Examples of sample()

@return a random value. @since 2.2
  • org.apache.commons.math3.distribution.RealDistribution.sample()
    Generate a random value sampled from this distribution. @return a random value.
  • org.apache.commons.math3.distribution.UniformIntegerDistribution.sample()
    {@inheritDoc}
  • org.apache.commons.math3.distribution.UniformRealDistribution.sample()
    {@inheritDoc}
  • org.apache.commons.math3.distribution.WeibullDistribution.sample()
    esus.ox.ac.uk/~clifford/a5/chap1/node5.html"> Inversion Method to generate exponentially distributed random values from uniform deviates.

    @return a random value. @since 2.2
  • org.apache.jmeter.protocol.http.sampler.HTTPSampler.sample()
  • org.apache.jmeter.protocol.http.sampler.HTTPSamplerBase.sample()
    Do a sampling and return its results. @param e Entry to be sampled @return results of the sampling
  • org.apache.jmeter.samplers.Sampler.sample()
    Obtains statistics about the given Entry, and packages the information into a SampleResult. @param e !ToDo (Parameter description) @return !ToDo (Return description)
  • org.apache.mahout.clustering.dirichlet.models.NormalModel.sample()
    TODO: Return a proper sample from the posterior. For now, return an instance with the same parameters @return an NormalModel
  • org.apache.mahout.clustering.lda.LDASampler.sample()
    @param topicDistribution vector of p(topicId) for all topicId < model.numTopics() @param numSamples the number of times to sample (with replacement) from the model @return array of length numSamples, with each entry being a sample from the model. Theremay be repeats
  • org.apache.mahout.knn.LumpyData.sample()
  • org.apache.mahout.math.random.ChineseRestaurant.sample()
  • org.apache.mahout.math.random.MultiNormal.sample()
  • org.apache.mahout.math.stats.Sampler.sample()
  • org.apache.openejb.api.Monitor.sample()
  • plm.hmm.DlmHiddenMarkovModel.sample()
    Sample a trajectory up to time T. Note: this method will add the filter's current input to the initial (and all other) states, so if you set the model input, the offset/input will be added twice to the initial state. @param random @param numSamples @return
  • prefuse.data.parser.TypeInferencer.sample()
    Sample the given text string for the given data column index. @param column the data column index of the sample @param value the text string sample
  • twitter4j.TwitterStream.sample()
    tter.com/Streaming-API-Documentation#sample">Twitter API Wiki / Streaming API Documentation - sample @since Twitter4J 2.0.10
  • vanilla.java.processingengine.testing.Histogram.sample()

    • Examples of org.apache.mahout.math.random.MultiNormal.sample()

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        s.addAllMatrixSlicesAsWeightedVectors(testData());

        MultiNormal noise = new MultiNormal(0.01, new DenseVector(20));
        for (MatrixSlice slice : queries) {
          Vector query = slice.vector();
          final Vector epsilon = noise.sample();
          List<WeightedThing<Vector>> r0 = s.search(query, 2);
          query = query.plus(epsilon);
          List<WeightedThing<Vector>> r = s.search(query, 2);
          r = s.search(query, 2);
          assertEquals("Distance has to be small", epsilon.norm(2), r.get(0).getWeight(), 1e-5);
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    Examples of org.apache.mahout.math.random.MultiNormal.sample()

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      @Test
      public void testOrdering() {
        Matrix queries = new DenseMatrix(100, 20);
        MultiNormal gen = new MultiNormal(20);
        for (int i = 0; i < 100; i++) {
          queries.viewRow(i).assign(gen.sample());
        }

        Searcher s = getSearch(20);
        // s.setSearchSize(200);
        s.addAllMatrixSlices(testData());
    View Full Code Here

    Examples of org.apache.mahout.math.random.MultiNormal.sample()

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        List<WeightedVector> data = Lists.newArrayListWithCapacity(K1 + 5000);
        int row = 0;

