Uses of Interface
elki.utilities.datastructures.arraylike.NumberArrayAdapter

Packages that use NumberArrayAdapter

Package	Description
elki.data	Basic classes for different data types, database object types and label types.
elki.data.projection	Data projections.
elki.data.uncertain.uncertainifier	Classes to generate uncertain objects from existing certain data.
elki.math.statistics	Statistical tests and methods.
elki.math.statistics.dependence	Statistical measures of dependence, such as correlation.
elki.math.statistics.dependence.mcde	Tests tailored to be used with MCDEDependence.
elki.math.statistics.distribution.estimator	Estimators for statistical distributions.
elki.math.statistics.distribution.estimator.meta	Meta estimators: estimators that do not actually estimate themselves, but instead use other estimators, e.g., on a trimmed data set, or as an ensemble.
elki.math.statistics.intrinsicdimensionality	Methods for estimating the intrinsic dimensionality.
elki.utilities.datastructures.arraylike	Common API for accessing objects that are "array-like", including lists, numerical vectors, database vectors and arrays.
elki.utilities.scaling.outlier	Scaling of outlier scores, that require a statistical analysis of the occurring values.

Uses of NumberArrayAdapter in elki.data

Methods in elki.data with parameters of type NumberArrayAdapter

Modifier and Type	Method	Description
<A> BitVector	BitVector.Factory.newNumberVector(A array, NumberArrayAdapter<?,? super A> adapter)
<A> ByteVector	ByteVector.Factory.newNumberVector(A array, NumberArrayAdapter<?,? super A> adapter)
<A> DoubleVector	DoubleVector.Factory.newNumberVector(A array, NumberArrayAdapter<?,? super A> adapter)
<A> FloatVector	FloatVector.Factory.newNumberVector(A array, NumberArrayAdapter<?,? super A> adapter)
<A> IntegerVector	IntegerVector.Factory.newNumberVector(A array, NumberArrayAdapter<?,? super A> adapter)
<A> V	NumberVector.Factory.newNumberVector(A array, NumberArrayAdapter<?,? super A> adapter)	Instantiate from any number-array like object.
<A> OneDimensionalDoubleVector	OneDimensionalDoubleVector.Factory.newNumberVector(A array, NumberArrayAdapter<?,? super A> adapter)
<A> ShortVector	ShortVector.Factory.newNumberVector(A array, NumberArrayAdapter<?,? super A> adapter)
<A> SparseByteVector	SparseByteVector.Factory.newNumberVector(A array, NumberArrayAdapter<?,? super A> adapter)
<A> SparseDoubleVector	SparseDoubleVector.Factory.newNumberVector(A array, NumberArrayAdapter<?,? super A> adapter)
<A> SparseFloatVector	SparseFloatVector.Factory.newNumberVector(A array, NumberArrayAdapter<?,? super A> adapter)
<A> SparseIntegerVector	SparseIntegerVector.Factory.newNumberVector(A array, NumberArrayAdapter<?,? super A> adapter)
<A> SparseShortVector	SparseShortVector.Factory.newNumberVector(A array, NumberArrayAdapter<?,? super A> adapter)

Uses of NumberArrayAdapter in elki.data.projection

Classes in elki.data.projection that implement NumberArrayAdapter

Modifier and Type	Class	Description
private class	FeatureSelection.ProjectedNumberFeatureVectorAdapter	Adapter for generating number vectors without reboxing.

Uses of NumberArrayAdapter in elki.data.uncertain.uncertainifier

Methods in elki.data.uncertain.uncertainifier with parameters of type NumberArrayAdapter

Modifier and Type	Method	Description
<A> SimpleGaussianContinuousUncertainObject	SimpleGaussianUncertainifier.newFeatureVector(java.util.Random rand, A array, NumberArrayAdapter<?,A> adapter)
<A> UO	Uncertainifier.newFeatureVector(java.util.Random rand, A array, NumberArrayAdapter<?,A> adapter)	Generate a new uncertain object.
<A> UniformContinuousUncertainObject	UniformUncertainifier.newFeatureVector(java.util.Random rand, A array, NumberArrayAdapter<?,A> adapter)
<A> UnweightedDiscreteUncertainObject	UnweightedDiscreteUncertainifier.newFeatureVector(java.util.Random rand, A array, NumberArrayAdapter<?,A> adapter)
<A> WeightedDiscreteUncertainObject	WeightedDiscreteUncertainifier.newFeatureVector(java.util.Random rand, A array, NumberArrayAdapter<?,A> adapter)

