Package org.apache.commons.math3.distribution

Examples of org.apache.commons.math3.distribution.UniformRealDistribution

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        }

        @Override
        public Distribution get()
        {
            return new DistributionBoundApache(new UniformRealDistribution(new JDKRandomGenerator(), min, max + 1), min, max);
        }
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        this.radius = radius;
        cX = new NormalDistribution(rng, x, xSigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        cY = new NormalDistribution(rng, y, ySigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        tP = new UniformRealDistribution(rng, 0, MathUtils.TWO_PI,
                                         UniformRealDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    }
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        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,
                                        UniformRealDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    }
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        this.radius = radius;
        cX = new NormalDistribution(rng, x, xSigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        cY = new NormalDistribution(rng, y, ySigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        tP = new UniformRealDistribution(rng, 0, MathUtils.TWO_PI,
                                         UniformRealDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    }
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        double[] permuted = new double[10];
        RandomDataImpl random = new RandomDataImpl();

        // 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 {
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* polynomial.
*/
public class PolynomialFitterTest {
    @Test
    public void testFit() {
        final RealDistribution rng = new UniformRealDistribution(-100, 100);
        rng.reseedRandomGenerator(64925784252L);

        final LevenbergMarquardtOptimizer optim = new LevenbergMarquardtOptimizer();
        final PolynomialFitter fitter = new PolynomialFitter(optim);
        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 });
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* polynomial.
*/
public class PolynomialFitterTest {
    @Test
    public void testFit() {
        final RealDistribution rng = new UniformRealDistribution(-100, 100);
        rng.reseedRandomGenerator(64925784252L);

        final LevenbergMarquardtOptimizer optim = new LevenbergMarquardtOptimizer();
        final PolynomialFitter fitter = new PolynomialFitter(optim);
        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 });
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     *
     * @return array of random double values
     */
    private double[] generateSample() {
        final IntegerDistribution size = new UniformIntegerDistribution(10, 100);
        final RealDistribution randomData = new UniformRealDistribution(-100, 100);
        final int sampleSize = size.sample();
        final double[] out = randomData.sample(sampleSize);
        return out;
    }
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        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,
                                        UniformRealDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    }
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    return ret;
  }
 
  public static Matrix uniform(RandomGenerator rng, int rows, int cols) {
 
    UniformRealDistribution uDist = new UniformRealDistribution(rng,0,1);
    Matrix U = new DenseMatrix(rows, cols);
    for (int r = 0; r < rows; r++) {
      for ( int c = 0; c < cols; c++ ) {
        U.set(r, c, uDist.sample());
      }
    }
   
    return U;
   
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