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
```
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

Constructors
Constructor and Description
`MappingDiffSM(AlphabetContainer originalAlphabetContainer, DifferentiableStatisticalModel nsf, DiscreteAlphabetMapping... mapping)` The main constructor creating a `MappingDiffSM`.
`MappingDiffSM(StringBuffer xml)` This is the constructor for `Storable`.

Method Summary

Methods
Modifier and Type	Method and Description
`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, toString

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

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

Class MappingDiffSM

Nested Class Summary

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

Field Summary

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

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

Constructor Summary

Method Summary

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel

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

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

Constructor Detail

MappingDiffSM

MappingDiffSM

Method Detail

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