Example usage for java.io PrintWriter println

List of usage examples for java.io PrintWriter println

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Introduction

In this page you can find the example usage for java.io PrintWriter println.

Prototype

public void println(Object x)

Source Link

Document

Prints an Object and then terminates the line.

Usage

From source file:com.jkoolcloud.jesl.simulator.TNT4JSimulator.java

public static void main(String[] args) {
    boolean isTTY = (System.console() != null);
    long startTime = System.currentTimeMillis();

    try {//w ww .j a  v  a 2 s .  c  om
        SAXParserFactory parserFactory = SAXParserFactory.newInstance();
        SAXParser theParser = parserFactory.newSAXParser();
        TNT4JSimulatorParserHandler xmlHandler = new TNT4JSimulatorParserHandler();

        processArgs(xmlHandler, args);

        TrackerConfig simConfig = DefaultConfigFactory.getInstance().getConfig(TNT4JSimulator.class.getName());
        logger = TrackingLogger.getInstance(simConfig.build());
        if (logger.isSet(OpLevel.TRACE))
            traceLevel = OpLevel.TRACE;
        else if (logger.isSet(OpLevel.DEBUG))
            traceLevel = OpLevel.DEBUG;

        if (runType == SimulatorRunType.RUN_SIM) {
            if (StringUtils.isEmpty(simFileName)) {
                simFileName = "tnt4j-sim.xml";
                String fileName = readFromConsole("Simulation file [" + simFileName + "]: ");

                if (!StringUtils.isEmpty(fileName))
                    simFileName = fileName;
            }

            StringBuffer simDef = new StringBuffer();
            BufferedReader simLoader = new BufferedReader(new FileReader(simFileName));
            String line;
            while ((line = simLoader.readLine()) != null)
                simDef.append(line).append("\n");
            simLoader.close();

            info("jKool Activity Simulator Run starting: file=" + simFileName + ", iterations=" + numIterations
                    + ", ttl.sec=" + ttl);
            startTime = System.currentTimeMillis();

            if (isTTY && numIterations > 1)
                System.out.print("Iteration: ");
            int itTrcWidth = 0;
            for (iteration = 1; iteration <= numIterations; iteration++) {
                itTrcWidth = printProgress("Executing Iteration", iteration, itTrcWidth);

                theParser.parse(new InputSource(new StringReader(simDef.toString())), xmlHandler);

                if (!Utils.isEmpty(jkFileName)) {
                    PrintWriter gwFile = new PrintWriter(new FileOutputStream(jkFileName, true));
                    gwFile.println("");
                    gwFile.close();
                }
            }
            if (numIterations > 1)
                System.out.println("");

            info("jKool Activity Simulator Run finished, elapsed time = "
                    + DurationFormatUtils.formatDurationHMS(System.currentTimeMillis() - startTime));
            printMetrics(xmlHandler.getSinkStats(), "Total Sink Statistics");
        } else if (runType == SimulatorRunType.REPLAY_SIM) {
            info("jKool Activity Simulator Replay starting: file=" + jkFileName + ", iterations="
                    + numIterations);
            connect();
            startTime = System.currentTimeMillis();

            // Determine number of lines in file
            BufferedReader gwFile = new BufferedReader(new java.io.FileReader(jkFileName));
            for (numIterations = 0; gwFile.readLine() != null; numIterations++)
                ;
            gwFile.close();

            // Reopen the file and
            gwFile = new BufferedReader(new java.io.FileReader(jkFileName));
            if (isTTY && numIterations > 1)
                System.out.print("Processing Line: ");
            int itTrcWidth = 0;
            String gwMsg;
            iteration = 0;
            while ((gwMsg = gwFile.readLine()) != null) {
                iteration++;
                if (isTTY)
                    itTrcWidth = printProgress("Processing Line", iteration, itTrcWidth);
                gwConn.write(gwMsg);
            }
            if (isTTY && numIterations > 1)
                System.out.println("");
            long endTime = System.currentTimeMillis();

            info("jKool Activity Simulator Replay finished, elasped.time = "
                    + DurationFormatUtils.formatDurationHMS(endTime - startTime));
        }
    } catch (Exception e) {
        if (e instanceof SAXParseException) {
            SAXParseException spe = (SAXParseException) e;
            error("Error at line: " + spe.getLineNumber() + ", column: " + spe.getColumnNumber(), e);
        } else {
            error("Error running simulator", e);
        }
    } finally {
        try {
            Thread.sleep(1000L);
        } catch (Exception e) {
        }
        TNT4JSimulator.disconnect();
    }

