Uses of Interface
com.yahoo.labs.samoa.instances.Instance
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Uses of Instance in com.yahoo.labs.samoa.instances
Subinterfaces of Instance in com.yahoo.labs.samoa.instances Modifier and Type Interface Description interface
MultiLabelInstance
The Interface MultiLabelInstance.Classes in com.yahoo.labs.samoa.instances that implement Instance Modifier and Type Class Description class
DenseInstance
The Class DenseInstance.class
FilteredSparseInstance
The Class FilteredSparseInstance.class
InstanceImpl
The Class InstanceImpl.class
SparseInstance
The Class SparseInstance.Fields in com.yahoo.labs.samoa.instances with type parameters of type Instance Modifier and Type Field Description protected List<Instance>
Instances. instances
The instances.Methods in com.yahoo.labs.samoa.instances that return Instance Modifier and Type Method Description Instance
Instance. copy()
Copy.Instance
InstanceImpl. copy()
Copy.Instance
Instances. get(int k)
Instance
Instances. instance(int num)
Instance.protected Instance
ArffLoader. newDenseInstance(int numberAttributes)
protected Instance
MultiTargetArffLoader. newDenseInstance(int numAttributes)
protected Instance
ArffLoader. newSparseInstance(double d)
protected Instance
ArffLoader. newSparseInstance(double d, double[] res)
protected Instance
MultiTargetArffLoader. newSparseInstance(double d, double[] res)
Instance
ArffLoader. readInstance()
Reads instance.Instance
ArffLoader. readInstanceDense()
Reads a dense instance from the file.Instance
WekaToSamoaInstanceConverter. samoaInstance(weka.core.Instance inst)
Samoa instance from weka instance.Methods in com.yahoo.labs.samoa.instances with parameters of type Instance Modifier and Type Method Description void
Instances. add(Instance inst)
Adds the.void
Instances. set(int i, Instance inst)
protected void
ArffLoader. setValue(Instance instance, int numAttribute, double value, boolean isNumber)
weka.core.Instance
SamoaToWekaInstanceConverter. wekaInstance(Instance inst)
Weka instance.Constructors in com.yahoo.labs.samoa.instances with parameters of type Instance Constructor Description DenseInstance(Instance inst)
Instantiates a new dense instance. -
Uses of Instance in moa.classifiers
Methods in moa.classifiers with parameters of type Instance Modifier and Type Method Description boolean
AbstractClassifier. correctlyClassifies(Instance inst)
boolean
Classifier. correctlyClassifies(Instance inst)
Gets whether this classifier correctly classifies an instance.double
OneClassClassifier. getAnomalyScore(Instance inst)
For use when an anomaly score is needed instead of a vote.Prediction
AbstractClassifier. getPredictionForInstance(Instance inst)
Prediction
AbstractMultiLabelLearner. getPredictionForInstance(Instance inst)
Prediction
Classifier. getPredictionForInstance(Instance inst)
Gets the reference to the header of the data stream.abstract double[]
AbstractClassifier. getVotesForInstance(Instance inst)
double[]
AbstractMultiLabelLearner. getVotesForInstance(Instance inst)
double[]
Classifier. getVotesForInstance(Instance inst)
Predicts the class memberships for a given instance.protected static int
AbstractClassifier. modelAttIndexToInstanceAttIndex(int index, Instance inst)
Gets the index of the attribute in the instance, given the index of the attribute in the learner.void
AbstractClassifier. trainOnInstance(Instance inst)
void
Classifier. trainOnInstance(Instance inst)
Trains this learner incrementally using the given example.abstract void
AbstractClassifier. trainOnInstanceImpl(Instance inst)
Trains this classifier incrementally using the given instance.
The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods.void
AbstractMultiLabelLearner. trainOnInstanceImpl(Instance instance)
Method parameters in moa.classifiers with type arguments of type Instance Modifier and Type Method Description Prediction
AbstractClassifier. getPredictionForInstance(Example<Instance> example)
Prediction
AbstractMultiLabelLearner. getPredictionForInstance(Example<Instance> example)
double[]
AbstractClassifier. getVotesForInstance(Example<Instance> example)
void
OneClassClassifier. initialize(Collection<Instance> trainingPoints)
Allows a one class classifier to be initialized with a starting set of training instances.void
AbstractClassifier. trainOnInstance(Example<Instance> example)
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Uses of Instance in moa.classifiers.active
Methods in moa.classifiers.active with parameters of type Instance Modifier and Type Method Description double[]
ALRandom. getVotesForInstance(Instance inst)
double[]
ALUncertainty. getVotesForInstance(Instance inst)
void
ALRandom. trainOnInstanceImpl(Instance inst)
void
ALUncertainty. trainOnInstanceImpl(Instance inst)
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Uses of Instance in moa.classifiers.bayes
Methods in moa.classifiers.bayes with parameters of type Instance Modifier and Type Method Description static double[]
NaiveBayes. doNaiveBayesPrediction(Instance inst, DoubleVector observedClassDistribution, AutoExpandVector<AttributeClassObserver> attributeObservers)
static double[]
NaiveBayes. doNaiveBayesPredictionLog(Instance inst, DoubleVector observedClassDistribution, AutoExpandVector<AttributeClassObserver> observers, AutoExpandVector<AttributeClassObserver> observers2)
double[]
NaiveBayes. getVotesForInstance(Instance inst)
double[]
NaiveBayesMultinomial. getVotesForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance.double
NaiveBayesMultinomial. totalSize(Instance instance)
void
NaiveBayes. trainOnInstanceImpl(Instance inst)
void
NaiveBayesMultinomial. trainOnInstanceImpl(Instance inst)
Trains the classifier with the given instance. -
Uses of Instance in moa.classifiers.core.conditionaltests
Methods in moa.classifiers.core.conditionaltests with parameters of type Instance Modifier and Type Method Description abstract int
InstanceConditionalTest. branchForInstance(Instance inst)
Returns the number of the branch for an instance, -1 if unknown.int
NominalAttributeBinaryTest. branchForInstance(Instance inst)
int
NominalAttributeMultiwayTest. branchForInstance(Instance inst)
int
NumericAttributeBinaryTest. branchForInstance(Instance inst)
boolean
InstanceConditionalTest. resultKnownForInstance(Instance inst)
Gets whether the number of the branch for an instance is known. -
Uses of Instance in moa.classifiers.core.statisticaltests
Methods in moa.classifiers.core.statisticaltests that return types with arguments of type Instance Modifier and Type Method Description static List<Instance>
Cramer. fileToInstances(String path)
Method parameters in moa.classifiers.core.statisticaltests with type arguments of type Instance Modifier and Type Method Description Cramer.CramerTest
Cramer. cramerTest(List<Instance> x, List<Instance> y)
Cramer.CramerTest
Cramer. cramerTest(List<Instance> x, List<Instance> y, double confLevel, int replicates, String sim, boolean justStatistic, int kernel, double maxM, int k)
double[]
KNN. mtsknn(List<Instance> x, List<Instance> y)
void
Cramer. set(List<Instance> x, List<Instance> y)
void
KNN. set(List<Instance> x, List<Instance> y)
void
StatisticalTest. set(List<Instance> x, List<Instance> y)
This method sets the instances for later use in concurrent scenarios.double
Cramer. test(List<Instance> x, List<Instance> y)
double
KNN. test(List<Instance> x, List<Instance> y)
double
StatisticalTest. test(List<Instance> x, List<Instance> y)
This method performs a test and returns the correspoding p-value. -
Uses of Instance in moa.classifiers.deeplearning
Methods in moa.classifiers.deeplearning with parameters of type Instance Modifier and Type Method Description static int
MLP. getFeatureValuesArraySize(Instance inst, boolean useOneHotEncoding)
double[]
MLP. getVotesForFeatureValues(Instance inst)
double[]
MLP. getVotesForFeatureValues(Instance inst, double[] featureValues)
double[]
CAND. getVotesForInstance(Instance instance)
double[]
MLP. getVotesForInstance(Instance inst)
void
MLP. initializeNetwork(Instance inst)
protected void
CAND. initNNs(Instance instance)
static void
MLP. setFeatureValuesArray(Instance inst, double[] featureValuesArrayToSet, boolean useOneHotEncoding, boolean testing, MLP.NormalizeInfo[] normalizeInfo, long samplesSeen)
void
CAND. trainOnInstanceImpl(Instance instance)
void
MLP. trainOnInstanceImpl(Instance inst)
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Uses of Instance in moa.classifiers.drift
Methods in moa.classifiers.drift with parameters of type Instance Modifier and Type Method Description double[]
DriftDetectionMethodClassifier. getVotesForInstance(Instance inst)
void
DriftDetectionMethodClassifier. trainOnInstanceImpl(Instance inst)
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Uses of Instance in moa.classifiers.functions
Methods in moa.classifiers.functions with parameters of type Instance Modifier and Type Method Description protected static double
SGD. dotProd(Instance inst1, DoubleVector weights, int classIndex)
protected static double
SGDMultiClass. dotProd(Instance inst1, DoubleVector weights, int classIndex)
protected static double
SPegasos. dotProd(Instance inst1, double[] weights, int classIndex)
double[]
MajorityClass. getVotesForInstance(Instance i)
double[]
NoChange. getVotesForInstance(Instance i)
double[]
Perceptron. getVotesForInstance(Instance inst)
double[]
SGD. getVotesForInstance(Instance inst)
Calculates the class membership probabilities for the given test instance.double[]
SGDMultiClass. getVotesForInstance(Instance inst)
Calculates the class membership probabilities for the given test instance.double[]
SPegasos. getVotesForInstance(Instance inst)
Calculates the class membership probabilities for the given test instance.double
Perceptron. prediction(Instance inst, int classVal)
void
AdaGrad. trainOnInstanceImpl(Instance instance)
Trains the classifier with the given instance.void
MajorityClass. trainOnInstanceImpl(Instance inst)
void
NoChange. trainOnInstanceImpl(Instance inst)
void
Perceptron. trainOnInstanceImpl(Instance inst)
void
SGD. trainOnInstanceImpl(Instance instance)
Trains the classifier with the given instance.void
SGDMultiClass. trainOnInstanceImpl(Instance instance)
Trains the classifier with the given instance.void
SGDMultiClass. trainOnInstanceImpl(Instance instance, int classLabel)
void
SPegasos. trainOnInstanceImpl(Instance instance)
