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BigFraction
.
FieldMatrix
methods regardless of the underlying storage.FractionFormat
and BigFractionFormat
.RandomGenerator
interface.StorelessUnivariateStatistic
interface.SubHyperplane
.UnivariateStatistic
interface.Adams-Bashforth
and
Adams-Moulton
integrators.Complex
whose value is
(this + addend)
.
Complex
whose value is (this + addend)
,
with addend
interpreted as a real number.
BigInteger
,
returning the result in reduced form.
m
to this matrix.
m
.
v
.
m
.
m
.
m
.
v
.
v
.
data
.
SummaryStatistics
from several data sets or
data set partitions.SummaryStatistics
for aggregation.(bracketed univariate real) root-finding algorithm
may accept as solutions.BOBYQAOptimizer.newPoint
, chosen by
altmov
.
double[]
arrays.
double[]
arrays.
Math
.FieldElement
[][] array to store entries.FieldMatrix<T>
with the supplied row and column dimensions.
FieldMatrix<T>
using the input array as the underlying
data array.
FieldMatrix<T>
using the input array as the underlying
data array.
FieldMatrix<T>
using the input array as the underlying
data array.
FieldMatrix<T>
using the input array as the underlying
data array.
FieldMatrix<T>
using v
as the
data for the unique column of the created matrix.
FieldMatrix<T>
using v
as the
data for the unique column of the created matrix.
RealMatrix
using a double[][]
array to
store entries.RealMatrix
using the input array as the underlying
data array.
v
as the
data for the unique column of the created matrix.
FieldVector
interface with a FieldElement
array.RealVector
interface with a double array.Cluster
.
BigDecimal
.
BigDecimal
following the passed
rounding mode.
BigDecimal
following the passed scale
and rounding mode.
BigFraction
equivalent to the passed BigInteger, ie
"num / 1".
BigFraction
given the numerator and denominator as
BigInteger
.
BigFraction
equivalent to the passed int, ie
"num / 1".
BigFraction
given the numerator and denominator as simple
int.
BigFraction
equivalent to the passed long, ie "num / 1".
BigFraction
given the numerator and denominator as simple
long.
FieldMatrix
/BigFraction
matrix to a RealMatrix
.
BinaryChromosome
s.n choose k
", the number of
k
-element subsets that can be selected from an
n
-element set.
double
representation of the Binomial
Coefficient, "n choose k
", the number of
k
-element subsets that can be selected from an
n
-element set.
log
of the Binomial
Coefficient, "n choose k
", the number of
k
-element subsets that can be selected from an
n
-element set.
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.
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.
(univariate real) root-finding
algorithms
that maintain a bracketed solution.100, 50
(see the
other constructor
).
BSP tree
nodes.WeibullDistribution.getNumericalMean()
ZipfDistribution.getNumericalMean()
.
FDistribution.getNumericalVariance()
HypergeometricDistribution.getNumericalVariance()
.
WeibullDistribution.getNumericalVariance()
ZipfDistribution.getNumericalVariance()
.
SymmLQ.MACH_PREC
.
P(D_n < d)
using method described in [1] with quick
decisions for extreme values given in [2] (see above).
P(D_n < d)
using method described in [1] with quick
decisions for extreme values given in [2] (see above).
P(D_n < d)
using method described in [1] with quick
decisions for extreme values given in [2] (see above).
ceil
function.true
if positive-definiteness of matrix and preconditioner should
be checked.
true
if symmetry of matrix and conditioner must be checked.
NaN
values returned.
solve
and
solveInPlace
,
and throws an exception if one of the checks fails.
solve
and
solveInPlace
,
and throws an exception if one of the checks fails.
representation
can represent a valid chromosome.
representation
can represent a valid chromosome.
representation
can represent a valid chromosome.
observed
and expected
frequency counts.
counts
array, viewed as a two-way table.
observed1
and observed2
.
observed
frequency counts to those in the expected
array.
alpha
.
counts
array, viewed as a two-way table.
alpha
.
observed1
and
observed2
.
Chromosome
objects.AbstractRandomGenerator.nextGaussian()
.
BitsStreamGenerator.nextGaussian
.
valuesFileURL
after use in REPLAY_MODE.
Clusterable
points.h(x) = combiner(f(x), g(x))
.
a * this + b * y
, the linear
combination of this
and y
.
a * this + b * y
, the linear
combination of this
and y
.
this
with the linear combination of this
and
y
.
this
with the linear combination of this
and
y
.
data
sorted by comparator
.
Comparable
arguments.
Complex
utilities functions.valuesFileURL
, using the default number of bins.
valuesFileURL
and binCount
bins.
n
-th roots of unity.
RealLinearOperator
.NonLinearConjugateGradientOptimizer
.ranks.
source
array.
source
array.
source
array.
source
array.
RandomVectorGenerator
that generates vectors with with
correlated components.Random
using the supplied
RandomGenerator
.
FieldMatrix
using the data from the input
array.
RealMatrix
using the data from the input
array.
Complex
from the specified two dimensional
array of real and imaginary parts.
SummaryStatistics
whose data will be
aggregated with those of this AggregateSummaryStatistics
.
dimension x dimension
identity matrix.
FieldMatrix
with specified dimensions.
FieldMatrix
whose entries are the the values in the
the input array.
FieldVector
using the data from the input array.
H
of size m x m
as described in [1] (see above).
dimension x dimension
identity matrix.
double
filled with the real
and imaginary parts of the specified Complex
numbers.
RealMatrix
with specified dimensions.
RealMatrix
whose entries are the the values in the
the input array.
RealVector
using the data from the input array.
FieldMatrix
using the data from the input
array.
RealMatrix
using the data from the input
array.
Comparator
for comparing
WeightedObservedPoint
instances.
OnePointCrossover.crossover(Chromosome, Chromosome)
.
X
whose values are distributed according
to this distribution, this method returns P(x0 < X <= x1)
.
X
whose values are distributed according
to this distribution, this method returns P(x0 < X <= x1)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(x0 < X <= x1)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(x0 < X <= x1)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(x0 < X <= x1)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(x0 < X <= x1)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
RealVector.SparseEntryIterator.next()
.
DAXPY
function, which carries out the
operation y ← a · x + y.
sequence
of objects of type T according to the
permutation this chromosome represents.
sequence
of objects of type T according to the
permutation this chromosome represents.
representation
and
returns a (generic) list with the permuted values.
CMAESOptimizer.checkFeasableCount
: 0.
CMAESOptimizer.diagonalOnly
: 0.
MultiDirectionalSimplex.gamma
: 0.5.
NelderMeadSimplex.gamma
: 0.5.
BOBYQAOptimizer.initialTrustRegionRadius
: 10.0 .
CMAESOptimizer.isActiveCMA
: true.
MultiDirectionalSimplex.khi
: 2.0.
NelderMeadSimplex.khi
: 2.0.
CMAESOptimizer.maxIterations
: 30000.
CMAESOptimizer.random
.
NelderMeadSimplex.rho
: 1.0.
NelderMeadSimplex.sigma
: 0.5.
CMAESOptimizer.stopFitness
: 0.0.
BOBYQAOptimizer.stoppingTrustRegionRadius
: 1.0E-8 .
FieldMatrixChangingVisitor
interface.FieldMatrixPreservingVisitor
interface.IterativeLinearSolverEvent
.MeasurementModel
for the use with a
KalmanFilter
.MeasurementModel
, taking double arrays as input
parameters for the respective measurement matrix and noise.
MeasurementModel
, taking RealMatrix
objects
as input parameters for the respective measurement matrix and noise.
ProcessModel
for the use with a
KalmanFilter
.ProcessModel
, taking double arrays as input
parameters.
ProcessModel
, taking double arrays as input
parameters.
ProcessModel
, taking double arrays as input
parameters.
RealMatrixChangingVisitor
interface.RealMatrixPreservingVisitor
interface.x
.
x
.
x
.
x
.
x
.
x
.
x
.
x
.
x
.
x
.
x
.
x
.
x
.
UnivariateFunction
.
ExceptionContext.context
.
ExceptionContext.msgPatterns
and ExceptionContext.msgArguments
.
RealMatrix
field in a class.
RealVector
field in a class.
Dfp
which hides the radix-10000 artifacts of the superclass.Dfp
.MultivariateFunction
representing a differentiable
multivariate real function.DifferentiableMultivariateOptimizer
interface adding multi-start features to an existing optimizer.scalar differentiable objective
functions
.MultivariateVectorFunction
representing a differentiable
multivariate vectorial function.DifferentiableMultivariateVectorOptimizer
interface addind multi-start features to an existing optimizer.vectorial differentiable
objective functions
.UnivariateFunction
representing a differentiable univariate real function.UnivariateMatrixFunction
representing a differentiable univariate matrix function.UnivariateVectorFunction
representing a differentiable univariate vectorial function.simplex.length - 1
).
i first or last elements of the array,
depending on the value of front
.
- discardFrontElements(int) -
Method in class org.apache.commons.math3.util.ResizableDoubleArray
- Discards the
i initial elements of the array.
- discardMostRecentElements(int) -
Method in class org.apache.commons.math3.util.ResizableDoubleArray
- Discards the
i last elements of the array.
- distance(Vector<Euclidean1D>) -
Method in class org.apache.commons.math3.geometry.euclidean.oned.Vector1D
- Compute the distance between the instance and another vector according to the L2 norm.
