Example usage for java.util ArrayList get

List of usage examples for java.util ArrayList get

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Introduction

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

Prototype

public E get(int index)

Source Link

Document

Returns the element at the specified position in this list.

Usage

From source file:com.aestel.chemistry.openEye.fp.DistMatrix.java

public static void main(String... args) throws IOException {
    long start = System.currentTimeMillis();

    // create command line Options object
    Options options = new Options();
    Option opt = new Option("i", true, "input file [.tsv from FingerPrinter]");
    opt.setRequired(true);/*from   w  ww.ja  v a  2 s  .c  o  m*/
    options.addOption(opt);

    opt = new Option("o", true, "outpur file [.tsv ");
    opt.setRequired(true);
    options.addOption(opt);

    CommandLineParser parser = new PosixParser();
    CommandLine cmd = null;
    try {
        cmd = parser.parse(options, args);
    } catch (Exception e) {
        System.err.println(e.getMessage());
        exitWithHelp(options);
    }
    args = cmd.getArgs();

    if (args.length != 0)
        exitWithHelp(options);

    String file = cmd.getOptionValue("i");
    BufferedReader in = new BufferedReader(new FileReader(file));

    file = cmd.getOptionValue("o");
    PrintWriter out = new PrintWriter(new BufferedWriter(new FileWriter(file)));

    ArrayList<Fingerprint> fps = new ArrayList<Fingerprint>();
    ArrayList<String> ids = new ArrayList<String>();
    String line;
    while ((line = in.readLine()) != null) {
        String[] parts = line.split("\t");
        if (parts.length == 3) {
            ids.add(parts[0]);
            fps.add(new ByteFingerprint(parts[2]));
        }
    }
    in.close();

    out.print("ID");
    for (int i = 0; i < ids.size(); i++) {
        out.print('\t');
        out.print(ids.get(i));
    }
    out.println();

    for (int i = 0; i < ids.size(); i++) {
        out.print(ids.get(i));
        Fingerprint fp1 = fps.get(i);

        for (int j = 0; j <= i; j++) {
            out.printf("\t%.4g", fp1.tanimoto(fps.get(j)));
        }
        out.println();
    }
    out.close();

    System.err.printf("Done %d fingerprints in %.2gsec\n", fps.size(),
            (System.currentTimeMillis() - start) / 1000D);
}

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

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

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

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

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

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

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

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

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

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

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

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

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

    PropertiesConfiguration config = new PropertiesConfiguration();
    config.setFile(new File("conf//GlobalMean.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());

    double totalMAE = 0;
    double totalRMSE = 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);
        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++) {
            testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()),
                    itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue());
        }
        System.out.println("Training: " + trainRatingMatrix.getTotalRatingNumber() + " vs Test: "
                + testRatingMatrix.getTotalRatingNumber());

        logger.println("Initialize a recommendation model based on global average method.");
        GlobalAverage algo = new GlobalAverage(trainRatingMatrix);
        algo.setLogger(logger);
        algo.build();
        algo.saveModel(".//localModels//" + config.getString("NAME"));
        logger.println("Save the model.");
        logger.flush();

        System.out.println(trainRatings.size() + " vs. " + testRatings.size());

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

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

    System.out.println("MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F);
    logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Final results: MAE: "
            + totalMAE / F + " RMSE: " + totalRMSE / F);
    logger.flush();
    logger.close();
    //MAE: 0.9338607074893257 RMSE: 1.1170971131112037 (MovieLens1M)
    //MAE: 0.9446876509332618 RMSE: 1.1256517870920375 (MovieLens100K)

}

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

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

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

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

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

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

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

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

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

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

}

From source file:com.ericsson.eiffel.remrem.semantics.clone.PrepareLocalEiffelSchemas.java

public static void main(String[] args) throws IOException {
    final PrepareLocalEiffelSchemas prepareLocalSchema = new PrepareLocalEiffelSchemas();
    final Proxy proxy = prepareLocalSchema.getProxy(httpProxyUrl, httpProxyPort, httpProxyUsername,
            httpProxyPassword);//from  w ww.j av  a  2s  .  c  om
    if (proxy != null) {
        prepareLocalSchema.setProxy(proxy);
    }
    final String eiffelRepoUrl = args[0];
    final String eiffelRepoBranch = args[1];
    final String operationRepoUrl = args[2];
    final String operationRepoBranch = args[3];

    final File localEiffelRepoPath = new File(
            System.getProperty(EiffelConstants.USER_HOME) + File.separator + EiffelConstants.EIFFEL);
    final File localOperationsRepoPath = new File(System.getProperty(EiffelConstants.USER_HOME) + File.separator
            + EiffelConstants.OPERATIONS_REPO_NAME);

