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  3. weka.core.Option

Java 类weka.core.Option 实例源码

项目：repo.kmeanspp.silhouette_score    文件：GeneticSearch.java    
/**
 * 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>(7);

  newVector
    .addElement(new Option("\tPopulation size", "L", 1, "-L <integer>"));
  newVector.addElement(new Option("\tDescendant population size", "A", 1,
    "-A <integer>"));
  newVector
    .addElement(new Option("\tNumber of runs", "U", 1, "-U <integer>"));
  newVector.addElement(new Option("\tUse mutation.\n\t(default true)", "M",
    0, "-M"));
  newVector.addElement(new Option("\tUse cross-over.\n\t(default true)", "C",
    0, "-C"));
  newVector
    .addElement(new Option(
      "\tUse tournament selection (true) or maximum subpopulatin (false).\n\t(default false)",
      "O", 0, "-O"));
  newVector
    .addElement(new Option("\tRandom number seed", "R", 1, "-R <seed>"));

  newVector.addAll(Collections.list(super.listOptions()));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：CheckSource.java    
/**
 * Executes the tests, use "-h" to list the commandline options.
 * 
 * @param args the commandline parameters
 * @throws Exception if something goes wrong
 */
public static void main(String[] args) throws Exception {
  CheckSource check;
  StringBuffer text;
  Enumeration<Option> enm;

  check = new CheckSource();
  if (Utils.getFlag('h', args)) {
    text = new StringBuffer();
    text.append("\nHelp requested:\n\n");
    enm = check.listOptions();
    while (enm.hasMoreElements()) {
      Option option = enm.nextElement();
      text.append(option.synopsis() + "\n");
      text.append(option.description() + "\n");
    }
    System.out.println("\n" + text + "\n");
  } else {
    check.setOptions(args);
    if (check.execute()) {
      System.out.println("Tests OK!");
    } else {
      System.out.println("Tests failed!");
    }
  }
}
项目：repo.kmeanspp.silhouette_score    文件：TabuSearch.java    
/**
 * 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>(4);

  newVector.addElement(new Option("\tTabu list length", "L", 1,
    "-L <integer>"));
  newVector
    .addElement(new Option("\tNumber of runs", "U", 1, "-U <integer>"));
  newVector.addElement(new Option("\tMaximum number of parents", "P", 1,
    "-P <nr of parents>"));
  newVector.addElement(new Option(
    "\tUse arc reversal operation.\n\t(default false)", "R", 0, "-R"));

  newVector.addAll(Collections.list(super.listOptions()));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：SubspaceCluster.java    
/**
 * Returns an enumeration describing the available options.
 * 
 * @return an enumeration of all the available options
 */
@Override
public Enumeration<Option> listOptions() {
  Vector<Option> result = enumToVector(super.listOptions());

  result.addElement(new Option("\tThe noise rate in percent (default "
    + defaultNoiseRate() + ").\n"
    + "\tCan be between 0% and 30%. (Remark: The original \n"
    + "\talgorithm only allows noise up to 10%.)", "P", 1, "-P <num>"));

  result.addElement(new Option("\tA cluster definition of class '"
    + SubspaceClusterDefinition.class.getName().replaceAll(".*\\.", "")
    + "'\n" + "\t(definition needs to be quoted to be recognized as \n"
    + "\ta single argument).", "C", 1, "-C <cluster-definition>"));

  result.addElement(new Option("", "", 0, "\nOptions specific to "
    + SubspaceClusterDefinition.class.getName() + ":"));

  result.addAll(enumToVector(new SubspaceClusterDefinition(this)
    .listOptions()));

  return result.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：ICSSearchAlgorithm.java    
/**
 * 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(
      "\tWhen determining whether an edge exists a search is performed \n"
        + "\tfor a set Z that separates the nodes. MaxCardinality determines \n"
        + "\tthe maximum size of the set Z. This greatly influences the \n"
        + "\tlength of the search. (default 2)", "cardinality", 1,
      "-cardinality <num>"));

  result.addAll(Collections.list(super.listOptions()));

  return result.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：TextDirectoryLoader.java    
/**
 * Lists the available options
 * 
 * @return an enumeration of the available options
 */
@Override
public Enumeration<Option> listOptions() {

