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## Uses of Class
cc.mallet.types.LabelVector

Packages that use LabelVector
cc.mallet.classify | Classes for training and classifying instances.
cc.mallet.fst | Transducers, including Conditional Random Fields (CRFs).
cc.mallet.types | Fundamental MALLET types, including FeatureVector, Instance, Label etc.

Uses of LabelVector in cc.mallet.classify

Methods in cc.mallet.classify that return LabelVector
`LabelVector` | `Classification.getLabelVector()`

Uses of LabelVector in cc.mallet.fst

Methods in cc.mallet.fst that return LabelVector
`LabelVector` | `SumLattice.getLabelingAtPosition(int outputPosition)`
`LabelVector` | `SumLatticeDefault.getLabelingAtPosition(int outputPosition)`
`LabelVector` | `SumLatticeBeam.getLabelingAtPosition(int outputPosition)`
`LabelVector` | `SumLatticeScaling.getLabelingAtPosition(int outputPosition)`

Uses of LabelVector in cc.mallet.types

Methods in cc.mallet.types that return LabelVector
`LabelVector` | `GainRatio.getBaseLabelDistribution()`
`LabelVector` | `InfoGain.getBaseLabelDistribution()`
`LabelVector` | `InstanceList.targetLabelDistribution()`
`LabelVector` | `Labeling.toLabelVector()`
`LabelVector` | `LabelVector.toLabelVector()`
`LabelVector` | `Label.toLabelVector()`

Methods in cc.mallet.types with parameters of type LabelVector
`PartiallyRankedFeatureVector` | `PartiallyRankedFeatureVector.Factory.newPartiallyRankedFeatureVector(InstanceList ilist, LabelVector[] posteriors)`
`PartiallyRankedFeatureVector[]` | `PartiallyRankedFeatureVector.PerLabelFactory.newPartiallyRankedFeatureVectors(InstanceList ilist, LabelVector[] posteriors)`

Constructors in cc.mallet.types with parameters of type LabelVector
`ExpGain.Factory(LabelVector[] classifications)`
`ExpGain.Factory(LabelVector[] classifications, double gaussianPriorVariance)`
`ExpGain(InstanceList ilist, LabelVector[] classifications, double gaussianPriorVariance)`
`GainRatio(Alphabet dataAlphabet, double[] gainRatios, double[] splitPoints, double baseEntropy, LabelVector baseLabelDistribution, int numSplitPointsForBestFeature, int minNumInsts)`
`GradientGain.Factory(LabelVector[] classifications)`
`GradientGain(InstanceList ilist, LabelVector[] classifications)`
`KLGain(InstanceList ilist, LabelVector[] classifications)`

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