Package org.apache.commons.cli2.builder

Examples of org.apache.commons.cli2.builder.ArgumentBuilder

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  public static DefaultOptionBuilder minClusterSizeOption() {
    return new DefaultOptionBuilder()
        .withLongName(MIN_CLUSTER_SIZE)
        .withRequired(false)
        .withArgument(
            new ArgumentBuilder().withName(MIN_CLUSTER_SIZE).withDefault("10")
                .withMinimum(1).withMaximum(1).create())
        .withDescription("Minimum points inside a cluster")
        .withShortName("mcs");
  }
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  public static DefaultOptionBuilder hashTypeOption() {
    return new DefaultOptionBuilder()
        .withLongName(HASH_TYPE)
        .withRequired(false)
        .withArgument(
            new ArgumentBuilder().withName(HASH_TYPE).withDefault("murmur")
                .withMinimum(1).withMaximum(1).create())
        .withDescription(
            "Type of hash function to use. Available types: (linear, polynomial, murmur) ")
        .withShortName("ht");
  }
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  public static DefaultOptionBuilder minVectorSizeOption() {
    return new DefaultOptionBuilder()
        .withLongName(MIN_VECTOR_SIZE)
        .withRequired(false)
        .withArgument(
            new ArgumentBuilder().withName(MIN_VECTOR_SIZE).withDefault("5")
                .withMinimum(1).withMaximum(1).create())
        .withDescription("Minimum size of vector to be hashed")
        .withShortName("mvs");
  }
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  public static DefaultOptionBuilder numHashFunctionsOption() {
    return new DefaultOptionBuilder()
        .withLongName(NUM_HASH_FUNCTIONS)
        .withRequired(false)
        .withArgument(
            new ArgumentBuilder().withName(NUM_HASH_FUNCTIONS)
                .withDefault("10").withMinimum(1).withMaximum(1).create())
        .withDescription("Number of hash functions to be used")
        .withShortName("nh");
  }
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  public static DefaultOptionBuilder keyGroupsOption() {
    return new DefaultOptionBuilder()
        .withLongName(KEY_GROUPS)
        .withRequired(false)
        .withArgument(
            new ArgumentBuilder().withName(KEY_GROUPS).withDefault("2")
                .withMinimum(1).withMaximum(1).create())
        .withDescription("Number of key groups to be used").withShortName("kg");
  }
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  /**
   * Returns a default command line option for input directory specification. Used by all clustering jobs plus others
   */
  public static DefaultOptionBuilder inputOption() {
    return new DefaultOptionBuilder().withLongName(INPUT_OPTION).withRequired(false).withShortName("i")
        .withArgument(new ArgumentBuilder().withName(INPUT_OPTION).withMinimum(1).withMaximum(1).create())
        .withDescription("Path to job input directory.");
  }
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  /**
   * Returns a default command line option for clusters input directory specification. Used by FuzzyKmeans, Kmeans
   */
  public static DefaultOptionBuilder clustersInOption() {
    return new DefaultOptionBuilder().withLongName(CLUSTERS_IN_OPTION).withRequired(true)
        .withArgument(new ArgumentBuilder().withName(CLUSTERS_IN_OPTION).withMinimum(1).withMaximum(1).create())
        .withDescription("The path to the initial clusters directory. Must be a SequenceFile of some type of Cluster")
        .withShortName("c");
  }
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  private PrepareTwentyNewsgroups() { }
 
  public static void main(String[] args) throws Exception {
    DefaultOptionBuilder obuilder = new DefaultOptionBuilder();
    ArgumentBuilder abuilder = new ArgumentBuilder();
    GroupBuilder gbuilder = new GroupBuilder();
    Option helpOpt = DefaultOptionCreator.helpOption();
   
    Option parentOpt = obuilder.withLongName("parent").withRequired(true).withArgument(
      abuilder.withName("parent").withMinimum(1).withMaximum(1).create()).withDescription(
      "Parent dir containing the newsgroups").withShortName("p").create();
   
    Option outputDirOpt = obuilder.withLongName("outputDir").withRequired(true).withArgument(
      abuilder.withName("outputDir").withMinimum(1).withMaximum(1).create()).withDescription(
      "The output directory").withShortName("o").create();
   
