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 | Method and Description |
---|---|
Prediction |
MultiLabelLearner.getPredictionForInstance(MultiLabelInstance instance) |
abstract Prediction |
AbstractMultiLabelLearner.getPredictionForInstance(MultiLabelInstance inst) |
Prediction |
MultiTargetLearnerSemiSupervised.getPredictionForInstance(MultiLabelInstance instance) |
void |
MultiLabelLearner.trainOnInstanceImpl(MultiLabelInstance instance) |
abstract void |
AbstractMultiLabelLearner.trainOnInstanceImpl(MultiLabelInstance instance) |
void |
MultiTargetLearnerSemiSupervised.trainOnInstanceImpl(MultiLabelInstance instance) |
Modifier and Type | Method and Description |
---|---|
Prediction |
MajorityLabelset.getPredictionForInstance(MultiLabelInstance x) |
Prediction |
MultilabelHoeffdingTree.getPredictionForInstance(MultiLabelInstance inst) |
Prediction |
MEKAClassifier.getPredictionForInstance(MultiLabelInstance samoaInstance) |
void |
MajorityLabelset.trainOnInstanceImpl(MultiLabelInstance x) |
void |
MultilabelHoeffdingTree.trainOnInstanceImpl(MultiLabelInstance instance) |
void |
MEKAClassifier.trainOnInstanceImpl(MultiLabelInstance samoaInstance) |
Modifier and Type | Method and Description |
---|---|
Prediction |
OzaBagAdwinML.getPredictionForInstance(MultiLabelInstance instance) |
Prediction |
OzaBagML.getPredictionForInstance(MultiLabelInstance instance) |
void |
OzaBagAdwinML.trainOnInstanceImpl(MultiLabelInstance instance) |
void |
OzaBagML.trainOnInstanceImpl(MultiLabelInstance inst) |
Modifier and Type | Method and Description |
---|---|
double[] |
ISOUPTree.getNormalizedError(MultiLabelInstance inst,
double[] prediction) |
double[] |
ISOUPTree.Node.getPrediction(MultiLabelInstance inst) |
double[] |
ISOUPTree.LeafNode.getPrediction(MultiLabelInstance inst) |
double[] |
ISOUPTree.SplitNode.getPrediction(MultiLabelInstance inst) |
Prediction |
ISOUPTree.getPredictionForInstance(MultiLabelInstance inst) |
double[] |
ISOUPTree.LeafNode.getPredictionModel(MultiLabelInstance inst)
Retrieve the class votes using the perceptron learner
|
double[] |
ISOUPTree.LeafNode.getPredictionTargetMean(MultiLabelInstance inst) |
int |
ISOUPTree.SplitNode.instanceChildIndex(MultiLabelInstance inst) |
void |
ISOUPTree.LeafNode.learnFromInstance(MultiLabelInstance inst,
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 observer
|
double[] |
ISOUPTree.normalizedInputVector(MultiLabelInstance inst) |
double[] |
ISOUPTree.normalizedTargetVector(MultiLabelInstance inst) |
double |
ISOUPTree.normalizeTargetValue(MultiLabelInstance inst,
int i) |
void |
ISOUPTree.processInstance(MultiLabelInstance inst,
ISOUPTree.Node node,
double[] prediction,
double[] normalError,
boolean growthAllowed,
boolean inAlternate) |
void |
ISOUPTree.trainOnInstanceImpl(MultiLabelInstance inst)
Method for updating (training) the model using a new instance
|
void |
ISOUPTree.MultitargetPerceptron.updatePerceptron(MultiLabelInstance inst)
Update the model using the provided instance
|
void |
ISOUPTree.MultitargetPerceptron.updateWeights(MultiLabelInstance inst,
double learningRatio) |
Modifier and Type | Method and Description |
---|---|
Prediction |
BasicMultiTargetRegressor.getPredictionForInstance(MultiLabelInstance instance) |
Prediction |
BasicMultiLabelLearner.getPredictionForInstance(MultiLabelInstance instance) |
void |
BasicMultiTargetRegressor.trainOnInstanceImpl(MultiLabelInstance instance) |
void |
BasicMultiLabelLearner.trainOnInstanceImpl(MultiLabelInstance instance) |
protected Instance |
BasicMultiTargetRegressor.transformInstance(MultiLabelInstance inst,
int outputIndex) |
protected Instance |
BasicMultiLabelLearner.transformInstance(MultiLabelInstance inst,
int outputIndex) |
