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.inference	Classes providing hypothesis testing and confidence interval construction.
org.apache.commons.math.stat.regression	Statistical routines involving multivariate data.
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	FunctionEvaluationException Exeption thrown when an error occurs evaluating a function.
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

UnivariateRealFunction

UnivariateRealInterpolator.interpolate(double[] xval, double[] yval) Computes an interpolating function for the data set.

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

Methods in org.apache.commons.math.distribution that throw MathException

double	TDistributionImpl.cumulativeProbability(double x) For this disbution, X, this method returns P(X < x).
double	TDistributionImpl.inverseCumulativeProbability(double p) For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
double	PoissonDistributionImpl.cumulativeProbability(int x) The probability distribution function P(X <= x) for a Poisson distribution.
double	PoissonDistributionImpl.normalApproximateProbability(int x) Calculates the Poisson distribution function using a normal approximation.
double	NormalDistributionImpl.cumulativeProbability(double x) For this disbution, X, this method returns P(X < x).
double	NormalDistributionImpl.inverseCumulativeProbability(double p) For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
double	HypergeometricDistributionImpl.cumulativeProbability(int x) For this disbution, X, this method returns P(X ≤ x).
double	GammaDistributionImpl.cumulativeProbability(double x) For this disbution, X, this method returns P(X < x).
double	GammaDistributionImpl.inverseCumulativeProbability(double p) For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
double	FDistributionImpl.cumulativeProbability(double x) For this disbution, X, this method returns P(X < x).
double	FDistributionImpl.inverseCumulativeProbability(double p) For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
double	ExponentialDistributionImpl.cumulativeProbability(double x) For this disbution, X, this method returns P(X < x).
double	ExponentialDistributionImpl.inverseCumulativeProbability(double p) For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
double	PoissonDistribution.normalApproximateProbability(int x) Calculates the Poisson distribution function using a normal approximation.
double	ChiSquaredDistributionImpl.cumulativeProbability(double x) For this disbution, X, this method returns P(X < x).
double	ChiSquaredDistributionImpl.inverseCumulativeProbability(double p) For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
double	BinomialDistributionImpl.cumulativeProbability(int x) For this distribution, X, this method returns P(X ≤ x).
int	BinomialDistributionImpl.inverseCumulativeProbability(double p) For this distribution, X, this method returns the largest x, such that P(X ≤ x) ≤ p.
double	IntegerDistribution.cumulativeProbability(int x) For a random variable X whose values are distributed according to this distribution, this method returns P(X ≤ x).
double	IntegerDistribution.cumulativeProbability(int x0, int x1) For this distribution, X, this method returns P(x0 ≤ X ≤ x1).
int	IntegerDistribution.inverseCumulativeProbability(double p) For this distribution, X, this method returns the largest x such that P(X ≤ x) <= p.
double	AbstractIntegerDistribution.cumulativeProbability(double x) For a random variable X whose values are distributed according to this distribution, this method returns P(X ≤ x).
abstract double	AbstractIntegerDistribution.cumulativeProbability(int x) For a random variable X whose values are distributed according to this distribution, this method returns P(X ≤ x).
double	AbstractIntegerDistribution.cumulativeProbability(int x0, int x1) For a random variable X whose values are distributed according to this distribution, this method returns P(x0 ≤ X ≤ x1).
int	AbstractIntegerDistribution.inverseCumulativeProbability(double p) For a random variable X whose values are distributed according to this distribution, this method returns the largest x, such that P(X ≤ x) ≤ p.
double	ContinuousDistribution.inverseCumulativeProbability(double p) For this disbution, X, this method returns x such that P(X < x) = p.
double	Distribution.cumulativeProbability(double x) For a random variable X whose values are distributed according to this distribution, this method returns P(X ≤ x).
double	Distribution.cumulativeProbability(double x0, double x1) For a random variable X whose values are distributed according to this distribution, this method returns P(x0 ≤ X ≤ x1).
double	AbstractDistribution.cumulativeProbability(double x0, double x1) For a random variable X whose values are distributed according to this distribution, this method returns P(x0 ≤ X ≤ x1).
double	AbstractContinuousDistribution.inverseCumulativeProbability(double p) For this distribution, X, this method returns the critical point x, such that P(X < x) = p.

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

Methods in org.apache.commons.math.special that throw MathException

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).
static double	Gamma.regularizedGammaQ(double a, double x) Returns the regularized gamma function Q(a, x) = 1 - P(a, x).
static double	Gamma.regularizedGammaQ(double a, double x, double epsilon, int maxIterations) Returns the regularized gamma function Q(a, x) = 1 - P(a, x).
static double	Erf.erf(double x) Returns the error function erf(x).
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).

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

Methods in org.apache.commons.math.stat.inference that throw MathException

double	TTestImpl.pairedT(double[] sample1, double[] sample2) Computes a paired, 2-sample t-statistic based on the data in the input arrays.
double	TTestImpl.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.
boolean	TTestImpl.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.
double	TTestImpl.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.
boolean	TTestImpl.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	TTestImpl.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.
boolean	TTestImpl.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.
double	TTestImpl.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.
double	TTestImpl.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.
boolean	TTestImpl.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	TTestImpl.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.
double	TTestImpl.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.
double	TTestImpl.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.
boolean	TTestImpl.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.
protected double	TTestImpl.tTest(double m, double mu, double v, double n) Computes p-value for 2-sided, 1-sample t-test.
protected double	TTestImpl.tTest(double m1, double m2, double v1, double v2, double n1, double n2) Computes p-value for 2-sided, 2-sample t-test.
protected double	TTestImpl.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.pairedT(double[] sample1, double[] sample2) Computes a paired, 2-sample t-statistic based on the data in the input arrays.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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	ChiSquareTestImpl.chiSquareTest(double[] expected, long[] observed)
boolean	ChiSquareTestImpl.chiSquareTest(double[] expected, long[] observed, double alpha)
double	ChiSquareTestImpl.chiSquareTest(long[][] counts)
boolean	ChiSquareTestImpl.chiSquareTest(long[][] counts, double alpha)
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.
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.
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.
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.

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

Methods in org.apache.commons.math.stat.regression that throw MathException

double	SimpleRegression.getSlopeConfidenceInterval() Returns the half-width of a 95% confidence interval for the slope estimate.
double	SimpleRegression.getSlopeConfidenceInterval(double alpha) Returns the half-width of a (100-100*alpha)% confidence interval for the slope estimate.
double	SimpleRegression.getSignificance() Returns the significance level of the slope (equiv) correlation.

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

Methods in org.apache.commons.math.util that throw MathException

double	TransformerMap.transform(Object o) Attempts to transform the Object against the map of NumberTransformers.
double	NumberTransformer.transform(Object o) Implementing this interface provides a facility to transform from Object to Double.
double	DefaultTransformer.transform(Object o)
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.