    Option analyzerNameOpt = obuilder.withLongName("analyzerName").withRequired(true).withArgument(
      abuilder.withName("analyzerName").withMinimum(1).withMaximum(1).create()).withDescription(
      "The class name of the analyzer").withShortName("a").create();
   
    Option charsetOpt = obuilder.withLongName("charset").withRequired(true).withArgument(
      abuilder.withName("charset").withMinimum(1).withMaximum(1).create()).withDescription(
      "The name of the character encoding of the input files").withShortName("c").create();
   
    Group group = gbuilder.withName("Options").withOption(analyzerNameOpt).withOption(charsetOpt).withOption(
      outputDirOpt).withOption(parentOpt).withOption(helpOpt).create();
    try {
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    Option help = builder.withLongName("help").withDescription("print this list").create();

    Option quiet = builder.withLongName("quiet").withDescription("be extra quiet").create();
    Option scores = builder.withLongName("scores").withDescription("output score diagnostics during training").create();

    ArgumentBuilder argumentBuilder = new ArgumentBuilder();
    Option inputFile = builder.withLongName("input")
            .withRequired(true)
            .withArgument(argumentBuilder.withName("input").withMaximum(1).create())
            .withDescription("where to get training data")
            .create();

    Option outputFile = builder.withLongName("output")
            .withRequired(true)
            .withArgument(argumentBuilder.withName("output").withMaximum(1).create())
            .withDescription("where to get training data")
            .create();

    Option predictors = builder.withLongName("predictors")
            .withRequired(true)
            .withArgument(argumentBuilder.withName("p").create())
            .withDescription("a list of predictor variables")
            .create();

    Option types = builder.withLongName("types")
            .withRequired(true)
            .withArgument(argumentBuilder.withName("t").create())
            .withDescription("a list of predictor variable types (numeric, word, or text)")
            .create();

    Option target = builder.withLongName("target")
            .withRequired(true)
            .withArgument(argumentBuilder.withName("target").withMaximum(1).create())
            .withDescription("the name of the target variable")
            .create();

    Option features = builder.withLongName("features")
            .withArgument(
                    argumentBuilder.withName("numFeatures")
                            .withDefault("1000")
                            .withMaximum(1).create())
            .withDescription("the number of internal hashed features to use")
            .create();

    Option passes = builder.withLongName("passes")
            .withArgument(
                    argumentBuilder.withName("passes")
                            .withDefault("2")
                            .withMaximum(1).create())
            .withDescription("the number of times to pass over the input data")
            .create();

    Option lambda = builder.withLongName("lambda")
            .withArgument(argumentBuilder.withName("lambda").withDefault("1e-4").withMaximum(1).create())
            .withDescription("the amount of coefficient decay to use")
            .create();

    Option rate = builder.withLongName("rate")
            .withArgument(argumentBuilder.withName("learningRate").withDefault("1e-3").withMaximum(1).create())
            .withDescription("the learning rate")
            .create();

    Option noBias = builder.withLongName("noBias")
            .withDescription("don't include a bias term")
            .create();

    Option targetCategories = builder.withLongName("categories")
            .withRequired(true)
            .withArgument(argumentBuilder.withName("number").withMaximum(1).create())
            .withDescription("the number of target categories to be considered")
            .create();

    Group normalArgs = new GroupBuilder()
            .withOption(help)
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    Option auc = builder.withLongName("auc").withDescription("print AUC").create();
    Option confusion = builder.withLongName("confusion").withDescription("print confusion matrix").create();

    Option scores = builder.withLongName("scores").withDescription("print scores").create();

    ArgumentBuilder argumentBuilder = new ArgumentBuilder();
    Option inputFileOption = builder.withLongName("input")
            .withRequired(true)
            .withArgument(argumentBuilder.withName("input").withMaximum(1).create())
            .withDescription("where to get training data")
            .create();

    Option modelFileOption = builder.withLongName("model")
            .withRequired(true)
            .withArgument(argumentBuilder.withName("model").withMaximum(1).create())
            .withDescription("where to get a model")
            .create();

    Group normalArgs = new GroupBuilder()
            .withOption(help)
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