- distance(Vector1D, Vector1D) -
Static method in class org.apache.commons.math3.geometry.euclidean.oned.Vector1D
- Compute the distance between two vectors according to the L2 norm.
- distance(Vector3D) -
Method in class org.apache.commons.math3.geometry.euclidean.threed.Line
- Compute the distance between the instance and a point.
- distance(Line) -
Method in class org.apache.commons.math3.geometry.euclidean.threed.Line
- Compute the shortest distance between the instance and another line.
- distance(Rotation, Rotation) -
Static method in class org.apache.commons.math3.geometry.euclidean.threed.Rotation
- Compute the distance between two rotations.
- distance(Vector<Euclidean3D>) -
Method in class org.apache.commons.math3.geometry.euclidean.threed.Vector3D
- Compute the distance between the instance and another vector according to the L2 norm.
- distance(Vector3D, Vector3D) -
Static method in class org.apache.commons.math3.geometry.euclidean.threed.Vector3D
- Compute the distance between two vectors according to the L2 norm.
- distance(Vector<Euclidean2D>) -
Method in class org.apache.commons.math3.geometry.euclidean.twod.Vector2D
- Compute the distance between the instance and another vector according to the L2 norm.
- distance(Vector2D, Vector2D) -
Static method in class org.apache.commons.math3.geometry.euclidean.twod.Vector2D
- Compute the distance between two vectors according to the L2 norm.
- distance(Vector<S>) -
Method in interface org.apache.commons.math3.geometry.Vector
- Compute the distance between the instance and another vector according to the L2 norm.
- distance(double[], double[]) -
Static method in class org.apache.commons.math3.util.MathArrays
- Calculates the L2 (Euclidean) distance between two points.
- distance(int[], int[]) -
Static method in class org.apache.commons.math3.util.MathArrays
- Calculates the L2 (Euclidean) distance between two points.
- distance1(Vector<Euclidean1D>) -
Method in class org.apache.commons.math3.geometry.euclidean.oned.Vector1D
- Compute the distance between the instance and another vector according to the L1 norm.
- distance1(Vector<Euclidean3D>) -
Method in class org.apache.commons.math3.geometry.euclidean.threed.Vector3D
- Compute the distance between the instance and another vector according to the L1 norm.
- distance1(Vector3D, Vector3D) -
Static method in class org.apache.commons.math3.geometry.euclidean.threed.Vector3D
- Compute the distance between two vectors according to the L1 norm.
- distance1(Vector<Euclidean2D>) -
Method in class org.apache.commons.math3.geometry.euclidean.twod.Vector2D
- Compute the distance between the instance and another vector according to the L1 norm.
- distance1(Vector<S>) -
Method in interface org.apache.commons.math3.geometry.Vector
- Compute the distance between the instance and another vector according to the L1 norm.
- distance1(double[], double[]) -
Static method in class org.apache.commons.math3.util.MathArrays
- Calculates the L1 (sum of abs) distance between two points.
- distance1(int[], int[]) -
Static method in class org.apache.commons.math3.util.MathArrays
- Calculates the L1 (sum of abs) distance between two points.
- distanceFrom(T) -
Method in interface org.apache.commons.math3.stat.clustering.Clusterable
- Returns the distance from the given point.
- distanceFrom(EuclideanIntegerPoint) -
Method in class org.apache.commons.math3.stat.clustering.EuclideanIntegerPoint
- Returns the distance from the given point.
- distanceInf(Vector<Euclidean1D>) -
Method in class org.apache.commons.math3.geometry.euclidean.oned.Vector1D
- Compute the distance between the instance and another vector according to the L∞ norm.
- distanceInf(Vector1D, Vector1D) -
Static method in class org.apache.commons.math3.geometry.euclidean.oned.Vector1D
- Compute the distance between two vectors according to the L∞ norm.
- distanceInf(Vector<Euclidean3D>) -
Method in class org.apache.commons.math3.geometry.euclidean.threed.Vector3D
- Compute the distance between the instance and another vector according to the L∞ norm.
- distanceInf(Vector3D, Vector3D) -
Static method in class org.apache.commons.math3.geometry.euclidean.threed.Vector3D
- Compute the distance between two vectors according to the L∞ norm.
- distanceInf(Vector<Euclidean2D>) -
Method in class org.apache.commons.math3.geometry.euclidean.twod.Vector2D
- Compute the distance between the instance and another vector according to the L∞ norm.
- distanceInf(Vector2D, Vector2D) -
Static method in class org.apache.commons.math3.geometry.euclidean.twod.Vector2D
- Compute the distance between two vectors according to the L∞ norm.
- distanceInf(Vector<S>) -
Method in interface org.apache.commons.math3.geometry.Vector
- Compute the distance between the instance and another vector according to the L∞ norm.
- distanceInf(double[], double[]) -
Static method in class org.apache.commons.math3.util.MathArrays
- Calculates the L∞ (max of abs) distance between two points.
- distanceInf(int[], int[]) -
Static method in class org.apache.commons.math3.util.MathArrays
- Calculates the L∞ (max of abs) distance between two points.
- distanceSq(Vector<Euclidean1D>) -
Method in class org.apache.commons.math3.geometry.euclidean.oned.Vector1D
- Compute the square of the distance between the instance and another vector.
- distanceSq(Vector1D, Vector1D) -
Static method in class org.apache.commons.math3.geometry.euclidean.oned.Vector1D
- Compute the square of the distance between two vectors.
- distanceSq(Vector<Euclidean3D>) -
Method in class org.apache.commons.math3.geometry.euclidean.threed.Vector3D
- Compute the square of the distance between the instance and another vector.
- distanceSq(Vector3D, Vector3D) -
Static method in class org.apache.commons.math3.geometry.euclidean.threed.Vector3D
- Compute the square of the distance between two vectors.
- distanceSq(Vector<Euclidean2D>) -
Method in class org.apache.commons.math3.geometry.euclidean.twod.Vector2D
- Compute the square of the distance between the instance and another vector.
- distanceSq(Vector2D, Vector2D) -
Static method in class org.apache.commons.math3.geometry.euclidean.twod.Vector2D
- Compute the square of the distance between two vectors.
- distanceSq(Vector<S>) -
Method in interface org.apache.commons.math3.geometry.Vector
- Compute the square of the distance between the instance and another vector.
- Divide - Class in org.apache.commons.math3.analysis.function
- Divide the first operand by the second.
- Divide() -
Constructor for class org.apache.commons.math3.analysis.function.Divide
-
- divide(Complex) -
Method in class org.apache.commons.math3.complex.Complex
- Returns a
Complex
whose value is
(this / divisor)
.
- divide(double) -
Method in class org.apache.commons.math3.complex.Complex
- Returns a
Complex
whose value is (this / divisor)
,
with divisor
interpreted as a real number.
- divide(Dfp) -
Method in class org.apache.commons.math3.dfp.Dfp
- Divide this by divisor.
- divide(int) -
Method in class org.apache.commons.math3.dfp.Dfp
- Divide by a single digit less than radix.
- divide(T) -
Method in interface org.apache.commons.math3.FieldElement
- Compute this ÷ a.
- divide(BigInteger) -
Method in class org.apache.commons.math3.fraction.BigFraction
-
Divide the value of this fraction by the passed
BigInteger
,
ie this * 1 / bg
, returning the result in reduced form.
- divide(int) -
Method in class org.apache.commons.math3.fraction.BigFraction
-
Divide the value of this fraction by the passed
int
, ie
this * 1 / i
, returning the result in reduced form.
- divide(long) -
Method in class org.apache.commons.math3.fraction.BigFraction
-
Divide the value of this fraction by the passed
long
, ie
this * 1 / l
, returning the result in reduced form.
- divide(BigFraction) -
Method in class org.apache.commons.math3.fraction.BigFraction
-
Divide the value of this fraction by another, returning the result in
reduced form.
- divide(Fraction) -
Method in class org.apache.commons.math3.fraction.Fraction
- Divide the value of this fraction by another.
- divide(int) -
Method in class org.apache.commons.math3.fraction.Fraction
- Divide the fraction by an integer.
- divide(RealMatrix, RealMatrix) -
Static method in class org.apache.commons.math3.optimization.direct.CMAESOptimizer
-
- divide(BigReal) -
Method in class org.apache.commons.math3.util.BigReal
- Compute this ÷ a.
- DIVIDE_TRAP -
Static variable in class org.apache.commons.math3.dfp.Dfp
- Name for traps triggered by division.
- DividedDifferenceInterpolator - Class in org.apache.commons.math3.analysis.interpolation
- Implements the
Divided Difference Algorithm for interpolation of real univariate
functions.
- DividedDifferenceInterpolator() -
Constructor for class org.apache.commons.math3.analysis.interpolation.DividedDifferenceInterpolator
-
- divideRow(int, double) -
Method in class org.apache.commons.math3.optimization.linear.SimplexTableau
- Subtracts a multiple of one row from another.
