Example usage for java.util Date Date

List of usage examples for java.util Date Date

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Introduction

In this page you can find the example usage for java.util Date Date.

Prototype

public Date()

Source Link

Document

Allocates a Date object and initializes it so that it represents the time at which it was allocated, measured to the nearest millisecond.

Usage

From source file:com.hp.mqm.atrf.Main.java

public static void main(String[] args) {

    long start = System.currentTimeMillis();
    setUncaughtExceptionHandler();//from ww  w.j  a  v a2  s  .  co  m

    CliParser cliParser = new CliParser();
    cliParser.handleHelpAndVersionOptions(args);

    configureLog4J();
    logger.info(System.lineSeparator() + System.lineSeparator());
    logger.info("************************************************************************************");
    DateFormat dateFormatter = DateFormat.getDateInstance(DateFormat.DEFAULT, Locale.getDefault());
    DateFormat timeFormatter = DateFormat.getTimeInstance(DateFormat.DEFAULT, Locale.getDefault());
    logger.info((String.format("Starting HPE ALM Test Result Collection Tool %s %s",
            dateFormatter.format(new Date()), timeFormatter.format(new Date()))));
    logger.info("************************************************************************************");

    FetchConfiguration configuration = cliParser.parse(args);
    ConfigurationUtilities.setConfiguration(configuration);

    App app = new App(configuration);
    app.start();

    long end = System.currentTimeMillis();
    logger.info(String.format("Finished creating tests and test results on ALM Octane in %s seconds",
            (end - start) / 1000));
    logger.info(System.lineSeparator());
}

From source file:io.minimum.minecraft.rbclean.RedisBungeeClean.java

public static void main(String... args) {
    Options options = new Options();

    Option hostOption = new Option("h", "host", true, "Sets the Redis host to use.");
    hostOption.setRequired(true);/*from ww w  . j a  v  a2  s . com*/
    options.addOption(hostOption);

    Option portOption = new Option("p", "port", true, "Sets the Redis port to use.");
    options.addOption(portOption);

    Option passwordOption = new Option("w", "password", true, "Sets the Redis password to use.");
    options.addOption(passwordOption);

    Option dryRunOption = new Option("d", "dry-run", false, "Performs a dry run (no data is modified).");
    options.addOption(dryRunOption);

    CommandLine commandLine;

    try {
        commandLine = new DefaultParser().parse(options, args);
    } catch (ParseException e) {
        HelpFormatter formatter = new HelpFormatter();
        formatter.printHelp("RedisBungeeClean", options);
        return;
    }

    int port = commandLine.hasOption('p') ? Integer.parseInt(commandLine.getOptionValue('p')) : 6379;

    try (Jedis jedis = new Jedis(commandLine.getOptionValue('h'), port, 0)) {
        if (commandLine.hasOption('w')) {
            jedis.auth(commandLine.getOptionValue('w'));
        }

        System.out.println("Fetching UUID cache...");
        Map<String, String> uuidCache = jedis.hgetAll("uuid-cache");
        Gson gson = new Gson();

        // Just in case we need it, compress everything in JSON format.
        if (!commandLine.hasOption('d')) {
            SimpleDateFormat dateFormat = new SimpleDateFormat("yyyy-MM-dd-hh-mm-ss");
            File file = new File("uuid-cache-previous-" + dateFormat.format(new Date()) + ".json.gz");
            try {
                file.createNewFile();
            } catch (IOException e) {
                System.out.println("Can't write backup of the UUID cache, will NOT proceed.");
                e.printStackTrace();
                return;
            }

            System.out.println("Creating backup (as " + file.getName() + ")...");

            try (OutputStreamWriter bw = new OutputStreamWriter(
                    new GZIPOutputStream(new FileOutputStream(file)))) {
                gson.toJson(uuidCache, bw);
            } catch (IOException e) {
                System.out.println("Can't write backup of the UUID cache, will NOT proceed.");
                e.printStackTrace();
                return;
            }
        }

