Uses of Class
cc.mallet.types.Alphabet

Packages that use Alphabet

cc.mallet.classify	Classes for training and classifying instances.
cc.mallet.classify.tui	Command line programs for document classification.
cc.mallet.cluster	Unsupervised clustering of Instance objects within an InstanceList.
cc.mallet.cluster.tui
cc.mallet.extract	Unimplemented.
cc.mallet.fst	Transducers, including Conditional Random Fields (CRFs).
cc.mallet.fst.semi_supervised
cc.mallet.grmm.learning
cc.mallet.grmm.learning.extract
cc.mallet.grmm.util
cc.mallet.pipe	Classes for processing arbitrary data into instances.
cc.mallet.pipe.iterator	Classes that generate instances from different kinds of input or data structures.
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 Alphabet in cc.mallet.classify

Methods in cc.mallet.classify that return Alphabet

Alphabet	NaiveBayesTrainer.getAlphabet()
Alphabet	Classifier.getAlphabet()
Alphabet[]	NaiveBayesTrainer.getAlphabets()
Alphabet[]	Classifier.getAlphabets()

Methods in cc.mallet.classify with parameters of type Alphabet

static java.util.ArrayList<java.lang.Integer>

FeatureConstraintUtil.selectTopLDAFeatures(int numSelFeatures, ParallelTopicModel lda, Alphabet alphabet) Select top features in LDA topics.

Uses of Alphabet in cc.mallet.classify.tui

Methods in cc.mallet.classify.tui with parameters of type Alphabet

static java.util.HashMap<java.lang.Integer,java.util.ArrayList<java.lang.Integer>>

Vectors2FeatureConstraints.readFeaturesAndLabelsFromFile(java.io.File file, Alphabet dataAlphabet, Alphabet targetAlphabet)

Uses of Alphabet in cc.mallet.cluster

Methods in cc.mallet.cluster that return Alphabet

Alphabet	Record.fieldAlphabet()
Alphabet	Record.valueAlphabet()

Constructors in cc.mallet.cluster with parameters of type Alphabet

Record(Alphabet fieldAlph, Alphabet valueAlph)

Record(Alphabet fieldAlph, Alphabet valueAlph, java.lang.String[][] vals)

Uses of Alphabet in cc.mallet.cluster.tui

Methods in cc.mallet.cluster.tui with parameters of type Alphabet

static int[]

Clusterings2Clusterer.string2ints(java.lang.String[] ss, Alphabet alph)

Uses of Alphabet in cc.mallet.extract

Methods in cc.mallet.extract that return Alphabet

Alphabet	Extractor.getInputAlphabet() Returns an alphabet of the features used by the extractor.
Alphabet	CRFExtractor.getInputAlphabet()

Uses of Alphabet in cc.mallet.fst

Fields in cc.mallet.fst declared as Alphabet

protected Alphabet	CRF.inputAlphabet
protected Alphabet	CRF.outputAlphabet
Alphabet	CRF.Factors.weightAlphabet

Methods in cc.mallet.fst that return Alphabet

Alphabet	HMM.getInputAlphabet()
Alphabet	FeatureTransducer.getInputAlphabet()
Alphabet	CRF.getInputAlphabet()
Alphabet	HMM.getOutputAlphabet()
Alphabet	FeatureTransducer.getOutputAlphabet()
Alphabet	CRF.getOutputAlphabet()

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

CRF(Alphabet inputAlphabet, Alphabet outputAlphabet)

FeatureTransducer(Alphabet dictionary)

FeatureTransducer(Alphabet inputAlphabet, Alphabet outputAlphabet)

HMM(Alphabet inputAlphabet, Alphabet outputAlphabet)

MEMM(Alphabet inputAlphabet, Alphabet outputAlphabet)

Uses of Alphabet in cc.mallet.fst.semi_supervised

Methods in cc.mallet.fst.semi_supervised that return Alphabet

Alphabet	StateLabelMap.getLabelAlphabet() Returns the label (target) alphabet.
Alphabet	StateLabelMap.getStateAlphabet() Returns the state alphabet.

