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## de.jstacs.scoringFunctions
Class UniformScoringFunction

    java.lang.Object
      de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
          de.jstacs.scoringFunctions.UniformScoringFunction

All Implemented Interfaces:
  NormalizableScoringFunction, ScoringFunction, Storable, Cloneable

    public class UniformScoringFunction

extends AbstractNormalizableScoringFunction

This `ScoringFunction` does nothing. So it is possible to save parameters in an optimization.

Author:
  Jens Keilwagen, Jan Grau

Field Summary

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

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

Constructor Summary
`UniformScoringFunction(AlphabetContainer alphabets, int length, double ess)` This is the main constructor that creates an instance of a `UniformScoringFunction` that models each sequence uniformly.
`UniformScoringFunction(StringBuffer xml)` This is the 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.
`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[]` | `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` | `getLogPriorTerm()` This method computes a value that is proportional to `NormalizableScoringFunction.getEss()` `* Math.log( `NormalizableScoringFunction.getNormalizationConstant()` ) + Math.log( prior ).`
`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 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.
`int` | `getNumberOfParameters()` Returns the number of parameters in this `ScoringFunction`.
`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.
`void` | `initializeFunction(int index, boolean meila, 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.
`boolean` | `isNormalized()` This method indicates whether the implemented score is already normalized to 1 or not.
`void` | `setParameters(double[] params, int start)` This method sets the internal parameters to the values of `params` between `start` and `start + `ScoringFunction.getNumberOfParameters()` - 1`
`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
`clone, getAlphabetContainer, getInitialClassParam, getLength, getLogScore, getLogScoreAndPartialDerivation, getNumberOfRecommendedStarts, isNormalized`

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

Constructor Detail

### UniformScoringFunction

    public UniformScoringFunction(AlphabetContainer alphabets,
                                  int length,
                                  double ess)

  This is the main constructor that creates an instance of a `UniformScoringFunction` that models each sequence uniformly.

  Parameters:
    `alphabets` - the `AlphabetContainer`
    `length` - the length of the modeled sequences
    `ess` - the equivalent sample size (ess) of the class

### UniformScoringFunction

    public UniformScoringFunction(StringBuffer xml)
                           throws NonParsableException

  This is the constructor for the interface `Storable`. Creates a new `UniformScoringFunction` out of its XML representation as returned by `fromXML(StringBuffer)`.

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

Method Detail

### getInstanceName

    public String getInstanceName()

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

  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`.

  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 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`

### 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`.

  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`

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

### toXML

    public StringBuffer toXML()

  Description copied from interface: `Storable`
  This method returns an XML representation as `StringBuffer` of an instance of the implementing class.

  Returns:
    the XML representation

### 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

### initializeFunction

    public void initializeFunction(int index,
                                   boolean meila,
                                   Sample[] data,
                                   double[][] weights)

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

  Parameters:
    `index` - the index of the class the `ScoringFunction` models
    `meila` - indicates whether the (reduced) parameterization is used
    `data` - the samples
    `weights` - the weights of the sequences in the samples

### 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`.

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

### 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

### 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()`

### 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.

  Returns:
    the equivalent sample size.

### toString

    public String toString()

  Overrides:
    `toString` in class `Object`

### getLogPriorTerm

    public double getLogPriorTerm()

  Description copied from interface: `NormalizableScoringFunction`
  This method computes a value that is proportional to

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

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

  Returns:
    a value that is proportional to `NormalizableScoringFunction.getEss()` `* Math.log( `NormalizableScoringFunction.getNormalizationConstant()` ) + Math.log( prior ).`
  See Also:
    `NormalizableScoringFunction.getEss()`, `NormalizableScoringFunction.getNormalizationConstant()`

### 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`.

  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()`

### 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.

  Returns:
    the current parameter values
  Throws:
    `Exception` - if no parameters exist (yet)

### 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[][])`.

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

### isNormalized

    public boolean isNormalized()

  Description copied from interface: `NormalizableScoringFunction`
  This method indicates whether the implemented score is already normalized to 1 or not. The standard implementation returns `false`.

  Specified by:
    `isNormalized` in interface `NormalizableScoringFunction`
  Overrides:
    `isNormalized` in class `AbstractNormalizableScoringFunction`
  Returns:
    `true` if the implemented score is already normalized to 1, `false` otherwise

### 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`.

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

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