Example usage for org.apache.commons.cli2.commandline Parser parseAndHelp

Introduction

In this page you can find the example usage for org.apache.commons.cli2.commandline Parser parseAndHelp.

Prototype

public CommandLine parseAndHelp(final String[] arguments) 

Source Link

Document

Parse the arguments according to the specified options and properties and displays the usage screen if the CommandLine is not valid or the help option was specified.

Usage

From source file:org.apache.mahout.regression.penalizedlinear.Job.java

private static boolean parseJobArgs(String[] args)
        throws IOException, InterruptedException, ClassNotFoundException {
    DefaultOptionBuilder builder = new DefaultOptionBuilder();

    Option help = builder.withLongName("help").withDescription("print this list").create();

    ArgumentBuilder argumentBuilder = new ArgumentBuilder();
    Option inputFile = builder.withLongName("input").withRequired(true)
            .withArgument(argumentBuilder.withName("input").withMaximum(1).create())
            .withDescription(/*from ww  w.  j ava2s  .c  o  m*/
                    "where to get training data (Mahout sequence file of VectorWritable or white-spaced TEXT file); in each line, the first element is response; rest are predictors.")
            .create();

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

    Option lambda = builder.withLongName("lambda")
            .withArgument(argumentBuilder.withName("lambda").withDefault("0").withMinimum(1).create())
            .withDescription("an increasing positive sequence of penalty coefficient, "
                    + "with length n >= 0; if lambda is not specified, the sequence is chosen by algorithm.")
            .create();

    Option alpha = builder.withLongName("alpha")
            .withArgument(
                    argumentBuilder.withName("alpha").withDefault("1").withMinimum(1).withMaximum(1).create())
            .withDescription("the elastic-net coefficient with default value 1 (LASSO)").create();

    Option bias = builder.withLongName("bias").withDescription("include a bias term").create();

    Option numOfCV = builder.withLongName("numOfCV")
            .withArgument(
                    argumentBuilder.withName("numOfCV").withDefault("5").withMinimum(0).withMaximum(1).create())
            .withDescription("number of cross validation, the rule of thumb is 5 or 10").create();

    Option convert = builder.withLongName("convert")
            .withDescription(
                    "pre-processing step if the input file is not Mahout sequence files of VectorWritable: "
                            + "converting space-delimited TEXT file containing floating point numbers into "
                            + "Mahout sequence files of VectorWritable suitable for input of Map-Reduce job.")
            .create();

    Group normalArgs = new GroupBuilder().withOption(help).withOption(inputFile).withOption(outputFile)
            .withOption(lambda).withOption(alpha).withOption(bias).withOption(numOfCV).withOption(convert)
            .create();

    Parser parser = new Parser();
    parser.setHelpOption(help);
    parser.setHelpTrigger("--help");
    parser.setGroup(normalArgs);
    parser.setHelpFormatter(new HelpFormatter(" ", "", " ", 130));
    CommandLine cmdLine = parser.parseAndHelp(args);
    if (cmdLine == null) {
        return false;
    }

    Path input = new Path((String) cmdLine.getValue(inputFile));
    Path output = new Path((String) cmdLine.getValue(outputFile), DIRECTORY_CONTAINING_CONVERTED_INPUT);
    if (cmdLine.hasOption(convert)) {
        jobArgs = new String[args.length - 1];
        int index = 0;
        for (int i = 0; i < args.length; ++i) {
            if (args[i].equals("--convert")) {
                continue;
            }
            jobArgs[index++] = args[i];
            if (args[i].equals("--input")) {
                args[i + 1] = output.toString();
                InputDriver.runJob(input, output, "org.apache.mahout.math.RandomAccessSparseVector");
            }
            if (args[i].equals("--output")) {
                args[i + 1] = (new Path((String) cmdLine.getValue(outputFile), DIRECTORY_CONTAINING_OUTPUT))
                        .toString();
            }
        }
    } else {
        jobArgs = args;
    }
    return true;
}

---

From source file:org.apache.mahout.regression.penalizedlinear.LinearCrossValidation.java

private boolean parseArgs(String[] args) {
    DefaultOptionBuilder builder = new DefaultOptionBuilder();

