BayesianNetworkDiffSM

java.lang.Object
- de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
- - de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
  - - de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM

All Implemented Interfaces:

InstantiableFromParameterSet, DifferentiableSequenceScore, QuickScanningSequenceScore, SequenceScore, DifferentiableStatisticalModel, StatisticalModel, Storable, Cloneable

Direct Known Subclasses:

MarkovModelDiffSM
```
public class BayesianNetworkDiffSM
extends AbstractDifferentiableStatisticalModel
implements InstantiableFromParameterSet, QuickScanningSequenceScore
```
This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network. This implementation also comprises well known specializations of Bayesian networks like Markov models of arbitrary order (including weight array matrix models (WAM) and position weight matrices (PWM)) or Bayesian trees. Different structures can be achieved by using the corresponding Measure, e.g. InhomogeneousMarkov for Markov models of arbitrary order.

This scoring function can be used in any ScoreClassifier, e.g. in a MSPClassifier to learn the parameters of the DifferentiableStatisticalModel using maximum conditional likelihood or maximum supervised posterior.

Author:

Jan Grau

Field Summary

Fields
Modifier and Type	Field and Description
`protected double`	`ess` The equivalent sample size.
`protected boolean`	`isTrained` Indicates if the instance has been trained.
`protected Double`	`logNormalizationConstant` Normalization constant to obtain normalized probabilities.
`protected Integer`	`numFreePars` The number of free parameters.
`protected int[]`	`nums` Used internally.
`protected int[][]`	`order` The network structure, used internally.
`protected BNDiffSMParameter[]`	`parameters` The parameters of the scoring function.
`protected boolean`	`plugInParameters` Indicates if plug-in parameters, i.e.
`protected Measure`	`structureMeasure` `Measure` that defines the network structure.
`protected BNDiffSMParameterTree[]`	`trees` The trees that represent the context of the random variable (i.e.

Fields inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
alphabets, length, r

Fields inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
UNKNOWN

Constructor Summary

Constructors
Constructor and Description
`BayesianNetworkDiffSM(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, Measure structureMeasure)` Creates a new `BayesianNetworkDiffSM` that has neither been initialized nor trained.
`BayesianNetworkDiffSM(BayesianNetworkDiffSMParameterSet parameters)` Creates a new `BayesianNetworkDiffSM` that has neither been initialized nor trained from a `BayesianNetworkDiffSMParameterSet`.
`BayesianNetworkDiffSM(StringBuffer xml)` The standard constructor for the interface `Storable`.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`addGradientOfLogPriorTerm(double[] grad, int start)` This method computes the gradient of `DifferentiableStatisticalModel.getLogPriorTerm()` for each parameter of this model.
`BayesianNetworkDiffSM`	`clone()` Creates a clone (deep copy) of the current `DifferentiableSequenceScore` instance.
`protected void`	`createTrees(DataSet[] data2, double[][] weights2)` Creates the tree structures that represent the context (array `trees`) and the parameter objects `parameters` using the given `Measure` `structureMeasure`.
`DataSet`	`emitDataSet(int numberOfSequences, int... seqLength)` This method returns a `DataSet` object containing artificial sequence(s).
`void`	`fillInfixScore(int[] seq, int start, int length, double[] scores)` Computes the position-wise scores of an infix of the sequence `seq` (which must be encoded by the same alphabet as this `QuickScanningSequenceScore`) beginning at `start` and extending for `length` positions.
`protected void`	`fromXML(StringBuffer source)` This method is called in the constructor for the `Storable` interface to create a scoring function from a `StringBuffer`.
`InstanceParameterSet`	`getCurrentParameterSet()` Returns the `InstanceParameterSet` that has been used to instantiate the current instance of the implementing class.
`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.
`boolean[][]`	`getInfixFilter(int kmer, double thresh, int... start)` Computes arrays that indicate, for a given set of starting positions and a given k-mer length, if a sequence containing this k-mer may yield a score above `threshold`, choosing the best-scoring option among all non-specified positions (i.e., those outside the k-mer).
`String`	`getInstanceName()` Should return a short instance name such as iMM(0), BN(2), ...
`double`	`getLogNormalizationConstant()` Returns the logarithm of the sum of the scores over all sequences of the event space.
`double`	`getLogPartialNormalizationConstant(int parameterIndex)` Returns the logarithm of the partial normalization constant for the parameter with index `parameterIndex`.
`double`	`getLogPriorTerm()` This method computes a value that is proportional to
`double`	`getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)` 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)` Returns the logarithmic score for the `Sequence` `seq` beginning at position `start` in the `Sequence`.
`byte`	`getMaximalMarkovOrder()` This method returns the maximal used Markov order, if possible.
`double`	`getMaximumScore()` Returns the maximum score of this `BayesianNetworkDiffSM` returned for a admissible input sequence.
`int`	`getNumberOfParameters()` Returns the number of parameters in this `DifferentiableSequenceScore`.
`double[]`	`getPositionDependentKMerProb(Sequence kmer)` Returns the probability of `kmer` for all possible positions in this `BayesianNetworkDiffSM` starting at position `kmer.getLength()-1`.
`int`	`getPositionForParameter(int index)` Returns the position in the sequence the parameter `index` is responsible for.
`double[][]`	`getPWM()` If this `BayesianNetworkDiffSM` is a PWM, i.e.
`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.
`protected void`	`precomputeNormalization()` Pre-computes all normalization constants.
`void`	`setParameters(double[] params, int start)` This method sets the internal parameters to the values of `params` between `start` and `start + DifferentiableSequenceScore.getNumberOfParameters() - 1`
`protected void`	`setPlugInParameters(int index, boolean freeParameters, DataSet[] data, double[][] weights)` Computes and sets the plug-in parameters (MAP estimated parameters) from `data` using `weights`.
`String`	`toHtml(NumberFormat nf)` Returns an HTML representation of this `BayesianNetworkDiffSM`.
`String`	`toString(NumberFormat nf)` This method returns a `String` representation of the instance.
`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.AbstractDifferentiableStatisticalModel
getInitialClassParam, getLogProbFor, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, isNormalized, isNormalized

