de.jstacs.scoringFunctions.homogeneous
Class HMMScoringFunction

java.lang.Object
  de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
      de.jstacs.scoringFunctions.VariableLengthScoringFunction
          de.jstacs.scoringFunctions.homogeneous.HomogeneousScoringFunction
              de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction

All Implemented Interfaces:

NormalizableScoringFunction, ScoringFunction, Storable, Cloneable

public class HMMScoringFunction
extends HomogeneousScoringFunction

This scoring function implements a homogeneous Markov model of arbitrary order for any sequence length. The scoring function uses the parametrization of Meila if one uses the free parameters, which yields in a non-concave lo conditional likelihood.

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

HMMScoringFunction(AlphabetContainer alphabets, int order, double classEss, double[] sumOfHyperParams, boolean plugIn, boolean optimize, int starts)
This is the main constructor.

HMMScoringFunction(StringBuffer xml)
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.
HMMScoringFunction	clone() Creates a clone (deep copy) of the current ScoringFunction instance.
Sample	emit(int numberOfSequences, int... seqLength) This method returns a Sample object containing artificial sequence(s).
protected void	fromXML(StringBuffer xml) 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, int length) This method computes the logarithm of the score for a given subsequence.
double	getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList dList) This method computes the logarithm of the score and the partial derivations for a given subsequence.
int	getMaximalMarkovOrder() Returns the maximal used markov oder.
double	getNormalizationConstant(int length) This method returns the normalization constant for a given sequence length.
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, int length) This method returns the partial normalization constant for a given parameter index and sequence length.
int	getSizeOfEventSpaceForRandomVariablesOfParameter(int index) Returns the size of the event space of the random variables that are affected by parameter no.
double[]	getStationarySymbolDistribution() This method returns the stationary symbol distribution.
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.
boolean	isNormalized() This method returns whether the implemented score is already normalized to 1.
void	setParameterOptimization(boolean optimize) This method allows the user specify whether the parameters should be optimized or not.
void	setParameters(double[] params, int start) This method sets the internal parameters to the values of params between start and start + this.getNumberOfParameters() - 1
void	setStatisticForHyperparameters(int[] length, double[] weight) This method sets the hyperparameters for the model parameters by evaluating the given statistic.
String	toString()
StringBuffer	toXML() This method returns an XML-representation of an instance of the implementing class.

Methods inherited from class de.jstacs.scoringFunctions.VariableLengthScoringFunction

getLogScore, getLogScoreAndPartialDerivation, getNormalizationConstant, getPartialNormalizationConstant

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

HMMScoringFunction

public HMMScoringFunction(AlphabetContainer alphabets,
                          int order,
                          double classEss,
                          double[] sumOfHyperParams,
                          boolean plugIn,
                          boolean optimize,
                          int starts)

This is the main constructor.

Parameters:

alphabets - the alphabet container

order - the oder of the model (has to be non-negative)

classEss - the ess of the class

sumOfHyperParams - the sum of the hyperparameter for each order (length has to be order, each entry has to be non-negative)

plugIn - a switch which enables to used the MAP-parameters as plug-in parameters

optimize - a switch which enables to optimize or fix the parameters

starts - the number of recommended starts

HMMScoringFunction

public HMMScoringFunction(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

clone

public HMMScoringFunction 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

getInstanceName

public String getInstanceName()

Description copied from interface: ScoringFunction

Returns a short instance name.

Returns:

a short instance name

getLogScore

public double getLogScore(Sequence seq,
                          int start,
                          int length)

Description copied from class: VariableLengthScoringFunction

This method computes the logarithm of the score for a given subsequence.

Specified by:

getLogScore in class VariableLengthScoringFunction

Parameters:

seq - the sequence

start - the start index

length - the end index

Returns:

the logarithm of the score

See Also:

ScoringFunction.getLogScore(Sequence, int)

getLogScoreAndPartialDerivation

public double getLogScoreAndPartialDerivation(Sequence seq,
                                              int start,
                                              int length,
                                              IntList indices,
                                              DoubleList dList)

Description copied from class: VariableLengthScoringFunction

This method computes the logarithm of the score and the partial derivations for a given subsequence.

