Uses of Class
org.apache.commons.math4.legacy.exception.NumberIsTooSmallException

Packages that use NumberIsTooSmallException

Package	Description
org.apache.commons.math4.legacy.analysis.differentiation	This package holds the main interfaces and basic building block classes dealing with differentiation.
org.apache.commons.math4.legacy.analysis.interpolation	Univariate real functions interpolation algorithms.
org.apache.commons.math4.legacy.analysis.polynomials	Univariate real polynomials implementations, seen as differentiable univariate real functions.
org.apache.commons.math4.legacy.analysis.solvers	Root finding algorithms, for univariate real functions.
org.apache.commons.math4.legacy.distribution.fitting	Fitting of parameters against distributions.
org.apache.commons.math4.legacy.exception	Specialized exceptions for algorithms errors.
org.apache.commons.math4.legacy.genetics	This package provides Genetic Algorithms components and implementations.
org.apache.commons.math4.legacy.linear	Linear algebra support.
org.apache.commons.math4.legacy.ode	This package provides classes to solve Ordinary Differential Equations problems.
org.apache.commons.math4.legacy.ode.nonstiff	This package provides classes to solve non-stiff Ordinary Differential Equations problems.
org.apache.commons.math4.legacy.stat	Data storage, manipulation and summary routines.
org.apache.commons.math4.legacy.stat.correlation	Correlations/Covariance computations.
org.apache.commons.math4.legacy.stat.inference	Classes providing hypothesis testing.

Uses of NumberIsTooSmallException in org.apache.commons.math4.legacy.analysis.differentiation

Constructors in org.apache.commons.math4.legacy.analysis.differentiation that throw NumberIsTooSmallException

Constructor	Description
FiniteDifferencesDifferentiator(int nbPoints, double stepSize)	Build a differentiator with number of points and step size when independent variable is unbounded.
FiniteDifferencesDifferentiator(int nbPoints, double stepSize, double tLower, double tUpper)	Build a differentiator with number of points and step size when independent variable is bounded.

Uses of NumberIsTooSmallException in org.apache.commons.math4.legacy.analysis.interpolation

Methods in org.apache.commons.math4.legacy.analysis.interpolation that throw NumberIsTooSmallException

Modifier and Type	Method	Description
protected static double[]	DividedDifferenceInterpolator.computeDividedDifference(double[] x, double[] y)	Return a copy of the divided difference array.
PolynomialSplineFunction	AkimaSplineInterpolator.interpolate(double[] xvals, double[] yvals)	Computes an interpolating function for the data set.
BicubicInterpolatingFunction	BicubicInterpolator.interpolate(double[] xval, double[] yval, double[][] fval)	Compute an interpolating function for the dataset.
BivariateFunction	BivariateGridInterpolator.interpolate(double[] xval, double[] yval, double[][] fval)	Compute an interpolating function for the dataset.
PolynomialSplineFunction	ClampedSplineInterpolator.interpolate(double[] x, double[] y, double fpo, double fpn)	Computes an interpolating function for the data set.
PolynomialFunctionNewtonForm	DividedDifferenceInterpolator.interpolate(double[] x, double[] y)	Compute an interpolating function for the dataset.
PolynomialSplineFunction	LinearInterpolator.interpolate(double[] x, double[] y)	Computes a linear interpolating function for the data set.
PolynomialSplineFunction	LoessInterpolator.interpolate(double[] xval, double[] yval)	Compute an interpolating function by performing a loess fit on the data at the original abscissae and then building a cubic spline with a SplineInterpolator on the resulting fit.
PolynomialFunctionLagrangeForm	NevilleInterpolator.interpolate(double[] x, double[] y)	Computes an interpolating function for the data set.
TricubicInterpolatingFunction	TricubicInterpolator.interpolate(double[] xval, double[] yval, double[] zval, double[][][] fval)	Compute an interpolating function for the dataset.
TrivariateFunction	TrivariateGridInterpolator.interpolate(double[] xval, double[] yval, double[] zval, double[][][] fval)	Compute an interpolating function for the dataset.
UnivariateFunction	UnivariatePeriodicInterpolator.interpolate(double[] xval, double[] yval)	Computes an interpolating function for the dataset.
double[]	LoessInterpolator.smooth(double[] xval, double[] yval)	Compute a loess fit on the data at the original abscissae.
double[]	LoessInterpolator.smooth(double[] xval, double[] yval, double[] weights)	Compute a weighted loess fit on the data at the original abscissae.