        System.out.println("Cleaning out the bird cage (this may take a while...)");
        int originalSize = uuidCache.size();
        for (Iterator<Map.Entry<String, String>> it = uuidCache.entrySet().iterator(); it.hasNext();) {
            CachedUUIDEntry entry = gson.fromJson(it.next().getValue(), CachedUUIDEntry.class);

            if (entry.expired()) {
                it.remove();
            }
        }
        int newSize = uuidCache.size();

        if (commandLine.hasOption('d')) {
            System.out.println(
                    (originalSize - newSize) + " records would be expunged if a dry run was not conducted.");
        } else {
            System.out.println("Expunging " + (originalSize - newSize) + " records...");
            Transaction transaction = jedis.multi();
            transaction.del("uuid-cache");
            transaction.hmset("uuid-cache", uuidCache);
            transaction.exec();
            System.out.println("Expunging complete.");
        }
    }
}

From source file:DateFormatBest.java

public static void main(String[] args) {
    Date today = new Date();

    DateFormat df = DateFormat.getInstance();
    System.out.println(df.format(today));

    DateFormat df_fr = DateFormat.getDateInstance(DateFormat.FULL, Locale.FRENCH);
    System.out.println(df_fr.format(today));
}

From source file:VarArgsDemo.java

public static void main(String[] args) {
    process(System.out, "Hello", "Goodbye");
    process(System.out, 42, 1066, 1776);
    process(System.out, "Foo", new Date(), new Object());
}

From source file:ch.epfl.lsir.xin.test.UserAverageTest.java

/**
 * @param args//from w  ww  .  java  2  s  .c  o m
 */
public static void main(String[] args) throws Exception {
    // TODO Auto-generated method stub

    PrintWriter logger = new PrintWriter(".//results//UserAverage");
    PropertiesConfiguration config = new PropertiesConfiguration();
    config.setFile(new File(".//conf//UserAverage.properties"));
    try {
        config.load();
    } catch (ConfigurationException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }

    logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Read rating data...");
    DataLoaderFile loader = new DataLoaderFile(".//data//MoveLens100k.txt");
    loader.readSimple();
    DataSetNumeric dataset = loader.getDataset();
    System.out.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: "
            + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size());
    logger.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: "
            + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size());
    logger.flush();

    double totalMAE = 0;
    double totalRMSE = 0;
    int F = 5;
    logger.println(F + "- folder cross validation.");
    ArrayList<ArrayList<NumericRating>> folders = new ArrayList<ArrayList<NumericRating>>();
    for (int i = 0; i < F; i++) {
        folders.add(new ArrayList<NumericRating>());
    }
    while (dataset.getRatings().size() > 0) {
        int index = new Random().nextInt(dataset.getRatings().size());
        int r = new Random().nextInt(F);
        folders.get(r).add(dataset.getRatings().get(index));
        dataset.getRatings().remove(index);
    }
    for (int folder = 1; folder <= F; folder++) {
        logger.println("Folder: " + folder);
        System.out.println("Folder: " + folder);
        ArrayList<NumericRating> trainRatings = new ArrayList<NumericRating>();
        ArrayList<NumericRating> testRatings = new ArrayList<NumericRating>();
        for (int i = 0; i < folders.size(); i++) {
            if (i == folder - 1)//test data
            {
                testRatings.addAll(folders.get(i));
            } else {//training data
                trainRatings.addAll(folders.get(i));
            }
        }

        //create rating matrix
        HashMap<String, Integer> userIDIndexMapping = new HashMap<String, Integer>();
        HashMap<String, Integer> itemIDIndexMapping = new HashMap<String, Integer>();
        for (int i = 0; i < dataset.getUserIDs().size(); i++) {
            userIDIndexMapping.put(dataset.getUserIDs().get(i), i);
        }
        for (int i = 0; i < dataset.getItemIDs().size(); i++) {
            itemIDIndexMapping.put(dataset.getItemIDs().get(i), i);
        }
        RatingMatrix trainRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(),
                dataset.getItemIDs().size());
        for (int i = 0; i < trainRatings.size(); i++) {
            trainRatingMatrix.set(userIDIndexMapping.get(trainRatings.get(i).getUserID()),
                    itemIDIndexMapping.get(trainRatings.get(i).getItemID()), trainRatings.get(i).getValue());
        }
        trainRatingMatrix.calculateGlobalAverage();
        RatingMatrix testRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(),
                dataset.getItemIDs().size());
        for (int i = 0; i < testRatings.size(); i++) {
            testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()),
                    itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue());
        }
        System.out.println("Training: " + trainRatingMatrix.getTotalRatingNumber() + " vs Test: "
                + testRatingMatrix.getTotalRatingNumber());

