jsat.classifiers.bayesian

Class NaiveBayesUpdateable

java.lang.Object

jsat.classifiers.BaseUpdateableClassifier

jsat.classifiers.bayesian.NaiveBayesUpdateable

All Implemented Interfaces:

Serializable, Cloneable, Classifier, UpdateableClassifier

public class NaiveBayesUpdateable
extends BaseUpdateableClassifier

An implementation of Gaussian Naive Bayes that can be updated in an online fashion. The ability to be updated comes at the cost to slower training and classification time. However NB is already very fast, so the difference is not significant.
The more advanced distribution detection of NaiveBayes is not possible in online form.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Modifier	Constructor and Description
	NaiveBayesUpdateable() Creates a new Naive Bayes classifier that assumes sparce input vectors
	NaiveBayesUpdateable(boolean sparse) Creates a new Naive Bayes classifier
protected	NaiveBayesUpdateable(NaiveBayesUpdateable other) Copy Constructor

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
NaiveBayesUpdateable	clone()
boolean	isSparseInput() Returns true if the input is assume sparse
void	setSparse(boolean sparseInput) Sets whether or not that classifier should behave as if the input vectors are sparse.
void	setUp(CategoricalData[] categoricalAttributes, int numericAttributes, CategoricalData predicting) Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.
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, trainC, trainC, trainEpochs

Methods inherited from class java.lang.Object

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

Constructor Detail

NaiveBayesUpdateable

public NaiveBayesUpdateable()

Creates a new Naive Bayes classifier that assumes sparce input vectors

NaiveBayesUpdateable

public NaiveBayesUpdateable(boolean sparse)

Creates a new Naive Bayes classifier

Parameters:

sparse - whether or not to assume input vectors are sparce

NaiveBayesUpdateable

protected NaiveBayesUpdateable(NaiveBayesUpdateable other)

Copy Constructor

Parameters:

other - the classifier to make a copy of

Method Detail

clone

public NaiveBayesUpdateable clone()

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.

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.

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.

Parameters:

data - the data point to classify

Returns:

the results of the classification.

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.

Returns:

true if the model supports weighted data, false otherwise

isSparseInput

public boolean isSparseInput()

Returns true if the input is assume sparse

Returns:

true if the input is assume sparse

setSparse

public void setSparse(boolean sparseInput)

Sets whether or not that classifier should behave as if the input vectors are sparse. This means zero values in the input will be ignored when performing classification.

Parameters:

sparseInput - true to use a sparse model