Package org.apache.hadoop.yarn.webapp.hamlet

Examples of org.apache.hadoop.yarn.webapp.hamlet.HamletSpec$OPTION

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    ToolRunner.run(new TransposeJob(), args);
  }

  @Override
  public int run(String[] strings) throws Exception {
    Option numRowsOpt = buildOption("numRows",
                                    "nr",
                                    "Number of rows of the input matrix");
    Option numColsOpt = buildOption("numCols",
                                    "nc",
                                    "Number of columns of the input matrix");
    Map<String,String> parsedArgs = parseArguments(strings, numRowsOpt, numColsOpt);

    String inputPathString = parsedArgs.get("--input");
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    ToolRunner.run(new MatrixMultiplicationJob(), args);
  }

  @Override
  public int run(String[] strings) throws Exception {
    Option numRowsAOpt = buildOption("numRowsA",
                                     "nra",
                                     "Number of rows of the first input matrix");
    Option numColsAOpt = buildOption("numColsA",
                                     "nca",
                                     "Number of columns of the first input matrix");
    Option numRowsBOpt = buildOption("numRowsB",
                                     "nrb",
                                     "Number of rows of the second input matrix");

    Option numColsBOpt = buildOption("numColsB",
                                     "ncb",
                                     "Number of columns of the second input matrix");
    Option inputPathA = buildOption("inputPathA",
                                    "ia",
                                    "Path to the first input matrix");
    Option inputPathB = buildOption("inputPathB",
                                    "ib",
                                    "Path to the second input matrix");

    Map<String, String> argMap = parseArguments(strings,
                                                numRowsAOpt,
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    DefaultOptionBuilder obuilder = new DefaultOptionBuilder();
    ArgumentBuilder abuilder = new ArgumentBuilder();
    GroupBuilder gbuilder = new GroupBuilder();
   
    Option pathOpt = obuilder.withLongName("path").withRequired(true).withArgument(
      abuilder.withName("path").withMinimum(1).withMaximum(1).create()).withDescription(
      "The local file system path").withShortName("m").create();
   
    Option classifyOpt = obuilder.withLongName("classify").withRequired(true).withArgument(
      abuilder.withName("classify").withMinimum(1).withMaximum(1).create()).withDescription(
      "The doc to classify").withShortName("").create();
   
    Option encodingOpt = obuilder.withLongName("encoding").withRequired(true).withArgument(
      abuilder.withName("encoding").withMinimum(1).withMaximum(1).create()).withDescription(
      "The file encoding.  Default: UTF-8").withShortName("e").create();
   
    Option analyzerOpt = obuilder.withLongName("analyzer").withRequired(true).withArgument(
      abuilder.withName("analyzer").withMinimum(1).withMaximum(1).create()).withDescription(
      "The Analyzer to use").withShortName("a").create();
   
    Option defaultCatOpt = obuilder.withLongName("defaultCat").withRequired(true).withArgument(
      abuilder.withName("defaultCat").withMinimum(1).withMaximum(1).create()).withDescription(
      "The default category").withShortName("d").create();
   
    Option gramSizeOpt = obuilder.withLongName("gramSize").withRequired(true).withArgument(
      abuilder.withName("gramSize").withMinimum(1).withMaximum(1).create()).withDescription(
      "Size of the n-gram").withShortName("ng").create();
   
    Option typeOpt = obuilder.withLongName("classifierType").withRequired(true).withArgument(
      abuilder.withName("classifierType").withMinimum(1).withMaximum(1).create()).withDescription(
      "Type of classifier").withShortName("type").create();
   
    Option dataSourceOpt = obuilder.withLongName("dataSource").withRequired(true).withArgument(
      abuilder.withName("dataSource").withMinimum(1).withMaximum(1).create()).withDescription(
      "Location of model: hdfs|hbase").withShortName("source").create();
   
    Group options = gbuilder.withName("Options").withOption(pathOpt).withOption(classifyOpt).withOption(
      encodingOpt).withOption(analyzerOpt).withOption(defaultCatOpt).withOption(gramSizeOpt).withOption(
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    }

    @Override
    public int run(String[] args) throws Exception {

      Option numRowsOpt = buildOption("numRows",
                                      "nr",
                                      "Number of rows of the input matrix");
      Option numColsOpt = buildOption("numCols",
                                      "nc",
                                      "Number of columns of the input matrix");
      Option desiredRankOpt = buildOption("rank",
                                          "r",
                                          "Desired decomposition rank (note: only roughly 1/4 to 1/3 "
                                        + "of these will have the top portion of the spectrum)");
      Option isSymmetricOpt = buildOption("symmetric",
                                          "sym",
                                          "Is the input matrix square and symmetric?");

