/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* Prior.java
* Copyright (C) 2008 Illinois Institute of Technology
*
*/
package weka.classifiers.bayes.blr;
import weka.classifiers.bayes.BayesianLogisticRegression;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionHandler;
import java.io.Serializable;
/**
* This is an interface to plug various priors into
* the Bayesian Logistic Regression Model.
*
* @version $Revision: 1.2 $
* @author Navendu Garg (gargnav@iit.edu)
*/
public abstract class Prior
implements Serializable, RevisionHandler {
protected Instances m_Instances;
protected double Beta = 0.0;
protected double Hyperparameter = 0.0;
protected double DeltaUpdate;
protected double[] R;
protected double Delta = 0.0;
protected double log_posterior = 0.0;
protected double log_likelihood = 0.0;
protected double penalty = 0.0;
/**
* Interface for the update functions for different types of
* priors.
*
*/
public double update(int j, Instances instances, double beta,
double hyperparameter, double[] r, double deltaV) {
return 0.0;
}
/**
* Function computes the log-likelihood value:
* -sum{1 to n}{ln(1+exp(-Beta*x(i)*y(i))}
* @param betas
* @param instances
*/
public void computelogLikelihood(double[] betas, Instances instances) {
Instance instance;
log_likelihood = 0.0;
for (int i = 0; i < instances.numInstances(); i++) {
instance = instances.instance(i);
double log_row = 0.0;
for (int j = 0; j < instance.numAttributes(); j++) {
if (instance.value(j) != 0.0) {
log_row += (betas[j] * instance.value(j) * instance.value(j));
}
}
log_row = log_row * BayesianLogisticRegression.classSgn(instance.classValue());
log_likelihood += Math.log(1.0 + Math.exp(0.0 - log_row));
}
log_likelihood = 0 - log_likelihood;
}
/**
* Skeleton function to compute penalty terms.
* @param betas
* @param hyperparameters
*/
public void computePenalty(double[] betas, double[] hyperparameters) {
//implement specific penalties in the prior implmentation.
}
/**
*
* @return log-likelihood value.
*/
public double getLoglikelihood() {
return log_likelihood;
}
/**
*
* @return regularized log posterior value.
*/
public double getLogPosterior() {
log_posterior = log_likelihood + penalty;
return log_posterior;
}
/**
*
* @return penalty term.
*/
public double getPenalty() {
return penalty;
}
}