Uses of Interface
com.aliasi.corpus.ObjectHandler

Packages that use ObjectHandler

Package	Description
com.aliasi.chunk	Classes for extracting meaningful chunks (spans) of text.
com.aliasi.classify	Classes for classifying data and evaluation.
com.aliasi.cluster	Classes for clustering data and evaluation.
com.aliasi.corpus	Classes for parsing and handling various corpora.
com.aliasi.crf	Classes and interfaces for conditional random fields.
com.aliasi.features	Classes for extracting feature vectors from objects and parsing objects for feature handlers.
com.aliasi.hmm	Classes for estimating and decoding hidden Markov models.
com.aliasi.lm	Classes for character- and token-based language models.
com.aliasi.sentences	Classes for sentence-boundary detection.
com.aliasi.spell	Classes for spelling correction and edit distance.
com.aliasi.stats	Classes for handling basic statical distributions and estimators.
com.aliasi.tag	Classes and interfaces for sequence tagging, including evaluators.

Uses of ObjectHandler in com.aliasi.chunk

Classes in com.aliasi.chunk that implement ObjectHandler

Modifier and Type	Class and Description
class	CharLmHmmChunker A CharLmHmmChunker employs a hidden Markov model estimator and tokenizer factory to learn a chunker.
class	CharLmRescoringChunker A CharLmRescoringChunker provides a long-distance character language model-based chunker that operates by rescoring the output of a contained character language model HMM chunker.
class	ChunkerEvaluator The ChunkerEvaulator class provides an evaluation framework for chunkers.
class	TrainTokenShapeChunker A TrainTokenShapeChunker is used to train a token and shape-based chunker.

Methods in com.aliasi.chunk that return ObjectHandler

Modifier and Type	Method and Description
static ObjectHandler<StringTagging>	TagChunkCodecAdapters.chunkingToStringTagging(TagChunkCodec codec, ObjectHandler<Chunking> handler) Return the string tagging handler that converts string taggings to chunkings.
static ObjectHandler<Tagging<String>>	TagChunkCodecAdapters.chunkingToTagging(TagChunkCodec codec, ObjectHandler<Chunking> handler) Returns the tagging handler that converts taggings to chunkings using the specified codec.
static ObjectHandler<Chunking>	TagChunkCodecAdapters.stringTaggingToChunking(TagChunkCodec codec, ObjectHandler<StringTagging> handler) Return the chunking handler that converts chunkings to taggings using the specified codec.
static ObjectHandler<Chunking>	TagChunkCodecAdapters.taggingToChunking(TagChunkCodec codec, ObjectHandler<Tagging<String>> handler) Return the chunking handler that converts chunkings to simple taggings using the specified codec.

Methods in com.aliasi.chunk with parameters of type ObjectHandler

Modifier and Type	Method and Description
static ObjectHandler<StringTagging>	TagChunkCodecAdapters.chunkingToStringTagging(TagChunkCodec codec, ObjectHandler<Chunking> handler) Return the string tagging handler that converts string taggings to chunkings.
static ObjectHandler<Tagging<String>>	TagChunkCodecAdapters.chunkingToTagging(TagChunkCodec codec, ObjectHandler<Chunking> handler) Returns the tagging handler that converts taggings to chunkings using the specified codec.
static ObjectHandler<Chunking>	TagChunkCodecAdapters.stringTaggingToChunking(TagChunkCodec codec, ObjectHandler<StringTagging> handler) Return the chunking handler that converts chunkings to taggings using the specified codec.
static ObjectHandler<Chunking>	TagChunkCodecAdapters.taggingToChunking(TagChunkCodec codec, ObjectHandler<Tagging<String>> handler) Return the chunking handler that converts chunkings to simple taggings using the specified codec.

