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BigFraction
.
BrentOptimizer
but will not pass the convergence check, so that the custom checker
will always decide when to stop the line search.
BrentOptimizer
but will not pass the convergence check, so that the custom checker
will always decide when to stop the line search.
AbstractUnivariateDifferentiableSolver
FieldMatrix
methods regardless of the underlying storage.FractionFormat
and BigFractionFormat
.AbstractIntegerDistribution.AbstractIntegerDistribution(RandomGenerator)
instead.
SimpleValueChecker.SimpleValueChecker()
RandomGenerator
interface.AbstractRealDistribution.AbstractRealDistribution(RandomGenerator)
instead.
SimpleValueChecker.SimpleValueChecker()
StorelessUnivariateStatistic
interface.SubHyperplane
.UnivariateStatistic
interface.Adams-Bashforth
and
Adams-Moulton
integrators.FunctionUtils.add(UnivariateDifferentiableFunction...)
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.
this
and m
.
m
to this matrix.
this
and m
.
this
and v
.
this
and v
.
v
.
this
and m
.
this
and m
.
m
.
this
and v
.
m
.
v
.
this
and m
.
v
.
this
and v
.
Collection
of chromosomes to this Population
.
data
.
ResizableDoubleArray.ExpansionMode.ADDITIVE
instead.
this
matrix.
this
matrix.
this
matrix.
this
matrix.
this
matrix.
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
.
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
.
SimpleValueChecker.SimpleValueChecker()
SimpleValueChecker.SimpleValueChecker()
SimpleVectorValueChecker.SimpleVectorValueChecker()
true
if the right-hand side vector is zero exactly.
true
if beta
is essentially zero.
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.
b == 0
(exact floating-point equality).
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
).
100, 50
(see the
other constructor
).
(lo, hi)
, this class
finds an approximation x
to the point at which the function
attains its minimum.BSP tree
nodes.byte
.
WeibullDistribution.getNumericalMean()
ZipfDistribution.getNumericalMean()
.
FDistribution.getNumericalVariance()
HypergeometricDistribution.getNumericalVariance()
.
WeibullDistribution.getNumericalVariance()
ZipfDistribution.getNumericalVariance()
.
SymmLQ.State.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.
true
if symmetry of matrix and conditioner must be checked.
ResizableDoubleArray.checkContractExpand(double,double)
instead.
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.lambda
must be
passed with the call to optimize
(whereas in the current code it is set to an undocumented value).
lambda
must be
passed with the call to optimize
(whereas in the current code it is set to an undocumented value)..
lambda
and inputSigma
must be
passed with the call to optimize
.
SimpleValueChecker.SimpleValueChecker()
lambda
and inputSigma
must be
passed with the call to optimize
.
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.
new Double(this.doubleValue()).compareTo(new
Double(o.doubleValue()))
Complex
utilities functions.FunctionUtils.compose(UnivariateDifferentiableFunction...)
valuesFileURL
, using the default number of bins.
valuesFileURL
and binCount
bins.
n
-th roots of unity.
RealLinearOperator
.ranks.
Complex
objects.
Number
type to double
source
array.
source
array.
source
array.
source
array.
RandomVectorGenerator
that generates vectors with with
correlated components.AbstractLeastSquaresOptimizer.setCost(double)
.
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.
OnePointCrossover.crossover(Chromosome, Chromosome)
.
X
whose values are distributed according
to this distribution, this method returns P(x0 < X <= x1)
.
AbstractRealDistribution.probability(double,double)
instead.
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)
.
RealDistribution.cumulativeProbability(double,double)
X
whose values are distributed according
to this distribution, this method returns P(X <= x)
.
RealDistribution.cumulativeProbability(double,double)
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)
.
probability(double x0, double 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)
.
RealVector.SparseEntryIterator.next()
.
CurveFitter.CurveFitter(MultivariateDifferentiableVectorOptimizer)
CycleCrossover
policy.
CycleCrossover
policy using the given randomStart
behavior.
DAXPY
function, which carries out the
operation y ← a · x + y.
double
value in an object.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.
ResizableDoubleArray.contractionCriterion
and ResizableDoubleArray.expansionFactor
.
CMAESOptimizer.diagonalOnly
: 0.
