Modifier and Type | Class and Description |
---|---|
class |
InstancesHeader
Class for storing the header or context of a data stream.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
WekaToSamoaInstanceConverter.samoaInstanceInformation |
Modifier and Type | Method and Description |
---|---|
Instances |
Instance.dataset()
Dataset.
|
Instances |
InstanceImpl.dataset()
Dataset.
|
Instances |
WekaToSamoaInstanceConverter.samoaInstances(weka.core.Instances instances)
Samoa instances from weka instances.
|
Instances |
WekaToSamoaInstanceConverter.samoaInstancesInformation(weka.core.Instances instances)
Samoa instances information.
|
Instances |
Instances.testCV(int numFolds,
int numFold)
Test cv.
|
Instances |
Instances.trainCV(int numFolds,
int numFold) |
Instances |
Instances.trainCV(int numFolds,
int numFold,
Random random)
Train cv.
|
Modifier and Type | Method and Description |
---|---|
protected void |
Instances.copyInstances(int from,
Instances dest,
int num) |
void |
Instance.setDataset(Instances dataset)
Sets the dataset.
|
void |
InstanceImpl.setDataset(Instances dataset)
Sets the dataset.
|
weka.core.Instances |
SamoaToWekaInstanceConverter.wekaInstances(Instances instances)
Weka instances.
|
weka.core.Instances |
SamoaToWekaInstanceConverter.wekaInstancesInformation(Instances instances)
Weka instances information.
|
Constructor and Description |
---|
Instances(Instances chunk)
Instantiates a new instances.
|
Instances(Instances chunk,
int capacity)
Instantiates a new instances.
|
Instances(Instances chunk,
int first,
int toCopy)
Instantiates a new instances.
|
InstancesHeader(Instances i) |
Modifier and Type | Method and Description |
---|---|
protected static int |
AbstractClassifier.modelAttIndexToInstanceAttIndex(int index,
Instances insts)
Gets the index of the attribute in a set of instances,
given the index of the attribute in the learner.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
NaiveBayesMultinomial.m_headerInfo
copy of header information for use in toString method
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
kNN.window |
Modifier and Type | Field and Description |
---|---|
protected Instances |
NormalizableDistance.m_Data
the instances used internally.
|
protected Instances |
NearestNeighbourSearch.m_Instances
The neighbourhood of instances to find neighbours in.
|
Modifier and Type | Method and Description |
---|---|
Instances |
NearestNeighbourSearch.getInstances()
returns the instances currently set.
|
Instances |
DistanceFunction.getInstances()
returns the instances currently set.
|
Instances |
NormalizableDistance.getInstances()
returns the instances currently set.
|
abstract Instances |
NearestNeighbourSearch.kNearestNeighbours(Instance target,
int k)
Returns k nearest instances in the current neighbourhood to the supplied
instance.
|
Instances |
LinearNNSearch.kNearestNeighbours(Instance target,
int kNN)
Returns k nearest instances in the current neighbourhood to the supplied
instance.
|
Instances |
KDTree.kNearestNeighbours(Instance target,
int k)
Returns the k nearest neighbours of the supplied instance.
|
Modifier and Type | Method and Description |
---|---|
void |
KDTree.assignSubToCenters(KDTreeNode node,
Instances centers,
int[] centList,
int[] assignments)
Assigns instances of this node to center.
|
protected void |
KDTree.buildKDTree(Instances instances)
Builds the KDTree on the supplied set of instances/points.
|
void |
KDTree.centerInstances(Instances centers,
int[] assignments,
double pc)
Assigns instances to centers using KDTree.
|
protected void |
KDTree.checkMissing(Instances instances)
Checks if there is any instance with missing values.
|
int |
EuclideanDistance.closestPoint(Instance instance,
Instances allPoints,
int[] pointList)
Returns the index of the closest point to the current instance.
|
protected void |
KDTree.determineAssignments(KDTreeNode node,
Instances centers,
int[] candidates,
int[] assignments,
double pc)
Assigns instances to the current centers called candidates.
|
protected int[] |
KDTree.refineOwners(KDTreeNode node,
Instances centers,
int[] candidates)
Refines the ownerlist.
|
void |
NearestNeighbourSearch.setInstances(Instances insts)
Sets the instances.
|
void |
DistanceFunction.setInstances(Instances insts)
Sets the instances.
|
void |
LinearNNSearch.setInstances(Instances insts)
Sets the instances comprising the current neighbourhood.
