| Package | Description |
|---|---|
| org.apache.commons.math3.distribution |
Implementations of common discrete and continuous distributions.
|
| org.apache.commons.math3.ml.neuralnet |
Neural networks.
|
| org.apache.commons.math3.random |
Random number and random data generators.
|
| org.apache.commons.math3.stat.inference |
Classes providing hypothesis testing.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractRealDistribution
Base class for probability distributions on the reals.
|
class |
BetaDistribution
Implements the Beta distribution.
|
class |
CauchyDistribution
Implementation of the Cauchy distribution.
|
class |
ChiSquaredDistribution
Implementation of the chi-squared distribution.
|
class |
ConstantRealDistribution
Implementation of the constant real distribution.
|
class |
EnumeratedRealDistribution
Implementation of a real-valued
EnumeratedDistribution. |
class |
ExponentialDistribution
Implementation of the exponential distribution.
|
class |
FDistribution
Implementation of the F-distribution.
|
class |
GammaDistribution
Implementation of the Gamma distribution.
|
class |
GumbelDistribution
This class implements the Gumbel distribution.
|
class |
LaplaceDistribution
This class implements the Laplace distribution.
|
class |
LevyDistribution
This class implements the
Lévy distribution.
|
class |
LogisticDistribution
This class implements the Logistic distribution.
|
class |
LogNormalDistribution
Implementation of the log-normal (gaussian) distribution.
|
class |
NakagamiDistribution
This class implements the Nakagami distribution.
|
class |
NormalDistribution
Implementation of the normal (gaussian) distribution.
|
class |
ParetoDistribution
Implementation of the Pareto distribution.
|
class |
TDistribution
Implementation of Student's t-distribution.
|
class |
TriangularDistribution
Implementation of the triangular real distribution.
|
class |
UniformRealDistribution
Implementation of the uniform real distribution.
|
class |
WeibullDistribution
Implementation of the Weibull distribution.
|
| Modifier and Type | Method and Description |
|---|---|
static FeatureInitializer |
FeatureInitializerFactory.randomize(RealDistribution random,
FeatureInitializer orig)
Adds some amount of random data to the given initializer.
|
| Modifier and Type | Class and Description |
|---|---|
class |
EmpiricalDistribution
Represents an
empirical probability distribution -- a probability distribution derived
from observed data without making any assumptions about the functional form
of the population distribution that the data come from.
|
| Modifier and Type | Method and Description |
|---|---|
protected RealDistribution |
EmpiricalDistribution.getKernel(SummaryStatistics bStats)
The within-bin smoothing kernel.
|
| Modifier and Type | Method and Description |
|---|---|
double |
RandomDataImpl.nextInversionDeviate(RealDistribution distribution)
Deprecated.
use the distribution's sample() method
|
| Modifier and Type | Method and Description |
|---|---|
static double |
TestUtils.kolmogorovSmirnovStatistic(RealDistribution dist,
double[] data) |
double |
KolmogorovSmirnovTest.kolmogorovSmirnovStatistic(RealDistribution distribution,
double[] data)
Computes the one-sample Kolmogorov-Smirnov test statistic, \(D_n=\sup_x |F_n(x)-F(x)|\) where
\(F\) is the distribution (cdf) function associated with
distribution, \(n\) is the
length of data and \(F_n\) is the empirical distribution that puts mass \(1/n\) at
each of the values in data. |
static double |
TestUtils.kolmogorovSmirnovTest(RealDistribution dist,
double[] data) |
double |
KolmogorovSmirnovTest.kolmogorovSmirnovTest(RealDistribution distribution,
double[] data)
Computes the p-value, or observed significance level, of a one-sample Kolmogorov-Smirnov test
evaluating the null hypothesis that
data conforms to distribution. |
static double |
TestUtils.kolmogorovSmirnovTest(RealDistribution dist,
double[] data,
boolean strict) |
double |
KolmogorovSmirnovTest.kolmogorovSmirnovTest(RealDistribution distribution,
double[] data,
boolean exact)
Computes the p-value, or observed significance level, of a one-sample Kolmogorov-Smirnov test
evaluating the null hypothesis that
data conforms to distribution. |
static boolean |
TestUtils.kolmogorovSmirnovTest(RealDistribution dist,
double[] data,
double alpha) |
boolean |
KolmogorovSmirnovTest.kolmogorovSmirnovTest(RealDistribution distribution,
double[] data,
double alpha)
Performs a Kolmogorov-Smirnov
test evaluating the null hypothesis that
data conforms to distribution. |
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