RealMatrix
objects.
MultiDirectionalSimplex.gamma
: 0.5.
NelderMeadSimplex.gamma
: 0.5.
MultiDirectionalSimplex.gamma
: 0.5.
NelderMeadSimplex.gamma
: 0.5.
BOBYQAOptimizer.initialTrustRegionRadius
: 10.0 .
BOBYQAOptimizer.initialTrustRegionRadius
: 10.0 .
CMAESOptimizer.isActiveCMA
: true.
MultiDirectionalSimplex.khi
: 2.0.
NelderMeadSimplex.khi
: 2.0.
MultiDirectionalSimplex.khi
: 2.0.
NelderMeadSimplex.khi
: 2.0.
CMAESOptimizer.maxIterations
: 30000.
CMAESOptimizer.random
.
NelderMeadSimplex.rho
: 1.0.
NelderMeadSimplex.rho
: 1.0.
NelderMeadSimplex.sigma
: 0.5.
NelderMeadSimplex.sigma
: 0.5.
CMAESOptimizer.stopFitness
: 0.0.
BOBYQAOptimizer.stoppingTrustRegionRadius
: 1.0E-8 .
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
.
x
.
x
.
x
.
x
.
shape / scale * sqrt(e / (2 * pi * (shape + g + 0.5))) / L(shape)
,
where L(shape)
is the Lanczos approximation returned by
Gamma.lanczos(double)
.
shape * sqrt(e / (2 * pi * (shape + g + 0.5))) / L(shape)
,
where L(shape)
is the Lanczos approximation returned by
Gamma.lanczos(double)
.
Acos.value(DerivativeStructure)
Acosh.value(DerivativeStructure)
Asin.value(DerivativeStructure)
Asinh.value(DerivativeStructure)
Atan.value(DerivativeStructure)
Atanh.value(DerivativeStructure)
Cbrt.value(DerivativeStructure)
Constant.value(DerivativeStructure)
Cos.value(DerivativeStructure)
Cosh.value(DerivativeStructure)
Exp.value(DerivativeStructure)
Expm1.value(DerivativeStructure)
Gaussian.value(DerivativeStructure)
HarmonicOscillator.value(DerivativeStructure)
Identity.value(DerivativeStructure)
Inverse.value(DerivativeStructure)
Log.value(DerivativeStructure)
Log10.value(DerivativeStructure)
Log1p.value(DerivativeStructure)
Logistic.value(DerivativeStructure)
Logit.value(DerivativeStructure)
Minus.value(DerivativeStructure)
Power.value(DerivativeStructure)
Sigmoid.value(DerivativeStructure)
Sin.value(DerivativeStructure)
Sinc.value(DerivativeStructure)
Sinh.value(DerivativeStructure)
Sqrt.value(DerivativeStructure)
Tan.value(DerivativeStructure)
Tanh.value(DerivativeStructure)
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
.MultivariateDifferentiableFunction
MultivariateDifferentiableVectorFunction
UnivariateDifferentiableFunction
UnivariateDifferentiableMatrixFunction
UnivariateDifferentiableSolver
UnivariateDifferentiableVectorFunction
differential
from a regular function
.
differential
from a regular vector function
.
differential
from a regular matrix function
.
differential
from a regular function
.
differential
from a regular matrix function
.
differential
from a regular vector function
.
simplex.length - 1
).
simplex.length - 1
).
i
first or last elements of the array,
depending on the value of front
.
i
initial elements of the array.
i
last elements of the array.
Complex
whose value is
(this / divisor)
.
Complex
whose value is (this / divisor)
,
with divisor
interpreted as a real number.
BigInteger
,
ie this * 1 / bg
, returning the result in reduced form.
int
, ie
this * 1 / i
, returning the result in reduced form.
long
, ie
this * 1 / l
, returning the result in reduced form.
v
.
v
.
DSCompiler.getCompiler(int, int)
.
Localizable
interface, without localization.Dfp
with value e.
RealVector
might lead to wrong results. Since there is no
satisfactory correction to this bug, this method is deprecated. Users who
want to preserve this feature are advised to implement
RealVectorPreservingVisitor
(possibly ignoring corner cases for
the sake of efficiency).