Uses of NumberArrayAdapter in elki.math.statistics

Methods in elki.math.statistics with parameters of type NumberArrayAdapter

Modifier and Type	Method	Description
static <A> double[]	ProbabilityWeightedMoments.alphaBetaPWM(A data, NumberArrayAdapter<?,A> adapter, int nmom)	Compute the alpha_r and beta_r factors in parallel using the method of probability-weighted moments.
static <A> double[]	ProbabilityWeightedMoments.alphaPWM(A data, NumberArrayAdapter<?,A> adapter, int nmom)	Compute the alpha_r factors using the method of probability-weighted moments.
static <A> double[]	ProbabilityWeightedMoments.betaPWM(A data, NumberArrayAdapter<?,A> adapter, int nmom)	Compute the beta_r factors using the method of probability-weighted moments.
static <A> double[]	ProbabilityWeightedMoments.samLMR(A sorted, NumberArrayAdapter<?,A> adapter, int nmom)	Compute the sample L-Moments using probability weighted moments.

Uses of NumberArrayAdapter in elki.math.statistics.dependence

Methods in elki.math.statistics.dependence with parameters of type NumberArrayAdapter

Modifier and Type	Method	Description
private <A> java.util.ArrayList<int[]>	MaximumConditionalEntropy.buildPartitions(NumberArrayAdapter<?,A> adapter1, A data1, int len, int depth)	Partitions an attribute.
protected static <A,B> double[]	HoeffdingsD.computeBivariateRanks(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2, int len)	Compute bivariate ranks.
protected static <A> double[]	DCor.computeDistances(NumberArrayAdapter<?,A> adapter, A data)	Compute the double-centered delta matrix.
static <A> double[]	Dependence.Utils.computeNormalizedRanks(NumberArrayAdapter<?,A> adapter, A data, int len)	Compute ranks of all objects, normalized to [0;1] (where 0 is the smallest value, 1 is the largest).
<A,B> double	DCor.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
<A> double[]	DCor.dependence(NumberArrayAdapter<?,A> adapter, java.util.List<? extends A> data)
default <A> double	Dependence.dependence(NumberArrayAdapter<?,A> adapter, A data1, A data2)	Measure the dependence of two variables.
<A,B> double	Dependence.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)	Measure the dependence of two variables.
default <A> double[]	Dependence.dependence(NumberArrayAdapter<?,A> adapter, java.util.List<? extends A> data)	Measure the dependence of two variables.
<A,B> double	HiCSDependence.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
<A,B> double	HoeffdingsD.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
<A,B> double	HoughSpaceMeasure.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
<A,B> double	JensenShannonEquiwidthDependence.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
<A,B> double	MaximumConditionalEntropy.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
<A,B> double	MCDEDependence.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
<A> double[]	MCDEDependence.dependence(NumberArrayAdapter<?,A> adapter, java.util.List<? extends A> data)
<A,B> double	MutualInformationEquiwidthDependence.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
<A,B> double	PearsonCorrelationDependence.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
<A> double[]	PearsonCorrelationDependence.dependence(NumberArrayAdapter<?,A> adapter, java.util.List<? extends A> data)
<A,B> double	SlopeDependence.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
<A,B> double	SlopeInversionDependence.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
<A,B> double	SpearmanCorrelationDependence.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
<A,B> double	SURFINGDependence.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
<A> double	MCDEDependence.higherOrderDependence(NumberArrayAdapter<?,A> adapter, java.util.List<? extends A> data)	Runs MCDE Algorithm with possibly more than two dimensions
static <A> double[]	Dependence.Utils.ranks(NumberArrayAdapter<?,A> adapter, A data, int len)	Compute ranks of all objects, ranging from 1 to len.
static <A> double[]	Dependence.Utils.ranks(NumberArrayAdapter<?,A> adapter, A data, int[] idx)	Compute ranks of all objects, ranging from 1 to len.
static <A,B> int	Dependence.Utils.size(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)	Validate the length of the two data sets (must be the same, and non-zero)
static <A> int	Dependence.Utils.size(NumberArrayAdapter<?,A> adapter, java.util.Collection<? extends A> data)	Validate the length of the two data sets (must be the same, and non-zero)
static <A> int[]	Dependence.Utils.sortedIndex(NumberArrayAdapter<?,A> adapter, A data, int len)	Build a sorted index of objects.

