Package | Description |
---|---|
org.apache.commons.math3.analysis |
Parent package for common numerical analysis procedures, including root finding,
function interpolation and integration.
|
org.apache.commons.math3.analysis.function |
The
function package contains function objects that wrap the
methods contained in Math , as well as common
mathematical functions such as the gaussian and sinc functions. |
org.apache.commons.math3.analysis.integration |
Numerical integration (quadrature) algorithms for univariate real functions.
|
org.apache.commons.math3.analysis.integration.gauss |
Gauss family of quadrature schemes.
|
org.apache.commons.math3.analysis.interpolation |
Univariate real functions interpolation algorithms.
|
org.apache.commons.math3.analysis.solvers |
Root finding algorithms, for univariate real functions.
|
org.apache.commons.math3.distribution |
Implementations of common discrete and continuous distributions.
|
org.apache.commons.math3.distribution.fitting |
Fitting of parameters against distributions.
|
org.apache.commons.math3.genetics |
This package provides Genetic Algorithms components and implementations.
|
org.apache.commons.math3.linear |
Linear algebra support.
|
org.apache.commons.math3.optim.nonlinear.scalar |
Algorithms for optimizing a scalar function.
|
org.apache.commons.math3.optim.nonlinear.scalar.noderiv |
This package provides optimization algorithms that do not require derivatives.
|
org.apache.commons.math3.optim.nonlinear.vector |
Algorithms for optimizing a vector function.
|
org.apache.commons.math3.optimization.direct |
This package provides optimization algorithms that don't require derivatives.
|
org.apache.commons.math3.random |
Random number and random data generators.
|
org.apache.commons.math3.stat.correlation |
Correlations/Covariance computations.
|
org.apache.commons.math3.stat.inference |
Classes providing hypothesis testing.
|
org.apache.commons.math3.stat.interval |
Classes providing binomial proportion confidence interval construction.
|
org.apache.commons.math3.transform |
Implementations of transform methods, including Fast Fourier transforms.
|
org.apache.commons.math3.util |
Convenience routines and common data structures used throughout the commons-math library.
|
Modifier and Type | Method and Description |
---|---|
static double[] |
FunctionUtils.sample(UnivariateFunction f,
double min,
double max,
int n)
Samples the specified univariate real function on the specified interval.
|
Modifier and Type | Method and Description |
---|---|
double[] |
Logistic.Parametric.gradient(double x,
double... param)
Computes the value of the gradient at
x . |
double[] |
Gaussian.Parametric.gradient(double x,
double... param)
Computes the value of the gradient at
x . |
double |
Logistic.Parametric.value(double x,
double... param)
Computes the value of the sigmoid at
x . |
double |
Gaussian.Parametric.value(double x,
double... param)
Computes the value of the Gaussian at
x . |
Constructor and Description |
---|
Gaussian(double mean,
double sigma)
Normalized gaussian with given mean and standard deviation.
|
Gaussian(double norm,
double mean,
double sigma)
Gaussian with given normalization factor, mean and standard deviation.
|
Logistic(double k,
double m,
double b,
double q,
double a,
double n) |
Constructor and Description |
---|
BaseAbstractUnivariateIntegrator(double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Construct an integrator with given accuracies and iteration counts.
|
BaseAbstractUnivariateIntegrator(int minimalIterationCount,
int maximalIterationCount)
Construct an integrator with given iteration counts.
|
IterativeLegendreGaussIntegrator(int n,
double relativeAccuracy,
double absoluteAccuracy)
Builds an integrator with given accuracies.
|
IterativeLegendreGaussIntegrator(int n,
double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Builds an integrator with given accuracies and iterations counts.
|
IterativeLegendreGaussIntegrator(int n,
int minimalIterationCount,
int maximalIterationCount)
Builds an integrator with given iteration counts.
|
LegendreGaussIntegrator(int n,
double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Deprecated.