Trains the classifier with the given instance. -
Uses of Instance in moa.classifiers.lazy
Methods in moa.classifiers.lazy with parameters of type Instance Modifier and Type Method Description double[]
kNN. getVotesForInstance(Instance inst)
double[]
SAMkNN. getVotesForInstance(Instance inst)
Predicts the label of a given sample by using the STM, LTM and the CM.void
kNN. trainOnInstanceImpl(Instance inst)
void
kNNwithPAW. trainOnInstanceImpl(Instance inst)
void
kNNwithPAWandADWIN. trainOnInstanceImpl(Instance inst)
void
SAMkNN. trainOnInstanceImpl(Instance inst)
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Uses of Instance in moa.classifiers.lazy.neighboursearch
Fields in moa.classifiers.lazy.neighboursearch declared as Instance Modifier and Type Field Description Instance
NearestNeighbourSearch.NeighborNode. m_Instance
The neighbor instance.Methods in moa.classifiers.lazy.neighboursearch that return Instance Modifier and Type Method Description Instance
KDTree. nearestNeighbour(Instance target)
Returns the nearest neighbour of the supplied target instance.Instance
LinearNNSearch. nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied instance.abstract Instance
NearestNeighbourSearch. nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied instance.Methods in moa.classifiers.lazy.neighboursearch with parameters of type Instance Modifier and Type Method Description void
KDTree. addInstanceInfo(Instance instance)
Adds one instance to KDTree loosly.void
LinearNNSearch. addInstanceInfo(Instance ins)
Adds the given instance info.void
NearestNeighbourSearch. addInstanceInfo(Instance ins)
Adds information from the given instance without modifying the datastructure a lot.protected void
KDTree. addInstanceToTree(Instance inst, KDTreeNode node)
Recursively adds an instance to the tree starting from the supplied KDTreeNode.protected boolean
KDTree. candidateIsFullOwner(KDTreeNode node, Instance candidate, Instance competitor)
Returns true if candidate is a full owner in respect to a competitor.protected void
KDTree. checkMissing(Instance ins)
Checks if there is any missing value in the given instance.protected boolean
KDTree. clipToInsideHrect(KDTreeNode node, Instance x)
Finds the closest point in the hyper rectangle to a given point.int
EuclideanDistance. closestPoint(Instance instance, Instances allPoints, int[] pointList)
Returns the index of the closest point to the current instance.double
DistanceFunction. distance(Instance first, Instance second)
Calculates the distance between two instances.double
DistanceFunction. distance(Instance first, Instance second, double cutOffValue)
Calculates the distance between two instances.double
EuclideanDistance. distance(Instance first, Instance second)
Calculates the distance between two instances.double
NormalizableDistance. distance(Instance first, Instance second)
Calculates the distance between two instances.double
NormalizableDistance. distance(Instance first, Instance second, double cutOffValue)
Calculates the distance between two instances.protected double
KDTree. distanceToHrect(KDTreeNode node, Instance x)
Returns the distance between a point and an hyperrectangle.protected void
KDTree. findNearestNeighbours(Instance target, KDTreeNode node, int k, NearestNeighbourSearch.MyHeap heap, double distanceToParents)
Returns (in the supplied heap object) the k nearest neighbours of the given instance starting from the give tree node.boolean
NormalizableDistance. inRanges(Instance instance, double[][] ranges)
Test if an instance is within the given ranges.void
NearestNeighbourSearch.NeighborList. insertSorted(double distance, Instance instance)
Inserts an instance neighbor into the list, maintaining the list sorted by distance.Instances
KDTree. kNearestNeighbours(Instance target, int k)
Returns the k nearest neighbours of the supplied instance.Instances
LinearNNSearch. kNearestNeighbours(Instance target, int kNN)
Returns k nearest instances in the current neighbourhood to the supplied instance.abstract Instances
NearestNeighbourSearch. kNearestNeighbours(Instance target, int k)
Returns k nearest instances in the current neighbourhood to the supplied instance.Instance
KDTree. nearestNeighbour(Instance target)
Returns the nearest neighbour of the supplied target instance.Instance
LinearNNSearch. nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied instance.abstract Instance
NearestNeighbourSearch. nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied instance.void
DistanceFunction. update(Instance ins)
Update the distance function (if necessary) for the newly added instance.void
KDTree. update(Instance instance)
Adds one instance to the KDTree.void
LinearNNSearch. update(Instance ins)
Updates the LinearNNSearch to cater for the new added instance.abstract void
NearestNeighbourSearch. update(Instance ins)
Updates the NearNeighbourSearch algorithm for the new added instance.void
NormalizableDistance. update(Instance ins)
Update the distance function (if necessary) for the newly added instance.void
NormalizableDistance. updateRanges(Instance instance)
Update the ranges if a new instance comes.double[][]
NormalizableDistance. updateRanges(Instance instance, double[][] ranges)
Updates the ranges given a new instance.void
NormalizableDistance. updateRanges(Instance instance, int numAtt, double[][] ranges)
Updates the minimum and maximum and width values for all the attributes based on a new instance.void
NormalizableDistance. updateRangesFirst(Instance instance, int numAtt, double[][] ranges)
Used to initialize the ranges.boolean
EuclideanDistance. valueIsSmallerEqual(Instance instance, int dim, double value)
Returns true if the value of the given dimension is smaller or equal the value to be compared with.Constructors in moa.classifiers.lazy.neighboursearch with parameters of type Instance Constructor Description NeighborNode(double distance, Instance instance)
Create a new neighbor node that doesn't link to any other nodes.NeighborNode(double distance, Instance instance, NearestNeighbourSearch.NeighborNode next)
Create a new neighbor node. -
Uses of Instance in moa.classifiers.meta
Fields in moa.classifiers.meta declared as Instance Modifier and Type Field Description protected Instance[]
PairedLearners. instances
Fields in moa.classifiers.meta with type parameters of type Instance Modifier and Type Field Description protected List<Instance>
RCD. currentChunk
protected List<Instance>
RCD. currentChunk2
protected List<Instance>
RCD. testChunk
Methods in moa.classifiers.meta that return Instance Modifier and Type Method Description Instance
TemporallyAugmentedClassifier. extendWithOldLabels(Instance instance)
static Instance
StreamingGradientBoostedTrees. getSubInstance(Instance instance, double weight, ArrayList<Integer> subSpaceFeaturesIndexes, boolean setNumericClassAttribute, double numericClassValue, boolean useOneHotEncoding)
static Instance
StreamingGradientBoostedTrees. newBinaryClassInstance(Instance instance)
protected Instance
RandomRules. transformInstance(Instance inst, int classifierIndex)
Methods in moa.classifiers.meta with parameters of type Instance Modifier and Type Method Description protected double
OnlineAccuracyUpdatedEnsemble. computeWeight(int i, Instance example)
Computes the weight of a learner before training a given example.boolean
StreamingGradientBoostedTrees. correctlyClassifies(Instance inst)
protected void
OnlineAccuracyUpdatedEnsemble. createNewClassifier(Instance inst)
Processes a chunk.Instance
TemporallyAugmentedClassifier. extendWithOldLabels(Instance instance)
Prediction
SelfOptimisingKNearestLeaves. getPredictionForInstance(Instance inst)
DoubleVector
StreamingGradientBoostedTrees.SGBT. getRawScoreForInstance(Instance inst)
double[]
StreamingGradientBoostedTrees.SGBT.BoostingCommittee. getScoresForInstance(Instance inst)
static Instance
StreamingGradientBoostedTrees. getSubInstance(Instance instance, double weight, ArrayList<Integer> subSpaceFeaturesIndexes, boolean setNumericClassAttribute, double numericClassValue, boolean useOneHotEncoding)
double[]
AccuracyUpdatedEnsemble. getVotesForInstance(Instance inst)
Predicts a class for an example.double[]
AccuracyWeightedEnsemble. getVotesForInstance(Instance inst)
Predicts a class for an example.double[]
AdaptiveRandomForest.ARFBaseLearner. getVotesForInstance(Instance instance)
double[]
AdaptiveRandomForest. getVotesForInstance(Instance instance)
double[]
AdaptiveRandomForestRegressor.ARFFIMTDDBaseLearner. getVotesForInstance(Instance instance)
double[]
AdaptiveRandomForestRegressor. getVotesForInstance(Instance instance)
double[]
ADOB. getVotesForInstance(Instance inst)
double[]
BOLE. getVotesForInstance(Instance inst)
double[]
DACC. getVotesForInstance(Instance inst)
double[]
DynamicWeightedMajority. getVotesForInstance(Instance inst)
double[]
HeterogeneousEnsembleAbstract. getVotesForInstance(Instance inst)
double[]
LearnNSE. getVotesForInstance(Instance inst)
double[]
LeveragingBag. getVotesForInstance(Instance inst)
double[]
LimAttClassifier. getVotesForInstance(Instance inst)
double[]
OCBoost. getVotesForInstance(Instance inst)
double[]
OnlineAccuracyUpdatedEnsemble. getVotesForInstance(Instance inst)
Predicts a class for an example.double[]
OnlineSmoothBoost. getVotesForInstance(Instance inst)
double[]
OzaBag. getVotesForInstance(Instance inst)
double[]
OzaBagAdwin. getVotesForInstance(Instance inst)
double[]
OzaBagASHT. getVotesForInstance(Instance inst)
double[]
OzaBoost. getVotesForInstance(Instance inst)
double[]
OzaBoostAdwin. getVotesForInstance(Instance inst)
double[]
PairedLearners. getVotesForInstance(Instance inst)
double[]
RandomRules. getVotesForInstance(Instance inst)
double[]
RCD. getVotesForInstance(Instance inst)
double[]
SelfOptimisingKNearestLeaves. getVotesForInstance(Instance instance)
double[]
SelfOptimisingKNearestLeaves.SelfOptimisingKNearestLeavesBaseLearner. getVotesForInstance(Instance instance)
double[]
StreamingGradientBoostedTrees. getVotesForInstance(Instance inst)
double[]
StreamingGradientBoostedTrees.SGBT. getVotesForInstance(Instance inst)
double[]
StreamingRandomPatches. getVotesForInstance(Instance instance)
double[]
StreamingRandomPatches.StreamingRandomPatchesClassifier. getVotesForInstance(Instance instance)
double[]
TemporallyAugmentedClassifier. getVotesForInstance(Instance instance)
double[]
WeightedMajorityAlgorithm. getVotesForInstance(Instance inst)
double[]
