Example usage for com.google.common.collect HashBasedTable create

Introduction

In this page you can find the example usage for com.google.common.collect HashBasedTable create.

Prototype

public static <R, C, V> HashBasedTable<R, C, V> create() 

Source Link

Document

Creates an empty HashBasedTable .

Usage

From source file:com.urey.flume.source.taildir.ReliableTaildirEventReader.java

/**
 * Create a ReliableTaildirEventReader to watch the given directory.
 *//*from w w  w .  j ava2  s  .  c om*/
private ReliableTaildirEventReader(Map<String, String> filePaths, Table<String, String, String> headerTable,
        String positionFilePath, boolean skipToEnd, boolean addByteOffset, boolean recursiveDirectorySearch,
        boolean annotateYarnApplicationId, boolean annotateYarnContainerId, boolean annotateFileName,
        String fileNameHeader) throws IOException {
    // Sanity checks
    Preconditions.checkNotNull(filePaths);
    Preconditions.checkNotNull(positionFilePath);

    if (logger.isDebugEnabled()) {
        logger.debug("Initializing {} with directory={}, metaDir={}",
                new Object[] { ReliableTaildirEventReader.class.getSimpleName(), filePaths });
    }

    Table<String, File, Pattern> tailFileTable = HashBasedTable.create();
    for (Entry<String, String> e : filePaths.entrySet()) {
        File f = new File(e.getValue());
        File parentDir = f.getParentFile();
        Preconditions.checkState(parentDir.exists(),
                "Directory does not exist: " + parentDir.getAbsolutePath());
        Pattern fileNamePattern = Pattern.compile(f.getName());
        tailFileTable.put(e.getKey(), parentDir, fileNamePattern);
    }
    logger.info("tailFileTable: " + tailFileTable.toString());
    logger.info("headerTable: " + headerTable.toString());

    this.tailFileTable = tailFileTable;
    this.headerTable = headerTable;
    this.addByteOffset = addByteOffset;

    this.recursiveDirectorySearch = recursiveDirectorySearch;
    this.annotateYarnApplicationId = annotateYarnApplicationId;
    this.annotateYarnContainerId = annotateYarnContainerId;

    this.annotateFileName = annotateFileName;
    this.fileNameHeader = fileNameHeader;

    updateTailFiles(skipToEnd);

    logger.info("Updating position from position file: " + positionFilePath);
    loadPositionFile(positionFilePath);
}

---

From source file:mtsar.processors.answer.KOSAggregator.java

@Override
@Nonnull/*  www  .  j av a2  s .co m*/
public Map<Integer, AnswerAggregation> aggregate(@Nonnull Collection<Task> tasks) {
    requireNonNull(stage, "the stage provider should not provide null");
    checkArgument(tasks.stream().allMatch(SINGLE_BINARY_TYPE),
            "tasks should be of the type single and have only two possible answers");
    if (tasks.isEmpty())
        return Collections.emptyMap();

    final List<Answer> answers = answerDAO.listForStage(stage.getId());
    if (answers.isEmpty())
        return Collections.emptyMap();

    final Map<Integer, Task> taskMap = taskDAO.listForStage(stage.getId()).stream().filter(SINGLE_BINARY_TYPE)
            .collect(Collectors.toMap(Task::getId, Function.identity()));

    final Map<Integer, BiMap<String, Short>> answerIndex = taskMap.values().stream()
            .collect(Collectors.toMap(Task::getId, task -> {
                final BiMap<String, Short> map = HashBiMap.create(2);
                map.put(task.getAnswers().get(0), (short) -1);
                map.put(task.getAnswers().get(1), (short) +1);
                return map;
            }));

    /* rows are tasks IDs, columns are worker IDs, values are answers */
    final Table<Integer, Integer, Short> graph = HashBasedTable.create();

    for (final Answer answer : answers) {
        if (!answer.getType().equalsIgnoreCase(AnswerDAO.ANSWER_TYPE_ANSWER))
            continue;
        graph.put(answer.getTaskId(), answer.getWorkerId(),
                answerIndex.get(answer.getTaskId()).get(answer.getAnswers().get(0)));
    }

    final Map<Integer, Double> estimations = converge(graph, getKMax());
    return estimations.entrySet().stream().collect(Collectors.toMap(Map.Entry::getKey, estimation -> {
        final String answer = answerIndex.get(estimation.getKey()).inverse()
                .get(estimation.getValue() < 0 ? (short) -1 : (short) +1);
        return new AnswerAggregation.Builder().setTask(taskMap.get(estimation.getKey())).addAnswers(answer)
                .build();
    }));
}