Build a Legendre-Gauss integrator with given accuracies and iterations counts.
|
MidPointIntegrator(double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Build a midpoint integrator with given accuracies and iterations counts.
|
MidPointIntegrator(int minimalIterationCount,
int maximalIterationCount)
Build a midpoint integrator with given iteration counts.
|
RombergIntegrator(double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Build a Romberg integrator with given accuracies and iterations counts.
|
RombergIntegrator(int minimalIterationCount,
int maximalIterationCount)
Build a Romberg integrator with given iteration counts.
|
SimpsonIntegrator(double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Build a Simpson integrator with given accuracies and iterations counts.
|
SimpsonIntegrator(int minimalIterationCount,
int maximalIterationCount)
Build a Simpson integrator with given iteration counts.
|
TrapezoidIntegrator(double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Build a trapezoid integrator with given accuracies and iterations counts.
|
TrapezoidIntegrator(int minimalIterationCount,
int maximalIterationCount)
Build a trapezoid integrator with given iteration counts.
|
Modifier and Type | Method and Description |
---|---|
Pair<double[],double[]> |
BaseRuleFactory.getRule(int numberOfPoints)
Gets a copy of the quadrature rule with the given number of integration
points.
|
GaussIntegrator |
GaussIntegratorFactory.legendre(int numberOfPoints,
double lowerBound,
double upperBound)
Creates a Gauss-Legendre integrator of the given order.
|
GaussIntegrator |
GaussIntegratorFactory.legendreHighPrecision(int numberOfPoints)
Creates a Gauss-Legendre integrator of the given order.
|
GaussIntegrator |
GaussIntegratorFactory.legendreHighPrecision(int numberOfPoints,
double lowerBound,
double upperBound)
Creates an integrator of the given order, and whose call to the
integrate method will perform an integration on the given interval. |
Constructor and Description |
---|
MicrosphereInterpolator(int elements,
int exponent)
Deprecated.
Create a microsphere interpolator.
|
Modifier and Type | Method and Description |
---|---|
static double[] |
UnivariateSolverUtils.bracket(UnivariateFunction function,
double initial,
double lowerBound,
double upperBound)
This method simply calls
bracket(function, initial, lowerBound, upperBound, q, r, maximumIterations)
with q and r set to 1.0 and maximumIterations set to Integer.MAX_VALUE . |
static double[] |
UnivariateSolverUtils.bracket(UnivariateFunction function,
double initial,
double lowerBound,
double upperBound,
int maximumIterations)
This method simply calls
bracket(function, initial, lowerBound, upperBound, q, r, maximumIterations)
with q and r set to 1.0. |
Modifier and Type | Method and Description |
---|---|
double[][] |
MultivariateRealDistribution.sample(int sampleSize)
Generates a list of a random value vectors from the distribution.
|
Object[] |
EnumeratedDistribution.sample(int sampleSize)
Generate a random sample from the distribution.
|
T[] |
EnumeratedDistribution.sample(int sampleSize,
T[] array)
Generate a random sample from the distribution.
|
Constructor and Description |
---|
ExponentialDistribution(RandomGenerator rng,
double mean)
Creates an exponential distribution.
|
ExponentialDistribution(RandomGenerator rng,
double mean,
double inverseCumAccuracy)
Creates an exponential distribution.
|
FDistribution(double numeratorDegreesOfFreedom,
double denominatorDegreesOfFreedom)
Creates an F distribution using the given degrees of freedom.
|
FDistribution(double numeratorDegreesOfFreedom,
double denominatorDegreesOfFreedom,
double inverseCumAccuracy)
Creates an F distribution using the given degrees of freedom
and inverse cumulative probability accuracy.
|
FDistribution(RandomGenerator rng,
double numeratorDegreesOfFreedom,
double denominatorDegreesOfFreedom)
Creates an F distribution.
|
FDistribution(RandomGenerator rng,
double numeratorDegreesOfFreedom,
double denominatorDegreesOfFreedom,
double inverseCumAccuracy)
Creates an F distribution.
|
GammaDistribution(double shape,
double scale)
Creates a new gamma distribution with specified values of the shape and
scale parameters.
|
GammaDistribution(double shape,
double scale,
double inverseCumAccuracy)
Creates a new gamma distribution with specified values of the shape and
scale parameters.
|
GammaDistribution(RandomGenerator rng,
double shape,
double scale)
Creates a Gamma distribution.
|
GammaDistribution(RandomGenerator rng,
double shape,
double scale,
double inverseCumAccuracy)
Creates a Gamma distribution.
|
HypergeometricDistribution(int populationSize,
int numberOfSuccesses,
int sampleSize)
Construct a new hypergeometric distribution with the specified population
size, number of successes in the population, and sample size.