    System.exit(0);
}

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

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

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

    PropertiesConfiguration config = new PropertiesConfiguration();
    config.setFile(new File("conf//biasedMF.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 biased matrix factorization recommendation model.");
        logger.flush();
        BiasedMF algo = new BiasedMF(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,
                    trainRatingMatrix);
            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:ch.epfl.lsir.xin.test.ItemBasedCFTest.java

/**
 * @param args/*from  w w w.j a  v a2 s  . c om*/
 */
public static void main(String[] args) throws Exception {
    // TODO Auto-generated method stub

    PrintWriter logger = new PrintWriter(".//results//ItemBasedCF");
    PropertiesConfiguration config = new PropertiesConfiguration();
    config.setFile(new File(".//conf//ItemBasedCF.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;
    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.");
    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();
        trainRatingMatrix.calculateItemsMean();
        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 item based collaborative filtering recommendation model.");
        ItemBasedCF algo = new ItemBasedCF(trainRatingMatrix);
        algo.setLogger(logger);
        algo.build();//if read local model, no need to build the model
        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);
                //               results.put(i, rec);
                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);
            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.append("Folder --- precision: " + precision + " recall: " + recall + " map: " + map
                    + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc + "\n");
        }
    }

    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);
    System.out.println("similarity: " + config.getString("SIMILARITY"));
    //MAE: 0.7227232762922241 RMSE: 0.9225576790122603 (MovieLens 100K, shrinkage 2500, neighbor size 40, PCC)
    //MAE: 0.7250636319353241 RMSE: 0.9242305485411567 (MovieLens 100K, shrinkage 25, neighbor size 40, PCC)
    //MAE: 0.7477213243604459 RMSE: 0.9512195004171138 (MovieLens 100K, shrinkage 2500, neighbor size 40, COSINE)

    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:ch.epfl.lsir.xin.test.MFTest.java

/**
 * @param args/* w w  w . j  av  a2s .c om*/
 */
public static void main(String[] args) throws Exception {
    // TODO Auto-generated method stub

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

    PropertiesConfiguration config = new PropertiesConfiguration();
    config.setFile(new File("conf//MF.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 matrix factorization based recommendation model.");
        logger.flush();
        MatrixFactorization algo = new MatrixFactorization(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);
                //               for( Map.Entry<Integer, Double> entry : testRatingMatrix.getRatingMatrix().get(i).entrySet() )
                //               {
                //                  System.out.print( entry.getKey() + "(" + entry.getValue() + ") , ");
                //               }
                //               System.out.println();
                //               for( int j = 0 ; j < rec.size() ; j++ )
                //               {
                //                  System.out.print(rec.get(j).getItemIndex() + "(" + rec.get(j).getPrediciton() +
                //                        ") , ");
                //               }
                //               System.out.println("**********");
            }
            RankResultGenerator generator = new RankResultGenerator(results, algo.getTopN(), testRatingMatrix,
                    trainRatingMatrix);
            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.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " MAE: " + MAE
                + " RMSE: " + RMSE);
        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:ch.epfl.lsir.xin.test.UserBasedCFTest.java

/**
 * @param args/*from www  .j a  v a  2  s  .  c  om*/
 */
public static void main(String[] args) throws Exception {
    // TODO Auto-generated method stub

