A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

A

abs() - Method in class net.sf.javaml.core.Complex
Takes the absolute value of this complex number.
abs(double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
 
AbstractClassifier - Class in net.sf.javaml.classification
 
AbstractClassifier() - Constructor for class net.sf.javaml.classification.AbstractClassifier
 
AbstractCorrelation - Class in net.sf.javaml.distance
Abstract super class for all correlation measures.
AbstractCorrelation() - Constructor for class net.sf.javaml.distance.AbstractCorrelation
 
AbstractDistance - Class in net.sf.javaml.distance
Abstract super class for all distance measures.
AbstractDistance() - Constructor for class net.sf.javaml.distance.AbstractDistance
 
AbstractFilter - Class in net.sf.javaml.filter
Umbrella class for filters that implements both the InstanceFilter and DatasetFilter interfaces.
AbstractFilter() - Constructor for class net.sf.javaml.filter.AbstractFilter
 
AbstractInstance - Class in net.sf.javaml.core
Implementation of some standard methods for instances.
AbstractInstance() - Constructor for class net.sf.javaml.core.AbstractInstance
 
AbstractInstance(Object) - Constructor for class net.sf.javaml.core.AbstractInstance
 
Abstraction - Class in net.sf.javaml.distance.fastdtw
 
Abstraction(int) - Constructor for class net.sf.javaml.distance.fastdtw.Abstraction
 
AbstractMeanClassifier - Class in net.sf.javaml.classification
Abstract classifier class that is the parent of all classifiers that require the mean of each class as training.
AbstractMeanClassifier() - Constructor for class net.sf.javaml.classification.AbstractMeanClassifier
 
AbstractSimilarity - Class in net.sf.javaml.distance
Abstract super class for all similarity measures.
AbstractSimilarity() - Constructor for class net.sf.javaml.distance.AbstractSimilarity
 
ActiveSetsOptimization - Class in net.sf.javaml.utils
Implementation of Active-sets method with BFGS update to solve optimization problem with only bounds constraints in multi-dimensions.
ActiveSetsOptimization() - Constructor for class net.sf.javaml.utils.ActiveSetsOptimization
 
add(int, int, double) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
adds a to the specified element, growing the matrix if necessary.
add(SparseVector) - Method in class net.sf.javaml.clustering.mcl.SparseVector
mutable add
add(int, double) - Method in class net.sf.javaml.clustering.mcl.SparseVector
mutable add
add(int[], int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
add a scalar to the vector
add(double) - Method in class net.sf.javaml.core.AbstractInstance
 
add(Instance) - Method in class net.sf.javaml.core.AbstractInstance
 
add(Instance) - Method in interface net.sf.javaml.core.Dataset
Add an instance to this data set.
add(Instance) - Method in class net.sf.javaml.core.DefaultDataset
 
add(int, Instance) - Method in class net.sf.javaml.core.DefaultDataset
 
add(Instance) - Method in interface net.sf.javaml.core.Instance
Add an instance to this instance and returns the results.
add(double) - Method in interface net.sf.javaml.core.Instance
Add a scalar value to this instance and returns the results.
add(double[], double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
Returns the sum of two array.
add(double[], double) - Static method in class net.sf.javaml.utils.ArrayUtils
Add a value to each value in an array.
addAll(Collection<? extends Instance>) - Method in class net.sf.javaml.core.DefaultDataset
 
addAll(int, Collection<? extends Instance>) - Method in class net.sf.javaml.core.DefaultDataset
 
addElement(Instance) - Method in class net.sf.javaml.core.DefaultDataset
 
addFirst(int, int) - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
addFirst(double, TimeSeriesPoint) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
addLast(int, int) - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
addLast(double, TimeSeriesPoint) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
adjustMaxIndex(int, int) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
adjusts the size of the matrix.
aggregatePtSize(int) - Method in class net.sf.javaml.distance.fastdtw.timeseries.PAA
 
AICScore - Class in net.sf.javaml.clustering.evaluation
XXX doc
AICScore() - Constructor for class net.sf.javaml.clustering.evaluation.AICScore
 
AngularDistance - Class in net.sf.javaml.distance
 
AngularDistance() - Constructor for class net.sf.javaml.distance.AngularDistance
 
AQBC - Class in net.sf.javaml.clustering
This class implements the Adaptive Quality-based Clustering Algorithm, based on the implementation in MATLAB by De Smet et al., ESAT - SCD (SISTA), K.U.Leuven, Belgium.
AQBC(double) - Constructor for class net.sf.javaml.clustering.AQBC
XXX write doc
AQBC() - Constructor for class net.sf.javaml.clustering.AQBC
XXX write doc default constructor
ARFFHandler - Class in net.sf.javaml.tools.data
Provides method to load data from ARFF formatted files.
ARFFHandler() - Constructor for class net.sf.javaml.tools.data.ARFFHandler
 
arithmicMean(double[]) - Static method in class net.sf.javaml.utils.MathUtils
 
array(Instance) - Static method in class net.sf.javaml.tools.InstanceTools
Create an array representation of the instance attributes.
Arrays - Class in net.sf.javaml.distance.fastdtw.util
 
Arrays() - Constructor for class net.sf.javaml.distance.fastdtw.util.Arrays
 
ArrayUtils - Class in net.sf.javaml.utils
 
ArrayUtils() - Constructor for class net.sf.javaml.utils.ArrayUtils
 
average(Dataset) - Static method in class net.sf.javaml.tools.DatasetTools
Creates an instance that contains the average values for the attributes.

B

Bagging - Class in net.sf.javaml.classification.meta
Bagging meta learner.
Bagging(Classifier[], Random) - Constructor for class net.sf.javaml.classification.meta.Bagging
Deprecated. 
Bagging(Classifier[], SamplingMethod, long) - Constructor for class net.sf.javaml.classification.meta.Bagging
 
Band - Class in net.sf.javaml.distance.fastdtw
 
Band(int) - Constructor for class net.sf.javaml.distance.fastdtw.Band
 
BICScore - Class in net.sf.javaml.clustering.evaluation
XXX DOC
BICScore() - Constructor for class net.sf.javaml.clustering.evaluation.BICScore
 
big - Static variable in class net.sf.javaml.utils.Statistics
 
biginv - Static variable in class net.sf.javaml.utils.Statistics
 
binomialStandardError(double, int) - Static method in class net.sf.javaml.utils.Statistics
Computes standard error for observed values of a binomial random variable.
bootstrap(Dataset, int, Random) - Static method in class net.sf.javaml.tools.DatasetTools
Deprecated. 
build(Dataset) - Method in class net.sf.javaml.featureselection.ensemble.LinearRankingEnsemble
 
build(Dataset) - Method in interface net.sf.javaml.featureselection.FeatureSelection
Build the attribute evaluation on the supplied data set.
build(Dataset) - Method in class net.sf.javaml.featureselection.ranking.RankingFromScoring
 
build(Dataset) - Method in class net.sf.javaml.featureselection.ranking.RecursiveFeatureEliminationSVM
 
build(Dataset) - Method in class net.sf.javaml.featureselection.scoring.GainRatio
 
build(Dataset) - Method in class net.sf.javaml.featureselection.scoring.KullbackLeiblerDivergence
 
build(Dataset) - Method in class net.sf.javaml.featureselection.scoring.RandomForestAttributeEvaluation
 
build(Dataset) - Method in class net.sf.javaml.featureselection.scoring.RELIEF
 
build(Dataset) - Method in class net.sf.javaml.featureselection.scoring.SymmetricalUncertainty
 
build(Dataset) - Method in class net.sf.javaml.featureselection.subset.GreedyBackwardElimination
 
build(Dataset) - Method in class net.sf.javaml.featureselection.subset.GreedyForwardSelection
 
build(Dataset) - Method in class net.sf.javaml.filter.AbstractFilter
 
build(Dataset) - Method in class net.sf.javaml.filter.ClassRemoveFilter
 
build(Dataset) - Method in class net.sf.javaml.filter.ClassRetainFilter
 
build(Dataset) - Method in interface net.sf.javaml.filter.DatasetFilter
This method can be used if the filter needs some training first
build(Dataset) - Method in class net.sf.javaml.filter.discretize.EqualWidthBinning
 
build(Dataset) - Method in class net.sf.javaml.filter.MissingClassFilter
 
build(Dataset) - Method in class net.sf.javaml.filter.missingvalue.KNearestNeighbors
 
build(Dataset) - Method in class net.sf.javaml.filter.missingvalue.RemoveMissingValue
 
build(Dataset) - Method in class net.sf.javaml.filter.normalize.InstanceNormalizeMidrange
 
build(Dataset) - Method in class net.sf.javaml.filter.normalize.NormalizeMeanIQR135
 
build(Dataset) - Method in class net.sf.javaml.filter.normalize.NormalizeMidrange
 
build(Dataset) - Method in class net.sf.javaml.tools.weka.WekaAttributeSelection
 
buildClassifier(Dataset) - Method in class libsvm.LibSVM
 
buildClassifier(Dataset) - Method in class libsvm.SelfOptimizingLinearLibSVM
 
buildClassifier(Dataset) - Method in class net.sf.javaml.classification.AbstractClassifier
 
buildClassifier(Dataset) - Method in class net.sf.javaml.classification.AbstractMeanClassifier
 
buildClassifier(Dataset) - Method in interface net.sf.javaml.classification.Classifier
Create a classifier from the given data set.
buildClassifier(Dataset) - Method in class net.sf.javaml.classification.KDtreeKNN
 
buildClassifier(Dataset) - Method in class net.sf.javaml.classification.KNearestNeighbors
 
buildClassifier(Dataset) - Method in class net.sf.javaml.classification.meta.Bagging
 
buildClassifier(Dataset) - Method in class net.sf.javaml.classification.meta.SimpleBagging
 
buildClassifier(Dataset) - Method in class net.sf.javaml.classification.SOM
 
buildClassifier(Dataset) - Method in class net.sf.javaml.classification.tree.RandomForest
 
buildClassifier(Dataset) - Method in class net.sf.javaml.classification.tree.RandomTree
 
buildClassifier(Dataset) - Method in class net.sf.javaml.classification.ZeroR
 
buildClassifier(Dataset) - Method in class net.sf.javaml.tools.weka.WekaClassifier
 

C

CachedDistance - Class in net.sf.javaml.distance
This class implements a wrapper around other distance measure to cache previously calculated distances.
CachedDistance(DistanceMeasure) - Constructor for class net.sf.javaml.distance.CachedDistance
 
calculateDistance(Instance, Instance) - Method in class net.sf.javaml.distance.EuclideanDistance
 
calcWarpCost(WarpPath, TimeSeries, TimeSeries) - Static method in class net.sf.javaml.distance.fastdtw.dtw.DTW
 
cast(double[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
cast a double[] to an int[]
cast(int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
cast a double[] to an int[]
CeilValueFilter - Class in net.sf.javaml.filter.instance
Filter to replace all values with their ceiled equivalent.
CeilValueFilter() - Constructor for class net.sf.javaml.filter.instance.CeilValueFilter
 
changeLength(double, double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
 
ChebychevDistance - Class in net.sf.javaml.distance
 
ChebychevDistance() - Constructor for class net.sf.javaml.distance.ChebychevDistance
 
chiSquared(double[][], boolean) - Static method in class net.sf.javaml.utils.ContingencyTables
Returns chi-squared probability for a given matrix.
chiSquaredProbability(double, double) - Static method in class net.sf.javaml.utils.Statistics
Returns chi-squared probability for given value and degrees of freedom.
chiVal(double[][], boolean) - Static method in class net.sf.javaml.utils.ContingencyTables
Computes chi-squared statistic for a contingency table.
chooseElements(double[][], int[], int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create a matrix that contains the rows and columns of the argument matrix in the order given by rows and cols
chooseElements(double[], int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create vector that contains the elements of the argument in the order as given by keep
chooseElements(int[][], int[], int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create a matrix that contains the rows and columns of the argument matrix in the order given by rows and cols
chooseElements(int[], int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create vector that contains the elements of the argument in the order as given by keep
CIndex - Class in net.sf.javaml.clustering.evaluation
TODO uitleg
CIndex(DistanceMeasure) - Constructor for class net.sf.javaml.clustering.evaluation.CIndex
 
classDistribution(Instance) - Method in class libsvm.SelfOptimizingLinearLibSVM
 
classDistribution(Instance) - Method in class net.sf.javaml.classification.AbstractClassifier
 
classDistribution(Instance) - Method in interface net.sf.javaml.classification.Classifier
Generate the membership distribution for this instance using this classifier.
classDistribution(Instance) - Method in class net.sf.javaml.classification.KDtreeKNN
 
classDistribution(Instance) - Method in class net.sf.javaml.classification.KNearestNeighbors
 
classDistribution(Instance) - Method in class net.sf.javaml.classification.MeanFeatureVotingClassifier
 
classDistribution(Instance) - Method in class net.sf.javaml.classification.meta.Bagging
 
classDistribution(Instance) - Method in class net.sf.javaml.classification.meta.SimpleBagging
 
classDistribution(Instance) - Method in class net.sf.javaml.classification.SOM
 
classDistribution(Instance) - Method in class net.sf.javaml.classification.tree.RandomForest
 
classDistribution(Instance) - Method in class net.sf.javaml.classification.tree.RandomTree
 
classDistribution(Instance) - Method in class net.sf.javaml.classification.ZeroR
 
classDistribution(Instance) - Method in class net.sf.javaml.tools.weka.WekaClassifier
 
classes() - Method in interface net.sf.javaml.core.Dataset
Returns a set containing all different classes in this data set.
classes() - Method in class net.sf.javaml.core.DefaultDataset
 
Classifier - Interface in net.sf.javaml.classification
Interface for all classifiers.
classify(Instance) - Method in class libsvm.LibSVM
 
classify(Instance) - Method in class libsvm.SelfOptimizingLinearLibSVM
 
classify(Instance) - Method in class net.sf.javaml.classification.AbstractClassifier
 
classify(Instance) - Method in interface net.sf.javaml.classification.Classifier
Classify the instance according to this classifier.
classify(Instance) - Method in class net.sf.javaml.classification.NearestMeanClassifier
 
classify(Instance) - Method in class net.sf.javaml.classification.tree.RandomForest
 
classify(Instance) - Method in class net.sf.javaml.classification.tree.RandomTree
 
classify(Instance) - Method in class net.sf.javaml.tools.weka.WekaClassifier
 
classIndex(Object) - Method in interface net.sf.javaml.core.Dataset
Returns the index of the class value in the supplied data set.
classIndex(Object) - Method in class net.sf.javaml.core.DefaultDataset
 
ClassRemoveFilter - Class in net.sf.javaml.filter
Removes all instances from a data set that have a specific class value
ClassRemoveFilter() - Constructor for class net.sf.javaml.filter.ClassRemoveFilter
 
ClassRemoveFilter(Object) - Constructor for class net.sf.javaml.filter.ClassRemoveFilter
 
ClassReplaceFilter - Class in net.sf.javaml.filter
Replaces a certain class value with another one.
ClassReplaceFilter(Object, Object) - Constructor for class net.sf.javaml.filter.ClassReplaceFilter
 
ClassRetainFilter - Class in net.sf.javaml.filter
Keeps all instances from a data set that have a specific class value
ClassRetainFilter() - Constructor for class net.sf.javaml.filter.ClassRetainFilter
 