|
HypergeometricDistribution(RandomGenerator rng,
int populationSize,
int numberOfSuccesses,
int sampleSize)
Creates a new hypergeometric distribution.
|
KolmogorovSmirnovDistribution(int n)
Deprecated.
|
LogNormalDistribution(double scale,
double shape)
Create a log-normal distribution using the specified scale and shape.
|
LogNormalDistribution(double scale,
double shape,
double inverseCumAccuracy)
Create a log-normal distribution using the specified scale, shape and
inverse cumulative distribution accuracy.
|
LogNormalDistribution(RandomGenerator rng,
double scale,
double shape)
Creates a log-normal distribution.
|
LogNormalDistribution(RandomGenerator rng,
double scale,
double shape,
double inverseCumAccuracy)
Creates a log-normal distribution.
|
NormalDistribution(double mean,
double sd)
Create a normal distribution using the given mean and standard deviation.
|
NormalDistribution(double mean,
double sd,
double inverseCumAccuracy)
Create a normal distribution using the given mean, standard deviation and
inverse cumulative distribution accuracy.
|
NormalDistribution(RandomGenerator rng,
double mean,
double sd)
Creates a normal distribution.
|
NormalDistribution(RandomGenerator rng,
double mean,
double sd,
double inverseCumAccuracy)
Creates a normal distribution.
|
ParetoDistribution(double scale,
double shape)
Create a Pareto distribution using the specified scale and shape.
|
ParetoDistribution(double scale,
double shape,
double inverseCumAccuracy)
Create a Pareto distribution using the specified scale, shape and
inverse cumulative distribution accuracy.
|
ParetoDistribution(RandomGenerator rng,
double scale,
double shape)
Creates a Pareto distribution.
|
ParetoDistribution(RandomGenerator rng,
double scale,
double shape,
double inverseCumAccuracy)
Creates a Pareto distribution.
|
PascalDistribution(int r,
double p)
Create a Pascal distribution with the given number of successes and
probability of success.
|
PascalDistribution(RandomGenerator rng,
int r,
double p)
Create a Pascal distribution with the given number of successes and
probability of success.
|
PoissonDistribution(double p)
Creates a new Poisson distribution with specified mean.
|
PoissonDistribution(double p,
double epsilon)
Creates a new Poisson distribution with the specified mean and
convergence criterion.
|
PoissonDistribution(double p,
double epsilon,
int maxIterations)
Creates a new Poisson distribution with specified mean, convergence
criterion and maximum number of iterations.
|
PoissonDistribution(RandomGenerator rng,
double p,
double epsilon,
int maxIterations)
Creates a new Poisson distribution with specified mean, convergence
criterion and maximum number of iterations.
|
TDistribution(double degreesOfFreedom)
Create a t distribution using the given degrees of freedom.
|
TDistribution(double degreesOfFreedom,
double inverseCumAccuracy)
Create a t distribution using the given degrees of freedom and the
specified inverse cumulative probability absolute accuracy.
|
TDistribution(RandomGenerator rng,
double degreesOfFreedom)
Creates a t distribution.
|
TDistribution(RandomGenerator rng,
double degreesOfFreedom,
double inverseCumAccuracy)
Creates a t distribution.
|
WeibullDistribution(double alpha,
double beta)
Create a Weibull distribution with the given shape and scale and a
location equal to zero.
|
WeibullDistribution(RandomGenerator rng,
double alpha,
double beta)
Creates a Weibull distribution.
|
WeibullDistribution(RandomGenerator rng,
double alpha,
double beta,
double inverseCumAccuracy)
Creates a Weibull distribution.
|
ZipfDistribution(RandomGenerator rng,
int numberOfElements,
double exponent)
Creates a Zipf distribution.
|
Modifier and Type | Method and Description |
---|---|
static MixtureMultivariateNormalDistribution |
MultivariateNormalMixtureExpectationMaximization.estimate(double[][] data,
int numComponents)
Helper method to create a multivariate normal mixture model which can be
used to initialize
MultivariateNormalMixtureExpectationMaximization.fit(MixtureMultivariateNormalDistribution) . |
void |
MultivariateNormalMixtureExpectationMaximization.fit(MixtureMultivariateNormalDistribution initialMixture)
Fit a mixture model to the data supplied to the constructor.
