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java.lang.Objectjbnc.util.BNTools
public final class BNTools
Utilities for Bayesian networks.
Field Summary | |
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static double |
beta_ijk
A some small value larger than zero. |
Constructor Summary | |
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BNTools()
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Method Summary | |
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static double |
gammaLn(double xx)
Returns the value ln[ gamma(xx)] for xx > 0 Implementation based on W.H. |
static double |
getASBMParamComponent(BayesianNetworks.BayesNet net,
DatasetInt dataset,
boolean usePriors,
double alphaK)
Return network parameters component of the asymptotic standard Bayesian measure (ASBM). |
static int |
getNetworkDimension(BayesianNetworks.BayesNet net)
Returns dimension of a Bayesian network. |
protected static InferenceGraphs.InferenceGraphNode[] |
getNodes(Dataset dataset,
InferenceGraphs.InferenceGraph graph)
Get node names from a graph in an order they appear in the dataset. |
protected static void |
learnParameters_old(BayesianNetworks.BayesNet net,
Dataset data,
boolean useDirihlet,
double alphaK)
Learns parameters for the current network structure. |
static void |
learnParameters(BayesianNetworks.BayesNet net,
DatasetInt data,
boolean useDirihlet,
double alphaK)
Learns parameters for the current network structure. |
static void |
learnParameters(BayesianNetworks.BayesNet net,
FrequencyCalc fc,
boolean useDirihlet,
double alphaK)
Learns parameters for the current network structure. |
static void |
main(java.lang.String[] args)
Description of the Method |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Field Detail |
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public static final double beta_ijk
Constructor Detail |
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public BNTools()
Method Detail |
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public static int getNetworkDimension(BayesianNetworks.BayesNet net) throws java.lang.Exception
Dimension of a Bayesian network: Let X be a set of random variables and B be a Bayesian network defined over X. The dimension of this network, Dim(B), is the number of free parameters required to completely specify the joint probability distribution of X.
net
- Description of Parameter
java.lang.Exception
- .public static final double getASBMParamComponent(BayesianNetworks.BayesNet net, DatasetInt dataset, boolean usePriors, double alphaK) throws java.lang.Exception
q = sumi =1...n sumj =1... qi sumk =1...ri Nijk log Nijk / Nij
where Nijk means that variable Xi is in configuration k and parents of variable Xi are in configuration j .
E. Castillo, J. M. Gutierrez and A. S. Hadi, Expert Systems and Probabilistic Network Models , Springer, 1997. p.494, eq.(11.28).
net
- Description of Parameterdataset
- Description of ParameterusePriors
- Description of ParameteralphaK
- Description of Parameter
java.lang.Exception
- .public static double gammaLn(double xx) throws java.lang.Exception
xx
-
java.lang.Exception
- When xx <= 0.public static void learnParameters(BayesianNetworks.BayesNet net, FrequencyCalc fc, boolean useDirihlet, double alphaK) throws java.lang.Exception
net
- Bayesian network.useDirihlet
- Indicates whether Dirihlet priors should be used for
network parameters.alphaK
- alphak parameter for Dirihlet priors. All
alphak are assumed to be the same and
greater than zero.fc
- Description of Parameter
java.lang.Exception
public static void learnParameters(BayesianNetworks.BayesNet net, DatasetInt data, boolean useDirihlet, double alphaK) throws java.lang.Exception
net
- Bayesian network.useDirihlet
- Indicates whether Dirihlet priors should be used for
network parameters.alphaK
- alphak parameter for Dirihlet priors. All
alphak are assumed to be the same and
greater than zero.data
- Description of Parameter
java.lang.Exception
public static void main(java.lang.String[] args)
args
- Description of Parameterprotected static InferenceGraphs.InferenceGraphNode[] getNodes(Dataset dataset, InferenceGraphs.InferenceGraph graph) throws java.lang.Exception
dataset
- Description of Parametergraph
- Description of Parameter
java.lang.Exception
- Description of Exceptionprotected static void learnParameters_old(BayesianNetworks.BayesNet net, Dataset data, boolean useDirihlet, double alphaK) throws java.lang.Exception
net
- Bayesian network.useDirihlet
- Indicates whether Dirihlet priors should be used for
network parameters.alphaK
- alphak parameter for Dirihlet priors. All
alphak are assumed to be the same and
greater than zero.data
- Description of Parameter
java.lang.Exception
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