Modifier and Type | Interface and Description |
---|---|
interface |
MultiLabelInstance
The Interface MultiLabelInstance.
|
Modifier and Type | Class and Description |
---|---|
class |
DenseInstance
The Class DenseInstance.
|
class |
FilteredSparseInstance
The Class FilteredSparseInstance.
|
class |
InstanceImpl
The Class InstanceImpl.
|
class |
SparseInstance
The Class SparseInstance.
|
Modifier and Type | Field and Description |
---|---|
protected List<Instance> |
Instances.instances
The instances.
|
Modifier and Type | Method and 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.
|
Modifier and Type | Method and 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.
|
Constructor and Description |
---|
DenseInstance(Instance inst)
Instantiates a new dense instance.
|
Modifier and Type | Method and Description |
---|---|
boolean |
Classifier.correctlyClassifies(Instance inst)
Gets whether this classifier correctly classifies an instance.
|
boolean |
AbstractClassifier.correctlyClassifies(Instance inst) |
Prediction |
Classifier.getPredictionForInstance(Instance inst)
Gets the reference to the header of the data stream.
|
Prediction |
AbstractMultiLabelLearner.getPredictionForInstance(Instance inst) |
Prediction |
AbstractClassifier.getPredictionForInstance(Instance inst) |
double[] |
Classifier.getVotesForInstance(Instance inst)
Predicts the class memberships for a given instance.
|
double[] |
AbstractMultiLabelLearner.getVotesForInstance(Instance inst) |
abstract double[] |
AbstractClassifier.getVotesForInstance(Instance inst) |
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 |
Classifier.trainOnInstance(Instance inst)
Trains this learner incrementally using the given example.
|
void |
AbstractClassifier.trainOnInstance(Instance inst) |
void |
AbstractMultiLabelLearner.trainOnInstanceImpl(Instance instance) |
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. |
Modifier and Type | Method and Description |
---|---|
Prediction |
AbstractMultiLabelLearner.getPredictionForInstance(Example<Instance> example) |
Prediction |
AbstractClassifier.getPredictionForInstance(Example<Instance> example) |
double[] |
AbstractClassifier.getVotesForInstance(Example<Instance> example) |
void |
AbstractClassifier.trainOnInstance(Example<Instance> example) |
Modifier and Type | Method and Description |
---|---|
double[] |
ALUncertainty.getVotesForInstance(Instance inst) |
double[] |
ALRandom.getVotesForInstance(Instance inst) |
void |
ALUncertainty.trainOnInstanceImpl(Instance inst) |
void |
ALRandom.trainOnInstanceImpl(Instance inst) |
Modifier and Type | Method and 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.
|
Modifier and Type | Method and Description |
---|---|
int |
NumericAttributeBinaryTest.branchForInstance(Instance inst) |
abstract int |
InstanceConditionalTest.branchForInstance(Instance inst)
Returns the number of the branch for an instance, -1 if unknown.
|
int |
NominalAttributeMultiwayTest.branchForInstance(Instance inst) |
int |
NominalAttributeBinaryTest.branchForInstance(Instance inst) |
boolean |
InstanceConditionalTest.resultKnownForInstance(Instance inst)
Gets whether the number of the branch for an instance is known.
|
Modifier and Type | Method and Description |
---|---|
static List<Instance> |
Cramer.fileToInstances(String path) |
Modifier and Type | Method and Description |
---|---|
Cramer.CramerTest |
Cramer.cramerTest(List<Instance> x,
List<Instance> y) |
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) |
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) |
double[] |
KNN.mtsknn(List<Instance> x,
List<Instance> y) |
void |
KNN.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.
|
void |
StatisticalTest.set(List<Instance> x,
List<Instance> y)
This method sets the instances for later use in concurrent scenarios.
|
void |
Cramer.set(List<Instance> x,
List<Instance> y) |
void |
Cramer.set(List<Instance> x,
List<Instance> y) |
double |
KNN.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.
|
double |
StatisticalTest.test(List<Instance> x,
List<Instance> y)
This method performs a test and returns the correspoding p-value.
|
double |
Cramer.test(List<Instance> x,
List<Instance> y) |
double |
Cramer.test(List<Instance> x,
List<Instance> y) |
Modifier and Type | Method and Description |
---|---|
double[] |
DriftDetectionMethodClassifier.getVotesForInstance(Instance inst) |
void |
DriftDetectionMethodClassifier.trainOnInstanceImpl(Instance inst) |
Modifier and Type | Method and Description |
---|---|
protected static double |
SPegasos.dotProd(Instance inst1,
double[] weights,
int classIndex) |
protected static double |
SGDMultiClass.dotProd(Instance inst1,
DoubleVector weights,
int classIndex) |
protected static double |
SGD.dotProd(Instance inst1,
DoubleVector weights,
int classIndex) |
double[] |
SPegasos.getVotesForInstance(Instance inst)
Calculates the class membership probabilities for the given test
instance.
|
double[] |
Perceptron.getVotesForInstance(Instance inst) |
double[] |
MajorityClass.getVotesForInstance(Instance i) |
double[] |
SGDMultiClass.getVotesForInstance(Instance inst)
Calculates the class membership probabilities for the given test
instance.
|
double[] |
SGD.getVotesForInstance(Instance inst)
Calculates the class membership probabilities for the given test
instance.
|
double[] |
NoChange.getVotesForInstance(Instance i) |
double |
Perceptron.prediction(Instance inst,
int classVal) |
void |
SPegasos.trainOnInstanceImpl(Instance instance)
Trains the classifier with the given instance.
|
void |
Perceptron.trainOnInstanceImpl(Instance inst) |
void |
MajorityClass.trainOnInstanceImpl(Instance inst) |
void |
AdaGrad.trainOnInstanceImpl(Instance instance)
Trains the classifier with the given instance.