    PrintWriter logger = new PrintWriter(".//results//UserBasedCF");
    PropertiesConfiguration config = new PropertiesConfiguration();
    config.setFile(new File(".//conf//UserBasedCF.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;
    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.");
    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();
        trainRatingMatrix.calculateUsersMean();
        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());
        }
        logger.println("Initialize a user based collaborative filtering recommendation model.");
        UserBasedCF algo = new UserBasedCF(trainRatingMatrix, false,
                ".//localModels//" + config.getString("NAME"));
        algo.setLogger(logger);
        algo.build();//if read local model, no need to build the model
        algo.saveModel(".//localModels//" + config.getString("NAME"));
        logger.println("Save the model.");
        System.out.println(trainRatings.size() + " vs. " + testRatings.size());
        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 (Double.isNaN(prediction)) {
                System.out.println("no prediction");
                continue;
            }
            if (prediction > algo.getMaxRating())
                prediction = algo.getMaxRating();
            if (prediction < algo.getMinRating())
                prediction = algo.getMinRating();
            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);
        logger.flush();
        //ranking accuracy
        if (algo.getTopN() > 0) {
            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);
                //               for( Map.Entry<Integer, Double> entry : testRatingMatrix.getRatingMatrix().get(i).entrySet() )
                //               {
                //                  System.out.print( entry.getKey() + "(" + entry.getValue() + ") , ");
                //               }
                //               System.out.println();
                //               for( int j = 0 ; j < rec.size() ; j++ )
                //               {
                //                  System.out.print(rec.get(j).getItemIndex() + "(" + rec.get(j).getPrediciton() +
                //                        ") , ");
                //               }
                //               System.out.println("**********");
            }
            RankResultGenerator generator = new RankResultGenerator(results, algo.getTopN(), testRatingMatrix,
                    trainRatingMatrix);
            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);
        }
    }

    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);
    // MovieLens100k
    //MAE: 0.7343907480119425 RMSE: 0.9405808357192891 (MovieLens 100K, shrinkage 25, neighbor size 60, PCC)
    //MAE: 0.7522376630596646 RMSE: 0.9520931265724659 (MovieLens 100K, no shrinkage , neighbor size 40, COSINE)
    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.github.lgi2p.obirs.utils.JSONConverter.java

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

    String annotdir = "/data/toxnuc/toxnuc_annots_5_11_14/annots";
    String annotsIndex = "/data/toxnuc/toxnuc_annots_5_11_14.json";

    File folder = new File(annotdir);
    File[] listOfFiles = folder.listFiles();

    PrintWriter printWriter = new PrintWriter(annotsIndex);

    int i = 0;//from  w  w w  .j  a  va 2  s. c om
    for (File file : listOfFiles) {
        if (file.isFile()) {
            System.out.println(file.getPath());
            String title = file.getName();
            String href = file.getPath();
            String json = toJSONindexFormat(i, title, Utils.readFileAsString(file), href);
            i++;

            printWriter.println(json);
        }
    }
    printWriter.close();
    System.out.println("consult: " + annotsIndex);
}

From source file:mx.unam.ecologia.gye.coalescence.app.RunExperiments.java

public static void main(String[] args) {
    BasicConfigurator.configure();//from  w  w w  . ja  v a 2s .co m
    SimulationParameters params = new SimulationParameters(args);
    //loop
    int num_beta = params.getBetaCount();
    int num_k = params.getKCount();
    int num_N = params.getNCount();
    int num_u = params.getUCount();
    PrintWriter pw;

    try {
        File csv = new File(params.getOutput());
        FileOutputStream fout = new FileOutputStream(csv);
        pw = new PrintWriter(fout);
    } catch (Exception ex) {
        pw = new PrintWriter(System.out);
    }
    for (int l = 0; l < num_beta; l++) {
        params.selectBeta(l);
        for (int m = 0; m < num_N; m++) {
            params.selectN(m);
            for (int n = 0; n < num_k; n++) {
                params.selectK(n);
                for (int o = 0; o < num_u; o++) {
                    params.selectU(o);
                    MicrosatelliteExperiment exp = new MicrosatelliteExperiment(params);
                    if (m + n + o == 0) {
                        pw.println(exp.getCSVHeader());
                        pw.flush();
                    }
                    exp.init();
                    exp.run();
                    pw.println(exp.resultsToCSV());
                    pw.flush();
                    System.gc();
                } //for u
            } //for k
        } //for N
    } //for beta
}

From source file:edu.ku.brc.specify.dbsupport.cleanuptools.FirstLastVerifier.java

/**
 * @param args//from  w ww.  j a  v  a 2s . co  m
 */
public static void main(String[] args) {
    if (true) {
        testLastNames();
        return;
    }
    FirstLastVerifier flv = new FirstLastVerifier();
    System.out.println(flv.isFirstName("Bill"));
    System.out.println(flv.isLastName("Bill"));

