de.jstacs.scoringFunctions
Class IndependentProductScoringFunction

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

All Implemented Interfaces:

MotifDiscoverer, MutableMotifDiscoverer, NormalizableScoringFunction, ScoringFunction, Storable, Cloneable

public class IndependentProductScoringFunction
extends AbstractNormalizableScoringFunction
implements MutableMotifDiscoverer

This class enables the user to model parts of a sequence independent of each other. For instance, the first part of the sequence is modeled by the first NormalizableScoringFunction and has the length of the first NormalizableScoringFunction, the second part starts directly after the first part, is modeled by the second NormalizableScoringFunction ... etc. It is also possible to use a NormalizableScoringFunction for more than one sequence part and in both orientations (if possible).

It is important to set the equivalent sample size (ESS) of each instance carefully, i.e., corresponding to the ESS of the parts.

Author:

Jens Keilwagen

Nested Class Summary

Nested classes/interfaces inherited from interface de.jstacs.motifDiscovery.MotifDiscoverer

MotifDiscoverer.KindOfProfile

Field Summary

Fields inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction

alphabets, length, r

Fields inherited from interface de.jstacs.scoringFunctions.ScoringFunction

UNKNOWN

Constructor Summary

IndependentProductScoringFunction(double ess, boolean plugIn, NormalizableScoringFunction... functions)
This constructor creates an instance of an IndependentProductScoringFunction from a given series of independent NormalizableScoringFunctions.

IndependentProductScoringFunction(double ess, boolean plugIn, NormalizableScoringFunction[] functions, int[] length)
This constructor creates an instance of an IndependentProductScoringFunction from given series of independent NormalizableScoringFunctions and lengths.

IndependentProductScoringFunction(double ess, boolean plugIn, NormalizableScoringFunction[] functions, int[] index, int[] length, boolean[] reverse)
This is the main constructor.

IndependentProductScoringFunction(StringBuffer source)
This is the constructor for the interface Storable.

Method Summary

void	addGradientOfLogPriorTerm(double[] grad, int start) This method computes the gradient of NormalizableScoringFunction.getLogPriorTerm() for each parameter of this model.
void	adjustHiddenParameters(int index, Sample[] data, double[][] weights) Adjusts all hidden parameters including duration and mixture parameters according to the current values of the remaining parameters.
IndependentProductScoringFunction	clone() Creates a clone (deep copy) of the current ScoringFunction instance.
int	extractSequenceParts(int scoringFunctionIndex, Sample[] data, Sample[] result) This method extracts the corresponding Sequence parts for a specific ScoringFunction.
double[][]	extractWeights(int number, double[][] weights) This method creates the weights for extractSequenceParts(int, Sample[], Sample[]).
protected void	fromXML(StringBuffer rep) 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[]	getFunctions() This method returns a deep copy of the internally used NormalizableScoringFunction.
int	getGlobalIndexOfMotifInComponent(int component, int motif) Returns the global index of the motif used in component.
int	getIndexOfMaximalComponentFor(Sequence sequence) Returns the index of the component with the maximal score for the sequence sequence.
int[]	getIndices() This method returns a deep copy of the internally used indices of the NormalizableScoringFunction for the parts.
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.
int	getMotifLength(int motif) This method returns the length of the motif with index motif .
int	getNumberOfComponents() Returns the number of components in this MotifDiscoverer.
int	getNumberOfMotifs() Returns the number of motifs for this MotifDiscoverer.
int	getNumberOfMotifsInComponent(int component) Returns the number of motifs that are used in the component component of this MotifDiscoverer.
int	getNumberOfParameters() Returns the number of parameters in this ScoringFunction.
int	getNumberOfRecommendedStarts() This method returns the number of recommended optimization starts.
int[]	getPartialLengths() This method returns a deep copy of the internally used partial lengths of the parts.
double[]	getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile dist) Returns the profile of the scores for component component and motif motif at all possible start positions of the motif in the sequence sequence beginning at startpos.
boolean[]	getReverseSwitches() This method returns a deep copy of the internally used switches for the parts whether to use the corresponding NormalizableScoringFunction forward or as reverse complement.
int	getSizeOfEventSpaceForRandomVariablesOfParameter(int index) Returns the size of the event space of the random variables that are affected by parameter no.
double[]	getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos) This method returns the probabilities of the strand orientations for a given subsequence if it is considered as site of the motif model in a specific component.
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	initializeMotif(int motifIndex, Sample data, double[] weights) This method allows to initialize the model of a motif manually using a weighted sample.
void	initializeMotifRandomly(int motif) This method initializes the motif with index motif randomly using for instance ScoringFunction.initializeFunctionRandomly(boolean).
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	modifyMotif(int motifIndex, int offsetLeft, int offsetRight) Manually modifies the motif model with index motifIndex.
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