        logger.println("Initialize a recommendation model based on user average method.");
        UserAverage algo = new UserAverage(trainRatingMatrix);
        algo.setLogger(logger);
        algo.build();
        algo.saveModel(".//localModels//" + config.getString("NAME"));
        logger.println("Save the model.");
        System.out.println(trainRatings.size() + " vs. " + testRatings.size());

        double RMSE = 0;
        double MAE = 0;
        int count = 0;
        for (int i = 0; i < testRatings.size(); i++) {
            NumericRating rating = testRatings.get(i);
            double prediction = algo.predict(userIDIndexMapping.get(rating.getUserID()),
                    itemIDIndexMapping.get(rating.getItemID()));
            if (Double.isNaN(prediction)) {
                System.out.println("no prediction");
                continue;
            }
            MAE = MAE + Math.abs(rating.getValue() - prediction);
            RMSE = RMSE + Math.pow((rating.getValue() - prediction), 2);
            count++;
        }
        MAE = MAE / count;
        RMSE = Math.sqrt(RMSE / count);

        logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " MAE: " + MAE
                + " RMSE: " + RMSE);
        logger.flush();
        totalMAE = totalMAE + MAE;
        totalRMSE = totalRMSE + RMSE;
    }

    System.out.println("MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F);
    logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Final results: MAE: "
            + totalMAE / F + " RMSE: " + totalRMSE / F);
    logger.flush();
    logger.close();
    //MAE: 0.8353035962363073 RMSE: 1.0422971886952053 (MovieLens 100k)
}

From source file:ch.epfl.lsir.xin.test.ItemAverageTest.java

/**
 * @param args// www .ja  va 2s  .com
 */
public static void main(String[] args) throws Exception {
    // TODO Auto-generated method stub

    PrintWriter logger = new PrintWriter(".//results//ItemAverage");
    PropertiesConfiguration config = new PropertiesConfiguration();
    config.setFile(new File(".//conf//ItemAverage.properties"));
    try {
        config.load();
    } catch (ConfigurationException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }

    logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Read rating data...");
    DataLoaderFile loader = new DataLoaderFile(".//data//MoveLens100k.txt");
    loader.readSimple();
    DataSetNumeric dataset = loader.getDataset();
    System.out.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: "
            + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size());
    logger.println("Number of ratings: " + dataset.getRatings().size() + ", Number of users: "
            + dataset.getUserIDs().size() + ", Number of items: " + dataset.getItemIDs().size());
    logger.flush();

    double totalMAE = 0;
    double totalRMSE = 0;
    int F = 5;
    logger.println(F + "- folder cross validation.");
    ArrayList<ArrayList<NumericRating>> folders = new ArrayList<ArrayList<NumericRating>>();
    for (int i = 0; i < F; i++) {
        folders.add(new ArrayList<NumericRating>());
    }
    while (dataset.getRatings().size() > 0) {
        int index = new Random().nextInt(dataset.getRatings().size());
        int r = new Random().nextInt(F);
        folders.get(r).add(dataset.getRatings().get(index));
        dataset.getRatings().remove(index);
    }
    for (int folder = 1; folder <= F; folder++) {
        logger.println("Folder: " + folder);
        logger.flush();
        System.out.println("Folder: " + folder);
        ArrayList<NumericRating> trainRatings = new ArrayList<NumericRating>();
        ArrayList<NumericRating> testRatings = new ArrayList<NumericRating>();
        for (int i = 0; i < folders.size(); i++) {
            if (i == folder - 1)//test data
            {
                testRatings.addAll(folders.get(i));
            } else {//training data
                trainRatings.addAll(folders.get(i));
            }
        }

