## Uses of Classorg.apache.commons.math3.exception.MaxCountExceededException

• Packages that use MaxCountExceededException
Package Description
org.apache.commons.math3.analysis.integration
Numerical integration (quadrature) algorithms for univariate real functions.
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.events
This package provides classes to handle discrete events occurring during Ordinary Differential Equations integration.
org.apache.commons.math3.ode.nonstiff
This package provides classes to solve non-stiff Ordinary Differential Equations problems.
org.apache.commons.math3.ode.sampling
This package provides classes to handle sampling steps during Ordinary Differential Equations integration.
org.apache.commons.math3.optimization.linear
This package provides optimization algorithms for linear constrained problems.
org.apache.commons.math3.stat.inference
Classes providing hypothesis testing.
org.apache.commons.math3.util
Convenience routines and common data structures used throughout the commons-math library.
• ### Uses of MaxCountExceededException in org.apache.commons.math3.analysis.integration

Methods in org.apache.commons.math3.analysis.integration that throw MaxCountExceededException
Modifier and Type Method and Description
protected double IterativeLegendreGaussIntegrator.doIntegrate()
Method for implementing actual integration algorithms in derived classes.
protected abstract double BaseAbstractUnivariateIntegrator.doIntegrate()
Method for implementing actual integration algorithms in derived classes.
protected double MidPointIntegrator.doIntegrate()
Method for implementing actual integration algorithms in derived classes.
protected double RombergIntegrator.doIntegrate()
Method for implementing actual integration algorithms in derived classes.
protected double LegendreGaussIntegrator.doIntegrate()
Deprecated.
Method for implementing actual integration algorithms in derived classes.
protected double TrapezoidIntegrator.doIntegrate()
Method for implementing actual integration algorithms in derived classes.
protected double SimpsonIntegrator.doIntegrate()
Method for implementing actual integration algorithms in derived classes.
double BaseAbstractUnivariateIntegrator.integrate(int maxEval, UnivariateFunction f, double lower, double upper)
Integrate the function in the given interval.
double UnivariateIntegrator.integrate(int maxEval, UnivariateFunction f, double min, double max)
Integrate the function in the given interval.
• ### Uses of MaxCountExceededException in org.apache.commons.math3.exception

Subclasses of MaxCountExceededException in org.apache.commons.math3.exception
Modifier and Type Class and 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.math3.linear

Methods in org.apache.commons.math3.linear that throw MaxCountExceededException
Modifier and Type Method and Description
RealVector PreconditionedIterativeLinearSolver.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)
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 PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealLinearOperator m, 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, RealVector x)
Returns an estimate of the solution to the linear system A · x = b.
RealVector IterativeLinearSolver.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)
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 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, RealVector b, RealVector x0)
Returns an estimate of the solution to the linear system A · 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 PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, RealLinearOperator m, 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.
abstract RealVector IterativeLinearSolver.solveInPlace(RealLinearOperator a, 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, RealVector b, RealVector x)
Returns an estimate of the solution to the linear system A · x = b.
• ### Uses of MaxCountExceededException in org.apache.commons.math3.ode

Methods in org.apache.commons.math3.ode that throw MaxCountExceededException
Modifier and Type Method and Description
protected double AbstractIntegrator.acceptStep(AbstractStepInterpolator interpolator, double[] y, double[] yDot, double tEnd)
Accept a step, triggering events and step handlers.
void ContinuousOutputModel.append(ContinuousOutputModel model)
Append another model at the end of the instance.
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.
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 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.
double FirstOrderIntegrator.integrate(FirstOrderDifferentialEquations equations, double t0, double[] y0, double t, double[] y)
Integrate the differential equations up to the given time.
protected void MultistepIntegrator.start(double t0, double[] y0, double t)
Start the integration.
• ### Uses of MaxCountExceededException in org.apache.commons.math3.ode.events

Methods in org.apache.commons.math3.ode.events that throw MaxCountExceededException
Modifier and Type Method and Description
boolean EventState.evaluateStep(StepInterpolator interpolator)
Evaluate the impact of the proposed step on the event handler.
void EventState.reinitializeBegin(StepInterpolator interpolator)
Reinitialize the beginning of the step.
• ### Uses of MaxCountExceededException in org.apache.commons.math3.ode.nonstiff

Methods in org.apache.commons.math3.ode.nonstiff that throw MaxCountExceededException
Modifier and Type Method and Description
double AdaptiveStepsizeIntegrator.initializeStep(boolean forward, int order, double[] scale, double t0, double[] y0, double[] yDot0, double[] y1, double[] yDot1)
Initialize the integration step.
void RungeKuttaIntegrator.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.
void AdamsMoultonIntegrator.integrate(ExpandableStatefulODE equations, double t)
Integrate a set of 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.
void AdamsBashforthIntegrator.integrate(ExpandableStatefulODE equations, double t)
Integrate a set of 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.
• ### Uses of MaxCountExceededException in org.apache.commons.math3.ode.sampling

