| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.data |
Basic classes for different data types, database object types and label types
|
| de.lmu.ifi.dbs.elki.data.uncertain.uncertainifier |
Classes to generate uncertain objects from existing certain data.
|
| de.lmu.ifi.dbs.elki.datasource.filter.normalization.columnwise |
Normalizations operating on columns / variates; where each column is treated independently.
|
| de.lmu.ifi.dbs.elki.math.statistics |
Statistical tests and methods
|
| de.lmu.ifi.dbs.elki.math.statistics.dependence |
Statistical measures of dependence, such as correlation
|
| de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator |
Estimators for statistical distributions.
|
| de.lmu.ifi.dbs.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.
|
| de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality |
Methods for estimating the intrinsic dimensionality.
|
| de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike |
Common API for accessing objects that are "array-like", including lists,
numerical vectors, database vectors and arrays.
|
| de.lmu.ifi.dbs.elki.utilities.scaling.outlier |
Scaling of outlier scores, that require a statistical analysis of the
occurring values
|
| Modifier and Type | Method and Description |
|---|---|
<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> SparseByteVector |
SparseByteVector.Factory.newNumberVector(A array,
NumberArrayAdapter<?,? super A> adapter) |
<A> ShortVector |
ShortVector.Factory.newNumberVector(A array,
NumberArrayAdapter<?,? super A> adapter) |
<A> SparseShortVector |
SparseShortVector.Factory.newNumberVector(A array,
NumberArrayAdapter<?,? super A> adapter) |
<A> DoubleVector |
DoubleVector.Factory.newNumberVector(A array,
NumberArrayAdapter<?,? super A> adapter) |
<A> SparseIntegerVector |
SparseIntegerVector.Factory.newNumberVector(A array,
NumberArrayAdapter<?,? super A> adapter) |
<A> SparseFloatVector |
SparseFloatVector.Factory.newNumberVector(A array,
NumberArrayAdapter<?,? super A> adapter) |
<A> BitVector |
BitVector.Factory.newNumberVector(A array,
NumberArrayAdapter<?,? super A> adapter) |
<A> IntegerVector |
IntegerVector.Factory.newNumberVector(A array,
NumberArrayAdapter<?,? super A> adapter) |
<A> SparseDoubleVector |
SparseDoubleVector.Factory.newNumberVector(A array,
NumberArrayAdapter<?,? super A> adapter) |
<A> ByteVector |
ByteVector.Factory.newNumberVector(A array,
NumberArrayAdapter<?,? super A> adapter) |
<A> FloatVector |
FloatVector.Factory.newNumberVector(A array,
NumberArrayAdapter<?,? super A> adapter) |
| Modifier and Type | Method and Description |
|---|---|
<A> UO |
Uncertainifier.newFeatureVector(java.util.Random rand,
A array,
NumberArrayAdapter<?,A> adapter)
Generate a new uncertain object.
|
<A> WeightedDiscreteUncertainObject |
WeightedDiscreteUncertainifier.newFeatureVector(java.util.Random rand,
A array,
NumberArrayAdapter<?,A> adapter) |
<A> UnweightedDiscreteUncertainObject |
UnweightedDiscreteUncertainifier.newFeatureVector(java.util.Random rand,
A array,
NumberArrayAdapter<?,A> adapter) |
<A> UniformContinuousUncertainObject |
UniformUncertainifier.newFeatureVector(java.util.Random rand,
A array,
NumberArrayAdapter<?,A> adapter) |
<A> SimpleGaussianContinuousUncertainObject |
SimpleGaussianUncertainifier.newFeatureVector(java.util.Random rand,
A array,
NumberArrayAdapter<?,A> adapter) |
| Modifier and Type | Class and Description |
|---|---|
protected static class |
AttributeWiseCDFNormalization.Adapter
Array adapter class for vectors.
|
| Modifier and Type | Method and 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.
|
| Modifier and Type | Method and Description |
|---|---|
private <A> java.util.ArrayList<int[]> |
MCEDependenceMeasure.buildPartitions(NumberArrayAdapter<?,A> adapter1,
A data1,
int len,
int depth)
Partitions an attribute.
|
protected static <A,B> double[] |
HoeffdingsDDependenceMeasure.computeBivariateRanks(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2,
int len)
Compute bivariate ranks.
