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

Packages that use FeatureVector
cc.mallet.cluster | Unsupervised clustering of `Instance` objects within an `InstanceList`.
cc.mallet.fst | Transducers, including Conditional Random Fields (CRFs).
cc.mallet.grmm.learning |
cc.mallet.grmm.learning.templates |
cc.mallet.types | Fundamental MALLET types, including FeatureVector, Instance, Label etc.
cc.mallet.util | Miscellaneous utilities including command line processing, math functions, lexing, logging.

Uses of FeatureVector in cc.mallet.cluster

Methods in cc.mallet.cluster that return FeatureVector
`FeatureVector` | `Record.values(int field)`
`FeatureVector` | `Record.values(java.lang.String field)`

Uses of FeatureVector in cc.mallet.fst

Methods in cc.mallet.fst with parameters of type FeatureVector
`Transducer.TransitionIterator` | `CRF.State.transitionIterator(FeatureVector fv, java.lang.String output)`

Constructors in cc.mallet.fst with parameters of type FeatureVector
`CRF.TransitionIterator(CRF.State source, FeatureVector fv, java.lang.String output, CRF crf)`
`MEMM.TransitionIterator(MEMM.State source, FeatureVector fv, java.lang.String output, CRF memm)`

Uses of FeatureVector in cc.mallet.grmm.learning

Methods in cc.mallet.grmm.learning that return FeatureVector
`FeatureVector` | `ACRF.UnrolledVarSet.getFv()`

Constructors in cc.mallet.grmm.learning with parameters of type FeatureVector
`ACRF.UnrolledVarSet(ACRF.UnrolledGraph graph, ACRF.Template tmpl, Variable[] vars, FeatureVector fv)`

Uses of FeatureVector in cc.mallet.grmm.learning.templates

Methods in cc.mallet.grmm.learning.templates with parameters of type FeatureVector
`java.lang.String` | `SimilarTokensTemplate.FeatureVectorBinner.computeBin(FeatureVector fv)`
`java.lang.String` | `SimilarTokensTemplate.WordFeatureBinner.computeBin(FeatureVector fv)`

Uses of FeatureVector in cc.mallet.types

Subclasses of FeatureVector in cc.mallet.types
`class` | `AugmentableFeatureVector`
`class` | `ExpGain`
`class` | `FeatureCounts`
`class` | `GainRatio` List of features along with their thresholds sorted in descending order of the ratio of (1) information gained by splitting instances on the feature at its associated threshold value, to (2) the split information.
`class` | `GradientGain`
`class` | `InfoGain`
`class` | `KLGain`
`class` | `LabelVector`
`class` | `Multinomial` A probability distribution over a set of features represented as a `FeatureVector`.
`static class` | `Multinomial.Logged` A Multinomial in which the values associated with each feature index fi is Math.log(probability[fi]) instead of probability[fi].
`class` | `PartiallyRankedFeatureVector`
`class` | `RankedFeatureVector`

Methods in cc.mallet.types that return FeatureVector
`FeatureVector` | `FeatureVectorSequence.get(int i)`
`FeatureVector` | `FeatureVectorSequence.getFeatureVector(int i)`
`static FeatureVector` | `FeatureVector.newFeatureVector(FeatureVector fv, Alphabet newVocab, FeatureSelection fs)` Construct a new FeatureVector, selecting only those features in fs, and having new (presumably more compact, dense) Alphabet.
`FeatureVector` | `FeatureVectorSequence.Iterator.next()`
`FeatureVector` | `Dirichlet.randomFeatureVector(Randoms r, int size)`
`FeatureVector` | `Multinomial.randomFeatureVector(Randoms r, int size)`
`FeatureVector` | `FeatureCounter.toFeatureVector()`
`FeatureVector` | `AugmentableFeatureVector.toFeatureVector()`
`FeatureVector` | `TokenSequence.toFeatureVector(Alphabet dict)`
`FeatureVector` | `PropertyHolder.toFeatureVector(Alphabet dict, boolean binary)`
`FeatureVector` | `Token.toFeatureVector(Alphabet dict, boolean binary)`

Methods in cc.mallet.types with parameters of type FeatureVector
`void` | `AugmentableFeatureVector.add(FeatureVector fv)` Adds all indices that are present in some other feature vector with value 1.0.
`void` | `AugmentableFeatureVector.add(FeatureVector fv, java.lang.String prefix)` Adds all features from some other feature vector with weight 1.0.
`void` | `AugmentableFeatureVector.add(FeatureVector fv, java.lang.String prefix, boolean binary)` Adds all features from some other feature vector with weight 1.0.
`void` | `Multinomial.Estimator.increment(FeatureVector fv)`
`void` | `Multinomial.Estimator.increment(FeatureVector fv, double scale)`
`static FeatureVector` | `FeatureVector.newFeatureVector(FeatureVector fv, Alphabet newVocab, FeatureSelection fs)` Construct a new FeatureVector, selecting only those features in fs, and having new (presumably more compact, dense) Alphabet.
`boolean` | `FeatureConjunction.satisfiedBy(FeatureVector fv)`

Constructors in cc.mallet.types with parameters of type FeatureVector
`AugmentableFeatureVector(FeatureVector fv)`
`FeatureVector(FeatureVector fv, Alphabet newVocab, FeatureSelection fsNarrow, FeatureSelection fsWide)`
`FeatureVector(FeatureVector fv, Alphabet newVocab, int[] conjunctions)` New feature vector containing all the features of "fv", plus new features created by making conjunctions between the features in "conjunctions" and all the other features.
`FeatureVectorSequence(FeatureVector[] featureVectors)`
`StringEditFeatureVectorSequence(FeatureVector[] featureVectors, java.lang.String s1, java.lang.String s2)`
`StringEditFeatureVectorSequence(FeatureVector[] featureVectors, java.lang.String s1, java.lang.String s2, char delimiter)`
`StringEditFeatureVectorSequence(FeatureVector[] featureVectors, java.lang.String s1, java.lang.String s2, char delimiter, java.util.HashMap lexic)`
`StringEditFeatureVectorSequence(FeatureVector[] featureVectors, java.lang.String s1, java.lang.String s2, java.util.HashMap lexic)`

Uses of FeatureVector in cc.mallet.util

Methods in cc.mallet.util that return FeatureVector
`static FeatureVector` | `MVNormal.nextFeatureVector(Alphabet alphabet, double[] mean, double[] precision, Randoms random)`

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