      DistributedLanczosSolver.this.parsedArgs = parseArguments(args,
                                                                numRowsOpt,
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    return 0;
  }

  public Map<String,String> handleArgs(String[] args) {
    Option eigenInputOpt = buildOption("eigenInput", "ei",
        "The Path for purported eigenVector input files (SequenceFile<WritableComparable,VectorWritable>.", null);
    Option corpusInputOpt = buildOption("corpusInput", "ci",
        "The Path for corpus input files (SequenceFile<WritableComparable,VectorWritable>.");
    Option outOpt = DefaultOptionCreator.outputOption().create();
    Option helpOpt = DefaultOptionCreator.helpOption();
    Option inMemOpt = buildOption("inMemory", "mem", "Buffer eigen matrix into memory (if you have enough!)", "false");
    Option errorOpt = buildOption("maxError", "err", "Maximum acceptable error", "0.05");
    Option minEigenValOpt = buildOption("minEigenvalue", "mev", "Minimum eigenvalue to keep the vector for", "0.0");

    GroupBuilder gBuilder = new GroupBuilder().withName("Options")
                                              .withOption(eigenInputOpt)
                                              .withOption(corpusInputOpt)
                                              .withOption(helpOpt)
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        .withArgument(arg).withDescription(description).create();
  }
 
  protected static Map<String,String> parseArguments(String[] args, Option... extraOpts) {
   
    Option inputOpt = DefaultOptionCreator.inputOption().create();
    Option tempDirOpt = buildOption("tempDir", "t", "Intermediate output directory", "temp");
    Option outputOpt = DefaultOptionCreator.outputOption().create();
    Option helpOpt = DefaultOptionCreator.helpOption();
    Option jarFileOpt = buildOption("jarFile", "m", "Implementation jar", false, null);
   
    GroupBuilder gBuilder = new GroupBuilder().withName("Options").withOption(inputOpt)
        .withOption(tempDirOpt).withOption(outputOpt).withOption(helpOpt).withOption(jarFileOpt);
   
    for (Option opt : extraOpts) {
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  public static void main(String[] args) throws Exception {
    DefaultOptionBuilder obuilder = new DefaultOptionBuilder();
    ArgumentBuilder abuilder = new ArgumentBuilder();
    GroupBuilder gbuilder = new GroupBuilder();
   
    Option inputDirOpt = obuilder.withLongName("input").withRequired(true).withArgument(
      abuilder.withName("input").withMinimum(1).withMaximum(1).create()).withDescription(
      "input dir containing the documents in sequence file format").withShortName("i").create();
   
    Option outputDirOpt = obuilder.withLongName("output").withRequired(true).withArgument(
      abuilder.withName("output").withMinimum(1).withMaximum(1).create()).withDescription(
      "The output directory").withShortName("o").create();
    Option minSupportOpt = obuilder.withLongName("minSupport").withArgument(
      abuilder.withName("minSupport").withMinimum(1).withMaximum(1).create()).withDescription(
      "(Optional) Minimum Support. Default Value: 2").withShortName("s").create();
   
    Option analyzerNameOpt = obuilder.withLongName("analyzerName").withArgument(
      abuilder.withName("analyzerName").withMinimum(1).withMaximum(1).create()).withDescription(
      "The class name of the analyzer").withShortName("a").create();
   
    Option chunkSizeOpt = obuilder.withLongName("chunkSize").withArgument(
      abuilder.withName("chunkSize").withMinimum(1).withMaximum(1).create()).withDescription(
      "The chunkSize in MegaBytes. 100-10000 MB").withShortName("chunk").create();
   
    Option weightOpt = obuilder.withLongName("weight").withRequired(false).withArgument(
      abuilder.withName("weight").withMinimum(1).withMaximum(1).create()).withDescription(
      "The kind of weight to use. Currently TF or TFIDF").withShortName("wt").create();
   
    Option minDFOpt = obuilder.withLongName("minDF").withRequired(false).withArgument(
      abuilder.withName("minDF").withMinimum(1).withMaximum(1).create()).withDescription(
      "The minimum document frequency.  Default is 1").withShortName("md").create();
   
    Option maxDFPercentOpt = obuilder.withLongName("maxDFPercent").withRequired(false).withArgument(
      abuilder.withName("maxDFPercent").withMinimum(1).withMaximum(1).create()).withDescription(
      "The max percentage of docs for the DF.  Can be used to remove really high frequency terms."
          + " Expressed as an integer between 0 and 100. Default is 99.").withShortName("x").create();
   
    Option minLLROpt = obuilder.withLongName("minLLR").withRequired(false).withArgument(
      abuilder.withName("minLLR").withMinimum(1).withMaximum(1).create()).withDescription(
      "(Optional)The minimum Log Likelihood Ratio(Float)  Default is " + LLRReducer.DEFAULT_MIN_LLR)
        .withShortName("ml").create();
   
