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java.lang.Objectde.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
de.jstacs.scoringFunctions.IndependentProductScoringFunction
public class IndependentProductScoringFunction
This class enables the user to model parts of the sequence independent of each other. The first part of the sequence is modeled by the first NormalizableScoringFunction and has the length of the first NormalizableScoringFunction, the second part starts directly after the first part, is modeled by the second ... .
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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IndependentProductScoringFunction(NormalizableScoringFunction... functions)
This constructor creates an instance of a given series of independent NormalizableScoringFunctions. |
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IndependentProductScoringFunction(NormalizableScoringFunction[] functions,
int[] length)
This constructor creates an instance of a given series of independent NormalizableScoringFunctions and lengths. |
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IndependentProductScoringFunction(StringBuffer source)
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. |
IndependentProductScoringFunction |
clone()
Creates a clone (deep copy) of the current ScoringFunction instance. |
protected void |
fromXML(StringBuffer rep)
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)
Returns the log score for the sequence |
double |
getLogScoreAndPartialDerivation(Sequence seq,
int start,
IntList indices,
DoubleList partialDer)
Returns the log score for the sequence and fills the list with the indices and the partial derivations. |
double |
getNormalizationConstant()
Returns the sum of the scores over all sequences of the event space. |
int |
getNumberOfParameters()
The number of parameters in this scoring function. |
int |
getNumberOfRecommendedStarts()
This method return the number of recommended optimization starts. |
double |
getPartialNormalizationConstant(int parameterIndex)
Returns the partial normalization constant for the parameter with index parameterIndex . |
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,
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. |
void |
setParameters(double[] params,
int start)
This method sets the internal parameters to the values of params between start and
start + this.getNumberOfParameters() - 1 |
String |
toString()
|
StringBuffer |
toXML()
This method returns an XML-representation of an instance of the implementing class. |
Methods inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction |
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getAlphabetContainer, getInitialClassParam, getLength, getLogScore, getLogScoreAndPartialDerivation, isNormalized, 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 IndependentProductScoringFunction(NormalizableScoringFunction... functions) throws CloneNotSupportedException, IllegalArgumentException
ScoringFunction.getLength()
.
So the length should not be 0.
functions
- the components
CloneNotSupportedException
- if at least one component could not be cloned
IllegalArgumentException
- if at least one component has length 0 or the components do not have the same essIndependentProductScoringFunction(NormalizableScoringFunction[], int[])
public IndependentProductScoringFunction(NormalizableScoringFunction[] functions, int[] length) throws CloneNotSupportedException
functions
- the componentslength
- the length of each component
CloneNotSupportedException
- if at least one component could not be cloned
IllegalArgumentException
- if the lengths and the components are not matching or the components do not have the same essIndependentProductScoringFunction(NormalizableScoringFunction[], int[])
public IndependentProductScoringFunction(StringBuffer source) throws NonParsableException
Storable
.
source
- the xml representation
NonParsableException
- if the representation could not be parsed.Method Detail |
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public IndependentProductScoringFunction clone() throws CloneNotSupportedException
ScoringFunction
ScoringFunction
instance.
clone
in interface ScoringFunction
clone
in class AbstractNormalizableScoringFunction
CloneNotSupportedException
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 getNormalizationConstant()
NormalizableScoringFunction
public double getPartialNormalizationConstant(int parameterIndex) throws Exception
NormalizableScoringFunction
parameterIndex
. This is
the partial derivation of the normalization constant for the parameter with index parameterIndex
\frac{\partial Z(\lambda)}{\partial \lambda_{index}}
.
parameterIndex
- the index of the parameter
Exception
- if something went wrong with the Normalizationpublic double getEss()
NormalizableScoringFunction
public void initializeFunction(int index, boolean freeParams, Sample[] data, double[][] weights) throws Exception
ScoringFunction
index
- the index of the class the scoring function modelsfreeParams
- if true, the (reduced) parameterization is useddata
- the samplesweights
- the weights of the sequences in the samples
- Throws:
Exception
protected void fromXML(StringBuffer rep) throws NonParsableException
AbstractNormalizableScoringFunction
fromXML
in class AbstractNormalizableScoringFunction
rep
- the XML representation
NonParsableException
- if the StringBuffer could not be parsed.public String getInstanceName()
ScoringFunction
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 double getLogScore(Sequence seq, int start)
ScoringFunction
seq
- the sequencestart
- the startposition in the sequence
public double getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)
ScoringFunction
seq
- the sequencestart
- the startposition in the sequenceindices
- after method invocation the list should contain the indices i where \frac{\partial \log
score(seq)}{\partial \lambda_i} is not zeropartialDer
- after method invocation the list should contain the corresponding \frac{\partial \log
score(seq)}{\partial \lambda_i}
public int getNumberOfParameters()
ScoringFunction
UNKNOWN
.
ScoringFunction.UNKNOWN
public int getNumberOfRecommendedStarts()
ScoringFunction
getNumberOfRecommendedStarts
in interface ScoringFunction
getNumberOfRecommendedStarts
in class AbstractNormalizableScoringFunction
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 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) throws Exception
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 enter
Exception
NormalizableScoringFunction.getLogPriorTerm()
public boolean isInitialized()
ScoringFunction
ScoringFunction.initializeFunction(int, boolean, Sample[], double[][])
.
true
if the model is initializedpublic void initializeFunctionRandomly(boolean freeParams) throws Exception
ScoringFunction
freeParams
- if true, the (reduced) parameterization is used
Exception
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