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.

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.

MRFScoringFunction(AlphabetContainer alphabets, int length, String constr)
This constructor creates an instance with ess 0.

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

Method Summary

void	addGradientOfLogPriorTerm(double[] grad, int start) This method computes the gradient of 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 to create a scoring function from a StringBuffer
double[]	getCurrentParameterValues() Returns a double array of dimension getNumberOfParameters() containing the current parameter values.
double	getEss() Returns the equivalent sample size 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	getLogPriorTerm() This method computes a value that is proportional to getESS()*Math.log( getNormalizationConstant() ) + Math.log( prior ).
double	getLogScore(Sequence seq, int start) Returns the log score for the sequence
double	getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer) Returns the log score for the sequence and fills the list with the indices and the partial derivations.
double	getNormalizationConstant() Returns the sum of the scores over all sequences of the event space.
int	getNumberOfParameters() The number of parameters in this scoring function.
double	getPartialNormalizationConstant(int parameterIndex) Returns the partial normalization constant for the parameter with index parameterIndex.
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 scoring function.
void	initializeFunctionRandomly(boolean freeParams) This method initializes the scoring function 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 + this.getNumberOfParameters() - 1
StringBuffer	toXML() This method returns an XML-representation of an instance of the implementing class.

Methods inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction

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

Methods inherited from class java.lang.Object

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

Constructor Detail

MRFScoringFunction

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

This constructor creates an instance with ess 0.

Parameters:

alphabets - the AlphabetContainer

length - the length of the sequences respectively 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.

Parameters:

alphabets - the AlphabetContainer

length - the length of the sequences respectively 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 Storable.

Parameters:

source - the xml representation

Throws:

NonParsableException - if the 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 to create a scoring function from a StringBuffer

Specified by:

fromXML in class AbstractNormalizableScoringFunction

Parameters:

representation - the XML representation

Throws:

NonParsableException - if the StringBuffer could not be parsed.

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

Throws:

CloneNotSupportedException

getLogScore

public double getLogScore(Sequence seq,
                          int start)

Description copied from interface: ScoringFunction

Returns the log score for the sequence

Parameters:

seq - the sequence

start - the startposition in the sequence

Returns:

the log score for the sequence

getLogScoreAndPartialDerivation

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

Description copied from interface: ScoringFunction

Returns the log score for the sequence and fills the list with the indices and the partial derivations.

Parameters:

seq - the sequence

start - the startposition in the sequence

indices - after method invocation the list should contain the indices i where \frac{\partial \log score(seq)}{\partial \lambda_i} is not zero

partialDer - after method invocation the list should contain the corresponding \frac{\partial \log score(seq)}{\partial \lambda_i}

Returns:

the log score

getNumberOfParameters

public int getNumberOfParameters()

Description copied from interface: ScoringFunction

The number of parameters in this scoring function. If the number of parameters is not known yet, the method returns UNKNOWN.

Returns:

the number of parameters in this scoring function

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 + this.getNumberOfParameters() - 1

Parameters:

params - the parameters

start - the start index

toXML

public StringBuffer toXML()

Description copied from interface: Storable

This method returns an XML-representation 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 scoring function.

Parameters:

index - the index of the class the scoring function models

freeParams - if true, the (reduced) parameterization is used

data - the samples

weights - the weights of the sequences in the samples

Throws:

Exception

initializeFunctionRandomly

public void initializeFunctionRandomly(boolean freeParams)
                                throws Exception

Description copied from interface: ScoringFunction

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

Parameters:

freeParams - if true, the (reduced) parameterization is used

Throws:

Exception

getNormalizationConstant

public double getNormalizationConstant()

Description copied from interface: NormalizableScoringFunction

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

Returns:

the normalization constant Z

getPartialNormalizationConstant

public double getPartialNormalizationConstant(int parameterIndex)
                                       throws Exception

Description copied from interface: NormalizableScoringFunction

Returns the partial normalization constant for the parameter with index parameterIndex. This is the partial derivation of the normalization constant for the parameter with index parameterIndex \frac{\partial Z(\lambda)}{\partial \lambda_{index}}.

Parameters:

parameterIndex - the index of the parameter

Returns:

the partial normalization constant

Throws:

Exception - if something went wrong with the Normalization

getEss

public double getEss()

Description copied from interface: NormalizableScoringFunction

Returns the equivalent sample size 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

getESS()*Math.log( getNormalizationConstant() ) + Math.log( prior ).

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

Returns:

getESS()*Math.log( getNormalizationConstant() ) + Math.log( prior )

See Also:

NormalizableScoringFunction.getEss(), NormalizableScoringFunction.getNormalizationConstant()

addGradientOfLogPriorTerm

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

Description copied from interface: NormalizableScoringFunction

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

Parameters:

grad - the gradient

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

See Also:

NormalizableScoringFunction.getLogPriorTerm()

getCurrentParameterValues

public double[] getCurrentParameterValues()
                                   throws Exception

Description copied from interface: ScoringFunction

Returns a double array of dimension getNumberOfParameters() containing the current parameter values. If on e 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 - is thrown 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 initialize you should invoke the method ScoringFunction.initializeFunction(int, boolean, Sample[], double[][]).

Returns:

true if the model is initialized