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.estimation This package provides classes to solve estimation problems. 
org.apache.commons.math.fraction Fraction number type and fraction number formatting. 
org.apache.commons.math.geometry This package provides basic 3D geometry components. 
org.apache.commons.math.ode This package provides classes to solve Ordinary Differential Equations problems. 
org.apache.commons.math.optimization This package provides parametric optimization algorithms. 
org.apache.commons.math.random Random number and random data generators. 
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.transform Implementations of transform methods, including Fast Fourier transforms. 
org.apache.commons.math.util Convenience routines and common data structures used throughout the commons-math library. 
 

Uses of MathException in org.apache.commons.math
 

Subclasses of MathException in org.apache.commons.math
 class ArgumentOutsideDomainException
          Error thrown when a method is called with an out of bounds argument.
 class ConvergenceException
          Error thrown when a numerical computation can not be performed because the numerical result failed to converge to a finite value.
 class DimensionMismatchException
          Error thrown when two dimensions differ.
 class DuplicateSampleAbscissaException
          Exeption thrown when a sample contains several entries at the same abscissa.
 class FunctionEvaluationException
          Exception thrown when an error occurs evaluating a function.
 class MathConfigurationException
          Signals a configuration problem with any of the factory methods.
 class MaxIterationsExceededException
          Error thrown when a numerical computation exceeds its allowed number of iterations.
 

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

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

Subclasses of MathException in org.apache.commons.math.estimation
 class EstimationException
          This class represents exceptions thrown by the estimation solvers.
 

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

Subclasses of MathException in org.apache.commons.math.fraction
 class FractionConversionException
          Error thrown when a double value cannot be converted to a fraction in the allowed number of iterations.
 

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

Subclasses of MathException in org.apache.commons.math.geometry
 class CardanEulerSingularityException
          This class represents exceptions thrown while extractiong Cardan or Euler angles from a rotation.
 class NotARotationMatrixException
          This class represents exceptions thrown while building rotations from matrices.
 

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

Subclasses of MathException in org.apache.commons.math.ode
 class DerivativeException
          This exception is made available to users to report the error conditions that are trigegred while computing the differential equations.
 class IntegratorException
          This exception is made available to users to report the error conditions that are triggered during integration
 

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

Subclasses of MathException in org.apache.commons.math.optimization
 class CostException
          This class represents exceptions thrown by cost functions.
 

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

Subclasses of MathException in org.apache.commons.math.random
 class NotPositiveDefiniteMatrixException
          This class represents exceptions thrown by the correlated random vector generator.
 

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

Methods in org.apache.commons.math.special that throw MathException
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, double epsilon, int maxIterations)
          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 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).
 

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

Methods in org.apache.commons.math.stat.inference that throw MathException
 double OneWayAnova.anovaFValue(java.util.Collection categoryData)
          Computes the ANOVA F-value for a collection of double[] arrays.
 double OneWayAnovaImpl.anovaFValue(java.util.Collection categoryData)
          Computes the ANOVA F-value for a collection of double[] arrays.
 double OneWayAnova.anovaPValue(java.util.Collection categoryData)
          Computes the ANOVA P-value for a collection of double[] arrays.
 double OneWayAnovaImpl.anovaPValue(java.util.Collection categoryData)
          Computes the ANOVA P-value for a collection of double[] arrays.
private  OneWayAnovaImpl.AnovaStats OneWayAnovaImpl.anovaStats(java.util.Collection categoryData)
          This method actually does the calculations (except P-value).
 boolean OneWayAnova.anovaTest(java.util.Collection categoryData, double alpha)
          Performs an ANOVA test, evaluating the null hypothesis that there is no difference among the means of the data categories.
 boolean OneWayAnovaImpl.anovaTest(java.util.Collection categoryData, double alpha)
          Performs an ANOVA test, evaluating the null hypothesis that there is no difference among the means of the data categories.
 double ChiSquareTestImpl.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.
 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 ChiSquareTestImpl.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.
 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 ChiSquareTestImpl.chiSquareTest(long[][] counts)
           
 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 ChiSquareTestImpl.chiSquareTest(long[][] counts, double alpha)
           
 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 ChiSquareTestImpl.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
           
 double UnknownDistributionChiSquareTest.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 ChiSquareTestImpl.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
           
 boolean UnknownDistributionChiSquareTest.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 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.
 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 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.
 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 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 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.
 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)
           
static double TestUtils.oneWayAnovaFValue(java.util.Collection categoryData)
           
static double TestUtils.oneWayAnovaPValue(java.util.Collection categoryData)
           
static boolean TestUtils.oneWayAnovaTest(java.util.Collection categoryData, double alpha)
           
 double TTestImpl.pairedT(double[] sample1, double[] sample2)
          Computes a paired, 2-sample t-statistic based on the data in the input arrays.
 double TTest.pairedT(double[] sample1, double[] sample2)
          Computes a paired, 2-sample t-statistic based on the data in the input arrays.
static double TestUtils.pairedT(double[] sample1, double[] sample2)
           
 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.
 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 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.
 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 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 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 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 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 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.
 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 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.
 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 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.
 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.
 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 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.
 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 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 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 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.
 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)
           
 

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

Methods in org.apache.commons.math.stat.regression that throw MathException
 double SimpleRegression.getSignificance()
          Returns the significance level of the slope (equiv) correlation.
 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.
 

