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## de.jstacs.scoringFunctions.homogeneous
Class UniformHomogeneousScoringFunction

    java.lang.Object
      de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
          de.jstacs.scoringFunctions.VariableLengthScoringFunction
              de.jstacs.scoringFunctions.homogeneous.HomogeneousScoringFunction
                  de.jstacs.scoringFunctions.homogeneous.UniformHomogeneousScoringFunction

All Implemented Interfaces:
  NormalizableScoringFunction, ScoringFunction, Storable, Cloneable

    public class UniformHomogeneousScoringFunction

extends HomogeneousScoringFunction

This scoring function 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
`UniformHomogeneousScoringFunction(AlphabetContainer alphabets, double ess)` This is the main constructor that creates an instance that models each sequence uniformly.
`UniformHomogeneousScoringFunction(StringBuffer xml)` This is the constructor for `Storable`.

Method Summary
`void` | `addGradientOfLogPriorTerm(double[] grad, int start)` This method computes the gradient of `getLogPriorTerm()` for each parameter of this model.
`protected void` | `fromXML(StringBuffer xml)` This method is called in the constructor to create a scoring function from a StringBuffer
`double[]` | `getCurrentParameterValues()` Returns a double array of dimension `getNumberOfParameters()` containing the current parameter values.
`double` | `getEss()` Returns the equivalent sample size 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 `getESS()*Math.log( getNormalizationConstant() ) + Math.log( prior ).`
`double` | `getLogScore(Sequence seq, int start, int length)` This method computes the logarithm of the score for a given subsequence.
`double` | `getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList dList)` This method computes the logarithm of the score and the partial derivations for a given subsequence.
`int` | `getMaximalMarkovOrder()` Returns the maximal used markov oder.
`double` | `getNormalizationConstant(int length)` This method returns the normalization constant for a given sequence length.
`int` | `getNumberOfParameters()` The number of parameters in this scoring function.
`double` | `getPartialNormalizationConstant(int parameterIndex, int length)` This method returns the partial normalization constant for a given parameter index and sequence length.
`int` | `getSizeOfEventSpaceForRandomVariablesOfParameter(int index)` Returns the size of the event space of the random variables that are affected by parameter no.
`double[]` | `getStationarySymbolDistribution()` This method returns the stationary symbol distribution.
`void` | `initializeFunction(int index, boolean meila, Sample[] data, double[][] weights)` This method creates the underlying structure of the scoring function.
`void` | `initializeFunctionRandomly(boolean freeParams)` This method initializes the scoring function randomly.
`boolean` | `isInitialized()` This method can be used to determine whether the model is initialized.
`boolean` | `isNormalized()` This method returns whether the implemented score is already normalized to 1.
`void` | `setParameters(double[] params, int start)` This method sets the internal parameters to the values of `params` between `start` and `start + this.getNumberOfParameters() - 1`
`void` | `setStatisticForHyperparameters(int[] length, double[] weights)` This method sets the hyperparameters for the model parameters by evaluating the given statistic.
`String` | `toString()`
`StringBuffer` | `toXML()` This method returns an XML-representation of an instance of the implementing class.

Methods inherited from class de.jstacs.scoringFunctions.VariableLengthScoringFunction
`getLogScore, getLogScoreAndPartialDerivation, getNormalizationConstant, getPartialNormalizationConstant`

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

### UniformHomogeneousScoringFunction

    public UniformHomogeneousScoringFunction(AlphabetContainer alphabets,
                                             double ess)

  This is the main constructor that creates an instance that models each sequence uniformly.

  Parameters:
    `alphabets` - the AlphabetContainer
    `ess` - the equivalent sample size (ess) for the class

### UniformHomogeneousScoringFunction

    public UniformHomogeneousScoringFunction(StringBuffer xml)
                                      throws NonParsableException

  This is the constructor for `Storable`.

  Parameters:
    `xml` - the xml representation
  Throws:
    `NonParsableException` - if the 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,
                              int length)

  Description copied from class: `VariableLengthScoringFunction`
  This method computes the logarithm of the score for a given subsequence.

  Specified by:
    `getLogScore` in class `VariableLengthScoringFunction`
  Parameters:
    `seq` - the sequence
    `start` - the start index
    `length` - the end index
  Returns:
    the logarithm of the score
  See Also:
    `ScoringFunction.getLogScore(Sequence, int)`

### getLogScoreAndPartialDerivation

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

  Description copied from class: `VariableLengthScoringFunction`
  This method computes the logarithm of the score and the partial derivations for a given subsequence.

