jsat.classifiers

Class RegressorToClassifier

java.lang.Object

jsat.classifiers.RegressorToClassifier

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, BinaryScoreClassifier, Classifier, Parameterized

public class RegressorToClassifier
extends java.lang.Object
implements BinaryScoreClassifier, Parameterized

This meta algorithm wraps a Regressor to perform binary classification. This is done my labeling class 0 data points as "-1" and class 1 points as "1". The sign of the outputs then determines the class. Not all regression algorithms will work well in this setting, and standard parameter values need to change.
The parameter values returned are exactly those provided by the given regressor, or an empty list if the regressor does not implement Parameterized

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
RegressorToClassifier(Regressor regressor) Creates a new Binary Classifier by using the given regressor

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
RegressorToClassifier	clone()
Parameter	getParameter(java.lang.String paramName) Returns the parameter with the given name.
java.util.List<Parameter>	getParameters() Returns the list of parameters that can be altered for this learner.
double	getScore(DataPoint dp) Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	train(ClassificationDataSet dataSet) Trains the classifier and constructs a model for classification using the given data set.
void	train(ClassificationDataSet dataSet, boolean parallel) Trains the classifier and constructs a model for classification using the given data set.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

RegressorToClassifier

public RegressorToClassifier(Regressor regressor)

Creates a new Binary Classifier by using the given regressor

Parameters:

regressor - the regressor to wrap as a binary classifier

Method Detail

getScore

public double getScore(DataPoint dp)

Description copied from interface: BinaryScoreClassifier

Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.

Specified by:

getScore in interface BinaryScoreClassifier

Parameters:

dp - the data point to predict the class label of

Returns:

the score for the given data point

clone

public RegressorToClassifier clone()

Specified by:

clone in interface BinaryScoreClassifier

Specified by:

clone in interface Classifier

Overrides:

clone in class java.lang.Object

classify

public CategoricalResults classify(DataPoint data)

Description copied from interface: Classifier

Performs classification on the given data point.

Specified by:

classify in interface Classifier

Parameters:

data - the data point to classify

Returns:

the results of the classification.

train

public void train(ClassificationDataSet dataSet,
                  boolean parallel)

Description copied from interface: Classifier

Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.

Specified by:

train in interface Classifier

Parameters:

dataSet - the data set to train on

parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.

train

public void train(ClassificationDataSet dataSet)

Description copied from interface: Classifier

Trains the classifier and constructs a model for classification using the given data set.

Specified by:

train in interface Classifier

Parameters:

dataSet - the data set to train on

supportsWeightedData

public boolean supportsWeightedData()

Description copied from interface: Classifier

Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.

Specified by:

supportsWeightedData in interface Classifier

Returns:

true if the model supports weighted data, false otherwise

getParameters

public java.util.List<Parameter> getParameters()

Description copied from interface: Parameterized

Returns the list of parameters that can be altered for this learner.

Specified by:

getParameters in interface Parameterized

Returns:

the list of parameters that can be altered for this learner.

getParameter

public Parameter getParameter(java.lang.String paramName)

Description copied from interface: Parameterized

Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.

Specified by:

getParameter in interface Parameterized

Parameters:

paramName - the name of the parameter to obtain

Returns:

the Parameter in question, or null if no such named Parameter exists.