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Packages that use RealVector | |
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de.lmu.ifi.dbs.elki.algorithm | Algorithms suitable as a task for the KDDTask main routine. |
de.lmu.ifi.dbs.elki.algorithm.clustering | Clustering algorithms
Clustering algorithms are supposed to implement the Algorithm -Interface. |
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation | Correlation clustering algorithms |
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace | Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces. |
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique | Helper classes for the CLIQUE algorithm. |
de.lmu.ifi.dbs.elki.algorithm.outlier | Outlier detection algorithms |
de.lmu.ifi.dbs.elki.algorithm.statistics | Statistical analysis algorithms The algorithms in this package perform statistical analysis of the data (e.g. compute distributions, distance distributions etc.) |
de.lmu.ifi.dbs.elki.data | Basic classes for different data types, database object types and label types. |
de.lmu.ifi.dbs.elki.data.model | Cluster models classes for various algorithms. |
de.lmu.ifi.dbs.elki.distance.distancefunction | Distance functions for use within ELKI. |
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation | Distance functions using correlations. |
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace | Distance functions based on subspaces. |
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel | Kernel functions. |
de.lmu.ifi.dbs.elki.math.linearalgebra.pca | Principal Component Analysis (PCA) and Eigenvector processing. |
de.lmu.ifi.dbs.elki.normalization | Data normalization (and reconstitution) of data sets. |
de.lmu.ifi.dbs.elki.parser | Parsers for different file formats and data types. |
de.lmu.ifi.dbs.elki.parser.meta | MetaParsers for different file formats and data types. |
de.lmu.ifi.dbs.elki.preprocessing | Preprocessors used for data preparation in a first step of various algorithms or distance and similarity measures. |
de.lmu.ifi.dbs.elki.utilities | Utility and helper classes - commonly used data structures, output formatting, exceptions, ... |
Uses of RealVector in de.lmu.ifi.dbs.elki.algorithm |
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Classes in de.lmu.ifi.dbs.elki.algorithm with type parameters of type RealVector | |
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class |
DependencyDerivator<V extends RealVector<V,?>,D extends Distance<D>>
Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA. |
class |
MaterializeDistances<V extends RealVector<V,?>,D extends NumberDistance<D,N>,N extends Number>
Algorithm to materialize all the distances in a data set. |
Uses of RealVector in de.lmu.ifi.dbs.elki.algorithm.clustering |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering with type parameters of type RealVector | |
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class |
EM<V extends RealVector<V,?>>
Provides the EM algorithm (clustering by expectation maximization). |
class |
KMeans<D extends Distance<D>,V extends RealVector<V,?>>
Provides the k-means algorithm. |
class |
ProjectedDBSCAN<V extends RealVector<V,?>>
Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor. |
Uses of RealVector in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with type parameters of type RealVector | |
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class |
COPAC<V extends RealVector<V,?>>
Provides the COPAC algorithm, an algorithm to partition a database according to the correlation dimension of its objects and to then perform an arbitrary clustering algorithm over the partitions. |
class |
ERiC<V extends RealVector<V,?>>
Performs correlation clustering on the data partitioned according to local correlation dimensionality and builds a hierarchy of correlation clusters that allows multiple inheritance from the clustering result. |
class |
FourC<O extends RealVector<O,?>>
4C identifies local subgroups of data objects sharing a uniform correlation. |
class |
ORCLUS<V extends RealVector<V,?>>
ORCLUS provides the ORCLUS algorithm, an algorithm to find clusters in high dimensional spaces. |
Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation declared as RealVector | |
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(package private) V |
ORCLUS.ORCLUSCluster.centroid
The centroid of this cluster. |
Uses of RealVector in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with type parameters of type RealVector | |
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class |
CLIQUE<V extends RealVector<V,?>>
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality. |
class |
DiSH<V extends RealVector<V,?>>
Algorithm for detecting subspace hierarchies. |
class |
PreDeCon<V extends RealVector<V,?>>
PreDeCon computes clusters of subspace preference weighted connected points. |
class |
PROCLUS<V extends RealVector<V,?>>
Provides the PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces. |
class |
ProjectedClustering<V extends RealVector<V,?>>
Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS . |
Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace declared as RealVector | |
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(package private) V |
PROCLUS.PROCLUSCluster.centroid
The centroids of this cluster along each dimension. |
Uses of RealVector in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique with type parameters of type RealVector | |