Constructors in cc.mallet.fst.semi_supervised with parameters of type Alphabet

StateLabelMap(Alphabet labelAlphabet, boolean isOneToOneMap)

StateLabelMap(Alphabet labelAlphabet, boolean isOneToOneMap, int startStateIndex)
Initializes the state and label maps.

Uses of Alphabet in cc.mallet.grmm.learning

Fields in cc.mallet.grmm.learning declared as Alphabet

Alphabet

DefaultAcrfTrainer.TestResults.alphabet

Methods in cc.mallet.grmm.learning that return Alphabet

Alphabet

ACRF.getInputAlphabet()

Uses of Alphabet in cc.mallet.grmm.learning.extract

Methods in cc.mallet.grmm.learning.extract that return Alphabet

Alphabet

ACRFExtractor.getInputAlphabet()

Uses of Alphabet in cc.mallet.grmm.util

Methods in cc.mallet.grmm.util that return Alphabet

Alphabet	LabelsAssignment.getAlphabet()
Alphabet[]	LabelsAssignment.getAlphabets()

Uses of Alphabet in cc.mallet.pipe

Methods in cc.mallet.pipe that return Alphabet

Alphabet	Pipe.getAlphabet()
Alphabet[]	Pipe.getAlphabets()
Alphabet	TokenSequence2FeatureSequenceWithBigrams.getBigramAlphabet()
Alphabet	Pipe.getDataAlphabet()
Alphabet	AddClassifierTokenPredictions.getDataAlphabet()
Alphabet	FeatureCountPipe.getPrunedAlphabet(int minimumCount) Returns a new alphabet that contains only features at or above the specified limit.
Alphabet	Pipe.getTargetAlphabet()

Methods in cc.mallet.pipe with parameters of type Alphabet

AugmentableFeatureVectorAddConjunctions	AugmentableFeatureVectorAddConjunctions.addConjunction(java.lang.String name, Alphabet v, int[] features, boolean[] negations)
protected void	Pipe.preceedingPipeDataAlphabetNotification(Alphabet a)
protected void	Pipe.preceedingPipeTargetAlphabetNotification(Alphabet a)
void	Pipe.setDataAlphabet(Alphabet dDict)
void	Pipe.setOrCheckDataAlphabet(Alphabet a)
void	Pipe.setOrCheckTargetAlphabet(Alphabet a)
void	Pipe.setTargetAlphabet(Alphabet tDict)

Constructors in cc.mallet.pipe with parameters of type Alphabet

Array2FeatureVector(Alphabet dataAlphabet, Alphabet targetAlphabet)
Construct a pipe based on the dimensions of the data and target.

FeatureCountPipe(Alphabet dataAlphabet, Alphabet targetAlphabet)

FeatureDocFreqPipe(Alphabet dataAlphabet, Alphabet targetAlphabet)

FeatureValueString2FeatureVector(Alphabet dataDict)

Noop(Alphabet dataDict, Alphabet targetDict)
Pass through input without change, but force the creation of Alphabet's, so it can be shared by future DictionariedPipe's.

Pipe(Alphabet dataDict, Alphabet targetDict)
Construct pipe with data and target dictionaries.

StringList2FeatureSequence(Alphabet dataDict)

Target2Label(Alphabet dataAlphabet, LabelAlphabet labelAlphabet)

Token2FeatureVector(Alphabet dataDict)

Token2FeatureVector(Alphabet dataDict, boolean binary, boolean augmentable)

TokenSequence2FeatureSequence(Alphabet dataDict)

TokenSequence2FeatureSequenceWithBigrams(Alphabet dataDict)

TokenSequence2FeatureSequenceWithBigrams(Alphabet dataDict, Alphabet bigramAlphabet)

TokenSequence2FeatureVectorSequence(Alphabet dataDict)

