Example usage for org.apache.commons.cli BasicParser parse

Introduction

In this page you can find the example usage for org.apache.commons.cli BasicParser parse.

Prototype

public CommandLine parse(Options options, String[] arguments) throws ParseException 

Source Link

Document

Parses the specified arguments based on the specifed Options .

Usage

From source file:org.fnlp.nlp.parser.dep.train.JointParerTester.java

public static void main(String[] args) throws Exception {

    Options opt = new Options();

    opt.addOption("h", false, "Print help for this application");

    BasicParser parser = new BasicParser();
    CommandLine cl;//ww  w .  j a  va 2 s.  co m
    try {
        cl = parser.parse(opt, args);
    } catch (Exception e) {
        System.err.println("Parameters format error");
        return;
    }

    if (args.length == 0 || cl.hasOption('h')) {
        HelpFormatter f = new HelpFormatter();
        f.printHelp("Tagger:\n" + "ParserTester [option] model_file test_file result_file;\n", opt);
        return;
    }

    String[] args1 = cl.getArgs();
    String modelfile = args1[0];
    String testfile = args1[1];
    String resultfile = args1[2];

    JointParerTester tester = new JointParerTester(modelfile);
    tester.test(testfile, resultfile, "UTF-8");
}

---

From source file:org.fnlp.nlp.parser.dep.train.JointParerTrainer.java

/**
 * /*from  w ww. jav  a2s . c  om*/
 * 
 * @param args
 *             
 * @throws Exception
 */
public static void main(String[] args) throws Exception {
    //      args = new String[2];
    //      args[0] = "./tmp/malt.train";
    //      args[1] = "./tmp/Malt2Model.gz";
    Options opt = new Options();

    opt.addOption("h", false, "Print help for this application");
    opt.addOption("iter", true, "iterative num, default 50");
    opt.addOption("c", true, "parameters 1, default 1");

    BasicParser parser = new BasicParser();
    CommandLine cl;
    try {
        cl = parser.parse(opt, args);
    } catch (Exception e) {
        System.err.println("Parameters format error");
        return;
    }

    if (args.length == 0 || cl.hasOption('h')) {
        HelpFormatter f = new HelpFormatter();
        f.printHelp("Tagger:\n" + "ParserTrainer [option] train_file model_file;\n", opt);
        return;
    }
    args = cl.getArgs();
    String datafile = args[0];
    String modelfile = args[1];
    int maxite = Integer.parseInt(cl.getOptionValue("iter", "50"));
    float c = Float.parseFloat(cl.getOptionValue("c", "1"));

    JointParerTrainer trainer = new JointParerTrainer(modelfile);
    trainer.train(datafile, maxite, c);
}

---

From source file:org.fnlp.nlp.parser.dep.train.ParserTrainer.java

/**
 * //from  ww  w. j a v a 2  s  .c om
 * 
 * @param args
 *             
 * @throws Exception
 */
public static void main(String[] args) throws Exception {
    args = new String[2];
    args[0] = "./tmp/CoNLL2009-ST-Chinese-train.txt";
    args[1] = "./tmp/modelConll.gz";
    Options opt = new Options();

    opt.addOption("h", false, "Print help for this application");
    opt.addOption("iter", true, "iterative num, default 50");
    opt.addOption("c", true, "parameters 1, default 1");

    BasicParser parser = new BasicParser();
    CommandLine cl;
    try {
        cl = parser.parse(opt, args);
    } catch (Exception e) {
        System.err.println("Parameters format error");
        return;
    }

    if (args.length == 0 || cl.hasOption('h')) {
        HelpFormatter f = new HelpFormatter();
        f.printHelp("Tagger:\n" + "ParserTrainer [option] train_file model_file;\n", opt);
        return;
    }
    args = cl.getArgs();
    String datafile = args[0];
    String modelfile = args[1];
    int maxite = Integer.parseInt(cl.getOptionValue("iter", "50"));
    float c = Float.parseFloat(cl.getOptionValue("c", "1"));

    ParserTrainer trainer = new ParserTrainer(modelfile);
    trainer.train(datafile, maxite, c);
}

---

From source file:org.fnlp.nlp.similarity.train.WordCluster.java

/**
 * @param args//from   w w w . j  a  v a  2 s.  c  o  m
 * @throws Exception 
 */
public static void main(String[] args) throws Exception {

    /**
     * ??
     */
    Options opt = new Options();

    opt.addOption("path", true, "?");
    opt.addOption("res", true, "?");
    opt.addOption("slot", true, "?");

    BasicParser parser = new BasicParser();
    CommandLine cl;
    try {
        cl = parser.parse(opt, args);
    } catch (Exception e) {
        System.err.println("Parameters format error");
        return;
    }

    int slotsize = Integer.parseInt(cl.getOptionValue("slot", "50"));
    System.out.println("?:" + slotsize);

