Uses of Class
org.apache.commons.math.MathException

Packages that use MathException
org.apache.commons.math Common classes used throughout the commons-math library. 
org.apache.commons.math.analysis Implementations of common numerical analysis procedures, including root finding and function interpolation. 
org.apache.commons.math.distribution Implementations of common discrete and continuous distributions. 
org.apache.commons.math.special Implementations of special functions such as Beta and Gamma. 
org.apache.commons.math.stat Data storage, manipulation and summary routines. 
org.apache.commons.math.util Convience routines and common data structure used throughout the commons-math library. 
 

Uses of MathException in org.apache.commons.math
 

Subclasses of MathException in org.apache.commons.math
 class ConvergenceException
          Error thrown when a numerical computation can not be performed because the numerical result failed to converge to a finite value.
 class MathConfigurationException
          Signals a configuration problem with any of the factory methods.
 

Uses of MathException in org.apache.commons.math.analysis
 

Methods in org.apache.commons.math.analysis that throw MathException
 double BisectionSolver.solve(double min, double max, double initial)
          Solve for a zero in the given interval.
 double BisectionSolver.solve(double min, double max)
          Solve for a zero root in the given interval.
 double BrentSolver.solve(double min, double max, double initial)
          Solve for a zero in the given interval.
 double BrentSolver.solve(double min, double max)
          Solve for a zero root in the given interval.
 double CubicSplineFunction.value(double x)
          Compute the value for the function.
 double CubicSplineFunction.firstDerivative(double x)
          Compute the value for the first derivative of the function.
 double CubicSplineFunction.secondDerivative(double x)
          Compute the value for the second derivative of the function.
 double PolynomialFunction.value(double x)
          Compute the value for the function.
 double PolynomialFunction.firstDerivative(double x)
          Compute the value for the first derivative of the function.
 double PolynomialFunction.secondDerivative(double x)
          Compute the value for the second derivative of the function.
 double SecantSolver.solve(double min, double max, double initial)
          Solve for a zero in the given interval.
 double SecantSolver.solve(double min, double max)
          Solve for a zero root in the given interval.
 double UnivariateRealFunction.value(double x)
          Compute the value for the function.
 UnivariateRealFunction UnivariateRealInterpolator.interpolate(double[] xval, double[] yval)
          Computes an interpolating function for the data set.
 void UnivariateRealSolver.setAbsoluteAccuracy(double accuracy)
          Set the absolute accuracy.
 void UnivariateRealSolver.setRelativeAccuracy(double accuracy)
          Set the relative accuracy.
 void UnivariateRealSolver.setFunctionValueAccuracy(double accuracy)
          Set the function value accuracy.
 double UnivariateRealSolver.solve(double min, double max)
          Solve for a zero root in the given interval.
 double UnivariateRealSolver.solve(double min, double max, double startValue)
          Solve for a zero in the given interval, start at startValue.
 double UnivariateRealSolver.getResult()
          Get the result of the last run of the solver.
 int UnivariateRealSolver.getIterationCount()
          Get the number of iterations in the last run of the solver.
 double UnivariateRealSolverImpl.getResult()
          Access the last computed root.
 int UnivariateRealSolverImpl.getIterationCount()
          Access the last iteration count.
 void UnivariateRealSolverImpl.setAbsoluteAccuracy(double accuracy)
          Set the absolute accuracy.
 void UnivariateRealSolverImpl.setRelativeAccuracy(double accuracy)
          Set the relative accuracy.
 void UnivariateRealSolverImpl.setFunctionValueAccuracy(double accuracy)
          Set the function value accuracy.
static double UnivariateRealSolverUtils.solve(UnivariateRealFunction f, double x0, double x1)
          Method to solve for zeros of real univariate functions.
static double UnivariateRealSolverUtils.solve(UnivariateRealFunction f, double x0, double x1, double absoluteAccuracy)
          Convience method to solve for zeros of real univariate functions.
static double[] UnivariateRealSolverUtils.bracket(UnivariateRealFunction function, double initial, double lowerBound, double upperBound)
          For a function, f, this method returns two values, a and b that bracket a root of f.
static double[] UnivariateRealSolverUtils.bracket(UnivariateRealFunction function, double initial, double lowerBound, double upperBound, int maximumIterations)
          For a function, f, this method returns two values, a and b that bracket a root of f.
 

Uses of MathException in org.apache.commons.math.distribution
 

Methods in org.apache.commons.math.distribution that throw MathException
 double AbstractContinuousDistribution.cummulativeProbability(double x0, double x1)
          For this distribution, X, this method returns P(x0 < X < x1).
 double AbstractContinuousDistribution.inverseCummulativeProbability(double p)
          For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
 double AbstractDiscreteDistribution.cummulativeProbability(int x0, int x1)
          For this disbution, X, this method returns P(x0 ≤ X ≤ x1).
 int AbstractDiscreteDistribution.inverseCummulativeProbability(double p)
          For this distribution, X, this method returns the critical point x, such that P(X ≤ x) ≤ p.
 double BinomialDistributionImpl.cummulativeProbability(int x)
          For this disbution, X, this method returns P(X ≤ x).
 double ChiSquaredDistributionImpl.cummulativeProbability(double x)
          For this disbution, X, this method returns P(X < x).
 double ContinuousDistribution.cummulativeProbability(double x)
          For this disbution, X, this method returns P(X < x).
 double ContinuousDistribution.cummulativeProbability(double x0, double x1)
          For this disbution, X, this method returns P(x0 < X < x1).
 double ContinuousDistribution.inverseCummulativeProbability(double p)
          For this disbution, X, this method returns x such that P(X < x) = p.
 double DiscreteDistribution.cummulativeProbability(int x)
          For this disbution, X, this method returns P(X ≤ x).
 double DiscreteDistribution.cummulativeProbability(int x0, int x1)
          For this disbution, X, this method returns P(x0 ≤ X ≤ x1).
 int DiscreteDistribution.inverseCummulativeProbability(double p)
          For this disbution, X, this method returns x such that P(X ≤ x) <= p.
 double ExponentialDistributionImpl.cummulativeProbability(double x)
          For this disbution, X, this method returns P(X < x).
 double ExponentialDistributionImpl.inverseCummulativeProbability(double p)
          For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
 double ExponentialDistributionImpl.cummulativeProbability(double x0, double x1)
          For this disbution, X, this method returns P(x0 < X < x1).
 double FDistributionImpl.cummulativeProbability(double x)
          For this disbution, X, this method returns P(X < x).
 double GammaDistributionImpl.cummulativeProbability(double x)
          For this disbution, X, this method returns P(X < x).
 double HypergeometricDistributionImpl.cummulativeProbability(int x)
          For this disbution, X, this method returns P(X ≤ x).
 double TDistributionImpl.cummulativeProbability(double x)
          For this disbution, X, this method returns P(X < x).
 

