Uses of Interface
cc.mallet.types.Sequence

Packages that use Sequence

Package	Description
cc.mallet.extract	Unimplemented.
cc.mallet.fst	Transducers, including Conditional Random Fields (CRFs).
cc.mallet.fst.confidence
cc.mallet.fst.semi_supervised.pr
cc.mallet.fst.semi_supervised.tui
cc.mallet.pipe.iterator	Classes that generate instances from different kinds of input or data structures.
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 Sequence in cc.mallet.extract

Subinterfaces of Sequence in cc.mallet.extract

Modifier and Type	Interface	Description
interface	Tokenization

Classes in cc.mallet.extract that implement Sequence

Modifier and Type	Class	Description
class	LabeledSpans	Created: Oct 31, 2004
class	StringTokenization

Methods in cc.mallet.extract that return Sequence

Modifier and Type	Method	Description
Sequence	DocumentExtraction.getPredictedLabels()
Sequence	CRFExtractor.pipeInput(java.lang.Object input)

Methods in cc.mallet.extract with parameters of type Sequence

Modifier and Type	Method	Description
LabeledSpans	BIOTokenizationFilter.constructLabeledSpans(LabelAlphabet dict, java.lang.Object document, Label backgroundTag, Tokenization input, Sequence seq)
LabeledSpans	ConfidenceTokenizationFilter.constructLabeledSpans(LabelAlphabet dict, java.lang.Object document, Label backgroundTag, Tokenization input, Sequence seq)
LabeledSpans	DefaultTokenizationFilter.constructLabeledSpans(LabelAlphabet dict, java.lang.Object document, Label backgroundTag, Tokenization input, Sequence seq)
LabeledSpans	HierarchicalTokenizationFilter.constructLabeledSpans(LabelAlphabet dict, java.lang.Object document, Label backgroundTag, Tokenization input, Sequence seq)
LabeledSpans	TokenizationFilter.constructLabeledSpans(LabelAlphabet dict, java.lang.Object document, Label backgroundTag, Tokenization input, Sequence seq)	Converts a the sequence of labels into a set of labeled spans.

Constructors in cc.mallet.extract with parameters of type Sequence

Constructor	Description
DocumentExtraction(java.lang.String name, LabelAlphabet dict, Tokenization input, Sequence predicted, Sequence target, java.lang.String background)
DocumentExtraction(java.lang.String name, LabelAlphabet dict, Tokenization input, Sequence predicted, Sequence target, java.lang.String background, TokenizationFilter filter)
DocumentExtraction(java.lang.String name, LabelAlphabet dict, Tokenization input, Sequence predicted, java.lang.String background)
Extraction(Extractor extractor, LabelAlphabet dict, java.lang.String name, Tokenization input, Sequence output, java.lang.String background)	Creates an extration given a sequence output by some kind of per-sequece labeler, like an HMM or a CRF.

Uses of Sequence in cc.mallet.fst

Methods in cc.mallet.fst that return Sequence

Modifier and Type	Method	Description
static Sequence[]	SimpleTagger.apply(Transducer model, Sequence input, int k)	Apply a transducer to an input sequence to produce the k highest-scoring output sequences.
Sequence<java.lang.Object>	MaxLattice.bestOutputSequence()
Sequence<java.lang.Object>	MaxLatticeDefault.bestOutputSequence()
Sequence<Transducer.State>	MaxLattice.bestStateSequence()
Sequence<Transducer.State>	MaxLatticeDefault.bestStateSequence()
Sequence	MaxLatticeDefault.getInput()
Sequence	Segment.getInput()
Sequence	SumLattice.getInput()
Sequence	SumLatticeBeam.getInput()
Sequence	SumLatticeDefault.getInput()
Sequence	SumLatticeScaling.getInput()
Sequence	Segment.getPredicted()
Sequence	MaxLatticeDefault.getProvidedOutput()
Sequence	Segment.getSegmentInputSequence()
Sequence	Segment.getTruth()
Sequence[]	CRF.predict(InstanceList testing)	Deprecated.
Sequence	Transducer.transduce(Sequence input)	Converts the given sequence into another sequence according to this transducer.