|
void |
SGDMultiClass.trainOnInstanceImpl(Instance instance)
Trains the classifier with the given instance.
|
void |
SGD.trainOnInstanceImpl(Instance instance)
Trains the classifier with the given instance.
|
void |
NoChange.trainOnInstanceImpl(Instance inst) |
void |
SGDMultiClass.trainOnInstanceImpl(Instance instance,
int classLabel) |
Modifier and Type | Method and 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 |
kNNwithPAWandADWIN.trainOnInstanceImpl(Instance inst) |
void |
kNN.trainOnInstanceImpl(Instance inst) |
void |
SAMkNN.trainOnInstanceImpl(Instance inst) |
void |
kNNwithPAW.trainOnInstanceImpl(Instance inst) |
Modifier and Type | Field and Description |
---|---|
Instance |
NearestNeighbourSearch.NeighborNode.m_Instance
The neighbor instance.
|
Modifier and Type | Method and Description |
---|---|
abstract Instance |
NearestNeighbourSearch.nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied
instance.
|
Instance |
LinearNNSearch.nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied
instance.
|
Instance |
KDTree.nearestNeighbour(Instance target)
Returns the nearest neighbour of the supplied target
instance.
|
Modifier and Type | Method and Description |
---|---|
void |
NearestNeighbourSearch.addInstanceInfo(Instance ins)
Adds information from the given instance without modifying the
datastructure a lot.
|
void |
LinearNNSearch.addInstanceInfo(Instance ins)
Adds the given instance info.
|
void |
KDTree.addInstanceInfo(Instance instance)
Adds one instance to KDTree loosly.
|
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 |
NormalizableDistance.distance(Instance first,
Instance second)
Calculates the distance between two instances.
|
double |
EuclideanDistance.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 |
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.
|
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.
|
abstract Instance |
NearestNeighbourSearch.nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied
instance.
|
Instance |
LinearNNSearch.nearestNeighbour(Instance target)
Returns the nearest instance in the current neighbourhood to the supplied
instance.
|
Instance |
KDTree.nearestNeighbour(Instance target)
Returns the nearest neighbour of the supplied target
instance.
|
abstract void |
NearestNeighbourSearch.update(Instance ins)
Updates the NearNeighbourSearch algorithm for the new added instance.
|
void |
DistanceFunction.update(Instance ins)
Update the distance function (if necessary) for the newly added instance.
|
void |
LinearNNSearch.update(Instance ins)
Updates the LinearNNSearch to cater for the new added instance.
|
void |
KDTree.update(Instance instance)
Adds one instance to the KDTree.
|
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.
|
Constructor and 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.
|
Modifier and Type | Field and Description |
---|---|
protected Instance[] |
PairedLearners.instances |
Modifier and Type | Field and Description |
---|---|
protected List<Instance> |
RCD.currentChunk |
protected List<Instance> |
RCD.currentChunk2 |
protected List<Instance> |
RCD.testChunk |
Modifier and Type | Method and Description |
---|---|
Instance |
TemporallyAugmentedClassifier.extendWithOldLabels(Instance instance) |
protected Instance |
RandomRules.transformInstance(Instance inst,
int classifierIndex) |
Modifier and Type | Method and Description |
---|---|
protected double |
OnlineAccuracyUpdatedEnsemble.computeWeight(int i,
Instance example)
Computes the weight of a learner before training a given example.
|
protected void |
OnlineAccuracyUpdatedEnsemble.createNewClassifier(Instance inst)
Processes a chunk.
|
Instance |
TemporallyAugmentedClassifier.extendWithOldLabels(Instance instance) |
double[] |
RCD.getVotesForInstance(Instance inst) |
double[] |
LeveragingBag.getVotesForInstance(Instance inst) |
double[] |
LimAttClassifier.getVotesForInstance(Instance inst) |
double[] |
OzaBagAdwin.getVotesForInstance(Instance inst) |
double[] |
OnlineSmoothBoost.getVotesForInstance(Instance inst) |
double[] |
OzaBag.getVotesForInstance(Instance inst) |
double[] |
HeterogeneousEnsembleAbstract.getVotesForInstance(Instance inst) |
double[] |
ADOB.getVotesForInstance(Instance inst) |
double[] |
OzaBoost.getVotesForInstance(Instance inst) |
double[] |
DynamicWeightedMajority.getVotesForInstance(Instance inst) |
double[] |
AccuracyWeightedEnsemble.getVotesForInstance(Instance inst)
Predicts a class for an example.
|
double[] |
LearnNSE.getVotesForInstance(Instance inst) |
double[] |
RandomRules.getVotesForInstance(Instance inst) |
double[] |
AdaptiveRandomForest.getVotesForInstance(Instance instance) |
double[] |
AdaptiveRandomForest.ARFBaseLearner.getVotesForInstance(Instance instance) |
double[] |
PairedLearners.getVotesForInstance(Instance inst) |
double[] |
BOLE.getVotesForInstance(Instance inst) |
double[] |
OnlineAccuracyUpdatedEnsemble.getVotesForInstance(Instance inst)
Predicts a class for an example.
|
double[] |
WeightedMajorityAlgorithm.getVotesForInstance(Instance inst) |
double[] |
DACC.getVotesForInstance(Instance inst) |
double[] |
WEKAClassifier.getVotesForInstance(Instance samoaInstance) |
double[] |
AccuracyUpdatedEnsemble.getVotesForInstance(Instance inst)
Predicts a class for an example.
