How to use

listOptions

method

in

weka.classifiers.SingleClassifierEnhancer

@Override
public Enumeration<Option> listOptions() {
 Vector<Option> options = new Vector<Option>();
 options.add(new Option("\tPath to pre-constructed filter to use.",
  "load-filter", 1,
  "-load-filter <path to serialized pre-constructed filter>"));
 Enumeration superOpts = super.listOptions();
 while (superOpts.hasMoreElements()) {
  options.add((Option) superOpts.nextElement());
 }
 return options.elements();
}

/**
 * Returns an enumeration describing the available options.
 *
 * @return an enumeration of all the available options.
 */
public Enumeration<Option> listOptions() {
 Vector<Option> newVector = new Vector<Option>(1);
 newVector.addElement(new Option(
    "\tRandom number seed.\n"
    + "\t(default 1)",
    "S", 1, "-S <num>"));
 newVector.addAll(Collections.list(super.listOptions()));
 
 return newVector.elements();
}

/**
 * Returns an enumeration describing the available options.
 *
 * @return an enumeration of all the available options.
 */
public Enumeration<Option> listOptions() {
 Vector<Option> newVector = new Vector<Option>(1);
 newVector.addElement(new Option(
    "\tRandom number seed.\n"
    + "\t(default 1)",
    "S", 1, "-S <num>"));
 newVector.addAll(Collections.list(super.listOptions()));
 
 return newVector.elements();
}

/**
 * Returns an enumeration describing the available options.
 * 
 * @return an enumeration of all the available options.
 */
@Override
public Enumeration<Option> listOptions() {
 Vector<Option> result = new Vector<Option>();
 result.addElement(new Option("\tThe method used in transformation:\n"
  + "\t1.arithmatic average; 2.geometric centor;\n"
  + "\t3.using minimax combined features of a bag (default: 1)\n\n"
  + "\tMethod 3:\n"
  + "\tDefine s to be the vector of the coordinate-wise maxima\n"
  + "\tand minima of X, ie., \n"
  + "\ts(X)=(minx1, ..., minxm, maxx1, ...,maxxm), transform\n"
  + "\tthe exemplars into mono-instance which contains attributes\n"
  + "\ts(X)", "M", 1, "-M [1|2|3]"));
 result.addAll(Collections.list(super.listOptions()));
 return result.elements();
}

/**
 * Returns an enumeration describing the available options.
 *
 * @return an enumeration of all the available options.
 */
public Enumeration<Option> listOptions() {
 Vector<Option> newVector = new Vector<Option>(2);
 newVector.addElement(new Option(
    "\tNumber of iterations.\n"
    + "\t(current value " + getNumIterations() + ")",
    "I", 1, "-I <num>"));
 newVector.addAll(Collections.list(super.listOptions()));
 
 return newVector.elements();
}

/**
 * Returns an enumeration describing the available options.
 *
 * @return an enumeration of all the available options.
 */
public Enumeration<Option> listOptions() {
 Vector<Option> newVector = new Vector<Option>(2);
 newVector.addElement(new Option(
    "\tNumber of iterations.\n"
    + "\t(current value " + getNumIterations() + ")",
    "I", 1, "-I <num>"));
 newVector.addAll(Collections.list(super.listOptions()));
 
 return newVector.elements();
}

/**
 * Returns an enumeration describing the available options
 * 
 * @return an enumeration of all the available options
 */
@Override
public Enumeration<Option> listOptions() {
 Vector<Option> result = new Vector<Option>();
 result.addElement(new Option("\tThe number of bins in discretization\n"
  + "\t(default 0, no discretization)", "B", 1, "-B <num>"));
 result.addElement(new Option("\tMaximum number of boost iterations.\n"
  + "\t(default 10)", "R", 1, "-R <num>"));
 result.addAll(Collections.list(super.listOptions()));
 return result.elements();
}

/**
 * Returns an enumeration describing the available options.
 *
 * @return an enumeration of all the available options.
 */
public Enumeration<Option> listOptions() {
 
 Vector<Option> newVector = new Vector<Option>(3);
 newVector.addElement(new Option("\tThe nearest neighbour search " +
                 "algorithm to use " +
                 "(default: weka.core.neighboursearch.LinearNNSearch).\n",
                 "A", 0, "-A"));
 newVector.addElement(new Option("\tSet the number of neighbours used to set"
         +" the kernel bandwidth.\n"
         +"\t(default all)",
         "K", 1, "-K <number of neighbours>"));
 newVector.addElement(new Option("\tSet the weighting kernel shape to use."
         +" 0=Linear, 1=Epanechnikov,\n"
         +"\t2=Tricube, 3=Inverse, 4=Gaussian.\n"
         +"\t(default 0 = Linear)",
         "U", 1,"-U <number of weighting method>"));
 
 newVector.addAll(Collections.list(super.listOptions()));
 return newVector.elements();
}