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class |
CLIQUESubspace<V extends RealVector<V,?>>
Represents a subspace of the original dataspace in the CLIQUE algorithm. |
class |
CLIQUEUnit<V extends RealVector<V,?>>
Represents a unit in the CLIQUE algorithm. |
Uses of RealVector in de.lmu.ifi.dbs.elki.algorithm.outlier |
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Classes in de.lmu.ifi.dbs.elki.algorithm.outlier with type parameters of type RealVector | |
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class |
ABOD<V extends RealVector<V,?>>
Angle-Based Outlier Detection Outlier detection using variance analysis on angles, especially for high dimensional data sets. |
class |
SOD<V extends RealVector<V,Double>,D extends Distance<D>>
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class |
SODModel<O extends RealVector<O,Double>>
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Fields in de.lmu.ifi.dbs.elki.algorithm.outlier declared as RealVector | |
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private O |
SODModel.center
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Uses of RealVector in de.lmu.ifi.dbs.elki.algorithm.statistics |
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Classes in de.lmu.ifi.dbs.elki.algorithm.statistics with type parameters of type RealVector | |
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class |
DistanceStatisticsWithClasses<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Algorithm to gather statistics over the distance distribution in the data set. |
class |
EvaluateRankingQuality<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Evaluate a distance function with respect to kNN queries. |
class |
RankingQualityHistogram<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Evaluate a distance function with respect to kNN queries. |
Uses of RealVector in de.lmu.ifi.dbs.elki.data |
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Classes in de.lmu.ifi.dbs.elki.data with type parameters of type RealVector | |
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class |
RealVector<V extends RealVector<V,N>,N extends Number>
RealVector is an abstract super class for all feature vectors having real numbers as values. |
class |
Subspace<V extends RealVector<V,?>>
Represents a subspace of the original data space. |
Subclasses of RealVector in de.lmu.ifi.dbs.elki.data | |
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class |
DoubleVector
A DoubleVector is to store real values approximately as double values. |
class |
FloatVector
A FloatVector is to store real values approximately as float values. |
class |
ParameterizationFunction
A parameterization function describes all lines in a d-dimensional feature space intersecting in one point p. |
class |
SparseFloatVector
A SparseFloatVector is to store real values approximately as float values. |
Uses of RealVector in de.lmu.ifi.dbs.elki.data.model |
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Classes in de.lmu.ifi.dbs.elki.data.model with type parameters of type RealVector | |
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class |
Bicluster<V extends RealVector<V,Double>>
Wrapper class to provide the basic properties of a bicluster. |
class |
BiclusterWithInverted<V extends RealVector<V,Double>>
This code was factored out of the Bicluster class, since not all biclusters have inverted rows. |
class |
CorrelationAnalysisSolution<V extends RealVector<V,?>>
A solution of correlation analysis is a matrix of equations describing the dependencies. |
class |
CorrelationModel<V extends RealVector<V,?>>
Cluster model using a filtered PCA result and an centroid. |
class |
EMModel<V extends RealVector<V,?>>
Cluster model of an EM cluster, providing a mean and a full covariance Matrix. |
Fields in de.lmu.ifi.dbs.elki.data.model declared as RealVector | |
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private V |
CorrelationModel.centroid
The centroid of this cluster. |
private V |
EMModel.mean
Cluster mean |
Uses of RealVector in de.lmu.ifi.dbs.elki.distance.distancefunction |
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Classes in de.lmu.ifi.dbs.elki.distance.distancefunction with type parameters of type RealVector | |
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class |
AbstractLocallyWeightedDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>>
Abstract super class for locally weighted distance functions using a preprocessor to compute the local weight matrix. |
class |
KernelBasedLocallyWeightedDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>>
Provides a kernel based locally weighted distance function. |
class |
LocallyWeightedDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>>
Provides a locally weighted distance function. |
Uses of RealVector in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation |
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Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation with type parameters of type RealVector | |
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class |
AbstractCorrelationDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>,D extends CorrelationDistance<D>>
Abstract super class for correlation based distance functions. |
class |
AbstractPreferenceVectorBasedCorrelationDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>>
