Uses of Class
org.apache.commons.math4.legacy.exception.NotStrictlyPositiveException

Packages that use NotStrictlyPositiveException

Package	Description
org.apache.commons.math4.legacy.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.math4.legacy.analysis.solvers	Root finding algorithms, for univariate real functions.
org.apache.commons.math4.legacy.distribution	Implementations of common discrete and continuous distributions.
org.apache.commons.math4.legacy.distribution.fitting	Fitting of parameters against distributions.
org.apache.commons.math4.legacy.genetics	This package provides Genetic Algorithms components and implementations.
org.apache.commons.math4.legacy.linear	Linear algebra support.
org.apache.commons.math4.legacy.optim.nonlinear.scalar	Algorithms for optimizing a scalar function.
org.apache.commons.math4.legacy.stat.correlation	Correlations/Covariance computations.
org.apache.commons.math4.legacy.stat.inference	Classes providing hypothesis testing.
org.apache.commons.math4.legacy.stat.interval	Classes providing binomial proportion confidence interval construction.

Uses of NotStrictlyPositiveException in org.apache.commons.math4.legacy.analysis.function

Methods in org.apache.commons.math4.legacy.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.math4.legacy.analysis.function that throw NotStrictlyPositiveException

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.math4.legacy.analysis.solvers

Methods in org.apache.commons.math4.legacy.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.math4.legacy.distribution

Methods in org.apache.commons.math4.legacy.distribution that throw NotStrictlyPositiveException

Modifier and Type	Method	Description
Object[]	EnumeratedDistribution.Sampler.sample(int sampleSize)	Generates a random sample from the distribution.
T[]	EnumeratedDistribution.Sampler.sample(int sampleSize, T[] array)	Generates a random sample from the distribution.

Uses of NotStrictlyPositiveException in org.apache.commons.math4.legacy.distribution.fitting

Methods in org.apache.commons.math4.legacy.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.math4.legacy.distribution.fitting that throw NotStrictlyPositiveException

Constructor	Description
MultivariateNormalMixtureExpectationMaximization(double[][] data)	Creates an object to fit a multivariate normal mixture model to data.

Uses of NotStrictlyPositiveException in org.apache.commons.math4.legacy.genetics

Constructors in org.apache.commons.math4.legacy.genetics that throw NotStrictlyPositiveException

Constructor	Description
NPointCrossover(int crossoverPoints)	Creates a new NPointCrossover policy using the given number of points.

Uses of NotStrictlyPositiveException in org.apache.commons.math4.legacy.linear

Methods in org.apache.commons.math4.legacy.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.math4.legacy.linear that throw NotStrictlyPositiveException

Constructor	Description
AbstractFieldMatrix(Field<T> field, int rowDimension, int columnDimension)	Create a new FieldMatrix<T> 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(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.math4.legacy.optim.nonlinear.scalar

Constructors in org.apache.commons.math4.legacy.optim.nonlinear.scalar that throw NotStrictlyPositiveException

Constructor	Description
PopulationSize(int size)

Uses of NotStrictlyPositiveException in org.apache.commons.math4.legacy.stat.correlation

Methods in org.apache.commons.math4.legacy.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.math4.legacy.stat.correlation that throw NotStrictlyPositiveException

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.math4.legacy.stat.inference

Methods in org.apache.commons.math4.legacy.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	InferenceTestUtils.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	InferenceTestUtils.chiSquareTest(double[] expected, long[] observed)
static boolean	InferenceTestUtils.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	InferenceTestUtils.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	InferenceTestUtils.gTest(double[] expected, long[] observed)
static boolean	InferenceTestUtils.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	InferenceTestUtils.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.math4.legacy.stat.interval

Methods in org.apache.commons.math4.legacy.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.