    Option numReduceTasksOpt = obuilder.withLongName("numReducers").withArgument(
      abuilder.withName("numReducers").withMinimum(1).withMaximum(1).create()).withDescription(
      "(Optional) Number of reduce tasks. Default Value: 1").withShortName("nr").create();
   
    Option powerOpt = obuilder.withLongName("norm").withRequired(false).withArgument(
      abuilder.withName("norm").withMinimum(1).withMaximum(1).create()).withDescription(
      "The norm to use, expressed as either a float or \"INF\" if you want to use the Infinite norm.  "
          + "Must be greater or equal to 0.  The default is not to normalize").withShortName("n").create();
    Option maxNGramSizeOpt = obuilder.withLongName("maxNGramSize").withRequired(false).withArgument(
      abuilder.withName("ngramSize").withMinimum(1).withMaximum(1).create())
        .withDescription(
          "(Optional) The maximum size of ngrams to create"
              + " (2 = bigrams, 3 = trigrams, etc) Default Value:2").withShortName("ng").create();
    Option sequentialAccessVectorOpt = obuilder.withLongName("sequentialAccessVector").withRequired(false)
        .withDescription(
          "(Optional) Whether output vectors should be SequentialAccessVectors If set true else false")
        .withShortName("seq").create();
   
    Option overwriteOutput = obuilder.withLongName("overwrite").withRequired(false).withDescription(
      "If set, overwrite the output directory").withShortName("w").create();
    Option helpOpt = obuilder.withLongName("help").withDescription("Print out help").withShortName("h")
        .create();
   
    Group group = gbuilder.withName("Options").withOption(minSupportOpt).withOption(analyzerNameOpt)
        .withOption(chunkSizeOpt).withOption(outputDirOpt).withOption(inputDirOpt).withOption(minDFOpt)
        .withOption(maxDFPercentOpt).withOption(weightOpt).withOption(powerOpt).withOption(minLLROpt)
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  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 inputDirOpt = obuilder.withLongName("input").withRequired(true).withArgument(
      abuilder.withName("input").withMinimum(1).withMaximum(1).create()).withDescription(
      "The Directory on HDFS containing the collapsed, properly formatted files").withShortName("i").create();
   
    Option outputOpt = obuilder.withLongName("output").withRequired(true).withArgument(
      abuilder.withName("output").withMinimum(1).withMaximum(1).create()).withDescription(
      "The location of the modelon the HDFS").withShortName("o").create();
   
    Option gramSizeOpt = obuilder.withLongName("gramSize").withRequired(true).withArgument(
      abuilder.withName("gramSize").withMinimum(1).withMaximum(1).create()).withDescription(
      "Size of the n-gram. Default Value: 1 ").withShortName("ng").create();
   
    Option alphaOpt = obuilder.withLongName("alpha").withRequired(false).withArgument(
      abuilder.withName("a").withMinimum(1).withMaximum(1).create()).withDescription(
      "Smoothing parameter Default Value: 1.0").withShortName("a").create();
   
    Option typeOpt = obuilder.withLongName("classifierType").withRequired(true).withArgument(
      abuilder.withName("classifierType").withMinimum(1).withMaximum(1).create()).withDescription(
      "Type of classifier: bayes|cbayes. Default: bayes").withShortName("type").create();
    Option dataSourceOpt = obuilder.withLongName("dataSource").withRequired(true).withArgument(
      abuilder.withName("dataSource").withMinimum(1).withMaximum(1).create()).withDescription(
      "Location of model: hdfs|hbase. Default Value: hdfs").withShortName("source").create();
   
    Group group = gbuilder.withName("Options").withOption(gramSizeOpt).withOption(helpOpt).withOption(
      inputDirOpt).withOption(outputOpt).withOption(typeOpt).withOption(dataSourceOpt).withOption(alphaOpt)
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  public static void main(String[] args) throws IOException, InvalidDatastoreException {
    DefaultOptionBuilder obuilder = new DefaultOptionBuilder();
    ArgumentBuilder abuilder = new ArgumentBuilder();
    GroupBuilder gbuilder = new GroupBuilder();
   
    Option pathOpt = obuilder.withLongName("model").withRequired(true).withArgument(
      abuilder.withName("model").withMinimum(1).withMaximum(1).create()).withDescription(
      "The path on HDFS / Name of Hbase Table as defined by the -source parameter").withShortName("m")
        .create();
   
    Option dirOpt = obuilder.withLongName("testDir").withRequired(true).withArgument(
      abuilder.withName("testDir").withMinimum(1).withMaximum(1).create()).withDescription(
      "The directory where test documents resides in").withShortName("d").create();
   
