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java.lang.Object de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
public class StrandDiffSM
This class enables the user to search on both strand. So the motif can be found on the forward or on the reverse complementary strand.
ComplementableDiscreteAlphabet
,
AlphabetContainer.isReverseComplementable()
Nested Class Summary | |
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static class |
StrandDiffSM.InitMethod
This enum defines the different types of plug-in initialization of a StrandDiffSM . |
Field Summary |
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Fields inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM |
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componentScore, dList, freeParams, function, hiddenParameter, hiddenPotential, iList, logGammaSum, logHiddenNorm, logHiddenPotential, norm, optimizeHidden, paramRef, partNorm |
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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StrandDiffSM(DifferentiableStatisticalModel function,
double forwardPartOfESS,
int starts,
boolean plugIn,
StrandDiffSM.InitMethod initMethod)
This constructor creates a StrandDiffSM that optimizes the usage of each strand. |
|
StrandDiffSM(DifferentiableStatisticalModel function,
int starts,
boolean plugIn,
StrandDiffSM.InitMethod initMethod,
double forward)
This constructor creates a StrandDiffSM that has a fixed frequency for the strand usage. |
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StrandDiffSM(StringBuffer xml)
This is the constructor for Storable . |
Method Summary | |
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protected void |
extractFurtherInformation(StringBuffer xml)
This method is the opposite of AbstractMixtureDiffSM.getFurtherInformation() . |
protected void |
fillComponentScores(Sequence seq,
int start)
Fills the internal array AbstractMixtureDiffSM.componentScore with the logarithmic
scores of the components given a Sequence . |
double |
getESS()
Returns the equivalent sample size (ess) of this model, i.e. |
double |
getForwardProbability()
This methoth returns the a-priori probability for the forward strand. |
protected StringBuffer |
getFurtherInformation()
This method is used to append further information of the instance to the XML representation. |
double |
getHyperparameterForHiddenParameter(int index)
This method returns the hyperparameter for the hidden parameter with index index . |
String |
getInstanceName()
Should return a short instance name such as iMM(0), BN(2), ... |
protected double |
getLogNormalizationConstantForComponent(int i)
Computes the logarithm of the normalization constant for the component i . |
double |
getLogPartialNormalizationConstant(int parameterIndex)
Returns the logarithm of the partial normalization constant for the parameter with index parameterIndex . |
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. |
static double[][][] |
getReverseComplementDistributions(ComplementableDiscreteAlphabet abc,
double[][][] condDistr)
This method computes the reverse complement distributions for given conditional distributions. |
StrandedLocatedSequenceAnnotationWithLength.Strand |
getStrand(Sequence seq,
int startPos)
This method returns the preferred StrandedLocatedSequenceAnnotationWithLength.Strand for a given subsequence. |
protected void |
init(boolean freeParams)
This method creates the underlying structure for the parameters. |
protected void |
initializeUsingPlugIn(int index,
boolean freeParams,
DataSet[] data,
double[][] weights)
This method initializes the functions using the data in some way. |
static boolean |
isStrandModel(DifferentiableStatisticalModel nsf)
Check whether a DifferentiableStatisticalModel is a StrandDiffSM . |
boolean |
modify(int offsetLeft,
int offsetRight)
Manually modifies the model. |
protected void |
setForwardProb(double forward)
This method can be used to set the forward strand probability. |
String |
toString(NumberFormat nf)
This method returns a String representation of the instance. |
Methods inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel |
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emitDataSet, getInitialClassParam, getLogProbFor, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, getMaximalMarkovOrder, isNormalized |
Methods inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore |
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getAlphabetContainer, getCharacteristics, getLength, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreFor, getLogScoreFor, getNumberOfStarts, getNumericalCharacteristics |
Methods inherited from class java.lang.Object |
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equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Methods inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore |
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getInitialClassParam, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation |
Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel |
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emitDataSet, getLogProbFor, getLogProbFor, getLogProbFor, getMaximalMarkovOrder |
Methods inherited from interface de.jstacs.sequenceScores.SequenceScore |
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getAlphabetContainer, getCharacteristics, getLength, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics |
Constructor Detail |
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public StrandDiffSM(DifferentiableStatisticalModel function, double forwardPartOfESS, int starts, boolean plugIn, StrandDiffSM.InitMethod initMethod) throws CloneNotSupportedException, WrongAlphabetException
function
- the DifferentiableSequenceScoreforwardPartOfESS
- the part of the full ESS that should be used as hyperparameter for the forward strandstarts
- the number of starts the should be done in an optimizationplugIn
- whether the initial parameters for an optimization should be related to the data or randomly drawninitMethod
- only used if plugIn==true
CloneNotSupportedException
- if function
could not be cloned
WrongAlphabetException
- if the alphabet of function
is not AlphabetContainer.isReverseComplementable()
and, hence, cannot be used for a strand mixtureStrandDiffSM.InitMethod
public StrandDiffSM(DifferentiableStatisticalModel function, int starts, boolean plugIn, StrandDiffSM.InitMethod initMethod, double forward) throws CloneNotSupportedException, WrongAlphabetException
function
- the DifferentiableSequenceScorestarts
- the number of starts the should be done in an optimizationplugIn
- whether the initial parameters for an optimization should be related to the data or randomly drawninitMethod
- only used if plugIn==true
forward
- the probability of a motif to be on the forward strand
CloneNotSupportedException
- if function
could not be cloned
WrongAlphabetException
- if the alphabet of function
is not AlphabetContainer.isReverseComplementable()
and, hence, cannot be used for a strand mixtureStrandDiffSM.InitMethod
public StrandDiffSM(StringBuffer xml) throws NonParsableException
Storable
.
