jsat.classifiers.bayesian

Class MultivariateNormals

java.lang.Object

jsat.classifiers.bayesian.BestClassDistribution

jsat.classifiers.bayesian.MultivariateNormals

All Implemented Interfaces:

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

public class MultivariateNormals
extends BestClassDistribution

This classifier can be seen as an extension of NaiveBayes. Instead of treating the variables as independent, each class uses all of its variables to fit a Multivariate Normal distribution. As such, it can only handle numerical attributes. However, if the classes are normally distributed, it will produce optimal classification results. The less normal the true distributions are, the less accurate the classifier will be.

See Also:

Serialized Form

Field Summary

Fields inherited from class jsat.classifiers.bayesian.BestClassDistribution

USE_PRIORS

Constructor Summary

Constructors

Constructor and Description
MultivariateNormals() Creates a new class for classification by feating each class to a Multivariate Normal Distribution.
MultivariateNormals(boolean usePriors)
MultivariateNormals(MultivariateNormals toCopy) Copy constructor

Method Summary

Modifier and Type	Method and Description
MultivariateNormals	clone()
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.

Methods inherited from class jsat.classifiers.bayesian.BestClassDistribution

classify, isUsePriors, setUsePriors, train

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.classifiers.Classifier

train

Methods inherited from interface jsat.parameters.Parameterized

getParameter, getParameters

Constructor Detail

MultivariateNormals

public MultivariateNormals(boolean usePriors)

MultivariateNormals

public MultivariateNormals()

Creates a new class for classification by feating each class to a Multivariate Normal Distribution.

MultivariateNormals

public MultivariateNormals(MultivariateNormals toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

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

Overrides:

supportsWeightedData in class BestClassDistribution

Returns:

true if the model supports weighted data, false otherwise

clone

public MultivariateNormals clone()

Specified by:

clone in interface Classifier

Overrides:

clone in class BestClassDistribution