de.jstacs.scoringFunctions
Class IndependentProductScoringFunction

java.lang.Object
  extended by de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
      extended by de.jstacs.scoringFunctions.IndependentProductScoringFunction
All Implemented Interfaces:
NormalizableScoringFunction, ScoringFunction, Storable, Cloneable

public class IndependentProductScoringFunction
extends AbstractNormalizableScoringFunction

This class enables the user to model parts of the sequence independent of each other. The first part of the sequence is modeled by the first NormalizableScoringFunction and has the length of the first NormalizableScoringFunction, the second part starts directly after the first part, is modeled by the second ... .

Author:
Jens Keilwagen

Field Summary
 
Fields inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
alphabets, length, r
 
Fields inherited from interface de.jstacs.scoringFunctions.ScoringFunction
UNKNOWN
 
Constructor Summary
IndependentProductScoringFunction(NormalizableScoringFunction... functions)
          This constructor creates an instance of a given series of independent NormalizableScoringFunctions.
IndependentProductScoringFunction(NormalizableScoringFunction[] functions, int[] length)
          This constructor creates an instance of a given series of independent NormalizableScoringFunctions and lengths.
IndependentProductScoringFunction(StringBuffer source)
          This is the constructor for Storable.
 
Method Summary
 void addGradientOfLogPriorTerm(double[] grad, int start)
          This method computes the gradient of getLogPriorTerm() for each parameter of this model.
 IndependentProductScoringFunction clone()
          Creates a clone (deep copy) of the current ScoringFunction instance.
protected  void fromXML(StringBuffer rep)
          This method is called in the constructor to create a scoring function from a StringBuffer
 double[] getCurrentParameterValues()
          Returns a double array of dimension getNumberOfParameters() containing the current parameter values.
 double getEss()
          Returns the equivalent sample size of this model, i.e. the equivalent sample size for the class or component that is represented by this model.
 String getInstanceName()
          Returns a short instance name.
 double getLogPriorTerm()
          This method computes a value that is proportional to getESS()*Math.log( getNormalizationConstant() ) + Math.log( prior ).
 double getLogScore(Sequence seq, int start)
          Returns the log score for the sequence
 double getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)
          Returns the log score for the sequence and fills the list with the indices and the partial derivations.
 double getNormalizationConstant()
          Returns the sum of the scores over all sequences of the event space.
 int getNumberOfParameters()
          The number of parameters in this scoring function.
 int getNumberOfRecommendedStarts()
          This method return the number of recommended optimization starts.
 double getPartialNormalizationConstant(int parameterIndex)
          Returns the partial normalization constant for the parameter with index parameterIndex.
 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, Sample[] data, double[][] weights)
          This method creates the underlying structure of the scoring function.
 void initializeFunctionRandomly(boolean freeParams)
          This method initializes the scoring function randomly.
 boolean isInitialized()
          This method can be used to determine whether the model is initialized.
 void setParameters(double[] params, int start)
          This method sets the internal parameters to the values of params between start and start + this.getNumberOfParameters() - 1
 String toString()
           
 StringBuffer toXML()
          This method returns an XML-representation of an instance of the implementing class.
 
Methods inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
getAlphabetContainer, getInitialClassParam, getLength, getLogScore, getLogScoreAndPartialDerivation, isNormalized, isNormalized
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Constructor Detail

IndependentProductScoringFunction

public IndependentProductScoringFunction(NormalizableScoringFunction... functions)
                                  throws CloneNotSupportedException,
                                         IllegalArgumentException
This constructor creates an instance of a given series of independent NormalizableScoringFunctions. The length that is modeled by each component is determined by ScoringFunction.getLength(). So the length should not be 0.

Parameters:
functions - the components
Throws:
CloneNotSupportedException - if at least one component could not be cloned
IllegalArgumentException - if at least one component has length 0 or the components do not have the same ess
See Also:
IndependentProductScoringFunction(NormalizableScoringFunction[], int[])

IndependentProductScoringFunction

public IndependentProductScoringFunction(NormalizableScoringFunction[] functions,
                                         int[] length)
                                  throws CloneNotSupportedException
This constructor creates an instance of a given series of independent NormalizableScoringFunctions and lengths.

Parameters:
functions - the components
length - the length of each component
Throws:
CloneNotSupportedException - if at least one component could not be cloned
IllegalArgumentException - if the lengths and the components are not matching or the components do not have the same ess
See Also:
IndependentProductScoringFunction(NormalizableScoringFunction[], int[])

IndependentProductScoringFunction

public IndependentProductScoringFunction(StringBuffer source)
                                  throws NonParsableException
This is the constructor for Storable.

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

clone

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

Specified by:
clone in interface ScoringFunction
Overrides:
clone in class AbstractNormalizableScoringFunction
Returns:
the cloned instance
Throws:
CloneNotSupportedException

getSizeOfEventSpaceForRandomVariablesOfParameter

public int getSizeOfEventSpaceForRandomVariablesOfParameter(int index)
Description copied from interface: NormalizableScoringFunction
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

getNormalizationConstant

public double getNormalizationConstant()
Description copied from interface: NormalizableScoringFunction
Returns the sum of the scores over all sequences of the event space.