ClassRetainFilter(Object) - Constructor for class net.sf.javaml.filter.ClassRetainFilter
 
classValue() - Method in class net.sf.javaml.core.AbstractInstance
 
classValue(int) - Method in interface net.sf.javaml.core.Dataset
Returns the class value of the supplied class index.
classValue(int) - Method in class net.sf.javaml.core.DefaultDataset
 
classValue() - Method in interface net.sf.javaml.core.Instance
Returns the class value for this instance.
clear() - Method in class net.sf.javaml.core.DefaultDataset
 
clear() - Method in class net.sf.javaml.core.DenseInstance
 
clear() - Method in class net.sf.javaml.core.SparseInstance
 
clear() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
cluster(Dataset) - Method in class net.sf.javaml.clustering.AQBC
XXX write doc FIXME remove output on the console
cluster(Dataset) - Method in interface net.sf.javaml.clustering.Clusterer
This method will execute the clustering algorithm on a particular data set.
cluster(Dataset) - Method in class net.sf.javaml.clustering.Cobweb
 
cluster(Dataset) - Method in class net.sf.javaml.clustering.DensityBasedSpatialClustering
 
cluster(Dataset) - Method in class net.sf.javaml.clustering.FarthestFirst
XXX DOC
cluster(Dataset) - Method in class net.sf.javaml.clustering.IterativeFarthestFirst
XXX DOC
cluster(Dataset) - Method in class net.sf.javaml.clustering.IterativeKMeans
XXX add doc
cluster(Dataset) - Method in class net.sf.javaml.clustering.IterativeMultiKMeans
XXX add doc
cluster(Dataset) - Method in class net.sf.javaml.clustering.KMeans
XXX add doc
cluster(Dataset) - Method in class net.sf.javaml.clustering.KMedoids
 
cluster(Dataset) - Method in class net.sf.javaml.clustering.mcl.MCL
 
cluster(Dataset) - Method in class net.sf.javaml.clustering.MultiKMeans
XXX add doc
cluster(Dataset) - Method in class net.sf.javaml.clustering.OPTICS
 
cluster(Dataset) - Method in class net.sf.javaml.clustering.SOM
 
cluster(Dataset) - Method in class net.sf.javaml.tools.weka.WekaClusterer
 
Clusterer - Interface in net.sf.javaml.clustering
A common interface for all clustering techniques.
ClusterEvaluation - Interface in net.sf.javaml.clustering.evaluation
This interface provides a frame for all measure that can be used to evaluate the quality of a clusterer.
Cobweb - Class in net.sf.javaml.clustering
Class implementing the Cobweb and Classit clustering algorithms.
Cobweb() - Constructor for class net.sf.javaml.clustering.Cobweb
default constructor
Cobweb(double, double) - Constructor for class net.sf.javaml.clustering.Cobweb
XXX DOC
cochransCriterion(double[][]) - Static method in class net.sf.javaml.utils.ContingencyTables
Tests if Cochran's criterion is full-filled for the given contingency table.
ColMajorCell - Class in net.sf.javaml.distance.fastdtw.matrix
 
ColMajorCell(int, int) - Constructor for class net.sf.javaml.distance.fastdtw.matrix.ColMajorCell
 
columns() - Method in class net.sf.javaml.matrix.Matrix
 
colwidth - Static variable in class net.sf.javaml.clustering.mcl.Vectors
 
compare(double, double) - Method in class net.sf.javaml.distance.AbstractCorrelation
 
compare(double, double) - Method in class net.sf.javaml.distance.AbstractDistance
 
compare(double, double) - Method in class net.sf.javaml.distance.AbstractSimilarity
 
compare(double, double) - Method in class net.sf.javaml.distance.CachedDistance
 
compare(double, double) - Method in interface net.sf.javaml.distance.DistanceMeasure
Returns whether the first distance, similarity or correlation is better than the second distance, similarity or correlation.
compare(double, double) - Method in class net.sf.javaml.distance.PolynomialKernel
 
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.AICScore
 
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.BICScore
 
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.CIndex
 
compareScore(double, double) - Method in interface net.sf.javaml.clustering.evaluation.ClusterEvaluation
Compares the two scores according to the criterion in the implementation.
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.Gamma
 
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.GPlus
 
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.HybridCentroidSimilarity
XXX DOC
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.HybridPairwiseSimilarities
XXX DOC
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.MinMaxCut
 
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.PointBiserial
 
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.SumOfAveragePairwiseSimilarities
XXX DOC
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.SumOfCentroidSimilarities
XXX DOC
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.SumOfSquaredErrors
 
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.Tau
 
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.TraceScatterMatrix
XXX DOC
compareScore(double, double) - Method in class net.sf.javaml.clustering.evaluation.WB
 
Complex - Class in net.sf.javaml.core
Implements a mutable Complex number.
Complex(double, double) - Constructor for class net.sf.javaml.core.Complex
Creates a new Complex number with the supplied real and complex part.
Complex() - Constructor for class net.sf.javaml.core.Complex
Creates a new Complex number with 0 for its real and imaginary part.
concat(double[], double[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
 
concat(double[], double[], double[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
w = [x y z]
conjugate() - Method in class net.sf.javaml.core.Complex
Takes the conjugate of this complex number.
ConsistencyIndex - Class in net.sf.javaml.distance
Consistency index for a pair of subsets.
ConsistencyIndex(int) - Constructor for class net.sf.javaml.distance.ConsistencyIndex
 
contains(boolean[], boolean) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
contains(byte[], byte) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
contains(char[], char) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
contains(double[], double) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
contains(float[], float) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
contains(int[], int) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
contains(long[], long) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
contains(short[], short) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
contains(String[], String) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
containsKey(Object) - Method in class net.sf.javaml.core.DenseInstance
 
containsKey(Object) - Method in class net.sf.javaml.core.SparseInstance
 
containsValue(Object) - Method in class net.sf.javaml.core.DenseInstance
 
containsValue(Object) - Method in class net.sf.javaml.core.SparseInstance
 
ContingencyTables - Class in net.sf.javaml.utils
Class implementing some statistical routines for contingency tables.
ContingencyTables() - Constructor for class net.sf.javaml.utils.ContingencyTables
 
convertClass(double) - Method in class net.sf.javaml.tools.weka.ToWekaUtils
 
copy() - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
copy the matrix and its elements
copy() - Method in class net.sf.javaml.clustering.mcl.SparseVector
copy the contents of the sparse vector
copy(double[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
copies a the source to the destination
copy(int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
copies a the source to the destination
copy() - Method in interface net.sf.javaml.core.Dataset
Create a deep copy of the data set.
copy() - Method in class net.sf.javaml.core.DefaultDataset
 
copy() - Method in class net.sf.javaml.core.DenseInstance
 
copy() - Method in interface net.sf.javaml.core.Instance
Create a deep copy of this instance
copy() - Method in class net.sf.javaml.core.SparseInstance
 
CosineDistance - Class in net.sf.javaml.distance
This similarity based distance measure actually measures the angle between two vectors.
CosineDistance() - Constructor for class net.sf.javaml.distance.CosineDistance
 
CosineSimilarity - Class in net.sf.javaml.distance
This similarity based distance measure actually measures the angle between two vectors.
CosineSimilarity() - Constructor for class net.sf.javaml.distance.CosineSimilarity
 
CramersV(double[][]) - Static method in class net.sf.javaml.utils.ContingencyTables
Computes Cramer's V for a contingency table.
create(int, int) - Static method in class net.sf.javaml.matrix.Matrix
 
createInstanceFromAttribute(Dataset, int) - Static method in class net.sf.javaml.tools.DatasetTools
Creates an Instance from the values of one particular attribute over all Instances in a data set.
createInstanceFromClass(Dataset) - Static method in class net.sf.javaml.tools.DatasetTools
Creates an Instance from the class labels over all Instances in a data set.
CrossValidation - Class in net.sf.javaml.classification.evaluation
Implementation of the cross-validation evaluation technique.
CrossValidation(Classifier) - Constructor for class net.sf.javaml.classification.evaluation.CrossValidation
 
crossValidation(Dataset, int, Random) - Method in class net.sf.javaml.classification.evaluation.CrossValidation
Performs cross validation with the specified parameters.
crossValidation(Dataset, int) - Method in class net.sf.javaml.classification.evaluation.CrossValidation
Performs cross validation with the specified parameters.
crossValidation(Dataset) - Method in class net.sf.javaml.classification.evaluation.CrossValidation
Performs cross validation with the specified parameters.
cumsum(double[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
cumulative sum of the elements, starting at element 0.

D

Dataset - Interface in net.sf.javaml.core
Interface for a data set.
DatasetFilter - Interface in net.sf.javaml.filter
The interface for filters that can be applied on an Dataset.
DatasetTools - Class in net.sf.javaml.tools
This class provides utility methods on data sets.
DatasetTools() - Constructor for class net.sf.javaml.tools.DatasetTools
 
debug() - Method in class net.sf.javaml.clustering.mcl.ExpDouble
 
DefaultDataset - Class in net.sf.javaml.core
Provides a standard data set implementation.
DefaultDataset(Collection<Instance>) - Constructor for class net.sf.javaml.core.DefaultDataset
Creates a data set that contains the provided instances
DefaultDataset() - Constructor for class net.sf.javaml.core.DefaultDataset
Creates an empty data set.
delete(double[]) - Method in class net.sf.javaml.core.kdtree.KDTree
Delete a node from a KD-tree.
DenseInstance - Class in net.sf.javaml.core
Implementation of a dense instance.
DenseInstance(double[]) - Constructor for class net.sf.javaml.core.DenseInstance
Creates a new instance with the provide value for the attributes.
DenseInstance(double[], Object) - Constructor for class net.sf.javaml.core.DenseInstance
Creates a new instance with the provided attribute values and the provided class label.
DenseInstance(int) - Constructor for class net.sf.javaml.core.DenseInstance
Creates an instance that has space for the supplied number of attributes.
DensityBasedSpatialClustering - Class in net.sf.javaml.clustering
Provides the density-based-spatial-scanning clustering algorithm.
DensityBasedSpatialClustering() - Constructor for class net.sf.javaml.clustering.DensityBasedSpatialClustering
Creates a density based clusterer with default parameters.
DensityBasedSpatialClustering(double, int) - Constructor for class net.sf.javaml.clustering.DensityBasedSpatialClustering
Create a new Density based clusterer with the provided parameters.
DensityBasedSpatialClustering(double, int, DistanceMeasure) - Constructor for class net.sf.javaml.clustering.DensityBasedSpatialClustering
Create a new Density based clusterer with the provided parameters.
DistanceMeasure - Interface in net.sf.javaml.distance
A distance measure is an algorithm to calculate the distance, similarity or correlation between two instances.
divide(double) - Method in class net.sf.javaml.core.AbstractInstance
 
divide(Instance) - Method in class net.sf.javaml.core.AbstractInstance
 
divide(double) - Method in interface net.sf.javaml.core.Instance
Divide each value of this instance by a scalar value and returns the results.
divide(Instance) - Method in interface net.sf.javaml.core.Instance
Divide each value in this instance with the corresponding value of the other instance and returns the results.
divide(double[], double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
 
dotProd(Instance, Instance) - Method in class net.sf.javaml.distance.PolynomialKernel
Calculates a dot product between two instances
doubleArrayToByteArray(double[]) - Static method in class net.sf.javaml.distance.fastdtw.lang.TypeConversions
 
DoubleFormat - Class in net.sf.javaml.clustering.mcl
DoubleFormat formats double numbers into a specified digit format.
DoubleFormat() - Constructor for class net.sf.javaml.clustering.mcl.DoubleFormat
 
doubleToByteArray(double) - Static method in class net.sf.javaml.distance.fastdtw.lang.TypeConversions
 
DTW - Class in net.sf.javaml.distance.fastdtw.dtw
 
DTW() - Constructor for class net.sf.javaml.distance.fastdtw.dtw.DTW
 
DTWSimilarity - Class in net.sf.javaml.distance.dtw
A similarity measure based on "Dynamic Time Warping".
DTWSimilarity() - Constructor for class net.sf.javaml.distance.dtw.DTWSimilarity
 

E

entropy(double[]) - Static method in class net.sf.javaml.utils.ContingencyTables
Computes the entropy of the given array.
entropyConditionedOnColumns(double[][]) - Static method in class net.sf.javaml.utils.ContingencyTables
Computes conditional entropy of the rows given the columns.
entropyConditionedOnRows(double[][]) - Static method in class net.sf.javaml.utils.ContingencyTables
Computes conditional entropy of the columns given the rows.
entropyConditionedOnRows(double[][], double[][], double) - Static method in class net.sf.javaml.utils.ContingencyTables
Computes conditional entropy of the columns given the rows of the test matrix with respect to the train matrix.
entropyOverColumns(double[][]) - Static method in class net.sf.javaml.utils.ContingencyTables
Computes the columns' entropy for the given contingency table.
entropyOverRows(double[][]) - Static method in class net.sf.javaml.utils.ContingencyTables
Computes the rows' entropy for the given contingency table.
entrySet() - Method in class net.sf.javaml.core.DenseInstance
 
entrySet() - Method in class net.sf.javaml.core.SparseInstance
 
eq(Complex, Complex) - Static method in class net.sf.javaml.utils.MathUtils
Tests if a is equal to b.
eq(double, double) - Static method in class net.sf.javaml.utils.MathUtils
Tests if a is equal to b.
eq(double[], double[]) - Static method in class net.sf.javaml.utils.MathUtils
XXX DOC
equals(Object) - Method in class net.sf.javaml.core.AbstractInstance
 
equals(Object) - Method in class net.sf.javaml.core.DenseInstance
 
equals(Object) - Method in class net.sf.javaml.core.Pair
 
equals(Object) - Method in class net.sf.javaml.core.SparseInstance
 
equals(Object) - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
equals(Object) - Method in class net.sf.javaml.distance.fastdtw.matrix.ColMajorCell
 
equals(Object) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeriesPoint
 
EqualWidthBinning - Class in net.sf.javaml.filter.discretize
A filter that discretizes a range of numeric attributes in the data set into nominal attributes.
EqualWidthBinning() - Constructor for class net.sf.javaml.filter.discretize.EqualWidthBinning
 
EqualWidthBinning(int) - Constructor for class net.sf.javaml.filter.discretize.EqualWidthBinning
 
EuclideanDistance - Class in net.sf.javaml.distance
This class implements the Euclidean distance.
EuclideanDistance() - Constructor for class net.sf.javaml.distance.EuclideanDistance
 
EvaluateDataset - Class in net.sf.javaml.classification.evaluation
Tests a classifier on a data set
EvaluateDataset() - Constructor for class net.sf.javaml.classification.evaluation.EvaluateDataset
 
evaluateGradient(double[]) - Method in class net.sf.javaml.utils.ActiveSetsOptimization
Subclass should implement this procedure to evaluate gradient of the objective function
evaluateHessian(double[], int) - Method in class net.sf.javaml.utils.ActiveSetsOptimization
Subclass is recommended to override this procedure to evaluate second-order gradient of the objective function.
exists(String) - Static method in class net.sf.javaml.tools.Serial
 
expand(SparseMatrix) - Method in class net.sf.javaml.clustering.mcl.MarkovClustering
expand stochastic quadratic matrix by sqaring it with itself: result = m * m.
ExpandedResWindow - Class in net.sf.javaml.distance.fastdtw.dtw
 
ExpandedResWindow(TimeSeries, TimeSeries, PAA, PAA, WarpPath, int) - Constructor for class net.sf.javaml.distance.fastdtw.dtw.ExpandedResWindow
 
expandWindow(int) - Method in class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 
ExpDouble - Class in net.sf.javaml.clustering.mcl
ExpDouble represents a double-precision number by a mantissa, a decimal exponent and the number of digits in the mantissa, in order to allow formatting of the represented double.
ExpDouble(double, int) - Constructor for class net.sf.javaml.clustering.mcl.ExpDouble
Creates an ExpDouble from a double, with the specified number of digits.
exportDataset(Dataset, File, boolean) - Static method in class net.sf.javaml.tools.data.FileHandler
Exports a data set to a file.
exportDataset(Dataset, File) - Static method in class net.sf.javaml.tools.data.FileHandler
Exports a data set to a file.
expSum(int) - Method in class net.sf.javaml.clustering.mcl.SparseVector
exponential sum, i.e., sum (elements^p)

