Examples of org.apache.commons.math3.random.RandomDataImpl

org.apache.commons.math3.random.RandomDataImpl
Generates random deviates and other random data using a {@link RandomGenerator}instance to generate non-secure data and a {@link java.security.SecureRandom}instance to provide data for the nextSecureXxx methods. If no RandomGenerator is provided in the constructor, the default is to use a {@link Well19937c} generator. To plug in a differentimplementation, either implement RandomGenerator directly or extend {@link AbstractRandomGenerator}.
Supports reseeding the underlying pseudo-random number generator (PRNG). The SecurityProvider and Algorithm used by the SecureRandom instance can also be reset.

For details on the default PRNGs, see {@link java.util.Random} and{@link java.security.SecureRandom}.

Usage Notes:
- Instance variables are used to maintain RandomGenerator and SecureRandom instances used in data generation. Therefore, to generate a random sequence of values or strings, you should use just one RandomDataGenerator instance repeatedly.
- The "secure" methods are *much* slower. These should be used only when a cryptographically secure random sequence is required. A secure random sequence is a sequence of pseudo-random values which, in addition to being well-dispersed (so no subsequence of values is an any more likely than other subsequence of the the same length), also has the additional property that knowledge of values generated up to any point in the sequence does not make it any easier to predict subsequent values.
- When a new RandomDataGenerator is created, the underlying random number generators are not initialized. If you do not explicitly seed the default non-secure generator, it is seeded with the current time in milliseconds plus the system identity hash code on first use. The same holds for the secure generator. If you provide a RandomGenerator to the constructor, however, this generator is not reseeded by the constructor nor is it reseeded on first use.
- The reSeed and reSeedSecure methods delegate to the corresponding methods on the underlying RandomGenerator and SecureRandom instances. Therefore, reSeed(long) fully resets the initial state of the non-secure random number generator (so that reseeding with a specific value always results in the same subsequent random sequence); whereas reSeedSecure(long) does not reinitialize the secure random number generator (so secure sequences started with calls to reseedSecure(long) won't be identical).
- This implementation is not synchronized. The underlying RandomGenerator or SecureRandom instances are not protected by synchronization and are not guaranteed to be thread-safe. Therefore, if an instance of this class is concurrently utilized by multiple threads, it is the responsibility of client code to synchronize access to seeding and data generation methods.
@deprecated to be removed in 4.0. Use {@link RandomDataGenerator} instead

     */
    public NaturalRanking(TiesStrategy tiesStrategy) {
        super();
        this.tiesStrategy = tiesStrategy;
        nanStrategy = DEFAULT_NAN_STRATEGY;
        randomData = new RandomDataImpl();
    }

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     */
    public NaturalRanking(NaNStrategy nanStrategy, TiesStrategy tiesStrategy) {
        super();
        this.nanStrategy = nanStrategy;
        this.tiesStrategy = tiesStrategy;
        randomData = new RandomDataImpl();
    }

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     */
    public NaturalRanking(RandomGenerator randomGenerator) {
        super();
        this.tiesStrategy = TiesStrategy.RANDOM;
        nanStrategy = DEFAULT_NAN_STRATEGY;
        randomData = new RandomDataImpl(randomGenerator);
    }

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    public NaturalRanking(NaNStrategy nanStrategy,
            RandomGenerator randomGenerator) {
        super();
        this.nanStrategy = nanStrategy;
        this.tiesStrategy = TiesStrategy.RANDOM;
        randomData = new RandomDataImpl(randomGenerator);
    }

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        int[] primeList = {19, 23, 53, 67, 73, 79, 101, 103, 111, 131};
        ArrayList<Integer> primes = new ArrayList<Integer>();
        for (int i = 0; i < primeList.length; i++) {
            primes.add(Integer.valueOf(primeList[i]));
        }
        RandomDataImpl randomData = new RandomDataImpl();
        for (int i = 0; i < 20; i++) {
            Object[] sample = randomData.nextSample(primes, 4);
            int p1 = ((Integer) sample[0]).intValue();
            int p2 = ((Integer) sample[1]).intValue();
            int p3 = ((Integer) sample[2]).intValue();
            int p4 = ((Integer) sample[3]).intValue();
            int i1 = p1 * p2 * p3;

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     */
    @Test
    public void testPermutedArrayHash() {
        double[] original = new double[10];
        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 {
            int[] permutation = random.nextPermutation(10, 10);
            for (int i = 0; i < 10; i++) {
                if (i != permutation[i]) {
                    isIdentity = false;
                }
                permuted[i] = original[permutation[i]];

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      }
    }
  }


  private RandomDataImpl seedRandomGenerator() {
    RandomDataImpl rnd = new RandomDataImpl();
    rnd.reSeed(MultilayerPerceptron.SEED);
    rnd.reSeedSecure(MultilayerPerceptron.SEED);
    return rnd;
  }

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      HashSet<DoubleVector> leftDistribution, long seed) {
    List<DoubleVector> lst = new ArrayList<>(100);


    double mean1 = 250;
    double mean2 = 750;
    RandomDataImpl random = new RandomDataImpl(new Well1024a(seed));
    for (int i = 0; i < 50; i++) {
      double nextGaussian1 = random.nextGaussian(mean1, Math.sqrt(100));
      assertTrue(nextGaussian1 >= 150 && nextGaussian1 <= 350);
      double nextGaussian2 = random.nextGaussian(mean2, Math.sqrt(100));
      assertTrue(nextGaussian2 >= 650 && nextGaussian2 <= 850);
      DenseDoubleVector lef = new DenseDoubleVector(
          new double[] { nextGaussian1 });
      lst.add(lef);
      if (leftDistribution != null) {

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     */
    @Test
    public void testPermutedArrayHash() {
        double[] original = new double[10];
        double[] permuted = new double[10];
        RandomDataImpl random = new RandomDataImpl();


        // Generate 10 distinct random values
        for (int i = 0; i < 10; i++) {
            original[i] = random.nextUniform(i + 0.5, i + 0.75);
        }


        // Generate a random permutation, making sure it is not the identity
        boolean isIdentity = true;
        do {
            int[] permutation = random.nextPermutation(10, 10);
            for (int i = 0; i < 10; i++) {
                if (i != permutation[i]) {
                    isIdentity = false;
                }
                permuted[i] = original[permutation[i]];

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    public void testWithInitialCapacity() {


        ResizableDoubleArray eDA2 = new ResizableDoubleArray(2);
        Assert.assertEquals("Initial number of elements should be 0", 0, eDA2.getNumElements());


        RandomData randomData = new RandomDataImpl();
        int iterations = randomData.nextInt(100, 1000);


        for( int i = 0; i < iterations; i++) {
            eDA2.addElement( i );
        }

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