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

Methods in org.apache.commons.math.transform that throw MathException
protected  double[] FastCosineTransformer.fct(double[] f)
          Perform the FCT algorithm (including inverse).
protected  Complex[] FastFourierTransformer.fft(Complex[] data)
          Perform the base-4 Cooley-Tukey FFT algorithm (including inverse).
protected  Complex[] FastFourierTransformer.fft(double[] f, boolean isInverse)
          Perform the base-4 Cooley-Tukey FFT algorithm (including inverse).
protected  double[] FastSineTransformer.fst(double[] f)
          Perform the FST algorithm (including inverse).
 Complex[] FastFourierTransformer.inversetransform(Complex[] f)
          Inversely transform the given complex data set.
 Complex[] FastFourierTransformer.inversetransform(double[] f)
          Inversely transform the given real data set.
 double[] FastCosineTransformer.inversetransform(double[] f)
          Inversely transform the given real data set.
 double[] FastSineTransformer.inversetransform(double[] f)
          Inversely transform the given real data set.
 Complex[] FastFourierTransformer.inversetransform(UnivariateRealFunction f, double min, double max, int n)
          Inversely transform the given real function, sampled on the given interval.
 double[] FastCosineTransformer.inversetransform(UnivariateRealFunction f, double min, double max, int n)
          Inversely transform the given real function, sampled on the given interval.
 double[] FastSineTransformer.inversetransform(UnivariateRealFunction f, double min, double max, int n)
          Inversely transform the given real function, sampled on the given interval.
 Complex[] FastFourierTransformer.inversetransform2(Complex[] f)
          Inversely transform the given complex data set.
 Complex[] FastFourierTransformer.inversetransform2(double[] f)
          Inversely transform the given real data set.
 double[] FastCosineTransformer.inversetransform2(double[] f)
          Inversely transform the given real data set.
 double[] FastSineTransformer.inversetransform2(double[] f)
          Inversely transform the given real data set.
 Complex[] FastFourierTransformer.inversetransform2(UnivariateRealFunction f, double min, double max, int n)
          Inversely transform the given real function, sampled on the given interval.
 double[] FastCosineTransformer.inversetransform2(UnivariateRealFunction f, double min, double max, int n)
          Inversely transform the given real function, sampled on the given interval.
 double[] FastSineTransformer.inversetransform2(UnivariateRealFunction f, double min, double max, int n)
          Inversely transform the given real function, sampled on the given interval.
static double[] FastFourierTransformer.sample(UnivariateRealFunction f, double min, double max, int n)
          Sample the given univariate real function on the given interval.
 Complex[] FastFourierTransformer.transform(Complex[] f)
          Transform the given complex data set.
 Complex[] FastFourierTransformer.transform(double[] f)
          Transform the given real data set.
 double[] FastCosineTransformer.transform(double[] f)
          Transform the given real data set.
 double[] FastSineTransformer.transform(double[] f)
          Transform the given real data set.
 Complex[] FastFourierTransformer.transform(UnivariateRealFunction f, double min, double max, int n)
          Transform the given real function, sampled on the given interval.
 double[] FastCosineTransformer.transform(UnivariateRealFunction f, double min, double max, int n)
          Transform the given real function, sampled on the given interval.
 double[] FastSineTransformer.transform(UnivariateRealFunction f, double min, double max, int n)
          Transform the given real function, sampled on the given interval.
 Complex[] FastFourierTransformer.transform2(Complex[] f)
          Transform the given complex data set.
 Complex[] FastFourierTransformer.transform2(double[] f)
          Transform the given real data set.
 double[] FastCosineTransformer.transform2(double[] f)
          Transform the given real data set.
 double[] FastSineTransformer.transform2(double[] f)
          Transform the given real data set.
 Complex[] FastFourierTransformer.transform2(UnivariateRealFunction f, double min, double max, int n)
          Transform the given real function, sampled on the given interval.
 double[] FastCosineTransformer.transform2(UnivariateRealFunction f, double min, double max, int n)
          Transform the given real function, sampled on the given interval.
 double[] FastSineTransformer.transform2(UnivariateRealFunction f, double min, double max, int n)
          Transform the given real function, sampled on the given interval.
 

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, double epsilon, int maxIterations)
           Evaluates the continued fraction at the value x.
 double ContinuedFraction.evaluate(double x, int maxIterations)
          Evaluates the continued fraction at the value x.
 double DefaultTransformer.transform(java.lang.Object o)
           
 double NumberTransformer.transform(java.lang.Object o)
          Implementing this interface provides a facility to transform from Object to Double.
 double TransformerMap.transform(java.lang.Object o)
          Attempts to transform the Object against the map of NumberTransformers.