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

Packages that use Alphabet
cc.mallet.classify | Classes for training and classifying instances.
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.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.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` | `CRFExtractor.getInputAlphabet()`
`Alphabet` | `Extractor.getInputAlphabet()` Returns an alphabet of the features used by the extractor.

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

Methods in cc.mallet.fst.semi_supervised with parameters of type Alphabet
`void` | `GECriteria.print(Alphabet targetAlphabet)` Prints the constraints.

Constructors in cc.mallet.fst.semi_supervised with parameters of type Alphabet
`StateLabelMap(Alphabet labelAlphabet, boolean isOneToOneMap)` 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.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)`
`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` | `ParallelTopicModel.alphabet`
`protected Alphabet` | `SimpleLDA.alphabet`
`protected Alphabet` | `LDAHyper.alphabet` Deprecated.
`protected Alphabet` | `NPTopicModel.alphabet`
`protected Alphabet[]` | `PolylingualTopicModel.alphabets`

Methods in cc.mallet.topics that return Alphabet
`Alphabet` | `ParallelTopicModel.getAlphabet()`
`Alphabet` | `SimpleLDA.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` | `FeatureVector.getAlphabet()`
`Alphabet` | `ROCData.getAlphabet()`
`Alphabet` | `InstanceList.getAlphabet()`
`Alphabet` | `FeatureSelection.getAlphabet()`
`Alphabet` | `Dirichlet.getAlphabet()`
`Alphabet` | `AlphabetCarrying.getAlphabet()`
`Alphabet` | `Multinomial.getAlphabet()`
`Alphabet` | `FeatureSequence.getAlphabet()`
`Alphabet` | `Labelings.getAlphabet()`
`Alphabet` | `FeatureVectorSequence.getAlphabet()`
`Alphabet` | `Labels.getAlphabet()`
`Alphabet` | `Label.getAlphabet()`
`Alphabet` | `Instance.getAlphabet()`
`Alphabet` | `LabelsSequence.getAlphabet()`
`Alphabet[]` | `FeatureVector.getAlphabets()`
`Alphabet[]` | `ROCData.getAlphabets()`
`Alphabet[]` | `InstanceList.getAlphabets()`
`Alphabet[]` | `FeatureSelection.getAlphabets()`
`Alphabet[]` | `AlphabetCarrying.getAlphabets()`
`Alphabet[]` | `FeatureSequence.getAlphabets()`
`Alphabet[]` | `Labelings.getAlphabets()`
`Alphabet[]` | `FeatureVectorSequence.getAlphabets()`
`Alphabet[]` | `Labels.getAlphabets()`
`Alphabet[]` | `Label.getAlphabets()`
`Alphabet[]` | `Instance.getAlphabets()`
`Alphabet[]` | `LabelsSequence.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` | `PropertyHolder.toFeatureVector(Alphabet dict, boolean binary)`
`FeatureVector` | `Token.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)`

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