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

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

All Implemented Interfaces:

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

Direct Known Subclasses:

HomogeneousMM0DiffSM, HomogeneousMMDiffSM, UniformHomogeneousDiffSM

public abstract class HomogeneousDiffSM
extends AbstractVariableLengthDiffSM
implements SamplingDifferentiableStatisticalModel

This is the main class for all homogeneous DifferentiableSequenceScores.

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

protected	HomogeneousDiffSM(AlphabetContainer alphabets) This is the main constructor that creates an instance of a HomogeneousDiffSM that models sequences of arbitrary length.
protected	HomogeneousDiffSM(AlphabetContainer alphabets, int length) This is the main constructor that creates an instance of a HomogeneousDiffSM that models sequences of a given length.
protected	HomogeneousDiffSM(StringBuffer source) This is the constructor for Storable.

Method Summary

abstract byte	getMaximalMarkovOrder() Returns the maximal used markov oder.
abstract void	initializeUniformly(boolean freeParams) This method allows to initialize the instance with an uniform distribution.

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

getLogNormalizationConstant, getLogPartialNormalizationConstant, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreFor, getLogScoreFor

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

clone, emitDataSet, getInitialClassParam, getLogProbFor, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, isNormalized, isNormalized

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

fromXML, getAlphabetContainer, getCharacteristics, getLength, getLogScoreAndPartialDerivation, getLogScoreFor, getNumberOfRecommendedStarts, getNumberOfStarts, getNumericalCharacteristics

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.differentiable.SamplingDifferentiableStatisticalModel

getSamplingGroups

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel

addGradientOfLogPriorTerm, getESS, getLogNormalizationConstant, getLogPartialNormalizationConstant, getLogPriorTerm, getSizeOfEventSpaceForRandomVariablesOfParameter, isNormalized

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

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

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel

emitDataSet, getLogProbFor, getLogProbFor, getLogProbFor

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

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

Methods inherited from interface de.jstacs.Storable

toXML

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.differentiable.VariableLengthDiffSM

getLogNormalizationConstant, getLogPartialNormalizationConstant, setStatisticForHyperparameters

Constructor Detail

HomogeneousDiffSM

protected HomogeneousDiffSM(AlphabetContainer alphabets)

This is the main constructor that creates an instance of a HomogeneousDiffSM that models sequences of arbitrary length.

Parameters:

alphabets - the AlphabetContainer

HomogeneousDiffSM

protected HomogeneousDiffSM(AlphabetContainer alphabets,
                            int length)

This is the main constructor that creates an instance of a HomogeneousDiffSM that models sequences of a given length.

Parameters:

alphabets - the AlphabetContainer

length - the length of the modeled sequences

HomogeneousDiffSM

protected HomogeneousDiffSM(StringBuffer source)
                     throws NonParsableException

This is the constructor for Storable. Creates a new HomogeneousDiffSM out of its XML representation.

Parameters:

source - the XML representation as StringBuffer

Throws:

NonParsableException - if the XML representation could not be parsed

Method Detail

getMaximalMarkovOrder

public abstract byte getMaximalMarkovOrder()

Returns the maximal used markov oder.

Specified by:

getMaximalMarkovOrder in interface StatisticalModel

Overrides:

getMaximalMarkovOrder in class AbstractDifferentiableStatisticalModel

Returns:

the maximal used markov oder

initializeUniformly

public abstract void initializeUniformly(boolean freeParams)

This method allows to initialize the instance with an uniform distribution.

Parameters:

freeParams - a switch whether to take only free parameters or to take all