Class RandomDataImpl

java.lang.Object
org.apache.commons.math.random.RandomDataImpl
All Implemented Interfaces:
Serializable, RandomData

public class RandomDataImpl extends Object implements RandomData, Serializable
Implements the RandomData interface using a RandomGenerator instance to generate non-secure data and a 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 Random. To plug in a different implementation, either implement RandomGenerator directly or extend 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 Random and 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) $
See Also:
  • Constructor Details

    • RandomDataImpl

      public RandomDataImpl()
      Construct a RandomDataImpl.
    • RandomDataImpl

      public RandomDataImpl(RandomGenerator rand)
      Construct a RandomDataImpl using the supplied RandomGenerator as the source of (non-secure) random data.
      Parameters:
      rand - the source of (non-secure) random data
      Since:
      1.1
  • Method Details

    • nextHexString

      public String nextHexString(int len)
      Generates a random string of hex characters of length len.

      The generated string will be random, but not cryptographically secure. To generate cryptographically secure strings, use nextSecureHexString

      Preconditions:

      • len > 0 (otherwise an IllegalArgumentException is thrown.)

      Algorithm Description: hex strings are generated using a 2-step process.

      1. len/2+1 binary bytes are generated using the underlying Random
      2. Each binary byte is translated into 2 hex digits

      Specified by:
      nextHexString in interface RandomData
      Parameters:
      len - the desired string length.
      Returns:
      the random string.
      Throws:
      NotStrictlyPositiveException - if len <= 0.
    • nextInt

      public int nextInt(int lower, int upper)
      Generate a random int value uniformly distributed between lower and upper, inclusive.
      Specified by:
      nextInt in interface RandomData
      Parameters:
      lower - the lower bound.
      upper - the upper bound.
      Returns:
      the random integer.
      Throws:
      NumberIsTooLargeException - if lower >= upper.
    • nextLong

      public long nextLong(long lower, long upper)
      Generate a random long value uniformly distributed between lower and upper, inclusive.
      Specified by:
      nextLong in interface RandomData
      Parameters:
      lower - the lower bound.
      upper - the upper bound.
      Returns:
      the random integer.
      Throws:
      NumberIsTooLargeException - if lower >= upper.
    • nextSecureHexString

      public String nextSecureHexString(int len)
      Generates a random string of hex characters from a secure random sequence.

      If cryptographic security is not required, use nextHexString().

      Preconditions:

      • len > 0 (otherwise an IllegalArgumentException is thrown.)

      Algorithm Description: hex strings are generated in 40-byte segments using a 3-step process.

      1. 20 random bytes are generated using the underlying SecureRandom.
      2. SHA-1 hash is applied to yield a 20-byte binary digest.
      3. Each byte of the binary digest is converted to 2 hex digits.

      Specified by:
      nextSecureHexString in interface RandomData
      Parameters:
      len - the length of the generated string
      Returns:
      the random string
      Throws:
      NotStrictlyPositiveException - if len <= 0.
    • nextSecureInt

      public int nextSecureInt(int lower, int upper)
      Generate a random int value uniformly distributed between lower and upper, inclusive. This algorithm uses a secure random number generator.
      Specified by:
      nextSecureInt in interface RandomData
      Parameters:
      lower - the lower bound.
      upper - the upper bound.
      Returns:
      the random integer.
      Throws:
      NumberIsTooLargeException - if lower >= upper.
    • nextSecureLong

      public long nextSecureLong(long lower, long upper)
      Generate a random long value uniformly distributed between lower and upper, inclusive. This algorithm uses a secure random number generator.
      Specified by:
      nextSecureLong in interface RandomData
      Parameters:
      lower - the lower bound.
      upper - the upper bound.
      Returns:
      the random integer.
      Throws:
      NumberIsTooLargeException - if lower >= upper.
    • nextPoisson

      public long nextPoisson(double mean)
      Generates a random value from the Poisson distribution with the given mean.

      Definition: Poisson Distribution

      Preconditions:

      • The specified mean must be positive (otherwise an IllegalArgumentException is thrown.)

      Algorithm Description:

      • For small means, uses simulation of a Poisson process using Uniform deviates, as described here. The Poisson process (and hence value returned) is bounded by 1000 * mean.
      • For large means, uses the rejection algorithm described in
        Devroye, Luc. (1981).The Computer Generation of Poisson Random Variables Computing vol. 26 pp. 197-207.

      Specified by:
      nextPoisson in interface RandomData
      Parameters:
      mean - mean of the Poisson distribution.
      Returns:
      the random Poisson value.
      Throws:
      NotStrictlyPositiveException - if mean <= 0.
    • nextGaussian

      public double nextGaussian(double mu, double sigma)
      Generate a random value from a Normal (a.k.a. Gaussian) distribution with the given mean, mu and the given standard deviation, sigma.
      Specified by:
      nextGaussian in interface RandomData
      Parameters:
      mu - the mean of the distribution
      sigma - the standard deviation of the distribution
      Returns:
      the random Normal value
      Throws:
      NotStrictlyPositiveException - if sigma <= 0.
    • nextExponential

      public double nextExponential(double mean)
      Returns a random value from an Exponential distribution with the given mean.

