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

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.HomogeneousMMDiffSM

All Implemented Interfaces:

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

public class HomogeneousMMDiffSM
extends HomogeneousDiffSM

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

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

HomogeneousMMDiffSM(AlphabetContainer alphabets, int order, double classEss, double[] sumOfHyperParams, boolean plugIn, boolean optimize, int starts)
This is the main constructor that creates an instance of a homogeneous Markov model of arbitrary order.

HomogeneousMMDiffSM(AlphabetContainer alphabets, int order, double classEss, int length)
This is a convenience constructor for creating an instance of a homogeneous Markov model of arbitrary order.

HomogeneousMMDiffSM(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.
HomogeneousMMDiffSM	clone() Creates a clone (deep copy) of the current DifferentiableSequenceScore instance.
DataSet	emit(int numberOfSequences, int... seqLength) This method returns a DataSet object containing artificial sequence(s).
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[][][]	getAllConditionalStationaryDistributions() This method returns the stationary conditional distributions.
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	getNumberOfRecommendedStarts() This method returns the number of recommended optimization starts.
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.
static double[]	getSumOfHyperParameters(int order, int length, double ess) This method returns an array that can be used in the constructor HomogeneousMMDiffSM(AlphabetContainer, int, double, double[], boolean, boolean, int) containing the sums of the specific hyperparameters.
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.
boolean	isNormalized() This method indicates whether the implemented score is already normalized to 1 or not.
void	setParameterOptimization(boolean optimize) This method allows the user to 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 + 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

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

getAlphabetContainer, getCharacteristics, getLength, getLogScoreAndPartialDerivation, getLogScoreFor, 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

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

getInitialClassParam, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation

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

HomogeneousMMDiffSM

public HomogeneousMMDiffSM(AlphabetContainer alphabets,
                           int order,
                           double classEss,
                           int length)

This is a convenience constructor for creating an instance of a homogeneous Markov model of arbitrary order.

Parameters:

alphabets - the AlphabetContainer

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

classEss - the equivalent sample size (ess) of the class

length - the sequence length (only used for computing the hyperparameters)

See Also:

getSumOfHyperParameters(int, int, double), HomogeneousMMDiffSM(AlphabetContainer, int, double, double[], boolean, boolean, int)

HomogeneousMMDiffSM

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

This is the main constructor that creates an instance of a homogeneous Markov model of arbitrary order.

Parameters:

alphabets - the AlphabetContainer

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

classEss - the equivalent sample size (ess) of the class

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

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

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

starts - the number of recommended starts

HomogeneousMMDiffSM

public HomogeneousMMDiffSM(StringBuffer xml)
                    throws NonParsableException

This is the constructor for Storable. Creates a new HomogeneousMMDiffSM 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

getSumOfHyperParameters

public static double[] getSumOfHyperParameters(int order,
                                               int length,
                                               double ess)

This method returns an array that can be used in the constructor HomogeneousMMDiffSM(AlphabetContainer, int, double, double[], boolean, boolean, int) containing the sums of the specific hyperparameters.

Parameters:

order - the order of the model

length - the sequence length

ess - the class ESS

Returns:

an array containing the sums of the specific hyperparameters

See Also:

HomogeneousMMDiffSM(AlphabetContainer, int, double, double[], boolean, boolean, int)

clone

public HomogeneousMMDiffSM 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()

isNormalized

public boolean isNormalized()

Description copied from interface: DifferentiableStatisticalModel

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

Specified by:

isNormalized in interface DifferentiableStatisticalModel

Overrides:

isNormalized in class AbstractDifferentiableStatisticalModel

Returns:

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

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

getNumberOfRecommendedStarts

public int getNumberOfRecommendedStarts()

Description copied from interface: DifferentiableSequenceScore

This method returns the number of recommended optimization starts. The standard implementation returns 1.

Specified by:

getNumberOfRecommendedStarts in interface DifferentiableSequenceScore

Overrides:

getNumberOfRecommendedStarts in class AbstractDifferentiableSequenceScore

Returns:

the number of recommended optimization starts

setParameterOptimization

public void setParameterOptimization(boolean optimize)

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

Parameters:

optimize - indicates if the parameters should be optimized or not

getAllConditionalStationaryDistributions

public double[][][] getAllConditionalStationaryDistributions()

This method returns the stationary conditional distributions. The first dimension of the result is used for the order, the second is used for encoding the context, and the third is used for the different values of the random variable.

For an homogeneous Markov model of order 2 it returns an array containing the stationary symbol distribution as first entry, the conditional stationary distribution of order 1 as second entry and the conditional distribution of order 2 as third entry.

Returns:

all conditional stationary distributions

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

emit

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

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

There are 2 different possibilities to create a DataSet:

1. emitDataSet( int n, int l ) returns a DataSet with n sequences of length l.
2. emitDataSet( int n, int[] l ) should return a DataSet 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 DataSet

seqLength - the length of the sequences

Returns:

a DataSet containing numberOfSequences artificial sequence(s)

Throws:

Exception - if the emission of the artificial DataSet did not succeed

See Also:

DataSet

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.