Uses of Class
org.apache.commons.math3.exception.NotStrictlyPositiveException

Packages that use NotStrictlyPositiveException
org.apache.commons.math3.analysis.function The function package contains function objects that wrap the methods contained in Math, as well as common mathematical functions such as the gaussian and sinc functions. 
org.apache.commons.math3.analysis.integration Numerical integration (quadrature) algorithms for univariate real functions. 
org.apache.commons.math3.analysis.integration.gauss Gauss family of quadrature schemes. 
org.apache.commons.math3.analysis.interpolation Univariate real functions interpolation algorithms. 
org.apache.commons.math3.analysis.solvers Root finding algorithms, for univariate real functions. 
org.apache.commons.math3.distribution Implementations of common discrete and continuous distributions. 
org.apache.commons.math3.genetics This package provides Genetic Algorithms components and implementations. 
org.apache.commons.math3.linear Linear algebra support. 
org.apache.commons.math3.optim.nonlinear.scalar Algorithms for optimizing a scalar function. 
org.apache.commons.math3.optim.nonlinear.scalar.noderiv This package provides optimization algorithms that do not require derivatives. 
org.apache.commons.math3.optim.nonlinear.vector Algorithms for optimizing a vector function. 
org.apache.commons.math3.optimization.direct This package provides optimization algorithms that don't require derivatives. 
org.apache.commons.math3.random Random number and random data generators. 
org.apache.commons.math3.stat.inference Classes providing hypothesis testing and confidence interval construction. 
org.apache.commons.math3.transform Implementations of transform methods, including Fast Fourier transforms. 
org.apache.commons.math3.util Convenience routines and common data structures used throughout the commons-math library. 
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis.function
 

Methods in org.apache.commons.math3.analysis.function that throw NotStrictlyPositiveException
 double[] Logistic.Parametric.gradient(double x, double... param)
          Computes the value of the gradient at x.
 double[] Gaussian.Parametric.gradient(double x, double... param)
          Computes the value of the gradient at x.
 double Logistic.Parametric.value(double x, double... param)
          Computes the value of the sigmoid at x.
 double Gaussian.Parametric.value(double x, double... param)
          Computes the value of the Gaussian at x.
 

Constructors in org.apache.commons.math3.analysis.function that throw NotStrictlyPositiveException
Gaussian(double mean, double sigma)
          Normalized gaussian with given mean and standard deviation.
Gaussian(double norm, double mean, double sigma)
          Gaussian with given normalization factor, mean and standard deviation.
Logistic(double k, double m, double b, double q, double a, double n)
           
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis.integration
 

Constructors in org.apache.commons.math3.analysis.integration that throw NotStrictlyPositiveException
BaseAbstractUnivariateIntegrator(double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)
          Construct an integrator with given accuracies and iteration counts.
BaseAbstractUnivariateIntegrator(int minimalIterationCount, int maximalIterationCount)
          Construct an integrator with given iteration counts.
IterativeLegendreGaussIntegrator(int n, double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)
          Builds an integrator with given accuracies and iterations counts.
LegendreGaussIntegrator(int n, double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)
          Deprecated. Build a Legendre-Gauss integrator with given accuracies and iterations counts.
RombergIntegrator(double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)
          Build a Romberg integrator with given accuracies and iterations counts.
RombergIntegrator(int minimalIterationCount, int maximalIterationCount)
          Build a Romberg integrator with given iteration counts.
SimpsonIntegrator(double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)
          Build a Simpson integrator with given accuracies and iterations counts.
SimpsonIntegrator(int minimalIterationCount, int maximalIterationCount)
          Build a Simpson integrator with given iteration counts.
TrapezoidIntegrator(double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)
          Build a trapezoid integrator with given accuracies and iterations counts.
TrapezoidIntegrator(int minimalIterationCount, int maximalIterationCount)
          Build a trapezoid integrator with given iteration counts.
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis.integration.gauss
 

