-M number <br> * Set minimum number of instances per leaf. (Default: 2) * * <p>-R <br> * Use reduced error pruning. No subtree raising is performed. * * <p>-N number <br> * Set number of folds for reduced error pruning. One fold is used as the pruning set. (Default: * 3) * * <p>-B <br> * Use binary splits for nominal attributes. * * <p>-S <br> * Don't perform subtree raising. * * <p>-L <br> * Do not clean up after the tree has been built. * * <p>-A <br> * If set, Laplace smoothing is used for predicted probabilites. * * <p> * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(9); newVector.addElement(new Option("\tUse unpruned tree.", "U", 0, "-U")); newVector.addElement( new Option( "\tSet confidence threshold for pruning.\n" + "\t(default 0.25)", "C", 1, "-C <pruning confidence>")); newVector.addElement( new Option( "\tSet minimum number of instances per leaf.\n" + "\t(default 2)", "M", 1, "-M <minimum number of instances>")); newVector.addElement(new Option("\tUse reduced error pruning.", "R", 0, "-R")); newVector.addElement( new Option( "\tSet number of folds for reduced error\n" + "\tpruning. One fold is used as pruning set.\n" + "\t(default 3)", "N", 1, "-N <number of folds>")); newVector.addElement(new Option("\tUse binary splits only.", "B", 0, "-B")); newVector.addElement(new Option("\tDon't perform subtree raising.", "S", 0, "-S")); newVector.addElement( new Option("\tDo not clean up after the tree has been built.", "L", 0, "-L")); newVector.addElement( new Option("\tLaplace smoothing for predicted probabilities.", "A", 0, "-A")); return newVector.elements(); }
/** * Returns an enumeration describing the available options.. * * <p>Valid options are: * * <p>-N <number of clusters> <br> * Specify the number of clusters to generate. If omitted, FarthestFirst will use cross validation * to select the number of clusters automatically. * * <p>-S <seed> <br> * Specify random number seed. * * <p> * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(2); newVector.addElement(new Option("\tnumber of clusters. (default = 2).", "N", 1, "-N <num>")); newVector.addElement(new Option("\trandom number seed.\n (default 10)", "S", 1, "-S <num>")); return newVector.elements(); }
/** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(7); newVector.addElement( new Option( "\tFull class name of search method, followed\n" + "\tby its options.\n" + "\teg: \"weka.attributeSelection.BestFirst -D 1\"\n" + "\t(default weka.attributeSelection.BestFirst)", "S", 1, "-S <search method specification>")); newVector.addElement( new Option( "\tUse cross validation to evaluate features.\n" + "\tUse number of folds = 1 for leave one out CV.\n" + "\t(Default = leave one out CV)", "X", 1, "-X <number of folds>")); newVector.addElement( new Option( "\tPerformance evaluation measure to use for selecting attributes.\n" + "\t(Default = accuracy for discrete class and rmse for numeric class)", "E", 1, "-E <acc | rmse | mae | auc>")); newVector.addElement( new Option("\tUse nearest neighbour instead of global table majority.", "I", 0, "-I")); newVector.addElement(new Option("\tDisplay decision table rules.\n", "R", 0, "-R")); newVector.addElement( new Option( "", "", 0, "\nOptions specific to search method " + m_search.getClass().getName() + ":")); Enumeration enu = ((OptionHandler) m_search).listOptions(); while (enu.hasMoreElements()) { newVector.addElement(enu.nextElement()); } return newVector.elements(); }
/** * Returns an enumeration describing the available options * * @return an enumeration of all the available options */ public Enumeration listOptions() { Vector newVector = new Vector(8); newVector.addElement( new Option("\tDesired size of ensemble.\n" + "\t(default 10)", "E", 1, "-E")); newVector.addElement( new Option( "\tFactor that determines number of artificial examples to generate.\n" + "\tSpecified proportional to training set size.\n" + "\t(default 1.0)", "R", 1, "-R")); Enumeration enu = super.listOptions(); while (enu.hasMoreElements()) { newVector.addElement(enu.nextElement()); } return newVector.elements(); }
/** * Returns an enumeration describing the available options. * * <p> * * @return an enumeration of all the available options */ public Enumeration listOptions() { return options.elements(); }