WEKAClassifier. getVotesForInstance(Instance samoaInstance)
double[]
LeveragingBag. getVotesForInstanceBinary(Instance inst)
double[]
OzaBoostAdwin. getVotesForInstanceBinary(Instance inst)
protected void
AdaptiveRandomForest. initEnsemble(Instance instance)
protected void
AdaptiveRandomForestRegressor. initEnsemble(Instance instance)
protected void
SelfOptimisingKNearestLeaves. initEnsemble(Instance instance)
void
StreamingGradientBoostedTrees.SGBT. initEnsemble(Instance inst)
protected void
StreamingRandomPatches. initEnsemble(Instance instance)
static Instance
StreamingGradientBoostedTrees. newBinaryClassInstance(Instance instance)
void
StreamingRandomPatches.StreamingRandomPatchesClassifier. prepareRandomSubspaceInstance(Instance instance, double weight)
void
StreamingRandomPatches.StreamingRandomPatchesClassifier. reset(Instance instance, long instancesSeen, Random random)
protected void
DACC. trainAndClassify(Instance inst)
Receives a training instance from the stream and updates the adaptive classifiers accordinglyvoid
StreamingGradientBoostedTrees.SGBT. trainBoosterUsingSoftmaxCrossEntropyLoss(Instance inst)
void
AdaptiveRandomForest.ARFBaseLearner. trainOnInstance(Instance instance, double weight, long instancesSeen)
void
AdaptiveRandomForestRegressor.ARFFIMTDDBaseLearner. trainOnInstance(Instance instance, double weight, long instancesSeen)
void
SelfOptimisingKNearestLeaves.SelfOptimisingKNearestLeavesBaseLearner. trainOnInstance(Instance instance, double weight, long instancesSeen)
void
StreamingGradientBoostedTrees.SGBT. trainOnInstance(Instance inst)
void
StreamingRandomPatches.StreamingRandomPatchesClassifier. trainOnInstance(Instance instance, double weight, long instancesSeen, Random random)
void
AccuracyUpdatedEnsemble. trainOnInstanceImpl(Instance inst)
void
AccuracyWeightedEnsemble. trainOnInstanceImpl(Instance inst)
void
ADACC. trainOnInstanceImpl(Instance inst)
void
AdaptiveRandomForest. trainOnInstanceImpl(Instance instance)
void
AdaptiveRandomForestRegressor. trainOnInstanceImpl(Instance instance)
void
ADOB. trainOnInstanceImpl(Instance inst)
void
BOLE. trainOnInstanceImpl(Instance inst)
void
DACC. trainOnInstanceImpl(Instance inst)
void
DynamicWeightedMajority. trainOnInstanceImpl(Instance inst)
void
HeterogeneousEnsembleBlast. trainOnInstanceImpl(Instance inst)
void
HeterogeneousEnsembleBlastFadingFactors. trainOnInstanceImpl(Instance inst)
void
LearnNSE. trainOnInstanceImpl(Instance inst)
void
LeveragingBag. trainOnInstanceImpl(Instance inst)
void
LimAttClassifier. trainOnInstanceImpl(Instance inst)
void
OCBoost. trainOnInstanceImpl(Instance inst)
void
OnlineAccuracyUpdatedEnsemble. trainOnInstanceImpl(Instance inst)
void
OnlineSmoothBoost. trainOnInstanceImpl(Instance inst)
void
OzaBag. trainOnInstanceImpl(Instance inst)
void
OzaBagAdwin. trainOnInstanceImpl(Instance inst)
void
OzaBagASHT. trainOnInstanceImpl(Instance inst)
void
OzaBoost. trainOnInstanceImpl(Instance inst)
void
OzaBoostAdwin. trainOnInstanceImpl(Instance inst)
void
PairedLearners. trainOnInstanceImpl(Instance inst)
void
RandomRules. trainOnInstanceImpl(Instance inst)
void
RCD. trainOnInstanceImpl(Instance inst)
void
SelfOptimisingKNearestLeaves. trainOnInstanceImpl(Instance instance)
void
StreamingGradientBoostedTrees.SGBT.BoostingCommittee. trainOnInstanceImpl(Instance inst)
void
StreamingGradientBoostedTrees.SGBT.BoostingCommittee. trainOnInstanceImpl(Instance[] instances, int multipleIterationByHessianCeiling, StreamingGradientBoostedTrees.SGBT.GradHess[] gradHess)
void
StreamingGradientBoostedTrees. trainOnInstanceImpl(Instance inst)
void
StreamingRandomPatches. trainOnInstanceImpl(Instance instance)
void
TemporallyAugmentedClassifier. trainOnInstanceImpl(Instance instance)
void
WeightedMajorityAlgorithm. trainOnInstanceImpl(Instance inst)
void
WEKAClassifier. trainOnInstanceImpl(Instance samoaInstance)
protected Instance
RandomRules. transformInstance(Instance inst, int classifierIndex)
void
StreamingRandomPatches.StreamingRandomPatchesClassifier. triggerWarning(Instance instance, long instancesSeen, Random random)
Constructors in moa.classifiers.meta with parameters of type Instance Constructor Description StreamingRandomPatchesClassifier(int indexOriginal, Classifier instantiatedClassifier, BasicClassificationPerformanceEvaluator evaluatorInstantiated, long instancesSeen, boolean disableBkgLearner, boolean disableDriftDetector, ClassOption driftOption, ClassOption warningOption, ArrayList<Integer> featuresIndexes, Instance instance, boolean isBackgroundLearner)
TrainingRunnable(AdaptiveRandomForest.ARFBaseLearner learner, Instance instance, double weight, long instancesSeen)
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Uses of Instance in moa.classifiers.meta.imbalanced
Fields in moa.classifiers.meta.imbalanced with type parameters of type Instance Modifier and Type Field Description protected ArrayList<Instance>
RebalanceStream. batch
protected ArrayList<Instance>
RebalanceStream. batchMajority
protected ArrayList<Instance>
RebalanceStream. batchMinority
protected HashMap<Instance,Integer>
CSMOTE. instanceGenerated
protected HashMap<Instance,Integer>
RebalanceStream. instanceGenerated
protected ArrayList<Instance>
RebalanceStream. resetBatch
protected ArrayList<Instance>
RebalanceStream. resetBatchMajority
protected ArrayList<Instance>
RebalanceStream. resetBatchMinority
protected ArrayList<Instance>
CSMOTE. W
Methods in moa.classifiers.meta.imbalanced that return Instance Modifier and Type Method Description protected Instance
OnlineSMOTEBagging. onlineSMOTE()
Methods in moa.classifiers.meta.imbalanced with parameters of type Instance Modifier and Type Method Description double[]
CSMOTE. getVotesForInstance(Instance instance)
double[]
OnlineAdaBoost. getVotesForInstance(Instance instance)
double[]
OnlineAdaC2. getVotesForInstance(Instance instance)
double[]
OnlineCSB2. getVotesForInstance(Instance instance)
double[]
OnlineRUSBoost. getVotesForInstance(Instance instance)
double[]
OnlineSMOTEBagging. getVotesForInstance(Instance instance)
double[]
OnlineUnderOverBagging. getVotesForInstance(Instance instance)
double[]
RebalanceStream. getVotesForInstance(Instance instance)
void
CSMOTE. trainOnInstanceImpl(Instance instance)
void
OnlineAdaBoost. trainOnInstanceImpl(Instance instance)
void
OnlineAdaC2. trainOnInstanceImpl(Instance instance)
void
OnlineCSB2. trainOnInstanceImpl(Instance instance)
void
OnlineRUSBoost. trainOnInstanceImpl(Instance instance)
void
OnlineSMOTEBagging. trainOnInstanceImpl(Instance instance)
void
OnlineUnderOverBagging. trainOnInstanceImpl(Instance instance)
void
RebalanceStream. trainOnInstanceImpl(Instance instance)
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Uses of Instance in moa.classifiers.multilabel
Methods in moa.classifiers.multilabel with parameters of type Instance Modifier and Type Method Description double[]
MultilabelHoeffdingTree.MultilabelLearningNodeClassifier. getClassVotes(Instance inst, HoeffdingTree ht)
Prediction
MultilabelHoeffdingTree.MultilabelLearningNodeClassifier. getPredictionForInstance(Instance inst, HoeffdingTree ht)
static List<Integer>
MultilabelHoeffdingTree. getRelevantLabels(Instance x)
double[]
MEKAClassifier. getVotesForInstance(Instance samoaInstance)
void
MultilabelHoeffdingTree.MultilabelInactiveLearningNode. learnFromInstance(Instance inst, HoeffdingTree ht)
void
MultilabelHoeffdingTree.MultilabelLearningNodeClassifier. learnFromInstance(Instance inst, HoeffdingTree ht)
void
MultilabelHoeffdingTree. trainOnInstance(Instance inst)
Method parameters in moa.classifiers.multilabel with type arguments of type Instance Modifier and Type Method Description Prediction
MultilabelHoeffdingTree. getPredictionForInstance(Example<Instance> example)
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Uses of Instance in moa.classifiers.multilabel.meta
Methods in moa.classifiers.multilabel.meta with parameters of type Instance Modifier and Type Method Description static Prediction
OzaBagML. combinePredictions(Prediction[] predictions, Instance inst)
static double[]
OzaBagML. compileVotes(Classifier[] h, Instance inst)
double[]
OzaBagAdwinML. getVotesForInstance(Instance inst)
double[]
OzaBagML. getVotesForInstance(Instance inst)
void
OzaBagAdwinML. trainOnInstanceImpl(Instance inst)
Method parameters in moa.classifiers.multilabel.meta with type arguments of type Instance Modifier and Type Method Description Prediction
OzaBagAdwinML. getPredictionForInstance(Example<Instance> example)
Prediction
OzaBagML. getPredictionForInstance(Example<Instance> example)
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Uses of Instance in moa.classifiers.multilabel.trees
Methods in moa.classifiers.multilabel.trees with parameters of type Instance Modifier and Type Method Description double[]
ISOUPTree. getNormalizedError(Instance inst, double[] prediction)
double[]
ISOUPTree.LeafNode. getPrediction(Instance inst)
abstract double[]
ISOUPTree.Node. getPrediction(Instance inst)
double[]
ISOUPTree.SplitNode. getPrediction(Instance inst)
double[]
ISOUPTree.LeafNode. getPredictionModel(Instance inst)
Retrieve the class votes using the perceptron learnerdouble[]
ISOUPTree.LeafNode. getPredictionTargetMean(Instance inst)
int
ISOUPTree.SplitNode. instanceChildIndex(Instance inst)
void
ISOUPTree.LeafNode. learnFromInstance(Instance instance, double[] prediction, boolean growthAllowed)
Method to learn from an instance that passes the new instance to the perceptron learner, and also prevents the class value from being truncated to an int when it is passed to the attribute observerdouble[]
ISOUPTree. normalizedInputVector(Instance inst)
double[]
ISOUPTree. normalizedTargetVector(Instance instance)
double
ISOUPTree. normalizeTargetValue(Instance inst, int i)
void
ISOUPTree. processInstance(Instance inst, ISOUPTree.Node node, double[] prediction, double[] normalError, boolean growthAllowed, boolean inAlternate)
void
ISOUPTree.MultitargetPerceptron. updatePerceptron(Instance inst)
Update the model using the provided instancevoid
ISOUPTree.MultitargetPerceptron. updateWeights(Instance inst, double learningRatio)
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Uses of Instance in moa.classifiers.multitarget
Methods in moa.classifiers.multitarget that return Instance Modifier and Type Method Description protected Instance
BasicMultiLabelLearner. transformInstance(MultiLabelInstance inst, int outputIndex)
protected Instance
