Example usage for java.util Collection contains

Introduction

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

Prototype

boolean contains(Object o);

Source Link

Document

Returns true if this collection contains the specified element.

Usage

From source file:podd.util.PoddWebappTestUtil.java

public static <T> void checkMembers(boolean checkGetterMethods, Set<String> skipList, Collection<T> collection,
        T... instances) {/*w w w  .  j a  v  a2  s  . c  o m*/
    if (null == instances) {
        assertTrue(collection.isEmpty());
    } else {
        assertEquals(instances.length, collection.size());
        for (T instance : instances) {
            assertTrue("Containing: " + instance, collection.contains(instance));
        }
        if (checkGetterMethods) {
            for (T obj1 : collection) {
                for (T obj2 : instances) {
                    if (obj1.equals(obj2)) {
                        checkGetterMethods(obj1, obj2, skipList);
                        break;
                    }
                }
            }
        }
    }
}

---

From source file:javadepchecker.Main.java

/**
 * Check if a dependency is needed by a given package
 *
 * @param pkg Gentoo package name/*from   w  w  w.j  a  va  2 s . c  o  m*/
 * @param deps collection of dependencies for the package
 * @return boolean if the dependency is needed or not
 * @throws IOException
 */
private static boolean depNeeded(String pkg, Collection<String> deps) throws IOException {
    Collection<String> jars = getPackageJars(pkg);

    // We have a virtual with VM provider here
    if (jars.isEmpty()) {
        return true;
    }
    for (String jarName : jars) {
        JarFile jar = new JarFile(jarName);
        for (Enumeration<JarEntry> e = jar.entries(); e.hasMoreElements();) {
            String name = e.nextElement().getName();
            if (deps.contains(name)) {
                return true;
            }
        }
    }
    return false;
}

---

From source file:com.cloudera.oryx.ml.mllib.als.AUC.java

static double areaUnderCurve(JavaSparkContext sparkContext, MatrixFactorizationModel mfModel,
        JavaRDD<Rating> positiveData) {

    // This does not use Spark's BinaryClassificationMetrics.areaUnderROC because it
    // is intended to operate on one large set of (score,label) pairs. The computation
    // here is really many small AUC problems, for which a much faster direct computation
    // is available.

    // Extract all positive (user,product) pairs
    JavaPairRDD<Integer, Integer> positiveUserProducts = positiveData
            .mapToPair(new PairFunction<Rating, Integer, Integer>() {
                @Override/*from  w w  w  .  ja  v a 2  s.com*/
                public Tuple2<Integer, Integer> call(Rating rating) {
                    return new Tuple2<>(rating.user(), rating.product());
                }
            });

    JavaPairRDD<Integer, Iterable<Rating>> positivePredictions = predictAll(mfModel, positiveData,
            positiveUserProducts);

    // All distinct item IDs, to be broadcast
    final Broadcast<List<Integer>> allItemIDsBC = sparkContext
            .broadcast(positiveUserProducts.values().distinct().collect());

    JavaPairRDD<Integer, Integer> negativeUserProducts = positiveUserProducts.groupByKey()
            .flatMapToPair(new PairFlatMapFunction<Tuple2<Integer, Iterable<Integer>>, Integer, Integer>() {
                private final RandomGenerator random = RandomManager.getRandom();

                @Override
                public Iterable<Tuple2<Integer, Integer>> call(
                        Tuple2<Integer, Iterable<Integer>> userIDsAndItemIDs) {
                    Integer userID = userIDsAndItemIDs._1();
                    Collection<Integer> positiveItemIDs = Sets.newHashSet(userIDsAndItemIDs._2());
                    int numPositive = positiveItemIDs.size();
                    Collection<Tuple2<Integer, Integer>> negative = new ArrayList<>(numPositive);
                    List<Integer> allItemIDs = allItemIDsBC.value();
                    int numItems = allItemIDs.size();
                    // Sample about as many negative examples as positive
                    for (int i = 0; i < numItems && negative.size() < numPositive; i++) {
                        Integer itemID = allItemIDs.get(random.nextInt(numItems));
                        if (!positiveItemIDs.contains(itemID)) {
                            negative.add(new Tuple2<>(userID, itemID));
                        }
                    }
                    return negative;
                }
            });

