Example usage for java.util Arrays stream

List of usage examples for java.util Arrays stream

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Introduction

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

Prototype

public static DoubleStream stream(double[] array)

Source Link

Document

Returns a sequential DoubleStream with the specified array as its source.

Usage

From source file:org.shredzone.commons.gravatar.impl.GravatarServiceImpl.java

/**
 * Cleans up the gravatar cache. Oldest entries are deleted until cache size is
 * valid again.//from www .  j av  a2s.  co  m
 */
@Scheduled(fixedDelay = CACHE_CLEANUP)
public void cacheCleanup() {
    Arrays.stream(new File(cachePath).listFiles()).filter(file -> file.isFile() && !file.isHidden())
            .sorted(comparing(File::lastModified).reversed()) // younger files first
            .skip(MAX_CACHE_ENTRIES).forEach(this::delete);
}

From source file:me.rkfg.xmpp.bot.plugins.CoolStoryPlugin.java

@Override
public String processCommand(Message message, Matcher matcher) throws LogicException, ClientAuthException {
    try {//  www .  ja va  2s.  c  o m
        final String command = matcher.group(1);

        final Optional<Website> website = Arrays.stream(WEBSITES)
                .filter(w -> ArrayUtils.contains(w.getCommands(), command.toLowerCase())).findFirst();
        if (!website.isPresent()) {
            return ERROR_WEBSITE_NOT_SUPPORTED;
        }

        return fetchStory(website.get());

    } catch (Exception e) {
        logger.error("{}", e);
        return null;
    }
}

From source file:com.marklogic.entityservices.examples.ExamplesBase.java

public void importRDF(Path referenceDataDir, String collection) {

    logger.info("RDF Load Job started");

    WriteHostBatcher batcher = moveMgr.newWriteHostBatcher().withBatchSize(10).withThreadCount(1)
            .withTransform(new ServerTransform("turtle-to-xml"))
            .onBatchSuccess((client, batch) -> logger.info("Loaded rdf data batch"))
            .onBatchFailure((client, batch, throwable) -> {
                logger.error("FAILURE on batch:" + batch.toString() + "\n", throwable);
                System.err.println(throwable.getMessage());
                System.err.println(Arrays.stream(batch.getItems()).map(item -> item.getTargetUri())
                        .collect(Collectors.joining("\n")));
                // throwable.printStackTrace();
            });/*from w w  w  .ja va 2s  .c  o m*/
    ;
    ticket = moveMgr.startJob(batcher);

    importOrDescend(referenceDataDir, batcher, collection, Format.TEXT);

    batcher.flush();

}

From source file:delfos.rs.collaborativefiltering.als.ALSRecommender.java

@Override
public MatrixFactorizationModel buildRecommendationModel(DatasetLoader<? extends Rating> datasetLoader)
        throws CannotLoadRatingsDataset, CannotLoadContentDataset, CannotLoadUsersDataset {

    int numIter = 1;
    int dimension = 5;
    long seed = getSeedValue();

    final double lambda = 0.1;

    Bias bias = new Bias(datasetLoader);

    Map<User, List<Double>> randomUserVectors = datasetLoader.getUsersDataset().parallelStream()
            .collect(Collectors.toMap(user -> user, user -> {
                Random random = new Random(seed + user.getId());
                List<Double> vector = random.doubles(-10, 10).limit(dimension).boxed()
                        .collect(Collectors.toList());
                return vector;
            }));/* w  w w .  j a va 2s  .  co  m*/

    Map<Item, List<Double>> randomItemVectors = datasetLoader.getContentDataset().parallelStream()
            .collect(Collectors.toMap(item -> item, item -> {
                Random random = new Random(seed + item.getId());
                List<Double> vector = random.doubles(-10, 10).limit(dimension).boxed()
                        .collect(Collectors.toList());
                return vector;
            }));

    MatrixFactorizationModel model = new MatrixFactorizationModel(randomUserVectors, randomItemVectors, bias);

    for (int iterationIndex = 0; iterationIndex < numIter; iterationIndex++) {

        final int iteration = iterationIndex;
        final MatrixFactorizationModel initialModel = model;

        double error = getModelError(bias, datasetLoader, initialModel);

        System.out.println("Error in iteration " + iterationIndex + " is " + error);

