Uses of Class
com.aliasi.io.Reporter

Packages that use Reporter

Package	Description
com.aliasi.classify	Classes for classifying data and evaluation.
com.aliasi.cluster	Classes for clustering data and evaluation.
com.aliasi.crf	Classes and interfaces for conditional random fields.
com.aliasi.dca	Classes for fitting and running discrete choice analysis (DCA) models.
com.aliasi.io	Classes to handle input, output, file selection, and logging.
com.aliasi.matrix	Classes for matrices and vectors.
com.aliasi.stats	Classes for handling basic statical distributions and estimators.

Uses of Reporter in com.aliasi.classify

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

Modifier and Type	Method and Description
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.

Uses of Reporter in com.aliasi.cluster

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

Modifier and Type	Method and Description
Set<Set<E>>	KMeansClusterer.cluster(Set<? extends E> elementSet, Random random, Reporter reporter) Return the k-means clustering for the specified set of elements, using the specified random number generator, sending progress reports to the specified reporter.
Set<Set<E>>	KMeansClusterer.recluster(Set<Set<E>> initialClustering, Set<E> unclusteredElements, Reporter reporter) Recluster the specified initial clustering, adding in the unclustered elements, reporting progress to the specified reporter.

Uses of Reporter in com.aliasi.crf

Methods in com.aliasi.crf with parameters of type Reporter

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 Reporter in com.aliasi.dca

Methods in com.aliasi.dca with parameters of type Reporter

Modifier and Type	Method and Description
static <F> DiscreteObjectChooser<F>	DiscreteObjectChooser.estimate(FeatureExtractor<F> featureExtractor, List<List<F>> alternativeObjectss, int[] choices, int minFeatureCount, RegressionPrior prior, int priorBlockSize, AnnealingSchedule annealingSchedule, double minImprovement, int minEpochs, int maxEpochs, Reporter reporter) Because intercepts are ignored, they are not added to feature maps and should not be treated as noninformative in the prior.
static DiscreteChooser	DiscreteChooser.estimate(Vector[][] alternativess, int[] choices, RegressionPrior prior, int priorBlockSize, AnnealingSchedule annealingSchedule, double minImprovement, int minEpochs, int maxEpochs, Reporter reporter) Returns a discrete choice model estimated from the specified training data, prior, and learning parameters.

Uses of Reporter in com.aliasi.io

Methods in com.aliasi.io that return Reporter

Modifier and Type	Method and Description
static Reporter	Reporters.file(File f, String encoding) Return a reporter that writes to the specified file using the specified encoding.
Reporter	Reporter.setLevel(LogLevel level) Sets the log level for this reporter to the specified level and returns the reporter.
static Reporter	Reporters.silent() Returns a silent reporter that writes its output nowhere.
static Reporter	Reporters.stdOut() Return a reporter that writes to standard output using the UTF-8 character encoding.
static Reporter	Reporters.stream(OutputStream out, String encoding) Return a reporter that writes to the specified output stream using the specified encoding.
static Reporter	Reporters.tee(Reporter... reporters) Returns a compound reporter that sends reports to all of the specified reporters.
static Reporter	Reporters.writer(Writer writer) Return a reporter that writes to the specified print writer.

Methods in com.aliasi.io with parameters of type Reporter

Modifier and Type	Method and Description
static Reporter	Reporters.tee(Reporter... reporters) Returns a compound reporter that sends reports to all of the specified reporters.

Uses of Reporter in com.aliasi.matrix

Methods in com.aliasi.matrix with parameters of type Reporter

Modifier and Type	Method and Description
static SvdMatrix	SvdMatrix.partialSvd(int[][] columnIds, double[][] values, int maxOrder, double featureInit, double initialLearningRate, double annealingRate, double regularization, Reporter reporter, double minImprovement, int minEpochs, int maxEpochs) Return the singular value decomposition of the specified partial matrix, using the specified search parameters.
static SvdMatrix	SvdMatrix.svd(double[][] values, int maxOrder, double featureInit, double initialLearningRate, double annealingRate, double regularization, Reporter reporter, double minImprovement, int minEpochs, int maxEpochs) Returns the signular value decomposition of the specified complete matrix of values.

Uses of Reporter in com.aliasi.stats

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

Modifier and Type	Method and Description
static LogisticRegression	LogisticRegression.estimate(Vector[] xs, int[] cs, RegressionPrior prior, AnnealingSchedule annealingSchedule, Reporter reporter, double minImprovement, int minEpochs, int maxEpochs) 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, 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.