Uses of Class
edu.illinois.cs.cogcomp.lbjava.learn.Learner

Packages that use Learner

Package	Description
edu.illinois.cs.cogcomp.lbjava
edu.illinois.cs.cogcomp.lbjava.infer
edu.illinois.cs.cogcomp.lbjava.learn
edu.illinois.cs.cogcomp.lbjava.util

Uses of Learner in edu.illinois.cs.cogcomp.lbjava

Fields in edu.illinois.cs.cogcomp.lbjava declared as Learner

Modifier and Type	Field and Description
protected Learner	Train.TrainingThread.learner The learning classifier being trained.

Uses of Learner in edu.illinois.cs.cogcomp.lbjava.infer

Fields in edu.illinois.cs.cogcomp.lbjava.infer declared as Learner

Modifier and Type	Field and Description
protected Learner	PropositionalVariable.classifier The classifier being applied.

Methods in edu.illinois.cs.cogcomp.lbjava.infer that return Learner

Modifier and Type	Method and Description
Learner	PropositionalVariable.getClassifier() Retrieves the classifier.
Learner	FirstOrderVariable.getClassifier() Retrieves the classifier.

Methods in edu.illinois.cs.cogcomp.lbjava.infer with parameters of type Learner

Modifier and Type	Method and Description
Normalizer	Inference.getNormalizer(Learner c) Returns the normalization function associated with the given classifier in this inference.
abstract String	Inference.valueOf(Learner c, Object o) Retrieves the value of the specified variable as identified by the classifier and the object that produce that variable.
String	ILPInference.valueOf(Learner c, Object o) Retrieves the value of the specified variable as identified by the classifier and the object that produce that variable.

Constructors in edu.illinois.cs.cogcomp.lbjava.infer with parameters of type Learner

Constructor and Description
FirstOrderVariable(Learner c, Object e) Initializing constructor.
PropositionalVariable(Learner c, Object e, String p) Initializing constructor; the value member variable is set to false .

Uses of Learner in edu.illinois.cs.cogcomp.lbjava.learn

Subclasses of Learner in edu.illinois.cs.cogcomp.lbjava.learn

Modifier and Type	Class and Description
class	AdaBoost Implementation of the AdaBoost binary classification learning algorithm.
class	AdaGrad AdaGrad - Adaptive Stochastic Gradient Method AdaGrad alters the update to adapt based on historical information, so that frequent occurring features in the gradients get small learning rates and infrequent features get higher ones.
class	BinaryMIRA The Binary MIRA learning algorithm implementation.
class	LinearThresholdUnit A LinearThresholdUnit is a Learner for binary classification in which a score is computed as a linear function a weight vector and the input example, and the decision is made by comparing the score to some threshold quantity.
class	MultiLabelLearner A simple implementation of a learner that learns from examples with multiple labels and is capable of predicting multiple labels on new examples.
class	MuxLearner A MuxLearner uses one of many Learners indexed by the first feature in an example to produce a classification.
class	NaiveBayes Naive Bayes is a multi-class learner that uses prediction value counts and feature counts given a particular prediction value to select the most likely prediction value.
class	PassiveAggressive The Passive Aggressive learning algorithm implementation.
class	SparseAveragedPerceptron An approximation to voted Perceptron, in which a weighted average of the weight vectors arrived at during training becomes the weight vector used to make predictions after training.
class	SparseConfidenceWeighted This is an implementation of the approximate "variance algorithm" of Confidence Weighted Linear Classification, Dredze, et.al (ICML, 2008).
class	SparseMIRA An implementation of the Margin Infused Relaxed Algorithm of Crammer and Singer.
class	SparseNetworkLearner A SparseNetworkLearner uses multiple LinearThresholdUnits to make a multi-class classification.
class	SparsePerceptron Simple sparse Perceptron implementation.
class	SparseWinnow Simple sparse Winnow implementation.
class	StochasticGradientDescent Gradient descent is a batch learning algorithm for function approximation in which the learner tries to follow the gradient of the error function to the solution of minimal error.
class	SupportVectorMachine Wrapper class for the liblinear library which supports support vector machine classification.
class	WekaWrapper Translates LBJava's internal problem representation into that which can be handled by WEKA learning algorithms.

Fields in edu.illinois.cs.cogcomp.lbjava.learn declared as Learner

Modifier and Type	Field and Description
protected Learner	MuxLearner.baseLearner Instances of this learning algorithm will be multiplexed; default null.
Learner	MuxLearner.Parameters.baseLearner Instances of this learning algorithm will be multiplexed; default null.
static Learner	MuxLearner.defaultBaseLearner Default for MuxLearner.baseLearner.
static Learner	AdaBoost.defaultWeakLearner Default for AdaBoost.weakLearner.
protected Learner	BatchTrainer.learner The learning classifier being trained.
protected Learner	AdaBoost.weakLearner The weak learning algorithm to be boosted.
protected Learner	AdaBoost.Parameters.weakLearner The weak learning algorithm to be boosted.
protected Learner[]	AdaBoost.weakLearners Will be filled with trained copies of the weak learner.