    String file = cl.getOptionValue("path", "./tmp/news.allsites.txt");
    System.out.println("?:" + file);

    String resfile = cl.getOptionValue("res", "./tmp/res.txt");
    System.out.println(":" + resfile);

    SougouCA sca = new SougouCA(file);

    WordCluster wc = new WordCluster();
    wc.slotsize = slotsize;
    wc.read(sca);

    wc.startClustering();
    wc.saveModel(resfile + ".m");
    wc.saveTxt(resfile);
    wc = WordCluster.loadFrom(resfile + ".m");
    wc.saveTxt(resfile + "1");
    System.out.println(new Date().toString());
    System.out.println("Done");
}

---

From source file:org.fnlp.nlp.similarity.train.WordClusterM.java

/**
 * @param args/*from w  w  w .  ja  va 2  s.  c  om*/
 * @throws Exception 
 */
public static void main(String[] args) throws Exception {

    /**
     * ??
     */
    Options opt = new Options();

    opt.addOption("path", true, "?");
    opt.addOption("res", true, "?");
    opt.addOption("slot", true, "?");
    opt.addOption("thd", true, "");

    BasicParser parser = new BasicParser();
    CommandLine cl;
    try {
        cl = parser.parse(opt, args);
    } catch (Exception e) {
        System.err.println("Parameters format error");
        return;
    }

    int threads = Integer.parseInt(cl.getOptionValue("thd", "3"));
    System.out.println("?:" + threads);

    int slotsize = Integer.parseInt(cl.getOptionValue("slot", "20"));
    System.out.println("?:" + slotsize);

    String file = cl.getOptionValue("path", "./tmp/SogouCA.mini.txt");
    System.out.println("?:" + file);

    String resfile = cl.getOptionValue("res", "./tmp/cluster.txt");
    System.out.println(":" + resfile);

    long starttime = System.currentTimeMillis();
    SougouCA sca = new SougouCA(file);

    WordClusterM wc = new WordClusterM(threads);
    wc.slotsize = slotsize;
    wc.read(sca);

    wc.startClustering();
    wc.saveModel(resfile + ".m");
    wc.saveTxt(resfile);
    wc = (WordClusterM) WordCluster.loadFrom(resfile + ".m");
    wc.saveTxt(resfile + "1");
    long endtime = System.currentTimeMillis();
    System.out.println("Total Time:" + (endtime - starttime) / 60000);
    System.out.println("Done");
    System.exit(0);
}

---

From source file:org.fnlp.nlp.tag.Tagger.java

/**
 * ??//from ww w .java  2s. c  o m
 *  java -classpath fnlp-core.jar org.fnlp.nlp.tag.Tagger -train template train model 
 *  java -classpath fnlp-core.jar org.fnlp.nlp.tag.Tagger [-haslabel] model test [result]
 * 
 * @param args
 * @throws Exception
 */
public static void main(String[] args) throws Exception {

    Options opt = new Options();

    opt.addOption("h", false, "Print help for this application");
    opt.addOption("iter", true, "iterative num, default 50");
    opt.addOption("c", true, "parameters C in PA algorithm, default 0.8");
    opt.addOption("train", false, "switch to training mode(Default: test model");
    opt.addOption("retrain", false, "switch to retraining mode(Default: test model");
    opt.addOption("margin", false, "use hamming loss as margin threshold");
    opt.addOption("interim", false, "save interim model file");
    opt.addOption("haslabel", false, "test file has includes label or not");

    BasicParser parser = new BasicParser();
    CommandLine cl;
    try {
        cl = parser.parse(opt, args);
    } catch (Exception e) {
        System.err.println("Parameters format error");
        return;
    }

    if (args.length == 0 || cl.hasOption('h')) {
        HelpFormatter f = new HelpFormatter();
        f.printHelp("Tagger:\n" + "tagger [option] -train templet_file train_file model_file [test_file];\n"
                + "tagger [option] -retrain train_file model_file newmodel_file [test_file];\n"
                + "tagger [option] -label model_file test_file output_file\n", opt);
        return;
    }
    Tagger tagger = new Tagger();
    tagger.iterNum = Integer.parseInt(cl.getOptionValue("iter", "50"));
    tagger.c = Float.parseFloat(cl.getOptionValue("c", "0.8"));
    tagger.useLoss = cl.hasOption("margin");
    tagger.interim = cl.hasOption("interim");
    tagger.hasLabel = cl.hasOption("haslabel");

