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
 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 $\frac{\partial \log score(seq)}{\partial \lambda_i}$ is not zero
partialDer - a DoubleList of partial derivations, after method invocation the list should contain the corresponding $\frac{\partial \log score(seq)}{\partial \lambda_i}$ 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