DSFactory factory
double[] data
private Object writeReplace()
int nbPoints
double stepSize
double halfSampleSpan
double tMin
double tMax
double value
double[] grad
double f0
double f1
private Object readResolve()
UnivariateDerivative1 zero
UnivariateDerivative1 one
DSFactory factory
DerivativeStructure
.double f0
double f1
double f2
private Object readResolve()
UnivariateDerivative2 zero
UnivariateDerivative2 one
DSFactory factory
DerivativeStructure
.double[] grid
int n
AtomicInteger cache
double bandwidth
A sensible value is usually 0.25 to 0.5.
int robustnessIters
A sensible value is usually 0 (just the initial fit without any robustness iterations) to 4.
double accuracy
double[] coefficients
protected final Object readResolve()
Complex.createComplex(double, double)
to
deserialize properly.double imaginary
double real
private Object readResolve()
double q0
double q1
double q2
double q3
int omegaCount
double[] omegaReal
double[] omegaImaginaryCounterClockwise
n
-th roots of unity, for positive values
of n
. In this array, the roots are stored in counter-clockwise
order.double[] omegaImaginaryClockwise
n
-th roots of unity, for negative values
of n
. In this array, the roots are stored in clockwise order.boolean isCounterClockWise
true
if RootsOfUnity.computeRoots(int)
was called with a positive
value of its argument n
. In this case, counter-clockwise ordering
of the roots of unity should be used.List<E> singletons
double[] probabilities
double solverAbsoluteAccuracy
double alpha
double beta
double z
double median
double scale
GammaDistribution gamma
double value
EnumeratedDistribution<T> innerDistribution
EnumeratedDistribution
(using the Double
wrapper)
used to generate the pmf.double mean
double logMean
double numeratorDegreesOfFreedom
double denominatorDegreesOfFreedom
double numericalVariance
double shape
double scale
double shiftedShape
double densityPrefactor1
shape / scale * sqrt(e / (2 * pi * (shape + g + 0.5))) / L(shape)
,
where L(shape)
is the Lanczos approximation returned by
Gamma.lanczos(double)
. This prefactor is used in
GammaDistribution.density(double)
, when no overflow occurs with the natural
calculation.double logDensityPrefactor1
log(shape / scale * sqrt(e / (2 * pi * (shape + g + 0.5))) / L(shape))
,
where L(shape)
is the Lanczos approximation returned by
Gamma.lanczos(double)
. This prefactor is used in
GammaDistribution.logDensity(double)
, when no overflow occurs with the natural
calculation.double densityPrefactor2
shape * sqrt(e / (2 * pi * (shape + g + 0.5))) / L(shape)
,
where L(shape)
is the Lanczos approximation returned by
Gamma.lanczos(double)
. This prefactor is used in
GammaDistribution.density(double)
, when overflow occurs with the natural
calculation.double logDensityPrefactor2
log(shape * sqrt(e / (2 * pi * (shape + g + 0.5))) / L(shape))
,
where L(shape)
is the Lanczos approximation returned by
Gamma.lanczos(double)
. This prefactor is used in
GammaDistribution.logDensity(double)
, when overflow occurs with the natural
calculation.double minY
y = x / scale
for the selection of the computation
method in GammaDistribution.density(double)
. For y <= minY
, the natural
calculation overflows.double maxLogY
log(y)
(y = x / scale
) for the selection
of the computation method in GammaDistribution.density(double)
. For
log(y) >= maxLogY
, the natural calculation overflows.double mu
double beta
double mu
double beta
double mu
double c
double halfC
double mu
double s
double location
double shape
double logShapePlusHalfLog2Pi
log(shape) + 0.5 * log(2*PI)
stored for faster computation.double mu
double omega
double mean
double standardDeviation
double logStandardDeviationPlusHalfLog2Pi
log(sd) + 0.5*log(2*pi)
stored for faster computation.double scale
double shape
double degreesOfFreedom