- doCopy() -
Method in class org.apache.commons.math3.ode.nonstiff.ClassicalRungeKuttaStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math3.ode.nonstiff.DormandPrince54StepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math3.ode.nonstiff.DormandPrince853StepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math3.ode.nonstiff.EulerStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math3.ode.nonstiff.GillStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math3.ode.nonstiff.GraggBulirschStoerStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math3.ode.nonstiff.HighamHall54StepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math3.ode.nonstiff.MidpointStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math3.ode.nonstiff.ThreeEighthesStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math3.ode.sampling.AbstractStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math3.ode.sampling.NordsieckStepInterpolator
- Really copy the finalized instance.
- doFinalize() -
Method in class org.apache.commons.math3.ode.nonstiff.DormandPrince853StepInterpolator
- Really finalize the step.
- doFinalize() -
Method in class org.apache.commons.math3.ode.sampling.AbstractStepInterpolator
- Really finalize the step.
- doIntegrate() -
Method in class org.apache.commons.math3.analysis.integration.BaseAbstractUnivariateIntegrator
- Method for implementing actual integration algorithms in derived
classes.
- doIntegrate() -
Method in class org.apache.commons.math3.analysis.integration.LegendreGaussIntegrator
- Method for implementing actual integration algorithms in derived
classes.
- doIntegrate() -
Method in class org.apache.commons.math3.analysis.integration.RombergIntegrator
- Method for implementing actual integration algorithms in derived
classes.
- doIntegrate() -
Method in class org.apache.commons.math3.analysis.integration.SimpsonIntegrator
- Method for implementing actual integration algorithms in derived
classes.
- doIntegrate() -
Method in class org.apache.commons.math3.analysis.integration.TrapezoidIntegrator
- Method for implementing actual integration algorithms in derived
classes.
- doIteration(SimplexTableau) -
Method in class org.apache.commons.math3.optimization.linear.SimplexSolver
- Runs one iteration of the Simplex method on the given model.
- doNormalizedStep(boolean) -
Method in class org.apache.commons.math3.ode.sampling.StepNormalizer
- Invokes the underlying step handler for the current normalized step.
- doOptimize() -
Method in class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer
- Perform the bulk of the optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer
- Perform the bulk of the optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math3.optimization.direct.BOBYQAOptimizer
- Perform the bulk of the optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math3.optimization.direct.CMAESOptimizer
- Perform the bulk of the optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math3.optimization.direct.PowellOptimizer
- Perform the bulk of the optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math3.optimization.direct.SimplexOptimizer
- Perform the bulk of the optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math3.optimization.general.GaussNewtonOptimizer
- Perform the bulk of the optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math3.optimization.general.LevenbergMarquardtOptimizer
- Perform the bulk of the optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math3.optimization.general.NonLinearConjugateGradientOptimizer
- Perform the bulk of the optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math3.optimization.linear.AbstractLinearOptimizer
- Perform the bulk of optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math3.optimization.linear.SimplexSolver
- Perform the bulk of optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math3.optimization.univariate.BaseAbstractUnivariateOptimizer
- Method for implementing actual optimization algorithms in derived
classes.
- doOptimize() -
Method in class org.apache.commons.math3.optimization.univariate.BrentOptimizer
- Method for implementing actual optimization algorithms in derived
classes.
- doRemove(int) -
Method in class org.apache.commons.math3.util.OpenIntToDoubleHashMap
- Remove an element at specified index.
- doRemove(int) -
Method in class org.apache.commons.math3.util.OpenIntToFieldHashMap
- Remove an element at specified index.
- DormandPrince54Integrator - Class in org.apache.commons.math3.ode.nonstiff
- This class implements the 5(4) Dormand-Prince integrator for Ordinary
Differential Equations.
- DormandPrince54Integrator(double, double, double, double) -
Constructor for class org.apache.commons.math3.ode.nonstiff.DormandPrince54Integrator
- Simple constructor.
- DormandPrince54Integrator(double, double, double[], double[]) -
Constructor for class org.apache.commons.math3.ode.nonstiff.DormandPrince54Integrator
- Simple constructor.
- DormandPrince54StepInterpolator - Class in org.apache.commons.math3.ode.nonstiff
- This class represents an interpolator over the last step during an
ODE integration for the 5(4) Dormand-Prince integrator.
- DormandPrince54StepInterpolator() -
Constructor for class org.apache.commons.math3.ode.nonstiff.DormandPrince54StepInterpolator
- Simple constructor.
- DormandPrince54StepInterpolator(DormandPrince54StepInterpolator) -
Constructor for class org.apache.commons.math3.ode.nonstiff.DormandPrince54StepInterpolator
- Copy constructor.
- DormandPrince853Integrator - Class in org.apache.commons.math3.ode.nonstiff
- This class implements the 8(5,3) Dormand-Prince integrator for Ordinary
Differential Equations.
- DormandPrince853Integrator(double, double, double, double) -
Constructor for class org.apache.commons.math3.ode.nonstiff.DormandPrince853Integrator
- Simple constructor.
- DormandPrince853Integrator(double, double, double[], double[]) -
Constructor for class org.apache.commons.math3.ode.nonstiff.DormandPrince853Integrator
- Simple constructor.
- DormandPrince853StepInterpolator - Class in org.apache.commons.math3.ode.nonstiff
- This class represents an interpolator over the last step during an
ODE integration for the 8(5,3) Dormand-Prince integrator.
- DormandPrince853StepInterpolator() -
Constructor for class org.apache.commons.math3.ode.nonstiff.DormandPrince853StepInterpolator
- Simple constructor.
- DormandPrince853StepInterpolator(DormandPrince853StepInterpolator) -
Constructor for class org.apache.commons.math3.ode.nonstiff.DormandPrince853StepInterpolator
- Copy constructor.
- doSolve() -
Method in class org.apache.commons.math3.analysis.solvers.BaseAbstractUnivariateSolver
- Method for implementing actual optimization algorithms in derived
classes.
- doSolve() -
Method in class org.apache.commons.math3.analysis.solvers.BaseSecantSolver
- Method for implementing actual optimization algorithms in derived
classes.
- doSolve() -
Method in class org.apache.commons.math3.analysis.solvers.BisectionSolver
- Method for implementing actual optimization algorithms in derived
classes.
- doSolve() -
Method in class org.apache.commons.math3.analysis.solvers.BracketingNthOrderBrentSolver
- Method for implementing actual optimization algorithms in derived
classes.
- doSolve() -
Method in class org.apache.commons.math3.analysis.solvers.BrentSolver
- Method for implementing actual optimization algorithms in derived
classes.
- doSolve() -
Method in class org.apache.commons.math3.analysis.solvers.LaguerreSolver
- Method for implementing actual optimization algorithms in derived
classes.
- doSolve() -
Method in class org.apache.commons.math3.analysis.solvers.MullerSolver
- Method for implementing actual optimization algorithms in derived
classes.
- doSolve() -
Method in class org.apache.commons.math3.analysis.solvers.MullerSolver2
- Method for implementing actual optimization algorithms in derived
classes.
- doSolve() -
Method in class org.apache.commons.math3.analysis.solvers.NewtonSolver
- Method for implementing actual optimization algorithms in derived
classes.
- doSolve() -
Method in class org.apache.commons.math3.analysis.solvers.RiddersSolver
- Method for implementing actual optimization algorithms in derived
classes.
- doSolve() -
Method in class org.apache.commons.math3.analysis.solvers.SecantSolver
- Method for implementing actual optimization algorithms in derived
classes.
- dotProduct(Vector<Euclidean1D>) -
Method in class org.apache.commons.math3.geometry.euclidean.oned.Vector1D
- Compute the dot-product of the instance and another vector.
- dotProduct(Vector<Euclidean3D>) -
Method in class org.apache.commons.math3.geometry.euclidean.threed.Vector3D
- Compute the dot-product of the instance and another vector.
- dotProduct(Vector3D, Vector3D) -
Static method in class org.apache.commons.math3.geometry.euclidean.threed.Vector3D
- Compute the dot-product of two vectors.
- dotProduct(Vector<Euclidean2D>) -
Method in class org.apache.commons.math3.geometry.euclidean.twod.Vector2D
- Compute the dot-product of the instance and another vector.
- dotProduct(Vector<S>) -
Method in interface org.apache.commons.math3.geometry.Vector
- Compute the dot-product of the instance and another vector.
- dotProduct(FieldVector<T>) -
Method in class org.apache.commons.math3.linear.ArrayFieldVector
- Compute the dot product.
- dotProduct(ArrayFieldVector<T>) -
Method in class org.apache.commons.math3.linear.ArrayFieldVector
- Compute the dot product.
- dotProduct(RealVector) -
Method in class org.apache.commons.math3.linear.ArrayRealVector
- Compute the dot product of this vector with
v
.
- dotProduct(FieldVector<T>) -
Method in interface org.apache.commons.math3.linear.FieldVector
- Compute the dot product.
- dotProduct(OpenMapRealVector) -
Method in class org.apache.commons.math3.linear.OpenMapRealVector
- Optimized method to compute the dot product with an OpenMapRealVector.
- dotProduct(RealVector) -
Method in class org.apache.commons.math3.linear.OpenMapRealVector
- Compute the dot product of this vector with
v
.
- dotProduct(RealVector) -
Method in class org.apache.commons.math3.linear.RealVector
- Compute the dot product of this vector with
v
.
- dotProduct(FieldVector<T>) -
Method in class org.apache.commons.math3.linear.SparseFieldVector
- Compute the dot product.
