jsat.classifiers

Class MultinomialLogisticRegression

java.lang.Object

jsat.classifiers.MultinomialLogisticRegression

All Implemented Interfaces:

Serializable, Cloneable, Classifier

public class MultinomialLogisticRegression
extends Object
implements Classifier

Multinomial Logistic Regression is an extension of LogisticRegression for classification when there are more then two target classes. The results of this extension can differ greatly from applying OneVSAll to Logistic Regression.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
MultinomialLogisticRegression()

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
MultinomialLogisticRegression	clone()
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	trainC(ClassificationDataSet dataSet) Trains the classifier and constructs a model for classification using the given data set.
void	trainC(ClassificationDataSet dataSet, ExecutorService threadPool) Trains the classifier and constructs a model for classification using the given data set.

Methods inherited from class java.lang.Object

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

Constructor Detail

MultinomialLogisticRegression

public MultinomialLogisticRegression()

Method Detail

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.

trainC

public void trainC(ClassificationDataSet dataSet,
                   ExecutorService threadPool)

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:

trainC in interface Classifier

Parameters:

dataSet - the data set to train on

threadPool - the source of threads to use.

trainC

public void trainC(ClassificationDataSet dataSet)

Description copied from interface: Classifier

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

Specified by:

trainC 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

clone

public MultinomialLogisticRegression clone()

Specified by:

clone in interface Classifier

Overrides:

clone in class Object