de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels
Class BayesianNetworkDiffSMParameterSet

java.lang.Object
  de.jstacs.parameters.ParameterSet
      de.jstacs.parameters.InstanceParameterSet<T>
          de.jstacs.parameters.SequenceScoringParameterSet
              de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSMParameterSet

All Implemented Interfaces:

GalaxyConvertible, Storable, Cloneable

public class BayesianNetworkDiffSMParameterSet
extends SequenceScoringParameterSet

Class for the parameters of a BayesianNetworkDiffSM. This class fulfills the requirements of a SequenceScoringParameterSet and can be used to create a new BayesianNetworkDiffSM.

Author:

Jan Grau

Nested Class Summary

Nested classes/interfaces inherited from class de.jstacs.parameters.ParameterSet

ParameterSet.ParameterList

Field Summary

Fields inherited from class de.jstacs.parameters.SequenceScoringParameterSet

alphabet, length

Fields inherited from class de.jstacs.parameters.ParameterSet

errorMessage, parameters, parent

Constructor Summary

BayesianNetworkDiffSMParameterSet()
Creates a new BayesianNetworkDiffSMParameterSet with empty parameter values.

BayesianNetworkDiffSMParameterSet(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, Measure structureMeasure)
Creates a new BayesianNetworkDiffSMParameterSet with pre-defined parameter values.

BayesianNetworkDiffSMParameterSet(StringBuffer representation)
Creates a new BayesianNetworkDiffSMParameterSet from its XML representation as defined by the Storable interface.

Method Summary

double	getEss() Returns the equivalent samples size (ess) defined in this set of parameters.
String	getInstanceComment() Returns a comment (a textual description) of the class that can be constructed using this ParameterSet.
String	getInstanceName() Returns the name of an instance of the class that can be constructed using this ParameterSet.
Measure	getMeasure() Returns the structure Measure defined by this set of parameters.
boolean	getPlugInParameters() Returns true if plug-in parameters shall be used when creating a BayesianNetworkDiffSM from this set of parameters.

Methods inherited from class de.jstacs.parameters.SequenceScoringParameterSet

clone, equals, fromXML, getAlphabetContainer, getLength, getNumberOfParameters, getParameterAt, hasDefaultOrIsSet, reset, toXML

Methods inherited from class de.jstacs.parameters.InstanceParameterSet

getInstance, getInstanceClass

Methods inherited from class de.jstacs.parameters.ParameterSet

fromGalaxy, getAllParameterNames, getComment, getComment, getErrorMessage, getIndex, getName, getName, getParameterForName, getParent, initParameterList, initParameterList, isAtomic, isComparable, parametersLoaded, setParent, toGalaxy

Methods inherited from class java.lang.Object

finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

BayesianNetworkDiffSMParameterSet

public BayesianNetworkDiffSMParameterSet(AlphabetContainer alphabet,
                                         int length,
                                         double ess,
                                         boolean plugInParameters,
                                         Measure structureMeasure)
                                  throws Exception

Creates a new BayesianNetworkDiffSMParameterSet with pre-defined parameter values.

Parameters:

alphabet - the alphabet of the scoring function boxed in an AlphabetContainer, e.g new AlphabetContainer(new DNAAlphabet())

length - the length of the scoring function, i.e. the length of the sequences this scoring function can handle

ess - the equivalent sample size

plugInParameters - indicates if plug-in parameters, i.e. generative (MAP) parameters, shall be used upon initialization

structureMeasure - the Measure used for the structure, e.g. InhomogeneousMarkov

Throws:

Exception - if the alphabet or the length are not in the expected range of values

BayesianNetworkDiffSMParameterSet

public BayesianNetworkDiffSMParameterSet()
                                  throws Exception

Creates a new BayesianNetworkDiffSMParameterSet with empty parameter values.

Throws:

Exception - if the parameter for the structure measures could not be created

BayesianNetworkDiffSMParameterSet

public BayesianNetworkDiffSMParameterSet(StringBuffer representation)
                                  throws NonParsableException

Creates a new BayesianNetworkDiffSMParameterSet from its XML representation as defined by the Storable interface.

Parameters:

representation - the XML code as StringBuffer

Throws:

NonParsableException - is thrown if the XML representation could not be parsed

Method Detail

getEss

public double getEss()

Returns the equivalent samples size (ess) defined in this set of parameters.

Returns:

the ess

getPlugInParameters

public boolean getPlugInParameters()

Returns true if plug-in parameters shall be used when creating a BayesianNetworkDiffSM from this set of parameters.

Returns:

if plug-in parameters shall be used

getMeasure

public Measure getMeasure()
                   throws ParameterSetParser.NotInstantiableException

Returns the structure Measure defined by this set of parameters.

Returns:

the structure Measure

Throws:

ParameterSetParser.NotInstantiableException - if the Measure could not be created from its own InstanceParameterSet

getInstanceComment

public String getInstanceComment()

Description copied from class: InstanceParameterSet

Returns a comment (a textual description) of the class that can be constructed using this ParameterSet.

Specified by:

getInstanceComment in class InstanceParameterSet

Returns:

the comment of the class

getInstanceName

public String getInstanceName()

Description copied from class: InstanceParameterSet

Returns the name of an instance of the class that can be constructed using this ParameterSet.

Specified by:

getInstanceName in class InstanceParameterSet

Returns:

the name of the class