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

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

All Implemented Interfaces:

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

public class StrandDiffSM
extends AbstractMixtureDiffSM
implements Mutable

This class enables the user to search on both strand. So the motif can be found on the forward or on the reverse complementary strand.

Author:

Jens Keilwagen

See Also:

ComplementableDiscreteAlphabet, AlphabetContainer.isReverseComplementable()

Nested Class Summary

static class

StrandDiffSM.InitMethod This enum defines the different types of plug-in initialization of a StrandDiffSM.

Field Summary

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM

componentScore, dList, freeParams, function, hiddenParameter, hiddenPotential, iList, logGammaSum, logHiddenNorm, logHiddenPotential, norm, optimizeHidden, paramRef, partNorm

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

alphabets, length, r

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

UNKNOWN

Constructor Summary

StrandDiffSM(DifferentiableStatisticalModel function, double forwardPartOfESS, int starts, boolean plugIn, StrandDiffSM.InitMethod initMethod)
This constructor creates a StrandDiffSM that optimizes the usage of each strand.

StrandDiffSM(DifferentiableStatisticalModel function, int starts, boolean plugIn, StrandDiffSM.InitMethod initMethod, double forward)
This constructor creates a StrandDiffSM that has a fixed frequency for the strand usage.

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

Method Summary

protected void	extractFurtherInformation(StringBuffer xml) This method is the opposite of AbstractMixtureDiffSM.getFurtherInformation().
protected void	fillComponentScores(Sequence seq, int start) Fills the internal array AbstractMixtureDiffSM.componentScore with the logarithmic scores of the components given a Sequence.
double	getESS() Returns the equivalent sample size (ess) of this model, i.e.
double	getForwardProbability() This methoth returns the a-priori probability for the forward strand.
protected StringBuffer	getFurtherInformation() This method is used to append further information of the instance to the XML representation.
double	getHyperparameterForHiddenParameter(int index) This method returns the hyperparameter for the hidden parameter with index index.
String	getInstanceName() Should return a short instance name such as iMM(0), BN(2), ...
protected double	getLogNormalizationConstantForComponent(int i) Computes the logarithm of the normalization constant for the component i.
double	getLogPartialNormalizationConstant(int parameterIndex) Returns the logarithm of the partial normalization constant for the parameter with index parameterIndex.
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 double[][][]	getReverseComplementDistributions(ComplementableDiscreteAlphabet abc, double[][][] condDistr) This method computes the reverse complement distributions for given conditional distributions.
StrandedLocatedSequenceAnnotationWithLength.Strand	getStrand(Sequence seq, int startPos) This method returns the preferred StrandedLocatedSequenceAnnotationWithLength.Strand for a given subsequence.
protected void	init(boolean freeParams) This method creates the underlying structure for the parameters.
protected void	initializeUsingPlugIn(int index, boolean freeParams, DataSet[] data, double[][] weights) This method initializes the functions using the data in some way.
static boolean	isStrandModel(DifferentiableStatisticalModel nsf) Check whether a DifferentiableStatisticalModel is a StrandDiffSM.
boolean	modify(int offsetLeft, int offsetRight) Manually modifies the model.
protected void	setForwardProb(double forward) This method can be used to set the forward strand probability.
String	toString(NumberFormat nf) This method returns a String representation of the instance.

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM

addGradientOfLogPriorTerm, clone, cloneFunctions, computeHiddenParameter, computeLogGammaSum, determineIsNormalized, fromXML, getCurrentParameterValues, getDifferentiableStatisticalModels, getFunction, getFunctions, getIndexOfMaximalComponentFor, getIndices, getLogNormalizationConstant, getLogPriorTerm, getLogScoreFor, getNumberOfComponents, getNumberOfParameters, getNumberOfRecommendedStarts, getProbsForComponent, getSamplingGroups, getSizeOfEventSpaceForRandomVariablesOfParameter, getXMLTag, initializeFunction, initializeFunctionRandomly, initializeHiddenPotentialRandomly, initializeHiddenUniformly, initWithLength, isInitialized, isNormalized, precomputeNorm, setHiddenParameters, setParameters, setParametersForFunction, toXML

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.differentiable.DifferentiableSequenceScore

getInitialClassParam, 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, getLength, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics

Constructor Detail

StrandDiffSM

public StrandDiffSM(DifferentiableStatisticalModel function,
                    double forwardPartOfESS,
                    int starts,
                    boolean plugIn,
                    StrandDiffSM.InitMethod initMethod)
             throws CloneNotSupportedException,
                    WrongAlphabetException

This constructor creates a StrandDiffSM that optimizes the usage of each strand.