Methods in cc.mallet.fst that return types with arguments of type Sequence

Modifier and Type	Method	Description
java.util.List<Sequence<java.lang.Object>>	MaxLattice.bestOutputSequences(int n)
java.util.List<Sequence<java.lang.Object>>	MaxLatticeDefault.bestOutputSequences(int n)
java.util.List<Sequence<Transducer.State>>	MaxLattice.bestStateSequences(int n)
java.util.List<Sequence<Transducer.State>>	MaxLatticeDefault.bestStateSequences(int n)

Methods in cc.mallet.fst with parameters of type Sequence

Modifier and Type	Method	Description
static Sequence[]	SimpleTagger.apply(Transducer model, Sequence input, int k)	Apply a transducer to an input sequence to produce the k highest-scoring output sequences.
double	MaxLattice.elementwiseAccuracy(Sequence referenceOutput)
double	MaxLatticeDefault.elementwiseAccuracy(Sequence referenceOutput)
MaxLattice	MaxLatticeDefault.Factory.newMaxLattice(Transducer trans, Sequence inputSequence, Sequence outputSequence)
MaxLattice	MaxLatticeFactory.newMaxLattice(Transducer trans, Sequence inputSequence)
abstract MaxLattice	MaxLatticeFactory.newMaxLattice(Transducer trans, Sequence inputSequence, Sequence outputSequence)
SumLattice	SumLatticeBeam.Factory.newSumLattice(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor, boolean saveXis, LabelAlphabet outputAlphabet)
SumLattice	SumLatticeDefault.Factory.newSumLattice(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor, boolean saveXis, LabelAlphabet outputAlphabet)
SumLattice	SumLatticeFactory.newSumLattice(Transducer trans, Sequence input)
SumLattice	SumLatticeFactory.newSumLattice(Transducer trans, Sequence input, Transducer.Incrementor incrementor)
SumLattice	SumLatticeFactory.newSumLattice(Transducer trans, Sequence input, Sequence output)
SumLattice	SumLatticeFactory.newSumLattice(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor)
SumLattice	SumLatticeFactory.newSumLattice(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor, boolean saveXis)
abstract SumLattice	SumLatticeFactory.newSumLattice(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor, boolean saveXis, LabelAlphabet outputAlphabet)	Returns a SumLattice object to run forward-backward.
SumLattice	SumLatticeFactory.newSumLattice(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor, LabelAlphabet outputAlphabet)
SumLattice	SumLatticeScaling.Factory.newSumLattice(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor, boolean saveXis, LabelAlphabet outputAlphabet)
int	MultiSegmentationEvaluator.numIncorrectSegments(Sequence trueOutput, Sequence predOutput)	Returns the number of incorrect segments in predOutput
void	Segment.setPredicted(Sequence predicted)
double	MaxLatticeDefault.tokenAccuracy(Sequence referenceOutput, java.io.PrintWriter out)
Sequence	Transducer.transduce(Sequence input)	Converts the given sequence into another sequence according to this transducer.
Transducer.TransitionIterator	CRF.State.transitionIterator(Sequence inputSequence, int inputPosition, Sequence outputSequence, int outputPosition)
Transducer.TransitionIterator	FeatureTransducer.State.transitionIterator(Sequence inputSequence, int inputPosition)
Transducer.TransitionIterator	FeatureTransducer.State.transitionIterator(Sequence input, int inputPosition, Sequence output, int outputPosition)
Transducer.TransitionIterator	HMM.State.transitionIterator(Sequence inputSequence, int inputPosition, Sequence outputSequence, int outputPosition)
Transducer.TransitionIterator	MEMM.State.transitionIterator(Sequence inputSequence, int inputPosition, Sequence outputSequence, int outputPosition)
Transducer.TransitionIterator	Transducer.State.transitionIterator(Sequence input, int inputPosition)
abstract Transducer.TransitionIterator	Transducer.State.transitionIterator(Sequence input, int inputPosition, Sequence output, int outputPosition)

Method parameters in cc.mallet.fst with type arguments of type Sequence

Modifier and Type	Method	Description
void	MultiSegmentationEvaluator.batchTest(InstanceList data, java.util.List<Sequence> predictedSequences, java.lang.String description, java.io.PrintStream viterbiOutputStream)	Tests segmentation using an ArrayList of predicted Sequences instead of a Transducer.

