# HG changeset patch
# User dwinter
# Date 1294131780 -3600
# Node ID e8ccd518555b43cdc80073092150ed58ea1da33e
# Parent 0b3f87acaabc339ed2ebc0d3943799c2766a4f6f
commons-math-2.1 added
diff -r 0b3f87acaabc -r e8ccd518555b libs/commons-math-2.1/docs/apidocs/allclasses-frame.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/libs/commons-math-2.1/docs/apidocs/allclasses-frame.html Tue Jan 04 10:03:00 2011 +0100
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+All Classes (Commons Math 2.1 API)
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+All Classes
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+All Classes (Commons Math 2.1 API)
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+All Classes
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+Constant Field Values (Commons Math 2.1 API)
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+Constant Field Values
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+Contents
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+org.apache.commons.math.special.Gamma
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+
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+public static final double GAMMA 0.5772156649015329
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+org.apache.commons.math.util.MathUtils
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+
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+public static final double EPSILON 1.1102230246251565E-16
+
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+public static final double SAFE_MIN 2.2250738585072014E-308
+
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+public static final double TWO_PI 6.283185307179586
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+org.apache.commons.math.util.OpenIntToDoubleHashMap
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+protected static final byte FREE 0
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+protected static final byte FULL 1
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+protected static final byte REMOVED 2
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+
The Apache Software Foundation . All Rights Reserved.
+
+
diff -r 0b3f87acaabc -r e8ccd518555b libs/commons-math-2.1/docs/apidocs/deprecated-list.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/libs/commons-math-2.1/docs/apidocs/deprecated-list.html Tue Jan 04 10:03:00 2011 +0100
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+
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+
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+Deprecated List (Commons Math 2.1 API)
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+
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+Deprecated API
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+Contents
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+
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+Deprecated Methods
+
+org.apache.commons.math.stat.Frequency.addValue(Integer)
+to be removed in math 3.0
+
+org.apache.commons.math.stat.Frequency.addValue(Object)
+use Frequency.addValue(Comparable)
+
+org.apache.commons.math.linear.MatrixUtils.createBigIdentityMatrix(int)
+since 2.0, replaced by MatrixUtils.createFieldIdentityMatrix(Field, int)
+
+org.apache.commons.math.linear.MatrixUtils.createBigMatrix(BigDecimal[][])
+since 2.0 replaced by MatrixUtils.createFieldMatrix(FieldElement[][])
+
+org.apache.commons.math.linear.MatrixUtils.createBigMatrix(BigDecimal[][], boolean)
+since 2.0 replaced by MatrixUtils.createFieldMatrix(FieldElement[][])
+
+org.apache.commons.math.linear.MatrixUtils.createBigMatrix(double[][])
+since 2.0 replaced by MatrixUtils.createFieldMatrix(FieldElement[][])
+
+org.apache.commons.math.linear.MatrixUtils.createBigMatrix(String[][])
+since 2.0 replaced by MatrixUtils.createFieldMatrix(FieldElement[][])
+
+org.apache.commons.math.linear.MatrixUtils.createColumnBigMatrix(BigDecimal[])
+since 2.0 replaced by MatrixUtils.createColumnFieldMatrix(FieldElement[])
+
+org.apache.commons.math.linear.MatrixUtils.createColumnBigMatrix(double[])
+since 2.0 replaced by MatrixUtils.createColumnFieldMatrix(FieldElement[])
+
+org.apache.commons.math.linear.MatrixUtils.createColumnBigMatrix(String[])
+since 2.0 replaced by MatrixUtils.createColumnFieldMatrix(FieldElement[])
+
+org.apache.commons.math.linear.MatrixUtils.createRowBigMatrix(BigDecimal[])
+since 2.0 replaced by MatrixUtils.createRowFieldMatrix(FieldElement[])
+
+org.apache.commons.math.linear.MatrixUtils.createRowBigMatrix(double[])
+since 2.0 replaced by MatrixUtils.createRowFieldMatrix(FieldElement[])
+
+org.apache.commons.math.linear.MatrixUtils.createRowBigMatrix(String[])
+since 2.0 replaced by MatrixUtils.createRowFieldMatrix(FieldElement[])
+
+org.apache.commons.math.distribution.NormalDistributionImpl.density(Double)
+
+
+org.apache.commons.math.distribution.GammaDistributionImpl.density(Double)
+
+
+org.apache.commons.math.distribution.ExponentialDistributionImpl.density(Double)
+- use density(double)
+
+org.apache.commons.math.distribution.ChiSquaredDistributionImpl.density(Double)
+
+
+org.apache.commons.math.distribution.BetaDistributionImpl.density(Double)
+
+
+org.apache.commons.math.stat.Frequency.getCount(Object)
+replaced by Frequency.getCount(Comparable)
+
+org.apache.commons.math.stat.Frequency.getCumFreq(Object)
+replaced by Frequency.getCumFreq(Comparable)
+
+org.apache.commons.math.stat.Frequency.getCumPct(Object)
+replaced by Frequency.getCumPct(Comparable)
+
+org.apache.commons.math.linear.RealMatrix.getDeterminant()
+as of release 2.0, replaced by
+ new LUDecompositionImpl(m)getDeterminant()
+
+org.apache.commons.math.linear.AbstractRealMatrix.getDeterminant()
+
+
+org.apache.commons.math.stat.Frequency.getPct(Object)
+replaced by Frequency.getPct(Comparable)
+
+org.apache.commons.math.analysis.solvers.LaguerreSolver.getPolynomialFunction()
+as of 2.0 the function is not stored anymore within the instance.
+
+org.apache.commons.math.util.ResizableDoubleArray.getValues()
+replaced by ResizableDoubleArray.getInternalValues()
+
+org.apache.commons.math.analysis.integration.UnivariateRealIntegrator.integrate(double, double)
+replaced by UnivariateRealIntegrator.integrate(UnivariateRealFunction, double, double)
+
+org.apache.commons.math.analysis.integration.TrapezoidIntegrator.integrate(double, double)
+
+
+org.apache.commons.math.analysis.integration.SimpsonIntegrator.integrate(double, double)
+
+
+org.apache.commons.math.analysis.integration.RombergIntegrator.integrate(double, double)
+
+
+org.apache.commons.math.analysis.integration.LegendreGaussIntegrator.integrate(double, double)
+
+
+org.apache.commons.math.linear.RealMatrix.inverse()
+as of release 2.0, replaced by
+ new LUDecompositionImpl(m)getSolver()getInverse()
+
+org.apache.commons.math.linear.AbstractRealMatrix.inverse()
+
+
+org.apache.commons.math.linear.RealMatrix.isSingular()
+as of release 2.0, replaced by the boolean negation of
+ new LUDecompositionImpl(m)getSolver()isNonSingular()
+
+org.apache.commons.math.linear.AbstractRealMatrix.isSingular()
+
+
+org.apache.commons.math.linear.AbstractRealMatrix.luDecompose()
+as of release 2.0, replaced by LUDecomposition
+
+org.apache.commons.math.distribution.GammaDistributionImpl.setAlpha(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.GammaDistribution.setAlpha(double)
+as of v2.1
+
+org.apache.commons.math.distribution.BetaDistributionImpl.setAlpha(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.BetaDistribution.setAlpha(double)
+as of 2.1
+
+org.apache.commons.math.distribution.GammaDistributionImpl.setBeta(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.GammaDistribution.setBeta(double)
+as of v2.1
+
+org.apache.commons.math.distribution.BetaDistributionImpl.setBeta(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.BetaDistribution.setBeta(double)
+as of 2.1
+
+org.apache.commons.math.distribution.TDistributionImpl.setDegreesOfFreedom(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.TDistribution.setDegreesOfFreedom(double)
+as of v2.1
+
+org.apache.commons.math.distribution.ChiSquaredDistributionImpl.setDegreesOfFreedom(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.ChiSquaredDistribution.setDegreesOfFreedom(double)
+as of v2.1
+
+org.apache.commons.math.distribution.FDistributionImpl.setDenominatorDegreesOfFreedom(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.FDistribution.setDenominatorDegreesOfFreedom(double)
+as of v2.1
+
+org.apache.commons.math.distribution.ZipfDistributionImpl.setExponent(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.ZipfDistribution.setExponent(double)
+as of v2.1
+
+org.apache.commons.math.distribution.ChiSquaredDistributionImpl.setGamma(GammaDistribution)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.PoissonDistributionImpl.setMean(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.PoissonDistribution.setMean(double)
+as of v2.1
+
+org.apache.commons.math.distribution.NormalDistributionImpl.setMean(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.NormalDistribution.setMean(double)
+as of v2.1
+
+org.apache.commons.math.distribution.ExponentialDistributionImpl.setMean(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.ExponentialDistribution.setMean(double)
+as of v2.1
+
+org.apache.commons.math.distribution.CauchyDistributionImpl.setMedian(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.CauchyDistribution.setMedian(double)
+as of v2.1
+
+org.apache.commons.math.distribution.PoissonDistributionImpl.setNormal(NormalDistribution)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.ZipfDistributionImpl.setNumberOfElements(int)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.ZipfDistribution.setNumberOfElements(int)
+as of v2.1
+
+org.apache.commons.math.distribution.PascalDistributionImpl.setNumberOfSuccesses(int)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.PascalDistribution.setNumberOfSuccesses(int)
+as of v2.1
+
+org.apache.commons.math.distribution.HypergeometricDistributionImpl.setNumberOfSuccesses(int)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.HypergeometricDistribution.setNumberOfSuccesses(int)
+as of v2.1
+
+org.apache.commons.math.distribution.BinomialDistributionImpl.setNumberOfTrials(int)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.BinomialDistribution.setNumberOfTrials(int)
+as of v2.1
+
+org.apache.commons.math.distribution.FDistributionImpl.setNumeratorDegreesOfFreedom(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.FDistribution.setNumeratorDegreesOfFreedom(double)
+as of v2.1
+
+org.apache.commons.math.distribution.HypergeometricDistributionImpl.setPopulationSize(int)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.HypergeometricDistribution.setPopulationSize(int)
+as of v2.1
+
+org.apache.commons.math.distribution.PascalDistributionImpl.setProbabilityOfSuccess(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.PascalDistribution.setProbabilityOfSuccess(double)
+as of v2.1
+
+org.apache.commons.math.distribution.BinomialDistributionImpl.setProbabilityOfSuccess(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.BinomialDistribution.setProbabilityOfSuccess(double)
+as of v2.1
+
+org.apache.commons.math.distribution.HypergeometricDistributionImpl.setSampleSize(int)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.HypergeometricDistribution.setSampleSize(int)
+as of v2.1
+
+org.apache.commons.math.distribution.WeibullDistributionImpl.setScale(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.WeibullDistribution.setScale(double)
+as of v2.1
+
+org.apache.commons.math.distribution.CauchyDistributionImpl.setScale(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.CauchyDistribution.setScale(double)
+as of v2.1
+
+org.apache.commons.math.distribution.WeibullDistributionImpl.setShape(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.WeibullDistribution.setShape(double)
+as of v2.1
+
+org.apache.commons.math.distribution.NormalDistributionImpl.setStandardDeviation(double)
+as of 2.1 (class will become immutable in 3.0)
+
+org.apache.commons.math.distribution.NormalDistribution.setStandardDeviation(double)
+as of v2.1
+
+org.apache.commons.math.linear.RealMatrix.solve(double[])
+as of release 2.0, replaced by DecompositionSolver.solve(double[])
+
+org.apache.commons.math.linear.AbstractRealMatrix.solve(double[])
+
+
+org.apache.commons.math.analysis.solvers.UnivariateRealSolver.solve(double, double)
+replaced by UnivariateRealSolver.solve(UnivariateRealFunction, double, double)
+
+org.apache.commons.math.analysis.solvers.SecantSolver.solve(double, double)
+
+
+org.apache.commons.math.analysis.solvers.RiddersSolver.solve(double, double)
+
+
+org.apache.commons.math.analysis.solvers.NewtonSolver.solve(double, double)
+
+
+org.apache.commons.math.analysis.solvers.MullerSolver.solve(double, double)
+
+
+org.apache.commons.math.analysis.solvers.LaguerreSolver.solve(double, double)
+
+
+org.apache.commons.math.analysis.solvers.BrentSolver.solve(double, double)
+
+
+org.apache.commons.math.analysis.solvers.BisectionSolver.solve(double, double)
+
+
+org.apache.commons.math.analysis.solvers.UnivariateRealSolver.solve(double, double, double)
+replaced by UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+
+org.apache.commons.math.analysis.solvers.SecantSolver.solve(double, double, double)
+
+
+org.apache.commons.math.analysis.solvers.RiddersSolver.solve(double, double, double)
+
+
+org.apache.commons.math.analysis.solvers.NewtonSolver.solve(double, double, double)
+
+
+org.apache.commons.math.analysis.solvers.MullerSolver.solve(double, double, double)
+
+
+org.apache.commons.math.analysis.solvers.LaguerreSolver.solve(double, double, double)
+
+
+org.apache.commons.math.analysis.solvers.BrentSolver.solve(double, double, double)
+
+
+org.apache.commons.math.analysis.solvers.BisectionSolver.solve(double, double, double)
+
+
+org.apache.commons.math.linear.RealMatrix.solve(RealMatrix)
+as of release 2.0, replaced by DecompositionSolver.solve(RealMatrix)
+
+org.apache.commons.math.linear.AbstractRealMatrix.solve(RealMatrix)
+
+
+org.apache.commons.math.analysis.solvers.MullerSolver.solve2(double, double)
+replaced by MullerSolver.solve2(UnivariateRealFunction, double, double)
+
+
+
+
+
+
+Deprecated Constructors
+
+org.apache.commons.math.analysis.solvers.BisectionSolver(UnivariateRealFunction)
+as of 2.0 the function to solve is passed as an argument
+ to the BisectionSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+
+org.apache.commons.math.analysis.solvers.BrentSolver(UnivariateRealFunction)
+as of 2.0 the function to solve is passed as an argument
+ to the BrentSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+
+org.apache.commons.math.distribution.ChiSquaredDistributionImpl(double, GammaDistribution)
+as of 2.1 (to avoid possibly inconsistent state, the
+ "GammaDistribution" will be instantiated internally)
+
+org.apache.commons.math.analysis.solvers.LaguerreSolver(UnivariateRealFunction)
+as of 2.0 the function to solve is passed as an argument
+ to the LaguerreSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+
+org.apache.commons.math.analysis.solvers.MullerSolver(UnivariateRealFunction)
+as of 2.0 the function to solve is passed as an argument
+ to the MullerSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+
+org.apache.commons.math.analysis.solvers.NewtonSolver(DifferentiableUnivariateRealFunction)
+as of 2.0 the function to solve is passed as an argument
+ to the NewtonSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+
+org.apache.commons.math.distribution.PoissonDistributionImpl(double, NormalDistribution)
+as of 2.1 (to avoid possibly inconsistent state, the
+ "NormalDistribution" will be instantiated internally)
+
+org.apache.commons.math.analysis.solvers.RiddersSolver(UnivariateRealFunction)
+as of 2.0 the function to solve is passed as an argument
+ to the RiddersSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+
+org.apache.commons.math.analysis.integration.RombergIntegrator(UnivariateRealFunction)
+as of 2.0 the integrand function is passed as an argument
+ to the RombergIntegrator.integrate(UnivariateRealFunction, double, double)
+
+org.apache.commons.math.analysis.solvers.SecantSolver(UnivariateRealFunction)
+as of 2.0 the function to solve is passed as an argument
+ to the SecantSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+
+org.apache.commons.math.analysis.integration.SimpsonIntegrator(UnivariateRealFunction)
+as of 2.0 the integrand function is passed as an argument
+ to the SimpsonIntegrator.integrate(UnivariateRealFunction, double, double)
+
+org.apache.commons.math.analysis.integration.TrapezoidIntegrator(UnivariateRealFunction)
+as of 2.0 the integrand function is passed as an argument
+ to the TrapezoidIntegrator.integrate(UnivariateRealFunction, double, double)
+
+org.apache.commons.math.analysis.integration.UnivariateRealIntegratorImpl(UnivariateRealFunction, int)
+as of 2.0 the integrand function is passed as an argument
+ to the UnivariateRealIntegrator.integrate(UnivariateRealFunction, double, double)
+
+org.apache.commons.math.analysis.solvers.UnivariateRealSolverImpl(UnivariateRealFunction, int, double)
+as of 2.0 the function to solve is passed as an argument
+ to the UnivariateRealSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+
+
+
+
The Apache Software Foundation . All Rights Reserved.
+
+
diff -r 0b3f87acaabc -r e8ccd518555b libs/commons-math-2.1/docs/apidocs/help-doc.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/libs/commons-math-2.1/docs/apidocs/help-doc.html Tue Jan 04 10:03:00 2011 +0100
@@ -0,0 +1,224 @@
+
+
+
+
+
+
+API Help (Commons Math 2.1 API)
+
+
+
+
+
+
+
+
+
+
+How This API Document Is Organized
+
+This API (Application Programming Interface) document has pages corresponding to the items in the navigation bar, described as follows.
+Overview
+
+
+
+The Overview page is the front page of this API document and provides a list of all packages with a summary for each. This page can also contain an overall description of the set of packages.
+
+Package
+
+
+
+Each package has a page that contains a list of its classes and interfaces, with a summary for each. This page can contain four categories:
+Interfaces (italic) Classes Enums Exceptions Errors Annotation Types
+
+
+Class/Interface
+
+
+
+Each class, interface, nested class and nested interface has its own separate page. Each of these pages has three sections consisting of a class/interface description, summary tables, and detailed member descriptions:
+Class inheritance diagram Direct Subclasses All Known Subinterfaces All Known Implementing Classes Class/interface declaration Class/interface description
+
+
Nested Class Summary Field Summary Constructor Summary Method Summary
+
+
Field Detail Constructor Detail Method Detail
+Each summary entry contains the first sentence from the detailed description for that item. The summary entries are alphabetical, while the detailed descriptions are in the order they appear in the source code. This preserves the logical groupings established by the programmer.
+
+
+Annotation Type
+
+
+
+Each annotation type has its own separate page with the following sections:
+Annotation Type declaration Annotation Type description Required Element Summary Optional Element Summary Element Detail
+
+
+
+Enum
+
+
+
+Each enum has its own separate page with the following sections:
+Enum declaration Enum description Enum Constant Summary Enum Constant Detail
+
+
+Use
+
+Each documented package, class and interface has its own Use page. This page describes what packages, classes, methods, constructors and fields use any part of the given class or package. Given a class or interface A, its Use page includes subclasses of A, fields declared as A, methods that return A, and methods and constructors with parameters of type A. You can access this page by first going to the package, class or interface, then clicking on the "Use" link in the navigation bar.
+
+Tree (Class Hierarchy)
+
+There is a Class Hierarchy page for all packages, plus a hierarchy for each package. Each hierarchy page contains a list of classes and a list of interfaces. The classes are organized by inheritance structure starting with java.lang.Object. The interfaces do not inherit from java.lang.Object.
+When viewing the Overview page, clicking on "Tree" displays the hierarchy for all packages. When viewing a particular package, class or interface page, clicking "Tree" displays the hierarchy for only that package.
+
+
+Deprecated API
+
+The Deprecated API page lists all of the API that have been deprecated. A deprecated API is not recommended for use, generally due to improvements, and a replacement API is usually given. Deprecated APIs may be removed in future implementations.
+
+Index
+
+The Index contains an alphabetic list of all classes, interfaces, constructors, methods, and fields.
+
+Prev/Next
+These links take you to the next or previous class, interface, package, or related page.
+Frames/No Frames
+These links show and hide the HTML frames. All pages are available with or without frames.
+
+
+Serialized Form
+Each serializable or externalizable class has a description of its serialization fields and methods. This information is of interest to re-implementors, not to developers using the API. While there is no link in the navigation bar, you can get to this information by going to any serialized class and clicking "Serialized Form" in the "See also" section of the class description.
+
+
+Constant Field Values
+The Constant Field Values page lists the static final fields and their values.
+
+
+
+This help file applies to API documentation generated using the standard doclet.
+
+
The Apache Software Foundation . All Rights Reserved.
+
+
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+
+
+
+
+
+
+Index (Commons Math 2.1 API)
+
+
+
+
+
+
+
+
+A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
+A
+
+ABS ComposableFunction
+The Math.abs method wrapped as a ComposableFunction abs() Complex
+Return the absolute value of this complex number.
+ abs() BigFraction
+
+ Returns the absolute value of this BigFraction abs() Fraction
+Returns the absolute value of this fraction.
+ absoluteAccuracy ConvergingAlgorithmImpl
+Maximum absolute error.
+ AbstractContinuousDistribution org.apache.commons.math.distribution Base class for continuous distributions. AbstractContinuousDistribution() AbstractContinuousDistribution
+Default constructor.
+ AbstractDistribution org.apache.commons.math.distribution Base class for probability distributions. AbstractDistribution() AbstractDistribution
+Default constructor.
+ AbstractEstimator org.apache.commons.math.estimation Deprecated. as of 2.0, everything in package org.apache.commons.math.estimation has
+ been deprecated and replaced by package org.apache.commons.math.optimization.general AbstractEstimator() AbstractEstimator
+Deprecated. Build an abstract estimator for least squares problems.
+AbstractFieldMatrix T extends FieldElement <T >> - Class in org.apache.commons.math.linear Basic implementation of FieldMatrix AbstractFieldMatrix() AbstractFieldMatrix
+Constructor for use with Serializable
+ AbstractFieldMatrix(Field<T>) AbstractFieldMatrix
+Creates a matrix with no data
+ AbstractFieldMatrix(Field<T>, int, int) AbstractFieldMatrix
+Create a new FieldMatrix with the supplied row and column dimensions.
+AbstractFormat org.apache.commons.math.fraction Common part shared by both FractionFormatBigFractionFormat AbstractFormat() AbstractFormat
+Create an improper formatting instance with the default number format
+ for the numerator and denominator.
+ AbstractFormat(NumberFormat) AbstractFormat
+Create an improper formatting instance with a custom number format for
+ both the numerator and denominator.
+ AbstractFormat(NumberFormat, NumberFormat) AbstractFormat
+Create an improper formatting instance with a custom number format for
+ the numerator and a custom number format for the denominator.
+ AbstractIntegerDistribution org.apache.commons.math.distribution Base class for integer-valued discrete distributions. AbstractIntegerDistribution() AbstractIntegerDistribution
+Default constructor.
+ AbstractIntegrator org.apache.commons.math.ode Base class managing common boilerplate for all integrators. AbstractIntegrator(String) AbstractIntegrator
+Build an instance.
+ AbstractIntegrator() AbstractIntegrator
+Build an instance with a null name.
+ AbstractLeastSquaresOptimizer org.apache.commons.math.optimization.general Base class for implementing least squares optimizers. AbstractLeastSquaresOptimizer() AbstractLeastSquaresOptimizer
+Simple constructor with default settings.
+ AbstractLinearOptimizer org.apache.commons.math.optimization.linear Base class for implementing linear optimizers. AbstractLinearOptimizer() AbstractLinearOptimizer
+Simple constructor with default settings.
+ AbstractListChromosome T > - Class in org.apache.commons.math.genetics Chromosome represented by an immutable list of a fixed length. AbstractListChromosome(List<T>) AbstractListChromosome
+Constructor.
+ AbstractListChromosome(T[]) AbstractListChromosome
+Constructor.
+ AbstractMultipleLinearRegression org.apache.commons.math.stat.regression Abstract base class for implementations of MultipleLinearRegression. AbstractMultipleLinearRegression() AbstractMultipleLinearRegression
+
+ AbstractRandomGenerator org.apache.commons.math.random Abstract class implementing the RandomGenerator AbstractRandomGenerator() AbstractRandomGenerator
+Construct a RandomGenerator.
+ AbstractRealMatrix org.apache.commons.math.linear Basic implementation of RealMatrix methods regardless of the underlying storage. AbstractRealMatrix() AbstractRealMatrix
+Creates a matrix with no data
+ AbstractRealMatrix(int, int) AbstractRealMatrix
+Create a new RealMatrix with the supplied row and column dimensions.
+ AbstractRealVector org.apache.commons.math.linear This class provides default basic implementations for many methods in the
+ RealVector AbstractRealVector() AbstractRealVector
+
+ AbstractRealVector.EntryImpl org.apache.commons.math.linear An entry in the vector. AbstractRealVector.EntryImpl() AbstractRealVector.EntryImpl
+Simple constructor.
+ AbstractRealVector.SparseEntryIterator org.apache.commons.math.linear This class should rare be used, but is here to provide
+ a default implementation of sparseIterator(), which is implemented
+ by walking over the entries, skipping those whose values are the default one. AbstractRealVector.SparseEntryIterator() AbstractRealVector.SparseEntryIterator
+Simple constructor.
+ AbstractScalarDifferentiableOptimizer org.apache.commons.math.optimization.general Base class for implementing optimizers for multivariate scalar functions. AbstractScalarDifferentiableOptimizer() AbstractScalarDifferentiableOptimizer
+Simple constructor with default settings.
+ AbstractStepInterpolator org.apache.commons.math.ode.sampling This abstract class represents an interpolator over the last step
+ during an ODE integration. AbstractStepInterpolator() AbstractStepInterpolator
+Simple constructor.
+ AbstractStepInterpolator(double[], boolean) AbstractStepInterpolator
+Simple constructor.
+ AbstractStepInterpolator(AbstractStepInterpolator) AbstractStepInterpolator
+Copy constructor.
+ AbstractStorelessUnivariateStatistic org.apache.commons.math.stat.descriptive Abstract implementation of the StorelessUnivariateStatistic AbstractStorelessUnivariateStatistic() AbstractStorelessUnivariateStatistic
+
+ AbstractUnivariateRealOptimizer org.apache.commons.math.optimization.univariate Provide a default implementation for several functions useful to generic
+ optimizers. AbstractUnivariateRealOptimizer(int, double) AbstractUnivariateRealOptimizer
+Construct a solver with given iteration count and accuracy.
+ AbstractUnivariateStatistic org.apache.commons.math.stat.descriptive Abstract base class for all implementations of the
+ UnivariateStatistic AbstractUnivariateStatistic() AbstractUnivariateStatistic
+
+ ACOS ComposableFunction
+The Math.abs method wrapped as a ComposableFunction acos() Complex
+Compute the
+
+ inverse cosine of this complex number.
+ AdamsBashforthIntegrator org.apache.commons.math.ode.nonstiff This class implements explicit Adams-Bashforth integrators for Ordinary
+ Differential Equations. AdamsBashforthIntegrator(int, double, double, double, double) AdamsBashforthIntegrator
+Build an Adams-Bashforth integrator with the given order and step control parameters.
+ AdamsBashforthIntegrator(int, double, double, double[], double[]) AdamsBashforthIntegrator
+Build an Adams-Bashforth integrator with the given order and step control parameters.
+ AdamsIntegrator org.apache.commons.math.ode.nonstiff Base class for Adams-BashforthAdams-Moulton AdamsIntegrator(String, int, int, double, double, double, double) AdamsIntegrator
+Build an Adams integrator with the given order and step control prameters.
+ AdamsIntegrator(String, int, int, double, double, double[], double[]) AdamsIntegrator
+Build an Adams integrator with the given order and step control parameters.
+ AdamsMoultonIntegrator org.apache.commons.math.ode.nonstiff This class implements implicit Adams-Moulton integrators for Ordinary
+ Differential Equations. AdamsMoultonIntegrator(int, double, double, double, double) AdamsMoultonIntegrator
+Build an Adams-Moulton integrator with the given order and error control parameters.
+ AdamsMoultonIntegrator(int, double, double, double[], double[]) AdamsMoultonIntegrator
+Build an Adams-Moulton integrator with the given order and error control parameters.
+ AdamsNordsieckTransformer org.apache.commons.math.ode.nonstiff Transformer to Nordsieck vectors for Adams integrators. AdaptiveStepsizeIntegrator org.apache.commons.math.ode.nonstiff This abstract class holds the common part of all adaptive
+ stepsize integrators for Ordinary Differential Equations. AdaptiveStepsizeIntegrator(String, double, double, double, double) AdaptiveStepsizeIntegrator
+Build an integrator with the given stepsize bounds.
+ AdaptiveStepsizeIntegrator(String, double, double, double[], double[]) AdaptiveStepsizeIntegrator
+Build an integrator with the given stepsize bounds.
+ ADD BinaryFunction
+The + operator method wrapped as a BinaryFunction add(UnivariateRealFunction) ComposableFunction
+Return a function adding the instance and another function.
+ add(double) ComposableFunction
+Return a function adding a constant term to the instance.
+ add(PolynomialFunction) PolynomialFunction
+Add a polynomial to the instance.
+ add(Complex) Complex
+Return the sum of this complex number and the given complex number.
+ add(T) FieldElement
+Compute this + a.
+ add(BigInteger) BigFraction
+
+ Adds the value of this fraction to the passed BigInteger add(int) BigFraction
+
+ Adds the value of this fraction to the passed integer , returning
+ the result in reduced form.
+ add(long) BigFraction
+
+ Adds the value of this fraction to the passed long , returning
+ the result in reduced form.
+ add(BigFraction) BigFraction
+
+ Adds the value of this fraction to another, returning the result in
+ reduced form.
+ add(Fraction) Fraction
+Adds the value of this fraction to another, returning the result in reduced form.
+ add(int) Fraction
+Add an integer to the fraction.
+ add(Vector3D) Vector3D
+Add a vector to the instance.
+ add(double, Vector3D) Vector3D
+Add a scaled vector to the instance.
+ add(FieldMatrix<T>) AbstractFieldMatrix
+Compute the sum of this and m.
+ add(RealMatrix) AbstractRealMatrix
+Compute the sum of this and m.
+ add(double[]) AbstractRealVector
+Compute the sum of this and v.
+ add(RealVector) AbstractRealVector
+Compute the sum of this and v.
+ add(FieldMatrix<T>) Array2DRowFieldMatrix
+Compute the sum of this and m.
+ add(Array2DRowFieldMatrix<T>) Array2DRowFieldMatrix
+Compute the sum of this and m.
+ add(RealMatrix) Array2DRowRealMatrix
+Compute the sum of this and m.
+ add(Array2DRowRealMatrix) Array2DRowRealMatrix
+Compute the sum of this and m.
+ add(FieldVector<T>) ArrayFieldVector
+Compute the sum of this and v.
+ add(T[]) ArrayFieldVector
+Compute the sum of this and v.
+ add(ArrayFieldVector<T>) ArrayFieldVector
+Compute the sum of this and v.
+ add(RealVector) ArrayRealVector
+Compute the sum of this and v.
+ add(double[]) ArrayRealVector
+Compute the sum of this and v.
+ add(ArrayRealVector) ArrayRealVector
+Compute the sum of this and v.
+ add(BigMatrix) BigMatrix
+Deprecated. Compute the sum of this and m.
+add(BigMatrix) BigMatrixImpl
+Deprecated. Compute the sum of this and m.
+add(BigMatrixImpl) BigMatrixImpl
+Deprecated. Compute the sum of this and m.
+add(FieldMatrix<T>) BlockFieldMatrix
+Compute the sum of this and m.
+ add(BlockFieldMatrix<T>) BlockFieldMatrix
+Compute the sum of this and m.
+ add(RealMatrix) BlockRealMatrix
+Compute the sum of this and m.
+ add(BlockRealMatrix) BlockRealMatrix
+Compute the sum of this and m.
+ add(FieldMatrix<T>) FieldMatrix
+Compute the sum of this and m.
+ add(FieldVector<T>) FieldVector
+Compute the sum of this and v.
+ add(T[]) FieldVector
+Compute the sum of this and v.
+ add(RealMatrix) OpenMapRealMatrix
+Compute the sum of this and m.
+ add(OpenMapRealMatrix) OpenMapRealMatrix
+Compute the sum of this and m.
+ add(RealVector) OpenMapRealVector
+Compute the sum of this and v.
+ add(OpenMapRealVector) OpenMapRealVector
+Optimized method to add two OpenMapRealVectors.
+ add(RealMatrix) RealMatrix
+Compute the sum of this and m.
+ add(RealMatrix) RealMatrixImpl
+Deprecated. Compute the sum of this and m.
+add(RealMatrixImpl) RealMatrixImpl
+Deprecated. Compute the sum of this and m.
+add(RealVector) RealVector
+Compute the sum of this and v.
+ add(double[]) RealVector
+Compute the sum of this and v.
+ add(SparseFieldVector<T>) SparseFieldVector
+Optimized method to add sparse vectors.
+ add(T[]) SparseFieldVector
+Compute the sum of this and v.
+ add(FieldVector<T>) SparseFieldVector
+Compute the sum of this and v.
+ add(BigReal) BigReal
+Compute this + a.
+ addAndCheck(int, int) MathUtils
+Add two integers, checking for overflow.
+ addAndCheck(long, long) MathUtils
+Add two long integers, checking for overflow.
+ addChromosome(Chromosome) ListPopulation
+Add the given chromosome to the population.
+ addChromosome(Chromosome) Population
+Add the given chromosome to the population.
+ addData(double, double) SimpleRegression
+Adds the observation (x,y) to the regression data set.
+ addData(double[][]) SimpleRegression
+Adds the observations represented by the elements in
+ data.
+ addElement(double) DoubleArray
+Adds an element to the end of this expandable array
+ addElement(double) ResizableDoubleArray
+Adds an element to the end of this expandable array.
+ addElementRolling(double) DoubleArray
+
+ Adds an element to the end of the array and removes the first
+ element in the array.
+ addElementRolling(double) ResizableDoubleArray
+
+ Adds an element to the end of the array and removes the first
+ element in the array.
+ addEndTimeChecker(double, double, CombinedEventsManager) AbstractIntegrator
+Add an event handler for end time checking.
+ addEventHandler(EventHandler, double, double, int) AbstractIntegrator
+Add an event handler to the integrator.
+ addEventHandler(EventHandler, double, double, int) CombinedEventsManager
+Add an events handler.
+ addEventHandler(EventHandlerWithJacobians, double, double, int) FirstOrderIntegratorWithJacobians
+Add an event handler to the integrator.
+ addEventHandler(EventHandler, double, double, int) GraggBulirschStoerIntegrator
+Add an event handler to the integrator.
+ addEventHandler(EventHandler, double, double, int) ODEIntegrator
+Add an event handler to the integrator.
+ ADDITIVE_MODE ResizableDoubleArray
+additive expansion mode
+ addMeasurement(WeightedMeasurement) SimpleEstimationProblem
+Deprecated. Add a new measurement to the set.
+addObservedPoint(double, double) CurveFitter
+Add an observed (x,y) point to the sample with unit weight.
+ addObservedPoint(double, double, double) CurveFitter
+Add an observed weighted (x,y) point to the sample.
+ addObservedPoint(WeightedObservedPoint) CurveFitter
+Add an observed weighted (x,y) point to the sample.
+ addObservedPoint(double, double, double) HarmonicFitter
+Add an observed weighted (x,y) point to the sample.
+ addObservedPoint(double, double, double) PolynomialFitter
+Add an observed weighted (x,y) point to the sample.
+ addParameter(EstimatedParameter) SimpleEstimationProblem
+Deprecated. Add a parameter to the problem.
+addPoint(T) Cluster
+Add a point to this cluster.
+ addStepHandler(StepHandler) AbstractIntegrator
+Add a step handler to this integrator.
+ addStepHandler(StepHandlerWithJacobians) FirstOrderIntegratorWithJacobians
+Add a step handler to this integrator.
+ addStepHandler(StepHandler) GraggBulirschStoerIntegrator
+Add a step handler to this integrator.
+ addStepHandler(StepHandler) ODEIntegrator
+Add a step handler to this integrator.
+ addToEntry(int, int, T) AbstractFieldMatrix
+Change an entry in the specified row and column.
+ addToEntry(int, int, double) AbstractRealMatrix
+Change an entry in the specified row and column.
+ addToEntry(int, int, T) Array2DRowFieldMatrix
+Change an entry in the specified row and column.
+ addToEntry(int, int, double) Array2DRowRealMatrix
+Change an entry in the specified row and column.
+ addToEntry(int, int, T) BlockFieldMatrix
+Change an entry in the specified row and column.
+ addToEntry(int, int, double) BlockRealMatrix
+Change an entry in the specified row and column.
+ addToEntry(int, int, T) FieldMatrix
+Change an entry in the specified row and column.
+ addToEntry(int, int, double) OpenMapRealMatrix
+Change an entry in the specified row and column.
+ addToEntry(int, int, double) RealMatrix
+Change an entry in the specified row and column.
+ addToEntry(int, int, double) RealMatrixImpl
+Deprecated. Change an entry in the specified row and column.
+addToEntry(int, int, T) SparseFieldMatrix
+Change an entry in the specified row and column.
+ addValue(double) DescriptiveStatistics
+Adds the value to the dataset.
+ addValue(double[]) MultivariateSummaryStatistics
+Add an n-tuple to the data
+ addValue(double) SummaryStatistics
+Add a value to the data
+ addValue(double) SynchronizedDescriptiveStatistics
+Adds the value to the dataset.
+ addValue(double[]) SynchronizedMultivariateSummaryStatistics
+Add an n-tuple to the data
+ addValue(double) SynchronizedSummaryStatistics
+Add a value to the data
+ addValue(Object) Frequency
+Deprecated. use Frequency.addValue(Comparable)
+addValue(Comparable<?>) Frequency
+Adds 1 to the frequency count for v.
+ addValue(int) Frequency
+Adds 1 to the frequency count for v.
+ addValue(Integer) Frequency
+Deprecated. to be removed in math 3.0
+addValue(long) Frequency
+Adds 1 to the frequency count for v.
+ addValue(char) Frequency
+Adds 1 to the frequency count for v.
+ advance(AbstractRealVector.EntryImpl) AbstractRealVector.SparseEntryIterator
+Advance an entry up to the next non null one.
+ advance() OpenIntToDoubleHashMap.Iterator
+Advance iterator one step further.
+ advance() OpenIntToFieldHashMap.Iterator
+Advance iterator one step further.
+ aggregate(Collection<SummaryStatistics>) AggregateSummaryStatistics
+Computes aggregate summary statistics.
+ AggregateSummaryStatistics org.apache.commons.math.stat.descriptive
+ An aggregator for SummaryStatistics from several data sets or
+ data set partitions. AggregateSummaryStatistics() AggregateSummaryStatistics
+Initializes a new AggregateSummaryStatistics with default statistics
+ implementations.
+ AggregateSummaryStatistics(SummaryStatistics) AggregateSummaryStatistics
+Initializes a new AggregateSummaryStatistics with the specified statistics
+ object as a prototype for contributing statistics and for the internal
+ aggregate statistics.
+ AggregateSummaryStatistics(SummaryStatistics, SummaryStatistics) AggregateSummaryStatistics
+Initializes a new AggregateSummaryStatistics with the specified statistics
+ object as a prototype for contributing statistics and for the internal
+ aggregate statistics.
+ angle(Vector3D, Vector3D) Vector3D
+Compute the angular separation between two vectors.
+ anovaFValue(Collection<double[]>) OneWayAnova
+Computes the ANOVA F-value for a collection of double[]
+ arrays.
+ anovaFValue(Collection<double[]>) OneWayAnovaImpl
+Computes the ANOVA F-value for a collection of double[]
+ arrays.
+ anovaPValue(Collection<double[]>) OneWayAnova
+Computes the ANOVA P-value for a collection of double[]
+ arrays.
+ anovaPValue(Collection<double[]>) OneWayAnovaImpl
+Computes the ANOVA P-value for a collection of double[]
+ arrays.
+ anovaTest(Collection<double[]>, double) OneWayAnova
+Performs an ANOVA test, evaluating the null hypothesis that there
+ is no difference among the means of the data categories.
+ anovaTest(Collection<double[]>, double) OneWayAnovaImpl
+Performs an ANOVA test, evaluating the null hypothesis that there
+ is no difference among the means of the data categories.
+ AnyMatrix org.apache.commons.math.linear Interface defining very basic matrix operations. append(FieldVector<T>) ArrayFieldVector
+Construct a vector by appending a vector to this vector.
+ append(ArrayFieldVector<T>) ArrayFieldVector
+Construct a vector by appending a vector to this vector.
+ append(T) ArrayFieldVector
+Construct a vector by appending a T to this vector.
+ append(T[]) ArrayFieldVector
+Construct a vector by appending a T array to this vector.
+ append(RealVector) ArrayRealVector
+Construct a vector by appending a vector to this vector.
+ append(ArrayRealVector) ArrayRealVector
+Construct a vector by appending a vector to this vector.
+ append(double) ArrayRealVector
+Construct a vector by appending a double to this vector.
+ append(double[]) ArrayRealVector
+Construct a vector by appending a double array to this vector.
+ append(FieldVector<T>) FieldVector
+Construct a vector by appending a vector to this vector.
+ append(T) FieldVector
+Construct a vector by appending a T to this vector.
+ append(T[]) FieldVector
+Construct a vector by appending a T array to this vector.
+ append(OpenMapRealVector) OpenMapRealVector
+Optimized method to append a OpenMapRealVector.
+ append(RealVector) OpenMapRealVector
+Construct a vector by appending a vector to this vector.
+ append(double) OpenMapRealVector
+Construct a vector by appending a double to this vector.
+ append(double[]) OpenMapRealVector
+Construct a vector by appending a double array to this vector.
+ append(RealVector) RealVector
+Construct a vector by appending a vector to this vector.
+ append(double) RealVector
+Construct a vector by appending a double to this vector.
+ append(double[]) RealVector
+Construct a vector by appending a double array to this vector.
+ append(SparseFieldVector<T>) SparseFieldVector
+Construct a vector by appending a vector to this vector.
+ append(FieldVector<T>) SparseFieldVector
+Construct a vector by appending a vector to this vector.
+ append(T) SparseFieldVector
+Construct a vector by appending a T to this vector.
+ append(T[]) SparseFieldVector
+Construct a vector by appending a T array to this vector.
+ append(ContinuousOutputModel) ContinuousOutputModel
+Append another model at the end of the instance.
+ apply(UnivariateStatistic) DescriptiveStatistics
+Apply the given statistic to the data associated with this set of statistics.
+ apply(UnivariateStatistic) SynchronizedDescriptiveStatistics
+Apply the given statistic to the data associated with this set of statistics.
+ applyInverseTo(Vector3D) Rotation
+Apply the inverse of the rotation to a vector.
+ applyInverseTo(Rotation) Rotation
+Apply the inverse of the instance to another rotation.
+ applyTo(Vector3D) Rotation
+Apply the rotation to a vector.
+ applyTo(Rotation) Rotation
+Apply the instance to another rotation.
+ ArgumentOutsideDomainException org.apache.commons.math Error thrown when a method is called with an out of bounds argument. ArgumentOutsideDomainException(double, double, double) ArgumentOutsideDomainException
+Constructs an exception with specified formatted detail message.
+ Array2DRowFieldMatrix T extends FieldElement <T >> - Class in org.apache.commons.math.linear Implementation of FieldMatrix using a FieldElementArray2DRowFieldMatrix(Field<T>) Array2DRowFieldMatrix
+Creates a matrix with no data
+ Array2DRowFieldMatrix(Field<T>, int, int) Array2DRowFieldMatrix
+Create a new FieldMatrix with the supplied row and column dimensions.
+Array2DRowFieldMatrix(T[][]) Array2DRowFieldMatrix
+Create a new FieldMatrix using the input array as the underlying
+ data array.
+Array2DRowFieldMatrix(T[][], boolean) Array2DRowFieldMatrix
+Create a new FieldMatrix using the input array as the underlying
+ data array.
+Array2DRowFieldMatrix(T[]) Array2DRowFieldMatrix
+Create a new (column) FieldMatrix using v as the
+ data for the unique column of the v.length x 1 matrix
+ created.
+Array2DRowRealMatrix org.apache.commons.math.linear Implementation of RealMatrix using a double[][] array to store entries and
+
+ LU decomposition to support linear system
+ solution and inverse. Array2DRowRealMatrix() Array2DRowRealMatrix
+Creates a matrix with no data
+ Array2DRowRealMatrix(int, int) Array2DRowRealMatrix
+Create a new RealMatrix with the supplied row and column dimensions.
+ Array2DRowRealMatrix(double[][]) Array2DRowRealMatrix
+Create a new RealMatrix using the input array as the underlying
+ data array.
+ Array2DRowRealMatrix(double[][], boolean) Array2DRowRealMatrix
+Create a new RealMatrix using the input array as the underlying
+ data array.
+ Array2DRowRealMatrix(double[]) Array2DRowRealMatrix
+Create a new (column) RealMatrix using v as the
+ data for the unique column of the v.length x 1 matrix
+ created.
+ ArrayFieldVector T extends FieldElement <T >> - Class in org.apache.commons.math.linear This class implements the FieldVectorFieldElement ArrayFieldVector(Field<T>) ArrayFieldVector
+Build a 0-length vector.
+ ArrayFieldVector(Field<T>, int) ArrayFieldVector
+Construct a (size)-length vector of zeros.
+ ArrayFieldVector(int, T) ArrayFieldVector
+Construct an (size)-length vector with preset values.
+ ArrayFieldVector(T[]) ArrayFieldVector
+Construct a vector from an array, copying the input array.
+ ArrayFieldVector(T[], boolean) ArrayFieldVector
+Create a new ArrayFieldVector using the input array as the underlying
+ data array.
+ ArrayFieldVector(T[], int, int) ArrayFieldVector
+Construct a vector from part of a array.
+ ArrayFieldVector(FieldVector<T>) ArrayFieldVector
+Construct a vector from another vector, using a deep copy.
+ ArrayFieldVector(ArrayFieldVector<T>) ArrayFieldVector
+Construct a vector from another vector, using a deep copy.
+ ArrayFieldVector(ArrayFieldVector<T>, boolean) ArrayFieldVector
+Construct a vector from another vector.
+ ArrayFieldVector(ArrayFieldVector<T>, ArrayFieldVector<T>) ArrayFieldVector
+Construct a vector by appending one vector to another vector.
+ ArrayFieldVector(ArrayFieldVector<T>, T[]) ArrayFieldVector
+Construct a vector by appending one vector to another vector.
+ ArrayFieldVector(T[], ArrayFieldVector<T>) ArrayFieldVector
+Construct a vector by appending one vector to another vector.
+ ArrayFieldVector(T[], T[]) ArrayFieldVector
+Construct a vector by appending one vector to another vector.
+ ArrayRealVector org.apache.commons.math.linear This class implements the RealVector ArrayRealVector() ArrayRealVector
+Build a 0-length vector.
+ ArrayRealVector(int) ArrayRealVector
+Construct a (size)-length vector of zeros.
+ ArrayRealVector(int, double) ArrayRealVector
+Construct an (size)-length vector with preset values.
+ ArrayRealVector(double[]) ArrayRealVector
+Construct a vector from an array, copying the input array.
+ ArrayRealVector(double[], boolean) ArrayRealVector
+Create a new ArrayRealVector using the input array as the underlying
+ data array.
+ ArrayRealVector(double[], int, int) ArrayRealVector
+Construct a vector from part of a array.
+ ArrayRealVector(Double[]) ArrayRealVector
+Construct a vector from an array.
+ ArrayRealVector(Double[], int, int) ArrayRealVector
+Construct a vector from part of a Double array
+ ArrayRealVector(RealVector) ArrayRealVector
+Construct a vector from another vector, using a deep copy.
+ ArrayRealVector(ArrayRealVector) ArrayRealVector
+Construct a vector from another vector, using a deep copy.
+ ArrayRealVector(ArrayRealVector, boolean) ArrayRealVector
+Construct a vector from another vector.
+ ArrayRealVector(ArrayRealVector, ArrayRealVector) ArrayRealVector
+Construct a vector by appending one vector to another vector.
+ ArrayRealVector(ArrayRealVector, RealVector) ArrayRealVector
+Construct a vector by appending one vector to another vector.
+ ArrayRealVector(RealVector, ArrayRealVector) ArrayRealVector
+Construct a vector by appending one vector to another vector.
+ ArrayRealVector(ArrayRealVector, double[]) ArrayRealVector
+Construct a vector by appending one vector to another vector.
+ ArrayRealVector(double[], ArrayRealVector) ArrayRealVector
+Construct a vector by appending one vector to another vector.
+ ArrayRealVector(double[], double[]) ArrayRealVector
+Construct a vector by appending one vector to another vector.
+ asCollector(BivariateRealFunction, double) ComposableFunction
+Generates a function that iteratively apply instance function on all
+ elements of an array.
+ asCollector(BivariateRealFunction) ComposableFunction
+Generates a function that iteratively apply instance function on all
+ elements of an array.
+ asCollector(double) ComposableFunction
+Generates a function that iteratively apply instance function on all
+ elements of an array.
+ asCollector() ComposableFunction
+Generates a function that iteratively apply instance function on all
+ elements of an array.
+ ASIN ComposableFunction
+The Math.asin method wrapped as a ComposableFunction asin() Complex
+Compute the
+
+ inverse sine of this complex number.
+ ATAN ComposableFunction
+The Math.atan method wrapped as a ComposableFunction atan() Complex
+Compute the
+
+ inverse tangent of this complex number.
+ ATAN2 BinaryFunction
+The Math.atan2 method wrapped as a BinaryFunction
+
+B
+
+Beta org.apache.commons.math.special This is a utility class that provides computation methods related to the
+ Beta family of functions. BetaDistribution org.apache.commons.math.distribution Computes the cumulative, inverse cumulative and density functions for the beta distribuiton. BetaDistributionImpl org.apache.commons.math.distribution Implements the Beta distribution. BetaDistributionImpl(double, double, double) BetaDistributionImpl
+Build a new instance.
+ BetaDistributionImpl(double, double) BetaDistributionImpl
+Build a new instance.
+ BicubicSplineInterpolatingFunction org.apache.commons.math.analysis.interpolation Function that implements the
+
+ bicubic spline interpolation . BicubicSplineInterpolatingFunction(double[], double[], double[][], double[][], double[][], double[][]) BicubicSplineInterpolatingFunction
+
+ bigDecimalValue() BigFraction
+
+ Gets the fraction as a BigDecimal.
+ bigDecimalValue(int) BigFraction
+
+ Gets the fraction as a BigDecimal following the passed
+ rounding mode.
+ bigDecimalValue(int, int) BigFraction
+
+ Gets the fraction as a BigDecimal following the passed scale
+ and rounding mode.
+ bigDecimalValue() BigReal
+Get the BigDecimal value corresponding to the instance.
+ BigFraction org.apache.commons.math.fraction Representation of a rational number without any overflow. BigFraction(BigInteger) BigFraction
+
+ Create a BigFractionBigInteger , ie
+ "num / 1".
+ BigFraction(BigInteger, BigInteger) BigFraction
+
+ Create a BigFractionBigInteger.
+ BigFraction(double) BigFraction
+Create a fraction given the double value.
+ BigFraction(double, double, int) BigFraction
+Create a fraction given the double value and maximum error allowed.
+ BigFraction(double, int) BigFraction
+Create a fraction given the double value and maximum denominator.
+ BigFraction(int) BigFraction
+
+ Create a BigFractionint , ie
+ "num / 1".
+ BigFraction(int, int) BigFraction
+
+ Create a BigFractionint .
+ BigFraction(long) BigFraction
+
+ Create a BigFraction BigFraction(long, long) BigFraction
+
+ Create a BigFractionlong .
+ BigFractionField org.apache.commons.math.fraction Representation of the fractional numbers without any overflow field. BigFractionFormat org.apache.commons.math.fraction Formats a BigFraction number in proper format or improper format. BigFractionFormat() BigFractionFormat
+Create an improper formatting instance with the default number format
+ for the numerator and denominator.
+ BigFractionFormat(NumberFormat) BigFractionFormat
+Create an improper formatting instance with a custom number format for
+ both the numerator and denominator.
+ BigFractionFormat(NumberFormat, NumberFormat) BigFractionFormat
+Create an improper formatting instance with a custom number format for
+ the numerator and a custom number format for the denominator.
+ bigFractionMatrixToRealMatrix(FieldMatrix<BigFraction>) MatrixUtils
+Convert a FieldMatrixBigFractionRealMatrix BigMatrix org.apache.commons.math.linear Deprecated. as of 2.0, replaced by FieldMatrixBigReal BigMatrixImpl org.apache.commons.math.linear Deprecated. as of 2.0, replaced by Array2DRowFieldMatrixBigReal BigMatrixImpl() BigMatrixImpl
+Deprecated. Creates a matrix with no data
+BigMatrixImpl(int, int) BigMatrixImpl
+Deprecated. Create a new BigMatrix with the supplied row and column dimensions.
+BigMatrixImpl(BigDecimal[][]) BigMatrixImpl
+Deprecated. Create a new BigMatrix using d as the underlying
+ data array.
+BigMatrixImpl(BigDecimal[][], boolean) BigMatrixImpl
+Deprecated. Create a new BigMatrix using the input array as the underlying
+ data array.
+BigMatrixImpl(double[][]) BigMatrixImpl
+Deprecated. Create a new BigMatrix using d as the underlying
+ data array.
+BigMatrixImpl(String[][]) BigMatrixImpl
+Deprecated. Create a new BigMatrix using the values represented by the strings in
+ d as the underlying data array.
+BigMatrixImpl(BigDecimal[]) BigMatrixImpl
+Deprecated. Create a new (column) BigMatrix using v as the
+ data for the unique column of the v.length x 1 matrix
+ created.
+BigReal org.apache.commons.math.util Arbitrary precision decimal number. BigReal(BigDecimal) BigReal
+Build an instance from a BigDecimal.
+ BigReal(BigInteger) BigReal
+Build an instance from a BigInteger.
+ BigReal(BigInteger, int) BigReal
+Build an instance from an unscaled BigInteger.
+ BigReal(BigInteger, int, MathContext) BigReal
+Build an instance from an unscaled BigInteger.
+ BigReal(BigInteger, MathContext) BigReal
+Build an instance from a BigInteger.
+ BigReal(char[]) BigReal
+Build an instance from a characters representation.
+ BigReal(char[], int, int) BigReal
+Build an instance from a characters representation.
+ BigReal(char[], int, int, MathContext) BigReal
+Build an instance from a characters representation.
+ BigReal(char[], MathContext) BigReal
+Build an instance from a characters representation.
+ BigReal(double) BigReal
+Build an instance from a double.
+ BigReal(double, MathContext) BigReal
+Build an instance from a double.
+ BigReal(int) BigReal
+Build an instance from an int.
+ BigReal(int, MathContext) BigReal
+Build an instance from an int.
+ BigReal(long) BigReal
+Build an instance from a long.
+ BigReal(long, MathContext) BigReal
+Build an instance from a long.
+ BigReal(String) BigReal
+Build an instance from a String representation.
+ BigReal(String, MathContext) BigReal
+Build an instance from a String representation.
+ BigRealField org.apache.commons.math.util Representation of real numbers with arbitrary precision field. BinaryChromosome org.apache.commons.math.genetics Chromosome represented by a vector of 0s and 1s. BinaryChromosome(List<Integer>) BinaryChromosome
+Constructor.
+ BinaryChromosome(Integer[]) BinaryChromosome
+Constructor.
+ BinaryFunction org.apache.commons.math.analysis Base class for BivariateRealFunction BinaryFunction() BinaryFunction
+
+ BinaryMutation org.apache.commons.math.genetics Mutation for BinaryChromosome BinaryMutation() BinaryMutation
+
+ binomialCoefficient(int, int) MathUtils
+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.
+ binomialCoefficientDouble(int, int) MathUtils
+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.
+ binomialCoefficientLog(int, int) MathUtils
+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.
+ BinomialDistribution org.apache.commons.math.distribution The Binomial Distribution. BinomialDistributionImpl org.apache.commons.math.distribution The default implementation of BinomialDistribution BinomialDistributionImpl(int, double) BinomialDistributionImpl
+Create a binomial distribution with the given number of trials and
+ probability of success.
+ BisectionSolver org.apache.commons.math.analysis.solvers Implements the
+ bisection algorithm for finding zeros of univariate real functions. BisectionSolver(UnivariateRealFunction) BisectionSolver
+Deprecated. as of 2.0 the function to solve is passed as an argument
+ to the BisectionSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+BisectionSolver() BisectionSolver
+Construct a solver.
+ BitsStreamGenerator org.apache.commons.math.random Base class for random number generators that generates bits streams. BitsStreamGenerator() BitsStreamGenerator
+Creates a new random number generator.
+ BivariateRealFunction org.apache.commons.math.analysis An interface representing a bivariate real function. BivariateRealGridInterpolator org.apache.commons.math.analysis.interpolation Interface representing a bivariate real interpolating function where the
+ sample points must be specified on a regular grid. BLOCK_SIZE BlockFieldMatrix
+Block size.
+ BLOCK_SIZE BlockRealMatrix
+Block size.
+ BlockFieldMatrix T extends FieldElement <T >> - Class in org.apache.commons.math.linear Cache-friendly implementation of FieldMatrix using a flat arrays to store
+ square blocks of the matrix. BlockFieldMatrix(Field<T>, int, int) BlockFieldMatrix
+Create a new matrix with the supplied row and column dimensions.
+ BlockFieldMatrix(T[][]) BlockFieldMatrix
+Create a new dense matrix copying entries from raw layout data.
+ BlockFieldMatrix(int, int, T[][], boolean) BlockFieldMatrix
+Create a new dense matrix copying entries from block layout data.
+ BlockRealMatrix org.apache.commons.math.linear Cache-friendly implementation of RealMatrix using a flat arrays to store
+ square blocks of the matrix. BlockRealMatrix(int, int) BlockRealMatrix
+Create a new matrix with the supplied row and column dimensions.
+ BlockRealMatrix(double[][]) BlockRealMatrix
+Create a new dense matrix copying entries from raw layout data.
+ BlockRealMatrix(int, int, double[][], boolean) BlockRealMatrix
+Create a new dense matrix copying entries from block layout data.
+ bracket(UnivariateRealFunction, double, double, double) UnivariateRealSolverUtils
+This method attempts to find two values a and b satisfying
+ lowerBound <= a < initial < b <= upperBound
+ f(a) * f(b) < 0
+
+ If f is continuous on [a,b], this means that a
+ and b bracket a root of f.
+ bracket(UnivariateRealFunction, double, double, double, int) UnivariateRealSolverUtils
+This method attempts to find two values a and b satisfying
+ lowerBound <= a < initial < b <= upperBound
+ f(a) * f(b) <= 0
+
+ If f is continuous on [a,b], this means that a
+ and b bracket a root of f.
+ BrentOptimizer org.apache.commons.math.optimization.univariate Implements Richard Brent's algorithm (from his book "Algorithms for
+ Minimization without Derivatives", p. BrentOptimizer() BrentOptimizer
+Construct a solver.
+ BrentSolver org.apache.commons.math.analysis.solvers Implements the
+ Brent algorithm for finding zeros of real univariate functions. BrentSolver(UnivariateRealFunction) BrentSolver
+Deprecated. as of 2.0 the function to solve is passed as an argument
+ to the BrentSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+BrentSolver() BrentSolver
+Construct a solver with default properties.
+ BrentSolver(double) BrentSolver
+Construct a solver with the given absolute accuracy.
+ BrentSolver(int, double) BrentSolver
+Contstruct a solver with the given maximum iterations and absolute accuracy.
+ buildArray(Field<T>, int, int) AbstractFieldMatrix
+Build an array of elements.
+ buildArray(Field<T>, int) AbstractFieldMatrix
+Build an array of elements.
+
+
+C
+
+calculateBeta() AbstractMultipleLinearRegression
+Calculates the beta of multiple linear regression in matrix notation.
+ calculateBeta() GLSMultipleLinearRegression
+Calculates beta by GLS.
+ calculateBeta() OLSMultipleLinearRegression
+Calculates regression coefficients using OLS.
+ calculateBetaVariance() AbstractMultipleLinearRegression
+Calculates the beta variance of multiple linear regression in matrix
+ notation.
+ calculateBetaVariance() GLSMultipleLinearRegression
+Calculates the variance on the beta by GLS.
+ calculateBetaVariance() OLSMultipleLinearRegression
+Calculates the variance on the beta by OLS.
+ calculateHat() OLSMultipleLinearRegression
+Compute the "hat" matrix.
+ calculateResiduals() AbstractMultipleLinearRegression
+Calculates the residuals of multiple linear regression in matrix
+ notation.
+ calculateYVariance() AbstractMultipleLinearRegression
+Calculates the Y variance of multiple linear regression.
+ calculateYVariance() GLSMultipleLinearRegression
+Calculates the variance on the y by GLS.
+ calculateYVariance() OLSMultipleLinearRegression
+Calculates the variance on the Y by OLS.
+ CardanEulerSingularityException org.apache.commons.math.geometry This class represents exceptions thrown while extractiong Cardan
+ or Euler angles from a rotation. CardanEulerSingularityException(boolean) CardanEulerSingularityException
+Simple constructor.
+ CauchyDistribution org.apache.commons.math.distribution Cauchy Distribution. CauchyDistributionImpl org.apache.commons.math.distribution Default implementation of
+ CauchyDistribution CauchyDistributionImpl() CauchyDistributionImpl
+Creates cauchy distribution with the medain equal to zero and scale
+ equal to one.
+ CauchyDistributionImpl(double, double) CauchyDistributionImpl
+Create a cauchy distribution using the given median and scale.
+ CauchyDistributionImpl(double, double, double) CauchyDistributionImpl
+Create a cauchy distribution using the given median and scale.
+ CBRT ComposableFunction
+The Math.cbrt method wrapped as a ComposableFunction CEIL ComposableFunction
+The Math.ceil method wrapped as a ComposableFunction centroidOf(Collection<T>) Clusterable
+Returns the centroid of the given Collection of points.
+ centroidOf(Collection<EuclideanIntegerPoint>) EuclideanIntegerPoint
+Returns the centroid of the given Collection of points.
+ checkAdditionCompatible(FieldMatrix<T>) AbstractFieldMatrix
+Check if a matrix is addition compatible with the instance
+ checkAdditionCompatible(AnyMatrix, AnyMatrix) MatrixUtils
+Check if matrices are addition compatible
+ checkColumnIndex(int) AbstractFieldMatrix
+Check if a column index is valid.
+ checkColumnIndex(AnyMatrix, int) MatrixUtils
+Check if a column index is valid.
+ checkContractExpand(float, float) ResizableDoubleArray
+Checks the expansion factor and the contraction criteria and throws an
+ IllegalArgumentException if the contractionCriteria is less than the
+ expansionCriteria
+ checker AbstractLeastSquaresOptimizer
+Convergence checker.
+ checker AbstractScalarDifferentiableOptimizer
+Convergence checker.
+ checkIndex(int) AbstractRealVector
+Check if an index is valid.
+ checkMultiplicationCompatible(FieldMatrix<T>) AbstractFieldMatrix
+Check if a matrix is multiplication compatible with the instance
+ checkMultiplicationCompatible(AnyMatrix, AnyMatrix) MatrixUtils
+Check if matrices are multiplication compatible
+ checkOrder(double[], int, boolean) MathUtils
+Checks that the given array is sorted.
+ checkResultComputed() UnivariateRealSolverImpl
+Check if a result has been computed.
+ checkResultComputed() AbstractUnivariateRealOptimizer
+Check if a result has been computed.
+ checkRowIndex(int) AbstractFieldMatrix
+Check if a row index is valid.
+ checkRowIndex(AnyMatrix, int) MatrixUtils
+Check if a row index is valid.
+ checkSubMatrixIndex(int, int, int, int) AbstractFieldMatrix
+Check if submatrix ranges indices are valid.
+ checkSubMatrixIndex(int[], int[]) AbstractFieldMatrix
+Check if submatrix ranges indices are valid.
+ checkSubMatrixIndex(AnyMatrix, int, int, int, int) MatrixUtils
+Check if submatrix ranges indices are valid.
+ checkSubMatrixIndex(AnyMatrix, int[], int[]) MatrixUtils
+Check if submatrix ranges indices are valid.
+ checkSubtractionCompatible(FieldMatrix<T>) AbstractFieldMatrix
+Check if a matrix is subtraction compatible with the instance
+ checkSubtractionCompatible(AnyMatrix, AnyMatrix) MatrixUtils
+Check if matrices are subtraction compatible
+ checkValidity(List<T>) AbstractListChromosome
+Asserts that representation can represent a valid chromosome.
+ checkValidity(List<Integer>) BinaryChromosome
+Asserts that representation can represent a valid chromosome.
+ checkValidity(List<Double>) RandomKey
+Asserts that representation can represent a valid chromosome.
+ checkVectorDimensions(RealVector) AbstractRealVector
+Check if instance and specified vectors have the same dimension.
+ checkVectorDimensions(int) AbstractRealVector
+Check if instance dimension is equal to some expected value.
+ checkVectorDimensions(FieldVector<T>) ArrayFieldVector
+Check if instance and specified vectors have the same dimension.
+ checkVectorDimensions(int) ArrayFieldVector
+Check if instance dimension is equal to some expected value.
+ checkVectorDimensions(RealVector) ArrayRealVector
+Check if instance and specified vectors have the same dimension.
+ checkVectorDimensions(int) ArrayRealVector
+Check if instance dimension is equal to some expected value.
+ checkVectorDimensions(int) SparseFieldVector
+Check if instance dimension is equal to some expected value.
+ chiSquare(double[], long[]) ChiSquareTest
+Computes the
+ Chi-Square statistic comparing observed and expected
+ frequency counts.
+ chiSquare(long[][]) ChiSquareTest
+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.
+ chiSquare(double[], long[]) ChiSquareTestImpl
+Computes the
+ Chi-Square statistic comparing observed and expected
+ frequency counts.
+ chiSquare(long[][]) ChiSquareTestImpl
+
+ chiSquare(double[], long[]) TestUtils
+
+ chiSquare(long[][]) TestUtils
+
+ chiSquareDataSetsComparison(long[], long[]) ChiSquareTestImpl
+
+ chiSquareDataSetsComparison(long[], long[]) TestUtils
+
+ chiSquareDataSetsComparison(long[], long[]) UnknownDistributionChiSquareTest
+Computes a
+
+ Chi-Square two sample test statistic comparing bin frequency counts
+ in observed1 and observed2.
+ ChiSquaredDistribution org.apache.commons.math.distribution The Chi-Squared Distribution. ChiSquaredDistributionImpl org.apache.commons.math.distribution The default implementation of ChiSquaredDistribution ChiSquaredDistributionImpl(double) ChiSquaredDistributionImpl
+Create a Chi-Squared distribution with the given degrees of freedom.
+ ChiSquaredDistributionImpl(double, GammaDistribution) ChiSquaredDistributionImpl
+Deprecated. as of 2.1 (to avoid possibly inconsistent state, the
+ "GammaDistribution" will be instantiated internally)
+ChiSquaredDistributionImpl(double, double) ChiSquaredDistributionImpl
+Create a Chi-Squared distribution with the given degrees of freedom and
+ inverse cumulative probability accuracy.
+ ChiSquareTest org.apache.commons.math.stat.inference An interface for Chi-Square tests. chiSquareTest(double[], long[]) ChiSquareTest
+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.
+ chiSquareTest(double[], long[], double) ChiSquareTest
+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.
+ chiSquareTest(long[][]) ChiSquareTest
+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.
+ chiSquareTest(long[][], double) ChiSquareTest
+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.
+ chiSquareTest(double[], long[]) ChiSquareTestImpl
+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.
+ chiSquareTest(double[], long[], double) ChiSquareTestImpl
+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.
+ chiSquareTest(long[][]) ChiSquareTestImpl
+
+ chiSquareTest(long[][], double) ChiSquareTestImpl
+
+ chiSquareTest(double[], long[], double) TestUtils
+
+ chiSquareTest(double[], long[]) TestUtils
+
+ chiSquareTest(long[][], double) TestUtils
+
+ chiSquareTest(long[][]) TestUtils
+
+ chiSquareTestDataSetsComparison(long[], long[]) ChiSquareTestImpl
+
+ chiSquareTestDataSetsComparison(long[], long[], double) ChiSquareTestImpl
+
+ chiSquareTestDataSetsComparison(long[], long[]) TestUtils
+
+ chiSquareTestDataSetsComparison(long[], long[], double) TestUtils
+
+ chiSquareTestDataSetsComparison(long[], long[]) UnknownDistributionChiSquareTest
+Returns the observed significance level , or
+ p-value , associated with a Chi-Square two sample test comparing
+ bin frequency counts in observed1 and
+ observed2.
+ chiSquareTestDataSetsComparison(long[], long[], double) UnknownDistributionChiSquareTest
+Performs a Chi-Square two sample test comparing two binned data
+ sets.
+ ChiSquareTestImpl org.apache.commons.math.stat.inference Implements Chi-Square test statistics defined in the
+ UnknownDistributionChiSquareTest ChiSquareTestImpl() ChiSquareTestImpl
+Construct a ChiSquareTestImpl
+ ChiSquareTestImpl(ChiSquaredDistribution) ChiSquareTestImpl
+Create a test instance using the given distribution for computing
+ inference statistics.
+ CholeskyDecomposition org.apache.commons.math.linear An interface to classes that implement an algorithm to calculate the
+ Cholesky decomposition of a real symmetric positive-definite matrix. CholeskyDecompositionImpl org.apache.commons.math.linear Calculates the Cholesky decomposition of a matrix. CholeskyDecompositionImpl(RealMatrix) CholeskyDecompositionImpl
+Calculates the Cholesky decomposition of the given matrix.
+ CholeskyDecompositionImpl(RealMatrix, double, double) CholeskyDecompositionImpl
+Calculates the Cholesky decomposition of the given matrix.
+ Chromosome org.apache.commons.math.genetics Individual in a population. Chromosome() Chromosome
+
+ ChromosomePair org.apache.commons.math.genetics A pair of Chromosome ChromosomePair(Chromosome, Chromosome) ChromosomePair
+Create a chromosome pair.
+ classes() TransformerMap
+Returns the Set of Classes used as keys in the map.
+ ClassicalRungeKuttaIntegrator org.apache.commons.math.ode.nonstiff This class implements the classical fourth order Runge-Kutta
+ integrator for Ordinary Differential Equations (it is the most
+ often used Runge-Kutta method). ClassicalRungeKuttaIntegrator(double) ClassicalRungeKuttaIntegrator
+Simple constructor.
+ clear() AbstractRandomGenerator
+Clears the cache used by the default implementation of
+ AbstractRandomGenerator.nextGaussian() clear() AbstractStorelessUnivariateStatistic
+Clears the internal state of the Statistic
+ clear() DescriptiveStatistics
+Resets all statistics and storage
+ clear() FirstMoment
+Clears the internal state of the Statistic
+ clear() FourthMoment
+Clears the internal state of the Statistic
+ clear() GeometricMean
+Clears the internal state of the Statistic
+ clear() Kurtosis
+Clears the internal state of the Statistic
+ clear() Mean
+Clears the internal state of the Statistic
+ clear() SecondMoment
+Clears the internal state of the Statistic
+ clear() Skewness
+Clears the internal state of the Statistic
+ clear() StandardDeviation
+Clears the internal state of the Statistic
+ clear() ThirdMoment
+Clears the internal state of the Statistic
+ clear() Variance
+Clears the internal state of the Statistic
+ clear() VectorialCovariance
+Clears the internal state of the Statistic
+ clear() MultivariateSummaryStatistics
+Resets all statistics and storage
+ clear() Max
+Clears the internal state of the Statistic
+ clear() Min
+Clears the internal state of the Statistic
+ clear() StorelessUnivariateStatistic
+Clears the internal state of the Statistic
+ clear() Product
+Clears the internal state of the Statistic
+ clear() Sum
+Clears the internal state of the Statistic
+ clear() SumOfLogs
+Clears the internal state of the Statistic
+ clear() SumOfSquares
+Clears the internal state of the Statistic
+ clear() SummaryStatistics
+Resets all statistics and storage
+ clear() SynchronizedDescriptiveStatistics
+Resets all statistics and storage
+ clear() SynchronizedMultivariateSummaryStatistics
+Resets all statistics and storage
+ clear() SynchronizedSummaryStatistics
+Resets all statistics and storage
+ clear() Frequency
+Clears the frequency table
+ clear() SimpleRegression
+Clears all data from the model.