Uses of ObjectHandler in com.aliasi.classify

Classes in com.aliasi.classify that implement ObjectHandler

Modifier and Type	Class and Description
class	BaseClassifierEvaluator<E> A BaseClassifierEvaluator provides an evaluation harness for first-best classifiers.
class	BernoulliClassifier<E> A BernoulliClassifier provides a feature-based classifier where feature values are reduced to booleans based on a specified threshold.
class	BinaryLMClassifier A BinaryLMClassifier is a boolean dynamic language model classifier for use when there are two categories, but training data is only available for one of the categories.
class	ConditionalClassifierEvaluator<E> A ConditionalClassifierEvaluator provides an evaluation harness for conditional probability-based n-best classifiers.
class	DynamicLMClassifier<L extends LanguageModel.Dynamic> A DynamicLMClassifier is a language model classifier that accepts training events of categorized character sequences.
class	JointClassifierEvaluator<E> A JointClassifierEvaluator provides an evaluation harness for joint probability-based n-best classifiers.
class	KnnClassifier<E> A KnnClassifier implements k-nearest-neighor classification based on feature extraction and a vector proximity or distance.
class	NaiveBayesClassifier A NaiveBayesClassifier provides a trainable naive Bayes text classifier, with tokens as features.
class	RankedClassifierEvaluator<E> A RankedClassifierEvaluator provides an evaluation harness for ranked classifiers.
class	ScoredClassifierEvaluator<E> A ScoredClassifierEvaluator provides an evaluation harness for score-based classifiers.
class	TfIdfClassifierTrainer<E> A TfIdfClassifierTrainer provides a framework for training discriminative classifiers based on term-frequency (TF) and inverse document frequency (IDF) weighting of features.
class	TradNaiveBayesClassifier A TradNaiveBayesClassifier implements a traditional token-based approach to naive Bayes text classification.

Methods in com.aliasi.classify with parameters of type ObjectHandler

Modifier and Type

Method and Description

static <F> LogisticRegressionClassifier<F>

LogisticRegressionClassifier.train(Corpus<ObjectHandler<Classified<F>>> corpus, FeatureExtractor<? super F> featureExtractor, int minFeatureCount, boolean addInterceptFeature, RegressionPrior prior, int blockSize, LogisticRegressionClassifier<F> hotStart, AnnealingSchedule annealingSchedule, double minImprovement, int rollingAverageSize, int minEpochs, int maxEpochs, ObjectHandler<LogisticRegressionClassifier<F>> classifierHandler, Reporter reporter) Returns a trained logistic regression classifier given the specified feature extractor, training corpus, model priors and search parameters.

Method parameters in com.aliasi.classify with type arguments of type ObjectHandler

