Example usage for com.google.common.base Stopwatch stop

List of usage examples for com.google.common.base Stopwatch stop

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Introduction

In this page you can find the example usage for com.google.common.base Stopwatch stop.

Prototype

public Stopwatch stop()

Source Link

Document

Stops the stopwatch.

Usage

From source file:GuavaStopwatch.java

private static void demonstrateStopwatch() {
    Stopwatch stopWatch = new Stopwatch();
    System.out.println("Is stopwatch running? " + stopWatch.isRunning());

    stopWatch.start();/*from  w ww  . j  a  v  a2 s.co m*/

    for (int i = 0; i < 1000; i++) {
        System.out.println("Is stopwatch running? " + stopWatch.isRunning());
    }

    stopWatch.stop();
    System.out.println("Our loop took : " + stopWatch.elapsedTime(TimeUnit.MILLISECONDS) + " milliseconds");
}

From source file:processing.BaselineCalculator.java

private static List<int[]> getPopularTags(BookmarkReader reader, int sampleSize, int limit) {
    timeString = "";
    List<int[]> tags = new ArrayList<int[]>();
    Stopwatch timer = new Stopwatch();
    timer.start();/* w  w w .  j a  va2 s.c  om*/

    int[] tagIDs = getPopularTagList(reader, limit);

    timer.stop();
    long trainingTime = timer.elapsed(TimeUnit.MILLISECONDS);
    timer = new Stopwatch();
    timer.start();
    for (int j = 0; j < sampleSize; j++) {
        tags.add(tagIDs);
    }
    timer.stop();
    long testTime = timer.elapsed(TimeUnit.MILLISECONDS);
    timeString += ("Full training time: " + trainingTime + "\n");
    timeString += ("Full test time: " + testTime + "\n");
    timeString += ("Average test time: " + testTime / sampleSize) + "\n";
    timeString += ("Total time: " + (trainingTime + testTime) + "\n");
    return tags;
}

From source file:edu.illinois.keshmesh.detector.Main.java

private static BasicAnalysisData initBytecodeAnalysis(IJavaProject javaProject, Reporter reporter,
        ConfigurationOptions configurationOptions) throws WALAInitializationException {
    KeshmeshCGModel model;//from ww w .  j a  v  a  2 s . c  o m
    try {
        String exclusionsFileName = FileProvider
                .getFileFromPlugin(Activator.getDefault(), "EclipseDefaultExclusions.txt").getAbsolutePath();
        model = new KeshmeshCGModel(javaProject, exclusionsFileName,
                configurationOptions.getObjectSensitivityLevel());
        Stopwatch stopWatch = Stopwatch.createStarted();
        model.buildGraph();
        stopWatch.stop();
        reporter.report(new KeyValuePair("CALL_GRAPH_CONSTRUCTION_TIME_IN_MILLISECONDS",
                String.valueOf(stopWatch.elapsed(TimeUnit.MILLISECONDS))));
        reportEntryPointStatistics(reporter, model.getEntryPoints());
        dumpEntryPoints(model.getEntryPoints());
    } catch (Exception e) {
        throw new Exceptions.WALAInitializationException(e);
    }
    CallGraph callGraph = model.getGraph();
    reportCallGraphStatistics(reporter, callGraph);
    PointerAnalysis pointerAnalysis = model.getPointerAnalysis();
    HeapModel heapModel = pointerAnalysis.getHeapModel();
    BasicHeapGraph heapGraph = new BasicHeapGraph(pointerAnalysis, callGraph);
    if (configurationOptions.shouldDumpHeapGraph()) {
        dumpHeapGraph(heapGraph);
    }
    reporter.report(
            new KeyValuePair("NUMBER_OF_NODES_OF_HEAP_GRAPH", String.valueOf(heapGraph.getNumberOfNodes())));
    if (!hasShownGraphs) {
        try {
            DisplayUtils.displayGraph(callGraph);
            DisplayUtils.displayGraph(heapGraph);
            hasShownGraphs = true;
        } catch (WalaException e) {
            throw new WALAInitializationException(e);
        }
    }
    IClassHierarchy classHierarchy = model.getClassHierarchy();
    reporter.report(new KeyValuePair("NUMBER_OF_CLASSES", String.valueOf(classHierarchy.getNumberOfClasses())));
    return new BasicAnalysisData(classHierarchy, callGraph, pointerAnalysis, heapModel, heapGraph);
}