RealVector
might lead to wrong results. Since there is no
satisfactory correction to this bug, this method is deprecated. Users who
want to preserve this feature are advised to implement
RealVectorPreservingVisitor
(possibly ignoring corner cases for
the sake of efficiency).
ElitisticListPopulation
instance.
ElitisticListPopulation
instance and initializes its inner chromosome list.
RandomGenerator
as the source of random data.
RandomGenerator
as the source of random data.
EmpiricalDistribution.EmpiricalDistribution(int,RandomGenerator)
instead.
EmpiricalDistribution.EmpiricalDistribution(RandomGenerator)
instead.
EmpiricalDistribution.randomData
instance variable.
DataAdapter
for data provided as array of doubles.sampleStats
and
beanStats
abstracting the source of data.DataAdapter
for data provided through some input stream1 + EPSILON
is numerically equal to 1.
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.
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)
.
true
if there is no double value strictly between the
arguments or the reltaive difference between them is smaller or equal
to the given tolerance.
Dfp
array with value e split in two pieces.
Clusterable
for points with double coordinates.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
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.PoissonDistribution.sample()
method.
n
(the product of the numbers 1 to n), as a
double
.
n
.
Math
and
StrictMath
for large scale computation.FastMath
.BracketFinder.hi
.
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
PolynomialFitter.fit(double[])
instead.
FixedElapsedTime
instance.
FixedElapsedTime
instance.
BracketFinder.lo
.
BracketFinder.lo
.
float
.
floor
function.BracketFinder.mid
.
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.
RealMatrixFormat.format(RealMatrix,StringBuffer,FieldPosition)
.
RealMatrix
object 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
.
observed
and expected
frequency counts.
Gaussian
function.norm
, mean
, and sigma
of a Gaussian.Parametric
based on the specified observed points.norm
, mean
, and sigma
of a Gaussian.Parametric
based on the specified observed points.integrating
a weighted
function.points
and weights
.
Gaussian integration rule
.SimpleVectorValueChecker.SimpleVectorValueChecker()
SimpleVectorValueChecker.SimpleVectorValueChecker()
observed1
and observed2
.
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
.
GammaDistribution.getShape()
should be preferred.
This method will be removed in version 4.0.
beta
.
GammaDistribution.getScale()
should be preferred.
This method will be removed in version 4.0.
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.
ResizableDoubleArray.getContractionCriterion()
instead.
getCorrelationStandardErrors().getEntry(i,j)
is the standard
error associated with getCorrelationMatrix.getEntry(i,j)
AbstractLeastSquaresOptimizer.computeCovariances(double[],double)
instead.
AbstractLeastSquaresOptimizer.computeCovariances(double[],double)
instead.
CrossoverPolicy
.
FieldVector.toArray()
method instead.
SparseFieldVector.toArray()
method instead.
BigInteger
.
DoubleArray
.
ResizableArray
.
EmpiricalDistribution
used when operating in 0.
ResizableDoubleArray.ExpansionMode
in 4.0.
BracketFinder.getHi()
.
BracketFinder.getHi()
.
Field
to which the instance belongs.
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.
Field
to which the instance belongs.
BracketFinder.getLo()
.
BracketFinder.getLo()
.
BracketFinder.getMid()
.
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.
ResizableDoubleArray.getCapacity()
instead.
this
event
is created.
Interval.getSize()
Interval.getInf()
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
Interval.getBarycenter()
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
point
.
valuesFileURL
.
BigInteger
.
optimize
.
optimize
.
optimize
.
optimize
.
optimize
.
optimize
.
optimize
.
index
.
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.
CrossoverPolicy
.
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.
this
distribution.
beta
.
this
distribution.
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.
Interval.getSup()
ValueServer.DIGEST_MODE
.
GoalType.MAXIMIZE
or GoalType.MINIMIZE
.
goodb
parameter.
x
.
x
.
x
.
x
.
x
.
BOBYQAOptimizer.originShift
+
BOBYQAOptimizer.trustRegionCenterOffset
.
BOBYQAOptimizer.originShift
+
BOBYQAOptimizer.trustRegionCenterOffset
.
observed
frequency counts to those in the expected
array.
alpha
.
observed1
and
observed2
.
