Package org.apache.commons.math3.random

Examples of org.apache.commons.math3.random.RandomGenerator

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        // check the initial state
        double[] expectedInitialState = new double[] { 0.0, 0.0 };
        assertVectorEquals(expectedInitialState, filter.getStateEstimation());

        RandomGenerator rand = new JDKRandomGenerator();

        RealVector tmpPNoise = new ArrayRealVector(
                new double[] { Math.pow(dt, 2d) / 2d, dt });

        RealVector mNoise = new ArrayRealVector(1);

        // iterate 60 steps
        for (int i = 0; i < 60; i++) {
            filter.predict(u);

            // Simulate the process
            RealVector pNoise = tmpPNoise.mapMultiply(accelNoise * rand.nextGaussian());

            // x = A * x + B * u + pNoise
            x = A.operate(x).add(B.operate(u)).add(pNoise);

            // Simulate the measurement
            mNoise.setEntry(0, measurementNoise * rand.nextGaussian());

            // z = H * x + m_noise
            RealVector z = H.operate(x).add(mNoise);

            filter.correct(z);
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                                            double b,
                                            double sigma,
                                            double lo,
                                            double hi,
                                            long seed) {
        final RandomGenerator rng = new Well44497b(seed);
        slope = a;
        intercept = b;
        error = new NormalDistribution(rng, 0, sigma,
                                       NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        x = new UniformRealDistribution(rng, lo, hi,
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     * with this error structure. Then verify that GLS estimated coefficients,
     * on average, perform better than OLS.
     */
    @Test
    public void testGLSEfficiency() {
        RandomGenerator rg = new JDKRandomGenerator();
        rg.setSeed(200);  // Seed has been selected to generate non-trivial covariance
       
        // Assume model has 16 observations (will use Longley data).  Start by generating
        // non-constant variances for the 16 error terms.
        final int nObs = 16;
        double[] sigma = new double[nObs];
        for (int i = 0; i < nObs; i++) {
            sigma[i] = 10 * rg.nextDouble();
        }
       
        // Now generate 1000 error vectors to use to estimate the covariance matrix
        // Columns are draws on N(0, sigma[col])
        final int numSeeds = 1000;
        RealMatrix errorSeeds = MatrixUtils.createRealMatrix(numSeeds, nObs);
        for (int i = 0; i < numSeeds; i++) {
            for (int j = 0; j < nObs; j++) {
                errorSeeds.setEntry(i, j, rg.nextGaussian() * sigma[j]);
            }
        }
       
        // Get covariance matrix for columns
        RealMatrix cov = (new Covariance(errorSeeds)).getCovarianceMatrix();
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        TestUtils.assertEquals(correctRanks, ranks, 0d);
    }

    @Test
    public void testNaNsFixedTiesRandom() {
        RandomGenerator randomGenerator = new JDKRandomGenerator();
        randomGenerator.setSeed(1000);
        NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
                randomGenerator);
        double[] ranks = ranking.rank(exampleData);
        double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
        TestUtils.assertEquals(correctRanks, ranks, 0d);
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    log.info("Building load test model...");

    System.gc();
    long startMemory = JVMUtils.getUsedMemory();

    RandomGenerator random = RandomManager.getRandom();
    PoissonDistribution itemPerUserDist = new PoissonDistribution(
        random,
        AVG_ITEMS_PER_USER,
        PoissonDistribution.DEFAULT_EPSILON,
        PoissonDistribution.DEFAULT_MAX_ITERATIONS);
    ALSServingModel model = new ALSServingModel(FEATURES, true);

    long totalEntries = 0;
    for (int user = 0; user < USERS; user++) {
      String userID = "U" + user;
      model.setUserVector(userID, randomVector(random));
      int itemsPerUser = itemPerUserDist.sample();
      totalEntries += itemsPerUser;
      Collection<String> knownIDs = new ArrayList<>(itemsPerUser);
      for (int i = 0; i < itemsPerUser; i++) {
        knownIDs.add("I" + random.nextInt(ITEMS));
      }
      model.addKnownItems(userID, knownIDs);
    }

    for (int item = 0; item < ITEMS; item++) {
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    producer.start();
  }

  public void start() throws InterruptedException, IOException {
    KafkaUtils.maybeCreateTopic("localhost", zkPort, topic);
    RandomGenerator random = RandomManager.getRandom();