F

factor(double) - Method in class net.sf.javaml.clustering.mcl.SparseVector
mutable factorisation
FarthestFirst - Class in net.sf.javaml.clustering
Cluster data using the FarthestFirst algorithm.
FarthestFirst() - Constructor for class net.sf.javaml.clustering.FarthestFirst
default constructor
FarthestFirst(int, DistanceMeasure) - Constructor for class net.sf.javaml.clustering.FarthestFirst
XXX DOC
FastDTW - Class in net.sf.javaml.distance.fastdtw.dtw
 
FastDTW() - Constructor for class net.sf.javaml.distance.fastdtw.dtw.FastDTW
 
FastDTW - Class in net.sf.javaml.distance.fastdtw
Implementation of the FastDTW algorithm as described by Salvador and Chan.
FastDTW(int) - Constructor for class net.sf.javaml.distance.fastdtw.FastDTW
 
FeatureRanking - Interface in net.sf.javaml.featureselection
Interface for algorithms that can generate an attribute ranking.
FeatureScoring - Interface in net.sf.javaml.featureselection
Interface for all attribute evaluation methods.
FeatureSelection - Interface in net.sf.javaml.featureselection
Top-level interface for feature selection algorithms.
FeatureSubsetSelection - Interface in net.sf.javaml.featureselection
Interface for all attribute subset selection algorithms.
FileHandler - Class in net.sf.javaml.tools.data
A class to load data sets from file and write them back.
FileHandler() - Constructor for class net.sf.javaml.tools.data.FileHandler
 
fillRandom(double[], Random) - Static method in class net.sf.javaml.utils.ArrayUtils
 
filter(Dataset) - Method in class net.sf.javaml.filter.AbstractFilter
 
filter(Instance) - Method in class net.sf.javaml.filter.AbstractFilter
 
filter(Dataset) - Method in class net.sf.javaml.filter.ClassRemoveFilter
 
filter(Instance) - Method in class net.sf.javaml.filter.ClassReplaceFilter
 
filter(Dataset) - Method in class net.sf.javaml.filter.ClassRetainFilter
 
filter(Dataset) - Method in interface net.sf.javaml.filter.DatasetFilter
Applies this filter to an dataset and return the modified dataset.
filter(Instance) - Method in class net.sf.javaml.filter.discretize.EqualWidthBinning
 
filter(Dataset) - Method in class net.sf.javaml.filter.discretize.EqualWidthBinning
 
filter(Instance) - Method in class net.sf.javaml.filter.instance.CeilValueFilter
 
filter(Instance) - Method in class net.sf.javaml.filter.instance.FloorValueFilter
 
filter(Instance) - Method in class net.sf.javaml.filter.instance.ReplaceValueFilter
 
filter(Instance) - Method in class net.sf.javaml.filter.instance.RoundValueFilter
 
filter(Instance) - Method in interface net.sf.javaml.filter.InstanceFilter
Applies this filter to an instance
filter(Dataset) - Method in class net.sf.javaml.filter.MissingClassFilter
 
filter(Dataset) - Method in class net.sf.javaml.filter.missingvalue.KNearestNeighbors
 
filter(Dataset) - Method in class net.sf.javaml.filter.missingvalue.RemoveMissingValue
 
filter(Instance) - Method in class net.sf.javaml.filter.missingvalue.ReplaceWithValue
 
filter(Dataset) - Method in class net.sf.javaml.filter.normalize.InstanceNormalizeMidrange
 
filter(Instance) - Method in class net.sf.javaml.filter.normalize.InstanceNormalizeMidrange
 
filter(Dataset) - Method in class net.sf.javaml.filter.normalize.NormalizeMean
 
filter(Instance) - Method in class net.sf.javaml.filter.normalize.NormalizeMean
 
filter(Dataset) - Method in class net.sf.javaml.filter.normalize.NormalizeMeanIQR135
 
filter(Instance) - Method in class net.sf.javaml.filter.normalize.NormalizeMeanIQR135
 
filter(Instance) - Method in class net.sf.javaml.filter.normalize.NormalizeMidrange
 
filter(Dataset) - Method in class net.sf.javaml.filter.normalize.NormalizeMidrange
 
filter(Instance) - Method in class net.sf.javaml.filter.RemoveAttributes
 
filter(Instance) - Method in class net.sf.javaml.filter.RetainAttributes
 
filter(Instance) - Method in class net.sf.javaml.filter.UnsetClassFilter
 
filter(Dataset) - Method in class net.sf.javaml.filter.UnsetClassFilter
 
find(int[], int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
find indices with val
findArgmin(double[], double[][]) - Method in class net.sf.javaml.utils.ActiveSetsOptimization
Main algorithm.
flipSign(double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
 
FloorValueFilter - Class in net.sf.javaml.filter.instance
Filter to replace all values with their rounded equivalent
FloorValueFilter() - Constructor for class net.sf.javaml.filter.instance.FloorValueFilter
 
fn - Variable in class net.sf.javaml.classification.evaluation.PerformanceMeasure
The number of false negatives.
folds(int, Random) - Method in interface net.sf.javaml.core.Dataset
Create a number of folds from the data set and return them.
folds(int, Random) - Method in class net.sf.javaml.core.DefaultDataset
 
format(double, int) - Static method in class net.sf.javaml.clustering.mcl.DoubleFormat
Format the number x with n significant digits.
formatExponential(double, int) - Method in class net.sf.javaml.clustering.mcl.ExpDouble
formats the number into a mantissa with interval in interval [1,10)E{ndigits} and an exponent.
fp - Variable in class net.sf.javaml.classification.evaluation.PerformanceMeasure
The number of false positives.
FProbability(double, int, int) - Static method in class net.sf.javaml.utils.Statistics
Computes probability of F-ratio.
FromWekaUtils - Class in net.sf.javaml.tools.weka
Provides utility methods to convert data from the WEKA format to the Java-ML format.
FromWekaUtils(Instances) - Constructor for class net.sf.javaml.tools.weka.FromWekaUtils
 
FullWindow - Class in net.sf.javaml.distance.fastdtw.dtw
 
FullWindow(TimeSeries, TimeSeries) - Constructor for class net.sf.javaml.distance.fastdtw.dtw.FullWindow
 

G

GainRatio - Class in net.sf.javaml.featureselection.scoring
Implements the Gain Ratio evaluation method for attributes.
GainRatio() - Constructor for class net.sf.javaml.featureselection.scoring.GainRatio
 
gainRatio(double[][]) - Static method in class net.sf.javaml.utils.ContingencyTables
Computes gain ratio for contingency table (split on rows).
Gamma - Class in net.sf.javaml.clustering.evaluation
TODO uitleg
Gamma(DistanceMeasure) - Constructor for class net.sf.javaml.clustering.evaluation.Gamma
 
gamma(double) - Static method in class net.sf.javaml.utils.GammaFunction
Returns the Gamma function of the argument.
GammaFunction - Class in net.sf.javaml.utils
 
GammaFunction() - Constructor for class net.sf.javaml.utils.GammaFunction
 
ge(double, double) - Static method in class net.sf.javaml.utils.MathUtils
Tests if a is greater or equal to b.
get(int, int) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
get number at index or 0. if not set.
get(Object) - Method in class net.sf.javaml.clustering.mcl.SparseVector
get ensures it returns 0 for empty hash values or if index exceeds length.
get(Object) - Method in class net.sf.javaml.core.DenseInstance
 
get(Object) - Method in class net.sf.javaml.core.SparseInstance
 
get(int) - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
get(int) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeriesPoint
 
get(int, int) - Method in class net.sf.javaml.matrix.Matrix
 
getAccuracy() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getBCR() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getC() - Method in class libsvm.SelfOptimizingLinearLibSVM
 
getCol() - Method in class net.sf.javaml.distance.fastdtw.matrix.ColMajorCell
 
getColum(int) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
get a column of the sparse matrix (expensive).
getCorrelation() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getCorrelationCoefficient() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getCost() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getDataset() - Method in class net.sf.javaml.tools.weka.FromWekaUtils
 
getDataset() - Method in class net.sf.javaml.tools.weka.ToWekaUtils
 
getDense() - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
create dense representation
getDense() - Method in class net.sf.javaml.clustering.mcl.SparseVector
get dense represenation
getDistance() - Method in class net.sf.javaml.distance.fastdtw.dtw.TimeWarpInfo
 
getErrorRate() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getFMeasure() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
Calculates F-score for beta equal to 1.
getFMeasure(int) - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
Returns the F-score.
getFMeasures() - Method in class libsvm.SelfOptimizingLinearLibSVM
Returns a map of all f-measure that were encountered while searching for the optimal C value.
getFNRate() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getFPRate() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getID() - Method in class net.sf.javaml.core.AbstractInstance
 
getID() - Method in interface net.sf.javaml.core.Instance
Returns a unique identifier for this instance.
getLabel(int) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
getLabels() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
getLabelsArr() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
getLength() - Method in class net.sf.javaml.clustering.mcl.SparseVector
get the length of the vector
getMatchingIndexesForI(int) - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
getMatchingIndexesForJ(int) - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
getMaximumDistance(Dataset) - Method in class net.sf.javaml.distance.SpearmanFootruleDistance
 
getMean(Object) - Method in class net.sf.javaml.classification.AbstractMeanClassifier
 
getMean() - Method in class net.sf.javaml.filter.normalize.NormalizeMeanIQR135
 
getMeasurement(int, int) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
getMeasurement(int, String) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
getMeasurement(double, int) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
getMeasurement(double, String) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
getMeasurementVector(int) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
getMeasurementVector(double) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
getMinFunction() - Method in class net.sf.javaml.utils.ActiveSetsOptimization
Get the minimal function value
getMinimumDistance(Dataset) - Method in class net.sf.javaml.distance.SpearmanFootruleDistance
 
getModCount() - Method in class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 
getOutOfBagErrorEstimate() - Method in class net.sf.javaml.classification.meta.Bagging
 
getOutOfBagErrorEstimate() - Method in class net.sf.javaml.classification.tree.RandomForest
 
getParameters() - Method in class libsvm.LibSVM
Returns a reference to the parameter configuration of the SVM
getPath() - Method in class net.sf.javaml.distance.fastdtw.dtw.TimeWarpInfo
 
getPrecision() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getQ9() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getRecall() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getRow() - Method in class net.sf.javaml.distance.fastdtw.matrix.ColMajorCell
 
getSize() - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
get the size of the matrix
getStd() - Method in class net.sf.javaml.filter.normalize.NormalizeMeanIQR135
 
getTimeAtNthPoint(int) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
getTNRate() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getTotal() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getTPRate() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
getVarbValues() - Method in class net.sf.javaml.utils.ActiveSetsOptimization
Get the variable values.
getWarpDistBetween(TimeSeries, TimeSeries) - Static method in class net.sf.javaml.distance.fastdtw.dtw.DTW
 
getWarpDistBetween(TimeSeries, TimeSeries, SearchWindow) - Static method in class net.sf.javaml.distance.fastdtw.dtw.DTW
 
getWarpDistBetween(TimeSeries, TimeSeries) - Static method in class net.sf.javaml.distance.fastdtw.dtw.FastDTW
 
getWarpDistBetween(TimeSeries, TimeSeries, int) - Static method in class net.sf.javaml.distance.fastdtw.dtw.FastDTW
 
getWarpInfoBetween(TimeSeries, TimeSeries) - Static method in class net.sf.javaml.distance.fastdtw.dtw.DTW
 
getWarpInfoBetween(TimeSeries, TimeSeries, SearchWindow) - Static method in class net.sf.javaml.distance.fastdtw.dtw.DTW
 
getWarpInfoBetween(TimeSeries, TimeSeries, int) - Static method in class net.sf.javaml.distance.fastdtw.dtw.FastDTW
 
getWarpPathBetween(TimeSeries, TimeSeries) - Static method in class net.sf.javaml.distance.fastdtw.dtw.DTW
 
getWarpPathBetween(TimeSeries, TimeSeries, SearchWindow) - Static method in class net.sf.javaml.distance.fastdtw.dtw.DTW
 
getWarpPathBetween(TimeSeries, TimeSeries) - Static method in class net.sf.javaml.distance.fastdtw.dtw.FastDTW
 
getWarpPathBetween(TimeSeries, TimeSeries, int) - Static method in class net.sf.javaml.distance.fastdtw.dtw.FastDTW
 
getWeights() - Method in class libsvm.LibSVM
Provides access to the weights the support vectors obtained during training.
getWeights() - Method in class libsvm.SelfOptimizingLinearLibSVM
 
GPlus - Class in net.sf.javaml.clustering.evaluation
TODO uitleg
GPlus(DistanceMeasure) - Constructor for class net.sf.javaml.clustering.evaluation.GPlus
 
GreedyBackwardElimination - Class in net.sf.javaml.featureselection.subset
Provides an implementation of the backward greedy attribute subset elimination algorithm.
GreedyBackwardElimination(int, DistanceMeasure) - Constructor for class net.sf.javaml.featureselection.subset.GreedyBackwardElimination
Creates a new GreedyForwardSelection that will select the supplied number of attributes.
GreedyForwardSelection - Class in net.sf.javaml.featureselection.subset
Provides an implementation of the forward greedy attribute subset selection.
GreedyForwardSelection(int, DistanceMeasure) - Constructor for class net.sf.javaml.featureselection.subset.GreedyForwardSelection
Creates a new GreedyForwardSelection that will select the supplied number of attributes.
gt(double, double) - Static method in class net.sf.javaml.utils.MathUtils
Tests if a is greater than b.