double factor
double a
double b
double c
double lower
double upper
double shape
double scale
int numberOfTrials
double probabilityOfSuccess
EnumeratedDistribution<T> innerDistribution
EnumeratedDistribution
instance (using the Integer
wrapper)
used to generate the pmf.double probabilityOfSuccess
double logProbabilityOfSuccess
log(p)
where p is the probability of success.double log1mProbabilityOfSuccess
log(1 - p)
where p is the probability of success.int numberOfSuccesses
int populationSize
int sampleSize
double numericalVariance
int numberOfSuccesses
double probabilityOfSuccess
double logProbabilityOfSuccess
log(p)
, where p
is the probability of success,
stored for faster computation.double log1mProbabilityOfSuccess
log(1-p)
, where p
is the probability of success,
stored for faster computation.NormalDistribution normal
double mean
int maxIterations
Gamma.regularizedGammaP(double, double, double, int)
or continued fraction approximation of
Gamma.regularizedGammaQ(double, double, double, int)
.double epsilon
int lower
int upper
int numberOfElements
double exponent
double nthHarmonic
double numericalMean
boolean numericalMeanIsCalculated
double numericalVariance
boolean numericalVarianceIsCalculated
String source
Localizable specifier
Object[] parts
Localizable specifier
Object[] parts
BigInteger numerator
BigInteger denominator
private Object readResolve()
int denominator
int numerator
private Object readResolve()
NumberFormat wholeFormat
NumberFormat wholeFormat
FieldElement<T extends FieldElement<T>>[][] data
double[][] data
FieldElement<T extends FieldElement<T>>[] data
Field<T extends FieldElement<T>> field
double[] data
FieldElement<T extends FieldElement<T>>[][] blocks
int rows
int columns
int blockRows
int blockColumns
double[][] blocks
int rows
int columns
int blockRows
int blockColumns
RealVector b
RealVector r
double rnorm
RealVector x
double[] data
int rows
int columns
OpenIntToDoubleHashMap entries
OpenIntToDoubleHashMap entries
int virtualSize
double epsilon
Field<T extends FieldElement<T>> field
OpenIntToFieldHashMap<T extends FieldElement<T>> entries
int virtualSize
int index
int[] v
int[] rsl
int[] mem
int count
int isaacA
int isaacB
int isaacC
int[] arr
int isaacX
int isaacI
int isaacJ
Random delegate
int[] mt
int mti
RandomGenerator randomGenerator
RandomGenerator randomGenerator
BigDecimal d
RoundingMode roundingMode
int scale
private Object readResolve()
double value
double
value of this object.private Object readResolve()
int iterations
PivotingStrategy pivotingStrategy
PivotingStrategy
used for pivoting.private void readObject(ObjectInputStream stream) throws IOException, ClassNotFoundException
IOException
- if object cannot be readClassNotFoundException
- if the class corresponding
to the serialized object cannot be foundint[] keys
double[] values
byte[] states
double missingEntries
int size
int mask
private void readObject(ObjectInputStream stream) throws IOException, ClassNotFoundException
IOException
- if object cannot be readClassNotFoundException
- if the class corresponding
to the serialized object cannot be foundField<T extends FieldElement<T>> field
int[] keys
FieldElement<T extends FieldElement<T>>[] values
byte[] states
FieldElement<T extends FieldElement<T>> missingEntries
int size
int mask
double contractionCriterion
double expansionFactor
internalArray.length * expansionFactor
if expansionMode
is set to MULTIPLICATIVE, or
internalArray.length + expansionFactor
if
expansionMode
is set to ADDITIVE.ResizableDoubleArray.ExpansionMode expansionMode
expansionFactor
is additive or multiplicative.double[] internalArray
int numElements
int startIndex
internalArray[startIndex],...,internalArray[startIndex + numElements - 1]
.Copyright © 2016-2022 CS GROUP. All rights reserved.