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 void testWithInitialCapacityAndExpansionFactor() {


        ResizableDoubleArray eDA3 = new ResizableDoubleArray(3, 3.0f, 3.5f);
        Assert.assertEquals("Initial number of elements should be 0", 0, eDA3.getNumElements() );


        RandomData randomData = new RandomDataImpl();
        int iterations = randomData.nextInt(100, 3000);


        for( int i = 0; i < iterations; i++) {
            eDA3.addElement( i );
        }

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     * uniformly distributed over [-100, 100].
     *
     * @return array of random double values
     */
    private double[] generateSample() {
        final RandomData randomData = new RandomDataImpl();
        final int sampleSize = randomData.nextInt(10,100);
        double[] out = new double[sampleSize];
        for (int i = 0; i < out.length; i++) {
            out[i] = randomData.nextUniform(-100, 100);
        }
        return out;
    }

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     * @return rectangular array with rows = subsamples
     */
    private double[][] generatePartition(double[] sample) {
        final int length = sample.length;
        final double[][] out = new double[5][];
        final RandomData randomData = new RandomDataImpl();
        int cur = 0;
        int offset = 0;
        int sampleCount = 0;
        for (int i = 0; i < 5; i++) {
            if (cur == length || offset == length) {
                break;
            }
            final int next = (i == 4 || cur == length - 1) ? length - 1 : randomData.nextInt(cur, length - 1);
            final int subLength = next - cur + 1;
            out[i] = new double[subLength];
            System.arraycopy(sample, offset, out[i], 0, subLength);
            cur = next + 1;
            sampleCount++;

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        final int len = 10;        // length of values array
        final double mu = 0;       // mean of test data
        final double sigma = 5;    // std dev of test data
        double[] values = new double[len];
        double[] weights = new double[len];
        RandomData randomData = new RandomDataImpl();


        // Fill weights array with random int values between 1 and 5
        int[] intWeights = new int[len];
        for (int i = 0; i < len; i++) {
            intWeights[i] = randomData.nextInt(1, 5);
            weights[i] = intWeights[i];
        }


        // 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
        List<Double> valuesList = new ArrayList<Double>();
        for (int i = 0; i < len; i++) {
            double value = randomData.nextGaussian(mu, sigma);
            values[i] = value;
            for (int j = 0; j < intWeights[i]; j++) {
                valuesList.add(new Double(value));
            }
        }

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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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    int haploidNum = 2;
    int groupNum = 1;
    int simSize = 1000000;
    CornSurface myCornSurface = new CornSurface(kernelNum,rowNum,haploidNum,groupNum);
    RandomGenerator rg = new Well44497b();
    RandomDataImpl rng = new RandomDataImpl(rg);
//    CornSimulation myCornSimulation = new CornSimulation(myCornSurface,rng,simSize);
//    System.out.println("The pvalue for four neighbor group is: " + myCornSimulation.simulate(NeighborType.FourNeighbor)+".");
//
//    System.out.println("The pvalue for eight neighbor group is: "+ myCornSimulation.simulate(NeighborType.EightNeighbor)+".");
  //  CornsSimulation corns = new CornsSimulation(2,rng,10000);

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 * 
 */
public class TestSpeed {
  public static void main(String[] args) {
    RandomGenerator rg = new Well44497b();
    RandomDataImpl rng = new RandomDataImpl(rg);
    Timer timer = new Timer();
    timer.begin();
    final int MAX_THREADS = Runtime.getRuntime().availableProcessors();
    ExecutorService pool = Executors.newFixedThreadPool(MAX_THREADS);
    for (int i = 0; i < 10000; i++) {

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public class TestRNGSpeed {
  public static void main(String[] args){
    //CommonMath
    RandomGenerator rg = new Well44497b(29);
    RandomDataImpl rng = new RandomDataImpl(rg);
    Timer timer = new Timer();
    timer.start();
    for(long i=0; i<130000000; ++i){
//      rng.nextPermutation(16, 8);
//      rng.nextGaussian(0, 1);
      rng.nextInt(0, 65779);
    }
    timer.stop();
    timer.printSeconds("generating random numbers");
  }

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public class TestFinal {
  public static void main(String[] args){
    RandomGenerator rg = new Well44497b(5);
    RandomDataImpl rng = new RandomDataImpl(rg);
    FinalClass fc = new FinalClass.Builder(rng).builder();
    System.out.println(fc.getRNG().nextInt(0, 10));
    System.out.println(fc.getRNG().nextInt(0, 10));
    System.out.println(fc.getRNG().nextInt(0, 10));
    System.out.println(fc.getRNG().nextInt(0,10));
    System.out.println(rng.nextInt(0, 10));
    System.out.println(rng.nextInt(0, 10));
//    rg = new Well44497b(11);
//    rng = new RandomDataImpl(rg);
    System.out.println(fc.getRNG().nextInt(0, 10));
    System.out.println(fc.getRNG().nextInt(0, 10));
    System.out.println(fc.getRNG().nextInt(0, 10));

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import org.apache.commons.math3.random.Well44497b;


public class TestRNG {
  public static void main(String[] args){
    RandomGenerator rg = new Well44497b(29);
    RandomDataImpl rng = new RandomDataImpl(rg);
    int numberOfElements = 12870;
    int upper = numberOfElements - 1;
    int[] freq = new int[numberOfElements];
    Arrays.fill(freq, 0);
    int averageFreq = 1000;
    for(int i=0; i<numberOfElements; ++i){
      for(int j=0; j<averageFreq; ++j){
        freq[rng.nextInt(0, upper)]++;
      }
    }
    Arrays.sort(freq);
    ArrayList<Frequency> hist = new ArrayList<Frequency>(100);
    Frequency currentFrequency = new Frequency(-1);

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