All Classes Interface Summary Class Summary Enum Summary Exception Summary
| Class |
Description |
| AbstractAugmentedSpaceSampler |
|
| AccessibleRandomTree |
Random Tree extension providing leaf node information of the constructed
tree.
|
| AggressiveAggregator |
An IntervalAggregator that makes predictions using the minimum of the
predictions as the lower bound and the maximum as the upper bound. |
| AllPairsTable |
|
| AMCTreeNode<C extends java.io.Serializable> |
|
| AugSpaceAllPairs |
|
| AWekaLearner<P extends org.api4.java.ai.ml.core.evaluation.IPrediction,B extends org.api4.java.ai.ml.core.evaluation.IPredictionBatch> |
|
| CategoricalFeatureDomain |
Description of a categorical feature domain.
|
| ChooseKAugSpaceSampler |
Samples interval-valued data from a dataset of precise points by sampling k precise points (with replacement)
and generating a point in the interval-valued augmented space by only considering those k points, i.e. choosing
respective minima and maxima for each attribute from the chosen precise points.
|
| ClassifierCache |
|
| ClassifierRankingForGroup |
|
| Cluster |
|
| ConfidenceIntervalClusteringBasedActiveDyadRanker |
A prototypical active dyad ranker based on clustering of pseudo confidence
intervals.
|
| ConstantClassifier |
|
| EMCNodeType |
|
| Ensemble |
|
| EnsembleProvider |
Class statically providing preconfigured ensembles as commonly used in TSC
implementations.
|
| ExactIntervalAugSpaceSampler |
Samples interval-valued data from a dataset of precise points.
|
| ExtendedM5Forest |
|
| ExtendedM5Tree |
|
| ExtendedRandomForest |
|
| ExtendedRandomTree |
Extension of a classic RandomTree to predict intervals.
|
| FeatureDomain |
Abstract description of a feature domain.
|
| FeatureGenerator |
|
| FeatureGeneratorTree |
|
| FeaturePreprocessor |
|
| FeatureSpace |
|
| HighProbClassifier |
|
| IAugmentedSpaceSampler |
Interface representing a class that samples interval-valued data from a set of precise data points.
|
| IAugSpaceSamplingFunction |
|
| IDistanceMetric<D,A,B> |
|
| IInstancesClassifier |
|
| ILearnShapeletsLearningAlgorithmConfig |
|
| InteractingFeatures |
|
| IntervalAggregator |
An IntervalAggeregator can aggregate from a list of intervals, more precisely
given a list of predictions in the leaf node, it can predict a range.
|
| ISplitter |
|
| ISplitterFactory<T extends ISplitter> |
|
| ITreeClassifier |
|
| IWekaClassifier |
|
| IWekaClassifierConfig |
|
| IWekaInstance |
|
| IWekaInstances |
|
| IWekaLearningAlgorithm |
|
| IWekaPreprocessingAlgorithm |
A WEKA preprocessing algorithm takes a labeled dataset and produces itself as to allow for applying the
obtained dimensionality reduction to some new data.
|
| Kmeans<A,D> |
|
| KNNAugSpaceSampler |
Samples interval-valued data from a dataset of precise points.
|
| L1DistanceMetric |
|
| LearnPatternSimilarityClassifier |
Class representing the Learn Pattern Similarity classifier as described in
Baydogan, Mustafa & Runger, George. (2015).
|
| LearnPatternSimilarityLearningAlgorithm |
|
| LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig |
|
| LearnShapeletsClassifier |
LearnShapeletsClassifier published in "J.
|
| LearnShapeletsLearningAlgorithm |
Generalized Shapelets Learning implementation for
LearnShapeletsClassifier published in "J.
|
| LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig |
|
| MajorityConfidenceVote |
Vote implementation for majority confidence.
|
| MCTreeMergeNode |
|
| MCTreeNode |
|
| MCTreeNodeLeaf |
|
| MCTreeNodeReD |
|
| MCTreeNodeReDLeaf |
|
| MLPipeline |
|
| MLSophisticatedPipeline |
|
| ModifiedISAC |
|
| ModifiedISACgMeans |
|
| ModifiedISACGroupBuilder |
|
| ModifiedISACInstanceCollector |
|
| ModifiedISACkMeans |
|
| Normalization |
|
| Normalizer |
|
| NumericFeatureDomain |
Description of a numeric feature domain.
|
| PCA |
|
| PolynomialFeatures |
|
| PredictionFailedException |
|
| PreprocessingException |
|
| QuantileAggregator |
|
| RandomSplitter |
|
| RangeQueryPredictor |
|
| RankingByPairwiseComparison |
|
| ReductionGraphGenerator |
|
| ReductionOptimizer |
|
| RPCConfig |
|
| RPNDSplitter |
|
| RQPHelper |
|
| RQPHelper.IntervalAndHeader |
|
| ShapeletTransformLearningAlgorithm |
Algorithm training a ShapeletTransform classifier as described in Jason
Lines, Luke M.
|
| ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig |
|
| ShapeletTransformTSClassifier |
Class for a ShapeletTransform classifier as described in Jason Lines, Luke M.
|
| Standardization |
|
| SupervisedFilterSelector |
|
| SuvervisedFilterPreprocessor |
|
| TimeSeriesBagOfFeaturesClassifier |
Implementation of the Time Series Bag-of-Features (TSBF) classifier as
described in Baydogan, Mustafa & Runger, George & Tuv, Eugene. (2013).
|
| TimeSeriesBagOfFeaturesLearningAlgorithm |
Algorithm to train a Time Series Bag-of-Features (TSBF) classifier as
described in Baydogan, Mustafa & Runger, George & Tuv, Eugene. (2013).
|
| TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig |
|
| TimeSeriesForestClassifier |
Time series forest classifier as described in Deng, Houtao et al.
|
| TimeSeriesForestLearningAlgorithm |
Algorithm to train a time series forest classifier as described in Deng,
Houtao et al.
|
| TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig |
|
| TimeSeriesTreeClassifier |
Time series tree as described in Deng, Houtao et al.
|
| TimeSeriesTreeLearningAlgorithm |
Algorithm to build a time series tree as described in Deng, Houtao et al.
|
| TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig |
|
| WekaClassifier |
|
| WekaInstance |
|
| WekaInstances |
|
| WekaInstancesFeatureUnion |
|
| WekaInstancesUtil |
|
| WekaLearningAlgorithm |
|
| WekaPreprocessorFitter |
|
| WekaRegressor |
|
| WekaTimeseriesUtil |
WekaUtil
|
| WekaUtil |
|
| ZeroShotUtil |
A collection of utility methods used to map the results of a input optimization of PLNetInputOptimizer back to Weka options for the respective classifiers.
|