  Vector<Option> result = new Vector<Option>();

  result.add(new Option("\tEnables debug output.\n" + "\t(default: off)",
    "D", 0, "-D"));

  result.add(new Option("\tStores the filename in an additional attribute.\n"
    + "\t(default: off)", "F", 0, "-F"));

  result.add(new Option("\tThe directory to work on.\n"
    + "\t(default: current directory)", "dir", 0, "-dir <directory>"));

  result.add(new Option("\tThe character set to use, e.g UTF-8.\n\t"
    + "(default: use the default character set)", "charset", 1,
    "-charset <charset name>"));

  return result.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：SpreadSubsample.java    
/**
 * 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>(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();
}
项目：repo.kmeanspp.silhouette_score    文件：DatabaseResultProducer.java    
/**
 * 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>(2);

  newVector.addElement(new Option(
    "\tThe name of the database field to cache over.\n"
      + "\teg: \"Fold\" (default none)", "F", 1, "-F <field name>"));
  newVector.addElement(new Option(
    "\tThe full class name of a ResultProducer.\n"
      + "\teg: weka.experiment.CrossValidationResultProducer", "W", 1,
    "-W <class name>"));

  if ((m_ResultProducer != null)
    && (m_ResultProducer instanceof OptionHandler)) {
    newVector.addElement(new Option("", "", 0,
      "\nOptions specific to result producer "
        + m_ResultProducer.getClass().getName() + ":"));
    newVector.addAll(Collections.list(((OptionHandler) m_ResultProducer)
      .listOptions()));
  }
  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：GlobalScoreSearchAlgorithm.java    
/**
 * 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>();

  newVector.addElement(new Option(
    "\tApplies a Markov Blanket correction to the network structure, \n"
      + "\tafter a network structure is learned. This ensures that all \n"
      + "\tnodes in the network are part of the Markov blanket of the \n"
      + "\tclassifier node.", "mbc", 0, "-mbc"));

  newVector.addElement(new Option(
    "\tScore type (LOO-CV,k-Fold-CV,Cumulative-CV)", "S", 1,
    "-S [LOO-CV|k-Fold-CV|Cumulative-CV]"));

  newVector.addElement(new Option(
    "\tUse probabilistic or 0/1 scoring.\n\t(default probabilistic scoring)",
    "Q", 0, "-Q"));

  newVector.addAll(Collections.list(super.listOptions()));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：MakeDensityBasedClusterer.java    
/**
 * 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(
    "\tminimum allowable standard deviation for normal density computation "
      + "\n\t(default 1e-6)", "M", 1, "-M <num>"));

  result.addElement(new Option("\tClusterer to wrap.\n" + "\t(default "
    + defaultClustererString() + ")", "W", 1, "-W <clusterer name>"));

  result.addAll(Collections.list(super.listOptions()));

  if ((m_wrappedClusterer != null)
    && (m_wrappedClusterer instanceof OptionHandler)) {
    result.addElement(new Option("", "", 0,
      "\nOptions specific to clusterer "
        + m_wrappedClusterer.getClass().getName() + ":"));
    result.addAll(Collections.list(((OptionHandler) m_wrappedClusterer)
      .listOptions()));
  }

  return result.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：MergeInfrequentNominalValues.java    
/**
 * 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>(3);

  result.addElement(new Option(
    "\tThe minimum frequency for a value to remain (default: 2).\n", "-N", 1,
    "-N <int>"));

  result
    .addElement(new Option(
      "\tSets list of attributes to act on (or its inverse). 'first and 'last' are accepted as well.'\n"
        + "\tE.g.: first-5,7,9,20-last\n" + "\t(default: 1,2)", "R", 1,
      "-R <range>"));
  result
    .addElement(new Option(
      "\tInvert matching sense (i.e. act on all attributes not specified in list)",
      "V", 0, "-V"));
  result.addElement(new Option("\tUse short IDs for merged attribute values.", "S", 0, "-S"));

  result.addAll(Collections.list(super.listOptions()));

  return result.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：AddNoise.java    
/**
 * 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>(4);

  newVector.addElement(new Option("\tIndex of the attribute to be changed \n"
    + "\t(default last attribute)", "C", 1, "-C <col>"));
  newVector.addElement(new Option(
    "\tTreat missing values as an extra value \n", "M", 1, "-M"));
  newVector.addElement(new Option(
    "\tSpecify the percentage of noise introduced \n"
      + "\tto the data (default 10)", "P", 1, "-P <num>"));
  newVector.addElement(new Option(
    "\tSpecify the random number seed (default 1)", "S", 1, "-S <num>"));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：RemoveRange.java    
/**
 * 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>(2);

  newVector.addElement(new Option(
    "\tSpecifies list of instances to select. First and last\n"
      + "\tare valid indexes. (required)\n", "R", 1,
    "-R <inst1,inst2-inst4,...>"));

