private Object writeReplace()
double[] data
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
protected final Object readResolve()
Complex.createComplex(double, double)
to
deserialize properly.double imaginary
double real
private Object readResolve()
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.RandomDataImpl randomData
AbstractIntegerDistribution.random
instance variable instead.RandomGenerator random
RandomDataImpl randomData
AbstractRealDistribution.random
instance variable instead.RandomGenerator random
double solverAbsoluteAccuracy
GammaDistribution gamma
double solverAbsoluteAccuracy
RandomGenerator random
List<E> singletons
double[] probabilities
double[] cumulativeProbabilities
EnumeratedDistribution<T> innerDistribution
EnumeratedDistribution
instance (using the Integer
wrapper)
used to generate the pmf.EnumeratedDistribution<T> innerDistribution
EnumeratedDistribution
(using the Double
wrapper)
used to generate the pmf.double numeratorDegreesOfFreedom
double denominatorDegreesOfFreedom
double solverAbsoluteAccuracy
double numericalVariance
boolean numericalVarianceIsCalculated
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 solverAbsoluteAccuracy
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
boolean numericalVarianceIsCalculated
double scale
double shape
double logShapePlusHalfLog2Pi
log(shape) + 0.5 * log(2*PI)
stored for faster computation.double solverAbsoluteAccuracy
double mean
double standardDeviation
double logStandardDeviationPlusHalfLog2Pi
log(sd) + 0.5*log(2*pi)
stored for faster computation.double solverAbsoluteAccuracy
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
ExponentialDistribution exponential
PoissonDistribution.sample()
method.double mean
int maxIterations
Gamma.regularizedGammaP(double, double, double, int)
or continued fraction approximation of
Gamma.regularizedGammaQ(double, double, double, int)
.double epsilon
double shape
double scale
double solverAbsoluteAccuracy
double numericalMean
boolean numericalMeanIsCalculated
double numericalVariance
boolean numericalVarianceIsCalculated
int numberOfElements
double exponent
double numericalMean
boolean numericalMeanIsCalculated
double numericalVariance
boolean numericalVarianceIsCalculated
ExceptionContext context
ExceptionContext context
Number argument
ExceptionContext context
ExceptionContext context
ExceptionContext context
Number max
MathArrays.OrderDirection direction
boolean strict
int index
Number previous
Number max
boolean boundIsAllowed
Number min
boolean boundIsAllowed
String source
private void readObject(ObjectInputStream in) throws IOException, ClassNotFoundException
IOException
- This should never happen.ClassNotFoundException
- This should never happen.private void writeObject(ObjectOutputStream out) throws IOException
IOException
- This should never happen.Throwable throwable
List<E> msgPatterns
List<E> msgArguments
ExceptionContext.msgPatterns
.Map<K,V> context
NumberFormat denominatorFormat
NumberFormat numeratorFormat
BigInteger numerator
BigInteger denominator
private Object readResolve()
private Object readResolve()
NumberFormat wholeFormat
NumberFormat wholeFormat
private Object readResolve()
private Object readResolve()
RealFieldElement<T> q0
RealFieldElement<T> q1
RealFieldElement<T> q2
RealFieldElement<T> q3
RealFieldElement<T> x
RealFieldElement<T> y
RealFieldElement<T> z
private Object writeReplace()
Vector3D v
double r
double theta
double phi
double[][] jacobian
double[][] rHessian
double[][] thetaHessian
double[][] phiHessian
private Object readResolve()
Vector2D[] vertices
double tolerance
double alpha
Vector2D vector
private Object readResolve()
double theta
double phi
Vector3D vector
private Object readResolve()
FieldElement<T>[][] data
FieldElement<T>[] data
Field<T> field
FieldElement<T>[][] blocks
int rows
int columns
int blockRows
int blockColumns
RealVector b
RealVector r
double rnorm
RealVector x
int rows
int columns
OpenIntToDoubleHashMap entries
OpenIntToDoubleHashMap entries
int virtualSize
double epsilon
Field<T> field
OpenIntToFieldHashMap<T extends FieldElement<T>> entries
int virtualSize
Clusterable center
private void readObject(ObjectInputStream in)
private Object writeReplace()
ConcurrentHashMap<K,V> neuronMap
AtomicLong nextId
int featureSize
ConcurrentHashMap<K,V> linkMap
private void readObject(ObjectInputStream in)
private Object writeReplace()
long identifier
int size
AtomicReference<V> features
AtomicLong numberOfAttemptedUpdates
AtomicLong numberOfSuccessfulUpdates
private void readObject(ObjectInputStream in)
private Object writeReplace()
Network network
int size
boolean wrap
long[] identifiers
NeuronString.network
instance).private void readObject(ObjectInputStream in)
private Object writeReplace()
Network network
int numberOfRows
int numberOfColumns
boolean wrapRows
boolean wrapColumns
SquareNeighbourhood neighbourhood
long[][] identifiers
NeuronSquareMesh2D.network
instance).String name
public abstract void readExternal(ObjectInput in) throws IOException, ClassNotFoundException
IOException
ClassNotFoundException
public abstract void writeExternal(ObjectOutput out) throws IOException
IOException
public void readExternal(ObjectInput in) throws IOException, ClassNotFoundException
IOException
ClassNotFoundException
public void writeExternal(ObjectOutput out) throws IOException
IOException
private Object writeReplace()
private Object writeReplace()
private void readObject(ObjectInputStream ois) throws ClassNotFoundException, IOException
ClassNotFoundException
- if a class in the stream cannot be foundIOException
