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## de.jstacs.scoringFunctions.directedGraphicalModels
Class BayesianNetworkScoringFunction

    java.lang.Object
      de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
          de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction

All Implemented Interfaces:
  InstantiableFromParameterSet, NormalizableScoringFunction, ScoringFunction, Storable, Cloneable

Direct Known Subclasses:
  MutableMarkovModelScoringFunction

    public class BayesianNetworkScoringFunction

extends AbstractNormalizableScoringFunction
implements InstantiableFromParameterSet

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 `ScoringFunction` using maximum conditional likelihood.

Author:
  Jan Grau

Field Summary
`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 Parameter[]` | `parameters` The parameters of the scoring function.
`protected boolean` | `plugInParameters` Indicates if plug-in parameters, i.e. generative (MAP) parameters shall be used upon initialization.
`protected Measure` | `structureMeasure` `Measure` that defines the network structure.
`protected ParameterTree[]` | `trees` The trees that represent the context of the random variable (i.e.

Fields inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
`alphabets, length, r`

Fields inherited from interface de.jstacs.scoringFunctions.ScoringFunction
`UNKNOWN`

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

Method Summary
`void` | `addGradientOfLogPriorTerm(double[] grad, int start)` This method computes the gradient of `NormalizableScoringFunction.getLogPriorTerm()` for each parameter of this model.
`BayesianNetworkScoringFunction` | `clone()` Creates a clone (deep copy) of the current `ScoringFunction` instance.
`protected void` | `createTrees(Sample[] data2, double[][] weights2)` Creates the tree structures that represent the context (array `trees`) and the parameter objects `parameters` using the given `Measure` `structureMeasure`.
`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 `ScoringFunction.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()` Returns a short instance name.
`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 ``NormalizableScoringFunction.getEss()` * `NormalizableScoringFunction.getLogNormalizationConstant()` + Math.log( prior )` where `prior` is the prior for the parameters of this model.
`double` | `getLogScore(Sequence seq, int start)` Returns the logarithmic score for the `Sequence` `seq` beginning at position `start` in the `Sequence`.
`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.
`int` | `getNumberOfParameters()` Returns the number of parameters in this `ScoringFunction`.
`double[]` | `getPositionDependentKMerProb(Sequence kmer)` Returns the probability of `kmer` for all possible positions in this `BayesianNetworkScoringFunction` 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 `BayesianNetworkScoringFunction` 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, Sample[] data, double[][] weights)` This method creates the underlying structure of the `ScoringFunction`.
`void` | `initializeFunctionRandomly(boolean freeParams)` This method initializes the `ScoringFunction` randomly.
`boolean` | `isInitialized()` This method can be used to determine whether the model 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 + `ScoringFunction.getNumberOfParameters()` - 1`
`protected void` | `setPlugInParameters(int index, boolean freeParameters, Sample[] data, double[][] weights)` Computes and sets the plug-in parameters (MAP estimated parameters) from `data` using `weights`.
`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.scoringFunctions.AbstractNormalizableScoringFunction
`getAlphabetContainer, getInitialClassParam, getLength, getLogScore, getLogScoreAndPartialDerivation, getNumberOfRecommendedStarts, getNumberOfStarts, isNormalized, isNormalized`

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

Field Detail

### parameters

    protected Parameter[] parameters

  The parameters of the scoring function. This comprises free as well as dependent parameters.

### trees

    protected ParameterTree[] 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

### BayesianNetworkScoringFunction

    public BayesianNetworkScoringFunction(AlphabetContainer alphabet,
                                          int length,
                                          double ess,
                                          boolean plugInParameters,
                                          Measure structureMeasure)
                                   throws Exception

  Creates a new `BayesianNetworkScoringFunction` 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

### BayesianNetworkScoringFunction

    public BayesianNetworkScoringFunction(BayesianNetworkScoringFunctionParameterSet parameters)
                                   throws ParameterSetParser.NotInstantiableException,
                                          Exception

  Creates a new `BayesianNetworkScoringFunction` that has neither been initialized nor trained from a `BayesianNetworkScoringFunctionParameterSet`.

