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

Packages that use NotStrictlyPositiveException
Package
Description
org.apache.commons.math3.analysis
Parent package for common numerical analysis procedures, including root finding, function interpolation and integration.
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.distribution.fitting
Fitting of parameters against 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.correlation
Correlations/Covariance computations.
org.apache.commons.math3.stat.inference
Classes providing hypothesis testing.
org.apache.commons.math3.stat.interval
Classes providing binomial proportion 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

Methods in org.apache.commons.math3.analysis that throw NotStrictlyPositiveException
Modifier and Type
Method
Description
`static double[]`
FunctionUtils.`sample(UnivariateFunction f, double min, double max, int n)`
Samples the specified univariate real function on the specified interval.

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

Methods in org.apache.commons.math3.analysis.function that throw NotStrictlyPositiveException
Modifier and Type
Method
Description
`double[]`
Gaussian.Parametric.`gradient(double x, double... param)`
Computes the value of the gradient at `x`.
`double[]`
Logistic.Parametric.`gradient(double x, double... param)`
Computes the value of the gradient at `x`.
`double`
Gaussian.Parametric.`value(double x, double... param)`
Computes the value of the Gaussian at `x`.
`double`
Logistic.Parametric.`value(double x, double... param)`
Computes the value of the sigmoid at `x`.
Constructors in org.apache.commons.math3.analysis.function that throw NotStrictlyPositiveException
Modifier
Constructor
Description
``
`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
Modifier
Constructor
Description
`protected`
`BaseAbstractUnivariateIntegrator(double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)`
Construct an integrator with given accuracies and iteration counts.
`protected`
`BaseAbstractUnivariateIntegrator(int minimalIterationCount, int maximalIterationCount)`
Construct an integrator with given iteration counts.
``
`IterativeLegendreGaussIntegrator(int n, double relativeAccuracy, double absoluteAccuracy)`
Builds an integrator with given accuracies.
``
`IterativeLegendreGaussIntegrator(int n, double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)`
Builds an integrator with given accuracies and iterations counts.
``
`IterativeLegendreGaussIntegrator(int n, int minimalIterationCount, int maximalIterationCount)`
Builds an integrator with given iteration counts.
``
`LegendreGaussIntegrator(int n, double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)`
Deprecated.
Build a Legendre-Gauss integrator with given accuracies and iterations counts.
``
`MidPointIntegrator(double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)`
Build a midpoint integrator with given accuracies and iterations counts.
``
`MidPointIntegrator(int minimalIterationCount, int maximalIterationCount)`
Build a midpoint integrator with given iteration 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
Modifier and Type
Method
Description
`Pair<double[],double[]>`
BaseRuleFactory.`getRule(int numberOfPoints)`
Gets a copy of the quadrature rule with the given number of integration points.
`GaussIntegrator`
GaussIntegratorFactory.`legendre(int numberOfPoints, double lowerBound, double upperBound)`
Creates a Gauss-Legendre integrator of the given order.
`GaussIntegrator`
GaussIntegratorFactory.`legendreHighPrecision(int numberOfPoints)`
Creates a Gauss-Legendre integrator of the given order.
`GaussIntegrator`
GaussIntegratorFactory.`legendreHighPrecision(int numberOfPoints, double lowerBound, double upperBound)`
Creates an integrator of the given order, and whose call to the `integrate` method will perform an integration on the given interval.

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

Constructors in org.apache.commons.math3.analysis.interpolation that throw NotStrictlyPositiveException
Modifier
Constructor
Description
``
`MicrosphereInterpolator(int elements, int exponent)`
Deprecated.
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
Modifier and Type
Method
Description
`static double[]`
UnivariateSolverUtils.`bracket(UnivariateFunction function, double initial, double lowerBound, double upperBound)`
This method simply calls `bracket(function, initial, lowerBound, upperBound, q, r, maximumIterations)` with `q` and `r` set to 1.0 and `maximumIterations` set to `Integer.MAX_VALUE`.
`static double[]`
UnivariateSolverUtils.`bracket(UnivariateFunction function, double initial, double lowerBound, double upperBound, int maximumIterations)`
This method simply calls `bracket(function, initial, lowerBound, upperBound, q, r, maximumIterations)` with `q` and `r` set to 1.0.