+ clear() DoubleArray
+Clear the double array
+ clear() ResizableDoubleArray
+Clear the array, reset the size to the initialCapacity and the number
+ of elements to zero.
+ clear() TransformerMap
+Clears all the Class to Transformer mappings.
+ clearEventHandlers() AbstractIntegrator
+Remove all the event handlers that have been added to the integrator.
+ clearEventHandlers() FirstOrderIntegratorWithJacobians
+Remove all the event handlers that have been added to the integrator.
+ clearEventHandlers() ODEIntegrator
+Remove all the event handlers that have been added to the integrator.
+ clearEventsHandlers() CombinedEventsManager
+Remove all the events handlers that have been added to the manager.
+ clearObservations() CurveFitter
+Remove all observations.
+ clearResult() UnivariateRealIntegratorImpl
+Convenience function for implementations.
+ clearResult() UnivariateRealSolverImpl
+Convenience function for implementations.
+ clearResult() AbstractUnivariateRealOptimizer
+Convenience function for implementations.
+ clearStepHandlers() AbstractIntegrator
+Remove all the step handlers that have been added to the integrator.
+ clearStepHandlers() FirstOrderIntegratorWithJacobians
+Remove all the step handlers that have been added to the integrator.
+ clearStepHandlers() ODEIntegrator
+Remove all the step handlers that have been added to the integrator.
+ closeReplayFile() ValueServer
+Closes valuesFileURL after use in REPLAY_MODE.
+ Cluster T extends Clusterable <T >> - Class in org.apache.commons.math.stat.clustering Cluster holding a set of Clusterable Cluster(T) Cluster
+Build a cluster centered at a specified point.
+ cluster(Collection<T>, int, int) KMeansPlusPlusClusterer
+Runs the K-means++ clustering algorithm.
+ Clusterable T > - Interface in org.apache.commons.math.stat.clustering Interface for points that can be clustered together. cols AbstractEstimator
+Deprecated. Number of columns of the jacobian matrix.
+cols AbstractLeastSquaresOptimizer
+Number of columns of the jacobian matrix.
+ combine(UnivariateRealFunction, BivariateRealFunction) ComposableFunction
+Return a function combining the instance and another function.
+ CombinedEventsManager org.apache.commons.math.ode.events This class manages several event handlers CombinedEventsManager() CombinedEventsManager
+Simple constructor.
+ comparatorPermutation(List<S>, Comparator<S>) RandomKey
+Generates a representation of a permutation corresponding to the
+ data sorted by comparator.
+ compareTo(BigFraction) BigFraction
+
+ Compares this object to another based on size.
+ compareTo(Fraction) Fraction
+Compares this object to another based on size.
+ compareTo(Chromosome) Chromosome
+Compares two chromosomes based on their fitness.
+ compareTo(BigReal) BigReal
+
+ compareTo(double, double, double) MathUtils
+Compares two numbers given some amount of allowed error.
+ Complex org.apache.commons.math.complex Representation of a Complex number - a number which has both a
+ real and imaginary part. Complex(double, double) Complex
+Create a complex number given the real and imaginary parts.
+ ComplexField org.apache.commons.math.complex Representation of the complex numbers field. ComplexFormat org.apache.commons.math.complex Formats a Complex number in cartesian format "Re(c) + Im(c)i". ComplexFormat() ComplexFormat
+Create an instance with the default imaginary character, 'i', and the
+ default number format for both real and imaginary parts.
+ ComplexFormat(NumberFormat) ComplexFormat
+Create an instance with a custom number format for both real and
+ imaginary parts.
+ ComplexFormat(NumberFormat, NumberFormat) ComplexFormat
+Create an instance with a custom number format for the real part and a
+ custom number format for the imaginary part.
+ ComplexFormat(String) ComplexFormat
+Create an instance with a custom imaginary character, and the default
+ number format for both real and imaginary parts.
+ ComplexFormat(String, NumberFormat) ComplexFormat
+Create an instance with a custom imaginary character, and a custom number
+ format for both real and imaginary parts.
+ ComplexFormat(String, NumberFormat, NumberFormat) ComplexFormat
+Create an instance with a custom imaginary character, a custom number
+ format for the real part, and a custom number format for the imaginary
+ part.
+ ComplexUtils org.apache.commons.math.complex Static implementations of common
+ Complex ComposableFunction org.apache.commons.math.analysis Base class for UnivariateRealFunction ComposableFunction() ComposableFunction
+
+ CompositeFormat org.apache.commons.math.util Base class for formatters of composite objects (complex numbers, vectors ...). CompositeFormat() CompositeFormat
+
+ computeCoefficients() PolynomialFunctionLagrangeForm
+Calculate the coefficients of Lagrange polynomial from the
+ interpolation data.
+ computeCoefficients() PolynomialFunctionNewtonForm
+Calculate the normal polynomial coefficients given the Newton form.
+ computeCorrelationMatrix(RealMatrix) PearsonsCorrelation
+Computes the correlation matrix for the columns of the
+ input matrix.
+ computeCorrelationMatrix(double[][]) PearsonsCorrelation
+Computes the correlation matrix for the columns of the
+ input rectangular array.
+ computeCorrelationMatrix(RealMatrix) SpearmansCorrelation
+Computes the Spearman's rank correlation matrix for the columns of the
+ input matrix.
+ computeCorrelationMatrix(double[][]) SpearmansCorrelation
+Computes the Spearman's rank correlation matrix for the columns of the
+ input rectangular array.
+ computeCovarianceMatrix(RealMatrix, boolean) Covariance
+Compute a covariance matrix from a matrix whose columns represent
+ covariates.
+ computeCovarianceMatrix(RealMatrix) Covariance
+Create a covariance matrix from a matrix whose columns represent
+ covariates.
+ computeCovarianceMatrix(double[][], boolean) Covariance
+Compute a covariance matrix from a rectangular array whose columns represent
+ covariates.
+ computeCovarianceMatrix(double[][]) Covariance
+Create a covariance matrix from a rectangual array whose columns represent
+ covariates.
+ computeDerivatives(double, double[], double[]) AbstractIntegrator
+Compute the derivatives and check the number of evaluations.
+ computeDerivatives(double, double[], double[]) FirstOrderConverter
+Get the current time derivative of the state vector.
+ computeDerivatives(double, double[], double[]) FirstOrderDifferentialEquations
+Get the current time derivative of the state vector.
+ computeDistribution() ValueServer
+Computes the empirical distribution using values from the file
+ in valuesFileURL, using the default number of bins.
+ computeDistribution(int) ValueServer
+Computes the empirical distribution using values from the file
+ in valuesFileURL and binCount bins.
+ computeDividedDifference(double[], double[]) DividedDifferenceInterpolator
+Returns a copy of the divided difference array.
+ computeInterpolatedStateAndDerivatives(double, double) AbstractStepInterpolator
+Compute the state and derivatives at the interpolated time.
+ computeInterpolatedStateAndDerivatives(double, double) DummyStepInterpolator
+Compute the state at the interpolated time.
+ computeInterpolatedStateAndDerivatives(double, double) NordsieckStepInterpolator
+Compute the state and derivatives at the interpolated time.
+ computeJacobians(double, double[], double[], double[][], double[][]) ODEWithJacobians
+Compute the partial derivatives of ODE with respect to state.
+ computeObjectiveGradient(double[]) AbstractScalarDifferentiableOptimizer
+Compute the gradient vector.
+ computeObjectiveValue(double[]) AbstractScalarDifferentiableOptimizer
+Compute the objective function value.
+ computeObjectiveValue(UnivariateRealFunction, double) AbstractUnivariateRealOptimizer
+Compute the objective function value.
+ computeSecondDerivatives(double, double[], double[], double[]) SecondOrderDifferentialEquations
+Get the current time derivative of the state vector.
+ computeStepGrowShrinkFactor(double) MultistepIntegrator
+Compute step grow/shrink factor according to normalized error.
+ conjugate() Complex
+Return the conjugate of this complex number.
+ ConjugateGradientFormula org.apache.commons.math.optimization.general Available choices of update formulas for the β parameter
+ in NonLinearConjugateGradientOptimizer CONSTANT_MODE ValueServer
+Always return mu
+ containsClass(Class<?>) TransformerMap
+Tests if a Class is present in the TransformerMap.
+ containsKey(int) OpenIntToDoubleHashMap
+Check if a value is associated with a key.
+ containsKey(int) OpenIntToFieldHashMap
+Check if a value is associated with a key.
+ containsTransformer(NumberTransformer) TransformerMap
+Tests if a NumberTransformer is present in the TransformerMap.
+ CONTINUE EventHandler
+Continue indicator.
+ CONTINUE EventHandlerWithJacobians
+Continue indicator.
+ ContinuedFraction org.apache.commons.math.util Provides a generic means to evaluate continued fractions. ContinuedFraction() ContinuedFraction
+Default constructor.
+ ContinuousDistribution org.apache.commons.math.distribution Base interface for continuous distributions. ContinuousOutputModel org.apache.commons.math.ode This class stores all information provided by an ODE integrator
+ during the integration process and build a continuous model of the
+ solution from this. ContinuousOutputModel() ContinuousOutputModel
+Simple constructor.
+ contract() ResizableDoubleArray
+Contracts the storage array to the (size of the element set) + 1 - to
+ avoid a zero length array.
+ contractionCriteria ResizableDoubleArray
+The contraction criteria determines when the internal array will be
+ contracted to fit the number of elements contained in the element
+ array + 1.
+ converged(int, RealPointValuePair, RealPointValuePair) RealConvergenceChecker
+Check if the optimization algorithm has converged considering the last points.
+ converged(int, RealPointValuePair, RealPointValuePair) SimpleRealPointChecker
+Check if the optimization algorithm has converged considering the last points.
+ converged(int, RealPointValuePair, RealPointValuePair) SimpleScalarValueChecker
+Check if the optimization algorithm has converged considering the last points.
+ converged(int, VectorialPointValuePair, VectorialPointValuePair) SimpleVectorialPointChecker
+Check if the optimization algorithm has converged considering the last points.
+ converged(int, VectorialPointValuePair, VectorialPointValuePair) SimpleVectorialValueChecker
+Check if the optimization algorithm has converged considering the last points.
+ converged(int, VectorialPointValuePair, VectorialPointValuePair) VectorialConvergenceChecker
+Check if the optimization algorithm has converged considering the last points.
+ ConvergenceException org.apache.commons.math Error thrown when a numerical computation can not be performed because the
+ numerical result failed to converge to a finite value. ConvergenceException() ConvergenceException
+Default constructor.
+ ConvergenceException(String, Object...) ConvergenceException
+Constructs an exception with specified formatted detail message.
+ ConvergenceException(Throwable) ConvergenceException
+Create an exception with a given root cause.
+ ConvergenceException(Throwable, String, Object...) ConvergenceException
+Constructs an exception with specified formatted detail message and root cause.
+ ConvergingAlgorithm org.apache.commons.math Interface for algorithms handling convergence settings. ConvergingAlgorithmImpl org.apache.commons.math Provide a default implementation for several functions useful to generic
+ converging algorithms. ConvergingAlgorithmImpl(int, double) ConvergingAlgorithmImpl
+Construct an algorithm with given iteration count and accuracy.
+ copy() AbstractFieldMatrix
+Returns a (deep) copy of this.
+ copy() AbstractRealMatrix
+Returns a (deep) copy of this.
+ copy() AbstractRealVector
+Returns a (deep) copy of this.
+ copy() Array2DRowFieldMatrix
+Returns a (deep) copy of this.
+ copy() Array2DRowRealMatrix
+Returns a (deep) copy of this.
+ copy() ArrayFieldVector
+Returns a (deep) copy of this.
+ copy() ArrayRealVector
+Returns a (deep) copy of this.
+ copy() BigMatrix
+Deprecated. Returns a (deep) copy of this.
+copy() BigMatrixImpl
+Deprecated. Create a new BigMatrix which is a copy of this.
+copy() BlockFieldMatrix
+Returns a (deep) copy of this.
+ copy() BlockRealMatrix
+Returns a (deep) copy of this.
+ copy() FieldMatrix
+Returns a (deep) copy of this.
+ copy() FieldVector
+Returns a (deep) copy of this.
+ copy() OpenMapRealMatrix
+Returns a (deep) copy of this.
+ copy() OpenMapRealVector
+Returns a (deep) copy of this.
+ copy() RealMatrix
+Returns a (deep) copy of this.
+ copy() RealMatrixImpl
+Deprecated. Returns a (deep) copy of this.
+copy() RealVector
+Returns a (deep) copy of this.
+ copy() SparseFieldMatrix
+Returns a (deep) copy of this.
+ copy() SparseFieldVector
+Returns a (deep) copy of this.
+ copy() StepInterpolatorWithJacobians
+Copy the instance.
+ copy() AbstractStepInterpolator
+Copy the instance.
+ copy() StepInterpolator
+Copy the instance.
+ copy() AbstractStorelessUnivariateStatistic
+Returns a copy of the statistic with the same internal state.
+ copy() AbstractUnivariateStatistic
+Returns a copy of the statistic with the same internal state.
+ copy() DescriptiveStatistics
+Returns a copy of this DescriptiveStatistics instance with the same internal state.
+ copy(DescriptiveStatistics, DescriptiveStatistics) DescriptiveStatistics
+Copies source to dest.
+ copy() FirstMoment
+Returns a copy of the statistic with the same internal state.
+ copy(FirstMoment, FirstMoment) FirstMoment
+Copies source to dest.
+ copy() FourthMoment
+Returns a copy of the statistic with the same internal state.
+ copy(FourthMoment, FourthMoment) FourthMoment
+Copies source to dest.
+ copy() GeometricMean
+Returns a copy of the statistic with the same internal state.
+ copy(GeometricMean, GeometricMean) GeometricMean
+Copies source to dest.
+ copy() Kurtosis
+Returns a copy of the statistic with the same internal state.
+ copy(Kurtosis, Kurtosis) Kurtosis
+Copies source to dest.
+ copy() Mean
+Returns a copy of the statistic with the same internal state.
+ copy(Mean, Mean) Mean
+Copies source to dest.
+ copy() SecondMoment
+Returns a copy of the statistic with the same internal state.
+ copy(SecondMoment, SecondMoment) SecondMoment
+Copies source to dest.
+ copy() SemiVariance
+Returns a copy of the statistic with the same internal state.
+ copy(SemiVariance, SemiVariance) SemiVariance
+Copies source to dest.
+ copy() Skewness
+Returns a copy of the statistic with the same internal state.
+ copy(Skewness, Skewness) Skewness
+Copies source to dest.
+ copy() StandardDeviation
+Returns a copy of the statistic with the same internal state.
+ copy(StandardDeviation, StandardDeviation) StandardDeviation
+Copies source to dest.
+ copy() ThirdMoment
+Returns a copy of the statistic with the same internal state.
+ copy(ThirdMoment, ThirdMoment) ThirdMoment
+Copies source to dest.
+ copy() Variance
+Returns a copy of the statistic with the same internal state.
+ copy(Variance, Variance) Variance
+Copies source to dest.
+ copy() Max
+Returns a copy of the statistic with the same internal state.
+ copy(Max, Max) Max
+Copies source to dest.
+ copy() Min
+Returns a copy of the statistic with the same internal state.
+ copy(Min, Min) Min
+Copies source to dest.
+ copy() Percentile
+Returns a copy of the statistic with the same internal state.
+ copy(Percentile, Percentile) Percentile
+Copies source to dest.
+ copy() StorelessUnivariateStatistic
+Returns a copy of the statistic with the same internal state.
+ copy() Product
+Returns a copy of the statistic with the same internal state.
+ copy(Product, Product) Product
+Copies source to dest.
+ copy() Sum
+Returns a copy of the statistic with the same internal state.
+ copy(Sum, Sum) Sum
+Copies source to dest.
+ copy() SumOfLogs
+Returns a copy of the statistic with the same internal state.
+ copy(SumOfLogs, SumOfLogs) SumOfLogs
+Copies source to dest.
+ copy() SumOfSquares
+Returns a copy of the statistic with the same internal state.
+ copy(SumOfSquares, SumOfSquares) SumOfSquares
+Copies source to dest.
+ copy() SummaryStatistics
+Returns a copy of this SummaryStatistics instance with the same internal state.
+ copy(SummaryStatistics, SummaryStatistics) SummaryStatistics
+Copies source to dest.
+ copy() SynchronizedDescriptiveStatistics
+Returns a copy of this SynchronizedDescriptiveStatistics instance with the
+ same internal state.
+ copy(SynchronizedDescriptiveStatistics, SynchronizedDescriptiveStatistics) SynchronizedDescriptiveStatistics
+Copies source to dest.
+ copy() SynchronizedSummaryStatistics
+Returns a copy of this SynchronizedSummaryStatistics instance with the
+ same internal state.
+ copy(SynchronizedSummaryStatistics, SynchronizedSummaryStatistics) SynchronizedSummaryStatistics
+Copies source to dest.
+ copy() UnivariateStatistic
+Returns a copy of the statistic with the same internal state.
+ copy(ResizableDoubleArray, ResizableDoubleArray) ResizableDoubleArray
+Copies source to dest, copying the underlying data, so dest is
+ a new, independent copy of source.
+ copy() ResizableDoubleArray
+Returns a copy of the ResizableDoubleArray.
+ copySubMatrix(int, int, int, int, T[][]) AbstractFieldMatrix
+Copy a submatrix.
+ copySubMatrix(int[], int[], T[][]) AbstractFieldMatrix
+Copy a submatrix.
+ copySubMatrix(int, int, int, int, double[][]) AbstractRealMatrix
+Copy a submatrix.
+ copySubMatrix(int[], int[], double[][]) AbstractRealMatrix
+Copy a submatrix.
+ copySubMatrix(int, int, int, int, T[][]) FieldMatrix
+Copy a submatrix.
+ copySubMatrix(int[], int[], T[][]) FieldMatrix
+Copy a submatrix.
+ copySubMatrix(int, int, int, int, double[][]) RealMatrix
+Copy a submatrix.
+ copySubMatrix(int[], int[], double[][]) RealMatrix
+Copy a submatrix.
+ CorrelatedRandomVectorGenerator org.apache.commons.math.random A RandomVectorGenerator CorrelatedRandomVectorGenerator(double[], RealMatrix, double, NormalizedRandomGenerator) CorrelatedRandomVectorGenerator
+Simple constructor.
+ CorrelatedRandomVectorGenerator(RealMatrix, double, NormalizedRandomGenerator) CorrelatedRandomVectorGenerator
+Simple constructor.
+ correlation(double[], double[]) PearsonsCorrelation
+Computes the Pearson's product-moment correlation coefficient between the two arrays.
+ correlation(double[], double[]) SpearmansCorrelation
+Computes the Spearman's rank correlation coefficient between the two arrays.
+ COS ComposableFunction
+The Math.cos method wrapped as a ComposableFunction cos() Complex
+Compute the
+
+ cosine
+ of this complex number.
+ COSH ComposableFunction
+The Math.cosh method wrapped as a ComposableFunction cosh() Complex
+Compute the
+
+ hyperbolic cosine of this complex number.
+ cosh(double) MathUtils
+Returns the
+ hyperbolic cosine of x.
+ cost AbstractEstimator
+Deprecated. Cost value (square root of the sum of the residuals).
+cost AbstractLeastSquaresOptimizer
+Cost value (square root of the sum of the residuals).
+ Covariance org.apache.commons.math.stat.correlation Computes covariances for pairs of arrays or columns of a matrix. Covariance() Covariance
+Create a Covariance with no data
+ Covariance(double[][], boolean) Covariance
+Create a Covariance matrix from a rectangular array
+ whose columns represent covariates.
+ Covariance(double[][]) Covariance
+Create a Covariance matrix from a rectangular array
+ whose columns represent covariates.
+ Covariance(RealMatrix, boolean) Covariance
+Create a covariance matrix from a matrix whose columns
+ represent covariates.
+ Covariance(RealMatrix) Covariance
+Create a covariance matrix from a matrix whose columns
+ represent covariates.
+ covariance(double[], double[], boolean) Covariance
+Computes the covariance between the two arrays.
+ covariance(double[], double[]) Covariance
+Computes the covariance between the two arrays, using the bias-corrected
+ formula.
+ covarianceToCorrelation(RealMatrix) PearsonsCorrelation
+Derives a correlation matrix from a covariance matrix.
+ createAdaptor(RandomGenerator) RandomAdaptor
+Factory method to create a Random using the supplied
+ RandomGenerator.
+ createArithmeticException(String, Object...) MathRuntimeException
+Constructs a new ArithmeticException with specified formatted detail message.
+ createArrayIndexOutOfBoundsException(String, Object...) MathRuntimeException
+Constructs a new ArrayIndexOutOfBoundsException with specified formatted detail message.
+ createBigIdentityMatrix(int) MatrixUtils
+Deprecated. since 2.0, replaced by MatrixUtils.createFieldIdentityMatrix(Field, int)
+createBigMatrix(double[][]) MatrixUtils
+Deprecated. since 2.0 replaced by MatrixUtils.createFieldMatrix(FieldElement[][])
+createBigMatrix(BigDecimal[][]) MatrixUtils
+Deprecated. since 2.0 replaced by MatrixUtils.createFieldMatrix(FieldElement[][])
+createBigMatrix(BigDecimal[][], boolean) MatrixUtils
+Deprecated. since 2.0 replaced by MatrixUtils.createFieldMatrix(FieldElement[][])
+createBigMatrix(String[][]) MatrixUtils
+Deprecated. since 2.0 replaced by MatrixUtils.createFieldMatrix(FieldElement[][])
+createBlocksLayout(Field<T>, int, int) BlockFieldMatrix
+Create a data array in blocks layout.
+ createBlocksLayout(int, int) BlockRealMatrix
+Create a data array in blocks layout.
+ createChebyshevPolynomial(int) PolynomialsUtils
+Create a Chebyshev polynomial of the first kind.
+ createColumnBigMatrix(double[]) MatrixUtils
+Deprecated. since 2.0 replaced by MatrixUtils.createColumnFieldMatrix(FieldElement[])
+createColumnBigMatrix(BigDecimal[]) MatrixUtils
+Deprecated. since 2.0 replaced by MatrixUtils.createColumnFieldMatrix(FieldElement[])
+createColumnBigMatrix(String[]) MatrixUtils
+Deprecated. since 2.0 replaced by MatrixUtils.createColumnFieldMatrix(FieldElement[])
+createColumnFieldMatrix(T[]) MatrixUtils
+Creates a column FieldMatrix createColumnRealMatrix(double[]) MatrixUtils
+Creates a column RealMatrix createComplex(double, double) Complex
+Create a complex number given the real and imaginary parts.
+ createConcurrentModificationException(String, Object...) MathRuntimeException
+Constructs a new ConcurrentModificationException with specified formatted detail message.
+ createContributingStatistics() AggregateSummaryStatistics
+Creates and returns a SummaryStatistics whose data will be
+ aggregated with those of this AggregateSummaryStatistics.
+ createEOFException(String, Object...) MathRuntimeException
+Constructs a new EOFException with specified formatted detail message.
+ createFieldDiagonalMatrix(T[]) MatrixUtils
+Returns a diagonal matrix with specified elements.
+ createFieldIdentityMatrix(Field<T>, int) MatrixUtils
+Returns dimension x dimension identity matrix.
+ createFieldMatrix(Field<T>, int, int) MatrixUtils
+Returns a FieldMatrix createFieldMatrix(T[][]) MatrixUtils
+Returns a FieldMatrix createFieldVector(T[]) MatrixUtils
+Creates a FieldVector createHermitePolynomial(int) PolynomialsUtils
+Create a Hermite polynomial.
+ createIllegalArgumentException(String, Object...) MathRuntimeException
+Constructs a new IllegalArgumentException with specified formatted detail message.
+ createIllegalArgumentException(Throwable) MathRuntimeException
+Constructs a new IllegalArgumentException with specified nested
+ Throwable root cause.
+ createIllegalStateException(String, Object...) MathRuntimeException
+Constructs a new IllegalStateException with specified formatted detail message.
+ createInternalError(Throwable) MathRuntimeException
+Create an RuntimeException createIOException(Throwable) MathRuntimeException
+Constructs a new IOException with specified nested
+ Throwable root cause.
+ createLaguerrePolynomial(int) PolynomialsUtils
+Create a Laguerre polynomial.
+ createLegendrePolynomial(int) PolynomialsUtils
+Create a Legendre polynomial.
+ createMatrix(int, int) AbstractFieldMatrix
+Create a new FieldMatrix of the same type as the instance with the supplied
+ row and column dimensions.
+createMatrix(int, int) AbstractRealMatrix
+Create a new RealMatrix of the same type as the instance with the supplied
+ row and column dimensions.
+ createMatrix(int, int) Array2DRowFieldMatrix
+Create a new FieldMatrix of the same type as the instance with the supplied
+ row and column dimensions.
+createMatrix(int, int) Array2DRowRealMatrix
+Create a new RealMatrix of the same type as the instance with the supplied
+ row and column dimensions.
+ createMatrix(int, int) BlockFieldMatrix
+Create a new FieldMatrix of the same type as the instance with the supplied
+ row and column dimensions.
+createMatrix(int, int) BlockRealMatrix
+Create a new RealMatrix of the same type as the instance with the supplied
+ row and column dimensions.
+ createMatrix(int, int) FieldMatrix
+Create a new FieldMatrix of the same type as the instance with the supplied
+ row and column dimensions.
+createMatrix(int, int) OpenMapRealMatrix
+Create a new RealMatrix of the same type as the instance with the supplied
+ row and column dimensions.
+ createMatrix(int, int) RealMatrix
+Create a new RealMatrix of the same type as the instance with the supplied
+ row and column dimensions.
+ createMatrix(int, int) RealMatrixImpl
+Deprecated. Create a new RealMatrix of the same type as the instance with the supplied
+ row and column dimensions.
+createMatrix(int, int) SparseFieldMatrix
+Create a new FieldMatrix of the same type as the instance with the supplied
+ row and column dimensions.
+createNoSuchElementException(String, Object...) MathRuntimeException
+Constructs a new NoSuchElementException with specified formatted detail message.
+ createNullPointerException(String, Object...) MathRuntimeException
+Constructs a new NullPointerException with specified formatted detail message.
+ createParseException(int, String, Object...) MathRuntimeException
+Constructs a new ParseException with specified
+ formatted detail message.
+ createRealDiagonalMatrix(double[]) MatrixUtils
+Returns a diagonal matrix with specified elements.
+ createRealIdentityMatrix(int) MatrixUtils
+Returns dimension x dimension identity matrix.
+ createRealMatrix(int, int) MatrixUtils
+Returns a RealMatrix createRealMatrix(double[][]) MatrixUtils
+Returns a RealMatrix createRealVector(double[]) MatrixUtils
+Creates a RealVector createRowBigMatrix(double[]) MatrixUtils
+Deprecated. since 2.0 replaced by MatrixUtils.createRowFieldMatrix(FieldElement[])
+createRowBigMatrix(BigDecimal[]) MatrixUtils
+Deprecated. since 2.0 replaced by MatrixUtils.createRowFieldMatrix(FieldElement[])
+createRowBigMatrix(String[]) MatrixUtils
+Deprecated. since 2.0 replaced by MatrixUtils.createRowFieldMatrix(FieldElement[])
+createRowFieldMatrix(T[]) MatrixUtils
+Creates a row FieldMatrix createRowRealMatrix(double[]) MatrixUtils
+Creates a row RealMatrix crossover(Chromosome, Chromosome) CrossoverPolicy
+Perform a crossover operation on the given chromosomes.
+ crossover(Chromosome, Chromosome) OnePointCrossover
+Performs one point crossover.
+ CrossoverPolicy org.apache.commons.math.genetics Policy used to create a pair of new chromosomes by performing a crossover
+ operation on a source pair of chromosomes. crossProduct(Vector3D, Vector3D) Vector3D
+Compute the cross-product of two vectors.
+ cumulativeProbability(double, double) AbstractDistribution
+For a random variable X whose values are distributed according
+ to this distribution, this method returns P(x0 ≤ X ≤ x1).
+ cumulativeProbability(double) AbstractIntegerDistribution
+For a random variable X whose values are distributed according
+ to this distribution, this method returns P(X ≤ x).
+ cumulativeProbability(double, double) AbstractIntegerDistribution
+For a random variable X whose values are distributed according
+ to this distribution, this method returns P(x0 ≤ X ≤ x1).
+ cumulativeProbability(int) AbstractIntegerDistribution
+For a random variable X whose values are distributed according
+ to this distribution, this method returns P(X ≤ x).
+ cumulativeProbability(int, int) AbstractIntegerDistribution
+For a random variable X whose values are distributed according
+ to this distribution, this method returns P(x0 ≤ X ≤ x1).
+ cumulativeProbability(double) BetaDistributionImpl
+For a random variable X whose values are distributed according
+ to this distribution, this method returns P(X ≤ x).
+ cumulativeProbability(double, double) BetaDistributionImpl
+For a random variable X whose values are distributed according
+ to this distribution, this method returns P(x0 ≤ X ≤ x1).
+ cumulativeProbability(int) BinomialDistributionImpl
+For this distribution, X, this method returns P(X ≤ x).
+ cumulativeProbability(double) CauchyDistributionImpl
+For this distribution, X, this method returns P(X < x).
+ cumulativeProbability(double) ChiSquaredDistributionImpl
+For this distribution, X, this method returns P(X < x).
+ cumulativeProbability(double) Distribution
+For a random variable X whose values are distributed according
+ to this distribution, this method returns P(X ≤ x).
+ cumulativeProbability(double, double) Distribution
+For a random variable X whose values are distributed according
+ to this distribution, this method returns P(x0 ≤ X ≤ x1).
+ cumulativeProbability(double) ExponentialDistributionImpl
+For this distribution, X, this method returns P(X < x).
+ cumulativeProbability(double) FDistributionImpl
+For this distribution, X, this method returns P(X < x).
+ cumulativeProbability(double) GammaDistributionImpl
+For this distribution, X, this method returns P(X < x).
+ cumulativeProbability(int) HypergeometricDistributionImpl
+For this distribution, X, this method returns P(X ≤ x).
+ cumulativeProbability(int) IntegerDistribution
+For a random variable X whose values are distributed according
+ to this distribution, this method returns P(X ≤ x).
+ cumulativeProbability(int, int) IntegerDistribution
+For this distribution, X, this method returns P(x0 ≤ X ≤ x1).
+ cumulativeProbability(double) NormalDistributionImpl
+For this distribution, X, this method returns P(X < x).
+ cumulativeProbability(int) PascalDistributionImpl
+For this distribution, X, this method returns P(X ≤ x).
+ cumulativeProbability(int) PoissonDistributionImpl
+The probability distribution function P(X <= x) for a Poisson
+ distribution.
+ cumulativeProbability(double) TDistributionImpl
+For this distribution, X, this method returns P(X < x).
+ cumulativeProbability(double) WeibullDistributionImpl
+For this distribution, X, this method returns P(X < x).
+ cumulativeProbability(int) ZipfDistributionImpl
+The probability distribution function P(X <= x) for a Zipf distribution.
+ currentState AbstractStepInterpolator
+current state
+ currentTime AbstractStepInterpolator
+current time
+ CurveFitter org.apache.commons.math.optimization.fitting Fitter for parametric univariate real functions y = f(x). CurveFitter(DifferentiableMultivariateVectorialOptimizer) CurveFitter
+Simple constructor.
+
+
+D
+
+data Array2DRowFieldMatrix
+Entries of the matrix
+ data Array2DRowRealMatrix
+Entries of the matrix
+ data ArrayFieldVector
+Entries of the vector.
+ data ArrayRealVector
+Entries of the vector.
+ data BigMatrixImpl
+Deprecated. Entries of the matrix
+data RealMatrixImpl
+Deprecated. Entries of the matrix
+decode(List<T>) PermutationChromosome
+Permutes the sequence of objects of type T according to the
+ permutation this chromosome represents.
+ decode(List<T>) RandomKey
+Permutes the sequence of objects of type T according to the
+ permutation this chromosome represents.
+ DecompositionSolver org.apache.commons.math.linear Interface handling decomposition algorithms that can solve A × X = B. DEFAULT_ABSOLUTE_ACCURACY BrentSolver
+Default absolute accuracy
+ DEFAULT_ABSOLUTE_POSITIVITY_THRESHOLD CholeskyDecompositionImpl
+Default threshold below which diagonal elements are considered null
+ and matrix not positive definite.
+ DEFAULT_ACCURACY LoessInterpolator
+Default value for accuracy.
+ DEFAULT_BANDWIDTH LoessInterpolator
+Default value of the bandwidth parameter.
+ DEFAULT_BRIGHTNESS_EXPONENT MicrosphereInterpolator
+Default exponent used the weights calculation.
+ DEFAULT_EPSILON PoissonDistributionImpl
+Default convergence criterion.
+ DEFAULT_INVERSE_ABSOLUTE_ACCURACY BetaDistributionImpl
+Default inverse cumulative probability accurac
+ DEFAULT_INVERSE_ABSOLUTE_ACCURACY CauchyDistributionImpl
+Default inverse cumulative probability accuracy
+ DEFAULT_INVERSE_ABSOLUTE_ACCURACY ChiSquaredDistributionImpl
+Default inverse cumulative probability accuracy
+ DEFAULT_INVERSE_ABSOLUTE_ACCURACY ExponentialDistributionImpl
+Default inverse cumulative probability accuracy
+ DEFAULT_INVERSE_ABSOLUTE_ACCURACY FDistributionImpl
+Default inverse cumulative probability accuracy
+ DEFAULT_INVERSE_ABSOLUTE_ACCURACY GammaDistributionImpl
+Default inverse cumulative probability accuracy
+ DEFAULT_INVERSE_ABSOLUTE_ACCURACY NormalDistributionImpl
+Default inverse cumulative probability accuracy
+ DEFAULT_INVERSE_ABSOLUTE_ACCURACY TDistributionImpl
+Default inverse cumulative probability accuracy
+ DEFAULT_INVERSE_ABSOLUTE_ACCURACY WeibullDistributionImpl
+Default inverse cumulative probability accuracy
+ DEFAULT_MAX_COST_EVALUATIONS AbstractEstimator
+Deprecated. Default maximal number of cost evaluations allowed.
+DEFAULT_MAX_ITERATIONS PoissonDistributionImpl
+Default maximum number of iterations for cumulative probability calculations.
+ DEFAULT_MAX_ITERATIONS AbstractLeastSquaresOptimizer
+Default maximal number of iterations allowed.
+ DEFAULT_MAX_ITERATIONS AbstractScalarDifferentiableOptimizer
+Default maximal number of iterations allowed.
+ DEFAULT_MAX_ITERATIONS AbstractLinearOptimizer
+Default maximal number of iterations allowed.
+ DEFAULT_MAXIMUM_ITERATIONS BrentSolver
+Default maximum number of iterations
+ DEFAULT_MICROSPHERE_ELEMENTS MicrosphereInterpolator
+Default number of surface elements that composes the microsphere.
+ DEFAULT_NAN_STRATEGY NaturalRanking
+default NaN strategy
+ DEFAULT_RELATIVE_SYMMETRY_THRESHOLD CholeskyDecompositionImpl
+Default threshold above which off-diagonal elements are considered too different
+ and matrix not symmetric.
+ DEFAULT_ROBUSTNESS_ITERS LoessInterpolator
+Default value of the number of robustness iterations.
+ DEFAULT_TIES_STRATEGY NaturalRanking
+default ties strategy
+ DEFAULT_ZERO_TOLERANCE OpenMapRealVector
+Default Tolerance for having a value considered zero.
+ defaultAbsoluteAccuracy ConvergingAlgorithmImpl
+Default maximum absolute error.
+ DefaultFieldMatrixChangingVisitor T extends FieldElement <T >> - Class in org.apache.commons.math.linear Default implementation of the FieldMatrixChangingVisitor DefaultFieldMatrixChangingVisitor(T) DefaultFieldMatrixChangingVisitor
+Build a new instance.
+ DefaultFieldMatrixPreservingVisitor T extends FieldElement <T >> - Class in org.apache.commons.math.linear Default implementation of the FieldMatrixPreservingVisitor DefaultFieldMatrixPreservingVisitor(T) DefaultFieldMatrixPreservingVisitor
+Build a new instance.
+ defaultFunctionValueAccuracy UnivariateRealSolverImpl
+Default maximum error of function.
+ defaultMaximalIterationCount ConvergingAlgorithmImpl
+Default maximum number of iterations.
+ defaultMinimalIterationCount UnivariateRealIntegratorImpl
+default minimum number of iterations
+ DefaultRealMatrixChangingVisitor org.apache.commons.math.linear Default implementation of the RealMatrixChangingVisitor DefaultRealMatrixChangingVisitor() DefaultRealMatrixChangingVisitor
+
+ DefaultRealMatrixPreservingVisitor org.apache.commons.math.linear Default implementation of the RealMatrixPreservingVisitor DefaultRealMatrixPreservingVisitor() DefaultRealMatrixPreservingVisitor
+
+ defaultRelativeAccuracy ConvergingAlgorithmImpl
+Default maximum relative error.
+ DefaultTransformer org.apache.commons.math.util A Default NumberTransformer for java.lang.Numbers and Numeric Strings. DefaultTransformer() DefaultTransformer
+
+ degree() PolynomialFunction
+Returns the degree of the polynomial
+ degree() PolynomialFunctionLagrangeForm
+Returns the degree of the polynomial.
+ degree() PolynomialFunctionNewtonForm
+Returns the degree of the polynomial.
+ denominatorFormat AbstractFormat
+The format used for the denominator.
+ density(double) AbstractContinuousDistribution
+Return the probability density for a particular point.
+ density(Double) BetaDistribution
+Return the probability density for a particular point.
+ density(Double) BetaDistributionImpl
+Deprecated. density(double) BetaDistributionImpl
+Return the probability density for a particular point.
+ density(double) CauchyDistributionImpl
+Returns the probability density for a particular point.
+ density(Double) ChiSquaredDistribution
+Return the probability density for a particular point.
+ density(Double) ChiSquaredDistributionImpl
+Deprecated. density(double) ChiSquaredDistributionImpl
+Return the probability density for a particular point.
+ density(Double) ExponentialDistribution
+Return the probability density for a particular point.
+ density(Double) ExponentialDistributionImpl
+Deprecated. - use density(double)
+density(double) ExponentialDistributionImpl
+Return the probability density for a particular point.
+ density(double) FDistributionImpl
+Returns the probability density for a particular point.
+ density(Double) GammaDistribution
+Return the probability density for a particular point.
+ density(double) GammaDistributionImpl
+Returns the probability density for a particular point.
+ density(Double) GammaDistributionImpl
+Deprecated. density(P) HasDensity
+Deprecated. Compute the probability density function.
+density(Double) NormalDistribution
+Return the probability density for a particular point.
+ density(Double) NormalDistributionImpl
+Deprecated. density(double) NormalDistributionImpl
+Returns the probability density for a particular point.
+ density(double) TDistributionImpl
+Returns the probability density for a particular point.
+ density(double) WeibullDistributionImpl
+Returns the probability density for a particular point.
+ derivative() DifferentiableUnivariateMatrixFunction
+Returns the derivative of the function
+ derivative() DifferentiableUnivariateRealFunction
+Returns the derivative of the function
+ derivative() DifferentiableUnivariateVectorialFunction
+Returns the derivative of the function
+ derivative() PolynomialFunction
+Returns the derivative as a UnivariateRealFunction
+ derivative() PolynomialSplineFunction
+Returns the derivative of the polynomial spline function as a UnivariateRealFunction
+ derivative() HarmonicFunction
+Returns the derivative of the function
+ DerivativeException org.apache.commons.math.ode This exception is made available to users to report
+ the error conditions that are triggered while computing
+ the differential equations. DerivativeException(String, Object...) DerivativeException
+Simple constructor.
+ DerivativeException(Throwable) DerivativeException
+Build an instance from an underlying cause.
+ DescriptiveStatistics org.apache.commons.math.stat.descriptive Maintains a dataset of values of a single variable and computes descriptive
+ statistics based on stored data. DescriptiveStatistics() DescriptiveStatistics
+Construct a DescriptiveStatistics instance with an infinite window
+ DescriptiveStatistics(int) DescriptiveStatistics
+Construct a DescriptiveStatistics instance with the specified window
+ DescriptiveStatistics(DescriptiveStatistics) DescriptiveStatistics
+Copy constructor.
+ deserializeRealMatrix(Object, String, ObjectInputStream) MatrixUtils
+Deserialize a RealMatrix deserializeRealVector(Object, String, ObjectInputStream) MatrixUtils
+Deserialize a RealVector dev FirstMoment
+Deviation of most recently added value from previous first moment.
+ df(double, double, double, double) TTestImpl
+Computes approximate degrees of freedom for 2-sample t-test.
+ DifferentiableMultivariateRealFunction org.apache.commons.math.analysis Extension of MultivariateRealFunction DifferentiableMultivariateRealOptimizer org.apache.commons.math.optimization This interface represents an optimization algorithm for scalar differentiable objective functions DifferentiableMultivariateVectorialFunction org.apache.commons.math.analysis Extension of MultivariateVectorialFunction DifferentiableMultivariateVectorialOptimizer org.apache.commons.math.optimization This interface represents an optimization algorithm for vectorial differentiable objective functions DifferentiableUnivariateMatrixFunction org.apache.commons.math.analysis Extension of UnivariateMatrixFunction DifferentiableUnivariateRealFunction org.apache.commons.math.analysis Extension of UnivariateRealFunction DifferentiableUnivariateVectorialFunction org.apache.commons.math.analysis Extension of UnivariateVectorialFunction differentiate(double[]) PolynomialFunction
+Returns the coefficients of the derivative of the polynomial with the given coefficients.
+ digamma(double) Gamma
+Computes the digamma function of x.
+ DIGEST_MODE ValueServer
+Use empirical distribution.
+ DimensionMismatchException org.apache.commons.math Error thrown when two dimensions differ. DimensionMismatchException(int, int) DimensionMismatchException
+Construct an exception from the mismatched dimensions
+ DirectSearchOptimizer org.apache.commons.math.optimization.direct This class implements simplex-based direct search optimization
+ algorithms. DirectSearchOptimizer() DirectSearchOptimizer
+Simple constructor.
+ discardFrontElements(int) ResizableDoubleArray
+Discards the i initial elements of the array.
+discardMostRecentElements(int) ResizableDoubleArray
+Discards the i last elements of the array.
+DiscreteDistribution org.apache.commons.math.distribution Base interface for discrete distributions. distance(Rotation, Rotation) Rotation
+Compute the distance between two rotations.
+ distance(Vector3D, Vector3D) Vector3D
+Compute the distance between two vectors according to the L2 norm.
+ distance(double[], double[]) MathUtils
+Calculates the L2 (Euclidean) distance between two points.
+ distance(int[], int[]) MathUtils
+Calculates the L2 (Euclidean) distance between two points.
+ distance1(Vector3D, Vector3D) Vector3D
+Compute the distance between two vectors according to the L1 norm.
+ distance1(double[], double[]) MathUtils
+Calculates the L1 (sum of abs) distance between two points.
+ distance1(int[], int[]) MathUtils
+Calculates the L1 (sum of abs) distance between two points.
+ distanceFrom(T) Clusterable
+Returns the distance from the given point.
+ distanceFrom(EuclideanIntegerPoint) EuclideanIntegerPoint
+Returns the distance from the given point.
+ distanceInf(Vector3D, Vector3D) Vector3D
+Compute the distance between two vectors according to the L∞ norm.
+ distanceInf(double[], double[]) MathUtils
+Calculates the L∞ (max of abs) distance between two points.
+ distanceInf(int[], int[]) MathUtils
+Calculates the L∞ (max of abs) distance between two points.
+ distanceSq(Vector3D, Vector3D) Vector3D
+Compute the square of the distance between two vectors.
+ Distribution org.apache.commons.math.distribution Base interface for probability distributions. DIVIDE BinaryFunction
+The / operator method wrapped as a BinaryFunction divide(UnivariateRealFunction) ComposableFunction
+Return a function dividing the instance by another function.
+ divide(Complex) Complex
+Return the quotient of this complex number and the given complex number.
+ divide(T) FieldElement
+Compute this ÷ a.
+ divide(BigInteger) BigFraction
+
+ Divide the value of this fraction by the passed BigInteger,
+ ie "this * 1 / bg", returning the result in reduced form.
+ divide(int) BigFraction
+
+ Divide the value of this fraction by the passed int , ie
+ "this * 1 / i", returning the result in reduced form.
+ divide(long) BigFraction
+
+ Divide the value of this fraction by the passed long , ie
+ "this * 1 / l", returning the result in reduced form.
+ divide(BigFraction) BigFraction
+
+ Divide the value of this fraction by another, returning the result in
+ reduced form.
+ divide(Fraction) Fraction
+Divide the value of this fraction by another.
+ divide(int) Fraction
+Divide the fraction by an integer.
+ divide(BigReal) BigReal
+Compute this ÷ a.
+ DividedDifferenceInterpolator org.apache.commons.math.analysis.interpolation Implements the
+ Divided Difference Algorithm for interpolation of real univariate
+ functions. DividedDifferenceInterpolator() DividedDifferenceInterpolator
+
+ doCopy() AbstractStepInterpolator
+Really copy the finalized instance.
+ doCopy() DummyStepInterpolator
+Really copy the finalized instance.
+ doCopy() NordsieckStepInterpolator
+Really copy the finalized instance.
+ doFinalize() AbstractStepInterpolator
+Really finalize the step.
+ doIteration(SimplexTableau) SimplexSolver
+Runs one iteration of the Simplex method on the given model.
+ doOptimize() AbstractLeastSquaresOptimizer
+Perform the bulk of optimization algorithm.
+ doOptimize() AbstractScalarDifferentiableOptimizer
+Perform the bulk of optimization algorithm.
+ doOptimize() GaussNewtonOptimizer
+Perform the bulk of optimization algorithm.
+ doOptimize() LevenbergMarquardtOptimizer
+Perform the bulk of optimization algorithm.
+ doOptimize() NonLinearConjugateGradientOptimizer
+Perform the bulk of optimization algorithm.
+ doOptimize() AbstractLinearOptimizer
+Perform the bulk of optimization algorithm.
+ doOptimize() SimplexSolver
+Perform the bulk of optimization algorithm.
+ DormandPrince54Integrator org.apache.commons.math.ode.nonstiff This class implements the 5(4) Dormand-Prince integrator for Ordinary
+ Differential Equations. DormandPrince54Integrator(double, double, double, double) DormandPrince54Integrator
+Simple constructor.
+ DormandPrince54Integrator(double, double, double[], double[]) DormandPrince54Integrator
+Simple constructor.
+ DormandPrince853Integrator org.apache.commons.math.ode.nonstiff This class implements the 8(5,3) Dormand-Prince integrator for Ordinary
+ Differential Equations. DormandPrince853Integrator(double, double, double, double) DormandPrince853Integrator
+Simple constructor.
+ DormandPrince853Integrator(double, double, double[], double[]) DormandPrince853Integrator
+Simple constructor.
+ dotProduct(Vector3D, Vector3D) Vector3D
+Compute the dot-product of two vectors.
+ dotProduct(double[]) AbstractRealVector
+Compute the dot product.
+ dotProduct(RealVector) AbstractRealVector
+Compute the dot product.
+ dotProduct(FieldVector<T>) ArrayFieldVector
+Compute the dot product.
+ dotProduct(T[]) ArrayFieldVector
+Compute the dot product.
+ dotProduct(ArrayFieldVector<T>) ArrayFieldVector
+Compute the dot product.
+ dotProduct(RealVector) ArrayRealVector
+Compute the dot product.
+ dotProduct(double[]) ArrayRealVector
+Compute the dot product.
+ dotProduct(ArrayRealVector) ArrayRealVector
+Compute the dot product.
+ dotProduct(FieldVector<T>) FieldVector
+Compute the dot product.
+ dotProduct(T[]) FieldVector
+Compute the dot product.
+ dotProduct(OpenMapRealVector) OpenMapRealVector
+Optimized method to compute the dot product with an OpenMapRealVector.
+ dotProduct(RealVector) OpenMapRealVector
+Compute the dot product.
+ dotProduct(RealVector) RealVector
+Compute the dot product.
+ dotProduct(double[]) RealVector
+Compute the dot product.
+ dotProduct(FieldVector<T>) SparseFieldVector
+Compute the dot product.
+ dotProduct(T[]) SparseFieldVector
+Compute the dot product.
+ DoubleArray org.apache.commons.math.util Provides a standard interface for double arrays. doubleValue() BigFraction
+
+ Gets the fraction as a double .
+ doubleValue() Fraction
+Gets the fraction as a double .
+ doubleValue() BigReal
+Get the double value corresponding to the instance.
+ DOWNSIDE_VARIANCE SemiVariance
+The DOWNSIDE Direction is used to specify that the observations below
+ the cutoff point will be used to calculate SemiVariance
+ DummyStepHandler org.apache.commons.math.ode.sampling This class is a step handler that does nothing. DummyStepInterpolator org.apache.commons.math.ode.sampling This class is a step interpolator that does nothing. DummyStepInterpolator() DummyStepInterpolator
+Simple constructor.
+ DummyStepInterpolator(double[], double[], boolean) DummyStepInterpolator
+Simple constructor.
+ DummyStepInterpolator(DummyStepInterpolator) DummyStepInterpolator
+Copy constructor.
+ DuplicateSampleAbscissaException org.apache.commons.math Exception thrown when a sample contains several entries at the same abscissa. DuplicateSampleAbscissaException(double, int, int) DuplicateSampleAbscissaException
+Construct an exception indicating the duplicate abscissa.
+
+
+E
+
+ebeDivide(double[]) AbstractRealVector
+Element-by-element division.
+ ebeDivide(FieldVector<T>) ArrayFieldVector
+Element-by-element division.
+ ebeDivide(T[]) ArrayFieldVector
+Element-by-element division.
+ ebeDivide(ArrayFieldVector<T>) ArrayFieldVector
+Element-by-element division.
+ ebeDivide(RealVector) ArrayRealVector
+Element-by-element division.
+ ebeDivide(double[]) ArrayRealVector
+Element-by-element division.
+ ebeDivide(ArrayRealVector) ArrayRealVector
+Element-by-element division.
+ ebeDivide(FieldVector<T>) FieldVector
+Element-by-element division.
+ ebeDivide(T[]) FieldVector
+Element-by-element division.
+ ebeDivide(RealVector) OpenMapRealVector
+Element-by-element division.
+ ebeDivide(double[]) OpenMapRealVector
+Element-by-element division.
+ ebeDivide(RealVector) RealVector
+Element-by-element division.
+ ebeDivide(double[]) RealVector
+Element-by-element division.
+ ebeDivide(FieldVector<T>) SparseFieldVector
+Element-by-element division.
+ ebeDivide(T[]) SparseFieldVector
+Element-by-element division.
+ ebeMultiply(double[]) AbstractRealVector
+Element-by-element multiplication.
+ ebeMultiply(FieldVector<T>) ArrayFieldVector
+Element-by-element multiplication.
+ ebeMultiply(T[]) ArrayFieldVector
+Element-by-element multiplication.
+ ebeMultiply(ArrayFieldVector<T>) ArrayFieldVector
+Element-by-element multiplication.
+ ebeMultiply(RealVector) ArrayRealVector
+Element-by-element multiplication.
+ ebeMultiply(double[]) ArrayRealVector
+Element-by-element multiplication.
+ ebeMultiply(ArrayRealVector) ArrayRealVector
+Element-by-element multiplication.
+ ebeMultiply(FieldVector<T>) FieldVector
+Element-by-element multiplication.
+ ebeMultiply(T[]) FieldVector
+Element-by-element multiplication.
+ ebeMultiply(RealVector) OpenMapRealVector
+Element-by-element multiplication.
+ ebeMultiply(double[]) OpenMapRealVector
+Element-by-element multiplication.
+ ebeMultiply(RealVector) RealVector
+Element-by-element multiplication.
+ ebeMultiply(double[]) RealVector
+Element-by-element multiplication.
+ ebeMultiply(FieldVector<T>) SparseFieldVector
+Element-by-element multiplication.
+ ebeMultiply(T[]) SparseFieldVector
+Element-by-element multiplication.
+ eDA DescriptiveStatistics
+Stored data values
+ EigenDecomposition org.apache.commons.math.linear An interface to classes that implement an algorithm to calculate the
+ eigen decomposition of a real matrix. EigenDecompositionImpl org.apache.commons.math.linear Calculates the eigen decomposition of a real symmetric
+ matrix. EigenDecompositionImpl(RealMatrix, double) EigenDecompositionImpl
+Calculates the eigen decomposition of the given symmetric matrix.
+ EigenDecompositionImpl(double[], double[], double) EigenDecompositionImpl
+Calculates the eigen decomposition of the symmetric tridiagonal
+ matrix.
+ ElitisticListPopulation org.apache.commons.math.genetics Population of chromosomes which uses elitism (certain percentace of the best
+ chromosomes is directly copied to the next generation). ElitisticListPopulation(List<Chromosome>, int, double) ElitisticListPopulation
+Creates a new ElitisticListPopulation instance.
+ ElitisticListPopulation(int, double) ElitisticListPopulation
+Creates a new ListPopulation instance and initializes its inner
+ chromosome list.
+ EmbeddedRungeKuttaIntegrator org.apache.commons.math.ode.nonstiff This class implements the common part of all embedded Runge-Kutta
+ integrators for Ordinary Differential Equations. EmbeddedRungeKuttaIntegrator(String, boolean, double[], double[][], double[], RungeKuttaStepInterpolator, double, double, double, double) EmbeddedRungeKuttaIntegrator
+Build a Runge-Kutta integrator with the given Butcher array.
+ EmbeddedRungeKuttaIntegrator(String, boolean, double[], double[][], double[], RungeKuttaStepInterpolator, double, double, double[], double[]) EmbeddedRungeKuttaIntegrator
+Build a Runge-Kutta integrator with the given Butcher array.
+ EmpiricalDistribution org.apache.commons.math.random Represents an
+ empirical probability distribution -- a probability distribution derived
+ from observed data without making any assumptions about the functional form
+ of the population distribution that the data come from. EmpiricalDistributionImpl org.apache.commons.math.random Implements EmpiricalDistribution interface. EmpiricalDistributionImpl() EmpiricalDistributionImpl
+Creates a new EmpiricalDistribution with the default bin count.
+ EmpiricalDistributionImpl(int) EmpiricalDistributionImpl
+Creates a new EmpiricalDistribution with the specified bin count.
+ end() DefaultFieldMatrixChangingVisitor
+End visiting a matrix.
+ end() DefaultFieldMatrixPreservingVisitor
+End visiting a matrix.
+ end() DefaultRealMatrixChangingVisitor
+End visiting a matrix.
+ end() DefaultRealMatrixPreservingVisitor
+End visiting a matrix.
+ end() FieldMatrixChangingVisitor
+End visiting a matrix.
+ end() FieldMatrixPreservingVisitor
+End visiting a matrix.
+ end() RealMatrixChangingVisitor
+End visiting a matrix.
+ end() RealMatrixPreservingVisitor
+End visiting a matrix.
+ epsilon SimplexSolver
+Amount of error to accept in floating point comparisons.
+ EPSILON MathUtils
+Smallest positive number such that 1 - EPSILON is not numerically equal to 1.
+ equals(Object) PolynomialFunction
+
+ equals(Object) Complex
+Test for the equality of two Complex objects.
+ equals(Object) BigFraction
+
+ Test for the equality of two fractions.
+ equals(Object) Fraction
+Test for the equality of two fractions.
+ equals(Object) Vector3D
+Test for the equality of two 3D vectors.
+ equals(Object) AbstractFieldMatrix
+Returns true iff object is a
+ FieldMatrix instance with the same dimensions as this
+ and all corresponding matrix entries are equal.
+ equals(Object) AbstractRealMatrix
+Returns true iff object is a
+ RealMatrix instance with the same dimensions as this
+ and all corresponding matrix entries are equal.
+ equals(Object) ArrayFieldVector
+Test for the equality of two real vectors.
+ equals(Object) ArrayRealVector
+Test for the equality of two real vectors.
+ equals(Object) BigMatrixImpl
+Deprecated. Returns true iff object is a
+ BigMatrixImpl instance with the same dimensions as this
+ and all corresponding matrix entries are equal.
+equals(Object) OpenMapRealVector
+ Implementation Note: This performs an exact comparison, and as a result
+ it is possible for a.subtract(b} to be the zero vector, while
+ a.equals(b) == false.
+ equals(Object) SparseFieldVector
+
+ equals(Object) LinearConstraint
+
+ equals(Object) LinearObjectiveFunction
+
+ equals(Object) EuclideanIntegerPoint
+
+ equals(Object) AbstractStorelessUnivariateStatistic
+Returns true iff object is an
+ AbstractStorelessUnivariateStatistic returning the same
+ values as this for getResult() and getN()
+ equals(Object) VectorialCovariance
+
+ equals(Object) VectorialMean
+
+ equals(Object) MultivariateSummaryStatistics
+Returns true iff object is a SummaryStatistics
+ instance and all statistics have the same values as this.
+ equals(Object) StatisticalSummaryValues
+Returns true iff object is a
+ StatisticalSummaryValues instance and all statistics have
+ the same values as this.
+ equals(Object) SummaryStatistics
+Returns true iff object is a
+ SummaryStatistics instance and all statistics have the
+ same values as this.
+ equals(Object) SynchronizedMultivariateSummaryStatistics
+Returns true iff object is a SummaryStatistics
+ instance and all statistics have the same values as this.
+ equals(Object) SynchronizedSummaryStatistics
+Returns true iff object is a
+ SummaryStatistics instance and all statistics have the
+ same values as this.
+ equals(Object) Frequency
+
+ equals(Object) BigReal
+
+ equals(Object) DefaultTransformer
+
+ equals(double, double) MathUtils
+Returns true iff both arguments are NaN or neither is NaN and they are
+ equal
+ equals(double, double, double) MathUtils
+Returns true iff both arguments are equal or within the range of allowed
+ error (inclusive).
+ equals(double, double, int) MathUtils
+Returns true iff both arguments are equal or within the range of allowed
+ error (inclusive).
+ equals(double[], double[]) MathUtils
+Returns true iff both arguments are null or have same dimensions
+ and all their elements are equals equals(Object) ResizableDoubleArray
+Returns true iff object is a ResizableDoubleArray with the same properties
+ as this and an identical internal storage array.
+ equals(Object) TransformerMap
+
+ Erf org.apache.commons.math.special This is a utility class that provides computation methods related to the
+ error functions. erf(double) Erf
+Returns the error function erf(x).
+ estimate(EstimationProblem) AbstractEstimator
+Deprecated. Solve an estimation problem.
+estimate EstimatedParameter
+Deprecated. Current value of the parameter
+estimate(EstimationProblem) Estimator
+Deprecated. Solve an estimation problem.
+estimate(EstimationProblem) GaussNewtonEstimator
+Deprecated. Solve an estimation problem using a least squares criterion.
+estimate(EstimationProblem) LevenbergMarquardtEstimator
+Deprecated. Solve an estimation problem using the Levenberg-Marquardt algorithm.
+EstimatedParameter org.apache.commons.math.estimation Deprecated. as of 2.0, everything in package org.apache.commons.math.estimation has
+ been deprecated and replaced by package org.apache.commons.math.optimization.general EstimatedParameter(String, double) EstimatedParameter
+Deprecated. Simple constructor.
+EstimatedParameter(String, double, boolean) EstimatedParameter
+Deprecated. Simple constructor.
+EstimatedParameter(EstimatedParameter) EstimatedParameter
+Deprecated. Copy constructor.
+estimateError(double[][], double[], double[], double) DormandPrince54Integrator
+Compute the error ratio.
+ estimateError(double[][], double[], double[], double) DormandPrince853Integrator
+Compute the error ratio.
+ estimateError(double[][], double[], double[], double) EmbeddedRungeKuttaIntegrator
+Compute the error ratio.
+ estimateError(double[][], double[], double[], double) HighamHall54Integrator
+Compute the error ratio.
+ estimateRegressandVariance() AbstractMultipleLinearRegression
+Returns the variance of the regressand, ie Var(y).
+ estimateRegressandVariance() MultipleLinearRegression
+Returns the variance of the regressand, ie Var(y).
+ estimateRegressionParameters() AbstractMultipleLinearRegression
+Estimates the regression parameters b.
+ estimateRegressionParameters() MultipleLinearRegression
+Estimates the regression parameters b.
+ estimateRegressionParametersStandardErrors() AbstractMultipleLinearRegression
+Returns the standard errors of the regression parameters.
+ estimateRegressionParametersStandardErrors() MultipleLinearRegression
+Returns the standard errors of the regression parameters.
+ estimateRegressionParametersVariance() AbstractMultipleLinearRegression
+Estimates the variance of the regression parameters, ie Var(b).
+ estimateRegressionParametersVariance() MultipleLinearRegression
+Estimates the variance of the regression parameters, ie Var(b).
+ estimateResiduals() AbstractMultipleLinearRegression
+Estimates the residuals, ie u = y - X*b.
+ estimateResiduals() MultipleLinearRegression
+Estimates the residuals, ie u = y - X*b.
+ EstimationException org.apache.commons.math.estimation Deprecated. as of 2.0, everything in package org.apache.commons.math.estimation has
+ been deprecated and replaced by package org.apache.commons.math.optimization.general EstimationException(String, Object...) EstimationException
+Deprecated. Simple constructor.
+EstimationProblem org.apache.commons.math.estimation Deprecated. as of 2.0, everything in package org.apache.commons.math.estimation has
+ been deprecated and replaced by package org.apache.commons.math.optimization.general Estimator org.apache.commons.math.estimation Deprecated. as of 2.0, everything in package org.apache.commons.math.estimation has
+ been deprecated and replaced by package org.apache.commons.math.optimization.general EuclideanIntegerPoint org.apache.commons.math.stat.clustering A simple implementation of Clusterable EuclideanIntegerPoint(int[]) EuclideanIntegerPoint
+Build an instance wrapping an integer array.
+ EulerIntegrator org.apache.commons.math.ode.nonstiff This class implements a simple Euler integrator for Ordinary
+ Differential Equations. EulerIntegrator(double) EulerIntegrator
+Simple constructor.
+ evaluate(double[], double) PolynomialFunction
+Uses Horner's Method to evaluate the polynomial with the given coefficients at
+ the argument.
+ evaluate(double[], double[], double) PolynomialFunctionLagrangeForm
+Evaluate the Lagrange polynomial using
+
+ Neville's Algorithm .
+ evaluate(double[], double[], double) PolynomialFunctionNewtonForm
+Evaluate the Newton polynomial using nested multiplication.
+ evaluate(double[]) DirectSearchOptimizer
+Evaluate the objective function on one point.
+ evaluate(double[]) AbstractStorelessUnivariateStatistic
+This default implementation calls AbstractStorelessUnivariateStatistic.clear()AbstractStorelessUnivariateStatistic.increment(double)AbstractStorelessUnivariateStatistic.getResult() evaluate(double[], int, int) AbstractStorelessUnivariateStatistic
+This default implementation calls AbstractStorelessUnivariateStatistic.clear()AbstractStorelessUnivariateStatistic.increment(double)AbstractStorelessUnivariateStatistic.getResult() evaluate(double[]) AbstractUnivariateStatistic
+Returns the result of evaluating the statistic over the input array.
+ evaluate(double[], int, int) AbstractUnivariateStatistic
+Returns the result of evaluating the statistic over the specified entries
+ in the input array.
+ evaluate(double[], int, int) GeometricMean
+Returns the geometric mean of the entries in the specified portion
+ of the input array.
+ evaluate(double[], int, int) Kurtosis
+Returns the kurtosis of the entries in the specified portion of the
+ input array.
+ evaluate(double[], int, int) Mean
+Returns the arithmetic mean of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ evaluate(double[], double[], int, int) Mean
+Returns the weighted arithmetic mean of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ evaluate(double[], double[]) Mean
+Returns the weighted arithmetic mean of the entries in the input array.
+ evaluate(double[]) SemiVariance
+This method calculates SemiVariance evaluate(double[], int, int) SemiVariance
+Returns the SemiVariance evaluate(double[], SemiVariance.Direction) SemiVariance
+This method calculates SemiVariance evaluate(double[], double) SemiVariance
+Returns the SemiVariance evaluate(double[], double, SemiVariance.Direction) SemiVariance
+Returns the SemiVariance evaluate(double[], double, SemiVariance.Direction, boolean, int, int) SemiVariance
+Returns the SemiVariance evaluate(double[], int, int) Skewness
+Returns the Skewness of the entries in the specifed portion of the
+ input array.
+ evaluate(double[]) StandardDeviation
+Returns the Standard Deviation of the entries in the input array, or
+ Double.NaN if the array is empty.
+ evaluate(double[], int, int) StandardDeviation
+Returns the Standard Deviation of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ evaluate(double[], double, int, int) StandardDeviation
+Returns the Standard Deviation of the entries in the specified portion of
+ the input array, using the precomputed mean value.
+ evaluate(double[], double) StandardDeviation
+Returns the Standard Deviation of the entries in the input array, using
+ the precomputed mean value.
+ evaluate(double[]) Variance
+Returns the variance of the entries in the input array, or
+ Double.NaN if the array is empty.
+ evaluate(double[], int, int) Variance
+Returns the variance of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ evaluate(double[], double[], int, int) Variance
+Returns the weighted variance of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ evaluate(double[], double[]) Variance
+
+ Returns the weighted variance of the entries in the the input array.
+ evaluate(double[], double, int, int) Variance
+Returns the variance of the entries in the specified portion of
+ the input array, using the precomputed mean value.
+ evaluate(double[], double) Variance
+Returns the variance of the entries in the input array, using the
+ precomputed mean value.
+ evaluate(double[], double[], double, int, int) Variance
+Returns the weighted variance of the entries in the specified portion of
+ the input array, using the precomputed weighted mean value.
+ evaluate(double[], double[], double) Variance
+Returns the weighted variance of the values in the input array, using
+ the precomputed weighted mean value.
+ evaluate(double[], int, int) Max
+Returns the maximum of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ evaluate(double[], int, int) Min
+Returns the minimum of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ evaluate(double[], double) Percentile
+Returns an estimate of the pth percentile of the values
+ in the values array.
+ evaluate(double[], int, int) Percentile
+Returns an estimate of the quantileth percentile of the
+ designated values in the values array.
+ evaluate(double[], int, int, double) Percentile
+Returns an estimate of the pth percentile of the values
+ in the values array, starting with the element in (0-based)
+ position begin in the array and including length
+ values.
+ evaluate(double[], int, int) Product
+Returns the product of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ evaluate(double[], double[], int, int) Product
+Returns the weighted product of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ evaluate(double[], double[]) Product
+Returns the weighted product of the entries in the input array.
+ evaluate(double[], int, int) Sum
+The sum of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ evaluate(double[], double[], int, int) Sum
+The weighted sum of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ evaluate(double[], double[]) Sum
+The weighted sum of the entries in the the input array.
+ evaluate(double[], int, int) SumOfLogs
+Returns the sum of the natural logs of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ evaluate(double[], int, int) SumOfSquares
+Returns the sum of the squares of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ evaluate(double[]) UnivariateStatistic
+Returns the result of evaluating the statistic over the input array.
+ evaluate(double[], int, int) UnivariateStatistic
+Returns the result of evaluating the statistic over the specified entries
+ in the input array.
+ evaluate(double[], double[]) WeightedEvaluation
+Returns the result of evaluating the statistic over the input array,
+ using the supplied weights.
+ evaluate(double[], double[], int, int) WeightedEvaluation
+Returns the result of evaluating the statistic over the specified entries
+ in the input array, using corresponding entries in the supplied weights array.
+ evaluate(double) ContinuedFraction
+Evaluates the continued fraction at the value x.
+ evaluate(double, double) ContinuedFraction
+Evaluates the continued fraction at the value x.
+ evaluate(double, int) ContinuedFraction
+Evaluates the continued fraction at the value x.
+ evaluate(double, double, int) ContinuedFraction
+
+ Evaluates the continued fraction at the value x.
+ evaluateSimplex(Comparator<RealPointValuePair>) DirectSearchOptimizer
+Evaluate all the non-evaluated points of the simplex.
+ evaluateStep(StepInterpolator) CombinedEventsManager
+Evaluate the impact of the proposed step on all managed
+ event handlers.
+ evaluateStep(StepInterpolator) EventState
+Evaluate the impact of the proposed step on the event handler.
+ EventException org.apache.commons.math.ode.events This exception is made available to users to report
+ the error conditions that are triggered by EventHandler EventException(String, Object...) EventException
+Simple constructor.
+ EventException(Throwable) EventException
+Create an exception with a given root cause.
+ EventHandler org.apache.commons.math.ode.events This interface represents a handler for discrete events triggered
+ during ODE integration. EventHandlerWithJacobians org.apache.commons.math.ode.jacobians This interface represents a handler for discrete events triggered
+ during ODE integration. eventOccurred(double, double[], boolean) EventHandler
+Handle an event and choose what to do next.
+ eventOccurred(double, double[], double[][], double[][], boolean) EventHandlerWithJacobians
+Handle an event and choose what to do next.
+ eventsHandlersManager AbstractIntegrator
+Events handlers manager.
+ EventState org.apache.commons.math.ode.events This class handles the state for one event handler EventState(EventHandler, double, double, int) EventState
+Simple constructor.
+ evolve(Population, StoppingCondition) GeneticAlgorithm
+Evolve the given population.
+ EXP ComposableFunction
+The Math.exp method wrapped as a ComposableFunction exp() Complex
+Compute the
+
+ exponential function of this complex number.
+ expand() ResizableDoubleArray
+Expands the internal storage array using the expansion factor.
+ expansionFactor ResizableDoubleArray
+The expansion factor of the array.
+ expansionMode ResizableDoubleArray
+Determines whether array expansion by expansionFactor
+ is additive or multiplicative.
+ EXPM1 ComposableFunction
+The Math.expm1 method wrapped as a ComposableFunction EXPONENTIAL_MODE ValueServer
+Exponential random deviates with mean = μ.
+ ExponentialDistribution org.apache.commons.math.distribution The Exponential Distribution. ExponentialDistributionImpl org.apache.commons.math.distribution The default implementation of ExponentialDistribution ExponentialDistributionImpl(double) ExponentialDistributionImpl
+Create a exponential distribution with the given mean.
+ ExponentialDistributionImpl(double, double) ExponentialDistributionImpl
+Create a exponential distribution with the given mean.
+ extractField(T[][]) AbstractFieldMatrix
+Get the elements type from an array.
+ extractField(T[]) AbstractFieldMatrix
+Get the elements type from an array.