Modifier and Type | Method and Description |
---|---|
Prediction |
MultiTargetNoChange.getPredictionForInstance(MultiLabelInstance inst) |
void |
MultiTargetNoChange.trainOnInstanceImpl(MultiLabelInstance inst) |
Modifier and Type | Method and Description |
---|---|
boolean |
NominalRulePredicate.evaluate(MultiLabelInstance instance) |
boolean |
NumericRulePredicate.evaluate(MultiLabelInstance instance) |
Modifier and Type | Method and Description |
---|---|
abstract boolean |
AbstractAnomalyDetector.updateAndCheckAnomalyDetection(MultiLabelInstance instance) |
boolean |
AnomalinessRatioScore.updateAndCheckAnomalyDetection(MultiLabelInstance instance) |
boolean |
NoAnomalyDetection.updateAndCheckAnomalyDetection(MultiLabelInstance instance) |
boolean |
AnomalyDetector.updateAndCheckAnomalyDetection(MultiLabelInstance instance)
Adding an instance to the anomaly detector
|
boolean |
OddsRatioScore.updateAndCheckAnomalyDetection(MultiLabelInstance instance) |
Modifier and Type | Method and Description |
---|---|
Prediction |
AMRulesMultiLabelLearnerSemiSuper.getPredictionForInstance(MultiLabelInstance inst) |
Prediction |
AMRulesMultiLabelLearner.getPredictionForInstance(MultiLabelInstance inst) |
ErrorWeightedVoteMultiLabel |
AMRulesMultiLabelLearnerSemiSuper.getVotes(MultiLabelInstance instance)
getVotes extension of the instance method getVotesForInstance
in moa.classifier.java
returns the prediction of the instance.
|
ErrorWeightedVoteMultiLabel |
AMRulesMultiLabelLearner.getVotes(MultiLabelInstance instance)
getVotes extension of the instance method getVotesForInstance
in moa.classifier.java
returns the prediction of the instance.
|
void |
AMRulesMultiLabelLearnerSemiSuper.trainOnInstanceImpl(MultiLabelInstance instance) |
void |
AMRulesMultiLabelLearner.trainOnInstanceImpl(MultiLabelInstance instance) |
protected void |
AMRulesMultiLabelLearnerSemiSuper.VerboseToConsole(MultiLabelInstance inst) |
protected void |
AMRulesMultiLabelLearner.VerboseToConsole(MultiLabelInstance inst) |
Modifier and Type | Method and Description |
---|---|
abstract void |
AbstractMultiLabelErrorMeasurer.addPrediction(Prediction prediction,
MultiLabelInstance inst) |
void |
AbstractMultiTargetErrorMeasurer.addPrediction(Prediction prediction,
MultiLabelInstance inst) |
void |
MultiLabelErrorMeasurer.addPrediction(Prediction prediction,
MultiLabelInstance inst) |
Modifier and Type | Method and Description |
---|---|
protected double[] |
StackedPredictor.getNormalizedInput(MultiLabelInstance instance) |
protected double[] |
StackedPredictor.getNormalizedOutput(MultiLabelInstance instance) |
Prediction |
StackedPredictor.getPredictionForInstance(MultiLabelInstance inst) |
Prediction |
DominantLabelsClassifier.getPredictionForInstance(MultiLabelInstance inst) |
Prediction |
AdaptiveMultiTargetRegressor.getPredictionForInstance(MultiLabelInstance inst) |
void |
StackedPredictor.trainOnInstanceImpl(MultiLabelInstance instance) |
void |
DominantLabelsClassifier.trainOnInstanceImpl(MultiLabelInstance instance) |
void |
AdaptiveMultiTargetRegressor.trainOnInstanceImpl(MultiLabelInstance instance) |
Modifier and Type | Method and Description |
---|---|
Prediction |
MultiLabelRandomAMRules.getPredictionForInstance(MultiLabelInstance inst) |
void |
MultiLabelRandomAMRules.trainOnInstanceImpl(MultiLabelInstance instance) |
Modifier and Type | Class and Description |
---|---|
class |
MultilabelInstance
Multilabel instance.
|
Modifier and Type | Class and Description |
---|---|
protected class |
CMM_GTAnalysis.CMMPoint
Wrapper class for data points to store CMM relevant attributes
|
Modifier and Type | Class and Description |
---|---|
class |
DataPoint |
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