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

Methods in org.apache.commons.math3.distribution that throw NotStrictlyPositiveException
Modifier and Type
Method
Description
`Object[]`
EnumeratedDistribution.`sample(int sampleSize)`
Generate a random sample from the distribution.
`T[]`
EnumeratedDistribution.`sample(int sampleSize, T[] array)`
Generate a random sample from the distribution.
`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
Modifier
Constructor
Description
``
`ExponentialDistribution(RandomGenerator rng, double mean)`
Creates an exponential distribution.
``
`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)`
Creates an F distribution.
``
`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)`
Creates a Gamma distribution.
``
`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)`
Deprecated.
``
`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)`
Creates a log-normal distribution.
``
`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)`
Creates a normal distribution.
``
`NormalDistribution(RandomGenerator rng, double mean, double sd, double inverseCumAccuracy)`
Creates a normal distribution.
``
`ParetoDistribution(double scale, double shape)`
Create a Pareto distribution using the specified scale and shape.
``
`ParetoDistribution(double scale, double shape, double inverseCumAccuracy)`
Create a Pareto distribution using the specified scale, shape and inverse cumulative distribution accuracy.
``
`ParetoDistribution(RandomGenerator rng, double scale, double shape)`
Creates a Pareto distribution.
``
`ParetoDistribution(RandomGenerator rng, double scale, double shape, double inverseCumAccuracy)`
Creates a Pareto 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)`
Creates a t distribution.
``
`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)`
Creates a Weibull distribution.
``
`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.distribution.fitting

Methods in org.apache.commons.math3.distribution.fitting that throw NotStrictlyPositiveException
Modifier and Type
Method
Description
`static MixtureMultivariateNormalDistribution`
MultivariateNormalMixtureExpectationMaximization.`estimate(double[][] data, int numComponents)`
Helper method to create a multivariate normal mixture model which can be used to initialize `MultivariateNormalMixtureExpectationMaximization.fit(MixtureMultivariateNormalDistribution)`.
`void`
MultivariateNormalMixtureExpectationMaximization.`fit(MixtureMultivariateNormalDistribution initialMixture)`
Fit a mixture model to the data supplied to the constructor.
`void`
MultivariateNormalMixtureExpectationMaximization.`fit(MixtureMultivariateNormalDistribution initialMixture, int maxIterations, double threshold)`
Fit a mixture model to the data supplied to the constructor.
Constructors in org.apache.commons.math3.distribution.fitting that throw NotStrictlyPositiveException
Modifier
Constructor
Description
``
`MultivariateNormalMixtureExpectationMaximization(double[][] data)`
Creates an object to fit a multivariate normal mixture model to data.

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

Constructors in org.apache.commons.math3.genetics that throw NotStrictlyPositiveException
Modifier
Constructor
Description
``
`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
Modifier and Type
Method
Description
`abstract FieldMatrix<T>`
AbstractFieldMatrix.`createMatrix(int rowDimension, int columnDimension)`
Create a new FieldMatrix<T> 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.
`FieldMatrix<T>`
Array2DRowFieldMatrix.`createMatrix(int rowDimension, int columnDimension)`
Create a new FieldMatrix<T> of the same type as the instance with the supplied row and column dimensions.
`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.
`FieldMatrix<T>`
BlockFieldMatrix.`createMatrix(int rowDimension, int columnDimension)`
Create a new FieldMatrix<T> 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.
`RealMatrix`
DiagonalMatrix.`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<T> of the same type as the instance with the supplied row and column dimensions.
`OpenMapRealMatrix`
OpenMapRealMatrix.`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.
Constructors in org.apache.commons.math3.linear that throw NotStrictlyPositiveException
Modifier
Constructor
Description
`protected`
`AbstractFieldMatrix(Field<T> field, int rowDimension, int columnDimension)`
Create a new FieldMatrix<T> with the supplied row and column dimensions.
`protected`
`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(int rows, int columns, T[][] blockData, boolean copyArray)`
Create a new dense matrix copying entries from block layout data.
``
`BlockFieldMatrix(Field<T> field, int rows, int columns)`
Create a new matrix with the supplied row and column dimensions.
``
`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.
``
`DiagonalMatrix(int dimension)`
Creates a matrix with the supplied dimension.
``
`OpenMapRealMatrix(int rowDimension, int columnDimension)`
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
Modifier
Constructor
Description
``
`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
Modifier
Constructor
Description
``
`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
Modifier
Constructor
Description
``
`MultiStartMultivariateVectorOptimizer(MultivariateVectorOptimizer optimizer, int starts, RandomVectorGenerator generator)`
Deprecated.
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
Modifier
Constructor
Description
``
`PopulationSize(int size)`

