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

Packages that use MaxCountExceededException

Package	Description
org.apache.commons.math4.legacy.core	Core math utilities.
org.apache.commons.math4.legacy.exception	Specialized exceptions for algorithms errors.
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.events	This package provides classes to handle discrete events occurring during Ordinary Differential Equations integration.
org.apache.commons.math4.legacy.ode.nonstiff	This package provides classes to solve non-stiff Ordinary Differential Equations problems.
org.apache.commons.math4.legacy.ode.sampling	This package provides classes to handle sampling steps during Ordinary Differential Equations integration.
org.apache.commons.math4.legacy.stat.inference	Classes providing hypothesis testing.

Uses of MaxCountExceededException in org.apache.commons.math4.legacy.core

Methods in org.apache.commons.math4.legacy.core that throw MaxCountExceededException

Modifier and Type	Method	Description
void	IntegerSequence.Incrementor.increment()	Adds the increment value to the current iteration count.
void	IntegerSequence.Incrementor.increment(int nTimes)	Performs multiple increments.
void	IntegerSequence.Incrementor.MaxCountExceededCallback.trigger(int maximalCount)	Function called when the maximal count has been reached.

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

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

Modifier and Type	Class	Description
class	TooManyEvaluationsException	Exception to be thrown when the maximal number of evaluations is exceeded.
class	TooManyIterationsException	Exception to be thrown when the maximal number of iterations is exceeded.

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

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

Modifier and Type	Method	Description
RealVector	IterativeLinearSolver.solve(RealLinearOperator a, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	IterativeLinearSolver.solve(RealLinearOperator a, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealLinearOperator m, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealLinearOperator m, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealLinearOperator m, RealVector b, boolean goodb, double shift)	Returns an estimate of the solution to the linear system (A - shift · I) · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealVector b, boolean goodb, double shift)	Returns the solution to the system (A - shift · I) · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealVector b, RealVector x)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	ConjugateGradient.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
abstract RealVector	IterativeLinearSolver.solveInPlace(RealLinearOperator a, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
abstract RealVector	PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x, boolean goodb, double shift)	Returns an estimate of the solution to the linear system (A - shift · I) · x = b.
RealVector	SymmLQ.solveInPlace(RealLinearOperator a, RealVector b, RealVector x)	Returns an estimate of the solution to the linear system A · x = b.

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

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

Modifier and Type	Method	Description
protected FieldODEStateAndDerivative<T>	AbstractFieldIntegrator.acceptStep(AbstractFieldStepInterpolator<T> interpolator, T tEnd)	Accept a step, triggering events and step handlers.
protected double	AbstractIntegrator.acceptStep(AbstractStepInterpolator interpolator, double[] y, double[] yDot, double tEnd)	Accept a step, triggering events and step handlers.
void	ContinuousOutputFieldModel.append(ContinuousOutputFieldModel<T> model)	Append another model at the end of the instance.
void	ContinuousOutputModel.append(ContinuousOutputModel model)	Append another model at the end of the instance.
T[]	AbstractFieldIntegrator.computeDerivatives(T t, T[] y)	Compute the derivatives and check the number of evaluations.
void	AbstractIntegrator.computeDerivatives(double t, double[] y, double[] yDot)	Compute the derivatives and check the number of evaluations.
void	ExpandableStatefulODE.computeDerivatives(double t, double[] y, double[] yDot)	Get the current time derivative of the complete state vector.
T[]	FieldExpandableODE.computeDerivatives(T t, T[] y)	Get the current time derivative of the complete state vector.
T[]	FieldSecondaryEquations.computeDerivatives(T t, T[] primary, T[] primaryDot, T[] secondary)	Compute the derivatives related to the secondary state parameters.
void	FirstOrderDifferentialEquations.computeDerivatives(double t, double[] y, double[] yDot)	Get the current time derivative of the state vector.
void	SecondaryEquations.computeDerivatives(double t, double[] primary, double[] primaryDot, double[] secondary, double[] secondaryDot)	Compute the derivatives related to the secondary state parameters.
void	MainStateJacobianProvider.computeMainStateJacobian(double t, double[] y, double[] yDot, double[][] dFdY)	Compute the jacobian matrix of ODE with respect to main state.
void	ParameterJacobianProvider.computeParameterJacobian(double t, double[] y, double[] yDot, String paramName, double[] dFdP)	Compute the Jacobian matrix of ODE with respect to one parameter.
double[]	ContinuousOutputModel.getInterpolatedDerivatives()	Get the derivatives of the state vector of the interpolated point.
double[]	ContinuousOutputModel.getInterpolatedSecondaryDerivatives(int secondaryStateIndex)	Get the interpolated secondary derivatives corresponding to the secondary equations.
double[]	ContinuousOutputModel.getInterpolatedSecondaryState(int secondaryStateIndex)	Get the interpolated secondary state corresponding to the secondary equations.
double[]	ContinuousOutputModel.getInterpolatedState()	Get the state vector of the interpolated point.
void	ContinuousOutputFieldModel.handleStep(FieldStepInterpolator<T> interpolator, boolean isLast)	Handle the last accepted step.
void	ContinuousOutputModel.handleStep(StepInterpolator interpolator, boolean isLast)	Handle the last accepted step.
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	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.

