de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Class MarkovModelDiffSM

java.lang.Object

de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore

de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel

de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM

de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.MarkovModelDiffSM

All Implemented Interfaces:

InstantiableFromParameterSet, Mutable, DifferentiableSequenceScore, QuickScanningSequenceScore, SequenceScore, DifferentiableStatisticalModel, SamplingDifferentiableStatisticalModel, StatisticalModel, Storable, Cloneable

public class MarkovModelDiffSM
extends BayesianNetworkDiffSM
implements Mutable, SamplingDifferentiableStatisticalModel

This class implements a AbstractDifferentiableStatisticalModel 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.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM

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

Fields inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore

alphabets, length, r

Fields inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore

UNKNOWN

Constructor Summary

Constructors

Constructor and Description
MarkovModelDiffSM(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, InhomogeneousMarkov structureMeasure) This constructor creates an instance without any prior for the modeled length.
MarkovModelDiffSM(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, InhomogeneousMarkov structureMeasure, DurationDiffSM lengthPenalty) This constructor creates an instance with an prior for the modeled length.
MarkovModelDiffSM(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, int order, DurationDiffSM lengthPenalty) This constructor creates an instance with an prior for the modeled length.
MarkovModelDiffSM(StringBuffer xml) The standard constructor for the interface Storable.

Method Summary

Modifier and Type	Method and Description
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
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.
void	normalizeParameters() Normalizes all parameters to log-probabilities.
StringBuffer	toXML() This method returns an XML representation as StringBuffer of an instance of the implementing class.

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM

addGradientOfLogPriorTerm, clone, createTrees, emitDataSet, fillInfixScore, getCurrentParameterSet, getCurrentParameterValues, getESS, getInfixFilter, getInstanceName, getLogNormalizationConstant, getLogPartialNormalizationConstant, getLogScoreAndPartialDerivation, getLogScoreFor, getMaximalMarkovOrder, getMaximumScore, getNumberOfParameters, getPositionDependentKMerProb, getPositionForParameter, getPWM, getSizeOfEventSpaceForRandomVariablesOfParameter, initializeFunction, initializeFunctionRandomly, isInitialized, precomputeNormalization, setParameters, setPlugInParameters, toHtml, toString

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel

getInitialClassParam, getLogProbFor, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, isNormalized, isNormalized

Methods inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore

getAlphabetContainer, getCharacteristics, getLength, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreFor, getLogScoreFor, getNumberOfRecommendedStarts, getNumberOfStarts, getNumericalCharacteristics, toString

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel

addGradientOfLogPriorTerm, getESS, getLogNormalizationConstant, getLogPartialNormalizationConstant, getSizeOfEventSpaceForRandomVariablesOfParameter, isNormalized

Methods inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore

clone, getCurrentParameterValues, getInitialClassParam, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getNumberOfParameters, getNumberOfRecommendedStarts, initializeFunction, initializeFunctionRandomly, setParameters

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel

emitDataSet, getLogProbFor, getLogProbFor, getLogProbFor, getMaximalMarkovOrder

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

getAlphabetContainer, getCharacteristics, getInstanceName, getLength, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics, isInitialized, toString

Constructor Detail

MarkovModelDiffSM

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

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

Parameters:

alphabet - the AlphabetContainer of the MarkovModelDiffSM

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

MarkovModelDiffSM

public MarkovModelDiffSM(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 MarkovModelDiffSM

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

MarkovModelDiffSM

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

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

Parameters:

alphabet - the AlphabetContainer of the MarkovModelDiffSM

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

MarkovModelDiffSM

public MarkovModelDiffSM(StringBuffer xml)
                  throws NonParsableException

The standard constructor for the interface Storable. Recreates a MarkovModelDiffSM 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: AbstractDifferentiableSequenceScore

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

Overrides:

fromXML in class BayesianNetworkDiffSM

Parameters:

source - the XML representation as StringBuffer

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

AbstractDifferentiableSequenceScore.AbstractDifferentiableSequenceScore(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 BayesianNetworkDiffSM

Returns:

the XML representation

getLogPriorTerm

public double getLogPriorTerm()

Description copied from interface: DifferentiableStatisticalModel

This method computes a value that is proportional to

DifferentiableStatisticalModel.getESS() * DifferentiableStatisticalModel.getLogNormalizationConstant() + Math.log( prior )

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

Specified by:

getLogPriorTerm in interface DifferentiableStatisticalModel

Specified by:

getLogPriorTerm in interface StatisticalModel

Overrides:

getLogPriorTerm in class BayesianNetworkDiffSM

Returns:

a value that is proportional to DifferentiableStatisticalModel.getESS() * DifferentiableStatisticalModel.getLogNormalizationConstant() + Math.log( prior ).

See Also:

DifferentiableStatisticalModel.getESS(), DifferentiableStatisticalModel.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

normalizeParameters

public void normalizeParameters()

Normalizes all parameters to log-probabilities.

getSamplingGroups

public int[][] getSamplingGroups(int parameterOffset)

Description copied from interface: SamplingDifferentiableStatisticalModel

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

Specified by:

getSamplingGroups in interface SamplingDifferentiableStatisticalModel

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.