public class MarkovModelDiffSM extends BayesianNetworkDiffSM implements Mutable, SamplingDifferentiableStatisticalModel
AbstractDifferentiableStatisticalModel
for an inhomogeneous Markov model.
The modeled length can be modified which might be very important for de-novo motif discovery.ess, isTrained, logNormalizationConstant, numFreePars, nums, order, parameters, plugInParameters, structureMeasure, trees
alphabets, length, r
UNKNOWN
Constructor and Description |
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MarkovModelDiffSM(AlphabetContainer alphabet,
int length,
double ess,
boolean plugInParameters,
InhomogeneousMarkov structureMeasure)
This constructor creates an instance without any prior for the modeled length.
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MarkovModelDiffSM(AlphabetContainer alphabet,
int length,
double ess,
boolean plugInParameters,
InhomogeneousMarkov structureMeasure,
DurationDiffSM lengthPenalty)
This constructor creates an instance with an prior for the modeled length.
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MarkovModelDiffSM(AlphabetContainer alphabet,
int length,
double ess,
boolean plugInParameters,
int order,
DurationDiffSM lengthPenalty)
This constructor creates an instance with an prior for the modeled length.
|
MarkovModelDiffSM(StringBuffer xml)
The standard constructor for the interface
Storable . |
Modifier and Type | Method and Description |
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protected void |
fromXML(StringBuffer source)
This method is called in the constructor for the
Storable
interface to create a scoring function from a StringBuffer . |
double |
getLogPriorTerm()
This method computes a value that is proportional to
|
int |
getOrder()
Returns the order of the inhomogeneous Markov model.
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int[][] |
getSamplingGroups(int parameterOffset)
Returns groups of indexes of parameters that shall be drawn
together in a sampling procedure
|
boolean |
modify(int offsetLeft,
int offsetRight)
Manually modifies the model.
|
void |
normalizeParameters()
Normalizes all parameters to log-probabilities.
|
StringBuffer |
toXML()
This method returns an XML representation as
StringBuffer of an
instance of the implementing class. |
addGradientOfLogPriorTerm, clone, createTrees, emitDataSet, fillInfixScore, getCurrentParameterSet, getCurrentParameterValues, getESS, getInfixFilter, getInstanceName, getLogNormalizationConstant, getLogPartialNormalizationConstant, getLogScoreAndPartialDerivation, getLogScoreFor, getMaximalMarkovOrder, getMaximumScore, getNumberOfParameters, getPositionDependentKMerProb, getPositionForParameter, getPWM, getSizeOfEventSpaceForRandomVariablesOfParameter, initializeFunction, initializeFunctionRandomly, isInitialized, precomputeNormalization, setParameters, setPlugInParameters, toHtml, toString
getInitialClassParam, getLogProbFor, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, isNormalized, isNormalized
getAlphabetContainer, getCharacteristics, getLength, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreFor, getLogScoreFor, getNumberOfRecommendedStarts, getNumberOfStarts, getNumericalCharacteristics, toString
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
addGradientOfLogPriorTerm, getESS, getLogNormalizationConstant, getLogPartialNormalizationConstant, getSizeOfEventSpaceForRandomVariablesOfParameter, isNormalized
clone, getCurrentParameterValues, getInitialClassParam, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getNumberOfParameters, getNumberOfRecommendedStarts, initializeFunction, initializeFunctionRandomly, setParameters
emitDataSet, getLogProbFor, getLogProbFor, getLogProbFor, getMaximalMarkovOrder
getAlphabetContainer, getCharacteristics, getInstanceName, getLength, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics, isInitialized, toString
public MarkovModelDiffSM(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, int order, DurationDiffSM lengthPenalty) throws Exception
alphabet
- the AlphabetContainer
of the MarkovModelDiffSM
length
- the initial length of the modeled sequencesess
- the equivalent sample sizeplugInParameters
- a switch whether to use plug-in parameters of notorder
- the order of the Markov modellengthPenalty
- the prior on the modeled sequence lengthException
- if super class constructor throws an Exception
or if the lengthPenalty
does not allow the initial lengthpublic MarkovModelDiffSM(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, InhomogeneousMarkov structureMeasure) throws Exception
alphabet
- the AlphabetContainer
of the MarkovModelDiffSM
length
- the initial length of the modeled sequencesess
- the equivalent sample sizeplugInParameters
- a switch whether to use plug-in parameters of notstructureMeasure
- an InhomogeneousMarkov
Measure
for the structureException
- if super class constructor throws an Exception
public MarkovModelDiffSM(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, InhomogeneousMarkov structureMeasure, DurationDiffSM lengthPenalty) throws Exception
alphabet
- the AlphabetContainer
of the MarkovModelDiffSM
length
- the initial length of the modeled sequencesess
- the equivalent sample sizeplugInParameters
- a switch whether to use plug-in parameters of notstructureMeasure
- a InhomogeneousMarkov
Measure
for the structurelengthPenalty
- the prior on the modeled sequence lengthException
- if super class constructor throws an Exception
or if the lengthPenalty
does not allow the initial lengthpublic MarkovModelDiffSM(StringBuffer xml) throws NonParsableException
Storable
.
Recreates a MarkovModelDiffSM
from its XML
representation as saved by the method toXML()
.xml
- the XML representation as StringBuffer
NonParsableException
- if the XML code could not be parsedprotected void fromXML(StringBuffer source) throws NonParsableException
AbstractDifferentiableSequenceScore
Storable
interface to create a scoring function from a StringBuffer
.fromXML
in class BayesianNetworkDiffSM
source
- the XML representation as StringBuffer
NonParsableException
- if the StringBuffer
could not be parsedAbstractDifferentiableSequenceScore.AbstractDifferentiableSequenceScore(StringBuffer)
public StringBuffer toXML()
Storable
StringBuffer
of an
instance of the implementing class.toXML
in interface Storable
toXML
in class BayesianNetworkDiffSM
public double getLogPriorTerm()
DifferentiableStatisticalModel
DifferentiableStatisticalModel.getESS()
* DifferentiableStatisticalModel.getLogNormalizationConstant()
+ Math.log( prior )
prior
is the prior for the parameters of this model.getLogPriorTerm
in interface DifferentiableStatisticalModel
getLogPriorTerm
in interface StatisticalModel
getLogPriorTerm
in class BayesianNetworkDiffSM
DifferentiableStatisticalModel.getESS()
* DifferentiableStatisticalModel.getLogNormalizationConstant()
+ Math.log( prior ).
DifferentiableStatisticalModel.getESS()
,
DifferentiableStatisticalModel.getLogNormalizationConstant()
public int getOrder()
public boolean modify(int offsetLeft, int offsetRight)
Mutable
offsetLeft
and offsetRight
define how many positions the left or
right border positions shall be moved. Negative numbers indicate moves to
the left while positive numbers correspond to moves to the right.public void normalizeParameters()
public int[][] getSamplingGroups(int parameterOffset)
SamplingDifferentiableStatisticalModel
getSamplingGroups
in interface SamplingDifferentiableStatisticalModel
parameterOffset
- a global offset on the parameter indexesparameterOffset
.