- if object cannot be read from the streamprivate void writeObject(ObjectOutputStream oos) throws IOException
IOException
- if object cannot be written to streamRelationship relationship
double value
private void readObject(ObjectInputStream ois) throws ClassNotFoundException, IOException
ClassNotFoundException
- if a class in the stream cannot be foundIOException
- if object cannot be read from the streamprivate void writeObject(ObjectOutputStream oos) throws IOException
IOException
- if object cannot be written to streamdouble constantTerm
private Object writeReplace()
private Object writeReplace()
private void readObject(ObjectInputStream ois) throws ClassNotFoundException, IOException
ClassNotFoundException
- if a class in the stream cannot be foundIOException
- if object cannot be read from the streamprivate void writeObject(ObjectOutputStream oos) throws IOException
IOException
- if object cannot be written to streamRelationship relationship
double value
private void readObject(ObjectInputStream ois) throws ClassNotFoundException, IOException
ClassNotFoundException
- if a class in the stream cannot be foundIOException
- if object cannot be read from the streamprivate void writeObject(ObjectOutputStream oos) throws IOException
IOException
- if object cannot be written to streamdouble constantTerm
int index
int[] v
int[] iRm1
int[] iRm2
int[] i1
int[] i2
int[] i3
RandomDataGenerator randomData
List<E> binStats
SummaryStatistics sampleStats
double max
double min
double delta
int binCount
boolean loaded
double[] upperBounds
int[] rsl
int[] mem
int count
int isaacA
int isaacB
int isaacC
int[] arr
int isaacX
int isaacI
int isaacJ
RandomGenerator randomGenerator
RandomGenerator rand
RandomGenerator secRand
RandomDataGenerator delegate
List<E> points
Clusterable<T> center
SummaryStatistics statisticsPrototype
SummaryStatistics statistics
int windowSize
ResizableDoubleArray eDA
UnivariateStatistic meanImpl
UnivariateStatistic geometricMeanImpl
UnivariateStatistic kurtosisImpl
UnivariateStatistic maxImpl
UnivariateStatistic minImpl
UnivariateStatistic percentileImpl
UnivariateStatistic skewnessImpl
UnivariateStatistic varianceImpl
UnivariateStatistic sumsqImpl
UnivariateStatistic sumImpl
int k
long n
StorelessUnivariateStatistic[] sumImpl
StorelessUnivariateStatistic[] sumSqImpl
StorelessUnivariateStatistic[] minImpl
StorelessUnivariateStatistic[] maxImpl
StorelessUnivariateStatistic[] sumLogImpl
StorelessUnivariateStatistic[] geoMeanImpl
StorelessUnivariateStatistic[] meanImpl
VectorialCovariance covarianceImpl
long n
SecondMoment secondMoment
Sum sum
SumOfSquares sumsq
Min min
Max max
SumOfLogs sumLog
GeometricMean geoMean
Mean mean
Variance variance
StorelessUnivariateStatistic sumImpl
StorelessUnivariateStatistic sumsqImpl
StorelessUnivariateStatistic minImpl
StorelessUnivariateStatistic maxImpl
StorelessUnivariateStatistic sumLogImpl
StorelessUnivariateStatistic geoMeanImpl
StorelessUnivariateStatistic meanImpl
StorelessUnivariateStatistic varianceImpl
StorelessUnivariateStatistic sumOfLogs
org.apache.commons.math3.stat.descriptive.moment.FourthMoment moment
boolean incMoment
Statistics based on (constructed from) external moments cannot be incremented or cleared.
org.apache.commons.math3.stat.descriptive.moment.FirstMoment moment
boolean incMoment
Statistics based on (constructed from) external moments cannot be incremented or cleared.
boolean biasCorrected
SemiVariance.Direction varianceDirection
org.apache.commons.math3.stat.descriptive.moment.ThirdMoment moment
boolean incMoment
Statistics based on (constructed from) external moments cannot be incremented or cleared.
Variance variance
SecondMoment moment
boolean incMoment
Variance.increment(double)
should increment
the internal second moment. When a Variance is constructed with an
external SecondMoment as a constructor parameter, this property is
set to false and increments must be applied to the second moment
directly.boolean isBiasCorrected
Variance
for details on the formula.Mean[] means
KthSelector kthSelector
Percentile.EstimationType estimationType
Percentile.EstimationType
s such as CM
can be used.NaNStrategy nanStrategy
NaNStrategy
double quantile
int[] cachedPivots
List<E> initialFive
double quantile
PSquarePercentile.PSquarePercentile(double)
ensures that passed in percentile is
divided by 100.PSquarePercentile.PSquareMarkers markers
double pValue
long countOfObservations
double[] parameters
double[][] varCovData
boolean isSymmetricVCD
int rank
long nobs
boolean containsConstant
double[] globalFitInfo
double sumX
double sumXX
double sumY
double sumYY
double sumXY
long n
double xbar
double ybar
boolean hasIntercept
DctNormalization normalization
DftNormalization normalization
DstNormalization normalization
BigDecimal d
RoundingMode roundingMode
int scale
private Object readResolve()
PivotingStrategyInterface pivotingStrategy
PivotingStrategyInterface
used for pivotingprivate 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> field
int[] keys
FieldElement<T>[] values
byte[] states
FieldElement<T> missingEntries
int size
int mask
RandomGenerator random
double contractionCriterion
double expansionFactor
internalArray.length * expansionFactor
if expansionMode
is set to MULTIPLICATIVE_MODE, or
internalArray.length + expansionFactor
if
expansionMode
is set to ADDITIVE_MODE.ResizableDoubleArray.ExpansionMode expansionMode
expansionFactor
is additive or multiplicative.double[] internalArray
int numElements
int startIndex
internalArray[startIndex],...,internalArray[startIndex + numElements - 1]
.NumberTransformer defaultTransformer
Map<K,V> map
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