weka.classifiers.bayes.blr
Class Prior
java.lang.Object
weka.classifiers.bayes.blr.Prior
- All Implemented Interfaces:
- java.io.Serializable, RevisionHandler
- Direct Known Subclasses:
- GaussianPriorImpl, LaplacePriorImpl
public abstract class Prior
- extends java.lang.Object
- implements java.io.Serializable, RevisionHandler
This is an interface to plug various priors into
the Bayesian Logistic Regression Model.
- Version:
- $Revision: 1.2 $
- Author:
- Navendu Garg (gargnav@iit.edu)
- See Also:
- Serialized Form
Constructor Summary |
Prior()
|
Method Summary |
void |
computelogLikelihood(double[] betas,
Instances instances)
Function computes the log-likelihood value:
-sum{1 to n}{ln(1+exp(-Beta*x(i)*y(i))} |
void |
computePenalty(double[] betas,
double[] hyperparameters)
Skeleton function to compute penalty terms. |
double |
getLoglikelihood()
|
double |
getLogPosterior()
|
double |
getPenalty()
|
double |
update(int j,
Instances instances,
double beta,
double hyperparameter,
double[] r,
double deltaV)
Interface for the update functions for different types of
priors. |
Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Prior
public Prior()
update
public double update(int j,
Instances instances,
double beta,
double hyperparameter,
double[] r,
double deltaV)
- Interface for the update functions for different types of
priors.
computelogLikelihood
public void computelogLikelihood(double[] betas,
Instances instances)
- Function computes the log-likelihood value:
-sum{1 to n}{ln(1+exp(-Beta*x(i)*y(i))}
- Parameters:
betas
- instances
-
computePenalty
public void computePenalty(double[] betas,
double[] hyperparameters)
- Skeleton function to compute penalty terms.
- Parameters:
betas
- hyperparameters
-
getLoglikelihood
public double getLoglikelihood()
- Returns:
- log-likelihood value.
getLogPosterior
public double getLogPosterior()
- Returns:
- regularized log posterior value.
getPenalty
public double getPenalty()
- Returns:
- penalty term.