    // Clone Eiffel Repo from GitHub 
    prepareLocalSchema.cloneEiffelRepo(eiffelRepoUrl, eiffelRepoBranch, localEiffelRepoPath);

    //Clone Eiffel operations Repo from GitHub 
    prepareLocalSchema.cloneEiffelRepo(operationRepoUrl, operationRepoBranch, localOperationsRepoPath);

    //Copy operations repo Schemas to location where Eiffel repo schemas available
    prepareLocalSchema.copyOperationSchemas(localOperationsRepoPath.getAbsolutePath(),
            localEiffelRepoPath.getAbsolutePath());

    // Read and Load JsonSchemas from Cloned Directory 
    final LocalRepo localRepo = new LocalRepo(localEiffelRepoPath);
    localRepo.readSchemas();

    final ArrayList<String> jsonEventNames = localRepo.getJsonEventNames();
    final ArrayList<File> jsonEventSchemas = localRepo.getJsonEventSchemas();

    // Schema changes 
    final SchemaFile schemaFile = new SchemaFile();

    // Iterate the Each jsonSchema file to Add and Modify the necessary properties 
    if (jsonEventNames != null && jsonEventSchemas != null) {
        for (int i = 0; i < jsonEventNames.size(); i++) {
            schemaFile.modify(jsonEventSchemas.get(i), jsonEventNames.get(i));
        }
    }

}

From source file:edu.usd.btl.ontology.ToolTree.java

public static void main(String[] args) throws Exception {
    try {/*from  ww  w.  ja  v a  2s  .com*/
        //File ontoInput = new File(".\\ontology_files\\EDAM_1.3.owl");
        ObjectMapper mapper = new ObjectMapper();
        OntologyFileRead ontFileRead = new OntologyFileRead();
        ArrayList<edu.usd.btl.ontology.BioPortalElement> nodeList = ontFileRead
                .readFile(".\\ontology_files\\EDAM_1.3.owl");
        //write nodelist to JSON string
        ObjectWriter treeWriter = mapper.writer().withDefaultPrettyPrinter();
        String edamJSON = mapper.writeValueAsString(nodeList);

        JsonNode rootNode = mapper.readValue(edamJSON, JsonNode.class);
        System.out.println("IsNull" + rootNode.toString());

        OntSearch ontSearch = new OntSearch();
        System.out.println(nodeList.get(0).getURI());
        String result = ontSearch.searchElementByURI("http://edamontology.org/topic_2817");
        System.out.println("RESULT = " + result);

        String topicSearchResult = ontSearch.findAllTopics();
        System.out.println("Topics Result = " + topicSearchResult);

        File ontFile = new File(".\\ontology_files\\EDAM_1.3.owl");
        String searchFromFileResult = ontSearch.searchNodeFromFile("http://edamontology.org/topic_2817",
                ".\\ontology_files\\EDAM_1.3.owl");
        System.out.println("File Response = " + searchFromFileResult.toString());
    } catch (IOException e) {
        System.out.println(e.getMessage());
    }

    //Hashmap stuff
    //        OntologyFileRead ontFileRead = new OntologyFileRead();
    //        ArrayList<edu.usd.btl.ontology.BioPortalElement> nodeList = ontFileRead.readFile(".\\ontology_files\\EDAM_1.3.owl");
    //        
    //        HashMap hm = new HashMap();
    //        
    //        //find topics
    //        ArrayList<OntologyNode> ontoTopicList = new ArrayList();
    //        
    //        for(BioPortalElement node : nodeList){
    //            //System.out.println("****" + node.getURI());
    //            hm.put(node.getURI(), node.getName());
    //        }
    //        
    //        Set set = hm.entrySet();
    //        Iterator i = set.iterator();
    //        while(i.hasNext()){
    //            Map.Entry me = (Map.Entry)i.next();
    //            System.out.println(me.getKey() + ": " + me.getValue());
    //        }
    //        System.out.println("HashMap Size = " + hm.size());
}