Methods in edu.illinois.cs.cogcomp.lbjava.learn that return Learner

Modifier and Type	Method and Description
Learner	Learner.emptyClone() Returns a new, emtpy learner into which all of the parameters that control the behavior of the algorithm have been copied.
Learner	BatchTrainer.preExtract(String exampleFile, boolean zip, Lexicon.CountPolicy countPolicy) Performs labeled feature vector pre-extraction into the specified file (or memory), replacing BatchTrainer.parser with one that reads from that file (or memory).
Learner	BatchTrainer.preExtract(String exampleFile, Lexicon.CountPolicy countPolicy) Performs labeled feature vector pre-extraction into the specified file (or memory), replacing BatchTrainer.parser with one that reads from that file (or memory).
static Learner	Learner.readLearner(edu.illinois.cs.cogcomp.core.datastructures.vectors.ExceptionlessInputStream in) Reads the binary representation of any type of learner (including the label lexicon, but not including the feature lexicon) from the given stream.
static Learner	Learner.readLearner(edu.illinois.cs.cogcomp.core.datastructures.vectors.ExceptionlessInputStream in, boolean whole) Reads the binary representation of any type of learner (including the label lexicon, but not including the feature lexicon), with the option of cutting off the reading process after the label lexicon and before any learned parameters.
static Learner	Learner.readLearner(String filename) Reads the binary representation of any type of learner (including the label lexicon, but not including the feature lexicon) from the given file.
static Learner	Learner.readLearner(String filename, boolean whole) Reads the binary representation of any type of learner (including the label lexicon, but not including the feature lexicon), with the option of cutting off the reading process after the label lexicon and before any learned parameters.
static Learner	Learner.readLearner(URL url) Reads the binary representation of any type of learner (including the label lexicon, but not including the feature lexicon) from the given location.
static Learner	Learner.readLearner(URL url, boolean whole) Reads the binary representation of any type of learner (including the label lexicon, but not including the feature lexicon), with the option of cutting off the reading process after the label lexicon and before any learned parameters.

Methods in edu.illinois.cs.cogcomp.lbjava.learn with parameters of type Learner

Modifier and Type	Method and Description
void	BatchTrainer.pruneDataset(String exampleFile, boolean zip, Lexicon.PruningPolicy policy, Learner preExtractLearner) Prunes the data returned by BatchTrainer.parser according to the given policy, under the assumption that feature counts have already been compiled in the given learner's lexicon.
void	BatchTrainer.pruneDataset(String exampleFile, Lexicon.PruningPolicy policy, Learner preExtractLearner) Prunes the data returned by BatchTrainer.parser according to the given policy, under the assumption that feature counts have already been compiled in the given learner's lexicon.
void	MuxLearner.setBase(Learner base) Sets MuxLearner.baseLearner.
void	SparseMIRA.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	PassiveAggressive.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	MuxLearner.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	BinaryMIRA.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	SupportVectorMachine.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	SparseWinnow.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	SparseNetworkLearner.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	SparseConfidenceWeighted.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	NaiveBayes.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	WekaWrapper.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	StochasticGradientDescent.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	SparseAveragedPerceptron.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	LinearThresholdUnit.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	Learner.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	AdaBoost.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	SparsePerceptron.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.
void	MultiLabelLearner.Parameters.setParameters(Learner l) Calls the appropriate Learner.setParameters(Parameters) method for this Parameters object.

Constructors in edu.illinois.cs.cogcomp.lbjava.learn with parameters of type Learner

Constructor and Description
AdaBoost(Learner w) Instantiates member variables.
AdaBoost(Learner w, int r) Instantiates member variables.
AdaBoost(String n, Learner w) Instantiates member variables.
AdaBoost(String n, Learner w, int r) Instantiates member variables.
BatchTrainer(Learner l, Parser p) Creates a new trainer that doesn't produce status messages.
BatchTrainer(Learner l, Parser p, int o) Creates a new trainer that produces status messages.
BatchTrainer(Learner l, Parser p, int o, String i) Creates a new trainer that produces status messages with the specified indentation spacing for status messages.
BatchTrainer(Learner l, String p) Creates a new trainer that doesn't produce status messages.
BatchTrainer(Learner l, String p, boolean z) Creates a new trainer that doesn't produce status messages.
BatchTrainer(Learner l, String p, boolean z, int o) Creates a new trainer that produces status messages.
BatchTrainer(Learner l, String p, boolean z, int o, String i) Creates a new trainer that produces status messages with the specified indentation spacing for status messages.
BatchTrainer(Learner l, String p, int o) Creates a new trainer that produces status messages.
BatchTrainer(Learner l, String p, int o, String i) Creates a new trainer that produces status messages with the specified indentation spacing for status messages.
MuxLearner(Learner base) Instantiates this multiplexed learner using the specified base learning algorithm.
MuxLearner(Learner base, String d) Instantiates this multiplexed learner using the specified base learning algorithm.
MuxLearner(String n, Learner base) Instantiates this multiplexed learner using the specified base learning algorithm.
MuxLearner(String n, Learner base, String d) Instantiates this multiplexed learner using the specified base learning algorithm.

Uses of Learner in edu.illinois.cs.cogcomp.lbjava.util

Methods in edu.illinois.cs.cogcomp.lbjava.util that return Learner

Modifier and Type	Method and Description
static Learner	ClassUtils.getLearner(String name) Retrieve a Learner by name using the no-argument constructor.
static Learner	ClassUtils.getLearner(String name, boolean exit) Retrieve a Learner by name using the no-argument constructor.
static Learner	ClassUtils.getLearner(String name, Class[] paramTypes, Object[] arguments) Retrieve a Learner by name using a constructor with arguments.
static Learner	ClassUtils.getLearner(String name, Class[] paramTypes, Object[] arguments, boolean exit) Retrieve a Learner by name using a constructor with arguments.