xml
- the xml representation
NonParsableException
- if the representation could not be parsed.Method Detail |
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protected void setForwardProb(double forward)
forward
- the forward strand probability in (0,1)protected double getLogNormalizationConstantForComponent(int i)
AbstractMixtureDiffSM
i
.
getLogNormalizationConstantForComponent
in class AbstractMixtureDiffSM
i
- the index of the component
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
,
getLogPartialNormalizationConstant
in interface DifferentiableStatisticalModel
parameterIndex
- the index of the parameter
Exception
- if something went wrong with the normalizationDifferentiableStatisticalModel.getLogNormalizationConstant()
public double getHyperparameterForHiddenParameter(int index)
AbstractMixtureDiffSM
index
.
getHyperparameterForHiddenParameter
in class AbstractMixtureDiffSM
index
- the index of the hidden parameter
public double getForwardProbability()
public double getESS()
DifferentiableStatisticalModel
getESS
in interface DifferentiableStatisticalModel
protected void initializeUsingPlugIn(int index, boolean freeParams, DataSet[] data, double[][] weights) throws Exception
AbstractMixtureDiffSM
initializeUsingPlugIn
in class AbstractMixtureDiffSM
index
- the class indexfreeParams
- if true, the (reduced) parameterization is useddata
- the dataweights
- the weights for the data
- Throws:
Exception
- if the initialization could not be done- See Also:
DifferentiableSequenceScore.initializeFunction(int,
boolean, DataSet[], double[][])
public String getInstanceName()
SequenceScore
getInstanceName
in interface SequenceScore
protected void fillComponentScores(Sequence seq, int start)
AbstractMixtureDiffSM
AbstractMixtureDiffSM.componentScore
with the logarithmic
scores of the components given a Sequence
.
fillComponentScores
in class AbstractMixtureDiffSM
seq
- the sequencestart
- the start position in seq
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.
getLogScoreAndPartialDerivation
in interface DifferentiableSequenceScore
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
is not zeropartialDer
- a DoubleList
of partial derivations, after method
invocation the list should contain the corresponding
that are not zero
Sequence
protected StringBuffer getFurtherInformation()
AbstractMixtureDiffSM
getFurtherInformation
in class AbstractMixtureDiffSM
StringBuffer
AbstractMixtureDiffSM.extractFurtherInformation(StringBuffer)
protected void extractFurtherInformation(StringBuffer xml) throws NonParsableException
AbstractMixtureDiffSM
AbstractMixtureDiffSM.getFurtherInformation()
. It
extracts further information of the instance from a XML representation.
extractFurtherInformation
in class AbstractMixtureDiffSM
xml
- the StringBuffer
containing the information to be
extracted as XML code
NonParsableException
- if the StringBuffer
could not be parsedAbstractMixtureDiffSM.getFurtherInformation()
protected void init(boolean freeParams)
AbstractMixtureDiffSM
init
in class AbstractMixtureDiffSM
freeParams
- indicates whether to use only free parameters or all
parameterspublic String toString(NumberFormat nf)
SequenceScore
String
representation of the instance.
toString
in interface SequenceScore
nf
- the NumberFormat
for the String
representation of parameters or probabilities
String
representation of the instancepublic 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.
modify
in interface Mutable
offsetLeft
- the offset on the left sideoffsetRight
- the offset on the right side
true
if the motif model was modified otherwise
false
public static double[][][] getReverseComplementDistributions(ComplementableDiscreteAlphabet abc, double[][][] condDistr)
abc
- the alphabetcondDistr
- the conditional distribution
public StrandedLocatedSequenceAnnotationWithLength.Strand getStrand(Sequence seq, int startPos)
StrandedLocatedSequenceAnnotationWithLength.Strand
for a given subsequence.
seq
- the sequencestartPos
- the start position
StrandedLocatedSequenceAnnotationWithLength.Strand
of this subsequenceAbstractMixtureDiffSM.getIndexOfMaximalComponentFor(Sequence, int)
public static boolean isStrandModel(DifferentiableStatisticalModel nsf)
DifferentiableStatisticalModel
is a StrandDiffSM
.
nsf
- the original DifferentiableStatisticalModel
true
if the DifferentiableStatisticalModel
is a StrandDiffSM
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