From source file:edu.oregonstate.eecs.mcplan.domains.toy.RelevantIrrelevant.java

public static void main(final String[] argv) throws NumberFormatException, IOException {
    final RandomGenerator rng = new MersenneTwister(42);
    final int T = 30;
    final int nr = 3;
    final int ni = 3;

    final Parameters params = new Parameters(T, nr, ni);
    final Actions actions = new Actions(params);
    final FsssModel model = new FsssModel(rng, params);

    State s = model.initialState();
    while (!s.isTerminal()) {
        System.out.println(s);/*from w w  w .  j  a  v a2s  .  c  o  m*/
        System.out.println("R(s): " + model.reward(s));
        actions.setState(s, 0);
        final ArrayList<Action> action_list = Fn.takeAll(actions);
        for (int i = 0; i < action_list.size(); ++i) {
            System.out.println(i + ": " + action_list.get(i));
        }
        System.out.print(">>> ");
        final BufferedReader cin = new BufferedReader(new InputStreamReader(System.in));
        final int choice = Integer.parseInt(cin.readLine());
        final Action a = action_list.get(choice);
        System.out.println("R(s, a): " + model.reward(s, a));
        s = model.sampleTransition(s, a);
    }

    //      // Estimate the value of a "good" policy.
    //      // Note: The "good" policy is to Invest when you can, and Sell if the
    //      // price is >= 2. This is not necessarily optimal because:
    //      //    1. You should Borrow once the episode will end before the loan must be repaid
    //      //   2. For some values of invest_period, you should pass on a low price
    //      //      early in the period to try to get a better one later.
    //      final int Ngames = 10000;
    //      double V = 0;
    //      int Ninvest = 0;
    //      for( int i = 0; i < Ngames; ++i ) {
    //         State s = model.initialState();
    //         double Vi = model.reward( s );
    //         while( !s.isTerminal() ) {
    //            final Action a;
    //
    //            // "Good" policy
    //            if( s.investment == 0 ) {
    //               a = new InvestAction();
    //               Ninvest += 1;
    //            }
    //            else if( s.investment > 0 && s.price >= 2 ) {
    //               if( s.invest_t < (params.invest_period - 1) || s.price > 2 ) {
    //                  a = new SellAction();
    //               }
    //               else {
    //                  a = new SaveAction();
    //               }
    ////               a = new SellAction();
    //            }
    //            else {
    //               a = new SaveAction();
    //            }
    //
    //            // "Borrow" policy
    ////            if( s.loan == 0 ) {
    ////               a = new BorrowAction();
    ////            }
    ////            else {
    ////               a = new SaveAction();
    ////            }
    //
    //            final double ra = model.reward( s, a );
    //            s = model.sampleTransition( s, a );
    //            Vi += ra + model.reward( s );
    //         }
    //         V += Vi;
    //      }
    //
    //      final double Vavg = V / Ngames;
    //      final double Navg = (Ninvest / ((double) Ngames));
    //      System.out.println( "Avg. value: " + Vavg );
    //      System.out.println( "Avg. Invest actions: " + Navg );
    //      System.out.println( "V(Invest) ~= " + ( 1 + (Vavg - params.T)/Navg ) );
}

From source file:challenge302.intermediate.ASCIIHistogramMaker.java

public static void main(String[] args) {
    String inFile = "/data/challenge302intermediate.txt";

    //        ArrayList<int[]> graphData = new ArrayList<int[]>();
    ArrayList<String> input = new ChallengeInput().getInputByLines(ASCIIHistogramMaker.class, inFile);
    //take first line as the chart bounds and create new BarChart with those bounds
    IntBasedBarChart chart = new IntBasedBarChart(input.remove(0).split(" "));

