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

Introduction

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

Prototype

public T sample() 

Source Link

Document

Generate a random value sampled from this distribution.

Usage

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

@SafeVarargs
public static <T, S> Math3ProbabilityDistribution<T> of(final EnumeratedDistribution<T> dist, //final PseudoRandom stream,
        final S... args) {
    Objects.requireNonNull(dist);
    final Math3ProbabilityDistribution<T> result = new Math3ProbabilityDistribution<T>() {
        @Override//from  w  w  w  .jav  a 2 s . co  m
        public T draw() {
            return dist.sample();
        }
    };
    //      result.stream = stream;
    //      result.params = Arrays.asList( args );
    return result;
}

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From source file:cc.kave.commons.pointsto.evaluation.UsagePruning.java

/**
 * @return The number of pruned usages//  w  w w .  j a  va  2  s  .co  m
 */
public int prune(final int maxUsages, Map<ProjectIdentifier, List<Usage>> usages) {
    final int initialNumUsages = usages.values().stream().mapToInt(Collection::size).sum();
    int numUsages = initialNumUsages;

    if (numUsages > maxUsages) {
        Random rnd = new Random(rndGenerator.nextLong());
        List<Pair<ProjectIdentifier, Double>> projectUsageCounts = new ArrayList<>(usages.size());
        for (Map.Entry<ProjectIdentifier, List<Usage>> entry : usages.entrySet()) {
            projectUsageCounts.add(Pair.create(entry.getKey(), (double) entry.getValue().size()));
            Collections.shuffle(entry.getValue(), rnd);
        }
        EnumeratedDistribution<ProjectIdentifier> distribution = new EnumeratedDistribution<>(rndGenerator,
                projectUsageCounts);

        while (numUsages > maxUsages) {
            ProjectIdentifier project = distribution.sample();
            List<Usage> projectUsages = usages.get(project);
            if (!projectUsages.isEmpty()) {
                projectUsages.remove(projectUsages.size() - 1);
                --numUsages;
            }
        }
    }

    return initialNumUsages - numUsages;
}

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From source file:com.github.tteofili.calabrize.impl.RNN.java

/**
 * sample a sequence of integers from the model, using current (hPrev) memory state, seedIx is seed letter for first time step
 *//*from   w  w  w  .ja v a 2s . c o  m*/
public String sample(int seedIx) {

    INDArray x = Nd4j.zeros(vocabSize, 1);
    x.putScalar(seedIx, 1);
    int sampleSize = 144;
    INDArray ixes = Nd4j.create(sampleSize);

    INDArray h = hPrev.dup();

    for (int t = 0; t < sampleSize; t++) {
        h = Transforms.tanh(wxh.mmul(x).add(whh.mmul(h)).add(bh));
        INDArray y = (why.mmul(h)).add(by);
        INDArray pm = Nd4j.getExecutioner().execAndReturn(new SoftMax(y)).ravel();

        List<Pair<Integer, Double>> d = new LinkedList<>();
        for (int pi = 0; pi < vocabSize; pi++) {
            d.add(new Pair<>(pi, pm.getDouble(0, pi)));
        }
        try {
            EnumeratedDistribution<Integer> distribution = new EnumeratedDistribution<>(d);

            int ix = distribution.sample();

            x = Nd4j.zeros(vocabSize, 1);
            x.putScalar(ix, 1);
            ixes.putScalar(t, ix);
        } catch (Exception e) {
        }
    }

    return getSampleString(ixes);
}

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From source file:io.coala.random.impl.RandomDistributionFactoryImpl.java

@Override
public <T> RandomDistribution<T> getEnumerated(final RandomNumberStream rng,
        final List<ProbabilityMass<T, Number>> probabilities) {
    final List<Pair<T, Double>> pmf = new ArrayList<>();
    if (probabilities != null)
        for (ProbabilityMass<T, Number> p : probabilities)
            pmf.add(Pair.create(p.getValue(), p.getMass().doubleValue()));
    final EnumeratedDistribution<T> dist = new EnumeratedDistribution<T>(
            RandomNumberStream.Util.asCommonsRandomGenerator(rng), pmf);
    return new RandomDistribution<T>() {
        @Override//from   w  w  w  . j  ava2s .  c o  m
        public T draw() {
            return dist.sample();
        }
    };
}

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From source file:cc.kave.commons.pointsto.evaluation.cv.StratifiedMethodsCVFoldBuilder.java

@Override
public List<List<Usage>> createFolds(Map<ProjectIdentifier, List<Usage>> projectUsages) {
    shuffleUsages(projectUsages.values());
    Map<ICoReMethodName, Integer> methods = countMethods(Iterables.concat(projectUsages.values()));
    EnumeratedDistribution<ICoReMethodName> distribution = buildDistribution(methods);
    MethodRegistry registry = new MethodRegistry(Iterables.concat(projectUsages.values()), methods.size());

    List<List<Usage>> folds = new ArrayList<>(numFolds);
    int avgFoldSize = calcAvgFoldSize(projectUsages.values());
    for (int f = 0; f < numFolds - 1; ++f) {
        List<Usage> fold = new ArrayList<>(avgFoldSize);
        folds.add(fold);//w w  w.  java  2s  . c  om
        for (int i = 0; i < avgFoldSize; ++i) {
            ICoReMethodName method;
            Usage usage;

            do {
                method = distribution.sample();
                usage = registry.next(method);
            } while (usage == null);

            fold.add(usage);
        }
    }
    folds.add(registry.remaining());

    return folds;
}

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From source file:cc.kave.commons.pointsto.evaluation.cv.StratifiedProjectsCVFoldBuilder.java

@Override
public List<List<Usage>> createFolds(Map<ProjectIdentifier, List<Usage>> projectUsages) {
    EnumeratedDistribution<ProjectIdentifier> projectDistribution;
    List<Pair<ProjectIdentifier, Double>> projectProbabilities = new ArrayList<>(projectUsages.size());

    // initialize distributions
    long totalUsages = 0;
    for (Map.Entry<ProjectIdentifier, List<Usage>> project : projectUsages.entrySet()) {
        projectProbabilities/*from   w w  w.  j av a 2 s .c o  m*/
                .add(new Pair<ProjectIdentifier, Double>(project.getKey(), (double) project.getValue().size()));
        totalUsages += project.getValue().size();
    }
    // normalization of the probability mass is done by the constructor
    projectDistribution = new EnumeratedDistribution<>(rndGenerator, projectProbabilities);

    shuffleUsages(projectUsages.values());

    List<List<Usage>> folds = new ArrayList<>(numFolds);
    int avgFoldSize = (int) (totalUsages / numFolds);
    for (int f = 0; f < numFolds - 1; ++f) {
        List<Usage> fold = new ArrayList<>(avgFoldSize);
        folds.add(fold);

        for (int i = 0; i < avgFoldSize; ++i) {
            ProjectIdentifier project;
            List<Usage> usages;
            do {
                project = projectDistribution.sample();
                usages = projectUsages.get(project);
            } while (usages.isEmpty());

            int lastIndex = usages.size() - 1;
            Usage usage = usages.remove(lastIndex);
            fold.add(usage);
        }
    }
    // add remaining usages to the last fold
    List<Usage> lastFold = new ArrayList<>(avgFoldSize);
    folds.add(lastFold);
    for (List<Usage> usages : projectUsages.values()) {
        lastFold.addAll(usages);
        usages.clear();
    }
    Collections.shuffle(lastFold, new Random(rndGenerator.nextLong()));

    return folds;
}

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