RandomDataGenerator (Apache Commons Math 3.5 API)

java.lang.Object
- org.apache.commons.math3.random.RandomDataGenerator

All Implemented Interfaces:

Serializable, RandomData
```
public class RandomDataGenerator
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 Well19937c generator. 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 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.
Since:

3.1

See Also:
Serialized Form

Constructor Summary

Constructors
Constructor and Description
`RandomDataGenerator()` Construct a RandomDataGenerator, using a default random generator as the source of randomness.
`RandomDataGenerator(RandomGenerator rand)` Construct a RandomDataGenerator using the supplied `RandomGenerator` as the source of (non-secure) random data.

Method Summary

Methods
Modifier and Type	Method and Description
`RandomGenerator`	`getRandomGenerator()` Returns the RandomGenerator used to generate non-secure random data.
`double`	`nextBeta(double alpha, double beta)` Generates a random value from the `Beta Distribution`.
`int`	`nextBinomial(int numberOfTrials, double probabilityOfSuccess)` Generates a random value from the `Binomial Distribution`.
`double`	`nextCauchy(double median, double scale)` Generates a random value from the `Cauchy Distribution`.
`double`	`nextChiSquare(double df)` Generates a random value from the `ChiSquare Distribution`.
`double`	`nextExponential(double mean)` Generates a random value from the exponential distribution with specified mean.
`double`	`nextF(double numeratorDf, double denominatorDf)` Generates a random value from the `F Distribution`.
`double`	`nextGamma(double shape, double scale)` Generates a random value from the `Gamma Distribution`.
`double`	`nextGaussian(double mu, double sigma)` Generates a random value from the Normal (or Gaussian) distribution with specified mean and standard deviation.
`String`	`nextHexString(int len)` Generates a random string of hex characters of length `len`.
`int`	`nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize)` Generates a random value from the `Hypergeometric Distribution`.
`int`	`nextInt(int lower, int upper)` Generates a uniformly distributed random integer between `lower` and `upper` (endpoints included).
`long`	`nextLong(long lower, long upper)` Generates a uniformly distributed random long integer between `lower` and `upper` (endpoints included).
`int`	`nextPascal(int r, double p)` Generates a random value from the `Pascal Distribution`.
`int[]`	`nextPermutation(int n, int k)` Generates an integer array of length `k` whose entries are selected randomly, without repetition, from the integers `0, ..., n - 1` (inclusive).
`long`	`nextPoisson(double mean)` Generates a random value from the Poisson distribution with the given mean.
`Object[]`	`nextSample(Collection<?> c, int k)` Returns an array of `k` objects selected randomly from the Collection `c`.
`String`	`nextSecureHexString(int len)` Generates a random string of hex characters from a secure random sequence.
`int`	`nextSecureInt(int lower, int upper)` Generates a uniformly distributed random integer between `lower` and `upper` (endpoints included) from a secure random sequence.
`long`	`nextSecureLong(long lower, long upper)` Generates a uniformly distributed random long integer between `lower` and `upper` (endpoints included) from a secure random sequence.
`double`	`nextT(double df)` Generates a random value from the `T Distribution`.
`double`	`nextUniform(double lower, double upper)` Generates a uniformly distributed random value from the open interval `(lower, upper)` (i.e., endpoints excluded).
`double`	`nextUniform(double lower, double upper, boolean lowerInclusive)` Generates a uniformly distributed random value from the interval `(lower, upper)` or the interval `[lower, upper)`.
`double`	`nextWeibull(double shape, double scale)` Generates a random value from the `Weibull Distribution`.
`int`	`nextZipf(int numberOfElements, double exponent)` Generates a random value from the `Zipf Distribution`.
`void`	`reSeed()` Reseeds the random number generator with `System.currentTimeMillis() + System.identityHashCode(this))`.
`void`	`reSeed(long seed)` Reseeds the random number generator with the supplied seed.
`void`	`reSeedSecure()` Reseeds the secure random number generator with the current time in milliseconds.
`void`	`reSeedSecure(long seed)` Reseeds the secure random number generator with the supplied seed.
`void`	`setSecureAlgorithm(String algorithm, String provider)` Sets the PRNG algorithm for the underlying SecureRandom instance using the Security Provider API.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - RandomDataGenerator
```
public RandomDataGenerator()
```
    Construct a RandomDataGenerator, using a default random generator as the source of randomness.
    The default generator is a Well19937c seeded with System.currentTimeMillis() + System.identityHashCode(this)). The generator is initialized and seeded on first use.
  - RandomDataGenerator
```
public RandomDataGenerator(RandomGenerator rand)
```
    Construct a RandomDataGenerator using the supplied RandomGenerator as the source of (non-secure) random data.
    
    Parameters:
    rand - the source of (non-secure) random data (may be null, resulting in the default generator)
- Method Detail
  - nextHexString
```
public String nextHexString(int len)
                     throws NotStrictlyPositiveException
```
    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
```
    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
```
    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
```
    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
```
    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
```
    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
```
    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 len <= 0
  - nextGaussian
```
public double nextGaussian(double mu,
                  double sigma)
                    throws NotStrictlyPositiveException
```
    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
```
    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.
  - nextGamma
```
public double nextGamma(double shape,
               double scale)
                 throws NotStrictlyPositiveException
```
    Generates a random value from the Gamma Distribution.
    
