Uses of Class
com.aliasi.stats.AnnealingSchedule

Packages that use AnnealingSchedule

Package	Description
com.aliasi.classify	Classes for classifying 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.stats	Classes for handling basic statical distributions and estimators.

Uses of AnnealingSchedule in com.aliasi.classify

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

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, 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 AnnealingSchedule in com.aliasi.crf

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

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 AnnealingSchedule in com.aliasi.dca

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

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 AnnealingSchedule in com.aliasi.stats

Methods in com.aliasi.stats that return AnnealingSchedule

Modifier and Type	Method and Description
static AnnealingSchedule	AnnealingSchedule.constant(double learningRate) Return the annealing schedule for the specified constant learning rate.
static AnnealingSchedule	AnnealingSchedule.exponential(double initialLearningRate, double base) Return the exponential annealing schedule with the specified initial learning rate and exponent.
static AnnealingSchedule	AnnealingSchedule.inverse(double initialLearningRate, double annealingRate) Return the inverse annealing schedule with the specified initial learning rate and annealing rate.

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

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.