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## 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` | `DiagonalMatrix.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.
`DiagonalMatrix(int dimension)` Creates a matrix with the supplied dimension.
`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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