+
+
+F
+
+f UnivariateRealIntegratorImpl
+Deprecated. as of 2.0 the integrand function is passed as an argument
+ to the UnivariateRealIntegrator.integrate(UnivariateRealFunction, double, double)
+f UnivariateRealSolverImpl
+Deprecated. as of 2.0 the function to solve is passed as an argument
+ to the UnivariateRealSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+factorial(int) MathUtils
+Returns n!.
+ factorialDouble(int) MathUtils
+Returns n!.
+ factorialLog(int) MathUtils
+Returns the natural logarithm of n!.
+ FastCosineTransformer org.apache.commons.math.transform Implements the Fast Cosine Transform
+ for transformation of one-dimensional data sets. FastCosineTransformer() FastCosineTransformer
+Construct a default transformer.
+ FastFourierTransformer org.apache.commons.math.transform Implements the
+ Fast Fourier Transform for transformation of one-dimensional data sets. FastFourierTransformer() FastFourierTransformer
+Construct a default transformer.
+ FastHadamardTransformer org.apache.commons.math.transform Implements the Fast Hadamard Transform (FHT). FastHadamardTransformer() FastHadamardTransformer
+
+ FastSineTransformer org.apache.commons.math.transform Implements the Fast Sine Transform
+ for transformation of one-dimensional data sets. FastSineTransformer() FastSineTransformer
+Construct a default transformer.
+ fct(double[]) FastCosineTransformer
+Perform the FCT algorithm (including inverse).
+ FDistribution org.apache.commons.math.distribution F-Distribution. FDistributionImpl org.apache.commons.math.distribution Default implementation of
+ FDistribution FDistributionImpl(double, double) FDistributionImpl
+Create a F distribution using the given degrees of freedom.
+ FDistributionImpl(double, double, double) FDistributionImpl
+Create a F distribution using the given degrees of freedom and inverse cumulative probability accuracy.
+ fft(double[], boolean) FastFourierTransformer
+Perform the base-4 Cooley-Tukey FFT algorithm (including inverse).
+ fft(Complex[]) FastFourierTransformer
+Perform the base-4 Cooley-Tukey FFT algorithm (including inverse).
+ fht(double[]) FastHadamardTransformer
+The FHT (Fast Hadamard Transformation) which uses only subtraction and addition.
+ fht(int[]) FastHadamardTransformer
+The FHT (Fast Hadamard Transformation) which uses only subtraction and addition.
+ Field T > - Interface in org.apache.commons.math Interface representing a field . FieldDecompositionSolver T extends FieldElement <T >> - Interface in org.apache.commons.math.linear Interface handling decomposition algorithms that can solve A × X = B. FieldElement T > - Interface in org.apache.commons.math Interface representing field elements. FieldLUDecomposition T extends FieldElement <T >> - Interface in org.apache.commons.math.linear An interface to classes that implement an algorithm to calculate the
+ LU-decomposition of a real matrix. FieldLUDecompositionImpl T extends FieldElement <T >> - Class in org.apache.commons.math.linear Calculates the LUP-decomposition of a square matrix. FieldLUDecompositionImpl(FieldMatrix<T>) FieldLUDecompositionImpl
+Calculates the LU-decomposition of the given matrix.
+ FieldMatrix T extends FieldElement <T >> - Interface in org.apache.commons.math.linear Interface defining field-valued matrix with basic algebraic operations. FieldMatrixChangingVisitor T extends FieldElement <?>> - Interface in org.apache.commons.math.linear Interface defining a visitor for matrix entries. FieldMatrixPreservingVisitor T extends FieldElement <?>> - Interface in org.apache.commons.math.linear Interface defining a visitor for matrix entries. FieldVector T extends FieldElement <T >> - Interface in org.apache.commons.math.linear Interface defining a field-valued vector with basic algebraic operations. fill(double[]) ValueServer
+Fills the input array with values generated using getNext() repeatedly.
+ fill(int) ValueServer
+Returns an array of length length with values generated
+ using getNext() repeatedly.
+ filterStep(double, boolean, boolean) AdaptiveStepsizeIntegrator
+Filter the integration step.
+ finalizeStep() AbstractStepInterpolator
+Finalize the step.
+ findSameChromosome(Population) Chromosome
+Searches the population for another chromosome with the same
+ representation.
+ FirstMoment org.apache.commons.math.stat.descriptive.moment Computes the first moment (arithmetic mean). FirstMoment() FirstMoment
+Create a FirstMoment instance
+ FirstMoment(FirstMoment) FirstMoment
+Copy constructor, creates a new FirstMoment identical
+ to the original
+ FirstOrderConverter org.apache.commons.math.ode This class converts second order differential equations to first
+ order ones. FirstOrderConverter(SecondOrderDifferentialEquations) FirstOrderConverter
+Simple constructor.
+ FirstOrderDifferentialEquations org.apache.commons.math.ode This interface represents a first order differential equations set. FirstOrderIntegrator org.apache.commons.math.ode This interface represents a first order integrator for
+ differential equations. FirstOrderIntegratorWithJacobians org.apache.commons.math.ode.jacobians This class enhances a first order integrator for differential equations to
+ compute also partial derivatives of the solution with respect to initial state
+ and parameters. FirstOrderIntegratorWithJacobians(FirstOrderIntegrator, ParameterizedODE, double[], double[], double[]) FirstOrderIntegratorWithJacobians
+Build an enhanced integrator using internal differentiation to compute jacobians.
+ FirstOrderIntegratorWithJacobians(FirstOrderIntegrator, ODEWithJacobians) FirstOrderIntegratorWithJacobians
+Build an enhanced integrator using ODE builtin jacobian computation features.
+ fit(ParametricRealFunction, double[]) CurveFitter
+Fit a curve.
+ fit() HarmonicFitter
+Fit an harmonic function to the observed points.
+ fit() PolynomialFitter
+Get the polynomial fitting the weighted (x, y) points.
+ Fitness org.apache.commons.math.genetics Fitness of a chromosome. fitness() Fitness
+Compute the fitness.
+ fix1stArgument(double) BinaryFunction
+Get a composable function by fixing the first argument of the instance.
+ fix2ndArgument(double) BinaryFunction
+Get a composable function by fixing the second argument of the instance.
+ FixedGenerationCount org.apache.commons.math.genetics Stops after a fixed number of generations. FixedGenerationCount(int) FixedGenerationCount
+Create a new FixedGenerationCount instance.
+ FixedStepHandler org.apache.commons.math.ode.sampling This interface represents a handler that should be called after
+ each successful fixed step. floatValue() BigFraction
+
+ Gets the fraction as a float .
+ floatValue() Fraction
+Gets the fraction as a float .
+ FLOOR ComposableFunction
+The Math.floor method wrapped as a ComposableFunction format(Complex, StringBuffer, FieldPosition) ComplexFormat
+Formats a Complex format(Object, StringBuffer, FieldPosition) ComplexFormat
+Formats a object to produce a string.
+ format(double, StringBuffer, FieldPosition) AbstractFormat
+Formats a double value as a fraction and appends the result to a StringBuffer.
+ format(long, StringBuffer, FieldPosition) AbstractFormat
+Formats a long value as a fraction and appends the result to a StringBuffer.
+ format(BigFraction, StringBuffer, FieldPosition) BigFractionFormat
+Formats a BigFraction format(Object, StringBuffer, FieldPosition) BigFractionFormat
+Formats an object and appends the result to a StringBuffer.
+ format(Fraction, StringBuffer, FieldPosition) FractionFormat
+Formats a Fraction format(Object, StringBuffer, FieldPosition) FractionFormat
+Formats an object and appends the result to a StringBuffer.
+ format(BigFraction, StringBuffer, FieldPosition) ProperBigFractionFormat
+Formats a BigFraction format(Fraction, StringBuffer, FieldPosition) ProperFractionFormat
+Formats a Fraction format(Vector3D, StringBuffer, FieldPosition) Vector3DFormat
+Formats a Vector3D format(Object, StringBuffer, FieldPosition) Vector3DFormat
+Formats a object to produce a string.
+ format(RealVector, StringBuffer, FieldPosition) RealVectorFormat
+Formats a RealVector format(Object, StringBuffer, FieldPosition) RealVectorFormat
+Formats a object to produce a string.
+ formatBigFraction(BigFraction) BigFractionFormat
+This static method calls formatBigFraction() on a default instance of
+ BigFractionFormat.
+ formatComplex(Complex) ComplexFormat
+This static method calls Format.format(Object) formatDouble(double, NumberFormat, StringBuffer, FieldPosition) CompositeFormat
+Formats a double value to produce a string.
+ formatFraction(Fraction) FractionFormat
+This static method calls formatFraction() on a default instance of
+ FractionFormat.
+ formatRealVector(RealVector) RealVectorFormat
+This static method calls Format.format(Object) formatVector3D(Vector3D) Vector3DFormat
+This static method calls Format.format(Object) FOUR_FIFTHS BigFraction
+A fraction representing "4/5".
+ FOUR_FIFTHS Fraction
+A fraction representing "4/5".
+ FourthMoment org.apache.commons.math.stat.descriptive.moment Computes a statistic related to the Fourth Central Moment. FourthMoment() FourthMoment
+Create a FourthMoment instance
+ FourthMoment(FourthMoment) FourthMoment
+Copy constructor, creates a new FourthMoment identical
+ to the original
+ Fraction org.apache.commons.math.fraction Representation of a rational number. Fraction(double) Fraction
+Create a fraction given the double value.
+ Fraction(double, double, int) Fraction
+Create a fraction given the double value and maximum error allowed.
+ Fraction(double, int) Fraction
+Create a fraction given the double value and maximum denominator.
+ Fraction(int) Fraction
+Create a fraction from an int.
+ Fraction(int, int) Fraction
+Create a fraction given the numerator and denominator.
+ FractionConversionException org.apache.commons.math.fraction Error thrown when a double value cannot be converted to a fraction
+ in the allowed number of iterations. FractionConversionException(double, int) FractionConversionException
+Constructs an exception with specified formatted detail message.
+ FractionConversionException(double, long, long) FractionConversionException
+Constructs an exception with specified formatted detail message.
+ FractionField org.apache.commons.math.fraction Representation of the fractional numbers field. FractionFormat org.apache.commons.math.fraction Formats a Fraction number in proper format or improper format. FractionFormat() FractionFormat
+Create an improper formatting instance with the default number format
+ for the numerator and denominator.
+ FractionFormat(NumberFormat) FractionFormat
+Create an improper formatting instance with a custom number format for
+ both the numerator and denominator.
+ FractionFormat(NumberFormat, NumberFormat) FractionFormat
+Create an improper formatting instance with a custom number format for
+ the numerator and a custom number format for the denominator.
+ fractionMatrixToRealMatrix(FieldMatrix<Fraction>) MatrixUtils
+Convert a FieldMatrixFractionRealMatrix FREE OpenIntToDoubleHashMap
+Status indicator for free table entries.
+ FREE OpenIntToFieldHashMap
+Status indicator for free table entries.
+ Frequency org.apache.commons.math.stat Maintains a frequency distribution. Frequency() Frequency
+Default constructor.
+ Frequency(Comparator<?>) Frequency
+Constructor allowing values Comparator to be specified.
+ fst(double[]) FastSineTransformer
+Perform the FST algorithm (including inverse).
+ FULL OpenIntToDoubleHashMap
+Status indicator for full table entries.
+ FULL OpenIntToFieldHashMap
+Status indicator for full table entries.
+ function AbstractLinearOptimizer
+Linear objective function.
+ FunctionEvaluationException org.apache.commons.math Exception thrown when an error occurs evaluating a function. FunctionEvaluationException(double) FunctionEvaluationException
+Construct an exception indicating the argument value
+ that caused the function evaluation to fail.
+ FunctionEvaluationException(double[]) FunctionEvaluationException
+Construct an exception indicating the argument value
+ that caused the function evaluation to fail.
+ FunctionEvaluationException(double, String, Object...) FunctionEvaluationException
+Constructs an exception with specified formatted detail message.
+ FunctionEvaluationException(double[], String, Object...) FunctionEvaluationException
+Constructs an exception with specified formatted detail message.
+ FunctionEvaluationException(Throwable, double) FunctionEvaluationException
+Constructs an exception with specified root cause.
+ FunctionEvaluationException(Throwable, double[]) FunctionEvaluationException
+Constructs an exception with specified root cause.
+ FunctionEvaluationException(Throwable, double, String, Object...) FunctionEvaluationException
+Constructs an exception with specified formatted detail message and root cause.
+ FunctionEvaluationException(Throwable, double[], String, Object...) FunctionEvaluationException
+Constructs an exception with specified formatted detail message and root cause.
+ functionValue UnivariateRealSolverImpl
+Value of the function at the last computed result.
+ functionValue AbstractUnivariateRealOptimizer
+Value of the function at the last computed result.
+ functionValueAccuracy UnivariateRealSolverImpl
+Maximum error of function.
+
+
+G
+
+g(double, double[]) EventHandler
+Compute the value of the switching function.
+ g(double, double[], double[][], double[][]) EventHandlerWithJacobians
+Compute the value of the switching function.
+ Gamma org.apache.commons.math.special This is a utility class that provides computation methods related to the
+ Gamma family of functions. GAMMA Gamma
+Euler-Mascheroni constant
+GammaDistribution org.apache.commons.math.distribution The Gamma Distribution. GammaDistributionImpl org.apache.commons.math.distribution The default implementation of GammaDistribution GammaDistributionImpl(double, double) GammaDistributionImpl
+Create a new gamma distribution with the given alpha and beta values.
+ GammaDistributionImpl(double, double, double) GammaDistributionImpl
+Create a new gamma distribution with the given alpha and beta values.
+ GAUSSIAN_MODE ValueServer
+Gaussian random deviates with mean = μ, std dev = σ.
+ GaussianRandomGenerator org.apache.commons.math.random This class is a gaussian normalized random generator for scalars. GaussianRandomGenerator(RandomGenerator) GaussianRandomGenerator
+Create a new generator.
+ GaussNewtonEstimator org.apache.commons.math.estimation Deprecated. as of 2.0, everything in package org.apache.commons.math.estimation has
+ been deprecated and replaced by package org.apache.commons.math.optimization.general GaussNewtonEstimator() GaussNewtonEstimator
+Deprecated. Simple constructor with default settings.
+GaussNewtonEstimator(int, double, double) GaussNewtonEstimator
+Deprecated. Simple constructor.
+GaussNewtonOptimizer org.apache.commons.math.optimization.general Gauss-Newton least-squares solver. GaussNewtonOptimizer(boolean) GaussNewtonOptimizer
+Simple constructor with default settings.
+ gcd(int, int) MathUtils
+
+ Gets the greatest common divisor of the absolute value of two numbers,
+ using the "binary gcd" method which avoids division and modulo
+ operations.
+ gcd(long, long) MathUtils
+
+ Gets the greatest common divisor of the absolute value of two numbers,
+ using the "binary gcd" method which avoids division and modulo
+ operations.
+ GeneticAlgorithm org.apache.commons.math.genetics Implementation of a genetic algorithm. GeneticAlgorithm(CrossoverPolicy, double, MutationPolicy, double, SelectionPolicy) GeneticAlgorithm
+
+ geoMean SummaryStatistics
+geoMean of values that have been added
+ GeometricMean org.apache.commons.math.stat.descriptive.moment Returns the
+ geometric mean of the available values. GeometricMean() GeometricMean
+Create a GeometricMean instance
+ GeometricMean(GeometricMean) GeometricMean
+Copy constructor, creates a new GeometricMean identical
+ to the original
+ GeometricMean(SumOfLogs) GeometricMean
+Create a GeometricMean instance using the given SumOfLogs instance
+ geometricMean(double[]) StatUtils
+Returns the geometric mean of the entries in the input array, or
+ Double.NaN if the array is empty.
+ geometricMean(double[], int, int) StatUtils
+Returns the geometric mean of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ get(int) OpenIntToDoubleHashMap
+Get the stored value associated with the given key
+ get(int) OpenIntToFieldHashMap
+Get the stored value associated with the given key
+ getA(int, double) ContinuedFraction
+Access the n-th a coefficient of the continued fraction.
+ getA1() RotationOrder
+Get the axis of the first rotation.
+ getA2() RotationOrder
+Get the axis of the second rotation.
+ getA3() RotationOrder
+Get the axis of the second rotation.
+ getAbsoluteAccuracy() ConvergingAlgorithm
+Get the actual absolute accuracy.
+ getAbsoluteAccuracy() ConvergingAlgorithmImpl
+Get the actual absolute accuracy.
+ getAbsoluteAccuracy() MultiStartUnivariateRealOptimizer
+Get the actual absolute accuracy.
+ getAllParameters() EstimationProblem
+Deprecated. Get all the parameters of the problem.
+getAllParameters() SimpleEstimationProblem
+Deprecated. Get all the parameters of the problem.
+getAlpha() BetaDistribution
+Access the shape parameter, alpha
+ getAlpha() BetaDistributionImpl
+Access the shape parameter, alpha
+ getAlpha() GammaDistribution
+Access the shape parameter, alpha
+ getAlpha() GammaDistributionImpl
+Access the shape parameter, alpha
+ getAlpha() Vector3D
+Get the azimuth of the vector.
+ getAmplitude() HarmonicFunction
+Get the amplitude a.
+ getAngle() Rotation
+Get the angle of the rotation.
+ getAngles(RotationOrder) Rotation
+Get the Cardan or Euler angles corresponding to the instance.
+ getArgument() Complex
+Compute the argument of this complex number.
+ getArgument() FunctionEvaluationException
+Returns the function argument that caused this exception.
+ getArguments() MathException
+Gets the arguments used to build the message of this throwable.
+ getArguments() MathRuntimeException
+Gets the arguments used to build the message of this throwable.
+ getArity() TournamentSelection
+Gets the arity (number of chromosomes drawn to the tournament).
+ getAvailableLocales() ComplexFormat
+Get the set of locales for which complex formats are available.
+ getAvailableLocales() BigFractionFormat
+Get the set of locales for which complex formats are available.
+ getAvailableLocales() FractionFormat
+Get the set of locales for which complex formats are available.
+ getAvailableLocales() Vector3DFormat
+Get the set of locales for which 3D vectors formats are available.
+ getAvailableLocales() RealVectorFormat
+Get the set of locales for which real vectors formats are available.
+ getAxis() Rotation
+Get the normalized axis of the rotation.
+ getB(int, double) ContinuedFraction
+Access the n-th b coefficient of the continued fraction.
+ getBeta() BetaDistribution
+Access the shape parameter, beta
+ getBeta() BetaDistributionImpl
+Access the shape parameter, beta
+ getBeta() GammaDistribution
+Access the scale parameter, beta
+ getBeta() GammaDistributionImpl
+Access the scale parameter, beta
+ getBinCount() EmpiricalDistribution
+Returns the number of bins.
+ getBinCount() EmpiricalDistributionImpl
+Returns the number of bins.
+ getBinStats() EmpiricalDistribution
+Returns a list of
+ SummaryStatistics getBinStats() EmpiricalDistributionImpl
+Returns a List of SummaryStatistics getCenter() Cluster
+Get the point chosen to be the center of this cluster.
+ getCenters() PolynomialFunctionNewtonForm
+Returns a copy of the centers array.
+ getChiSquare(EstimationProblem) AbstractEstimator
+Deprecated. Get the Chi-Square value.
+getChiSquare() AbstractLeastSquaresOptimizer
+Get the Chi-Square value.
+ getChiSquareTest() TestUtils
+Return a (singleton) ChiSquareTest instance.
+ getChromosomes() ListPopulation
+Access the list of chromosomes.
+ getCoefficients() PolynomialFunction
+Returns a copy of the coefficients array.
+ getCoefficients() PolynomialFunctionLagrangeForm
+Returns a copy of the coefficients array.
+ getCoefficients() PolynomialFunctionNewtonForm
+Returns a copy of the coefficients array.
+ getCoefficients() LinearConstraint
+Get the coefficients of the constraint (left hand side).
+ getCoefficients() LinearObjectiveFunction
+Get the coefficients of the linear equation being optimized.
+ getColumn(int) AbstractFieldMatrix
+Returns the entries in column number col as an array.
+ getColumn(int) AbstractRealMatrix
+Returns the entries in column number col as an array.
+ getColumn(int) BigMatrix
+Deprecated. Returns the entries in column number col as an array.
+getColumn(int) BigMatrixImpl
+Deprecated. Returns the entries in column number col as an array.
+getColumn(int) BlockFieldMatrix
+Returns the entries in column number col as an array.
+ getColumn(int) BlockRealMatrix
+Returns the entries in column number col as an array.
+ getColumn(int) FieldMatrix
+Returns the entries in column number col as an array.
+ getColumn(int) RealMatrix
+Returns the entries in column number col as an array.
+ getColumnAsDoubleArray(int) BigMatrix
+Deprecated. Returns the entries in column number col as an array
+ of double values.
+getColumnAsDoubleArray(int) BigMatrixImpl
+Deprecated. Returns the entries in column number col as an array
+ of double values.
+getColumnDimension() AbstractFieldMatrix
+Returns the number of columns in the matrix.
+ getColumnDimension() AbstractRealMatrix
+Returns the number of columns in the matrix.
+ getColumnDimension() AnyMatrix
+Returns the number of columns in the matrix.
+ getColumnDimension() Array2DRowFieldMatrix
+Returns the number of columns in the matrix.
+ getColumnDimension() Array2DRowRealMatrix
+Returns the number of columns in the matrix.
+ getColumnDimension() BigMatrixImpl
+Deprecated. Returns the number of columns in the matrix.
+getColumnDimension() BlockFieldMatrix
+Returns the number of columns in the matrix.
+ getColumnDimension() BlockRealMatrix
+Returns the number of columns in the matrix.
+ getColumnDimension() OpenMapRealMatrix
+Returns the number of columns in the matrix.
+ getColumnDimension() RealMatrixImpl
+Deprecated. Returns the number of columns in the matrix.
+getColumnDimension() SparseFieldMatrix
+Returns the number of columns in the matrix.
+ getColumnMatrix(int) AbstractFieldMatrix
+Returns the entries in column number column
+ as a column matrix.
+ getColumnMatrix(int) AbstractRealMatrix
+Returns the entries in column number column
+ as a column matrix.
+ getColumnMatrix(int) BigMatrix
+Deprecated. Returns the entries in column number column
+ as a column matrix.
+getColumnMatrix(int) BigMatrixImpl
+Deprecated. Returns the entries in column number column
+ as a column matrix.
+getColumnMatrix(int) BlockFieldMatrix
+Returns the entries in column number column
+ as a column matrix.
+ getColumnMatrix(int) BlockRealMatrix
+Returns the entries in column number column
+ as a column matrix.
+ getColumnMatrix(int) FieldMatrix
+Returns the entries in column number column
+ as a column matrix.
+ getColumnMatrix(int) RealMatrix
+Returns the entries in column number column
+ as a column matrix.
+ getColumnVector(int) AbstractFieldMatrix
+Returns the entries in column number column
+ as a vector.
+ getColumnVector(int) AbstractRealMatrix
+Returns the entries in column number column
+ as a vector.
+ getColumnVector(int) BlockFieldMatrix
+Returns the entries in column number column
+ as a vector.
+ getColumnVector(int) BlockRealMatrix
+Returns the entries in column number column
+ as a vector.
+ getColumnVector(int) FieldMatrix
+Returns the entries in column number column
+ as a vector.
+ getColumnVector(int) RealMatrix
+Returns the entries in column number column
+ as a vector.
+ getConditionNumber() SingularValueDecomposition
+Return the condition number of the matrix.
+ getConditionNumber() SingularValueDecompositionImpl
+Return the condition number of the matrix.
+ getConstantTerm() LinearObjectiveFunction
+Get the constant of the linear equation being optimized.
+ getContents() MessagesResources_fr
+Get the non-translated/translated messages arrays from this resource bundle.
+ getContractionCriteria() ResizableDoubleArray
+The contraction criteria defines when the internal array will contract
+ to store only the number of elements in the element array.
+ getConvergence() EventState
+Get the convergence threshold for event localization.
+ getConvergenceChecker() DifferentiableMultivariateRealOptimizer
+Get the convergence checker.
+ getConvergenceChecker() DifferentiableMultivariateVectorialOptimizer
+Get the convergence checker.
+ getConvergenceChecker() DirectSearchOptimizer
+Get the convergence checker.
+ getConvergenceChecker() AbstractLeastSquaresOptimizer
+Get the convergence checker.
+ getConvergenceChecker() AbstractScalarDifferentiableOptimizer
+Get the convergence checker.
+ getConvergenceChecker() MultiStartDifferentiableMultivariateRealOptimizer
+Get the convergence checker.
+ getConvergenceChecker() MultiStartDifferentiableMultivariateVectorialOptimizer
+Get the convergence checker.
+ getConvergenceChecker() MultiStartMultivariateRealOptimizer
+Get the convergence checker.
+ getConvergenceChecker() MultivariateRealOptimizer
+Get the convergence checker.
+ getCorrelationMatrix() PearsonsCorrelation
+Returns the correlation matrix
+ getCorrelationMatrix() SpearmansCorrelation
+Calculate the Spearman Rank Correlation Matrix.
+ getCorrelationPValues() PearsonsCorrelation
+Returns a matrix of p-values associated with the (two-sided) null
+ hypothesis that the corresponding correlation coefficient is zero.
+ getCorrelationStandardErrors() PearsonsCorrelation
+Returns a matrix of standard errors associated with the estimates
+ in the correlation matrix.getCorrelationStandardErrors().getEntry(i,j) is the standard
+ error associated with getCorrelationMatrix.getEntry(i,j)
+ getCostEvaluations() AbstractEstimator
+Deprecated. Get the number of cost evaluations.
+getCount(Object) Frequency
+Deprecated. replaced by Frequency.getCount(Comparable)
+getCount(Comparable<?>) Frequency
+Returns the number of values = v.
+ getCount(int) Frequency
+Returns the number of values = v.
+ getCount(long) Frequency
+Returns the number of values = v.
+ getCount(char) Frequency
+Returns the number of values = v.
+ getCovariance(double) SingularValueDecomposition
+Returns the n × n covariance matrix.
+ getCovariance(double) SingularValueDecompositionImpl
+Returns the n × n covariance matrix.
+ getCovariance() MultivariateSummaryStatistics
+Returns the covariance matrix of the values that have been added.
+ getCovariance() StatisticalMultivariateSummary
+Returns the covariance of the available values.
+ getCovariance() SynchronizedMultivariateSummaryStatistics
+Returns the covariance matrix of the values that have been added.
+ getCovarianceMatrix() Covariance
+Returns the covariance matrix
+ getCovariances(EstimationProblem) AbstractEstimator
+Deprecated. Get the covariance matrix of unbound estimated parameters.
+getCovariances(EstimationProblem) Estimator
+Deprecated. Get the covariance matrix of estimated parameters.
+getCovariances() AbstractLeastSquaresOptimizer
+Get the covariance matrix of optimized parameters.
+ getCrossoverPolicy() GeneticAlgorithm
+Returns the crossover policy.
+ getCrossoverRate() GeneticAlgorithm
+Returns the crossover rate.
+ getCumFreq(Object) Frequency
+Deprecated. replaced by Frequency.getCumFreq(Comparable)
+getCumFreq(Comparable<?>) Frequency
+Returns the cumulative frequency of values less than or equal to v.
+ getCumFreq(int) Frequency
+Returns the cumulative frequency of values less than or equal to v.
+ getCumFreq(long) Frequency
+Returns the cumulative frequency of values less than or equal to v.
+ getCumFreq(char) Frequency
+Returns the cumulative frequency of values less than or equal to v.
+ getCumPct(Object) Frequency
+Deprecated. replaced by Frequency.getCumPct(Comparable)
+getCumPct(Comparable<?>) Frequency
+Returns the cumulative percentage of values less than or equal to v
+ (as a proportion between 0 and 1).
+ getCumPct(int) Frequency
+Returns the cumulative percentage of values less than or equal to v
+ (as a proportion between 0 and 1).
+ getCumPct(long) Frequency
+Returns the cumulative percentage of values less than or equal to v
+ (as a proportion between 0 and 1).
+ getCumPct(char) Frequency
+Returns the cumulative percentage of values less than or equal to v
+ (as a proportion between 0 and 1).
+ getCurrentSignedStepsize() AbstractIntegrator
+Get the current signed value of the integration stepsize.
+ getCurrentSignedStepsize() FirstOrderIntegratorWithJacobians
+Get the current signed value of the integration stepsize.
+ getCurrentSignedStepsize() ODEIntegrator
+Get the current signed value of the integration stepsize.
+ getCurrentStepStart() AbstractIntegrator
+Get the current value of the step start time ti .
+ getCurrentStepStart() FirstOrderIntegratorWithJacobians
+Get the current value of the step start time ti .
+ getCurrentStepStart() AdaptiveStepsizeIntegrator
+Get the current value of the step start time ti .
+ getCurrentStepStart() ODEIntegrator
+Get the current value of the step start time ti .
+ getCurrentTime() StepInterpolatorWithJacobians
+Get the current grid point time.
+ getCurrentTime() AbstractStepInterpolator
+Get the current grid point time.
+ getCurrentTime() StepInterpolator
+Get the current grid point time.
+ getD() EigenDecomposition
+Returns the block diagonal matrix D of the decomposition.
+ getD() EigenDecompositionImpl
+Returns the block diagonal matrix D of the decomposition.
+ getData() AbstractFieldMatrix
+Returns matrix entries as a two-dimensional array.
+ getData() AbstractRealMatrix
+Returns matrix entries as a two-dimensional array.
+ getData() AbstractRealVector
+Returns vector entries as a double array.
+ getData() Array2DRowFieldMatrix
+Returns matrix entries as a two-dimensional array.
+ getData() Array2DRowRealMatrix
+Returns matrix entries as a two-dimensional array.
+ getData() ArrayFieldVector
+Returns vector entries as a T array.
+ getData() ArrayRealVector
+Returns vector entries as a double array.
+ getData() BigMatrix
+Deprecated. Returns matrix entries as a two-dimensional array.
+getData() BigMatrixImpl
+Deprecated. Returns matrix entries as a two-dimensional array.
+getData() BlockFieldMatrix
+Returns matrix entries as a two-dimensional array.
+ getData() BlockRealMatrix
+Returns matrix entries as a two-dimensional array.
+ getData() FieldMatrix
+Returns matrix entries as a two-dimensional array.
+ getData() FieldVector
+Returns vector entries as a T array.
+ getData() OpenMapRealVector
+Returns vector entries as a double array.
+ getData() RealMatrix
+Returns matrix entries as a two-dimensional array.
+ getData() RealMatrixImpl
+Deprecated. Returns matrix entries as a two-dimensional array.
+getData() RealVector
+Returns vector entries as a double array.
+ getData() SparseFieldVector
+Returns vector entries as a T array.
+ getDataAsDoubleArray() BigMatrix
+Deprecated. Returns matrix entries as a two-dimensional array.
+getDataAsDoubleArray() BigMatrixImpl
+Deprecated. Returns matrix entries as a two-dimensional array.
+getDataRef() Array2DRowFieldMatrix
+Returns a reference to the underlying data array.
+ getDataRef() Array2DRowRealMatrix
+Returns a reference to the underlying data array.
+ getDataRef() ArrayFieldVector
+Returns a reference to the underlying data array.
+ getDataRef() ArrayRealVector
+Returns a reference to the underlying data array.
+ getDataRef() BigMatrixImpl
+Deprecated. Returns a reference to the underlying data array.
+getDataRef() RealMatrixImpl
+Deprecated. Returns a reference to the underlying data array.
+getDefaultNumberFormat() AbstractFormat
+Create a default number format.
+ getDefaultNumberFormat(Locale) AbstractFormat
+Create a default number format.
+ getDefaultNumberFormat() FractionFormat
+Create a default number format.
+ getDefaultNumberFormat() CompositeFormat
+Create a default number format.
+ getDefaultNumberFormat(Locale) CompositeFormat
+Create a default number format.
+ getDegreesOfFreedom() ChiSquaredDistribution
+Access the degrees of freedom.
+ getDegreesOfFreedom() ChiSquaredDistributionImpl
+Access the degrees of freedom.
+ getDegreesOfFreedom() TDistribution
+Access the degrees of freedom.
+ getDegreesOfFreedom() TDistributionImpl
+Access the degrees of freedom.
+ getDelta() Vector3D
+Get the elevation of the vector.
+ getDenominator() BigFraction
+
+ Access the denominator as a BigInteger.
+ getDenominator() Fraction
+Access the denominator.
+ getDenominatorAsInt() BigFraction
+
+ Access the denominator as a int .
+ getDenominatorAsLong() BigFraction
+
+ Access the denominator as a long .
+ getDenominatorDegreesOfFreedom() FDistribution
+Access the denominator degrees of freedom.
+ getDenominatorDegreesOfFreedom() FDistributionImpl
+Access the denominator degrees of freedom.
+ getDenominatorFormat() AbstractFormat
+Access the denominator format.
+ getDeterminant() AbstractRealMatrix
+Deprecated.
+getDeterminant() BigMatrix
+Deprecated. Returns the determinant of this matrix.
+getDeterminant() BigMatrixImpl
+Deprecated. Returns the determinant of this matrix.
+getDeterminant() CholeskyDecomposition
+Return the determinant of the matrix
+ getDeterminant() CholeskyDecompositionImpl
+Return the determinant of the matrix
+ getDeterminant() EigenDecomposition
+Return the determinant of the matrix
+ getDeterminant() EigenDecompositionImpl
+Return the determinant of the matrix
+ getDeterminant() FieldLUDecomposition
+Return the determinant of the matrix
+ getDeterminant() FieldLUDecompositionImpl
+Return the determinant of the matrix
+ getDeterminant() LUDecomposition
+Return the determinant of the matrix
+ getDeterminant() LUDecompositionImpl
+Return the determinant of the matrix
+ getDeterminant() RealMatrix
+Deprecated. as of release 2.0, replaced by
+ new LUDecompositionImpl(m)getDeterminant()
+getDimension() ArrayFieldVector
+Returns the size of the vector.
+ getDimension() ArrayRealVector
+Returns the size of the vector.
+ getDimension() FieldVector
+Returns the size of the vector.
+ getDimension() OpenMapRealVector
+Returns the size of the vector.
+ getDimension() RealVector
+Returns the size of the vector.
+ getDimension() SparseFieldVector
+Returns the size of the vector.
+ getDimension() FirstOrderConverter
+Get the dimension of the problem.
+ getDimension() FirstOrderDifferentialEquations
+Get the dimension of the problem.
+ getDimension() SecondOrderDifferentialEquations
+Get the dimension of the problem.
+ getDimension() MultivariateSummaryStatistics
+Returns the dimension of the data
+ getDimension() StatisticalMultivariateSummary
+Returns the dimension of the data
+ getDimension() SynchronizedMultivariateSummaryStatistics
+Returns the dimension of the data
+ getDimension1() DimensionMismatchException
+Get the first dimension
+ getDimension2() DimensionMismatchException
+Get the second dimension
+ getDistance(RealVector) AbstractRealVector
+Distance between two vectors.
+ getDistance(double[]) AbstractRealVector
+Distance between two vectors.
+ getDistance(RealVector) ArrayRealVector
+Distance between two vectors.
+ getDistance(double[]) ArrayRealVector
+Distance between two vectors.
+ getDistance(ArrayRealVector) ArrayRealVector
+Distance between two vectors.
+ getDistance(OpenMapRealVector) OpenMapRealVector
+Optimized method to compute distance.
+ getDistance(RealVector) OpenMapRealVector
+Distance between two vectors.
+ getDistance(double[]) OpenMapRealVector
+Distance between two vectors.
+ getDistance(RealVector) RealVector
+Distance between two vectors.
+ getDistance(double[]) RealVector
+Distance between two vectors.
+ getDomainLowerBound(double) AbstractContinuousDistribution
+Access the domain value lower bound, based on p, used to
+ bracket a CDF root.
+ getDomainLowerBound(double) AbstractIntegerDistribution
+Access the domain value lower bound, based on p, used to
+ bracket a PDF root.
+ getDomainLowerBound(double) BetaDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a CDF root.
+ getDomainLowerBound(double) BinomialDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a PDF root.
+ getDomainLowerBound(double) CauchyDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a CDF root.
+ getDomainLowerBound(double) ChiSquaredDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a CDF root.
+ getDomainLowerBound(double) ExponentialDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a CDF root.
+ getDomainLowerBound(double) FDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a CDF root.
+ getDomainLowerBound(double) GammaDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a CDF root.
+ getDomainLowerBound(double) HypergeometricDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a PDF root.
+ getDomainLowerBound(double) NormalDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a CDF root.
+ getDomainLowerBound(double) PascalDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a PDF root.
+ getDomainLowerBound(double) PoissonDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a CDF root.
+ getDomainLowerBound(double) TDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a CDF root.
+ getDomainLowerBound(double) WeibullDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a CDF root.
+ getDomainLowerBound(double) ZipfDistributionImpl
+Access the domain value lower bound, based on p, used to
+ bracket a PDF root.
+ getDomainUpperBound(double) AbstractContinuousDistribution
+Access the domain value upper bound, based on p, used to
+ bracket a CDF root.
+ getDomainUpperBound(double) AbstractIntegerDistribution
+Access the domain value upper bound, based on p, used to
+ bracket a PDF root.
+ getDomainUpperBound(double) BetaDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a CDF root.
+ getDomainUpperBound(double) BinomialDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a PDF root.
+ getDomainUpperBound(double) CauchyDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a CDF root.
+ getDomainUpperBound(double) ChiSquaredDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a CDF root.
+ getDomainUpperBound(double) ExponentialDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a CDF root.
+ getDomainUpperBound(double) FDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a CDF root.
+ getDomainUpperBound(double) GammaDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a CDF root.
+ getDomainUpperBound(double) HypergeometricDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a PDF root.
+ getDomainUpperBound(double) NormalDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a CDF root.
+ getDomainUpperBound(double) PascalDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a PDF root.
+ getDomainUpperBound(double) PoissonDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a CDF root.
+ getDomainUpperBound(double) TDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a CDF root.
+ getDomainUpperBound(double) WeibullDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a CDF root.
+ getDomainUpperBound(double) ZipfDistributionImpl
+Access the domain value upper bound, based on p, used to
+ bracket a PDF root.
+ getDuplicateAbscissa() DuplicateSampleAbscissaException
+Get the duplicate abscissa.
+ getEigenvector(int) EigenDecomposition
+Returns a copy of the ith eigenvector of the original matrix.
+ getEigenvector(int) EigenDecompositionImpl
+Returns a copy of the ith eigenvector of the original matrix.
+ getElement(int) DescriptiveStatistics
+Returns the element at the specified index
+ getElement(int) SynchronizedDescriptiveStatistics
+Returns the element at the specified index
+ getElement(int) DoubleArray
+Returns the element at the specified index.
+ getElement(int) ResizableDoubleArray
+Returns the element at the specified index
+ getElements() DoubleArray
+Returns a double[] array containing the elements of this
+ DoubleArray.
+ getElements() ResizableDoubleArray
+Returns a double array containing the elements of this
+ ResizableArray.
+ getElitismRate() ElitisticListPopulation
+Access the elitism rate.
+ getEmpiricalDistribution() ValueServer
+Getter for property empiricalDistribution.
+ getEntry(int, int) AbstractFieldMatrix
+Returns the entry in the specified row and column.
+ getEntry(int, int) AbstractRealMatrix
+Returns the entry in the specified row and column.
+ getEntry(int, int) Array2DRowFieldMatrix
+Returns the entry in the specified row and column.
+ getEntry(int, int) Array2DRowRealMatrix
+Returns the entry in the specified row and column.
+ getEntry(int) ArrayFieldVector
+Returns the entry in the specified index.
+ getEntry(int) ArrayRealVector
+Returns the entry in the specified index.
+ getEntry(int, int) BigMatrix
+Deprecated. Returns the entry in the specified row and column.
+getEntry(int, int) BigMatrixImpl
+Deprecated. Returns the entry in the specified row and column.
+getEntry(int, int) BlockFieldMatrix
+Returns the entry in the specified row and column.
+ getEntry(int, int) BlockRealMatrix
+Returns the entry in the specified row and column.
+ getEntry(int, int) FieldMatrix
+Returns the entry in the specified row and column.
+ getEntry(int) FieldVector
+Returns the entry in the specified index.
+ getEntry(int, int) OpenMapRealMatrix
+Returns the entry in the specified row and column.
+ getEntry(int) OpenMapRealVector
+Returns the entry in the specified index.
+ getEntry(int, int) RealMatrix
+Returns the entry in the specified row and column.
+ getEntry(int, int) RealMatrixImpl
+Deprecated. Returns the entry in the specified row and column.
+getEntry(int) RealVector
+Returns the entry in the specified index.
+ getEntry(int, int) SparseFieldMatrix
+Returns the entry in the specified row and column.
+ getEntry(int) SparseFieldVector
+Returns the entry in the specified index.
+ getEntryAsDouble(int, int) BigMatrix
+Deprecated. Returns the entry in the specified row and column as a double.
+getEntryAsDouble(int, int) BigMatrixImpl
+Deprecated. Returns the entry in the specified row and column as a double.
+getEstimate() EstimatedParameter
+Deprecated. Get the current estimate of the parameter
+getEvaluations() AbstractIntegrator
+Get the number of evaluations of the differential equations function.
+ getEvaluations() FirstOrderIntegratorWithJacobians
+Get the number of evaluations of the differential equations function.
+ getEvaluations() ODEIntegrator
+Get the number of evaluations of the differential equations function.
+ getEvaluations() DifferentiableMultivariateRealOptimizer
+Get the number of evaluations of the objective function.
+ getEvaluations() DifferentiableMultivariateVectorialOptimizer
+Get the number of evaluations of the objective function.
+ getEvaluations() DirectSearchOptimizer
+Get the number of evaluations of the objective function.
+ getEvaluations() AbstractLeastSquaresOptimizer
+Get the number of evaluations of the objective function.
+ getEvaluations() AbstractScalarDifferentiableOptimizer
+Get the number of evaluations of the objective function.
+ getEvaluations() MultiStartDifferentiableMultivariateRealOptimizer
+Get the number of evaluations of the objective function.
+ getEvaluations() MultiStartDifferentiableMultivariateVectorialOptimizer
+Get the number of evaluations of the objective function.
+ getEvaluations() MultiStartMultivariateRealOptimizer
+Get the number of evaluations of the objective function.
+ getEvaluations() MultiStartUnivariateRealOptimizer
+Get the number of evaluations of the objective function.
+ getEvaluations() MultivariateRealOptimizer
+Get the number of evaluations of the objective function.
+ getEvaluations() AbstractUnivariateRealOptimizer
+Get the number of evaluations of the objective function.
+ getEvaluations() UnivariateRealOptimizer
+Get the number of evaluations of the objective function.
+ getEventHandler() EventState
+Get the underlying event handler.
+ getEventHandlers() AbstractIntegrator
+Get all the event handlers that have been added to the integrator.
+ getEventHandlers() FirstOrderIntegratorWithJacobians
+Get all the event handlers that have been added to the integrator.
+ getEventHandlers() ODEIntegrator
+Get all the event handlers that have been added to the integrator.
+ getEventsHandlers() CombinedEventsManager
+Get all the events handlers that have been added to the manager.
+ getEventsStates() CombinedEventsManager
+Get all the events state wrapping the handlers that have been added to the manager.
+ getEventTime() CombinedEventsManager
+Get the occurrence time of the first event triggered in the
+ last evaluated step.
+ getEventTime() EventState
+Get the occurrence time of the event triggered in the current
+ step.
+ getExpansionFactor() ResizableDoubleArray
+The expansion factor controls the size of a new array when an array
+ needs to be expanded.
+ getExpansionMode() ResizableDoubleArray
+The expansionMode determines whether the internal storage
+ array grows additively (ADDITIVE_MODE) or multiplicatively
+ (MULTIPLICATIVE_MODE) when it is expanded.
+ getExponent() ZipfDistribution
+Get the exponent characterising the distribution.
+ getExponent() ZipfDistributionImpl
+Get the exponent characterising the distribution.
+ getField() Complex
+Get the Field getField() FieldElement
+Get the Field getField() BigFraction
+Get the Field getField() Fraction
+Get the Field getField() AbstractFieldMatrix
+Get the type of field elements of the matrix.
+ getField() ArrayFieldVector
+Get the type of field elements of the vector.
+ getField() FieldMatrix
+Get the type of field elements of the matrix.
+ getField() FieldVector
+Get the type of field elements of the vector.
+ getField() SparseFieldVector
+Get the type of field elements of the vector.
+ getField() BigReal
+Get the Field getFinalTime() ContinuousOutputModel
+Get the final integration time.
+ getFirst() ChromosomePair
+Access the first chromosome.
+ getFitness() Chromosome
+Access the fitness of this chromosome.
+ getFittestChromosome() ListPopulation
+Access the fittest chromosome in this population.
+ getFittestChromosome() Population
+Access the fittest chromosome in this population.
+ getFormat() Vector3DFormat
+Get the components format.
+ getFormat() RealVectorFormat
+Get the components format.
+ getFrobeniusNorm() AbstractRealMatrix
+Returns the
+ Frobenius norm of the matrix.
+ getFrobeniusNorm() BlockRealMatrix
+Returns the
+ Frobenius norm of the matrix.
+ getFrobeniusNorm() RealMatrix
+Returns the
+ Frobenius norm of the matrix.
+ getFunctionValue() UnivariateRealSolver
+Get the result of the last run of the solver.
+ getFunctionValue() UnivariateRealSolverImpl
+Get the result of the last run of the solver.
+ getFunctionValue() MultiStartUnivariateRealOptimizer
+Get the result of the last run of the optimizer.
+ getFunctionValue() AbstractUnivariateRealOptimizer
+Get the result of the last run of the optimizer.
+ getFunctionValue() UnivariateRealOptimizer
+Get the result of the last run of the optimizer.
+ getFunctionValueAccuracy() UnivariateRealSolver
+Get the actual function value accuracy.
+ getFunctionValueAccuracy() UnivariateRealSolverImpl
+Get the actual function value accuracy.
+ getGenerationsEvolved() GeneticAlgorithm
+Returns the number of generations evolved to
+ reach StoppingCondition getGenerator() CorrelatedRandomVectorGenerator
+Get the underlying normalized components generator.
+ getGeneratorUpperBounds() EmpiricalDistributionImpl
+Returns a fresh copy of the array of upper bounds of the subintervals
+ of [0,1] used in generating data from the empirical distribution.
+ getGeoMeanImpl() MultivariateSummaryStatistics
+Returns the currently configured geometric mean implementation
+ getGeoMeanImpl() SummaryStatistics
+Returns the currently configured geometric mean implementation
+ getGeoMeanImpl() SynchronizedMultivariateSummaryStatistics
+Returns the currently configured geometric mean implementation
+ getGeoMeanImpl() SynchronizedSummaryStatistics
+Returns the currently configured geometric mean implementation
+ getGeometricMean() AggregateSummaryStatistics
+Returns the geometric mean of all the aggregated data.
+ getGeometricMean() DescriptiveStatistics
+Returns the
+ geometric mean of the available values
+ getGeometricMean() MultivariateSummaryStatistics
+Returns an array whose ith entry is the geometric mean of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getGeometricMean() StatisticalMultivariateSummary
+Returns an array whose ith entry is the
+ geometric mean of the ith entries of the arrays
+ that correspond to each multivariate sample
+ getGeometricMean() SummaryStatistics
+Returns the geometric mean of the values that have been added.
+ getGeometricMean() SynchronizedMultivariateSummaryStatistics
+Returns an array whose ith entry is the geometric mean of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getGeometricMean() SynchronizedSummaryStatistics
+Returns the geometric mean of the values that have been added.
+ getGeometricMeanImpl() DescriptiveStatistics
+Returns the currently configured geometric mean implementation.
+ getGradientEvaluations() DifferentiableMultivariateRealOptimizer
+Get the number of evaluations of the objective function gradient.
+ getGradientEvaluations() AbstractScalarDifferentiableOptimizer
+Get the number of evaluations of the objective function gradient.
+ getGradientEvaluations() MultiStartDifferentiableMultivariateRealOptimizer
+Get the number of evaluations of the objective function gradient.
+ getGuessedAmplitude() HarmonicCoefficientsGuesser
+Get the guessed amplitude a.
+ getGuessedPhase() HarmonicCoefficientsGuesser
+Get the guessed phase φ.
+ getGuessedPulsation() HarmonicCoefficientsGuesser
+Get the guessed pulsation ω.
+ getH() QRDecomposition
+Returns the Householder reflector vectors.
+ getH() QRDecompositionImpl
+Returns the Householder reflector vectors.
+ getImagEigenvalue(int) EigenDecomposition
+Returns the imaginary part of the ith eigenvalue of the original matrix.
+ getImagEigenvalue(int) EigenDecompositionImpl
+Returns the imaginary part of the ith eigenvalue of the original matrix.
+ getImagEigenvalues() EigenDecomposition
+Returns a copy of the imaginary parts of the eigenvalues of the original matrix.
+ getImagEigenvalues() EigenDecompositionImpl
+Returns a copy of the imaginary parts of the eigenvalues of the original matrix.
+ getImaginary() Complex
+Access the imaginary part.
+ getImaginaryCharacter() ComplexFormat
+Access the imaginaryCharacter.
+ getImaginaryFormat() ComplexFormat
+Access the imaginaryFormat.
+ getImproperInstance() BigFractionFormat
+Returns the default complex format for the current locale.
+ getImproperInstance(Locale) BigFractionFormat
+Returns the default complex format for the given locale.
+ getImproperInstance() FractionFormat
+Returns the default complex format for the current locale.
+ getImproperInstance(Locale) FractionFormat
+Returns the default complex format for the given locale.
+ getIndex() OpenMapRealVector.OpenMapEntry
+Get the index of the entry.
+ getIndex() RealVector.Entry
+Get the index of the entry.
+ getInitialDomain(double) AbstractContinuousDistribution
+Access the initial domain value, based on p, used to
+ bracket a CDF root.
+ getInitialDomain(double) BetaDistributionImpl
+Access the initial domain value, based on p, used to
+ bracket a CDF root.
+ getInitialDomain(double) CauchyDistributionImpl
+Access the initial domain value, based on p, used to
+ bracket a CDF root.
+ getInitialDomain(double) ChiSquaredDistributionImpl
+Access the initial domain value, based on p, used to
+ bracket a CDF root.
+ getInitialDomain(double) ExponentialDistributionImpl
+Access the initial domain value, based on p, used to
+ bracket a CDF root.
+ getInitialDomain(double) FDistributionImpl
+Access the initial domain value, based on p, used to
+ bracket a CDF root.
+ getInitialDomain(double) GammaDistributionImpl
+Access the initial domain value, based on p, used to
+ bracket a CDF root.
+ getInitialDomain(double) NormalDistributionImpl
+Access the initial domain value, based on p, used to
+ bracket a CDF root.
+ getInitialDomain(double) TDistributionImpl
+Access the initial domain value, based on p, used to
+ bracket a CDF root.
+ getInitialDomain(double) WeibullDistributionImpl
+Access the initial domain value, based on p, used to
+ bracket a CDF root.
+ getInitialTime() ContinuousOutputModel
+Get the initial integration time.
+ getInstance() ComplexField
+Get the unique instance.
+ getInstance() ComplexFormat
+Returns the default complex format for the current locale.
+ getInstance(Locale) ComplexFormat
+Returns the default complex format for the given locale.
+ getInstance() BigFractionField
+Get the unique instance.
+ getInstance() FractionField
+Get the unique instance.
+ getInstance() Vector3DFormat
+Returns the default 3D vector format for the current locale.
+ getInstance(Locale) Vector3DFormat
+Returns the default 3D vector format for the given locale.
+ getInstance() RealVectorFormat
+Returns the default real vector format for the current locale.
+ getInstance(Locale) RealVectorFormat
+Returns the default real vector format for the given locale.
+ getInstance(int) AdamsNordsieckTransformer
+Get the Nordsieck transformer for a given number of steps.
+ getInstance() DummyStepHandler
+Get the only instance.
+ getInstance() BigRealField
+Get the unique instance.
+ getIntercept() SimpleRegression
+Returns the intercept of the estimated regression line.
+ getInterceptStdErr() SimpleRegression
+Returns the
+ standard error of the intercept estimate ,
+ usually denoted s(b0).
+ getInternalValues() ResizableDoubleArray
+Returns the internal storage array.
+ getInterpolatedDerivatives() AbstractStepInterpolator
+Get the derivatives of the state vector of the interpolated point.
+ getInterpolatedDerivatives() StepInterpolator
+Get the derivatives of the state vector of the interpolated point.
+ getInterpolatedDyDp() StepInterpolatorWithJacobians
+Get the partial derivatives of the state vector with respect to
+ the ODE parameters of the interpolated point.
+ getInterpolatedDyDpDot() StepInterpolatorWithJacobians
+Get the time derivatives of the jacobian of the state vector
+ with respect to the ODE parameters of the interpolated point.
+ getInterpolatedDyDy0() StepInterpolatorWithJacobians
+Get the partial derivatives of the state vector with respect to
+ the initial state of the interpolated point.
+ getInterpolatedDyDy0Dot() StepInterpolatorWithJacobians
+Get the time derivatives of the jacobian of the state vector
+ with respect to the initial state of the interpolated point.
+ getInterpolatedState() ContinuousOutputModel
+Get the state vector of the interpolated point.
+ getInterpolatedState() AbstractStepInterpolator
+Get the state vector of the interpolated point.
+ getInterpolatedState() StepInterpolator
+Get the state vector of the interpolated point.
+ getInterpolatedStateVariation() NordsieckStepInterpolator
+Get the state vector variation from current to interpolated state.
+ getInterpolatedTime() ContinuousOutputModel
+Get the time of the interpolated point.
+ getInterpolatedTime() StepInterpolatorWithJacobians
+Get the time of the interpolated point.
+ getInterpolatedTime() AbstractStepInterpolator
+Get the time of the interpolated point.
+ getInterpolatedTime() StepInterpolator
+Get the time of the interpolated point.
+ getInterpolatedY() StepInterpolatorWithJacobians
+Get the state vector of the interpolated point.
+ getInterpolatedYDot() StepInterpolatorWithJacobians
+Get the time derivatives of the state vector of the interpolated point.
+ getInterpolatingPoints() PolynomialFunctionLagrangeForm
+Returns a copy of the interpolating points array.
+ getInterpolatingValues() PolynomialFunctionLagrangeForm
+Returns a copy of the interpolating values array.
+ getInverse() DecompositionSolver
+Get the inverse (or pseudo-inverse) of the decomposed matrix.
+ getInverse() FieldDecompositionSolver
+Get the inverse (or pseudo-inverse) of the decomposed matrix.
+ getIterationCount() ConvergingAlgorithm
+Get the number of iterations in the last run of the algorithm.
+ getIterationCount() ConvergingAlgorithmImpl
+Get the number of iterations in the last run of the algorithm.
+ getIterationCount() MultiStartUnivariateRealOptimizer
+Get the number of iterations in the last run of the algorithm.
+ getIterations() DifferentiableMultivariateRealOptimizer
+Get the number of iterations realized by the algorithm.
+ getIterations() DifferentiableMultivariateVectorialOptimizer
+Get the number of iterations realized by the algorithm.
+ getIterations() DirectSearchOptimizer
+Get the number of iterations realized by the algorithm.
+ getIterations() AbstractLeastSquaresOptimizer
+Get the number of iterations realized by the algorithm.
+ getIterations() AbstractScalarDifferentiableOptimizer
+Get the number of iterations realized by the algorithm.
+ getIterations() AbstractLinearOptimizer
+Get the number of iterations realized by the algorithm.
+ getIterations() LinearOptimizer
+Get the number of iterations realized by the algorithm.
+ getIterations() MultiStartDifferentiableMultivariateRealOptimizer
+Get the number of iterations realized by the algorithm.
+ getIterations() MultiStartDifferentiableMultivariateVectorialOptimizer
+Get the number of iterations realized by the algorithm.
+ getIterations() MultiStartMultivariateRealOptimizer
+Get the number of iterations realized by the algorithm.
+ getIterations() MultivariateRealOptimizer
+Get the number of iterations realized by the algorithm.
+ getJacobianEvaluations() AbstractEstimator
+Deprecated. Get the number of jacobian evaluations.
+getJacobianEvaluations() DifferentiableMultivariateVectorialOptimizer
+Get the number of evaluations of the objective function jacobian .
+ getJacobianEvaluations() AbstractLeastSquaresOptimizer
+Get the number of evaluations of the objective function jacobian .
+ getJacobianEvaluations() MultiStartDifferentiableMultivariateVectorialOptimizer
+Get the number of evaluations of the objective function jacobian .
+ getKnots() PolynomialSplineFunction
+Returns an array copy of the knot points.
+ getKurtosis() DescriptiveStatistics
+Returns the Kurtosis of the available values.
+ getKurtosisImpl() DescriptiveStatistics
+Returns the currently configured kurtosis implementation.
+ getL() CholeskyDecomposition
+Returns the matrix L of the decomposition.
+ getL() CholeskyDecompositionImpl
+Returns the matrix L of the decomposition.
+ getL() FieldLUDecomposition
+Returns the matrix L of the decomposition.
+ getL() FieldLUDecompositionImpl
+Returns the matrix L of the decomposition.
+ getL() LUDecomposition
+Returns the matrix L of the decomposition.
+ getL() LUDecompositionImpl
+Returns the matrix L of the decomposition.
+ getL1Distance(RealVector) AbstractRealVector
+Distance between two vectors.
+ getL1Distance(double[]) AbstractRealVector
+Distance between two vectors.
+ getL1Distance(RealVector) ArrayRealVector
+Distance between two vectors.
+ getL1Distance(double[]) ArrayRealVector
+Distance between two vectors.
+ getL1Distance(ArrayRealVector) ArrayRealVector
+Distance between two vectors.
+ getL1Distance(OpenMapRealVector) OpenMapRealVector
+Distance between two vectors.
+ getL1Distance(RealVector) OpenMapRealVector
+Distance between two vectors.
+ getL1Distance(double[]) OpenMapRealVector
+Distance between two vectors.
+ getL1Distance(RealVector) RealVector
+Distance between two vectors.
+ getL1Distance(double[]) RealVector
+Distance between two vectors.
+ getL1Norm() AbstractRealVector
+Returns the L1 norm of the vector.
+ getL1Norm() ArrayRealVector
+Returns the L1 norm of the vector.
+ getL1Norm() RealVector
+Returns the L1 norm of the vector.
+ getLength() AbstractListChromosome
+Returns the length of the chromosome.
+ getLInfDistance(RealVector) AbstractRealVector
+Distance between two vectors.
+ getLInfDistance(double[]) AbstractRealVector
+Distance between two vectors.
+ getLInfDistance(RealVector) ArrayRealVector
+Distance between two vectors.
+ getLInfDistance(double[]) ArrayRealVector
+Distance between two vectors.
+ getLInfDistance(ArrayRealVector) ArrayRealVector
+Distance between two vectors.
+ getLInfDistance(RealVector) OpenMapRealVector
+Distance between two vectors.
+ getLInfDistance(double[]) OpenMapRealVector
+Distance between two vectors.
+ getLInfDistance(RealVector) RealVector
+Distance between two vectors.
+ getLInfDistance(double[]) RealVector
+Distance between two vectors.
+ getLInfNorm() AbstractRealVector
+Returns the L∞ norm of the vector.
+ getLInfNorm() ArrayRealVector
+Returns the L∞ norm of the vector.
+ getLInfNorm() RealVector
+Returns the L∞ norm of the vector.
+ getLocalizedMessage() MathException
+
+ getLocalizedMessage() MathRuntimeException
+
+ getLT() CholeskyDecomposition
+Returns the transpose of the matrix L of the decomposition.
+ getLT() CholeskyDecompositionImpl
+Returns the transpose of the matrix L of the decomposition.
+ getLUMatrix() BigMatrixImpl
+Deprecated. Returns the LU decomposition as a BigMatrix.
+getMatrix() Rotation
+Get the 3X3 matrix corresponding to the instance
+ getMax() AggregateSummaryStatistics
+Returns the maximum of the available values
+ getMax() DescriptiveStatistics
+Returns the maximum of the available values
+ getMax() MultivariateSummaryStatistics
+Returns an array whose ith entry is the maximum of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getMax() StatisticalMultivariateSummary
+Returns an array whose ith entry is the
+ maximum of the ith entries of the arrays
+ that correspond to each multivariate sample
+ getMax() StatisticalSummary
+Returns the maximum of the available values
+ getMax() StatisticalSummaryValues
+
+ getMax() SummaryStatistics
+Returns the maximum of the values that have been added.
+ getMax() SynchronizedMultivariateSummaryStatistics
+Returns an array whose ith entry is the maximum of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getMax() SynchronizedSummaryStatistics
+Returns the maximum of the values that have been added.
+ getMaxCheckInterval() EventState
+Get the maximal time interval between events handler checks.
+ getMaxEvaluations() MaxEvaluationsExceededException
+Get the maximal number of evaluations allowed.
+ getMaxEvaluations() AbstractIntegrator
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() FirstOrderIntegratorWithJacobians
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() ODEIntegrator
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() DifferentiableMultivariateRealOptimizer
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() DifferentiableMultivariateVectorialOptimizer
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() DirectSearchOptimizer
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() AbstractLeastSquaresOptimizer
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() AbstractScalarDifferentiableOptimizer
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() MultiStartDifferentiableMultivariateRealOptimizer
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() MultiStartDifferentiableMultivariateVectorialOptimizer
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() MultiStartMultivariateRealOptimizer
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() MultiStartUnivariateRealOptimizer
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() MultivariateRealOptimizer
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() AbstractUnivariateRealOptimizer
+Get the maximal number of functions evaluations.
+ getMaxEvaluations() UnivariateRealOptimizer
+Get the maximal number of functions evaluations.
+ getMaxGrowth() MultistepIntegrator
+Get the maximal growth factor for stepsize control.
+ getMaxGrowth() EmbeddedRungeKuttaIntegrator
+Get the maximal growth factor for stepsize control.
+ getMaximalIterationCount() ConvergingAlgorithm
+Get the upper limit for the number of iterations.
+ getMaximalIterationCount() ConvergingAlgorithmImpl
+Get the upper limit for the number of iterations.
+ getMaximalIterationCount() MultiStartUnivariateRealOptimizer
+Get the upper limit for the number of iterations.
+ getMaxImpl() DescriptiveStatistics
+Returns the currently configured maximum implementation.
+ getMaxImpl() MultivariateSummaryStatistics
+Returns the currently configured maximum implementation
+ getMaxImpl() SummaryStatistics
+Returns the currently configured maximum implementation
+ getMaxImpl() SynchronizedMultivariateSummaryStatistics
+Returns the currently configured maximum implementation
+ getMaxImpl() SynchronizedSummaryStatistics
+Returns the currently configured maximum implementation
+ getMaxIndex() AbstractRealVector
+Get the index of the maximum entry.
+ getMaxIterationCount() EventState
+Get the upper limit in the iteration count for event localization.
+ getMaxIterations() MaxIterationsExceededException
+Get the maximal number of iterations allowed.
+ getMaxIterations() DifferentiableMultivariateRealOptimizer
+Get the maximal number of iterations of the algorithm.
+ getMaxIterations() DifferentiableMultivariateVectorialOptimizer
+Get the maximal number of iterations of the algorithm.
+ getMaxIterations() DirectSearchOptimizer
+Get the maximal number of iterations of the algorithm.
+ getMaxIterations() AbstractLeastSquaresOptimizer
+Get the maximal number of iterations of the algorithm.
+ getMaxIterations() AbstractScalarDifferentiableOptimizer
+Get the maximal number of iterations of the algorithm.
+ getMaxIterations() AbstractLinearOptimizer
+Get the maximal number of iterations of the algorithm.
+ getMaxIterations() LinearOptimizer
+Get the maximal number of iterations of the algorithm.
+ getMaxIterations() MultiStartDifferentiableMultivariateRealOptimizer
+Get the maximal number of iterations of the algorithm.
+ getMaxIterations() MultiStartDifferentiableMultivariateVectorialOptimizer
+Get the maximal number of iterations of the algorithm.
+ getMaxIterations() MultiStartMultivariateRealOptimizer
+Get the maximal number of iterations of the algorithm.
+ getMaxIterations() MultivariateRealOptimizer
+Get the maximal number of iterations of the algorithm.
+ getMaxStep() AdaptiveStepsizeIntegrator
+Get the maximal step.
+ getMaxValue() AbstractRealVector
+Get the value of the maximum entry.
+ getMean() ExponentialDistribution
+Access the mean.
+ getMean() ExponentialDistributionImpl
+Access the mean.
+ getMean() NormalDistribution
+Access the mean.
+ getMean() NormalDistributionImpl
+Access the mean.
+ getMean() PoissonDistribution
+Get the mean for the distribution.
+ getMean() PoissonDistributionImpl
+Get the Poisson mean for the distribution.
+ getMean() AggregateSummaryStatistics
+Returns the
+ arithmetic mean of the available values
+ getMean() DescriptiveStatistics
+Returns the
+ arithmetic mean of the available values
+ getMean() MultivariateSummaryStatistics
+Returns an array whose ith entry is the mean of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getMean() StatisticalMultivariateSummary
+Returns an array whose ith entry is the
+ mean of the ith entries of the arrays
+ that correspond to each multivariate sample
+ getMean() StatisticalSummary
+Returns the
+ arithmetic mean of the available values
+ getMean() StatisticalSummaryValues
+
+ getMean() SummaryStatistics
+Returns the mean of the values that have been added.
+ getMean() SynchronizedMultivariateSummaryStatistics
+Returns an array whose ith entry is the mean of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getMean() SynchronizedSummaryStatistics
+Returns the mean of the values that have been added.
+ getMeanImpl() DescriptiveStatistics
+Returns the currently configured mean implementation.
+ getMeanImpl() MultivariateSummaryStatistics
+Returns the currently configured mean implementation
+ getMeanImpl() SummaryStatistics
+Returns the currently configured mean implementation
+ getMeanImpl() SynchronizedMultivariateSummaryStatistics
+Returns the currently configured mean implementation
+ getMeanImpl() SynchronizedSummaryStatistics
+Returns the currently configured mean implementation
+ getMeanSquareError() SimpleRegression
+Returns the sum of squared errors divided by the degrees of freedom,
+ usually abbreviated MSE.
+ getMeasuredValue() WeightedMeasurement
+Deprecated. Get the measured value
+getMeasurements() EstimationProblem
+Deprecated. Get the measurements of an estimation problem.
+getMeasurements() SimpleEstimationProblem
+Deprecated. Get the measurements of an estimation problem.
+getMedian() CauchyDistribution
+Access the median.
+ getMedian() CauchyDistributionImpl
+Access the median.
+ getMessage(Locale) MathException
+Gets the message in a specified locale.
+ getMessage() MathException
+
+ getMessage(Locale) MathRuntimeException
+Gets the message in a specified locale.
+ getMessage() MathRuntimeException
+
+ getMin() AggregateSummaryStatistics
+Returns the minimum of the available values
+ getMin() DescriptiveStatistics
+Returns the minimum of the available values
+ getMin() MultivariateSummaryStatistics
+Returns an array whose ith entry is the minimum of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getMin() StatisticalMultivariateSummary
+Returns an array whose ith entry is the
+ minimum of the ith entries of the arrays
+ that correspond to each multivariate sample
+ getMin() StatisticalSummary
+Returns the minimum of the available values
+ getMin() StatisticalSummaryValues
+
+ getMin() SummaryStatistics
+Returns the minimum of the values that have been added.
+ getMin() SynchronizedMultivariateSummaryStatistics
+Returns an array whose ith entry is the minimum of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getMin() SynchronizedSummaryStatistics
+Returns the minimum of the values that have been added.
+ getMinimalIterationCount() UnivariateRealIntegrator
+Get the lower limit for the number of iterations.
+ getMinimalIterationCount() UnivariateRealIntegratorImpl
+Get the lower limit for the number of iterations.
+ getMinImpl() DescriptiveStatistics
+Returns the currently configured minimum implementation.
+ getMinImpl() MultivariateSummaryStatistics
+Returns the currently configured minimum implementation
+ getMinImpl() SummaryStatistics
+Returns the currently configured minimum implementation
+ getMinImpl() SynchronizedMultivariateSummaryStatistics
+Returns the currently configured minimum implementation
+ getMinImpl() SynchronizedSummaryStatistics
+Returns the currently configured minimum implementation
+ getMinIndex() AbstractRealVector
+Get the index of the minimum entry.
+ getMinReduction() MultistepIntegrator
+Get the minimal reduction factor for stepsize control.
+ getMinReduction() EmbeddedRungeKuttaIntegrator
+Get the minimal reduction factor for stepsize control.
+ getMinStep() AdaptiveStepsizeIntegrator
+Get the minimal step.
+ getMinValue() AbstractRealVector
+Get the value of the minimum entry.
+ getMode() ValueServer
+Getter for property mode.
+ getMu() ValueServer
+Getter for property mu.
+ getMutationPolicy() GeneticAlgorithm
+Returns the mutation policy.
+ getMutationRate() GeneticAlgorithm
+Returns the mutation rate.
+ getN() PolynomialSplineFunction
+Returns the number of spline segments = the number of polynomials
+ = the number of knot points - 1.
+ getN() Covariance
+Returns the number of observations (length of covariate vectors)
+ getN() AggregateSummaryStatistics
+Returns the number of available values
+ getN() DescriptiveStatistics
+Returns the number of available values
+ getN() FirstMoment
+Returns the number of values that have been added.
+ getN() GeometricMean
+Returns the number of values that have been added.
+ getN() Kurtosis
+Returns the number of values that have been added.
+ getN() Mean
+Returns the number of values that have been added.
+ getN() Skewness
+Returns the number of values that have been added.
+ getN() StandardDeviation
+Returns the number of values that have been added.
+ getN() Variance
+Returns the number of values that have been added.
+ getN() VectorialCovariance
+Get the number of vectors in the sample.
+ getN() VectorialMean
+Get the number of vectors in the sample.
+ getN() MultivariateSummaryStatistics
+Returns the number of available values
+ getN() Max
+Returns the number of values that have been added.
+ getN() Min
+Returns the number of values that have been added.
+ getN() StatisticalMultivariateSummary
+Returns the number of available values
+ getN() StatisticalSummary
+Returns the number of available values
+ getN() StatisticalSummaryValues
+
+ getN() StorelessUnivariateStatistic
+Returns the number of values that have been added.
+ getN() Product
+Returns the number of values that have been added.
+ getN() Sum
+Returns the number of values that have been added.
+ getN() SumOfLogs
+Returns the number of values that have been added.
+ getN() SumOfSquares
+Returns the number of values that have been added.
+ getN() SummaryStatistics
+Returns the number of available values
+ getN() SynchronizedDescriptiveStatistics
+Returns the number of available values
+ getN() SynchronizedMultivariateSummaryStatistics
+Returns the number of available values
+ getN() SynchronizedSummaryStatistics
+Returns the number of available values
+ getN() SimpleRegression
+Returns the number of observations that have been added to the model.
+ getName() EstimatedParameter
+Deprecated. get the name of the parameter
+getName() AbstractIntegrator
+Get the name of the method.
+ getName() ODEIntegrator
+Get the name of the method.
+ getNanStrategy() NaturalRanking
+Return the NaNStrategy
+ getNewtonCoefficients() PolynomialFunctionNewtonForm
+Returns a copy of coefficients in Newton form formula.
