de.jstacs.scoringFunctions
Class MRFScoringFunction

java.lang.Object
  de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
      de.jstacs.scoringFunctions.MRFScoringFunction

All Implemented Interfaces:

NormalizableScoringFunction, ScoringFunction, Storable, Cloneable

public final class MRFScoringFunction
extends AbstractNormalizableScoringFunction

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

Author:

Jens Keilwagen

Field Summary

Fields inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction

alphabets, length, r

Fields inherited from interface de.jstacs.scoringFunctions.ScoringFunction

UNKNOWN

Constructor Summary

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

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

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

Method Summary

void	addGradientOfLogPriorTerm(double[] grad, int start) This method computes the gradient of NormalizableScoringFunction.getLogPriorTerm() for each parameter of this model.
MRFScoringFunction	clone() Creates a clone (deep copy) of the current ScoringFunction instance.
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 ScoringFunction.getNumberOfParameters() containing the current parameter values.
double	getEss() 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.
String	getInstanceName() Returns a short instance name.
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 NormalizableScoringFunction.getEss() * NormalizableScoringFunction.getLogNormalizationConstant() + Math.log( prior ) where prior is the prior for the parameters of this model.
double	getLogScore(Sequence seq, int start) Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.
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.
int	getNumberOfParameters() Returns the number of parameters in this ScoringFunction.
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, Sample[] data, double[][] weights) This method creates the underlying structure of the ScoringFunction.
void	initializeFunctionRandomly(boolean freeParams) This method initializes the ScoringFunction randomly.
boolean	isInitialized() This method can be used to determine whether the model is initialized.
void	setParameters(double[] params, int start) This method sets the internal parameters to the values of params between start and start + ScoringFunction.getNumberOfParameters() - 1
String	toString()
StringBuffer	toXML() This method returns an XML representation as StringBuffer of an instance of the implementing class.

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

Constructor Detail

MRFScoringFunction

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

This constructor creates an instance of a MRFScoringFunction 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:

MRFScoringFunction(AlphabetContainer, int, double, String)

MRFScoringFunction

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

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

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)

MRFScoringFunction

public MRFScoringFunction(StringBuffer source)
                   throws NonParsableException

This is the constructor for the interface Storable. Creates a new MRFScoringFunction 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: AbstractNormalizableScoringFunction

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

Specified by:

fromXML in class AbstractNormalizableScoringFunction

Parameters:

representation - the XML representation as StringBuffer

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

AbstractNormalizableScoringFunction.AbstractNormalizableScoringFunction(StringBuffer)

clone

public MRFScoringFunction clone()
                         throws CloneNotSupportedException

Description copied from interface: ScoringFunction

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

Specified by:

clone in interface ScoringFunction

Overrides:

clone in class AbstractNormalizableScoringFunction

Returns:

the cloned instance of the current ScoringFunction

Throws:

CloneNotSupportedException - if something went wrong while cloning the ScoringFunction

getLogScore

public double getLogScore(Sequence seq,
                          int start)

Description copied from interface: ScoringFunction

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

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: ScoringFunction

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

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: ScoringFunction

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

Returns:

the number of parameters in this ScoringFunction

See Also:

ScoringFunction.UNKNOWN

getInstanceName

public String getInstanceName()

Description copied from interface: ScoringFunction

Returns a short instance name.

Returns:

a short instance name

setParameters

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

Description copied from interface: ScoringFunction

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

Parameters:

params - the new parameters

start - the start index in params

toString

public String toString()

Overrides:

toString in class Object

toXML

public StringBuffer toXML()

Description copied from interface: Storable

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

Returns:

the XML representation

initializeFunction

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

Description copied from interface: ScoringFunction

This method creates the underlying structure of the ScoringFunction.

Parameters:

index - the index of the class the ScoringFunction models

freeParams - indicates whether the (reduced) parameterization is used

data - the samples

weights - the weights of the sequences in the samples

Throws:

Exception - if something went wrong

initializeFunctionRandomly

public void initializeFunctionRandomly(boolean freeParams)
                                throws Exception

Description copied from interface: ScoringFunction

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

Parameters:

freeParams - indicates whether the (reduced) parameterization is used

Throws:

Exception - if something went wrong

getLogNormalizationConstant

public double getLogNormalizationConstant()

Description copied from interface: NormalizableScoringFunction

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

Returns:

the logarithm of the normalization constant Z

getLogPartialNormalizationConstant

public double getLogPartialNormalizationConstant(int parameterIndex)
                                          throws Exception

Description copied from interface: NormalizableScoringFunction

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,

.

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:

NormalizableScoringFunction.getLogNormalizationConstant()

getEss

public double getEss()

Description copied from interface: NormalizableScoringFunction

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.

Returns:

the equivalent sample size.

getSizeOfEventSpaceForRandomVariablesOfParameter

public int getSizeOfEventSpaceForRandomVariablesOfParameter(int index)

Description copied from interface: NormalizableScoringFunction

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, ...

Parameters:

index - the index of the parameter

Returns:

the size of the event space

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.

Returns:

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

See Also:

NormalizableScoringFunction.getEss(), NormalizableScoringFunction.getLogNormalizationConstant()

addGradientOfLogPriorTerm

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

Description copied from interface: NormalizableScoringFunction

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

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:

NormalizableScoringFunction.getLogPriorTerm()

getCurrentParameterValues

public double[] getCurrentParameterValues()
                                   throws Exception

Description copied from interface: ScoringFunction

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

Returns:

the current parameter values

Throws:

Exception - if no parameters exist (yet)

isInitialized

public boolean isInitialized()

Description copied from interface: ScoringFunction

This method can be used to determine whether the model is initialized. If the model is not initialized you should invoke the method ScoringFunction.initializeFunction(int, boolean, Sample[], double[][]).

Returns:

true if the model is initialized, false otherwise