Uses of Class
org.tribuo.anomaly.Event

Packages that use Event

Package	Description
org.tribuo.anomaly	Provides classes and infrastructure for anomaly detection problems.
org.tribuo.anomaly.evaluation	Evaluation classes for anomaly detection.
org.tribuo.anomaly.example	Provides anomaly data generators used for demos and testing implementations.
org.tribuo.anomaly.liblinear	Provides an interface to LibLinear-java for anomaly detection problems.
org.tribuo.anomaly.libsvm	Provides an interface to LibSVM for anomaly detection problems.

Uses of Event in org.tribuo.anomaly

Fields in org.tribuo.anomaly declared as Event

Modifier and Type	Field	Description
static final Event	AnomalyFactory.ANOMALOUS_EVENT	The anomalous event.
static final Event	AnomalyFactory.EXPECTED_EVENT	The expected event.
static final Event	AnomalyFactory.UNKNOWN_EVENT	The unknown event.

Methods in org.tribuo.anomaly that return Event

Modifier and Type	Method	Description
Event	Event.copy()
<V> Event	AnomalyFactory.generateOutput(V label)
Event	ImmutableAnomalyInfo.getOutput(int id)
Event	AnomalyFactory.getUnknownOutput()

Methods in org.tribuo.anomaly that return types with arguments of type Event

Modifier and Type	Method	Description
ImmutableOutputInfo<Event>	AnomalyFactory.constructInfoForExternalModel(Map<Event,Integer> mapping)
ImmutableOutputInfo<Event>	AnomalyInfo.generateImmutableOutputInfo()
MutableOutputInfo<Event>	AnomalyFactory.generateInfo()
MutableOutputInfo<Event>	AnomalyInfo.generateMutableOutputInfo()
Set<Event>	AnomalyInfo.getDomain()	Returns the set of possible Events.
Evaluator<Event,AnomalyEvaluation>	AnomalyFactory.getEvaluator()
Iterator<com.oracle.labs.mlrg.olcut.util.Pair<Integer,Event>>	ImmutableAnomalyInfo.iterator()

Methods in org.tribuo.anomaly with parameters of type Event

Modifier and Type	Method	Description
boolean	Event.fullEquals(Event o)
int	ImmutableAnomalyInfo.getID(Event output)
void	MutableAnomalyInfo.observe(Event output)

Method parameters in org.tribuo.anomaly with type arguments of type Event

Modifier and Type	Method	Description
ImmutableOutputInfo<Event>	AnomalyFactory.constructInfoForExternalModel(Map<Event,Integer> mapping)
boolean	ImmutableAnomalyInfo.domainAndIDEquals(ImmutableOutputInfo<Event> other)

Uses of Event in org.tribuo.anomaly.evaluation

Methods in org.tribuo.anomaly.evaluation that return types with arguments of type Event

Modifier and Type	Method	Description
MetricTarget<Event>	AnomalyMetric.getTarget()

Method parameters in org.tribuo.anomaly.evaluation with type arguments of type Event

Modifier and Type	Method	Description
protected org.tribuo.anomaly.evaluation.AnomalyMetric.Context	AnomalyEvaluator.createContext(Model<Event> model, List<Prediction<Event>> predictions)
protected org.tribuo.anomaly.evaluation.AnomalyMetric.Context	AnomalyEvaluator.createContext(Model<Event> model, List<Prediction<Event>> predictions)
org.tribuo.anomaly.evaluation.AnomalyMetric.Context	AnomalyMetric.createContext(Model<Event> model, List<Prediction<Event>> predictions)
org.tribuo.anomaly.evaluation.AnomalyMetric.Context	AnomalyMetric.createContext(Model<Event> model, List<Prediction<Event>> predictions)
protected AnomalyEvaluation	AnomalyEvaluator.createEvaluation(org.tribuo.anomaly.evaluation.AnomalyMetric.Context context, Map<MetricID<Event>,Double> results, EvaluationProvenance provenance)
protected Set<AnomalyMetric>	AnomalyEvaluator.createMetrics(Model<Event> model)

Constructor parameters in org.tribuo.anomaly.evaluation with type arguments of type Event

Modifier	Constructor	Description
	AnomalyMetric(MetricTarget<Event> target, String name, ToDoubleBiFunction<MetricTarget<Event>,org.tribuo.anomaly.evaluation.AnomalyMetric.Context> impl)	Creates an anomaly detection metric, with a specific name, using the supplied evaluation function.
	AnomalyMetric(MetricTarget<Event> target, String name, ToDoubleBiFunction<MetricTarget<Event>,org.tribuo.anomaly.evaluation.AnomalyMetric.Context> impl)	Creates an anomaly detection metric, with a specific name, using the supplied evaluation function.