- dotrap(int, String, Dfp, Dfp) -
Method in class org.apache.commons.math3.dfp.Dfp
- Raises a trap.
- DoubleArray - Interface in org.apache.commons.math3.util
- Provides a standard interface for double arrays.
- doubleHighPart(double) -
Static method in class org.apache.commons.math3.util.FastMath
- Get the high order bits from the mantissa.
- doubleValue() -
Method in class org.apache.commons.math3.fraction.BigFraction
-
Gets the fraction as a double.
- doubleValue() -
Method in class org.apache.commons.math3.fraction.Fraction
- Gets the fraction as a double.
- doubleValue() -
Method in class org.apache.commons.math3.util.BigReal
- Get the double value corresponding to the instance.
- DOWNSIDE_VARIANCE -
Static variable in class org.apache.commons.math3.stat.descriptive.moment.SemiVariance
- The DOWNSIDE Direction is used to specify that the observations below
the cutoff point will be used to calculate SemiVariance
- dropPhase1Objective() -
Method in class org.apache.commons.math3.optimization.linear.SimplexTableau
- Removes the phase 1 objective function, positive cost non-artificial variables,
and the non-basic artificial variables from this tableau.
- DstNormalization - Enum in org.apache.commons.math3.transform
- This enumeration defines the various types of normalizations that can be
applied to discrete sine transforms (DST).
- DstNormalization() -
Constructor for enum org.apache.commons.math3.transform.DstNormalization
-
- DummyLocalizable - Class in org.apache.commons.math3.exception.util
- Dummy implementation of the
Localizable
interface, without localization. - DummyLocalizable(String) -
Constructor for class org.apache.commons.math3.exception.util.DummyLocalizable
- Simple constructor.
- DummyStepHandler - Class in org.apache.commons.math3.ode.sampling
- This class is a step handler that does nothing.
- DummyStepHandler() -
Constructor for class org.apache.commons.math3.ode.sampling.DummyStepHandler
- Private constructor.
- DummyStepHandler.LazyHolder - Class in org.apache.commons.math3.ode.sampling
- Holder for the instance.
- DummyStepHandler.LazyHolder() -
Constructor for class org.apache.commons.math3.ode.sampling.DummyStepHandler.LazyHolder
-
Dfp
with value e.
RandomGenerator
as the source of random data.
RandomGenerator
as the source of random data.
RandomDataImpl
instance as the source of random data.
RandomDataImpl
as the source of random data.
DataAdapter
for data provided as array of doubles.sampleStats
and
beanStats
abstracting the source of data.DataAdapter
objects.DataAdapter
for data provided through some input stream1 - EPSILON
is not
numerically equal to 1: 1.1102230246251565E-16.
object
is a
FieldMatrix
instance with the same dimensions as this
and all corresponding matrix entries are equal.
object
is a
RealMatrix
instance with the same dimensions as this
and all corresponding matrix entries are equal.
a.subtract(b
} to be the zero vector, while
a.equals(b) == false
.
object
is an
AbstractStorelessUnivariateStatistic
returning the same
values as this for getResult()
and getN()
object
is a
SummaryStatistics
instance and all statistics have the
same values as this.
object
is a MultivariateSummaryStatistics
instance and all statistics have the same values as this.
object
is a
StatisticalSummaryValues
instance and all statistics have
the same values as this.
object
is a
SummaryStatistics
instance and all statistics have the
same values as this.
object
is a MultivariateSummaryStatistics
instance and all statistics have the same values as this.
object
is a
SummaryStatistics
instance and all statistics have the
same values as this.
Precision.equals(float,float)
.
true
iff both arguments are null
or have same
dimensions and all their elements are equal as defined by
Precision.equals(double,double)
.
equals(x, y, 1)
.
equals(x, y, 1)
.
true
if there is no double value strictly between the
arguments or the difference between them is within the range of allowed
error (inclusive).
this method
.
true
iff both arguments are null
or have same
dimensions and all their elements are equal as defined by
this method
.
equals(x, y, 1)
.
equals(x, y, maxUlps)
.
equals(x, y, 1)
.
equals(x, y, maxUlps)
.
Dfp
array with value e split in two pieces.
Clusterable
for points with integer coordinates.AbstractStorelessUnivariateStatistic.clear()
, then invokes
AbstractStorelessUnivariateStatistic.increment(double)
in a loop over the the input array, and then uses
AbstractStorelessUnivariateStatistic.getResult()
to compute the return value.
AbstractStorelessUnivariateStatistic.clear()
, then invokes
AbstractStorelessUnivariateStatistic.increment(double)
in a loop over the specified portion of the input
array, and then uses AbstractStorelessUnivariateStatistic.getResult()
to compute the return value.
Double.NaN
if the designated subarray
is empty.
Double.NaN
if the designated subarray
is empty.
SemiVariance
for the entire array against the mean, using
instance properties varianceDirection and biasCorrection.
SemiVariance
of the designated values against the mean, using
instance properties varianceDirection and biasCorrection.
SemiVariance
for the entire array against the mean, using
the current value of the biasCorrection instance property.
SemiVariance
of the designated values against the cutoff, using
instance properties variancDirection and biasCorrection.
SemiVariance
of the designated values against the cutoff in the
given direction, using the current value of the biasCorrection instance property.
SemiVariance
of the designated values against the cutoff
in the given direction with the provided bias correction.
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
Double.NaN
if the designated subarray
is empty.
Double.NaN
if the designated subarray
is empty.
Double.NaN
if the designated subarray
is empty.
p
th percentile of the values
in the values
array.
quantile
th percentile of the
designated values in the values
array.
p
th percentile of the values
in the values
array, starting with the element in (0-based)
position begin
in the array and including length
values.
Double.NaN
if the designated subarray
is empty.
Double.NaN
if the designated subarray
is empty.
Double.NaN
if the designated subarray
is empty.
Double.NaN
if the designated subarray
is empty.
event handler
during integration steps.P(D_n < d)
using method described
in [1] and BigFraction
(see
above).
int
.
ExceptionContext
interface.expansionFactor
is additive or multiplicative.
ex-1
function.n
(the product of the numbers 1 to n), as a
double
.
n
.
Math
and
StrictMath
for large scale computation.FastMath
.BracketFinder.hi
.
length
with values generated
using getNext() repeatedly.
data[i] = value
for each i in tiesTrace.
population
for another chromosome with the same
representation.
FirstMoment
identical
to the original
BracketFinder.lo
.
floor
function.BracketFinder.mid
.
ComplexFormat.format(Object,StringBuffer,FieldPosition)
.
ComplexFormat.format(Object,StringBuffer,FieldPosition)
.
Complex
object to produce a string.
BigFraction
object to produce a string.
Fraction
object to produce a string.
BigFraction
object to produce a string.
Fraction
object to produce a string.
Vector
object to produce a string.
Vector3D
object to produce a string.
Vector
object to produce a string.
Vector
object to produce a string.
Vector
object to produce a string.
Vector
to produce a string.
RealVectorFormat.format(RealVector,StringBuffer,FieldPosition)
.
RealVector
object to produce a string.
FourthMoment
identical
to the original
FieldMatrix
/Fraction
matrix to a RealMatrix
.
alpha
and
beta
values.
alpha
and
beta
values.
Gaussian
function.norm
, mean
, and sigma
of a Gaussian.Parametric
based on the specified observed points.StoppingCondition
in the last run.
GeometricMean
identical
to the original
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
alpha
.
alpha
shape parameter.
beta
.
beta
scale parameter.
SummaryStatistics
instances containing
statistics describing the values in each of the bins.
true
if positive-definiteness should be checked for both
matrix and preconditioner.
true
if symmetry of the matrix, and symmetry as well as
positive definiteness of the preconditioner should be checked.
col
as an array.
col
as an array.
col
as an array.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a vector.
column
as a vector.
column
as a vector.
getCorrelationStandardErrors().getEntry(i,j)
is the standard
error associated with getCorrelationMatrix.getEntry(i,j)
BigInteger
.
DoubleArray
.
ResizableArray
.
EmpiricalDistribution
used when operating in 0.
expansionMode
determines whether the internal storage
array grows additively (ADDITIVE_MODE) or multiplicatively
(MULTIPLICATIVE_MODE) when it is expanded.
BracketFinder.getHi()
.
Field
to which the instance belongs.
Field
(really a DfpField
) to which the instance belongs.
Field
to which the instance belongs.
Field
to which the instance belongs.
Field
to which the instance belongs.
Field
to which the instance belongs.
BracketFinder.getLo()
.
BracketFinder.getMid()
.
StoppingCondition
in the last run.
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
k
-th n
-th root of unity.
SimpleRegression.hasIntercept()
is true; otherwise 0.
this
event
is created.
this
event
is created.
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
c
of this distribution.
ValueServer.GAUSSIAN_MODE
, ValueServer.EXPONENTIAL_MODE
or ValueServer.UNIFORM_MODE
.
Covariance
method is not supported by a StorelessCovariance
,
since the number of bivariate observations does not have to be the same for different
pairs of covariates - i.e., N as defined in Covariance.getN()
is undefined.
ranks
is NaN.
Cluster
to the given point
valuesFileURL
.
BigInteger
.
optimize
.
optimize
.
optimize
.
index
.
Cluster
with the largest number of points
Cluster
with the largest distance variance.
Dfp
instances built by this factory.
