de.jstacs.sequenceScores.statisticalModels.differentiable
Class MarkovRandomFieldDiffSM

java.lang.Object
  de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
      de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
          de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM

All Implemented Interfaces:

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

public final class MarkovRandomFieldDiffSM
extends AbstractDifferentiableStatisticalModel
implements Mutable, SamplingDifferentiableStatisticalModel

This class implements the scoring function for any MRF (Markov Random Field).

Author:

Jens Keilwagen

Field Summary

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

alphabets, length, r

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

UNKNOWN

Constructor Summary

MarkovRandomFieldDiffSM(AlphabetContainer alphabets, int length, double ess, String constr)
This is the main constructor that creates an instance of a MarkovRandomFieldDiffSM.

MarkovRandomFieldDiffSM(AlphabetContainer alphabets, int length, String constr)
This constructor creates an instance of a MarkovRandomFieldDiffSM with equivalent sample size (ess) 0.

MarkovRandomFieldDiffSM(StringBuffer source)
This is the constructor for the interface Storable.

Method Summary

void	addGradientOfLogPriorTerm(double[] grad, int start) This method computes the gradient of DifferentiableStatisticalModel.getLogPriorTerm() for each parameter of this model.
MarkovRandomFieldDiffSM	clone() Creates a clone (deep copy) of the current DifferentiableSequenceScore instance.
DataSet	emitDataSet(int numberOfSequences, int... seqLength) This method returns a DataSet object containing artificial sequence(s).
protected void	fromXML(StringBuffer representation) This method is called in the constructor for the Storable interface to create a scoring function from a StringBuffer.
double[]	getCurrentParameterValues() Returns a double array of dimension DifferentiableSequenceScore.getNumberOfParameters() containing the current parameter values.
double	getESS() Returns the equivalent sample size (ess) of this model, i.e.
String	getInstanceName() Should return a short instance name such as iMM(0), BN(2), ...
double	getLogNormalizationConstant() Returns the logarithm of the sum of the scores over all sequences of the event space.
double	getLogPartialNormalizationConstant(int parameterIndex) Returns the logarithm of the partial normalization constant for the parameter with index parameterIndex.
double	getLogPriorTerm() This method computes a value that is proportional to
double	getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer) Returns the logarithmic score for a Sequence beginning at position start in the Sequence and fills lists with the indices and the partial derivations.
double	getLogScoreFor(Sequence seq, int start) Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.
int	getNumberOfParameters() Returns the number of parameters in this DifferentiableSequenceScore.
int[][]	getSamplingGroups(int parameterOffset) Returns groups of indexes of parameters that shall be drawn together in a sampling procedure
int	getSizeOfEventSpaceForRandomVariablesOfParameter(int index) Returns the size of the event space of the random variables that are affected by parameter no.
void	initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights) This method creates the underlying structure of the DifferentiableSequenceScore.
void	initializeFunctionRandomly(boolean freeParams) This method initializes the DifferentiableSequenceScore randomly.
boolean	isInitialized() This method can be used to determine whether the instance is initialized.
boolean	modify(int offsetLeft, int offsetRight) Manually modifies the model.
void	setParameters(double[] params, int start) This method sets the internal parameters to the values of params between start and start + DifferentiableSequenceScore.getNumberOfParameters() - 1
String	toString(NumberFormat nf) This method returns a String representation of the instance.
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.AbstractDifferentiableStatisticalModel

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

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

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

Methods inherited from class java.lang.Object

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

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

isNormalized

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

getInitialClassParam, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getNumberOfRecommendedStarts

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

getLogProbFor, getLogProbFor, getLogProbFor, getMaximalMarkovOrder

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

getAlphabetContainer, getCharacteristics, getLength, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics

Constructor Detail

MarkovRandomFieldDiffSM

public MarkovRandomFieldDiffSM(AlphabetContainer alphabets,
                               int length,
                               String constr)

This constructor creates an instance of a MarkovRandomFieldDiffSM with equivalent sample size (ess) 0.

Parameters:

alphabets - the AlphabetContainer

length - the length of the sequences and accordingly the model

constr - the constraints that are used for the model, see ConstraintManager.extract(int, String)

See Also:

MarkovRandomFieldDiffSM(AlphabetContainer, int, double, String)

MarkovRandomFieldDiffSM

public MarkovRandomFieldDiffSM(AlphabetContainer alphabets,
                               int length,
                               double ess,
                               String constr)

This is the main constructor that creates an instance of a MarkovRandomFieldDiffSM.

Parameters:

alphabets - the AlphabetContainer

length - the length of the sequences and accordingly the model

ess - the equivalent sample size (ess)

constr - the constraints that are used for the model, see ConstraintManager.extract(int, String)

MarkovRandomFieldDiffSM

public MarkovRandomFieldDiffSM(StringBuffer source)
                        throws NonParsableException

This is the constructor for the interface Storable. Creates a new MarkovRandomFieldDiffSM out of a StringBuffer as returned by toXML().

