Examples of org.apache.commons.math3.random.RandomData.nextGaussian()

Class org.apache.commons.math3.random.RandomData

Examples of org.apache.commons.math3.random.RandomData.nextGaussian()

org.apache.commons.math3.random.RandomData.nextGaussian()
tl.nist.gov/div898/handbook/eda/section3/eda3661.htm"> Normal Distribution
@param mu the mean of the distribution @param sigma the standard deviation of the distribution @return a random value following the specified Gaussian distribution @throws NotStrictlyPositiveException if {@code sigma <= 0}.

        // 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

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            // 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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        // 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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        // 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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        // Fill values array with random data from N(mu, sigma)
        // and fill valuesList with values from values array with
        // values[i] repeated weights[i] times, each i
        List<Double> valuesList = new ArrayList<Double>();
        for (int i = 0; i < len; i++) {
            double value = randomData.nextGaussian(mu, sigma);
            values[i] = value;
            for (int j = 0; j < intWeights[i]; j++) {
                valuesList.add(new Double(value));
            }
        }

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    double mean1 = 250;
    double mean2 = 750;
    RandomDataImpl random = new RandomDataImpl(new Well1024a(seed));
    for (int i = 0; i < 50; i++) {
      double nextGaussian1 = random.nextGaussian(mean1, Math.sqrt(100));
      assertTrue(nextGaussian1 >= 150 && nextGaussian1 <= 350);
      double nextGaussian2 = random.nextGaussian(mean2, Math.sqrt(100));
      assertTrue(nextGaussian2 >= 650 && nextGaussian2 <= 850);
      DenseDoubleVector lef = new DenseDoubleVector(
          new double[] { nextGaussian1 });

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    double mean2 = 750;
    RandomDataImpl random = new RandomDataImpl(new Well1024a(seed));
    for (int i = 0; i < 50; i++) {
      double nextGaussian1 = random.nextGaussian(mean1, Math.sqrt(100));
      assertTrue(nextGaussian1 >= 150 && nextGaussian1 <= 350);
      double nextGaussian2 = random.nextGaussian(mean2, Math.sqrt(100));
      assertTrue(nextGaussian2 >= 650 && nextGaussian2 <= 850);
      DenseDoubleVector lef = new DenseDoubleVector(
          new double[] { nextGaussian1 });
      lst.add(lef);
      if (leftDistribution != null) {

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        // Fill values array with random data from N(mu, sigma)
        // and fill valuesList with values from values array with
        // values[i] repeated weights[i] times, each i
        List<Double> valuesList = new ArrayList<Double>();
        for (int i = 0; i < len; i++) {
            double value = randomData.nextGaussian(mu, sigma);
            values[i] = value;
            for (int j = 0; j < intWeights[i]; j++) {
                valuesList.add(new Double(value));
            }
        }

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


            // Simulate the process
            pNoise.setEntry(0, processNoise * rand.nextGaussian());


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


            // Simulate the measurement

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            // x = A * x + p_noise
            x = A.operate(x).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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