Uses of NumberIsTooSmallException in org.apache.commons.math4.legacy.analysis.polynomials

Methods in org.apache.commons.math4.legacy.analysis.polynomials that throw NumberIsTooSmallException

Modifier and Type	Method	Description
static double	PolynomialFunctionLagrangeForm.evaluate(double[] x, double[] y, double z)	Evaluate the Lagrange polynomial using Neville's Algorithm.
static boolean	PolynomialFunctionLagrangeForm.verifyInterpolationArray(double[] x, double[] y, boolean abort)	Check that the interpolation arrays are valid.

Constructors in org.apache.commons.math4.legacy.analysis.polynomials that throw NumberIsTooSmallException

Constructor	Description
PolynomialFunctionLagrangeForm(double[] x, double[] y)	Construct a Lagrange polynomial with the given abscissas and function values.
PolynomialSplineFunction(double[] knots, PolynomialFunction[] polynomials)	Construct a polynomial spline function with the given segment delimiters and interpolating polynomials.

Uses of NumberIsTooSmallException in org.apache.commons.math4.legacy.analysis.solvers

Constructors in org.apache.commons.math4.legacy.analysis.solvers that throw NumberIsTooSmallException

Constructor	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.
FieldBracketingNthOrderBrentSolver(T relativeAccuracy, T absoluteAccuracy, T functionValueAccuracy, int maximalOrder)	Construct a solver.

Uses of NumberIsTooSmallException in org.apache.commons.math4.legacy.distribution.fitting

Constructors in org.apache.commons.math4.legacy.distribution.fitting that throw NumberIsTooSmallException

Constructor	Description
MultivariateNormalMixtureExpectationMaximization(double[][] data)	Creates an object to fit a multivariate normal mixture model to data.

Uses of NumberIsTooSmallException in org.apache.commons.math4.legacy.exception

Subclasses of NumberIsTooSmallException in org.apache.commons.math4.legacy.exception

Modifier and Type	Class	Description
class	NotPositiveException	Exception to be thrown when the argument is negative.
class	NotStrictlyPositiveException	Exception to be thrown when the argument is not greater than 0.

Uses of NumberIsTooSmallException in org.apache.commons.math4.legacy.genetics

Methods in org.apache.commons.math4.legacy.genetics that throw NumberIsTooSmallException

Modifier and Type	Method	Description
void	ListPopulation.setPopulationLimit(int populationLimit)	Sets the maximal population size.

Constructors in org.apache.commons.math4.legacy.genetics that throw NumberIsTooSmallException

Constructor	Description
FixedElapsedTime(long maxTime)	Create a new FixedElapsedTime instance.
FixedElapsedTime(long maxTime, TimeUnit unit)	Create a new FixedElapsedTime instance.
FixedGenerationCount(int maxGenerations)	Create a new FixedGenerationCount instance.

Uses of NumberIsTooSmallException in org.apache.commons.math4.legacy.linear

Subclasses of NumberIsTooSmallException in org.apache.commons.math4.legacy.linear

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

Methods in org.apache.commons.math4.legacy.linear that throw NumberIsTooSmallException