    System.out.println(flv.isFirstName("Johnson"));
    System.out.println(flv.isLastName("Johnson"));

    try {
        if (false) {
            for (String nm : new String[] { "firstnames", "lastnames" }) {
                File file = new File("/Users/rods/Downloads/" + nm + ".txt");
                try {
                    PrintWriter pw = new PrintWriter("/Users/rods/Downloads/" + nm + ".list");
                    for (String line : (List<String>) FileUtils.readLines(file)) {
                        String[] toks = StringUtils.split(line, '\t');
                        if (toks != null && toks.length > 0)
                            pw.println(toks[0]);
                    }
                    pw.close();

                } catch (Exception e) {
                    e.printStackTrace();
                }
            }
        }

        Vector<String> lnames = new Vector<String>();
        File file = XMLHelper.getConfigDir("lastnames.list");
        if (false) {
            for (String name : (List<String>) FileUtils.readLines(file)) {
                if (flv.isFirstName(name)) {
                    System.out.println(name + " is first.");
                } else {
                    lnames.add(name);
                }
            }
            Collections.sort(lnames);
            FileUtils.writeLines(file, lnames);
        }

        lnames.clear();
        file = XMLHelper.getConfigDir("firstnames.list");
        for (String name : (List<String>) FileUtils.readLines(file)) {
            if (flv.isLastName(name)) {
                System.out.println(name + " is first.");
            } else {
                lnames.add(name);
            }
        }
        Collections.sort(lnames);
        //FileUtils.writeLines(file, lnames);

    } catch (Exception ex) {
        ex.printStackTrace();
    }
}

From source file:edu.usc.ee599.CommunityStats.java

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

    File dir = new File("results5");
    PrintWriter writer = new PrintWriter(new FileWriter("results5_stats.txt"));

    File[] files = dir.listFiles();

    DescriptiveStatistics statistics1 = new DescriptiveStatistics();
    DescriptiveStatistics statistics2 = new DescriptiveStatistics();
    for (File file : files) {

        BufferedReader reader = new BufferedReader(new FileReader(file));

        String line1 = reader.readLine();
        String line2 = reader.readLine();

        int balanced = Integer.parseInt(line1.split(",")[1]);
        int unbalanced = Integer.parseInt(line2.split(",")[1]);

        double bp = (double) balanced / (double) (balanced + unbalanced);
        double up = (double) unbalanced / (double) (balanced + unbalanced);

        statistics1.addValue(bp);/*from  ww w . jav a 2s . c o  m*/
        statistics2.addValue(up);

    }

    writer.println("AVG Balanced %: " + statistics1.getMean());
    writer.println("AVG Unbalanced %: " + statistics2.getMean());

    writer.println("STD Balanced %: " + statistics1.getStandardDeviation());
    writer.println("STD Unbalanced %: " + statistics2.getStandardDeviation());

    writer.flush();
    writer.close();

}

From source file:it.tidalwave.imageio.example.stats.FocalLengthStats.java

public static void main(final String[] args) {
    try {/*from  ww  w .j av  a2  s .c  om*/
        final PrintWriter out = new PrintWriter(new File(args[1]));
        new DirectoryWalker() {
            @Override
            protected void handleFile(final File file, final int depth, final Collection results)
                    throws IOException {
                if (file.getName().toUpperCase().endsWith(".NEF")) {
                    System.out.printf("Processing %s...\n", file.getCanonicalPath());
                    final ImageReader reader = (ImageReader) ImageIO.getImageReaders(file).next();
                    reader.setInput(ImageIO.createImageInputStream(file));
                    final IIOMetadata metadata = reader.getImageMetadata(0);
                    final NEFMetadata nefMetadata = (NEFMetadata) metadata;
                    final IFD exifIFD = nefMetadata.getExifIFD();
                    final TagRational focalLength = exifIFD.getFocalLength();
                    out.println(focalLength.doubleValue());
                }
            }

            public void start() throws IOException {
                super.walk(new File(args[0]), new ArrayList<Object>());
            }
        }.start();

        out.flush();
        out.close();
    } catch (Exception e) {
        e.printStackTrace();
    }
}