Uses of Event in org.tribuo.anomaly.example

Methods in org.tribuo.anomaly.example that return types with arguments of type Event

Modifier and Type	Method	Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.denseTrainTest()	Makes a simple dataset for training and testing.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.denseTrainTest()	Makes a simple dataset for training and testing.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.denseTrainTest(double negate)	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.denseTrainTest(double negate)	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
static Example<Event>	AnomalyDataGenerator.emptyExample()	Generates an example with no features.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.gaussianAnomaly()	Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.gaussianAnomaly()	Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.gaussianAnomaly(long size, double fractionAnomalous)	Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.gaussianAnomaly(long size, double fractionAnomalous)	Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
static MutableDataset<Event>	GaussianAnomalyDataSource.generateDataset(int numSamples, double[] expectedMeans, double[] expectedVariances, double[] anomalousMeans, double[] anomalousVariances, float fractionAnomalous, long seed)	Generates an anomaly detection dataset sampling each feature uniformly from a univariate Gaussian.
OutputFactory<Event>	GaussianAnomalyDataSource.getOutputFactory()
static Example<Event>	AnomalyDataGenerator.invalidSparseExample()	Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.
Iterator<Example<Event>>	GaussianAnomalyDataSource.iterator()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.sparseTrainTest()	Makes a simple dataset for training and testing.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.sparseTrainTest()	Makes a simple dataset for training and testing.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.sparseTrainTest(double negate)	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.sparseTrainTest(double negate)	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.

Uses of Event in org.tribuo.anomaly.liblinear

Methods in org.tribuo.anomaly.liblinear that return types with arguments of type Event

Modifier and Type	Method	Description
protected LibLinearModel<Event>	LibLinearAnomalyTrainer.createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Event> outputIDInfo, List<de.bwaldvogel.liblinear.Model> models)
protected Excuse<Event>	LibLinearAnomalyModel.innerGetExcuse(Example<Event> e, double[][] allFeatureWeights)	The call to model.getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses.
Prediction<Event>	LibLinearAnomalyModel.predict(Example<Event> example)

Method parameters in org.tribuo.anomaly.liblinear with type arguments of type Event

Modifier and Type	Method	Description
protected LibLinearModel<Event>	LibLinearAnomalyTrainer.createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Event> outputIDInfo, List<de.bwaldvogel.liblinear.Model> models)
protected com.oracle.labs.mlrg.olcut.util.Pair<de.bwaldvogel.liblinear.FeatureNode[][],double[][]>	LibLinearAnomalyTrainer.extractData(Dataset<Event> data, ImmutableOutputInfo<Event> outputInfo, ImmutableFeatureMap featureMap)
protected com.oracle.labs.mlrg.olcut.util.Pair<de.bwaldvogel.liblinear.FeatureNode[][],double[][]>	LibLinearAnomalyTrainer.extractData(Dataset<Event> data, ImmutableOutputInfo<Event> outputInfo, ImmutableFeatureMap featureMap)
protected Excuse<Event>	LibLinearAnomalyModel.innerGetExcuse(Example<Event> e, double[][] allFeatureWeights)	The call to model.getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses.
Prediction<Event>	LibLinearAnomalyModel.predict(Example<Event> example)
protected de.bwaldvogel.liblinear.Parameter	LibLinearAnomalyTrainer.setupParameters(ImmutableOutputInfo<Event> labelIDMap)

Uses of Event in org.tribuo.anomaly.libsvm

Methods in org.tribuo.anomaly.libsvm that return types with arguments of type Event

Modifier and Type	Method	Description
protected LibSVMModel<Event>	LibSVMAnomalyTrainer.createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Event> outputIDInfo, List<libsvm.svm_model> models)
Prediction<Event>	LibSVMAnomalyModel.predict(Example<Event> example)
LibSVMModel<Event>	LibSVMAnomalyTrainer.train(Dataset<Event> dataset, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)

Methods in org.tribuo.anomaly.libsvm with parameters of type Event

Modifier and Type	Method	Description
protected double	LibSVMAnomalyTrainer.extractOutput(Event output)	Converts an output into a double for use in training.

Method parameters in org.tribuo.anomaly.libsvm with type arguments of type Event

Modifier and Type	Method	Description
protected LibSVMModel<Event>	LibSVMAnomalyTrainer.createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Event> outputIDInfo, List<libsvm.svm_model> models)
protected com.oracle.labs.mlrg.olcut.util.Pair<libsvm.svm_node[][],double[][]>	LibSVMAnomalyTrainer.extractData(Dataset<Event> data, ImmutableOutputInfo<Event> outputInfo, ImmutableFeatureMap featureMap)
protected com.oracle.labs.mlrg.olcut.util.Pair<libsvm.svm_node[][],double[][]>	LibSVMAnomalyTrainer.extractData(Dataset<Event> data, ImmutableOutputInfo<Event> outputInfo, ImmutableFeatureMap featureMap)
Prediction<Event>	LibSVMAnomalyModel.predict(Example<Event> example)
LibSVMModel<Event>	LibSVMAnomalyTrainer.train(Dataset<Event> dataset, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)

Constructor parameters in org.tribuo.anomaly.libsvm with type arguments of type Event

Modifier	Constructor	Description
	LibSVMAnomalyTrainer(SVMParameters<Event> parameters)	Creates a one-class LibSVM trainer using the supplied parameters and Trainer.DEFAULT_SEED.
	LibSVMAnomalyTrainer(SVMParameters<Event> parameters, long seed)	Creates a one-class LibSVM trainer using the supplied parameters and RNG seed.