H

hadamardPower(double) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
mutable m2 = m .^ s
hadamardPower(double) - Method in class net.sf.javaml.clustering.mcl.SparseVector
mutable Hadamard power
hadamardProduct(SparseMatrix) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
mutable Hadamard product
hadamardProduct(SparseVector) - Method in class net.sf.javaml.clustering.mcl.SparseVector
mutable Hadamard product (elementwise multiplication)
harmonicMean(double[]) - Static method in class net.sf.javaml.utils.MathUtils
Calculates the harmonic mean of the values in the supplied array.
hashCode() - Method in class net.sf.javaml.core.AbstractInstance
 
hashCode() - Method in class net.sf.javaml.core.DenseInstance
 
hashCode() - Method in class net.sf.javaml.core.Pair
 
hashCode() - Method in class net.sf.javaml.core.SparseInstance
 
hashCode() - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
hashCode() - Method in class net.sf.javaml.distance.fastdtw.matrix.ColMajorCell
 
hashCode() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeriesPoint
 
hasMissingValues(Instance) - Static method in class net.sf.javaml.tools.InstanceTools
Checks if there are any missing values in the instance.
HybridCentroidSimilarity - Class in net.sf.javaml.clustering.evaluation
H_2 from the Zhao 2001 paper TODO uitleg
HybridCentroidSimilarity() - Constructor for class net.sf.javaml.clustering.evaluation.HybridCentroidSimilarity
 
HybridPairwiseSimilarities - Class in net.sf.javaml.clustering.evaluation
H_1 from the Zhao 2001 paper TODO uitleg
HybridPairwiseSimilarities() - Constructor for class net.sf.javaml.clustering.evaluation.HybridPairwiseSimilarities
 

I

I - Static variable in class net.sf.javaml.core.Complex
The imaginary constant I
im - Variable in class net.sf.javaml.core.Complex
The imaginary part of this complex number.
incfill(int) - Static method in class net.sf.javaml.tools.ListTools
Create a list of the specified size filled with integers from 0 to size-1
incompleteBeta(double, double, double) - Static method in class net.sf.javaml.utils.Statistics
Returns the Incomplete Beta Function evaluated from zero to xx.
increaseSize(double[], int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create new vector of larger size and data of the argument.
increaseSize(double[][], int, int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create new matrix of larger size and data of the argument.
increaseSize(int[], int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create new vector of larger size and data of the argument.
increaseSize(int[][], int, int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create new matrix of larger size and data of the argument.
inflate(SparseMatrix, double, double) - Method in class net.sf.javaml.clustering.mcl.MarkovClustering
inflate stochastic matrix by Hadamard (elementwise) exponentiation, pruning and normalisation : result = Gamma ( m, p ) = normalise ( prune ( m .^ p ) ).
insert(double[], Object) - Method in class net.sf.javaml.core.kdtree.KDTree
Insert a node in a KD-tree.
insertElementAt(Instance, int) - Method in class net.sf.javaml.core.DefaultDataset
 
instance(int) - Method in interface net.sf.javaml.core.Dataset
Get the instance with a certain index.
instance(int) - Method in class net.sf.javaml.core.DefaultDataset
 
Instance - Interface in net.sf.javaml.core
The interface for instances in a data set.
InstanceFilter - Interface in net.sf.javaml.filter
The interface for filters that can be applied on an Instance without the need for a reference Dataset.
instanceFromWeka(Instance) - Method in class net.sf.javaml.tools.weka.FromWekaUtils
 
InstanceNormalizeMidrange - Class in net.sf.javaml.filter.normalize
This filter will normalize all the attributes in an instance to a certain interval determined by a mid-range and a range.
InstanceNormalizeMidrange() - Constructor for class net.sf.javaml.filter.normalize.InstanceNormalizeMidrange
A normalization filter to the interval [-1,1]
InstanceNormalizeMidrange(double, double) - Constructor for class net.sf.javaml.filter.normalize.InstanceNormalizeMidrange
 
InstanceTools - Class in net.sf.javaml.tools
Provides utility methods for manipulating, creating and modifying instances.
InstanceTools() - Constructor for class net.sf.javaml.tools.InstanceTools
 
instanceToWeka(Instance) - Method in class net.sf.javaml.tools.weka.ToWekaUtils
 
intersection(Set<? extends Integer>, Set<? extends Integer>) - Static method in class net.sf.javaml.tools.SetTools
Returns the intersection of the two sets provided as arguments.
invert() - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
invertedCopy() - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
isEmpty() - Method in class net.sf.javaml.core.DenseInstance
 
isEmpty() - Method in class net.sf.javaml.core.SparseInstance
 
isInWindow(int, int) - Method in class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 
IterativeFarthestFirst - Class in net.sf.javaml.clustering
XXX DOC
IterativeFarthestFirst(ClusterEvaluation) - Constructor for class net.sf.javaml.clustering.IterativeFarthestFirst
default constructor
IterativeFarthestFirst(int, int, DistanceMeasure, ClusterEvaluation) - Constructor for class net.sf.javaml.clustering.IterativeFarthestFirst
XXX DOC
IterativeKMeans - Class in net.sf.javaml.clustering
This class implements an extension of KMeans.
IterativeKMeans(ClusterEvaluation) - Constructor for class net.sf.javaml.clustering.IterativeKMeans
default constructor
IterativeKMeans(int, int, ClusterEvaluation) - Constructor for class net.sf.javaml.clustering.IterativeKMeans
XXX add doc
IterativeKMeans(int, int, int, DistanceMeasure, ClusterEvaluation) - Constructor for class net.sf.javaml.clustering.IterativeKMeans
XXX add doc
IterativeMultiKMeans - Class in net.sf.javaml.clustering
This class implements an extension of KMeans, combining Iterative- en MultiKMeans.
IterativeMultiKMeans(ClusterEvaluation) - Constructor for class net.sf.javaml.clustering.IterativeMultiKMeans
default constructor
IterativeMultiKMeans(int, int, ClusterEvaluation) - Constructor for class net.sf.javaml.clustering.IterativeMultiKMeans
XXX add doc
IterativeMultiKMeans(int, int, int, int, DistanceMeasure, ClusterEvaluation) - Constructor for class net.sf.javaml.clustering.IterativeMultiKMeans
XXX add doc
iterator() - Method in class net.sf.javaml.core.AbstractInstance
 
iterator() - Method in class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 

J

JaccardIndexDistance - Class in net.sf.javaml.distance
Jaccard index.
JaccardIndexDistance() - Constructor for class net.sf.javaml.distance.JaccardIndexDistance
 
JaccardIndexSimilarity - Class in net.sf.javaml.distance
Jaccard index.
JaccardIndexSimilarity() - Constructor for class net.sf.javaml.distance.JaccardIndexSimilarity
 

K

KDTree - Class in net.sf.javaml.core.kdtree
KDTree is a class supporting KD-tree insertion, deletion, equality search, range search, and nearest neighbor(s) using double-precision floating-point keys.
KDTree(int) - Constructor for class net.sf.javaml.core.kdtree.KDTree
Creates a KD-tree with specified number of dimensions.
KDtreeKNN - Class in net.sf.javaml.classification
Implementation of the K nearest neighbor (KNN) classification algorithm with KDtree support.
KDtreeKNN(int) - Constructor for class net.sf.javaml.classification.KDtreeKNN
Instantiate the k-nearest neighbors algorithm with a specified number of neighbors.
KeyDuplicateException - Exception in net.sf.javaml.core.kdtree
KeyDuplicateException is thrown when the KDTree.insert method is invoked on a key already in the KDTree.
KeyDuplicateException() - Constructor for exception net.sf.javaml.core.kdtree.KeyDuplicateException
 
keySet() - Method in class net.sf.javaml.core.DenseInstance
 
keySet() - Method in interface net.sf.javaml.core.Instance
 
keySet() - Method in class net.sf.javaml.core.SparseInstance
 
KeySizeException - Exception in net.sf.javaml.core.kdtree
KeySizeException is thrown when a KDTree method is invoked on a key whose size (array length) mismatches the one used in the that KDTree's constructor.
KeySizeException() - Constructor for exception net.sf.javaml.core.kdtree.KeySizeException
 
KMeans - Class in net.sf.javaml.clustering
Implements the K-means algorithms as described by Mac Queen in 1967.
KMeans() - Constructor for class net.sf.javaml.clustering.KMeans
Constuct a default K-means clusterer with 100 iterations, 4 clusters, a default random generator and using the Euclidean distance.
KMeans(int) - Constructor for class net.sf.javaml.clustering.KMeans
Constuct a default K-means clusterer with the specified number of clusters, 100 iterations, a default random generator and using the Euclidean distance.
KMeans(int, int) - Constructor for class net.sf.javaml.clustering.KMeans
Create a new Simple K-means clusterer with the given number of clusters and iterations.
KMeans(int, int, DistanceMeasure) - Constructor for class net.sf.javaml.clustering.KMeans
Create a new K-means clusterer with the given number of clusters and iterations.
KMedoids - Class in net.sf.javaml.clustering
Implementation of the K-medoids algorithm.
KMedoids() - Constructor for class net.sf.javaml.clustering.KMedoids
default constructor
KMedoids(int, int, DistanceMeasure) - Constructor for class net.sf.javaml.clustering.KMedoids
Creates a new instance of the k-medoids algorithm with the specified parameters.
kNearest(int, Instance, DistanceMeasure) - Method in interface net.sf.javaml.core.Dataset
Returns the k closest instances.
kNearest(int, Instance, DistanceMeasure) - Method in class net.sf.javaml.core.DefaultDataset
Returns the k instances of the given data set that are the closest to the instance that is given as a parameter.
KNearestNeighbors - Class in net.sf.javaml.classification
Implementation of the K nearest neighbor (KNN) classification algorithm.
KNearestNeighbors(int) - Constructor for class net.sf.javaml.classification.KNearestNeighbors
Instantiate the k-nearest neighbors algorithm with a specified number of neighbors.
KNearestNeighbors(int, DistanceMeasure) - Constructor for class net.sf.javaml.classification.KNearestNeighbors
Instantiate the k-nearest neighbors algorithm with a specified number of neighbors.
KNearestNeighbors - Class in net.sf.javaml.filter.missingvalue
Replaces the missing value with the average of the values of its nearest neighbors.
KNearestNeighbors() - Constructor for class net.sf.javaml.filter.missingvalue.KNearestNeighbors
 
KullbackLeiblerDivergence - Class in net.sf.javaml.featureselection.scoring
Feature scoring algorithm based on Kullback-Leibler divergence of the value distributions of features.
KullbackLeiblerDivergence() - Constructor for class net.sf.javaml.featureselection.scoring.KullbackLeiblerDivergence
 
KullbackLeiblerDivergence(int) - Constructor for class net.sf.javaml.featureselection.scoring.KullbackLeiblerDivergence
 

L

le(double, double) - Static method in class net.sf.javaml.utils.MathUtils
Tests if a is smaller or equal to b.
libsvm - package libsvm
 
LibSVM - Class in libsvm
Wrapper for the libSVM library by Chih-Chung Chang and Chih-Jen Lin.
LibSVM() - Constructor for class libsvm.LibSVM
Create a new instance of libsvm.
LinearKernel - Class in net.sf.javaml.distance
 
LinearKernel() - Constructor for class net.sf.javaml.distance.LinearKernel
 
LinearRankingEnsemble - Class in net.sf.javaml.featureselection.ensemble
Provides a linear aggregation feature selection ensemble as described in Saeys et al. 2008.
LinearRankingEnsemble(FeatureRanking[]) - Constructor for class net.sf.javaml.featureselection.ensemble.LinearRankingEnsemble
Creates a ranking ensemble with the provided single feature rankers.
LinearRankingEnsemble(FeatureRanking[], Random) - Constructor for class net.sf.javaml.featureselection.ensemble.LinearRankingEnsemble
Creates a ranking ensemble with the provided single feature rankers and a specified random generator used for the generation of bootstraps.
LinearWindow - Class in net.sf.javaml.distance.fastdtw.dtw
 
LinearWindow(TimeSeries, TimeSeries, int) - Constructor for class net.sf.javaml.distance.fastdtw.dtw.LinearWindow
 
LinearWindow(TimeSeries, TimeSeries) - Constructor for class net.sf.javaml.distance.fastdtw.dtw.LinearWindow
 
ListTools - Class in net.sf.javaml.tools
Implements additional operations for lists
ListTools() - Constructor for class net.sf.javaml.tools.ListTools
 
lnFactorial(double) - Static method in class net.sf.javaml.utils.SpecialFunctions
Returns natural logarithm of factorial using gamma function.
lnGamma(double) - Static method in class net.sf.javaml.utils.Statistics
Returns natural logarithm of gamma function.
lnsrch(double[], double[], double[], double, boolean[], double[][], ActiveSetsOptimization.DynamicIntArray) - Method in class net.sf.javaml.utils.ActiveSetsOptimization
Find a new point x in the direction p from a point xold at which the value of the function has decreased sufficiently, the positive definiteness of B matrix (approximation of the inverse of the Hessian) is preserved and no bound constraints are violated.
load(File) - Static method in class net.sf.javaml.tools.Serial
 
load(String) - Static method in class net.sf.javaml.tools.Serial
 
loadARFF(File) - Static method in class net.sf.javaml.tools.data.ARFFHandler
Load a data set from an ARFF formatted file.
loadARFF(File, int) - Static method in class net.sf.javaml.tools.data.ARFFHandler
Load a data set from an ARFF formatted file.
loadDataset(File, String) - Static method in class net.sf.javaml.tools.data.FileHandler
Utility method to load from a file without class set.
loadDataset(File, int) - Static method in class net.sf.javaml.tools.data.FileHandler
This method will load the data stored in a file..
loadDataset(File) - Static method in class net.sf.javaml.tools.data.FileHandler
 
loadDataset(File, int, String) - Static method in class net.sf.javaml.tools.data.FileHandler
Load the data from a file.
loadSparseDataset(File, int) - Static method in class net.sf.javaml.tools.data.FileHandler
 
loadSparseDataset(File, int, String, String) - Static method in class net.sf.javaml.tools.data.FileHandler
 
log2(double) - Static method in class net.sf.javaml.utils.MathUtils
 
log2Binomial(double, double) - Static method in class net.sf.javaml.utils.SpecialFunctions
Returns base 2 logarithm of binomial coefficient using gamma function.
log2Multinomial(double, double[]) - Static method in class net.sf.javaml.utils.SpecialFunctions
Returns base 2 logarithm of multinomial using gamma function.
log2MultipleHypergeometric(double[][]) - Static method in class net.sf.javaml.utils.ContingencyTables
Returns negative base 2 logarithm of multiple hypergeometric probability for a contingency table.
logGamma(double) - Static method in class net.sf.javaml.utils.GammaFunction
Returns the natural logarithm of the gamma function; formerly named lgamma.
logLikelihood(Dataset) - Method in class net.sf.javaml.utils.LogLikelihoodFunction
 
logLikelihoodC(Dataset) - Method in class net.sf.javaml.utils.LogLikelihoodFunction
 
LogLikelihoodFunction - Class in net.sf.javaml.utils
 
LogLikelihoodFunction() - Constructor for class net.sf.javaml.utils.LogLikelihoodFunction
 
logLikelihoodFunction(double, double, double) - Method in class net.sf.javaml.utils.LogLikelihoodFunction
 
loglikelihoodsum(Dataset[]) - Method in class net.sf.javaml.utils.LogLikelihoodFunction
 
LOGPI - Static variable in class net.sf.javaml.utils.Statistics
 
lt(double, double) - Static method in class net.sf.javaml.utils.MathUtils
Tests if a is smaller than b.

