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Packages that use NotStrictlyPositiveException | |
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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 |
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Methods in org.apache.commons.math3.analysis.function that throw NotStrictlyPositiveException | |
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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 | |
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Gaussian(double mean,
double sigma)
Normalized gaussian with given mean and standard deviation. |
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Gaussian(double norm,
double mean,
double sigma)
Gaussian with given normalization factor, mean and standard deviation. |
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Logistic(double k,
double m,
double b,
double q,
double a,
double n)
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Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis.integration |
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Constructors in org.apache.commons.math3.analysis.integration that throw NotStrictlyPositiveException | |
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BaseAbstractUnivariateIntegrator(double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Construct an integrator with given accuracies and iteration counts. |
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BaseAbstractUnivariateIntegrator(int minimalIterationCount,
int maximalIterationCount)
Construct an integrator with given iteration counts. |
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IterativeLegendreGaussIntegrator(int n,
double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Builds an integrator with given accuracies and iterations counts. |
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LegendreGaussIntegrator(int n,
double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Deprecated. Build a Legendre-Gauss integrator with given accuracies and iterations counts. |
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RombergIntegrator(double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Build a Romberg integrator with given accuracies and iterations counts. |
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RombergIntegrator(int minimalIterationCount,
int maximalIterationCount)
Build a Romberg integrator with given iteration counts. |
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SimpsonIntegrator(double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Build a Simpson integrator with given accuracies and iterations counts. |
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SimpsonIntegrator(int minimalIterationCount,
int maximalIterationCount)
Build a Simpson integrator with given iteration counts. |
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TrapezoidIntegrator(double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Build a trapezoid integrator with given accuracies and iterations counts. |
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TrapezoidIntegrator(int minimalIterationCount,
int maximalIterationCount)
Build a trapezoid integrator with given iteration counts. |
Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis.integration.gauss |
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Methods in org.apache.commons.math3.analysis.integration.gauss that throw NotStrictlyPositiveException | |
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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 |
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Constructors in org.apache.commons.math3.analysis.interpolation that throw NotStrictlyPositiveException | |
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MicrosphereInterpolator(int elements,
int exponent)
Create a microsphere interpolator. |
Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis.solvers |
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Methods in org.apache.commons.math3.analysis.solvers that throw NotStrictlyPositiveException | |
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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 |
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Methods in org.apache.commons.math3.distribution that throw NotStrictlyPositiveException | |
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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 | |
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ExponentialDistribution(RandomGenerator rng,
double mean,
double inverseCumAccuracy)
Creates an exponential distribution. |
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FDistribution(double numeratorDegreesOfFreedom,
double denominatorDegreesOfFreedom)
Creates an F distribution using the given degrees of freedom. |
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FDistribution(double numeratorDegreesOfFreedom,
double denominatorDegreesOfFreedom,
double inverseCumAccuracy)
Creates an F distribution using the given degrees of freedom and inverse cumulative probability accuracy. |
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FDistribution(RandomGenerator rng,
double numeratorDegreesOfFreedom,
double denominatorDegreesOfFreedom,
double inverseCumAccuracy)
Creates an F distribution. |
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GammaDistribution(double shape,
double scale)
Creates a new gamma distribution with specified values of the shape and scale parameters. |
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GammaDistribution(double shape,
double scale,
double inverseCumAccuracy)
Creates a new gamma distribution with specified values of the shape and scale parameters. |
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GammaDistribution(RandomGenerator rng,
double shape,
double scale,
double inverseCumAccuracy)
Creates a Gamma distribution. |
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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. |
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HypergeometricDistribution(RandomGenerator rng,
int populationSize,
int numberOfSuccesses,
int sampleSize)
Creates a new hypergeometric distribution. |
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KolmogorovSmirnovDistribution(int n)
|
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LogNormalDistribution(double scale,
double shape)
Create a log-normal distribution using the specified scale and shape. |
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LogNormalDistribution(double scale,
double shape,
double inverseCumAccuracy)
Create a log-normal distribution using the specified scale, shape and inverse cumulative distribution accuracy. |
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LogNormalDistribution(RandomGenerator rng,
double scale,
double shape,
double inverseCumAccuracy)
Creates a log-normal distribution. |
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NormalDistribution(double mean,
double sd)
Create a normal distribution using the given mean and standard deviation. |
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NormalDistribution(double mean,
double sd,
double inverseCumAccuracy)
Create a normal distribution using the given mean, standard deviation and inverse cumulative distribution accuracy. |
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NormalDistribution(RandomGenerator rng,
double mean,
double sd,
double inverseCumAccuracy)
Creates a normal distribution. |