    //take next line as the number of chart elements
    int size = Integer.parseInt(input.remove(0));

    for (int i = 0; i < size; i++) {
        //            System.out.println("Parsing: graph data line=\""+input.get(i)+"\"");
        chart.addData(stringArraytoIntArray(input.get(i).split(" ")));
    } //end adding all lines

    printChart(chart, size);/*from w w  w  .j a  v a  2s. com*/

}

From source file:ch.cyclops.gatekeeper.Main.java

public static void main(String[] args) throws Exception {
    CompositeConfiguration config = new CompositeConfiguration();
    config.addConfiguration(new SystemConfiguration());
    if (args.length > 0)
        config.addConfiguration(new PropertiesConfiguration(args[args.length - 1]));

    //setting up the logging framework now
    Logger.getRootLogger().getLoggerRepository().resetConfiguration();
    ConsoleAppender console = new ConsoleAppender(); //create appender
    //configure the appender
    String PATTERN = "%d [%p|%C{1}|%M|%L] %m%n";
    console.setLayout(new PatternLayout(PATTERN));
    String logConsoleLevel = config.getProperty("log.level.console").toString();
    switch (logConsoleLevel) {
    case ("INFO"):
        console.setThreshold(Level.INFO);
        break;// ww  w.j av  a  2s. c  o m
    case ("DEBUG"):
        console.setThreshold(Level.DEBUG);
        break;
    case ("WARN"):
        console.setThreshold(Level.WARN);
        break;
    case ("ERROR"):
        console.setThreshold(Level.ERROR);
        break;
    case ("FATAL"):
        console.setThreshold(Level.FATAL);
        break;
    case ("OFF"):
        console.setThreshold(Level.OFF);
        break;
    default:
        console.setThreshold(Level.ALL);
    }

    console.activateOptions();
    //add appender to any Logger (here is root)
    Logger.getRootLogger().addAppender(console);

    String logFileLevel = config.getProperty("log.level.file").toString();
    String logFile = config.getProperty("log.file").toString();
    if (logFile != null && logFile.length() > 0) {
        FileAppender fa = new FileAppender();
        fa.setName("FileLogger");

        fa.setFile(logFile);
        fa.setLayout(new PatternLayout("%d %-5p [%c{1}] %m%n"));

        switch (logFileLevel) {
        case ("INFO"):
            fa.setThreshold(Level.INFO);
            break;
        case ("DEBUG"):
            fa.setThreshold(Level.DEBUG);
            break;
        case ("WARN"):
            fa.setThreshold(Level.WARN);
            break;
        case ("ERROR"):
            fa.setThreshold(Level.ERROR);
            break;
        case ("FATAL"):
            fa.setThreshold(Level.FATAL);
            break;
        case ("OFF"):
            fa.setThreshold(Level.OFF);
            break;
        default:
            fa.setThreshold(Level.ALL);
        }

        fa.setAppend(true);
        fa.activateOptions();

        //add appender to any Logger (here is root)
        Logger.getRootLogger().addAppender(fa);
    }
    //now logger configuration is done, we can start using it.
    Logger mainLogger = Logger.getLogger("gatekeeper-driver.Main");

    mainLogger.debug("Driver loaded properly");
    if (args.length > 0) {
        GKDriver gkDriver = new GKDriver(args[args.length - 1], 1, "Eq7K8h9gpg");
        System.out.println("testing if admin: " + gkDriver.isAdmin(1, 0));
        ArrayList<String> uList = gkDriver.getUserList(0); //the argument is the starting count of number of allowed
                                                           //internal attempts.
        if (uList != null) {
            mainLogger.info("Received user list from Gatekeeper! Count: " + uList.size());
            for (int i = 0; i < uList.size(); i++)
                mainLogger.info(uList.get(i));
        }

        boolean authResponse = gkDriver.simpleAuthentication(1, "Eq7K8h9gpg");
        if (authResponse)
            mainLogger.info("Authentication attempt was successful.");
        else
            mainLogger.warn("Authentication attempt failed!");

        String sName = "myservice-" + System.currentTimeMillis();
        HashMap<String, String> newService = gkDriver.registerService(sName, "this is my new cool service", 0);
        String sKey = "";
        if (newService != null) {
            mainLogger.info("Service registration was successful! Got:" + newService.get("uri") + ", Key="
                    + newService.get("key"));
            sKey = newService.get("key");
        } else {
            mainLogger.warn("Service registration failed!");
        }

        int newUserId = gkDriver.registerUser("user-" + System.currentTimeMillis(), "pass1234", false, sName,
                0);
        if (newUserId != -1)
            mainLogger.info("User registration was successful. Received new id: " + newUserId);
        else
            mainLogger.warn("User registration failed!");

