de.jstacs.scoringFunctions.mix.motifSearch
Class SkewNormalLikeScoringFunction

java.lang.Object
  de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
      de.jstacs.scoringFunctions.mix.motifSearch.PositionScoringFunction
          de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction
              de.jstacs.scoringFunctions.mix.motifSearch.SkewNormalLikeScoringFunction

All Implemented Interfaces:

NormalizableScoringFunction, ScoringFunction, Storable, Cloneable

public class SkewNormalLikeScoringFunction
extends DurationScoringFunction

This class implements a skew normal like discrete truncated distribution.

Author:

Jens Keilwagen

Field Summary

Fields inherited from class de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction

delta, ess, max, min

Fields inherited from class de.jstacs.scoringFunctions.mix.motifSearch.PositionScoringFunction

internal

Fields inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction

alphabets, length, r

Fields inherited from interface de.jstacs.scoringFunctions.ScoringFunction

UNKNOWN

Constructor Summary

SkewNormalLikeScoringFunction(int min, int max, boolean trainMean, double hyperMeanMean, double hyperMeanSigma, boolean trainPrecision, double hyperPrec1, double hyperPrec2, boolean trainSkew, double hyperSkewMean, double hyperSkewStdev, int starts)
This is the constructor that allows the most flexible handling of the parameters.

SkewNormalLikeScoringFunction(int min, int max, double param0, double param1, double param2)
This is the main constructor if the parameters are fixed.

SkewNormalLikeScoringFunction(StringBuffer source)
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.
void	adjust(int[] length, double[] weight) This method adjust the parameter based on the given statistic.
SkewNormalLikeScoringFunction	clone() Creates a clone (deep copy) of the current ScoringFunction instance.
protected void	fromXML(StringBuffer rep) 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.
String	getInstanceName() Returns a short instance name.
double	getLogPriorTerm() This method computes a value that is proportional to NormalizableScoringFunction.getEss() * NormalizableScoringFunction.getLogNormalizationConstant() + Math.log( prior ) where prior is the prior for the parameters of this model.
double	getLogScore(int... values) This method enables the user to get the log-score without using a sequence object.
double	getLogScoreAndPartialDerivation(IntList indices, DoubleList partialDer, int... values) This method enables the user to get the log-score and the partial derivations without using a sequence object.
int	getNumberOfParameters() Returns the number of parameters in this ScoringFunction.
int	getNumberOfRecommendedStarts() This method returns the number of recommended optimization starts.
protected String	getRNotation(String distributionName) This method returns the distribution in R notation.
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	initializeUniformly() This method set special parameters that lead to an uniform distribution.
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.
void	modify(int delta) This method modifies the underlying AlphabetContainer.
void	setParameters(double[] params, int start) This method sets the internal parameters to the values of params between start and start + ScoringFunction.getNumberOfParameters() - 1
void	setParameters(double par0, double par1, double par2) this method can be used to set the parameters even if the parameters are not allowed to be optimized.
StringBuffer	toXML() This method returns an XML representation as StringBuffer of an instance of the implementing class.

Methods inherited from class de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction

getEss, getLogNormalizationConstant, getLogPartialNormalizationConstant, getMax, getMin, getNumberOfPossibilities, getSizeOfEventSpaceForRandomVariablesOfParameter, isPossible, next, reset, toString

Methods inherited from class de.jstacs.scoringFunctions.mix.motifSearch.PositionScoringFunction

getInternalPosition, getLogScore, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivationForInternal, getLogScoreForInternal, getValuesFromSequence

Methods inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction

getAlphabetContainer, getInitialClassParam, getLength, getLogScore, getLogScoreAndPartialDerivation, getNumberOfStarts, isNormalized

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

SkewNormalLikeScoringFunction

public SkewNormalLikeScoringFunction(int min,
                                     int max,
                                     double param0,
                                     double param1,
                                     double param2)

This is the main constructor if the parameters are fixed.

Parameters:

min - the minimal value

max - the maximal value

param0 - the fixed parameter value for the first parameter (mean)

param1 - the fixed parameter value for the second parameter (precision)

param2 - the fixed parameter value for the third parameter (skew)

SkewNormalLikeScoringFunction

public SkewNormalLikeScoringFunction(int min,
                                     int max,
                                     boolean trainMean,
                                     double hyperMeanMean,
                                     double hyperMeanSigma,
                                     boolean trainPrecision,
                                     double hyperPrec1,
                                     double hyperPrec2,
                                     boolean trainSkew,
                                     double hyperSkewMean,
                                     double hyperSkewStdev,
                                     int starts)

This is the constructor that allows the most flexible handling of the parameters.