        ProgressChangedController userProgress = new ProgressChangedController(
                getAlias() + " for dataset " + datasetLoader.getAlias() + " userOptimization iteration "
                        + iteration,
                datasetLoader.getUsersDataset().size(), this::fireBuildingProgressChangedEvent);

        Map<User, List<Double>> trainedUserVectors = datasetLoader.getUsersDataset().parallelStream()
                .collect(Collectors.toMap(user -> user, (User user) -> {
                    Map<Integer, ? extends Rating> userRatings = datasetLoader.getRatingsDataset()
                            .getUserRatingsRated(user.getId());

                    ObjectiveFunction objectiveFunction = new ObjectiveFunction((double[] pu) -> {
                        List<Double> userVector = Arrays.stream(pu).boxed().collect(Collectors.toList());
                        double predictionError = userRatings.values().parallelStream()
                                .map(bias.getBiasApplier()).map(rating -> {
                                    List<Double> itemVector = initialModel.getItemFeatures(rating.getItem());
                                    double prediction = IntStream.range(0, userVector.size())
                                            .mapToDouble(index -> userVector.get(index) * itemVector.get(index))
                                            .sum();

                                    double value = rating.getRatingValue().doubleValue();

                                    double errorThisRating = prediction - value;

                                    return errorThisRating;
                                }).map(value -> Math.pow(value, 2)).mapToDouble(value -> value).sum();

                        double penalty = Arrays.stream(pu).map(value -> Math.pow(value, 2)).sum();
                        double objectiveFunctionValue = predictionError + lambda * penalty;
                        return objectiveFunctionValue;
                    });

                    SimplexOptimizer simplexOptimizer = new SimplexOptimizer(0, 0);

                    double[] initialGuess = new Random(seed + user.getId()).doubles(-10, 10).limit(dimension)
                            .toArray();

                    List<Double> initialGuessList = Arrays.stream(initialGuess).boxed()
                            .collect(Collectors.toList());

                    double initialGuessPenalty = objectiveFunction.getObjectiveFunction().value(initialGuess);

                    try {
                        PointValuePair optimize = simplexOptimizer.optimize(
                                new MultiDirectionalSimplex(dimension), new InitialGuess(initialGuess),
                                objectiveFunction, GoalType.MINIMIZE, MAX_EVAL, MAX_ITER);
                        double optimizedPenalty = optimize.getValue();
                        userProgress.setTaskFinished();

                        List<Double> optimizedUserVector = Arrays.stream(optimize.getPoint()).boxed()
                                .collect(Collectors.toList());
                        return optimizedUserVector;
                    } catch (Exception ex) {
                        System.out.println("Vector cannot be optimized for user " + user + " (numRatings="
                                + userRatings.size() + ")");
                        return initialModel.getUserFeatures(user);
                    }
                }));

        ProgressChangedController itemProgress = new ProgressChangedController(
                getAlias() + " for dataset " + datasetLoader.getAlias() + " item optimization iteration "
                        + iteration,
                datasetLoader.getContentDataset().size(), this::fireBuildingProgressChangedEvent);

        Map<Item, List<Double>> trainedItemVectors = datasetLoader.getContentDataset().parallelStream()
                .collect(Collectors.toMap(item -> item, item -> {
                    Map<Integer, ? extends Rating> itemRatings = datasetLoader.getRatingsDataset()
                            .getItemRatingsRated(item.getId());

                    ObjectiveFunction objectiveFunction = new ObjectiveFunction((double[] pu) -> {
                        List<Double> itemVector = Arrays.stream(pu).boxed().collect(Collectors.toList());
                        double predictionError = itemRatings.values().parallelStream()
                                .map(bias.getBiasApplier()).map(rating -> {
                                    List<Double> userVector = initialModel.getUserFeatures(rating.getUser());
                                    double prediction = IntStream.range(0, userVector.size())
                                            .mapToDouble(index -> userVector.get(index) * itemVector.get(index))
                                            .sum();

                                    double value = rating.getRatingValue().doubleValue();

                                    double errorThisRating = prediction - value;

                                    return errorThisRating;
                                }).map(value -> Math.pow(value, 2)).mapToDouble(value -> value).sum();

                        double penalty = Arrays.stream(pu).map(value -> Math.pow(value, 2)).sum();
                        double objectiveFunctionValue = predictionError + lambda * penalty;
                        return objectiveFunctionValue;
                    });

