de.jstacs.sequenceScores.statisticalModels.differentiable.mixture
Class AbstractMixtureDiffSM

java.lang.Object
  de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
      de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
          de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM

All Implemented Interfaces:

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

Direct Known Subclasses:

ExtendedZOOPSDiffSM, MixtureDiffSM, StrandDiffSM

public abstract class AbstractMixtureDiffSM
extends AbstractDifferentiableStatisticalModel
implements SamplingDifferentiableStatisticalModel

This main abstract class for any mixture scoring function (e.g. "real" mixture, strand mixture, hidden motif mixture, ...). The potential for the hidden variables is parameterized depending on the parameterization of the given DifferentiableStatisticalModels. If these are already normalized (see DifferentiableStatisticalModel.isNormalized() ) the potential is parameterized using the Meila-parameterization, otherwise it is parameterized using the unnormalized MRF(Markov Random Fields)-parameterization.

Author:

Jens Keilwagen

Field Summary

protected double[]	componentScore This array is used while computing the score.
protected DoubleList[]	dList This array contains some DoubleLists that are used while computing the partial derivation.
protected boolean	freeParams This boolean indicates whether free parameterization or all parameters are used.
protected DifferentiableStatisticalModel[]	function This array contains the internal DifferentiableStatisticalModels that are used to determine the score.
protected double[]	hiddenParameter This array contains the hidden parameters of the instance.
protected double[]	hiddenPotential This array contains the hidden potentials of the instance.
protected IntList[]	iList This array contains some IntLists that are used while computing the partial derivation.
protected double	logGammaSum This double contains the sum of the logarithm of the gamma functions used in the prior.
protected double	logHiddenNorm This double contains the logarithm of the normalization constant of hidden parameters of the instance.
protected double[]	logHiddenPotential This array contains the logarithm of the hidden potentials of the instance.
protected double	norm This double contains the normalization constant of the instance.
protected boolean	optimizeHidden This boolean indicates whether to optimize the hidden variables of this instance.
protected int[]	paramRef This array contains the references/indices for the parameters.
protected double[]	partNorm This array contains the partial normalization constants, i.e. the normalization constant for each component.

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

alphabets, length, r

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

UNKNOWN

Constructor Summary

protected	AbstractMixtureDiffSM(int length, int starts, int dimension, boolean optimizeHidden, boolean plugIn, DifferentiableStatisticalModel... function) This constructor creates a new AbstractMixtureDiffSM.
protected	AbstractMixtureDiffSM(StringBuffer xml) 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.
AbstractMixtureDiffSM	clone() Creates a clone (deep copy) of the current DifferentiableSequenceScore instance.
protected void	cloneFunctions(DifferentiableStatisticalModel[] originalFunctions) This method clones the given array of functions and enables the user to do some post-processing.
protected void	computeHiddenParameter(double[] statistic, boolean add) This method has to be invoked during an initialization.
protected void	computeLogGammaSum() This method is used to pre-compute the sum of the logarithm of the gamma functions that is used in the prior.
protected boolean	determineIsNormalized() This method is used to determine the value that is returned by the method isNormalized().
protected void	extractFurtherInformation(StringBuffer xml) This method is the opposite of getFurtherInformation().
protected abstract void	fillComponentScores(Sequence seq, int start) Fills the internal array componentScore with the logarithmic scores of the components given a Sequence.
protected void	fromXML(StringBuffer b) 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.
DifferentiableStatisticalModel[]	getDifferentiableStatisticalModels() Returns a deep copy of all internal used DifferentiableStatisticalModels.
DifferentiableStatisticalModel	getFunction(int index) This method returns a specific internal function.
DifferentiableStatisticalModel[]	getFunctions() This method returns an array of clones of the internal used functions.
protected StringBuffer	getFurtherInformation() This method is used to append further information of the instance to the XML representation.
abstract double	getHyperparameterForHiddenParameter(int index) This method returns the hyperparameter for the hidden parameter with index index.
int	getIndexOfMaximalComponentFor(Sequence seq, int start) Returns the index of the component that has the greatest impact on the complete score for a Sequence.
protected int[]	getIndices(int index) This array is used to compute the relative indices of a parameter index.
double	getLogNormalizationConstant() Returns the logarithm of the sum of the scores over all sequences of the event space.
protected abstract double	getLogNormalizationConstantForComponent(int i) Computes the logarithm of the normalization constant for the component i.
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	getLogScoreFor(Sequence seq, int start) Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.
int	getNumberOfComponents() Returns the number of different components of this AbstractMixtureDiffSM.
int	getNumberOfParameters() Returns the number of parameters in this DifferentiableSequenceScore.
int	getNumberOfRecommendedStarts() This method returns the number of recommended optimization starts.
double[]	getProbsForComponent(Sequence seq) Returns the probabilities for each component given a Sequence.
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.
protected String	getXMLTag() This method returns the XML tag of the instance that is used to build a XML representation.
protected void	init(boolean freeParams) This method creates the underlying structure for the parameters.
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.
protected void	initializeHiddenPotentialRandomly() This method initializes the hidden potential (and the corresponding parameters) randomly.
void	initializeHiddenUniformly() This method initializes the hidden parameters of the instance uniformly.
protected abstract void	initializeUsingPlugIn(int index, boolean freeParams, DataSet[] data, double[][] weights) This method initializes the functions using the data in some way.
protected void	initWithLength(boolean freeParams, int len) This method is used to create the underlying structure, e.g.
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.
protected void	precomputeNorm() Pre-computes the normalization constant.
protected void	setHiddenParameters(double[] params, int start) This method sets the hidden parameters of 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
protected void	setParametersForFunction(int index, double[] params, int start) This method allows to set the parameters for specific functions.
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, toString, wait, wait, wait

