de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

Interface TransitionWithSufficientStatistic

All Superinterfaces:

Cloneable, Storable, Transition

All Known Subinterfaces:

SamplingTransition, TrainableAndDifferentiableTransition, TrainableTransition

All Known Implementing Classes:

BasicHigherOrderTransition, HigherOrderTransition

public interface TransitionWithSufficientStatistic
extends Transition

This interface defines method for reseting and filling an internal sufficient statistic. Using this statistic the interfaces TrainableTransition and SamplingTransition can be used to estimate the parameters (cf. TrainableTransition.estimateFromStatistic()) or to draw new parameters (cf. SamplingFromStatistic.drawParametersFromStatistic()).

Author:

Jens Keilwagen

See Also:

TrainableTransition, SamplingTransition

Method Summary

Methods

Modifier and Type	Method and Description
void	addToStatistic(int layer, int index, int childIdx, double weight, Sequence sequence, int sequencePosition) This method allows to add a certain weight to the sufficient statistic of a specific transition.
double	getLogGammaScoreFromStatistic() This method calculates a score for the current statistics, which is independent from the current parameters In general the gamma-score is a product of gamma-functions parameterized with the current statistics
void	joinStatistics(Transition... transitions) This method joins the statistics of different instances and sets this joined statistic as statistic of each instance.
void	resetStatistic() This method reset the internal sufficient statistics that can be used for estimating the parameters.

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition

clone, fillTransitionInformation, getChildIdx, getGraphizNetworkRepresentation, getLastContextState, getLogPriorTerm, getLogScoreFor, getMaximalInDegree, getMaximalMarkovOrder, getMaximalNumberOfChildren, getNumberOfChildren, getNumberOfIndexes, getNumberOfStates, hasAnySelfTransitions, initializeRandomly, isAbsoring, setParameters, toString

Methods inherited from interface de.jstacs.Storable

toXML

Method Detail

resetStatistic

void resetStatistic()

This method reset the internal sufficient statistics that can be used for estimating the parameters.

addToStatistic

void addToStatistic(int layer,
                  int index,
                  int childIdx,
                  double weight,
                  Sequence sequence,
                  int sequencePosition)

This method allows to add a certain weight to the sufficient statistic of a specific transition.

Parameters:

layer - the layer of the matrix

index - the index encoding the context

childIdx - the index of the child

weight - the weight added to the sufficient statistic

sequence - the sequence

sequencePosition - the position within the sequence

joinStatistics

void joinStatistics(Transition... transitions)

This method joins the statistics of different instances and sets this joined statistic as statistic of each instance. This method might be used for instance in a multi-threaded optimization to join partial statistics.

Parameters:

transitions - the transitions to be joined

getLogGammaScoreFromStatistic

double getLogGammaScoreFromStatistic()

This method calculates a score for the current statistics, which is independent from the current parameters In general the gamma-score is a product of gamma-functions parameterized with the current statistics

Returns:

the logarithm of the gamma-score for the current statistics