        MultiNormal g = new MultiNormal(radius, new ConstantVector(0, 10));
        for (int i = 0; i < K1; i++) {
          data.add(new WeightedVector(g.sample(), 1, row++));
        }

        for (int i = 0; i < 5; i++) {
          Vector m = new DenseVector(10);
          m.set(i, i == 0 ? 6 : 6);
    View Full Code Here

    Examples of org.apache.mahout.math.random.MultiNormal.sample()

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        for (int i = 0; i < 5; i++) {
          Vector m = new DenseVector(10);
          m.set(i, i == 0 ? 6 : 6);
          MultiNormal gx = new MultiNormal(radius, m);
          for (int j = 0; j < 1000; j++) {
            data.add(new WeightedVector(gx.sample(), 1, row++));
          }
        }
        return data;
      }
    }
    View Full Code Here

    Examples of org.apache.mahout.math.random.MultiNormal.sample()

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      public static void main(String[] args) {
        Sampler<Vector> rand = new MultiNormal(new ConstantVector(1, VECTOR_DIMENSION));
        List<WeightedVector> referenceVectors = Lists.newArrayListWithExpectedSize(REFERENCE_SIZE);
        for (int i = 0; i < REFERENCE_SIZE; ++i) {
          referenceVectors.add(new WeightedVector(rand.sample(), 1, i));
        }
        System.out.printf("Generated reference matrix.\n");

        List<WeightedVector> queryVectors = Lists.newArrayListWithExpectedSize(QUERY_SIZE);
        for (int i = 0; i < QUERY_SIZE; ++i) {
    View Full Code Here

    Examples of org.apache.mahout.math.random.MultiNormal.sample()

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        }
        System.out.printf("Generated reference matrix.\n");

        List<WeightedVector> queryVectors = Lists.newArrayListWithExpectedSize(QUERY_SIZE);
        for (int i = 0; i < QUERY_SIZE; ++i) {
          queryVectors.add(new WeightedVector(rand.sample(), 1, i));
        }
        System.out.printf("Generated query matrix.\n");

        for (int threads : new int[]{1, 2, 3, 4, 5, 6, 10, 20, 50}) {
          for (int block : new int[]{1, 10, 50}) {
    View Full Code Here

    Examples of org.apache.mahout.math.stats.Sampler.sample()

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        Preconditions.checkArgument(topicDistribution.size() == samplers.length,
            "topicDistribution must have same cardinality as the sampling model");
        int[] samples = new int[numSamples];
        Sampler topicSampler = new Sampler(random, topicDistribution);
        for(int i = 0; i < numSamples; i++) {
          samples[i] = samplers[topicSampler.sample()].sample();
        }
        return samples;
      }
    }
    View Full Code Here

    Examples of org.apache.openejb.api.Monitor.sample()

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            private final String method;

            public Stats(Method method, Monitor classAnnotation) {
                Monitor methodAnnotation = method.getAnnotation(Monitor.class);

                int window = (methodAnnotation != null) ? methodAnnotation.sample() : (classAnnotation != null) ? classAnnotation.sample() : 2000;

                this.samples = new SynchronizedDescriptiveStatistics(window);
                String s = ",";

                StringBuilder sb = new StringBuilder(method.getName());
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    Examples of plm.hmm.DlmHiddenMarkovModel.sample()

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        String outputFilename = args[0] + "/hmm-nar-wf-rs-10000-class-errors-m1.csv";
        final int K = 10;
        final int T = 300;

        List<SimHmmObservedValue<Vector, Vector>> simulation = dlmHmm.sample(random, T);

        wfFilter.setNumParticles(N);
        wfFilter.setResampleOnly(false);
        rsFilter.setNumParticles(N);
    View Full Code Here

    Examples of prefuse.data.parser.TypeInferencer.sample()

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                public void readValue(int line, int col, String value)
                    throws DataParseException
                {
                    // sample value to determine data type
                    if ( line > 1 || !m_hasHeader ) {
                        di.sample(col-1, value);
                       
                        // update num rows
                        if ( line != prevLine ) {
                            prevLine = line;
                            dim[0]++;
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