|
void |
MultivariateNormalMixtureExpectationMaximization.fit(MixtureMultivariateNormalDistribution initialMixture,
int maxIterations,
double threshold)
Fit a mixture model to the data supplied to the constructor.
|
Constructor and Description |
---|
MultivariateNormalMixtureExpectationMaximization(double[][] data)
Creates an object to fit a multivariate normal mixture model to data.
|
Constructor and Description |
---|
NPointCrossover(int crossoverPoints)
Creates a new
NPointCrossover policy using the given number of points. |
Modifier and Type | Method and Description |
---|---|
FieldMatrix<T> |
Array2DRowFieldMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new FieldMatrix
|
RealMatrix |
DiagonalMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new RealMatrix of the same type as the instance with the
supplied
row and column dimensions.
|
BlockRealMatrix |
BlockRealMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new RealMatrix of the same type as the instance with the
supplied
row and column dimensions.
|
abstract RealMatrix |
AbstractRealMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new RealMatrix of the same type as the instance with the
supplied
row and column dimensions.
|
abstract FieldMatrix<T> |
AbstractFieldMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new FieldMatrix
|
RealMatrix |
RealMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new RealMatrix of the same type as the instance with the
supplied
row and column dimensions.
|
RealMatrix |
Array2DRowRealMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new RealMatrix of the same type as the instance with the
supplied
row and column dimensions.
|
FieldMatrix<T> |
BlockFieldMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new FieldMatrix
|
OpenMapRealMatrix |
OpenMapRealMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new RealMatrix of the same type as the instance with the
supplied
row and column dimensions.
|
FieldMatrix<T> |
FieldMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new FieldMatrix
|
Constructor and Description |
---|
AbstractFieldMatrix(Field<T> field,
int rowDimension,
int columnDimension)
Create a new FieldMatrix
|
AbstractRealMatrix(int rowDimension,
int columnDimension)
Create a new RealMatrix with the supplied row and column dimensions.
|
Array2DRowFieldMatrix(Field<T> field,
int rowDimension,
int columnDimension)
Create a new
FieldMatrix<T> with the supplied row and column dimensions. |
Array2DRowRealMatrix(int rowDimension,
int columnDimension)
Create a new RealMatrix with the supplied row and column dimensions.
|
BlockFieldMatrix(Field<T> field,
int rows,
int columns)
Create a new matrix with the supplied row and column dimensions.
|
BlockFieldMatrix(int rows,
int columns,
T[][] blockData,
boolean copyArray)
Create a new dense matrix copying entries from block layout data.
|
BlockRealMatrix(double[][] rawData)
Create a new dense matrix copying entries from raw layout data.
|
BlockRealMatrix(int rows,
int columns)
Create a new matrix with the supplied row and column dimensions.
|
BlockRealMatrix(int rows,
int columns,
double[][] blockData,
boolean copyArray)
Create a new dense matrix copying entries from block layout data.
|
DiagonalMatrix(int dimension)
Creates a matrix with the supplied dimension.
|
OpenMapRealMatrix(int rowDimension,
int columnDimension)
Build a sparse matrix with the supplied row and column dimensions.
|
Constructor and Description |
---|
MultiStartMultivariateOptimizer(MultivariateOptimizer optimizer,
int starts,
RandomVectorGenerator generator)
Create a multi-start optimizer from a single-start optimizer.
|
Constructor and Description |
---|
CMAESOptimizer.PopulationSize(int size) |
Constructor and Description |
---|
MultiStartMultivariateVectorOptimizer(MultivariateVectorOptimizer optimizer,
int starts,
RandomVectorGenerator generator)
Deprecated.
Create a multi-start optimizer from a single-start optimizer.
|
Constructor and Description |
---|
CMAESOptimizer.PopulationSize(int size) |
Modifier and Type | Method and Description |
---|---|
double |
RandomDataGenerator.nextExponential(double mean)
Generates a random value from the exponential distribution
with specified mean.
|
double |
RandomData.nextExponential(double mean)
Deprecated.
Generates a random value from the exponential distribution
with specified mean.
|
double |
RandomDataImpl.nextExponential(double mean)
Deprecated.
