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 com.statslibextensions.statistics.distribution.ScaledInverseGammaCovDistribution.sample()

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              updatedBetaMean, observation.getObservedData());

          final Vector betaMeanError = postBetaSmoothedSample.minus(priorBetaSmoothedSample);
          final ScaledInverseGammaCovDistribution updatedBetaCov = particle.getPriorBetaCov().clone();
          updateCovariancePrior(updatedBetaCov, betaMeanError);
          final Matrix betaCovSmpl = updatedBetaCov.sample(random);
          Preconditions.checkState(betaCovSmpl.getElement(0, 0) >= 0d);
          updatedBetaMean.setCovariance(lambdaSamplesMatrix.times(betaCovSmpl
              .times(updatedBetaMean.getCovariance())));

          /*
     
    View Full Code Here

    Examples of engine.graphics.synthesis.texture.CacheTileManager.TileCacheEntry.sample()

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              float u = (float) (x + px * img.getWidth()) / (float) globalXres;
              float v = (float) (y + py * img.getHeight()) / (float) globalYres;

              final Vector4 col;
              if (tce == null) col = c.valueRGBA(u, v);
              else col = tce.sample(x, y);
             
              final Vector3 color = new Vector3();
              int val;
              // !!UGH TODO: optimize this!!
              if (mode == 0) {
    View Full Code Here

    Examples of gov.sandia.cognition.statistics.distribution.InverseGammaDistribution.sample()

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          final CountedDataDistribution<GaussianArHpWfParticle> initialParticles =
              CountedDataDistribution.create(numParticles, true);
          for (int i = 0; i < numParticles; i++) {

            final InverseGammaDistribution thisSigma2Prior = this.initialPriorSigma2.clone();
            final double sigma2Sample = thisSigma2Prior.sample(this.rng);

            final KalmanFilter thisKf = this.initialKf.clone();
            final MultivariateGaussian thisPsiPrior = initialPriorPsi.clone();
            // TODO FIXME use t-distribution
            final MultivariateGaussian thisPsiPriorSmpler = thisPsiPrior.clone();
    View Full Code Here

    Examples of gov.sandia.cognition.statistics.distribution.MultivariateGaussian.sample()

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            final KalmanFilter thisKf = this.initialKf.clone();
            final MultivariateGaussian thisPsiPrior = initialPriorPsi.clone();
            // TODO FIXME use t-distribution
            final MultivariateGaussian thisPsiPriorSmpler = thisPsiPrior.clone();
            thisPsiPriorSmpler.getCovariance().scaleEquals(sigma2Sample);
            final Vector psiSample = thisPsiPriorSmpler.sample(this.rng);

            final Vector alphaTerm = psiSample.subVector(0,
                psiSample.getDimensionality()/2 - 1);
            thisKf.getModel().setState(alphaTerm);
            thisKf.setCurrentInput(alphaTerm);
    View Full Code Here

    Examples of gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution.sample()

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          final Matrix postStatePrec = posteriorState.getCovarianceInverse().scale(
              sigma2SS.getShape()/sigma2SS.getScale());
          MultivariateStudentTDistribution postStateMarginal = new MultivariateStudentTDistribution(
              sigma2SS.getShape(),
              posteriorState.getMean(), postStatePrec);
          final Vector postStateSample = postStateMarginal.sample(this.rng);
         
          final Vector psiPriorSmpl = predState.getPsiSample();
          // x_t
          final Vector xHdiff = postStateSample.minus(H.times(psiPriorSmpl));
    View Full Code Here

    Examples of org.apache.commons.math3.distribution.BetaDistribution.sample()

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             *  Generate a sequence of deviates using inversion - the distribution function
             *  evaluated at the random value from the distribution should match the uniform
             *  random value used to generate it, which is stored in the quantiles[] array.
             */
            for (int i = 0; i < 10; i++) {
                double value = betaDistribution.sample();
                Assert.assertEquals(betaDistribution.cumulativeProbability(value), quantiles[i], 10E-9);
            }
        }

        @Test
    View Full Code Here

    Examples of org.apache.commons.math3.distribution.ExponentialDistribution.sample()

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        ed.reseedRandomGenerator(rng.nextLong());
        final List<Long> times = newArrayList();

        long sum = 0;
        while (sum < 1000) {
          sum += DoubleMath.roundToLong(ed.sample(), RoundingMode.HALF_DOWN);
          if (sum < 1000) {
            times.add(sum);
          }
        }
        return asTimes(1000, times);
    View Full Code Here

    Examples of org.apache.commons.math3.distribution.IntegerDistribution.sample()

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            ResizableDoubleArray eDA2 = new ResizableDoubleArray(2);
            Assert.assertEquals("Initial number of elements should be 0", 0, eDA2.getNumElements());

            final IntegerDistribution randomData = new UniformIntegerDistribution(100, 1000);
            final int iterations = randomData.sample();

            for( int i = 0; i < iterations; i++) {
                eDA2.addElement( i );
            }
    View Full Code Here

    Examples of org.apache.commons.math3.distribution.IntegerDistribution.sample()

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            ResizableDoubleArray eDA3 = new ResizableDoubleArray(3, 3.0, 3.5);
            Assert.assertEquals("Initial number of elements should be 0", 0, eDA3.getNumElements() );

            final IntegerDistribution randomData = new UniformIntegerDistribution(100, 3000);
            final int iterations = randomData.sample();

            for( int i = 0; i < iterations; i++) {
                eDA3.addElement( i );
            }
    View Full Code Here

    Examples of org.apache.commons.math3.distribution.IntegerDistribution.sample()

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            // Fill weights array with random int values between 1 and 5
            int[] intWeights = new int[len];
            final IntegerDistribution weightDist = new UniformIntegerDistribution(1, 5);
            for (int i = 0; i < len; i++) {
                intWeights[i] = weightDist.sample();
                weights[i] = intWeights[i];
            }

            // Fill values array with random data from N(mu, sigma)
            // and fill valuesList with values from values array with
    View Full Code Here
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