Uses of Class
org.apache.commons.math3.exception.NumberIsTooSmallException

Packages that use NumberIsTooSmallException

Package	Description
org.apache.commons.math3.analysis.integration	Numerical integration (quadrature) algorithms for univariate real functions.
org.apache.commons.math3.analysis.solvers	Root finding algorithms, for univariate real functions.
org.apache.commons.math3.dfp	Decimal floating point library for Java
org.apache.commons.math3.distribution	Implementations of common discrete and continuous distributions.
org.apache.commons.math3.exception	Specialized exceptions for algorithms errors.
org.apache.commons.math3.linear	Linear algebra support.
org.apache.commons.math3.ode	This package provides classes to solve Ordinary Differential Equations problems.
org.apache.commons.math3.ode.nonstiff	This package provides classes to solve non-stiff Ordinary Differential Equations problems.
org.apache.commons.math3.stat.correlation	Correlations/Covariance computations.
org.apache.commons.math3.stat.inference	Classes providing hypothesis testing and confidence interval construction.

Uses of NumberIsTooSmallException in org.apache.commons.math3.analysis.integration

Constructors in org.apache.commons.math3.analysis.integration that throw NumberIsTooSmallException

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.
LegendreGaussIntegrator(int n, double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount) Build a Legendre-Gauss integrator with given accuracies and iterations 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.

Uses of NumberIsTooSmallException in org.apache.commons.math3.analysis.solvers

Constructors in org.apache.commons.math3.analysis.solvers that throw NumberIsTooSmallException

Constructor and Description
BracketingNthOrderBrentSolver(double relativeAccuracy, double absoluteAccuracy, double functionValueAccuracy, int maximalOrder) Construct a solver.
BracketingNthOrderBrentSolver(double relativeAccuracy, double absoluteAccuracy, int maximalOrder) Construct a solver.
BracketingNthOrderBrentSolver(double absoluteAccuracy, int maximalOrder) Construct a solver.

Uses of NumberIsTooSmallException in org.apache.commons.math3.dfp

Constructors in org.apache.commons.math3.dfp that throw NumberIsTooSmallException

Constructor and Description
BracketingNthOrderBrentSolverDFP(Dfp relativeAccuracy, Dfp absoluteAccuracy, Dfp functionValueAccuracy, int maximalOrder) Construct a solver.

Uses of NumberIsTooSmallException in org.apache.commons.math3.distribution

Constructors in org.apache.commons.math3.distribution that throw NumberIsTooSmallException

Constructor and Description
TriangularDistribution(double a, double c, double b) Create a triangular real distribution using the given lower limit, upper limit, and mode.

Uses of NumberIsTooSmallException in org.apache.commons.math3.exception

Subclasses of NumberIsTooSmallException in org.apache.commons.math3.exception

Modifier and Type	Class and Description
class	NotPositiveException Exception to be thrown when the argument is negative.
class	NotStrictlyPositiveException Exception to be thrown when the argument is negative.

Uses of NumberIsTooSmallException in org.apache.commons.math3.linear

Subclasses of NumberIsTooSmallException in org.apache.commons.math3.linear

Modifier and Type	Class and Description
class	NonPositiveDefiniteMatrixException Exception to be thrown when a positive definite matrix is expected.

Uses of NumberIsTooSmallException in org.apache.commons.math3.ode

Methods in org.apache.commons.math3.ode that throw NumberIsTooSmallException

Modifier and Type	Method and Description
protected void	AbstractIntegrator.sanityChecks(ExpandableStatefulODE equations, double t) Check the integration span.

Uses of NumberIsTooSmallException in org.apache.commons.math3.ode.nonstiff

Methods in org.apache.commons.math3.ode.nonstiff that throw NumberIsTooSmallException

Modifier and Type	Method and Description
protected double	AdaptiveStepsizeIntegrator.filterStep(double h, boolean forward, boolean acceptSmall) Filter the integration step.
protected void	AdaptiveStepsizeIntegrator.sanityChecks(ExpandableStatefulODE equations, double t) Check the integration span.

Uses of NumberIsTooSmallException in org.apache.commons.math3.stat.correlation

Methods in org.apache.commons.math3.stat.correlation that throw NumberIsTooSmallException

Modifier and Type	Method and Description
double	StorelessCovariance.getCovariance(int xIndex, int yIndex) Get the covariance for an individual element of the covariance matrix.
RealMatrix	StorelessCovariance.getCovarianceMatrix() Returns the covariance matrix
double[][]	StorelessCovariance.getData() Return the covariance matrix as two-dimensional array.

