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.commons.math3.distribution.NormalDistribution.sample()

    193
    194
    195
    196
    197
    198
    199
    200
    201
    202
    203
            // and fill valuesList with values from values array with
            // values[i] repeated weights[i] times, each i
            final RealDistribution valueDist = new NormalDistribution(mu, sigma);
            List<Double> valuesList = new ArrayList<Double>();
            for (int i = 0; i < len; i++) {
                double value = valueDist.sample();
                values[i] = value;
                for (int j = 0; j < intWeights[i]; j++) {
                    valuesList.add(new Double(value));
                }
            }
    View Full Code Here

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

    394
    395
    396
    397
    398
    399
    400
    401
    402
    403
    404
                // get the "real" cannonball position
                double x = cannonball.getX();
                double y = cannonball.getY();
               
                // apply measurement noise to current cannonball position
                double nx = x + dist.sample();
                double ny = y + dist.sample();

                cannonball.step();
               
                filter.predict(controlVector);
    View Full Code Here

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

    395
    396
    397
    398
    399
    400
    401
    402
    403
    404
    405
                double x = cannonball.getX();
                double y = cannonball.getY();
               
                // apply measurement noise to current cannonball position
                double nx = x + dist.sample();
                double ny = y + dist.sample();

                cannonball.step();
               
                filter.predict(controlVector);
                // correct the filter with our measurements
    View Full Code Here

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

    395
    396
    397
    398
    399
    400
    401
    402
    403
    404
    405
                // get the "real" cannonball position
                double x = cannonball.getX();
                double y = cannonball.getY();
               
                // apply measurement noise to current cannonball position
                double nx = x + dist.sample();
                double ny = y + dist.sample();

                cannonball.step();
               
                filter.predict(controlVector);
    View Full Code Here

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

    396
    397
    398
    399
    400
    401
    402
    403
    404
    405
    406
                double x = cannonball.getX();
                double y = cannonball.getY();
               
                // apply measurement noise to current cannonball position
                double nx = x + dist.sample();
                double ny = y + dist.sample();

                cannonball.step();
               
                filter.predict(controlVector);
                // correct the filter with our measurements
    View Full Code Here

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

    1238
    1239
    1240
    1241
    1242
    1243
    1244
    1245
    1246
    1247
    1248
         * MATH-720
         */
        public void testReseed() {
            PoissonDistribution x = new PoissonDistribution(3.0);
            x.reseedRandomGenerator(0);
            final double u = x.sample();
            PoissonDistribution y = new PoissonDistribution(3.0);
            y.reseedRandomGenerator(0);
            Assert.assertEquals(u, y.sample(), 0);
        }

    View Full Code Here

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

    1241
    1242
    1243
    1244
    1245
    1246
    1247
    1248
    1249
            PoissonDistribution x = new PoissonDistribution(3.0);
            x.reseedRandomGenerator(0);
            final double u = x.sample();
            PoissonDistribution y = new PoissonDistribution(3.0);
            y.reseedRandomGenerator(0);
            Assert.assertEquals(u, y.sample(), 0);
        }

    }
    View Full Code Here

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

    193
    194
    195
    196
    197
    198
    199
    200
    201
    202
    203
            // and fill valuesList with values from values array with
            // values[i] repeated weights[i] times, each i
            final RealDistribution valueDist = new NormalDistribution(mu, sigma);
            List<Double> valuesList = new ArrayList<Double>();
            for (int i = 0; i < len; i++) {
                double value = valueDist.sample();
                values[i] = value;
                for (int j = 0; j < intWeights[i]; j++) {
                    valuesList.add(new Double(value));
                }
            }
    View Full Code Here

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

    97
    98
    99
    100
    101
    102
    103
    104
    105
    106
    107
            RandomDataGenerator random = new RandomDataGenerator();

            // Generate 10 distinct random values
            for (int i = 0; i < 10; i++) {
                final RealDistribution u = new UniformRealDistribution(i + 0.5, i + 0.75);
                original[i] = u.sample();
            }

            // Generate a random permutation, making sure it is not the identity
            boolean isIdentity = true;
            do {
    View Full Code Here

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

    50
    51
    52
    53
    54
    55
    56
    57
    58
    59
    60
            final double[] coeff = { 12.9, -3.4, 2.1 }; // 12.9 - 3.4 x + 2.1 x^2
            final PolynomialFunction f = new PolynomialFunction(coeff);

            // Collect data from a known polynomial.
            for (int i = 0; i < 100; i++) {
                final double x = rng.sample();
                fitter.addObservedPoint(x, f.value(x));
            }

            // Start fit from initial guesses that are far from the optimal values.
            final double[] best = fitter.fit(new double[] { -1e-20, 3e15, -5e25 });
    View Full Code Here
    TOP
    Copyright © 2018 www.massapi.com. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.