Examples of org.apache.commons.math3.random.ISAACRandom.nextDouble()

Class org.apache.commons.math3.random.ISAACRandom

Examples of org.apache.commons.math3.random.ISAACRandom.nextDouble()

org.apache.commons.math3.random.ISAACRandom.nextDouble()
Returns the next pseudorandom, uniformly distributed double value between 0.0 and 1.0 from this random number generator's sequence. @return the next pseudorandom, uniformly distributeddouble value between 0.0 and 1.0 from this random number generator's sequence


        for (int i = 0; i < 10000; ++i) {
            final DerivativeStructure[] u = new DerivativeStructure[4];
            final DerivativeStructure[] v = new DerivativeStructure[4];
            for (int j = 0; j < u.length; ++j) {
                u[j] = new DerivativeStructure(u.length, 1, j, 1e17 * random.nextDouble());
                v[j] = new DerivativeStructure(u.length, 1, 1e17 * random.nextDouble());
            }


            DerivativeStructure lin = u[0].linearCombination(u[0], v[0], u[1], v[1]);
            double ref = u[0].getValue() * v[0].getValue() +

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        for (int i = 0; i < 10000; ++i) {
            double ux = 10000 * random.nextDouble();
            double uy = 10000 * random.nextDouble();
            double uz = 10000 * random.nextDouble();
            double vx = 10000 * random.nextDouble();
            double vy = 10000 * random.nextDouble();
            double vz = 10000 * random.nextDouble();
            double sNaive = ux * vx + uy * vy + uz * vz;
            double sAccurate = new Vector3D(ux, uy, uz).dotProduct(new Vector3D(vx, vy, vz));
            Assert.assertEquals(sNaive, sAccurate, 2.5e-16 * sAccurate);
        }

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        for (int i = 0; i < 10000; ++i) {
            final DerivativeStructure[] u = new DerivativeStructure[4];
            final DerivativeStructure[] v = new DerivativeStructure[4];
            for (int j = 0; j < u.length; ++j) {
                u[j] = new DerivativeStructure(u.length, 1, j, 1e17 * random.nextDouble());
                v[j] = new DerivativeStructure(u.length, 1, 1e17 * random.nextDouble());
            }


            DerivativeStructure lin = u[0].linearCombination(u[0], v[0], u[1], v[1]);
            double ref = u[0].getValue() * v[0].getValue() +
                         u[1].getValue() * v[1].getValue();

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            double ux = 10000 * random.nextDouble();
            double uy = 10000 * random.nextDouble();
            double uz = 10000 * random.nextDouble();
            double vx = 10000 * random.nextDouble();
            double vy = 10000 * random.nextDouble();
            double vz = 10000 * random.nextDouble();
            double sNaive = ux * vx + uy * vy + uz * vz;
            double sAccurate = new Vector3D(ux, uy, uz).dotProduct(new Vector3D(vx, vy, vz));
            Assert.assertEquals(sNaive, sAccurate, 2.5e-16 * sAccurate);
        }
    }

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        for (int i = 0; i < 10000; ++i) {
            final double[] u = new double[4];
            final DerivativeStructure[] v = new DerivativeStructure[4];
            for (int j = 0; j < u.length; ++j) {
                u[j] = 1e17 * random.nextDouble();
                v[j] = new DerivativeStructure(u.length, 1, j, 1e17 * random.nextDouble());
            }


            DerivativeStructure lin = v[0].linearCombination(u[0], v[0], u[1], v[1]);
            double ref = u[0] * v[0].getValue() +

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        for (int i = 0; i < 10000; ++i) {
            final double[] u = new double[4];
            final DerivativeStructure[] v = new DerivativeStructure[4];
            for (int j = 0; j < u.length; ++j) {
                u[j] = 1e17 * random.nextDouble();
                v[j] = new DerivativeStructure(u.length, 1, j, 1e17 * random.nextDouble());
            }


            DerivativeStructure lin = v[0].linearCombination(u[0], v[0], u[1], v[1]);
            double ref = u[0] * v[0].getValue() +
                         u[1] * v[1].getValue();

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    public void testCrossProduct() {
        // we compare accurate versus naive cross product implementations
        // on regular vectors (i.e. not extreme cases like in the previous test)
        Well1024a random = new Well1024a(885362227452043214l);
        for (int i = 0; i < 10000; ++i) {
            double ux = 10000 * random.nextDouble();
            double uy = 10000 * random.nextDouble();
            double uz = 10000 * random.nextDouble();
            double vx = 10000 * random.nextDouble();
            double vy = 10000 * random.nextDouble();
            double vz = 10000 * random.nextDouble();

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        // we compare accurate versus naive cross product implementations
        // on regular vectors (i.e. not extreme cases like in the previous test)
        Well1024a random = new Well1024a(885362227452043214l);
        for (int i = 0; i < 10000; ++i) {
            double ux = 10000 * random.nextDouble();
            double uy = 10000 * random.nextDouble();
            double uz = 10000 * random.nextDouble();
            double vx = 10000 * random.nextDouble();
            double vy = 10000 * random.nextDouble();
            double vz = 10000 * random.nextDouble();
            Vector3D cNaive = new Vector3D(uy * vz - uz * vy, uz * vx - ux * vz, ux * vy - uy * vx);

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        // on regular vectors (i.e. not extreme cases like in the previous test)
        Well1024a random = new Well1024a(885362227452043214l);
        for (int i = 0; i < 10000; ++i) {
            double ux = 10000 * random.nextDouble();
            double uy = 10000 * random.nextDouble();
            double uz = 10000 * random.nextDouble();
            double vx = 10000 * random.nextDouble();
            double vy = 10000 * random.nextDouble();
            double vz = 10000 * random.nextDouble();
            Vector3D cNaive = new Vector3D(uy * vz - uz * vy, uz * vx - ux * vz, ux * vy - uy * vx);
            Vector3D cAccurate = new Vector3D(ux, uy, uz).crossProduct(new Vector3D(vx, vy, vz));

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        Well1024a random = new Well1024a(885362227452043214l);
        for (int i = 0; i < 10000; ++i) {
            double ux = 10000 * random.nextDouble();
            double uy = 10000 * random.nextDouble();
            double uz = 10000 * random.nextDouble();
            double vx = 10000 * random.nextDouble();
            double vy = 10000 * random.nextDouble();
            double vz = 10000 * random.nextDouble();
            Vector3D cNaive = new Vector3D(uy * vz - uz * vy, uz * vx - ux * vz, ux * vy - uy * vx);
            Vector3D cAccurate = new Vector3D(ux, uy, uz).crossProduct(new Vector3D(vx, vy, vz));
            Assert.assertEquals(0.0, cAccurate.distance(cNaive), 6.0e-15 * cAccurate.getNorm());

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