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PascalDistribution(int r,
double p)
Create a Pascal distribution with the given number of successes and probability of success. |
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PascalDistribution(RandomGenerator rng,
int r,
double p)
Create a Pascal distribution with the given number of successes and probability of success. |
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PoissonDistribution(double p)
Creates a new Poisson distribution with specified mean. |
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PoissonDistribution(double p,
double epsilon)
Creates a new Poisson distribution with the specified mean and convergence criterion. |
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PoissonDistribution(double p,
double epsilon,
int maxIterations)
Creates a new Poisson distribution with specified mean, convergence criterion and maximum number of iterations. |
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PoissonDistribution(RandomGenerator rng,
double p,
double epsilon,
int maxIterations)
Creates a new Poisson distribution with specified mean, convergence criterion and maximum number of iterations. |
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TDistribution(double degreesOfFreedom)
Create a t distribution using the given degrees of freedom. |
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TDistribution(double degreesOfFreedom,
double inverseCumAccuracy)
Create a t distribution using the given degrees of freedom and the specified inverse cumulative probability absolute accuracy. |
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TDistribution(RandomGenerator rng,
double degreesOfFreedom,
double inverseCumAccuracy)
Creates a t distribution. |
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WeibullDistribution(double alpha,
double beta)
Create a Weibull distribution with the given shape and scale and a location equal to zero. |
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WeibullDistribution(RandomGenerator rng,
double alpha,
double beta,
double inverseCumAccuracy)
Creates a Weibull distribution. |
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ZipfDistribution(RandomGenerator rng,
int numberOfElements,
double exponent)
Creates a Zipf distribution. |
Uses of NotStrictlyPositiveException in org.apache.commons.math3.genetics |
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Constructors in org.apache.commons.math3.genetics that throw NotStrictlyPositiveException | |
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NPointCrossover(int crossoverPoints)
Creates a new NPointCrossover policy using the given number of points. |
Uses of NotStrictlyPositiveException in org.apache.commons.math3.linear |
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Methods in org.apache.commons.math3.linear that throw NotStrictlyPositiveException | |
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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 |
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 |
abstract FieldMatrix<T> |
AbstractFieldMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new FieldMatrix |
FieldMatrix<T> |
BlockFieldMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new FieldMatrix |
Constructors in org.apache.commons.math3.linear that throw NotStrictlyPositiveException | |
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AbstractFieldMatrix(Field<T> field,
int rowDimension,
int columnDimension)
Create a new FieldMatrix |
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AbstractRealMatrix(int rowDimension,
int columnDimension)
Create a new RealMatrix with the supplied row and column dimensions. |
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Array2DRowFieldMatrix(Field<T> field,
int rowDimension,
int columnDimension)
Create a new FieldMatrix<T> with the supplied row and column dimensions. |
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Array2DRowRealMatrix(int rowDimension,
int columnDimension)
Create a new RealMatrix with the supplied row and column dimensions. |
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BlockFieldMatrix(Field<T> field,
int rows,
int columns)
Create a new matrix with the supplied row and column dimensions. |
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BlockFieldMatrix(int rows,
int columns,
T[][] blockData,
boolean copyArray)
Create a new dense matrix copying entries from block layout data. |
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BlockRealMatrix(double[][] rawData)
Create a new dense matrix copying entries from raw layout data. |
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BlockRealMatrix(int rows,
int columns)
Create a new matrix with the supplied row and column dimensions. |
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BlockRealMatrix(int rows,
int columns,
double[][] blockData,
boolean copyArray)
Create a new dense matrix copying entries from block layout data. |
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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 |
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Constructors in org.apache.commons.math3.optim.nonlinear.scalar that throw NotStrictlyPositiveException | |
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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 |
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Constructors in org.apache.commons.math3.optim.nonlinear.scalar.noderiv that throw NotStrictlyPositiveException | |
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CMAESOptimizer.PopulationSize(int size)
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Uses of NotStrictlyPositiveException in org.apache.commons.math3.optim.nonlinear.vector |
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Constructors in org.apache.commons.math3.optim.nonlinear.vector that throw NotStrictlyPositiveException | |
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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 |
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Constructors in org.apache.commons.math3.optimization.direct that throw NotStrictlyPositiveException | |
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CMAESOptimizer.PopulationSize(int size)
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Uses of NotStrictlyPositiveException in org.apache.commons.math3.random |
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Methods in org.apache.commons.math3.random that throw NotStrictlyPositiveException | |
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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 |
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Methods in org.apache.commons.math3.stat.inference that throw NotStrictlyPositiveException | |
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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 |
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Methods in org.apache.commons.math3.transform that throw NotStrictlyPositiveException | |
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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 |
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Methods in org.apache.commons.math3.util that throw NotStrictlyPositiveException | |
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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 | |
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MultidimensionalCounter(int... size)
Create a counter. |
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