Overview  Package   Class  Use  Tree  Deprecated  Index  Help 
 PREV CLASS   NEXT CLASS FRAMES    NO FRAMES    All Classes
SUMMARY: NESTED | FIELD | CONSTR | METHOD DETAIL: FIELD | CONSTR | METHOD

de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous
Class UniformHomogeneousDiffSM

java.lang.Object
  extended by de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
      extended by de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
          extended by de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractVariableLengthDiffSM
              extended by de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousDiffSM
                  extended by de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.UniformHomogeneousDiffSM
All Implemented Interfaces:
DifferentiableSequenceScore, SequenceScore, DifferentiableStatisticalModel, SamplingDifferentiableStatisticalModel, VariableLengthDiffSM, StatisticalModel, Storable, Cloneable
public class UniformHomogeneousDiffSM
extends HomogeneousDiffSM

This scoring function does nothing. So it is possible to save parameters in an optimization.

Author:
Jens Keilwagen, Jan Grau

Field Summary
 
Fields inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
alphabets, length, r
 
Fields inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
UNKNOWN
 
Constructor Summary
UniformHomogeneousDiffSM(AlphabetContainer alphabets, double ess)
          This is the main constructor that creates an instance of a UniformHomogeneousDiffSM that models each sequence uniformly.
UniformHomogeneousDiffSM(StringBuffer xml)
          This is the constructor for Storable.
 
Method Summary
 void addGradientOfLogPriorTerm(double[] grad, int start)
          This method computes the gradient of DifferentiableStatisticalModel.getLogPriorTerm() for each parameter of this model.
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.
 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 DifferentiableStatisticalModel.getESS() * DifferentiableStatisticalModel.getLogNormalizationConstant() + Math.log( prior ) 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.
 byte getMaximalMarkovOrder()
          Returns the maximal used markov oder.
 int getNumberOfParameters()
          Returns the number of parameters in this DifferentiableSequenceScore.
 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 meila, DataSet[] data, double[][] weights)
          This method creates the underlying structure of the DifferentiableSequenceScore.
 void initializeFunctionRandomly(boolean freeParams)
          This method initializes the DifferentiableSequenceScore randomly.
 void initializeUniformly(boolean freeParams)
          This method allows to initialize the instance with an uniform distribution.
 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 setParameters(double[] params, int start)
          This method sets the internal parameters to the values of params between start and start + DifferentiableSequenceScore.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 as StringBuffer of an instance of the implementing class.
 
Methods inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractVariableLengthDiffSM
getLogNormalizationConstant, getLogPartialNormalizationConstant, getLogScoreAndPartialDerivation, getLogScoreFor
 
Methods inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
clone, emitDataSet, getInitialClassParam, getLogProbFor, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, isNormalized
 
Methods inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
getAlphabetContainer, getCharacteristics, getLength, getLogScoreAndPartialDerivation, getLogScoreFor, getNumberOfRecommendedStarts, getNumberOfStarts, getNumericalCharacteristics
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 
Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel
getLogNormalizationConstant, getLogPartialNormalizationConstant
 
Methods inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
clone, getInitialClassParam, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getNumberOfRecommendedStarts
 
Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel
emitDataSet, getLogProbFor, getLogProbFor, getLogProbFor
 
Methods inherited from interface de.jstacs.sequenceScores.SequenceScore
getAlphabetContainer, getCharacteristics, getLength, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics
 

Constructor Detail

UniformHomogeneousDiffSM

public UniformHomogeneousDiffSM(AlphabetContainer alphabets,
                                double ess)
This is the main constructor that creates an instance of a UniformHomogeneousDiffSM that models each sequence uniformly.

Parameters:
alphabets - the AlphabetContainer
ess - the equivalent sample size (ess) for the class

UniformHomogeneousDiffSM

public UniformHomogeneousDiffSM(StringBuffer xml)
                         throws NonParsableException
This is the constructor for Storable. Creates a new UniformHomogeneousDiffSM out of its XML representation as returned by fromXML(StringBuffer).

Parameters:
xml - the XML representation as StringBuffer
Throws:
NonParsableException - if the XML representation could not be parsed
Method Detail

getInstanceName

public String getInstanceName()
Description copied from interface: SequenceScore
Should return a short instance name such as iMM(0), BN(2), ...

Returns:
a short instance name

getLogScoreFor

public double getLogScoreFor(Sequence seq,
                             int start,
                             int end)
Description copied from interface: SequenceScore
Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.