M

m_ALF - Variable in class net.sf.javaml.utils.ActiveSetsOptimization
 
m_BETA - Variable in class net.sf.javaml.utils.ActiveSetsOptimization
 
m_Debug - Static variable in class net.sf.javaml.utils.ActiveSetsOptimization
 
m_Epsilon - Static variable in class net.sf.javaml.utils.ActiveSetsOptimization
Compute machine precision
m_f - Variable in class net.sf.javaml.utils.ActiveSetsOptimization
function value
m_MAXITS - Variable in class net.sf.javaml.utils.ActiveSetsOptimization
 
m_STPMX - Variable in class net.sf.javaml.utils.ActiveSetsOptimization
 
m_TOLX - Variable in class net.sf.javaml.utils.ActiveSetsOptimization
 
m_Zero - Static variable in class net.sf.javaml.utils.ActiveSetsOptimization
Compute machine precision
MACHEP - Static variable in class net.sf.javaml.utils.Statistics
Some constants
MahalanobisDistance - Class in net.sf.javaml.distance
 
MahalanobisDistance() - Constructor for class net.sf.javaml.distance.MahalanobisDistance
 
main(String[]) - Static method in class net.sf.javaml.distance.fastdtw.Abstraction
 
main(String[]) - Static method in class net.sf.javaml.distance.fastdtw.Band
 
main(String[]) - Static method in class net.sf.javaml.utils.ContingencyTables
Main method for testing this class.
main(String[]) - Static method in class net.sf.javaml.utils.SpecialFunctions
Main method for testing this class.
main(String[]) - Static method in class net.sf.javaml.utils.Statistics
Main method for testing this class.
main(String[]) - Static method in class tutorials.classification.TutorialCrossValidation
Default cross-validation with little options.
main(String[]) - Static method in class tutorials.classification.TutorialCVSameFolds
Default cross-validation with the same folds for multiple runs.
main(String[]) - Static method in class tutorials.classification.TutorialEvaluateDataset
Shows the default usage of the KNN algorithm.
main(String[]) - Static method in class tutorials.classification.TutorialKDtreeKNN
Shows the default usage of the KNN algorithm.
main(String[]) - Static method in class tutorials.classification.TutorialKNN
Shows the default usage of the KNN algorithm.
main(String[]) - Static method in class tutorials.classification.TutorialLibSVM
Shows the default usage of the LibSVM algorithm.
main(String[]) - Static method in class tutorials.classification.TutorialRandomForest
Shows the default usage of the random forest algorithm.
main(String[]) - Static method in class tutorials.clustering.TutorialClusterEvaluation
 
main(String[]) - Static method in class tutorials.clustering.TutorialKMeans
Tests the k-means algorithm with default parameter settings.
main(String[]) - Static method in class tutorials.core.TutorialDataset
Create a data set and put some instances in it.
main(String[]) - Static method in class tutorials.core.TutorialDenseInstance
Shows how to construct an instance.
main(String[]) - Static method in class tutorials.core.TutorialSparseInstance
Shows how to construct a SparseInstance.
main(String[]) - Static method in class tutorials.featureselection.TutorialEnsembleFeatureSelection
Shows the basic steps to use ensemble feature selection
main(String[]) - Static method in class tutorials.featureselection.TutorialFeatureRanking
Shows the basic steps to create use a feature ranking algorithm.
main(String[]) - Static method in class tutorials.featureselection.TutorialFeatureScoring
Shows the basic steps to create use a feature scoring algorithm.
main(String[]) - Static method in class tutorials.featureselection.TutorialFeatureSubsetSelection
 
main(String[]) - Static method in class tutorials.featureselection.TutorialWekaAttributeSelection
 
main(String[]) - Static method in class tutorials.tools.TutorialARFFLoader
 
main(String[]) - Static method in class tutorials.tools.TutorialDataLoader
 
main(String[]) - Static method in class tutorials.tools.TutorialStoreData
 
main(String[]) - Static method in class tutorials.tools.TutorialWekaClassifier
 
main(String[]) - Static method in class tutorials.tools.TutorialWekaClusterer
 
ManhattanDistance - Class in net.sf.javaml.distance
The Manhattan distance is the sum of the (absolute) differences of their coordinates.
ManhattanDistance() - Constructor for class net.sf.javaml.distance.ManhattanDistance
 
MarkovClustering - Class in net.sf.javaml.clustering.mcl
MarkovClustering implements the Markov clustering (MCL) algorithm for graphs, using a HashMap-based sparse representation of a Markov matrix, i.e., an adjacency matrix m that is normalised to one.
MarkovClustering() - Constructor for class net.sf.javaml.clustering.mcl.MarkovClustering
 
markVisited(int, int) - Method in class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 
MathUtils - Class in net.sf.javaml.utils
A class that provides some utility math methods. - Comparing doubles for equality and order with a precision.
MathUtils() - Constructor for class net.sf.javaml.utils.MathUtils
 
Matrix - Class in net.sf.javaml.matrix
 
Matrix() - Constructor for class net.sf.javaml.matrix.Matrix
 
matrixTimes(SparseMatrix) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
immutable multiply matrix M with this (A) : M * A
max() - Method in class net.sf.javaml.clustering.mcl.SparseVector
maximum element value
max(int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
maximum value in vec
max(double[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
maximum value in vec
max(double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
Return the maximum value in this array.
maxAttributes(Dataset) - Static method in class net.sf.javaml.tools.DatasetTools
Create an instance that contains all the maximum values for the attributes.
MAXGAM - Static variable in class net.sf.javaml.utils.Statistics
 
maxI() - Method in class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 
maxI() - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
maxIndex(double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
 
maxJ() - Method in class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 
maxJ() - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
maxJforI(int) - Method in class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 
MAXLOG - Static variable in class net.sf.javaml.utils.Statistics
 
MaxProductSimilarity - Class in net.sf.javaml.distance
Specialized similarity that takes the maximum product of two feature values.
MaxProductSimilarity() - Constructor for class net.sf.javaml.distance.MaxProductSimilarity
 
MCL - Class in net.sf.javaml.clustering.mcl
 
MCL(DistanceMeasure) - Constructor for class net.sf.javaml.clustering.mcl.MCL
XXX doc
MCL(DistanceMeasure, double, double, double, double) - Constructor for class net.sf.javaml.clustering.mcl.MCL
XXX doc
mean - Variable in class net.sf.javaml.classification.AbstractMeanClassifier
 
MeanFeatureVotingClassifier - Class in net.sf.javaml.classification
This classifier calculates the mean for each class.
MeanFeatureVotingClassifier() - Constructor for class net.sf.javaml.classification.MeanFeatureVotingClassifier
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.AngularDistance
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.CachedDistance
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.ChebychevDistance
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.ConsistencyIndex
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.CosineDistance
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.CosineSimilarity
 
measure(Instance, Instance) - Method in interface net.sf.javaml.distance.DistanceMeasure
Calculates the distance between two instances.
measure(Instance, Instance) - Method in class net.sf.javaml.distance.dtw.DTWSimilarity
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.fastdtw.Abstraction
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.fastdtw.Band
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.fastdtw.FastDTW
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.JaccardIndexDistance
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.JaccardIndexSimilarity
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.MahalanobisDistance
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.ManhattanDistance
Calculates the Manhattan distance as the sum of the absolute differences of their coordinates.
measure(Instance, Instance) - Method in class net.sf.javaml.distance.MaxProductSimilarity
XXX doc
measure(Instance, Instance) - Method in class net.sf.javaml.distance.NormalizedEuclideanDistance
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.NormalizedEuclideanSimilarity
XXX DOC
measure(Instance, Instance) - Method in class net.sf.javaml.distance.NormDistance
XXX add doc
measure(Instance, Instance) - Method in class net.sf.javaml.distance.PearsonCorrelationCoefficient
Measures the Pearson Correlation Coefficient between the two supplied instances.
measure(Instance, Instance) - Method in class net.sf.javaml.distance.PolynomialKernel
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.RBFKernel
XXX DOC
measure(Instance, Instance) - Method in class net.sf.javaml.distance.RBFKernelDistance
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.SpearmanFootruleDistance
 
measure(Instance, Instance) - Method in class net.sf.javaml.distance.SpearmanRankCorrelation
 
merge(Dataset...) - Static method in class net.sf.javaml.tools.DatasetTools
All data will be merged together in the first supplied data set.
min(int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
minimum value in vec
min(double[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
minimum value in vec
min(double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
Return the minimum value in this array.
min(double[]) - Static method in class net.sf.javaml.utils.MathUtils
XXX DOC
minAttributes(Dataset) - Static method in class net.sf.javaml.tools.DatasetTools
Create an instance that contains all the minimum values for the attributes.
minI() - Method in class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 
minI() - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
minJ() - Method in class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 
minJ() - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
minJforI(int) - Method in class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 
MinkowskiDistance - Class in net.sf.javaml.distance
 
MinkowskiDistance() - Constructor for class net.sf.javaml.distance.MinkowskiDistance
 
MINLOG - Static variable in class net.sf.javaml.utils.Statistics
 
MinMaxCut - Class in net.sf.javaml.clustering.evaluation
G_1 from the Zhao 2001 paper TODO uitleg
MinMaxCut(DistanceMeasure) - Constructor for class net.sf.javaml.clustering.evaluation.MinMaxCut
 
minus(Instance) - Method in class net.sf.javaml.core.AbstractInstance
 
minus(double) - Method in class net.sf.javaml.core.AbstractInstance
 
minus(Complex) - Method in class net.sf.javaml.core.Complex
Subtracts a complex number from this one.
minus(Instance) - Method in interface net.sf.javaml.core.Instance
Subtract an instance from this instance and returns the results.
minus(double) - Method in interface net.sf.javaml.core.Instance
Subtract a scalar from this instance and returns the results.
MissingClassFilter - Class in net.sf.javaml.filter
Filters all instances from a data set that have their class value not set
MissingClassFilter() - Constructor for class net.sf.javaml.filter.MissingClassFilter
 
mult(int, double) - Method in class net.sf.javaml.clustering.mcl.SparseVector
mutable mult
mult(double[], double) - Static method in class net.sf.javaml.clustering.mcl.Vectors
multiplicates the vector with a scalar.
mult(double[], double[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
multiplicates the vector with a vector (inner product).
MultiKMeans - Class in net.sf.javaml.clustering
This class implements an extension of KMeans (SKM).
MultiKMeans(ClusterEvaluation) - Constructor for class net.sf.javaml.clustering.MultiKMeans
default constructor
MultiKMeans(int, int, int, DistanceMeasure, ClusterEvaluation) - Constructor for class net.sf.javaml.clustering.MultiKMeans
XXX add doc
multiply(double) - Method in class net.sf.javaml.core.AbstractInstance
 
multiply(Instance) - Method in class net.sf.javaml.core.AbstractInstance
 
multiply(Complex, double) - Static method in class net.sf.javaml.core.Complex
Multiplies a complex number with a real value.
multiply(Complex, Complex) - Static method in class net.sf.javaml.core.Complex
Multiplies two complex numbers and return the resulting complex number.
multiply(double) - Method in interface net.sf.javaml.core.Instance
Multiply each value of this instance with a scalar value and return the result.
multiply(Instance) - Method in interface net.sf.javaml.core.Instance
Multiply each value in this instance with the corresponding value in provide instance.
multiply(double[], double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
 

N

ndigits - Static variable in class net.sf.javaml.clustering.mcl.Vectors
 
nearest(double[]) - Method in class net.sf.javaml.core.kdtree.KDTree
Find KD-tree node whose key is nearest neighbor to key.
nearest(double[], int) - Method in class net.sf.javaml.core.kdtree.KDTree
Find KD-tree nodes whose keys are n nearest neighbors to key.
NearestMeanClassifier - Class in net.sf.javaml.classification
Nearest mean classifier.
NearestMeanClassifier() - Constructor for class net.sf.javaml.classification.NearestMeanClassifier
 
net.sf.javaml.classification - package net.sf.javaml.classification
Provides several classification algorithms.
net.sf.javaml.classification.evaluation - package net.sf.javaml.classification.evaluation
Provides algorithms and measures to evaluate classification algorithms.
net.sf.javaml.classification.meta - package net.sf.javaml.classification.meta
Provides meta-classifiers like for example the Bagging algorithm.
net.sf.javaml.classification.tree - package net.sf.javaml.classification.tree
Provides classification trees and derivative algorithms like forests.
net.sf.javaml.clustering - package net.sf.javaml.clustering
Provides algorithms to cluster data.
net.sf.javaml.clustering.evaluation - package net.sf.javaml.clustering.evaluation
Provides scores to evaluate the result of a clustering algorithm.
net.sf.javaml.clustering.mcl - package net.sf.javaml.clustering.mcl
Provides an implementation of the MCL clustering algorithm.
net.sf.javaml.core - package net.sf.javaml.core
The core of Java-ML: Instance and Dataset interfaces with their implementation can be found here.
net.sf.javaml.core.exception - package net.sf.javaml.core.exception
Provides exceptions that are used throughout Java-ML.
net.sf.javaml.core.kdtree - package net.sf.javaml.core.kdtree
Provides a KD-tree implementation for fast range- and nearest-neighbors-queries.
net.sf.javaml.distance - package net.sf.javaml.distance
Implements algorithms that can measure the distance, similarity or correlation between Instances.
net.sf.javaml.distance.dtw - package net.sf.javaml.distance.dtw
Provides DTW (dynamic time-warping) based distance measures.
net.sf.javaml.distance.fastdtw - package net.sf.javaml.distance.fastdtw
Provides the fast DTW (dynamic time-warping) approximation by Stan Salvador and Philip Chan.
net.sf.javaml.distance.fastdtw.dtw - package net.sf.javaml.distance.fastdtw.dtw
 
net.sf.javaml.distance.fastdtw.lang - package net.sf.javaml.distance.fastdtw.lang
 
net.sf.javaml.distance.fastdtw.matrix - package net.sf.javaml.distance.fastdtw.matrix
 
net.sf.javaml.distance.fastdtw.timeseries - package net.sf.javaml.distance.fastdtw.timeseries
 
net.sf.javaml.distance.fastdtw.util - package net.sf.javaml.distance.fastdtw.util
 
net.sf.javaml.featureselection - package net.sf.javaml.featureselection
Provides algorithms to evaluation the worth of attributes and attribute sets.
net.sf.javaml.featureselection.ensemble - package net.sf.javaml.featureselection.ensemble
Provides ensemble feature selection algorithms.
net.sf.javaml.featureselection.ranking - package net.sf.javaml.featureselection.ranking
Provides feature ranking algorithms.
net.sf.javaml.featureselection.scoring - package net.sf.javaml.featureselection.scoring
Provides feature scoring algorithms.
net.sf.javaml.featureselection.subset - package net.sf.javaml.featureselection.subset
Provides feature selection tutorials.
net.sf.javaml.filter - package net.sf.javaml.filter
Provides filters for instances and data sets.
net.sf.javaml.filter.discretize - package net.sf.javaml.filter.discretize
Provides filters that can discretize continuous values net.sf.javaml.filter.instance - package net.sf.javaml.filter.instance
Filters that can be run on a single instance at once.
net.sf.javaml.filter.missingvalue - package net.sf.javaml.filter.missingvalue
Contains filters that can be used to replace missing values with more sensible information.
net.sf.javaml.filter.normalize - package net.sf.javaml.filter.normalize
Provides filters to normalize values.
net.sf.javaml.matrix - package net.sf.javaml.matrix
Implements a matrix which can be backed by a memory- or file-based array.
net.sf.javaml.tools - package net.sf.javaml.tools
Provides useful tools for the library.
net.sf.javaml.tools.data - package net.sf.javaml.tools.data
Provides tools to load and data sets to various sources and destinations
net.sf.javaml.tools.sampling - package net.sf.javaml.tools.sampling
 
net.sf.javaml.tools.weka - package net.sf.javaml.tools.weka
Provides Weka tools: converting to data to Weka, wrappers for algorithms, etc.
net.sf.javaml.utils - package net.sf.javaml.utils
Provides utilities for algorithms; this package will disappear at some point!
noAttributes() - Method in interface net.sf.javaml.core.Dataset
The number of attributes in each instance.
noAttributes() - Method in class net.sf.javaml.core.DefaultDataset
 
noAttributes() - Method in class net.sf.javaml.core.DenseInstance
 
noAttributes() - Method in interface net.sf.javaml.core.Instance
Returns the number of attributes this instance has.
noAttributes() - Method in class net.sf.javaml.core.SparseInstance
 
noAttributes() - Method in class net.sf.javaml.featureselection.ensemble.LinearRankingEnsemble
 
noAttributes() - Method in interface net.sf.javaml.featureselection.FeatureSelection
Returns the number of attributes that have been ranked, scored or selected.
noAttributes() - Method in class net.sf.javaml.featureselection.ranking.RankingFromScoring
 
noAttributes() - Method in class net.sf.javaml.featureselection.ranking.RecursiveFeatureEliminationSVM
 
noAttributes() - Method in class net.sf.javaml.featureselection.scoring.GainRatio
 
noAttributes() - Method in class net.sf.javaml.featureselection.scoring.KullbackLeiblerDivergence
 
noAttributes() - Method in class net.sf.javaml.featureselection.scoring.RandomForestAttributeEvaluation
 
noAttributes() - Method in class net.sf.javaml.featureselection.scoring.RELIEF
 
noAttributes() - Method in class net.sf.javaml.featureselection.scoring.SymmetricalUncertainty
 
noAttributes() - Method in class net.sf.javaml.featureselection.subset.GreedyBackwardElimination
 
noAttributes() - Method in class net.sf.javaml.featureselection.subset.GreedyForwardSelection
 
noAttributes() - Method in class net.sf.javaml.tools.weka.WekaAttributeSelection
 
norm(double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
Returns the norm of the vector
NormalBootstrapping - Class in net.sf.javaml.tools.sampling
Implements normal bootstrapping.
NormalBootstrapping() - Constructor for class net.sf.javaml.tools.sampling.NormalBootstrapping
 
normalInverse(double) - Static method in class net.sf.javaml.utils.Statistics
Returns the value, x, for which the area under the Normal (Gaussian) probability density function (integrated from minus infinity to x) is equal to the argument y (assumes mean is zero, variance is one).
normalise(double) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
normalise rows to rowsum
normalise() - Method in class net.sf.javaml.clustering.mcl.SparseVector
normalises the vector to 1.
normalise(double) - Method in class net.sf.javaml.clustering.mcl.SparseVector
normalises the vector to newsum
normaliseCols() - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
normalise by minor dimension (columns), expensive.
normaliseRows() - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
normalise by major dimension (rows)
normalize() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
normalize(double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class net.sf.javaml.utils.ArrayUtils
Normalizes the doubles in the array using the given value.
NormalizedEuclideanDistance - Class in net.sf.javaml.distance
A normalized version of the Euclidean distance.
NormalizedEuclideanDistance(Dataset) - Constructor for class net.sf.javaml.distance.NormalizedEuclideanDistance
 