Modifier and Type	Method	Description
protected void	RealVector.checkIndices(int start, int end)	Checks that the indices of a subvector are valid.
protected void	AbstractFieldMatrix.checkSubMatrixIndex(int startRow, int endRow, int startColumn, int endColumn)	Check if submatrix ranges indices are valid.
static void	MatrixUtils.checkSubMatrixIndex(AnyMatrix m, int startRow, int endRow, int startColumn, int endColumn)	Check if submatrix ranges indices are valid.
void	AbstractFieldMatrix.copySubMatrix(int startRow, int endRow, int startColumn, int endColumn, T[][] destination)	Copy a submatrix.
void	AbstractRealMatrix.copySubMatrix(int startRow, int endRow, int startColumn, int endColumn, double[][] destination)	Copy a submatrix.
void	FieldMatrix.copySubMatrix(int startRow, int endRow, int startColumn, int endColumn, T[][] destination)	Copy a submatrix.
void	RealMatrix.copySubMatrix(int startRow, int endRow, int startColumn, int endColumn, double[][] destination)	Copy a submatrix.
FieldMatrix<T>	AbstractFieldMatrix.getSubMatrix(int startRow, int endRow, int startColumn, int endColumn)	Get a submatrix.
RealMatrix	AbstractRealMatrix.getSubMatrix(int startRow, int endRow, int startColumn, int endColumn)	Gets a submatrix.
RealMatrix	Array2DRowRealMatrix.getSubMatrix(int startRow, int endRow, int startColumn, int endColumn)	Gets a submatrix.
FieldMatrix<T>	BlockFieldMatrix.getSubMatrix(int startRow, int endRow, int startColumn, int endColumn)	Get a submatrix.
BlockRealMatrix	BlockRealMatrix.getSubMatrix(int startRow, int endRow, int startColumn, int endColumn)	Gets a submatrix.
FieldMatrix<T>	FieldMatrix.getSubMatrix(int startRow, int endRow, int startColumn, int endColumn)	Get a submatrix.
RealMatrix	RealMatrix.getSubMatrix(int startRow, int endRow, int startColumn, int endColumn)	Gets a submatrix.
T	AbstractFieldMatrix.walkInColumnOrder(FieldMatrixChangingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in column order.
T	AbstractFieldMatrix.walkInColumnOrder(FieldMatrixPreservingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in column order.
double	AbstractRealMatrix.walkInColumnOrder(RealMatrixChangingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in column order.
double	AbstractRealMatrix.walkInColumnOrder(RealMatrixPreservingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in column order.
T	Array2DRowFieldMatrix.walkInColumnOrder(FieldMatrixChangingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in column order.
T	Array2DRowFieldMatrix.walkInColumnOrder(FieldMatrixPreservingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in column order.
double	Array2DRowRealMatrix.walkInColumnOrder(RealMatrixChangingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in column order.
double	Array2DRowRealMatrix.walkInColumnOrder(RealMatrixPreservingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in column order.
T	FieldMatrix.walkInColumnOrder(FieldMatrixChangingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in column order.
T	FieldMatrix.walkInColumnOrder(FieldMatrixPreservingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in column order.
double	RealMatrix.walkInColumnOrder(RealMatrixChangingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in column order.
double	RealMatrix.walkInColumnOrder(RealMatrixPreservingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in column order.
T	ArrayFieldVector.walkInDefaultOrder(FieldVectorChangingVisitor<T> visitor, int start, int end)	Visits (and possibly alters) some entries of this vector in default order (increasing index).
T	ArrayFieldVector.walkInDefaultOrder(FieldVectorPreservingVisitor<T> visitor, int start, int end)	Visits (but does not alter) some entries of this vector in default order (increasing index).
double	ArrayRealVector.walkInDefaultOrder(RealVectorChangingVisitor visitor, int start, int end)	Visits (and possibly alters) some entries of this vector in default order (increasing index).
double	ArrayRealVector.walkInDefaultOrder(RealVectorPreservingVisitor visitor, int start, int end)	Visits (but does not alter) some entries of this vector in default order (increasing index).
double	RealVector.walkInDefaultOrder(RealVectorChangingVisitor visitor, int start, int end)	Visits (and possibly alters) some entries of this vector in default order (increasing index).
double	RealVector.walkInDefaultOrder(RealVectorPreservingVisitor visitor, int start, int end)	Visits (but does not alter) some entries of this vector in default order (increasing index).
T	SparseFieldVector.walkInDefaultOrder(FieldVectorChangingVisitor<T> visitor, int start, int end)	Visits (and possibly alters) some entries of this vector in default order (increasing index).
T	SparseFieldVector.walkInDefaultOrder(FieldVectorPreservingVisitor<T> visitor, int start, int end)	Visits (but does not alter) some entries of this vector in default order (increasing index).
T	AbstractFieldMatrix.walkInOptimizedOrder(FieldMatrixChangingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries using the fastest possible order.