Methods inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
getAlphabetContainer, getCharacteristics, getLength, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreFor, getLogScoreFor, getNumberOfRecommendedStarts, getNumberOfStarts, getNumericalCharacteristics, toString

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore
getAlphabetContainer, getCharacteristics, getLength, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics

Methods inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getNumberOfRecommendedStarts

- Field Detail
  - parameters
```
protected BNDiffSMParameter[] parameters
```
    The parameters of the scoring function. This comprises free as well as dependent parameters.
  - trees
```
protected BNDiffSMParameterTree[] trees
```
    The trees that represent the context of the random variable (i.e. configuration of parent random variables) of the parameters.
  - isTrained
```
protected boolean isTrained
```
    Indicates if the instance has been trained.
  - ess
```
protected double ess
```
    The equivalent sample size.
  - numFreePars
```
protected Integer numFreePars
```
    The number of free parameters.
  - nums
```
protected int[] nums
```
    Used internally. Mapping from indexes of free parameters to indexes of all parameters.
  - structureMeasure
```
protected Measure structureMeasure
```
    Measure that defines the network structure.
  - plugInParameters
```
protected boolean plugInParameters
```
    Indicates if plug-in parameters, i.e. generative (MAP) parameters shall be used upon initialization.
  - order
```
protected int[][] order
```
    The network structure, used internally.
  - logNormalizationConstant
```
protected Double logNormalizationConstant
```
    Normalization constant to obtain normalized probabilities.
- Constructor Detail
  - BayesianNetworkDiffSM
```
public BayesianNetworkDiffSM(AlphabetContainer alphabet,
                             int length,
                             double ess,
                             boolean plugInParameters,
                             Measure structureMeasure)
                      throws Exception
```
    Creates a new BayesianNetworkDiffSM that has neither been initialized nor trained.
    
