weka.classifiers.bayes.net.estimate

Class DiscreteEstimatorBayes

java.lang.Object

weka.estimators.Estimator

weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Scoreable, CapabilitiesHandler, OptionHandler, RevisionHandler

Direct Known Subclasses:

DiscreteEstimatorFullBayes

public class DiscreteEstimatorBayes
extends Estimator
implements Scoreable

Symbolic probability estimator based on symbol counts and a prior.

Version:

$Revision: 1.7 $

Author:

Remco Bouckaert (rrb@xm.co.nz)

See Also:

Serialized Form

Field Summary

Fields inherited from interface weka.classifiers.bayes.net.search.local.Scoreable

AIC, BAYES, BDeu, ENTROPY, MDL

Constructor Summary

Constructors

Constructor and Description
DiscreteEstimatorBayes(int nSymbols, double fPrior) Constructor

Method Summary

Modifier and Type	Method and Description
void	addValue(double data, double weight) Add a new data value to the current estimator.
double	getCount(double data) Get a counts for a value
int	getNumSymbols() Gets the number of symbols this estimator operates with
double	getProbability(double data) Get a probability estimate for a value
java.lang.String	getRevision() Returns the revision string.
double	logScore(int nType, int nCardinality) Gets the log score contribution of this distribution
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	toString() Display a representation of this estimator

Methods inherited from class weka.estimators.Estimator

addValues, addValues, addValues, addValues, buildEstimator, buildEstimator, clone, debugTipText, equals, forName, getCapabilities, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions, testCapabilities

Methods inherited from class java.lang.Object

getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

DiscreteEstimatorBayes

public DiscreteEstimatorBayes(int nSymbols,
                              double fPrior)

Constructor

Parameters:

nSymbols - the number of possible symbols (remember to include 0)

fPrior -

Method Detail

addValue

public void addValue(double data,
                     double weight)

Add a new data value to the current estimator.

Overrides:

addValue in class Estimator

Parameters:

data - the new data value

weight - the weight assigned to the data value

getProbability

public double getProbability(double data)

Get a probability estimate for a value

Specified by:

getProbability in class Estimator

Parameters:

data - the value to estimate the probability of

Returns:

the estimated probability of the supplied value

getCount

public double getCount(double data)

Get a counts for a value

Parameters:

data - the value to get the counts for

Returns:

the count of the supplied value

getNumSymbols

public int getNumSymbols()

Gets the number of symbols this estimator operates with

Returns:

the number of estimator symbols

logScore

public double logScore(int nType,
                       int nCardinality)

Gets the log score contribution of this distribution

Specified by:

logScore in interface Scoreable

Parameters:

nType - score type

Returns:

the score

toString

public java.lang.String toString()

Display a representation of this estimator

Overrides:

toString in class java.lang.Object

Returns:

a string representation of the estimator

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Returns:

the revision

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - should contain a sequence of integers which will be treated as symbolic.