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

Constructor	Description
MaxLatticeDefault(Transducer t, Sequence inputSequence)
MaxLatticeDefault(Transducer t, Sequence inputSequence, Sequence outputSequence)
MaxLatticeDefault(Transducer t, Sequence inputSequence, Sequence outputSequence, int maxCaches)	Initiate Viterbi decoding of the inputSequence, contrained to match non-null parts of the outputSequence.
Segment(Sequence input, Sequence pred, Sequence truth, int start, int end, java.lang.Object startTag, java.lang.Object inTag)	Initializes the segment.
SumLatticeBeam(Transducer t, Sequence input, Sequence output, Transducer.Incrementor incrementor)
SumLatticeBeam(Transducer t, Sequence input, Sequence output, Transducer.Incrementor incrementor, boolean saveXis)
SumLatticeBeam(Transducer t, Sequence input, Sequence output, Transducer.Incrementor incrementor, boolean saveXis, LabelAlphabet outputAlphabet)
SumLatticeBeam(Transducer t, Sequence input, Sequence output, Transducer.Incrementor incrementor, LabelAlphabet outputAlphabet, int[] constraints)	Create a lattice that constrains its transitions such that the pairs in "constraints" are adhered to.
SumLatticeConstrained(Transducer t, Sequence input, Sequence output, Segment requiredSegment, Sequence constrainedSequence)
SumLatticeConstrained(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor, LabelAlphabet outputAlphabet, int[] constraints)	Create a lattice that constrains its transitions such that the pairs in "constraints" are adhered to.
SumLatticeDefault(Transducer trans, Sequence input)
SumLatticeDefault(Transducer trans, Sequence input, boolean saveXis)
SumLatticeDefault(Transducer trans, Sequence input, Transducer.Incrementor incrementor)
SumLatticeDefault(Transducer trans, Sequence input, Sequence output)
SumLatticeDefault(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor)
SumLatticeDefault(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor, boolean saveXis)
SumLatticeDefault(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor, boolean saveXis, LabelAlphabet outputAlphabet)
SumLatticeDefault(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor, LabelAlphabet outputAlphabet)
SumLatticeScaling(Transducer trans, Sequence input)
SumLatticeScaling(Transducer trans, Sequence input, boolean saveXis)
SumLatticeScaling(Transducer trans, Sequence input, Transducer.Incrementor incrementor)
SumLatticeScaling(Transducer trans, Sequence input, Sequence output)
SumLatticeScaling(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor)
SumLatticeScaling(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor, boolean saveXis)
SumLatticeScaling(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor, boolean saveXis, LabelAlphabet outputAlphabet)
SumLatticeScaling(Transducer trans, Sequence input, Sequence output, Transducer.Incrementor incrementor, LabelAlphabet outputAlphabet)

Uses of Sequence in cc.mallet.fst.confidence

Constructors in cc.mallet.fst.confidence with parameters of type Sequence

Constructor	Description
EntityConfidence(double conf, boolean corr, Sequence input, int start, int end)
InstanceWithConfidence(Instance inst, double c, Sequence predicted)

Uses of Sequence in cc.mallet.fst.semi_supervised.pr

Methods in cc.mallet.fst.semi_supervised.pr that return Sequence

Modifier and Type	Method	Description
Sequence	SumLatticeDefaultCachedDot.getInput()
Sequence	SumLatticeKL.getInput()
Sequence	SumLatticePR.getInput()