/**
 * Returns an enumeration describing the available options.
 * 
 * @return an enumeration of all the available options.
 */
@Override
public Enumeration<Option> listOptions() {
 Vector<Option> result = new Vector<Option>();
 result.addElement(new Option("\tThe method used in testing:\n"
  + "\t1.arithmetic average\n" + "\t2.geometric average\n"
  + "\t3.max probability of positive bag.\n" + "\t(default: 1)", "P", 1,
  "-P [1|2|3]"));
 result.addElement(new Option(
  "\tThe type of weight setting for each single-instance:\n"
   + "\t0.keep the weight to be the same as the original value;\n"
   + "\t1.weight = 1.0\n"
   + "\t2.weight = 1.0/Total number of single-instance in the\n"
   + "\t\tcorresponding bag\n"
   + "\t3. weight = Total number of single-instance / (Total\n"
   + "\t\tnumber of bags * Total number of single-instance \n"
   + "\t\tin the corresponding bag).\n" + "\t(default: 3)", "A", 1,
  "-A [0|1|2|3]"));
 result.addAll(Collections.list(super.listOptions()));
 return result.elements();
}

/**
 * Returns an enumeration describing the available options.
 *
 * @return an enumeration of all the available options.
 */
public Enumeration<Option> listOptions() {
 
 Vector<Option> newVector = new Vector<Option>(3);
 newVector.addElement(new Option("\tThe nearest neighbour search " +
                 "algorithm to use " +
                 "(default: weka.core.neighboursearch.LinearNNSearch).\n",
                 "A", 0, "-A"));
 newVector.addElement(new Option("\tSet the number of neighbours used to set"
         +" the kernel bandwidth.\n"
         +"\t(default all)",
         "K", 1, "-K <number of neighbours>"));
 newVector.addElement(new Option("\tSet the weighting kernel shape to use."
         +" 0=Linear, 1=Epanechnikov,\n"
         +"\t2=Tricube, 3=Inverse, 4=Gaussian.\n"
         +"\t(default 0 = Linear)",
         "U", 1,"-U <number of weighting method>"));
 
 newVector.addAll(Collections.list(super.listOptions()));
 return newVector.elements();
}

/**
 * Returns an enumeration describing the available options.
 * 
 * @return an enumeration of all the available options.
 */
@Override
public Enumeration<Option> listOptions() {
 Vector<Option> newVector = new Vector<Option>(1);
 newVector.addElement(new Option(
  "\tPartition generator to use, including options.\n"
   + "\tQuotes are needed when options are specified.\n"
   + "\t(default: weka.classifiers.trees.J48)", "P", 1,
  "-P \"<name and options of partition generator>\""));
 newVector.addAll(Collections.list(super.listOptions()));
 newVector.addElement(new Option("", "", 0,
  "\nOptions specific to partition generator "
   + getPartitionGenerator().getClass().getName() + ":"));
 newVector.addAll(Collections.list(((OptionHandler) getPartitionGenerator())
  .listOptions()));
 return newVector.elements();
}

/**
 * Returns an enumeration describing the available options.
 *
 * @return an enumeration of all the available options.
 */
public Enumeration<Option> listOptions() {
 Vector<Option> newVector = new Vector<Option>(5);
 newVector.addElement(new Option(
  "\tNumber of bins for equal-width discretization\n"
   + "\t(default 10).\n",
   "B", 1, "-B <int>"));
 newVector.addElement(new Option(
  "\tWhether to delete empty bins after discretization\n"
   + "\t(default false).\n",
   "E", 0, "-E"));
 newVector.addElement(new Option(
  "\tWhether to minimize absolute error, rather than squared error.\n"
   + "\t(default false).\n",
   "A", 0, "-A"));
 newVector.addElement(new Option(
  "\tUse equal-frequency instead of equal-width discretization.",
  "F", 0, "-F"));
 newVector.addElement(new Option(
  "\tThe density estimator to use (including parameters).",
  "K", 1, "-K <estimator name and parameters"));
 newVector.addAll(Collections.list(super.listOptions()));
 return newVector.elements();
}

/**
 * Returns an enumeration describing the available options.
 *
 * @return an enumeration of all the available options.
 */
public Enumeration<Option> listOptions() {
 Vector<Option> newVector = new Vector<Option>(5);
 newVector.addElement(new Option(
  "\tNumber of bins for equal-width discretization\n"
   + "\t(default 10).\n",
   "B", 1, "-B <int>"));
 newVector.addElement(new Option(
  "\tWhether to delete empty bins after discretization\n"
   + "\t(default false).\n",
   "E", 0, "-E"));
 newVector.addElement(new Option(
  "\tWhether to minimize absolute error, rather than squared error.\n"
   + "\t(default false).\n",
   "A", 0, "-A"));
 newVector.addElement(new Option(
  "\tUse equal-frequency instead of equal-width discretization.",
  "F", 0, "-F"));
 newVector.addElement(new Option(
  "\tThe density estimator to use (including parameters).",
  "K", 1, "-K <estimator name and parameters"));
 newVector.addAll(Collections.list(super.listOptions()));
 return newVector.elements();
}

 "-L <path to model to load>"));
newVector.addAll(Collections.list(super.listOptions()));

 "-L <path to model to load>"));
newVector.addAll(Collections.list(super.listOptions()));

   "S", 1, "-S <search method specification>"));
newVector.addAll(Collections.list(super.listOptions()));

   "S", 1, "-S <search method specification>"));
newVector.addAll(Collections.list(super.listOptions()));