/*
* RapidMiner
*
* Copyright (C) 2001-2014 by RapidMiner and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapidminer.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 instance-averaging utility function WRAcc.
*
* @author Dirk Dach
*/
public class WRAcc extends InstanceAveraging {
/** Constructs new WRAcc with the given default probability.*/
public WRAcc (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 g*(p-this.priors[Hypothesis.POSITIVE_CLASS]);
}
else {
return g*(p-this.priors[Hypothesis.NEGATIVE_CLASS]);
}
}
/** Calculates the empirical variance. */
@Override
public double variance(double totalWeight, double totalPositiveWeight, Hypothesis hypo) {
double p0;
if (hypo.getPrediction()==Hypothesis.POSITIVE_CLASS) {
p0=this.priors[Hypothesis.POSITIVE_CLASS];
}
else {
p0=this.priors[Hypothesis.NEGATIVE_CLASS];
}
double mean=this.utility(totalWeight,totalPositiveWeight,hypo);
double innerTerm=hypo.getPositiveWeight()*Math.pow(1.0-p0-mean,2) +
(hypo.getCoveredWeight()-hypo.getPositiveWeight())*Math.pow(0.0-p0-mean,2) +
(totalWeight-hypo.getCoveredWeight())*Math.pow(0.0-mean,2);
return Math.sqrt(innerTerm)/totalWeight;
}
/** 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 ((g+conf)*(1.0-p0));
}
}