Uses of Class
org.apache.commons.math3.exception.NotPositiveException

Packages that use NotPositiveException
org.apache.commons.math3.analysis.differentiation This package holds the main interfaces and basic building block classes dealing with differentiation. 
org.apache.commons.math3.analysis.interpolation Univariate real functions interpolation algorithms. 
org.apache.commons.math3.complex Complex number type and implementations of complex transcendental 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.ml.clustering Clustering algorithms. 
org.apache.commons.math3.optim.nonlinear.scalar.noderiv This package provides optimization algorithms that do not require derivatives. 
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.clustering All classes and sub-packages of this package are deprecated. 
org.apache.commons.math3.stat.inference Classes providing hypothesis testing and confidence interval construction. 
org.apache.commons.math3.util Convenience routines and common data structures used throughout the commons-math library. 
 

Uses of NotPositiveException in org.apache.commons.math3.analysis.differentiation
 

Constructors in org.apache.commons.math3.analysis.differentiation that throw NotPositiveException
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.math3.analysis.interpolation
 

Constructors in org.apache.commons.math3.analysis.interpolation that throw NotPositiveException
LoessInterpolator(double bandwidth, int robustnessIters, double accuracy)
          Construct a new LoessInterpolator with given bandwidth, number of robustness iterations and accuracy.
MicrosphereInterpolator(int elements, int exponent)
          Create a microsphere interpolator.
SmoothingPolynomialBicubicSplineInterpolator(int degree)
           
SmoothingPolynomialBicubicSplineInterpolator(int xDegree, int yDegree)
           
 

Uses of NotPositiveException in org.apache.commons.math3.complex
 

Methods in org.apache.commons.math3.complex that throw NotPositiveException
 List<Complex> Complex.nthRoot(int n)
          Computes the n-th roots of this complex number.
 

Uses of NotPositiveException in org.apache.commons.math3.distribution
 

Constructors in org.apache.commons.math3.distribution that throw NotPositiveException
EnumeratedDistribution(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 distribution using the given probability mass function enumeration.
EnumeratedRealDistribution(RandomGenerator rng, double[] singletons, double[] probabilities)
          Create a discrete 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 NotPositiveException
 void ListPopulation.setPopulationLimit(int populationLimit)
          Sets the maximal population size.
 

Constructors in org.apache.commons.math3.genetics that throw NotPositiveException
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.math3.linear
 

Methods in org.apache.commons.math3.linear that throw NotPositiveException
abstract  RealVector RealVector.getSubVector(int index, int n)
          Get a subvector from consecutive elements.
 FieldVector<T> ArrayFieldVector.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.
 FieldVector<T> SparseFieldVector.getSubVector(int index, int n)
          Deprecated. Get a subvector from consecutive elements.
 RealVector ArrayRealVector.getSubVector(int index, int n)
          Get a subvector from consecutive elements.
 OpenMapRealVector OpenMapRealVector.getSubVector(int index, int n)
          Deprecated. Get a subvector from consecutive elements.
 FieldMatrix<T> FieldMatrix.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.
 RealMatrix RealMatrix.power(int p)
          Returns the result of multiplying this with itself p times.
 FieldMatrix<T> AbstractFieldMatrix.power(int p)
          Returns the result multiplying this with itself p times.
 

Uses of NotPositiveException in org.apache.commons.math3.ml.clustering
 

Constructors in org.apache.commons.math3.ml.clustering that throw NotPositiveException
DBSCANClusterer(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
CMAESOptimizer.Sigma(double[] s)
           
 

Uses of NotPositiveException in org.apache.commons.math3.optimization.direct
 

Constructors in org.apache.commons.math3.optimization.direct that throw NotPositiveException
CMAESOptimizer.Sigma(double[] s)
           
 

Uses of NotPositiveException in org.apache.commons.math3.random
 

Methods in org.apache.commons.math3.random that throw NotPositiveException
 int RandomDataImpl.nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize)
          Deprecated. Generates a random value from the Hypergeometric Distribution.
 int RandomDataGenerator.nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize)
          Generates a random value from the Hypergeometric Distribution.
 

Uses of NotPositiveException in org.apache.commons.math3.stat.clustering
 

Constructors in org.apache.commons.math3.stat.clustering that throw NotPositiveException
DBSCANClusterer(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 NotPositiveException
 double ChiSquareTest.chiSquare(double[] expected, long[] observed)
          Computes the Chi-Square statistic comparing observed and expected frequency counts.
static double TestUtils.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 TestUtils.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 TestUtils.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 TestUtils.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 TestUtils.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 TestUtils.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 TestUtils.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 TestUtils.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 TestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double 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.gDataSetsComparison(long[] observed1, long[] observed2)
           
 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 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.gTestDataSetsComparison(long[] observed1, long[] observed2)
           
 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 boolean TestUtils.gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
           
 boolean GTest.gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
          Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned data sets.
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. 2009.
static double TestUtils.rootLogLikelihoodRatio(long k11, long k12, long k21, long k22)
           
 

Uses of NotPositiveException in org.apache.commons.math3.util
 

Methods in org.apache.commons.math3.util that throw NotPositiveException
static long ArithmeticUtils.binomialCoefficient(int n, int k)
          Returns an exact representation of the Binomial Coefficient, "n choose k", the number of k-element subsets that can be selected from an n-element set.
static double ArithmeticUtils.binomialCoefficientDouble(int n, int k)
          Returns a double representation of the Binomial Coefficient, "n choose k", the number of k-element subsets that can be selected from an n-element set.
static double ArithmeticUtils.binomialCoefficientLog(int n, int k)
          Returns the natural log of the Binomial Coefficient, "n choose k", the number of k-element subsets that can be selected from an n-element set.
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 ArithmeticUtils.factorial(int n)
          Returns n!.
static double ArithmeticUtils.factorialDouble(int n)
          Compute n!
static double ArithmeticUtils.factorialLog(int n)
          Compute the natural logarithm of the factorial of n.
static BigInteger ArithmeticUtils.pow(BigInteger k, BigInteger e)
          Raise a BigInteger to a BigInteger power.
static BigInteger ArithmeticUtils.pow(BigInteger k, int e)
          Raise a BigInteger to an int power.
static BigInteger ArithmeticUtils.pow(BigInteger k, long e)
          Raise a BigInteger to a long power.
static int ArithmeticUtils.pow(int k, int e)
          Raise an int to an int power.
static int ArithmeticUtils.pow(int k, long e)
          Raise an int to a long power.
static long ArithmeticUtils.pow(long k, int e)
          Raise a long to an int power.
static long ArithmeticUtils.pow(long k, long e)
          Raise a long to a long power.
static long ArithmeticUtils.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.
 



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