Uses of Class
org.apache.commons.math3.exception.NotPositiveException
Packages that use NotPositiveException
Package
Description
This package holds the main interfaces and basic building block classes
dealing with differentiation.
Univariate real functions interpolation algorithms.
Complex number type and implementations of complex transcendental
functions.
Implementations of common discrete and continuous distributions.
This package provides Genetic Algorithms components and implementations.
Linear algebra support.
Clustering algorithms.
This package provides optimization algorithms that do not require derivatives.
This package provides optimization algorithms that don't require derivatives.
Random number and random data generators.
All classes and sub-packages of this package are deprecated.
Classes providing hypothesis testing.
Classes providing binomial proportion confidence interval construction.
Convenience routines and common data structures used throughout the commons-math library.
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Uses of NotPositiveException in org.apache.commons.math3.analysis.differentiation
Constructors in org.apache.commons.math3.analysis.differentiation that throw NotPositiveExceptionModifierConstructorDescriptionFiniteDifferencesDifferentiator(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.math3.analysis.interpolation
Constructors in org.apache.commons.math3.analysis.interpolation that throw NotPositiveExceptionModifierConstructorDescriptionLoessInterpolator(double bandwidth, int robustnessIters, double accuracy) Construct a newLoessInterpolatorwith given bandwidth, number of robustness iterations and accuracy.MicrosphereInterpolator(int elements, int exponent) Deprecated.Create a microsphere interpolator.MicrosphereProjectionInterpolator(InterpolatingMicrosphere microsphere, double exponent, boolean sharedSphere, double noInterpolationTolerance) Create a microsphere interpolator.SmoothingPolynomialBicubicSplineInterpolator(int degree) Deprecated.SmoothingPolynomialBicubicSplineInterpolator(int xDegree, int yDegree) Deprecated. -
Uses of NotPositiveException in org.apache.commons.math3.complex
Methods in org.apache.commons.math3.complex that throw NotPositiveException -
Uses of NotPositiveException in org.apache.commons.math3.distribution
Constructors in org.apache.commons.math3.distribution that throw NotPositiveExceptionModifierConstructorDescriptionEnumeratedDistribution(List<Pair<T, Double>> pmf) Create an enumerated distribution using the given probability mass function enumeration.EnumeratedDistribution(RandomGenerator rng, 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 using the given probability mass function definition.EnumeratedIntegerDistribution(RandomGenerator rng, int[] singletons, double[] probabilities) Create a discrete distribution using the given random number generator and probability mass function definition.EnumeratedRealDistribution(double[] singletons, double[] probabilities) Create a discrete real-valued distribution using the given probability mass function enumeration.EnumeratedRealDistribution(RandomGenerator rng, double[] singletons, double[] probabilities) Create a discrete real-valued distribution using the given random number generator and probability mass function enumeration.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.MixtureMultivariateNormalDistribution(RandomGenerator rng, 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.math3.genetics
Methods in org.apache.commons.math3.genetics that throw NotPositiveExceptionModifier and TypeMethodDescriptionvoidListPopulation.setPopulationLimit(int populationLimit) Sets the maximal population size.Constructors in org.apache.commons.math3.genetics that throw NotPositiveExceptionModifierConstructorDescriptionElitisticListPopulation(int populationLimit, double elitismRate) Creates a newElitisticListPopulationinstance and initializes its inner chromosome list.ElitisticListPopulation(List<Chromosome> chromosomes, int populationLimit, double elitismRate) Creates a newElitisticListPopulationinstance.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.math3.linear
