Example usage for java.io ObjectOutputStream flush

List of usage examples for java.io ObjectOutputStream flush

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Introduction

In this page you can find the example usage for java.io ObjectOutputStream flush.

Prototype

public void flush() throws IOException

Source Link

Document

Flushes the stream.

Usage

From source file:com.openteach.diamond.network.waverider.command.Command.java

public static void main(String[] args) {

    ByteArrayOutputStream bout = null;
    ObjectOutputStream objOutputStream = null;

    try {/*from   w  ww .  j ava2s .c  o m*/
        bout = new ByteArrayOutputStream();
        objOutputStream = new ObjectOutputStream(bout);
        SlaveState slaveState = new SlaveState();
        slaveState.setId(1L);
        slaveState.setIsMasterCandidate(false);
        objOutputStream.writeObject(slaveState);
        objOutputStream.flush();
        Command command = CommandFactory.createHeartbeatCommand(ByteBuffer.wrap(bout.toByteArray()));

        ByteBuffer buffer = command.marshall();
        Command cmd = Command.unmarshall(buffer);
        SlaveState ss = SlaveState.fromByteBuffer(cmd.getPayLoad());
        System.out.println(cmd.toString());
    } catch (IOException e) {
        throw new RuntimeException(e);
    } finally {
        try {
            if (objOutputStream != null) {
                objOutputStream.close();
            }
            if (bout != null) {
                bout.close();
            }
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

From source file:com.icesoft.faces.webapp.parser.TagToComponentMap.java

/**
 * Main method for when this class is run to build the serialized data from
 * a set of TLDS.//from   www  .  ja  v  a2  s  . co  m
 *
 * @param args The runtime arguements.
 */
public static void main(String args[]) {

    /* arg[0] is "new" to create serialzed data or 'old' to read serialized data
       arg[1] is filename for serialized data;
       arg[2...] are tld's to process */

    FileInputStream tldFile = null;

    TagToComponentMap map = new TagToComponentMap();

    if (args[0].equals("new")) {
        // Build new component map from tlds and serialize it;

        for (int i = 2; i < args.length; i++) {
            try {
                tldFile = new FileInputStream(args[i]);
                map.addTagAttrib((InputStream) tldFile);
            } catch (IOException e) {
                e.printStackTrace();
                return;
            }
        }

        try {
            FileOutputStream fos = new FileOutputStream(args[1]);
            ObjectOutputStream oos = new ObjectOutputStream(fos);
            oos.writeObject(map);
            oos.flush();
            oos.close();
        } catch (Exception e) {
            e.printStackTrace();
        }
    } else if (args[0].equals("old")) {
        // Build component from serialized data;
        try {
            FileInputStream fis = new FileInputStream(args[1]);
            ObjectInputStream ois = new ObjectInputStream(fis);
            map = (TagToComponentMap) ois.readObject();
        } catch (Exception e) {
            e.printStackTrace();
        }
    } else if (args[0].equals("facelets")) {
        // Build new component map from tld, and use that to
        //  generate a Facelets taglib.xml
        // args[0] is command
        // args[1] is output taglib.xml
        // args[2] is input tld

        try {
            FileWriter faceletsTaglibXmlWriter = new FileWriter(args[1]);
            String preamble = "<?xml version=\"1.0\"?>\n"
                    + "<facelet-taglib xmlns=\"http://java.sun.com/xml/ns/javaee\"\n"
                    + "xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\"\n"
                    + "xsi:schemaLocation=\"http://java.sun.com/xml/ns/javaee "
                    + "http://java.sun.com/xml/ns/javaee/web-facelettaglibrary_2_0.xsd\"\n"
                    + "version=\"2.0\">\n";

            String trailer = "</facelet-taglib>\n";
            faceletsTaglibXmlWriter.write(preamble);

            map.setFaceletsTaglibXmlWriter(faceletsTaglibXmlWriter);
            tldFile = new FileInputStream(args[2]);
            map.addTagAttrib((InputStream) tldFile);

            faceletsTaglibXmlWriter.write(trailer);
            faceletsTaglibXmlWriter.flush();
            faceletsTaglibXmlWriter.close();
        } catch (IOException e) {
            e.printStackTrace();
            return;
        }
    }
}