AbstractLeastSquaresOptimizer.computeSigma(double[],double)
should be used
instead. It should be emphasized that guessParametersErrors
and
computeSigma
are not strictly equivalent.
true
if the default convergence criterion is verified.
true
if the default stopping criterion is fulfilled.
new Double(this.doubleValue()).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)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
.
BaseMultiStartMultivariateOptimizer.optimData
where the updated start value
will be stored.
StorelessBivariateCovariance
instances.
Well19937c
generator seeded with
System.currentTimeMillis() + System.identityHashCode(this))
.
X
, this method returns
P(x0 <= X <= x1)
.
f(x) * w(x)
,
where w
is a weight function that depends on the actual
flavor of the Gauss integration scheme.
SplineInterpolator
on the resulting fit.
BOBYQAOptimizer.originShift
.
BOBYQAOptimizer.originShift
.
int
.
A0
defined in DGAM1
.
A1
defined in DGAM1
.
B1
defined in DGAM1
.
B2
defined in DGAM1
.
B3
defined in DGAM1
.
B4
defined in DGAM1
.
B5
defined in DGAM1
.
B6
defined in DGAM1
.
B7
defined in DGAM1
.
B8
defined in DGAM1
.
C
defined in DGAM1
.
C0
defined in DGAM1
.
C1
defined in DGAM1
.
C10
defined in DGAM1
.
C11
defined in DGAM1
.
C12
defined in DGAM1
.
C13
defined in DGAM1
.
C2
defined in DGAM1
.
C3
defined in DGAM1
.
C4
defined in DGAM1
.
C5
defined in DGAM1
.
C6
defined in DGAM1
.
C7
defined in DGAM1
.
C8
defined in DGAM1
.
C9
defined in DGAM1
.
P0
defined in DGAM1
.
P1
defined in DGAM1
.
P2
defined in DGAM1
.
P3
defined in DGAM1
.
P4
defined in DGAM1
.
P5
defined in DGAM1
.
P6
defined in DGAM1
.
Q1
defined in DGAM1
.
Q2
defined in DGAM1
.
Q3
defined in DGAM1
.
Q4
defined in DGAM1
.
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
.
true
if this
double precision number is infinite
(Double.POSITIVE_INFINITY
or Double.NEGATIVE_INFINITY
).
NaN
.
NaN
.
NaN
.
NaN
.
true
if this
double precision number is
Not-a-Number (NaN
), false otherwise.
true
iff another
has the same representation and therefore the same fitness.
true
iff another
is a RandomKey and
encodes the same permutation.
true
if this operator supports
RealLinearOperator.operateTranspose(RealVector)
.
SimplePointChecker.maxIterationCount
is set to this value, the number of
iterations will never cause SimplePointChecker.converged(int, Pair, Pair)
to return true
.
SimpleValueChecker.maxIterationCount
is set to this value, the number of
iterations will never cause
SimpleValueChecker.converged(int,PointValuePair,PointValuePair)
to return true
.
SimpleVectorValueChecker.maxIterationCount
is set to this value, the number of
iterations will never cause
SimpleVectorValueChecker.converged(int,PointVectorValuePair,PointVectorValuePair)
to return true
.
SimpleUnivariateValueChecker.maxIterationCount
is set to this value, the number of
iterations will never cause
SimpleUnivariateValueChecker.converged(int,UnivariatePointValuePair,UnivariatePointValuePair)
to return true
.
SimplePointChecker.maxIterationCount
is set to this value, the number of
iterations will never cause SimplePointChecker.converged(int, Pair, Pair)
to return true
.
SimpleValueChecker.maxIterationCount
is set to this value, the number of
iterations will never cause
SimpleValueChecker.converged(int,PointValuePair,PointValuePair)
to return true
.
SimpleVectorValueChecker.maxIterationCount
is set to this value, the number of
iterations will never cause
SimpleVectorValueChecker.converged(int,PointVectorValuePair,PointVectorValuePair)
to return true
.
SimpleUnivariateValueChecker.maxIterationCount
is set to this value, the number of
iterations will never cause
SimpleUnivariateValueChecker.converged(int,UnivariatePointValuePair,UnivariatePointValuePair)
to return true
.
IterationManager
should be derived.IterativeLinearSolver
.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
g
constant in the Lanczos approximation, see
Gamma.lanczos(double)
.