---

From source file:es.upm.dit.xsdinferencer.statistics.ComplexTypeStatisticsEntry.java

/**
 * Default constructor/*from  w ww  . j ava 2s.com*/
 * @param inputDocumentsCount how many input documents are there
 */
public ComplexTypeStatisticsEntry(int inputDocumentsCount) {
    this.inputDocumentsCount = inputDocumentsCount;
    this.elementInfo = new HashMap<SchemaElement, BasicStatisticsEntry>();
    this.subpatternsInfo = new HashMap<List<SchemaElement>, Integer>();
    this.attributeOccurrencesInfo = new HashMap<SchemaAttribute, BasicStatisticsEntry>();
    this.valuesInfo = HashBasedTable.create();
    this.lastAllValuesTablesHash = valuesInfo.hashCode();
}

---

From source file:com.rackspacecloud.blueflood.io.astyanax.AEnumIO.java

/**
 * Read the metrics_preaggregated_{granularity} for specific locators. Organize
 * the data as a table of locator, timestamp, and enum object
 *
 * @param locators/*from ww  w  .  j  av  a2  s  .  c  o  m*/
 * @param columnFamily
 * @return
 */
@Override
public Table<Locator, Long, BluefloodEnumRollup> getEnumRollupsForLocators(List<Locator> locators,
        String columnFamily, Range range) {
    AstyanaxReader reader = AstyanaxReader.getInstance();
    Map<Locator, ColumnList<Long>> locatorToColumnList = reader.getColumnsFromDB(locators,
            CassandraModel.getColumnFamily(columnFamily), range);
    Table<Locator, Long, BluefloodEnumRollup> locatorHashRollup = HashBasedTable.create();

    for (Map.Entry<Locator, ColumnList<Long>> entry : locatorToColumnList.entrySet()) {
        ColumnList<Long> columnList = entry.getValue();
        for (Column<Long> column : columnList) {
            locatorHashRollup.put(entry.getKey(), column.getName(),
                    column.getValue(Serializers.enumRollupInstance));
        }
    }
    return locatorHashRollup;
}

---

From source file:org.apache.sentry.provider.db.generic.UpdatableCache.java

/**
 * Build cache replica with latest values
 *
 * @return cache replica with latest values
 *//*from  ww w  .  j  a  v  a2 s  . c  o  m*/
private Table<String, String, Set<String>> loadFromRemote() throws Exception {
    Table<String, String, Set<String>> tempCache = HashBasedTable.create();
    String requestor;
    requestor = UserGroupInformation.getLoginUser().getShortUserName();

    final SentryGenericServiceClient client = getClient();
    final Set<TSentryRole> tSentryRoles = client.listAllRoles(requestor, componentType);

    for (TSentryRole tSentryRole : tSentryRoles) {
        final String roleName = tSentryRole.getRoleName();
        final Set<TSentryPrivilege> tSentryPrivileges = client.listPrivilegesByRoleName(requestor, roleName,
                componentType, serviceName);
        for (String group : tSentryRole.getGroups()) {
            Set<String> currentPrivileges = tempCache.get(group, roleName);
            if (currentPrivileges == null) {
                currentPrivileges = new HashSet<>();
                tempCache.put(group, roleName, currentPrivileges);
            }
            for (TSentryPrivilege tSentryPrivilege : tSentryPrivileges) {
                currentPrivileges.add(tSentryPrivilegeConverter.toString(tSentryPrivilege));
            }
        }
    }
    return tempCache;
}

---

From source file:de.uni_potsdam.hpi.asg.logictool.synthesis.model.EspressoTable.java

public static EspressoTable importFromFile(File file) {
    List<String> lines = FileHelper.getInstance().readFile(file);
    if (lines == null) {
        logger.error("Could not read-in results from espresso");
        return null;
    }/*w  ww . java  2 s  .c  o m*/