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

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：AbstractClassifier.java    
/**
 * Returns an enumeration describing the available options.
 *
 * @return an enumeration of all the available options.
 */
@Override
public Enumeration<Option> listOptions() {

  Vector<Option> newVector =
    Option.listOptionsForClassHierarchy(this.getClass(),
      AbstractClassifier.class);

  newVector.addElement(new Option(
    "\tIf set, classifier is run in debug mode and\n"
      + "\tmay output additional info to the console", "output-debug-info",
    0, "-output-debug-info"));
  newVector
    .addElement(new Option(
      "\tIf set, classifier capabilities are not checked before classifier is built\n"
        + "\t(use with caution).", "-do-not-check-capabilities", 0,
      "-do-not-check-capabilities"));

  newVector.addElement(new Option(
    "\tThe number of decimal places for the output of numbers in the model"
      + " (default " + m_numDecimalPlaces + ").", "num-decimal-places", 1,
    "-num-decimal-places"));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：SwapValues.java    
/**
 * 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>(3);

  newVector.addElement(new Option(
    "\tSets the attribute index (default last).", "C", 1, "-C <col>"));

  newVector.addElement(new Option(
    "\tSets the first value's index (default first).", "F", 1,
    "-F <value index>"));

  newVector.addElement(new Option(
    "\tSets the second value's index (default last).", "S", 1,
    "-S <value index>"));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：MiddleOutConstructor.java    
/**
 * 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>();

  newVector.addElement(new Option(
    "\tThe seed for the random number generator used\n"
      + "\tin selecting random anchor.\n" + "(default: 1)", "S", 1,
    "-S <num>"));

  newVector.addElement(new Option("\tUse randomly chosen initial anchors.",
    "R", 0, "-R"));

  newVector.addAll(Collections.list(super.listOptions()));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：XRFFSaver.java    
/**
 * 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 class index (first and last are valid as well).\n"
      + "\t(default: last)", "C", 1, "-C <class index>"));

  result.addElement(new Option("\tCompresses the data (uses '"
    + XRFFLoader.FILE_EXTENSION_COMPRESSED + "' as extension instead of '"
    + XRFFLoader.FILE_EXTENSION + "')\n" + "\t(default: off)", "compress", 0,
    "-compress"));

  result.addAll(Collections.list(super.listOptions()));

  return result.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：CheckSource.java    
/**
 * Returns an enumeration describing the available options.
 *
 * @return an enumeration of all the available options.
 */
public Enumeration<Option> listOptions() {
  Vector<Option> result = new Vector<Option>();

  result.addElement(new Option(
      "\tThe classifier (incl. options) that was used to generate\n"
      + "\tthe source code.",
      "W", 1, "-W <classname and options>"));

  result.addElement(new Option(
      "\tThe classname of the generated source code.",
      "S", 1, "-S <classname>"));

  result.addElement(new Option(
      "\tThe training set with which the source code was generated.",
      "t", 1, "-t <file>"));

  result.addElement(new Option(
      "\tThe class index of the training set. 'first' and 'last' are\n"
      + "\tvalid indices.\n"
      + "\t(default: last)",
      "c", 1, "-c <index>"));

  return result.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：PrincipalComponents.java    
/**
 * 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("\tCenter (rather than standardize) the"
    + "\n\tdata and compute PCA using the covariance (rather"
    + "\n\t than the correlation) matrix.", "C", 0, "-C"));

  result.addElement(new Option("\tRetain enough PC attributes to account\n"
    + "\tfor this proportion of variance in the original data.\n"
    + "\t(default: 0.95)", "R", 1, "-R <num>"));

  result.addElement(new Option(
    "\tMaximum number of attributes to include in \n"
      + "\ttransformed attribute names.\n"
      + "\t(-1 = include all, default: 5)", "A", 1, "-A <num>"));

  result.addElement(new Option(
    "\tMaximum number of PC attributes to retain.\n"
      + "\t(-1 = include all, default: -1)", "M", 1, "-M <num>"));

  return result.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：AbstractFileBasedStopwords.java    
/**
 * 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>();

