de.jstacs.sequenceScores.statisticalModels.differentiable

Interface VariableLengthDiffSM

All Superinterfaces:

Cloneable, DifferentiableSequenceScore, DifferentiableStatisticalModel, SequenceScore, StatisticalModel, Storable

All Known Implementing Classes:

AbstractVariableLengthDiffSM, CyclicMarkovModelDiffSM, HomogeneousDiffSM, HomogeneousMM0DiffSM, HomogeneousMMDiffSM, UniformHomogeneousDiffSM, VariableLengthMixtureDiffSM

public interface VariableLengthDiffSM
extends DifferentiableStatisticalModel

This is an interface for all DifferentiableStatisticalModels that allow to score subsequences of arbitrary length. This DifferentiableStatisticalModel should be the super class for non-motif DifferentiableStatisticalModels like homogeneous Markov models, cyclic Markov models, ... etc.

Author:

Jens Keilwagen

Field Summary

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

UNKNOWN

Method Summary

Methods

Modifier and Type	Method and Description
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	getLogScoreAndPartialDerivation(Sequence seq, int startpos, int endpos, IntList indices, DoubleList partialDer) 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 startpos, int endpos) Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.
void	setStatisticForHyperparameters(int[] length, double[] weight) This method sets the hyperparameters for the model parameters by evaluating the given statistic.

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, getNumberOfParameters, getNumberOfRecommendedStarts, initializeFunction, initializeFunctionRandomly, setParameters

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

emitDataSet, getLogProbFor, getLogProbFor, getLogProbFor, getMaximalMarkovOrder

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

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

Methods inherited from interface de.jstacs.Storable

toXML

Method Detail

getLogNormalizationConstant

double getLogNormalizationConstant(int length)

This method returns the logarithm of the normalization constant for a given sequence length.

Parameters:

length - the sequence length

Returns:

the logarithm of the normalization constant

See Also:

DifferentiableStatisticalModel.getLogNormalizationConstant()

getLogPartialNormalizationConstant

double getLogPartialNormalizationConstant(int parameterIndex,
                                        int length)
                                          throws Exception

This method returns the logarithm of the partial normalization constant for a given parameter index and a sequence length.

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)

getLogScoreFor

double getLogScoreFor(Sequence seq,
                    int startpos,
                    int endpos)

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

Parameters:

seq - the Sequence

startpos - the start position in the Sequence

endpos - the end position (inclusive) in the Sequence

Returns:

the logarithmic score for the Sequence

getLogScoreAndPartialDerivation

double getLogScoreAndPartialDerivation(Sequence seq,
                                     int startpos,
                                     int endpos,
                                     IntList indices,
                                     DoubleList partialDer)

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

Parameters:

seq - the Sequence

startpos - the start position in the Sequence

endpos - 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

partialDer - 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

setStatisticForHyperparameters

void setStatisticForHyperparameters(int[] length,
                                  double[] weight)
                                    throws Exception

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.

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