edu.illinois.cs.cogcomp.lbjava.learn

Class MuxLearner

java.lang.Object

edu.illinois.cs.cogcomp.lbjava.classify.Classifier

edu.illinois.cs.cogcomp.lbjava.learn.Learner

edu.illinois.cs.cogcomp.lbjava.learn.MuxLearner

All Implemented Interfaces:

Serializable, Cloneable

public class MuxLearner
extends Learner

A MuxLearner uses one of many Learners indexed by the first feature in an example to produce a classification. During training, the features produced by the first child classifier of this classifier's composite generator feature extractor are taken to determine which Learners will learn from the training object. For any given example, there must be one Feature produced by the labeler for each Feature produced by the first child classifier. If this classifier's feature extractor is not a composite generator, the first feature it produces is the only one taken.

It is assumed that the Learner being multiplexed expects a single label feature on each training example, and that the feature(s) used to do the multiplexing are DiscreteFeature(s). Furthermore, if this classifier's feature extractor is a composite generator, it must produce the same number of features as this classifier's labeler, and they must correspond to each other in the order produced.

This algorithm's user-configurable parameters are stored in member fields of this class. They may be set via either a constructor that names each parameter explicitly or a constructor that takes an instance of Parameters as input. The documentation in each member field in this class indicates the default value of the associated parameter when using the former type of constructor. The documentation of the associated member field in the Parameters class indicates the default value of the parameter when using the latter type of constructor.

Author:

Nick Rizzolo

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	MuxLearner.Parameters Simply a container for all of MuxLearner's configurable parameters.

Field Summary

Fields

Modifier and Type	Field and Description
protected Learner	baseLearner Instances of this learning algorithm will be multiplexed; default null.
static Learner	defaultBaseLearner Default for baseLearner.
static String	defaultDefaultPrediction Default for defaultPrediction.
protected Feature	defaultFeature A feature whose value is defaultPrediction.
protected String	defaultPrediction This string is returned during testing when the multiplexed Learner doesn't exist; default defaultDefaultPrediction.
protected edu.illinois.cs.cogcomp.core.datastructures.vectors.OVector	network A map from feature values to learners.

Fields inherited from class edu.illinois.cs.cogcomp.lbjava.learn.Learner

candidates, encoding, extractor, labeler, labelLexicon, lcFilePath, lexFilePath, lexicon, lossFlag, predictions, readLexiconOnDemand

Fields inherited from class edu.illinois.cs.cogcomp.lbjava.classify.Classifier

containingPackage, name

Constructor Summary

Constructors

Constructor and Description
MuxLearner() For the LBJava compiler; not for use by the LBJava user.
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(MuxLearner.Parameters p) Initializing constructor.
MuxLearner(String n) For the LBJava compiler; not for use by the LBJava user.
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.
MuxLearner(String n, MuxLearner.Parameters p) Initializing constructor.

Method Summary

Modifier and Type	Method and Description
FeatureVector	classify(int[] exampleFeatures, double[] exampleValues) This method performs the multiplexing and returns the output of the selected Learner.
Object	clone() Returns a deep clone of this learning algorithm.
String	discreteValue(int[] exampleFeatures, double[] exampleValues) Returns the value of the discrete feature that would be returned by this classifier.
Feature	featureValue(int[] f, double[] v) Returns the classification of the given example as a single feature instead of a FeatureVector.
void	forget() Clears the network.
Learner.Parameters	getParameters() Retrieves the parameters that are set in this learner.
void	learn(int[] exampleFeatures, double[] exampleValues, int[] exampleLabels, double[] labelValues) The training example is multiplexed to the appropriate Learner(s).
void	read(edu.illinois.cs.cogcomp.core.datastructures.vectors.ExceptionlessInputStream in) Reads the binary representation of a learner with this object's run-time type, overwriting any and all learned or manually specified parameters as well as the label lexicon but without modifying the feature lexicon.
double	realValue(int[] f, double[] v) Returns the value of the real feature that would be returned by this classifier.
ScoreSet	scores(int[] exampleFeatures, double[] exampleValues) Produces a set of scores indicating the degree to which each possible discrete classification value is associated with the given example object.
void	setBase(Learner base) Sets baseLearner.
protected void	setDefaultFeature() Sets the value of defaultFeature according to the current value of defaultPrediction.
void	setLabeler(Classifier l) Sets the labeler.
void	setLabelLexicon(Lexicon l) Sets the label lexicon.
void	setParameters(MuxLearner.Parameters p) Sets the values of parameters that control the behavior of this learning algorithm.
void	write(edu.illinois.cs.cogcomp.core.datastructures.vectors.ExceptionlessOutputStream out) Writes the learned function's internal representation in binary form.
void	write(PrintStream out) Writes the algorithm's internal representation as text.

