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

     * Make sure that permuted arrays do not hash to the same value.
     */
    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]];

View Full Code Here

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 );
        }

View Full Code Here

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

     * 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;
    }

View Full Code Here

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

     * @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++;

View Full Code Here

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

        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));
            }
        }

View Full Code Here

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 );
        }

View Full Code Here

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 );
        }

View Full Code Here

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 );
        }

View Full Code Here

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

     * Make sure that permuted arrays do not hash to the same value.
     */
    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]];

View Full Code Here

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

     * 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;     
    }

View Full Code Here

0 1 2 3 4 5

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.