Uses of Class
org.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.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

Modifier and Type	Method and Description
protected double	RombergIntegrator.doIntegrate() Method for implementing actual integration algorithms in derived classes.
protected double	SimpsonIntegrator.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	TrapezoidIntegrator.doIntegrate() Method for implementing actual integration algorithms in derived classes.
protected double	LegendreGaussIntegrator.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

Modifier and Type	Class and Description
class	TooManyEvaluationsException Exception to be thrown when the maximal number of evaluations 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 minv, RealVector b) Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealLinearOperator minv, RealVector b) Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealLinearOperator minv, 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 minv, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealLinearOperator minv, 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.
abstract RealVector	PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solveInPlace(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x) Returns an estimate of the solution to the linear system A · x = b.
RealVector	ConjugateGradient.solveInPlace(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solveInPlace(RealLinearOperator a, RealLinearOperator minv, 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
void	AbstractIntegrator.computeDerivatives(double t, double[] y, double[] yDot) Compute the derivatives and check the number of evaluations.

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) Runs one iteration of the Simplex method on the given model.
PointValuePair	SimplexSolver.doOptimize() Perform the bulk of optimization algorithm.
protected void	AbstractLinearOptimizer.incrementIterationsCounter() Increment the iterations counter by 1.
protected void	SimplexSolver.solvePhase1(org.apache.commons.math3.optimization.linear.SimplexTableau tableau) 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.
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)
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

Modifier and Type	Method and Description
void	IterationManager.incrementIterationCount() Increments the iteration count by one, and throws an exception if the maximum number of iterations is reached.