+ getNext() ValueServer
+Returns the next generated value, generated according
+ to the mode value (see MODE constants).
+ getNextValue() EmpiricalDistribution
+Generates a random value from this distribution.
+ getNextValue() EmpiricalDistributionImpl
+Generates a random value from this distribution.
+ getNorm() Vector3D
+Get the L2 norm for the vector.
+ getNorm() AbstractRealMatrix
+Returns the
+ maximum absolute row sum norm of the matrix.
+ getNorm() AbstractRealVector
+Returns the L2 norm of the vector.
+ getNorm() ArrayRealVector
+Returns the L2 norm of the vector.
+ getNorm() BigMatrix
+Deprecated. Returns the
+ maximum absolute row sum norm of the matrix.
+getNorm() BigMatrixImpl
+Deprecated. Returns the
+ maximum absolute row sum norm of the matrix.
+getNorm() BlockRealMatrix
+Returns the
+ maximum absolute row sum norm of the matrix.
+ getNorm() RealMatrix
+Returns the
+ maximum absolute row sum norm of the matrix.
+ getNorm() RealVector
+Returns the L2 norm of the vector.
+ getNorm() SingularValueDecomposition
+Returns the L2 norm of the matrix.
+ getNorm() SingularValueDecompositionImpl
+Returns the L2 norm of the matrix.
+ getNorm1() Vector3D
+Get the L1 norm for the vector.
+ getNormInf() Vector3D
+Get the L∞ norm for the vector.
+ getNormSq() Vector3D
+Get the square of the norm for the vector.
+ getNSteps() AdamsNordsieckTransformer
+Get the number of steps of the method
+ (excluding the one being computed).
+ getNumberOfElements() ZipfDistribution
+Get the number of elements (e.g.
+ getNumberOfElements() ZipfDistributionImpl
+Get the number of elements (e.g.
+ getNumberOfSuccesses() HypergeometricDistribution
+Access the number of successes.
+ getNumberOfSuccesses() HypergeometricDistributionImpl
+Access the number of successes.
+ getNumberOfSuccesses() PascalDistribution
+Access the number of successes for this distribution.
+ getNumberOfSuccesses() PascalDistributionImpl
+Access the number of successes for this distribution.
+ getNumberOfTrials() BinomialDistribution
+Access the number of trials for this distribution.
+ getNumberOfTrials() BinomialDistributionImpl
+Access the number of trials for this distribution.
+ getNumElements() DoubleArray
+Returns the number of elements currently in the array.
+ getNumElements() ResizableDoubleArray
+Returns the number of elements currently in the array.
+ getNumerator() BigFraction
+
+ Access the numerator as a BigInteger.
+ getNumerator() Fraction
+Access the numerator.
+ getNumeratorAsInt() BigFraction
+
+ Access the numerator as a int .
+ getNumeratorAsLong() BigFraction
+
+ Access the numerator as a long .
+ getNumeratorDegreesOfFreedom() FDistribution
+Access the numerator degrees of freedom.
+ getNumeratorDegreesOfFreedom() FDistributionImpl
+Access the numerator degrees of freedom.
+ getNumeratorFormat() AbstractFormat
+Access the numerator format.
+ getNumGenerations() FixedGenerationCount
+
+ getObservations() CurveFitter
+Get the observed points.
+ getOmegaInverse() GLSMultipleLinearRegression
+Get the inverse of the covariance.
+ getOne() ComplexField
+Get the multiplicative identity of the field.
+ getOne() Field
+Get the multiplicative identity of the field.
+ getOne() BigFractionField
+Get the multiplicative identity of the field.
+ getOne() FractionField
+Get the multiplicative identity of the field.
+ getOne() BigRealField
+Get the multiplicative identity of the field.
+ getOneWayAnova() TestUtils
+Return a (singleton) OneWayAnova instance.
+ getOptima() MultiStartDifferentiableMultivariateRealOptimizer
+Get all the optima found during the last call to optimize getOptima() MultiStartDifferentiableMultivariateVectorialOptimizer
+Get all the optima found during the last call to optimize getOptima() MultiStartMultivariateRealOptimizer
+Get all the optima found during the last call to optimize getOptima() MultiStartUnivariateRealOptimizer
+Get all the optima found during the last call to optimize getOptimaValues() MultiStartUnivariateRealOptimizer
+Get all the function values at optima found during the last call to optimize getOrder() DormandPrince54Integrator
+Get the order of the method.
+ getOrder() DormandPrince853Integrator
+Get the order of the method.
+ getOrder() EmbeddedRungeKuttaIntegrator
+Get the order of the method.
+ getOrder() HighamHall54Integrator
+Get the order of the method.
+ getP() FieldLUDecomposition
+Returns the P rows permutation matrix.
+ getP() FieldLUDecompositionImpl
+Returns the P rows permutation matrix.
+ getP() LUDecomposition
+Returns the P rows permutation matrix.
+ getP() LUDecompositionImpl
+Returns the P rows permutation matrix.
+ getParametersDimension() ODEWithJacobians
+Get the number of parameters.
+ getParametersDimension() ParameterizedODE
+Get the number of parameters.
+ getPartial(EstimatedParameter) WeightedMeasurement
+Deprecated. Get the partial derivative of the theoretical valuegetPattern() MathException
+Gets the pattern used to build the message of this throwable.
+ getPattern() MathRuntimeException
+Gets the pattern used to build the message of this throwable.
+ getPct(Object) Frequency
+Deprecated. replaced by Frequency.getPct(Comparable)
+getPct(Comparable<?>) Frequency
+Returns the percentage of values that are equal to v
+ (as a proportion between 0 and 1).
+ getPct(int) Frequency
+Returns the percentage of values that are equal to v
+ (as a proportion between 0 and 1).
+ getPct(long) Frequency
+Returns the percentage of values that are equal to v
+ (as a proportion between 0 and 1).
+ getPct(char) Frequency
+Returns the percentage of values that are equal to v
+ (as a proportion between 0 and 1).
+ getPercentile(double) DescriptiveStatistics
+Returns an estimate for the pth percentile of the stored values.
+ getPercentileImpl() DescriptiveStatistics
+Returns the currently configured percentile implementation.
+ getPermutation() BigMatrixImpl
+Deprecated. Returns the permutation associated with the lu decomposition.
+getPhase() HarmonicFunction
+Get the phase φ.
+ getPivot() FieldLUDecomposition
+Returns the pivot permutation vector.
+ getPivot() FieldLUDecompositionImpl
+Returns the pivot permutation vector.
+ getPivot() LUDecomposition
+Returns the pivot permutation vector.
+ getPivot() LUDecompositionImpl
+Returns the pivot permutation vector.
+ getPoint() RealPointValuePair
+Get the point.
+ getPoint() VectorialPointValuePair
+Get the point.
+ getPoint() EuclideanIntegerPoint
+Get the n-dimensional point in integer space.
+ getPointRef() RealPointValuePair
+Get a reference to the point.
+ getPointRef() VectorialPointValuePair
+Get a reference to the point.
+ getPoints() Cluster
+Get the points contained in the cluster.
+ getPolynomialFunction() LaguerreSolver
+Deprecated. as of 2.0 the function is not stored anymore within the instance.
+getPolynomials() PolynomialSplineFunction
+Returns a copy of the interpolating polynomials array.
+ getPopulationLimit() ListPopulation
+Access the maximum population size.
+ getPopulationLimit() Population
+Access the maximum population size.
+ getPopulationSize() HypergeometricDistribution
+Access the population size.
+ getPopulationSize() HypergeometricDistributionImpl
+Access the population size.
+ getPopulationSize() ListPopulation
+Access the current population size.
+ getPopulationSize() Population
+Access the current population size.
+ getPrefix() Vector3DFormat
+Get the format prefix.
+ getPrefix() RealVectorFormat
+Get the format prefix.
+ getPreviousTime() StepInterpolatorWithJacobians
+Get the previous grid point time.
+ getPreviousTime() AbstractStepInterpolator
+Get the previous grid point time.
+ getPreviousTime() StepInterpolator
+Get the previous grid point time.
+ getProbabilityOfSuccess() BinomialDistribution
+Access the probability of success for this distribution.
+ getProbabilityOfSuccess() BinomialDistributionImpl
+Access the probability of success for this distribution.
+ getProbabilityOfSuccess() PascalDistribution
+Access the probability of success for this distribution.
+ getProbabilityOfSuccess() PascalDistributionImpl
+Access the probability of success for this distribution.
+ getProperInstance() BigFractionFormat
+Returns the default complex format for the current locale.
+ getProperInstance(Locale) BigFractionFormat
+Returns the default complex format for the given locale.
+ getProperInstance() FractionFormat
+Returns the default complex format for the current locale.
+ getProperInstance(Locale) FractionFormat
+Returns the default complex format for the given locale.
+ getPulsation() HarmonicFunction
+Get the pulsation ω.
+ getQ() QRDecomposition
+Returns the matrix Q of the decomposition.
+ getQ() QRDecompositionImpl
+Returns the matrix Q of the decomposition.
+ getQ0() Rotation
+Get the scalar coordinate of the quaternion.
+ getQ1() Rotation
+Get the first coordinate of the vectorial part of the quaternion.
+ getQ2() Rotation
+Get the second coordinate of the vectorial part of the quaternion.
+ getQ3() Rotation
+Get the third coordinate of the vectorial part of the quaternion.
+ getQT() QRDecomposition
+Returns the transpose of the matrix Q of the decomposition.
+ getQT() QRDecompositionImpl
+Returns the transpose of the matrix Q of the decomposition.
+ getQuantile() Percentile
+Returns the value of the quantile field (determines what percentile is
+ computed when evaluate() is called with no quantile argument).
+ getR() QRDecomposition
+Returns the matrix R of the decomposition.
+ getR() QRDecompositionImpl
+Returns the matrix R of the decomposition.
+ getR() SimpleRegression
+Returns
+ Pearson's product moment correlation coefficient ,
+ usually denoted r.
+ getRandomGenerator() GeneticAlgorithm
+Returns the (static) random generator.
+ getRank() SingularValueDecomposition
+Return the effective numerical matrix rank.
+ getRank() SingularValueDecompositionImpl
+Return the effective numerical matrix rank.
+ getRank() CorrelatedRandomVectorGenerator
+Get the rank of the covariance matrix.
+ getRankCorrelation() SpearmansCorrelation
+Returns a PearsonsCorrelation getReal() Complex
+Access the real part.
+ getRealEigenvalue(int) EigenDecomposition
+Returns the real part of the ith eigenvalue of the original matrix.
+ getRealEigenvalue(int) EigenDecompositionImpl
+Returns the real part of the ith eigenvalue of the original matrix.
+ getRealEigenvalues() EigenDecomposition
+Returns a copy of the real parts of the eigenvalues of the original matrix.
+ getRealEigenvalues() EigenDecompositionImpl
+Returns a copy of the real parts of the eigenvalues of the original matrix.
+ getRealFormat() ComplexFormat
+Access the realFormat.
+ getReducedFraction(int, int) BigFraction
+
+ Creates a BigFraction instance with the 2 parts of a fraction
+ Y/Z.
+ getReducedFraction(int, int) Fraction
+Creates a Fraction instance with the 2 parts
+ of a fraction Y/Z.
+ getRegressionSumSquares() SimpleRegression
+Returns the sum of squared deviations of the predicted y values about
+ their mean (which equals the mean of y).
+ getRelationship() LinearConstraint
+Get the relationship between left and right hand sides.
+ getRelativeAccuracy() ConvergingAlgorithm
+Get the actual relative accuracy.
+ getRelativeAccuracy() ConvergingAlgorithmImpl
+Get the actual relative accuracy.
+ getRelativeAccuracy() MultiStartUnivariateRealOptimizer
+Get the actual relative accuracy.
+ getRepresentation() AbstractListChromosome
+Returns the (immutable) inner representation of the chromosome.
+ getResidual() WeightedMeasurement
+Deprecated. Get the residual for this measurement
+ The residual is the measured value minus the theoretical value.
+getResult() UnivariateRealIntegrator
+Get the result of the last run of the integrator.
+ getResult() UnivariateRealIntegratorImpl
+Access the last computed integral.
+ getResult() UnivariateRealSolver
+Get the result of the last run of the solver.
+ getResult() UnivariateRealSolverImpl
+Get the result of the last run of the solver.
+ getResult() MultiStartUnivariateRealOptimizer
+Get the result of the last run of the optimizer.
+ getResult() AbstractUnivariateRealOptimizer
+Get the result of the last run of the optimizer.
+ getResult() UnivariateRealOptimizer
+Get the result of the last run of the optimizer.
+ getResult() AbstractStorelessUnivariateStatistic
+Returns the current value of the Statistic.
+ getResult() FirstMoment
+Returns the current value of the Statistic.
+ getResult() FourthMoment
+Returns the current value of the Statistic.
+ getResult() GeometricMean
+Returns the current value of the Statistic.
+ getResult() Kurtosis
+Returns the current value of the Statistic.
+ getResult() Mean
+Returns the current value of the Statistic.
+ getResult() SecondMoment
+Returns the current value of the Statistic.
+ getResult() Skewness
+Returns the value of the statistic based on the values that have been added.
+ getResult() StandardDeviation
+Returns the current value of the Statistic.
+ getResult() ThirdMoment
+Returns the current value of the Statistic.
+ getResult() Variance
+Returns the current value of the Statistic.
+ getResult() VectorialCovariance
+Get the covariance matrix.
+ getResult() VectorialMean
+Get the mean vector.
+ getResult() Max
+Returns the current value of the Statistic.
+ getResult() Min
+Returns the current value of the Statistic.
+ getResult() StorelessUnivariateStatistic
+Returns the current value of the Statistic.
+ getResult() Product
+Returns the current value of the Statistic.
+ getResult() Sum
+Returns the current value of the Statistic.
+ getResult() SumOfLogs
+Returns the current value of the Statistic.
+ getResult() SumOfSquares
+Returns the current value of the Statistic.
+ getRMS(EstimationProblem) AbstractEstimator
+Deprecated. Get the Root Mean Square value.
+getRMS(EstimationProblem) Estimator
+Deprecated. Get the Root Mean Square value.
+getRMS() AbstractLeastSquaresOptimizer
+Get the Root Mean Square value.
+ getRootMatrix() CorrelatedRandomVectorGenerator
+Get the root of the covariance matrix.
+ getRoundingMode() BigMatrix
+Deprecated. Gets the rounding mode
+getRoundingMode() BigMatrixImpl
+Deprecated. Gets the rounding mode for division operations
+ The default is BigDecimal.ROUND_HALF_UPgetRoundingMode() BigReal
+Gets the rounding mode for division operations
+ The default is RoundingMode.HALF_UP
+ getRow(int) AbstractFieldMatrix
+Returns the entries in row number row as an array.
+ getRow(int) AbstractRealMatrix
+Returns the entries in row number row as an array.
+ getRow(int) BigMatrix
+Deprecated. Returns the entries in row number row as an array.
+getRow(int) BigMatrixImpl
+Deprecated. Returns the entries in row number row as an array.
+getRow(int) BlockFieldMatrix
+Returns the entries in row number row as an array.
+ getRow(int) BlockRealMatrix
+Returns the entries in row number row as an array.
+ getRow(int) FieldMatrix
+Returns the entries in row number row as an array.
+ getRow(int) RealMatrix
+Returns the entries in row number row as an array.
+ getRowAsDoubleArray(int) BigMatrix
+Deprecated. Returns the entries in row number row as an array
+ of double values.
+getRowAsDoubleArray(int) BigMatrixImpl
+Deprecated. Returns the entries in row number row as an array
+ of double values.
+getRowDimension() AbstractFieldMatrix
+Returns the number of rows in the matrix.
+ getRowDimension() AbstractRealMatrix
+Returns the number of rows in the matrix.
+ getRowDimension() AnyMatrix
+Returns the number of rows in the matrix.
+ getRowDimension() Array2DRowFieldMatrix
+Returns the number of rows in the matrix.
+ getRowDimension() Array2DRowRealMatrix
+Returns the number of rows in the matrix.
+ getRowDimension() BigMatrixImpl
+Deprecated. Returns the number of rows in the matrix.
+getRowDimension() BlockFieldMatrix
+Returns the number of rows in the matrix.
+ getRowDimension() BlockRealMatrix
+Returns the number of rows in the matrix.
+ getRowDimension() OpenMapRealMatrix
+Returns the number of rows in the matrix.
+ getRowDimension() RealMatrixImpl
+Deprecated. Returns the number of rows in the matrix.
+getRowDimension() SparseFieldMatrix
+Returns the number of rows in the matrix.
+ getRowMatrix(int) AbstractFieldMatrix
+Returns the entries in row number row
+ as a row matrix.
+ getRowMatrix(int) AbstractRealMatrix
+Returns the entries in row number row
+ as a row matrix.
+ getRowMatrix(int) BigMatrix
+Deprecated. Returns the entries in row number row
+ as a row matrix.
+getRowMatrix(int) BigMatrixImpl
+Deprecated. Returns the entries in row number row
+ as a row matrix.
+getRowMatrix(int) BlockFieldMatrix
+Returns the entries in row number row
+ as a row matrix.
+ getRowMatrix(int) BlockRealMatrix
+Returns the entries in row number row
+ as a row matrix.
+ getRowMatrix(int) FieldMatrix
+Returns the entries in row number row
+ as a row matrix.
+ getRowMatrix(int) RealMatrix
+Returns the entries in row number row
+ as a row matrix.
+ getRowVector(int) AbstractFieldMatrix
+Returns the entries in row number row
+ as a vector.
+ getRowVector(int) AbstractRealMatrix
+Returns the entries in row number row
+ as a vector.
+ getRowVector(int) BlockFieldMatrix
+Returns the entries in row number row
+ as a vector.
+ getRowVector(int) BlockRealMatrix
+Returns the entries in row number row
+ as a vector.
+ getRowVector(int) FieldMatrix
+Returns the entries in row number row
+ as a vector.
+ getRowVector(int) RealMatrix
+Returns the entries in row number row
+ as a vector.
+ getRSquare() SimpleRegression
+Returns the
+ coefficient of determination ,
+ usually denoted r-square.
+ getS() SingularValueDecomposition
+Returns the diagonal matrix Σ of the decomposition.
+ getS() SingularValueDecompositionImpl
+Returns the diagonal matrix Σ of the decomposition.
+ getSafety() MultistepIntegrator
+Get the safety factor for stepsize control.
+ getSafety() EmbeddedRungeKuttaIntegrator
+Get the safety factor for stepsize control.
+ getSampleSize() HypergeometricDistribution
+Access the sample size.
+ getSampleSize() HypergeometricDistributionImpl
+Access the sample size.
+ getSampleStats() EmpiricalDistribution
+Returns a
+ StatisticalSummary getSampleStats() EmpiricalDistributionImpl
+Returns a StatisticalSummary getScale() CauchyDistribution
+Access the scale parameter.
+ getScale() CauchyDistributionImpl
+Access the scale parameter.
+ getScale() WeibullDistribution
+Access the scale parameter.
+ getScale() WeibullDistributionImpl
+Access the scale parameter.
+ getScale() BigMatrixImpl
+Deprecated. Sets the scale for division operations.
+getScale() BigReal
+Sets the scale for division operations.
+ getSecond() ChromosomePair
+Access the second chromosome.
+ getSecondMoment() AggregateSummaryStatistics
+Returns a statistic related to the Second Central Moment.
+ getSecondMoment() SummaryStatistics
+Returns a statistic related to the Second Central Moment.
+ getSelectionPolicy() GeneticAlgorithm
+Returns the selection policy.
+ getSeparator() Vector3DFormat
+Get the format separator between components.
+ getSeparator() RealVectorFormat
+Get the format separator between components.
+ getShape() WeibullDistribution
+Access the shape parameter.
+ getShape() WeibullDistributionImpl
+Access the shape parameter.
+ getSigma() ValueServer
+Getter for property sigma.
+ getSignificance() SimpleRegression
+Returns the significance level of the slope (equiv) correlation.
+ getSingularValues() SingularValueDecomposition
+Returns the diagonal elements of the matrix Σ of the decomposition.
+ getSingularValues() SingularValueDecompositionImpl
+Returns the diagonal elements of the matrix Σ of the decomposition.
+ getSkewness() DescriptiveStatistics
+Returns the skewness of the available values.
+ getSkewnessImpl() DescriptiveStatistics
+Returns the currently configured skewness implementation.
+ getSlope() SimpleRegression
+Returns the slope of the estimated regression line.
+ getSlopeConfidenceInterval() SimpleRegression
+Returns the half-width of a 95% confidence interval for the slope
+ estimate.
+ getSlopeConfidenceInterval(double) SimpleRegression
+Returns the half-width of a (100-100*alpha)% confidence interval for
+ the slope estimate.
+ getSlopeStdErr() SimpleRegression
+Returns the standard
+ error of the slope estimate ,
+ usually denoted s(b1).
+ getSolver() CholeskyDecomposition
+Get a solver for finding the A × X = B solution in least square sense.
+ getSolver() CholeskyDecompositionImpl
+Get a solver for finding the A × X = B solution in least square sense.
+ getSolver() EigenDecomposition
+Get a solver for finding the A × X = B solution in exact linear sense.
+ getSolver() EigenDecompositionImpl
+Get a solver for finding the A × X = B solution in exact linear sense.
+ getSolver() FieldLUDecomposition
+Get a solver for finding the A × X = B solution in exact linear sense.
+ getSolver() FieldLUDecompositionImpl
+Get a solver for finding the A × X = B solution in exact linear sense.
+ getSolver() LUDecomposition
+Get a solver for finding the A × X = B solution in exact linear sense.
+ getSolver() LUDecompositionImpl
+Get a solver for finding the A × X = B solution in exact linear sense.
+ getSolver() QRDecomposition
+Get a solver for finding the A × X = B solution in least square sense.
+ getSolver() QRDecompositionImpl
+Get a solver for finding the A × X = B solution in least square sense.
+ getSolver() SingularValueDecomposition
+Get a solver for finding the A × X = B solution in least square sense.
+ getSolver() SingularValueDecompositionImpl
+Get a solver for finding the A × X = B solution in least square sense.
+ getSolverAbsoluteAccuracy() AbstractContinuousDistribution
+Returns the solver absolute accuracy for inverse cum computation.
+ getSolverAbsoluteAccuracy() BetaDistributionImpl
+Return the absolute accuracy setting of the solver used to estimate
+ inverse cumulative probabilities.
+ getSolverAbsoluteAccuracy() CauchyDistributionImpl
+Return the absolute accuracy setting of the solver used to estimate
+ inverse cumulative probabilities.
+ getSolverAbsoluteAccuracy() ChiSquaredDistributionImpl
+Return the absolute accuracy setting of the solver used to estimate
+ inverse cumulative probabilities.
+ getSolverAbsoluteAccuracy() ExponentialDistributionImpl
+Return the absolute accuracy setting of the solver used to estimate
+ inverse cumulative probabilities.
+ getSolverAbsoluteAccuracy() FDistributionImpl
+Return the absolute accuracy setting of the solver used to estimate
+ inverse cumulative probabilities.
+ getSolverAbsoluteAccuracy() GammaDistributionImpl
+Return the absolute accuracy setting of the solver used to estimate
+ inverse cumulative probabilities.
+ getSolverAbsoluteAccuracy() NormalDistributionImpl
+Return the absolute accuracy setting of the solver used to estimate
+ inverse cumulative probabilities.
+ getSolverAbsoluteAccuracy() TDistributionImpl
+Return the absolute accuracy setting of the solver used to estimate
+ inverse cumulative probabilities.
+ getSolverAbsoluteAccuracy() WeibullDistributionImpl
+Return the absolute accuracy setting of the solver used to estimate
+ inverse cumulative probabilities.
+ getSortedValues() DescriptiveStatistics
+Returns the current set of values in an array of double primitives,
+ sorted in ascending order.
+ getSparcity() OpenMapRealVector
+
+ getStandardDeviation() NormalDistribution
+Access the standard deviation.
+ getStandardDeviation() NormalDistributionImpl
+Access the standard deviation.
+ getStandardDeviation() AggregateSummaryStatistics
+Returns the standard deviation of the available values.
+ getStandardDeviation() DescriptiveStatistics
+Returns the standard deviation of the available values.
+ getStandardDeviation() MultivariateSummaryStatistics
+Returns an array whose ith entry is the standard deviation of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getStandardDeviation() StatisticalMultivariateSummary
+Returns an array whose ith entry is the
+ standard deviation of the ith entries of the arrays
+ that correspond to each multivariate sample
+ getStandardDeviation() StatisticalSummary
+Returns the standard deviation of the available values.
+ getStandardDeviation() StatisticalSummaryValues
+
+ getStandardDeviation() SummaryStatistics
+Returns the standard deviation of the values that have been added.
+ getStandardDeviation() SynchronizedDescriptiveStatistics
+Returns the standard deviation of the available values.
+ getStandardDeviation() SynchronizedMultivariateSummaryStatistics
+Returns an array whose ith entry is the standard deviation of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getStandardDeviation() SynchronizedSummaryStatistics
+Returns the standard deviation of the values that have been added.
+ getStarterIntegrator() MultistepIntegrator
+Get the starter integrator.
+ getStepHandlers() AbstractIntegrator
+Get all the step handlers that have been added to the integrator.
+ getStepHandlers() FirstOrderIntegratorWithJacobians
+Get all the step handlers that have been added to the integrator.
+ getStepHandlers() ODEIntegrator
+Get all the step handlers that have been added to the integrator.
+ getSubMatrix(int, int, int, int) AbstractFieldMatrix
+Gets a submatrix.
+ getSubMatrix(int[], int[]) AbstractFieldMatrix
+Gets a submatrix.
+ getSubMatrix(int, int, int, int) AbstractRealMatrix
+Gets a submatrix.
+ getSubMatrix(int[], int[]) AbstractRealMatrix
+Gets a submatrix.
+ getSubMatrix(int, int, int, int) BigMatrix
+Deprecated. Gets a submatrix.
+getSubMatrix(int[], int[]) BigMatrix
+Deprecated. Gets a submatrix.
+getSubMatrix(int, int, int, int) BigMatrixImpl
+Deprecated. Gets a submatrix.
+getSubMatrix(int[], int[]) BigMatrixImpl
+Deprecated. Gets a submatrix.
+getSubMatrix(int, int, int, int) BlockFieldMatrix
+Gets a submatrix.
+ getSubMatrix(int, int, int, int) BlockRealMatrix
+Gets a submatrix.
+ getSubMatrix(int, int, int, int) FieldMatrix
+Gets a submatrix.
+ getSubMatrix(int[], int[]) FieldMatrix
+Gets a submatrix.
+ getSubMatrix(int, int, int, int) RealMatrix
+Gets a submatrix.
+ getSubMatrix(int[], int[]) RealMatrix
+Gets a submatrix.
+ getSubVector(int, int) ArrayFieldVector
+Get a subvector from consecutive elements.
+ getSubVector(int, int) ArrayRealVector
+Get a subvector from consecutive elements.
+ getSubVector(int, int) FieldVector
+Get a subvector from consecutive elements.
+ getSubVector(int, int) OpenMapRealVector
+Get a subvector from consecutive elements.
+ getSubVector(int, int) RealVector
+Get a subvector from consecutive elements.
+ getSubVector(int, int) SparseFieldVector
+Get a subvector from consecutive elements.
+ getSuffix() Vector3DFormat
+Get the format suffix.
+ getSuffix() RealVectorFormat
+Get the format suffix.
+ getSum() AggregateSummaryStatistics
+Returns the sum of the values that have been added to Univariate.
+ getSum() DescriptiveStatistics
+Returns the sum of the values that have been added to Univariate.
+ getSum() MultivariateSummaryStatistics
+Returns an array whose ith entry is the sum of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getSum() StatisticalMultivariateSummary
+Returns an array whose ith entry is the
+ sum of the ith entries of the arrays
+ that correspond to each multivariate sample
+ getSum() StatisticalSummary
+Returns the sum of the values that have been added to Univariate.
+ getSum() StatisticalSummaryValues
+
+ getSum() SummaryStatistics
+Returns the sum of the values that have been added
+ getSum() SynchronizedMultivariateSummaryStatistics
+Returns an array whose ith entry is the sum of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getSum() SynchronizedSummaryStatistics
+Returns the sum of the values that have been added
+ getSumFreq() Frequency
+Returns the sum of all frequencies.
+ getSumImpl() DescriptiveStatistics
+Returns the currently configured sum implementation.
+ getSumImpl() MultivariateSummaryStatistics
+Returns the currently configured Sum implementation
+ getSumImpl() SummaryStatistics
+Returns the currently configured Sum implementation
+ getSumImpl() SynchronizedMultivariateSummaryStatistics
+Returns the currently configured Sum implementation
+ getSumImpl() SynchronizedSummaryStatistics
+Returns the currently configured Sum implementation
+ getSumLog() MultivariateSummaryStatistics
+Returns an array whose ith entry is the sum of logs of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getSumLog() StatisticalMultivariateSummary
+Returns an array whose ith entry is the
+ sum of logs of the ith entries of the arrays
+ that correspond to each multivariate sample
+ getSumLog() SynchronizedMultivariateSummaryStatistics
+Returns an array whose ith entry is the sum of logs of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getSumLogImpl() GeometricMean
+Returns the currently configured sum of logs implementation
+ getSumLogImpl() MultivariateSummaryStatistics
+Returns the currently configured sum of logs implementation
+ getSumLogImpl() SummaryStatistics
+Returns the currently configured sum of logs implementation
+ getSumLogImpl() SynchronizedMultivariateSummaryStatistics
+Returns the currently configured sum of logs implementation
+ getSumLogImpl() SynchronizedSummaryStatistics
+Returns the currently configured sum of logs implementation
+ getSummary() AggregateSummaryStatistics
+Return a StatisticalSummaryValues getSummary() SummaryStatistics
+Return a StatisticalSummaryValues getSummary() SynchronizedSummaryStatistics
+Return a StatisticalSummaryValues getSumOfCrossProducts() SimpleRegression
+Returns the sum of crossproducts, xi *yi .
+ getSumOfLogs() AggregateSummaryStatistics
+Returns the sum of the logs of all the aggregated data.
+ getSumOfLogs() SummaryStatistics
+Returns the sum of the logs of the values that have been added.
+ getSumsq() AggregateSummaryStatistics
+Returns the sum of the squares of all the aggregated data.
+ getSumsq() DescriptiveStatistics
+Returns the sum of the squares of the available values.
+ getSumSq() MultivariateSummaryStatistics
+Returns an array whose ith entry is the sum of squares of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getSumSq() StatisticalMultivariateSummary
+Returns an array whose ith entry is the
+ sum of squares of the ith entries of the arrays
+ that correspond to each multivariate sample
+ getSumsq() SummaryStatistics
+Returns the sum of the squares of the values that have been added.
+ getSumSq() SynchronizedMultivariateSummaryStatistics
+Returns an array whose ith entry is the sum of squares of the
+ ith entries of the arrays that have been added using
+ MultivariateSummaryStatistics.addValue(double[]) getSumsq() SynchronizedSummaryStatistics
+Returns the sum of the squares of the values that have been added.
+ getSumsqImpl() DescriptiveStatistics
+Returns the currently configured sum of squares implementation.
+ getSumsqImpl() MultivariateSummaryStatistics
+Returns the currently configured sum of squares implementation
+ getSumsqImpl() SummaryStatistics
+Returns the currently configured sum of squares implementation
+ getSumsqImpl() SynchronizedMultivariateSummaryStatistics
+Returns the currently configured sum of squares implementation
+ getSumsqImpl() SynchronizedSummaryStatistics
+Returns the currently configured sum of squares implementation
+ getSumSquaredErrors() SimpleRegression
+Returns the
+ sum of squared errors (SSE) associated with the regression
+ model.
+ getTheoreticalValue() WeightedMeasurement
+Deprecated. Get the theoretical value expected for this measurement
+getTiesStrategy() NaturalRanking
+Return the TiesStrategy
+ getTotalSumSquares() SimpleRegression
+Returns the sum of squared deviations of the y values about their mean.
+ getTrace() AbstractFieldMatrix
+Returns the
+ trace of the matrix (the sum of the elements on the main diagonal).
+ getTrace() AbstractRealMatrix
+Returns the
+ trace of the matrix (the sum of the elements on the main diagonal).
+ getTrace() BigMatrix
+Deprecated. Returns the
+ trace of the matrix (the sum of the elements on the main diagonal).
+getTrace() BigMatrixImpl
+Deprecated. Returns the
+ trace of the matrix (the sum of the elements on the main diagonal).
+getTrace() FieldMatrix
+Returns the
+ trace of the matrix (the sum of the elements on the main diagonal).
+ getTrace() RealMatrix
+Returns the
+ trace of the matrix (the sum of the elements on the main diagonal).
+ getTransformer(Class<?>) TransformerMap
+Returns the Transformer that is mapped to a class
+ if mapping is not present, this returns null.
+ getTTest() TestUtils
+Return a (singleton) TTest instance.
+ getU() FieldLUDecomposition
+Returns the matrix U of the decomposition.
+ getU() FieldLUDecompositionImpl
+Returns the matrix U of the decomposition.
+ getU() LUDecomposition
+Returns the matrix U of the decomposition.
+ getU() LUDecompositionImpl
+Returns the matrix U of the decomposition.
+ getU() SingularValueDecomposition
+Returns the matrix U of the decomposition.
+ getU() SingularValueDecompositionImpl
+Returns the matrix U of the decomposition.
+ getUnboundParameters() EstimationProblem
+Deprecated. Get the unbound parameters of the problem.
+getUnboundParameters() SimpleEstimationProblem
+Deprecated. Get the unbound parameters of the problem.
+getUnknownDistributionChiSquareTest() TestUtils
+Return a (singleton) UnknownDistributionChiSquareTest instance.
+ getUpperBounds() EmpiricalDistribution
+Returns the array of upper bounds for the bins.
+ getUpperBounds() EmpiricalDistributionImpl
+Returns a fresh copy of the array of upper bounds for the bins.
+ getUT() SingularValueDecomposition
+Returns the transpose of the matrix U of the decomposition.
+ getUT() SingularValueDecompositionImpl
+Returns the transpose of the matrix U of the decomposition.
+ getV() EigenDecomposition
+Returns the matrix V of the decomposition.
+ getV() EigenDecompositionImpl
+Returns the matrix V of the decomposition.
+ getV() SingularValueDecomposition
+Returns the matrix V of the decomposition.
+ getV() SingularValueDecompositionImpl
+Returns the matrix V of the decomposition.
+ getValue() AbstractRealVector.EntryImpl
+Get the value of the entry.
+ getValue() OpenMapRealVector.OpenMapEntry
+Get the value of the entry.
+ getValue() RealVector.Entry
+Get the value of the entry.
+ getValue() LinearConstraint
+Get the value of the constraint (right hand side).
+ getValue(double[]) LinearObjectiveFunction
+Compute the value of the linear equation at the current point
+ getValue(RealVector) LinearObjectiveFunction
+Compute the value of the linear equation at the current point
+ getValue() RealPointValuePair
+Get the value of the objective function.
+ getValue() VectorialPointValuePair
+Get the value of the objective function.
+ getValueRef() VectorialPointValuePair
+Get a reference to the value of the objective function.
+ getValues() DescriptiveStatistics
+Returns the current set of values in an array of double primitives.
+ getValues() SynchronizedDescriptiveStatistics
+Returns the current set of values in an array of double primitives.
+ getValues() ResizableDoubleArray
+Deprecated. replaced by ResizableDoubleArray.getInternalValues()
+getValuesFileURL() ValueServer
+Getter for valuesFileURL
+getVariance() AggregateSummaryStatistics
+Returns the variance of the available values.
+ getVariance() DescriptiveStatistics
+Returns the variance of the available values.
+ getVariance() StatisticalSummary
+Returns the variance of the available values.
+ getVariance() StatisticalSummaryValues
+
+ getVariance() SummaryStatistics
+Returns the variance of the values that have been added.
+ getVariance() SynchronizedSummaryStatistics
+Returns the variance of the values that have been added.
+ getVarianceDirection() SemiVariance
+Returns the varianceDirection property.
+ getVarianceImpl() DescriptiveStatistics
+Returns the currently configured variance implementation.
+ getVarianceImpl() SummaryStatistics
+Returns the currently configured variance implementation
+ getVarianceImpl() SynchronizedSummaryStatistics
+Returns the currently configured variance implementation
+ getVT() EigenDecomposition
+Returns the transpose of the matrix V of the decomposition.
+ getVT() EigenDecompositionImpl
+Returns the transpose of the matrix V of the decomposition.
+ getVT() SingularValueDecomposition
+Returns the transpose of the matrix V of the decomposition.
+ getVT() SingularValueDecompositionImpl
+Returns the transpose of the matrix V of the decomposition.
+ getWeight() WeightedMeasurement
+Deprecated. Get the weight of the measurement in the least squares problem
+getWeight() WeightedObservedPoint
+Get the weight of the measurement in the fitting process.
+ getWholeFormat() ProperBigFractionFormat
+Access the whole format.
+ getWholeFormat() ProperFractionFormat
+Access the whole format.
+ getWindowSize() DescriptiveStatistics
+Returns the maximum number of values that can be stored in the
+ dataset, or INFINITE_WINDOW (-1) if there is no limit.
+ getWindowSize() SynchronizedDescriptiveStatistics
+Returns the maximum number of values that can be stored in the
+ dataset, or INFINITE_WINDOW (-1) if there is no limit.
+ getX() Vector3D
+Get the abscissa of the vector.
+ getX() WeightedObservedPoint
+Get the abscissa of the point.
+ getXSumSquares() SimpleRegression
+Returns the sum of squared deviations of the x values about their mean.
+ getY() Vector3D
+Get the ordinate of the vector.
+ getY() WeightedObservedPoint
+Get the observed value of the function at x.
+ getZ() Vector3D
+Get the height of the vector.
+ getZero() ComplexField
+Get the additive identity of the field.
+ getZero() Field
+Get the additive identity of the field.
+ getZero() BigFractionField
+Get the additive identity of the field.
+ getZero() FractionField
+Get the additive identity of the field.
+ getZero() BigRealField
+Get the additive identity of the field.
+ GillIntegrator org.apache.commons.math.ode.nonstiff This class implements the Gill fourth order Runge-Kutta
+ integrator for Ordinary Differential Equations . GillIntegrator(double) GillIntegrator
+Simple constructor.
+ GLSMultipleLinearRegression org.apache.commons.math.stat.regression The GLS implementation of the multiple linear regression. GLSMultipleLinearRegression() GLSMultipleLinearRegression
+
+ goal AbstractScalarDifferentiableOptimizer
+Type of optimization.
+ goal AbstractLinearOptimizer
+Type of optimization goal: either GoalType.MAXIMIZEGoalType.MINIMIZE GoalType org.apache.commons.math.optimization Goal type for an optimization problem. gradient() DifferentiableMultivariateRealFunction
+Returns the gradient function.
+ gradient(double, double[]) ParametricRealFunction
+Compute the gradient of the function with respect to its parameters.
+ GraggBulirschStoerIntegrator org.apache.commons.math.ode.nonstiff This class implements a Gragg-Bulirsch-Stoer integrator for
+ Ordinary Differential Equations. GraggBulirschStoerIntegrator(double, double, double, double) GraggBulirschStoerIntegrator
+Simple constructor.
+ GraggBulirschStoerIntegrator(double, double, double[], double[]) GraggBulirschStoerIntegrator
+Simple constructor.
+ guess() HarmonicCoefficientsGuesser
+Estimate a first guess of the coefficients.
+ guessParametersErrors(EstimationProblem) AbstractEstimator
+Deprecated. Guess the errors in unbound estimated parameters.
+guessParametersErrors(EstimationProblem) Estimator
+Deprecated. Guess the errors in estimated parameters.
+guessParametersErrors() AbstractLeastSquaresOptimizer
+Guess the errors in optimized parameters.
+
+
+H
+
+h AbstractStepInterpolator
+current time step
+ handleStep(StepInterpolator, boolean) ContinuousOutputModel
+Handle the last accepted step.
+ handleStep(StepInterpolatorWithJacobians, boolean) StepHandlerWithJacobians
+Handle the last accepted step
+ handleStep(StepInterpolator, boolean) DummyStepHandler
+Handle the last accepted step.
+ handleStep(double, double[], double[], boolean) FixedStepHandler
+Handle the last accepted step
+ handleStep(StepInterpolator, boolean) StepHandler
+Handle the last accepted step
+ handleStep(StepInterpolator, boolean) StepNormalizer
+Handle the last accepted step
+ HarmonicCoefficientsGuesser org.apache.commons.math.optimization.fitting This class guesses harmonic coefficients from a sample. HarmonicCoefficientsGuesser(WeightedObservedPoint[]) HarmonicCoefficientsGuesser
+Simple constructor.
+ HarmonicFitter org.apache.commons.math.optimization.fitting This class implements a curve fitting specialized for sinusoids. HarmonicFitter(DifferentiableMultivariateVectorialOptimizer) HarmonicFitter
+Simple constructor.
+ HarmonicFitter(DifferentiableMultivariateVectorialOptimizer, double[]) HarmonicFitter
+Simple constructor.
+ HarmonicFunction org.apache.commons.math.optimization.fitting Harmonic function of the form f (t) = a cos (ω t + φ). HarmonicFunction(double, double, double) HarmonicFunction
+Simple constructor.
+ HasDensity P > - Interface in org.apache.commons.math.distribution Deprecated. to be removed in math 3.0 hash(double) MathUtils
+Returns an integer hash code representing the given double value.
+ hash(double[]) MathUtils
+Returns an integer hash code representing the given double array.
+ hashCode() PolynomialFunction
+
+ hashCode() Complex
+Get a hashCode for the complex number.
+ hashCode() BigFraction
+
+ Gets a hashCode for the fraction.
+ hashCode() Fraction
+Gets a hashCode for the fraction.
+ hashCode() Vector3D
+Get a hashCode for the 3D vector.
+ hashCode() AbstractFieldMatrix
+Computes a hashcode for the matrix.
+ hashCode() AbstractRealMatrix
+Computes a hashcode for the matrix.
+ hashCode() ArrayFieldVector
+Get a hashCode for the real vector.
+ hashCode() ArrayRealVector
+Get a hashCode for the real vector.
+ hashCode() BigMatrixImpl
+Deprecated. Computes a hashcode for the matrix.
+hashCode() OpenMapRealVector
+
+ hashCode() SparseFieldVector
+
+ hashCode() LinearConstraint
+
+ hashCode() LinearObjectiveFunction
+
+ hashCode() EuclideanIntegerPoint
+
+ hashCode() AbstractStorelessUnivariateStatistic
+Returns hash code based on getResult() and getN()
+ hashCode() VectorialCovariance
+
+ hashCode() VectorialMean
+
+ hashCode() MultivariateSummaryStatistics
+Returns hash code based on values of statistics
+ hashCode() StatisticalSummaryValues
+Returns hash code based on values of statistics
+ hashCode() SummaryStatistics
+Returns hash code based on values of statistics
+ hashCode() SynchronizedMultivariateSummaryStatistics
+Returns hash code based on values of statistics
+ hashCode() SynchronizedSummaryStatistics
+Returns hash code based on values of statistics
+ hashCode() Frequency
+
+ hashCode() BigReal
+
+ hashCode() DefaultTransformer
+
+ hashCode() ResizableDoubleArray
+Returns a hash code consistent with equals.
+ hashCode() TransformerMap
+
+ hasNext() AbstractRealVector.SparseEntryIterator
+
+ hasNext() OpenMapRealVector.OpenMapSparseIterator
+
+ hasNext() OpenIntToDoubleHashMap.Iterator
+Check if there is a next element in the map.
+ hasNext() OpenIntToFieldHashMap.Iterator
+Check if there is a next element in the map.
+ HighamHall54Integrator org.apache.commons.math.ode.nonstiff This class implements the 5(4) Higham and Hall integrator for
+ Ordinary Differential Equations. HighamHall54Integrator(double, double, double, double) HighamHall54Integrator
+Simple constructor.
+ HighamHall54Integrator(double, double, double[], double[]) HighamHall54Integrator
+Simple constructor.
+ homoscedasticT(double[], double[]) TestUtils
+
+ homoscedasticT(StatisticalSummary, StatisticalSummary) TestUtils
+
+ homoscedasticT(double[], double[]) TTest
+Computes a 2-sample t statistic, under the hypothesis of equal
+ subpopulation variances.
+ homoscedasticT(StatisticalSummary, StatisticalSummary) TTest
+Computes a 2-sample t statistic, comparing the means of the datasets
+ described by two StatisticalSummary homoscedasticT(double[], double[]) TTestImpl
+Computes a 2-sample t statistic, under the hypothesis of equal
+ subpopulation variances.
+ homoscedasticT(StatisticalSummary, StatisticalSummary) TTestImpl
+Computes a 2-sample t statistic, comparing the means of the datasets
+ described by two StatisticalSummary homoscedasticT(double, double, double, double, double, double) TTestImpl
+Computes t test statistic for 2-sample t-test under the hypothesis
+ of equal subpopulation variances.
+ homoscedasticTTest(double[], double[], double) TestUtils
+
+ homoscedasticTTest(double[], double[]) TestUtils
+
+ homoscedasticTTest(StatisticalSummary, StatisticalSummary) TestUtils
+
+ homoscedasticTTest(double[], double[]) TTest
+Returns the observed significance level , or
+ p-value , associated with a two-sample, two-tailed t-test
+ comparing the means of the input arrays, under the assumption that
+ the two samples are drawn from subpopulations with equal variances.
+ homoscedasticTTest(double[], double[], double) TTest
+Performs a
+
+ two-sided t-test evaluating the null hypothesis that sample1
+ and sample2 are drawn from populations with the same mean,
+ with significance level alpha, assuming that the
+ subpopulation variances are equal.
+ homoscedasticTTest(StatisticalSummary, StatisticalSummary) TTest
+Returns the observed significance level , or
+ p-value , associated with a two-sample, two-tailed t-test
+ comparing the means of the datasets described by two StatisticalSummary
+ instances, under the hypothesis of equal subpopulation variances.
+ homoscedasticTTest(double[], double[]) TTestImpl
+Returns the observed significance level , or
+ p-value , associated with a two-sample, two-tailed t-test
+ comparing the means of the input arrays, under the assumption that
+ the two samples are drawn from subpopulations with equal variances.
+ homoscedasticTTest(double[], double[], double) TTestImpl
+Performs a
+
+ two-sided t-test evaluating the null hypothesis that sample1
+ and sample2 are drawn from populations with the same mean,
+ with significance level alpha, assuming that the
+ subpopulation variances are equal.
+ homoscedasticTTest(StatisticalSummary, StatisticalSummary) TTestImpl
+Returns the observed significance level , or
+ p-value , associated with a two-sample, two-tailed t-test
+ comparing the means of the datasets described by two StatisticalSummary
+ instances, under the hypothesis of equal subpopulation variances.
+ homoscedasticTTest(double, double, double, double, double, double) TTestImpl
+Computes p-value for 2-sided, 2-sample t-test, under the assumption
+ of equal subpopulation variances.
+ HypergeometricDistribution org.apache.commons.math.distribution The Hypergeometric Distribution. HypergeometricDistributionImpl org.apache.commons.math.distribution The default implementation of HypergeometricDistribution HypergeometricDistributionImpl(int, int, int) HypergeometricDistributionImpl
+Construct a new hypergeometric distribution with the given the population
+ size, the number of successes in the population, and the sample size.
+
+
+I
+
+I Complex
+The square root of -1.
+ IDENTITY ComposableFunction
+The identity function.
+ IDENTITY Rotation
+Identity rotation.
+ identityPermutation(int) RandomKey
+Generates a representation corresponding to an identity permutation of
+ length l which can be passed to the RandomKey constructor.
+ incMoment Kurtosis
+Determines whether or not this statistic can be incremented or cleared.
+ incMoment Mean
+Determines whether or not this statistic can be incremented or cleared.
+ incMoment Skewness
+Determines whether or not this statistic can be incremented or cleared.
+ incMoment Variance
+Boolean test to determine if this Variance should also increment
+ the second moment, this evaluates to false when this Variance is
+ constructed with an external SecondMoment as a parameter.
+ increment(double) AbstractStorelessUnivariateStatistic
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) FirstMoment
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) FourthMoment
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) GeometricMean
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) Kurtosis
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) Mean
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) SecondMoment
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) Skewness
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) StandardDeviation
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) ThirdMoment
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) Variance
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double[]) VectorialCovariance
+Add a new vector to the sample.
+ increment(double[]) VectorialMean
+Add a new vector to the sample.
+ increment(double) Max
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) Min
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) StorelessUnivariateStatistic
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) Product
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) Sum
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) SumOfLogs
+Updates the internal state of the statistic to reflect the addition of the new value.
+ increment(double) SumOfSquares
+Updates the internal state of the statistic to reflect the addition of the new value.
+ incrementAll(double[]) AbstractStorelessUnivariateStatistic
+This default implementation just calls AbstractStorelessUnivariateStatistic.increment(double) incrementAll(double[], int, int) AbstractStorelessUnivariateStatistic
+This default implementation just calls AbstractStorelessUnivariateStatistic.increment(double) incrementAll(double[]) StorelessUnivariateStatistic
+Updates the internal state of the statistic to reflect addition of
+ all values in the values array.
+ incrementAll(double[], int, int) StorelessUnivariateStatistic
+Updates the internal state of the statistic to reflect addition of
+ the values in the designated portion of the values array.
+ incrementIterationsCounter() DirectSearchOptimizer
+Increment the iterations counter by 1.
+ incrementIterationsCounter() AbstractLeastSquaresOptimizer
+Increment the iterations counter by 1.
+ incrementIterationsCounter() AbstractScalarDifferentiableOptimizer
+Increment the iterations counter by 1.
+ incrementIterationsCounter() AbstractLinearOptimizer
+Increment the iterations counter by 1.
+ incrementJacobianEvaluationsCounter() AbstractEstimator
+Deprecated. Increment the jacobian evaluations counter.
+indicator(byte) MathUtils
+For a byte value x, this method returns (byte)(+1) if x >= 0 and
+ (byte)(-1) if x < 0.
+ indicator(double) MathUtils
+For a double precision value x, this method returns +1.0 if x >= 0 and
+ -1.0 if x < 0.
+ indicator(float) MathUtils
+For a float value x, this method returns +1.0F if x >= 0 and -1.0F if x <
+ 0.
+ indicator(int) MathUtils
+For an int value x, this method returns +1 if x >= 0 and -1 if x < 0.
+ indicator(long) MathUtils
+For a long value x, this method returns +1L if x >= 0 and -1L if x < 0.
+ indicator(short) MathUtils
+For a short value x, this method returns (short)(+1) if x >= 0 and
+ (short)(-1) if x < 0.
+ inducedPermutation(List<S>, List<S>) RandomKey
+Generates a representation of a permutation corresponding to a
+ permutation which yields permutedData when applied to
+ originalData.
+ INF Complex
+A complex number representing "+INF + INFi"
+ INFINITE_WINDOW DescriptiveStatistics
+Represents an infinite window size.
+ initialCapacity ResizableDoubleArray
+The initial capacity of the array.
+ initializeEstimate(EstimationProblem) AbstractEstimator
+Deprecated. Initialization of the common parts of the estimation.
+initializeHighOrderDerivatives(double[], double[][]) MultistepIntegrator
+Initialize the high order scaled derivatives at step start.
+ initializeHighOrderDerivatives(double[], double[][]) MultistepIntegrator.NordsieckTransformer
+Initialize the high order scaled derivatives at step start.
+ initializeHighOrderDerivatives(double[], double[][]) AdamsIntegrator
+Initialize the high order scaled derivatives at step start.
+ initializeHighOrderDerivatives(double[], double[][]) AdamsNordsieckTransformer
+Initialize the high order scaled derivatives at step start.
+ initializeStep(FirstOrderDifferentialEquations, boolean, int, double[], double, double[], double[], double[], double[]) AdaptiveStepsizeIntegrator
+Initialize the integration step.
+ IntegerDistribution org.apache.commons.math.distribution Interface for discrete distributions of integer-valued random variables. integrate(double, double) LegendreGaussIntegrator
+Deprecated.
+integrate(UnivariateRealFunction, double, double) LegendreGaussIntegrator
+Integrate the function in the given interval.
+ integrate(double, double) RombergIntegrator
+Deprecated.
+integrate(UnivariateRealFunction, double, double) RombergIntegrator
+Integrate the function in the given interval.
+ integrate(double, double) SimpsonIntegrator
+Deprecated.
+integrate(UnivariateRealFunction, double, double) SimpsonIntegrator
+Integrate the function in the given interval.
+ integrate(double, double) TrapezoidIntegrator
+Deprecated.
+integrate(UnivariateRealFunction, double, double) TrapezoidIntegrator
+Integrate the function in the given interval.
+ integrate(double, double) UnivariateRealIntegrator
+Deprecated. replaced by UnivariateRealIntegrator.integrate(UnivariateRealFunction, double, double)
+integrate(UnivariateRealFunction, double, double) UnivariateRealIntegrator
+Integrate the function in the given interval.
+ integrate(FirstOrderDifferentialEquations, double, double[], double, double[]) FirstOrderIntegrator
+Integrate the differential equations up to the given time.
+ integrate(double, double[], double[][], double, double[], double[][], double[][]) FirstOrderIntegratorWithJacobians
+Integrate the differential equations and the variational equations up to the given time.
+ integrate(FirstOrderDifferentialEquations, double, double[], double, double[]) AdamsBashforthIntegrator
+Integrate the differential equations up to the given time.
+ integrate(FirstOrderDifferentialEquations, double, double[], double, double[]) AdamsIntegrator
+Integrate the differential equations up to the given time.
+ integrate(FirstOrderDifferentialEquations, double, double[], double, double[]) AdamsMoultonIntegrator
+Integrate the differential equations up to the given time.
+ integrate(FirstOrderDifferentialEquations, double, double[], double, double[]) AdaptiveStepsizeIntegrator
+Integrate the differential equations up to the given time.
+ integrate(FirstOrderDifferentialEquations, double, double[], double, double[]) EmbeddedRungeKuttaIntegrator
+Integrate the differential equations up to the given time.
+ integrate(FirstOrderDifferentialEquations, double, double[], double, double[]) GraggBulirschStoerIntegrator
+Integrate the differential equations up to the given time.
+ integrate(FirstOrderDifferentialEquations, double, double[], double, double[]) RungeKuttaIntegrator
+Integrate the differential equations up to the given time.
+ integrate(SecondOrderDifferentialEquations, double, double[], double[], double, double[], double[]) SecondOrderIntegrator
+Integrate the differential equations up to the given time
+ IntegratorException org.apache.commons.math.ode This exception is made available to users to report
+ the error conditions that are triggered during integration IntegratorException(String, Object...) IntegratorException
+Simple constructor.
+ IntegratorException(Throwable) IntegratorException
+Create an exception with a given root cause.
+ internalArray ResizableDoubleArray
+The internal storage array.
+ interpolate(double[], double[], double[][]) BivariateRealGridInterpolator
+Computes an interpolating function for the data set.
+ interpolate(double[], double[]) DividedDifferenceInterpolator
+Computes an interpolating function for the data set.
+ interpolate(double[], double[]) LoessInterpolator
+Compute an interpolating function by performing a loess fit
+ on the data at the original abscissae and then building a cubic spline
+ with a
+ SplineInterpolator interpolate(double[][], double[]) MicrosphereInterpolator
+Computes an interpolating function for the data set.
+ interpolate(double[][], double[]) MultivariateRealInterpolator
+Computes an interpolating function for the data set.
+ interpolate(double[], double[]) NevilleInterpolator
+Computes an interpolating function for the data set.
+ interpolate(double[], double[], double[][]) SmoothingBicubicSplineInterpolator
+Computes an interpolating function for the data set.
+ interpolate(double[], double[]) SplineInterpolator
+Computes an interpolating function for the data set.
+ interpolate(double[], double[]) UnivariateRealInterpolator
+Computes an interpolating function for the data set.
+ interpolatedDerivatives AbstractStepInterpolator
+interpolated derivatives
+ interpolatedState AbstractStepInterpolator
+interpolated state
+ interpolatedTime AbstractStepInterpolator
+interpolated time
+ intValue() BigFraction
+
+ Gets the fraction as an int .
+ intValue() Fraction
+Gets the fraction as an int .
+ InvalidMatrixException org.apache.commons.math.linear Thrown when a system attempts an operation on a matrix, and
+ that matrix does not satisfy the preconditions for the
+ aforementioned operation. InvalidMatrixException(String, Object...) InvalidMatrixException
+Construct an exception with the given message.
+ InvalidMatrixException(Throwable) InvalidMatrixException
+Construct an exception with the given message.
+ InvalidRepresentationException org.apache.commons.math.genetics Exception indicating that the representation of a chromosome is not valid. InvalidRepresentationException() InvalidRepresentationException
+Constructor
+ InvalidRepresentationException(String) InvalidRepresentationException
+Construct an InvalidRepresentationException
+ InvalidRepresentationException(Throwable) InvalidRepresentationException
+Construct an InvalidRepresentationException
+ InvalidRepresentationException(String, Throwable) InvalidRepresentationException
+Construct an InvalidRepresentationException
+ inverse() AbstractRealMatrix
+Deprecated.
+inverse() BigMatrix
+Deprecated. Returns the inverse of this matrix.
+inverse() BigMatrixImpl
+Deprecated. Returns the inverse matrix if this matrix is invertible.
+inverse() RealMatrix
+Deprecated. as of release 2.0, replaced by
+ new LUDecompositionImpl(m)getSolver()getInverse()
+inverseCumulativeProbability(double) AbstractContinuousDistribution
+For this distribution, X, this method returns the critical point x, such
+ that P(X < x) = p.
+ inverseCumulativeProbability(double) AbstractIntegerDistribution
+For a random variable X whose values are distributed according
+ to this distribution, this method returns the largest x, such
+ that P(X ≤ x) ≤ p.
+ inverseCumulativeProbability(double) BetaDistributionImpl
+For this distribution, X, this method returns the critical point x, such
+ that P(X < x) = p.
+ inverseCumulativeProbability(double) BinomialDistributionImpl
+For this distribution, X, this method returns the largest x, such that
+ P(X ≤ x) ≤ p.
+ inverseCumulativeProbability(double) CauchyDistributionImpl
+For this distribution, X, this method returns the critical point x, such
+ that P(X < x) = p.
+ inverseCumulativeProbability(double) ChiSquaredDistributionImpl
+For this distribution, X, this method returns the critical point x, such
+ that P(X < x) = p.
+ inverseCumulativeProbability(double) ContinuousDistribution
+For this distribution, X, this method returns x such that P(X < x) = p.
+ inverseCumulativeProbability(double) ExponentialDistributionImpl
+For this distribution, X, this method returns the critical point x, such
+ that P(X < x) = p.
+ inverseCumulativeProbability(double) FDistributionImpl
+For this distribution, X, this method returns the critical point x, such
+ that P(X < x) = p.
+ inverseCumulativeProbability(double) GammaDistributionImpl
+For this distribution, X, this method returns the critical point x, such
+ that P(X < x) = p.
+ inverseCumulativeProbability(double) IntegerDistribution
+For this distribution, X, this method returns the largest x such that
+ P(X ≤ x) <= p.
+ inverseCumulativeProbability(double) NormalDistributionImpl
+For this distribution, X, this method returns the critical point x, such
+ that P(X < x) = p.
+ inverseCumulativeProbability(double) PascalDistributionImpl
+For this distribution, X, this method returns the largest x, such that
+ P(X ≤ x) ≤ p.
+ inverseCumulativeProbability(double) TDistributionImpl
+For this distribution, X, this method returns the critical point x, such
+ that P(X < x) = p.
+ inverseCumulativeProbability(double) WeibullDistributionImpl
+For this distribution, X, this method returns the critical point x, such
+ that P(X < x) = p.
+ inversetransform(double[]) FastCosineTransformer
+Inversely transform the given real data set.
+ inversetransform(UnivariateRealFunction, double, double, int) FastCosineTransformer
+Inversely transform the given real function, sampled on the given interval.
+ inversetransform(double[]) FastFourierTransformer
+Inversely transform the given real data set.
+ inversetransform(UnivariateRealFunction, double, double, int) FastFourierTransformer
+Inversely transform the given real function, sampled on the given interval.
+ inversetransform(Complex[]) FastFourierTransformer
+Inversely transform the given complex data set.
+ inversetransform(double[]) FastHadamardTransformer
+Inversely transform the given real data set.
+ inversetransform(UnivariateRealFunction, double, double, int) FastHadamardTransformer
+Inversely transform the given real function, sampled on the given interval.
+ inversetransform(double[]) FastSineTransformer
+Inversely transform the given real data set.
+ inversetransform(UnivariateRealFunction, double, double, int) FastSineTransformer
+Inversely transform the given real function, sampled on the given interval.
+ inversetransform(double[]) RealTransformer
+Inversely transform the given real data set.
+ inversetransform(UnivariateRealFunction, double, double, int) RealTransformer
+Inversely transform the given real function, sampled on the given interval.
+ inversetransform2(double[]) FastCosineTransformer
+Inversely transform the given real data set.
+ inversetransform2(UnivariateRealFunction, double, double, int) FastCosineTransformer
+Inversely transform the given real function, sampled on the given interval.
+ inversetransform2(double[]) FastFourierTransformer
+Inversely transform the given real data set.
+ inversetransform2(UnivariateRealFunction, double, double, int) FastFourierTransformer
+Inversely transform the given real function, sampled on the given interval.
+ inversetransform2(Complex[]) FastFourierTransformer
+Inversely transform the given complex data set.
+ inversetransform2(double[]) FastSineTransformer
+Inversely transform the given real data set.
+ inversetransform2(UnivariateRealFunction, double, double, int) FastSineTransformer
+Inversely transform the given real function, sampled on the given interval.
+ INVERT ComposableFunction
+The invert operator wrapped as a ComposableFunction isBiasCorrected() SemiVariance
+Returns true iff biasCorrected property is set to true.
+ isBiasCorrected() StandardDeviation
+
+ isBiasCorrected() Variance
+
+ isBound() EstimatedParameter
+Deprecated. Check if the parameter is bound
+isBracketing(double, double, UnivariateRealFunction) UnivariateRealSolverImpl
+Returns true iff the function takes opposite signs at the endpoints.
+ isDefaultValue(double) OpenMapRealVector
+Determine if this value is within epsilon of zero.
+ isEmpty() CombinedEventsManager
+Check if the manager does not manage any event handlers.
+ isForward() StepInterpolatorWithJacobians
+Check if the natural integration direction is forward.
+ isForward() AbstractStepInterpolator
+Check if the natural integration direction is forward.
+ isForward() StepInterpolator
+Check if the natural integration direction is forward.
+ isIgnored() WeightedMeasurement
+Deprecated. Check if this measurement should be ignored
+isInfinite() Complex
+Returns true if either the real or imaginary part of this complex number
+ takes an infinite value (either Double.POSITIVE_INFINITY or
+ Double.NEGATIVE_INFINITY) and neither part
+ is NaN.
+ isInfinite() Vector3D
+Returns true if any coordinate of this vector is infinite and none are NaN;
+ false otherwise
+ isInfinite() ArrayRealVector
+Returns true if any coordinate of this vector is infinite and none are NaN;
+ false otherwise
+ isInfinite() OpenMapRealVector
+Returns true if any coordinate of this vector is infinite and none are NaN;
+ false otherwise
+ isInfinite() RealVector
+Returns true if any coordinate of this vector is infinite and none are NaN;
+ false otherwise
+ isLoaded() EmpiricalDistribution
+Property indicating whether or not the distribution has been loaded.
+ isLoaded() EmpiricalDistributionImpl
+Property indicating whether or not the distribution has been loaded.
+ isNaN() Complex
+Returns true if either or both parts of this complex number is NaN;
+ false otherwise
+ isNaN() Vector3D
+Returns true if any coordinate of this vector is NaN; false otherwise
+ isNaN() ArrayRealVector
+Returns true if any coordinate of this vector is NaN; false otherwise
+ isNaN() OpenMapRealVector
+Returns true if any coordinate of this vector is NaN; false otherwise
+ isNaN() RealVector
+Returns true if any coordinate of this vector is NaN; false otherwise
+ isNonSingular() DecompositionSolver
+Check if the decomposed matrix is non-singular.
+ isNonSingular() FieldDecompositionSolver
+Check if the decomposed matrix is non-singular.
+ isPowerOf2(long) FastFourierTransformer
+Returns true if the argument is power of 2.
+ isRootOK(double, double, Complex) LaguerreSolver
+Returns true iff the given complex root is actually a real zero
+ in the given interval, within the solver tolerance level.
+ isSame(Chromosome) BinaryChromosome
+Returns true iff another has the same
+ representation and therefore the same fitness.
+isSame(Chromosome) Chromosome
+Returns true iff another has the same
+ representation and therefore the same fitness.
+isSame(Chromosome) RandomKey
+Returns true iff another is a RandomKey and
+ encodes the same permutation.
+ isSatisfied(Population) FixedGenerationCount
+Determine whether or not the given number of generations have passed.
+ isSatisfied(Population) StoppingCondition
+Determine whether or not the given population satisfies the stopping
+ condition.
+ isSequence(double, double, double) UnivariateRealSolverImpl
+Returns true if the arguments form a (strictly) increasing sequence
+ isSingular() AbstractRealMatrix
+Deprecated.
+isSingular() BigMatrixImpl
+Deprecated. Is this a singular matrix?
+isSingular() RealMatrix
+Deprecated. as of release 2.0, replaced by the boolean negation of
+ new LUDecompositionImpl(m)getSolver()isNonSingular()
+isSquare() AbstractFieldMatrix
+Is this a square matrix?
+ isSquare() AbstractRealMatrix
+Is this a square matrix?
+ isSquare() AnyMatrix
+Is this a square matrix?
+ isSquare() BigMatrixImpl
+Deprecated. Is this a square matrix?
+iterateSimplex(Comparator<RealPointValuePair>) DirectSearchOptimizer
+Compute the next simplex of the algorithm.
+ iterateSimplex(Comparator<RealPointValuePair>) MultiDirectional
+Compute the next simplex of the algorithm.
+ iterateSimplex(Comparator<RealPointValuePair>) NelderMead
+Compute the next simplex of the algorithm.
+ iterationCount ConvergingAlgorithmImpl
+The last iteration count.
+ iterator() ListPopulation
+Chromosome list iterator
+ iterator() AbstractRealVector
+Generic dense iterator - starts with index == zero, and hasNext() == true until index == getDimension();
+ iterator() RealVector
+Generic dense iterator - starts with index == zero, and hasNext() == true until index == getDimension();
+ iterator() OpenIntToDoubleHashMap
+Get an iterator over map elements.
+ iterator() OpenIntToFieldHashMap
+Get an iterator over map elements.
+
+
+J
+
+jacobian() DifferentiableMultivariateVectorialFunction
+Returns the jacobian function.
+ jacobian AbstractEstimator
+Deprecated. Jacobian matrix.
+jacobian AbstractLeastSquaresOptimizer
+Jacobian matrix.
+ JDKRandomGenerator org.apache.commons.math.random Extension of java.util.Random to implement
+ RandomGenerator JDKRandomGenerator() JDKRandomGenerator
+
+
+
+K
+
+key() OpenIntToDoubleHashMap.Iterator
+Get the key of current entry.
+ key() OpenIntToFieldHashMap.Iterator
+Get the key of current entry.
+ KMeansPlusPlusClusterer T extends Clusterable <T >> - Class in org.apache.commons.math.stat.clustering Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm. KMeansPlusPlusClusterer(Random) KMeansPlusPlusClusterer
+Build a clusterer.
+ Kurtosis org.apache.commons.math.stat.descriptive.moment Computes the Kurtosis of the available values. Kurtosis() Kurtosis
+Construct a Kurtosis
+ Kurtosis(FourthMoment) Kurtosis
+Construct a Kurtosis from an external moment
+ Kurtosis(Kurtosis) Kurtosis
+Copy constructor, creates a new Kurtosis identical
+ to the original
+
+
+L
+
+LaguerreSolver org.apache.commons.math.analysis.solvers Implements the
+ Laguerre's Method for root finding of real coefficient polynomials. LaguerreSolver(UnivariateRealFunction) LaguerreSolver
+Deprecated. as of 2.0 the function to solve is passed as an argument
+ to the LaguerreSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+LaguerreSolver() LaguerreSolver
+Construct a solver.
+ lcm(int, int) MathUtils
+
+ Returns the least common multiple of the absolute value of two numbers,
+ using the formula lcm(a,b) = (a / gcd(a,b)) * b.
+ lcm(long, long) MathUtils
+
+ Returns the least common multiple of the absolute value of two numbers,
+ using the formula lcm(a,b) = (a / gcd(a,b)) * b.
+ LeastSquaresConverter org.apache.commons.math.optimization This class converts vectorial
+ objective functionsscalar objective functions LeastSquaresConverter(MultivariateVectorialFunction, double[]) LeastSquaresConverter
+Build a simple converter for uncorrelated residuals with the same weight.
+ LeastSquaresConverter(MultivariateVectorialFunction, double[], double[]) LeastSquaresConverter
+Build a simple converter for uncorrelated residuals with the specific weights.
+ LeastSquaresConverter(MultivariateVectorialFunction, double[], RealMatrix) LeastSquaresConverter
+Build a simple converter for correlated residuals with the specific weights.
+ LegendreGaussIntegrator org.apache.commons.math.analysis.integration Implements the
+ Legendre-Gauss quadrature formula. LegendreGaussIntegrator(int, int) LegendreGaussIntegrator
+Build a Legendre-Gauss integrator.
+ LevenbergMarquardtEstimator org.apache.commons.math.estimation Deprecated. as of 2.0, everything in package org.apache.commons.math.estimation has
+ been deprecated and replaced by package org.apache.commons.math.optimization.general LevenbergMarquardtEstimator() LevenbergMarquardtEstimator
+Deprecated. Build an estimator for least squares problems.
+LevenbergMarquardtOptimizer org.apache.commons.math.optimization.general This class solves a least squares problem using the Levenberg-Marquardt algorithm. LevenbergMarquardtOptimizer() LevenbergMarquardtOptimizer
+Build an optimizer for least squares problems.
+ LinearConstraint org.apache.commons.math.optimization.linear A linear constraint for a linear optimization problem. LinearConstraint(double[], Relationship, double) LinearConstraint
+Build a constraint involving a single linear equation.
+ LinearConstraint(RealVector, Relationship, double) LinearConstraint
+Build a constraint involving a single linear equation.
+ LinearConstraint(double[], double, Relationship, double[], double) LinearConstraint
+Build a constraint involving two linear equations.
+ LinearConstraint(RealVector, double, Relationship, RealVector, double) LinearConstraint
+Build a constraint involving two linear equations.
+ linearConstraints AbstractLinearOptimizer
+Linear constraints.
+ LinearObjectiveFunction org.apache.commons.math.optimization.linear An objective function for a linear optimization problem. LinearObjectiveFunction(double[], double) LinearObjectiveFunction
+
+ LinearObjectiveFunction(RealVector, double) LinearObjectiveFunction
+
+ LinearOptimizer org.apache.commons.math.optimization.linear This interface represents an optimization algorithm for linear problems. ListPopulation org.apache.commons.math.genetics Population of chromosomes represented by a List ListPopulation(List<Chromosome>, int) ListPopulation
+Creates a new ListPopulation instance.