    JavaPairRDD<Integer, Iterable<Rating>> negativePredictions = predictAll(mfModel, positiveData,
            negativeUserProducts);

    return positivePredictions.join(negativePredictions).values()
            .mapToDouble(new DoubleFunction<Tuple2<Iterable<Rating>, Iterable<Rating>>>() {
                @Override
                public double call(Tuple2<Iterable<Rating>, Iterable<Rating>> t) {
                    // AUC is also the probability that random positive examples
                    // rank higher than random examples at large. Here we compare all random negative
                    // examples to all positive examples and report the totals as an alternative
                    // computation for AUC
                    long correct = 0;
                    long total = 0;
                    for (Rating positive : t._1()) {
                        for (Rating negative : t._2()) {
                            if (positive.rating() > negative.rating()) {
                                correct++;
                            }
                            total++;
                        }
                    }
                    return (double) correct / total;
                }
            }).mean();
}

---

From source file:com.cloudera.oryx.app.batch.mllib.als.Evaluation.java

/**
 * Computes AUC (area under the ROC curve) as a recommender evaluation metric.
 * Really, it computes what might be described as "Mean AUC", as it computes AUC per
 * user and averages them.//  w  ww  .jav a2 s.c om
 */
static double areaUnderCurve(JavaSparkContext sparkContext, MatrixFactorizationModel mfModel,
        JavaRDD<Rating> positiveData) {

    // This does not use Spark's BinaryClassificationMetrics.areaUnderROC because it
    // is intended to operate on one large set of (score,label) pairs. The computation
    // here is really many small AUC problems, for which a much faster direct computation
    // is available.

    // Extract all positive (user,product) pairs
    JavaPairRDD<Integer, Integer> positiveUserProducts = positiveData
            .mapToPair(rating -> new Tuple2<>(rating.user(), rating.product()));

    JavaPairRDD<Integer, Iterable<Rating>> positivePredictions = predictAll(mfModel, positiveData,
            positiveUserProducts);

    // All distinct item IDs, to be broadcast
    Broadcast<List<Integer>> allItemIDsBC = sparkContext
            .broadcast(positiveUserProducts.values().distinct().collect());

    JavaPairRDD<Integer, Integer> negativeUserProducts = positiveUserProducts.groupByKey()
            .flatMapToPair(new PairFlatMapFunction<Tuple2<Integer, Iterable<Integer>>, Integer, Integer>() {
                private final RandomGenerator random = RandomManager.getRandom();

                @Override
                public Iterable<Tuple2<Integer, Integer>> call(
                        Tuple2<Integer, Iterable<Integer>> userIDsAndItemIDs) {
                    Integer userID = userIDsAndItemIDs._1();
                    Collection<Integer> positiveItemIDs = Sets.newHashSet(userIDsAndItemIDs._2());
                    int numPositive = positiveItemIDs.size();
                    Collection<Tuple2<Integer, Integer>> negative = new ArrayList<>(numPositive);
                    List<Integer> allItemIDs = allItemIDsBC.value();
                    int numItems = allItemIDs.size();
                    // Sample about as many negative examples as positive
                    for (int i = 0; i < numItems && negative.size() < numPositive; i++) {
                        Integer itemID = allItemIDs.get(random.nextInt(numItems));
                        if (!positiveItemIDs.contains(itemID)) {
                            negative.add(new Tuple2<>(userID, itemID));
                        }
                    }
                    return negative;
                }
            });

    JavaPairRDD<Integer, Iterable<Rating>> negativePredictions = predictAll(mfModel, positiveData,
            negativeUserProducts);

    return positivePredictions.join(negativePredictions).values().mapToDouble(t -> {
        // AUC is also the probability that random positive examples
        // rank higher than random examples at large. Here we compare all random negative
        // examples to all positive examples and report the totals as an alternative
        // computation for AUC
        long correct = 0;
        long total = 0;
        for (Rating positive : t._1()) {
            for (Rating negative : t._2()) {
                if (positive.rating() > negative.rating()) {
                    correct++;
                }
                total++;
            }
        }
        if (total == 0) {
            return 0.0;
        }
        return (double) correct / total;
    }).mean();
}