Methods in org.apache.commons.math3.linear that throw NotPositiveExceptionModifier and TypeMethodDescriptionArrayFieldVector.getSubVector(int index, int n) Get a subvector from consecutive elements.ArrayRealVector.getSubVector(int index, int n) Get a subvector from consecutive elements.FieldVector.getSubVector(int index, int n) Get a subvector from consecutive elements.OpenMapRealVector.getSubVector(int index, int n) Get a subvector from consecutive elements.abstract RealVectorRealVector.getSubVector(int index, int n) Get a subvector from consecutive elements.SparseFieldVector.getSubVector(int index, int n) Get a subvector from consecutive elements.AbstractFieldMatrix.power(int p) Returns the result multiplying this with itselfptimes.AbstractRealMatrix.power(int p) Returns the result of multiplyingthiswith itselfptimes.FieldMatrix.power(int p) Returns the result multiplying this with itselfptimes.RealMatrix.power(int p) Returns the result of multiplyingthiswith itselfptimes. -
Uses of NotPositiveException in org.apache.commons.math3.ml.clustering
Constructors in org.apache.commons.math3.ml.clustering that throw NotPositiveExceptionModifierConstructorDescriptionDBSCANClusterer(double eps, int minPts) Creates a new instance of a DBSCANClusterer.DBSCANClusterer(double eps, int minPts, DistanceMeasure measure) Creates a new instance of a DBSCANClusterer. -
Uses of NotPositiveException in org.apache.commons.math3.optim.nonlinear.scalar.noderiv
Constructors in org.apache.commons.math3.optim.nonlinear.scalar.noderiv that throw NotPositiveException -
Uses of NotPositiveException in org.apache.commons.math3.optimization.direct
Constructors in org.apache.commons.math3.optimization.direct that throw NotPositiveException -
Uses of NotPositiveException in org.apache.commons.math3.random
Methods in org.apache.commons.math3.random that throw NotPositiveExceptionModifier and TypeMethodDescriptionintRandomDataGenerator.nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize) Generates a random value from theHypergeometric Distribution.intRandomDataImpl.nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize) Deprecated.Generates a random value from theHypergeometric Distribution.double[]HaltonSequenceGenerator.skipTo(int index) Skip to the i-th point in the Halton sequence.double[]SobolSequenceGenerator.skipTo(int index) Skip to the i-th point in the Sobol sequence. -
Uses of NotPositiveException in org.apache.commons.math3.stat.clustering
Constructors in org.apache.commons.math3.stat.clustering that throw NotPositiveExceptionModifierConstructorDescriptionDBSCANClusterer(double eps, int minPts) Deprecated.Creates a new instance of a DBSCANClusterer. -
Uses of NotPositiveException in org.apache.commons.math3.stat.inference
Methods in org.apache.commons.math3.stat.inference that throw NotPositiveExceptionModifier and TypeMethodDescriptiondoubleChiSquareTest.chiSquare(double[] expected, long[] observed) doubleChiSquareTest.chiSquare(long[][] counts) Computes the Chi-Square statistic associated with a chi-square test of independence based on the inputcountsarray, viewed as a two-way table.static doubleTestUtils.chiSquare(double[] expected, long[] observed) static doubleTestUtils.chiSquare(long[][] counts) doubleChiSquareTest.chiSquareDataSetsComparison(long[] observed1, long[] observed2) Computes a Chi-Square two sample test statistic comparing bin frequency counts inobserved1andobserved2.static doubleTestUtils.chiSquareDataSetsComparison(long[] observed1, long[] observed2) doubleChiSquareTest.chiSquareTest(double[] expected, long[] observed) Returns the observed significance level, or p-value, associated with a Chi-square goodness of fit test comparing theobservedfrequency counts to those in theexpectedarray.booleanChiSquareTest.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 levelalpha.doubleChiSquareTest.chiSquareTest(long[][] counts) Returns the observed significance level, or p-value, associated with a chi-square test of independence based on the inputcountsarray, viewed as a two-way table.booleanChiSquareTest.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 levelalpha.static doubleTestUtils.chiSquareTest(double[] expected, long[] observed) static booleanTestUtils.chiSquareTest(double[] expected, long[] observed, double alpha) static doubleTestUtils.chiSquareTest(long[][] counts) static booleanTestUtils.chiSquareTest(long[][] counts, double alpha) doubleChiSquareTest.