Uses of MaxCountExceededException in org.apache.commons.math4.legacy.ode.events

Methods in org.apache.commons.math4.legacy.ode.events that throw MaxCountExceededException

Modifier and Type	Method	Description
boolean	EventState.evaluateStep(StepInterpolator interpolator)	Evaluate the impact of the proposed step on the event handler.
boolean	FieldEventState.evaluateStep(FieldStepInterpolator<T> interpolator)	Evaluate the impact of the proposed step on the event handler.
void	EventState.reinitializeBegin(StepInterpolator interpolator)	Reinitialize the beginning of the step.
void	FieldEventState.reinitializeBegin(FieldStepInterpolator<T> interpolator)	Reinitialize the beginning of the step.

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

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

Modifier and Type	Method	Description
T	AdaptiveStepsizeFieldIntegrator.initializeStep(boolean forward, int order, T[] scale, FieldODEStateAndDerivative<T> state0, FieldEquationsMapper<T> mapper)	Initialize the integration step.
double	AdaptiveStepsizeIntegrator.initializeStep(boolean forward, int order, double[] scale, double t0, double[] y0, double[] yDot0, double[] y1, double[] yDot1)	Initialize 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.

Uses of MaxCountExceededException in org.apache.commons.math4.legacy.ode.sampling

Methods in org.apache.commons.math4.legacy.ode.sampling that throw MaxCountExceededException

Modifier and Type	Method	Description
protected abstract FieldODEStateAndDerivative<T>	AbstractFieldStepInterpolator.computeInterpolatedStateAndDerivatives(FieldEquationsMapper<T> equationsMapper, T time, T theta, T thetaH, T oneMinusThetaH)	Compute the state and derivatives at the interpolated time.
protected abstract void	AbstractStepInterpolator.computeInterpolatedStateAndDerivatives(double theta, double oneMinusThetaH)	Compute the state and derivatives at the interpolated time.
StepInterpolator	AbstractStepInterpolator.copy()	Copy the instance.
StepInterpolator	StepInterpolator.copy()	Copy the instance.
protected void	AbstractStepInterpolator.doFinalize()	Really finalize the step.
void	AbstractStepInterpolator.finalizeStep()	Finalize the step.
double[]	AbstractStepInterpolator.getInterpolatedDerivatives()	Get the derivatives of the state vector of the interpolated point.
double[]	StepInterpolator.getInterpolatedDerivatives()	Get the derivatives of the state vector of the interpolated point.
double[]	AbstractStepInterpolator.getInterpolatedSecondaryDerivatives(int index)	Get the interpolated secondary derivatives corresponding to the secondary equations.
double[]	StepInterpolator.getInterpolatedSecondaryDerivatives(int index)	Get the interpolated secondary derivatives corresponding to the secondary equations.
double[]	AbstractStepInterpolator.getInterpolatedSecondaryState(int index)	Get the interpolated secondary state corresponding to the secondary equations.
double[]	StepInterpolator.getInterpolatedSecondaryState(int index)	Get the interpolated secondary state corresponding to the secondary equations.
double[]	AbstractStepInterpolator.getInterpolatedState()	Get the state vector of the interpolated point.
double[]	StepInterpolator.getInterpolatedState()	Get the state vector of the interpolated point.
double[]	NordsieckStepInterpolator.getInterpolatedStateVariation()	Get the state vector variation from current to interpolated state.
void	FieldStepHandler.handleStep(FieldStepInterpolator<T> interpolator, boolean isLast)	Handle the last accepted step.
void	FieldStepNormalizer.handleStep(FieldStepInterpolator<T> interpolator, boolean isLast)	Handle the last accepted step.
void	StepHandler.handleStep(StepInterpolator interpolator, boolean isLast)	Handle the last accepted step.
void	StepNormalizer.handleStep(StepInterpolator interpolator, boolean isLast)	Handle the last accepted step.