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.integrate
method will perform an integration on the natural interval
[-1 , 1]
.
integrate
method will perform an integration on the given interval.
IterativeLegendreGaussIntegrator
instead.integrate
method will perform an integration on the natural interval
[-1 , 1]
.
integrate
method will perform an integration on the given interval.
DECIMAL128
.
other contructor
.
other contructor
.
other contructor
.
other contructor
.
AffineTransform
.
linear constraints
.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.Beta.logBeta(double, double)
.
normally distributed
natural
logarithm of the log-normal distribution are equal to zero and one
respectively.
long
.
BaseMultivariateOptimizer.getLowerBound()
- BOBYQAOptimizer.originShift
.
BaseAbstractMultivariateSimpleBoundsOptimizer.getLowerBound()
- BOBYQAOptimizer.originShift
.
first order
differential equations
in order to compute exactly the main state jacobian
matrix for partial derivatives equations
.CycleCrossover.crossover(Chromosome, Chromosome)
.
NPointCrossover.crossover(Chromosome, Chromosome)
.
OrderedCrossover.crossover(Chromosome, Chromosome)
.
UniformCrossover.crossover(Chromosome, Chromosome)
.
NullArgumentException
) inherit from this class.FieldMatrix
/BigFraction
.FieldMatrix
/Fraction
.Max
identical
to the original
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
BaseMultiStartMultivariateOptimizer.optimData
where the updated maximum
number of evaluations will be stored.
MultiStartUnivariateOptimizer.optimData
where the updated maximum
number of evaluations will be stored.
SimplePointChecker.converged(int, Pair, Pair)
method
will return true (unless the check is disabled).
SimpleValueChecker.converged(int,PointValuePair,PointValuePair)
method
will return true (unless the check is disabled).
SimpleVectorValueChecker.converged(int,PointVectorValuePair,PointVectorValuePair)
method
will return true (unless the check is disabled).
SimpleUnivariateValueChecker.converged(int,UnivariatePointValuePair,UnivariatePointValuePair)
method will return true (unless the check is disabled).
SimplePointChecker.converged(int, Pair, Pair)
method
will return true (unless the check is disabled).
SimpleValueChecker.converged(int,PointValuePair,PointValuePair)
method
will return true (unless the check is disabled).
SimpleVectorValueChecker.converged(int,PointVectorValuePair,PointVectorValuePair)
method
will return true (unless the check is disabled).
SimpleUnivariateValueChecker.converged(int,UnivariatePointValuePair,UnivariatePointValuePair)
method will return true (unless the check is disabled).
log(y)
(y = x / scale
) for the selection
of the computation method in GammaDistribution.density(double)
.
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
Collection
of Frequency
objects into this instance.
UpdatingMultipleLinearRegression
interface.Min
identical
to the original
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
-1
.
y = x / scale
for the selection of the computation
method in GammaDistribution.density(double)
.
ResizableDoubleArray.ExpansionMode.MULTIPLICATIVE
instead.
FunctionUtils.multiply(UnivariateDifferentiableFunction...)
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
.
this
by m
.
m
.
this
by m
.
m
.
this
by m
.
this
by m
.
m
.
m
.
this
by m
.
m
.
this
by m
.
this
matrix by the
specified value.
this
matrix by the
specified value.
this
matrix by the
specified value.
this
matrix by the
specified value.
this
matrix by the
specified value.
UnivariateOptimizer
interface
adding multi-start features to an existing optimizer.MultivariateFunction
representing a
multivariate differentiable real function.MultivariateVectorFunction
representing a
multivariate differentiable vectorial function.MultivariateFunction
to unbounded ones.MultivariateFunction
to an unbouded
domain using a penalty function.addValue
method.Double.NaN
as a Decimal64
.
Complex
whose value is (-this)
.
this
element.
this
element.
this
element.
Double.NEGATIVE_INFINITY
as a
Decimal64
.
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
.
trsbox
or altmov
.
NewtonRaphsonSolver
RealVector.SparseEntryIterator.next()
to return.
Beta Distribution
.
Beta Distribution
.
Binomial 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.
boolean
value from this random number generator's
sequence.
Cauchy Distribution
.
Cauchy Distribution
.
