de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous
Class GaussianEmission

java.lang.Object
  de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission

All Implemented Interfaces:

DifferentiableEmission, Emission, Storable, Cloneable

Direct Known Subclasses:

PluginGaussianEmission

public class GaussianEmission
extends Object
implements DifferentiableEmission

Emission for continuous values following a Gaussian distribution. The Gaussian density is parameterized in terms of mean and (log) precision. The prior of the Gaussian density is a normal-gamma density parameterized in terms of shape and rate.

Author:

Jan Grau

Constructor Summary

GaussianEmission(AlphabetContainer con)
Creates a GaussianEmission which can be used for maximum likelihood.

GaussianEmission(AlphabetContainer con, double ess, double priorMu, double priorAlpha, double priorBeta, boolean transformed)
Creates a GaussianEmission with normal-gamma prior by directly defining the hyper-parameters of the prior.

GaussianEmission(double ess, AlphabetContainer con, double priorMu, double expectedPrecision, double sdPrecision, boolean transformed)
Creates a GaussianEmission with normal-gamma prior by defining the expected precision and the expected standard deviation of the precision, i.e. via the expectation and variance of the gamma part of the normal-gamma prior.

GaussianEmission(StringBuffer xml)
Creates a GaussianEmission from its XML representation.

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.
GaussianEmission	clone()
void	estimateFromStatistic() This method estimates the parameters from the internal sufficient statistic.
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.
protected void	fromXML(StringBuffer xml) This method is internally used by the constructor GaussianEmission(StringBuffer).
AlphabetContainer	getAlphabetContainer() This method returns the AlphabetContainer of this emission.
double	getLogPriorTerm() Returns a value that is proportional to the log of the prior.
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.
String	getNodeLabel(double weight, String name, NumberFormat nf) Returns the graphviz label of the node containing this emission.
String	getNodeShape(boolean forward) Returns the graphviz string for the shape of the node.
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	initializeFunctionRandomly() This method initializes the emission randomly.
void	joinStatistics(Emission... emissions) This method joins the statistics of different instances and sets this joined statistic as statistic of each instance.
protected void	precompute() This method precomputes some normalization constant.
void	resetStatistic() This method resets the internal sufficient statistic.
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.
void	setParameters(Emission t) Set values of parameters of the instance to the value of the parameters of the given instance.
String	toString()
StringBuffer	toXML() This method returns an XML representation as StringBuffer of an instance of the implementing class.

Methods inherited from class java.lang.Object

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

Constructor Detail

GaussianEmission

public GaussianEmission(AlphabetContainer con)

Creates a GaussianEmission which can be used for maximum likelihood.

Parameters:

con - the alphabet of the emissions

GaussianEmission

public GaussianEmission(AlphabetContainer con,
                        double ess,
                        double priorMu,
                        double priorAlpha,
                        double priorBeta,
                        boolean transformed)

Creates a GaussianEmission with normal-gamma prior by directly defining the hyper-parameters of the prior.

Parameters:

con - the alphabet of the emissions

ess - the equivalent sample size of the normal-gamma prior

priorMu - the a-priori mean of the normal part of the prior

priorAlpha - the shape parameter of the gamma part of the prior

priorBeta - the rate parameter of the gamma part of the prior

transformed - use the transformed Gaussian density, i.e. exponential precision, ignored for numerical optimization

GaussianEmission

public GaussianEmission(double ess,
                        AlphabetContainer con,
                        double priorMu,
                        double expectedPrecision,
                        double sdPrecision,
                        boolean transformed)

Creates a GaussianEmission with normal-gamma prior by defining the expected precision and the expected standard deviation of the precision, i.e. via the expectation and variance of the gamma part of the normal-gamma prior.

Parameters:

ess - the equivalent sample size of the normal-gamma prior

con - the alphabet of the emissions

priorMu - the a-priori mean of the normal part of the prior

expectedPrecision - the expected value of the precision

sdPrecision - the expected standard deviation of the precision

transformed - use the transformed Gaussian density, i.e. exponential precision, ignored for numerical optimization

GaussianEmission

public GaussianEmission(StringBuffer xml)
                 throws NonParsableException

Creates a GaussianEmission from its XML representation.

Parameters:

xml - the XML representation.

Throws:

NonParsableException - if the XML representation could not be parsed

Method Detail

clone

public GaussianEmission clone()
                       throws CloneNotSupportedException

Overrides:

clone in class Object

Throws:

CloneNotSupportedException

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

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)

joinStatistics

public void joinStatistics(Emission... emissions)

Description copied from interface: Emission

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.