+ ListPopulation(int) ListPopulation
+Creates a new ListPopulation instance and initializes its inner
+ chromosome list.
+ load(double[]) EmpiricalDistribution
+Computes the empirical distribution from the provided
+ array of numbers.
+ load(File) EmpiricalDistribution
+Computes the empirical distribution from the input file.
+ load(URL) EmpiricalDistribution
+Computes the empirical distribution using data read from a URL.
+ load(double[]) EmpiricalDistributionImpl
+Computes the empirical distribution from the provided
+ array of numbers.
+ load(URL) EmpiricalDistributionImpl
+Computes the empirical distribution using data read from a URL.
+ load(File) EmpiricalDistributionImpl
+Computes the empirical distribution from the input file.
+ LoessInterpolator org.apache.commons.math.analysis.interpolation Implements the
+ Local Regression Algorithm (also Loess, Lowess) for interpolation of
+ real univariate functions. LoessInterpolator() LoessInterpolator
+Constructs a new LoessInterpolatorLoessInterpolator.DEFAULT_BANDWIDTHLoessInterpolator.DEFAULT_ROBUSTNESS_ITERS LoessInterpolator(double, int) LoessInterpolator
+Constructs a new LoessInterpolator LoessInterpolator(double, int, double) LoessInterpolator
+Constructs a new LoessInterpolator LOG ComposableFunction
+The Math.log method wrapped as a ComposableFunction log() Complex
+Compute the
+
+ natural logarithm of this complex number.
+ log(double, double) MathUtils
+Returns the
+ logarithm
+ for base b of x.
+ LOG10 ComposableFunction
+The Math.log10 method wrapped as a ComposableFunction LOG1P ComposableFunction
+The Math.log1p method wrapped as a ComposableFunction logBeta(double, double) Beta
+Returns the natural logarithm of the beta function B(a, b).
+ logBeta(double, double, double, int) Beta
+Returns the natural logarithm of the beta function B(a, b).
+ logGamma(double) Gamma
+Returns the natural logarithm of the gamma function Γ(x).
+ longValue() BigFraction
+
+ Gets the fraction as a long .
+ longValue() Fraction
+Gets the fraction as a long .
+ lu BigMatrixImpl
+Deprecated. Entries of cached LU decomposition.
+luDecompose() AbstractRealMatrix
+Deprecated. as of release 2.0, replaced by LUDecomposition
+luDecompose() BigMatrixImpl
+Deprecated. Computes a new
+
+ LU decompostion for this matrix, storing the result for use by other methods.
+LUDecomposition org.apache.commons.math.linear An interface to classes that implement an algorithm to calculate the
+ LU-decomposition of a real matrix. LUDecompositionImpl org.apache.commons.math.linear Calculates the LUP-decomposition of a square matrix. LUDecompositionImpl(RealMatrix) LUDecompositionImpl
+Calculates the LU-decomposition of the given matrix.
+ LUDecompositionImpl(RealMatrix, double) LUDecompositionImpl
+Calculates the LU-decomposition of the given matrix.
+
+
+M
+
+m1 FirstMoment
+First moment of values that have been added
+ m2 SecondMoment
+second moment of values that have been added
+ m3 ThirdMoment
+third moment of values that have been added
+ m4 FourthMoment
+fourth moment of values that have been added
+ map(UnivariateRealFunction) AbstractRealVector
+Acts as if implemented as:
+ return copy().map(function);
+ map(UnivariateRealFunction) RealVector
+Acts as if implemented as:
+ return copy().map(function);
+ mapAbs() AbstractRealVector
+Map the Math.abs(double) mapAbs() RealVector
+Map the Math.abs(double) mapAbsToSelf() AbstractRealVector
+Map the Math.abs(double) mapAbsToSelf() ArrayRealVector
+Map the Math.abs(double) mapAbsToSelf() RealVector
+Map the Math.abs(double) mapAcos() AbstractRealVector
+Map the Math.acos(double) mapAcos() RealVector
+Map the Math.acos(double) mapAcosToSelf() AbstractRealVector
+Map the Math.acos(double) mapAcosToSelf() ArrayRealVector
+Map the Math.acos(double) mapAcosToSelf() RealVector
+Map the Math.acos(double) mapAdd(double) AbstractRealVector
+Map an addition operation to each entry.
+ mapAdd(T) ArrayFieldVector
+Map an addition operation to each entry.
+ mapAdd(T) FieldVector
+Map an addition operation to each entry.
+ mapAdd(double) OpenMapRealVector
+Map an addition operation to each entry.
+ mapAdd(double) RealVector
+Map an addition operation to each entry.
+ mapAdd(T) SparseFieldVector
+Map an addition operation to each entry.
+ mapAddToSelf(double) AbstractRealVector
+Map an addition operation to each entry.
+ mapAddToSelf(T) ArrayFieldVector
+Map an addition operation to each entry.
+ mapAddToSelf(double) ArrayRealVector
+Map an addition operation to each entry.
+ mapAddToSelf(T) FieldVector
+Map an addition operation to each entry.
+ mapAddToSelf(double) OpenMapRealVector
+Map an addition operation to each entry.
+ mapAddToSelf(double) RealVector
+Map an addition operation to each entry.
+ mapAddToSelf(T) SparseFieldVector
+Map an addition operation to each entry.
+ mapAsin() AbstractRealVector
+Map the Math.asin(double) mapAsin() RealVector
+Map the Math.asin(double) mapAsinToSelf() AbstractRealVector
+Map the Math.asin(double) mapAsinToSelf() ArrayRealVector
+Map the Math.asin(double) mapAsinToSelf() RealVector
+Map the Math.asin(double) mapAtan() AbstractRealVector
+Map the Math.atan(double) mapAtan() RealVector
+Map the Math.atan(double) mapAtanToSelf() AbstractRealVector
+Map the Math.atan(double) mapAtanToSelf() ArrayRealVector
+Map the Math.atan(double) mapAtanToSelf() RealVector
+Map the Math.atan(double) mapCbrt() AbstractRealVector
+Map the Math.cbrt(double) mapCbrt() RealVector
+Map the Math.cbrt(double) mapCbrtToSelf() AbstractRealVector
+Map the Math.cbrt(double) mapCbrtToSelf() ArrayRealVector
+Map the Math.cbrt(double) mapCbrtToSelf() RealVector
+Map the Math.cbrt(double) mapCeil() AbstractRealVector
+Map the Math.ceil(double) mapCeil() RealVector
+Map the Math.ceil(double) mapCeilToSelf() AbstractRealVector
+Map the Math.ceil(double) mapCeilToSelf() ArrayRealVector
+Map the Math.ceil(double) mapCeilToSelf() RealVector
+Map the Math.ceil(double) mapCos() AbstractRealVector
+Map the Math.cos(double) mapCos() RealVector
+Map the Math.cos(double) mapCosh() AbstractRealVector
+Map the Math.cosh(double) mapCosh() RealVector
+Map the Math.cosh(double) mapCoshToSelf() AbstractRealVector
+Map the Math.cosh(double) mapCoshToSelf() ArrayRealVector
+Map the Math.cosh(double) mapCoshToSelf() RealVector
+Map the Math.cosh(double) mapCosToSelf() AbstractRealVector
+Map the Math.cos(double) mapCosToSelf() ArrayRealVector
+Map the Math.cos(double) mapCosToSelf() RealVector
+Map the Math.cos(double) mapDivide(double) AbstractRealVector
+Map a division operation to each entry.
+ mapDivide(T) ArrayFieldVector
+Map a division operation to each entry.
+ mapDivide(T) FieldVector
+Map a division operation to each entry.
+ mapDivide(double) RealVector
+Map a division operation to each entry.
+ mapDivide(T) SparseFieldVector
+Map a division operation to each entry.
+ mapDivideToSelf(double) AbstractRealVector
+Map a division operation to each entry.
+ mapDivideToSelf(T) ArrayFieldVector
+Map a division operation to each entry.
+ mapDivideToSelf(double) ArrayRealVector
+Map a division operation to each entry.
+ mapDivideToSelf(T) FieldVector
+Map a division operation to each entry.
+ mapDivideToSelf(double) RealVector
+Map a division operation to each entry.
+ mapDivideToSelf(T) SparseFieldVector
+Map a division operation to each entry.
+ mapExp() AbstractRealVector
+Map the Math.exp(double) mapExp() RealVector
+Map the Math.exp(double) mapExpm1() AbstractRealVector
+Map the Math.expm1(double) mapExpm1() RealVector
+Map the Math.expm1(double) mapExpm1ToSelf() AbstractRealVector
+Map the Math.expm1(double) mapExpm1ToSelf() ArrayRealVector
+Map the Math.expm1(double) mapExpm1ToSelf() RealVector
+Map the Math.expm1(double) mapExpToSelf() AbstractRealVector
+Map the Math.exp(double) mapExpToSelf() ArrayRealVector
+Map the Math.exp(double) mapExpToSelf() RealVector
+Map the Math.exp(double) mapFloor() AbstractRealVector
+Map the Math.floor(double) mapFloor() RealVector
+Map the Math.floor(double) mapFloorToSelf() AbstractRealVector
+Map the Math.floor(double) mapFloorToSelf() ArrayRealVector
+Map the Math.floor(double) mapFloorToSelf() RealVector
+Map the Math.floor(double) mapInv() AbstractRealVector
+Map the 1/x function to each entry.
+ mapInv() ArrayFieldVector
+Map the 1/x function to each entry.
+ mapInv() FieldVector
+Map the 1/x function to each entry.
+ mapInv() RealVector
+Map the 1/x function to each entry.
+ mapInv() SparseFieldVector
+Map the 1/x function to each entry.
+ mapInvToSelf() AbstractRealVector
+Map the 1/x function to each entry.
+ mapInvToSelf() ArrayFieldVector
+Map the 1/x function to each entry.
+ mapInvToSelf() ArrayRealVector
+Map the 1/x function to each entry.
+ mapInvToSelf() FieldVector
+Map the 1/x function to each entry.
+ mapInvToSelf() RealVector
+Map the 1/x function to each entry.
+ mapInvToSelf() SparseFieldVector
+Map the 1/x function to each entry.
+ mapLog() AbstractRealVector
+Map the Math.log(double) mapLog() RealVector
+Map the Math.log(double) mapLog10() AbstractRealVector
+Map the Math.log10(double) mapLog10() RealVector
+Map the Math.log10(double) mapLog10ToSelf() AbstractRealVector
+Map the Math.log10(double) mapLog10ToSelf() ArrayRealVector
+Map the Math.log10(double) mapLog10ToSelf() RealVector
+Map the Math.log10(double) mapLog1p() AbstractRealVector
+Map the Math.log1p(double) mapLog1p() RealVector
+Map the Math.log1p(double) mapLog1pToSelf() AbstractRealVector
+Map the Math.log1p(double) mapLog1pToSelf() ArrayRealVector
+Map the Math.log1p(double) mapLog1pToSelf() RealVector
+Map the Math.log1p(double) mapLogToSelf() AbstractRealVector
+Map the Math.log(double) mapLogToSelf() ArrayRealVector
+Map the Math.log(double) mapLogToSelf() RealVector
+Map the Math.log(double) mapMultiply(double) AbstractRealVector
+Map a multiplication operation to each entry.
+ mapMultiply(T) ArrayFieldVector
+Map a multiplication operation to each entry.
+ mapMultiply(T) FieldVector
+Map a multiplication operation to each entry.
+ mapMultiply(double) RealVector
+Map a multiplication operation to each entry.
+ mapMultiply(T) SparseFieldVector
+Map a multiplication operation to each entry.
+ mapMultiplyToSelf(double) AbstractRealVector
+Map a multiplication operation to each entry.
+ mapMultiplyToSelf(T) ArrayFieldVector
+Map a multiplication operation to each entry.
+ mapMultiplyToSelf(double) ArrayRealVector
+Map a multiplication operation to each entry.
+ mapMultiplyToSelf(T) FieldVector
+Map a multiplication operation to each entry.
+ mapMultiplyToSelf(double) RealVector
+Map a multiplication operation to each entry.
+ mapMultiplyToSelf(T) SparseFieldVector
+Map a multiplication operation to each entry.
+ mapPow(double) AbstractRealVector
+Map a power operation to each entry.
+ mapPow(double) RealVector
+Map a power operation to each entry.
+ mapPowToSelf(double) AbstractRealVector
+Map a power operation to each entry.
+ mapPowToSelf(double) ArrayRealVector
+Map a power operation to each entry.
+ mapPowToSelf(double) RealVector
+Map a power operation to each entry.
+ mapRint() AbstractRealVector
+Map the Math.rint(double) mapRint() RealVector
+Map the Math.rint(double) mapRintToSelf() AbstractRealVector
+Map the Math.rint(double) mapRintToSelf() ArrayRealVector
+Map the Math.rint(double) mapRintToSelf() RealVector
+Map the Math.rint(double) mapSignum() AbstractRealVector
+Map the Math.signum(double) mapSignum() RealVector
+Map the Math.signum(double) mapSignumToSelf() AbstractRealVector
+Map the Math.signum(double) mapSignumToSelf() ArrayRealVector
+Map the Math.signum(double) mapSignumToSelf() RealVector
+Map the Math.signum(double) mapSin() AbstractRealVector
+Map the Math.sin(double) mapSin() RealVector
+Map the Math.sin(double) mapSinh() AbstractRealVector
+Map the Math.sinh(double) mapSinh() RealVector
+Map the Math.sinh(double) mapSinhToSelf() AbstractRealVector
+Map the Math.sinh(double) mapSinhToSelf() ArrayRealVector
+Map the Math.sinh(double) mapSinhToSelf() RealVector
+Map the Math.sinh(double) mapSinToSelf() AbstractRealVector
+Map the Math.sin(double) mapSinToSelf() ArrayRealVector
+Map the Math.sin(double) mapSinToSelf() RealVector
+Map the Math.sin(double) mapSqrt() AbstractRealVector
+Map the Math.sqrt(double) mapSqrt() RealVector
+Map the Math.sqrt(double) mapSqrtToSelf() AbstractRealVector
+Map the Math.sqrt(double) mapSqrtToSelf() ArrayRealVector
+Map the Math.sqrt(double) mapSqrtToSelf() RealVector
+Map the Math.sqrt(double) mapSubtract(double) AbstractRealVector
+Map a subtraction operation to each entry.
+ mapSubtract(T) ArrayFieldVector
+Map a subtraction operation to each entry.
+ mapSubtract(T) FieldVector
+Map a subtraction operation to each entry.
+ mapSubtract(double) RealVector
+Map a subtraction operation to each entry.
+ mapSubtract(T) SparseFieldVector
+Map a subtraction operation to each entry.
+ mapSubtractToSelf(double) AbstractRealVector
+Map a subtraction operation to each entry.
+ mapSubtractToSelf(T) ArrayFieldVector
+Map a subtraction operation to each entry.
+ mapSubtractToSelf(double) ArrayRealVector
+Map a subtraction operation to each entry.
+ mapSubtractToSelf(T) FieldVector
+Map a subtraction operation to each entry.
+ mapSubtractToSelf(double) RealVector
+Map a subtraction operation to each entry.
+ mapSubtractToSelf(T) SparseFieldVector
+Map a subtraction operation to each entry.
+ mapTan() AbstractRealVector
+Map the Math.tan(double) mapTan() RealVector
+Map the Math.tan(double) mapTanh() AbstractRealVector
+Map the Math.tanh(double) mapTanh() RealVector
+Map the Math.tanh(double) mapTanhToSelf() AbstractRealVector
+Map the Math.tanh(double) mapTanhToSelf() ArrayRealVector
+Map the Math.tanh(double) mapTanhToSelf() RealVector
+Map the Math.tanh(double) mapTanToSelf() AbstractRealVector
+Map the Math.tan(double) mapTanToSelf() ArrayRealVector
+Map the Math.tan(double) mapTanToSelf() RealVector
+Map the Math.tan(double) mapToSelf(UnivariateRealFunction) AbstractRealVector
+Acts as if it is implemented as:
+ Entry e = null;
+ for(Iterator it = iterator(); it.hasNext(); e = it.next()) {
+ e.setValue(function.value(e.getValue()));
+ }
+mapToSelf(UnivariateRealFunction) RealVector
+Acts as if it is implemented as:
+ Entry e = null;
+ for(Iterator it = iterator(); it.hasNext(); e = it.next()) {
+ e.setValue(function.value(e.getValue()));
+ }
+mapUlp() AbstractRealVector
+Map the Math.ulp(double) mapUlp() RealVector
+Map the Math.ulp(double) mapUlpToSelf() AbstractRealVector
+Map the Math.ulp(double) mapUlpToSelf() ArrayRealVector
+Map the Math.ulp(double) mapUlpToSelf() RealVector
+Map the Math.ulp(double) MathConfigurationException org.apache.commons.math Signals a configuration problem with any of the factory methods. MathConfigurationException() MathConfigurationException
+Default constructor.
+ MathConfigurationException(String, Object...) MathConfigurationException
+Constructs an exception with specified formatted detail message.
+ MathConfigurationException(Throwable) MathConfigurationException
+Create an exception with a given root cause.
+ MathConfigurationException(Throwable, String, Object...) MathConfigurationException
+Constructs an exception with specified formatted detail message and root cause.
+ MathException org.apache.commons.math Base class for commons-math checked exceptions. MathException() MathException
+Constructs a new MathException with no
+ detail message.
+ MathException(String, Object...) MathException
+Constructs a new MathException with specified
+ formatted detail message.
+ MathException(Throwable) MathException
+Constructs a new MathException with specified
+ nested Throwable root cause.
+ MathException(Throwable, String, Object...) MathException
+Constructs a new MathException with specified
+ formatted detail message and nested Throwable root cause.
+ MathRuntimeException org.apache.commons.math Base class for commons-math unchecked exceptions. MathRuntimeException(String, Object...) MathRuntimeException
+Constructs a new MathRuntimeException with specified
+ formatted detail message.
+ MathRuntimeException(Throwable) MathRuntimeException
+Constructs a new MathRuntimeException with specified
+ nested Throwable root cause.
+ MathRuntimeException(Throwable, String, Object...) MathRuntimeException
+Constructs a new MathRuntimeException with specified
+ formatted detail message and nested Throwable root cause.
+ MathUtils org.apache.commons.math.util Some useful additions to the built-in functions in Math MatrixIndexException org.apache.commons.math.linear Thrown when an operation addresses a matrix coordinate (row, col)
+ which is outside of the dimensions of a matrix. MatrixIndexException(String, Object...) MatrixIndexException
+Constructs a new instance with specified formatted detail message.
+ MatrixUtils org.apache.commons.math.linear A collection of static methods that operate on or return matrices. MatrixVisitorException org.apache.commons.math.linear Thrown when a visitor encounters an error while processing a matrix entry. MatrixVisitorException(String, Object[]) MatrixVisitorException
+Constructs a new instance with specified formatted detail message.
+ Max org.apache.commons.math.stat.descriptive.rank Returns the maximum of the available values. Max() Max
+Create a Max instance
+ Max(Max) Max
+Copy constructor, creates a new Max identical
+ to the original
+ max SummaryStatistics
+max of values that have been added
+ max(double[]) StatUtils
+Returns the maximum of the entries in the input array, or
+ Double.NaN if the array is empty.
+ max(double[], int, int) StatUtils
+Returns the maximum of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ MaxEvaluationsExceededException org.apache.commons.math Error thrown when a numerical computation exceeds its allowed
+ number of functions evaluations. MaxEvaluationsExceededException(int) MaxEvaluationsExceededException
+Constructs an exception with specified formatted detail message.
+ MaxEvaluationsExceededException(int, String, Object...) MaxEvaluationsExceededException
+Constructs an exception with specified formatted detail message.
+ maximalIterationCount ConvergingAlgorithmImpl
+Maximum number of iterations.
+ MaxIterationsExceededException org.apache.commons.math Error thrown when a numerical computation exceeds its allowed
+ number of iterations. MaxIterationsExceededException(int) MaxIterationsExceededException
+Constructs an exception with specified formatted detail message.
+ MaxIterationsExceededException(int, String, Object...) MaxIterationsExceededException
+Constructs an exception with specified formatted detail message.
+ mdfft(Object, boolean) FastFourierTransformer
+Performs a multi-dimensional Fourier transform on a given array.
+ Mean org.apache.commons.math.stat.descriptive.moment Computes the arithmetic mean of a set of values. Mean() Mean
+Constructs a Mean.
+ Mean(FirstMoment) Mean
+Constructs a Mean with an External Moment.
+ Mean(Mean) Mean
+Copy constructor, creates a new Mean identical
+ to the original
+ mean SummaryStatistics
+mean of values that have been added
+ mean(double[]) StatUtils
+Returns the arithmetic mean of the entries in the input array, or
+ Double.NaN if the array is empty.
+ mean(double[], int, int) StatUtils
+Returns the arithmetic mean of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ meanDifference(double[], double[]) StatUtils
+Returns the mean of the (signed) differences between corresponding elements of the
+ input arrays -- i.e., sum(sample1[i] - sample2[i]) / sample1.length.
+ measurements AbstractEstimator
+Deprecated. Array of measurements.
+Median org.apache.commons.math.stat.descriptive.rank Returns the median of the available values. Median() Median
+Default constructor.
+ Median(Median) Median
+Copy constructor, creates a new Median identical
+ to the original
+ MersenneTwister org.apache.commons.math.random This class implements a powerful pseudo-random number generator
+ developed by Makoto Matsumoto and Takuji Nishimura during
+ 1996-1997. MersenneTwister() MersenneTwister
+Creates a new random number generator.
+ MersenneTwister(int) MersenneTwister
+Creates a new random number generator using a single int seed.
+ MersenneTwister(int[]) MersenneTwister
+Creates a new random number generator using an int array seed.
+ MersenneTwister(long) MersenneTwister
+Creates a new random number generator using a single long seed.
+ MessagesResources_fr org.apache.commons.math French localization message resources for the commons-math library. MessagesResources_fr() MessagesResources_fr
+Simple constructor.
+ MicrosphereInterpolatingFunction org.apache.commons.math.analysis.interpolation Interpolating function that implements the
+ Microsphere Projection . MicrosphereInterpolatingFunction(double[][], double[], int, int, UnitSphereRandomVectorGenerator) MicrosphereInterpolatingFunction
+
+ MicrosphereInterpolator org.apache.commons.math.analysis.interpolation Interpolator that implements the algorithm described in
+ William Dudziak 's
+ MS thesis MicrosphereInterpolator() MicrosphereInterpolator
+Create a microsphere interpolator with default settings.
+ MicrosphereInterpolator(int, int) MicrosphereInterpolator
+Create a microsphere interpolator.
+ midpoint(double, double) UnivariateRealSolverUtils
+Compute the midpoint of two values.
+ MidpointIntegrator org.apache.commons.math.ode.nonstiff This class implements a second order Runge-Kutta integrator for
+ Ordinary Differential Equations. MidpointIntegrator(double) MidpointIntegrator
+Simple constructor.
+ Min org.apache.commons.math.stat.descriptive.rank Returns the minimum of the available values. Min() Min
+Create a Min instance
+ Min(Min) Min
+Copy constructor, creates a new Min identical
+ to the original
+ min SummaryStatistics
+min of values that have been added
+ min(double[]) StatUtils
+Returns the minimum of the entries in the input array, or
+ Double.NaN if the array is empty.
+ min(double[], int, int) StatUtils
+Returns the minimum of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ minimalIterationCount UnivariateRealIntegratorImpl
+minimum number of iterations
+ MINUS_I Vector3D
+Opposite of the first canonical vector (coordinates: -1, 0, 0).
+ MINUS_J Vector3D
+Opposite of the second canonical vector (coordinates: 0, -1, 0).
+ MINUS_K Vector3D
+Opposite of the third canonical vector (coordinates: 0, 0, -1).
+ MINUS_ONE BigFraction
+A fraction representing "-1 / 1".
+ MINUS_ONE Fraction
+A fraction representing "-1 / 1".
+ moment Kurtosis
+Fourth Moment on which this statistic is based
+ moment Mean
+First moment on which this statistic is based.
+ moment Skewness
+Third moment on which this statistic is based
+ moment Variance
+SecondMoment is used in incremental calculation of Variance
+ mulAndCheck(int, int) MathUtils
+Multiply two integers, checking for overflow.
+ mulAndCheck(long, long) MathUtils
+Multiply two long integers, checking for overflow.
+ MullerSolver org.apache.commons.math.analysis.solvers Implements the
+ Muller's Method for root finding of real univariate functions. MullerSolver(UnivariateRealFunction) MullerSolver
+Deprecated. as of 2.0 the function to solve is passed as an argument
+ to the MullerSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+MullerSolver() MullerSolver
+Construct a solver.
+ MultiDirectional org.apache.commons.math.optimization.direct This class implements the multi-directional direct search method. MultiDirectional() MultiDirectional
+Build a multi-directional optimizer with default coefficients.
+ MultiDirectional(double, double) MultiDirectional
+Build a multi-directional optimizer with specified coefficients.
+ MultipleLinearRegression org.apache.commons.math.stat.regression The multiple linear regression can be represented in matrix-notation. MULTIPLICATIVE_MODE ResizableDoubleArray
+multiplicative expansion mode
+ MULTIPLY BinaryFunction
+The * operator method wrapped as a BinaryFunction multiply(UnivariateRealFunction) ComposableFunction
+Return a function multiplying the instance and another function.
+ multiply(double) ComposableFunction
+Return a function scaling the instance by a constant factor.
+ multiply(PolynomialFunction) PolynomialFunction
+Multiply the instance by a polynomial.
+ multiply(Complex) Complex
+Return the product of this complex number and the given complex number.
+ multiply(double) Complex
+Return the product of this complex number and the given scalar number.
+ multiply(T) FieldElement
+Compute this × a.
+ multiply(BigInteger) BigFraction
+
+ Multiplies the value of this fraction by the passed
+ BigInteger, returning the result in reduced form.
+ multiply(int) BigFraction
+
+ Multiply the value of this fraction by the passed int , returning
+ the result in reduced form.
+ multiply(long) BigFraction
+
+ Multiply the value of this fraction by the passed long ,
+ returning the result in reduced form.
+ multiply(BigFraction) BigFraction
+
+ Multiplies the value of this fraction by another, returning the result in
+ reduced form.
+ multiply(Fraction) Fraction
+Multiplies the value of this fraction by another, returning the
+ result in reduced form.
+ multiply(int) Fraction
+Multiply the fraction by an integer.
+ multiply(FieldMatrix<T>) AbstractFieldMatrix
+Returns the result of postmultiplying this by m.
+ multiply(RealMatrix) AbstractRealMatrix
+Returns the result of postmultiplying this by m.
+ multiply(FieldMatrix<T>) Array2DRowFieldMatrix
+Returns the result of postmultiplying this by m.
+ multiply(Array2DRowFieldMatrix<T>) Array2DRowFieldMatrix
+Returns the result of postmultiplying this by m.
+ multiply(RealMatrix) Array2DRowRealMatrix
+Returns the result of postmultiplying this by m.
+ multiply(Array2DRowRealMatrix) Array2DRowRealMatrix
+Returns the result of postmultiplying this by m.
+ multiply(BigMatrix) BigMatrix
+Deprecated. Returns the result of postmultiplying this by m.
+multiply(BigMatrix) BigMatrixImpl
+Deprecated. Returns the result of postmultiplying this by m.
+multiply(BigMatrixImpl) BigMatrixImpl
+Deprecated. Returns the result of postmultiplying this by m.
+multiply(FieldMatrix<T>) BlockFieldMatrix
+Returns the result of postmultiplying this by m.
+ multiply(BlockFieldMatrix<T>) BlockFieldMatrix
+Returns the result of postmultiplying this by m.
+ multiply(RealMatrix) BlockRealMatrix
+Returns the result of postmultiplying this by m.
+ multiply(BlockRealMatrix) BlockRealMatrix
+Returns the result of postmultiplying this by m.
+ multiply(FieldMatrix<T>) FieldMatrix
+Returns the result of postmultiplying this by m.
+ multiply(RealMatrix) OpenMapRealMatrix
+Returns the result of postmultiplying this by m.
+ multiply(OpenMapRealMatrix) OpenMapRealMatrix
+Returns the result of postmultiplying this by m.
+ multiply(RealMatrix) RealMatrix
+Returns the result of postmultiplying this by m.
+ multiply(RealMatrix) RealMatrixImpl
+Deprecated. Returns the result of postmultiplying this by m.
+multiply(RealMatrixImpl) RealMatrixImpl
+Deprecated. Returns the result of postmultiplying this by m.
+multiply(BigReal) BigReal
+Compute this × a.
+ multiplyEntry(int, int, T) AbstractFieldMatrix
+Change an entry in the specified row and column.
+ multiplyEntry(int, int, double) AbstractRealMatrix
+Change an entry in the specified row and column.
+ multiplyEntry(int, int, T) Array2DRowFieldMatrix
+Change an entry in the specified row and column.
+ multiplyEntry(int, int, double) Array2DRowRealMatrix
+Change an entry in the specified row and column.
+ multiplyEntry(int, int, T) BlockFieldMatrix
+Change an entry in the specified row and column.
+ multiplyEntry(int, int, double) BlockRealMatrix
+Change an entry in the specified row and column.
+ multiplyEntry(int, int, T) FieldMatrix
+Change an entry in the specified row and column.
+ multiplyEntry(int, int, double) OpenMapRealMatrix
+Change an entry in the specified row and column.
+ multiplyEntry(int, int, double) RealMatrix
+Change an entry in the specified row and column.
+ multiplyEntry(int, int, double) RealMatrixImpl
+Deprecated. Change an entry in the specified row and column.
+multiplyEntry(int, int, T) SparseFieldMatrix
+Change an entry in the specified row and column.
+ MultiStartDifferentiableMultivariateRealOptimizer org.apache.commons.math.optimization Special implementation of the DifferentiableMultivariateRealOptimizer MultiStartDifferentiableMultivariateRealOptimizer(DifferentiableMultivariateRealOptimizer, int, RandomVectorGenerator) MultiStartDifferentiableMultivariateRealOptimizer
+Create a multi-start optimizer from a single-start optimizer
+ MultiStartDifferentiableMultivariateVectorialOptimizer org.apache.commons.math.optimization Special implementation of the DifferentiableMultivariateVectorialOptimizer MultiStartDifferentiableMultivariateVectorialOptimizer(DifferentiableMultivariateVectorialOptimizer, int, RandomVectorGenerator) MultiStartDifferentiableMultivariateVectorialOptimizer
+Create a multi-start optimizer from a single-start optimizer
+ MultiStartMultivariateRealOptimizer org.apache.commons.math.optimization Special implementation of the MultivariateRealOptimizer MultiStartMultivariateRealOptimizer(MultivariateRealOptimizer, int, RandomVectorGenerator) MultiStartMultivariateRealOptimizer
+Create a multi-start optimizer from a single-start optimizer
+ MultiStartUnivariateRealOptimizer org.apache.commons.math.optimization Special implementation of the UnivariateRealOptimizer MultiStartUnivariateRealOptimizer(UnivariateRealOptimizer, int, RandomGenerator) MultiStartUnivariateRealOptimizer
+Create a multi-start optimizer from a single-start optimizer
+ MultistepIntegrator org.apache.commons.math.ode This class is the base class for multistep integrators for Ordinary
+ Differential Equations. MultistepIntegrator(String, int, int, double, double, double, double) MultistepIntegrator
+Build a multistep integrator with the given stepsize bounds.
+ MultistepIntegrator(String, int, int, double, double, double[], double[]) MultistepIntegrator
+Build a multistep integrator with the given stepsize bounds.
+ MultistepIntegrator.NordsieckTransformer org.apache.commons.math.ode Transformer used to convert the first step to Nordsieck representation. MultivariateMatrixFunction org.apache.commons.math.analysis An interface representing a multivariate matrix function. MultivariateRealFunction org.apache.commons.math.analysis An interface representing a multivariate real function. MultivariateRealInterpolator org.apache.commons.math.analysis.interpolation Interface representing a univariate real interpolating function. MultivariateRealOptimizer org.apache.commons.math.optimization This interface represents an optimization algorithm for scalar objective functions MultivariateSummaryStatistics org.apache.commons.math.stat.descriptive Computes summary statistics for a stream of n-tuples added using the
+ addValue MultivariateSummaryStatistics(int, boolean) MultivariateSummaryStatistics
+Construct a MultivariateSummaryStatistics instance
+ MultivariateVectorialFunction org.apache.commons.math.analysis An interface representing a multivariate vectorial function. mutate(Chromosome) BinaryMutation
+Mutate the given chromosome.
+ mutate(Chromosome) MutationPolicy
+Mutate the given chromosome.
+ mutate(Chromosome) RandomKeyMutation
+Mutate the given chromosome.
+ MutationPolicy org.apache.commons.math.genetics Algorithm used to mutate a chrommosome.
+
+N
+
+n FirstMoment
+Count of values that have been added
+ n SummaryStatistics
+count of values that have been added
+ NaN Complex
+A complex number representing "NaN + NaNi"
+ NaN Vector3D
+A vector with all coordinates set to NaN.
+ NaNStrategy org.apache.commons.math.stat.ranking Strategies for handling NaN values in rank transformations. NaturalRanking org.apache.commons.math.stat.ranking Ranking based on the natural ordering on doubles. NaturalRanking() NaturalRanking
+Create a NaturalRanking with default strategies for handling ties and NaNs.
+ NaturalRanking(TiesStrategy) NaturalRanking
+Create a NaturalRanking with the given TiesStrategy.
+ NaturalRanking(NaNStrategy) NaturalRanking
+Create a NaturalRanking with the given NaNStrategy.
+ NaturalRanking(NaNStrategy, TiesStrategy) NaturalRanking
+Create a NaturalRanking with the given NaNStrategy and TiesStrategy.
+ NaturalRanking(RandomGenerator) NaturalRanking
+Create a NaturalRanking with TiesStrategy.RANDOM and the given
+ RandomGenerator as the source of random data.
+ NaturalRanking(NaNStrategy, RandomGenerator) NaturalRanking
+Create a NaturalRanking with the given NaNStrategy, TiesStrategy.RANDOM
+ and the given source of random data.
+ nDev FirstMoment
+Deviation of most recently added value from previous first moment,
+ normalized by previous sample size.
+ nDevSq ThirdMoment
+Square of deviation of most recently added value from previous first
+ moment, normalized by previous sample size.
+ NEGATE ComposableFunction
+The - operator wrapped as a ComposableFunction negate() PolynomialFunction
+Negate the instance.
+ negate() Complex
+Return the additive inverse of this complex number.
+ negate() BigFraction
+
+ Return the additive inverse of this fraction, returning the result in
+ reduced form.
+ negate() Fraction
+Return the additive inverse of this fraction.
+ negate() Vector3D
+Get the opposite of the instance.
+ NEGATIVE_INFINITY Vector3D
+A vector with all coordinates set to negative infinity.
+ NelderMead org.apache.commons.math.optimization.direct This class implements the Nelder-Mead direct search method. NelderMead() NelderMead
+Build a Nelder-Mead optimizer with default coefficients.
+ NelderMead(double, double, double, double) NelderMead
+Build a Nelder-Mead optimizer with specified coefficients.
+ NevilleInterpolator org.apache.commons.math.analysis.interpolation Implements the
+ Neville's Algorithm for interpolation of real univariate functions. NevilleInterpolator() NevilleInterpolator
+
+ newBisectionSolver() UnivariateRealSolverFactory
+Create a new UnivariateRealSolver newBisectionSolver() UnivariateRealSolverFactoryImpl
+Create a new UnivariateRealSolver newBrentSolver() UnivariateRealSolverFactory
+Create a new UnivariateRealSolver newBrentSolver() UnivariateRealSolverFactoryImpl
+Create a new UnivariateRealSolver newCovarianceData(double[][]) GLSMultipleLinearRegression
+Add the covariance data.
+ newDefaultSolver() UnivariateRealSolverFactory
+Create a new UnivariateRealSolver newDefaultSolver() UnivariateRealSolverFactoryImpl
+Create a new UnivariateRealSolver newFixedLengthChromosome(List<T>) AbstractListChromosome
+Creates a new instance of the same class as this is, with a
+ given arrayRepresentation.
+ newInstance() UnivariateRealSolverFactory
+Create a new factory.
+ newNewtonSolver() UnivariateRealSolverFactory
+Create a new UnivariateRealSolver newNewtonSolver() UnivariateRealSolverFactoryImpl
+Create a new UnivariateRealSolver newSampleData(double[], int, int) AbstractMultipleLinearRegression
+Loads model x and y sample data from a flat array of data, overriding any previous sample.
+ newSampleData(double[], double[][], double[][]) GLSMultipleLinearRegression
+Replace sample data, overriding any previous sample.
+ newSampleData(double[], double[][]) OLSMultipleLinearRegression
+Loads model x and y sample data, overriding any previous sample.
+ newSampleData(double[], int, int) OLSMultipleLinearRegression
+Loads model x and y sample data from a flat array of data, overriding any previous sample.
+ newSecantSolver() UnivariateRealSolverFactory
+Create a new UnivariateRealSolver newSecantSolver() UnivariateRealSolverFactoryImpl
+Create a new UnivariateRealSolver NewtonSolver org.apache.commons.math.analysis.solvers Implements
+ Newton's Method for finding zeros of real univariate functions. NewtonSolver(DifferentiableUnivariateRealFunction) NewtonSolver
+Deprecated. as of 2.0 the function to solve is passed as an argument
+ to the NewtonSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+NewtonSolver() NewtonSolver
+Construct a solver.
+ newXSampleData(double[][]) AbstractMultipleLinearRegression
+Loads new x sample data, overriding any previous sample
+ newXSampleData(double[][]) OLSMultipleLinearRegression
+Loads new x sample data, overriding any previous sample
+ newYSampleData(double[]) AbstractMultipleLinearRegression
+Loads new y sample data, overriding any previous sample
+ next() AbstractRealVector.SparseEntryIterator
+
+ next() OpenMapRealVector.OpenMapSparseIterator
+
+ next(int) BitsStreamGenerator
+Generate next pseudorandom number.
+ next(int) MersenneTwister
+Generate next pseudorandom number.
+ nextAfter(double, double) MathUtils
+Get the next machine representable number after a number, moving
+ in the direction of another number.
+ nextBoolean() AbstractRandomGenerator
+Returns the next pseudorandom, uniformly distributed
+ boolean value from this random number generator's
+ sequence.
+ nextBoolean() BitsStreamGenerator
+Returns the next pseudorandom, uniformly distributed
+ boolean value from this random number generator's
+ sequence.
+ nextBoolean() RandomAdaptor
+Returns the next pseudorandom, uniformly distributed
+ boolean value from this random number generator's
+ sequence.
+ nextBoolean() RandomGenerator
+Returns the next pseudorandom, uniformly distributed
+ boolean value from this random number generator's
+ sequence.
+ nextBytes(byte[]) AbstractRandomGenerator
+Generates random bytes and places them into a user-supplied
+ byte array.
+ nextBytes(byte[]) BitsStreamGenerator
+Generates random bytes and places them into a user-supplied
+ byte array.
+ nextBytes(byte[]) RandomAdaptor
+Generates random bytes and places them into a user-supplied
+ byte array.
+ nextBytes(byte[]) RandomGenerator
+Generates random bytes and places them into a user-supplied
+ byte array.
+ nextDouble() AbstractRandomGenerator
+Returns the next pseudorandom, uniformly distributed
+ double value between 0.0 and
+ 1.0 from this random number generator's sequence.
+ nextDouble() BitsStreamGenerator
+Returns the next pseudorandom, uniformly distributed
+ double value between 0.0 and
+ 1.0 from this random number generator's sequence.
+ nextDouble() RandomAdaptor
+Returns the next pseudorandom, uniformly distributed
+ double value between 0.0 and
+ 1.0 from this random number generator's sequence.
+ nextDouble() RandomGenerator
+Returns the next pseudorandom, uniformly distributed
+ double value between 0.0 and
+ 1.0 from this random number generator's sequence.
+ nextExponential(double) RandomData
+Generates a random value from the exponential distribution
+ with expected value = mean.
+ nextExponential(double) RandomDataImpl
+Returns a random value from an Exponential distribution with the given
+ mean.
+ nextFloat() AbstractRandomGenerator
+Returns the next pseudorandom, uniformly distributed float
+ value between 0.0 and 1.0 from this random
+ number generator's sequence.
+ nextFloat() BitsStreamGenerator
+Returns the next pseudorandom, uniformly distributed float
+ value between 0.0 and 1.0 from this random
+ number generator's sequence.
+ nextFloat() RandomAdaptor
+Returns the next pseudorandom, uniformly distributed float
+ value between 0.0 and 1.0 from this random
+ number generator's sequence.
+ nextFloat() RandomGenerator
+Returns the next pseudorandom, uniformly distributed float
+ value between 0.0 and 1.0 from this random
+ number generator's sequence.
+ nextGaussian() AbstractRandomGenerator
+Returns the next pseudorandom, Gaussian ("normally") distributed
+ double value with mean 0.0 and standard
+ deviation 1.0 from this random number generator's sequence.
+ nextGaussian() BitsStreamGenerator
+Returns the next pseudorandom, Gaussian ("normally") distributed
+ double value with mean 0.0 and standard
+ deviation 1.0 from this random number generator's sequence.
+ nextGaussian() RandomAdaptor
+Returns the next pseudorandom, Gaussian ("normally") distributed
+ double value with mean 0.0 and standard
+ deviation 1.0 from this random number generator's sequence.
+ nextGaussian(double, double) RandomData
+Generates a random value from the
+ Normal (or Gaussian) distribution with the given mean
+ and standard deviation.
+ nextGaussian(double, double) RandomDataImpl
+Generate a random value from a Normal (a.k.a.
+ nextGaussian() RandomGenerator
+Returns the next pseudorandom, Gaussian ("normally") distributed
+ double value with mean 0.0 and standard
+ deviation 1.0 from this random number generator's sequence.
+ nextGeneration() ElitisticListPopulation
+Start the population for the next generation.
+ nextGeneration(Population) GeneticAlgorithm
+Evolve the given population into the next generation.
+ nextGeneration() Population
+Start the population for the next generation.
+ nextHexString(int) RandomData
+Generates a random string of hex characters of length
+ len.
+ nextHexString(int) RandomDataImpl
+Generates a random string of hex characters of length
+ len.
+ nextInt() AbstractRandomGenerator
+Returns the next pseudorandom, uniformly distributed int
+ value from this random number generator's sequence.
+ nextInt(int) AbstractRandomGenerator
+Returns a pseudorandom, uniformly distributed int value
+ between 0 (inclusive) and the specified value (exclusive), drawn from
+ this random number generator's sequence.
+ nextInt() BitsStreamGenerator
+Returns the next pseudorandom, uniformly distributed int
+ value from this random number generator's sequence.
+ nextInt(int) BitsStreamGenerator
+Returns a pseudorandom, uniformly distributed int value
+ between 0 (inclusive) and the specified value (exclusive), drawn from
+ this random number generator's sequence.
+ nextInt() RandomAdaptor
+Returns the next pseudorandom, uniformly distributed int
+ value from this random number generator's sequence.
+ nextInt(int) RandomAdaptor
+Returns a pseudorandom, uniformly distributed int value
+ between 0 (inclusive) and the specified value (exclusive), drawn from
+ this random number generator's sequence.
+ nextInt(int, int) RandomData
+Generates a uniformly distributed random integer between
+ lower and upper (endpoints included).
+ nextInt(int, int) RandomDataImpl
+Generate a random int value uniformly distributed between
+ lower and upper, inclusive.
+ nextInt() RandomGenerator
+Returns the next pseudorandom, uniformly distributed int
+ value from this random number generator's sequence.
+ nextInt(int) RandomGenerator
+Returns a pseudorandom, uniformly distributed int value
+ between 0 (inclusive) and the specified value (exclusive), drawn from
+ this random number generator's sequence.
+ nextLong() AbstractRandomGenerator
+Returns the next pseudorandom, uniformly distributed long
+ value from this random number generator's sequence.
+ nextLong() BitsStreamGenerator
+Returns the next pseudorandom, uniformly distributed long
+ value from this random number generator's sequence.
+ nextLong() RandomAdaptor
+Returns the next pseudorandom, uniformly distributed long
+ value from this random number generator's sequence.
+ nextLong(long, long) RandomData
+Generates a uniformly distributed random long integer between
+ lower and upper (endpoints included).
+ nextLong(long, long) RandomDataImpl
+Generate a random long value uniformly distributed between
+ lower and upper, inclusive.
+ nextLong() RandomGenerator
+Returns the next pseudorandom, uniformly distributed long
+ value from this random number generator's sequence.
+ nextNormalizedDouble() GaussianRandomGenerator
+Generate a random scalar with null mean and unit standard deviation.
+ nextNormalizedDouble() NormalizedRandomGenerator
+Generate a random scalar with null mean and unit standard deviation.
+ nextNormalizedDouble() UniformRandomGenerator
+Generate a random scalar with null mean and unit standard deviation.
+ nextPermutation(int, int) RandomData
+Generates an integer array of length k whose entries
+ are selected randomly, without repetition, from the integers
+ 0 through n-1 (inclusive).
+ nextPermutation(int, int) RandomDataImpl
+Generates an integer array of length k whose entries are
+ selected randomly, without repetition, from the integers
+ 0 through n-1 (inclusive).
+ nextPoisson(double) RandomData
+Generates a random value from the Poisson distribution with
+ the given mean.
+ nextPoisson(double) RandomDataImpl
+Generates a random value from the Poisson distribution with
+ the given mean.
+ nextSample(Collection<?>, int) RandomData
+Returns an array of k objects selected randomly
+ from the Collection c.
+ nextSample(Collection<?>, int) RandomDataImpl
+Uses a 2-cycle permutation shuffle to generate a random permutation.
+ nextSecureHexString(int) RandomData
+Generates a random string of hex characters from a secure random
+ sequence.
+ nextSecureHexString(int) RandomDataImpl
+Generates a random string of hex characters from a secure random
+ sequence.
+ nextSecureInt(int, int) RandomData
+Generates a uniformly distributed random integer between
+ lower and upper (endpoints included)
+ from a secure random sequence.
+ nextSecureInt(int, int) RandomDataImpl
+Generate a random int value uniformly distributed between
+ lower and upper, inclusive.
+ nextSecureLong(long, long) RandomData
+Generates a random long integer between lower
+ and upper (endpoints included).
+ nextSecureLong(long, long) RandomDataImpl
+Generate a random long value uniformly distributed between
+ lower and upper, inclusive.
+ nextUniform(double, double) RandomData
+Generates a uniformly distributed random value from the open interval
+ (lower,upper) (i.e., endpoints excluded).
+ nextUniform(double, double) RandomDataImpl
+Generates a uniformly distributed random value from the open interval
+ (lower,upper) (i.e., endpoints excluded).
+ nextVector() CorrelatedRandomVectorGenerator
+Generate a correlated random vector.
+ nextVector() RandomVectorGenerator
+Generate a random vector.
+ nextVector() UncorrelatedRandomVectorGenerator
+Generate an uncorrelated random vector.
+ nextVector() UnitSphereRandomVectorGenerator
+Generate a random vector.
+ NoFeasibleSolutionException org.apache.commons.math.optimization.linear This class represents exceptions thrown by optimizers when no solution
+ fulfills the constraints. NoFeasibleSolutionException() NoFeasibleSolutionException
+Simple constructor using a default message.
+ NonLinearConjugateGradientOptimizer org.apache.commons.math.optimization.general Non-linear conjugate gradient optimizer. NonLinearConjugateGradientOptimizer(ConjugateGradientFormula) NonLinearConjugateGradientOptimizer
+Simple constructor with default settings.
+ nonNegative AbstractLinearOptimizer
+Whether to restrict the variables to non-negative values.
+ NonSquareMatrixException org.apache.commons.math.linear Thrown when an operation defined only for square matrices is applied to non-square ones. NonSquareMatrixException(int, int) NonSquareMatrixException
+Construct an exception with the given message.
+ nordsieck MultistepIntegrator
+Nordsieck matrix of the higher scaled derivatives.
+ NordsieckStepInterpolator org.apache.commons.math.ode.sampling This class implements an interpolator for integrators using Nordsieck representation. NordsieckStepInterpolator() NordsieckStepInterpolator
+Simple constructor.
+ NordsieckStepInterpolator(NordsieckStepInterpolator) NordsieckStepInterpolator
+Copy constructor.
+ normalApproximateProbability(int) PoissonDistribution
+Calculates the Poisson distribution function using a normal approximation.
+ normalApproximateProbability(int) PoissonDistributionImpl
+Calculates the Poisson distribution function using a normal
+ approximation.
+ NormalDistribution org.apache.commons.math.distribution Normal (Gauss) Distribution. NormalDistributionImpl org.apache.commons.math.distribution Default implementation of
+ NormalDistribution NormalDistributionImpl(double, double) NormalDistributionImpl
+Create a normal distribution using the given mean and standard deviation.
+ NormalDistributionImpl(double, double, double) NormalDistributionImpl
+Create a normal distribution using the given mean, standard deviation and
+ inverse cumulative distribution accuracy.
+ NormalDistributionImpl() NormalDistributionImpl
+Creates normal distribution with the mean equal to zero and standard
+ deviation equal to one.
+ normalize() Vector3D
+Get a normalized vector aligned with the instance.
+ normalizeAngle(double, double) MathUtils
+Normalize an angle in a 2&pi wide interval around a center value.
+ normalizeArray(double[], double) MathUtils
+Normalizes an array to make it sum to a specified value.
+ NormalizedRandomGenerator org.apache.commons.math.random This interface represent a normalized random generator for
+ scalars. NotARotationMatrixException org.apache.commons.math.geometry This class represents exceptions thrown while building rotations
+ from matrices. NotARotationMatrixException(String, Object...) NotARotationMatrixException
+Simple constructor.
+ NotPositiveDefiniteMatrixException org.apache.commons.math.linear This class represents exceptions thrown when a matrix expected to
+ be positive definite is not. NotPositiveDefiniteMatrixException() NotPositiveDefiniteMatrixException
+Simple constructor.
+ NotSymmetricMatrixException org.apache.commons.math.linear This class represents exceptions thrown when a matrix expected to
+ be symmetric is not NotSymmetricMatrixException() NotSymmetricMatrixException
+Simple constructor.
+ nthRoot(int) Complex
+Computes the n-th roots of this complex number.
+ NumberTransformer org.apache.commons.math.util Subclasses implementing this interface can transform Objects to doubles. numElements ResizableDoubleArray
+The number of addressable elements in the array.
+ numeratorFormat AbstractFormat
+The format used for the numerator.
+
+
+O
+
+objective AbstractLeastSquaresOptimizer
+Current objective function value.
+ ODEIntegrator org.apache.commons.math.ode This interface defines the common parts shared by integrators
+ for first and second order differential equations. ODEWithJacobians org.apache.commons.math.ode.jacobians This interface represents first order differential equations of(UnivariateRealFunction) ComposableFunction
+Precompose the instance with another function.
+ OLSMultipleLinearRegression org.apache.commons.math.stat.regression Implements ordinary least squares (OLS) to estimate the parameters of a
+ multiple linear regression model. OLSMultipleLinearRegression() OLSMultipleLinearRegression
+
+ ONE ComposableFunction
+The constant function always returning 1.
+ ONE Complex
+A complex number representing "1.0 + 0.0i"
+ ONE BigFraction
+A fraction representing "1".
+ ONE Fraction
+A fraction representing "1".
+ ONE BigReal
+A big real representing 1.
+ ONE_FIFTH BigFraction
+A fraction representing "1/5".
+ ONE_FIFTH Fraction
+A fraction representing "1/5".
+ ONE_HALF BigFraction
+A fraction representing "1/2".
+ ONE_HALF Fraction
+A fraction representing "1/2".
+ ONE_QUARTER BigFraction
+A fraction representing "1/4".
+ ONE_QUARTER Fraction
+A fraction representing "1/4".
+ ONE_THIRD BigFraction
+A fraction representing "1/3".
+ ONE_THIRD Fraction
+A fraction representing "1/3".
+ OnePointCrossover T > - Class in org.apache.commons.math.genetics One point crossover policy. OnePointCrossover() OnePointCrossover
+
+ OneWayAnova org.apache.commons.math.stat.inference An interface for one-way ANOVA (analysis of variance). oneWayAnovaFValue(Collection<double[]>) TestUtils
+
+ OneWayAnovaImpl org.apache.commons.math.stat.inference Implements one-way ANOVA statistics defined in the OneWayAnovaImpl OneWayAnovaImpl() OneWayAnovaImpl
+Default constructor.
+ oneWayAnovaPValue(Collection<double[]>) TestUtils
+
+ oneWayAnovaTest(Collection<double[]>, double) TestUtils
+
+ OpenIntToDoubleHashMap org.apache.commons.math.util Open addressed map from int to double. OpenIntToDoubleHashMap() OpenIntToDoubleHashMap
+Build an empty map with default size and using NaN for missing entries.
+ OpenIntToDoubleHashMap(double) OpenIntToDoubleHashMap
+Build an empty map with default size
+ OpenIntToDoubleHashMap(int) OpenIntToDoubleHashMap
+Build an empty map with specified size and using NaN for missing entries.
+ OpenIntToDoubleHashMap(int, double) OpenIntToDoubleHashMap
+Build an empty map with specified size.
+ OpenIntToDoubleHashMap(OpenIntToDoubleHashMap) OpenIntToDoubleHashMap
+Copy constructor.
+ OpenIntToDoubleHashMap.Iterator org.apache.commons.math.util Iterator class for the map. OpenIntToFieldHashMap T extends FieldElement <T >> - Class in org.apache.commons.math.util Open addressed map from int to FieldElement. OpenIntToFieldHashMap(Field<T>) OpenIntToFieldHashMap
+Build an empty map with default size and using zero for missing entries.
+ OpenIntToFieldHashMap(Field<T>, T) OpenIntToFieldHashMap
+Build an empty map with default size
+ OpenIntToFieldHashMap(Field<T>, int) OpenIntToFieldHashMap
+Build an empty map with specified size and using zero for missing entries.
+ OpenIntToFieldHashMap(Field<T>, int, T) OpenIntToFieldHashMap
+Build an empty map with specified size.
+ OpenIntToFieldHashMap(OpenIntToFieldHashMap<T>) OpenIntToFieldHashMap
+Copy constructor.
+ OpenIntToFieldHashMap.Iterator org.apache.commons.math.util Iterator class for the map. OpenMapRealMatrix org.apache.commons.math.linear Sparse matrix implementation based on an open addressed map. OpenMapRealMatrix(int, int) OpenMapRealMatrix
+Build a sparse matrix with the supplied row and column dimensions.
+ OpenMapRealMatrix(OpenMapRealMatrix) OpenMapRealMatrix
+Build a matrix by copying another one.
+ OpenMapRealVector org.apache.commons.math.linear This class implements the RealVectorOpenIntToDoubleHashMap OpenMapRealVector() OpenMapRealVector
+Build a 0-length vector.
+ OpenMapRealVector(int) OpenMapRealVector
+Construct a (dimension)-length vector of zeros.
+ OpenMapRealVector(int, double) OpenMapRealVector
+Construct a (dimension)-length vector of zeros, specifying zero tolerance.
+ OpenMapRealVector(OpenMapRealVector, int) OpenMapRealVector
+Build a resized vector, for use with append.
+ OpenMapRealVector(int, int) OpenMapRealVector
+Build a vector with known the sparseness (for advanced use only).
+ OpenMapRealVector(int, int, double) OpenMapRealVector
+Build a vector with known the sparseness and zero tolerance setting (for advanced use only).
+ OpenMapRealVector(double[]) OpenMapRealVector
+Create from a double array.
+ OpenMapRealVector(double[], double) OpenMapRealVector
+Create from a double array, specifying zero tolerance.
+ OpenMapRealVector(Double[]) OpenMapRealVector
+Create from a Double array.
+ OpenMapRealVector(Double[], double) OpenMapRealVector
+Create from a Double array.
+ OpenMapRealVector(OpenMapRealVector) OpenMapRealVector
+Copy constructor.
+ OpenMapRealVector(RealVector) OpenMapRealVector
+Generic copy constructor.
+ OpenMapRealVector.OpenMapEntry org.apache.commons.math.linear Implementation of Entry optimized for OpenMap. OpenMapRealVector.OpenMapEntry(OpenIntToDoubleHashMap.Iterator) OpenMapRealVector.OpenMapEntry
+Build an entry from an iterator point to an element.
+ OpenMapRealVector.OpenMapSparseIterator org.apache.commons.math.linear Iterator class to do iteration over just the non-zero elements. OpenMapRealVector.OpenMapSparseIterator() OpenMapRealVector.OpenMapSparseIterator
+Simple constructor.
+ operate(T[]) AbstractFieldMatrix
+Returns the result of multiplying this by the vector v.
+ operate(FieldVector<T>) AbstractFieldMatrix
+Returns the result of multiplying this by the vector v.
+ operate(double[]) AbstractRealMatrix
+Returns the result of multiplying this by the vector v.
+ operate(RealVector) AbstractRealMatrix
+Returns the result of multiplying this by the vector v.
+ operate(T[]) Array2DRowFieldMatrix
+Returns the result of multiplying this by the vector v.
+ operate(double[]) Array2DRowRealMatrix
+Returns the result of multiplying this by the vector v.
+ operate(BigDecimal[]) BigMatrix
+Deprecated. Returns the result of multiplying this by the vector v.
+operate(BigDecimal[]) BigMatrixImpl
+Deprecated. Returns the result of multiplying this by the vector v.
+operate(double[]) BigMatrixImpl
+Deprecated. Returns the result of multiplying this by the vector v.
+operate(T[]) BlockFieldMatrix
+Returns the result of multiplying this by the vector v.
+ operate(double[]) BlockRealMatrix
+Returns the result of multiplying this by the vector v.
+ operate(T[]) FieldMatrix
+Returns the result of multiplying this by the vector v.
+ operate(FieldVector<T>) FieldMatrix
+Returns the result of multiplying this by the vector v.
+ operate(double[]) RealMatrix
+Returns the result of multiplying this by the vector v.
+ operate(RealVector) RealMatrix
+Returns the result of multiplying this by the vector v.
+ operate(double[]) RealMatrixImpl
+Deprecated. Returns the result of multiplying this by the vector v.
+oppositeRelationship() Relationship
+Get the relationship obtained when multiplying all coefficients by -1.
+ OptimizationException org.apache.commons.math.optimization This class represents exceptions thrown by optimizers. OptimizationException(String, Object...) OptimizationException
+Simple constructor.
+ OptimizationException(Throwable) OptimizationException
+Create an exception with a given root cause.
+ optimize(DifferentiableMultivariateRealFunction, GoalType, double[]) DifferentiableMultivariateRealOptimizer
+Optimizes an objective function.
+ optimize(DifferentiableMultivariateVectorialFunction, double[], double[], double[]) DifferentiableMultivariateVectorialOptimizer
+Optimizes an objective function.
+ optimize(MultivariateRealFunction, GoalType, double[]) DirectSearchOptimizer
+Optimizes an objective function.
+ optimize(DifferentiableMultivariateVectorialFunction, double[], double[], double[]) AbstractLeastSquaresOptimizer
+Optimizes an objective function.
+ optimize(DifferentiableMultivariateRealFunction, GoalType, double[]) AbstractScalarDifferentiableOptimizer
+Optimizes an objective function.
+ optimize(LinearObjectiveFunction, Collection<LinearConstraint>, GoalType, boolean) AbstractLinearOptimizer
+Optimizes an objective function.
+ optimize(LinearObjectiveFunction, Collection<LinearConstraint>, GoalType, boolean) LinearOptimizer
+Optimizes an objective function.
+ optimize(DifferentiableMultivariateRealFunction, GoalType, double[]) MultiStartDifferentiableMultivariateRealOptimizer
+Optimizes an objective function.
+ optimize(DifferentiableMultivariateVectorialFunction, double[], double[], double[]) MultiStartDifferentiableMultivariateVectorialOptimizer
+Optimizes an objective function.
+ optimize(MultivariateRealFunction, GoalType, double[]) MultiStartMultivariateRealOptimizer
+Optimizes an objective function.
+ optimize(UnivariateRealFunction, GoalType, double, double) MultiStartUnivariateRealOptimizer
+Find an optimum in the given interval.
+ optimize(UnivariateRealFunction, GoalType, double, double, double) MultiStartUnivariateRealOptimizer
+Find an optimum in the given interval, start at startValue.
+ optimize(MultivariateRealFunction, GoalType, double[]) MultivariateRealOptimizer
+Optimizes an objective function.
+ optimize(UnivariateRealFunction, GoalType, double, double, double) BrentOptimizer
+Find an optimum in the given interval, start at startValue.
+ optimize(UnivariateRealFunction, GoalType, double, double) BrentOptimizer
+Find an optimum in the given interval.
+ optimize(UnivariateRealFunction, GoalType, double, double) UnivariateRealOptimizer
+Find an optimum in the given interval.
+ optimize(UnivariateRealFunction, GoalType, double, double, double) UnivariateRealOptimizer
+Find an optimum in the given interval, start at startValue.
+ org.apache.commons.math Common classes used throughout the commons-math library. org.apache.commons.math.analysis
+ Parent package for common numerical analysis procedures, including root finding,
+ function interpolation and integration. org.apache.commons.math.analysis.integration Numerical integration (quadrature) algorithms for univariate real functions. org.apache.commons.math.analysis.interpolation Univariate real functions interpolation algorithms. org.apache.commons.math.analysis.polynomials Univariate real polynomials implementations, seen as differentiable
+ univariate real functions. org.apache.commons.math.analysis.solvers Root finding algorithms, for univariate real functions. org.apache.commons.math.complex Complex number type and implementations of complex transcendental
+ functions. org.apache.commons.math.distribution Implementations of common discrete and continuous distributions. org.apache.commons.math.estimation This package provided classes to solve estimation problems, it is deprecated since 2.0. org.apache.commons.math.fraction Fraction number type and fraction number formatting. org.apache.commons.math.genetics
+This package provides Genetic Algorithms components and implementations. org.apache.commons.math.geometry
+This package provides basic 3D geometry components. org.apache.commons.math.linear Linear algebra support. org.apache.commons.math.ode
+This package provides classes to solve Ordinary Differential Equations problems. org.apache.commons.math.ode.events
+This package provides classes to handle discrete events occurring during
+Ordinary Differential Equations integration. org.apache.commons.math.ode.jacobians
+This package provides classes to solve Ordinary Differential Equations problems
+and also compute derivatives of the solution. org.apache.commons.math.ode.nonstiff
+This package provides classes to solve non-stiff Ordinary Differential Equations problems. org.apache.commons.math.ode.sampling
+This package provides classes to handle sampling steps during
+Ordinary Differential Equations integration. org.apache.commons.math.optimization
+This package provides common interfaces for the optimization algorithms
+provided in sub-packages. org.apache.commons.math.optimization.direct
+This package provides optimization algorithms that don't require derivatives. org.apache.commons.math.optimization.fitting This package provides classes to perform curve fitting. org.apache.commons.math.optimization.general This package provides optimization algorithms that require derivatives. org.apache.commons.math.optimization.linear This package provides optimization algorithms for linear constrained problems. org.apache.commons.math.optimization.univariate Univariate real functions minimum finding algorithms. org.apache.commons.math.random Random number and random data generators. org.apache.commons.math.special Implementations of special functions such as Beta and Gamma. org.apache.commons.math.stat Data storage, manipulation and summary routines. org.apache.commons.math.stat.clustering Clustering algorithms org.apache.commons.math.stat.correlation Correlations/Covariance computations. org.apache.commons.math.stat.descriptive Generic univariate summary statistic objects. org.apache.commons.math.stat.descriptive.moment Summary statistics based on moments. org.apache.commons.math.stat.descriptive.rank Summary statistics based on ranks. org.apache.commons.math.stat.descriptive.summary Other summary statistics. org.apache.commons.math.stat.inference Classes providing hypothesis testing and confidence interval
+ construction. org.apache.commons.math.stat.ranking Classes providing rank transformations. org.apache.commons.math.stat.regression Statistical routines involving multivariate data. org.apache.commons.math.transform Implementations of transform methods, including Fast Fourier transforms. org.apache.commons.math.util Convenience routines and common data structures used throughout the commons-math library. orthogonal() Vector3D
+Get a vector orthogonal to the instance.
+ outerProduct(RealVector) AbstractRealVector
+Compute the outer product.
+ outerProduct(double[]) AbstractRealVector
+Compute the outer product.
+ outerProduct(FieldVector<T>) ArrayFieldVector
+Compute the outer product.
+ outerProduct(ArrayFieldVector<T>) ArrayFieldVector
+Compute the outer product.
+ outerProduct(T[]) ArrayFieldVector
+Compute the outer product.
+ outerProduct(RealVector) ArrayRealVector
+Compute the outer product.
+ outerProduct(ArrayRealVector) ArrayRealVector
+Compute the outer product.
+ outerProduct(double[]) ArrayRealVector
+Compute the outer product.
+ outerProduct(FieldVector<T>) FieldVector
+Compute the outer product.
+ outerProduct(T[]) FieldVector
+Compute the outer product.
+ outerProduct(double[]) OpenMapRealVector
+Compute the outer product.
+ outerProduct(RealVector) RealVector
+Compute the outer product.
+ outerProduct(double[]) RealVector
+Compute the outer product.
+ outerProduct(SparseFieldVector<T>) SparseFieldVector
+Optimized method to compute outer product when both vectors are sparse.
+ outerProduct(T[]) SparseFieldVector
+Compute the outer product.
+ outerProduct(FieldVector<T>) SparseFieldVector
+Compute the outer product.
+
+
+P
+
+pairedT(double[], double[]) TestUtils
+
+ pairedT(double[], double[]) TTest
+Computes a paired, 2-sample t-statistic based on the data in the input
+ arrays.
+ pairedT(double[], double[]) TTestImpl
+Computes a paired, 2-sample t-statistic based on the data in the input
+ arrays.
+ pairedTTest(double[], double[], double) TestUtils
+
+ pairedTTest(double[], double[]) TestUtils
+
+ pairedTTest(double[], double[]) TTest
+Returns the observed significance level , or
+ p-value , associated with a paired, two-sample, two-tailed t-test
+ based on the data in the input arrays.
+ pairedTTest(double[], double[], double) TTest
+Performs a paired t-test evaluating the null hypothesis that the
+ mean of the paired differences between sample1 and
+ sample2 is 0 in favor of the two-sided alternative that the
+ mean paired difference is not equal to 0, with significance level
+ alpha.
+ pairedTTest(double[], double[]) TTestImpl
+Returns the observed significance level , or
+ p-value , associated with a paired, two-sample, two-tailed t-test
+ based on the data in the input arrays.
+ pairedTTest(double[], double[], double) TTestImpl
+Performs a paired t-test evaluating the null hypothesis that the
+ mean of the paired differences between sample1 and
+ sample2 is 0 in favor of the two-sided alternative that the
+ mean paired difference is not equal to 0, with significance level
+ alpha.
+ ParameterizedODE org.apache.commons.math.ode.jacobians This interface represents first order differential equations parameters AbstractEstimator
+Deprecated. Array of parameters.
+ParametricRealFunction org.apache.commons.math.optimization.fitting An interface representing a real function that depends on one independent
+ variable plus some extra parameters. parity BigMatrixImpl
+Deprecated. Parity of the permutation associated with the LU decomposition
+parse(String) ComplexFormat
+Parses a string to produce a Complex parse(String, ParsePosition) ComplexFormat
+Parses a string to produce a Complex parse(String) BigFractionFormat
+Parses a string to produce a BigFraction parse(String, ParsePosition) BigFractionFormat
+Parses a string to produce a BigFraction parse(String) FractionFormat
+Parses a string to produce a Fraction parse(String, ParsePosition) FractionFormat
+Parses a string to produce a Fraction parse(String, ParsePosition) ProperBigFractionFormat
+Parses a string to produce a BigFraction parse(String, ParsePosition) ProperFractionFormat
+Parses a string to produce a Fraction parse(String) Vector3DFormat
+Parses a string to produce a Vector3D parse(String, ParsePosition) Vector3DFormat
+Parses a string to produce a Vector3D parse(String) RealVectorFormat
+Parses a string to produce a RealVector parse(String, ParsePosition) RealVectorFormat
+Parses a string to produce a RealVector parseAndIgnoreWhitespace(String, ParsePosition) AbstractFormat
+Parses source until a non-whitespace character is found.
+ parseAndIgnoreWhitespace(String, ParsePosition) CompositeFormat
+Parses source until a non-whitespace character is found.
+ parseFixedstring(String, String, ParsePosition) CompositeFormat
+Parse source for an expected fixed string.
+ parseNextBigInteger(String, ParsePosition) BigFractionFormat
+Parses a string to produce a BigInteger.
+ parseNextCharacter(String, ParsePosition) AbstractFormat
+Parses source until a non-whitespace character is found.
+ parseNextCharacter(String, ParsePosition) CompositeFormat
+Parses source until a non-whitespace character is found.
+ parseNumber(String, NumberFormat, ParsePosition) CompositeFormat
+Parses source for a number.
+ parseObject(String, ParsePosition) ComplexFormat
+Parses a string to produce a object.
+ parseObject(String, ParsePosition) Vector3DFormat
+Parses a string to produce a object.
+ parseObject(String, ParsePosition) RealVectorFormat
+Parses a string to produce a object.
+ partialDerivative(int) DifferentiableMultivariateRealFunction
+Returns the partial derivative of the function with respect to a point coordinate.
+ PascalDistribution org.apache.commons.math.distribution The Pascal distribution. PascalDistributionImpl org.apache.commons.math.distribution The default implementation of PascalDistribution PascalDistributionImpl(int, double) PascalDistributionImpl
+Create a binomial distribution with the given number of trials and
+ probability of success.
+ PearsonsCorrelation org.apache.commons.math.stat.correlation Computes Pearson's product-moment correlation coefficients for pairs of arrays
+ or columns of a matrix. PearsonsCorrelation() PearsonsCorrelation
+Create a PearsonsCorrelation instance without data
+ PearsonsCorrelation(double[][]) PearsonsCorrelation
+Create a PearsonsCorrelation from a rectangular array
+ whose columns represent values of variables to be correlated.
+ PearsonsCorrelation(RealMatrix) PearsonsCorrelation
+Create a PearsonsCorrelation from a RealMatrix whose columns
+ represent variables to be correlated.
+ PearsonsCorrelation(Covariance) PearsonsCorrelation
+Create a PearsonsCorrelation from a Covariance PearsonsCorrelation(RealMatrix, int) PearsonsCorrelation
+Create a PearsonsCorrelation from a covariance matrix.
+ percentageValue() BigFraction
+
+ Gets the fraction percentage as a double .
+ Percentile org.apache.commons.math.stat.descriptive.rank Provides percentile computation. Percentile() Percentile
+Constructs a Percentile with a default quantile
+ value of 50.0.
+ Percentile(double) Percentile
+Constructs a Percentile with the specific quantile value.
+ Percentile(Percentile) Percentile
+Copy constructor, creates a new Percentile identical
+ to the original
+ percentile(double[], double) StatUtils
+Returns an estimate of the pth percentile of the values
+ in the values array.
+ percentile(double[], int, int, double) StatUtils
+Returns an estimate of the pth percentile of the values
+ in the values array, starting with the element in (0-based)
+ position begin in the array and including length
+ values.