|
double[] |
OzaBoostAdwin.getVotesForInstance(Instance inst) |
double[] |
OzaBagASHT.getVotesForInstance(Instance inst) |
double[] |
TemporallyAugmentedClassifier.getVotesForInstance(Instance instance) |
double[] |
OCBoost.getVotesForInstance(Instance inst) |
double[] |
LeveragingBag.getVotesForInstanceBinary(Instance inst) |
double[] |
OzaBoostAdwin.getVotesForInstanceBinary(Instance inst) |
protected void |
AdaptiveRandomForest.initEnsemble(Instance instance) |
protected void |
DACC.trainAndClassify(Instance inst)
Receives a training instance from the stream and
updates the adaptive classifiers accordingly
|
void |
AdaptiveRandomForest.ARFBaseLearner.trainOnInstance(Instance instance,
double weight,
long instancesSeen) |
void |
RCD.trainOnInstanceImpl(Instance inst) |
void |
LeveragingBag.trainOnInstanceImpl(Instance inst) |
void |
HeterogeneousEnsembleBlast.trainOnInstanceImpl(Instance inst) |
void |
LimAttClassifier.trainOnInstanceImpl(Instance inst) |
void |
OzaBagAdwin.trainOnInstanceImpl(Instance inst) |
void |
OnlineSmoothBoost.trainOnInstanceImpl(Instance inst) |
void |
OzaBag.trainOnInstanceImpl(Instance inst) |
void |
ADOB.trainOnInstanceImpl(Instance inst) |
void |
OzaBoost.trainOnInstanceImpl(Instance inst) |
void |
DynamicWeightedMajority.trainOnInstanceImpl(Instance inst) |
void |
AccuracyWeightedEnsemble.trainOnInstanceImpl(Instance inst) |
void |
LearnNSE.trainOnInstanceImpl(Instance inst) |
void |
RandomRules.trainOnInstanceImpl(Instance inst) |
void |
AdaptiveRandomForest.trainOnInstanceImpl(Instance instance) |
void |
PairedLearners.trainOnInstanceImpl(Instance inst) |
void |
BOLE.trainOnInstanceImpl(Instance inst) |
void |
OnlineAccuracyUpdatedEnsemble.trainOnInstanceImpl(Instance inst) |
void |
WeightedMajorityAlgorithm.trainOnInstanceImpl(Instance inst) |
void |
DACC.trainOnInstanceImpl(Instance inst) |
void |
WEKAClassifier.trainOnInstanceImpl(Instance samoaInstance) |
void |
HeterogeneousEnsembleBlastFadingFactors.trainOnInstanceImpl(Instance inst) |
void |
AccuracyUpdatedEnsemble.trainOnInstanceImpl(Instance inst) |
void |
OzaBoostAdwin.trainOnInstanceImpl(Instance inst) |
void |
ADACC.trainOnInstanceImpl(Instance inst) |
void |
OzaBagASHT.trainOnInstanceImpl(Instance inst) |
void |
TemporallyAugmentedClassifier.trainOnInstanceImpl(Instance instance) |
void |
OCBoost.trainOnInstanceImpl(Instance inst) |
protected Instance |
RandomRules.transformInstance(Instance inst,
int classifierIndex) |
Constructor and Description |
---|
DetectingRunnable(Classifier learner,
ADWIN ADError,
Instance inst) |
TrainingRunnable(AdaptiveRandomForest.ARFBaseLearner learner,
Instance instance,
double weight,
long instancesSeen) |
TrainingRunnable(Classifier learner,
Instance weightedInst) |
TrainingRunnable(Classifier learner,
Instance weightedInst) |
Modifier and Type | Method and 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) |
Modifier and Type | Method and Description |
---|---|
Prediction |
MultilabelHoeffdingTree.getPredictionForInstance(Example<Instance> example) |
Modifier and Type | Method and 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) |
Modifier and Type | Method and Description |
---|---|
Prediction |
OzaBagAdwinML.getPredictionForInstance(Example<Instance> example) |
Prediction |
OzaBagML.getPredictionForInstance(Example<Instance> example) |
Modifier and Type | Method and Description |
---|---|
protected Instance |
BasicMultiTargetRegressor.transformInstance(MultiLabelInstance inst,
int outputIndex) |
protected Instance |
BasicMultiLabelLearner.transformInstance(MultiLabelInstance inst,
int outputIndex) |
Modifier and Type | Field and Description |
---|---|
protected Instance |
RuleClassifier.instance |
Modifier and Type | Method and 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[] |
BinaryClassifierFromRegressor.getVotesForInstance(Instance inst) |
double[] |
RuleClassifierNBayes.getVotesForInstance(Instance inst) |
double[] |
AbstractAMRules.getVotesForInstance(Instance instance)
getVotesForInstance extension of the instance method getVotesForInstance
in moa.classifier.java
returns the prediction of the instance.
