de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete
Class PhyloDiscreteEmission

java.lang.Object
  de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission
      de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.DiscreteEmission
          de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission

All Implemented Interfaces:

SamplingComponent, SamplingFromStatistic, DifferentiableEmission, Emission, SamplingEmission, Storable, Cloneable

public class PhyloDiscreteEmission
extends DiscreteEmission
implements SamplingEmission

Phylogenetic discrete emission This class uses a phylogenetic tree to describe multidimensional data It implements Felsensteins model for nucleotide substitution (F81)

Author:

Michael Scharfe

Field Summary

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission

con, counter, ess, grad, hyperParams, logNorm, offset, params, paramsFile, probs, reader, samplingIndex, statistic, writer

Constructor Summary

PhyloDiscreteEmission(AlphabetContainer con, double[] hyperParams, PhyloTree t)
This is a simple constructor for a DiscreteEmission defining the individual hyper parameters.

PhyloDiscreteEmission(AlphabetContainer con, double ess, PhyloTree t)
This is a simple constructor for a PhyloDiscreteEmission based on the equivalent sample size.

Method Summary

void	addGradientOfLogPriorTerm(double[] gradient, int offset) This method computes the gradient of Emission.getLogPriorTerm() for each parameter of this model.
void	addToStatistic(boolean forward, int startPos, int endPos, double weight, Sequence seq) This method adds the weight to the internal sufficient statistic.
protected void	appendFurtherInformation(StringBuffer xml) This method appends further information to the XML representation.
PhyloDiscreteEmission	clone()
void	estimateFromStatistic() This method estimates the parameters from the internal sufficient statistic.
protected void	extractFurtherInformation(StringBuffer xml) This method extracts further information from the XML representation.
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
double	getLogPosteriorFromStatistic() This method calculates the a-posteriori probability for the current statistics
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.
double	getLogProbFor(boolean forward, int startPos, int endPos, Sequence seq) This method computes the logarithm of the likelihood.
double	getLogProposalPosteriorFromStatistic() Returns the log posterior of the proposal distribution for the current statistic
void	resetStatistic() This method resets the internal sufficient statistic.

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.DiscreteEmission

getConditionIndex, toString

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission

acceptParameters, drawParametersFromStatistic, extendSampling, fillCurrentParameter, fillSamplingGroups, finalize, fromXML, getAlphabetContainer, getHyperParams, getLogPriorTerm, getNodeLabel, getNodeShape, getNumberOfParameters, getSizeOfEventSpace, initForSampling, initializeFunctionRandomly, isInSamplingMode, joinStatistics, parseNextParameterSet, parseParameterSet, precompute, samplingStopped, setLinear, setParameter, setParameterOffset, setParameters, setShape, toXML

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface de.jstacs.sampling.SamplingFromStatistic

drawParametersFromStatistic

Methods inherited from interface de.jstacs.sampling.SamplingComponent

acceptParameters, extendSampling, initForSampling, isInSamplingMode, parseNextParameterSet, parseParameterSet, samplingStopped

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

getAlphabetContainer, getLogPriorTerm, getNodeLabel, getNodeShape, initializeFunctionRandomly, joinStatistics, setParameters

Methods inherited from interface de.jstacs.Storable

toXML

Constructor Detail

PhyloDiscreteEmission

public PhyloDiscreteEmission(AlphabetContainer con,
                             double ess,
                             PhyloTree t)

This is a simple constructor for a PhyloDiscreteEmission based on the equivalent sample size.

Parameters:

con - the AlphabetContainer of this emission

ess - the equivalent sample size (ess) of this emission that is equally distributed over all parameters

t - the phylogenetic tree PhyloTree

PhyloDiscreteEmission

public PhyloDiscreteEmission(AlphabetContainer con,
                             double[] hyperParams,
                             PhyloTree t)

This is a simple constructor for a DiscreteEmission defining the individual hyper parameters.

Parameters:

con - the AlphabetContainer of this emission

hyperParams - the individual hyper parameters for each parameter

t - the phylogenetic tree PhyloTree

Method Detail

clone

public PhyloDiscreteEmission clone()
                            throws CloneNotSupportedException

Overrides:

clone in class AbstractConditionalDiscreteEmission

Throws:

CloneNotSupportedException

getLogProbFor

public double getLogProbFor(boolean forward,
                            int startPos,
                            int endPos,
                            Sequence seq)
                     throws OperationNotSupportedException

Description copied from interface: Emission

This method computes the logarithm of the likelihood.

