de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous
Class HomogeneousMM0DiffSM

java.lang.Object
  de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
      de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
          de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractVariableLengthDiffSM
              de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousDiffSM
                  de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMM0DiffSM

All Implemented Interfaces:

DifferentiableSequenceScore, SequenceScore, DifferentiableStatisticalModel, SamplingDifferentiableStatisticalModel, VariableLengthDiffSM, StatisticalModel, Storable, Cloneable

public class HomogeneousMM0DiffSM
extends HomogeneousDiffSM

This scoring function implements a homogeneous Markov model of order zero (hMM(0)) for a fixed sequence length.

Author:

Jens Keilwagen

Field Summary

Fields inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore

alphabets, length, r

Fields inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore

UNKNOWN

Constructor Summary

HomogeneousMM0DiffSM(AlphabetContainer alphabets, int length, double ess, boolean plugIn, boolean optimize)
The main constructor that creates an instance of a homogeneous Markov model of order 0.

HomogeneousMM0DiffSM(StringBuffer xml)
This is the constructor for Storable.

Method Summary

void	addGradientOfLogPriorTerm(double[] grad, int start) This method computes the gradient of DifferentiableStatisticalModel.getLogPriorTerm() for each parameter of this model.
HomogeneousMM0DiffSM	clone() Creates a clone (deep copy) of the current DifferentiableSequenceScore instance.
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 DifferentiableSequenceScore.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.
String	getInstanceName() Should return a short instance name such as iMM(0), BN(2), ...
double	getLogNormalizationConstant(int length) This method returns the logarithm of the normalization constant for a given sequence length.
double	getLogPartialNormalizationConstant(int parameterIndex, int length) This method returns the logarithm of the partial normalization constant for a given parameter index and a sequence length.
double	getLogPriorTerm() This method computes a value that is proportional to DifferentiableStatisticalModel.getESS() * DifferentiableStatisticalModel.getLogNormalizationConstant() + Math.log( prior ) where prior is the prior for the parameters of this model.
double	getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList dList) 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	getLogScoreFor(Sequence seq, int start, int end) Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.
byte	getMaximalMarkovOrder() Returns the maximal used markov oder.
int	getNumberOfParameters() Returns the number of parameters in this DifferentiableSequenceScore.
int[][]	getSamplingGroups(int parameterOffset) Returns groups of indexes of parameters that shall be drawn together in a sampling procedure
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, DataSet[] data, double[][] weights) This method creates the underlying structure of the DifferentiableSequenceScore.
void	initializeFunctionRandomly(boolean freeParams) This method initializes the DifferentiableSequenceScore randomly.
void	initializeUniformly(boolean freeParams) This method allows to initialize the instance with an uniform distribution.
boolean	isInitialized() This method can be used to determine whether the instance is initialized.
void	setParameters(double[] params, int start) This method sets the internal parameters to the values of params between start and start + DifferentiableSequenceScore.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 as StringBuffer of an instance of the implementing class.

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractVariableLengthDiffSM

getLogNormalizationConstant, getLogPartialNormalizationConstant, getLogScoreAndPartialDerivation, getLogScoreFor

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel

emitDataSet, getInitialClassParam, getLogProbFor, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, isNormalized, isNormalized

Methods inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore

getAlphabetContainer, getCharacteristics, getLength, getLogScoreAndPartialDerivation, getLogScoreFor, getNumberOfRecommendedStarts, getNumberOfStarts, getNumericalCharacteristics

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel

getLogNormalizationConstant, getLogPartialNormalizationConstant, isNormalized

Methods inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore

getInitialClassParam, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getNumberOfRecommendedStarts

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel

emitDataSet, getLogProbFor, getLogProbFor, getLogProbFor

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

getAlphabetContainer, getCharacteristics, getLength, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics

Constructor Detail

HomogeneousMM0DiffSM

public HomogeneousMM0DiffSM(AlphabetContainer alphabets,
                            int length,
                            double ess,
                            boolean plugIn,
                            boolean optimize)

The main constructor that creates an instance of a homogeneous Markov model of order 0.

