@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()));