Generates a random value from the exponential distribution
with specified mean.
|
double |
RandomDataGenerator.nextF(double numeratorDf,
double denominatorDf)
Generates a random value from the
F Distribution . |
double |
RandomDataImpl.nextF(double numeratorDf,
double denominatorDf)
Deprecated.
Generates a random value from the
F Distribution . |
double |
RandomDataGenerator.nextGamma(double shape,
double scale)
Generates a random value from the
Gamma Distribution . |
double |
RandomDataImpl.nextGamma(double shape,
double scale)
Deprecated.
Generates a random value from the
Gamma Distribution . |
double |
RandomDataGenerator.nextGaussian(double mu,
double sigma)
Generates a random value from the Normal (or Gaussian) distribution with
specified mean and standard deviation.
|
double |
RandomData.nextGaussian(double mu,
double sigma)
Deprecated.
Generates a random value from the Normal (or Gaussian) distribution with
specified mean and standard deviation.
|
double |
RandomDataImpl.nextGaussian(double mu,
double sigma)
Deprecated.
Generates a random value from the Normal (or Gaussian) distribution with
specified mean and standard deviation.
|
String |
RandomDataGenerator.nextHexString(int len)
Generates a random string of hex characters of length
len . |
String |
RandomData.nextHexString(int len)
Deprecated.
Generates a random string of hex characters of length
len . |
String |
RandomDataImpl.nextHexString(int len)
Deprecated.
Generates a random string of hex characters of length
len . |
int |
RandomDataGenerator.nextHypergeometric(int populationSize,
int numberOfSuccesses,
int sampleSize)
Generates a random value from the
Hypergeometric Distribution . |
int |
RandomDataImpl.nextHypergeometric(int populationSize,
int numberOfSuccesses,
int sampleSize)
Deprecated.
Generates a random value from the
Hypergeometric Distribution . |
int |
RandomDataGenerator.nextPascal(int r,
double p)
Generates a random value from the
Pascal Distribution . |
int |
RandomDataImpl.nextPascal(int r,
double p)
Deprecated.
Generates a random value from the
Pascal Distribution . |
int[] |
RandomDataGenerator.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). |
int[] |
RandomData.nextPermutation(int n,
int k)
Deprecated.
Generates an integer array of length
k whose entries are selected
randomly, without repetition, from the integers 0, ..., n - 1
(inclusive). |
int[] |
RandomDataImpl.nextPermutation(int n,
int k)
Deprecated.
Generates an integer array of length
k whose entries are selected
randomly, without repetition, from the integers 0, ..., n - 1
(inclusive). |
long |
RandomDataGenerator.nextPoisson(double mean)
Generates a random value from the Poisson distribution with the given
mean.
|
long |
RandomData.nextPoisson(double mean)
Deprecated.
Generates a random value from the Poisson distribution with the given
mean.
|
long |
RandomDataImpl.nextPoisson(double mean)
Deprecated.
Generates a random value from the Poisson distribution with the given
mean.
|
Object[] |
RandomDataGenerator.nextSample(Collection<?> c,
int k)
Returns an array of
k objects selected randomly from the
Collection c . |
Object[] |
RandomData.nextSample(Collection<?> c,
int k)
Deprecated.
Returns an array of
k objects selected randomly from the
Collection c . |
Object[] |
RandomDataImpl.nextSample(Collection<?> c,
int k)
Deprecated.
Returns an array of
k objects selected randomly from the
Collection c . |
String |
RandomDataGenerator.nextSecureHexString(int len)
Generates a random string of hex characters from a secure random
sequence.
|
String |
RandomData.nextSecureHexString(int len)
Deprecated.
Generates a random string of hex characters from a secure random
sequence.
|
String |
RandomDataImpl.nextSecureHexString(int len)
Deprecated.
Generates a random string of hex characters from a secure random
sequence.
|
double |
RandomDataGenerator.nextT(double df)
Generates a random value from the
T Distribution . |
double |
RandomDataImpl.nextT(double df)
Deprecated.
Generates a random value from the
T Distribution . |
double |
RandomDataGenerator.nextWeibull(double shape,
double scale)
Generates a random value from the
Weibull Distribution . |
double |
RandomDataImpl.nextWeibull(double shape,
double scale)
Deprecated.
