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de.jstacs.sequenceScores.statisticalModels.differentiable
Class MappingDiffSM

java.lang.Object
  extended by de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
      extended by de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
          extended by 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
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. the equivalent sample size for the class or component that is represented by this model.
 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 DifferentiableStatisticalModel.getESS() * DifferentiableStatisticalModel.getLogNormalizationConstant() + Math.log( prior ) where prior is the prior for the parameters of this model.
 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 sample.
 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()
           
 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, 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 samples
weights - the weights of the sequences in the samples
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()
Overrides:
toString in class Object

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 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 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].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
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
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