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de.jstacs.sequenceScores.statisticalModels.differentiable
Class MarkovRandomFieldDiffSM

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

This class implements the scoring function for any MRF (Markov Random Field).

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
MarkovRandomFieldDiffSM(AlphabetContainer alphabets, int length, double ess, String constr)
          This is the main constructor that creates an instance of a MarkovRandomFieldDiffSM.
MarkovRandomFieldDiffSM(AlphabetContainer alphabets, int length, String constr)
          This constructor creates an instance of a MarkovRandomFieldDiffSM with equivalent sample size (ess) 0.
MarkovRandomFieldDiffSM(StringBuffer source)
          This is the constructor for the interface Storable.
 
Method Summary
 void addGradientOfLogPriorTerm(double[] grad, int start)
          This method computes the gradient of DifferentiableStatisticalModel.getLogPriorTerm() for each parameter of this model.
 MarkovRandomFieldDiffSM clone()
          Creates a clone (deep copy) of the current DifferentiableSequenceScore instance.
protected  void fromXML(StringBuffer representation)
          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.
 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 getNumberOfParameters()
          Returns the number of parameters in this DifferentiableSequenceScore.
 int getSizeOfEventSpaceForRandomVariablesOfParameter(int index)
          Returns the size of the event space of the random variables that are affected by parameter no.
 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.
 boolean isInitialized()
          This method can be used to determine whether the instance is initialized.
 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

MarkovRandomFieldDiffSM

public MarkovRandomFieldDiffSM(AlphabetContainer alphabets,
                               int length,
                               String constr)
This constructor creates an instance of a MarkovRandomFieldDiffSM with equivalent sample size (ess) 0.

Parameters:
alphabets - the AlphabetContainer
length - the length of the sequences and accordingly the model
constr - the constraints that are used for the model, see ConstraintManager.extract(int, String)
See Also:
MarkovRandomFieldDiffSM(AlphabetContainer, int, double, String)

MarkovRandomFieldDiffSM

public MarkovRandomFieldDiffSM(AlphabetContainer alphabets,
                               int length,
                               double ess,
                               String constr)
This is the main constructor that creates an instance of a MarkovRandomFieldDiffSM.

Parameters:
alphabets - the AlphabetContainer
length - the length of the sequences and accordingly the model
ess - the equivalent sample size (ess)
constr - the constraints that are used for the model, see ConstraintManager.extract(int, String)

MarkovRandomFieldDiffSM

public MarkovRandomFieldDiffSM(StringBuffer source)
                        throws NonParsableException
This is the constructor for the interface Storable. Creates a new MarkovRandomFieldDiffSM out of a StringBuffer as returned by toXML().

Parameters:
source - the XML representation as StringBuffer
Throws:
NonParsableException - if the XML representation could not be parsed
Method Detail

fromXML

protected void fromXML(StringBuffer representation)
                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:
representation - the XML representation as StringBuffer
Throws:
NonParsableException - if the StringBuffer could not be parsed
See Also:
AbstractDifferentiableSequenceScore.AbstractDifferentiableSequenceScore(StringBuffer)

clone

public MarkovRandomFieldDiffSM 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

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.

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.

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.

Returns:
the number of parameters in this DifferentiableSequenceScore
See Also:
DifferentiableSequenceScore.UNKNOWN

getInstanceName

public String getInstanceName()
Description copied from interface: SequenceScore
Should return a short instance name such as iMM(0), BN(2), ...

Returns:
a short instance name

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

Parameters:
params - the new parameters
start - the start index in params

toString

public String toString()
Overrides:
toString in class Object

toXML

public StringBuffer toXML()
Description copied from interface: Storable
This method returns an XML representation as StringBuffer of an instance of the implementing class.

Returns:
the XML representation

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.

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.

Parameters:
freeParams - indicates whether the (reduced) parameterization is used
Throws:
Exception - if something went wrong

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.

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,
\[\log \frac{\partial Z(\underline{\lambda})}{\partial \lambda_{parameterindex}}\]
.

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.

Returns:
the equivalent sample size.

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, ...

Parameters:
index - the index of the parameter
Returns:
the size of the event space

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.

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)
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.

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
See Also:
DifferentiableStatisticalModel.getLogPriorTerm()

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.

Returns:
the current parameter values
Throws:
Exception - if no parameters exist (yet)

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).

Returns:
true if the instance is initialized, false otherwise
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