|
double[] |
RuleClassifier.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 |
BinaryClassifierFromRegressor.trainOnInstanceImpl(Instance inst) |
void |
AbstractAMRules.trainOnInstanceImpl(Instance instance) |
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) |
Modifier and Type | Method and Description |
---|---|
abstract double |
RuleActiveLearningNode.computeError(Instance instance) |
double |
Rule.computeError(Instance instance) |
double |
RuleActiveRegressionNode.computeError(Instance instance) |
protected void |
RuleActiveRegressionNode.debuganomaly(Instance instance,
double uni,
double multi,
double probability) |
boolean |
Predicate.evaluate(Instance instance) |
boolean |
NominalRulePredicate.evaluate(Instance instance) |
boolean |
RuleSplitNode.evaluate(Instance instance) |
boolean |
NumericRulePredicate.evaluate(Instance instance) |
abstract int |
RuleActiveLearningNode.getLearnerToUse(Instance instance,
int predictionMode) |
int |
RuleActiveRegressionNode.getLearnerToUse(Instance instance,
int predMode) |
double |
RuleActiveRegressionNode.getNormalizedPrediction(Instance instance) |
double[] |
RuleActiveLearningNode.getPrediction(Instance instance) |
double[] |
Rule.getPrediction(Instance instance) |
abstract double[] |
RuleActiveLearningNode.getPrediction(Instance instance,
int predictionMode) |
double[] |
Rule.getPrediction(Instance instance,
int mode) |
double[] |
RuleActiveRegressionNode.getPrediction(Instance instance,
int predictionMode) |
abstract boolean |
RuleActiveLearningNode.isAnomaly(Instance instance,
double uniVariateAnomalyProbabilityThreshold,
double multiVariateAnomalyProbabilityThreshold,
int numberOfInstanceesForAnomaly) |
boolean |
Rule.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 |
RuleActiveRegressionNode.learnFromInstance(Instance inst) |
void |
RuleActiveLearningNode.learnFromInstance(Instance inst,
HoeffdingTree ht) |
void |
RuleActiveLearningNode.updateStatistics(Instance instance) |
void |
Rule.updateStatistics(Instance instance) |
void |
RuleActiveRegressionNode.updateStatistics(Instance instance) |
Modifier and Type | Method and Description |
---|---|
protected void |
OddsRatioScore.printAnomaly(Instance inst,
double anomaly) |
Modifier and Type | Method and Description |
---|---|
int |
NumericAttributeBinaryRulePredicate.branchForInstance(Instance inst) |
boolean |
NominalAttributeBinaryRulePredicate.evaluate(Instance inst) |
boolean |
NumericAttributeBinaryRulePredicate.evaluate(Instance inst) |
Modifier and Type | Method and Description |
---|---|
abstract void |
ErrorMeasurement.addPrediction(double[] prediction,
Instance inst) |
void |
MeanAbsoluteDeviation.addPrediction(double[] prediction,
Instance inst) |
void |
RootMeanSquaredError.addPrediction(double[] prediction,
Instance inst) |
Modifier and Type | Method and Description |
---|---|
double[] |
AdaptiveNodePredictor.getVotesForInstance(Instance inst) |
double[] |
LowPassFilteredLearner.getVotesForInstance(Instance inst) |
double[] |
Perceptron.getVotesForInstance(Instance inst) |
double[] |
TargetMean.getVotesForInstance(Instance inst) |
double[] |
FadingTargetMean.getVotesForInstance(Instance inst) |
double[] |
Perceptron.normalizedInstance(Instance inst) |
double |
Perceptron.normalizedPrediction(Instance inst) |
void |
AdaptiveNodePredictor.trainOnInstanceImpl(Instance inst) |
void |
LowPassFilteredLearner.trainOnInstanceImpl(Instance inst) |
void |
Perceptron.trainOnInstanceImpl(Instance inst)
Update the model using the provided instance
|
void |
TargetMean.trainOnInstanceImpl(Instance inst) |
void |
FadingTargetMean.trainOnInstanceImpl(Instance inst) |
protected void |
TargetMean.updateAccumulatedError(Instance inst) |
void |
Perceptron.updateWeights(Instance inst,
double learningRatio) |
Modifier and Type | Method and Description |
---|---|
double[] |
RandomAMRulesOld.getVotesForInstance(Instance inst) |
void |
RandomAMRulesOld.trainOnInstanceImpl(Instance instance) |
Modifier and Type | Method and Description |
---|---|
boolean |
Literal.evaluate(Instance inst) |
protected double[] |
LearningLiteralClassification.getNormalizedErrors(Prediction prediction,
Instance instance) |
protected double[] |
LearningLiteralRegression.getNormalizedErrors(Prediction prediction,
Instance instance) |
protected abstract double[] |
LearningLiteral.getNormalizedErrors(Prediction prediction,
Instance inst) |
Modifier and Type | Method and Description |
---|---|
Instance |
InstanceOutputAttributesSelector.sourceInstanceToTarget(Instance sourceInstance) |
Instance |
InstanceTransformer.sourceInstanceToTarget(Instance sourceInstance) |
Instance |
NoInstanceTransformation.sourceInstanceToTarget(Instance sourceInstance) |
Instance |
InstanceAttributesSelector.sourceInstanceToTarget(Instance sourceInstance) |
Modifier and Type | Method and Description |
---|---|
Instance |
InstanceOutputAttributesSelector.sourceInstanceToTarget(Instance sourceInstance) |
Instance |
InstanceTransformer.sourceInstanceToTarget(Instance sourceInstance) |
Instance |
NoInstanceTransformation.sourceInstanceToTarget(Instance sourceInstance) |
Instance |
InstanceAttributesSelector.sourceInstanceToTarget(Instance sourceInstance) |
Modifier and Type | Method and Description |
---|---|
FIMTDD.Node |
FIMTDD.SplitNode.descendOneStep(Instance inst) |
HoeffdingTree.FoundNode |