PearsonsCorrelation
instance constructed from the
ranked input data.
k
-th n
-th root of unity.
BigFraction
instance with the 2 parts of a fraction
Y/Z.
Fraction
instance with the 2 parts
of a fraction Y/Z.
RoundingMode.HALF_UP
row
as an array.
row
as an array.
row
as an array.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a vector.
row
as a vector.
row
as a vector.
row
as a vector.
row
as a vector.
row
as a vector.
StatisticalSummary
describing this distribution.
beta
.
alpha
.
ValueServer.GAUSSIAN_MODE
.
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
StatisticalSummaryValues
instance reporting current
aggregate statistics.
StatisticalSummaryValues
instance reporting current
statistics.
StatisticalSummaryValues
instance reporting current
statistics.
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
Transform
embedding an affine transform.
ValueServer.DIGEST_MODE
.
GoalType.MAXIMIZE
or GoalType.MINIMIZE
.
goodb
parameter.
x
.
x
.
x
.
x
.
x
.
BOBYQAOptimizer.originShift
+
BOBYQAOptimizer.trustRegionCenterOffset
.
true
if the default convergence criterion is verified.
a.subtract(b)
to be the zero vector, while
a.hashCode() != b.hashCode()
.
StatisticalSummary
instances, under the
assumption of equal subpopulation variances.
sample1
and sample2
are drawn from populations with the same mean,
with significance level alpha
, assuming that the
subpopulation variances are equal.
x
and y
- sqrt(x2 +y2)RealLinearOperator
is too high.Variance.increment(double)
should increment
the internal second moment.
AbstractStorelessUnivariateStatistic.increment(double)
in a loop over
the input array.
AbstractStorelessUnivariateStatistic.increment(double)
in a loop over
the specified portion of the input array.
MaxCountExceededException
.permutedData
when applied to
originalData
.
StorelessBivariateCovariance
instances.
Well19937c
generator seeded with
System.currentTimeMillis() + System.identityHashCode(this))
.
X
, this method returns
P(x0 <= X <= x1)
.
SplineInterpolator
on the resulting fit.
BOBYQAOptimizer.originShift
.
true
if RootsOfUnity.computeRoots(int)
was called with a positive
value of its argument n
.
true
if RootsOfUnity.computeRoots(int)
was called with a
positive value of its argument n
.
Double.POSITIVE_INFINITY
or
Double.NEGATIVE_INFINITY
) and neither part
is NaN
.
NaN
.
NaN
.
NaN
.
NaN
.
NaN
.
NaN
.
NaN
.
true iff another
has the same
representation and therefore the same fitness.
- isSame(Chromosome) -
Method in class org.apache.commons.math3.genetics.Chromosome
- Returns
true iff another
has the same
representation and therefore the same fitness.
- isSame(Chromosome) -
Method in class org.apache.commons.math3.genetics.RandomKey
- Returns
true
iff another
is a RandomKey and
encodes the same permutation.
- isSatisfied(Population) -
Method in class org.apache.commons.math3.genetics.FixedGenerationCount
- Determine whether or not the given number of generations have passed.
- isSatisfied(Population) -
Method in interface org.apache.commons.math3.genetics.StoppingCondition
- Determine whether or not the given population satisfies the stopping
condition.
- isSequence(double, double, double) -
Method in class org.apache.commons.math3.analysis.solvers.BaseAbstractUnivariateSolver
- Check whether the arguments form a (strictly) increasing sequence.
- isSequence(double, double, double) -
Static method in class org.apache.commons.math3.analysis.solvers.UnivariateSolverUtils
- Check whether the arguments form a (strictly) increasing sequence.
- isSimilarTo(Line) -
Method in class org.apache.commons.math3.geometry.euclidean.threed.Line
- Check if the instance is similar to another line.
- isSimilarTo(Plane) -
Method in class org.apache.commons.math3.geometry.euclidean.threed.Plane
- Check if the instance is similar to another plane.
- isSquare() -
Method in class org.apache.commons.math3.linear.AbstractFieldMatrix
- Is this a square matrix?
- isSquare() -
Method in class org.apache.commons.math3.linear.AbstractRealMatrix
- Is this a square matrix?
- isSquare() -
Method in interface org.apache.commons.math3.linear.AnyMatrix
- Is this a square matrix?
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.BetaDistribution
- Use this method to get information about whether the support is connected,
i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.BinomialDistribution
- Use this method to get information about whether the support is
connected, i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.CauchyDistribution
- Use this method to get information about whether the support is connected,
i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.ChiSquaredDistribution
- Use this method to get information about whether the support is connected,
i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.ExponentialDistribution
- Use this method to get information about whether the support is connected,
i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.FDistribution
- Use this method to get information about whether the support is connected,
i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.GammaDistribution
- Use this method to get information about whether the support is connected,
i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.HypergeometricDistribution
- Use this method to get information about whether the support is
connected, i.e.
- isSupportConnected() -
Method in interface org.apache.commons.math3.distribution.IntegerDistribution
- Use this method to get information about whether the support is
connected, i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.LogNormalDistribution
- Use this method to get information about whether the support is connected,
i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.NormalDistribution
- Use this method to get information about whether the support is connected,
i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.PascalDistribution
- Use this method to get information about whether the support is
connected, i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.PoissonDistribution
- Use this method to get information about whether the support is
connected, i.e.
- isSupportConnected() -
Method in interface org.apache.commons.math3.distribution.RealDistribution
- Use this method to get information about whether the support is connected,
i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.TDistribution
- Use this method to get information about whether the support is connected,
i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.TriangularDistribution
- Use this method to get information about whether the support is connected,
i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.UniformIntegerDistribution
- Use this method to get information about whether the support is
connected, i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.UniformRealDistribution
- Use this method to get information about whether the support is connected,
i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.WeibullDistribution
- Use this method to get information about whether the support is connected,
i.e.
- isSupportConnected() -
Method in class org.apache.commons.math3.distribution.ZipfDistribution
- Use this method to get information about whether the support is
connected, i.e.
- isSupported(String) -
Method in class org.apache.commons.math3.ode.AbstractParameterizable
- Check if a parameter is supported.
- isSupported(String) -
Method in interface org.apache.commons.math3.ode.Parameterizable
- Check if a parameter is supported.
- isSupported(String) -
Method in class org.apache.commons.math3.ode.ParameterizedWrapper
- Check if a parameter is supported.
- isSupported(String) -
Method in class org.apache.commons.math3.ode.ParameterJacobianWrapper
- Check if a parameter is supported.
- isSupportLowerBoundInclusive() -
Method in class org.apache.commons.math3.distribution.BetaDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportLowerBoundInclusive() -
Method in class org.apache.commons.math3.distribution.CauchyDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportLowerBoundInclusive() -
Method in class org.apache.commons.math3.distribution.ChiSquaredDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportLowerBoundInclusive() -
Method in class org.apache.commons.math3.distribution.ExponentialDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportLowerBoundInclusive() -
Method in class org.apache.commons.math3.distribution.FDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportLowerBoundInclusive() -
Method in class org.apache.commons.math3.distribution.GammaDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportLowerBoundInclusive() -
Method in class org.apache.commons.math3.distribution.LogNormalDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportLowerBoundInclusive() -
Method in class org.apache.commons.math3.distribution.NormalDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportLowerBoundInclusive() -
Method in interface org.apache.commons.math3.distribution.RealDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportLowerBoundInclusive() -
Method in class org.apache.commons.math3.distribution.TDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportLowerBoundInclusive() -
Method in class org.apache.commons.math3.distribution.TriangularDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportLowerBoundInclusive() -
Method in class org.apache.commons.math3.distribution.UniformRealDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportLowerBoundInclusive() -
Method in class org.apache.commons.math3.distribution.WeibullDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in class org.apache.commons.math3.distribution.BetaDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in class org.apache.commons.math3.distribution.CauchyDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in class org.apache.commons.math3.distribution.ChiSquaredDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in class org.apache.commons.math3.distribution.ExponentialDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in class org.apache.commons.math3.distribution.FDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in class org.apache.commons.math3.distribution.GammaDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in class org.apache.commons.math3.distribution.LogNormalDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in class org.apache.commons.math3.distribution.NormalDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in interface org.apache.commons.math3.distribution.RealDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in class org.apache.commons.math3.distribution.TDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in class org.apache.commons.math3.distribution.TriangularDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in class org.apache.commons.math3.distribution.UniformRealDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in class org.apache.commons.math3.distribution.WeibullDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSymmetric(RealMatrix, boolean) -
Method in class org.apache.commons.math3.linear.EigenDecomposition
- Check if a matrix is symmetric.
- isSymmetricVCD -
Variable in class org.apache.commons.math3.stat.regression.RegressionResults
- boolean flag for variance covariance matrix in symm compressed storage
- isTransposable() -
Method in class org.apache.commons.math3.linear.RealLinearOperator
- Returns
true
if this operator supports
RealLinearOperator.operateTranspose(RealVector)
.
- isUpperBiDiagonal() -
Method in class org.apache.commons.math3.linear.BiDiagonalTransformer
- Check if the matrix is transformed to upper bi-diagonal.
- isZero() -
Method in class org.apache.commons.math3.dfp.Dfp
- Check if instance is equal to zero.