Returns:
the normalization constant Z

getPartialNormalizationConstant

public double getPartialNormalizationConstant(int parameterIndex)
                                       throws Exception
Description copied from interface: NormalizableScoringFunction
Returns the partial normalization constant for the parameter with index parameterIndex. This is the partial derivation of the normalization constant for the parameter with index parameterIndex \frac{\partial Z(\lambda)}{\partial \lambda_{index}}.

Parameters:
parameterIndex - the index of the parameter
Returns:
the partial normalization constant
Throws:
Exception - if something went wrong with the Normalization

getEss

public double getEss()
Description copied from interface: NormalizableScoringFunction
Returns the equivalent sample size 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.

initializeFunction

public void initializeFunction(int index,
                               boolean freeParams,
                               Sample[] data,
                               double[][] weights)
                        throws Exception
Description copied from interface: ScoringFunction
This method creates the underlying structure of the scoring function.

Parameters:
index - the index of the class the scoring function models
freeParams - if true, the (reduced) parameterization is used
data - the samples
weights - the weights of the sequences in the samples
Throws:
Exception

fromXML

protected void fromXML(StringBuffer rep)
                throws NonParsableException
Description copied from class: AbstractNormalizableScoringFunction
This method is called in the constructor to create a scoring function from a StringBuffer

Specified by:
fromXML in class AbstractNormalizableScoringFunction
Parameters:
rep - the XML representation
Throws:
NonParsableException - if the StringBuffer could not be parsed.

getInstanceName

public String getInstanceName()
Description copied from interface: ScoringFunction
Returns a short instance name.

Returns:
a short instance name

getCurrentParameterValues

public double[] getCurrentParameterValues()
                                   throws Exception
Description copied from interface: ScoringFunction
Returns a double array of dimension getNumberOfParameters() containing the current parameter values. If on e likes to use these parameters to start an optimization it is highly recommended to invoke ScoringFunction.initializeFunction(int, boolean, Sample[], double[][]) before. After an optimization this method can be used to get the current parameter values.

Returns:
the current parameter values
Throws:
Exception - is thrown if no parameters exist, yet

getLogScore

public double getLogScore(Sequence seq,
                          int start)
Description copied from interface: ScoringFunction
Returns the log score for the sequence

Parameters:
seq - the sequence
start - the startposition in the sequence
Returns:
the log score for the sequence

getLogScoreAndPartialDerivation

public double getLogScoreAndPartialDerivation(Sequence seq,
                                              int start,
                                              IntList indices,
                                              DoubleList partialDer)
Description copied from interface: ScoringFunction
Returns the log score for the sequence and fills the list with the indices and the partial derivations.

Parameters:
seq - the sequence
start - the startposition in the sequence
indices - after method invocation the list should contain the indices i where \frac{\partial \log score(seq)}{\partial \lambda_i} is not zero
partialDer - after method invocation the list should contain the corresponding \frac{\partial \log score(seq)}{\partial \lambda_i}
Returns:
the log score

getNumberOfParameters

public int getNumberOfParameters()
Description copied from interface: ScoringFunction
The number of parameters in this scoring function. If the number of parameters is not known yet, the method returns UNKNOWN.

Returns:
the number of parameters in this scoring function
See Also:
ScoringFunction.UNKNOWN

getNumberOfRecommendedStarts

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

Specified by:
getNumberOfRecommendedStarts in interface ScoringFunction
Overrides:
getNumberOfRecommendedStarts in class AbstractNormalizableScoringFunction
Returns:
the number of recommended optimization starts

setParameters

public void setParameters(double[] params,
                          int start)
Description copied from interface: ScoringFunction
This method sets the internal parameters to the values of params between start and start + this.getNumberOfParameters() - 1

Parameters:
params - the parameters
start - the start index

toXML

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

Returns:
the XML-representation

toString

public String toString()
Overrides:
toString in class Object

getLogPriorTerm

public double getLogPriorTerm()
Description copied from interface: NormalizableScoringFunction
This method computes a value that is proportional to

getESS()*Math.log( getNormalizationConstant() ) + Math.log( prior ).

where prior is the prior for the parameters of this model.

Returns:
getESS()*Math.log( getNormalizationConstant() ) + Math.log( prior )
See Also:
NormalizableScoringFunction.getEss(), NormalizableScoringFunction.getNormalizationConstant()

addGradientOfLogPriorTerm

public void addGradientOfLogPriorTerm(double[] grad,
                                      int start)
                               throws Exception
Description copied from interface: NormalizableScoringFunction
This method computes the gradient of getLogPriorTerm() for each parameter of this model. The results are added to the array grad beginning at index start.

Parameters:
grad - the gradient
start - the start index in the grad array, where the partial derivations for the parameters of this models shall be enter
Throws:
Exception
See Also:
NormalizableScoringFunction.getLogPriorTerm()

isInitialized

public boolean isInitialized()
Description copied from interface: ScoringFunction
This method can be used to determine whether the model is initialized. If the model is not initialize you should invoke the method ScoringFunction.initializeFunction(int, boolean, Sample[], double[][]).

Returns:
true if the model is initialized

initializeFunctionRandomly

public void initializeFunctionRandomly(boolean freeParams)
                                throws Exception
Description copied from interface: ScoringFunction
This method initializes the scoring function randomly. It has to create the underlying structure of the scoring function.

Parameters:
freeParams - if true, the (reduced) parameterization is used
Throws:
Exception