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java.lang.Objectde.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
public final class MarkovRandomFieldDiffSM
This class implements the scoring function for any MRF (Markov Random Field).
Field Summary |
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Fields inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore |
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alphabets, length, r |
Fields inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore |
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UNKNOWN |
Constructor Summary | |
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MarkovRandomFieldDiffSM(AlphabetContainer alphabets,
int length,
double ess,
String constr)
This is the main constructor that creates an instance of a MarkovRandomFieldDiffSM . |
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MarkovRandomFieldDiffSM(AlphabetContainer alphabets,
int length,
String constr)
This constructor creates an instance of a MarkovRandomFieldDiffSM with
equivalent sample size (ess) 0. |
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MarkovRandomFieldDiffSM(StringBuffer source)
This is the constructor for the interface Storable . |
Method Summary | |
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void |
addGradientOfLogPriorTerm(double[] grad,
int start)
This method computes the gradient of DifferentiableStatisticalModel.getLogPriorTerm() for each
parameter of this model. |
MarkovRandomFieldDiffSM |
clone()
Creates a clone (deep copy) of the current DifferentiableSequenceScore
instance. |
protected void |
fromXML(StringBuffer representation)
This method is called in the constructor for the Storable
interface to create a scoring function from a StringBuffer . |
double[] |
getCurrentParameterValues()
Returns a double array of dimension
DifferentiableSequenceScore.getNumberOfParameters() containing the current parameter values. |
double |
getESS()
Returns the equivalent sample size (ess) of this model, i.e. the equivalent sample size for the class or component that is represented by this model. |
String |
getInstanceName()
Should return a short instance name such as iMM(0), BN(2), ... |
double |
getLogNormalizationConstant()
Returns the logarithm of the sum of the scores over all sequences of the event space. |
double |
getLogPartialNormalizationConstant(int parameterIndex)
Returns the logarithm of the partial normalization constant for the parameter with index parameterIndex . |
double |
getLogPriorTerm()
This method computes a value that is proportional to
where prior is the prior for the parameters of this model. |
double |
getLogScoreAndPartialDerivation(Sequence seq,
int start,
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 start)
Returns the logarithmic score for the Sequence seq
beginning at position start in the Sequence . |
int |
getNumberOfParameters()
Returns the number of parameters in this DifferentiableSequenceScore . |
int |
getSizeOfEventSpaceForRandomVariablesOfParameter(int index)
Returns the size of the event space of the random variables that are affected by parameter no. |
void |
initializeFunction(int index,
boolean freeParams,
DataSet[] data,
double[][] weights)
This method creates the underlying structure of the DifferentiableSequenceScore . |
void |
initializeFunctionRandomly(boolean freeParams)
This method initializes the DifferentiableSequenceScore randomly. |
boolean |
isInitialized()
This method can be used to determine whether the instance is initialized. |
void |
setParameters(double[] params,
int start)
This method sets the internal parameters to the values of params between start and
start + |
String |
toString()
|
StringBuffer |
toXML()
This method returns an XML representation as StringBuffer of an
instance of the implementing class. |
Methods inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel |
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emitDataSet, getInitialClassParam, getLogProbFor, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, getMaximalMarkovOrder, isNormalized, isNormalized |
Methods inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore |
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getAlphabetContainer, getCharacteristics, getLength, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreFor, getLogScoreFor, getNumberOfRecommendedStarts, getNumberOfStarts, getNumericalCharacteristics |
Methods inherited from class java.lang.Object |
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equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Methods inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore |
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getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getNumberOfRecommendedStarts |
Methods inherited from interface de.jstacs.sequenceScores.SequenceScore |
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getAlphabetContainer, getCharacteristics, getLength, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics |
Constructor Detail |
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public MarkovRandomFieldDiffSM(AlphabetContainer alphabets, int length, String constr)
MarkovRandomFieldDiffSM
with
equivalent sample size (ess) 0.
alphabets
- the AlphabetContainer
length
- the length of the sequences and accordingly the modelconstr
- the constraints that are used for the model, see
ConstraintManager.extract(int, String)
MarkovRandomFieldDiffSM(AlphabetContainer, int,
double, String)
public MarkovRandomFieldDiffSM(AlphabetContainer alphabets, int length, double ess, String constr)
MarkovRandomFieldDiffSM
.
alphabets
- the AlphabetContainer
length
- the length of the sequences and accordingly the modeless
- the equivalent sample size (ess)constr
- the constraints that are used for the model, see
ConstraintManager.extract(int, String)
public MarkovRandomFieldDiffSM(StringBuffer source) throws NonParsableException
Storable
.