From source file:org.apache.fop.hyphenation.HyphenationTree.java

/**
 * Main entry point for this hyphenation utility application.
 * @param argv array of command linee arguments
 * @throws Exception in case an exception is raised but not caught
 *//*from w w w  .  ja va2  s  .c o m*/
public static void main(String[] argv) throws Exception {
    HyphenationTree ht = null;
    int minCharCount = 2;
    BufferedReader in = new BufferedReader(new java.io.InputStreamReader(System.in));
    while (true) {
        System.out.print("l:\tload patterns from XML\n" + "L:\tload patterns from serialized object\n"
                + "s:\tset minimum character count\n" + "w:\twrite hyphenation tree to object file\n"
                + "h:\thyphenate\n" + "f:\tfind pattern\n" + "b:\tbenchmark\n" + "q:\tquit\n\n" + "Command:");
        String token = in.readLine();
        if (token == null) {
            break;
        }
        token = token.trim();
        if (token.equals("f")) {
            System.out.print("Pattern: ");
            token = in.readLine();
            if (token == null) {
                break;
            }
            token = token.trim();
            System.out.println("Values: " + ht.findPattern(token));
        } else if (token.equals("s")) {
            System.out.print("Minimun value: ");
            token = in.readLine();
            if (token == null) {
                break;
            }
            token = token.trim();
            minCharCount = Integer.parseInt(token);
        } else if (token.equals("l")) {
            ht = new HyphenationTree();
            System.out.print("XML file name: ");
            token = in.readLine();
            if (token == null) {
                break;
            }
            token = token.trim();
            ht.loadPatterns(token);
        } else if (token.equals("L")) {
            ObjectInputStream ois = null;
            System.out.print("Object file name: ");
            token = in.readLine();
            if (token == null) {
                break;
            }
            token = token.trim();
            try {
                ois = new ObjectInputStream(new FileInputStream(token));
                ht = (HyphenationTree) ois.readObject();
            } catch (Exception e) {
                e.printStackTrace();
            } finally {
                if (ois != null) {
                    try {
                        ois.close();
                    } catch (IOException e) {
                        //ignore
                    }
                }
            }
        } else if (token.equals("w")) {
            System.out.print("Object file name: ");
            token = in.readLine();
            if (token == null) {
                break;
            }
            token = token.trim();
            ObjectOutputStream oos = null;
            try {
                oos = new ObjectOutputStream(new FileOutputStream(token));
                oos.writeObject(ht);
            } catch (Exception e) {
                e.printStackTrace();
            } finally {
                if (oos != null) {
                    try {
                        oos.flush();
                    } catch (IOException e) {
                        //ignore
                    }
                    try {
                        oos.close();
                    } catch (IOException e) {
                        //ignore
                    }
                }
            }
        } else if (token.equals("h")) {
            System.out.print("Word: ");
            token = in.readLine();
            if (token == null) {
                break;
            }
            token = token.trim();
            System.out.print("Hyphenation points: ");
            System.out.println(ht.hyphenate(token, minCharCount, minCharCount));
        } else if (token.equals("b")) {
            if (ht == null) {
                System.out.println("No patterns have been loaded.");
                break;
            }
            System.out.print("Word list filename: ");
            token = in.readLine();
            if (token == null) {
                break;
            }
            token = token.trim();
            long starttime = 0;
            int counter = 0;
            BufferedReader reader = null;
            try {
                reader = new BufferedReader(new FileReader(token));
                String line;

                starttime = System.currentTimeMillis();
                while ((line = reader.readLine()) != null) {
                    // System.out.print("\nline: ");
                    Hyphenation hyp = ht.hyphenate(line, minCharCount, minCharCount);
                    if (hyp != null) {
                        String hword = hyp.toString();
                        // System.out.println(line);
                        // System.out.println(hword);
                    } else {
                        // System.out.println("No hyphenation");
                    }
                    counter++;
                }
            } catch (Exception ioe) {
                System.out.println("Exception " + ioe);
                ioe.printStackTrace();
            } finally {
                IOUtils.closeQuietly(reader);
            }
            long endtime = System.currentTimeMillis();
            long result = endtime - starttime;
            System.out.println(counter + " words in " + result + " Milliseconds hyphenated");