    Parameters:
    
    alphabet - the alphabet of the scoring function boxed in an AlphabetContainer, e.g new AlphabetContainer(new DNAAlphabet())
    
    length - the length of the scoring function, i.e. the length of the sequences this scoring function can handle
    
    ess - the equivalent sample size
    
    plugInParameters - indicates if plug-in parameters, i.e. generative (MAP) parameters, shall be used upon initialization
    
    structureMeasure - the Measure used for the structure, e.g. InhomogeneousMarkov
    
    Throws:
    
    Exception - if the length of the scoring function is not admissible (<=0) or the alphabet is not discrete
  - BayesianNetworkDiffSM
```
public BayesianNetworkDiffSM(BayesianNetworkDiffSMParameterSet parameters)
                      throws ParameterSetParser.NotInstantiableException,
                             Exception
```
    Creates a new BayesianNetworkDiffSM that has neither been initialized nor trained from a BayesianNetworkDiffSMParameterSet.
    
    Parameters:
    
    parameters - the parameter set
    
    Throws:
    
    ParameterSetParser.NotInstantiableException - if the BayesianNetworkDiffSM could not be instantiated from the BayesianNetworkDiffSMParameterSet
    
    Exception - if the length of the scoring function is not admissible (<=0) or the alphabet is not discrete
  - BayesianNetworkDiffSM
```
public BayesianNetworkDiffSM(StringBuffer xml)
                      throws NonParsableException
```
    The standard constructor for the interface Storable. Recreates a BayesianNetworkDiffSM from its XML representation as saved by the method toXML().
    
    Parameters:
    
    xml - the XML representation as StringBuffer
    
    Throws:
    
    NonParsableException - if the XML code could not be parsed
- Method Detail
  - clone
```
public BayesianNetworkDiffSM clone()
                            throws CloneNotSupportedException
```
    Description copied from interface: DifferentiableSequenceScore
    
    Creates a clone (deep copy) of the current DifferentiableSequenceScore instance.
    
    Specified by:
    
    clone in interface DifferentiableSequenceScore
    
    Specified by:
    
    clone in interface SequenceScore
    
    Overrides:
    
    clone in class AbstractDifferentiableStatisticalModel
    
    Returns:
    
    the cloned instance of the current DifferentiableSequenceScore
    
    Throws:
    
    CloneNotSupportedException - if something went wrong while cloning the DifferentiableSequenceScore
  - getLogPartialNormalizationConstant
```
public double getLogPartialNormalizationConstant(int parameterIndex)
                                          throws Exception
```
    Description copied from interface: DifferentiableStatisticalModel
    
    Returns the logarithm of the partial normalization constant for the parameter with index parameterIndex. This is the logarithm of the partial derivation of the normalization constant for the parameter with index parameterIndex,
    $\log \frac{\partial Z(\underline{\lambda})}{\partial \lambda_{parameterindex}}$
    .
    
    Specified by:
    
    getLogPartialNormalizationConstant in interface DifferentiableStatisticalModel
    
    Parameters:
    
    parameterIndex - the index of the parameter
    
    Returns:
    
    the logarithm of the partial normalization constant
    
    Throws:
    
    Exception - if something went wrong with the normalization
    
    See Also:
    
    DifferentiableStatisticalModel.getLogNormalizationConstant()
  - initializeFunction
```
public void initializeFunction(int index,
                               boolean freeParams,
                               DataSet[] data,
                               double[][] weights)
                        throws Exception
```
    Description copied from interface: DifferentiableSequenceScore
    
    This method creates the underlying structure of the DifferentiableSequenceScore.
    
    Specified by:
    
    initializeFunction in interface DifferentiableSequenceScore
    
    Parameters:
    
    index - the index of the class the DifferentiableSequenceScore models
    
    freeParams - indicates whether the (reduced) parameterization is used
    
    data - the data sets
    
    weights - the weights of the sequences in the data sets
    
    Throws:
    
    Exception - if something went wrong
  - createTrees
```
protected void createTrees(DataSet[] data2,
                           double[][] weights2)
                    throws Exception
```
    Creates the tree structures that represent the context (array trees) and the parameter objects parameters using the given Measure structureMeasure.
    
    Parameters:
    
    data2 - the data that is used to compute the structure
    
    weights2 - the weights on the sequences in data2
    
    Throws:
    
    Exception - if the structure is no moral graph or if the lengths of data and scoring function do not match or other problems concerning the data occur
  - setPlugInParameters
```
protected void setPlugInParameters(int index,
                                   boolean freeParameters,
                                   DataSet[] data,
                                   double[][] weights)
```
    Computes and sets the plug-in parameters (MAP estimated parameters) from data using weights.
    