- iter -
Variable in class org.apache.commons.math3.linear.OpenMapRealVector.OpenMapEntry
- Iterator pointing to the entry.
- iter -
Variable in class org.apache.commons.math3.linear.OpenMapRealVector.OpenMapSparseIterator
- Underlying iterator.
- iterate(MultivariateFunction, Comparator<PointValuePair>) -
Method in class org.apache.commons.math3.optimization.direct.AbstractSimplex
- Compute the next simplex of the algorithm.
- iterate(MultivariateFunction, Comparator<PointValuePair>) -
Method in class org.apache.commons.math3.optimization.direct.MultiDirectionalSimplex
- Compute the next simplex of the algorithm.
- iterate(MultivariateFunction, Comparator<PointValuePair>) -
Method in class org.apache.commons.math3.optimization.direct.NelderMeadSimplex
- Compute the next simplex of the algorithm.
- IterationEvent - Class in org.apache.commons.math3.util
- The root class from which all events occurring while running an
IterationManager
should be derived. - IterationEvent(Object, int) -
Constructor for class org.apache.commons.math3.util.IterationEvent
- Creates a new instance of this class.
- IterationListener - Interface in org.apache.commons.math3.util
- The listener interface for receiving events occurring in an iterative
algorithm.
- IterationManager - Class in org.apache.commons.math3.util
- This abstract class provides a general framework for managing iterative
algorithms.
- IterationManager(int) -
Constructor for class org.apache.commons.math3.util.IterationManager
- Creates a new instance of this class.
- iterationPerformed(IterationEvent) -
Method in interface org.apache.commons.math3.util.IterationListener
- Invoked each time an iteration is completed (in the main iteration loop).
- iterations -
Variable in class org.apache.commons.math3.analysis.integration.BaseAbstractUnivariateIntegrator
- The iteration count.
- iterations -
Variable in class org.apache.commons.math3.optimization.direct.CMAESOptimizer
- Number of iterations already performed.
- iterations -
Variable in class org.apache.commons.math3.optimization.linear.AbstractLinearOptimizer
- Number of iterations already performed.
- iterations -
Variable in class org.apache.commons.math3.util.IterationEvent
- The number of iterations performed so far.
- iterations -
Variable in class org.apache.commons.math3.util.IterationManager
- Keeps a count of the number of iterations.
- iterationStarted(IterationEvent) -
Method in interface org.apache.commons.math3.util.IterationListener
- Invoked each time a new iteration is completed (in the main iteration
loop).
- IterativeLinearSolver - Class in org.apache.commons.math3.linear
- This abstract class defines an iterative solver for the linear system A
· x = b.
- IterativeLinearSolver(int) -
Constructor for class org.apache.commons.math3.linear.IterativeLinearSolver
- Creates a new instance of this class, with default iteration manager.
- IterativeLinearSolver(IterationManager) -
Constructor for class org.apache.commons.math3.linear.IterativeLinearSolver
- Creates a new instance of this class, with custom iteration manager.
- IterativeLinearSolverEvent - Class in org.apache.commons.math3.linear
- This is the base class for all events occuring during the iterations of a
IterativeLinearSolver
. - IterativeLinearSolverEvent(Object, int) -
Constructor for class org.apache.commons.math3.linear.IterativeLinearSolverEvent
- Creates a new instance of this class.
- iterator() -
Method in class org.apache.commons.math3.genetics.ListPopulation
- Chromosome list iterator
- iterator() -
Method in class org.apache.commons.math3.linear.RealVector
- Generic dense iterator.
- iterator() -
Method in class org.apache.commons.math3.util.MultidimensionalCounter
- Create an iterator over this counter.
- iterator() -
Method in class org.apache.commons.math3.util.OpenIntToDoubleHashMap
- Get an iterator over map elements.
- iterator() -
Method in class org.apache.commons.math3.util.OpenIntToFieldHashMap
- Get an iterator over map elements.
secondary equations
to
compute the Jacobian matrices with respect to the initial state vector and, if
any, to some parameters of the primary ODE set.FirstOrderDifferentialEquations
into a MainStateJacobianProvider
.
java.util.Random
to implement
RandomGenerator
.Kurtosis
identical
to the original
lcm(a,b) = (a / gcd(a,b)) * b
.
lcm(a,b) = (a / gcd(a,b)) * b
.
vectorial
objective functions
to scalar objective functions
when the goal is to minimize them.other contructor
.
other contructor
.
AffineTransform
.
List
.Dfp
with value ln(10).
Dfp
with value ln(2).
Dfp
array with value ln(2) split in two pieces.
Dfp
with value ln(5).
Dfp
array with value ln(5) split in two pieces.
LoessInterpolator
with a bandwidth of LoessInterpolator.DEFAULT_BANDWIDTH
,
LoessInterpolator.DEFAULT_ROBUSTNESS_ITERS
robustness iterations
and an accuracy of {#link #DEFAULT_ACCURACY}.
LoessInterpolator
with given bandwidth and number of robustness iterations.
LoessInterpolator
with given bandwidth, number of robustness iterations and accuracy.
log(1 + p)
function.normally distributed
natural
logarithm of the log-normal distribution are equal to zero and one
respectively.
BaseAbstractMultivariateSimpleBoundsOptimizer.getLowerBound()
- BOBYQAOptimizer.originShift
.
first order
differential equations
in order to compute exactly the main state jacobian
matrix for partial derivatives equations
.FieldMatrix
/BigFraction
.FieldMatrix
/Fraction
.Max
identical
to the original
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
Mean
identical
to the original
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
KalmanFilter
.Median
identical
to the original
UpdatingMultipleLinearRegression
interface.Min
identical
to the original
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
Complex
whose value is this * factor
.
Complex
whose value is this * factor
, with factor
interpreted as a integer number.
Complex
whose value is this * factor
, with factor
interpreted as a real number.
BigInteger
, returning the result in reduced form.
m
.
m
.
m
.
m
.
m
.
m
.
m
.
MultivariateFunction
to unbounded ones.MultivariateFunction
to an unbouded
domain using a penalty function.MultivariateOptimizer
interface adding
multi-start features to an existing optimizer.scalar objective functions
.addValue
method.Complex
whose value is (-this)
.
this
element.
this
element.
Dfp
with a value of 0.
Dfp
given a String representation.
Dfp
with a non-finite value.
this
is, with a
given arrayRepresentation
.
trsbox
or altmov
.
RealVector.SparseEntryIterator.next()
to return.
Beta Distribution
.
Binomial Distribution
.
boolean
value from this random number generator's
sequence.
boolean
value from this random number generator's
sequence.
boolean
value from this random number generator's
sequence.
boolean
value from this random number generator's
sequence.
Cauchy Distribution
.
ChiSquare Distribution
.
double
value between 0.0
and
1.0
from this random number generator's sequence.
double
value between 0.0
and
1.0
from this random number generator's sequence.
double
value between 0.0
and
1.0
from this random number generator's sequence.
double
value between 0.0
and
1.0
from this random number generator's sequence.
F Distribution
.
float
value between 0.0
and 1.0
from this random
number generator's sequence.
float
value between 0.0
and 1.0
from this random
number generator's sequence.
float
value between 0.0
and 1.0
from this random
number generator's sequence.
float
value between 0.0
and 1.0
from this random
number generator's sequence.
Gamma Distribution
.
double
value with mean 0.0
and standard
deviation 1.0
from this random number generator's sequence.
double
value with mean 0.0
and standard
deviation 1.0
from this random number generator's sequence.
double
value with mean 0.0
and standard
deviation 1.0
from this random number generator's sequence.
double
value with mean 0.0
and standard
deviation 1.0
from this random number generator's sequence.
len
.
len
.
Hypergeometric Distribution
.
int
value from this random number generator's sequence.
int
value
between 0 (inclusive) and the specified value (exclusive), drawn from
this random number generator's sequence.
int
value from this random number generator's sequence.
int
value from this random number generator's sequence.
lower
and upper
(endpoints included).
lower
and upper
(endpoints included).
int
value from this random number generator's sequence.
long
value from this random number generator's sequence.
long
value from this random number generator's sequence.
long
value from this random number generator's sequence.
lower
and upper
(endpoints included).
lower
and upper
(endpoints included).
long
value from this random number generator's sequence.
j
such that
j > i && (j == weights.length || weights[j] != 0)
.
Pascal Distribution
.
k
whose entries are selected
randomly, without repetition, from the integers 0, ..., n - 1
(inclusive).
k
whose entries are selected
randomly, without repetition, from the integers 0, ..., n - 1
(inclusive).
k
objects selected randomly from the
Collection c
.
k
objects selected randomly from the
Collection c
.
lower
and upper
(endpoints included) from a secure random sequence.
lower
and upper
(endpoints included) from a secure random sequence.
lower
and upper
(endpoints included) from a secure random
sequence.
lower
and upper
(endpoints included) from a secure random
sequence.
T Distribution
.
(lower, upper)
(i.e., endpoints excluded).
(lower, upper)
or the interval [lower, upper)
.
(lower, upper)
(i.e., endpoints excluded).
(lower, upper)
or the interval [lower, upper)
.
Weibull Distribution
.
Zipf Distribution
.
checker
,
line search solver
and
preconditioner
.
line search solver
and
preconditioner
.
preconditioner
.
