Example usage for java.io PrintWriter flush

List of usage examples for java.io PrintWriter flush

  1. HOME
  2. Java
  3. java
  4. java.io.*
  5. PrintWriter
  6. flush

Introduction

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

Prototype

public void flush()

Source Link

Document

Flushes the stream.

Usage

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

/**
 * @param args/*from   w ww .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//SocialReg");
    PropertiesConfiguration config = new PropertiesConfiguration();
    config.setFile(new File("conf//SocialReg.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//Epinions-ratings.txt");
    loader.readSimple();
    //read social information
    loader.readRelation(".//data//Epinions-trust.txt");
    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 = dataset.getUserIDMapping();
        HashMap<String, Integer> itemIDIndexMapping = dataset.getItemIDMapping();
        //         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++) {
            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 social regularization recommendation model.");
        logger.flush();
        SocialReg algo = new SocialReg(trainRatingMatrix, dataset.getRelationships(), false,
                ".//localModels//" + config.getString("NAME"));
        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());

        //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()));
            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);
        //            }
        //            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:ch.epfl.lsir.xin.test.BiasedMFTest.java

/**
 * @param args/*from   w  ww .  j  ava 2s .  c o  m*/
 */
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.MFTest.java

/**
 * @param args//  ww w.  ja  v  a  2  s.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:mx.unam.ecologia.gye.coalescence.app.RunExperiments.java

public static void main(String[] args) {
    BasicConfigurator.configure();//from  w  w  w  .j av a 2 s. c o 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:com.occamlab.te.parsers.ImageParser.java

public static void main(String[] args) throws Exception {
    if (args.length < 2) {
        System.err.println("Parameters: xml_url image_url");
        return;//from   w w w.  j a v  a 2  s  . co m
    }

    java.net.URL xml_url;
    try {
        xml_url = new java.net.URL(args[0]);
    } catch (Exception e) {
        jlogger.log(Level.INFO, "Error building xmlurl, will prefix file://", e);

        xml_url = new java.net.URL("file://" + args[0]);
    }

    java.net.URL image_url;
    try {
        image_url = new java.net.URL(args[1]);
    } catch (Exception e) {
        jlogger.log(Level.INFO, "Error building xmlurl, will prefix file://", e);

        image_url = new java.net.URL("file://" + args[1]);
    }

    DocumentBuilderFactory dbf = DocumentBuilderFactory.newInstance();
    dbf.setNamespaceAware(true);
    DocumentBuilder db = dbf.newDocumentBuilder();
    Document doc = db.parse(xml_url.openStream());
    // Element instruction = (Element)
    // doc.getElementsByTagNameNS("http://www.occamlab.com/te/parsers",
    // "ImageParser").item(0);
    Element instruction = (Element) doc.getDocumentElement();

    PrintWriter logger = new PrintWriter(System.out);
    InputStream image_is = image_url.openConnection().getInputStream();

    Document result = parse(image_is, instruction, logger);
    logger.flush();

    if (result != null) {
        TransformerFactory tf = TransformerFactory.newInstance();
        try {
            tf.setAttribute("http://saxon.sf.net/feature/strip-whitespace", "all");
        } catch (IllegalArgumentException e) {
            jlogger.log(Level.INFO, "setAttribute(\"http://saxon.sf.net/feature/strip-whitespace\", \"all\");",
                    e);

        }
        Transformer t = tf.newTransformer();
        t.setOutputProperty(OutputKeys.INDENT, "yes");
        t.transform(new DOMSource(result), new StreamResult(System.out));
    }

    System.exit(0);
}

From source file:GenericClient.java

public static void main(String[] args) throws IOException {
    try {/*w w w . j av  a  2  s .c  om*/
        // Check the number of arguments
        if (args.length != 2)
            throw new IllegalArgumentException("Wrong number of args");

        // Parse the host and port specifications
        String host = args[0];
        int port = Integer.parseInt(args[1]);