        String token = gkDriver.generateToken(newUserId, "pass1234");
        boolean isValidToken = gkDriver.validateToken(token, newUserId);

        if (isValidToken)
            mainLogger.info("The token: " + token + " is successfully validated for user-id: " + newUserId);
        else
            mainLogger.warn("Token validation was unsuccessful! Token: " + token + ", user-id: " + newUserId);

        ArrayList<String> sList = gkDriver.getServiceList(0); //the argument is the starting count of number of allowed
                                                              //internal attempts.
        if (sList != null) {
            mainLogger.info("Received service list from Gatekeeper! Count: " + sList.size());
            for (int i = 0; i < sList.size(); i++)
                mainLogger.info(sList.get(i));
        }

        isValidToken = gkDriver.validateToken(token, sKey);
        if (isValidToken)
            mainLogger.info("The token: " + token + " is successfully validated for user-id: " + newUserId
                    + " against s-key:" + sKey);
        else
            mainLogger.warn("Token validation was unsuccessful! Token: " + token + ", user-id: " + newUserId
                    + ", s-key: " + sKey);

        boolean deleteResult = gkDriver.deleteUser(newUserId, 0);
        if (deleteResult)
            mainLogger.info("User with id: " + newUserId + " was deleted successfully.");
        else
            mainLogger.warn("User with id: " + newUserId + " could not be deleted successfully!");
    }
}

From source file:edu.oregonstate.eecs.mcplan.ml.LinearDiscriminantAnalysis.java

public static void main(final String[] args) throws FileNotFoundException {
    final File root = new File("test/LinearDiscriminantAnalysis");
    root.mkdirs();/*from  w w w.  j  av a2 s . c  o  m*/
    final int seed = 42;
    final int N = 30;
    final double shrinkage = 1e-6;

    final RandomGenerator rng = new MersenneTwister(seed);
    final Pair<ArrayList<double[]>, int[]> dataset = Datasets.twoVerticalGaussian2D(rng, N);
    final ArrayList<double[]> data = dataset.first;
    final int[] label = dataset.second;
    final int Nlabels = 2;
    final int[] shuffle_idx = Fn.linspace(0, Nlabels * N);
    Fn.shuffle(rng, shuffle_idx);
    final ArrayList<double[]> shuffled = new ArrayList<double[]>();
    final int[] shuffled_label = new int[label.length];
    for (int i = 0; i < data.size(); ++i) {
        shuffled.add(Fn.copy(data.get(shuffle_idx[i])));
        shuffled_label[i] = label[shuffle_idx[i]];
    }

    final Csv.Writer data_writer = new Csv.Writer(new PrintStream(new File(root, "data.csv")));
    for (final double[] v : data) {
        for (int i = 0; i < v.length; ++i) {
            data_writer.cell(v[i]);
        }
        data_writer.newline();
    }
    data_writer.close();

    System.out.println("[Training]");
    //      final KernelPrincipalComponentsAnalysis<RealVector> kpca
    //         = new KernelPrincipalComponentsAnalysis<RealVector>( shuffled, new RadialBasisFunctionKernel( 0.5 ), 1e-6 );
    final LinearDiscriminantAnalysis lda = new LinearDiscriminantAnalysis(shuffled, shuffled_label, Nlabels,
            shrinkage);
    System.out.println("[Finished]");
    for (final RealVector ev : lda.eigenvectors) {
        System.out.println(ev);
    }

    System.out.println("Transformed data:");
    final LinearDiscriminantAnalysis.Transformer transformer = lda.makeTransformer();
    final Csv.Writer transformed_writer = new Csv.Writer(new PrintStream(new File(root, "transformed.csv")));
    for (final double[] u : data) {
        final RealVector uvec = new ArrayRealVector(u);
        System.out.println(uvec);
        final RealVector v = transformer.transform(uvec);
        System.out.println("-> " + v);
        for (int i = 0; i < v.getDimension(); ++i) {
            transformed_writer.cell(v.getEntry(i));
        }
        transformed_writer.newline();
    }
    transformed_writer.close();
}