HoeffdingTree.Node.filterInstanceToLeaf(Instance inst,
HoeffdingTree.SplitNode parent,
int parentBranch) |
HoeffdingTree.FoundNode |
HoeffdingTree.SplitNode.filterInstanceToLeaf(Instance inst,
HoeffdingTree.SplitNode parent,
int parentBranch) |
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) |
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) |
void |
HoeffdingAdaptiveTree.AdaSplitNode.filterInstanceToLeaves(Instance inst,
HoeffdingTree.SplitNode myparent,
int parentBranch,
List<HoeffdingTree.FoundNode> foundNodes,
boolean updateSplitterCounts) |
void |
HoeffdingAdaptiveTree.AdaLearningNode.filterInstanceToLeaves(Instance inst,
HoeffdingTree.SplitNode splitparent,
int parentBranch,
List<HoeffdingTree.FoundNode> foundNodes,
boolean updateSplitterCounts) |
double[] |
HoeffdingOptionTree.Node.getClassVotes(Instance inst,
HoeffdingOptionTree ht) |
double[] |
HoeffdingOptionTree.LearningNodeNB.getClassVotes(Instance inst,
HoeffdingOptionTree hot) |
double[] |
HoeffdingOptionTree.LearningNodeNBAdaptive.getClassVotes(Instance inst,
HoeffdingOptionTree ht) |
double[] |
AdaHoeffdingOptionTree.AdaLearningNode.getClassVotes(Instance inst,
HoeffdingOptionTree ht) |
double[] |
HoeffdingAdaptiveTree.AdaLearningNode.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[] |
HoeffdingAdaptiveTreeClassifLeaves.LearningNodeHATClassifier.getClassVotes(Instance inst,
HoeffdingTree ht) |
double[] |
RandomHoeffdingTree.LearningNodeNB.getClassVotes(Instance inst,
HoeffdingTree ht) |
double[] |
RandomHoeffdingTree.LearningNodeNBAdaptive.getClassVotes(Instance inst,
HoeffdingTree ht) |
double[] |
ARFHoeffdingTree.LearningNodeNB.getClassVotes(Instance inst,
HoeffdingTree ht) |
double[] |
ARFHoeffdingTree.LearningNodeNBAdaptive.getClassVotes(Instance inst,
HoeffdingTree ht) |
double[] |
HoeffdingTree.Node.getClassVotes(Instance inst,
HoeffdingTree ht) |
double[] |
HoeffdingTree.LearningNodeNB.getClassVotes(Instance inst,
HoeffdingTree ht) |
double[] |
HoeffdingTree.LearningNodeNBAdaptive.getClassVotes(Instance inst,
HoeffdingTree ht) |
double |
FIMTDD.getNormalizedError(Instance inst,
double prediction) |
double |
FIMTDD.Node.getPrediction(Instance inst) |
double |
FIMTDD.LeafNode.getPrediction(Instance inst) |
double |
FIMTDD.SplitNode.getPrediction(Instance inst) |
double |
ORTO.OptionNode.getPrediction(Instance inst,
ORTO tree) |
double |
FIMTDD.LeafNode.getPredictionModel(Instance inst)
Retrieve the class votes using the perceptron learner
|
double |
FIMTDD.LeafNode.getPredictionTargetMean(Instance inst) |
double[] |
HoeffdingAdaptiveTree.getVotesForInstance(Instance inst) |
double[] |
DecisionStump.getVotesForInstance(Instance inst) |
double[] |
HoeffdingOptionTree.getVotesForInstance(Instance inst) |
double[] |
FIMTDD.getVotesForInstance(Instance inst) |
double[] |
HoeffdingTree.getVotesForInstance(Instance inst) |
int |
HoeffdingOptionTree.SplitNode.instanceChildIndex(Instance inst) |
int |
FIMTDD.SplitNode.instanceChildIndex(Instance inst) |
int |
HoeffdingTree.SplitNode.instanceChildIndex(Instance inst) |
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 observer
|
void |
HoeffdingAdaptiveTree.NewNode.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.AdaLearningNode.learnFromInstance(Instance inst,
HoeffdingAdaptiveTree ht,
HoeffdingTree.SplitNode parent,
int parentBranch) |
abstract void |
HoeffdingOptionTree.LearningNode.learnFromInstance(Instance inst,
HoeffdingOptionTree ht) |
void |
HoeffdingOptionTree.InactiveLearningNode.learnFromInstance(Instance inst,
HoeffdingOptionTree ht) |
void |
HoeffdingOptionTree.ActiveLearningNode.learnFromInstance(Instance inst,
HoeffdingOptionTree ht) |
void |
HoeffdingOptionTree.LearningNodeNBAdaptive.learnFromInstance(Instance inst,
HoeffdingOptionTree hot) |
void |
AdaHoeffdingOptionTree.AdaLearningNode.learnFromInstance(Instance inst,
HoeffdingOptionTree hot) |
void |
HoeffdingTreeClassifLeaves.LearningNodeClassifier.learnFromInstance(Instance inst,
HoeffdingTree ht) |
void |
LimAttHoeffdingTree.LimAttLearningNode.learnFromInstance(Instance inst,
HoeffdingTree ht) |
void |
LimAttHoeffdingTree.LearningNodeNBAdaptive.learnFromInstance(Instance inst,
HoeffdingTree ht) |
void |
HoeffdingAdaptiveTreeClassifLeaves.LearningNodeHATClassifier.learnFromInstance(Instance inst,
HoeffdingTree ht) |
void |
RandomHoeffdingTree.RandomLearningNode.learnFromInstance(Instance inst,
HoeffdingTree ht) |
void |
RandomHoeffdingTree.LearningNodeNBAdaptive.learnFromInstance(Instance inst,
HoeffdingTree ht) |
void |
ARFHoeffdingTree.RandomLearningNode.learnFromInstance(Instance inst,
HoeffdingTree ht) |
void |
ARFHoeffdingTree.LearningNodeNBAdaptive.learnFromInstance(Instance inst,
HoeffdingTree ht) |
abstract void |
HoeffdingTree.LearningNode.learnFromInstance(Instance inst,
HoeffdingTree ht) |
void |
HoeffdingTree.InactiveLearningNode.learnFromInstance(Instance inst,
HoeffdingTree ht) |
void |
HoeffdingTree.ActiveLearningNode.learnFromInstance(Instance inst,
HoeffdingTree ht) |
void |
HoeffdingTree.LearningNodeNBAdaptive.learnFromInstance(Instance inst,
HoeffdingTree ht) |
DoubleVector |
FIMTDD.FIMTDDPerceptron.normalizedInstance(Instance inst) |
protected double |
FIMTDD.FIMTDDPerceptron.prediction(Instance inst) |
void |
ORTO.processInstance(Instance inst,
FIMTDD.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.processInstanceOptionNode(Instance inst,
ORTO.OptionNode node,