Modifier and Type	Method and Description
static Iterator<TradNaiveBayesClassifier>	TradNaiveBayesClassifier.emIterator(TradNaiveBayesClassifier initialClassifier, Factory<TradNaiveBayesClassifier> classifierFactory, Corpus<ObjectHandler<Classified<CharSequence>>> labeledData, Corpus<ObjectHandler<CharSequence>> unlabeledData, double minTokenCount) Apply the expectation maximization (EM) algorithm to train a traditional naive Bayes classifier using the specified labeled and unabled data, initial classifier and factory for creating subsequent factories.
static Iterator<TradNaiveBayesClassifier>	TradNaiveBayesClassifier.emIterator(TradNaiveBayesClassifier initialClassifier, Factory<TradNaiveBayesClassifier> classifierFactory, Corpus<ObjectHandler<Classified<CharSequence>>> labeledData, Corpus<ObjectHandler<CharSequence>> unlabeledData, double minTokenCount) Apply the expectation maximization (EM) algorithm to train a traditional naive Bayes classifier using the specified labeled and unabled data, initial classifier and factory for creating subsequent factories.
static TradNaiveBayesClassifier	TradNaiveBayesClassifier.emTrain(TradNaiveBayesClassifier initialClassifier, Factory<TradNaiveBayesClassifier> classifierFactory, Corpus<ObjectHandler<Classified<CharSequence>>> labeledData, Corpus<ObjectHandler<CharSequence>> unlabeledData, double minTokenCount, int maxEpochs, double minImprovement, Reporter reporter) Apply the expectation maximization (EM) algorithm to train a traditional naive Bayes classifier using the specified labeled and unabled data, initial classifier and factory for creating subsequent factories, maximum number of epochs, minimum improvement per epoch, and reporter to which progress reports are sent.
static TradNaiveBayesClassifier	TradNaiveBayesClassifier.emTrain(TradNaiveBayesClassifier initialClassifier, Factory<TradNaiveBayesClassifier> classifierFactory, Corpus<ObjectHandler<Classified<CharSequence>>> labeledData, Corpus<ObjectHandler<CharSequence>> unlabeledData, double minTokenCount, int maxEpochs, double minImprovement, Reporter reporter) Apply the expectation maximization (EM) algorithm to train a traditional naive Bayes classifier using the specified labeled and unabled data, initial classifier and factory for creating subsequent factories, maximum number of epochs, minimum improvement per epoch, and reporter to which progress reports are sent.
static <F> LogisticRegressionClassifier<F>	LogisticRegressionClassifier.train(Corpus<ObjectHandler<Classified<F>>> corpus, FeatureExtractor<? super F> featureExtractor, int minFeatureCount, boolean addInterceptFeature, RegressionPrior prior, AnnealingSchedule annealingSchedule, double minImprovement, int minEpochs, int maxEpochs, Reporter reporter) Returns a trained logistic regression classifier given the specified feature extractor, training corpus, model priors and search parameters.
static <F> LogisticRegressionClassifier<F>	LogisticRegressionClassifier.train(Corpus<ObjectHandler<Classified<F>>> corpus, FeatureExtractor<? super F> featureExtractor, int minFeatureCount, boolean addInterceptFeature, RegressionPrior prior, int blockSize, LogisticRegressionClassifier<F> hotStart, AnnealingSchedule annealingSchedule, double minImprovement, int rollingAverageSize, int minEpochs, int maxEpochs, ObjectHandler<LogisticRegressionClassifier<F>> classifierHandler, Reporter reporter) Returns a trained logistic regression classifier given the specified feature extractor, training corpus, model priors and search parameters.

Constructor parameters in com.aliasi.classify with type arguments of type ObjectHandler

Constructor and Description

PerceptronClassifier(Corpus<ObjectHandler<Classified<E>>> corpus, FeatureExtractor<? super E> featureExtractor, KernelFunction kernelFunction, String corpusAcceptCategory, int numIterations, String outputAcceptCategory, String outputRejectCategory) Construct a perceptron classifier from the specified feature extractor, corpus with designated accept category, polynomial kernel degree and number of training iterations, and output accept and reject categories.

Uses of ObjectHandler in com.aliasi.cluster

Methods in com.aliasi.cluster with parameters of type ObjectHandler

Modifier and Type	Method and Description
static LatentDirichletAllocation.GibbsSample	LatentDirichletAllocation.gibbsSampler(int[][] docWords, short numTopics, double docTopicPrior, double topicWordPrior, int burninEpochs, int sampleLag, int numSamples, Random random, ObjectHandler<LatentDirichletAllocation.GibbsSample> handler) Run Gibbs sampling for the specified multinomial data, number of topics, priors, search parameters, randomization and callback sample handler.

Uses of ObjectHandler in com.aliasi.corpus

Classes in com.aliasi.corpus that implement ObjectHandler

Modifier and Type	Class and Description
class	XValidatingObjectCorpus<E> An XValidatingObjectCorpus holds a list of items which it uses to provide training and testing items using cross-validation.

Methods in com.aliasi.corpus with parameters of type ObjectHandler

Modifier and Type	Method and Description
void	XValidatingObjectCorpus.visitCorpus(ObjectHandler<E> handler)
void	XValidatingObjectCorpus.visitCorpus(ObjectHandler<E> trainHandler, ObjectHandler<E> testHandler)
void	XValidatingObjectCorpus.visitCorpus(ObjectHandler<E> trainHandler, ObjectHandler<E> testHandler)
void	XValidatingObjectCorpus.visitTest(ObjectHandler<E> handler) Send all of the test items to the specified handler.
void	ListCorpus.visitTest(ObjectHandler<E> handler)
void	XValidatingObjectCorpus.visitTest(ObjectHandler<E> handler, int fold) Visit the test portion of the specified fold with the specified handler.
void	XValidatingObjectCorpus.visitTrain(ObjectHandler<E> handler) Send all of the training items to the specified handler.
void	ListCorpus.visitTrain(ObjectHandler<E> handler)
void	XValidatingObjectCorpus.visitTrain(ObjectHandler<E> handler, int fold) Visit the training portion of the specified fold with the specified handler.