    Option help = builder.withLongName("help").withDescription("print this list").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 (CSV or white-spaced TEXT file)").create();

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

    Option dependent = builder.withLongName("dependent").withRequired(true)
            .withArgument(argumentBuilder.withName("dependent").withMinimum(1).withMaximum(1).create())
            .withDescription("the dependent features").create();

    Option independent = builder.withLongName("independent").withRequired(true)
            .withArgument(argumentBuilder.withName("independent").create())
            .withDescription("the independent features").create();

    Option interaction = builder.withLongName("interaction").withRequired(true)
            .withArgument(argumentBuilder.withName("interaction").withMinimum(0).create())
            .withDescription(/*from w  ww . j av a2  s. c  o  m*/
                    "the interactions of features, the format is: feature1:feature2 (identical features are OK)")
            .create();

    Option bias = builder.withLongName("bias").withDescription("include a bias term").create();

    Option lambda = builder.withLongName("lambda")
            .withArgument(argumentBuilder.withName("lambda").withDefault("0").withMinimum(1).create())
            .withDescription("an increasing positive sequence of penalty coefficient, "
                    + "with length n >= 0; if lambda is not specified, the sequence is chosen by algorithm.")
            .create();

    Option alpha = builder.withLongName("alpha")
            .withArgument(
                    argumentBuilder.withName("alpha").withDefault("1").withMinimum(1).withMaximum(1).create())
            .withDescription("the elastic-net coefficient with default value 1 (LASSO)").create();

    Option numOfCV = builder.withLongName("numOfCV")
            .withArgument(
                    argumentBuilder.withName("numOfCV").withDefault("5").withMinimum(0).withMaximum(1).create())
            .withDescription("number of cross validation, the rule of thumb is 5 or 10").create();

    Group normalArgs = new GroupBuilder().withOption(help).withOption(inputFile).withOption(outputFile)
            .withOption(dependent).withOption(independent).withOption(interaction).withOption(bias)
            .withOption(lambda).withOption(alpha).withOption(numOfCV).create();

    Parser parser = new Parser();
    parser.setHelpOption(help);
    parser.setHelpTrigger("--help");
    parser.setGroup(normalArgs);
    parser.setHelpFormatter(new HelpFormatter(" ", "", " ", 130));
    CommandLine cmdLine = parser.parseAndHelp(args);
    if (cmdLine == null) {
        return false;
    }

    parameter = new LinearCrossValidationParameter();
    parameter.numOfCV = Integer.parseInt((String) cmdLine.getValue(numOfCV));
    parameter.alpha = Float.parseFloat((String) cmdLine.getValue(alpha));
    parameter.intercept = cmdLine.hasOption(bias);
    parameter.dependent = (String) cmdLine.getValue(dependent);
    String independentString = "";
    for (Object x : cmdLine.getValues(independent)) {
        independentString += x.toString() + ",";
    }
    parameter.independent = independentString.substring(0, Math.max(independentString.length() - 1, 0));
    String interactionString = "";
    for (Object x : cmdLine.getValues(interaction)) {
        interactionString += x.toString() + ",";
    }
    parameter.interaction = interactionString.substring(0, Math.max(interactionString.length() - 1, 0));

    if (!processLambda(parameter, cmdLine, lambda) || parameter.alpha < 0.0 || parameter.alpha > 1.0
            || parameter.numOfCV < 1 || parameter.numOfCV > 20) {
        log.error(
                "please make sure the lambda sequence is positive and increasing, and 0.0 <= alphaValue <= 1.0 and 1 <= numofCV <= 20");
        return false;
    }

    input = (String) cmdLine.getValue(inputFile);
    output = (String) cmdLine.getValue(outputFile);
    return true;
}

---

From source file:org.apache.mahout.regression.penalizedlinear.LinearRegularizePath.java

private boolean parseArgs(String[] args) {
    DefaultOptionBuilder builder = new DefaultOptionBuilder();

    Option help = builder.withLongName("help").withDescription("print this list").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 (CSV or white-spaced TEXT file)").create();