Generates a random value from the
Weibull Distribution . |
int |
RandomDataGenerator.nextZipf(int numberOfElements,
double exponent)
Generates a random value from the
Zipf Distribution . |
int |
RandomDataImpl.nextZipf(int numberOfElements,
double exponent)
Deprecated.
Generates a random value from the
Zipf Distribution . |
Constructor and Description |
---|
SobolSequenceGenerator(int dimension,
InputStream is)
Construct a new Sobol sequence generator for the given space dimension with
direction vectors loaded from the given stream.
|
Modifier and Type | Method and Description |
---|---|
protected RealMatrix |
Covariance.computeCovarianceMatrix(double[][] data)
Create a covariance matrix from a rectangular array whose columns represent
covariates.
|
protected RealMatrix |
Covariance.computeCovarianceMatrix(double[][] data,
boolean biasCorrected)
Compute a covariance matrix from a rectangular array whose columns represent
covariates.
|
Constructor and Description |
---|
Covariance(double[][] data)
Create a Covariance matrix from a rectangular array
whose columns represent covariates.
|
Covariance(double[][] data,
boolean biasCorrected)
Create a Covariance matrix from a rectangular array
whose columns represent covariates.
|
Modifier and Type | Method and Description |
---|---|
static double |
TestUtils.chiSquare(double[] expected,
long[] observed) |
double |
ChiSquareTest.chiSquare(double[] expected,
long[] observed)
|
static double |
TestUtils.chiSquareTest(double[] expected,
long[] observed) |
double |
ChiSquareTest.chiSquareTest(double[] expected,
long[] observed)
Returns the observed significance level, or
p-value, associated with a
Chi-square goodness of fit test comparing the
observed
frequency counts to those in the expected array. |
static boolean |
TestUtils.chiSquareTest(double[] expected,
long[] observed,
double alpha) |
boolean |
ChiSquareTest.chiSquareTest(double[] expected,
long[] observed,
double alpha)
Performs a
Chi-square goodness of fit test evaluating the null hypothesis that the
observed counts conform to the frequency distribution described by the expected
counts, with significance level
alpha . |
static double |
TestUtils.g(double[] expected,
long[] observed) |
double |
GTest.g(double[] expected,
long[] observed)
|
static double |
TestUtils.gTest(double[] expected,
long[] observed) |
double |
GTest.gTest(double[] expected,
long[] observed)
Returns the observed significance level, or p-value,
associated with a G-Test for goodness of fit comparing the
observed frequency counts to those in the expected array. |
static boolean |
TestUtils.gTest(double[] expected,
long[] observed,
double alpha) |
boolean |
GTest.gTest(double[] expected,
long[] observed,
double alpha)
Performs a G-Test (Log-Likelihood Ratio Test) for goodness of fit
evaluating the null hypothesis that the observed counts conform to the
frequency distribution described by the expected counts, with
significance level
alpha . |
static double |
TestUtils.gTestIntrinsic(double[] expected,
long[] observed) |
double |
GTest.gTestIntrinsic(double[] expected,
long[] observed)
Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described
in p64-69 of McDonald, J.H.
|
protected double |
TTest.homoscedasticTTest(double m1,
double m2,
double v1,
double v2,
double n1,
double n2)
Computes p-value for 2-sided, 2-sample t-test, under the assumption
of equal subpopulation variances.
|
protected double |
TTest.tTest(double m1,
double m2,
double v1,
double v2,
double n1,
double n2)
Computes p-value for 2-sided, 2-sample t-test.
|
Modifier and Type | Method and Description |
---|---|
ConfidenceInterval |
BinomialConfidenceInterval.createInterval(int numberOfTrials,
int numberOfSuccesses,
double confidenceLevel)
Create a confidence interval for the true probability of success
of an unknown binomial distribution with the given observed number
of trials, successes and confidence level.
|
Modifier and Type | Method and Description |
---|---|
double[] |
RealTransformer.transform(UnivariateFunction f,
double min,
double max,
int n,
TransformType type)
Returns the (forward, inverse) transform of the specified real function,
sampled on the specified interval.
|
Modifier and Type | Method and Description |
---|---|
static void |
MathArrays.checkPositive(double[] in)
Check that all entries of the input array are strictly positive.
|
Constructor and Description |
---|
MultidimensionalCounter(int... size)
Create a counter.
|
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