BasicMultiTargetRegressor. transformInstance(MultiLabelInstance inst, int outputIndex)
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Uses of Instance in moa.classifiers.oneclass
Methods in moa.classifiers.oneclass with parameters of type Instance Modifier and Type Method Description double
Autoencoder. getAnomalyScore(Instance inst)
Returns the squared error between the input value and the reconstructed value as the anomaly score for the argument instance.double
HSTrees. getAnomalyScore(Instance inst)
Returns the anomaly score for the argument instance.double
NearestNeighbourDescription. getAnomalyScore(Instance inst)
Returns the anomaly score for an argument instance based on the distance from it to its nearest neighbour compared to the distance from its nearest neighbour to the neighbour's nearest neighbour.double[]
Autoencoder. getVotesForInstance(Instance inst)
Calculates the error between the autoencoder's reconstruction of the input and the argument instances.double[]
HSTrees. getVotesForInstance(Instance inst)
Combine the anomaly scores from each HSTree in the forest and convert into a vote score.double[]
NearestNeighbourDescription. getVotesForInstance(Instance inst)
Calculates the distance between the argument instance and its nearest neighbour as well as the distance between that nearest neighbour and its own nearest neighbour.double
HSTreeNode. score(Instance inst, int sizeLimit)
If this node is a leaf node or it has a mass profile of less than sizeLimit, this returns the anomaly score for the argument instance.void
Autoencoder. trainOnInstanceImpl(Instance inst)
Uses backpropagation to update the weights in the autoencoder.void
HSTrees. trainOnInstanceImpl(Instance inst)
Update the forest with the argument instancevoid
NearestNeighbourDescription. trainOnInstanceImpl(Instance inst)
The classifier adds the argument instance to its neighbourhood.void
HSTreeNode. updateMass(Instance inst, boolean referenceWindow)
Update the mass profile of this node.Method parameters in moa.classifiers.oneclass with type arguments of type Instance Modifier and Type Method Description void
Autoencoder. initialize(Collection<Instance> trainingPoints)
Initializes the Autoencoder classifier on the argument trainingPoints.void
HSTrees. initialize(Collection<Instance> trainingPoints)
Initializes the Streaming HS-Trees classifier on the argument trainingPoints.void
NearestNeighbourDescription. initialize(Collection<Instance> trainingPoints)
Initializes the Nearest Neighbour Distance (NN-d) classifier with the argument training points.Constructor parameters in moa.classifiers.oneclass with type arguments of type Instance Constructor Description NearestNeighbourDescription(List<Instance> trainingSet)
Constructor for a Nearest Neighbour Description classifier based on an argument training set of instances. -
Uses of Instance in moa.classifiers.rules
Fields in moa.classifiers.rules declared as Instance Modifier and Type Field Description protected Instance
RuleClassifier. instance
Methods in moa.classifiers.rules with parameters of type Instance Modifier and Type Method Description double
RuleClassifier. computeAnomalySupervised(RuleClassification rl, int ruleIndex, Instance inst)
double
RuleClassifier. computeAnomalyUnsupervised(RuleClassification rl, int ruleIndex, Instance inst)
void
RuleClassifier. createRule(Instance inst)
boolean
Predicates. evaluate(Instance inst)
void
RuleClassifier. expandeRule(RuleClassification rl, Instance inst, int ruleIndex)
protected double[]
RuleClassifier. firstHit(Instance inst)
protected double[]
RuleClassifierNBayes. firstHitNB(Instance inst)
int
AbstractAMRules. getModelAttIndexToInstanceAttIndex(int index, Instance inst)
Vote
AbstractAMRules. getVotes(Instance instance)
getVotes extension of the instance method getVotesForInstance in moa.classifier.java returns the prediction of the instance.double[]
AbstractAMRules. getVotesForInstance(Instance instance)
getVotesForInstance extension of the instance method getVotesForInstance in moa.classifier.java returns the prediction of the instance.double[]
BinaryClassifierFromRegressor. getVotesForInstance(Instance inst)
double[]
RuleClassifier. getVotesForInstance(Instance inst)
double[]
RuleClassifierNBayes. getVotesForInstance(Instance inst)
void
RuleClassifier. initializeRuleStatistics(RuleClassification rl, Predicates pred, Instance inst)
static int
AbstractAMRules. modelAttIndexToInstanceAttIndex(int index, Instance inst)
Gets the index of the attribute in the instance, given the index of the attribute in the learner.protected double[]
RuleClassifier. oberversDistribProb(Instance inst, DoubleVector classDistrib)
boolean
RuleClassification. ruleEvaluate(Instance inst)
void
RuleClassifier. theBestAttributes(Instance instance, AutoExpandVector<AttributeClassObserver> observersParameter)
void
AbstractAMRules. trainOnInstanceImpl(Instance instance)
void
BinaryClassifierFromRegressor. trainOnInstanceImpl(Instance inst)
void
RuleClassifier. trainOnInstanceImpl(Instance inst)
void
RuleClassifier. updateRuleAttribStatistics(Instance inst, RuleClassification rl, int ruleIndex)
protected void
AbstractAMRules. VerboseToConsole(Instance inst)
protected double[]
RuleClassifier. weightedMax(Instance inst)
protected double[]
RuleClassifierNBayes. weightedMaxNB(Instance inst)
protected double[]
RuleClassifier. weightedSum(Instance inst)
protected double[]
RuleClassifierNBayes. weightedSumNB(Instance inst)
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Uses of Instance in moa.classifiers.rules.core
Methods in moa.classifiers.rules.core with parameters of type Instance Modifier and Type Method Description double
Rule. computeError(Instance instance)
abstract double
RuleActiveLearningNode. computeError(Instance instance)
double
RuleActiveRegressionNode. computeError(Instance instance)
protected void
RuleActiveRegressionNode. debuganomaly(Instance instance, double uni, double multi, double probability)
boolean
NominalRulePredicate. evaluate(Instance instance)
boolean
NumericRulePredicate. evaluate(Instance instance)
boolean
Predicate. evaluate(Instance instance)
boolean
RuleSplitNode. evaluate(Instance instance)
abstract int
RuleActiveLearningNode. getLearnerToUse(Instance instance, int predictionMode)
int
RuleActiveRegressionNode. getLearnerToUse(Instance instance, int predMode)
double
RuleActiveRegressionNode. getNormalizedPrediction(Instance instance)
double[]
Rule. getPrediction(Instance instance)
double[]
Rule. getPrediction(Instance instance, int mode)
double[]
RuleActiveLearningNode. getPrediction(Instance instance)
abstract double[]
RuleActiveLearningNode. getPrediction(Instance instance, int predictionMode)
double[]
RuleActiveRegressionNode. getPrediction(Instance instance, int predictionMode)
boolean
Rule. isAnomaly(Instance instance, double uniVariateAnomalyProbabilityThreshold, double multiVariateAnomalyProbabilityThreshold, int numberOfInstanceesForAnomaly)
abstract boolean
RuleActiveLearningNode. isAnomaly(Instance instance, double uniVariateAnomalyProbabilityThreshold, double multiVariateAnomalyProbabilityThreshold, int numberOfInstanceesForAnomaly)
boolean
RuleActiveRegressionNode. isAnomaly(Instance instance, double uniVariateAnomalyProbabilityThreshold, double multiVariateAnomalyProbabilityThreshold, int numberOfInstanceesForAnomaly)
boolean
Rule. isCovering(Instance inst)
abstract void
RuleActiveLearningNode. learnFromInstance(Instance inst)
void
RuleActiveLearningNode. learnFromInstance(Instance inst, HoeffdingTree ht)
void
RuleActiveRegressionNode. learnFromInstance(Instance inst)
void
Rule. updateStatistics(Instance instance)
void
RuleActiveLearningNode. updateStatistics(Instance instance)
void
RuleActiveRegressionNode. updateStatistics(Instance instance)
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Uses of Instance in moa.classifiers.rules.core.anomalydetection
Methods in moa.classifiers.rules.core.anomalydetection with parameters of type Instance Modifier and Type Method Description protected void
OddsRatioScore. printAnomaly(Instance inst, double anomaly)
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Uses of Instance in moa.classifiers.rules.core.conditionaltests
Methods in moa.classifiers.rules.core.conditionaltests with parameters of type Instance Modifier and Type Method Description int
NumericAttributeBinaryRulePredicate. branchForInstance(Instance inst)
boolean
NominalAttributeBinaryRulePredicate. evaluate(Instance inst)
boolean
NumericAttributeBinaryRulePredicate. evaluate(Instance inst)
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Uses of Instance in moa.classifiers.rules.errormeasurers
Methods in moa.classifiers.rules.errormeasurers with parameters of type Instance Modifier and Type Method Description abstract void
ErrorMeasurement. addPrediction(double[] prediction, Instance inst)
void
MeanAbsoluteDeviation. addPrediction(double[] prediction, Instance inst)
void
RootMeanSquaredError. addPrediction(double[] prediction, Instance inst)
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Uses of Instance in moa.classifiers.rules.functions
Methods in moa.classifiers.rules.functions with parameters of type Instance Modifier and Type Method Description double[]
AdaptiveNodePredictor. getVotesForInstance(Instance inst)
double[]
FadingTargetMean. getVotesForInstance(Instance inst)
double[]
LowPassFilteredLearner. getVotesForInstance(Instance inst)
double[]
Perceptron. getVotesForInstance(Instance inst)
double[]
TargetMean. getVotesForInstance(Instance inst)
double[]
Perceptron. normalizedInstance(Instance inst)
double
Perceptron. normalizedPrediction(Instance inst)
void
AdaptiveNodePredictor. trainOnInstanceImpl(Instance inst)
void
FadingTargetMean. trainOnInstanceImpl(Instance inst)
void
LowPassFilteredLearner. trainOnInstanceImpl(Instance inst)
void
Perceptron. trainOnInstanceImpl(Instance inst)
Update the model using the provided instancevoid
TargetMean. trainOnInstanceImpl(Instance inst)
protected void
TargetMean. updateAccumulatedError(Instance inst)
void
Perceptron. updateWeights(Instance inst, double learningRatio)
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Uses of Instance in moa.classifiers.rules.meta
Methods in moa.classifiers.rules.meta with parameters of type Instance Modifier and Type Method Description double[]
RandomAMRulesOld. getVotesForInstance(Instance inst)
void
RandomAMRulesOld. trainOnInstanceImpl(Instance instance)