---

From source file:fll.scheduler.TableOptimizer.java

/**
 * Check if any elements in set2 are in set1.
 *//*from  w ww .  j  a  va2 s  .c  o  m*/
private static boolean containsAny(final Collection<String> set1, final Collection<String> set2) {
    for (final String needle : set2) {
        if (set1.contains(needle)) {
            return true;
        }
    }
    return false;
}

---

From source file:de.tudarmstadt.ukp.dkpro.core.corenlp.internal.DKPro2CoreNlp.java

@SuppressWarnings("unchecked")
public static <T extends HasWord> List<T> applyPtbEscaping(List<T> words, Collection<String> quoteBegin,
        Collection<String> quoteEnd) {
    PTBEscapingProcessor<T, String, Word> escaper = new PTBEscapingProcessor<T, String, Word>();
    // Apply escaper to the whole sentence, not to each token individually. The
    // escaper takes context into account, e.g. when transforming regular double
    // quotes into PTB opening and closing quotes (`` and '').
    words = (List<T>) escaper.apply(words);

    for (HasWord w : words) {
        if (quoteBegin != null && quoteBegin.contains(w.word())) {
            w.setWord("``");
        } else if (quoteEnd != null && quoteEnd.contains(w.word())) {
            w.setWord("\'\'");
        }//from ww w . j av a2 s  .co m
    }

    return words;
}

---

From source file:com.cloudera.oryx.app.mllib.als.Evaluation.java

/**
 * Computes AUC (area under the ROC curve) as a recommender evaluation metric.
 * Really, it computes what might be described as "Mean AUC", as it computes AUC per
 * user and averages them.//ww  w .j av  a2  s  .c  o  m
 */
static double areaUnderCurve(JavaSparkContext sparkContext, MatrixFactorizationModel mfModel,
        JavaRDD<Rating> positiveData) {

    // This does not use Spark's BinaryClassificationMetrics.areaUnderROC because it
    // is intended to operate on one large set of (score,label) pairs. The computation
    // here is really many small AUC problems, for which a much faster direct computation
    // is available.

    // Extract all positive (user,product) pairs
    JavaPairRDD<Integer, Integer> positiveUserProducts = positiveData
            .mapToPair(new PairFunction<Rating, Integer, Integer>() {
                @Override
                public Tuple2<Integer, Integer> call(Rating rating) {
                    return new Tuple2<>(rating.user(), rating.product());
                }
            });

    JavaPairRDD<Integer, Iterable<Rating>> positivePredictions = predictAll(mfModel, positiveData,
            positiveUserProducts);

    // All distinct item IDs, to be broadcast
    final Broadcast<List<Integer>> allItemIDsBC = sparkContext
            .broadcast(positiveUserProducts.values().distinct().collect());

    JavaPairRDD<Integer, Integer> negativeUserProducts = positiveUserProducts.groupByKey()
            .flatMapToPair(new PairFlatMapFunction<Tuple2<Integer, Iterable<Integer>>, Integer, Integer>() {
                private final RandomGenerator random = RandomManager.getRandom();

                @Override
                public Iterable<Tuple2<Integer, Integer>> call(
                        Tuple2<Integer, Iterable<Integer>> userIDsAndItemIDs) {
                    Integer userID = userIDsAndItemIDs._1();
                    Collection<Integer> positiveItemIDs = Sets.newHashSet(userIDsAndItemIDs._2());
                    int numPositive = positiveItemIDs.size();
                    Collection<Tuple2<Integer, Integer>> negative = new ArrayList<>(numPositive);
                    List<Integer> allItemIDs = allItemIDsBC.value();
                    int numItems = allItemIDs.size();
                    // Sample about as many negative examples as positive
                    for (int i = 0; i < numItems && negative.size() < numPositive; i++) {
                        Integer itemID = allItemIDs.get(random.nextInt(numItems));
                        if (!positiveItemIDs.contains(itemID)) {
                            negative.add(new Tuple2<>(userID, itemID));
                        }
                    }
                    return negative;
                }
            });