Specified by:

joinStatistics in interface Emission

Parameters:

emissions - the emissions to be joined

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

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

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

fillCurrentParameter

public void fillCurrentParameter(double[] params)

Description copied from interface: DifferentiableEmission

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

Specified by:

fillCurrentParameter in interface DifferentiableEmission

Parameters:

params - the global parameter array of the HMM

See Also:

DifferentiableEmission.setParameterOffset(int)

setParameter

public void setParameter(double[] params,
                         int offset)

Description copied from interface: DifferentiableEmission

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

Specified by:

setParameter in interface DifferentiableEmission

Parameters:

params - the global parameter array of the classifier

offset - the offset of the HMM

See Also:

DifferentiableEmission.setParameterOffset(int)

setParameterOffset

public int setParameterOffset(int offset)

Description copied from interface: DifferentiableEmission

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

Specified by:

setParameterOffset in interface DifferentiableEmission

Parameters:

offset - the offset to be set

Returns:

the new parameter offset

fromXML

protected void fromXML(StringBuffer xml)
                throws NonParsableException

This method is internally used by the constructor GaussianEmission(StringBuffer).

Parameters:

xml - the StringBuffer containing the xml representation of an instance

Throws:

NonParsableException - if the StringBuffer is not parsable

See Also:

GaussianEmission(StringBuffer)

getLogPriorTerm

public double getLogPriorTerm()

Description copied from interface: Emission

Returns a value that is proportional to the log of the prior. For maximum likelihood (ML) 0 should be returned.

Specified by:

getLogPriorTerm in interface Emission

Returns:

a value that is proportional to the log of the prior

See Also:

StatisticalModel.getLogPriorTerm()

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

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

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

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

initializeFunctionRandomly

public void initializeFunctionRandomly()

Description copied from interface: Emission

This method initializes the emission randomly.

Specified by:

initializeFunctionRandomly in interface Emission

precompute

protected void precompute()

This method precomputes some normalization constant.

resetStatistic

public void resetStatistic()

Description copied from interface: Emission

This method resets the internal sufficient statistic.

Specified by:

resetStatistic in interface Emission

toXML

public StringBuffer toXML()

Description copied from interface: Storable

This method returns an XML representation as StringBuffer of an instance of the implementing class.

Specified by:

toXML in interface Storable

Returns:

the XML representation

getAlphabetContainer

public AlphabetContainer getAlphabetContainer()

Description copied from interface: Emission

This method returns the AlphabetContainer of this emission.

Specified by:

getAlphabetContainer in interface Emission

Returns:

the AlphabetContainer of this emission

toString

public String toString()

Overrides:

toString in class Object

getNodeShape

public String getNodeShape(boolean forward)

Description copied from interface: Emission

Returns the graphviz string for the shape of the node.

Specified by:

getNodeShape in interface Emission

Parameters:

forward - if this emission is used on the forward strand

Returns:

the shape

getNodeLabel

public String getNodeLabel(double weight,
                           String name,
                           NumberFormat nf)

Description copied from interface: Emission

Returns the graphviz label of the node containing this emission.

Specified by:

getNodeLabel in interface Emission

Parameters:

weight - the weight of the node which is represented by the color of the node, or -1 for no representation, i.e., white background

name - the name of the state using this emission

nf - the NumberFormat for formatting the textual representation of this emission

Returns:

the label

fillSamplingGroups

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

Description copied from interface: DifferentiableEmission

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

Specified by:

fillSamplingGroups in interface DifferentiableEmission

Parameters:

parameterOffset - the external parameter offset

list - the list of sampling groups

getNumberOfParameters

public int getNumberOfParameters()

Description copied from interface: DifferentiableEmission

Returns the number of parameters of this emission.

Specified by:

getNumberOfParameters in interface DifferentiableEmission

Returns:

the number of parameters

getSizeOfEventSpace

public int getSizeOfEventSpace()

Description copied from interface: DifferentiableEmission

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

Specified by:

getSizeOfEventSpace in interface DifferentiableEmission

Returns:

the size of the event space

setParameters

public void setParameters(Emission t)
                   throws IllegalArgumentException

Description copied from interface: Emission

Set values of parameters of the instance to the value of the parameters of the given instance. It can be assumed that the given instance and the current instance are from the same class. This method might be used for instance in a multi-threaded optimization to broadcast the parameters.

Specified by:

setParameters in interface Emission

Parameters:

t - the emission with the parameters to be set

Throws:

IllegalArgumentException - if the assumption about the same class for given and current instance is wrong