Parameters:

min - the minimal value

max - the maximal value

trainMean - a switch whether to optimize the first parameter

hyperMeanMean - the mean hyper parameter for the first parameter

hyperMeanSigma - the standard deviation hyper parameter for the first parameter

trainPrecision - a switch whether to optimize the second parameter

hyperPrec1 - the first hyper parameter for the precision (first parameter of the transformed gamma density); this is value is used to determine the ess: hyperPrec1 = 0.5*ess

hyperPrec2 - the second hyper parameter for the precision (second parameter of the transformed gamma density)

trainSkew - a switch whether to optimize the third parameter

hyperSkewMean - the mean hyper parameter for the third parameter

hyperSkewStdev - the standard deviation hyper parameter for the third parameter

starts - the number of recommended starts

SkewNormalLikeScoringFunction

public SkewNormalLikeScoringFunction(StringBuffer source)
                              throws NonParsableException

This is the constructor for Storable. Creates a new SkewNormalLikeScoringFunction out of a StringBuffer.

Parameters:

source - the XML representation as StringBuffer

Throws:

NonParsableException - if the XML representation could not be parsed

Method Detail

clone

public SkewNormalLikeScoringFunction 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 PositionScoringFunction

Returns:

the cloned instance of the current ScoringFunction

Throws:

CloneNotSupportedException - if something went wrong while cloning the ScoringFunction

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.

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

adjust

public void adjust(int[] length,
                   double[] weight)

Description copied from class: DurationScoringFunction

This method adjust the parameter based on the given statistic.

Specified by:

adjust in class DurationScoringFunction

Parameters:

length - an array containing length values

weight - an array containing corresponding weight values

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.

Parameters:

freeParams - indicates whether the (reduced) parameterization is used

Throws:

Exception - if something went wrong

fromXML

protected void fromXML(StringBuffer rep)
                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.

Overrides:

fromXML in class DurationScoringFunction

Parameters:

rep - 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.

Returns:

a short instance name

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.

Returns:

the current parameter values

Throws:

Exception - if no parameters exist (yet)

getLogScore

public double getLogScore(int... values)

Description copied from class: PositionScoringFunction

This method enables the user to get the log-score without using a sequence object.

Specified by:

getLogScore in class PositionScoringFunction

Parameters:

values - the values

Returns:

the score

getLogScoreAndPartialDerivation

public double getLogScoreAndPartialDerivation(IntList indices,
                                              DoubleList partialDer,
                                              int... values)

Description copied from class: PositionScoringFunction

This method enables the user to get the log-score and the partial derivations without using a sequence object.

Specified by:

getLogScoreAndPartialDerivation in class PositionScoringFunction

Parameters:

indices - a list for the indices of the parameters

partialDer - a list of the partial derivations

values - the values

Returns:

the score

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.

Returns:

the number of parameters in this ScoringFunction

See Also:

ScoringFunction.UNKNOWN

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

Parameters:

params - the new parameters

start - the start index in params

setParameters

public void setParameters(double par0,
                          double par1,
                          double par2)

this method can be used to set the parameters even if the parameters are not allowed to be optimized.

Parameters:

par0 - the first parameter (for the mean or maximum)

par1 - the second parameter (for the precision)

par2 - the third parameter (for the skew)

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

Overrides:

toXML in class DurationScoringFunction

Returns:

the XML representation

getRNotation

protected String getRNotation(String distributionName)

Description copied from class: DurationScoringFunction

This method returns the distribution in R notation.

Specified by:

getRNotation in class DurationScoringFunction

Parameters:

distributionName - the name of the distribution, e.g., "p"

Returns:

the distribution in R notation

See Also:

REnvironment

getLogPriorTerm

public double getLogPriorTerm()

Description copied from interface: NormalizableScoringFunction

This method computes a value that is proportional to

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

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

Returns:

a value that is proportional to NormalizableScoringFunction.getEss() * NormalizableScoringFunction.getLogNormalizationConstant() + Math.log( prior ).

See Also:

NormalizableScoringFunction.getEss(), NormalizableScoringFunction.getLogNormalizationConstant()

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.

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()

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[][]).

Returns:

true if the model is initialized, false otherwise

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

initializeUniformly

public void initializeUniformly()

Description copied from class: DurationScoringFunction

This method set special parameters that lead to an uniform distribution.

Specified by:

initializeUniformly in class DurationScoringFunction

modify

public void modify(int delta)

Description copied from class: DurationScoringFunction

This method modifies the underlying AlphabetContainer. This might be necessary if the motif length changed.

Overrides:

modify in class DurationScoringFunction

Parameters:

delta - the change

See Also:

Mutable.modify(int, int), MutableMotifDiscoverer.modifyMotif(int, int, int)

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