Examples of RandomDataImpl

org.apache.commons.math.random.RandomDataImpl
Implements the {@link RandomData} interface 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 generator based on {@link java.util.Random}. To plug in a different implementation, 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 RandomDataImpl 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 RandomDataImpl 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 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.
@version $Revision: 1061496 $ $Date: 2011-01-20 21:32:16 +0100 (jeu. 20 janv. 2011) $
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

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

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

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

    return (T) pickArray(args);
  }


  public static Number pickRange(NumberRange range) {
    Number result = null;
    RandomData randomData = new RandomDataImpl();


    if (range.getMinimumNumber() instanceof Integer && range.getMaximumNumber() instanceof Integer) {
      result = randomData.nextInt(range.getMinimumInteger(), range.getMaximumInteger());
    }


    if (range.getMinimumNumber() instanceof Long && range.getMaximumNumber() instanceof Long) {
      result = randomData.nextLong(range.getMinimumLong(), range.getMaximumLong());
    }


    if (range.getMinimumNumber() instanceof Float && range.getMaximumNumber() instanceof Float) {
      result = randomData.nextUniform(range.getMinimumFloat(), range.getMaximumFloat());
    }


    if (range.getMinimumNumber() instanceof Double && range.getMaximumNumber() instanceof Double) {
      result = randomData.nextUniform(range.getMinimumDouble(), range.getMaximumDouble());
    }


    return result;
  }

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

    return null;
  }
  
  public static double getRandom(double mean, double stdDev)
  {
    RandomDataImpl randomDataImpl = new RandomDataImpl();
    randomDataImpl.reSeedSecure(1000);
    return randomDataImpl.nextGaussian(mean, stdDev);
  }

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

    public void testWithInitialCapacity() {
        
        ResizableDoubleArray eDA2 = new ResizableDoubleArray(2);
        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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Examples of org.apache.commons.math.random.RandomDataImpl

    public void testWithInitialCapacityAndExpansionFactor() {
        
        ResizableDoubleArray eDA3 = new ResizableDoubleArray(3, 3.0f, 3.5f);
        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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Examples of org.apache.commons.math.random.RandomDataImpl

        double[] test1 = {5,4,3,2,1};
        double[] test2 = {5,2,1,3,4,0};
        double[] test3 = {1};
        int[] testi = null;
        double[] test4 = null;
        RandomData rd = new RandomDataImpl();
        tstGetSortedValues(test1);
        tstGetSortedValues(test2);
        tstGetSortedValues(test3);
        for (int i = 0; i < 10; i++) {
            testi = rd.nextPermutation(10,6);
            test4 = new double[6];
            for (int j = 0; j < testi.length; j++) {
                test4[j] = (double) testi[j];
            }
            tstGetSortedValues(test4);
        }
        for (int i = 0; i < 10; i++) {
            testi = rd.nextPermutation(10,5);
            test4 = new double[5];
            for (int j = 0; j < testi.length; j++) {
                test4[j] = (double) testi[j];
            }
            tstGetSortedValues(test4);

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

        assertEquals("Q1",1,u.getPercentile(25),10E-12);
        assertEquals("Q3",1,u.getPercentile(75),10E-12);
        assertEquals("Q2",1,u.getPercentile(50),10E-12);
        
        u.clear();
        RandomData rd = new RandomDataImpl();
        int[] testi = rd.nextPermutation(100,100); // will contain 0-99
        for (int j = 0; j < testi.length; j++) {
            u.addValue((double) testi[j]);  //OK, laugh at me for the cast
        }
        for (int i = 1; i < 100; i++) {
            assertEquals("percentile " + i,

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

        double[] test1 = {5,4,3,2,1};
        double[] test2 = {5,2,1,3,4,0};
        double[] test3 = {1};
        int[] testi = null;
        double[] test4 = null;
        RandomData rd = new RandomDataImpl();
        tstGetSortedValues(test1);
        tstGetSortedValues(test2);
        tstGetSortedValues(test3);
        for (int i = 0; i < 10; i++) {
            testi = rd.nextPermutation(10,6);
            test4 = new double[6];
            for (int j = 0; j < testi.length; j++) {
                test4[j] = (double) testi[j];
            }
            tstGetSortedValues(test4);
        }
        for (int i = 0; i < 10; i++) {
            testi = rd.nextPermutation(10,5);
            test4 = new double[5];
            for (int j = 0; j < testi.length; j++) {
                test4[j] = (double) testi[j];
            }
            tstGetSortedValues(test4);

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

        assertEquals("Q1",1,u.getPercentile(25),10E-12);
        assertEquals("Q3",1,u.getPercentile(75),10E-12);
        assertEquals("Q2",1,u.getPercentile(50),10E-12);
        
        u.clear();
        RandomData rd = new RandomDataImpl();
        int[] testi = rd.nextPermutation(100,100); // will contain 0-99
        for (int j = 0; j < testi.length; j++) {
            u.addValue((double) testi[j]);  //OK, laugh at me for the cast
        }
        for (int i = 1; i < 100; i++) {
            assertEquals("percentile " + i,

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

        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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