Class RandomDataImpl

Method Details

• nextHexString

  public String nextHexString(int len) throws NotStrictlyPositiveException

  Deprecated.

  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 RandomData.nextSecureHexString(int).

  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) throws NumberIsTooLargeException

  Deprecated.

  Generates a uniformly distributed random integer between lower and upper (endpoints included).

  The generated integer will be random, but not cryptographically secure. To generate cryptographically secure integer sequences, use RandomData.nextSecureInt(int, int).

  Specified by:

  nextInt in interface RandomData

  Parameters:

  lower - lower bound for generated integer

  upper - upper bound for generated integer

  Returns:

  a random integer greater than or equal to lower and less than or equal to upper

  Throws:

  NumberIsTooLargeException - if lower >= upper

• nextLong

  public long nextLong(long lower, long upper) throws NumberIsTooLargeException

  Deprecated.

  Generates a uniformly distributed random long integer between lower and upper (endpoints included).

  The generated long integer values will be random, but not cryptographically secure. To generate cryptographically secure sequences of longs, use RandomData.nextSecureLong(long, long).

  Specified by:

  nextLong in interface RandomData

  Parameters:

  lower - lower bound for generated long integer

  upper - upper bound for generated long integer

  Returns:

  a random long integer greater than or equal to lower and less than or equal to upper

  Throws:

  NumberIsTooLargeException - if lower >= upper

• nextSecureHexString

  public String nextSecureHexString(int len) throws NotStrictlyPositiveException

  Deprecated.

  Generates a random string of hex characters from a secure random sequence.

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

  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 string to be generated

  Returns:

  a random string of hex characters of length len

  Throws:

  NotStrictlyPositiveException - if len <= 0

• nextSecureInt

  public int nextSecureInt(int lower, int upper) throws NumberIsTooLargeException

  Deprecated.

  Generates a uniformly distributed random integer between lower and upper (endpoints included) from a secure random sequence.

  Sequences of integers generated using this method will be cryptographically secure. If cryptographic security is not required, RandomData.nextInt(int, int) should be used instead of this method.

  Definition: Secure Random Sequence

  Specified by:

  nextSecureInt in interface RandomData

  Parameters:

  lower - lower bound for generated integer

  upper - upper bound for generated integer

  Returns:

  a random integer greater than or equal to lower and less than or equal to upper.

  Throws:

  NumberIsTooLargeException - if lower >= upper.

• nextSecureLong

  public long nextSecureLong(long lower, long upper) throws NumberIsTooLargeException

  Deprecated.

  Generates a uniformly distributed random long integer between lower and upper (endpoints included) from a secure random sequence.

  Sequences of long values generated using this method will be cryptographically secure. If cryptographic security is not required, RandomData.nextLong(long, long) should be used instead of this method.

  Definition: Secure Random Sequence

  Specified by:

  nextSecureLong in interface RandomData

  Parameters:

  lower - lower bound for generated integer

  upper - upper bound for generated integer

  Returns:

  a random long integer greater than or equal to lower and less than or equal to upper.

  Throws:

  NumberIsTooLargeException - if lower >= upper.

• nextPoisson

  public long nextPoisson(double mean) throws NotStrictlyPositiveException

  Deprecated.

  Generates a random value from the Poisson distribution with the given mean.

  Definition: Poisson Distribution

  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 - the mean of the Poisson distribution

  Returns:

  a random value following the specified Poisson distribution

  Throws:

  NotStrictlyPositiveException - if mean <= 0.

• nextGaussian

  public double nextGaussian(double mu, double sigma) throws NotStrictlyPositiveException

  Deprecated.

  Generates a random value from the Normal (or Gaussian) distribution with specified mean and standard deviation.

  Definition: Normal Distribution

  Specified by:

  nextGaussian in interface RandomData

  Parameters:

  mu - the mean of the distribution

  sigma - the standard deviation of the distribution

  Returns:

  a random value following the specified Gaussian distribution

  Throws:

  NotStrictlyPositiveException - if sigma <= 0.

• nextExponential

  public double nextExponential(double mean) throws NotStrictlyPositiveException

  Deprecated.

  Generates a random value from the exponential distribution with specified mean.

  Definition: Exponential Distribution

  Algorithm Description: Uses the Algorithm SA (Ahrens) from p. 876 in: [1]: Ahrens, J. H. and Dieter, U. (1972). Computer methods for sampling from the exponential and normal distributions. Communications of the ACM, 15, 873-882.

  Specified by:

  nextExponential in interface RandomData

  Parameters:

  mean - the mean of the distribution

  Returns:

  a random value following the specified exponential distribution

  Throws:

  NotStrictlyPositiveException - if mean <= 0.

• nextUniform

  public double nextUniform(double lower, double upper) throws NumberIsTooLargeException, NotFiniteNumberException, NotANumberException

  Deprecated.

  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.

  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 exclusive lower bound of the support

  upper - the exclusive upper bound of the support

  Returns:

  a uniformly distributed random value between lower and upper (exclusive)

  Throws:

  NumberIsTooLargeException - if lower >= upper

  NotFiniteNumberException - if one of the bounds is infinite

  NotANumberException - if one of the bounds is NaN

• nextUniform

  public double nextUniform(double lower, double upper, boolean lowerInclusive) throws NumberIsTooLargeException, NotFiniteNumberException, NotANumberException

  Deprecated.

  Generates a uniformly distributed random value from the interval (lower, upper) or the interval [lower, upper). The lower bound is thus optionally included, while the upper bound is always excluded.