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Uses of Instance in moa.classifiers.rules.multilabel.core
Methods in moa.classifiers.rules.multilabel.core with parameters of type Instance Modifier and Type Method Description boolean
Literal. evaluate(Instance inst)
protected abstract double[]
LearningLiteral. getNormalizedErrors(Prediction prediction, Instance inst)
protected double[]
LearningLiteralClassification. getNormalizedErrors(Prediction prediction, Instance instance)
protected double[]
LearningLiteralRegression. getNormalizedErrors(Prediction prediction, Instance instance)
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Uses of Instance in moa.classifiers.rules.multilabel.instancetransformers
Methods in moa.classifiers.rules.multilabel.instancetransformers that return Instance Modifier and Type Method Description Instance
InstanceAttributesSelector. sourceInstanceToTarget(Instance sourceInstance)
Instance
InstanceOutputAttributesSelector. sourceInstanceToTarget(Instance sourceInstance)
Instance
InstanceTransformer. sourceInstanceToTarget(Instance sourceInstance)
Instance
NoInstanceTransformation. sourceInstanceToTarget(Instance sourceInstance)
Methods in moa.classifiers.rules.multilabel.instancetransformers with parameters of type Instance Modifier and Type Method Description Instance
InstanceAttributesSelector. sourceInstanceToTarget(Instance sourceInstance)
Instance
InstanceOutputAttributesSelector. sourceInstanceToTarget(Instance sourceInstance)
Instance
InstanceTransformer. sourceInstanceToTarget(Instance sourceInstance)
Instance
NoInstanceTransformation. sourceInstanceToTarget(Instance sourceInstance)
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Uses of Instance in moa.classifiers.trees
Methods in moa.classifiers.trees with parameters of type Instance Modifier and Type Method Description ARFFIMTDD.Node
ARFFIMTDD.SplitNode. descendOneStep(Instance inst)
FIMTDD.Node
FIMTDD.SplitNode. descendOneStep(Instance inst)
SelfOptimisingBaseTree.Node
SelfOptimisingBaseTree.SplitNode. descendOneStep(Instance inst)
EFDT.FoundNode
EFDT.Node. filterInstanceToLeaf(Instance inst, EFDT.SplitNode parent, int parentBranch)
EFDT.FoundNode
EFDT.SplitNode. filterInstanceToLeaf(Instance inst, EFDT.SplitNode parent, int parentBranch)
HoeffdingTree.FoundNode
HoeffdingTree.Node. filterInstanceToLeaf(Instance inst, HoeffdingTree.SplitNode parent, int parentBranch)
HoeffdingTree.FoundNode
HoeffdingTree.SplitNode. filterInstanceToLeaf(Instance inst, HoeffdingTree.SplitNode parent, int parentBranch)
void
HoeffdingAdaptiveTree.AdaLearningNode. filterInstanceToLeaves(Instance inst, HoeffdingTree.SplitNode splitparent, int parentBranch, List<HoeffdingTree.FoundNode> foundNodes, boolean updateSplitterCounts)
void
HoeffdingAdaptiveTree.AdaSplitNode. filterInstanceToLeaves(Instance inst, HoeffdingTree.SplitNode myparent, int parentBranch, List<HoeffdingTree.FoundNode> foundNodes, boolean updateSplitterCounts)
HoeffdingTree.FoundNode[]
HoeffdingAdaptiveTree. filterInstanceToLeaves(Instance inst, HoeffdingTree.SplitNode parent, int parentBranch, boolean updateSplitterCounts)
void
HoeffdingAdaptiveTree.NewNode. filterInstanceToLeaves(Instance inst, HoeffdingTree.SplitNode myparent, int parentBranch, List<HoeffdingTree.FoundNode> foundNodes, boolean updateSplitterCounts)
HoeffdingOptionTree.FoundNode[]
HoeffdingOptionTree.Node. filterInstanceToLeaves(Instance inst, HoeffdingOptionTree.SplitNode parent, int parentBranch, boolean updateSplitterCounts)
void
HoeffdingOptionTree.Node. filterInstanceToLeaves(Instance inst, HoeffdingOptionTree.SplitNode splitparent, int parentBranch, List<HoeffdingOptionTree.FoundNode> foundNodes, boolean updateSplitterCounts)
void
HoeffdingOptionTree.SplitNode. filterInstanceToLeaves(Instance inst, HoeffdingOptionTree.SplitNode myparent, int parentBranch, List<HoeffdingOptionTree.FoundNode> foundNodes, boolean updateSplitterCounts)
double[]
AdaHoeffdingOptionTree.AdaLearningNode. getClassVotes(Instance inst, HoeffdingOptionTree ht)
double[]
ARFHoeffdingTree.LearningNodeNB. getClassVotes(Instance inst, HoeffdingTree ht)
double[]
ARFHoeffdingTree.LearningNodeNBAdaptive. getClassVotes(Instance inst, HoeffdingTree ht)
double[]
EFDT.LearningNodeNB. getClassVotes(Instance inst, EFDT ht)
double[]
EFDT.LearningNodeNBAdaptive. getClassVotes(Instance inst, EFDT ht)
double[]
EFDT.Node. getClassVotes(Instance inst, EFDT ht)
double[]
HoeffdingAdaptiveTree.AdaLearningNode. getClassVotes(Instance inst, HoeffdingTree ht)
double[]
HoeffdingAdaptiveTreeClassifLeaves.LearningNodeHATClassifier. getClassVotes(Instance inst, HoeffdingTree ht)
double[]
HoeffdingOptionTree.LearningNodeNB. getClassVotes(Instance inst, HoeffdingOptionTree hot)
double[]
HoeffdingOptionTree.LearningNodeNBAdaptive. getClassVotes(Instance inst, HoeffdingOptionTree ht)
double[]
HoeffdingOptionTree.Node. getClassVotes(Instance inst, HoeffdingOptionTree ht)
double[]
HoeffdingTree.LearningNodeNB. getClassVotes(Instance inst, HoeffdingTree ht)
double[]
HoeffdingTree.LearningNodeNBAdaptive. getClassVotes(Instance inst, HoeffdingTree ht)
double[]
HoeffdingTree.Node. getClassVotes(Instance inst, HoeffdingTree ht)
double[]
HoeffdingTreeClassifLeaves.LearningNodeClassifier. getClassVotes(Instance inst, HoeffdingTree ht)
double[]
LimAttHoeffdingTree.LearningNodeNB. getClassVotes(Instance inst, HoeffdingTree ht)
double[]
LimAttHoeffdingTree.LearningNodeNBAdaptive. getClassVotes(Instance inst, HoeffdingTree ht)
double[]
RandomHoeffdingTree.LearningNodeNB. getClassVotes(Instance inst, HoeffdingTree ht)
double[]
RandomHoeffdingTree.LearningNodeNBAdaptive. getClassVotes(Instance inst, HoeffdingTree ht)
SelfOptimisingBaseTree.Node
SelfOptimisingBaseTree. getLeafForInstance(Instance inst, SelfOptimisingBaseTree.Node node)
double
ARFFIMTDD. getNormalizedError(Instance inst, double prediction)
double
FIMTDD. getNormalizedError(Instance inst, double prediction)
double
SelfOptimisingBaseTree. getNormalizedError(Instance inst, double prediction)
double
ARFFIMTDD.LeafNode. getPrediction(Instance inst)
double
ARFFIMTDD.Node. getPrediction(Instance inst)
double
ARFFIMTDD.SplitNode. getPrediction(Instance inst)
double
FIMTDD.LeafNode. getPrediction(Instance inst)
double
FIMTDD.Node. getPrediction(Instance inst)
double
FIMTDD.SplitNode. getPrediction(Instance inst)
double
ORTO.OptionNode. getPrediction(Instance inst, ORTO tree)
double
SelfOptimisingBaseTree.LeafNode. getPrediction(Instance inst)
double
SelfOptimisingBaseTree.Node. getPrediction(Instance inst)
double
SelfOptimisingBaseTree.SplitNode. getPrediction(Instance inst)
double
ARFFIMTDD.LeafNode. getPredictionModel(Instance inst)
Retrieve the class votes using the perceptron learnerdouble
FIMTDD.LeafNode. getPredictionModel(Instance inst)
Retrieve the class votes using the perceptron learnerdouble
SelfOptimisingBaseTree.LeafNode. getPredictionModel(Instance inst)
Retrieve the class votes using the perceptron learnerdouble
ARFFIMTDD.LeafNode. getPredictionTargetMean(Instance inst)
double
FIMTDD.LeafNode. getPredictionTargetMean(Instance inst)
double
SelfOptimisingBaseTree.LeafNode. getPredictionTargetMean(Instance inst)
double[]
ARFFIMTDD. getVotesForInstance(Instance inst)
double[]
DecisionStump. getVotesForInstance(Instance inst)
double[]
EFDT. getVotesForInstance(Instance inst)
double[]
FIMTDD. getVotesForInstance(Instance inst)
double[]
HoeffdingAdaptiveTree. getVotesForInstance(Instance inst)
double[]
HoeffdingOptionTree. getVotesForInstance(Instance inst)
double[]
HoeffdingTree. getVotesForInstance(Instance inst)
double[]
SelfOptimisingBaseTree. getVotesForInstance(Instance inst)
int
ARFFIMTDD.SplitNode. instanceChildIndex(Instance inst)
int
EFDT.SplitNode. instanceChildIndex(Instance inst)
int
FIMTDD.SplitNode. instanceChildIndex(Instance inst)
int
HoeffdingOptionTree.SplitNode. instanceChildIndex(Instance inst)
int
HoeffdingTree.SplitNode. instanceChildIndex(Instance inst)
int
SelfOptimisingBaseTree.SplitNode. instanceChildIndex(Instance inst)
void
AdaHoeffdingOptionTree.AdaLearningNode. learnFromInstance(Instance inst, HoeffdingOptionTree hot)
void
ARFFIMTDD.LeafNode. learnFromInstance(Instance inst, boolean growthAllowed, ARFFIMTDD arffimtdd)
Method to learn from an instance that passes the new instance to the perceptron learner, and also prevents the class value from being truncated to an int when it is passed to the attribute observervoid
ARFHoeffdingTree.LearningNodeNBAdaptive. learnFromInstance(Instance inst, HoeffdingTree ht)
void
ARFHoeffdingTree.RandomLearningNode. learnFromInstance(Instance inst, HoeffdingTree ht)
void
EFDT.ActiveLearningNode. learnFromInstance(Instance inst, EFDT ht)
void
EFDT.EFDTLearningNode. learnFromInstance(Instance inst, EFDT ht)
void
EFDT.EFDTLearningNode. learnFromInstance(Instance inst, EFDT ht, EFDT.EFDTSplitNode parent, int parentBranch)
void
EFDT.EFDTNode. learnFromInstance(Instance inst, EFDT ht, EFDT.EFDTSplitNode parent, int parentBranch)
void
EFDT.EFDTSplitNode. learnFromInstance(Instance inst, EFDT ht, EFDT.EFDTSplitNode parent, int parentBranch)
void
EFDT.InactiveLearningNode. learnFromInstance(Instance inst, EFDT ht)
abstract void
EFDT.LearningNode. learnFromInstance(Instance inst, EFDT ht)
void
EFDT.LearningNodeNBAdaptive. learnFromInstance(Instance inst, EFDT ht)
void
FIMTDD.LeafNode. learnFromInstance(Instance inst, boolean growthAllowed)
Method to learn from an instance that passes the new instance to the perceptron learner, and also prevents the class value from being truncated to an int when it is passed to the attribute observervoid
HoeffdingAdaptiveTree.AdaLearningNode. learnFromInstance(Instance inst, HoeffdingAdaptiveTree ht, HoeffdingTree.SplitNode parent, int parentBranch)
void
HoeffdingAdaptiveTree.AdaSplitNode. learnFromInstance(Instance inst, HoeffdingAdaptiveTree ht, HoeffdingTree.SplitNode parent, int parentBranch)
void
HoeffdingAdaptiveTree.NewNode. learnFromInstance(Instance inst, HoeffdingAdaptiveTree ht, HoeffdingTree.SplitNode parent, int parentBranch)
void
HoeffdingAdaptiveTreeClassifLeaves.LearningNodeHATClassifier. learnFromInstance(Instance inst, HoeffdingTree ht)
void
HoeffdingOptionTree.ActiveLearningNode. learnFromInstance(Instance inst, HoeffdingOptionTree ht)
void
HoeffdingOptionTree.InactiveLearningNode. learnFromInstance(Instance inst, HoeffdingOptionTree ht)
abstract void
HoeffdingOptionTree.LearningNode. learnFromInstance(Instance inst, HoeffdingOptionTree ht)
