de.jstacs.sequenceScores.statisticalModels.differentiable

Class UniformDiffSM

java.lang.Object

de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore

de.jstacs.sequenceScores.differentiable.UniformDiffSS

de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM

All Implemented Interfaces:

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

public class UniformDiffSM
extends UniformDiffSS
implements SamplingDifferentiableStatisticalModel

This DifferentiableStatisticalModel does nothing. So it is possible to save parameters in an optimization.

Author:

Jens Keilwagen, Jan Grau

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

Constructors

Constructor and Description
UniformDiffSM(AlphabetContainer alphabets, int length, double ess) This is the main constructor that creates an instance of a UniformDiffSM that models each sequence uniformly.
UniformDiffSM(StringBuffer xml) This is the constructor for the interface Storable.

Method Summary

Methods

Modifier and Type	Method and Description
void	addGradientOfLogPriorTerm(double[] grad, int start) This method computes the gradient of DifferentiableStatisticalModel.getLogPriorTerm() for each parameter of this model.
DataSet	emitDataSet(int numberOfSequences, int... seqLength) This method returns a DataSet object containing artificial sequence(s).
protected void	extractFurtherInformation(StringBuffer xml) This method is the opposite of UniformDiffSS.getFurtherInformation().
double	getESS() Returns the equivalent sample size (ess) of this model, i.e.
protected StringBuffer	getFurtherInformation() This method is used to append further information of the instance to the XML representation.
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
double	getLogProbFor(Sequence sequence) Returns the logarithm of the probability of the given sequence given the model.
double	getLogProbFor(Sequence sequence, int startpos) Returns the logarithm of the probability of (a part of) the given sequence given the model.
double	getLogProbFor(Sequence sequence, int startpos, int endpos) Returns the logarithm of the probability of (a part of) the given sequence given the model.
double	getLogScoreAndPartialDerivation(Sequence seq, int start, 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) Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.
byte	getMaximalMarkovOrder() This method returns the maximal used Markov order, if possible.
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 meila, DataSet[] data, double[][] weights) This method creates the underlying structure of the DifferentiableSequenceScore.
boolean	isNormalized() This method indicates whether the implemented score is already normalized to 1 or not.
String	toString(NumberFormat nf) This method returns a String representation of the instance.

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

fromXML, getCurrentParameterValues, getInstanceName, getNumberOfParameters, initializeFunctionRandomly, isInitialized, setParameters, toXML

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

clone, getAlphabetContainer, getCharacteristics, getInitialClassParam, getLength, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumberOfRecommendedStarts, getNumberOfStarts, getNumericalCharacteristics, toString

Methods inherited from class java.lang.Object

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

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

clone, getCurrentParameterValues, getInitialClassParam, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getNumberOfParameters, getNumberOfRecommendedStarts, initializeFunctionRandomly, setParameters

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

getAlphabetContainer, getCharacteristics, getInstanceName, getLength, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics, isInitialized

Methods inherited from interface de.jstacs.Storable

toXML

Constructor Detail

UniformDiffSM

public UniformDiffSM(AlphabetContainer alphabets,
             int length,
             double ess)

This is the main constructor that creates an instance of a UniformDiffSM that models each sequence uniformly.

Parameters:

alphabets - the AlphabetContainer

length - the length of the modeled sequences

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

UniformDiffSM

public UniformDiffSM(StringBuffer xml)
              throws NonParsableException

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

Parameters:

xml - the XML representation as StringBuffer

Throws:

NonParsableException - if the XML representation could not be parsed

Method Detail

getLogScoreFor

public double getLogScoreFor(Sequence seq,
                    int start)

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

Overrides:

getLogScoreFor in class UniformDiffSS

Parameters:

seq - the Sequence

start - the start position in the Sequence

Returns:

the logarithmic score for the Sequence

getLogScoreAndPartialDerivation

public double getLogScoreAndPartialDerivation(Sequence seq,
                                     int start,
                                     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

Overrides:

getLogScoreAndPartialDerivation in class UniformDiffSS

Parameters:

seq - the Sequence

start - the start position 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

getFurtherInformation

protected StringBuffer getFurtherInformation()

Description copied from class: UniformDiffSS

This method is used to append further information of the instance to the XML representation. This method is designed to allow subclasses to add information to the XML representation.

Overrides:

getFurtherInformation in class UniformDiffSS

Returns:

the further information as XML code in a StringBuffer

See Also:

UniformDiffSS.extractFurtherInformation(StringBuffer)

extractFurtherInformation

protected void extractFurtherInformation(StringBuffer xml)
                                  throws NonParsableException

Description copied from class: UniformDiffSS

This method is the opposite of UniformDiffSS.getFurtherInformation(). It extracts further information of the instance from a XML representation.

Overrides:

extractFurtherInformation in class UniformDiffSS

Parameters:

xml - the StringBuffer containing the information to be extracted as XML code

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

UniformDiffSS.getFurtherInformation()

getLogNormalizationConstant

public double getLogNormalizationConstant()

Description copied from interface: DifferentiableStatisticalModel

Returns the logarithm of the sum of the scores over all sequences of the event space.

Specified by:

getLogNormalizationConstant in interface DifferentiableStatisticalModel

Returns:

the logarithm of the normalization constant Z

initializeFunction

public void initializeFunction(int index,
                      boolean meila,
                      DataSet[] data,
                      double[][] weights)

Description copied from interface: DifferentiableSequenceScore

This method creates the underlying structure of the DifferentiableSequenceScore.