    Parameters:
    
    index - the index of the class the scoring function is responsible for, the parameters are estimated from data[index] and weights[index]
    
    freeParameters - indicates if only the free parameters or all parameters should be used, this also affects the initialization
    
    data - the data used for initialization
    
    weights - the weights on the data
  - fromXML
```
protected void fromXML(StringBuffer source)
                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:
    
    source - the XML representation as StringBuffer
    
    Throws:
    
    NonParsableException - if the StringBuffer could not be parsed
    
    See Also:
    
    AbstractDifferentiableSequenceScore.AbstractDifferentiableSequenceScore(StringBuffer)
  - toString
```
public String toString(NumberFormat nf)
```
    Description copied from interface: SequenceScore
    
    This method returns a String representation of the instance.
    
    Specified by:
    
    toString in interface SequenceScore
    
    Parameters:
    
    nf - the NumberFormat for the String representation of parameters or probabilities
    
    Returns:
    
    a String representation of the instance
  - getInstanceName
```
public String getInstanceName()
```
    Description copied from interface: SequenceScore
    
    Should return a short instance name such as iMM(0), BN(2), ...
    
    Specified by:
    
    getInstanceName in interface SequenceScore
    
    Returns:
    
    a short instance name
  - getLogScoreFor
```
public double getLogScoreFor(Sequence seq,
                             int start)
```
    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
    
    Parameters:
    
    seq - the Sequence
    
    start - the start position in the Sequence
    
    Returns:
    
    the logarithmic score for the Sequence
  - getInfixFilter
```
public boolean[][] getInfixFilter(int kmer,
                                  double thresh,
                                  int... start)
```
    Description copied from interface: QuickScanningSequenceScore
    
    Computes arrays that indicate, for a given set of starting positions and a given k-mer length, if a sequence containing this k-mer may yield a score above threshold, choosing the best-scoring option among all non-specified positions (i.e., those outside the k-mer). This method is implemented as an upper bound on the scores, i.e., there may be k-mers that are considered to score above threshold (i.e., that have entry true) although they are not, but there may not be k-mers that are considered to be below threshold (i.e., that have entry false), although there exist sequences containing this k-mer that do. The returned array is indexed by the starting positions (in the same order as provided in starts) in the first dimension, and in the second dimension it is indexed by an integer representation of the k-mers, assigning the highest priority to the first k-mer position, i.e.,
    $\sum_{\ell=0}^{L-1} A^{L-1-\ell} \cdot e(x_\ell)$
    where denotes the size of the alphabet, the length of the k-mer (starting at 0 in this case), and $e(\ldots)$ denotes the function encoding symbols from the alphabet as integers (see DiscreteAlphabet).
    
    Specified by:
    
    getInfixFilter in interface QuickScanningSequenceScore
    
    Parameters:
    
    kmer - the k-mer length
    
    thresh - the threshold
    
    start - the starting position(s)
    
    Returns:
    
    the indicator arrays
  - getLogScoreAndPartialDerivation
```
public double getLogScoreAndPartialDerivation(Sequence seq,
                                              int start,
                                              IntList indices,
                                              DoubleList partialDer)
```
    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
    
    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
    
    partialDer - 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
  - getLogNormalizationConstant
```
public double getLogNormalizationConstant()
                                   throws RuntimeException
```
    Description copied from interface: DifferentiableStatisticalModel
    
    Returns the logarithm of the sum of the scores over all sequences of the event space.
    
    Specified by:
    
    getLogNormalizationConstant in interface DifferentiableStatisticalModel
    
    Returns:
    
    the logarithm of the normalization constant Z
    
    Throws:
    
    RuntimeException
  - 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.
    
    Specified by:
    
    getNumberOfParameters in interface DifferentiableSequenceScore
    
    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
    
    Specified by:
    
    setParameters in interface DifferentiableSequenceScore
    
    Parameters:
    
    params - the new parameters
    
