de.jstacs.scoringFunctions
Class NormalizedScoringFunction

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

public final class NormalizedScoringFunction
extends AbstractNormalizableScoringFunction
implements Mutable

This class makes an unnormalized ScoringFunction to a normalized ScoringFunction. 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.scoringFunctions.AbstractNormalizableScoringFunction
alphabets, length, r
 
Fields inherited from interface de.jstacs.scoringFunctions.ScoringFunction
UNKNOWN
 
Constructor Summary
NormalizedScoringFunction(NormalizableScoringFunction nsf, int starts)
          Creates a new instance using a given NormalizableScoringFunction.
NormalizedScoringFunction(StringBuffer xml)
          This is the constructor for Storable.
 
Method Summary
 void addGradientOfLogPriorTerm(double[] grad, int start)
          This method computes the gradient of NormalizableScoringFunction.getLogPriorTerm() for each parameter of this model.
 NormalizedScoringFunction clone()
          Creates a clone (deep copy) of the current ScoringFunction instance.
 int[] determineNotSignificantPositions(double samples, double[] weightsLeft, double[] weightsRight, double[][][][] contrastLeft, double[][][][] contrastRight, double sign)
          This method determines the number of not significant positions from each side of the motif using the the significance level sign and the contrast distributions of the left or right side, contrastLeft and contrastRight, respectively.
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 ScoringFunction.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.
 NormalizableScoringFunction getFunction()
          This method returns the internal function.
 String getInstanceName()
          Returns a short instance name.
 double getLogPriorTerm()
          This method computes a value that is proportional to NormalizableScoringFunction.getEss() * Math.log( NormalizableScoringFunction.getNormalizationConstant() ) + Math.log( prior ).
 double getLogScore(Sequence seq, int start)
          Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.
 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 getNormalizationConstant()
          Returns the sum of the scores over all sequences of the event space.
static NormalizableScoringFunction getNormalizedVersion(NormalizableScoringFunction nsf, int starts)
          This method returns a normalized version of a NormalizableScoringFunction.
 int getNumberOfParameters()
          Returns the number of parameters in this ScoringFunction.
 int getNumberOfRecommendedStarts()
          This method returns 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.
 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, Sample[] data, double[][] weights)
          This method creates the underlying structure of the ScoringFunction.
 void initializeFunctionRandomly(boolean freeParams)
          This method initializes the ScoringFunction randomly.
 void initializeHiddenUniformly()
          This method initializes the hidden parameters of the internal NormalizableScoringFunction uniformly if it is a AbstractMixtureScoringFunction.
 boolean isInitialized()
          This method can be used to determine whether the model is initialized.
 boolean isNormalized()
          This method indicates whether the implemented score is already normalized to 1 or not.
 boolean isStrandScoringFunction()
          This method returns true if the internal NormalizableScoringFunction is a StrandScoringFunction otherwise false.
 boolean modify(double[] weightsLeft, double[] weightsRight, double[][][][] replacementLeft, double[][][][] replacementRight, 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 + ScoringFunction.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.scoringFunctions.AbstractNormalizableScoringFunction
getAlphabetContainer, getInitialClassParam, getLength, getLogScore, getLogScoreAndPartialDerivation, isNormalized
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Constructor Detail

NormalizedScoringFunction

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

Parameters:
nsf - the function to be used internal
starts - the number of recommended starts (ScoringFunction.getNumberOfRecommendedStarts())
Throws:
Exception

NormalizedScoringFunction

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

Parameters:
nsf - the NormalizableScoringFunction to be normalized
starts - the number of recommended starts for a NormalizedScoringFunction
Returns:
a normalized scoring function
Throws:
Exception

clone

public NormalizedScoringFunction 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 of the current ScoringFunction
Throws:
CloneNotSupportedException - if something went wrong while cloning the ScoringFunction

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, ...

Specified by:
getSizeOfEventSpaceForRandomVariablesOfParameter in interface NormalizableScoringFunction
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.

Specified by:
getNormalizationConstant in interface NormalizableScoringFunction
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, in LaTex notation: \frac{\partial Z(\lambda)}{\partial \lambda_{index}}.

Specified by:
getPartialNormalizationConstant in interface NormalizableScoringFunction
Parameters:
parameterIndex - the index of the parameter
Returns:
the partial normalization constant
Throws:
Exception - if something went wrong with the normalization
See Also:
NormalizableScoringFunction.getNormalizationConstant()

getEss

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

getLogPriorTerm

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

NormalizableScoringFunction.getEss() * Math.log( NormalizableScoringFunction.getNormalizationConstant() ) + Math.log( prior ).

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

Specified by:
getLogPriorTerm in interface NormalizableScoringFunction
Returns:
a value that is proportional to NormalizableScoringFunction.getEss() * Math.log( NormalizableScoringFunction.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 NormalizableScoringFunction.getLogPriorTerm() for each parameter of this model. The results are added to the array grad beginning at index start.

