org.openimaj.math.model

Class UnivariateGaussianNaiveBayesModel<T>

java.lang.Object

org.openimaj.math.model.UnivariateGaussianNaiveBayesModel<T>

Type Parameters:

T - The type of class/category predicted by the model

All Implemented Interfaces:

Cloneable, EstimatableModel<Double,T>, Model<Double,T>

public class UnivariateGaussianNaiveBayesModel<T>
extends Object
implements EstimatableModel<Double,T>

An implementation of a EstimatableModel that uses a VectorNaiveBayesCategorizer to associate a univariate (a Double) with a category.

Author:

Jonathon Hare (jsh2@ecs.soton.ac.uk)

Constructor Summary

Constructors

Constructor and Description
UnivariateGaussianNaiveBayesModel() Default constructor.
UnivariateGaussianNaiveBayesModel(gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer<T,gov.sandia.cognition.statistics.distribution.UnivariateGaussian.PDF> model) Construct with a pre-trained model.

Method Summary

Methods

Modifier and Type	Method and Description
UnivariateGaussianNaiveBayesModel<T>	clone() Clone the model
boolean	estimate(List<? extends IndependentPair<Double,T>> data) Estimates the model from the observations in the list of data.
Map<T,gov.sandia.cognition.statistics.distribution.UnivariateGaussian>	getClassDistribution() Get the class distribution for all classes.
gov.sandia.cognition.statistics.distribution.UnivariateGaussian	getClassDistribution(T clz) Get the class distribution for the given class.
gov.sandia.cognition.statistics.DataHistogram<T>	getClassPriors()
static void	main(String[] args) Testing
int	numItemsToEstimate()
T	predict(Double data) Uses the model to predict dependent data from an independent value.

Methods inherited from class java.lang.Object

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

Constructor Detail

UnivariateGaussianNaiveBayesModel

public UnivariateGaussianNaiveBayesModel()

Default constructor.

UnivariateGaussianNaiveBayesModel

public UnivariateGaussianNaiveBayesModel(gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer<T,gov.sandia.cognition.statistics.distribution.UnivariateGaussian.PDF> model)

Construct with a pre-trained model.

Parameters:

model - the pre-trained model.

Method Detail

estimate

public boolean estimate(List<? extends IndependentPair<Double,T>> data)

Description copied from interface: EstimatableModel

Estimates the model from the observations in the list of data. The data must contain at least EstimatableModel.numItemsToEstimate() pairs of dependent and independent data. It may contain more, in which case the estimate method may choose to make use of this data for validation, or obtaining a better model by a least squares method for example.

Specified by:

estimate in interface EstimatableModel<Double,T>

Parameters:

data - Data with which to estimate the model

Returns:

true if a successful estimate was made, false otherwise

See Also:

EstimatableModel.numItemsToEstimate()

predict

public T predict(Double data)

Description copied from interface: Model

Uses the model to predict dependent data from an independent value.

Specified by:

predict in interface Model<Double,T>

Parameters:

data - the data (independent variable)

Returns:

Dependent variable(s) predicted from the independent ones.

numItemsToEstimate

public int numItemsToEstimate()

Specified by:

numItemsToEstimate in interface EstimatableModel<Double,T>

Returns:

The minimum number of observations required to estimate the model.

clone

public UnivariateGaussianNaiveBayesModel<T> clone()

Description copied from interface: EstimatableModel

Clone the model

Specified by:

clone in interface EstimatableModel<Double,T>

Overrides:

clone in class Object

Returns:

a cloned copy

getClassDistribution

public gov.sandia.cognition.statistics.distribution.UnivariateGaussian getClassDistribution(T clz)

Get the class distribution for the given class.

Parameters:

clz - the class

Returns:

the univariate gaussian distribution.

getClassDistribution

public Map<T,gov.sandia.cognition.statistics.distribution.UnivariateGaussian> getClassDistribution()

Get the class distribution for all classes.

Returns:

a map of classes to distributions

getClassPriors

public gov.sandia.cognition.statistics.DataHistogram<T> getClassPriors()

Returns:

The priors for each class

main

public static void main(String[] args)

Testing

Parameters:

args -