T	AbstractFieldMatrix.walkInOptimizedOrder(FieldMatrixPreservingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries using the fastest possible order.
double	AbstractRealMatrix.walkInOptimizedOrder(RealMatrixChangingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries using the fastest possible order.
double	AbstractRealMatrix.walkInOptimizedOrder(RealMatrixPreservingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries using the fastest possible order.
T	ArrayFieldVector.walkInOptimizedOrder(FieldVectorChangingVisitor<T> visitor, int start, int end)	Visits (and possibly change) some entries of this vector in optimized order.
T	ArrayFieldVector.walkInOptimizedOrder(FieldVectorPreservingVisitor<T> visitor, int start, int end)	Visits (but does not alter) some entries of this vector in optimized order.
double	ArrayRealVector.walkInOptimizedOrder(RealVectorChangingVisitor visitor, int start, int end)	Visits (and possibly change) some entries of this vector in optimized order.
double	ArrayRealVector.walkInOptimizedOrder(RealVectorPreservingVisitor visitor, int start, int end)	Visits (but does not alter) some entries of this vector in optimized order.
T	BlockFieldMatrix.walkInOptimizedOrder(FieldMatrixChangingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries using the fastest possible order.
T	BlockFieldMatrix.walkInOptimizedOrder(FieldMatrixPreservingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries using the fastest possible order.
double	BlockRealMatrix.walkInOptimizedOrder(RealMatrixChangingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries using the fastest possible order.
double	BlockRealMatrix.walkInOptimizedOrder(RealMatrixPreservingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries using the fastest possible order.
T	FieldMatrix.walkInOptimizedOrder(FieldMatrixChangingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries using the fastest possible order.
T	FieldMatrix.walkInOptimizedOrder(FieldMatrixPreservingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries using the fastest possible order.
double	RealMatrix.walkInOptimizedOrder(RealMatrixChangingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries using the fastest possible order.
double	RealMatrix.walkInOptimizedOrder(RealMatrixPreservingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries using the fastest possible order.
double	RealVector.walkInOptimizedOrder(RealVectorChangingVisitor visitor, int start, int end)	Visits (and possibly change) some entries of this vector in optimized order.
double	RealVector.walkInOptimizedOrder(RealVectorPreservingVisitor visitor, int start, int end)	Visits (but does not alter) some entries of this vector in optimized order.
T	SparseFieldVector.walkInOptimizedOrder(FieldVectorChangingVisitor<T> visitor, int start, int end)	Visits (and possibly change) some entries of this vector in optimized order.
T	SparseFieldVector.walkInOptimizedOrder(FieldVectorPreservingVisitor<T> visitor, int start, int end)	Visits (but does not alter) some entries of this vector in optimized order.
T	AbstractFieldMatrix.walkInRowOrder(FieldMatrixChangingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in row order.
T	AbstractFieldMatrix.walkInRowOrder(FieldMatrixPreservingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in row order.
double	AbstractRealMatrix.walkInRowOrder(RealMatrixChangingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in row order.
double	AbstractRealMatrix.walkInRowOrder(RealMatrixPreservingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in row order.
T	Array2DRowFieldMatrix.walkInRowOrder(FieldMatrixChangingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in row order.
T	Array2DRowFieldMatrix.walkInRowOrder(FieldMatrixPreservingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in row order.
double	Array2DRowRealMatrix.walkInRowOrder(RealMatrixChangingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in row order.
double	Array2DRowRealMatrix.walkInRowOrder(RealMatrixPreservingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in row order.
T	BlockFieldMatrix.walkInRowOrder(FieldMatrixChangingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in row order.
T	BlockFieldMatrix.walkInRowOrder(FieldMatrixPreservingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in row order.
double	BlockRealMatrix.walkInRowOrder(RealMatrixChangingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in row order.
double	BlockRealMatrix.walkInRowOrder(RealMatrixPreservingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in row order.
T	FieldMatrix.walkInRowOrder(FieldMatrixChangingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in row order.
T	FieldMatrix.walkInRowOrder(FieldMatrixPreservingVisitor<T> visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in row order.
double	RealMatrix.walkInRowOrder(RealMatrixChangingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (and possibly change) some matrix entries in row order.
double	RealMatrix.walkInRowOrder(RealMatrixPreservingVisitor visitor, int startRow, int endRow, int startColumn, int endColumn)	Visit (but don't change) some matrix entries in row order.