    String[] inputs = null;
    String[] outputs = null;
    Table<EspressoTerm, String, EspressoValue> table = HashBasedTable.create();

    Matcher m = null;
    for (String str : lines) {
        m = tablePattern.matcher(str);
        if (m.matches()) {
            if (outputs != null && inputs != null) {
                EspressoTerm t = new EspressoTerm(m.group(1), inputs);
                int i = 0;
                for (String out : outputs) {
                    switch (m.group(2).charAt(i)) {
                    case '0':
                        table.put(t, out, EspressoValue.zero);
                        break;
                    case '1':
                        table.put(t, out, EspressoValue.one);
                        break;
                    case '-':
                        table.put(t, out, EspressoValue.dontcare);
                        break;
                    default:
                        System.err.println("Unknown char in table: " + m.group(2).charAt(i));
                    }
                    i++;
                }
            } else {
                System.err.println("No in/outputs");
                return null;
            }
            continue;
        }
        m = inputsPattern.matcher(str);
        if (m.matches()) {
            continue;
        }
        m = outputsPattern.matcher(str);
        if (m.matches()) {
            continue;
        }
        m = inputNamePattern.matcher(str);
        if (m.matches()) {
            inputs = m.group(1).split(" ");
            continue;
        }
        m = outputNamePattern.matcher(str);
        if (m.matches()) {
            outputs = m.group(1).split(" ");
            continue;
        }
        m = tableEntriesPattern.matcher(str);
        if (m.matches()) {
            continue;
        }
        m = endPattern.matcher(str);
        if (m.matches()) {
            break;
        }

        System.err.println("No match: " + str);
    }

    return new EspressoTable(inputs, table);
}

---

From source file:net.librec.recommender.cf.rating.GPLSARecommender.java

@Override
protected void setup() throws LibrecException {
    super.setup();

    numTopics = conf.getInt("rec.topic.number", 10);

    // Initialize users' conditional probabilities
    userTopicProbs = new DenseMatrix(numUsers, numTopics);
    for (int u = 0; u < numUsers; u++) {
        double[] probs = Randoms.randProbs(numTopics);
        for (int z = 0; z < numTopics; z++) {
            userTopicProbs.set(u, z, probs[z]);
        }//from  ww  w  . jav a2s.  c  om
    }

    double mean = trainMatrix.mean();
    double sd = Stats.sd(trainMatrix.getData(), mean);

    userMu = new DenseVector(numUsers);
    userSigma = new DenseVector(numUsers);
    smoothWeight = conf.getInt("rec.recommender.smoothWeight");
    for (int u = 0; u < numUsers; u++) {
        SparseVector userRow = trainMatrix.row(u);
        int numRatings = userRow.size();
        if (numRatings < 1) {
            continue;
        }

        // Compute mu_u
        double mu_u = (userRow.sum() + smoothWeight * mean) / (numRatings + smoothWeight);
        userMu.set(u, mu_u);

        // Compute sigma_u
        double sum = 0;
        for (VectorEntry ve : userRow) {
            sum += Math.pow(ve.get() - mu_u, 2);
        }
        sum += smoothWeight * Math.pow(sd, 2);
        double sigma_u = Math.sqrt(sum / (numRatings + smoothWeight));
        userSigma.set(u, sigma_u);
    }

    // Initialize Q
    Q = HashBasedTable.create();

    for (MatrixEntry trainMatrixEntry : trainMatrix) {
        int userIdx = trainMatrixEntry.row();
        int itemIdx = trainMatrixEntry.column();
        double rating = trainMatrixEntry.get();

        double r = (rating - userMu.get(userIdx)) / userSigma.get(userIdx);
        trainMatrix.set(userIdx, itemIdx, r);

