de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
Class DurationDiffSM

java.lang.Object
  de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
      de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
          de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM
              de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM

All Implemented Interfaces:

DifferentiableSequenceScore, SequenceScore, DifferentiableStatisticalModel, StatisticalModel, Storable, Cloneable

Direct Known Subclasses:

MixtureDurationDiffSM, SkewNormalLikeDurationDiffSM, UniformDurationDiffSM

public abstract class DurationDiffSM
extends PositionDiffSM

This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.

Author:

Jens Keilwagen

See Also:

ExtendedZOOPSDiffSM

Field Summary

protected int	delta The difference of maximal and minimal value.
protected double	ess The equivalent sample size.
protected int	max The maximal value.
protected int	min The minimal value.

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.PositionDiffSM

internal

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

alphabets, length, r

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

UNKNOWN

Constructor Summary

protected	DurationDiffSM(int min, int max, double ess) The default constructor.
protected	DurationDiffSM(StringBuffer source) This is the constructor for Storable.

Method Summary

abstract void	adjust(int[] length, double[] weight) This method adjust the parameter based on the given statistic.
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	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.
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.
int	getMax() Returns the maximal value that can be scored.
int	getMin() Returns the minimal value that can be scored.
int	getNumberOfPossibilities() Returns the number of different possibilities that can be scored.
protected abstract String	getRNotation(String distributionName) This method returns the distribution in R notation.
int	getSizeOfEventSpaceForRandomVariablesOfParameter(int index) Returns the size of the event space of the random variables that are affected by parameter no.
abstract void	initializeUniformly() This method set special parameters that lead to an uniform distribution.
boolean	isPossible(int... positions) This method returns true if the given positions are in the domain of the PositionDiffSM.
void	modify(int delta) This method modifies the underlying AlphabetContainer.
boolean	next() This method steps to the next reasonable outcome if possible.
void	reset() This method resets the iterator to the initial state (first reasonable output) so that it can be used again.
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.mixture.motif.PositionDiffSM

clone, getInternalPosition, getLogScore, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivationForInternal, getLogScoreFor, getLogScoreForInternal, getValuesFromSequence

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

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

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

getAlphabetContainer, getCharacteristics, getLength, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreFor, 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

addGradientOfLogPriorTerm, getLogPriorTerm

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

getCurrentParameterValues, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getNumberOfParameters, getNumberOfRecommendedStarts, initializeFunction, initializeFunctionRandomly, setParameters

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

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

Field Detail

ess

protected double ess

The equivalent sample size.

min

protected int min

The minimal value.

max

protected int max

The maximal value.

delta

protected int delta

The difference of maximal and minimal value.

Constructor Detail

DurationDiffSM

protected DurationDiffSM(int min,
                         int max,
                         double ess)

The default constructor.

Parameters:

min - the minimal value

max - the maximal value

ess - the equivalent sample size

DurationDiffSM

protected DurationDiffSM(StringBuffer source)
                  throws NonParsableException

This is the constructor for Storable. Creates a new DurationDiffSM out of a StringBuffer.

Parameters:

source - the XML representation as StringBuffer

Throws:

NonParsableException - if the XML representation could not be parsed

Method Detail

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

Overrides:

toXML in class PositionDiffSM

Returns:

the XML representation

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.

Overrides:

fromXML in class PositionDiffSM

Parameters:

xml - the XML representation as StringBuffer

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

AbstractDifferentiableSequenceScore.AbstractDifferentiableSequenceScore(StringBuffer)

reset

public void reset()

Description copied from class: PositionDiffSM

This method resets the iterator to the initial state (first reasonable output) so that it can be used again.

Specified by:

reset in class PositionDiffSM

next

public boolean next()

Description copied from class: PositionDiffSM

This method steps to the next reasonable outcome if possible.

Specified by:

next in class PositionDiffSM

Returns:

true if a next reasonable outcome could be set, otherwise false

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

getESS

public final 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.

isPossible

public boolean isPossible(int... positions)

Description copied from class: PositionDiffSM

This method returns true if the given positions are in the domain of the PositionDiffSM.

Specified by:

isPossible in class PositionDiffSM

Parameters:

positions - the positions to be tested

Returns:

true if the given positions are in the domain of the PositionDiffSM

getMin

public final int getMin()

Returns the minimal value that can be scored.

Returns:

the minimal value that can be scored.

getMax

public final int getMax()

Returns the maximal value that can be scored.

Returns:

the maximal value that can be scored.

getNumberOfPossibilities

public int getNumberOfPossibilities()

Returns the number of different possibilities that can be scored.

Returns:

the number of different possibilities that can be scored.

initializeUniformly

public abstract void initializeUniformly()

This method set special parameters that lead to an uniform distribution.

adjust

public abstract void adjust(int[] length,
                            double[] weight)

This method adjust the parameter based on the given statistic.

Parameters:

length - an array containing length values

weight - an array containing corresponding weight values

modify

public void modify(int delta)

This method modifies the underlying AlphabetContainer. This might be necessary if the motif length changed.

Parameters:

delta - the change

See Also:

Mutable.modify(int, int), MutableMotifDiscoverer.modifyMotif(int, int, int)

getLogNormalizationConstant

public final double getLogNormalizationConstant()

Description copied from interface: DifferentiableStatisticalModel

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

Returns:

the logarithm of the normalization constant Z

getLogPartialNormalizationConstant

public final 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,

.

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()

getRNotation

protected abstract String getRNotation(String distributionName)

This method returns the distribution in R notation.

Parameters:

distributionName - the name of the distribution, e.g., "p"

Returns:

the distribution in R notation

See Also:

REnvironment

toString

public String toString()

Overrides:

toString in class Object