Uses of NumberIsTooSmallException in org.apache.commons.math4.legacy.ode

Methods in org.apache.commons.math4.legacy.ode that throw NumberIsTooSmallException

Modifier and Type	Method	Description
abstract void	AbstractIntegrator.integrate(ExpandableStatefulODE equations, double t)	Integrate a set of differential equations up to the given time.
double	AbstractIntegrator.integrate(FirstOrderDifferentialEquations equations, double t0, double[] y0, double t, double[] y)	Integrate the differential equations up to the given time.
FieldODEStateAndDerivative<T>	FirstOrderFieldIntegrator.integrate(FieldExpandableODE<T> equations, FieldODEState<T> initialState, T finalTime)	Integrate the differential equations up to the given time.
double	FirstOrderIntegrator.integrate(FirstOrderDifferentialEquations equations, double t0, double[] y0, double t, double[] y)	Integrate the differential equations up to the given time.
protected void	AbstractFieldIntegrator.sanityChecks(FieldODEState<T> eqn, T t)	Check the integration span.
protected void	AbstractIntegrator.sanityChecks(ExpandableStatefulODE equations, double t)	Check the integration span.
protected void	MultistepFieldIntegrator.start(FieldExpandableODE<T> equations, FieldODEState<T> initialState, T t)	Start the integration.
protected void	MultistepIntegrator.start(double t0, double[] y0, double t)	Start the integration.

Constructors in org.apache.commons.math4.legacy.ode that throw NumberIsTooSmallException

Constructor	Description
MultistepFieldIntegrator(Field<T> field, String name, int nSteps, int order, double minStep, double maxStep, double scalAbsoluteTolerance, double scalRelativeTolerance)	Build a multistep integrator with the given stepsize bounds.
MultistepIntegrator(String name, int nSteps, int order, double minStep, double maxStep, double scalAbsoluteTolerance, double scalRelativeTolerance)	Build a multistep integrator with the given stepsize bounds.

Uses of NumberIsTooSmallException in org.apache.commons.math4.legacy.ode.nonstiff

Methods in org.apache.commons.math4.legacy.ode.nonstiff that throw NumberIsTooSmallException

Modifier and Type	Method	Description
protected T	AdaptiveStepsizeFieldIntegrator.filterStep(T h, boolean forward, boolean acceptSmall)	Filter the integration step.
protected double	AdaptiveStepsizeIntegrator.filterStep(double h, boolean forward, boolean acceptSmall)	Filter the integration step.
FieldODEStateAndDerivative<T>	AdamsBashforthFieldIntegrator.integrate(FieldExpandableODE<T> equations, FieldODEState<T> initialState, T finalTime)	Integrate the differential equations up to the given time.
void	AdamsBashforthIntegrator.integrate(ExpandableStatefulODE equations, double t)	Integrate a set of differential equations up to the given time.
abstract FieldODEStateAndDerivative<T>	AdamsFieldIntegrator.integrate(FieldExpandableODE<T> equations, FieldODEState<T> initialState, T finalTime)	Integrate the differential equations up to the given time.
abstract void	AdamsIntegrator.integrate(ExpandableStatefulODE equations, double t)	Integrate a set of differential equations up to the given time.
FieldODEStateAndDerivative<T>	AdamsMoultonFieldIntegrator.integrate(FieldExpandableODE<T> equations, FieldODEState<T> initialState, T finalTime)	Integrate the differential equations up to the given time.
void	AdamsMoultonIntegrator.integrate(ExpandableStatefulODE equations, double t)	Integrate a set of differential equations up to the given time.
abstract void	AdaptiveStepsizeIntegrator.integrate(ExpandableStatefulODE equations, double t)	Integrate a set of differential equations up to the given time.
FieldODEStateAndDerivative<T>	EmbeddedRungeKuttaFieldIntegrator.integrate(FieldExpandableODE<T> equations, FieldODEState<T> initialState, T finalTime)	Integrate the differential equations up to the given time.
void	EmbeddedRungeKuttaIntegrator.integrate(ExpandableStatefulODE equations, double t)	Integrate a set of differential equations up to the given time.
void	GraggBulirschStoerIntegrator.integrate(ExpandableStatefulODE equations, double t)	Integrate a set of differential equations up to the given time.
FieldODEStateAndDerivative<T>	RungeKuttaFieldIntegrator.integrate(FieldExpandableODE<T> equations, FieldODEState<T> initialState, T finalTime)	Integrate the differential equations up to the given time.
void	RungeKuttaIntegrator.integrate(ExpandableStatefulODE equations, double t)	Integrate a set of differential equations up to the given time.
protected void	AdaptiveStepsizeFieldIntegrator.sanityChecks(FieldODEState<T> eqn, T t)	Check the integration span.
protected void	AdaptiveStepsizeIntegrator.sanityChecks(ExpandableStatefulODE equations, double t)	Check the integration span.