        } else if (token.equals("q")) {
            break;
        }
    }

}

From source file:com.yahoo.labs.yamall.local.Yamall.java

public static void main(String[] args) {
    String[] remainingArgs = null;
    String inputFile = null;// ww w .ja  v  a2s .c  o  m
    String predsFile = null;
    String saveModelFile = null;
    String initialModelFile = null;
    String lossName = null;
    String parserName = null;
    String linkName = null;
    String invertHashName = null;
    double learningRate = 1;
    String minPredictionString = null;
    String maxPredictionString = null;
    String fmNumberFactorsString = null;
    int bitsHash;
    int numberPasses;
    int holdoutPeriod = 10;

    boolean testOnly = false;
    boolean exponentialProgress;
    double progressInterval;

    options.addOption("h", "help", false, "displays this help");
    options.addOption("t", false, "ignore label information and just test");
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("binary")
            .desc("reports loss as binary classification with -1,1 labels").build());
    options.addOption(
            Option.builder().hasArg(false).required(false).longOpt("solo").desc("uses SOLO optimizer").build());
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("pcsolo")
            .desc("uses Per Coordinate SOLO optimizer").build());
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("pistol")
            .desc("uses PiSTOL optimizer").build());
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("kt")
            .desc("(EXPERIMENTAL) uses KT optimizer").build());
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("pckt")
            .desc("(EXPERIMENTAL) uses Per Coordinate KT optimizer").build());
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("pccocob")
            .desc("(EXPERIMENTAL) uses Per Coordinate COCOB optimizer").build());
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("cocob")
            .desc("(EXPERIMENTAL) uses COCOB optimizer").build());
    options.addOption(
            Option.builder().hasArg(false).required(false).longOpt("fm").desc("Factorization Machine").build());
    options.addOption(Option.builder("f").hasArg(true).required(false).desc("final regressor to save")
            .type(String.class).longOpt("final_regressor").build());
    options.addOption(Option.builder("p").hasArg(true).required(false).desc("file to output predictions to")
            .longOpt("predictions").type(String.class).build());
    options.addOption(
            Option.builder("i").hasArg(true).required(false).desc("initial regressor(s) to load into memory")
                    .longOpt("initial_regressor").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false).desc(
            "specify the loss function to be used. Currently available ones are: absolute, squared (default), hinge, logistic")
            .longOpt("loss_function").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false).desc(
            "specify the link function used in the output of the predictions. Currently available ones are: identity (default), logistic")
            .longOpt("link").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false)
            .desc("output human-readable final regressor with feature names").longOpt("invert_hash")
            .type(String.class).build());
    options.addOption(
            Option.builder("l").hasArg(true).required(false).desc("set (initial) learning Rate, default = 1.0")
                    .longOpt("learning_rate").type(String.class).build());
    options.addOption(Option.builder("b").hasArg(true).required(false)
            .desc("number of bits in the feature table, default = 18").longOpt("bit_precision")
            .type(String.class).build());
    options.addOption(Option.builder("P").hasArg(true).required(false)
            .desc("progress update frequency, integer: additive; float: multiplicative, default = 2.0")
            .longOpt("progress").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false)
            .desc("smallest prediction to output, before the link function, default = -50")
            .longOpt("min_prediction").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false)
            .desc("smallest prediction to output, before the link function, default = 50")
            .longOpt("max_prediction").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false)
            .desc("ignore namespaces beginning with the characters in <arg>").longOpt("ignore")
            .type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false).desc("number of training passes")
            .longOpt("passes").type(String.class).build());
    options.addOption(
            Option.builder().hasArg(true).required(false).desc("holdout period for test only, default = 10")
                    .longOpt("holdout_period").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false)
            .desc("number of factors for Factorization Machines default = 8").longOpt("fmNumberFactors")
            .type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false)
            .desc("specify the parser to use. Currently available ones are: vw (default), libsvm, tsv")
            .longOpt("parser").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false).desc("schema file for the TSV input")
            .longOpt("schema").type(String.class).build());

    CommandLineParser parser = new DefaultParser();
    CommandLine cmd = null;
    try {
        cmd = parser.parse(options, args);
    } catch (ParseException e) {
        System.out.println("Unrecognized option");
        help();
    }
    if (cmd.hasOption("h"))
        help();
    if (cmd.hasOption("t"))
        testOnly = true;
    if (cmd.hasOption("binary")) {
        binary = true;
        System.out.println("Reporting binary loss");
    }
    initialModelFile = cmd.getOptionValue("i");
    predsFile = cmd.getOptionValue("p");
    lossName = cmd.getOptionValue("loss_function", "squared");
    linkName = cmd.getOptionValue("link", "identity");
    saveModelFile = cmd.getOptionValue("f");
    learningRate = Double.parseDouble(cmd.getOptionValue("l", "1.0"));
    bitsHash = Integer.parseInt(cmd.getOptionValue("b", "18"));
    invertHashName = cmd.getOptionValue("invert_hash");
    minPredictionString = cmd.getOptionValue("min_prediction", "-50");
    maxPredictionString = cmd.getOptionValue("max_prediction", "50");
    fmNumberFactorsString = cmd.getOptionValue("fmNumberFactors", "8");
    parserName = cmd.getOptionValue("parser", "vw");

