/**
* Computes the error in classification on the given data.
*
* @param data the instances to be classified
* @return classification error
* @exception Exception if error can not be computed successfully
*/
protected double computeError(Instances data) throws Exception {
double error = 0.0;
int numInstances = data.numInstances();
Instance curr;
for (int i = 0; i < numInstances; i++) {
curr = data.instance(i);
// Check if the instance has been misclassified
if (curr.classValue() != ((int) classifyInstance(curr))) error++;
}
return (error / numInstances);
}
/**
* Labels the artificially generated data.
*
* @param artData the artificially generated instances
* @exception Exception if instances cannot be labeled successfully
*/
protected void labelData(Instances artData) throws Exception {
Instance curr;
double[] probs;
for (int i = 0; i < artData.numInstances(); i++) {
curr = artData.instance(i);
// compute the class membership probs predicted by the current ensemble
probs = distributionForInstance(curr);
// select class label inversely proportional to the ensemble predictions
curr.setClassValue(inverseLabel(probs));
}
}
/**
* Calculates the class membership probabilities for the given test instance.
*
* @param instance the instance to be classified
* @return predicted class probability distribution
* @exception Exception if distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
if (instance.classAttribute().isNumeric()) {
throw new UnsupportedClassTypeException("Decorate can't handle a numeric class!");
}
double[] sums = new double[instance.numClasses()], newProbs;
Classifier curr;
for (int i = 0; i < m_Committee.size(); i++) {
curr = (Classifier) m_Committee.get(i);
newProbs = curr.distributionForInstance(instance);
for (int j = 0; j < newProbs.length; j++) sums[j] += newProbs[j];
}
if (Utils.eq(Utils.sum(sums), 0)) {
return sums;
} else {
Utils.normalize(sums);
return sums;
}
}