Abstract super class for all preference vector based correlation distance functions. |
class |
ERiCDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>>
Provides a distance function for building the hierarchiy in the ERiC algorithm. |
class |
PCABasedCorrelationDistanceFunction<V extends RealVector<V,?>,P extends HiCOPreprocessor<V>,D extends CorrelationDistance<D>>
Provides the correlation distance for real valued vectors. |
Uses of RealVector in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace |
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Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace with type parameters of type RealVector | |
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class |
DiSHDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>>
Distance function used in the DiSH algorithm. |
class |
HiSCDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>>
Distance function used in the HiSC algorithm. |
class |
SubspaceDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>>
Provides a distance function to determine a kind of correlation distance between two points, which is a pair consisting of the distance between the two subspaces spanned by the strong eigenvectors of the two points and the affine distance between the two subspaces. |
Uses of RealVector in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel |
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Classes in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with type parameters of type RealVector | |
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class |
ArbitraryKernelFunctionWrapper<O extends RealVector<O,?>>
Provides a wrapper for arbitrary kernel functions whose kernel matrix has been precomputed. |
class |
KernelMatrix<O extends RealVector<O,?>>
Provides a class for storing the kernel matrix and several extraction methods for convenience. |
Method parameters in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with type arguments of type RealVector | |
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static Matrix |
KernelMatrix.centerKernelMatrix(KernelMatrix<? extends RealVector<?,? extends Number>> kernelMatrix)
Centers the Kernel Matrix in Feature Space according to Smola et. |
Uses of RealVector in de.lmu.ifi.dbs.elki.math.linearalgebra.pca |
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Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with type parameters of type RealVector | |
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class |
CovarianceMatrixBuilder<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Abstract class with the task of computing a Covariance matrix to be used in PCA. |
class |
KernelCovarianceMatrixBuilder<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Kernel Covariance Matrix Builder. |
class |
PCAFilteredRunner<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
PCA runner that will do dimensionality reduction. |
class |
PCARunner<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Class to run PCA on given data. |
class |
StandardCovarianceMatrixBuilder<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Class for building a "traditional" covariance matrix. |
class |
WeightedCovarianceMatrixBuilder<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
CovarianceMatrixBuilder with weights. |
Uses of RealVector in de.lmu.ifi.dbs.elki.normalization |
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Classes in de.lmu.ifi.dbs.elki.normalization with type parameters of type RealVector | |
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class |
AttributeWiseMinMaxNormalization<V extends RealVector<V,?>>
Class to perform and undo a normalization on real vectors with respect to given minimum and maximum in each dimension. |
class |
AttributeWiseVarianceNormalization<V extends RealVector<V,?>>
Class to perform and undo a normalization on real vectors with respect to given mean and standard deviation in each dimension. |
Methods in de.lmu.ifi.dbs.elki.normalization with parameters of type RealVector | |
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private void |
AttributeWiseVarianceNormalization.determineMeanVariance(V[] featureVectors)
Determines the mean and standard deviations in each dimension of the given featureVectors. |
private void |
AttributeWiseMinMaxNormalization.determineMinMax(V[] featureVectors)
Determines the minima and maxima values in each dimension of the given featureVectors. |
Uses of RealVector in de.lmu.ifi.dbs.elki.parser |
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Classes in de.lmu.ifi.dbs.elki.parser with type parameters of type RealVector | |
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class |
RealVectorLabelParser<V extends RealVector<?,?>>
Provides a parser for parsing one point per line, attributes separated by whitespace. |
Uses of RealVector in de.lmu.ifi.dbs.elki.parser.meta |
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Classes in de.lmu.ifi.dbs.elki.parser.meta with type parameters of type RealVector | |
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class |
ProjectionParser<V extends RealVector<V,?>>
A ProjectionParser projects the ParsingResult of its base parser
onto a subspace specified by a BitSet. |
class |
RandomProjectionParser<V extends RealVector<V,?>>
A RandomProjectionParser selects a subset of attributes randomly for projection of a ParsingResult. |
Uses of RealVector in de.lmu.ifi.dbs.elki.preprocessing |