  Parameters:
    `parameters` - the parameter set
  Throws:
    `ParameterSetParser.NotInstantiableException` - if the `BayesianNetworkScoringFunction` could not be instantiated from the `BayesianNetworkScoringFunctionParameterSet`
    `Exception` - if the length of the scoring function is not admissible (<=0) or the alphabet is not discrete

### BayesianNetworkScoringFunction

    public BayesianNetworkScoringFunction(StringBuffer xml)
                                   throws NonParsableException

  The standard constructor for the interface `Storable`. Recreates a `BayesianNetworkScoringFunction` 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 BayesianNetworkScoringFunction clone()
                                         throws CloneNotSupportedException

  Description copied from interface: `ScoringFunction`
  Creates a clone (deep copy) of the current `ScoringFunction` instance.

  Specified by:
    `clone` in interface `ScoringFunction`
  Overrides:
    `clone` in class `AbstractNormalizableScoringFunction`
  Returns:
    the cloned instance of the current `ScoringFunction`
  Throws:
    `CloneNotSupportedException` - if something went wrong while cloning the `ScoringFunction`

### getLogPartialNormalizationConstant

    public double getLogPartialNormalizationConstant(int parameterIndex)
                                              throws Exception

  Description copied from interface: `NormalizableScoringFunction`
  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`,
  .

  Specified by:
    `getLogPartialNormalizationConstant` in interface `NormalizableScoringFunction`
  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:
    `NormalizableScoringFunction.getLogNormalizationConstant()`

### initializeFunction

    public void initializeFunction(int index,
                                   boolean freeParams,
                                   Sample[] data,
                                   double[][] weights)
                            throws Exception

  Description copied from interface: `ScoringFunction`
  This method creates the underlying structure of the `ScoringFunction`.

  Specified by:
    `initializeFunction` in interface `ScoringFunction`
  Parameters:
    `index` - the index of the class the `ScoringFunction` models
    `freeParams` - indicates whether the (reduced) parameterization is used
    `data` - the samples
    `weights` - the weights of the sequences in the samples
  Throws:
    `Exception` - if something went wrong

### createTrees

    protected void createTrees(Sample[] 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,
                                       Sample[] 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: `AbstractNormalizableScoringFunction`
  This method is called in the constructor for the `Storable` interface to create a scoring function from a `StringBuffer`.

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

### toString

    public String toString()

  Overrides:
    `toString` in class `Object`

### getInstanceName

    public String getInstanceName()

  Description copied from interface: `ScoringFunction`
  Returns a short instance name.

  Specified by:
    `getInstanceName` in interface `ScoringFunction`
  Returns:
    a short instance name

### 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`.

  Specified by:
    `getLogScore` in interface `ScoringFunction`
  Parameters:
    `seq` - the `Sequence`
    `start` - the start position in the `Sequence`
  Returns:
    the logarithmic score for the `Sequence`

### getLogScoreAndPartialDerivation

    public double getLogScoreAndPartialDerivation(Sequence seq,
                                                  int start,
                                                  IntList indices,
                                                  DoubleList partialDer)

  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.

  Specified by:
    `getLogScoreAndPartialDerivation` in interface `ScoringFunction`
  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 is not zero
    `partialDer` - a `DoubleList` of partial derivations, after method invocation the list should contain the corresponding that are not zero
  Returns:
    the logarithmic score for the `Sequence`

### getLogNormalizationConstant

    public double getLogNormalizationConstant()
                                       throws RuntimeException

  Description copied from interface: `NormalizableScoringFunction`
  Returns the logarithm of the sum of the scores over all sequences of the event space.

  Specified by:
    `getLogNormalizationConstant` in interface `NormalizableScoringFunction`
  Returns:
    the logarithm of the normalization constant Z
  Throws:
    `RuntimeException`

### getNumberOfParameters

    public int getNumberOfParameters()

  Description copied from interface: `ScoringFunction`
  Returns the number of parameters in this `ScoringFunction`. If the number of parameters is not known yet, the method returns `ScoringFunction.UNKNOWN`.