    start - the start index in params
  - precomputeNormalization
```
protected void precomputeNormalization()
```
    Pre-computes all normalization constants.
  - 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.
    
    Specified by:
    
    getCurrentParameterValues in interface DifferentiableSequenceScore
    
    Returns:
    
    the current parameter values
    
    Throws:
    
    Exception - if no parameters exist (yet)
  - toXML
```
public StringBuffer toXML()
```
    Description copied from interface: Storable
    
    This method returns an XML representation as StringBuffer of an instance of the implementing class.
    
    Specified by:
    
    toXML in interface Storable
    
    Returns:
    
    the XML representation
  - 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.
    
    Specified by:
    
    getLogPriorTerm in interface DifferentiableStatisticalModel
    
    Specified by:
    
    getLogPriorTerm in interface StatisticalModel
    
    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.
    
    Specified by:
    
    addGradientOfLogPriorTerm in interface DifferentiableStatisticalModel
    
    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()
  - 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.
    
    Specified by:
    
    getESS in interface DifferentiableStatisticalModel
    
    Returns:
    
    the equivalent sample size.
  - getPositionForParameter
```
public int getPositionForParameter(int index)
```
    Returns the position in the sequence the parameter index is responsible for.
    
    Parameters:
    
    index - the index of the parameter
    
    Returns:
    
    the position in the sequence
  - getPositionDependentKMerProb
```
public double[] getPositionDependentKMerProb(Sequence kmer)
                                      throws Exception
```
    Returns the probability of kmer for all possible positions in this BayesianNetworkDiffSM starting at position kmer.getLength()-1.
    
    Parameters:
    
    kmer - the k-mer
    
    Returns:
    
    the position-dependent probabilities of this k-mer for position kmer.getLength()-1 to AbstractDifferentiableSequenceScore.getLength()-1
    
    Throws:
    
    Exception - if the method is called for non-Markov model structures
  - 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, ...
    
    Specified by:
    
    getSizeOfEventSpaceForRandomVariablesOfParameter in interface DifferentiableStatisticalModel
    
    Parameters:
    
    index - the index of the parameter
    
    Returns:
    
    the size of the event space
  - 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.
    
    Specified by:
    
    initializeFunctionRandomly in interface DifferentiableSequenceScore
    
    Parameters:
    
    freeParams - indicates whether the (reduced) parameterization is used
    
    Throws:
    
    Exception - if something went wrong
  - 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).
    
    Specified by:
    
    isInitialized in interface SequenceScore
    
    Returns:
    
    true if the instance is initialized, false otherwise
  - getPWM
```
public double[][] getPWM()
                  throws Exception
```
    If this BayesianNetworkDiffSM is a PWM, i.e. structureMeasure=new InhomogeneousMarkov(0)}}, this method returns the normalized PWM as a double array of dimension AbstractDifferentiableSequenceScore.getLength() x size-of-alphabet.
    
    Returns:
    
    the PWM as a two-dimensional array
    
    Throws:
    
    Exception - if this method is called for a BayesianNetworkDiffSM that is not a PWM
  - getMaximalMarkovOrder
```
public byte getMaximalMarkovOrder()
                           throws UnsupportedOperationException
```
    Description copied from interface: StatisticalModel
    
    This method returns the maximal used Markov order, if possible.
    
    Specified by:
    
    getMaximalMarkovOrder in interface StatisticalModel
    
    Overrides:
    
    getMaximalMarkovOrder in class AbstractDifferentiableStatisticalModel
    
    Returns:
    
    maximal used Markov order
    
    Throws:
    
    UnsupportedOperationException - if the model can't give a proper answer
  - getMaximumScore
```
public double getMaximumScore()
                       throws Exception
```
    Returns the maximum score of this BayesianNetworkDiffSM returned for a admissible input sequence. Currently only implemented for position weight matrices.
    
    Returns:
    
    the maximum score
    
    Throws:
    
    Exception - if the model is of higher order than 0
  - getCurrentParameterSet
```
public InstanceParameterSet getCurrentParameterSet()
                                            throws Exception
```
    Description copied from interface: InstantiableFromParameterSet
    