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

    Option dependent = builder.withLongName("dependent").withRequired(true)
            .withArgument(argumentBuilder.withName("dependent").withMinimum(1).withMaximum(1).create())
            .withDescription("the dependent features").create();

    Option independent = builder.withLongName("independent").withRequired(true)
            .withArgument(argumentBuilder.withName("independent").create())
            .withDescription("the independent features").create();

    Option interaction = builder.withLongName("interaction").withRequired(true)
            .withArgument(argumentBuilder.withName("interaction").withMinimum(0).create())
            .withDescription(//from   w ww . j a v  a2s. c o  m
                    "the interactions of features, the format is: feature1:feature2 (identical features are OK)")
            .create();

    Option bias = builder.withLongName("bias").withDescription("include a bias term").create();

    Option lambda = builder.withLongName("lambda")
            .withArgument(argumentBuilder.withName("lambda").withDefault("0").withMinimum(1).create())
            .withDescription("an increasing positive sequence of penalty coefficient, "
                    + "with length n >= 0; if lambda is not specified, the sequence is chosen by algorithm.")
            .create();

    Option alpha = builder.withLongName("alpha")
            .withArgument(
                    argumentBuilder.withName("alpha").withDefault("1").withMinimum(1).withMaximum(1).create())
            .withDescription("the elastic-net coefficient with default value 1 (LASSO)").create();

    Group normalArgs = new GroupBuilder().withOption(help).withOption(inputFile).withOption(outputFile)
            .withOption(dependent).withOption(independent).withOption(interaction).withOption(bias)
            .withOption(lambda).withOption(alpha).create();

    Parser parser = new Parser();
    parser.setHelpOption(help);
    parser.setHelpTrigger("--help");
    parser.setGroup(normalArgs);
    parser.setHelpFormatter(new HelpFormatter(" ", "", " ", 130));
    CommandLine cmdLine = parser.parseAndHelp(args);
    if (cmdLine == null) {
        return false;
    }

    parameter = new LinearRegularizePathParameter();
    parameter.numOfCV = 1;
    parameter.alpha = Float.parseFloat((String) cmdLine.getValue(alpha));
    parameter.intercept = cmdLine.hasOption(bias);
    parameter.dependent = (String) cmdLine.getValue(dependent);
    String independentString = "";
    for (Object x : cmdLine.getValues(independent)) {
        independentString += x.toString() + ",";
    }
    parameter.independent = independentString.substring(0, Math.max(independentString.length() - 1, 0));
    String interactionString = "";
    for (Object x : cmdLine.getValues(interaction)) {
        interactionString += x.toString() + ",";
    }
    parameter.interaction = interactionString.substring(0, Math.max(interactionString.length() - 1, 0));

    if (!processLambda(parameter, cmdLine, lambda) || parameter.alpha < 0.0 || parameter.alpha > 1.0
            || parameter.numOfCV < 1 || parameter.numOfCV > 20) {
        log.error(
                "please make sure the lambda sequence is positive and increasing, and 0.0 <= alphaValue <= 1.0 and 1 <= numofCV <= 20");
        return false;
    }

    input = (String) cmdLine.getValue(inputFile);
    output = (String) cmdLine.getValue(outputFile);
    return true;
}

---

From source file:org.apache.mahout.regression.penalizedlinear.PenalizedLinearDriver.java

private boolean parseArgs(String[] args) {
    DefaultOptionBuilder builder = new DefaultOptionBuilder();

    Option help = builder.withLongName("help").withDescription("print this list").create();

    ArgumentBuilder argumentBuilder = new ArgumentBuilder();
    Option inputFile = builder.withLongName("input").withRequired(true)
            .withArgument(argumentBuilder.withName("input").withMaximum(1).create())
            .withDescription(/*  w  w  w.j a  v a 2 s  .  c  o m*/
                    "where to get training data (Mahout sequence file of VectorWritable); in each line, the first element is response; rest are predictors.")
            .create();