Parameters:

function - the DifferentiableSequenceScore

forwardPartOfESS - the part of the full ESS that should be used as hyperparameter for the forward strand

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

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

initMethod - only used if plugIn==true
whether the initial parameters for an optimization should be related to the data of the forward strand, the backward strand or both strands

Throws:

CloneNotSupportedException - if function could not be cloned

WrongAlphabetException - if the alphabet of function is not AlphabetContainer.isReverseComplementable() and, hence, cannot be used for a strand mixture

See Also:

StrandDiffSM.InitMethod

StrandDiffSM

public StrandDiffSM(DifferentiableStatisticalModel function,
                    int starts,
                    boolean plugIn,
                    StrandDiffSM.InitMethod initMethod,
                    double forward)
             throws CloneNotSupportedException,
                    WrongAlphabetException

This constructor creates a StrandDiffSM that has a fixed frequency for the strand usage.

Parameters:

function - the DifferentiableSequenceScore

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

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

initMethod - only used if plugIn==true
whether the initial parameters for an optimization should be related to the data of the forward strand, the backward strand or both strands

forward - the probability of a motif to be on the forward strand

Throws:

CloneNotSupportedException - if function could not be cloned

WrongAlphabetException - if the alphabet of function is not AlphabetContainer.isReverseComplementable() and, hence, cannot be used for a strand mixture

See Also:

StrandDiffSM.InitMethod

StrandDiffSM

public StrandDiffSM(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

setForwardProb

protected void setForwardProb(double forward)

This method can be used to set the forward strand probability.

Parameters:

forward - the forward strand probability in (0,1)

getLogNormalizationConstantForComponent

protected double getLogNormalizationConstantForComponent(int i)

Description copied from class: AbstractMixtureDiffSM

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

Specified by:

getLogNormalizationConstantForComponent in class AbstractMixtureDiffSM

Parameters:

i - the index of the component

Returns:

the logarithm of the normalization constant of the component

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()

getHyperparameterForHiddenParameter

public double getHyperparameterForHiddenParameter(int index)

Description copied from class: AbstractMixtureDiffSM

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

Specified by:

getHyperparameterForHiddenParameter in class AbstractMixtureDiffSM

Parameters:

index - the index of the hidden parameter

Returns:

the hyperparameter for the hidden parameter

getForwardProbability

public double getForwardProbability()

This methoth returns the a-priori probability for the forward strand.

Returns:

the a-priori probability for the forward strand

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.

initializeUsingPlugIn

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

Description copied from class: AbstractMixtureDiffSM

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

Specified by:

initializeUsingPlugIn in class AbstractMixtureDiffSM

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[][])

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

fillComponentScores

protected void fillComponentScores(Sequence seq,
                                   int start)

Description copied from class: AbstractMixtureDiffSM

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

Specified by:

fillComponentScores in class AbstractMixtureDiffSM

Parameters:

seq - the sequence

start - the start position in seq

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

getFurtherInformation

protected StringBuffer getFurtherInformation()

Description copied from class: AbstractMixtureDiffSM

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.

Overrides:

getFurtherInformation in class AbstractMixtureDiffSM

Returns:

the further information as XML code in a StringBuffer

See Also:

AbstractMixtureDiffSM.extractFurtherInformation(StringBuffer)

extractFurtherInformation

protected void extractFurtherInformation(StringBuffer xml)
                                  throws NonParsableException

Description copied from class: AbstractMixtureDiffSM

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

Overrides:

extractFurtherInformation in class AbstractMixtureDiffSM

Parameters:

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

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

AbstractMixtureDiffSM.getFurtherInformation()

init

protected void init(boolean freeParams)

Description copied from class: AbstractMixtureDiffSM

This method creates the underlying structure for the parameters.

Overrides:

init in class AbstractMixtureDiffSM

Parameters:

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

toString

public String toString(NumberFormat nf)

Description copied from interface: SequenceScore

This method returns a String representation of the instance.

Specified by:

toString in interface SequenceScore

Parameters:

nf - the NumberFormat for the String representation of parameters or probabilities

Returns:

a String representation of the instance

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

getReverseComplementDistributions

public static double[][][] getReverseComplementDistributions(ComplementableDiscreteAlphabet abc,
                                                             double[][][] condDistr)

This method computes the reverse complement distributions for given conditional distributions. This method is used to determine the context of a motif.

Parameters:

abc - the alphabet

condDistr - the conditional distribution

Returns:

the complement of the conditional distribution that can be used for computing a combing conditional distribution

getStrand

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

This method returns the preferred StrandedLocatedSequenceAnnotationWithLength.Strand for a given subsequence.

Parameters:

seq - the sequence

startPos - the start position

Returns:

the StrandedLocatedSequenceAnnotationWithLength.Strand of this subsequence

See Also:

AbstractMixtureDiffSM.getIndexOfMaximalComponentFor(Sequence, int)

isStrandModel

public static boolean isStrandModel(DifferentiableStatisticalModel nsf)

Check whether a DifferentiableStatisticalModel is a StrandDiffSM.

Parameters:

nsf - the original DifferentiableStatisticalModel

Returns:

true if the DifferentiableStatisticalModel is a StrandDiffSM