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 inobserved1andobserved2.booleanChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha) Performs a Chi-Square two sample test comparing two binned data sets.static doubleTestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2) static booleanTestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha) doubleGTest.g(double[] expected, long[] observed) static doubleTestUtils.g(double[] expected, long[] observed) doubleGTest.gDataSetsComparison(long[] observed1, long[] observed2) Computes a G (Log-Likelihood Ratio) two sample test statistic for independence comparing frequency counts inobserved1andobserved2.static doubleTestUtils.gDataSetsComparison(long[] observed1, long[] observed2) doubleGTest.gTest(double[] expected, long[] observed) Returns the observed significance level, or p-value, associated with a G-Test for goodness of fit comparing theobservedfrequency counts to those in theexpectedarray.booleanGTest.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 levelalpha.static doubleTestUtils.gTest(double[] expected, long[] observed) static booleanTestUtils.gTest(double[] expected, long[] observed, double alpha) doubleGTest.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 inobserved1andobserved2.booleanGTest.gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha) Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned data sets.static doubleTestUtils.gTestDataSetsComparison(long[] observed1, long[] observed2) static booleanTestUtils.gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha) doubleGTest.gTestIntrinsic(double[] expected, long[] observed) Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described in p64-69 of McDonald, J.H.static doubleTestUtils.gTestIntrinsic(double[] expected, long[] observed) static doubleTestUtils.rootLogLikelihoodRatio(long k11, long k12, long k21, long k22) -
Uses of NotPositiveException in org.apache.commons.math3.stat.interval
Methods in org.apache.commons.math3.stat.interval that throw NotPositiveExceptionModifier and TypeMethodDescriptionBinomialConfidenceInterval.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.math3.util
Methods in org.apache.commons.math3.util that throw NotPositiveExceptionModifier and TypeMethodDescriptionstatic longArithmeticUtils.binomialCoefficient(int n, int k) Deprecated.static longCombinatoricsUtils.binomialCoefficient(int n, int k) Returns an exact representation of the Binomial Coefficient, "n choose k", the number ofk-element subsets that can be selected from ann-element set.static doubleArithmeticUtils.binomialCoefficientDouble(int n, int k) Deprecated.static doubleCombinatoricsUtils.binomialCoefficientDouble(int n, int k) Returns adoublerepresentation of the Binomial Coefficient, "n choose k", the number ofk-element subsets that can be selected from ann-element set.static doubleArithmeticUtils.binomialCoefficientLog(int n, int k) Deprecated.static doubleCombinatoricsUtils.binomialCoefficientLog(int n, int k) Returns the naturallogof the Binomial Coefficient, "n choose k", the number ofk-element subsets that can be selected from ann-element set.static voidCombinatoricsUtils.checkBinomial(int n, int k) Check binomial preconditions.static voidMathArrays.checkNonNegative(long[] in) Check that all entries of the input array are >= 0.static voidMathArrays.checkNonNegative(long[][] in) Check all entries of the input array are >= 0.static longArithmeticUtils.factorial(int n) Deprecated.static longCombinatoricsUtils.factorial(int n) Returns n!.static doubleArithmeticUtils.factorialDouble(int n) Deprecated.static doubleCombinatoricsUtils.factorialDouble(int n) static doubleArithmeticUtils.factorialLog(int n) Deprecated.static doubleCombinatoricsUtils.factorialLog(int n) Compute the natural logarithm of the factorial ofn.static intArithmeticUtils.pow(int k, int e) Raise an int to an int power.static intArithmeticUtils.pow(int k, long e) Deprecated.As of 3.3.static longArithmeticUtils.pow(long k, int e) Raise a long to an int power.static longArithmeticUtils.pow(long k, long e) Deprecated.As of 3.3.static BigIntegerArithmeticUtils.pow(BigInteger k, int e) Raise a BigInteger to an int power.static BigIntegerArithmeticUtils.pow(BigInteger k, long e) Raise a BigInteger to a long power.static BigIntegerArithmeticUtils.pow(BigInteger k, BigInteger e) Raise a BigInteger to a BigInteger power.static longArithmeticUtils.stirlingS2(int n, int k) Deprecated.static longCombinatoricsUtils.stirlingS2(int n, int k) Returns the Stirling number of the second kind, "S(n,k)", the number of ways of partitioning ann-element set intoknon-empty subsets.
CombinatoricsUtils.binomialCoefficient(int, int)