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

Class org.apache.commons.math3.random.JDKRandomGenerator

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

org.apache.commons.math3.random.JDKRandomGenerator.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

        // 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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        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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            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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        Well1024a random = new Well1024a(0x180b41cfeeffaf67l);
        UnitSphereRandomVectorGenerator g = new UnitSphereRandomVectorGenerator(3, random);
        for (int i = 0; i < 10; ++i) {
            double[] unit = g.nextVector();
            FieldRotation<DerivativeStructure> r = new FieldRotation<DerivativeStructure>(createVector(unit[0], unit[1], unit[2]),
                                          createAngle(random.nextDouble()));


            for (double x = -0.9; x < 0.9; x += 0.2) {
                for (double y = -0.9; y < 0.9; y += 0.2) {
                    for (double z = -0.9; z < 0.9; z += 0.2) {
                        FieldVector3D<DerivativeStructure> uds   = createVector(x, y, z);

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        Well1024a random = new Well1024a(0x180b41cfeeffaf67l);
        UnitSphereRandomVectorGenerator g = new UnitSphereRandomVectorGenerator(3, random);
        for (int i = 0; i < 10; ++i) {
            double[] unit1 = g.nextVector();
            Rotation r1 = new Rotation(new Vector3D(unit1[0], unit1[1], unit1[2]),
                                      random.nextDouble());
            FieldRotation<DerivativeStructure> r1Prime = new FieldRotation<DerivativeStructure>(new DerivativeStructure(4, 1, 0, r1.getQ0()),
                                                new DerivativeStructure(4, 1, 1, r1.getQ1()),
                                                new DerivativeStructure(4, 1, 2, r1.getQ2()),
                                                new DerivativeStructure(4, 1, 3, r1.getQ3()),
                                                false);

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                                                new DerivativeStructure(4, 1, 2, r1.getQ2()),
                                                new DerivativeStructure(4, 1, 3, r1.getQ3()),
                                                false);
            double[] unit2 = g.nextVector();
            FieldRotation<DerivativeStructure> r2 = new FieldRotation<DerivativeStructure>(createVector(unit2[0], unit2[1], unit2[2]),
                                           createAngle(random.nextDouble()));


            FieldRotation<DerivativeStructure> rA = FieldRotation.applyTo(r1, r2);
            FieldRotation<DerivativeStructure> rB = r1Prime.applyTo(r2);
            FieldRotation<DerivativeStructure> rC = FieldRotation.applyInverseTo(r1, r2);
            FieldRotation<DerivativeStructure> rD = r1Prime.applyInverseTo(r2);

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    public void testDotProduct() {
        // we compare accurate versus naive dot product implementations
        // on regular vectors (i.e. not extreme cases like in the previous test)
        Well1024a random = new Well1024a(553267312521321234l);
        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 dot product implementations
        // on regular vectors (i.e. not extreme cases like in the previous test)
        Well1024a random = new Well1024a(553267312521321234l);
        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;

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        // on regular vectors (i.e. not extreme cases like in the previous test)
        Well1024a random = new Well1024a(553267312521321234l);
        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;

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