void
HoeffdingOptionTree.LearningNodeNBAdaptive. learnFromInstance(Instance inst, HoeffdingOptionTree hot)
void
HoeffdingTree.ActiveLearningNode. learnFromInstance(Instance inst, HoeffdingTree ht)
void
HoeffdingTree.InactiveLearningNode. learnFromInstance(Instance inst, HoeffdingTree ht)
abstract void
HoeffdingTree.LearningNode. learnFromInstance(Instance inst, HoeffdingTree ht)
void
HoeffdingTree.LearningNodeNBAdaptive. learnFromInstance(Instance inst, HoeffdingTree ht)
void
HoeffdingTreeClassifLeaves.LearningNodeClassifier. learnFromInstance(Instance inst, HoeffdingTree ht)
void
LimAttHoeffdingTree.LearningNodeNBAdaptive. learnFromInstance(Instance inst, HoeffdingTree ht)
void
LimAttHoeffdingTree.LimAttLearningNode. learnFromInstance(Instance inst, HoeffdingTree ht)
void
RandomHoeffdingTree.LearningNodeNBAdaptive. learnFromInstance(Instance inst, HoeffdingTree ht)
void
RandomHoeffdingTree.RandomLearningNode. learnFromInstance(Instance inst, HoeffdingTree ht)
void
SelfOptimisingBaseTree.LeafNode. learnFromInstance(Instance inst, boolean growthAllowed, SelfOptimisingBaseTree selfOptimisingBaseTree, SelfOptimisingBaseTree.LeafNode node)
Method to learn from an instance that passes the new instance to the perceptron learner, and also prevents the class value from being truncated to an int when it is passed to the attribute observerDoubleVector
ARFFIMTDD.FIMTDDPerceptron. normalizedInstance(Instance inst)
DoubleVector
FIMTDD.FIMTDDPerceptron. normalizedInstance(Instance inst)
DoubleVector
SelfOptimisingBaseTree.FIMTDDPerceptron. normalizedInstance(Instance inst)
protected double
ARFFIMTDD.FIMTDDPerceptron. prediction(Instance inst)
protected double
FIMTDD.FIMTDDPerceptron. prediction(Instance inst)
protected double
SelfOptimisingBaseTree.FIMTDDPerceptron. prediction(Instance inst)
void
ARFFIMTDD. processInstance(Instance inst, ARFFIMTDD.Node node, double prediction, double normalError, boolean growthAllowed, boolean inAlternate)
void
FIMTDD. processInstance(Instance inst, FIMTDD.Node node, double prediction, double normalError, boolean growthAllowed, boolean inAlternate)
void
ORTO. processInstance(Instance inst, FIMTDD.Node node, double prediction, double normalError, boolean growthAllowed, boolean inAlternate)
void
SelfOptimisingBaseTree. processInstance(Instance inst, SelfOptimisingBaseTree.Node node, double prediction, double normalError, boolean growthAllowed, boolean inAlternate)
void
ORTO. processInstanceOptionNode(Instance inst, ORTO.OptionNode node, double prediction, double normalError, boolean growthAllowed, boolean inAlternate)
void
ARFFIMTDD. trainOnInstanceImpl(Instance inst)
Method for updating (training) the model using a new instancevoid
ASHoeffdingTree. trainOnInstanceImpl(Instance inst)
void
DecisionStump. trainOnInstanceImpl(Instance inst)
void
EFDT. trainOnInstanceImpl(Instance inst)
void
FIMTDD. trainOnInstanceImpl(Instance inst)
Method for updating (training) the model using a new instancevoid
HoeffdingAdaptiveTree. trainOnInstanceImpl(Instance inst)
void
HoeffdingOptionTree. trainOnInstanceImpl(Instance inst)
void
HoeffdingTree. trainOnInstanceImpl(Instance inst)
void
SelfOptimisingBaseTree. trainOnInstanceImpl(Instance inst)
Method for updating (training) the model using a new instancevoid
ARFFIMTDD.FIMTDDPerceptron. updatePerceptron(Instance inst)
Update the model using the provided instancevoid
FIMTDD.FIMTDDPerceptron. updatePerceptron(Instance inst)
Update the model using the provided instancevoid
SelfOptimisingBaseTree.FIMTDDPerceptron. updatePerceptron(Instance inst)
Update the model using the provided instancevoid
ARFFIMTDD.FIMTDDPerceptron. updateWeights(Instance inst, double learningRatio)
void
FIMTDD.FIMTDDPerceptron. updateWeights(Instance inst, double learningRatio)
void
SelfOptimisingBaseTree.FIMTDDPerceptron. updateWeights(Instance inst, double learningRatio)
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Uses of Instance in moa.classifiers.trees.iadem
Methods in moa.classifiers.trees.iadem with parameters of type Instance Modifier and Type Method Description void
Iadem2.LeafNode. attemptToSplit(Instance instance)
void
Iadem3.AdaptiveLeafNode. attemptToSplit(Instance instance)
void
Iadem2. createRoot(Instance instance)
void
Iadem3. createRoot(Instance instance)
protected void
Iadem2.LeafNode. createVirtualNodes(IademNumericAttributeObserver numericObserver, boolean onlyMultiwayTest, boolean onlyBinaryTest, Instance instance)
protected void
Iadem3.AdaptiveLeafNode. createVirtualNodes(IademNumericAttributeObserver numericAttClassObserver, boolean onlyMultiwayTest, boolean onlyBinaryTest, Instance instance)
Iadem2.LeafNode[]
Iadem2.LeafNode. doSplit(IademAttributeSplitSuggestion bestSuggestion, Instance instance)
Iadem3.AdaptiveLeafNode[]
Iadem3.AdaptiveLeafNode. doSplit(IademAttributeSplitSuggestion mejorExpansion, Instance instance)
IademAttributeSplitSuggestion
Iadem2.LeafNode. getBestSplitSuggestion(Instance instance)
IademAttributeSplitSuggestion
Iadem2.LeafNode. getBestSplitSuggestionIADEM(Instance instance)
double[]
Iadem2. getClassVotes(Instance instance)
double[]
Iadem2.LeafNode. getClassVotes(Instance obs)
double[]
Iadem2.LeafNodeNB. getClassVotes(Instance inst)
double[]
Iadem2.LeafNodeNBKirkby. getClassVotes(Instance instance)
double[]
Iadem2.LeafNodeWeightedVote. getClassVotes(Instance instance)
abstract double[]
Iadem2.Node. getClassVotes(Instance instance)
double[]
Iadem2.SplitNode. getClassVotes(Instance inst)
double[]
Iadem2.VirtualNode. getClassVotes(Instance inst)
double[]
Iadem3.AdaptiveLeafNodeNB. getClassVotes(Instance inst)
double[]
Iadem3.AdaptiveLeafNodeNBAdaptive. getClassVotes(Instance instance)
double[]
Iadem3.AdaptiveLeafNodeNBKirkby. getClassVotes(Instance instance)
double[]
Iadem3.AdaptiveLeafNodeWeightedVote. getClassVotes(Instance instance)
double[]
Iadem3.AdaptiveSplitNode. getClassVotes(Instance observacion)
double[]
Iadem3. getClassVotes(Instance instance)
protected void
Iadem3. getClassVotesFromLeaf(Instance instance)
protected IademAttributeSplitSuggestion
Iadem2.LeafNode. getFastSplitSuggestion(Instance instance)
double
Iadem2.VirtualNode. getHeuristicMeasureLower(Instance instance)
double
Iadem2.VirtualNode. getHeuristicMeasureUpper(Instance instance)
double[]
Iadem2.LeafNode. getMajorityClassVotes(Instance instance)
protected double[]
Iadem2.LeafNodeNB. getNaiveBayesPrediction(Instance obs)
protected double[]
Iadem3.AdaptiveLeafNodeNB. getNaiveBayesPrediction(Instance inst)
Iadem2.SplitNode
Iadem2.NominalVirtualNode. getNewSplitNode(long newTotal, Iadem2.Node parent, IademAttributeSplitSuggestion bestSuggestion, Instance instance)
Iadem2.SplitNode
Iadem2.NumericVirtualNode. getNewSplitNode(long newTotal, Iadem2.Node parent, IademAttributeSplitSuggestion bestSuggestion, Instance instance)
abstract Iadem2.SplitNode
Iadem2.VirtualNode. getNewSplitNode(long newInstancesSeen, Iadem2.Node parent, IademAttributeSplitSuggestion bestSuggestion, Instance instance)
Iadem2.SplitNode
Iadem3.AdaptiveNominalVirtualNode. getNewSplitNode(long counter, Iadem2.Node parent, IademAttributeSplitSuggestion bestSplit, Instance instance)
Iadem2.SplitNode
Iadem3.AdaptiveNumericVirtualNode. getNewSplitNode(long counter, Iadem2.Node parent, IademAttributeSplitSuggestion bestSplit, Instance instance)
int
Iadem2. getValuesOfNominalAttributes(int attIndex, Instance instance)
double[]
Iadem2. getVotesForInstance(Instance inst)
int
Iadem2.SplitNode. instanceChildIndex(Instance inst)
Iadem2.Node
Iadem2.LeafNode. learnFromInstance(Instance inst)
Iadem2.Node
Iadem2.LeafNodeNBKirkby. learnFromInstance(Instance inst)
Iadem2.Node
Iadem2.LeafNodeWeightedVote. learnFromInstance(Instance inst)
void
Iadem2. learnFromInstance(Instance instance)
abstract Iadem2.Node
Iadem2.Node. learnFromInstance(Instance instance)
Iadem2.Node
Iadem2.NominalVirtualNode. learnFromInstance(Instance inst)
Iadem2.Node
Iadem2.NumericVirtualNode. learnFromInstance(Instance instance)
Iadem2.Node
Iadem2.SplitNode. learnFromInstance(Instance inst)
Iadem2.Node
Iadem3.AdaptiveLeafNode. learnFromInstance(Instance inst)
Iadem2.Node
Iadem3.AdaptiveLeafNodeNBAdaptive. learnFromInstance(Instance inst)
Iadem2.Node
Iadem3.AdaptiveLeafNodeNBKirkby. learnFromInstance(Instance inst)
Iadem2.Node
Iadem3.AdaptiveNominalVirtualNode. learnFromInstance(Instance inst)
Iadem2.Node
Iadem3.AdaptiveNumericVirtualNode. learnFromInstance(Instance inst)
Iadem2.Node
Iadem3.AdaptiveSplitNode. learnFromInstance(Instance instance)
void
Iadem3. learnFromInstance(Instance instance)
void
Iadem3Subtree. learnFromInstance(Instance instance)
Iadem2.LeafNode
Iadem2. newLeafNode(Iadem2.Node parent, long instTreeCountSinceVirtual, long instNodeCountSinceVirtual, double[] classDist, Instance instance)
Iadem2.LeafNode
Iadem3. newLeafNode(Iadem2.Node parent, long instTreeCountSinceVirtual, long instNodeCountSinceVirtual, double[] initialClassCount, Instance instance)
protected ArrayList<Integer>
Iadem2.LeafNode. nominalAttUsed(Instance instance)
void
Iadem2. trainOnInstanceImpl(Instance inst)
void
Iadem2.NominalVirtualNode. updateHeuristicMeasure(Instance instance)
void
Iadem2.NumericVirtualNode. updateHeuristicMeasure(Instance instance)
abstract void
Iadem2.VirtualNode. updateHeuristicMeasure(Instance instance)
void
Iadem2.NominalVirtualNode. updateHeuristicMeasureBinaryTest(Instance instance)
void
Iadem2.NominalVirtualNode. updateHeuristicMeasureMultiwayTest(Instance instance)
Constructors in moa.classifiers.trees.iadem with parameters of type Instance Constructor Description AdaptiveLeafNode(Iadem3 arbol, Iadem2.Node parent, long instTreeCountSinceVirtual, long instNodeCountSinceVirtual, double[] initialClassCount, IademNumericAttributeObserver numericAttClassObserver, AbstractChangeDetector estimator, boolean onlyMultiwayTest, boolean onlyBinaryTest, Instance instance)
AdaptiveLeafNodeNB(Iadem3 tree, Iadem2.Node parent, long instTreeCountSinceVirtual, long instNodeCountSinceVirtual, double[] initialClassCount, IademNumericAttributeObserver numericAttClassObserver, int limitNaiveBayes, AbstractChangeDetector estimator, boolean onlyMultiwayTest, boolean onlyBinaryTest, Instance instance)
AdaptiveLeafNodeNBAdaptive(Iadem3 tree, Iadem2.Node parent, long instancesProcessedByTheTree, long instancesProcessedByThisLeaf, double[] classDist, IademNumericAttributeObserver observadorContinuos, int naiveBayesLimit, boolean onlyMultiwayTest, boolean onlyBinaryTest, AbstractChangeDetector estimator, Instance instance)
