jsat.classifiers.linear

Class ROMMA

java.lang.Object

jsat.classifiers.BaseUpdateableClassifier

jsat.classifiers.linear.ROMMA

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, BinaryScoreClassifier, Classifier, UpdateableClassifier, SimpleWeightVectorModel, SingleWeightVectorModel

public class ROMMA
extends BaseUpdateableClassifier
implements BinaryScoreClassifier, SingleWeightVectorModel

Provides an implementation of the linear Relaxed online Maximum Margin algorithm, which finds a similar solution to SVMs. By default, the aggressive variant with an implicit bias term is used, which is the suggested form from the paper. It is a binary classifier.

See: Li, Y.,&Long, P. M. (2002). The Relaxed Online Maximum Margin Algorithm. Machine Learning, 46(1-3), 361–387. doi:10.1023/A:1012435301888

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ROMMA() Creates a new aggressive ROMMA classifier
ROMMA(boolean aggressive) Creates a new ROMMA classifier

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
ROMMA	clone()
double	getBias() Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.
double	getBias(int index) Returns the bias term used with the weight vector for the given class index.
Vec	getRawWeight() Returns the only weight vector used for the model
Vec	getRawWeight(int index) Returns the raw weight vector associated with the given class index.
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.
Vec	getWeightVec() Returns the weight vector used to compute results via a dot product.
boolean	isAggressive() Returns whether or not the aggressive variant of ROMMA is used
boolean	isUseBias() Returns whether or not an implicit bias term is in use
int	numWeightsVecs() Returns the number of weight vectors that can be returned.
void	setAggressive(boolean aggressive) Determines whether the normal or aggressive ROMMA algorithm will be used.
void	setUp(CategoricalData[] categoricalAttributes, int numericAttributes, CategoricalData predicting) Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.
void	setUseBias(boolean useBias) Sets whether or not an implicit bias term will be added to the data set
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	update(DataPoint dataPoint, int targetClass) Updates the classifier by giving it a new data point to learn from.

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier

getEpochs, setEpochs, train, train, trainEpochs

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.classifiers.Classifier

train, train

Constructor Detail

ROMMA

public ROMMA()

Creates a new aggressive ROMMA classifier

ROMMA

public ROMMA(boolean aggressive)

Creates a new ROMMA classifier

Parameters:

aggressive - whether or not to use the aggressive variant

Method Detail

clone

public ROMMA clone()

Specified by:

clone in interface BinaryScoreClassifier

Specified by:

clone in interface Classifier

Specified by:

clone in interface UpdateableClassifier

Specified by:

clone in class BaseUpdateableClassifier

setAggressive

public void setAggressive(boolean aggressive)

Determines whether the normal or aggressive ROMMA algorithm will be used.

Parameters:

aggressive - true to use the aggressive variant

isAggressive

public boolean isAggressive()

Returns whether or not the aggressive variant of ROMMA is used

Returns:

true if the aggressive variant of ROMMA is used

setUseBias

public void setUseBias(boolean useBias)

Sets whether or not an implicit bias term will be added to the data set

Parameters:

useBias - true to add an implicit bias term

isUseBias

public boolean isUseBias()

Returns whether or not an implicit bias term is in use

Returns:

true if a bias term is in use

getWeightVec

public Vec getWeightVec()

Returns the weight vector used to compute results via a dot product.
Do not modify this value, or you will alter the results returned.

Returns:

the learned weight vector for prediction

getRawWeight

public Vec getRawWeight()

Description copied from interface: SingleWeightVectorModel

Returns the only weight vector used for the model

Specified by:

getRawWeight in interface SingleWeightVectorModel

Returns:

the only weight vector used for the model

getBias

public double getBias()

Description copied from interface: SingleWeightVectorModel

Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.

Specified by:

getBias in interface SingleWeightVectorModel

Returns:

the bias term for the model

getRawWeight

public Vec getRawWeight(int index)

Description copied from interface: SimpleWeightVectorModel

Returns the raw weight vector associated with the given class index. If the given class is an implicit zero vector, a ConstantVector object may be returned.
Do not alter the returned weight vector, as it will change the model's values.

If a regression problem, only index = 0 should be used

Specified by:

getRawWeight in interface SimpleWeightVectorModel

Parameters:

index - the class index to get the weight vector for

Returns:

the weight vector used for the specified class

getBias

public double getBias(int index)

Description copied from interface: SimpleWeightVectorModel

Returns the bias term used with the weight vector for the given class index. If the model does not support or was not trained with bias weights, 0 will be returned.

If a regression problem, only index = 0 should be used

Specified by:

getBias in interface SimpleWeightVectorModel

Parameters:

index - the class index to get the weight vector for

Returns:

the bias term for the specified class

numWeightsVecs

public int numWeightsVecs()

Description copied from interface: SimpleWeightVectorModel

Returns the number of weight vectors that can be returned. For binary classification problems the value may be 1 if only a single weight vector's sign is used to determine the class. For multi-class problems, the weight vector count includes the implicit zero vector (if one is being used).

Specified by:

numWeightsVecs in interface SimpleWeightVectorModel

Returns:

the number of weight vectors for which SimpleWeightVectorModel.getRawWeight(int) can be called.

setUp

public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes,
                  CategoricalData predicting)

Description copied from interface: UpdateableClassifier

Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.

Specified by:

setUp in interface UpdateableClassifier

Parameters:

categoricalAttributes - an array containing the categorical attributes that will be in each data point

numericAttributes - the number of numeric attributes that will be in each data point

predicting - the information for the target class that will be predicted

update

public void update(DataPoint dataPoint,
                   int targetClass)

Description copied from interface: UpdateableClassifier

Updates the classifier by giving it a new data point to learn from.

Specified by:

update in interface UpdateableClassifier

Parameters:

dataPoint - the data point to learn

targetClass - the target class of the data point

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.

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

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