ChiSquare 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.
double
value between 0.0
and
1.0
from this random number generator's sequence.
F Distribution
.
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.
float
value between 0.0
and 1.0
from this random
number generator's sequence.
Gamma Distribution
.
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.
double
value with mean 0.0
and standard
deviation 1.0
from this random number generator's sequence.
len
.
len
.
len
.
Hypergeometric Distribution
.
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).
lower
and upper
(endpoints included).
int
value from this random number generator's sequence.
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).
lower
and upper
(endpoints included).
long
value from this random number generator's sequence.
long
value from this random number generator's sequence.
j
such that
j > i && (j == weights.length || weights[j] != 0)
.
Pascal Distribution
.
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
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
.
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.
lower
and upper
(endpoints included) from a secure random
sequence.
lower
and upper
(endpoints included) from a secure random
sequence.
T Distribution
.
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)
.
(lower, upper)
(i.e., endpoints excluded).
(lower, upper)
or the interval [lower, upper)
.
Weibull Distribution
.
Weibull Distribution
.
Zipf Distribution
.
Zipf Distribution
.
line search solver
and
preconditioner
.
preconditioner
.
SimpleValueChecker.SimpleValueChecker()
line search solver
and
preconditioner
.
preconditioner
.
RealLinearOperator
is expected.RealLinearOperator
is expected.NPointCrossover
policy using the given number of points.
null
argument must throw
this exception.RealMatrix
objects compatible with octave.
CurveFitter.optimizer
n
-th roots of unity, for negative values
of n
.
n
-th roots of unity, for positive values
of n
.
Dfp
with value 1.
1d
as a Decimal64
.
BigInteger
representation of 100.
0.5
.
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).
method
.
method
.
BaseAbstractMultivariateOptimizer.optimize(int,MultivariateFunction,GoalType,OptimizationData[])
instead.
BaseAbstractMultivariateVectorOptimizer.optimize(int,MultivariateVectorFunction,OptimizationData[])
instead.
optimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)
instead.
optimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)
instead.
BaseAbstractMultivariateOptimizer.optimize(int,MultivariateFunction,GoalType,OptimizationData[])
instead.
BaseAbstractMultivariateVectorOptimizer.optimizeInternal(int,MultivariateVectorFunction,OptimizationData[])
instead.
AbstractDifferentiableOptimizer.optimizeInternal(int,MultivariateDifferentiableFunction,GoalType,OptimizationData[])
instead.
MultivariateDifferentiableVectorFunction
.
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.minimize
or
maximize
a scalar function, called the
objective
function
.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.
RealMatrix
object.
RealMatrix
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
.
curve fitting
.PolynomialFitter.PolynomialFitter(DifferentiableMultivariateVectorOptimizer)
instead.
Double.NaN
if the array is empty.
Double.NaN
if the designated subarray
is empty.
Double.POSITIVE_INFINITY
as a
Decimal64
.
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.
this
with itself p
times.
p
times.
this
with itself 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
.
this
by m
.
v
.
v
.
v
.
v
.
v
.
v
.
m
.
v
.
v
.
this
by m
.
v
.
v
.
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(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)
.
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
.
AbstractIntegerDistribution.random
instance variable instead.
AbstractRealDistribution.random
instance variable instead.
RandomDataGenerator
directlyRandomData
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.
RandomDataGenerator
insteadRandomGenerator
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
DerivativeStructure
.
PointValuePair
.
PointValuePair
.
PointValuePair
.
PointValuePair
.
double
)
vector spaces.nxm
matrix in components list format
"{{a00,a01, ...,
a0m-1},{a10,
a11, ..., a1m-1},{...},{
an-10, an-11, ...,
an-1m-1}}".this
element.
this
element.
this
element.
this
element.
this
element.
this
element.
BigFraction
to its lowest terms.
|a - offset|
to the primary interval
[0, |period|)
.
Region
.BrentOptimizer
but will not pass the convergence check, so that the custom checker
will always decide when to stop the line search.
BrentOptimizer
but will not pass the convergence check, so that the custom checker
will always decide when to stop the line search.
LinearConstraint
.data
.
this
object.
delta
close to originalDelta
with
the property that
EmpiricalDistribution.getNextValue()
.