Methods in org.apache.commons.math3.analysis.integration.gauss that throw NotStrictlyPositiveException
protected  Pair<Double[],Double[]> LegendreRuleFactory.computeRule(int numberOfPoints)
          Computes the rule for the given order.
 Pair<double[],double[]> BaseRuleFactory.getRule(int numberOfPoints)
          Gets a copy of the quadrature rule with the given number of integration points.
protected  Pair<T[],T[]> BaseRuleFactory.getRuleInternal(int numberOfPoints)
          Gets a rule.
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis.interpolation
 

Constructors in org.apache.commons.math3.analysis.interpolation that throw NotStrictlyPositiveException
MicrosphereInterpolator(int elements, int exponent)
          Create a microsphere interpolator.
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis.solvers
 

Methods in org.apache.commons.math3.analysis.solvers that throw NotStrictlyPositiveException
static double[] UnivariateSolverUtils.bracket(UnivariateFunction function, double initial, double lowerBound, double upperBound)
          This method attempts to find two values a and b satisfying lowerBound <= a < initial < b <= upperBound f(a) * f(b) < 0 If f is continuous on [a,b], this means that a and b bracket a root of f.
static double[] UnivariateSolverUtils.bracket(UnivariateFunction function, double initial, double lowerBound, double upperBound, int maximumIterations)
          This method attempts to find two values a and b satisfying lowerBound <= a < initial < b <= upperBound f(a) * f(b) <= 0 If f is continuous on [a,b], this means that a and b bracket a root of f.
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.distribution
 

Methods in org.apache.commons.math3.distribution that throw NotStrictlyPositiveException
 double[][] MultivariateRealDistribution.sample(int sampleSize)
          Generates a list of a random value vectors from the distribution.
 

Constructors in org.apache.commons.math3.distribution that throw NotStrictlyPositiveException
ExponentialDistribution(RandomGenerator rng, double mean, double inverseCumAccuracy)
          Creates an exponential distribution.
FDistribution(double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom)
          Creates an F distribution using the given degrees of freedom.
FDistribution(double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom, double inverseCumAccuracy)
          Creates an F distribution using the given degrees of freedom and inverse cumulative probability accuracy.
FDistribution(RandomGenerator rng, double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom, double inverseCumAccuracy)
          Creates an F distribution.
GammaDistribution(double shape, double scale)
          Creates a new gamma distribution with specified values of the shape and scale parameters.
GammaDistribution(double shape, double scale, double inverseCumAccuracy)
          Creates a new gamma distribution with specified values of the shape and scale parameters.
GammaDistribution(RandomGenerator rng, double shape, double scale, double inverseCumAccuracy)
          Creates a Gamma distribution.
HypergeometricDistribution(int populationSize, int numberOfSuccesses, int sampleSize)
          Construct a new hypergeometric distribution with the specified population size, number of successes in the population, and sample size.
HypergeometricDistribution(RandomGenerator rng, int populationSize, int numberOfSuccesses, int sampleSize)
          Creates a new hypergeometric distribution.
KolmogorovSmirnovDistribution(int n)
           