        //create rating matrix
        HashMap<String, Integer> userIDIndexMapping = new HashMap<String, Integer>();
        HashMap<String, Integer> itemIDIndexMapping = new HashMap<String, Integer>();
        for (int i = 0; i < dataset.getUserIDs().size(); i++) {
            userIDIndexMapping.put(dataset.getUserIDs().get(i), i);
        }
        for (int i = 0; i < dataset.getItemIDs().size(); i++) {
            itemIDIndexMapping.put(dataset.getItemIDs().get(i), i);
        }
        RatingMatrix trainRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(),
                dataset.getItemIDs().size());
        for (int i = 0; i < trainRatings.size(); i++) {
            trainRatingMatrix.set(userIDIndexMapping.get(trainRatings.get(i).getUserID()),
                    itemIDIndexMapping.get(trainRatings.get(i).getItemID()), trainRatings.get(i).getValue());
        }
        trainRatingMatrix.calculateGlobalAverage();
        RatingMatrix testRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(),
                dataset.getItemIDs().size());
        for (int i = 0; i < testRatings.size(); i++) {
            testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()),
                    itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue());
        }
        System.out.println("Training: " + trainRatingMatrix.getTotalRatingNumber() + " vs Test: "
                + testRatingMatrix.getTotalRatingNumber());

        logger.println("Initialize a recommendation model based on item average method.");
        ItemAverage algo = new ItemAverage(trainRatingMatrix);
        algo.setLogger(logger);
        algo.build();
        algo.saveModel(".//localModels//" + config.getString("NAME"));
        logger.println("Save the model.");
        logger.flush();
        System.out.println(trainRatings.size() + " vs. " + testRatings.size());

        double RMSE = 0;
        double MAE = 0;
        int count = 0;
        for (int i = 0; i < testRatings.size(); i++) {
            NumericRating rating = testRatings.get(i);
            double prediction = algo.predict(userIDIndexMapping.get(rating.getUserID()),
                    itemIDIndexMapping.get(rating.getItemID()));
            if (Double.isNaN(prediction)) {
                System.out.println("no prediction");
                continue;
            }
            MAE = MAE + Math.abs(rating.getValue() - prediction);
            RMSE = RMSE + Math.pow((rating.getValue() - prediction), 2);
            count++;
        }
        MAE = MAE / count;
        RMSE = Math.sqrt(RMSE / count);

        logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " MAE: " + MAE
                + " RMSE: " + RMSE);
        logger.flush();
        //         System.out.println("MAE: " + MAE + " RMSE: " + RMSE);
        totalMAE = totalMAE + MAE;
        totalRMSE = totalRMSE + RMSE;
    }

    System.out.println("MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F);
    logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Final results: MAE: "
            + totalMAE / F + " RMSE: " + totalRMSE / F);
    logger.flush();
    //MAE: 0.8173633324758338 RMSE: 1.0251973503888645 (MovieLens 100K)

}

From source file:ch.epfl.lsir.xin.test.MostPopularTest.java

/**
 * @param args/*from  w w  w .ja v  a2s .co m*/
 */
public static void main(String[] args) throws Exception {
    // TODO Auto-generated method stub

    PrintWriter logger = new PrintWriter(".//results//MostPopular");
    PropertiesConfiguration config = new PropertiesConfiguration();
    config.setFile(new File(".//conf//MostPopular.properties"));
    try {
        config.load();
    } catch (ConfigurationException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }

    logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Read rating data...");
    DataLoaderFile loader = new DataLoaderFile(".//data//MoveLens100k.txt");
    loader.readSimple();
    DataSetNumeric dataset = loader.getDataset();
    System.out.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: "
            + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size());
    logger.println("Number of ratings: " + dataset.getRatings().size() + ", Number of users: "
            + dataset.getUserIDs().size() + ", Number of items: " + dataset.getItemIDs().size());
    logger.flush();