RealLinearOperator
is expected.RealLinearOperator
is expected.null
argument must throw
this exception.n
-th roots of unity, for negative values
of n
.
n
-th roots of unity, for positive values
of n
.
Dfp
with value 1.
BigInteger
representation of 100.
RealVector
interface with a
OpenIntToDoubleHashMap
backing store.Entry
optimized for OpenMap.v
.
v
.
v
.
this
by the vector x
.
v
.
v
.
v
.
v
.
v
.
v
.
this
by the vector x
.
this
by the vector x
.
v
.
v
.
this
operator
by the vector x
(optional operation).
function
package contains function objects that wrap the
methods contained in Math
, as well as common
mathematical functions such as the gaussian and sinc functions.polyhedrons sets
outlines.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
.
partial derivatives equations
.basic simple
ODE instances to be used when processing JacobianMatrices
.partial derivatives equations
.ParameterizedODE
into a ParameterJacobianProvider
.
Complex
object.
Complex
object.
BigFraction
object.
BigFraction
object.
Fraction
object.
Fraction
object.
BigFraction
object.
Fraction
object.
Vector
object.
Vector
object.
Vector3D
object.
Vector3D
object.
Vector
object.
Vector
object.
Vector
object.
Vector
object.
RealVector
object.
RealVector
object.
source
until a non-whitespace character is found.
source
until a non-whitespace character is found.
source
for an expected fixed string.
BigInteger
.
source
until a non-whitespace character is found.
source
until a non-whitespace character is found.
source
for special double values.
source
for a number.
Covariance
.
Percentile
identical
to the original
p
th percentile of the values
in the values
array.
p
th percentile of the values
in the values
array, starting with the element in (0-based)
position begin
in the array and including length
values.
Dfp
with value π.
Dfp
array with value π split in two pieces.
PolynomialFunction
.
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
x
.
x
.
BigFraction
whose value is
(this<sup>exponent</sup>)
, returning the result in reduced form.
BigFraction
whose value is
(thisexponent), returning the result in reduced form.
BigFraction
whose value is
(thisexponent), returning the result in reduced form.
double
whose value is
(thisexponent), returning the result in reduced form.
p
times.
p
times.
p
times.
p
times.
y
value associated with the
supplied x
value, based on the data that has been
added to the model when this method is activated.
m
.
v
.
v
.
m
.
v
.
v
.
v
.
v
.
v
.
v
.
m
.
v
.
v
.
m
.
v
.
v
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
, defined by the given hypergeometric
distribution parameters, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
X
whose values are distributed according
to this distribution, this method returns P(X = x)
.
KalmanFilter
.Product
identical
to the original
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
true
if IterativeLinearSolverEvent.getResidual()
is supported.
true
if IterativeLinearSolverEvent.getResidual()
is supported.
java.util.Random
wrapping a
RandomGenerator
.length
.
RandomData
interface using a RandomGenerator
instance to generate non-secure data and a SecureRandom
instance to provide data for the nextSecureXxx
methods.RandomGenerator
as
the source of (non-secure) random data.
java.util.Random
.RandomKey
s.data
using the natural ordering on Doubles, with
NaN values handled according to nanStrategy
and ties
resolved using tiesStrategy.
matrix
using the current rankingAlgorithm
double
)
vector spaces.this
element.
this
element.
this
element.
this
element.
BigFraction
to its lowest terms.
|a - offset|
to the primary interval
[0, |period|)
.
Region
.LinearConstraint
.data
.
this
object.
delta
close to originalDelta
with
the property that
EmpiricalDistribution.getNextValue()
.
System.currentTimeMillis() + System.identityHashCode(this))
.
valuesFileURL
.
DoubleArray
implementation that automatically
handles expanding and contracting its internal storage array as elements
are added and removed.TiesStrategy
.
ranks[i] = Double.NaN
for each i in nanPositions.
rint
function.RombergIntegrator.ROMBERG_MAX_ITERATIONS_COUNT
)
n
-th roots of
unity.n
-th roots of unity.
P(D_n < d)
using method described in [1] and doubles
(see above).
1 / SAFE_MIN
does not overflow.
d
.
d
.
d
.
f(p + alpha * d)
.
SecondMoment
identical
to the original
biasCorrected
property and default (Downside) varianceDirection
property.
biasCorrected
property and default (Downside) varianceDirection
property.
Direction
property
and default (true) biasCorrected
property
isBiasCorrected
property and the specified Direction
property.
SemiVariance
identical
to the original
ExceptionContext.context
.
ExceptionContext.msgPatterns
and ExceptionContext.msgArguments
.
RealMatrix
.
RealVector
.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a vector.
column
as a vector.
column
as a vector.
expansionMode
.
mean
used in data generation.
DescriptiveStatistics.getPercentile(double)
.
index
.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a vector.
row
as a vector.
row
as a vector.
int
seed.
int
array seed.
long
seed.
int
seed.
int
array seed.
long
seed.
int
seed.
long
seed.
int
array seed.
int
seed.
int
array seed.
int
seed.
int
array seed.
long
seed.
int
seed.
int
array seed.
long
seed.
standard deviation
used in ValueServer.GAUSSIAN_MODE
.
(row, column)
using data in
the input subMatrix
array.
row, column
using data in
the input subMatrix
array.
(row, column)
using data in
the input subMatrix
array.
row, column
using data in
the input subMatrix
array.
(row, column)
using data in
the input subMatrix
array.
row, column
using data in
the input subMatrix
array.
(row, column)
using data in
the input subMatrix
array.
row, column
using data in
the input subMatrix
array.
values file URL
using a string
URL representation.
values file URL
.
Ps(x)
whose values at point x
will be the same as the those from the
original polynomial P(x)
when computed at x + shift
.
shift
parameter.
hyperplane
of a space.signum
function.ConvergenceChecker
interface using
only point coordinates.ConvergenceChecker
interface using
only objective function values.ConvergenceChecker
interface using
only objective function values.convergence
checker
.
sin(x) / x
.
Skewness
identical
to the original
startValue
.
startValue
.
startValue
.
startValue
.
startValue
.
startValue
.
startValue
.
startValue
.
min
and max
.
startValue
.
AbstractIntegerDistribution.inverseCumulativeProbability(double)
.
RealMatrix
.
RealMatrix
.
null
elements.
null
elements.
null
elements.
FieldVector
interface with a OpenIntToFieldHashMap
backing store.RealMatrix
implementations that require sparse backing storageDfp
's.
Dfp
into 2 Dfp
's such that their sum is equal to the input Dfp
.
Dfp
with value √2.
Dfp
with value √2 / 2.
Dfp
array with value √2 split in two pieces.
Dfp
with value √3.
Dfp
with value √3 / 3.
1 - this2
for this complex
number.
StandardDeviation
identical
to the original
isBiasCorrected
property.
isBiasCorrected
property and the supplied external moment.
FixedStepHandler
into a StepHandler
.Step normalizer
bounds settings.Step normalizer
modes.StorelessBivariateCovariance
instance with
bias correction.
StorelessBivariateCovariance
instance.
UnivariateStatistic
with
StorelessUnivariateStatistic.increment(double)
and StorelessUnivariateStatistic.incrementAll(double[])
methods for adding
values and updating internal state.split
method.Line
.Line
.OrientedPoint
.Plane
.value
for the most recently added value.
Complex
whose value is
(this - subtrahend)
.
Complex
whose value is
(this - subtrahend)
.
BigInteger
from the value of this
BigFraction
, returning the result in reduced form.
integer
from the value of this
BigFraction
, returning the result in reduced form.
long
from the value of this
BigFraction
, returning the result in reduced form.
m
from this matrix.
m
from this matrix.
m
from this matrix.
v
from this vector.
m
from this matrix.
m
.
m
from this matrix.
m
from this matrix.
m
from this matrix.
v
from this vector.
v
from this vector.
Sum
identical
to the original
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
addValue
method.SumOfLogs
identical
to the original
SumOfSquares
identical
to the original
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
DescriptiveStatistics
that
is safe to use in a multithreaded environment.MultivariateSummaryStatistics
that
is safe to use in a multithreaded environment.SummaryStatistics
that
is safe to use in a multithreaded environment.sampleStats
to mu
.
StatisticalSummary
instances, without the
assumption of equal subpopulation variances.
evaluate(double[], int, int)
methods
to verify that the input parameters designate a subarray of positive length.
evaluate(double[], int, int)
methods
to verify that the input parameters designate a subarray of positive length.
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.
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.
ThirdMoment
identical
to the original
NonPositiveDefiniteOperatorException
with
appropriate context.
double
s.
double
s.
double
s.
String
representing this fraction, ie
"num / dem" or just "num" if the denominator is one.
String
representing this fraction, ie
"num / dem" or just "num" if the denominator is one.
TournamentSelection.select(Population)
.
BOBYQAOptimizer.trustRegionCenterOffset
which is usually
BOBYQAOptimizer.newPoint
- BOBYQAOptimizer.trustRegionCenterOffset
.
Tricubic interpolation in three dimensions
F.- TricubicSplineInterpolatingFunction(double[], double[], double[], double[][][], double[][][], double[][][], double[][][], double[][][], double[][][], double[][][], double[][][]) - Constructor for class org.apache.commons.math3.analysis.interpolation.TricubicSplineInterpolatingFunction
- TricubicSplineInterpolator - Class in org.apache.commons.math3.analysis.interpolation
- Generates a tricubic interpolating function.