Uses of NumberArrayAdapter in elki.math.statistics.dependence.mcde

Methods in elki.math.statistics.dependence.mcde with parameters of type NumberArrayAdapter

Modifier and Type	Method	Description
<A> R	MCDETest.correctedRanks(NumberArrayAdapter<?,A> adapter, A data, int len)	Compute the corrected rank index.
<A> MWPTest.MWPRanking	MWPTest.correctedRanks(NumberArrayAdapter<?,A> adapter, A data, int len)

Uses of NumberArrayAdapter in elki.math.statistics.distribution.estimator

Methods in elki.math.statistics.distribution.estimator with parameters of type NumberArrayAdapter

Modifier and Type	Method	Description
<A> D	DistributionEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)	General form of the parameter estimation
<A> ExpGammaDistribution	ExpGammaExpMOMEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
<A> GammaDistribution	GammaChoiWetteEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
<A> InverseGaussianDistribution	InverseGaussianMLEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
<A> LaplaceDistribution	LaplaceMLEEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
default <A> D	LMMDistributionEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
default <A> D	LogMADDistributionEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
default <A> D	LogMeanVarianceEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
default <A> D	LogMOMDistributionEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
<A> LogNormalDistribution	LogNormalLevenbergMarquardtKDEEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
default <A> D	MADDistributionEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
default <A> D	MeanVarianceDistributionEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
default <A> D	MOMDistributionEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
<A> NormalDistribution	NormalLevenbergMarquardtKDEEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
<A> RayleighDistribution	RayleighMLEEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
<A> UniformDistribution	UniformEnhancedMinMaxEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
<A> UniformDistribution	UniformMinMaxEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
static <A> double	LogMOMDistributionEstimator.min(A data, NumberArrayAdapter<?,A> adapter, double minmin, double margin)	Utility function to find minimum and maximum values.

Uses of NumberArrayAdapter in elki.math.statistics.distribution.estimator.meta

Methods in elki.math.statistics.distribution.estimator.meta with parameters of type NumberArrayAdapter

Modifier and Type	Method	Description
<A> Distribution	BestFitEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
<A> D	TrimmedEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
<A> D	WinsorizingEstimator.estimate(A data, NumberArrayAdapter<?,A> adapter)
static <A> double[]	TrimmedEstimator.toPrimitiveDoubleArray(A data, NumberArrayAdapter<?,A> adapter)	Local copy, see ArrayLikeUtil.toPrimitiveDoubleArray.

Uses of NumberArrayAdapter in elki.math.statistics.intrinsicdimensionality

Fields in elki.math.statistics.intrinsicdimensionality declared as NumberArrayAdapter

Modifier and Type	Field	Description
private NumberArrayAdapter<?,? super A>	LMomentsEstimator.ReverseAdapter.inner	Adapter class.

Methods in elki.math.statistics.intrinsicdimensionality with parameters of type NumberArrayAdapter

Modifier and Type	Method	Description
static <A> int	DistanceBasedIntrinsicDimensionalityEstimator.countLeadingZeros(A data, NumberArrayAdapter<?,? super A> adapter, int end)
<A> double	AggregatedHillEstimator.estimate(A data, NumberArrayAdapter<?,? super A> adapter, int end)
default <A> double	DistanceBasedIntrinsicDimensionalityEstimator.estimate(A data, NumberArrayAdapter<?,? super A> adapter)	Estimate from a distance list.
<A> double	DistanceBasedIntrinsicDimensionalityEstimator.estimate(A data, NumberArrayAdapter<?,? super A> adapter, int size)	Estimate from a distance list.
<A> double	EnsembleEstimator.estimate(A data, NumberArrayAdapter<?,? super A> adapter, int end)
<A> double	GEDEstimator.estimate(A data, NumberArrayAdapter<?,? super A> adapter, int end)
<A> double	HillEstimator.estimate(A data, NumberArrayAdapter<?,? super A> adapter, int end)
<A> double	LMomentsEstimator.estimate(A data, NumberArrayAdapter<?,? super A> adapter, int end)
<A> double	MOMEstimator.estimate(A data, NumberArrayAdapter<?,? super A> adapter, int end)
<A> double	PWM2Estimator.estimate(A data, NumberArrayAdapter<?,? super A> adapter, int end)
<A> double	PWMEstimator.estimate(A data, NumberArrayAdapter<?,? super A> adapter, int end)
<A> double	RABIDEstimator.estimate(A data, NumberArrayAdapter<?,? super A> adapter, int size)
<A> double	RVEstimator.estimate(A data, NumberArrayAdapter<?,? super A> adapter, int end)
<A> double	ZipfEstimator.estimate(A data, NumberArrayAdapter<?,? super A> adapter, int end)