Specified by:

getLogScoreAndPartialDerivation in class VariableLengthScoringFunction

Parameters:

seq - the sequence

start - the start index

length - the end index

indices - the list for the indices of the parameters

dList - the list for the partial derivations

Returns:

the logarithm of the score

See Also:

ScoringFunction.getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList)

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

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

getCurrentParameterValues

public double[] getCurrentParameterValues()

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

initializeFunction

public void initializeFunction(int index,
                               boolean freeParams,
                               Sample[] data,
                               double[][] weights)

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

initializeFunctionRandomly

public void initializeFunctionRandomly(boolean freeParams)

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

fromXML

protected void fromXML(StringBuffer xml)
                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:

xml - the XML representation

Throws:

NonParsableException - if the StringBuffer could not be parsed.

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(int length)

Description copied from class: VariableLengthScoringFunction

This method returns the normalization constant for a given sequence length.

Specified by:

getNormalizationConstant in class VariableLengthScoringFunction

Parameters:

length - the sequence length

Returns:

the normalization constant

See Also:

NormalizableScoringFunction.getNormalizationConstant()

getPartialNormalizationConstant

public double getPartialNormalizationConstant(int parameterIndex,
                                              int length)
                                       throws Exception

Description copied from class: VariableLengthScoringFunction

This method returns the partial normalization constant for a given parameter index and sequence length.

Specified by:

getPartialNormalizationConstant in class VariableLengthScoringFunction

Parameters:

parameterIndex - the index of the parameter

length - the sequence length

Returns:

the partial normalization constant

Throws:

Exception - if something went wrong

See Also:

NormalizableScoringFunction.getPartialNormalizationConstant(int)

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.

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)

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

See Also:

NormalizableScoringFunction.getLogPriorTerm()

isNormalized

public boolean isNormalized()

Description copied from interface: NormalizableScoringFunction

This method returns whether the implemented score is already normalized to 1. The standard implementation returns false.

Specified by:

isNormalized in interface NormalizableScoringFunction

Overrides:

isNormalized in class AbstractNormalizableScoringFunction

Returns:

whether the implemented score is already normalized to 1

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

getMaximalMarkovOrder

public int getMaximalMarkovOrder()

Description copied from class: HomogeneousScoringFunction

Returns the maximal used markov oder.

Specified by:

getMaximalMarkovOrder in class HomogeneousScoringFunction

Returns:

the maximal used markov oder

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

setParameterOptimization

public void setParameterOptimization(boolean optimize)

This method allows the user specify whether the parameters should be optimized or not.

Parameters:

optimize - whether the parameters should be optimized or not.

getStationarySymbolDistribution

public double[] getStationarySymbolDistribution()

Description copied from class: VariableLengthScoringFunction

This method returns the stationary symbol distribution. For DNA this is the stationary mono nucleotide distribution. This method is used to determine the contrast for de.jstacs.motifDiscovery.Mutable#expand(int, double[], double[], double), de.jstacs.motifDiscovery.Mutable#shift(double[], double[], double) and de.jstacs.motifDiscovery.Mutable#shrink(double[], double[], double).

Specified by:

getStationarySymbolDistribution in class VariableLengthScoringFunction

Returns:

the stationary symbol distribution

See Also:

Mutable

setStatisticForHyperparameters

public void setStatisticForHyperparameters(int[] length,
                                           double[] weight)
                                    throws Exception

Description copied from class: VariableLengthScoringFunction

This method sets the hyperparameters for the model parameters by evaluating the given statistic. The statistic can be interpreted as follows: The model has seen a number of sequences. From these sequences it is only known how long (length) and how often (weight) they have been seen.

Specified by:

setStatisticForHyperparameters in class VariableLengthScoringFunction

Parameters:

length - the non-negative lengths of the sequences

weight - the non-negative weight for the corresponding sequence

Throws:

Exception - if something went wrong

See Also:

Mutable

emit

public Sample emit(int numberOfSequences,
                   int... seqLength)
            throws Exception

This method returns a Sample object containing artificial sequence(s).

There are 2 different possibilities to create a sample:

1. emitSample( int n, int l ) returns a sample with n sequences of length l.
2. emitSample( int n, int[] l ) should return a sample with n sequences which have a sequence length corresponding to the entry in the array

Parameters:

numberOfSequences - the number of sequences that should be contained in the returned sample

seqLength - the length of the sequences

Returns:

Sample containing the artificial sequence(s)

Throws:

Exception - an Exception should be thrown if the emission did not succeed.

NotTrainedException - a NotTrainedException should be thrown if the model is not trained yet.

See Also:

Sample