TokenSequence2FeatureVectorSequence(Alphabet dataDict, boolean binary, boolean augmentable)

Uses of Alphabet in cc.mallet.pipe.iterator

Methods in cc.mallet.pipe.iterator that return Alphabet

Alphabet	RandomTokenSequenceIterator.getAlphabet()
Alphabet	RandomFeatureVectorIterator.getAlphabet()

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

RandomFeatureVectorIterator(Randoms r, Alphabet vocab, java.lang.String[] classnames)

RandomTokenSequenceIterator(Randoms r, Alphabet vocab, java.lang.String[] classnames)

Uses of Alphabet in cc.mallet.topics

Fields in cc.mallet.topics declared as Alphabet

protected Alphabet	SimpleLDA.alphabet
Alphabet	ParallelTopicModel.alphabet
protected Alphabet	NPTopicModel.alphabet
protected Alphabet	LDAHyper.alphabet Deprecated.
protected Alphabet[]	PolylingualTopicModel.alphabets

Methods in cc.mallet.topics that return Alphabet

Alphabet	SimpleLDA.getAlphabet()
Alphabet	ParallelTopicModel.getAlphabet()
Alphabet	LDAHyper.getAlphabet() Deprecated.

Constructors in cc.mallet.topics with parameters of type Alphabet

TopicInferencer(int[][] typeTopicCounts, int[] tokensPerTopic, Alphabet alphabet, double[] alpha, double beta, double betaSum)

Uses of Alphabet in cc.mallet.types

Subclasses of Alphabet in cc.mallet.types

class

LabelAlphabet A mapping from arbitrary objects (usually String's) to integers (and corresponding Label objects) and back.

Methods in cc.mallet.types that return Alphabet

static Alphabet	AlphabetFactory.alphabetOfSize(int n) Create a dummy alphabet with n dimensions
Alphabet	ROCData.getAlphabet()
Alphabet	NullLabel.getAlphabet()
Alphabet	Multinomial.getAlphabet()
Alphabet	LabelsSequence.getAlphabet()
Alphabet	Labels.getAlphabet()
Alphabet	Labelings.getAlphabet()
Alphabet	Label.getAlphabet()
Alphabet	InstanceList.getAlphabet()
Alphabet	Instance.getAlphabet()
Alphabet	FeatureVectorSequence.getAlphabet()
Alphabet	FeatureVector.getAlphabet()
Alphabet	FeatureSequence.getAlphabet()
Alphabet	FeatureSelection.getAlphabet()
Alphabet	Dirichlet.getAlphabet()
Alphabet	AlphabetCarrying.getAlphabet()
Alphabet[]	ROCData.getAlphabets()
Alphabet[]	NullLabel.getAlphabets()
Alphabet[]	LabelsSequence.getAlphabets()
Alphabet[]	Labels.getAlphabets()
Alphabet[]	Labelings.getAlphabets()
Alphabet[]	Label.getAlphabets()
Alphabet[]	InstanceList.getAlphabets()
Alphabet[]	Instance.getAlphabets()
Alphabet[]	FeatureVectorSequence.getAlphabets()
Alphabet[]	FeatureVector.getAlphabets()
Alphabet[]	FeatureSequence.getAlphabets()
Alphabet[]	FeatureSelection.getAlphabets()
Alphabet[]	AlphabetCarrying.getAlphabets()
Alphabet	FeatureSequenceWithBigrams.getBiAlphabet()
Alphabet	InstanceList.getDataAlphabet() Returns the Alphabet mapping features of the data to integers.
Alphabet	Instance.getDataAlphabet()
Alphabet	InstanceList.getTargetAlphabet() Returns the Alphabet mapping target output labels to integers.
Alphabet	Instance.getTargetAlphabet()
static Alphabet	AlphabetFactory.loadFromFile(java.io.File alphabetFile) Load an alphabet from a file, one item per line

