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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* 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 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see .
*/
/*
* InfoGainSplitCrit.java
* Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.trees.j48;
import weka.core.RevisionUtils;
import weka.core.Utils;
/**
* Class for computing the information gain for a given distribution.
*
* @author Eibe Frank ([email protected])
* @version $Revision: 10169 $
*/
public final class InfoGainSplitCrit extends EntropyBasedSplitCrit {
/** for serialization */
private static final long serialVersionUID = 4892105020180728499L;
/**
* This method is a straightforward implementation of the information gain
* criterion for the given distribution.
*/
@Override
public final double splitCritValue(Distribution bags) {
double numerator;
numerator = oldEnt(bags) - newEnt(bags);
// Splits with no gain are useless.
if (Utils.eq(numerator, 0)) {
return Double.MAX_VALUE;
}
// We take the reciprocal value because we want to minimize the
// splitting criterion's value.
return bags.total() / numerator;
}
/**
* This method computes the information gain in the same way C4.5 does.
*
* @param bags the distribution
* @param totalNoInst weight of ALL instances (including the ones with missing
* values).
*/
public final double splitCritValue(Distribution bags, double totalNoInst) {
double numerator;
double noUnknown;
double unknownRate;
noUnknown = totalNoInst - bags.total();
unknownRate = noUnknown / totalNoInst;
numerator = (oldEnt(bags) - newEnt(bags));
numerator = (1 - unknownRate) * numerator;
// Splits with no gain are useless.
if (Utils.eq(numerator, 0)) {
return 0;
}
return numerator / bags.total();
}
/**
* This method computes the information gain in the same way C4.5 does.
*
* @param bags the distribution
* @param totalNoInst weight of ALL instances
* @param oldEnt entropy with respect to "no-split"-model.
*/
public final double splitCritValue(Distribution bags, double totalNoInst,
double oldEnt) {
double numerator;
double noUnknown;
double unknownRate;
noUnknown = totalNoInst - bags.total();
unknownRate = noUnknown / totalNoInst;
numerator = (oldEnt - newEnt(bags));
numerator = (1 - unknownRate) * numerator;
// Splits with no gain are useless.
if (Utils.eq(numerator, 0)) {
return 0;
}
return numerator / bags.total();
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 10169 $");
}
}