    String[] arg = cl.getArgs();
    if (cl.hasOption("train") && arg.length == 3) {
        tagger.templateFile = arg[0];
        tagger.train = arg[1];
        tagger.model = arg[2];
        System.out.println("Training model ...");
        tagger.train();
    } else if (cl.hasOption("train") && arg.length == 4) {
        tagger.templateFile = arg[0];
        tagger.train = arg[1];
        tagger.model = arg[2];
        tagger.testfile = arg[3];
        System.out.println("Training model ...");
        tagger.train();
    } else if (cl.hasOption("train") && arg.length == 5) {
        tagger.templateFile = arg[0];
        tagger.train = arg[1];
        tagger.model = arg[2];
        tagger.testfile = arg[3];
        System.out.println("Training model ...");
        tagger.train();
        System.gc();
        tagger.output = arg[4];
        tagger.test();
    } else if (cl.hasOption("retrain") && arg.length == 3) {
        tagger.train = arg[0];
        tagger.model = arg[1];
        tagger.newmodel = arg[2];
        System.out.println("Re-Training model ...");
        tagger.loadFrom(tagger.model);
        tagger.train();
    } else if (cl.hasOption("retrain") && arg.length == 4) {
        tagger.train = arg[0];
        tagger.model = arg[1];
        tagger.newmodel = arg[2];
        tagger.testfile = arg[3];
        System.out.println("Re-Training model ...");
        tagger.loadFrom(tagger.model);
        tagger.train();
    } else if (cl.hasOption("retrain") && arg.length == 5) {
        tagger.train = arg[0];
        tagger.model = arg[1];
        tagger.newmodel = arg[2];
        tagger.testfile = arg[3];
        System.out.println("Re-Training model ...");
        tagger.loadFrom(tagger.model);
        tagger.train();
        System.gc();
        tagger.output = arg[4];
        tagger.test();
    } else if (arg.length == 3) {
        tagger.model = arg[0];
        tagger.testfile = arg[1];
        tagger.output = arg[2];
        tagger.test();
    } else if (arg.length == 2) {
        tagger.model = arg[0];
        tagger.testfile = arg[1];
        tagger.test();
    } else {
        System.err.println("paramenters format error!");
        System.err.println("Print option \"-h\" for help.");
        return;
    }

    System.gc();

}

---

From source file:org.fnlp.train.tag.addedTagger.java

/**
 *  java -classpath fudannlp.jar edu.fudan.nlp.tag.Tagger -train template train model 
 *  java -classpath fudannlp.jar edu.fudan.nlp.tag.Tagger model test [result]
 * /*from  w  ww.j ava  2  s . c o  m*/
 * @param args
 * @throws Exception
 */
public static void main(String[] args) throws Exception {

    Options opt = new Options();

    opt.addOption("h", false, "Print help for this application");
    opt.addOption("iter", true, "iterative num, default 50");
    opt.addOption("c1", true, "parameters 1, default 1");
    opt.addOption("c2", true, "parameters 2, default 0.1");
    opt.addOption("train", false, "switch to training mode(Default: test model");
    opt.addOption("labelwise", false, "switch to labelwise mode(Default: viterbi model");
    opt.addOption("margin", false, "use hamming loss as margin threshold");
    opt.addOption("interim", false, "save interim model file");

    BasicParser parser = new BasicParser();
    CommandLine cl;
    try {
        cl = parser.parse(opt, args);
    } catch (Exception e) {
        System.err.println("Parameters format error");
        return;
    }

    if (args.length == 0 || cl.hasOption('h')) {
        HelpFormatter f = new HelpFormatter();
        f.printHelp("Tagger:\n" + "tagger [option] -train templet_file train_file model_file [test_file];\n"
                + "tagger [option] model_file test_file output_file\n", opt);
        return;
    }
    addedTagger tagger = new addedTagger();
    tagger.iterNum = Integer.parseInt(cl.getOptionValue("iter", "50"));
    tagger.c1 = Float.parseFloat(cl.getOptionValue("c1", "1"));
    tagger.c2 = Float.parseFloat(cl.getOptionValue("c2", "0.1"));
    tagger.useLoss = cl.hasOption("margin");
    tagger.interim = cl.hasOption("interim");

    String[] arg = cl.getArgs();
    if (cl.hasOption("train") && arg.length == 3) {
        tagger.templateFile = arg[0];
        tagger.train = arg[1];
        tagger.model = arg[2];
        System.out.println("Training model ...");
        tagger.train();
    } else if (cl.hasOption("train") && arg.length == 4) {
        tagger.templateFile = arg[0];
        tagger.train = arg[1];
        tagger.model = arg[2];
        tagger.testfile = arg[3];
        System.out.println("Training model ...");
        tagger.train();
    } else if (cl.hasOption("train") && arg.length == 5) {
        tagger.templateFile = arg[0];
        tagger.train = arg[1];
        tagger.model = arg[2];
        tagger.testfile = arg[3];
        System.out.println("Training model ...");
        tagger.train();
        System.gc();
        tagger.output = arg[4];
        tagger.test();
    } else if (arg.length == 3) {
        tagger.model = arg[0];
        tagger.testfile = arg[1];
        tagger.output = arg[2];
        tagger.test();
    } else if (arg.length == 2) {
        tagger.model = arg[0];
        tagger.testfile = arg[1];
        tagger.test();
    } else {
        System.err.println("paramenters format error!");
        System.err.println("Print option \"-h\" for help.");
        return;
    }

