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## de.jstacs.scoringFunctions
Interface ScoringFunction

All Superinterfaces:
  Cloneable, Storable

All Known Subinterfaces:
  NormalizableScoringFunction, VariableLengthScoringFunction

All Known Implementing Classes:
  AbstractMixtureScoringFunction, AbstractNormalizableScoringFunction, AbstractVariableLengthScoringFunction, BayesianNetworkScoringFunction, CMMScoringFunction, DurationScoringFunction, HiddenMotifsMixture, HMM0ScoringFunction, HMMScoringFunction, HomogeneousScoringFunction, IndependentProductScoringFunction, MixtureDuration, MixtureScoringFunction, MRFScoringFunction, MutableMarkovModelScoringFunction, NormalizedScoringFunction, PositionScoringFunction, SkewNormalLikeScoringFunction, StrandScoringFunction, UniformDurationScoringFunction, UniformHomogeneousScoringFunction, UniformScoringFunction, VariableLengthMixtureScoringFunction

    public interface ScoringFunction

extends Storable, Cloneable

This interface is the main part of any `ScoreClassifier`.

Author:
  Jens Keilwagen, Jan Grau

Field Summary
`static int` | `UNKNOWN` Indicates that the number of parameters of this `ScoringFunction` is not known (yet).

Method Summary
`ScoringFunction` | `clone()` Creates a clone (deep copy) of the current `ScoringFunction` instance.
`AlphabetContainer` | `getAlphabetContainer()` Returns the `AlphabetContainer` for this `ScoringFunction`.
`double[]` | `getCurrentParameterValues()` Returns a `double` array of dimension `getNumberOfParameters()` containing the current parameter values.
`double` | `getInitialClassParam(double classProb)` Returns the initial class parameter for the class this `ScoringFunction` is responsible for, based on the class probability `classProb`.
`String` | `getInstanceName()` Returns a short instance name.
`int` | `getLength()` Returns the length of this `ScoringFunction`, i.e. the length of the `Sequence`s this `ScoringFunction` can handle.
`double` | `getLogScore(Sequence seq)` Returns the logarithmic score for the `Sequence` `seq`.
`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.
`double` | `getLogScoreAndPartialDerivation(Sequence seq, IntList indices, DoubleList partialDer)` Returns the logarithmic score for a `Sequence` `seq` and fills lists with the indices and the partial derivations.
`int` | `getNumberOfParameters()` Returns the number of parameters in this `ScoringFunction`.
`int` | `getNumberOfRecommendedStarts()` This method returns the number of recommended optimization starts.
`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.
`void` | `setParameters(double[] params, int start)` This method sets the internal parameters to the values of `params` between `start` and `start + `getNumberOfParameters()` - 1`

Methods inherited from interface de.jstacs.Storable
`toXML`

Field Detail

### UNKNOWN

    static final int UNKNOWN

  Indicates that the number of parameters of this `ScoringFunction` is not known (yet).

  See Also:
    Constant Field Values

Method Detail

### clone

    ScoringFunction clone()
                          throws CloneNotSupportedException

  Creates a clone (deep copy) of the current `ScoringFunction` instance.

  Returns:
    the cloned instance of the current `ScoringFunction`
  Throws:
    `CloneNotSupportedException` - if something went wrong while cloning the `ScoringFunction`

### initializeFunction

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

  This method creates the underlying structure of the `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

### initializeFunctionRandomly

    void initializeFunctionRandomly(boolean freeParams)
                                    throws Exception

  This method initializes the `ScoringFunction` randomly. It has to create the underlying structure of the `ScoringFunction`.

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

### getAlphabetContainer

    AlphabetContainer getAlphabetContainer()

  Returns the `AlphabetContainer` for this `ScoringFunction`. Only `Sequence`s with a comparable `AlphabetContainer` can be modeled.

  Returns:
    the `AlphabetContainer`

### getInstanceName

    String getInstanceName()

  Returns a short instance name.

  Returns:
    a short instance name

### getLength

    int getLength()

  Returns the length of this `ScoringFunction`, i.e. the length of the `Sequence`s this `ScoringFunction` can handle. For homogeneous `ScoringFunction`s, i.e. `ScoringFunction`s that support `Sequence`s of different lengths, the method should return `0`.

  Returns:
    the length of this `ScoringFunction`

### getLogScore

    double getLogScore(Sequence seq)

  Returns the logarithmic score for the `Sequence` `seq`.

  Parameters:
    `seq` - the sequence
  Returns:
    the logarithmic score for the sequence

### getLogScore

    double getLogScore(Sequence seq,
                       int start)

  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`

### getLogScoreAndPartialDerivation

    double getLogScoreAndPartialDerivation(Sequence seq,
                                           IntList indices,
                                           DoubleList partialDer)

  Returns the logarithmic score for a `Sequence` `seq` and fills lists with the indices and the partial derivations.

  Parameters:
    `seq` - 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`

### getLogScoreAndPartialDerivation

    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.

  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`

### getNumberOfParameters

    int getNumberOfParameters()

  Returns the number of parameters in this `ScoringFunction`. If the number of parameters is not known yet, the method returns `UNKNOWN`.

  Returns:
    the number of parameters in this `ScoringFunction`
  See Also:
    `UNKNOWN`

### getNumberOfRecommendedStarts

    int getNumberOfRecommendedStarts()

  This method returns the number of recommended optimization starts. The standard implementation returns 1.

  Returns:
    the number of recommended optimization starts

### getCurrentParameterValues

    double[] getCurrentParameterValues()
                                       throws Exception

  Returns a `double` array of dimension `getNumberOfParameters()` containing the current parameter values. If one likes to use these parameters to start an optimization it is highly recommended to invoke `initializeFunction(int, boolean, Sample[], 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)

### setParameters

    void setParameters(double[] params,
                       int start)

  This method sets the internal parameters to the values of `params` between `start` and `start + `getNumberOfParameters()` - 1`

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

### isInitialized

    boolean isInitialized()

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

  Returns:
    `true` if the model is initialized, `false` otherwise

### getInitialClassParam

    double getInitialClassParam(double classProb)

  Returns the initial class parameter for the class this `ScoringFunction` is responsible for, based on the class probability `classProb`.

  Parameters:
    `classProb` - the class probability
  Returns:
    the initial class parameter

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