Uses of Class
cc.mallet.types.FeatureVector

Packages that use FeatureVector

Package	Description
cc.mallet.classify.constraints.ge
cc.mallet.classify.constraints.pr
cc.mallet.cluster	Unsupervised clustering of Instance objects within an InstanceList.
cc.mallet.fst	Transducers, including Conditional Random Fields (CRFs).
cc.mallet.fst.semi_supervised.constraints
cc.mallet.fst.semi_supervised.pr.constraints
cc.mallet.topics
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.classify.constraints.ge

Methods in cc.mallet.classify.constraints.ge with parameters of type FeatureVector

Modifier and Type	Method	Description
void	MaxEntFLGEConstraints.computeExpectations(FeatureVector input, double[] dist, double weight)
void	MaxEntGEConstraint.computeExpectations(FeatureVector fv, double[] dist, double weight)	Compute expectations using provided distribution over labels.
void	MaxEntRangeL2FLGEConstraints.computeExpectations(FeatureVector input, double[] dist, double weight)
double	MaxEntFLGEConstraints.getCompositeConstraintFeatureValue(FeatureVector input, int label)
double	MaxEntGEConstraint.getCompositeConstraintFeatureValue(FeatureVector input, int label)	Computes the composite constraint feature value (over all constraint features) for FeatureVector fv and label label.
double	MaxEntRangeL2FLGEConstraints.getCompositeConstraintFeatureValue(FeatureVector input, int label)
void	MaxEntFLGEConstraints.preProcess(FeatureVector input)
void	MaxEntGEConstraint.preProcess(FeatureVector input)	Gives the constraint the option to do some caching using only the FeatureVector.
void	MaxEntRangeL2FLGEConstraints.preProcess(FeatureVector input)

Uses of FeatureVector in cc.mallet.classify.constraints.pr

Methods in cc.mallet.classify.constraints.pr with parameters of type FeatureVector

Modifier and Type	Method	Description
double	MaxEntL2FLPRConstraints.getScore(FeatureVector input, int label, double[] parameters)
double	MaxEntPRConstraint.getScore(FeatureVector input, int label, double[] parameters)
void	MaxEntFLPRConstraints.incrementExpectations(FeatureVector input, double[] dist, double weight)
void	MaxEntPRConstraint.incrementExpectations(FeatureVector fv, double[] dist, double weight)
void	MaxEntFLPRConstraints.preProcess(FeatureVector input)
void	MaxEntPRConstraint.preProcess(FeatureVector input)	Gives the constraint the option to do some caching using only the FeatureVector.

Uses of FeatureVector in cc.mallet.cluster

Methods in cc.mallet.cluster that return FeatureVector

Modifier and Type	Method	Description
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

Modifier and Type	Method	Description
Transducer.TransitionIterator	CRF.State.transitionIterator(FeatureVector fv, java.lang.String output)

Constructors in cc.mallet.fst with parameters of type FeatureVector

Constructor	Description
TransitionIterator(CRF.State source, FeatureVector fv, java.lang.String output, CRF crf)
TransitionIterator(MEMM.State source, FeatureVector fv, java.lang.String output, CRF memm)

Uses of FeatureVector in cc.mallet.fst.semi_supervised.constraints

Methods in cc.mallet.fst.semi_supervised.constraints with parameters of type FeatureVector

Modifier and Type	Method	Description
double	GEConstraint.getCompositeConstraintFeatureValue(FeatureVector input, int inputPosition, int srcIndex, int destIndex)	Computes the composite constraint feature value (over all constraint features) for FeatureVector fv and a transition from state li1 to li2.
double	OneLabelGEConstraints.getCompositeConstraintFeatureValue(FeatureVector fv, int ip, int si1, int si2)
double	OneLabelL2RangeGEConstraints.getCompositeConstraintFeatureValue(FeatureVector fv, int ip, int si1, int si2)
double	SelfTransitionGEConstraint.getCompositeConstraintFeatureValue(FeatureVector fv, int ip, int si1, int si2)
double	TwoLabelGEConstraints.getCompositeConstraintFeatureValue(FeatureVector fv, int ip, int si1, int si2)
void	GEConstraint.preProcess(FeatureVector input)	Gives the constraint the option to do some caching using only the FeatureVector.
void	OneLabelGEConstraints.preProcess(FeatureVector fv)
void	OneLabelL2RangeGEConstraints.preProcess(FeatureVector fv)
void	SelfTransitionGEConstraint.preProcess(FeatureVector fv)
void	TwoLabelGEConstraints.preProcess(FeatureVector fv)

Uses of FeatureVector in cc.mallet.fst.semi_supervised.pr.constraints

Methods in cc.mallet.fst.semi_supervised.pr.constraints with parameters of type FeatureVector

Modifier and Type	Method	Description
double	OneLabelL2IndPRConstraints.getScore(FeatureVector input, int inputPosition, int srcIndex, int destIndex, double[] parameters)
double	OneLabelL2PRConstraints.getScore(FeatureVector input, int inputPosition, int srcIndex, int destIndex, double[] parameters)
double	PRConstraint.getScore(FeatureVector input, int inputPosition, int srcIndex, int destIndex, double[] parameters)
void	OneLabelL2IndPRConstraints.incrementExpectations(FeatureVector input, int inputPosition, int srcIndex, int destIndex, double prob)
void	OneLabelL2PRConstraints.incrementExpectations(FeatureVector input, int inputPosition, int srcIndex, int destIndex, double prob)
void	PRConstraint.incrementExpectations(FeatureVector input, int inputPosition, int srcIndex, int destIndex, double prob)
void	OneLabelL2IndPRConstraints.preProcess(FeatureVector fv)
void	OneLabelL2PRConstraints.preProcess(FeatureVector fv)
void	PRConstraint.preProcess(FeatureVector input)	Gives the constraint the option to do some caching using only the FeatureVector.

Uses of FeatureVector in cc.mallet.topics

Methods in cc.mallet.topics with parameters of type FeatureVector

Modifier and Type	Method	Description
protected void	LabeledLDA.sampleTopicsForOneDoc(FeatureSequence tokenSequence, FeatureVector labels, FeatureSequence topicSequence)

Uses of FeatureVector in cc.mallet.types

Subclasses of FeatureVector in cc.mallet.types

Modifier and Type	Class	Description
class	AugmentableFeatureVector
class	BiNormalSeparation	Bi-Normal Separation is a feature weighting algorithm introduced in: An Extensive Empirical Study of Feature Selection Metrics for Text Classification, George Forman, Journal of Machine Learning Research, 3:1289--1305, 2003.
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

Modifier and Type	Method	Description
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	AugmentableFeatureVector.toFeatureVector()
FeatureVector	FeatureCounter.toFeatureVector()
FeatureVector	PropertyHolder.toFeatureVector(Alphabet dict, boolean binary)
FeatureVector	Token.toFeatureVector(Alphabet dict, boolean binary)
FeatureVector	TokenSequence.toFeatureVector(Alphabet dict)

Methods in cc.mallet.types with parameters of type FeatureVector

Modifier and Type	Method	Description
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

Constructor	Description
AugmentableFeatureVector(FeatureVector fv)
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.
FeatureVector(FeatureVector fv, Alphabet newVocab, FeatureSelection fsNarrow, FeatureSelection fsWide)
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

Modifier and Type	Method	Description
static FeatureVector	MVNormal.nextFeatureVector(Alphabet alphabet, double[] mean, double[] precision, Randoms random)