Parameters:

alphabets - the AlphabetContainer of the model

length - the length of sequences the model can handle

ess - the equivalent sample size (ess)

plugIn - indicates if a plug-in strategy to initialize the parameters should be used

optimize - indicates if the parameters should be optimized or not after they have been initialized

HomogeneousMM0DiffSM

public HomogeneousMM0DiffSM(StringBuffer xml)
                     throws NonParsableException

This is the constructor for Storable. Creates a new HomogeneousMM0DiffSM out of its XML representation as returned by fromXML(StringBuffer).

Parameters:

xml - the XML representation as StringBuffer

Throws:

NonParsableException - if the StringBuffer representation could not be parsed

Method Detail

clone

public HomogeneousMM0DiffSM clone()
                           throws CloneNotSupportedException

Description copied from interface: DifferentiableSequenceScore

Creates a clone (deep copy) of the current DifferentiableSequenceScore instance.

Specified by:

clone in interface DifferentiableSequenceScore

Specified by:

clone in interface SequenceScore

Overrides:

clone in class AbstractDifferentiableStatisticalModel

Returns:

the cloned instance of the current DifferentiableSequenceScore

Throws:

CloneNotSupportedException - if something went wrong while cloning the DifferentiableSequenceScore

getInstanceName

public String getInstanceName()

Description copied from interface: SequenceScore

Should return a short instance name such as iMM(0), BN(2), ...

Returns:

a short instance name

getLogScoreFor

public double getLogScoreFor(Sequence seq,
                             int start,
                             int end)

Description copied from interface: SequenceScore

Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.

Specified by:

getLogScoreFor in interface SequenceScore

Specified by:

getLogScoreFor in interface VariableLengthDiffSM

Specified by:

getLogScoreFor in class AbstractVariableLengthDiffSM

Parameters:

seq - the Sequence

start - the start position in the Sequence

end - the end position (inclusive) in the Sequence

Returns:

the logarithmic score for the Sequence

getLogScoreAndPartialDerivation

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

Description copied from interface: DifferentiableSequenceScore

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 DifferentiableSequenceScore

Specified by:

getLogScoreAndPartialDerivation in interface VariableLengthDiffSM

Specified by:

getLogScoreAndPartialDerivation in class AbstractVariableLengthDiffSM

Parameters:

seq - the Sequence

start - the start position in the Sequence

end - the end position (inclusive) in the Sequence

indices - an IntList of indices, after method invocation the list should contain the indices i where is not zero

dList - a DoubleList of partial derivations, after method invocation the list should contain the corresponding that are not zero

Returns:

the logarithmic score for the Sequence

getNumberOfParameters

public int getNumberOfParameters()

Description copied from interface: DifferentiableSequenceScore

Returns the number of parameters in this DifferentiableSequenceScore. If the number of parameters is not known yet, the method returns DifferentiableSequenceScore.UNKNOWN.

Returns:

the number of parameters in this DifferentiableSequenceScore

See Also:

DifferentiableSequenceScore.UNKNOWN

setParameters

public void setParameters(double[] params,
                          int start)

Description copied from interface: DifferentiableSequenceScore

This method sets the internal parameters to the values of params between start and start + DifferentiableSequenceScore.getNumberOfParameters() - 1

Parameters:

params - the new parameters

start - the start index in params

toXML

public StringBuffer toXML()

Description copied from interface: Storable

This method returns an XML representation as StringBuffer of an instance of the implementing class.

Returns:

the XML representation

getCurrentParameterValues

public double[] getCurrentParameterValues()

Description copied from interface: DifferentiableSequenceScore

Returns a double array of dimension DifferentiableSequenceScore.getNumberOfParameters() containing the current parameter values. If one likes to use these parameters to start an optimization it is highly recommended to invoke DifferentiableSequenceScore.initializeFunction(int, boolean, DataSet[], 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,
                               DataSet[] data,
                               double[][] weights)

Description copied from interface: DifferentiableSequenceScore

This method creates the underlying structure of the DifferentiableSequenceScore.