IndependentProductScoringFunction

public IndependentProductScoringFunction(double ess,
                                         boolean plugIn,
                                         NormalizableScoringFunction... functions)
                                  throws CloneNotSupportedException,
                                         IllegalArgumentException,
                                         WrongAlphabetException

This constructor creates an instance of an IndependentProductScoringFunction from a given series of independent NormalizableScoringFunctions. The length that is modeled by each component is determined by ScoringFunction.getLength(). So the length should not be 0.

Parameters:

ess - the equivalent sample size

plugIn - whether to use plugIn parameters for the parts, otherwise the last parameters are used for parts that are instance of HomogeneousScoringFunction

functions - the components, i.e. the given series of independent NormalizableScoringFunctions

Throws:

CloneNotSupportedException - if at least one element of functions could not be cloned

IllegalArgumentException - if at least one component has length 0 or if the equivalent sample size (ess) is smaller than zero (0)

WrongAlphabetException - if the user tries to use an alphabet for a reverse complement that can not be used for a reverse complement.

See Also:

IndependentProductScoringFunction(double, boolean, NormalizableScoringFunction[], int[])

IndependentProductScoringFunction

public IndependentProductScoringFunction(double ess,
                                         boolean plugIn,
                                         NormalizableScoringFunction[] functions,
                                         int[] length)
                                  throws CloneNotSupportedException,
                                         IllegalArgumentException,
                                         WrongAlphabetException

This constructor creates an instance of an IndependentProductScoringFunction from given series of independent NormalizableScoringFunctions and lengths.

Parameters:

ess - the equivalent sample size

plugIn - whether to use plugIn parameters for the parts, otherwise the last parameters are used for parts that are instance of HomogeneousScoringFunction

functions - the components, i.e. the given series of independent NormalizableScoringFunctions

length - the lengths, one for each component

Throws:

CloneNotSupportedException - if at least one component could not be cloned

IllegalArgumentException - if the lengths and the components are not matching or if the equivalent sample size (ess) is smaller than zero (0)

WrongAlphabetException - if the user tries to use an alphabet for a reverse complement that can not be used for a reverse complement.

See Also:

IndependentProductScoringFunction(double, boolean, NormalizableScoringFunction[], int[], int[], boolean[])

IndependentProductScoringFunction

public IndependentProductScoringFunction(double ess,
                                         boolean plugIn,
                                         NormalizableScoringFunction[] functions,
                                         int[] index,
                                         int[] length,
                                         boolean[] reverse)
                                  throws CloneNotSupportedException,
                                         IllegalArgumentException,
                                         WrongAlphabetException

This is the main constructor.

Parameters:

ess - the equivalent sample size

plugIn - whether to use plugIn parameters for the parts, otherwise the last parameters are used for parts that are instance of HomogeneousScoringFunction

functions - the NormalizableScoringFunction

index - the index of the NormalizableScoringFunction at each part

length - the length of each part

reverse - a switch whether to use it directly or the reverse complementary strand

Throws:

CloneNotSupportedException - if at least one component could not be cloned

IllegalArgumentException - if the lengths and the components are not matching or if the equivalent sample size (ess) is smaller than zero (0)

WrongAlphabetException - if the user tries to use an alphabet for a reverse complement that can not be used for a reverse complement.

IndependentProductScoringFunction

public IndependentProductScoringFunction(StringBuffer source)
                                  throws NonParsableException

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

Parameters:

source - the XML representation as StringBuffer

Throws:

NonParsableException - if the XML representation could not be parsed

Method Detail

clone

public IndependentProductScoringFunction clone()
                                        throws CloneNotSupportedException

Description copied from interface: ScoringFunction

Creates a clone (deep copy) of the current ScoringFunction instance.