    Properties producerProps = new Properties();
    producerProps.setProperty("metadata.broker.list", "localhost:" + kafkaPort);
    producerProps.setProperty("serializer.class", "kafka.serializer.StringEncoder");
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   * @param args No args.
   */
  public static void main(String[] args) {
    final String string = "AgentWise";
    final List<Point> points = measureString(string, 30, 30, 0);
    final RandomGenerator rng = new MersenneTwister(123);
    final Simulator sim = new Simulator(rng, Measure.valueOf(1000L,
        SI.MILLI(SI.SECOND)));
    sim.register(new PlaneRoadModel(new Point(0, 0), new Point(4500, 1200),
        SI.METER, Measure.valueOf(1000d, NonSI.KILOMETERS_PER_HOUR)));
    sim.configure();
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  }

  public static void run(boolean testing) {
    // initialize a random generator which we use throughout this
    // 'experiment'
    final RandomGenerator rnd = new MersenneTwister(123);

    // initialize a new Simulator instance
    final Simulator sim = new Simulator(rnd, Measure.valueOf(1000L,
        SI.MILLI(SI.SECOND)));
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  public static Simulator run(boolean testing, final long endTime,
      String graphFile,
      @Nullable Display display, @Nullable Monitor m, @Nullable Listener list) {

    // create a new simulator
    final RandomGenerator rng = new MersenneTwister(123);
    final Simulator simulator = new Simulator(rng, Measure.valueOf(1000L,
        SI.MILLI(SI.SECOND)));

    // use map of leuven
    final RoadModel roadModel = new GraphRoadModel(loadGraph(graphFile));
    final DefaultPDPModel pdpModel = new DefaultPDPModel();

    // configure simulator with models
    simulator.register(roadModel);
    simulator.register(pdpModel);
    simulator.configure();

    // add depots, taxis and parcels to simulator
    for (int i = 0; i < NUM_DEPOTS; i++) {
      simulator.register(new TaxiBase(roadModel.getRandomPosition(rng),
          DEPOT_CAPACITY));
    }
    for (int i = 0; i < NUM_TAXIS; i++) {
      simulator.register(new Taxi(roadModel.getRandomPosition(rng),
          TAXI_CAPACITY));
    }
    for (int i = 0; i < NUM_CUSTOMERS; i++) {
      simulator.register(new Customer(roadModel.getRandomPosition(rng),
          roadModel.getRandomPosition(rng), SERVICE_DURATION, SERVICE_DURATION,
          1 + rng.nextInt(3)));
    }

    simulator.addTickListener(new TickListener() {
      @Override
      public void tick(TimeLapse time) {
        if (time.getStartTime() > endTime) {
          simulator.stop();
        } else if (rng.nextDouble() < .007) {
          simulator.register(new Customer(
              roadModel.getRandomPosition(rng), roadModel
                  .getRandomPosition(rng), SERVICE_DURATION, SERVICE_DURATION,
              1 + rng.nextInt(3)));
        }
      }

      @Override
      public void afterTick(TimeLapse timeLapse) {}
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      NUM_VECHICLES = 12;
    }
    // projector
    else {}

    final RandomGenerator rng = new MersenneTwister(123);
    final Simulator simulator = new Simulator(rng, Measure.valueOf(1000L,
        SI.MILLI(SI.SECOND)));

    final ImmutableList.Builder<ImmutableList<Point>> pointBuilder = ImmutableList
        .builder();
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