From source file:hr.fer.tel.rovkp.lab01.Program.java

public static void zad2(String from, String to) {
    Stopwatch timer = new Stopwatch().start();

    FileReaderWriter frw = new FileReaderWriter();
    try {//from  w  w w .j  a  v  a2 s  .c  o m
        frw.work(from, to, StandardCharsets.ISO_8859_1);
    } catch (URISyntaxException | IOException ex) {
        System.err.println(ex);
    } catch (Exception ex) {
        System.err.println(ex);
    }

    timer.stop();
    System.out.println("Read " + frw.readLinesCount() + " lines from " + frw.readFilesCount() + " files.");
    System.out.println("Program finished in " + timer.elapsedTime(TimeUnit.SECONDS) + " seconds.");
}

From source file:processing.MPurCalculator.java

public static List<Map<Integer, Double>> startLanguageModelCreation(BookmarkReader reader, int sampleSize,
        boolean sorting, boolean userBased, boolean resBased, int beta) {
    int size = reader.getBookmarks().size();
    int trainSize = size - sampleSize;

    Stopwatch timer = new Stopwatch();
    timer.start();//from   w w  w  .  jav  a 2  s  .c om
    MPurCalculator calculator = new MPurCalculator(reader, trainSize, beta, userBased, resBased);
    timer.stop();
    long trainingTime = timer.elapsed(TimeUnit.MILLISECONDS);
    List<Map<Integer, Double>> results = new ArrayList<Map<Integer, Double>>();
    if (trainSize == size) {
        trainSize = 0;
    }

    timer.reset();
    timer.start();
    for (int i = trainSize; i < size; i++) { // the test-set
        Bookmark data = reader.getBookmarks().get(i);
        Map<Integer, Double> map = calculator.getRankedTagList(data.getUserID(), data.getResourceID(), sorting);
        results.add(map);
    }
    timer.stop();
    long testTime = timer.elapsed(TimeUnit.MILLISECONDS);

    timeString = PerformanceMeasurement.addTimeMeasurement(timeString, true, trainingTime, testTime,
            sampleSize);
    return results;
}

From source file:org.litecoinj.crypto.MnemonicCode.java

/**
 * Convert mnemonic word list to seed./*  ww w  .  j av a2 s  .  c o  m*/
 */
public static byte[] toSeed(List<String> words, String passphrase) {

    // To create binary seed from mnemonic, we use PBKDF2 function
    // with mnemonic sentence (in UTF-8) used as a password and
    // string "mnemonic" + passphrase (again in UTF-8) used as a
    // salt. Iteration count is set to 4096 and HMAC-SHA512 is
    // used as a pseudo-random function. Desired length of the
    // derived key is 512 bits (= 64 bytes).
    //
    String pass = Utils.join(words);
    String salt = "mnemonic" + passphrase;

    final Stopwatch watch = Stopwatch.createStarted();
    byte[] seed = PBKDF2SHA512.derive(pass, salt, PBKDF2_ROUNDS, 64);
    watch.stop();
    log.info("PBKDF2 took {}", watch);
    return seed;
}

From source file:org.bitcoinj.crypto.MnemonicCode.java

/**
 * Convert mnemonic word list to seed.//from   ww  w  .j av a  2s . c o  m
 */
public static byte[] toSeed(List<String> words, String passphrase) {

    // To create binary seed from mnemonic, we use PBKDF2 function
    // with mnemonic sentence (in UTF-8) used as a password and
    // string "mnemonic" + passphrase (again in UTF-8) used as a
    // salt. Iteration count is set to 4096 and HMAC-SHA512 is
    // used as a pseudo-random function. Desired length of the
    // derived key is 512 bits (= 64 bytes).
    //
    String pass = Utils.SPACE_JOINER.join(words);
    String salt = "mnemonic" + passphrase;

    final Stopwatch watch = Stopwatch.createStarted();
    byte[] seed = PBKDF2SHA512.derive(pass, salt, PBKDF2_ROUNDS, 64);
    watch.stop();
    log.info("PBKDF2 took {}", watch);
    return seed;
}

From source file:processing.LanguageModelCalculator.java

public static List<Map<Integer, Double>> startLanguageModelCreation(BookmarkReader reader, int sampleSize,
        boolean sorting, boolean userBased, boolean resBased, int beta, boolean smoothing) {
    timeString = "";
    int size = reader.getUserLines().size();
    int trainSize = size - sampleSize;