|
void |
KDTree.setInstances(Instances instances)
Builds the KDTree on the given set of instances.
|
void |
NormalizableDistance.setInstances(Instances insts)
Sets the instances.
|
Constructor and Description |
---|
EuclideanDistance(Instances data)
Constructs an Euclidean Distance object and automatically initializes the
ranges.
|
KDTree(Instances insts)
Creates a new instance of KDTree.
|
LinearNNSearch(Instances insts)
Constructor that uses the supplied set of
instances.
|
NearestNeighbourSearch(Instances insts)
Constructor.
|
NormalizableDistance(Instances data)
Initializes the distance function and automatically initializes the
ranges.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
KDTreeNodeSplitter.m_Instances
The instances that'll be used for tree construction.
|
Modifier and Type | Method and Description |
---|---|
protected static int |
KMeansInpiredMethod.partition(Instances insts,
int[] index,
int attidx,
int l,
int r)
Partitions the instances around a pivot.
|
protected static void |
KMeansInpiredMethod.quickSort(Instances insts,
int[] indices,
int attidx,
int left,
int right)
Sorts the instances according to the given attribute/dimension.
|
void |
KDTreeNodeSplitter.setInstances(Instances inst)
Sets the training instances on which the tree is (or is
to be) built.
|
Constructor and Description |
---|
KDTreeNodeSplitter(int[] instList,
Instances insts,
EuclideanDistance e)
Creates a new instance of KDTreeNodeSplitter.
|
Modifier and Type | Field and Description |
---|---|
protected Instances |
LearnNSE.buffer |
protected Instances |
AccuracyWeightedEnsemble.currentChunk |
protected Instances |
AccuracyUpdatedEnsemble.currentChunk
Current chunk of instances.
|
protected Instances |
TemporallyAugmentedClassifier.header |
protected Instances |
ADACC.recentChunk
Last chunk of data of size (tau_size) to compute the stability index
|
Modifier and Type | Method and Description |
---|---|
protected double |
AccuracyWeightedEnsemble.computeCandidateWeight(Classifier candidate,
Instances chunk,
int numFolds)
Computes the weight of a candidate classifier.
|
protected double |
AccuracyUpdatedEnsemble.computeMse(Classifier learner,
Instances chunk)
Computes the MSE of a learner for a given chunk of examples.
|
protected double |
AccuracyWeightedEnsemble.computeWeight(Classifier learner,
Instances chunk)
Computes the weight of a given classifie.
|
void |
TemporallyAugmentedClassifier.initHeader(Instances dataset) |
Modifier and Type | Method and Description |
---|---|
Instances |
WekaClusteringAlgorithm.getDataset(int numdim,
int numclass) |
Modifier and Type | Method and Description |
---|---|
protected static int |
AbstractClusterer.modelAttIndexToInstanceAttIndex(int index,
Instances insts) |
Modifier and Type | Class and Description |
---|---|
class |
MultilabelInstancesHeader
Class for storing the header or context of a multilabel data stream.
|
Constructor and Description |
---|
MultilabelInstancesHeader(Instances i,
int numLabels) |
Modifier and Type | Field and Description |
---|---|
protected Instances |
Converter.m_InstancesTemplate |
Modifier and Type | Method and Description |
---|---|
Instances |
Converter.createTemplate(Instances i) |
Modifier and Type | Method and Description |
---|---|
Instances |
Converter.createTemplate(Instances i) |
Constructor and Description |
---|
WekaExplorer(Instances instances) |
Modifier and Type | Field and Description |
---|---|
protected Instances |
ArffFileStream.instances |
protected Instances |
MultiTargetArffFileStream.instances |
protected Instances[] |
ImbalancedStream.instancesBuffer |
protected Instances |
CachedInstancesStream.toStream |
Constructor and Description |
---|
CachedInstancesStream(Instances toStream) |
Modifier and Type | Field and Description |
---|---|
protected Instances |
SimpleCSVStream.dataset |
protected Instances |
FileStream.instances |
Modifier and Type | Field and Description |
---|---|
protected Instances |
MetaMultilabelGenerator.multilabelStreamTemplate |
Modifier and Type | Method and Description |
---|---|
protected MultilabelInstancesHeader |
MetaMultilabelGenerator.generateMultilabelHeader(Instances si)
GenerateMultilabelHeader.
|
Copyright © 2019 University of Waikato, Hamilton, NZ. All Rights Reserved.