LogNormalDistribution(double scale, double shape)
          Create a log-normal distribution using the specified scale and shape.
LogNormalDistribution(double scale, double shape, double inverseCumAccuracy)
          Create a log-normal distribution using the specified scale, shape and inverse cumulative distribution accuracy.
LogNormalDistribution(RandomGenerator rng, double scale, double shape, double inverseCumAccuracy)
          Creates a log-normal distribution.
NormalDistribution(double mean, double sd)
          Create a normal distribution using the given mean and standard deviation.
NormalDistribution(double mean, double sd, double inverseCumAccuracy)
          Create a normal distribution using the given mean, standard deviation and inverse cumulative distribution accuracy.
NormalDistribution(RandomGenerator rng, double mean, double sd, double inverseCumAccuracy)
          Creates a normal distribution.
PascalDistribution(int r, double p)
          Create a Pascal distribution with the given number of successes and probability of success.
PascalDistribution(RandomGenerator rng, int r, double p)
          Create a Pascal distribution with the given number of successes and probability of success.
PoissonDistribution(double p)
          Creates a new Poisson distribution with specified mean.
PoissonDistribution(double p, double epsilon)
          Creates a new Poisson distribution with the specified mean and convergence criterion.
PoissonDistribution(double p, double epsilon, int maxIterations)
          Creates a new Poisson distribution with specified mean, convergence criterion and maximum number of iterations.
PoissonDistribution(RandomGenerator rng, double p, double epsilon, int maxIterations)
          Creates a new Poisson distribution with specified mean, convergence criterion and maximum number of iterations.
TDistribution(double degreesOfFreedom)
          Create a t distribution using the given degrees of freedom.
TDistribution(double degreesOfFreedom, double inverseCumAccuracy)
          Create a t distribution using the given degrees of freedom and the specified inverse cumulative probability absolute accuracy.
TDistribution(RandomGenerator rng, double degreesOfFreedom, double inverseCumAccuracy)
          Creates a t distribution.
WeibullDistribution(double alpha, double beta)
          Create a Weibull distribution with the given shape and scale and a location equal to zero.
WeibullDistribution(RandomGenerator rng, double alpha, double beta, double inverseCumAccuracy)
          Creates a Weibull distribution.
ZipfDistribution(RandomGenerator rng, int numberOfElements, double exponent)
          Creates a Zipf distribution.
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.genetics
 

Constructors in org.apache.commons.math3.genetics that throw NotStrictlyPositiveException
NPointCrossover(int crossoverPoints)
          Creates a new NPointCrossover policy using the given number of points.
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.linear
 

Methods in org.apache.commons.math3.linear that throw NotStrictlyPositiveException
 RealMatrix Array2DRowRealMatrix.createMatrix(int rowDimension, int columnDimension)
          Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.
abstract  RealMatrix AbstractRealMatrix.createMatrix(int rowDimension, int columnDimension)
          Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.
 RealMatrix RealMatrix.createMatrix(int rowDimension, int columnDimension)
          Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.
 FieldMatrix<T> FieldMatrix.createMatrix(int rowDimension, int columnDimension)
          Create a new FieldMatrix of the same type as the instance with the supplied row and column dimensions.
 BlockRealMatrix BlockRealMatrix.createMatrix(int rowDimension, int columnDimension)
          Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.
 OpenMapRealMatrix OpenMapRealMatrix.createMatrix(int rowDimension, int columnDimension)
          Deprecated. Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.
 FieldMatrix<T> Array2DRowFieldMatrix.createMatrix(int rowDimension, int columnDimension)
          Create a new FieldMatrix of the same type as the instance with the supplied row and column dimensions.
abstract  FieldMatrix<T> AbstractFieldMatrix.createMatrix(int rowDimension, int columnDimension)
          Create a new FieldMatrix of the same type as the instance with the supplied row and column dimensions.
 FieldMatrix<T> BlockFieldMatrix.createMatrix(int rowDimension, int columnDimension)
          Create a new FieldMatrix of the same type as the instance with the supplied row and column dimensions.
 

Constructors in org.apache.commons.math3.linear that throw NotStrictlyPositiveException
AbstractFieldMatrix(Field<T> field, int rowDimension, int columnDimension)
          Create a new FieldMatrix with the supplied row and column dimensions.
AbstractRealMatrix(int rowDimension, int columnDimension)
          Create a new RealMatrix with the supplied row and column dimensions.
Array2DRowFieldMatrix(Field<T> field, int rowDimension, int columnDimension)
          Create a new FieldMatrix<T> with the supplied row and column dimensions.
Array2DRowRealMatrix(int rowDimension, int columnDimension)
          Create a new RealMatrix with the supplied row and column dimensions.
BlockFieldMatrix(Field<T> field, int rows, int columns)
          Create a new matrix with the supplied row and column dimensions.
BlockFieldMatrix(int rows, int columns, T[][] blockData, boolean copyArray)
          Create a new dense matrix copying entries from block layout data.
BlockRealMatrix(double[][] rawData)
          Create a new dense matrix copying entries from raw layout data.
BlockRealMatrix(int rows, int columns)
          Create a new matrix with the supplied row and column dimensions.
BlockRealMatrix(int rows, int columns, double[][] blockData, boolean copyArray)
          Create a new dense matrix copying entries from block layout data.
OpenMapRealMatrix(int rowDimension, int columnDimension)
          Deprecated. Build a sparse matrix with the supplied row and column dimensions.
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.optim.nonlinear.scalar
 