NormalizedEuclideanSimilarity - Class in net.sf.javaml.distance
XXX DOC
NormalizedEuclideanSimilarity(Dataset) - Constructor for class net.sf.javaml.distance.NormalizedEuclideanSimilarity
XXX DOC
NormalizeMean - Class in net.sf.javaml.filter.normalize
This filter will normalize the data set with mean 0 and standard deviation 1 The normalization will be done on the attributes, so each attribute will have mean 0 and std 1.
NormalizeMean() - Constructor for class net.sf.javaml.filter.normalize.NormalizeMean
 
NormalizeMeanIQR135 - Class in net.sf.javaml.filter.normalize
This filter will normalize the data set with mean 0 and standard deviation 1 The normalization will be done on the attributes, so each attribute will have mean 0 and std 1.
NormalizeMeanIQR135() - Constructor for class net.sf.javaml.filter.normalize.NormalizeMeanIQR135
 
NormalizeMidrange - Class in net.sf.javaml.filter.normalize
This filter will normalize the data set with a certain mid-range and a certain range for each attribute.
NormalizeMidrange() - Constructor for class net.sf.javaml.filter.normalize.NormalizeMidrange
A normalization filter to the interval [-1,1]
NormalizeMidrange(double, double) - Constructor for class net.sf.javaml.filter.normalize.NormalizeMidrange
 
normalProbability(double) - Static method in class net.sf.javaml.utils.Statistics
Returns the area under the Normal (Gaussian) probability density function, integrated from minus infinity to x (assumes mean is zero, variance is one).
NormDistance - Class in net.sf.javaml.distance
The norm distance or This class implements the Norm distance.
NormDistance() - Constructor for class net.sf.javaml.distance.NormDistance
XXX add doc
NormDistance(double) - Constructor for class net.sf.javaml.distance.NormDistance
XXX add doc
num_attr - Variable in class net.sf.javaml.tools.weka.WekaAttributeSelection
 
numbers - Variable in class net.sf.javaml.tools.weka.WekaAttributeSelection
 
numOfDimensions() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
numOfPts() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 

O

objectiveFunction(double[]) - Method in class net.sf.javaml.utils.ActiveSetsOptimization
Subclass should implement this procedure to evaluate objective function to be minimized
ones(int, double) - Static method in class net.sf.javaml.clustering.mcl.Vectors
 
ones(int, int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
 
ones(int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
 
OPTICS - Class in net.sf.javaml.clustering
Mihael Ankerst, Markus M.
OPTICS() - Constructor for class net.sf.javaml.clustering.OPTICS
XXX doc default constructor
OPTICS(double, int) - Constructor for class net.sf.javaml.clustering.OPTICS
XXX doc
OPTICS(double, int, DistanceMeasure) - Constructor for class net.sf.javaml.clustering.OPTICS
XXX doc
orderOfMagnitude(double) - Static method in class net.sf.javaml.clustering.mcl.ExpDouble
 
originalSize() - Method in class net.sf.javaml.distance.fastdtw.timeseries.PAA
 

P

P0 - Static variable in class net.sf.javaml.utils.Statistics
COEFFICIENTS FOR METHOD normalInverse() *
P1 - Static variable in class net.sf.javaml.utils.Statistics
 
p1evl(double, double[], int) - Static method in class net.sf.javaml.utils.Polynomial
Evaluates the given polynomial of degree N at x, assuming coefficient of N is 1.0.
P2 - Static variable in class net.sf.javaml.utils.Statistics
 
PAA - Class in net.sf.javaml.distance.fastdtw.timeseries
 
PAA(TimeSeries, int) - Constructor for class net.sf.javaml.distance.fastdtw.timeseries.PAA
 
Pair<S,T> - Class in net.sf.javaml.core
Represents a pair or couple of objects.
Pair(S, T) - Constructor for class net.sf.javaml.core.Pair
Create a pair
ParallelogramWindow - Class in net.sf.javaml.distance.fastdtw.dtw
 
ParallelogramWindow(TimeSeries, TimeSeries, int) - Constructor for class net.sf.javaml.distance.fastdtw.dtw.ParallelogramWindow
 
parentClasses - Variable in class net.sf.javaml.classification.AbstractClassifier
 
parts() - Method in class net.sf.javaml.clustering.mcl.ExpDouble
 
PearsonCorrelationCoefficient - Class in net.sf.javaml.distance
Calculates the Pearson Correlation Coeffient between two vectors.
PearsonCorrelationCoefficient() - Constructor for class net.sf.javaml.distance.PearsonCorrelationCoefficient
 
percentile(Dataset, double) - Static method in class net.sf.javaml.tools.DatasetTools
Calculates the percentile hinge for a given percentile.
PerformanceMeasure - Class in net.sf.javaml.classification.evaluation
Class implementing several performance measures commonly used for classification algorithms.
PerformanceMeasure(double, double, double, double) - Constructor for class net.sf.javaml.classification.evaluation.PerformanceMeasure
Constructs a new performance measure using the supplied arguments.
PerformanceMeasure() - Constructor for class net.sf.javaml.classification.evaluation.PerformanceMeasure
Default constructor for a new performance measure, all values (TP,TN,FP and FN) will be set zero
plus(Complex) - Method in class net.sf.javaml.core.Complex
Adds a complex number to this one.
plus(Complex, Complex) - Static method in class net.sf.javaml.core.Complex
Adds two complex numbers and return the result.
PointBiserial - Class in net.sf.javaml.clustering.evaluation
TODO uitleg
PointBiserial(DistanceMeasure) - Constructor for class net.sf.javaml.clustering.evaluation.PointBiserial
 
polevl(double, double[], int) - Static method in class net.sf.javaml.utils.Polynomial
Evaluates the given polynomial of degree N at x.
Polynomial - Class in net.sf.javaml.utils
Polynomial functions.
Polynomial() - Constructor for class net.sf.javaml.utils.Polynomial
Makes this class non instantiable, but still let's others inherit from it.
PolynomialKernel - Class in net.sf.javaml.distance
 
PolynomialKernel(double) - Constructor for class net.sf.javaml.distance.PolynomialKernel
 
print(double[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
prints a double representation of the vector.
print(double[][]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
prints a double representation of an array.
print(int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
prints a double representation of the vector.
print(int[][]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
prints a double representation of an array.
prune(double) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
prune all values whose magnitude is below threshold
prune(double) - Method in class net.sf.javaml.clustering.mcl.SparseVector
remove all elements whose magnitude is < threshold
put(Integer, Double) - Method in class net.sf.javaml.clustering.mcl.SparseVector
put increases the matrix size if the index exceeds the current size.
put(Integer, Double) - Method in class net.sf.javaml.core.DenseInstance
 
put(Integer, Double) - Method in class net.sf.javaml.core.SparseInstance
 
put(int, int, double) - Method in class net.sf.javaml.matrix.Matrix
 
putAll(Map<? extends Integer, ? extends Double>) - Method in class net.sf.javaml.core.DenseInstance
 
putAll(Map<? extends Integer, ? extends Double>) - Method in class net.sf.javaml.core.SparseInstance
 

Q

Q0 - Static variable in class net.sf.javaml.utils.Statistics
 
Q1 - Static variable in class net.sf.javaml.utils.Statistics
 
Q2 - Static variable in class net.sf.javaml.utils.Statistics
 

R

RandomForest - Class in net.sf.javaml.classification.tree
 
RandomForest(int) - Constructor for class net.sf.javaml.classification.tree.RandomForest
 
RandomForest(int, boolean, int, Random) - Constructor for class net.sf.javaml.classification.tree.RandomForest
 
RandomForestAttributeEvaluation - Class in net.sf.javaml.featureselection.scoring
Random Forest based attribute evaluation.
RandomForestAttributeEvaluation(int, Object, Random) - Constructor for class net.sf.javaml.featureselection.scoring.RandomForestAttributeEvaluation
 
randomGaussianInstance(int) - Static method in class net.sf.javaml.tools.InstanceTools
Creates a random instance with the given number of attributes.
randomInstance(int) - Static method in class net.sf.javaml.tools.InstanceTools
Creates a random instance with the given number of attributes.
RandomTree - Class in net.sf.javaml.classification.tree
Simple and fast implementation of the RandomTree classifier.
RandomTree(int, Random) - Constructor for class net.sf.javaml.classification.tree.RandomTree
 
range(int, int, int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
 
range(int, int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
 
range(double, double, double) - Static method in class net.sf.javaml.clustering.mcl.Vectors
create sequence [start : step : end] of double values.
range(double, double) - Static method in class net.sf.javaml.clustering.mcl.Vectors
 
range(double[], double[]) - Method in class net.sf.javaml.core.kdtree.KDTree
Range search in a KD-tree.
rangeComplement(int[], int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
return the complement of the sorted subset of the set 0:length-1 in Matlab notation
rank(int) - Method in class net.sf.javaml.featureselection.ensemble.LinearRankingEnsemble
 
rank(int) - Method in interface net.sf.javaml.featureselection.FeatureRanking
Get the ranking of the given attribute.
rank(int) - Method in class net.sf.javaml.featureselection.ranking.RankingFromScoring
 
rank(int) - Method in class net.sf.javaml.featureselection.ranking.RecursiveFeatureEliminationSVM
 
rank(int) - Method in class net.sf.javaml.tools.weka.WekaAttributeSelection
 
RankingFromScoring - Class in net.sf.javaml.featureselection.ranking
Creates an attribute ranking from an attribute evaluation technique.
RankingFromScoring(FeatureScoring) - Constructor for class net.sf.javaml.featureselection.ranking.RankingFromScoring
 
rawDecisionValues(Instance) - Method in class libsvm.LibSVM
 
RBFKernel - Class in net.sf.javaml.distance
The kernel method for measuring similarities between instances.
RBFKernel() - Constructor for class net.sf.javaml.distance.RBFKernel
 
RBFKernel(double) - Constructor for class net.sf.javaml.distance.RBFKernel
Create a new RBF kernel with gamma as a parameter
RBFKernelDistance - Class in net.sf.javaml.distance
 
RBFKernelDistance() - Constructor for class net.sf.javaml.distance.RBFKernelDistance
 
re - Variable in class net.sf.javaml.core.Complex
The real part of this imaginary number
RecursiveFeatureEliminationSVM - Class in net.sf.javaml.featureselection.ranking
Implements the recursive feature elimination procedure for linear support vector machines.
RecursiveFeatureEliminationSVM(double) - Constructor for class net.sf.javaml.featureselection.ranking.RecursiveFeatureEliminationSVM
 
RecursiveFeatureEliminationSVM(double, boolean) - Constructor for class net.sf.javaml.featureselection.ranking.RecursiveFeatureEliminationSVM
 
RecursiveFeatureEliminationSVM(double, boolean, int) - Constructor for class net.sf.javaml.featureselection.ranking.RecursiveFeatureEliminationSVM
 
reduceMatrix(double[][]) - Static method in class net.sf.javaml.utils.ContingencyTables
Reduces a matrix by deleting all zero rows and columns.
RELIEF - Class in net.sf.javaml.featureselection.scoring
Implementation of the RELIEF attribute evaluation algorithm.
RELIEF() - Constructor for class net.sf.javaml.featureselection.scoring.RELIEF
 
RELIEF(int, Random) - Constructor for class net.sf.javaml.featureselection.scoring.RELIEF
 
remove(Object) - Method in class net.sf.javaml.core.DenseInstance
 
remove(Object) - Method in class net.sf.javaml.core.SparseInstance
 
removeAttribute(int) - Method in class net.sf.javaml.core.DenseInstance
 
removeAttribute(int) - Method in interface net.sf.javaml.core.Instance
Removes an attribute from the instance.
removeAttribute(int) - Method in class net.sf.javaml.core.SparseInstance
 
removeAttributes(Set<Integer>) - Method in class net.sf.javaml.core.DenseInstance
 
removeAttributes(Set<Integer>) - Method in interface net.sf.javaml.core.Instance
Removes a set of attributes from the instance.
removeAttributes(Set<Integer>) - Method in class net.sf.javaml.core.SparseInstance
 
RemoveAttributes - Class in net.sf.javaml.filter
 
RemoveAttributes(Set<Integer>) - Constructor for class net.sf.javaml.filter.RemoveAttributes
Construct a remove filter that removes all the attributes with the indices given in the array as parameter.
removeElement(double[], int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create new vector with data of the argument and removed element.
removeElement(int[], int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create new vector with data of the argument and removed element.
removeElements(double[][], int[], int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create new matrix with data of the argument and removed rows and columns.
removeElements(double[], int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create new vector with data of the argument and removed elements.
removeElements(int[][], int[], int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create new matrix with data of the argument and removed rows and columns.
removeElements(int[], int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
Create new vector with data of the argument and removed elements.
removeFirst() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
removeLast() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
RemoveMissingValue - Class in net.sf.javaml.filter.missingvalue
Removes all instances that have missing values.
RemoveMissingValue() - Constructor for class net.sf.javaml.filter.missingvalue.RemoveMissingValue
 
ReplaceValueFilter - Class in net.sf.javaml.filter.instance
 
ReplaceValueFilter(Double, Double) - Constructor for class net.sf.javaml.filter.instance.ReplaceValueFilter
 
ReplaceWithValue - Class in net.sf.javaml.filter.missingvalue
Replaces all missing values with a fixed value.
ReplaceWithValue(double) - Constructor for class net.sf.javaml.filter.missingvalue.ReplaceWithValue
 
RetainAttributes - Class in net.sf.javaml.filter
Filter to retain a set of wanted attributes and remove all others
RetainAttributes(int[]) - Constructor for class net.sf.javaml.filter.RetainAttributes
Construct a filter that retains all the attributes with the indices given in the array as parameter.
RetainAttributes(Set<Integer>) - Constructor for class net.sf.javaml.filter.RetainAttributes
Construct a filter that retains all the attributes with the indices given in the array as parameter.
reverse(int[]) - Static method in class net.sf.javaml.utils.ArrayUtils
 
reverse(double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
 
round(int) - Method in class net.sf.javaml.clustering.mcl.ExpDouble
Round this instance to the number of significant digits
RoundValueFilter - Class in net.sf.javaml.filter.instance
Filter to replace all values with their rounded equivalent
RoundValueFilter() - Constructor for class net.sf.javaml.filter.instance.RoundValueFilter
 
rows() - Method in class net.sf.javaml.matrix.Matrix
 
run(SparseMatrix, double, double, double, double) - Method in class net.sf.javaml.clustering.mcl.MarkovClustering
run the MCL process.

