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java.lang.Objectde.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
de.jstacs.scoringFunctions.VariableLengthScoringFunction
de.jstacs.scoringFunctions.homogeneous.HomogeneousScoringFunction
de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction
public class UniformHomogeneousScoringFunction
This scoring function does nothing. So it is possible to save parameters in an optimization.
Field Summary |
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Fields inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction |
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alphabets, length, r |
Fields inherited from interface de.jstacs.scoringFunctions.ScoringFunction |
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UNKNOWN |
Constructor Summary | |
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UniformHomogeneousScoringFunction(AlphabetContainer alphabets,
double ess)
This is the main constructor that creates an instance that models each sequence uniformly. |
|
UniformHomogeneousScoringFunction(StringBuffer xml)
This is the constructor for Storable . |
Method Summary | |
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void |
addGradientOfLogPriorTerm(double[] grad,
int start)
This method computes the gradient of getLogPriorTerm() for each parameter of this model. |
protected void |
fromXML(StringBuffer xml)
This method is called in the constructor to create a scoring function from a StringBuffer |
double[] |
getCurrentParameterValues()
Returns a double array of dimension getNumberOfParameters() containing the current parameter
values. |
double |
getEss()
Returns the equivalent sample size of this model, i.e. the equivalent sample size for the class or component that is represented by this model. |
String |
getInstanceName()
Returns a short instance name. |
double |
getLogPriorTerm()
This method computes a value that is proportional to getESS()*Math.log( getNormalizationConstant() ) + Math.log( prior ). |
double |
getLogScore(Sequence seq,
int start,
int length)
This method computes the logarithm of the score for a given subsequence. |
double |
getLogScoreAndPartialDerivation(Sequence seq,
int start,
int length,
IntList indices,
DoubleList dList)
This method computes the logarithm of the score and the partial derivations for a given subsequence. |
int |
getMaximalMarkovOrder()
Returns the maximal used markov oder. |
double |
getNormalizationConstant(int length)
This method returns the normalization constant for a given sequence length. |
int |
getNumberOfParameters()
The number of parameters in this scoring function. |
double |
getPartialNormalizationConstant(int parameterIndex,
int length)
This method returns the partial normalization constant for a given parameter index and sequence length. |
int |
getSizeOfEventSpaceForRandomVariablesOfParameter(int index)
Returns the size of the event space of the random variables that are affected by parameter no. |
double[] |
getStationarySymbolDistribution()
This method returns the stationary symbol distribution. |
void |
initializeFunction(int index,
boolean meila,
Sample[] data,
double[][] weights)
This method creates the underlying structure of the scoring function. |
void |
initializeFunctionRandomly(boolean freeParams)
This method initializes the scoring function randomly. |
boolean |
isInitialized()
This method can be used to determine whether the model is initialized. |
boolean |
isNormalized()
This method returns whether the implemented score is already normalized to 1. |
void |
setParameters(double[] params,
int start)
This method sets the internal parameters to the values of params between start and
start + this.getNumberOfParameters() - 1 |
void |
setStatisticForHyperparameters(int[] length,
double[] weights)
This method sets the hyperparameters for the model parameters by evaluating the given statistic. |
String |
toString()
|
StringBuffer |
toXML()
This method returns an XML-representation of an instance of the implementing class. |
Methods inherited from class de.jstacs.scoringFunctions.VariableLengthScoringFunction |
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getLogScore, getLogScoreAndPartialDerivation, getNormalizationConstant, getPartialNormalizationConstant |
Methods inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction |
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clone, getAlphabetContainer, getInitialClassParam, getLength, getLogScore, getLogScoreAndPartialDerivation, getNumberOfRecommendedStarts, isNormalized |
Methods inherited from class java.lang.Object |
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equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public UniformHomogeneousScoringFunction(AlphabetContainer alphabets, double ess)
alphabets
- the AlphabetContaineress
- the equivalent sample size (ess) for the classpublic UniformHomogeneousScoringFunction(StringBuffer xml) throws NonParsableException
Storable
.
xml
- the xml representation
NonParsableException
- if the representation could not be parsed.Method Detail |
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public String getInstanceName()
ScoringFunction
public double getLogScore(Sequence seq, int start, int length)
VariableLengthScoringFunction
getLogScore
in class VariableLengthScoringFunction
seq
- the sequencestart
- the start indexlength
- the end index
ScoringFunction.getLogScore(Sequence, int)
public double getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList dList)
VariableLengthScoringFunction
getLogScoreAndPartialDerivation
in class VariableLengthScoringFunction
seq
- the sequencestart
- the start indexlength
- the end indexindices
- the list for the indices of the parametersdList
- the list for the partial derivations
ScoringFunction.getLogScoreAndPartialDerivation(Sequence, int, IntList,
DoubleList)
public int getNumberOfParameters()
ScoringFunction
UNKNOWN
.