Specified by:

getLogProbFor in interface Emission

Overrides:

getLogProbFor in class AbstractConditionalDiscreteEmission

Parameters:

forward - whether to use the forward or the reverse strand

startPos - the start position

endPos - the end position

seq - the sequence

Returns:

the logarithm of the probability

Throws:

OperationNotSupportedException - if forward=false and the reverse complement of the sequence seq is not defined

getLogProbAndPartialDerivationFor

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

Description copied from interface: DifferentiableEmission

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:

getLogProbAndPartialDerivationFor in interface DifferentiableEmission

Overrides:

getLogProbAndPartialDerivationFor in class AbstractConditionalDiscreteEmission

Parameters:

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

seq - the Sequence

Returns:

the logarithmic score for the Sequence

Throws:

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

addGradientOfLogPriorTerm

public void addGradientOfLogPriorTerm(double[] gradient,
                                      int offset)

Description copied from interface: DifferentiableEmission

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).

Specified by:

addGradientOfLogPriorTerm in interface DifferentiableEmission

Overrides:

addGradientOfLogPriorTerm in class AbstractConditionalDiscreteEmission

Parameters:

gradient - 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(), DifferentiableEmission.setParameterOffset(int)

estimateFromStatistic

public void estimateFromStatistic()

Description copied from interface: Emission

This method estimates the parameters from the internal sufficient statistic.

Specified by:

estimateFromStatistic in interface Emission

Overrides:

estimateFromStatistic in class AbstractConditionalDiscreteEmission

appendFurtherInformation

protected void appendFurtherInformation(StringBuffer xml)

Description copied from class: AbstractConditionalDiscreteEmission

This method appends further information to the XML representation. It allows subclasses to save further parameters that are not defined in the superclass.

Overrides:

appendFurtherInformation in class AbstractConditionalDiscreteEmission

Parameters:

xml - the XML representation

extractFurtherInformation

protected void extractFurtherInformation(StringBuffer xml)
                                  throws NonParsableException

Description copied from class: AbstractConditionalDiscreteEmission

This method extracts further information from the XML representation. It allows subclasses to cast further parameters that are not defined in the superclass.

Overrides:

extractFurtherInformation in class AbstractConditionalDiscreteEmission

Parameters:

xml - the XML representation

Throws:

NonParsableException - if the information could not be reconstructed out of the StringBuffer xml

getLogProposalPosteriorFromStatistic

public double getLogProposalPosteriorFromStatistic()

Returns the log posterior of the proposal distribution for the current statistic

Returns:

the log posterior

getLogPosteriorFromStatistic

public double getLogPosteriorFromStatistic()

Description copied from interface: SamplingFromStatistic

This method calculates the a-posteriori probability for the current statistics

Specified by:

getLogPosteriorFromStatistic in interface SamplingFromStatistic

Overrides:

getLogPosteriorFromStatistic in class AbstractConditionalDiscreteEmission

Returns:

the logarithm of the a-posteriori probability

resetStatistic

public void resetStatistic()

Description copied from interface: Emission

This method resets the internal sufficient statistic.

Specified by:

resetStatistic in interface Emission

Overrides:

resetStatistic in class AbstractConditionalDiscreteEmission

addToStatistic

public void addToStatistic(boolean forward,
                           int startPos,
                           int endPos,
                           double weight,
                           Sequence seq)
                    throws OperationNotSupportedException

Description copied from interface: Emission

This method adds the weight to the internal sufficient statistic.

Specified by:

addToStatistic in interface Emission

Overrides:

addToStatistic in class AbstractConditionalDiscreteEmission

Parameters:

forward - whether to use the forward or the reverse strand

startPos - the start position

endPos - the end position

weight - the weight of the sequence

seq - the sequence

Throws:

OperationNotSupportedException - if forward=false and the reverse complement of the sequence seq is not defined

getLogGammaScoreFromStatistic

public double getLogGammaScoreFromStatistic()

Description copied from interface: SamplingEmission

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

Specified by:

getLogGammaScoreFromStatistic in interface SamplingEmission

Overrides:

getLogGammaScoreFromStatistic in class AbstractConditionalDiscreteEmission

Returns:

the logarithm of the gamma-score for the current statistics