jsat.classifiers.linear

Class ALMA2

java.lang.Object

jsat.classifiers.BaseUpdateableClassifier

jsat.classifiers.linear.ALMA2

All Implemented Interfaces:

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

public class ALMA2
extends BaseUpdateableClassifier
implements BinaryScoreClassifier, SingleWeightVectorModel

Provides a linear implementation of the ALMAp algorithm for p = 2, which is considerably more efficient to compute. It is a binary classifier for numeric features.
ALMA requires one major parameter alpha to be set, the other two have default behavior / values that have provable convergence.

See: Gentile, C. (2002). A New Approximate Maximal Margin Classification Algorithm. The Journal of Machine Learning Research, 2, 213–242. Retrieved from here

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ALMA2() Creates a new ALMA learner using an alpha of 0.8
ALMA2(double alpha) Creates a new ALMA learner using the given alpha

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
ALMA2	clone()
double	getAlpha() Returns the approximation coefficient used
double	getB() Returns the B value of the ALMA algorithm
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.
double	getC()
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	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	setAlpha(double alpha) Alpha controls the approximation of the large margin formed by ALMA, with larger values causing more updates.
void	setB(double B) Sets the B variable of the ALMA algorithm, this is set automatically by setAlpha(double).
void	setC(double C) Sets the C value of the ALMA algorithm.
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

ALMA2

public ALMA2()

Creates a new ALMA learner using an alpha of 0.8

ALMA2

public ALMA2(double alpha)

Creates a new ALMA learner using the given alpha

Parameters:

alpha - the alpha value to use

See Also:

setAlpha(double)

Method Detail

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

setAlpha

public void setAlpha(double alpha)

Alpha controls the approximation of the large margin formed by ALMA, with larger values causing more updates. A value of 1.0 will update only on mistakes, while smaller values update if the error was not far enough away from the margin.

NOTE: Whenever alpha is set, the value of B will also be set to an appropriate value. This is not the only possible value that will lead to convergence, and can be set manually after alpha is set to another value.

Parameters:

alpha - the approximation scale in (0.0, 1.0]

getAlpha

public double getAlpha()

Returns the approximation coefficient used

Returns:

the approximation coefficient used

setB

public void setB(double B)

Sets the B variable of the ALMA algorithm, this is set automatically by setAlpha(double).

Parameters:

B - the value for B

getB

public double getB()

Returns the B value of the ALMA algorithm

Returns:

the B value of the ALMA algorithm

setC

public void setC(double C)

Sets the C value of the ALMA algorithm. The default value is the one suggested in the paper.

Parameters:

C - the C value of ALMA

getC

public double getC()

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

clone

public ALMA2 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

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

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.