Constructors in org.apache.commons.math4.legacy.ode.nonstiff that throw NumberIsTooSmallException

Constructor	Description
AdamsBashforthFieldIntegrator(Field<T> field, int nSteps, double minStep, double maxStep, double scalAbsoluteTolerance, double scalRelativeTolerance)	Build an Adams-Bashforth integrator with the given order and step control parameters.
AdamsBashforthIntegrator(int nSteps, double minStep, double maxStep, double scalAbsoluteTolerance, double scalRelativeTolerance)	Build an Adams-Bashforth integrator with the given order and step control parameters.
AdamsFieldIntegrator(Field<T> field, String name, int nSteps, int order, double minStep, double maxStep, double scalAbsoluteTolerance, double scalRelativeTolerance)	Build an Adams integrator with the given order and step control parameters.
AdamsIntegrator(String name, int nSteps, int order, double minStep, double maxStep, double scalAbsoluteTolerance, double scalRelativeTolerance)	Build an Adams integrator with the given order and step control parameters.
AdamsMoultonFieldIntegrator(Field<T> field, int nSteps, double minStep, double maxStep, double scalAbsoluteTolerance, double scalRelativeTolerance)	Build an Adams-Moulton integrator with the given order and error control parameters.
AdamsMoultonIntegrator(int nSteps, double minStep, double maxStep, double scalAbsoluteTolerance, double scalRelativeTolerance)	Build an Adams-Moulton integrator with the given order and error control parameters.

Uses of NumberIsTooSmallException in org.apache.commons.math4.legacy.stat

Methods in org.apache.commons.math4.legacy.stat that throw NumberIsTooSmallException

Modifier and Type	Method	Description
static double	StatUtils.varianceDifference(double[] sample1, double[] sample2, double meanDifference)	Returns the variance of the (signed) differences between corresponding elements of the input arrays -- i.e., var(sample1[i] - sample2[i]).

Uses of NumberIsTooSmallException in org.apache.commons.math4.legacy.stat.correlation

Methods in org.apache.commons.math4.legacy.stat.correlation that throw NumberIsTooSmallException

Modifier and Type	Method	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.math4.legacy.stat.inference

Methods in org.apache.commons.math4.legacy.stat.inference that throw NumberIsTooSmallException

Modifier and Type	Method	Description
static double	InferenceTestUtils.homoscedasticT(double[] sample1, double[] sample2)
static double	InferenceTestUtils.homoscedasticT(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
double	TTest.homoscedasticT(double[] sample1, double[] sample2)	Computes a 2-sample t statistic, under the hypothesis of equal subpopulation variances.
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	InferenceTestUtils.homoscedasticTTest(double[] sample1, double[] sample2)
static boolean	InferenceTestUtils.homoscedasticTTest(double[] sample1, double[] sample2, double alpha)
static double	InferenceTestUtils.homoscedasticTTest(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.
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.
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	InferenceTestUtils.pairedT(double[] sample1, double[] sample2)
double	TTest.pairedT(double[] sample1, double[] sample2)	Computes a paired, 2-sample t-statistic based on the data in the input arrays.
static double	InferenceTestUtils.pairedTTest(double[] sample1, double[] sample2)
static boolean	InferenceTestUtils.pairedTTest(double[] sample1, double[] sample2, double alpha)
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.
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 double	InferenceTestUtils.t(double[] sample1, double[] sample2)
static double	InferenceTestUtils.t(double mu, double[] observed)
static double	InferenceTestUtils.t(double mu, StatisticalSummary sampleStats)
static double	InferenceTestUtils.t(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
double	TTest.t(double[] sample1, double[] sample2)	Computes a 2-sample t statistic, without the hypothesis of equal subpopulation variances.
double	TTest.t(double mu, double[] observed)	Computes a t statistic given observed values and a comparison constant.
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.
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	InferenceTestUtils.tTest(double[] sample1, double[] sample2)
static boolean	InferenceTestUtils.tTest(double[] sample1, double[] sample2, double alpha)
static double	InferenceTestUtils.tTest(double mu, double[] sample)
static boolean	InferenceTestUtils.tTest(double mu, double[] sample, double alpha)
static double	InferenceTestUtils.tTest(double mu, StatisticalSummary sampleStats)
static boolean	InferenceTestUtils.tTest(double mu, StatisticalSummary sampleStats, double alpha)
static double	InferenceTestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
static boolean	InferenceTestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)
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.
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.
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.
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.
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.
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.
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.
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.