                    SimplexOptimizer simplexOptimizer = new SimplexOptimizer(0, 0);

                    double[] initialGuess = new Random(seed + item.getId()).doubles(-10, 10).limit(dimension)
                            .toArray();

                    List<Double> initialGuessList = Arrays.stream(initialGuess).boxed()
                            .collect(Collectors.toList());

                    double initialGuessPenalty = objectiveFunction.getObjectiveFunction().value(initialGuess);

                    try {
                        PointValuePair optimize = simplexOptimizer.optimize(
                                new MultiDirectionalSimplex(dimension), new InitialGuess(initialGuess),
                                objectiveFunction, GoalType.MINIMIZE, MAX_EVAL, MAX_ITER);
                        double optimizedPenalty = optimize.getValue();
                        itemProgress.setTaskFinished();

                        List<Double> optimizedVector = Arrays.stream(optimize.getPoint()).boxed()
                                .collect(Collectors.toList());

                        return optimizedVector;
                    } catch (Exception ex) {
                        System.out.println("Vector cannot be optimized " + item
                                + " cannot be optimized (numRatings=" + itemRatings.size() + ")");
                        return initialModel.getItemFeatures(item);
                    }
                }));

        model = new MatrixFactorizationModel(trainedUserVectors, trainedItemVectors, bias);

    }
    return model;

}

From source file:io.promagent.internal.HookMetadataParser.java

/**
 * See {@link #parse()}./*from   www . ja va  2s  .c o m*/
 *
 * The classNameFilter is used to parse only specific classes from the JAR files.
 */
public SortedSet<HookMetadata> parse(Predicate<String> classNameFilter)
        throws IOException, ClassNotFoundException {
    SortedSet<HookMetadata> result = new TreeSet<>();
    for (String className : listAllJavaClasses(hookJars, classNameFilter)) {
        byte[] binaryRepresentation = readBinaryRepresentation(className);
        ClassReader classReader = new ClassReader(binaryRepresentation);
        HookMetadataBuilder hookMetadata = new HookMetadataBuilder(className);
        classReader.accept(new ClassVisitor(Opcodes.ASM5) {
            @Override
            public AnnotationVisitor visitAnnotation(String desc, boolean visible) {
                if (visible && typeEquals(desc, io.promagent.annotations.Hook.class)) {
                    return new AnnotationValueCollector("instruments", hookMetadata::addInstruments,
                            Opcodes.ASM5, super.visitAnnotation(desc, visible));
                } else {
                    return super.visitAnnotation(desc, visible);
                }
            }

            @Override
            public MethodVisitor visitMethod(int i, String method, String desc, String signature,
                    String[] strings) {
                List<String> parameterTypes = Arrays.stream(Type.getArgumentTypes(desc)).map(Type::getClassName)
                        .collect(Collectors.toList());
                MethodSignatureBuilder builder = hookMetadata.newMethodSignature(parameterTypes);
                return new MethodVisitor(Opcodes.ASM5, super.visitMethod(i, method, desc, signature, strings)) {
                    @Override
                    public AnnotationVisitor visitParameterAnnotation(int parameter, String desc,
                            boolean visible) {
                        if (visible && typeEquals(desc, Returned.class, Thrown.class)) {
                            builder.markReturnedOrThrown(parameter);
                        }
                        return super.visitParameterAnnotation(parameter, desc, visible);
                    }

                    @Override
                    public AnnotationVisitor visitAnnotation(String desc, boolean visible) {
                        if (visible && typeEquals(desc, Before.class, After.class)) {
                            return new AnnotationValueCollector("method", builder::addMethodName, Opcodes.ASM5,
                                    super.visitAnnotation(desc, visible));
                        } else {
                            return super.visitAnnotation(desc, visible);
                        }
                    }
                };
            }

        }, ClassReader.SKIP_CODE);
        if (hookMetadata.isComplete()) {
            result.add(hookMetadata.build());
        }
    }
    return result;
}

From source file:com.github.erchu.beancp.MapperBuilder.java

/**
 * If two data types has no mapping defined by
 * {@link #addMap(java.lang.Class, java.lang.Class, com.github.erchu.beancp.DeclarativeMapSetup)}
 * or any of {@code addConverter} methods then this convention will be used.
 *
 * @param conventions convention to add.
 *
 * @return this (for method chaining)/*from  ww w .jav a2 s  . c o m*/
 */
public MapperBuilder addMapAnyByConvention(final MapConvention... conventions)
        throws MapperConfigurationException {
    List<MapConventionExecutor> conventionExecutors = Arrays.stream(conventions)
            .map(i -> new MapConventionExecutor(i)).collect(Collectors.toList());

    this._mapAnyConventions.addAll(conventionExecutors);

    return this;
}

From source file:com.thinkbiganalytics.metadata.modeshape.security.role.JcrAbstractRoleMembership.java

