Examples of RealDistribution

com.facebook.LinkBench.RealDistribution
org.apache.commons.math3.distribution.RealDistribution
Base interface for distributions on the reals. @since 3.0

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

        int i = 0;
        while (cumBinP(i) < p) {
            i++;
        }


        final RealDistribution kernel = getKernel(binStats.get(i));
        final double kB = kB(i);
        final double[] binBounds = getUpperBounds();
        final double lower = i == 0 ? min : binBounds[i - 1];
        final double kBminus = kernel.cumulativeProbability(lower);
        final double pB = pB(i);
        final double pBminus = pBminus(i);
        final double pCrit = p - pBminus;
        if (pCrit <= 0) {
            return lower;
        }
        return kernel.inverseCumulativeProbability(kBminus + pCrit * kB / pB);
    }

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

     * upper and lower endpoints of bin i
     */
    @SuppressWarnings("deprecation")
    private double kB(int i) {
        final double[] binBounds = getUpperBounds();
        final RealDistribution kernel = getKernel(binStats.get(i));
        return i == 0 ? kernel.cumulativeProbability(min, binBounds[0]) :
            kernel.cumulativeProbability(binBounds[i - 1], binBounds[i]);
    }

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

     * across no more than 3 bin boundaries.
     */
    @Override
    @Test
    public void testDensityIntegrals() {
        final RealDistribution distribution = makeDistribution();
        final double tol = 1.0e-9;
        final BaseAbstractUnivariateIntegrator integrator =
            new IterativeLegendreGaussIntegrator(5, 1.0e-12, 1.0e-10);
        final UnivariateFunction d = new UnivariateFunction() {
            public double value(double x) {
                return distribution.density(x);
            }
        };
        final double[] lower = {0, 5, 1000, 5001, 9995};
        final double[] upper = {5, 12, 1030, 5010, 10000};
        for (int i = 1; i < 5; i++) {
            Assert.assertEquals(
                    distribution.cumulativeProbability( 
                            lower[i], upper[i]),
                            integrator.integrate(
                                    1000000, // Triangle integrals are very slow to converge
                                    d, lower[i], upper[i]), tol);
        }

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

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

 * 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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Examples of org.apache.commons.math3.distribution.RealDistribution

                binBounds[bin - 1];
            final double upper = binBounds[bin];
            // Compute bMinus = sum or mass of bins below the bin containing the point
            // First bin has mass 11 / 10000, the rest have mass 10 / 10000.
            final double bMinus = bin == 0 ? 0 : (bin - 1) * binMass + firstBinMass;
            final RealDistribution kernel = findKernel(lower, upper);
            final double withinBinKernelMass = kernel.cumulativeProbability(lower, upper);
            final double kernelCum = kernel.cumulativeProbability(lower, testPoints[i]);
            cumValues[i] = bMinus + (bin == 0 ? firstBinMass : binMass) * kernelCum/withinBinKernelMass;
        }
        return cumValues;
    }

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

 * 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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Examples of org.apache.commons.math3.distribution.RealDistribution

        for (int i = 0; i < testPoints.length; i++) {
            final int bin = findBin(testPoints[i]);
            final double lower = bin == 0 ? empiricalDistribution.getSupportLowerBound() :
                binBounds[bin - 1];
            final double upper = binBounds[bin];
            final RealDistribution kernel = findKernel(lower, upper);
            final double withinBinKernelMass = kernel.cumulativeProbability(lower, upper);
            final double density = kernel.density(testPoints[i]);
            densityValues[i] = density * (bin == 0 ? firstBinMass : binMass) / withinBinKernelMass;   
        }
        return densityValues;
    }

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

     *
     * @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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Examples of org.apache.commons.math3.distribution.RealDistribution

        }


        // 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
        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));
            }
        }

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