de.jstacs.scoringFunctions.directedGraphicalModels
Class MutableMarkovModelScoringFunction

java.lang.Object
  de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
      de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
          de.jstacs.scoringFunctions.directedGraphicalModels.MutableMarkovModelScoringFunction

All Implemented Interfaces:

InstantiableFromParameterSet, Mutable, NormalizableScoringFunction, SamplingScoringFunction, ScoringFunction, Storable, Cloneable

public class MutableMarkovModelScoringFunction
extends BayesianNetworkScoringFunction
implements Mutable, SamplingScoringFunction

This class implements a AbstractNormalizableScoringFunction for an inhomogeneous Markov model. The modeled length can be modified which might be very important for de-novo motif discovery.

Author:

Jan Grau, Jens Keilwagen

Field Summary

Fields inherited from class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction

ess, isTrained, logNormalizationConstant, numFreePars, nums, order, parameters, plugInParameters, structureMeasure, trees

Fields inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction

alphabets, length, r

Fields inherited from interface de.jstacs.scoringFunctions.ScoringFunction

UNKNOWN

Constructor Summary

MutableMarkovModelScoringFunction(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, InhomogeneousMarkov structureMeasure)
This constructor creates an instance without any prior for the modeled length.

MutableMarkovModelScoringFunction(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, InhomogeneousMarkov structureMeasure, DurationScoringFunction lengthPenalty)
This constructor creates an instance with an prior for the modeled length.

MutableMarkovModelScoringFunction(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, int order, DurationScoringFunction lengthPenalty)
This constructor creates an instance with an prior for the modeled length.

MutableMarkovModelScoringFunction(StringBuffer xml)
The standard constructor for the interface Storable.

Method Summary

protected void	fromXML(StringBuffer source) This method is called in the constructor for the Storable interface to create a scoring function from a StringBuffer.
double	getLogPriorTerm() This method computes a value that is proportional to NormalizableScoringFunction.getEss() * NormalizableScoringFunction.getLogNormalizationConstant() + Math.log( prior ) where prior is the prior for the parameters of this model.
int	getOrder() Returns the order of the inhomogeneous Markov model.
int[][]	getSamplingGroups(int parameterOffset) Returns groups of indexes of parameters that shall be drawn together in a sampling procedure
boolean	modify(int offsetLeft, int offsetRight) Manually modifies the model.
StringBuffer	toXML() This method returns an XML representation as StringBuffer of an instance of the implementing class.

Methods inherited from class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction

addGradientOfLogPriorTerm, clone, createTrees, getCurrentParameterSet, getCurrentParameterValues, getEss, getInstanceName, getLogNormalizationConstant, getLogPartialNormalizationConstant, getLogScore, getLogScoreAndPartialDerivation, getNumberOfParameters, getPositionDependentKMerProb, getPositionForParameter, getPWM, getSizeOfEventSpaceForRandomVariablesOfParameter, initializeFunction, initializeFunctionRandomly, isInitialized, precomputeNormalization, setParameters, setPlugInParameters, toString

Methods inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction

getAlphabetContainer, getInitialClassParam, getLength, getLogScore, getLogScoreAndPartialDerivation, getNumberOfRecommendedStarts, getNumberOfStarts, isNormalized, isNormalized

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface de.jstacs.scoringFunctions.NormalizableScoringFunction

addGradientOfLogPriorTerm, getEss, getInitialClassParam, getLogNormalizationConstant, getLogPartialNormalizationConstant, getSizeOfEventSpaceForRandomVariablesOfParameter, isNormalized

Methods inherited from interface de.jstacs.scoringFunctions.ScoringFunction

clone, getAlphabetContainer, getCurrentParameterValues, getInstanceName, getLength, getLogScore, getLogScore, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getNumberOfParameters, getNumberOfRecommendedStarts, initializeFunction, initializeFunctionRandomly, isInitialized, setParameters

Constructor Detail

MutableMarkovModelScoringFunction

public MutableMarkovModelScoringFunction(AlphabetContainer alphabet,
                                         int length,
                                         double ess,
                                         boolean plugInParameters,
                                         int order,
                                         DurationScoringFunction lengthPenalty)
                                  throws Exception

This constructor creates an instance with an prior for the modeled length.