Methods inherited from class edu.illinois.cs.cogcomp.lbjava.learn.Learner

classify, classify, classify, classify, countFeatures, createPrediction, createPrediction, demandLexicon, discreteValue, discreteValue, doneLearning, doneWithRound, emptyClone, featureValue, featureValue, getCurrentLexicon, getExampleArray, getExampleArray, getExtractor, getLabeler, getLabelLexicon, getLexicon, getLexiconDiscardCounts, getLexiconLocation, getModelLocation, getPrunedLexiconSize, initialize, learn, learn, learn, learn, read, readLabelLexicon, readLearner, readLearner, readLearner, readLearner, readLearner, readLearner, readLexicon, readLexicon, readLexiconOnDemand, readLexiconOnDemand, readModel, readModel, readParameters, realValue, realValue, save, saveLexicon, saveModel, scores, scores, scoresAugmented, setCandidates, setEncoding, setExtractor, setLexicon, setLexiconLocation, setLexiconLocation, setLossFlag, setModelLocation, setModelLocation, setParameters, setReadLexiconOnDemand, unclone, unsetLossFlag, write, writeLexicon, writeModel, writeParameters

Methods inherited from class edu.illinois.cs.cogcomp.lbjava.classify.Classifier

allowableValues, classify, discreteValueArray, getCompositeChildren, getInputType, getOutputType, realValueArray, test, toString, valueIndexOf

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

defaultBaseLearner

public static final Learner defaultBaseLearner

Default for baseLearner.

defaultDefaultPrediction

public static final String defaultDefaultPrediction

Default for defaultPrediction.

baseLearner

protected Learner baseLearner

Instances of this learning algorithm will be multiplexed; default null.

network

protected edu.illinois.cs.cogcomp.core.datastructures.vectors.OVector network

A map from feature values to learners.

defaultPrediction

protected String defaultPrediction

This string is returned during testing when the multiplexed Learner doesn't exist; default defaultDefaultPrediction.

defaultFeature

protected Feature defaultFeature

A feature whose value is defaultPrediction.

Constructor Detail

MuxLearner

public MuxLearner()

For the LBJava compiler; not for use by the LBJava user.

MuxLearner

public MuxLearner(Learner base)

Instantiates this multiplexed learner using the specified base learning algorithm. This constructor will normally only be called by the compiler.

Parameters:

base - Instances of this learner will be multiplexed.

MuxLearner

public MuxLearner(Learner base,
                  String d)

Instantiates this multiplexed learner using the specified base learning algorithm.

Parameters:

base - Instances of this learner will be multiplexed.

d - This prediction will be returned during testing when the multiplexed Learner does not exist.

MuxLearner

public MuxLearner(MuxLearner.Parameters p)

Initializing constructor. Sets all member variables to their associated settings in the MuxLearner.Parameters object.

Parameters:

p - The settings of all parameters.

MuxLearner

public MuxLearner(String n)

For the LBJava compiler; not for use by the LBJava user.

MuxLearner

public MuxLearner(String n,
                  Learner base)

Instantiates this multiplexed learner using the specified base learning algorithm.

Parameters:

n - The name of the classifier.

base - Instances of this learner will be multiplexed.

MuxLearner

public MuxLearner(String n,
                  Learner base,
                  String d)

Instantiates this multiplexed learner using the specified base learning algorithm.

Parameters:

n - The name of the classifier.

base - Instances of this learner will be multiplexed.

d - This prediction will be returned during testing when the multiplexed Learner does not exist.

MuxLearner

public MuxLearner(String n,
                  MuxLearner.Parameters p)

Initializing constructor. Sets all member variables to their associated settings in the MuxLearner.Parameters object.