    JavaPairRDD<Integer, Iterable<Rating>> negativePredictions = predictAll(mfModel, positiveData,
            negativeUserProducts);

    return positivePredictions.join(negativePredictions).values()
            .mapToDouble(new DoubleFunction<Tuple2<Iterable<Rating>, Iterable<Rating>>>() {
                @Override
                public double call(Tuple2<Iterable<Rating>, Iterable<Rating>> t) {
                    // AUC is also the probability that random positive examples
                    // rank higher than random examples at large. Here we compare all random negative
                    // examples to all positive examples and report the totals as an alternative
                    // computation for AUC
                    long correct = 0;
                    long total = 0;
                    for (Rating positive : t._1()) {
                        for (Rating negative : t._2()) {
                            if (positive.rating() > negative.rating()) {
                                correct++;
                            }
                            total++;
                        }
                    }
                    return (double) correct / total;
                }
            }).mean();
}

---

From source file:eu.ggnet.dwoss.report.assist.ReportUtil.java

/**
 * Returns all Lines of the Report for Category Invoiced.
 * This consists of://from  w w w  .  ja  v  a2 s .  co  m
 * <ul>
 * <li>Position of Type Capital Asset</li>
 * <li>Position of Type Invoice, with no References</li>
 * <li>Position of Type UNIT_ANNEX in DocumentType CREDIT_MEMO/ANNULATIION_INVOICE and a Referencing Invoice in the same report.</li>
 * </ul>
 * <p>
 * It's not allowed to have a null value in the collection.
 * <p>
 * @param lines
 * @return all Lines of the Report for Category Invoiced.
 */
//TODO: We could also substract the value of a unitannex from the invoice and not return the unit annex at all.
//But consider the impact in the ui, especially if we allow selection of such a "combined" line.
public static NavigableSet<ReportLine> filterInvoiced(Collection<ReportLine> lines) {
    NavigableSet<ReportLine> result = new TreeSet<>();
    for (ReportLine line : lines) {
        if (line.getDocumentType() == CAPITAL_ASSET)
            result.add(line);
        if (line.getDocumentType() == INVOICE && !line.isFullRepayedIn(lines))
            result.add(line);
        if (line.isPartialRepayment() && !line.isFullRepayedIn(lines)) {
            ReportLine invoiceRef = line.getSingleReference(INVOICE);
            if (invoiceRef == null)
                /*  No Invoice exists, probably before 2014 */ result.add(line);
            else if (lines.contains(invoiceRef))
                result.add(line);
        }
    }
    return result;
}

---

From source file:doc.action.SelectedDocsUtils.java

public static Collection saveSelectedDocsIDs(HttpServletRequest request) {
    //System.out.println("start");
    Collection documents = (Collection) request.getSession().getAttribute(Constant.SELECTED_PRESIDENTS);
    //System.out.println(documents);
    if (documents == null) {
        documents = new ArrayList();
        request.getSession().setAttribute(Constant.SELECTED_PRESIDENTS, documents);
    }//from   w ww  .  j a  v  a 2s .  co  m

    Enumeration parameterNames = request.getParameterNames();
    while (parameterNames.hasMoreElements()) {
        String parameterName = (String) parameterNames.nextElement();
        if (parameterName.startsWith("chkbx_")) {
            String docId = StringUtils.substringAfter(parameterName, "chkbx_");
            String parameterValue = request.getParameter(parameterName);
            if (parameterValue.equals(Constant.SELECTED)) {
                if (!documents.contains(docId)) {
                    documents.add(docId);
                }
            } else {
                documents.remove(docId);
            }
        }
    }

    return documents;
}

---

From source file:fr.certu.chouette.manager.AbstractNeptuneManager.java

/**
 * merge source collection in target one : <br/>
 * add source objects if not already in target
 * // www  .  j  a v a 2  s .  co m
 * @param <U>
 *           type of source and target entries
 * @param target
 *           collection to fill
 * @param source
 *           objects to add
 */
protected static <U> void mergeCollection(Collection<U> target, Collection<U> source) {
    if (source == null || source.isEmpty())
        return;
    for (U object : source) {
        if (!target.contains(object)) {
            target.add(object);
        }
    }
}

---

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