Example usage for org.apache.commons.math3.distribution IntegerDistribution sample

Introduction

In this page you can find the example usage for org.apache.commons.math3.distribution IntegerDistribution sample.

Prototype

int sample();

Source Link

Document

Generate a random value sampled from this distribution.

Usage

From source file:io.coala.math3.Math3ProbabilityDistribution.java

@SafeVarargs
public static <S> Math3ProbabilityDistribution<Long> of(final IntegerDistribution dist, //final PseudoRandom stream,
        final S... args) {
    Objects.requireNonNull(dist);
    final Math3ProbabilityDistribution<Long> result = new Math3ProbabilityDistribution<Long>() {
        @Override//www  .j  a v a  2s.c  om
        public Long draw() {
            return Long.valueOf(dist.sample());
        }
    };
    //      result.stream = stream;
    return result;
}

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From source file:net.myrrix.online.som.SelfOrganizingMaps.java

/**
 * @return map of initialized {@link Node}s, where each node is empty and initialized to a randomly chosen
 *  input vector normalized to unit length
 *///from www. j  av  a2  s .c  o m
private static Node[][] buildInitialMap(FastByIDMap<float[]> vectors, int mapSize) {

    double p = ((double) mapSize * mapSize) / vectors.size(); // Choose mapSize^2 out of # vectors
    IntegerDistribution pascalDistribution;
    if (p >= 1.0) {
        // No sampling at all, we can't fill the map with one pass even
        pascalDistribution = null;
    } else {
        // Number of un-selected elements to skip between selections is geometrically distributed with
        // parameter p; this is the same as a negative binomial / Pascal distribution with r=1:
        pascalDistribution = new PascalDistribution(RandomManager.getRandom(), 1, p);
    }

    LongPrimitiveIterator keyIterator = vectors.keySetIterator();
    Node[][] map = new Node[mapSize][mapSize];
    for (Node[] mapRow : map) {
        for (int j = 0; j < mapSize; j++) {
            if (pascalDistribution != null) {
                keyIterator.skip(pascalDistribution.sample());
            }
            while (!keyIterator.hasNext()) {
                keyIterator = vectors.keySetIterator(); // Start over, a little imprecise but affects it not much
                Preconditions.checkState(keyIterator.hasNext());
                if (pascalDistribution != null) {
                    keyIterator.skip(pascalDistribution.sample());
                }
            }
            float[] sampledVector = vectors.get(keyIterator.nextLong());
            mapRow[j] = new Node(sampledVector);
        }
    }
    return map;
}

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From source file:com.vmware.photon.controller.rootscheduler.simulator.CloudStoreLoader.java

/**
 * Creates host documents in cloudstore.
 *
 * @param cloudstore CloudStore test environment to create documents in.
 * @param numHosts The number of host documents to create.
 * @param hostConfigurations A map from {@link HostConfiguration} to the probability that this
 *                           host configuration is used in the deployment. The sum of all the
 *                           values of this map must be 1.
 * @param numDatastores The number of datastores.
 * @param numDatastoresDistribution Distribution for number of datastores on each host. This
 *                                  distribution is expected to generate samples in the range
 *                                  [0, numDatastores].
 * @throws Throwable/*from   w  w  w  .j  av a  2 s  .c om*/
 */
public static void loadHosts(TestEnvironment cloudstore, int numHosts,
        Map<HostConfiguration, Double> hostConfigurations, int numDatastores,
        IntegerDistribution numDatastoresDistribution) throws Throwable {
    int[] indices = new int[hostConfigurations.size()];
    HostConfiguration[] configs = new HostConfiguration[hostConfigurations.size()];
    double[] probabilities = new double[hostConfigurations.size()];
    int i = 0;
    for (Map.Entry<HostConfiguration, Double> entry : hostConfigurations.entrySet()) {
        indices[i] = i;
        configs[i] = entry.getKey();
        probabilities[i] = entry.getValue();
        i++;
    }
    EnumeratedIntegerDistribution configDistribution = new EnumeratedIntegerDistribution(indices,
            probabilities);
    for (i = 0; i < numHosts; i++) {
        HostService.State host = new HostService.State();
        host.hostAddress = "host" + i;
        host.state = HostState.READY;
        host.userName = "username";
        host.password = "password";
        host.reportedDatastores = new HashSet<>();
        int numDatastoresPerHost = numDatastoresDistribution.sample();
        assertThat(numDatastoresPerHost >= 0, is(true));
        assertThat(numDatastoresPerHost <= numDatastores, is(true));
        while (host.reportedDatastores.size() < numDatastoresPerHost) {
            int randomInt = random.nextInt(numDatastores);
            host.reportedDatastores.add(new UUID(0, randomInt).toString());
        }
        host.reportedNetworks = new HashSet<>();
        host.usageTags = new HashSet<>(Arrays.asList(UsageTag.CLOUD.name()));
        int configIndex = configDistribution.sample();
        host.cpuCount = configs[configIndex].numCpus;
        host.memoryMb = configs[configIndex].memoryMb;
        host.documentSelfLink = new UUID(0, i).toString();
        // TODO(mmutsuzaki) Support availability zones.
        Operation result = cloudstore.sendPostAndWait(HostServiceFactory.SELF_LINK, host);
        assertThat(result.getStatusCode(), is(200));
        logger.debug("Created a host document: {}", Utils.toJson(true, false, host));
    }
}