Specified by:

clone in interface MotifDiscoverer

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

See Also:

Cloneable

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.

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

extractSequenceParts

public int extractSequenceParts(int scoringFunctionIndex,
                                Sample[] data,
                                Sample[] result)
                         throws Exception

This method extracts the corresponding Sequence parts for a specific ScoringFunction.

Parameters:

scoringFunctionIndex - the index of the ScoringFunction

data - the original data

result - an array for the resulting Samples of Sequences; has to have same length as data

Returns:

the number how often the ScoringFunction was used

Throws:

Exception - if the Sample can not be created

extractWeights

public double[][] extractWeights(int number,
                                 double[][] weights)

This method creates the weights for extractSequenceParts(int, Sample[], Sample[]).

Parameters:

number - the number how often the weights should be copied after each other.

weights - the original weights

Returns:

the new weights (might be null)

See Also:

extractSequenceParts(int, Sample[], Sample[])

fromXML

protected void fromXML(StringBuffer rep)
                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:

rep - 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

getFunctions

public NormalizableScoringFunction[] getFunctions()
                                           throws Exception

This method returns a deep copy of the internally used NormalizableScoringFunction.

Returns:

a deep copy of the internally used NormalizableScoringFunction

Throws:

Exception - if at least one NormalizableScoringFunction could not be cloned

See Also:

getIndices(), getPartialLengths(), getReverseSwitches()

getIndices

public int[] getIndices()

This method returns a deep copy of the internally used indices of the NormalizableScoringFunction for the parts.

Returns:

a deep copy of the internally used indices of the NormalizableScoringFunction for the parts

See Also:

getFunctions(), getPartialLengths(), getReverseSwitches()

getPartialLengths

public int[] getPartialLengths()

This method returns a deep copy of the internally used partial lengths of the parts.

Returns:

a deep copy of the internally used partial lengths of the parts

See Also:

getFunctions(), getIndices(), getReverseSwitches()

getReverseSwitches

public boolean[] getReverseSwitches()

This method returns a deep copy of the internally used switches for the parts whether to use the corresponding NormalizableScoringFunction forward or as reverse complement.

Returns:

a deep copy of the internally used switches for the parts whether to use the corresponding NormalizableScoringFunction forward or as reverse complement

See Also:

getFunctions(), getIndices(), getPartialLengths()

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)

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

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

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

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

toString

public String toString()

Overrides:

toString in class Object

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()

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

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

initializeMotif

public void initializeMotif(int motifIndex,
                            Sample data,
                            double[] weights)
                     throws Exception

Description copied from interface: MutableMotifDiscoverer

This method allows to initialize the model of a motif manually using a weighted sample.

Specified by:

initializeMotif in interface MutableMotifDiscoverer

Parameters:

motifIndex - the index of the motif in the motif discoverer

data - the sample of sequences

weights - either null or an array of length data.getNumberofElements() with non-negative weights.

Throws:

Exception - if initialize was not possible

initializeMotifRandomly

public void initializeMotifRandomly(int motif)
                             throws Exception

Description copied from interface: MutableMotifDiscoverer

This method initializes the motif with index motif randomly using for instance ScoringFunction.initializeFunctionRandomly(boolean). Furthermore, if available, it also initializes the positional distribution.

Specified by:

initializeMotifRandomly in interface MutableMotifDiscoverer

Parameters:

motif - the index of the motif

Throws:

Exception - either if the index is wrong or if it is thrown by the method ScoringFunction.initializeFunctionRandomly(boolean)

modifyMotif

public boolean modifyMotif(int motifIndex,
                           int offsetLeft,
                           int offsetRight)
                    throws Exception

Description copied from interface: MutableMotifDiscoverer

Manually modifies the motif model with index motifIndex. 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. The distribution for sequences to the left and right side of the motif shall be computed internally.

Specified by:

modifyMotif in interface MutableMotifDiscoverer

Parameters:

motifIndex - the index of the motif in the motif discoverer

offsetLeft - the offset on the left side

offsetRight - the offset on the right side

Returns:

true if the motif model was modified otherwise false

Throws:

Exception - if some unexpected error occurred during the modification

See Also:

MutableMotifDiscoverer.modifyMotif(int, int, int), Mutable.modify(int, int)

getGlobalIndexOfMotifInComponent

public int getGlobalIndexOfMotifInComponent(int component,
                                            int motif)

Description copied from interface: MotifDiscoverer

Returns the global index of the motif used in component. The index returned must be at least 0 and less than MotifDiscoverer.getNumberOfMotifs().