Uses of MathException in org.apache.commons.math.special
 

Methods in org.apache.commons.math.special that throw MathException
static double Beta.regularizedBeta(double x, double a, double b)
          Returns the regularized beta function I(x, a, b).
static double Beta.regularizedBeta(double x, double a, double b, double epsilon)
          Returns the regularized beta function I(x, a, b).
static double Beta.regularizedBeta(double x, double a, double b, int maxIterations)
          Returns the regularized beta function I(x, a, b).
static double Beta.regularizedBeta(double x, double a, double b, double epsilon, int maxIterations)
          Returns the regularized beta function I(x, a, b).
static double Gamma.regularizedGammaP(double a, double x)
          Returns the regularized gamma function P(a, x).
static double Gamma.regularizedGammaP(double a, double x, double epsilon, int maxIterations)
          Returns the regularized gamma function P(a, x).
 

Uses of MathException in org.apache.commons.math.stat
 

Methods in org.apache.commons.math.stat that throw MathException
 double BivariateRegression.getSlopeConfidenceInterval()
          Returns the half-width of a 95% confidence interval for the slope estimate.
 double BivariateRegression.getSlopeConfidenceInterval(double alpha)
          Returns the half-width of a (100-100*alpha)% confidence interval for the slope estimate.
 double BivariateRegression.getSignificance()
          Returns the significance level of the slope (equiv) correlation.
 double TestStatistic.chiSquare(double[] expected, double[] observed)
          Computes the Chi-Square statistic comparing observed and expected freqeuncy counts.
 double TestStatistic.chiSquareTest(double[] expected, double[] 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 TestStatistic.chiSquareTest(double[] expected, double[] 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 TestStatistic.t(double mu, double[] observed)
          Computes a t statistic given observed values and a comparison constant.
 double TestStatistic.t(double[] sample1, double[] sample2)
          Computes a 2-sample t statistic , without the assumption of equal sample variances.
 double TestStatistic.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 TestStatistic.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.
 boolean TestStatistic.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.
 double TestStatistic.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.
 double TestStatistic.t(double mu, DescriptiveStatistics sampleStats)
          Computes a t statistic to use in comparing the dataset described by sampleStats to mu.
 double TestStatistic.t(DescriptiveStatistics sampleStats1, DescriptiveStatistics sampleStats2)
          Computes a 2-sample t statistic , comparing the datasets described by two Univariates without the assumption of equal sample variances.
 double TestStatistic.tTest(DescriptiveStatistics sampleStats1, DescriptiveStatistics 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 Univariates.
 boolean TestStatistic.tTest(DescriptiveStatistics sampleStats1, DescriptiveStatistics 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.
 boolean TestStatistic.tTest(double mu, DescriptiveStatistics 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 TestStatistic.tTest(double mu, DescriptiveStatistics 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.
 double TestStatisticImpl.chiSquareTest(double[] expected, double[] observed)
           
 boolean TestStatisticImpl.chiSquareTest(double[] expected, double[] observed, double alpha)
           
 boolean TestStatisticImpl.tTest(double mu, double[] sample, double alpha)
           
 double TestStatisticImpl.tTest(double[] sample1, double[] sample2)
           
 boolean TestStatisticImpl.tTest(double[] sample1, double[] sample2, double alpha)
           
 double TestStatisticImpl.tTest(double mu, double[] sample)
           
 double TestStatisticImpl.tTest(DescriptiveStatistics sampleStats1, DescriptiveStatistics sampleStats2)
           
 boolean TestStatisticImpl.tTest(DescriptiveStatistics sampleStats1, DescriptiveStatistics sampleStats2, double alpha)
           
 boolean TestStatisticImpl.tTest(double mu, DescriptiveStatistics sampleStats, double alpha)
           
 double TestStatisticImpl.tTest(double mu, DescriptiveStatistics sampleStats)
           
 

Uses of MathException in org.apache.commons.math.util
 

Methods in org.apache.commons.math.util that throw MathException
 double ContinuedFraction.evaluate(double x)
          Evaluates the continued fraction at the value x.
 double ContinuedFraction.evaluate(double x, double epsilon)
          Evaluates the continued fraction at the value x.
 double ContinuedFraction.evaluate(double x, int maxIterations)
          Evaluates the continued fraction at the value x.
 double ContinuedFraction.evaluate(double x, double epsilon, int maxIterations)
          Evaluates the continued fraction at the value x.
 double DefaultTransformer.transform(Object o)
           
 double NumberTransformer.transform(Object o)
          Implementing this interface provides a facility to transform from Object to Double.
 double TransformerMap.transform(Object o)
          Attempts to transform the Object against the map of NumberTransformers.
 



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