jsat.classifiers.bayesian

Class BestClassDistribution

java.lang.Object

jsat.classifiers.bayesian.BestClassDistribution

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized

Direct Known Subclasses:

MultivariateNormals

public class BestClassDistribution
extends Object
implements Classifier, Parameterized

BestClassDistribution is a generic class for performing classification by fitting a MultivariateDistribution to each class. The distribution is supplied by the user, and each class if fit to the same type of distribution. Classification is then performed by returning the class of the most likely distribution given the data point.

Author:

Edward Raff

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static boolean	USE_PRIORS The default value for whether or not to use the prior probability of a class when making classification decisions is true.

Constructor Summary

Constructors

Constructor and Description
BestClassDistribution(BestClassDistribution toCopy) Copy constructor
BestClassDistribution(MultivariateDistribution baseDist)
BestClassDistribution(MultivariateDistribution baseDist, boolean usePriors)

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
Classifier	clone()
Parameter	getParameter(String paramName) Returns the parameter with the given name.
List<Parameter>	getParameters() Returns the list of parameters that can be altered for this learner.
boolean	isUsePriors() Returns whether or not this object uses the prior probabilities for classification.
void	setUsePriors(boolean usePriors) Controls whether or not the priors will be used for classification.
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

Field Detail

USE_PRIORS

public static final boolean USE_PRIORS

The default value for whether or not to use the prior probability of a class when making classification decisions is true.

See Also:

Constant Field Values

Constructor Detail

BestClassDistribution

public BestClassDistribution(MultivariateDistribution baseDist)

BestClassDistribution

public BestClassDistribution(MultivariateDistribution baseDist,
                             boolean usePriors)

BestClassDistribution

public BestClassDistribution(BestClassDistribution toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

setUsePriors

public void setUsePriors(boolean usePriors)

Controls whether or not the priors will be used for classification. This value can be changed at any time, before or after training has occurred.

Parameters:

usePriors - true to use the prior probabilities for each class, false to ignore them.

isUsePriors

public boolean isUsePriors()

Returns whether or not this object uses the prior probabilities for classification.

Returns:

true if the prior probabilities are being used, false if not.

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 Classifier clone()

Specified by:

clone in interface Classifier

Overrides:

clone in class Object

getParameters

public 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(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.