jsat.classifiers.bayesian

Class ODE

java.lang.Object

jsat.classifiers.BaseUpdateableClassifier

jsat.classifiers.bayesian.ODE

All Implemented Interfaces:

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

public class ODE
extends BaseUpdateableClassifier

One-Dependence Estimators (ODE) is an extension of Naive Bayes that, instead of assuming all features are independent, assumes all features are dependent on one other feature besides the target class. Because of this extra dependence requirement, the implementation only allows for categorical features.
This class is primarily for use by AODE

See: Webb, G., & Boughton, J. (2005). Not so naive bayes: Aggregating one-dependence estimators. Machine Learning, 1–24. Retrieved from here

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ODE(int dependent) Creates a new ODE classifier

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
ODE	clone()
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, train, train, trainEpochs

Methods inherited from class java.lang.Object

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

Constructor Detail

ODE

public ODE(int dependent)

Creates a new ODE classifier

Parameters:

dependent - the categorical feature to be dependent of

Method Detail

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

clone

public ODE 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