de.jstacs.sequenceScores.statisticalModels.differentiable
Class NormalizedDiffSM

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

All Implemented Interfaces:

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

public final class NormalizedDiffSM
extends AbstractDifferentiableStatisticalModel
implements Mutable

This class makes an unnormalized DifferentiableStatisticalModel to a normalized DifferentiableStatisticalModel. However, the class allows to use only DifferentiableStatisticalModel that do not implement VariableLengthDiffSM. 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.sequenceScores.differentiable.AbstractDifferentiableSequenceScore

alphabets, length, r

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

UNKNOWN

Constructor Summary

NormalizedDiffSM(DifferentiableStatisticalModel nsf, int starts)
Creates a new instance using a given DifferentiableStatisticalModel.

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

Method Summary

void	addGradientOfLogPriorTerm(double[] grad, int start) This method computes the gradient of DifferentiableStatisticalModel.getLogPriorTerm() for each parameter of this model.
NormalizedDiffSM	clone() Creates a clone (deep copy) of the current DifferentiableSequenceScore 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 DifferentiableSequenceScore.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.
DifferentiableStatisticalModel	getFunction() This method returns the internal function.
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 DifferentiableStatisticalModel.getESS() * DifferentiableStatisticalModel.getLogNormalizationConstant() + Math.log( prior ) where prior is the prior for the parameters of this model.
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.
static DifferentiableStatisticalModel	getNormalizedVersion(DifferentiableStatisticalModel nsf, int starts) This method returns a normalized version of a DifferentiableStatisticalModel.
int	getNumberOfParameters() Returns the number of parameters in this DifferentiableSequenceScore.
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, DataSet[] data, double[][] weights) This method creates the underlying structure of the DifferentiableSequenceScore.
void	initializeFunctionRandomly(boolean freeParams) This method initializes the DifferentiableSequenceScore randomly.
void	initializeHiddenUniformly() This method initializes the hidden parameters of the internal DifferentiableStatisticalModel uniformly if it is a AbstractMixtureDiffSM.
boolean	isInitialized() This method can be used to determine whether the instance is initialized.
boolean	isNormalized() This method indicates whether the implemented score is already normalized to 1 or not.
boolean	isStrandModel() This method returns true if the internal DifferentiableStatisticalModel is a StrandDiffSM 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 + DifferentiableSequenceScore.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.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel

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

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

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

Methods inherited from class java.lang.Object

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

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

getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

getAlphabetContainer, getCharacteristics, getLength, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics

Constructor Detail

NormalizedDiffSM

public NormalizedDiffSM(DifferentiableStatisticalModel nsf,
                        int starts)
                 throws Exception

Creates a new instance using a given DifferentiableStatisticalModel.

Parameters:

nsf - the function to be used internal

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

Throws:

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

NormalizedDiffSM

public NormalizedDiffSM(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 DifferentiableStatisticalModel getNormalizedVersion(DifferentiableStatisticalModel nsf,
                                                                        int starts)
                                                                 throws Exception

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

Parameters:

nsf - the DifferentiableStatisticalModel to be normalized

starts - the number of recommended starts for a NormalizedDiffSM

Returns:

a normalized scoring function

Throws:

Exception - if nsf could not be cloned

clone

public NormalizedDiffSM 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

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

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.

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)
                               throws Exception

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

Throws:

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

See Also:

DifferentiableStatisticalModel.getLogPriorTerm()

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 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: 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

fromXML

protected void fromXML(StringBuffer xml)
                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:

xml - the XML representation as StringBuffer

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

AbstractDifferentiableSequenceScore.AbstractDifferentiableSequenceScore(StringBuffer)

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

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

getCurrentParameterValues

public double[] getCurrentParameterValues()
                                   throws Exception

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

Throws:

Exception - if no parameters exist (yet)

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

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

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: DifferentiableSequenceScore

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

Specified by:

getNumberOfRecommendedStarts in interface DifferentiableSequenceScore

Overrides:

getNumberOfRecommendedStarts in class AbstractDifferentiableSequenceScore

Returns:

the number of recommended optimization starts

isNormalized

public boolean isNormalized()

Description copied from interface: DifferentiableStatisticalModel

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

Specified by:

isNormalized in interface DifferentiableStatisticalModel

Overrides:

isNormalized in class AbstractDifferentiableStatisticalModel

Returns:

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

toString

public String toString()

Overrides:

toString in class Object

getFunction

public DifferentiableStatisticalModel 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

isStrandModel

public boolean isStrandModel()

This method returns true if the internal DifferentiableStatisticalModel is a StrandDiffSM otherwise false.

Returns:

true if the internal DifferentiableStatisticalModel is a StrandDiffSM 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 DifferentiableStatisticalModel uniformly if it is a AbstractMixtureDiffSM.