S

sample(Dataset, SamplingMethod, int, long) - Static method in class net.sf.javaml.tools.DatasetTools
 
sample(Dataset, SamplingMethod, int) - Static method in class net.sf.javaml.tools.DatasetTools
 
sample(List<Integer>, int, long) - Method in class net.sf.javaml.tools.sampling.NormalBootstrapping
 
sample(List<Integer>) - Method in class net.sf.javaml.tools.sampling.SamplingMethod
Samples a set of integers and returns a new set of integers that is the result of the sampling.
sample(List<Integer>, int) - Method in class net.sf.javaml.tools.sampling.SamplingMethod
Samples a set of integers and returns a new set of integers that is the result of the sampling.
sample(List<Integer>, int, long) - Method in class net.sf.javaml.tools.sampling.SamplingMethod
Samples a set of integers and returns a new set of integers that is the result of the sampling.
sample(List<Integer>, int, long) - Method in class net.sf.javaml.tools.sampling.SubSampling
 
SamplingMethod - Class in net.sf.javaml.tools.sampling
Defines sampling methods to select a subset of a set integers.
SamplingMethod() - Constructor for class net.sf.javaml.tools.sampling.SamplingMethod
 
save(File) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
scalarMultiply(double, double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
Computes the scalar product of this vector with a scalar
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.AICScore
 
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.BICScore
 
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.CIndex
 
score(Dataset[]) - Method in interface net.sf.javaml.clustering.evaluation.ClusterEvaluation
Returns the score the current clusterer obtains on the dataset.
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.Gamma
 
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.GPlus
 
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.HybridCentroidSimilarity
XXX DOC
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.HybridPairwiseSimilarities
XXX DOC
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.MinMaxCut
 
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.PointBiserial
 
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.SumOfAveragePairwiseSimilarities
XXX DOC
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.SumOfCentroidSimilarities
XXX DOC
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.SumOfSquaredErrors
 
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.Tau
 
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.TraceScatterMatrix
XXX DOC
score(Dataset[]) - Method in class net.sf.javaml.clustering.evaluation.WB
 
score(int) - Method in interface net.sf.javaml.featureselection.FeatureScoring
Evaluate a single attribute.
score(int) - Method in class net.sf.javaml.featureselection.scoring.GainRatio
Evaluates an individual attribute by measuring the gain ratio between it and the class label.
score(int) - Method in class net.sf.javaml.featureselection.scoring.KullbackLeiblerDivergence
 
score(int) - Method in class net.sf.javaml.featureselection.scoring.RandomForestAttributeEvaluation
 
score(int) - Method in class net.sf.javaml.featureselection.scoring.RELIEF
 
score(int) - Method in class net.sf.javaml.featureselection.scoring.SymmetricalUncertainty
Evaluates an individual attribute by measuring the symmetrical uncertainty between it and the class.
score(int) - Method in class net.sf.javaml.tools.weka.WekaAttributeSelection
 
search(double[]) - Method in class net.sf.javaml.core.kdtree.KDTree
Find KD-tree node whose key is identical to key.
SearchWindow - Class in net.sf.javaml.distance.fastdtw.dtw
 
SearchWindow(int, int) - Constructor for class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 
selectedAttributes() - Method in interface net.sf.javaml.featureselection.FeatureSubsetSelection
Returns the set of indices of attributes that are selected by the algorithm.
selectedAttributes() - Method in class net.sf.javaml.featureselection.subset.GreedyBackwardElimination
 
selectedAttributes() - Method in class net.sf.javaml.featureselection.subset.GreedyForwardSelection
 
SelfOptimizingLinearLibSVM - Class in libsvm
A svm variant the optimizes the C-paramater by itself.
SelfOptimizingLinearLibSVM() - Constructor for class libsvm.SelfOptimizingLinearLibSVM
 
SelfOptimizingLinearLibSVM(int, int) - Constructor for class libsvm.SelfOptimizingLinearLibSVM
 
SelfOptimizingLinearLibSVM(int, int, int) - Constructor for class libsvm.SelfOptimizingLinearLibSVM
 
Serial - Class in net.sf.javaml.tools
Class with utility methods for serialization.
Serial() - Constructor for class net.sf.javaml.tools.Serial
 
serialVersionUID - Static variable in exception net.sf.javaml.core.kdtree.KeyDuplicateException
 
serialVersionUID - Static variable in exception net.sf.javaml.core.kdtree.KeySizeException
 
set(int, SparseVector) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
set the sparse vector at index i.
set(int, int, double) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
set the value at the index i,j, returning the old value or 0.
set(int, double) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeriesPoint
 
setCalculateOutOfBagErrorEstimate(boolean) - Method in class net.sf.javaml.classification.meta.Bagging
 
setClass(Object) - Method in class net.sf.javaml.filter.ClassRemoveFilter
 
setClass(Object) - Method in class net.sf.javaml.filter.ClassRetainFilter
 
setClassValue(Object) - Method in class net.sf.javaml.core.AbstractInstance
 
setClassValue(Object) - Method in interface net.sf.javaml.core.Instance
 
setDebug(boolean) - Method in class net.sf.javaml.utils.ActiveSetsOptimization
Set whether in debug mode
setElementAt(Instance, int) - Method in class net.sf.javaml.core.DefaultDataset
 
setFolds(int) - Method in class libsvm.SelfOptimizingLinearLibSVM
 
setK(int) - Method in class net.sf.javaml.featureselection.scoring.RandomForestAttributeEvaluation
 
setK(int) - Method in class net.sf.javaml.filter.missingvalue.KNearestNeighbors
 
setLabels(String[]) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
setLabels(ArrayList<String>) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
setLength(int) - Method in class net.sf.javaml.clustering.mcl.SparseVector
set the new length of the vector (regardless of the maximum index).
setMaxCapacity(int) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
setMaxIteration(int) - Method in class net.sf.javaml.utils.ActiveSetsOptimization
Set the maximal number of iterations in searching (Default 200)
setMeasurement(int, int, double) - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
setNoAttributes(int) - Method in class net.sf.javaml.core.SparseInstance
Sets the number of attributes that this sparse instance has.
setNumAttributes(int) - Method in class net.sf.javaml.classification.tree.RandomForest
 
setNumNeigbors(int) - Method in class net.sf.javaml.featureselection.scoring.RELIEF
 
setParameters(svm_parameter) - Method in class libsvm.LibSVM
Set the parameters that will be used for training.
setPerturbations(int) - Method in class net.sf.javaml.featureselection.scoring.RandomForestAttributeEvaluation
 
setSubVector(int[], int[], int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
set the elements of vec at indices with the respective replacements.
setSubVector(int[], int[], int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
set the elements of vec at indices with the replacement.
setSubVectorCopy(int[], int[], int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
set the elements of a copy of vec at indices with the respective replacements.
SetTools - Class in net.sf.javaml.tools
Implements additional operations on sets.
SetTools() - Constructor for class net.sf.javaml.tools.SetTools
 
SimpleBagging - Class in net.sf.javaml.classification.meta
Bootstrap aggregating (Bagging) meta learner.
SimpleBagging(Classifier[]) - Constructor for class net.sf.javaml.classification.meta.SimpleBagging
 
SimpleBagging(Classifier[], Random) - Constructor for class net.sf.javaml.classification.meta.SimpleBagging
 
SineWave - Class in net.sf.javaml.distance.fastdtw.timeseries
 
SineWave(int, double, double) - Constructor for class net.sf.javaml.distance.fastdtw.timeseries.SineWave
 
size() - Method in class net.sf.javaml.core.DenseInstance
Deprecated. 
size() - Method in interface net.sf.javaml.core.Instance
Deprecated. 
size() - Method in class net.sf.javaml.core.SparseInstance
Deprecated. 
size() - Method in class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 
size() - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
size() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
size() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeriesPoint
 
solveTriangle(Matrix, double[], boolean, boolean[]) - Static method in class net.sf.javaml.utils.ActiveSetsOptimization
Solve the linear equation of TX=B where T is a triangle matrix It can be solved using back/forward substitution, with O(N^2) complexity
SOM - Class in net.sf.javaml.classification
Classifier based on the Self-organized map clustering
SOM(int, int, SOM.GridType, int, double, int, SOM.LearningType, SOM.NeighbourhoodFunction) - Constructor for class net.sf.javaml.classification.SOM
Create a new self-organizing map with the provided parameters.
SOM - Class in net.sf.javaml.clustering
An implementation of the Self Organizing Maps algorithm as proposed by Kohonen.
SOM() - Constructor for class net.sf.javaml.clustering.SOM
Create a 2 by 2 Self-organizing map, using a hexagonal grid, 1000 iteration, 0.1 learning rate, 8 initial radius, linear learning, a step-wise neighborhood function and the Euclidean distance as distance measure.
SOM(int, int, SOM.GridType, int, double, int, SOM.LearningType, SOM.NeighbourhoodFunction) - Constructor for class net.sf.javaml.clustering.SOM
Create a new self-organizing map with the provided parameters and the Euclidean distance as distance metric.
SOM(int, int, SOM.GridType, int, double, int, SOM.LearningType, SOM.NeighbourhoodFunction, DistanceMeasure) - Constructor for class net.sf.javaml.clustering.SOM
Create a new self-organizing map with the provided parameters.
SOM.GridType - Enum in net.sf.javaml.clustering
Enumeration of all grid types that are supported in a self organizing map.
SOM.LearningType - Enum in net.sf.javaml.clustering
 
SOM.NeighbourhoodFunction - Enum in net.sf.javaml.clustering
 
sort(double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
SparseInstance - Class in net.sf.javaml.core
Implementation of a sparse instance.
SparseInstance() - Constructor for class net.sf.javaml.core.SparseInstance
 
SparseInstance(int) - Constructor for class net.sf.javaml.core.SparseInstance
 
SparseInstance(int, double) - Constructor for class net.sf.javaml.core.SparseInstance
 
SparseInstance(int, Object) - Constructor for class net.sf.javaml.core.SparseInstance
 
SparseInstance(int, double, Object) - Constructor for class net.sf.javaml.core.SparseInstance
 
SparseInstance(double[]) - Constructor for class net.sf.javaml.core.SparseInstance
 
SparseInstance(double[], double) - Constructor for class net.sf.javaml.core.SparseInstance
 
SparseInstance(double[], Object) - Constructor for class net.sf.javaml.core.SparseInstance
 
SparseInstance(double[], double, Object) - Constructor for class net.sf.javaml.core.SparseInstance
 
SparseMatrix - Class in net.sf.javaml.clustering.mcl
SparseMatrix is a sparse matrix with row-major format.
SparseMatrix() - Constructor for class net.sf.javaml.clustering.mcl.SparseMatrix
empty sparse matrix
SparseMatrix(int, int) - Constructor for class net.sf.javaml.clustering.mcl.SparseMatrix
empty sparse matrix with allocated number of rows
SparseMatrix(double[][]) - Constructor for class net.sf.javaml.clustering.mcl.SparseMatrix
create sparse matrix from full matrix
SparseMatrix(SparseMatrix) - Constructor for class net.sf.javaml.clustering.mcl.SparseMatrix
copy contructor
SparseVector - Class in net.sf.javaml.clustering.mcl
SparseVector represents a sparse vector.
SparseVector() - Constructor for class net.sf.javaml.clustering.mcl.SparseVector
create empty vector
SparseVector(int) - Constructor for class net.sf.javaml.clustering.mcl.SparseVector
create empty vector with length
SparseVector(double[]) - Constructor for class net.sf.javaml.clustering.mcl.SparseVector
create vector from dense vector
SparseVector(SparseVector) - Constructor for class net.sf.javaml.clustering.mcl.SparseVector
copy constructor
SpearmanFootruleDistance - Class in net.sf.javaml.distance
TODO WRITE DOC
SpearmanFootruleDistance() - Constructor for class net.sf.javaml.distance.SpearmanFootruleDistance
 
SpearmanRankCorrelation - Class in net.sf.javaml.distance
Calculates the Spearman rank correlation of two instances.
SpearmanRankCorrelation() - Constructor for class net.sf.javaml.distance.SpearmanRankCorrelation
 
SpecialFunctions - Class in net.sf.javaml.utils
Class implementing some mathematical functions.
SpecialFunctions() - Constructor for class net.sf.javaml.utils.SpecialFunctions
 
sqrt() - Method in class net.sf.javaml.core.AbstractInstance
 
sqrt() - Method in interface net.sf.javaml.core.Instance
Take square root of all attributes.
SQRTH - Static variable in class net.sf.javaml.utils.Statistics
 
SQTPI - Static variable in class net.sf.javaml.utils.Statistics
 
standardDeviation(Dataset, Instance) - Static method in class net.sf.javaml.tools.DatasetTools
Creates an instance that contains the standard deviation of the values for each attribute.
Statistics - Class in net.sf.javaml.utils
Class implementing some distributions, tests, etc.
Statistics() - Constructor for class net.sf.javaml.utils.Statistics
 
stirlingFormula(double) - Static method in class net.sf.javaml.utils.GammaFunction
Returns the Gamma function computed by Stirling's formula; formerly named stirf.
store(Object, String) - Static method in class net.sf.javaml.tools.Serial
 
SubSampling - Class in net.sf.javaml.tools.sampling
Implements regular subsampling without replacement.
SubSampling() - Constructor for class net.sf.javaml.tools.sampling.SubSampling
 
substract(double[], double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
Subtract the second array from the first one and returns the result.
subVector(double[], int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
returns a copy of the vector elements with the given indices in the original vector.
subVector(int[], int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
returns a copy of the vector elements with the given indices in the original vector.
subVector(double[], int, int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
 
sum(double) - Method in class net.sf.javaml.clustering.mcl.SparseVector
power sum of the elements
sum(double[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
sum the elements of vec
sum(int[]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
sum the elements of vec
sum(double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
Computes the sum of the elements of an array of doubles.
sum(double[], double[]) - Static method in class net.sf.javaml.utils.ArrayUtils
 
SumOfAveragePairwiseSimilarities - Class in net.sf.javaml.clustering.evaluation
I_1 from the Zhao 2001 paper TODO uitleg
SumOfAveragePairwiseSimilarities() - Constructor for class net.sf.javaml.clustering.evaluation.SumOfAveragePairwiseSimilarities
 
SumOfCentroidSimilarities - Class in net.sf.javaml.clustering.evaluation
TODO uitleg I_2 from Zhao 2001
SumOfCentroidSimilarities() - Constructor for class net.sf.javaml.clustering.evaluation.SumOfCentroidSimilarities
 
SumOfSquaredErrors - Class in net.sf.javaml.clustering.evaluation
I_3 from the Zhao 2001 paper TODO uitleg
SumOfSquaredErrors() - Constructor for class net.sf.javaml.clustering.evaluation.SumOfSquaredErrors
Construct a new SumOfSquaredErrors cluster evaluation measure that will use the Euclidean distance to measure the errors.
SumOfSquaredErrors(DistanceMeasure) - Constructor for class net.sf.javaml.clustering.evaluation.SumOfSquaredErrors
Construct a new SumOfSquaredErrors cluster evaluation measure that will use the supplied distance metric to measure the errors.
SymmetricalUncertainty - Class in net.sf.javaml.featureselection.scoring
Implements the Symmetrical Uncertainty (SU) evaluation method for attributes.
SymmetricalUncertainty() - Constructor for class net.sf.javaml.featureselection.scoring.SymmetricalUncertainty
 
symmetricalUncertainty(double[][]) - Static method in class net.sf.javaml.utils.ContingencyTables
Calculates the symmetrical uncertainty for base 2.

