| 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.
|
| 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.
|
| 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. |
| 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.
|
| 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.
|
| 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.
|
Copyright © 2016 Alias-i, Inc.. All rights reserved.