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## de.jstacs.classifier.scoringFunctionBased.logPrior
Class SeparateGaussianLogPrior

    java.lang.Object
      de.jstacs.algorithms.optimization.DifferentiableFunction
          de.jstacs.classifier.scoringFunctionBased.logPrior.LogPrior
              de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLogPrior
                  de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateGaussianLogPrior

All Implemented Interfaces:
  Function, Storable, Cloneable

    public class SeparateGaussianLogPrior

extends SeparateLogPrior

Class for a `LogPrior` that defines a Gaussian prior on the parameters of a set of `NormalizableScoringFunction`s and a set of class parameters. The variances `v[i]` for a parameter `i` of a `NormalizableScoringFunction` `fun[j]` are determined from the base variance `v[j]` as
`v[i] = v[j]*funs[j].getSizeOfEventSpaceForRandomVariablesOfParameter(j)` .
The variances for the class parameters are used as defined by the user. The mean parameters are set to 0 for the parameters of the `NormalizableScoringFunction`s and to the user-specified means for the class parameters.

Author:
  Jan Grau, Jens Keilwagen

Field Summary

Fields inherited from class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLogPrior
`classMus, classVars, freeParameters, funs, vars`

Fields inherited from class de.jstacs.classifier.scoringFunctionBased.logPrior.LogPrior
`UNKNOWN`

Constructor Summary
`SeparateGaussianLogPrior(double[] vars, double[] classVars, double[] classMus)` Creates a new `SeparateGaussianLogPrior` from a set of base variances `vars`, a set of class variances `classVars` and a set of class means `classMus`.
`SeparateGaussianLogPrior(StringBuffer xml)` The standard constructor for the interface `Storable`.

Method Summary
`void` | `addGradientFor(double[] params, double[] grad)` Adds the gradient of the log-prior using the current parameters to a given vector.
`double` | `evaluateFunction(double[] x)` Evaluates the function at a certain vector (in mathematical sense) `x`.
`int` | `getDimensionOfScope()` Returns the dimension of the scope of the `Function`.
`String` | `getInstanceName()` Returns a short instance name.
`void` | `unset()` Resets all internally pre-computed values, e.g. the hyperparameters for each parameter.

Methods inherited from class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLogPrior
`getNewInstance, set, toXML`

Methods inherited from class de.jstacs.classifier.scoringFunctionBased.logPrior.LogPrior
`evaluateGradientOfFunction`

Methods inherited from class de.jstacs.algorithms.optimization.DifferentiableFunction
`findOneDimensionalMin`

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

Constructor Detail

### SeparateGaussianLogPrior

    public SeparateGaussianLogPrior(double[] vars,
                                    double[] classVars,
                                    double[] classMus)

  Creates a new `SeparateGaussianLogPrior` from a set of base variances `vars`, a set of class variances `classVars` and a set of class means `classMus`.

  Parameters:
    `vars` - the base variances for each class
    `classVars` - the class variances
    `classMus` - the class means
  See Also:
    `SeparateLogPrior.SeparateLogPrior(double[], double[], double[])`

### SeparateGaussianLogPrior

    public SeparateGaussianLogPrior(StringBuffer xml)
                             throws NonParsableException

  The standard constructor for the interface `Storable`. Creates a new `SeparateGaussianLogPrior` out of its XML representation.

  Parameters:
    `xml` - the XML representation as `StringBuffer`
  Throws:
    `NonParsableException` - if the `SeparateGaussianLogPrior` could not be reconstructed out of the XML representation (the `StringBuffer` could not be parsed)
  See Also:
    `SeparateLogPrior.SeparateLogPrior(StringBuffer)`, `Storable`

Method Detail

### unset

    public void unset()

  Description copied from class: `SeparateLogPrior`
  Resets all internally pre-computed values, e.g. the hyperparameters for each parameter.

  Specified by:
    `unset` in class `SeparateLogPrior`

### addGradientFor

    public void addGradientFor(double[] params,
                               double[] grad)

  Description copied from class: `LogPrior`
  Adds the gradient of the log-prior using the current parameters to a given vector.

  Specified by:
    `addGradientFor` in class `LogPrior`
  Parameters:
    `params` - the parameters
    `grad` - the vector

### evaluateFunction

    public double evaluateFunction(double[] x)
                            throws DimensionException,
                                   EvaluationException

  Description copied from interface: `Function`
  Evaluates the function at a certain vector (in mathematical sense) `x`.

  Parameters:
    `x` - the current vector
  Returns:
    the evaluation of the function
  Throws:
    `DimensionException` - if `dim(x) != n`, with `f: R^n -> R`
    `EvaluationException` - if there was something wrong during the evaluation of the function

### getDimensionOfScope

    public int getDimensionOfScope()

  Description copied from interface: `Function`
  Returns the dimension of the scope of the `Function`.

  Returns:
    the dimension of the scope: `n` with `f: R^n -> R`

### getInstanceName

    public String getInstanceName()

  Description copied from class: `LogPrior`
  Returns a short instance name.

  Specified by:
    `getInstanceName` in class `LogPrior`
  Returns:
    a short instance name

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