  Specified by:
    `getNumberOfParameters` in interface `ScoringFunction`
  Returns:
    the number of parameters in this `ScoringFunction`
  See Also:
    `ScoringFunction.UNKNOWN`

### setParameters

    public void setParameters(double[] params,
                              int start)

  Description copied from interface: `ScoringFunction`
  This method sets the internal parameters to the values of `params` between `start` and `start + `ScoringFunction.getNumberOfParameters()` - 1`

  Specified by:
    `setParameters` in interface `ScoringFunction`
  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: `ScoringFunction`
  Returns a `double` array of dimension `ScoringFunction.getNumberOfParameters()` containing the current parameter values. If one 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.

  Specified by:
    `getCurrentParameterValues` in interface `ScoringFunction`
  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: `NormalizableScoringFunction`
  This method computes a value that is proportional to

  ``NormalizableScoringFunction.getEss()` * `NormalizableScoringFunction.getLogNormalizationConstant()` + Math.log( prior )`

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

  Specified by:
    `getLogPriorTerm` in interface `NormalizableScoringFunction`
  Returns:
    a value that is proportional to ``NormalizableScoringFunction.getEss()` * `NormalizableScoringFunction.getLogNormalizationConstant()` + Math.log( prior ).`
  See Also:
    `NormalizableScoringFunction.getEss()`, `NormalizableScoringFunction.getLogNormalizationConstant()`

### addGradientOfLogPriorTerm

    public void addGradientOfLogPriorTerm(double[] grad,
                                          int start)

  Description copied from interface: `NormalizableScoringFunction`
  This method computes the gradient of `NormalizableScoringFunction.getLogPriorTerm()` for each parameter of this model. The results are added to the array `grad` beginning at index `start`.

  Specified by:
    `addGradientOfLogPriorTerm` in interface `NormalizableScoringFunction`
  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:
    `NormalizableScoringFunction.getLogPriorTerm()`

### getEss

    public double getEss()

  Description copied from interface: `NormalizableScoringFunction`
  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 `NormalizableScoringFunction`
  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 `BayesianNetworkScoringFunction` 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 ``AbstractNormalizableScoringFunction.getLength()`-1```
  Throws:
    `Exception` - if the method is called for non-Markov model structures

### getSizeOfEventSpaceForRandomVariablesOfParameter

    public int getSizeOfEventSpaceForRandomVariablesOfParameter(int index)

  Description copied from interface: `NormalizableScoringFunction`
  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 `NormalizableScoringFunction`
  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: `ScoringFunction`
  This method initializes the `ScoringFunction` randomly. It has to create the underlying structure of the `ScoringFunction`.

  Specified by:
    `initializeFunctionRandomly` in interface `ScoringFunction`
  Parameters:
    `freeParams` - indicates whether the (reduced) parameterization is used
  Throws:
    `Exception` - if something went wrong

### isInitialized

    public boolean isInitialized()

  Description copied from interface: `ScoringFunction`
  This method can be used to determine whether the model is initialized. If the model is not initialized you should invoke the method `ScoringFunction.initializeFunction(int, boolean, Sample[], double[][])`.

  Specified by:
    `isInitialized` in interface `ScoringFunction`
  Returns:
    `true` if the model is initialized, `false` otherwise

### getPWM

    public double[][] getPWM()
                      throws Exception

  If this `BayesianNetworkScoringFunction` is a PWM, i.e. `structureMeasure`=new `InhomogeneousMarkov`(0)}}, this method returns the normalized PWM as a `double` array of dimension `AbstractNormalizableScoringFunction.getLength()` x size-of-alphabet.

  Returns:
    the PWM as a two-dimensional array
  Throws:
    `Exception` - if this method is called for a `BayesianNetworkScoringFunction` that is not a PWM

### 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

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