    System.gc();

}

---

From source file:org.fnlp.train.tag.CWSTrain.java

/**
 * ??/*w  ww . j a  v a 2s .  com*/
 *  java -classpath fudannlp.jar org.fnlp.nlp.tag.Tagger -train template train model 
 *  java -classpath fudannlp.jar org.fnlp.nlp.tag.Tagger model test [result]
 * 
 * @param args
 * @throws Exception
 */
public static void main(String[] args) throws Exception {

    Options opt = new Options();

    opt.addOption("iter", true, "iterative num, default 50");
    opt.addOption("c", true, "parameters C in PA algorithm, default 0.8");

    BasicParser parser = new BasicParser();
    CommandLine cl;
    try {
        cl = parser.parse(opt, args);
    } catch (Exception e) {
        System.err.println("Parameters format error");
        return;
    }

    CWSTrain tagger = new CWSTrain();
    tagger.iterNum = Integer.parseInt(cl.getOptionValue("iter", "50"));
    tagger.c = Float.parseFloat(cl.getOptionValue("c", "0.8"));

    String[] arg = cl.getArgs();

    tagger.templateFile = arg[0];
    tagger.train = arg[1];
    tagger.model = arg[2];
    System.out.println("Training model ...");
    tagger.train();

    System.gc();

}

---

From source file:org.fnlp.train.tag.POSTrain.java

/**
 * ??/*w  w w  .  j  a v a 2  s.com*/
 *  java -classpath fudannlp.jar edu.fudan.nlp.tag.Tagger -train template train model 
 *  java -classpath fudannlp.jar edu.fudan.nlp.tag.Tagger model test [result]
 * 
 * @param args
 * @throws Exception
 */
public static void main(String[] args) throws Exception {

    Options opt = new Options();

    opt.addOption("iter", true, "iterative num, default 50");
    opt.addOption("c", true, "parameters C in PA algorithm, default 0.8");

    BasicParser parser = new BasicParser();
    CommandLine cl;
    try {
        cl = parser.parse(opt, args);
    } catch (Exception e) {
        System.err.println("Parameters format error");
        return;
    }

    POSTrain tagger = new POSTrain();
    tagger.iterNum = Integer.parseInt(cl.getOptionValue("iter", "50"));
    tagger.c = Float.parseFloat(cl.getOptionValue("c", "0.8"));

    String[] arg = cl.getArgs();

    tagger.templateFile = arg[0];
    tagger.train = arg[1];
    tagger.model = arg[2];
    System.out.println("Training model ...");
    tagger.train();

    System.gc();

}

---

From source file:org.freeeed.main.FreeEedMain.java

/**
 * Process the command line arguments/*from w w w.  j  a va 2 s . c  o m*/
 *
 * @param args command line arguments
 */
private void processOptions(String[] args) {
    String customParameterFile;
    Project project = null;
    try {
        BasicParser parser = new BasicParser();
        commandLine = parser.parse(options, args);

        // one-time actions
        if (commandLine.hasOption(CommandLineOption.HELP.getName()) || commandLine.getOptions().length == 0) {
            HelpFormatter f = new HelpFormatter();
            f.printHelp("java -jar FreeEed.jar [options]\n\n" + "where options include:", options);
        } else if (commandLine.hasOption(CommandLineOption.VERSION.getName())) {
            System.out.println(Version.getVersionAndBuild());
        } else if (commandLine.hasOption(CommandLineOption.GUI.getName())) {
            openGUI();
        } else if (commandLine.hasOption(CommandLineOption.ENRON.getName())) {
            processEnronDataSet();
        } else {
            if (commandLine.hasOption(CommandLineOption.PARAM_FILE.getName())) {
                // independent actions
                customParameterFile = commandLine.getOptionValue(CommandLineOption.PARAM_FILE.getName());
                project = Project.loadFromFile(new File(customParameterFile));
            }
            if (commandLine.hasOption(CommandLineOption.DRY.getName())) {
                System.out.println("Dry run - exiting now.");
            } else {
                if (project.isStage()) {
                    stagePackageInput();
                }
                String runWhere = project.getProcessWhere();
                if (runWhere != null) {
                    process(runWhere);
                }
            }
        }
    } catch (Exception e) {
        logger.error("Error in processing", e);
    }
}

---

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