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

static FeatureSelection	FeatureSelection.createFromRegex(Alphabet dictionary, java.util.regex.Pattern regex) Creates a FeatureSelection that includes only those features whose names match a given regex.
static boolean	FeatureConjunction.featuresOverlap(Alphabet dictionary, int feature1, int feature2)
static int[]	FeatureConjunction.getFeatureIndices(Alphabet dictionary, java.lang.String featureConjunctionName)
static java.lang.String	FeatureConjunction.getName(Alphabet dictionary, int[] features)
static java.lang.String	FeatureConjunction.getName(Alphabet dictionary, int[] features, boolean[] negations)
static java.lang.String	FeatureConjunction.getName(Alphabet dictionary, int feature1, int feature2)
static int[]	FeatureVector.getObjectIndices(java.lang.Object[] entries, Alphabet dict, boolean addIfNotPresent)
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.
void	FeatureSequence.prune(double[] counts, Alphabet newAlphabet, int cutoff) Remove features from the sequence that occur fewer than cutoff times in the corpus, as indicated by the provided counts.
void	Multinomial.Estimator.setAlphabet(Alphabet d)
FeatureSequence	TokenSequence.toFeatureSequence(Alphabet dict)
FeatureVector	TokenSequence.toFeatureVector(Alphabet dict)
FeatureVector	Token.toFeatureVector(Alphabet dict, boolean binary)
FeatureVector	PropertyHolder.toFeatureVector(Alphabet dict, boolean binary)

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

AugmentableFeatureVector(Alphabet dict)

AugmentableFeatureVector(Alphabet dict, boolean binary)

AugmentableFeatureVector(Alphabet dict, double[] values)

AugmentableFeatureVector(Alphabet dict, double[] values, int capacity)

AugmentableFeatureVector(Alphabet dict, int[] indices, double[] values, int capacity)

AugmentableFeatureVector(Alphabet dict, int[] indices, double[] values, int capacity, boolean copy)

AugmentableFeatureVector(Alphabet dict, int[] indices, double[] values, int capacity, boolean copy, boolean checkIndicesSorted)

AugmentableFeatureVector(Alphabet dict, int[] indices, double[] values, int capacity, int size, boolean copy, boolean checkIndicesSorted, boolean removeDuplicates)
To make a binary vector, pass null for "values"

AugmentableFeatureVector(Alphabet dict, int capacity, boolean binary)

AugmentableFeatureVector(Alphabet dict, PropertyList pl, boolean binary)

AugmentableFeatureVector(Alphabet dict, PropertyList pl, boolean binary, boolean growAlphabet)

Dirichlet(Alphabet dict)
A symmetric Dirichlet with alpha_i = 1.0 and the number of dimensions of the given alphabet.

Dirichlet(Alphabet dict, double alpha)
A symmetric Dirichlet with alpha_i = alpha and the number of dimensions of the given alphabet.

Dirichlet(double[] alphas, Alphabet dict)
Constructor that takes an alphabet representing the meaning of each dimension

FeatureConjunction(Alphabet dictionary, int[] features)

FeatureConjunction(Alphabet dictionary, int[] features, boolean[] negations)

FeatureConjunction(java.lang.String name, Alphabet dictionary, int[] features, boolean[] negations)

FeatureConjunction(java.lang.String name, Alphabet dictionary, int[] features, boolean[] negations, boolean checkSorted)

FeatureConjunction(java.lang.String name, Alphabet dictionary, int[] features, boolean[] negations, boolean checkSorted, boolean copyFeatures, boolean copyNegations)
If negations[i] is true, insist that the feature has non-zero value; if false, insist that it has zero value.

FeatureCounter(Alphabet alphabet)

FeatureCounts(Alphabet vocab, double[] counts)

FeatureSelection(Alphabet dictionary)

FeatureSelection(Alphabet dictionary, java.util.BitSet selectedFeatures)

FeatureSequence(Alphabet dict)

FeatureSequence(Alphabet dict, int capacity)

FeatureSequence(Alphabet dict, int[] features)
Creates a FeatureSequence given all of the objects in the sequence.