Uses of NumberIsTooSmallException in org.apache.commons.math3.stat.inference

Methods in org.apache.commons.math3.stat.inference that throw NumberIsTooSmallException

Modifier and Type	Method and Description
double	TTest.homoscedasticT(double[] sample1, double[] sample2) Computes a 2-sample t statistic, under the hypothesis of equal subpopulation variances.
static double	TestUtils.homoscedasticT(double[] sample1, double[] sample2)
double	TTest.homoscedasticT(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) Computes a 2-sample t statistic, comparing the means of the datasets described by two StatisticalSummary instances, under the assumption of equal subpopulation variances.
static double	TestUtils.homoscedasticT(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
double	TTest.homoscedasticTTest(double[] sample1, double[] sample2) Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays, under the assumption that the two samples are drawn from subpopulations with equal variances.
static double	TestUtils.homoscedasticTTest(double[] sample1, double[] sample2)
boolean	TTest.homoscedasticTTest(double[] sample1, double[] sample2, double alpha) Performs a two-sided t-test evaluating the null hypothesis that sample1 and sample2 are drawn from populations with the same mean, with significance level alpha, assuming that the subpopulation variances are equal.
static boolean	TestUtils.homoscedasticTTest(double[] sample1, double[] sample2, double alpha)
double	TTest.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances, under the hypothesis of equal subpopulation variances.
static double	TestUtils.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
double	TTest.pairedT(double[] sample1, double[] sample2) Computes a paired, 2-sample t-statistic based on the data in the input arrays.
static double	TestUtils.pairedT(double[] sample1, double[] sample2)
double	TTest.pairedTTest(double[] sample1, double[] sample2) Returns the observed significance level, or p-value, associated with a paired, two-sample, two-tailed t-test based on the data in the input arrays.
static double	TestUtils.pairedTTest(double[] sample1, double[] sample2)
boolean	TTest.pairedTTest(double[] sample1, double[] sample2, double alpha) Performs a paired t-test evaluating the null hypothesis that the mean of the paired differences between sample1 and sample2 is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0, with significance level alpha.
static boolean	TestUtils.pairedTTest(double[] sample1, double[] sample2, double alpha)
double	TTest.t(double[] sample1, double[] sample2) Computes a 2-sample t statistic, without the hypothesis of equal subpopulation variances.
static double	TestUtils.t(double[] sample1, double[] sample2)
double	TTest.t(double mu, double[] observed) Computes a t statistic given observed values and a comparison constant.
static double	TestUtils.t(double mu, double[] observed)
double	TTest.t(double mu, StatisticalSummary sampleStats) Computes a t statistic to use in comparing the mean of the dataset described by sampleStats to mu.
static double	TestUtils.t(double mu, StatisticalSummary sampleStats)
double	TTest.t(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) Computes a 2-sample t statistic , comparing the means of the datasets described by two StatisticalSummary instances, without the assumption of equal subpopulation variances.
static double	TestUtils.t(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
double	TTest.tTest(double[] sample1, double[] sample2) Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays.
static double	TestUtils.tTest(double[] sample1, double[] sample2)
boolean	TTest.tTest(double[] sample1, double[] sample2, double alpha) Performs a two-sided t-test evaluating the null hypothesis that sample1 and sample2 are drawn from populations with the same mean, with significance level alpha.
static boolean	TestUtils.tTest(double[] sample1, double[] sample2, double alpha)
double	TTest.tTest(double mu, double[] sample) Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the input array with the constant mu.
static double	TestUtils.tTest(double mu, double[] sample)
boolean	TTest.tTest(double mu, double[] sample, double alpha) Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which sample is drawn equals mu.
static boolean	TestUtils.tTest(double mu, double[] sample, double alpha)
double	TTest.tTest(double mu, StatisticalSummary sampleStats) Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the dataset described by sampleStats with the constant mu.
static double	TestUtils.tTest(double mu, StatisticalSummary sampleStats)
boolean	TTest.tTest(double mu, StatisticalSummary sampleStats, double alpha) Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which the dataset described by stats is drawn equals mu.
static boolean	TestUtils.tTest(double mu, StatisticalSummary sampleStats, double alpha)
double	TTest.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances.
static double	TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
boolean	TTest.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha) Performs a two-sided t-test evaluating the null hypothesis that sampleStats1 and sampleStats2 describe datasets drawn from populations with the same mean, with significance level alpha.
static boolean	TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)