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

Modifier and Type	Method	Description
double	PRAuxiliaryModel.getWeight(int index, int position, Sequence input, Transducer.TransitionIterator iter)
void	PRAuxiliaryModel.incrementTransition(int index, int position, Sequence input, Transducer.TransitionIterator iter, double prob)
void	PRAuxiliaryModel.preProcess(int index, int position, Sequence input)

Constructors in cc.mallet.fst.semi_supervised.pr with parameters of type Sequence

Constructor	Description
CachedDotTransitionIterator(CRF.State source, Sequence inputSeq, int inputPosition, java.lang.String output, double[] dots)
SumLatticeDefaultCachedDot(Transducer trans, Sequence input, Sequence output, double[][][] cachedDots, Transducer.Incrementor incrementor, boolean saveXis, LabelAlphabet outputAlphabet)
SumLatticeKL(Transducer trans, Sequence input, double[] initProbs, double[] finalProbs, double[][][] xis, double[][][] cachedDots, Transducer.Incrementor incrementor)
SumLatticePR(Transducer trans, int index, Sequence input, Sequence output, PRAuxiliaryModel auxModel, double[][][] cachedDots, boolean incrementConstraints, Transducer.Incrementor incrementor, LabelAlphabet outputAlphabet, boolean saveXis)

Uses of Sequence in cc.mallet.fst.semi_supervised.tui

Methods in cc.mallet.fst.semi_supervised.tui that return Sequence

Modifier and Type	Method	Description
static Sequence[]	SimpleTaggerWithConstraints.apply(Transducer model, Sequence input, int k)	Apply a transducer to an input sequence to produce the k highest-scoring output sequences.

Methods in cc.mallet.fst.semi_supervised.tui with parameters of type Sequence

Modifier and Type	Method	Description
static Sequence[]	SimpleTaggerWithConstraints.apply(Transducer model, Sequence input, int k)	Apply a transducer to an input sequence to produce the k highest-scoring output sequences.

Uses of Sequence in cc.mallet.pipe.iterator

Constructors in cc.mallet.pipe.iterator with parameters of type Sequence

Constructor	Description
SegmentIterator(Instance instance, java.lang.Object[] startTags, java.lang.Object[] inTags, Sequence prediction)	Iterate over segments in one instance.
SegmentIterator(Sequence input, Sequence predicted, Sequence truth, java.lang.Object[] startTags, java.lang.Object[] inTags)	Iterate over segments in one labeled sequence

Uses of Sequence in cc.mallet.types

Classes in cc.mallet.types that implement Sequence

Modifier and Type	Class	Description
class	ArrayListSequence<E>
class	ArraySequence<E>
class	FeatureSequence	An implementation of Sequence that ensures that every Object in the sequence has the same class.
class	FeatureSequenceWithBigrams	A FeatureSequence with a parallel record of bigrams, kept in a separate dictionary
class	FeatureVectorSequence
class	LabelSequence
class	LabelsSequence	A simple Sequence implementation where all of the elements must be Labels.
class	StringEditFeatureVectorSequence
class	TokenSequence	A representation of a piece of text, usually a single word, to which we can attach properties.

Fields in cc.mallet.types declared as Sequence

Modifier and Type	Field	Description
protected Sequence<I>	SequencePair.input
protected Sequence<O>	SequencePair.output

Methods in cc.mallet.types that return Sequence

Modifier and Type	Method	Description
Sequence<I>	SequencePair.input()
Sequence<O>	SequencePair.output()

Constructors in cc.mallet.types with parameters of type Sequence

Constructor	Description
ArraySequence(Sequence<E> s, boolean copy)
SequencePair(Sequence<I> input, Sequence<O> output)
SequencePairAlignment(Sequence<I> input, Sequence<O> output, double weight)

Uses of Sequence in cc.mallet.util

Methods in cc.mallet.util with parameters of type Sequence

Modifier and Type	Method	Description
static double	Sequences.elementwiseAccuracy(Sequence truth, Sequence predicted)