Uses of ObjectHandler in com.aliasi.crf

Method parameters in com.aliasi.crf with type arguments of type ObjectHandler

Modifier and Type	Method and Description
static ChainCrfChunker	ChainCrfChunker.estimate(Corpus<ObjectHandler<Chunking>> chunkingCorpus, TagChunkCodec codec, TokenizerFactory tokenizerFactory, ChainCrfFeatureExtractor<String> featureExtractor, boolean addInterceptFeature, int minFeatureCount, boolean cacheFeatureVectors, RegressionPrior prior, int priorBlockSize, AnnealingSchedule annealingSchedule, double minImprovement, int minEpochs, int maxEpochs, Reporter reporter) Return the chain CRF-based chunker estimated from the specified corpus, which is converted to a tagging corpus using the specified coder/decoder and tokenizer factory, then passed to the chain CRF estimate method along with the rest of the arguments.
static <F> ChainCrf<F>	ChainCrf.estimate(Corpus<ObjectHandler<Tagging<F>>> corpus, ChainCrfFeatureExtractor<F> featureExtractor, boolean addInterceptFeature, int minFeatureCount, boolean cacheFeatureVectors, boolean allowUnseenTransitions, RegressionPrior prior, int priorBlockSize, AnnealingSchedule annealingSchedule, double minImprovement, int minEpochs, int maxEpochs, Reporter reporter) Return the CRF estimated using stochastic gradient descent with the specified prior from the specified corpus of taggings of type F pruned to the specified minimum feature count, using the specified feature extractor, automatically adding an intercept feature if the flag is true, allow unseen tag transitions as specified, using the specified training parameters for annealing, measuring convergence, and reporting the incremental results to the specified reporter.

Uses of ObjectHandler in com.aliasi.features

Constructor parameters in com.aliasi.features with type arguments of type ObjectHandler

Constructor and Description
ZScoreFeatureExtractor(Corpus<ObjectHandler<Classified<E>>> corpus, FeatureExtractor<? super E> extractor) Construct a z-core feature extractor from the specified base feature extractor and the training section of the supplied corpus.

Uses of ObjectHandler in com.aliasi.hmm

Classes in com.aliasi.hmm that implement ObjectHandler

Modifier and Type	Class and Description
class	AbstractHmmEstimator An HmmEstimator may be used to train a hidden Markov model (HMM).
class	HmmCharLmEstimator An HmmCharLmEstimator employs a maximum a posteriori transition estimator and a bounded character language model emission estimator.

Uses of ObjectHandler in com.aliasi.lm

Subinterfaces of ObjectHandler in com.aliasi.lm

Modifier and Type	Interface and Description
static interface	LanguageModel.Dynamic A LanguageModel.Dynamic accepts training events in the form of character slices or sequences.

Classes in com.aliasi.lm that implement ObjectHandler

Modifier and Type	Class and Description
class	NGramBoundaryLM An NGramBoundaryLM provides a dynamic sequence language model for which training, estimation and pruning may be interleaved.
class	NGramProcessLM An NGramProcessLM provides a dynamic conditional process language model process for which training, estimation, and pruning may be interleaved.
class	TokenizedLM A TokenizedLM provides a dynamic sequence language model which models token sequences with an n-gram model, and whitespace and unknown tokens with their own sequence language models.
class	UniformBoundaryLM A UniformBoundaryLM implements a uniform sequence language model with a specified number of outcomes and the same probability assigned to the end-of-stream marker.
class	UniformProcessLM A UniformLM.Sequence implements a uniform sequence language model with a specified number of outcomes and the same probability assigned to the end-of-stream marker.

