org.apache.commons.math3.random

## Class RandomDataGenerator

- 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

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