de.jstacs.sequenceScores.statisticalModels.differentiable
Class CyclicMarkovModelDiffSM

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.CyclicMarkovModelDiffSM

All Implemented Interfaces:

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

public class CyclicMarkovModelDiffSM
extends AbstractVariableLengthDiffSM
implements SamplingDifferentiableStatisticalModel

This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length. The scoring function uses the parametrization of Meila.

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

CyclicMarkovModelDiffSM(AlphabetContainer alphabets, double[] frameHyper, double[][][] hyper, boolean plugIn, boolean optimize, int starts, int initFrame)
This constructor allows to create an instance with specific hyper-parameters for all conditional distributions.

CyclicMarkovModelDiffSM(AlphabetContainer alphabets, int order, int period, double classEss, double[] sumOfHyperParams, boolean plugIn, boolean optimize, int starts, int initFrame)
The main constructor.

CyclicMarkovModelDiffSM(StringBuffer source)
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.
CyclicMarkovModelDiffSM	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.
static double[][][]	getHyperParams(int alphabetSize, int length, double ess, double[] frameProb, double[][][] prob) This method returns the hyper-parameters for a model given some a-priori probabilities.
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.
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.
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.
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	setFrameParameterOptimization(boolean optimize) This method enables the user to choose whether the frame parameters should be optimized or not.
void	setParameterOptimization(boolean optimize) This method enables the user to choose 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, getMaximalMarkovOrder, 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, getMaximalMarkovOrder

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

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

Constructor Detail

CyclicMarkovModelDiffSM

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

The main constructor.

Parameters:

alphabets - the alphabet container

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

period - the period

classEss - the ess of the class

sumOfHyperParams - the sum of the hyper parameter for each order (length has to be order+1, each entry has to be non-negative), the sum also sums over the period

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

initFrame - the frame which should be used for plug-in initialization, negative for random initialization

See Also:

getHyperParams(int, int, double, double[], double[][][]), CyclicMarkovModelDiffSM(AlphabetContainer, double[], double[][][], boolean, boolean, int, int)

CyclicMarkovModelDiffSM

public CyclicMarkovModelDiffSM(AlphabetContainer alphabets,
                               double[] frameHyper,
                               double[][][] hyper,
                               boolean plugIn,
                               boolean optimize,
                               int starts,
                               int initFrame)

This constructor allows to create an instance with specific hyper-parameters for all conditional distributions.

Parameters:

alphabets - the alphabet container

frameHyper - the hyper-parameters for the frame, the length of this array also defines the period of the model

hyper - the hyper-parameters for each frame

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

initFrame - the frame which should be used for plug-in initialization, negative for random initialization

CyclicMarkovModelDiffSM

public CyclicMarkovModelDiffSM(StringBuffer source)
                        throws NonParsableException

This is the constructor for Storable.

Parameters:

source - the xml representation

Throws:

NonParsableException - if the representation could not be parsed.

Method Detail

getHyperParams

public static double[][][] getHyperParams(int alphabetSize,
                                          int length,
                                          double ess,
                                          double[] frameProb,
                                          double[][][] prob)

This method returns the hyper-parameters for a model given some a-priori probabilities.

Parameters:

alphabetSize - the size of the alphabet

length - the expected sequence length

ess - the equivalent sample size (ess) of the model

frameProb - the a-priori probabilities for each frame

prob - the a-priori probabilities for each frame and order

Returns:

specific hyper-parameters

clone

public CyclicMarkovModelDiffSM 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), ...

Specified by:

getInstanceName in interface SequenceScore

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.

Specified by:

getNumberOfParameters in interface DifferentiableSequenceScore

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

Specified by:

setParameters in interface DifferentiableSequenceScore

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.

Specified by:

toXML in interface Storable

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.

Specified by:

getCurrentParameterValues in interface DifferentiableSequenceScore

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.

Specified by:

initializeFunction in interface 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.

Specified by:

initializeFunctionRandomly in interface 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, ...

Specified by:

getSizeOfEventSpaceForRandomVariablesOfParameter in interface DifferentiableStatisticalModel

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.

Specified by:

getLogNormalizationConstant in interface VariableLengthDiffSM

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.

Specified by:

getLogPartialNormalizationConstant in interface VariableLengthDiffSM

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.

Specified by:

getESS in interface DifferentiableStatisticalModel

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.

Specified by:

getLogPriorTerm in interface DifferentiableStatisticalModel

Specified by:

getLogPriorTerm in interface StatisticalModel

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.

Specified by:

addGradientOfLogPriorTerm in interface DifferentiableStatisticalModel

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).

Specified by:

isInitialized in interface SequenceScore

Returns:

true if the instance is initialized, false otherwise

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 enables the user to choose whether the parameters should be optimized or not.

Parameters:

optimize - the switch for optimization of the parameters

setFrameParameterOptimization

public void setFrameParameterOptimization(boolean optimize)

This method enables the user to choose whether the frame parameters should be optimized or not.

Parameters:

optimize - the switch for optimization of the frame parameters

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.

Specified by:

setStatisticForHyperparameters in interface VariableLengthDiffSM

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

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

Specified by:

getSamplingGroups in interface SamplingDifferentiableStatisticalModel

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.