    TrainTestSplitter splitter = new TrainTestSplitter(dataset);
    splitter.splitFraction(config.getDouble("TRAIN_FRACTION"));
    ArrayList<NumericRating> trainRatings = splitter.getTrain();
    ArrayList<NumericRating> testRatings = splitter.getTest();

    HashMap<String, Integer> userIDIndexMapping = new HashMap<String, Integer>();
    HashMap<String, Integer> itemIDIndexMapping = new HashMap<String, Integer>();
    //create rating matrix
    for (int i = 0; i < dataset.getUserIDs().size(); i++) {
        userIDIndexMapping.put(dataset.getUserIDs().get(i), i);
    }
    for (int i = 0; i < dataset.getItemIDs().size(); i++) {
        itemIDIndexMapping.put(dataset.getItemIDs().get(i), i);
    }
    RatingMatrix trainRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size());
    for (int i = 0; i < trainRatings.size(); i++) {
        trainRatingMatrix.set(userIDIndexMapping.get(trainRatings.get(i).getUserID()),
                itemIDIndexMapping.get(trainRatings.get(i).getItemID()), trainRatings.get(i).getValue());
    }
    RatingMatrix testRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size());
    for (int i = 0; i < testRatings.size(); i++) {
        //only consider 5-star rating in the test set
        //         if( testRatings.get(i).getValue() < 5 )
        //            continue;
        testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()),
                itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue());
    }
    System.out.println("Training: " + trainRatingMatrix.getTotalRatingNumber() + " vs Test: "
            + testRatingMatrix.getTotalRatingNumber());

    logger.println("Initialize a most popular based recommendation model.");
    MostPopular algo = new MostPopular(trainRatingMatrix);
    algo.setLogger(logger);
    algo.build();
    algo.saveModel(".//localModels//" + config.getString("NAME"));
    logger.println("Save the model.");
    logger.flush();

    HashMap<Integer, ArrayList<ResultUnit>> results = new HashMap<Integer, ArrayList<ResultUnit>>();
    for (int i = 0; i < testRatingMatrix.getRow(); i++) {
        ArrayList<ResultUnit> rec = algo.getRecommendationList(i);
        if (rec == null)
            continue;
        int total = testRatingMatrix.getUserRatingNumber(i);
        if (total == 0)//this user is ignored
            continue;
        results.put(i, rec);
    }

    RankResultGenerator generator = new RankResultGenerator(results, algo.getTopN(), testRatingMatrix,
            trainRatingMatrix);
    System.out.println("Precision@N: " + generator.getPrecisionN());
    System.out.println("Recall@N: " + generator.getRecallN());
    System.out.println("MAP@N: " + generator.getMAPN());
    System.out.println("MRR@N: " + generator.getMRRN());
    System.out.println("NDCG@N: " + generator.getNDCGN());
    System.out.println("AUC@N: " + generator.getAUC());
    logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + "\n" + "Precision@N: "
            + generator.getPrecisionN() + "\n" + "Recall@N: " + generator.getRecallN() + "\n" + "MAP@N: "
            + generator.getMAPN() + "\n" + "MRR@N: " + generator.getMRRN() + "\n" + "NDCG@N: "
            + generator.getNDCGN() + "\n" + "AUC@N: " + generator.getAUC());
    logger.flush();
    logger.close();
}

From source file:ch.epfl.lsir.xin.test.SVDPPTest.java

/**
 * @param args//from  ww  w. j av a 2  s .co m
 */
public static void main(String[] args) throws Exception {
    // TODO Auto-generated method stub

    PrintWriter logger = new PrintWriter(".//results//SVDPP");