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From source file:lda.inference.internal.CollapsedGibbsSampler.java

/**
 * Run collapsed Gibbs sampling [Griffiths and Steyvers 2004].
 *//*from w  ww .  j  av  a  2 s .c o m*/
void runSampling() {
    for (Document d : documents.getDocuments()) {
        for (int w = 0; w < d.getDocLength(); ++w) {
            final Topic oldTopic = topics.get(d.getTopicID(w));
            d.decrementTopicCount(oldTopic.id());

            final Vocabulary v = d.getVocabulary(w);
            oldTopic.decrementVocabCount(v.id());

            IntegerDistribution distribution = getFullConditionalDistribution(lda.getNumTopics(), d.id(),
                    v.id());

            final int newTopicID = distribution.sample();
            d.setTopicID(w, newTopicID);

            d.incrementTopicCount(newTopicID);
            final Topic newTopic = topics.get(newTopicID);
            newTopic.incrementVocabCount(v.id());
        }
    }
}

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From source file:fr.inria.atlanmod.dag.instantiator.DagGenerator.java

protected void generateManyContainmentReference(EObject eObject, EReference eReference,
        ListMultimap<EClass, String> indexByKind, ImmutableMultiset<EClass> eAllConcreteSubTypesOrSelf,
        IntegerDistribution distribution) {

    @SuppressWarnings("unchecked")
    List<EObject> values = (List<EObject>) eObject.eGet(eReference);
    verticesSize = distribution.sample();
    LOGGER.fine(MessageFormat.format("Generating {0} values for EReference ''{1}'' in EObject {2}",
            verticesSize, eReference.getName(), eObject.toString()));
    IntegerDistribution distributionId = configuration.getDistributionFor(dagPck.getVertex_Id());
    for (int i = 0; i < verticesSize; i++) {
        final Optional<EObject> nextEObject = generateVertex(vertexClass, indexByKind, distributionId);
        if (nextEObject.isPresent()) {
            values.add(nextEObject.get());
        }/*from www. j  ava2  s.  com*/
    }
}

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From source file:fr.obeo.emf.specimen.SpecimenGenerator.java

private boolean booleanInDistribution(IntegerDistribution distribution) {
    int sample = distribution.sample();
    return sample < distribution.getNumericalMean();
}

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From source file:fr.inria.atlanmod.dag.instantiator.DagGenerator.java

@SuppressWarnings("unchecked")
protected void generateCrossReferencesForVertex(EObject eObject, ListMultimap<EClass, String> indexByKind) {

    EReference eReference = dagPck.getVertex_Outgoing();

    //EClass eReferenceType = eReference.getEReferenceType();
    IntegerDistribution distribution = configuration.getDistributionFor(eReference);

    //List<Object> values = (List<Object>) eObject.eGet(eReference);
    int sample = distribution.sample();
    LOGGER.fine(MessageFormat.format("Generating {0} values for EReference ''{1}'' in EObject {2}", sample,
            eReference.getName(), eObject.toString()));
    for (int i = 0; i < Math.min(sample, verticesSize - i); i++) {
        // check the edge does not already exist
        IntegerDistribution distId = configuration.getDistributionFor(dagPck.getEdge_Id());
        EObject nextEObject = null;//from ww w . j av  a 2s .  c om
        int count = verticesSize - i;
        while (count-- > 0) {// limiting max iteration to the number of remaining elements 
            nextEObject = ((List<EObject>) rootObject.eGet(dagPck.getDAG_Vertices()))
                    .get(generator.nextInt(verticesSize - i) + i);
            if (!existingEdge((Vertex) eObject, (Vertex) nextEObject))
                break;

        }
        if (nextEObject != null && !nextEObject.equals(eObject)) {
            Optional<Edge> edge = generateEdge(eObject, nextEObject, distId);
            if (edge.isPresent()) {
                //values.add(edge.get());
                ((List<EObject>) rootObject.eGet(dagPck.getDAG_Edges())).add(edge.get());
            }
        }
    }
}

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From source file:fr.obeo.emf.specimen.DirectWriteSpecimenGenerator.java

protected boolean booleanInDistribution(IntegerDistribution distribution) {
    int sample = distribution.sample();
    return sample <= distribution.getNumericalMean();
}

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From source file:eu.opensourceprojects.mondo.benchmarks.transformationzoo.instantiator.SpecimenGenerator.java

private boolean booleanInDistribution(IntegerDistribution distribution) {
    int sample = distribution.sample();
    //System.out.println(sample < distribution.getNumericalMean());
    return sample < distribution.getNumericalMean();
}

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From source file:eu.opensourceprojects.mondo.benchmarks.transformationzoo.instantiator.SpecimenGenerator.java

private void setNextResourceSizeForType(Map<EClass, Integer> resourcesSize, EClass eClass) {
    IntegerDistribution sizeDistribution = c.getResourceSizeDistribution(eClass);
    int desiredSize = sizeDistribution.sample();
    resourcesSize.put(eClass, desiredSize);
}

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