    Option helpOpt = DefaultOptionCreator.helpOption();
   
    Option encodingOpt = obuilder.withLongName("encoding").withArgument(
      abuilder.withName("encoding").withMinimum(1).withMaximum(1).create()).withDescription(
      "The file encoding.  Defaults to UTF-8").withShortName("e").create();
   
    Option defaultCatOpt = obuilder.withLongName("defaultCat").withArgument(
      abuilder.withName("defaultCat").withMinimum(1).withMaximum(1).create()).withDescription(
      "The default category Default Value: unknown").withShortName("default").create();
   
    Option gramSizeOpt = obuilder.withLongName("gramSize").withRequired(true).withArgument(
      abuilder.withName("gramSize").withMinimum(1).withMaximum(1).create()).withDescription(
      "Size of the n-gram. Default Value: 1").withShortName("ng").create();
   
    Option alphaOpt = obuilder.withLongName("alpha").withRequired(false).withArgument(
      abuilder.withName("a").withMinimum(1).withMaximum(1).create()).withDescription(
      "Smoothing parameter Default Value: 1.0").withShortName("a").create();
   
    Option verboseOutputOpt = obuilder.withLongName("verbose").withRequired(false).withDescription(
      "Output which values were correctly and incorrectly classified").withShortName("v").create();
   
    Option typeOpt = obuilder.withLongName("classifierType").withRequired(true).withArgument(
      abuilder.withName("classifierType").withMinimum(1).withMaximum(1).create()).withDescription(
      "Type of classifier: bayes|cbayes. Default Value: bayes").withShortName("type").create();
   
    Option dataSourceOpt = obuilder.withLongName("dataSource").withRequired(true).withArgument(
      abuilder.withName("dataSource").withMinimum(1).withMaximum(1).create()).withDescription(
      "Location of model: hdfs|hbase Default Value: hdfs").withShortName("source").create();
   
    Option methodOpt = obuilder.withLongName("method").withRequired(false).withArgument(
      abuilder.withName("method").withMinimum(1).withMaximum(1).create()).withDescription(
      "Method of Classification: sequential|mapreduce. Default Value: sequential").withShortName("method")
        .create();
   
    Group group = gbuilder.withName("Options").withOption(defaultCatOpt).withOption(dirOpt).withOption(
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  public static void main(String[] args) throws Exception {
    DefaultOptionBuilder obuilder = new DefaultOptionBuilder();
    ArgumentBuilder abuilder = new ArgumentBuilder();
    GroupBuilder gbuilder = new GroupBuilder();
   
    Option inputOpt = DefaultOptionCreator.inputOption().create();
    Option outputOpt = DefaultOptionCreator.outputOption().create();
    Option maxIterOpt = DefaultOptionCreator.maxIterOption().create();
    Option topicsOpt = DefaultOptionCreator.kOption().create();
    Option helpOpt = DefaultOptionCreator.helpOption();
   
    Option mOpt = obuilder.withLongName("alpha").withRequired(true).withShortName("m").withArgument(
      abuilder.withName("alpha").withMinimum(1).withMaximum(1).create()).withDescription(
      "The alpha0 value for the DirichletDistribution.").create();
   
    Option modelOpt = obuilder.withLongName("modelClass").withRequired(true).withShortName("d").withArgument(
      abuilder.withName("modelClass").withMinimum(1).withMaximum(1).create()).withDescription(
      "The ModelDistribution class name. "
          + "Defaults to org.apache.mahout.clustering.dirichlet.models.NormalModelDistribution").create();
   
    Option prototypeOpt = obuilder.withLongName("modelPrototypeClass").withRequired(false).withShortName("p")
        .withArgument(abuilder.withName("prototypeClass").withMinimum(1).withMaximum(1).create())
        .withDescription(
          "The ModelDistribution prototype Vector class name. "
              + "Defaults to org.apache.mahout.math.RandomAccessSparseVector").create();
   
    Option sizeOpt = obuilder.withLongName("prototypeSize").withRequired(true).withShortName("s")
        .withArgument(abuilder.withName("prototypeSize").withMinimum(1).withMaximum(1).create())
        .withDescription("The ModelDistribution prototype Vector size. ").create();
   
    Option numRedOpt = obuilder.withLongName("maxRed").withRequired(true).withShortName("r").withArgument(
      abuilder.withName("maxRed").withMinimum(1).withMaximum(1).create()).withDescription(
      "The number of reduce tasks.").create();
   
    Group group = gbuilder.withName("Options").withOption(inputOpt).withOption(outputOpt)
        .withOption(modelOpt).withOption(prototypeOpt).withOption(sizeOpt).withOption(maxIterOpt).withOption(
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