    numberPasses = Integer.parseInt(cmd.getOptionValue("passes", "1"));
    System.out.println("Number of passes = " + numberPasses);
    if (numberPasses > 1) {
        holdoutPeriod = Integer.parseInt(cmd.getOptionValue("holdout_period", "10"));
        System.out.println("Holdout period = " + holdoutPeriod);
    }

    remainingArgs = cmd.getArgs();
    if (remainingArgs.length == 1)
        inputFile = remainingArgs[0];

    InstanceParser instanceParser = null;
    if (parserName.equals("vw"))
        instanceParser = new VWParser(bitsHash, cmd.getOptionValue("ignore"), (invertHashName != null));
    else if (parserName.equals("libsvm"))
        instanceParser = new LIBSVMParser(bitsHash, (invertHashName != null));
    else if (parserName.equals("tsv")) {
        String schema = cmd.getOptionValue("schema");
        if (schema == null) {
            System.out.println("TSV parser requires a schema file.");
            System.exit(0);
        } else {
            String spec = null;
            try {
                spec = new String(Files.readAllBytes(Paths.get(schema)));
            } catch (IOException e) {
                System.out.println("Error reading the TSV schema file.");
                e.printStackTrace();
                System.exit(0);
            }
            instanceParser = new TSVParser(bitsHash, cmd.getOptionValue("ignore"), (invertHashName != null),
                    spec);
        }
    } else {
        System.out.println("Unknown parser.");
        System.exit(0);
    }
    System.out.println("Num weight bits = " + bitsHash);

    // setup progress
    String progress = cmd.getOptionValue("P", "2.0");
    if (progress.indexOf('.') >= 0) {
        exponentialProgress = true;
        progressInterval = (double) Double.parseDouble(progress);
    } else {
        exponentialProgress = false;
        progressInterval = (double) Integer.parseInt(progress);
    }

    // min and max predictions
    minPrediction = (double) Double.parseDouble(minPredictionString);
    maxPrediction = (double) Double.parseDouble(maxPredictionString);

    // number of factors for Factorization Machines
    fmNumberFactors = (int) Integer.parseInt(fmNumberFactorsString);

    // configure the learner
    Loss lossFnc = null;
    LinkFunction link = null;
    if (initialModelFile == null) {
        if (cmd.hasOption("kt")) {
            learner = new KT(bitsHash);
        } else if (cmd.hasOption("pckt")) {
            learner = new PerCoordinateKT(bitsHash);
        } else if (cmd.hasOption("pcsolo")) {
            learner = new PerCoordinateSOLO(bitsHash);
        } else if (cmd.hasOption("solo")) {
            learner = new SOLO(bitsHash);
        } else if (cmd.hasOption("pccocob")) {
            learner = new PerCoordinateCOCOB(bitsHash);
        } else if (cmd.hasOption("cocob")) {
            learner = new COCOB(bitsHash);
        } else if (cmd.hasOption("pistol")) {
            learner = new PerCoordinatePiSTOL(bitsHash);
        } else if (cmd.hasOption("fm")) {
            learner = new SGD_FM(bitsHash, fmNumberFactors);
        } else
            learner = new SGD_VW(bitsHash);
    } else {
        learner = IOLearner.loadLearner(initialModelFile);
    }

    // setup link function
    if (linkName.equals("identity")) {
        link = new IdentityLinkFunction();
    } else if (linkName.equals("logistic")) {
        link = new LogisticLinkFunction();
    } else {
        System.out.println("Unknown link function.");
        System.exit(0);
    }

    // setup loss function
    if (lossName.equals("squared")) {
        lossFnc = new SquareLoss();
    } else if (lossName.equals("hinge")) {
        lossFnc = new HingeLoss();
    } else if (lossName.equals("logistic")) {
        lossFnc = new LogisticLoss();
    } else if (lossName.equals("absolute")) {
        lossFnc = new AbsLoss();
    } else {
        System.out.println("Unknown loss function.");
        System.exit(0);
    }

    learner.setLoss(lossFnc);
    learner.setLearningRate(learningRate);