Parameters:

index - the index of the class the DifferentiableSequenceScore models

freeParams - indicates whether 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: DifferentiableSequenceScore

This method initializes the DifferentiableSequenceScore randomly. It has to create the underlying structure of the DifferentiableSequenceScore.

Parameters:

freeParams - indicates whether the (reduced) parameterization is used

fromXML

protected void fromXML(StringBuffer xml)
                throws NonParsableException

Description copied from class: AbstractDifferentiableSequenceScore

This method is called in the constructor for the Storable interface to create a scoring function from a StringBuffer.

Specified by:

fromXML in class AbstractDifferentiableSequenceScore

Parameters:

xml - the XML representation as StringBuffer

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

AbstractDifferentiableSequenceScore.AbstractDifferentiableSequenceScore(StringBuffer)

getSizeOfEventSpaceForRandomVariablesOfParameter

public int getSizeOfEventSpaceForRandomVariablesOfParameter(int index)

Description copied from interface: DifferentiableStatisticalModel

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

public double getLogNormalizationConstant(int length)

Description copied from interface: VariableLengthDiffSM

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

Parameters:

length - the sequence length

Returns:

the logarithm of the normalization constant

See Also:

DifferentiableStatisticalModel.getLogNormalizationConstant()

getLogPartialNormalizationConstant

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

Description copied from interface: VariableLengthDiffSM

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

Parameters:

parameterIndex - the index of the parameter

length - the sequence length

Returns:

the logarithm of the partial normalization constant

Throws:

Exception - if something went wrong

See Also:

DifferentiableStatisticalModel.getLogPartialNormalizationConstant(int)

getESS

public double getESS()

Description copied from interface: DifferentiableStatisticalModel

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.

toString

public String toString()

Overrides:

toString in class Object

getLogPriorTerm

public double getLogPriorTerm()

Description copied from interface: DifferentiableStatisticalModel

This method computes a value that is proportional to

DifferentiableStatisticalModel.getESS() * DifferentiableStatisticalModel.getLogNormalizationConstant() + Math.log( prior )

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

Returns:

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

See Also:

DifferentiableStatisticalModel.getESS(), DifferentiableStatisticalModel.getLogNormalizationConstant()

addGradientOfLogPriorTerm

public void addGradientOfLogPriorTerm(double[] grad,
                                      int start)

Description copied from interface: DifferentiableStatisticalModel

This method computes the gradient of DifferentiableStatisticalModel.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

See Also:

DifferentiableStatisticalModel.getLogPriorTerm()

isInitialized

public boolean isInitialized()

Description copied from interface: SequenceScore

This method can be used to determine whether the instance is initialized. If the instance is initialized you should be able to invoke SequenceScore.getLogScoreFor(Sequence).

Returns:

true if the instance is initialized, false otherwise

getMaximalMarkovOrder

public byte getMaximalMarkovOrder()

Description copied from class: HomogeneousDiffSM

Returns the maximal used markov oder.

Specified by:

getMaximalMarkovOrder in interface StatisticalModel

Specified by:

getMaximalMarkovOrder in class HomogeneousDiffSM

Returns:

the maximal used markov oder

setStatisticForHyperparameters

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

Description copied from interface: VariableLengthDiffSM

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.

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

initializeUniformly

public void initializeUniformly(boolean freeParams)

Description copied from class: HomogeneousDiffSM

This method allows to initialize the instance with an uniform distribution.

Specified by:

initializeUniformly in class HomogeneousDiffSM

Parameters:

freeParams - a switch whether to take only free parameters or to take all

getSamplingGroups

public int[][] getSamplingGroups(int parameterOffset)

Description copied from interface: SamplingDifferentiableStatisticalModel

Returns groups of indexes of parameters that shall be drawn together in a sampling procedure

Parameters:

parameterOffset - a global offset on the parameter indexes

Returns:

the groups of indexes. The first dimension represents the different groups while the second dimension contains the parameters that shall be sampled together. Internal parameter indexes need to be increased by parameterOffset.