Specified by:
getLogScoreFor in interface SequenceScore
Specified by:
getLogScoreFor in interface VariableLengthDiffSM
Specified by:
getLogScoreFor in class AbstractVariableLengthDiffSM
Parameters:
seq - the Sequence
start - the start position in the Sequence
end - the end position (inclusive) in the Sequence
Returns:
the logarithmic score for the Sequence

getLogScoreAndPartialDerivation

public double getLogScoreAndPartialDerivation(Sequence seq,
                                              int start,
                                              int end,
                                              IntList indices,
                                              DoubleList dList)
Description copied from interface: DifferentiableSequenceScore
Returns the logarithmic score for a Sequence beginning at position start in the Sequence and fills lists with the indices and the partial derivations.

Specified by:
getLogScoreAndPartialDerivation in interface DifferentiableSequenceScore
Specified by:
getLogScoreAndPartialDerivation in interface VariableLengthDiffSM
Specified by:
getLogScoreAndPartialDerivation in class AbstractVariableLengthDiffSM
Parameters:
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 $\frac{\partial \log score(seq)}{\partial \lambda_i}$ is not zero
dList - a DoubleList of partial derivations, after method invocation the list should contain the corresponding $\frac{\partial \log score(seq)}{\partial \lambda_i}$ that are not zero
Returns:
the logarithmic score for the Sequence

getNumberOfParameters

public int getNumberOfParameters()
Description copied from interface: DifferentiableSequenceScore
Returns the number of parameters in this DifferentiableSequenceScore. If the number of parameters is not known yet, the method returns DifferentiableSequenceScore.UNKNOWN.

Returns:
the number of parameters in this DifferentiableSequenceScore
See Also:
DifferentiableSequenceScore.UNKNOWN

setParameters

public void setParameters(double[] params,
                          int start)
Description copied from interface: DifferentiableSequenceScore
This method sets the internal parameters to the values of params between start and start + DifferentiableSequenceScore.getNumberOfParameters() - 1

Parameters:
params - the new parameters
start - the start index in params

toXML

public StringBuffer toXML()
Description copied from interface: Storable
This method returns an XML representation as StringBuffer of an instance of the implementing class.

Returns:
the XML representation

getLogNormalizationConstant

public double getLogNormalizationConstant(int length)
Description copied from interface: VariableLengthDiffSM
This method returns the logarithm of the normalization constant for a given sequence length.

Parameters:
length - the sequence length
Returns:
the logarithm of the normalization constant
See Also:
DifferentiableStatisticalModel.getLogNormalizationConstant()

initializeFunction

public void initializeFunction(int index,
                               boolean meila,
                               DataSet[] data,
                               double[][] weights)
Description copied from interface: DifferentiableSequenceScore
This method creates the underlying structure of the DifferentiableSequenceScore.

Parameters:
index - the index of the class the DifferentiableSequenceScore models
meila - indicates whether the (reduced) parameterization is used
data - the samples
weights - the weights of the sequences in the samples

fromXML

protected void fromXML(StringBuffer xml)
                throws NonParsableException
Description copied from class: AbstractDifferentiableSequenceScore
This method is called in the constructor for the Storable interface to create a scoring function from a StringBuffer.

Specified by:
fromXML in class AbstractDifferentiableSequenceScore
Parameters:
xml - the XML representation as StringBuffer
Throws:
NonParsableException - if the StringBuffer could not be parsed
See Also:
AbstractDifferentiableSequenceScore.AbstractDifferentiableSequenceScore(StringBuffer)

getSizeOfEventSpaceForRandomVariablesOfParameter

public int getSizeOfEventSpaceForRandomVariablesOfParameter(int index)
Description copied from interface: DifferentiableStatisticalModel
Returns the size of the event space of the random variables that are affected by parameter no. 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, ...

Parameters:
index - the index of the parameter
Returns:
the size of the event space

getLogPartialNormalizationConstant

public double getLogPartialNormalizationConstant(int parameterIndex,
                                                 int length)
                                          throws Exception
Description copied from interface: VariableLengthDiffSM
This method returns the logarithm of the partial normalization constant for a given parameter index and a sequence length.

Parameters:
parameterIndex - the index of the parameter
length - the sequence length
Returns:
the logarithm of the partial normalization constant
Throws:
Exception - if something went wrong
See Also:
DifferentiableStatisticalModel.getLogPartialNormalizationConstant(int)

getESS

public double getESS()
Description copied from interface: DifferentiableStatisticalModel
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.