  Enumeration<Option> enm = super.listOptions();
  while (enm.hasMoreElements())
    result.add(enm.nextElement());

  result.addElement(new Option(
    "\t" + stopwordsTipText() + "\n"
    + "\t(default: .)",
    "stopwords", 1, "-stopwords <file>"));

  return result.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：ClusterMembership.java    
/**
 * 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>(2);

  newVector.addElement(new Option("\tFull name of clusterer to use. eg:\n"
    + "\t\tweka.clusterers.EM\n" + "\tAdditional options after the '--'.\n"
    + "\t(default: weka.clusterers.EM)", "W", 1, "-W <clusterer name>"));

  newVector.addElement(new Option(
    "\tThe range of attributes the clusterer should ignore."
      + "\n\t(the class attribute is automatically ignored)", "I", 1,
    "-I <att1,att2-att4,...>"));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：WekaScoringHadoopJob.java    
@Override
public Enumeration<Option> listOptions() {
  Vector<Option> result = new Vector<Option>();

  result.add(new Option(
    "\tPath to model file to use for scoring (can be \n\t"
      + "local or in HDFS", "model-file", 1,
    "-model-file <path to model file>"));

  result.add(new Option(
    "\tColumns to output in the scored data. Specify as\n\t"
      + "a range, e.g. 1,4,5,10-last (default = first-last).",
    "columns-to-output", 1, "-columns-to-output"));

  result.add(new Option("", "", 0,
    "\nOptions specific to ARFF training header creation:"));

  ArffHeaderHadoopJob tempArffJob = new ArffHeaderHadoopJob();
  Enumeration<Option> arffOpts = tempArffJob.listOptions();
  while (arffOpts.hasMoreElements()) {
    result.add(arffOpts.nextElement());
  }

  return result.elements();
}
项目：miniML    文件：Model.java    
/**
 * get information from the classifier about what options (parameters) it accepts
 * so that we can build our parameter search space dynamically.
 * TODO this should be used in future iterations if possible; weka conventions do
 * TODO not make this economical at the moment, but it should be kept in mind!
 */
public String[] get_info(){
    Enumeration<Option> info = classifier.listOptions();
    ArrayList<String> parameter_descriptions = new ArrayList<String>();
    while(info.hasMoreElements()){
        Option elt = info.nextElement();
        parameter_descriptions.add(elt.synopsis());
        parameter_descriptions.add(elt.description());
    }
    String[] arr = parameter_descriptions.toArray(new String[parameter_descriptions.size()]);
    return(arr);
}
项目：seqcode-core    文件：BaggedRandomForest.java    
/**
 * 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>();

  newVector.addElement(new Option("\tNumber of trees to build.\n\t(default 100)", "I", 1,
    "-I <number of trees>"));

  newVector.addElement(new Option(
    "\tNumber of features to consider (<1=int(log_2(#predictors)+1)).\n\t(default 0)", "K", 1,
    "-K <number of features>"));

  newVector.addElement(new Option("\tSeed for random number generator.\n"
    + "\t(default 1)", "S", 1, "-S"));

  newVector.addElement(new Option(
    "\tThe maximum depth of the trees, 0 for unlimited.\n" + "\t(default 0)",
    "depth", 1, "-depth <num>"));

  newVector.addElement(new Option("\tDon't calculate the out of bag error.",
    "O", 0, "-O"));

  newVector.addElement(new Option(
    "\tPrint the individual trees in the output", "print", 0, "-print"));

  newVector.addElement(new Option("\tNumber of execution slots.\n"
    + "\t(default 1 - i.e. no parallelism)", "num-slots", 1,
    "-num-slots <num>"));

  newVector.addAll(Collections.list(super.listOptions()));

  return newVector.elements();
}
项目：seqcode-core    文件：AttributeRandomTree.java    
/**
 * Lists the command-line options for this classifier.
 * 
 * @return an enumeration over all possible options
 */
@Override
public Enumeration<Option> listOptions() {