Specified by:
addGradientOfLogPriorTerm in interface NormalizableScoringFunction
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:
NormalizableScoringFunction.getLogPriorTerm()

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 ScoringFunction.

Specified by:
initializeFunction in interface ScoringFunction
Parameters:
index - the index of the class the ScoringFunction 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: ScoringFunction
This method initializes the ScoringFunction randomly. It has to create the underlying structure of the ScoringFunction.

Specified by:
initializeFunctionRandomly in interface ScoringFunction
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: AbstractNormalizableScoringFunction
This method is called in the constructor for the Storable interface to create a scoring function from a StringBuffer.

Specified by:
fromXML in class AbstractNormalizableScoringFunction
Parameters:
xml - the XML representation as StringBuffer
Throws:
NonParsableException - if the StringBuffer could not be parsed
See Also:
AbstractNormalizableScoringFunction.AbstractNormalizableScoringFunction(StringBuffer)

getInstanceName

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

Specified by:
getInstanceName in interface ScoringFunction
Returns:
a short instance name

getLogScore

public double getLogScore(Sequence seq,
                          int start)
Description copied from interface: ScoringFunction
Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.

Specified by:
getLogScore in interface ScoringFunction
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: ScoringFunction
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 ScoringFunction
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}
Returns:
the logarithmic score for the Sequence

getNumberOfParameters

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

Specified by:
getNumberOfParameters in interface ScoringFunction
Returns:
the number of parameters in this ScoringFunction
See Also:
ScoringFunction.UNKNOWN

getCurrentParameterValues

public double[] getCurrentParameterValues()
                                   throws Exception
Description copied from interface: ScoringFunction
Returns a double array of dimension ScoringFunction.getNumberOfParameters() containing the current parameter values. If one 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.

Specified by:
getCurrentParameterValues in interface ScoringFunction
Returns:
the current parameter values
Throws:
Exception - if no parameters exist (yet)

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 + ScoringFunction.getNumberOfParameters() - 1

Specified by:
setParameters in interface ScoringFunction
Parameters:
params - the new parameters
start - the start index in params

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 initialized you should invoke the method ScoringFunction.initializeFunction(int, boolean, Sample[], double[][]).

Specified by:
isInitialized in interface ScoringFunction
Returns:
true if the model 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: ScoringFunction
This method returns 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

isNormalized

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

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

toString

public String toString()
Overrides:
toString in class Object

getFunction

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

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

determineNotSignificantPositions

public int[] determineNotSignificantPositions(double samples,
                                              double[] weightsLeft,
                                              double[] weightsRight,
                                              double[][][][] contrastLeft,
                                              double[][][][] contrastRight,
                                              double sign)
Description copied from interface: Mutable
This method determines the number of not significant positions from each side of the motif using the the significance level sign and the contrast distributions of the left or right side, contrastLeft and contrastRight, respectively. The contrast array have four dimensions.
  1. The first is used for the possibility of having different contrast (caused by different flanking models).
  2. The second is used for the order of the contrast.
  3. The third is used for the (encoded) context.
  4. The fourth is used for the the realization of the random variable
For example, if we have only one flanking model which is a homogeneous Markov model of order 0 for a DNAAlphabet, the contrast array has the dimension new double[1][1][1][4]. For the same example but with order 1, the contrast array has the dimension new double[1][1][4][4]. Left and right contrast can have different dimensions.

Specified by:
determineNotSignificantPositions in interface Mutable
Parameters:
samples - the summed weights of Sequence containing this motif
weightsLeft - the weights for the left contrast distributions
weightsRight - the weights for the right contrast distributions
contrastLeft - the left contrast distributions
contrastRight - the right contrast distributions
sign - the significance level
Returns:
a two dimensional array containing at position 0 the number of not significant positions from the left side using contrastLeft and at position 1 the number of not significant positions from the right side using contrastRight
See Also:
Mutable.modify(double[], double[], double[][][][], double[][][][], int, int)

modify

public boolean modify(double[] weightsLeft,
                      double[] weightsRight,
                      double[][][][] replacementLeft,
                      double[][][][] replacementRight,
                      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:
weightsLeft - the weights for the left replacement distributions
weightsRight - the weights for the left replacement distributions
replacementLeft - the replacement distribution for the left side
replacementRight - the replacement distribution for the right side
offsetLeft - the offset on the left side
offsetRight - the offset on the right side
Returns:
true if the motif model was modified otherwise false

isStrandScoringFunction

public boolean isStrandScoringFunction()
This method returns true if the internal NormalizableScoringFunction is a StrandScoringFunction otherwise false.

Returns:
true if the internal NormalizableScoringFunction is a StrandScoringFunction 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 NormalizableScoringFunction uniformly if it is a AbstractMixtureScoringFunction.