Uses of Class
org.tribuo.anomaly.Event
Packages that use Event
Package
Description
Provides classes and infrastructure for anomaly detection problems.
Evaluation classes for anomaly detection.
Provides a anomaly data generator used for testing implementations.
Provides an interface to LibLinear-java for anomaly detection problems.
Provides an interface to LibSVM for anomaly detection problems.
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Uses of Event in org.tribuo.anomaly
Classes in org.tribuo.anomaly that implement interfaces with type arguments of type EventModifier and TypeClassDescriptionfinal classA factory for generating events.classThe base class for tracking anomalous events.final classfinal classAnImmutableOutputInfoobject forEvents.final classAnMutableOutputInfoobject forEvents.Fields in org.tribuo.anomaly declared as EventModifier and TypeFieldDescriptionstatic final EventAnomalyFactory.ANOMALOUS_EVENTThe anomalous event.static final EventAnomalyFactory.EXPECTED_EVENTThe expected event.static final EventAnomalyFactory.UNKNOWN_EVENTThe unknown event.Methods in org.tribuo.anomaly that return EventModifier and TypeMethodDescriptionEvent.copy()<V> EventAnomalyFactory.generateOutput(V label) ImmutableAnomalyInfo.getOutput(int id) AnomalyFactory.getUnknownOutput()Methods in org.tribuo.anomaly that return types with arguments of type EventModifier and TypeMethodDescriptionAnomalyFactory.constructInfoForExternalModel(Map<Event, Integer> mapping) AnomalyInfo.generateImmutableOutputInfo()AnomalyFactory.generateInfo()AnomalyInfo.generateMutableOutputInfo()AnomalyInfo.getDomain()Returns the set of possibleEvents.AnomalyFactory.getEvaluator()ImmutableAnomalyInfo.iterator()Methods in org.tribuo.anomaly with parameters of type EventMethod parameters in org.tribuo.anomaly with type arguments of type EventModifier and TypeMethodDescriptionAnomalyFactory.constructInfoForExternalModel(Map<Event, Integer> mapping) -
Uses of Event in org.tribuo.anomaly.evaluation
Subclasses with type arguments of type Event in org.tribuo.anomaly.evaluationModifier and TypeClassDescriptionclassSubinterfaces with type arguments of type Event in org.tribuo.anomaly.evaluationClasses in org.tribuo.anomaly.evaluation that implement interfaces with type arguments of type EventModifier and TypeClassDescriptionclassA metric for evaluating anomaly detection problems.Methods in org.tribuo.anomaly.evaluation that return types with arguments of type EventMethod parameters in org.tribuo.anomaly.evaluation with type arguments of type EventModifier and TypeMethodDescriptionprotected org.tribuo.anomaly.evaluation.AnomalyMetric.ContextAnomalyEvaluator.createContext(Model<Event> model, List<Prediction<Event>> predictions) protected org.tribuo.anomaly.evaluation.AnomalyMetric.ContextAnomalyEvaluator.createContext(Model<Event> model, List<Prediction<Event>> predictions) org.tribuo.anomaly.evaluation.AnomalyMetric.ContextAnomalyMetric.createContext(Model<Event> model, List<Prediction<Event>> predictions) org.tribuo.anomaly.evaluation.AnomalyMetric.ContextAnomalyMetric.createContext(Model<Event> model, List<Prediction<Event>> predictions) protected AnomalyEvaluationAnomalyEvaluator.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 EventModifierConstructorDescriptionAnomalyMetric(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 EventModifier and TypeMethodDescriptionAnomalyDataGenerator.denseTrainTest()Makes a simple dataset for training and testing.AnomalyDataGenerator.denseTrainTest()Makes a simple dataset for training and testing.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}.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}.AnomalyDataGenerator.emptyExample()Generates an example with no features.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.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.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.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.AnomalyDataGenerator.invalidSparseExample()Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.AnomalyDataGenerator.sparseTrainTest()Makes a simple dataset for training and testing.AnomalyDataGenerator.sparseTrainTest()Makes a simple dataset for training and testing.AnomalyDataGenerator.sparseTrainTest(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.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
Subclasses with type arguments of type Event in org.tribuo.anomaly.liblinearModifier and TypeClassDescriptionclassAModelwhich wraps a LibLinear-java anomaly detection model.classATrainerwhich wraps a liblinear-java anomaly detection trainer using a one-class SVM.Classes in org.tribuo.anomaly.liblinear that implement interfaces with type arguments of type EventModifier and TypeClassDescriptionfinal classThe carrier type for liblinear anomaly detection modes.Methods in org.tribuo.anomaly.liblinear that return types with arguments of type EventModifier and TypeMethodDescriptionprotected LibLinearModel<Event> LibLinearAnomalyTrainer.createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Event> outputIDInfo, List<de.bwaldvogel.liblinear.Model> models) 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.Method parameters in org.tribuo.anomaly.liblinear with type arguments of type EventModifier and TypeMethodDescriptionprotected 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) 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.protected de.bwaldvogel.liblinear.ParameterLibLinearAnomalyTrainer.setupParameters(ImmutableOutputInfo<Event> labelIDMap) -
Uses of Event in org.tribuo.anomaly.libsvm
Subclasses with type arguments of type Event in org.tribuo.anomaly.libsvmModifier and TypeClassDescriptionclassA anomaly detection model that uses an underlying libSVM model to make the predictions.classA trainer for anomaly models that uses LibSVM.Classes in org.tribuo.anomaly.libsvm that implement interfaces with type arguments of type EventModifier and TypeClassDescriptionclassThe carrier type for LibSVM anomaly detection modes.Methods in org.tribuo.anomaly.libsvm that return types with arguments of type EventModifier and TypeMethodDescriptionprotected LibSVMModel<Event> LibSVMAnomalyTrainer.createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Event> outputIDInfo, List<libsvm.svm_model> models) 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 EventModifier and TypeMethodDescriptionprotected doubleLibSVMAnomalyTrainer.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 EventModifier and TypeMethodDescriptionprotected 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) 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 EventModifierConstructorDescriptionLibSVMAnomalyTrainer(SVMParameters<Event> parameters) Creates a one-class LibSVM trainer using the supplied parameters andTrainer.DEFAULT_SEED.LibSVMAnomalyTrainer(SVMParameters<Event> parameters, long seed) Creates a one-class LibSVM trainer using the supplied parameters and RNG seed.