        // Connect to the specified host and port
        Socket s = new Socket(host, port);

        // Set up streams for reading from and writing to the server.
        // The from_server stream is final for use in the inner class below
        final Reader from_server = new InputStreamReader(s.getInputStream());
        PrintWriter to_server = new PrintWriter(s.getOutputStream());

        // Set up streams for reading from and writing to the console
        // The to_user stream is final for use in the anonymous class below
        BufferedReader from_user = new BufferedReader(new InputStreamReader(System.in));
        // Pass true for auto-flush on println()
        final PrintWriter to_user = new PrintWriter(System.out, true);

        // Tell the user that we've connected
        to_user.println("Connected to " + s.getInetAddress() + ":" + s.getPort());

        // Create a thread that gets output from the server and displays
        // it to the user. We use a separate thread for this so that we
        // can receive asynchronous output
        Thread t = new Thread() {
            public void run() {
                char[] buffer = new char[1024];
                int chars_read;
                try {
                    // Read characters from the server until the
                    // stream closes, and write them to the console
                    while ((chars_read = from_server.read(buffer)) != -1) {
                        to_user.write(buffer, 0, chars_read);
                        to_user.flush();
                    }
                } catch (IOException e) {
                    to_user.println(e);
                }

                // When the server closes the connection, the loop above
                // will end. Tell the user what happened, and call
                // System.exit(), causing the main thread to exit along
                // with this one.
                to_user.println("Connection closed by server.");
                System.exit(0);
            }
        };

        // Now start the server-to-user thread
        t.start();

        // In parallel, read the user's input and pass it on to the server.
        String line;
        while ((line = from_user.readLine()) != null) {
            to_server.print(line + "\r\n");
            to_server.flush();
        }

        // If the user types a Ctrl-D (Unix) or Ctrl-Z (Windows) to end
        // their input, we'll get an EOF, and the loop above will exit.
        // When this happens, we stop the server-to-user thread and close
        // the socket.

        s.close();
        to_user.println("Connection closed by client.");
        System.exit(0);
    }
    // If anything goes wrong, print an error message
    catch (Exception e) {
        System.err.println(e);
        System.err.println("Usage: java GenericClient <hostname> <port>");
    }
}

From source file:be.dnsbelgium.rdap.client.RDAPCLI.java

public static void main(String[] args) {

    LOGGER.debug("Create the command line parser");
    CommandLineParser parser = new GnuParser();

    LOGGER.debug("Create the options");
    Options options = new RDAPOptions(Locale.ENGLISH);

    try {/*from w  w  w  .ja  v a2 s .c  om*/
        LOGGER.debug("Parse the command line arguments");
        CommandLine line = parser.parse(options, args);

        if (line.hasOption("help")) {
            printHelp(options);
            return;
        }

        if (line.getArgs().length == 0) {
            throw new IllegalArgumentException("You must provide a query");
        }
        String query = line.getArgs()[0];

        Type type = (line.getArgs().length == 2) ? Type.valueOf(line.getArgs()[1].toUpperCase())
                : guessQueryType(query);

        LOGGER.debug("Query: {}, Type: {}", query, type);

        try {
            SSLContextBuilder sslContextBuilder = SSLContexts.custom();
            if (line.hasOption(RDAPOptions.TRUSTSTORE)) {
                sslContextBuilder.loadTrustMaterial(
                        RDAPClient.getKeyStoreFromFile(new File(line.getOptionValue(RDAPOptions.TRUSTSTORE)),
                                line.getOptionValue(RDAPOptions.TRUSTSTORE_TYPE, RDAPOptions.DEFAULT_STORETYPE),
                                line.getOptionValue(RDAPOptions.TRUSTSTORE_PASS, RDAPOptions.DEFAULT_PASS)));
            }
            if (line.hasOption(RDAPOptions.KEYSTORE)) {
                sslContextBuilder.loadKeyMaterial(
                        RDAPClient.getKeyStoreFromFile(new File(line.getOptionValue(RDAPOptions.KEYSTORE)),
                                line.getOptionValue(RDAPOptions.KEYSTORE_TYPE, RDAPOptions.DEFAULT_STORETYPE),
                                line.getOptionValue(RDAPOptions.KEYSTORE_PASS, RDAPOptions.DEFAULT_PASS)),
                        line.getOptionValue(RDAPOptions.KEYSTORE_PASS, RDAPOptions.DEFAULT_PASS).toCharArray());
            }
            SSLContext sslContext = sslContextBuilder.build();

