de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions
Interface DifferentiableEmission

All Superinterfaces:

Cloneable, Emission, Storable

All Known Implementing Classes:

AbstractConditionalDiscreteEmission, DiscreteEmission, GaussianEmission, PhyloDiscreteEmission, PluginGaussianEmission, ReferenceSequenceDiscreteEmission, SilentEmission, UniformEmission

public interface DifferentiableEmission
extends Emission

This interface declares all methods needed in an emission during a numerical optimization of HMM.

Author:

Jens Keilwagen

Method Summary

void	addGradientOfLogPriorTerm(double[] grad, int offset) This method computes the gradient of Emission.getLogPriorTerm() for each parameter of this model.
void	fillCurrentParameter(double[] params) Fills the current parameters in the global code>params array using the internal offset.
void	fillSamplingGroups(int parameterOffset, LinkedList<int[]> list) Adds the groups of indexes of those parameters of this emission that should be sampled together in one step of a grouped sampling procedure, each as an int[], into list.
double	getLogProbAndPartialDerivationFor(boolean forward, int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq) Returns the logarithmic score for a Sequence beginning at position start in the Sequence and fills lists with the indices and the partial derivations.
int	getNumberOfParameters() Returns the number of parameters of this emission.
int	getSizeOfEventSpace() Returns the size of the event space, i.e., the number of possible outcomes, for the random variables of this emission
void	setParameter(double[] params, int offset) This method sets the internal parameters using the given global parameter array, the global offset of the HMM and the internal offset.
int	setParameterOffset(int offset) This method sets the internal parameter offset and returns the new parameter offset for further use.

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.Emission

addToStatistic, estimateFromStatistic, getAlphabetContainer, getLogPriorTerm, getLogProbFor, getNodeLabel, getNodeShape, initializeFunctionRandomly, joinStatistics, resetStatistic, setParameters

Methods inherited from interface de.jstacs.Storable

toXML

Method Detail

fillCurrentParameter

void fillCurrentParameter(double[] params)

Fills the current parameters in the global code>params array using the internal offset.

Parameters:

params - the global parameter array of the HMM

See Also:

setParameterOffset(int)

setParameter

void setParameter(double[] params,
                  int offset)

This method sets the internal parameters using the given global parameter array, the global offset of the HMM and the internal offset.

Parameters:

params - the global parameter array of the classifier

offset - the offset of the HMM

See Also:

setParameterOffset(int)

setParameterOffset

int setParameterOffset(int offset)

This method sets the internal parameter offset and returns the new parameter offset for further use.

Parameters:

offset - the offset to be set

Returns:

the new parameter offset

getLogProbAndPartialDerivationFor

double getLogProbAndPartialDerivationFor(boolean forward,
                                         int startPos,
                                         int endPos,
                                         IntList indices,
                                         DoubleList partDer,
                                         Sequence seq)
                                         throws OperationNotSupportedException

Returns the logarithmic score for a Sequence beginning at position start in the Sequence and fills lists with the indices and the partial derivations.

Parameters:

forward - a switch whether to use the forward or the reverse complementary strand of the sequence

seq - the Sequence

startPos - the start position in the Sequence

endPos - the end position in the Sequence

indices - an IntList of indices, after method invocation the list should contain the indices i where is not zero

partDer - 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

Throws:

OperationNotSupportedException - if forward==false and the reverse complement of the sequence can not be computed

addGradientOfLogPriorTerm

void addGradientOfLogPriorTerm(double[] grad,
                               int offset)

This method computes the gradient of Emission.getLogPriorTerm() for each parameter of this model. The results are added to the array grad beginning at index (offset + internal offset).

Parameters:

grad - the array of gradients

offset - the start index of the HMM in the grad array, where the partial derivations for the parameters of the HMM shall be entered

See Also:

Emission.getLogPriorTerm(), setParameterOffset(int)

fillSamplingGroups

void fillSamplingGroups(int parameterOffset,
                        LinkedList<int[]> list)

Adds the groups of indexes of those parameters of this emission that should be sampled together in one step of a grouped sampling procedure, each as an int[], into list. In most cases, one group should contain the parameters that are living on a common simplex. The internal indexes of the parameters are incremeneted by an external parameterOffset

Parameters:

parameterOffset - the external parameter offset

list - the list of sampling groups

getNumberOfParameters

int getNumberOfParameters()

Returns the number of parameters of this emission.

Returns:

the number of parameters

getSizeOfEventSpace

int getSizeOfEventSpace()

Returns the size of the event space, i.e., the number of possible outcomes, for the random variables of this emission

Returns:

the size of the event space