Parameters:

alphabet - the AlphabetContainer of the MutableMarkovModelScoringFunction

length - the initial length of the modeled sequences

ess - the equivalent sample size

plugInParameters - a switch whether to use plug-in parameters of not

order - the order of the Markov model

lengthPenalty - the prior on the modeled sequence length

Throws:

Exception - if super class constructor throws an Exception or if the lengthPenalty does not allow the initial length

MutableMarkovModelScoringFunction

public MutableMarkovModelScoringFunction(AlphabetContainer alphabet,
                                         int length,
                                         double ess,
                                         boolean plugInParameters,
                                         InhomogeneousMarkov structureMeasure)
                                  throws Exception

This constructor creates an instance without any prior for the modeled length.

Parameters:

alphabet - the AlphabetContainer of the MutableMarkovModelScoringFunction

length - the initial length of the modeled sequences

ess - the equivalent sample size

plugInParameters - a switch whether to use plug-in parameters of not

structureMeasure - an InhomogeneousMarkov Measure for the structure

Throws:

Exception - if super class constructor throws an Exception

MutableMarkovModelScoringFunction

public MutableMarkovModelScoringFunction(AlphabetContainer alphabet,
                                         int length,
                                         double ess,
                                         boolean plugInParameters,
                                         InhomogeneousMarkov structureMeasure,
                                         DurationScoringFunction lengthPenalty)
                                  throws Exception

This constructor creates an instance with an prior for the modeled length.

Parameters:

alphabet - the AlphabetContainer of the MutableMarkovModelScoringFunction

length - the initial length of the modeled sequences

ess - the equivalent sample size

plugInParameters - a switch whether to use plug-in parameters of not

structureMeasure - a InhomogeneousMarkov Measure for the structure

lengthPenalty - the prior on the modeled sequence length

Throws:

Exception - if super class constructor throws an Exception or if the lengthPenalty does not allow the initial length

MutableMarkovModelScoringFunction

public MutableMarkovModelScoringFunction(StringBuffer xml)
                                  throws NonParsableException

The standard constructor for the interface Storable. Recreates a MutableMarkovModelScoringFunction from its XML representation as saved by the method toXML().

Parameters:

xml - the XML representation as StringBuffer

Throws:

NonParsableException - if the XML code could not be parsed

Method Detail

fromXML

protected void fromXML(StringBuffer source)
                throws NonParsableException

Description copied from class: AbstractNormalizableScoringFunction

This method is called in the constructor for the Storable interface to create a scoring function from a StringBuffer.

Overrides:

fromXML in class BayesianNetworkScoringFunction

Parameters:

source - the XML representation as StringBuffer

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

AbstractNormalizableScoringFunction.AbstractNormalizableScoringFunction(StringBuffer)

toXML

public StringBuffer toXML()

Description copied from interface: Storable

This method returns an XML representation as StringBuffer of an instance of the implementing class.

Specified by:

toXML in interface Storable

Overrides:

toXML in class BayesianNetworkScoringFunction

Returns:

the XML representation

getLogPriorTerm

public double getLogPriorTerm()

Description copied from interface: NormalizableScoringFunction

This method computes a value that is proportional to

NormalizableScoringFunction.getEss() * NormalizableScoringFunction.getLogNormalizationConstant() + Math.log( prior )

where prior is the prior for the parameters of this model.

Specified by:

getLogPriorTerm in interface NormalizableScoringFunction

Overrides:

getLogPriorTerm in class BayesianNetworkScoringFunction

Returns:

a value that is proportional to NormalizableScoringFunction.getEss() * NormalizableScoringFunction.getLogNormalizationConstant() + Math.log( prior ).

See Also:

NormalizableScoringFunction.getEss(), NormalizableScoringFunction.getLogNormalizationConstant()

getOrder

public int getOrder()

Returns the order of the inhomogeneous Markov model.

Returns:

the order

modify

public boolean modify(int offsetLeft,
                      int offsetRight)

Description copied from interface: Mutable

Manually modifies the model. The two offsets offsetLeft and offsetRight define how many positions the left or right border positions shall be moved. Negative numbers indicate moves to the left while positive numbers correspond to moves to the right.

Specified by:

modify in interface Mutable

Parameters:

offsetLeft - the offset on the left side

offsetRight - the offset on the right side

Returns:

true if the motif model was modified otherwise false

getSamplingGroups

public int[][] getSamplingGroups(int parameterOffset)

Description copied from interface: SamplingScoringFunction

Returns groups of indexes of parameters that shall be drawn together in a sampling procedure

Specified by:

getSamplingGroups in interface SamplingScoringFunction

Parameters:

parameterOffset - a global offset on the parameter indexes

Returns:

the groups of indexes. The first dimension represents the different groups while the second dimension contains the parameters that shall be sampled together. Internal parameter indexes need to be increased by parameterOffset.