de.jstacs.scoringFunctions
Class NormalizedScoringFunction

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

All Implemented Interfaces:

Mutable, NormalizableScoringFunction, ScoringFunction, Storable, Cloneable

public final class NormalizedScoringFunction
extends AbstractNormalizableScoringFunction
implements Mutable

This class makes an unnormalized NormalizableScoringFunction to a normalized NormalizableScoringFunction. However, the class allows to use only NormalizableScoringFunction that do not implement VariableLengthScoringFunction. This class should be used only in cases when it is not possible to avoid its usage.

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

NormalizedScoringFunction(NormalizableScoringFunction nsf, int starts)
Creates a new instance using a given NormalizableScoringFunction.

NormalizedScoringFunction(StringBuffer xml)
This is the constructor for Storable.

Method Summary

void	addGradientOfLogPriorTerm(double[] grad, int start) This method computes the gradient of NormalizableScoringFunction.getLogPriorTerm() for each parameter of this model.
NormalizedScoringFunction	clone() Creates a clone (deep copy) of the current ScoringFunction instance.
protected void	fromXML(StringBuffer xml) 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.
NormalizableScoringFunction	getFunction() This method returns the internal function.
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.
static NormalizableScoringFunction	getNormalizedVersion(NormalizableScoringFunction nsf, int starts) This method returns a normalized version of a NormalizableScoringFunction.
int	getNumberOfParameters() Returns the number of parameters in this ScoringFunction.
int	getNumberOfRecommendedStarts() This method returns the number of recommended optimization starts.
int	getSizeOfEventSpaceForRandomVariablesOfParameter(int index) Returns the size of the event space of the random variables that are affected by parameter no.
StrandedLocatedSequenceAnnotationWithLength.Strand	getStrand(Sequence seq, int startPos) This method return the preferred StrandedLocatedSequenceAnnotationWithLength.Strand for a Sequence beginning at startPos.
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.
void	initializeHiddenUniformly() This method initializes the hidden parameters of the internal NormalizableScoringFunction uniformly if it is a AbstractMixtureScoringFunction.
boolean	isInitialized() This method can be used to determine whether the model is initialized.
boolean	isNormalized() This method indicates whether the implemented score is already normalized to 1 or not.
boolean	isStrandScoringFunction() This method returns true if the internal NormalizableScoringFunction is a StrandScoringFunction otherwise false.
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 + 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, getNumberOfStarts, isNormalized

Methods inherited from class java.lang.Object

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

Constructor Detail

NormalizedScoringFunction

public NormalizedScoringFunction(NormalizableScoringFunction nsf,
                                 int starts)
                          throws Exception

Creates a new instance using a given NormalizableScoringFunction.

Parameters:

nsf - the function to be used internal

starts - the number of recommended starts (ScoringFunction.getNumberOfRecommendedStarts())

Throws:

Exception - is nsf could not be cloned or some error occurred during computation of some values

NormalizedScoringFunction

public NormalizedScoringFunction(StringBuffer xml)
                          throws NonParsableException

This is the constructor for Storable.

Parameters:

xml - the xml representation

Throws:

NonParsableException - if the representation could not be parsed.

Method Detail

getNormalizedVersion

public static final NormalizableScoringFunction getNormalizedVersion(NormalizableScoringFunction nsf,
                                                                     int starts)
                                                              throws Exception

This method returns a normalized version of a NormalizableScoringFunction. Either it returns a clone of nsf or an instance of NormalizedScoringFunction using nsf and starts.

Parameters:

nsf - the NormalizableScoringFunction to be normalized

starts - the number of recommended starts for a NormalizedScoringFunction

Returns:

a normalized scoring function

Throws:

Exception - if nsf could not be cloned

clone

public NormalizedScoringFunction 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

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, ...

Specified by:

getSizeOfEventSpaceForRandomVariablesOfParameter in interface NormalizableScoringFunction

Parameters:

index - the index of the parameter

Returns:

the size of the event space

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.