Methods in org.apache.commons.math3.ode.sampling that throw MaxCountExceededException
Modifier and Type Method and Description
protected abstract void AbstractStepInterpolator.computeInterpolatedStateAndDerivatives(double theta, double oneMinusThetaH)
Compute the state and derivatives at the interpolated time.
StepInterpolator StepInterpolator.copy()
Copy the instance.
StepInterpolator AbstractStepInterpolator.copy()
Copy the instance.
protected void AbstractStepInterpolator.doFinalize()
Really finalize the step.
void AbstractStepInterpolator.finalizeStep()
Finalize the step.
double[] StepInterpolator.getInterpolatedDerivatives()
Get the derivatives of the state vector of the interpolated point.
double[] AbstractStepInterpolator.getInterpolatedDerivatives()
Get the derivatives of the state vector of the interpolated point.
double[] StepInterpolator.getInterpolatedSecondaryDerivatives(int index)
Get the interpolated secondary derivatives corresponding to the secondary equations.
double[] AbstractStepInterpolator.getInterpolatedSecondaryDerivatives(int index)
Get the interpolated secondary derivatives corresponding to the secondary equations.
double[] StepInterpolator.getInterpolatedSecondaryState(int index)
Get the interpolated secondary state corresponding to the secondary equations.
double[] AbstractStepInterpolator.getInterpolatedSecondaryState(int index)
Get the interpolated secondary state corresponding to the secondary equations.
double[] StepInterpolator.getInterpolatedState()
Get the state vector of the interpolated point.
double[] AbstractStepInterpolator.getInterpolatedState()
Get the state vector of the interpolated point.
double[] NordsieckStepInterpolator.getInterpolatedStateVariation()
Get the state vector variation from current to interpolated state.
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.math3.optimization.linear

Methods in org.apache.commons.math3.optimization.linear that throw MaxCountExceededException
Modifier and Type Method and Description
protected void SimplexSolver.doIteration(org.apache.commons.math3.optimization.linear.SimplexTableau tableau)
Deprecated.
Runs one iteration of the Simplex method on the given model.
PointValuePair SimplexSolver.doOptimize()
Deprecated.
Perform the bulk of optimization algorithm.
protected void AbstractLinearOptimizer.incrementIterationsCounter()
Deprecated.
Increment the iterations counter by 1.
protected void SimplexSolver.solvePhase1(org.apache.commons.math3.optimization.linear.SimplexTableau tableau)
Deprecated.
Solves Phase 1 of the Simplex method.
• ### Uses of MaxCountExceededException in org.apache.commons.math3.stat.inference

Methods in org.apache.commons.math3.stat.inference that throw MaxCountExceededException
Modifier and Type Method and 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.
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.chiSquareTest(long[][] counts)
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.
static boolean TestUtils.chiSquareTest(long[][] counts, double alpha)
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 TestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
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.
static boolean TestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
boolean ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
Performs a Chi-Square two sample test comparing two binned data sets.
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.gTestDataSetsComparison(long[] observed1, long[] observed2)
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.
static boolean TestUtils.gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
boolean GTest.gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned data sets.
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.
static double TestUtils.homoscedasticTTest(double[] sample1, double[] sample2)
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 boolean TestUtils.homoscedasticTTest(double[] sample1, double[] sample2, double alpha)
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.
static double TestUtils.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
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 TestUtils.oneWayAnovaPValue(Collection<double[]> categoryData)
static boolean TestUtils.oneWayAnovaTest(Collection<double[]> categoryData, double alpha)
static double TestUtils.pairedTTest(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 boolean TestUtils.pairedTTest(double[] sample1, double[] sample2, double alpha)
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 TestUtils.tTest(double[] sample1, double[] sample2)
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 boolean TestUtils.tTest(double[] sample1, double[] sample2, double alpha)
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 double TestUtils.tTest(double mu, double[] sample)
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 boolean TestUtils.tTest(double mu, double[] sample, double alpha)
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.
static double TestUtils.tTest(double mu, StatisticalSummary sampleStats)
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 boolean TestUtils.tTest(double mu, StatisticalSummary sampleStats, double alpha)
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 double TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
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 boolean TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)
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.
• ### Uses of MaxCountExceededException in org.apache.commons.math3.util

Methods in org.apache.commons.math3.util that throw MaxCountExceededException
Modifier and Type Method and Description
double ContinuedFraction.evaluate(double x, double epsilon, int maxIterations)
Evaluates the continued fraction at the value x.
double ContinuedFraction.evaluate(double x, int maxIterations)
Evaluates the continued fraction at the value x.
void Incrementor.incrementCount()
Adds one to the current iteration count.
void Incrementor.incrementCount(int value)
Performs multiple increments.
void IterationManager.incrementIterationCount()
Increments the iteration count by one, and throws an exception if the maximum number of iterations is reached.
void Incrementor.MaxCountExceededCallback.trigger(int maximalCount)
Function called when the maximal count has been reached.