            final String url = line.getOptionValue(RDAPOptions.URL);
            final HttpHost host = Utils.httpHost(url);

            HashSet<Header> headers = new HashSet<Header>();
            headers.add(new BasicHeader("Accept-Language",
                    line.getOptionValue(RDAPOptions.LANG, Locale.getDefault().toString())));
            HttpClientBuilder httpClientBuilder = HttpClients.custom().setDefaultHeaders(headers)
                    .setSSLSocketFactory(new SSLConnectionSocketFactory(sslContext,
                            (line.hasOption(RDAPOptions.INSECURE) ? new AllowAllHostnameVerifier()
                                    : new BrowserCompatHostnameVerifier())));

            if (line.hasOption(RDAPOptions.USERNAME) && line.hasOption(RDAPOptions.PASSWORD)) {
                BasicCredentialsProvider credentialsProvider = new BasicCredentialsProvider();
                credentialsProvider.setCredentials(new AuthScope(host.getHostName(), host.getPort()),
                        new UsernamePasswordCredentials(line.getOptionValue(RDAPOptions.USERNAME),
                                line.getOptionValue(RDAPOptions.PASSWORD)));
                httpClientBuilder.setDefaultCredentialsProvider(credentialsProvider);
            }

            RDAPClient rdapClient = new RDAPClient(httpClientBuilder.build(), url);
            ObjectMapper mapper = new ObjectMapper();

            JsonNode json = null;
            switch (type) {
            case DOMAIN:
                json = rdapClient.getDomainAsJson(query);
                break;
            case ENTITY:
                json = rdapClient.getEntityAsJson(query);
                break;
            case AUTNUM:
                json = rdapClient.getAutNum(query);
                break;
            case IP:
                json = rdapClient.getIp(query);
                break;
            case NAMESERVER:
                json = rdapClient.getNameserver(query);
                break;
            }
            PrintWriter out = new PrintWriter(System.out, true);
            if (line.hasOption(RDAPOptions.RAW)) {
                mapper.writer().writeValue(out, json);
            } else if (line.hasOption(RDAPOptions.PRETTY)) {
                mapper.writer(new DefaultPrettyPrinter()).writeValue(out, json);
            } else if (line.hasOption(RDAPOptions.YAML)) {
                DumperOptions dumperOptions = new DumperOptions();
                dumperOptions.setPrettyFlow(true);
                dumperOptions.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK);
                dumperOptions.setSplitLines(true);
                Yaml yaml = new Yaml(dumperOptions);
                Map data = mapper.convertValue(json, Map.class);
                yaml.dump(data, out);
            } else {
                mapper.writer(new MinimalPrettyPrinter()).writeValue(out, json);
            }
            out.flush();
        } catch (Exception e) {
            LOGGER.error(e.getMessage(), e);
            System.exit(-1);
        }
    } catch (org.apache.commons.cli.ParseException e) {
        printHelp(options);
        System.exit(-1);
    }
}

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);// w  w w .  jav  a2 s.  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:ch.epfl.lsir.xin.test.MostPopularTest.java

/**
 * @param args//from  w w  w. ja v a  2 s.c om
 */
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:it.tidalwave.imageio.example.stats.FocalLengthStats.java

public static void main(final String[] args) {
    try {//from   w w  w  . j a va  2  s.c  o  m
        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();
    }
}