Methods in com.aliasi.lm with parameters of type ObjectHandler

Modifier and Type	Method and Description
void	TrieIntSeqCounter.handleNGrams(int nGram, int minCount, ObjectHandler<int[]> handler) Supplies each n-gram of the specified length and with greater than or equal to the specified minimum count to the specified handler.
void	TokenizedLM.handleNGrams(int nGramLength, int minCount, ObjectHandler<String[]> handler) Visits the n-grams of the specified length with at least the specified minimum count stored in the underlying counter of this tokenized language model and passes them to the specified handler.

Uses of ObjectHandler in com.aliasi.sentences

Classes in com.aliasi.sentences that implement ObjectHandler

Modifier and Type	Class and Description
class	SentenceEvaluator A SentenceEvaluator handles reference chunkings by constructing a response chunking and adding them to a sentence evaluation.

Uses of ObjectHandler in com.aliasi.spell

Classes in com.aliasi.spell that implement ObjectHandler

Modifier and Type	Class and Description
class	TfIdfDistance The TfIdfDistance class provides a string distance based on term frequency (TF) and inverse document frequency (IDF).
class	TrainSpellChecker A TrainSpellChecker instance provides a mechanism for collecting training data for a compiled spell checker.

Uses of ObjectHandler in com.aliasi.stats

Methods in com.aliasi.stats with parameters of type ObjectHandler

Modifier and Type	Method and Description
static LogisticRegression	LogisticRegression.estimate(Vector[] xs, int[] cs, RegressionPrior prior, int blockSize, LogisticRegression hotStart, AnnealingSchedule annealingSchedule, double minImprovement, int rollingAverageSize, int minEpochs, int maxEpochs, ObjectHandler<LogisticRegression> handler, Reporter reporter) Estimate a logistic regression model from the specified input data using the specified Gaussian prior, initial learning rate and annealing rate, the minimum improvement per epoch, the minimum and maximum number of estimation epochs, and a reporter.
static LogisticRegression	LogisticRegression.estimate(Vector[] xs, Vector[] cs, RegressionPrior prior, int blockSize, LogisticRegression hotStart, AnnealingSchedule annealingSchedule, double minImprovement, int rollingAverageSize, int minEpochs, int maxEpochs, ObjectHandler<LogisticRegression> handler, Reporter reporter) Estimate a logistic regression model from the specified input data using the specified Gaussian prior, initial learning rate and annealing rate, the minimum improvement per epoch, the minimum and maximum number of estimation epochs, and a reporter.

Uses of ObjectHandler in com.aliasi.tag

Classes in com.aliasi.tag that implement ObjectHandler

Modifier and Type	Class and Description
class	MarginalTaggerEvaluator<E> A MarginalTaggerEvaluator evaluates marginal taggers either directly or by adding their outputs.
class	NBestTaggerEvaluator<E> An NBestTaggerEvaluator provides an evaluation framework for n-best taggers.
class	TaggerEvaluator<E> A TaggerEvaluator provides evaluation for first-best taggers implementing the Tagger interface.

Methods in com.aliasi.tag that return types with arguments of type ObjectHandler

Modifier and Type	Method and Description
static <F> Corpus<ObjectHandler<Classified<ClassifierTagger.State<F>>>>	ClassifierTagger.toClassifiedCorpus(Corpus<ObjectHandler<Tagging<F>>> taggingCorpus) Return a corpus consisting of classified tagger states derived from the specified corpus of taggings.

Method parameters in com.aliasi.tag with type arguments of type ObjectHandler

Modifier and Type	Method and Description
static <F> Corpus<ObjectHandler<Classified<ClassifierTagger.State<F>>>>	ClassifierTagger.toClassifiedCorpus(Corpus<ObjectHandler<Tagging<F>>> taggingCorpus) Return a corpus consisting of classified tagger states derived from the specified corpus of taggings.