Uses of MaxCountExceededException in org.apache.commons.math4.legacy.stat.inference

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

Modifier and Type	Method	Description
double	OneWayAnova.anovaPValue(Collection<double[]> categoryData)	Computes the ANOVA P-value for a collection of double[] arrays.
double	OneWayAnova.anovaPValue(Collection<SummaryStatistics> categoryData, boolean allowOneElementData)	Computes the ANOVA P-value for a collection of SummaryStatistics.
boolean	OneWayAnova.anovaTest(Collection<double[]> categoryData, double alpha)	Performs an ANOVA test, evaluating the null hypothesis that there is no difference among the means of the data categories.
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.
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.
double	ChiSquareTest.chiSquareTest(long[][] counts)	Returns the observed significance level, or p-value, associated with a chi-square test of independence based on the input counts array, viewed as a two-way table.
boolean	ChiSquareTest.chiSquareTest(long[][] counts, double alpha)	Performs a chi-square test of independence evaluating the null hypothesis that the classifications represented by the counts in the columns of the input 2-way table are independent of the rows, with significance level alpha.
static double	InferenceTestUtils.chiSquareTest(double[] expected, long[] observed)
static boolean	InferenceTestUtils.chiSquareTest(double[] expected, long[] observed, double alpha)
static double	InferenceTestUtils.chiSquareTest(long[][] counts)
static boolean	InferenceTestUtils.chiSquareTest(long[][] counts, double alpha)
double	ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)	Returns the observed significance level, or p-value, associated with a Chi-Square two sample test comparing bin frequency counts in observed1 and observed2.
boolean	ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)	Performs a Chi-Square two sample test comparing two binned data sets.
static double	InferenceTestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
static boolean	InferenceTestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
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.
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	InferenceTestUtils.gTest(double[] expected, long[] observed)
static boolean	InferenceTestUtils.gTest(double[] expected, long[] observed, double alpha)
double	GTest.gTestDataSetsComparison(long[] observed1, long[] observed2)	Returns the observed significance level, or p-value, associated with a G-Value (Log-Likelihood Ratio) for two sample test comparing bin frequency counts in observed1 and observed2.
boolean	GTest.gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)	Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned data sets.
static double	InferenceTestUtils.gTestDataSetsComparison(long[] observed1, long[] observed2)
static boolean	InferenceTestUtils.gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
double	GTest.gTestIntrinsic(double[] expected, long[] observed)	Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described in p64-69 of McDonald, J.H.
static double	InferenceTestUtils.gTestIntrinsic(double[] expected, long[] observed)
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.
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.
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.
double	MannWhitneyUTest.mannWhitneyUTest(double[] x, double[] y)	Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U statistic comparing mean for two independent samples.
static double	InferenceTestUtils.oneWayAnovaPValue(Collection<double[]> categoryData)
static boolean	InferenceTestUtils.oneWayAnovaTest(Collection<double[]> categoryData, double alpha)
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.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.
protected double	TTest.tTest(double m, double mu, double v, double n)	Computes p-value for 2-sided, 1-sample t-test.
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.
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.
double	WilcoxonSignedRankTest.wilcoxonSignedRankTest(double[] x, double[] y, boolean exactPValue)	Returns the observed significance level, or p-value, associated with a Wilcoxon signed ranked statistic comparing mean for two related samples or repeated measurements on a single sample.