Specified by:

getLogNormalizationConstant in interface NormalizableScoringFunction

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,

.

Specified by:

getLogPartialNormalizationConstant in interface NormalizableScoringFunction

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.

Specified by:

getEss in interface NormalizableScoringFunction

Returns:

the equivalent sample size.

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

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)
                               throws Exception

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.

Specified by:

addGradientOfLogPriorTerm in interface NormalizableScoringFunction

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

Throws:

Exception - if something went wrong with the computing of the gradients

See Also:

NormalizableScoringFunction.getLogPriorTerm()

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.

Specified by:

initializeFunction in interface 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.

Specified by:

initializeFunctionRandomly in interface ScoringFunction

Parameters:

freeParams - indicates whether the (reduced) parameterization is used

Throws:

Exception - if something went wrong

fromXML

protected void fromXML(StringBuffer xml)
                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:

xml - the XML representation as StringBuffer

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

AbstractNormalizableScoringFunction.AbstractNormalizableScoringFunction(StringBuffer)

getInstanceName

public String getInstanceName()

Description copied from interface: ScoringFunction

Returns a short instance name.

Specified by:

getInstanceName in interface ScoringFunction

Returns:

a short instance name

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.

Specified by:

getLogScore in interface ScoringFunction

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.

Specified by:

getLogScoreAndPartialDerivation in interface ScoringFunction

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.

Specified by:

getNumberOfParameters in interface ScoringFunction

Returns:

the number of parameters in this ScoringFunction

See Also:

ScoringFunction.UNKNOWN

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.

Specified by:

getCurrentParameterValues in interface ScoringFunction

Returns:

the current parameter values

Throws:

Exception - if no parameters exist (yet)

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

Specified by:

setParameters in interface ScoringFunction

Parameters:

params - the new parameters

start - the start index in params

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[][]).

Specified by:

isInitialized in interface ScoringFunction

Returns:

true if the model is initialized, false otherwise

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

getNumberOfRecommendedStarts

public int getNumberOfRecommendedStarts()

Description copied from interface: ScoringFunction

This method returns the number of recommended optimization starts. The standard implementation returns 1.

Specified by:

getNumberOfRecommendedStarts in interface ScoringFunction

Overrides:

getNumberOfRecommendedStarts in class AbstractNormalizableScoringFunction

Returns:

the number of recommended optimization starts

isNormalized

public boolean isNormalized()

Description copied from interface: NormalizableScoringFunction

This method indicates whether the implemented score is already normalized to 1 or not. The standard implementation returns false.

Specified by:

isNormalized in interface NormalizableScoringFunction

Overrides:

isNormalized in class AbstractNormalizableScoringFunction

Returns:

true if the implemented score is already normalized to 1, false otherwise

toString

public String toString()

Overrides:

toString in class Object

getFunction

public NormalizableScoringFunction getFunction()
                                        throws CloneNotSupportedException

This method returns the internal function.

Returns:

the internal function

Throws:

CloneNotSupportedException - if the internal function could not be cloned

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

isStrandScoringFunction

public boolean isStrandScoringFunction()

This method returns true if the internal NormalizableScoringFunction is a StrandScoringFunction otherwise false.

Returns:

true if the internal NormalizableScoringFunction is a StrandScoringFunction otherwise false

getStrand

public StrandedLocatedSequenceAnnotationWithLength.Strand getStrand(Sequence seq,
                                                                    int startPos)

This method return the preferred StrandedLocatedSequenceAnnotationWithLength.Strand for a Sequence beginning at startPos.

Parameters:

seq - the sequence

startPos - the start position

Returns:

the preferred StrandedLocatedSequenceAnnotationWithLength.Strand

initializeHiddenUniformly

public void initializeHiddenUniformly()

This method initializes the hidden parameters of the internal NormalizableScoringFunction uniformly if it is a AbstractMixtureScoringFunction.