System.currentTimeMillis() + System.identityHashCode(this))
.
System.currentTimeMillis() + System.identityHashCode(this))
.
valuesFileURL
.
DoubleArray
implementation that automatically
handles expanding and contracting its internal storage array as elements
are added and removed.double[]
with the
initial capacity and numElements corresponding to the size of
the supplied double[]
array.
ResizableDoubleArray.ResizableDoubleArray(int,double)
instead.
ResizableDoubleArray.ResizableDoubleArray(int,double,double)
instead.
ResizableDoubleArray.ResizableDoubleArray(int,double,double,ExpansionMode,double[])
instead.
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
to each entry of this
.
d
to each entry of this
.
d
to each entry of this
.
d
.
this
by
d
.
d
.
this
by
d
.
d
.
this
by
d
.
f(p + alpha * d)
.
f(p + alpha * d)
.
MultiStartUnivariateOptimizer.optimData
where the updated start value
will be stored.
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
.
ListPopulation.addChromosomes(Collection)
instead
column
as a column matrix.
column
of this
matrix to the entries
of the specified array
.
column
as a column matrix.
column
of this
matrix to the entries
of the specified array
.
column
as a column matrix.
column
of this
matrix to the entries
of the specified array
.
column
as a column matrix.
column
of this
matrix to the entries
of the specified column matrix
.
column
as a column matrix.
column
as a column matrix.
column
of this
matrix to the entries
of the specified column matrix
.
column
as a column matrix.
column
as a column matrix.
column
of this
matrix to the entries
of the specified column matrix
.
column
as a vector.
column
of this
matrix to the entries
of the specified vector
.
column
as a vector.
column
of this
matrix to the entries
of the specified vector
.
column
as a vector.
column
of this
matrix to the entries
of the specified vector
.
ResizableDoubleArray.setExpansionMode(ExpansionMode)
instead.
mean
used in data generation.
DescriptiveStatistics.getPercentile(double)
.
index
.
index
.
row
as a row matrix.
row
of this
matrix to the entries
of the specified array
.
row
as a row matrix.
row
of this
matrix to the entries
of the specified array
.
row
as a row matrix.
row
of this
matrix to the entries
of the specified array
.
row
as a row matrix.
row
of this
matrix to the entries of
the specified row matrix
.
row
as a row matrix.
row
as a row matrix.
row
of this
matrix to the entries of
the specified row matrix
.
row
as a row matrix.
row
as a row matrix.
row
of this
matrix to the entries of
the specified row matrix
.
row
as a vector.
row
of this
matrix to the entries of
the specified vector
.
row
as a vector.
row
of this
matrix to the entries of
the specified vector
.
row
as a vector.
row
of this
matrix to the entries of
the specified 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.
int
seed.
int
array seed.
long
seed.
standard deviation
used in ValueServer.GAUSSIAN_MODE
.
BaseAbstractMultivariateOptimizer.optimize(int,MultivariateFunction,GoalType,OptimizationData[])
method.
(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.
input
parsed by this base
class.
input
parsed by this base
class.
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.
shape + g + 0.5
, where g
is the
Lanczos constant Gamma.LANCZOS_G
.
short
.
hyperplane
of a space.signum
function.ConvergenceChecker
interface using
only point coordinates.AbstractConvergenceChecker.AbstractConvergenceChecker()
ConvergenceChecker
interface
that uses only objective function values.AbstractConvergenceChecker.AbstractConvergenceChecker()
ConvergenceChecker
interface using
only objective function values.AbstractConvergenceChecker.AbstractConvergenceChecker()
ConvergenceChecker
interface using
only objective function values.AbstractConvergenceChecker.AbstractConvergenceChecker()
SimpleValueChecker.SimpleValueChecker()
sin(x) / x
.
Skewness
identical
to the original
startValue
.
startValue
.
startValue
.
startValue
.
startValue
.
startValue
.
startValue
.
startValue
.
min
and max
.
min
and max
.
startValue
.
AbstractIntegerDistribution.inverseCumulativeProbability(double)
.
RealMatrix
.
RealMatrix
.
null
elements.
null
elements.
null
elements.
null
elements.
Dfp
'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.
this
diagonal operator.
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.S(n,k)
", the number of
ways of partitioning an n
-element set into k
non-empty
subsets.
