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de.jstacs.sequenceScores.statisticalModels.differentiable
Class NormalizedDiffSM

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.NormalizedDiffSM
All Implemented Interfaces:
Mutable, DifferentiableSequenceScore, SequenceScore, DifferentiableStatisticalModel, StatisticalModel, Storable, Cloneable
public final class NormalizedDiffSM
extends AbstractDifferentiableStatisticalModel
implements Mutable

This class makes an unnormalized DifferentiableStatisticalModel to a normalized DifferentiableStatisticalModel. However, the class allows to use only DifferentiableStatisticalModel that do not implement VariableLengthDiffSM. This class should be used only in cases when it is not possible to avoid its usage.

Author:
Jens Keilwagen

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
NormalizedDiffSM(DifferentiableStatisticalModel nsf, int starts)
          Creates a new instance using a given DifferentiableStatisticalModel.
NormalizedDiffSM(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.
 NormalizedDiffSM 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 returns the internal function.
 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.
static DifferentiableStatisticalModel getNormalizedVersion(DifferentiableStatisticalModel nsf, int starts)
          This method returns a normalized version of a DifferentiableStatisticalModel.
 int getNumberOfParameters()
          Returns the number of parameters in this DifferentiableSequenceScore.
 int getNumberOfRecommendedStarts()
          This method returns the number of recommended optimization starts.
 int getSizeOfEventSpaceForRandomVariablesOfParameter(int index)
          Returns the size of the event space of the random variables that are affected by parameter no.
 StrandedLocatedSequenceAnnotationWithLength.Strand getStrand(Sequence seq, int startPos)
          This method return the preferred StrandedLocatedSequenceAnnotationWithLength.Strand for a Sequence beginning at startPos.
 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 initializeHiddenUniformly()
          This method initializes the hidden parameters of the internal DifferentiableStatisticalModel uniformly if it is a AbstractMixtureDiffSM.
 boolean isInitialized()
          This method can be used to determine whether the instance is initialized.
 boolean isNormalized()
          This method indicates whether the implemented score is already normalized to 1 or not.
 boolean isStrandModel()
          This method returns true if the internal DifferentiableStatisticalModel is a StrandDiffSM otherwise false.
 boolean modify(int offsetLeft, int offsetRight)
          Manually modifies the model.
 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
 
Methods inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
getAlphabetContainer, getCharacteristics, getLength, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreFor, getLogScoreFor, getNumberOfStarts, getNumericalCharacteristics
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 
Methods inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation
 
Methods inherited from interface de.jstacs.sequenceScores.SequenceScore
getAlphabetContainer, getCharacteristics, getLength, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics
 

Constructor Detail

NormalizedDiffSM

public NormalizedDiffSM(DifferentiableStatisticalModel nsf,
                        int starts)
                 throws Exception
Creates a new instance using a given DifferentiableStatisticalModel.

Parameters:
nsf - the function to be used internal
starts - the number of recommended starts (DifferentiableSequenceScore.getNumberOfRecommendedStarts())
Throws:
Exception - is nsf could not be cloned or some error occurred during computation of some values

NormalizedDiffSM

public NormalizedDiffSM(StringBuffer xml)
                 throws NonParsableException
This is the constructor for Storable.

Parameters:
xml - the xml representation
Throws:
NonParsableException - if the representation could not be parsed.
Method Detail

getNormalizedVersion

public static final DifferentiableStatisticalModel getNormalizedVersion(DifferentiableStatisticalModel nsf,
                                                                        int starts)
                                                                 throws Exception
This method returns a normalized version of a DifferentiableStatisticalModel. Either it returns a clone of nsf or an instance of NormalizedDiffSM using nsf and starts.

Parameters:
nsf - the DifferentiableStatisticalModel to be normalized
starts - the number of recommended starts for a NormalizedDiffSM
Returns:
a normalized scoring function
Throws:
Exception - if nsf could not be cloned

clone

public NormalizedDiffSM clone()
                       throws CloneNotSupportedException
Description copied from interface: DifferentiableSequenceScore
Creates a clone (deep copy) of the current DifferentiableSequenceScore instance.

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

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

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()

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.

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()

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()

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

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)

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

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)

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

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

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

getNumberOfRecommendedStarts

public int getNumberOfRecommendedStarts()
Description copied from interface: DifferentiableSequenceScore
This method returns the number of recommended optimization starts. The standard implementation returns 1.

Specified by:
getNumberOfRecommendedStarts in interface DifferentiableSequenceScore
Overrides:
getNumberOfRecommendedStarts in class AbstractDifferentiableSequenceScore
Returns:
the number of recommended optimization starts

isNormalized

public boolean isNormalized()
Description copied from interface: DifferentiableStatisticalModel
This method indicates whether the implemented score is already normalized to 1 or not. The standard implementation returns false.

Specified by:
isNormalized in interface DifferentiableStatisticalModel
Overrides:
isNormalized in class AbstractDifferentiableStatisticalModel
Returns:
true if the implemented score is already normalized to 1, false otherwise

toString

public String toString()
Overrides:
toString in class Object

getFunction

public DifferentiableStatisticalModel getFunction()
                                           throws CloneNotSupportedException
This method returns the internal function.

Returns:
the internal function
Throws:
CloneNotSupportedException - if the internal function could not be cloned

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

isStrandModel

public boolean isStrandModel()
This method returns true if the internal DifferentiableStatisticalModel is a StrandDiffSM otherwise false.

Returns:
true if the internal DifferentiableStatisticalModel is a StrandDiffSM otherwise false

getStrand

public StrandedLocatedSequenceAnnotationWithLength.Strand getStrand(Sequence seq,
                                                                    int startPos)
This method return the preferred StrandedLocatedSequenceAnnotationWithLength.Strand for a Sequence beginning at startPos.

Parameters:
seq - the sequence
startPos - the start position
Returns:
the preferred StrandedLocatedSequenceAnnotationWithLength.Strand

initializeHiddenUniformly

public void initializeHiddenUniformly()
This method initializes the hidden parameters of the internal DifferentiableStatisticalModel uniformly if it is a AbstractMixtureDiffSM.

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