    PropertiesConfiguration config = new PropertiesConfiguration();
    config.setFile(new File("conf//SVDPlusPlus.properties"));
    try {
        config.load();
    } catch (ConfigurationException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }

    logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Read rating data...");
    logger.flush();
    DataLoaderFile loader = new DataLoaderFile(".//data//MoveLens100k.txt");
    loader.readSimple();
    DataSetNumeric dataset = loader.getDataset();
    System.out.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: "
            + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size());
    logger.println("Number of ratings: " + dataset.getRatings().size() + ", Number of users: "
            + dataset.getUserIDs().size() + ", Number of items: " + dataset.getItemIDs().size());
    logger.flush();

    double totalMAE = 0;
    double totalRMSE = 0;
    double totalPrecision = 0;
    double totalRecall = 0;
    double totalMAP = 0;
    double totalNDCG = 0;
    double totalMRR = 0;
    double totalAUC = 0;
    int F = 5;
    logger.println(F + "- folder cross validation.");
    logger.flush();
    ArrayList<ArrayList<NumericRating>> folders = new ArrayList<ArrayList<NumericRating>>();
    for (int i = 0; i < F; i++) {
        folders.add(new ArrayList<NumericRating>());
    }
    while (dataset.getRatings().size() > 0) {
        int index = new Random().nextInt(dataset.getRatings().size());
        int r = new Random().nextInt(F);
        folders.get(r).add(dataset.getRatings().get(index));
        dataset.getRatings().remove(index);
    }

    for (int folder = 1; folder <= F; folder++) {
        System.out.println("Folder: " + folder);
        logger.println("Folder: " + folder);
        logger.flush();
        ArrayList<NumericRating> trainRatings = new ArrayList<NumericRating>();
        ArrayList<NumericRating> testRatings = new ArrayList<NumericRating>();
        for (int i = 0; i < folders.size(); i++) {
            if (i == folder - 1)//test data
            {
                testRatings.addAll(folders.get(i));
            } else {//training data
                trainRatings.addAll(folders.get(i));
            }
        }

        //create rating matrix
        HashMap<String, Integer> userIDIndexMapping = new HashMap<String, Integer>();
        HashMap<String, Integer> itemIDIndexMapping = new HashMap<String, Integer>();
        for (int i = 0; i < dataset.getUserIDs().size(); i++) {
            userIDIndexMapping.put(dataset.getUserIDs().get(i), i);
        }
        for (int i = 0; i < dataset.getItemIDs().size(); i++) {
            itemIDIndexMapping.put(dataset.getItemIDs().get(i), i);
        }
        RatingMatrix trainRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(),
                dataset.getItemIDs().size());
        for (int i = 0; i < trainRatings.size(); i++) {
            trainRatingMatrix.set(userIDIndexMapping.get(trainRatings.get(i).getUserID()),
                    itemIDIndexMapping.get(trainRatings.get(i).getItemID()), trainRatings.get(i).getValue());
        }
        RatingMatrix testRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(),
                dataset.getItemIDs().size());
        for (int i = 0; i < testRatings.size(); i++) {
            if (testRatings.get(i).getValue() < 5)
                continue;
            testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()),
                    itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue());
        }
        System.out.println("Training: " + trainRatingMatrix.getTotalRatingNumber() + " vs Test: "
                + testRatingMatrix.getTotalRatingNumber());

        logger.println("Initialize a SVD++ recommendation model.");
        logger.flush();
        SVDPlusPlus algo = new SVDPlusPlus(trainRatingMatrix, false,
                ".//localModels//" + config.getString("NAME"));
        algo.setLogger(logger);
        algo.build();
        algo.saveModel(".//localModels//" + config.getString("NAME"));
        logger.println("Save the model.");
        logger.flush();