    // maximum range predictions
    System.out.println("Max prediction = " + maxPrediction + ", Min Prediction = " + minPrediction);
    // print information about the learner
    System.out.println(learner.toString());
    // print information about the link function
    System.out.println(link.toString());
    // print information about the parser
    System.out.println(instanceParser.toString());
    // print information about ignored namespaces
    System.out.println("Ignored namespaces = " + cmd.getOptionValue("ignore", ""));

    long start = System.nanoTime();
    FileInputStream fstream;
    try {
        BufferedReader br = null;
        if (inputFile != null) {
            fstream = new FileInputStream(inputFile);
            System.out.println("Reading datafile = " + inputFile);
            br = new BufferedReader(new InputStreamReader(fstream));
        } else {
            System.out.println("Reading from console");
            br = new BufferedReader(new InputStreamReader(System.in));
        }

        File fout = null;
        FileOutputStream fos = null;
        BufferedWriter bw = null;
        if (predsFile != null) {
            fout = new File(predsFile);
            fos = new FileOutputStream(fout);
            bw = new BufferedWriter(new OutputStreamWriter(fos));
        }

        try {
            System.out.println("average       example  current  current  current");
            System.out.println("loss          counter    label  predict  features");
            int iter = 0;
            double cumLoss = 0;
            double weightedSampleSum = 0;
            double sPlus = 0;
            double sMinus = 0;
            Instance sample = null;
            boolean justPrinted = false;
            int pass = 0;
            ObjectOutputStream ooutTr = null;
            ObjectOutputStream ooutHO = null;
            ObjectInputStream oinTr = null;
            double pred = 0;
            int limit = 1;
            double hError = Double.MAX_VALUE;
            double lastHError = Double.MAX_VALUE;
            int numTestSample = 0;
            int numTrainingSample = 0;
            int idx = 0;

            if (numberPasses > 1) {
                ooutTr = new ObjectOutputStream(new FileOutputStream("cache_training.bin"));
                ooutHO = new ObjectOutputStream(new FileOutputStream("cache_holdout.bin"));
                oinTr = new ObjectInputStream(new FileInputStream("cache_training.bin"));
            }

            do {
                while (true) {
                    double score;

                    if (pass > 0 && numberPasses > 1) {
                        Instance tmp = (Instance) oinTr.readObject();
                        if (tmp != null)
                            sample = tmp;
                        else
                            break;
                    } else {
                        String strLine = br.readLine();
                        if (strLine != null)
                            sample = instanceParser.parse(strLine);
                        else
                            break;
                    }

                    justPrinted = false;
                    idx++;

                    if (numberPasses > 1 && pass == 0 && idx % holdoutPeriod == 0) {
                        // store the current sample for the holdout set
                        ooutHO.writeObject(sample);
                        ooutHO.reset();
                        numTestSample++;
                    } else {
                        if (numberPasses > 1 && pass == 0) {
                            ooutTr.writeObject(sample);
                            ooutTr.reset();
                            numTrainingSample++;
                        }

                        iter++;
                        if (testOnly) {
                            // predict the sample
                            score = learner.predict(sample);
                        } else {
                            // predict the sample and update the classifier using the sample
                            score = learner.update(sample);
                        }
                        score = Math.min(Math.max(score, minPrediction), maxPrediction);
                        pred = link.apply(score);
                        if (!binary)
                            cumLoss += learner.getLoss().lossValue(score, sample.getLabel())
                                    * sample.getWeight();
                        else if (Math.signum(score) != sample.getLabel())
                            cumLoss += sample.getWeight();

                        weightedSampleSum += sample.getWeight();
                        if (sample.getLabel() > 0)
                            sPlus = sPlus + sample.getWeight();
                        else
                            sMinus = sMinus + sample.getWeight();

                        // output predictions to file
                        if (predsFile != null) {
                            bw.write(String.format("%.6f %s", pred, sample.getTag()));
                            bw.newLine();
                        }

                        // print statistics to screen
                        if (iter == limit) {
                            justPrinted = true;
                            System.out.printf("%.6f %12d  % .4f  % .4f  %d\n", cumLoss / weightedSampleSum,
                                    iter, sample.getLabel(), pred, sample.getVector().size());
                            if (exponentialProgress)
                                limit *= progressInterval;
                            else
                                limit += progressInterval;
                        }
                    }
                }
                if (numberPasses > 1) {
                    if (pass == 0) { // finished first pass of many
                        // write a null at the end of the files
                        ooutTr.writeObject(null);
                        ooutHO.writeObject(null);
                        ooutTr.flush();
                        ooutHO.flush();
                        ooutTr.close();
                        ooutHO.close();

