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java.lang.Objectde.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractVariableLengthDiffSM
de.jstacs.sequenceScores.statisticalModels.differentiable.CyclicMarkovModelDiffSM
public class CyclicMarkovModelDiffSM
This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length. The scoring function uses the parametrization of Meila.
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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CyclicMarkovModelDiffSM(AlphabetContainer alphabets,
double[] frameHyper,
double[][][] hyper,
boolean plugIn,
boolean optimize,
int starts,
int initFrame)
This constructor allows to create an instance with specific hyper-parameters for all conditional distributions. |
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CyclicMarkovModelDiffSM(AlphabetContainer alphabets,
int order,
int period,
double classEss,
double[] sumOfHyperParams,
boolean plugIn,
boolean optimize,
int starts,
int initFrame)
The main constructor. |
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CyclicMarkovModelDiffSM(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 DifferentiableStatisticalModel.getLogPriorTerm() for each
parameter of this model. |
CyclicMarkovModelDiffSM |
clone()
Creates a clone (deep copy) of the current DifferentiableSequenceScore
instance. |
protected void |
fromXML(StringBuffer xml)
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. |
static double[][][] |
getHyperParams(int alphabetSize,
int length,
double ess,
double[] frameProb,
double[][][] prob)
This method returns the hyper-parameters for a model given some a-priori probabilities. |
String |
getInstanceName()
Should return a short instance name such as iMM(0), BN(2), ... |
double |
getLogNormalizationConstant(int length)
This method returns the logarithm of the normalization constant for a given sequence length. |
double |
getLogPartialNormalizationConstant(int parameterIndex,
int length)
This method returns the logarithm of the partial normalization constant for a given parameter index and a sequence length. |
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,
int end,
IntList indices,
DoubleList dList)
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,
int end)
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 |
getNumberOfRecommendedStarts()
This method returns the number of recommended optimization starts. |
int[][] |
getSamplingGroups(int parameterOffset)
Returns groups of indexes of parameters that shall be drawn together in a sampling procedure |
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. |
boolean |
isNormalized()
This method indicates whether the implemented score is already normalized to 1 or not. |
void |
setFrameParameterOptimization(boolean optimize)
This method enables the user to choose whether the frame parameters should be optimized or not. |
void |
setParameterOptimization(boolean optimize)
This method enables the user to choose whether the parameters should be optimized or not. |
void |
setParameters(double[] params,
int start)
This method sets the internal parameters to the values of params between start and
start + |
void |
setStatisticForHyperparameters(int[] length,
double[] weight)
This method sets the hyperparameters for the model parameters by evaluating the given statistic. |
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.AbstractVariableLengthDiffSM |
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getLogNormalizationConstant, getLogPartialNormalizationConstant, getLogScoreAndPartialDerivation, getLogScoreFor |
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, getLogScoreFor, 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.statisticalModels.differentiable.DifferentiableStatisticalModel |
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getLogNormalizationConstant, getLogPartialNormalizationConstant |
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 CyclicMarkovModelDiffSM(AlphabetContainer alphabets, int order, int period, double classEss, double[] sumOfHyperParams, boolean plugIn, boolean optimize, int starts, int initFrame)
alphabets
- the alphabet containerorder
- the oder of the model (has to be non-negative)period
- the periodclassEss
- the ess of the classsumOfHyperParams
- the sum of the hyper parameter for each order (length has to be order
+1, each entry has to be non-negative), the sum also sums over the periodplugIn
- a switch which enables to used the MAP-parameters as plug-in parametersoptimize
- a switch which enables to optimize or fix the parametersstarts
- the number of recommended startsinitFrame
- the frame which should be used for plug-in initialization, negative for random initializationgetHyperParams(int, int, double, double[], double[][][])
,
CyclicMarkovModelDiffSM(AlphabetContainer, double[], double[][][], boolean, boolean, int, int)
public CyclicMarkovModelDiffSM(AlphabetContainer alphabets, double[] frameHyper, double[][][] hyper, boolean plugIn, boolean optimize, int starts, int initFrame)
alphabets
- the alphabet containerframeHyper
- the hyper-parameters for the frame, the length of this array also defines the period of the modelhyper
- the hyper-parameters for each frameplugIn
- a switch which enables to used the MAP-parameters as plug-in parametersoptimize
- a switch which enables to optimize or fix the parametersstarts
- the number of recommended startsinitFrame
- the frame which should be used for plug-in initialization, negative for random initializationpublic CyclicMarkovModelDiffSM(StringBuffer source) throws NonParsableException
Storable
.
source
- the xml representation
NonParsableException
- if the representation could not be parsed.Method Detail |
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public static double[][][] getHyperParams(int alphabetSize, int length, double ess, double[] frameProb, double[][][] prob)
alphabetSize
- the size of the alphabetlength
- the expected sequence lengthess
- the equivalent sample size (ess) of the modelframeProb
- the a-priori probabilities for each frameprob
- the a-priori probabilities for each frame and order
public CyclicMarkovModelDiffSM 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 String getInstanceName()
SequenceScore
getInstanceName
in interface SequenceScore
public double getLogScoreFor(Sequence seq, int start, int end)
SequenceScore
Sequence
seq
beginning at position start
in the Sequence
.
