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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.
AbstractUnivariateDifferentiableSolverFieldMatrix 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.
BinaryChromosomes.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.MultivariateDifferentiableFunctionMultivariateDifferentiableVectorFunctionUnivariateDifferentiableFunctionUnivariateDifferentiableMatrixFunctionUnivariateDifferentiableSolverUnivariateDifferentiableVectorFunctiondifferential 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.
pth percentile of the values
in the values array.
quantileth percentile of the
designated values in the values array.
pth percentile of the values
in the values array, starting with the element in (0-based)
position begin in the array and including length
values.
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.
NewtonRaphsonSolverRealVector.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
pth percentile of the values
in the values array.
pth percentile of the values
in the values array, starting with the element in (0-based)
position begin in the array and including length
values.
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.RandomKeys.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.
doubles.
doubles.
doubles.
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.originShiftof the trust region center.- trustRegionCenterOffset - Variable in class org.apache.commons.math3.optimization.direct.BOBYQAOptimizer
- Deprecated. Displacement from
BOBYQAOptimizer.originShiftof 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
sampleis 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
sampleStatswith 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
statsis 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
sample1andsample2are 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
sampleStats1andsampleStats2describe 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
Dfpwith 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.trustRegionCenterOffseti ≤
upperBoundi,BOBYQAOptimizer.trustRegionCenterOffset is
on a constraint boundary.
BaseAbstractMultivariateSimpleBoundsOptimizer.getUpperBound() - BOBYQAOptimizer.originShift
All the components of every BOBYQAOptimizer.trustRegionCenterOffset are going
to satisfy the boundsBOBYQAOptimizer.trustRegionCenterOffseti ≤
upperBoundi,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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