double prediction,
double normalError,
boolean growthAllowed,
boolean inAlternate) |
void |
HoeffdingAdaptiveTree.trainOnInstanceImpl(Instance inst) |
void |
ASHoeffdingTree.trainOnInstanceImpl(Instance inst) |
void |
DecisionStump.trainOnInstanceImpl(Instance inst) |
void |
HoeffdingOptionTree.trainOnInstanceImpl(Instance inst) |
void |
FIMTDD.trainOnInstanceImpl(Instance inst)
Method for updating (training) the model using a new instance
|
void |
HoeffdingTree.trainOnInstanceImpl(Instance inst) |
void |
FIMTDD.FIMTDDPerceptron.updatePerceptron(Instance inst)
Update the model using the provided instance
|
void |
FIMTDD.FIMTDDPerceptron.updateWeights(Instance inst,
double learningRatio) |
Modifier and Type | Method and Description |
---|---|
void |
Iadem3.AdaptiveLeafNode.attemptToSplit(Instance instance) |
void |
Iadem2.LeafNode.attemptToSplit(Instance instance) |
void |
Iadem3.createRoot(Instance instance) |
void |
Iadem2.createRoot(Instance instance) |
protected void |
Iadem3.AdaptiveLeafNode.createVirtualNodes(IademNumericAttributeObserver numericAttClassObserver,
boolean onlyMultiwayTest,
boolean onlyBinaryTest,
Instance instance) |
protected void |
Iadem2.LeafNode.createVirtualNodes(IademNumericAttributeObserver numericObserver,
boolean onlyMultiwayTest,
boolean onlyBinaryTest,
Instance instance) |
Iadem3.AdaptiveLeafNode[] |
Iadem3.AdaptiveLeafNode.doSplit(IademAttributeSplitSuggestion mejorExpansion,
Instance instance) |
Iadem2.LeafNode[] |
Iadem2.LeafNode.doSplit(IademAttributeSplitSuggestion bestSuggestion,
Instance instance) |
IademAttributeSplitSuggestion |
Iadem2.LeafNode.getBestSplitSuggestion(Instance instance) |
IademAttributeSplitSuggestion |
Iadem2.LeafNode.getBestSplitSuggestionIADEM(Instance instance) |
double[] |
Iadem3.getClassVotes(Instance instance) |
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[] |
Iadem2.getClassVotes(Instance instance) |
abstract double[] |
Iadem2.Node.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) |
double[] |
Iadem2.VirtualNode.getClassVotes(Instance inst) |
double[] |
Iadem2.SplitNode.getClassVotes(Instance inst) |
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[] |
Iadem3.AdaptiveLeafNodeNB.getNaiveBayesPrediction(Instance inst) |
protected double[] |
Iadem2.LeafNodeNB.getNaiveBayesPrediction(Instance obs) |
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) |
abstract Iadem2.SplitNode |
Iadem2.VirtualNode.getNewSplitNode(long newInstancesSeen,
Iadem2.Node parent,
IademAttributeSplitSuggestion bestSuggestion,
Instance instance) |
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) |
int |
Iadem2.getValuesOfNominalAttributes(int attIndex,
Instance instance) |
double[] |
Iadem2.getVotesForInstance(Instance inst) |
int |
Iadem2.SplitNode.instanceChildIndex(Instance inst) |
void |
Iadem3.learnFromInstance(Instance instance) |
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 |
Iadem2.learnFromInstance(Instance instance) |
abstract Iadem2.Node |
Iadem2.Node.learnFromInstance(Instance instance) |
Iadem2.Node |
Iadem2.LeafNode.learnFromInstance(Instance inst) |
Iadem2.Node |
Iadem2.LeafNodeNBKirkby.learnFromInstance(Instance inst) |
Iadem2.Node |
Iadem2.LeafNodeWeightedVote.learnFromInstance(Instance inst) |
Iadem2.Node |
Iadem2.NominalVirtualNode.learnFromInstance(Instance inst) |
Iadem2.Node |
Iadem2.NumericVirtualNode.learnFromInstance(Instance instance) |
Iadem2.Node |
Iadem2.SplitNode.learnFromInstance(Instance inst) |
void |
Iadem3Subtree.learnFromInstance(Instance instance) |
Iadem2.LeafNode |
Iadem3.newLeafNode(Iadem2.Node parent,
long instTreeCountSinceVirtual,
long instNodeCountSinceVirtual,
double[] initialClassCount,
Instance instance) |
Iadem2.LeafNode |
Iadem2.newLeafNode(Iadem2.Node parent,
long instTreeCountSinceVirtual,
long instNodeCountSinceVirtual,
double[] classDist,
Instance instance) |
protected ArrayList<Integer> |
Iadem2.LeafNode.nominalAttUsed(Instance instance) |
void |
Iadem2.trainOnInstanceImpl(Instance inst) |
abstract void |
Iadem2.VirtualNode.updateHeuristicMeasure(Instance instance) |
void |
Iadem2.NominalVirtualNode.updateHeuristicMeasure(Instance instance) |
void |
Iadem2.NumericVirtualNode.updateHeuristicMeasure(Instance instance) |
void |
Iadem2.NominalVirtualNode.updateHeuristicMeasureBinaryTest(Instance instance) |
void |
Iadem2.NominalVirtualNode.updateHeuristicMeasureMultiwayTest(Instance instance) |
Constructor and 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) |
Modifier and Type | Method and Description |
---|---|
Instance |
SphereCluster.sample(Random random)
Samples this cluster by returning a point from inside it.
|
abstract Instance |
Cluster.sample(Random random)
Samples this cluster by returning a point from inside it.
|
Modifier and Type | Method and Description |
---|---|
double |
SphereCluster.getCenterDistance(Instance instance) |
double[] |
SphereCluster.getDistanceVector(Instance instance) |
double |
SphereCluster.getInclusionProbability(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 |
Clustering.getMaxInclusionProbability(Instance point) |
Modifier and Type | Method and Description |
---|---|
static HashMap<Integer,Integer> |
Clustering.classValues(List<? extends Instance> points) |
Constructor and Description |
---|
CFCluster(Instance instance,
int dimensions)
Instantiates an empty kernel with the given dimensionality.