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Classes in de.lmu.ifi.dbs.elki.preprocessing with type parameters of type RealVector | |
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class |
DiSHPreprocessor<V extends RealVector<V,N>,N extends Number>
Preprocessor for DiSH preference vector assignment to objects of a certain database. |
class |
FourCPreprocessor<D extends Distance<D>,V extends RealVector<V,?>>
Preprocessor for 4C local dimensionality and locally weighted matrix assignment to objects of a certain database. |
class |
HiCOPreprocessor<V extends RealVector<V,?>>
Abstract superclass for preprocessors for HiCO correlation dimension assignment to objects of a certain database. |
class |
HiSCPreprocessor<V extends RealVector<V,?>>
Preprocessor for HiSC preference vector assignment to objects of a certain database. |
class |
KnnQueryBasedHiCOPreprocessor<V extends RealVector<V,?>>
Computes the HiCO correlation dimension of objects of a certain database. |
class |
PreDeConPreprocessor<D extends Distance<D>,V extends RealVector<V,?>>
Preprocessor for PreDeCon local dimensionality and locally weighted matrix assignment to objects of a certain database. |
class |
ProjectedDBSCANPreprocessor<D extends Distance<D>,V extends RealVector<V,?>>
Abstract superclass for preprocessor of algorithms extending the ProjectedDBSCAN algorithm. |
class |
RangeQueryBasedHiCOPreprocessor<V extends RealVector<V,?>>
Computes the HiCO correlation dimension of objects of a certain database. |
Uses of RealVector in de.lmu.ifi.dbs.elki.utilities |
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Methods in de.lmu.ifi.dbs.elki.utilities with type parameters of type RealVector | ||
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static
|
DatabaseUtil.centroid(Database<O> database)
Returns the centroid as a RealVector object of the specified database. |
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static
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DatabaseUtil.centroid(Database<V> database,
Collection<Integer> ids)
Returns the centroid as a RealVector object of the specified objects stored in the given database. |
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static
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DatabaseUtil.centroid(Database<V> database,
Collection<Integer> ids,
BitSet bitSet)
Returns the centroid w.r.t. the dimensions specified by the given BitSet as a RealVector object of the specified objects stored in the given database. |
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static
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DatabaseUtil.centroid(Database<V> database,
Iterator<Integer> iter,
BitSet bitSet)
Returns the centroid w.r.t. the dimensions specified by the given BitSet as a RealVector object of the specified objects stored in the given database. |
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static
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DatabaseUtil.covarianceMatrix(Database<O> database)
Determines the covariance matrix of the objects stored in the given database. |
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static
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DatabaseUtil.covarianceMatrix(Database<O> database,
O centroid)
Determines the covariance matrix of the objects stored in the given database w.r.t. the given centroid. |
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static
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DatabaseUtil.covarianceMatrix(Database<V> database,
Collection<Integer> ids)
Determines the covariance matrix of the objects stored in the given database. |
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static
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DatabaseUtil.variances(Database<O> database)
Determines the variances in each dimension of all objects stored in the given database. |
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static
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DatabaseUtil.variances(Database<V> database,
Collection<Integer> ids)
Determines the variances in each dimension of the specified objects stored in the given database. |
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static
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DatabaseUtil.variances(Database<V> database,
V centroid,
Collection<Integer> ids)
Determines the variances in each dimension of the specified objects stored in the given database. |
Methods in de.lmu.ifi.dbs.elki.utilities with parameters of type RealVector | |
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static double[] |
DatabaseUtil.variances(Database<RealVector<?,?>> database,
RealVector<?,?> centroid,
Collection<Integer>[] ids)
Determines the variances in each dimension of the specified objects stored in the given database. |
Method parameters in de.lmu.ifi.dbs.elki.utilities with type arguments of type RealVector | |
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static double[][] |
DatabaseUtil.min_max(Database<RealVector<?,?>> database)
Determines the minimum and maximum values in each dimension of all objects stored in the given database. |
static double[] |
DatabaseUtil.variances(Database<RealVector<?,?>> database,
RealVector<?,?> centroid,
Collection<Integer>[] ids)
Determines the variances in each dimension of the specified objects stored in the given database. |
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