de.jstacs.scoringFunctions
Class VariableLengthScoringFunction

java.lang.Object
  extended by de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
      extended by de.jstacs.scoringFunctions.VariableLengthScoringFunction
All Implemented Interfaces:
NormalizableScoringFunction, ScoringFunction, Storable, Cloneable
Direct Known Subclasses:
HomogeneousScoringFunction

public abstract class VariableLengthScoringFunction
extends AbstractNormalizableScoringFunction

This is the main class for all ScoringFunctions that allow to score subsequences of arbitrary length. This ScoringFunction should be the super class for non-motif ScoringFunctions like homogeneous Markov models, cyclic Markov models, ... etc.

Author:
Jens Keilwagen

Field Summary
 
Fields inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
alphabets, length, r
 
Fields inherited from interface de.jstacs.scoringFunctions.ScoringFunction
UNKNOWN
 
Constructor Summary
protected VariableLengthScoringFunction(AlphabetContainer alphabets)
          This is the main constructor that creates an instance of a VariableLengthScoringFunction that models sequences of arbitrary length.
protected VariableLengthScoringFunction(AlphabetContainer alphabets, int length)
          This is the main constructor that creates an instance of a VariableLengthScoringFunction that models sequences of a given length.
protected VariableLengthScoringFunction(StringBuffer source)
          This is the constructor for the interface Storable.
 
Method Summary
abstract  double[][][] getAllConditionalStationaryDistributions()
          This method returns the stationary conditional distributions.
 double getLogScore(Sequence seq, int start)
          Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.
abstract  double getLogScore(Sequence seq, int start, int length)
          This method computes the logarithm of the score for a given subsequence.
abstract  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.
 double getLogScoreAndPartialDerivation(Sequence seq, int start, 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 getNormalizationConstant()
          Returns the sum of the scores over all sequences of the event space.
abstract  double getNormalizationConstant(int length)
          This method returns the normalization constant for a given sequence length.
 double getPartialNormalizationConstant(int parameterIndex)
          Returns the partial normalization constant for the parameter with index parameterIndex.
abstract  double getPartialNormalizationConstant(int parameterIndex, int length)
          This method returns the partial normalization constant for a given parameter index and a sequence length.
abstract  void setStatisticForHyperparameters(int[] length, double[] weight)
          This method sets the hyperparameters for the model parameters by evaluating the given statistic.
 
Methods inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
clone, fromXML, getAlphabetContainer, getInitialClassParam, getLength, getLogScore, getLogScoreAndPartialDerivation, getNumberOfRecommendedStarts, isNormalized, isNormalized
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 
Methods inherited from interface de.jstacs.scoringFunctions.NormalizableScoringFunction
addGradientOfLogPriorTerm, getEss, getLogPriorTerm, getSizeOfEventSpaceForRandomVariablesOfParameter
 
Methods inherited from interface de.jstacs.scoringFunctions.ScoringFunction
getCurrentParameterValues, getInstanceName, getNumberOfParameters, initializeFunction, initializeFunctionRandomly, isInitialized, setParameters
 
Methods inherited from interface de.jstacs.Storable
toXML
 

Constructor Detail

VariableLengthScoringFunction

protected VariableLengthScoringFunction(AlphabetContainer alphabets)
This is the main constructor that creates an instance of a VariableLengthScoringFunction that models sequences of arbitrary length.

Parameters:
alphabets - the AlphabetContainer of this VariableLengthScoringFunction
See Also:
VariableLengthScoringFunction(AlphabetContainer, int)

VariableLengthScoringFunction

protected VariableLengthScoringFunction(AlphabetContainer alphabets,
                                        int length)
This is the main constructor that creates an instance of a VariableLengthScoringFunction that models sequences of a given length.

Parameters:
alphabets - the AlphabetContainer of this VariableLengthScoringFunction
length - the length of the modeled sequences

VariableLengthScoringFunction

protected VariableLengthScoringFunction(StringBuffer source)
                                 throws NonParsableException
This is the constructor for the interface Storable. Creates a new VariableLengthScoringFunction out of its XML representation.

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

getNormalizationConstant

public double getNormalizationConstant()
Description copied from interface: NormalizableScoringFunction
Returns the sum of the scores over all sequences of the event space.

Returns:
the normalization constant Z

getNormalizationConstant

public abstract double getNormalizationConstant(int length)
This method returns the normalization constant for a given sequence length.

Parameters:
length - the sequence length
Returns:
the normalization constant
See Also:
NormalizableScoringFunction.getNormalizationConstant()

getPartialNormalizationConstant

public double getPartialNormalizationConstant(int parameterIndex)
                                       throws Exception
Description copied from interface: NormalizableScoringFunction
Returns the partial normalization constant for the parameter with index parameterIndex. This is the partial derivation of the normalization constant for the parameter with index parameterIndex, in LaTex notation: \frac{\partial Z(\lambda)}{\partial \lambda_{index}}.

Parameters:
parameterIndex - the index of the parameter
Returns:
the partial normalization constant
Throws:
Exception - if something went wrong with the normalization
See Also:
NormalizableScoringFunction.getNormalizationConstant()

getPartialNormalizationConstant

public abstract double getPartialNormalizationConstant(int parameterIndex,
                                                       int length)
                                                throws Exception
This method returns 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 partial normalization constant
Throws:
Exception - if something went wrong
See Also:
NormalizableScoringFunction.getPartialNormalizationConstant(int)

getLogScore

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

Parameters:
seq - the Sequence
start - the start position in the Sequence
Returns:
the logarithmic score for the Sequence

getLogScore

public abstract double getLogScore(Sequence seq,
                                   int start,
                                   int length)
This method computes the logarithm of the score for a given subsequence.

Parameters:
seq - the Sequence
start - the start index in the Sequence
length - the end index in the Sequence
Returns:
the logarithm of the score for the subsequence
See Also:
ScoringFunction.getLogScore(Sequence, int)

getLogScoreAndPartialDerivation

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

Parameters:
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 \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}
Returns:
the logarithmic score for the Sequence

getLogScoreAndPartialDerivation

public abstract 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.

Parameters:
seq - the Sequence
start - the start index in the Sequence
length - the end index in the Sequence
indices - an IntList of indices, after method invocation the list should contain the indices i where \frac{\partial \log score(seq,start,length)}{\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,start,length)}{\partial \lambda_i}
Returns:
the logarithm of the score
See Also:
ScoringFunction.getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList)

getAllConditionalStationaryDistributions

public abstract double[][][] getAllConditionalStationaryDistributions()
This method returns the stationary conditional distributions. The first dimension of the result is used for the order, the second is used for encoding the context, and the third is used for the different values of the random variable.

For an homogeneous Markov model of order 2 it returns an array containing the stationary symbol distribution as first entry, the conditional stationary distribution of order 1 as second entry and the conditional distribution of order 2 as third entry.

This method is used to determine the contrast for Mutable.determineNotSignificantPositions(double, double[], double[], double[][][][], double[][][][], double) .

Returns:
all conditional stationary distributions
See Also:
Mutable

setStatisticForHyperparameters

public abstract void setStatisticForHyperparameters(int[] length,
                                                    double[] weight)
                                             throws Exception
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
weight - the non-negative weight for the corresponding sequence
Throws:
Exception - if something went wrong
See Also:
Mutable