Specified by:

getGlobalIndexOfMotifInComponent in interface MotifDiscoverer

Parameters:

component - the component index

motif - the motif index in the component

Returns:

the global index of the motif in component

getIndexOfMaximalComponentFor

public int getIndexOfMaximalComponentFor(Sequence sequence)
                                  throws Exception

Description copied from interface: MotifDiscoverer

Returns the index of the component with the maximal score for the sequence sequence.

Specified by:

getIndexOfMaximalComponentFor in interface MotifDiscoverer

Parameters:

sequence - the given sequence

Returns:

the index of the component with the maximal score for the given sequence

Throws:

Exception - if the index could not be computed for any reasons

getMotifLength

public int getMotifLength(int motif)

Description copied from interface: MotifDiscoverer

This method returns the length of the motif with index motif .

Specified by:

getMotifLength in interface MotifDiscoverer

Parameters:

motif - the index of the motif

Returns:

the length of the motif with index motif

getNumberOfComponents

public int getNumberOfComponents()

Description copied from interface: MotifDiscoverer

Returns the number of components in this MotifDiscoverer.

Specified by:

getNumberOfComponents in interface MotifDiscoverer

Returns:

the number of components

getNumberOfMotifs

public int getNumberOfMotifs()

Description copied from interface: MotifDiscoverer

Returns the number of motifs for this MotifDiscoverer.

Specified by:

getNumberOfMotifs in interface MotifDiscoverer

Returns:

the number of motifs

getNumberOfMotifsInComponent

public int getNumberOfMotifsInComponent(int component)

Description copied from interface: MotifDiscoverer

Returns the number of motifs that are used in the component component of this MotifDiscoverer.

Specified by:

getNumberOfMotifsInComponent in interface MotifDiscoverer

Parameters:

component - the component of the MotifDiscoverer

Returns:

the number of motifs

getProfileOfScoresFor

public double[] getProfileOfScoresFor(int component,
                                      int motif,
                                      Sequence sequence,
                                      int startpos,
                                      MotifDiscoverer.KindOfProfile dist)
                               throws Exception

Description copied from interface: MotifDiscoverer

Returns the profile of the scores for component component and motif motif at all possible start positions of the motif in the sequence sequence beginning at startpos. This array should be of length
sequence.length() - startpos - motifs[motif].length() + 1.
A high score should encode for a probable start position.

Specified by:

getProfileOfScoresFor in interface MotifDiscoverer

Parameters:

component - the component index

motif - the index of the motif in the component

sequence - the given sequence

startpos - the start position in the sequence

dist - indicates the kind of profile

Returns:

the profile of scores

Throws:

Exception - if the score could not be computed for any reasons

getStrandProbabilitiesFor

public double[] getStrandProbabilitiesFor(int component,
                                          int motif,
                                          Sequence sequence,
                                          int startpos)
                                   throws Exception

Description copied from interface: MotifDiscoverer

This method returns the probabilities of the strand orientations for a given subsequence if it is considered as site of the motif model in a specific component.

Specified by:

getStrandProbabilitiesFor in interface MotifDiscoverer

Parameters:

component - the component index

motif - the index of the motif in the component

sequence - the given sequence

startpos - the start position in the sequence

Returns:

the probabilities of the strand orientations

Throws:

Exception - if the strand could not be computed for any reasons

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

adjustHiddenParameters

public void adjustHiddenParameters(int index,
                                   Sample[] data,
                                   double[][] weights)
                            throws Exception

Description copied from interface: MutableMotifDiscoverer

Adjusts all hidden parameters including duration and mixture parameters according to the current values of the remaining parameters.

Specified by:

adjustHiddenParameters in interface MutableMotifDiscoverer

Parameters:

index - the index of the class of this MutableMotifDiscoverer

data - the array of data for all classes

weights - the weights for all sequences in data

Throws:

Exception - thrown if the hidden parameters could not be adjusted