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

    Option lambda = builder.withLongName("lambda")
            .withArgument(argumentBuilder.withName("lambda").withDefault("0").withMinimum(1).create())
            .withDescription("an increasing positive sequence of penalty coefficient, "
                    + "with length n >= 0; if lambda is not specified, the sequence is chosen by algorithm.")
            .create();

    Option alpha = builder.withLongName("alpha")
            .withArgument(
                    argumentBuilder.withName("alpha").withDefault("1").withMinimum(1).withMaximum(1).create())
            .withDescription("the elastic-net coefficient with default value 1 (LASSO)").create();

    Option bias = builder.withLongName("bias").withDescription("include a bias term").create();

    Option numOfCV = builder.withLongName("numOfCV")
            .withArgument(
                    argumentBuilder.withName("numOfCV").withDefault("5").withMinimum(0).withMaximum(1).create())
            .withDescription("number of cross validation, the rule of thumb is 5 or 10").create();

    Group normalArgs = new GroupBuilder().withOption(help).withOption(inputFile).withOption(outputFile)
            .withOption(lambda).withOption(alpha).withOption(bias).withOption(numOfCV).create();

    Parser parser = new Parser();
    parser.setHelpOption(help);
    parser.setHelpTrigger("--help");
    parser.setGroup(normalArgs);
    parser.setHelpFormatter(new HelpFormatter(" ", "", " ", 130));
    CommandLine cmdLine = parser.parseAndHelp(args);
    if (cmdLine == null) {
        return false;
    }

    parameter = new PenalizedLinearParameter();
    parameter.setNumOfCV(Integer.parseInt((String) cmdLine.getValue(numOfCV)));
    parameter.setAlpha(Float.parseFloat((String) cmdLine.getValue(alpha)));
    parameter.setIntercept(cmdLine.hasOption(bias));

    if (!processLambda(parameter, cmdLine, lambda) || parameter.alpha < 0.0 || parameter.alpha > 1.0
            || parameter.numOfCV < 1 || parameter.numOfCV > 20) {
        log.error(
                "please make sure the lambda sequence is positive and increasing, and 0.0 <= alphaValue <= 1.0 and 1 <= numOfCV <= 20");
        return false;
    }

    input = (String) cmdLine.getValue(inputFile);
    output = (String) cmdLine.getValue(outputFile);

    return true;
}

---

From source file:org.rvsnoop.ui.RvSnoopApplication.java

private CommandLine parseCommandLine(String[] args, Option helpOption, Option projectOption) {
    Group group = new GroupBuilder().withOption(helpOption).withOption(projectOption).create();
    Parser parser = new Parser();
    parser.setGroup(group);/*from   w  w w  .j  a  va2 s. co  m*/
    parser.setHelpOption(helpOption);
    parser.setHelpFormatter(new HelpFormatter());
    CommandLine line = parser.parseAndHelp(args);
    if (line.hasOption(helpOption)) {
        System.exit(0);
    }
    return line;
}

---

From source file:org.rzo.yajsw.WrapperExe.java

/**
 * Parses the command./* ww w .  java 2  s  .co m*/
 * 
 * @param args
 *            the args
 */
private static void parseCommand(String[] args) {
    Parser parser = new Parser();

    // configure a HelpFormatter
    HelpFormatter hf = new HelpFormatter();
    DefaultOptionBuilder oBuilder = new DefaultOptionBuilder();
    ;

    // configure a parser
    Parser p = new Parser();
    p.setGroup(group);
    p.setHelpFormatter(hf);
    p.setHelpOption(oBuilder.withLongName("help").withShortName("?").create());
    cl = p.parseAndHelp(args);

    // abort application if no CommandLine was parsed
    if (cl == null) {
        System.exit(-1);
    }
    cmds = cl.getOptions();
    try {
        confFile = (String) cl.getValue(CONF_FILE);
    } catch (Exception ex) {
        System.out.println("no wrapper config file found ");
    }
    try {
        defaultFile = (String) cl.getValue(cl.getOption("-d"));
        if (defaultFile != null)
            defaultFile = new File(defaultFile).getCanonicalPath();
    } catch (Exception ex) {
        // no defaults -> maybe ok
    }
    properties = cl.getValues(PROPERTIES);

}

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