Uses of Class org.apache.commons.math3.exception.MaxCountExceededException (Commons Math 3.2 API)

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org.apache.commons.math3.exception.MaxCountExceededException

Packages that use MaxCountExceededException
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 and confidence interval construction.
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
`protected double`	`LegendreGaussIntegrator.doIntegrate()` Deprecated. Method for implementing actual integration algorithms in derived classes.
`protected double`	`IterativeLegendreGaussIntegrator.doIntegrate()` Method for implementing actual integration algorithms in derived classes.
`protected double`	`TrapezoidIntegrator.doIntegrate()` Method for implementing actual integration algorithms in derived classes.
`protected double`	`RombergIntegrator.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`	`SimpsonIntegrator.doIntegrate()` Method for implementing actual integration algorithms in derived classes.
`double`	`UnivariateIntegrator.integrate(int maxEval, UnivariateFunction f, double min, double max)` Integrate the function in the given interval.
`double`	`BaseAbstractUnivariateIntegrator.integrate(int maxEval, UnivariateFunction f, double lower, double upper)` 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
`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
`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`	`PreconditionedIterativeLinearSolver.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)` 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`	`PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealVector b, RealVector x0)` 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`	`SymmLQ.solve(RealLinearOperator a, RealVector b, RealVector x)` 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`	`ConjugateGradient.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.
`RealVector`	`PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, 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.
`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
`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.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`	`FirstOrderIntegrator.integrate(FirstOrderDifferentialEquations equations, double t0, double[] y0, double t, double[] y)` Integrate the 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.
`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
`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
`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.
`void`	`GraggBulirschStoerIntegrator.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`	`AdamsBashforthIntegrator.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`	`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.

Uses of MaxCountExceededException in org.apache.commons.math3.ode.sampling

Methods in org.apache.commons.math3.ode.sampling that throw MaxCountExceededException
`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`	`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
`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
`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.
`static double`	`TestUtils.chiSquareTest(double[] expected, long[] observed)`
`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 boolean`	`TestUtils.chiSquareTest(double[] expected, long[] observed, double 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.
`static double`	`TestUtils.chiSquareTest(long[][] counts)`
`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 boolean`	`TestUtils.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`.
`static double`	`TestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)`
`boolean`	`ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)` Performs a Chi-Square two sample test comparing two binned data sets.
`static boolean`	`TestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)`
`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. 2009.
`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)`
`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.
`static double`	`TestUtils.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
`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)`
`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.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)`
`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`.
`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)`
`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
`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.

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

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