## 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.

Copyright © 2003-2012 The Apache Software Foundation. All Rights Reserved.