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java.lang.Objectde.jstacs.models.AbstractModel
de.jstacs.models.discrete.DiscreteGraphicalModel
de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM
de.jstacs.models.discrete.inhomogeneous.DAGModel
de.jstacs.models.discrete.inhomogeneous.BayesianNetworkModel
public class BayesianNetworkModel
The class implements a Bayesian network of fixed order. It allows the user to specify some kinds of sub models including inhomogeneous Markov model (iMM), permuted Markov model (pMM) or Bayesian network (BN)
StructureLearner.ModelType
Field Summary |
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Fields inherited from class de.jstacs.models.discrete.inhomogeneous.DAGModel |
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constraints |
Fields inherited from class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM |
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DEFAULT_STREAM, sostream |
Fields inherited from class de.jstacs.models.discrete.DiscreteGraphicalModel |
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params, trained |
Fields inherited from class de.jstacs.models.AbstractModel |
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alphabets, length |
Constructor Summary | |
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BayesianNetworkModel(BayesianNetworkModelParameterSet params)
The default constructor. |
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BayesianNetworkModel(StringBuffer representation)
The constructor for a model in xml format. |
Method Summary | |
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BayesianNetworkModel |
clone()
Follows the conventions of Object 's clone-method. |
protected int[] |
count(int[][] structure,
byte maxOrder)
Counts the occurrence of the different indegrees and check if the conventions are met. |
String |
getInstanceName()
Should return a short instance name such as iMM(0), BN(2), ... |
double |
getLogPriorTerm()
Returns a value that is proportional to the log of the prior. |
byte |
getMaximalMarkovOrder()
This method returns the maximal used markov order if possible. |
protected String |
getXMLTag()
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protected void |
set(DGMParameterSet parameter,
boolean trained)
Sets the parameters as internal parameters and does some essential computations. |
void |
train(Sample data,
double[] weights)
Trains the Model object given the data as Sample using the specified weights. |
Methods inherited from class de.jstacs.models.discrete.inhomogeneous.DAGModel |
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checkAcyclic, createConstraints, drawParameters, emitSample, estimateParameters, getFurtherModelInfos, getLogProbFor, getNumericalCharacteristics, getProbFor, getStructure, setFurtherModelInfos, toString |
Methods inherited from class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM |
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check, setOutputStream |
Methods inherited from class de.jstacs.models.discrete.DiscreteGraphicalModel |
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fromXML, getCurrentParameterSet, getDescription, getESS, isTrained, toXML |
Methods inherited from class de.jstacs.models.AbstractModel |
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getAlphabetContainer, getCharacteristics, getLength, getLogProbFor, getLogProbFor, getLogProbFor, getLogProbFor, getPriorTerm, getProbFor, getProbFor, set, setNewAlphabetContainerInstance, train |
Methods inherited from class java.lang.Object |
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equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public BayesianNetworkModel(BayesianNetworkModelParameterSet params) throws CloneNotSupportedException, IllegalArgumentException, NonParsableException
params
- the parameter set
CloneNotSupportedException
- if the parameter set could not be cloned
IllegalArgumentException
- if the parameter set is not instantiated
NonParsableException
- if the parameter set is not parsablepublic BayesianNetworkModel(StringBuffer representation) throws NonParsableException
representation
- the model in xml format
NonParsableException
- if the StringBuffer could not be parsedMethod Detail |
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public BayesianNetworkModel clone() throws CloneNotSupportedException
AbstractModel
Object
's clone-method.
clone
in interface Model
clone
in class DAGModel
AbstractModel
(the member-AlphabetContainer
isn't deeply cloned since it is assumed to be immutable). The type of the returned object is defined by
the class X
directly inherited from AbstractModel
. Hence X
's
clone-method should work as:Object o = (X)super.clone();
2. all additional member variables of o
defined by X
that are not of simple data-types like int, double, ... , have to be deeply
copied 3. return o
CloneNotSupportedException
public String getInstanceName()
Model
protected String getXMLTag()
getXMLTag
in class DiscreteGraphicalModel
DiscreteGraphicalModel.fromXML(StringBuffer)
,
DiscreteGraphicalModel.toXML()
public void train(Sample data, double[] weights) throws Exception
Model
Sample
using the specified weights. The weight
at position i belongs to the element at position i. So the array weight
should have the number of
sequences in the sample as dimension. (Optionally it is possible to use weight == null
if all
weights have the value one.)
data
- the given sequencesweights
- the weights of the elements, each weight should be non-negative
Exception
- an Exception should be thrown if the training did not succeed (e.g. the weights dimension of weights
and number of samples does not match).Sample.getElementAt(int)
,
Sample.ElementEnumerator
public double getLogPriorTerm() throws Exception
Model
getLogPriorTerm
in interface Model
getLogPriorTerm
in class DAGModel
Exception
- if something went wrongModel.getPriorTerm()
public byte getMaximalMarkovOrder()
Model
getMaximalMarkovOrder
in interface Model
getMaximalMarkovOrder
in class AbstractModel
protected int[] count(int[][] structure, byte maxOrder)
structure
- the structuremaxOrder
- the maximal order
protected void set(DGMParameterSet parameter, boolean trained) throws CloneNotSupportedException, NonParsableException
DiscreteGraphicalModel
set
in class InhomogeneousDGM
parameter
- the new ParameterSettrained
- the indicator for the model
CloneNotSupportedException
- if the parmeterSet could not be cloned
NonParsableException
- if the parameters of the model could not be parsed
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