|
Constructor and Description |
---|
Clustering(List<? extends Instance> points) |
SphereCluster(List<? extends Instance> instances,
int dimension) |
Modifier and Type | Method and Description |
---|---|
double[] |
ClusterGenerator.getVotesForInstance(Instance inst) |
double[] |
WekaClusteringAlgorithm.getVotesForInstance(Instance inst) |
double[] |
Clusterer.getVotesForInstance(Instance inst) |
double[] |
CobWeb.getVotesForInstance(Instance instance)
Classifies a given instance.
|
protected static int |
AbstractClusterer.modelAttIndexToInstanceAttIndex(int index,
Instance inst) |
void |
Clusterer.trainOnInstance(Instance inst) |
void |
AbstractClusterer.trainOnInstance(Instance inst) |
void |
ClusterGenerator.trainOnInstanceImpl(Instance inst) |
void |
WekaClusteringAlgorithm.trainOnInstanceImpl(Instance inst) |
void |
CobWeb.trainOnInstanceImpl(Instance newInstance)
Adds an instance to the clusterer.
|
abstract void |
AbstractClusterer.trainOnInstanceImpl(Instance inst) |
Modifier and Type | Method and Description |
---|---|
double |
ClustreamKernel.getInclusionProbability(Instance instance)
See interface
Cluster |
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) |
Constructor and Description |
---|
ClustreamKernel(Instance instance,
int dimensions,
long timestamp,
double t,
int m) |
Modifier and Type | Method and Description |
---|---|
double |
ClusKernel.getInclusionProbability(Instance instance) |
double[] |
ClusTree.getVotesForInstance(Instance inst) |
void |
ClusTree.trainOnInstanceImpl(Instance instance) |
Modifier and Type | Method and Description |
---|---|
double |
MicroCluster.getInclusionProbability(Instance instance) |
double[] |
WithDBSCAN.getVotesForInstance(Instance inst) |
void |
MicroCluster.insert(Instance instance,
long timestamp) |
void |
WithDBSCAN.trainOnInstanceImpl(Instance inst) |
Constructor and Description |
---|
MicroCluster(Instance instance,
int dimensions,
long timestamp,
double lambda,
Timestamp currentTimestamp) |
Modifier and Type | Method and Description |
---|---|
double |
GridCluster.getInclusionProbability(Instance instance)
Iterates through the DensityGrids in the cluster and calculates the inclusion probability for each.
|
double |
DensityGrid.getInclusionProbability(Instance instance)
Provides the probability of the argument instance belonging to the density grid in question.
|
double[] |
Dstream.getVotesForInstance(Instance inst) |
void |
Dstream.printInst(Instance inst) |
void |
Dstream.trainOnInstanceImpl(Instance inst) |
Modifier and Type | Method and Description |
---|---|
double[] |
BICO.getVotesForInstance(Instance inst) |
void |
BICO.trainOnInstanceImpl(Instance inst) |
Modifier and Type | Method and Description |
---|---|
double |
NonConvexCluster.getInclusionProbability(Instance instance) |
Modifier and Type | Field and Description |
---|---|
Instance |
MyBaseOutlierDetector.Outlier.inst |
Modifier and Type | Method and 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) |
Constructor and Description |
---|
Outlier(Instance inst,
long id,
Object obj) |
Modifier and Type | Field and Description |
---|---|
Instance |
ISBIndex.ISBNode.inst |
Modifier and Type | Method and Description |
---|---|
protected void |
AbstractC.ProcessNewStreamObj(Instance inst) |
Constructor and Description |
---|
ISBNode(Instance inst,
StreamObj obj,
Long id) |
Modifier and Type | Field and Description |
---|---|
Instance |
ISBIndex.ISBNode.inst |
Modifier and Type | Method and Description |
---|---|
protected void |
ExactSTORM.ProcessNewStreamObj(Instance inst) |
protected void |
ApproxSTORM.ProcessNewStreamObj(Instance inst) |
Constructor and 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) |
Modifier and Type | Method and Description |
---|---|
protected void |
AnyOut.ProcessNewStreamObj(Instance i) |
Modifier and Type | Method and Description |
---|---|
Instance |
DataObject.getInstance()
Return the
Instance of the DataObject . |
Constructor and Description |
---|
DataObject(int idCounter,
Instance inst)
Standard constructor for
DataObject . |
Modifier and Type | Field and Description |
---|---|
Instance |
ISBIndex.ISBNode.inst |
Modifier and Type | Method and Description |
---|---|
protected void |
MCOD.ProcessNewStreamObj(Instance inst) |
Constructor and Description |
---|
ISBNode(Instance inst,
StreamObj obj,
Long id) |
Modifier and Type | Field and Description |
---|---|
Instance |
ISBIndex.ISBNode.inst |
Modifier and Type | Method and Description |
---|---|
protected void |
SimpleCOD.ProcessNewStreamObj(Instance inst) |
Constructor and Description |
---|
ISBNode(Instance inst,
StreamObj obj,
Long id) |
Modifier and Type | Method and Description |
---|---|
double[] |
StreamKM.getVotesForInstance(Instance inst) |
void |
StreamKM.trainOnInstanceImpl(Instance inst) |
Constructor and Description |
---|
Point(Instance inst,
int id) |
Modifier and Type | Class and Description |
---|---|
class |
MultilabelInstance
Multilabel instance.