AdaptiveLeafNodeNBKirkby(Iadem3 tree, Iadem2.Node parent, long instancesProcessedByTheTree, long instancesProcessedByThisLeaf, double[] classDist, IademNumericAttributeObserver observadorContinuos, int naiveBayesLimit, boolean onlyMultiwayTest, boolean onlyBinaryTest, AbstractChangeDetector estimator, Instance instance)
AdaptiveLeafNodeWeightedVote(Iadem3 tree, Iadem2.Node parent, long instTreeCountSinceVirtual, long instNodeCountSinceVirtual, double[] classDist, IademNumericAttributeObserver observadorContinuos, int naiveBayesLimit, boolean onlyMultiwayTest, boolean onlyBinaryTest, AbstractChangeDetector estimator, Instance instance)
Iadem3Subtree(Iadem2.Node node, int treeLevel, Iadem3 mainTree, Instance instance)
LeafNode(Iadem2 tree, Iadem2.Node parent, long instTreeCountSinceVirtual, long instNodeCountSinceVirtual, double[] initialClassCount, IademNumericAttributeObserver numericAttClassObserver, boolean onlyMultiwayTest, boolean onlyBinaryTest, Instance instance)
LeafNodeNB(Iadem2 tree, Iadem2.Node parent, long instTreeCountSinceVirtual, long instNodeCountSinceVirtual, double[] initialClassVotes, IademNumericAttributeObserver numericAttClassObserver, int naiveBayesLimit, boolean onlyMultiwayTest, boolean onlyBinaryTest, Instance instance)
LeafNodeNBKirkby(Iadem2 tree, Iadem2.Node parent, long instancesProcessedByTheTree, long instancesProcessedByThisLeaf, double[] classDist, IademNumericAttributeObserver numericAttClassObserver, int naiveBayesLimit, boolean onlyMultiwayTest, boolean onlyBinaryTest, AbstractChangeDetector estimator, Instance instance)
LeafNodeWeightedVote(Iadem2 tree, Iadem2.Node parent, long instancesProcessedByTheTree, long instancesProcessedByThisLeaf, double[] classDist, IademNumericAttributeObserver observadorContinuos, int naiveBayesLimit, boolean onlyMultiwayTest, boolean onlyBinaryTest, AbstractChangeDetector estimator, Instance instance)
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Uses of Instance in moa.cluster
Methods in moa.cluster that return Instance Modifier and Type Method Description abstract Instance
Cluster. sample(Random random)
Samples this cluster by returning a point from inside it.Instance
SphereCluster. sample(Random random)
Samples this cluster by returning a point from inside it.Methods in moa.cluster with parameters of type Instance Modifier and Type Method Description double
SphereCluster. getCenterDistance(Instance instance)
double[]
SphereCluster. getDistanceVector(Instance instance)
abstract double
CFCluster. getInclusionProbability(Instance instance)
abstract double
Cluster. getInclusionProbability(Instance instance)
Returns the probability of the given point belonging to this cluster.double
SphereCluster. getInclusionProbability(Instance instance)
double
Clustering. getMaxInclusionProbability(Instance point)
Method parameters in moa.cluster with type arguments of type Instance Modifier and Type Method Description static HashMap<Integer,Integer>
Clustering. classValues(List<? extends Instance> points)
Constructors in moa.cluster with parameters of type Instance Constructor Description CFCluster(Instance instance, int dimensions)
Instantiates an empty kernel with the given dimensionality.Constructor parameters in moa.cluster with type arguments of type Instance Constructor Description Clustering(List<? extends Instance> points)
SphereCluster(List<? extends Instance> instances, int dimension)
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Uses of Instance in moa.clusterers
Methods in moa.clusterers with parameters of type Instance Modifier and Type Method Description double[]
Clusterer. getVotesForInstance(Instance inst)
double[]
ClusterGenerator. getVotesForInstance(Instance inst)
double[]
CobWeb. getVotesForInstance(Instance instance)
Classifies a given instance.double[]
WekaClusteringAlgorithm. getVotesForInstance(Instance inst)
protected static int
AbstractClusterer. modelAttIndexToInstanceAttIndex(int index, Instance inst)
void
AbstractClusterer. trainOnInstance(Instance inst)
void
Clusterer. trainOnInstance(Instance inst)
abstract void
AbstractClusterer. trainOnInstanceImpl(Instance inst)
void
ClusterGenerator. trainOnInstanceImpl(Instance inst)
void
CobWeb. trainOnInstanceImpl(Instance newInstance)
Adds an instance to the clusterer.void
WekaClusteringAlgorithm. trainOnInstanceImpl(Instance inst)
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Uses of Instance in moa.clusterers.clustream
Methods in moa.clusterers.clustream with parameters of type Instance Modifier and Type Method Description double
ClustreamKernel. getInclusionProbability(Instance instance)
See interfaceCluster
double[]
Clustream. getVotesForInstance(Instance inst)
double[]
WithKmeans. getVotesForInstance(Instance inst)
void
ClustreamKernel. insert(Instance instance, long timestamp)
void
Clustream. trainOnInstanceImpl(Instance instance)
void
WithKmeans. trainOnInstanceImpl(Instance instance)
Constructors in moa.clusterers.clustream with parameters of type Instance Constructor Description ClustreamKernel(Instance instance, int dimensions, long timestamp, double t, int m)
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Uses of Instance in moa.clusterers.clustree
Methods in moa.clusterers.clustree with parameters of type Instance Modifier and Type Method Description double
ClusKernel. getInclusionProbability(Instance instance)
double[]
ClusTree. getVotesForInstance(Instance inst)
void
ClusTree. trainOnInstanceImpl(Instance instance)
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Uses of Instance in moa.clusterers.denstream
Methods in moa.clusterers.denstream with parameters of type Instance Modifier and Type Method Description double
MicroCluster. getInclusionProbability(Instance instance)
double[]
WithDBSCAN. getVotesForInstance(Instance inst)
void
MicroCluster. insert(Instance instance, long timestamp)
void
WithDBSCAN. trainOnInstanceImpl(Instance inst)
Constructors in moa.clusterers.denstream with parameters of type Instance Constructor Description MicroCluster(Instance instance, int dimensions, long timestamp, double lambda, Timestamp currentTimestamp)
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Uses of Instance in moa.clusterers.dstream
Methods in moa.clusterers.dstream with parameters of type Instance Modifier and Type Method Description double
DensityGrid. getInclusionProbability(Instance instance)
Provides the probability of the argument instance belonging to the density grid in question.double
GridCluster. getInclusionProbability(Instance instance)
Iterates through the DensityGrids in the cluster and calculates the inclusion probability for each.double[]
Dstream. getVotesForInstance(Instance inst)
void
Dstream. printInst(Instance inst)
void
Dstream. trainOnInstanceImpl(Instance inst)
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Uses of Instance in moa.clusterers.kmeanspm
Methods in moa.clusterers.kmeanspm with parameters of type Instance Modifier and Type Method Description double[]
BICO. getVotesForInstance(Instance inst)
void
BICO. trainOnInstanceImpl(Instance inst)
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Uses of Instance in moa.clusterers.macro
Methods in moa.clusterers.macro with parameters of type Instance Modifier and Type Method Description double
NonConvexCluster. getInclusionProbability(Instance instance)
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Uses of Instance in moa.clusterers.meta
Methods in moa.clusterers.meta with parameters of type Instance Modifier and Type Method Description double[]
EnsembleClustererAbstract. getVotesForInstance(Instance inst)
void
EnsembleClustererAbstract. trainOnInstanceImpl(Instance inst)
Constructors in moa.clusterers.meta with parameters of type Instance Constructor Description EnsembleRunnable(AbstractClusterer clusterer, Instance instance)
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Uses of Instance in moa.clusterers.outliers
Fields in moa.clusterers.outliers declared as Instance Modifier and Type Field Description Instance
MyBaseOutlierDetector.Outlier. inst
Methods in moa.clusterers.outliers with parameters of type Instance Modifier and Type Method Description double[]
MyBaseOutlierDetector. getInstanceValues(Instance inst)
double[]
MyBaseOutlierDetector. getVotesForInstance(Instance inst)
void
MyBaseOutlierDetector. PrintInstance(Instance inst)
void
MyBaseOutlierDetector. processNewInstanceImpl(Instance inst)
protected void
MyBaseOutlierDetector. ProcessNewStreamObj(Instance inst)
void
MyBaseOutlierDetector. trainOnInstanceImpl(Instance inst)
Constructors in moa.clusterers.outliers with parameters of type Instance Constructor Description Outlier(Instance inst, long id, Object obj)
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Uses of Instance in moa.clusterers.outliers.AbstractC
Fields in moa.clusterers.outliers.AbstractC declared as Instance Modifier and Type Field Description Instance
ISBIndex.ISBNode. inst
Methods in moa.clusterers.outliers.AbstractC with parameters of type Instance Modifier and Type Method Description protected void
AbstractC. ProcessNewStreamObj(Instance inst)
Constructors in moa.clusterers.outliers.AbstractC with parameters of type Instance Constructor Description ISBNode(Instance inst, StreamObj obj, Long id)
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Uses of Instance in moa.clusterers.outliers.Angiulli
Fields in moa.clusterers.outliers.Angiulli declared as Instance Modifier and Type Field Description Instance
ISBIndex.ISBNode. inst
Methods in moa.clusterers.outliers.Angiulli with parameters of type Instance Modifier and Type Method Description protected void
ApproxSTORM. ProcessNewStreamObj(Instance inst)
protected void
ExactSTORM. ProcessNewStreamObj(Instance inst)
Constructors in moa.clusterers.outliers.Angiulli with parameters of type Instance Constructor Description ISBNode(Instance inst, StreamObj obj, Long id)
ISBNodeAppr(Instance inst, StreamObj obj, Long id, int k)
ISBNodeExact(Instance inst, StreamObj obj, Long id, int k)
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Uses of Instance in moa.clusterers.outliers.AnyOut
Methods in moa.clusterers.outliers.AnyOut with parameters of type Instance Modifier and Type Method Description protected void
AnyOut. ProcessNewStreamObj(Instance i)
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Uses of Instance in moa.clusterers.outliers.AnyOut.util