ScoringFunction.UNKNOWN
public void setParameters(double[] params, int start)
ScoringFunction
params
between start
and
start + this.getNumberOfParameters() - 1
params
- the parametersstart
- the start indexpublic StringBuffer toXML()
Storable
public double getNormalizationConstant(int length)
VariableLengthScoringFunction
getNormalizationConstant
in class VariableLengthScoringFunction
length
- the sequence length
NormalizableScoringFunction.getNormalizationConstant()
public void initializeFunction(int index, boolean meila, Sample[] data, double[][] weights)
ScoringFunction
index
- the index of the class the scoring function modelsmeila
- if true, the (reduced) parameterization is useddata
- the samplesweights
- the weights of the sequences in the samples
protected void fromXML(StringBuffer xml) throws NonParsableException
AbstractNormalizableScoringFunction
fromXML
in class AbstractNormalizableScoringFunction
xml
- the XML representation
NonParsableException
- if the StringBuffer could not be parsed.public int getSizeOfEventSpaceForRandomVariablesOfParameter(int index)
NormalizableScoringFunction
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 getPartialNormalizationConstant(int parameterIndex, int length) throws Exception
VariableLengthScoringFunction
getPartialNormalizationConstant
in class VariableLengthScoringFunction
parameterIndex
- the index of the parameterlength
- the sequence length
Exception
- if something went wrongNormalizableScoringFunction.getPartialNormalizationConstant(int)
public double getEss()
NormalizableScoringFunction
public String toString()
toString
in class Object
public double getLogPriorTerm()
NormalizableScoringFunction
getESS()*Math.log( getNormalizationConstant() ) + Math.log( prior ).
prior is the prior for the parameters of this model.
- Returns:
getESS()*Math.log( getNormalizationConstant() ) + Math.log( prior )
- See Also:
NormalizableScoringFunction.getEss()
,
NormalizableScoringFunction.getNormalizationConstant()
public void addGradientOfLogPriorTerm(double[] grad, int start)
NormalizableScoringFunction
getLogPriorTerm()
for each parameter of this model. The
results are added to the array grad
beginning at index start
.
grad
- the gradientstart
- the start index in the grad
array, where the partial derivations for the parameters of
this models shall be enterNormalizableScoringFunction.getLogPriorTerm()
public double[] getCurrentParameterValues() throws Exception
ScoringFunction
getNumberOfParameters()
containing the current parameter
values. If on e likes to use these parameters to start an optimization it is highly recommended to invoke
ScoringFunction.initializeFunction(int, boolean, Sample[], double[][])
before. After an optimization
this method can be used to get the current parameter values.
Exception
- is thrown if no parameters exist, yetpublic boolean isInitialized()
ScoringFunction
ScoringFunction.initializeFunction(int, boolean, Sample[], double[][])
.
true
if the model is initializedpublic boolean isNormalized()
NormalizableScoringFunction
false
.
isNormalized
in interface NormalizableScoringFunction
isNormalized
in class AbstractNormalizableScoringFunction
public int getMaximalMarkovOrder()
HomogeneousScoringFunction
getMaximalMarkovOrder
in class HomogeneousScoringFunction
public void initializeFunctionRandomly(boolean freeParams) throws Exception
ScoringFunction
freeParams
- if true, the (reduced) parameterization is used
Exception
public double[] getStationarySymbolDistribution()
VariableLengthScoringFunction
de.jstacs.motifDiscovery.Mutable#expand(int, double[], double[], double)
,
de.jstacs.motifDiscovery.Mutable#shift(double[], double[], double)
and
de.jstacs.motifDiscovery.Mutable#shrink(double[], double[], double)
.
getStationarySymbolDistribution
in class VariableLengthScoringFunction
Mutable
public void setStatisticForHyperparameters(int[] length, double[] weights) throws Exception
VariableLengthScoringFunction
length
) and how often (weight
) they have been seen.
setStatisticForHyperparameters
in class VariableLengthScoringFunction
length
- the non-negative lengths of the sequencesweights
- the non-negative weight for the corresponding sequence
Exception
- if something went wrongMutable
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