Creates a new MarkovRandomFieldDiffSM
out of a StringBuffer
as
returned by toXML()
.
source
- the XML representation as StringBuffer
NonParsableException
- if the XML representation could not be parsedMethod Detail |
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protected void fromXML(StringBuffer representation) throws NonParsableException
AbstractDifferentiableSequenceScore
Storable
interface to create a scoring function from a StringBuffer
.
fromXML
in class AbstractDifferentiableSequenceScore
representation
- the XML representation as StringBuffer
NonParsableException
- if the StringBuffer
could not be parsedAbstractDifferentiableSequenceScore.AbstractDifferentiableSequenceScore(StringBuffer)
public MarkovRandomFieldDiffSM clone() throws CloneNotSupportedException
DifferentiableSequenceScore
DifferentiableSequenceScore
instance.
clone
in interface DifferentiableSequenceScore
clone
in interface SequenceScore
clone
in class AbstractDifferentiableStatisticalModel
DifferentiableSequenceScore
CloneNotSupportedException
- if something went wrong while cloning the
DifferentiableSequenceScore
public double getLogScoreFor(Sequence seq, int start)
SequenceScore
Sequence
seq
beginning at position start
in the Sequence
.
seq
- the Sequence
start
- the start position in the Sequence
Sequence
public double getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)
DifferentiableSequenceScore
Sequence
beginning at
position start
in the Sequence
and fills lists with
the indices and the partial derivations.
seq
- the Sequence
start
- the start position in the Sequence
indices
- an IntList
of indices, after method invocation the
list should contain the indices i where
partialDer
- a DoubleList
of partial derivations, after method
invocation the list should contain the corresponding
Sequence
public int getNumberOfParameters()
DifferentiableSequenceScore
DifferentiableSequenceScore
. If the
number of parameters is not known yet, the method returns
DifferentiableSequenceScore.UNKNOWN
.
DifferentiableSequenceScore
DifferentiableSequenceScore.UNKNOWN
public String getInstanceName()
SequenceScore
public void setParameters(double[] params, int start)
DifferentiableSequenceScore
params
between start
and
start + DifferentiableSequenceScore.getNumberOfParameters()
- 1
params
- the new parametersstart
- the start index in params
public String toString()
toString
in class Object
public StringBuffer toXML()
Storable
StringBuffer
of an
instance of the implementing class.
public void initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights) throws Exception
DifferentiableSequenceScore
DifferentiableSequenceScore
.
index
- the index of the class the DifferentiableSequenceScore
modelsfreeParams
- indicates whether the (reduced) parameterization is useddata
- the samplesweights
- the weights of the sequences in the samples
Exception
- if something went wrongpublic void initializeFunctionRandomly(boolean freeParams) throws Exception
DifferentiableSequenceScore
DifferentiableSequenceScore
randomly. It has to
create the underlying structure of the DifferentiableSequenceScore
.
freeParams
- indicates whether the (reduced) parameterization is used
Exception
- if something went wrongpublic double getLogNormalizationConstant()
DifferentiableStatisticalModel
public double getLogPartialNormalizationConstant(int parameterIndex) throws Exception
DifferentiableStatisticalModel
parameterIndex
. This is the logarithm of the partial derivation of the
normalization constant for the parameter with index
parameterIndex
,
parameterIndex
- the index of the parameter
Exception
- if something went wrong with the normalizationDifferentiableStatisticalModel.getLogNormalizationConstant()
public double getESS()
DifferentiableStatisticalModel
public int getSizeOfEventSpaceForRandomVariablesOfParameter(int index)
DifferentiableStatisticalModel
index
, i.e. the product of the
sizes of the alphabets at the position of each random variable affected
by parameter index
. For DNA alphabets this corresponds to 4
for a PWM, 16 for a WAM except position 0, ...
index
- the index of the parameter
public double getLogPriorTerm()
DifferentiableStatisticalModel
DifferentiableStatisticalModel.getESS()
* DifferentiableStatisticalModel.getLogNormalizationConstant()
+ Math.log( prior )
prior
is the prior for the parameters of this model.
DifferentiableStatisticalModel.getESS()
* DifferentiableStatisticalModel.getLogNormalizationConstant()
+ Math.log( prior ).
DifferentiableStatisticalModel.getESS()
,
DifferentiableStatisticalModel.getLogNormalizationConstant()
public void addGradientOfLogPriorTerm(double[] grad, int start)
DifferentiableStatisticalModel
DifferentiableStatisticalModel.getLogPriorTerm()
for each
parameter of this model. The results are added to the array
grad
beginning at index start
.
grad
- the array of gradientsstart
- the start index in the grad
array, where the
partial derivations for the parameters of this models shall be
enteredDifferentiableStatisticalModel.getLogPriorTerm()
public double[] getCurrentParameterValues() throws Exception
DifferentiableSequenceScore
double
array of dimension
DifferentiableSequenceScore.getNumberOfParameters()
containing the current parameter values.
If one likes to use these parameters to start an optimization it is
highly recommended to invoke
DifferentiableSequenceScore.initializeFunction(int, boolean, DataSet[], double[][])
before.
After an optimization this method can be used to get the current
parameter values.
Exception
- if no parameters exist (yet)public boolean isInitialized()
SequenceScore
SequenceScore.getLogScoreFor(Sequence)
.
true
if the instance is initialized, false
otherwise
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