+ permutation BigMatrixImpl
+Deprecated. Permutation associated with LU decomposition
+PermutationChromosome T > - Interface in org.apache.commons.math.genetics Interface indicating that the chromosome represents a permutation of objects. PLUS_I Vector3D
+First canonical vector (coordinates: 1, 0, 0).
+ PLUS_J Vector3D
+Second canonical vector (coordinates: 0, 1, 0).
+ PLUS_K Vector3D
+Third canonical vector (coordinates: 0, 0, 1).
+ point AbstractLeastSquaresOptimizer
+Current point.
+ point AbstractScalarDifferentiableOptimizer
+Current point set.
+ PoissonDistribution org.apache.commons.math.distribution Interface representing the Poisson Distribution. PoissonDistributionImpl org.apache.commons.math.distribution Implementation for the PoissonDistribution PoissonDistributionImpl(double) PoissonDistributionImpl
+Create a new Poisson distribution with the given the mean.
+ PoissonDistributionImpl(double, double, int) PoissonDistributionImpl
+Create a new Poisson distribution with the given mean, convergence criterion
+ and maximum number of iterations.
+ PoissonDistributionImpl(double, double) PoissonDistributionImpl
+Create a new Poisson distribution with the given mean and convergence criterion.
+ PoissonDistributionImpl(double, int) PoissonDistributionImpl
+Create a new Poisson distribution with the given mean and maximum number of iterations.
+ PoissonDistributionImpl(double, NormalDistribution) PoissonDistributionImpl
+Deprecated. as of 2.1 (to avoid possibly inconsistent state, the
+ "NormalDistribution" will be instantiated internally)
+polar2Complex(double, double) ComplexUtils
+Creates a complex number from the given polar representation.
+ polynomialDerivative() PolynomialFunction
+Returns the derivative as a PolynomialRealFunction
+ PolynomialFitter org.apache.commons.math.optimization.fitting This class implements a curve fitting specialized for polynomials. PolynomialFitter(int, DifferentiableMultivariateVectorialOptimizer) PolynomialFitter
+Simple constructor.
+ PolynomialFunction org.apache.commons.math.analysis.polynomials Immutable representation of a real polynomial function with real coefficients. PolynomialFunction(double[]) PolynomialFunction
+Construct a polynomial with the given coefficients.
+ PolynomialFunctionLagrangeForm org.apache.commons.math.analysis.polynomials Implements the representation of a real polynomial function in
+
+ Lagrange Form . PolynomialFunctionLagrangeForm(double[], double[]) PolynomialFunctionLagrangeForm
+Construct a Lagrange polynomial with the given abscissas and function
+ values.
+ PolynomialFunctionNewtonForm org.apache.commons.math.analysis.polynomials Implements the representation of a real polynomial function in
+ Newton Form. PolynomialFunctionNewtonForm(double[], double[]) PolynomialFunctionNewtonForm
+Construct a Newton polynomial with the given a[] and c[].
+ polynomialSplineDerivative() PolynomialSplineFunction
+Returns the derivative of the polynomial spline function as a PolynomialSplineFunction
+ PolynomialSplineFunction org.apache.commons.math.analysis.polynomials Represents a polynomial spline function. PolynomialSplineFunction(double[], PolynomialFunction[]) PolynomialSplineFunction
+Construct a polynomial spline function with the given segment delimiters
+ and interpolating polynomials.
+ PolynomialsUtils org.apache.commons.math.analysis.polynomials A collection of static methods that operate on or return polynomials. Population org.apache.commons.math.genetics A collection of chromosomes that facilitates generational evolution. POSITIVE_INFINITY Vector3D
+A vector with all coordinates set to positive infinity.
+ postCompose(UnivariateRealFunction) ComposableFunction
+Postcompose the instance with another function.
+ POW BinaryFunction
+The Math.pow method wrapped as a BinaryFunction pow(Complex) Complex
+Returns of value of this complex number raised to the power of x.
+ pow(int) BigFraction
+
+ Returns a integer whose value is
+ (thisexponent ) , returning the result in reduced form.
+ pow(long) BigFraction
+
+ Returns a BigFraction whose value is
+ (thisexponent ) , returning the result in reduced form.
+ pow(BigInteger) BigFraction
+
+ Returns a BigFraction whose value is
+ (thisexponent ) , returning the result in reduced form.
+ pow(double) BigFraction
+
+ Returns a double whose value is
+ (thisexponent ) , returning the result in reduced form.
+ pow(int, int) MathUtils
+Raise an int to an int power.
+ pow(int, long) MathUtils
+Raise an int to a long power.
+ pow(long, int) MathUtils
+Raise a long to an int power.
+ pow(long, long) MathUtils
+Raise a long to a long power.
+ pow(BigInteger, int) MathUtils
+Raise a BigInteger to an int power.
+ pow(BigInteger, long) MathUtils
+Raise a BigInteger to a long power.
+ pow(BigInteger, BigInteger) MathUtils
+Raise a BigInteger to a BigInteger power.
+ precondition(double[], double[]) Preconditioner
+Precondition a search direction.
+ Preconditioner org.apache.commons.math.optimization.general This interface represents a preconditioner for differentiable scalar
+ objective function optimizers. predict(double) SimpleRegression
+Returns the "predicted" y value associated with the
+ supplied x value, based on the data that has been
+ added to the model when this method is activated.
+ preMultiply(FieldMatrix<T>) AbstractFieldMatrix
+Returns the result premultiplying this by m.
+ preMultiply(T[]) AbstractFieldMatrix
+Returns the (row) vector result of premultiplying this by the vector v.
+ preMultiply(FieldVector<T>) AbstractFieldMatrix
+Returns the (row) vector result of premultiplying this by the vector v.
+ preMultiply(RealMatrix) AbstractRealMatrix
+Returns the result premultiplying this by m.
+ preMultiply(double[]) AbstractRealMatrix
+Returns the (row) vector result of premultiplying this by the vector v.
+ preMultiply(RealVector) AbstractRealMatrix
+Returns the (row) vector result of premultiplying this by the vector v.
+ preMultiply(T[]) Array2DRowFieldMatrix
+Returns the (row) vector result of premultiplying this by the vector v.
+ preMultiply(double[]) Array2DRowRealMatrix
+Returns the (row) vector result of premultiplying this by the vector v.
+ preMultiply(BigMatrix) BigMatrix
+Deprecated. Returns the result premultiplying this by m.
+preMultiply(BigDecimal[]) BigMatrix
+Deprecated. Returns the (row) vector result of premultiplying this by the vector v.
+preMultiply(BigMatrix) BigMatrixImpl
+Deprecated. Returns the result premultiplying this by m.
+preMultiply(BigDecimal[]) BigMatrixImpl
+Deprecated. Returns the (row) vector result of premultiplying this by the vector v.
+preMultiply(T[]) BlockFieldMatrix
+Returns the (row) vector result of premultiplying this by the vector v.
+ preMultiply(double[]) BlockRealMatrix
+Returns the (row) vector result of premultiplying this by the vector v.
+ preMultiply(FieldMatrix<T>) FieldMatrix
+Returns the result premultiplying this by m.
+ preMultiply(T[]) FieldMatrix
+Returns the (row) vector result of premultiplying this by the vector v.
+ preMultiply(FieldVector<T>) FieldMatrix
+Returns the (row) vector result of premultiplying this by the vector v.
+ preMultiply(RealMatrix) RealMatrix
+Returns the result premultiplying this by m.
+ preMultiply(double[]) RealMatrix
+Returns the (row) vector result of premultiplying this by the vector v.
+ preMultiply(RealVector) RealMatrix
+Returns the (row) vector result of premultiplying this by the vector v.
+ preMultiply(double[]) RealMatrixImpl
+Deprecated. Returns the (row) vector result of premultiplying this by the vector v.
+previousTime AbstractStepInterpolator
+previous time
+ printStackTrace() MathException
+Prints the stack trace of this exception to the standard error stream.
+ printStackTrace(PrintStream) MathException
+Prints the stack trace of this exception to the specified stream.
+ printStackTrace() MathRuntimeException
+Prints the stack trace of this exception to the standard error stream.
+ printStackTrace(PrintStream) MathRuntimeException
+Prints the stack trace of this exception to the specified stream.
+ probability(double) AbstractIntegerDistribution
+For a random variable X whose values are distributed according
+ to this distribution, this method returns P(X = x).
+ probability(int) BinomialDistributionImpl
+For this distribution, X, this method returns P(X = x).
+ probability(double) DiscreteDistribution
+For a random variable X whose values are distributed according
+ to this distribution, this method returns P(X = x).
+ probability(int) HypergeometricDistributionImpl
+For this distribution, X, this method returns P(X = x).
+ probability(int) IntegerDistribution
+For a random variable X whose values are distributed according
+ to this distribution, this method returns P(X = x).
+ probability(int) PascalDistributionImpl
+For this distribution, X, this method returns P(X = x).
+ probability(int) PoissonDistributionImpl
+The probability mass function P(X = x) for a Poisson distribution.
+ probability(int) ZipfDistributionImpl
+The probability mass function P(X = x) for a Zipf distribution.
+ Product org.apache.commons.math.stat.descriptive.summary Returns the product of the available values. Product() Product
+Create a Product instance
+ Product(Product) Product
+Copy constructor, creates a new Product identical
+ to the original
+ product(double[]) StatUtils
+Returns the product of the entries in the input array, or
+ Double.NaN if the array is empty.
+ product(double[], int, int) StatUtils
+Returns the product of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ projection(double[]) AbstractRealVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(FieldVector<T>) ArrayFieldVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(T[]) ArrayFieldVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(ArrayFieldVector<T>) ArrayFieldVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(RealVector) ArrayRealVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(double[]) ArrayRealVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(ArrayRealVector) ArrayRealVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(FieldVector<T>) FieldVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(T[]) FieldVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(RealVector) OpenMapRealVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(double[]) OpenMapRealVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(RealVector) RealVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(double[]) RealVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(FieldVector<T>) SparseFieldVector
+Find the orthogonal projection of this vector onto another vector.
+ projection(T[]) SparseFieldVector
+Find the orthogonal projection of this vector onto another vector.
+ ProperBigFractionFormat org.apache.commons.math.fraction Formats a BigFraction number in proper format. ProperBigFractionFormat() ProperBigFractionFormat
+Create a proper formatting instance with the default number format for
+ the whole, numerator, and denominator.
+ ProperBigFractionFormat(NumberFormat) ProperBigFractionFormat
+Create a proper formatting instance with a custom number format for the
+ whole, numerator, and denominator.
+ ProperBigFractionFormat(NumberFormat, NumberFormat, NumberFormat) ProperBigFractionFormat
+Create a proper formatting instance with a custom number format for each
+ of the whole, numerator, and denominator.
+ ProperFractionFormat org.apache.commons.math.fraction Formats a Fraction number in proper format. ProperFractionFormat() ProperFractionFormat
+Create a proper formatting instance with the default number format for
+ the whole, numerator, and denominator.
+ ProperFractionFormat(NumberFormat) ProperFractionFormat
+Create a proper formatting instance with a custom number format for the
+ whole, numerator, and denominator.
+ ProperFractionFormat(NumberFormat, NumberFormat, NumberFormat) ProperFractionFormat
+Create a proper formatting instance with a custom number format for each
+ of the whole, numerator, and denominator.
+ put(int, double) OpenIntToDoubleHashMap
+Put a value associated with a key in the map.
+ put(int, T) OpenIntToFieldHashMap
+Put a value associated with a key in the map.
+ putTransformer(Class<?>, NumberTransformer) TransformerMap
+Sets a Class to Transformer Mapping in the Map.
+
+
+Q
+
+QRDecomposition org.apache.commons.math.linear An interface to classes that implement an algorithm to calculate the
+ QR-decomposition of a real matrix. QRDecompositionImpl org.apache.commons.math.linear Calculates the QR-decomposition of a matrix. QRDecompositionImpl(RealMatrix) QRDecompositionImpl
+Calculates the QR-decomposition of the given matrix.
+
+
+R
+
+RandomAdaptor org.apache.commons.math.random Extension of java.util.Random wrapping a
+ RandomGenerator RandomAdaptor(RandomGenerator) RandomAdaptor
+Construct a RandomAdaptor wrapping the supplied RandomGenerator.
+ randomBinaryRepresentation(int) BinaryChromosome
+Returns a representation of a random binary array of length length.
+ RandomData org.apache.commons.math.random Random data generation utilities. RandomDataImpl org.apache.commons.math.random Implements the RandomDataRandomGeneratorSecureRandomnextSecureXxx methods. RandomDataImpl() RandomDataImpl
+Construct a RandomDataImpl.
+ RandomDataImpl(RandomGenerator) RandomDataImpl
+Construct a RandomDataImpl using the supplied RandomGenerator RandomGenerator org.apache.commons.math.random Interface extracted from java.util.Random. RandomKey T > - Class in org.apache.commons.math.genetics
+ Random Key chromosome is used for permutation representation. RandomKey(List<Double>) RandomKey
+Constructor.
+ RandomKey(Double[]) RandomKey
+Constructor.
+ RandomKeyMutation org.apache.commons.math.genetics Mutation operator for RandomKey RandomKeyMutation() RandomKeyMutation
+
+ randomPermutation(int) RandomKey
+Generates a representation corresponding to a random permutation of
+ length l which can be passed to the RandomKey constructor.
+ RandomVectorGenerator org.apache.commons.math.random This interface represents a random generator for whole vectors. rank(double[]) NaturalRanking
+Rank data using the natural ordering on Doubles, with
+ NaN values handled according to nanStrategy and ties
+ resolved using tiesStrategy.
+ rank(double[]) RankingAlgorithm
+Performs a rank transformation on the input data, returning an array
+ of ranks.
+ RankingAlgorithm org.apache.commons.math.stat.ranking Interface representing a rank transformation. readBaseExternal(ObjectInput) AbstractStepInterpolator
+Read the base state of the instance.
+ readExternal(ObjectInput) AbstractStepInterpolator
+
+ readExternal(ObjectInput) DummyStepInterpolator
+Read the instance from an input channel.
+ readExternal(ObjectInput) NordsieckStepInterpolator
+
+ readResolve() Complex
+Resolve the transient fields in a deserialized Complex Object.
+ RealConvergenceChecker org.apache.commons.math.optimization This interface specifies how to check if an optimization
+ algorithm RealMatrix org.apache.commons.math.linear Interface defining a real-valued matrix with basic algebraic operations. RealMatrixChangingVisitor org.apache.commons.math.linear Interface defining a visitor for matrix entries. RealMatrixImpl org.apache.commons.math.linear Deprecated. as of 2.0 replaced by Array2DRowRealMatrix RealMatrixImpl() RealMatrixImpl
+Deprecated. Creates a matrix with no data
+RealMatrixImpl(int, int) RealMatrixImpl
+Deprecated. Create a new RealMatrix with the supplied row and column dimensions.
+RealMatrixImpl(double[][]) RealMatrixImpl
+Deprecated. Create a new RealMatrix using the input array as the underlying
+ data array.
+RealMatrixImpl(double[][], boolean) RealMatrixImpl
+Deprecated. Create a new RealMatrix using the input array as the underlying
+ data array.
+RealMatrixImpl(double[]) RealMatrixImpl
+Deprecated. Create a new (column) RealMatrix using v as the
+ data for the unique column of the v.length x 1 matrix
+ created.
+RealMatrixPreservingVisitor org.apache.commons.math.linear Interface defining a visitor for matrix entries. RealPointValuePair org.apache.commons.math.optimization This class holds a point and the value of an objective function at this point. RealPointValuePair(double[], double) RealPointValuePair
+Build a point/objective function value pair.
+ RealPointValuePair(double[], double, boolean) RealPointValuePair
+Build a point/objective function value pair.
+ RealTransformer org.apache.commons.math.transform Interface for one-dimensional data sets transformations producing real results. RealVector org.apache.commons.math.linear Interface defining a real-valued vector with basic algebraic operations. RealVector.Entry org.apache.commons.math.linear Class representing a modifiable entry in the vector. RealVector.Entry() RealVector.Entry
+
+ RealVectorFormat org.apache.commons.math.linear Formats a vector in components list format "{v0; v1; ...; vk-1}". RealVectorFormat() RealVectorFormat
+Create an instance with default settings.
+ RealVectorFormat(NumberFormat) RealVectorFormat
+Create an instance with a custom number format for components.
+ RealVectorFormat(String, String, String) RealVectorFormat
+Create an instance with custom prefix, suffix and separator.
+ RealVectorFormat(String, String, String, NumberFormat) RealVectorFormat
+Create an instance with custom prefix, suffix, separator and format
+ for components.
+ reciprocal() BigFraction
+
+ Return the multiplicative inverse of this fraction.
+ reciprocal() Fraction
+Return the multiplicative inverse of this fraction.
+ reduce() BigFraction
+
+ Reduce this BigFraction to its lowest terms.
+ regularizedBeta(double, double, double) Beta
+Returns the
+
+ regularized beta function I(x, a, b).
+ regularizedBeta(double, double, double, double) Beta
+Returns the
+
+ regularized beta function I(x, a, b).
+ regularizedBeta(double, double, double, int) Beta
+Returns the regularized beta function I(x, a, b).
+ regularizedBeta(double, double, double, double, int) Beta
+Returns the regularized beta function I(x, a, b).
+ regularizedGammaP(double, double) Gamma
+Returns the regularized gamma function P(a, x).
+ regularizedGammaP(double, double, double, int) Gamma
+Returns the regularized gamma function P(a, x).
+ regularizedGammaQ(double, double) Gamma
+Returns the regularized gamma function Q(a, x) = 1 - P(a, x).
+ regularizedGammaQ(double, double, double, int) Gamma
+Returns the regularized gamma function Q(a, x) = 1 - P(a, x).
+ reinitialize(double[], boolean) AbstractStepInterpolator
+Reinitialize the instance
+ reinitialize(double[], boolean) NordsieckStepInterpolator
+Reinitialize the instance.
+ reinitialize(double, double, double[], Array2DRowRealMatrix) NordsieckStepInterpolator
+Reinitialize the instance.
+ reinitializeBegin(double, double[]) EventState
+Reinitialize the beginning of the step.
+ Relationship org.apache.commons.math.optimization.linear Types of relationships between two cells in a Solver LinearConstraint relativeAccuracy ConvergingAlgorithmImpl
+Maximum relative error.
+ remove() AbstractRealVector.SparseEntryIterator
+
+ remove() OpenMapRealVector.OpenMapSparseIterator
+
+ remove(int) OpenIntToDoubleHashMap
+Remove the value associated with a key.
+ remove(int) OpenIntToFieldHashMap
+Remove the value associated with a key.
+ REMOVED OpenIntToDoubleHashMap
+Status indicator for removed table entries.
+ REMOVED OpenIntToFieldHashMap
+Status indicator for removed table entries.
+ removeData(double, double) SimpleRegression
+Removes the observation (x,y) from the regression data set.
+ removeData(double[][]) SimpleRegression
+Removes observations represented by the elements in data.
+ removeMostRecentValue() DescriptiveStatistics
+Removes the most recent value from the dataset.
+ removeTransformer(Class<?>) TransformerMap
+Removes a Class to Transformer Mapping in the Map.
+ replaceMostRecentValue(double) DescriptiveStatistics
+Replaces the most recently stored value with the given value.
+ replaceWorstPoint(RealPointValuePair, Comparator<RealPointValuePair>) DirectSearchOptimizer
+Replace the worst point of the simplex by a new point.
+ REPLAY_MODE ValueServer
+Replay data from valuesFilePath.
+ requiresDenseOutput() AbstractIntegrator
+Check if one of the step handlers requires dense output.
+ requiresDenseOutput() ContinuousOutputModel
+Determines whether this handler needs dense output.
+ requiresDenseOutput() StepHandlerWithJacobians
+Determines whether this handler needs dense output.
+ requiresDenseOutput() DummyStepHandler
+Determines whether this handler needs dense output.
+ requiresDenseOutput() StepHandler
+Determines whether this handler needs dense output.
+ requiresDenseOutput() StepNormalizer
+Determines whether this handler needs dense output.
+ rescale(double) NordsieckStepInterpolator
+Rescale the instance.
+ reSeed(long) RandomDataImpl
+Reseeds the random number generator with the supplied seed.
+ reSeed() RandomDataImpl
+Reseeds the random number generator with the current time in
+ milliseconds.
+ reSeedSecure() RandomDataImpl
+Reseeds the secure random number generator with the current time in
+ milliseconds.
+ reSeedSecure(long) RandomDataImpl
+Reseeds the secure random number generator with the supplied seed.
+ reset() ContinuousOutputModel
+Reset the step handler.
+ reset(double, double[]) CombinedEventsManager
+Let the event handlers reset the state if they want.
+ reset(double, double[]) EventState
+Let the event handler reset the state if it wants.
+ reset() StepHandlerWithJacobians
+Reset the step handler.
+ reset() DummyStepHandler
+Reset the step handler.
+ reset() StepHandler
+Reset the step handler.
+ reset() StepNormalizer
+Reset the step handler.
+ RESET_DERIVATIVES EventHandler
+Reset derivatives indicator.
+ RESET_DERIVATIVES EventHandlerWithJacobians
+Reset derivatives indicator.
+ RESET_STATE EventHandler
+Reset state indicator.
+ RESET_STATE EventHandlerWithJacobians
+Reset state indicator.
+ resetAbsoluteAccuracy() ConvergingAlgorithm
+Reset the absolute accuracy to the default.
+ resetAbsoluteAccuracy() ConvergingAlgorithmImpl
+Reset the absolute accuracy to the default.
+ resetAbsoluteAccuracy() MultiStartUnivariateRealOptimizer
+Reset the absolute accuracy to the default.
+ resetEvaluations() AbstractIntegrator
+Reset the number of evaluations to zero.
+ resetFunctionValueAccuracy() UnivariateRealSolver
+Reset the actual function accuracy to the default.
+ resetFunctionValueAccuracy() UnivariateRealSolverImpl
+Reset the actual function accuracy to the default.
+ resetInternalState() AdaptiveStepsizeIntegrator
+Reset internal state to dummy values.
+ resetMaximalIterationCount() ConvergingAlgorithm
+Reset the upper limit for the number of iterations to the default.
+ resetMaximalIterationCount() ConvergingAlgorithmImpl
+Reset the upper limit for the number of iterations to the default.
+ resetMaximalIterationCount() MultiStartUnivariateRealOptimizer
+Reset the upper limit for the number of iterations to the default.
+ resetMinimalIterationCount() UnivariateRealIntegrator
+Reset the lower limit for the number of iterations to the default.
+ resetMinimalIterationCount() UnivariateRealIntegratorImpl
+Reset the lower limit for the number of iterations to the default.
+ resetRelativeAccuracy() ConvergingAlgorithm
+Reset the relative accuracy to the default.
+ resetRelativeAccuracy() ConvergingAlgorithmImpl
+Reset the relative accuracy to the default.
+ resetRelativeAccuracy() MultiStartUnivariateRealOptimizer
+Reset the relative accuracy to the default.
+ resetReplayFile() ValueServer
+Resets REPLAY_MODE file pointer to the beginning of the valuesFileURL.
+ resetState(double, double[]) EventHandler
+Reset the state prior to continue the integration.
+ resetState(double, double[], double[][], double[][]) EventHandlerWithJacobians
+Reset the state prior to continue the integration.
+ residuals AbstractEstimator
+Deprecated. Residuals array.
+residuals AbstractLeastSquaresOptimizer
+Current residuals.
+ residualsWeights AbstractLeastSquaresOptimizer
+Weight for the least squares cost computation.
+ ResizableDoubleArray org.apache.commons.math.util
+ A variable length DoubleArray ResizableDoubleArray() ResizableDoubleArray
+Create a ResizableArray with default properties.
+ ResizableDoubleArray(int) ResizableDoubleArray
+Create a ResizableArray with the specified initial capacity.
+ ResizableDoubleArray(int, float) ResizableDoubleArray
+
+ Create a ResizableArray with the specified initial capacity
+ and expansion factor.
+ ResizableDoubleArray(int, float, float) ResizableDoubleArray
+
+ Create a ResizableArray with the specified initialCapacity,
+ expansionFactor, and contractionCriteria.
+ ResizableDoubleArray(int, float, float, int) ResizableDoubleArray
+
+ Create a ResizableArray with the specified properties.
+ ResizableDoubleArray(ResizableDoubleArray) ResizableDoubleArray
+Copy constructor.
+ result UnivariateRealIntegratorImpl
+the last computed integral
+ result UnivariateRealSolverImpl
+The last computed root.
+ result AbstractUnivariateRealOptimizer
+The last computed root.
+ resultComputed UnivariateRealIntegratorImpl
+indicates whether an integral has been computed
+ resultComputed UnivariateRealSolverImpl
+Indicates where a root has been computed.
+ resultComputed AbstractUnivariateRealOptimizer
+Indicates where a root has been computed.
+ revert() Rotation
+Revert a rotation.
+ RiddersSolver org.apache.commons.math.analysis.solvers Implements the
+ Ridders' Method for root finding of real univariate functions. RiddersSolver(UnivariateRealFunction) RiddersSolver
+Deprecated. as of 2.0 the function to solve is passed as an argument
+ to the RiddersSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+RiddersSolver() RiddersSolver
+Construct a solver.
+ RINT ComposableFunction
+The Math.rint method wrapped as a ComposableFunction RombergIntegrator org.apache.commons.math.analysis.integration Implements the
+ Romberg Algorithm for integration of real univariate functions. RombergIntegrator(UnivariateRealFunction) RombergIntegrator
+Deprecated. as of 2.0 the integrand function is passed as an argument
+ to the RombergIntegrator.integrate(UnivariateRealFunction, double, double)
+RombergIntegrator() RombergIntegrator
+Construct an integrator.
+ Rotation org.apache.commons.math.geometry This class implements rotations in a three-dimensional space. Rotation(double, double, double, double, boolean) Rotation
+Build a rotation from the quaternion coordinates.
+ Rotation(Vector3D, double) Rotation
+Build a rotation from an axis and an angle.
+ Rotation(double[][], double) Rotation
+Build a rotation from a 3X3 matrix.
+ Rotation(Vector3D, Vector3D, Vector3D, Vector3D) Rotation
+Build the rotation that transforms a pair of vector into another pair.
+ Rotation(Vector3D, Vector3D) Rotation
+Build one of the rotations that transform one vector into another one.
+ Rotation(RotationOrder, double, double, double) Rotation
+Build a rotation from three Cardan or Euler elementary rotations.
+ RotationOrder org.apache.commons.math.geometry This class is a utility representing a rotation order specification
+ for Cardan or Euler angles specification. round(double, int) MathUtils
+Round the given value to the specified number of decimal places.
+ round(double, int, int) MathUtils
+Round the given value to the specified number of decimal places.
+ round(float, int) MathUtils
+Round the given value to the specified number of decimal places.
+ round(float, int, int) MathUtils
+Round the given value to the specified number of decimal places.
+ rows AbstractEstimator
+Deprecated. Number of rows of the jacobian matrix.
+rows AbstractLeastSquaresOptimizer
+Number of rows of the jacobian matrix.
+ RungeKuttaIntegrator org.apache.commons.math.ode.nonstiff This class implements the common part of all fixed step Runge-Kutta
+ integrators for Ordinary Differential Equations. RungeKuttaIntegrator(String, double[], double[][], double[], RungeKuttaStepInterpolator, double) RungeKuttaIntegrator
+Simple constructor.
+
+
+S
+
+SAFE_MIN MathUtils
+Safe minimum, such that 1 / SAFE_MIN does not overflow.
+ sample(UnivariateRealFunction, double, double, int) FastFourierTransformer
+Sample the given univariate real function on the given interval.
+ sanityChecks(FirstOrderDifferentialEquations, double, double[], double, double[]) AbstractIntegrator
+Perform some sanity checks on the integration parameters.
+ sanityChecks(FirstOrderDifferentialEquations, double, double[], double, double[]) AdaptiveStepsizeIntegrator
+Perform some sanity checks on the integration parameters.
+ scalAbsoluteTolerance AdaptiveStepsizeIntegrator
+Allowed absolute scalar error.
+ scalarAdd(T) AbstractFieldMatrix
+Returns the result of adding d to each entry of this.
+ scalarAdd(double) AbstractRealMatrix
+Returns the result of adding d to each entry of this.
+ scalarAdd(BigDecimal) BigMatrix
+Deprecated. Returns the result of adding d to each entry of this.
+scalarAdd(BigDecimal) BigMatrixImpl
+Deprecated. Returns the result of adding d to each entry of this.
+scalarAdd(T) BlockFieldMatrix
+Returns the result of adding d to each entry of this.
+ scalarAdd(double) BlockRealMatrix
+Returns the result of adding d to each entry of this.
+ scalarAdd(T) FieldMatrix
+Returns the result of adding d to each entry of this.
+ scalarAdd(double) RealMatrix
+Returns the result of adding d to each entry of this.
+ scalarMultiply(double) Vector3D
+Multiply the instance by a scalar
+ scalarMultiply(T) AbstractFieldMatrix
+Returns the result multiplying each entry of this by d.
+ scalarMultiply(double) AbstractRealMatrix
+Returns the result multiplying each entry of this by d.
+ scalarMultiply(BigDecimal) BigMatrix
+Deprecated. Returns the result multiplying each entry of this by d.
+scalarMultiply(BigDecimal) BigMatrixImpl
+Deprecated. Returns the result of multiplying each entry of this by d
+scalarMultiply(T) BlockFieldMatrix
+Returns the result multiplying each entry of this by d.
+ scalarMultiply(double) BlockRealMatrix
+Returns the result multiplying each entry of this by d.
+ scalarMultiply(T) FieldMatrix
+Returns the result multiplying each entry of this by d.
+ scalarMultiply(double) RealMatrix
+Returns the result multiplying each entry of this by d.
+ scalb(double, int) MathUtils
+Scale a number by 2scaleFactor .
+ scaleArray(double[], double) FastFourierTransformer
+Multiply every component in the given real array by the
+ given real number.
+ scaleArray(Complex[], double) FastFourierTransformer
+Multiply every component in the given complex array by the
+ given real number.
+ scaled MultistepIntegrator
+First scaled derivative (h y').
+ scalRelativeTolerance AdaptiveStepsizeIntegrator
+Allowed relative scalar error.
+ searchForFitnessUpdate(Population) Chromosome
+Searches the population for a chromosome representing the same solution,
+ and if it finds one, updates the fitness to its value.
+ SecantSolver org.apache.commons.math.analysis.solvers Implements a modified version of the
+ secant method
+ for approximating a zero of a real univariate function. SecantSolver(UnivariateRealFunction) SecantSolver
+Deprecated. as of 2.0 the function to solve is passed as an argument
+ to the SecantSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+SecantSolver() SecantSolver
+Construct a solver.
+ SecondMoment org.apache.commons.math.stat.descriptive.moment Computes a statistic related to the Second Central Moment. SecondMoment() SecondMoment
+Create a SecondMoment instance
+ SecondMoment(SecondMoment) SecondMoment
+Copy constructor, creates a new SecondMoment identical
+ to the original
+ secondMoment SummaryStatistics
+SecondMoment is used to compute the mean and variance
+ SecondOrderDifferentialEquations org.apache.commons.math.ode This interface represents a second order differential equations set. SecondOrderIntegrator org.apache.commons.math.ode This interface represents a second order integrator for
+ differential equations. select(Population) SelectionPolicy
+Select two chromosomes from the population.
+ select(Population) TournamentSelection
+Select two chromosomes from the population.
+ SelectionPolicy org.apache.commons.math.genetics Algorithm used to select a chromosome pair from a population. SemiVariance org.apache.commons.math.stat.descriptive.moment Computes the semivariance of a set of values with respect to a given cutoff value. SemiVariance() SemiVariance
+Constructs a SemiVariance with default (true) biasCorrected
+ property and default (Downside) varianceDirection property.
+ SemiVariance(boolean) SemiVariance
+Constructs a SemiVariance with the specified biasCorrected
+ property and default (Downside) varianceDirection property.
+ SemiVariance(SemiVariance.Direction) SemiVariance
+Constructs a SemiVariance with the specified Direction property
+ and default (true) biasCorrected property
+ SemiVariance(boolean, SemiVariance.Direction) SemiVariance
+Constructs a SemiVariance with the specified isBiasCorrected
+ property and the specified Direction property.
+ SemiVariance(SemiVariance) SemiVariance
+Copy constructor, creates a new SemiVariance identical
+ to the original
+ SemiVariance.Direction org.apache.commons.math.stat.descriptive.moment The direction of the semivariance - either upside or downside. serializeRealMatrix(RealMatrix, ObjectOutputStream) MatrixUtils
+Serialize a RealMatrix serializeRealVector(RealVector, ObjectOutputStream) MatrixUtils
+Serialize a RealVector set(double) AbstractRealVector
+Set all elements to a single value.
+ set(int, ArrayFieldVector<T>) ArrayFieldVector
+Set a set of consecutive elements.
+ set(T) ArrayFieldVector
+Set all elements to a single value.
+ set(int, ArrayRealVector) ArrayRealVector
+Set a set of consecutive elements.
+ set(double) ArrayRealVector
+Set all elements to a single value.
+ set(T) FieldVector
+Set all elements to a single value.
+ set(double) OpenMapRealVector
+Set all elements to a single value.
+ set(double) RealVector
+Set all elements to a single value.
+ set(T) SparseFieldVector
+Set all elements to a single value.
+ setAbsoluteAccuracy(double) ConvergingAlgorithm
+Set the absolute accuracy.
+ setAbsoluteAccuracy(double) ConvergingAlgorithmImpl
+Set the absolute accuracy.
+ setAbsoluteAccuracy(double) MultiStartUnivariateRealOptimizer
+Set the absolute accuracy.
+ setAlpha(double) BetaDistribution
+Deprecated. as of 2.1
+setAlpha(double) BetaDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setAlpha(double) GammaDistribution
+Deprecated. as of v2.1
+setAlpha(double) GammaDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setArity(int) TournamentSelection
+Sets the arity (number of chromosomes drawn to the tournament).
+ setBeta(double) BetaDistribution
+Deprecated. as of 2.1
+setBeta(double) BetaDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setBeta(double) GammaDistribution
+Deprecated. as of v2.1
+setBeta(double) GammaDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setBiasCorrected(boolean) SemiVariance
+Sets the biasCorrected property.
+ setBiasCorrected(boolean) StandardDeviation
+
+ setBiasCorrected(boolean) Variance
+
+ setBound(boolean) EstimatedParameter
+Deprecated. Set the bound flag of the parameter
+setBrightnessExponent(int) MicrosphereInterpolator
+Set the brightness exponent.
+ setChiSquareTest(TTest) TestUtils
+Set the (singleton) TTest instance.
+ setChiSquareTest(ChiSquareTest) TestUtils
+Set the (singleton) ChiSquareTest instance.
+ setChromosomes(List<Chromosome>) ListPopulation
+Sets the list of chromosomes.
+ setColumn(int, T[]) AbstractFieldMatrix
+Sets the entries in column number column
+ as a column matrix.
+ setColumn(int, double[]) AbstractRealMatrix
+Sets the entries in column number column
+ as a column matrix.
+ setColumn(int, T[]) BlockFieldMatrix
+Sets the entries in column number column
+ as a column matrix.
+ setColumn(int, double[]) BlockRealMatrix
+Sets the entries in column number column
+ as a column matrix.
+ setColumn(int, T[]) FieldMatrix
+Sets the entries in column number column
+ as a column matrix.
+ setColumn(int, double[]) RealMatrix
+Sets the entries in column number column
+ as a column matrix.
+ setColumnMatrix(int, FieldMatrix<T>) AbstractFieldMatrix
+Sets the entries in column number column
+ as a column matrix.
+ setColumnMatrix(int, RealMatrix) AbstractRealMatrix
+Sets the entries in column number column
+ as a column matrix.
+ setColumnMatrix(int, FieldMatrix<T>) BlockFieldMatrix
+Sets the entries in column number column
+ as a column matrix.
+ setColumnMatrix(int, RealMatrix) BlockRealMatrix
+Sets the entries in column number column
+ as a column matrix.
+ setColumnMatrix(int, FieldMatrix<T>) FieldMatrix
+Sets the entries in column number column
+ as a column matrix.
+ setColumnMatrix(int, RealMatrix) RealMatrix
+Sets the entries in column number column
+ as a column matrix.
+ setColumnVector(int, FieldVector<T>) AbstractFieldMatrix
+Sets the entries in column number column
+ as a vector.
+ setColumnVector(int, RealVector) AbstractRealMatrix
+Sets the entries in column number column
+ as a vector.
+ setColumnVector(int, FieldVector<T>) BlockFieldMatrix
+Sets the entries in column number column
+ as a vector.
+ setColumnVector(int, RealVector) BlockRealMatrix
+Sets the entries in column number column
+ as a vector.
+ setColumnVector(int, FieldVector<T>) FieldMatrix
+Sets the entries in column number column
+ as a vector.
+ setColumnVector(int, RealVector) RealMatrix
+Sets the entries in column number column
+ as a vector.
+ setContractionCriteria(float) ResizableDoubleArray
+Sets the contraction criteria for this ExpandContractDoubleArray.
+ setConvergence(double) GaussNewtonEstimator
+Deprecated. Set the convergence criterion threshold.
+setConvergenceChecker(RealConvergenceChecker) DifferentiableMultivariateRealOptimizer
+Set the convergence checker.
+ setConvergenceChecker(VectorialConvergenceChecker) DifferentiableMultivariateVectorialOptimizer
+Set the convergence checker.
+ setConvergenceChecker(RealConvergenceChecker) DirectSearchOptimizer
+Set the convergence checker.
+ setConvergenceChecker(VectorialConvergenceChecker) AbstractLeastSquaresOptimizer
+Set the convergence checker.
+ setConvergenceChecker(RealConvergenceChecker) AbstractScalarDifferentiableOptimizer
+Set the convergence checker.
+ setConvergenceChecker(RealConvergenceChecker) MultiStartDifferentiableMultivariateRealOptimizer
+Set the convergence checker.
+ setConvergenceChecker(VectorialConvergenceChecker) MultiStartDifferentiableMultivariateVectorialOptimizer
+Set the convergence checker.
+ setConvergenceChecker(RealConvergenceChecker) MultiStartMultivariateRealOptimizer
+Set the convergence checker.
+ setConvergenceChecker(RealConvergenceChecker) MultivariateRealOptimizer
+Set the convergence checker.
+ setCostRelativeTolerance(double) LevenbergMarquardtEstimator
+Deprecated. Set the desired relative error in the sum of squares.
+setCostRelativeTolerance(double) LevenbergMarquardtOptimizer
+Set the desired relative error in the sum of squares.
+ setDegreesOfFreedom(double) ChiSquaredDistribution
+Deprecated. as of v2.1
+setDegreesOfFreedom(double) ChiSquaredDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setDegreesOfFreedom(double) TDistribution
+Deprecated. as of v2.1
+setDegreesOfFreedom(double) TDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setDenominatorDegreesOfFreedom(double) FDistribution
+Deprecated. as of v2.1
+setDenominatorDegreesOfFreedom(double) FDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setDenominatorFormat(NumberFormat) AbstractFormat
+Modify the denominator format.
+ setDistribution(ChiSquaredDistribution) ChiSquareTestImpl
+Modify the distribution used to compute inference statistics.
+ setDistribution(TDistribution) TTestImpl
+Modify the distribution used to compute inference statistics.
+ setDistribution(TDistribution) SimpleRegression
+Modify the distribution used to compute inference statistics.
+ setElement(int, double) DoubleArray
+Sets the element at the specified index.
+ setElement(int, double) ResizableDoubleArray
+Sets the element at the specified index.
+ setElitismRate(double) ElitisticListPopulation
+Sets the elitism rate, i.e.
+ setEntry(int, int, T) AbstractFieldMatrix
+Set the entry in the specified row and column.
+ setEntry(int, int, double) AbstractRealMatrix
+Set the entry in the specified row and column.
+ setEntry(int, int, T) Array2DRowFieldMatrix
+Set the entry in the specified row and column.
+ setEntry(int, int, double) Array2DRowRealMatrix
+Set the entry in the specified row and column.
+ setEntry(int, T) ArrayFieldVector
+Set a single element.
+ setEntry(int, double) ArrayRealVector
+Set a single element.
+ setEntry(int, int, T) BlockFieldMatrix
+Set the entry in the specified row and column.
+ setEntry(int, int, double) BlockRealMatrix
+Set the entry in the specified row and column.
+ setEntry(int, int, T) FieldMatrix
+Set the entry in the specified row and column.
+ setEntry(int, T) FieldVector
+Set a single element.
+ setEntry(int, int, double) OpenMapRealMatrix
+Set the entry in the specified row and column.
+ setEntry(int, double) OpenMapRealVector
+Set a single element.
+ setEntry(int, int, double) RealMatrix
+Set the entry in the specified row and column.
+ setEntry(int, int, double) RealMatrixImpl
+Deprecated. Set the entry in the specified row and column.
+setEntry(int, double) RealVector
+Set a single element.
+ setEntry(int, int, T) SparseFieldMatrix
+Set the entry in the specified row and column.
+ setEntry(int, T) SparseFieldVector
+Set a single element.
+ setEquations(FirstOrderDifferentialEquations) AbstractIntegrator
+Set the differential equations.
+ setEstimate(double) EstimatedParameter
+Deprecated. Set a new estimated value for the parameter.
+setExpansionFactor(float) ResizableDoubleArray
+Sets the expansionFactor.
+ setExpansionMode(int) ResizableDoubleArray
+Sets the expansionMode.
+ setExponent(double) ZipfDistribution
+Deprecated. as of v2.1
+setExponent(double) ZipfDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setFunctionValueAccuracy(double) UnivariateRealSolver
+Set the function value accuracy.
+ setFunctionValueAccuracy(double) UnivariateRealSolverImpl
+Set the function value accuracy.
+ setGamma(GammaDistribution) ChiSquaredDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setGeoMeanImpl(StorelessUnivariateStatistic[]) MultivariateSummaryStatistics
+Sets the implementation for the geometric mean.
+ setGeoMeanImpl(StorelessUnivariateStatistic) SummaryStatistics
+
+ Sets the implementation for the geometric mean.
+ setGeoMeanImpl(StorelessUnivariateStatistic[]) SynchronizedMultivariateSummaryStatistics
+Sets the implementation for the geometric mean.
+ setGeoMeanImpl(StorelessUnivariateStatistic) SynchronizedSummaryStatistics
+
+ Sets the implementation for the geometric mean.
+ setGeometricMeanImpl(UnivariateStatistic) DescriptiveStatistics
+Sets the implementation for the gemoetric mean.
+ setIgnored(boolean) WeightedMeasurement
+Deprecated. Set the ignore flag to the specified value
+ Setting the ignore flag to true allow to reject wrong
+ measurements, which sometimes can be detected only rather late.
+setImaginaryCharacter(String) ComplexFormat
+Modify the imaginaryCharacter.
+ setImaginaryFormat(NumberFormat) ComplexFormat
+Modify the imaginaryFormat.
+ setIndex(int) RealVector.Entry
+Set the index of the entry.
+ setInitialCapacity(int) ResizableDoubleArray
+Sets the initial capacity.
+ setInitialStep(double) NonLinearConjugateGradientOptimizer
+Set the initial step used to bracket the optimum in line search.
+ setInitialStepBoundFactor(double) LevenbergMarquardtEstimator
+Deprecated. Set the positive input variable used in determining the initial step bound.
+setInitialStepBoundFactor(double) LevenbergMarquardtOptimizer
+Set the positive input variable used in determining the initial step bound.
+ setInitialStepSize(double) AdaptiveStepsizeIntegrator
+Set the initial step size.
+ setInterpolatedTime(double) ContinuousOutputModel
+Set the time of the interpolated point.
+ setInterpolatedTime(double) StepInterpolatorWithJacobians
+Set the time of the interpolated point.
+ setInterpolatedTime(double) AbstractStepInterpolator
+Set the time of the interpolated point.
+ setInterpolatedTime(double) StepInterpolator
+Set the time of the interpolated point.
+ setInterpolationControl(boolean, int) GraggBulirschStoerIntegrator
+Set the interpolation order control parameter.
+ setKurtosisImpl(UnivariateStatistic) DescriptiveStatistics
+Sets the implementation for the kurtosis.
+ setLineSearchSolver(UnivariateRealSolver) NonLinearConjugateGradientOptimizer
+Set the solver to use during line search.
+ setMaxCostEval(int) AbstractEstimator
+Deprecated. Set the maximal number of cost evaluations allowed.
+setMaxEvaluations(int) AbstractIntegrator
+Set the maximal number of differential equations function evaluations.
+ setMaxEvaluations(int) FirstOrderIntegratorWithJacobians
+Set the maximal number of differential equations function evaluations.
+ setMaxEvaluations(int) ODEIntegrator
+Set the maximal number of differential equations function evaluations.
+ setMaxEvaluations(int) DifferentiableMultivariateRealOptimizer
+Set the maximal number of functions evaluations.
+ setMaxEvaluations(int) DifferentiableMultivariateVectorialOptimizer
+Set the maximal number of functions evaluations.
+ setMaxEvaluations(int) DirectSearchOptimizer
+Set the maximal number of functions evaluations.
+ setMaxEvaluations(int) AbstractLeastSquaresOptimizer
+Set the maximal number of functions evaluations.
+ setMaxEvaluations(int) AbstractScalarDifferentiableOptimizer
+Set the maximal number of functions evaluations.
+ setMaxEvaluations(int) MultiStartDifferentiableMultivariateRealOptimizer
+Set the maximal number of functions evaluations.
+ setMaxEvaluations(int) MultiStartDifferentiableMultivariateVectorialOptimizer
+Set the maximal number of functions evaluations.
+ setMaxEvaluations(int) MultiStartMultivariateRealOptimizer
+Set the maximal number of functions evaluations.
+ setMaxEvaluations(int) MultiStartUnivariateRealOptimizer
+Set the maximal number of functions evaluations.
+ setMaxEvaluations(int) MultivariateRealOptimizer
+Set the maximal number of functions evaluations.
+ setMaxEvaluations(int) AbstractUnivariateRealOptimizer
+Set the maximal number of functions evaluations.
+ setMaxEvaluations(int) UnivariateRealOptimizer
+Set the maximal number of functions evaluations.
+ setMaxGrowth(double) MultistepIntegrator
+Set the maximal growth factor for stepsize control.
+ setMaxGrowth(double) EmbeddedRungeKuttaIntegrator
+Set the maximal growth factor for stepsize control.
+ setMaximalIterationCount(int) ConvergingAlgorithm
+Set the upper limit for the number of iterations.
+ setMaximalIterationCount(int) ConvergingAlgorithmImpl
+Set the upper limit for the number of iterations.
+ setMaximalIterationCount(int) MultiStartUnivariateRealOptimizer
+Set the upper limit for the number of iterations.
+ setMaxImpl(UnivariateStatistic) DescriptiveStatistics
+Sets the implementation for the maximum.
+ setMaxImpl(StorelessUnivariateStatistic[]) MultivariateSummaryStatistics
+Sets the implementation for the maximum.
+ setMaxImpl(StorelessUnivariateStatistic) SummaryStatistics
+
+ Sets the implementation for the maximum.
+ setMaxImpl(StorelessUnivariateStatistic[]) SynchronizedMultivariateSummaryStatistics
+Sets the implementation for the maximum.
+ setMaxImpl(StorelessUnivariateStatistic) SynchronizedSummaryStatistics
+
+ Sets the implementation for the maximum.
+ setMaxIterations(int) DifferentiableMultivariateRealOptimizer
+Set the maximal number of iterations of the algorithm.
+ setMaxIterations(int) DifferentiableMultivariateVectorialOptimizer
+Set the maximal number of iterations of the algorithm.
+ setMaxIterations(int) DirectSearchOptimizer
+Set the maximal number of iterations of the algorithm.
+ setMaxIterations(int) AbstractLeastSquaresOptimizer
+Set the maximal number of iterations of the algorithm.
+ setMaxIterations(int) AbstractScalarDifferentiableOptimizer
+Set the maximal number of iterations of the algorithm.
+ setMaxIterations(int) AbstractLinearOptimizer
+Set the maximal number of iterations of the algorithm.
+ setMaxIterations(int) LinearOptimizer
+Set the maximal number of iterations of the algorithm.
+ setMaxIterations(int) MultiStartDifferentiableMultivariateRealOptimizer
+Set the maximal number of iterations of the algorithm.
+ setMaxIterations(int) MultiStartDifferentiableMultivariateVectorialOptimizer
+Set the maximal number of iterations of the algorithm.
+ setMaxIterations(int) MultiStartMultivariateRealOptimizer
+Set the maximal number of iterations of the algorithm.
+ setMaxIterations(int) MultivariateRealOptimizer
+Set the maximal number of iterations of the algorithm.
+ setMean(double) ExponentialDistribution
+Deprecated. as of v2.1
+setMean(double) ExponentialDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setMean(double) NormalDistribution
+Deprecated. as of v2.1
+setMean(double) NormalDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setMean(double) PoissonDistribution
+Deprecated. as of v2.1
+setMean(double) PoissonDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setMeanImpl(UnivariateStatistic) DescriptiveStatistics
+Sets the implementation for the mean.
+ setMeanImpl(StorelessUnivariateStatistic[]) MultivariateSummaryStatistics
+Sets the implementation for the mean.
+ setMeanImpl(StorelessUnivariateStatistic) SummaryStatistics
+
+ Sets the implementation for the mean.
+ setMeanImpl(StorelessUnivariateStatistic[]) SynchronizedMultivariateSummaryStatistics
+Sets the implementation for the mean.
+ setMeanImpl(StorelessUnivariateStatistic) SynchronizedSummaryStatistics
+
+ Sets the implementation for the mean.
+ setMedian(double) CauchyDistribution
+Deprecated. as of v2.1
+setMedian(double) CauchyDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setMicropshereElements(int) MicrosphereInterpolator
+Set the number of microsphere elements.
+ setMinimalIterationCount(int) UnivariateRealIntegrator
+Set the lower limit for the number of iterations.
+ setMinimalIterationCount(int) UnivariateRealIntegratorImpl
+Set the lower limit for the number of iterations.
+ setMinImpl(UnivariateStatistic) DescriptiveStatistics
+Sets the implementation for the minimum.
+ setMinImpl(StorelessUnivariateStatistic[]) MultivariateSummaryStatistics
+Sets the implementation for the minimum.
+ setMinImpl(StorelessUnivariateStatistic) SummaryStatistics
+
+ Sets the implementation for the minimum.
+ setMinImpl(StorelessUnivariateStatistic[]) SynchronizedMultivariateSummaryStatistics
+Sets the implementation for the minimum.
+ setMinImpl(StorelessUnivariateStatistic) SynchronizedSummaryStatistics
+
+ Sets the implementation for the minimum.
+ setMinReduction(double) MultistepIntegrator
+Set the minimal reduction factor for stepsize control.
+ setMinReduction(double) EmbeddedRungeKuttaIntegrator
+Set the minimal reduction factor for stepsize control.
+ setMode(int) ValueServer
+Setter for property mode.
+ setMu(double) ValueServer
+Setter for property mu.
+ setNormal(NormalDistribution) PoissonDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setNumberOfElements(int) ZipfDistribution
+Deprecated. as of v2.1
+setNumberOfElements(int) ZipfDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setNumberOfSuccesses(int) HypergeometricDistribution
+Deprecated. as of v2.1
+setNumberOfSuccesses(int) HypergeometricDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setNumberOfSuccesses(int) PascalDistribution
+Deprecated. as of v2.1
+setNumberOfSuccesses(int) PascalDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setNumberOfTrials(int) BinomialDistribution
+Deprecated. as of v2.1
+setNumberOfTrials(int) BinomialDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setNumElements(int) ResizableDoubleArray
+This function allows you to control the number of elements contained
+ in this array, and can be used to "throw out" the last n values in an
+ array.
+ setNumeratorDegreesOfFreedom(double) FDistribution
+Deprecated. as of v2.1
+setNumeratorDegreesOfFreedom(double) FDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setNumeratorFormat(NumberFormat) AbstractFormat
+Modify the numerator format.
+ setOneWayAnova(OneWayAnova) TestUtils
+Set the (singleton) OneWayAnova instance
+ setOrderControl(int, double, double) GraggBulirschStoerIntegrator
+Set the order control parameters.
+ setOrthoTolerance(double) LevenbergMarquardtEstimator
+Deprecated. Set the desired max cosine on the orthogonality.
+setOrthoTolerance(double) LevenbergMarquardtOptimizer
+Set the desired max cosine on the orthogonality.
+ setParameter(int, double) ParameterizedODE
+Set a parameter.
+ setParRelativeTolerance(double) LevenbergMarquardtEstimator
+Deprecated. Set the desired relative error in the approximate solution parameters.
+setParRelativeTolerance(double) LevenbergMarquardtOptimizer
+Set the desired relative error in the approximate solution parameters.
+ setPercentileImpl(UnivariateStatistic) DescriptiveStatistics
+Sets the implementation to be used by DescriptiveStatistics.getPercentile(double) setPopulationLimit(int) ListPopulation
+Sets the maximal population size.
+ setPopulationSize(int) HypergeometricDistribution
+Deprecated. as of v2.1
+setPopulationSize(int) HypergeometricDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setPreconditioner(Preconditioner) NonLinearConjugateGradientOptimizer
+Set the preconditioner.
+ setProbabilityOfSuccess(double) BinomialDistribution
+Deprecated. as of v2.1
+setProbabilityOfSuccess(double) BinomialDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setProbabilityOfSuccess(double) PascalDistribution
+Deprecated. as of v2.1
+setProbabilityOfSuccess(double) PascalDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setQuantile(double) Percentile
+Sets the value of the quantile field (determines what percentile is
+ computed when evaluate() is called with no quantile argument).
+ setRandomGenerator(RandomGenerator) GeneticAlgorithm
+Set the (static) random generator.
+ setRealFormat(NumberFormat) ComplexFormat
+Modify the realFormat.
+ setRelativeAccuracy(double) ConvergingAlgorithm
+Set the relative accuracy.
+ setRelativeAccuracy(double) ConvergingAlgorithmImpl
+Set the relative accuracy.
+ setRelativeAccuracy(double) MultiStartUnivariateRealOptimizer
+Set the relative accuracy.
+ setResult(double, int) UnivariateRealIntegratorImpl
+Convenience function for implementations.
+ setResult(double, int) UnivariateRealSolverImpl
+Convenience function for implementations.
+ setResult(double, double, int) UnivariateRealSolverImpl
+Convenience function for implementations.
+ setResult(double, double, int) AbstractUnivariateRealOptimizer
+Convenience function for implementations.
+ setRoundingMode(int) BigMatrixImpl
+Deprecated. Sets the rounding mode for decimal divisions.
+setRoundingMode(RoundingMode) BigReal
+Sets the rounding mode for decimal divisions.
+ setRow(int, T[]) AbstractFieldMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRow(int, double[]) AbstractRealMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRow(int, T[]) BlockFieldMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRow(int, double[]) BlockRealMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRow(int, T[]) FieldMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRow(int, double[]) RealMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRowMatrix(int, FieldMatrix<T>) AbstractFieldMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRowMatrix(int, RealMatrix) AbstractRealMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRowMatrix(int, FieldMatrix<T>) BlockFieldMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRowMatrix(int, BlockFieldMatrix<T>) BlockFieldMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRowMatrix(int, RealMatrix) BlockRealMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRowMatrix(int, BlockRealMatrix) BlockRealMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRowMatrix(int, FieldMatrix<T>) FieldMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRowMatrix(int, RealMatrix) RealMatrix
+Sets the entries in row number row
+ as a row matrix.
+ setRowVector(int, FieldVector<T>) AbstractFieldMatrix
+Sets the entries in row number row
+ as a vector.
+ setRowVector(int, RealVector) AbstractRealMatrix
+Sets the entries in row number row
+ as a vector.
+ setRowVector(int, FieldVector<T>) BlockFieldMatrix
+Sets the entries in row number row
+ as a vector.
+ setRowVector(int, RealVector) BlockRealMatrix
+Sets the entries in row number row
+ as a vector.
+ setRowVector(int, FieldVector<T>) FieldMatrix
+Sets the entries in row number row
+ as a vector.
+ setRowVector(int, RealVector) RealMatrix
+Sets the entries in row number row
+ as a vector.
+ setSafety(double) MultistepIntegrator
+Set the safety factor for stepsize control.
+ setSafety(double) EmbeddedRungeKuttaIntegrator
+Set the safety factor for stepsize control.
+ setSampleSize(int) HypergeometricDistribution
+Deprecated. as of v2.1
+setSampleSize(int) HypergeometricDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setScale(double) CauchyDistribution
+Deprecated. as of v2.1
+setScale(double) CauchyDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setScale(double) WeibullDistribution
+Deprecated. as of v2.1
+setScale(double) WeibullDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setScale(int) BigMatrixImpl
+Deprecated. Sets the scale for division operations.
+setScale(int) BigReal
+Sets the scale for division operations.
+ setSecureAlgorithm(String, String) RandomDataImpl
+Sets the PRNG algorithm for the underlying SecureRandom instance using
+ the Security Provider API.
+ setSeed(int) AbstractRandomGenerator
+Sets the seed of the underyling random number generator using an
+ int seed.
+ setSeed(int[]) AbstractRandomGenerator
+Sets the seed of the underyling random number generator using an
+ int array seed.
+ setSeed(long) AbstractRandomGenerator
+Sets the seed of the underyling random number generator using a
+ long seed.
+ setSeed(int) BitsStreamGenerator
+Sets the seed of the underyling random number generator using an
+ int seed.
+ setSeed(int[]) BitsStreamGenerator
+Sets the seed of the underyling random number generator using an
+ int array seed.
+ setSeed(long) BitsStreamGenerator
+Sets the seed of the underyling random number generator using a
+ long seed.
+ setSeed(int) JDKRandomGenerator
+Sets the seed of the underyling random number generator using an
+ int seed.
+ setSeed(int[]) JDKRandomGenerator
+Sets the seed of the underyling random number generator using an
+ int array seed.
+ setSeed(int) MersenneTwister
+Reinitialize the generator as if just built with the given int seed.
+ setSeed(int[]) MersenneTwister
+Reinitialize the generator as if just built with the given int array seed.
+ setSeed(long) MersenneTwister
+Reinitialize the generator as if just built with the given long seed.
+ setSeed(int) RandomAdaptor
+Sets the seed of the underyling random number generator using an
+ int seed.
+ setSeed(int[]) RandomAdaptor
+Sets the seed of the underyling random number generator using an
+ int array seed.
+ setSeed(long) RandomAdaptor
+Sets the seed of the underyling random number generator using a
+ long seed.
+ setSeed(int) RandomGenerator
+Sets the seed of the underyling random number generator using an
+ int seed.
+ setSeed(int[]) RandomGenerator
+Sets the seed of the underyling random number generator using an
+ int array seed.
+ setSeed(long) RandomGenerator
+Sets the seed of the underyling random number generator using a
+ long seed.
+ setShape(double) WeibullDistribution
+Deprecated. as of v2.1
+setShape(double) WeibullDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setSigma(double) ValueServer
+Setter for property sigma.
+ setSkewnessImpl(UnivariateStatistic) DescriptiveStatistics
+Sets the implementation for the skewness.
+ setStabilityCheck(boolean, int, int, double) GraggBulirschStoerIntegrator
+Set the stability check controls.
+ setStandardDeviation(double) NormalDistribution
+Deprecated. as of v2.1
+setStandardDeviation(double) NormalDistributionImpl
+Deprecated. as of 2.1 (class will become immutable in 3.0)
+setStartConfiguration(double[]) DirectSearchOptimizer
+Set start configuration for simplex.
+ setStartConfiguration(double[][]) DirectSearchOptimizer
+Set start configuration for simplex.
+ setStarterIntegrator(FirstOrderIntegrator) MultistepIntegrator
+Set the starter integrator.
+ setSteadyStateThreshold(double) GaussNewtonEstimator
+Deprecated. Set the steady state detection threshold.
+setStepsizeControl(double, double, double, double) GraggBulirschStoerIntegrator
+Set the step size control factors.
+ setSubMatrix(T[][], int, int) AbstractFieldMatrix
+Replace the submatrix starting at row, column using data in
+ the input subMatrix array.
+ setSubMatrix(double[][], int, int) AbstractRealMatrix
+Replace the submatrix starting at row, column using data in
+ the input subMatrix array.
+ setSubMatrix(T[][], int, int) Array2DRowFieldMatrix
+Replace the submatrix starting at row, column using data in
+ the input subMatrix array.
+ setSubMatrix(double[][], int, int) Array2DRowRealMatrix
+Replace the submatrix starting at row, column using data in
+ the input subMatrix array.
+ setSubMatrix(BigDecimal[][], int, int) BigMatrixImpl
+Deprecated. Replace the submatrix starting at row, column using data in
+ the input subMatrix array.
+setSubMatrix(T[][], int, int) BlockFieldMatrix
+Replace the submatrix starting at row, column using data in
+ the input subMatrix array.
+ setSubMatrix(double[][], int, int) BlockRealMatrix
+Replace the submatrix starting at row, column using data in
+ the input subMatrix array.
+ setSubMatrix(T[][], int, int) FieldMatrix
+Replace the submatrix starting at row, column using data in
+ the input subMatrix array.
+ setSubMatrix(double[][], int, int) RealMatrix
+Replace the submatrix starting at row, column using data in
+ the input subMatrix array.
+ setSubMatrix(double[][], int, int) RealMatrixImpl
+Deprecated. Replace the submatrix starting at row, column using data in
+ the input subMatrix array.
+setSubVector(int, RealVector) AbstractRealVector
+Set a set of consecutive elements.
+ setSubVector(int, double[]) AbstractRealVector
+Set a set of consecutive elements.
+ setSubVector(int, FieldVector<T>) ArrayFieldVector
+Set a set of consecutive elements.
+ setSubVector(int, T[]) ArrayFieldVector
+Set a set of consecutive elements.
+ setSubVector(int, RealVector) ArrayRealVector
+Set a set of consecutive elements.
+ setSubVector(int, double[]) ArrayRealVector
+Set a set of consecutive elements.
+ setSubVector(int, FieldVector<T>) FieldVector
+Set a set of consecutive elements.
+ setSubVector(int, T[]) FieldVector
+Set a set of consecutive elements.
+ setSubVector(int, RealVector) OpenMapRealVector
+Set a set of consecutive elements.
+ setSubVector(int, double[]) OpenMapRealVector
+Set a set of consecutive elements.
+ setSubVector(int, RealVector) RealVector
+Set a set of consecutive elements.
+ setSubVector(int, double[]) RealVector
+Set a set of consecutive elements.
+ setSubVector(int, FieldVector<T>) SparseFieldVector
+Set a set of consecutive elements.
+ setSubVector(int, T[]) SparseFieldVector
+Set a set of consecutive elements.
+ setSumImpl(UnivariateStatistic) DescriptiveStatistics
+Sets the implementation for the sum.
+ setSumImpl(StorelessUnivariateStatistic[]) MultivariateSummaryStatistics
+Sets the implementation for the Sum.
+ setSumImpl(StorelessUnivariateStatistic) SummaryStatistics
+
+ Sets the implementation for the Sum.
+ setSumImpl(StorelessUnivariateStatistic[]) SynchronizedMultivariateSummaryStatistics
+Sets the implementation for the Sum.
+ setSumImpl(StorelessUnivariateStatistic) SynchronizedSummaryStatistics
+
+ Sets the implementation for the Sum.
+ setSumLogImpl(StorelessUnivariateStatistic) GeometricMean
+Sets the implementation for the sum of logs.
+ setSumLogImpl(StorelessUnivariateStatistic[]) MultivariateSummaryStatistics
+Sets the implementation for the sum of logs.
+ setSumLogImpl(StorelessUnivariateStatistic) SummaryStatistics
+
+ Sets the implementation for the sum of logs.
+ setSumLogImpl(StorelessUnivariateStatistic[]) SynchronizedMultivariateSummaryStatistics
+Sets the implementation for the sum of logs.
+ setSumLogImpl(StorelessUnivariateStatistic) SynchronizedSummaryStatistics
+
+ Sets the implementation for the sum of logs.
+ setSumsqImpl(UnivariateStatistic) DescriptiveStatistics
+Sets the implementation for the sum of squares.
+ setSumsqImpl(StorelessUnivariateStatistic[]) MultivariateSummaryStatistics
+Sets the implementation for the sum of squares.
+ setSumsqImpl(StorelessUnivariateStatistic) SummaryStatistics
+
+ Sets the implementation for the sum of squares.
+ setSumsqImpl(StorelessUnivariateStatistic[]) SynchronizedMultivariateSummaryStatistics
+Sets the implementation for the sum of squares.
+ setSumsqImpl(StorelessUnivariateStatistic) SynchronizedSummaryStatistics
+
+ Sets the implementation for the sum of squares.
+ setUnknownDistributionChiSquareTest(UnknownDistributionChiSquareTest) TestUtils
+Set the (singleton) UnknownDistributionChiSquareTest instance.
+ setValue(double) AbstractRealVector.EntryImpl
+Set the value of the entry.
+ setValue(double) OpenMapRealVector.OpenMapEntry
+Set the value of the entry.
+ setValue(double) RealVector.Entry
+Set the value of the entry.
+ setValuesFileURL(String) ValueServer
+Sets the valuesFileURL using a string URL representation
+ setValuesFileURL(URL) ValueServer
+Sets the valuesFileURL
+ setVarianceDirection(SemiVariance.Direction) SemiVariance
+Sets the variance direction
+ setVarianceImpl(UnivariateStatistic) DescriptiveStatistics
+Sets the implementation for the variance.
+ setVarianceImpl(StorelessUnivariateStatistic) SummaryStatistics
+
+ Sets the implementation for the variance.
+ setVarianceImpl(StorelessUnivariateStatistic) SynchronizedSummaryStatistics
+
+ Sets the implementation for the variance.
+ setWholeFormat(NumberFormat) ProperBigFractionFormat
+Modify the whole format.
+ setWholeFormat(NumberFormat) ProperFractionFormat
+Modify the whole format.
+ setWindowSize(int) DescriptiveStatistics
+WindowSize controls the number of values which contribute
+ to the reported statistics.
+ setWindowSize(int) SynchronizedDescriptiveStatistics
+WindowSize controls the number of values which contribute
+ to the reported statistics.
+ shift() AbstractStepInterpolator
+Shift one step forward.
+ sign(byte) MathUtils
+Returns the sign
+ for byte value x.
+ sign(double) MathUtils
+Returns the sign
+ for double precision x.
+ sign(float) MathUtils
+Returns the sign
+ for float value x.
+ sign(int) MathUtils
+Returns the sign
+ for int value x.
+ sign(long) MathUtils
+Returns the sign
+ for long value x.
+ sign(short) MathUtils
+Returns the sign
+ for short value x.
+ SIGNUM ComposableFunction
+The Math.signum method wrapped as a ComposableFunction SimpleEstimationProblem org.apache.commons.math.estimation Deprecated. as of 2.0, everything in package org.apache.commons.math.estimation has
+ been deprecated and replaced by package org.apache.commons.math.optimization.general SimpleEstimationProblem() SimpleEstimationProblem
+Deprecated. Build an empty instance without parameters nor measurements.
+SimpleRealPointChecker org.apache.commons.math.optimization Simple implementation of the RealConvergenceChecker SimpleRealPointChecker() SimpleRealPointChecker
+Build an instance with default threshold.
+ SimpleRealPointChecker(double, double) SimpleRealPointChecker
+Build an instance with a specified threshold.
+ SimpleRegression org.apache.commons.math.stat.regression Estimates an ordinary least squares regression model
+ with one independent variable. SimpleRegression() SimpleRegression
+Create an empty SimpleRegression instance
+ SimpleRegression(TDistribution) SimpleRegression
+Create an empty SimpleRegression using the given distribution object to
+ compute inference statistics.
+ SimpleScalarValueChecker org.apache.commons.math.optimization Simple implementation of the RealConvergenceChecker SimpleScalarValueChecker() SimpleScalarValueChecker
+Build an instance with default threshold.
+ SimpleScalarValueChecker(double, double) SimpleScalarValueChecker
+Build an instance with a specified threshold.
+ SimpleVectorialPointChecker org.apache.commons.math.optimization Simple implementation of the VectorialConvergenceChecker SimpleVectorialPointChecker() SimpleVectorialPointChecker
+Build an instance with default threshold.
+ SimpleVectorialPointChecker(double, double) SimpleVectorialPointChecker
+Build an instance with a specified threshold.
+ SimpleVectorialValueChecker org.apache.commons.math.optimization Simple implementation of the VectorialConvergenceChecker SimpleVectorialValueChecker() SimpleVectorialValueChecker
+Build an instance with default threshold.
+ SimpleVectorialValueChecker(double, double) SimpleVectorialValueChecker
+Build an instance with a specified threshold.
+ simplex DirectSearchOptimizer
+Simplex.
+ SimplexSolver org.apache.commons.math.optimization.linear Solves a linear problem using the Two-Phase Simplex Method. SimplexSolver() SimplexSolver
+Build a simplex solver with default settings.
+ SimplexSolver(double) SimplexSolver
+Build a simplex solver with a specified accepted amount of error
+ SimpsonIntegrator org.apache.commons.math.analysis.integration Implements the
+ Simpson's Rule for integration of real univariate functions. SimpsonIntegrator(UnivariateRealFunction) SimpsonIntegrator
+Deprecated. as of 2.0 the integrand function is passed as an argument
+ to the SimpsonIntegrator.integrate(UnivariateRealFunction, double, double)
+SimpsonIntegrator() SimpsonIntegrator
+Construct an integrator.
+ SIN ComposableFunction
+The Math.sin method wrapped as a ComposableFunction sin() Complex
+Compute the
+
+ sine
+ of this complex number.
+ SingularMatrixException org.apache.commons.math.linear Thrown when a matrix is singular. SingularMatrixException() SingularMatrixException
+Construct an exception with a default message.
+ SingularValueDecomposition org.apache.commons.math.linear An interface to classes that implement an algorithm to calculate the
+ Singular Value Decomposition of a real matrix. SingularValueDecompositionImpl org.apache.commons.math.linear Calculates the compact Singular Value Decomposition of a matrix. SingularValueDecompositionImpl(RealMatrix) SingularValueDecompositionImpl
+Calculates the compact Singular Value Decomposition of the given matrix.
+ SINH ComposableFunction
+The Math.sinh method wrapped as a ComposableFunction sinh() Complex
+Compute the
+
+ hyperbolic sine of this complex number.
+ sinh(double) MathUtils
+Returns the
+ hyperbolic sine of x.
+ size() OpenIntToDoubleHashMap
+Get the number of elements stored in the map.
+ size() OpenIntToFieldHashMap
+Get the number of elements stored in the map.
+ Skewness org.apache.commons.math.stat.descriptive.moment Computes the skewness of the available values. Skewness() Skewness
+Constructs a Skewness
+ Skewness(ThirdMoment) Skewness
+Constructs a Skewness with an external moment
+ Skewness(Skewness) Skewness
+Copy constructor, creates a new Skewness identical
+ to the original
+ smooth(double[], double[], double[]) LoessInterpolator
+Compute a weighted loess fit on the data at the original abscissae.
+ smooth(double[], double[]) LoessInterpolator
+Compute a loess fit on the data at the original abscissae.
