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## weka.classifiers.bayes.blr
Class LaplacePriorImpl

    java.lang.Object
      weka.classifiers.bayes.blr.Prior
          weka.classifiers.bayes.blr.LaplacePriorImpl

All Implemented Interfaces:
  java.io.Serializable, RevisionHandler

    public class LaplacePriorImpl

extends Prior

Implementation of the Gaussian Prior update function based on modified CLG Algorithm (CLG-Lasso) with a certain Trust Region Update based on Laplace Priors.

Version:
  $Revision: 1.2 $
Author:
  Navendu Garg(gargnav@iit.edu)
See Also:
  Serialized Form

Constructor Summary
`LaplacePriorImpl()`

Method Summary
`void` | `computeLogLikelihood(double[] betas, Instances instances)` Computes the log-likelihood values using the implementation in the Prior class.
`void` | `computePenalty(double[] betas, double[] hyperparameters)` This function computes the penalty term specific to Laplacian distribution.
`java.lang.String` | `getRevision()` Returns the revision string.
`double` | `laplaceUpdate(int j, double sign)` This is the CLG-lasso update function described in the
`double` | `update(int j, Instances instances, double beta, double hyperparameter, double[] r, double deltaV)` Update function specific to Laplace Prior.

Methods inherited from class weka.classifiers.bayes.blr.Prior
`computelogLikelihood, getLoglikelihood, getLogPosterior, getPenalty`

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

Constructor Detail

### LaplacePriorImpl

    public LaplacePriorImpl()

Method Detail

### update

    public double update(int j,
                         Instances instances,
                         double beta,
                         double hyperparameter,
                         double[] r,
                         double deltaV)

  Update function specific to Laplace Prior.

  Overrides:
    `update` in class `Prior`

### laplaceUpdate

    public double laplaceUpdate(int j,
                                double sign)

  This is the CLG-lasso update function described in the

       @TechReport{blrtext04,
      author = {Alexander Genkin and David D. Lewis and David Madigan},
      title = {Large-scale bayesian logistic regression for text categorization},
      institution = {DIMACS},
      year = {2004},
      url = "http://www.stat.rutgers.edu/~madigan/PAPERS/shortFat-v3a.pdf",
      OPTannote = {}
      }

  Parameters:
    `j` -
  Returns:
    double value

### computeLogLikelihood

    public void computeLogLikelihood(double[] betas,
                                     Instances instances)

  Computes the log-likelihood values using the implementation in the Prior class.

  Parameters:
    `betas` -
    `instances` -
    `hyperparameter` -

### computePenalty

    public void computePenalty(double[] betas,
                               double[] hyperparameters)

  This function computes the penalty term specific to Laplacian distribution.

  Overrides:
    `computePenalty` in class `Prior`
  Parameters:
    `betas` -
    `hyperparameters` -

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Returns:
    the revision

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