/*
* 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.
*/
/*
* LabelBasedAUC.java
* Copyright (C) 2009-2010 Aristotle University of Thessaloniki, Thessaloniki, Greece
*/
package mulan.evaluation.measure;
import weka.classifiers.evaluation.NominalPrediction;
import weka.core.FastVector;
/**
* Implementation of the label-based macro precision measure.
*
* @author Grigorios Tsoumakas
* @version 2010.12.04
*/
public abstract class LabelBasedAUC extends ConfidenceMeasureBase {
/** The number of labels */
protected int numOfLabels;
/** The predictions for each label */
protected FastVector[] m_Predictions;
/** The predictions for all labels */
protected FastVector all_Predictions;
/**
* Creates a new instance of this class
*
* @param numOfLabels the number of labels
*/
public LabelBasedAUC(int numOfLabels) {
this.numOfLabels = numOfLabels;
m_Predictions = new FastVector[numOfLabels];
for (int labelIndex = 0; labelIndex < numOfLabels; labelIndex++) {
m_Predictions[labelIndex] = new FastVector();
}
all_Predictions = new FastVector();
}
public void reset() {
for (int labelIndex = 0; labelIndex < numOfLabels; labelIndex++) {
m_Predictions[labelIndex] = new FastVector();
}
all_Predictions = new FastVector();
}
public double getIdealValue() {
return 1;
}
protected void updateConfidence(double[] confidences, boolean[] truth) {
for (int labelIndex = 0; labelIndex < numOfLabels; labelIndex++) {
int classValue;
boolean actual = truth[labelIndex];
if (actual) {
classValue = 1;
} else {
classValue = 0;
}
double[] dist = new double[2];
dist[1] = confidences[labelIndex];
dist[0] = 1 - dist[1];
m_Predictions[labelIndex].addElement(new NominalPrediction(classValue, dist, 1));
all_Predictions.addElement(new NominalPrediction(classValue, dist, 1));
}
}
}