        Q.put(userIdx, itemIdx, new HashMap<Integer, Double>());
    }

    // Initialize Mu, Sigma
    topicItemMu = new DenseMatrix(numItems, numTopics);
    topicItemSigma = new DenseMatrix(numItems, numTopics);
    for (int i = 0; i < numItems; i++) {
        SparseVector itemColumn = trainMatrix.column(i);
        int numRatings = itemColumn.size();
        if (numRatings < 1) {
            continue;
        }

        double mu_i = itemColumn.mean();

        double sum = 0;
        for (VectorEntry ve : itemColumn) {
            sum += Math.pow(ve.get() - mu_i, 2);
        }

        double sd_i = Math.sqrt(sum / numRatings);

        for (int z = 0; z < numTopics; z++) {
            topicItemMu.set(i, z, mu_i + smallValue * Randoms.uniform());
            topicItemSigma.set(i, z, sd_i + smallValue * Randoms.uniform());
        }
    }
}

---

From source file:net.librec.recommender.cf.rating.URPRecommender.java

@Override
protected void setup() throws LibrecException {
    super.setup();
    numTopics = conf.getInt("rec.pgm.number", 10);
    numRatingLevels = trainMatrix.getValueSet().size();

    // cumulative parameters
    userTopicSumProbs = new DenseMatrix(numUsers, numTopics);
    topicItemRatingSumProbs = new double[numTopics][numItems][numRatingLevels];

    // initialize count variables
    userTopicNum = new DenseMatrix(numUsers, numTopics);
    userNum = new DenseVector(numUsers);

    topicItemRatingNum = new int[numTopics][numItems][numRatingLevels];
    topicItemNum = new DenseMatrix(numTopics, numItems);

    alpha = new DenseVector(numTopics);
    double initAlpha = conf.getDouble("rec.pgm.bucm.alpha", 1.0 / numTopics);
    alpha.setAll(initAlpha);//from www . j  a v  a2s .c  o  m

    beta = new DenseVector(numRatingLevels);
    double initBeta = conf.getDouble("rec.pgm.bucm.beta", 1.0 / numTopics);
    beta.setAll(initBeta);

    // initialize topics
    topics = HashBasedTable.create();
    for (MatrixEntry me : trainMatrix) {
        int u = me.row();
        int i = me.column();
        double rui = me.get();

        int r = ratingScale.indexOf(rui); // rating level 0 ~ numLevels
        int t = (int) (Randoms.uniform() * numTopics); // 0 ~ k-1

        // Assign a topic t to pair (u, i)
        topics.put(u, i, t);
        // number of pairs (u, t) in (u, i, t)
        userTopicNum.add(u, t, 1);
        // total number of items of user u
        userNum.add(u, 1);

        // number of pairs (t, i, r)
        topicItemRatingNum[t][i][r]++;
        // total number of words assigned to topic t
        topicItemNum.add(t, i, 1);
    }
}

---

From source file:rapture.sheet.memory.InMemorySheet.java

public void setCell(int dim, int row, int col, String val) {
    Table<Integer, Integer, RaptureSheetCell> table = dimToData.get(dim);
    if (table == null) {
        table = HashBasedTable.create();
        dimToData.put(dim, table);/*from  w  ww.j  av a 2  s. c  o  m*/
    }

    setCell(table, row, col, val);

}

---

From source file:co.cask.cdap.internal.app.AbstractInMemoryProgramRunner.java

protected Table<String, Integer, ProgramController> startPrograms(Program program, RunId runId,
        ProgramOptions options, ProgramRunnerFactory.Type type, int numInstances) {
    Table<String, Integer, ProgramController> components = HashBasedTable.create();
    try {//  w  w  w  .j  av a2 s. c  o  m
        startComponent(program, program.getName(), numInstances, runId, options, components, type);
    } catch (Throwable t) {
        LOG.error("Failed to start all program instances", t);
        try {
            // Need to stop all started components
            Futures.successfulAsList(Iterables.transform(components.values(),
                    new Function<ProgramController, ListenableFuture<?>>() {
                        @Override
                        public ListenableFuture<?> apply(ProgramController controller) {
                            return controller.stop();
                        }
                    })).get();
            throw Throwables.propagate(t);
        } catch (Exception e) {
            LOG.error("Failed to stop all program instances upon startup failure.", e);
            throw Throwables.propagate(e);
        }
    }
    return components;
}

---

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