    This implementation uses the following algorithms:
    
    For 0 < shape < 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.
  - nextHypergeometric
```
public int nextHypergeometric(int populationSize,
                     int numberOfSuccesses,
                     int sampleSize)
                       throws NotPositiveException,
                              NotStrictlyPositiveException,
                              NumberIsTooLargeException
```
    Generates a random value from the Hypergeometric Distribution.
    
    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.
  - nextPascal
```
public int nextPascal(int r,
             double p)
               throws NotStrictlyPositiveException,
                      OutOfRangeException
```
    Generates a random value from the Pascal Distribution.
    
    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].
  - nextT
```
public double nextT(double df)
             throws NotStrictlyPositiveException
```
    Generates a random value from the T Distribution.
    
    Parameters:
    df - the degrees of freedom of the T distribution
    
    Returns:
    random value from the T(df) distribution
    
    Throws:
    
    NotStrictlyPositiveException - if df <= 0
  - nextWeibull
```
public double nextWeibull(double shape,
                 double scale)
                   throws NotStrictlyPositiveException
```
    Generates a random value from the Weibull Distribution.
    
    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.
  - nextZipf
```
public int nextZipf(int numberOfElements,
           double exponent)
             throws NotStrictlyPositiveException
```
    Generates a random value from the Zipf Distribution.
    
    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.
  - nextBeta
```
public double nextBeta(double alpha,
              double beta)
```
    Generates a random value from the Beta Distribution.
    
    Parameters:
    alpha - first distribution shape parameter
    beta - second distribution shape parameter
    
    Returns:
    random value sampled from the beta(alpha, beta) distribution
  - nextBinomial
```
public int nextBinomial(int numberOfTrials,
               double probabilityOfSuccess)
```
    Generates a random value from the Binomial Distribution.
    
    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
  - nextCauchy
```
public double nextCauchy(double median,
                double scale)
```
    Generates a random value from the Cauchy Distribution.
    
    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
  - nextChiSquare
```
public double nextChiSquare(double df)
```
    Generates a random value from the ChiSquare Distribution.
    
    Parameters:
    df - the degrees of freedom of the ChiSquare distribution
    
    Returns:
    random value sampled from the ChiSquare(df) distribution
  - nextF
```
public double nextF(double numeratorDf,
           double denominatorDf)
             throws NotStrictlyPositiveException
```
    Generates a random value from the F Distribution.
    
    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.
  - nextUniform
```
public double nextUniform(double lower,
                 double upper)
                   throws NumberIsTooLargeException,
                          NotFiniteNumberException,
                          NotANumberException
```
    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
```
    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
  - nextPermutation
```
public int[] nextPermutation(int n,
                    int k)
                      throws NumberIsTooLargeException,
                             NotStrictlyPositiveException
```
    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.
    This method calls MathArrays.shuffle in order to create a random shuffle of the set of natural numbers { 0, 1, ..., n - 1 }.
    
    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:
    
    NumberIsTooLargeException - if k > n.
    
    NotStrictlyPositiveException - if k <= 0.
  - nextSample
```
public Object[] nextSample(Collection<?> c,
                  int k)
                    throws NumberIsTooLargeException,
                           NotStrictlyPositiveException
```
    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.
    This method calls nextPermutation(c.size(), k) in order to sample the collection.
    
    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:
    
    NumberIsTooLargeException - if k > c.size().
    
    NotStrictlyPositiveException - if k <= 0.
  - 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 System.currentTimeMillis() + System.identityHashCode(this)).
  - 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
  - getRandomGenerator
```
public RandomGenerator getRandomGenerator()
```
    Returns the RandomGenerator used to generate non-secure random data.
    Creates and initializes a default generator if null. Uses a Well19937c generator with System.currentTimeMillis() + System.identityHashCode(this)) as the default seed.
    
    Returns:
    the Random used to generate random data
    Since:
    
    3.2

Class RandomDataGenerator

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

RandomDataGenerator

RandomDataGenerator

Method Detail

nextHexString

nextInt

nextLong

nextSecureHexString

nextSecureInt

nextSecureLong

nextPoisson

nextGaussian

nextExponential

nextGamma

nextHypergeometric

nextPascal

nextT

nextWeibull

nextZipf

nextBeta

nextBinomial

nextCauchy

nextChiSquare

nextF

nextUniform

nextUniform

nextPermutation

nextSample

reSeed

reSeedSecure

reSeedSecure

reSeed

setSecureAlgorithm

getRandomGenerator