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

getESS, getLogPartialNormalizationConstant

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

getInitialClassParam, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation

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

emitDataSet, getLogProbFor, getLogProbFor, getLogProbFor, getMaximalMarkovOrder

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

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

Field Detail

paramRef

protected int[] paramRef

This array contains the references/indices for the parameters. Only the start index for each new function is stored.

optimizeHidden

protected boolean optimizeHidden

This boolean indicates whether to optimize the hidden variables of this instance. (It is not used recursive.)

freeParams

protected boolean freeParams

This boolean indicates whether free parameterization or all parameters are used.

function

protected DifferentiableStatisticalModel[] function

This array contains the internal DifferentiableStatisticalModels that are used to determine the score.

hiddenParameter

protected double[] hiddenParameter

This array contains the hidden parameters of the instance.

logHiddenPotential

protected double[] logHiddenPotential

This array contains the logarithm of the hidden potentials of the instance.

hiddenPotential

protected double[] hiddenPotential

This array contains the hidden potentials of the instance.

componentScore

protected double[] componentScore

This array is used while computing the score. It stores the scores of the components and is used to avoid creating a new array every time.

partNorm

protected double[] partNorm

This array contains the partial normalization constants, i.e. the normalization constant for each component.

norm

protected double norm

This double contains the normalization constant of the instance.

logHiddenNorm

protected double logHiddenNorm

This double contains the logarithm of the normalization constant of hidden parameters of the instance.

logGammaSum

protected double logGammaSum

This double contains the sum of the logarithm of the gamma functions used in the prior.

See Also:

computeLogGammaSum()

dList

protected DoubleList[] dList

This array contains some DoubleLists that are used while computing the partial derivation.

iList

protected IntList[] iList

This array contains some IntLists that are used while computing the partial derivation.

Constructor Detail

AbstractMixtureDiffSM

protected AbstractMixtureDiffSM(int length,
                                int starts,
                                int dimension,
                                boolean optimizeHidden,
                                boolean plugIn,
                                DifferentiableStatisticalModel... function)
                         throws CloneNotSupportedException

This constructor creates a new AbstractMixtureDiffSM.

Parameters:

length - the sequence length that should be modeled

starts - the number of starts that should be done in an optimization

dimension - the number of different mixture components

optimizeHidden - indicates whether the parameters for the hidden variables should be optimized or not

plugIn - indicates whether the initial parameters for an optimization should be related to the data or randomly drawn

function - the DifferentiableStatisticalModels

Throws:

CloneNotSupportedException - if an element of function could not be cloned

AbstractMixtureDiffSM

protected AbstractMixtureDiffSM(StringBuffer xml)
                         throws NonParsableException

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

Parameters:

xml - the XML representation as StringBuffer

Throws:

NonParsableException - if the representation could not be parsed

Method Detail

computeLogGammaSum

protected void computeLogGammaSum()

This method is used to pre-compute the sum of the logarithm of the gamma functions that is used in the prior.

clone

public AbstractMixtureDiffSM 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

cloneFunctions

protected void cloneFunctions(DifferentiableStatisticalModel[] originalFunctions)
                       throws CloneNotSupportedException

This method clones the given array of functions and enables the user to do some post-processing. This method is only used in clone().

Parameters:

originalFunctions - the array of functions to be cloned

Throws:

CloneNotSupportedException - if an element of originalFunctions could not be cloned

getHyperparameterForHiddenParameter

public abstract double getHyperparameterForHiddenParameter(int index)

This method returns the hyperparameter for the hidden parameter with index index.

Parameters:

index - the index of the hidden parameter

Returns:

the hyperparameter for the hidden parameter

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()

getIndexOfMaximalComponentFor

public int getIndexOfMaximalComponentFor(Sequence seq,
                                         int start)

Returns the index of the component that has the greatest impact on the complete score for a Sequence.

Parameters:

seq - the sequence

start - the start position

Returns:

the index of the component

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)

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

getLogNormalizationConstant

public final 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

getNumberOfComponents

public final int getNumberOfComponents()

Returns the number of different components of this AbstractMixtureDiffSM.

Returns:

the number of different components

getNumberOfParameters

public final 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

getNumberOfRecommendedStarts

public final 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

getProbsForComponent

public double[] getProbsForComponent(Sequence seq)

Returns the probabilities for each component given a Sequence.