Parameters:

source - the XML representation as StringBuffer

Throws:

NonParsableException - if the XML representation could not be parsed

Method Detail

fromXML

protected void fromXML(StringBuffer representation)
                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.

Specified by:

fromXML in class AbstractDifferentiableSequenceScore

Parameters:

representation - the XML representation as StringBuffer

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

AbstractDifferentiableSequenceScore.AbstractDifferentiableSequenceScore(StringBuffer)

clone

public MarkovRandomFieldDiffSM clone()
                              throws CloneNotSupportedException

Description copied from interface: DifferentiableSequenceScore

Creates a clone (deep copy) of the current DifferentiableSequenceScore instance.

Specified by:

clone in interface DifferentiableSequenceScore

Specified by:

clone in interface SequenceScore

Overrides:

clone in class AbstractDifferentiableStatisticalModel

Returns:

the cloned instance of the current DifferentiableSequenceScore

Throws:

CloneNotSupportedException - if something went wrong while cloning the DifferentiableSequenceScore

getLogScoreFor

public double getLogScoreFor(Sequence seq,
                             int start)

Description copied from interface: SequenceScore

Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.

Specified by:

getLogScoreFor in interface SequenceScore

Parameters:

seq - the Sequence

start - the start position in the Sequence

Returns:

the logarithmic score for the Sequence

getLogScoreAndPartialDerivation

public double getLogScoreAndPartialDerivation(Sequence seq,
                                              int start,
                                              IntList indices,
                                              DoubleList partialDer)

Description copied from interface: DifferentiableSequenceScore

Returns the logarithmic score for a Sequence beginning at position start in the Sequence and fills lists with the indices and the partial derivations.

Specified by:

getLogScoreAndPartialDerivation in interface DifferentiableSequenceScore

Parameters:

seq - the Sequence

start - the start position in the Sequence

indices - an IntList of indices, after method invocation the list should contain the indices i where is not zero

partialDer - a DoubleList of partial derivations, after method invocation the list should contain the corresponding that are not zero

Returns:

the logarithmic score for the Sequence

getNumberOfParameters

public int getNumberOfParameters()

Description copied from interface: DifferentiableSequenceScore

Returns the number of parameters in this DifferentiableSequenceScore. If the number of parameters is not known yet, the method returns DifferentiableSequenceScore.UNKNOWN.

Specified by:

getNumberOfParameters in interface DifferentiableSequenceScore

Returns:

the number of parameters in this DifferentiableSequenceScore

See Also:

DifferentiableSequenceScore.UNKNOWN

getInstanceName

public String getInstanceName()

Description copied from interface: SequenceScore

Should return a short instance name such as iMM(0), BN(2), ...

Specified by:

getInstanceName in interface SequenceScore

Returns:

a short instance name

setParameters

public void setParameters(double[] params,
                          int start)

Description copied from interface: DifferentiableSequenceScore

This method sets the internal parameters to the values of params between start and start + DifferentiableSequenceScore.getNumberOfParameters() - 1

Specified by:

setParameters in interface DifferentiableSequenceScore

Parameters:

params - the new parameters

start - the start index in params

toString

public String toString(NumberFormat nf)

Description copied from interface: SequenceScore

This method returns a String representation of the instance.

Specified by:

toString in interface SequenceScore

Parameters:

nf - the NumberFormat for the String representation of parameters or probabilities

Returns:

a String representation of the instance

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

Returns:

the XML representation

initializeFunction

public void initializeFunction(int index,
                               boolean freeParams,
                               DataSet[] data,
                               double[][] weights)
                        throws Exception

Description copied from interface: DifferentiableSequenceScore

This method creates the underlying structure of the DifferentiableSequenceScore.

Specified by:

initializeFunction in interface DifferentiableSequenceScore

Parameters:

index - the index of the class the DifferentiableSequenceScore models

freeParams - indicates whether the (reduced) parameterization is used

data - the data sets

weights - the weights of the sequences in the data sets

Throws:

Exception - if something went wrong

initializeFunctionRandomly

public void initializeFunctionRandomly(boolean freeParams)
                                throws Exception

Description copied from interface: DifferentiableSequenceScore

This method initializes the DifferentiableSequenceScore randomly. It has to create the underlying structure of the DifferentiableSequenceScore.

Specified by:

initializeFunctionRandomly in interface DifferentiableSequenceScore

Parameters:

freeParams - indicates whether the (reduced) parameterization is used

Throws:

Exception - if something went wrong

getLogNormalizationConstant

public double getLogNormalizationConstant()

Description copied from interface: DifferentiableStatisticalModel

Returns the logarithm of the sum of the scores over all sequences of the event space.

