/**
* Get a shallow copy of this rule
*
* @return the copy
*/
public Object copy() {
RipperRule copy = new RipperRule();
copy.setConsequent(getConsequent());
copy.m_Antds = (FastVector) this.m_Antds.copyElements();
copy.aprioriDistribution = this.aprioriDistribution.clone();
return copy;
}
public void calculateConfidences(Instances data) throws Exception {
RipperRule tempRule = (RipperRule) this.copy();
while (tempRule.hasAntds()) {
double acc = 0;
double cov = 0;
for (int i = 0; i < data.numInstances(); i++) {
double membershipValue = tempRule.coverageDegree(data.instance(i));
cov += membershipValue;
if (m_Consequent == data.instance(i).classValue()) {
acc += membershipValue;
}
}
// m-estimate
double m = 2.0;
((Antd) this.m_Antds.elementAt((int) tempRule.size() - 1)).m_confidence =
(acc + m * (aprioriDistribution[(int) m_Consequent] / Utils.sum(aprioriDistribution)))
/ (cov + m);
tempRule.m_Antds.removeElementAt(tempRule.m_Antds.size() - 1);
}
}