  Vector<Option> newVector = new Vector<Option>();

  newVector.addElement(new Option(
    "\tNumber of attributes to randomly investigate.\t(default 0)\n"
      + "\t(<0 = int(log_2(#predictors)+1)).", "K", 1,
    "-K <number of attributes>"));

  newVector.addElement(new Option(
    "\tSet minimum number of instances per leaf.\n\t(default 1)", "M", 1,
    "-M <minimum number of instances>"));

  newVector.addElement(new Option(
    "\tSet minimum numeric class variance proportion\n"
      + "\tof train variance for split (default 1e-3).", "V", 1,
    "-V <minimum variance for split>"));

  newVector.addElement(new Option("\tSeed for random number generator.\n"
    + "\t(default 1)", "S", 1, "-S <num>"));

  newVector.addElement(new Option(
    "\tThe maximum depth of the tree, 0 for unlimited.\n" + "\t(default 0)",
    "depth", 1, "-depth <num>"));

  newVector.addElement(new Option("\tNumber of folds for backfitting "
    + "(default 0, no backfitting).", "N", 1, "-N <num>"));
  newVector.addElement(new Option("\tAllow unclassified instances.", "U", 0,
    "-U"));

  newVector.addAll(Collections.list(super.listOptions()));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：UnivariateMixtureEstimator.java    
/**
 * Returns an enumeration that lists the command-line options that are available
 * 
 * @return the list of options as an enumeration
 */
@Override
public Enumeration<Option> listOptions() {

  Vector<Option> options = new Vector<Option>();
  options.addElement(new Option("\tNumber of components to use (default: -1).", "N", 1, "-N"));
  options.addElement(new Option("\tMaximum number of components to use (default: 5).", "M", 1, "-M"));
  options.addElement(new Option("\tSeed for the random number generator (default: 1).", "S", 1, "-S"));
  options.addElement(new Option("\tThe number of bootstrap runs to use (default: 10).", "B", 1, "-B"));
  options.addElement(new Option("\tUse normalized entropy instead of bootstrap.", "E", 1, "-E"));
  return options.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：RandomizableSingleClassifierEnhancer.java    
/**
 * 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();
}
项目：repo.kmeanspp.silhouette_score    文件：MultiNomialBMAEstimator.java    
/**
 * 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("\tWhether to use K2 prior.\n", "k2", 0,
    "-k2"));

  newVector.addAll(Collections.list(super.listOptions()));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：TopDownConstructor.java    
/**
 * 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>();

  newVector.addElement(new Option("\tBall splitting algorithm to use.", "S",
    1, "-S <classname and options>"));

  newVector.addAll(Collections.list(super.listOptions()));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：CostSensitiveClassifierSplitEvaluator.java    
/**
 * 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.addAll(Collections.list(super.listOptions()));

  newVector.addElement(new Option(
    "\tName of a directory to search for cost files when loading\n"
      + "\tcosts on demand (default current directory).", "D", 1,
    "-D <directory>"));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：StringToNominal.java    
/**
 * 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(
    "\tSets the range of attribute indices (default last).", "R", 1,
    "-R <col>"));

  newVector.addElement(new Option("\tInvert the range specified by -R.", "V",
    1, "-V <col>"));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：BayesNetGenerator.java    
/**
 * prints all the options to stdout
 */
protected static void printOptions(OptionHandler o) {
  Enumeration<Option> enm = o.listOptions();

  System.out.println("Options for " + o.getClass().getName() + ":\n");

  while (enm.hasMoreElements()) {
    Option option = enm.nextElement();
    System.out.println(option.synopsis());
    System.out.println(option.description());
  }
}
项目：repo.kmeanspp.silhouette_score    文件：InfoGainAttributeEval.java    
/**
 * 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>(2);
  newVector.addElement(new Option("\ttreat missing values as a seperate "
    + "value.", "M", 0, "-M"));
  newVector.addElement(new Option(
    "\tjust binarize numeric attributes instead \n"
      + "\tof properly discretizing them.", "B", 0, "-B"));
  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：ReplaceMissingWithUserConstant.java    
@Override
public Enumeration<Option> listOptions() {

