## Uses of Classorg.apache.commons.math4.exception.NotPositiveException

• Packages that use NotPositiveException
Package Description
org.apache.commons.math4.analysis.differentiation
This package holds the main interfaces and basic building block classes dealing with differentiation.
org.apache.commons.math4.analysis.interpolation
Univariate real functions interpolation algorithms.
org.apache.commons.math4.distribution
Implementations of common discrete and continuous distributions.
org.apache.commons.math4.genetics
This package provides Genetic Algorithms components and implementations.
org.apache.commons.math4.linear
Linear algebra support.
org.apache.commons.math4.optim.nonlinear.scalar.noderiv
This package provides optimization algorithms that do not require derivatives.
org.apache.commons.math4.random
Random Data Generation
org.apache.commons.math4.stat.inference
Classes providing hypothesis testing.
org.apache.commons.math4.stat.interval
Classes providing binomial proportion confidence interval construction.
org.apache.commons.math4.util
Convenience routines and common data structures used throughout the commons-math library.
• ### Uses of NotPositiveException in org.apache.commons.math4.analysis.differentiation

Constructors in org.apache.commons.math4.analysis.differentiation that throw NotPositiveException
Constructor and 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.analysis.interpolation

Constructors in org.apache.commons.math4.analysis.interpolation that throw NotPositiveException
Constructor and 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.distribution

Constructors in org.apache.commons.math4.distribution that throw NotPositiveException
Constructor and 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.genetics

Methods in org.apache.commons.math4.genetics that throw NotPositiveException
Modifier and Type Method and Description
void ListPopulation.setPopulationLimit(int populationLimit)
Sets the maximal population size.
Constructors in org.apache.commons.math4.genetics that throw NotPositiveException
Constructor and 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)
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.linear

Methods in org.apache.commons.math4.linear that throw NotPositiveException
Modifier and Type Method and Description
FieldVector<T> ArrayFieldVector.getSubVector(int index, int n)
Get a subvector from consecutive elements.
OpenMapRealVector OpenMapRealVector.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.
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.
RealVector ArrayRealVector.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.
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.
RealMatrix AbstractRealMatrix.power(int p)
Returns the result of multiplying this with itself p times.
• ### Uses of NotPositiveException in org.apache.commons.math4.optim.nonlinear.scalar.noderiv

Constructors in org.apache.commons.math4.optim.nonlinear.scalar.noderiv that throw NotPositiveException
Constructor and Description
Sigma(double[] s)
• ### Uses of NotPositiveException in org.apache.commons.math4.random

Methods in org.apache.commons.math4.random that throw NotPositiveException
Modifier and Type Method and Description
double[] SobolSequenceGenerator.skipTo(int index)
double[] HaltonSequenceGenerator.skipTo(int index)
• ### Uses of NotPositiveException in org.apache.commons.math4.stat.inference

Methods in org.apache.commons.math4.stat.inference that throw NotPositiveException
Modifier and Type Method and 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.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(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.
static double InferenceTestUtils.chiSquareTest(double[] expected, long[] observed)
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 boolean InferenceTestUtils.chiSquareTest(double[] expected, long[] observed, double 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.
static double InferenceTestUtils.chiSquareTest(long[][] counts)
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 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.
static double InferenceTestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
boolean ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
Performs a Chi-Square two sample test comparing two binned data sets.
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.
static double InferenceTestUtils.gTest(double[] expected, long[] observed)
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 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.
static double InferenceTestUtils.gTestDataSetsComparison(long[] observed1, long[] observed2)
boolean GTest.gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned data sets.
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.stat.interval

Methods in org.apache.commons.math4.stat.interval that throw NotPositiveException
Modifier and Type Method and 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 NotPositiveException in org.apache.commons.math4.util

Methods in org.apache.commons.math4.util that throw NotPositiveException
Modifier and Type Method and Description
static void MathArrays.checkNonNegative(long[] in)
Check that all entries of the input array are >= 0.
static void MathArrays.checkNonNegative(long[][] in)
Check all entries of the input array are >= 0.
static long CombinatoricsUtils.stirlingS2(int n, int k)
Returns the Stirling number of the second kind, "S(n,k)", the number of ways of partitioning an n-element set into k non-empty subsets.