  Specified by:
    `getLogScoreAndPartialDerivation` in class `VariableLengthScoringFunction`
  Parameters:
    `seq` - the sequence
    `start` - the start index
    `length` - the end index
    `indices` - the list for the indices of the parameters
    `dList` - the list for the partial derivations
  Returns:
    the logarithm of the score
  See Also:
    `ScoringFunction.getLogScoreAndPartialDerivation(Sequence, int, IntList, DoubleList)`

### getNumberOfParameters

    public int getNumberOfParameters()

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

  Returns:
    the number of parameters in this scoring function
  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 + this.getNumberOfParameters() - 1`

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

### toXML

    public StringBuffer toXML()

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

  Returns:
    the XML-representation

### getNormalizationConstant

    public double getNormalizationConstant(int length)

  Description copied from class: `VariableLengthScoringFunction`
  This method returns the normalization constant for a given sequence length.

  Specified by:
    `getNormalizationConstant` in class `VariableLengthScoringFunction`
  Parameters:
    `length` - the sequence length
  Returns:
    the normalization constant
  See Also:
    `NormalizableScoringFunction.getNormalizationConstant()`

### 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 scoring function.

  Parameters:
    `index` - the index of the class the scoring function models
    `meila` - if `true`, 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 to create a scoring function from a StringBuffer

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

### 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,
                                                  int length)
                                           throws Exception

  Description copied from class: `VariableLengthScoringFunction`
  This method returns the partial normalization constant for a given parameter index and sequence length.

  Specified by:
    `getPartialNormalizationConstant` in class `VariableLengthScoringFunction`
  Parameters:
    `parameterIndex` - the index of the parameter
    `length` - the sequence length
  Returns:
    the partial normalization constant
  Throws:
    `Exception` - if something went wrong
  See Also:
    `NormalizableScoringFunction.getPartialNormalizationConstant(int)`

### getEss

    public double getEss()

  Description copied from interface: `NormalizableScoringFunction`
  Returns the equivalent sample size 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

  `getESS()*Math.log( getNormalizationConstant() ) + Math.log( prior ).`

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

  ``
  Returns:
    `getESS()*Math.log( 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 `getLogPriorTerm()` for each parameter of this model. The results are added to the array `grad` beginning at index `start`.

  Parameters:
    `grad` - the gradient
    `start` - the start index in the `grad` array, where the partial derivations for the parameters of this models shall be enter
  See Also:
    `NormalizableScoringFunction.getLogPriorTerm()`

### getCurrentParameterValues

    public double[] getCurrentParameterValues()
                                       throws Exception

  Description copied from interface: `ScoringFunction`
  Returns a double array of dimension `getNumberOfParameters()` containing the current parameter values. If on e 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` - is thrown 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 initialize you should invoke the method `ScoringFunction.initializeFunction(int, boolean, Sample[], double[][])`.

  Returns:
    `true` if the model is initialized

### isNormalized

    public boolean isNormalized()

  Description copied from interface: `NormalizableScoringFunction`
  This method returns whether the implemented score is already normalized to 1. The standard implementation returns `false`.

  Specified by:
    `isNormalized` in interface `NormalizableScoringFunction`
  Overrides:
    `isNormalized` in class `AbstractNormalizableScoringFunction`
  Returns:
    whether the implemented score is already normalized to 1

### getMaximalMarkovOrder

    public int getMaximalMarkovOrder()

  Description copied from class: `HomogeneousScoringFunction`
  Returns the maximal used markov oder.

  Specified by:
    `getMaximalMarkovOrder` in class `HomogeneousScoringFunction`
  Returns:
    the maximal used markov oder

### initializeFunctionRandomly

    public void initializeFunctionRandomly(boolean freeParams)
                                    throws Exception

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

  Parameters:
    `freeParams` - if true, the (reduced) parameterization is used
  Throws:
    `Exception`

### getStationarySymbolDistribution

    public double[] getStationarySymbolDistribution()

  Description copied from class: `VariableLengthScoringFunction`
  This method returns the stationary symbol distribution. For DNA this is the stationary mono nucleotide distribution. This method is used to determine the contrast for `de.jstacs.motifDiscovery.Mutable#expand(int, double[], double[], double)`, `de.jstacs.motifDiscovery.Mutable#shift(double[], double[], double)` and `de.jstacs.motifDiscovery.Mutable#shrink(double[], double[], double)`.

  Specified by:
    `getStationarySymbolDistribution` in class `VariableLengthScoringFunction`
  Returns:
    the stationary symbol distribution
  See Also:
    `Mutable`

### setStatisticForHyperparameters

    public void setStatisticForHyperparameters(int[] length,
                                               double[] weights)
                                        throws Exception

  Description copied from class: `VariableLengthScoringFunction`
  This method sets the hyperparameters for the model parameters by evaluating the given statistic. The statistic can be interpreted as follows: The model has seen a number of sequences. From these sequences it is only known how long (`length`) and how often (`weight`) they have been seen.

  Specified by:
    `setStatisticForHyperparameters` in class `VariableLengthScoringFunction`
  Parameters:
    `length` - the non-negative lengths of the sequences
    `weights` - the non-negative weight for the corresponding sequence
  Throws:
    `Exception` - if something went wrong
  See Also:
    `Mutable`

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