  Vector<Option> opts = new Vector<Option>(5);

  opts
    .addElement(new Option(
      "\tSpecify list of attributes to replace missing values for "
        + "\n\t(as weka range list of indices or a comma separated list of attribute names).\n"
        + "\t(default: consider all attributes)", "R", 1,
      "-A <index1,index2-index4,... | att-name1,att-name2,...>"));

  opts.addElement(new Option(
    "\tSpecify the replacement constant for nominal/string attributes", "N",
    1, "-N"));
  opts.addElement(new Option(
    "\tSpecify the replacement constant for numeric attributes"
      + "\n\t(default: 0)", "R", 1, "-R"));
  opts.addElement(new Option(
    "\tSpecify the replacement constant for date attributes", "D", 1, "-D"));
  opts.addElement(new Option(
    "\tSpecify the date format for parsing the replacement date constant"
      + "\n\t(default: yyyy-MM-dd'T'HH:mm:ss)", "F", 1, "-F"));

  opts.addAll(Collections.list(super.listOptions()));

  return opts.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：PolyKernel.java    
/**
 * 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 Exponent to use.\n"
    + "\t(default: 1.0)", "E", 1, "-E <num>"));

  result.addElement(new Option("\tUse lower-order terms.\n"
    + "\t(default: no)", "L", 0, "-L"));

  result.addAll(Collections.list(super.listOptions()));

  return result.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：Script.java    
/**
 * Make up the help string giving all the command line options.
 * 
 * @param script the script to include options for
 * @return a string detailing the valid command line options
 */
protected static String makeOptionString(Script script) {
  StringBuffer result;
  Enumeration<Option> enm;
  Option option;

  result = new StringBuffer("");

  result.append("\nHelp requested:\n\n");
  result.append("-h or -help\n");
  result.append("\tDisplays this help screen.\n");
  result.append("-s <file>\n");
  result.append("\tThe script to execute.\n");

  enm = script.listOptions();
  while (enm.hasMoreElements()) {
    option = enm.nextElement();
    result.append(option.synopsis() + '\n');
    result.append(option.description() + "\n");
  }

  result.append("\n");
  result.append("Any additional options are passed on to the script as\n");
  result.append("command-line parameters.\n");
  result.append("\n");

  return result.toString();
}
项目：repo.kmeanspp.silhouette_score    文件：HoeffdingTree.java    
/**
 * 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>();

  newVector.add(new Option("\tThe leaf prediction strategy to use. 0 = "
      + "majority class, 1 = naive Bayes, 2 = naive Bayes adaptive.\n\t"
      + "(default = 2)", "L", 1, "-L"));

  newVector.add(new Option("\tThe splitting criterion to use. 0 = "
      + "Gini, 1 = Info gain\n\t" + "(default = 1)", "S", 1, "-S"));
  newVector.add(new Option("\tThe allowable error in a split decision "
      + "- values closer to zero will take longer to decide\n\t"
      + "(default = 1e-7)", "E", 1, "-E"));
  newVector.add(new Option(
      "\tThreshold below which a split will be forced to "
          + "break ties\n\t(default = 0.05)", "H", 1, "-H"));
  newVector.add(new Option(
      "\tMinimum fraction of weight required down at least two "
          + "branches for info gain splitting\n\t(default = 0.01)", "M", 1,
      "-M"));
  newVector.add(new Option("\tGrace period - the number of instances "
      + "a leaf should observe between split attempts\n\t"
      + "(default = 200)", "G", 1, "-G"));
  newVector
      .add(new Option("\tThe number of instances (weight) a leaf "
          + "should observe before allowing naive Bayes to make "
          + "predictions (NB or NB adaptive only)\n\t(default = 0)", "N", 1,
          "-N"));
  newVector.add(new Option("\tPrint leaf models when using naive Bayes "
      + "at the leaves.", "P", 0, "-P"));

  return newVector.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：CorrelationMatrixMapTask.java    
@Override
public Enumeration<Option> listOptions() {
  Vector<Option> opts = new Vector<Option>();

  opts.add(new Option(
    "\tIgnore missing values (rather than replace with mean).",
    "ignore-missing", 0, "-ignore-missing"));
  opts.add(new Option("\tKeep class attribute (if set).", "keep-class", 0,
    "-keep-class"));
  opts.add(new Option(
    "\tFinal result is covariance rather than correlation.", "covariance", 0,
    "-covariance"));

  return opts.elements();
}
项目：repo.kmeanspp.silhouette_score    文件：RandomizableParallelMultipleClassifiersCombiner.java    
/**
 * 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();
}
项目：repo.kmeanspp.silhouette_score    文件：PotentialClassIgnorer.java    
/**
 * 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(
    "\tUnsets the class index temporarily before the filter is\n"
      + "\tapplied to the data.\n" + "\t(default: no)",
    "unset-class-temporarily", 1, "-unset-class-temporarily"));

  return result.elements();
}