getLogScoreFor
in interface SequenceScore
getLogScoreFor
in interface VariableLengthDiffSM
getLogScoreFor
in class AbstractVariableLengthDiffSM
seq
- the Sequence
start
- the start position in the Sequence
end
- the end position (inclusive) in the Sequence
Sequence
public double getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList dList)
DifferentiableSequenceScore
Sequence
beginning at
position start
in the Sequence
and fills lists with
the indices and the partial derivations.
getLogScoreAndPartialDerivation
in interface DifferentiableSequenceScore
getLogScoreAndPartialDerivation
in interface VariableLengthDiffSM
getLogScoreAndPartialDerivation
in class AbstractVariableLengthDiffSM
seq
- the Sequence
start
- the start position in the Sequence
end
- the end position (inclusive) in the Sequence
indices
- an IntList
of indices, after method invocation the
list should contain the indices i where
dList
- 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
.
getNumberOfParameters
in interface DifferentiableSequenceScore
DifferentiableSequenceScore
DifferentiableSequenceScore.UNKNOWN
public void setParameters(double[] params, int start)
DifferentiableSequenceScore
params
between start
and
start + DifferentiableSequenceScore.getNumberOfParameters()
- 1
setParameters
in interface DifferentiableSequenceScore
params
- the new parametersstart
- the start index in params
public StringBuffer toXML()
Storable
StringBuffer
of an
instance of the implementing class.
toXML
in interface Storable
public double[] getCurrentParameterValues()
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.
getCurrentParameterValues
in interface DifferentiableSequenceScore
public void initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)
DifferentiableSequenceScore
DifferentiableSequenceScore
.
initializeFunction
in interface 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 samplespublic void initializeFunctionRandomly(boolean freeParams)
DifferentiableSequenceScore
DifferentiableSequenceScore
randomly. It has to
create the underlying structure of the DifferentiableSequenceScore
.
initializeFunctionRandomly
in interface DifferentiableSequenceScore
freeParams
- indicates whether the (reduced) parameterization is usedprotected void fromXML(StringBuffer xml) throws NonParsableException
AbstractDifferentiableSequenceScore
Storable
interface to create a scoring function from a StringBuffer
.
fromXML
in class AbstractDifferentiableSequenceScore
xml
- the XML representation as StringBuffer
NonParsableException
- if the StringBuffer
could not be parsedAbstractDifferentiableSequenceScore.AbstractDifferentiableSequenceScore(StringBuffer)
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, ...
getSizeOfEventSpaceForRandomVariablesOfParameter
in interface DifferentiableStatisticalModel
index
- the index of the parameter
public double getLogNormalizationConstant(int length)
VariableLengthDiffSM
getLogNormalizationConstant
in interface VariableLengthDiffSM
length
- the sequence length
DifferentiableStatisticalModel.getLogNormalizationConstant()
public double getLogPartialNormalizationConstant(int parameterIndex, int length) throws Exception
VariableLengthDiffSM
getLogPartialNormalizationConstant
in interface VariableLengthDiffSM
parameterIndex
- the index of the parameterlength
- the sequence length
Exception
- if something went wrongDifferentiableStatisticalModel.getLogPartialNormalizationConstant(int)
public double getESS()
DifferentiableStatisticalModel
getESS
in interface DifferentiableStatisticalModel
public String toString()
toString
in class Object
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
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
.
addGradientOfLogPriorTerm
in interface DifferentiableStatisticalModel
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 boolean isNormalized()
DifferentiableStatisticalModel
false
.
isNormalized
in interface DifferentiableStatisticalModel
isNormalized
in class AbstractDifferentiableStatisticalModel
true
if the implemented score is already normalized
to 1, false
otherwisepublic boolean isInitialized()
SequenceScore
SequenceScore.getLogScoreFor(Sequence)
.
isInitialized
in interface SequenceScore
true
if the instance is initialized, false
otherwisepublic int getNumberOfRecommendedStarts()
DifferentiableSequenceScore
getNumberOfRecommendedStarts
in interface DifferentiableSequenceScore
getNumberOfRecommendedStarts
in class AbstractDifferentiableSequenceScore
public void setParameterOptimization(boolean optimize)
optimize
- the switch for optimization of the parameterspublic void setFrameParameterOptimization(boolean optimize)
optimize
- the switch for optimization of the frame parameterspublic void setStatisticForHyperparameters(int[] length, double[] weight) throws Exception
VariableLengthDiffSM
length
) and how often (
weight
) they have been seen.
setStatisticForHyperparameters
in interface VariableLengthDiffSM
length
- the non-negative lengths of the sequencesweight
- the non-negative weight for the corresponding sequence
Exception
- if something went wrongMutable
public int[][] getSamplingGroups(int parameterOffset)
SamplingDifferentiableStatisticalModel
getSamplingGroups
in interface SamplingDifferentiableStatisticalModel
parameterOffset
- a global offset on the parameter indexes
parameterOffset
.
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