                        System.out.println("finished first epoch");
                        System.out.println(numTrainingSample + " training samples");
                        System.out.println(numTestSample + " holdout samples saved");
                    }
                    lastHError = hError;
                    hError = evalHoldoutError();
                }
                if (numberPasses > 1) {
                    System.out.printf("Weighted loss on holdout on epoch %d = %.6f\n", pass + 1, hError);

                    oinTr.close();
                    oinTr = new ObjectInputStream(new FileInputStream("cache_training.bin"));

                    if (hError > lastHError) {
                        System.out.println("Early stopping");
                        break;
                    }
                }
                pass++;
            } while (pass < numberPasses);

            if (justPrinted == false) {
                System.out.printf("%.6f %12d  % .4f  % .4f  %d\n", cumLoss / weightedSampleSum, iter,
                        sample.getLabel(), pred, sample.getVector().size());
            }
            System.out.println("finished run");

            System.out.println(String.format("average loss best constant predictor: %.6f",
                    lossFnc.lossConstantBinaryLabels(sPlus, sMinus)));

            if (saveModelFile != null)
                IOLearner.saveLearner(learner, saveModelFile);
            if (invertHashName != null)
                IOLearner.saveInvertHash(learner.getWeights(), instanceParser.getInvertHashMap(),
                        invertHashName);
        } catch (IOException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        } catch (ClassNotFoundException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }

        // close the input stream
        try {
            br.close();
        } catch (IOException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
        // close the output stream
        if (predsFile != null) {
            try {
                bw.close();
            } catch (IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
        }
        long millis = System.nanoTime() - start;
        System.out.printf("Elapsed time: %d min, %d sec\n", TimeUnit.NANOSECONDS.toMinutes(millis),
                TimeUnit.NANOSECONDS.toSeconds(millis) - 60 * TimeUnit.NANOSECONDS.toMinutes(millis));
    } catch (

    FileNotFoundException e) {
        System.out.println("Error opening the input file");
        e.printStackTrace();
    }

}

From source file:Main.java

private static void writeToFile(String filename, Object object) throws Exception {
    ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(new File(filename)));
    oos.writeObject(object);/*from  w  ww . java  2  s.c  om*/
    oos.flush();
    oos.close();
}

From source file:Main.java

public static final byte[] serialize(Object o) throws IOException {
    ByteArrayOutputStream baos = new ByteArrayOutputStream();
    ObjectOutputStream oos = new ObjectOutputStream(baos);
    oos.writeObject(o);/*from www . ja va2  s. c  o  m*/
    oos.flush();
    oos.close();
    return baos.toByteArray();
}

From source file:Main.java

public static byte[] getBytes(Object obj) throws IOException {
    ByteArrayOutputStream bout = new ByteArrayOutputStream();
    ObjectOutputStream out = new ObjectOutputStream(bout);
    out.writeObject(obj);/*from  w ww.  j  a  va 2 s . c  o  m*/
    out.flush();
    byte[] bytes = bout.toByteArray();
    bout.close();
    out.close();
    return bytes;
}

From source file:Main.java

public static byte[] serialize(Object obj) throws Exception {
    //System.err.println(" ser ##"+obj);
    ByteArrayOutputStream bout = new ByteArrayOutputStream();
    ObjectOutputStream oout = new ObjectOutputStream(bout);
    oout.writeObject(obj);/*from www . j a v a2s . com*/
    oout.flush();
    byte array[] = bout.toByteArray();
    oout.close();
    bout.close();
    //System.err.println(" ser #"+new String(array));
    return array;
}

From source file:Main.java

public static void writeGZipCompressedObject(Object o, OutputStream out) throws IOException {
    GZIPOutputStream gos = new GZIPOutputStream(out);
    ObjectOutputStream oos = new ObjectOutputStream(gos);
    oos.writeObject(o);/* w ww.  j  av a2 s.com*/
    oos.flush();
    gos.finish();
}

From source file:Main.java

public static boolean writeFileByObject(String fileName, Object object) {
    try {//  www  .j ava2s  . c o  m
        FileOutputStream fos = new FileOutputStream(fileName);
        ObjectOutputStream oos = new ObjectOutputStream(fos);
        oos.writeObject(object);
        oos.flush();
        oos.close();
        fos.close();
        return true;
    } catch (Exception e) {
        e.printStackTrace();
        return false;
    }
}