Uses of Class
org.apache.commons.math4.legacy.exception.NotPositiveException
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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 probability 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. -
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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 newLoessInterpolator
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 newElitisticListPopulation
instance and initializes its inner chromosome list.ElitisticListPopulation(List<Chromosome> chromosomes, int populationLimit, double elitismRate)
Creates a newElitisticListPopulation
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 itselfp
times.RealMatrix
AbstractRealMatrix. power(int p)
Returns the result of multiplyingthis
with itselfp
times.FieldMatrix<T>
FieldMatrix. power(int p)
Returns the result multiplying this with itselfp
times.RealMatrix
RealMatrix. power(int p)
Returns the result of multiplyingthis
with itselfp
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)
double
ChiSquareTest. chiSquare(long[][] counts)
Computes the Chi-Square statistic associated with a chi-square test of independence based on the inputcounts
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 inobserved1
andobserved2
.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 theobserved
frequency counts to those in theexpected
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 levelalpha
.double
ChiSquareTest. chiSquareTest(long[][] counts)
Returns the observed significance level, or p-value, associated with a chi-square test of independence based on the inputcounts
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 levelalpha
.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 inobserved1
andobserved2
.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)
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 inobserved1
andobserved2
.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 theobserved
frequency counts to those in theexpected
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 levelalpha
.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 inobserved1
andobserved2
.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)
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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.
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