Methods in moa.clusterers.outliers.AnyOut.util that return Instance Modifier and Type Method Description Instance
DataObject. getInstance()
Return theInstance
of theDataObject
.Constructors in moa.clusterers.outliers.AnyOut.util with parameters of type Instance Constructor Description DataObject(int idCounter, Instance inst)
Standard constructor forDataObject
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Uses of Instance in moa.clusterers.outliers.MCOD
Fields in moa.clusterers.outliers.MCOD declared as Instance Modifier and Type Field Description Instance
ISBIndex.ISBNode. inst
Methods in moa.clusterers.outliers.MCOD with parameters of type Instance Modifier and Type Method Description protected void
MCOD. ProcessNewStreamObj(Instance inst)
Constructors in moa.clusterers.outliers.MCOD with parameters of type Instance Constructor Description ISBNode(Instance inst, StreamObj obj, Long id)
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Uses of Instance in moa.clusterers.outliers.SimpleCOD
Fields in moa.clusterers.outliers.SimpleCOD declared as Instance Modifier and Type Field Description Instance
ISBIndex.ISBNode. inst
Methods in moa.clusterers.outliers.SimpleCOD with parameters of type Instance Modifier and Type Method Description protected void
SimpleCOD. ProcessNewStreamObj(Instance inst)
Constructors in moa.clusterers.outliers.SimpleCOD with parameters of type Instance Constructor Description ISBNode(Instance inst, StreamObj obj, Long id)
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Uses of Instance in moa.clusterers.streamkm
Methods in moa.clusterers.streamkm with parameters of type Instance Modifier and Type Method Description double[]
StreamKM. getVotesForInstance(Instance inst)
void
StreamKM. trainOnInstanceImpl(Instance inst)
Constructors in moa.clusterers.streamkm with parameters of type Instance Constructor Description Point(Instance inst, int id)
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Uses of Instance in moa.core
Classes in moa.core that implement Instance Modifier and Type Class Description class
MultilabelInstance
Multilabel instance.Fields in moa.core declared as Instance Modifier and Type Field Description Instance
InstanceExample. instance
Methods in moa.core that return Instance Modifier and Type Method Description Instance
InstanceExample. getData()
Constructors in moa.core with parameters of type Instance Constructor Description InstanceExample(Instance inst)
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Uses of Instance in moa.core.utils
Methods in moa.core.utils that return Instance Modifier and Type Method Description Instance
Converter. formatInstance(Instance original)
Methods in moa.core.utils with parameters of type Instance Modifier and Type Method Description Instance
Converter. formatInstance(Instance original)
List<Integer>
Converter. getRelevantLabels(Instance x)
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Uses of Instance in moa.evaluation
Classes in moa.evaluation that implement Instance Modifier and Type Class Description protected class
CMM_GTAnalysis.CMMPoint
Wrapper class for data points to store CMM relevant attributesMethod parameters in moa.evaluation with type arguments of type Instance Modifier and Type Method Description void
BasicAUCImbalancedPerformanceEvaluator. addResult(Example<Instance> exampleInstance, double[] classVotes)
void
BasicAUCImbalancedPerformanceEvaluator. addResult(Example<Instance> arg0, Prediction arg1)
void
BasicClassificationPerformanceEvaluator. addResult(Example<Instance> example, double[] classVotes)
void
BasicClassificationPerformanceEvaluator. addResult(Example<Instance> testInst, Prediction prediction)
void
BasicConceptDriftPerformanceEvaluator. addResult(Example<Instance> example, double[] classVotes)
void
BasicConceptDriftPerformanceEvaluator. addResult(Example<Instance> testInst, Prediction prediction)
void
BasicMultiLabelPerformanceEvaluator. addResult(Example<Instance> example, double[] classVotes)
void
BasicMultiLabelPerformanceEvaluator. addResult(Example<Instance> example, Prediction y)
void
BasicMultiTargetPerformanceEvaluator. addResult(Example<Instance> example, double[] classVotes)
void
BasicMultiTargetPerformanceEvaluator. addResult(Example<Instance> example, Prediction prediction)
void
BasicMultiTargetPerformanceRelativeMeasuresEvaluator. addResult(Example<Instance> example, double[] classVotes)
void
BasicMultiTargetPerformanceRelativeMeasuresEvaluator. addResult(Example<Instance> example, Prediction prediction)
void
BasicRegressionPerformanceEvaluator. addResult(Example<Instance> example, double[] prediction)
void
BasicRegressionPerformanceEvaluator. addResult(Example<Instance> example, Prediction prediction)
void
MultiTargetWindowRegressionPerformanceEvaluator. addResult(Example<Instance> example, double[] prediction)
void
MultiTargetWindowRegressionPerformanceEvaluator. addResult(Example<Instance> testInst, Prediction prediction)
void
MultiTargetWindowRegressionPerformanceRelativeMeasuresEvaluator. addResult(Example<Instance> example, double[] prediction)
void
MultiTargetWindowRegressionPerformanceRelativeMeasuresEvaluator. addResult(Example<Instance> testInst, Prediction prediction)
void
WindowAUCImbalancedPerformanceEvaluator. addResult(Example<Instance> exampleInstance, double[] classVotes)
void
WindowAUCImbalancedPerformanceEvaluator. addResult(Example<Instance> arg0, Prediction arg1)
void
WindowRegressionPerformanceEvaluator. addResult(Example<Instance> example, double[] prediction)
void
WindowRegressionPerformanceEvaluator. addResult(Example<Instance> testInst, Prediction prediction)
void
ALClassificationPerformanceEvaluator. doLabelAcqReport(Example<Instance> trainInst, int labelAcquired)
Reports if a label of an instance was acquired.void
ALWindowClassificationPerformanceEvaluator. doLabelAcqReport(Example<Instance> trainInst, int labelAcquired)
Receives the information if a label has been acquired and increases counters. -
Uses of Instance in moa.gui.visualization
Classes in moa.gui.visualization that implement Instance Modifier and Type Class Description class
DataPoint
Constructors in moa.gui.visualization with parameters of type Instance Constructor Description DataPoint(Instance nextInstance, Integer timestamp)
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Uses of Instance in moa.learners
Methods in moa.learners with parameters of type Instance Modifier and Type Method Description double[]
ChangeDetectorLearner. getVotesForInstance(Instance inst)
void
ChangeDetectorLearner. trainOnInstanceImpl(Instance inst)
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Uses of Instance in moa.learners.featureanalysis
Methods in moa.learners.featureanalysis with parameters of type Instance Modifier and Type Method Description double[]
ClassifierWithFeatureImportance. getVotesForInstance(Instance instance)
double[]
FeatureImportanceHoeffdingTree. getVotesForInstance(Instance instance)
double[]
FeatureImportanceHoeffdingTreeEnsemble. getVotesForInstance(Instance instance)
void
ClassifierWithFeatureImportance. trainOnInstanceImpl(Instance instance)
void
FeatureImportanceHoeffdingTree. trainOnInstanceImpl(Instance instance)
void
FeatureImportanceHoeffdingTreeEnsemble. trainOnInstanceImpl(Instance instance)
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Uses of Instance in moa.streams
Fields in moa.streams declared as Instance Modifier and Type Field Description protected Instance
ConceptDriftRealStream. driftInstance
protected Instance
ConceptDriftRealStream. inputInstance
Fields in moa.streams with type parameters of type Instance Modifier and Type Field Description protected Example<Instance>
BootstrappedStream. queuedInstance
Methods in moa.streams that return types with arguments of type Instance Modifier and Type Method Description Example<Instance>
BootstrappedStream. nextInstance()
Example<Instance>
ImbalancedStream. nextInstance()
Example<Instance>
IrrelevantFeatureAppenderStream. nextInstance()
Example<Instance>
PartitioningStream. nextInstance()
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Uses of Instance in moa.streams.filters
Fields in moa.streams.filters with type parameters of type Instance Modifier and Type Field Description protected ExampleStream<Example<Instance>>
AbstractMultiLabelStreamFilter. inputStream
The input stream to this filter.Methods in moa.streams.filters that return Instance Modifier and Type Method Description Instance
AbstractStreamFilter. filterInstance(Instance inst)
Instance
AddNoiseFilter. filterInstance(Instance inst)
Instance
NormalisationFilter. filterInstance(Instance inst)
Instance
ReLUFilter. filterInstance(Instance x)
Filter an instance.Instance
StandardisationFilter. filterInstance(Instance inst)
Instance
StreamFilter. filterInstance(Instance inst)
Methods in moa.streams.filters with parameters of type Instance Modifier and Type Method Description Instance
AbstractStreamFilter. filterInstance(Instance inst)
Instance
AddNoiseFilter. filterInstance(Instance inst)
Instance
NormalisationFilter. filterInstance(Instance inst)
Instance
ReLUFilter. filterInstance(Instance x)
Filter an instance.Instance
StandardisationFilter. filterInstance(Instance inst)
Instance
StreamFilter. filterInstance(Instance inst)
double[]
HashingTrickFilter. hashVector(Instance instance, int n, com.google.common.hash.HashFunction hashFunction)
double[]
RandomProjectionFilter. randomProjection(Instance instance, double[][] gm)
DenseInstance
HashingTrickFilter. transformedInstance(Instance sparseInst, double[] hashVal)
DenseInstance
RandomProjectionFilter. transformedInstance(Instance sparseInst, double[] val)
Method parameters in moa.streams.filters with type arguments of type Instance Modifier and Type Method Description void
AbstractMultiLabelStreamFilter. setInputStream(ExampleStream<Example<Instance>> stream)
void
MultiLabelStreamFilter. setInputStream(ExampleStream<Example<Instance>> stream)
Sets the input stream to the filter -
Uses of Instance in moa.streams.generators
Methods in moa.streams.generators that return Instance Modifier and Type Method Description Instance
AssetNegotiationGenerator.ClassFunction. makeTrue(Instance intnc)
Methods in moa.streams.generators with parameters of type Instance Modifier and Type Method Description Instance
AssetNegotiationGenerator.ClassFunction. makeTrue(Instance intnc)
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