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

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
                  de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.UniformDurationDiffSM

All Implemented Interfaces:

DifferentiableSequenceScore, SequenceScore, DifferentiableStatisticalModel, StatisticalModel, Storable, Cloneable

public final class UniformDurationDiffSM
extends DurationDiffSM

This scoring function implements a uniform distribution for positions. The class has no parameters, so the distribution does not change and it is possible to save parameters in an optimization.

Field Summary

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

delta, ess, max, min

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

UniformDurationDiffSM(int min, int max)
This is the main constructor that creates an instance for the given interval.

UniformDurationDiffSM(int min, int max, double ess)
This is the main constructor that creates an instance for the given interval and given ESS.

UniformDurationDiffSM(StringBuffer b)
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.
void	adjust(int[] length, double[] weight) This method adjust the parameter based on the given statistic.
void	drawPosition(int[] positions) This method draws from the distribution and returns the result in the given array.
double[]	getCurrentParameterValues() Returns a double array of dimension DifferentiableSequenceScore.getNumberOfParameters() containing the current parameter values.
String	getInstanceName() Should return a short instance name such as iMM(0), BN(2), ...
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	getLogScore(int... values) This method enables the user to get the log-score without using a sequence object.
double	getLogScoreAndPartialDerivation(IntList indices, DoubleList partialDer, int... values) This method enables the user to get the log-score and the partial derivations without using a sequence object.
int	getNumberOfParameters() Returns the number of parameters in this DifferentiableSequenceScore.
protected String	getRNotation(String distributionName) This method returns the distribution in R notation.
void	initializeFunction(int index, boolean meila, 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() This method set special parameters that lead to 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	setParameters(double[] params, int start) This method sets the internal parameters to the values of params between start and start + DifferentiableSequenceScore.getNumberOfParameters() - 1

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM

fromXML, getESS, getLogNormalizationConstant, getLogPartialNormalizationConstant, getMax, getMin, getNumberOfPossibilities, getSizeOfEventSpaceForRandomVariablesOfParameter, isPossible, modify, next, reset, toString, toXML

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

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

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, 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.differentiable.DifferentiableSequenceScore

getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getNumberOfRecommendedStarts

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

getAlphabetContainer, getCharacteristics, getLength, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics

Constructor Detail

UniformDurationDiffSM

public UniformDurationDiffSM(int min,
                             int max)

This is the main constructor that creates an instance for the given interval.

Parameters:

min - the minimal value

max - the maximal value

UniformDurationDiffSM

public UniformDurationDiffSM(int min,
                             int max,
                             double ess)

This is the main constructor that creates an instance for the given interval and given ESS.

Parameters:

min - the minimal value

max - the maximal value

ess - the equivalent sample size (used for the class probability)

UniformDurationDiffSM

public UniformDurationDiffSM(StringBuffer b)
                      throws NonParsableException

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

Parameters:

b - the XML representation as StringBuffer

Throws:

NonParsableException - if the XML representation could not be parsed

Method Detail

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

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

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.

Parameters:

index - the index of the class the DifferentiableSequenceScore models

meila - indicates whether the (reduced) parameterization is used

data - the samples

weights - the weights of the sequences in the samples

getRNotation

protected String getRNotation(String distributionName)

Description copied from class: DurationDiffSM

This method returns the distribution in R notation.

Specified by:

getRNotation in class DurationDiffSM

Parameters:

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

Returns:

the distribution in R notation

See Also:

REnvironment

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()

getLogScore

public double getLogScore(int... values)

Description copied from class: PositionDiffSM

This method enables the user to get the log-score without using a sequence object.

Specified by:

getLogScore in class PositionDiffSM

Parameters:

values - the values

Returns:

the score

getLogScoreAndPartialDerivation

public double getLogScoreAndPartialDerivation(IntList indices,
                                              DoubleList partialDer,
                                              int... values)

Description copied from class: PositionDiffSM

This method enables the user to get the log-score and the partial derivations without using a sequence object.

Specified by:

getLogScoreAndPartialDerivation in class PositionDiffSM

Parameters:

indices - a list for the indices of the parameters

partialDer - a list of the partial derivations

values - the values

Returns:

the score

drawPosition

public void drawPosition(int[] positions)

This method draws from the distribution and returns the result in the given array.

Parameters:

positions - an array for the result.

getCurrentParameterValues

public double[] getCurrentParameterValues()
                                   throws Exception

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

Throws:

Exception - if no parameters exist (yet)

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

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

initializeFunctionRandomly

public void initializeFunctionRandomly(boolean freeParams)
                                throws Exception

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

Throws:

Exception - if something went wrong

initializeUniformly

public void initializeUniformly()

Description copied from class: DurationDiffSM

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

Specified by:

initializeUniformly in class DurationDiffSM

adjust

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

Description copied from class: DurationDiffSM

This method adjust the parameter based on the given statistic.

Specified by:

adjust in class DurationDiffSM

Parameters:

length - an array containing length values

weight - an array containing corresponding weight values