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.
this
minus m
.
m
from this matrix.
this
minus m
.
this
minus v
.
this
minus v
.
v
from this vector.
m
from this matrix.
this - m
.
this
minus m
.
m
from this matrix.
m
from this matrix.
this
minus v
.
this
minus m
.
m
from this matrix.
v
from this vector.
this
minus m
.
v
from this vector.
this
minus v
.
this
minus v
.
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.RandomGenerator
implementation can be thread-safe if it
is used through an instance of this class.RandomGenerator
instance.
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.
DifferentiableMultivariateFunction
interface itself is deprecated
DifferentiableMultivariateVectorFunction
interface itself is deprecated
DifferentiableUnivariateFunction
interface itself is deprecated
DifferentiableMultivariateFunction
interface itself is deprecated
DifferentiableMultivariateFunction
interface itself is deprecated
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.
String
is equal to
Double.toString(this.doubleValue())
DifferentiableUnivariateFunction
interface itself is deprecated
TournamentSelection.select(Population)
.
[a, b]
.
BOBYQAOptimizer.trustRegionCenterOffset
which is usually
BOBYQAOptimizer.newPoint
- BOBYQAOptimizer.trustRegionCenterOffset
.
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 class org.apache.commons.math3.optim.BaseOptimizer.MaxEvalCallback
- Function called when the maximal count has been reached.
- trigger(int) - Method in class org.apache.commons.math3.optim.BaseOptimizer.MaxIterCallback
- Function called when the maximal count has been reached.
- 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.optim.nonlinear.scalar.noderiv.CMAESOptimizer
- triu(RealMatrix, int) - Static method in class org.apache.commons.math3.optimization.direct.CMAESOptimizer
- Deprecated.
- 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.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer
- A version of the truncated conjugate gradient is applied.
- trsbox(double, ArrayRealVector, ArrayRealVector, ArrayRealVector, ArrayRealVector, ArrayRealVector) - Method in class org.apache.commons.math3.optimization.direct.BOBYQAOptimizer
- Deprecated. 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.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer
- Index of the interpolation point at the trust region center.
- trustRegionCenterInterpolationPointIndex - Variable in class org.apache.commons.math3.optimization.direct.BOBYQAOptimizer
- Deprecated. Index of the interpolation point at the trust region center.
- trustRegionCenterOffset - Variable in class org.apache.commons.math3.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer
- Displacement from
BOBYQAOptimizer.originShift
of the trust region center.- trustRegionCenterOffset - Variable in class org.apache.commons.math3.optimization.direct.BOBYQAOptimizer
- Deprecated. 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.analysis.integration.gauss.LegendreHighPrecisionRuleFactory
- The number
2
.- 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.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer
- TWO - Static variable in class org.apache.commons.math3.optimization.direct.BOBYQAOptimizer
- Deprecated.
- 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.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer
- TWO_HUNDRED_FIFTY - Static variable in class org.apache.commons.math3.optimization.direct.BOBYQAOptimizer
- Deprecated.
- 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_POWER_53 - Static variable in class org.apache.commons.math3.util.FastMath
- 2^53 - double numbers this large must be even.
- 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.UniformCrossover
policy using the given mixing ratio.
MersenneTwister
),
in order to generate the individual components.
Dfp
function.UnivariateMatrixFunction
representing a univariate differentiable matrix function.UnivariateVectorFunction
representing a univariate differentiable vectorial function.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.
AbstractLeastSquaresOptimizer.computeWeightedJacobian(double[])
instead.
AbstractLeastSquaresOptimizer.computeResiduals(double[])
,
BaseAbstractMultivariateVectorOptimizer.computeObjectiveValue(double[])
, AbstractLeastSquaresOptimizer.computeCost(double[])
and AbstractLeastSquaresOptimizer.setCost(double)
instead.
X
, this method returns P(X >= x)
.
BaseMultivariateOptimizer.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.
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
.
double
value of this object.
ValueServer.ValueServer(RandomGenerator)
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
.AbstractLeastSquaresOptimizer.computeWeightedJacobian(double[])
instead.
X_CRIT
is used by Erf.erf(double, double)
internally.
Dfp
with value 0.
0d
as a Decimal64
.
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