@Override
public void setMemebers(GroupPrincipal... principals) {
    Set<GroupPrincipal> newMembers = Arrays.stream(principals).collect(Collectors.toSet());
    Set<GroupPrincipal> oldMembers = streamGroups().collect(Collectors.toSet());

    newMembers.stream().filter(u -> !oldMembers.contains(u)).forEach(this::addMember);

    oldMembers.stream().filter(u -> !newMembers.contains(u)).forEach(this::removeMember);
}

From source file:com.synopsys.integration.blackduck.service.model.PolicyStatusDescription.java

private void getPolicySeverityMessage(final StringBuilder stringBuilder) {
    stringBuilder.append("Policy Severity counts: ");
    // let's loop over the actual enum values for a consistently ordered output
    final String policySeverityItems = Arrays.stream(PolicySeverityType.values())
            .filter(policySeverityCount::containsKey)
            .map(policySeverityType -> fixMatchPlural("%d %s a severity level of %s",
                    policySeverityCount.get(policySeverityType).value, policySeverityType))
            .collect(Collectors.joining(", "));
    stringBuilder.append(policySeverityItems);
}

From source file:com.thinkbiganalytics.schema.QueryRunner.java

/**
 * Tests that the specified query is a SHOW, SELECT, DESC, or DESCRIBE query.
 *
 * @param query the query to test/*from  w w  w . ja v a2  s .co m*/
 * @return {@code true} if the query is valid, or {@code false} otherwise
 */
private boolean validateQuery(@Nonnull final String query) {
    final String testQuery = StringUtils.trimToEmpty(query);
    return Arrays.stream(new String[] { "show", "select", "desc", "describe" })
            .anyMatch(prefix -> StringUtils.startsWithIgnoreCase(testQuery, prefix));
}

From source file:com.netflix.spinnaker.halyard.deploy.spinnaker.v1.service.distributed.kubernetes.v2.KubernetesV2Utils.java

static public boolean isReady(KubernetesAccount account, String namespace, String service) {
    log.info("Checking readiness for " + service);
    List<String> command = kubectlPrefix(account);

    if (StringUtils.isNotEmpty(namespace)) {
        command.add("-n=" + namespace);
    }//from  w w  w .j  a v a  2  s . c  o  m

    command.add("get");
    command.add("po");

    command.add("-l=cluster=" + service);
    command.add("-o=jsonpath='{.items[*].status.containerStatuses[*].ready}'");
    // This command returns a space-separated string of true/false values indicating whether each of
    // the pod's containers are READY.
    // e.g., if we are querying two spin-orca pods and both pods' monitoring-daemon containers are
    // READY but the orca containers are not READY, the output may be 'true false true false'.

    JobRequest request = new JobRequest().setTokenizedCommand(command);

    String jobId = DaemonTaskHandler.getJobExecutor().startJob(request);

    JobStatus status;
    try {
        status = DaemonTaskHandler.getJobExecutor().backoffWait(jobId);
    } catch (InterruptedException e) {
        throw new DaemonTaskInterrupted(e);
    }

    if (status.getState() != JobStatus.State.COMPLETED) {
        throw new HalException(Problem.Severity.FATAL,
                String.join("\n", "Unterminated readiness check for " + service + " in " + namespace,
                        status.getStdErr(), status.getStdOut()));
    }

    if (status.getResult() == JobStatus.Result.SUCCESS) {
        String readyStatuses = status.getStdOut();
        if (readyStatuses.isEmpty()) {
            return false;
        }
        readyStatuses = readyStatuses.substring(1, readyStatuses.length() - 1); // Strip leading and trailing single quote
        if (readyStatuses.isEmpty()) {
            return false;
        }
        return Arrays.stream(readyStatuses.split(" ")).allMatch(s -> s.equals("true"));
    } else {
        throw new HalException(Problem.Severity.FATAL,
                String.join("\n", "Failed readiness check for " + service + " in " + namespace,
                        status.getStdErr(), status.getStdOut()));
    }
}