/**
* 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();
}