      Algorithm Description: Uses the Inversion Method to generate exponentially distributed random values from uniform deviates.

      Specified by:
      nextExponential in interface RandomData
      Parameters:
      mean - the mean of the distribution
      Returns:
      the random Exponential value
      Throws:
      NotStrictlyPositiveException - if mean <= 0.
    • nextUniform

      public double nextUniform(double lower, double upper)
      Generates a uniformly distributed random value from the open interval (lower,upper) (i.e., endpoints excluded).

      Definition: Uniform Distribution lower and upper - lower are the location and scale parameters, respectively.

      Preconditions:

      • lower < upper (otherwise an IllegalArgumentException is thrown.)

      Algorithm Description: scales the output of Random.nextDouble(), but rejects 0 values (i.e., will generate another random double if Random.nextDouble() returns 0). This is necessary to provide a symmetric output interval (both endpoints excluded).

      Specified by:
      nextUniform in interface RandomData
      Parameters:
      lower - the lower bound.
      upper - the upper bound.
      Returns:
      a uniformly distributed random value from the interval (lower, upper)
      Throws:
      NumberIsTooLargeException - if lower >= upper.
    • nextBeta

      public double nextBeta(double alpha, double beta) throws MathException
      Generates a random value from the Beta Distribution. This implementation uses inversion to generate random values.
      Parameters:
      alpha - first distribution shape parameter
      beta - second distribution shape parameter
      Returns:
      random value sampled from the beta(alpha, beta) distribution
      Throws:
      MathException - if an error occurs generating the random value
      Since:
      2.2
    • nextBinomial

      public int nextBinomial(int numberOfTrials, double probabilityOfSuccess) throws MathException
      Generates a random value from the Binomial Distribution. This implementation uses inversion to generate random values.
      Parameters:
      numberOfTrials - number of trials of the Binomial distribution
      probabilityOfSuccess - probability of success of the Binomial distribution
      Returns:
      random value sampled from the Binomial(numberOfTrials, probabilityOfSuccess) distribution
      Throws:
      MathException - if an error occurs generating the random value
      Since:
      2.2
    • nextCauchy

      public double nextCauchy(double median, double scale) throws MathException
      Generates a random value from the Cauchy Distribution. This implementation uses inversion to generate random values.
      Parameters:
      median - the median of the Cauchy distribution
      scale - the scale parameter of the Cauchy distribution
      Returns:
      random value sampled from the Cauchy(median, scale) distribution
      Throws:
      MathException - if an error occurs generating the random value
      Since:
      2.2
    • nextChiSquare

      public double nextChiSquare(double df) throws MathException
      Generates a random value from the ChiSquare Distribution. This implementation uses inversion to generate random values.
      Parameters:
      df - the degrees of freedom of the ChiSquare distribution
      Returns:
      random value sampled from the ChiSquare(df) distribution
      Throws:
      MathException - if an error occurs generating the random value
      Since:
      2.2
    • nextF

      public double nextF(double numeratorDf, double denominatorDf) throws MathException
      Generates a random value from the F Distribution. This implementation uses inversion to generate random values.
      Parameters:
      numeratorDf - the numerator degrees of freedom of the F distribution
      denominatorDf - the denominator degrees of freedom of the F distribution
      Returns:
      random value sampled from the F(numeratorDf, denominatorDf) distribution
      Throws:
      MathException - if an error occurs generating the random value
      Since:
      2.2
    • nextGamma

      public double nextGamma(double shape, double scale) throws MathException
      Generates a random value from the Gamma Distribution. This implementation uses inversion to generate random values.
      Parameters:
      shape - the median of the Gamma distribution
      scale - the scale parameter of the Gamma distribution
      Returns:
      random value sampled from the Gamma(shape, scale) distribution
      Throws:
      MathException - if an error occurs generating the random value
      Since:
      2.2
    • nextHypergeometric

      public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize) throws MathException
      Generates a random value from the Hypergeometric Distribution. This implementation uses inversion to generate random values.
      Parameters:
      populationSize - the population size of the Hypergeometric distribution
      numberOfSuccesses - number of successes in the population of the Hypergeometric distribution
      sampleSize - the sample size of the Hypergeometric distribution
      Returns:
      random value sampled from the Hypergeometric(numberOfSuccesses, sampleSize) distribution
      Throws:
      MathException - if an error occurs generating the random value
      Since:
      2.2
    • nextPascal