|
protected static <A,B> double[] |
HoeffdingsDDependenceMeasure.computeBivariateRanks(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2,
int len)
Compute bivariate ranks.
|
protected static <A> double[] |
DistanceCorrelationDependenceMeasure.computeDistances(NumberArrayAdapter<?,A> adapter,
A data)
Compute the double-centered delta matrix.
|
protected static <A> double[] |
AbstractDependenceMeasure.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).
|
default <A> double |
DependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter,
A data1,
A data2)
Measure the dependence of two variables.
|
<A,B> double |
HiCSDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
HiCSDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
SlopeInversionDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
SlopeInversionDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
DependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2)
Measure the dependence of two variables.
|
<A,B> double |
DependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2)
Measure the dependence of two variables.
|
<A,B> double |
CorrelationDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
CorrelationDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
SlopeDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
SlopeDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
HSMDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
HSMDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
JensenShannonEquiwidthDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
JensenShannonEquiwidthDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
HoeffdingsDDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
HoeffdingsDDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
DistanceCorrelationDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
DistanceCorrelationDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
SURFINGDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
SURFINGDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
SpearmanCorrelationDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
SpearmanCorrelationDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
MCEDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
MCEDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
MutualInformationEquiwidthDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
<A,B> double |
MutualInformationEquiwidthDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
default <A> double[] |
DependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter,
java.util.List<? extends A> data)
Measure the dependence of two variables.
|
<A> double[] |
CorrelationDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter,
java.util.List<? extends A> data) |
<A> double[] |
DistanceCorrelationDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter,
java.util.List<? extends A> data) |
protected static <A> int[] |
AbstractDependenceMeasure.discretize(NumberArrayAdapter<?,A> adapter,
A data,
int len,
int bins)
Discretize a data set into equi-width bin numbers.
|
protected static <A> double[] |
AbstractDependenceMeasure.ranks(NumberArrayAdapter<?,A> adapter,
A data,
int len)
Compute ranks of all objects, ranging from 1 to len.
|
protected static <A> double[] |
AbstractDependenceMeasure.ranks(NumberArrayAdapter<?,A> adapter,
A data,
int[] idx)
Compute ranks of all objects, ranging from 1 to len.
|
protected static <A,B> int |
AbstractDependenceMeasure.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)
|
protected static <A,B> int |
AbstractDependenceMeasure.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)
|
protected static <A> int |
AbstractDependenceMeasure.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)
|
protected static <A> int[] |
AbstractDependenceMeasure.sortedIndex(NumberArrayAdapter<?,A> adapter,
A data,
int len)
Build a sorted index of objects.
|
| Modifier and Type | Method and Description |
|---|---|
default <A> D |
LogMeanVarianceEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
default <A> D |
LogMADDistributionEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
<A> UniformDistribution |
UniformEnhancedMinMaxEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
<A> ExpGammaDistribution |
ExpGammaExpMOMEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
<A> GammaDistribution |
GammaChoiWetteEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
default <A> D |
MeanVarianceDistributionEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
<A> InverseGaussianDistribution |
InverseGaussianMLEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
default <A> D |
LogMOMDistributionEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
<A> D |
DistributionEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter)
General form of the parameter estimation
|
<A> LogNormalDistribution |
LogNormalLevenbergMarquardtKDEEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
<A> UniformDistribution |
UniformMinMaxEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
default <A> D |
MOMDistributionEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
<A> RayleighDistribution |
RayleighMLEEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
<A> NormalDistribution |
NormalLevenbergMarquardtKDEEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
<A> LaplaceDistribution |
LaplaceMLEEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
default <A> D |
MADDistributionEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
default <A> D |
LMMDistributionEstimator.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.
|
| Modifier and Type | Method and Description |
|---|---|
<A> D |
WinsorizingEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
<A> D |
TrimmedEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
<A> Distribution |
BestFitEstimator.estimate(A data,
NumberArrayAdapter<?,A> adapter) |
static <A> double[] |
TrimmedEstimator.toPrimitiveDoubleArray(A data,
NumberArrayAdapter<?,A> adapter)
Local copy, see ArrayLikeUtil.toPrimitiveDoubleArray.
|
| Modifier and Type | Class and Description |
|---|---|
private static class |
LMomentsEstimator.ReverseAdapter<A>
Adapter to process an array in reverse order.
