MappingDiffSM

Overview

Package

Class

Use

Tree

Deprecated

Index

Help

PREV CLASS NEXT CLASS

FRAMES NO FRAMES

SUMMARY: NESTED | FIELD | CONSTR | METHOD

DETAIL: FIELD | CONSTR | METHOD

de.jstacs.sequenceScores.statisticalModels.differentiable
Class MappingDiffSM

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

All Implemented Interfaces:: MotifDiscoverer, Mutable, MutableMotifDiscoverer, DifferentiableSequenceScore, SequenceScore, DifferentiableStatisticalModel, StatisticalModel, Storable, Cloneable

public final class MappingDiffSM
extends AbstractDifferentiableStatisticalModel
implements MutableMotifDiscoverer, Mutable
extends AbstractDifferentiableStatisticalModel
implements MutableMotifDiscoverer, Mutable

This class implements a DifferentiableStatisticalModel that works on mapped Sequences. For instance this can be useful for protein sequences to reduce the alphabet of size 20 to a smaller alphabet using for instance some chemical properties of the amino acids. Be careful with references to Sequences in the internal DifferentiableStatisticalModel, since the Sequences might be unexpectedly mutable.

Author:: Jens Keilwagen
See Also:: MappedDiscreteSequence, DiscreteAlphabetMapping

Nested Class Summary

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

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
`MappingDiffSM(AlphabetContainer originalAlphabetContainer, DifferentiableStatisticalModel nsf, DiscreteAlphabetMapping... mapping)` The main constructor creating a `MappingDiffSM`.
`MappingDiffSM(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.
`void`	`adjustHiddenParameters(int index, DataSet[] data, double[][] weights)` Adjusts all hidden parameters including duration and mixture parameters according to the current values of the remaining parameters.
`MappingDiffSM`	`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.
`DifferentiableStatisticalModel`	`getFunction()` This method return the internal function.
`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`.
`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
`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`.
`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 `DifferentiableSequenceScore`.
`double[]`	`getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile kind)` 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`.
`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, DataSet[] data, double[][] weights)` This method creates the underlying structure of the `DifferentiableSequenceScore`.
`void`	`initializeFunctionRandomly(boolean freeParams)` This method initializes the `DifferentiableSequenceScore` randomly.
`void`	`initializeMotif(int motifIndex, DataSet data, double[] weights)` This method allows to initialize the model of a motif manually using a weighted data set.
`void`	`initializeMotifRandomly(int motif)` This method initializes the motif with index `motif` randomly using for instance `DifferentiableSequenceScore.initializeFunctionRandomly(boolean)`.
`boolean`	`isInitialized()` This method can be used to determine whether the instance is initialized.
`boolean`	`modify(int offsetLeft, int offsetRight)` Manually modifies the model.
`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 + DifferentiableSequenceScore.getNumberOfParameters() - 1`
`String`	`toString(NumberFormat nf)` This method returns a `String` representation of the instance.
`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, isNormalized`

Methods inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
`getAlphabetContainer, getCharacteristics, getLength, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreFor, getLogScoreFor, getNumberOfRecommendedStarts, 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
`getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getNumberOfRecommendedStarts`

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore
`getAlphabetContainer, getCharacteristics, getLength, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics`

Constructor Detail

MappingDiffSM

public MappingDiffSM(AlphabetContainer originalAlphabetContainer,
                     DifferentiableStatisticalModel nsf,
                     DiscreteAlphabetMapping... mapping)
              throws WrongAlphabetException,
                     CloneNotSupportedException

The main constructor creating a MappingDiffSM.

Parameters:: originalAlphabetContainer - the original AlphabetContainer; nsf - the internally used DifferentiableStatisticalModel; mapping - the DiscreteAlphabetMappings defining the transformation from the original AlphabetContainer to the AlphabetContainer of the DifferentiableStatisticalModel nsf
Throws:: WrongAlphabetException - if there is a problem with the mapping of the Alphabets; CloneNotSupportedException - if the DifferentiableStatisticalModel could not be cloned

MappingDiffSM

public MappingDiffSM(StringBuffer xml)
              throws NonParsableException

This is the constructor for Storable. Creates a new MappingDiffSM out of a StringBuffer.

Parameters:: xml - the XML representation as StringBuffer
Throws:: NonParsableException - if the XML representation could not be parsed

Method Detail

clone

public MappingDiffSM clone()
                    throws CloneNotSupportedException

Description copied from interface: DifferentiableSequenceScore

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

Specified by:: clone in interface MotifDiscoverer
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
See Also:: Cloneable

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

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)

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()

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.

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,

$\log \frac{\partial Z(\underline{\lambda})}{\partial \lambda_{parameterindex}}$

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()

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()

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

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)

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 $\frac{\partial \log score(seq)}{\partial \lambda_i}$ is not zero; partialDer - a DoubleList of partial derivations, after method invocation the list should contain the corresponding $\frac{\partial \log score(seq)}{\partial \lambda_i}$ 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

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 data sets; weights - the weights of the sequences in the data sets
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

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

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

getFunction

public DifferentiableStatisticalModel getFunction()
                                           throws CloneNotSupportedException

This method return the internal function.

Returns:: the DifferentiableStatisticalModel that is internally used
Throws:: CloneNotSupportedException - if the DifferentiableStatisticalModel could not be cloned

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

adjustHiddenParameters

public void adjustHiddenParameters(int index,
                                   DataSet[] 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

initializeMotif

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

Description copied from interface: MutableMotifDiscoverer

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

Specified by:: initializeMotif in interface MutableMotifDiscoverer

Parameters:: motifIndex - the index of the motif in the motif discoverer; data - the data set 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 DifferentiableSequenceScore.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 DifferentiableSequenceScore.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





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 kind)
                               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].getLength() + 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
kind - 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





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














  
      Overview 
      Package 
    Class 
      Use 
      Tree 
      Deprecated 
      Index 
      Help 
  









 PREV CLASS 
 NEXT CLASS

  FRAMES   
 NO FRAMES   
 







  SUMMARY: NESTED | FIELD | CONSTR | METHOD

DETAIL: FIELD | CONSTR | METHOD

de.jstacs.sequenceScores.statisticalModels.differentiable Class MappingDiffSM

MappingDiffSM

MappingDiffSM

clone

toXML

fromXML

addGradientOfLogPriorTerm

getESS

getLogNormalizationConstant

getLogPartialNormalizationConstant

getLogPriorTerm

getSizeOfEventSpaceForRandomVariablesOfParameter

getCurrentParameterValues

getInstanceName

getLogScoreFor

getLogScoreAndPartialDerivation

getNumberOfParameters

initializeFunction

initializeFunctionRandomly

isInitialized

setParameters

toString

getFunction

getNumberOfMotifs

adjustHiddenParameters

initializeMotif

initializeMotifRandomly

modifyMotif

getGlobalIndexOfMotifInComponent

getIndexOfMaximalComponentFor

getMotifLength

getNumberOfComponents

getNumberOfMotifsInComponent

getProfileOfScoresFor

getStrandProbabilitiesFor

modify

de.jstacs.sequenceScores.statisticalModels.differentiable
Class MappingDiffSM