Parameters:

seq - the Sequence

Returns:

an array containing the probability of component i (=p(i|class,seq)) in entry i

getDifferentiableStatisticalModels

public DifferentiableStatisticalModel[] getDifferentiableStatisticalModels()
                                                                    throws CloneNotSupportedException

Returns a deep copy of all internal used DifferentiableStatisticalModels.

Returns:

a deep copy of all internal used DifferentiableStatisticalModels

Throws:

CloneNotSupportedException - if one element of the internal used DifferentiableStatisticalModels could not be cloned

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

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

initializeUsingPlugIn

protected abstract void initializeUsingPlugIn(int index,
                                              boolean freeParams,
                                              DataSet[] data,
                                              double[][] weights)
                                       throws Exception

This method initializes the functions using the data in some way.

Parameters:

index - the class index

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

data - the data

weights - the weights for the data

Throws:

Exception - if the initialization could not be done

See Also:

DifferentiableSequenceScore.initializeFunction(int, boolean, DataSet[], double[][])

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

initializeHiddenPotentialRandomly

protected void initializeHiddenPotentialRandomly()

This method initializes the hidden potential (and the corresponding parameters) randomly.

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

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

determineIsNormalized

protected boolean determineIsNormalized()

This method is used to determine the value that is returned by the method isNormalized().

Returns:

the value that is set to an internal variable and that will be returned by isNormalized()

initializeHiddenUniformly

public void initializeHiddenUniformly()

This method initializes the hidden parameters of the instance uniformly.

setHiddenParameters

protected void setHiddenParameters(double[] params,
                                   int start)

This method sets the hidden parameters of the model.

Parameters:

params - the parameter vector

start - the start index in params

setParametersForFunction

protected void setParametersForFunction(int index,
                                        double[] params,
                                        int start)

This method allows to set the parameters for specific functions.

Parameters:

index - the function index

params - the parameter vector

start - the start index in params

toXML

public final 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

fromXML

protected final void fromXML(StringBuffer b)
                      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:

b - the XML representation as StringBuffer

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

AbstractDifferentiableSequenceScore.AbstractDifferentiableSequenceScore(StringBuffer)

getFurtherInformation

protected StringBuffer getFurtherInformation()

This method is used to append further information of the instance to the XML representation. This method is designed to allow subclasses to add information to the XML representation.

Returns:

the further information as XML code in a StringBuffer

See Also:

extractFurtherInformation(StringBuffer)

extractFurtherInformation

protected void extractFurtherInformation(StringBuffer xml)
                                  throws NonParsableException

This method is the opposite of getFurtherInformation(). It extracts further information of the instance from a XML representation.

Parameters:

xml - the StringBuffer containing the information to be extracted as XML code

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

getFurtherInformation()

getIndices

protected int[] getIndices(int index)

This array is used to compute the relative indices of a parameter index.

Parameters:

index - the parameter index

Returns:

the relative indices of the parameter index

See Also:

paramRef

getXMLTag

protected String getXMLTag()

This method returns the XML tag of the instance that is used to build a XML representation.

Returns:

the XML tag of the instance

See Also:

Class.getSimpleName()

init

protected void init(boolean freeParams)

This method creates the underlying structure for the parameters.

Parameters:

freeParams - indicates whether to use only free parameters or all parameters

initWithLength

protected final void initWithLength(boolean freeParams,
                                    int len)

This method is used to create the underlying structure, e.g. paramRef.

Parameters:

freeParams - indicates whether to use free parameters or all

len - the length of the array paramRef

computeHiddenParameter

protected void computeHiddenParameter(double[] statistic,
                                      boolean add)

This method has to be invoked during an initialization.

Parameters:

statistic - a statistic for the initialization of the hidden parameters

add - a switch for adding hyperparameters to the statistic

See Also:

DifferentiableSequenceScore.initializeFunction(int, boolean, DataSet[], double[][])

precomputeNorm

protected void precomputeNorm()

Pre-computes the normalization constant.

getLogNormalizationConstantForComponent

protected abstract double getLogNormalizationConstantForComponent(int i)

Computes the logarithm of the normalization constant for the component i.

Parameters:

i - the index of the component

Returns:

the logarithm of the normalization constant of the component

fillComponentScores

protected abstract void fillComponentScores(Sequence seq,
                                            int start)

Fills the internal array componentScore with the logarithmic scores of the components given a Sequence.

Parameters:

seq - the sequence

start - the start position in seq

isNormalized

public final 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

getFunction

public DifferentiableStatisticalModel getFunction(int index)
                                           throws CloneNotSupportedException

This method returns a specific internal function.

Parameters:

index - the index of the specific function

Returns:

a clone of the specific function

Throws:

CloneNotSupportedException - if the function could not be cloned

getFunctions

public DifferentiableStatisticalModel[] getFunctions()
                                              throws CloneNotSupportedException

This method returns an array of clones of the internal used functions.

Returns:

an array of clones of the internal used functions

Throws:

CloneNotSupportedException - if at least one function could not be cloned

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.