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

  Algorithm Description: if the lower bound is excluded, 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 of the support

  upper - the exclusive upper bound of the support

  lowerInclusive - true if the lower bound is inclusive

  Returns:

  uniformly distributed random value in the (lower, upper) interval, if lowerInclusive is false, or in the [lower, upper) interval, if lowerInclusive is true

  Throws:

  NumberIsTooLargeException - if lower >= upper

  NotFiniteNumberException - if one of the bounds is infinite

  NotANumberException - if one of the bounds is NaN

  Since:

  3.0

• nextBeta

  public double nextBeta(double alpha, double beta)

  Deprecated.

  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

  Since:

  2.2

• nextBinomial

  public int nextBinomial(int numberOfTrials, double probabilityOfSuccess)

  Deprecated.

  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

  Since:

  2.2

• nextCauchy

  public double nextCauchy(double median, double scale)

  Deprecated.

  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

  Since:

  2.2

• nextChiSquare

  public double nextChiSquare(double df)

  Deprecated.

  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

  Since:

  2.2

• nextF

  public double nextF(double numeratorDf, double denominatorDf) throws NotStrictlyPositiveException

  Deprecated.

  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:

  NotStrictlyPositiveException - if numeratorDf <= 0 or denominatorDf <= 0.

  Since:

  2.2

• nextGamma

  public double nextGamma(double shape, double scale) throws NotStrictlyPositiveException

  Deprecated.

  Generates a random value from the Gamma Distribution.

  This implementation uses the following algorithms:

  For 0 invalid input: '<' shape invalid input: '<' 1:
  Ahrens, J. H. and Dieter, U., Computer methods for sampling from gamma, beta, Poisson and binomial distributions. Computing, 12, 223-246, 1974.

  For shape >= 1:
  Marsaglia and Tsang, A Simple Method for Generating Gamma Variables. ACM Transactions on Mathematical Software, Volume 26 Issue 3, September, 2000.

  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:

  NotStrictlyPositiveException - if shape <= 0 or scale <= 0.

  Since:

  2.2

• nextHypergeometric

  public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize) throws NotPositiveException, NotStrictlyPositiveException, NumberIsTooLargeException

  Deprecated.

  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:

  NumberIsTooLargeException - if numberOfSuccesses > populationSize, or sampleSize > populationSize.

  NotStrictlyPositiveException - if populationSize <= 0.

  NotPositiveException - if numberOfSuccesses < 0.

  Since:

  2.2

• nextPascal

  public int nextPascal(int r, double p) throws NotStrictlyPositiveException, OutOfRangeException

  Deprecated.

  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:

  NotStrictlyPositiveException - if the number of successes is not positive

  OutOfRangeException - if the probability of success is not in the range [0, 1].

  Since:

  2.2

• nextT

  public double nextT(double df) throws NotStrictlyPositiveException

  Deprecated.

  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:

  NotStrictlyPositiveException - if df <= 0

  Since:

  2.2

• nextWeibull

  public double nextWeibull(double shape, double scale) throws NotStrictlyPositiveException

  Deprecated.

  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:

  NotStrictlyPositiveException - if shape <= 0 or scale <= 0.

  Since:

  2.2

• nextZipf

  public int nextZipf(int numberOfElements, double exponent) throws NotStrictlyPositiveException

  Deprecated.

  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:

  NotStrictlyPositiveException - if numberOfElements <= 0 or exponent <= 0.

  Since:

  2.2

• reSeed

  public void reSeed(long seed)

  Deprecated.

  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()

  Deprecated.

  Reseeds the secure random number generator with the current time in milliseconds.

  Will create and initialize if null.

• reSeedSecure

  public void reSeedSecure(long seed)

  Deprecated.

  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()

  Deprecated.

  Reseeds the random number generator with System.currentTimeMillis() + System.identityHashCode(this)).

• setSecureAlgorithm

  public void setSecureAlgorithm(String algorithm, String provider) throws NoSuchAlgorithmException, NoSuchProviderException

  Deprecated.

  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 invalid input: '&' 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) throws NotStrictlyPositiveException, NumberIsTooLargeException

  Deprecated.

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

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

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

  Specified by:

  nextPermutation in interface RandomData

  Parameters:

  n - the domain of the permutation

  k - the size of the permutation

  Returns:

  a random k-permutation of n, as an array of integers

  Throws:

  NotStrictlyPositiveException - if k <= 0.

  NumberIsTooLargeException - if k > n.

• nextSample

  public Object[] nextSample(Collection<?> c, int k) throws NotStrictlyPositiveException, NumberIsTooLargeException

  Deprecated.

  Returns an array of k objects selected randomly from the Collection c.

  Sampling from c is without replacement; but if c contains identical objects, the sample may include repeats. If all elements of c are distinct, the resulting object array represents a Simple Random Sample of size k from the elements of c.

  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 - the collection to be sampled

  k - the size of the sample

  Returns:

  a random sample of k elements from c

  Throws:

  NotStrictlyPositiveException - if k <= 0.

  NumberIsTooLargeException - if k > c.size().

• nextInversionDeviate

  @Deprecated public double nextInversionDeviate(RealDistribution distribution) throws MathIllegalArgumentException

  Deprecated.

  use the distribution's sample() method

  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:

  MathIllegalArgumentException - if the underlynig distribution throws one

  Since:

  2.2

• nextInversionDeviate

  @Deprecated public int nextInversionDeviate(IntegerDistribution distribution) throws MathIllegalArgumentException

  Deprecated.

  use the distribution's sample() method

  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:

  MathIllegalArgumentException - if the underlynig distribution throws one

  Since:

  2.2