+ SmoothingBicubicSplineInterpolator org.apache.commons.math.analysis.interpolation Generates a bicubic interpolation function. SmoothingBicubicSplineInterpolator() SmoothingBicubicSplineInterpolator
+
+ solve(double, double, double) BisectionSolver
+Deprecated.
+solve(double, double) BisectionSolver
+Deprecated.
+solve(UnivariateRealFunction, double, double, double) BisectionSolver
+Solve for a zero in the given interval, start at startValue.
+ solve(UnivariateRealFunction, double, double) BisectionSolver
+Solve for a zero root in the given interval.
+ solve(double, double) BrentSolver
+Deprecated.
+solve(double, double, double) BrentSolver
+Deprecated.
+solve(UnivariateRealFunction, double, double, double) BrentSolver
+Find a zero in the given interval with an initial guess.
+ solve(UnivariateRealFunction, double, double) BrentSolver
+Find a zero in the given interval.
+ solve(double, double) LaguerreSolver
+Deprecated.
+solve(double, double, double) LaguerreSolver
+Deprecated.
+solve(UnivariateRealFunction, double, double, double) LaguerreSolver
+Find a real root in the given interval with initial value.
+ solve(UnivariateRealFunction, double, double) LaguerreSolver
+Find a real root in the given interval.
+ solve(Complex[], Complex) LaguerreSolver
+Find a complex root for the polynomial with the given coefficients,
+ starting from the given initial value.
+ solve(double, double) MullerSolver
+Deprecated.
+solve(double, double, double) MullerSolver
+Deprecated.
+solve(UnivariateRealFunction, double, double, double) MullerSolver
+Find a real root in the given interval with initial value.
+ solve(UnivariateRealFunction, double, double) MullerSolver
+Find a real root in the given interval.
+ solve(double, double) NewtonSolver
+Deprecated.
+solve(double, double, double) NewtonSolver
+Deprecated.
+solve(UnivariateRealFunction, double, double) NewtonSolver
+Find a zero near the midpoint of min and max.
+ solve(UnivariateRealFunction, double, double, double) NewtonSolver
+Find a zero near the value startValue.
+ solve(double, double) RiddersSolver
+Deprecated.
+solve(double, double, double) RiddersSolver
+Deprecated.
+solve(UnivariateRealFunction, double, double, double) RiddersSolver
+Find a root in the given interval with initial value.
+ solve(UnivariateRealFunction, double, double) RiddersSolver
+Find a root in the given interval.
+ solve(double, double) SecantSolver
+Deprecated.
+solve(double, double, double) SecantSolver
+Deprecated.
+solve(UnivariateRealFunction, double, double, double) SecantSolver
+Find a zero in the given interval.
+ solve(UnivariateRealFunction, double, double) SecantSolver
+Find a zero in the given interval.
+ solve(double, double) UnivariateRealSolver
+Deprecated. replaced by UnivariateRealSolver.solve(UnivariateRealFunction, double, double)
+solve(UnivariateRealFunction, double, double) UnivariateRealSolver
+Solve for a zero root in the given interval.
+ solve(double, double, double) UnivariateRealSolver
+Deprecated. replaced by UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+solve(UnivariateRealFunction, double, double, double) UnivariateRealSolver
+Solve for a zero in the given interval, start at startValue.
+ solve(UnivariateRealFunction, double, double) UnivariateRealSolverUtils
+Convenience method to find a zero of a univariate real function.
+ solve(UnivariateRealFunction, double, double, double) UnivariateRealSolverUtils
+Convenience method to find a zero of a univariate real function.
+ solve(double[]) AbstractRealMatrix
+Deprecated.
+solve(RealMatrix) AbstractRealMatrix
+Deprecated.
+solve(BigDecimal[]) BigMatrix
+Deprecated. Returns the solution vector for a linear system with coefficient
+ matrix = this and constant vector = b.
+solve(BigMatrix) BigMatrix
+Deprecated. Returns a matrix of (column) solution vectors for linear systems with
+ coefficient matrix = this and constant vectors = columns of
+ b.
+solve(BigDecimal[]) BigMatrixImpl
+Deprecated. Returns a matrix of (column) solution vectors for linear systems with
+ coefficient matrix = this and constant vectors = columns of
+ b.
+solve(double[]) BigMatrixImpl
+Deprecated. Returns a matrix of (column) solution vectors for linear systems with
+ coefficient matrix = this and constant vectors = columns of
+ b.
+solve(BigMatrix) BigMatrixImpl
+Deprecated. Returns a matrix of (column) solution vectors for linear systems with
+ coefficient matrix = this and constant vectors = columns of
+ b.
+solve(double[]) DecompositionSolver
+Solve the linear equation A × X = B for matrices A.
+ solve(RealVector) DecompositionSolver
+Solve the linear equation A × X = B for matrices A.
+ solve(RealMatrix) DecompositionSolver
+Solve the linear equation A × X = B for matrices A.
+ solve(T[]) FieldDecompositionSolver
+Solve the linear equation A × X = B for matrices A.
+ solve(FieldVector<T>) FieldDecompositionSolver
+Solve the linear equation A × X = B for matrices A.
+ solve(FieldMatrix<T>) FieldDecompositionSolver
+Solve the linear equation A × X = B for matrices A.
+ solve(double[]) RealMatrix
+Deprecated. as of release 2.0, replaced by DecompositionSolver.solve(double[])
+solve(RealMatrix) RealMatrix
+Deprecated. as of release 2.0, replaced by DecompositionSolver.solve(RealMatrix)
+solve2(double, double) MullerSolver
+Deprecated. replaced by MullerSolver.solve2(UnivariateRealFunction, double, double)
+solve2(UnivariateRealFunction, double, double) MullerSolver
+Find a real root in the given interval.
+ solveAll(double[], double) LaguerreSolver
+Find all complex roots for the polynomial with the given coefficients,
+ starting from the given initial value.
+ solveAll(Complex[], Complex) LaguerreSolver
+Find all complex roots for the polynomial with the given coefficients,
+ starting from the given initial value.
+ solvePhase1(SimplexTableau) SimplexSolver
+Solves Phase 1 of the Simplex method.
+ SparseFieldMatrix T extends FieldElement <T >> - Class in org.apache.commons.math.linear Sparse matrix implementation based on an open addressed map. SparseFieldMatrix(Field<T>) SparseFieldMatrix
+Creates a matrix with no data.
+ SparseFieldMatrix(Field<T>, int, int) SparseFieldMatrix
+Create a new SparseFieldMatrix with the supplied row and column dimensions.
+SparseFieldMatrix(SparseFieldMatrix<T>) SparseFieldMatrix
+Copy constructor.
+ SparseFieldMatrix(FieldMatrix<T>) SparseFieldMatrix
+Generic copy constructor.
+ SparseFieldVector T extends FieldElement <T >> - Class in org.apache.commons.math.linear This class implements the FieldVectorOpenIntToFieldHashMap SparseFieldVector(Field<T>) SparseFieldVector
+Build a 0-length vector.
+ SparseFieldVector(Field<T>, int) SparseFieldVector
+Construct a (dimension)-length vector of zeros.
+ SparseFieldVector(SparseFieldVector<T>, int) SparseFieldVector
+Build a resized vector, for use with append.
+ SparseFieldVector(Field<T>, int, int) SparseFieldVector
+Build a vector with known the sparseness (for advanced use only).
+ SparseFieldVector(Field<T>, T[]) SparseFieldVector
+Create from a Field array.
+ SparseFieldVector(SparseFieldVector<T>) SparseFieldVector
+Copy constructor.
+ sparseIterator() AbstractRealVector
+Specialized implementations may choose to not iterate over all dimensions, either because those values are
+ unset, or are equal to defaultValue(), or are small enough to be ignored for the purposes of iteration.
+ sparseIterator() OpenMapRealVector
+Specialized implementations may choose to not iterate over all dimensions, either because those values are
+ unset, or are equal to defaultValue(), or are small enough to be ignored for the purposes of iteration.
+ sparseIterator() RealVector
+Specialized implementations may choose to not iterate over all dimensions, either because those values are
+ unset, or are equal to defaultValue(), or are small enough to be ignored for the purposes of iteration.
+ SparseRealMatrix org.apache.commons.math.linear Marker interface for RealMatrix SparseRealVector org.apache.commons.math.linear Marker interface for RealVectors that require sparse backing storage SpearmansCorrelation org.apache.commons.math.stat.correlation Spearman's rank correlation. SpearmansCorrelation(RealMatrix, RankingAlgorithm) SpearmansCorrelation
+Create a SpearmansCorrelation with the given input data matrix
+ and ranking algorithm.
+ SpearmansCorrelation(RealMatrix) SpearmansCorrelation
+Create a SpearmansCorrelation from the given data matrix.
+ SpearmansCorrelation() SpearmansCorrelation
+Create a SpearmansCorrelation without data.
+ SplineInterpolator org.apache.commons.math.analysis.interpolation Computes a natural (also known as "free", "unclamped") cubic spline interpolation for the data set. SplineInterpolator() SplineInterpolator
+
+ SQRT ComposableFunction
+The Math.sqrt method wrapped as a ComposableFunction sqrt() Complex
+Compute the
+
+ square root of this complex number.
+ sqrt1z() Complex
+Compute the
+
+ square root of 1 - this2 for this complex
+ number.
+ StandardDeviation org.apache.commons.math.stat.descriptive.moment Computes the sample standard deviation. StandardDeviation() StandardDeviation
+Constructs a StandardDeviation.
+ StandardDeviation(SecondMoment) StandardDeviation
+Constructs a StandardDeviation from an external second moment.
+ StandardDeviation(StandardDeviation) StandardDeviation
+Copy constructor, creates a new StandardDeviation identical
+ to the original
+ StandardDeviation(boolean) StandardDeviation
+Contructs a StandardDeviation with the specified value for the
+ isBiasCorrected property.
+ StandardDeviation(boolean, SecondMoment) StandardDeviation
+Contructs a StandardDeviation with the specified value for the
+ isBiasCorrected property and the supplied external moment.
+ start(int, int, int, int, int, int) DefaultFieldMatrixChangingVisitor
+Start visiting a matrix.
+ start(int, int, int, int, int, int) DefaultFieldMatrixPreservingVisitor
+Start visiting a matrix.
+ start(int, int, int, int, int, int) DefaultRealMatrixChangingVisitor
+Start visiting a matrix.
+ start(int, int, int, int, int, int) DefaultRealMatrixPreservingVisitor
+Start visiting a matrix.
+ start(int, int, int, int, int, int) FieldMatrixChangingVisitor
+Start visiting a matrix.
+ start(int, int, int, int, int, int) FieldMatrixPreservingVisitor
+Start visiting a matrix.
+ start(int, int, int, int, int, int) RealMatrixChangingVisitor
+Start visiting a matrix.
+ start(int, int, int, int, int, int) RealMatrixPreservingVisitor
+Start visiting a matrix.
+ start(double, double[], double) MultistepIntegrator
+Start the integration.
+ start() ResizableDoubleArray
+Returns the starting index of the internal array.
+ startIndex ResizableDoubleArray
+The position of the first addressable element in the internal storage
+ array.
+ stateVariation NordsieckStepInterpolator
+State variation.
+ StatisticalMultivariateSummary org.apache.commons.math.stat.descriptive Reporting interface for basic multivariate statistics. StatisticalSummary org.apache.commons.math.stat.descriptive Reporting interface for basic univariate statistics. StatisticalSummaryValues org.apache.commons.math.stat.descriptive Value object representing the results of a univariate statistical summary. StatisticalSummaryValues(double, double, long, double, double, double) StatisticalSummaryValues
+Constructor
+ StatUtils org.apache.commons.math.stat StatUtils provides static methods for computing statistics based on data
+ stored in double[] arrays. stepAccepted(double, double[]) CombinedEventsManager
+Inform the event handlers that the step has been accepted
+ by the integrator.
+ stepAccepted(double, double[]) EventState
+Acknowledge the fact the step has been accepted by the integrator.
+ StepHandler org.apache.commons.math.ode.sampling This interface represents a handler that should be called after
+ each successful step. stepHandlers AbstractIntegrator
+Step handler.
+ StepHandlerWithJacobians org.apache.commons.math.ode.jacobians This interface represents a handler that should be called after
+ each successful step. StepInterpolator org.apache.commons.math.ode.sampling This interface represents an interpolator over the last step
+ during an ODE integration. StepInterpolatorWithJacobians org.apache.commons.math.ode.jacobians This interface represents an interpolator over the last step
+ during an ODE integration. StepNormalizer org.apache.commons.math.ode.sampling This class wraps an object implementing FixedStepHandlerStepHandler StepNormalizer(double, FixedStepHandler) StepNormalizer
+Simple constructor.
+ stepSize AbstractIntegrator
+Current stepsize.
+ stepStart AbstractIntegrator
+Current step start time.
+ stop() CombinedEventsManager
+Check if the integration should be stopped at the end of the
+ current step.
+ STOP EventHandler
+Stop indicator.
+ stop() EventState
+Check if the integration should be stopped at the end of the
+ current step.
+ STOP EventHandlerWithJacobians
+Stop indicator.
+ StoppingCondition org.apache.commons.math.genetics Algorithm used to determine when to stop evolution. StorelessUnivariateStatistic org.apache.commons.math.stat.descriptive Extends the definition of UnivariateStatisticStorelessUnivariateStatistic.increment(double)StorelessUnivariateStatistic.incrementAll(double[]) storeTime(double) AbstractStepInterpolator
+Store the current step time.
+ subAndCheck(int, int) MathUtils
+Subtract two integers, checking for overflow.
+ subAndCheck(long, long) MathUtils
+Subtract two long integers, checking for overflow.
+ substituteMostRecentElement(double) ResizableDoubleArray
+Substitutes value for the most recently added value.
+ SUBTRACT BinaryFunction
+The - operator method wrapped as a BinaryFunction subtract(UnivariateRealFunction) ComposableFunction
+Return a function subtracting another function from the instance.
+ subtract(PolynomialFunction) PolynomialFunction
+Subtract a polynomial from the instance.
+ subtract(Complex) Complex
+Return the difference between this complex number and the given complex
+ number.
+ subtract(T) FieldElement
+Compute this - a.
+ subtract(BigInteger) BigFraction
+
+ Subtracts the value of an BigInteger subtract(int) BigFraction
+
+ Subtracts the value of an integer from the value of this one,
+ returning the result in reduced form.
+ subtract(long) BigFraction
+
+ Subtracts the value of an integer from the value of this one,
+ returning the result in reduced form.
+ subtract(BigFraction) BigFraction
+
+ Subtracts the value of another fraction from the value of this one,
+ returning the result in reduced form.
+ subtract(Fraction) Fraction
+Subtracts the value of another fraction from the value of this one,
+ returning the result in reduced form.
+ subtract(int) Fraction
+Subtract an integer from the fraction.
+ subtract(Vector3D) Vector3D
+Subtract a vector from the instance.
+ subtract(double, Vector3D) Vector3D
+Subtract a scaled vector from the instance.
+ subtract(FieldMatrix<T>) AbstractFieldMatrix
+Compute this minus m.
+ subtract(RealMatrix) AbstractRealMatrix
+Compute this minus m.
+ subtract(double[]) AbstractRealVector
+Compute this minus v.
+ subtract(RealVector) AbstractRealVector
+Compute this minus v.
+ subtract(FieldMatrix<T>) Array2DRowFieldMatrix
+Compute this minus m.
+ subtract(Array2DRowFieldMatrix<T>) Array2DRowFieldMatrix
+Compute this minus m.
+ subtract(RealMatrix) Array2DRowRealMatrix
+Compute this minus m.
+ subtract(Array2DRowRealMatrix) Array2DRowRealMatrix
+Compute this minus m.
+ subtract(FieldVector<T>) ArrayFieldVector
+Compute this minus v.
+ subtract(T[]) ArrayFieldVector
+Compute this minus v.
+ subtract(ArrayFieldVector<T>) ArrayFieldVector
+Compute this minus v.
+ subtract(RealVector) ArrayRealVector
+Compute this minus v.
+ subtract(double[]) ArrayRealVector
+Compute this minus v.
+ subtract(ArrayRealVector) ArrayRealVector
+Compute this minus v.
+ subtract(BigMatrix) BigMatrix
+Deprecated. Compute this minus m.
+subtract(BigMatrix) BigMatrixImpl
+Deprecated. Compute this minus m.
+subtract(BigMatrixImpl) BigMatrixImpl
+Deprecated. Compute this minus m.
+subtract(FieldMatrix<T>) BlockFieldMatrix
+Compute this minus m.
+ subtract(BlockFieldMatrix<T>) BlockFieldMatrix
+Compute this minus m.
+ subtract(RealMatrix) BlockRealMatrix
+Compute this minus m.
+ subtract(BlockRealMatrix) BlockRealMatrix
+Compute this minus m.
+ subtract(FieldMatrix<T>) FieldMatrix
+Compute this minus m.
+ subtract(FieldVector<T>) FieldVector
+Compute this minus v.
+ subtract(T[]) FieldVector
+Compute this minus v.
+ subtract(RealMatrix) OpenMapRealMatrix
+Compute this minus m.
+ subtract(OpenMapRealMatrix) OpenMapRealMatrix
+Compute this minus m.
+ subtract(OpenMapRealVector) OpenMapRealVector
+Optimized method to subtract OpenMapRealVectors.
+ subtract(RealVector) OpenMapRealVector
+Compute this minus v.
+ subtract(double[]) OpenMapRealVector
+Compute this minus v.
+ subtract(RealMatrix) RealMatrix
+Compute this minus m.
+ subtract(RealMatrix) RealMatrixImpl
+Deprecated. Compute this minus m.
+subtract(RealMatrixImpl) RealMatrixImpl
+Deprecated. Compute this minus m.
+subtract(RealVector) RealVector
+Compute this minus v.
+ subtract(double[]) RealVector
+Compute this minus v.
+ subtract(SparseFieldVector<T>) SparseFieldVector
+Optimized method to subtract SparseRealVectors.
+ subtract(FieldVector<T>) SparseFieldVector
+Compute this minus v.
+ subtract(T[]) SparseFieldVector
+Compute this minus v.
+ subtract(BigReal) BigReal
+Compute this - a.
+ Sum org.apache.commons.math.stat.descriptive.summary Returns the sum of the available values. Sum() Sum
+Create a Sum instance
+ Sum(Sum) Sum
+Copy constructor, creates a new Sum identical
+ to the original
+ sum SummaryStatistics
+sum of values that have been added
+ sum(double[]) StatUtils
+Returns the sum of the values in the input array, or
+ Double.NaN if the array is empty.
+ sum(double[], int, int) StatUtils
+Returns the sum of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ sumDifference(double[], double[]) StatUtils
+Returns the sum of the (signed) differences between corresponding elements of the
+ input arrays -- i.e., sum(sample1[i] - sample2[i]).
+ sumLog SummaryStatistics
+sumLog of values that have been added
+ sumLog(double[]) StatUtils
+Returns the sum of the natural logs of the entries in the input array, or
+ Double.NaN if the array is empty.
+ sumLog(double[], int, int) StatUtils
+Returns the sum of the natural logs of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ SummaryStatistics org.apache.commons.math.stat.descriptive
+ Computes summary statistics for a stream of data values added using the
+ addValue SummaryStatistics() SummaryStatistics
+Construct a SummaryStatistics instance
+ SummaryStatistics(SummaryStatistics) SummaryStatistics
+A copy constructor.
+ SumOfLogs org.apache.commons.math.stat.descriptive.summary Returns the sum of the natural logs for this collection of values. SumOfLogs() SumOfLogs
+Create a SumOfLogs instance
+ SumOfLogs(SumOfLogs) SumOfLogs
+Copy constructor, creates a new SumOfLogs identical
+ to the original
+ SumOfSquares org.apache.commons.math.stat.descriptive.summary Returns the sum of the squares of the available values. SumOfSquares() SumOfSquares
+Create a SumOfSquares instance
+ SumOfSquares(SumOfSquares) SumOfSquares
+Copy constructor, creates a new SumOfSquares identical
+ to the original
+ sumsq SummaryStatistics
+sum of the square of each value that has been added
+ sumSq(double[]) StatUtils
+Returns the sum of the squares of the entries in the input array, or
+ Double.NaN if the array is empty.
+ sumSq(double[], int, int) StatUtils
+Returns the sum of the squares of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ SynchronizedDescriptiveStatistics org.apache.commons.math.stat.descriptive Implementation of
+ DescriptiveStatistics SynchronizedDescriptiveStatistics() SynchronizedDescriptiveStatistics
+Construct an instance with infinite window
+ SynchronizedDescriptiveStatistics(int) SynchronizedDescriptiveStatistics
+Construct an instance with finite window
+ SynchronizedDescriptiveStatistics(SynchronizedDescriptiveStatistics) SynchronizedDescriptiveStatistics
+A copy constructor.
+ SynchronizedMultivariateSummaryStatistics org.apache.commons.math.stat.descriptive Implementation of
+ MultivariateSummaryStatistics SynchronizedMultivariateSummaryStatistics(int, boolean) SynchronizedMultivariateSummaryStatistics
+Construct a SynchronizedMultivariateSummaryStatistics instance
+ SynchronizedSummaryStatistics org.apache.commons.math.stat.descriptive Implementation of
+ SummaryStatistics SynchronizedSummaryStatistics() SynchronizedSummaryStatistics
+Construct a SynchronizedSummaryStatistics instance
+ SynchronizedSummaryStatistics(SynchronizedSummaryStatistics) SynchronizedSummaryStatistics
+A copy constructor.
+
+
+T
+
+t(double, double[]) TestUtils
+
+ t(double, StatisticalSummary) TestUtils
+
+ t(double[], double[]) TestUtils
+
+ t(StatisticalSummary, StatisticalSummary) TestUtils
+
+ t(double, double[]) TTest
+Computes a
+ t statistic given observed values and a comparison constant.
+ t(double, StatisticalSummary) TTest
+Computes a
+ t statistic to use in comparing the mean of the dataset described by
+ sampleStats to mu.
+ t(double[], double[]) TTest
+Computes a 2-sample t statistic, without the hypothesis of equal
+ subpopulation variances.
+ t(StatisticalSummary, StatisticalSummary) TTest
+Computes a 2-sample t statistic , comparing the means of the datasets
+ described by two StatisticalSummary t(double, double[]) TTestImpl
+Computes a
+ t statistic given observed values and a comparison constant.
+ t(double, StatisticalSummary) TTestImpl
+Computes a
+ t statistic to use in comparing the mean of the dataset described by
+ sampleStats to mu.
+ t(double[], double[]) TTestImpl
+Computes a 2-sample t statistic, without the hypothesis of equal
+ subpopulation variances.
+ t(StatisticalSummary, StatisticalSummary) TTestImpl
+Computes a 2-sample t statistic , comparing the means of the datasets
+ described by two StatisticalSummary t(double, double, double, double) TTestImpl
+Computes t test statistic for 1-sample t-test.
+ t(double, double, double, double, double, double) TTestImpl
+Computes t test statistic for 2-sample t-test.
+ TAN ComposableFunction
+The Math.tan method wrapped as a ComposableFunction tan() Complex
+Compute the
+
+ tangent of this complex number.
+ TANH ComposableFunction
+The Math.tanh method wrapped as a ComposableFunction tanh() Complex
+Compute the
+
+ hyperbolic tangent of this complex number.
+ targetValues AbstractLeastSquaresOptimizer
+Target value for the objective functions at optimum.
+ TDistribution org.apache.commons.math.distribution Student's t-Distribution. TDistributionImpl org.apache.commons.math.distribution Default implementation of
+ TDistribution TDistributionImpl(double, double) TDistributionImpl
+Create a t distribution using the given degrees of freedom and the
+ specified inverse cumulative probability absolute accuracy.
+ TDistributionImpl(double) TDistributionImpl
+Create a t distribution using the given degrees of freedom.
+ test(double[], int, int) AbstractUnivariateStatistic
+This method is used by evaluate(double[], int, int) methods
+ to verify that the input parameters designate a subarray of positive length.
+ test(double[], double[], int, int) AbstractUnivariateStatistic
+This method is used by evaluate(double[], double[], int, int) methods
+ to verify that the begin and length parameters designate a subarray of positive length
+ and the weights are all non-negative, non-NaN, finite, and not all zero.
+ TestUtils org.apache.commons.math.stat.inference A collection of static methods to create inference test instances or to
+ perform inference tests. TestUtils() TestUtils
+Prevent instantiation.
+ ThirdMoment org.apache.commons.math.stat.descriptive.moment Computes a statistic related to the Third Central Moment. ThirdMoment() ThirdMoment
+Create a FourthMoment instance
+ ThirdMoment(ThirdMoment) ThirdMoment
+Copy constructor, creates a new ThirdMoment identical
+ to the original
+ THREE_FIFTHS BigFraction
+A fraction representing "3/5".
+ THREE_FIFTHS Fraction
+A fraction representing "3/5".
+ THREE_QUARTERS BigFraction
+A fraction representing "3/4".
+ THREE_QUARTERS Fraction
+A fraction representing "3/4".
+ ThreeEighthesIntegrator org.apache.commons.math.ode.nonstiff This class implements the 3/8 fourth order Runge-Kutta
+ integrator for Ordinary Differential Equations. ThreeEighthesIntegrator(double) ThreeEighthesIntegrator
+Simple constructor.
+ TiesStrategy org.apache.commons.math.stat.ranking Strategies for handling tied values in rank transformations. toArray() AbstractRealVector
+Convert the vector to a double array.
+ toArray() ArrayFieldVector
+Convert the vector to a T array.
+ toArray() ArrayRealVector
+Convert the vector to a double array.
+ toArray() FieldVector
+Convert the vector to a T array.
+ toArray() OpenMapRealVector
+Convert the vector to a double array.
+ toArray() RealVector
+Convert the vector to a double array.
+ toArray() SparseFieldVector
+Convert the vector to a T array.
+ toBlocksLayout(T[][]) BlockFieldMatrix
+Convert a data array from raw layout to blocks layout.
+ toBlocksLayout(double[][]) BlockRealMatrix
+Convert a data array from raw layout to blocks layout.
+ toString() PolynomialFunction
+Returns a string representation of the polynomial.
+ toString() BigFraction
+
+ Returns the String representing this fraction, ie
+ "num / dem" or just "num" if the denominator is one.
+ toString() Fraction
+
+ Returns the String representing this fraction, ie
+ "num / dem" or just "num" if the denominator is one.
+ toString() AbstractListChromosome
+
+ toString() ChromosomePair
+
+ toString() ListPopulation
+
+ toString() RandomKey
+
+ toString() RotationOrder
+Get a string representation of the instance.
+ toString() Vector3D
+Get a string representation of this vector.
+ toString() AbstractFieldMatrix
+Get a string representation for this matrix.
+ toString() AbstractRealMatrix
+Get a string representation for this matrix.
+ toString() ArrayRealVector
+
+ toString() BigMatrixImpl
+Deprecated. Get a string representation for this matrix.
+toString() Relationship
+
+ toString() EuclideanIntegerPoint
+
+ toString() DescriptiveStatistics
+Generates a text report displaying univariate statistics from values
+ that have been added.
+ toString() MultivariateSummaryStatistics
+Generates a text report displaying
+ summary statistics from values that
+ have been added.
+ toString() SummaryStatistics
+Generates a text report displaying summary statistics from values that
+ have been added.
+ toString() SynchronizedDescriptiveStatistics
+Generates a text report displaying univariate statistics from values
+ that have been added.
+ toString() SynchronizedMultivariateSummaryStatistics
+Generates a text report displaying
+ summary statistics from values that
+ have been added.
+ toString() SynchronizedSummaryStatistics
+Generates a text report displaying summary statistics from values that
+ have been added.
+ toString() Frequency
+Return a string representation of this frequency
+ distribution.
+ TournamentSelection org.apache.commons.math.genetics Tournament selection scheme. TournamentSelection(int) TournamentSelection
+Creates a new TournamentSelection instance.
+ transform(double[]) FastCosineTransformer
+Transform the given real data set.
+ transform(UnivariateRealFunction, double, double, int) FastCosineTransformer
+Transform the given real function, sampled on the given interval.
+ transform(double[]) FastFourierTransformer
+Transform the given real data set.
+ transform(UnivariateRealFunction, double, double, int) FastFourierTransformer
+Transform the given real function, sampled on the given interval.
+ transform(Complex[]) FastFourierTransformer
+Transform the given complex data set.
+ transform(double[]) FastHadamardTransformer
+Transform the given real data set.
+ transform(UnivariateRealFunction, double, double, int) FastHadamardTransformer
+Transform the given real function, sampled on the given interval.
+ transform(int[]) FastHadamardTransformer
+Transform the given real data set.
+ transform(double[]) FastSineTransformer
+Transform the given real data set.
+ transform(UnivariateRealFunction, double, double, int) FastSineTransformer
+Transform the given real function, sampled on the given interval.
+ transform(double[]) RealTransformer
+Transform the given real data set.
+ transform(UnivariateRealFunction, double, double, int) RealTransformer
+Transform the given real function, sampled on the given interval.
+ transform(Object) DefaultTransformer
+
+ transform(Object) NumberTransformer
+Implementing this interface provides a facility to transform
+ from Object to Double.
+ transform(Object) TransformerMap
+Attempts to transform the Object against the map of
+ NumberTransformers.
+ transform2(double[]) FastCosineTransformer
+Transform the given real data set.
+ transform2(UnivariateRealFunction, double, double, int) FastCosineTransformer
+Transform the given real function, sampled on the given interval.
+ transform2(double[]) FastFourierTransformer
+Transform the given real data set.
+ transform2(UnivariateRealFunction, double, double, int) FastFourierTransformer
+Transform the given real function, sampled on the given interval.
+ transform2(Complex[]) FastFourierTransformer
+Transform the given complex data set.
+ transform2(double[]) FastSineTransformer
+Transform the given real data set.
+ transform2(UnivariateRealFunction, double, double, int) FastSineTransformer
+Transform the given real function, sampled on the given interval.
+ TransformerMap org.apache.commons.math.util This TansformerMap automates the transformation of mixed object types. TransformerMap() TransformerMap
+Build a map containing only the default transformer.
+ transformers() TransformerMap
+Returns the Set of NumberTransformers used as values
+ in the map.
+ transpose() AbstractFieldMatrix
+Returns the transpose of this matrix.
+ transpose() AbstractRealMatrix
+Returns the transpose of this matrix.
+ transpose() BigMatrix
+Deprecated. Returns the transpose of this matrix.
+transpose() BigMatrixImpl
+Deprecated. Returns the transpose matrix.
+transpose() BlockFieldMatrix
+Returns the transpose of this matrix.
+ transpose() BlockRealMatrix
+Returns the transpose of this matrix.
+ transpose() FieldMatrix
+Returns the transpose of this matrix.
+ transpose() RealMatrix
+Returns the transpose of this matrix.
+ TrapezoidIntegrator org.apache.commons.math.analysis.integration Implements the
+ Trapezoidal Rule for integration of real univariate functions. TrapezoidIntegrator(UnivariateRealFunction) TrapezoidIntegrator
+Deprecated. as of 2.0 the integrand function is passed as an argument
+ to the TrapezoidIntegrator.integrate(UnivariateRealFunction, double, double)
+TrapezoidIntegrator() TrapezoidIntegrator
+Construct an integrator.
+ trigamma(double) Gamma
+Computes the trigamma function of x.
+ tTest(double, double[], double) TestUtils
+
+ tTest(double, double[]) TestUtils
+
+ tTest(double, StatisticalSummary, double) TestUtils
+
+ tTest(double, StatisticalSummary) TestUtils
+
+ tTest(double[], double[], double) TestUtils
+
+ tTest(double[], double[]) TestUtils
+
+ tTest(StatisticalSummary, StatisticalSummary, double) TestUtils
+
+ tTest(StatisticalSummary, StatisticalSummary) TestUtils
+
+ TTest org.apache.commons.math.stat.inference An interface for Student's t-tests. tTest(double, double[]) TTest
+Returns the observed significance level , or
+ p-value , associated with a one-sample, two-tailed t-test
+ comparing the mean of the input array with the constant mu.
+ tTest(double, double[], double) TTest
+Performs a
+ two-sided t-test evaluating the null hypothesis that the mean of the population from
+ which sample is drawn equals mu.
+ tTest(double, StatisticalSummary) TTest
+Returns the observed significance level , or
+ p-value , associated with a one-sample, two-tailed t-test
+ comparing the mean of the dataset described by sampleStats
+ with the constant mu.
+ tTest(double, StatisticalSummary, double) TTest
+Performs a
+ two-sided t-test evaluating the null hypothesis that the mean of the
+ population from which the dataset described by stats is
+ drawn equals mu.
+ tTest(double[], double[]) TTest
+Returns the observed significance level , or
+ p-value , associated with a two-sample, two-tailed t-test
+ comparing the means of the input arrays.
+ tTest(double[], double[], double) TTest
+Performs a
+
+ two-sided t-test evaluating the null hypothesis that sample1
+ and sample2 are drawn from populations with the same mean,
+ with significance level alpha.
+ tTest(StatisticalSummary, StatisticalSummary) TTest
+Returns the observed significance level , or
+ p-value , associated with a two-sample, two-tailed t-test
+ comparing the means of the datasets described by two StatisticalSummary
+ instances.
+ tTest(StatisticalSummary, StatisticalSummary, double) TTest
+Performs a
+
+ two-sided t-test evaluating the null hypothesis that
+ sampleStats1 and sampleStats2 describe
+ datasets drawn from populations with the same mean, with significance
+ level alpha.
+ tTest(double, double[]) TTestImpl
+Returns the observed significance level , or
+ p-value , associated with a one-sample, two-tailed t-test
+ comparing the mean of the input array with the constant mu.
+ tTest(double, double[], double) TTestImpl
+Performs a
+ two-sided t-test evaluating the null hypothesis that the mean of the population from
+ which sample is drawn equals mu.
+ tTest(double, StatisticalSummary) TTestImpl
+Returns the observed significance level , or
+ p-value , associated with a one-sample, two-tailed t-test
+ comparing the mean of the dataset described by sampleStats
+ with the constant mu.
+ tTest(double, StatisticalSummary, double) TTestImpl
+Performs a
+ two-sided t-test evaluating the null hypothesis that the mean of the
+ population from which the dataset described by stats is
+ drawn equals mu.
+ tTest(double[], double[]) TTestImpl
+Returns the observed significance level , or
+ p-value , associated with a two-sample, two-tailed t-test
+ comparing the means of the input arrays.
+ tTest(double[], double[], double) TTestImpl
+Performs a
+
+ two-sided t-test evaluating the null hypothesis that sample1
+ and sample2 are drawn from populations with the same mean,
+ with significance level alpha.
+ tTest(StatisticalSummary, StatisticalSummary) TTestImpl
+Returns the observed significance level , or
+ p-value , associated with a two-sample, two-tailed t-test
+ comparing the means of the datasets described by two StatisticalSummary
+ instances.
+ tTest(StatisticalSummary, StatisticalSummary, double) TTestImpl
+Performs a
+
+ two-sided t-test evaluating the null hypothesis that
+ sampleStats1 and sampleStats2 describe
+ datasets drawn from populations with the same mean, with significance
+ level alpha.
+ tTest(double, double, double, double) TTestImpl
+Computes p-value for 2-sided, 1-sample t-test.
+ tTest(double, double, double, double, double, double) TTestImpl
+Computes p-value for 2-sided, 2-sample t-test.
+ TTestImpl org.apache.commons.math.stat.inference Implements t-test statistics defined in the TTest TTestImpl() TTestImpl
+Default constructor.
+ TTestImpl(TDistribution) TTestImpl
+Create a test instance using the given distribution for computing
+ inference statistics.
+ TWO BigFraction
+A fraction representing "2 / 1".
+ TWO Fraction
+A fraction representing "2 / 1".
+ TWO_FIFTHS BigFraction
+A fraction representing "2/5".
+ TWO_FIFTHS Fraction
+A fraction representing "2/5".
+ TWO_PI MathUtils
+2 π.
+ TWO_QUARTERS BigFraction
+A fraction representing "2/4".
+ TWO_QUARTERS Fraction
+A fraction representing "2/4".
+ TWO_THIRDS BigFraction
+A fraction representing "2/3".
+ TWO_THIRDS Fraction
+A fraction representing "2/3".
+
+
+U
+
+ULP ComposableFunction
+The Math.ulp method wrapped as a ComposableFunction UnboundedSolutionException org.apache.commons.math.optimization.linear This class represents exceptions thrown by optimizers when a solution
+ escapes to infinity. UnboundedSolutionException() UnboundedSolutionException
+Simple constructor using a default message.
+ UncorrelatedRandomVectorGenerator org.apache.commons.math.random A RandomVectorGenerator UncorrelatedRandomVectorGenerator(double[], double[], NormalizedRandomGenerator) UncorrelatedRandomVectorGenerator
+Simple constructor.
+ UncorrelatedRandomVectorGenerator(int, NormalizedRandomGenerator) UncorrelatedRandomVectorGenerator
+Simple constructor.
+ UNIFORM_MODE ValueServer
+Uniform random deviates with mean = μ.
+ UniformRandomGenerator org.apache.commons.math.random This class implements a normalized uniform random generator. UniformRandomGenerator(RandomGenerator) UniformRandomGenerator
+Create a new generator.
+ unitize() AbstractRealVector
+Converts this vector into a unit vector.
+ unitize() ArrayRealVector
+Converts this vector into a unit vector.
+ unitize() OpenMapRealVector
+Converts this vector into a unit vector.
+ unitize() RealVector
+Converts this vector into a unit vector.
+ UnitSphereRandomVectorGenerator org.apache.commons.math.random Generate random vectors isotropically located on the surface of a sphere. UnitSphereRandomVectorGenerator(int, RandomGenerator) UnitSphereRandomVectorGenerator
+
+ UnitSphereRandomVectorGenerator(int) UnitSphereRandomVectorGenerator
+Create an object that will use a default RNG (MersenneTwister unitVector() AbstractRealVector
+Creates a unit vector pointing in the direction of this vector.
+ unitVector() ArrayRealVector
+Creates a unit vector pointing in the direction of this vector.
+ unitVector() OpenMapRealVector
+Creates a unit vector pointing in the direction of this vector.
+ unitVector() RealVector
+Creates a unit vector pointing in the direction of this vector.
+ UnivariateMatrixFunction org.apache.commons.math.analysis An interface representing a univariate matrix function. UnivariateRealFunction org.apache.commons.math.analysis An interface representing a univariate real function. UnivariateRealIntegrator org.apache.commons.math.analysis.integration Interface for univariate real integration algorithms. UnivariateRealIntegratorImpl org.apache.commons.math.analysis.integration Provide a default implementation for several generic functions. UnivariateRealIntegratorImpl(UnivariateRealFunction, int) UnivariateRealIntegratorImpl
+Deprecated. as of 2.0 the integrand function is passed as an argument
+ to the UnivariateRealIntegrator.integrate(UnivariateRealFunction, double, double)
+UnivariateRealIntegratorImpl(int) UnivariateRealIntegratorImpl
+Construct an integrator with given iteration count and accuracy.
+ UnivariateRealInterpolator org.apache.commons.math.analysis.interpolation Interface representing a univariate real interpolating function. UnivariateRealOptimizer org.apache.commons.math.optimization Interface for (univariate real) optimization algorithms. UnivariateRealSolver org.apache.commons.math.analysis.solvers Interface for (univariate real) rootfinding algorithms. UnivariateRealSolverFactory org.apache.commons.math.analysis.solvers Abstract factory class used to create UnivariateRealSolver UnivariateRealSolverFactory() UnivariateRealSolverFactory
+Default constructor.
+ UnivariateRealSolverFactoryImpl org.apache.commons.math.analysis.solvers A concrete UnivariateRealSolverFactory UnivariateRealSolverFactoryImpl() UnivariateRealSolverFactoryImpl
+Default constructor.
+ UnivariateRealSolverImpl org.apache.commons.math.analysis.solvers Provide a default implementation for several functions useful to generic
+ solvers. UnivariateRealSolverImpl(UnivariateRealFunction, int, double) UnivariateRealSolverImpl
+Deprecated. as of 2.0 the function to solve is passed as an argument
+ to the UnivariateRealSolver.solve(UnivariateRealFunction, double, double)UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
+UnivariateRealSolverImpl(int, double) UnivariateRealSolverImpl
+Construct a solver with given iteration count and accuracy.
+ UnivariateRealSolverUtils org.apache.commons.math.analysis.solvers Utility routines for UnivariateRealSolver UnivariateStatistic org.apache.commons.math.stat.descriptive Base interface implemented by all statistics. UnivariateVectorialFunction org.apache.commons.math.analysis An interface representing a univariate vectorial function. UnknownDistributionChiSquareTest org.apache.commons.math.stat.inference An interface for Chi-Square tests for unknown distributions. updateHighOrderDerivativesPhase1(Array2DRowRealMatrix) AdamsIntegrator
+Update the high order scaled derivatives for Adams integrators (phase 1).
+ updateHighOrderDerivativesPhase1(Array2DRowRealMatrix) AdamsNordsieckTransformer
+Update the high order scaled derivatives for Adams integrators (phase 1).
+ updateHighOrderDerivativesPhase2(double[], double[], Array2DRowRealMatrix) AdamsIntegrator
+Update the high order scaled derivatives Adams integrators (phase 2).
+ updateHighOrderDerivativesPhase2(double[], double[], Array2DRowRealMatrix) AdamsNordsieckTransformer
+Update the high order scaled derivatives Adams integrators (phase 2).
+ updateJacobian() AbstractEstimator
+Deprecated. Update the jacobian matrix.
+updateJacobian() AbstractLeastSquaresOptimizer
+Update the jacobian matrix.
+ updateResidualsAndCost() AbstractEstimator
+Deprecated. Update the residuals array and cost function value.
+updateResidualsAndCost() AbstractLeastSquaresOptimizer
+Update the residuals array and cost function value.
+ upperCumulativeProbability(int) HypergeometricDistributionImpl
+For this distribution, X, this method returns P(X ≥ x).
+ UPSIDE_VARIANCE SemiVariance
+The UPSIDE Direction is used to specify that the observations above the
+ cutoff point will be used to calculate SemiVariance.
+
+
+V
+
+validateCovarianceData(double[][], double[][]) AbstractMultipleLinearRegression
+Validates sample data.
+ validateSampleData(double[][], double[]) AbstractMultipleLinearRegression
+Validates sample data.
+ value(double, double) BinaryFunction
+Compute the value for the function.
+ value(double, double) BivariateRealFunction
+Compute the value for the function.
+ value(double) ComposableFunction
+Compute the value for the function.
+ value(double, double) BicubicSplineInterpolatingFunction
+Compute the value for the function.
+ value(double[]) MicrosphereInterpolatingFunction
+
+ value(double[]) MultivariateMatrixFunction
+Compute the value for the function at the given point.
+ value(double[]) MultivariateRealFunction
+Compute the value for the function at the given point.
+ value(double[]) MultivariateVectorialFunction
+Compute the value for the function at the given point.
+ value(double) PolynomialFunction
+Compute the value of the function for the given argument.
+ value(double) PolynomialFunctionLagrangeForm
+Calculate the function value at the given point.
+ value(double) PolynomialFunctionNewtonForm
+Calculate the function value at the given point.
+ value(double) PolynomialSplineFunction
+Compute the value for the function.
+ value(double) UnivariateMatrixFunction
+Compute the value for the function.
+ value(double) UnivariateRealFunction
+Compute the value for the function.
+ value(double) UnivariateVectorialFunction
+Compute the value for the function.
+ value(double) HarmonicFunction
+Compute the value for the function.
+ value(double, double[]) ParametricRealFunction
+Compute the value of the function.
+ value(double[]) LeastSquaresConverter
+Compute the value for the function at the given point.
+ value() OpenIntToDoubleHashMap.Iterator
+Get the value of current entry.
+ value() OpenIntToFieldHashMap.Iterator
+Get the value of current entry.
+ valueOf(String) ConjugateGradientFormula
+Returns the enum constant of this type with the specified name.
+ valueOf(String) GoalType
+Returns the enum constant of this type with the specified name.
+ valueOf(String) Relationship
+Returns the enum constant of this type with the specified name.
+ valueOf(String) SemiVariance.Direction
+Returns the enum constant of this type with the specified name.
+ valueOf(String) NaNStrategy
+Returns the enum constant of this type with the specified name.
+ valueOf(String) TiesStrategy
+Returns the enum constant of this type with the specified name.
+ values() ConjugateGradientFormula
+Returns an array containing the constants of this enum type, in
+the order they are declared.
+ values() GoalType
+Returns an array containing the constants of this enum type, in
+the order they are declared.
+ values() Relationship
+Returns an array containing the constants of this enum type, in
+the order they are declared.
+ values() SemiVariance.Direction
+Returns an array containing the constants of this enum type, in
+the order they are declared.
+ values() NaNStrategy
+Returns an array containing the constants of this enum type, in
+the order they are declared.
+ values() TiesStrategy
+Returns an array containing the constants of this enum type, in
+the order they are declared.
+ ValueServer org.apache.commons.math.random Generates values for use in simulation applications. ValueServer() ValueServer
+Creates new ValueServer
+ ValueServer(RandomData) ValueServer
+Construct a ValueServer instance using a RandomData as its source
+ of random data.
+ valuesIterator() Frequency
+Returns an Iterator over the set of values that have been added.
+ Variance org.apache.commons.math.stat.descriptive.moment Computes the variance of the available values. Variance() Variance
+Constructs a Variance with default (true) isBiasCorrected
+ property.
+ Variance(SecondMoment) Variance
+Constructs a Variance based on an external second moment.
+ Variance(boolean) Variance
+Constructs a Variance with the specified isBiasCorrected
+ property
+ Variance(boolean, SecondMoment) Variance
+Constructs a Variance with the specified isBiasCorrected
+ property and the supplied external second moment.
+ Variance(Variance) Variance
+Copy constructor, creates a new Variance identical
+ to the original
+ variance SummaryStatistics
+variance of values that have been added
+ variance(double[]) StatUtils
+Returns the variance of the entries in the input array, or
+ Double.NaN if the array is empty.
+ variance(double[], int, int) StatUtils
+Returns the variance of the entries in the specified portion of
+ the input array, or Double.NaN if the designated subarray
+ is empty.
+ variance(double[], double, int, int) StatUtils
+Returns the variance of the entries in the specified portion of
+ the input array, using the precomputed mean value.
+ variance(double[], double) StatUtils
+Returns the variance of the entries in the input array, using the
+ precomputed mean value.
+ varianceDifference(double[], double[], double) StatUtils
+Returns the variance of the (signed) differences between corresponding elements of the
+ input arrays -- i.e., var(sample1[i] - sample2[i]).
+ vecAbsoluteTolerance AdaptiveStepsizeIntegrator
+Allowed absolute vectorial error.
+ vecRelativeTolerance AdaptiveStepsizeIntegrator
+Allowed relative vectorial error.
+ Vector3D org.apache.commons.math.geometry This class implements vectors in a three-dimensional space. Vector3D(double, double, double) Vector3D
+Simple constructor.
+ Vector3D(double, double) Vector3D
+Simple constructor.
+ Vector3D(double, Vector3D) Vector3D
+Multiplicative constructor
+ Build a vector from another one and a scale factor.
+ Vector3D(double, Vector3D, double, Vector3D) Vector3D
+Linear constructor
+ Build a vector from two other ones and corresponding scale factors.
+ Vector3D(double, Vector3D, double, Vector3D, double, Vector3D) Vector3D
+Linear constructor
+ Build a vector from three other ones and corresponding scale factors.
+ Vector3D(double, Vector3D, double, Vector3D, double, Vector3D, double, Vector3D) Vector3D
+Linear constructor
+ Build a vector from four other ones and corresponding scale factors.
+ Vector3DFormat org.apache.commons.math.geometry Formats a 3D vector in components list format "{x; y; z}". Vector3DFormat() Vector3DFormat
+Create an instance with default settings.
+ Vector3DFormat(NumberFormat) Vector3DFormat
+Create an instance with a custom number format for components.
+ Vector3DFormat(String, String, String) Vector3DFormat
+Create an instance with custom prefix, suffix and separator.
+ Vector3DFormat(String, String, String, NumberFormat) Vector3DFormat
+Create an instance with custom prefix, suffix, separator and format
+ for components.
+ VectorialConvergenceChecker org.apache.commons.math.optimization This interface specifies how to check if a optimization algorithm VectorialCovariance org.apache.commons.math.stat.descriptive.moment Returns the covariance matrix of the available vectors. VectorialCovariance(int, boolean) VectorialCovariance
+Constructs a VectorialCovariance.
+ VectorialMean org.apache.commons.math.stat.descriptive.moment Returns the arithmetic mean of the available vectors. VectorialMean(int) VectorialMean
+Constructs a VectorialMean.
+ VectorialPointValuePair org.apache.commons.math.optimization This class holds a point and the vectorial value of an objective function at this point. VectorialPointValuePair(double[], double[]) VectorialPointValuePair
+Build a point/objective function value pair.
+ VectorialPointValuePair(double[], double[], boolean) VectorialPointValuePair
+Build a point/objective function value pair.
+ verifyBracketing(double, double, UnivariateRealFunction) UnivariateRealSolverImpl
+Verifies that the endpoints specify an interval and the function takes
+ opposite signs at the enpoints, throws IllegalArgumentException if not
+ verifyDataSet(double[]) FastFourierTransformer
+Verifies that the data set has length of power of 2.
+ verifyDataSet(Object[]) FastFourierTransformer
+Verifies that the data set has length of power of 2.
+ verifyInputArray(double[], double[]) PolynomialFunctionNewtonForm
+Verifies that the input arrays are valid.
+ verifyInterpolationArray(double[], double[]) PolynomialFunctionLagrangeForm
+Verifies that the interpolation arrays are valid.
+ verifyInterval(double, double) UnivariateRealIntegratorImpl
+Verifies that the endpoints specify an interval.
+ verifyInterval(double, double) UnivariateRealSolverImpl
+Verifies that the endpoints specify an interval,
+ throws IllegalArgumentException if not
+ verifyInterval(double, double) FastFourierTransformer
+Verifies that the endpoints specify an interval.
+ verifyIterationCount() RombergIntegrator
+Verifies that the upper and lower limits of iterations are valid.
+ verifyIterationCount() SimpsonIntegrator
+Verifies that the upper and lower limits of iterations are valid.
+ verifyIterationCount() TrapezoidIntegrator
+Verifies that the upper and lower limits of iterations are valid.
+ verifyIterationCount() UnivariateRealIntegratorImpl
+Verifies that the upper and lower limits of iterations are valid.
+ verifySequence(double, double, double) UnivariateRealSolverImpl
+Verifies that lower < initial < upper
+ throws IllegalArgumentException if not
+ visit(int, int, T) DefaultFieldMatrixChangingVisitor
+Visit one matrix entry.
+ visit(int, int, T) DefaultFieldMatrixPreservingVisitor
+Visit one matrix entry.
+ visit(int, int, double) DefaultRealMatrixChangingVisitor
+Visit one matrix entry.
+ visit(int, int, double) DefaultRealMatrixPreservingVisitor
+Visit one matrix entry.
+ visit(int, int, T) FieldMatrixChangingVisitor
+Visit one matrix entry.
+ visit(int, int, T) FieldMatrixPreservingVisitor
+Visit one matrix entry.
+ visit(int, int, double) RealMatrixChangingVisitor
+Visit one matrix entry.
+ visit(int, int, double) RealMatrixPreservingVisitor
+Visit one matrix entry.
+
+
+W
+
+walkInColumnOrder(FieldMatrixChangingVisitor<T>) AbstractFieldMatrix
+Visit (and possibly change) all matrix entries in column order.
+ walkInColumnOrder(FieldMatrixPreservingVisitor<T>) AbstractFieldMatrix
+Visit (but don't change) all matrix entries in column order.
+ walkInColumnOrder(FieldMatrixChangingVisitor<T>, int, int, int, int) AbstractFieldMatrix
+Visit (and possibly change) some matrix entries in column order.
+ walkInColumnOrder(FieldMatrixPreservingVisitor<T>, int, int, int, int) AbstractFieldMatrix
+Visit (but don't change) some matrix entries in column order.
+ walkInColumnOrder(RealMatrixChangingVisitor) AbstractRealMatrix
+Visit (and possibly change) all matrix entries in column order.
+ walkInColumnOrder(RealMatrixPreservingVisitor) AbstractRealMatrix
+Visit (but don't change) all matrix entries in column order.
+ walkInColumnOrder(RealMatrixChangingVisitor, int, int, int, int) AbstractRealMatrix
+Visit (and possibly change) some matrix entries in column order.
+ walkInColumnOrder(RealMatrixPreservingVisitor, int, int, int, int) AbstractRealMatrix
+Visit (but don't change) some matrix entries in column order.
+ walkInColumnOrder(FieldMatrixChangingVisitor<T>) Array2DRowFieldMatrix
+Visit (and possibly change) all matrix entries in column order.
+ walkInColumnOrder(FieldMatrixPreservingVisitor<T>) Array2DRowFieldMatrix
+Visit (but don't change) all matrix entries in column order.
+ walkInColumnOrder(FieldMatrixChangingVisitor<T>, int, int, int, int) Array2DRowFieldMatrix
+Visit (and possibly change) some matrix entries in column order.
+ walkInColumnOrder(FieldMatrixPreservingVisitor<T>, int, int, int, int) Array2DRowFieldMatrix
+Visit (but don't change) some matrix entries in column order.
+ walkInColumnOrder(RealMatrixChangingVisitor) Array2DRowRealMatrix
+Visit (and possibly change) all matrix entries in column order.
+ walkInColumnOrder(RealMatrixPreservingVisitor) Array2DRowRealMatrix
+Visit (but don't change) all matrix entries in column order.
+ walkInColumnOrder(RealMatrixChangingVisitor, int, int, int, int) Array2DRowRealMatrix
+Visit (and possibly change) some matrix entries in column order.
+ walkInColumnOrder(RealMatrixPreservingVisitor, int, int, int, int) Array2DRowRealMatrix
+Visit (but don't change) some matrix entries in column order.
+ walkInColumnOrder(FieldMatrixChangingVisitor<T>) FieldMatrix
+Visit (and possibly change) all matrix entries in column order.
+ walkInColumnOrder(FieldMatrixPreservingVisitor<T>) FieldMatrix
+Visit (but don't change) all matrix entries in column order.
+ walkInColumnOrder(FieldMatrixChangingVisitor<T>, int, int, int, int) FieldMatrix
+Visit (and possibly change) some matrix entries in column order.
+ walkInColumnOrder(FieldMatrixPreservingVisitor<T>, int, int, int, int) FieldMatrix
+Visit (but don't change) some matrix entries in column order.
+ walkInColumnOrder(RealMatrixChangingVisitor) RealMatrix
+Visit (and possibly change) all matrix entries in column order.
+ walkInColumnOrder(RealMatrixPreservingVisitor) RealMatrix
+Visit (but don't change) all matrix entries in column order.
+ walkInColumnOrder(RealMatrixChangingVisitor, int, int, int, int) RealMatrix
+Visit (and possibly change) some matrix entries in column order.
+ walkInColumnOrder(RealMatrixPreservingVisitor, int, int, int, int) RealMatrix
+Visit (but don't change) some matrix entries in column order.
+ walkInColumnOrder(RealMatrixChangingVisitor) RealMatrixImpl
+Deprecated. Visit (and possibly change) all matrix entries in column order.
+walkInColumnOrder(RealMatrixPreservingVisitor) RealMatrixImpl
+Deprecated. Visit (but don't change) all matrix entries in column order.
+walkInColumnOrder(RealMatrixChangingVisitor, int, int, int, int) RealMatrixImpl
+Deprecated. Visit (and possibly change) some matrix entries in column order.
+walkInColumnOrder(RealMatrixPreservingVisitor, int, int, int, int) RealMatrixImpl
+Deprecated. Visit (but don't change) some matrix entries in column order.
+walkInOptimizedOrder(FieldMatrixChangingVisitor<T>) AbstractFieldMatrix
+Visit (and possibly change) all matrix entries using the fastest possible order.
+ walkInOptimizedOrder(FieldMatrixPreservingVisitor<T>) AbstractFieldMatrix
+Visit (but don't change) all matrix entries using the fastest possible order.
+ walkInOptimizedOrder(FieldMatrixChangingVisitor<T>, int, int, int, int) AbstractFieldMatrix
+Visit (and possibly change) some matrix entries using the fastest possible order.
+ walkInOptimizedOrder(FieldMatrixPreservingVisitor<T>, int, int, int, int) AbstractFieldMatrix
+Visit (but don't change) some matrix entries using the fastest possible order.
+ walkInOptimizedOrder(RealMatrixChangingVisitor) AbstractRealMatrix
+Visit (and possibly change) all matrix entries using the fastest possible order.
+ walkInOptimizedOrder(RealMatrixPreservingVisitor) AbstractRealMatrix
+Visit (but don't change) all matrix entries using the fastest possible order.
+ walkInOptimizedOrder(RealMatrixChangingVisitor, int, int, int, int) AbstractRealMatrix
+Visit (and possibly change) some matrix entries using the fastest possible order.
+ walkInOptimizedOrder(RealMatrixPreservingVisitor, int, int, int, int) AbstractRealMatrix
+Visit (but don't change) some matrix entries using the fastest possible order.
+ walkInOptimizedOrder(FieldMatrixChangingVisitor<T>) BlockFieldMatrix
+Visit (and possibly change) all matrix entries using the fastest possible order.
+ walkInOptimizedOrder(FieldMatrixPreservingVisitor<T>) BlockFieldMatrix
+Visit (but don't change) all matrix entries using the fastest possible order.
+ walkInOptimizedOrder(FieldMatrixChangingVisitor<T>, int, int, int, int) BlockFieldMatrix
+Visit (and possibly change) some matrix entries using the fastest possible order.
+ walkInOptimizedOrder(FieldMatrixPreservingVisitor<T>, int, int, int, int) BlockFieldMatrix
+Visit (but don't change) some matrix entries using the fastest possible order.
+ walkInOptimizedOrder(RealMatrixChangingVisitor) BlockRealMatrix
+Visit (and possibly change) all matrix entries using the fastest possible order.
+ walkInOptimizedOrder(RealMatrixPreservingVisitor) BlockRealMatrix
+Visit (but don't change) all matrix entries using the fastest possible order.
+ walkInOptimizedOrder(RealMatrixChangingVisitor, int, int, int, int) BlockRealMatrix
+Visit (and possibly change) some matrix entries using the fastest possible order.
+ walkInOptimizedOrder(RealMatrixPreservingVisitor, int, int, int, int) BlockRealMatrix
+Visit (but don't change) some matrix entries using the fastest possible order.
+ walkInOptimizedOrder(FieldMatrixChangingVisitor<T>) FieldMatrix
+Visit (and possibly change) all matrix entries using the fastest possible order.
+ walkInOptimizedOrder(FieldMatrixPreservingVisitor<T>) FieldMatrix
+Visit (but don't change) all matrix entries using the fastest possible order.
+ walkInOptimizedOrder(FieldMatrixChangingVisitor<T>, int, int, int, int) FieldMatrix
+Visit (and possibly change) some matrix entries using the fastest possible order.
+ walkInOptimizedOrder(FieldMatrixPreservingVisitor<T>, int, int, int, int) FieldMatrix
+Visit (but don't change) some matrix entries using the fastest possible order.
+ walkInOptimizedOrder(RealMatrixChangingVisitor) RealMatrix
+Visit (and possibly change) all matrix entries using the fastest possible order.
+ walkInOptimizedOrder(RealMatrixPreservingVisitor) RealMatrix
+Visit (but don't change) all matrix entries using the fastest possible order.
+ walkInOptimizedOrder(RealMatrixChangingVisitor, int, int, int, int) RealMatrix
+Visit (and possibly change) some matrix entries using the fastest possible order.
+ walkInOptimizedOrder(RealMatrixPreservingVisitor, int, int, int, int) RealMatrix
+Visit (but don't change) some matrix entries using the fastest possible order.
+ walkInRowOrder(FieldMatrixChangingVisitor<T>) AbstractFieldMatrix
+Visit (and possibly change) all matrix entries in row order.
+ walkInRowOrder(FieldMatrixPreservingVisitor<T>) AbstractFieldMatrix
+Visit (but don't change) all matrix entries in row order.
+ walkInRowOrder(FieldMatrixChangingVisitor<T>, int, int, int, int) AbstractFieldMatrix
+Visit (and possibly change) some matrix entries in row order.
+ walkInRowOrder(FieldMatrixPreservingVisitor<T>, int, int, int, int) AbstractFieldMatrix
+Visit (but don't change) some matrix entries in row order.
+ walkInRowOrder(RealMatrixChangingVisitor) AbstractRealMatrix
+Visit (and possibly change) all matrix entries in row order.
+ walkInRowOrder(RealMatrixPreservingVisitor) AbstractRealMatrix
+Visit (but don't change) all matrix entries in row order.
+ walkInRowOrder(RealMatrixChangingVisitor, int, int, int, int) AbstractRealMatrix
+Visit (and possibly change) some matrix entries in row order.
+ walkInRowOrder(RealMatrixPreservingVisitor, int, int, int, int) AbstractRealMatrix
+Visit (but don't change) some matrix entries in row order.
+ walkInRowOrder(FieldMatrixChangingVisitor<T>) Array2DRowFieldMatrix
+Visit (and possibly change) all matrix entries in row order.
+ walkInRowOrder(FieldMatrixPreservingVisitor<T>) Array2DRowFieldMatrix
+Visit (but don't change) all matrix entries in row order.
+ walkInRowOrder(FieldMatrixChangingVisitor<T>, int, int, int, int) Array2DRowFieldMatrix
+Visit (and possibly change) some matrix entries in row order.
+ walkInRowOrder(FieldMatrixPreservingVisitor<T>, int, int, int, int) Array2DRowFieldMatrix
+Visit (but don't change) some matrix entries in row order.
+ walkInRowOrder(RealMatrixChangingVisitor) Array2DRowRealMatrix
+Visit (and possibly change) all matrix entries in row order.
+ walkInRowOrder(RealMatrixPreservingVisitor) Array2DRowRealMatrix
+Visit (but don't change) all matrix entries in row order.
+ walkInRowOrder(RealMatrixChangingVisitor, int, int, int, int) Array2DRowRealMatrix
+Visit (and possibly change) some matrix entries in row order.
+ walkInRowOrder(RealMatrixPreservingVisitor, int, int, int, int) Array2DRowRealMatrix
+Visit (but don't change) some matrix entries in row order.
+ walkInRowOrder(FieldMatrixChangingVisitor<T>) BlockFieldMatrix
+Visit (and possibly change) all matrix entries in row order.
+ walkInRowOrder(FieldMatrixPreservingVisitor<T>) BlockFieldMatrix
+Visit (but don't change) all matrix entries in row order.
+ walkInRowOrder(FieldMatrixChangingVisitor<T>, int, int, int, int) BlockFieldMatrix
+Visit (and possibly change) some matrix entries in row order.
+ walkInRowOrder(FieldMatrixPreservingVisitor<T>, int, int, int, int) BlockFieldMatrix
+Visit (but don't change) some matrix entries in row order.
+ walkInRowOrder(RealMatrixChangingVisitor) BlockRealMatrix
+Visit (and possibly change) all matrix entries in row order.
+ walkInRowOrder(RealMatrixPreservingVisitor) BlockRealMatrix
+Visit (but don't change) all matrix entries in row order.
+ walkInRowOrder(RealMatrixChangingVisitor, int, int, int, int) BlockRealMatrix
+Visit (and possibly change) some matrix entries in row order.
+ walkInRowOrder(RealMatrixPreservingVisitor, int, int, int, int) BlockRealMatrix
+Visit (but don't change) some matrix entries in row order.
+ walkInRowOrder(FieldMatrixChangingVisitor<T>) FieldMatrix
+Visit (and possibly change) all matrix entries in row order.
+ walkInRowOrder(FieldMatrixPreservingVisitor<T>) FieldMatrix
+Visit (but don't change) all matrix entries in row order.
+ walkInRowOrder(FieldMatrixChangingVisitor<T>, int, int, int, int) FieldMatrix
+Visit (and possibly change) some matrix entries in row order.
+ walkInRowOrder(FieldMatrixPreservingVisitor<T>, int, int, int, int) FieldMatrix
+Visit (but don't change) some matrix entries in row order.
+ walkInRowOrder(RealMatrixChangingVisitor) RealMatrix
+Visit (and possibly change) all matrix entries in row order.
+ walkInRowOrder(RealMatrixPreservingVisitor) RealMatrix
+Visit (but don't change) all matrix entries in row order.
+ walkInRowOrder(RealMatrixChangingVisitor, int, int, int, int) RealMatrix
+Visit (and possibly change) some matrix entries in row order.
+ walkInRowOrder(RealMatrixPreservingVisitor, int, int, int, int) RealMatrix
+Visit (but don't change) some matrix entries in row order.
+ walkInRowOrder(RealMatrixChangingVisitor) RealMatrixImpl
+Deprecated. Visit (and possibly change) all matrix entries in row order.
+walkInRowOrder(RealMatrixPreservingVisitor) RealMatrixImpl
+Deprecated. Visit (but don't change) all matrix entries in row order.
+walkInRowOrder(RealMatrixChangingVisitor, int, int, int, int) RealMatrixImpl
+Deprecated. Visit (and possibly change) some matrix entries in row order.
+walkInRowOrder(RealMatrixPreservingVisitor, int, int, int, int) RealMatrixImpl
+Deprecated. Visit (but don't change) some matrix entries in row order.
+WeibullDistribution org.apache.commons.math.distribution Weibull Distribution. WeibullDistributionImpl org.apache.commons.math.distribution Default implementation of
+ WeibullDistribution WeibullDistributionImpl(double, double) WeibullDistributionImpl
+Creates weibull distribution with the given shape and scale and a
+ location equal to zero.
+ WeibullDistributionImpl(double, double, double) WeibullDistributionImpl
+Creates weibull distribution with the given shape, scale and inverse
+ cumulative probability accuracy and a location equal to zero.
+ WeightedEvaluation org.apache.commons.math.stat.descriptive Weighted evaluation for statistics. WeightedMeasurement org.apache.commons.math.estimation Deprecated. as of 2.0, everything in package org.apache.commons.math.estimation has
+ been deprecated and replaced by package org.apache.commons.math.optimization.general WeightedMeasurement(double, double) WeightedMeasurement
+Deprecated. Simple constructor.
+WeightedMeasurement(double, double, boolean) WeightedMeasurement
+Deprecated. Simple constructor.
+WeightedObservedPoint org.apache.commons.math.optimization.fitting This class is a simple container for weighted observed point in
+ curve fitting WeightedObservedPoint(double, double, double) WeightedObservedPoint
+Simple constructor.
+ windowSize DescriptiveStatistics
+hold the window size
+ writeBaseExternal(ObjectOutput) AbstractStepInterpolator
+Save the base state of the instance.
+ writeExternal(ObjectOutput) AbstractStepInterpolator
+
+ writeExternal(ObjectOutput) DummyStepInterpolator
+Write the instance to an output channel.
+ writeExternal(ObjectOutput) NordsieckStepInterpolator
+
+
+
+X
+
+X AbstractMultipleLinearRegression
+X sample data.
+ XYX RotationOrder
+Set of Euler angles.
+ XYZ RotationOrder
+Set of Cardan angles.
+ XZX RotationOrder
+Set of Euler angles.
+ XZY RotationOrder
+Set of Cardan angles.
+
+
+Y
+
+Y AbstractMultipleLinearRegression
+Y sample data.
+ YXY RotationOrder
+Set of Euler angles.
+ YXZ RotationOrder
+Set of Cardan angles.
+ YZX RotationOrder
+Set of Cardan angles.
+ YZY RotationOrder
+Set of Euler angles.
+
+
+Z
+
+ZERO ComposableFunction
+The constant function always returning 0.
+ ZERO Complex
+A complex number representing "0.0 + 0.0i"
+ ZERO BigFraction
+A fraction representing "0".
+ ZERO Fraction
+A fraction representing "0".
+ ZERO Vector3D
+Null vector (coordinates: 0, 0, 0).
+ ZERO BigReal
+A big real representing 0.
+ ZipfDistribution org.apache.commons.math.distribution The Zipf (or zeta) Distribution. ZipfDistributionImpl org.apache.commons.math.distribution Implementation for the ZipfDistribution ZipfDistributionImpl(int, double) ZipfDistributionImpl
+Create a new Zipf distribution with the given number of elements and
+ exponent.
+ ZXY RotationOrder
+Set of Cardan angles.
+ ZXZ RotationOrder
+Set of Euler angles.
+ ZYX RotationOrder
+Set of Cardan angles.
+ ZYZ RotationOrder
+Set of Euler angles.
+
+A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
+
+
+The Apache Software Foundation . All Rights Reserved.
+
+
diff -r 0b3f87acaabc -r e8ccd518555b libs/commons-math-2.1/docs/apidocs/index.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/libs/commons-math-2.1/docs/apidocs/index.html Tue Jan 04 10:03:00 2011 +0100
@@ -0,0 +1,40 @@
+
+
+
+
+
+
+Commons Math 2.1 API
+
+
+
+
+
+
+
+
+<![CDATA[
+<H2>
+Frame Alert</H2>
+
+<P>
+This document is designed to be viewed using the frames feature. If you see this message, you are using a non-frame-capable web client.
+<BR>
+Link to<A HREF="overview-summary.html">Non-frame version.</A>
+]]>
+
+
diff -r 0b3f87acaabc -r e8ccd518555b libs/commons-math-2.1/docs/apidocs/options
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/libs/commons-math-2.1/docs/apidocs/options Tue Jan 04 10:03:00 2011 +0100
@@ -0,0 +1,28 @@
+-classpath
+'/Users/philsteitz/newMath/tags/MATH_2_1_RC3/target/classes'
+-encoding
+'iso-8859-1'
+-protected
+-quiet
+-source
+'1.5'
+-sourcepath
+'/Users/philsteitz/newMath/tags/MATH_2_1_RC3/src/main/java'
+-author
+-bottom
+'Copyright © 2003-2010 The Apache Software Foundation . All Rights Reserved.'
+-charset
+'UTF-8'
+-d
+'/Users/philsteitz/newMath/tags/MATH_2_1_RC3/target/site/apidocs'
+-docencoding
+'UTF-8'
+-doctitle
+'Commons Math 2.1 API'
+-link
+'http://java.sun.com/javase/6/docs/api'
+-linksource
+-use
+-version
+-windowtitle
+'Commons Math 2.1 API'
\ No newline at end of file
diff -r 0b3f87acaabc -r e8ccd518555b libs/commons-math-2.1/docs/apidocs/overview-frame.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/libs/commons-math-2.1/docs/apidocs/overview-frame.html Tue Jan 04 10:03:00 2011 +0100
@@ -0,0 +1,117 @@
+
+
+
+
+
+
+Overview List (Commons Math 2.1 API)
+
+
+
+
+
+
diff -r 0b3f87acaabc -r e8ccd518555b libs/commons-math-2.1/docs/apidocs/overview-summary.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/libs/commons-math-2.1/docs/apidocs/overview-summary.html Tue Jan 04 10:03:00 2011 +0100
@@ -0,0 +1,323 @@
+
+
+
+
+
+
+Overview (Commons Math 2.1 API)
+
+
+
+
+
+
+
+
+
+
+Commons Math 2.1 API
+
+
+
+
+
+
+
The Apache Software Foundation . All Rights Reserved.
