Uses of Class
com.aliasi.stats.RegressionPrior

Packages that use RegressionPrior

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 RegressionPrior in com.aliasi.classify

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

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 RegressionPrior in com.aliasi.crf

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

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 RegressionPrior in com.aliasi.dca

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

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 RegressionPrior in com.aliasi.stats

Methods in com.aliasi.stats that return RegressionPrior

Modifier and Type	Method and Description
static RegressionPrior	RegressionPrior.cauchy(double[] priorSquaredScales) Returns the Cauchy prior for the specified squared scales.
static RegressionPrior	RegressionPrior.cauchy(double priorSquaredScale, boolean noninformativeIntercept) Returns the Cauchy prior with the specified prior squared scales for the dimensions.
static RegressionPrior	RegressionPrior.elasticNet(double laplaceWeight, double scale, boolean noninformativeIntercept) Returns the elastic net prior with the specified weight on the Laplace prior, the specified scale parameter for the elastic net and a noninformative prior on the intercept (dimension 0) if the specified flag is set.
static RegressionPrior	RegressionPrior.gaussian(double[] priorVariances) Returns the Gaussian prior with the specified priors for each dimension.
static RegressionPrior	RegressionPrior.gaussian(double priorVariance, boolean noninformativeIntercept) Returns the Gaussian prior with the specified prior variance and indication of whether the intercept is given a noninformative prior.
static RegressionPrior	RegressionPrior.laplace(double[] priorVariances) Returns the Laplace prior with the specified prior variances for the dimensions.
static RegressionPrior	RegressionPrior.laplace(double priorVariance, boolean noninformativeIntercept) Returns the Laplace prior with the specified prior variance and number of dimensions and indication of whether the intecept dimension is given a noninformative prior.
static RegressionPrior	RegressionPrior.logInterpolated(double alpha, RegressionPrior prior1, RegressionPrior prior2) Returns the prior that interpolates its log probability between the specified priors with the weight going to the first prior.
static RegressionPrior	RegressionPrior.noninformative() Returns the noninformative or uniform prior to use for maximum likelihood regression fitting.
static RegressionPrior	RegressionPrior.shiftMeans(double[] shifts, RegressionPrior prior) Returns the prior that shifts the means of the specified prior by the specified values.

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

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.
static LogisticRegression	LogisticRegression.estimate(Vector[] xs, Vector[] 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, 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.
static RegressionPrior	RegressionPrior.logInterpolated(double alpha, RegressionPrior prior1, RegressionPrior prior2) Returns the prior that interpolates its log probability between the specified priors with the weight going to the first prior.
static RegressionPrior	RegressionPrior.shiftMeans(double[] shifts, RegressionPrior prior) Returns the prior that shifts the means of the specified prior by the specified values.