Returns:
the equivalent sample size.

toString

public String toString()
Overrides:
toString in class Object

getLogPriorTerm

public double getLogPriorTerm()
Description copied from interface: DifferentiableStatisticalModel
This method computes a value that is proportional to

DifferentiableStatisticalModel.getESS() * DifferentiableStatisticalModel.getLogNormalizationConstant() + Math.log( prior )

where prior is the prior for the parameters of this model.

Returns:
a value that is proportional to DifferentiableStatisticalModel.getESS() * DifferentiableStatisticalModel.getLogNormalizationConstant() + Math.log( prior ).
See Also:
DifferentiableStatisticalModel.getESS(), DifferentiableStatisticalModel.getLogNormalizationConstant()

addGradientOfLogPriorTerm

public void addGradientOfLogPriorTerm(double[] grad,
                                      int start)
Description copied from interface: DifferentiableStatisticalModel
This method computes the gradient of DifferentiableStatisticalModel.getLogPriorTerm() for each parameter of this model. The results are added to the array grad beginning at index start.

Parameters:
grad - the array of gradients
start - the start index in the grad array, where the partial derivations for the parameters of this models shall be entered
See Also:
DifferentiableStatisticalModel.getLogPriorTerm()

getCurrentParameterValues

public double[] getCurrentParameterValues()
                                   throws Exception
Description copied from interface: DifferentiableSequenceScore
Returns a 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.

Returns:
the current parameter values
Throws:
Exception - if no parameters exist (yet)

isInitialized

public boolean isInitialized()
Description copied from interface: SequenceScore
This method can be used to determine whether the instance is initialized. If the instance is initialized you should be able to invoke SequenceScore.getLogScoreFor(Sequence).

Returns:
true if the instance is initialized, false otherwise

isNormalized

public boolean isNormalized()
Description copied from interface: DifferentiableStatisticalModel
This method indicates whether the implemented score is already normalized to 1 or not. The standard implementation returns false.

Specified by:
isNormalized in interface DifferentiableStatisticalModel
Overrides:
isNormalized in class AbstractDifferentiableStatisticalModel
Returns:
true if the implemented score is already normalized to 1, false otherwise

getMaximalMarkovOrder

public byte getMaximalMarkovOrder()
Description copied from class: HomogeneousDiffSM
Returns the maximal used markov oder.

Specified by:
getMaximalMarkovOrder in interface StatisticalModel
Specified by:
getMaximalMarkovOrder in class HomogeneousDiffSM
Returns:
the maximal used markov oder

initializeFunctionRandomly

public void initializeFunctionRandomly(boolean freeParams)
                                throws Exception
Description copied from interface: DifferentiableSequenceScore
This method initializes the DifferentiableSequenceScore randomly. It has to create the underlying structure of the DifferentiableSequenceScore.

Parameters:
freeParams - indicates whether the (reduced) parameterization is used
Throws:
Exception - if something went wrong

setStatisticForHyperparameters

public void setStatisticForHyperparameters(int[] length,
                                           double[] weights)
                                    throws Exception
Description copied from interface: VariableLengthDiffSM
This method sets the hyperparameters for the model parameters by evaluating the given statistic. The statistic can be interpreted as follows: The model has seen a number of sequences. From these sequences it is only known how long (length) and how often ( weight) they have been seen.

Parameters:
length - the non-negative lengths of the sequences
weights - the non-negative weight for the corresponding sequence
Throws:
Exception - if something went wrong
See Also:
Mutable

initializeUniformly

public void initializeUniformly(boolean freeParams)
Description copied from class: HomogeneousDiffSM
This method allows to initialize the instance with an uniform distribution.

Specified by:
initializeUniformly in class HomogeneousDiffSM
Parameters:
freeParams - a switch whether to take only free parameters or to take all

getSamplingGroups

public int[][] getSamplingGroups(int parameterOffset)
Description copied from interface: SamplingDifferentiableStatisticalModel
Returns groups of indexes of parameters that shall be drawn together in a sampling procedure

Parameters:
parameterOffset - a global offset on the parameter indexes
Returns:
the groups of indexes. The first dimension represents the different groups while the second dimension contains the parameters that shall be sampled together. Internal parameter indexes need to be increased by parameterOffset.
Overview  Package   Class  Use  Tree  Deprecated  Index  Help 
 PREV CLASS   NEXT CLASS FRAMES    NO FRAMES    All Classes
SUMMARY: NESTED | FIELD | CONSTR | METHOD DETAIL: FIELD | CONSTR | METHOD