Parameters:

n - The name of the classifier.

p - The settings of all parameters.

Method Detail

setParameters

public void setParameters(MuxLearner.Parameters p)

Sets the values of parameters that control the behavior of this learning algorithm.

Parameters:

p - The parameters.

setDefaultFeature

protected void setDefaultFeature()

Sets the value of defaultFeature according to the current value of defaultPrediction.

getParameters

public Learner.Parameters getParameters()

Retrieves the parameters that are set in this learner.

Overrides:

getParameters in class Learner

Returns:

An object containing all the values of the parameters that control the behavior of this learning algorithm.

setBase

public void setBase(Learner base)

Sets baseLearner. This method will not have any effect on the learners that already exist in the network. However, new learners created after this method is executed will be of the same type as the object specified.

Parameters:

base - The new base learning algorithm.

setLabeler

public void setLabeler(Classifier l)

Sets the labeler.

Overrides:

setLabeler in class Learner

Parameters:

l - A labeling classifier.

setLabelLexicon

public void setLabelLexicon(Lexicon l)

Sets the label lexicon.

Overrides:

setLabelLexicon in class Learner

Parameters:

l - A feature lexicon.

learn

public void learn(int[] exampleFeatures,
                  double[] exampleValues,
                  int[] exampleLabels,
                  double[] labelValues)

The training example is multiplexed to the appropriate Learner(s).

Specified by:

learn in class Learner

Parameters:

exampleFeatures - The example's array of feature indices.

exampleValues - The example's array of feature values.

exampleLabels - The example's label(s).

labelValues - The labels' values.

forget

public void forget()

Clears the network.

Overrides:

forget in class Learner

scores

public ScoreSet scores(int[] exampleFeatures,
                       double[] exampleValues)

Produces a set of scores indicating the degree to which each possible discrete classification value is associated with the given example object. These scores are just the scores produced by the multiplexed Learner's scores(Object) method.

Specified by:

scores in class Learner

Parameters:

exampleFeatures - The example's array of feature indices.

exampleValues - The example's array of feature values.

Returns:

A set of scores indicating the degree to which each possible discrete classification value is associated with the given example object.

See Also:

Learner.scores(Object)

discreteValue

public String discreteValue(int[] exampleFeatures,
                            double[] exampleValues)

Returns the value of the discrete feature that would be returned by this classifier.

Overrides:

discreteValue in class Learner

Parameters:

exampleFeatures - The example's array of feature indices.

exampleValues - The example's array of feature values.

Returns:

A single value with the winning linear threshold unit's associated value.

realValue

public double realValue(int[] f,
                        double[] v)

Returns the value of the real feature that would be returned by this classifier.

Overrides:

realValue in class Learner

Parameters:

f - The features array.

v - The values array.

Returns:

The value of the feature produced for the input object.

featureValue

public Feature featureValue(int[] f,
                            double[] v)

Returns the classification of the given example as a single feature instead of a FeatureVector.

Overrides:

featureValue in class Learner

Parameters:

f - The features array.

v - The values array.

Returns:

The classification of the example as a feature.

classify

public FeatureVector classify(int[] exampleFeatures,
                              double[] exampleValues)

This method performs the multiplexing and returns the output of the selected Learner.

Specified by:

classify in class Learner

Parameters:

exampleFeatures - The example's array of feature indices.

exampleValues - The example's array of feature values.

Returns:

The output of the selected Learner.

write

public void write(PrintStream out)

Writes the algorithm's internal representation as text.

Specified by:

write in class Learner

Parameters:

out - The output stream.

write

public void write(edu.illinois.cs.cogcomp.core.datastructures.vectors.ExceptionlessOutputStream out)

Writes the learned function's internal representation in binary form.

Overrides:

write in class Learner

Parameters:

out - The output stream.

read

public void read(edu.illinois.cs.cogcomp.core.datastructures.vectors.ExceptionlessInputStream in)

Reads the binary representation of a learner with this object's run-time type, overwriting any and all learned or manually specified parameters as well as the label lexicon but without modifying the feature lexicon.

Overrides:

read in class Learner

Parameters:

in - The input stream.

clone

public Object clone()

Returns a deep clone of this learning algorithm.

Overrides:

clone in class Learner

Returns:

A shallow clone.