    Stopwatch timer = new Stopwatch();
    timer.start();/*from ww w  .j  ava  2  s.  co  m*/
    LanguageModelCalculator calculator = new LanguageModelCalculator(reader, trainSize, beta, userBased,
            resBased);
    timer.stop();
    long trainingTime = timer.elapsed(TimeUnit.MILLISECONDS);
    List<Map<Integer, Double>> results = new ArrayList<Map<Integer, Double>>();
    if (trainSize == size) {
        trainSize = 0;
    }

    timer = new Stopwatch();
    timer.start();
    for (int i = trainSize; i < size; i++) { // the test-set
        UserData data = reader.getUserLines().get(i);
        Map<Integer, Double> map = calculator.getRankedTagList(data.getUserID(), data.getWikiID(), sorting,
                smoothing);
        results.add(map);
    }
    timer.stop();
    long testTime = timer.elapsed(TimeUnit.MILLISECONDS);
    timeString += ("Full training time: " + trainingTime + "\n");
    timeString += ("Full test time: " + testTime + "\n");
    timeString += ("Average test time: " + testTime / (double) sampleSize) + "\n";
    timeString += ("Total time: " + (trainingTime + testTime) + "\n");
    return results;
}

From source file:processing.LayersCalculator.java

public static void predictSample(String filename, int trainSize, int sampleSize, int beta) {
    //filename += "_res";
    BookmarkReader reader = new BookmarkReader(trainSize, false);
    reader.readFile(filename);//from  www .j av  a 2  s  . c  om

    List<int[]> predictionValues = new ArrayList<int[]>();
    Stopwatch timer = new Stopwatch();
    timer.start();
    LayersCalculator calculator = new LayersCalculator(reader, trainSize, beta);
    timer.stop();
    long trainingTime = timer.elapsed(TimeUnit.MILLISECONDS);

    timer = new Stopwatch();
    timer.start();
    for (int i = trainSize; i < trainSize + sampleSize; i++) { // the test-set
        UserData data = reader.getUserLines().get(i);
        Map<Integer, Double> map = calculator.getRankedTagList(data.getUserID(), data.getWikiID(),
                data.getCategories());
        predictionValues.add(Ints.toArray(map.keySet()));
    }
    timer.stop();
    long testTime = timer.elapsed(TimeUnit.MILLISECONDS);
    timeString += ("Full training time: " + trainingTime + "\n");
    timeString += ("Full test time: " + testTime + "\n");
    timeString += ("Average test time: " + testTime / (double) sampleSize) + "\n";
    timeString += ("Total time: " + (trainingTime + testTime) + "\n");
    String outputFile = filename + "_3layers";
    Utilities.writeStringToFile("./data/metrics/" + outputFile + "_TIME.txt", timeString);

    reader.setUserLines(reader.getUserLines().subList(trainSize, reader.getUserLines().size()));
    PredictionFileWriter writer = new PredictionFileWriter(reader, predictionValues);
    writer.writeFile(outputFile);
}

From source file:edu.mit.streamjit.test.apps.TriangleContainment.java

private static int runThreads() {
    Iterator<String> taskIterator = generateInput().iterator();
    AtomicInteger result = new AtomicInteger(0);
    List<Thread> threads = new ArrayList<>(NUM_THREADS);
    List<Semaphore> readSemaphores = new ArrayList<>(NUM_THREADS),
            writeSemaphores = new ArrayList<>(NUM_THREADS);
    for (int i = 0; i < NUM_THREADS; ++i) {
        readSemaphores.add(new Semaphore(i == 0 ? 1 : 0));
        writeSemaphores.add(new Semaphore(i == 0 ? 1 : 0));
    }// w w  w.j av  a  2  s.  c  o m
    for (int i = 0; i < NUM_THREADS; ++i)
        threads.add(new ComputeThread(taskIterator, result, readSemaphores.get(i),
                readSemaphores.get((i + 1) % readSemaphores.size()), writeSemaphores.get(i),
                writeSemaphores.get((i + 1) % writeSemaphores.size())));
    Stopwatch stopwatch = Stopwatch.createStarted();
    for (Thread t : threads)
        t.start();
    for (Thread t : threads)
        Uninterruptibles.joinUninterruptibly(t);
    System.out.println("Thread impl ran in " + stopwatch.stop().elapsed(TimeUnit.MILLISECONDS));
    return result.get();
}