T

Tau - Class in net.sf.javaml.clustering.evaluation
TODO uitleg
Tau(DistanceMeasure) - Constructor for class net.sf.javaml.clustering.evaluation.Tau
 
tauVal(double[][]) - Static method in class net.sf.javaml.utils.ContingencyTables
Computes Goodman and Kruskal's tau-value for a contingency table.
testDataset(Classifier, Dataset) - Static method in class net.sf.javaml.classification.evaluation.EvaluateDataset
Tests a classifier on a data set
times(SparseVector) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
immutable multiply this times the vector: A * x, i.e., rowwise.
times(SparseMatrix) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
immutable multiply this matrix (A) with M : A * M
times(SparseVector) - Method in class net.sf.javaml.clustering.mcl.SparseVector
immutable scalar product
times(Complex) - Method in class net.sf.javaml.core.Complex
Multiplies this complex number with another one.
times(double) - Method in class net.sf.javaml.core.Complex
Multiplies with real number
TimeSeries - Class in net.sf.javaml.distance.fastdtw.timeseries
 
TimeSeries(int) - Constructor for class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
TimeSeries(TimeSeries) - Constructor for class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
TimeSeries(String, boolean) - Constructor for class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
TimeSeries(String, char) - Constructor for class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
TimeSeries(String, boolean, char) - Constructor for class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
TimeSeries(String, boolean, boolean, char) - Constructor for class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
TimeSeries(String, int[], boolean) - Constructor for class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
TimeSeries(String, int[], boolean, boolean, char) - Constructor for class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
TimeSeries(Instance) - Constructor for class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
TimeSeriesPoint - Class in net.sf.javaml.distance.fastdtw.timeseries
 
TimeSeriesPoint(double[]) - Constructor for class net.sf.javaml.distance.fastdtw.timeseries.TimeSeriesPoint
 
TimeSeriesPoint(Collection) - Constructor for class net.sf.javaml.distance.fastdtw.timeseries.TimeSeriesPoint
 
timesTransposed(SparseMatrix) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
mutable multiply this matrix (A) with M : A * M'
TimeWarpInfo - Class in net.sf.javaml.distance.fastdtw.dtw
 
tn - Variable in class net.sf.javaml.classification.evaluation.PerformanceMeasure
The number of true negatives.
toArray() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeriesPoint
 
toCollection(boolean[]) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
toCollection(byte[]) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
toCollection(char[]) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
toCollection(double[]) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
toCollection(float[]) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
toCollection(int[]) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
toCollection(long[]) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
toCollection(short[]) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
toCollection(String[]) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
toDouble() - Method in class net.sf.javaml.clustering.mcl.ExpDouble
 
toDoubleUnsigned() - Method in class net.sf.javaml.clustering.mcl.ExpDouble
 
toExpString() - Method in class net.sf.javaml.clustering.mcl.ExpDouble
 
toIntArray(Collection) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
toPrimitiveArray(Integer[]) - Static method in class net.sf.javaml.distance.fastdtw.util.Arrays
 
toString() - Method in class net.sf.javaml.classification.evaluation.PerformanceMeasure
 
toString() - Method in class net.sf.javaml.clustering.mcl.ExpDouble
 
toString() - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
 
toString() - Method in class net.sf.javaml.clustering.mcl.SparseVector
 
toString() - Method in enum net.sf.javaml.clustering.SOM.GridType
 
toString() - Method in enum net.sf.javaml.clustering.SOM.LearningType
 
toString() - Method in enum net.sf.javaml.clustering.SOM.NeighbourhoodFunction
 
toString() - Method in class net.sf.javaml.core.Complex
toString() - Method in class net.sf.javaml.core.DenseInstance
 
toString() - Method in class net.sf.javaml.core.kdtree.KDTree
 
toString() - Method in class net.sf.javaml.core.Pair
 
toString() - Method in class net.sf.javaml.core.SparseInstance
 
toString() - Method in class net.sf.javaml.distance.fastdtw.dtw.SearchWindow
 
toString() - Method in class net.sf.javaml.distance.fastdtw.dtw.TimeWarpInfo
 
toString() - Method in class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
toString() - Method in class net.sf.javaml.distance.fastdtw.matrix.ColMajorCell
 
toString() - Method in class net.sf.javaml.distance.fastdtw.timeseries.PAA
 
toString() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeries
 
toString() - Method in class net.sf.javaml.distance.fastdtw.timeseries.TimeSeriesPoint
 
toStringDense() - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
prints a dense representation
toStringDense() - Method in class net.sf.javaml.clustering.mcl.SparseVector
create string representation of dense equivalent.
ToWekaUtils - Class in net.sf.javaml.tools.weka
Provides utility methods to convert data to the WEKA format.
ToWekaUtils(Dataset) - Constructor for class net.sf.javaml.tools.weka.ToWekaUtils
 
tp - Variable in class net.sf.javaml.classification.evaluation.PerformanceMeasure
The number of true positives.
TraceScatterMatrix - Class in net.sf.javaml.clustering.evaluation
E_1 from the Zhao 2001 paper XXX DOC Distance measure has to be CosineSimilarity TODO uitleg
TraceScatterMatrix() - Constructor for class net.sf.javaml.clustering.evaluation.TraceScatterMatrix
 
TrainingRequiredException - Exception in net.sf.javaml.core.exception
Indicates that the algorithm that throws this exception should have been trained prior to point the exception was thrown.
TrainingRequiredException() - Constructor for exception net.sf.javaml.core.exception.TrainingRequiredException
 
TrainingRequiredException(String) - Constructor for exception net.sf.javaml.core.exception.TrainingRequiredException
 
TrainingRequiredException(Throwable) - Constructor for exception net.sf.javaml.core.exception.TrainingRequiredException
 
TrainingRequiredException(String, Throwable) - Constructor for exception net.sf.javaml.core.exception.TrainingRequiredException
 
transpose() - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
immutable transpose.
transpose(double[][]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
transpose the matrix
transpose(int[][]) - Static method in class net.sf.javaml.clustering.mcl.Vectors
transpose the matrix
TutorialARFFLoader - Class in tutorials.tools
Demonstrates how you can load data from an ARFF formatted file.
TutorialARFFLoader() - Constructor for class tutorials.tools.TutorialARFFLoader
 
TutorialClusterEvaluation - Class in tutorials.clustering
Shows how to use the different cluster evaluation measure that are implemented in Java-ML.
TutorialClusterEvaluation() - Constructor for class tutorials.clustering.TutorialClusterEvaluation
 
TutorialCrossValidation - Class in tutorials.classification
This tutorial shows how you can do cross-validation with Java-ML
TutorialCrossValidation() - Constructor for class tutorials.classification.TutorialCrossValidation
 
TutorialCVSameFolds - Class in tutorials.classification
This tutorial shows how you can do multiple cross-validations with the same folds.
TutorialCVSameFolds() - Constructor for class tutorials.classification.TutorialCVSameFolds
 
TutorialDataLoader - Class in tutorials.tools
This tutorial shows how to load data from a local file.
TutorialDataLoader() - Constructor for class tutorials.tools.TutorialDataLoader
 
TutorialDataset - Class in tutorials.core
This tutorial show how to create a Dataset from a collection of instances.
TutorialDataset() - Constructor for class tutorials.core.TutorialDataset
 
TutorialDenseInstance - Class in tutorials.core
This tutorial shows the very first step in using Java-ML.
TutorialDenseInstance() - Constructor for class tutorials.core.TutorialDenseInstance
 
TutorialEnsembleFeatureSelection - Class in tutorials.featureselection
Tutorial to illustrate ensemble feature selection.
TutorialEnsembleFeatureSelection() - Constructor for class tutorials.featureselection.TutorialEnsembleFeatureSelection
 
TutorialEvaluateDataset - Class in tutorials.classification
This tutorial show how to use the EvaluateDataset class to test the performance of a classifier on a data set.
TutorialEvaluateDataset() - Constructor for class tutorials.classification.TutorialEvaluateDataset
 
TutorialFeatureRanking - Class in tutorials.featureselection
 
TutorialFeatureRanking() - Constructor for class tutorials.featureselection.TutorialFeatureRanking
 
TutorialFeatureScoring - Class in tutorials.featureselection
 
TutorialFeatureScoring() - Constructor for class tutorials.featureselection.TutorialFeatureScoring
 
TutorialFeatureSubsetSelection - Class in tutorials.featureselection
Shows the basic steps to create use a feature subset selection algorithm.
TutorialFeatureSubsetSelection() - Constructor for class tutorials.featureselection.TutorialFeatureSubsetSelection
 
TutorialKDtreeKNN - Class in tutorials.classification
This tutorial show how to use a the k-nearest neighbors classifier.
TutorialKDtreeKNN() - Constructor for class tutorials.classification.TutorialKDtreeKNN
 
TutorialKMeans - Class in tutorials.clustering
This tutorial shows how to use a clustering algorithm to cluster a data set.
TutorialKMeans() - Constructor for class tutorials.clustering.TutorialKMeans
 
TutorialKNN - Class in tutorials.classification
This tutorial show how to use a the k-nearest neighbors classifier.
TutorialKNN() - Constructor for class tutorials.classification.TutorialKNN
 
TutorialLibSVM - Class in tutorials.classification
This tutorial show how to use a the LibSVM classifier.
TutorialLibSVM() - Constructor for class tutorials.classification.TutorialLibSVM
 
TutorialRandomForest - Class in tutorials.classification
Tutorial for the random forest classifier.
TutorialRandomForest() - Constructor for class tutorials.classification.TutorialRandomForest
 
tutorials.classification - package tutorials.classification
Provides tutorials for the Classifier interface and the associated classes for evaluation and performance assessment.
tutorials.clustering - package tutorials.clustering
Provides tutorials for the Clustering interface.
tutorials.core - package tutorials.core
Provides tutorials for the Instance and Dataset core interfaces.
tutorials.featureselection - package tutorials.featureselection
Provides ensemble feature selection algorithms.
tutorials.tools - package tutorials.tools
Provides tutorials for the tools package.
TutorialSparseInstance - Class in tutorials.core
Shows how to create a SparseInstance.
TutorialSparseInstance() - Constructor for class tutorials.core.TutorialSparseInstance
 
TutorialStoreData - Class in tutorials.tools
Demonstrates how you can store data to a file.
TutorialStoreData() - Constructor for class tutorials.tools.TutorialStoreData
 
TutorialWekaAttributeSelection - Class in tutorials.featureselection
Tutorial how to use the Bridge to WEKA AS Evaluation , AS Search and Evaluator algorithms in Java-ML
TutorialWekaAttributeSelection() - Constructor for class tutorials.featureselection.TutorialWekaAttributeSelection
 
TutorialWekaClassifier - Class in tutorials.tools
Tutorial how to use a Weka classifier in Java-ML.
TutorialWekaClassifier() - Constructor for class tutorials.tools.TutorialWekaClassifier
 
TutorialWekaClusterer - Class in tutorials.tools
Tutorial how to use a Weka classifier in Java-ML.
TutorialWekaClusterer() - Constructor for class tutorials.tools.TutorialWekaClusterer
 
TypeConversions - Class in net.sf.javaml.distance.fastdtw.lang
 
TypeConversions() - Constructor for class net.sf.javaml.distance.fastdtw.lang.TypeConversions
 

U

union(Set<? extends Integer>, Set<? extends Integer>) - Static method in class net.sf.javaml.tools.SetTools
Returns the union of the two sets provided as arguments.
UnsetClassFilter - Class in net.sf.javaml.filter
Filter to remove class information from a data set or instance.
UnsetClassFilter() - Constructor for class net.sf.javaml.filter.UnsetClassFilter
 
updateCholeskyFactor(Matrix, double[], double[], double, boolean[]) - Method in class net.sf.javaml.utils.ActiveSetsOptimization
One rank update of the Cholesky factorization of B matrix in BFGS updates, i.e.

V

value(int) - Method in class net.sf.javaml.core.DenseInstance
 
value(int) - Method in interface net.sf.javaml.core.Instance
 
value(int) - Method in class net.sf.javaml.core.SparseInstance
 
valueOf(String) - Static method in enum net.sf.javaml.clustering.SOM.GridType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum net.sf.javaml.clustering.SOM.LearningType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum net.sf.javaml.clustering.SOM.NeighbourhoodFunction
Returns the enum constant of this type with the specified name.
values() - Static method in enum net.sf.javaml.clustering.SOM.GridType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum net.sf.javaml.clustering.SOM.LearningType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum net.sf.javaml.clustering.SOM.NeighbourhoodFunction
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Method in class net.sf.javaml.core.DenseInstance
 
values() - Method in class net.sf.javaml.core.SparseInstance
 
Vectors - Class in net.sf.javaml.clustering.mcl
Static vector manipulation routines for Matlab porting and other numeric operations.
Vectors() - Constructor for class net.sf.javaml.clustering.mcl.Vectors
 
vectorTimes(SparseVector) - Method in class net.sf.javaml.clustering.mcl.SparseMatrix
immutable multiply the vector times this: x' * A, i.e., colwise.

W

WarpPath - Class in net.sf.javaml.distance.fastdtw.dtw
 
WarpPath() - Constructor for class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
WarpPath(int) - Constructor for class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
WarpPath(String) - Constructor for class net.sf.javaml.distance.fastdtw.dtw.WarpPath
 
WarpPathWindow - Class in net.sf.javaml.distance.fastdtw.dtw
 
WarpPathWindow(WarpPath, int) - Constructor for class net.sf.javaml.distance.fastdtw.dtw.WarpPathWindow
 
WB - Class in net.sf.javaml.clustering.evaluation
TODO uitleg
WB(DistanceMeasure) - Constructor for class net.sf.javaml.clustering.evaluation.WB
 
WekaAttributeSelection - Class in net.sf.javaml.tools.weka
Provides a bridge between Java-ML and the Attribute Selection algorithms in WEKA.
WekaAttributeSelection(ASEvaluation, ASSearch) - Constructor for class net.sf.javaml.tools.weka.WekaAttributeSelection
 
WekaClassifier - Class in net.sf.javaml.tools.weka
 
WekaClassifier(Classifier) - Constructor for class net.sf.javaml.tools.weka.WekaClassifier
 
WekaClusterer - Class in net.sf.javaml.tools.weka
Provides a bridge between Java-ML and the clustering algorithms in WEKA.
WekaClusterer(Clusterer) - Constructor for class net.sf.javaml.tools.weka.WekaClusterer
 
WekaException - Exception in net.sf.javaml.tools.weka
This exception should be thrown when something went wrong with calls to the WEKA library.
WekaException() - Constructor for exception net.sf.javaml.tools.weka.WekaException
 
WekaException(String, Throwable) - Constructor for exception net.sf.javaml.tools.weka.WekaException
 
WekaException(String) - Constructor for exception net.sf.javaml.tools.weka.WekaException
 
WekaException(Throwable) - Constructor for exception net.sf.javaml.tools.weka.WekaException
 

X

x() - Method in class net.sf.javaml.core.Pair
Get the first object

Y

y() - Method in class net.sf.javaml.core.Pair
Get the second object

Z

zero(double) - Static method in class net.sf.javaml.utils.MathUtils
Test whether a is zero with acceptable error.
ZeroR - Class in net.sf.javaml.classification
ZeroR classifier implementation.
ZeroR() - Constructor for class net.sf.javaml.classification.ZeroR
 
zeros(int) - Static method in class net.sf.javaml.clustering.mcl.Vectors
 

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