weka.classifiers.bayes.net.estimate
Class DiscreteEstimatorBayes

java.lang.Object
  extended by weka.estimators.Estimator
      extended by 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
DiscreteEstimatorBayes(int nSymbols, double fPrior)
          Constructor
 
Method Summary
 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.