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## de.jstacs.scoringFunctions.mix.motifSearch
Class DurationScoringFunction

    java.lang.Object
      de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
          de.jstacs.scoringFunctions.mix.motifSearch.PositionScoringFunction
              de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction

All Implemented Interfaces:
  NormalizableScoringFunction, ScoringFunction, Storable, Cloneable

Direct Known Subclasses:
  SkewNormalLikeScoringFunction, UniformDurationScoringFunction

    public abstract class DurationScoringFunction

extends PositionScoringFunction

This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.

Author:
  Jens Keilwagen
See Also:
  `HiddenMotifsMixture`

Field Summary
`protected int` | `delta` The difference of maximal and minimal value.
`protected double` | `ess` The equivalent sample size.
`protected int` | `max` The maximal value.
`protected int` | `min` The minimal value.

Fields inherited from class de.jstacs.scoringFunctions.mix.motifSearch.PositionScoringFunction
`internal`

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

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

Constructor Summary
`protected` | `DurationScoringFunction(int min, int max, double ess)` The default constructor.
`protected` | `DurationScoringFunction(StringBuffer source)` This is the constructor for `Storable`.

Method Summary
`abstract void` | `adjust(int[] length, double[] weight)` This method adjust the parameter based on the given statistic.
`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` | `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.
`int` | `getMax()` Returns the maximal value that can be scored.
`int` | `getMin()` Returns the minimal value that can be scored.
`double` | `getNormalizationConstant()` Returns the sum of the scores over all sequences of the event space.
`int` | `getNumberOfPossibilities()` Returns the number of different possibilities that can be scored.
`double` | `getPartialNormalizationConstant(int parameterIndex)` Returns the partial normalization constant for the parameter with index `parameterIndex`.
`int` | `getSizeOfEventSpaceForRandomVariablesOfParameter(int index)` Returns the size of the event space of the random variables that are affected by parameter no.
`abstract void` | `initializeUniformly()` This method set special parameters that lead to an uniform distribution.
`boolean` | `isPossible(int... positions)` This method returns `true` if the given `positions` are in the domain of the PositionScoringFunction.
`void` | `modify(int delta)` This method modifies the underlying `AlphabetContainer`.
`boolean` | `next()` This method steps to the next reasonable outcome if possible.
`void` | `reset()` This method resets the iterator to the initial state (first reasonable output) so that it can be used again.
`StringBuffer` | `toXML()` This method returns an XML representation as `StringBuffer` of an instance of the implementing class.

Methods inherited from class de.jstacs.scoringFunctions.mix.motifSearch.PositionScoringFunction
`clone, getInternalPosition, getLogScore, getLogScore, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivationForInternal, getLogScoreForInternal, getValuesFromSequence`

Methods inherited from class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
`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, getLogPriorTerm`

Methods inherited from interface de.jstacs.scoringFunctions.ScoringFunction
`getCurrentParameterValues, getInstanceName, getNumberOfParameters, initializeFunction, initializeFunctionRandomly, isInitialized, setParameters`

Field Detail

### ess

    protected double ess

  The equivalent sample size.

### min

    protected int min

  The minimal value.

### max

    protected int max

  The maximal value.

### delta

    protected int delta

  The difference of maximal and minimal value.

Constructor Detail

### DurationScoringFunction

    protected DurationScoringFunction(int min,
                                      int max,
                                      double ess)

  The default constructor.

  Parameters:
    `min` - the minimal value
    `max` - the maximal value
    `ess` - the equivalent sample size

### DurationScoringFunction

    protected DurationScoringFunction(StringBuffer source)
                               throws NonParsableException

  This is the constructor for `Storable`. Creates a new `DurationScoringFunction` out of a `StringBuffer`.

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

Method Detail

### 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`
  Overrides:
    `toXML` in class `PositionScoringFunction`
  Returns:
    the XML representation

### fromXML

    protected void fromXML(StringBuffer xml)
                    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`.

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

### reset

    public void reset()

  Description copied from class: `PositionScoringFunction`
  This method resets the iterator to the initial state (first reasonable output) so that it can be used again.

  Specified by:
    `reset` in class `PositionScoringFunction`

### next

    public boolean next()

  Description copied from class: `PositionScoringFunction`
  This method steps to the next reasonable outcome if possible.

  Specified by:
    `next` in class `PositionScoringFunction`
  Returns:
    `true` if a next reasonale outcome could be set, otherwise `false`

### 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, ...

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

### getEss

    public final 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.

  Returns:
    the equivalent sample size.

### isPossible

    public boolean isPossible(int... positions)

  Description copied from class: `PositionScoringFunction`
  This method returns `true` if the given `positions` are in the domain of the PositionScoringFunction.

  Specified by:
    `isPossible` in class `PositionScoringFunction`
  Parameters:
    `positions` - the positions to be tested
  Returns:
    `true` if the given `positions` are in the domain of the PositionScoringFunction

### getMin

    public final int getMin()

  Returns the minimal value that can be scored.

  Returns:
    the minimal value that can be scored.

### getMax

    public final int getMax()

  Returns the maximal value that can be scored.

  Returns:
    the maximal value that can be scored.

### getNumberOfPossibilities

    public int getNumberOfPossibilities()

  Returns the number of different possibilities that can be scored.

  Returns:
    the number of different possibilities that can be scored.

### initializeUniformly

    public abstract void initializeUniformly()

  This method set special parameters that lead to an uniform distribution.

### adjust

    public abstract void adjust(int[] length,
                                double[] weight)

  This method adjust the parameter based on the given statistic.

  Parameters:
    `length` - an array containing length values
    `weight` - an array containing corresponding weight values

### modify

    public void modify(int delta)

  This method modifies the underlying `AlphabetContainer`. This might be necessary if the motif length changed.

  Parameters:
    `delta` - the change
  See Also:
    `Mutable.modify(double[], double[], double[][][][], double[][][][], int, int)`, `MutableMotifDiscoverer.modifyMotif(int, int, int)`, `MutableMotifDiscoverer.modifyMotif(int, double[], double[], double[][][][], double[][][][], int, int)`

### getNormalizationConstant

    public final 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

### getPartialNormalizationConstant

    public final 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()`

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