Constructors in org.apache.commons.math3.optim.nonlinear.scalar that throw NotStrictlyPositiveException
MultiStartMultivariateOptimizer(MultivariateOptimizer optimizer, int starts, RandomVectorGenerator generator)
          Create a multi-start optimizer from a single-start optimizer.
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.optim.nonlinear.scalar.noderiv
 

Constructors in org.apache.commons.math3.optim.nonlinear.scalar.noderiv that throw NotStrictlyPositiveException
CMAESOptimizer.PopulationSize(int size)
           
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.optim.nonlinear.vector
 

Constructors in org.apache.commons.math3.optim.nonlinear.vector that throw NotStrictlyPositiveException
MultiStartMultivariateVectorOptimizer(MultivariateVectorOptimizer optimizer, int starts, RandomVectorGenerator generator)
          Create a multi-start optimizer from a single-start optimizer.
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.optimization.direct
 

Constructors in org.apache.commons.math3.optimization.direct that throw NotStrictlyPositiveException
CMAESOptimizer.PopulationSize(int size)
           
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.random
 

Methods in org.apache.commons.math3.random that throw NotStrictlyPositiveException
 double RandomDataGenerator.nextExponential(double mean)
          Generates a random value from the exponential distribution with specified mean.
 double RandomData.nextExponential(double mean)
          Deprecated. Generates a random value from the exponential distribution with specified mean.
 double RandomDataImpl.nextExponential(double mean)
          Deprecated. Generates a random value from the exponential distribution with specified mean.
 double RandomDataGenerator.nextF(double numeratorDf, double denominatorDf)
          Generates a random value from the F Distribution.
 double RandomDataImpl.nextF(double numeratorDf, double denominatorDf)
          Deprecated. Generates a random value from the F Distribution.
 double RandomDataGenerator.nextGamma(double shape, double scale)
          Generates a random value from the Gamma Distribution.
 double RandomDataImpl.nextGamma(double shape, double scale)
          Deprecated. Generates a random value from the Gamma Distribution.
 double RandomDataGenerator.nextGaussian(double mu, double sigma)
          Generates a random value from the Normal (or Gaussian) distribution with specified mean and standard deviation.
 double RandomData.nextGaussian(double mu, double sigma)
          Deprecated. Generates a random value from the Normal (or Gaussian) distribution with specified mean and standard deviation.
 double RandomDataImpl.nextGaussian(double mu, double sigma)
          Deprecated. Generates a random value from the Normal (or Gaussian) distribution with specified mean and standard deviation.
 String RandomDataGenerator.nextHexString(int len)
          Generates a random string of hex characters of length len.
 String RandomData.nextHexString(int len)
          Deprecated. Generates a random string of hex characters of length len.
 String RandomDataImpl.nextHexString(int len)
          Deprecated. Generates a random string of hex characters of length len.
 int RandomDataGenerator.nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize)
          Generates a random value from the Hypergeometric Distribution.
 int RandomDataImpl.nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize)
          Deprecated. Generates a random value from the Hypergeometric Distribution.
 int RandomDataGenerator.nextPascal(int r, double p)
          Generates a random value from the Pascal Distribution.
 int RandomDataImpl.nextPascal(int r, double p)
          Deprecated. Generates a random value from the Pascal Distribution.
 int[] RandomDataGenerator.nextPermutation(int n, int k)
          Generates an integer array of length k whose entries are selected randomly, without repetition, from the integers 0, ..., n - 1 (inclusive).
 int[] RandomData.nextPermutation(int n, int k)
          Deprecated. Generates an integer array of length k whose entries are selected randomly, without repetition, from the integers 0, ..., n - 1 (inclusive).
 int[] RandomDataImpl.nextPermutation(int n, int k)
          Deprecated. Generates an integer array of length k whose entries are selected randomly, without repetition, from the integers 0, ..., n - 1 (inclusive).
 long RandomDataGenerator.nextPoisson(double mean)
          Generates a random value from the Poisson distribution with the given mean.
 long RandomData.nextPoisson(double mean)
          Deprecated. Generates a random value from the Poisson distribution with the given mean.
 long RandomDataImpl.nextPoisson(double mean)
          Deprecated. Generates a random value from the Poisson distribution with the given mean.
 Object[] RandomDataGenerator.nextSample(Collection<?> c, int k)
          Returns an array of k objects selected randomly from the Collection c.
 Object[] RandomData.nextSample(Collection<?> c, int k)
          Deprecated. Returns an array of k objects selected randomly from the Collection c.
 Object[] RandomDataImpl.nextSample(Collection<?> c, int k)
          Deprecated. Returns an array of k objects selected randomly from the Collection c.
 String RandomDataGenerator.nextSecureHexString(int len)
          Generates a random string of hex characters from a secure random sequence.
 String RandomData.nextSecureHexString(int len)
          Deprecated. Generates a random string of hex characters from a secure random sequence.
 String RandomDataImpl.nextSecureHexString(int len)
          Deprecated. Generates a random string of hex characters from a secure random sequence.
 double RandomDataGenerator.nextT(double df)
          Generates a random value from the T Distribution.
 double RandomDataImpl.nextT(double df)
          Deprecated. Generates a random value from the T Distribution.
 double RandomDataGenerator.nextWeibull(double shape, double scale)
          Generates a random value from the Weibull Distribution.
 double RandomDataImpl.nextWeibull(double shape, double scale)
          Deprecated. Generates a random value from the Weibull Distribution.
 int RandomDataGenerator.nextZipf(int numberOfElements, double exponent)
          Generates a random value from the Zipf Distribution.
 int RandomDataImpl.nextZipf(int numberOfElements, double exponent)
          Deprecated. Generates a random value from the Zipf Distribution.
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.stat.inference
 

