org.apache.commons.math.random

## Class RandomDataImpl

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

```public class RandomDataImpl
extends java.lang.Object
implements RandomData, java.io.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.

Serialized Form
• ### Constructor Summary

Constructors
Constructor and Description
`RandomDataImpl()`
Construct a RandomDataImpl.
`RandomDataImpl(RandomGenerator rand)`
Construct a RandomDataImpl using the supplied `RandomGenerator` as the source of (non-secure) random data.
• ### Method Summary

All Methods
Modifier and Type Method and Description
`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)`
Returns a random value from an Exponential distribution with the given 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)```
Generate a random value from a Normal (a.k.a.
`java.lang.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)```
Generate a random int value uniformly distributed between `lower` and `upper`, inclusive.
`double` `nextInversionDeviate(ContinuousDistribution distribution)`
Generate a random deviate from the given distribution using the inversion method.
`int` `nextInversionDeviate(IntegerDistribution distribution)`
Generate a random deviate from the given distribution using the inversion method.
`long` ```nextLong(long lower, long upper)```
Generate a random long value uniformly distributed between `lower` and `upper`, inclusive.
`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 through n-1` (inclusive).
`long` `nextPoisson(double mean)`
Generates a random value from the Poisson distribution with the given mean.
`java.lang.Object[]` ```nextSample(java.util.Collection<?> c, int k)```
Uses a 2-cycle permutation shuffle to generate a random permutation.
`java.lang.String` `nextSecureHexString(int len)`
Generates a random string of hex characters from a secure random sequence.
`int` ```nextSecureInt(int lower, int upper)```
Generate a random int value uniformly distributed between `lower` and `upper`, inclusive.
`long` ```nextSecureLong(long lower, long upper)```
Generate a random long value uniformly distributed between `lower` and `upper`, inclusive.
`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` ```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 the current time in milliseconds.
`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(java.lang.String algorithm, java.lang.String provider)```
Sets the PRNG algorithm for the underlying SecureRandom instance using the Security Provider API.
• ### Methods inherited from class java.lang.Object

`equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• ### Constructor Detail

• #### 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 Detail

• #### nextHexString

`public java.lang.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 java.lang.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(java.lang.String algorithm,
java.lang.String provider)
throws java.security.NoSuchAlgorithmException,
java.security.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:
`java.security.NoSuchAlgorithmException` - if the specified algorithm is not available
`java.security.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 java.lang.Object[] nextSample(java.util.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
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