        //rating prediction accuracy
        double RMSE = 0;
        double MAE = 0;
        double precision = 0;
        double recall = 0;
        double map = 0;
        double ndcg = 0;
        double mrr = 0;
        double auc = 0;
        int count = 0;
        for (int i = 0; i < testRatings.size(); i++) {
            NumericRating rating = testRatings.get(i);
            double prediction = algo.predict(userIDIndexMapping.get(rating.getUserID()),
                    itemIDIndexMapping.get(rating.getItemID()), false);
            if (prediction > algo.getMaxRating())
                prediction = algo.getMaxRating();
            if (prediction < algo.getMinRating())
                prediction = algo.getMinRating();
            if (Double.isNaN(prediction)) {
                System.out.println("no prediction");
                continue;
            }
            MAE = MAE + Math.abs(rating.getValue() - prediction);
            RMSE = RMSE + Math.pow((rating.getValue() - prediction), 2);
            count++;
        }
        MAE = MAE / count;
        RMSE = Math.sqrt(RMSE / count);
        totalMAE = totalMAE + MAE;
        totalRMSE = totalRMSE + RMSE;
        System.out.println("Folder --- MAE: " + MAE + " RMSE: " + RMSE);
        logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Folder --- MAE: "
                + MAE + " RMSE: " + RMSE);
        //ranking accuracy
        if (algo.getTopN() > 0) {
            HashMap<Integer, ArrayList<ResultUnit>> results = new HashMap<Integer, ArrayList<ResultUnit>>();
            for (int i = 0; i < trainRatingMatrix.getRow(); i++) {
                ArrayList<ResultUnit> rec = algo.getRecommendationList(i);
                if (rec == null)
                    continue;
                int total = testRatingMatrix.getUserRatingNumber(i);
                if (total == 0)//this user is ignored
                    continue;
                results.put(i, rec);
            }
            RankResultGenerator generator = new RankResultGenerator(results, algo.getTopN(), testRatingMatrix);
            precision = generator.getPrecisionN();
            totalPrecision = totalPrecision + precision;
            recall = generator.getRecallN();
            totalRecall = totalRecall + recall;
            map = generator.getMAPN();
            totalMAP = totalMAP + map;
            ndcg = generator.getNDCGN();
            totalNDCG = totalNDCG + ndcg;
            mrr = generator.getMRRN();
            totalMRR = totalMRR + mrr;
            auc = generator.getAUC();
            totalAUC = totalAUC + auc;
            System.out.println("Folder --- precision: " + precision + " recall: " + recall + " map: " + map
                    + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc);
            logger.println("Folder --- precision: " + precision + " recall: " + recall + " map: " + map
                    + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc);
        }

        logger.flush();
    }

    System.out.println("MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F);
    System.out.println("Precision@N: " + totalPrecision / F);
    System.out.println("Recall@N: " + totalRecall / F);
    System.out.println("MAP@N: " + totalMAP / F);
    System.out.println("MRR@N: " + totalMRR / F);
    System.out.println("NDCG@N: " + totalNDCG / F);
    System.out.println("AUC@N: " + totalAUC / F);

    logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + "\n" + "MAE: "
            + totalMAE / F + " RMSE: " + totalRMSE / F + "\n" + "Precision@N: " + totalPrecision / F + "\n"
            + "Recall@N: " + totalRecall / F + "\n" + "MAP@N: " + totalMAP / F + "\n" + "MRR@N: " + totalMRR / F
            + "\n" + "NDCG@N: " + totalNDCG / F + "\n" + "AUC@N: " + totalAUC / F);
    logger.flush();
    logger.close();
}

From source file:com.taobao.tddl.common.DynamicLogTest.java

public static void main(String[] args) throws InterruptedException {
    MockServer.setUpMockServer();//from   ww  w  .  jav a  2s .c o m

    DynamicLog dynamicLog = DynamicLog.getInstance("test");
    new Thread(new Runnable() {
        @Override
        public void run() {
            for (int i = 0; i < 300; i++) {
                try {
                    Thread.sleep(1000);
                } catch (InterruptedException e) {
                }
                MockServer.setConfigInfo("com.taobao.tddl.v1_test_buryPoints", getSpringXmlString("test", //
                        i + "+ 'th:date='+(java.util.Date)args[0]"));
            }
        }
    }).start();

    for (int i = 0; i < 300; i++) {
        dynamicLog.warn("test", new Object[] { new Date() }, "defaultLog", log);
        Thread.sleep(1000);
    }

    MockServer.tearDownMockServer();
}

From source file:com.mmone.gpdati.config.GpDatiProperties.java

public static void main(String[] args) {
    String s = "C:/svnprjects/mauro_netbprj/abs-ota-soapui-listener/test/FILE_DISPO__%s.txt";
    String td = DateFormatUtils.format(new Date(), "yyyyMMdd");
    System.out.println(String.format(s, td));
}