Methods in org.apache.commons.math3.stat.inference that throw NotStrictlyPositiveException
static double TestUtils.chiSquare(double[] expected, long[] observed)
           
 double ChiSquareTest.chiSquare(double[] expected, long[] observed)
          Computes the Chi-Square statistic comparing observed and expected frequency counts.
static double TestUtils.chiSquareTest(double[] expected, long[] observed)
           
 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 boolean TestUtils.chiSquareTest(double[] expected, long[] observed, double 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 double TestUtils.g(double[] expected, long[] observed)
           
 double GTest.g(double[] expected, long[] observed)
          Computes the G statistic for Goodness of Fit comparing observed and expected frequency counts.
static double TestUtils.gTest(double[] expected, long[] observed)
           
 double GTest.gTest(double[] expected, long[] observed)
          Returns the observed significance level, or p-value, associated with a G-Test for goodness of fit comparing the observed frequency counts to those in the expected array.
static boolean TestUtils.gTest(double[] expected, long[] observed, double alpha)
           
 boolean GTest.gTest(double[] expected, long[] observed, double alpha)
          Performs a G-Test (Log-Likelihood Ratio Test) for goodness of fit evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance level alpha.
static double TestUtils.gTestIntrinsic(double[] expected, long[] observed)
           
 double GTest.gTestIntrinsic(double[] expected, long[] observed)
          Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described in p64-69 of McDonald, J.H.
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.
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.
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.transform
 

Methods in org.apache.commons.math3.transform that throw NotStrictlyPositiveException
 double[] RealTransformer.transform(UnivariateFunction f, double min, double max, int n, TransformType type)
          Returns the (forward, inverse) transform of the specified real function, sampled on the specified interval.
 

Uses of NotStrictlyPositiveException in org.apache.commons.math3.util
 

Methods in org.apache.commons.math3.util that throw NotStrictlyPositiveException
static void MathArrays.checkPositive(double[] in)
          Check that all entries of the input array are strictly positive.
 

Constructors in org.apache.commons.math3.util that throw NotStrictlyPositiveException
MultidimensionalCounter(int... size)
          Create a counter.
 



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