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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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