de.jstacs.scoringFunctions
Interface NormalizableScoringFunction

All Superinterfaces:

Cloneable, ScoringFunction, Storable

All Known Subinterfaces:

SamplingScoringFunction, VariableLengthScoringFunction

All Known Implementing Classes:

AbstractMixtureScoringFunction, AbstractNormalizableScoringFunction, AbstractVariableLengthScoringFunction, BayesianNetworkScoringFunction, CMMScoringFunction, DifferentiableHigherOrderHMM, DurationScoringFunction, HiddenMotifsMixture, HMM0ScoringFunction, HMMScoringFunction, HomogeneousScoringFunction, IndependentProductScoringFunction, MappingScoringFunction, MixtureDuration, MixtureScoringFunction, MRFScoringFunction, MutableMarkovModelScoringFunction, NormalizedScoringFunction, PositionScoringFunction, SkewNormalLikeScoringFunction, StrandScoringFunction, UniformDurationScoringFunction, UniformHomogeneousScoringFunction, UniformScoringFunction, VariableLengthMixtureScoringFunction

public interface NormalizableScoringFunction
extends ScoringFunction

The interface for normalizable ScoringFunctions.

Author:

Jens Keilwagen, Jan Grau

Field Summary

Fields inherited from interface de.jstacs.scoringFunctions.ScoringFunction

UNKNOWN

Method Summary

void	addGradientOfLogPriorTerm(double[] grad, int start) This method computes the gradient of getLogPriorTerm() for each parameter of this model.
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.
double	getInitialClassParam(double classProb) Returns the initial class parameter for the class this ScoringFunction is responsible for, based on the class probability classProb.
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 getEss() * getLogNormalizationConstant() + Math.log( prior ) where prior is the prior for the parameters of this model.
int	getSizeOfEventSpaceForRandomVariablesOfParameter(int index) Returns the size of the event space of the random variables that are affected by parameter no.
boolean	isNormalized() This method indicates whether the implemented score is already normalized to 1 or not.

Methods inherited from interface de.jstacs.scoringFunctions.ScoringFunction

clone, getAlphabetContainer, getCurrentParameterValues, getInstanceName, getLength, getLogScore, getLogScore, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getNumberOfParameters, getNumberOfRecommendedStarts, initializeFunction, initializeFunctionRandomly, isInitialized, setParameters

Methods inherited from interface de.jstacs.Storable

toXML

Method Detail

getSizeOfEventSpaceForRandomVariablesOfParameter

int getSizeOfEventSpaceForRandomVariablesOfParameter(int index)

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

getLogNormalizationConstant

double getLogNormalizationConstant()

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

double getLogPartialNormalizationConstant(int parameterIndex)
                                          throws Exception

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,

.

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:

getLogNormalizationConstant()

getEss

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.

Returns:

the equivalent sample size.

getLogPriorTerm

double getLogPriorTerm()

This method computes a value that is proportional to

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

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

Returns:

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

See Also:

getEss(), getLogNormalizationConstant()

addGradientOfLogPriorTerm

void addGradientOfLogPriorTerm(double[] grad,
                               int start)
                               throws Exception

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 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:

getLogPriorTerm()

isNormalized

boolean isNormalized()

This method indicates whether the implemented score is already normalized to 1 or not. The standard implementation returns false.

Returns:

true if the implemented score is already normalized to 1, false otherwise

getInitialClassParam

double getInitialClassParam(double classProb)

Description copied from interface: ScoringFunction

Returns the initial class parameter for the class this ScoringFunction is responsible for, based on the class probability classProb.

Specified by:

getInitialClassParam in interface ScoringFunction

Parameters:

classProb - the class probability

Returns:

the initial class parameter