+
+
diff -r 0b3f87acaabc -r e8ccd518555b libs/commons-math-2.1/docs/apidocs/overview-tree.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/libs/commons-math-2.1/docs/apidocs/overview-tree.html Tue Jan 04 10:03:00 2011 +0100
@@ -0,0 +1,518 @@
+
+
+
+
+
+
+Class Hierarchy (Commons Math 2.1 API)
+
+
+
+
+
+
+
+
+
+
+Hierarchy For All Packages
+
+
+Package Hierarchies: org.apache.commons.math , org.apache.commons.math.analysis , org.apache.commons.math.analysis.integration , org.apache.commons.math.analysis.interpolation , org.apache.commons.math.analysis.polynomials , org.apache.commons.math.analysis.solvers , org.apache.commons.math.complex , org.apache.commons.math.distribution , org.apache.commons.math.estimation , org.apache.commons.math.fraction , org.apache.commons.math.genetics , org.apache.commons.math.geometry , org.apache.commons.math.linear , org.apache.commons.math.ode , org.apache.commons.math.ode.events , org.apache.commons.math.ode.jacobians , org.apache.commons.math.ode.nonstiff , org.apache.commons.math.ode.sampling , org.apache.commons.math.optimization , org.apache.commons.math.optimization.direct , org.apache.commons.math.optimization.fitting , org.apache.commons.math.optimization.general , org.apache.commons.math.optimization.linear , org.apache.commons.math.optimization.univariate , org.apache.commons.math.random , org.apache.commons.math.special , org.apache.commons.math.stat , org.apache.commons.math.stat.clustering , org.apache.commons.math.stat.correlation , org.apache.commons.math.stat.descriptive , org.apache.commons.math.stat.descriptive.moment , org.apache.commons.math.stat.descriptive.rank , org.apache.commons.math.stat.descriptive.summary , org.apache.commons.math.stat.inference , org.apache.commons.math.stat.ranking , org.apache.commons.math.stat.regression , org.apache.commons.math.transform , org.apache.commons.math.util
+
+Class Hierarchy
+
+
+java.lang.Object
+org.apache.commons.math.distribution.AbstractDistribution Distribution , java.io.Serializable )
+
+ org.apache.commons.math.estimation.AbstractEstimator Estimator )
+
+ org.apache.commons.math.linear.AbstractFieldMatrix FieldMatrix <T>)
+
+ org.apache.commons.math.ode.AbstractIntegrator FirstOrderIntegrator )
+
+ org.apache.commons.math.optimization.general.AbstractLeastSquaresOptimizer DifferentiableMultivariateVectorialOptimizer )
+
+ org.apache.commons.math.optimization.linear.AbstractLinearOptimizer LinearOptimizer )
+
+ org.apache.commons.math.stat.regression.AbstractMultipleLinearRegression MultipleLinearRegression )
+
+ org.apache.commons.math.random.AbstractRandomGenerator RandomGenerator )
+ org.apache.commons.math.linear.AbstractRealMatrix RealMatrix )
+
+ org.apache.commons.math.linear.AbstractRealVector RealVector )
+
+ org.apache.commons.math.linear.AbstractRealVector.SparseEntryIterator Iterator <E>)
+ org.apache.commons.math.optimization.general.AbstractScalarDifferentiableOptimizer DifferentiableMultivariateRealOptimizer )
+
+ org.apache.commons.math.ode.sampling.AbstractStepInterpolator StepInterpolator )
+
+ org.apache.commons.math.stat.descriptive.AbstractUnivariateStatistic UnivariateStatistic )
+
+ org.apache.commons.math.ode.nonstiff.AdamsNordsieckTransformer org.apache.commons.math.stat.descriptive.AggregateSummaryStatistics Serializable , org.apache.commons.math.stat.descriptive.StatisticalSummary )
+ org.apache.commons.math.linear.ArrayFieldVector FieldVector <T>, java.io.Serializable )
+ org.apache.commons.math.special.Beta org.apache.commons.math.analysis.interpolation.BicubicSplineInterpolatingFunction BivariateRealFunction )
+ org.apache.commons.math.fraction.BigFractionField Field <T>, java.io.Serializable )
+ org.apache.commons.math.linear.BigMatrixImpl BigMatrix , java.io.Serializable )
+ org.apache.commons.math.util.BigReal Comparable <T>, org.apache.commons.math.FieldElement <T>, java.io.Serializable )
+ org.apache.commons.math.util.BigRealField Field <T>, java.io.Serializable )
+ org.apache.commons.math.analysis.BinaryFunction BivariateRealFunction )
+ org.apache.commons.math.genetics.BinaryMutation MutationPolicy )
+ org.apache.commons.math.random.BitsStreamGenerator RandomGenerator )
+
+ org.apache.commons.math.stat.inference.ChiSquareTestImpl UnknownDistributionChiSquareTest )
+ org.apache.commons.math.linear.CholeskyDecompositionImpl CholeskyDecomposition )
+ org.apache.commons.math.genetics.Chromosome Comparable <T>, org.apache.commons.math.genetics.Fitness )
+
+ org.apache.commons.math.genetics.ChromosomePair org.apache.commons.math.stat.clustering.Cluster Serializable )
+ org.apache.commons.math.ode.events.CombinedEventsManager org.apache.commons.math.complex.Complex FieldElement <T>, java.io.Serializable )
+ org.apache.commons.math.complex.ComplexField Field <T>, java.io.Serializable )
+ org.apache.commons.math.complex.ComplexUtils org.apache.commons.math.analysis.ComposableFunction UnivariateRealFunction )
+ org.apache.commons.math.util.ContinuedFraction org.apache.commons.math.ode.ContinuousOutputModel Serializable , org.apache.commons.math.ode.sampling.StepHandler )
+ org.apache.commons.math.ConvergingAlgorithmImpl ConvergingAlgorithm )
+
+ org.apache.commons.math.random.CorrelatedRandomVectorGenerator RandomVectorGenerator )
+ org.apache.commons.math.stat.correlation.Covariance org.apache.commons.math.optimization.fitting.CurveFitter org.apache.commons.math.linear.DefaultFieldMatrixChangingVisitor FieldMatrixChangingVisitor <T>)
+ org.apache.commons.math.linear.DefaultFieldMatrixPreservingVisitor FieldMatrixPreservingVisitor <T>)
+ org.apache.commons.math.linear.DefaultRealMatrixChangingVisitor RealMatrixChangingVisitor )
+ org.apache.commons.math.linear.DefaultRealMatrixPreservingVisitor RealMatrixPreservingVisitor )
+ org.apache.commons.math.util.DefaultTransformer NumberTransformer , java.io.Serializable )
+ org.apache.commons.math.stat.descriptive.DescriptiveStatistics Serializable , org.apache.commons.math.stat.descriptive.StatisticalSummary )
+
+ org.apache.commons.math.optimization.direct.DirectSearchOptimizer MultivariateRealOptimizer )
+
+ org.apache.commons.math.analysis.interpolation.DividedDifferenceInterpolator Serializable , org.apache.commons.math.analysis.interpolation.UnivariateRealInterpolator )
+ org.apache.commons.math.ode.sampling.DummyStepHandler StepHandler )
+ org.apache.commons.math.linear.EigenDecompositionImpl EigenDecomposition )
+ org.apache.commons.math.random.EmpiricalDistributionImpl EmpiricalDistribution , java.io.Serializable )
+ org.apache.commons.math.special.Erf org.apache.commons.math.estimation.EstimatedParameter Serializable )
+ org.apache.commons.math.stat.clustering.EuclideanIntegerPoint Clusterable <T>, java.io.Serializable )
+ org.apache.commons.math.ode.events.EventState org.apache.commons.math.transform.FastCosineTransformer RealTransformer )
+ org.apache.commons.math.transform.FastFourierTransformer Serializable )
+ org.apache.commons.math.transform.FastHadamardTransformer RealTransformer )
+ org.apache.commons.math.transform.FastSineTransformer RealTransformer )
+ org.apache.commons.math.linear.FieldLUDecompositionImpl FieldLUDecomposition <T>)
+ org.apache.commons.math.ode.FirstOrderConverter FirstOrderDifferentialEquations )
+ org.apache.commons.math.ode.jacobians.FirstOrderIntegratorWithJacobians org.apache.commons.math.genetics.FixedGenerationCount StoppingCondition )
+ java.text.Format Cloneable , java.io.Serializable )
+
+ org.apache.commons.math.fraction.FractionField Field <T>, java.io.Serializable )
+ org.apache.commons.math.stat.Frequency Serializable )
+ org.apache.commons.math.special.Gamma org.apache.commons.math.random.GaussianRandomGenerator NormalizedRandomGenerator )
+ org.apache.commons.math.genetics.GeneticAlgorithm org.apache.commons.math.optimization.fitting.HarmonicCoefficientsGuesser org.apache.commons.math.optimization.fitting.HarmonicFitter org.apache.commons.math.optimization.fitting.HarmonicFunction DifferentiableUnivariateRealFunction )
+ org.apache.commons.math.stat.clustering.KMeansPlusPlusClusterer org.apache.commons.math.optimization.LeastSquaresConverter MultivariateRealFunction )
+ org.apache.commons.math.optimization.linear.LinearConstraint Serializable )
+ org.apache.commons.math.optimization.linear.LinearObjectiveFunction Serializable )
+ org.apache.commons.math.genetics.ListPopulation Population )
+
+ org.apache.commons.math.analysis.interpolation.LoessInterpolator Serializable , org.apache.commons.math.analysis.interpolation.UnivariateRealInterpolator )
+ org.apache.commons.math.linear.LUDecompositionImpl LUDecomposition )
+ org.apache.commons.math.util.MathUtils org.apache.commons.math.linear.MatrixUtils org.apache.commons.math.analysis.interpolation.MicrosphereInterpolatingFunction MultivariateRealFunction )
+ org.apache.commons.math.analysis.interpolation.MicrosphereInterpolator MultivariateRealInterpolator )
+ org.apache.commons.math.optimization.MultiStartDifferentiableMultivariateRealOptimizer DifferentiableMultivariateRealOptimizer )
+ org.apache.commons.math.optimization.MultiStartDifferentiableMultivariateVectorialOptimizer DifferentiableMultivariateVectorialOptimizer )
+ org.apache.commons.math.optimization.MultiStartMultivariateRealOptimizer MultivariateRealOptimizer )
+ org.apache.commons.math.optimization.MultiStartUnivariateRealOptimizer UnivariateRealOptimizer )
+ org.apache.commons.math.stat.descriptive.MultivariateSummaryStatistics Serializable , org.apache.commons.math.stat.descriptive.StatisticalMultivariateSummary )
+
+ org.apache.commons.math.stat.ranking.NaturalRanking RankingAlgorithm )
+ org.apache.commons.math.analysis.interpolation.NevilleInterpolator Serializable , org.apache.commons.math.analysis.interpolation.UnivariateRealInterpolator )
+ java.lang.Number Serializable )
+
+ org.apache.commons.math.genetics.OnePointCrossover CrossoverPolicy )
+ org.apache.commons.math.stat.inference.OneWayAnovaImpl OneWayAnova )
+ org.apache.commons.math.util.OpenIntToDoubleHashMap Serializable )
+ org.apache.commons.math.util.OpenIntToDoubleHashMap.Iterator org.apache.commons.math.util.OpenIntToFieldHashMap Serializable )
+ org.apache.commons.math.util.OpenIntToFieldHashMap.Iterator org.apache.commons.math.linear.OpenMapRealVector.OpenMapSparseIterator Iterator <E>)
+ org.apache.commons.math.stat.correlation.PearsonsCorrelation org.apache.commons.math.optimization.fitting.PolynomialFitter org.apache.commons.math.analysis.polynomials.PolynomialFunction DifferentiableUnivariateRealFunction , java.io.Serializable )
+ org.apache.commons.math.analysis.polynomials.PolynomialFunctionLagrangeForm UnivariateRealFunction )
+ org.apache.commons.math.analysis.polynomials.PolynomialFunctionNewtonForm UnivariateRealFunction )
+ org.apache.commons.math.analysis.polynomials.PolynomialSplineFunction DifferentiableUnivariateRealFunction )
+ org.apache.commons.math.analysis.polynomials.PolynomialsUtils org.apache.commons.math.linear.QRDecompositionImpl QRDecomposition )
+ java.util.Random Serializable )
+
+ org.apache.commons.math.random.RandomDataImpl RandomData , java.io.Serializable )
+ org.apache.commons.math.genetics.RandomKeyMutation MutationPolicy )
+ org.apache.commons.math.optimization.RealPointValuePair Serializable )
+ org.apache.commons.math.linear.RealVector.Entry org.apache.commons.math.util.ResizableDoubleArray DoubleArray , java.io.Serializable )
+ java.util.ResourceBundle org.apache.commons.math.geometry.Rotation Serializable )
+ org.apache.commons.math.geometry.RotationOrder org.apache.commons.math.estimation.SimpleEstimationProblem EstimationProblem )
+ org.apache.commons.math.optimization.SimpleRealPointChecker RealConvergenceChecker )
+ org.apache.commons.math.stat.regression.SimpleRegression Serializable )
+ org.apache.commons.math.optimization.SimpleScalarValueChecker RealConvergenceChecker )
+ org.apache.commons.math.optimization.SimpleVectorialPointChecker VectorialConvergenceChecker )
+ org.apache.commons.math.optimization.SimpleVectorialValueChecker VectorialConvergenceChecker )
+ org.apache.commons.math.linear.SingularValueDecompositionImpl SingularValueDecomposition )
+ org.apache.commons.math.analysis.interpolation.SmoothingBicubicSplineInterpolator BivariateRealGridInterpolator )
+ org.apache.commons.math.linear.SparseFieldVector FieldVector <T>, java.io.Serializable )
+ org.apache.commons.math.stat.correlation.SpearmansCorrelation org.apache.commons.math.analysis.interpolation.SplineInterpolator UnivariateRealInterpolator )
+ org.apache.commons.math.stat.descriptive.StatisticalSummaryValues Serializable , org.apache.commons.math.stat.descriptive.StatisticalSummary )
+ org.apache.commons.math.stat.StatUtils org.apache.commons.math.ode.sampling.StepNormalizer StepHandler )
+ org.apache.commons.math.stat.descriptive.SummaryStatistics Serializable , org.apache.commons.math.stat.descriptive.StatisticalSummary )
+
+ org.apache.commons.math.stat.inference.TestUtils java.lang.Throwable Serializable )
+
+ org.apache.commons.math.genetics.TournamentSelection SelectionPolicy )
+ org.apache.commons.math.util.TransformerMap NumberTransformer , java.io.Serializable )
+ org.apache.commons.math.stat.inference.TTestImpl TTest )
+ org.apache.commons.math.random.UncorrelatedRandomVectorGenerator RandomVectorGenerator )
+ org.apache.commons.math.random.UniformRandomGenerator NormalizedRandomGenerator )
+ org.apache.commons.math.random.UnitSphereRandomVectorGenerator RandomVectorGenerator )
+ org.apache.commons.math.analysis.solvers.UnivariateRealSolverFactory org.apache.commons.math.analysis.solvers.UnivariateRealSolverUtils org.apache.commons.math.random.ValueServer org.apache.commons.math.geometry.Vector3D Serializable )
+ org.apache.commons.math.stat.descriptive.moment.VectorialCovariance Serializable )
+ org.apache.commons.math.stat.descriptive.moment.VectorialMean Serializable )
+ org.apache.commons.math.optimization.VectorialPointValuePair Serializable )
+ org.apache.commons.math.estimation.WeightedMeasurement Serializable )
+ org.apache.commons.math.optimization.fitting.WeightedObservedPoint Serializable )
+
+
+
+Interface Hierarchy
+
+
+
+Enum Hierarchy
+
+
+The Apache Software Foundation . All Rights Reserved.
+
+
diff -r 0b3f87acaabc -r e8ccd518555b libs/commons-math-2.1/docs/apidocs/package-list
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/libs/commons-math-2.1/docs/apidocs/package-list Tue Jan 04 10:03:00 2011 +0100
@@ -0,0 +1,38 @@
+org.apache.commons.math
+org.apache.commons.math.analysis
+org.apache.commons.math.analysis.integration
+org.apache.commons.math.analysis.interpolation
+org.apache.commons.math.analysis.polynomials
+org.apache.commons.math.analysis.solvers
+org.apache.commons.math.complex
+org.apache.commons.math.distribution
+org.apache.commons.math.estimation
+org.apache.commons.math.fraction
+org.apache.commons.math.genetics
+org.apache.commons.math.geometry
+org.apache.commons.math.linear
+org.apache.commons.math.ode
+org.apache.commons.math.ode.events
+org.apache.commons.math.ode.jacobians
+org.apache.commons.math.ode.nonstiff
+org.apache.commons.math.ode.sampling
+org.apache.commons.math.optimization
+org.apache.commons.math.optimization.direct
+org.apache.commons.math.optimization.fitting
+org.apache.commons.math.optimization.general
+org.apache.commons.math.optimization.linear
+org.apache.commons.math.optimization.univariate
+org.apache.commons.math.random
+org.apache.commons.math.special
+org.apache.commons.math.stat
+org.apache.commons.math.stat.clustering
+org.apache.commons.math.stat.correlation
+org.apache.commons.math.stat.descriptive
+org.apache.commons.math.stat.descriptive.moment
+org.apache.commons.math.stat.descriptive.rank
+org.apache.commons.math.stat.descriptive.summary
+org.apache.commons.math.stat.inference
+org.apache.commons.math.stat.ranking
+org.apache.commons.math.stat.regression
+org.apache.commons.math.transform
+org.apache.commons.math.util
diff -r 0b3f87acaabc -r e8ccd518555b libs/commons-math-2.1/docs/apidocs/packages
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/libs/commons-math-2.1/docs/apidocs/packages Tue Jan 04 10:03:00 2011 +0100
@@ -0,0 +1,38 @@
+org.apache.commons.math.analysis
+org.apache.commons.math.analysis.integration
+org.apache.commons.math.analysis.interpolation
+org.apache.commons.math.analysis.polynomials
+org.apache.commons.math.analysis.solvers
+org.apache.commons.math
+org.apache.commons.math.complex
+org.apache.commons.math.distribution
+org.apache.commons.math.estimation
+org.apache.commons.math.fraction
+org.apache.commons.math.genetics
+org.apache.commons.math.geometry
+org.apache.commons.math.linear
+org.apache.commons.math.ode
+org.apache.commons.math.ode.events
+org.apache.commons.math.ode.jacobians
+org.apache.commons.math.ode.nonstiff
+org.apache.commons.math.ode.sampling
+org.apache.commons.math.optimization
+org.apache.commons.math.optimization.direct
+org.apache.commons.math.optimization.fitting
+org.apache.commons.math.optimization.general
+org.apache.commons.math.optimization.linear
+org.apache.commons.math.optimization.univariate
+org.apache.commons.math.random
+org.apache.commons.math.special
+org.apache.commons.math.stat.clustering
+org.apache.commons.math.stat.correlation
+org.apache.commons.math.stat.descriptive
+org.apache.commons.math.stat.descriptive.moment
+org.apache.commons.math.stat.descriptive.rank
+org.apache.commons.math.stat.descriptive.summary
+org.apache.commons.math.stat
+org.apache.commons.math.stat.inference
+org.apache.commons.math.stat.ranking
+org.apache.commons.math.stat.regression
+org.apache.commons.math.transform
+org.apache.commons.math.util
\ No newline at end of file
diff -r 0b3f87acaabc -r e8ccd518555b libs/commons-math-2.1/docs/apidocs/serialized-form.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/libs/commons-math-2.1/docs/apidocs/serialized-form.html Tue Jan 04 10:03:00 2011 +0100
@@ -0,0 +1,6536 @@
+
+
+
+
+
+
+Serialized Form (Commons Math 2.1 API)
+
+
+
+
+
+
+
+
+
+
+Serialized Form
+
+
+
+
+Package org.apache.commons.math
+
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+serialVersionUID: -1316089546353786411L
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+
+
+
+
+Serialized Fields
+
+
+
+dimension1
+
+int dimension1
+
+First dimension.
+
+
+
+
+
+dimension2
+
+int dimension2
+
+Second dimension.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -2271007547170169872L
+
+
+
+
+
+
+
+serialVersionUID: -4305020489115478365L
+
+
+
+
+
+Serialized Fields
+
+
+
+argument
+
+double[] argument
+
+Argument causing function evaluation failure
+
+
+
+
+
+
+
+
+
+serialVersionUID: 5261476508226103366L
+
+
+
+
+
+
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+serialVersionUID: -9004610152740737812L
+
+
+
+
+
+Serialized Fields
+
+
+
+pattern
+
+String pattern
+
+Pattern used to build the message.
+
+
+
+
+
+arguments
+
+Object [] arguments
+
+Arguments used to build the message.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -5128983364075381060L
+
+
+
+
+
+Serialized Fields
+
+
+
+pattern
+
+String pattern
+
+Pattern used to build the message.
+
+
+
+
+
+arguments
+
+Object [] arguments
+
+Arguments used to build the message.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -5921271447220129118L
+
+
+
+
+
+Serialized Fields
+
+
+
+maxEvaluations
+
+int maxEvaluations
+
+Maximal number of evaluations allowed.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -7821226672760574694L
+
+
+
+
+
+Serialized Fields
+
+
+
+maxIterations
+
+int maxIterations
+
+Maximal number of iterations allowed.
+
+
+
+
+
+
+
+Package org.apache.commons.math.analysis.interpolation
+
+
+
+
+
+
+serialVersionUID: 107049519551235069L
+
+
+
+
+
+
+
+serialVersionUID: 5204927143605193821L
+
+
+
+
+
+Serialized Fields
+
+
+
+bandwidth
+
+double bandwidth
+
+The bandwidth parameter: when computing the loess fit at
+ a particular point, this fraction of source points closest
+ to the current point is taken into account for computing
+ a least-squares regression.
+
+ A sensible value is usually 0.25 to 0.5.
+
+
+
+
+
+robustnessIters
+
+int robustnessIters
+
+The number of robustness iterations parameter: this many
+ robustness iterations are done.
+
+ A sensible value is usually 0 (just the initial fit without any
+ robustness iterations) to 4.
+
+
+
+
+
+accuracy
+
+double accuracy
+
+If the median residual at a certain robustness iteration
+ is less than this amount, no more iterations are done.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 3003707660147873733L
+
+
+
+
+
+Package org.apache.commons.math.analysis.polynomials
+
+
+
+
+
+
+serialVersionUID: -7726511984200295583L
+
+
+
+
+
+Serialized Fields
+
+
+
+coefficients
+
+double[] coefficients
+
+The coefficients of the polynomial, ordered by degree -- i.e.,
+ coefficients[0] is the constant term and coefficients[n] is the
+ coefficient of x^n where n is the degree of the polynomial.
+
+
+
+
+
+
+
+Package org.apache.commons.math.complex
+
+
+
+
+
+
+serialVersionUID: -6195664516687396620L
+
+
+
+
+
+Serialization Methods
+
+
+
+
+readResolve
+
+protected final Object readResolve
+
+Resolve the transient fields in a deserialized Complex Object.
+ Subclasses will need to override Complex.createComplex(double, double)
+
+
+Since: 2.0
+
+
+
+
+
+Serialized Fields
+
+
+
+imaginary
+
+double imaginary
+
+The imaginary part.
+
+
+
+
+
+real
+
+double real
+
+The real part.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -6130362688700788798L
+
+
+
+
+
+Serialization Methods
+
+
+
+
+readResolve
+
+private Object readResolve
+
+Handle deserialization of the singleton.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -3343698360149467646L
+
+
+
+
+
+Serialized Fields
+
+
+
+imaginaryCharacter
+
+String imaginaryCharacter
+
+The notation used to signify the imaginary part of the complex number.
+
+
+
+
+
+imaginaryFormat
+
+NumberFormat imaginaryFormat
+
+The format used for the imaginary part.
+
+
+
+
+
+realFormat
+
+NumberFormat realFormat
+
+The format used for the real part.
+
+
+
+
+
+
+
+Package org.apache.commons.math.distribution
+
+
+
+
+
+
+serialVersionUID: -38038050983108802L
+
+
+
+
+
+Serialized Fields
+
+
+
+solverAbsoluteAccuracy
+
+double solverAbsoluteAccuracy
+
+Solver absolute accuracy for inverse cum computation
+
+
+Since: 2.1
+
+
+
+
+
+
+
+serialVersionUID: -38038050983108802L
+
+
+
+
+
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+
+serialVersionUID: -1146319659338487221L
+
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+
+
+
+
+
+serialVersionUID: -1221965979403477668L
+
+
+
+
+
+Serialized Fields
+
+
+
+alpha
+
+double alpha
+
+First shape parameter.
+
+
+
+
+
+beta
+
+double beta
+
+Second shape parameter.
+
+
+
+
+
+z
+
+double z
+
+Normalizing factor used in density computations.
+ updated whenever alpha or beta are changed.
+
+
+
+
+
+solverAbsoluteAccuracy
+
+double solverAbsoluteAccuracy
+
+Inverse cumulative probability accuracy
+
+
+
+
+
+
+
+
+
+serialVersionUID: 6751309484392813623L
+
+
+
+
+
+Serialized Fields
+
+
+
+numberOfTrials
+
+int numberOfTrials
+
+The number of trials.
+
+
+
+
+
+probabilityOfSuccess
+
+double probabilityOfSuccess
+
+The probability of success.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 8589540077390120676L
+
+
+
+
+
+Serialized Fields
+
+
+
+median
+
+double median
+
+The median of this distribution.
+
+
+
+
+
+scale
+
+double scale
+
+The scale of this distribution.
+
+
+
+
+
+solverAbsoluteAccuracy
+
+double solverAbsoluteAccuracy
+
+Inverse cumulative probability accuracy
+
+
+
+
+
+
+
+
+
+serialVersionUID: -8352658048349159782L
+
+
+
+
+
+Serialized Fields
+
+
+
+gamma
+
+GammaDistribution gamma
+
+Internal Gamma distribution.
+
+
+
+
+
+solverAbsoluteAccuracy
+
+double solverAbsoluteAccuracy
+
+Inverse cumulative probability accuracy
+
+
+
+
+
+
+
+
+
+serialVersionUID: 2401296428283614780L
+
+
+
+
+
+Serialized Fields
+
+
+
+mean
+
+double mean
+
+The mean of this distribution.
+
+
+
+
+
+solverAbsoluteAccuracy
+
+double solverAbsoluteAccuracy
+
+Inverse cumulative probability accuracy
+
+
+
+
+
+
+
+
+
+serialVersionUID: -8516354193418641566L
+
+
+
+
+
+Serialized Fields
+
+
+
+numeratorDegreesOfFreedom
+
+double numeratorDegreesOfFreedom
+
+The numerator degrees of freedom
+
+
+
+
+
+denominatorDegreesOfFreedom
+
+double denominatorDegreesOfFreedom
+
+The numerator degrees of freedom
+
+
+
+
+
+solverAbsoluteAccuracy
+
+double solverAbsoluteAccuracy
+
+Inverse cumulative probability accuracy
+
+
+
+
+
+
+
+
+
+serialVersionUID: -3239549463135430361L
+
+
+
+
+
+Serialized Fields
+
+
+
+alpha
+
+double alpha
+
+The shape parameter.
+
+
+
+
+
+beta
+
+double beta
+
+The scale parameter.
+
+
+
+
+
+solverAbsoluteAccuracy
+
+double solverAbsoluteAccuracy
+
+Inverse cumulative probability accuracy
+
+
+
+
+
+
+
+
+
+serialVersionUID: -436928820673516179L
+
+
+
+
+
+Serialized Fields
+
+
+
+numberOfSuccesses
+
+int numberOfSuccesses
+
+The number of successes in the population.
+
+
+
+
+
+populationSize
+
+int populationSize
+
+The population size.
+
+
+
+
+
+sampleSize
+
+int sampleSize
+
+The sample size.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 8589540077390120676L
+
+
+
+
+
+Serialized Fields
+
+
+
+mean
+
+double mean
+
+The mean of this distribution.
+
+
+
+
+
+standardDeviation
+
+double standardDeviation
+
+The standard deviation of this distribution.
+
+
+
+
+
+solverAbsoluteAccuracy
+
+double solverAbsoluteAccuracy
+
+Inverse cumulative probability accuracy
+
+
+
+
+
+
+
+
+
+serialVersionUID: 6751309484392813623L
+
+
+
+
+
+Serialized Fields
+
+
+
+numberOfSuccesses
+
+int numberOfSuccesses
+
+The number of successes
+
+
+
+
+
+probabilityOfSuccess
+
+double probabilityOfSuccess
+
+The probability of success
+
+
+
+
+
+
+
+
+
+serialVersionUID: -3349935121172596109L
+
+
+
+
+
+Serialized Fields
+
+
+
+normal
+
+NormalDistribution normal
+
+Distribution used to compute normal approximation.
+
+
+
+
+
+mean
+
+double mean
+
+Holds the Poisson mean for the distribution.
+
+
+
+
+
+maxIterations
+
+int maxIterations
+
+Maximum number of iterations for cumulative probability.
+
+ Cumulative probabilities are estimated using either Lanczos series approximation of
+ Gamma#regularizedGammaP or continued fraction approximation of Gamma#regularizedGammaQ.
+
+
+
+
+
+epsilon
+
+double epsilon
+
+Convergence criterion for cumulative probability.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -5852615386664158222L
+
+
+
+
+
+Serialized Fields
+
+
+
+degreesOfFreedom
+
+double degreesOfFreedom
+
+The degrees of freedom
+
+
+
+
+
+solverAbsoluteAccuracy
+
+double solverAbsoluteAccuracy
+
+Inverse cumulative probability accuracy
+
+
+
+
+
+
+
+
+
+serialVersionUID: 8589540077390120676L
+
+
+
+
+
+Serialized Fields
+
+
+
+shape
+
+double shape
+
+The shape parameter.
+
+
+
+
+
+scale
+
+double scale
+
+The scale parameter.
+
+
+
+
+
+solverAbsoluteAccuracy
+
+double solverAbsoluteAccuracy
+
+Inverse cumulative probability accuracy
+
+
+
+
+
+
+
+
+
+serialVersionUID: -140627372283420404L
+
+
+
+
+
+Serialized Fields
+
+
+
+numberOfElements
+
+int numberOfElements
+
+Number of elements.
+
+
+
+
+
+exponent
+
+double exponent
+
+Exponent parameter of the distribution.
+
+
+
+
+
+
+
+Package org.apache.commons.math.estimation
+
+
+
+
+
+
+serialVersionUID: -555440800213416949L
+
+
+
+
+
+Serialized Fields
+
+
+
+estimate
+
+double estimate
+
+Deprecated. Current value of the parameter
+
+
+
+
+
+name
+
+String name
+
+Deprecated. Name of the parameter
+
+
+
+
+
+bound
+
+boolean bound
+
+Deprecated. Indicator for bound parameters
+ (ie parameters that should not be estimated)
+
+
+
+
+
+
+
+
+
+serialVersionUID: -573038581493881337L
+
+
+
+
+
+
+
+serialVersionUID: 5485001826076289109L
+
+
+
+
+
+Serialized Fields
+
+
+
+steadyStateThreshold
+
+double steadyStateThreshold
+
+Deprecated. Threshold for cost steady state detection.
+
+
+
+
+
+convergence
+
+double convergence
+
+Deprecated. Threshold for cost convergence.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -5705952631533171019L
+
+
+
+
+
+Serialized Fields
+
+
+
+solvedCols
+
+int solvedCols
+
+Deprecated. Number of solved variables.
+
+
+
+
+
+diagR
+
+double[] diagR
+
+Deprecated. Diagonal elements of the R matrix in the Q.R. decomposition.
+
+
+
+
+
+jacNorm
+
+double[] jacNorm
+
+Deprecated. Norms of the columns of the jacobian matrix.
+
+
+
+
+
+beta
+
+double[] beta
+
+Deprecated. Coefficients of the Householder transforms vectors.
+
+
+
+
+
+permutation
+
+int[] permutation
+
+Deprecated. Columns permutation array.
+
+
+
+
+
+rank
+
+int rank
+
+Deprecated. Rank of the jacobian matrix.
+
+
+
+
+
+lmPar
+
+double lmPar
+
+Deprecated. Levenberg-Marquardt parameter.
+
+
+
+
+
+lmDir
+
+double[] lmDir
+
+Deprecated. Parameters evolution direction associated with lmPar.
+
+
+
+
+
+initialStepBoundFactor
+
+double initialStepBoundFactor
+
+Deprecated. Positive input variable used in determining the initial step bound.
+
+
+
+
+
+costRelativeTolerance
+
+double costRelativeTolerance
+
+Deprecated. Desired relative error in the sum of squares.
+
+
+
+
+
+parRelativeTolerance
+
+double parRelativeTolerance
+
+Deprecated. Desired relative error in the approximate solution parameters.
+
+
+
+
+
+orthoTolerance
+
+double orthoTolerance
+
+Deprecated. Desired max cosine on the orthogonality between the function vector
+ and the columns of the jacobian.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 4360046376796901941L
+
+
+
+
+
+Serialized Fields
+
+
+
+weight
+
+double weight
+
+Deprecated. Measurement weight.
+
+
+
+
+
+measuredValue
+
+double measuredValue
+
+Deprecated. Value of the measurements.
+
+
+
+
+
+ignored
+
+boolean ignored
+
+Deprecated. Ignore measurement indicator.
+
+
+
+
+
+
+
+Package org.apache.commons.math.fraction
+
+
+
+
+
+
+serialVersionUID: -6981118387974191891L
+
+
+
+
+
+Serialized Fields
+
+
+
+denominatorFormat
+
+NumberFormat denominatorFormat
+
+The format used for the denominator.
+
+
+
+
+
+numeratorFormat
+
+NumberFormat numeratorFormat
+
+The format used for the numerator.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -5630213147331578515L
+
+
+
+
+
+Serialized Fields
+
+
+
+numerator
+
+BigInteger numerator
+
+The numerator.
+
+
+
+
+
+denominator
+
+BigInteger denominator
+
+The denominator.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -1699294557189741703L
+
+
+
+
+
+Serialization Methods
+
+
+
+
+readResolve
+
+private Object readResolve
+
+Handle deserialization of the singleton.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -2932167925527338976L
+
+
+
+
+
+
+
+serialVersionUID: 3698073679419233275L
+
+
+
+
+
+Serialized Fields
+
+
+
+denominator
+
+int denominator
+
+The denominator.
+
+
+
+
+
+numerator
+
+int numerator
+
+The numerator.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -4661812640132576263L
+
+
+
+
+
+
+
+serialVersionUID: -1257768487499119313L
+
+
+
+
+
+Serialization Methods
+
+
+
+
+readResolve
+
+private Object readResolve
+
+Handle deserialization of the singleton.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 3008655719530972611L
+
+
+
+
+
+
+
+serialVersionUID: -6337346779577272307L
+
+
+
+
+
+Serialized Fields
+
+
+
+wholeFormat
+
+NumberFormat wholeFormat
+
+The format used for the whole number.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 760934726031766749L
+
+
+
+
+
+Serialized Fields
+
+
+
+wholeFormat
+
+NumberFormat wholeFormat
+
+The format used for the whole number.
+
+
+
+
+
+
+
+Package org.apache.commons.math.genetics
+
+
+
+
+
+
+serialVersionUID: 1L
+
+
+
+
+
+Package org.apache.commons.math.geometry
+
+
+
+
+
+
+serialVersionUID: -1360952845582206770L
+
+
+
+
+
+
+
+serialVersionUID: 5647178478658937642L
+
+
+
+
+
+
+
+serialVersionUID: -2153622329907944313L
+
+
+
+
+
+Serialized Fields
+
+
+
+q0
+
+double q0
+
+Scalar coordinate of the quaternion.
+
+
+
+
+
+q1
+
+double q1
+
+First coordinate of the vectorial part of the quaternion.
+
+
+
+
+
+q2
+
+double q2
+
+Second coordinate of the vectorial part of the quaternion.
+
+
+
+
+
+q3
+
+double q3
+
+Third coordinate of the vectorial part of the quaternion.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 5133268763396045979L
+
+
+
+
+
+Serialized Fields
+
+
+
+x
+
+double x
+
+Abscissa.
+
+
+
+
+
+y
+
+double y
+
+Ordinate.
+
+
+
+
+
+z
+
+double z
+
+Height.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -5447606608652576301L
+
+
+
+
+
+Serialized Fields
+
+
+
+prefix
+
+String prefix
+
+Prefix.
+
+
+
+
+
+suffix
+
+String suffix
+
+Suffix.
+
+
+
+
+
+separator
+
+String separator
+
+Separator.
+
+
+
+
+
+trimmedPrefix
+
+String trimmedPrefix
+
+Trimmed prefix.
+
+
+
+
+
+trimmedSuffix
+
+String trimmedSuffix
+
+Trimmed suffix.
+
+
+
+
+
+trimmedSeparator
+
+String trimmedSeparator
+
+Trimmed separator.
+
+
+
+
+
+format
+
+NumberFormat format
+
+The format used for components.
+
+
+
+
+
+
+
+Package org.apache.commons.math.linear
+
+
+
+
+
+
+serialVersionUID: 7260756672015356458L
+
+
+
+
+
+Serialized Fields
+
+
+
+data
+
+FieldElement <T >[][] data
+
+Entries of the matrix
+
+
+
+
+
+
+
+
+
+serialVersionUID: -1067294169172445528L
+
+
+
+
+
+Serialized Fields
+
+
+
+data
+
+double[][] data
+
+Entries of the matrix
+
+
+
+
+
+
+
+
+
+serialVersionUID: 7648186910365927050L
+
+
+
+
+
+Serialized Fields
+
+
+
+data
+
+FieldElement <T >[] data
+
+Entries of the vector.
+
+
+
+
+
+field
+
+Field <T > field
+
+Field to which the elements belong.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -1097961340710804027L
+
+
+
+
+
+Serialized Fields
+
+
+
+data
+
+double[] data
+
+Entries of the vector.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -1011428905656140431L
+
+
+
+
+
+Serialized Fields
+
+
+
+data
+
+BigDecimal [][] data
+
+Deprecated. Entries of the matrix
+
+
+
+
+
+lu
+
+BigDecimal [][] lu
+
+Deprecated. Entries of cached LU decomposition.
+ All updates to data (other than luDecompose()) *must* set this to null
+
+
+
+
+
+permutation
+
+int[] permutation
+
+Deprecated. Permutation associated with LU decomposition
+
+
+
+
+
+parity
+
+int parity
+
+Deprecated. Parity of the permutation associated with the LU decomposition
+
+
+
+
+
+roundingMode
+
+int roundingMode
+
+Deprecated. Rounding mode for divisions
+
+
+
+
+
+scale
+
+int scale
+
+Deprecated. BigDecimal scale
+
+
+
+
+
+
+
+
+
+serialVersionUID: -4602336630143123183L
+
+
+
+
+
+Serialized Fields
+
+
+
+blocks
+
+FieldElement <T >[][] blocks
+
+Blocks of matrix entries.
+
+
+
+
+
+rows
+
+int rows
+
+Number of rows of the matrix.
+
+
+
+
+
+columns
+
+int columns
+
+Number of columns of the matrix.
+
+
+
+
+
+blockRows
+
+int blockRows
+
+Number of block rows of the matrix.
+
+
+
+
+
+blockColumns
+
+int blockColumns
+
+Number of block columns of the matrix.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 4991895511313664478L
+
+
+
+
+
+Serialized Fields
+
+
+
+blocks
+
+double[][] blocks
+
+Blocks of matrix entries.
+
+
+
+
+
+rows
+
+int rows
+
+Number of rows of the matrix.
+
+
+
+
+
+columns
+
+int columns
+
+Number of columns of the matrix.
+
+
+
+
+
+blockRows
+
+int blockRows
+
+Number of block rows of the matrix.
+
+
+
+
+
+blockColumns
+
+int blockColumns
+
+Number of block columns of the matrix.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 1135533765052675495L
+
+
+
+
+
+
+
+serialVersionUID: -2382324504109300625L
+
+
+
+
+
+
+
+serialVersionUID: 3814333035048617048L
+
+
+
+
+
+
+
+serialVersionUID: 8996207526636673730L
+
+
+
+
+
+
+
+serialVersionUID: 4122929125438624648L
+
+
+
+
+
+
+
+serialVersionUID: -7012803946709786097L
+
+
+
+
+
+
+
+serialVersionUID: -5962461716457143437L
+
+
+
+
+
+Serialized Fields
+
+
+
+rows
+
+int rows
+
+Number of rows of the matrix.
+
+
+
+
+
+columns
+
+int columns
+
+Number of columns of the matrix.
+
+
+
+
+
+entries
+
+OpenIntToDoubleHashMap entries
+
+Storage for (sparse) matrix elements.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 8772222695580707260L
+
+
+
+
+
+Serialized Fields
+
+
+
+entries
+
+OpenIntToDoubleHashMap entries
+
+Entries of the vector.
+
+
+
+
+
+virtualSize
+
+int virtualSize
+
+Dimension of the vector.
+
+
+
+
+
+epsilon
+
+double epsilon
+
+Tolerance for having a value considered zero.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -1067294169172445528L
+
+
+
+
+
+Serialized Fields
+
+
+
+data
+
+double[][] data
+
+Deprecated. Entries of the matrix
+
+
+
+
+
+
+
+
+
+serialVersionUID: -708767813036157690L
+
+
+
+
+
+Serialized Fields
+
+
+
+prefix
+
+String prefix
+
+Prefix.
+
+
+
+
+
+suffix
+
+String suffix
+
+Suffix.
+
+
+
+
+
+separator
+
+String separator
+
+Separator.
+
+
+
+
+
+trimmedPrefix
+
+String trimmedPrefix
+
+Trimmed prefix.
+
+
+
+
+
+trimmedSuffix
+
+String trimmedSuffix
+
+Trimmed suffix.
+
+
+
+
+
+trimmedSeparator
+
+String trimmedSeparator
+
+Trimmed separator.
+
+
+
+
+
+format
+
+NumberFormat format
+
+The format used for components.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -7379143356784298432L
+
+
+
+
+
+
+
+serialVersionUID: 7841233292190413362L
+
+
+
+
+
+Serialized Fields
+
+
+
+field
+
+Field <T > field
+
+Field to which the elements belong.
+
+
+
+
+
+entries
+
+OpenIntToFieldHashMap <T extends FieldElement <T >> entries
+
+Entries of the vector.
+
+
+
+
+
+virtualSize
+
+int virtualSize
+
+Dimension of the vector.
+
+
+
+
+
+
+
+Package org.apache.commons.math.ode
+
+
+
+
+
+
+serialVersionUID: -1417964919405031606L
+
+
+
+
+
+Serialized Fields
+
+
+
+initialTime
+
+double initialTime
+
+Initial integration time.
+
+
+
+
+
+finalTime
+
+double finalTime
+
+Final integration time.
+
+
+
+
+
+forward
+
+boolean forward
+
+Integration direction indicator.
+
+
+
+
+
+index
+
+int index
+
+Current interpolator index.
+
+
+
+
+
+steps
+
+List <E > steps
+
+Steps table.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 5666710788967425123L
+
+
+
+
+
+
+
+serialVersionUID: -1607588949778036796L
+
+
+
+
+
+Package org.apache.commons.math.ode.events
+
+
+
+
+
+
+serialVersionUID: -898215297400035290L
+
+
+
+
+
+Package org.apache.commons.math.ode.jacobians
+
+
+
+
+Package org.apache.commons.math.ode.sampling
+
+
+
+
+
+
+
+
+
+Serialization Methods
+
+
+
+
+readExternal
+
+public abstract void readExternal ObjectInput in)
+ throws IOException ,
+ ClassNotFoundException
+
+
+
+
+
+Throws:
+IOException ClassNotFoundException
+
+
+writeExternal
+
+public abstract void writeExternal ObjectOutput out)
+ throws IOException
+
+
+
+
+
+Throws:
+IOException
+
+
+
+
+
+
+serialVersionUID: 1708010296707839488L
+
+
+
+
+
+Serialization Methods
+
+
+
+
+readExternal
+
+public void readExternal ObjectInput in)
+ throws IOException
+
+Read the instance from an input channel.
+
+
+
+Throws:
+IOException
+
+
+writeExternal
+
+public void writeExternal ObjectOutput out)
+ throws IOException
+
+Write the instance to an output channel.
+
+
+
+Throws:
+IOException
+
+
+
+
+
+
+serialVersionUID: -7179861704951334960L
+
+
+
+
+
+Serialization Methods
+
+
+
+
+readExternal
+
+public void readExternal ObjectInput in)
+ throws IOException ,
+ ClassNotFoundException
+
+
+
+
+
+Throws:
+IOException ClassNotFoundException
+
+
+writeExternal
+
+public void writeExternal ObjectOutput out)
+ throws IOException
+
+
+
+
+
+Throws:
+IOException
+
+
+
+
+Package org.apache.commons.math.optimization
+
+
+
+
+
+
+serialVersionUID: -357696069587075016L
+
+
+
+
+
+
+
+serialVersionUID: 1003888396256744753L
+
+
+
+
+
+Serialized Fields
+
+
+
+point
+
+double[] point
+
+Point coordinates.
+
+
+
+
+
+value
+
+double value
+
+Value of the objective function at the point.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 1003888396256744753L
+
+
+
+
+
+Serialized Fields
+
+
+
+point
+
+double[] point
+
+Point coordinates.
+
+
+
+
+
+value
+
+double[] value
+
+Vectorial value of the objective function at the point.
+
+
+
+
+
+
+
+Package org.apache.commons.math.optimization.fitting
+
+
+
+
+
+
+serialVersionUID: 5306874947404636157L
+
+
+
+
+
+Serialized Fields
+
+
+
+weight
+
+double weight
+
+Weight of the measurement in the fitting process.
+
+
+
+
+
+x
+
+double x
+
+Abscissa of the point.
+
+
+
+
+
+y
+
+double y
+
+Observed value of the function at x.
+
+
+
+
+
+
+
+Package org.apache.commons.math.optimization.linear
+
+
+
+
+
+
+serialVersionUID: -764632794033034092L
+
+
+
+
+
+Serialization Methods
+
+
+
+
+readObject
+
+private void readObject ObjectInputStream ois)
+ throws ClassNotFoundException ,
+ IOException
+
+Deserialize the instance.
+
+
+
+Throws:
+ClassNotFoundException IOException
+
+
+writeObject
+
+private void writeObject ObjectOutputStream oos)
+ throws IOException
+
+Serialize the instance.
+
+
+
+Throws:
+IOException
+
+
+
+
+Serialized Fields
+
+
+
+relationship
+
+Relationship relationship
+
+Relationship between left and right hand sides (=, <=, >=).
+
+
+
+
+
+value
+
+double value
+
+Value of the constraint (right hand side).
+
+
+
+
+
+
+
+
+
+serialVersionUID: -4531815507568396090L
+
+
+
+
+
+Serialization Methods
+
+
+
+
+readObject
+
+private void readObject ObjectInputStream ois)
+ throws ClassNotFoundException ,
+ IOException
+
+Deserialize the instance.
+
+
+
+Throws:
+ClassNotFoundException IOException
+
+
+writeObject
+
+private void writeObject ObjectOutputStream oos)
+ throws IOException
+
+Serialize the instance.
+
+
+
+Throws:
+IOException
+
+
+
+
+Serialized Fields
+
+
+
+constantTerm
+
+double constantTerm
+
+Constant term of the linear equation.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -3044253632189082760L
+
+
+
+
+
+
+
+serialVersionUID: 940539497277290619L
+
+
+
+
+
+Package org.apache.commons.math.random
+
+
+
+
+
+
+serialVersionUID: 5729073523949762654L
+
+
+
+
+
+Serialized Fields
+
+
+
+binStats
+
+List <E > binStats
+
+List of SummaryStatistics objects characterizing the bins
+
+
+
+
+
+sampleStats
+
+SummaryStatistics sampleStats
+
+Sample statistics
+
+
+
+
+
+max
+
+double max
+
+Max loaded value
+
+
+
+
+
+min
+
+double min
+
+Min loaded value
+
+
+
+
+
+delta
+
+double delta
+
+Grid size
+
+
+
+
+
+binCount
+
+int binCount
+
+number of bins
+
+
+
+
+
+loaded
+
+boolean loaded
+
+is the distribution loaded?
+
+
+
+
+
+upperBounds
+
+double[] upperBounds
+
+upper bounds of subintervals in (0,1) "belonging" to the bins
+
+
+
+
+
+randomData
+
+RandomData randomData
+
+RandomData instance to use in repeated calls to getNext()
+
+
+
+
+
+
+
+
+
+serialVersionUID: -7745277476784028798L
+
+
+
+
+
+
+
+serialVersionUID: 8661194735290153518L
+
+
+
+
+
+Serialized Fields
+
+
+
+mt
+
+int[] mt
+
+Bytes pool.
+
+
+
+
+
+mti
+
+int mti
+
+Current index in the bytes pool.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 2306581345647615033L
+
+
+
+
+
+Serialized Fields
+
+
+
+randomGenerator
+
+RandomGenerator randomGenerator
+
+Wrapped randomGenerator instance
+
+
+
+
+
+
+
+
+
+serialVersionUID: -626730818244969716L
+
+
+
+
+
+Serialized Fields
+
+
+
+rand
+
+RandomGenerator rand
+
+underlying random number generator
+
+
+
+
+
+secRand
+
+SecureRandom secRand
+
+underlying secure random number generator
+
+
+
+
+
+
+
+Package org.apache.commons.math.stat
+
+
+
+
+
+
+serialVersionUID: -3845586908418844111L
+
+
+
+
+
+Serialized Fields
+
+
+
+freqTable
+
+TreeMap <K ,V > freqTable
+
+underlying collection
+
+
+
+
+
+
+
+Package org.apache.commons.math.stat.clustering
+
+
+
+
+
+
+serialVersionUID: -3442297081515880464L
+
+
+
+
+
+Serialized Fields
+
+
+
+points
+
+List <E > points
+
+The points contained in this cluster.
+
+
+
+
+
+center
+
+Clusterable <T > center
+
+Center of the cluster.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 3946024775784901369L
+
+
+
+
+
+Serialized Fields
+
+
+
+point
+
+int[] point
+
+Point coordinates.
+
+
+
+
+
+
+
+Package org.apache.commons.math.stat.descriptive
+
+
+
+
+
+
+serialVersionUID: -8207112444016386906L
+
+
+
+
+
+Serialized Fields
+
+
+
+statisticsPrototype
+
+SummaryStatistics statisticsPrototype
+
+A SummaryStatistics serving as a prototype for creating SummaryStatistics
+ contributing to this aggregate
+
+
+
+
+
+statistics
+
+SummaryStatistics statistics
+
+The SummaryStatistics in which aggregate statistics are accumulated.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 4133067267405273064L
+
+
+
+
+
+Serialized Fields
+
+
+
+windowSize
+
+int windowSize
+
+hold the window size
+
+
+
+
+
+eDA
+
+ResizableDoubleArray eDA
+
+Stored data values
+
+
+
+
+
+meanImpl
+
+UnivariateStatistic meanImpl
+
+Mean statistic implementation - can be reset by setter.
+
+
+
+
+
+geometricMeanImpl
+
+UnivariateStatistic geometricMeanImpl
+
+Geometric mean statistic implementation - can be reset by setter.
+
+
+
+
+
+kurtosisImpl
+
+UnivariateStatistic kurtosisImpl
+
+Kurtosis statistic implementation - can be reset by setter.
+
+
+
+
+
+maxImpl
+
+UnivariateStatistic maxImpl
+
+Maximum statistic implementation - can be reset by setter.
+
+
+
+
+
+minImpl
+
+UnivariateStatistic minImpl
+
+Minimum statistic implementation - can be reset by setter.
+
+
+
+
+
+percentileImpl
+
+UnivariateStatistic percentileImpl
+
+Percentile statistic implementation - can be reset by setter.
+
+
+
+
+
+skewnessImpl
+
+UnivariateStatistic skewnessImpl
+
+Skewness statistic implementation - can be reset by setter.
+
+
+
+
+
+varianceImpl
+
+UnivariateStatistic varianceImpl
+
+Variance statistic implementation - can be reset by setter.
+
+
+
+
+
+sumsqImpl
+
+UnivariateStatistic sumsqImpl
+
+Sum of squares statistic implementation - can be reset by setter.
+
+
+
+
+
+sumImpl
+
+UnivariateStatistic sumImpl
+
+Sum statistic implementation - can be reset by setter.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 2271900808994826718L
+
+
+
+
+
+Serialized Fields
+
+
+
+k
+
+int k
+
+Dimension of the data.
+
+
+
+
+
+n
+
+long n
+
+Count of values that have been added
+
+
+
+
+
+sumImpl
+
+StorelessUnivariateStatistic [] sumImpl
+
+Sum statistic implementation - can be reset by setter.
+
+
+
+
+
+sumSqImpl
+
+StorelessUnivariateStatistic [] sumSqImpl
+
+Sum of squares statistic implementation - can be reset by setter.
+
+
+
+
+
+minImpl
+
+StorelessUnivariateStatistic [] minImpl
+
+Minimum statistic implementation - can be reset by setter.
+
+
+
+
+
+maxImpl
+
+StorelessUnivariateStatistic [] maxImpl
+
+Maximum statistic implementation - can be reset by setter.
+
+
+
+
+
+sumLogImpl
+
+StorelessUnivariateStatistic [] sumLogImpl
+
+Sum of log statistic implementation - can be reset by setter.
+
+
+
+
+
+geoMeanImpl
+
+StorelessUnivariateStatistic [] geoMeanImpl
+
+Geometric mean statistic implementation - can be reset by setter.
+
+
+
+
+
+meanImpl
+
+StorelessUnivariateStatistic [] meanImpl
+
+Mean statistic implementation - can be reset by setter.
+
+
+
+
+
+covarianceImpl
+
+VectorialCovariance covarianceImpl
+
+Covariance statistic implementation - cannot be reset.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -5108854841843722536L
+
+
+
+
+
+Serialized Fields
+
+
+
+mean
+
+double mean
+
+The sample mean
+
+
+
+
+
+variance
+
+double variance
+
+The sample variance
+
+
+
+
+
+n
+
+long n
+
+The number of observations in the sample
+
+
+
+
+
+max
+
+double max
+
+The maximum value
+
+
+
+
+
+min
+
+double min
+
+The minimum value
+
+
+
+
+
+sum
+
+double sum
+
+The sum of the sample values
+
+
+
+
+
+
+
+
+
+serialVersionUID: -2021321786743555871L
+
+
+
+
+
+Serialized Fields
+
+
+
+n
+
+long n
+
+count of values that have been added
+
+
+
+
+
+secondMoment
+
+SecondMoment secondMoment
+
+SecondMoment is used to compute the mean and variance
+
+
+
+
+
+sum
+
+Sum sum
+
+sum of values that have been added
+
+
+
+
+
+sumsq
+
+SumOfSquares sumsq
+
+sum of the square of each value that has been added
+
+
+
+
+
+min
+
+Min min
+
+min of values that have been added
+
+
+
+
+
+max
+
+Max max
+
+max of values that have been added
+
+
+
+
+
+sumLog
+
+SumOfLogs sumLog
+
+sumLog of values that have been added
+
+
+
+
+
+geoMean
+
+GeometricMean geoMean
+
+geoMean of values that have been added
+
+
+
+
+
+mean
+
+Mean mean
+
+mean of values that have been added
+
+
+
+
+
+variance
+
+Variance variance
+
+variance of values that have been added
+
+
+
+
+
+sumImpl
+
+StorelessUnivariateStatistic sumImpl
+
+Sum statistic implementation - can be reset by setter.
+
+
+
+
+
+sumsqImpl
+
+StorelessUnivariateStatistic sumsqImpl
+
+Sum of squares statistic implementation - can be reset by setter.
+
+
+
+
+
+minImpl
+
+StorelessUnivariateStatistic minImpl
+
+Minimum statistic implementation - can be reset by setter.
+
+
+
+
+
+maxImpl
+
+StorelessUnivariateStatistic maxImpl
+
+Maximum statistic implementation - can be reset by setter.
+
+
+
+
+
+sumLogImpl
+
+StorelessUnivariateStatistic sumLogImpl
+
+Sum of log statistic implementation - can be reset by setter.
+
+
+
+
+
+geoMeanImpl
+
+StorelessUnivariateStatistic geoMeanImpl
+
+Geometric mean statistic implementation - can be reset by setter.
+
+
+
+
+
+meanImpl
+
+StorelessUnivariateStatistic meanImpl
+
+Mean statistic implementation - can be reset by setter.
+
+
+
+
+
+varianceImpl
+
+StorelessUnivariateStatistic varianceImpl
+
+Variance statistic implementation - can be reset by setter.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 1L
+
+
+
+
+
+
+
+serialVersionUID: 7099834153347155363L
+
+
+
+
+
+
+
+serialVersionUID: 1909861009042253704L
+
+
+
+
+
+Package org.apache.commons.math.stat.descriptive.moment
+
+
+
+
+
+
+serialVersionUID: 6112755307178490473L
+
+
+
+
+
+Serialized Fields
+
+
+
+n
+
+long n
+
+Count of values that have been added
+
+
+
+
+
+m1
+
+double m1
+
+First moment of values that have been added
+
+
+
+
+
+dev
+
+double dev
+
+Deviation of most recently added value from previous first moment.
+ Retained to prevent repeated computation in higher order moments.
+
+
+
+
+
+nDev
+
+double nDev
+
+Deviation of most recently added value from previous first moment,
+ normalized by previous sample size. Retained to prevent repeated
+ computation in higher order moments
+
+
+
+
+
+
+
+
+
+serialVersionUID: 4763990447117157611L
+
+
+
+
+
+Serialized Fields
+
+
+
+m4
+
+double m4
+
+fourth moment of values that have been added
+
+
+
+
+
+
+
+
+
+serialVersionUID: -8178734905303459453L
+
+
+
+
+
+Serialized Fields
+
+
+
+sumOfLogs
+
+StorelessUnivariateStatistic sumOfLogs
+
+Wrapped SumOfLogs instance
+
+
+
+
+
+
+
+
+
+serialVersionUID: 2784465764798260919L
+
+
+
+
+
+Serialized Fields
+
+
+
+moment
+
+FourthMoment moment
+
+Fourth Moment on which this statistic is based
+
+
+
+
+
+incMoment
+
+boolean incMoment
+
+Determines whether or not this statistic can be incremented or cleared.
+
+ Statistics based on (constructed from) external moments cannot
+ be incremented or cleared.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -1296043746617791564L
+
+
+
+
+
+Serialized Fields
+
+
+
+moment
+
+FirstMoment moment
+
+First moment on which this statistic is based.
+
+
+
+
+
+incMoment
+
+boolean incMoment
+
+Determines whether or not this statistic can be incremented or cleared.
+
+ Statistics based on (constructed from) external moments cannot
+ be incremented or cleared.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 3942403127395076445L
+
+
+
+
+
+Serialized Fields
+
+
+
+m2
+
+double m2
+
+second moment of values that have been added
+
+
+
+
+
+
+
+
+
+serialVersionUID: -2653430366886024994L
+
+
+
+
+
+Serialized Fields
+
+
+
+biasCorrected
+
+boolean biasCorrected
+
+Determines whether or not bias correction is applied when computing the
+ value of the statisic. True means that bias is corrected.
+
+
+
+
+
+varianceDirection
+
+SemiVariance.Direction varianceDirection
+
+Determines whether to calculate downside or upside SemiVariance.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 7101857578996691352L
+
+
+
+
+
+Serialized Fields
+
+
+
+moment
+
+ThirdMoment moment
+
+Third moment on which this statistic is based
+
+
+
+
+
+incMoment
+
+boolean incMoment
+
+Determines whether or not this statistic can be incremented or cleared.
+
+ Statistics based on (constructed from) external moments cannot
+ be incremented or cleared.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 5728716329662425188L
+
+
+
+
+
+Serialized Fields
+
+
+
+variance
+
+Variance variance
+
+Wrapped Variance instance
+
+
+
+
+
+
+
+
+
+serialVersionUID: -7818711964045118679L
+
+
+
+
+
+Serialized Fields
+
+
+
+m3
+
+double m3
+
+third moment of values that have been added
+
+
+
+
+
+nDevSq
+
+double nDevSq
+
+Square of deviation of most recently added value from previous first
+ moment, normalized by previous sample size. Retained to prevent
+ repeated computation in higher order moments. nDevSq = nDev * nDev.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -9111962718267217978L
+
+
+
+
+
+Serialized Fields
+
+
+
+moment
+
+SecondMoment moment
+
+SecondMoment is used in incremental calculation of Variance
+
+
+
+
+
+incMoment
+
+boolean incMoment
+
+Boolean test to determine if this Variance should also increment
+ the second moment, this evaluates to false when this Variance is
+ constructed with an external SecondMoment as a parameter.
+
+
+
+
+
+isBiasCorrected
+
+boolean isBiasCorrected
+
+Determines whether or not bias correction is applied when computing the
+ value of the statisic. True means that bias is corrected. See
+ Variance
+
+
+
+
+
+
+
+
+serialVersionUID: 4118372414238930270L
+
+
+
+
+
+Serialized Fields
+
+
+
+sums
+
+double[] sums
+
+Sums for each component.
+
+
+
+
+
+productsSums
+
+double[] productsSums
+
+Sums of products for each component.
+
+
+
+
+
+isBiasCorrected
+
+boolean isBiasCorrected
+
+Indicator for bias correction.
+
+
+
+
+
+n
+
+long n
+
+Number of vectors in the sample.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 8223009086481006892L
+
+
+
+
+
+Serialized Fields
+
+
+
+means
+
+Mean [] means
+
+Means for each component.
+
+
+
+
+
+
+
+Package org.apache.commons.math.stat.descriptive.rank
+
+
+
+
+
+
+serialVersionUID: -5593383832225844641L
+
+
+
+
+
+Serialized Fields
+
+
+
+n
+
+long n
+
+Number of values that have been added
+
+
+
+
+
+value
+
+double value
+
+Current value of the statistic
+
+
+
+
+
+
+
+
+
+serialVersionUID: -3961477041290915687L
+
+
+
+
+
+
+
+serialVersionUID: -2941995784909003131L
+
+
+
+
+
+Serialized Fields
+
+
+
+n
+
+long n
+
+Number of values that have been added
+
+
+
+
+
+value
+
+double value
+
+Current value of the statistic
+
+
+
+
+
+
+
+
+
+serialVersionUID: -8091216485095130416L
+
+
+
+
+
+Serialized Fields
+
+
+
+quantile
+
+double quantile
+
+Determines what percentile is computed when evaluate() is activated
+ with no quantile argument
+
+
+
+
+
+
+
+Package org.apache.commons.math.stat.descriptive.summary
+
+
+
+
+
+
+serialVersionUID: 2824226005990582538L
+
+
+
+
+
+Serialized Fields
+
+
+
+n
+
+long n
+
+The number of values that have been added
+
+
+
+
+
+value
+
+double value
+
+The current Running Product.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -8231831954703408316L
+
+
+
+
+
+Serialized Fields
+
+
+
+n
+
+long n
+
+
+
+
+
+value
+
+double value
+
+The currently running sum.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -370076995648386763L
+
+
+
+
+
+Serialized Fields
+
+
+
+n
+
+int n
+
+Number of values that have been added
+
+
+
+
+
+value
+
+double value
+
+The currently running value
+
+
+
+
+
+
+
+
+
+serialVersionUID: 1460986908574398008L
+
+
+
+
+
+Serialized Fields
+
+
+
+n
+
+long n
+
+
+
+
+
+value
+
+double value
+
+The currently running sumSq
+
+
+
+
+
+
+
+Package org.apache.commons.math.stat.regression
+
+
+
+
+
+
+serialVersionUID: -3004689053607543335L
+
+
+
+
+
+Serialized Fields
+
+
+
+distribution
+
+TDistribution distribution
+
+the distribution used to compute inference statistics.
+
+
+
+
+
+sumX
+
+double sumX
+
+sum of x values
+
+
+
+
+
+sumXX
+
+double sumXX
+
+total variation in x (sum of squared deviations from xbar)
+
+
+
+
+
+sumY
+
+double sumY
+
+sum of y values
+
+
+
+
+
+sumYY
+
+double sumYY
+
+total variation in y (sum of squared deviations from ybar)
+
+
+
+
+
+sumXY
+
+double sumXY
+
+sum of products
+
+
+
+
+
+n
+
+long n
+
+number of observations
+
+
+
+
+
+xbar
+
+double xbar
+
+mean of accumulated x values, used in updating formulas
+
+
+
+
+
+ybar
+
+double ybar
+
+mean of accumulated y values, used in updating formulas
+
+
+
+
+
+
+
+Package org.apache.commons.math.transform
+
+
+
+
+
+
+serialVersionUID: 5138259215438106000L
+
+
+
+
+
+Serialized Fields
+
+
+
+roots
+
+org.apache.commons.math.transform.FastFourierTransformer.RootsOfUnity roots
+
+roots of unity
+
+
+
+
+
+
+
+Package org.apache.commons.math.util
+
+
+
+
+
+
+serialVersionUID: 4984534880991310382L
+
+
+
+
+
+Serialized Fields
+
+
+
+d
+
+BigDecimal d
+
+Underlying BigDecimal.
+
+
+
+
+
+roundingMode
+
+RoundingMode roundingMode
+
+Rounding mode for divisions.
+
+
+
+
+
+scale
+
+int scale
+
+BigDecimal scale
+
+
+
+
+
+
+
+
+
+serialVersionUID: 4756431066541037559L
+
+
+
+
+
+Serialization Methods
+
+
+
+
+readResolve
+
+private Object readResolve
+
+Handle deserialization of the singleton.
+
+
+
+
+
+
+
+
+
+serialVersionUID: 5358685519349262494L
+
+
+
+
+
+
+
+serialVersionUID: 4019938025047800455L
+
+
+
+
+
+
+
+serialVersionUID: -3646337053166149105L
+
+
+
+
+
+Serialization Methods
+
+
+
+
+readObject
+
+private void readObject ObjectInputStream stream)
+ throws IOException ,
+ ClassNotFoundException
+
+Read a serialized object.
+
+
+
+Throws:
+IOException ClassNotFoundException
+
+
+
+
+Serialized Fields
+
+
+
+keys
+
+int[] keys
+
+Keys table.
+
+
+
+
+
+values
+
+double[] values
+
+Values table.
+
+
+
+
+
+states
+
+byte[] states
+
+States table.
+
+
+
+
+
+missingEntries
+
+double missingEntries
+
+Return value for missing entries.
+
+
+
+
+
+size
+
+int size
+
+Current size of the map.
+
+
+
+
+
+mask
+
+int mask
+
+Bit mask for hash values.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -9179080286849120720L
+
+
+
+
+
+Serialization Methods
+
+
+
+
+readObject
+
+private void readObject ObjectInputStream stream)
+ throws IOException ,
+ ClassNotFoundException
+
+Read a serialized object.
+
+
+
+Throws:
+IOException ClassNotFoundException
+
+
+
+
+Serialized Fields
+
+
+
+field
+
+Field <T > field
+
+Field to which the elements belong.
+
+
+
+
+
+keys
+
+int[] keys
+
+Keys table.
+
+
+
+
+
+values
+
+FieldElement <T >[] values
+
+Values table.
+
+
+
+
+
+states
+
+byte[] states
+
+States table.
+
+
+
+
+
+missingEntries
+
+FieldElement <T > missingEntries
+
+Return value for missing entries.
+
+
+
+
+
+size
+
+int size
+
+Current size of the map.
+
+
+
+
+
+mask
+
+int mask
+
+Bit mask for hash values.
+
+
+
+
+
+
+
+
+
+serialVersionUID: -3485529955529426875L
+
+
+
+
+
+Serialized Fields
+
+
+
+contractionCriteria
+
+float contractionCriteria
+
+The contraction criteria determines when the internal array will be
+ contracted to fit the number of elements contained in the element
+ array + 1.
+
+
+
+
+
+expansionFactor
+
+float expansionFactor
+
+The expansion factor of the array. When the array needs to be expanded,
+ the new array size will be
+ internalArray.length * expansionFactor
+ if expansionMode is set to MULTIPLICATIVE_MODE, or
+ internalArray.length + expansionFactor if
+ expansionMode is set to ADDITIVE_MODE.
+
+
+
+
+
+expansionMode
+
+int expansionMode
+
+Determines whether array expansion by expansionFactor
+ is additive or multiplicative.
+
+
+
+
+
+initialCapacity
+
+int initialCapacity
+
+The initial capacity of the array. Initial capacity is not exposed as a
+ property as it is only meaningful when passed to a constructor.
+
+
+
+
+
+internalArray
+
+double[] internalArray
+
+The internal storage array.
+
+
+
+
+
+numElements
+
+int numElements
+
+The number of addressable elements in the array. Note that this
+ has nothing to do with the length of the internal storage array.
+
+
+
+
+
+startIndex
+
+int startIndex
+
+The position of the first addressable element in the internal storage
+ array. The addressable elements in the array are
+ internalArray[startIndex],...,internalArray[startIndex + numElements -1]
+
+
+
+
+
+
+
+
+
+
+serialVersionUID: 4605318041528645258L
+
+
+
+
+
+Serialized Fields
+
+
+
+defaultTransformer
+
+NumberTransformer defaultTransformer
+
+A default Number Transformer for Numbers and numeric Strings.
+
+
+
+
+
+map
+
+Map <K ,V > map
+
+The internal Map.
+
+
+
+
+
+
+
The Apache Software Foundation . All Rights Reserved.
+
+
diff -r 0b3f87acaabc -r e8ccd518555b libs/commons-math-2.1/docs/apidocs/stylesheet.css
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/libs/commons-math-2.1/docs/apidocs/stylesheet.css Tue Jan 04 10:03:00 2011 +0100
@@ -0,0 +1,29 @@
+/* Javadoc style sheet */
+
+/* Define colors, fonts and other style attributes here to override the defaults */
+
+/* Page background color */
+body { background-color: #FFFFFF; color:#000000 }
+
+/* Headings */
+h1 { font-size: 145% }
+
+/* Table colors */
+.TableHeadingColor { background: #CCCCFF; color:#000000 } /* Dark mauve */
+.TableSubHeadingColor { background: #EEEEFF; color:#000000 } /* Light mauve */
+.TableRowColor { background: #FFFFFF; color:#000000 } /* White */
+
+/* Font used in left-hand frame lists */
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+.FrameHeadingFont { font-size: 90%; font-family: Helvetica, Arial, sans-serif; color:#000000 }
+.FrameItemFont { font-size: 90%; font-family: Helvetica, Arial, sans-serif; color:#000000 }
+
+/* Navigation bar fonts and colors */
+.NavBarCell1 { background-color:#EEEEFF; color:#000000} /* Light mauve */
+.NavBarCell1Rev { background-color:#00008B; color:#FFFFFF} /* Dark Blue */
+.NavBarFont1 { font-family: Arial, Helvetica, sans-serif; color:#000000;color:#000000;}
+.NavBarFont1Rev { font-family: Arial, Helvetica, sans-serif; color:#FFFFFF;color:#FFFFFF;}
+
+.NavBarCell2 { font-family: Arial, Helvetica, sans-serif; background-color:#FFFFFF; color:#000000}
+.NavBarCell3 { font-family: Arial, Helvetica, sans-serif; background-color:#FFFFFF; color:#000000}
+