Examples of com.cloudera.cdk.morphline.shaded.org.apache.commons.math3.random.Well1024a

Package com.cloudera.cdk.morphline.shaded.org.apache.commons.math3.random

Examples of com.cloudera.cdk.morphline.shaded.org.apache.commons.math3.random.Well1024a

org.apache.commons.math3.random.Well1024a
ro.umontreal.ca/~lecuyer/myftp/papers/wellrng.pdf">Improved Long-Period Generators Based on Linear Recurrences Modulo 2 ACM Transactions on Mathematical Software, 32, 1 (2006). The errata for the paper are in wellrng-errata.txt.
@see WELL Random number generator @version $Id: Well1024a.java 1416643 2012-12-03 19:37:14Z tn $ @since 2.2

        }
    }


    @Test
    public void testLinearCombinationDF2() {
        RandomGenerator r = new Well1024a(0xdf2l);
        for (int i = 0; i < 50; ++i) {
            double[] aD = generateDouble(r, 2);
            double[] bD = generateDouble(r, 2);
            T[] bF      = toFieldArray(bD);
            checkRelative(MathArrays.linearCombination(aD[0], bD[0], aD[1], bD[1]),

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        }
    }


    @Test
    public void testLinearCombinationFF3() {
        RandomGenerator r = new Well1024a(0xff3l);
        for (int i = 0; i < 50; ++i) {
            double[] aD = generateDouble(r, 3);
            double[] bD = generateDouble(r, 3);
            T[] aF      = toFieldArray(aD);
            T[] bF      = toFieldArray(bD);

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        }
    }


    @Test
    public void testLinearCombinationDF3() {
        RandomGenerator r = new Well1024a(0xdf3l);
        for (int i = 0; i < 50; ++i) {
            double[] aD = generateDouble(r, 3);
            double[] bD = generateDouble(r, 3);
            T[] bF      = toFieldArray(bD);
            checkRelative(MathArrays.linearCombination(aD[0], bD[0], aD[1], bD[1], aD[2], bD[2]),

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        }
    }


    @Test
    public void testLinearCombinationFF4() {
        RandomGenerator r = new Well1024a(0xff4l);
        for (int i = 0; i < 50; ++i) {
            double[] aD = generateDouble(r, 4);
            double[] bD = generateDouble(r, 4);
            T[] aF      = toFieldArray(aD);
            T[] bF      = toFieldArray(bD);

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        }
    }


    @Test
    public void testLinearCombinationDF4() {
        RandomGenerator r = new Well1024a(0xdf4l);
        for (int i = 0; i < 50; ++i) {
            double[] aD = generateDouble(r, 4);
            double[] bD = generateDouble(r, 4);
            T[] bF      = toFieldArray(bD);
            checkRelative(MathArrays.linearCombination(aD[0], bD[0], aD[1], bD[1], aD[2], bD[2], aD[3], bD[3]),

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    }


    @Test
    public void testDoubleVectors() throws MathIllegalArgumentException {


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


            for (double x = -0.9; x < 0.9; x += 0.4) {
                for (double y = -0.9; y < 0.9; y += 0.4) {
                    for (double z = -0.9; z < 0.9; z += 0.4) {
                        FieldVector3D<Dfp> uds   = createVector(x, y, z);

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    @Test
    public void testDoubleRotations() throws MathIllegalArgumentException {


        DfpField field = new DfpField(20);
        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<Dfp> r1Prime = new FieldRotation<Dfp>(field.newDfp(r1.getQ0()),
                                                                field.newDfp(r1.getQ1()),
                                                                field.newDfp(r1.getQ2()),
                                                                field.newDfp(r1.getQ3()),
                                                                false);
            double[] unit2 = g.nextVector();
            FieldRotation<Dfp> r2 = new FieldRotation<Dfp>(createVector(unit2[0], unit2[1], unit2[2]),
                                           createAngle(random.nextDouble()));


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

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            int[] minIndices,
            int[] maxIndices,
            boolean withSymmetries,
            boolean generateNewDescriptors,
            long seed) {
        this(minDiffNDs, maxDiffNDs, minIndices, maxIndices, withSymmetries, generateNewDescriptors, new Well1024a(seed));
    }

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            case MERSENNE_TWISTER:
                return new RandomAdaptor(randomSeed == null ? new MersenneTwister() : new MersenneTwister(randomSeed));
            case WELL512A:
                return new RandomAdaptor(randomSeed == null ? new Well512a() : new Well512a(randomSeed));
            case WELL1024A:
                return new RandomAdaptor(randomSeed == null ? new Well1024a() : new Well1024a(randomSeed));
            case WELL19937A:
                return new RandomAdaptor(randomSeed == null ? new Well19937a() : new Well19937a(randomSeed));
            case WELL19937C:
                return new RandomAdaptor(randomSeed == null ? new Well19937c() : new Well19937c(randomSeed));
            case WELL44497A:

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    @Test
    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();
            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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