    Returns the InstanceParameterSet that has been used to instantiate the current instance of the implementing class. If the current instance was not created using an InstanceParameterSet, an equivalent InstanceParameterSet should be returned, so that an instance created using this InstanceParameterSet would be in principle equal to the current instance.
    
    Specified by:
    
    getCurrentParameterSet in interface InstantiableFromParameterSet
    
    Returns:
    
    the current InstanceParameterSet
    
    Throws:
    
    Exception - if the InstanceParameterSet could not be returned
  - emitDataSet
```
public DataSet emitDataSet(int numberOfSequences,
                           int... seqLength)
                    throws NotTrainedException,
                           Exception
```
    Description copied from interface: StatisticalModel
    This method returns a DataSet object containing artificial sequence(s).
    
    There are two different possibilities to create a data set for a model with length 0 (homogeneous models).
    1. emitDataSet( int n, int l ) should return a data set with n sequences of length l.
    2. emitDataSet( int n, int[] l ) should return a data set with n sequences which have a sequence length corresponding to the entry in the given array l.
    There are two different possibilities to create a data set for a model with length greater than 0 (inhomogeneous models).
    emitDataSet( int n ) and emitDataSet( int n, null ) should return a data set with n sequences of length of the model ( SequenceScore.getLength()).
    
    The standard implementation throws an Exception.
    Specified by:
    
    emitDataSet in interface StatisticalModel
    
    Overrides:
    
    emitDataSet in class AbstractDifferentiableStatisticalModel
    
    Parameters:
    
    numberOfSequences - the number of sequences that should be contained in the returned data set
    
    seqLength - the length of the sequences for a homogeneous model; for an inhomogeneous model this parameter should be null or an array of size 0.
    
    Returns:
    
    a DataSet containing the artificial sequence(s)
    
    Throws:
    
    NotTrainedException - if the model is not trained yet
    
    Exception - if the emission did not succeed
    
    See Also:
    
    DataSet
  - toHtml
```
public String toHtml(NumberFormat nf)
```
    Returns an HTML representation of this BayesianNetworkDiffSM.
    
    Parameters:
    
    nf - the number format
    
    Returns:
    
    the HTML representation
  - fillInfixScore
```
public void fillInfixScore(int[] seq,
                           int start,
                           int length,
                           double[] scores)
```
    Description copied from interface: QuickScanningSequenceScore
    
    Computes the position-wise scores of an infix of the sequence seq (which must be encoded by the same alphabet as this QuickScanningSequenceScore) beginning at start and extending for length positions. The scores are computed per position and filled into the provided array scores, which is of the same length as seq. This must be implemented such that ToolBox.sum(double...) applied to scores computed from 0 to seq.length returns the same value as SequenceScore.getLogScoreFor(de.jstacs.data.sequences.Sequence) called on the IntSequence created from seq.
    
    Specified by:
    
    fillInfixScore in interface QuickScanningSequenceScore
    
    Parameters:
    
    seq - the sequence
    
    start - the start of the infix
    
    length - the length of the infix
    
    scores - the array of scores to be (partly) filled

Class BayesianNetworkDiffSM

Field Summary

Fields inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore

Fields inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore

Constructor Summary

Method Summary

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel

Methods inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore

Methods inherited from class java.lang.Object

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

Methods inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore

Field Detail

parameters

trees

isTrained

ess

numFreePars

nums

structureMeasure

plugInParameters

order

logNormalizationConstant

Constructor Detail

BayesianNetworkDiffSM

BayesianNetworkDiffSM

BayesianNetworkDiffSM

Method Detail

clone

getLogPartialNormalizationConstant

initializeFunction

createTrees

setPlugInParameters

fromXML

toString

getInstanceName

getLogScoreFor

getInfixFilter

getLogScoreAndPartialDerivation

getLogNormalizationConstant

getNumberOfParameters

setParameters

precomputeNormalization

getCurrentParameterValues

toXML

getLogPriorTerm

addGradientOfLogPriorTerm

getESS

getPositionForParameter

getPositionDependentKMerProb

getSizeOfEventSpaceForRandomVariablesOfParameter

initializeFunctionRandomly

isInitialized

getPWM

getMaximalMarkovOrder

getMaximumScore

getCurrentParameterSet

emitDataSet

toHtml

fillInfixScore