Examples of org.apache.commons.math.random.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) $

     * @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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     */
    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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        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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        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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        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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        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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    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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