Specified by:

getLogNormalizationConstant in interface DifferentiableStatisticalModel

Returns:

the logarithm of the normalization constant Z

getLogPartialNormalizationConstant

public double getLogPartialNormalizationConstant(int parameterIndex)
                                          throws Exception

Description copied from interface: DifferentiableStatisticalModel

Returns the logarithm of the partial normalization constant for the parameter with index parameterIndex. This is the logarithm of the partial derivation of the normalization constant for the parameter with index parameterIndex,

.

Specified by:

getLogPartialNormalizationConstant in interface DifferentiableStatisticalModel

Parameters:

parameterIndex - the index of the parameter

Returns:

the logarithm of the partial normalization constant

Throws:

Exception - if something went wrong with the normalization

See Also:

DifferentiableStatisticalModel.getLogNormalizationConstant()

getESS

public double getESS()

Description copied from interface: DifferentiableStatisticalModel

Returns the equivalent sample size (ess) of this model, i.e. the equivalent sample size for the class or component that is represented by this model.

Specified by:

getESS in interface DifferentiableStatisticalModel

Returns:

the equivalent sample size.

getSizeOfEventSpaceForRandomVariablesOfParameter

public int getSizeOfEventSpaceForRandomVariablesOfParameter(int index)

Description copied from interface: DifferentiableStatisticalModel

Returns the size of the event space of the random variables that are affected by parameter no. index, i.e. the product of the sizes of the alphabets at the position of each random variable affected by parameter index. For DNA alphabets this corresponds to 4 for a PWM, 16 for a WAM except position 0, ...

Specified by:

getSizeOfEventSpaceForRandomVariablesOfParameter in interface DifferentiableStatisticalModel

Parameters:

index - the index of the parameter

Returns:

the size of the event space

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

Returns:

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

See Also:

DifferentiableStatisticalModel.getESS(), DifferentiableStatisticalModel.getLogNormalizationConstant()

addGradientOfLogPriorTerm

public void addGradientOfLogPriorTerm(double[] grad,
                                      int start)

Description copied from interface: DifferentiableStatisticalModel

This method computes the gradient of DifferentiableStatisticalModel.getLogPriorTerm() for each parameter of this model. The results are added to the array grad beginning at index start.

Specified by:

addGradientOfLogPriorTerm in interface DifferentiableStatisticalModel

Parameters:

grad - the array of gradients

start - the start index in the grad array, where the partial derivations for the parameters of this models shall be entered

See Also:

DifferentiableStatisticalModel.getLogPriorTerm()

getCurrentParameterValues

public double[] getCurrentParameterValues()

Description copied from interface: DifferentiableSequenceScore

Returns a double array of dimension DifferentiableSequenceScore.getNumberOfParameters() containing the current parameter values. If one likes to use these parameters to start an optimization it is highly recommended to invoke DifferentiableSequenceScore.initializeFunction(int, boolean, DataSet[], double[][]) before. After an optimization this method can be used to get the current parameter values.

Specified by:

getCurrentParameterValues in interface DifferentiableSequenceScore

Returns:

the current parameter values

isInitialized

public boolean isInitialized()

Description copied from interface: SequenceScore

This method can be used to determine whether the instance is initialized. If the instance is initialized you should be able to invoke SequenceScore.getLogScoreFor(Sequence).

Specified by:

isInitialized in interface SequenceScore

Returns:

true if the instance is initialized, false otherwise

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: 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.

emitDataSet

public DataSet emitDataSet(int numberOfSequences,
                           int... seqLength)
                    throws NotTrainedException,
                           Exception

Description copied from interface: StatisticalModel

This method returns a DataSet object containing artificial sequence(s).

There are two different possibilities to create a data set for a model with length 0 (homogeneous models).

1. emitDataSet( int n, int l ) should return a data set with n sequences of length l.
2. emitDataSet( int n, int[] l ) should return a data set with n sequences which have a sequence length corresponding to the entry in the given array l.

There are two different possibilities to create a data set for a model with length greater than 0 (inhomogeneous models).
emitDataSet( int n ) and emitDataSet( int n, null ) should return a data set with n sequences of length of the model ( SequenceScore.getLength()).

The standard implementation throws an Exception.

Specified by:

emitDataSet in interface StatisticalModel

Overrides:

emitDataSet in class AbstractDifferentiableStatisticalModel

Parameters:

numberOfSequences - the number of sequences that should be contained in the returned data set

seqLength - the length of the sequences for a homogeneous model; for an inhomogeneous model this parameter should be null or an array of size 0.

Returns:

a DataSet containing the artificial sequence(s)

Throws:

NotTrainedException - if the model is not trained yet

Exception - if the emission did not succeed

See Also:

DataSet