Constructors in elki.math.statistics.intrinsicdimensionality with parameters of type NumberArrayAdapter

Constructor	Description
ReverseAdapter(NumberArrayAdapter<?,? super A> inner, int begin, int end)	Constructor.

Uses of NumberArrayAdapter in elki.utilities.datastructures.arraylike

Classes in elki.utilities.datastructures.arraylike that implement NumberArrayAdapter

Modifier and Type	Class	Description
class	DoubleArray	Array of double values (primitive, avoiding the boxing overhead of ArrayList).
class	DoubleArrayAdapter	Use a double[] in the ArrayAdapter API.
class	FloatArrayAdapter	Use a float[] in the ArrayAdapter API.
class	IntegerArray	Array of int values (primitive, avoiding the boxing overhead of ArrayList).
class	NumberVectorAdapter	Adapter to use a feature vector as an array of features.

Fields in elki.utilities.datastructures.arraylike declared as NumberArrayAdapter

Modifier and Type	Field	Description
static NumberArrayAdapter<java.lang.Double,double[]>	ArrayLikeUtil.DOUBLEARRAYADAPTER	Use a double array in the array API.
static NumberArrayAdapter<java.lang.Float,float[]>	ArrayLikeUtil.FLOATARRAYADAPTER	Use a float array in the array API.

Methods in elki.utilities.datastructures.arraylike with parameters of type NumberArrayAdapter

Modifier and Type	Method	Description
static <A> int	ArrayLikeUtil.getIndexOfMaximum(A array, NumberArrayAdapter<?,A> adapter)	Returns the index of the maximum of the given values.
static <A> double[]	ArrayLikeUtil.toPrimitiveDoubleArray(A array, NumberArrayAdapter<?,? super A> adapter)	Convert a numeric array-like to a double[].
static <A> float[]	ArrayLikeUtil.toPrimitiveFloatArray(A array, NumberArrayAdapter<?,? super A> adapter)	Convert a numeric array-like to a float[].
static <A> int[]	ArrayLikeUtil.toPrimitiveIntegerArray(A array, NumberArrayAdapter<?,? super A> adapter)	Convert a numeric array-like to a int[].

Uses of NumberArrayAdapter in elki.utilities.scaling.outlier

Methods in elki.utilities.scaling.outlier with parameters of type NumberArrayAdapter

Modifier and Type	Method	Description
private <A> double[]	SigmoidOutlierScaling.MStepLevenbergMarquardt(double a, double b, long[] t, A array, NumberArrayAdapter<?,A> adapter)	M-Step using a modified Levenberg-Marquardt method.
<A> void	COPOutlierScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)
<A> void	HeDESNormalizationOutlierScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)
<A> void	LogRankingPseudoOutlierScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)
<A> void	MixtureModelOutlierScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)
<A> void	MultiplicativeInverseScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)
<A> void	OutlierGammaScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)
<A> void	OutlierLinearScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)
<A> void	OutlierMinusLogScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)
<A> void	OutlierScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)	Prepare is called once for each data set, before getScaled() will be called.
<A> void	OutlierSqrtScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)
<A> void	RankingPseudoOutlierScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)
<A> void	SigmoidOutlierScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)
<A> void	SqrtStandardDeviationScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)
<A> void	StandardDeviationScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)
<A> void	TopKOutlierScaling.prepare(A array, NumberArrayAdapter<?,A> adapter)