/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 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 Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.learner.igss.utility;
import com.rapidminer.operator.learner.igss.hypothesis.Hypothesis;
/** The utility function Binomial.
*
* @author Dirk Dach
* @version $Id: Binomial.java,v 1.3 2008/05/09 19:23:23 ingomierswa Exp $
*/
public class Binomial extends AbstractUtility{
/** Constructs a new Binomial with the given default probability.*/
public Binomial (double[] priors, int large) {
super(priors,large);
}
/** Calculates the utility for the given number of examples,positive examples and hypothesis*/
public double utility (double totalWeight, double totalPositiveWeight, Hypothesis hypo) {
double g=hypo.getCoveredWeight()/totalWeight;
double p=hypo.getPositiveWeight()/hypo.getCoveredWeight();
if (hypo.getPrediction()==Hypothesis.POSITIVE_CLASS) {
return Math.sqrt(g)*(p-this.priors[Hypothesis.POSITIVE_CLASS]);
}
else {
return Math.sqrt(g)*(p-this.priors[Hypothesis.NEGATIVE_CLASS]);
}
}
/** Calculate confidence intervall without a specific rule */
public double conf (double totalWeight, double delta) {
double inverseNormal=inverseNormal(1-delta/4);
return Math.sqrt(inverseNormal/(2*Math.sqrt(totalWeight))) +
inverseNormal/(2*Math.sqrt(totalWeight)) +
Math.pow(inverseNormal/(2*Math.sqrt(totalWeight)),1.5d);
}
/** Calculate confidence intervall for a specific rule.*/
public double conf (double totalWeight, double totalPositiveWeight, Hypothesis hypo, double delta) {
double g=hypo.getCoveredWeight()/totalWeight;
double p=hypo.getPositiveWeight()/hypo.getCoveredWeight();
double sg=variance(g,totalWeight);
double sp=variance(p,hypo.getCoveredWeight());
double inverseNormal=inverseNormal(1-delta/4);
return Math.sqrt(sg*inverseNormal)+sp*inverseNormal+Math.sqrt(sg*inverseNormal)*sp*inverseNormal;
}
/** Calculates the variance for a binomial distribution. */
private double variance(double p, double totalWeight) {
return (p*(1-p))/totalWeight;
}
/** Calculate confidence intervall without a specific rule for small m. */
public double confSmallM (double totalExampleWeight, double delta) {
double term=Math.log(4/delta)/(2*totalExampleWeight);
return Math.sqrt(term)+Math.pow(term,0.25)+Math.pow(term,0.75);
}
/** Returns an upper bound for the utility of refinements for the given hypothesis. */
public double getUpperBound(double totalWeight, double totalPositiveWeight, Hypothesis hypo, double delta) {
double p0;
if (hypo.getPrediction()==Hypothesis.POSITIVE_CLASS) {
p0=this.priors[Hypothesis.POSITIVE_CLASS];
}
else {
p0=this.priors[Hypothesis.NEGATIVE_CLASS];
}
Utility cov=new Coverage(this.priors,this.large);
Hypothesis h=hypo.clone();
h.setCoveredWeight(hypo.getPositiveWeight()); // all fp become tn
double g=cov.utility(totalWeight,totalPositiveWeight,h);
double conf=cov.confidenceIntervall(totalWeight,delta);
return (Math.sqrt(g+conf)*(1.0d-p0));
}
}