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

Packages that use NotPositiveException

Package	Description
org.apache.commons.math4.legacy.analysis.differentiation	This package holds the main interfaces and basic building block classes dealing with differentiation.
org.apache.commons.math4.legacy.analysis.interpolation	Univariate real functions interpolation algorithms.
org.apache.commons.math4.legacy.distribution	Implementations of common discrete and continuous 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.stat.inference	Classes providing hypothesis testing.
org.apache.commons.math4.legacy.stat.interval	Classes providing binomial proportion confidence interval construction.

Uses of NotPositiveException in org.apache.commons.math4.legacy.analysis.differentiation

Constructors in org.apache.commons.math4.legacy.analysis.differentiation that throw NotPositiveException

Constructor	Description
FiniteDifferencesDifferentiator(int nbPoints, double stepSize)	Build a differentiator with number of points and step size when independent variable is unbounded.
FiniteDifferencesDifferentiator(int nbPoints, double stepSize, double tLower, double tUpper)	Build a differentiator with number of points and step size when independent variable is bounded.

Uses of NotPositiveException in org.apache.commons.math4.legacy.analysis.interpolation

Constructors in org.apache.commons.math4.legacy.analysis.interpolation that throw NotPositiveException

Constructor	Description
LoessInterpolator(double bandwidth, int robustnessIters, double accuracy)	Construct a new LoessInterpolator with given bandwidth, number of robustness iterations and accuracy.
MicrosphereProjectionInterpolator(InterpolatingMicrosphere microsphere, double exponent, boolean sharedSphere, double noInterpolationTolerance)	Create a microsphere interpolator.

Uses of NotPositiveException in org.apache.commons.math4.legacy.distribution

Constructors in org.apache.commons.math4.legacy.distribution that throw NotPositiveException

Constructor	Description
EnumeratedDistribution(List<Pair<T,Double>> pmf)	Create an enumerated distribution using the given random number generator and probability mass function enumeration.
EnumeratedIntegerDistribution(int[] singletons, double[] probabilities)	Create a discrete distribution.
EnumeratedRealDistribution(double[] singletons, double[] probabilities)	Create a discrete real-valued distribution using the given random number generator and probability mass function enumeration.
MixtureMultivariateNormalDistribution(double[] weights, double[][] means, double[][][] covariances)	Creates a multivariate normal mixture distribution.
MixtureMultivariateNormalDistribution(List<Pair<Double,MultivariateNormalDistribution>> components)	Creates a mixture model from a list of distributions and their associated weights.

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

Methods in org.apache.commons.math4.legacy.genetics that throw NotPositiveException

Modifier and Type	Method	Description
void	ListPopulation.setPopulationLimit(int populationLimit)	Sets the maximal population size.

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

Constructor	Description
ElitisticListPopulation(int populationLimit, double elitismRate)	Creates a new ElitisticListPopulation instance and initializes its inner chromosome list.
ElitisticListPopulation(List<Chromosome> chromosomes, int populationLimit, double elitismRate)	Creates a new ElitisticListPopulation instance.
ListPopulation(int populationLimit)	Creates a new ListPopulation instance and initializes its inner chromosome list.
ListPopulation(List<Chromosome> chromosomes, int populationLimit)	Creates a new ListPopulation instance.

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

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

Modifier and Type	Method	Description
FieldVector<T>	ArrayFieldVector.getSubVector(int index, int n)	Get a subvector from consecutive elements.
RealVector	ArrayRealVector.getSubVector(int index, int n)	Get a subvector from consecutive elements.
FieldVector<T>	FieldVector.getSubVector(int index, int n)	Get a subvector from consecutive elements.
OpenMapRealVector	OpenMapRealVector.getSubVector(int index, int n)	Get a subvector from consecutive elements.
abstract RealVector	RealVector.getSubVector(int index, int n)	Get a subvector from consecutive elements.
FieldVector<T>	SparseFieldVector.getSubVector(int index, int n)	Get a subvector from consecutive elements.
FieldMatrix<T>	AbstractFieldMatrix.power(int p)	Returns the result multiplying this with itself p times.
RealMatrix	AbstractRealMatrix.power(int p)	Returns the result of multiplying this with itself p times.
FieldMatrix<T>	FieldMatrix.power(int p)	Returns the result multiplying this with itself p times.
RealMatrix	RealMatrix.power(int p)	Returns the result of multiplying this with itself p times.

Uses of NotPositiveException in org.apache.commons.math4.legacy.stat.inference

Methods in org.apache.commons.math4.legacy.stat.inference that throw NotPositiveException

Modifier and Type	Method	Description
double	ChiSquareTest.chiSquare(double[] expected, long[] observed)	Computes the Chi-Square statistic comparing observed and expected frequency counts.
double	ChiSquareTest.chiSquare(long[][] counts)	Computes the Chi-Square statistic associated with a chi-square test of independence based on the input counts array, viewed as a two-way table.
static double	InferenceTestUtils.chiSquare(double[] expected, long[] observed)
static double	InferenceTestUtils.chiSquare(long[][] counts)
double	ChiSquareTest.chiSquareDataSetsComparison(long[] observed1, long[] observed2)	Computes a Chi-Square two sample test statistic comparing bin frequency counts in observed1 and observed2.
static double	InferenceTestUtils.chiSquareDataSetsComparison(long[] observed1, long[] observed2)
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.
double	ChiSquareTest.chiSquareTest(long[][] counts)	Returns the observed significance level, or p-value, associated with a chi-square test of independence based on the input counts array, viewed as a two-way table.
boolean	ChiSquareTest.chiSquareTest(long[][] counts, double alpha)	Performs a chi-square test of independence evaluating the null hypothesis that the classifications represented by the counts in the columns of the input 2-way table are independent of the rows, with significance level alpha.
static double	InferenceTestUtils.chiSquareTest(double[] expected, long[] observed)
static boolean	InferenceTestUtils.chiSquareTest(double[] expected, long[] observed, double alpha)
static double	InferenceTestUtils.chiSquareTest(long[][] counts)
static boolean	InferenceTestUtils.chiSquareTest(long[][] counts, double alpha)
double	ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)	Returns the observed significance level, or p-value, associated with a Chi-Square two sample test comparing bin frequency counts in observed1 and observed2.
boolean	ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)	Performs a Chi-Square two sample test comparing two binned data sets.
static double	InferenceTestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
static boolean	InferenceTestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, 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.gDataSetsComparison(long[] observed1, long[] observed2)	Computes a G (Log-Likelihood Ratio) two sample test statistic for independence comparing frequency counts in observed1 and observed2.
static double	InferenceTestUtils.gDataSetsComparison(long[] observed1, long[] observed2)
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.gTestDataSetsComparison(long[] observed1, long[] observed2)	Returns the observed significance level, or p-value, associated with a G-Value (Log-Likelihood Ratio) for two sample test comparing bin frequency counts in observed1 and observed2.
boolean	GTest.gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)	Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned data sets.
static double	InferenceTestUtils.gTestDataSetsComparison(long[] observed1, long[] observed2)
static boolean	InferenceTestUtils.gTestDataSetsComparison(long[] observed1, long[] observed2, 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)
static double	InferenceTestUtils.rootLogLikelihoodRatio(long k11, long k12, long k21, long k22)

Uses of NotPositiveException in org.apache.commons.math4.legacy.stat.interval

Methods in org.apache.commons.math4.legacy.stat.interval that throw NotPositiveException

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.