C# Class AIMA.Core.Probability.BayesNet

Datei anzeigen Open project: PaulMineau/AIMA.Net Class Usage Examples

Public Methods

Method Description
BayesNet ( BayesNetNode root ) : AIMA.Core.Util
BayesNet ( BayesNetNode root1, BayesNetNode root2 ) : AIMA.Core.Util
BayesNet ( BayesNetNode root1, BayesNetNode root2, BayesNetNode root3 ) : AIMA.Core.Util
BayesNet ( List rootNodes ) : AIMA.Core.Util
getPriorSample ( ) : bool>.Dictionary
getPriorSample ( Randomizer r ) : bool>.Dictionary
getVariables ( ) : List
likelihoodWeighting ( String X, bool>.Dictionary evidence, int numberOfSamples ) : double[]
likelihoodWeighting ( String X, bool>.Dictionary evidence, int numberOfSamples, Randomizer r ) : double[]
mcmcAsk ( String X, bool>.Dictionary evidence, int numberOfVariables ) : double[]
mcmcAsk ( String X, bool>.Dictionary evidence, int numberOfVariables, Randomizer r ) : double[]
probabilityOf ( String Y, bool value, bool>.Dictionary evidence ) : double
rejectionSample ( String X, bool>.Dictionary evidence, int numberOfSamples ) : double[]
rejectionSample ( String X, bool>.Dictionary evidence, int numberOfSamples, Randomizer r ) : double[]

Private Methods

Method Description
consistent ( bool>.Dictionary sample, bool>.Dictionary evidence ) : bool
createMBValues ( List markovBlanket, bool>.Dictionary evt ) : bool>.Dictionary
createRandomEvent ( List nonEvidenceVariables, bool>.Dictionary evidence, Randomizer r ) : bool>.Dictionary
getNodeOf ( String y ) : BayesNetNode
getVariableNodes ( ) : List
markovBlanket ( BayesNetNode node ) : List
markovBlanket ( BayesNetNode node, List soFar ) : List
nonEvidenceVariables ( bool>.Dictionary evidence, String query ) : List
truthValue ( double ds, Randomizer r ) : bool

Method Details

BayesNet() public method

public BayesNet ( BayesNetNode root ) : AIMA.Core.Util
root BayesNetNode
return AIMA.Core.Util

BayesNet() public method

public BayesNet ( BayesNetNode root1, BayesNetNode root2 ) : AIMA.Core.Util
root1 BayesNetNode
root2 BayesNetNode
return AIMA.Core.Util

BayesNet() public method

public BayesNet ( BayesNetNode root1, BayesNetNode root2, BayesNetNode root3 ) : AIMA.Core.Util
root1 BayesNetNode
root2 BayesNetNode
root3 BayesNetNode
return AIMA.Core.Util

BayesNet() public method

public BayesNet ( List rootNodes ) : AIMA.Core.Util
rootNodes List
return AIMA.Core.Util

getPriorSample() public method

public getPriorSample ( ) : bool>.Dictionary
return bool>.Dictionary

getPriorSample() public method

public getPriorSample ( Randomizer r ) : bool>.Dictionary
r Randomizer
return bool>.Dictionary

getVariables() public method

public getVariables ( ) : List
return List

likelihoodWeighting() public method

public likelihoodWeighting ( String X, bool>.Dictionary evidence, int numberOfSamples ) : double[]
X String
evidence bool>.Dictionary
numberOfSamples int
return double[]

likelihoodWeighting() public method

public likelihoodWeighting ( String X, bool>.Dictionary evidence, int numberOfSamples, Randomizer r ) : double[]
X String
evidence bool>.Dictionary
numberOfSamples int
r Randomizer
return double[]

mcmcAsk() public method

public mcmcAsk ( String X, bool>.Dictionary evidence, int numberOfVariables ) : double[]
X String
evidence bool>.Dictionary
numberOfVariables int
return double[]

mcmcAsk() public method

public mcmcAsk ( String X, bool>.Dictionary evidence, int numberOfVariables, Randomizer r ) : double[]
X String
evidence bool>.Dictionary
numberOfVariables int
r Randomizer
return double[]

probabilityOf() public method

public probabilityOf ( String Y, bool value, bool>.Dictionary evidence ) : double
Y String
value bool
evidence bool>.Dictionary
return double

rejectionSample() public method

public rejectionSample ( String X, bool>.Dictionary evidence, int numberOfSamples ) : double[]
X String
evidence bool>.Dictionary
numberOfSamples int
return double[]

rejectionSample() public method

public rejectionSample ( String X, bool>.Dictionary evidence, int numberOfSamples, Randomizer r ) : double[]
X String
evidence bool>.Dictionary
numberOfSamples int
r Randomizer
return double[]