      public int nextPascal(int r, double p) throws MathException
      Generates a random value from the Pascal Distribution. This implementation uses inversion to generate random values.
      Parameters:
      r - the number of successes of the Pascal distribution
      p - the probability of success of the Pascal distribution
      Returns:
      random value sampled from the Pascal(r, p) distribution
      Throws:
      MathException - if an error occurs generating the random value
      Since:
      2.2
    • nextT

      public double nextT(double df) throws MathException
      Generates a random value from the T Distribution. This implementation uses inversion to generate random values.
      Parameters:
      df - the degrees of freedom of the T distribution
      Returns:
      random value from the T(df) distribution
      Throws:
      MathException - if an error occurs generating the random value
      Since:
      2.2
    • nextWeibull

      public double nextWeibull(double shape, double scale) throws MathException
      Generates a random value from the Weibull Distribution. This implementation uses inversion to generate random values.
      Parameters:
      shape - the shape parameter of the Weibull distribution
      scale - the scale parameter of the Weibull distribution
      Returns:
      random value sampled from the Weibull(shape, size) distribution
      Throws:
      MathException - if an error occurs generating the random value
      Since:
      2.2
    • nextZipf

      public int nextZipf(int numberOfElements, double exponent) throws MathException
      Generates a random value from the Zipf Distribution. This implementation uses inversion to generate random values.
      Parameters:
      numberOfElements - the number of elements of the ZipfDistribution
      exponent - the exponent of the ZipfDistribution
      Returns:
      random value sampled from the Zipf(numberOfElements, exponent) distribution
      Throws:
      MathException - if an error occurs generating the random value
      Since:
      2.2
    • reSeed

      public void reSeed(long seed)
      Reseeds the random number generator with the supplied seed.

      Will create and initialize if null.

      Parameters:
      seed - the seed value to use
    • reSeedSecure

      public void reSeedSecure()
      Reseeds the secure random number generator with the current time in milliseconds.

      Will create and initialize if null.

    • reSeedSecure

      public void reSeedSecure(long seed)
      Reseeds the secure random number generator with the supplied seed.

      Will create and initialize if null.

      Parameters:
      seed - the seed value to use
    • reSeed

      public void reSeed()
      Reseeds the random number generator with the current time in milliseconds.
    • setSecureAlgorithm

      public void setSecureAlgorithm(String algorithm, String provider) throws NoSuchAlgorithmException, NoSuchProviderException
      Sets the PRNG algorithm for the underlying SecureRandom instance using the Security Provider API. The Security Provider API is defined in Java Cryptography Architecture API Specification & Reference.

      USAGE NOTE: This method carries significant overhead and may take several seconds to execute.

      Parameters:
      algorithm - the name of the PRNG algorithm
      provider - the name of the provider
      Throws:
      NoSuchAlgorithmException - if the specified algorithm is not available
      NoSuchProviderException - if the specified provider is not installed
    • nextPermutation

      public int[] nextPermutation(int n, int k)
      Generates an integer array of length k whose entries are selected randomly, without repetition, from the integers 0 through n-1 (inclusive).

      Generated arrays represent permutations of n taken k at a time.

      Preconditions:

      • k <= n
      • n > 0
      If the preconditions are not met, an IllegalArgumentException is thrown.

      Uses a 2-cycle permutation shuffle. The shuffling process is described here.

      Specified by:
      nextPermutation in interface RandomData
      Parameters:
      n - domain of the permutation (must be positive)
      k - size of the permutation (must satisfy 0 < k <= n).
      Returns:
      the random permutation as an int array
      Throws:
      NumberIsTooLargeException - if k > n.
      NotStrictlyPositiveException - if k <= 0.
    • nextSample

      public Object[] nextSample(Collection<?> c, int k)
      Uses a 2-cycle permutation shuffle to generate a random permutation. Algorithm Description: Uses a 2-cycle permutation shuffle to generate a random permutation of c.size() and then returns the elements whose indexes correspond to the elements of the generated permutation. This technique is described, and proven to generate random samples, here
      Specified by:
      nextSample in interface RandomData
      Parameters:
      c - Collection to sample from.
      k - sample size.
      Returns:
      the random sample.
      Throws:
      NumberIsTooLargeException - if k > c.size().
      NotStrictlyPositiveException - if k <= 0.
    • nextInversionDeviate

      public double nextInversionDeviate(ContinuousDistribution distribution) throws MathException
      Generate a random deviate from the given distribution using the inversion method.
      Parameters:
      distribution - Continuous distribution to generate a random value from
      Returns:
      a random value sampled from the given distribution
      Throws:
      MathException - if an error occurs computing the inverse cumulative distribution function
      Since:
      2.2
    • nextInversionDeviate

      public int nextInversionDeviate(IntegerDistribution distribution) throws MathException
      Generate a random deviate from the given distribution using the inversion method.
      Parameters:
      distribution - Integer distribution to generate a random value from
      Returns:
      a random value sampled from the given distribution
      Throws:
      MathException - if an error occurs computing the inverse cumulative distribution function
      Since:
      2.2