| Package | Description |
|---|---|
| org.hipparchus.clustering |
Clustering algorithms.
|
| org.hipparchus.distribution.multivariate |
Implementations of multivariate distributions.
|
| org.hipparchus.optim.nonlinear.scalar.noderiv |
This package provides optimization algorithms that do not require derivatives.
|
| org.hipparchus.optim.univariate |
One-dimensional optimization algorithms.
|
| org.hipparchus.random |
Random number and random data generators.
|
| org.hipparchus.samples |
Various examples.
|
| org.hipparchus.stat.descriptive.rank |
Summary statistics based on ranks.
|
| org.hipparchus.stat.fitting |
Statistical methods for fitting distributions.
|
| org.hipparchus.stat.ranking |
Classes providing rank transformations.
|
| org.hipparchus.util |
Convenience routines and common data structures used throughout the Hipparchus library.
|
| Modifier and Type | Method and Description |
|---|---|
RandomGenerator |
KMeansPlusPlusClusterer.getRandomGenerator()
Returns the random generator this instance will use.
|
RandomGenerator |
FuzzyKMeansClusterer.getRandomGenerator()
Returns the random generator this instance will use.
|
| Constructor and Description |
|---|
FuzzyKMeansClusterer(int k,
double fuzziness,
int maxIterations,
DistanceMeasure measure,
double epsilon,
RandomGenerator random)
Creates a new instance of a FuzzyKMeansClusterer.
|
KMeansPlusPlusClusterer(int k,
int maxIterations,
DistanceMeasure measure,
RandomGenerator random)
Build a clusterer.
|
KMeansPlusPlusClusterer(int k,
int maxIterations,
DistanceMeasure measure,
RandomGenerator random,
KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)
Build a clusterer.
|
| Modifier and Type | Field and Description |
|---|---|
protected RandomGenerator |
AbstractMultivariateRealDistribution.random
RNG instance used to generate samples from the distribution.
|
| Constructor and Description |
|---|
AbstractMultivariateRealDistribution(RandomGenerator rng,
int n) |
MixtureMultivariateNormalDistribution(RandomGenerator rng,
List<Pair<Double,MultivariateNormalDistribution>> components)
Creates a mixture model from a list of distributions and their
associated weights.
|
MixtureMultivariateRealDistribution(RandomGenerator rng,
List<Pair<Double,T>> components)
Creates a mixture model from a list of distributions and their
associated weights.
|
MultivariateNormalDistribution(RandomGenerator rng,
double[] means,
double[][] covariances)
Creates a multivariate normal distribution with the given mean vector and
covariance matrix.
|
| Constructor and Description |
|---|
CMAESOptimizer(int maxIterations,
double stopFitness,
boolean isActiveCMA,
int diagonalOnly,
int checkFeasableCount,
RandomGenerator random,
boolean generateStatistics,
ConvergenceChecker<PointValuePair> checker) |
| Constructor and Description |
|---|
MultiStartUnivariateOptimizer(UnivariateOptimizer optimizer,
int starts,
RandomGenerator generator)
Create a multi-start optimizer from a single-start optimizer.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractWell
This abstract class implements the WELL class of pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
|
class |
ISAACRandom
A fast cryptographic pseudo-random number generator.
|
class |
JDKRandomGenerator
A
RandomGenerator adapter that delegates the random number
generation to the standard Random class. |
class |
MersenneTwister
This class implements a powerful pseudo-random number generator
developed by Makoto Matsumoto and Takuji Nishimura during
1996-1997.
|
class |
RandomAdaptor
Extension of
Random wrapping a
RandomGenerator. |
class |
RandomDataGenerator
A class for generating random data.
|
class |
SynchronizedRandomGenerator
Any
RandomGenerator implementation can be thread-safe if it
is used through an instance of this class. |
class |
Well1024a
This class implements the WELL1024a pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
|
class |
Well19937a
This class implements the WELL19937a pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
|
class |
Well19937c
This class implements the WELL19937c pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
|
class |
Well44497a
This class implements the WELL44497a pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
|
class |
Well44497b
This class implements the WELL44497b pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
|
class |
Well512a
This class implements the WELL512a pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
|
| Modifier and Type | Method and Description |
|---|---|
protected RandomGenerator |
RandomDataGenerator.delegate()
Returns the backing delegate instance that methods are forwarded to.
|
| Modifier and Type | Method and Description |
|---|---|
static RandomDataGenerator |
RandomDataGenerator.of(RandomGenerator randomGenerator)
Factory method to create a
RandomData instance using the supplied
RandomGenerator. |
static Random |
RandomAdaptor.of(RandomGenerator randomGenerator)
Factory method to create a
Random using the supplied
RandomGenerator. |
| Constructor and Description |
|---|
GaussianRandomGenerator(RandomGenerator generator)
Create a new generator.
|
RandomAdaptor(RandomGenerator randomGenerator)
Construct a RandomAdaptor wrapping the supplied RandomGenerator.
|
StableRandomGenerator(RandomGenerator generator,
double alpha,
double beta)
Create a new generator.
|
SynchronizedRandomGenerator(RandomGenerator rng)
Creates a synchronized wrapper for the given
RandomGenerator
instance. |
UniformRandomGenerator(RandomGenerator generator)
Create a new generator.
|
UnitSphereRandomVectorGenerator(int dimension,
RandomGenerator rand) |
| Modifier and Type | Method and Description |
|---|---|
static Vector2D |
ClusterAlgorithmComparison.generateNoiseVector(RandomGenerator randomGenerator,
double noise) |
static List<Vector2D> |
ClusterAlgorithmComparison.makeBlobs(int samples,
int centers,
double clusterStd,
double min,
double max,
boolean shuffle,
RandomGenerator random) |
static List<Vector2D> |
ClusterAlgorithmComparison.makeCircles(int samples,
boolean shuffle,
double noise,
double factor,
RandomGenerator random) |
static List<Vector2D> |
ClusterAlgorithmComparison.makeMoons(int samples,
boolean shuffle,
double noise,
RandomGenerator random) |
| Constructor and Description |
|---|
RandomPercentile(double epsilon,
RandomGenerator randomGenerator)
Constructs a
RandomPercentile with quantile estimation error
epsilon using randomGenerator as its source of random data. |
RandomPercentile(RandomGenerator randomGenerator)
Constructs a
RandomPercentile with default estimation error
using randomGenerator as its source of random data. |
| Constructor and Description |
|---|
EmpiricalDistribution(int binCount,
RandomGenerator generator)
Creates a new EmpiricalDistribution with the specified bin count using the
provided
RandomGenerator as the source of random data. |
EmpiricalDistribution(RandomGenerator generator)
Creates a new EmpiricalDistribution with default bin count using the
provided
RandomGenerator as the source of random data. |
| Constructor and Description |
|---|
NaturalRanking(NaNStrategy nanStrategy,
RandomGenerator randomGenerator)
Create a NaturalRanking with the given NaNStrategy, TiesStrategy.RANDOM
and the given source of random data.
|
NaturalRanking(RandomGenerator randomGenerator)
Create a NaturalRanking with TiesStrategy.RANDOM and the given
RandomGenerator as the source of random data.
|
| Modifier and Type | Method and Description |
|---|---|
static void |
MathArrays.shuffle(int[] list,
int start,
MathArrays.Position pos,
RandomGenerator rng)
Shuffle the entries of the given array, using the
Fisher–Yates algorithm.
|
static void |
MathArrays.shuffle(int[] list,
RandomGenerator rng)
Shuffle the entries of the given array.
|
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