FeatureSequence(Alphabet dict, int[] features, int len)

FeatureSequenceWithBigrams(Alphabet dict, Alphabet bigramDictionary, TokenSequence ts)

FeatureVector(Alphabet dict, double[] values)
Create a dense vector

FeatureVector(Alphabet dict, int[] featureIndices)
Create binary vector

FeatureVector(Alphabet dict, int[] featureIndices, double[] values)
Create non-binary vector, possibly dense if "featureIndices" or possibly sparse, if not

FeatureVector(Alphabet dict, int[] indices, double[] values, int capacity, int size, boolean copy, boolean checkIndicesSorted, boolean removeDuplicates)

FeatureVector(Alphabet dict, java.lang.Object[] keys, double[] values)

FeatureVector(Alphabet dict, PropertyList pl, boolean binary)

FeatureVector(Alphabet dict, PropertyList pl, boolean binary, boolean growAlphabet)

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(Alphabet dict, TokenSequence tokens)

FeatureVectorSequence(Alphabet dict, TokenSequence tokens, boolean binary, boolean augmentable)

FeatureVectorSequence(Alphabet dict, TokenSequence tokens, boolean binary, boolean augmentable, boolean growAlphabet)

GainRatio(Alphabet dataAlphabet, double[] gainRatios, double[] splitPoints, double baseEntropy, LabelVector baseLabelDistribution, int numSplitPointsForBestFeature, int minNumInsts)

InfoGain(Alphabet vocab, double[] infogains)

InstanceList(Alphabet dataAlphabet, Alphabet targetAlphabet)
Construct an InstanceList with initial capacity of 10, with a Noop default pipe.

InstanceList(Randoms r, Alphabet vocab, java.lang.String[] classNames, int meanInstancesPerLabel)

LabelSequence(Alphabet dict)

Multinomial.Estimator(Alphabet dictionary)

Multinomial.Estimator(double[] counts, Alphabet dictionary)

Multinomial.Estimator(double[] counts, int size, Alphabet dictionary)

Multinomial.LaplaceEstimator(Alphabet dictionary)

Multinomial.Logged(double[] probabilities, Alphabet dictionary)

Multinomial.Logged(double[] probabilities, Alphabet dictionary, boolean areLoggedAlready)

Multinomial.Logged(double[] probabilities, Alphabet dictionary, int size)

Multinomial.Logged(double[] probabilities, Alphabet dictionary, int size, boolean areLoggedAlready)

Multinomial.MEstimator(Alphabet dictionary, double m)

Multinomial.MLEstimator(Alphabet dictionary)

Multinomial(double[] probabilities, Alphabet dictionary)

Multinomial(double[] probabilities, Alphabet dictionary, int size, boolean copy, boolean checkSum)

PartiallyRankedFeatureVector(Alphabet dict, AugmentableFeatureVector v)

PartiallyRankedFeatureVector(Alphabet dict, DenseVector v)

PartiallyRankedFeatureVector(Alphabet dict, double[] values)

PartiallyRankedFeatureVector(Alphabet dict, int[] indices, double[] values)

PartiallyRankedFeatureVector(Alphabet dict, SparseVector v)

RankedFeatureVector(Alphabet dict, AugmentableFeatureVector v)

RankedFeatureVector(Alphabet dict, DenseVector v)

RankedFeatureVector(Alphabet dict, double[] values)

RankedFeatureVector(Alphabet dict, double[] values, int begin, int length)

RankedFeatureVector(Alphabet dict, int[] indices, double[] values)

RankedFeatureVector(Alphabet dict, SparseVector v)

SparseVector(Alphabet dict, PropertyList pl, boolean binary)

SparseVector(Alphabet dict, PropertyList pl, boolean binary, boolean growAlphabet)

Uses of Alphabet in cc.mallet.util

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

static FeatureVector

MVNormal.nextFeatureVector(Alphabet alphabet, double[] mean, double[] precision, Randoms random)