|
| Modifier and Type | Field and Description |
|---|---|
private NumberArrayAdapter<?,? super A> |
LMomentsEstimator.ReverseAdapter.inner
Adapter class.
|
| Modifier and Type | Method and Description |
|---|---|
static <A> int |
IntrinsicDimensionalityEstimator.countLeadingZeros(A data,
NumberArrayAdapter<?,? super A> adapter,
int end) |
default <A> double |
IntrinsicDimensionalityEstimator.estimate(A data,
NumberArrayAdapter<?,? super A> adapter)
Estimate from a distance list.
|
<A> double |
AggregatedHillEstimator.estimate(A data,
NumberArrayAdapter<?,? super A> adapter,
int end) |
<A> double |
IntrinsicDimensionalityEstimator.estimate(A data,
NumberArrayAdapter<?,? super A> adapter,
int size)
Estimate from a distance list.
|
<A> double |
ZipfEstimator.estimate(A data,
NumberArrayAdapter<?,? super A> adapter,
int end) |
<A> double |
PWM2Estimator.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 |
MOMEstimator.estimate(A data,
NumberArrayAdapter<?,? super A> adapter,
int end) |
<A> double |
LMomentsEstimator.estimate(A data,
NumberArrayAdapter<?,? super A> adapter,
int end) |
<A> double |
EnsembleEstimator.estimate(A data,
NumberArrayAdapter<?,? super A> adapter,
int end) |
<A> double |
RVEstimator.estimate(A data,
NumberArrayAdapter<?,? super A> adapter,
int end) |
<A> double |
ALIDEstimator.estimate(A data,
NumberArrayAdapter<?,? super A> adapter,
int size) |
<A> double |
PWMEstimator.estimate(A data,
NumberArrayAdapter<?,? super A> adapter,
int end) |
| Constructor and Description |
|---|
ReverseAdapter(NumberArrayAdapter<?,? super A> inner,
int begin,
int end)
Constructor.
|
| Modifier and Type | Class and 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 |
NumberListArrayAdapter<T extends java.lang.Number>
Static adapter class to use a
List in an array of number
API. |
class |
NumberVectorAdapter
Adapter to use a feature vector as an array of features.
|
class |
SubsetNumberArrayAdapter<T extends java.lang.Number,A>
Subset array adapter (allows reordering and projection)
|
| Modifier and Type | Field and 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.
|
(package private) NumberArrayAdapter<T,? super A> |
SubsetNumberArrayAdapter.wrapped
Wrapped adapter
|
| Modifier and Type | Method and Description |
|---|---|
static <T extends java.lang.Number> |
ArrayLikeUtil.numberListAdapter(java.util.List<? extends T> dummy)
Cast the static instance.
|
| Modifier and Type | Method and 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[]. |
| Constructor and Description |
|---|
SubsetNumberArrayAdapter(NumberArrayAdapter<T,? super A> wrapped,
int[] offs)
Constructor.
|
| Modifier and Type | Method and 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 |
HeDESNormalizationOutlierScaling.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 |
RankingPseudoOutlierScaling.prepare(A array,
NumberArrayAdapter<?,A> adapter) |
<A> void |
SigmoidOutlierScaling.prepare(A array,
NumberArrayAdapter<?,A> adapter) |
<A> void |
OutlierLinearScaling.prepare(A array,
NumberArrayAdapter<?,A> adapter) |
<A> void |
MultiplicativeInverseScaling.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 |
MixtureModelOutlierScaling.prepare(A array,
NumberArrayAdapter<?,A> adapter) |
<A> void |
TopKOutlierScaling.prepare(A array,
NumberArrayAdapter<?,A> adapter) |
<A> void |
LogRankingPseudoOutlierScaling.prepare(A array,
NumberArrayAdapter<?,A> adapter) |
<A> void |
OutlierSqrtScaling.prepare(A array,
NumberArrayAdapter<?,A> adapter) |
<A> void |
OutlierGammaScaling.prepare(A array,
NumberArrayAdapter<?,A> adapter) |
<A> void |
OutlierMinusLogScaling.prepare(A array,
NumberArrayAdapter<?,A> adapter) |
<A> void |
COPOutlierScaling.prepare(A array,
NumberArrayAdapter<?,A> adapter) |
Copyright © 2019 ELKI Development Team. License information.