Specified by:

initializeFunction in interface DifferentiableSequenceScore

Overrides:

initializeFunction in class UniformDiffSS

Parameters:

index - the index of the class the DifferentiableSequenceScore models

meila - indicates whether the (reduced) parameterization is used

data - the data sets

weights - the weights of the sequences in the data sets

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

getLogPartialNormalizationConstant

public double getLogPartialNormalizationConstant(int parameterIndex)
                                          throws Exception

Description copied from interface: DifferentiableStatisticalModel

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,

.

Specified by:

getLogPartialNormalizationConstant in interface DifferentiableStatisticalModel

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:

DifferentiableStatisticalModel.getLogNormalizationConstant()

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(NumberFormat nf)

Description copied from interface: SequenceScore

This method returns a String representation of the instance.

Specified by:

toString in interface SequenceScore

Overrides:

toString in class UniformDiffSS

Parameters:

nf - the NumberFormat for the String representation of parameters or probabilities

Returns:

a String representation of the instance

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

Returns:

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

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.

getLogProbFor

public double getLogProbFor(Sequence sequence,
                   int startpos)
                     throws Exception

Description copied from interface: StatisticalModel

Returns the logarithm of the probability of (a part of) the given sequence given the model. If at least one random variable is continuous the value of density function is returned.

If the length of the sequences, whose probability should be returned, is fixed (e.g. in a inhomogeneous model) and the given sequence is longer than their fixed length, the start position within the given sequence is given by startpos. E.g. the fixed length is 12. The length of the given sequence is 30 and the startpos=15 the logarithm of the probability of the part from position 15 to 26 (inclusive) given the model should be returned.
The length and the alphabets define the type of data that can be modeled and therefore both has to be checked.

Specified by:

getLogProbFor in interface StatisticalModel

Parameters:

sequence - the given sequence

startpos - the start position within the given sequence

Returns:

the logarithm of the probability or the value of the density function of (the part of) the given sequence given the model

Throws:

Exception - if the sequence could not be handled by the model

NotTrainedException - if the model is not trained yet

See Also:

StatisticalModel.getLogProbFor(Sequence, int, int)

getLogProbFor

public double getLogProbFor(Sequence sequence)
                     throws Exception

Description copied from interface: StatisticalModel

Returns the logarithm of the probability of the given sequence given the model. If at least one random variable is continuous the value of density function is returned.

The length and the alphabets define the type of data that can be modeled and therefore both has to be checked.

Specified by:

getLogProbFor in interface StatisticalModel

Parameters:

sequence - the given sequence for which the logarithm of the probability/the value of the density function should be returned

Returns:

the logarithm of the probability or the value of the density function of the part of the given sequence given the model

Throws:

Exception - if the sequence could not be handled by the model

NotTrainedException - if the model is not trained yet

See Also:

StatisticalModel.getLogProbFor(Sequence, int, int)

getLogProbFor

public double getLogProbFor(Sequence sequence,
                   int startpos,
                   int endpos)
                     throws Exception

Description copied from interface: StatisticalModel

Returns the logarithm of the probability of (a part of) the given sequence given the model. If at least one random variable is continuous the value of density function is returned.

It extends the possibility given by the method StatisticalModel.getLogProbFor(Sequence, int) by the fact, that the model could be e.g. homogeneous and therefore the length of the sequences, whose probability should be returned, is not fixed. Additionally, the end position of the part of the given sequence is given and the probability of the part from position startpos to endpos (inclusive) should be returned.
The length and the alphabets define the type of data that can be modeled and therefore both has to be checked.

Specified by:

getLogProbFor in interface StatisticalModel

Parameters:

sequence - the given sequence

startpos - the start position within the given sequence

endpos - the last position to be taken into account

Returns:

the logarithm of the probability or the value of the density function of (the part of) the given sequence given the model

Throws:

Exception - if the sequence could not be handled (e.g. startpos > , endpos > sequence.length, ...) by the model

NotTrainedException - if the model is not trained yet

emitDataSet

public DataSet emitDataSet(int numberOfSequences,
                  int... seqLength)
                    throws NotTrainedException,
                           Exception

Description copied from interface: StatisticalModel

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

There are two different possibilities to create a data set for a model with length 0 (homogeneous models).

1. emitDataSet( int n, int l ) should return a data set with n sequences of length l.
2. emitDataSet( int n, int[] l ) should return a data set with n sequences which have a sequence length corresponding to the entry in the given array l.

There are two different possibilities to create a data set for a model with length greater than 0 (inhomogeneous models).
emitDataSet( int n ) and emitDataSet( int n, null ) should return a data set with n sequences of length of the model ( SequenceScore.getLength()).

The standard implementation throws an Exception.

Specified by:

emitDataSet in interface StatisticalModel

Parameters:

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

seqLength - the length of the sequences for a homogeneous model; for an inhomogeneous model this parameter should be null or an array of size 0.

Returns:

a DataSet containing the artificial sequence(s)

Throws:

NotTrainedException - if the model is not trained yet

Exception - if the emission did not succeed

See Also:

DataSet

getMaximalMarkovOrder

public byte getMaximalMarkovOrder()
                           throws UnsupportedOperationException

Description copied from interface: StatisticalModel

This method returns the maximal used Markov order, if possible.

Specified by:

getMaximalMarkovOrder in interface StatisticalModel

Returns:

maximal used Markov order

Throws:

UnsupportedOperationException - if the model can't give a proper answer