- TricubicSplineInterpolator() - Constructor for class org.apache.commons.math3.analysis.interpolation.TricubicSplineInterpolator
- TriDiagonalTransformer - Class in org.apache.commons.math3.linear
- Class transforming a symmetrical matrix to tridiagonal shape.
- TriDiagonalTransformer(RealMatrix) - Constructor for class org.apache.commons.math3.linear.TriDiagonalTransformer
- Build the transformation to tridiagonal shape of a symmetrical matrix.
- trigamma(double) - Static method in class org.apache.commons.math3.special.Gamma
- Computes the trigamma function of x.
- trigger(int) - Method in interface org.apache.commons.math3.util.Incrementor.MaxCountExceededCallback
- Function called when the maximal count has been reached.
- trimmedPrefix - Variable in class org.apache.commons.math3.geometry.VectorFormat
- Trimmed prefix.
- trimmedPrefix - Variable in class org.apache.commons.math3.linear.RealVectorFormat
- Trimmed prefix.
- trimmedSeparator - Variable in class org.apache.commons.math3.geometry.VectorFormat
- Trimmed separator.
- trimmedSeparator - Variable in class org.apache.commons.math3.linear.RealVectorFormat
- Trimmed separator.
- trimmedSuffix - Variable in class org.apache.commons.math3.geometry.VectorFormat
- Trimmed suffix.
- trimmedSuffix - Variable in class org.apache.commons.math3.linear.RealVectorFormat
- Trimmed suffix.
- triu(RealMatrix, int) - Static method in class org.apache.commons.math3.optimization.direct.CMAESOptimizer
- TrivariateFunction - Interface in org.apache.commons.math3.analysis
- An interface representing a trivariate real function.
- TrivariateGridInterpolator - Interface in org.apache.commons.math3.analysis.interpolation
- Interface representing a trivariate real interpolating function where the sample points must be specified on a regular grid.
- trsbox(double, ArrayRealVector, ArrayRealVector, ArrayRealVector, ArrayRealVector, ArrayRealVector) - Method in class org.apache.commons.math3.optimization.direct.BOBYQAOptimizer
- A version of the truncated conjugate gradient is applied.
- trunc(DfpField.RoundingMode) - Method in class org.apache.commons.math3.dfp.Dfp
- Does the integer conversions with the specified rounding.
- TRUNC_TRAP - Static variable in class org.apache.commons.math3.dfp.Dfp
- Name for traps triggered by truncation.
- trustRegionCenterInterpolationPointIndex - Variable in class org.apache.commons.math3.optimization.direct.BOBYQAOptimizer
- Index of the interpolation point at the trust region center.
- trustRegionCenterOffset - Variable in class org.apache.commons.math3.optimization.direct.BOBYQAOptimizer
- Displacement from
BOBYQAOptimizer.originShift
of the trust region center.- tryStep(double, double[], double, int, double[], double[][], double[], double[], double[]) - Method in class org.apache.commons.math3.ode.nonstiff.GraggBulirschStoerIntegrator
- Perform integration over one step using substeps of a modified midpoint method.
- tTest(double, double[], double) - Static method in class org.apache.commons.math3.stat.inference.TestUtils
- tTest(double, double[]) - Static method in class org.apache.commons.math3.stat.inference.TestUtils
- tTest(double, StatisticalSummary, double) - Static method in class org.apache.commons.math3.stat.inference.TestUtils
- tTest(double, StatisticalSummary) - Static method in class org.apache.commons.math3.stat.inference.TestUtils
- tTest(double[], double[], double) - Static method in class org.apache.commons.math3.stat.inference.TestUtils
- tTest(double[], double[]) - Static method in class org.apache.commons.math3.stat.inference.TestUtils
- tTest(StatisticalSummary, StatisticalSummary, double) - Static method in class org.apache.commons.math3.stat.inference.TestUtils
- tTest(StatisticalSummary, StatisticalSummary) - Static method in class org.apache.commons.math3.stat.inference.TestUtils
- TTest - Class in org.apache.commons.math3.stat.inference
- An implementation for Student's t-tests.
- TTest() - Constructor for class org.apache.commons.math3.stat.inference.TTest
- tTest(double, double[]) - Method in class org.apache.commons.math3.stat.inference.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) - Method in class org.apache.commons.math3.stat.inference.TTest
- Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which
sample
is drawn equalsmu
.- tTest(double, StatisticalSummary) - Method in class org.apache.commons.math3.stat.inference.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 constantmu
.- tTest(double, StatisticalSummary, double) - Method in class org.apache.commons.math3.stat.inference.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 equalsmu
.- tTest(double[], double[]) - Method in class org.apache.commons.math3.stat.inference.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) - Method in class org.apache.commons.math3.stat.inference.TTest
- Performs a two-sided t-test evaluating the null hypothesis that
sample1
andsample2
are drawn from populations with the same mean, with significance levelalpha
.- tTest(StatisticalSummary, StatisticalSummary) - Method in class org.apache.commons.math3.stat.inference.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) - Method in class org.apache.commons.math3.stat.inference.TTest
- Performs a two-sided t-test evaluating the null hypothesis that
sampleStats1
andsampleStats2
describe datasets drawn from populations with the same mean, with significance levelalpha
.- tTest(double, double, double, double) - Method in class org.apache.commons.math3.stat.inference.TTest
- Computes p-value for 2-sided, 1-sample t-test.
- tTest(double, double, double, double, double, double) - Method in class org.apache.commons.math3.stat.inference.TTest
- Computes p-value for 2-sided, 2-sample t-test.
- two - Variable in class org.apache.commons.math3.dfp.DfpField
- A
Dfp
with value 2.- TWO - Static variable in class org.apache.commons.math3.fraction.BigFraction
- A fraction representing "2 / 1".
- TWO - Static variable in class org.apache.commons.math3.fraction.Fraction
- A fraction representing "2 / 1".
- TWO - Static variable in class org.apache.commons.math3.optimization.direct.BOBYQAOptimizer
- TWO_FIFTHS - Static variable in class org.apache.commons.math3.fraction.BigFraction
- A fraction representing "2/5".
- TWO_FIFTHS - Static variable in class org.apache.commons.math3.fraction.Fraction
- A fraction representing "2/5".
- TWO_HUNDRED_FIFTY - Static variable in class org.apache.commons.math3.optimization.direct.BOBYQAOptimizer
- TWO_PI - Static variable in class org.apache.commons.math3.util.MathUtils
- 2 π.
- TWO_POWER_52 - Static variable in class org.apache.commons.math3.util.FastMath
- 2^52 - double numbers this large must be integral (no fraction) or NaN or Infinite
- TWO_QUARTERS - Static variable in class org.apache.commons.math3.fraction.BigFraction
- A fraction representing "2/4".
- TWO_QUARTERS - Static variable in class org.apache.commons.math3.fraction.Fraction
- A fraction representing "2/4".
- TWO_THIRDS - Static variable in class org.apache.commons.math3.fraction.BigFraction
- A fraction representing "2/3".
- TWO_THIRDS - Static variable in class org.apache.commons.math3.fraction.Fraction
- A fraction representing "2/3".
ulp
function.RandomVectorGenerator
that generates vectors with uncorrelated
components.MersenneTwister
),
in order to generate the individual components.
Dfp
function.UnivariateOptimizer
interface
adding multi-start features to an existing optimizer.UnivariateInterpolator
interface.UnivariateSolver
objects.xval[i-1]
, update the interval so that it
embraces the same number of points closest to xval[i]
,
ignoring zero weights.
X
, this method returns P(X >= x)
.
BaseAbstractMultivariateSimpleBoundsOptimizer.getUpperBound()
- BOBYQAOptimizer.originShift
All the components of every BOBYQAOptimizer.trustRegionCenterOffset
are going
to satisfy the boundsBOBYQAOptimizer.trustRegionCenterOffset
i ≤
upperBound
i,BOBYQAOptimizer.trustRegionCenterOffset
is
on a constraint boundary.
Gaussian.Parametric.value(double,double[])
and Gaussian.Parametric.gradient(double,double[])
methods.
HarmonicOscillator.Parametric.value(double,double[])
and HarmonicOscillator.Parametric.gradient(double,double[])
methods.
Logistic.Parametric.value(double,double[])
and Logistic.Parametric.gradient(double,double[])
methods.
Logit.Parametric.value(double,double[])
and Logit.Parametric.gradient(double,double[])
methods.
Sigmoid.Parametric.value(double,double[])
and Sigmoid.Parametric.gradient(double,double[])
methods.
x
.
x
.
x
.
x
.
x
.
isBiasCorrected
property.
isBiasCorrected
property
isBiasCorrected
property and the supplied external second moment.
Variance
identical
to the original
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
lower < initial < upper
.
lower < initial < upper
.
W_SUB_N_I[i]
is the imaginary part of
exp(- 2 * i * pi / n)
:
W_SUB_N_I[i] = -sin(2 * pi/ n)
, where n = 2^i
.
W_SUB_N_R[i]
is the real part of
exp(- 2 * i * pi / n)
:
W_SUB_N_R[i] = cos(2 * pi/ n)
, where n = 2^i
.
curve fitting
.X_CRIT
is used by Erf.erf(double, double)
internally.
Dfp
with value 0.
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