|
Modifier and Type | Field and Description |
---|---|
Instance |
InstanceExample.instance |
Modifier and Type | Method and Description |
---|---|
Instance |
InstanceExample.getData() |
Constructor and Description |
---|
InstanceExample(Instance inst) |
Modifier and Type | Method and Description |
---|---|
Instance |
Converter.formatInstance(Instance original) |
Modifier and Type | Method and Description |
---|---|
Instance |
Converter.formatInstance(Instance original) |
List<Integer> |
Converter.getRelevantLabels(Instance x) |
Modifier and Type | Class and Description |
---|---|
protected class |
CMM_GTAnalysis.CMMPoint
Wrapper class for data points to store CMM relevant attributes
|
Modifier and Type | Method and Description |
---|---|
void |
BasicMultiTargetPerformanceEvaluator.addResult(Example<Instance> example,
double[] classVotes) |
void |
BasicAUCImbalancedPerformanceEvaluator.addResult(Example<Instance> exampleInstance,
double[] classVotes) |
void |
BasicMultiLabelPerformanceEvaluator.addResult(Example<Instance> example,
double[] classVotes) |
void |
BasicClassificationPerformanceEvaluator.addResult(Example<Instance> example,
double[] classVotes) |
void |
WindowRegressionPerformanceEvaluator.addResult(Example<Instance> example,
double[] prediction) |
void |
MultiTargetWindowRegressionPerformanceEvaluator.addResult(Example<Instance> example,
double[] prediction) |
void |
BasicConceptDriftPerformanceEvaluator.addResult(Example<Instance> example,
double[] classVotes) |
void |
BasicRegressionPerformanceEvaluator.addResult(Example<Instance> example,
double[] prediction) |
void |
MultiTargetWindowRegressionPerformanceRelativeMeasuresEvaluator.addResult(Example<Instance> example,
double[] prediction) |
void |
WindowAUCImbalancedPerformanceEvaluator.addResult(Example<Instance> exampleInstance,
double[] classVotes) |
void |
BasicMultiTargetPerformanceRelativeMeasuresEvaluator.addResult(Example<Instance> example,
double[] classVotes) |
void |
BasicMultiTargetPerformanceEvaluator.addResult(Example<Instance> example,
Prediction prediction) |
void |
BasicAUCImbalancedPerformanceEvaluator.addResult(Example<Instance> arg0,
Prediction arg1) |
void |
BasicMultiLabelPerformanceEvaluator.addResult(Example<Instance> example,
Prediction y) |
void |
BasicClassificationPerformanceEvaluator.addResult(Example<Instance> testInst,
Prediction prediction) |
void |
WindowRegressionPerformanceEvaluator.addResult(Example<Instance> testInst,
Prediction prediction) |
void |
MultiTargetWindowRegressionPerformanceEvaluator.addResult(Example<Instance> testInst,
Prediction prediction) |
void |
BasicConceptDriftPerformanceEvaluator.addResult(Example<Instance> testInst,
Prediction prediction) |
void |
BasicRegressionPerformanceEvaluator.addResult(Example<Instance> example,
Prediction prediction) |
void |
MultiTargetWindowRegressionPerformanceRelativeMeasuresEvaluator.addResult(Example<Instance> testInst,
Prediction prediction) |
void |
WindowAUCImbalancedPerformanceEvaluator.addResult(Example<Instance> arg0,
Prediction arg1) |
void |
BasicMultiTargetPerformanceRelativeMeasuresEvaluator.addResult(Example<Instance> example,
Prediction prediction) |
void |
BasicClassificationPerformanceEvaluator.addResultDelay(List<Instance> instances) |
void |
BasicRegressionPerformanceEvaluator.addResultDelay(List<Instance> instances) |
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.
|
Modifier and Type | Class and Description |
---|---|
class |
DataPoint |
Constructor and Description |
---|
DataPoint(Instance nextInstance,
Integer timestamp) |
Modifier and Type | Method and Description |
---|---|
double[] |
ChangeDetectorLearner.getVotesForInstance(Instance inst) |
void |
ChangeDetectorLearner.trainOnInstanceImpl(Instance inst) |
Modifier and Type | Field and Description |
---|---|
protected Instance |
ConceptDriftRealStream.driftInstance |
protected Instance |
ConceptDriftRealStream.inputInstance |
Modifier and Type | Field and Description |
---|---|
protected Example<Instance> |
BootstrappedStream.queuedInstance |
Modifier and Type | Method and Description |
---|---|
Example<Instance> |
PartitioningStream.nextInstance() |
Example<Instance> |
ImbalancedStream.nextInstance() |
Example<Instance> |
IrrelevantFeatureAppenderStream.nextInstance() |
Example<Instance> |
BootstrappedStream.nextInstance() |
Modifier and Type | Field and Description |
---|---|
protected ExampleStream<Example<Instance>> |
AbstractMultiLabelStreamFilter.inputStream
The input stream to this filter.
|
Modifier and Type | Method and Description |
---|---|
Instance |
AbstractStreamFilter.filterInstance(Instance inst) |
Instance |
StreamFilter.filterInstance(Instance inst) |
Instance |
AddNoiseFilter.filterInstance(Instance inst) |
Instance |
ReLUFilter.filterInstance(Instance x)
Filter an instance.
|
Modifier and Type | Method and Description |
---|---|
Instance |
AbstractStreamFilter.filterInstance(Instance inst) |
Instance |
StreamFilter.filterInstance(Instance inst) |
Instance |
AddNoiseFilter.filterInstance(Instance inst) |
Instance |
ReLUFilter.filterInstance(Instance x)
Filter an instance.
|
Modifier and Type | Method and Description |
---|---|
void |
AbstractMultiLabelStreamFilter.setInputStream(ExampleStream<Example<Instance>> stream) |
void |
MultiLabelStreamFilter.setInputStream(ExampleStream<Example<Instance>> stream)
Sets the input stream to the filter
|
Modifier and Type | Method and Description |
---|---|
Instance |
AssetNegotiationGenerator.ClassFunction.makeTrue(Instance intnc) |
Modifier and Type | Method and Description |
---|---|
Instance |
AssetNegotiationGenerator.ClassFunction.makeTrue(Instance intnc) |
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