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

Methods in org.apache.commons.math3.random that throw NotStrictlyPositiveException
Modifier and Type
Method
Description
`double`
RandomData.`nextExponential(double mean)`
Deprecated.
Generates a random value from the exponential distribution with specified mean.
`double`
RandomDataGenerator.`nextExponential(double mean)`
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`
RandomData.`nextGaussian(double mu, double sigma)`
Deprecated.
Generates a random value from the Normal (or Gaussian) distribution with specified mean and standard deviation.
`double`
RandomDataGenerator.`nextGaussian(double mu, double sigma)`
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`
RandomData.`nextHexString(int len)`
Deprecated.
Generates a random string of hex characters of length `len`.
`String`
RandomDataGenerator.`nextHexString(int len)`
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[]`
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[]`
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[]`
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`
RandomData.`nextPoisson(double mean)`
Deprecated.
Generates a random value from the Poisson distribution with the given mean.
`long`
RandomDataGenerator.`nextPoisson(double mean)`
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[]`
RandomData.`nextSample(Collection<?> c, int k)`
Deprecated.
Returns an array of `k` objects selected randomly from the Collection `c`.
`Object[]`
RandomDataGenerator.`nextSample(Collection<?> c, int k)`
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`
RandomData.`nextSecureHexString(int len)`
Deprecated.
Generates a random string of hex characters from a secure random sequence.
`String`
RandomDataGenerator.`nextSecureHexString(int len)`
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`.
Constructors in org.apache.commons.math3.random that throw NotStrictlyPositiveException
Modifier
Constructor
Description
``
`SobolSequenceGenerator(int dimension, InputStream is)`
Construct a new Sobol sequence generator for the given space dimension with direction vectors loaded from the given stream.

## Uses of NotStrictlyPositiveException in org.apache.commons.math3.stat.correlation

Methods in org.apache.commons.math3.stat.correlation that throw NotStrictlyPositiveException
Modifier and Type
Method
Description
`protected RealMatrix`
Covariance.`computeCovarianceMatrix(double[][] data)`
Create a covariance matrix from a rectangular array whose columns represent covariates.
`protected RealMatrix`
Covariance.`computeCovarianceMatrix(double[][] data, boolean biasCorrected)`
Compute a covariance matrix from a rectangular array whose columns represent covariates.
Constructors in org.apache.commons.math3.stat.correlation that throw NotStrictlyPositiveException
Modifier
Constructor
Description
``
`Covariance(double[][] data)`
Create a Covariance matrix from a rectangular array whose columns represent covariates.
``
`Covariance(double[][] data, boolean biasCorrected)`
Create a Covariance matrix from a rectangular array whose columns represent covariates.

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

Methods in org.apache.commons.math3.stat.inference that throw NotStrictlyPositiveException
Modifier and Type
Method
Description
`double`
ChiSquareTest.`chiSquare(double[] expected, long[] observed)`
Computes the Chi-Square statistic comparing `observed` and `expected` frequency counts.
`static double`
TestUtils.`chiSquare(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.
`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.`chiSquareTest(double[] expected, long[] observed)`
`static boolean`
TestUtils.`chiSquareTest(double[] expected, long[] observed, double alpha)`
`double`
GTest.`g(double[] expected, long[] observed)`
Computes the G statistic for Goodness of Fit comparing `observed` and `expected` frequency counts.
`static double`
TestUtils.`g(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.
`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.`gTest(double[] expected, long[] observed)`
`static boolean`
TestUtils.`gTest(double[] expected, long[] observed, double alpha)`
`double`
GTest.`gTestIntrinsic(double[] expected, long[] observed)`
Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described in p64-69 of McDonald, J.H.
`static double`
TestUtils.`gTestIntrinsic(double[] expected, long[] observed)`
`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.stat.interval

Methods in org.apache.commons.math3.stat.interval that throw NotStrictlyPositiveException
Modifier and Type
Method
Description
`ConfidenceInterval`
BinomialConfidenceInterval.`createInterval(int numberOfTrials, int numberOfSuccesses, double confidenceLevel)`
Create a confidence interval for the true probability of success of an unknown binomial distribution with the given observed number of trials, successes and confidence level.

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

Methods in org.apache.commons.math3.transform that throw NotStrictlyPositiveException
Modifier and Type
Method
Description
`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
Modifier and Type
Method
Description
`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
Modifier
Constructor
Description
``
`MultidimensionalCounter(int... size)`
Create a counter.